diff --git a/.gitmodules b/.gitmodules
index a1367c97b2f5..6ef740e33153 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -10,3 +10,6 @@
 [submodule "3rdparty/vta-hw"]
 	path = 3rdparty/vta-hw
 	url = https://github.com/apache/incubator-tvm-vta
+[submodule "3rdparty/libbacktrace"]
+	path = 3rdparty/libbacktrace
+	url = https://github.com/tlc-pack/libbacktrace.git
diff --git a/3rdparty/dmlc-core b/3rdparty/dmlc-core
index 6c401e242c59..21cc7de0dc9f 160000
--- a/3rdparty/dmlc-core
+++ b/3rdparty/dmlc-core
@@ -1 +1 @@
-Subproject commit 6c401e242c59a1f4c913918246591bb13fd714e7
+Subproject commit 21cc7de0dc9fd6acb796e1be6181fa8e6b6c8f41
diff --git a/3rdparty/libbacktrace b/3rdparty/libbacktrace
new file mode 160000
index 000000000000..08f7c7e69f8e
--- /dev/null
+++ b/3rdparty/libbacktrace
@@ -0,0 +1 @@
+Subproject commit 08f7c7e69f8ea61a0c4151359bc8023be8e9217b
diff --git a/3rdparty/vta-hw b/3rdparty/vta-hw
index 57db5a718c74..87ce9acfae55 160000
--- a/3rdparty/vta-hw
+++ b/3rdparty/vta-hw
@@ -1 +1 @@
-Subproject commit 57db5a718c74a788c98120ebbe1230797be698c8
+Subproject commit 87ce9acfae550d1a487746e9d06c2e250076e54c
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6929dd66e0ef..1aa3e68ffd14 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -35,7 +35,8 @@ tvm_option(USE_THREADS "Build with thread support" ON)
 tvm_option(USE_LLVM "Build with LLVM, can be set to specific llvm-config path" OFF)
 tvm_option(USE_STACKVM_RUNTIME "Include stackvm into the runtime" OFF)
 tvm_option(USE_GRAPH_RUNTIME "Build with tiny graph runtime" ON)
-tvm_option(USE_GRAPH_RUNTIME_DEBUG "Build with tiny graph runtime debug mode" OFF)
+tvm_option(USE_GRAPH_RUNTIME_CUDA_GRAPH "Build with tiny graph runtime with CUDA Graph for GPUs" OFF)
+tvm_option(USE_PROFILER "Build profiler for the VM and graph runtime" ON)
 tvm_option(USE_OPENMP "Build with OpenMP thread pool implementation" OFF)
 tvm_option(USE_RELAY_DEBUG "Building Relay in debug mode..." OFF)
 tvm_option(USE_RTTI "Build with RTTI" ON)
@@ -47,6 +48,11 @@ tvm_option(USE_TF_TVMDSOOP "Build with TensorFlow TVMDSOOp" OFF)
 tvm_option(USE_FALLBACK_STL_MAP "Use TVM's POD compatible Map" OFF)
 tvm_option(USE_ETHOSN "Build with Arm Ethos-N" OFF)
 tvm_option(INDEX_DEFAULT_I64 "Defaults the index datatype to int64" ON)
+set(_LIBBACKTRACE_DEFAULT OFF)
+if(CMAKE_SYSTEM_NAME MATCHES "Darwin" OR CMAKE_SYSTEM_NAME MATCHES "Linux")
+  set(_LIBBACKTRACE_DEFAULT ON)
+endif()
+tvm_option(USE_LIBBACKTRACE "Build libbacktrace to supply linenumbers on stack traces" ${_LIBBACKTRACE_DEFAULT})
 
 # 3rdparty libraries
 tvm_option(DLPACK_PATH "Path to DLPACK" "3rdparty/dlpack/include")
@@ -74,6 +80,7 @@ tvm_option(USE_CPP_RPC "Build CPP RPC" OFF)
 tvm_option(USE_TFLITE "Build with tflite support" OFF)
 tvm_option(USE_TENSORFLOW_PATH "TensorFlow root path when use TFLite" none)
 tvm_option(USE_COREML "Build with coreml support" OFF)
+tvm_option(USE_BNNS "Build with BNNS support" OFF)
 tvm_option(USE_TARGET_ONNX "Build with ONNX Codegen support" OFF)
 tvm_option(USE_ARM_COMPUTE_LIB "Build with Arm Compute Library" OFF)
 tvm_option(USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME "Build with Arm Compute Library graph runtime" OFF)
@@ -130,6 +137,14 @@ if(MSVC)
   add_compile_options(/wd4180)
   # DLL interface warning in c++
   add_compile_options(/wd4251)
+  # destructor was implicitly defined as deleted
+  add_compile_options(/wd4624)
+  # unary minus operator applied to unsigned type, result still unsigned
+  add_compile_options(/wd4146)
+  # 'inline': used more than once
+  add_compile_options(/wd4141)
+  # unknown pragma
+  add_compile_options(/wd4068)
 else(MSVC)
   set(WARNING_FLAG -Wall)
   if ("${CMAKE_BUILD_TYPE}" STREQUAL "Debug")
@@ -151,7 +166,6 @@ else(MSVC)
       CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 7.0)
     set(CMAKE_CXX_FLAGS "-faligned-new ${CMAKE_CXX_FLAGS}")
   endif()
-  include(cmake/modules/ClangFlags.cmake)
 
   # Detect if we're compiling for Hexagon.
   set(TEST_FOR_HEXAGON_CXX
@@ -256,13 +270,6 @@ list(APPEND COMPILER_SRCS ${RELAY_BACKEND_SRCS})
 list(APPEND COMPILER_SRCS ${RELAY_IR_SRCS})
 list(APPEND COMPILER_SRCS ${RELAY_QNN_SRCS})
 
-
-if(USE_VM_PROFILER)
-  message(STATUS "Build compiler with Relay VM profiler support...")
-  file(GLOB BACKEND_VM_PROFILER_SRCS src/relay/backend/vm/profiler/*.cc)
-  list(APPEND COMPILER_SRCS ${BACKEND_VM_PROFILER_SRCS})
-endif(USE_VM_PROFILER)
-
 file(GLOB DATATYPE_SRCS src/target/datatype/*.cc)
 list(APPEND COMPILER_SRCS ${DATATYPE_SRCS})
 list(APPEND COMPILER_SRCS "src/target/datatype/myfloat/myfloat.cc")
@@ -309,20 +316,29 @@ if(USE_GRAPH_RUNTIME)
   file(GLOB RUNTIME_GRAPH_SRCS src/runtime/graph/*.cc)
   list(APPEND RUNTIME_SRCS ${RUNTIME_GRAPH_SRCS})
 
-  if(USE_GRAPH_RUNTIME_DEBUG)
-    message(STATUS "Build with Graph runtime debug support...")
-    file(GLOB RUNTIME_GRAPH_DEBUG_SRCS src/runtime/graph/debug/*.cc)
-    list(APPEND RUNTIME_SRCS ${RUNTIME_GRAPH_DEBUG_SRCS})
-    set_source_files_properties(${RUNTIME_GRAPH_SRCS}
-      PROPERTIES COMPILE_DEFINITIONS "TVM_GRAPH_RUNTIME_DEBUG")
-  endif(USE_GRAPH_RUNTIME_DEBUG)
 endif(USE_GRAPH_RUNTIME)
 
+# convert old options for profiler
+if(USE_GRAPH_RUNTIME_DEBUG)
+  unset(USE_GRAPH_RUNTIME_DEBUG CACHE)
+  set(USE_PROFILER ON)
+endif()
 if(USE_VM_PROFILER)
-  message(STATUS "Build with Relay VM profiler support...")
+  unset(USE_VM_PROFILER CACHE)
+  set(USE_PROFILER ON)
+endif()
+
+if(USE_PROFILER)
+  message(STATUS "Build with profiler...")
+
+  file(GLOB RUNTIME_GRAPH_DEBUG_SRCS src/runtime/graph/debug/*.cc)
+  list(APPEND RUNTIME_SRCS ${RUNTIME_GRAPH_DEBUG_SRCS})
+  set_source_files_properties(${RUNTIME_GRAPH_SRCS}
+    PROPERTIES COMPILE_DEFINITIONS "TVM_GRAPH_RUNTIME_DEBUG")
+
   file(GLOB RUNTIME_VM_PROFILER_SRCS src/runtime/vm/profiler/*.cc)
   list(APPEND RUNTIME_SRCS ${RUNTIME_VM_PROFILER_SRCS})
-endif(USE_VM_PROFILER)
+endif(USE_PROFILER)
 
 # Module rules
 include(cmake/modules/VTA.cmake)
@@ -349,6 +365,7 @@ include(cmake/modules/contrib/HybridDump.cmake)
 include(cmake/modules/contrib/TFLite.cmake)
 include(cmake/modules/contrib/TF_TVMDSOOP.cmake)
 include(cmake/modules/contrib/CoreML.cmake)
+include(cmake/modules/contrib/BNNS.cmake)
 include(cmake/modules/contrib/ONNX.cmake)
 include(cmake/modules/contrib/ArmComputeLib.cmake)
 include(cmake/modules/contrib/TensorRT.cmake)
@@ -371,13 +388,33 @@ endif()
 
 add_lib_info(${CMAKE_CURRENT_LIST_DIR}/src/support/libinfo.cc)
 
-add_library(tvm_objs OBJECT ${COMPILER_SRCS} ${RUNTIME_SRCS})
+add_library(tvm_objs OBJECT ${COMPILER_SRCS})
 add_library(tvm_runtime_objs OBJECT ${RUNTIME_SRCS})
 
-add_library(tvm SHARED $<TARGET_OBJECTS:tvm_objs>)
-set_property(TARGET tvm APPEND PROPERTY LINK_OPTIONS "${TVM_VISIBILITY_FLAGS}")
+add_library(tvm SHARED $<TARGET_OBJECTS:tvm_objs> $<TARGET_OBJECTS:tvm_runtime_objs>)
+set_property(TARGET tvm APPEND PROPERTY LINK_OPTIONS "${TVM_VISIBILITY_FLAG}")
 add_library(tvm_runtime SHARED $<TARGET_OBJECTS:tvm_runtime_objs>)
-set_property(TARGET tvm_runtime APPEND PROPERTY LINK_OPTIONS "${TVM_VISIBILITY_FLAGS}")
+set_property(TARGET tvm_runtime APPEND PROPERTY LINK_OPTIONS "${TVM_VISIBILITY_FLAG}")
+
+target_compile_definitions(tvm_objs PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
+target_compile_definitions(tvm_runtime_objs PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
+target_compile_definitions(tvm PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
+target_compile_definitions(tvm_runtime PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
+if(USE_LIBBACKTRACE)
+  message(STATUS "Building with libbacktrace...")
+  include(cmake/modules/Libbacktrace.cmake)
+  target_link_libraries(tvm PRIVATE libbacktrace)
+  target_link_libraries(tvm_runtime PRIVATE libbacktrace)
+  add_dependencies(tvm_runtime_objs libbacktrace)
+  # pre 3.12 versions of cmake cannot propagate include directories from imported targets so we set them manually
+  target_include_directories(tvm PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/include")
+  target_include_directories(tvm_objs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/include")
+  target_include_directories(tvm_runtime PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/include")
+  target_include_directories(tvm_runtime_objs PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/include")
+else()
+  target_compile_definitions(tvm_objs PRIVATE TVM_BACKTRACE_DISABLED)
+  target_compile_definitions(tvm_runtime_objs PRIVATE TVM_BACKTRACE_DISABLED)
+endif()
 
 if(USE_MICRO)
   # NOTE: cmake doesn't track dependencies at the file level across subdirectories. For the
@@ -393,17 +430,22 @@ endif()
 if(USE_RELAY_DEBUG)
   message(STATUS "Building Relay in debug mode...")
   target_compile_definitions(tvm_objs PRIVATE "USE_RELAY_DEBUG")
-  target_compile_definitions(tvm_objs PRIVATE "DMLC_LOG_DEBUG")
+  target_compile_definitions(tvm_objs PRIVATE "TVM_LOG_DEBUG")
+  target_compile_definitions(tvm_runtime_objs PRIVATE "USE_RELAY_DEBUG")
+  target_compile_definitions(tvm_runtime_objs PRIVATE "TVM_LOG_DEBUG")
 else()
   target_compile_definitions(tvm_objs PRIVATE "NDEBUG")
+  target_compile_definitions(tvm_runtime_objs PRIVATE "NDEBUG")
 endif(USE_RELAY_DEBUG)
 
 if(USE_FALLBACK_STL_MAP)
   message(STATUS "Building with STL Map...")
   target_compile_definitions(tvm_objs PRIVATE "USE_FALLBACK_STL_MAP=1")
+  target_compile_definitions(tvm_runtime_objs PRIVATE "USE_FALLBACK_STL_MAP=1")
 else()
   message(STATUS "Building with TVM Map...")
   target_compile_definitions(tvm_objs PRIVATE "USE_FALLBACK_STL_MAP=0")
+  target_compile_definitions(tvm_runtime_objs PRIVATE "USE_FALLBACK_STL_MAP=0")
 endif(USE_FALLBACK_STL_MAP)
 
 if(BUILD_FOR_HEXAGON)
@@ -430,6 +472,9 @@ endif()
 target_link_libraries(tvm PRIVATE ${TVM_LINKER_LIBS} ${TVM_RUNTIME_LINKER_LIBS})
 target_link_libraries(tvm_runtime PRIVATE ${TVM_RUNTIME_LINKER_LIBS})
 
+# Set flags for clang
+include(cmake/modules/ClangFlags.cmake)
+
 # Related headers
 target_include_directories(
   tvm
@@ -447,7 +492,7 @@ target_include_directorieS(
 
 set(TVM_TEST_LIBRARY_NAME tvm)
 if (HIDE_PRIVATE_SYMBOLS AND NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-  add_library(tvm_allvisible SHARED $<TARGET_OBJECTS:tvm_objs>)
+  add_library(tvm_allvisible SHARED $<TARGET_OBJECTS:tvm_objs> $<TARGET_OBJECTS:tvm_runtime_objs>)
   target_include_directories(tvm_allvisible PUBLIC "$<TARGET_PROPERTY:tvm,INCLUDE_DIRECTORIES>")
   target_link_libraries(tvm_allvisible PRIVATE "$<TARGET_PROPERTY:tvm,LINK_LIBRARIES>")
   set(TVM_TEST_LIBRARY_NAME tvm_allvisible)
@@ -458,6 +503,7 @@ if (HIDE_PRIVATE_SYMBOLS AND NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
   # once minimum CMake version is bumped up to 3.13 or above.
   target_link_libraries(tvm PRIVATE ${HIDE_SYMBOLS_LINKER_FLAGS})
   target_link_libraries(tvm_runtime PRIVATE ${HIDE_SYMBOLS_LINKER_FLAGS})
+  target_compile_definitions(tvm_allvisible PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
 endif()
 
 # Tests
@@ -526,3 +572,33 @@ if(MSVC)
   target_compile_definitions(tvm_objs PRIVATE -DTVM_EXPORTS)
   target_compile_definitions(tvm_runtime_objs PRIVATE -DTVM_EXPORTS)
 endif()
+
+set(TVM_IS_DEBUG_BUILD OFF)
+if(CMAKE_BUILD_TYPE STREQUAL "Debug" OR CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo" OR CMAKE_CXX_FLAGS MATCHES "-g")
+  set(TVM_IS_DEBUG_BUILD ON)
+endif()
+
+# Change relative paths in backtrace to absolute ones
+if(TVM_IS_DEBUG_BUILD)
+  set(FILE_PREFIX_MAP_FLAG "-ffile-prefix-map=..=${CMAKE_CURRENT_SOURCE_DIR}")
+  target_compile_options(tvm PRIVATE "${FILE_PREFIX_MAP_FLAG}")
+  CHECK_CXX_COMPILER_FLAG("${FILE_PREFIX_MAP_FLAG}" FILE_PREFIX_MAP_SUPPORTED)
+  if(FILE_PREFIX_MAP_SUPPORTED)
+    target_compile_options(tvm PRIVATE $<$<COMPILE_LANGUAGE:CXX>:${FILE_PREFIX_MAP_FLAG}>)
+    target_compile_options(tvm_objs PRIVATE $<$<COMPILE_LANGUAGE:CXX>:${FILE_PREFIX_MAP_FLAG}>)
+    target_compile_options(tvm_runtime PRIVATE $<$<COMPILE_LANGUAGE:CXX>:${FILE_PREFIX_MAP_FLAG}>)
+    target_compile_options(tvm_runtime_objs PRIVATE $<$<COMPILE_LANGUAGE:CXX>:${FILE_PREFIX_MAP_FLAG}>)
+  endif()
+endif()
+
+# Run dsymutil to generate debugging symbols for backtraces
+if(APPLE AND TVM_IS_DEBUG_BUILD)
+  find_program(DSYMUTIL dsymutil)
+  mark_as_advanced(DSYMUTIL)
+  add_custom_command(TARGET tvm
+      POST_BUILD
+      COMMAND ${DSYMUTIL} ARGS $<TARGET_FILE:tvm>
+      COMMENT "Running dsymutil"
+      VERBATIM
+		  )
+endif()
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 909bdb700722..eb2af2151acc 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -42,38 +42,40 @@ We do encourage everyone to work anything they are interested in.
 - [Aditya Atluri](https://github.com/adityaatluri): @adityaatluri - rocm
 - [Matthew Barrett](https://github.com/mbaret): @mbaret - byoc, arm
 - [Matthew Brookhart](https://github.com/mbrookhart): @mbrookhart - relay, frontends
-- [Tianqi Chen](https://github.com/tqchen) (PPMC): @tqchen - topi, compiler, relay, docs
+- [Tianqi Chen](https://github.com/tqchen) (PMC): @tqchen - topi, compiler, relay, docs
 - [Liangfu Chen](https://github.com/liangfu): @liangfu - vta, chisel, intel FPGA, c runtime
 - [Wei Chen](https://github.com/wweic): @wweic - runtime, relay, vm
-- [Zhi Chen](https://github.com/zhiics) (PPMC): @zhiics - relay, quantization, pass manager
+- [Zhi Chen](https://github.com/zhiics) (PMC): @zhiics - relay, quantization, pass manager
 - [Chenfan](https://github.com/jcf94): @jcf94 - autoscheduling
+- [Josh Fromm](https://github.com/jwfromm): @jwfromm - frontends, quantization, topi
 - [Yuwei Hu](https://github.com/Huyuwei): @Huyuwei - topi, frontends
 - [Nick Hynes](https://github.com/nhynes): @nhynes: - sgx, rust
 - [Animesh Jain](https://github.com/anijain2305): @anijain2305 - quantization, relay
-- [Ziheng Jiang](https://github.com/ZihengJiang) (PPMC): @ZihengJiang - relay, compiler
+- [Ziheng Jiang](https://github.com/ZihengJiang) (PMC): @ZihengJiang - relay, compiler
 - [Marisa Kirisame](https://github.com/MarisaKirisame): @MarisaKirisame - relay
 - [Wuwei Lin](https://github.com/vinx13): @vinx13 - relay, topi
-- [Yizhi Liu](https://github.com/yzhliu) (PPMC): @yzhliu - jvm, topi, relay
+- [Yizhi Liu](https://github.com/yzhliu) (PMC): @yzhliu - jvm, topi, relay
 - [Hao Lu](https://github.com/hlu1): @hlu1 - nnpack, frontends
-- [Masahiro Masuda](https://github.com/masahi) (PPMC): @masahi - topi, relay
-- [Thierry Moreau](https://github.com/tmoreau89) (PPMC): @tmoreau89 - vta
+- [Masahiro Masuda](https://github.com/masahi) (PMC): @masahi - topi, relay
+- [Thierry Moreau](https://github.com/tmoreau89) (PMC): @tmoreau89 - vta
 - [Kazutaka Morita](https://github.com/kazum): @kazum - frontends, opencl
 - [Krzysztof Parzyszek](https://github.com/kparzysz-quic): @kparzysz-quic - hexagon, llvm
-- [Jared Roesch](https://github.com/jroesch) (PPMC): @jroesch - relay
+- [Andrew Reusch](https://github.com/areusch): @areusch - runtime, µTVM
+- [Jared Roesch](https://github.com/jroesch) (PMC): @jroesch - relay
 - [Siju Samuel](https://github.com/siju-samuel): @siju-samuel - frontends
 - [Siva](https://github.com/srkreddy1238): @srkreddy1238 - frontends, golang
 - [Junru Shao](https://github.com/junrushao1994) @junrushao1994 - relay, compiler
-- [Haichen Shen](https://github.com/icemelon9) (PPMC): @icemelon9 - relay, topi
+- [Haichen Shen](https://github.com/icemelon9) (PMC): @icemelon9 - relay, topi
 - [Zhixun Tan](https://github.com/phisiart): @phisiart - opengl, web
 - [Andrew Tulloch](https://github.com/ajtulloch): @ajtulloch - topi, compiler, runtime
 - [Luis Vega](https://github.com/vegaluisjose): @vegaluisjose - vta, chisel
-- [Leyuan Wang](https://github.com/Laurawly): @Laurawly: - topi
+- [Leyuan Wang](https://github.com/Laurawly) (PMC): @Laurawly: - topi
 - [Yao Wang](https://github.com/kevinthesun): @kevinthesun: - topi, vision
 - [Jian Weng](https://github.com/were): @were: - hybrid script
 - [Zhao Wu](https://github.com/FrozenGene): @FrozenGene - runtime, topi, frontends
-- [Eddie Yan](https://github.com/eqy) (PPMC): @eqy - runtime, autotvm, rpc, topi
+- [Eddie Yan](https://github.com/eqy) (PMC): @eqy - runtime, autotvm, rpc, topi
 - [Hao Yu](https://github.com/comaniac): @comaniac - relay, byoc, ansor
-- [Lianmin Zheng](https://github.com/merrymercy) (PPMC): @merrymercy - autotvm, topi, relay
+- [Lianmin Zheng](https://github.com/merrymercy) (PMC): @merrymercy - autotvm, topi, relay
 
 ## Reviewers
 
@@ -88,6 +90,7 @@ We do encourage everyone to work anything they are interested in.
 - [Neo Chien](https://github.com/cchung100m): @cchung100m
 - [Meghan Cowan](https://github.com/cowanmeg): @cowanmeg
 - [Balint Cristian](https://github.com/cbalint13): @cbalint13
+- [Haozheng Fan](https://github.com/hzfan): @hzfan
 - [Josh Fromm](https://github.com/jwfromm): @jwfromm
 - [Sergei Grechanik](https://github.com/sgrechanik-h): @sgrechanik-h
 - [Hao Lu](https://github.com/hlu1): @hlu1
@@ -102,6 +105,7 @@ We do encourage everyone to work anything they are interested in.
 - [Xiaoqiang Dan](https://github.com/xqdan): @xqdan
 - [Ziheng Jiang](https://github.com/ZihengJiang): @ZihengJiang
 - [Marisa Kirisame](https://github.com/MarisaKirisame): @MarisaKirisame
+- [Tristan Konolige](https://github.com/tkonolige): @tkonolige
 - [Wuwei Lin](https://github.com/vinx13): @vinx13
 - [Andrew Liu](https://github.com/hypercubestart): @hypercubestart
 - [Henry Liu](https://github.com/optima2005): @optima2005
@@ -110,6 +114,7 @@ We do encourage everyone to work anything they are interested in.
 - [Sergey Mironov](https://github.com/grwlf): @grwlf
 - [Thierry Moreau](https://github.com/tmoreau89): @tmoreau89
 - [Kazutaka Morita](https://github.com/kazum): @kazum
+- [Trevor Morris](https://github.com/trevor-m): @trevor-m
 - [Tatsuya Nishiyama](https://github.com/nishi-t): @nishi-t
 - [Wei Pan](https://github.com/wpan11nv): @wpan11nv
 - [Krzysztof Parzyszek](https://github.com/kparzysz-quic): @kparzysz-quic
@@ -117,6 +122,7 @@ We do encourage everyone to work anything they are interested in.
 - [Josh Pollock](https://github.com/joshpoll): @joshpoll
 - [Jared Roesch](https://github.com/jroesch): @jroesch
 - [Andrew Reusch](https://github.com/areusch): @areusch
+- [Dmitriy Smirnov](https://github.com/d-smirnov): @d-smirnov
 - [Siva](https://github.com/srkreddy1238): @srkreddy1238
 - [Siju Samuel](https://github.com/siju-samuel): @siju-samuel
 - [Junru Shao](https://github.com/junrushao1994): @junrushao1994
diff --git a/DISCLAIMER b/DISCLAIMER
deleted file mode 100644
index 986b2c84f6b4..000000000000
--- a/DISCLAIMER
+++ /dev/null
@@ -1,12 +0,0 @@
-Apache TVM (incubating) is an effort undergoing incubation at The
-Apache Software Foundation (ASF), sponsored by the Apache Incubator PMC.
-
-Incubation is required of all newly accepted
-projects until a further review indicates that the
-infrastructure, communications, and decision making process have
-stabilized in a manner consistent with other successful ASF
-projects.
-
-While incubation status is not necessarily a reflection
-of the completeness or stability of the code, it does indicate
-that the project has yet to be fully endorsed by the ASF.
diff --git a/Jenkinsfile b/Jenkinsfile
index 81439e95be16..506dcab4e306 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -46,11 +46,11 @@
 // NOTE: these lines are scanned by docker/dev_common.sh. Please update the regex as needed. -->
 ci_lint = "tlcpack/ci-lint:v0.62"
 ci_gpu = "tlcpack/ci-gpu:v0.72"
-ci_cpu = "tlcpack/ci-cpu:v0.71"
+ci_cpu = "tlcpack/ci-cpu:v0.72-t0"
 ci_wasm = "tlcpack/ci-wasm:v0.70"
-ci_i386 = "tlcpack/ci-i386:v0.71"
+ci_i386 = "tlcpack/ci-i386:v0.72-t0"
 ci_qemu = "tlcpack/ci-qemu:v0.01"
-ci_arm = "tlcpack/ci-arm:v0.01"
+ci_arm = "tlcpack/ci-arm:v0.02"
 // <--- End of regex-scanned config.
 
 // tvm libraries
@@ -65,7 +65,7 @@ tvm_multilib = "build/libtvm.so, " +
 // command to start a docker container
 docker_run = 'docker/bash.sh'
 // timeout in minutes
-max_time = 120
+max_time = 240
 
 def per_exec_ws(folder) {
   return "workspace/exec_${env.EXECUTOR_NUMBER}/" + folder
@@ -80,7 +80,7 @@ def init_git() {
   checkout scm
   retry(5) {
     timeout(time: 2, unit: 'MINUTES') {
-      sh 'git submodule update --init'
+      sh 'git submodule update --init -f'
     }
   }
 }
@@ -89,7 +89,7 @@ def init_git_win() {
     checkout scm
     retry(5) {
         timeout(time: 2, unit: 'MINUTES') {
-            bat 'git submodule update --init'
+            bat 'git submodule update --init -f'
         }
     }
 }
@@ -181,13 +181,14 @@ stage('Build') {
         make(ci_cpu, 'build', '-j2')
         pack_lib('cpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_cpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_cpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_unittest.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_integration.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_vta_fsim.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_vta_tsim.sh"
           // sh "${docker_run} ${ci_cpu} ./tests/scripts/task_golang.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_rust.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -199,7 +200,7 @@ stage('Build') {
         sh "${docker_run} ${ci_wasm} ./tests/scripts/task_config_build_wasm.sh"
         make(ci_wasm, 'build', '-j2')
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_wasm} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_wasm} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_wasm} ./tests/scripts/task_web_wasm.sh"
         }
       }
@@ -232,8 +233,9 @@ stage('Build') {
         sh "${docker_run} ${ci_qemu} ./tests/scripts/task_config_build_qemu.sh"
         make(ci_qemu, 'build', '-j2')
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_qemu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_qemu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_qemu} ./tests/scripts/task_python_microtvm.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -247,10 +249,11 @@ stage('Unit Test') {
         init_git()
         unpack_lib('gpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_sphinx_precheck.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_unittest_gpuonly.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_integration_gpuonly.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -261,10 +264,11 @@ stage('Unit Test') {
         init_git()
         unpack_lib('i386', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_i386} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_i386} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_i386} ./tests/scripts/task_python_unittest.sh"
           sh "${docker_run} ${ci_i386} ./tests/scripts/task_python_integration.sh"
           sh "${docker_run} ${ci_i386} ./tests/scripts/task_python_vta_fsim.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -275,8 +279,9 @@ stage('Unit Test') {
         init_git()
         unpack_lib('arm', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_arm} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_arm} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_arm} ./tests/scripts/task_python_unittest.sh"
+          junit "build/pytest-results/*.xml"
           // sh "${docker_run} ${ci_arm} ./tests/scripts/task_python_integration.sh"
         }
       }
@@ -288,7 +293,7 @@ stage('Unit Test') {
         init_git()
         unpack_lib('gpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_java_unittest.sh"
         }
       }
@@ -303,8 +308,9 @@ stage('Integration Test') {
         init_git()
         unpack_lib('gpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_topi.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -315,8 +321,9 @@ stage('Integration Test') {
         init_git()
         unpack_lib('gpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_frontend.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -327,8 +334,9 @@ stage('Integration Test') {
         init_git()
         unpack_lib('cpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_cpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_frontend_cpu.sh"
+          junit "build/pytest-results/*.xml"
         }
       }
     }
@@ -339,7 +347,7 @@ stage('Integration Test') {
         init_git()
         unpack_lib('gpu', tvm_multilib)
         timeout(time: max_time, unit: 'MINUTES') {
-          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_python_setup.sh"
+          sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_docs.sh"
         }
         pack_lib('mydocs', 'docs.tgz')
diff --git a/NOTICE b/NOTICE
index edb1bd250000..a4b747830dcf 100644
--- a/NOTICE
+++ b/NOTICE
@@ -1,5 +1,5 @@
-Apache TVM (incubating)
-Copyright 2019-2020 The Apache Software Foundation
+Apache TVM
+Copyright 2019-2021 The Apache Software Foundation
 
 This product includes software developed at
 The Apache Software Foundation (http://www.apache.org/).
diff --git a/README.md b/README.md
index 13a04f66d5aa..eec5bfd5797d 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@
 [![Build Status](https://ci.tlcpack.ai/buildStatus/icon?job=tvm/main)](https://ci.tlcpack.ai/job/tvm/job/main/)
 [![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)
 
-Apache TVM (incubating) is a compiler stack for deep learning systems. It is designed to close the gap between the
+Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
 productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.
 TVM works with deep learning frameworks to provide end to end compilation to different backends.
 
@@ -36,7 +36,7 @@ License
 Contribute to TVM
 -----------------
 TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community.
-Checkout the [Contributor Guide](https://tvm.apache.org/docs/contribute/)
+Check out the [Contributor Guide](https://tvm.apache.org/docs/contribute/).
 
 Acknowledgement
 ---------------
diff --git a/apps/android_camera/app/src/main/jni/Application.mk b/apps/android_camera/app/src/main/jni/Application.mk
index 63a79458ef94..5c8774889685 100644
--- a/apps/android_camera/app/src/main/jni/Application.mk
+++ b/apps/android_camera/app/src/main/jni/Application.mk
@@ -31,7 +31,7 @@ include $(config)
 APP_ABI ?= all
 APP_STL := c++_shared
 
-APP_CPPFLAGS += -DDMLC_LOG_STACK_TRACE=0 -DTVM4J_ANDROID=1 -std=c++14 -Oz -frtti
+APP_CPPFLAGS += -DTVM4J_ANDROID=1 -std=c++14 -Oz -frtti
 ifeq ($(USE_OPENCL), 1)
     APP_CPPFLAGS += -DTVM_OPENCL_RUNTIME=1
 endif
@@ -43,4 +43,4 @@ endif
 
 ifeq ($(USE_SORT), 1)
     APP_CPPFLAGS += -DUSE_SORT=1
-endif
\ No newline at end of file
+endif
diff --git a/apps/android_camera/app/src/main/jni/tvm_runtime.h b/apps/android_camera/app/src/main/jni/tvm_runtime.h
index 5f3db04274a1..47a3a3de6bba 100644
--- a/apps/android_camera/app/src/main/jni/tvm_runtime.h
+++ b/apps/android_camera/app/src/main/jni/tvm_runtime.h
@@ -25,17 +25,13 @@
 
 #include <fstream>
 
-/* Enable custom logging - this will cause TVM to pass every log message
- * through CustomLogMessage instead of LogMessage. By enabling this, we must
- * implement dmlc::CustomLogMessage::Log. We use this to pass TVM log
- * messages to Android logcat.
+#define DMLC_USE_LOGGING_LIBRARY <tvm/runtime/logging.h>
+#define TVM_BACKTRACE_DISABLED 1
+/* Enable custom logging - this will cause TVM to use a custom implementation
+ * of tvm::runtime::detail::LogMessage. We use this to pass TVM log messages to
+ * Android logcat.
  */
-#define DMLC_LOG_CUSTOMIZE 1
-
-/* Ensure that fatal errors are passed to the logger before throwing
- * in LogMessageFatal
- */
-#define DMLC_LOG_BEFORE_THROW 1
+#define TVM_LOG_CUSTOMIZE 1
 
 #include "../src/runtime/c_runtime_api.cc"
 #include "../src/runtime/cpu_device_api.cc"
@@ -72,8 +68,20 @@
 
 #include <android/log.h>
 
-void dmlc::CustomLogMessage::Log(const std::string& msg) {
-  // This is called for every message logged by TVM.
-  // We pass the message to logcat.
-  __android_log_write(ANDROID_LOG_DEBUG, "TVM_RUNTIME", msg.c_str());
-}
\ No newline at end of file
+namespace tvm {
+namespace runtime {
+namespace detail {
+// Override logging mechanism
+void LogFatalImpl(const std::string& file, int lineno, const std::string& message) {
+  std::string m = file + ":" + std::to_string(lineno) + ": " + message;
+  __android_log_write(ANDROID_LOG_DEBUG, "TVM_RUNTIME", m.c_str());
+  throw InternalError(file, lineno, message);
+}
+void LogMessageImpl(const std::string& file, int lineno, const std::string& message) {
+  std::string m = file + ":" + std::to_string(lineno) + ": " + message;
+  __android_log_write(ANDROID_LOG_DEBUG, "TVM_RUNTIME", m.c_str());
+}
+
+}  // namespace detail
+}  // namespace runtime
+}  // namespace tvm
diff --git a/apps/android_camera/models/prepare_model.py b/apps/android_camera/models/prepare_model.py
index ab20e028c2ad..f155d46c31a4 100644
--- a/apps/android_camera/models/prepare_model.py
+++ b/apps/android_camera/models/prepare_model.py
@@ -106,7 +106,7 @@ def main(model_str, output_path):
         f.write(graph)
     print("dumping params...")
     with open(output_path_str + "/" + "deploy_param.params", "wb") as f:
-        f.write(relay.save_param_dict(params))
+        f.write(runtime.save_param_dict(params))
     print("dumping labels...")
     synset_url = "".join(
         [
diff --git a/apps/android_deploy/app/src/main/jni/Application.mk b/apps/android_deploy/app/src/main/jni/Application.mk
index a50a40bf5cd1..42c4f232a553 100644
--- a/apps/android_deploy/app/src/main/jni/Application.mk
+++ b/apps/android_deploy/app/src/main/jni/Application.mk
@@ -27,7 +27,7 @@ include $(config)
 
 APP_STL := c++_static
 
-APP_CPPFLAGS += -DDMLC_LOG_STACK_TRACE=0 -DTVM4J_ANDROID=1 -std=c++14 -Oz -frtti
+APP_CPPFLAGS += -DTVM4J_ANDROID=1 -std=c++14 -Oz -frtti
 ifeq ($(USE_OPENCL), 1)
 	APP_CPPFLAGS += -DTVM_OPENCL_RUNTIME=1
 endif
diff --git a/apps/android_deploy/app/src/main/jni/tvm_runtime.h b/apps/android_deploy/app/src/main/jni/tvm_runtime.h
index 362d278c38c4..4412e9c62e9d 100644
--- a/apps/android_deploy/app/src/main/jni/tvm_runtime.h
+++ b/apps/android_deploy/app/src/main/jni/tvm_runtime.h
@@ -25,6 +25,9 @@
 
 #include <fstream>
 
+#define DMLC_USE_LOGGING_LIBRARY <tvm/runtime/logging.h>
+#define TVM_BACKTRACE_DISABLED 1
+
 #include "../src/runtime/c_runtime_api.cc"
 #include "../src/runtime/cpu_device_api.cc"
 #include "../src/runtime/dso_library.cc"
diff --git a/apps/android_rpc/app/src/main/jni/Application.mk b/apps/android_rpc/app/src/main/jni/Application.mk
index 5f885f1c6f14..088eeed750b8 100644
--- a/apps/android_rpc/app/src/main/jni/Application.mk
+++ b/apps/android_rpc/app/src/main/jni/Application.mk
@@ -31,7 +31,7 @@ include $(config)
 APP_ABI ?= armeabi-v7a arm64-v8a x86 x86_64 mips
 APP_STL := c++_shared
 
-APP_CPPFLAGS += -DDMLC_LOG_STACK_TRACE=0 -DTVM4J_ANDROID=1 -std=c++14 -Oz -frtti
+APP_CPPFLAGS += -DTVM4J_ANDROID=1 -std=c++14 -Oz -frtti
 ifeq ($(USE_OPENCL), 1)
     APP_CPPFLAGS += -DTVM_OPENCL_RUNTIME=1
 endif
diff --git a/apps/android_rpc/app/src/main/jni/tvm_runtime.h b/apps/android_rpc/app/src/main/jni/tvm_runtime.h
index 2005568c608c..40e6279fb386 100644
--- a/apps/android_rpc/app/src/main/jni/tvm_runtime.h
+++ b/apps/android_rpc/app/src/main/jni/tvm_runtime.h
@@ -25,17 +25,13 @@
 
 #include <fstream>
 
-/* Enable custom logging - this will cause TVM to pass every log message
- * through CustomLogMessage instead of LogMessage. By enabling this, we must
- * implement dmlc::CustomLogMessage::Log. We use this to pass TVM log
- * messages to Android logcat.
+#define DMLC_USE_LOGGING_LIBRARY <tvm/runtime/logging.h>
+#define TVM_BACKTRACE_DISABLED 1
+/* Enable custom logging - this will cause TVM to use a custom implementation
+ * of tvm::runtime::detail::LogMessage. We use this to pass TVM log messages to
+ * Android logcat.
  */
-#define DMLC_LOG_CUSTOMIZE 1
-
-/* Ensure that fatal errors are passed to the logger before throwing
- * in LogMessageFatal
- */
-#define DMLC_LOG_BEFORE_THROW 1
+#define TVM_LOG_CUSTOMIZE 1
 
 #include "../src/runtime/c_runtime_api.cc"
 #include "../src/runtime/cpu_device_api.cc"
@@ -47,6 +43,7 @@
 #include "../src/runtime/module.cc"
 #include "../src/runtime/ndarray.cc"
 #include "../src/runtime/object.cc"
+#include "../src/runtime/profiling.cc"
 #include "../src/runtime/registry.cc"
 #include "../src/runtime/rpc/rpc_channel.cc"
 #include "../src/runtime/rpc/rpc_endpoint.cc"
@@ -80,8 +77,20 @@
 
 #include <android/log.h>
 
-void dmlc::CustomLogMessage::Log(const std::string& msg) {
-  // This is called for every message logged by TVM.
-  // We pass the message to logcat.
-  __android_log_write(ANDROID_LOG_DEBUG, "TVM_RUNTIME", msg.c_str());
+namespace tvm {
+namespace runtime {
+namespace detail {
+// Override logging mechanism
+void LogFatalImpl(const std::string& file, int lineno, const std::string& message) {
+  std::string m = file + ":" + std::to_string(lineno) + ": " + message;
+  __android_log_write(ANDROID_LOG_DEBUG, "TVM_RUNTIME", m.c_str());
+  throw InternalError(file, lineno, message);
 }
+void LogMessageImpl(const std::string& file, int lineno, const std::string& message) {
+  std::string m = file + ":" + std::to_string(lineno) + ": " + message;
+  __android_log_write(ANDROID_LOG_DEBUG, "TVM_RUNTIME", m.c_str());
+}
+
+}  // namespace detail
+}  // namespace runtime
+}  // namespace tvm
diff --git a/apps/bundle_deploy/Makefile b/apps/bundle_deploy/Makefile
index 8a5f1cf95571..8e23a92afa93 100644
--- a/apps/bundle_deploy/Makefile
+++ b/apps/bundle_deploy/Makefile
@@ -32,12 +32,14 @@ PKG_CXXFLAGS = ${PKG_COMPILE_OPTS} -std=c++14 \
 	-I${TVM_ROOT}/include \
 	-I${DMLC_CORE}/include \
 	-I${TVM_ROOT}/3rdparty/dlpack/include \
-	-Icrt_config
+	-Icrt_config \
+	-DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>
 PKG_CFLAGS = ${PKG_COMPILE_OPTS} \
 	-I${TVM_ROOT}/include \
 	-I${DMLC_CORE}/include \
 	-I${TVM_ROOT}/3rdparty/dlpack/include \
-	-Icrt_config
+	-Icrt_config \
+	-DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>
 
 PKG_LDFLAGS = -pthread -lm
 
@@ -62,6 +64,9 @@ $(endif)
 
 CRT_SRCS = $(shell find $(CRT_ROOT))
 
+MODEL_OBJ = $(build_dir)/model_c/devc.o $(build_dir)/model_c/lib0.o $(build_dir)/model_c/lib1.o
+TEST_MODEL_OBJ = $(build_dir)/test_model_c/devc.o $(build_dir)/test_model_c/lib0.o $(build_dir)/test_model_c/lib1.o
+
 demo_dynamic: $(build_dir)/demo_dynamic $(build_dir)/bundle.so $(build_dir)/bundle_c.so $(build_dir)/bundle.so $(build_dir)/graph_cpp.json $(build_dir)/graph_c.json $(build_dir)/params_cpp.bin $(build_dir)/params_c.bin $(build_dir)/cat.bin
 	$(QUIET)TVM_NUM_THREADS=1 $(build_dir)/demo_dynamic $(build_dir)/bundle.so $(build_dir)/graph_cpp.json $(build_dir)/params_cpp.bin $(build_dir)/cat.bin
 	$(QUIET)TVM_NUM_THREADS=1 $(build_dir)/demo_dynamic $(build_dir)/bundle_c.so $(build_dir)/graph_c.json $(build_dir)/params_c.bin $(build_dir)/cat.bin
@@ -93,11 +98,11 @@ $(build_dir)/test_dynamic: test.cc ${build_dir}/test_graph_c.json ${build_dir}/t
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)g++ $(PKG_CXXFLAGS) -o $@ test.cc $(BACKTRACE_OBJS) $(BACKTRACE_LDFLAGS)
 
-$(build_dir)/demo_static: demo_static.c ${build_dir}/bundle_static.o ${build_dir}/model_c.o ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a ${build_dir}/graph_c.json.c ${build_dir}/params_c.bin.c $(BACKTRACE_OBJS)
+$(build_dir)/demo_static: demo_static.c ${build_dir}/bundle_static.o $(MODEL_OBJ) ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a ${build_dir}/graph_c.json.c ${build_dir}/params_c.bin.c $(BACKTRACE_OBJS)
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)gcc $(PKG_CFLAGS) -o $@ $^ $(PKG_LDFLAGS) $(BACKTRACE_LDFLAGS) $(BACKTRACE_CFLAGS)
 
-$(build_dir)/test_static: test_static.c ${build_dir}/bundle_static.o ${build_dir}/test_model_c.o ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a $(BACKTRACE_OBJS)
+$(build_dir)/test_static: test_static.c ${build_dir}/bundle_static.o $(TEST_MODEL_OBJ) ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a $(BACKTRACE_OBJS)
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)gcc $(PKG_CFLAGS) -o $@ $^ $(BACKTRACE_LDFLAGS)
 
@@ -119,11 +124,15 @@ $(build_dir)/params_c.bin.c: $(build_dir)/params_c.bin
 $(build_dir)/params_cpp.bin.c: $(build_dir)/params_cpp.bin
 	$(QUIET)xxd -i $^  > $@
 
-$(build_dir)/model_c.o $(build_dir)/graph_c.json $(build_dir)/model_cpp.o $(build_dir)/graph_cpp.json $(build_dir)/params.bin $(build_dir)/cat.bin: build_model.py
+$(MODEL_OBJ) $(build_dir)/graph_c.json $(build_dir)/model_cpp.o $(build_dir)/graph_cpp.json $(build_dir)/params.bin $(build_dir)/cat.bin: build_model.py
 	$(QUIET)python3 $< -o $(build_dir)
+	$(QUIET)mkdir -p build/model_c
+	$(QUIET)tar -C build/model_c -xvf build/model_c.tar
 
-$(build_dir)/test_model_c.o $(build_dir)/test_graph_c.json $(build_dir)/test_params_c.bin $(build_dir)/test_data_c.bin $(build_dir)/test_output_c.bin $(build_dir)/test_model_cpp.o $(build_dir)/test_graph_cpp.json $(build_dir)/test_params_cpp.bin $(build_dir)/test_data_cpp.bin $(build_dir)/test_output_cpp.bin: build_model.py
+$(TEST_MODEL_OBJ) $(build_dir)/test_graph_c.json $(build_dir)/test_params_c.bin $(build_dir)/test_data_c.bin $(build_dir)/test_output_c.bin $(build_dir)/test_model_cpp.o $(build_dir)/test_graph_cpp.json $(build_dir)/test_params_cpp.bin $(build_dir)/test_data_cpp.bin $(build_dir)/test_output_cpp.bin: build_model.py
 	$(QUIET)python3 $< -o $(build_dir) --test
+	$(QUIET)mkdir -p build/test_model_c
+	$(QUIET)tar -C build/test_model_c -xvf build/test_model_c.tar
 
 # Build our bundle against the serialized bundle.c API, the runtime.cc API, and
 # the serialized graph.json and params.bin
@@ -131,7 +140,7 @@ $(build_dir)/bundle.so: bundle.cc runtime.cc $(build_dir)/model_cpp.o
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)g++ -shared $(PKG_CXXFLAGS) -fvisibility=hidden -o $@  $^ $(PKG_LDFLAGS)
 
-$(build_dir)/bundle_c.so: bundle.c $(build_dir)/model_c.o ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a $(BACKTRACE_OBJS)
+$(build_dir)/bundle_c.so: bundle.c $(MODEL_OBJ) ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a $(BACKTRACE_OBJS)
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)gcc -shared $(PKG_CFLAGS) -fvisibility=hidden -o $@  $^ $(PKG_LDFLAGS) $(BACKTRACE_LDFLAGS) $(BACKTRACE_CFLAGS)
 
@@ -139,7 +148,7 @@ $(build_dir)/test_bundle.so: bundle.cc runtime.cc $(build_dir)/test_model_cpp.o
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)g++ -shared $(PKG_CXXFLAGS) -fvisibility=hidden -o $@  $^ $(PKG_LDFLAGS)
 
-$(build_dir)/test_bundle_c.so: bundle.c $(build_dir)/test_model_c.o ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a $(BACKTRACE_OBJS)
+$(build_dir)/test_bundle_c.so: bundle.c $(TEST_MODEL_OBJ) ${build_dir}/crt/libmemory.a ${build_dir}/crt/libgraph_runtime.a ${build_dir}/crt/libcommon.a $(BACKTRACE_OBJS)
 	$(QUIET)mkdir -p $(@D)
 	$(QUIET)gcc -shared $(PKG_CFLAGS) -fvisibility=hidden -o $@  $^ $(PKG_LDFLAGS) $(BACKTRACE_LDFLAGS) $(BACKTRACE_CFLAGS)
 
diff --git a/apps/bundle_deploy/build_model.py b/apps/bundle_deploy/build_model.py
index a2513c8a46d0..8fbc01bcf4a6 100644
--- a/apps/bundle_deploy/build_model.py
+++ b/apps/bundle_deploy/build_model.py
@@ -20,9 +20,10 @@
 import os
 from tvm import relay
 import tvm
-from tvm import te
+from tvm import te, runtime
 import logging
 import json
+from tvm.contrib import cc as _cc
 
 RUNTIMES = {
     "c": "{name}_c.{ext}",
@@ -51,7 +52,17 @@ def build_module(opts):
         build_dir = os.path.abspath(opts.out_dir)
         if not os.path.isdir(build_dir):
             os.makedirs(build_dir)
-        lib.save(os.path.join(build_dir, file_format_str.format(name="model", ext="o")))
+        ext = "tar" if runtime_name == "c" else "o"
+        lib_file_name = os.path.join(build_dir, file_format_str.format(name="model", ext=ext))
+        if runtime_name == "c":
+            lib.export_library(lib_file_name)
+        else:
+            # NOTE: at present, export_libarary will always create _another_ shared object, and you
+            # can't stably combine two shared objects together (in this case, init_array is not
+            # populated correctly when you do that). So for now, must continue to use save() with the
+            # C++ library.
+            # TODO(areusch): Obliterate runtime.cc and replace with libtvm_runtime.so.
+            lib.save(lib_file_name)
         with open(
             os.path.join(build_dir, file_format_str.format(name="graph", ext="json")), "w"
         ) as f_graph_json:
@@ -59,7 +70,7 @@ def build_module(opts):
         with open(
             os.path.join(build_dir, file_format_str.format(name="params", ext="bin")), "wb"
         ) as f_params:
-            f_params.write(relay.save_param_dict(params))
+            f_params.write(runtime.save_param_dict(params))
 
 
 def build_test_module(opts):
@@ -84,7 +95,17 @@ def build_test_module(opts):
         build_dir = os.path.abspath(opts.out_dir)
         if not os.path.isdir(build_dir):
             os.makedirs(build_dir)
-        lib.save(os.path.join(build_dir, file_format_str.format(name="test_model", ext="o")))
+        ext = "tar" if runtime_name == "c" else "o"
+        lib_file_name = os.path.join(build_dir, file_format_str.format(name="test_model", ext=ext))
+        if runtime_name == "c":
+            lib.export_library(lib_file_name)
+        else:
+            # NOTE: at present, export_libarary will always create _another_ shared object, and you
+            # can't stably combine two shared objects together (in this case, init_array is not
+            # populated correctly when you do that). So for now, must continue to use save() with the
+            # C++ library.
+            # TODO(areusch): Obliterate runtime.cc and replace with libtvm_runtime.so.
+            lib.save(lib_file_name)
         with open(
             os.path.join(build_dir, file_format_str.format(name="test_graph", ext="json")), "w"
         ) as f_graph_json:
@@ -92,7 +113,7 @@ def build_test_module(opts):
         with open(
             os.path.join(build_dir, file_format_str.format(name="test_params", ext="bin")), "wb"
         ) as f_params:
-            f_params.write(relay.save_param_dict(lowered_params))
+            f_params.write(runtime.save_param_dict(lowered_params))
         with open(
             os.path.join(build_dir, file_format_str.format(name="test_data", ext="bin")), "wb"
         ) as fp:
diff --git a/apps/bundle_deploy/runtime.cc b/apps/bundle_deploy/runtime.cc
index 3224028b60a1..2f7e3848b4bf 100644
--- a/apps/bundle_deploy/runtime.cc
+++ b/apps/bundle_deploy/runtime.cc
@@ -23,6 +23,7 @@
 #include <tvm/runtime/registry.h>
 
 #include "../../src/runtime/c_runtime_api.cc"
+#include "../../src/runtime/container.cc"
 #include "../../src/runtime/cpu_device_api.cc"
 #include "../../src/runtime/file_utils.cc"
 #include "../../src/runtime/graph/graph_runtime.cc"
diff --git a/apps/cpp_rpc/CMakeLists.txt b/apps/cpp_rpc/CMakeLists.txt
index ad8ae1488498..ccac53fc3ca0 100644
--- a/apps/cpp_rpc/CMakeLists.txt
+++ b/apps/cpp_rpc/CMakeLists.txt
@@ -1,4 +1,6 @@
-set(TVM_RPC_SOURCES 
+cmake_policy(SET CMP0069 NEW) # suppress cmake warning about IPO
+
+set(TVM_RPC_SOURCES
   main.cc
   rpc_env.cc
   rpc_server.cc
@@ -11,7 +13,12 @@ endif()
 # Set output to same directory as the other TVM libs
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})
 add_executable(tvm_rpc ${TVM_RPC_SOURCES})
-set_property(TARGET tvm_rpc PROPERTY INTERPROCEDURAL_OPTIMIZATION_RELEASE TRUE)
+
+include(CheckIPOSupported)
+check_ipo_supported(RESULT result OUTPUT output)
+if(result)
+  set_property(TARGET tvm_rpc PROPERTY INTERPROCEDURAL_OPTIMIZATION_RELEASE TRUE)
+endif()
 
 if(WIN32)
   target_compile_definitions(tvm_rpc PUBLIC -DNOMINMAX)
@@ -35,5 +42,5 @@ target_include_directories(
   PUBLIC DLPACK_PATH
   PUBLIC DMLC_PATH
 )
- 
-target_link_libraries(tvm_rpc tvm_runtime)
\ No newline at end of file
+
+target_link_libraries(tvm_rpc tvm_runtime)
diff --git a/apps/cpp_rpc/rpc_env.cc b/apps/cpp_rpc/rpc_env.cc
index 5b351725b1f1..ea19cfa3979d 100644
--- a/apps/cpp_rpc/rpc_env.cc
+++ b/apps/cpp_rpc/rpc_env.cc
@@ -86,7 +86,13 @@ void CleanDir(const std::string& dirname);
 std::string BuildSharedLibrary(std::string file_in);
 
 RPCEnv::RPCEnv() {
-#ifndef _WIN32
+#if defined(ANDROID) || defined(__ANDROID__)
+  char cwd[PATH_MAX];
+  auto cmdline = fopen("/proc/self/cmdline", "r");
+  fread(cwd, 1, sizeof(cwd), cmdline);
+  fclose(cmdline);
+  base_ = "/data/data/" + std::string(cwd) + "/cache/rpc";
+#elif !defined(_WIN32)
   char cwd[PATH_MAX];
   if (getcwd(cwd, sizeof(cwd))) {
     base_ = std::string(cwd) + "/rpc";
diff --git a/apps/cpp_rpc/rpc_server.cc b/apps/cpp_rpc/rpc_server.cc
index 83b9a18c5f21..a4028ff61eca 100644
--- a/apps/cpp_rpc/rpc_server.cc
+++ b/apps/cpp_rpc/rpc_server.cc
@@ -168,14 +168,14 @@ class RPCServer {
         if (timer_pid == 0) {
           // Timer process
           sleep(timeout);
-          exit(0);
+          _exit(0);
         }
 
         const pid_t worker_pid = fork();
         if (worker_pid == 0) {
           // Worker process
           ServerLoopProc(conn, addr);
-          exit(0);
+          _exit(0);
         }
 
         int status = 0;
diff --git a/apps/dso_plugin_module/Makefile b/apps/dso_plugin_module/Makefile
index c2ce3306870a..438d9db223a8 100644
--- a/apps/dso_plugin_module/Makefile
+++ b/apps/dso_plugin_module/Makefile
@@ -19,7 +19,8 @@ TVM_ROOT=$(shell cd ../..; pwd)
 PKG_CFLAGS = -std=c++14 -O2 -fPIC\
 	-I${TVM_ROOT}/include\
 	-I${TVM_ROOT}/3rdparty/dmlc-core/include\
-	-I${TVM_ROOT}/3rdparty/dlpack/include
+	-I${TVM_ROOT}/3rdparty/dlpack/include\
+	-DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>
 
 PKG_LDFLAGS =-L${TVM_ROOT}/build
 UNAME_S := $(shell uname -s)
diff --git a/apps/extension/Makefile b/apps/extension/Makefile
index 91d914aba63b..6eba941f7c98 100644
--- a/apps/extension/Makefile
+++ b/apps/extension/Makefile
@@ -20,7 +20,8 @@ TVM_ROOT=$(shell cd ../..; pwd)
 PKG_CFLAGS = -std=c++14 -O2 -fPIC\
 	-I${TVM_ROOT}/include\
 	-I${TVM_ROOT}/3rdparty/dmlc-core/include\
-	-I${TVM_ROOT}/3rdparty/dlpack/include
+	-I${TVM_ROOT}/3rdparty/dlpack/include\
+	-DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>
 
 
 PKG_LDFLAGS =-L${TVM_ROOT}/build
diff --git a/apps/ios_rpc/tvmrpc.xcodeproj/project.pbxproj b/apps/ios_rpc/tvmrpc.xcodeproj/project.pbxproj
index b33c892cf002..28079e710a38 100644
--- a/apps/ios_rpc/tvmrpc.xcodeproj/project.pbxproj
+++ b/apps/ios_rpc/tvmrpc.xcodeproj/project.pbxproj
@@ -349,6 +349,8 @@
 				GCC_PREPROCESSOR_DEFINITIONS = (
 					"DEBUG=1",
 					"$(inherited)",
+					"DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>",
+					"TVM_BACKTRACE_DISABLED=1",
 				);
 				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
 				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
@@ -393,6 +395,10 @@
 				ENABLE_STRICT_OBJC_MSGSEND = YES;
 				GCC_C_LANGUAGE_STANDARD = gnu99;
 				GCC_NO_COMMON_BLOCKS = YES;
+				GCC_PREPROCESSOR_DEFINITIONS = (
+					"DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>",
+					"TVM_BACKTRACE_DISABLED=1",
+				);
 				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
 				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
 				GCC_WARN_UNDECLARED_SELECTOR = YES;
diff --git a/apps/ios_rpc/tvmrpc/TVMRuntime.h b/apps/ios_rpc/tvmrpc/TVMRuntime.h
index f6a6dc64c53a..0d172fc3eaa1 100644
--- a/apps/ios_rpc/tvmrpc/TVMRuntime.h
+++ b/apps/ios_rpc/tvmrpc/TVMRuntime.h
@@ -22,7 +22,7 @@
  */
 #import <Foundation/Foundation.h>
 // Customize logging mechanism, redirect to NSLOG
-#define DMLC_LOG_CUSTOMIZE 1
+#define TVM_LOG_CUSTOMIZE 1
 #define TVM_METAL_RUNTIME 1
 
 #include <tvm/runtime/packed_func.h>
diff --git a/apps/ios_rpc/tvmrpc/TVMRuntime.mm b/apps/ios_rpc/tvmrpc/TVMRuntime.mm
index fbe4850e1b57..87cb6f9b4c69 100644
--- a/apps/ios_rpc/tvmrpc/TVMRuntime.mm
+++ b/apps/ios_rpc/tvmrpc/TVMRuntime.mm
@@ -53,9 +53,19 @@
 // CoreML
 #include "../../../src/runtime/contrib/coreml/coreml_runtime.mm"
 
-namespace dmlc {
+namespace tvm {
+namespace runtime {
+namespace detail {
 // Override logging mechanism
-void CustomLogMessage::Log(const std::string& msg) { NSLog(@"%s", msg.c_str()); }
+void LogFatalImpl(const std::string& file, int lineno, const std::string& message) {
+  throw tvm::runtime::InternalError(file, lineno, message);
+}
+
+void LogMessageImpl(const std::string& file, int lineno, const std::string& message) {
+  NSLog(@"%s:%d: %s", file.c_str(), lineno, message.c_str());
+}
+}
+}
 }  // namespace dmlc
 
 namespace tvm {
@@ -69,7 +79,7 @@ size_t Send(const void* data, size_t size) final {
     ssize_t nbytes = [stream_ write:reinterpret_cast<const uint8_t*>(data) maxLength:size];
     if (nbytes < 0) {
       NSLog(@"%@", [stream_ streamError].localizedDescription);
-      throw dmlc::Error("Stream error");
+      throw tvm::Error("Stream error");
     }
     return nbytes;
   }
diff --git a/apps/ios_rpc/tvmrpc/ViewController.mm b/apps/ios_rpc/tvmrpc/ViewController.mm
index 910c650aedc1..879ed2334a84 100644
--- a/apps/ios_rpc/tvmrpc/ViewController.mm
+++ b/apps/ios_rpc/tvmrpc/ViewController.mm
@@ -100,7 +100,7 @@ - (void)onReadAvailable {
         if (flag == 2) {
           [self onShutdownReceived];
         }
-      } catch (const dmlc::Error& e) {
+      } catch (const tvm::Error& e) {
         [self close];
       }
     }
@@ -123,7 +123,7 @@ - (void)onWriteAvailable {
       if (flag == 2) {
         [self onShutdownReceived];
       }
-    } catch (const dmlc::Error& e) {
+    } catch (const tvm::Error& e) {
       [self close];
     }
   }
diff --git a/apps/microtvm/reference-vm/base-box-tool.py b/apps/microtvm/reference-vm/base-box-tool.py
index c317a373bd8b..0e82dc2e9c0e 100755
--- a/apps/microtvm/reference-vm/base-box-tool.py
+++ b/apps/microtvm/reference-vm/base-box-tool.py
@@ -18,6 +18,7 @@
 
 
 import argparse
+import copy
 import json
 import logging
 import os
@@ -38,6 +39,7 @@
 ALL_PROVIDERS = (
     "parallels",
     "virtualbox",
+    "vmware_desktop",
 )
 
 
@@ -141,9 +143,27 @@ def attach_parallels(uuid, vid_hex=None, pid_hex=None, serial=None):
     )
 
 
+def attach_vmware(uuid, vid_hex=None, pid_hex=None, serial=None):
+    print("NOTE: vmware doesn't seem to support automatic attaching of devices :(")
+    print("The VMWare VM UUID is {uuid}")
+    print("Please attach the following usb device using the VMWare GUI:")
+    if vid_hex is not None:
+        print(f" - VID: {vid_hex}")
+    if pid_hex is not None:
+        print(f" - PID: {pid_hex}")
+    if serial is not None:
+        print(f" - Serial: {serial}")
+    if vid_hex is None and pid_hex is None and serial is None:
+        print(" - (no specifications given for USB device)")
+    print()
+    print("Press [Enter] when the USB device is attached")
+    input()
+
+
 ATTACH_USB_DEVICE = {
     "parallels": attach_parallels,
     "virtualbox": attach_virtualbox,
+    "vmware_desktop": attach_vmware,
 }
 
 
@@ -153,6 +173,7 @@ def generate_packer_config(file_path, providers):
         builders.append(
             {
                 "type": "vagrant",
+                "box_name": f"microtvm-base-{provider_name}",
                 "output_dir": f"output-packer-{provider_name}",
                 "communicator": "ssh",
                 "source_path": "generic/ubuntu1804",
@@ -175,10 +196,19 @@ def generate_packer_config(file_path, providers):
 def build_command(args):
     generate_packer_config(
         os.path.join(THIS_DIR, args.platform, "base-box", "packer.json"),
-        args.provider.split(",") or ALL_PROVIDERS,
+        args.provider or ALL_PROVIDERS,
     )
+    env = None
+    packer_args = ["packer", "build"]
+    if args.debug_packer:
+        env = copy.copy(os.environ)
+        env["PACKER_LOG"] = "1"
+        env["PACKER_LOG_PATH"] = "packer.log"
+        packer_args += ["-debug"]
+
+    packer_args += ["packer.json"]
     subprocess.check_call(
-        ["packer", "build", "packer.json"], cwd=os.path.join(THIS_DIR, args.platform, "base-box")
+        packer_args, cwd=os.path.join(THIS_DIR, args.platform, "base-box"), env=env
     )
 
 
@@ -318,16 +348,17 @@ def test_command(args):
 
 
 def release_command(args):
-    subprocess.check_call(
-        [
-            "vagrant",
-            "cloud",
-            "version",
-            "create",
-            f"tlcpack/microtvm-{args.platform}",
-            args.release_version,
-        ]
-    )
+    if not args.skip_creating_release_version:
+        subprocess.check_call(
+            [
+                "vagrant",
+                "cloud",
+                "version",
+                "create",
+                f"tlcpack/microtvm-{args.platform}",
+                args.release_version,
+            ]
+        )
     if not args.release_version:
         sys.exit(f"--release-version must be specified")
 
@@ -399,6 +430,19 @@ def parse_args():
         "--release-version",
         help="Version to release, in the form 'x.y.z'. Must be specified with release.",
     )
+    parser.add_argument(
+        "--skip-creating-release-version",
+        action="store_true",
+        help="With release, skip creating the version and just upload for this provider.",
+    )
+    parser.add_argument(
+        "--debug-packer",
+        action="store_true",
+        help=(
+            "When the build command is given, run packer in debug mode, and write log to the "
+            "base-box directory"
+        ),
+    )
 
     return parser.parse_args()
 
diff --git a/apps/microtvm/reference-vm/zephyr/Vagrantfile b/apps/microtvm/reference-vm/zephyr/Vagrantfile
index 5a73d1f5e79b..b7f9e4d2363d 100644
--- a/apps/microtvm/reference-vm/zephyr/Vagrantfile
+++ b/apps/microtvm/reference-vm/zephyr/Vagrantfile
@@ -57,4 +57,14 @@ Vagrant.configure("2") do |config|
     end
   end
 
+  config.vm.provider "vmware_desktop" do |vm, overrides|
+    vm.vmx["usb_xhci.present"] = "TRUE"
+    vm.vmx["usb.present"] = "TRUE"
+    vm.vmx["ehci.present"] = "TRUE"
+    dirs_to_mount.each do |d|
+      overrides.vm.synced_folder d.to_s, d.to_s
+    end
+    vm.gui = true
+  end
+
 end
diff --git a/apps/microtvm/reference-vm/zephyr/base-box/Vagrantfile.packer-template b/apps/microtvm/reference-vm/zephyr/base-box/Vagrantfile.packer-template
index b1fff9c63806..38f9a20b56cf 100644
--- a/apps/microtvm/reference-vm/zephyr/base-box/Vagrantfile.packer-template
+++ b/apps/microtvm/reference-vm/zephyr/base-box/Vagrantfile.packer-template
@@ -36,5 +36,12 @@ Vagrant.configure("2") do |config|
     config.vm.synced_folder ".", "/vagrant", disabled: true
   {{- end}}
 
+
+  {{ if eq .BoxName "microtvm-base-vmware_desktop" -}}
+  config.vm.provision "shell", inline: "touch ~/skip_zeroing_disk", privileged: false
+  {{- end}}
+
+  # NOTE: setup.sh resides in the parent directory (../) because this template is expanded into a
+  # sub-directory of base-box (output-packer-*).
   config.vm.provision "shell", path: "../setup.sh", privileged: false
 end
diff --git a/apps/microtvm/reference-vm/zephyr/base-box/setup.sh b/apps/microtvm/reference-vm/zephyr/base-box/setup.sh
index fd758064f4ca..52af947c3e89 100644
--- a/apps/microtvm/reference-vm/zephyr/base-box/setup.sh
+++ b/apps/microtvm/reference-vm/zephyr/base-box/setup.sh
@@ -18,6 +18,13 @@
 
 set -e
 
+skip_zeroing_disk=0
+if [ -e "$HOME/skip_zeroing_disk" ]; then
+    echo "NOTE: will not zero disk at the end due to VMWare Fusion bug"
+    echo "See: https://communities.vmware.com/t5/VMware-Fusion-Discussions/VMWare-Fusion-Pro-11-15-6-16696540-causes-macOS-crash-during/m-p/2284011#M139190"
+    skip_zeroing_disk=1
+fi
+
 sudo apt update
 sudo apt install -y build-essential
 sudo apt-get --purge remove modemmanager  # required to access serial ports.
@@ -96,10 +103,15 @@ sed -i "/^# If not running interactively,/ i\\ " ~/.bashrc
 
 # Clean box for packaging as a base box
 sudo apt-get clean
-EMPTY_FILE="$HOME/EMPTY"
-dd if=/dev/zero "of=${EMPTY_FILE}" bs=1M || /bin/true
-if [ ! -e "${EMPTY_FILE}" ]; then
-    echo "failed to zero empty sectors on disk"
-    exit 2
+if [ $skip_zeroing_disk -eq 0 ]; then
+    echo "Zeroing disk..."
+    EMPTY_FILE="$HOME/EMPTY"
+    dd if=/dev/zero "of=${EMPTY_FILE}" bs=1M || /bin/true
+    if [ ! -e "${EMPTY_FILE}" ]; then
+        echo "failed to zero empty sectors on disk"
+        exit 2
+    fi
+    rm -f "${EMPTY_FILE}"
+else
+    echo "NOTE: skipping zeroing disk due to command-line argument."
 fi
-rm -f "${EMPTY_FILE}"
diff --git a/apps/microtvm/reference-vm/zephyr/pyproject.toml b/apps/microtvm/reference-vm/zephyr/pyproject.toml
index ed8182584e36..b4cfc544df58 100644
--- a/apps/microtvm/reference-vm/zephyr/pyproject.toml
+++ b/apps/microtvm/reference-vm/zephyr/pyproject.toml
@@ -64,6 +64,9 @@ scipy = "^1.4"
 python = "^3.6"
 tornado = "^6"
 typed_ast = "^1.4"
+pyyaml = "^5.4.1"
+pyserial = "^3.5"
+
 
 # AutoTVM
 xgboost = {version = "^1.1", optional = true}
@@ -117,13 +120,13 @@ importer-keras = ["tensorflow", "tensorflow-estimator"]
 importer-onnx = ["onnx", "onnxruntime", "torch", "torchvision", "future"]
 importer-pytorch = ["torch", "torchvision", "future"]
 importer-tensorflow = ["tensorflow", "tensorflow-estimator"]
-importer-tflite = ["tlfite", "tensorflow", "tensorflow-estimator"]
+importer-tflite = ["tflite", "tensorflow", "tensorflow-estimator"]
 
 [tool.poetry.dev-dependencies]
 autodocsumm = "^0.1"
 black = "^19.10b0"
 sphinx = "^3.0"
-sphinx-gallery = "^0.4"
+sphinx-gallery = "^0.8"
 sphinx-rtd-theme = "^0.4"
 matplotlib = "^3.2"
 Image = "^1.5"
diff --git a/apps/sgx/src/build_model.py b/apps/sgx/src/build_model.py
index 868d3bcb9fc4..1fc297d8a094 100755
--- a/apps/sgx/src/build_model.py
+++ b/apps/sgx/src/build_model.py
@@ -23,7 +23,7 @@
 from os import path as osp
 import sys
 
-from tvm import relay
+from tvm import relay, runtime
 from tvm.relay import testing
 import tvm
 from tvm import te
@@ -49,7 +49,7 @@ def main():
     with open(osp.join(build_dir, "graph.json"), "w") as f_graph_json:
         f_graph_json.write(graph)
         with open(osp.join(build_dir, "params.bin"), "wb") as f_params:
-            f_params.write(relay.save_param_dict(params))
+            f_params.write(runtime.save_param_dict(params))
 
 
 if __name__ == "__main__":
diff --git a/apps/topi_recipe/gemm/cuda_gemm_square.py b/apps/topi_recipe/gemm/cuda_gemm_square.py
index 25d14f9abdf3..0d548dc0b554 100644
--- a/apps/topi_recipe/gemm/cuda_gemm_square.py
+++ b/apps/topi_recipe/gemm/cuda_gemm_square.py
@@ -21,6 +21,7 @@
 from tvm.contrib import nvcc
 from tvm.contrib import spirv
 import numpy as np
+import tvm.testing
 
 TASK = "gemm"
 USE_MANUAL_CODE = False
diff --git a/apps/wasm-standalone/wasm-graph/tools/build_graph_lib.py b/apps/wasm-standalone/wasm-graph/tools/build_graph_lib.py
index 42695d28fadb..3d8a349b8744 100644
--- a/apps/wasm-standalone/wasm-graph/tools/build_graph_lib.py
+++ b/apps/wasm-standalone/wasm-graph/tools/build_graph_lib.py
@@ -24,7 +24,7 @@
 
 import onnx
 import tvm
-from tvm import relay
+from tvm import relay, runtime
 
 
 def _get_mod_and_params(model_file):
@@ -60,7 +60,7 @@ def build_graph_lib(model_file, opt_level):
         f_graph.write(graph_json)
 
     with open(os.path.join(out_dir, "graph.params"), "wb") as f_params:
-        f_params.write(relay.save_param_dict(params))
+        f_params.write(runtime.save_param_dict(params))
 
 
 if __name__ == "__main__":
diff --git a/cmake/config.cmake b/cmake/config.cmake
index cd0f4b8e75e9..8c090dce741e 100644
--- a/cmake/config.cmake
+++ b/cmake/config.cmake
@@ -99,11 +99,11 @@ set(USE_STACKVM_RUNTIME OFF)
 # Whether enable tiny embedded graph runtime.
 set(USE_GRAPH_RUNTIME ON)
 
-# Whether enable additional graph debug functions
-set(USE_GRAPH_RUNTIME_DEBUG OFF)
+# Whether enable tiny graph runtime with CUDA Graph
+set(USE_GRAPH_RUNTIME_CUDA_GRAPH OFF)
 
-# Whether enable additional vm profiler functions
-set(USE_VM_PROFILER OFF)
+# Whether to enable the profiler for the graph runtime and vm
+set(USE_PROFILER ON)
 
 # Whether enable uTVM standalone runtime
 set(USE_MICRO_STANDALONE_RUNTIME OFF)
@@ -116,7 +116,7 @@ set(USE_MICRO_STANDALONE_RUNTIME OFF)
 # - OFF: disable llvm, note this will disable CPU codegen
 #        which is needed for most cases
 # - /path/to/llvm-config: enable specific LLVM when multiple llvm-dev is available.
-set(USE_LLVM ON)
+set(USE_LLVM OFF)
 
 #---------------------------------------------
 # Contrib libraries
@@ -174,7 +174,10 @@ set(USE_FLATBUFFERS_PATH none)
 # - /path/to/edgetpu: use specific path to edgetpu library
 set(USE_EDGETPU OFF)
 
-# Whether use CuDNN
+# Possible values:
+# - ON: enable cuDNN with cmake's auto search in CUDA directory
+# - OFF: disable cuDNN
+# - /path/to/cudnn: use specific path to cuDNN path
 set(USE_CUDNN OFF)
 
 # Whether use cuBLAS
@@ -232,8 +235,8 @@ set(USE_TENSORRT_RUNTIME OFF)
 # Whether use VITIS-AI codegen
 set(USE_VITIS_AI OFF)
 
-# Build Verilator codegen and runtime, example located in 3rdparty/vta-hw/apps/verilator
-set(USE_VERILATOR_HW OFF)
+# Build Verilator codegen and runtime
+set(USE_VERILATOR OFF)
 
 # Build ANTLR parser for Relay text format
 # Possible values:
@@ -269,3 +272,11 @@ set(USE_HEXAGON_SDK /path/to/sdk)
 
 # Whether to use ONNX codegen
 set(USE_TARGET_ONNX OFF)
+
+# Whether enable BNNS runtime
+set(USE_BNNS OFF)
+
+# Whether to use libbacktrace
+# Libbacktrace provides line and column information on stack traces from errors. It is only
+# supported on linux and macOS.
+# set(USE_LIBBACKTRACE OFF)
diff --git a/cmake/modules/CUDA.cmake b/cmake/modules/CUDA.cmake
index 1e104218a456..262a4e6e7123 100644
--- a/cmake/modules/CUDA.cmake
+++ b/cmake/modules/CUDA.cmake
@@ -16,12 +16,12 @@
 # under the License.
 
 # CUDA Module
-find_cuda(${USE_CUDA})
+find_cuda(${USE_CUDA} ${USE_CUDNN})
 
 if(CUDA_FOUND)
   # always set the includedir when cuda is available
   # avoid global retrigger of cmake
-	include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+  include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
 endif(CUDA_FOUND)
 
 if(USE_CUDA)
@@ -40,6 +40,7 @@ if(USE_CUDA)
 
   if(USE_CUDNN)
     message(STATUS "Build with cuDNN support")
+    include_directories(SYSTEM ${CUDA_CUDNN_INCLUDE_DIRS})
     file(GLOB CONTRIB_CUDNN_SRCS src/runtime/contrib/cudnn/*.cc)
     list(APPEND RUNTIME_SRCS ${CONTRIB_CUDNN_SRCS})
     list(APPEND TVM_RUNTIME_LINKER_LIBS ${CUDA_CUDNN_LIBRARY})
@@ -64,6 +65,17 @@ if(USE_CUDA)
     list(APPEND RUNTIME_SRCS ${CONTRIB_THRUST_SRC})
   endif(USE_THRUST)
 
+  if(USE_GRAPH_RUNTIME_CUDA_GRAPH)
+    if(NOT USE_GRAPH_RUNTIME)
+      message(FATAL_ERROR "CUDA Graph is only supported by graph runtime, please set USE_GRAPH_RUNTIME=ON")
+    endif()
+    if(CUDAToolkit_VERSION_MAJOR LESS "10")
+      message(FATAL_ERROR "CUDA Graph requires CUDA 10 or above, got=" ${CUDAToolkit_VERSION})
+    endif()
+    message(STATUS "Build with Graph runtime with CUDA Graph support...")
+    file(GLOB RUNTIME_CUDA_GRAPH_SRCS src/runtime/graph/cuda_graph/*.cc)
+    list(APPEND RUNTIME_SRCS ${RUNTIME_CUDA_GRAPH_SRCS})
+  endif()
 else(USE_CUDA)
   list(APPEND COMPILER_SRCS src/target/opt/build_cuda_off.cc)
 endif(USE_CUDA)
diff --git a/cmake/modules/ClangFlags.cmake b/cmake/modules/ClangFlags.cmake
index 53d0e3631caf..841570dc2e12 100644
--- a/cmake/modules/ClangFlags.cmake
+++ b/cmake/modules/ClangFlags.cmake
@@ -28,9 +28,9 @@ if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
       (CLANG_VERSION VERSION_GREATER ${CLANG_MINIMUM_VERSION}))
     message(STATUS "Setting enhanced clang warning flags")
 
-    # These warnings are only enabled when clang's -Weverything flag is enabled
-    # but there is no harm in turning them off for all cases.
-    add_compile_options(
+    set(warning_opts
+      # These warnings are only enabled when clang's -Weverything flag is enabled
+      # but there is no harm in turning them off for all cases.
       -Wno-c++98-compat
       -Wno-c++98-compat-extra-semi
       -Wno-c++98-compat-pedantic
@@ -61,17 +61,13 @@ if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
       -Wno-implicit-fallthrough
       -Wno-unreachable-code-return
       -Wno-non-virtual-dtor
-    )
-
-    # Here we have non-standard warnings that clang has available and are useful
-    # so enable them if we are using clang.
-    add_compile_options(
+      # Here we have non-standard warnings that clang has available and are useful
+      # so enable them if we are using clang.
       -Wreserved-id-macro
       -Wused-but-marked-unused
       -Wdocumentation-unknown-command
       -Wcast-qual
       -Wzero-as-null-pointer-constant
-
       # These warnings should be enabled one at a time and fixed.
       # To enable one of these warnings remove the `no-` after -W so
       # -Wno-documentation -> -Wdocumentation
@@ -85,7 +81,10 @@ if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
       -Wno-old-style-cast
       -Wno-gnu-anonymous-struct
       -Wno-nested-anon-types
-      )
+    )
+  target_compile_options(tvm_objs PRIVATE $<$<COMPILE_LANGUAGE:CXX>: ${warning_opts}>)
+  target_compile_options(tvm_runtime_objs PRIVATE $<$<COMPILE_LANGUAGE:CXX>: ${warning_opts}>)
+
 
   endif ()
 endif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
diff --git a/cmake/modules/LibInfo.cmake b/cmake/modules/LibInfo.cmake
index deaa6d9d8362..131dceeb345d 100644
--- a/cmake/modules/LibInfo.cmake
+++ b/cmake/modules/LibInfo.cmake
@@ -75,6 +75,7 @@ function(add_lib_info src_file)
     TVM_INFO_USE_ARM_COMPUTE_LIB="${USE_ARM_COMPUTE_LIB}"
     TVM_INFO_USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME="${USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME}"
     TVM_INFO_INDEX_DEFAULT_I64="${INDEX_DEFAULT_I64}"
+    TVM_CXX_COMPILER_PATH="${CMAKE_CXX_COMPILER}"
   )
 
 endfunction()
diff --git a/cmake/modules/Libbacktrace.cmake b/cmake/modules/Libbacktrace.cmake
new file mode 100644
index 000000000000..742855358809
--- /dev/null
+++ b/cmake/modules/Libbacktrace.cmake
@@ -0,0 +1,45 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+include(ExternalProject)
+
+ExternalProject_Add(project_libbacktrace
+  PREFIX libbacktrace
+  SOURCE_DIR ${CMAKE_CURRENT_LIST_DIR}/../../3rdparty/libbacktrace
+  BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR}/libbacktrace
+  CONFIGURE_COMMAND "${CMAKE_CURRENT_LIST_DIR}/../../3rdparty/libbacktrace/configure"
+                    "--prefix=${CMAKE_CURRENT_BINARY_DIR}/libbacktrace" --with-pic
+  INSTALL_DIR "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace"
+  BUILD_COMMAND make
+  INSTALL_COMMAND make install
+  BUILD_BYPRODUCTS "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/lib/libbacktrace.a"
+                   "${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/include/backtrace.h"
+  )
+
+# Custom step to rebuild libbacktrace if any of the source files change
+file(GLOB LIBBACKTRACE_SRCS "${CMAKE_CURRENT_LIST_DIR}/../../3rdparty/libbacktrace/*.c")
+ExternalProject_Add_Step(project_libbacktrace checkout
+  DEPENDERS configure
+  DEPENDEES download
+  DEPENDS ${LIBBACKTRACE_SRCS}
+)
+
+add_library(libbacktrace STATIC IMPORTED)
+add_dependencies(libbacktrace project_libbacktrace)
+set_property(TARGET libbacktrace
+  PROPERTY IMPORTED_LOCATION ${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/lib/libbacktrace.a)
+# create include directory so cmake doesn't complain
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/libbacktrace/include)
diff --git a/cmake/modules/ROCM.cmake b/cmake/modules/ROCM.cmake
index ec348f8b57f6..b908df2f869b 100644
--- a/cmake/modules/ROCM.cmake
+++ b/cmake/modules/ROCM.cmake
@@ -48,6 +48,23 @@ if(USE_ROCM)
     list(APPEND RUNTIME_SRCS ${ROCBLAS_CONTRIB_SRCS})
     list(APPEND TVM_RUNTIME_LINKER_LIBS ${ROCM_ROCBLAS_LIBRARY})
   endif(USE_ROCBLAS)
+
+  if(USE_THRUST)
+    message(STATUS "Build with rocThrust support")
+    # We need to override CXX to hipcc. This is required by rocthrust
+    if (${CMAKE_CXX_COMPILER} MATCHES "hipcc$")
+      message(STATUS "Using hipcc compiler to compile rocthrust code.")
+    else()
+      message(FATAL_ERROR "Set CXX=hipcc to compile rocthrust code.")
+    endif()
+
+    find_package(rocprim REQUIRED)
+    find_package(rocthrust REQUIRED)
+    set_source_files_properties(src/runtime/contrib/thrust/thrust.cu PROPERTIES LANGUAGE CXX)
+    list(APPEND RUNTIME_SRCS src/runtime/contrib/thrust/thrust.cu)
+    list(APPEND TVM_RUNTIME_LINKER_LIBS roc::rocthrust)
+  endif(USE_THRUST)
+
 else(USE_ROCM)
   list(APPEND COMPILER_SRCS src/target/opt/build_rocm_off.cc)
 endif(USE_ROCM)
diff --git a/cmake/modules/StandaloneCrt.cmake b/cmake/modules/StandaloneCrt.cmake
index 411d0383faf4..dc1b3b2665f2 100644
--- a/cmake/modules/StandaloneCrt.cmake
+++ b/cmake/modules/StandaloneCrt.cmake
@@ -45,12 +45,14 @@ if(USE_MICRO)
          "src/runtime/crt/common *.c -> src/runtime/crt/common"
          "src/runtime/crt/graph_runtime *.c -> src/runtime/crt/graph_runtime"
          "src/runtime/crt/graph_runtime_module *.c -> src/runtime/crt/graph_runtime_module"
-         "src/runtime/crt/host crt_config.h -> src/runtime/crt/host"
+         "src/runtime/crt/host crt_config.h -> template/host"
+         "src/runtime/crt/host *.cc -> template/host"
          "src/runtime/crt/memory *.c -> src/runtime/crt/memory"
          "src/runtime/crt/utvm_rpc_common *.cc -> src/runtime/crt/utvm_rpc_common"
          "src/runtime/crt/utvm_rpc_server *.cc -> src/runtime/crt/utvm_rpc_server"
          "src/runtime/minrpc *.h -> src/runtime/minrpc"
          "src/support generic_arena.h -> src/support"
+         "src/runtime/crt crt_config-template.h -> template"
          )
 
     set(standalone_crt_base "${CMAKE_CURRENT_BINARY_DIR}/standalone_crt")
@@ -101,9 +103,7 @@ if(USE_MICRO)
     endforeach()
 
     set(make_common_args
-        "DLPACK_INCLUDE_DIR=${CMAKE_SOURCE_DIR}/3rdparty/dlpack/include"
-        "TVM_INCLUDE_DIR=${CMAKE_CURRENT_BINARY_DIR}/standalone_crt/include"
-        "CRT_CONFIG=src/runtime/crt/host/crt_config.h"
+        "CRT_CONFIG=template/host/crt_config.h"
         "BUILD_DIR=${host_build_dir_abspath}"
         "EXTRA_CFLAGS=-fPIC"
         "EXTRA_CXXFLAGS=-fPIC"
diff --git a/cmake/modules/VTA.cmake b/cmake/modules/VTA.cmake
index 115216680fff..58b58d231d83 100644
--- a/cmake/modules/VTA.cmake
+++ b/cmake/modules/VTA.cmake
@@ -60,6 +60,7 @@ elseif(PYTHON)
     # Target lib: vta_fsim
     add_library(vta_fsim SHARED ${FSIM_RUNTIME_SRCS})
     target_include_directories(vta_fsim SYSTEM PUBLIC ${VTA_HW_PATH}/include)
+    target_compile_definitions(vta_fsim PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
     foreach(__def ${VTA_DEFINITIONS})
       string(SUBSTRING ${__def} 3 -1 __strip_def)
       target_compile_definitions(vta_fsim PUBLIC ${__strip_def})
@@ -81,6 +82,7 @@ elseif(PYTHON)
     # Target lib: vta_tsim
     add_library(vta_tsim SHARED ${TSIM_RUNTIME_SRCS})
     target_include_directories(vta_tsim SYSTEM PUBLIC ${VTA_HW_PATH}/include)
+    target_compile_definitions(vta_tsim PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
     foreach(__def ${VTA_DEFINITIONS})
       string(SUBSTRING ${__def} 3 -1 __strip_def)
       target_compile_definitions(vta_tsim PUBLIC ${__strip_def})
@@ -107,6 +109,7 @@ elseif(PYTHON)
     add_library(vta SHARED ${FPGA_RUNTIME_SRCS})
     target_include_directories(vta PUBLIC vta/runtime)
     target_include_directories(vta PUBLIC ${VTA_HW_PATH}/include)
+    target_compile_definitions(vta PUBLIC DMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>)
     foreach(__def ${VTA_DEFINITIONS})
       string(SUBSTRING ${__def} 3 -1 __strip_def)
       target_compile_definitions(vta PUBLIC ${__strip_def})
diff --git a/cmake/modules/Vulkan.cmake b/cmake/modules/Vulkan.cmake
index 4df8986c800c..095790f08547 100644
--- a/cmake/modules/Vulkan.cmake
+++ b/cmake/modules/Vulkan.cmake
@@ -26,16 +26,11 @@ IF USE_VULKAN)
 tvm_option(USE_VULKAN_VALIDATION "Enable Vulkan API validation layers" OFF
   IF USE_VULKAN)
 
-if(Vulkan_FOUND)
-  # always set the includedir
-  # avoid global retrigger of cmake
-  include_directories(SYSTEM ${Vulkan_INCLUDE_DIRS})
-endif(Vulkan_FOUND)
-
 if(USE_VULKAN)
   if(NOT Vulkan_FOUND)
     message(FATAL_ERROR "Cannot find Vulkan, USE_VULKAN=" ${USE_VULKAN})
   endif()
+  include_directories(SYSTEM ${Vulkan_INCLUDE_DIRS})
   message(STATUS "Build with Vulkan support")
   file(GLOB RUNTIME_VULKAN_SRCS src/runtime/vulkan/vulkan.cc)
   file(GLOB COMPILER_VULKAN_SRCS src/target/spirv/*.cc)
diff --git a/cmake/modules/contrib/ArmComputeLib.cmake b/cmake/modules/contrib/ArmComputeLib.cmake
index 0a75f607acf3..ba082505125b 100644
--- a/cmake/modules/contrib/ArmComputeLib.cmake
+++ b/cmake/modules/contrib/ArmComputeLib.cmake
@@ -23,7 +23,9 @@ if(USE_ARM_COMPUTE_LIB)
     file(GLOB ACL_RELAY_CONTRIB_SRC src/relay/backend/contrib/arm_compute_lib/*.cc)
     file(GLOB ACL_RUNTIME_MODULE src/runtime/contrib/arm_compute_lib/acl_runtime.cc)
     list(APPEND COMPILER_SRCS ${ACL_RELAY_CONTRIB_SRC})
-    list(APPEND COMPILER_SRCS ${ACL_RUNTIME_MODULE})
+    if(NOT USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME)
+        list(APPEND COMPILER_SRCS ${ACL_RUNTIME_MODULE})
+    endif()
     message(STATUS "Build with Arm Compute Library support...")
 endif()
 
diff --git a/cmake/modules/contrib/BNNS.cmake b/cmake/modules/contrib/BNNS.cmake
new file mode 100644
index 000000000000..e14aa2857ebc
--- /dev/null
+++ b/cmake/modules/contrib/BNNS.cmake
@@ -0,0 +1,30 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+if(USE_BNNS STREQUAL "ON")
+  add_definitions(-DUSE_JSON_RUNTIME=1)
+  file(GLOB BNNS_RELAY_CONTRIB_SRC src/relay/backend/contrib/bnns/*.cc)
+  list(APPEND COMPILER_SRCS ${BNNS_RELAY_CONTRIB_SRC})
+  list(APPEND COMPILER_SRCS ${JSON_RELAY_CONTRIB_SRC})
+
+  list(APPEND TVM_RUNTIME_LINKER_LIBS "-framework Accelerate")
+
+  file(GLOB BNNS_CONTRIB_SRC src/runtime/contrib/bnns/*.cc)
+  list(APPEND RUNTIME_SRCS ${BNNS_CONTRIB_SRC})
+  message(STATUS "Build with BNNS JSON runtime: " ${EXTERN_LIBRARY_BNNS})
+endif()
+
diff --git a/cmake/modules/contrib/TensorRT.cmake b/cmake/modules/contrib/TensorRT.cmake
index 24a8241a2229..0c7e43c0fcf8 100644
--- a/cmake/modules/contrib/TensorRT.cmake
+++ b/cmake/modules/contrib/TensorRT.cmake
@@ -28,7 +28,9 @@ if(USE_TENSORRT_CODEGEN)
     file(GLOB RUNTIME_TENSORRT_SRCS src/runtime/contrib/tensorrt/tensorrt_runtime.cc)
     set_source_files_properties(${RUNTIME_TENSORRT_SRCS} PROPERTIES COMPILE_FLAGS "-Wno-deprecated-declarations")
     list(APPEND COMPILER_SRCS ${COMPILER_TENSORRT_SRCS})
-    list(APPEND COMPILER_SRCS ${RUNTIME_TENSORRT_SRCS})
+    if(NOT USE_TENSORRT_RUNTIME)
+        list(APPEND COMPILER_SRCS ${RUNTIME_TENSORRT_SRCS})
+    endif()
 endif()
 
 # TensorRT Runtime
diff --git a/cmake/modules/contrib/Verilator.cmake b/cmake/modules/contrib/Verilator.cmake
index d3c1a7161182..4947d44064a0 100644
--- a/cmake/modules/contrib/Verilator.cmake
+++ b/cmake/modules/contrib/Verilator.cmake
@@ -15,14 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 
-if(USE_VERILATOR_HW STREQUAL "ON")
-  execute_process(COMMAND make --directory ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/vta-hw/apps/verilator)
+if(USE_VERILATOR STREQUAL "ON")
   file(GLOB VERILATOR_RELAY_CONTRIB_SRC src/relay/backend/contrib/verilator/codegen.cc)
-  list(APPEND COMPILER_SRCS ${VERILATOR_RELAY_CONTRIB_SRC})
-  list(APPEND COMPILER_SRCS ${JSON_RELAY_CONTRIB_SRC})
-  find_library(EXTERN_LIBRARY_VERILATOR NAMES verilator PATHS ${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/vta-hw/apps/verilator)
-  list(APPEND TVM_RUNTIME_LINKER_LIBS ${EXTERN_LIBRARY_VERILATOR})
   file(GLOB VERILATOR_CONTRIB_SRC src/runtime/contrib/verilator/verilator_runtime.cc)
+  list(APPEND COMPILER_SRCS ${VERILATOR_RELAY_CONTRIB_SRC})
   list(APPEND RUNTIME_SRCS ${VERILATOR_CONTRIB_SRC})
 endif()
 
diff --git a/cmake/utils/FindCUDA.cmake b/cmake/utils/FindCUDA.cmake
index 564b837515a7..aaddfb054366 100644
--- a/cmake/utils/FindCUDA.cmake
+++ b/cmake/utils/FindCUDA.cmake
@@ -19,10 +19,12 @@
 # Enhanced version of find CUDA.
 #
 # Usage:
-#   find_cuda(${USE_CUDA})
+#   find_cuda(${USE_CUDA} ${USE_CUDNN})
 #
 # - When USE_CUDA=ON, use auto search
 # - When USE_CUDA=/path/to/cuda-path, use the cuda path
+# - When USE_CUDNN=ON, use auto search
+# - When USE_CUDNN=/path/to/cudnn-path, use the cudnn path
 #
 # Provide variables:
 #
@@ -32,10 +34,11 @@
 # - CUDA_CUDA_LIBRARY
 # - CUDA_CUDART_LIBRARY
 # - CUDA_NVRTC_LIBRARY
+# - CUDA_CUDNN_INCLUDE_DIRS
 # - CUDA_CUDNN_LIBRARY
 # - CUDA_CUBLAS_LIBRARY
 #
-macro(find_cuda use_cuda)
+macro(find_cuda use_cuda use_cudnn)
   set(__use_cuda ${use_cuda})
   if(${__use_cuda} MATCHES ${IS_TRUE_PATTERN})
     find_package(CUDA QUIET)
@@ -64,9 +67,6 @@ macro(find_cuda use_cuda)
       find_library(CUDA_NVRTC_LIBRARY nvrtc
         ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
         ${CUDA_TOOLKIT_ROOT_DIR}/lib/Win32)
-      find_library(CUDA_CUDNN_LIBRARY cudnn
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib/Win32)
       find_library(CUDA_CUBLAS_LIBRARY cublas
         ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
         ${CUDA_TOOLKIT_ROOT_DIR}/lib/Win32)
@@ -85,12 +85,6 @@ macro(find_cuda use_cuda)
         PATHS ${CUDA_TOOLKIT_ROOT_DIR}
         PATH_SUFFIXES lib lib64 targets/x86_64-linux/lib targets/x86_64-linux/lib/stubs lib64/stubs lib/x86_64-linux-gnu
         NO_DEFAULT_PATH)
-      find_library(CUDA_CUDNN_LIBRARY cudnn
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib64
-        ${CUDA_TOOLKIT_ROOT_DIR}/lib
-        NO_DEFAULT_PATH)
-      # search default path if cannot find cudnn in non-default
-      find_library(CUDA_CUDNN_LIBRARY cudnn)
       find_library(CUDA_CUBLAS_LIBRARY cublas
         ${CUDA_TOOLKIT_ROOT_DIR}/lib64
         ${CUDA_TOOLKIT_ROOT_DIR}/lib
@@ -102,10 +96,38 @@ macro(find_cuda use_cuda)
         ${CUDA_TOOLKIT_ROOT_DIR}/lib
         NO_DEFAULT_PATH)
     endif(MSVC)
+
+    # find cuDNN
+    set(__use_cudnn ${use_cudnn})
+    if(${__use_cudnn} MATCHES ${IS_TRUE_PATTERN})
+      set(CUDA_CUDNN_INCLUDE_DIRS ${CUDA_INCLUDE_DIRS})
+      if(MSVC)
+        find_library(CUDA_CUDNN_LIBRARY cudnn
+          ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
+          ${CUDA_TOOLKIT_ROOT_DIR}/lib/Win32)
+      else(MSVC)
+        find_library(CUDA_CUDNN_LIBRARY cudnn
+          ${CUDA_TOOLKIT_ROOT_DIR}/lib64
+          ${CUDA_TOOLKIT_ROOT_DIR}/lib
+          NO_DEFAULT_PATH)
+        # search default path if cannot find cudnn in non-default
+        find_library(CUDA_CUDNN_LIBRARY cudnn)
+      endif(MSVC)
+    elseif(IS_DIRECTORY ${__use_cudnn})
+      # cuDNN doesn't necessarily live in the CUDA dir
+      set(CUDA_CUDNN_ROOT_DIR ${__use_cudnn})
+      set(CUDA_CUDNN_INCLUDE_DIRS ${CUDA_CUDNN_ROOT_DIR}/include)
+      find_library(CUDA_CUDNN_LIBRARY cudnn
+        ${CUDA_CUDNN_ROOT_DIR}/lib64
+        ${CUDA_CUDNN_ROOT_DIR}/lib
+        NO_DEFAULT_PATH)
+    endif()
+
     message(STATUS "Found CUDA_TOOLKIT_ROOT_DIR=" ${CUDA_TOOLKIT_ROOT_DIR})
     message(STATUS "Found CUDA_CUDA_LIBRARY=" ${CUDA_CUDA_LIBRARY})
     message(STATUS "Found CUDA_CUDART_LIBRARY=" ${CUDA_CUDART_LIBRARY})
     message(STATUS "Found CUDA_NVRTC_LIBRARY=" ${CUDA_NVRTC_LIBRARY})
+    message(STATUS "Found CUDA_CUDNN_INCLUDE_DIRS=" ${CUDA_CUDNN_INCLUDE_DIRS})
     message(STATUS "Found CUDA_CUDNN_LIBRARY=" ${CUDA_CUDNN_LIBRARY})
     message(STATUS "Found CUDA_CUBLAS_LIBRARY=" ${CUDA_CUBLAS_LIBRARY})
     message(STATUS "Found CUDA_CUBLASLT_LIBRARY=" ${CUDA_CUBLASLT_LIBRARY})
diff --git a/cmake/utils/FindEthosN.cmake b/cmake/utils/FindEthosN.cmake
index d33b55f0c7a9..26d00a462b39 100644
--- a/cmake/utils/FindEthosN.cmake
+++ b/cmake/utils/FindEthosN.cmake
@@ -59,6 +59,7 @@ macro(find_ethosn use_ethosn)
     find_library(ETHOSN_COMPILER_LIBRARY NAMES EthosNSupport)
 
     set(ETHOSN_PACKAGE_VERSION "0.1.1")
+    set(ETHOSN_DEFINITIONS -DETHOSN_API_VERSION=${USE_ETHOSN_API_VERSION})
 
     if(${USE_ETHOSN_HW} MATCHES ${IS_TRUE_PATTERN})
       # Runtime hardware support
@@ -70,7 +71,7 @@ macro(find_ethosn use_ethosn)
       find_library(ETHOSN_RUNTIME_LIBRARY NAMES EthosNDriver
         PATHS ${__ethosn_stack}/lib)
       find_library(ETHOSN_RUNTIME_LIBRARY NAMES EthosNDriver)
-      set(ETHOSN_DEFINITIONS -DETHOSN_HW)
+      set(ETHOSN_DEFINITIONS -DETHOSN_HW -DETHOSN_API_VERSION=${USE_ETHOSN_API_VERSION})
     endif ()
 
     if(ETHOSN_COMPILER_LIBRARY)
diff --git a/cmake/utils/FindLLVM.cmake b/cmake/utils/FindLLVM.cmake
index b8c5bf815bf5..9fc4df24b813 100644
--- a/cmake/utils/FindLLVM.cmake
+++ b/cmake/utils/FindLLVM.cmake
@@ -120,7 +120,7 @@ macro(find_llvm use_llvm)
     string(STRIP ${TVM_LLVM_VERSION} TVM_LLVM_VERSION)
     # definitions
     string(REGEX MATCHALL "(^| )-D[A-Za-z0-9_]*" __llvm_defs ${__llvm_cxxflags})
-    set(LLVM_DEFINTIIONS "")
+    set(LLVM_DEFINITIONS "")
     foreach(__flag IN ITEMS ${__llvm_defs})
       string(STRIP "${__flag}" __llvm_def)
       list(APPEND LLVM_DEFINITIONS "${__llvm_def}")
diff --git a/conda/build-environment.yaml b/conda/build-environment.yaml
index 31b39bfafcd0..7c7831e25b1b 100644
--- a/conda/build-environment.yaml
+++ b/conda/build-environment.yaml
@@ -35,3 +35,4 @@ dependencies:
   - bzip2
   - make
   - scipy
+  - pillow
diff --git a/docker/Dockerfile.ci_arm b/docker/Dockerfile.ci_arm
index 020792700ee9..671ce04e8c1d 100644
--- a/docker/Dockerfile.ci_arm
+++ b/docker/Dockerfile.ci_arm
@@ -16,7 +16,7 @@
 # under the License.
 
 # CI docker arm env
-# tag: v0.10
+# tag: v0.02
 
 FROM ubuntu:18.04
 
diff --git a/docker/Dockerfile.ci_gpu b/docker/Dockerfile.ci_gpu
index ac76af6b0a1e..a44677f5ce56 100644
--- a/docker/Dockerfile.ci_gpu
+++ b/docker/Dockerfile.ci_gpu
@@ -107,8 +107,8 @@ ENV PATH=/usr/local/nvidia/bin:${PATH}
 ENV PATH=/usr/local/cuda/bin:${PATH}
 ENV CPLUS_INCLUDE_PATH=/usr/local/cuda/include:${CPLUS_INCLUDE_PATH}
 ENV C_INCLUDE_PATH=/usr/local/cuda/include:${C_INCLUDE_PATH}
-ENV LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/compact:${LIBRARY_PATH}
-ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/compact:${LD_LIBRARY_PATH}
+ENV LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/compat:${LIBRARY_PATH}
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/compat:${LD_LIBRARY_PATH}
 
 ENV LD_LIBRARY_PATH=/opt/rocm/lib:${LD_LIBRARY_PATH}
 ENV PATH=/node_modules/.bin:${PATH}
diff --git a/docker/Dockerfile.demo_rocm b/docker/Dockerfile.demo_rocm
new file mode 100644
index 000000000000..c336be41934f
--- /dev/null
+++ b/docker/Dockerfile.demo_rocm
@@ -0,0 +1,36 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Demo docker for ROCm
+FROM ubuntu:18.04
+
+COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
+RUN bash /install/ubuntu_install_core.sh
+
+COPY install/ubuntu1804_install_python.sh /install/ubuntu1804_install_python.sh
+RUN bash /install/ubuntu1804_install_python.sh
+
+COPY install/ubuntu_install_python_package.sh /install/ubuntu_install_python_package.sh
+RUN bash /install/ubuntu_install_python_package.sh
+
+COPY install/ubuntu1804_install_llvm.sh /install/ubuntu1804_install_llvm.sh
+RUN bash /install/ubuntu1804_install_llvm.sh
+
+COPY install/ubuntu_install_rocm.sh /install/ubuntu_install_rocm.sh
+RUN bash /install/ubuntu_install_rocm.sh
+
+ENV PATH "${PATH}:/opt/rocm/bin"
diff --git a/docker/Dockerfile.demo_vitis_ai b/docker/Dockerfile.demo_vitis_ai
index 58326b66bf0c..8cc623e2f38c 100644
--- a/docker/Dockerfile.demo_vitis_ai
+++ b/docker/Dockerfile.demo_vitis_ai
@@ -20,10 +20,13 @@ FROM xilinx/vitis-ai:latest
 
 RUN apt-get update --fix-missing
 
-
 COPY install/ubuntu_install_core.sh /install/ubuntu_install_core.sh
 RUN bash /install/ubuntu_install_core.sh
 
+# Install Vitis-AI ubuntu dependencies
+COPY install/ubuntu_install_vitis_ai_core.sh /install/ubuntu_install_vitis_ai_core.sh
+RUN bash /install/ubuntu_install_vitis_ai_core.sh
+
 COPY install/ubuntu_install_python.sh /install/ubuntu_install_python.sh
 RUN bash /install/ubuntu_install_python.sh
 
@@ -43,10 +46,6 @@ ENV PATH $PATH:$CARGO_HOME/bin:/usr/lib/go-1.10/bin
 COPY install/ubuntu_install_java.sh /install/ubuntu_install_java.sh
 RUN bash /install/ubuntu_install_java.sh
 
-# Install Vitis-AI ubuntu dependencies
-COPY install/ubuntu_install_vitis_ai_core.sh /install/ubuntu_install_vitis_ai_core.sh
-RUN bash /install/ubuntu_install_vitis_ai_core.sh
-
 # Install dependencies inside vitis-ai-tensorflow conda
 RUN . $VAI_ROOT/conda/etc/profile.d/conda.sh && \
     conda activate vitis-ai-tensorflow && \
diff --git a/docker/bash.sh b/docker/bash.sh
index a615d180b9ed..51fb68265b73 100755
--- a/docker/bash.sh
+++ b/docker/bash.sh
@@ -27,6 +27,11 @@
 #     Execute command in the docker image, default non-interactive
 #     With -i, execute interactively.
 #
+
+set -e
+
+source "$(dirname $0)/dev_common.sh" || exit 2
+
 interactive=0
 if [ "$1" == "-i" ]; then
     interactive=1
@@ -38,7 +43,10 @@ if [ "$#" -lt 1 ]; then
     exit -1
 fi
 
-DOCKER_IMAGE_NAME=("$1")
+DOCKER_IMAGE_NAME=$(lookup_image_spec "$1")
+if [ -z "${DOCKER_IMAGE_NAME}" ]; then
+    DOCKER_IMAGE_NAME=("$1")
+fi
 
 CI_DOCKER_EXTRA_PARAMS=( )
 if [ "$#" -eq 1 ]; then
@@ -88,6 +96,9 @@ else
     CI_ADDON_ENV=""
 fi
 
+DOCKER_ENVS=""
+DOCKER_DEVICES=""
+WORKSPACE_VOLUMES=""
 # If the Vitis-AI docker image is selected, expose the Xilinx FPGA devices and required volumes containing e.g. DSA's and overlays
 if [[ "${DOCKER_IMAGE_NAME}" == *"demo_vitis_ai"* && -d "/dev/shm" && -d "/opt/xilinx/dsa" && -d "/opt/xilinx/overlaybins" ]]; then
     WORKSPACE_VOLUMES="-v /dev/shm:/dev/shm -v /opt/xilinx/dsa:/opt/xilinx/dsa -v /opt/xilinx/overlaybins:/opt/xilinx/overlaybins"
@@ -103,12 +114,14 @@ if [[ "${DOCKER_IMAGE_NAME}" == *"demo_vitis_ai"* && -d "/dev/shm" && -d "/opt/x
     do
         DOCKER_DEVICES+="--device=$i "
     done
-
-else
-    DOCKER_DEVICES=""
-    WORKSPACE_VOLUMES=""
 fi
 
+# Add ROCm devices and set ROCM_ENABLED=1 which is used in the with_the_same_user script
+# to add the user to the video group
+if [[ "${DOCKER_IMAGE_NAME}" == *"rocm"* && -d "/dev/dri" ]]; then
+    DOCKER_DEVICES+="--device=/dev/kfd --device=/dev/dri "
+    DOCKER_ENVS+="-e ROCM_ENABLED=1 "
+fi
 
 # Print arguments.
 echo "WORKSPACE: ${WORKSPACE}"
@@ -143,6 +156,7 @@ ${DOCKER_BINARY} run --rm --pid=host\
     -e "CI_BUILD_GID=$(id -g)" \
     -e "CI_PYTEST_ADD_OPTIONS=$CI_PYTEST_ADD_OPTIONS" \
     -e "CI_IMAGE_NAME=${DOCKER_IMAGE_NAME}" \
+    ${DOCKER_ENVS} \
     ${CI_ADDON_ENV} \
     ${CUDA_ENV} \
     "${CI_DOCKER_EXTRA_PARAMS[@]}" \
diff --git a/docker/dev_common.sh b/docker/dev_common.sh
index 559a66469e37..68b9f8d28760 100644
--- a/docker/dev_common.sh
+++ b/docker/dev_common.sh
@@ -28,13 +28,39 @@ INVOCATION_PWD="$(pwd)"
 GIT_TOPLEVEL=$(cd $(dirname ${BASH_SOURCE[0]}) && git rev-parse --show-toplevel)
 
 
+function filter_jenkinsfile() {
+    local echo_on=0;
+    while read line; do
+        if [ "${line}" == "// NOTE: these lines are scanned by docker/dev_common.sh. Please update the regex as needed. -->" ]; then
+            echo_on=1
+        elif [ "${line}" == "// <--- End of regex-scanned config." ]; then
+            break
+        elif [ ${echo_on} -eq 1 ]; then
+            echo "$line"
+        fi
+    done
+}
+
+
+function lookup_image_spec() {
+    img_line=$(cat "${GIT_TOPLEVEL}/Jenkinsfile" | filter_jenkinsfile | grep -E "^${1} = ")
+    if [ -n "${img_line}" ]; then
+        img_spec=$(echo "${img_line}" | sed -E "s/${1} = \"([^\"]*)\"/\1/")
+        has_similar_docker_image=1
+        docker inspect "${1}" &>/dev/null || has_similar_docker_image=0
+        if [ ${has_similar_docker_image} -ne 0 ]; then
+            echo "WARNING: resolved docker image through Jenkinsfile to \"${img_spec}\"" >&2
+        fi
+        echo "${img_spec}"
+    fi
+}
+
+
 function run_docker() {
     image_name="$1"  # Name of the Jenkinsfile var to find
     shift
 
-    image_spec=$(cat "${GIT_TOPLEVEL}/Jenkinsfile" | \
-                     grep -E "^${image_name} = " | \
-                     sed -E "s/${image_name} = \"([^\"]*)\"/\1/")
+    image_spec=$(lookup_image_spec "${image_name}")
     if [ -z "${image_spec}" ]; then
         echo "${image_name}: not found in ${GIT_TOPLEVEL}/Jenkinsfile" >&2
         exit 2
diff --git a/docker/install/ubuntu_install_python.sh b/docker/install/ubuntu_install_python.sh
index 58d72f327aa6..d3af336491cc 100755
--- a/docker/install/ubuntu_install_python.sh
+++ b/docker/install/ubuntu_install_python.sh
@@ -34,7 +34,7 @@ apt-get install -y python-pip python-dev python3.6 python3.6-dev
 rm -f /usr/bin/python3 && ln -s /usr/bin/python3.6 /usr/bin/python3
 
 # Install pip
-cd /tmp && wget -q https://bootstrap.pypa.io/get-pip.py && python2 get-pip.py && python3.6 get-pip.py
+cd /tmp && wget -q https://bootstrap.pypa.io/get-pip.py && python3.6 get-pip.py
 
 # Pin pip version
 pip3 install pip==19.3.1
diff --git a/docker/install/ubuntu_install_qemu.sh b/docker/install/ubuntu_install_qemu.sh
old mode 100644
new mode 100755
diff --git a/docker/install/ubuntu_install_rocm.sh b/docker/install/ubuntu_install_rocm.sh
index 196f4134db6e..0945c582489f 100755
--- a/docker/install/ubuntu_install_rocm.sh
+++ b/docker/install/ubuntu_install_rocm.sh
@@ -23,4 +23,8 @@ set -o pipefail
 # Install ROCm cross compilation toolchain.
 wget -qO - http://repo.radeon.com/rocm/apt/debian/rocm.gpg.key | sudo apt-key add -
 echo deb [arch=amd64] http://repo.radeon.com/rocm/apt/debian/ xenial main > /etc/apt/sources.list.d/rocm.list
-apt-get update && apt-get install -y rocm-dev
+apt-get update && apt-get install -y \
+    rocm-dev \
+    lld && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
diff --git a/docker/install/ubuntu_install_vitis_ai_core.sh b/docker/install/ubuntu_install_vitis_ai_core.sh
index ea05ffd170fe..a2d7c2ebe332 100644
--- a/docker/install/ubuntu_install_vitis_ai_core.sh
+++ b/docker/install/ubuntu_install_vitis_ai_core.sh
@@ -21,9 +21,9 @@ set -u
 set -o pipefail
 
 # install libraries for building Vitis-AI on ubuntu
-apt-get update && apt-get install -y --no-install-recommends \
-    graphviz\
-    gnupg2
-
-apt-get update && apt-get install -y gcc-aarch64-linux-gnu
-
+apt-get update && apt-get install -y \
+    graphviz \
+    gnupg2 \
+    gpg-agent \
+    gcc-aarch64-linux-gnu \
+    && rm -rf /var/lib/apt/lists/*
diff --git a/docker/install/ubuntu_install_vitis_ai_packages_ci.sh b/docker/install/ubuntu_install_vitis_ai_packages_ci.sh
index c34ed3addce2..774d85dcf68a 100644
--- a/docker/install/ubuntu_install_vitis_ai_packages_ci.sh
+++ b/docker/install/ubuntu_install_vitis_ai_packages_ci.sh
@@ -23,7 +23,7 @@ set -o pipefail
 export PYXIR_HOME=/opt/pyxir
 mkdir "$PYXIR_HOME"
 
-pip3 install progressbar
+pip3 install progressbar h5py==2.10.0
 
-git clone --recursive --branch v0.1.3 https://github.com/Xilinx/pyxir.git "${PYXIR_HOME}"
+git clone --recursive --branch v0.1.6 --depth 1 https://github.com/Xilinx/pyxir.git "${PYXIR_HOME}"
 cd "${PYXIR_HOME}" && python3 setup.py install
diff --git a/docker/with_the_same_user b/docker/with_the_same_user
index 459978409be5..a7ea8c009b58 100644
--- a/docker/with_the_same_user
+++ b/docker/with_the_same_user
@@ -41,6 +41,12 @@ getent passwd "${CI_BUILD_UID}" || adduser --gid "${CI_BUILD_GID}" --uid "${CI_B
     --gecos "${CI_BUILD_USER} (generated by with_the_same_user script)" \
     --disabled-password --home "${CI_BUILD_HOME}" --quiet "${CI_BUILD_USER}"
 usermod -a -G sudo "${CI_BUILD_USER}"
+
+# Add user to video group for ROCm
+if [[ ! -z $ROCM_ENABLED ]]; then
+  usermod -a -G video "${CI_BUILD_USER}"
+fi
+
 # This is a grotesque hack to get PYTEST_ADD_OPTS available to all task scripts.
 echo "${CI_BUILD_USER} ALL=(ALL) NOPASSWD:ALL" > /etc/sudoers.d/90-nopasswd-sudo
 
diff --git a/docs/conf.py b/docs/conf.py
index ad838f767f80..c9c68706998b 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -210,10 +210,11 @@
 # The unlisted files always appear after listed files.
 within_subsection_order = {
     "get_started": [
-        "relay_quick_start.py",
-        "tensor_expr_get_started.py",
         "tvmc_command_line_driver.py",
+        "tensor_expr_get_started.py",
+        "autoschedule_matmul.py",
         "cross_compilation_and_rpc.py",
+        "relay_quick_start.py",
     ],
     "frontend": [
         "from_pytorch.py",
diff --git a/docs/deploy/android.rst b/docs/deploy/android.rst
index 8c8fcfb49679..256978d00607 100644
--- a/docs/deploy/android.rst
+++ b/docs/deploy/android.rst
@@ -31,7 +31,7 @@ The code below will save the compilation output which is required on android tar
     with open("deploy_graph.json", "w") as fo:
         fo.write(graph.json())
     with open("deploy_param.params", "wb") as fo:
-        fo.write(relay.save_param_dict(params))
+        fo.write(runtime.save_param_dict(params))
 
 deploy_lib.so, deploy_graph.json, deploy_param.params will go to android target.
 
diff --git a/docs/deploy/arm_compute_lib.rst b/docs/deploy/arm_compute_lib.rst
index a2eaa5fb5662..5d11241c1a34 100644
--- a/docs/deploy/arm_compute_lib.rst
+++ b/docs/deploy/arm_compute_lib.rst
@@ -15,7 +15,7 @@
     specific language governing permissions and limitations
     under the License.
 
-Relay Arm :sup:`®` Compute Library Integration
+Relay Arm:sup:`®` Compute Library Integration
 ==============================================
 **Author**: `Luke Hutton <https://github.com/lhutton1>`_
 
@@ -195,12 +195,14 @@ Operator support
 |                      |   Simple: nn.conv2d                                                     |
 |                      |   Composite: nn.pad?, nn.conv2d, nn.bias_add?, nn.relu?                 |
 |                      |                                                                         |
-|                      | (only groups = 1 supported)                                             |
+|                      | Normal and depth-wise (when kernel is 3x3 or 5x5 and strides are 1x1    |
+|                      | or 2x2) convolution supported. Grouped convolution is not supported.    |
 +----------------------+-------------------------------------------------------------------------+
 | qnn.conv2d           | uint8:                                                                  |
 |                      |   Composite: nn.pad?, nn.conv2d, nn.bias_add?, nn.relu?, qnn.requantize |
 |                      |                                                                         |
-|                      | (only groups = 1 supported)                                             |
+|                      | Normal and depth-wise (when kernel is 3x3 or 5x5 and strides are 1x1    |
+|                      | or 2x2) convolution supported. Grouped convolution is not supported.    |
 +----------------------+-------------------------------------------------------------------------+
 | nn.dense             | fp32:                                                                   |
 |                      |   Simple: nn.dense                                                      |
diff --git a/docs/deploy/bnns.rst b/docs/deploy/bnns.rst
new file mode 100644
index 000000000000..cb15a4f3bd54
--- /dev/null
+++ b/docs/deploy/bnns.rst
@@ -0,0 +1,183 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+Relay BNNS Integration
+======================
+**Author**: `Egor Churaev <https://github.com/echuraev>`_
+
+Introduction
+------------
+
+Apple BNNS library is a collection of functions that can be used to construct neural networks
+for inference (and train). It’s supported in macOS, iOS, tvOS, and watchOS. BNNS provides
+primitives executed on all CPU supported on those platforms and optimized for high performance
+and low-energy consumption. This integration will offload as many operators as possible from Relay to BNNS.
+
+BNNS runtime is a part of platform API and available on all modern Apple operating systems.
+Application using BNNS will not depends on any additional external dependencies.
+
+BNNS functions uses Apple private hardware capabilities which are not exposed yet by Apple. Example
+of such capabilities can be AMX Apple cpu extension.
+
+This guide will demonstrate how to build TVM with BNNS codegen and runtime enabled. It will also provide example
+code to compile and run models using BNNS runtime. Finally, we document the supported operators.
+
+Building TVM with BNNS support
+------------------------------
+
+To turn on TVM BNNS codegen and TVM BNNS runtime you need to turn on the only USE_BNNS flag
+
+* USE_BNNS=ON/OFF - This flag will enable compiling a network with offloading subgraphs to BNNS primitives
+  and will link tvm library to the BNNS runtime module.
+
+Enabling of this flag will cause to search the default Accelerate Frameworks on current target SDK.
+The minimal versions of required SDK is macOS 11.0, iOS 14.0, tvOS 14.0 and watchOS 7.0.
+
+Example setting in config.cmake file:
+
+.. code:: cmake
+
+    set(USE_BNNS ON)
+
+BNNS partitioning of Relay graph
+--------------------------------
+
+Operations to be offloaded on BNNS execution must be annotated before passing of module for compilation.
+All ops annotated by `partition_for_bnns` will be offloaded for BNNS execution. The rest of the ops
+will go through the LLVM compilation and code generation.
+
+Important note: BNNS support primitives only with constant weights. To satisfy this requirements we have
+to map constants to related tensor abstraction in relay representation. To freeze tensors and operate
+with them as constants you may need to call ONNX importer with special flag "freeze_params=True"
+or performer binding manually. In general cases all relay importers don't do that by default.
+For your convenience "partition_for_bnns" can do this for you if params dictionary is passed as the argument.
+
+.. code:: python
+
+    from tvm.relay.op.contrib.bnns import partition_for_bnns
+    model = partition_for_bnns(model, params=params)
+
+
+Input data layout for operations to be offloaded to BNNS execution
+------------------------------------------------------------------
+
+BNNS kernels support only planar format of input data. The partitioner will require to have NCHW input
+layout for conv2d input.
+
+To use BNNS integration for models with interleave input layout, they should be converted before
+passing of module to `partition_for_bnns`. The layout conversion will happen only for explicitly
+enumerated types of ops. It might happen that depending on topology there might be regular data reorder
+around conv2d to interleave and planar layout. This will be reflected in performance penalties and affect
+execution time. It is recommended to analyze the whole topology and extend below list to convert all
+intermediate tensors to NCHW data layout.
+
+Example of input layouts change:
+
+.. code:: python
+
+    # For models with NHWC input layout
+    with tvm.transform.PassContext(opt_level=3):
+        mod = relay.transform.InferType()(mod)
+        mod = relay.transform.ConvertLayout({"nn.conv2d": ["NCHW", "default"],
+                                            "nn.bias_add": ["NCHW", "default"],
+                                            "nn.relu": ["NCHW"]})(mod)
+
+
+Example: Build and Deploy Mobilenet v2 1.0 with BNNS
+----------------------------------------------------
+
+Create a Relay graph from a MXNet Mobilenet v2 1.0 model.
+
+.. code:: python
+
+    import tvm
+    from tvm import relay
+    import mxnet
+    from mxnet.gluon.model_zoo.vision import get_model
+
+    dtype = "float32"
+    input_shape = (1, 3, 224, 224)
+    block = get_model('mobilenetv2_1.0', pretrained=True)
+    module, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
+
+
+Markup the parts of graphs to be offloaded to BNNS primitives. All ops which are supported by the BNNS
+integration will be handled by BNNS invocations, the rest of the ops will go through the
+regular TVM llvm compilation and code generation.
+
+After that you need to compile new module with target corresponding to required Apple platform
+
+.. code:: python
+
+    from tvm.relay.op.contrib.bnns import partition_for_bnns
+
+    # target for macOS Big Sur 11.1:
+    target = "llvm -mtriple=x86_64-apple-darwin20.2.0"
+
+    model = partition_for_bnns(model, params=params)  # to markup operations to be offloaded to BNNS
+    with tvm.transform.PassContext(opt_level=3):
+        lib = relay.build(model, target=target, target_host=target, params=params)
+
+Export the module.
+
+.. code:: python
+
+    lib.export_library('compiled.dylib')
+
+
+Load module and run inference on the target machine with TVM  built with ``USE_BNNS`` enabled
+
+.. code:: python
+
+    import tvm
+    import numpy as np
+    from tvm.contrib import graph_runtime
+
+    ctx = tvm.cpu(0)
+    loaded_lib = tvm.runtime.load_module('compiled.dylib')
+    gen_module = tvm.contrib.graph_runtime.GraphModule(loaded_lib['default'](ctx))
+
+    dtype = "float32"
+    input_shape = (1, 3, 224, 224)
+    input_data = np.random.uniform(0, 1, input_shape).astype(dtype)
+    gen_module.run(data=input_data)
+
+
+
+Operator support
+----------------
+
++------------------------+------------------------------------------------------------------------------+
+|       Relay Node       |              Remarks                                                         |
++========================+==============================================================================+
+| nn.conv2d              |                                                                              |
++------------------------+------------------------------------------------------------------------------+
+| nn.batch_norm          | Supported by BNNS integration only in nn.conv2d-batch_norm pattern           |
++------------------------+------------------------------------------------------------------------------+
+| nn.dense               |                                                                              |
++------------------------+------------------------------------------------------------------------------+
+| nn.batch_matmul        |                                                                              |
++------------------------+------------------------------------------------------------------------------+
+| nn.bias_add            | Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense   |
+|                        | fusion                                                                       |
++------------------------+------------------------------------------------------------------------------+
+| add                    | Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense fusion |
++------------------------+------------------------------------------------------------------------------+
+| nn.relu                | Supported by BNNS integration only as a part of nn.conv2d or nn.dense fusion |
++------------------------+------------------------------------------------------------------------------+
+| nn.gelu                | Supported by BNNS integration only as a part of nn.conv2d or nn.dense fusion |
++------------------------+------------------------------------------------------------------------------+
diff --git a/docs/deploy/index.rst b/docs/deploy/index.rst
index 2b37f734c3c3..3cbbb10bd74b 100644
--- a/docs/deploy/index.rst
+++ b/docs/deploy/index.rst
@@ -71,3 +71,4 @@ target device without relying on RPC. see the following resources on how to do s
    arm_compute_lib
    tensorrt
    vitis_ai
+   bnns
diff --git a/docs/deploy/vitis_ai.rst b/docs/deploy/vitis_ai.rst
index df29f16f9d8d..7de8f58ce54f 100755
--- a/docs/deploy/vitis_ai.rst
+++ b/docs/deploy/vitis_ai.rst
@@ -304,15 +304,22 @@ Edge hardware setup
   This section provides instructions for setting up with the `Pynq <http://www.pynq.io/>`__ platform but
   Petalinux based flows are also supported.
 
-1. Download the Pynq v2.5 image for your target (use Z1 or Z2 for
+1. Download the Pynq v2.6 image for your target (use Z1 or Z2 for
    Ultra96 target depending on board version) Link to image:
-   https://github.com/Xilinx/PYNQ/releases/tag/v2.5
+   https://github.com/Xilinx/PYNQ/releases/tag/v2.6.0
 2. Follow Pynq instructions for setting up the board: `pynq
    setup <https://pynq.readthedocs.io/en/latest/getting_started.html>`__
-3. After connecting to the board, make sure to run as root. Execute
+3. After connecting to the board, make sure to run as root. **Execute**
    ``su``
-4. Set up DPU on Pynq by following the steps here: `DPU Pynq
-   setup <https://github.com/Xilinx/DPU-PYNQ>`__
+4. Set up DPU on Pynq:
+
+    .. code:: bash
+
+     git clone --branch v1.2.0 --recursive --shallow-submodules https://github.com/Xilinx/DPU-PYNQ.git
+     cd DPU-PYNQ/upgrade
+     make
+     pip3 install pynq-dpu==1.2.0
+
 5. Run the following command to download the DPU bitstream:
 
    .. code:: bash
@@ -343,7 +350,7 @@ interface between TVM and Vitis-AI tools.
    .. code:: bash
 
       apt-get install libhdf5-dev
-      pip3 install pydot h5py
+      pip3 install pydot==1.4.1 h5py==2.8.0
 
 2. Install PyXIR
 
@@ -362,16 +369,17 @@ interface between TVM and Vitis-AI tools.
       mkdir build
       cp cmake/config.cmake build
       cd build
+      echo set\(USE_LLVM OFF\) >> config.cmake
       echo set\(USE_VITIS_AI ON\) >> config.cmake
       cmake ..
-      make
+      make tvm_runtime -j$(nproc)
 
 4. Install TVM
 
    .. code:: bash
 
       cd tvm/python
-      pip3 install -e . --user
+      pip3 install -e .
 
 5. Check whether the setup was successful in the Python shell:
 
@@ -441,7 +449,7 @@ TVM.
    import tvm
    import tvm.relay as relay
    from tvm.contrib.target import vitis_ai
-   from tvm.contrib import util, graph_runtime
+   from tvm.contrib import utils, graph_runtime
    from tvm.relay.build_module import bind_params_by_name
    from tvm.relay.op.contrib.vitis_ai import annotation
 
@@ -524,6 +532,8 @@ model in TVM with Vitis-AI at the edge. The first couple of steps will
 have to be run on the host machine and take care of quantization and
 compilation for deployment at the edge.
 
+A complete ResNet 18 example can be found `here <https://github.com/Xilinx/pyxir/tree/master/examples/tvm>`__.
+
 Host steps
 ^^^^^^^^^^
 
@@ -541,7 +551,7 @@ TVM.
    import tvm
    import tvm.relay as relay
    from tvm.contrib.target import vitis_ai
-   from tvm.contrib import util, graph_runtime
+   from tvm.contrib import utils, graph_runtime
    from tvm.relay.build_module import bind_params_by_name
    from tvm.relay.op.contrib.vitis_ai import annotation
 
@@ -549,12 +559,47 @@ After importing a convolutional neural network model using the usual
 Relay API's, annotate the Relay expression for the given Vitis-AI DPU
 target and partition the graph.
 
+.. note::
+
+    We recommend converting DPU convolutions' data layouts to NHWC and CPU convolutions'
+    data layouts to NCHW for best DPU and out of the box CPU performance. You can use the
+    ConvertLayout transformation pass two times to achieve this as demonstrated in the code
+    block underneath. You can also leave the CPU convolution layouts in NHWC and tune ARM CPU
+    performance for this data layout to avoid the layout transformation overheads introduced by
+    executing DPU convolutions in NHWC and CPU convolutions in NCHW
+    (check out the `AutoScheduling <https://tvm.apache.org/docs/tutorials/index.html#autoscheduler-template-free-auto-scheduling>`__
+    and `AutoTuning <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_arm.html>`__
+    tutorials for this).
+
 .. code:: python
 
    mod["main"] = bind_params_by_name(mod["main"], params)
+   
+   # For edge DPU we recommend converting the convolutions' data layout
+   #    to NHWC for best performance. Therefore, we first convert the layouts
+   #    of all convolutions to NHWC before partitioning. Afterwards, we can
+   #    convert any remaining convolutions (to be executed on CPU) back to NCHW.
+   desired_layouts = {'nn.conv2d': ['NHWC', 'default']}
+   seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
+                                   relay.transform.ConvertLayout(desired_layouts),
+                                   relay.transform.FoldConstant()])
+   with tvm.transform.PassContext(opt_level=3):
+       mod = seq(mod)
+            
+   # Annotate and partition the Relay expression for the given target
    mod = annotation(mod, params, target)
    mod = relay.transform.MergeCompilerRegions()(mod)
    mod = relay.transform.PartitionGraph()(mod)
+   
+   # After partitioning we recommend transforming the remaining convolutions
+   #    (that will be executed on CPU, if any) back to NCHW data layout
+   #    for best CPU performance
+   desired_layouts = {'nn.conv2d': ['NCHW', 'default']}
+   seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
+                                   relay.transform.ConvertLayout(desired_layouts),
+                                   relay.transform.FoldConstant()])
+   with tvm.transform.PassContext(opt_level=3):
+       mod = seq(mod)
 
 Now, we can build the TVM runtime library for executing the model. The
 TVM target is 'llvm' as the operations that can't be handled by the DPU
@@ -572,13 +617,9 @@ can be included.
 
 .. code:: python
 
-   from tvm.contrib import util
-
-   temp = util.tempdir()
-
    tvm_target = 'llvm'
    target='DPUCZDX8G-zcu104'
-   export_rt_mod_file = temp.relpath("vitis_ai.rtmod")
+   export_rt_mod_file = "vitis_ai.rtmod"
 
    with tvm.transform.PassContext(opt_level=3, config= {'relay.ext.vitis_ai.options.target': target,
    						        'relay.ext.vitis_ai.options.export_runtime_module': export_rt_mod_file}):
@@ -604,9 +645,9 @@ Save the TVM lib module so that the Vitis-AI runtime module will also be exporte
 
 .. code:: python
 
-   from tvm.contrib import util
+   from tvm.contrib import utils
 
-   temp = util.tempdir()
+   temp = utils.tempdir()
    lib.export_library(temp.relpath("tvm_lib.so"))
 
 After quantizing and compiling the model for Vitis-AI acceleration using the
@@ -638,15 +679,31 @@ Edge steps
 ^^^^^^^^^^
 
 After setting up TVM with Vitis-AI on the edge device, you can now load
-the TVM runtime module into memory and feed inputs for inference.
+the TVM runtime module into memory and feed inputs for inference. A nearly
+complete runtiem script can be found underneath. Make sure to run the script
+as root (execute ``su`` in terminal to log into root).
+
+
+.. note::
+
+    You will see a warning about the 'cpu-tf' runtime not being found. This warning is
+    expected on the board and can be ignored. Note also that you **shouldn't** import the
+    PyXIR targets in the run script (``import pyxir.contrib.target.DPUCZDX8G``).
 
 .. code:: python
 
+   import pyxir
+   import tvm
+   from tvm.contrib import graph_runtime
+
    ctx = tvm.cpu()
+   
+   # input_name = ...
+   # input_data = ...
 
    # load the module into memory
    lib = tvm.runtime.load_module("tvm_dpu_arm.so")
 
    module = graph_runtime.GraphModule(lib["default"](tvm.cpu()))
-   module.set_input(name, data)
+   module.set_input(input_name, input_data)
    module.run()
diff --git a/docs/dev/index.rst b/docs/dev/index.rst
index 71ae5d4ec68d..a098df12f1c1 100644
--- a/docs/dev/index.rst
+++ b/docs/dev/index.rst
@@ -49,7 +49,7 @@ In this guide, we will study an example compilation flow in the compiler. The fi
 - Runtime Execution: the user loads back a `runtime.Module` and runs the compiled functions in the supported runtime environment.
 
 
-.. figure:: https://raw.githubusercontent.com/tlcpack/web-data/main/images/design/tvm_dyn_workflow.svg
+.. figure:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/design/tvm_dyn_workflow.svg
    :align: center
    :width: 85%
 
@@ -201,7 +201,7 @@ except that the data structure of interest changes from the numpy.ndarray to tvm
 Logical Architecture Components
 -------------------------------
 
-.. figure:: https://raw.githubusercontent.com/tlcpack/web-data/main/images/design/tvm_static_overview.svg
+.. figure:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/design/tvm_static_overview.svg
    :align: center
    :width: 85%
 
@@ -396,3 +396,11 @@ Security
    :maxdepth: 1
 
    security
+
+
+microTVM
+--------
+.. toctree::
+   :maxdepth: 1
+
+   microtvm_design
diff --git a/docs/dev/microtvm_design.rst b/docs/dev/microtvm_design.rst
new file mode 100644
index 000000000000..2c3eeb2faea3
--- /dev/null
+++ b/docs/dev/microtvm_design.rst
@@ -0,0 +1,349 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+..    http://www.apache.org/licenses/LICENSE-2.0
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+**************************
+microTVM Design Document
+**************************
+
+.. contents:: Table of Contents
+    :depth: 3
+
+Background
+===========
+
+TVM is a model deployment framework that has demonstrated good performance across a wide range of
+models on traditional operating systems. Given TVM's layered approach to compilation, it is a
+natural extension to target bare metal devices. While most of the compilation flow does not need to
+change for a proof-of-concept implementation on such devices, the runtime cannot depend on:
+
+* **Virtual Memory**, and by extension any system-provided ``malloc``. Additionally, bare metal
+  devices typically have very limited memory (measured in KB). Because of this, libraries designed
+  for such platforms typically need to be more judicious in using memory, and need to release
+  memory when it is not in use.
+* Traditional OS abstractions, such as **files**, **libraries**, and **kernel functions**. Some
+  projects implement support for these, but they are by no means standard.
+* Support for programming languages other than **C**.
+
+Such changes require a different approach from the TVM C++ runtime typically used on traditional
+Operating Systems.
+
+Typical Use
+===========
+
+This section discusses our vision of the "typical" microTVM use case. Each component used to achieve
+this typical use case is intended to be designed for flexibility, but this unifying vision serves to
+motivate the inclusion of each part of the design.
+
+.. figure:: https://raw.githubusercontent.com/tvmai/web-data/main/images/dev/microtvm_workflow.svg
+   :align: center
+   :width: 85%
+
+The parts of this process are described below:
+
+#. **Model Import**. The user imports an existing model or describes a new model to TVM, producing a
+   *Relay module*.
+
+#. **Model Transformations**. The user can apply transformations, such as quantization, to the
+   model. After each transformation, the user should still have a Relay module.
+
+#. **Compilation** (Scheduling and Code Generation). TVM implements each operator into Tensor IR by
+   assigning a schedule and schedule configuration to each Relay operator. Then, code (C source or
+   compiled object) is generated for each operator.
+
+#. **Integration**. The generated code is integrated along with the TVM C Runtime library into a
+   user-supplied binary project. In some cases (such as when the project is standardized across
+   multiple SoC/development boards), this process is handled automatically.
+
+#. **Deployment**. The project is built and the residual firmware binary is flashed onto the device.
+   Model inference is driven either by TVM using an on-device RPC server, or on the device using the
+   on-device Graph Runtime.
+
+Design Goals
+============
+
+microTVM aims to achieve these design goals:
+
+1. **Portable Code**. microTVM can translate any Relay model into C code that can compile with only
+   a C standard library.
+2. **Minimal Overhead**. microTVM generates target-specific, highly optimized code. As much overhead
+   from the runtime should be removed.
+3. **Accessible Code**. microTVM considers C source code as a first-class output mechanism so that
+   it is easier for a firmware engineer to understand and tweak.
+
+Overview
+========
+
+microTVM requires changes at all levels of the TVM compiler stack. The following sub-sections enumerate
+these changes at a high level, and follow-on sections discuss the specifics in more detail.
+
+Modeling Target Platforms
+-------------------------
+
+TVM's search-based optimization approach allows it to largely avoid system-level modeling of targets
+in favor of experimental results. However, some modeling is necessary in order to ensure TVM is
+comparing apples-to-apples search results, and to avoid wasting time during the search by attempting
+to compile invalid code for a target.
+
+microTVM models these parts of the target:
+
+* The CPU used, through the ``-mcpu`` and ``-march`` target flags.
+* The presence or absence of accelerators, through the device components of the target (Currently
+  only the absence of accelerators can be expressed, but this mechanism should extend well).
+
+microTVM aims to model these parts of the target in the future:
+
+* Memory, modeled as a set of disjoint memory spaces, each with a label and size and prefetch/flush
+  behavior. Some memory may be shared with accelerators.
+* Target runtime configuration (i.e. clock tree configuration, clock speed, etc). This is intended
+  only to contribute to the AutoTVM schedule key and not for any other use.
+
+At this time, TVM does not intend to model:
+
+* Size, type, or relationship of caches, with the exception of prefetching or cache flushing.
+
+
+TVM Targets for microTVM
+-------------------------
+
+A central data structure in the compilation process is the ``tvm::target::Target`` class. TVM uses
+Target to decide which TIR schedules to enable and how to configure the code generator. The Target
+class should also uniquely identify the generated code for a particular operator, as autotuning
+logs use it to rank measured performance (but see Future Work).
+
+Targets are currently represented as strings structured similarly to command-line arguments. An
+example target is shown below:
+
+    ``c -keys=arm_cpu -mcpu=cortex-m7 -link-params -model=stm32f746xx -runtime=c -system-lib=1``
+
+The relevant parts to microTVM are:
+
+ * Code generator (``llvm`` or ``c``)
+ * ``-mcpu=cortex-m7``: used by TOPI to enable Cortex-M schedules, and, when the C source code
+   generator is selected, included in the output as a comment to help identify the code and
+   configure the downstream C compiler.
+ * ``-link-params``: include parameters as global constants to load from flash.
+ * ``-runtime=c``: build glue code to allow operators to work with the C runtime
+ * ``-system-lib=1``: emit a system library (i.e. which can be loaded by calling the PackedFunc
+   ``runtime.SystemLib``.
+
+Writing Schedules for microTVM
+------------------------------
+
+For operations scheduled on the CPU, microTVM initially plans to make use of specialized
+instructions and extern (i.e. hand-optimized) functions to achieve good performance. In TVM, this
+approach is generally accomplished through tensorization, in which TVM breaks a computation into
+small pieces, and a TIR extern function accelerates each small piece.
+
+TVM currently accommodates both approaches using ``tir.call_extern``. First, a pragma is attached to
+the schedule defining the extern function in portable C.
+
+    ``sched[output].pragma(n, "import_c", "void call_asm(int32_t* a, int32_t* b) { /* ... */ }")``
+
+Next, ``tensorize`` is used to split the computation.
+
+    ``sched[output].tensorize(owi, gemm)``
+
+There are a couple of caveats to this approach, all which could be resolved by linking generated
+code against external libraries:
+
+* Inline assembly is compiler-specific. While Clang and GCC have standardized on one syntax, this
+  may not be portable to other compilers. SDKs solve this by conditionally including a header file
+  depending on the compiler being used. However, taking this approach means that the generated code
+  needs additional compiler flags (i.e. ``-Isystempath/to/header``).
+* It may be helpful to reference helper functions from the generated code (e.g. to inline common
+  sequences of hand-optimized assembly).
+* Finally, the extern function invoked may be wholly written in an external library. If those
+  functions can be wholly inlined, this caveat is the same as the previous. If not, then additional
+  C code needs to be compiled and linked against the operator.
+
+At present, microTVM presumes that all eligible schedules can be compiled. This means that the user-
+supplied project (see next section) must include all libraries that are used by the generated code.
+When not using autotuning, TVM randomly chooses a fallback schedule, so all libraries would need to
+be supported. When using autotuning, TVM selects the best-performing schedule, so only that library
+is needed. There isn't currently a way to force TVM to pick a particular schedule outside of
+autotuning logs, but that would be a good addition.
+
+Finally, when using the ``llvm`` backend, the process is similar except that LLVM bitcode is included
+in the generated code (with an ``import_llvm`` pragma). LLVM bitcode provides a portable way to call
+inline assembly. However, it may be more complex to call external C functions, and helper functions
+are of course not easy to use from LLVM bitcode.
+
+Executing Models
+----------------
+
+The TVM compiler traditionally outputs three pieces:
+
+1. Model operator implementations, as discussed above;
+2. A model execution graph, encoded as JSON; and
+3. Simplified parameters.
+
+To correctly execute the model, a Graph Runtime needs to reconstruct the graph in memory, load the
+parameters, and then invoke the operator implementations in the correct order.
+
+microTVM supports two ways to do this:
+
+1. **Host-Driven**. The Graph Runtime can run on the host and carry out execution by issuing
+   commands to the device using an RPC link with a UART-like transport.
+2. **Standalone**. A C Graph Runtime is available to be compiled on-device, but it is not
+   particularly memory efficient. This way enables standalone execution without any attached host.
+
+Host-Driven is designed for experimenting with models on-device and, like AutoTVM, uses the RPC server to
+drive computation on-device. Standalone is intended for deployment.
+
+Host-Driven Execution
+^^^^^^^^^^^^^^^^^^^^^
+
+In Host-Driven execution, the firmware binary is the following:
+
+1. Generated operator implementations from TVM.
+2. The TVM C runtime.
+3. SoC-specific initialization.
+4. The TVM RPC server.
+5. (optional) Simplified Parameters.
+
+This firmware image is flashed onto the device and a GraphRuntime instance is created on the host.
+The GraphRuntime drives execution by sending RPC commands over a UART:
+
+.. figure:: https://raw.githubusercontent.com/tvmai/web-data/main/images/dev/microtvm_host_driven.svg
+   :align: center
+   :width: 85%
+
+Standalone Execution
+^^^^^^^^^^^^^^^^^^^^
+
+In Standalone execution, the GraphRuntime is instantiated on device:
+
+.. figure:: https://raw.githubusercontent.com/tvmai/web-data/main/images/dev/microtvm_standalone.svg
+   :align: center
+   :width: 85%
+
+microTVM Firmware
+------------------
+
+We can now discuss how microTVM firmware should behave. An important task common to both model
+execution strategies is configuring the SoC to match the way it performs in production. microTVM
+considers this task project- and SoC-dependent. Whether for AutoTVM, host-driven model inference, or
+in standalone deployment, the user is expected to supply a project whose main() does the following:
+
+1. Configure the SoC to match deployment performance.
+2. Initialize the TVM C Runtime.
+
+When configuring for host-driven inference or AutoTVM, the remaining tasks are well-defined:
+
+3. Initialize a transport (i.e. a UART) for use with the TVM RPC server.
+4. Launch the TVM RPC Server.
+
+When configuring for standalone deployment, the firmware needs to:
+
+1. Instantiate the system library by calling the ``runtime.SystemLib`` PackedFunc.
+2. Instantiate a GraphRuntime passing the system library module.
+3. Configure parameters and inputs as needed.
+4. Run the model.
+
+Parts of a microTVM Binary
+--------------------------
+
+To summarize, a microTVM firwmare binary image must contain these parts:
+
+1. Operator implementations, produced by TVM.
+2. The TVM C runtime library, supplied by TVM as a static library.
+3. SoC Initialization, supplied by the user.
+
+For Host-driven model execution, firmware also needs:
+
+4. The TVM RPC Server library.
+
+For Standalone model execution, firmware also needs:
+
+4. The TVM C GraphRuntime library, supplied by TVM as a static library.
+5. The remaining compiler outputs (Simplified Parameters and Graph JSON).
+
+The Automated Build Flow
+------------------------
+
+Once code generation is complete, ``tvm.relay.build`` returns a ``tvm.runtime.Module`` and the
+user can save the generated C source or binary object to a ``.c`` or ``.o`` file. From this point, TVM
+can theoretically step back and the user can compile and run the code separately.
+
+However, for AutoTVM, TVM needs some automated flow to handle the following tasks:
+
+1. Integrate operator implementations, the TVM C Runtime library, and the TVM RPC Server library into the
+   firmware project containing user-supplied SoC Initialization.
+2. Build the resulting project.
+3. Program the built firmware onto a (specific) attached device.
+4. Identify the serial port or other transport to be used by TVM to drive remote execution.
+
+At present, TVM expects the user to supply an implementation of the ``tvm.micro.Compiler``,
+``tvm.micro.Flasher``, and ``tvm.micro.Transport`` interfaces. TVM then:
+
+1. Builds each piece separately as a library.
+2. Builds the libraries into a binary firmware image.
+3. Programs the firmware image onto an attached device.
+4. Opens a serial port to serve as the RPC server transport.
+
+This design was chosen to reduce build times for microTVM (the common libraries need to be built
+only once per candidate operator implemmentation). In practice, these projects are extremely small
+and compile relatively quickly. Compared with the added complexity of this tighter build integration
+with TVM, the performance gains are likely not worth it. A future design will consolidate the build
+tasks into a single step and narrow the interface to provide a better integration.
+
+Measuring operator performance
+------------------------------
+
+The TVM C runtime depends on user-supplied functions to measure time on-device. Users should implement
+``TVMPlatformTimerStart`` and ``TVMPlatformTimerStop``. These functions should measure wall clock time, so there
+are some pitfalls in implementing these functions:
+
+1. If the CPU could halt or sleep during a computation (i.e. if it is being done on an accelerator),
+   a cycle counter should likely not be used as these tend to stop counting while the CPU is asleep.
+2. The granularity of these functions can be relaxed as needed to extend the range of the timer
+   device. However, if granularity is too coarse, a sub-optimal schedule may be used.
+3. An error should be raised if the timer overflows.
+4. The timer should not interrupt computation unless absolutely necessary. Doing so may affect the
+   accuracy of the results.
+5. Calibrating the output against a wall clock is ideal, but it will likely be too cumbersome. A
+   future PR could enable some characterization of the platform timer by, e.g., measuring the internal
+   oscillator against a reference such as an external crystal.
+
+Future Work
+===========
+
+Ahead-of-Time Runtime
+----------------------
+
+A limitation of the Graph Runtime is the amount of memory overhead required in parsing the JSON.
+The current implementation contributes significantly to the dynamic memory usage of microTVM,
+limiting its utility. An ahead-of-time runtime can avoid the need for any Graph JSON parsing and
+improve inference speed by generating C code to call the generated operator implementations directly
+rather than relying on a data-driven approach with the Graph Runtime.
+
+Memory Planning
+----------------
+
+The current memory planner attempts to limit the number of ``TVMBackendDeviceAlloc()`` calls
+issued for intermediate tensors only. Because scratchpads can vary widely, and because the planner
+coalesces memory allocations within 16x of each other, this strategy typically results in high
+peak memory usage.
+
+Heterogeneous Execution
+-----------------------
+
+Newer Cortex-M SoCs can contain multiple CPUs and onboard ML accelerators.
+
+
+Autotuning Target
+-----------------
+
+As discussed previously,
diff --git a/docs/index.rst b/docs/index.rst
index f407fa2d4f29..3131be5381fc 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -44,6 +44,7 @@ For Developers
    contribute/index
    deploy/index
    dev/how_to
+   microtvm/index
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/langref/relay_pattern.rst b/docs/langref/relay_pattern.rst
index ff02e50eb5fb..d77a51980f23 100644
--- a/docs/langref/relay_pattern.rst
+++ b/docs/langref/relay_pattern.rst
@@ -230,6 +230,39 @@ The next example is matching function nodes with a specific attribute:
         f = relay.Function([x, y], x + y).with_attr("Composite", "add")
         assert pattern.match(f)
 
+A Relay ``If`` expression can be matched if all of its condition, true branch and false branch
+are matched:
+
+.. code-block:: python
+
+    def test_match_if():
+        x = is_var("x")
+        y = is_var("y")
+        pat = is_if(is_op("less")(x, y), x, y)
+
+        x = relay.var("x")
+        y = relay.var("y")
+        cond = x < y
+
+        assert pat.match(relay.expr.If(cond, x, y))
+
+
+A Relay ``Let`` expression can be matched if all of its variable, value, and body
+are matched:
+
+.. code-block:: python
+
+  def test_match_let():
+      x = is_var("x")
+      y = is_var("y")
+      let_var = is_var("let")
+      pat = is_let(let_var, is_op("less")(x, y), let_var)
+
+      x = relay.var("x")
+      y = relay.var("y")
+      lv = relay.var("let")
+      cond = x < y
+      assert pat.match(relay.expr.Let(lv, cond, lv))
 
 Matching Diamonds and Post-Dominator Graphs
 *******************************************
@@ -294,6 +327,8 @@ The high level design is to introduce a language of patterns for now we propose
             | is_op(op_name)
             | is_tuple()
             | is_tuple_get_item(pattern, index = None)
+            | is_if(cond, tru, fls)
+            | is_let(var, value, body)
             | pattern1 `|` pattern2
             | dominates(parent_pattern, path_pattern, child_pattern)
             | FunctionPattern(params, body)
@@ -351,6 +386,16 @@ Function Pattern
 
 Match a Function with a body and parameters
 
+If Pattern
+**********
+
+Match an If with condition, true branch, and false branch
+
+Let Pattern
+***********
+
+Match a Let with a variable, value, and body
+
 Applications
 ============
 
diff --git a/docs/microtvm/index.rst b/docs/microtvm/index.rst
new file mode 100644
index 000000000000..2371219af27f
--- /dev/null
+++ b/docs/microtvm/index.rst
@@ -0,0 +1,73 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+.. _microtvm-index:
+
+microTVM: TVM on bare-metal
+===========================
+
+microTVM runs TVM models on bare-metal (i.e. IoT) devices. microTVM depends only on the C standard
+library, and doesn't require an operating system to execute. microTVM is currently under heavy
+development.
+
+.. figure:: https://raw.githubusercontent.com/tvmai/web-data/main/images/dev/microtvm_workflow.svg
+   :align: center
+   :width: 85%
+
+microTVM is:
+
+* an extension to TVM's compiler to allow it to target microcontrollers
+* a way to run the TVM RPC server on-device, to allow autotuning
+* a minimal C runtime that supports standalone model inference on bare metal devices.
+
+Supported Hardware
+~~~~~~~~~~~~~~~~~~
+
+microTVM currently tests against Cortex-M microcontrollers with the Zephyr RTOS; however, it is
+flexible and portable to other processors such as RISC-V and does not require Zephyr. The current
+demos run against QEMU and the following hardware:
+
+* `STM Nucleo-F746ZG <https://www.st.com/en/evaluation-tools/nucleo-f746zg.html>`_
+* `STM STM32F746 Discovery <https://www.st.com/en/evaluation-tools/32f746gdiscovery.html>`_
+* `nRF 5340 Preview Development Kit <https://www.nordicsemi.com/Software-and-tools/Development-Kits/nRF5340-PDK>`_
+
+
+Getting Started with microTVM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Before working with microTVM, we recommend you have a supported development board. Then, follow these
+tutorials to get started with microTVM:
+
+1. :ref:`Start the microTVM Reference VM <tutorial-micro-reference-vm>`. The microTVM tutorials
+   depend on Zephyr and on a compiler toolchain for your hardware. The reference VM is a convenient
+   way to install those dependencies.
+2. Try the :doc:`microTVM with TFLite Tutorial </tutorials/micro/micro_tflite>`.
+3. Try running a more complex `CIFAR10-CNN model <https://github.com/areusch/microtvm-blogpost-eval>`_.
+
+
+How microTVM Works
+~~~~~~~~~~~~~~~~~~
+
+
+You can read more about the design of these pieces at the :doc:`microTVM Design Document </dev/microtvm_design>`.
+
+
+Help and Discussion
+~~~~~~~~~~~~~~~~~~~
+
+The `TVM Discuss Forum <https://discuss.tvm.ai>`_ is a great place to collaborate on microTVM tasks,
+and maintains a searchable history of past problems.
diff --git a/golang/Makefile b/golang/Makefile
index 6fd77996e119..137e2a488e29 100644
--- a/golang/Makefile
+++ b/golang/Makefile
@@ -25,7 +25,7 @@ NATIVE_SRC = tvm_runtime_pack.cc
 GOPATH=$(CURDIR)/gopath
 GOPATHDIR=${GOPATH}/src/${TARGET}/
 CGO_CPPFLAGS="-I. -I${TVM_BASE}/ -I${TVM_BASE}/3rdparty/dmlc-core/include -I${TVM_BASE}/include -I${TVM_BASE}/3rdparty/dlpack/include/"
-CGO_CXXFLAGS="-std=c++14"
+CGO_CXXFLAGS="-std=c++14 -DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>"
 CGO_CFLAGS="-I${TVM_BASE}"
 CGO_LDFLAGS="-ldl -lm"
 
diff --git a/golang/sample/gen_mobilenet_lib.py b/golang/sample/gen_mobilenet_lib.py
index b82e0c476b9f..12f215b4fd9c 100644
--- a/golang/sample/gen_mobilenet_lib.py
+++ b/golang/sample/gen_mobilenet_lib.py
@@ -16,7 +16,7 @@
 # under the License.
 
 import os
-from tvm import relay, transform
+from tvm import relay, transform, runtime
 from tvm.contrib.download import download_testdata
 
 
@@ -94,4 +94,4 @@ def extract(path):
     fo.write(graph)
 
 with open("./mobilenet.params", "wb") as fo:
-    fo.write(relay.save_param_dict(params))
+    fo.write(runtime.save_param_dict(params))
diff --git a/include/tvm/arith/bound.h b/include/tvm/arith/bound.h
index 12b91cc033e5..f8e63ed5857a 100644
--- a/include/tvm/arith/bound.h
+++ b/include/tvm/arith/bound.h
@@ -25,7 +25,7 @@
 
 #include <tvm/arith/int_set.h>
 #include <tvm/ir/expr.h>
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/tir/expr.h>
 #include <tvm/tir/stmt.h>
 
diff --git a/include/tvm/arith/pattern.h b/include/tvm/arith/pattern.h
index 301d95636ca4..3f1096b10a8b 100644
--- a/include/tvm/arith/pattern.h
+++ b/include/tvm/arith/pattern.h
@@ -25,7 +25,7 @@
 #define TVM_ARITH_PATTERN_H_
 
 #include <tvm/ir/expr.h>
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/tir/expr.h>
 
 namespace tvm {
diff --git a/include/tvm/auto_scheduler/compute_dag.h b/include/tvm/auto_scheduler/compute_dag.h
index 1e3f09721279..a87563e348f7 100755
--- a/include/tvm/auto_scheduler/compute_dag.h
+++ b/include/tvm/auto_scheduler/compute_dag.h
@@ -262,6 +262,13 @@ class ComputeDAG : public ObjectRef {
    */
   String PrintStepsAsPython(const Array<Step>& transform_steps) const;
 
+  /*!
+   * \brief Print the compute DAG to a string. This is also used to generate the ComputeDAG hash.
+   * \param simple_mode Simple mode will only include the op names and brief compute.
+   * \return The ComputeDAG in a string.
+   */
+  String PrintDAG(bool simple_mode = false) const;
+
   /*!
    * \brief Fill the correct bound information for a given state by calling ir_pass::InferBound.
    * The states can lose complete bound information after some transform steps (e.g., compute_at).
diff --git a/include/tvm/auto_scheduler/measure_record.h b/include/tvm/auto_scheduler/measure_record.h
index ec40611d49b4..c82ed076eca7 100755
--- a/include/tvm/auto_scheduler/measure_record.h
+++ b/include/tvm/auto_scheduler/measure_record.h
@@ -34,7 +34,7 @@
 namespace tvm {
 namespace auto_scheduler {
 
-const std::string AUTO_SCHEDULER_LOG_VERSION = "v0.5";  // NOLINT(*)
+const std::string AUTO_SCHEDULER_LOG_VERSION = "v0.6";  // NOLINT(*)
 
 /*! \brief Callback for logging the input and results of measurements to file */
 class RecordToFileNode : public MeasureCallbackNode {
diff --git a/include/tvm/auto_scheduler/search_task.h b/include/tvm/auto_scheduler/search_task.h
index 9e7d3aa2cd32..14bf55abb447 100755
--- a/include/tvm/auto_scheduler/search_task.h
+++ b/include/tvm/auto_scheduler/search_task.h
@@ -26,6 +26,7 @@
 #define TVM_AUTO_SCHEDULER_SEARCH_TASK_H_
 
 #include <tvm/auto_scheduler/compute_dag.h>
+#include <tvm/runtime/ndarray.h>
 #include <tvm/target/target.h>
 
 namespace tvm {
@@ -120,6 +121,8 @@ class SearchTaskNode : public Object {
   HardwareParams hardware_params;
   /*! \brief The layout rewrite option used for measuring programs. */
   LayoutRewriteOption layout_rewrite_option;
+  /*! \brief Names of some user defined input data used in program measuring. */
+  Array<String> task_input_names;
 
   void VisitAttrs(tvm::AttrVisitor* v) {
     v->Visit("compute_dag", &compute_dag);
@@ -128,6 +131,7 @@ class SearchTaskNode : public Object {
     v->Visit("target_host", &target_host);
     v->Visit("hardware_params", &hardware_params);
     v->Visit("layout_rewrite_option", &layout_rewrite_option);
+    v->Visit("task_input_names", &task_input_names);
   }
 
   static constexpr const char* _type_key = "auto_scheduler.SearchTask";
@@ -148,9 +152,11 @@ class SearchTask : public ObjectRef {
    * \param target_host The target host device of this search task.
    * \param hardware_params Hardware parameters used in this search task.
    * \param layout_rewrite_option The layout rewrite option used for measuring programs.
+   * \param task_input_names Names of some user defined input data used in program measuring.
    */
   SearchTask(ComputeDAG compute_dag, String workload_key, Target target, Target target_host,
-             Optional<HardwareParams> hardware_params, LayoutRewriteOption layout_rewrite_option);
+             Optional<HardwareParams> hardware_params, LayoutRewriteOption layout_rewrite_option,
+             Array<String> task_input_names);
 
   TVM_DEFINE_OBJECT_REF_METHODS(SearchTask, ObjectRef, SearchTaskNode);
 };
diff --git a/include/tvm/ir/adt.h b/include/tvm/ir/adt.h
index 466a4f00fd5f..231c04e69821 100644
--- a/include/tvm/ir/adt.h
+++ b/include/tvm/ir/adt.h
@@ -29,8 +29,8 @@
 
 #include <tvm/ir/expr.h>
 #include <tvm/ir/type.h>
-#include <tvm/node/container.h>
 #include <tvm/node/node.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/object.h>
 
 #include <string>
diff --git a/include/tvm/ir/attrs.h b/include/tvm/ir/attrs.h
index 13bfd715cdfb..da7bc12619bd 100644
--- a/include/tvm/ir/attrs.h
+++ b/include/tvm/ir/attrs.h
@@ -92,12 +92,12 @@ inline DataType NullValue<DataType>() {
 }
 
 /*! \brief Error thrown during attribute checking. */
-struct AttrError : public dmlc::Error {
+struct AttrError : public Error {
   /*!
    * \brief constructor
    * \param msg error message
    */
-  explicit AttrError(std::string msg) : dmlc::Error("AttributeError:" + msg) {}
+  explicit AttrError(std::string msg) : Error("AttributeError:" + msg) {}
 };
 
 /*!
@@ -146,7 +146,7 @@ class BaseAttrsNode : public Object {
   virtual void VisitAttrs(AttrVisitor* v) {}
   /*!
    * \brief Initialize the attributes by sequence of arguments
-   * \param args The postional arguments in the form
+   * \param args The positional arguments in the form
    *        [key0, value0, key1, value1, ..., key_n, value_n]
    */
   template <typename... Args>
diff --git a/include/tvm/ir/diagnostic.h b/include/tvm/ir/diagnostic.h
index 2053a295a3b8..41130a5be0aa 100644
--- a/include/tvm/ir/diagnostic.h
+++ b/include/tvm/ir/diagnostic.h
@@ -37,6 +37,15 @@ namespace tvm {
 using tvm::parser::SourceMap;
 using tvm::runtime::TypedPackedFunc;
 
+/*! \brief The diagnostic level, controls the printing of the message. */
+enum class DiagnosticLevel : int {
+  kBug = 10,
+  kError = 20,
+  kWarning = 30,
+  kNote = 40,
+  kHelp = 50,
+};
+
 class DiagnosticBuilder;
 
 /*! \brief A compiler diagnostic. */
diff --git a/include/tvm/ir/error.h b/include/tvm/ir/error.h
index ac7b96a3bd59..6ff61781ac44 100644
--- a/include/tvm/ir/error.h
+++ b/include/tvm/ir/error.h
@@ -36,11 +36,11 @@ namespace tvm {
 /*!
  * \brief A wrapper around std::stringstream to build error.
  *
- * Can be consumed by Error to construct an error.
+ * Can be consumed by CompileError to construct an error.
  *
  * \code
  *
- * void ReportError(const Error& err);
+ * void ReportError(const CompileError& err);
  *
  * void Test(int number) {
  *   // Use error reporter to construct an error.
@@ -59,13 +59,13 @@ struct ErrorBuilder {
 
  private:
   std::stringstream stream_;
-  friend class Error;
+  friend class CompileError;
 };
 
 /*!
  * \brief Custom Error class to be thrown during compilation.
  */
-class Error : public dmlc::Error {
+class CompileError : public Error {
  public:
   /*! \brief Location of the error */
   Span span;
@@ -73,20 +73,20 @@ class Error : public dmlc::Error {
    * \brief construct error from message.
    * \param msg The message
    */
-  explicit Error(const std::string& msg) : dmlc::Error(msg), span(nullptr) {}
+  explicit CompileError(const std::string& msg) : Error(msg), span(nullptr) {}
   /*!
    * \brief construct error from error builder.
    * \param err The error builder
    */
-  Error(const ErrorBuilder& err) : dmlc::Error(err.stream_.str()), span(nullptr) {}  // NOLINT(*)
+  CompileError(const ErrorBuilder& err) : Error(err.stream_.str()), span(nullptr) {}  // NOLINT(*)
   /*!
    * \brief copy constructor.
    * \param other The other ereor.
    */
-  Error(const Error& other) : dmlc::Error(other.what()), span(other.span) {}  // NOLINT(*)
+  CompileError(const CompileError& other) : Error(other.what()), span(other.span) {}  // NOLINT(*)
   /*!
    * \brief default constructor. */
-  Error() : dmlc::Error(""), span(nullptr) {}
+  CompileError() : Error(""), span(nullptr) {}
 };
 
 /*!
@@ -115,13 +115,13 @@ class ErrorReporter {
   ErrorReporter() : errors_(), node_to_error_() {}
 
   /*!
-   * \brief Report a tvm::Error.
+   * \brief Report a CompileError.
    *
    * This API is useful for reporting spanned errors.
    *
    * \param err The error to report.
    */
-  void Report(const Error& err) {
+  void Report(const CompileError& err) {
     if (!err.span.defined()) {
       throw err;
     }
@@ -143,7 +143,7 @@ class ErrorReporter {
    */
   void ReportAt(const GlobalVar& global, const ObjectRef& node, std::stringstream& err) {
     std::string err_msg = err.str();
-    this->ReportAt(global, node, Error(err_msg));
+    this->ReportAt(global, node, CompileError(err_msg));
   }
 
   /*!
@@ -158,7 +158,7 @@ class ErrorReporter {
    * \param node The expression or type to report the error at.
    * \param err The error to report.
    */
-  void ReportAt(const GlobalVar& global, const ObjectRef& node, const Error& err);
+  void ReportAt(const GlobalVar& global, const ObjectRef& node, const CompileError& err);
 
   /*!
    * \brief Render all reported errors and exit the program.
@@ -176,7 +176,7 @@ class ErrorReporter {
   inline bool AnyErrors() { return errors_.size() != 0; }
 
  private:
-  std::vector<Error> errors_;
+  std::vector<CompileError> errors_;
   std::unordered_map<ObjectRef, std::vector<size_t>, ObjectPtrHash, ObjectPtrEqual> node_to_error_;
   std::unordered_map<ObjectRef, GlobalVar, ObjectPtrHash, ObjectPtrEqual> node_to_gv_;
 };
diff --git a/include/tvm/ir/expr.h b/include/tvm/ir/expr.h
index 5302a55bfff3..2295baa0297b 100644
--- a/include/tvm/ir/expr.h
+++ b/include/tvm/ir/expr.h
@@ -26,8 +26,8 @@
 
 #include <tvm/ir/span.h>
 #include <tvm/ir/type.h>
-#include <tvm/node/container.h>
 #include <tvm/node/node.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/object.h>
 
 #include <algorithm>
diff --git a/include/tvm/ir/module.h b/include/tvm/ir/module.h
index d6fb6a20b58a..07d582a298e4 100644
--- a/include/tvm/ir/module.h
+++ b/include/tvm/ir/module.h
@@ -28,8 +28,8 @@
 #include <tvm/ir/expr.h>
 #include <tvm/ir/function.h>
 #include <tvm/ir/type.h>
-#include <tvm/node/container.h>
 #include <tvm/parser/source_map.h>
+#include <tvm/runtime/container.h>
 
 #include <string>
 #include <unordered_map>
diff --git a/include/tvm/ir/op.h b/include/tvm/ir/op.h
index c73be3c1e564..9456ea80d860 100644
--- a/include/tvm/ir/op.h
+++ b/include/tvm/ir/op.h
@@ -146,7 +146,7 @@ class OpNode : public RelayExprNode {
   // Internal function to compute if it is primitive op
   bool IsPrimitiveOp_() const {
     const auto& fn_ty = this->op_type;
-    ICHECK(fn_ty.get() != nullptr);
+    ICHECK(fn_ty.get() != nullptr) << "op_type of " << this->name << "is not registered";
     if (fn_ty->type_constraints.size() != 1) return false;
     const TypeRelationNode* rel = fn_ty->type_constraints[0].as<TypeRelationNode>();
     if (rel == nullptr) return false;
diff --git a/include/tvm/ir/transform.h b/include/tvm/ir/transform.h
index 56905ded5201..50c6f8dd8c3a 100644
--- a/include/tvm/ir/transform.h
+++ b/include/tvm/ir/transform.h
@@ -59,7 +59,6 @@
 #include <tvm/ir/diagnostic.h>
 #include <tvm/ir/error.h>
 #include <tvm/ir/module.h>
-#include <tvm/node/container.h>
 #include <tvm/runtime/container.h>
 #include <tvm/support/with.h>
 
@@ -349,11 +348,8 @@ class Pass : public ObjectRef {
    *
    * \return The transformed module.
    */
-  IRModule operator()(IRModule mod) const {
-    const PassNode* node = operator->();
-    ICHECK(node != nullptr);
-    return node->operator()(std::move(mod));
-  }
+  IRModule operator()(IRModule mod) const;
+
   /*!
    * \brief Transform mod using a functor under a given pass context.
    *
@@ -362,11 +358,7 @@ class Pass : public ObjectRef {
    *
    * \return The transformed module.
    */
-  IRModule operator()(IRModule mod, const PassContext& pass_ctx) const {
-    const PassNode* node = operator->();
-    ICHECK(node != nullptr);
-    return node->operator()(std::move(mod), pass_ctx);
-  }
+  IRModule operator()(IRModule mod, const PassContext& pass_ctx) const;
 
   TVM_DEFINE_OBJECT_REF_METHODS(Pass, ObjectRef, PassNode);
 };
diff --git a/include/tvm/ir/type.h b/include/tvm/ir/type.h
index 19b1ad0a0d83..b93a41e0c098 100644
--- a/include/tvm/ir/type.h
+++ b/include/tvm/ir/type.h
@@ -50,8 +50,8 @@
 #define TVM_IR_TYPE_H_
 
 #include <tvm/ir/span.h>
-#include <tvm/node/container.h>
 #include <tvm/node/node.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/data_type.h>
 #include <tvm/runtime/object.h>
 
diff --git a/include/tvm/ir/type_relation.h b/include/tvm/ir/type_relation.h
index 462588006c9b..dd6861750a10 100644
--- a/include/tvm/ir/type_relation.h
+++ b/include/tvm/ir/type_relation.h
@@ -29,7 +29,7 @@
 #include <tvm/ir/env_func.h>
 #include <tvm/ir/module.h>
 #include <tvm/ir/type.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 namespace tvm {
 
diff --git a/include/tvm/node/attr_registry_map.h b/include/tvm/node/attr_registry_map.h
index 552aa7114657..6acd2e7dbdd8 100644
--- a/include/tvm/node/attr_registry_map.h
+++ b/include/tvm/node/attr_registry_map.h
@@ -23,7 +23,7 @@
 #ifndef TVM_NODE_ATTR_REGISTRY_MAP_H_
 #define TVM_NODE_ATTR_REGISTRY_MAP_H_
 
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 
 #include <utility>
 #include <vector>
diff --git a/include/tvm/node/container.h b/include/tvm/node/container.h
deleted file mode 100644
index 209bb9e72f33..000000000000
--- a/include/tvm/node/container.h
+++ /dev/null
@@ -1,1485 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-/*!
- * \file tvm/node/container.h
- * \brief Array/Map container in the DSL graph.
- */
-#ifndef TVM_NODE_CONTAINER_H_
-#define TVM_NODE_CONTAINER_H_
-
-#ifndef USE_FALLBACK_STL_MAP
-#define USE_FALLBACK_STL_MAP 0
-#endif
-
-#include <tvm/runtime/container.h>
-#include <tvm/runtime/memory.h>
-#include <tvm/runtime/object.h>
-#include <tvm/runtime/packed_func.h>
-
-#include <algorithm>
-#include <string>
-#include <utility>
-
-namespace tvm {
-
-using runtime::Array;
-using runtime::ArrayNode;
-using runtime::Downcast;
-using runtime::IterAdapter;
-using runtime::make_object;
-using runtime::Object;
-using runtime::ObjectEqual;
-using runtime::ObjectHash;
-using runtime::ObjectPtr;
-using runtime::ObjectPtrEqual;
-using runtime::ObjectPtrHash;
-using runtime::ObjectRef;
-using runtime::String;
-using runtime::StringObj;
-
-#if (USE_FALLBACK_STL_MAP != 0)
-
-/*! \brief Shared content of all specializations of hash map */
-class MapNode : public Object {
- public:
-  /*! \brief Type of the keys in the hash map */
-  using key_type = ObjectRef;
-  /*! \brief Type of the values in the hash map */
-  using mapped_type = ObjectRef;
-  /*! \brief Type of the actual underlying container */
-  using ContainerType = std::unordered_map<ObjectRef, ObjectRef, ObjectHash, ObjectEqual>;
-  /*! \brief Iterator class */
-  using iterator = ContainerType::iterator;
-  /*! \brief Iterator class */
-  using const_iterator = ContainerType::const_iterator;
-  /*! \brief Type of value stored in the hash map */
-  using KVType = ContainerType::value_type;
-
-  static_assert(std::is_standard_layout<KVType>::value, "KVType is not standard layout");
-  static_assert(sizeof(KVType) == 16 || sizeof(KVType) == 8, "sizeof(KVType) incorrect");
-
-  static constexpr const uint32_t _type_index = runtime::TypeIndex::kRuntimeMap;
-  static constexpr const char* _type_key = "Map";
-  TVM_DECLARE_FINAL_OBJECT_INFO(MapNode, Object);
-
-  /*!
-   * \brief Number of elements in the SmallMapNode
-   * \return The result
-   */
-  size_t size() const { return data_.size(); }
-  /*!
-   * \brief Count the number of times a key exists in the hash map
-   * \param key The indexing key
-   * \return The result, 0 or 1
-   */
-  size_t count(const key_type& key) const { return data_.count(key); }
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The const reference to the value
-   */
-  const mapped_type& at(const key_type& key) const { return data_.at(key); }
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The mutable reference to the value
-   */
-  mapped_type& at(const key_type& key) { return data_.at(key); }
-  /*! \return begin iterator */
-  iterator begin() { return data_.begin(); }
-  /*! \return const begin iterator */
-  const_iterator begin() const { return data_.begin(); }
-  /*! \return end iterator */
-  iterator end() { return data_.end(); }
-  /*! \return end iterator */
-  const_iterator end() const { return data_.end(); }
-  /*!
-   * \brief Index value associated with a key
-   * \param key The indexing key
-   * \return The iterator of the entry associated with the key, end iterator if not exists
-   */
-  const_iterator find(const key_type& key) const { return data_.find(key); }
-  /*!
-   * \brief Index value associated with a key
-   * \param key The indexing key
-   * \return The iterator of the entry associated with the key, end iterator if not exists
-   */
-  iterator find(const key_type& key) { return data_.find(key); }
-  /*!
-   * \brief Erase the entry associated with the iterator
-   * \param position The iterator
-   */
-  void erase(const iterator& position) { data_.erase(position); }
-  /*!
-   * \brief Erase the entry associated with the key, do nothing if not exists
-   * \param key The indexing key
-   */
-  void erase(const key_type& key) { data_.erase(key); }
-  /*!
-   * \brief Create an empty container
-   * \return The object created
-   */
-  static ObjectPtr<MapNode> Empty() { return make_object<MapNode>(); }
-
- protected:
-  /*!
-   * \brief Create the map using contents from the given iterators.
-   * \param first Begin of iterator
-   * \param last End of iterator
-   * \tparam IterType The type of iterator
-   * \return ObjectPtr to the map created
-   */
-  template <typename IterType>
-  static ObjectPtr<Object> CreateFromRange(IterType first, IterType last) {
-    ObjectPtr<MapNode> p = make_object<MapNode>();
-    p->data_ = ContainerType(first, last);
-    return p;
-  }
-  /*!
-   * \brief InsertMaybeReHash an entry into the given hash map
-   * \param kv The entry to be inserted
-   * \param map The pointer to the map, can be changed if re-hashing happens
-   */
-  static void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
-    MapNode* map_node = static_cast<MapNode*>(map->get());
-    map_node->data_[kv.first] = kv.second;
-  }
-  /*!
-   * \brief Create an empty container with elements copying from another MapNode
-   * \param from The source container
-   * \return The object created
-   */
-  static ObjectPtr<MapNode> CopyFrom(MapNode* from) {
-    ObjectPtr<MapNode> p = make_object<MapNode>();
-    p->data_ = ContainerType(from->data_.begin(), from->data_.end());
-    return p;
-  }
-  /*! \brief The real container storing data */
-  ContainerType data_;
-  template <typename, typename, typename, typename>
-  friend class Map;
-};
-
-#else
-
-/*! \brief Shared content of all specializations of hash map */
-class MapNode : public Object {
- public:
-  /*! \brief Type of the keys in the hash map */
-  using key_type = ObjectRef;
-  /*! \brief Type of the values in the hash map */
-  using mapped_type = ObjectRef;
-  /*! \brief Type of value stored in the hash map */
-  using KVType = std::pair<ObjectRef, ObjectRef>;
-  /*! \brief Iterator class */
-  class iterator;
-
-  static_assert(std::is_standard_layout<KVType>::value, "KVType is not standard layout");
-  static_assert(sizeof(KVType) == 16 || sizeof(KVType) == 8, "sizeof(KVType) incorrect");
-
-  static constexpr const uint32_t _type_index = runtime::TypeIndex::kRuntimeMap;
-  static constexpr const char* _type_key = "Map";
-  TVM_DECLARE_FINAL_OBJECT_INFO(MapNode, Object);
-
-  /*!
-   * \brief Number of elements in the SmallMapNode
-   * \return The result
-   */
-  size_t size() const { return size_; }
-  /*!
-   * \brief Count the number of times a key exists in the hash map
-   * \param key The indexing key
-   * \return The result, 0 or 1
-   */
-  size_t count(const key_type& key) const;
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The const reference to the value
-   */
-  const mapped_type& at(const key_type& key) const;
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The mutable reference to the value
-   */
-  mapped_type& at(const key_type& key);
-  /*! \return begin iterator */
-  iterator begin() const;
-  /*! \return end iterator */
-  iterator end() const;
-  /*!
-   * \brief Index value associated with a key
-   * \param key The indexing key
-   * \return The iterator of the entry associated with the key, end iterator if not exists
-   */
-  iterator find(const key_type& key) const;
-  /*!
-   * \brief Erase the entry associated with the iterator
-   * \param position The iterator
-   */
-  void erase(const iterator& position);
-  /*!
-   * \brief Erase the entry associated with the key, do nothing if not exists
-   * \param key The indexing key
-   */
-  void erase(const key_type& key) { erase(find(key)); }
-
-  class iterator {
-   public:
-    using iterator_category = std::forward_iterator_tag;
-    using difference_type = int64_t;
-    using value_type = KVType;
-    using pointer = KVType*;
-    using reference = KVType&;
-    /*! \brief Default constructor */
-    iterator() : index(0), self(nullptr) {}
-    /*! \brief Compare iterators */
-    bool operator==(const iterator& other) const {
-      return index == other.index && self == other.self;
-    }
-    /*! \brief Compare iterators */
-    bool operator!=(const iterator& other) const { return !(*this == other); }
-    /*! \brief De-reference iterators */
-    pointer operator->() const;
-    /*! \brief De-reference iterators */
-    reference operator*() const { return *((*this).operator->()); }
-    /*! \brief Prefix self increment, e.g. ++iter */
-    iterator& operator++();
-    /*! \brief Prefix self decrement, e.g. --iter */
-    iterator& operator--();
-    /*! \brief Suffix self increment */
-    iterator operator++(int) {
-      iterator copy = *this;
-      ++(*this);
-      return copy;
-    }
-    /*! \brief Suffix self decrement */
-    iterator operator--(int) {
-      iterator copy = *this;
-      --(*this);
-      return copy;
-    }
-
-   protected:
-    /*! \brief Construct by value */
-    iterator(uint64_t index, const MapNode* self) : index(index), self(self) {}
-    /*! \brief The position on the array */
-    uint64_t index;
-    /*! \brief The container it points to */
-    const MapNode* self;
-
-    friend class DenseMapNode;
-    friend class SmallMapNode;
-  };
-  /*!
-   * \brief Create an empty container
-   * \return The object created
-   */
-  static inline ObjectPtr<MapNode> Empty();
-
- protected:
-  /*!
-   * \brief Create the map using contents from the given iterators.
-   * \param first Begin of iterator
-   * \param last End of iterator
-   * \tparam IterType The type of iterator
-   * \return ObjectPtr to the map created
-   */
-  template <typename IterType>
-  static inline ObjectPtr<Object> CreateFromRange(IterType first, IterType last);
-  /*!
-   * \brief InsertMaybeReHash an entry into the given hash map
-   * \param kv The entry to be inserted
-   * \param map The pointer to the map, can be changed if re-hashing happens
-   */
-  static inline void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map);
-  /*!
-   * \brief Create an empty container with elements copying from another SmallMapNode
-   * \param from The source container
-   * \return The object created
-   */
-  static inline ObjectPtr<MapNode> CopyFrom(MapNode* from);
-  /*! \brief number of slots minus 1 */
-  uint64_t slots_;
-  /*! \brief number of entries in the container */
-  uint64_t size_;
-  // Reference class
-  template <typename, typename, typename, typename>
-  friend class Map;
-};
-
-/*! \brief A specialization of small-sized hash map */
-class SmallMapNode : public MapNode,
-                     public runtime::InplaceArrayBase<SmallMapNode, MapNode::KVType> {
- private:
-  static constexpr uint64_t kInitSize = 2;
-  static constexpr uint64_t kMaxSize = 4;
-
- public:
-  using MapNode::iterator;
-  using MapNode::KVType;
-
-  /*! \brief Defaults to the destructor of InplaceArrayBase */
-  ~SmallMapNode() = default;
-  /*!
-   * \brief Count the number of times a key exists in the SmallMapNode
-   * \param key The indexing key
-   * \return The result, 0 or 1
-   */
-  size_t count(const key_type& key) const { return find(key).index < size_; }
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The const reference to the value
-   */
-  const mapped_type& at(const key_type& key) const {
-    iterator itr = find(key);
-    ICHECK(itr.index < size_) << "IndexError: key is not in Map";
-    return itr->second;
-  }
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The mutable reference to the value
-   */
-  mapped_type& at(const key_type& key) {
-    iterator itr = find(key);
-    ICHECK(itr.index < size_) << "IndexError: key is not in Map";
-    return itr->second;
-  }
-  /*! \return begin iterator */
-  iterator begin() const { return iterator(0, this); }
-  /*! \return end iterator */
-  iterator end() const { return iterator(size_, this); }
-  /*!
-   * \brief Index value associated with a key
-   * \param key The indexing key
-   * \return The iterator of the entry associated with the key, end iterator if not exists
-   */
-  iterator find(const key_type& key) const {
-    KVType* ptr = static_cast<KVType*>(AddressOf(0));
-    for (uint64_t i = 0; i < size_; ++i, ++ptr) {
-      if (ObjectEqual()(ptr->first, key)) {
-        return iterator(i, this);
-      }
-    }
-    return iterator(size_, this);
-  }
-  /*!
-   * \brief Erase the entry associated with the iterator
-   * \param position The iterator
-   */
-  void erase(const iterator& position) { Erase(position.index); }
-
- private:
-  /*!
-   * \brief Remove a position in SmallMapNode
-   * \param index The position to be removed
-   */
-  void Erase(const uint64_t index) {
-    if (index >= size_) {
-      return;
-    }
-    KVType* begin = static_cast<KVType*>(AddressOf(0));
-    KVType* last = begin + (size_ - 1);
-    if (index + 1 == size_) {
-      last->first.ObjectRef::~ObjectRef();
-      last->second.ObjectRef::~ObjectRef();
-    } else {
-      *(begin + index) = std::move(*last);
-    }
-    size_ -= 1;
-  }
-  /*!
-   * \brief Create an empty container
-   * \param n Number of empty slots
-   * \return The object created
-   */
-  static ObjectPtr<SmallMapNode> Empty(uint64_t n = kInitSize) {
-    using ::tvm::runtime::make_inplace_array_object;
-    ObjectPtr<SmallMapNode> p = make_inplace_array_object<SmallMapNode, KVType>(n);
-    p->size_ = 0;
-    p->slots_ = n;
-    return p;
-  }
-  /*!
-   * \brief Create an empty container initialized with a given range
-   * \param n Number of empty slots
-   * \param first begin of iterator
-   * \param last end of iterator
-   * \tparam IterType The type of iterator
-   * \return The object created
-   */
-  template <typename IterType>
-  static ObjectPtr<SmallMapNode> CreateFromRange(uint64_t n, IterType first, IterType last) {
-    ObjectPtr<SmallMapNode> p = Empty(n);
-    KVType* ptr = static_cast<KVType*>(p->AddressOf(0));
-    for (; first != last; ++first, ++p->size_) {
-      new (ptr++) KVType(*first);
-    }
-    return p;
-  }
-  /*!
-   * \brief Create an empty container with elements copying from another SmallMapNode
-   * \param from The source container
-   * \return The object created
-   */
-  static ObjectPtr<SmallMapNode> CopyFrom(SmallMapNode* from) {
-    KVType* first = static_cast<KVType*>(from->AddressOf(0));
-    KVType* last = first + from->size_;
-    return CreateFromRange(from->size_, first, last);
-  }
-  /*!
-   * \brief InsertMaybeReHash an entry into the given hash map
-   * \param kv The entry to be inserted
-   * \param map The pointer to the map, can be changed if re-hashing happens
-   */
-  static void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
-    SmallMapNode* map_node = static_cast<SmallMapNode*>(map->get());
-    iterator itr = map_node->find(kv.first);
-    if (itr.index < map_node->size_) {
-      itr->second = kv.second;
-      return;
-    }
-    if (map_node->size_ < map_node->slots_) {
-      KVType* ptr = static_cast<KVType*>(map_node->AddressOf(map_node->size_));
-      new (ptr) KVType(kv);
-      ++map_node->size_;
-      return;
-    }
-    uint64_t next_size = std::max(map_node->slots_ * 2, uint64_t(kInitSize));
-    next_size = std::min(next_size, uint64_t(kMaxSize));
-    ICHECK_GT(next_size, map_node->slots_);
-    ObjectPtr<Object> new_map = CreateFromRange(next_size, map_node->begin(), map_node->end());
-    InsertMaybeReHash(kv, &new_map);
-    *map = std::move(new_map);
-  }
-  /*!
-   * \brief Increment the pointer
-   * \param index The pointer to be incremented
-   * \return The increased pointer
-   */
-  uint64_t IncItr(uint64_t index) const { return index + 1 < size_ ? index + 1 : size_; }
-  /*!
-   * \brief Decrement the pointer
-   * \param index The pointer to be decremented
-   * \return The decreased pointer
-   */
-  uint64_t DecItr(uint64_t index) const { return index > 0 ? index - 1 : size_; }
-  /*!
-   * \brief De-reference the pointer
-   * \param index The pointer to be dereferenced
-   * \return The result
-   */
-  KVType* DeRefItr(uint64_t index) const { return static_cast<KVType*>(AddressOf(index)); }
-  /*! \brief A size function used by InplaceArrayBase */
-  uint64_t GetSize() const { return size_; }
-
- protected:
-  friend class MapNode;
-  friend class DenseMapNode;
-  friend class runtime::InplaceArrayBase<SmallMapNode, MapNode::KVType>;
-};
-
-/*! \brief A specialization of hash map that implements the idea of array-based hash map.
- * Another reference implementation can be found [1].
- *
- * A. Overview
- *
- * DenseMapNode did several improvements over traditional separate chaining hash,
- * in terms of cache locality, memory footprints and data organization.
- *
- * A1. Implicit linked list. For better cache locality, instead of using linked list
- * explicitly for each bucket, we store list data into a single array that spans contiguously
- * in memory, and then carefully design access patterns to make sure most of them fall into
- * a single cache line.
- *
- * A2. 1-byte metadata. There is only 1 byte overhead for each slot in the array to indexing and
- * traversal. This can be divided in 3 parts.
- * 1) Reserved code: (0b11111111)_2 indicates a slot is empty; (0b11111110)_2 indicates protected,
- * which means the slot is empty but not allowed to be written.
- * 2) If not empty or protected, the highest bit is used to indicate whether data in the slot is
- * head of a linked list.
- * 3) The rest 7 bits are used as the "next pointer" (i.e. pointer to the next element). On 64-bit
- * architecture, an ordinary pointer can take up to 8 bytes, which is not acceptable overhead when
- * dealing with 16-byte ObjectRef pairs. Based on a commonly noticed fact that the lists are
- * relatively short (length <= 3) in hash maps, we follow [1]'s idea that only allows the pointer to
- * be one of the 126 possible values, i.e. if the next element of i-th slot is (i + x)-th element,
- * then x must be one of the 126 pre-defined values.
- *
- * A3. Data blocking. We organize the array in the way that every 16 elements forms a data block.
- * The 16-byte metadata of those 16 elements are stored together, followed by the real data, i.e.
- * 16 key-value pairs.
- *
- * B. Implementation details
- *
- * B1. Power-of-2 table size and Fibonacci Hashing. We use power-of-two as table size to avoid
- * modulo for more efficient arithmetics. To make the hash-to-slot mapping distribute more evenly,
- * we use the Fibonacci Hashing [2] trick.
- *
- * B2. Traverse a linked list in the array.
- * 1) List head. Assume Fibonacci Hashing maps a given key to slot i, if metadata at slot i
- * indicates that it is list head, then we found the head; otherwise the list is empty. No probing
- * is done in this procedure. 2) Next element. To find the next element of a non-empty slot i, we
- * look at the last 7 bits of the metadata at slot i. If they are all zeros, then it is the end of
- * list; otherwise, we know that the next element is (i + candidates[the-last-7-bits]).
- *
- * B3. InsertMaybeReHash an element. Following B2, we first traverse the linked list to see if this
- * element is in the linked list, and if not, we put it at the end by probing the next empty
- * position in one of the 126 candidate positions. If the linked list does not even exist, but the
- * slot for list head has been occupied by another linked list, we should find this intruder another
- * place.
- *
- * B4. Quadratic probing with triangle numbers. In open address hashing, it is provable that probing
- * with triangle numbers can traverse power-of-2-sized table [3]. In our algorithm, we follow the
- * suggestion in [1] that also use triangle numbers for "next pointer" as well as sparing for list
- * head.
- *
- * [1] https://github.com/skarupke/flat_hash_map
- * [2] https://programmingpraxis.com/2018/06/19/fibonacci-hash/
- * [3] https://fgiesen.wordpress.com/2015/02/22/triangular-numbers-mod-2n/
- */
-class DenseMapNode : public MapNode {
- private:
-  /*! \brief The number of elements in a memory block */
-  static constexpr int kBlockCap = 16;
-  /*! \brief Maximum load factor of the hash map */
-  static constexpr double kMaxLoadFactor = 0.99;
-  /*! \brief Binary representation of the metadata of an empty slot */
-  static constexpr uint8_t kEmptySlot = uint8_t(0b11111111);
-  /*! \brief Binary representation of the metadata of a protected slot */
-  static constexpr uint8_t kProtectedSlot = uint8_t(0b11111110);
-  /*! \brief Number of probing choices available */
-  static constexpr int kNumJumpDists = 126;
-  /*! \brief Head of the implicit linked list */
-  struct ListNode;
-  /*! \brief POD type of a block of memory */
-  struct Block {
-    uint8_t bytes[kBlockCap + kBlockCap * sizeof(KVType)];
-  };
-  static_assert(sizeof(Block) == kBlockCap * (sizeof(KVType) + 1), "sizeof(Block) incorrect");
-  static_assert(std::is_standard_layout<Block>::value, "Block is not standard layout");
-
- public:
-  using MapNode::iterator;
-
-  /*!
-   * \brief Destroy the DenseMapNode
-   */
-  ~DenseMapNode() { this->Reset(); }
-  /*! \return The number of elements of the key */
-  size_t count(const key_type& key) const { return !Search(key).IsNone(); }
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The const reference to the value
-   */
-  const mapped_type& at(const key_type& key) const { return At(key); }
-  /*!
-   * \brief Index value associated with a key, throw exception if the key does not exist
-   * \param key The indexing key
-   * \return The mutable reference to the value
-   */
-  mapped_type& at(const key_type& key) { return At(key); }
-  /*!
-   * \brief Index value associated with a key
-   * \param key The indexing key
-   * \return The iterator of the entry associated with the key, end iterator if not exists
-   */
-  iterator find(const key_type& key) const {
-    ListNode node = Search(key);
-    return node.IsNone() ? end() : iterator(node.index, this);
-  }
-  /*!
-   * \brief Erase the entry associated with the iterator
-   * \param position The iterator
-   */
-  void erase(const iterator& position) {
-    uint64_t index = position.index;
-    if (position.self != nullptr && index <= this->slots_) {
-      Erase(ListNode(index, this));
-    }
-  }
-  /*! \return begin iterator */
-  iterator begin() const {
-    if (slots_ == 0) {
-      return iterator(0, this);
-    }
-    for (uint64_t index = 0; index <= slots_; ++index) {
-      if (!ListNode(index, this).IsEmpty()) {
-        return iterator(index, this);
-      }
-    }
-    return iterator(slots_ + 1, this);
-  }
-  /*! \return end iterator */
-  iterator end() const { return slots_ == 0 ? iterator(0, this) : iterator(slots_ + 1, this); }
-
- private:
-  /*!
-   * \brief Search for the given key
-   * \param key The key
-   * \return ListNode that associated with the key
-   */
-  ListNode Search(const key_type& key) const {
-    if (this->size_ == 0) {
-      return ListNode();
-    }
-    for (ListNode iter = GetListHead(ObjectHash()(key)); !iter.IsNone(); iter.MoveToNext(this)) {
-      if (ObjectEqual()(key, iter.Key())) {
-        return iter;
-      }
-    }
-    return ListNode();
-  }
-  /*!
-   * \brief Search for the given key, throw exception if not exists
-   * \param key The key
-   * \return ListNode that associated with the key
-   */
-  mapped_type& At(const key_type& key) const {
-    ListNode iter = Search(key);
-    ICHECK(!iter.IsNone()) << "IndexError: key is not in Map";
-    return iter.Val();
-  }
-  /*!
-   * \brief Try to insert a key, or do nothing if already exists
-   * \param key The indexing key
-   * \param result The linked-list entry found or just constructed
-   * \return A boolean, indicating if actual insertion happens
-   */
-  bool TryInsert(const key_type& key, ListNode* result) {
-    if (slots_ == 0) {
-      return false;
-    }
-    // required that `iter` to be the head of a linked list through which we can iterator
-    ListNode iter = IndexFromHash(ObjectHash()(key));
-    // `iter` can be: 1) empty; 2) body of an irrelevant list; 3) head of the relevant list
-    // Case 1: empty
-    if (iter.IsEmpty()) {
-      iter.NewHead(KVType(key, ObjectRef(nullptr)));
-      this->size_ += 1;
-      *result = iter;
-      return true;
-    }
-    // Case 2: body of an irrelevant list
-    if (!iter.IsHead()) {
-      // we move the elements around and construct the single-element linked list
-      return IsFull() ? false : TrySpareListHead(iter, key, result);
-    }
-    // Case 3: head of the relevant list
-    // we iterate through the linked list until the end
-    // make sure `iter` is the previous element of `next`
-    ListNode next = iter;
-    do {
-      // find equal item, do not insert
-      if (ObjectEqual()(key, next.Key())) {
-        *result = next;
-        return true;
-      }
-      // make sure `iter` is the previous element of `next`
-      iter = next;
-    } while (next.MoveToNext(this));
-    // `iter` is the tail of the linked list
-    // always check capacity before insertion
-    if (IsFull()) {
-      return false;
-    }
-    // find the next empty slot
-    uint8_t jump;
-    if (!iter.GetNextEmpty(this, &jump, result)) {
-      return false;
-    }
-    result->NewTail(KVType(key, ObjectRef(nullptr)));
-    // link `iter` to `empty`, and move forward
-    iter.SetJump(jump);
-    this->size_ += 1;
-    return true;
-  }
-  /*!
-   * \brief Spare an entry to be the head of a linked list.
-   * As described in B3, during insertion, it is possible that the entire linked list does not
-   * exist, but the slot of its head has been occupied by other linked lists. In this case, we need
-   * to spare the slot by moving away the elements to another valid empty one to make insertion
-   * possible.
-   * \param target The given entry to be spared
-   * \param key The indexing key
-   * \param result The linked-list entry constructed as the head
-   * \return A boolean, if actual insertion happens
-   */
-  bool TrySpareListHead(ListNode target, const key_type& key, ListNode* result) {
-    // `target` is not the head of the linked list
-    // move the original item of `target` (if any)
-    // and construct new item on the position `target`
-    // To make `target` empty, we
-    // 1) find `w` the previous element of `target` in the linked list
-    // 2) copy the linked list starting from `r = target`
-    // 3) paste them after `w`
-    // read from the linked list after `r`
-    ListNode r = target;
-    // write to the tail of `w`
-    ListNode w = target.FindPrev(this);
-    // after `target` is moved, we disallow writing to the slot
-    bool is_first = true;
-    uint8_t r_meta, jump;
-    ListNode empty;
-    do {
-      // `jump` describes how `w` is jumped to `empty`
-      // rehash if there is no empty space after `w`
-      if (!w.GetNextEmpty(this, &jump, &empty)) {
-        return false;
-      }
-      // move `r` to `empty`
-      empty.NewTail(std::move(r.Data()));
-      // clear the metadata of `r`
-      r_meta = r.Meta();
-      if (is_first) {
-        is_first = false;
-        r.SetProtected();
-      } else {
-        r.SetEmpty();
-      }
-      // link `w` to `empty`, and move forward
-      w.SetJump(jump);
-      w = empty;
-      // move `r` forward as well
-    } while (r.MoveToNext(this, r_meta));
-    // finally we have done moving the linked list
-    // fill data_ into `target`
-    target.NewHead(KVType(key, ObjectRef(nullptr)));
-    this->size_ += 1;
-    *result = target;
-    return true;
-  }
-  /*!
-   * \brief Remove a ListNode
-   * \param iter The node to be removed
-   */
-  void Erase(const ListNode& iter) {
-    this->size_ -= 1;
-    if (!iter.HasNext()) {
-      // `iter` is the last
-      if (!iter.IsHead()) {
-        // cut the link if there is any
-        iter.FindPrev(this).SetJump(0);
-      }
-      iter.Data().KVType::~KVType();
-      iter.SetEmpty();
-    } else {
-      ListNode last = iter, prev = iter;
-      for (last.MoveToNext(this); last.HasNext(); prev = last, last.MoveToNext(this)) {
-      }
-      iter.Data() = std::move(last.Data());
-      last.SetEmpty();
-      prev.SetJump(0);
-    }
-  }
-  /*! \brief Clear the container to empty, release all entries and memory acquired */
-  void Reset() {
-    uint64_t n_blocks = CalcNumBlocks(this->slots_);
-    for (uint64_t bi = 0; bi < n_blocks; ++bi) {
-      uint8_t* meta_ptr = data_[bi].bytes;
-      KVType* data_ptr = reinterpret_cast<KVType*>(data_[bi].bytes + kBlockCap);
-      for (int j = 0; j < kBlockCap; ++j, ++meta_ptr, ++data_ptr) {
-        uint8_t& meta = *meta_ptr;
-        if (meta != uint8_t(kProtectedSlot) && meta != uint8_t(kEmptySlot)) {
-          meta = uint8_t(kEmptySlot);
-          data_ptr->KVType::~KVType();
-        }
-      }
-    }
-    ReleaseMemory();
-  }
-  /*! \brief Release the memory acquired by the container without deleting its entries stored inside
-   */
-  void ReleaseMemory() {
-    delete[] data_;
-    data_ = nullptr;
-    slots_ = 0;
-    size_ = 0;
-    fib_shift_ = 63;
-  }
-  /*!
-   * \brief Create an empty container
-   * \param fib_shift The fib shift provided
-   * \param n_slots Number of slots required, should be power-of-two
-   * \return The object created
-   */
-  static ObjectPtr<DenseMapNode> Empty(uint32_t fib_shift, uint64_t n_slots) {
-    ICHECK_GT(n_slots, uint64_t(SmallMapNode::kMaxSize));
-    ObjectPtr<DenseMapNode> p = make_object<DenseMapNode>();
-    uint64_t n_blocks = CalcNumBlocks(n_slots - 1);
-    Block* block = p->data_ = new Block[n_blocks];
-    p->slots_ = n_slots - 1;
-    p->size_ = 0;
-    p->fib_shift_ = fib_shift;
-    for (uint64_t i = 0; i < n_blocks; ++i, ++block) {
-      std::fill(block->bytes, block->bytes + kBlockCap, uint8_t(kEmptySlot));
-    }
-    return p;
-  }
-  /*!
-   * \brief Create an empty container with elements copying from another DenseMapNode
-   * \param from The source container
-   * \return The object created
-   */
-  static ObjectPtr<DenseMapNode> CopyFrom(DenseMapNode* from) {
-    ObjectPtr<DenseMapNode> p = make_object<DenseMapNode>();
-    uint64_t n_blocks = CalcNumBlocks(from->slots_);
-    p->data_ = new Block[n_blocks];
-    p->slots_ = from->slots_;
-    p->size_ = from->size_;
-    p->fib_shift_ = from->fib_shift_;
-    for (uint64_t bi = 0; bi < n_blocks; ++bi) {
-      uint8_t* meta_ptr_from = from->data_[bi].bytes;
-      KVType* data_ptr_from = reinterpret_cast<KVType*>(from->data_[bi].bytes + kBlockCap);
-      uint8_t* meta_ptr_to = p->data_[bi].bytes;
-      KVType* data_ptr_to = reinterpret_cast<KVType*>(p->data_[bi].bytes + kBlockCap);
-      for (int j = 0; j < kBlockCap;
-           ++j, ++meta_ptr_from, ++data_ptr_from, ++meta_ptr_to, ++data_ptr_to) {
-        uint8_t& meta = *meta_ptr_to = *meta_ptr_from;
-        ICHECK(meta != kProtectedSlot);
-        if (meta != uint8_t(kEmptySlot)) {
-          new (data_ptr_to) KVType(*data_ptr_from);
-        }
-      }
-    }
-    return p;
-  }
-  /*!
-   * \brief InsertMaybeReHash an entry into the given hash map
-   * \param kv The entry to be inserted
-   * \param map The pointer to the map, can be changed if re-hashing happens
-   */
-  static void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
-    DenseMapNode* map_node = static_cast<DenseMapNode*>(map->get());
-    ListNode iter;
-    // Try to insert. If succeed, we simply return
-    if (map_node->TryInsert(kv.first, &iter)) {
-      iter.Val() = kv.second;
-      return;
-    }
-    ICHECK_GT(map_node->slots_, uint64_t(SmallMapNode::kMaxSize));
-    // Otherwise, start rehash
-    ObjectPtr<Object> p = Empty(map_node->fib_shift_ - 1, map_node->slots_ * 2 + 2);
-    // Insert the given `kv` into the new hash map
-    InsertMaybeReHash(kv, &p);
-    uint64_t n_blocks = CalcNumBlocks(map_node->slots_);
-    // Then Insert data from the original block.
-    for (uint64_t bi = 0; bi < n_blocks; ++bi) {
-      uint8_t* meta_ptr = map_node->data_[bi].bytes;
-      KVType* data_ptr = reinterpret_cast<KVType*>(map_node->data_[bi].bytes + kBlockCap);
-      for (int j = 0; j < kBlockCap; ++j, ++meta_ptr, ++data_ptr) {
-        uint8_t& meta = *meta_ptr;
-        if (meta != uint8_t(kProtectedSlot) && meta != uint8_t(kEmptySlot)) {
-          meta = uint8_t(kEmptySlot);
-          KVType kv = std::move(*data_ptr);
-          InsertMaybeReHash(kv, &p);
-        }
-      }
-    }
-    map_node->ReleaseMemory();
-    *map = p;
-  }
-  /*!
-   * \brief Check whether the hash table is full
-   * \return A boolean indicating whether hash table is full
-   */
-  bool IsFull() const { return size_ + 1 > (slots_ + 1) * kMaxLoadFactor; }
-  /*!
-   * \brief Increment the pointer
-   * \param index The pointer to be incremented
-   * \return The increased pointer
-   */
-  uint64_t IncItr(uint64_t index) const {
-    for (++index; index <= slots_; ++index) {
-      if (!ListNode(index, this).IsEmpty()) {
-        return index;
-      }
-    }
-    return slots_ + 1;
-  }
-  /*!
-   * \brief Decrement the pointer
-   * \param index The pointer to be decremented
-   * \return The decreased pointer
-   */
-  uint64_t DecItr(uint64_t index) const {
-    while (index != 0) {
-      index -= 1;
-      if (!ListNode(index, this).IsEmpty()) {
-        return index;
-      }
-    }
-    return slots_ + 1;
-  }
-  /*!
-   * \brief De-reference the pointer
-   * \param index The pointer to be dereferenced
-   * \return The result
-   */
-  KVType* DeRefItr(uint64_t index) const { return &ListNode(index, this).Data(); }
-  /*! \brief Construct from hash code */
-  ListNode IndexFromHash(uint64_t hash_value) const {
-    return ListNode(FibHash(hash_value, fib_shift_), this);
-  }
-  /*! \brief Construct from hash code if the position is head of list */
-  ListNode GetListHead(uint64_t hash_value) const {
-    ListNode node = IndexFromHash(hash_value);
-    return node.IsHead() ? node : ListNode();
-  }
-  /*! \brief Construct the number of blocks in the hash table */
-  static uint64_t CalcNumBlocks(uint64_t n_slots_m1) {
-    uint64_t n_slots = n_slots_m1 > 0 ? n_slots_m1 + 1 : 0;
-    return (n_slots + kBlockCap - 1) / kBlockCap;
-  }
-  /*!
-   * \brief Calculate the power-of-2 table size given the lower-bound of required capacity.
-   * \param cap The lower-bound of the required capacity
-   * \param fib_shift The result shift for Fibonacci Hashing
-   * \param n_slots The result number of slots
-   */
-  static void CalcTableSize(uint64_t cap, uint32_t* fib_shift, uint64_t* n_slots) {
-    uint32_t shift = 64;
-    uint64_t slots = 1;
-    for (uint64_t c = cap; c; c >>= 1) {
-      shift -= 1;
-      slots <<= 1;
-    }
-    ICHECK_GT(slots, cap);
-    if (slots < cap * 2) {
-      *fib_shift = shift - 1;
-      *n_slots = slots << 1;
-    } else {
-      *fib_shift = shift;
-      *n_slots = slots;
-    }
-  }
-  /*!
-   * \brief Fibonacci Hashing, maps a hash code to an index in a power-of-2-sized table.
-   * See also: https://programmingpraxis.com/2018/06/19/fibonacci-hash/.
-   * \param hash_value The raw hash value
-   * \param fib_shift The shift in Fibonacci Hashing
-   * \return An index calculated using Fibonacci Hashing
-   */
-  static uint64_t FibHash(uint64_t hash_value, uint32_t fib_shift) {
-    constexpr uint64_t coeff = 11400714819323198485ull;
-    return (coeff * hash_value) >> fib_shift;
-  }
-  /*! \brief The implicit in-place linked list used to index a chain */
-  struct ListNode {
-    /*! \brief Construct None */
-    ListNode() : index(0), block(nullptr) {}
-    /*! \brief Construct from position */
-    ListNode(uint64_t index, const DenseMapNode* self)
-        : index(index), block(self->data_ + (index / kBlockCap)) {}
-    /*! \brief Metadata on the entry */
-    uint8_t& Meta() const { return *(block->bytes + index % kBlockCap); }
-    /*! \brief Data on the entry */
-    KVType& Data() const {
-      return *(reinterpret_cast<KVType*>(block->bytes + kBlockCap +
-                                         (index % kBlockCap) * sizeof(KVType)));
-    }
-    /*! \brief Key on the entry */
-    key_type& Key() const { return Data().first; }
-    /*! \brief Value on the entry */
-    mapped_type& Val() const { return Data().second; }
-    /*! \brief If the entry is head of linked list */
-    bool IsHead() const { return (Meta() & 0b10000000) == 0b00000000; }
-    /*! \brief If the entry is none */
-    bool IsNone() const { return block == nullptr; }
-    /*! \brief If the entry is empty slot */
-    bool IsEmpty() const { return Meta() == uint8_t(kEmptySlot); }
-    /*! \brief If the entry is protected slot */
-    bool IsProtected() const { return Meta() == uint8_t(kProtectedSlot); }
-    /*! \brief Set the entry to be empty */
-    void SetEmpty() const { Meta() = uint8_t(kEmptySlot); }
-    /*! \brief Set the entry to be protected */
-    void SetProtected() const { Meta() = uint8_t(kProtectedSlot); }
-    /*! \brief Set the entry's jump to its next entry */
-    void SetJump(uint8_t jump) const { (Meta() &= 0b10000000) |= jump; }
-    /*! \brief Construct a head of linked list in-place */
-    void NewHead(KVType v) const {
-      Meta() = 0b00000000;
-      new (&Data()) KVType(std::move(v));
-    }
-    /*! \brief Construct a tail of linked list in-place */
-    void NewTail(KVType v) const {
-      Meta() = 0b10000000;
-      new (&Data()) KVType(std::move(v));
-    }
-    /*! \brief If the entry has next entry on the linked list */
-    bool HasNext() const { return kNextProbeLocation[Meta() & 0b01111111] != 0; }
-    /*! \brief Move the entry to the next entry on the linked list */
-    bool MoveToNext(const DenseMapNode* self, uint8_t meta) {
-      uint64_t offset = kNextProbeLocation[meta & 0b01111111];
-      if (offset == 0) {
-        index = 0;
-        block = nullptr;
-        return false;
-      }
-      index = (index + offset) & (self->slots_);
-      block = self->data_ + (index / kBlockCap);
-      return true;
-    }
-    /*! \brief Move the entry to the next entry on the linked list */
-    bool MoveToNext(const DenseMapNode* self) { return MoveToNext(self, Meta()); }
-    /*! \brief Get the previous entry on the linked list */
-    ListNode FindPrev(const DenseMapNode* self) const {
-      // start from the head of the linked list, which must exist
-      ListNode next = self->IndexFromHash(ObjectHash()(Key()));
-      // `prev` is always the previous item of `next`
-      ListNode prev = next;
-      for (next.MoveToNext(self); index != next.index; prev = next, next.MoveToNext(self)) {
-      }
-      return prev;
-    }
-    /*! \brief Get the next empty jump */
-    bool GetNextEmpty(const DenseMapNode* self, uint8_t* jump, ListNode* result) const {
-      for (uint8_t idx = 1; idx < kNumJumpDists; ++idx) {
-        ListNode candidate((index + kNextProbeLocation[idx]) & (self->slots_), self);
-        if (candidate.IsEmpty()) {
-          *jump = idx;
-          *result = candidate;
-          return true;
-        }
-      }
-      return false;
-    }
-    /*! \brief Index on the real array */
-    uint64_t index;
-    /*! \brief Pointer to the actual block */
-    Block* block;
-  };
-
- protected:
-  /*! \brief fib shift in Fibonacci Hashing */
-  uint32_t fib_shift_;
-  /*! \brief array of data blocks */
-  Block* data_;
-  /* clang-format off */
-  /*! \brief Candidates of probing distance */
-  TVM_DLL static constexpr uint64_t kNextProbeLocation[kNumJumpDists] {
-    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
-    // Quadratic probing with triangle numbers. See also:
-    // 1) https://en.wikipedia.org/wiki/Quadratic_probing
-    // 2) https://fgiesen.wordpress.com/2015/02/22/triangular-numbers-mod-2n/
-    // 3) https://github.com/skarupke/flat_hash_map
-    21, 28, 36, 45, 55, 66, 78, 91, 105, 120,
-    136, 153, 171, 190, 210, 231, 253, 276, 300, 325,
-    351, 378, 406, 435, 465, 496, 528, 561, 595, 630,
-    666, 703, 741, 780, 820, 861, 903, 946, 990, 1035,
-    1081, 1128, 1176, 1225, 1275, 1326, 1378, 1431, 1485, 1540,
-    1596, 1653, 1711, 1770, 1830, 1891, 1953, 2016, 2080, 2145,
-    2211, 2278, 2346, 2415, 2485, 2556, 2628,
-    // larger triangle numbers
-    8515, 19110, 42778, 96141, 216153,
-    486591, 1092981, 2458653, 5532801, 12442566,
-    27993903, 62983476, 141717030, 318844378, 717352503,
-    1614057336, 3631522476, 8170957530, 18384510628, 41364789378,
-    93070452520, 209408356380, 471168559170, 1060128894105, 2385289465695,
-    5366898840628, 12075518705635, 27169915244790, 61132312065111, 137547689707000,
-    309482283181501, 696335127828753, 1566753995631385, 3525196511162271, 7931691992677701,
-    17846306936293605, 40154190677507445, 90346928918121501, 203280589587557251, 457381325854679626,
-    1029107982097042876, 2315492959180353330, 5209859154120846435,
-  };
-  /* clang-format on */
-  friend class MapNode;
-};
-
-#define TVM_DISPATCH_MAP(base, var, body)     \
-  {                                           \
-    using TSmall = SmallMapNode*;             \
-    using TDense = DenseMapNode*;             \
-    uint64_t slots = base->slots_;            \
-    if (slots <= SmallMapNode::kMaxSize) {    \
-      TSmall var = static_cast<TSmall>(base); \
-      body;                                   \
-    } else {                                  \
-      TDense var = static_cast<TDense>(base); \
-      body;                                   \
-    }                                         \
-  }
-
-#define TVM_DISPATCH_MAP_CONST(base, var, body) \
-  {                                             \
-    using TSmall = const SmallMapNode*;         \
-    using TDense = const DenseMapNode*;         \
-    uint64_t slots = base->slots_;              \
-    if (slots <= SmallMapNode::kMaxSize) {      \
-      TSmall var = static_cast<TSmall>(base);   \
-      body;                                     \
-    } else {                                    \
-      TDense var = static_cast<TDense>(base);   \
-      body;                                     \
-    }                                           \
-  }
-
-inline MapNode::iterator::pointer MapNode::iterator::operator->() const {
-  TVM_DISPATCH_MAP_CONST(self, p, { return p->DeRefItr(index); });
-}
-
-inline MapNode::iterator& MapNode::iterator::operator++() {
-  TVM_DISPATCH_MAP_CONST(self, p, {
-    index = p->IncItr(index);
-    return *this;
-  });
-}
-
-inline MapNode::iterator& MapNode::iterator::operator--() {
-  TVM_DISPATCH_MAP_CONST(self, p, {
-    index = p->IncItr(index);
-    return *this;
-  });
-}
-
-inline size_t MapNode::count(const key_type& key) const {
-  TVM_DISPATCH_MAP_CONST(this, p, { return p->count(key); });
-}
-
-inline const MapNode::mapped_type& MapNode::at(const MapNode::key_type& key) const {
-  TVM_DISPATCH_MAP_CONST(this, p, { return p->at(key); });
-}
-
-inline MapNode::mapped_type& MapNode::at(const MapNode::key_type& key) {
-  TVM_DISPATCH_MAP(this, p, { return p->at(key); });
-}
-
-inline MapNode::iterator MapNode::begin() const {
-  TVM_DISPATCH_MAP_CONST(this, p, { return p->begin(); });
-}
-
-inline MapNode::iterator MapNode::end() const {
-  TVM_DISPATCH_MAP_CONST(this, p, { return p->end(); });
-}
-
-inline MapNode::iterator MapNode::find(const MapNode::key_type& key) const {
-  TVM_DISPATCH_MAP_CONST(this, p, { return p->find(key); });
-}
-
-inline void MapNode::erase(const MapNode::iterator& position) {
-  TVM_DISPATCH_MAP(this, p, { return p->erase(position); });
-}
-
-#undef TVM_DISPATCH_MAP
-#undef TVM_DISPATCH_MAP_CONST
-
-inline ObjectPtr<MapNode> MapNode::Empty() { return SmallMapNode::Empty(); }
-
-inline ObjectPtr<MapNode> MapNode::CopyFrom(MapNode* from) {
-  if (from->slots_ <= SmallMapNode::kMaxSize) {
-    return SmallMapNode::CopyFrom(static_cast<SmallMapNode*>(from));
-  } else {
-    return DenseMapNode::CopyFrom(static_cast<DenseMapNode*>(from));
-  }
-}
-
-template <typename IterType>
-inline ObjectPtr<Object> MapNode::CreateFromRange(IterType first, IterType last) {
-  int64_t _cap = std::distance(first, last);
-  if (_cap < 0) {
-    return SmallMapNode::Empty();
-  }
-  uint64_t cap = static_cast<uint64_t>(_cap);
-  if (cap < SmallMapNode::kMaxSize) {
-    return SmallMapNode::CreateFromRange(cap, first, last);
-  }
-  uint32_t fib_shift;
-  uint64_t n_slots;
-  DenseMapNode::CalcTableSize(cap, &fib_shift, &n_slots);
-  ObjectPtr<Object> obj = DenseMapNode::Empty(fib_shift, n_slots);
-  for (; first != last; ++first) {
-    KVType kv(*first);
-    DenseMapNode::InsertMaybeReHash(kv, &obj);
-  }
-  return obj;
-}
-
-inline void MapNode::InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
-  constexpr uint64_t kSmallMapMaxSize = SmallMapNode::kMaxSize;
-  MapNode* base = static_cast<MapNode*>(map->get());
-  if (base->slots_ < kSmallMapMaxSize) {
-    SmallMapNode::InsertMaybeReHash(kv, map);
-  } else if (base->slots_ == kSmallMapMaxSize) {
-    if (base->size_ < base->slots_) {
-      SmallMapNode::InsertMaybeReHash(kv, map);
-    } else {
-      ObjectPtr<Object> new_map = MapNode::CreateFromRange(base->begin(), base->end());
-      DenseMapNode::InsertMaybeReHash(kv, &new_map);
-      *map = std::move(new_map);
-    }
-  } else {
-    DenseMapNode::InsertMaybeReHash(kv, map);
-  }
-}
-
-namespace runtime {
-template <>
-inline ObjectPtr<MapNode> make_object<>() = delete;
-}  // namespace runtime
-
-#endif
-
-/*!
- * \brief Map container of NodeRef->NodeRef in DSL graph.
- *  Map implements copy on write semantics, which means map is mutable
- *  but copy will happen when array is referenced in more than two places.
- *
- * operator[] only provide const acces, use Set to mutate the content.
- * \tparam K The key NodeRef type.
- * \tparam V The value NodeRef type.
- */
-template <typename K, typename V,
-          typename = typename std::enable_if<std::is_base_of<ObjectRef, K>::value>::type,
-          typename = typename std::enable_if<std::is_base_of<ObjectRef, V>::value>::type>
-class Map : public ObjectRef {
- public:
-  using key_type = K;
-  using mapped_type = V;
-  class iterator;
-  /*!
-   * \brief default constructor
-   */
-  Map() { data_ = MapNode::Empty(); }
-  /*!
-   * \brief move constructor
-   * \param other source
-   */
-  Map(Map<K, V>&& other) { data_ = std::move(other.data_); }
-  /*!
-   * \brief copy constructor
-   * \param other source
-   */
-  Map(const Map<K, V>& other) : ObjectRef(other.data_) {}
-  /*!
-   * \brief copy assign operator
-   * \param other The source of assignment
-   * \return reference to self.
-   */
-  Map<K, V>& operator=(Map<K, V>&& other) {
-    data_ = std::move(other.data_);
-    return *this;
-  }
-  /*!
-   * \brief move assign operator
-   * \param other The source of assignment
-   * \return reference to self.
-   */
-  Map<K, V>& operator=(const Map<K, V>& other) {
-    data_ = other.data_;
-    return *this;
-  }
-  /*!
-   * \brief constructor from pointer
-   * \param n the container pointer
-   */
-  explicit Map(ObjectPtr<Object> n) : ObjectRef(n) {}
-  /*!
-   * \brief constructor from iterator
-   * \param begin begin of iterator
-   * \param end end of iterator
-   * \tparam IterType The type of iterator
-   */
-  template <typename IterType>
-  Map(IterType begin, IterType end) {
-    data_ = MapNode::CreateFromRange(begin, end);
-  }
-  /*!
-   * \brief constructor from initializer list
-   * \param init The initalizer list
-   */
-  Map(std::initializer_list<std::pair<K, V>> init) {
-    data_ = MapNode::CreateFromRange(init.begin(), init.end());
-  }
-  /*!
-   * \brief constructor from unordered_map
-   * \param init The unordered_map
-   */
-  template <typename Hash, typename Equal>
-  Map(const std::unordered_map<K, V, Hash, Equal>& init) {  // NOLINT(*)
-    data_ = MapNode::CreateFromRange(init.begin(), init.end());
-  }
-  /*!
-   * \brief Read element from map.
-   * \param key The key
-   * \return the corresonding element.
-   */
-  const V at(const K& key) const { return DowncastNoCheck<V>(GetMapNode()->at(key)); }
-  /*!
-   * \brief Read element from map.
-   * \param key The key
-   * \return the corresonding element.
-   */
-  const V operator[](const K& key) const { return this->at(key); }
-  /*! \return The size of the array */
-  size_t size() const {
-    MapNode* n = GetMapNode();
-    return n == nullptr ? 0 : n->size();
-  }
-  /*! \return The number of elements of the key */
-  size_t count(const K& key) const {
-    MapNode* n = GetMapNode();
-    return n == nullptr ? 0 : GetMapNode()->count(key);
-  }
-  /*! \return whether array is empty */
-  bool empty() const { return size() == 0; }
-  /*!
-   * \brief set the Map.
-   * \param key The index key.
-   * \param value The value to be setted.
-   */
-  void Set(const K& key, const V& value) {
-    CopyOnWrite();
-    MapNode::InsertMaybeReHash(MapNode::KVType(key, value), &data_);
-  }
-  /*! \return begin iterator */
-  iterator begin() const { return iterator(GetMapNode()->begin()); }
-  /*! \return end iterator */
-  iterator end() const { return iterator(GetMapNode()->end()); }
-  /*! \return find the key and returns the associated iterator */
-  iterator find(const K& key) const { return iterator(GetMapNode()->find(key)); }
-
-  void erase(const K& key) { CopyOnWrite()->erase(key); }
-
-  /*!
-   * \brief copy on write semantics
-   *  Do nothing if current handle is the unique copy of the array.
-   *  Otherwise make a new copy of the array to ensure the current handle
-   *  hold a unique copy.
-   *
-   * \return Handle to the internal node container(which ganrantees to be unique)
-   */
-  MapNode* CopyOnWrite() {
-    if (data_.get() == nullptr) {
-      data_ = MapNode::Empty();
-    } else if (!data_.unique()) {
-      data_ = MapNode::CopyFrom(GetMapNode());
-    }
-    return GetMapNode();
-  }
-  /*! \brief specify container node */
-  using ContainerType = MapNode;
-
-  /*! \brief Iterator of the hash map */
-  class iterator {
-   public:
-    using iterator_category = std::bidirectional_iterator_tag;
-    using difference_type = int64_t;
-    using value_type = const std::pair<K, V>;
-    using pointer = value_type*;
-    using reference = value_type;
-
-    iterator() : itr() {}
-
-    /*! \brief Compare iterators */
-    bool operator==(const iterator& other) const { return itr == other.itr; }
-    /*! \brief Compare iterators */
-    bool operator!=(const iterator& other) const { return itr != other.itr; }
-    /*! \brief De-reference iterators is not allowed */
-    pointer operator->() const = delete;
-    /*! \brief De-reference iterators */
-    reference operator*() const {
-      auto& kv = *itr;
-      return std::make_pair(DowncastNoCheck<K>(kv.first), DowncastNoCheck<V>(kv.second));
-    }
-    /*! \brief Prefix self increment, e.g. ++iter */
-    iterator& operator++() {
-      ++itr;
-      return *this;
-    }
-    /*! \brief Suffix self increment */
-    iterator operator++(int) {
-      iterator copy = *this;
-      ++(*this);
-      return copy;
-    }
-
-   private:
-    iterator(const MapNode::iterator& itr)  // NOLINT(*)
-        : itr(itr) {}
-
-    template <typename, typename, typename, typename>
-    friend class Map;
-
-    MapNode::iterator itr;
-  };
-
- private:
-  /*! \brief Return data_ as type of pointer of MapNode */
-  MapNode* GetMapNode() const { return static_cast<MapNode*>(data_.get()); }
-};
-
-/*!
- * \brief Merge two Maps.
- * \param lhs the first Map to merge.
- * \param rhs the second Map to merge.
- * @return The merged Array. Original Maps are kept unchanged.
- */
-template <typename K, typename V,
-          typename = typename std::enable_if<std::is_base_of<ObjectRef, K>::value>::type,
-          typename = typename std::enable_if<std::is_base_of<ObjectRef, V>::value>::type>
-inline Map<K, V> Merge(Map<K, V> lhs, const Map<K, V>& rhs) {
-  for (const auto& p : rhs) {
-    lhs.Set(p.first, p.second);
-  }
-  return std::move(lhs);
-}
-
-}  // namespace tvm
-
-namespace tvm {
-namespace runtime {
-// Additional overloads for PackedFunc checking.
-template <typename T>
-struct ObjectTypeChecker<Array<T>> {
-  static bool Check(const Object* ptr) {
-    if (ptr == nullptr) return true;
-    if (!ptr->IsInstance<ArrayNode>()) return false;
-    const ArrayNode* n = static_cast<const ArrayNode*>(ptr);
-    for (const ObjectRef& p : *n) {
-      if (!ObjectTypeChecker<T>::Check(p.get())) {
-        return false;
-      }
-    }
-    return true;
-  }
-  static std::string TypeName() { return "Array[" + ObjectTypeChecker<T>::TypeName() + "]"; }
-};
-
-template <typename K, typename V>
-struct ObjectTypeChecker<Map<K, V>> {
-  static bool Check(const Object* ptr) {
-    if (ptr == nullptr) return true;
-    if (!ptr->IsInstance<MapNode>()) return false;
-    const MapNode* n = static_cast<const MapNode*>(ptr);
-    for (const auto& kv : *n) {
-      if (!ObjectTypeChecker<K>::Check(kv.first.get())) return false;
-      if (!ObjectTypeChecker<V>::Check(kv.second.get())) return false;
-    }
-    return true;
-  }
-  static std::string TypeName() {
-    return "Map[" + ObjectTypeChecker<K>::TypeName() + ", " + ObjectTypeChecker<V>::TypeName() +
-           ']';
-  }
-};
-}  // namespace runtime
-}  // namespace tvm
-#endif  // TVM_NODE_CONTAINER_H_
diff --git a/include/tvm/node/node.h b/include/tvm/node/node.h
index 59295c2ce427..7b2a9f8061b4 100644
--- a/include/tvm/node/node.h
+++ b/include/tvm/node/node.h
@@ -34,7 +34,6 @@
 #ifndef TVM_NODE_NODE_H_
 #define TVM_NODE_NODE_H_
 
-#include <tvm/node/container.h>
 #include <tvm/node/reflection.h>
 #include <tvm/node/repr_printer.h>
 #include <tvm/node/structural_equal.h>
diff --git a/include/tvm/node/structural_equal.h b/include/tvm/node/structural_equal.h
index 9424f6dc30f2..d5309bca894d 100644
--- a/include/tvm/node/structural_equal.h
+++ b/include/tvm/node/structural_equal.h
@@ -23,8 +23,8 @@
 #ifndef TVM_NODE_STRUCTURAL_EQUAL_H_
 #define TVM_NODE_STRUCTURAL_EQUAL_H_
 
-#include <tvm/node/container.h>
 #include <tvm/node/functor.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/data_type.h>
 
 #include <string>
diff --git a/include/tvm/node/structural_hash.h b/include/tvm/node/structural_hash.h
index ed89d841cd65..a661a852780d 100644
--- a/include/tvm/node/structural_hash.h
+++ b/include/tvm/node/structural_hash.h
@@ -23,8 +23,8 @@
 #ifndef TVM_NODE_STRUCTURAL_HASH_H_
 #define TVM_NODE_STRUCTURAL_HASH_H_
 
-#include <tvm/node/container.h>
 #include <tvm/node/functor.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/data_type.h>
 
 #include <functional>
diff --git a/include/tvm/relay/analysis.h b/include/tvm/relay/analysis.h
index 5dd837038731..f88b04994099 100644
--- a/include/tvm/relay/analysis.h
+++ b/include/tvm/relay/analysis.h
@@ -29,7 +29,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/function.h>
 #include <tvm/relay/type.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <string>
 #include <unordered_map>
diff --git a/include/tvm/relay/attrs/random.h b/include/tvm/relay/attrs/random.h
new file mode 100644
index 000000000000..8238f102dab8
--- /dev/null
+++ b/include/tvm/relay/attrs/random.h
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file tvm/relay/attrs/vision.h
+ * \brief Auxiliary attributes for random operators.
+ */
+#ifndef TVM_RELAY_ATTRS_RANDOM_H_
+#define TVM_RELAY_ATTRS_RANDOM_H_
+
+#include <tvm/ir/attrs.h>
+
+namespace tvm {
+namespace relay {
+
+struct ThreefryGenerateAttrs : public tvm::AttrsNode<ThreefryGenerateAttrs> {
+  Array<Integer> out_shape;
+
+  TVM_DECLARE_ATTRS(ThreefryGenerateAttrs, "relay.attrs.ThreefryGenerateAttrs") {
+    TVM_ATTR_FIELD(out_shape).describe("Shape of random numbers to generate");
+  }
+};
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_ATTRS_RANDOM_H_
diff --git a/include/tvm/relay/attrs/transform.h b/include/tvm/relay/attrs/transform.h
index cbe989f93558..ff344f5e1a85 100644
--- a/include/tvm/relay/attrs/transform.h
+++ b/include/tvm/relay/attrs/transform.h
@@ -54,7 +54,7 @@ struct ExpandDimsAttrs : public tvm::AttrsNode<ExpandDimsAttrs> {
         "If `axis < 0`, it is the first axis inserted;"
         "If `axis >= 0`, it is the last axis inserted in Python's negative indexing.");
     TVM_ATTR_FIELD(num_newaxis)
-        .describe("Number of axises to be inserted. Should be >= 0.")
+        .describe("Number of axes to be inserted. Should be >= 0.")
         .set_lower_bound(0)
         .set_default(1);
   }
@@ -83,13 +83,9 @@ struct TransposeAttrs : public tvm::AttrsNode<TransposeAttrs> {
 /*! \brief Attributes used in reshape operators */
 struct ReshapeAttrs : public tvm::AttrsNode<ReshapeAttrs> {
   Array<Integer> newshape;
-  bool reverse;
   TVM_DECLARE_ATTRS(ReshapeAttrs, "relay.attrs.ReshapeAttrs") {
     TVM_ATTR_FIELD(newshape).describe(
         "The new shape. Should be compatible with the original shape.");
-    TVM_ATTR_FIELD(reverse)
-        .describe("Infer the special values from right to left if true")
-        .set_default(false);
   }
 };  // struct ReshapeAttrs
 
@@ -442,6 +438,32 @@ struct MatrixSetDiagAttrs : public tvm::AttrsNode<MatrixSetDiagAttrs> {
   }
 };  // struct MatrixSetDiagAttrs
 
+/*! \brief Attributes used in cumsum operator */
+struct CumsumAttrs : public tvm::AttrsNode<CumsumAttrs> {
+  Integer axis;
+  DataType dtype;
+  Integer exclusive;
+  TVM_DECLARE_ATTRS(CumsumAttrs, "relay.attrs.CumsumAttrs") {
+    TVM_ATTR_FIELD(axis).describe("The axis to sum over").set_default(NullValue<Integer>());
+    TVM_ATTR_FIELD(dtype).describe("Output data type").set_default(NullValue<DataType>());
+    TVM_ATTR_FIELD(exclusive)
+        .describe("The first element is not included")
+        .set_default(NullValue<Integer>());
+  }
+};
+
+/*! \brief Attributes used in unique operator */
+struct UniqueAttrs : public tvm::AttrsNode<UniqueAttrs> {
+  bool sorted;
+  bool return_counts;
+  TVM_DECLARE_ATTRS(UniqueAttrs, "relay.attrs.UniqueAttrs") {
+    TVM_ATTR_FIELD(sorted).describe("Whether the unique elements are sorted").set_default(true);
+    TVM_ATTR_FIELD(return_counts)
+        .describe("Whether to return an additional tensor with counts of each unique elements")
+        .set_default(false);
+  }
+};  // struct UniqueAttrs
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_TRANSFORM_H_
diff --git a/include/tvm/relay/attrs/vision.h b/include/tvm/relay/attrs/vision.h
index ca2c4a2b837d..4a96d391430e 100644
--- a/include/tvm/relay/attrs/vision.h
+++ b/include/tvm/relay/attrs/vision.h
@@ -124,6 +124,7 @@ struct ROIAlignAttrs : public tvm::AttrsNode<ROIAlignAttrs> {
   double spatial_scale;
   int sample_ratio;
   std::string layout;
+  std::string mode;
   TVM_DECLARE_ATTRS(ROIAlignAttrs, "relay.attrs.ROIAlignAttrs") {
     TVM_ATTR_FIELD(pooled_size).describe("Output size of roi align.");
     TVM_ATTR_FIELD(spatial_scale)
@@ -139,6 +140,8 @@ struct ROIAlignAttrs : public tvm::AttrsNode<ROIAlignAttrs> {
         "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
         "dimensions respectively. Convolution is applied on the 'H' and"
         "'W' dimensions.");
+    TVM_ATTR_FIELD(mode).set_default("avg").describe(
+        "Mode for ROI Align. Can be 'avg' or 'max'. The default mode is 'avg'.");
   }
 };
 
diff --git a/include/tvm/relay/dataflow_pattern.h b/include/tvm/relay/dataflow_pattern.h
index 909a4fe44eb1..99ef9a237de2 100644
--- a/include/tvm/relay/dataflow_pattern.h
+++ b/include/tvm/relay/dataflow_pattern.h
@@ -27,6 +27,9 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/type.h>
 
+#include <string>
+#include <vector>
+
 namespace tvm {
 namespace relay {
 
@@ -46,6 +49,29 @@ class DFPatternNode : public Object {
  */
 class DFPattern : public ObjectRef {
  public:
+  /*! \brief Syntatic Sugar for creating a CallPattern */
+  DFPattern operator()(const std::vector<DFPattern>& args);
+  /*! \brief Syntatic Sugar for creating a CallPattern with an "add" op */
+  DFPattern operator+(const DFPattern& other);
+  /*! \brief Syntatic Sugar for creating a CallPattern with a "subtract" op */
+  DFPattern operator-(const DFPattern& other);
+  /*! \brief Syntatic Sugar for creating a CallPattern with a "multiply" op */
+  DFPattern operator*(const DFPattern& other);
+  /*! \brief Syntatic Sugar for creating a CallPattern with a "divide" op */
+  DFPattern operator/(const DFPattern& other);
+  /*! \brief Syntatic Sugar for creating an AltPattern */
+  DFPattern operator||(const DFPattern& other);
+  /*! \brief Syntatic Sugar for creating an AttrPattern */
+  DFPattern HasAttr(const Map<String, ObjectRef>& attrs);
+  /*! \brief Syntatic Sugar for creating a TypePattern */
+  DFPattern HasType(const Type& type);
+  /*! \brief Syntatic Sugar for creating a DataTypePattern with a DataType */
+  DFPattern HasDtype(const DataType& dtype);
+  /*! \brief Syntatic Sugar for creating a DataTypePattern with a data type's name */
+  DFPattern HasDtype(const std::string& dtype);
+  /*! \brief Syntatic Sugar for creating a ShapePattern */
+  DFPattern HasShape(const Array<PrimExpr> shape);
+
   TVM_DEFINE_OBJECT_REF_METHODS(DFPattern, ObjectRef, DFPatternNode);
 };
 
@@ -86,20 +112,11 @@ class VarPatternNode : public DFPatternNode {
    * \brief The name of the Var (optional).
    */
   String name;
-  /*!
-   * \brief type annotation of the variable.
-   * This field records user provided type annotation of the Var.
-   * This field is optional and can be None.
-   */
-  Type type_annotation;
 
   /*! \return The name hint of the variable */
   const String& name_hint() const { return name; }
 
-  void VisitAttrs(tvm::AttrVisitor* v) {
-    v->Visit("name", &name);
-    v->Visit("type_annotation", &type_annotation);
-  }
+  void VisitAttrs(tvm::AttrVisitor* v) { v->Visit("name", &name); }
 
   static constexpr const char* _type_key = "relay.dataflow_pattern.VarPattern";
   TVM_DECLARE_FINAL_OBJECT_INFO(VarPatternNode, DFPatternNode);
@@ -107,7 +124,7 @@ class VarPatternNode : public DFPatternNode {
 
 class VarPattern : public DFPattern {
  public:
-  TVM_DLL VarPattern(String name_hint, Type type_annotation);
+  TVM_DLL VarPattern(String name_hint);
   TVM_DEFINE_OBJECT_REF_METHODS(VarPattern, DFPattern, VarPatternNode);
 };
 
@@ -205,6 +222,42 @@ class FunctionPattern : public DFPattern {
   TVM_DEFINE_OBJECT_REF_COW_METHOD(FunctionPatternNode);
 };
 
+/*! \brief A binding of a sub-network. */
+class LetPatternNode : public DFPatternNode {
+ public:
+  /*! \brief The variable we bind to */
+  DFPattern var;
+  /*! \brief The value we bind var to */
+  DFPattern value;
+  /*! \brief The body of the let binding */
+  DFPattern body;
+
+  void VisitAttrs(tvm::AttrVisitor* v) {
+    v->Visit("var", &var);
+    v->Visit("value", &value);
+    v->Visit("body", &body);
+  }
+
+  static constexpr const char* _type_key = "relay.dataflow_pattern.LetPattern";
+  TVM_DECLARE_FINAL_OBJECT_INFO(LetPatternNode, DFPatternNode);
+};
+
+/*!
+ * \brief Let binding that binds a local var
+ */
+class LetPattern : public DFPattern {
+ public:
+  /*!
+   * \brief The constructor
+   * \param var The variable that is bound to.
+   * \param value The value used to bind to the variable.
+   * \param body The body of the let binding.
+   */
+  TVM_DLL LetPattern(DFPattern var, DFPattern value, DFPattern body);
+
+  TVM_DEFINE_OBJECT_REF_METHODS(LetPattern, DFPattern, LetPatternNode);
+};
+
 /*! \brief Tuple of multiple Exprs */
 class TuplePattern;
 /*! \brief Tuple container */
@@ -243,6 +296,26 @@ class TupleGetItemPatternNode : public DFPatternNode {
   TVM_DECLARE_FINAL_OBJECT_INFO(TupleGetItemPatternNode, DFPatternNode);
 };
 
+class IfPatternNode : public DFPatternNode {
+ public:
+  DFPattern cond, true_branch, false_branch;
+
+  void VisitAttrs(tvm::AttrVisitor* v) {
+    v->Visit("cond", &cond);
+    v->Visit("true_branch", &true_branch);
+    v->Visit("false_branch", &false_branch);
+  }
+
+  static constexpr const char* _type_key = "relay.dataflow_pattern.IfPattern";
+  TVM_DECLARE_FINAL_OBJECT_INFO(IfPatternNode, DFPatternNode);
+};
+
+class IfPattern : public DFPattern {
+ public:
+  TVM_DLL IfPattern(DFPattern cond, DFPattern then_clause, DFPattern else_clause);
+  TVM_DEFINE_OBJECT_REF_METHODS(IfPattern, DFPattern, IfPatternNode);
+};
+
 class TupleGetItemPattern : public DFPattern {
  public:
   TVM_DLL TupleGetItemPattern(DFPattern tuple, int index);
@@ -393,7 +466,7 @@ class AttrPatternNode : public DFPatternNode {
   /*! \brief The pattern. */
   DFPattern pattern;
   /*! \brief The attribute to match */
-  Attrs attrs;
+  DictAttrs attrs;
 
   void VisitAttrs(tvm::AttrVisitor* v) {
     v->Visit("pattern", &pattern);
@@ -409,7 +482,7 @@ class AttrPatternNode : public DFPatternNode {
  */
 class AttrPattern : public DFPattern {
  public:
-  TVM_DLL AttrPattern(DFPattern pattern, Attrs attrs);
+  TVM_DLL AttrPattern(DFPattern pattern, DictAttrs attrs);
   TVM_DEFINE_OBJECT_REF_METHODS(AttrPattern, DFPattern, AttrPatternNode);
 };
 
@@ -447,6 +520,21 @@ class DominatorPattern : public DFPattern {
   TVM_DEFINE_OBJECT_REF_METHODS(DominatorPattern, DFPattern, DominatorPatternNode);
 };
 
+/*! \brief Syntatic Sugar for creating a VarPattern with a name */
+DFPattern IsVar(const String& name);
+/*! \brief Syntatic Sugar for creating a ConstantPattern */
+DFPattern IsConstant();
+/*! \brief Syntatic Sugar for creating a WildcardPattern */
+DFPattern IsWildcard();
+/*! \brief Syntatic Sugar for creating a ExprPattern */
+DFPattern IsExpr(const Expr& expr);
+/*! \brief Syntatic Sugar for creating a ExprPattern base on an Op*/
+DFPattern IsOp(const String& op_name);
+/*! \brief Syntatic Sugar for creating a TuplePattern*/
+DFPattern IsTuple(const Array<DFPattern>& fields);
+/*! \brief Syntatic Sugar for creating a TupleGetItemPattern*/
+DFPattern IsTupleGetItem(const DFPattern tuple, int index = -1);
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_DATAFLOW_PATTERN_H_
diff --git a/include/tvm/relay/dataflow_pattern_functor.h b/include/tvm/relay/dataflow_pattern_functor.h
index f04977b86ccb..490cdc5e3f9d 100644
--- a/include/tvm/relay/dataflow_pattern_functor.h
+++ b/include/tvm/relay/dataflow_pattern_functor.h
@@ -84,17 +84,19 @@ class DFPatternFunctor<R(const DFPattern& n, Args...)> {
   virtual R VisitDFPattern_(const AltPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const AttrPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const CallPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
+  virtual R VisitDFPattern_(const ConstantPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const DataTypePatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const DominatorPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const ExprPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const FunctionPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
+  virtual R VisitDFPattern_(const IfPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
+  virtual R VisitDFPattern_(const LetPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const ShapePatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const TupleGetItemPatternNode* op,
                             Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const TuplePatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const TypePatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const VarPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
-  virtual R VisitDFPattern_(const ConstantPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPattern_(const WildcardPatternNode* op, Args... args) DFPATTERN_FUNCTOR_DEFAULT;
   virtual R VisitDFPatternDefault_(const Object* op, Args...) {
     LOG(FATAL) << "Do not have a default for " << op->GetTypeKey();
@@ -114,6 +116,8 @@ class DFPatternFunctor<R(const DFPattern& n, Args...)> {
     RELAY_DFPATTERN_FUNCTOR_DISPATCH(DominatorPatternNode);
     RELAY_DFPATTERN_FUNCTOR_DISPATCH(ExprPatternNode);
     RELAY_DFPATTERN_FUNCTOR_DISPATCH(FunctionPatternNode);
+    RELAY_DFPATTERN_FUNCTOR_DISPATCH(IfPatternNode);
+    RELAY_DFPATTERN_FUNCTOR_DISPATCH(LetPatternNode);
     RELAY_DFPATTERN_FUNCTOR_DISPATCH(ShapePatternNode);
     RELAY_DFPATTERN_FUNCTOR_DISPATCH(TupleGetItemPatternNode);
     RELAY_DFPATTERN_FUNCTOR_DISPATCH(TuplePatternNode);
@@ -141,6 +145,8 @@ class DFPatternVisitor : public DFPatternFunctor<void(const DFPattern&)> {
   void VisitDFPattern_(const DominatorPatternNode* op) override;
   void VisitDFPattern_(const ExprPatternNode* op) override;
   void VisitDFPattern_(const FunctionPatternNode* op) override;
+  void VisitDFPattern_(const IfPatternNode* op) override;
+  void VisitDFPattern_(const LetPatternNode* op) override;
   void VisitDFPattern_(const ShapePatternNode* op) override;
   void VisitDFPattern_(const TupleGetItemPatternNode* op) override;
   void VisitDFPattern_(const TuplePatternNode* op) override;
diff --git a/include/tvm/relay/expr_functor.h b/include/tvm/relay/expr_functor.h
index 8589f8cc4f16..e6eec61a7e9d 100644
--- a/include/tvm/relay/expr_functor.h
+++ b/include/tvm/relay/expr_functor.h
@@ -88,7 +88,8 @@ class ExprFunctor<R(const Expr& n, Args...)> {
    * \return The result of the call
    */
   virtual R VisitExpr(const Expr& n, Args... args) {
-    ICHECK(n.defined());
+    ICHECK(n.defined()) << "Found null pointer node while traversing AST. The previous pass may "
+                           "have generated invalid data.";
     static FType vtable = InitVTable();
     return vtable(n, this, std::forward<Args>(args)...);
   }
@@ -476,6 +477,10 @@ void ExpandDataflow(Expr expr, FCheckVisited fcheck_visited, FVisitLeaf fvisit_l
     }
   }
 }
+
+void ExpandANormalForm(const LetNode* op, std::function<void(const LetNode*)> pre_visit,
+                       std::function<void(const LetNode*)> post_visit);
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_EXPR_FUNCTOR_H_
diff --git a/include/tvm/relay/feature.h b/include/tvm/relay/feature.h
index 7df881938f50..4a5de33af4b9 100644
--- a/include/tvm/relay/feature.h
+++ b/include/tvm/relay/feature.h
@@ -25,8 +25,8 @@
 #define TVM_RELAY_FEATURE_H_
 
 #include <tvm/ir/module.h>
-#include <tvm/node/container.h>
 #include <tvm/relay/expr.h>
+#include <tvm/runtime/container.h>
 
 #include <bitset>
 #include <string>
diff --git a/include/tvm/relay/op_attr_types.h b/include/tvm/relay/op_attr_types.h
index 1e9b86d9e0bc..f916dbeb713f 100644
--- a/include/tvm/relay/op_attr_types.h
+++ b/include/tvm/relay/op_attr_types.h
@@ -83,9 +83,9 @@ using TOpIsStateful = bool;
 using TNonComputational = bool;
 
 /*!
- * \brief Mark the operator whether output shape is data dependant.
+ * \brief Mark the operator whether output shape is data dependent.
  */
-using TShapeDataDependant = bool;
+using TShapeDataDependent = Array<Integer>;
 
 /*!
  * \brief Computation description interface.
diff --git a/include/tvm/relay/qnn/attrs.h b/include/tvm/relay/qnn/attrs.h
index c5213fe07471..f0280a90c604 100644
--- a/include/tvm/relay/qnn/attrs.h
+++ b/include/tvm/relay/qnn/attrs.h
@@ -75,6 +75,18 @@ struct QuantizeAttrs : public tvm::AttrsNode<QuantizeAttrs> {
   }
 };
 
+struct SimulatedQuantizeAttrs : public tvm::AttrsNode<SimulatedQuantizeAttrs> {
+  int axis;
+
+  TVM_DECLARE_ATTRS(SimulatedQuantizeAttrs, "relay.attrs.SimulatedQuantizeAttrs") {
+    TVM_ATTR_FIELD(axis)
+        .describe(
+            "The output channel axis for channel wise quantization. Default value is -1,"
+            "which corresponds to the last axis.")
+        .set_default(-1);
+  }
+};
+
 /*! \brief Attribute for dequantize operator */
 struct DequantizeAttrs : public tvm::AttrsNode<DequantizeAttrs> {
   int axis;
diff --git a/include/tvm/relay/transform.h b/include/tvm/relay/transform.h
index e4b39da85206..123b7e395faa 100644
--- a/include/tvm/relay/transform.h
+++ b/include/tvm/relay/transform.h
@@ -31,6 +31,7 @@
 #include <tvm/relay/op.h>
 #include <tvm/relay/op_attr_types.h>
 #include <tvm/runtime/container.h>
+#include <tvm/target/target.h>
 
 #include <string>
 
@@ -419,6 +420,17 @@ TVM_DLL Pass RemoveUnusedFunctions(Array<runtime::String> entry_functions);
  */
 TVM_DLL Pass SimplifyExpr();
 
+/*!
+ * \brief A pass for manifesting explicit memory allocations and rewriting
+ * specific dialects.
+ *
+ * \param target_host The target used by the host for compliation.
+ * \param targets The device type and target pairs for compliation.
+ *
+ * \return The pass.
+ */
+TVM_DLL Pass ManifestAlloc(Target target_host, Map<tvm::Integer, tvm::Target> targets);
+
 }  // namespace transform
 
 /*!
diff --git a/include/tvm/runtime/c_runtime_api.h b/include/tvm/runtime/c_runtime_api.h
index 467e69a60827..59316a0bace0 100644
--- a/include/tvm/runtime/c_runtime_api.h
+++ b/include/tvm/runtime/c_runtime_api.h
@@ -559,6 +559,23 @@ TVM_DLL int TVMByteArrayFree(TVMByteArray* arr);
 TVM_DLL int TVMDeviceAllocDataSpace(DLContext ctx, size_t nbytes, size_t alignment,
                                     DLDataType type_hint, void** out_data);
 
+/*!
+ * \brief Allocate a data space on device with special memory scope.
+ * \note The memory could use a special multi-dimensional memory layout.
+ *       That is why we pass shape and dtype instead of raw number of bytes.
+ * \param ctx The device context to perform operation.
+ * \param ndim The number of dimension of the tensor.
+ * \param shape The shape of the tensor.
+ * \param dtype The type of elements.
+ * \param mem_scope The memory scope of the tensor,
+ *        can be nullptr, which indicate the default global DRAM
+ * \param out_data The allocated device pointer.
+ * \return 0 when success, -1 when failure happens
+ */
+TVM_DLL int TVMDeviceAllocDataSpaceWithScope(DLContext ctx, int ndim, const int64_t* shape,
+                                             DLDataType dtype, const char* mem_scope,
+                                             void** out_data);
+
 /*!
  * \brief Free a data space on device.
  * \param ctx The device context to perform operation.
@@ -569,22 +586,14 @@ TVM_DLL int TVMDeviceFreeDataSpace(TVMContext ctx, void* ptr);
 
 /*!
  * \brief Copy data from one place to another.
- * \param from The source array.
- * \param from_offset The byte offeset in the from.
- * \param to The target array.
- * \param to_offset The byte offset in the to.
- * \param num_bytes The size of the memory in bytes
- * \param ctx_from The source context
- * \param ctx_to The target context
- * \param type_hint The type of elements, only neded by certain backends.
- *                  can be useful for cross device endian converison.
+ * \note This API is designed to support special memory with shape dependent layout.
+ *       We pass in DLTensor* with shape information to support these cases.
+ * \param from The source tensor.
+ * \param to The target tensor.
  * \param stream Optional stream object.
  * \return 0 when success, -1 when failure happens.
  */
-TVM_DLL int TVMDeviceCopyDataFromTo(const void* from, size_t from_offset, void* to,
-                                    size_t to_offset, size_t num_bytes, TVMContext ctx_from,
-                                    TVMContext ctx_to, DLDataType type_hint,
-                                    TVMStreamHandle stream);
+TVM_DLL int TVMDeviceCopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream);
 
 /*!
  * \brief Check that an object is derived from another.
diff --git a/include/tvm/runtime/container.h b/include/tvm/runtime/container.h
index 796ab7b113c1..362582f4dab9 100644
--- a/include/tvm/runtime/container.h
+++ b/include/tvm/runtime/container.h
@@ -24,7 +24,13 @@
 #ifndef TVM_RUNTIME_CONTAINER_H_
 #define TVM_RUNTIME_CONTAINER_H_
 
+#ifndef USE_FALLBACK_STL_MAP
+#define USE_FALLBACK_STL_MAP 0
+#endif
+
 #include <dmlc/logging.h>
+#include <tvm/runtime/container.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/memory.h>
 #include <tvm/runtime/object.h>
 
@@ -34,6 +40,7 @@
 #include <memory>
 #include <string>
 #include <unordered_map>
+#include <utility>
 // We use c++14 std::experimental::string_view for optimizing hash computation
 // only right now, its usage is limited in this file. Any broader usage of
 // std::experiment in our core codebase is discouraged and needs community
@@ -1688,11 +1695,1413 @@ class Closure : public ObjectRef {
   TVM_DEFINE_OBJECT_REF_METHODS(Closure, ObjectRef, ClosureObj);
 };
 
+#if (USE_FALLBACK_STL_MAP != 0)
+
+/*! \brief Shared content of all specializations of hash map */
+class MapNode : public Object {
+ public:
+  /*! \brief Type of the keys in the hash map */
+  using key_type = ObjectRef;
+  /*! \brief Type of the values in the hash map */
+  using mapped_type = ObjectRef;
+  /*! \brief Type of the actual underlying container */
+  using ContainerType = std::unordered_map<ObjectRef, ObjectRef, ObjectHash, ObjectEqual>;
+  /*! \brief Iterator class */
+  using iterator = ContainerType::iterator;
+  /*! \brief Iterator class */
+  using const_iterator = ContainerType::const_iterator;
+  /*! \brief Type of value stored in the hash map */
+  using KVType = ContainerType::value_type;
+
+  static_assert(std::is_standard_layout<KVType>::value, "KVType is not standard layout");
+  static_assert(sizeof(KVType) == 16 || sizeof(KVType) == 8, "sizeof(KVType) incorrect");
+
+  static constexpr const uint32_t _type_index = runtime::TypeIndex::kRuntimeMap;
+  static constexpr const char* _type_key = "Map";
+  TVM_DECLARE_FINAL_OBJECT_INFO(MapNode, Object);
+
+  /*!
+   * \brief Number of elements in the SmallMapNode
+   * \return The result
+   */
+  size_t size() const { return data_.size(); }
+  /*!
+   * \brief Count the number of times a key exists in the hash map
+   * \param key The indexing key
+   * \return The result, 0 or 1
+   */
+  size_t count(const key_type& key) const { return data_.count(key); }
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The const reference to the value
+   */
+  const mapped_type& at(const key_type& key) const { return data_.at(key); }
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The mutable reference to the value
+   */
+  mapped_type& at(const key_type& key) { return data_.at(key); }
+  /*! \return begin iterator */
+  iterator begin() { return data_.begin(); }
+  /*! \return const begin iterator */
+  const_iterator begin() const { return data_.begin(); }
+  /*! \return end iterator */
+  iterator end() { return data_.end(); }
+  /*! \return end iterator */
+  const_iterator end() const { return data_.end(); }
+  /*!
+   * \brief Index value associated with a key
+   * \param key The indexing key
+   * \return The iterator of the entry associated with the key, end iterator if not exists
+   */
+  const_iterator find(const key_type& key) const { return data_.find(key); }
+  /*!
+   * \brief Index value associated with a key
+   * \param key The indexing key
+   * \return The iterator of the entry associated with the key, end iterator if not exists
+   */
+  iterator find(const key_type& key) { return data_.find(key); }
+  /*!
+   * \brief Erase the entry associated with the iterator
+   * \param position The iterator
+   */
+  void erase(const iterator& position) { data_.erase(position); }
+  /*!
+   * \brief Erase the entry associated with the key, do nothing if not exists
+   * \param key The indexing key
+   */
+  void erase(const key_type& key) { data_.erase(key); }
+  /*!
+   * \brief Create an empty container
+   * \return The object created
+   */
+  static ObjectPtr<MapNode> Empty() { return make_object<MapNode>(); }
+
+ protected:
+  /*!
+   * \brief Create the map using contents from the given iterators.
+   * \param first Begin of iterator
+   * \param last End of iterator
+   * \tparam IterType The type of iterator
+   * \return ObjectPtr to the map created
+   */
+  template <typename IterType>
+  static ObjectPtr<Object> CreateFromRange(IterType first, IterType last) {
+    ObjectPtr<MapNode> p = make_object<MapNode>();
+    p->data_ = ContainerType(first, last);
+    return p;
+  }
+  /*!
+   * \brief InsertMaybeReHash an entry into the given hash map
+   * \param kv The entry to be inserted
+   * \param map The pointer to the map, can be changed if re-hashing happens
+   */
+  static void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
+    MapNode* map_node = static_cast<MapNode*>(map->get());
+    map_node->data_[kv.first] = kv.second;
+  }
+  /*!
+   * \brief Create an empty container with elements copying from another MapNode
+   * \param from The source container
+   * \return The object created
+   */
+  static ObjectPtr<MapNode> CopyFrom(MapNode* from) {
+    ObjectPtr<MapNode> p = make_object<MapNode>();
+    p->data_ = ContainerType(from->data_.begin(), from->data_.end());
+    return p;
+  }
+  /*! \brief The real container storing data */
+  ContainerType data_;
+  template <typename, typename, typename, typename>
+  friend class Map;
+};
+
+#else
+
+/*! \brief Shared content of all specializations of hash map */
+class MapNode : public Object {
+ public:
+  /*! \brief Type of the keys in the hash map */
+  using key_type = ObjectRef;
+  /*! \brief Type of the values in the hash map */
+  using mapped_type = ObjectRef;
+  /*! \brief Type of value stored in the hash map */
+  using KVType = std::pair<ObjectRef, ObjectRef>;
+  /*! \brief Iterator class */
+  class iterator;
+
+  static_assert(std::is_standard_layout<KVType>::value, "KVType is not standard layout");
+  static_assert(sizeof(KVType) == 16 || sizeof(KVType) == 8, "sizeof(KVType) incorrect");
+
+  static constexpr const uint32_t _type_index = runtime::TypeIndex::kRuntimeMap;
+  static constexpr const char* _type_key = "Map";
+  TVM_DECLARE_FINAL_OBJECT_INFO(MapNode, Object);
+
+  /*!
+   * \brief Number of elements in the SmallMapNode
+   * \return The result
+   */
+  size_t size() const { return size_; }
+  /*!
+   * \brief Count the number of times a key exists in the hash map
+   * \param key The indexing key
+   * \return The result, 0 or 1
+   */
+  size_t count(const key_type& key) const;
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The const reference to the value
+   */
+  const mapped_type& at(const key_type& key) const;
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The mutable reference to the value
+   */
+  mapped_type& at(const key_type& key);
+  /*! \return begin iterator */
+  iterator begin() const;
+  /*! \return end iterator */
+  iterator end() const;
+  /*!
+   * \brief Index value associated with a key
+   * \param key The indexing key
+   * \return The iterator of the entry associated with the key, end iterator if not exists
+   */
+  iterator find(const key_type& key) const;
+  /*!
+   * \brief Erase the entry associated with the iterator
+   * \param position The iterator
+   */
+  void erase(const iterator& position);
+  /*!
+   * \brief Erase the entry associated with the key, do nothing if not exists
+   * \param key The indexing key
+   */
+  void erase(const key_type& key) { erase(find(key)); }
+
+  class iterator {
+   public:
+    using iterator_category = std::forward_iterator_tag;
+    using difference_type = int64_t;
+    using value_type = KVType;
+    using pointer = KVType*;
+    using reference = KVType&;
+    /*! \brief Default constructor */
+    iterator() : index(0), self(nullptr) {}
+    /*! \brief Compare iterators */
+    bool operator==(const iterator& other) const {
+      return index == other.index && self == other.self;
+    }
+    /*! \brief Compare iterators */
+    bool operator!=(const iterator& other) const { return !(*this == other); }
+    /*! \brief De-reference iterators */
+    pointer operator->() const;
+    /*! \brief De-reference iterators */
+    reference operator*() const { return *((*this).operator->()); }
+    /*! \brief Prefix self increment, e.g. ++iter */
+    iterator& operator++();
+    /*! \brief Prefix self decrement, e.g. --iter */
+    iterator& operator--();
+    /*! \brief Suffix self increment */
+    iterator operator++(int) {
+      iterator copy = *this;
+      ++(*this);
+      return copy;
+    }
+    /*! \brief Suffix self decrement */
+    iterator operator--(int) {
+      iterator copy = *this;
+      --(*this);
+      return copy;
+    }
+
+   protected:
+    /*! \brief Construct by value */
+    iterator(uint64_t index, const MapNode* self) : index(index), self(self) {}
+    /*! \brief The position on the array */
+    uint64_t index;
+    /*! \brief The container it points to */
+    const MapNode* self;
+
+    friend class DenseMapNode;
+    friend class SmallMapNode;
+  };
+  /*!
+   * \brief Create an empty container
+   * \return The object created
+   */
+  static inline ObjectPtr<MapNode> Empty();
+
+ protected:
+  /*!
+   * \brief Create the map using contents from the given iterators.
+   * \param first Begin of iterator
+   * \param last End of iterator
+   * \tparam IterType The type of iterator
+   * \return ObjectPtr to the map created
+   */
+  template <typename IterType>
+  static inline ObjectPtr<Object> CreateFromRange(IterType first, IterType last);
+  /*!
+   * \brief InsertMaybeReHash an entry into the given hash map
+   * \param kv The entry to be inserted
+   * \param map The pointer to the map, can be changed if re-hashing happens
+   */
+  static inline void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map);
+  /*!
+   * \brief Create an empty container with elements copying from another SmallMapNode
+   * \param from The source container
+   * \return The object created
+   */
+  static inline ObjectPtr<MapNode> CopyFrom(MapNode* from);
+  /*! \brief number of slots minus 1 */
+  uint64_t slots_;
+  /*! \brief number of entries in the container */
+  uint64_t size_;
+  // Reference class
+  template <typename, typename, typename, typename>
+  friend class Map;
+};
+
+/*! \brief A specialization of small-sized hash map */
+class SmallMapNode : public MapNode,
+                     public runtime::InplaceArrayBase<SmallMapNode, MapNode::KVType> {
+ private:
+  static constexpr uint64_t kInitSize = 2;
+  static constexpr uint64_t kMaxSize = 4;
+
+ public:
+  using MapNode::iterator;
+  using MapNode::KVType;
+
+  /*! \brief Defaults to the destructor of InplaceArrayBase */
+  ~SmallMapNode() = default;
+  /*!
+   * \brief Count the number of times a key exists in the SmallMapNode
+   * \param key The indexing key
+   * \return The result, 0 or 1
+   */
+  size_t count(const key_type& key) const { return find(key).index < size_; }
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The const reference to the value
+   */
+  const mapped_type& at(const key_type& key) const {
+    iterator itr = find(key);
+    ICHECK(itr.index < size_) << "IndexError: key is not in Map";
+    return itr->second;
+  }
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The mutable reference to the value
+   */
+  mapped_type& at(const key_type& key) {
+    iterator itr = find(key);
+    ICHECK(itr.index < size_) << "IndexError: key is not in Map";
+    return itr->second;
+  }
+  /*! \return begin iterator */
+  iterator begin() const { return iterator(0, this); }
+  /*! \return end iterator */
+  iterator end() const { return iterator(size_, this); }
+  /*!
+   * \brief Index value associated with a key
+   * \param key The indexing key
+   * \return The iterator of the entry associated with the key, end iterator if not exists
+   */
+  iterator find(const key_type& key) const {
+    KVType* ptr = static_cast<KVType*>(AddressOf(0));
+    for (uint64_t i = 0; i < size_; ++i, ++ptr) {
+      if (ObjectEqual()(ptr->first, key)) {
+        return iterator(i, this);
+      }
+    }
+    return iterator(size_, this);
+  }
+  /*!
+   * \brief Erase the entry associated with the iterator
+   * \param position The iterator
+   */
+  void erase(const iterator& position) { Erase(position.index); }
+
+ private:
+  /*!
+   * \brief Remove a position in SmallMapNode
+   * \param index The position to be removed
+   */
+  void Erase(const uint64_t index) {
+    if (index >= size_) {
+      return;
+    }
+    KVType* begin = static_cast<KVType*>(AddressOf(0));
+    KVType* last = begin + (size_ - 1);
+    if (index + 1 == size_) {
+      last->first.ObjectRef::~ObjectRef();
+      last->second.ObjectRef::~ObjectRef();
+    } else {
+      *(begin + index) = std::move(*last);
+    }
+    size_ -= 1;
+  }
+  /*!
+   * \brief Create an empty container
+   * \param n Number of empty slots
+   * \return The object created
+   */
+  static ObjectPtr<SmallMapNode> Empty(uint64_t n = kInitSize) {
+    using ::tvm::runtime::make_inplace_array_object;
+    ObjectPtr<SmallMapNode> p = make_inplace_array_object<SmallMapNode, KVType>(n);
+    p->size_ = 0;
+    p->slots_ = n;
+    return p;
+  }
+  /*!
+   * \brief Create an empty container initialized with a given range
+   * \param n Number of empty slots
+   * \param first begin of iterator
+   * \param last end of iterator
+   * \tparam IterType The type of iterator
+   * \return The object created
+   */
+  template <typename IterType>
+  static ObjectPtr<SmallMapNode> CreateFromRange(uint64_t n, IterType first, IterType last) {
+    ObjectPtr<SmallMapNode> p = Empty(n);
+    KVType* ptr = static_cast<KVType*>(p->AddressOf(0));
+    for (; first != last; ++first, ++p->size_) {
+      new (ptr++) KVType(*first);
+    }
+    return p;
+  }
+  /*!
+   * \brief Create an empty container with elements copying from another SmallMapNode
+   * \param from The source container
+   * \return The object created
+   */
+  static ObjectPtr<SmallMapNode> CopyFrom(SmallMapNode* from) {
+    KVType* first = static_cast<KVType*>(from->AddressOf(0));
+    KVType* last = first + from->size_;
+    return CreateFromRange(from->size_, first, last);
+  }
+  /*!
+   * \brief InsertMaybeReHash an entry into the given hash map
+   * \param kv The entry to be inserted
+   * \param map The pointer to the map, can be changed if re-hashing happens
+   */
+  static void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
+    SmallMapNode* map_node = static_cast<SmallMapNode*>(map->get());
+    iterator itr = map_node->find(kv.first);
+    if (itr.index < map_node->size_) {
+      itr->second = kv.second;
+      return;
+    }
+    if (map_node->size_ < map_node->slots_) {
+      KVType* ptr = static_cast<KVType*>(map_node->AddressOf(map_node->size_));
+      new (ptr) KVType(kv);
+      ++map_node->size_;
+      return;
+    }
+    uint64_t next_size = std::max(map_node->slots_ * 2, uint64_t(kInitSize));
+    next_size = std::min(next_size, uint64_t(kMaxSize));
+    ICHECK_GT(next_size, map_node->slots_);
+    ObjectPtr<Object> new_map = CreateFromRange(next_size, map_node->begin(), map_node->end());
+    InsertMaybeReHash(kv, &new_map);
+    *map = std::move(new_map);
+  }
+  /*!
+   * \brief Increment the pointer
+   * \param index The pointer to be incremented
+   * \return The increased pointer
+   */
+  uint64_t IncItr(uint64_t index) const { return index + 1 < size_ ? index + 1 : size_; }
+  /*!
+   * \brief Decrement the pointer
+   * \param index The pointer to be decremented
+   * \return The decreased pointer
+   */
+  uint64_t DecItr(uint64_t index) const { return index > 0 ? index - 1 : size_; }
+  /*!
+   * \brief De-reference the pointer
+   * \param index The pointer to be dereferenced
+   * \return The result
+   */
+  KVType* DeRefItr(uint64_t index) const { return static_cast<KVType*>(AddressOf(index)); }
+  /*! \brief A size function used by InplaceArrayBase */
+  uint64_t GetSize() const { return size_; }
+
+ protected:
+  friend class MapNode;
+  friend class DenseMapNode;
+  friend class runtime::InplaceArrayBase<SmallMapNode, MapNode::KVType>;
+};
+
+/*! \brief A specialization of hash map that implements the idea of array-based hash map.
+ * Another reference implementation can be found [1].
+ *
+ * A. Overview
+ *
+ * DenseMapNode did several improvements over traditional separate chaining hash,
+ * in terms of cache locality, memory footprints and data organization.
+ *
+ * A1. Implicit linked list. For better cache locality, instead of using linked list
+ * explicitly for each bucket, we store list data into a single array that spans contiguously
+ * in memory, and then carefully design access patterns to make sure most of them fall into
+ * a single cache line.
+ *
+ * A2. 1-byte metadata. There is only 1 byte overhead for each slot in the array to indexing and
+ * traversal. This can be divided in 3 parts.
+ * 1) Reserved code: (0b11111111)_2 indicates a slot is empty; (0b11111110)_2 indicates protected,
+ * which means the slot is empty but not allowed to be written.
+ * 2) If not empty or protected, the highest bit is used to indicate whether data in the slot is
+ * head of a linked list.
+ * 3) The rest 7 bits are used as the "next pointer" (i.e. pointer to the next element). On 64-bit
+ * architecture, an ordinary pointer can take up to 8 bytes, which is not acceptable overhead when
+ * dealing with 16-byte ObjectRef pairs. Based on a commonly noticed fact that the lists are
+ * relatively short (length <= 3) in hash maps, we follow [1]'s idea that only allows the pointer to
+ * be one of the 126 possible values, i.e. if the next element of i-th slot is (i + x)-th element,
+ * then x must be one of the 126 pre-defined values.
+ *
+ * A3. Data blocking. We organize the array in the way that every 16 elements forms a data block.
+ * The 16-byte metadata of those 16 elements are stored together, followed by the real data, i.e.
+ * 16 key-value pairs.
+ *
+ * B. Implementation details
+ *
+ * B1. Power-of-2 table size and Fibonacci Hashing. We use power-of-two as table size to avoid
+ * modulo for more efficient arithmetics. To make the hash-to-slot mapping distribute more evenly,
+ * we use the Fibonacci Hashing [2] trick.
+ *
+ * B2. Traverse a linked list in the array.
+ * 1) List head. Assume Fibonacci Hashing maps a given key to slot i, if metadata at slot i
+ * indicates that it is list head, then we found the head; otherwise the list is empty. No probing
+ * is done in this procedure. 2) Next element. To find the next element of a non-empty slot i, we
+ * look at the last 7 bits of the metadata at slot i. If they are all zeros, then it is the end of
+ * list; otherwise, we know that the next element is (i + candidates[the-last-7-bits]).
+ *
+ * B3. InsertMaybeReHash an element. Following B2, we first traverse the linked list to see if this
+ * element is in the linked list, and if not, we put it at the end by probing the next empty
+ * position in one of the 126 candidate positions. If the linked list does not even exist, but the
+ * slot for list head has been occupied by another linked list, we should find this intruder another
+ * place.
+ *
+ * B4. Quadratic probing with triangle numbers. In open address hashing, it is provable that probing
+ * with triangle numbers can traverse power-of-2-sized table [3]. In our algorithm, we follow the
+ * suggestion in [1] that also use triangle numbers for "next pointer" as well as sparing for list
+ * head.
+ *
+ * [1] https://github.com/skarupke/flat_hash_map
+ * [2] https://programmingpraxis.com/2018/06/19/fibonacci-hash/
+ * [3] https://fgiesen.wordpress.com/2015/02/22/triangular-numbers-mod-2n/
+ */
+class DenseMapNode : public MapNode {
+ private:
+  /*! \brief The number of elements in a memory block */
+  static constexpr int kBlockCap = 16;
+  /*! \brief Maximum load factor of the hash map */
+  static constexpr double kMaxLoadFactor = 0.99;
+  /*! \brief Binary representation of the metadata of an empty slot */
+  static constexpr uint8_t kEmptySlot = uint8_t(0b11111111);
+  /*! \brief Binary representation of the metadata of a protected slot */
+  static constexpr uint8_t kProtectedSlot = uint8_t(0b11111110);
+  /*! \brief Number of probing choices available */
+  static constexpr int kNumJumpDists = 126;
+  /*! \brief Head of the implicit linked list */
+  struct ListNode;
+  /*! \brief POD type of a block of memory */
+  struct Block {
+    uint8_t bytes[kBlockCap + kBlockCap * sizeof(KVType)];
+  };
+  static_assert(sizeof(Block) == kBlockCap * (sizeof(KVType) + 1), "sizeof(Block) incorrect");
+  static_assert(std::is_standard_layout<Block>::value, "Block is not standard layout");
+
+ public:
+  using MapNode::iterator;
+
+  /*!
+   * \brief Destroy the DenseMapNode
+   */
+  ~DenseMapNode() { this->Reset(); }
+  /*! \return The number of elements of the key */
+  size_t count(const key_type& key) const { return !Search(key).IsNone(); }
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The const reference to the value
+   */
+  const mapped_type& at(const key_type& key) const { return At(key); }
+  /*!
+   * \brief Index value associated with a key, throw exception if the key does not exist
+   * \param key The indexing key
+   * \return The mutable reference to the value
+   */
+  mapped_type& at(const key_type& key) { return At(key); }
+  /*!
+   * \brief Index value associated with a key
+   * \param key The indexing key
+   * \return The iterator of the entry associated with the key, end iterator if not exists
+   */
+  iterator find(const key_type& key) const {
+    ListNode node = Search(key);
+    return node.IsNone() ? end() : iterator(node.index, this);
+  }
+  /*!
+   * \brief Erase the entry associated with the iterator
+   * \param position The iterator
+   */
+  void erase(const iterator& position) {
+    uint64_t index = position.index;
+    if (position.self != nullptr && index <= this->slots_) {
+      Erase(ListNode(index, this));
+    }
+  }
+  /*! \return begin iterator */
+  iterator begin() const {
+    if (slots_ == 0) {
+      return iterator(0, this);
+    }
+    for (uint64_t index = 0; index <= slots_; ++index) {
+      if (!ListNode(index, this).IsEmpty()) {
+        return iterator(index, this);
+      }
+    }
+    return iterator(slots_ + 1, this);
+  }
+  /*! \return end iterator */
+  iterator end() const { return slots_ == 0 ? iterator(0, this) : iterator(slots_ + 1, this); }
+
+ private:
+  /*!
+   * \brief Search for the given key
+   * \param key The key
+   * \return ListNode that associated with the key
+   */
+  ListNode Search(const key_type& key) const {
+    if (this->size_ == 0) {
+      return ListNode();
+    }
+    for (ListNode iter = GetListHead(ObjectHash()(key)); !iter.IsNone(); iter.MoveToNext(this)) {
+      if (ObjectEqual()(key, iter.Key())) {
+        return iter;
+      }
+    }
+    return ListNode();
+  }
+  /*!
+   * \brief Search for the given key, throw exception if not exists
+   * \param key The key
+   * \return ListNode that associated with the key
+   */
+  mapped_type& At(const key_type& key) const {
+    ListNode iter = Search(key);
+    ICHECK(!iter.IsNone()) << "IndexError: key is not in Map";
+    return iter.Val();
+  }
+  /*!
+   * \brief Try to insert a key, or do nothing if already exists
+   * \param key The indexing key
+   * \param result The linked-list entry found or just constructed
+   * \return A boolean, indicating if actual insertion happens
+   */
+  bool TryInsert(const key_type& key, ListNode* result) {
+    if (slots_ == 0) {
+      return false;
+    }
+    // required that `iter` to be the head of a linked list through which we can iterator
+    ListNode iter = IndexFromHash(ObjectHash()(key));
+    // `iter` can be: 1) empty; 2) body of an irrelevant list; 3) head of the relevant list
+    // Case 1: empty
+    if (iter.IsEmpty()) {
+      iter.NewHead(KVType(key, ObjectRef(nullptr)));
+      this->size_ += 1;
+      *result = iter;
+      return true;
+    }
+    // Case 2: body of an irrelevant list
+    if (!iter.IsHead()) {
+      // we move the elements around and construct the single-element linked list
+      return IsFull() ? false : TrySpareListHead(iter, key, result);
+    }
+    // Case 3: head of the relevant list
+    // we iterate through the linked list until the end
+    // make sure `iter` is the previous element of `next`
+    ListNode next = iter;
+    do {
+      // find equal item, do not insert
+      if (ObjectEqual()(key, next.Key())) {
+        *result = next;
+        return true;
+      }
+      // make sure `iter` is the previous element of `next`
+      iter = next;
+    } while (next.MoveToNext(this));
+    // `iter` is the tail of the linked list
+    // always check capacity before insertion
+    if (IsFull()) {
+      return false;
+    }
+    // find the next empty slot
+    uint8_t jump;
+    if (!iter.GetNextEmpty(this, &jump, result)) {
+      return false;
+    }
+    result->NewTail(KVType(key, ObjectRef(nullptr)));
+    // link `iter` to `empty`, and move forward
+    iter.SetJump(jump);
+    this->size_ += 1;
+    return true;
+  }
+  /*!
+   * \brief Spare an entry to be the head of a linked list.
+   * As described in B3, during insertion, it is possible that the entire linked list does not
+   * exist, but the slot of its head has been occupied by other linked lists. In this case, we need
+   * to spare the slot by moving away the elements to another valid empty one to make insertion
+   * possible.
+   * \param target The given entry to be spared
+   * \param key The indexing key
+   * \param result The linked-list entry constructed as the head
+   * \return A boolean, if actual insertion happens
+   */
+  bool TrySpareListHead(ListNode target, const key_type& key, ListNode* result) {
+    // `target` is not the head of the linked list
+    // move the original item of `target` (if any)
+    // and construct new item on the position `target`
+    // To make `target` empty, we
+    // 1) find `w` the previous element of `target` in the linked list
+    // 2) copy the linked list starting from `r = target`
+    // 3) paste them after `w`
+    // read from the linked list after `r`
+    ListNode r = target;
+    // write to the tail of `w`
+    ListNode w = target.FindPrev(this);
+    // after `target` is moved, we disallow writing to the slot
+    bool is_first = true;
+    uint8_t r_meta, jump;
+    ListNode empty;
+    do {
+      // `jump` describes how `w` is jumped to `empty`
+      // rehash if there is no empty space after `w`
+      if (!w.GetNextEmpty(this, &jump, &empty)) {
+        return false;
+      }
+      // move `r` to `empty`
+      empty.NewTail(std::move(r.Data()));
+      // clear the metadata of `r`
+      r_meta = r.Meta();
+      if (is_first) {
+        is_first = false;
+        r.SetProtected();
+      } else {
+        r.SetEmpty();
+      }
+      // link `w` to `empty`, and move forward
+      w.SetJump(jump);
+      w = empty;
+      // move `r` forward as well
+    } while (r.MoveToNext(this, r_meta));
+    // finally we have done moving the linked list
+    // fill data_ into `target`
+    target.NewHead(KVType(key, ObjectRef(nullptr)));
+    this->size_ += 1;
+    *result = target;
+    return true;
+  }
+  /*!
+   * \brief Remove a ListNode
+   * \param iter The node to be removed
+   */
+  void Erase(const ListNode& iter) {
+    this->size_ -= 1;
+    if (!iter.HasNext()) {
+      // `iter` is the last
+      if (!iter.IsHead()) {
+        // cut the link if there is any
+        iter.FindPrev(this).SetJump(0);
+      }
+      iter.Data().KVType::~KVType();
+      iter.SetEmpty();
+    } else {
+      ListNode last = iter, prev = iter;
+      for (last.MoveToNext(this); last.HasNext(); prev = last, last.MoveToNext(this)) {
+      }
+      iter.Data() = std::move(last.Data());
+      last.SetEmpty();
+      prev.SetJump(0);
+    }
+  }
+  /*! \brief Clear the container to empty, release all entries and memory acquired */
+  void Reset() {
+    uint64_t n_blocks = CalcNumBlocks(this->slots_);
+    for (uint64_t bi = 0; bi < n_blocks; ++bi) {
+      uint8_t* meta_ptr = data_[bi].bytes;
+      KVType* data_ptr = reinterpret_cast<KVType*>(data_[bi].bytes + kBlockCap);
+      for (int j = 0; j < kBlockCap; ++j, ++meta_ptr, ++data_ptr) {
+        uint8_t& meta = *meta_ptr;
+        if (meta != uint8_t(kProtectedSlot) && meta != uint8_t(kEmptySlot)) {
+          meta = uint8_t(kEmptySlot);
+          data_ptr->KVType::~KVType();
+        }
+      }
+    }
+    ReleaseMemory();
+  }
+  /*! \brief Release the memory acquired by the container without deleting its entries stored inside
+   */
+  void ReleaseMemory() {
+    delete[] data_;
+    data_ = nullptr;
+    slots_ = 0;
+    size_ = 0;
+    fib_shift_ = 63;
+  }
+  /*!
+   * \brief Create an empty container
+   * \param fib_shift The fib shift provided
+   * \param n_slots Number of slots required, should be power-of-two
+   * \return The object created
+   */
+  static ObjectPtr<DenseMapNode> Empty(uint32_t fib_shift, uint64_t n_slots) {
+    ICHECK_GT(n_slots, uint64_t(SmallMapNode::kMaxSize));
+    ObjectPtr<DenseMapNode> p = make_object<DenseMapNode>();
+    uint64_t n_blocks = CalcNumBlocks(n_slots - 1);
+    Block* block = p->data_ = new Block[n_blocks];
+    p->slots_ = n_slots - 1;
+    p->size_ = 0;
+    p->fib_shift_ = fib_shift;
+    for (uint64_t i = 0; i < n_blocks; ++i, ++block) {
+      std::fill(block->bytes, block->bytes + kBlockCap, uint8_t(kEmptySlot));
+    }
+    return p;
+  }
+  /*!
+   * \brief Create an empty container with elements copying from another DenseMapNode
+   * \param from The source container
+   * \return The object created
+   */
+  static ObjectPtr<DenseMapNode> CopyFrom(DenseMapNode* from) {
+    ObjectPtr<DenseMapNode> p = make_object<DenseMapNode>();
+    uint64_t n_blocks = CalcNumBlocks(from->slots_);
+    p->data_ = new Block[n_blocks];
+    p->slots_ = from->slots_;
+    p->size_ = from->size_;
+    p->fib_shift_ = from->fib_shift_;
+    for (uint64_t bi = 0; bi < n_blocks; ++bi) {
+      uint8_t* meta_ptr_from = from->data_[bi].bytes;
+      KVType* data_ptr_from = reinterpret_cast<KVType*>(from->data_[bi].bytes + kBlockCap);
+      uint8_t* meta_ptr_to = p->data_[bi].bytes;
+      KVType* data_ptr_to = reinterpret_cast<KVType*>(p->data_[bi].bytes + kBlockCap);
+      for (int j = 0; j < kBlockCap;
+           ++j, ++meta_ptr_from, ++data_ptr_from, ++meta_ptr_to, ++data_ptr_to) {
+        uint8_t& meta = *meta_ptr_to = *meta_ptr_from;
+        ICHECK(meta != kProtectedSlot);
+        if (meta != uint8_t(kEmptySlot)) {
+          new (data_ptr_to) KVType(*data_ptr_from);
+        }
+      }
+    }
+    return p;
+  }
+  /*!
+   * \brief InsertMaybeReHash an entry into the given hash map
+   * \param kv The entry to be inserted
+   * \param map The pointer to the map, can be changed if re-hashing happens
+   */
+  static void InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
+    DenseMapNode* map_node = static_cast<DenseMapNode*>(map->get());
+    ListNode iter;
+    // Try to insert. If succeed, we simply return
+    if (map_node->TryInsert(kv.first, &iter)) {
+      iter.Val() = kv.second;
+      return;
+    }
+    ICHECK_GT(map_node->slots_, uint64_t(SmallMapNode::kMaxSize));
+    // Otherwise, start rehash
+    ObjectPtr<Object> p = Empty(map_node->fib_shift_ - 1, map_node->slots_ * 2 + 2);
+    // Insert the given `kv` into the new hash map
+    InsertMaybeReHash(kv, &p);
+    uint64_t n_blocks = CalcNumBlocks(map_node->slots_);
+    // Then Insert data from the original block.
+    for (uint64_t bi = 0; bi < n_blocks; ++bi) {
+      uint8_t* meta_ptr = map_node->data_[bi].bytes;
+      KVType* data_ptr = reinterpret_cast<KVType*>(map_node->data_[bi].bytes + kBlockCap);
+      for (int j = 0; j < kBlockCap; ++j, ++meta_ptr, ++data_ptr) {
+        uint8_t& meta = *meta_ptr;
+        if (meta != uint8_t(kProtectedSlot) && meta != uint8_t(kEmptySlot)) {
+          meta = uint8_t(kEmptySlot);
+          KVType kv = std::move(*data_ptr);
+          InsertMaybeReHash(kv, &p);
+        }
+      }
+    }
+    map_node->ReleaseMemory();
+    *map = p;
+  }
+  /*!
+   * \brief Check whether the hash table is full
+   * \return A boolean indicating whether hash table is full
+   */
+  bool IsFull() const { return size_ + 1 > (slots_ + 1) * kMaxLoadFactor; }
+  /*!
+   * \brief Increment the pointer
+   * \param index The pointer to be incremented
+   * \return The increased pointer
+   */
+  uint64_t IncItr(uint64_t index) const {
+    for (++index; index <= slots_; ++index) {
+      if (!ListNode(index, this).IsEmpty()) {
+        return index;
+      }
+    }
+    return slots_ + 1;
+  }
+  /*!
+   * \brief Decrement the pointer
+   * \param index The pointer to be decremented
+   * \return The decreased pointer
+   */
+  uint64_t DecItr(uint64_t index) const {
+    while (index != 0) {
+      index -= 1;
+      if (!ListNode(index, this).IsEmpty()) {
+        return index;
+      }
+    }
+    return slots_ + 1;
+  }
+  /*!
+   * \brief De-reference the pointer
+   * \param index The pointer to be dereferenced
+   * \return The result
+   */
+  KVType* DeRefItr(uint64_t index) const { return &ListNode(index, this).Data(); }
+  /*! \brief Construct from hash code */
+  ListNode IndexFromHash(uint64_t hash_value) const {
+    return ListNode(FibHash(hash_value, fib_shift_), this);
+  }
+  /*! \brief Construct from hash code if the position is head of list */
+  ListNode GetListHead(uint64_t hash_value) const {
+    ListNode node = IndexFromHash(hash_value);
+    return node.IsHead() ? node : ListNode();
+  }
+  /*! \brief Construct the number of blocks in the hash table */
+  static uint64_t CalcNumBlocks(uint64_t n_slots_m1) {
+    uint64_t n_slots = n_slots_m1 > 0 ? n_slots_m1 + 1 : 0;
+    return (n_slots + kBlockCap - 1) / kBlockCap;
+  }
+  /*!
+   * \brief Calculate the power-of-2 table size given the lower-bound of required capacity.
+   * \param cap The lower-bound of the required capacity
+   * \param fib_shift The result shift for Fibonacci Hashing
+   * \param n_slots The result number of slots
+   */
+  static void CalcTableSize(uint64_t cap, uint32_t* fib_shift, uint64_t* n_slots) {
+    uint32_t shift = 64;
+    uint64_t slots = 1;
+    for (uint64_t c = cap; c; c >>= 1) {
+      shift -= 1;
+      slots <<= 1;
+    }
+    ICHECK_GT(slots, cap);
+    if (slots < cap * 2) {
+      *fib_shift = shift - 1;
+      *n_slots = slots << 1;
+    } else {
+      *fib_shift = shift;
+      *n_slots = slots;
+    }
+  }
+  /*!
+   * \brief Fibonacci Hashing, maps a hash code to an index in a power-of-2-sized table.
+   * See also: https://programmingpraxis.com/2018/06/19/fibonacci-hash/.
+   * \param hash_value The raw hash value
+   * \param fib_shift The shift in Fibonacci Hashing
+   * \return An index calculated using Fibonacci Hashing
+   */
+  static uint64_t FibHash(uint64_t hash_value, uint32_t fib_shift) {
+    constexpr uint64_t coeff = 11400714819323198485ull;
+    return (coeff * hash_value) >> fib_shift;
+  }
+  /*! \brief The implicit in-place linked list used to index a chain */
+  struct ListNode {
+    /*! \brief Construct None */
+    ListNode() : index(0), block(nullptr) {}
+    /*! \brief Construct from position */
+    ListNode(uint64_t index, const DenseMapNode* self)
+        : index(index), block(self->data_ + (index / kBlockCap)) {}
+    /*! \brief Metadata on the entry */
+    uint8_t& Meta() const { return *(block->bytes + index % kBlockCap); }
+    /*! \brief Data on the entry */
+    KVType& Data() const {
+      return *(reinterpret_cast<KVType*>(block->bytes + kBlockCap +
+                                         (index % kBlockCap) * sizeof(KVType)));
+    }
+    /*! \brief Key on the entry */
+    key_type& Key() const { return Data().first; }
+    /*! \brief Value on the entry */
+    mapped_type& Val() const { return Data().second; }
+    /*! \brief If the entry is head of linked list */
+    bool IsHead() const { return (Meta() & 0b10000000) == 0b00000000; }
+    /*! \brief If the entry is none */
+    bool IsNone() const { return block == nullptr; }
+    /*! \brief If the entry is empty slot */
+    bool IsEmpty() const { return Meta() == uint8_t(kEmptySlot); }
+    /*! \brief If the entry is protected slot */
+    bool IsProtected() const { return Meta() == uint8_t(kProtectedSlot); }
+    /*! \brief Set the entry to be empty */
+    void SetEmpty() const { Meta() = uint8_t(kEmptySlot); }
+    /*! \brief Set the entry to be protected */
+    void SetProtected() const { Meta() = uint8_t(kProtectedSlot); }
+    /*! \brief Set the entry's jump to its next entry */
+    void SetJump(uint8_t jump) const { (Meta() &= 0b10000000) |= jump; }
+    /*! \brief Construct a head of linked list in-place */
+    void NewHead(KVType v) const {
+      Meta() = 0b00000000;
+      new (&Data()) KVType(std::move(v));
+    }
+    /*! \brief Construct a tail of linked list in-place */
+    void NewTail(KVType v) const {
+      Meta() = 0b10000000;
+      new (&Data()) KVType(std::move(v));
+    }
+    /*! \brief If the entry has next entry on the linked list */
+    bool HasNext() const { return kNextProbeLocation[Meta() & 0b01111111] != 0; }
+    /*! \brief Move the entry to the next entry on the linked list */
+    bool MoveToNext(const DenseMapNode* self, uint8_t meta) {
+      uint64_t offset = kNextProbeLocation[meta & 0b01111111];
+      if (offset == 0) {
+        index = 0;
+        block = nullptr;
+        return false;
+      }
+      index = (index + offset) & (self->slots_);
+      block = self->data_ + (index / kBlockCap);
+      return true;
+    }
+    /*! \brief Move the entry to the next entry on the linked list */
+    bool MoveToNext(const DenseMapNode* self) { return MoveToNext(self, Meta()); }
+    /*! \brief Get the previous entry on the linked list */
+    ListNode FindPrev(const DenseMapNode* self) const {
+      // start from the head of the linked list, which must exist
+      ListNode next = self->IndexFromHash(ObjectHash()(Key()));
+      // `prev` is always the previous item of `next`
+      ListNode prev = next;
+      for (next.MoveToNext(self); index != next.index; prev = next, next.MoveToNext(self)) {
+      }
+      return prev;
+    }
+    /*! \brief Get the next empty jump */
+    bool GetNextEmpty(const DenseMapNode* self, uint8_t* jump, ListNode* result) const {
+      for (uint8_t idx = 1; idx < kNumJumpDists; ++idx) {
+        ListNode candidate((index + kNextProbeLocation[idx]) & (self->slots_), self);
+        if (candidate.IsEmpty()) {
+          *jump = idx;
+          *result = candidate;
+          return true;
+        }
+      }
+      return false;
+    }
+    /*! \brief Index on the real array */
+    uint64_t index;
+    /*! \brief Pointer to the actual block */
+    Block* block;
+  };
+
+ protected:
+  /*! \brief fib shift in Fibonacci Hashing */
+  uint32_t fib_shift_;
+  /*! \brief array of data blocks */
+  Block* data_;
+  /* clang-format off */
+  /*! \brief Candidates of probing distance */
+  TVM_DLL static constexpr uint64_t kNextProbeLocation[kNumJumpDists] {
+    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    // Quadratic probing with triangle numbers. See also:
+    // 1) https://en.wikipedia.org/wiki/Quadratic_probing
+    // 2) https://fgiesen.wordpress.com/2015/02/22/triangular-numbers-mod-2n/
+    // 3) https://github.com/skarupke/flat_hash_map
+    21, 28, 36, 45, 55, 66, 78, 91, 105, 120,
+    136, 153, 171, 190, 210, 231, 253, 276, 300, 325,
+    351, 378, 406, 435, 465, 496, 528, 561, 595, 630,
+    666, 703, 741, 780, 820, 861, 903, 946, 990, 1035,
+    1081, 1128, 1176, 1225, 1275, 1326, 1378, 1431, 1485, 1540,
+    1596, 1653, 1711, 1770, 1830, 1891, 1953, 2016, 2080, 2145,
+    2211, 2278, 2346, 2415, 2485, 2556, 2628,
+    // larger triangle numbers
+    8515, 19110, 42778, 96141, 216153,
+    486591, 1092981, 2458653, 5532801, 12442566,
+    27993903, 62983476, 141717030, 318844378, 717352503,
+    1614057336, 3631522476, 8170957530, 18384510628, 41364789378,
+    93070452520, 209408356380, 471168559170, 1060128894105, 2385289465695,
+    5366898840628, 12075518705635, 27169915244790, 61132312065111, 137547689707000,
+    309482283181501, 696335127828753, 1566753995631385, 3525196511162271, 7931691992677701,
+    17846306936293605, 40154190677507445, 90346928918121501, 203280589587557251, 457381325854679626,
+    1029107982097042876, 2315492959180353330, 5209859154120846435,
+  };
+  /* clang-format on */
+  friend class MapNode;
+};
+
+#define TVM_DISPATCH_MAP(base, var, body)     \
+  {                                           \
+    using TSmall = SmallMapNode*;             \
+    using TDense = DenseMapNode*;             \
+    uint64_t slots = base->slots_;            \
+    if (slots <= SmallMapNode::kMaxSize) {    \
+      TSmall var = static_cast<TSmall>(base); \
+      body;                                   \
+    } else {                                  \
+      TDense var = static_cast<TDense>(base); \
+      body;                                   \
+    }                                         \
+  }
+
+#define TVM_DISPATCH_MAP_CONST(base, var, body) \
+  {                                             \
+    using TSmall = const SmallMapNode*;         \
+    using TDense = const DenseMapNode*;         \
+    uint64_t slots = base->slots_;              \
+    if (slots <= SmallMapNode::kMaxSize) {      \
+      TSmall var = static_cast<TSmall>(base);   \
+      body;                                     \
+    } else {                                    \
+      TDense var = static_cast<TDense>(base);   \
+      body;                                     \
+    }                                           \
+  }
+
+inline MapNode::iterator::pointer MapNode::iterator::operator->() const {
+  TVM_DISPATCH_MAP_CONST(self, p, { return p->DeRefItr(index); });
+}
+
+inline MapNode::iterator& MapNode::iterator::operator++() {
+  TVM_DISPATCH_MAP_CONST(self, p, {
+    index = p->IncItr(index);
+    return *this;
+  });
+}
+
+inline MapNode::iterator& MapNode::iterator::operator--() {
+  TVM_DISPATCH_MAP_CONST(self, p, {
+    index = p->DecItr(index);
+    return *this;
+  });
+}
+
+inline size_t MapNode::count(const key_type& key) const {
+  TVM_DISPATCH_MAP_CONST(this, p, { return p->count(key); });
+}
+
+inline const MapNode::mapped_type& MapNode::at(const MapNode::key_type& key) const {
+  TVM_DISPATCH_MAP_CONST(this, p, { return p->at(key); });
+}
+
+inline MapNode::mapped_type& MapNode::at(const MapNode::key_type& key) {
+  TVM_DISPATCH_MAP(this, p, { return p->at(key); });
+}
+
+inline MapNode::iterator MapNode::begin() const {
+  TVM_DISPATCH_MAP_CONST(this, p, { return p->begin(); });
+}
+
+inline MapNode::iterator MapNode::end() const {
+  TVM_DISPATCH_MAP_CONST(this, p, { return p->end(); });
+}
+
+inline MapNode::iterator MapNode::find(const MapNode::key_type& key) const {
+  TVM_DISPATCH_MAP_CONST(this, p, { return p->find(key); });
+}
+
+inline void MapNode::erase(const MapNode::iterator& position) {
+  TVM_DISPATCH_MAP(this, p, { return p->erase(position); });
+}
+
+#undef TVM_DISPATCH_MAP
+#undef TVM_DISPATCH_MAP_CONST
+
+inline ObjectPtr<MapNode> MapNode::Empty() { return SmallMapNode::Empty(); }
+
+inline ObjectPtr<MapNode> MapNode::CopyFrom(MapNode* from) {
+  if (from->slots_ <= SmallMapNode::kMaxSize) {
+    return SmallMapNode::CopyFrom(static_cast<SmallMapNode*>(from));
+  } else {
+    return DenseMapNode::CopyFrom(static_cast<DenseMapNode*>(from));
+  }
+}
+
+template <typename IterType>
+inline ObjectPtr<Object> MapNode::CreateFromRange(IterType first, IterType last) {
+  int64_t _cap = std::distance(first, last);
+  if (_cap < 0) {
+    return SmallMapNode::Empty();
+  }
+  uint64_t cap = static_cast<uint64_t>(_cap);
+  if (cap < SmallMapNode::kMaxSize) {
+    return SmallMapNode::CreateFromRange(cap, first, last);
+  }
+  uint32_t fib_shift;
+  uint64_t n_slots;
+  DenseMapNode::CalcTableSize(cap, &fib_shift, &n_slots);
+  ObjectPtr<Object> obj = DenseMapNode::Empty(fib_shift, n_slots);
+  for (; first != last; ++first) {
+    KVType kv(*first);
+    DenseMapNode::InsertMaybeReHash(kv, &obj);
+  }
+  return obj;
+}
+
+inline void MapNode::InsertMaybeReHash(const KVType& kv, ObjectPtr<Object>* map) {
+  constexpr uint64_t kSmallMapMaxSize = SmallMapNode::kMaxSize;
+  MapNode* base = static_cast<MapNode*>(map->get());
+  if (base->slots_ < kSmallMapMaxSize) {
+    SmallMapNode::InsertMaybeReHash(kv, map);
+  } else if (base->slots_ == kSmallMapMaxSize) {
+    if (base->size_ < base->slots_) {
+      SmallMapNode::InsertMaybeReHash(kv, map);
+    } else {
+      ObjectPtr<Object> new_map = MapNode::CreateFromRange(base->begin(), base->end());
+      DenseMapNode::InsertMaybeReHash(kv, &new_map);
+      *map = std::move(new_map);
+    }
+  } else {
+    DenseMapNode::InsertMaybeReHash(kv, map);
+  }
+}
+
+template <>
+inline ObjectPtr<MapNode> make_object<>() = delete;
+
+#endif
+
+/*!
+ * \brief Map container of NodeRef->NodeRef in DSL graph.
+ *  Map implements copy on write semantics, which means map is mutable
+ *  but copy will happen when array is referenced in more than two places.
+ *
+ * operator[] only provide const acces, use Set to mutate the content.
+ * \tparam K The key NodeRef type.
+ * \tparam V The value NodeRef type.
+ */
+template <typename K, typename V,
+          typename = typename std::enable_if<std::is_base_of<ObjectRef, K>::value>::type,
+          typename = typename std::enable_if<std::is_base_of<ObjectRef, V>::value>::type>
+class Map : public ObjectRef {
+ public:
+  using key_type = K;
+  using mapped_type = V;
+  class iterator;
+  /*!
+   * \brief default constructor
+   */
+  Map() { data_ = MapNode::Empty(); }
+  /*!
+   * \brief move constructor
+   * \param other source
+   */
+  Map(Map<K, V>&& other) { data_ = std::move(other.data_); }
+  /*!
+   * \brief copy constructor
+   * \param other source
+   */
+  Map(const Map<K, V>& other) : ObjectRef(other.data_) {}
+  /*!
+   * \brief copy assign operator
+   * \param other The source of assignment
+   * \return reference to self.
+   */
+  Map<K, V>& operator=(Map<K, V>&& other) {
+    data_ = std::move(other.data_);
+    return *this;
+  }
+  /*!
+   * \brief move assign operator
+   * \param other The source of assignment
+   * \return reference to self.
+   */
+  Map<K, V>& operator=(const Map<K, V>& other) {
+    data_ = other.data_;
+    return *this;
+  }
+  /*!
+   * \brief constructor from pointer
+   * \param n the container pointer
+   */
+  explicit Map(ObjectPtr<Object> n) : ObjectRef(n) {}
+  /*!
+   * \brief constructor from iterator
+   * \param begin begin of iterator
+   * \param end end of iterator
+   * \tparam IterType The type of iterator
+   */
+  template <typename IterType>
+  Map(IterType begin, IterType end) {
+    data_ = MapNode::CreateFromRange(begin, end);
+  }
+  /*!
+   * \brief constructor from initializer list
+   * \param init The initalizer list
+   */
+  Map(std::initializer_list<std::pair<K, V>> init) {
+    data_ = MapNode::CreateFromRange(init.begin(), init.end());
+  }
+  /*!
+   * \brief constructor from unordered_map
+   * \param init The unordered_map
+   */
+  template <typename Hash, typename Equal>
+  Map(const std::unordered_map<K, V, Hash, Equal>& init) {  // NOLINT(*)
+    data_ = MapNode::CreateFromRange(init.begin(), init.end());
+  }
+  /*!
+   * \brief Read element from map.
+   * \param key The key
+   * \return the corresonding element.
+   */
+  const V at(const K& key) const { return DowncastNoCheck<V>(GetMapNode()->at(key)); }
+  /*!
+   * \brief Read element from map.
+   * \param key The key
+   * \return the corresonding element.
+   */
+  const V operator[](const K& key) const { return this->at(key); }
+  /*! \return The size of the array */
+  size_t size() const {
+    MapNode* n = GetMapNode();
+    return n == nullptr ? 0 : n->size();
+  }
+  /*! \return The number of elements of the key */
+  size_t count(const K& key) const {
+    MapNode* n = GetMapNode();
+    return n == nullptr ? 0 : GetMapNode()->count(key);
+  }
+  /*! \return whether array is empty */
+  bool empty() const { return size() == 0; }
+  /*!
+   * \brief set the Map.
+   * \param key The index key.
+   * \param value The value to be setted.
+   */
+  void Set(const K& key, const V& value) {
+    CopyOnWrite();
+    MapNode::InsertMaybeReHash(MapNode::KVType(key, value), &data_);
+  }
+  /*! \return begin iterator */
+  iterator begin() const { return iterator(GetMapNode()->begin()); }
+  /*! \return end iterator */
+  iterator end() const { return iterator(GetMapNode()->end()); }
+  /*! \return find the key and returns the associated iterator */
+  iterator find(const K& key) const { return iterator(GetMapNode()->find(key)); }
+
+  void erase(const K& key) { CopyOnWrite()->erase(key); }
+
+  /*!
+   * \brief copy on write semantics
+   *  Do nothing if current handle is the unique copy of the array.
+   *  Otherwise make a new copy of the array to ensure the current handle
+   *  hold a unique copy.
+   *
+   * \return Handle to the internal node container(which ganrantees to be unique)
+   */
+  MapNode* CopyOnWrite() {
+    if (data_.get() == nullptr) {
+      data_ = MapNode::Empty();
+    } else if (!data_.unique()) {
+      data_ = MapNode::CopyFrom(GetMapNode());
+    }
+    return GetMapNode();
+  }
+  /*! \brief specify container node */
+  using ContainerType = MapNode;
+
+  /*! \brief Iterator of the hash map */
+  class iterator {
+   public:
+    using iterator_category = std::bidirectional_iterator_tag;
+    using difference_type = int64_t;
+    using value_type = const std::pair<K, V>;
+    using pointer = value_type*;
+    using reference = value_type;
+
+    iterator() : itr() {}
+
+    /*! \brief Compare iterators */
+    bool operator==(const iterator& other) const { return itr == other.itr; }
+    /*! \brief Compare iterators */
+    bool operator!=(const iterator& other) const { return itr != other.itr; }
+    /*! \brief De-reference iterators is not allowed */
+    pointer operator->() const = delete;
+    /*! \brief De-reference iterators */
+    reference operator*() const {
+      auto& kv = *itr;
+      return std::make_pair(DowncastNoCheck<K>(kv.first), DowncastNoCheck<V>(kv.second));
+    }
+    /*! \brief Prefix self increment, e.g. ++iter */
+    iterator& operator++() {
+      ++itr;
+      return *this;
+    }
+    /*! \brief Suffix self increment */
+    iterator operator++(int) {
+      iterator copy = *this;
+      ++(*this);
+      return copy;
+    }
+
+   private:
+    iterator(const MapNode::iterator& itr)  // NOLINT(*)
+        : itr(itr) {}
+
+    template <typename, typename, typename, typename>
+    friend class Map;
+
+    MapNode::iterator itr;
+  };
+
+ private:
+  /*! \brief Return data_ as type of pointer of MapNode */
+  MapNode* GetMapNode() const { return static_cast<MapNode*>(data_.get()); }
+};
+
+/*!
+ * \brief Merge two Maps.
+ * \param lhs the first Map to merge.
+ * \param rhs the second Map to merge.
+ * @return The merged Array. Original Maps are kept unchanged.
+ */
+template <typename K, typename V,
+          typename = typename std::enable_if<std::is_base_of<ObjectRef, K>::value>::type,
+          typename = typename std::enable_if<std::is_base_of<ObjectRef, V>::value>::type>
+inline Map<K, V> Merge(Map<K, V> lhs, const Map<K, V>& rhs) {
+  for (const auto& p : rhs) {
+    lhs.Set(p.first, p.second);
+  }
+  return std::move(lhs);
+}
+
 }  // namespace runtime
 
 // expose the functions to the root namespace.
+using runtime::Array;
+using runtime::ArrayNode;
+using runtime::Downcast;
+using runtime::IterAdapter;
+using runtime::make_object;
+using runtime::Map;
+using runtime::MapNode;
+using runtime::Object;
+using runtime::ObjectEqual;
+using runtime::ObjectHash;
+using runtime::ObjectPtr;
+using runtime::ObjectPtrEqual;
+using runtime::ObjectPtrHash;
+using runtime::ObjectRef;
 using runtime::Optional;
 using runtime::String;
+using runtime::StringObj;
 constexpr runtime::NullOptType NullOpt{};
 }  // namespace tvm
 
diff --git a/include/tvm/runtime/crt/platform.h b/include/tvm/runtime/crt/platform.h
index 8e0383912f50..d1226e388f73 100644
--- a/include/tvm/runtime/crt/platform.h
+++ b/include/tvm/runtime/crt/platform.h
@@ -97,6 +97,25 @@ tvm_crt_error_t TVMPlatformTimerStart();
  */
 tvm_crt_error_t TVMPlatformTimerStop(double* elapsed_time_seconds);
 
+/*! \brief Fill a buffer with random data.
+ *
+ * Cryptographically-secure random data is NOT required. This function is intended for use
+ * cases such as filling autotuning input tensors and choosing the nonce used for microTVM RPC.
+ *
+ * This function does not need to be implemented for inference tasks. It is used only by
+ * AutoTVM and the RPC server. When not implemented, an internal weak-linked stub is provided.
+ *
+ * Please take care that across successive resets, this function returns different sequences of
+ * values. If e.g. the random number generator is seeded with the same value, it may make it
+ * difficult for a host to detect device resets during autotuning or host-driven inference.
+ *
+ * \param buffer Pointer to the 0th byte to write with random data. `num_bytes` of random data
+ * should be written here.
+ * \param num_bytes Number of bytes to write.
+ * \return kTvmErrorNoError if successful; a descriptive error code otherwise.
+ */
+tvm_crt_error_t TVMPlatformGenerateRandom(uint8_t* buffer, size_t num_bytes);
+
 #ifdef __cplusplus
 }  // extern "C"
 #endif
diff --git a/include/tvm/runtime/crt/rpc_common/session.h b/include/tvm/runtime/crt/rpc_common/session.h
index 9e6a9f380554..eee1de6072d2 100644
--- a/include/tvm/runtime/crt/rpc_common/session.h
+++ b/include/tvm/runtime/crt/rpc_common/session.h
@@ -78,9 +78,9 @@ class Session {
   /*! \brief An invalid nonce value that typically indicates an unknown nonce. */
   static constexpr const uint8_t kInvalidNonce = 0;
 
-  Session(uint8_t initial_session_nonce, Framer* framer, FrameBuffer* receive_buffer,
-          MessageReceivedFunc message_received_func, void* message_received_func_context)
-      : local_nonce_{initial_session_nonce},
+  Session(Framer* framer, FrameBuffer* receive_buffer, MessageReceivedFunc message_received_func,
+          void* message_received_func_context)
+      : local_nonce_{kInvalidNonce},
         session_id_{0},
         state_{State::kReset},
         receiver_{this},
@@ -99,9 +99,11 @@ class Session {
 
   /*!
    * \brief Send a session terminate message, usually done at startup to interrupt a hanging remote.
+   * \param initial_session_nonce Initial nonce that should be used on the first session start
+   *      message. Callers should ensure this is different across device resets.
    * \return kTvmErrorNoError on success, or an error code otherwise.
    */
-  tvm_crt_error_t Initialize();
+  tvm_crt_error_t Initialize(uint8_t initial_session_nonce);
 
   /*!
    * \brief Terminate any previously-established session.
diff --git a/include/tvm/runtime/data_type.h b/include/tvm/runtime/data_type.h
index d705be6c4deb..b4fdcbff58b4 100644
--- a/include/tvm/runtime/data_type.h
+++ b/include/tvm/runtime/data_type.h
@@ -25,7 +25,7 @@
 #define TVM_RUNTIME_DATA_TYPE_H_
 
 #include <tvm/runtime/c_runtime_api.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <string>
 #include <type_traits>
@@ -160,12 +160,19 @@ class DataType {
    */
   static DataType UInt(int bits, int lanes = 1) { return DataType(kDLUInt, bits, lanes); }
   /*!
-   * \brief Construct an uint type.
+   * \brief Construct an float type.
    * \param bits The number of bits in the type.
    * \param lanes The number of lanes
    * \return The constructed data type.
    */
   static DataType Float(int bits, int lanes = 1) { return DataType(kDLFloat, bits, lanes); }
+  /*!
+   * \brief Construct an bfloat type.
+   * \param bits The number of bits in the type.
+   * \param lanes The number of lanes
+   * \return The constructed data type.
+   */
+  static DataType BFloat(int bits, int lanes = 1) { return DataType(kDLBfloat, bits, lanes); }
   /*!
    * \brief Construct a bool type.
    * \param lanes The number of lanes
diff --git a/include/tvm/runtime/device_api.h b/include/tvm/runtime/device_api.h
index a6f5624de084..1276663a2bc3 100644
--- a/include/tvm/runtime/device_api.h
+++ b/include/tvm/runtime/device_api.h
@@ -90,6 +90,17 @@ class TVM_DLL DeviceAPI {
    */
   virtual void* AllocDataSpace(TVMContext ctx, size_t nbytes, size_t alignment,
                                DLDataType type_hint) = 0;
+  /*!
+   * \brief Allocate a data space on device with memory scope support.
+   * \param ctx The device context to perform operation.
+   * \param ndim The number of dimension of allocated tensor.
+   * \param shape The shape of allocated tensor.
+   * \param dtype The type of elements.
+   * \param mem_scope The memory scope of allocated tensor.
+   * \return The allocated device pointer.
+   */
+  virtual void* AllocDataSpace(TVMContext ctx, int ndim, const int64_t* shape, DLDataType dtype,
+                               Optional<String> mem_scope = NullOpt);
   /*!
    * \brief Free a data space on device.
    * \param ctx The device context to perform operation.
@@ -98,20 +109,13 @@ class TVM_DLL DeviceAPI {
   virtual void FreeDataSpace(TVMContext ctx, void* ptr) = 0;
   /*!
    * \brief copy data from one place to another
+   * \note This API is designed to support special memory with shape dependent layout.
+   *       We pass in DLTensor* with shape information to support these cases.
    * \param from The source array.
-   * \param from_offset The byte offeset in the from.
    * \param to The target array.
-   * \param to_offset The byte offset in the to.
-   * \param num_bytes The size of the memory in bytes
-   * \param ctx_from The source context
-   * \param ctx_to The target context
-   * \param type_hint The type of elements, only neded by certain backends.
-   *                  can be useful for cross device endian converison.
    * \param stream Optional stream object.
    */
-  virtual void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
-                              size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
-                              DLDataType type_hint, TVMStreamHandle stream) = 0;
+  virtual void CopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream);
   /*!
    * \brief Create a new stream of execution.
    *
@@ -194,6 +198,24 @@ class TVM_DLL DeviceAPI {
   static bool NeedSetDeviceContext(int device_type) {
     return device_type != kDLCPU && device_type != kDLMicroDev;
   }
+
+ protected:
+  /*!
+   * \brief copy data from one place to another
+   * \param from The source array.
+   * \param from_offset The byte offeset in the from.
+   * \param to The target array.
+   * \param to_offset The byte offset in the to.
+   * \param num_bytes The size of the memory in bytes
+   * \param ctx_from The source context
+   * \param ctx_to The target context
+   * \param type_hint The type of elements, only neded by certain backends.
+   *                  can be useful for cross device endian converison.
+   * \param stream Optional stream object.
+   */
+  virtual void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
+                              size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
+                              DLDataType type_hint, TVMStreamHandle stream);
 };
 
 /*! \brief The device type bigger than this is RPC device */
diff --git a/include/tvm/runtime/logging.h b/include/tvm/runtime/logging.h
new file mode 100644
index 000000000000..952a5ffec637
--- /dev/null
+++ b/include/tvm/runtime/logging.h
@@ -0,0 +1,438 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file tvm/runtime/logging.h
+ * \brief logging utilities
+ *
+ * We define our own CHECK and LOG macros to replace those from dmlc-core.
+ * These macros are then injected into dmlc-core via the
+ * DMLC_USE_LOGGING_LIBRARY define. dmlc-core will #include this file wherever
+ * it needs logging.
+ */
+#ifndef TVM_RUNTIME_LOGGING_H_
+#define TVM_RUNTIME_LOGGING_H_
+
+#include <dmlc/common.h>
+
+#include <ctime>
+#include <iomanip>
+#include <iostream>
+#include <sstream>
+#include <string>
+
+#include "tvm/runtime/c_runtime_api.h"
+
+// a technique that enables overriding macro names on the number of parameters. This is used
+// to define other macros below
+#define GET_MACRO(_1, _2, _3, _4, _5, NAME, ...) NAME
+
+/*!
+ * \brief COND_X calls COND_X_N where N is the number of parameters passed to COND_X
+ * X can be any of CHECK_GE, CHECK_EQ, CHECK, or LOG COND_X (but not COND_X_N)
+ * are supposed to be used outside this file.
+ * The first parameter of COND_X (and therefore, COND_X_N), which we call 'quit_on_assert',
+ * is a boolean. The rest of the parameters of COND_X is the same as the parameters of X.
+ * quit_on_assert determines the overall behavior of COND_X. If it's true COND_X
+ * quits the program on assertion failure. If it's false, then it moves on and somehow reports
+ * the assertion failure back to the macro caller in an appropriate manner (e.g, 'return false'
+ * in a function, or 'continue' or 'break' in a loop)
+ * The default behavior when quit_on_assertion is false, is to 'return false'. If this is not
+ * desirable, the macro caller can pass one more last parameter to COND_X to tell COND_X what
+ * to do when when quit_on_assertion is false and the assertion fails.
+ *
+ * Rationale: These macros were designed to implement functions that have two behaviors
+ * in a concise way. Those behaviors are quitting on assertion failures, or trying to
+ * move on from assertion failures. Note that these macros hide lots of control flow in them,
+ * and therefore, makes the logic of the whole code slightly harder to understand. However,
+ * in pieces of code that use these macros frequently, it will significantly shorten the
+ * amount of code needed to be read, and we won't need to clutter the main logic of the
+ * function by repetitive control flow structure. The first problem
+ * mentioned will be improved over time as the developer gets used to the macro.
+ *
+ * Here is an example of how to use it
+ * \code
+ * bool f(..., bool quit_on_assertion) {
+ *   int a = 0, b = 0;
+ *   ...
+ *   a = ...
+ *   b = ...
+ *   // if quit_on_assertion is true, if a==b, continue, otherwise quit.
+ *   // if quit_on_assertion is false, if a==b, continue, otherwise 'return false' (default
+ * behaviour) COND_CHECK_EQ(quit_on_assertion, a, b) << "some error message when  quiting"
+ *   ...
+ *   for (int i = 0; i < N; i++) {
+ *     a = ...
+ *     b = ...
+ *     // if quit_on_assertion is true, if a==b, continue, otherwise quit.
+ *     // if quit_on_assertion is false, if a==b, continue, otherwise 'break' (non-default
+ *     // behaviour, therefore, has to be explicitly specified)
+ *     COND_CHECK_EQ(quit_on_assertion, a, b, break) << "some error message when  quiting"
+ *   }
+ * }
+ * \endcode
+ */
+#define COND_CHECK_GE(...) \
+  GET_MACRO(__VA_ARGS__, COND_CHECK_GE_5, COND_CHECK_GE_4, COND_CHECK_GE_3)(__VA_ARGS__)
+#define COND_CHECK_EQ(...) \
+  GET_MACRO(__VA_ARGS__, COND_CHECK_EQ_5, COND_CHECK_EQ_4, COND_CHECK_EQ_3)(__VA_ARGS__)
+#define COND_CHECK(...) \
+  GET_MACRO(__VA_ARGS__, COND_CHECK_5, COND_CHECK_4, COND_CHECK_3, COND_CHECK_2)(__VA_ARGS__)
+#define COND_LOG(...) \
+  GET_MACRO(__VA_ARGS__, COND_LOG_5, COND_LOG_4, COND_LOG_3, COND_LOG_2)(__VA_ARGS__)
+
+// Not supposed to be used by users directly.
+#define COND_CHECK_OP(quit_on_assert, x, y, what, op) \
+  if (!quit_on_assert) {                              \
+    if (!((x)op(y))) what;                            \
+  } else /* NOLINT(*) */                              \
+    CHECK_##op(x, y)
+
+#define COND_CHECK_EQ_4(quit_on_assert, x, y, what) COND_CHECK_OP(quit_on_assert, x, y, what, ==)
+#define COND_CHECK_GE_4(quit_on_assert, x, y, what) COND_CHECK_OP(quit_on_assert, x, y, what, >=)
+
+#define COND_CHECK_3(quit_on_assert, x, what) \
+  if (!quit_on_assert) {                      \
+    if (!(x)) what;                           \
+  } else /* NOLINT(*) */                      \
+    CHECK(x)
+
+#define COND_LOG_3(quit_on_assert, x, what) \
+  if (!quit_on_assert) {                    \
+    what;                                   \
+  } else /* NOLINT(*) */                    \
+    LOG(x)
+
+#define COND_CHECK_EQ_3(quit_on_assert, x, y) COND_CHECK_EQ_4(quit_on_assert, x, y, return false)
+#define COND_CHECK_GE_3(quit_on_assert, x, y) COND_CHECK_GE_4(quit_on_assert, x, y, return false)
+#define COND_CHECK_2(quit_on_assert, x) COND_CHECK_3(quit_on_assert, x, return false)
+#define COND_LOG_2(quit_on_assert, x) COND_LOG_3(quit_on_assert, x, return false)
+
+#ifdef _MSC_VER
+#define TVM_THROW_EXCEPTION noexcept(false) __declspec(noreturn)
+#else
+#define TVM_THROW_EXCEPTION noexcept(false)
+#endif
+
+namespace tvm {
+namespace runtime {
+
+/* \brief Generate a backtrace when called.
+ * \return A multiline string of the backtrace. There will be either one or two lines per frame.
+ */
+std::string Backtrace();
+
+/*! \brief Base error type for TVM. Wraps a string message. */
+class Error : public ::dmlc::Error {  // for backwards compatibility
+ public:
+  /*! \brief Construct an error.
+   * \param s The message to be displayed with the error.
+   */
+  explicit Error(const std::string& s) : ::dmlc::Error(s) {}
+};
+
+/*! \brief Error type for errors from CHECK, ICHECK, and LOG(FATAL). This error
+ * contains a backtrace of where it occured.
+ */
+class InternalError : public Error {
+ public:
+  /*! \brief Construct an error. Not recommended to use directly. Instead use LOG(FATAL).
+   *
+   * \param file The file where the error occurred.
+   * \param lineno The line number where the error occurred.
+   * \param message The error message to display.
+   * \param time The time at which the error occurred. This should be in local time.
+   * \param backtrace Backtrace from when the error occurred.
+   */
+  InternalError(std::string file, int lineno, std::string message,
+                std::time_t time = std::time(nullptr), std::string backtrace = Backtrace())
+      : Error(""),
+        file_(file),
+        lineno_(lineno),
+        message_(message),
+        time_(time),
+        backtrace_(backtrace) {
+    std::ostringstream s;
+    // XXX: Do not change this format, otherwise all error handling in python will break (because it
+    // parses the message to reconstruct the error type).
+    // TODO(tkonolige): Convert errors to Objects, so we can avoid the mess of formatting/parsing
+    // error messages correctly.
+    s << "[" << std::put_time(std::localtime(&time), "%H:%M:%S") << "] " << file << ":" << lineno
+      << ": " << message << std::endl;
+    if (backtrace.size() > 0) {
+      s << backtrace << std::endl;
+    }
+    full_message_ = s.str();
+  }
+  /*! \return The file in which the error occurred. */
+  const std::string& file() const { return file_; }
+  /*! \return The message associated with this error. */
+  const std::string& message() const { return message_; }
+  /*! \return Formatted error message including file, linenumber, backtrace, and message. */
+  const std::string& full_message() const { return full_message_; }
+  /*! \return The backtrace from where this error occurred. */
+  const std::string& backtrace() const { return backtrace_; }
+  /*! \return The time at which this error occurred. */
+  const std::time_t& time() const { return time_; }
+  /*! \return The line number at which this error occurred. */
+  int lineno() const { return lineno_; }
+  virtual const char* what() const noexcept { return full_message_.c_str(); }
+
+ private:
+  std::string file_;
+  int lineno_;
+  std::string message_;
+  std::time_t time_;
+  std::string backtrace_;
+  std::string full_message_;  // holds the full error string
+};
+
+namespace detail {
+#ifndef TVM_LOG_CUSTOMIZE
+
+/*! \brief Class to accumulate an error message and throw it. Do not use
+ * directly, instead use LOG(FATAL).
+ */
+class LogFatal {
+ public:
+  LogFatal(const std::string& file, int lineno) : file_(file), lineno_(lineno) {}
+#ifdef _MSC_VER
+#pragma disagnostic push
+#pragma warning(disable : 4722)
+#endif
+  ~LogFatal() noexcept(false) { throw InternalError(file_, lineno_, stream_.str()); }
+#ifdef _MSC_VER
+#pragma disagnostic pop
+#endif
+  std::ostringstream& stream() { return stream_; }
+
+ private:
+  std::ostringstream stream_;
+  std::string file_;
+  int lineno_;
+};
+
+/*! \brief Class to accumulate an log message. Do not use directly, instead use
+ * LOG(INFO), LOG(WARNING), LOG(ERROR).
+ */
+class LogMessage {
+ public:
+  LogMessage(const std::string& file, int lineno) {
+    std::time_t t = std::time(nullptr);
+    stream_ << "[" << std::put_time(std::localtime(&t), "%H:%M:%S") << "] " << file << ":" << lineno
+            << ": ";
+  }
+  ~LogMessage() { std::cerr << stream_.str() << std::endl; }
+  std::ostringstream& stream() { return stream_; }
+
+ private:
+  std::ostringstream stream_;
+};
+#else
+// Custom implementations of LogFatal and LogMessage that allow the user to
+// override handling of the message. The user must implement LogFatalImpl and LogMessageImpl
+void LogFatalImpl(const std::string& file, int lineno, const std::string& message);
+class LogFatal {
+ public:
+  LogFatal(const std::string& file, int lineno) : file_(file), lineno_(lineno) {}
+  ~LogFatal() TVM_THROW_EXCEPTION { LogFatalImpl(file_, lineno_, stream_.str()); }
+  std::ostringstream& stream() { return stream_; }
+
+ private:
+  std::ostringstream stream_;
+  std::string file_;
+  int lineno_;
+};
+
+void LogMessageImpl(const std::string& file, int lineno, const std::string& message);
+class LogMessage {
+ public:
+  LogMessage(const std::string& file, int lineno) : file_(file), lineno_(lineno) {}
+  ~LogMessage() { LogMessageImpl(file_, lineno_, stream_.str()); }
+  std::ostringstream& stream() { return stream_; }
+
+ private:
+  std::string file_;
+  int lineno_;
+  std::ostringstream stream_;
+};
+#endif
+
+// Below is from dmlc-core
+// This class is used to explicitly ignore values in the conditional
+// logging macros.  This avoids compiler warnings like "value computed
+// is not used" and "statement has no effect".
+class LogMessageVoidify {
+ public:
+  LogMessageVoidify() {}
+  // This has to be an operator with a precedence lower than << but
+  // higher than "?:". See its usage.
+  void operator&(std::ostream&) {}
+};
+
+// Also from dmlc-core
+inline bool DebugLoggingEnabled() {
+  static int state = 0;
+  if (state == 0) {
+    if (auto var = std::getenv("TVM_LOG_DEBUG")) {
+      if (std::string(var) == "1") {
+        state = 1;
+      } else {
+        state = -1;
+      }
+    } else {
+      // by default hide debug logging.
+      state = -1;
+    }
+  }
+  return state == 1;
+}
+
+constexpr const char* kTVM_INTERNAL_ERROR_MESSAGE =
+    "---------------------------------------------------------------\n"
+    "An internal invariant was violated during the execution of TVM.\n"
+    "Please read TVM's error reporting guidelines.\n"
+    "More details can be found here: https://discuss.tvm.ai/t/error-reporting/7793.\n"
+    "---------------------------------------------------------------\n";
+
+// Inline _Pragma in macros does not work reliably on old version of MVSC and
+// GCC. We wrap all comparisons in a function so that we can use #pragma to
+// silence bad comparison warnings.
+#define TVM_CHECK_FUNC(name, op)                                   \
+  template <typename A, typename B>                                \
+  DMLC_ALWAYS_INLINE bool LogCheck##name(const A& a, const B& b) { \
+    return a op b;                                                 \
+  }
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wsign-compare"
+TVM_CHECK_FUNC(_LT, <)
+TVM_CHECK_FUNC(_GT, >)
+TVM_CHECK_FUNC(_LE, <=)
+TVM_CHECK_FUNC(_GE, >=)
+TVM_CHECK_FUNC(_EQ, ==)
+TVM_CHECK_FUNC(_NE, !=)
+#pragma GCC diagnostic pop
+}  // namespace detail
+
+#define LOG(level) LOG_##level
+#define LOG_FATAL ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream()
+#define LOG_INFO ::tvm::runtime::detail::LogMessage(__FILE__, __LINE__).stream()
+#define LOG_ERROR (::tvm::runtime::detail::LogMessage(__FILE__, __LINE__).stream() << "error: ")
+#define LOG_WARNING (::tvm::runtime::detail::LogMessage(__FILE__, __LINE__).stream() << "warning: ")
+
+#define TVM_CHECK_BINARY_OP(name, op, x, y)                     \
+  if (!::tvm::runtime::detail::LogCheck##name(x, y))            \
+  ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream() \
+      << "Check failed: " << #x " " #op " " #y << ": "
+
+#define CHECK(x)                                                \
+  if (!(x))                                                     \
+  ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream() \
+      << "Check failed: " #x << " == false: "
+
+#define CHECK_LT(x, y) TVM_CHECK_BINARY_OP(_LT, <, x, y)
+#define CHECK_GT(x, y) TVM_CHECK_BINARY_OP(_GT, >, x, y)
+#define CHECK_LE(x, y) TVM_CHECK_BINARY_OP(_LE, <=, x, y)
+#define CHECK_GE(x, y) TVM_CHECK_BINARY_OP(_GE, >=, x, y)
+#define CHECK_EQ(x, y) TVM_CHECK_BINARY_OP(_EQ, ==, x, y)
+#define CHECK_NE(x, y) TVM_CHECK_BINARY_OP(_NE, !=, x, y)
+#define CHECK_NOTNULL(x)                                                          \
+  ((x) == nullptr ? ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream() \
+                        << "Check not null: " #x << ' ',                          \
+   (x) : (x))  // NOLINT(*)
+
+#define LOG_IF(severity, condition) \
+  !(condition) ? (void)0 : ::tvm::runtime::detail::LogMessageVoidify() & LOG(severity)
+
+#if TVM_LOG_DEBUG
+
+#define LOG_DFATAL LOG_FATAL
+#define DFATAL FATAL
+#define DLOG(severity) LOG_IF(severity, ::tvm::runtime::detail::DebugLoggingEnabled())
+#define DLOG_IF(severity, condition) \
+  LOG_IF(severity, ::tvm::runtime::detail::DebugLoggingEnabled() && (condition))
+
+#else
+
+#define LOG_DFATAL LOG_ERROR
+#define DFATAL ERROR
+#define DLOG(severity) true ? (void)0 : ::tvm::runtime::detail::LogMessageVoidify() & LOG(severity)
+#define DLOG_IF(severity, condition) \
+  (true || !(condition)) ? (void)0 : ::tvm::runtime::detail::LogMessageVoidify() & LOG(severity)
+
+#endif
+
+#if TVM_LOG_DEBUG
+#define DCHECK(x) \
+  while (false) CHECK(x)
+#define DCHECK_LT(x, y) \
+  while (false) CHECK((x) < (y))
+#define DCHECK_GT(x, y) \
+  while (false) CHECK((x) > (y))
+#define DCHECK_LE(x, y) \
+  while (false) CHECK((x) <= (y))
+#define DCHECK_GE(x, y) \
+  while (false) CHECK((x) >= (y))
+#define DCHECK_EQ(x, y) \
+  while (false) CHECK((x) == (y))
+#define DCHECK_NE(x, y) \
+  while (false) CHECK((x) != (y))
+#else
+#define DCHECK(x) CHECK(x)
+#define DCHECK_LT(x, y) CHECK((x) < (y))
+#define DCHECK_GT(x, y) CHECK((x) > (y))
+#define DCHECK_LE(x, y) CHECK((x) <= (y))
+#define DCHECK_GE(x, y) CHECK((x) >= (y))
+#define DCHECK_EQ(x, y) CHECK((x) == (y))
+#define DCHECK_NE(x, y) CHECK((x) != (y))
+#endif
+
+#define TVM_ICHECK_INDENT "  "
+
+#define ICHECK_BINARY_OP(name, op, x, y)                                  \
+  if (!::tvm::runtime::detail::LogCheck##name(x, y))                      \
+  ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream()           \
+      << ::tvm::runtime::detail::kTVM_INTERNAL_ERROR_MESSAGE << std::endl \
+      << TVM_ICHECK_INDENT << "Check failed: " << #x " " #op " " #y << ": "
+
+#define ICHECK(x)                                                                 \
+  if (!(x))                                                                       \
+  ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream()                   \
+      << ::tvm::runtime::detail::kTVM_INTERNAL_ERROR_MESSAGE << TVM_ICHECK_INDENT \
+      << "Check failed: " #x << " == false: "
+
+#define ICHECK_LT(x, y) ICHECK_BINARY_OP(_LT, <, x, y)
+#define ICHECK_GT(x, y) ICHECK_BINARY_OP(_GT, >, x, y)
+#define ICHECK_LE(x, y) ICHECK_BINARY_OP(_LE, <=, x, y)
+#define ICHECK_GE(x, y) ICHECK_BINARY_OP(_GE, >=, x, y)
+#define ICHECK_EQ(x, y) ICHECK_BINARY_OP(_EQ, ==, x, y)
+#define ICHECK_NE(x, y) ICHECK_BINARY_OP(_NE, !=, x, y)
+#define ICHECK_NOTNULL(x)                                                         \
+  ((x) == nullptr ? ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream() \
+                        << ::tvm::runtime::detail::kTVM_INTERNAL_ERROR_MESSAGE    \
+                        << TVM_ICHECK_INDENT << "Check not null: " #x << ' ',     \
+   (x) : (x))  // NOLINT(*)
+
+}  // namespace runtime
+// Re-export error types
+using runtime::Error;
+using runtime::InternalError;
+}  // namespace tvm
+#endif  // TVM_RUNTIME_LOGGING_H_
diff --git a/include/tvm/runtime/ndarray.h b/include/tvm/runtime/ndarray.h
index 0ff171d4821f..a884b5c6838f 100644
--- a/include/tvm/runtime/ndarray.h
+++ b/include/tvm/runtime/ndarray.h
@@ -25,6 +25,7 @@
 #define TVM_RUNTIME_NDARRAY_H_
 
 #include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/data_type.h>
 #include <tvm/runtime/object.h>
 #include <tvm/runtime/serializer.h>
@@ -133,10 +134,12 @@ class NDArray : public ObjectRef {
    * \brief Create an empty NDArray.
    * \param shape The shape of the new array.
    * \param dtype The data type of the new array.
-   * \param ctx The context of the Array.
+   * \param ctx The context of the array.
+   * \param mem_scope The memory scope of the array.
    * \return The created Array
    */
-  TVM_DLL static NDArray Empty(std::vector<int64_t> shape, DLDataType dtype, DLContext ctx);
+  TVM_DLL static NDArray Empty(std::vector<int64_t> shape, DLDataType dtype, DLContext ctx,
+                               Optional<String> mem_scope = NullOpt);
   /*!
    * \brief Create a NDArray backed by a dlpack tensor.
    *
diff --git a/include/tvm/runtime/object.h b/include/tvm/runtime/object.h
index b5cf77d590f6..048fc1d5af54 100644
--- a/include/tvm/runtime/object.h
+++ b/include/tvm/runtime/object.h
@@ -24,7 +24,7 @@
 #define TVM_RUNTIME_OBJECT_H_
 
 #include <tvm/runtime/c_runtime_api.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <string>
 #include <type_traits>
@@ -185,7 +185,11 @@ class TVM_DLL Object {
    */
   template <typename TargetType>
   inline bool IsInstance() const;
-
+  /*!
+   * \return Whether the cell has only one reference
+   * \note We use stl style naming to be consistent with known API in shared_ptr.
+   */
+  inline bool unique() const;
   /*!
    * \brief Get the type key of the corresponding index from runtime.
    * \param tindex The type index.
@@ -333,7 +337,7 @@ inline RelayRefType GetRef(const ObjectType* ptr);
 /*!
  * \brief Downcast a base reference type to a more specific type.
  *
- * \param ref The inptut reference
+ * \param ref The input reference
  * \return The corresponding SubRef.
  * \tparam SubRef The target specific reference type.
  * \tparam BaseRef the current reference type.
@@ -412,7 +416,7 @@ class ObjectPtr {
     return *get();
   }
   /*!
-   * \brief copy assignmemt
+   * \brief copy assignment
    * \param other The value to be assigned.
    * \return reference to self.
    */
@@ -423,7 +427,7 @@ class ObjectPtr {
     return *this;
   }
   /*!
-   * \brief move assignmemt
+   * \brief move assignment
    * \param other The value to be assigned.
    * \return reference to self.
    */
@@ -628,7 +632,7 @@ struct ObjectPtrEqual {
 };
 
 /*!
- * \brief helper macro to declare a base object type that can be inheritated.
+ * \brief helper macro to declare a base object type that can be inherited.
  * \param TypeName The name of the current type.
  * \param ParentType The name of the ParentType
  */
@@ -644,10 +648,10 @@ struct ObjectPtrEqual {
     return _GetOrAllocRuntimeTypeIndex();                                                      \
   }                                                                                            \
   static uint32_t _GetOrAllocRuntimeTypeIndex() {                                              \
-    static uint32_t tidx = Object::GetOrAllocRuntimeTypeIndex(                                 \
+    static uint32_t tindex = Object::GetOrAllocRuntimeTypeIndex(                               \
         TypeName::_type_key, TypeName::_type_index, ParentType::_GetOrAllocRuntimeTypeIndex(), \
         TypeName::_type_child_slots, TypeName::_type_child_slots_can_overflow);                \
-    return tidx;                                                                               \
+    return tindex;                                                                             \
   }
 
 /*!
@@ -660,7 +664,7 @@ struct ObjectPtrEqual {
   static const constexpr int _type_child_slots = 0;         \
   TVM_DECLARE_BASE_OBJECT_INFO(TypeName, ParentType)
 
-/*! \brief helper macro to supress unused warning */
+/*! \brief helper macro to suppress unused warning */
 #if defined(__GNUC__)
 #define TVM_ATTRIBUTE_UNUSED __attribute__((unused))
 #else
@@ -682,7 +686,7 @@ struct ObjectPtrEqual {
   TVM_STR_CONCAT(TVM_OBJECT_REG_VAR_DEF, __COUNTER__) = TypeName::_GetOrAllocRuntimeTypeIndex()
 
 /*
- * \brief Define the default copy/move constructor and assign opeator
+ * \brief Define the default copy/move constructor and assign operator
  * \param TypeName The class typename.
  */
 #define TVM_DEFINE_DEFAULT_COPY_MOVE_AND_ASSIGN(TypeName) \
@@ -823,7 +827,7 @@ inline bool Object::IsInstance() const {
       if (!TargetType::_type_child_slots_can_overflow) return false;
       // Invariance: parent index is always smaller than the child.
       if (self->type_index_ < TargetType::RuntimeTypeIndex()) return false;
-      // The rare slower-path, check type hierachy.
+      // The rare slower-path, check type hierarchy.
       return self->DerivedFrom(TargetType::RuntimeTypeIndex());
     }
   } else {
@@ -831,6 +835,8 @@ inline bool Object::IsInstance() const {
   }
 }
 
+inline bool Object::unique() const { return use_count() == 1; }
+
 template <typename ObjectType>
 inline const ObjectType* ObjectRef::as() const {
   if (data_ != nullptr && data_->IsInstance<ObjectType>()) {
diff --git a/include/tvm/runtime/packed_func.h b/include/tvm/runtime/packed_func.h
index ee4ab82cd4d3..7113863a6fb3 100644
--- a/include/tvm/runtime/packed_func.h
+++ b/include/tvm/runtime/packed_func.h
@@ -24,10 +24,10 @@
 #ifndef TVM_RUNTIME_PACKED_FUNC_H_
 #define TVM_RUNTIME_PACKED_FUNC_H_
 
-#include <dmlc/logging.h>
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/container.h>
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/module.h>
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/object.h>
@@ -60,7 +60,7 @@ namespace runtime {
 // forward declarations
 class TVMArgs;
 class TVMArgValue;
-class TVMMovableArgValue_;
+class TVMMovableArgValueWithContext_;
 class TVMRetValue;
 class TVMArgsSetter;
 
@@ -215,7 +215,7 @@ class TypedPackedFunc<R(Args...)> {
    * \brief constructor from TVMMovableArgValue_
    * \param value The TVMMovableArgValue_
    */
-  inline TypedPackedFunc(TVMMovableArgValue_&& value);  // NOLINT(*)
+  inline TypedPackedFunc(TVMMovableArgValueWithContext_&& value);  // NOLINT(*)
   /*!
    * \brief construct from a lambda function with the same signature.
    *
@@ -223,6 +223,30 @@ class TypedPackedFunc<R(Args...)> {
    * \code
    * auto typed_lambda = [](int x)->int { return x + 1; }
    * // construct from packed function
+   * TypedPackedFunc<int(int)> ftyped(typed_lambda, "add_one");
+   * // call the typed version.
+   * ICHECK_EQ(ftyped(1), 2);
+   * \endcode
+   *
+   * \param typed_lambda typed lambda function.
+   * \param name the name of the lambda function.
+   * \tparam FLambda the type of the lambda function.
+   */
+  template <typename FLambda, typename = typename std::enable_if<std::is_convertible<
+                                  FLambda, std::function<R(Args...)>>::value>::type>
+  TypedPackedFunc(const FLambda& typed_lambda, std::string name) {  // NOLINT(*)
+    this->AssignTypedLambda(typed_lambda, name);
+  }
+  /*!
+   * \brief construct from a lambda function with the same signature.
+   *
+   * This version does not take a name. It is highly recommend you use the
+   * version that takes a name for the lambda.
+   *
+   * Example usage:
+   * \code
+   * auto typed_lambda = [](int x)->int { return x + 1; }
+   * // construct from packed function
    * TypedPackedFunc<int(int)> ftyped(typed_lambda);
    * // call the typed version.
    * ICHECK_EQ(ftyped(1), 2);
@@ -231,9 +255,8 @@ class TypedPackedFunc<R(Args...)> {
    * \param typed_lambda typed lambda function.
    * \tparam FLambda the type of the lambda function.
    */
-  template <typename FLambda, typename = typename std::enable_if<
-                                  std::is_convertible<FLambda,
-                                                      std::function<R(Args...)>>::value>::type>
+  template <typename FLambda, typename = typename std::enable_if<std::is_convertible<
+                                  FLambda, std::function<R(Args...)>>::value>::type>
   TypedPackedFunc(const FLambda& typed_lambda) {  // NOLINT(*)
     this->AssignTypedLambda(typed_lambda);
   }
@@ -297,6 +320,17 @@ class TypedPackedFunc<R(Args...)> {
    * \brief Assign the packed field using a typed lambda function.
    *
    * \param flambda The lambda function.
+   * \param name The name associated with this lambda.
+   * \tparam FLambda The lambda function type.
+   * \note We capture the lambda when possible for maximum efficiency.
+   */
+  template <typename FLambda>
+  inline void AssignTypedLambda(FLambda flambda, std::string name);
+  /*!
+   * \brief Assign the packed field using a typed lambda function. This variant is for functions
+   * without names.
+   *
+   * \param flambda The lambda function.
    * \tparam FLambda The lambda function type.
    * \note We capture the lambda when possible for maximum efficiency.
    */
@@ -337,7 +371,7 @@ inline const char* ArgTypeCode2Str(int type_code);
 
 // macro to check type code.
 #define TVM_CHECK_TYPE_CODE(CODE, T) \
-  ICHECK_EQ(CODE, T) << " expected " << ArgTypeCode2Str(T) << " but get " << ArgTypeCode2Str(CODE)
+  ICHECK_EQ(CODE, T) << "expected " << ArgTypeCode2Str(T) << " but got " << ArgTypeCode2Str(CODE)
 
 /*!
  * \brief Type traits for runtime type check during FFI conversion.
@@ -345,6 +379,33 @@ inline const char* ArgTypeCode2Str(int type_code);
  */
 template <typename T>
 struct ObjectTypeChecker {
+  /*!
+   * \brief Check if an object matches the template type and return the
+   *        mismatched type if it exists.
+   * \param ptr The object to check the type of.
+   * \return An Optional containing the actual type of the pointer if it does not match the
+   *         template type. If the Optional does not contain a value, then the types match.
+   */
+  static Optional<String> CheckAndGetMismatch(const Object* ptr) {
+    using ContainerType = typename T::ContainerType;
+    if (ptr == nullptr) {
+      if (T::_type_is_nullable) {
+        return NullOpt;
+      } else {
+        return String("nullptr");
+      }
+    }
+    if (ptr->IsInstance<ContainerType>()) {
+      return NullOpt;
+    } else {
+      return String(ptr->GetTypeKey());
+    }
+  }
+  /*!
+   * \brief Check if an object matches the template type.
+   * \param ptr The object to check the type of.
+   * \return Whether or not the template type matches the objects type.
+   */
   static bool Check(const Object* ptr) {
     using ContainerType = typename T::ContainerType;
     if (ptr == nullptr) return T::_type_is_nullable;
@@ -356,6 +417,74 @@ struct ObjectTypeChecker {
   }
 };
 
+// Additional overloads for PackedFunc checking.
+template <typename T>
+struct ObjectTypeChecker<Array<T>> {
+  static Optional<String> CheckAndGetMismatch(const Object* ptr) {
+    if (ptr == nullptr) {
+      return NullOpt;
+    }
+    if (!ptr->IsInstance<ArrayNode>()) {
+      return String(ptr->GetTypeKey());
+    }
+    const ArrayNode* n = static_cast<const ArrayNode*>(ptr);
+    for (size_t i = 0; i < n->size(); i++) {
+      const ObjectRef& p = (*n)[i];
+      Optional<String> check_subtype = ObjectTypeChecker<T>::CheckAndGetMismatch(p.get());
+      if (check_subtype.defined()) {
+        return String("Array[index " + std::to_string(i) + ": " + check_subtype.value() + "]");
+      }
+    }
+    return NullOpt;
+  }
+  static bool Check(const Object* ptr) {
+    if (ptr == nullptr) return true;
+    if (!ptr->IsInstance<ArrayNode>()) return false;
+    const ArrayNode* n = static_cast<const ArrayNode*>(ptr);
+    for (const ObjectRef& p : *n) {
+      if (!ObjectTypeChecker<T>::Check(p.get())) {
+        return false;
+      }
+    }
+    return true;
+  }
+  static std::string TypeName() { return "Array[" + ObjectTypeChecker<T>::TypeName() + "]"; }
+};
+template <typename K, typename V>
+struct ObjectTypeChecker<Map<K, V>> {
+  static Optional<String> CheckAndGetMismatch(const Object* ptr) {
+    if (ptr == nullptr) return NullOpt;
+    if (!ptr->IsInstance<MapNode>()) return String(ptr->GetTypeKey());
+    const MapNode* n = static_cast<const MapNode*>(ptr);
+    for (const auto& kv : *n) {
+      Optional<String> key_type = ObjectTypeChecker<K>::CheckAndGetMismatch(kv.first.get());
+      Optional<String> value_type = ObjectTypeChecker<K>::CheckAndGetMismatch(kv.first.get());
+      if (key_type.defined() || value_type.defined()) {
+        std::string key_name =
+            key_type.defined() ? std::string(key_type.value()) : ObjectTypeChecker<K>::TypeName();
+        std::string value_name = value_type.defined() ? std::string(value_type.value())
+                                                      : ObjectTypeChecker<V>::TypeName();
+        return String("Map[" + key_name + ", " + value_name + "]");
+      }
+    }
+    return NullOpt;
+  }
+  static bool Check(const Object* ptr) {
+    if (ptr == nullptr) return true;
+    if (!ptr->IsInstance<MapNode>()) return false;
+    const MapNode* n = static_cast<const MapNode*>(ptr);
+    for (const auto& kv : *n) {
+      if (!ObjectTypeChecker<K>::Check(kv.first.get())) return false;
+      if (!ObjectTypeChecker<V>::Check(kv.second.get())) return false;
+    }
+    return true;
+  }
+  static std::string TypeName() {
+    return "Map[" + ObjectTypeChecker<K>::TypeName() + ", " + ObjectTypeChecker<V>::TypeName() +
+           ']';
+  }
+};
+
 /*!
  * \brief Internal base class to
  *  handle conversion to POD values.
@@ -401,8 +530,8 @@ class TVMPODValue_ {
       return static_cast<DLTensor*>(value_.v_handle);
     } else {
       if (type_code_ == kTVMNullptr) return nullptr;
-      LOG(FATAL) << "Expect "
-                 << "DLTensor* or NDArray but get " << ArgTypeCode2Str(type_code_);
+      LOG(FATAL) << "Expected "
+                 << "DLTensor* or NDArray but got " << ArgTypeCode2Str(type_code_);
       return nullptr;
     }
   }
@@ -442,6 +571,7 @@ class TVMPODValue_ {
  protected:
   friend class TVMArgsSetter;
   friend class TVMRetValue;
+  friend class TVMMovableArgValue_;
   TVMPODValue_() : type_code_(kTVMNullptr) {}
   TVMPODValue_(TVMValue value, int type_code) : value_(value), type_code_(type_code) {}
 
@@ -562,6 +692,44 @@ class TVMMovableArgValue_ : public TVMPODValue_ {
   TVMArgValue AsArgValue() const { return TVMArgValue(value_, type_code_); }
 };
 
+/*!
+ * \brief Internal auxiliary struct for TypedPackedFunc to indicate a movable argument with
+ * additional context information (function name and argument index) for better error reporting.
+ *
+ * \sa MovableArgValue_
+ * \note For internal development purpose only.
+ */
+class TVMMovableArgValueWithContext_ {
+ public:
+  /*!
+   * \brief move constructor from another return value.
+   * \param value The other return value.
+   * \param type_code The code associated with the type of the value.
+   * \param arg_index In a function call, this argument is at index arg_index (0-indexed).
+   * \param optional_name Name of the function being called. Can be nullptr if the function is not
+   * named.
+   */
+  TVMMovableArgValueWithContext_(TVMValue value, int type_code, int arg_index,
+                                 const std::string* optional_name)
+      : value_(value, type_code), arg_index_(arg_index), optional_name_(optional_name) {}
+
+  template <typename T>
+  operator T() const {
+    try {
+      return value_;  // implicit conversion happens here
+    } catch (dmlc::Error& e) {
+      LOG(FATAL) << "In function " << (optional_name_ == nullptr ? "<anonymous>" : *optional_name_)
+                 << ": error while converting argument " << arg_index_ << ": " << e.what();
+      throw;  // never reached, LOG(FATAL) throws, but this silences a warning.
+    }
+  }
+
+ private:
+  TVMMovableArgValue_ value_;
+  int arg_index_;
+  const std::string* optional_name_;
+};
+
 /*!
  * \brief Return Value container,
  *  Unlike TVMArgValue, which only holds reference and do not delete
@@ -910,15 +1078,15 @@ struct PackedFuncValueConverter {
 #define TVM_DLL_EXPORT_PACKED_FUNC(ExportName, Function)                                    \
   extern "C" {                                                                              \
   TVM_DLL int ExportName(TVMValue* args, int* type_code, int num_args, TVMValue* out_value, \
-                         int* out_type_code);                                               \
+                         int* out_type_code, void* resource_handle);                        \
   int ExportName(TVMValue* args, int* type_code, int num_args, TVMValue* out_value,         \
-                 int* out_type_code) {                                                      \
+                 int* out_type_code, void* resource_handle) {                               \
     try {                                                                                   \
       ::tvm::runtime::TVMRetValue rv;                                                       \
       Function(::tvm::runtime::TVMArgs(args, type_code, num_args), &rv);                    \
       rv.MoveToCHost(out_value, out_type_code);                                             \
       return 0;                                                                             \
-    } catch (const ::std::runtime_error& _except_) {                                        \
+    } catch (const ::std::exception& _except_) {                                            \
       TVMAPISetLastError(_except_.what());                                                  \
       return -1;                                                                            \
     }                                                                                       \
@@ -963,7 +1131,7 @@ struct PackedFuncValueConverter {
 #define TVM_DLL_EXPORT_TYPED_FUNC(ExportName, Function)                                     \
   extern "C" {                                                                              \
   TVM_DLL int ExportName(TVMValue* args, int* type_code, int num_args, TVMValue* out_value, \
-                         int* out_type_code) {                                              \
+                         int* out_type_code, void* resource_handle) {                       \
     try {                                                                                   \
       auto f = Function;                                                                    \
       using FType = ::tvm::runtime::detail::function_signature<decltype(f)>::FType;         \
@@ -972,7 +1140,7 @@ struct PackedFuncValueConverter {
           f, ::tvm::runtime::TVMArgs(args, type_code, num_args), &rv);                      \
       rv.MoveToCHost(out_value, out_type_code);                                             \
       return 0;                                                                             \
-    } catch (const ::std::runtime_error& _except_) {                                        \
+    } catch (const ::std::exception& _except_) {                                            \
       TVMAPISetLastError(_except_.what());                                                  \
       return -1;                                                                            \
     }                                                                                       \
@@ -1070,7 +1238,7 @@ struct func_signature_helper<R (T::*)(Args...) const> {
 
 /*!
  * \brief template class to get function signature of a function or functor.
- * \tparam T The funtion/functor type.
+ * \tparam T The function/functor type.
  */
 template <typename T>
 struct function_signature {
@@ -1213,20 +1381,23 @@ namespace detail {
 template <typename R, int nleft, int index, typename F>
 struct unpack_call_dispatcher {
   template <typename... Args>
-  TVM_ALWAYS_INLINE static void run(const F& f, const TVMArgs& args_pack, TVMRetValue* rv,
+  TVM_ALWAYS_INLINE static void run(const std::string* optional_name, const F& f,
+                                    const TVMArgs& args_pack, TVMRetValue* rv,
                                     Args&&... unpacked_args) {
     // construct a movable argument value
     // which allows potential move of argument to the input of F.
     unpack_call_dispatcher<R, nleft - 1, index + 1, F>::run(
-        f, args_pack, rv, std::forward<Args>(unpacked_args)...,
-        TVMMovableArgValue_(args_pack.values[index], args_pack.type_codes[index]));
+        optional_name, f, args_pack, rv, std::forward<Args>(unpacked_args)...,
+        TVMMovableArgValueWithContext_(args_pack.values[index], args_pack.type_codes[index], index,
+                                       optional_name));
   }
 };
 
 template <typename R, int index, typename F>
 struct unpack_call_dispatcher<R, 0, index, F> {
   template <typename... Args>
-  TVM_ALWAYS_INLINE static void run(const F& f, const TVMArgs& args_pack, TVMRetValue* rv,
+  TVM_ALWAYS_INLINE static void run(const std::string* optional_name, const F& f,
+                                    const TVMArgs& args_pack, TVMRetValue* rv,
                                     Args&&... unpacked_args) {
     using RetType = decltype(f(std::forward<Args>(unpacked_args)...));
     if (std::is_same<RetType, R>::value) {
@@ -1240,16 +1411,21 @@ struct unpack_call_dispatcher<R, 0, index, F> {
 template <int index, typename F>
 struct unpack_call_dispatcher<void, 0, index, F> {
   template <typename... Args>
-  TVM_ALWAYS_INLINE static void run(const F& f, const TVMArgs& args_pack, TVMRetValue* rv,
+  TVM_ALWAYS_INLINE static void run(const std::string* optional_name, const F& f,
+                                    const TVMArgs& args_pack, TVMRetValue* rv,
                                     Args&&... unpacked_args) {
     f(std::forward<Args>(unpacked_args)...);
   }
 };
 
 template <typename R, int nargs, typename F>
-TVM_ALWAYS_INLINE void unpack_call(const F& f, const TVMArgs& args, TVMRetValue* rv) {
-  ICHECK_EQ(nargs, args.size()) << "Expect " << nargs << " arguments but get " << args.size();
-  unpack_call_dispatcher<R, nargs, 0, F>::run(f, args, rv);
+TVM_ALWAYS_INLINE void unpack_call(const std::string* optional_name, const F& f,
+                                   const TVMArgs& args, TVMRetValue* rv) {
+  CHECK_EQ(nargs, args.size()) << "Function "
+                               << (optional_name == nullptr ? "<anonymous>" : *optional_name)
+                               << " expects " << nargs << " arguments but " << args.size()
+                               << " were provided";
+  unpack_call_dispatcher<R, nargs, 0, F>::run(optional_name, f, args, rv);
 }
 
 template <typename FType>
@@ -1259,7 +1435,7 @@ template <typename R, typename... Args>
 struct unpack_call_by_signature<R(Args...)> {
   template <typename F>
   TVM_ALWAYS_INLINE static void run(const F& f, const TVMArgs& args, TVMRetValue* rv) {
-    unpack_call<R, sizeof...(Args)>(f, args, rv);
+    unpack_call<R, sizeof...(Args)>(nullptr, f, args, rv);
   }
 };
 
@@ -1297,14 +1473,30 @@ TypedPackedFunc<R(Args...)>::TypedPackedFunc(const TVMArgValue& value)
     : packed_(value.operator PackedFunc()) {}
 
 template <typename R, typename... Args>
-TypedPackedFunc<R(Args...)>::TypedPackedFunc(TVMMovableArgValue_&& value)
+TypedPackedFunc<R(Args...)>::TypedPackedFunc(TVMMovableArgValueWithContext_&& value)
     : packed_(value.operator PackedFunc()) {}
 
+template <typename R, typename... Args>
+template <typename FType>
+inline void TypedPackedFunc<R(Args...)>::AssignTypedLambda(FType flambda, std::string name) {
+  packed_ = PackedFunc([flambda, name](const TVMArgs& args, TVMRetValue* rv) {
+    if (args.size() != sizeof...(Args)) {
+      LOG(FATAL) << "Function " << name << " expects " << sizeof...(Args) << " arguments, but "
+                 << args.size() << " were provided.";
+    }
+    detail::unpack_call<R, sizeof...(Args)>(&name, flambda, args, rv);
+  });
+}
+
 template <typename R, typename... Args>
 template <typename FType>
 inline void TypedPackedFunc<R(Args...)>::AssignTypedLambda(FType flambda) {
   packed_ = PackedFunc([flambda](const TVMArgs& args, TVMRetValue* rv) {
-    detail::unpack_call<R, sizeof...(Args)>(flambda, args, rv);
+    if (args.size() != sizeof...(Args)) {
+      LOG(FATAL) << "Function <anonymous> expects " << sizeof...(Args) << " arguments, but "
+                 << args.size() << " were provided.";
+    }
+    detail::unpack_call<R, sizeof...(Args)>(nullptr, flambda, args, rv);
   });
 }
 
@@ -1377,7 +1569,7 @@ inline TObjectRef TVMPODValue_::AsObjectRef() const {
   using ContainerType = typename TObjectRef::ContainerType;
 
   if (type_code_ == kTVMNullptr) {
-    ICHECK(TObjectRef::_type_is_nullable)
+    CHECK(TObjectRef::_type_is_nullable)
         << "Expect a not null value of " << ContainerType::_type_key;
     return TObjectRef(ObjectPtr<Object>(nullptr));
   }
@@ -1387,30 +1579,30 @@ inline TObjectRef TVMPODValue_::AsObjectRef() const {
     TVM_CHECK_TYPE_CODE(type_code_, kTVMNDArrayHandle);
     ObjectPtr<Object> data =
         NDArray::FFIDataFromHandle(static_cast<TVMArrayHandle>(value_.v_handle));
-    ICHECK(data->IsInstance<ContainerType>())
-        << "Expect " << ContainerType::_type_key << " but get " << data->GetTypeKey();
+    CHECK(data->IsInstance<ContainerType>())
+        << "Expected " << ContainerType::_type_key << " but got " << data->GetTypeKey();
     return TObjectRef(data);
   }
   if (std::is_base_of<Module::ContainerType, ContainerType>::value) {
     // Casting to a sub-class of Module
     TVM_CHECK_TYPE_CODE(type_code_, kTVMModuleHandle);
     ObjectPtr<Object> data = GetObjectPtr<Object>(static_cast<Object*>(value_.v_handle));
-    ICHECK(data->IsInstance<ContainerType>())
-        << "Expect " << ContainerType::_type_key << " but get " << data->GetTypeKey();
+    CHECK(data->IsInstance<ContainerType>())
+        << "Expected " << ContainerType::_type_key << " but got " << data->GetTypeKey();
     return TObjectRef(data);
   }
   if (type_code_ == kTVMObjectHandle) {
     // normal object type check.
     Object* ptr = static_cast<Object*>(value_.v_handle);
-    ICHECK(ObjectTypeChecker<TObjectRef>::Check(ptr))
-        << "Expect " << ObjectTypeChecker<TObjectRef>::TypeName() << " but get "
-        << ptr->GetTypeKey();
+    Optional<String> checked_type = ObjectTypeChecker<TObjectRef>::CheckAndGetMismatch(ptr);
+    ICHECK(!checked_type.defined()) << "Expected " << ObjectTypeChecker<TObjectRef>::TypeName()
+                                    << ", but got " << checked_type.value();
     return TObjectRef(GetObjectPtr<Object>(ptr));
   } else if (type_code_ == kTVMObjectRValueRefArg) {
     Object* ptr = *static_cast<Object**>(value_.v_handle);
-    ICHECK(ObjectTypeChecker<TObjectRef>::Check(ptr))
-        << "Expect " << ObjectTypeChecker<TObjectRef>::TypeName() << " but get "
-        << ptr->GetTypeKey();
+    Optional<String> checked_type = ObjectTypeChecker<TObjectRef>::CheckAndGetMismatch(ptr);
+    ICHECK(!checked_type.defined()) << "Expected " << ObjectTypeChecker<TObjectRef>::TypeName()
+                                    << ", but got " << checked_type.value();
     return TObjectRef(GetObjectPtr<Object>(ptr));
   } else if (std::is_base_of<ContainerType, NDArray::ContainerType>::value &&
              type_code_ == kTVMNDArrayHandle) {
diff --git a/include/tvm/runtime/profiling.h b/include/tvm/runtime/profiling.h
new file mode 100644
index 000000000000..45b60ea18acc
--- /dev/null
+++ b/include/tvm/runtime/profiling.h
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file include/tvm/runtime/profiling.h
+ * \brief Runtime profiling including timers.
+ */
+#ifndef TVM_RUNTIME_PROFILING_H_
+#define TVM_RUNTIME_PROFILING_H_
+
+#include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/device_api.h>
+#include <tvm/runtime/object.h>
+#include <tvm/runtime/registry.h>
+
+#include <string>
+
+namespace tvm {
+namespace runtime {
+
+/*! \brief Base class for all implementations.
+ *
+ * New implementations of this interface should make sure that `Start` and `Stop`
+ * are as lightweight as possible. Expensive state synchronization should be
+ * done in `SyncAndGetElapsedNanos`.
+ */
+class TimerNode : public Object {
+ public:
+  /*! \brief Start the timer.
+   *
+   * Note: this function should only be called once per object.
+   */
+  virtual void Start() = 0;
+  /*! \brief Stop the timer.
+   *
+   * Note: this function should only be called once per object.
+   */
+  virtual void Stop() = 0;
+  /*! \brief Synchronize timer state and return elapsed time between `Start` and `Stop`.
+   * \return The time in nanoseconds between `Start` and `Stop`.
+   *
+   * This function is necessary because we want to avoid timing the overhead of
+   * doing timing. When using multiple timers, it is recommended to stop all of
+   * them before calling `SyncAndGetElapsedNanos` on any of them.
+   *
+   * Note: this function should be only called once per object. It may incur
+   * a large synchronization overhead (for example, with GPUs).
+   */
+  virtual int64_t SyncAndGetElapsedNanos() = 0;
+
+  virtual ~TimerNode() {}
+
+  static constexpr const char* _type_key = "TimerNode";
+  TVM_DECLARE_BASE_OBJECT_INFO(TimerNode, Object);
+};
+
+/*! \brief Timer for a specific device.
+ *
+ * This is a managed reference to a TimerNode.
+ *
+ * \sa TimerNode
+ */
+class Timer : public ObjectRef {
+ public:
+  /*!
+   * \brief Get a device specific timer.
+   * \param ctx The device context to time.
+   * \return A `Timer` that has already been started.
+   *
+   * Use this function to time runtime of arbitrary regions of code on a specific
+   * device. The code that you want to time should be running on the device
+   * otherwise the timer will not return correct results. This is a lower level
+   * interface than TimeEvaluator and only runs the timed code once
+   * (TimeEvaluator runs the code multiple times).
+   *
+   * A default timer is used if a device specific one does not exist. This
+   * timer performs synchronization between the device and CPU, which can lead
+   * to overhead in the reported results.
+   *
+   * Example usage:
+   * \code{.cpp}
+   * Timer t = Timer::Start(TVMContext::cpu());
+   * my_long_running_function();
+   * t->Stop();
+   * ... // some more computation
+   * int64_t nanosecs = t->SyncAndGetElapsedNanos() // elapsed time in nanoseconds
+   * \endcode
+   *
+   * To add a new device-specific timer, register a new function
+   * "profiler.timer.my_device" (where `my_device` is the `DeviceName` of your
+   * device). This function should accept a `TVMContext` and return a new `Timer`
+   * that has already been started.
+   *
+   * For example, this is how the CPU timer is implemented:
+   * \code{.cpp}
+   *  class CPUTimerNode : public TimerNode {
+   *   public:
+   *    virtual void Start() { start_ = std::chrono::high_resolution_clock::now(); }
+   *    virtual void Stop() { duration_ = std::chrono::high_resolution_clock::now() - start_; }
+   *    virtual int64_t SyncAndGetElapsedNanos() { return duration_.count(); }
+   *    virtual ~CPUTimerNode() {}
+   *
+   *    static constexpr const char* _type_key = "CPUTimerNode";
+   *    TVM_DECLARE_FINAL_OBJECT_INFO(CPUTimerNode, TimerNode);
+   *
+   *   private:
+   *    std::chrono::high_resolution_clock::time_point start_;
+   *    std::chrono::duration<int64_t, std::nano> duration_;
+   *  };
+   *  TVM_REGISTER_OBJECT_TYPE(CPUTimerNode);
+   *
+   *  TVM_REGISTER_GLOBAL("profiling.timer.cpu").set_body_typed([](TVMContext ctx) {
+   *    return Timer(make_object<CPUTimerNode>());
+   *  });
+   * \endcode
+   */
+  static TVM_DLL Timer Start(TVMContext ctx);
+
+  TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(Timer, ObjectRef, TimerNode);
+};
+
+/*!
+ * \brief Default timer if one does not exist for the context.
+ * \param ctx The context to time on.
+ *
+ * Note that this timer performs synchronization between the device and CPU,
+ * which can lead to overhead in the reported results.
+ */
+Timer DefaultTimer(TVMContext ctx);
+
+}  // namespace runtime
+}  // namespace tvm
+
+#endif  // TVM_RUNTIME_PROFILING_H_
diff --git a/include/tvm/runtime/registry.h b/include/tvm/runtime/registry.h
index 86e3706b2058..859a8ace1abe 100644
--- a/include/tvm/runtime/registry.h
+++ b/include/tvm/runtime/registry.h
@@ -93,7 +93,7 @@ class Registry {
   template <typename FLambda>
   Registry& set_body_typed(FLambda f) {
     using FType = typename detail::function_signature<FLambda>::FType;
-    return set_body(TypedPackedFunc<FType>(std::move(f)).packed());
+    return set_body(TypedPackedFunc<FType>(std::move(f), name_).packed());
   }
   /*!
    * \brief set the body of the function to be the passed method pointer.
@@ -122,7 +122,7 @@ class Registry {
       // call method pointer
       return (target.*f)(params...);
     };
-    return set_body(TypedPackedFunc<R(T, Args...)>(fwrap));
+    return set_body(TypedPackedFunc<R(T, Args...)>(fwrap, name_));
   }
 
   /*!
@@ -152,7 +152,7 @@ class Registry {
       // call method pointer
       return (target.*f)(params...);
     };
-    return set_body(TypedPackedFunc<R(const T, Args...)>(fwrap));
+    return set_body(TypedPackedFunc<R(const T, Args...)>(fwrap, name_));
   }
 
   /*!
@@ -194,7 +194,7 @@ class Registry {
       // call method pointer
       return (target->*f)(params...);
     };
-    return set_body(TypedPackedFunc<R(TObjectRef, Args...)>(fwrap));
+    return set_body(TypedPackedFunc<R(TObjectRef, Args...)>(fwrap, name_));
   }
 
   /*!
@@ -236,7 +236,7 @@ class Registry {
       // call method pointer
       return (target->*f)(params...);
     };
-    return set_body(TypedPackedFunc<R(TObjectRef, Args...)>(fwrap));
+    return set_body(TypedPackedFunc<R(TObjectRef, Args...)>(fwrap, name_));
   }
 
   /*!
diff --git a/include/tvm/runtime/vm/bytecode.h b/include/tvm/runtime/vm/bytecode.h
index e858c4458054..72a557fa93b1 100644
--- a/include/tvm/runtime/vm/bytecode.h
+++ b/include/tvm/runtime/vm/bytecode.h
@@ -25,7 +25,7 @@
 #define TVM_RUNTIME_VM_BYTECODE_H_
 
 #include <tvm/runtime/data_type.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <iostream>
 #include <vector>
diff --git a/include/tvm/support/logging.h b/include/tvm/support/logging.h
deleted file mode 100644
index d98363ea1c1b..000000000000
--- a/include/tvm/support/logging.h
+++ /dev/null
@@ -1,158 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * \file tvm/support/logging.h
- * \brief logging utilities on top of dmlc-core
- */
-#ifndef TVM_SUPPORT_LOGGING_H_
-#define TVM_SUPPORT_LOGGING_H_
-
-#include <dmlc/logging.h>
-
-// a technique that enables overriding macro names on the number of parameters. This is used
-// to define other macros below
-#define GET_MACRO(_1, _2, _3, _4, _5, NAME, ...) NAME
-
-/*!
- * \brief COND_X calls COND_X_N where N is the number of parameters passed to COND_X
- * X can be any of CHECK_GE, CHECK_EQ, CHECK, or LOG (defined dmlc-core/include/dmlc/logging.h.)
- * COND_X (but not COND_X_N) are supposed to be used outside this file.
- * The first parameter of COND_X (and therefore, COND_X_N), which we call 'quit_on_assert',
- * is a boolean. The rest of the parameters of COND_X is the same as the parameters of X.
- * quit_on_assert determines the overall behaviour of COND_X. If it's true COND_X
- * quits the program on assertion failure. If it's false, then it moves on and somehow reports
- * the assertion failure back to the macro caller in an appropriate manner (e.g, 'return false'
- * in a function, or 'continue' or 'break' in a loop)
- * The default behavior when quit_on_assertion is false, is to 'return false'. If this is not
- * desirable, the macro caller can pass one more last parameter to COND_X to tell COND_X what
- * to do when when quit_on_assertion is false and the assertion fails.
- *
- * Rationale: These macros were designed to implement functions that have two behaviours
- * in a concise way. Those behaviours are quitting on assertion failures, or trying to
- * move on from assertion failures. Note that these macros hide lots of control flow in them,
- * and therefore, makes the logic of the whole code slightly harder to understand. However,
- * in pieces of code that use these macros frequently, it will significantly shorten the
- * amount of code needed to be read, and we won't need to clutter the main logic of the
- * function by repetitive control flow structure. The first problem
- * mentioned will be improved over time as the developer gets used to the macro.
- *
- * Here is an example of how to use it
- * \code
- * bool f(..., bool quit_on_assertion) {
- *   int a = 0, b = 0;
- *   ...
- *   a = ...
- *   b = ...
- *   // if quit_on_assertion is true, if a==b, continue, otherwise quit.
- *   // if quit_on_assertion is false, if a==b, continue, otherwise 'return false' (default
- * behaviour) COND_CHECK_EQ(quit_on_assertion, a, b) << "some error message when  quiting"
- *   ...
- *   for (int i = 0; i < N; i++) {
- *     a = ...
- *     b = ...
- *     // if quit_on_assertion is true, if a==b, continue, otherwise quit.
- *     // if quit_on_assertion is false, if a==b, continue, otherwise 'break' (non-default
- *     // behaviour, therefore, has to be explicitly specified)
- *     COND_CHECK_EQ(quit_on_assertion, a, b, break) << "some error message when  quiting"
- *   }
- * }
- * \endcode
- */
-#define COND_CHECK_GE(...) \
-  GET_MACRO(__VA_ARGS__, COND_CHECK_GE_5, COND_CHECK_GE_4, COND_CHECK_GE_3)(__VA_ARGS__)
-#define COND_CHECK_EQ(...) \
-  GET_MACRO(__VA_ARGS__, COND_CHECK_EQ_5, COND_CHECK_EQ_4, COND_CHECK_EQ_3)(__VA_ARGS__)
-#define COND_CHECK(...) \
-  GET_MACRO(__VA_ARGS__, COND_CHECK_5, COND_CHECK_4, COND_CHECK_3, COND_CHECK_2)(__VA_ARGS__)
-#define COND_LOG(...) \
-  GET_MACRO(__VA_ARGS__, COND_LOG_5, COND_LOG_4, COND_LOG_3, COND_LOG_2)(__VA_ARGS__)
-
-// Not supposed to be used by users directly.
-#define COND_CHECK_OP(quit_on_assert, x, y, what, op) \
-  if (!quit_on_assert) {                              \
-    if (!((x)op(y))) what;                            \
-  } else /* NOLINT(*) */                              \
-    CHECK_##op(x, y)
-
-#define COND_CHECK_EQ_4(quit_on_assert, x, y, what) COND_CHECK_OP(quit_on_assert, x, y, what, ==)
-#define COND_CHECK_GE_4(quit_on_assert, x, y, what) COND_CHECK_OP(quit_on_assert, x, y, what, >=)
-
-#define COND_CHECK_3(quit_on_assert, x, what) \
-  if (!quit_on_assert) {                      \
-    if (!(x)) what;                           \
-  } else /* NOLINT(*) */                      \
-    CHECK(x)
-
-#define COND_LOG_3(quit_on_assert, x, what) \
-  if (!quit_on_assert) {                    \
-    what;                                   \
-  } else /* NOLINT(*) */                    \
-    LOG(x)
-
-#define COND_CHECK_EQ_3(quit_on_assert, x, y) COND_CHECK_EQ_4(quit_on_assert, x, y, return false)
-#define COND_CHECK_GE_3(quit_on_assert, x, y) COND_CHECK_GE_4(quit_on_assert, x, y, return false)
-#define COND_CHECK_2(quit_on_assert, x) COND_CHECK_3(quit_on_assert, x, return false)
-#define COND_LOG_2(quit_on_assert, x) COND_LOG_3(quit_on_assert, x, return false)
-
-namespace tvm {
-
-constexpr const char* kTVM_INTERNAL_ERROR_MESSAGE =
-    "\n---------------------------------------------------------------\n"
-    "An internal invariant was violated during the execution of TVM.\n"
-    "Please read TVM's error reporting guidelines.\n"
-    "More details can be found here: https://discuss.tvm.ai/t/error-reporting/7793.\n"
-    "---------------------------------------------------------------\n";
-
-#define ICHECK_INDENT "  "
-
-#define ICHECK_BINARY_OP(name, op, x, y)                           \
-  if (dmlc::LogCheckError _check_err = dmlc::LogCheck##name(x, y)) \
-  dmlc::LogMessageFatal(__FILE__, __LINE__).stream()               \
-      << tvm::kTVM_INTERNAL_ERROR_MESSAGE << std::endl             \
-      << ICHECK_INDENT << "Check failed: " << #x " " #op " " #y << *(_check_err.str) << ": "
-
-#define ICHECK(x)                                    \
-  if (!(x))                                          \
-  dmlc::LogMessageFatal(__FILE__, __LINE__).stream() \
-      << tvm::kTVM_INTERNAL_ERROR_MESSAGE << ICHECK_INDENT << "Check failed: " #x << " == false: "
-
-#define ICHECK_LT(x, y) ICHECK_BINARY_OP(_LT, <, x, y)
-#define ICHECK_GT(x, y) ICHECK_BINARY_OP(_GT, >, x, y)
-#define ICHECK_LE(x, y) ICHECK_BINARY_OP(_LE, <=, x, y)
-#define ICHECK_GE(x, y) ICHECK_BINARY_OP(_GE, >=, x, y)
-#define ICHECK_EQ(x, y) ICHECK_BINARY_OP(_EQ, ==, x, y)
-#define ICHECK_NE(x, y) ICHECK_BINARY_OP(_NE, !=, x, y)
-#define ICHECK_NOTNULL(x)                                                                        \
-  ((x) == nullptr ? dmlc::LogMessageFatal(__FILE__, __LINE__).stream()                           \
-                        << tvm::kTVM_INTERNAL_ERROR_MESSAGE << __INDENT << "Check not null: " #x \
-                        << ' ',                                                                  \
-   (x) : (x))  // NOLINT(*)
-
-/*! \brief The diagnostic level, controls the printing of the message. */
-enum class DiagnosticLevel : int {
-  kBug = 10,
-  kError = 20,
-  kWarning = 30,
-  kNote = 40,
-  kHelp = 50,
-};
-
-}  // namespace tvm
-#endif  // TVM_SUPPORT_LOGGING_H_
diff --git a/include/tvm/support/with.h b/include/tvm/support/with.h
index 90c82c4f3a06..d4547a304e8f 100644
--- a/include/tvm/support/with.h
+++ b/include/tvm/support/with.h
@@ -25,7 +25,7 @@
 #ifndef TVM_SUPPORT_WITH_H_
 #define TVM_SUPPORT_WITH_H_
 
-#include <dmlc/logging.h>
+#include <dmlc/common.h>
 
 #include <utility>
 
diff --git a/include/tvm/target/target.h b/include/tvm/target/target.h
index 2abdb558baf8..64bd251c0ded 100644
--- a/include/tvm/target/target.h
+++ b/include/tvm/target/target.h
@@ -44,6 +44,8 @@ class TargetNode : public Object {
  public:
   /*! \brief The kind of the target device */
   TargetKind kind;
+  /*! \brief Target host information, must be Target type */
+  Optional<ObjectRef> host;
   /*! \brief Tag of the the target, can be empty */
   String tag;
   /*! \brief Keys for this target */
@@ -64,6 +66,7 @@ class TargetNode : public Object {
     v->Visit("tag", &tag);
     v->Visit("keys", &keys);
     v->Visit("attrs", &attrs);
+    v->Visit("host", &host);
   }
 
   /*!
@@ -122,12 +125,12 @@ class Target : public ObjectRef {
   TVM_DLL explicit Target(std::nullptr_t) { data_ = nullptr; }
   /*!
    * \brief Construct a Target given a string
-   * \param tag_or_config_or_target_str the string to parse
+   * \param tag_or_config_or_target_str the string to parse for target
    */
   TVM_DLL explicit Target(const String& tag_or_config_or_target_str);
   /*!
    * \brief Construct a Target using a JSON-like configuration
-   * \param config The JSON-like configuration
+   * \param config The JSON-like configuration for target
    */
   TVM_DLL explicit Target(const Map<String, ObjectRef>& config);
   /*!
@@ -139,7 +142,13 @@ class Target : public ObjectRef {
    * allow_not_defined is true.
    */
   TVM_DLL static tvm::Target Current(bool allow_not_defined = true);
-
+  /*!
+   * \brief Construct a Target given target and host
+   * \param target The Target typed object with host field undefined for target
+   * \param host The Target typed object for target host
+   * \return The Target with given target and host context information
+   */
+  TVM_DLL explicit Target(Target target, Target host);
   TVM_DEFINE_OBJECT_REF_METHODS(Target, ObjectRef, TargetNode);
 
  private:
diff --git a/include/tvm/target/target_kind.h b/include/tvm/target/target_kind.h
index c9ef736f7aee..e7da2dd413a0 100644
--- a/include/tvm/target/target_kind.h
+++ b/include/tvm/target/target_kind.h
@@ -196,6 +196,11 @@ class TargetKindRegEntry {
   inline TargetKindRegEntry& add_attr_option(const String& key, ObjectRef default_value);
   /*! \brief Set name of the TargetKind to be the same as registry if it is empty */
   inline TargetKindRegEntry& set_name();
+  /*!
+   * \brief List all the entry names in the registry.
+   * \return The entry names.
+   */
+  TVM_DLL static Array<String> ListTargetKinds();
   /*!
    * \brief Register or get a new entry.
    * \param target_kind_name The name of the TargetKind.
@@ -371,7 +376,8 @@ inline TargetKindRegEntry& TargetKindRegEntry::set_name() {
           .add_attr_option<String>("tag")                         \
           .add_attr_option<String>("device")                      \
           .add_attr_option<String>("model")                       \
-          .add_attr_option<Array<String>>("libs")
+          .add_attr_option<Array<String>>("libs")                 \
+          .add_attr_option<Target>("host")
 
 }  // namespace tvm
 
diff --git a/include/tvm/te/schedule_pass.h b/include/tvm/te/schedule_pass.h
index a4efa7a94990..32e74f6ef9d5 100644
--- a/include/tvm/te/schedule_pass.h
+++ b/include/tvm/te/schedule_pass.h
@@ -41,6 +41,13 @@ namespace te {
  */
 void AutoInlineElemWise(Schedule sch);
 
+/*!
+ * \brief To automatically inline the broadcast operations.
+ *
+ * \param sch The schedule to be inlined.
+ */
+void AutoInlineBroarcast(Schedule sch);
+
 /*!
  * \brief To automatically inline operations with injective writes
  *   (i.e. writes without reduction or sequential loops). Note
diff --git a/include/tvm/te/tensor.h b/include/tvm/te/tensor.h
index 2f9fa2f534c5..401ba102c2f4 100644
--- a/include/tvm/te/tensor.h
+++ b/include/tvm/te/tensor.h
@@ -25,7 +25,7 @@
 #define TVM_TE_TENSOR_H_
 
 #include <tvm/arith/bound.h>
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/tir/expr.h>
 #include <tvm/tir/op.h>
 
diff --git a/include/tvm/tir/analysis.h b/include/tvm/tir/analysis.h
index e5b2c2b6957c..1ad78596586a 100644
--- a/include/tvm/tir/analysis.h
+++ b/include/tvm/tir/analysis.h
@@ -56,6 +56,22 @@ struct ExprDeepEqual {
   TVM_DLL bool operator()(const PrimExpr& lhs, const PrimExpr& rhs) const;
 };
 
+/*!
+ * \brief Visit the PrimFuncs in the IRModule
+ * \tparam FLambda The type of the PrimFunc visitor
+ * \param mod The IRModule to be visited
+ * \param fvisit The visitor to the PrimFuncs in the IRModule
+ */
+template <class FLambda>
+inline void VisitPrimFuncs(const IRModule& mod, FLambda fvisit) {
+  for (const auto& kv : mod->functions) {
+    const BaseFunc& base_func = kv.second;
+    if (const auto* prim_func = base_func.as<PrimFuncNode>()) {
+      fvisit(prim_func);
+    }
+  }
+}
+
 /*!
  * \brief Find undefined vars in the statement.
  * \param stmt The function to be checked.
diff --git a/include/tvm/tir/buffer.h b/include/tvm/tir/buffer.h
index 839e7c1b7c1c..83f228da9475 100644
--- a/include/tvm/tir/buffer.h
+++ b/include/tvm/tir/buffer.h
@@ -25,7 +25,7 @@
 #define TVM_TIR_BUFFER_H_
 
 #include <tvm/ir/expr.h>
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/tir/var.h>
 
 #include <string>
diff --git a/include/tvm/tir/builtin.h b/include/tvm/tir/builtin.h
index a150595ab551..6a40d86b8984 100644
--- a/include/tvm/tir/builtin.h
+++ b/include/tvm/tir/builtin.h
@@ -41,6 +41,10 @@ namespace tir {
 
 /*! \brief Collection of builtin intrinsics as ops */
 namespace builtin {
+/*!
+ * \brief Return value.
+ */
+TVM_DLL const Op& ret();
 /*!
  * \brief Reinterpret the value using the target type.
  */
diff --git a/include/tvm/tir/expr.h b/include/tvm/tir/expr.h
index c7ff9e19014c..7cab1970f478 100644
--- a/include/tvm/tir/expr.h
+++ b/include/tvm/tir/expr.h
@@ -26,10 +26,10 @@
 #define TVM_TIR_EXPR_H_
 
 #include <tvm/ir/expr.h>
-#include <tvm/node/container.h>
 #include <tvm/node/functor.h>
 #include <tvm/node/node.h>
 #include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/data_type.h>
 #include <tvm/tir/buffer.h>
 #include <tvm/tir/var.h>
diff --git a/include/tvm/tir/op.h b/include/tvm/tir/op.h
index 61481d931763..b5a62c907ed6 100644
--- a/include/tvm/tir/op.h
+++ b/include/tvm/tir/op.h
@@ -70,6 +70,15 @@ TVM_DLL Type GetType(const PrimExpr& expr);
  */
 TVM_DLL runtime::DataType GetRuntimeDataType(const Type& type);
 
+/*!
+ * \brief Return the value.
+ *
+ * \param value The returned value.
+ * \param span The location of this operation in the source.
+ * \return The return expression.
+ */
+TVM_DLL PrimExpr ret(PrimExpr value, Span span = Span());
+
 /*!
  * Query the maximum possible value of dtype.
  * \param dtype The data type.
@@ -1241,7 +1250,7 @@ inline void DivAmbiguityError(const TA& a) {
                 "please call div, indexdiv/indexmod, "
                 "floordiv/floormod or truncdiv/truncmod directly "
                 "to avoid ambiguity in the code. "
-                "Checkout these functions in expr_operator.h.");
+                "Checkout these functions in tir/op.h.");
 }
 
 // The following code are not intended to be used in the codebase.
diff --git a/include/tvm/tir/op_attr_types.h b/include/tvm/tir/op_attr_types.h
index ec7fc172cde8..3dcc4b943a79 100644
--- a/include/tvm/tir/op_attr_types.h
+++ b/include/tvm/tir/op_attr_types.h
@@ -74,7 +74,11 @@ enum class CallEffectKind : int {
   /*!
    * \brief Embed opaque information in the Expr, cannot be codegen.
    */
-  kEmbedInfo = 5
+  kEmbedInfo = 5,
+  /*!
+   * \brief Function that changes control flow
+   */
+  kControlJump = 6,
 };
 
 /*! \brief Use integer to record the kind. */
diff --git a/include/tvm/tir/stmt.h b/include/tvm/tir/stmt.h
index 2b7f1e67bda5..6445bb1fe73f 100644
--- a/include/tvm/tir/stmt.h
+++ b/include/tvm/tir/stmt.h
@@ -752,23 +752,34 @@ class Evaluate : public Stmt {
   TVM_DEFINE_OBJECT_REF_METHODS(Evaluate, Stmt, EvaluateNode);
 };
 
-/*! \brief Additional annotation of for loop. */
-enum class ForType : int {
-  /*! \brief serial execution. */
-  Serial = 0,
-  /*! \brief parallel execution on CPU. */
-  Parallel = 1,
-  /*! \brief Vector SIMD loop annotaion. */
-  Vectorized = 2,
-  /*! \brief Unroll annotation. */
-  Unrolled = 3
+/*!
+ * \brief The kind of the loop.
+ *
+ *  ForKind can change the control flow semantics
+ *  of the loop. So the kind field needs to be considered
+ *  in all TIR passes.
+ */
+enum class ForKind : int {
+  /*! \brief default semantics -- serial execution. */
+  kSerial = 0,
+  /*! \brief Parallel execution on CPU. */
+  kParallel = 1,
+  /*!
+   * \brief Vector SIMD loop.
+   *  The loop body will be vectorized.
+   */
+  kVectorized = 2,
+  /*! \brief The loop body must be unrolled. */
+  kUnrolled = 3,
+  /*!
+   * \brief The loop variable is bound to a thread in
+   * an environment. In the final stage of lowering,
+   * the loop is simply removed and the loop variable is
+   * mapped to the corresponding context thread.
+   */
+  kThreadBinding = 4
 };
 
-// Kevice api of for loop
-// kept for backward compatibility
-// consider refactor and remove later.
-enum class DeviceAPI : int { None = 0 };
-
 /*!
  * \brief A for loop, with poissible type annotations.
  *
@@ -787,39 +798,50 @@ class ForNode : public StmtNode {
   PrimExpr min;
   /*! \brief The extent of the iteration. */
   PrimExpr extent;
-  /*! \brief The type of the for loop. */
-  ForType for_type;
-  /*!
-   * \brief Deprecated, reserved for backward compatibility.
-   *  Consider refactor and remove later.
-   */
-  DeviceAPI device_api;
+  /*! \brief The kind of the for loop. */
+  ForKind kind;
   /*! \brief The body of the for loop. */
   Stmt body;
+  /*!
+   * \brief Only valid when kind == ForKind::kThreadBinding
+   * The context thread that this loop variable bounds to.
+   */
+  Optional<IterVar> thread_binding;
+  /*!
+   * \brief Additional annotations about the loop.
+   *
+   *  These annotations can be used as auxiliary hint
+   *  to future transformations. An annotation should
+   *  not change the control flow semantics of the loop
+   *  and can be ignored in most passes.
+   */
+  Map<String, ObjectRef> annotations;
 
   void VisitAttrs(AttrVisitor* v) {
     v->Visit("loop_var", &loop_var);
     v->Visit("min", &min);
     v->Visit("extent", &extent);
-    v->Visit("for_type", &for_type);
-    v->Visit("device_api", &device_api);
+    v->Visit("kind", &kind);
     v->Visit("body", &body);
+    v->Visit("thread_binding", &thread_binding);
+    v->Visit("annotations", &annotations);
     v->Visit("span", &span);
   }
 
   bool SEqualReduce(const ForNode* other, SEqualReducer equal) const {
     return equal.DefEqual(loop_var, other->loop_var) && equal(min, other->min) &&
-           equal(extent, other->extent) && equal(for_type, other->for_type) &&
-           equal(device_api, other->device_api) && equal(body, other->body);
+           equal(extent, other->extent) && equal(kind, other->kind) && equal(body, other->body) &&
+           equal(thread_binding, other->thread_binding) && equal(annotations, other->annotations);
   }
 
   void SHashReduce(SHashReducer hash_reduce) const {
     hash_reduce.DefHash(loop_var);
     hash_reduce(min);
     hash_reduce(extent);
-    hash_reduce(for_type);
-    hash_reduce(device_api);
+    hash_reduce(kind);
     hash_reduce(body);
+    hash_reduce(thread_binding);
+    hash_reduce(annotations);
   }
 
   static constexpr const char* _type_key = "tir.For";
@@ -832,14 +854,62 @@ class ForNode : public StmtNode {
  */
 class For : public Stmt {
  public:
-  TVM_DLL For(Var loop_var, PrimExpr min, PrimExpr extent, ForType for_type, DeviceAPI device_api,
-              Stmt body, Span span = Span());
+  TVM_DLL For(Var loop_var, PrimExpr min, PrimExpr extent, ForKind kind, Stmt body,
+              Optional<IterVar> thread_binding = NullOpt,
+              Map<String, ObjectRef> annotations = Map<String, ObjectRef>(), Span span = Span());
 
   TVM_DEFINE_OBJECT_REF_METHODS(For, Stmt, ForNode);
 };
 
 /*!
- * \brief A prefetch hint for abuffer
+ * \brief A While loop
+ *
+ * \code
+ *
+ *  while (condition)
+ *    body
+ *
+ * \endcode
+ */
+class WhileNode : public StmtNode {
+ public:
+  /*! \brief The termination condition. */
+  PrimExpr condition;
+  /*! \brief The body of the while loop. */
+  Stmt body;
+
+  void VisitAttrs(AttrVisitor* v) {
+    v->Visit("condition", &condition);
+    v->Visit("body", &body);
+    v->Visit("span", &span);
+  }
+
+  bool SEqualReduce(const WhileNode* other, SEqualReducer equal) const {
+    return equal.DefEqual(condition, other->condition) && equal.DefEqual(body, other->body);
+  }
+
+  void SHashReduce(SHashReducer hash_reduce) const {
+    hash_reduce.DefHash(condition);
+    hash_reduce.DefHash(body);
+  }
+
+  static constexpr const char* _type_key = "tir.While";
+  TVM_DECLARE_FINAL_OBJECT_INFO(WhileNode, StmtNode);
+};
+
+/*!
+ * \brief Managed reference to WhileNode.
+ * \sa WhileNode
+ */
+class While : public Stmt {
+ public:
+  TVM_DLL While(PrimExpr condition, Stmt body, Span span = Span());
+
+  TVM_DEFINE_OBJECT_REF_METHODS(While, Stmt, WhileNode);
+};
+
+/*!
+ * \brief A prefetch hint for a buffer
  */
 class PrefetchNode : public StmtNode {
  public:
@@ -882,6 +952,252 @@ class Prefetch : public Stmt {
   TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS(Prefetch, Stmt, PrefetchNode);
 };
 
+/*!
+ * \brief Representing the region of multi-dimensional buffer access.
+ */
+class BufferRegionNode : public Object {
+ public:
+  /*! \brief The buffer of the buffer region. */
+  Buffer buffer;
+  /*! \brief The region array of the buffer region. */
+  Array<Range> region;
+
+  void VisitAttrs(AttrVisitor* v) {
+    v->Visit("buffer", &buffer);
+    v->Visit("region", &region);
+  }
+
+  bool SEqualReduce(const BufferRegionNode* other, SEqualReducer equal) const {
+    return equal(buffer, other->buffer) && equal(region, other->region);
+  }
+
+  void SHashReduce(SHashReducer hash_reduce) const {
+    hash_reduce(buffer);
+    hash_reduce(region);
+  }
+
+  static constexpr const char* _type_key = "tir.BufferRegion";
+  static constexpr const bool _type_has_method_sequal_reduce = true;
+  static constexpr const bool _type_has_method_shash_reduce = true;
+  TVM_DECLARE_FINAL_OBJECT_INFO(BufferRegionNode, Object);
+};
+
+/*!
+ * \brief Managed reference to BufferRegionNode.
+ * \sa BufferRegionNode
+ */
+class BufferRegion : public ObjectRef {
+ public:
+  TVM_DLL explicit BufferRegion(Buffer buffer, Array<Range> region);
+
+  /*!
+   * \brief Create a BufferRegion which is full region of the given buffer..
+   * \param buffer The buffer to generate full BufferRegion.
+   * \return The BufferRegion which covers all region of the given buffer
+   */
+  TVM_DLL static BufferRegion FullRegion(Buffer buffer);
+
+  TVM_DEFINE_OBJECT_REF_METHODS(BufferRegion, ObjectRef, BufferRegionNode);
+};
+
+/*!
+ * \brief Match introduces a constraint that the source buffer region can be remapped to the data
+ * layout specified by the buffer field. The constraint can be checked in later part of lowering (or
+ * optionally during runtime).
+ *
+ * MatchBufferRegion provides a mechanism to represent data layout and compactness constraints in
+ * low-level hardware primitives in the IR and defer the check after the sequence of
+ * transformations.
+ */
+class MatchBufferRegionNode : public Object {
+ public:
+  /*! \brief The target buffer. */
+  Buffer buffer;
+  /*! \brief The source buffer region. */
+  BufferRegion source;
+
+  void VisitAttrs(AttrVisitor* v) {
+    v->Visit("buffer", &buffer);
+    v->Visit("source", &source);
+  }
+
+  bool SEqualReduce(const MatchBufferRegionNode* other, SEqualReducer equal) const {
+    return equal(buffer, other->buffer) && equal(source, other->source);
+  }
+
+  void SHashReduce(SHashReducer hash_reduce) const {
+    hash_reduce(buffer);
+    hash_reduce(source);
+  }
+
+  static constexpr const char* _type_key = "tir.MatchBufferRegion";
+  static constexpr const bool _type_has_method_sequal_reduce = true;
+  static constexpr const bool _type_has_method_shash_reduce = true;
+  TVM_DECLARE_FINAL_OBJECT_INFO(MatchBufferRegionNode, Object);
+};
+
+/*!
+ * \brief Managed reference to MatchBufferRegionNode.
+ * \sa MatchBufferRegionNode
+ */
+class MatchBufferRegion : public ObjectRef {
+ public:
+  TVM_DLL explicit MatchBufferRegion(Buffer buffer, BufferRegion source);
+
+  TVM_DEFINE_OBJECT_REF_METHODS(MatchBufferRegion, ObjectRef, MatchBufferRegionNode);
+};
+
+/*!
+ * \brief A block is a basic schedule unit in TIR.
+ * \note Block's body is parameterized by iter vars.
+ * \code
+ *
+ *  with tir.block([extent0, extent1, ...], name) as [v0, v1, ...]:
+ *      tir.bind(v0, value0)
+ *      tir.bind(v1, value1)
+ *      ...
+ *      tir.reads([buffer0[start:end, ...], ...])
+ *      tir.writes([buffer1[start:end, ...], ...])
+ *      tir.where(predicate)
+ *      buffer2 = tir.alloc_buffer(shape, dtype)
+ *      buffer3 = tir.match_buffer(source_buffer[start:end, ...])
+ *      tir.attr({attr_key: attr_value, ...})
+ *      with tir.init():
+ *          // init body
+ *      // body
+ *
+ * \endcode
+ */
+class BlockNode : public StmtNode {
+ public:
+  /*! \brief The variables of the block. */
+  Array<IterVar> iter_vars;
+  /*! \brief The read buffer regions of the block. */
+  Array<BufferRegion> reads;
+  /*! \brief The write buffer regions of the block. */
+  Array<BufferRegion> writes;
+  /*! \brief The name_hint of the block. */
+  String name_hint;
+  /*! \brief The body of the block. */
+  Stmt body;
+  /*!
+   * \brief The init statement is executed during the first iteration of reduction loops in a
+   *  reduction block. The optional init field allows us to represent initialization and
+   *  reduction update in a single block and transform them collectively.
+   *  We also provide primitives to decompose the init into a separate block during scheduling.
+   *  Init field is `NullOpt` if there is no reduction iter_vars
+   */
+  Optional<Stmt> init;
+  /*! \brief The buffer allocated in the block. */
+  Array<Buffer> alloc_buffers;
+  /*! \brief The match buffer regions. */
+  Array<MatchBufferRegion> match_buffers;
+  /*! \brief The annotation of the block. */
+  Map<String, ObjectRef> annotations;
+
+  void VisitAttrs(AttrVisitor* v) {
+    v->Visit("iter_vars", &iter_vars);
+    v->Visit("reads", &reads);
+    v->Visit("writes", &writes);
+    v->Visit("name_hint", &name_hint);
+    v->Visit("body", &body);
+    v->Visit("init", &init);
+    v->Visit("alloc_buffers", &alloc_buffers);
+    v->Visit("match_buffers", &match_buffers);
+    v->Visit("annotations", &annotations);
+  }
+
+  bool SEqualReduce(const BlockNode* other, SEqualReducer equal) const {
+    // Need first reduce iter_vars, alloc_buffers and match_buffers to define new vars
+    return equal.DefEqual(iter_vars, other->iter_vars) &&
+           equal(alloc_buffers, other->alloc_buffers) &&
+           equal(match_buffers, other->match_buffers) && equal(reads, other->reads) &&
+           equal(writes, other->writes) && equal(body, other->body) && equal(init, other->init) &&
+           equal(annotations, other->annotations);
+  }
+
+  void SHashReduce(SHashReducer hash_reduce) const {
+    hash_reduce.DefHash(iter_vars);
+    hash_reduce(alloc_buffers);
+    hash_reduce(match_buffers);
+    hash_reduce(reads);
+    hash_reduce(writes);
+    hash_reduce(body);
+    hash_reduce(init);
+    hash_reduce(annotations);
+  }
+
+  static constexpr const char* _type_key = "tir.Block";
+  TVM_DECLARE_FINAL_OBJECT_INFO(BlockNode, StmtNode);
+};
+
+/*!
+ * \brief Managed reference to BlockNode.
+ * \sa BlockNode
+ */
+class Block : public Stmt {
+ public:
+  TVM_DLL explicit Block(Array<IterVar> iter_vars, Array<BufferRegion> reads,
+                         Array<BufferRegion> writes, String name_hint, Stmt body,
+                         Optional<Stmt> init = NullOpt,
+                         Array<Buffer> alloc_buffers = Array<Buffer>(),
+                         Array<MatchBufferRegion> match_buffers = Array<MatchBufferRegion>(),
+                         Map<String, ObjectRef> annotations = Map<String, ObjectRef>(),
+                         Span span = Span());
+
+  TVM_DEFINE_OBJECT_REF_METHODS(Block, Stmt, BlockNode);
+  TVM_DEFINE_OBJECT_REF_COW_METHOD(BlockNode);
+};
+
+/*!
+ * \brief A block realization node represents execution of the block at the binding values.
+ */
+class BlockRealizeNode : public StmtNode {
+ public:
+  /*! \brief The corresponding values of the iter vars. */
+  Array<PrimExpr> iter_values;
+  /*!
+   * \brief The predicate of the block realization, the block will only be executed when the
+   * predicate is true.
+   */
+  PrimExpr predicate;
+  /*! \brief The block to be realized. */
+  Block block;
+
+  void VisitAttrs(AttrVisitor* v) {
+    v->Visit("iter_values", &iter_values);
+    v->Visit("predicate", &predicate);
+    v->Visit("block", &block);
+  }
+
+  bool SEqualReduce(const BlockRealizeNode* other, SEqualReducer equal) const {
+    return equal(iter_values, other->iter_values) && equal(predicate, other->predicate) &&
+           equal(block, other->block);
+  }
+
+  void SHashReduce(SHashReducer hash_reduce) const {
+    hash_reduce(iter_values);
+    hash_reduce(predicate);
+    hash_reduce(block);
+  }
+
+  static constexpr const char* _type_key = "tir.BlockRealize";
+  TVM_DECLARE_FINAL_OBJECT_INFO(BlockRealizeNode, StmtNode);
+};
+
+/*!
+ * \brief Managed reference to BlockRealizeNode
+ * \sa BlockRealizeNode
+ */
+class BlockRealize : public Stmt {
+ public:
+  TVM_DLL explicit BlockRealize(Array<PrimExpr> iter_values, PrimExpr predicate, Block block,
+                                Span span = Span());
+
+  TVM_DEFINE_OBJECT_REF_METHODS(BlockRealize, Stmt, BlockRealizeNode);
+  TVM_DEFINE_OBJECT_REF_COW_METHOD(BlockRealizeNode);
+};
+
 /*! \brief namespace of possible attribute sin AttrStmt.attr_key */
 namespace attr {
 // The above attr does not pass to ir stage.
@@ -996,6 +1312,10 @@ constexpr const char* fragment_shape = "fragment_shape";
  */
 constexpr const char* fragment_layout = "fragment_layout";
 
+/*!
+ * \brief Mark that the kernel is hand threaded and doesn't need syncs inserted
+ */
+constexpr const char* hand_threaded = "hand_threaded";
 /*!
  * \brief Check if attr_key is a pragma key extension
  * \param attr_key The attr key to be compared
@@ -1015,7 +1335,7 @@ inline bool IsPragmaKey(const std::string& attr_key) {
 TVM_DLL PrimExpr TypeAnnotation(DataType dtype, Span span = Span());
 
 // overload printing of for type.
-TVM_DLL std::ostream& operator<<(std::ostream& os, ForType for_type);
+TVM_DLL std::ostream& operator<<(std::ostream& os, ForKind kind);
 
 }  // namespace tir
 }  // namespace tvm
diff --git a/include/tvm/tir/stmt_functor.h b/include/tvm/tir/stmt_functor.h
index 0f4238deeebd..c1c618f0c22f 100644
--- a/include/tvm/tir/stmt_functor.h
+++ b/include/tvm/tir/stmt_functor.h
@@ -26,8 +26,8 @@
 #ifndef TVM_TIR_STMT_FUNCTOR_H_
 #define TVM_TIR_STMT_FUNCTOR_H_
 
-#include <tvm/node/container.h>
 #include <tvm/node/functor.h>
+#include <tvm/runtime/container.h>
 #include <tvm/tir/expr.h>
 #include <tvm/tir/expr_functor.h>
 #include <tvm/tir/stmt.h>
@@ -86,6 +86,7 @@ class StmtFunctor<R(const Stmt& n, Args... args)> {
   virtual R VisitStmt_(const AttrStmtNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const IfThenElseNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const ForNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
+  virtual R VisitStmt_(const WhileNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const AllocateNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const StoreNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const BufferStoreNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
@@ -96,6 +97,8 @@ class StmtFunctor<R(const Stmt& n, Args... args)> {
   virtual R VisitStmt_(const PrefetchNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const SeqStmtNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmt_(const EvaluateNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
+  virtual R VisitStmt_(const BlockNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
+  virtual R VisitStmt_(const BlockRealizeNode* op, Args... args) STMT_FUNCTOR_DEFAULT;
   virtual R VisitStmtDefault_(const Object* op, Args...) {
     LOG(FATAL) << "Do not have a default for " << op->GetTypeKey();
     return R();
@@ -109,6 +112,7 @@ class StmtFunctor<R(const Stmt& n, Args... args)> {
     IR_STMT_FUNCTOR_DISPATCH(AttrStmtNode);
     IR_STMT_FUNCTOR_DISPATCH(IfThenElseNode);
     IR_STMT_FUNCTOR_DISPATCH(ForNode);
+    IR_STMT_FUNCTOR_DISPATCH(WhileNode);
     IR_STMT_FUNCTOR_DISPATCH(AllocateNode);
     IR_STMT_FUNCTOR_DISPATCH(StoreNode);
     IR_STMT_FUNCTOR_DISPATCH(AssertStmtNode);
@@ -119,6 +123,8 @@ class StmtFunctor<R(const Stmt& n, Args... args)> {
     IR_STMT_FUNCTOR_DISPATCH(EvaluateNode);
     IR_STMT_FUNCTOR_DISPATCH(BufferStoreNode);
     IR_STMT_FUNCTOR_DISPATCH(BufferRealizeNode);
+    IR_STMT_FUNCTOR_DISPATCH(BlockNode);
+    IR_STMT_FUNCTOR_DISPATCH(BlockRealizeNode);
     return vtable;
   }
 };
@@ -148,6 +154,7 @@ class TVM_DLL StmtVisitor : protected StmtFunctor<void(const Stmt&)> {
   void VisitStmt_(const IfThenElseNode* op) override;
   void VisitStmt_(const LetStmtNode* op) override;
   void VisitStmt_(const ForNode* op) override;
+  void VisitStmt_(const WhileNode* op) override;
   void VisitStmt_(const AllocateNode* op) override;
   void VisitStmt_(const StoreNode* op) override;
   void VisitStmt_(const BufferStoreNode* op) override;
@@ -158,6 +165,8 @@ class TVM_DLL StmtVisitor : protected StmtFunctor<void(const Stmt&)> {
   void VisitStmt_(const PrefetchNode* op) override;
   void VisitStmt_(const SeqStmtNode* op) override;
   void VisitStmt_(const EvaluateNode* op) override;
+  void VisitStmt_(const BlockNode* op) override;
+  void VisitStmt_(const BlockRealizeNode* op) override;
 };
 
 /*!
@@ -239,6 +248,7 @@ class TVM_DLL StmtMutator : protected StmtFunctor<Stmt(const Stmt&)> {
   Stmt VisitStmt_(const IfThenElseNode* op) override;
   Stmt VisitStmt_(const LetStmtNode* op) override;
   Stmt VisitStmt_(const ForNode* op) override;
+  Stmt VisitStmt_(const WhileNode* op) override;
   Stmt VisitStmt_(const AllocateNode* op) override;
   Stmt VisitStmt_(const StoreNode* op) override;
   Stmt VisitStmt_(const BufferStoreNode* op) override;
@@ -249,6 +259,8 @@ class TVM_DLL StmtMutator : protected StmtFunctor<Stmt(const Stmt&)> {
   Stmt VisitStmt_(const PrefetchNode* op) override;
   Stmt VisitStmt_(const SeqStmtNode* op) override;
   Stmt VisitStmt_(const EvaluateNode* op) override;
+  Stmt VisitStmt_(const BlockNode* op) override;
+  Stmt VisitStmt_(const BlockRealizeNode* op) override;
   /*!
    * \brief Alternative advance method for SeqStmtNode.
    *
@@ -374,6 +386,15 @@ inline T Substitute(T input, const std::unordered_map<const VarNode*, PrimExpr>&
   return Substitute(std::move(input), vmap);
 }
 
+/*!
+ * \brief Recursively visit the IR in pre DFS order node, apply fvisit.
+ * If fvisit returns false, it won't visit the children of the node.
+ * \param stmt_or_expr The ir to be visited.
+ * \param fvisit The visitor function to be applied. If fvisit returns false, it won't visit the
+ * children of the node
+ */
+TVM_DLL void PreOrderVisit(const ObjectRef& stmt_or_expr,
+                           const std::function<bool(const ObjectRef&)>& fvisit);
 }  // namespace tir
 }  // namespace tvm
 
diff --git a/include/tvm/topi/detail/constant_utils.h b/include/tvm/topi/detail/constant_utils.h
index 49ce21b5732e..95e68f5f6d61 100644
--- a/include/tvm/topi/detail/constant_utils.h
+++ b/include/tvm/topi/detail/constant_utils.h
@@ -48,7 +48,8 @@ using namespace tvm::te;
 inline bool IsConstInt(PrimExpr expr) { return expr->IsInstance<tvm::tir::IntImmNode>(); }
 
 /*!
- * \brief Test whether the given Array has every element as constant integer
+ * \brief Test whether the given Array has every element as constant integer.
+ * Undefined elements are also treat as constants.
  *
  * \param array the array to query
  *
@@ -57,7 +58,7 @@ inline bool IsConstInt(PrimExpr expr) { return expr->IsInstance<tvm::tir::IntImm
 inline bool IsConstIntArray(Array<PrimExpr> array) {
   bool is_const_int = true;
   for (auto const& elem : array) {
-    is_const_int &= elem->IsInstance<tvm::tir::IntImmNode>();
+    is_const_int &= !elem.defined() || elem->IsInstance<tvm::tir::IntImmNode>();
   }
   return is_const_int;
 }
@@ -118,12 +119,11 @@ inline std::vector<int64_t> GetConstInt64Values(Array<PrimExpr> exprs,
 }
 
 /*!
- * \brief Check weather the two expressions are equal or not, if not simplify the expressions and
- * check again \note This is stronger equality check than tvm::tir::Equal
- *
- * \param lhs First expreesion
- * \param rhs Second expreesion
- *
+ * \brief Check whether the two expressions are equal or not, if not simplify the expressions and
+ * check again
+ * \note This is stronger equality check than tvm::tir::Equal
+ * \param lhs First expression
+ * \param rhs Second expression
  * \return result True if both expressions are equal, else false
  */
 inline bool EqualCheck(PrimExpr lhs, PrimExpr rhs) {
diff --git a/include/tvm/topi/detail/tensor_utils.h b/include/tvm/topi/detail/tensor_utils.h
index 65a760b1397c..397c70c9451e 100644
--- a/include/tvm/topi/detail/tensor_utils.h
+++ b/include/tvm/topi/detail/tensor_utils.h
@@ -26,6 +26,7 @@
 
 #include <tvm/te/operation.h>
 
+#include <vector>
 namespace tvm {
 namespace topi {
 namespace detail {
@@ -64,29 +65,36 @@ inline bool is_empty_shape(const Array<PrimExpr>& x) {
  */
 inline PrimExpr bilinear_sample_nchw(const Tensor& input, const Array<PrimExpr>& indices,
                                      const PrimExpr max_y, const PrimExpr max_x) {
+  auto batch_id = indices[0];
+  auto channel_id = indices[1];
   auto in_y = indices[2];
-  auto yf = tvm::floor(in_y);
-  auto yc = tvm::cast(DataType::Int(32), tvm::ceil(in_y));
-
-  auto y0 = tvm::cast(DataType::Int(32), tvm::floor(in_y));
-  auto y1 = tvm::if_then_else((yc > max_y), max_y, yc);
-  auto y_lerp = in_y - yf;
-
   auto in_x = indices[3];
-  auto xf = tvm::floor(in_x);
-  auto xc = tvm::cast(DataType::Int(32), tvm::ceil(in_x));
-
-  auto x0 = tvm::cast(DataType::Int(32), tvm::floor(in_x));
-  auto x1 = tvm::if_then_else((xc > max_x), max_x, xc);
-  auto x_lerp = in_x - xf;
 
-  auto A = input(indices[0], indices[1], y0, x0);
-  auto B = input(indices[0], indices[1], y0, x1);
-  auto C = input(indices[0], indices[1], y1, x0);
-  auto D = input(indices[0], indices[1], y1, x1);
-
-  return A * (1 - x_lerp) * (1 - y_lerp) + B * x_lerp * (1 - y_lerp) + C * (1 - x_lerp) * y_lerp +
-         D * x_lerp * y_lerp;
+  auto y_low = tvm::cast(DataType::Int(32), tvm::floor(in_y));
+  auto y_high = y_low + 1;
+
+  auto x_low = tvm::cast(DataType::Int(32), tvm::floor(in_x));
+  auto x_high = x_low + 1;
+
+  auto wy_h = in_y - y_low;
+  auto wx_h = in_x - x_low;
+  auto wy_l = 1 - wy_h;
+  auto wx_l = 1 - wx_h;
+
+  PrimExpr val = 0;
+  std::vector<std::vector<PrimExpr>> wx_xp{{wx_l, x_low}, {wx_h, x_high}};
+  std::vector<std::vector<PrimExpr>> wy_yp{{wy_l, y_low}, {wy_h, y_high}};
+  for (auto wx_xp_ele : wx_xp) {
+    for (auto wy_yp_ele : wy_yp) {
+      auto wx = wx_xp_ele[0];
+      auto xp = wx_xp_ele[1];
+      auto wy = wy_yp_ele[0];
+      auto yp = wy_yp_ele[1];
+      val += tvm::if_then_else(0 <= yp && yp <= max_y && 0 <= xp && xp <= max_x,
+                               wx * wy * input(batch_id, channel_id, yp, xp), 0);
+    }
+  }
+  return val;
 }
 
 /*!
@@ -101,29 +109,36 @@ inline PrimExpr bilinear_sample_nchw(const Tensor& input, const Array<PrimExpr>&
  */
 inline PrimExpr bilinear_sample_nhwc(const Tensor& input, const Array<PrimExpr>& indices,
                                      const PrimExpr max_y, const PrimExpr max_x) {
+  auto batch_id = indices[0];
+  auto channel_id = indices[3];
   auto in_y = indices[1];
-  auto yf = tvm::floor(in_y);
-  auto yc = tvm::cast(DataType::Int(32), tvm::ceil(in_y));
-
-  auto y0 = tvm::cast(DataType::Int(32), tvm::floor(in_y));
-  auto y1 = tvm::if_then_else((yc > max_y), max_y, yc);
-  auto y_lerp = in_y - yf;
-
   auto in_x = indices[2];
-  auto xf = tvm::floor(in_x);
-  auto xc = tvm::cast(DataType::Int(32), tvm::ceil(in_x));
-
-  auto x0 = tvm::cast(DataType::Int(32), tvm::floor(in_x));
-  auto x1 = tvm::if_then_else((xc > max_x), max_x, xc);
-  auto x_lerp = in_x - xf;
 
-  auto A = input(indices[0], y0, x0, indices[3]);
-  auto B = input(indices[0], y0, x1, indices[3]);
-  auto C = input(indices[0], y1, x0, indices[3]);
-  auto D = input(indices[0], y1, x1, indices[3]);
-
-  return A * (1 - x_lerp) * (1 - y_lerp) + B * x_lerp * (1 - y_lerp) + C * (1 - x_lerp) * y_lerp +
-         D * x_lerp * y_lerp;
+  auto y_low = tvm::cast(DataType::Int(32), tvm::floor(in_y));
+  auto y_high = y_low + 1;
+
+  auto x_low = tvm::cast(DataType::Int(32), tvm::floor(in_x));
+  auto x_high = x_low + 1;
+
+  auto wy_h = in_y - y_low;
+  auto wx_h = in_x - x_low;
+  auto wy_l = 1 - wy_h;
+  auto wx_l = 1 - wx_h;
+
+  PrimExpr val = 0;
+  std::vector<std::vector<PrimExpr>> wx_xp{{wx_l, x_low}, {wx_h, x_high}};
+  std::vector<std::vector<PrimExpr>> wy_yp{{wy_l, y_low}, {wy_h, y_high}};
+  for (auto wx_xp_ele : wx_xp) {
+    for (auto wy_yp_ele : wy_yp) {
+      auto wx = wx_xp_ele[0];
+      auto xp = wx_xp_ele[1];
+      auto wy = wy_yp_ele[0];
+      auto yp = wy_yp_ele[1];
+      val += tvm::if_then_else(0 <= yp && yp <= max_y && 0 <= xp && xp <= max_x,
+                               wx * wy * input(batch_id, yp, xp, channel_id), 0);
+    }
+  }
+  return val;
 }
 
 }  // namespace detail
diff --git a/include/tvm/topi/einsum.h b/include/tvm/topi/einsum.h
new file mode 100644
index 000000000000..a0c4039909ad
--- /dev/null
+++ b/include/tvm/topi/einsum.h
@@ -0,0 +1,943 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file topi/einsum.h
+ * \brief Einstein summation op
+ */
+#ifndef TVM_TOPI_EINSUM_H_
+#define TVM_TOPI_EINSUM_H_
+
+#define LABELRANGE 128
+#define NPY_MAXDIMS 16
+#define NPY_MAXARGS 16
+
+#include <tvm/te/operation.h>
+#include <tvm/tir/data_layout.h>
+#include <tvm/topi/detail/constant_utils.h>
+#include <tvm/topi/detail/ravel_unravel.h>
+#include <tvm/topi/detail/tensor_utils.h>
+#include <tvm/topi/tags.h>
+
+#include <algorithm>
+#include <bitset>
+#include <iterator>
+#include <string>
+#include <tuple>
+#include <unordered_set>
+#include <vector>
+
+namespace tvm {
+namespace topi {
+
+using namespace tvm::te;
+using namespace topi::detail;
+
+/*!
+ * \brief Compute the stride of the given shape.
+ *
+ * \param shape for the operation.
+ *
+ * \return the stride of the shape.
+ */
+inline Array<PrimExpr> GetStride(const Array<PrimExpr> shape) {
+  size_t ndim = shape.size();
+  int prod = 1;
+  Array<PrimExpr> stride = Array<PrimExpr>(ndim, -1);
+  for (int i = ndim - 1; i >= 0; i--) {
+    stride.Set(i, if_then_else(shape[i] > 1, prod, 0));
+    prod = prod * GetConstInt(shape[i]);
+  }
+  return stride;
+}
+
+/*!
+ * \brief Pad the shape with 1.
+ *
+ * \param shape the input shape to be padded
+ * \param odim the padding size of the objective shape.
+ *
+ * \return the padded shape.
+ */
+inline Array<PrimExpr> Pad(const Array<PrimExpr> shape, int odim) {
+  int ndim = shape.size();
+  CHECK_GE(odim, ndim);
+  Array<PrimExpr> ret(static_cast<size_t>(odim), 1);
+  for (int idim = 0; idim < ndim; ++idim) {
+    ret.Set(idim, shape[idim]);
+  }
+  return ret;
+}
+
+/*!
+ * \brief Parse the subscripts for one operand into an output of 'ndim' labels.
+ *
+ * \param subscripts the subscripts for to be parsed.
+ * \param length subscripts[0: length] represents the current operand.
+ * \param ndim the ndim of current operand.
+ * \param iop the index of the operand.
+ * \param op_labels the parsing result.
+ *        For Example:
+ *           subscripts="abbcbc",  ndim=6 -> op_labels=[97, 98, -1, 99, -3, -2].
+ *           subscripts="ab...bc", ndim=6 -> op_labels=[97, 98, 0, 0, -3, 99].
+ * \param label_counts Count the number the label appears.
+ * \param min_label Save the minimal label according to ASCII.
+ * \param max_label Save the maximal label according to ASCII.
+ *
+ * \return 0.
+ */
+inline int ParseOperandSubscripts(const char* subscripts, int length, int ndim, int iop,
+                                  char* op_labels, char* label_counts, int* min_label,
+                                  int* max_label) {
+  int i;
+  int idim = 0;
+  int ellipsis = -1;
+
+  /* Process all labels for this operand */
+  for (i = 0; i < length; ++i) {
+    int label = subscripts[i];
+
+    /* A proper label for an axis. */
+    if (label > 0 && isalpha(label)) {
+      /* Check we don't exceed the operator dimensions. */
+      CHECK(idim < ndim) << "einstein sum subscripts string contains "
+                         << "too many subscripts for operand " << iop;
+
+      op_labels[idim++] = label;
+      if (label < *min_label) {
+        *min_label = label;
+      }
+      if (label > *max_label) {
+        *max_label = label;
+      }
+      label_counts[label]++;
+    } else if (label == '.') {
+      /* The beginning of the ellipsis. */
+      /* Check it's a proper ellipsis. */
+      CHECK(
+          !(ellipsis != -1 || i + 2 >= length || subscripts[++i] != '.' || subscripts[++i] != '.'))
+          << "einstein sum subscripts string contains a "
+          << "'.' that is not part of an ellipsis ('...') "
+          << "in operand " << iop;
+
+      ellipsis = idim;
+    } else {
+      CHECK(label == ' ') << "invalid subscript '" << static_cast<char>(label)
+                          << "' in einstein sum "
+                          << "subscripts string, subscripts must "
+                          << "be letters";
+    }
+  }
+
+  /* No ellipsis found, labels must match dimensions exactly. */
+  if (ellipsis == -1) {
+    CHECK(idim == ndim) << "operand has more dimensions than subscripts "
+                        << "given in einstein sum, but no '...' ellipsis "
+                        << "provided to broadcast the extra dimensions.";
+  } else if (idim < ndim) {
+    /* Ellipsis found, may have to add broadcast dimensions. */
+    /* Move labels after ellipsis to the end. */
+    for (i = 0; i < idim - ellipsis; ++i) {
+      op_labels[ndim - i - 1] = op_labels[idim - i - 1];
+    }
+    /* Set all broadcast dimensions to zero. */
+    for (i = 0; i < ndim - idim; ++i) {
+      op_labels[ellipsis + i] = 0;
+    }
+  }
+
+  /*
+   * Find any labels duplicated for this operand, and turn them
+   * into negative offsets to the axis to merge with.
+   *
+   * In C, the char type may be signed or unsigned, but with
+   * twos complement arithmetic the char is ok either way here, and
+   * later where it matters the char is cast to a signed char.
+   */
+  for (idim = 0; idim < ndim - 1; ++idim) {
+    int label = op_labels[idim];
+    /* If it is a proper label, find any duplicates of it. */
+    if (label > 0) {
+      /* Search for the next matching label. */
+      char* next = reinterpret_cast<char*>(memchr(op_labels + idim + 1, label, ndim - idim - 1));
+
+      while (next != nullptr) {
+        /* The offset from next to op_labels[idim] (negative). */
+        *next = static_cast<char>((op_labels + idim) - next);
+        /* Search for the next matching label. */
+        next = reinterpret_cast<char*>(memchr(next + 1, label, op_labels + ndim - 1 - next));
+      }
+    }
+  }
+  return 0;
+}
+
+/*!
+ * \brief Parse the subscripts for the output into an output that includes 'ndim_broadcast'
+ *        unlabeled dimensions.
+ *
+ * \param subscripts the subscripts for to be parsed.
+ * \param length subscripts[0: length] represents the output operand.
+ * \param ndim_broadcast the broadcast dimension number.
+ * \param label_counts Count the number the label appears.
+ * \param out_labels similar to the op_labels in ParseOperandSubscripts, for each
+ *        dimension, the ASCII code of the corresponding label. zero for the broadcasting dim.
+ *
+ * \return the total number of output dimensions or -1 if there is an error.
+ */
+inline int ParseOutputSubscripts(const char* subscripts, int length, int ndim_broadcast,
+                                 const char* label_counts, char* out_labels) {
+  int i, bdim;
+  int ndim = 0;
+  int ellipsis = 0;
+
+  /* Process all the output labels. */
+  for (i = 0; i < length; ++i) {
+    int label = subscripts[i];
+
+    /* A proper label for an axis. */
+    if (label > 0 && isalpha(label)) {
+      /* Check that it doesn't occur again. */
+      CHECK(memchr(subscripts + i + 1, label, length - i - 1) == nullptr)
+          << "einstein sum subscripts string includes "
+          << "output subscript '" << static_cast<char>(label) << "' multiple times";
+
+      /* Check that it was used in the inputs. */
+      CHECK(label_counts[label] != 0)
+          << "einstein sum subscripts string included "
+          << "output subscript '" << static_cast<char>(label) << "' which never appeared "
+          << "in an input";
+
+      /* Check that there is room in out_labels for this label. */
+      CHECK(ndim < NPY_MAXDIMS) << "einstein sum subscripts string contains "
+                                << "too many subscripts in the output";
+
+      out_labels[ndim++] = label;
+    } else if (label == '.') {
+      /* The beginning of the ellipsis. */
+      /* Check it is a proper ellipsis. */
+      CHECK(!(ellipsis || i + 2 >= length || subscripts[++i] != '.' || subscripts[++i] != '.'))
+          << "einstein sum subscripts string "
+          << "contains a '.' that is not part of "
+          << "an ellipsis ('...') in the output";
+
+      /* Check there is room in out_labels for broadcast dims. */
+      CHECK(ndim + ndim_broadcast <= NPY_MAXDIMS) << "einstein sum subscripts string contains "
+                                                  << "too many subscripts in the output";
+
+      ellipsis = 1;
+      for (bdim = 0; bdim < ndim_broadcast; ++bdim) {
+        out_labels[ndim++] = 0;
+      }
+    } else {
+      CHECK(label == ' ') << "invalid subscript '" << static_cast<char>(label)
+                          << "' in einstein sum "
+                          << "subscripts string, subscripts must "
+                          << "be letters";
+    }
+  }
+
+  /* If no ellipsis was found there should be no broadcast dimensions. */
+  CHECK(!(!ellipsis && ndim_broadcast > 0)) << "output has more dimensions than subscripts "
+                                            << "given in einstein sum, but no '...' ellipsis "
+                                            << "provided to broadcast the extra dimensions.";
+
+  return ndim;
+}
+
+/*!
+ * \brief If any dimensions are combined, create a view that combines them.
+ *        Shows in newshape and newstride.
+ *
+ * \param op the operand tensor.
+ * \param iop the index of the operand.
+ * \param labels the op_labels fot the operand. Like [97, 98, -2] for "aba".
+ * \param newshape The combined shape.
+ * \param newstride The combined stride.
+ *
+ * For example:
+ *  "aba -> ab",              shape = [2,3,2] stride = [6,2,1]
+ *  op_labels = [97, 98, -2], newshape = [2,3], newstride = [7,2]
+ */
+inline void GetCombinedDimsView(const Tensor& op, int iop, char* labels, Array<PrimExpr>* newshape,
+                                Array<PrimExpr>* newstride) {
+  int idim, ndim, icombine, combineoffset;
+  int icombinemap[NPY_MAXDIMS];
+  int newdim;
+
+  Array<PrimExpr> shape = op->shape;
+  Array<PrimExpr> stride = GetStride(shape);
+  ndim = op.ndim();
+  newdim = newshape->size();
+
+  /* Initialize the dimensions and strides to zero */
+  for (idim = 0; idim < newdim; ++idim) {
+    newshape->Set(idim, 0);
+    newstride->Set(idim, 0);
+  }
+
+  /* Copy the dimensions and strides, except when collapsing */
+  icombine = 0;
+  for (idim = 0; idim < ndim; ++idim) {
+    /*
+     * The char type may be either signed or unsigned, we
+     * need it to be signed here.
+     */
+    int label = (signed char)labels[idim];
+    /* If this label says to merge axes, get the actual label */
+    if (label < 0) {
+      combineoffset = label;
+      label = labels[idim + label];
+    } else {
+      combineoffset = 0;
+      if (icombine != idim) {
+        labels[icombine] = labels[idim];
+      }
+      icombinemap[idim] = icombine;
+    }
+    /* If the label is 0, it's an unlabeled broadcast dimension */
+    if (label == 0) {
+      newshape->Set(icombine, shape[idim]);
+      newstride->Set(icombine, stride[idim]);
+    } else {
+      /* Update the combined axis dimensions and strides */
+      int i = icombinemap[idim + combineoffset];
+      CHECK(!((combineoffset < 0) &&
+              GetConstInt((*newshape)[i] != 0 && (*newshape)[i] != shape[idim])))
+          << "dimensions in operand " << iop << " for collapsing index '" << label
+          << "' don't match (" << GetConstInt((*newshape)[i]) << " != " << shape[idim] << ")";
+      newshape->Set(i, shape[idim]);
+      newstride->Set(i, (*newstride)[i] + stride[idim]);
+    }
+
+    /* If the label didn't say to combine axes, increment dest i */
+    if (combineoffset == 0) {
+      icombine++;
+    }
+  }
+}
+
+/*!
+ * \brief Prepare the operand axes to match each stride or shape pair.
+ *
+ * \param ndim the ndim of the operand tensor.
+ * \param iop the index of the operand.
+ * \param labels the op_labels fot the operand. [97, 98, -1, 99, -3, -2] for "abbcbc".
+ * \param axes The matched axes to be calculated.
+ * \param ndim_iter the dimension of iterating. Subscripts "ab, bc -> ac" ndim_iter = 3.
+ * \param iter_labels output_labels with the iterating label. ['a', 'c', 'b'] for the case above.
+ */
+inline static int PrepareOpAxes(int ndim, int iop, char* labels, int* axes, int ndim_iter,
+                                char* iter_labels) {
+  int i, label, ibroadcast;
+
+  ibroadcast = ndim - 1;
+  for (i = ndim_iter - 1; i >= 0; --i) {
+    label = iter_labels[i];
+    /*
+     * If it's an unlabeled broadcast dimension, choose
+     * the next broadcast dimension from the operand.
+     */
+    if (label == 0) {
+      while (ibroadcast >= 0 && labels[ibroadcast] != 0) {
+        --ibroadcast;
+      }
+      /*
+       * If we used up all the operand broadcast dimensions,
+       * extend it with a "newaxis"
+       */
+      if (ibroadcast < 0) {
+        axes[i] = -1;
+      } else {
+        /* Otherwise map to the broadcast axis */
+        axes[i] = ibroadcast;
+        --ibroadcast;
+      }
+    } else {
+      /* It's a labeled dimension, find the matching one */
+      char* match = reinterpret_cast<char*>(memchr(labels, label, ndim));
+      /* If the op doesn't have the label, broadcast it */
+      if (match == nullptr) {
+        axes[i] = -1;
+      } else {
+        /* Otherwise use it */
+        axes[i] = match - labels;
+      }
+    }
+  }
+  return 0;
+}
+
+/*!
+ * \brief Count SubString.
+ * \param str the object string
+ * \param sub the pattern string
+ *
+ * \return number of substring
+ */
+inline int CountSubstring(const std::string& str, const std::string& sub) {
+  int count = 0;
+  std::string::size_type pos = 0;
+  while ((pos = str.find(sub, pos)) != std::string::npos) {
+    ++count;
+    pos += sub.length();
+  }
+  return count;
+}
+
+/*!
+ * \brief Transfer string to.
+ * \param str input string.
+ *
+ * \return bitset.
+ */
+inline std::bitset<LABELRANGE> Str2Set(const std::string& str) {
+  std::bitset<LABELRANGE> ret;
+  for (const char& c : str) {
+    ret.set(static_cast<int>(c));
+  }
+  return ret;
+}
+
+/*!
+ * \brief Split str according to substring.
+ * \param str input string.
+ * \param sub the split pattern string.
+ *
+ * \return vector contains the splited substring.
+ */
+inline std::vector<std::string> Split(const std::string& str, const std::string& sub) {
+  std::string::size_type pos = 0;
+  std::string::size_type start = 0;
+  std::vector<std::string> ret;
+  while ((pos = str.find(sub, start)) != std::string::npos) {
+    ret.push_back(str.substr(start, pos - start));
+    start = pos + sub.length();
+  }
+  ret.push_back(str.substr(start));
+  return ret;
+}
+
+/*!
+ * \brief Parse the input subscripts into a vector of strings.
+ * \param subscripts input subscripts.
+ * \param operands operand tensors.
+ *
+ * \return vector of strings, vector[0] represents the input part, vector[1] represents the output.
+ * if no output, the vector[1] is NULL.
+ * "ab, bc -> ac" => ["ab,bc", "ac"]
+ */
+inline std::tuple<std::string, std::string> ParseEinsumInput(
+    std::string subscripts, const std::vector<Array<PrimExpr>>& operands) {
+  const std::string einsum_symbols = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ";
+  std::bitset<LABELRANGE> einsum_symbols_set;
+  for (const char& c : einsum_symbols) {
+    einsum_symbols_set.set(c);
+  }
+
+  CHECK_NE(operands.size(), 0U) << "No input operands";
+
+  auto end_pos = std::remove(subscripts.begin(), subscripts.end(), ' ');
+  subscripts.erase(end_pos, subscripts.end());
+
+  // Ensure all characters are valid
+  for (const char& c : subscripts) {
+    if (c == '.' || c == ',' || c == '-' || c == '>') {
+      continue;
+    }
+    CHECK(einsum_symbols_set.test(c)) << "Character " << c << " is not a valid symbol.";
+  }
+
+  // Check for proper "->"
+  if (subscripts.find('-') != std::string::npos || subscripts.find('>') != std::string::npos) {
+    bool invalid = (std::count(subscripts.begin(), subscripts.end(), '-') > 1 ||
+                    std::count(subscripts.begin(), subscripts.end(), '>') > 1);
+    CHECK(!invalid && CountSubstring(subscripts, "->") == 1)
+        << "Subscripts can only contain one '->'.";
+  }
+
+  // Parse ellipses
+  if (subscripts.find('.') != std::string::npos) {
+    std::string used = subscripts;
+    used.erase(
+        std::remove_if(used.begin(), used.end(),
+                       [](const char& c) { return c == '.' || c == ',' || c == '-' || c == '>'; }),
+        used.end());
+
+    std::bitset<LABELRANGE> used_set = Str2Set(used);
+    std::string ellipse_inds = "";
+    for (const char& c : einsum_symbols) {
+      if (!used_set.test(static_cast<int>(c))) {
+        ellipse_inds.append(1, c);
+      }
+    }
+    int longest = 0;
+    std::string input_tmp, output_sub;
+    std::vector<std::string> split_subscripts;
+    bool out_sub;
+
+    if (subscripts.find("->") != std::string::npos) {
+      std::vector<std::string> tmp = Split(subscripts, "->");
+      input_tmp = tmp[0];
+      output_sub = tmp[1];
+      split_subscripts = Split(input_tmp, ",");
+      out_sub = true;
+    } else {
+      split_subscripts = Split(subscripts, ",");
+      out_sub = false;
+    }
+
+    size_t size_split_subscripts = split_subscripts.size();
+    subscripts = "";
+    for (size_t i = 0; i < size_split_subscripts; ++i) {
+      const std::string& sub = split_subscripts[i];
+      if (sub.find('.') != std::string::npos) {
+        CHECK_EQ(std::count(sub.begin(), sub.end(), '.'), 3) << "Invalid Ellipses";
+        CHECK_EQ(CountSubstring(sub, "..."), 1) << "Invalid Ellipses";
+
+        // Take into account numerical values
+        int ellipse_count = 0;
+        if (operands[i].size() == 0) {
+          ellipse_count = 0;
+        } else {
+          ellipse_count = std::max(operands[i].size(), static_cast<size_t>(1));
+          ellipse_count -= sub.length() - 3;
+        }
+
+        if (ellipse_count > longest) {
+          longest = ellipse_count;
+        }
+
+        CHECK_GE(ellipse_count, 0) << "Ellipses lengths do not match.";
+        if (ellipse_count == 0) {
+          split_subscripts[i].erase(sub.find("..."), 3);
+        } else {
+          std::string rep_inds = ellipse_inds.substr(ellipse_inds.length() - ellipse_count);
+          split_subscripts[i].replace(sub.find("..."), 3, rep_inds);
+        }
+      }
+      subscripts += split_subscripts[i];
+      if (i + 1 < size_split_subscripts) {
+        subscripts += ",";
+      }
+    }
+    std::string out_ellipse;
+    if (longest == 0) {
+      out_ellipse = "";
+    } else {
+      out_ellipse = ellipse_inds.substr(ellipse_inds.length() - longest);
+    }
+
+    if (out_sub) {
+      output_sub.replace(output_sub.find("..."), 3, out_ellipse);
+      subscripts += "->" + output_sub;
+    } else {
+      // Special care for outputless ellipses
+      std::bitset<LABELRANGE> out_ellipse_set = Str2Set(out_ellipse);
+      std::string tmp_subscripts = subscripts, output_subscript = "";
+      size_t len_tmp_subscripts = tmp_subscripts.length();
+      std::sort(tmp_subscripts.begin(), tmp_subscripts.end());
+      for (size_t i = 0; i < len_tmp_subscripts; ++i) {
+        const char& c = tmp_subscripts[i];
+        if (c == ',') {
+          continue;
+        }
+        CHECK(einsum_symbols_set.test(c)) << "Character " << c << " is not a valid symbol.";
+        if ((i == 0 || tmp_subscripts[i - 1] != c) &&
+            (i == len_tmp_subscripts - 1 || tmp_subscripts[i + 1] != c) &&
+            !out_ellipse_set.test(c)) {
+          output_subscript.append(1, c);
+        }
+      }
+      subscripts += "->" + out_ellipse + output_subscript;
+    }
+  }
+
+  // Build output string if does not exist
+  std::tuple<std::string, std::string> ret;
+  if (subscripts.find("->") != std::string::npos) {
+    std::vector<std::string> tmp(2);
+    tmp = Split(subscripts, "->");
+    ret = std::make_tuple(tmp[0], tmp[1]);
+  } else {
+    std::string first = subscripts;
+    std::string second = "";
+    // Build output subscripts
+    std::string tmp_subscripts = subscripts;
+    size_t len_tmp_subscripts = tmp_subscripts.length();
+    std::sort(tmp_subscripts.begin(), tmp_subscripts.end());
+    for (size_t i = 0; i < len_tmp_subscripts; ++i) {
+      const char& c = tmp_subscripts[i];
+      if (c == ',') {
+        continue;
+      }
+      CHECK(einsum_symbols_set.test(c)) << "Character " << c << " is not a valid symbol.";
+      if ((i == 0 || tmp_subscripts[i - 1] != c) &&
+          (i == len_tmp_subscripts - 1 || tmp_subscripts[i + 1] != c)) {
+        second.append(1, c);
+      }
+    }
+    ret = std::make_tuple(first, second);
+  }
+
+  // Make sure output subscripts are in the input
+  std::bitset<LABELRANGE> input_subscripts_set = Str2Set(std::get<0>(ret));
+  for (const char& c : std::get<1>(ret)) {
+    CHECK(input_subscripts_set.test(c))
+        << "Output character " << c << " did not appear in the input";
+  }
+
+  // Make sure number operands is equivalent to the number of terms
+  CHECK_EQ(std::count(std::get<0>(ret).begin(), std::get<0>(ret).end(), ',') + 1, operands.size())
+      << "Number of einsum subscripts must be equal to the "
+      << "number of operands.";
+
+  return ret;
+}
+
+/*!
+ * \brief Compute the shape of the output.
+ * \param subscripts input subscripts.
+ * \param operands operand tensors.
+ *
+ * \return the shape of the output.
+ */
+inline Array<PrimExpr> NumpyEinsumShape(const std::string subscripts,
+                                        const std::vector<Array<PrimExpr>>& operands) {
+  // Parsing
+  std::tuple<std::string, std::string> parsed_subscripts = ParseEinsumInput(subscripts, operands);
+
+  // Build a few useful list and sets
+  std::vector<std::string> input_list = Split(std::get<0>(parsed_subscripts), ",");
+  size_t isize = input_list.size();
+
+  // Get length of each unique dimension and ensure all dimensions are correct
+  int dimension_dict[LABELRANGE];
+  memset(dimension_dict, -1, sizeof(dimension_dict));
+  for (size_t i = 0; i < isize; ++i) {
+    const std::string& term = input_list[i];
+    const Array<PrimExpr>& sh = operands[i];
+    CHECK_EQ(sh.size(), term.length())
+        << "Einstein sum subscript " << input_list[i] << " does not contain the "
+        << "correct number of indices for operand " << i << ".";
+    size_t len_term = term.length();
+    for (size_t j = 0; j < len_term; ++j) {
+      int64_t dim = GetConstInt(sh[j]);
+      const char& c = term[j];
+
+      if (dimension_dict[static_cast<int>(c)] != -1) {
+        // For broadcasting cases we always want the largest dim size
+        if (dimension_dict[static_cast<int>(c)] == 1) {
+          dimension_dict[static_cast<int>(c)] = dim;
+        }
+        CHECK(dim == 1 || dim == dimension_dict[static_cast<int>(c)])
+            << "Size of label '" << c << "' for operand  " << i << " ("
+            << dimension_dict[static_cast<int>(c)] << ") does not match previous terms (" << dim
+            << ").";
+      } else {
+        dimension_dict[static_cast<int>(c)] = dim;
+      }
+    }
+  }
+
+  // Get oshape
+  const std::string& output_str = std::get<1>(parsed_subscripts);
+  size_t odim = output_str.size();
+  Array<PrimExpr> oshape(odim, -1);
+  for (size_t i = 0; i < odim; ++i) {
+    oshape.Set(i, dimension_dict[static_cast<int>(output_str[i])]);
+  }
+  // Neglecting oshape assign check temporally
+  return oshape;
+}
+
+/*!
+ * \brief Evaluates the Einstein summation convention on the operands.
+ *
+ * \param subscripts_str Specifies the subscripts for summation as comma separated list of
+ * subscript labels.
+ * \param inputs Arrays for the operation.
+ * \param name The name of the operation.
+ * \param tag The tag to mark the operation.
+ *
+ * \return The calculation based on the Einstein summation convention.
+ */
+inline Tensor einsum(const std::string& subscripts_str, const Array<Tensor> inputs,
+                     std::string name = "T_einsum", std::string tag = kEinsum) {
+  bool back = false;
+  const char* subscripts = subscripts_str.data();
+  const char* head = subscripts;
+  const int nop = inputs.size();
+
+  /* Step 1: Parse the subscripts string into label_counts and op_labels */
+  int iop, idim, min_label = LABELRANGE - 1, max_label = 0;
+  char label_counts[LABELRANGE], op_labels[NPY_MAXARGS][NPY_MAXDIMS];
+  memset(label_counts, 0, sizeof(label_counts));
+  for (iop = 0; iop < nop; ++iop) {
+    int length = static_cast<int>(strcspn(subscripts, ",-"));
+
+    CHECK(!(iop == nop - 1 && subscripts[length] == ','))
+        << "more operands provided to einstein sum function "
+        << "than specified in the subscripts string";
+    CHECK(!(iop < nop - 1 && subscripts[length] != ','))
+        << "fewer operands provided to einstein sum function "
+        << "than specified in the subscripts string";
+    CHECK_EQ(ParseOperandSubscripts(subscripts, length, inputs[iop + back].ndim(), iop,
+                                    op_labels[iop], label_counts, &min_label, &max_label),
+             0);
+
+    /* Move subscripts to the start of the labels for the next op */
+    subscripts += length;
+
+    if (iop < nop - 1) {
+      CHECK_LT(subscripts - head, subscripts_str.length()) << "subscripts out of range";
+      subscripts++;
+    }
+  }
+  /*
+   * Find the number of broadcast dimensions, which is the maximum
+   * number of labels == 0 in an op_labels array.
+   */
+  int ndim_broadcast = 0;
+  for (iop = 0; iop < nop; ++iop) {
+    int count_zeros = 0;
+    int ndim;
+    char* labels = op_labels[iop];
+
+    ndim = inputs[iop + back].ndim();
+    for (idim = 0; idim < ndim; ++idim) {
+      if (labels[idim] == 0) {
+        ++count_zeros;
+      }
+    }
+
+    if (count_zeros > ndim_broadcast) {
+      ndim_broadcast = count_zeros;
+    }
+  }
+
+  /*
+   * If there is no output signature, fill output_labels and ndim_output
+   * using each label that appeared once, in alphabetical order.
+   */
+  int label, ndim_output;
+  char output_labels[NPY_MAXDIMS];
+  if (subscripts[0] == '\0') {
+    /* If no output was specified, always broadcast left, as usual. */
+    for (ndim_output = 0; ndim_output < ndim_broadcast; ++ndim_output) {
+      output_labels[ndim_output] = 0;
+    }
+    for (label = min_label; label <= max_label; ++label) {
+      if (label_counts[label] == 1) {
+        CHECK(ndim_output < NPY_MAXDIMS) << "einstein sum subscript string has too many "
+                                         << "distinct labels";
+        output_labels[ndim_output++] = label;
+      }
+    }
+  } else {
+    CHECK(subscripts[0] == '-' && subscripts[1] == '>') << "einstein sum subscript string does not "
+                                                        << "contain proper '->' output specified";
+    subscripts += 2;
+
+    /* Parse the output subscript string. */
+    ndim_output = ParseOutputSubscripts(subscripts, strlen(subscripts), ndim_broadcast,
+                                        label_counts, output_labels);
+    CHECK_GE(ndim_output, 0);
+  }
+
+  /*
+   * Step 2:
+   * Process all the input ops, combining dimensions into their
+   * diagonal where specified.
+   */
+  std::vector<Array<PrimExpr>> opshape(nop), opstride_true(nop);
+  for (iop = 0; iop < nop; ++iop) {
+    char* labels = op_labels[iop];
+    int combine, ndim;
+
+    ndim = inputs[iop + back].ndim();
+
+    /*
+     * Check whether any dimensions need to be combined
+     *
+     * The char type may be either signed or unsigned, we
+     * need it to be signed here.
+     */
+    combine = 0;
+    for (idim = 0; idim < ndim; ++idim) {
+      if ((signed char)labels[idim] < 0) {
+        combine++;
+      }
+    }
+    /* If any dimensions are combined, create a view which combines them */
+    if (combine) {
+      Array<PrimExpr> tshape(static_cast<size_t>(ndim - combine), -1);
+      Array<PrimExpr> tstride(static_cast<size_t>(ndim - combine), -1);
+      GetCombinedDimsView(inputs[iop + back], iop, labels, &tshape, &tstride);
+      opshape[iop] = tshape;
+      opstride_true[iop] = tstride;
+    } else {
+      /* No combining needed */
+      opshape[iop] = inputs[iop + back]->shape;
+      opstride_true[iop] = GetStride(opshape[iop]);
+    }
+  }
+  /*
+   * Step 3:
+   * Set up the labels for the iterator (output + combined labels).
+   * Can just share the output_labels memory, because iter_labels
+   * is output_labels with some more labels appended.
+   */
+  char* iter_labels = output_labels;
+  int ndim_iter = ndim_output;
+  for (label = min_label; label <= max_label; ++label) {
+    if (label_counts[label] > 0 && memchr(output_labels, label, ndim_output) == nullptr) {
+      CHECK(ndim_iter < NPY_MAXDIMS) << "too many subscripts in einsum";
+      iter_labels[ndim_iter++] = label;
+    }
+  }
+  /* Step 4: Set up the op_axes for the iterator */
+  Array<PrimExpr> itershape(static_cast<size_t>(ndim_iter), -1);
+  std::vector<Array<PrimExpr>> iterstride(nop + 1,
+                                          Array<PrimExpr>(static_cast<size_t>(ndim_iter), 0));
+
+  // output_shape
+  std::vector<Array<PrimExpr>> operands;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    operands.push_back(inputs[i]->shape);
+  }
+  Array<PrimExpr> oshape = NumpyEinsumShape(subscripts_str, operands);
+  Array<PrimExpr> ostride_true = GetStride(oshape);
+  Array<PrimExpr> reduceshape;
+  std::vector<Array<PrimExpr>> remainshape(nop);
+  int op_axes_arrays[NPY_MAXARGS][NPY_MAXDIMS];
+  int* op_axes[NPY_MAXARGS];
+  for (iop = 0; iop < nop; ++iop) {
+    op_axes[iop] = op_axes_arrays[iop];
+    CHECK_GE(PrepareOpAxes(opshape[iop].size(), iop, op_labels[iop], op_axes[iop], ndim_iter,
+                           iter_labels),
+             0);
+    for (idim = 0; idim < ndim_iter; idim++) {
+      if (op_axes[iop][idim] != -1) {
+        iterstride[iop].Set(idim, opstride_true[iop][op_axes[iop][idim]]);
+        if (GetConstInt(itershape[idim]) != -1) {
+          if (GetConstInt(itershape[idim]) == 1) {
+            itershape.Set(idim, opshape[iop][op_axes[iop][idim]]);
+          }
+        } else {
+          itershape.Set(idim, opshape[iop][op_axes[iop][idim]]);
+        }
+      }
+    }
+  }
+  for (idim = 0; idim < ndim_output; ++idim) {
+    iterstride[nop].Set(idim, ostride_true[idim]);
+  }
+  reduceshape = Array<PrimExpr>(static_cast<size_t>(ndim_iter - ndim_output), 0);
+  for (idim = ndim_output; idim < ndim_iter; ++idim) {
+    reduceshape.Set(idim - ndim_output, itershape[idim]);
+  }
+  for (iop = 0; iop < nop; iop++) {
+    Array<Integer> rsh;
+    for (idim = 0; idim < ndim_iter; idim++) {
+      if (op_axes_arrays[iop][idim] == -1) {
+        rsh.push_back(GetConstInt(itershape[idim]));
+      } else {
+        if (GetConstInt(itershape[idim] != opshape[iop][op_axes_arrays[iop][idim]])) {
+          rsh.push_back(GetConstInt(itershape[idim]));
+        }
+      }
+    }
+    remainshape[iop] = Array<PrimExpr>(rsh.begin(), rsh.end());
+  }
+  // exclude the 0-dim case
+  if (ndim_iter == 0) {
+    ndim_iter = 1;
+  }
+  itershape = Pad(itershape, ndim_iter);
+  for (iop = 0; iop <= nop; ++iop) {
+    iterstride[iop] = Pad(iterstride[iop], ndim_iter);
+  }
+  // oshape = Pad(oshape, ndim_iter);
+  reduceshape = Pad(reduceshape, ndim_iter);
+  for (iop = 0; iop < nop; ++iop) {
+    opshape[iop] = Pad(opshape[iop], ndim_iter);
+    remainshape[iop] = Pad(remainshape[iop], ndim_iter);
+  }
+  // ostride and rstride
+  Array<Array<PrimExpr>> ostride;
+  Array<Array<PrimExpr>> rstride;
+
+  for (iop = 0; iop < nop; ++iop) {
+    Array<PrimExpr> otmp(static_cast<size_t>(ndim_iter), 0);
+    Array<PrimExpr> rtmp(static_cast<size_t>(ndim_iter), 0);
+    for (idim = 0; idim < ndim_iter; ++idim) {
+      otmp.Set(idim, idim < ndim_output ? iterstride[iop][idim] : 1);
+      rtmp.Set(idim, idim < ndim_iter - ndim_output ? iterstride[iop][idim + ndim_output] : 1);
+    }
+    ostride.push_back(otmp);
+    rstride.push_back(rtmp);
+  }
+
+  // func: input indices => return cooresponding value
+  auto func = [inputs, oshape, ostride, reduceshape, ndim_iter, rstride,
+               nop](const Array<Var>& input_indices) -> PrimExpr {
+    for (int rdim = 0; rdim < ndim_iter; ++rdim) {
+      if (GetConstInt(reduceshape[rdim]) == 0) {
+        return 0;  //
+      }
+    }
+    Array<PrimExpr> ridx = UnravelIndex(0, reduceshape);
+
+    PrimExpr sum = 0;
+    bool rec_flag = false;
+    do {
+      PrimExpr tmp = 1;
+      for (int iop = 0; iop < nop; ++iop) {
+        if (iop != -1) {
+          PrimExpr k = 0;
+
+          for (size_t i = 0; i < input_indices.size(); ++i) {
+            k += input_indices[i] * ostride[iop][i];
+          }
+          for (size_t i = 0; i < ridx.size(); ++i) {
+            k += ridx[i] * rstride[iop][i];
+          }
+          Array<PrimExpr> temp_indices = UnravelIndex(k, inputs[iop]->shape);
+          tmp = tmp * inputs[iop](temp_indices);
+        }
+      }
+      sum += tmp;
+      ridx.Set(ridx.size() - 1, ridx[ridx.size() - 1] + 1);
+      for (int i = static_cast<int>(ridx.size() - 1);
+           (i > 0) && GetConstInt(ridx[i] >= reduceshape[i]); --i) {
+        ridx.Set(i, ridx[i] - reduceshape[i]);
+        ridx.Set(i - 1, ridx[i - 1] + 1);
+      }
+      rec_flag = GetConstInt(ridx[0] < reduceshape[0]);
+    } while (rec_flag);
+    return sum;
+  };
+
+  return compute(oshape, func, name, tag);
+}
+
+}  // namespace topi
+}  // namespace tvm
+#endif  // TVM_TOPI_EINSUM_H_
diff --git a/include/tvm/topi/tags.h b/include/tvm/topi/tags.h
index 3b748ca60ce5..c3641ae0de12 100644
--- a/include/tvm/topi/tags.h
+++ b/include/tvm/topi/tags.h
@@ -41,6 +41,7 @@ constexpr auto kDepthwiseConv2dNCHW = "depthwise_conv2d_nchw";
 constexpr auto kDepthwiseConv2dNHWC = "depthwise_conv2d_nhwc";
 constexpr auto kDepthwiseConv2dBackInputNHWC = "depthwise_conv2d_back_input_nhwc";
 constexpr auto kDepthwiseConv2dBackWeightNHWC = "depthwise_conv2d_back_weight_nhwc";
+constexpr auto kEinsum = "einsum";
 constexpr auto kGroupConv2d = "group_conv2d";
 
 inline bool is_broadcast(std::string tag) {
diff --git a/include/tvm/topi/transform.h b/include/tvm/topi/transform.h
index a04762f28feb..3ad230560f3a 100644
--- a/include/tvm/topi/transform.h
+++ b/include/tvm/topi/transform.h
@@ -612,6 +612,7 @@ inline Tensor strided_slice(const Tensor& x, const Array<PrimExpr>& begin,
 
   Array<PrimExpr> out_shape;
   if (!is_static) {
+    ICHECK_EQ(strides.size(), src_tensor_dim);
     for (size_t i = 0; i < src_tensor_dim; ++i) {
       out_shape.push_back(indexdiv(end[i] - begin[i], strides[i]));
     }
@@ -1133,6 +1134,9 @@ inline Tensor gather(const Tensor& data, int axis, const Tensor& indices,
   size_t ndim_i = indices->shape.size();
   ICHECK_GE(ndim_d, 1) << "Cannot gather from a scalar.";
   ICHECK_EQ(ndim_d, ndim_i);
+  if (axis < 0) {
+    axis += ndim_d;
+  }
   ICHECK_GE(axis, 0);
   ICHECK_LT(axis, ndim_d);
   size_t indices_dim_i = static_cast<size_t>(GetConstInt(indices->shape[axis]));
diff --git a/licenses/LICENSE.libbacktrace.txt b/licenses/LICENSE.libbacktrace.txt
new file mode 100644
index 000000000000..097d2774e5df
--- /dev/null
+++ b/licenses/LICENSE.libbacktrace.txt
@@ -0,0 +1,29 @@
+# Copyright (C) 2012-2016 Free Software Foundation, Inc.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are
+# met:
+
+#     (1) Redistributions of source code must retain the above copyright
+#     notice, this list of conditions and the following disclaimer. 
+
+#     (2) Redistributions in binary form must reproduce the above copyright
+#     notice, this list of conditions and the following disclaimer in
+#     the documentation and/or other materials provided with the
+#     distribution.  
+    
+#     (3) The name of the author may not be used to
+#     endorse or promote products derived from this software without
+#     specific prior written permission.
+
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+# INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+# STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+# IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
diff --git a/nnvm/include/nnvm/graph.h b/nnvm/include/nnvm/graph.h
index 6f624b758fa9..475494e62c4d 100644
--- a/nnvm/include/nnvm/graph.h
+++ b/nnvm/include/nnvm/graph.h
@@ -229,7 +229,7 @@ inline void DFSVisit(const std::vector<NodeEntry>& heads, FVisit fvisit);
 template <typename T>
 inline const T& Graph::GetAttr(const std::string& attr_name) const {
   auto it = attrs.find(attr_name);
-  ICHECK(it != attrs.end()) << "Cannot find attribute " << attr_name << " in the graph";
+  CHECK(it != attrs.end()) << "Cannot find attribute " << attr_name << " in the graph";
   return nnvm::unsafe_get<T>(*it->second);
 }
 
@@ -241,7 +241,7 @@ inline bool Graph::HasAttr(const std::string& attr_name) const {
 template <typename T>
 inline T Graph::MoveCopyAttr(const std::string& attr_name) {
   auto it = attrs.find(attr_name);
-  ICHECK(it != attrs.end()) << "Cannot find attribute " << attr_name << " in the graph";
+  CHECK(it != attrs.end()) << "Cannot find attribute " << attr_name << " in the graph";
   std::shared_ptr<any> sptr = it->second;
   attrs.erase(it);
   if (sptr.unique()) {
diff --git a/nnvm/include/nnvm/layout.h b/nnvm/include/nnvm/layout.h
index 6c46f9de9e0f..e2e99784c99e 100644
--- a/nnvm/include/nnvm/layout.h
+++ b/nnvm/include/nnvm/layout.h
@@ -220,7 +220,7 @@ class Layout {
     for (size_t i = pos; i < pos + len; ++i) {
       if (is_subdim(layout_simplified_[i])) {
         auto block_size = this->subsizeof(layout_simplified_[i]);
-        ICHECK_GT(block_size, 0);
+        CHECK_GT(block_size, 0);
         new_layout << block_size;
       }
       new_layout << layout_simplified_[i];
@@ -235,7 +235,7 @@ class Layout {
     for (int64_t i = this->ndim() - 1; i >= 0; --i) {
       if (is_subdim(layout_simplified_[i])) {
         auto block_size = this->subsizeof(layout_simplified_[i]);
-        ICHECK_GT(block_size, 0);
+        CHECK_GT(block_size, 0);
         new_layout << block_size;
       }
       new_layout << layout_simplified_[i];
@@ -251,13 +251,13 @@ class Layout {
    * \return A newly constructed Layout object.
    */
   inline Layout split(LayoutDim dim, size_t target_pos, uint32_t size) const {
-    ICHECK(target_pos <= this->ndim())
+    CHECK(target_pos <= this->ndim())
         << "Invalid split position " << target_pos << " for layout " << name_;
-    ICHECK(is_superdim(dim)) << "Cannot split a sub-dimension " << dim;
-    ICHECK(this->contains(dim)) << "Axis " << dim << " does not exist in " << name_;
-    ICHECK(!this->contains(to_subdim(dim)))
+    CHECK(is_superdim(dim)) << "Cannot split a sub-dimension " << dim;
+    CHECK(this->contains(dim)) << "Axis " << dim << " does not exist in " << name_;
+    CHECK(!this->contains(to_subdim(dim)))
         << "Dimension " << dim << " has already been split in " << name_;
-    ICHECK(size > 0) << "Invalid split size " << size;
+    CHECK(size > 0) << "Invalid split size " << size;
     std::ostringstream new_layout;
     for (size_t i = 0; i <= this->ndim(); ++i) {
       if (i == target_pos) {
@@ -293,11 +293,11 @@ class Layout {
    * \return the description of the dimension.
    */
   inline std::string at(size_t i) const {
-    ICHECK_LT(i, this->ndim()) << "position " << i << " exceeds ndim=" << this->ndim();
+    CHECK_LT(i, this->ndim()) << "position " << i << " exceeds ndim=" << this->ndim();
     std::ostringstream repr;
     if (is_subdim(layout_simplified_[i])) {
       auto factor = subsizeof(layout_simplified_[i]);
-      ICHECK_GT(factor, 0);
+      CHECK_GT(factor, 0);
       repr << factor;
     }
     repr << layout_simplified_[i];
@@ -328,7 +328,7 @@ class Layout {
    *         Return -1 if \p dim is not in the layout or the layout is undefined.
    */
   inline int64_t subsizeof(LayoutDim dim) const {
-    ICHECK(is_superdim(dim) || is_subdim(dim)) << "Invalid dim " << dim;
+    CHECK(is_superdim(dim) || is_subdim(dim)) << "Invalid dim " << dim;
     if (!this->defined() || !this->contains(to_subdim(dim))) {
       return -1;
     }
@@ -409,34 +409,34 @@ class Layout {
       const LayoutDim c = layout.at(i);
       if (is_superdim(c)) {
         int pos = c - 'A';
-        ICHECK_EQ(factor, 0) << "Invalid layout " << layout << ": invalid factor size " << factor
-                             << " before dimension " << c;
-        ICHECK_EQ(superdim_pos_[pos], -1)
+        CHECK_EQ(factor, 0) << "Invalid layout " << layout << ": invalid factor size " << factor
+                            << " before dimension " << c;
+        CHECK_EQ(superdim_pos_[pos], -1)
             << "Invalid layout " << layout << ": duplicate dimension " << c;
         superdim_pos_[pos] = curr++;
         layout_simplified_.push_back(c);
       } else if (is_subdim(c)) {
         int pos = c - 'a';
-        ICHECK_GT(factor, 0) << "Invalid layout " << layout << ": invalid factor size " << factor
-                             << " for dimension " << c;
-        ICHECK_EQ(subdim_pos_[pos], -1)
+        CHECK_GT(factor, 0) << "Invalid layout " << layout << ": invalid factor size " << factor
+                            << " for dimension " << c;
+        CHECK_EQ(subdim_pos_[pos], -1)
             << "Invalid layout " << layout << ": duplicate dimension " << c;
-        ICHECK_EQ(subdim_size_[pos], -1)
+        CHECK_EQ(subdim_size_[pos], -1)
             << "Invalid layout " << layout << ": duplicate dimension " << c;
         subdim_pos_[pos] = curr++;
         subdim_size_[pos] = factor;
         layout_simplified_.push_back(c);
         factor = 0;
       } else if (c >= '0' && c <= '9') {
-        ICHECK(factor >= 0) << "Invalid layout " << layout << ": _ is adjacent to a number.";
+        CHECK(factor >= 0) << "Invalid layout " << layout << ": _ is adjacent to a number.";
         factor = factor * 10 + c - '0';
       } else {
         LOG(FATAL) << "Invalid layout " << layout;
       }
     }
-    ICHECK(!layout_simplified_.empty()) << "Invalid layout " << layout;
+    CHECK(!layout_simplified_.empty()) << "Invalid layout " << layout;
     for (LayoutDim dim : layout_simplified_) {
-      ICHECK(is_superdim(dim) || superdim_pos_[dim - 'a'] >= 0)
+      CHECK(is_superdim(dim) || superdim_pos_[dim - 'a'] >= 0)
           << "Invalid layout " << layout << ": missing axis " << static_cast<char>(dim - 'a' + 'A');
     }
   }
diff --git a/nnvm/include/nnvm/op.h b/nnvm/include/nnvm/op.h
index be52b08ebe62..f53e0f25ee37 100644
--- a/nnvm/include/nnvm/op.h
+++ b/nnvm/include/nnvm/op.h
@@ -452,7 +452,7 @@ inline const OpMap<ValueType>& Op::GetAttr(const std::string& key) {
 template <typename ValueType>
 inline Op& Op::set_attr(  // NOLINT(*)
     const std::string& attr_name, const ValueType& value, int plevel) {
-  ICHECK_GT(plevel, 0) << "plevel in set_attr must be greater than 0";
+  CHECK_GT(plevel, 0) << "plevel in set_attr must be greater than 0";
   // update the attribute map of the key by creating new empty if needed.
   UpdateAttrMap(attr_name, [this, attr_name, value, plevel](any* pmap) {
     // the callback is in lockscope so is threadsafe.
@@ -461,7 +461,7 @@ inline Op& Op::set_attr(  // NOLINT(*)
       pm.attr_name_ = attr_name;
       *pmap = std::move(pm);
     }
-    ICHECK(pmap->type() == typeid(OpMap<ValueType>))
+    CHECK(pmap->type() == typeid(OpMap<ValueType>))
         << "Attribute " << attr_name << " of operator " << this->name
         << " is registered as inconsistent types"
         << " previously " << pmap->type().name() << " current " << typeid(OpMap<ValueType>).name();
@@ -471,8 +471,8 @@ inline Op& Op::set_attr(  // NOLINT(*)
       vec.resize(index_ + 1, std::make_pair(ValueType(), 0));
     }
     std::pair<ValueType, int>& p = vec[index_];
-    ICHECK(p.second != plevel) << "Attribute " << attr_name << " of operator " << this->name
-                               << " is already registered with same plevel=" << plevel;
+    CHECK(p.second != plevel) << "Attribute " << attr_name << " of operator " << this->name
+                              << " is already registered with same plevel=" << plevel;
     if (p.second < plevel) {
       vec[index_] = std::make_pair(value, plevel);
     }
@@ -547,9 +547,9 @@ inline bool OpMap<ValueType>::contains(const Op* op) const {
 
 template <typename ValueType>
 inline const ValueType& OpMap<ValueType>::operator[](const Op* op) const {
-  ICHECK(op != nullptr);
+  CHECK(op != nullptr);
   const uint32_t idx = op->index_;
-  ICHECK(idx < data_.size() && data_[idx].second)
+  CHECK(idx < data_.size() && data_[idx].second)
       << "Attribute " << attr_name_ << " has not been registered for Operator " << op->name;
   return data_[idx].first;
 }
diff --git a/nnvm/include/nnvm/tuple.h b/nnvm/include/nnvm/tuple.h
index af800e77dd07..c6d6125aa194 100644
--- a/nnvm/include/nnvm/tuple.h
+++ b/nnvm/include/nnvm/tuple.h
@@ -435,7 +435,7 @@ class TShape : public Tuple<dim_t> {
    */
   template <int dim>
   inline mshadow::Shape<dim> get() const {
-    ICHECK_EQ(dim, static_cast<int>(ndim()))
+    CHECK_EQ(dim, static_cast<int>(ndim()))
         << "dimension do not match target dimension " << dim << " vs " << ndim();
     const dim_t* d = this->data();
     mshadow::Shape<dim> s;
@@ -467,7 +467,7 @@ class TShape : public Tuple<dim_t> {
    * \return the flat 3d shape
    */
   inline mshadow::Shape<3> FlatTo3D(size_t axis_begin, size_t axis_end) const {
-    ICHECK(axis_end >= axis_begin);
+    CHECK(axis_end >= axis_begin);
     mshadow::Shape<3> s;
     if (ndim() == 0) return mshadow::Shape3(0, 0, 0);
     const dim_t* d = this->data();
diff --git a/nnvm/src/core/graph.cc b/nnvm/src/core/graph.cc
index 81dc9bc35992..e5042802906c 100644
--- a/nnvm/src/core/graph.cc
+++ b/nnvm/src/core/graph.cc
@@ -54,7 +54,7 @@ static void SubgraphSanityCheck(const std::vector<std::shared_ptr<Symbol>>& subg
         nnvm::Node* node = n.get();
         // if the node is visited, but on a different level, then check failed
         // if check failed here or before, we stop doing anything, but raise an error
-        ICHECK(!node2level.count(node) || node2level[node] == level)
+        CHECK(!node2level.count(node) || node2level[node] == level)
             << "A subgraph should not depend on the outputs of nodes on higher levels";
         // otherwise, this node belongs to the current level
         node2level[node] = level;
@@ -76,9 +76,9 @@ IndexedGraph::IndexedGraph(const Graph& g) {
   DFSVisit(g.outputs, [this, &inputs_rptr, &control_rptr, &subgraphs](const ObjectPtr& n) {
     const auto& is_ghost = Op::GetAttr<TIsGhost>("TIsGhost");
     if (!n->is_variable() && is_ghost.get(n->op(), false)) return;
-    ICHECK_LT(nodes_.size(), std::numeric_limits<uint32_t>::max());
+    CHECK_LT(nodes_.size(), std::numeric_limits<uint32_t>::max());
     uint32_t nid = static_cast<uint32_t>(nodes_.size());
-    ICHECK(n);
+    CHECK(n);
     for (const auto& subgraph : n->attrs.subgraphs) subgraphs.push_back(subgraph);
     // nodes_
     IndexedGraph::Node new_node;
@@ -96,7 +96,7 @@ IndexedGraph::IndexedGraph(const Graph& g) {
     // input entries
     for (const auto& e : n->inputs) {
       auto it = node2index_.find(e.node.get());
-      ICHECK(it != node2index_.end() && it->first == e.node.get());
+      CHECK(it != node2index_.end() && it->first == e.node.get());
       input_entries_.emplace_back(NodeEntry{it->second, e.index, e.version});
     }
     inputs_rptr.push_back(input_entries_.size());
@@ -104,7 +104,7 @@ IndexedGraph::IndexedGraph(const Graph& g) {
     for (const auto& nptr : n->control_deps) {
       if (!nptr->is_variable() && is_ghost.get(nptr->op(), false)) continue;
       auto it = node2index_.find(nptr.get());
-      ICHECK(it != node2index_.end()) << "control dep not found in graph";
+      CHECK(it != node2index_.end()) << "control dep not found in graph";
       control_deps_.push_back(it->second);
     }
     control_rptr.push_back(control_deps_.size());
diff --git a/nnvm/src/core/op.cc b/nnvm/src/core/op.cc
index 7f5d1999780d..08a11dff9a02 100644
--- a/nnvm/src/core/op.cc
+++ b/nnvm/src/core/op.cc
@@ -70,7 +70,7 @@ Op& Op::add_alias(const std::string& alias) {  // NOLINT(*)
 // find operator by name
 const Op* Op::Get(const std::string& name) {
   const Op* op = dmlc::Registry<Op>::Find(name);
-  ICHECK(op != nullptr) << "Operator " << name << " is not registered";
+  CHECK(op != nullptr) << "Operator " << name << " is not registered";
   return op;
 }
 
diff --git a/nnvm/src/core/pass.cc b/nnvm/src/core/pass.cc
index 9966d3d42300..974cd2b35918 100644
--- a/nnvm/src/core/pass.cc
+++ b/nnvm/src/core/pass.cc
@@ -45,7 +45,7 @@ Graph ApplyPasses(Graph g, const std::vector<std::string>& pass) {
   std::vector<const PassFunctionReg*> fpass;
   for (auto& name : pass) {
     auto* reg = dmlc::Registry<PassFunctionReg>::Find(name);
-    ICHECK(reg != nullptr) << "Cannot find pass " << name << " in the registry";
+    CHECK(reg != nullptr) << "Cannot find pass " << name << " in the registry";
     fpass.push_back(reg);
   }
 
diff --git a/nnvm/src/core/symbolic.cc b/nnvm/src/core/symbolic.cc
index 18d31dd3a937..48f834b28535 100644
--- a/nnvm/src/core/symbolic.cc
+++ b/nnvm/src/core/symbolic.cc
@@ -58,7 +58,7 @@ inline void UpdateNodeVersion(Node* n) {
   if (fmutate_inputs.count(n->op()) != 0) {
     for (uint32_t i : fmutate_inputs[n->op()](n->attrs)) {
       NodeEntry& e = n->inputs[i];
-      ICHECK(e.node->is_variable()) << "Mutation target can only be Variable";
+      CHECK(e.node->is_variable()) << "Mutation target can only be Variable";
       // increase the version of the variable.
       e.version = ++nnvm::get<VariableParam>(e.node->attrs.parsed).version;
     }
@@ -186,7 +186,7 @@ void Symbol::Print(std::ostream& os) const {
 
 Symbol Symbol::operator[](size_t index) const {
   size_t nreturn = outputs.size();
-  ICHECK_LT(index, nreturn) << "Symbol only accept nonnegative index";
+  CHECK_LT(index, nreturn) << "Symbol only accept nonnegative index";
   if (nreturn == 1) {
     return *this;
   } else {
@@ -240,7 +240,7 @@ std::vector<std::string> Symbol::ListInputNames(ListInputOption option) const {
 }
 
 std::vector<std::string> Symbol::ListOutputNames() const {
-  static auto& flist_ouputs = Op::GetAttr<FListOutputNames>("FListOutputNames");
+  static auto& flist_outputs = Op::GetAttr<FListOutputNames>("FListOutputNames");
 
   std::vector<std::string> ret;
   ret.reserve(outputs.size());
@@ -250,7 +250,7 @@ std::vector<std::string> Symbol::ListOutputNames() const {
     } else {
       const std::string& hname = head.node->attrs.name;
       std::string rname;
-      FListOutputNames fn = flist_ouputs.get(head.node->op(), nullptr);
+      FListOutputNames fn = flist_outputs.get(head.node->op(), nullptr);
       if (fn != nullptr) {
         rname = fn(head.node->attrs)[head.index];
       } else {
@@ -298,13 +298,13 @@ void Symbol::Compose(const array_view<const Symbol*>& args,
   for (size_t i = 0; i < args.size(); ++i) {
     // If the argument isn't a graph, it should have only one output.
     if (garg_idx.empty() || std::find(garg_idx.begin(), garg_idx.end(), i) == garg_idx.end())
-      ICHECK_EQ(args[i]->outputs.size(), 1U)
+      CHECK_EQ(args[i]->outputs.size(), 1U)
           << "Argument " << i << " is a tuple, single value is required";
   }
   for (const auto& kv : kwargs) {
     if (garg_names.empty() ||
         std::find(garg_names.begin(), garg_names.end(), kv.first) == garg_names.end())
-      ICHECK_EQ(kv.second->outputs.size(), 1U)
+      CHECK_EQ(kv.second->outputs.size(), 1U)
           << "Keyword Argument " << kv.first << " is a tuple, single value is required";
   }
   // assign new name
@@ -325,7 +325,7 @@ void Symbol::Compose(const array_view<const Symbol*>& args,
           sym = arg_vec[idx];
         } else {
           auto it = kwarg_map.find(arg_names[idx]);
-          ICHECK(it != kwarg_map.end());
+          CHECK(it != kwarg_map.end());
           sym = it->second;
           kwarg_map.erase(it);
         }
@@ -346,7 +346,7 @@ void Symbol::Compose(const array_view<const Symbol*>& args,
 
     if (n_req != kVarg) {
       n->inputs.resize(n_req);
-      ICHECK_LE(arg_vec.size(), n_req)
+      CHECK_LE(arg_vec.size(), n_req)
           << "Incorrect number of arguments, requires " << n_req << ", provided " << arg_vec.size();
       for (size_t i = 0; i < arg_vec.size(); ++i) {
         n->inputs[i] = arg_vec[i]->outputs[0];
@@ -378,7 +378,7 @@ void Symbol::Compose(const array_view<const Symbol*>& args,
         }
       }
     } else {
-      ICHECK_EQ(kwarg_map.size(), 0U) << "Variable length function do not accept kwargs";
+      CHECK_EQ(kwarg_map.size(), 0U) << "Variable length function do not accept kwargs";
       n->inputs.reserve(arg_vec.size());
       for (const Symbol* s : arg_vec) {
         n->inputs.push_back(s->outputs[0]);
@@ -396,7 +396,7 @@ void Symbol::Compose(const array_view<const Symbol*>& args,
     }
   } else {
     // general composition
-    ICHECK_EQ(args.size(), 0U) << "General composition only support kwargs for now";
+    CHECK_EQ(args.size(), 0U) << "General composition only support kwargs for now";
     size_t nmatched = 0;
     size_t arg_counter = 0;
     std::unordered_map<Node*, const NodeEntry*> replace_map;
@@ -456,7 +456,7 @@ void Symbol::Compose(const array_view<const Symbol*>& args,
     // update outputs in case the composed variable is part of outputs.
     for (size_t i = 0; i < outputs.size(); ++i) {
       if (outputs[i].node->is_variable()) {
-        ICHECK_EQ(args.size(), 0) << "Variable composition only supports keyword arguments";
+        CHECK_EQ(args.size(), 0) << "Variable composition only supports keyword arguments";
         const auto it = kwargs.find(outputs[i].node->attrs.name);
         if (it != kwargs.end()) outputs[i] = it->second->outputs[0];
       }
@@ -473,7 +473,7 @@ Symbol Symbol::operator()(const array_view<const Symbol*>& args,
 }
 
 void Symbol::AddControlDeps(const Symbol& src) {
-  ICHECK_EQ(outputs.size(), 1U) << "AddControlDeps only works for nongrouped symbol";
+  CHECK_EQ(outputs.size(), 1U) << "AddControlDeps only works for nongrouped symbol";
   Node* n = outputs[0].node.get();
   for (const NodeEntry& sp : src.outputs) {
     n->control_deps.push_back(sp.node);
@@ -517,7 +517,7 @@ Symbol Symbol::GetChildren() const {
 void Symbol::SetAttrs(const std::vector<std::pair<std::string, std::string> >& attrs) {
   Node* node = outputs[0].node.get();
   for (const NodeEntry& e : outputs) {
-    ICHECK(node == e.node.get()) << "Symbol.SetAttrs only works for non-grouped symbol";
+    CHECK(node == e.node.get()) << "Symbol.SetAttrs only works for non-grouped symbol";
   }
   for (const auto& kv : attrs) {
     if (kv.first == "name") {
diff --git a/nnvm/src/pass/correct_layout.cc b/nnvm/src/pass/correct_layout.cc
index 3a8cc16511ff..b9024a56d143 100644
--- a/nnvm/src/pass/correct_layout.cc
+++ b/nnvm/src/pass/correct_layout.cc
@@ -64,7 +64,7 @@ nnvm::Graph CorrectLayout(nnvm::Graph src) {
     if (new_node->is_variable()) {
       // Variable node. No operator. Only one output entry.
       auto input_iter = std::find(idx.input_nodes().cbegin(), idx.input_nodes().cend(), nid);
-      ICHECK(input_iter != idx.input_nodes().cend());
+      CHECK(input_iter != idx.input_nodes().cend());
       int64_t input_id = std::distance(idx.input_nodes().cbegin(), input_iter);
       if (src.HasAttr("layout_inputs")) {
         new_layouts[new_node.get()] = {
@@ -83,11 +83,11 @@ nnvm::Graph CorrectLayout(nnvm::Graph src) {
     for (size_t i = 0; i < num_inputs; ++i) {
       const IndexedGraph::NodeEntry& input_entry = inode.inputs[i];
       const ObjectPtr& new_input_node = mirror_vec[input_entry.node_id];
-      ICHECK(new_input_node != nullptr);
+      CHECK(new_input_node != nullptr);
 
       // fill inputs by previous node (DFS order) inferred layouts.
       const auto& layouts_iter = new_layouts.find(new_input_node.get());
-      ICHECK(layouts_iter != new_layouts.end());
+      CHECK(layouts_iter != new_layouts.end());
       request_ilayouts[i] = layouts_iter->second[input_entry.index];
     }
     // layouts produced by previous node.
@@ -108,10 +108,10 @@ nnvm::Graph CorrectLayout(nnvm::Graph src) {
 
     if (op_correct_layout.count(new_node->op())) {
       const auto& flayout = op_correct_layout[new_node->op()];
-      ICHECK(flayout(new_node->attrs, &request_ilayouts, &last_request_ilayouts, &produce_olayouts))
+      CHECK(flayout(new_node->attrs, &request_ilayouts, &last_request_ilayouts, &produce_olayouts))
           << "Layout infer fail";
-      ICHECK_EQ(request_ilayouts.size(), num_inputs);
-      ICHECK_EQ(produce_olayouts.size(), num_outputs);
+      CHECK_EQ(request_ilayouts.size(), num_inputs);
+      CHECK_EQ(produce_olayouts.size(), num_outputs);
     }
 
     // update new layouts
diff --git a/nnvm/src/pass/gradient.cc b/nnvm/src/pass/gradient.cc
index 902a968b102d..1df3af7ffaaf 100644
--- a/nnvm/src/pass/gradient.cc
+++ b/nnvm/src/pass/gradient.cc
@@ -85,10 +85,10 @@ Graph Gradient(Graph src) {
   using MirrorFun = std::function<int(const Node& node)>;
   using AttrHintFun = std::function<NodeEntry(const NodeEntry& src, const NodeEntry& like)>;
 
-  ICHECK_NE(src.attrs.count("grad_ys"), 0U) << "Gradient require grad_ys to be presented.";
-  ICHECK_NE(src.attrs.count("grad_ys_out_grad"), 0U)
+  CHECK_NE(src.attrs.count("grad_ys"), 0U) << "Gradient require grad_ys to be presented.";
+  CHECK_NE(src.attrs.count("grad_ys_out_grad"), 0U)
       << "Gradient require grad_ys_out_grad to be presented.";
-  ICHECK_NE(src.attrs.count("grad_xs"), 0U) << "Gradient require grad_xs to be presented.";
+  CHECK_NE(src.attrs.count("grad_xs"), 0U) << "Gradient require grad_xs to be presented.";
   const std::vector<NodeEntry>& ys = src.GetAttr<std::vector<NodeEntry> >("grad_ys");
   const std::vector<NodeEntry>& ys_out_grad =
       src.GetAttr<std::vector<NodeEntry> >("grad_ys_out_grad");
@@ -124,7 +124,7 @@ Graph Gradient(Graph src) {
     topo_order.push_back(node);
   });
 
-  ICHECK_EQ(ys.size(), ys_out_grad.size());
+  CHECK_EQ(ys.size(), ys_out_grad.size());
   for (size_t i = 0; i < ys.size(); ++i) {
     NodeEntry ograd = ys_out_grad[i];
     output_grads[ys[i].node.get()][ys[i].index].grads = {ograd};
@@ -132,7 +132,7 @@ Graph Gradient(Graph src) {
 
   // Check that all xs are reachable from ys
   for (size_t i = 0; i < xs.size(); ++i) {
-    ICHECK(output_grads.find(xs[i].node.get()) != output_grads.end())
+    CHECK(output_grads.find(xs[i].node.get()) != output_grads.end())
         << "Cannot differentiate with respect to the " << i + 1 << "-th variable "
         << "because it is unreachable from the outputs.";
   }
@@ -182,7 +182,7 @@ Graph Gradient(Graph src) {
       // Check for FGradient
       if (grad_fun_map.contains(ptr->op())) {
         input_grads = grad_fun_map[ptr->op()](fwd_node, out_agg_grads);
-        ICHECK_EQ((*rit)->inputs.size(), input_grads.size())
+        CHECK_EQ((*rit)->inputs.size(), input_grads.size())
             << "Gradient function not returning enough gradient";
       } else if (CheckGradAllZero(out_agg_grads, zero_ops)) {
         for (size_t i = 0; i < fwd_node->num_inputs(); ++i) {
@@ -206,9 +206,9 @@ Graph Gradient(Graph src) {
         LOG(FATAL) << "Operator " << fwd_node->op()->name << " is non-differentiable "
                    << "because it didn't register FGradient attribute.";
       }
-      for (const auto& nodeEntry : input_grads) ICHECK(nodeEntry.node);
+      for (const auto& nodeEntry : input_grads) CHECK(nodeEntry.node);
       auto git = input_grads.begin();
-      ICHECK((*rit)->inputs.size() <= input_grads.size());
+      CHECK((*rit)->inputs.size() <= input_grads.size());
       for (auto it = (*rit)->inputs.begin(); it != (*rit)->inputs.end(); ++it, ++git) {
         auto& output_grad_entry = output_grads[it->node.get()][it->index];
         // if any of the backward op can do shape inference, the hint is not necessary.
diff --git a/nnvm/src/pass/graph_algorithm.h b/nnvm/src/pass/graph_algorithm.h
index 4620079a0ab2..b305c08bc05f 100644
--- a/nnvm/src/pass/graph_algorithm.h
+++ b/nnvm/src/pass/graph_algorithm.h
@@ -45,7 +45,7 @@ namespace pass {
 inline uint32_t FindBestPath(const IndexedGraph& graph, const std::vector<uint32_t>& node_reward,
                              std::vector<uint32_t>* path) {
   const uint32_t num_nodes = static_cast<uint32_t>(graph.num_nodes());
-  ICHECK_EQ(num_nodes, node_reward.size());
+  CHECK_EQ(num_nodes, node_reward.size());
 
   std::vector<uint32_t> best_reward(node_reward.size(), 0);
   std::vector<uint32_t> next_node(node_reward.size(), num_nodes);
@@ -73,7 +73,7 @@ inline uint32_t FindBestPath(const IndexedGraph& graph, const std::vector<uint32
     path->push_back(nid);
     reward += node_reward[nid];
   }
-  ICHECK_EQ(reward, best_solution);
+  CHECK_EQ(reward, best_solution);
   return best_solution;
 }
 
@@ -90,8 +90,8 @@ inline uint32_t FindBestPath(const IndexedGraph& graph, const std::vector<uint32
  */
 inline uint32_t ColorNodeGroup(const IndexedGraph& graph, std::vector<uint32_t> node_importance,
                                uint32_t max_ncolor, std::vector<uint32_t>* color) {
-  ICHECK_NE(max_ncolor, 0U);
-  ICHECK_EQ(graph.num_nodes(), node_importance.size());
+  CHECK_NE(max_ncolor, 0U);
+  CHECK_EQ(graph.num_nodes(), node_importance.size());
 
   color->clear();
   color->resize(graph.num_nodes(), max_ncolor);
@@ -105,7 +105,7 @@ inline uint32_t ColorNodeGroup(const IndexedGraph& graph, std::vector<uint32_t>
     if (reward == 0) break;
     for (uint32_t nid : path) {
       if (node_importance[nid] != 0) {
-        ICHECK_EQ(color->at(nid), max_ncolor);
+        CHECK_EQ(color->at(nid), max_ncolor);
         color->at(nid) = cindex;
         // make the importance 0 after color is decided.
         node_importance[nid] = 0;
diff --git a/nnvm/src/pass/infer_shape_type.cc b/nnvm/src/pass/infer_shape_type.cc
index 859c5b385c4a..fde1691ee96a 100644
--- a/nnvm/src/pass/infer_shape_type.cc
+++ b/nnvm/src/pass/infer_shape_type.cc
@@ -49,7 +49,7 @@ Graph InferAttr(Graph&& ret, const AttrType empty_val, const char* infer_name,
 
   if (ret.attrs.count(input_name) != 0) {
     const AttrVector& shape_args = ret.GetAttr<AttrVector>(input_name);
-    ICHECK_LE(shape_args.size(), idx.input_nodes().size())
+    CHECK_LE(shape_args.size(), idx.input_nodes().size())
         << "More provided shapes than number of arguments.";
     for (size_t i = 0; i < shape_args.size(); ++i) {
       rshape[idx.entry_id(idx.input_nodes()[i], 0)] = shape_args[i];
@@ -88,22 +88,22 @@ Graph InferAttr(Graph&& ret, const AttrType empty_val, const char* infer_name,
     const uint32_t num_outputs = inode.source->num_outputs();
     if (inode.source->is_variable()) {
       // Variable node. No operator. Only one output entry.
-      ICHECK(inode.source->op() == nullptr);
-      ICHECK_EQ(num_outputs, 1U);
+      CHECK(inode.source->op() == nullptr);
+      CHECK_EQ(num_outputs, 1U);
       const uint32_t out_ent_id = idx.entry_id(nid, 0);
       if (shape_attr_key.length() != 0 && fis_none(rshape[out_ent_id])) {
         auto it = inode.source->attrs.dict.find(shape_attr_key);
         if (it != inode.source->attrs.dict.end()) {
           std::istringstream is(it->second);
-          ICHECK(is >> rshape[out_ent_id]) << "Invalid attribute";
+          CHECK(is >> rshape[out_ent_id]) << "Invalid attribute";
         }
       }
     } else if (is_backward.get(inode.source->op(), false) && inode.control_deps.size()) {
-      ICHECK_GE(inode.control_deps.size(), 1U)
+      CHECK_GE(inode.control_deps.size(), 1U)
           << "BackwardOp need to have control_deps to its forward op";
       const IndexedGraph::Node& fnode = idx[inode.control_deps[0]];
       ObjectPtr fwd_ptr = inode.source->control_deps[0];
-      ICHECK(fwd_ptr->op() != nullptr) << "Forward op cannot be a variable";
+      CHECK(fwd_ptr->op() != nullptr) << "Forward op cannot be a variable";
       // use gradient function to find out the correspondence.
       std::vector<NodeEntry> ograd(fwd_ptr->num_outputs());
       for (size_t i = 0; i < ograd.size(); ++i) {
@@ -119,18 +119,18 @@ Graph InferAttr(Graph&& ret, const AttrType empty_val, const char* infer_name,
           if (fis_none(rshape[eid])) {
             rshape[eid] = rshape[idx.entry_id(fnode.inputs[i])];
           } else if (!fis_none(rshape[idx.entry_id(fnode.inputs[i])])) {
-            ICHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
+            CHECK_EQ(rshape[eid], rshape[idx.entry_id(fnode.inputs[i])])
                 << "Backward shape inconsistent with the forward shape";
           }
           if (igrad_node == nullptr) {
             igrad_node = igrad[i].node.get();
           } else {
-            ICHECK(igrad_node == igrad[i].node.get());
+            CHECK(igrad_node == igrad[i].node.get());
           }
         }
       }
       // out grad entries
-      ICHECK(igrad_node != nullptr)
+      CHECK(igrad_node != nullptr)
           << "Cannot find matching backward op for " << inode.source->attrs.name;
       for (size_t i = 0; i < igrad_node->inputs.size(); ++i) {
         const NodeEntry& e = igrad_node->inputs[i];
@@ -164,9 +164,9 @@ Graph InferAttr(Graph&& ret, const AttrType empty_val, const char* infer_name,
             throw dmlc::Error("Error in operator " + inode.source->attrs.name + ": " + e.what());
           }
         } else {
-          ICHECK(!last_iter) << "Attribute " << infer_name << " is not registered by op "
-                             << inode.source->op()->name
-                             << " we are not able to complete the inference because of this";
+          CHECK(!last_iter) << "Attribute " << infer_name << " is not registered by op "
+                            << inode.source->op()->name
+                            << " we are not able to complete the inference because of this";
         }
       }
       // Save to the result map.
diff --git a/nnvm/src/pass/place_device.cc b/nnvm/src/pass/place_device.cc
index 4a9d93465de8..d45658ae24ab 100644
--- a/nnvm/src/pass/place_device.cc
+++ b/nnvm/src/pass/place_device.cc
@@ -33,11 +33,11 @@ namespace {
 // simply logic to place device according to device_group hint
 // insert copy node when there is
 Graph PlaceDevice(Graph src) {
-  ICHECK(src.attrs.count("device_group_attr_key"))
+  CHECK(src.attrs.count("device_group_attr_key"))
       << "Need graph attribute \"device_group_attr_key\" in PlaceDevice";
-  ICHECK(src.attrs.count("device_assign_map"))
+  CHECK(src.attrs.count("device_assign_map"))
       << "Need graph attribute \"device_assign_map\" in PlaceDevice";
-  ICHECK(src.attrs.count("device_copy_op"))
+  CHECK(src.attrs.count("device_copy_op"))
       << "Need graph attribute \"device_copy_op\" in PlaceDevice";
   std::string device_group_attr_key = src.GetAttr<std::string>("device_group_attr_key");
   const Op* copy_op = Op::Get(src.GetAttr<std::string>("device_copy_op"));
@@ -48,7 +48,7 @@ Graph PlaceDevice(Graph src) {
   // copy on write semanatics
   if (src.attrs.count("device") != 0) {
     device = src.MoveCopyAttr<DeviceVector>("device");
-    ICHECK_EQ(device.size(), idx.num_nodes());
+    CHECK_EQ(device.size(), idx.num_nodes());
   } else {
     device.resize(idx.num_nodes(), -1);
   }
@@ -60,7 +60,7 @@ Graph PlaceDevice(Graph src) {
     if (it != inode.source->attrs.dict.end()) {
       const std::string& device_group = it->second;
       auto dit = device_assign_map.find(device_group);
-      ICHECK(dit != device_assign_map.end())
+      CHECK(dit != device_assign_map.end())
           << "The device assignment not found for group " << device_group;
       device[nid] = dit->second;
     } else {
@@ -139,7 +139,7 @@ Graph PlaceDevice(Graph src) {
       }
     }
     if (inode.source->is_variable()) {
-      ICHECK(!need_mutate) << "consistency check";
+      CHECK(!need_mutate) << "consistency check";
     }
     if (need_mutate) {
       ObjectPtr new_node = Node::Create();
diff --git a/nnvm/src/pass/plan_memory.cc b/nnvm/src/pass/plan_memory.cc
index 42c54e366039..2c36cd2eef5a 100644
--- a/nnvm/src/pass/plan_memory.cc
+++ b/nnvm/src/pass/plan_memory.cc
@@ -112,7 +112,7 @@ class GraphAllocator {
   }
   // release a memory space.
   void Release(StorageID id, uint32_t node_id) {
-    ICHECK_NE(id, kBadStorageID);
+    CHECK_NE(id, kBadStorageID);
     if (id == kExternalStorageID || id == kDynamicStorageID) return;
     StorageEntry* e = data_[id].get();
     e->released_by_node = node_id;
@@ -219,7 +219,7 @@ size_t AllocMemory(const Graph& ret, const IndexedGraph& idx,
       std::vector<bool> identity;
       if (finplace_identity.count(inode.source->op()) != 0) {
         identity = finplace_identity[inode.source->op()](inode.source->attrs);
-        ICHECK_EQ(identity.size(), inplace_pairs.size())
+        CHECK_EQ(identity.size(), inplace_pairs.size())
             << "FInplaceOption and FInplaceIdentity returned vectors of different "
             << "size for operator " << inode.source->op()->name;
       } else {
diff --git a/nnvm/src/pass/print_graph_ir.cc b/nnvm/src/pass/print_graph_ir.cc
index 6604d810f288..4fe92e665961 100644
--- a/nnvm/src/pass/print_graph_ir.cc
+++ b/nnvm/src/pass/print_graph_ir.cc
@@ -41,7 +41,7 @@ AttrPrinter GetVectorPrinter_(const T& vec) {
 
 AttrPrinter GetVectorPrinter(const Graph& graph, const std::string& key) {
   auto it = graph.attrs.find(key);
-  ICHECK(it != graph.attrs.end()) << "Cannot find " << key << " in graph attr";
+  CHECK(it != graph.attrs.end()) << "Cannot find " << key << " in graph attr";
   const any& value = *(it->second);
   if (value.type() == typeid(std::vector<TShape>)) {
     return GetVectorPrinter_(nnvm::get<std::vector<TShape> >(value));
diff --git a/nnvm/src/pass/saveload_json.cc b/nnvm/src/pass/saveload_json.cc
index dbd8ee0f83d4..3916da43618d 100644
--- a/nnvm/src/pass/saveload_json.cc
+++ b/nnvm/src/pass/saveload_json.cc
@@ -72,13 +72,13 @@ struct JSONNode {
     }
     void Load(dmlc::JSONReader* reader) {
       reader->BeginArray();
-      ICHECK(reader->NextArrayItem()) << "invalid json format";
+      CHECK(reader->NextArrayItem()) << "invalid json format";
       reader->Read(&node_id);
-      ICHECK(reader->NextArrayItem()) << "invalid json format";
+      CHECK(reader->NextArrayItem()) << "invalid json format";
       reader->Read(&index);
       if (reader->NextArrayItem()) {
         reader->Read(&version);
-        ICHECK(!reader->NextArrayItem()) << "invalid json format";
+        CHECK(!reader->NextArrayItem()) << "invalid json format";
       } else {
         version = 0;
       }
@@ -226,12 +226,12 @@ std::shared_ptr<Symbol> JSONGraph2Symbol(const JSONGraph& jgraph, bool no_parse)
   for (const JSONNode& n : jgraph.nodes) {
     n.node->inputs.reserve(n.inputs.size());
     for (const JSONNode::Entry& e : n.inputs) {
-      ICHECK(e.node_id < jgraph.nodes.size());
+      CHECK(e.node_id < jgraph.nodes.size());
       n.node->inputs.emplace_back(NodeEntry{jgraph.nodes[e.node_id].node, e.index, e.version});
     }
     n.node->control_deps.reserve(n.control_deps.size());
     for (uint32_t nid : n.control_deps) {
-      ICHECK(nid < jgraph.nodes.size());
+      CHECK(nid < jgraph.nodes.size());
       n.node->control_deps.push_back(jgraph.nodes[nid].node);
     }
     for (const JSONGraph& subgraph : n.subgraphs) {
@@ -252,13 +252,13 @@ std::shared_ptr<Symbol> JSONGraph2Symbol(const JSONGraph& jgraph, bool no_parse)
   }
   // consistency check
   for (uint32_t nid : jgraph.arg_nodes) {
-    ICHECK(nid < jgraph.nodes.size());
-    ICHECK(jgraph.nodes[nid].node->is_variable());
+    CHECK(nid < jgraph.nodes.size());
+    CHECK(jgraph.nodes[nid].node->is_variable());
   }
   std::shared_ptr<Symbol> symbol = std::make_shared<Symbol>();
   symbol->outputs.reserve(jgraph.heads.size());
   for (const JSONNode::Entry& e : jgraph.heads) {
-    ICHECK(e.node_id < jgraph.nodes.size());
+    CHECK(e.node_id < jgraph.nodes.size());
     symbol->outputs.emplace_back(NodeEntry{jgraph.nodes[e.node_id].node, e.index, e.version});
   }
   return symbol;
@@ -266,7 +266,7 @@ std::shared_ptr<Symbol> JSONGraph2Symbol(const JSONGraph& jgraph, bool no_parse)
 
 // Load a graph from JSON file.
 Graph LoadJSON(Graph src) {
-  ICHECK_NE(src.attrs.count("json"), 0U) << "Load JSON require json to be presented.";
+  CHECK_NE(src.attrs.count("json"), 0U) << "Load JSON require json to be presented.";
   const std::string& json_str = nnvm::get<std::string>(*src.attrs.at("json"));
   bool no_parse = false;
   if (src.attrs.count("load_json_no_parse")) {
diff --git a/nnvm/tests/cpp/op_test.cc b/nnvm/tests/cpp/op_test.cc
index 39a998a4eebe..2ebd14688f46 100644
--- a/nnvm/tests/cpp/op_test.cc
+++ b/nnvm/tests/cpp/op_test.cc
@@ -35,7 +35,7 @@ TEST(Op, GetAttr) {
   auto add = Op::Get("add");
   auto nick = Op::GetAttr<std::string>("nick_name");
 
-  ICHECK_EQ(nick[add], "plus");
+  CHECK_EQ(nick[add], "plus");
 }
 
 int main(int argc, char** argv) {
diff --git a/nnvm/tests/cpp/tuple_test.cc b/nnvm/tests/cpp/tuple_test.cc
index e28ecd89f6fa..2c2c307aadce 100644
--- a/nnvm/tests/cpp/tuple_test.cc
+++ b/nnvm/tests/cpp/tuple_test.cc
@@ -28,18 +28,18 @@ TEST(Tuple, Basic) {
   Tuple<int> y{1, 2, 3, 5, 6};
   x = std::move(y);
 
-  ICHECK_EQ(x.ndim(), 5);
+  CHECK_EQ(x.ndim(), 5);
   Tuple<int> z{1, 2, 3, 5, 6};
   std::ostringstream os;
   os << z;
-  ICHECK_EQ(os.str(), "[1,2,3,5,6]");
+  CHECK_EQ(os.str(), "[1,2,3,5,6]");
   std::istringstream is(os.str());
   is >> y;
-  ICHECK_EQ(x, y);
+  CHECK_EQ(x, y);
   Tuple<nnvm::dim_t> ss{1, 2, 3};
   TShape s = ss;
   s = std::move(ss);
-  ICHECK((s == TShape{1, 2, 3}));
+  CHECK((s == TShape{1, 2, 3}));
 }
 
 int main(int argc, char** argv) {
diff --git a/python/.gitignore b/python/.gitignore
index a4d2483a90e2..4c6fde5b68b5 100644
--- a/python/.gitignore
+++ b/python/.gitignore
@@ -1,3 +1,4 @@
 build
 dist
 *.cpp
+requirements/*.txt
diff --git a/python/gen_requirements.py b/python/gen_requirements.py
new file mode 100755
index 000000000000..6869e4829d98
--- /dev/null
+++ b/python/gen_requirements.py
@@ -0,0 +1,615 @@
+#!/usr/bin/env python3
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""TVM Python requriements.txt generator.
+
+This script generates a set of requirements.txt files (stored in `./requirements`) that describe
+TVM's Python dependencies.
+
+## Pieces
+
+TVM can be roughly broken into these named pieces along the lines of Python dependencies:
+
+- "core": A core piece, which is intended to be buildable with very few external dependencies. Users
+  can use Relay, compile models, and run autotuning with this part.
+- "importer-<tool>": Model importers, which convert models defined in various other tools (i.e.
+  TensorFlow, PyTorch, etc) into Relay models.
+- Extra features (i.e. XGBoost in AutoTVM). These enhance TVM's functionality, but aren't required
+  for basic operation.
+
+## What this tool does
+
+From these pieces, this tool builds:
+ - requirements/<name>.txt - Python dependencies for each named piece above, `<name>` is the same as
+   the quoted piece name.
+ - requirements/all.txt - Consolidated Python dependencies for all pieces, excluding dev below.
+ - requirements/dev.txt - Python dependencies needed to develop TVM, such as lint and test tools.
+
+The data representing each piece is contained in the two maps below.
+"""
+
+import argparse
+import collections
+import os
+import re
+import textwrap
+import sys
+import typing
+
+
+RequirementsByPieceType = typing.List[typing.Tuple[str, typing.Tuple[str, typing.List[str]]]]
+
+
+# Maps named TVM piece (see description above) to a list of names of Python packages. Please use
+# alphabetical order for each package list, and do not add version constraints here!
+REQUIREMENTS_BY_PIECE: RequirementsByPieceType = [
+    # Base requirements needed to install tvm.
+    (
+        "core",
+        (
+            "Base requirements needed to install tvm",
+            [
+                "attrs",
+                "cloudpickle",
+                "decorator",
+                "numpy",
+                "psutil",
+                "scipy",
+                "synr",
+                "tornado",
+            ],
+        ),
+    ),
+    # Relay frontends.
+    (
+        "importer-caffe2",
+        (
+            "Requirements for the Caffe2 importer",
+            [
+                "future",  # Hidden dependency of torch.
+                "torch",
+            ],
+        ),
+    ),
+    ("importer-coreml", ("Requirements for the CoreML importer", ["coremltools"])),
+    ("importer-darknet", ("Requirements for the DarkNet importer", ["opencv-python"])),
+    (
+        "importer-keras",
+        ("Requirements for the Keras importer", ["tensorflow", "tensorflow-estimator"]),
+    ),
+    (
+        "importer-onnx",
+        (
+            "Requirements for the ONNX importer",
+            [
+                "future",  # Hidden dependency of torch.
+                "onnx",
+                "onnxruntime",
+                "torch",
+                "torchvision",
+            ],
+        ),
+    ),
+    (
+        "importer-pytorch",
+        (
+            "Requirements for the PyTorch importer",
+            [
+                "future",  # Hidden dependency of torch.
+                "torch",
+                "torchvision",
+            ],
+        ),
+    ),
+    (
+        "importer-tensorflow",
+        ("Requirements for the TensorFlow importer", ["tensorflow", "tensorflow-estimator"]),
+    ),
+    (
+        "importer-tflite",
+        ("Requirements for the TFLite importer", ["tensorflow", "tensorflow-estimator", "tflite"]),
+    ),
+    (
+        "tvmc",
+        (
+            "Requirements for the tvmc command-line tool",
+            [
+                "future",  # Hidden dependency of torch.
+                "onnx",
+                "onnxruntime",
+                "tensorflow",
+                "tflite",
+                "torch",
+                "torchvision",
+                "xgboost",
+            ],
+        ),
+    ),
+    # XGBoost, useful for autotuning on some targets.
+    (
+        "xgboost",
+        (
+            "Requirements for XGBoost autotuning",
+            [
+                "future",  # Hidden dependency of torch.
+                "torch",
+                "xgboost",
+            ],
+        ),
+    ),
+    # Development requirements
+    (
+        "dev",
+        (
+            "Requirements to develop TVM -- lint, docs, testing, etc.",
+            [
+                "astroid",  # pylint requirement, listed so a hard constraint can be included.
+                "autodocsumm",
+                "black",
+                "commonmark",
+                "cpplint",
+                "docutils",
+                "image",
+                "matplotlib",
+                "pillow",
+                "pylint",
+                "sphinx",
+                "sphinx_autodoc_annotation",
+                "sphinx_gallery",
+                "sphinx_rtd_theme",
+            ],
+        ),
+    ),
+]
+
+ConstraintsType = typing.List[typing.Tuple[str, typing.Union[None, str]]]
+
+# Maps a named Python package (which should appear in REQUIREMENTS_BY_PIECE above) to a
+# semver or pip version constraint. Semver constraints are translated into requirements.txt-friendly
+# constraints.
+#
+# These constraints serve only to record technical reasons why a particular version can't be used.
+# They are the default install_requires used in setup.py. These can be further narrowed to restrict
+# dependencies to those tested or used in CI; however, that process is not done here.
+#
+# Policy for constraints listed here:
+# 1. Each package specified in REQUIREMENTS_BY_PIECE must be included here.
+# 2. If TVM will functionally break against an old version of a dependency, specify a >= relation
+#    here. Include a comment linking to context or explaining why the constraint is in place.
+CONSTRAINTS = [
+    ("astroid", None),
+    ("attrs", None),
+    ("autodocsumm", None),
+    ("black", None),
+    ("cloudpickle", None),
+    ("commonmark", ">=0.7.3"),  # From PR #213.
+    ("coremltools", None),
+    ("cpplint", None),
+    ("decorator", None),
+    ("docutils", None),
+    ("future", None),
+    ("image", None),
+    ("matplotlib", None),
+    ("numpy", None),
+    ("onnx", None),
+    ("onnxruntime", None),
+    ("opencv-python", None),
+    ("pillow", None),
+    ("psutil", None),
+    ("pylint", None),
+    ("scipy", None),
+    ("sphinx", None),
+    ("sphinx_autodoc_annotation", None),
+    ("sphinx_gallery", None),
+    ("sphinx_rtd_theme", None),
+    ("synr", ">=0.2.1"),  # Requires bugfix commit ee0b12a61c08f01604475f36ff37d4cb110bdc27
+    ("tensorflow", None),
+    ("tensorflow-estimator", None),
+    ("tflite", None),
+    ("torch", None),
+    ("torchvision", None),
+    ("tornado", None),
+    ("xgboost", ">=1.1.0"),  # From PR #4953.
+]
+
+################################################################################
+# End of configuration options.
+################################################################################
+
+
+# Required keys in REQUIREMENTS_BY_PIECE.
+REQUIRED_PIECES: typing.List[str] = ["core", "dev"]
+
+# Regex to validates piece names.
+PIECE_REGEX: typing.Pattern = re.compile(r"^[a-z0-9][a-z0-9-]*", re.IGNORECASE)
+
+# Regex to match a constraint specification. Multiple constraints are not supported.
+CONSTRAINT_REGEX: typing.Pattern = re.compile(r"(?:\^|\<|(?:~=)|(?:<=)|(?:==)|(?:>=)|\>)[^<>=\^,]+")
+
+# Regex for parsing semantic versions. See
+# https://semver.org/#is-there-a-suggested-regular-expression-regex-to-check-a-semver-string
+SEMVER_REGEX: typing.Pattern = re.compile(
+    r"^(?P<major>0|[1-9]\d*)\.(?P<minor>0|[1-9]\d*)\.(?P<patch>0|[1-9]\d*)(?:-(?P<prerelease>(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\.(?:0|[1-9]\d*|\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\+(?P<buildmetadata>[0-9a-zA-Z-]+(?:\.[0-9a-zA-Z-]+)*))?$"
+)
+
+
+def validate_requirements_by_piece() -> typing.List[str]:
+    """Validate REQUIREMENTS_BY_PIECE, returning a list of problems.
+
+    Returns
+    -------
+    list[str] :
+        A list of strings, each one describing a distinct problem with REQUIREMENTS_BY_PIECE.
+    """
+    problems = []
+
+    unseen_required_pieces = set(REQUIRED_PIECES)
+    seen_pieces = set()
+
+    # Ensure that core is listed first and dev is listed last.
+    saw_core = False
+    saw_dev = False
+
+    if not isinstance(REQUIREMENTS_BY_PIECE, (list, tuple)):
+        problems.append(f"must be list or tuple, see {REQUIREMENTS_BY_PIECE!r}")
+        return problems
+
+    for piece, value in REQUIREMENTS_BY_PIECE:
+        if not isinstance(piece, str):
+            problems.append(f"piece {piece!r}: must be str")
+            continue
+
+        if piece in unseen_required_pieces:
+            unseen_required_pieces.remove(piece)
+
+        piece_lower = piece.lower()
+        if piece_lower in seen_pieces:
+            problems.append(f"piece {piece}: listed twice")
+
+        seen_pieces.add(piece_lower)
+
+        if not saw_core and piece != "core":
+            problems.append(f'piece {piece}: must list after "core" (core must be first)')
+        elif piece == "core":
+            saw_core = True
+
+        if saw_dev:
+            problems.append(f'piece {piece}: must list before "dev" (dev must be last)')
+        elif piece == "dev":
+            saw_dev = True
+
+        if not isinstance(value, (tuple, list)) or len(value) != 2:
+            problems.append(
+                f'piece {piece}: should be formatted like ("{piece}", ("<requirements.txt comment>", ["dep1", "dep2", ...])). got: {value!r}'
+            )
+            continue
+
+        description, deps = value
+
+        if not isinstance(description, str):
+            problems.append(f"piece {piece}: description should be a string, got {description!r}")
+
+        if not isinstance(deps, (list, tuple)) or any(not isinstance(d, str) for d in deps):
+            problems.append(f"piece {piece}: deps should be a list of strings, got {deps!r}")
+            continue
+
+        if list(sorted(deps)) != list(deps):
+            problems.append(
+                f"piece {piece}: deps must be sorted. Correct order:\n  {list(sorted(deps))!r}"
+            )
+
+        piece_deps = set()
+        for d in deps:
+            if CONSTRAINT_REGEX.search(d):
+                problems.append(
+                    f"piece {piece}: dependency {d} should not specify a version. "
+                    "Add it to CONSTRAINTS instead."
+                )
+
+            if d.lower() in piece_deps:
+                problems.append(f"piece {piece}: dependency {d} listed twice")
+
+            piece_deps.add(d.lower())
+
+    extras_pieces = [
+        k for (k, _) in REQUIREMENTS_BY_PIECE if k not in ("dev", "core") if isinstance(k, str)
+    ]
+    sorted_extras_pieces = list(sorted(extras_pieces))
+    if sorted_extras_pieces != list(extras_pieces):
+        problems.append(
+            'pieces other than "core" and "dev" must appear in alphabetical order: '
+            f"{sorted_extras_pieces}"
+        )
+
+    return problems
+
+
+def parse_semver(
+    package: str, constraint: str, problems: typing.List[str]
+) -> typing.Tuple[typing.List[str], int, int]:
+    """Parse a semantic versioning constraint of the form "^X.[.Y[.Z[...]]]]"
+
+    Parameters
+    ----------
+    package : str
+        Name of the package specifying this constraint, for reporting problems.
+    constraint : str
+        The semver constraint. Must start with "^"
+    problems : List[str]
+        A list of strings describing problems that have occurred validating the configuration.
+        Problems encountered while validating constraint are appended to this list.
+
+    Returns
+    -------
+    tuple[list[str], int, int] :
+        A 3-tuple. The first element is a list containing an entry for each component in the
+        semver string (components separated by "."). The second element is the index of the
+        component in the list which must not change to meet the semver constraint. The third element
+        is an integer, the numeric value of the changing component (this can be non-trivial when
+        the patch is the changing part but pre-, post-release, or build metadta.
+
+        See "Caret requirements" at https://python-poetry.org/docs/versions/.
+    """
+    m = SEMVER_REGEX.match(constraint[1:])
+    if not m:
+        problems.append(f"{package}: invalid semver constraint {constraint}")
+        return [], 0, 0
+
+    min_ver_parts = [
+        m.group("major"),
+        m.group("minor"),
+        m.group("patch")
+        + (f"-{m.group('prerelease')}" if m.group("prerelease") else "")
+        + (f"+{m.group('buildmetadata')}" if m.group("buildmetadata") else ""),
+    ]
+
+    # Major/minor version handling is simple
+    for i, p in enumerate(min_ver_parts[:2]):
+        x = int(p.strip())
+        if x:
+            return min_ver_parts, i, x
+
+    # For patch version, consult only the numeric patch
+    if m.group("patch"):
+        patch_int = int(m.group("patch"))
+        if patch_int or min_ver_parts[2] != m.group("patch"):
+            return min_ver_parts, 2, patch_int
+
+    # All 0's
+    return min_ver_parts, 0, 0
+
+
+def validate_constraints() -> typing.List[str]:
+    """Validate CONSTRAINTS, returning a list of problems found.
+
+    Returns
+    -------
+    list[str] :
+        A list of strings, each one describing a distinct problem found in CONSTRAINTS.
+    """
+    problems = []
+
+    if not isinstance(CONSTRAINTS, (list, tuple)):
+        problems.append(f"must be list or tuple, see: {CONSTRAINTS!r}")
+
+    seen_packages = set()
+    all_deps = set()
+    for _, (_, deps) in REQUIREMENTS_BY_PIECE:
+        for d in deps:
+            all_deps.add(d.lower())
+
+    for package, constraint in CONSTRAINTS:
+        if package in seen_packages:
+            problems.append(f"{package}: specified twice")
+        seen_packages.add(package)
+
+        if package.lower() not in all_deps:
+            problems.append(f"{package}: not specified in REQUIREMENTS_BY_PIECE")
+
+        if constraint is None:  # None is just a placeholder that allows for comments.
+            continue
+
+        if not CONSTRAINT_REGEX.match(constraint):
+            problems.append(
+                f'{package}: constraint "{constraint}" does not look like a valid constraint'
+            )
+
+        if constraint.startswith("^"):
+            parse_semver(package, constraint, problems)
+
+    all_constrained_packages = [p for (p, _) in CONSTRAINTS]
+    sorted_constrained_packages = list(sorted(all_constrained_packages))
+    if sorted_constrained_packages != all_constrained_packages:
+        problems.append(
+            "CONSTRAINTS entries should be in this sorted order: " f"{sorted_constrained_packages}"
+        )
+
+    return problems
+
+
+class ValidationError(Exception):
+    """Raised when a validation error occurs."""
+
+    @staticmethod
+    def format_problems(config: str, problems: typing.List[str]) -> str:
+        """Format a list of problems with a global config variable into human-readable output.
+
+        Parameters
+        ----------
+        config : str
+            Name of the global configuration variable of concern. Prepended to the output.
+        problems: list[str]
+            A list of strings, each one a distinct problem with that config variable.
+
+        Returns
+        -------
+        str :
+            A human-readable string suitable for console, listing the problems as bullet points.
+        """
+        formatted = []
+        for p in problems:
+            assert isinstance(p, str), f"problems element not a str: {p}"
+            formatted.append(
+                "\n".join(
+                    textwrap.wrap(
+                        f"{config}: {p}", width=80, initial_indent=" * ", subsequent_indent="   "
+                    )
+                )
+            )
+
+        return "\n".join(formatted)
+
+    def __init__(self, config: str, problems: typing.List[str]):
+        """Describes an error that occurs validating one of the global config variables.
+
+        Parameters
+        ----------
+        config : str
+            Name of the global configuration variable of concern. Prepended to the output.
+        problems: list[str]
+            A list of strings, each one a distinct problem with that config variable.
+        """
+        super(ValidationError, self).__init__(self.format_problems(config, problems))
+        self.problems = problems
+
+
+def validate_or_raise():
+    problems = validate_requirements_by_piece()
+    if problems:
+        raise ValidationError("REQUIREMENTS_BY_PIECE", problems)
+
+    problems = validate_constraints()
+    if problems:
+        raise ValidationError("CONSTRAINTS", problems)
+
+
+def semver_to_requirements(dep: str, constraint: str, joined_deps: typing.List[str]):
+    """Convert a SemVer-style constraint to a setuptools-compatible constraint.
+
+    Parameters
+    ----------
+    dep : str
+        Name of the PyPI package to depend on.
+    constraint : str
+        The SemVer constraint, of the form "^<semver constraint>"
+    joined_deps : list[str]
+        A list of strings, each a setuptools-compatible constraint which could be written to
+        a line in requirements.txt. The converted constraint is appended to this list.
+    """
+    problems: typing.List[str] = []
+    min_ver_parts, fixed_index, fixed_part = parse_semver(dep, constraint, problems)
+    text_problems = "\n" + "\n".join(f" * {p}" for p in problems)
+    assert (
+        not problems
+    ), f"should not happen: validated semver {constraint} parses with problems:{text_problems}"
+
+    max_ver_parts = (
+        min_ver_parts[:fixed_index]
+        + [str(fixed_part + 1)]
+        + ["0" for _ in min_ver_parts[fixed_index + 1 :]]
+    )
+    joined_deps.append(f'{dep}>={".".join(min_ver_parts)},<{".".join(max_ver_parts)}')
+
+
+def join_requirements() -> typing.Dict[str, typing.Tuple[str, typing.List[str]]]:
+    """Validate, then join REQUIRMENTS_BY_PIECE against CONSTRAINTS and return the result.
+
+    Returns
+    -------
+    An OrderedDict containing REQUIREMENTS_BY_PIECE, except any dependency mentioned in CONSTRAINTS
+    is replaced by a setuptools-compatible constraint.
+    """
+    validate_or_raise()
+
+    constraints_map = collections.OrderedDict([(p.lower(), c) for (p, c) in CONSTRAINTS])
+
+    to_return = collections.OrderedDict()
+    all_deps = set()
+    for piece, (description, deps) in REQUIREMENTS_BY_PIECE:
+        joined_deps = []
+        for d in deps:
+            constraint = constraints_map.get(d.lower())
+            if constraint is None:
+                joined_deps.append(d)
+                continue
+
+            if constraint[0] == "^":
+                semver_to_requirements(d, constraint, joined_deps)
+            else:
+                joined_deps.append(f"{d}{constraint}")
+
+        if piece != "dev":
+            all_deps.update(joined_deps)
+
+        to_return[piece] = (description, joined_deps)
+
+    to_return["all-prod"] = (
+        "Combined dependencies for all TVM pieces, excluding dev",
+        list(sorted(all_deps)),
+    )
+
+    return to_return
+
+
+def join_and_write_requirements(args: argparse.Namespace):
+    try:
+        joined_deps = join_requirements()
+    except ValidationError as e:
+        print(f"ERROR: invalid requirements configuration in {__file__}:", file=sys.stderr)
+        print(str(e), file=sys.stderr)
+        sys.exit(2)
+
+    if args.lint:
+        sys.exit(0)
+
+    output_dir = os.path.join(os.path.dirname(__file__), "requirements")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    elif not os.path.isdir(output_dir):
+        print(
+            f"ERROR: output directory {output_dir} exists but is not a dir. Delete it",
+            file=sys.stderr,
+        )
+        sys.exit(2)
+
+    for piece, (description, deps) in joined_deps.items():
+        with open(os.path.join(output_dir, f"{piece}.txt"), "w") as f:
+            f.write(
+                f"# AUTOGENERATED by python/gen_requirements.py{os.linesep}"
+                f"#{os.linesep}"
+                f"# {description}{os.linesep}"
+            )
+            for d in deps:
+                f.write(f"{d}{os.linesep}")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lint", action="store_true", help="Just lint dependencies, don't generate anything"
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    join_and_write_requirements(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/setup.py b/python/setup.py
index 8af62f9c9102..b47e5b14f6a7 100644
--- a/python/setup.py
+++ b/python/setup.py
@@ -94,7 +94,7 @@ def config_cython():
             subdir = "_cy2"
         ret = []
         path = "tvm/_ffi/_cython"
-        extra_compile_args = ["-std=c++14"]
+        extra_compile_args = ["-std=c++14", "-DDMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>"]
         if os.name == "nt":
             library_dirs = ["tvm", "../build/Release", "../build"]
             libraries = ["tvm"]
@@ -171,38 +171,26 @@ def get_package_data_files():
     return ["relay/std/prelude.rly", "relay/std/core.rly"]
 
 
+# Temporarily add this directory to the path so we can import the requirements generator
+# tool.
+sys.path.insert(0, os.path.dirname(__file__))
+import gen_requirements
+
+sys.path.pop(0)
+
+requirements = gen_requirements.join_requirements()
+extras_require = {
+    piece: deps for piece, (_, deps) in requirements.items() if piece not in ("all", "core")
+}
+
 setup(
     name="tvm",
     version=__version__,
     description="TVM: An End to End Tensor IR/DSL Stack for Deep Learning Systems",
     zip_safe=False,
     entry_points={"console_scripts": ["tvmc = tvm.driver.tvmc.main:main"]},
-    install_requires=[
-        "numpy",
-        "scipy",
-        "decorator",
-        "attrs",
-        "psutil",
-        "synr>=0.2.1",
-    ],
-    extras_require={
-        "test": ["pillow<7", "matplotlib"],
-        "extra_feature": [
-            "tornado",
-            "psutil",
-            "xgboost>=1.1.0",
-            "mypy",
-            "orderedset",
-        ],
-        "tvmc": [
-            "tensorflow>=2.1.0",
-            "tflite>=2.1.0",
-            "onnx>=1.7.0",
-            "onnxruntime>=1.0.0",
-            "torch>=1.4.0",
-            "torchvision>=0.5.0",
-        ],
-    },
+    install_requires=requirements["core"][1],
+    extras_require=extras_require,
     packages=find_packages(),
     package_dir={"tvm": "tvm"},
     package_data={"tvm": get_package_data_files()},
diff --git a/python/tvm/__init__.py b/python/tvm/__init__.py
index c2b4fdb2d00e..7a5f553ccdd5 100644
--- a/python/tvm/__init__.py
+++ b/python/tvm/__init__.py
@@ -68,6 +68,11 @@
 from .contrib import rocm as _rocm, nvcc as _nvcc, sdaccel as _sdaccel
 
 
+# NOTE: This file should be python2 compatible so we can
+# raise proper error message when user run the package using
+# an older version of the python
+
+
 def _should_print_backtrace():
     in_pytest = "PYTEST_CURRENT_TEST" in os.environ
     tvm_backtrace = os.environ.get("TVM_BACKTRACE", "0")
@@ -76,7 +81,7 @@ def _should_print_backtrace():
         tvm_backtrace = bool(int(tvm_backtrace))
     except ValueError:
         raise ValueError(
-            f"invalid value for TVM_BACKTRACE `{tvm_backtrace}`, please set to 0 or 1."
+            "invalid value for TVM_BACKTRACE {}, please set to 0 or 1.".format(tvm_backtrace)
         )
 
     return in_pytest or tvm_backtrace
diff --git a/python/tvm/_ffi/_ctypes/object.py b/python/tvm/_ffi/_ctypes/object.py
index d30026adf9cc..fc510b7b6504 100644
--- a/python/tvm/_ffi/_ctypes/object.py
+++ b/python/tvm/_ffi/_ctypes/object.py
@@ -106,7 +106,12 @@ class ObjectBase(object):
 
     def __del__(self):
         if _LIB is not None:
-            check_call(_LIB.TVMObjectFree(self.handle))
+            try:
+                handle = self.handle
+            except AttributeError:
+                return
+
+            check_call(_LIB.TVMObjectFree(handle))
 
     def __init_handle_by_constructor__(self, fconstructor, *args):
         """Initialize the handle by calling constructor function.
diff --git a/python/tvm/_ffi/base.py b/python/tvm/_ffi/base.py
index 397090618ade..0496195fd73f 100644
--- a/python/tvm/_ffi/base.py
+++ b/python/tvm/_ffi/base.py
@@ -253,7 +253,9 @@ def c2pyerror(err_msg):
     message = []
     for line in arr:
         if trace_mode:
-            if line.startswith("  "):
+            if line.startswith("        "):
+                stack_trace[-1] += "\n" + line
+            elif line.startswith("  "):
                 stack_trace.append(line)
             else:
                 trace_mode = False
diff --git a/python/tvm/_ffi/libinfo.py b/python/tvm/_ffi/libinfo.py
index 28614d072f01..8d67313e2e61 100644
--- a/python/tvm/_ffi/libinfo.py
+++ b/python/tvm/_ffi/libinfo.py
@@ -167,7 +167,6 @@ def find_include_path(name=None, search_path=None, optional=False):
     """
     ffi_dir = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
     source_dir = os.path.join(ffi_dir, "..", "..", "..")
-    install_include_dir = os.path.join(ffi_dir, "..", "..", "..", "..")
 
     third_party_dir = os.path.join(source_dir, "3rdparty")
 
@@ -176,7 +175,6 @@ def find_include_path(name=None, search_path=None, optional=False):
     if os.environ.get("TVM_INCLUDE_PATH", None):
         header_path.append(os.environ["TVM_INCLUDE_PATH"])
 
-    header_path.append(install_include_dir)
     header_path.append(source_dir)
     header_path.append(third_party_dir)
 
diff --git a/python/tvm/auto_scheduler/__init__.py b/python/tvm/auto_scheduler/__init__.py
index a03e156cc10f..ff6d82a0242c 100644
--- a/python/tvm/auto_scheduler/__init__.py
+++ b/python/tvm/auto_scheduler/__init__.py
@@ -33,7 +33,7 @@
 # Shortcut
 from .compute_dag import ComputeDAG, LayoutRewriteOption, get_shape_from_rewritten_layout
 from .cost_model import RandomModel, XGBModel
-from .dispatcher import DispatchContext, ApplyHistoryBest
+from .dispatcher import DispatchContext, ApplyHistoryBest, ApplyHistoryBestOrSample
 from .measure import (
     MeasureInput,
     MeasureResult,
@@ -41,6 +41,7 @@
     LocalRunner,
     RPCRunner,
     LocalRPCMeasureContext,
+    register_task_input_check_func,
 )
 from .measure_record import RecordToFile, RecordReader, load_best_record, load_records, save_records
 from .relay_integration import (
@@ -50,6 +51,11 @@
     is_auto_scheduler_enabled,
 )
 from .search_task import SearchTask, TuningOptions, HardwareParams, create_task, auto_schedule
-from .search_policy import EmptyPolicy, SketchPolicy, PreloadMeasuredStates
+from .search_policy import (
+    EmptyPolicy,
+    SketchPolicy,
+    PreloadMeasuredStates,
+    PreloadCustomSketchRule,
+)
 from .task_scheduler import TaskScheduler
 from .workload_registry import register_workload, make_workload_key
diff --git a/python/tvm/auto_scheduler/compute_dag.py b/python/tvm/auto_scheduler/compute_dag.py
index a7f200aa5cdd..948f277034db 100755
--- a/python/tvm/auto_scheduler/compute_dag.py
+++ b/python/tvm/auto_scheduler/compute_dag.py
@@ -19,11 +19,11 @@
 """ The auto-scheduler's computational graph and related program analyses. """
 
 import hashlib
+import json
 
 import tvm._ffi
 from tvm.runtime import Object
 from tvm.runtime._ffi_node_api import LoadJSON, SaveJSON
-from tvm.te import ComputeOp, PlaceholderOp
 
 from . import _ffi_api
 from .loop_state import State, StateObject
@@ -220,32 +220,23 @@ def rewrite_layout_from_state(self, state):
         state_obj = state if isinstance(state, StateObject) else state.state_object
         return _ffi_api.ComputeDAGRewriteLayoutFromState(self, state_obj)
 
-    def hash_key(self):
-        """Return the hash key of this compute DAG.
+    def workload_key(self):
+        """Return the workload key of this compute DAG.
+        The workload key is a JSON string from a tuple of (hash-key, tensor shapes...)
 
         Returns
         -------
         key: str
-            The hash key of this compute DAG
+            The workload key of this compute DAG
         """
-        # TODO(merrymercy): Implement this more carefully and move this to c++ as a member function
-        # of ComputeDAG
-        str_key = ""
-        for op in self.ops:
-            t = op.output(0)
-            if isinstance(op, PlaceholderOp):
-                str_key += "placeholder,"
-                str_key += str(get_const_tuple(t.shape)) + ","
-                str_key += t.dtype + ";"
-            elif isinstance(op, ComputeOp):
-                str_key += str(t.op.body) + ","
-                str_key += str(get_const_tuple(t.shape)) + ","
-                str_key += t.dtype + ";"
-            else:
-                raise ValueError("Invalid op: " + op)
-
-        str_key = str_key.encode(encoding="utf-8")
-        return hashlib.md5(str_key).hexdigest()
+        str_dag = _ffi_api.ComputeDAGPrintDAG(self, True)
+        str_dag = str_dag.encode(encoding="utf-8")
+        hash_key = hashlib.md5(str_dag).hexdigest()
+
+        io_shapes = []
+        for tensor in self.tensors:
+            io_shapes += get_const_tuple(tensor.shape)
+        return json.dumps([hash_key] + io_shapes)
 
     def __str__(self):
         # pretty print
diff --git a/python/tvm/auto_scheduler/cost_model/cost_model.py b/python/tvm/auto_scheduler/cost_model/cost_model.py
index 32e276b31c6a..9ef4bcac7a99 100644
--- a/python/tvm/auto_scheduler/cost_model/cost_model.py
+++ b/python/tvm/auto_scheduler/cost_model/cost_model.py
@@ -15,7 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
-""" Cost model that estimates the performance of programs """
+""" Cost models that estimate the performance of programs """
 import ctypes
 import numpy as np
 
@@ -31,7 +31,7 @@ class CostModel(Object):
 
 @tvm._ffi.register_object("auto_scheduler.RandomModel")
 class RandomModel(CostModel):
-    """A model returns random estimation for all inputs"""
+    """A model that returns random estimation for all inputs"""
 
     def __init__(self):
         self.__init_handle_by_constructor__(_ffi_api.RandomModel)
diff --git a/python/tvm/auto_scheduler/cost_model/xgb_model.py b/python/tvm/auto_scheduler/cost_model/xgb_model.py
index eb14dff0815c..3cf65954be7f 100644
--- a/python/tvm/auto_scheduler/cost_model/xgb_model.py
+++ b/python/tvm/auto_scheduler/cost_model/xgb_model.py
@@ -86,19 +86,43 @@ class XGBModel(PythonBasedModel):
     of several samples, so we implemented a custom loss function and call it pack-sum-rmse.
     It is called "pack-sum" because we combine several samples into a "pack" and sum up
     their predictions.
+
+    Parameters
+    ----------
+    verbose_eval: int = 25
+        Print training log every `verbose_eval` iterations.
+    num_warmup_sample: int = 100
+        The minimum number of samples to start to use the trained model.
+        If the number of samples is less than this number, the model outputs random predictions.
+    seed: Optional[int]
+        The random seed
+    model_file: Optional[str]
+        If is not None, save model to this file after every update.
+    adapative_training: bool = False
+        Whether to use adapatie training, which reduces the training frequency when there are
+        too many logs.
     """
 
-    def __init__(self, verbose_eval=25, num_warmup_sample=100, seed=None):
+    def __init__(
+        self,
+        verbose_eval=25,
+        num_warmup_sample=100,
+        seed=None,
+        model_file=None,
+        adapative_training=False,
+    ):
         global xgb
         try:
             if xgb is None:
                 xgb = __import__("xgboost")
         except ImportError:
+            # add "from Node" to silence
+            # "During handling of the above exception, another exception occurred"
             raise ImportError(
                 "XGBoost is required for XGBModel. "
                 "Please install its python package first. "
                 "Help: (https://xgboost.readthedocs.io/en/latest/) "
-            )
+            ) from None
 
         self.xgb_params = {
             "max_depth": 10,
@@ -116,12 +140,15 @@ def __init__(self, verbose_eval=25, num_warmup_sample=100, seed=None):
         self.plan_size = 32
         self.num_warmup_sample = num_warmup_sample
         self.verbose_eval = verbose_eval
+        self.model_file = model_file
+        self.adapative_training = adapative_training
 
         super().__init__()
 
         # cache measurement input/result pairs and extracted features
         self.inputs = []
         self.results = []
+        self.last_train_length = 0
         self.inputs_feature_cache = []
 
     def update(self, inputs, results):
@@ -141,6 +168,15 @@ def update(self, inputs, results):
         self.inputs.extend(inputs)
         self.results.extend(results)
 
+        if (
+            self.adapative_training
+            and len(self.inputs) - self.last_train_length < self.last_train_length / 5
+        ):
+            # Set a training threshold related to `last_train_length` to reduce the training
+            # overhead when there're too many logs
+            return
+        self.last_train_length = len(self.inputs)
+
         # extract feature
         n_cached = len(self.inputs_feature_cache)
         features, normalized_throughputs, task_ids = get_per_store_features_from_measure_pairs(
@@ -176,6 +212,10 @@ def update(self, inputs, results):
             ],
         )
 
+        # Update the model file if it has been set
+        if self.model_file:
+            self.save(self.model_file)
+
     def predict(self, task, states):
         """Predict the scores of states
         Parameters
diff --git a/python/tvm/auto_scheduler/dispatcher.py b/python/tvm/auto_scheduler/dispatcher.py
index b0b98d8d0f56..6a25960fe7b7 100644
--- a/python/tvm/auto_scheduler/dispatcher.py
+++ b/python/tvm/auto_scheduler/dispatcher.py
@@ -28,8 +28,14 @@
 
 import numpy as np
 
+from tvm.contrib.utils import tempdir
 from tvm.tir.expr import FloatImm
-from .measure_record import load_records
+from .cost_model import RandomModel, XGBModel
+from .measure import LocalRPCMeasureContext
+from .measure_record import RecordToFile, load_records
+from .search_policy import PreloadMeasuredStates, SketchPolicy
+from .search_task import SearchTask, TuningOptions
+from .utils import calc_workload_dis_factor, decode_workload_key
 
 logger = logging.getLogger("auto_scheduler")
 
@@ -126,18 +132,53 @@ class ApplyHistoryBest(DispatchContext):
         If is str, then it should be the filename of a records log file.
         Each row of this file is an encoded record pair. Otherwise, it is an iterator.
     n_lines: Optional[int]
-        if it is not None, only load the first `n_lines` lines of log
+        if it is not None, only load the first `n_lines` lines of log.
+    include_compatible: bool
+        When set to True, compatible records will also be considered.
     """
 
-    def __init__(self, records, n_lines=None):
+    def __init__(self, records, n_lines=None, include_compatible=False):
         super(ApplyHistoryBest, self).__init__()
+        self.include_compatible = include_compatible
 
+        # Dict[str (target key),
+        #   Dict[str (workload hash),
+        #     Dict[tuple (workload args), tuple (State, cost)]]]
         self.best_by_targetkey = {}
         self.best_by_model = {}
         self._best_user_defined = {}
 
         self.load(records, n_lines)
 
+    @staticmethod
+    def get_workload_entry(best_records, target_key, workload_key):
+        """Get the entry of the target key and workload key hash in the given best record map.
+
+        Parameters
+        ----------
+        best_records: Dict[str, Dict[str, Dict[str, Any]]]
+            The best record map.
+        target_key: str
+            The first key to the best_records.
+        workload_key: str
+            The workload key that can be decoded to workload hash and args.
+
+        Returns
+        -------
+        entry: Dict[str, Any]
+            The entry in best_records with target key and workload hash.
+        workload_hash: str
+            The workload hash decoded from workload_key.
+        workload_args: Tuple[Any, ...]
+            The hashable tuple of workload args decoded from workload_key.
+        """
+        workload_hash, workload_args = decode_workload_key(workload_key)
+        if target_key not in best_records:
+            best_records[target_key] = {}
+        if workload_hash not in best_records[target_key]:
+            best_records[target_key][workload_hash] = {}
+        return best_records[target_key][workload_hash], workload_hash, workload_args
+
     def load(self, records, n_lines=None):
         """Load records to this dispatch context
 
@@ -171,29 +212,32 @@ def load(self, records, n_lines=None):
             if res.error_no != 0:
                 continue
 
+            costs = [x.value for x in res.costs if isinstance(x, FloatImm)]
+            cost = np.mean(costs)
+
             # use target keys in tvm target system as key to build best map
             for k in inp.task.target.keys:
-                key = (k, inp.task.workload_key)
-                if key not in best_by_targetkey:
-                    best_by_targetkey[key] = (inp, res)
+                entry, _, workload_args = self.get_workload_entry(
+                    best_by_targetkey, k, inp.task.workload_key
+                )
+                if workload_args not in entry:
+                    entry[workload_args] = (inp.state, cost)
                 else:
-                    _, other_res = best_by_targetkey[key]
-                    other_costs = [x.value for x in other_res.costs if isinstance(x, FloatImm)]
-                    costs = [x.value for x in res.costs if isinstance(x, FloatImm)]
-                    if np.mean(other_costs) > np.mean(costs):
-                        best_by_targetkey[key] = (inp, res)
+                    _, other_cost = entry[workload_args]
+                    if other_cost > cost:
+                        entry[workload_args] = (inp.state, cost)
 
             # use model as key to build best map
-            key = (inp.task.target.model, inp.task.workload_key)
-            if key not in best_by_model:
+            entry, _, workload_args = self.get_workload_entry(
+                best_by_model, inp.task.target.model, inp.task.workload_key
+            )
+            if workload_args not in entry:
                 if inp.task.target.model != "unknown":
-                    best_by_model[key] = (inp, res)
+                    entry[workload_args] = (inp.state, cost)
             else:
-                _, other_res = best_by_model[key]
-                other_costs = [x.value for x in other_res.costs if isinstance(x, FloatImm)]
-                costs = [x.value for x in res.costs if isinstance(x, FloatImm)]
-                if np.mean(other_costs) > np.mean(costs):
-                    best_by_model[key] = (inp, res)
+                _, other_cost = entry[workload_args]
+                if other_cost > cost:
+                    entry[workload_args] = (inp.state, cost)
 
         logger.debug("Finish loading %d records", counter)
 
@@ -205,31 +249,147 @@ def _query_inside(self, target, workload_key):
                 " above the dispatcher call. So does other target. "
             )
 
+        def match_record(best_records, target_key, workload_key):
+            """The helper function to match the record in the given map
+            and return the matched state, or None if no match.
+            """
+            ret = None
+
+            entry, workload_hash, workload_args = self.get_workload_entry(
+                best_records, target_key, workload_key
+            )
+            if workload_args in entry:
+                ret = entry[workload_args][0]
+            elif self.include_compatible:
+                best_cost = float("inf")
+                for args, val in entry.items():
+                    dis_f = calc_workload_dis_factor(
+                        (workload_hash, workload_args), (workload_hash, args)
+                    )
+                    if dis_f == float("inf"):
+                        continue
+
+                    state, cost = val
+                    cost *= dis_f
+                    if ret is None or cost < best_cost:
+                        best_cost = cost
+                        ret = state
+            return ret
+
         # first try matching by model
-        key = (target.model, workload_key)
-        if key in self._best_user_defined:
-            return self._best_user_defined[key]
-        if key in self.best_by_model:
-            return self.best_by_model[key][0].state
+        ret = match_record(self._best_user_defined, target.model, workload_key)
+        if ret is not None:
+            return ret
+        ret = match_record(self.best_by_model, target.model, workload_key)
+        if ret is not None:
+            return ret
 
         # then try matching by target key
         for k in target.keys:
-            key = (k, workload_key)
-            if key in self._best_user_defined:
-                return self._best_user_defined[key]
-            if key in self.best_by_targetkey:
-                return self.best_by_targetkey[key][0].state
+            ret = match_record(self._best_user_defined, k, workload_key)
+            if ret is not None:
+                return ret
+            ret = match_record(self.best_by_targetkey, k, workload_key)
+            if ret is not None:
+                return ret
 
         return None
 
     def update(self, target, workload_key, state):
-        model = target.model
-        key = (model, workload_key)
-        self._best_user_defined[key] = state
+        entry, _, workload_args = self.get_workload_entry(
+            self._best_user_defined, target.model, workload_key
+        )
+        entry[workload_args] = (state, 1)
 
         for k in target.keys:
-            key = (k, workload_key)
-            self._best_user_defined[key] = state
+            entry, _, _ = self.get_workload_entry(self._best_user_defined, k, workload_key)
+            entry[workload_args] = (state, 1)
+
+
+class ApplyHistoryBestOrSample(ApplyHistoryBest):
+    """
+    Apply the history best config, or sample a valid schedule if no config is found.
+
+    Parameters
+    ----------
+    records : str or iterator of (auto_scheduler.measure.MeasureInput,\
+                                  auto_scheduler.measure.MeasureResult)
+        Collection of tuning records.
+        If is str, then it should be the filename of a records log file.
+        Each row of this file is an encoded record pair. Otherwise, it is an iterator.
+    sample_simple_workloads: bool
+        When False, sampling will not apply to simple workloads (w/o reduction).
+    cost_model_file: str
+        The filename of the pre-trained XGBoost cost model. If not present, then random
+        model will be used.
+    num_measure: int
+        Meausre the top-N rank of sampled schedules on the device. The default -1 means
+        no measurement and simply return the top-1 schedule ranked by the cost model.
+    """
+
+    def __init__(
+        self, records, sample_simple_workloads=False, cost_model_file=None, num_measure=-1
+    ):
+        self.sample_simple_workloads = sample_simple_workloads
+        self.num_measure = num_measure
+        self.log_dir = tempdir()
+        if cost_model_file is None:
+            self.cost_model = RandomModel()
+        else:
+            self.cost_model = XGBModel()
+            self.cost_model.load(cost_model_file)
+
+        super(ApplyHistoryBestOrSample, self).__init__(
+            records, n_lines=None, include_compatible=True
+        )
+
+    def query(self, target, workload_key, has_complex_op, dag):
+        if has_complex_op or self.sample_simple_workloads:
+            ret = self._query_inside(target, workload_key)
+        else:
+            ret = super(ApplyHistoryBestOrSample, self)._query_inside(target, workload_key)
+
+        if ret is None:
+            ret = self._old_ctx.query(target, workload_key, has_complex_op, dag)
+        return ret
+
+    def _query_inside(self, target, workload_key):
+        ret = super(ApplyHistoryBestOrSample, self)._query_inside(target, workload_key)
+        if ret is not None:
+            return ret
+
+        # Sampling valid schedules when no existing records can be used.
+        task = SearchTask(workload_key=workload_key, target=target)
+        measure_ctx = LocalRPCMeasureContext(min_repeat_ms=300)
+
+        log_file = self.log_dir.relpath("%s.log" % decode_workload_key(workload_key)[0])
+
+        while ret is None:
+            tune_option = TuningOptions(
+                num_measure_trials=self.num_measure,
+                runner=measure_ctx.runner,
+                measure_callbacks=[RecordToFile(log_file)],
+                verbose=0,
+            )
+            search_policy = SketchPolicy(
+                task,
+                self.cost_model,
+                params={
+                    "eps_greedy": 0.01,
+                    "sample_init_min_population": 64,
+                    "evolutionary_search_num_iters": 0,
+                },
+                init_search_callbacks=[PreloadMeasuredStates(log_file)],
+                verbose=0,
+            )
+            task.tune(tune_option, search_policy)
+
+            # Load the sampled records and query again.
+            self.load(log_file)
+            ret = super(ApplyHistoryBestOrSample, self)._query_inside(target, workload_key)
+
+        del measure_ctx
+        return ret
 
 
 class FallbackContext(DispatchContext):
diff --git a/python/tvm/auto_scheduler/feature.py b/python/tvm/auto_scheduler/feature.py
index 4c1883ad263f..ec7cf6334f98 100644
--- a/python/tvm/auto_scheduler/feature.py
+++ b/python/tvm/auto_scheduler/feature.py
@@ -80,7 +80,7 @@ def unpack_feature(byte_arr: bytearray) -> Tuple[np.ndarray, np.ndarray, np.ndar
       ... // until i == n - 1
 
       float throughputs[sizes[n]];  // The normalized throughputs for n records
-      int   task_ids[size[n+1];   // The task ids for n records
+      int   task_ids[size[n+1]];    // The task ids for n records
 
     }
     To implement this format, we also store int as float, so we can store all numbers
@@ -120,7 +120,7 @@ def unpack_feature(byte_arr: bytearray) -> Tuple[np.ndarray, np.ndarray, np.ndar
             tmp_vec_len = (size - 1) // n_stmts
             assert (
                 tmp_vec_len == vec_len
-            ), "The lenght of feature vector is wrong. " "Expected %d but got %d." % (
+            ), "The length of feature vector is wrong. Expected %d but got %d." % (
                 vec_len,
                 tmp_vec_len,
             )
@@ -135,7 +135,7 @@ def unpack_feature(byte_arr: bytearray) -> Tuple[np.ndarray, np.ndarray, np.ndar
     # unpack normalized_throughputs
     m = sizes[-2]
     normalized_throughputs = struct.unpack_from("%df" % m, byte_arr, offset=offset)
-    offset += m * SIZE_OF_INT32
+    offset += m * SIZE_OF_FLOAT32
 
     # unpack task_ids
     m = sizes[-1]
@@ -211,7 +211,7 @@ def get_per_store_features_from_measure_pairs(
 
 def get_per_store_features_from_states(
     states: List[Union[State, StateObject]], task: "SearchTask", max_n_bufs: Optional[int] = None
-) -> List[np.ndarray]:
+) -> np.ndarray:
     """Get per-store features from measurement input/result pairs
 
     Parameters
@@ -227,10 +227,6 @@ def get_per_store_features_from_states(
     -------
     features: np.ndarray
         Feature vectors
-    normalized_throughputs: np.ndarray
-        Normalized throughputs
-    task_ids: np.ndarray
-        Task ids
     """
     if isinstance(states[0], State):
         state_objects = [s.state_object for s in states]
diff --git a/python/tvm/auto_scheduler/measure.py b/python/tvm/auto_scheduler/measure.py
index 2f177a242835..d02dcff3bba0 100644
--- a/python/tvm/auto_scheduler/measure.py
+++ b/python/tvm/auto_scheduler/measure.py
@@ -36,6 +36,7 @@
 import shutil
 import tempfile
 import multiprocessing
+import logging
 
 import tvm._ffi
 from tvm.runtime import Object, module, ndarray
@@ -50,6 +51,7 @@
     call_func_with_timeout,
     check_remote,
     get_const_tuple,
+    get_func_name,
     make_traceback_info,
     request_remote,
 )
@@ -58,12 +60,26 @@
     deserialize_workload_registry_entry,
 )
 
+# pylint: disable=invalid-name
+logger = logging.getLogger("auto_scheduler")
 
 # The time cost for measurements with errors
 # We use 1e10 instead of sys.float_info.max for better readability in log
 MAX_FLOAT = 1e10
 
 
+class BuildFunc:
+    """store build_func name and callable to class variable.
+    name: str = "default"
+        The name of registered build function.
+    build_func: callable = tar.tar
+        The callable of registered build function.
+    """
+
+    name = "default"
+    build_func = tar.tar
+
+
 @tvm._ffi.register_object("auto_scheduler.MeasureCallback")
 class MeasureCallback(Object):
     """ The base class of measurement callback functions. """
@@ -211,6 +227,7 @@ def recover_measure_input(inp, rebuild_state=False):
         target_host=task.target_host,
         hardware_params=task.hardware_params,
         layout_rewrite_option=task.layout_rewrite_option,
+        task_inputs=list(task.task_input_names),
     )
 
     if rebuild_state:
@@ -303,12 +320,28 @@ class LocalBuilder(ProgramBuilder):
         This is used in a wrapper of the multiprocessing.Process.join().
     n_parallel : int = multiprocessing.cpu_count()
         Number of threads used to build in parallel.
-    build_func : str = 'default'
-        The name of registered build function.
+    build_func: callable or str = "default"
+        If is 'default', use default build function
+        If is 'ndk', use function for android ndk
+        If is callable, use it as custom build function, expect lib_format field.
     """
 
     def __init__(self, timeout=15, n_parallel=multiprocessing.cpu_count(), build_func="default"):
-        self.__init_handle_by_constructor__(_ffi_api.LocalBuilder, timeout, n_parallel, build_func)
+        if build_func == "default":
+            BuildFunc.name = "default"
+            BuildFunc.build_func = tar.tar
+        elif build_func == "ndk":
+            BuildFunc.name = "ndk"
+            BuildFunc.build_func = ndk.create_shared
+        elif callable(build_func):
+            BuildFunc.name = "custom"
+            BuildFunc.build_func = build_func
+        else:
+            raise ValueError("Invalid build_func" + build_func)
+
+        self.__init_handle_by_constructor__(
+            _ffi_api.LocalBuilder, timeout, n_parallel, BuildFunc.name
+        )
 
 
 @tvm._ffi.register_object("auto_scheduler.LocalRunner")
@@ -624,12 +657,10 @@ def local_build_worker(args):
         The build result of this Builder thread.
     """
     inp, build_func, timeout, verbose = args
-    if build_func == "default":
-        build_func = tar.tar
-    elif build_func == "ndk":
-        build_func = ndk.create_shared
-    else:
-        raise ValueError("Invalid build_func" + build_func)
+    assert build_func == BuildFunc.name, (
+        "BuildFunc.name: " + BuildFunc.name + ", but args is: " + build_func
+    )
+    build_func = BuildFunc.build_func
 
     res = call_func_with_timeout(timeout, _timed_func, args=(inp, build_func, verbose))
     if isinstance(res, TimeoutError):
@@ -693,6 +724,97 @@ def local_builder_build(inputs, timeout, n_parallel, build_func="default", verbo
     return results
 
 
+TASK_INPUT_CHECK_FUNC_REGISTRY = {}
+
+
+def register_task_input_check_func(func_name, f=None, override=False):
+    """Register a function that checks the input buffer map.
+
+    The input function should take a list of Tensor wich indicate the Input/output Tensor of a TVM
+    subgraph and return a Map from the input Tensor to its buffer name.
+
+    Parameters
+    ----------
+    func_name : Union[Function, str]
+        The check function that returns the compute declaration Tensors or its function name.
+    f : Optional[Function]
+        The check function to be registered.
+    override : boolean = False
+        Whether to override existing entry.
+
+    Examples
+    --------
+    .. code-block:: python
+
+      @auto_scheduler.register_task_input_check_func
+      def check_task_input_by_placeholder_name(args : List[Tensor]):
+          tensor_input_map = {}
+          for arg in args:
+              if isinstance(arg.op, tvm.te.PlaceholderOp):
+                  if arg.op.name != "placeholder":
+                      tensor_input_map[arg] = arg.op.name
+          return tensor_input_map
+    """
+    global TASK_INPUT_CHECK_FUNC_REGISTRY
+
+    if callable(func_name):
+        f = func_name
+        func_name = get_func_name(f)
+    if not isinstance(func_name, str):
+        raise ValueError("expect string function name")
+
+    def register(myf):
+        """internal register function"""
+        if func_name in TASK_INPUT_CHECK_FUNC_REGISTRY and not override:
+            raise RuntimeError("%s has been registered already" % func_name)
+        TASK_INPUT_CHECK_FUNC_REGISTRY[func_name] = myf
+        return myf
+
+    if f:
+        return register(f)
+    return register
+
+
+def _prepare_input_map(args):
+    """This function deals with special task inputs. Map the input Tensor of a TVM subgraph
+    to a specific buffer name in the global buffer map.
+
+    Parameters
+    ----------
+    args : List[Tensor]
+        Input/output Tensor of a TVM subgraph.
+
+    Returns
+    -------
+    Dict[Tensor, str] :
+        Map from the input Tensor to its buffer name.
+
+    Notes
+    -----
+    The buffer name is specially designed, and these buffer should be provided in
+    `SearchTask(..., task_inputs={...})`.
+    """
+    # pylint: disable=import-outside-toplevel
+
+    global TASK_INPUT_CHECK_FUNC_REGISTRY
+
+    # A dict that maps the input tensor arg to a buffer name
+    tensor_input_map = {}
+
+    # Case 0: Check placeholder name
+    for arg in args:
+        if isinstance(arg.op, tvm.te.PlaceholderOp):
+            if arg.op.name != "placeholder":
+                tensor_input_map[arg] = arg.op.name
+
+    # Case 1: Check specific tensor inputs
+    for func_name in TASK_INPUT_CHECK_FUNC_REGISTRY:
+        func = TASK_INPUT_CHECK_FUNC_REGISTRY[func_name]
+        tensor_input_map.update(func(args))
+
+    return tensor_input_map
+
+
 def _timed_eval_func(
     inp_serialized,
     build_res,
@@ -703,7 +825,11 @@ def _timed_eval_func(
     enable_cpu_cache_flush,
     verbose,
 ):
+    # pylint: disable=import-outside-toplevel
+    from .search_task import get_task_input_buffer  # lazily import to avoid recursive dependency
+
     inp = MeasureInput.deserialize(inp_serialized)
+    task_input_names = inp.task.task_input_names
     tic = time.time()
     error_no = 0
     error_msg = None
@@ -732,11 +858,35 @@ def _timed_eval_func(
 
     if error_no == 0:
         try:
-            args = [ndarray.empty(get_const_tuple(x.shape), x.dtype, ctx) for x in build_res.args]
             random_fill = tvm.get_global_func("tvm.contrib.random.random_fill", True)
             assert random_fill, "Please make sure USE_RANDOM is ON in the config.cmake"
-            for arg in args:
-                random_fill(arg)
+
+            tensor_input_map = _prepare_input_map(build_res.args) if task_input_names else {}
+            args = []
+            task_inputs_count = 0
+            for arg in build_res.args:
+                if arg in tensor_input_map:
+                    tensor_name = tensor_input_map[arg]
+                    if tensor_name in task_input_names:
+                        args.append(
+                            ndarray.array(
+                                get_task_input_buffer(inp.task.workload_key, tensor_name), ctx
+                            )
+                        )
+                        task_inputs_count += 1
+                    else:
+                        raise ValueError(
+                            "%s not found in task_inputs, " % (tensor_name)
+                            + "should provide with `SearchTask(..., task_inputs={...})`"
+                        )
+                else:
+                    empty_array = ndarray.empty(get_const_tuple(arg.shape), arg.dtype, ctx)
+                    random_fill(empty_array)
+                    args.append(empty_array)
+            if task_inputs_count != len(task_input_names):
+                logger.warning(
+                    "task_inputs not fully matched, check if there's any unexpected error"
+                )
             ctx.sync()
             costs = time_f(*args).results
         # pylint: disable=broad-except
@@ -885,7 +1035,11 @@ def _timed_rpc_run(
     enable_cpu_cache_flush,
     verbose,
 ):
+    # pylint: disable=import-outside-toplevel
+    from .search_task import get_task_input_buffer  # lazily import to avoid recursive dependency
+
     inp = MeasureInput.deserialize(inp_serialized)
+    task_input_names = inp.task.task_input_names
     tic = time.time()
     error_no = 0
     error_msg = None
@@ -917,18 +1071,40 @@ def _timed_rpc_run(
 
     if error_no == 0:
         try:
-            args = [ndarray.empty(get_const_tuple(x.shape), x.dtype, ctx) for x in build_res.args]
-            try:
-                random_fill = remote.get_function("tvm.contrib.random.random_fill")
-            except AttributeError:
-                raise AttributeError(
-                    "Please make sure USE_RANDOM is ON in the config.cmake " "on the remote devices"
+            random_fill = remote.get_function("tvm.contrib.random.random_fill")
+            assert (
+                random_fill
+            ), "Please make sure USE_RANDOM is ON in the config.cmake on the remote devices"
+
+            tensor_input_map = _prepare_input_map(build_res.args) if task_input_names else {}
+            args = []
+            task_inputs_count = 0
+            for arg in build_res.args:
+                if arg in tensor_input_map:
+                    tensor_name = tensor_input_map[arg]
+                    if tensor_name in task_input_names:
+                        args.append(
+                            ndarray.array(
+                                get_task_input_buffer(inp.task.workload_key, tensor_name), ctx
+                            )
+                        )
+                        task_inputs_count += 1
+                    else:
+                        raise ValueError(
+                            "%s not found in task_inputs, " % (tensor_name)
+                            + "should provide with `SearchTask(..., task_inputs={...})`"
+                        )
+                else:
+                    empty_array = ndarray.empty(get_const_tuple(arg.shape), arg.dtype, ctx)
+                    random_fill(empty_array)
+                    args.append(empty_array)
+            if task_inputs_count != len(task_input_names):
+                logger.warning(
+                    "task_inputs not fully matched, check if there's any unexpected error"
                 )
-            for arg in args:
-                random_fill(arg)
             ctx.sync()
-
             costs = time_f(*args).results
+
             # clean up remote files
             remote.remove(build_res.filename)
             remote.remove(os.path.splitext(build_res.filename)[0] + ".so")
diff --git a/python/tvm/auto_scheduler/measure_record.py b/python/tvm/auto_scheduler/measure_record.py
index 35e5e9b68a43..ee671cd9b23a 100644
--- a/python/tvm/auto_scheduler/measure_record.py
+++ b/python/tvm/auto_scheduler/measure_record.py
@@ -27,6 +27,7 @@
 import tvm._ffi
 from tvm.runtime import Object
 from .measure import MeasureErrorNo, MeasureCallback
+from .utils import calc_workload_dis_factor, decode_workload_key
 from . import _ffi_api
 
 logger = logging.getLogger("auto_scheduler")
@@ -59,8 +60,37 @@ class RecordReader(Object):
     """
 
     def __init__(self, filename):
+        # a set to prevent print duplicated message
+        self.messages = set()
+
         self.__init_handle_by_constructor__(_ffi_api.RecordReader, filename)
 
+    def check_workload_key(self, inputs):
+        """Check and throw warnings for records with old format workload key.
+
+        Parameters
+        ----------
+        inputs: List[MeasureInput]
+            The measure inputs to be checked.
+
+        Notes
+        -----
+        This checker could be deprecated in the future.
+        """
+        for inp in inputs:
+            _, args = decode_workload_key(inp.task.workload_key)
+            if args is None:
+                continue
+            if not args:
+                msg = (
+                    "MeasureInput with old format workload key %s should be updated "
+                    "using the script from https://github.com/apache/tvm/pull/7317."
+                    % inp.task.workload_key
+                )
+                if msg not in self.messages:
+                    self.messages.add(msg)
+                    logger.warning(msg)
+
     def read_lines(self, max_lines=None, skip_lines=0):
         """Read multiple lines from the log file.
 
@@ -88,6 +118,7 @@ def read_lines(self, max_lines=None, skip_lines=0):
         inputs, results = _ffi_api.RecordReaderReadLines(
             self, max_lines if max_lines else -1, skip_lines
         )
+        self.check_workload_key(inputs)
         return inputs, results
 
     def __iter__(self):
@@ -95,6 +126,7 @@ def __iter__(self):
             ret = _ffi_api.RecordReaderReadNext(self)
             if not ret:
                 break
+            self.check_workload_key([ret[0]])
             yield ret[0], ret[1]  # (input, result)
 
 
@@ -174,7 +206,7 @@ def save_records(filename, inputs, results):
     _ffi_api.SaveRecords(filename, inputs, results)
 
 
-def load_best_record(filename, workload_key=None, target=None):
+def load_best_record(filename, workload_key=None, target=None, include_compatible=False):
     """Return the best measurement pair form a log file. This may return none results if
     there is no legal measure pair with the specified workload_key/target found from the log file.
 
@@ -188,6 +220,8 @@ def load_best_record(filename, workload_key=None, target=None):
     target : Optional[tvm.target.Target]
         The target device.
         With `None`, this returns the best measure pair of all target devices.
+    include_compatible: bool
+        When set to True, all compatible records in the log file will be considered.
 
     Returns
     -------
@@ -204,13 +238,25 @@ def load_best_record(filename, workload_key=None, target=None):
     for inp, res in log_reader:
         if res.error_no != MeasureErrorNo.NO_ERROR:
             continue
-        if workload_key and inp.task.workload_key != workload_key:
-            continue
         if target and inp.task.target.kind.name != target.kind.name:
             continue
 
         costs = [v.value for v in res.costs]
         cost = np.mean(costs)
+
+        if workload_key is not None:
+            dis_f = calc_workload_dis_factor(
+                decode_workload_key(workload_key), decode_workload_key(inp.task.workload_key)
+            )
+            if dis_f == float("inf"):
+                continue
+            if not include_compatible and dis_f != 1:
+                continue
+
+            # Since different workloads have different FLOPS, we multiply the factor to
+            # eliminate this difference, which is basically the concept of throughput.
+            cost *= dis_f
+
         if cost < best_cost:
             best_cost = cost
             best_inp = inp
@@ -240,26 +286,42 @@ def distill_record_file(in_file, out_file):
     if os.path.isfile(out_file):
         out_context = load_records(out_file)
         context = itertools.chain(context, out_context)
-    context, context_clone = itertools.tee(context)
-    best_context = ApplyHistoryBest(context)
-    best_set = set()
 
     def measure_input_str_key(inp):
         return _ffi_api.SerializeMeasureInput(inp)
 
-    for v in best_context.best_by_model.values():
-        best_set.add(measure_input_str_key(v[0]))
+    # Dict[target key,
+    #   Dict[workload hash,
+    #     Dict[workload args, (cost, (MeasureInput, MeasureResult))]]]
+    # Full type: Dict[str, Dict[str, Dict[Tuple, Tuple[float, Tuple[Measureinput, MeasureResult]]]]]
+    best_records = {}
+
+    for inp, res in context:
+        if res.error_no != 0:
+            continue
 
-    for v in best_context.best_by_targetkey.values():
-        best_set.add(measure_input_str_key(v[0]))
+        # Keep the best record for each target and workload.
+        costs = [x.value for x in res.costs if isinstance(x, tvm.tir.expr.FloatImm)]
+        cost = np.mean(costs)
+        for k in inp.task.target.keys:
+            entry, _, workload_args = ApplyHistoryBest.get_workload_entry(
+                best_records, k, inp.task.workload_key
+            )
+            if workload_args not in entry or cost < entry[workload_args][0]:
+                entry[workload_args] = (cost, (inp, res))
+
+    # Remove duplications by multiple target keys.
+    out_records = {}
+    for target_entry in best_records.values():
+        for workload_entry in target_entry.values():
+            for _, (inp, res) in workload_entry.values():
+                out_records[measure_input_str_key(inp)] = (inp, res)
 
     inputs = []
     results = []
-    for inp, res in context_clone:
-        if measure_input_str_key(inp) in best_set:
-            inputs.append(inp)
-            results.append(res)
-            best_set.remove(measure_input_str_key(inp))
+    for inp, res in out_records.values():
+        inputs.append(inp)
+        results.append(res)
 
     # create a new file and save the best records
     open(out_file, "w")
@@ -267,21 +329,26 @@ def measure_input_str_key(inp):
     logger.info("Extract %d best records from %s to %s", len(inputs), in_file, out_file)
 
 
-"""
-Usage:
-* Distill the best entries from a large log file
-e.g. python -m tvm.auto_scheduler.measure_record --mode distill --i input.json
-"""
-if __name__ == "__main__":
+def main():
+    """The main function for CLI."""
     parser = argparse.ArgumentParser()
-    parser.add_argument("--mode", choices=["distill"], required=True)
-    parser.add_argument("--i", type=str, help="input file")
-    parser.add_argument("--o", type=str, default=None, help="output file")
+    parser.add_argument("--mode", choices=["distill"], default="distill")
+    parser.add_argument("-i", "--input", type=str, help="input file")
+    parser.add_argument("-o", "--output", type=str, default=None, help="output file")
 
     args = parser.parse_args()
     logging.basicConfig()
     logger.setLevel(logging.INFO)
 
     if args.mode == "distill":
-        args.o = args.o or args.i + ".best.json"
-        distill_record_file(args.i, args.o)
+        args.output = args.output or args.input + ".best.json"
+        distill_record_file(args.input, args.output)
+
+
+"""
+Usage:
+* Distill the best entries from a large log file
+e.g. python -m tvm.auto_scheduler.measure_record --mode distill -i input.json
+"""
+if __name__ == "__main__":
+    main()
diff --git a/python/tvm/auto_scheduler/relay_integration.py b/python/tvm/auto_scheduler/relay_integration.py
index 3287f3d4a1e5..6cce30f2f559 100644
--- a/python/tvm/auto_scheduler/relay_integration.py
+++ b/python/tvm/auto_scheduler/relay_integration.py
@@ -23,19 +23,21 @@
 """
 
 import logging
-import json
 import threading
 
 import tvm
-from tvm import autotvm, te, transform
+from tvm import autotvm, transform
 from tvm.ir.transform import PassContext
 from tvm.runtime import convert_to_object
 from tvm.te.tensor import ComputeOp, PlaceholderOp, Tensor
+from tvm.tir import Reduce
 from tvm.tir import expr as _expr
+
 from . import _ffi_api
 from .compute_dag import ComputeDAG, LayoutRewriteOption
 from .dispatcher import DispatchContext
 from .search_task import SearchTask
+from .utils import get_const_tuple
 from .workload_registry import register_workload_tensors
 
 logger = logging.getLogger("auto_scheduler")
@@ -53,12 +55,26 @@ def call_all_topi_funcs(mod, params, target):
 
     with transform.PassContext(
         opt_level=3,
-        config={"relay.backend.use_auto_scheduler": True},
+        config={
+            "relay.backend.use_auto_scheduler": True,
+            "relay.backend.disable_compile_engine_cache": True,
+        },
         disabled_pass={"AutoSchedulerLayoutRewrite"},
     ):
-        opt_mod, _ = relay.optimize(mod, target, params)
-        grc = graph_runtime_codegen.GraphRuntimeCodegen(None, target)
-        grc.codegen(opt_mod["main"])
+        try:
+            opt_mod, _ = relay.optimize(mod, target, params)
+            grc = graph_runtime_codegen.GraphRuntimeCodegen(None, target)
+            grc.codegen(opt_mod["main"])
+        except tvm.TVMError:
+            print(
+                "Get errors with GraphRuntimeCodegen for task extraction. "
+                "Fallback to VMCompiler."
+            )
+            compiler = relay.vm.VMCompiler()
+            if params:
+                compiler.set_params(params)
+            mod = tvm.IRModule.from_expr(mod) if isinstance(mod, relay.Function) else mod
+            compiler.lower(mod, target)
 
     autotvm.GLOBAL_SCOPE.silent = old_autotvm_silent
 
@@ -91,7 +107,6 @@ def extract_tasks(
         The weight (i.e. the number of appearance) of extracted tasks
     """
     # pylint: disable=import-outside-toplevel
-    from tvm import relay
 
     if isinstance(target, str):
         target = tvm.target.Target(target)
@@ -102,24 +117,22 @@ def extract_tasks(
     env = TracingEnvironment(
         TracingMode.EXTRACT_TASK if include_simple_tasks else TracingMode.EXTRACT_COMPLEX_TASK_ONLY
     )
+
+    dispatch_ctx = DispatchContext.current
+    old_verbose = dispatch_ctx.verbose
+    dispatch_ctx.verbose = 0
     with env:
         # Wrap build call in a new thread to avoid the conflict
         # between python's multiprocessing and tvm's thread pool
         build_thread = threading.Thread(target=call_all_topi_funcs, args=(mod, params, target))
         build_thread.start()
         build_thread.join()
-
-    # query the compile engine to get the number of occurrence of all tasks
-    engine = relay.backend.compile_engine.get()
-    use_count_dict = {}
-    for k, v in engine.items():
-        use_count_dict[k] = v.use_count
+    dispatch_ctx.verbose = old_verbose
 
     # create search tasks
     tasks = []
     weights = []
-    for wkl_key, ccache_key in env.wkl_key_to_ccache_key.items():
-        dag = ComputeDAG(wkl_key)
+    for wkl_key, weight in env.wkl_key_to_weight.items():
         tasks.append(
             SearchTask(
                 workload_key=wkl_key,
@@ -131,10 +144,7 @@ def extract_tasks(
                 layout_rewrite_option=LayoutRewriteOption.get_target_default(target, True),
             )
         )
-        weights.append(use_count_dict[ccache_key] + 1)
-
-    # clean the cached lowering results
-    engine.clear()
+        weights.append(weight)
 
     return tasks, weights
 
@@ -155,7 +165,7 @@ class TracingEnvironment:
     def __init__(self, tracing_mode):
         self.tracing_mode = tracing_mode
         self.relay_disable_build_cache = "false"
-        self.wkl_key_to_ccache_key = {}
+        self.wkl_key_to_weight = {}
 
     def __enter__(self):
         TracingEnvironment.current = self
@@ -164,17 +174,17 @@ def __enter__(self):
     def __exit__(self, exc_type, exc_val, exc_tb):
         TracingEnvironment.current = None
 
-    def add_workload_key(self, workload_key, ccache_key):
+    def add_workload_key(self, workload_key):
         """Add the workload key of a search task
 
         Parameters
         ----------
         workload_key: str
             The workload key of a task
-        ccache_key: CCacheKey
-            The corresponding ccache_key of the task
         """
-        self.wkl_key_to_ccache_key[workload_key] = ccache_key
+        if workload_key not in self.wkl_key_to_weight:
+            self.wkl_key_to_weight[workload_key] = 0
+        self.wkl_key_to_weight[workload_key] += 1
 
 
 @tvm._ffi.register_func("auto_scheduler.enter_layout_rewrite")
@@ -192,7 +202,8 @@ def exit_layout_rewrite():
 
 
 def traverse_to_get_io_tensors(outs):
-    """Traverse from a list of output tensors to get both input and output tensors
+    """Traverse from a list of output tensors to get input/output tensors and
+    other useful information.
 
     Parameters
     ----------
@@ -202,36 +213,50 @@ def traverse_to_get_io_tensors(outs):
     Returns
     -------
     io_tensors: List[Tensor]
-        The input and output tensors
+        The input and output tensors with static shape
     has_layout_free: bool
         Whether the compute DAG has layout_free placeholders
+    has_complex_op: bool
+        Whether the topi compute function includes at least one complex (reduce) op
     """
     layout_free_ops = []
     inputs = []
 
+    has_complex_op = False
     visited = set()
 
     def traverse(t):
-        if t in visited:
+        nonlocal has_complex_op
+
+        # We cannot directly add tensors to the set, because the comparison of
+        # two tensors with ndim=0 is ambiguous.
+        assert t.handle is not None
+        if t.handle.value in visited:
             return
         if isinstance(t.op, PlaceholderOp):
             inputs.append(t)
         elif isinstance(t.op, ComputeOp):
+            has_complex_op = has_complex_op or any([isinstance(e, Reduce) for e in t.op.body])
             if "layout_free_placeholders" in t.op.attrs:
                 layout_free_ops.append(t.op)
             for x in t.op.input_tensors:
                 traverse(x)
-        visited.add(t)
+        visited.add(t.handle.value)
 
     for t in outs:
         traverse(t)
 
-    has_layout_free = len(layout_free_ops) > 0
-    return inputs + list(outs), has_layout_free
+    io_tensors = inputs + list(outs)
+    for tensor in io_tensors:
+        # Reject the compute if any of its I/O tensors has dynamic shape.
+        if any([not isinstance(v, int) for v in get_const_tuple(tensor.shape)]):
+            return ([], False, False)
+
+    return (io_tensors, len(layout_free_ops) > 0, has_complex_op)
 
 
 @tvm._ffi.register_func("auto_scheduler.relay_integration.auto_schedule_topi_compute")
-def auto_schedule_topi(outs, has_complex_op):
+def auto_schedule_topi(outs):
     """Use auto-scheduler to schedule any topi compute function.
 
     Note: This is used internally for relay integration. Do
@@ -241,62 +266,59 @@ def auto_schedule_topi(outs, has_complex_op):
     ----------
     outs: List[Tensor]
         The output tensors of topi compute functions
-    has_complex_op: bool
-        Whether the topi compute function includes at least one complex op.
 
     Returns
     -------
     sch: Optional[te.Schedule]
         A tuned schedule or none (if not tuned) in the final build mode;
-        An initial schdule in the tracing mode.
+        None in the tracing mode so that the fallback topi schedule will be used.
     """
     # pylint: disable=import-outside-toplevel
-    from tvm import relay
 
-    io_tensors, has_layout_free = traverse_to_get_io_tensors(outs)
+    io_tensors, has_layout_free, has_complex_op = traverse_to_get_io_tensors(outs)
+    if not io_tensors:  # The compute includes dynamic shapes which are not supported yet.
+        return None
+
     try:
         dag = ComputeDAG(io_tensors)
     except tvm.error.TVMError as err:
         logger.info("Failed to create a ComputeDAG for auto_scheduler: %s", str(err))
         return None
 
-    key = register_workload_tensors(dag.hash_key(), io_tensors)
-
+    key = register_workload_tensors(dag.workload_key(), io_tensors)
     target = tvm.target.Target.current()
 
+    dispatch_ctx = DispatchContext.current
+    state = dispatch_ctx.query(target, key, has_complex_op, dag)
+    schedule = None
+
     env = TracingEnvironment.current
     if env is None:
         # in the final build mode
-        state = DispatchContext.current.query(target, key, has_complex_op, dag)
         if state is None:
             return None
 
         schedule, _ = dag.apply_steps_from_state(state)
-    elif env.tracing_mode in [TracingMode.EXTRACT_TASK, TracingMode.EXTRACT_COMPLEX_TASK_ONLY]:
+        return schedule
+
+    if env.tracing_mode in [TracingMode.EXTRACT_TASK, TracingMode.EXTRACT_COMPLEX_TASK_ONLY]:
         # in the task extraction mode
         if has_complex_op or env.tracing_mode == TracingMode.EXTRACT_TASK:
-            engine = relay.backend.compile_engine.get()
-            ccache_key = engine.get_current_ccache_key()
-            env.add_workload_key(key, ccache_key)
-        schedule = te.create_schedule([x.op for x in outs])
+            env.add_workload_key(key)
     elif env.tracing_mode == TracingMode.PREPARE_LAYOUT_REWRITE:
         # in prepare_layout_rewrite mode
         if (
             LayoutRewriteOption.get_target_default(target, True) != LayoutRewriteOption.NO_REWRITE
             and has_layout_free
         ):
-            dispatch_ctx = DispatchContext.current
-            state = dispatch_ctx.query(target, key, has_complex_op, dag)
             if state is None:
                 return None
 
             # rewrite the layout and update the context for the new dag
-            dag = ComputeDAG(outs)
             new_dag = dag.rewrite_layout_from_state(state)
-            new_key = json.dumps((new_dag.hash_key(),))
+            new_key = new_dag.workload_key()
             if new_key != key:
                 dispatch_ctx.update(target, new_key, state)
-        return te.create_schedule([x.op for x in outs])
     else:
         raise ValueError("Invalid tracing mode: " + env.tracing_mode)
 
diff --git a/python/tvm/auto_scheduler/search_policy.py b/python/tvm/auto_scheduler/search_policy.py
index 5b15a48943d2..f0388a886c5f 100644
--- a/python/tvm/auto_scheduler/search_policy.py
+++ b/python/tvm/auto_scheduler/search_policy.py
@@ -61,6 +61,39 @@ def __init__(self, filename):
         self.__init_handle_by_constructor__(_ffi_api.PreloadMeasuredStates, filename)
 
 
+@tvm._ffi.register_object("auto_scheduler.PreloadCustomSketchRule")
+class PreloadCustomSketchRule(SearchCallback):
+    """
+    A SearchCallback for SketchSearchPolicy that allows users to add
+    custom sketch rule.
+
+    Notes
+    -----
+    This is an advanced feature. Make sure you're clear how it works and this should only be used
+    in SketchSearchPolicy.
+
+    Parameters
+    ----------
+    meet_condition_func: Callable
+        A function with `(policy, state, stage_id) -> int`. Should return one of the result
+        enumeration.
+    apply_func: Callable
+        A function with `(policy, state, stage_id) -> [[State, int], ...]`.
+    rule_name: str = "CustomSketchRule"
+        The name of this custom sketch rule.
+    """
+
+    # Result enumeration of the condition function.
+    PASS = 0  # Skip this rule and continue to try the next rules.
+    APPLY = 1  # Apply this rule and continue to try the next rules.
+    APPLY_AND_SKIP_REST = 2  # Apply this rule and skip the rest rules.
+
+    def __init__(self, meet_condition_func, apply_func, rule_name="CustomSketchRule"):
+        self.__init_handle_by_constructor__(
+            _ffi_api.PreloadCustomSketchRule, meet_condition_func, apply_func, rule_name
+        )
+
+
 @tvm._ffi.register_object("auto_scheduler.SearchPolicy")
 class SearchPolicy(Object):
     """ The base class of search policies. """
@@ -141,8 +174,6 @@ class SketchPolicy(SearchPolicy):
 
           - auto_scheduler.PreloadMeasuredStates
           - auto_scheduler.PreloadCustomSketchRule
-
-        TODO(jcf94): Add these search callback implementations.
     """
 
     DEFAULT_PARAMS = {
diff --git a/python/tvm/auto_scheduler/search_task.py b/python/tvm/auto_scheduler/search_task.py
index bfa596a1dc61..57e239cf79e8 100644
--- a/python/tvm/auto_scheduler/search_task.py
+++ b/python/tvm/auto_scheduler/search_task.py
@@ -19,8 +19,12 @@
 
 import json
 
+import os
+import logging
+import numpy as np
+
 import tvm._ffi
-from tvm.runtime import Object
+from tvm.runtime import Object, ndarray
 
 from tvm.driver.build_module import build
 from tvm.target import Target
@@ -30,9 +34,12 @@
 from .compute_dag import ComputeDAG, LayoutRewriteOption
 from .cost_model import XGBModel
 from .search_policy import SketchPolicy
-from .workload_registry import register_workload_tensors
+from .workload_registry import WORKLOAD_FUNC_REGISTRY, register_workload_tensors
 from . import _ffi_api
 
+# pylint: disable=invalid-name
+logger = logging.getLogger("auto_scheduler")
+
 
 @tvm._ffi.register_object("auto_scheduler.HardwareParams")
 class HardwareParams(Object):
@@ -157,6 +164,156 @@ def __init__(
         )
 
 
+# The map stores special registered buffer for measurement.
+# This can be used for sparse workloads when we cannot use random tensors for measurment.
+# {
+#     "workload_key_0": {
+#         "task_input_0": Tensor(...),
+#         "task_input_1": Tensor(...)
+#     },
+#     "workload_key_1": {
+#         "task_input_2": Tensor(...),
+#         "task_input_3": Tensor(...)
+#     },
+#     ...
+# }
+TASK_INPUT_BUFFER_TABLE = {}
+
+
+def _save_buffer_to_file(buffer_name, buffer_data):
+    """Save the current Tensor buffer to a numpy file.
+
+    File name will be: {buffer_name}.{buffer_shape}_{buffer_data_type}.npy
+    """
+    np_data = buffer_data.asnumpy()
+
+    buffer_name += "."
+    for i in np_data.shape:
+        buffer_name += "%d_" % (i)
+    buffer_name += "%s" % (np_data.dtype)
+    buffer_name += ".npy"
+
+    np_data.tofile(buffer_name, " ")
+
+
+def _try_load_buffer_from_file(buffer_name):
+    """Try to load buffer from a numpy file, if not found, return None.
+
+    File name has a same format as `_save_buffer_to_file`.
+    """
+    filelist = os.listdir()
+
+    for file in filelist:
+        if file.startswith(buffer_name + "."):
+            meta_info = file.split(".")[-2].split("_")
+            shape = [int(i) for i in meta_info[:-1]]
+            dtype = meta_info[-1]
+            buffer_data = np.fromfile(file, dtype=dtype, sep=" ")
+            buffer_data = buffer_data.reshape(shape)
+            return ndarray.array(buffer_data)
+
+    return None
+
+
+def register_task_input_buffer(
+    workload_key,
+    input_name,
+    input_data,
+    overwrite=False,
+    save_to_file=False,
+):
+    """Register special buffer for measurement.
+
+    Parameters
+    ----------
+    workload_key : str
+        The workload key of the SearchTask.
+
+    input_name : str
+        The name of input buffer.
+
+    input_data : tvm.nd.NDArray
+        The input Tensor data.
+
+    overwrite : bool = False
+        Whether to overwrite the data if a name has already registered.
+
+    save_to_file : bool = False
+        Whether to save the data to a local file as well. This can be reused to resume the last
+        tuning process.
+
+    Returns
+    -------
+    tvm.nd.NDArray
+        The actual registered Tensor data of this input_name. With `overwrite` set to False, will
+        return the original one if the name has already registered before.
+    """
+    global TASK_INPUT_BUFFER_TABLE
+
+    if workload_key not in TASK_INPUT_BUFFER_TABLE:
+        TASK_INPUT_BUFFER_TABLE[workload_key] = {}
+    input_table = TASK_INPUT_BUFFER_TABLE[workload_key]
+
+    if not overwrite:
+        if input_name not in input_table.keys():
+            # Try to load buffer data from local file
+            tensor_from_file = _try_load_buffer_from_file(input_name)
+            if tensor_from_file:
+                input_table[input_name] = tensor_from_file
+
+        if input_name in input_table.keys():
+            logger.warning(
+                "Tensor %s exists in TASK_INPUT_BUFFER_TABLE, %s",
+                input_name,
+                "set overwrite to True or this Tensor will not be registered",
+            )
+            return input_table[input_name]
+
+    input_table[input_name] = input_data
+    if save_to_file:
+        _save_buffer_to_file(input_name, input_data)
+    return input_data
+
+
+def get_task_input_buffer(workload_key, input_name):
+    """Get special buffer for measurement.
+
+    The buffers are registered by `register_task_input_buffer`.
+
+    Parameters
+    ----------
+    workload_key : str
+        The workload key of the SearchTask.
+
+    input_name : str
+        The name of input buffer.
+
+    Returns
+    -------
+    tvm.nd.NDArray
+        The registered input buffer.
+    """
+    global TASK_INPUT_BUFFER_TABLE
+
+    if workload_key not in TASK_INPUT_BUFFER_TABLE:
+        TASK_INPUT_BUFFER_TABLE[workload_key] = {}
+    input_table = TASK_INPUT_BUFFER_TABLE[workload_key]
+
+    if input_name not in input_table.keys():
+        # Try to load buffer data from local file
+        tensor_from_file = _try_load_buffer_from_file(input_name)
+        if tensor_from_file:
+            input_table[input_name] = tensor_from_file
+
+    if input_name in input_table.keys():
+        return input_table[input_name]
+
+    raise ValueError(
+        "%s not found in TASK_INPUT_BUFFER_TABLE, " % (input_name)
+        + "should provide with `SearchTask(..., task_inputs={...})`"
+    )
+
+
 @tvm._ffi.register_object("auto_scheduler.SearchTask")
 class SearchTask(Object):
     """The computation information and hardware parameters for a schedule search task.
@@ -185,6 +342,16 @@ class SearchTask(Object):
         The NO_REWRITE and INSERT_TRANSFORM_STAGE are expected to be used when tuning a standalone
         op, and the REWRITE_FOR_PRE_TRANSFORMED is expected to be used when tuning ops inside a
         network.
+    task_inputs : Union[Dict[str, tvm.nd.NDArray], List[str]]
+        A dict maps the input names to input tensors or a list of input names.
+        Some special Tensor used as inputs in program measuring. Usually we do not need to care
+        about it, but for special workloads like Sparse computation the Sparse Tensor input are
+        meaningful that we cannot use random input directly.
+    task_inputs_overwrite : bool = False
+        Whether to overwrite the data if a name has already in the global table.
+    task_inputs_save_to_file : bool = False
+        Whether to save the data to a local file as well. This can be reused to resume the last
+        tuning process.
 
     Examples
     --------
@@ -212,6 +379,9 @@ def __init__(
         target_host=None,
         hardware_params=None,
         layout_rewrite_option=None,
+        task_inputs=None,
+        task_inputs_overwrite=False,
+        task_inputs_save_to_file=False,
     ):
         assert (
             func is not None or workload_key is not None
@@ -228,6 +398,25 @@ def __init__(
         if isinstance(target_host, str):
             target_host = Target(target_host)
 
+        if layout_rewrite_option is None:
+            layout_rewrite_option = LayoutRewriteOption.get_target_default(target)
+
+        task_input_names = []
+        if isinstance(task_inputs, list):
+            task_input_names = task_inputs
+        elif isinstance(task_inputs, dict):
+            for input_name in task_inputs:
+                register_task_input_buffer(
+                    workload_key,
+                    input_name,
+                    task_inputs[input_name],
+                    task_inputs_overwrite,
+                    task_inputs_save_to_file,
+                )
+                task_input_names.append(input_name)
+        elif task_inputs is not None:
+            raise ValueError("task_inputs should be a dict or a list.")
+
         self.__init_handle_by_constructor__(
             _ffi_api.SearchTask,
             compute_dag,
@@ -235,7 +424,8 @@ def __init__(
             target,
             target_host,
             hardware_params,
-            layout_rewrite_option or LayoutRewriteOption.get_target_default(target),
+            layout_rewrite_option,
+            task_input_names,
         )
 
     def tune(self, tuning_options, search_policy=None):
@@ -254,13 +444,15 @@ def tune(self, tuning_options, search_policy=None):
 
         _ffi_api.AutoSchedule(search_policy, tuning_options)
 
-    def apply_best(self, log_file, layout_rewrite_option=None):
+    def apply_best(self, log_file, include_compatible=False, layout_rewrite_option=None):
         """Apply the history best from a log file and return the schedule.
 
         Parameters
         ----------
         log_file : str
            The name of the log file.
+        include_compatible: bool
+            When set to True, all compatible records in the log file will be considered.
         layout_rewrite_option : Optional[LayoutRewriteOption]
            The layout rewrite option.
 
@@ -269,7 +461,9 @@ def apply_best(self, log_file, layout_rewrite_option=None):
         -------
             A `te.Schedule` and the a list of `te.Tensor` to be used in `tvm.lower` or `tvm.build`.
         """
-        inp, _ = load_best_record(log_file, self.workload_key)
+        inp, _ = load_best_record(
+            log_file, self.workload_key, include_compatible=include_compatible
+        )
         if inp is None:
             raise RuntimeError(
                 "Cannot find any valid schedule for %s in file %s" % (self.workload_key, log_file)
@@ -319,6 +513,7 @@ def __getstate__(self):
             "target_host": self.target_host,
             "hardware_params": self.hardware_params,
             "layout_rewrite_option": self.layout_rewrite_option,
+            "task_input_names": self.task_input_names,
         }
 
     def __setstate__(self, state):
@@ -328,11 +523,12 @@ def __setstate__(self, state):
         except Exception:  # pylint: disable=broad-except
             raise RuntimeError("Invalid workload key %s" % state["workload_key"])
 
-        # The workload from a compute DAG does not have arguments and is not registered
-        # by default so we register it here. If the workload has already been registered,
-        # the later registration overrides the prvious one.
-        if len(workload) == 1:
-            register_workload_tensors(workload[0], state["compute_dag"].tensors)
+        # workload[0] is either the compute function name or the ComputeDAG hash.
+        # The compute functions are already registered when importing TVM, so here
+        # we only register the ComputeDAG workloads. If the same workload has
+        # already been registered, the later registration overrides the prvious one.
+        if workload[0] not in WORKLOAD_FUNC_REGISTRY:
+            register_workload_tensors(state["workload_key"], state["compute_dag"].tensors)
 
         self.__init_handle_by_constructor__(
             _ffi_api.SearchTask,
@@ -342,6 +538,7 @@ def __setstate__(self, state):
             state["target_host"],
             state["hardware_params"],
             state["layout_rewrite_option"],
+            state["task_input_names"],
         )
 
 
diff --git a/python/tvm/auto_scheduler/task_scheduler.py b/python/tvm/auto_scheduler/task_scheduler.py
index ab83ff40c461..0221870badcf 100644
--- a/python/tvm/auto_scheduler/task_scheduler.py
+++ b/python/tvm/auto_scheduler/task_scheduler.py
@@ -47,6 +47,7 @@ def make_search_policies(
     verbose,
     load_model_file=None,
     load_log_file=None,
+    adapative_training=False,
 ):
     """Make a list of search policies for a list of search tasks.
     It creates one policy per task.
@@ -70,6 +71,9 @@ def make_search_policies(
     load_log_file: Optional[str]
         Load measurement records from this file. If it is not None, the status of the
         task scheduler, search policies and cost models will be restored according to this file.
+    adapative_training: bool = False
+        Option used by XGBModel to reduce the model training frequency when there're too
+        many logs.
 
     Returns
     -------
@@ -82,11 +86,16 @@ def make_search_policies(
     if isinstance(search_policy, str):
         policy_type, model_type = search_policy.split(".")
         if model_type == "xgb":
-            cost_model = XGBModel(num_warmup_sample=len(tasks) * num_measures_per_round)
-            if load_model_file:
+            cost_model = XGBModel(
+                num_warmup_sample=len(tasks) * num_measures_per_round,
+                model_file=load_model_file,
+                adapative_training=adapative_training,
+            )
+            if load_model_file and os.path.isfile(load_model_file):
                 logger.info("TaskScheduler: Load pretrained model...")
                 cost_model.load(load_model_file)
             elif load_log_file:
+                logger.info("TaskScheduler: Reload measured states and train the model...")
                 cost_model.update_from_file(load_log_file)
         elif model_type == "random":
             cost_model = RandomModel()
@@ -237,6 +246,9 @@ def __init__(
         # task_cts[i] saves how many times task i is tuned
         self.task_cts = [0 for _ in range(len(self.tasks))]
 
+        # task_best_cts[i] saves the round task i found the best latency
+        self.task_best_cts = [0 for _ in range(len(self.tasks))]
+
         # task_costs_history[i] saves the latency history of task i
         self.task_costs_history = [[] for _ in range(len(self.tasks))]
 
@@ -266,13 +278,20 @@ def __init__(
                 self.group_task_ids.append([])
             self.group_task_ids[self.tag_to_group_id[tag]].append(i)
 
-    def tune(self, tune_option, search_policy="default", search_policy_params=None):
+    def tune(
+        self,
+        tune_option,
+        search_policy="default",
+        search_policy_params=None,
+        adapative_training=False,
+        per_task_early_stopping=None,
+    ):
         """Tune a batch of tasks together.
 
         Parameters
         ----------
         tune_option: TuningOptions
-            The options of tuning
+            The tuning options applied to all tasks.
         search_policy: : Union[str, List[SearchPolicy]] = "default"
             The list of search policies.
             If it is str,
@@ -281,10 +300,20 @@ def tune(self, tune_option, search_policy="default", search_policy_params=None):
             "sketch.random" for SketchPolicy + RandomModel.
         search_policy_params : Optional[Dict[str, Any]]
             The parameters of the search policy
+        adapative_training : bool = False
+            Option used by XGBModel to reduce the model training frequency when there're
+            too many logs.
+        per_task_early_stopping : Optional[int]
+            Stop tuning a task early if getting no improvement after n measurements.
         """
         # init members
         self.tune_option = tune_option
-        early_stopping = 1e20 if tune_option.early_stopping < 0 else tune_option.early_stopping
+        self.early_stopping_all = (
+            1e20 if tune_option.early_stopping < 0 else tune_option.early_stopping
+        )
+        self.early_stopping_task = (
+            1e20 if per_task_early_stopping is None else per_task_early_stopping
+        )
 
         self.measurer = ProgramMeasurer(
             tune_option.builder,
@@ -315,6 +344,7 @@ def tune(self, tune_option, search_policy="default", search_policy_params=None):
             tune_option.verbose,
             self.load_model_file,
             self.load_log_file,
+            adapative_training,
         )
 
         # do a round robin first to warm up
@@ -398,13 +428,13 @@ def tune(self, tune_option, search_policy="default", search_policy_params=None):
             if self.cur_score < self.best_score:
                 self.best_score = self.cur_score
                 self.best_ct = self.ct
-            elif self.ct - self.best_ct >= early_stopping and all(
+            elif self.ct - self.best_ct >= self.early_stopping_all and all(
                 cost < 1e9 for cost in self.best_costs
             ):
                 if self.tune_option.verbose >= 1:
                     print(
                         "Stop early since no performance improvement in the last "
-                        + str(early_stopping)
+                        + str(self.early_stopping_all)
                         + " measurement trials."
                     )
                 break
@@ -420,15 +450,22 @@ def _tune_task(self, task_idx):
             self.num_measures_per_round, self.measurer
         )
 
+        self.task_cts[task_idx] += 1
+
         for res in measure_results:
             cost = array_mean(res.costs)
             if cost < self.best_costs[task_idx]:
+                self.task_best_cts[task_idx] = self.task_cts[task_idx]
                 self.best_costs[task_idx] = cost
 
-        if len(measure_inputs) == 0:
+        # Stop tuning this task in the rest of the process if its search space has been
+        # fully explored or it has no improvement for a long while.
+        no_change_trials = (
+            self.task_cts[task_idx] - self.task_best_cts[task_idx]
+        ) * self.num_measures_per_round
+        if len(measure_inputs) == 0 or no_change_trials > self.early_stopping_task:
             self.dead_tasks.add(task_idx)
 
-        self.task_cts[task_idx] += 1
         self.task_costs_history[task_idx].append(self.best_costs[task_idx])
 
         self.ct += len(measure_inputs)
@@ -475,17 +512,24 @@ def _restore_status(self, log_file, num_measures_per_round):
             if task_idx is None:
                 continue
 
+            self.task_cts[task_idx] += 1
+
             if res.error_no == 0:
-                self.best_costs[task_idx] = min(self.best_costs[task_idx], array_mean(res.costs))
+                cost = array_mean(res.costs)
+                if cost < self.best_costs[task_idx]:
+                    self.best_costs[task_idx] = cost
+                    self.task_best_cts[task_idx] = self.task_cts[task_idx]
 
-            self.task_cts[task_idx] += 1
+        for idx in range(len(self.tasks)):
+            if self.task_cts[idx] - self.task_best_cts[idx] > self.early_stopping_task:
+                self.dead_tasks.add(idx)
 
-        for i in range(len(self.tasks)):
             # The computation of taks_cts is just an estimation.
             # The estimation may not be accurate if the log file is changed externally or
             # `num_measures_per_round` is different from the last tuning.
-            self.task_cts[i] = int(self.task_cts[i] / num_measures_per_round + 0.5)
-            self.task_costs_history[i].append(self.best_costs[i])
+            self.task_cts[idx] = int(self.task_cts[idx] / num_measures_per_round + 0.5)
+            self.task_best_cts[idx] = int(self.task_best_cts[idx] / num_measures_per_round + 0.5)
+            self.task_costs_history[idx].append(self.best_costs[idx])
 
         self.cur_score = self._compute_score(self.best_costs)
 
diff --git a/python/tvm/auto_scheduler/utils.py b/python/tvm/auto_scheduler/utils.py
index f3698fa7fd6a..14dc5b8984c3 100644
--- a/python/tvm/auto_scheduler/utils.py
+++ b/python/tvm/auto_scheduler/utils.py
@@ -19,6 +19,7 @@
 """ Common utilities for auto_scheduler. """
 
 from typing import Hashable
+import json
 import multiprocessing
 import multiprocessing.pool
 import queue
@@ -34,6 +35,7 @@
 except ImportError:
     psutil = None
 
+import tvm
 from tvm import rpc
 from tvm.tir import expr
 from tvm.tir.transform import Simplify
@@ -41,6 +43,91 @@
 from ..te import Tensor, placeholder
 
 
+def decode_workload_key(workload_key):
+    """Decode the workload key from a string to the name and arguments. The wokrload key
+    is expected to be a list of "[func_name/hash, args ...]" in a JSON string. If not,
+    then simply return the workload key as the name without arguments.
+
+    Parameters
+    ----------
+    workload_key: str
+        The workload key in string. Format: "[func_name/hash, args ...]".
+
+    Returns
+    -------
+    name: str
+        The workload function name or the DAG hash.
+    args: Optional[Tuple[Any, ...]]
+        The flatten arguments in a tuple, or None if the workload key format is not decodeable.
+    """
+
+    def flatten_list(inp):
+        ret = []
+        for elt in inp:
+            if isinstance(elt, list):
+                ret += flatten_list(elt)
+            else:
+                ret.append(elt)
+        return ret
+
+    try:
+        key_list = json.loads(workload_key)
+        if isinstance(key_list, list) and len(key_list) >= 1:
+            return key_list[0], tuple(flatten_list(key_list[1:]))
+    except json.decoder.JSONDecodeError:
+        pass
+    return workload_key, None
+
+
+def calc_workload_dis_factor(target_workload_pair, workload_pair):
+    """Calculate the distance factor of the workload to the target workload.
+    If two workloads are not compatible at all (i.e., different compute DAG or function),
+    then the distance factor is "inf". Otherwise, we calculate the factor by traversing
+    the workload arguments, which are the arguments of the compute function,
+    or the output shapes for the ComputeDAG. The factor is calculated by the following rules:
+
+    1. For non-zero integer values: `product(target_arg / candidate_arg)`.
+    2. For non-integer or zero values: "inf" if not equal else 1.
+
+    As a result, factor=1 is the optimal when two workloads are identical.
+
+    Parameters
+    ----------
+    target_workload_pair: Tuple[str, Optional[Tuple[Any, ...]]]
+        The target workload pair: (hash, argument tuple).
+
+    workload_pair: Tuple[str, Optional[Tuple[Any, ...]]]
+        The candidate workload pair: (hash, argument tuple).
+
+    Returns
+    -------
+    dis_f: float
+        The distance factor.
+    """
+    target_key, target_args = target_workload_pair
+    target_args = target_args if target_args is not None else []
+    key, args = workload_pair
+    args = args if args is not None else []
+
+    # Not even the same func/DAG.
+    if key != target_key or len(target_args) != len(args):
+        return float("inf")
+
+    dis_f = 1
+    for target_arg, arg in zip(target_args, args):
+        if isinstance(target_arg, int):
+            if target_arg == 0 or arg == 0:
+                if target_arg != arg:
+                    return float("inf")
+            elif target_arg % arg != 0:
+                return float("inf")
+            else:
+                dis_f *= target_arg / arg
+        elif target_arg != arg:
+            return float("inf")
+    return dis_f
+
+
 def get_func_name(func):
     """Get name of a function.
 
@@ -90,10 +177,16 @@ def get_const_tuple(in_tuple):
 
     Returns
     -------
-    out_tuple : Tuple[int]
-        The output.
+    out_tuple : Tuple[Union[int,tvm.tir.Var,tvm.tir.Any]]
+        The output tuple of int. The dynamic shape variables (Var or Any) will be preserved.
     """
-    return tuple(get_const_int(x) for x in in_tuple)
+    ret = []
+    for elem in in_tuple:
+        if isinstance(elem, (tvm.tir.Var, tvm.tir.expr.Any)):
+            ret.append(elem)
+        else:
+            ret.append(get_const_int(elem))
+    return tuple(ret)
 
 
 def list_to_tuple(x):
@@ -108,6 +201,9 @@ def serialize_args(args):
     Currently this is mainly used for tvm.tensor.Tensor
     """
     ret = []
+    if args is None:
+        return tuple(ret)
+
     for t in args:
         if isinstance(t, Tensor):
             t = ("TENSOR", get_const_tuple(t.shape), t.dtype)
diff --git a/python/tvm/auto_scheduler/workload_registry.py b/python/tvm/auto_scheduler/workload_registry.py
index 9a7c15c877aa..cd8f8c9d1a3e 100644
--- a/python/tvm/auto_scheduler/workload_registry.py
+++ b/python/tvm/auto_scheduler/workload_registry.py
@@ -35,6 +35,7 @@
 import json
 
 import tvm._ffi
+from tvm.runtime._ffi_node_api import LoadJSON, SaveJSON
 from .utils import serialize_args, deserialize_args, get_func_name
 
 logger = logging.getLogger("auto_scheduler")
@@ -98,14 +99,14 @@ def register(myf):
     return register
 
 
-def register_workload_tensors(func_name, tensors, override=True):
+def register_workload_tensors(workload_key, tensors, override=True):
     """Register a workload by provding input/output tensors. Since this function is used
     when extracting/deserializing tasks, it expects duplicated registrations by default.
 
     Parameters
     ----------
-    func_name: str
-        The function name or the hash key of the compute DAG.
+    workload_key: str
+        The wokrload key of the compute DAG in JSON string.
     tensors: List[Tensor]
         The input/output tensors of a compute DAG
     override : boolean = True
@@ -113,11 +114,11 @@ def register_workload_tensors(func_name, tensors, override=True):
 
     Returns
     -------
-    key: str
-        The serialized JSON string as the workload key.
+    workload_key: str
+        The wokrload key of the compute DAG in JSON string.
     """
-    register_workload(func_name, override=override)(tensors)
-    return json.dumps((func_name,))
+    register_workload(workload_key, override=override)(tensors)
+    return workload_key
 
 
 def make_workload_key(func, args):
@@ -169,7 +170,8 @@ def workload_key_to_tensors(workload_key):
     Parameters
     ----------
     workload_key : str
-        The input workload key.
+        The input workload key in JSON string. The format is either (func_name, arguments...)
+        for compute functions, or (hash, shapes...) for ComputeDAG.
 
     Returns
     -------
@@ -178,16 +180,21 @@ def workload_key_to_tensors(workload_key):
     """
     global WORKLOAD_FUNC_REGISTRY
 
+    # We register ComputeDAG with both hash and argumetns, which are fixed in ComputeDAG,
+    # so we use an entire workload key to query the ComputeDAG.
+    if workload_key in WORKLOAD_FUNC_REGISTRY:
+        return WORKLOAD_FUNC_REGISTRY[workload_key]
+
+    # We register compute function with only the function name since
+    # it does not bind to specific arguments, so we use the function name to query
+    # the function and call the function with arguments to get the tensors.
     workload = json.loads(workload_key)
     name = workload[0]
     value = WORKLOAD_FUNC_REGISTRY[name]
+    assert callable(value)
 
-    # "value" can be either a function or a list of tensors
-    if callable(value):  # if it is a func
-        args = deserialize_args(workload[1:])
-        return value(*args)
-    # otherwise, it is a list of tensors
-    return value
+    args = deserialize_args(workload[1:])
+    return value(*args)
 
 
 def serialize_workload_registry_entry(workload_key):
@@ -209,11 +216,18 @@ def serialize_workload_registry_entry(workload_key):
     """
     global WORKLOAD_FUNC_REGISTRY
 
-    workload = json.loads(workload_key)
-    name = workload[0]
-    value = WORKLOAD_FUNC_REGISTRY[name]
+    if workload_key in WORKLOAD_FUNC_REGISTRY:
+        sname = workload_key
+    else:
+        workload = json.loads(workload_key)
+        sname = workload[0]
+
+    svalue = WORKLOAD_FUNC_REGISTRY[sname]
+    if not callable(svalue):
+        # pylint: disable=assignment-from-no-return
+        svalue = SaveJSON(svalue)
 
-    return name, value
+    return sname, svalue
 
 
 def deserialize_workload_registry_entry(data):
@@ -230,7 +244,8 @@ def deserialize_workload_registry_entry(data):
 
     name, value = data
     if name not in WORKLOAD_FUNC_REGISTRY:
-        WORKLOAD_FUNC_REGISTRY[name] = value
+        # pylint: disable=assignment-from-no-return
+        WORKLOAD_FUNC_REGISTRY[name] = LoadJSON(value)
 
 
 def save_workload_func_registry(filename):
diff --git a/python/tvm/autotvm/measure/__init__.py b/python/tvm/autotvm/measure/__init__.py
index 0c32ae0ca9bf..c4c0dc92b116 100644
--- a/python/tvm/autotvm/measure/__init__.py
+++ b/python/tvm/autotvm/measure/__init__.py
@@ -23,6 +23,12 @@
     measure_option,
     create_measure_batch,
 )
-from .measure_methods import LocalBuilder, LocalRunner, RPCRunner, request_remote
+from .measure_methods import (
+    LocalBuilder,
+    LocalRunner,
+    RPCRunner,
+    default_module_loader,
+    request_remote,
+)
 from .executor import Executor
 from .local_executor import LocalExecutor
diff --git a/python/tvm/autotvm/measure/measure_methods.py b/python/tvm/autotvm/measure/measure_methods.py
index 4d6c5daad378..b68767bd0528 100644
--- a/python/tvm/autotvm/measure/measure_methods.py
+++ b/python/tvm/autotvm/measure/measure_methods.py
@@ -22,21 +22,21 @@
 remote devices, recording the running time costs, and checking the correctness of the output.
 """
 
+import contextlib
 import logging
 import shutil
 import os
 import threading
 import time
+import typing
 from random import getrandbits
 from collections import namedtuple
 import tempfile
-
 import numpy as np
 
 import tvm._ffi
 import tvm.ir.transform
 from tvm import nd, rpc as _rpc
-from tvm.target import Target
 from tvm.error import TVMError
 from tvm.driver import build
 from tvm.contrib import nvcc, ndk, tar
@@ -195,16 +195,15 @@ class RPCRunner(Runner):
         will be automatically increased.
     cooldown_interval: float, optional
         The cool down interval between two measurements.
-    check_correctness: bool, optional
-        Whether check correctness after measurement. This will use llvm cpu target to
-        call your template and get the reference output.
-        This can work for TOPI templates, but may not work for your custom template.
     enable_cpu_cache_flush: bool
         Whether to flush cache on CPU between repeated measurements.
         Flushing cache can make the measured latency of one operator closer to
         its actual latency during end-to-end inference.
         To make this option effective, the argument `number` should also be set to 1.
         This is only has effect on CPU task.
+    module_loader : ModuleLoader
+        If given, a context manager that loads the module to be timed into the remote runtime.
+        If not given, default_module_loader is used.
     """
 
     def __init__(
@@ -219,8 +218,8 @@ def __init__(
         repeat=3,
         min_repeat_ms=0,
         cooldown_interval=0.1,
-        check_correctness=False,
         enable_cpu_cache_flush=False,
+        module_loader=None,
     ):
         super(RPCRunner, self).__init__(timeout, n_parallel)
 
@@ -234,11 +233,9 @@ def __init__(
         self.repeat = repeat
         self.min_repeat_ms = min_repeat_ms
 
-        self.ref_input = None
-        self.ref_output = None
         self.enable_cpu_cache_flush = enable_cpu_cache_flush
-        self.check_correctness = check_correctness
         self.cooldown_interval = cooldown_interval
+        self.module_loader = module_loader
 
         self.executor = LocalExecutor(timeout=timeout * (self.n_parallel + 1))
 
@@ -255,19 +252,6 @@ def set_task(self, task):
                 "and make sure you have free devices on the queue status."
             )
 
-        if self.check_correctness:
-            # use llvm cpu to generate a reference input/output
-            # this option works for tuning topi, but might not work for you custom op
-            with Target("llvm"):
-                s, arg_bufs = task.instantiate(task.config_space.get(0))
-            self.ref_input = [
-                np.random.uniform(size=get_const_tuple(x.shape)).astype(x.dtype) for x in arg_bufs
-            ]
-            func = build(s, arg_bufs, "llvm")
-            tvm_buf = [nd.array(x) for x in self.ref_input]
-            func(*tvm_buf)
-            self.ref_output = [x.asnumpy() for x in tvm_buf]
-
     def get_build_kwargs(self):
         kwargs = {}
         if (
@@ -296,13 +280,24 @@ def get_build_kwargs(self):
 
     def run(self, measure_inputs, build_results):
         results = []
-        remote_args = (self.key, self.host, self.port, self.priority, self.timeout)
+        remote_kwargs = dict(
+            device_key=self.key,
+            host=self.host,
+            port=self.port,
+            priority=self.priority,
+            timeout=self.timeout,
+        )
 
         for i in range(0, len(measure_inputs), self.n_parallel):
             futures = []
             for measure_inp, build_res in zip(
                 measure_inputs[i : i + self.n_parallel], build_results[i : i + self.n_parallel]
             ):
+                module_loader = (
+                    self.module_loader
+                    if self.module_loader is not None
+                    else default_module_loader()
+                )
                 ret = self.executor.submit(
                     run_through_rpc,
                     measure_inp,
@@ -311,10 +306,9 @@ def run(self, measure_inputs, build_results):
                     self.repeat,
                     self.min_repeat_ms,
                     self.cooldown_interval,
-                    remote_args,
-                    self.ref_input,
-                    self.ref_output,
+                    remote_kwargs,
                     self.enable_cpu_cache_flush,
+                    module_loader,
                 )
                 futures.append(ret)
 
@@ -357,10 +351,6 @@ class LocalRunner(RPCRunner):
         will be automatically increased.
     cooldown_interval: float, optional
         The cool down interval between two measurements.
-    check_correctness: bool, optional
-        Whether check correctness after measurement. This will use llvm cpu target to
-        call your template and get the reference output.
-        This can work for TOPI templates, but may not work for your custom template.
     enable_cpu_cache_flush: bool
         Whether to flush cache on CPU between repeated measurements.
         Flushing cache can make the measured latency of one operator closer to
@@ -380,8 +370,8 @@ def __init__(
         repeat=3,
         min_repeat_ms=0,
         cooldown_interval=0.1,
-        check_correctness=False,
         enable_cpu_cache_flush=False,
+        module_loader=None,
     ):
         super(LocalRunner, self).__init__(
             "",
@@ -394,8 +384,8 @@ def __init__(
             repeat=repeat,
             min_repeat_ms=min_repeat_ms,
             cooldown_interval=cooldown_interval,
-            check_correctness=check_correctness,
             enable_cpu_cache_flush=enable_cpu_cache_flush,
+            module_loader=module_loader,
         )
         self.tracker = None
         self.server = None
@@ -504,6 +494,11 @@ def __call__(self, measure_input, tmp_dir, **kwargs):
         return BuildResult(filename, arg_info, None, time.time() - tic)
 
 
+ModuleLoader = typing.Callable[
+    [dict, dict], typing.ContextManager[typing.Tuple[tvm.rpc.RPCSession, tvm.runtime.Module]]
+]
+
+
 def run_through_rpc(
     measure_input,
     build_result,
@@ -511,10 +506,9 @@ def run_through_rpc(
     repeat,
     min_repeat_ms,
     cooldown_interval,
-    remote_args,
-    ref_input=None,
-    ref_output=None,
+    remote_kwargs,
     enable_cpu_cache_flush=False,
+    module_loader=None,
 ):
     """Run a generated library through rpc
 
@@ -542,18 +536,16 @@ def run_through_rpc(
         will be automatically increased.
     cooldown_interval: float
         The cool down interval between two measurements
-    remote_args: Tuple
-        The argument for request_remote
-    ref_input: List of np.ndarray
-        The reference input used for checking correctness
-    ref_output: List of np.ndarray
-        The reference output used for checking correctness
+    remote_kwargs: dict
+        Passed to module_loader(). Ultimately, keyword args to request_remote().
     enable_cpu_cache_flush: bool
         Whether to flush cache on CPU between repeated measurements.
         Flushing cache can make the measured latency of one operator closer to
         its actual latency during end-to-end inference.
         To make this option effective, the argument `number` should also be set to 1.
         This is only has effect on CPU task.
+    module_loader: ModuleLoader
+        A function that returns a ContextManager used to establish and teardown the remote session.
     """
     if isinstance(build_result, MeasureResult):
         return build_result
@@ -562,69 +554,43 @@ def run_through_rpc(
     errno = MeasureErrorNo.NO_ERROR
     try:
         # upload built module
-        remote = request_remote(*remote_args)
-        # Program the FPGA every single time when targeting VTA
-        if (
-            hasattr(measure_input.target, "device_name")
-            and measure_input.target.device_name == "vta"
-        ):
-            # pylint: disable=import-outside-toplevel
-            from vta import program_fpga, reconfig_runtime
-
-            program_fpga(remote, None)
-            reconfig_runtime(remote)
-        remote.upload(build_result.filename)
-        func = remote.load_module(os.path.split(build_result.filename)[1])
-        ctx = remote.context(str(measure_input.target), 0)
-
-        # Limitation:
-        # We can not get PackFunction directly in the remote mode as it is wrapped
-        # under the std::function. We could lift the restriction later once we fold
-        # the PackedFunc as an object. Currently, we pass function name to work
-        # around it.
-        f_prepare = "cache_flush_cpu_non_first_arg" if enable_cpu_cache_flush else ""
-        time_f = func.time_evaluator(
-            func.entry_name,
-            ctx,
-            number=number,
-            repeat=repeat,
-            min_repeat_ms=min_repeat_ms,
-            f_preproc=f_prepare,
-        )
+        with module_loader(remote_kwargs, build_result) as (remote, mod):
+            ctx = remote.context(str(measure_input.target), 0)
+
+            # Limitation:
+            # We can not get PackFunction directly in the remote mode as it is wrapped
+            # under the std::function. We could lift the restriction later once we fold
+            # the PackedFunc as an object. Currently, we pass function name to work
+            # around it.
+            f_prepare = "cache_flush_cpu_non_first_arg" if enable_cpu_cache_flush else ""
+            time_f = mod.time_evaluator(
+                mod.entry_name,
+                ctx,
+                number=number,
+                repeat=repeat,
+                min_repeat_ms=min_repeat_ms,
+                f_preproc=f_prepare,
+            )
 
-        # set input
-        if ref_input:
-            args = [nd.array(x, ctx=ctx) for x in ref_input]
-        else:
             try:
                 random_fill = remote.get_function("tvm.contrib.random.random_fill")
             except AttributeError:
                 raise AttributeError(
                     "Please make sure USE_RANDOM is ON in the config.cmake " "on the remote devices"
                 )
-            args = [nd.empty(x[0], dtype=x[1], ctx=ctx) for x in build_result.arg_info]
-            for arg in args:
-                random_fill(arg)
+            args = [nd.array(np.zeros(x[0], dtype=x[1]), ctx=ctx) for x in build_result.arg_info]
+            if "scatter" not in measure_input.task.name:
+                # the index tensor of scatter op cannot be randomly initialized
+                for arg in args:
+                    random_fill(arg)
             ctx.sync()
 
-        costs = time_f(*args).results
-
-        # clean up remote files
-        remote.remove(build_result.filename)
-        remote.remove(os.path.splitext(build_result.filename)[0] + ".so")
-        remote.remove("")
+            costs = time_f(*args).results
 
         if len(costs) > 2:  # remove largest and smallest value to reduce variance
             costs = list(costs)
             costs.sort()
             costs = tuple(costs[1:-1])
-
-        # check correctness of output
-        if ref_output:
-            for expected, real in zip(ref_output, args):
-                if not np.allclose(expected, real.asnumpy(), rtol=1e-4):
-                    logger.warning("Wrong Answer!")
-                    errno = MeasureErrorNo.WRONG_ANSWER
     except TVMError as exc:
         msg = str(exc)
         if "Stack trace returned" in msg:
@@ -638,6 +604,40 @@ def run_through_rpc(
     return MeasureResult(costs, errno, tstamp - tic + build_result.time_cost, tstamp)
 
 
+def default_module_loader(pre_load_function=None):
+    """Returns a default function that can be passed as module_loader to run_through_rpc.
+
+    Parameters
+    ----------
+    pre_load_function : Optional[Function[tvm.rpc.Session, tvm.runtime.Module]]
+        Invoked after a session is established and before the default code-loading RPC calls are
+        issued. Allows performing pre-upload actions, e.g. resetting the remote runtime environment.
+
+    Returns
+    -------
+    ModuleLoader :
+        A function that can be passed as module_loader to run_through_rpc.
+    """
+
+    @contextlib.contextmanager
+    def default_module_loader_mgr(remote_kwargs, build_result):
+        remote = request_remote(**remote_kwargs)
+        if pre_load_function is not None:
+            pre_load_function(remote, build_result)
+
+        remote.upload(build_result.filename)
+        try:
+            yield remote, remote.load_module(os.path.split(build_result.filename)[1])
+
+        finally:
+            # clean up remote files
+            remote.remove(build_result.filename)
+            remote.remove(os.path.splitext(build_result.filename)[0] + ".so")
+            remote.remove("")
+
+    return default_module_loader_mgr
+
+
 def request_remote(device_key, host=None, port=None, priority=1, timeout=60):
     """Request a remote session
 
diff --git a/python/tvm/autotvm/task/space.py b/python/tvm/autotvm/task/space.py
index cf9cd809aa8d..afbfb4c03988 100644
--- a/python/tvm/autotvm/task/space.py
+++ b/python/tvm/autotvm/task/space.py
@@ -19,7 +19,7 @@
 """
 Template configuration space.
 
-Each template function can be parametrized by a ConfigSpace.
+Each template function can be parameterized by a ConfigSpace.
 The space is declared when we invoke the template function with ConfigSpace.
 During evaluation, we pass in a ConfigEntity, which contains a specific
 entity in the space. This entity contains deterministic parameters.
@@ -63,7 +63,7 @@ class TransformSpace(object):
         Each operator has some tunable parameters (e.g. the split factor).
         Then the tuning process is just to find good parameters of these op.
 
-    So the all the combinations of the parameters of these op forms our search space.
+    So all the combinations of the parameters of these op form our search space.
 
     Naming convention:
     We call the set of all possible values as XXXSpace. (XXX can be Split, Reorder, Config ...)
@@ -797,7 +797,7 @@ def add_flop(self, flop):
 
     def raise_error(self, msg):
         """register error in config
-        Using this to actively detect error when scheudling.
+        Using this to actively detect error when scheduling.
         Otherwise these error will occur during runtime, which
         will cost more time.
 
@@ -848,6 +848,8 @@ def get(self, index):
         index: int
             index in the space
         """
+        if index < 0 or index >= len(self):
+            raise IndexError("Index out of range: size {}, got index {}".format(len(self), index))
         entities = OrderedDict()
         t = index
         for name, space in self.space_map.items():
diff --git a/python/tvm/autotvm/task/task.py b/python/tvm/autotvm/task/task.py
index c8b50ad33741..52f0996c800c 100644
--- a/python/tvm/autotvm/task/task.py
+++ b/python/tvm/autotvm/task/task.py
@@ -580,6 +580,7 @@ def traverse(ops):
                 pass
             else:
                 raise FlopCalculationError(
+                    f"{op.name} is not supported by autotvm. "
                     "Only support te.compute currently. "
                     "Other ops like tvm.te.scan/te.extern is not supported"
                 )
diff --git a/python/tvm/autotvm/tuner/xgboost_tuner.py b/python/tvm/autotvm/tuner/xgboost_tuner.py
index 8f8ddfe7bd4e..2f4d0ee88ce9 100644
--- a/python/tvm/autotvm/tuner/xgboost_tuner.py
+++ b/python/tvm/autotvm/tuner/xgboost_tuner.py
@@ -64,7 +64,7 @@ class XGBTuner(ModelBasedTuner):
         top-(plan_size * diversity_filter_ratio) candidates according to the cost model
         and then pick batch_size of them according to the diversity metric.
 
-    log_interval: int, optional
+    log_interval: int = 50
         The verbose level.
         If is 0, output nothing.
         Otherwise, output debug information every `verbose` iterations.
diff --git a/python/tvm/contrib/cc.py b/python/tvm/contrib/cc.py
index 9643d9b650fd..f48ae395fbcd 100644
--- a/python/tvm/contrib/cc.py
+++ b/python/tvm/contrib/cc.py
@@ -47,7 +47,7 @@ def create_shared(output, objects, options=None, cc="g++"):
     ):
         _linux_compile(output, objects, options, cc, compile_shared=True)
     elif sys.platform == "win32":
-        _windows_shared(output, objects, options)
+        _windows_compile(output, objects, options)
     else:
         raise ValueError("Unsupported platform")
 
@@ -71,6 +71,8 @@ def create_executable(output, objects, options=None, cc="g++"):
     """
     if sys.platform == "darwin" or sys.platform.startswith("linux"):
         _linux_compile(output, objects, options, cc)
+    elif sys.platform == "win32":
+        _windows_compile(output, objects, options)
     else:
         raise ValueError("Unsupported platform")
 
@@ -190,12 +192,16 @@ def _fcompile(outputs, objects, options=None):
 
 def _linux_compile(output, objects, options, compile_cmd="g++", compile_shared=False):
     cmd = [compile_cmd]
-    if compile_shared or output.endswith(".so") or output.endswith(".dylib"):
-        cmd += ["-shared", "-fPIC"]
-        if sys.platform == "darwin":
-            cmd += ["-undefined", "dynamic_lookup"]
-    elif output.endswith(".obj"):
-        cmd += ["-c"]
+    if compile_cmd != "nvcc":
+        if compile_shared or output.endswith(".so") or output.endswith(".dylib"):
+            cmd += ["-shared", "-fPIC"]
+            if sys.platform == "darwin":
+                cmd += ["-undefined", "dynamic_lookup"]
+        elif output.endswith(".obj"):
+            cmd += ["-c"]
+    else:
+        if compile_shared or output.endswith(".so") or output.endswith(".dylib"):
+            cmd += ["--shared"]
     cmd += ["-o", output]
     if isinstance(objects, str):
         cmd += [objects]
@@ -212,9 +218,9 @@ def _linux_compile(output, objects, options, compile_cmd="g++", compile_shared=F
         raise RuntimeError(msg)
 
 
-def _windows_shared(output, objects, options):
+def _windows_compile(output, objects, options):
     cmd = ["clang"]
-    cmd += ["-O2", "-flto=full", "-fuse-ld=lld-link"]
+    cmd += ["-O2"]
 
     if output.endswith(".so") or output.endswith(".dll"):
         cmd += ["-shared"]
@@ -240,6 +246,7 @@ def _windows_shared(output, objects, options):
         )
     if proc.returncode != 0:
         msg = "Compilation error:\n"
+        msg += " ".join(cmd) + "\n"
         msg += py_str(out)
 
         raise RuntimeError(msg)
diff --git a/python/tvm/contrib/cublas.py b/python/tvm/contrib/cublas.py
index 9a36fa52ce4b..e01b09c3e4ee 100644
--- a/python/tvm/contrib/cublas.py
+++ b/python/tvm/contrib/cublas.py
@@ -48,7 +48,7 @@ def matmul(lhs, rhs, transa=False, transb=False, dtype=None):
             "tvm.contrib.cublas.matmul", ins[0], ins[1], outs[0], transa, transb
         ),
         dtype=dtype,
-        name="C",
+        name="matmul_cublas",
     )
 
 
@@ -82,5 +82,5 @@ def batch_matmul(lhs, rhs, transa=False, transb=False, dtype=None):
             "tvm.contrib.cublas.batch_matmul", ins[0], ins[1], outs[0], transa, transb
         ),
         dtype=dtype,
-        name="C",
+        name="batch_matmul_cublas",
     )
diff --git a/python/tvm/contrib/cuda_graph/__init__.py b/python/tvm/contrib/cuda_graph/__init__.py
new file mode 100644
index 000000000000..13a83393a912
--- /dev/null
+++ b/python/tvm/contrib/cuda_graph/__init__.py
@@ -0,0 +1,16 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
diff --git a/python/tvm/contrib/cuda_graph/cuda_graph_runtime.py b/python/tvm/contrib/cuda_graph/cuda_graph_runtime.py
new file mode 100644
index 000000000000..45ec89d37b3d
--- /dev/null
+++ b/python/tvm/contrib/cuda_graph/cuda_graph_runtime.py
@@ -0,0 +1,134 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Graph runtime with CUDA Graph"""
+import tvm._ffi
+
+from tvm._ffi.base import string_types
+from tvm.contrib import graph_runtime
+
+
+def create(graph_json_str, libmod, ctx):
+    """Create a runtime executor module given a graph and module.
+
+    Parameters
+    ----------
+    graph_json_str : str
+        The graph to be deployed in json format output by json graph.
+        The graph can contain operator(tvm_op) that points to the name
+        of PackedFunc in the libmod.
+
+    libmod : tvm.runtime.Module
+        The module of the corresponding function
+
+    ctx : TVMContext
+        The context to deploy the module, only supports CUDA GPU
+
+    Returns
+    -------
+    graph_module : GraphModuleCudaGraph
+        CUDA graph runtime module that can be used to execute the graph.
+
+    Note
+    ----
+    See also :py:class:`tvm.contrib.cuda_graph.cuda_graph_runtime.GraphModuleCudaGraph`
+    for examples to directly construct a GraphModuleCudaGraph from an exported
+    relay compiled library.
+    """
+    assert isinstance(graph_json_str, string_types)
+    try:
+        ctx, num_rpc_ctx, device_type_id = graph_runtime.get_device_ctx(libmod, ctx)
+        if num_rpc_ctx == len(ctx):
+            fcreate = ctx[0]._rpc_sess.get_function("tvm.graph_runtime_cuda_graph.create")
+        else:
+            fcreate = tvm._ffi.get_global_func("tvm.graph_runtime_cuda_graph.create")
+    except ValueError:
+        raise ValueError(
+            "To enable CUDA graph support (experimental), please set "
+            "'(USE_GRAPH_RUNTIME_CUGRAPH ON)' in config.cmake and rebuild TVM"
+        )
+
+    return GraphModuleCudaGraph(fcreate(graph_json_str, libmod, *device_type_id))
+
+
+class GraphModuleCudaGraph(graph_runtime.GraphModule):
+    """CUDA graph runtime module.
+
+    This is a CUDA graph runtime wrapper over the TVM runtime.
+    Runtime interfaces are wrapped with CUDA graph functionalities.
+
+    Parameters
+    ----------
+    module : Module
+        The internal tvm module that holds the actual graph functions.
+    """
+
+    def __init__(self, module):
+        self._start_capture = module["start_capture"]
+        self._end_capture = module["end_capture"]
+        self._run_cuda_graph = module["run_cuda_graph"]
+        self._cuda_graph_captured = False
+        graph_runtime.GraphModule.__init__(self, module)
+
+    def capture_cuda_graph(self):
+        """Capture a CUDA graph for tvm_op graph
+
+        This should be called before run_cuda_graph() to capture and
+        instantiate a CUDA graph instance.
+        """
+        self._run()  # call cuModuleLoadData before cudaStream API
+        self._start_capture()
+        self._run()
+        self._end_capture()
+        self._cuda_graph_captured = True
+
+    def run_cuda_graph(self):
+        """Run the CUDA graph for tvm_op graph
+
+        Run the captured CUDA graph instance instead of the
+        for-loop kernel launch of default graph runtime
+        """
+        self._run_cuda_graph()
+
+    def run(self, **input_dict):
+        """A run wrapper for graph capture / launch, user can just
+        change default graph runtime to cuda graph runtime, and
+        the first call will capture a cuda graph for future launch
+
+        Parameters
+        ----------
+        input_dict: dict of str to NDArray
+            List of input values to be feed to
+        """
+        if input_dict:
+            self.set_input(**input_dict)
+        if not self._cuda_graph_captured:
+            self.capture_cuda_graph()
+        else:
+            self._run_cuda_graph()
+
+    def debug_get_output(self, node, out):
+        """Run graph up to node and get the output to out
+
+        Parameters
+        ----------
+        node : int / str
+            The node index or name
+
+        out : NDArray
+            The output array container
+        """
+        raise NotImplementedError("Please use debugger.debug_runtime as graph_runtime instead.")
diff --git a/python/tvm/contrib/cudnn.py b/python/tvm/contrib/cudnn.py
index 6dc04c9f58fd..0e22e0c09274 100644
--- a/python/tvm/contrib/cudnn.py
+++ b/python/tvm/contrib/cudnn.py
@@ -342,36 +342,57 @@ def conv_forward(x, w, pad, stride, dilation, conv_mode, tensor_format, algo, co
     conv_dtype = x.dtype if conv_dtype is None else conv_dtype
     pad, stride, dilation, _, _ = _prepare_global_func_params(dims - 2, pad, stride, dilation)
 
-    oshape = conv_output_shape(
-        tensor_format,
-        pad,
-        stride,
-        dilation,
-        list(x.shape),
-        list(w.shape),
-        x.dtype,
-        conv_dtype,
-        groups,
-    )
-    if algo == -1:
-        # For now if we try to call `cudnnFindConvolutionForwardAlgorithm` when
-        # using INT8 data type, CuDNN will crash down.
-        # On the other hand, CuDNN only support IMPLICIT_PRECOMP_GEMM at NHWC format
-        if tensor_format == 1 and conv_dtype == "int32":
-            algo = 1
-        else:
-            algo = conv_find_algo(
-                tensor_format,
-                pad,
-                stride,
-                dilation,
-                list(x.shape),
-                list(w.shape),
-                oshape,
-                x.dtype,
-                conv_dtype,
-                groups,
-            )
+    x_shape = list(x.shape)
+
+    if isinstance(x.shape[0], tvm.tir.expr.IntImm):
+        oshape = conv_output_shape(
+            tensor_format,
+            pad,
+            stride,
+            dilation,
+            x_shape,
+            list(w.shape),
+            x.dtype,
+            conv_dtype,
+            groups,
+        )
+        if algo == -1:
+            # For now if we try to call `cudnnFindConvolutionForwardAlgorithm` when
+            # using INT8 data type, CuDNN will crash down.
+            # On the other hand, CuDNN only support IMPLICIT_PRECOMP_GEMM at NHWC format
+            if tensor_format == 1 and conv_dtype == "int32":
+                algo = 1
+            else:
+                algo = conv_find_algo(
+                    tensor_format,
+                    pad,
+                    stride,
+                    dilation,
+                    list(x.shape),
+                    list(w.shape),
+                    oshape,
+                    x.dtype,
+                    conv_dtype,
+                    groups,
+                )
+    else:
+        # The dynamic batch size case, pretend this is a single batch
+        x_shape[0] = 1
+        oshape = conv_output_shape(
+            tensor_format,
+            pad,
+            stride,
+            dilation,
+            x_shape,
+            list(w.shape),
+            x.dtype,
+            conv_dtype,
+            groups,
+        )
+        oshape[0] = x.shape[0]
+        # This picks CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM
+        # It seems this is the fastest among algorithms that are always applicable
+        algo = 1
 
     if dims == 4:
         return te.extern(
diff --git a/python/tvm/contrib/debugger/debug_result.py b/python/tvm/contrib/debugger/debug_result.py
index 0b9810e74bb1..f58947f0766f 100644
--- a/python/tvm/contrib/debugger/debug_result.py
+++ b/python/tvm/contrib/debugger/debug_result.py
@@ -212,7 +212,7 @@ def get_debug_result(self, sort_by_time=True):
                     continue
                 name = node["name"]
                 shape = str(self._output_tensor_list[eid].shape)
-                time_us = round(time[0] * 1000000, 3)
+                time_us = round(time[0] * 1e6, 3)
                 time_percent = round(((time[0] / total_time) * 100), 3)
                 inputs = str(node["attrs"]["num_inputs"])
                 outputs = str(node["attrs"]["num_outputs"])
@@ -224,8 +224,8 @@ def get_debug_result(self, sort_by_time=True):
             # Sort on the basis of execution time. Prints the most expensive ops in the start.
             data = sorted(data, key=lambda x: x[2], reverse=True)
             # Insert a row for total time at the end.
-            rounded_total_time = round(total_time * 1000000, 3)
-            data.append(["Total_time", "-", rounded_total_time, "-", "-", "-", "-", "-"])
+            rounded_total_time_us = round(total_time * 1e6, 3)
+            data.append(["Total_time", "-", rounded_total_time_us, "-", "-", "-", "-", "-"])
 
         fmt = ""
         for i, _ in enumerate(header):
@@ -264,8 +264,4 @@ def save_tensors(params):
     """
     _save_tensors = tvm.get_global_func("tvm.relay._save_param_dict")
 
-    args = []
-    for k, v in params.items():
-        args.append(k)
-        args.append(tvm.nd.array(v))
-    return _save_tensors(*args)
+    return _save_tensors(params)
diff --git a/python/tvm/contrib/debugger/debug_runtime.py b/python/tvm/contrib/debugger/debug_runtime.py
index 4d2fab4358ba..289ac4c467e0 100644
--- a/python/tvm/contrib/debugger/debug_runtime.py
+++ b/python/tvm/contrib/debugger/debug_runtime.py
@@ -175,7 +175,7 @@ def _run_debug(self):
         Time consumed for each execution will be set as debug output.
 
         """
-        self.debug_datum._time_list = [[float(t) * 1e-6] for t in self.run_individual(10, 1, 1)]
+        self.debug_datum._time_list = [[float(t)] for t in self.run_individual(10, 1, 1)]
         for i, node in enumerate(self.debug_datum.get_graph_nodes()):
             num_outputs = self.debug_datum.get_graph_node_output_num(node)
             for j in range(num_outputs):
diff --git a/python/tvm/contrib/graph_runtime.py b/python/tvm/contrib/graph_runtime.py
index a960e552f68f..59db716e917c 100644
--- a/python/tvm/contrib/graph_runtime.py
+++ b/python/tvm/contrib/graph_runtime.py
@@ -141,11 +141,11 @@ class GraphModule(object):
         lib = relay.build(...)
         lib.export_library("compiled_lib.so")
         # load it back as a runtime
-        lib:tvm.runtime.Module = tvm.runtime.load_module("compiled_lib.so")
+        lib: tvm.runtime.Module = tvm.runtime.load_module("compiled_lib.so")
         # Call the library factory function for default and create
         # a new runtime.Module, wrap with graph module.
         gmod = graph_runtime.GraphModule(lib["default"](ctx))
-        # use the gmod
+        # use the graph module.
         gmod.set_input("x", data)
         gmod.run()
     """
diff --git a/python/tvm/contrib/nvcc.py b/python/tvm/contrib/nvcc.py
index bc11e4a867e4..99844f799d7a 100644
--- a/python/tvm/contrib/nvcc.py
+++ b/python/tvm/contrib/nvcc.py
@@ -89,6 +89,12 @@ def compile_cuda(code, target="ptx", arch=None, options=None, path_target=None):
     cmd += ["-o", file_target]
     cmd += [temp_code]
 
+    cxx_compiler_path = tvm.support.libinfo().get("TVM_CXX_COMPILER_PATH")
+    if cxx_compiler_path != "":
+        # This tells nvcc where to find the c++ compiler just in case it is not in the path.
+        # On Windows it is not in the path by default.
+        cmd += ["-ccbin", cxx_compiler_path]
+
     proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
 
     (out, _) = proc.communicate()
@@ -186,7 +192,7 @@ def find_libdevice_path(arch):
     selected_ver = 0
     selected_path = None
     cuda_ver = get_cuda_version(cuda_path)
-    if cuda_ver in (9.0, 9.1, 10.0, 10.1, 10.2, 11.0, 11.1):
+    if cuda_ver in (9.0, 9.1, 10.0, 10.1, 10.2, 11.0, 11.1, 11.2):
         path = os.path.join(lib_path, "libdevice.10.bc")
     else:
         for fn in os.listdir(lib_path):
@@ -210,6 +216,47 @@ def callback_libdevice_path(arch):
         return ""
 
 
+def get_target_compute_version(target=None):
+    """Utility function to get compute capability of compilation target.
+
+    Looks for the arch in three different places, first in the target attributes, then the global
+    scope, and finally the GPU device (if it exists).
+
+    Parameters
+    ----------
+    target : tvm.target.Target, optional
+        The compilation target
+
+    Returns
+    -------
+    compute_version : str
+        compute capability of a GPU (e.g. "8.0")
+    """
+    # 1. Target
+    if target:
+        if "arch" in target.attrs:
+            compute_version = target.attrs["arch"]
+            major, minor = compute_version.split("_")[1]
+            return major + "." + minor
+
+    # 2. Global scope
+    from tvm.autotvm.env import AutotvmGlobalScope  # pylint: disable=import-outside-toplevel
+
+    if AutotvmGlobalScope.current.cuda_target_arch:
+        major, minor = AutotvmGlobalScope.current.cuda_target_arch.split("_")[1]
+        return major + "." + minor
+
+    # 3. GPU
+    if tvm.gpu(0).exist:
+        return tvm.gpu(0).compute_version
+
+    warnings.warn(
+        "No CUDA architecture was specified or GPU detected."
+        "Try specifying it by adding '-arch=sm_xx' to your target."
+    )
+    return None
+
+
 def parse_compute_version(compute_version):
     """Parse compute capability string to divide major and minor version
 
@@ -296,8 +343,34 @@ def have_tensorcore(compute_version=None, target=None):
             major, minor = compute_version.split("_")[1]
             compute_version = major + "." + minor
     major, _ = parse_compute_version(compute_version)
+    if major >= 7:
+        return True
+
+    return False
 
-    if major == 7:
+
+def have_cudagraph():
+    """Either CUDA Graph support is provided"""
+    try:
+        cuda_path = find_cuda_path()
+        cuda_ver = get_cuda_version(cuda_path)
+        if cuda_ver < 10.0:
+            return False
+        return True
+    except RuntimeError:
+        return False
+
+
+def have_bf16(compute_version):
+    """Either bf16 support is provided in the compute capability or not
+
+    Parameters
+    ----------
+    compute_version : str
+        compute capability of a GPU (e.g. "8.0")
+    """
+    major, _ = parse_compute_version(compute_version)
+    if major >= 8:
         return True
 
     return False
diff --git a/python/tvm/contrib/target/vitis_ai.py b/python/tvm/contrib/target/vitis_ai.py
index d4931d9e3f48..f319fd799829 100644
--- a/python/tvm/contrib/target/vitis_ai.py
+++ b/python/tvm/contrib/target/vitis_ai.py
@@ -132,14 +132,14 @@ def vitis_ai_compiler(ref):
         layers = xgraph.get_layers()
 
         # Get the output tensor names using XGraph and output Relay ids
-        out_tensor_names = []
+        out_tensor_names = ["unknown_name"] * len(output_relay_ids)
         for layer in layers:
             if not layer.internal:
                 for relay_id in layer.attrs["relay_id"]:
                     if relay_id in output_relay_ids:
-                        out_tensor_names.append(layer.name)
+                        out_tensor_names[output_relay_ids.index(relay_id)] = layer.name
                         break
-        if not out_tensor_names:
+        if any([name == "unkown_name" for name in out_tensor_names]):
             raise ValueError(
                 "During codegeneration the loading of subexpression \
                              failed due to output tensor name mismatch in Relay PyXIR interface."
diff --git a/python/tvm/contrib/thrust.py b/python/tvm/contrib/thrust.py
new file mode 100644
index 000000000000..7fe0077c2b42
--- /dev/null
+++ b/python/tvm/contrib/thrust.py
@@ -0,0 +1,45 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Utilities for thrust"""
+import logging
+
+from tvm._ffi import get_global_func
+
+
+def maybe_warn(target, func_name):
+    if get_global_func(func_name, allow_missing=True) and not "thrust" in target.libs:
+        logging.warning("TVM is built with thrust but thrust is not used.")
+    if "thrust" in target.libs and get_global_func(func_name, allow_missing=True) is None:
+        logging.warning("thrust is requested but TVM is not built with thrust.")
+
+
+def can_use_thrust(target, func_name):
+    maybe_warn(target, func_name)
+    return (
+        target.kind.name in ["cuda", "nvptx"]
+        and "thrust" in target.libs
+        and get_global_func(func_name, allow_missing=True)
+    )
+
+
+def can_use_rocthrust(target, func_name):
+    maybe_warn(target, func_name)
+    return (
+        target.kind.name == "rocm"
+        and "thrust" in target.libs
+        and get_global_func(func_name, allow_missing=True)
+    )
diff --git a/python/tvm/driver/build_module.py b/python/tvm/driver/build_module.py
index 7ad48e19a1db..5eaecb422163 100644
--- a/python/tvm/driver/build_module.py
+++ b/python/tvm/driver/build_module.py
@@ -428,12 +428,19 @@ def build(inputs, args=None, target=None, target_host=None, name="default_functi
     if not isinstance(target_host, Target):
         target_host = Target(target_host)
     if (
-        "system-lib" in target_host.attrs
-        and target_host.attrs["system-lib"].value == 1
-        and target_host.kind.name == "c"
+        target_host.attrs.get("runtime", tvm.runtime.String("c++")) == "c"
+        and target_host.attrs.get("system-lib", 0).value == 1
     ):
-        create_csource_metadata_module = tvm._ffi.get_global_func(
-            "runtime.CreateCSourceMetadataModule"
-        )
-        return create_csource_metadata_module([rt_mod_host], target_host)
+        if target_host.kind.name == "c":
+            create_csource_crt_metadata_module = tvm._ffi.get_global_func(
+                "runtime.CreateCSourceCrtMetadataModule"
+            )
+            return create_csource_crt_metadata_module([rt_mod_host], target_host)
+
+        if target_host.kind.name == "llvm":
+            create_llvm_crt_metadata_module = tvm._ffi.get_global_func(
+                "runtime.CreateLLVMCrtMetadataModule"
+            )
+            return create_llvm_crt_metadata_module([rt_mod_host], target_host)
+
     return rt_mod_host
diff --git a/python/tvm/driver/tvmc/autotuner.py b/python/tvm/driver/tvmc/autotuner.py
index 71ccc8546e8b..187b7c5d2a31 100644
--- a/python/tvm/driver/tvmc/autotuner.py
+++ b/python/tvm/driver/tvmc/autotuner.py
@@ -29,7 +29,7 @@
 from tvm.autotvm.tuner import RandomTuner
 from tvm.autotvm.tuner import XGBTuner
 
-from . import common, frontends
+from . import common, composite_target, frontends
 from .common import TVMCException
 from .main import register_parser
 
@@ -210,6 +210,13 @@ def add_tune_parser(subparsers):
     #     can be improved in future to add integration with a modelzoo
     #     or URL, for example.
     parser.add_argument("FILE", help="path to the input model file")
+    parser.add_argument(
+        "--input-shapes",
+        help="specify non-generic shapes for model to run, format is "
+        '"input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]"',
+        type=common.parse_shape_string,
+        default=None,
+    )
 
 
 def drive_tune(args):
@@ -234,8 +241,13 @@ def drive_tune(args):
                 "need to provide an RPC tracker key (--rpc-key) for remote tuning"
             )
 
-    target = common.target_from_cli(args.target)
-    mod, params = frontends.load_model(args.FILE, args.model_format)
+    target, extra_targets = common.target_from_cli(args.target)
+    mod, params = frontends.load_model(args.FILE, args.model_format, shape_dict=args.input_shapes)
+
+    for codegen_from_cli in extra_targets:
+        codegen = composite_target.get_codegen_by_target(codegen_from_cli["name"])
+        partition_function = codegen["pass_pipeline"]
+        mod = partition_function(mod, params)
 
     # min_repeat_ms should be:
     # a. the value provided by the user, if any, or
diff --git a/python/tvm/driver/tvmc/common.py b/python/tvm/driver/tvmc/common.py
index 9db22f3f3390..864c3a9bddb4 100644
--- a/python/tvm/driver/tvmc/common.py
+++ b/python/tvm/driver/tvmc/common.py
@@ -17,8 +17,11 @@
 """
 Common utility functions shared by TVMC modules.
 """
+import re
+import json
 import logging
 import os.path
+import argparse
 
 from urllib.parse import urlparse
 
@@ -76,6 +79,183 @@ def convert_graph_layout(mod, desired_layout):
             )
 
 
+def validate_targets(parse_targets):
+    """
+    Apply a series of validations in the targets provided via CLI.
+    """
+    tvm_target_kinds = tvm.target.Target.list_kinds()
+    targets = [t["name"] for t in parse_targets]
+
+    if len(targets) > len(set(targets)):
+        raise TVMCException("Duplicate target definitions are not allowed")
+
+    if targets[-1] not in tvm_target_kinds:
+        tvm_target_names = ", ".join(tvm_target_kinds)
+        raise TVMCException(
+            f"The last target needs to be a TVM target. Choices: {tvm_target_names}"
+        )
+
+    tvm_targets = [t for t in targets if t in tvm_target_kinds]
+    if len(tvm_targets) > 1:
+        verbose_tvm_targets = ", ".join(tvm_targets)
+        raise TVMCException(
+            f"Only one of the following targets can be used at a time. "
+            "Found: {verbose_tvm_targets}."
+        )
+
+
+def tokenize_target(target):
+    """
+    Extract a list of tokens from a target specification text.
+
+    It covers some corner-cases that are not covered by the built-in
+    module 'shlex', such as the use of "+" as a punctuation character.
+
+
+    Example
+    -------
+
+    For the input `foo -op1=v1 -op2="v ,2", bar -op3=v-4` we
+    should obtain:
+
+        ["foo", "-op1=v1", "-op2="v ,2"", ",", "bar", "-op3=v-4"]
+
+    Parameters
+    ----------
+    target : str
+        Target options sent via CLI arguments
+
+    Returns
+    -------
+    list of str
+        a list of parsed tokens extracted from the target string
+    """
+
+    # Regex to tokenize the "--target" value. It is split into five parts
+    # to match with:
+    #  1. target and option names e.g. llvm, -mattr=, -mcpu=
+    #  2. option values, all together, without quotes e.g. -mattr=+foo,+opt
+    #  3. option values, when single quotes are used e.g. -mattr='+foo, +opt'
+    #  4. option values, when double quotes are used e.g. -mattr="+foo ,+opt"
+    #  5. commas that separate different targets e.g. "my-target, llvm"
+    target_pattern = (
+        r"(\-{0,2}[\w\-]+\=?"
+        r"(?:[\w\+\-\.]+(?:,[\w\+\-\.])*"
+        r"|[\'][\w\+\-,\s\.]+[\']"
+        r"|[\"][\w\+\-,\s\.]+[\"])*"
+        r"|,)"
+    )
+
+    return re.findall(target_pattern, target)
+
+
+def parse_target(target):
+    """
+    Parse a plain string of targets provided via a command-line
+    argument.
+
+    To send more than one codegen, a comma-separated list
+    is expected. Options start with -<option_name>=<value>.
+
+    We use python standard library 'shlex' to parse the argument in
+    a POSIX compatible way, so that if options are defined as
+    strings with spaces or commas, for example, this is considered
+    and parsed accordingly.
+
+
+    Example
+    -------
+
+    For the input `--target="foo -op1=v1 -op2="v ,2", bar -op3=v-4"` we
+    should obtain:
+
+      [
+        {
+            name: "foo",
+            opts: {"op1":"v1", "op2":"v ,2"},
+            raw: 'foo -op1=v1 -op2="v ,2"'
+        },
+        {
+            name: "bar",
+            opts: {"op3":"v-4"},
+            raw: 'bar -op3=v-4'
+        }
+      ]
+
+    Parameters
+    ----------
+    target : str
+        Target options sent via CLI arguments
+
+    Returns
+    -------
+    codegens : list of dict
+        This list preserves the order in which codegens were
+        provided via command line. Each Dict contains three keys:
+        'name', containing the name of the codegen; 'opts' containing
+        a key-value for all options passed via CLI; 'raw',
+        containing the plain string for this codegen
+    """
+    codegens = []
+
+    parsed_tokens = tokenize_target(target)
+
+    split_codegens = []
+    current_codegen = []
+    split_codegens.append(current_codegen)
+    for token in parsed_tokens:
+        # every time there is a comma separating
+        # two codegen definitions, prepare for
+        # a new codegen
+        if token == ",":
+            current_codegen = []
+            split_codegens.append(current_codegen)
+        else:
+            # collect a new token for the current
+            # codegen being parsed
+            current_codegen.append(token)
+
+    # at this point we have a list of lists,
+    # each item on the first list is a codegen definition
+    # in the comma-separated values
+    for codegen_def in split_codegens:
+        # the first is expected to be the name
+        name = codegen_def[0]
+        raw_target = " ".join(codegen_def)
+        all_opts = codegen_def[1:] if len(codegen_def) > 1 else []
+        opts = {}
+        for opt in all_opts:
+            try:
+                # deal with -- prefixed flags
+                if opt.startswith("--"):
+                    opt_name = opt[2:]
+                    opt_value = True
+                else:
+                    opt = opt[1:] if opt.startswith("-") else opt
+                    opt_name, opt_value = opt.split("=", maxsplit=1)
+
+                    # remove quotes from the value: quotes are only parsed if they match,
+                    # so it is safe to assume that if the string starts with quote, it ends
+                    # with quote.
+                    opt_value = opt_value[1:-1] if opt_value[0] in ('"', "'") else opt_value
+            except ValueError:
+                raise ValueError(f"Error when parsing '{opt}'")
+
+            opts[opt_name] = opt_value
+
+        codegens.append({"name": name, "opts": opts, "raw": raw_target})
+
+    return codegens
+
+
+def is_inline_json(target):
+    try:
+        json.loads(target)
+        return True
+    except json.decoder.JSONDecodeError:
+        return False
+
+
 def target_from_cli(target):
     """
     Create a tvm.target.Target instance from a
@@ -91,18 +271,33 @@ def target_from_cli(target):
     -------
     tvm.target.Target
         an instance of target device information
+    extra_targets : list of dict
+        This list preserves the order in which extra targets were
+        provided via command line. Each Dict contains three keys:
+        'name', containing the name of the codegen; 'opts' containing
+        a key-value for all options passed via CLI; 'raw',
+        containing the plain string for this codegen
     """
+    extra_targets = []
 
-    if os.path.exists(target):
+    if os.path.isfile(target):
         with open(target) as target_file:
-            logger.info("using target input from file: %s", target)
+            logger.debug("target input is a path: %s", target)
             target = "".join(target_file.readlines())
+    elif is_inline_json(target):
+        logger.debug("target input is inline JSON: %s", target)
+    else:
+        logger.debug("target input is plain text: %s", target)
+        try:
+            parsed_targets = parse_target(target)
+        except ValueError as ex:
+            raise TVMCException(f"Error parsing target string '{target}'.\nThe error was: {ex}")
 
-    # TODO(@leandron) We don't have an API to collect a list of supported
-    #       targets yet
-    logger.debug("creating target from input: %s", target)
+        validate_targets(parsed_targets)
+        target = parsed_targets[-1]["raw"]
+        extra_targets = parsed_targets[:-1] if len(parsed_targets) > 1 else []
 
-    return tvm.target.Target(target)
+    return tvm.target.Target(target), extra_targets
 
 
 def tracker_host_port_from_cli(rpc_tracker_str):
@@ -136,3 +331,40 @@ def tracker_host_port_from_cli(rpc_tracker_str):
         logger.info("RPC tracker port: %s", rpc_port)
 
     return rpc_hostname, rpc_port
+
+
+def parse_shape_string(inputs_string):
+    """Parse an input shape dictionary string to a usable dictionary.
+
+    Parameters
+    ----------
+    inputs_string: str
+        A string of the form "input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]" that
+        indicates the desired shape for specific model inputs.
+
+    Returns
+    -------
+    shape_dict: dict
+        A dictionary mapping input names to their shape for use in relay frontend converters.
+    """
+
+    # Create a regex pattern that extracts each separate input mapping.
+    pattern = r"\w+\:\s*\[\-?\d+(?:\,\s*\-?\d+)*\]"
+    input_mappings = re.findall(pattern, inputs_string)
+    if not input_mappings:
+        raise argparse.ArgumentTypeError(
+            "--input-shapes argument must be of the form "
+            '"input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]"'
+        )
+    shape_dict = {}
+    for mapping in input_mappings:
+        # Remove whitespace.
+        mapping = mapping.replace(" ", "")
+        # Split mapping into name and shape.
+        name, shape_string = mapping.split(":")
+        # Convert shape string into a list of integers or Anys if negative.
+        shape = [int(x) if int(x) > 0 else relay.Any() for x in shape_string.strip("][").split(",")]
+        # Add parsed mapping to shape dictionary.
+        shape_dict[name] = shape
+
+    return shape_dict
diff --git a/python/tvm/driver/tvmc/compiler.py b/python/tvm/driver/tvmc/compiler.py
index 90b0aceaa17a..83791e50f6d5 100644
--- a/python/tvm/driver/tvmc/compiler.py
+++ b/python/tvm/driver/tvmc/compiler.py
@@ -24,11 +24,11 @@
 
 import tvm
 from tvm import autotvm, auto_scheduler
-from tvm import relay
+from tvm import relay, runtime
 from tvm.contrib import cc
 from tvm.contrib import utils
 
-from . import common, frontends
+from . import common, composite_target, frontends
 from .main import register_parser
 
 
@@ -72,7 +72,7 @@ def add_compile_parser(subparsers):
     )
     parser.add_argument(
         "--target",
-        help="compilation target as plain string, inline JSON or path to a JSON file",
+        help="compilation targets as comma separated string, inline JSON or path to a JSON file.",
         required=True,
     )
     parser.add_argument(
@@ -87,6 +87,13 @@ def add_compile_parser(subparsers):
     #     can be improved in future to add integration with a modelzoo
     #     or URL, for example.
     parser.add_argument("FILE", help="path to the input model file")
+    parser.add_argument(
+        "--input-shapes",
+        help="specify non-generic shapes for model to run, format is "
+        '"input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]"',
+        type=common.parse_shape_string,
+        default=None,
+    )
 
 
 def drive_compile(args):
@@ -98,7 +105,7 @@ def drive_compile(args):
         Arguments from command line parser.
 
     Returns
-    --------
+    -------
     int
         Zero if successfully completed
 
@@ -112,6 +119,7 @@ def drive_compile(args):
         args.model_format,
         args.tuning_records,
         args.desired_layout,
+        args.input_shapes,
     )
 
     if dumps:
@@ -129,6 +137,7 @@ def compile_model(
     model_format=None,
     tuning_records=None,
     alter_layout=None,
+    shape_dict=None,
 ):
     """Compile a model from a supported framework into a TVM module.
 
@@ -158,6 +167,9 @@ def compile_model(
         The layout to convert the graph to. Note, the convert layout
         pass doesn't currently guarantee the whole of the graph will
         be converted to the chosen layout.
+    shape_dict: dict, optional
+        A mapping from input names to their shape. When present,
+        the default shapes in the model will be overwritten.
 
     Returns
     -------
@@ -172,14 +184,22 @@ def compile_model(
 
     """
     dump_code = [x.strip() for x in dump_code.split(",")] if dump_code else None
-    mod, params = frontends.load_model(path, model_format)
+    mod, params = frontends.load_model(path, model_format, shape_dict)
+    config = {}
 
     if alter_layout:
         mod = common.convert_graph_layout(mod, alter_layout)
 
-    tvm_target = common.target_from_cli(target)
+    tvm_target, extra_targets = common.target_from_cli(target)
     target_host = tvm_target if not target_host else target_host
 
+    for codegen_from_cli in extra_targets:
+        codegen = composite_target.get_codegen_by_target(codegen_from_cli["name"])
+        partition_function = codegen["pass_pipeline"]
+        mod = partition_function(mod, params)
+        if codegen["config_key"] is not None:
+            config[codegen["config_key"]] = codegen_from_cli["opts"]
+
     if tuning_records and os.path.exists(tuning_records):
         logger.debug("tuning records file provided: %s", tuning_records)
 
@@ -191,22 +211,21 @@ def compile_model(
 
         if use_autoscheduler:
             with auto_scheduler.ApplyHistoryBest(tuning_records):
-                with tvm.transform.PassContext(
-                    opt_level=3, config={"relay.backend.use_auto_scheduler": True}
-                ):
+                config["relay.backend.use_auto_scheduler"] = True
+                with tvm.transform.PassContext(opt_level=3, config=config):
                     logger.debug("building relay graph with autoscheduler")
                     graph_module = relay.build(
                         mod, target=target, params=params, target_host=target_host
                     )
         else:
             with autotvm.apply_history_best(tuning_records):
-                with tvm.transform.PassContext(opt_level=3):
+                with tvm.transform.PassContext(opt_level=3, config=config):
                     logger.debug("building relay graph with tuning records")
                     graph_module = relay.build(
                         mod, tvm_target, params=params, target_host=target_host
                     )
     else:
-        with tvm.transform.PassContext(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3, config=config):
             logger.debug("building relay graph (no tuning records provided)")
             graph_module = relay.build(mod, tvm_target, params=params, target_host=target_host)
 
@@ -263,7 +282,7 @@ def save_module(module_path, graph, lib, params, cross=None):
 
     with open(temp.relpath(param_name), "wb") as params_file:
         logger.debug("writing params to file to %s", params_file.name)
-        params_file.write(relay.save_param_dict(params))
+        params_file.write(runtime.save_param_dict(params))
 
     logger.debug("saving module as tar file to %s", module_path)
     with tarfile.open(module_path, "w") as tar:
diff --git a/python/tvm/driver/tvmc/composite_target.py b/python/tvm/driver/tvmc/composite_target.py
new file mode 100644
index 000000000000..886160ad000c
--- /dev/null
+++ b/python/tvm/driver/tvmc/composite_target.py
@@ -0,0 +1,73 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Provides support to composite target on TVMC.
+"""
+import logging
+
+from tvm.relay.op.contrib.arm_compute_lib import partition_for_arm_compute_lib
+from tvm.relay.op.contrib.ethosn import partition_for_ethosn
+from tvm.relay.op.contrib.bnns import partition_for_bnns
+
+from .common import TVMCException
+
+
+# pylint: disable=invalid-name
+logger = logging.getLogger("TVMC")
+
+# Global dictionary to map targets with the configuration key
+# to be used in the PassContext (if any), and a function
+# responsible for partitioning to that target.
+REGISTERED_CODEGEN = {
+    "compute-library": {
+        "config_key": None,
+        "pass_pipeline": partition_for_arm_compute_lib,
+    },
+    "ethos-n77": {
+        "config_key": "relay.ext.ethos-n.options",
+        "pass_pipeline": partition_for_ethosn,
+    },
+    "bnns": {
+        "config_key": None,
+        "pass_pipeline": partition_for_bnns,
+    },
+}
+
+
+def get_codegen_names():
+    """Return a list of all registered codegens.
+
+    Returns
+    -------
+    list of str
+        all registered targets
+    """
+    return list(REGISTERED_CODEGEN.keys())
+
+
+def get_codegen_by_target(name):
+    """Return a codegen entry by name.
+
+    Returns
+    -------
+    dict
+        requested target information
+    """
+    try:
+        return REGISTERED_CODEGEN[name]
+    except KeyError:
+        raise TVMCException("Composite target %s is not defined in TVMC." % name)
diff --git a/python/tvm/driver/tvmc/frontends.py b/python/tvm/driver/tvmc/frontends.py
index bb54b82cceca..0488223c782f 100644
--- a/python/tvm/driver/tvmc/frontends.py
+++ b/python/tvm/driver/tvmc/frontends.py
@@ -54,13 +54,15 @@ def suffixes():
         """File suffixes (extensions) used by this frontend"""
 
     @abstractmethod
-    def load(self, path):
+    def load(self, path, shape_dict=None, **kwargs):
         """Load a model from a given path.
 
         Parameters
         ----------
         path: str
             Path to a file
+        shape_dict: dict, optional
+            Mapping from input names to their shapes.
 
         Returns
         -------
@@ -99,7 +101,7 @@ def name():
     def suffixes():
         return ["h5"]
 
-    def load(self, path):
+    def load(self, path, shape_dict=None, **kwargs):
         # pylint: disable=C0103
         tf, keras = import_keras()
 
@@ -125,8 +127,11 @@ def load(self, path):
                 )
 
         inputs = [np.random.uniform(size=shape, low=-1.0, high=1.0) for shape in in_shapes]
-        shape_dict = {name: x.shape for (name, x) in zip(model.input_names, inputs)}
-        return relay.frontend.from_keras(model, shape_dict, layout="NHWC")
+        input_shapes = {name: x.shape for (name, x) in zip(model.input_names, inputs)}
+        if shape_dict is not None:
+            input_shapes.update(shape_dict)
+        kwargs.setdefault("layout", "NHWC")
+        return relay.frontend.from_keras(model, input_shapes, **kwargs)
 
     def is_sequential_p(self, model):
         _, keras = import_keras()
@@ -154,14 +159,14 @@ def name():
     def suffixes():
         return ["onnx"]
 
-    def load(self, path):
+    def load(self, path, shape_dict=None, **kwargs):
         # pylint: disable=C0415
         import onnx
 
         # pylint: disable=E1101
         model = onnx.load(path)
 
-        return relay.frontend.from_onnx(model)
+        return relay.frontend.from_onnx(model, shape=shape_dict, **kwargs)
 
 
 class TensorflowFrontend(Frontend):
@@ -175,7 +180,7 @@ def name():
     def suffixes():
         return ["pb"]
 
-    def load(self, path):
+    def load(self, path, shape_dict=None, **kwargs):
         # pylint: disable=C0415
         import tensorflow as tf
         import tvm.relay.testing.tf as tf_testing
@@ -188,25 +193,12 @@ def load(self, path):
         graph_def = tf_testing.ProcessGraphDefParam(graph_def)
 
         logger.debug("parse TensorFlow model and convert into Relay computation graph")
-        return relay.frontend.from_tensorflow(graph_def)
+        return relay.frontend.from_tensorflow(graph_def, shape=shape_dict, **kwargs)
 
 
 class TFLiteFrontend(Frontend):
     """ TFLite frontend for TVMC """
 
-    _tflite_m = {
-        0: "float32",
-        1: "float16",
-        2: "int32",
-        3: "uint8",
-        4: "int64",
-        5: "string",
-        6: "bool",
-        7: "int16",
-        8: "complex64",
-        9: "int8",
-    }
-
     @staticmethod
     def name():
         return "tflite"
@@ -215,7 +207,7 @@ def name():
     def suffixes():
         return ["tflite"]
 
-    def load(self, path):
+    def load(self, path, shape_dict=None, **kwargs):
         # pylint: disable=C0415
         import tflite.Model as model
 
@@ -237,41 +229,10 @@ def load(self, path):
         if version != 3:
             raise TVMCException("input file not tflite version 3")
 
-        logger.debug("tflite_input_type")
-        shape_dict, dtype_dict = TFLiteFrontend._input_type(tflite_model)
-
         logger.debug("parse TFLite model and convert into Relay computation graph")
-        mod, params = relay.frontend.from_tflite(
-            tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict
-        )
+        mod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict, **kwargs)
         return mod, params
 
-    @staticmethod
-    def _decode_type(n):
-        return TFLiteFrontend._tflite_m[n]
-
-    @staticmethod
-    def _input_type(model):
-        subgraph_count = model.SubgraphsLength()
-        assert subgraph_count > 0
-        shape_dict = {}
-        dtype_dict = {}
-        for subgraph_index in range(subgraph_count):
-            subgraph = model.Subgraphs(subgraph_index)
-            inputs_count = subgraph.InputsLength()
-            assert inputs_count >= 1
-            for input_index in range(inputs_count):
-                input_ = subgraph.Inputs(input_index)
-                assert subgraph.TensorsLength() > input_
-                tensor = subgraph.Tensors(input_)
-                input_shape = tuple(tensor.ShapeAsNumpy())
-                tensor_type = tensor.Type()
-                input_name = tensor.Name().decode("utf8")
-                shape_dict[input_name] = input_shape
-                dtype_dict[input_name] = TFLiteFrontend._decode_type(tensor_type)
-
-        return shape_dict, dtype_dict
-
 
 class PyTorchFrontend(Frontend):
     """ PyTorch frontend for TVMC """
@@ -285,20 +246,21 @@ def suffixes():
         # Torch Script is a zip file, but can be named pth
         return ["pth", "zip"]
 
-    def load(self, path):
+    def load(self, path, shape_dict=None, **kwargs):
         # pylint: disable=C0415
         import torch
 
-        traced_model = torch.jit.load(path)
-
-        inputs = list(traced_model.graph.inputs())[1:]
-        input_shapes = [inp.type().sizes() for inp in inputs]
+        if shape_dict is None:
+            raise TVMCException("--input-shapes must be specified for %s" % self.name())
 
+        traced_model = torch.jit.load(path)
         traced_model.eval()  # Switch to inference mode
-        input_shapes = [("input{}".format(idx), shape) for idx, shape in enumerate(shapes)]
+
+        # Convert shape dictionary to list for Pytorch frontend compatibility
+        input_shapes = list(shape_dict.items())
 
         logger.debug("parse Torch model and convert into Relay computation graph")
-        return relay.frontend.from_pytorch(traced_model, input_shapes)
+        return relay.frontend.from_pytorch(traced_model, input_shapes, **kwargs)
 
 
 ALL_FRONTENDS = [
@@ -378,7 +340,7 @@ def guess_frontend(path):
     raise TVMCException("failed to infer the model format. Please specify --model-format")
 
 
-def load_model(path, model_format=None):
+def load_model(path, model_format=None, shape_dict=None, **kwargs):
     """Load a model from a supported framework and convert it
     into an equivalent relay representation.
 
@@ -389,6 +351,8 @@ def load_model(path, model_format=None):
     model_format : str, optional
         The underlying framework used to create the model.
         If not specified, this will be inferred from the file type.
+    shape_dict : dict, optional
+        Mapping from input names to their shapes.
 
     Returns
     -------
@@ -404,6 +368,6 @@ def load_model(path, model_format=None):
     else:
         frontend = guess_frontend(path)
 
-    mod, params = frontend.load(path)
+    mod, params = frontend.load(path, shape_dict, **kwargs)
 
     return mod, params
diff --git a/python/tvm/driver/tvmc/main.py b/python/tvm/driver/tvmc/main.py
index fee04db820fb..1d360d98206e 100644
--- a/python/tvm/driver/tvmc/main.py
+++ b/python/tvm/driver/tvmc/main.py
@@ -23,7 +23,7 @@
 import logging
 import sys
 
-import pkg_resources
+import tvm
 
 from tvm.driver.tvmc.common import TVMCException
 
@@ -75,8 +75,7 @@ def _main(argv):
     logging.getLogger("TVMC").setLevel(40 - args.verbose * 10)
 
     if args.version:
-        version = pkg_resources.get_distribution("tvm").version
-        sys.stdout.write("%s\n" % version)
+        sys.stdout.write("%s\n" % tvm.__version__)
         return 0
 
     if not hasattr(args, "func"):
diff --git a/python/tvm/driver/tvmc/runner.py b/python/tvm/driver/tvmc/runner.py
index dec0e9842a37..1d23ccfb0c00 100644
--- a/python/tvm/driver/tvmc/runner.py
+++ b/python/tvm/driver/tvmc/runner.py
@@ -24,11 +24,11 @@
 import tempfile
 
 import numpy as np
-import tvm
 from tvm import rpc
 from tvm.autotvm.measure import request_remote
 from tvm.contrib import graph_runtime as runtime
 from tvm.contrib.debugger import debug_runtime
+from tvm.relay import load_param_dict
 
 from . import common
 from .common import TVMCException
@@ -163,9 +163,8 @@ def get_input_info(graph_str, params):
 
     shape_dict = {}
     dtype_dict = {}
-    # Use a special function to load the binary params back into a dict
-    load_arr = tvm.get_global_func("tvm.relay._load_param_dict")(params)
-    param_names = [v.name for v in load_arr]
+    params_dict = load_param_dict(params)
+    param_names = [k for (k, v) in params_dict.items()]
     graph = json.loads(graph_str)
     for node_id in graph["arg_nodes"]:
         node = graph["nodes"][node_id]
@@ -427,7 +426,7 @@ def get_top_results(outputs, max_results):
         The first row is the indices and the second is the values.
 
     """
-    output = outputs["output_0"]
+    output = np.copy(outputs["output_0"])
     sorted_labels = output.argsort()[0][-max_results:][::-1]
     output.sort()
     sorted_values = output[0][-max_results:][::-1]
diff --git a/python/tvm/ir/container.py b/python/tvm/ir/container.py
index a87d67992953..5222f7a97a7c 100644
--- a/python/tvm/ir/container.py
+++ b/python/tvm/ir/container.py
@@ -19,7 +19,7 @@
 
 from tvm.runtime import Object
 from tvm.runtime.container import getitem_helper
-from tvm.runtime import _ffi_node_api
+from tvm.runtime import _ffi_api
 
 
 @tvm._ffi.register_object("Array")
@@ -33,10 +33,10 @@ class Array(Object):
     """
 
     def __getitem__(self, idx):
-        return getitem_helper(self, _ffi_node_api.ArrayGetItem, len(self), idx)
+        return getitem_helper(self, _ffi_api.ArrayGetItem, len(self), idx)
 
     def __len__(self):
-        return _ffi_node_api.ArraySize(self)
+        return _ffi_api.ArraySize(self)
 
 
 @tvm._ffi.register_object
@@ -49,18 +49,18 @@ class Map(Object):
     """
 
     def __getitem__(self, k):
-        return _ffi_node_api.MapGetItem(self, k)
+        return _ffi_api.MapGetItem(self, k)
 
     def __contains__(self, k):
-        return _ffi_node_api.MapCount(self, k) != 0
+        return _ffi_api.MapCount(self, k) != 0
 
     def items(self):
         """Get the items from the map"""
-        akvs = _ffi_node_api.MapItems(self)
+        akvs = _ffi_api.MapItems(self)
         return [(akvs[i], akvs[i + 1]) for i in range(0, len(akvs), 2)]
 
     def __len__(self):
-        return _ffi_node_api.MapSize(self)
+        return _ffi_api.MapSize(self)
 
     def get(self, key, default=None):
         """Get an element with a default value.
diff --git a/python/tvm/ir/transform.py b/python/tvm/ir/transform.py
index bb230cad0c9c..36e06eeb8b23 100644
--- a/python/tvm/ir/transform.py
+++ b/python/tvm/ir/transform.py
@@ -330,3 +330,26 @@ def PrintIR(header="", show_meta_data=False):
     The pass
     """
     return _ffi_transform_api.PrintIR(header, show_meta_data)
+
+
+def render_pass_profiles():
+    """Returns a string render of the pass profiling data. The format of each output line is
+    `{name}: {time} [{time excluding sub-passes}] ({% of total}; {% of parent})`.
+    The indentation of each line corresponds to nesting of passes.
+    """
+    return _ffi_transform_api.render_pass_profiles()
+
+
+def clear_pass_profiles():
+    """Clears all stored pass profiling data."""
+    _ffi_transform_api.clear_pass_profiles()
+
+
+def enable_pass_profiling():
+    """Enables pass profiling."""
+    _ffi_transform_api.enable_pass_profiling()
+
+
+def disable_pass_profiling():
+    """Disables pass profiling."""
+    _ffi_transform_api.disable_pass_profiling()
diff --git a/python/tvm/micro/__init__.py b/python/tvm/micro/__init__.py
index a6e24343e378..ade63f2da9e4 100644
--- a/python/tvm/micro/__init__.py
+++ b/python/tvm/micro/__init__.py
@@ -17,11 +17,17 @@
 """MicroTVM module for bare-metal backends"""
 
 from .artifact import Artifact
-from .build import build_static_runtime, default_options, TVM_ROOT_DIR
-from .build import CRT_ROOT_DIR, Workspace
+from .build import build_static_runtime, default_options, get_standalone_crt_dir
+from .build import get_standalone_crt_lib, Workspace
 from .compiler import Compiler, DefaultCompiler, Flasher
 from .debugger import GdbRemoteDebugger
 from .micro_library import MicroLibrary
 from .micro_binary import MicroBinary
-from .session import create_local_graph_runtime, Session, SessionTerminatedError
+from .model_library_format import export_model_library_format, UnsupportedInModelLibraryFormatError
+from .session import (
+    create_local_graph_runtime,
+    create_local_debug_runtime,
+    Session,
+    SessionTerminatedError,
+)
 from .transport import TransportLogger, DebugWrapperTransport, SubprocessTransport
diff --git a/python/tvm/micro/build.py b/python/tvm/micro/build.py
index cad385b9b190..d95f14f0349e 100644
--- a/python/tvm/micro/build.py
+++ b/python/tvm/micro/build.py
@@ -21,9 +21,11 @@
 import logging
 import os
 import re
+import typing
 from tvm.contrib import utils
 
 from .micro_library import MicroLibrary
+from .._ffi import libinfo
 
 
 _LOG = logging.getLogger(__name__)
@@ -55,69 +57,137 @@ def path(self):
 CRT_RUNTIME_LIB_NAMES = ["utvm_rpc_server", "utvm_rpc_common", "common"]
 
 
-TVM_ROOT_DIR = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
+STANDALONE_CRT_DIR = None
 
 
-CRT_ROOT_DIR = os.path.join(TVM_ROOT_DIR, "src", "runtime", "crt")
+class CrtNotFoundError(Exception):
+    """Raised when the standalone CRT dirtree cannot be found."""
 
 
-RUNTIME_LIB_SRC_DIRS = [os.path.join(CRT_ROOT_DIR, n) for n in CRT_RUNTIME_LIB_NAMES] + [
-    os.path.join(TVM_ROOT_DIR, "3rdparty/libcrc/src")
-]
+def get_standalone_crt_dir() -> str:
+    """Find the standalone_crt directory.
 
+    Though the C runtime source lives in the tvm tree, it is intended to be distributed with any
+    binary build of TVM. This source tree is intended to be integrated into user projects to run
+    models targeted with --runtime=c.
+
+    Returns
+    -------
+    str :
+        The path to the standalone_crt
+    """
+    global STANDALONE_CRT_DIR
+    if STANDALONE_CRT_DIR is None:
+        for path in libinfo.find_lib_path():
+            crt_path = os.path.join(os.path.dirname(path), "standalone_crt")
+            if os.path.isdir(crt_path):
+                STANDALONE_CRT_DIR = crt_path
+                break
+
+        else:
+            raise CrtNotFoundError()
+
+    return STANDALONE_CRT_DIR
+
+
+def get_standalone_crt_lib(name: str) -> str:
+    """Find a source library directory in the standalone_crt.
+
+    The standalone C runtime is split into various libraries (one per directory underneath
+    src/runtime/crt). This convenience function returns the full path to one of those libraries
+    located in get_standalone_crt_dir().
+
+    Parameters
+    ----------
+    name : str
+        Name of the library subdirectory underneath src/runtime/crt.
+
+    Returns
+    -------
+    str :
+         The full path to the the library.
+    """
+    return os.path.join(get_standalone_crt_dir(), "src", "runtime", "crt", name)
 
-RUNTIME_SRC_REGEX = re.compile(r"^.*\.cc?$", re.IGNORECASE)
 
+def get_runtime_libs() -> str:
+    """Return abspath to all CRT directories which contain source (i.e. not header) files."""
+    return [get_standalone_crt_lib(n) for n in CRT_RUNTIME_LIB_NAMES]
 
-_COMMON_CFLAGS = ["-Wall", "-Werror"]
 
+RUNTIME_SRC_REGEX = re.compile(r"^.*\.cc?$", re.IGNORECASE)
 
-_CRT_DEFAULT_OPTIONS = {
-    "cflags": ["-std=c11"] + _COMMON_CFLAGS,
-    "ccflags": ["-std=c++11"] + _COMMON_CFLAGS,
-    "ldflags": ["-std=c++11"],
-    "include_dirs": [
-        f"{TVM_ROOT_DIR}/include",
-        f"{TVM_ROOT_DIR}/3rdparty/dlpack/include",
-        f"{TVM_ROOT_DIR}/3rdparty/libcrc/include",
-        f"{TVM_ROOT_DIR}/3rdparty/dmlc-core/include",
-        f"{CRT_ROOT_DIR}/include",
-    ],
-}
 
+_COMMON_CFLAGS = ["-Wall", "-Werror", "-DDMLC_USE_LOGGING_LIBRARY=<tvm/runtime/logging.h>"]
 
-_CRT_GENERATED_LIB_OPTIONS = copy.copy(_CRT_DEFAULT_OPTIONS)
 
+def _build_default_compiler_options(standalone_crt_dir: typing.Optional[str] = None) -> str:
+    """Return a dict containing base compile flags for the CRT under gcc common to .
 
-# Disable due to limitation in the TVM C codegen, which generates lots of local variable
-# declarations at the top of generated code without caring whether they're used.
-# Example:
-#   void* arg0 = (((TVMValue*)args)[0].v_handle);
-#   int32_t arg0_code = ((int32_t*)arg_type_ids)[(0)];
-_CRT_GENERATED_LIB_OPTIONS["cflags"].append("-Wno-unused-variable")
-_CRT_GENERATED_LIB_OPTIONS["ccflags"].append("-Wno-unused-variable")
+    Parameters
+    ----------
+    standalone_crt_dir : Optional[str]
+        If given, the path to the standalone_crt
+    """
+    if standalone_crt_dir is None:
+        standalone_crt_dir = get_standalone_crt_dir()
+    return {
+        "cflags": ["-std=c11"] + _COMMON_CFLAGS,
+        "ccflags": ["-std=c++11"] + _COMMON_CFLAGS,
+        "ldflags": ["-std=c++11"],
+        "include_dirs": [os.path.join(standalone_crt_dir, "include")],
+    }
 
 
-# Many TVM-intrinsic operators (i.e. expf, in particular)
-_CRT_GENERATED_LIB_OPTIONS["cflags"].append("-fno-builtin")
+def default_options(crt_config_include_dir, standalone_crt_dir=None):
+    """Return default opts passed to Compile commands.
 
+    Parameters
+    ----------
+    crt_config_include_dir : str
+        Path to a directory containing crt_config.h for the target. This will be appended
+        to the include path for cflags and ccflags.
+    standalone_crt_dir : Optional[str]
+
+    Returns
+    -------
+    Dict :
+        A dictionary containing 3 subkeys, each whose value is _build_default_compiler_options()
+        plus additional customization.
+         - "bin_opts" - passed as "options" to Compiler.binary() when building MicroBinary.
+         - "lib_opts" - passed as "options" to Compiler.library() when building bundled CRT
+           libraries (or otherwise, non-generated libraries).
+         - "generated_lib_opts" - passed as "options" to Compiler.library() when building the
+           generated library.
+    """
+    bin_opts = _build_default_compiler_options(standalone_crt_dir)
+    bin_opts["include_dirs"].append(crt_config_include_dir)
 
-def default_options(target_include_dir):
-    """Return default opts passed to Compile commands."""
-    bin_opts = copy.deepcopy(_CRT_DEFAULT_OPTIONS)
-    bin_opts["include_dirs"].append(target_include_dir)
-    lib_opts = copy.deepcopy(bin_opts)
+    lib_opts = _build_default_compiler_options(standalone_crt_dir)
     lib_opts["cflags"] = ["-Wno-error=incompatible-pointer-types"]
-    return {"bin_opts": bin_opts, "lib_opts": lib_opts}
+    lib_opts["include_dirs"].append(crt_config_include_dir)
+
+    generated_lib_opts = copy.copy(lib_opts)
+
+    # Disable due to limitation in the TVM C codegen, which generates lots of local variable
+    # declarations at the top of generated code without caring whether they're used.
+    # Example:
+    #   void* arg0 = (((TVMValue*)args)[0].v_handle);
+    #   int32_t arg0_code = ((int32_t*)arg_type_ids)[(0)];
+    generated_lib_opts["cflags"].append("-Wno-unused-variable")
+    generated_lib_opts["ccflags"].append("-Wno-unused-variable")
+
+    # Many TVM-intrinsic operators (i.e. expf, in particular)
+    generated_lib_opts["cflags"].append("-fno-builtin")
+
+    return {"bin_opts": bin_opts, "lib_opts": lib_opts, "generated_lib_opts": generated_lib_opts}
 
 
 def build_static_runtime(
     workspace,
     compiler,
     module,
-    lib_opts=None,
-    bin_opts=None,
-    generated_lib_opts=None,
+    compiler_options,
     extra_libs=None,
 ):
     """Build the on-device runtime, statically linking the given modules.
@@ -130,15 +200,11 @@ def build_static_runtime(
     module : IRModule
         Module to statically link.
 
-    lib_opts : Optional[dict]
-        The `options` parameter passed to compiler.library().
-
-    bin_opts : Optional[dict]
-        The `options` parameter passed to compiler.binary().
-
-    generated_lib_opts : Optional[dict]
-        The `options` parameter passed to compiler.library() when compiling the generated TVM C
-        source module.
+    compiler_options : dict
+        The return value of tvm.micro.default_options(), with any keys overridden to inject
+        compiler options specific to this build. If not given, tvm.micro.default_options() is
+        used. This dict contains the `options` parameter passed to Compiler.library() and
+        Compiler.binary() at various stages in the compilation process.
 
     extra_libs : Optional[List[MicroLibrary|str]]
         If specified, extra libraries to be compiled into the binary. If a MicroLibrary, it is
@@ -151,18 +217,12 @@ def build_static_runtime(
     MicroBinary :
         The compiled runtime.
     """
-    lib_opts = _CRT_DEFAULT_OPTIONS if lib_opts is None else lib_opts
-    bin_opts = _CRT_DEFAULT_OPTIONS if bin_opts is None else bin_opts
-    generated_lib_opts = (
-        _CRT_GENERATED_LIB_OPTIONS if generated_lib_opts is None else generated_lib_opts
-    )
-
     mod_build_dir = workspace.relpath(os.path.join("build", "module"))
     os.makedirs(mod_build_dir)
     mod_src_dir = workspace.relpath(os.path.join("src", "module"))
 
     libs = []
-    for mod_or_src_dir in (extra_libs or []) + RUNTIME_LIB_SRC_DIRS:
+    for mod_or_src_dir in (extra_libs or []) + get_runtime_libs():
         if isinstance(mod_or_src_dir, MicroLibrary):
             libs.append(mod_or_src_dir)
             continue
@@ -177,7 +237,7 @@ def build_static_runtime(
             if RUNTIME_SRC_REGEX.match(p):
                 lib_srcs.append(os.path.join(lib_src_dir, p))
 
-        libs.append(compiler.library(lib_build_dir, lib_srcs, lib_opts))
+        libs.append(compiler.library(lib_build_dir, lib_srcs, compiler_options["lib_opts"]))
 
     mod_src_dir = workspace.relpath(os.path.join("src", "module"))
     os.makedirs(mod_src_dir)
@@ -185,10 +245,12 @@ def build_static_runtime(
         module.export_library(
             mod_build_dir,
             workspace_dir=mod_src_dir,
-            fcompile=lambda bdir, srcs, **kwargs: compiler.library(bdir, srcs, generated_lib_opts),
+            fcompile=lambda bdir, srcs, **kwargs: compiler.library(
+                bdir, srcs, compiler_options["generated_lib_opts"]
+            ),
         )
     )
 
     runtime_build_dir = workspace.relpath(f"build/runtime")
     os.makedirs(runtime_build_dir)
-    return compiler.binary(runtime_build_dir, libs, bin_opts)
+    return compiler.binary(runtime_build_dir, libs, compiler_options["bin_opts"])
diff --git a/python/tvm/micro/compiler.py b/python/tvm/micro/compiler.py
index 3b62e9347c7f..5bc5aba8a1be 100644
--- a/python/tvm/micro/compiler.py
+++ b/python/tvm/micro/compiler.py
@@ -24,7 +24,6 @@
 import subprocess
 
 import tvm.target
-from . import build
 from . import class_factory
 from . import debugger
 from . import transport
@@ -82,6 +81,9 @@ def _target_from_sources(cls, sources):
         target_strs = set()
 
         for obj in sources:
+            if os.path.splitext(obj)[1] not in (".cc", ".c"):
+                continue
+
             with open(obj) as obj_f:
                 for line in obj_f:
                     m = cls.TVM_TARGET_RE.match(line)
@@ -96,7 +98,7 @@ def _target_from_sources(cls, sources):
             )
 
         target_str = next(iter(target_strs))
-        return tvm.target.create(target_str)
+        return tvm.target.Target(target_str)
 
     # Maps regexes identifying CPUs to the default toolchain prefix for that CPU.
     TOOLCHAIN_PREFIX_BY_CPU_REGEX = {
@@ -106,6 +108,12 @@ def _target_from_sources(cls, sources):
     }
 
     def _autodetect_toolchain_prefix(self, target):
+        # Treat absence of -mcpu as if -mcpu=native is specified. The gcc shipped with OS X
+        # complains if -mcpu=native is given, so this approach allows model targets to avoid
+        # specifying this flag e.g. for tutorials.
+        if "mcpu" not in target.attrs:
+            return self.TOOLCHAIN_PREFIX_BY_CPU_REGEX["native"]
+
         matches = []
         for regex, prefix in self.TOOLCHAIN_PREFIX_BY_CPU_REGEX.items():
             if re.match(regex, target.attrs["mcpu"]):
@@ -241,7 +249,8 @@ def library(self, output, sources, options=None):
             )
 
         prefix = self._autodetect_toolchain_prefix(target)
-        outputs = []
+        outputs = [s for s in sources if os.path.splitext(s)[1] == ".o"]
+        sources = [s for s in sources if s not in outputs]
         for src in sources:
             src_base, src_ext = os.path.splitext(os.path.basename(src))
 
@@ -285,7 +294,9 @@ def binary(self, output, objects, options=None, link_main=True, main_options=Non
         args.extend(["-g", "-o", output_abspath])
 
         if link_main:
-            host_main_srcs = glob.glob(os.path.join(build.CRT_ROOT_DIR, "host", "*.cc"))
+            host_main_srcs = glob.glob(
+                os.path.join(tvm.micro.get_standalone_crt_dir(), "template", "host", "*.cc")
+            )
             if main_options:
                 main_lib = self.library(os.path.join(output, "host"), host_main_srcs, main_options)
                 for lib_name in main_lib.library_files:
diff --git a/python/tvm/micro/contrib/zephyr.py b/python/tvm/micro/contrib/zephyr.py
index 66254987cb8b..cd9c23cd2f9d 100644
--- a/python/tvm/micro/contrib/zephyr.py
+++ b/python/tvm/micro/contrib/zephyr.py
@@ -55,7 +55,11 @@ def run(self, cmd, **kw):
         for k, v in self.default_overrides.items():
             env[k] = v
 
-        return subprocess.check_output(cmd, env=env, **kw)
+        return subprocess.check_output(cmd, env=env, **kw, universal_newlines=True)
+
+
+class ProjectNotFoundError(Exception):
+    """Raised when the project_dir supplied to ZephyrCompiler does not exist."""
 
 
 class FlashRunnerNotSupported(Exception):
@@ -95,6 +99,13 @@ def __init__(
             If given, additional environment variables present when invoking west, cmake, or make.
         """
         self._project_dir = project_dir
+        if not os.path.exists(project_dir):
+            # Raise this error instead of a potentially-more-cryptic compiler error due to a missing
+            # prj.conf.
+            raise ProjectNotFoundError(
+                f"project_dir supplied to ZephyrCompiler does not exist: {project_dir}"
+            )
+
         self._board = board
         if west_cmd is None:
             self._west_cmd = [sys.executable, "-mwest.app.main"]
@@ -180,7 +191,7 @@ def library(self, output, sources, options=None):
         with open(os.path.join(output, "main.c"), "w"):
             pass
 
-        # expecetd not to exist after populate_tvm_libs
+        # expected not to exist after populate_tvm_libs
         build_dir = os.path.join(output, "__tvm_build")
         os.mkdir(build_dir)
         self._subprocess_env.run(
@@ -193,6 +204,25 @@ def library(self, output, sources, options=None):
         )
         return tvm.micro.MicroLibrary(build_dir, [f"lib{project_name}.a"])
 
+    def _print_make_statistics(self, output):
+        output = output.splitlines()
+        lines = iter(output)
+        for line in lines:
+            if line.startswith("Memory region"):
+                # print statistics header
+                _LOG.info(line)
+                _LOG.info("--------------------- ---------- ------------ ---------")
+                line = next(lines)
+                # while there is a region print it
+                try:
+                    while ":" in line:
+                        _LOG.info(line)
+                        line = next(lines)
+                    else:
+                        break
+                except StopIteration:
+                    pass
+
     def binary(self, output, objects, options=None, link_main=True, main_options=None):
         assert link_main, "Must pass link_main=True"
         assert self._project_dir is not None, "Must supply project_dir= to build binaries"
@@ -213,7 +243,9 @@ def binary(self, output, objects, options=None, link_main=True, main_options=Non
         cmake_args.append(f'-DTVM_LIBS={";".join(copied_libs)}')
         self._subprocess_env.run(cmake_args, cwd=output)
 
-        self._subprocess_env.run(["make"], cwd=output)
+        make_output = self._subprocess_env.run(["make"], cwd=output)
+
+        self._print_make_statistics(make_output)
 
         return tvm.micro.MicroBinary(
             output,
@@ -230,11 +262,12 @@ def binary(self, output, objects, options=None, link_main=True, main_options=Non
     def flasher_factory(self):
         return compiler.FlasherFactory(
             ZephyrFlasher,
-            (self._west_cmd,),
+            (self._board,),
             dict(
                 zephyr_base=self._zephyr_base,
                 project_dir=self._project_dir,
                 subprocess_env=self._subprocess_env.default_overrides,
+                west_cmd=self._west_cmd,
             ),
         )
 
@@ -280,7 +313,7 @@ class ZephyrFlasher(tvm.micro.compiler.Flasher):
 
     def __init__(
         self,
-        west_cmd,
+        board,
         zephyr_base=None,
         project_dir=None,
         subprocess_env=None,
@@ -289,6 +322,7 @@ def __init__(
         flash_args=None,
         debug_rpc_session=None,
         serial_timeouts=None,
+        west_cmd=None,
     ):
         zephyr_base = zephyr_base or os.environ["ZEPHYR_BASE"]
         sys.path.insert(0, os.path.join(zephyr_base, "scripts", "dts"))
@@ -299,6 +333,7 @@ def __init__(
         finally:
             sys.path.pop(0)
 
+        self._board = board
         self._zephyr_base = zephyr_base
         self._project_dir = project_dir
         self._west_cmd = west_cmd
@@ -341,6 +376,7 @@ def _get_nrf_device_args(self):
     # kwargs passed to usb.core.find to find attached boards for the openocd flash runner.
     BOARD_USB_FIND_KW = {
         "nucleo_f746zg": {"idVendor": 0x0483, "idProduct": 0x374B},
+        "stm32f746g_disco": {"idVendor": 0x0483, "idProduct": 0x374B},
     }
 
     def openocd_serial(self, cmake_entries):
@@ -376,7 +412,7 @@ def _get_flash_runner(cls, cmake_entries):
             return flash_runner
 
         with open(cmake_entries["ZEPHYR_RUNNERS_YAML"]) as f:
-            doc = yaml.load(f)
+            doc = yaml.load(f, Loader=yaml.FullLoader)
         return doc["flash-runner"]
 
     def _get_device_args(self, cmake_entries):
@@ -402,6 +438,20 @@ def flash(self, micro_binary):
         build_dir = os.path.dirname(
             micro_binary.abspath(micro_binary.labelled_files["cmake_cache"][0])
         )
+
+        # The nRF5340DK requires an additional `nrfjprog --recover` before each flash cycle.
+        # This is because readback protection is enabled by default when this device is flashed.
+        # Otherwise, flashing may fail with an error such as the following:
+        #  ERROR: The operation attempted is unavailable due to readback protection in
+        #  ERROR: your device. Please use --recover to unlock the device.
+        if (
+            self._board.startswith("nrf5340dk")
+            and self._get_flash_runner(cmake_entries) == "nrfjprog"
+        ):
+            recover_args = ["nrfjprog", "--recover"]
+            recover_args.extend(self._get_nrf_device_args())
+            self._subprocess_env.run(recover_args, cwd=build_dir)
+
         west_args = (
             self._west_cmd
             + ["flash", "--build-dir", build_dir, "--skip-rebuild"]
@@ -487,7 +537,7 @@ class QemuStartupFailureError(Exception):
 
 
 class QemuFdTransport(file_descriptor.FdTransport):
-    """An FdTransport subclass that escapes written data to accomodate the QEMU monitor.
+    """An FdTransport subclass that escapes written data to accommodate the QEMU monitor.
 
     It's supposedly possible to disable the monitor, but Zephyr controls most of the command-line
     arguments for QEMU and there are too many options which implictly enable the monitor, so this
diff --git a/python/tvm/micro/model_library_format.py b/python/tvm/micro/model_library_format.py
new file mode 100644
index 000000000000..4ce80be647c1
--- /dev/null
+++ b/python/tvm/micro/model_library_format.py
@@ -0,0 +1,171 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Defines functions for exporting to Model Library Format."""
+
+import datetime
+import json
+import os
+import re
+import tarfile
+
+from ..contrib import utils
+from ..relay.backend import graph_runtime_factory
+from ..relay import param_dict
+
+
+class UnsupportedInModelLibraryFormatError(Exception):
+    """Raised when export_model_library_format does not support the given Module tree."""
+
+
+def _populate_codegen_dir(mod, codegen_dir: str):
+    """Populate the codegen sub-directory as part of a Model Library Format export.
+
+    Parameters
+    ----------
+    mod : tvm.runtime.Module
+        Module which should be written to codegen_dir.
+    codegen_dir : str
+        Path to the codegen directory on disk.
+    """
+    dso_modules = mod._collect_dso_modules()
+    dso_module_handles = [m.handle.value for m in dso_modules]
+    non_dso_modules = mod._collect_from_import_tree(lambda m: m not in dso_modules)
+    if non_dso_modules:
+        raise UnsupportedInModelLibraryFormatError(
+            f"Don't know how to export non-c or non-llvm modules; found: {non_dso_modules!r}"
+        )
+
+    mod_indices = {"lib": 0, "src": 0}
+    host_codegen_dir = os.path.join(codegen_dir, "host")
+    for dso_mod in dso_modules:
+        if dso_mod.type_key == "c":
+            index = mod_indices["src"]
+            mod_indices["src"] += 1
+            parent_dir = os.path.join(host_codegen_dir, "src")
+            file_name = os.path.join(parent_dir, f"lib{index}.c")
+        elif dso_mod.type_key == "llvm":
+            index = mod_indices["lib"]
+            mod_indices["lib"] += 1
+            parent_dir = os.path.join(host_codegen_dir, "lib")
+            file_name = os.path.join(parent_dir, f"lib{index}.o")
+        else:
+            assert (
+                False
+            ), f"do not expect module with type_key={mod.type_key} from _collect_dso_modules"
+
+        if not os.path.exists(parent_dir):
+            os.makedirs(parent_dir)
+        dso_mod.save(file_name)
+
+
+def _build_memory_map(graph_json):
+    """Build a simpler memory map from graph JSON.
+
+    Parameters
+    ----------
+    graph_json : str
+        String representation of the graph_json created from tvm.relay.build().
+
+    Returns
+    -------
+    list :
+        A list with one entry per storage id describing that memory.
+    """
+    graph = json.loads(graph_json)
+
+    seen_storage_ids = set()
+    memory_map = []
+    for node_id, storage_id in enumerate(graph["attrs"]["storage_id"][1]):
+        if storage_id in seen_storage_ids:
+            continue
+
+        seen_storage_ids.add(storage_id)
+        num_elements = 1
+        for dim in graph["attrs"]["shape"][1][storage_id]:
+            num_elements *= dim
+
+        dltype = graph["attrs"]["dltype"][1][storage_id]
+        m = re.match(r"^[a-zA-Z]+([0-9]+)$", dltype)
+        assert m, f"Exported graph contains unknown dltype {dltype}"
+
+        elem_bits = int(m.group(1))
+
+        map_entry = {
+            "storage_id": storage_id,
+            "size_bytes": (num_elements * elem_bits + 7) // 8,
+        }
+        if node_id in graph["arg_nodes"]:
+            map_entry["input_binding"] = graph["nodes"][node_id]["name"]
+
+        memory_map.append(map_entry)
+
+    return memory_map
+
+
+def export_model_library_format(mod: graph_runtime_factory.GraphRuntimeFactoryModule, file_name):
+    """Export the build artifact in Model Library Format.
+
+    This function creates a .tar archive containing the build artifacts in a standardized
+    layout. It's intended to allow downstream automation to build TVM artifacts against the C
+    runtime.
+
+    Parameters
+    ----------
+    mod : tvm.relay.backend.graph_runtime_factory.GraphRuntimeFactoryModule
+        The return value of tvm.relay.build, which will be exported into Model Library Format.
+    file_name : str
+        Path to the .tar archive to generate.
+    """
+    tempdir = utils.tempdir()
+    metadata = {
+        "version": 1,
+        "model_name": mod.libmod_name,
+        "export_datetime": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%SZ"),
+        "memory": _build_memory_map(mod.graph_json),
+        "target": {int(k): str(v) for k, v in mod.target.items()},
+        "runtimes": ["graph"],
+    }
+    with open(tempdir.relpath("metadata.json"), "w") as json_f:
+        json.dump(metadata, json_f, indent=2, sort_keys=True)
+
+    codegen_dir_path = tempdir.relpath("codegen")
+    os.mkdir(codegen_dir_path)
+    _populate_codegen_dir(mod.lib, codegen_dir_path)
+
+    parameters_dir_path = tempdir.relpath("parameters")
+    os.mkdir(parameters_dir_path)
+    param_filename = os.path.join(parameters_dir_path, f"{mod.libmod_name}.params")
+    with open(param_filename, "wb") as f:
+        f.write(param_dict.save_param_dict(mod.params))
+
+    with open(tempdir.relpath("relay.txt"), "w") as f:
+        f.write(str(mod.ir_mod))
+
+    graph_config_dir_path = tempdir.relpath(os.path.join("runtime-config", "graph"))
+    os.makedirs(graph_config_dir_path)
+    with open(os.path.join(graph_config_dir_path, "graph.json"), "w") as f:
+        f.write(mod.graph_json)
+
+    with tarfile.open(file_name, "w") as tar_f:
+
+        def reset(tarinfo):
+            tarinfo.uid = tarinfo.gid = 0
+            tarinfo.uname = tarinfo.gname = "root"
+            return tarinfo
+
+        tar_f.add(tempdir.temp_dir, arcname=".", filter=reset)
diff --git a/python/tvm/micro/transport/serial.py b/python/tvm/micro/transport/serial.py
index 6640bb5a8a0c..b72dee1397b1 100644
--- a/python/tvm/micro/transport/serial.py
+++ b/python/tvm/micro/transport/serial.py
@@ -67,7 +67,7 @@ def open(self):
         if self._port_path is not None:
             port_path = self._port_path
         else:
-            ports = list(serial.tools.list_ports.grep(self._grep, include_links=True))
+            ports = list(serial.tools.list_ports.grep(self._grep))
             if len(ports) != 1:
                 raise SerialPortNotFoundError(
                     f"grep expression should find 1 serial port; found {ports!r}"
diff --git a/python/tvm/relay/__init__.py b/python/tvm/relay/__init__.py
index cd96ecc7ee33..89c8fcb17d73 100644
--- a/python/tvm/relay/__init__.py
+++ b/python/tvm/relay/__init__.py
@@ -45,6 +45,7 @@
 from .op import vision
 from .op import contrib
 from .op import dyn
+from .op import random
 from .op.reduce import *
 from .op.tensor import *
 from .op.transform import *
@@ -60,7 +61,6 @@
 from .scope_builder import ScopeBuilder
 
 # Load Memory Passes
-from .transform import memory_alloc
 from .transform import memory_plan
 
 # Required to traverse large programs
diff --git a/python/tvm/relay/analysis/analysis.py b/python/tvm/relay/analysis/analysis.py
index 7e49461dff52..48e9ce0643a9 100644
--- a/python/tvm/relay/analysis/analysis.py
+++ b/python/tvm/relay/analysis/analysis.py
@@ -20,9 +20,9 @@
 This file contains the set of passes for Relay, which exposes an interface for
 configuring the passes and scripting them in Python.
 """
-from tvm.ir import IRModule
-from tvm.relay import transform, build_module
-from tvm.runtime.ndarray import cpu
+from ...ir import IRModule
+from ...relay import transform, build_module
+from ...runtime.ndarray import cpu
 
 from . import _ffi_api
 from .feature import Feature
diff --git a/python/tvm/relay/analysis/annotated_regions.py b/python/tvm/relay/analysis/annotated_regions.py
index 437b97b0fa16..a18ccb97836b 100644
--- a/python/tvm/relay/analysis/annotated_regions.py
+++ b/python/tvm/relay/analysis/annotated_regions.py
@@ -17,7 +17,7 @@
 # pylint: disable=no-else-return, unidiomatic-typecheck, invalid-name, unused-import
 """Regions used in Relay."""
 
-from tvm.runtime import Object
+from ...runtime import Object
 from . import _ffi_api
 
 
diff --git a/python/tvm/relay/analysis/call_graph.py b/python/tvm/relay/analysis/call_graph.py
index 966659aac494..fd9704d0af1f 100644
--- a/python/tvm/relay/analysis/call_graph.py
+++ b/python/tvm/relay/analysis/call_graph.py
@@ -17,8 +17,8 @@
 # pylint: disable=no-else-return, unidiomatic-typecheck, invalid-name, unused-import
 """Call graph used in Relay."""
 
-from tvm.ir import IRModule
-from tvm.runtime import Object
+from ...ir import IRModule
+from ...runtime import Object
 from ..expr import GlobalVar
 from . import _ffi_api
 
diff --git a/python/tvm/relay/backend/compile_engine.py b/python/tvm/relay/backend/compile_engine.py
index a39f72e2e61f..68397cc0cef6 100644
--- a/python/tvm/relay/backend/compile_engine.py
+++ b/python/tvm/relay/backend/compile_engine.py
@@ -386,6 +386,18 @@ def items(self):
         assert len(res) % 2 == 0
         return [(res[2 * i], res[2 * i + 1]) for i in range(len(res) // 2)]
 
+    def shape_func_items(self):
+        """List items in the shape_func_cache.
+
+        Returns
+        -------
+        item_list : List[Tuple[CCacheKey, CCacheValue]]
+            The list of shape_func_items.
+        """
+        res = _backend._CompileEngineListShapeFuncItems(self)
+        assert len(res) % 2 == 0
+        return [(res[2 * i], res[2 * i + 1]) for i in range(len(res) // 2)]
+
     def get_current_ccache_key(self):
         return _backend._CompileEngineGetCurrentCCacheKey(self)
 
@@ -405,7 +417,28 @@ def dump(self):
             res += "target={}\n".format(k.target)
             res += "use_count={}\n".format(v.use_count)
             res += "func_name={}\n".format(v.cached_func.func_name)
+            res += "----relay function----\n"
+            res += k.source_func.astext() + "\n"
+            res += "----tir function----- \n"
+            res += "inputs={}\n".format(v.cached_func.inputs)
+            res += "outputs={}\n".format(v.cached_func.outputs)
+            res += "function: \n"
+            res += v.cached_func.funcs.astext() + "\n"
+        res += "===================================\n"
+        shape_func_items = self.shape_func_items()
+        res += "%d shape_func_items cached\n" % len(shape_func_items)
+        for k, v in shape_func_items:
+            res += "------------------------------------\n"
+            res += "target={}\n".format(k.target)
+            res += "use_count={}\n".format(v.use_count)
+            res += "func_name={}\n".format(v.cached_func.func_name)
+            res += "----relay function----\n"
             res += k.source_func.astext() + "\n"
+            res += "----tir function----- \n"
+            res += "inputs={}\n".format(v.cached_func.inputs)
+            res += "outputs={}\n".format(v.cached_func.outputs)
+            res += "function: \n"
+            res += v.cached_func.funcs.astext() + "\n"
         res += "===================================\n"
         return res
 
diff --git a/python/tvm/relay/backend/graph_runtime_factory.py b/python/tvm/relay/backend/graph_runtime_factory.py
index 4c6ac47b71b4..e92ae710ca0b 100644
--- a/python/tvm/relay/backend/graph_runtime_factory.py
+++ b/python/tvm/relay/backend/graph_runtime_factory.py
@@ -16,12 +16,12 @@
 # under the License.
 """Graph runtime factory."""
 import warnings
-from tvm._ffi.base import string_types
-from tvm._ffi.registry import get_global_func
-from tvm.runtime import ndarray
+from ..._ffi.base import string_types
+from ..._ffi.registry import get_global_func
+from ...runtime import ndarray
 
 
-class GraphRuntimeFactoryModule(object):
+class GraphRuntimeFactoryModule:
     """Graph runtime factory module.
     This is a module of graph runtime factory
 
@@ -31,6 +31,8 @@ class GraphRuntimeFactoryModule(object):
         The graph to be deployed in json format output by graph compiler.
         The graph can contain operator(tvm_op) that points to the name of
         PackedFunc in the libmod.
+    target : tvm.Target
+        The Target used to build this module.
     libmod : tvm.Module
         The module of the corresponding function
     libmod_name: str
@@ -39,13 +41,15 @@ class GraphRuntimeFactoryModule(object):
         The parameters of module
     """
 
-    def __init__(self, graph_json_str, libmod, libmod_name, params):
+    def __init__(self, ir_mod, target, graph_json_str, libmod, libmod_name, params):
         assert isinstance(graph_json_str, string_types)
         fcreate = get_global_func("tvm.graph_runtime_factory.create")
         args = []
         for k, v in params.items():
             args.append(k)
             args.append(ndarray.array(v))
+        self.ir_mod = ir_mod
+        self.target = target
         self.module = fcreate(graph_json_str, libmod, libmod_name, *args)
         self.graph_json = graph_json_str
         self.lib = libmod
diff --git a/python/tvm/relay/build_module.py b/python/tvm/relay/build_module.py
index 20cdc24ebc69..8e69d288df12 100644
--- a/python/tvm/relay/build_module.py
+++ b/python/tvm/relay/build_module.py
@@ -25,7 +25,7 @@
 
 from tvm.ir.transform import PassContext
 from tvm.tir import expr as tvm_expr
-from .. import nd as _nd, autotvm
+from .. import nd as _nd, autotvm, register_func
 from ..target import Target
 from ..contrib import graph_runtime as _graph_rt
 from . import _build_module
@@ -110,14 +110,8 @@ def build(self, mod, target=None, target_host=None, params=None):
 
         Returns
         -------
-        graph_json : str
-            The json string that can be accepted by graph runtime.
-
-        mod : tvm.Module
-            The module containing necessary libraries.
-
-        params : dict
-            The parameters of the final graph.
+        factory_module : tvm.relay.backend.graph_runtime_factory.GraphRuntimeFactoryModule
+            The runtime factory for the TVM graph runtime.
         """
         target = _update_target(target)
 
@@ -200,14 +194,28 @@ def get_params(self):
         return ret
 
 
-def build(mod, target=None, target_host=None, params=None, mod_name="default"):
+@register_func("tvm.relay.module_export_library")
+def _module_export(module, file_name):  # fcompile, addons, kwargs?
+    return module.export_library(file_name)
+
+
+@register_func("tvm.relay.build")
+def _build_module_no_factory(mod, target=None, target_host=None, params=None, mod_name="default"):
+    """A wrapper around build which discards the Python GraphFactoryRuntime.
+    This wrapper is suitable to be used from other programming languages as
+    the runtime::Module can be freely passed between language boundaries.
+    """
+    return build(mod, target, target_host, params, mod_name).module
+
+
+def build(ir_mod, target=None, target_host=None, params=None, mod_name="default"):
     # fmt: off
     # pylint: disable=line-too-long
     """Helper function that builds a Relay function to run on TVM graph runtime.
 
     Parameters
     ----------
-    mod : :py:class:`~tvm.IRModule`
+    ir_mod : :py:class:`~tvm.IRModule`
         The IR module to build. Using relay.Function is deprecated.
 
     target : str, :any:`tvm.target.Target`, or dict of str(i.e. device/context name) to str/tvm.target.Target, optional
@@ -243,13 +251,13 @@ def build(mod, target=None, target_host=None, params=None, mod_name="default"):
     """
     # pylint: enable=line-too-long
     # fmt: on
-    if not isinstance(mod, (IRModule, _function.Function)):
+    if not isinstance(ir_mod, (IRModule, _function.Function)):
         raise ValueError("Type of input parameter mod must be tvm.IRModule")
 
-    if isinstance(mod, _function.Function):
+    if isinstance(ir_mod, _function.Function):
         if params:
-            mod = bind_params_by_name(mod, params)
-        mod = IRModule.from_expr(mod)
+            ir_mod = bind_params_by_name(ir_mod, params)
+        ir_mod = IRModule.from_expr(ir_mod)
         warnings.warn(
             "Please use input parameter mod (tvm.IRModule) "
             "instead of deprecated parameter mod (tvm.relay.function.Function)",
@@ -272,9 +280,11 @@ def build(mod, target=None, target_host=None, params=None, mod_name="default"):
 
     with tophub_context:
         bld_mod = BuildModule()
-        graph_json, mod, params = bld_mod.build(mod, target, target_host, params)
-        mod = _graph_runtime_factory.GraphRuntimeFactoryModule(graph_json, mod, mod_name, params)
-        return mod
+        graph_json, runtime_mod, params = bld_mod.build(ir_mod, target, target_host, params)
+        runtime_mod = _graph_runtime_factory.GraphRuntimeFactoryModule(
+            ir_mod, target, graph_json, runtime_mod, mod_name, params
+        )
+        return runtime_mod
 
 
 def optimize(mod, target=None, params=None):
@@ -383,10 +393,20 @@ def _make_executor(self, expr=None):
         ret_type = self.mod["main"].checked_type.ret_type
         if _ty.is_dynamic(ret_type):
             raise ValueError("Graph Runtime only supports static graphs, got output type", ret_type)
-        num_outputs = len(ret_type.fields) if isinstance(ret_type, _ty.TupleType) else 1
         mod = build(self.mod, target=self.target)
         gmodule = _graph_rt.GraphModule(mod["default"](self.ctx))
 
+        def _unflatten(flat_iter, cur_type):
+            if isinstance(cur_type, _ty.TensorType):
+                return next(flat_iter)
+            if isinstance(cur_type, _ty.TupleType):
+                fields = []
+                for field_type in cur_type.fields:
+                    field = _unflatten(flat_iter, field_type)
+                    fields.append(field)
+                return fields
+            raise ValueError("Return type", ret_type, "contains unsupported type", cur_type)
+
         def _graph_wrapper(*args, **kwargs):
             args = self._convert_args(self.mod["main"], args, kwargs)
             # Create map of inputs.
@@ -394,13 +414,11 @@ def _graph_wrapper(*args, **kwargs):
                 gmodule.set_input(i, arg)
             # Run the module, and fetch the output.
             gmodule.run()
-            # make a copy so multiple invocation won't hurt perf.
-            if num_outputs == 1:
-                return gmodule.get_output(0).copyto(_nd.cpu(0))
-            outputs = []
-            for i in range(num_outputs):
-                outputs.append(gmodule.get_output(i).copyto(_nd.cpu(0)))
-            return outputs
+            flattened = []
+            for i in range(gmodule.get_num_outputs()):
+                flattened.append(gmodule.get_output(i).copyto(_nd.cpu(0)))
+            unflattened = _unflatten(iter(flattened), ret_type)
+            return unflattened
 
         return _graph_wrapper
 
diff --git a/python/tvm/relay/dataflow_pattern/__init__.py b/python/tvm/relay/dataflow_pattern/__init__.py
index 233c696fd716..d4a8481d106e 100644
--- a/python/tvm/relay/dataflow_pattern/__init__.py
+++ b/python/tvm/relay/dataflow_pattern/__init__.py
@@ -314,6 +314,52 @@ def is_tuple_get_item(tuple_value: "DFPattern", index: Optional[int] = None) ->
     return TupleGetItemPattern(tuple_value, index)
 
 
+def is_if(cond, true_branch, false_branch):
+    """
+    Syntatic sugar for creating an IfPattern.
+
+    Parameters
+    ----------
+    cond: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the condition of If.
+
+    true_branch: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the true branch of If.
+
+    false_branch: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the false branch of If.
+
+    Returns
+    -------
+    result: tvm.relay.dataflow_pattern.DFPattern
+        The resulting pattern.
+    """
+    return IfPattern(cond, true_branch, false_branch)
+
+
+def is_let(var, value, body):
+    """
+    Syntatic sugar for creating a LetPattern.
+
+    Parameters
+    ----------
+    var: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the variable of Let.
+
+    value: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the value of Let.
+
+    body: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the body where the binding is in effect.
+
+    Returns
+    -------
+    result: tvm.relay.dataflow_pattern.DFPattern
+        The resulting pattern.
+    """
+    return LetPattern(var, value, body)
+
+
 def wildcard() -> "DFPattern":
     """
     Syntatic sugar for creating a WildcardPattern.
@@ -480,8 +526,8 @@ class VarPattern(DFPattern):
         The type annotation on the variable.
     """
 
-    def __init__(self, name_hint: str = "", type_annotation: Optional[tvm.ir.type.Type] = None):
-        self.__init_handle_by_constructor__(ffi.VarPattern, name_hint, type_annotation)
+    def __init__(self, name_hint: str = ""):
+        self.__init_handle_by_constructor__(ffi.VarPattern, name_hint)
 
 
 @register_df_node
@@ -536,6 +582,47 @@ def __init__(
         self.__init_handle_by_constructor__(ffi.FunctionPattern, params, body)
 
 
+@register_df_node
+class IfPattern(DFPattern):
+    """A patern matching a Relay If.
+
+    Parameters
+    ----------
+    cond: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the condition of If.
+
+    true_branch: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the true branch of If.
+
+    false_branch: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the false branch of If.
+    """
+
+    def __init__(self, cond: "DFPattern", true_branch: "DFPattern", false_branch: "DFPattern"):
+        self.__init_handle_by_constructor__(ffi.IfPattern, cond, true_branch, false_branch)
+
+
+@register_df_node
+class LetPattern(DFPattern):
+    """A patern matching a Relay Let.
+
+    Parameters
+    ----------
+    var: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the variable of Let.
+
+    value: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the value of Let.
+
+    body: tvm.relay.dataflow_pattern.DFPattern
+        The pattern describing the body where the binding is in effect.
+
+    """
+
+    def __init__(self, var: "DFPattern", value: "DFPattern", body: "DFPattern"):
+        self.__init_handle_by_constructor__(ffi.LetPattern, var, value, body)
+
+
 @register_df_node
 class TuplePattern(DFPattern):
     """A patern matching a Relay Tuple.
diff --git a/python/tvm/relay/expr.py b/python/tvm/relay/expr.py
index 7b6e4b4ccf80..8d73a090ed6f 100644
--- a/python/tvm/relay/expr.py
+++ b/python/tvm/relay/expr.py
@@ -488,7 +488,7 @@ def const(value, dtype=None):
         The constant value.
 
     dtype: str, optional
-        The data type of the value.
+        The data type of the resulting constant.
 
     Note
     ----
@@ -504,13 +504,13 @@ def const(value, dtype=None):
 
     if not dtype:
         # when dtype is None: int maps to "int32", float maps to "float32"
-        map_dtype = {_np.dtype("int64"): _np.int32, _np.dtype("float64"): _np.float32}.get(
+        dtype = {_np.dtype("int64"): _np.int32, _np.dtype("float64"): _np.float32}.get(
             value.dtype, None
         )
-        if map_dtype:
-            value = value.astype(map_dtype)
 
     if isinstance(value, (_np.ndarray, _np.generic)):
+        if dtype is not None:
+            value = value.astype(dtype)
         value = _nd.array(value)
 
     if not isinstance(value, _nd.NDArray):
diff --git a/python/tvm/relay/frontend/__init__.py b/python/tvm/relay/frontend/__init__.py
index 7e16499ccc44..aa8ac4fc7434 100644
--- a/python/tvm/relay/frontend/__init__.py
+++ b/python/tvm/relay/frontend/__init__.py
@@ -20,9 +20,6 @@
 Contains the model importers currently defined
 for Relay.
 """
-
-from __future__ import absolute_import
-
 from .mxnet import from_mxnet
 from .mxnet_qnn_op_utils import quantize_conv_bias_mkldnn_from_var
 from .keras import from_keras
diff --git a/python/tvm/relay/frontend/common.py b/python/tvm/relay/frontend/common.py
index 6323c63ab9b3..2db420a40992 100644
--- a/python/tvm/relay/frontend/common.py
+++ b/python/tvm/relay/frontend/common.py
@@ -491,6 +491,12 @@ def infer_type(node, mod=None):
     return ret
 
 
+def fold_constant(node, mod=None):
+    if mod is None:
+        mod = IRModule.from_expr(node)
+    return _transform.FoldConstantExpr(node, mod)
+
+
 def infer_channels(inputs, transpose=False):
     """A hack for getting 'channels' or 'units' since caffe2 does not provide
     these attributes. We check the shape of weights provided to get the number.
diff --git a/python/tvm/relay/frontend/coreml.py b/python/tvm/relay/frontend/coreml.py
index 4efe014b9ffd..f850750fad51 100644
--- a/python/tvm/relay/frontend/coreml.py
+++ b/python/tvm/relay/frontend/coreml.py
@@ -524,7 +524,7 @@ def coreml_op_to_relay(op, inname, outnames, etab):
             outname = outnames if isinstance(outnames, _base.string_types) else outnames[0]
             etab.set_expr(outname, outs, force_override=True)
         else:
-            # the number of ouputs from model op and tvm relay must be same
+            # the number of outputs from model op and tvm relay must be same
             assert len(outnames) == len(outs)
             for outname, out in zip(outnames, outs):
                 etab.set_expr(outname, out, force_override=True)
diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index 4bdca2c4d533..eb16bf2a25b4 100644
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -864,29 +864,14 @@ def _convert_reshape(inexpr, keras_layer, etab):
     _check_data_format(keras_layer)
     inshape = keras_layer.input_shape  # includes batch
     tshape = keras_layer.target_shape  # no batch
-    if len(inshape) == 3 and len(tshape) == 1:
-        # (?, a, b) -> (-1, ab)
-        shape = (-1, tshape[0])
-    elif len(inshape) in [2, 3] and len(tshape) == 2:
-        # (?, cc) -> (-1, c, c)
-        # (?, a, b) -> (-1, c, c)
-        assert tshape[0] == tshape[1], "Only supports square target shapes, but got {}".format(
-            tshape
-        )
-        shape = (-1,) + tshape
-    else:
-        # (?, h, w, c) -> (-1, c, H, W)
-        # (?, h, w, c) -> (-1, c, hw)
-        # (?, hw, c) -> (-1, c, h, w)
-        ch = inshape[-1]
-        assert ch == tshape[-1], (
-            "Only supports last dimension in target shape being equal to "
-            "the channel number of input tensor."
-        )
-        if etab.data_layout == "NCHW":
-            shape = (-1, ch) + tshape[:-1]
-        else:
-            shape = (-1,) + tshape[:-1] + (ch,)
+    shape = (-1,) + tshape
+
+    if etab.data_layout == "NCHW" and (len(inshape) > 3 or len(tshape) > 2):
+        # Perform reshape in original NHWC format.
+        inexpr = _op.transpose(inexpr, [0] + list(range(2, len(inshape))) + [1])
+        inexpr = _op.reshape(inexpr, newshape=shape)
+        return _op.transpose(inexpr, axes=[0, -1] + list(range(1, len(shape) - 1)))
+
     return _op.reshape(inexpr, newshape=shape)
 
 
diff --git a/python/tvm/relay/frontend/mxnet.py b/python/tvm/relay/frontend/mxnet.py
index f2330c72e1f4..5415c77097a2 100644
--- a/python/tvm/relay/frontend/mxnet.py
+++ b/python/tvm/relay/frontend/mxnet.py
@@ -495,6 +495,19 @@ def _mx_layer_norm(inputs, attrs):
     return _op.nn.layer_norm(*inputs, **new_attrs)
 
 
+def _mx_group_norm(inputs, attrs):
+    assert len(inputs) == 3
+    if attrs.get_bool("output_mean_var", False):
+        raise tvm.error.OpAttributeUnimplemented(
+            'Attribute "output_mean_var" is not supported for operator Group Norm.'
+        )
+    new_attrs = {}
+    new_attrs["axis"] = 1
+    new_attrs["num_groups"] = attrs.get_int("num_groups", 1)
+    new_attrs["epsilon"] = attrs.get_float("eps", 1e-5)
+    return _op.nn.group_norm(*inputs, **new_attrs)
+
+
 def _mx_slice(inputs, attrs):
     new_attrs = {}
     begin = list(attrs.get_int_tuple("begin", None))
@@ -1221,7 +1234,7 @@ def _mx_topk(inputs, attrs):
     new_attrs = {}
     new_attrs["k"] = attrs.get_int("k", 1)
     new_attrs["axis"] = attrs.get_int("axis", -1)
-    new_attrs["is_ascend"] = attrs.get_bool("is_ascend", True)
+    new_attrs["is_ascend"] = attrs.get_bool("is_ascend", False)
     ret_type = attrs.get_str("ret_typ", "indices")
     if ret_type == "mask":
         raise tvm.error.OpAttributeUnimplemented(
@@ -2335,6 +2348,14 @@ def _mx_npi_concatenate(inputs, attrs):
         return _op.concatenate(tuple(inputs), axis=int(axis))
 
 
+def _mx_npi_stack(inputs, attrs):
+    axis = attrs.get_str("axis", "0")
+    if axis == "None":
+        return _op.reshape(_op.stack(tuple(inputs), axis=0), (-1,))
+    else:
+        return _op.stack(tuple(inputs), axis=int(axis))
+
+
 def _mx_npx_reshape(inputs, attrs):
     shape = attrs.get_int_tuple("newshape")
     reverse = attrs.get_bool("reverse", False)
@@ -2591,6 +2612,7 @@ def _mx_npi_where_rscalar(inputs, attrs):
     "_contrib_SyncBatchNorm": _mx_batch_norm,
     "InstanceNorm": _mx_instance_norm,
     "LayerNorm": _mx_layer_norm,
+    "GroupNorm": _mx_group_norm,
     "LRN": _mx_lrn,
     "L2Normalization": _mx_l2_normalize,
     "slice": _mx_slice,
@@ -2693,11 +2715,14 @@ def _mx_npi_where_rscalar(inputs, attrs):
     "_npi_multiply_scalar": _binop_scalar(_op.multiply),
     "_npi_add": _rename(_op.add),
     "_npi_add_scalar": _binop_scalar(_op.add),
+    "_npi_subtract": _rename(_op.subtract),
+    "_npi_subtract_scalar": _binop_scalar(_op.subtract),
     "_npi_where_rscalar": _mx_npi_where_rscalar,
     "_npi_less": _rename(_op.less),
     "_npi_less_equal": _mx_compare(_op.less_equal, _rename),
     "_npi_tanh": _rename(_op.tanh),
     "_npi_true_divide_scalar": _binop_scalar(_op.divide),
+    "_npi_stack": _mx_npi_stack,
 }
 
 # set identity list
diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 6122c81d321a..391eaaab5f64 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -17,6 +17,7 @@
 # pylint: disable=invalid-name, import-self, len-as-condition, unused-argument, too-many-lines
 # pylint: disable=import-outside-toplevel
 """ONNX: Open Neural Network Exchange frontend for Relay."""
+import copy
 import warnings
 import numpy as np
 import tvm
@@ -33,7 +34,7 @@
 from .. import ty as _ty
 
 from .common import AttrCvt, Renamer
-from .common import get_relay_op, new_var, infer_shape, infer_channels
+from .common import get_relay_op, new_var, infer_shape, infer_channels, infer_value, fold_constant
 from .common import infer_type, get_name
 
 
@@ -167,7 +168,7 @@ def get_pad_pair(input1d, kernel1d, stride1d):
     return [pad_before, pad_after]
 
 
-def onnx_default_layout(dims):
+def onnx_default_layout(dims, op_name):
     if dims == 1:
         return "NCW"
     if dims == 2:
@@ -175,11 +176,11 @@ def onnx_default_layout(dims):
     if dims == 3:
         return "NCDHW"
 
-    msg = "Only 1D, 2D and 3D layouts are currently supported"
+    msg = "Only 1D, 2D and 3D layouts are currently supported for operator {}."
     raise tvm.error.OpAttributeInvalid(msg.format(op_name))
 
 
-def onnx_storage_order2layout(storage_order, dims=2):
+def onnx_storage_order2layout(storage_order, dims, op_name):
     """converter of onnx storage order parameter to tvm storage order format"""
     if storage_order not in (0, 1):
         raise tvm.error.OpAttributeInvalid("Mode of storage_order must be either 0 or 1")
@@ -191,7 +192,7 @@ def onnx_storage_order2layout(storage_order, dims=2):
     if dims == 3:
         return "NCDHW" if storage_order == 0 else "NDHWC"
 
-    msg = "Only 1D, 2D and 3D layouts are currently supported"
+    msg = "Only 1D, 2D and 3D layouts are currently supported for operator {}."
     raise tvm.error.OpAttributeInvalid(msg.format(op_name))
 
 
@@ -300,10 +301,10 @@ def _impl_v1(cls, inputs, attr, params):
 
         if "storage_order" in attr:
             attr["layout"] = onnx_storage_order2layout(
-                attr["storage_order"], dims=(len(input_shape) - 2)
+                attr["storage_order"], dims=(len(input_shape) - 2), op_name=cls.name
             )
         else:
-            attr["layout"] = onnx_default_layout(dims=(len(input_shape) - 2))
+            attr["layout"] = onnx_default_layout(dims=(len(input_shape) - 2), op_name=cls.name)
 
         return AttrCvt(
             op_name=dimension_picker(cls.name),
@@ -363,7 +364,7 @@ def autopad(data, strides, kernel_shape, dilations, ndim, pad_type="constant", d
         ),
         dtype="int64",
     )
-    shape = _op.strided_slice(_op.shape_of(data, dtype="int64"), [2], [ndim])
+    shape = _op.strided_slice(shape_of(data, dtype="int64"), [2], [ndim])
     # get input shape
 
     # set up integer constants
@@ -445,7 +446,7 @@ def _impl_v1(cls, inputs, attr, params):
         # get number of channels
         channels = infer_channels(inputs[1], True)
         attr["channels"] = channels
-        groups = attr.pop("group")
+        groups = attr.get("group", 1)
         attr["groups"] = groups
         # infer pads for auto_pad
         data = inputs[0]
@@ -512,7 +513,9 @@ class Gemm(OnnxOpConverter):
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
-        assert len(inputs) == 3, "Gemm op take 3 inputs, {} given".format(len(inputs))
+        assert len(inputs) == 3 or len(inputs) == 2, "Gemm op take 2 or 3 inputs, {} given".format(
+            len(inputs)
+        )
         # Y = alpha * A * B + beta * C
         alpha = float(attr.get("alpha", 1.0))
         beta = float(attr.get("beta", 1.0))
@@ -530,11 +533,9 @@ def _impl_v1(cls, inputs, attr, params):
             inputs[0] *= _expr.const(alpha)
         out = _op.nn.dense(inputs[0], inputs[1], units=channels)
 
-        # skip (beta * C) if zero
-        C_array = params[inputs[2].name_hint].asnumpy()
-        if (beta == 0.0) or np.array_equal(C_array, np.array([0])):
-            return out
-        return _op.nn.bias_add(out, _expr.const(beta) * inputs[2])
+        if len(inputs) == 3:
+            return _op.nn.bias_add(out, _expr.const(beta) * inputs[2])
+        return out
 
 
 class MatMul(OnnxOpConverter):
@@ -544,9 +545,9 @@ class MatMul(OnnxOpConverter):
     def _impl_v1(cls, inputs, attr, params):
         assert len(inputs) == 2, "MatMul op take 2 inputs, {} given".format(len(inputs))
         # Need to check input shape as batch matmul must be supported.
-        a_shape = _op.shape_of(inputs[0])
+        a_shape = shape_of(inputs[0])
         a_rank = infer_shape(a_shape)[0]
-        b_shape = _op.shape_of(inputs[1])
+        b_shape = shape_of(inputs[1])
         b_rank = infer_shape(b_shape)[0]
         # When performing a batch matmul, we need to properly handle N-dim shapes.
         if a_rank > 2 or b_rank > 2:
@@ -554,9 +555,13 @@ def _impl_v1(cls, inputs, attr, params):
             def flatten_to_3d(x, x_shape):
                 ndims = infer_shape(x_shape)[0]
                 newshape = _op.concatenate(
-                    [_expr.const([-1]), _op.strided_slice(x_shape, [ndims - 2], [ndims])], 0
+                    [
+                        _expr.const([-1], dtype=infer_type(x_shape).checked_type.dtype),
+                        _op.strided_slice(x_shape, [ndims - 2], [ndims]),
+                    ],
+                    0,
                 )
-                out = _op.reshape(x, newshape)
+                out = _op.reshape(x, fold_constant(newshape))
                 return out
 
             # Convert a and b into 3 dimensional tensors.
@@ -597,7 +602,7 @@ def flatten_to_3d(x, x_shape):
                 ],
                 0,
             )
-            return _op.reshape(output, final_shape)
+            return _op.reshape(output, fold_constant(final_shape))
         # Otherwise a simple dense op will get the job done.
         input_1_t = _op.transpose(inputs[1], axes=(1, 0))
         return _op.nn.dense(inputs[0], input_1_t)
@@ -645,7 +650,7 @@ def _impl_v11(cls, inputs, attr, params):
         multiplier = _op.concatenate(
             [_expr.const([1, 1], dtype="int64"), _expr.const(list(strides), dtype="int64")], axis=0
         )
-        total_output_shape = multiplier * _op.shape_of(data, dtype="int64")
+        total_output_shape = multiplier * shape_of(data, dtype="int64")
         # Add extra dimensions from kernel size and stride mismatch
         total_output_shape += _op.concatenate(
             [_expr.const([0, 0], "int64"), _expr.const(list(kernel_shape), "int64")], axis=0
@@ -709,10 +714,10 @@ def _impl_v1(cls, inputs, attr, params):
 
         if "storage_order" in attr:
             attr["layout"] = onnx_storage_order2layout(
-                attr["storage_order"], dims=(len(input_shape) - 2)
+                attr["storage_order"], dims=(len(input_shape) - 2), op_name="LpPool"
             )
         else:
-            attr["layout"] = onnx_default_layout(dims=(len(input_shape) - 2))
+            attr["layout"] = onnx_default_layout(dims=(len(input_shape) - 2), op_name="LpPool")
 
         p = _expr.const(attr["p"], dtype)
         reci_p = _expr.const(1.0 / attr["p"], dtype)
@@ -791,11 +796,11 @@ def _impl_v2(cls, inputs, attr, params):
     def _impl_v11(cls, inputs, attr, params):
         pads = inputs[1]
         if len(inputs) == 3:
-            value = _op.take(inputs[2], _op.const(0))
+            value = fold_constant(_op.take(inputs[2], _op.const(0)))
         else:
             value = 0
 
-        pad_width_expr = _op.transpose(_op.reshape(pads, (2, -1)))
+        pad_width_expr = fold_constant(_op.transpose(_op.reshape(pads, (2, -1))))
         pad_mode = attr.get("mode", b"constant").decode("utf-8")
 
         if not pad_mode in ["constant", "edge", "reflect"]:
@@ -822,13 +827,11 @@ class Prelu(OnnxOpConverter):
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
         assert len(inputs) == 2, "Prelu need 2 inputs, {} given".format(len(inputs))
-        input_channels = infer_shape(inputs[0])[1]
-        alpha_shape = infer_shape(inputs[1])
-        if len(alpha_shape) != 1:
-            alpha = _op.reshape(inputs[1], (-1,))
-        else:
-            alpha = inputs[1]
-        return _op.nn.prelu(inputs[0], _op.broadcast_to(alpha, [input_channels]))
+        input_shape = shape_of(inputs[0])
+        alpha = _op.broadcast_to_like(inputs[1], inputs[0])
+        alpha = _op.reshape(alpha, [-1])
+        output = _op.nn.prelu(_op.reshape(inputs[0], [-1]), alpha, axis=0)
+        return _op.reshape(output, input_shape)
 
 
 class Reciprocal(OnnxOpConverter):
@@ -836,7 +839,8 @@ class Reciprocal(OnnxOpConverter):
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
-        return _expr.const(1.0) / inputs[0]
+        dtype = infer_type(inputs[0]).checked_type.dtype
+        return _expr.const(1.0, dtype=dtype) / inputs[0]
 
 
 class Flatten(OnnxOpConverter):
@@ -876,7 +880,6 @@ class DepthToSpace(OnnxOpConverter):
 
     @classmethod
     def _impl_v11(cls, inputs, attr, params):
-
         block_size = int(attr["blocksize"])
         mode = attr.get("mode", b"DCR").decode("utf-8")
         return _op.nn.depth_to_space(inputs[0], block_size, mode=mode)
@@ -932,14 +935,6 @@ def _impl_v1(cls, inputs, attr, params):
         return _op.tanh(_expr.const(beta) * inputs[0]) * _expr.const(alpha)
 
 
-class SoftPlus(OnnxOpConverter):
-    """Operator converter for SoftPlus."""
-
-    @classmethod
-    def _impl_v1(cls, inputs, attr, params):
-        return _op.log(_op.exp(inputs[0]) + _expr.const(1.0))
-
-
 class Softsign(OnnxOpConverter):
     """Operator converter for Softsign."""
 
@@ -1024,8 +1019,9 @@ def _impl_v9(cls, inputs, attr, params):
                 scales = params[inputs[1].name_hint].asnumpy()
             else:
                 scales = inputs[1]
-
-        if not isinstance(scales, _expr.Call):
+        if isinstance(scales, _expr.Constant):
+            scales = list(scales.data.asnumpy())
+        if not isinstance(scales, _expr.Expr):
             assert scales[0] == 1.0 and scales[1] == 1.0
 
         mode = attr.get("mode")
@@ -1038,10 +1034,6 @@ def _impl_v9(cls, inputs, attr, params):
                 'Value {} in attribute "mode" of operator Upsample is not valid.'.format(mode)
             )
 
-        if method == "nearest_neighbor":
-            align_corners = False
-        else:
-            align_corners = True
         # in 3d case, we use the purely static op
         if dims == 5:
             if isinstance(scales, _expr.Call):
@@ -1075,17 +1067,47 @@ def _impl_v9(cls, inputs, attr, params):
                 scale_w,
                 layout=layout,
                 method=method,
-                align_corners=align_corners,
+                align_corners=False,
             )
         return out
 
 
+def shape_of(x, dtype="int64"):
+    ttype = infer_type(x).checked_type
+    if not _ty.is_dynamic(ttype):
+        shape = list(ttype.shape)
+        return _expr.const(shape, dtype)
+    return _op.shape_of(x, dtype)
+
+
 class Shape(OnnxOpConverter):
     """Operator converter for Shape."""
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
-        return _op.shape_of(inputs[0], "int64")
+        return shape_of(inputs[0], "int64")
+
+
+class CumSum(OnnxOpConverter):
+    """Operator converter for CumSum."""
+
+    @classmethod
+    def _impl_v1(cls, inputs, attr, params):
+        data = inputs[0]
+        dim = inputs[1]
+
+        if dim is not None:
+            dim = int(infer_value(dim, params).asnumpy())
+
+        exclusive = attr.get("exclusive", 0)
+        reverse = attr.get("reverse", 0)
+
+        if reverse != 0:
+            out = _op.reverse(data, axis=dim)
+            out = _op.cumsum(out, axis=dim, exclusive=exclusive)
+            return _op.reverse(out, axis=dim)
+
+        return _op.cumsum(data, axis=dim, exclusive=exclusive)
 
 
 class Cast(OnnxOpConverter):
@@ -1121,17 +1143,22 @@ class Split(OnnxOpConverter):
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
-        splits = attr.get("split", False)
-        if splits:
+        splits = attr.get("split", None)
+        if splits is not None:
+            indices = []
             attr["indices_or_sections"] = []
             index = 0
             for i in splits[:-1]:
                 index += i
-                attr["indices_or_sections"].append(index)
+                indices.append(index)
         # When splits isnt specified divide evenly over axis.
         else:
-            attr["indices_or_sections"] = attr["tvm_custom"]["num_outputs"]
-        return AttrCvt("split", ignores=["split"])(inputs, attr, params)
+            indices = attr["tvm_custom"]["num_outputs"]
+        output = _op.split(inputs[0], indices, attr.get("axis", 0))
+        # If the output of split is a single value, unpack if from the TupleWrapper
+        if len(output) == 1:
+            output = output[0]
+        return output
 
 
 class Slice(OnnxOpConverter):
@@ -1190,7 +1217,7 @@ def _impl_v10(cls, inputs, attr, params):
 
         # Update the starts and ends according to axes if required.
         if axes is not None:
-            data_shape = _op.shape_of(inputs[0], dtype=infer_type(ends).checked_type.dtype)
+            data_shape = shape_of(inputs[0], dtype=infer_type(ends).checked_type.dtype)
             starts = _op.scatter(
                 _op.const([0] * data_rank, dtype=infer_type(starts).checked_type.dtype),
                 axes,
@@ -1209,7 +1236,9 @@ def _impl_v10(cls, inputs, attr, params):
         if steps is None:
             steps = _op.const([1] * data_rank, dtype=infer_type(starts).checked_type.dtype)
 
-        return _op.strided_slice(inputs[0], starts, ends, steps)
+        return _op.strided_slice(
+            inputs[0], fold_constant(starts), fold_constant(ends), fold_constant(steps)
+        )
 
 
 class Gather(OnnxOpConverter):
@@ -1237,7 +1266,9 @@ class GatherND(OnnxOpConverter):
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
-        return _op.gather_nd(inputs[0], inputs[1])
+        indices_dims = len(infer_shape(inputs[1]))
+        indices = _op.transpose(inputs[1], axes=[-1] + list(range(indices_dims - 1)))
+        return _op.gather_nd(inputs[0], indices)
 
 
 class Scatter(OnnxOpConverter):
@@ -1457,7 +1488,7 @@ def _impl_v1(cls, inputs, attr, params):
         axis = attr.get("axis", 0)
         keepdims = attr.get("keepdims", True)
         attr = {"axis": axis, "keepdims": keepdims}
-        return AttrCvt("argmax")(inputs, attr)
+        return _op.cast(AttrCvt("argmax")(inputs, attr), "int64")
 
 
 class ArgMin(OnnxOpConverter):
@@ -1468,7 +1499,7 @@ def _impl_v1(cls, inputs, attr, params):
         axis = attr.get("axis", 0)
         keepdims = attr.get("keepdims", True)
         attr = {"axis": axis, "keepdims": keepdims}
-        return AttrCvt("argmin")(inputs, attr)
+        return _op.cast(AttrCvt("argmin")(inputs, attr), "int64")
 
 
 class Softmax(OnnxOpConverter):
@@ -1515,6 +1546,19 @@ def _impl_v9(cls, inputs, attr, params):
         return output
 
 
+class Constant(OnnxOpConverter):
+    """Operator converter for ConstantOfShape."""
+
+    @classmethod
+    def _impl_v9(cls, inputs, attr, params):
+        if "value" not in attr:
+            raise "No Value in Constant"
+        np_value = get_numpy(attr.pop("value"))
+        dtype = np_value.dtype.name
+        value = _expr.const(np_value, dtype)
+        return value
+
+
 class Sign(OnnxOpConverter):
     """Operator converter for Sign."""
 
@@ -1548,15 +1592,6 @@ def _impl_v1(cls, inputs, attr, params):
 class Tile(Elemwise):
     """Operator converter for Tile"""
 
-    @classmethod
-    def _impl_v1(cls, inputs, attr, params):
-        if "repeats" not in attr:
-            raise tvm.error.OpAttributeInvalid(
-                'Attribute "repeats" should be set ' "for operator Tile."
-            )
-        reps = attr.pop("repeats")  # The number of times repeating the tensor data.
-        return _op.tile(inputs[0], reps)
-
     @classmethod
     def _impl_v6(cls, inputs, attr, params):
         return _op.tile(inputs[0], inputs[1])
@@ -1575,34 +1610,28 @@ class Where(OnnxOpConverter):
 
     @classmethod
     def _impl_v9(cls, inputs, attr, params):
-        condition_shape = infer_shape(inputs[0])
-        x_shape = infer_shape(inputs[1])
-        y_shape = infer_shape(inputs[2])
-
-        # condition, x, and y can all be broadcasted.
-        # broadcast each of them to the longest shape.
-        # if two shapes have the same number of dimensions,
-        # try to choose the one that doesn't have "1" as
-        # a dimension.
-        shapes = [condition_shape, x_shape, y_shape]
-        shape_lens = [len(shape) for shape in shapes]
-        max_size = max(shape_lens)
-        max_size_idxs = [i for i, x in enumerate(shape_lens) if x == max_size]
-        broadcast_idx = max_size_idxs[0]
-        if len(max_size_idxs) > 1:
-            for idx in max_size_idxs:
-                if 1 not in shapes[idx]:
-                    broadcast_idx = idx
-
-        broadcast_shape = shapes[broadcast_idx]
-
-        if condition_shape != broadcast_shape:
-            inputs[0] = _op.broadcast_to(inputs[0], broadcast_shape)
-        if x_shape != broadcast_shape:
-            inputs[1] = _op.broadcast_to(inputs[1], broadcast_shape)
-        if y_shape != broadcast_shape:
-            inputs[2] = _op.broadcast_to(inputs[2], broadcast_shape)
-        return _op.where(inputs[0], inputs[1], inputs[2])
+        condition_rank = len(infer_shape(inputs[0]))
+        x_rank = len(infer_shape(inputs[1]))
+        y_rank = len(infer_shape(inputs[2]))
+        ranks = [condition_rank, x_rank, y_rank]
+
+        # If one rank is longer than others, then we can broadcast
+        # to that shape.
+        max_rank = max(ranks)
+        max_rank_idxs = [i for i, x in enumerate(ranks) if x == max_rank]
+        broadcast_shape = shape_of(inputs[max_rank_idxs[0]])
+        # If two or more inputs have the same rank, compute the broadcast
+        # shape by taking the maximum value of each dimensions.
+        if len(max_rank_idxs) > 1:
+            for idx in max_rank_idxs:
+                broadcast_shape = _op.maximum(broadcast_shape, shape_of(inputs[idx]))
+
+        broadcast_shape = fold_constant(broadcast_shape)
+
+        condition = _op.broadcast_to(inputs[0], broadcast_shape)
+        x = _op.broadcast_to(inputs[1], broadcast_shape)
+        y = _op.broadcast_to(inputs[2], broadcast_shape)
+        return _op.where(condition, x, y)
 
 
 class Or(Elemwise):
@@ -1619,7 +1648,7 @@ class Expand(OnnxOpConverter):
     @classmethod
     def _impl_v8(cls, inputs, attr, params):
         dtype = infer_type(inputs[1]).checked_type.dtype
-        in_shape = _op.shape_of(inputs[0], dtype=dtype)
+        in_shape = shape_of(inputs[0], dtype=dtype)
         shape = inputs[1]
 
         # Currently 'op.broadcast_to' expect the rank of the given 'shape'
@@ -1637,6 +1666,7 @@ def expand_shape(in_shape, shape):
             """
             in_dims = infer_shape(in_shape)[0]
             new_dims = infer_shape(shape)[0]
+
             if in_dims < new_dims:
                 in_shape = _op.concatenate(
                     [
@@ -1668,7 +1698,7 @@ def expand_shape(in_shape, shape):
             new_shape = _op.maximum(in_shape, shape)
             return new_shape
 
-        shape = expand_shape(in_shape, shape)
+        shape = fold_constant(expand_shape(in_shape, shape))
         return _op.broadcast_to(inputs[0], shape=shape)
 
 
@@ -1724,7 +1754,7 @@ def _impl_v7(cls, inputs, attr, params):
         P = inputs[7]
 
         num_directions = infer_shape(W)[0]
-        W_dtype = infer_type(W).type_annotation.dtype
+        W_dtype = infer_type(W).checked_type.dtype
 
         if num_directions != 1:
             raise NotImplementedError("Bidirectional LSTMs not yet supported.")
@@ -1836,7 +1866,7 @@ def _impl_v7(cls, inputs, attr, params):
         linear_before_reset = attr.get("linear_before_reset", 0)
 
         num_directions = infer_shape(W)[0]
-        W_dtype = infer_type(W).type_annotation.dtype
+        W_dtype = infer_type(W).checked_type.dtype
 
         if num_directions != 1:
             raise NotImplementedError("Bidirectional GRUs not yet supported.")
@@ -1943,10 +1973,9 @@ def _impl_v10(cls, inputs, attr, params):
             )
 
         scale = inputs[1]
-        size = _op.cast(_op.shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
-
+        size = _op.cast(shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
         layout = "NCHW"  # ONNX assumes NCHW layout
-        out_size = _op.strided_slice(size, [2], [4])
+        out_size = fold_constant(_op.strided_slice(size, [2], [4]))
         return _op.image.resize(inputs[0], out_size, layout, method, "asymmetric")
 
     @classmethod
@@ -1970,7 +1999,7 @@ def _impl_v11(cls, inputs, attr, params):
             size = inputs[3]
         else:
             assert len(scale_shape) != 0, "One of scale or size should be passed."
-            size = _op.cast(_op.shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
+            size = _op.cast(shape_of(inputs[0]), infer_type(scale).checked_type.dtype) * scale
 
         coord_trans = attr.get("coordinate_transformation_mode")
         if coord_trans in [b"pytorch_half_pixel", b"half_pixel"]:
@@ -1984,7 +2013,7 @@ def _impl_v11(cls, inputs, attr, params):
                 "Unsupported coordinate_transformation_mode: {}".format(coord_trans)
             )
         layout = "NCHW"  # ONNX assumes NCHW layout
-        out_size = _op.strided_slice(size, [2], [4])
+        out_size = fold_constant(_op.strided_slice(size, [2], [4]))
         return _op.image.resize(inputs[0], out_size, layout, method, coord_trans)
 
 
@@ -2015,7 +2044,7 @@ def _impl_v1(cls, inputs, attr, params):
         if largest == 0:
             raise ValueError("TVM only supports finding TopK largest elements")
 
-        return _op.topk(inputs[0], inputs[1], axis=axis)
+        return _op.topk(inputs[0], inputs[1], axis=axis, dtype="int64")
 
 
 class Range(OnnxOpConverter):
@@ -2056,9 +2085,9 @@ def _impl_v1(cls, inputs, attr, params):
         x = inputs[0]
         rois = inputs[1]
         batch_indices = inputs[2]
-        mode = attr.get("mode", "avg")
-        if mode != b"avg":
-            raise ValueError("RoiAlign in Relay only uses avg mode")
+        mode = attr.get("mode", b"avg")
+        if mode not in (b"avg", b"max"):
+            raise ValueError("RoiAlign in Relay only uses avg and max modes")
         output_height = attr.get("output_height", 1)
         output_width = attr.get("output_width", 1)
 
@@ -2066,11 +2095,11 @@ def _impl_v1(cls, inputs, attr, params):
         spatial_scale = attr.get("spatial_scale", 1.0)
 
         batch_indices = _op.expand_dims(batch_indices, axis=1, num_newaxis=1)
-        batch_indices = _op.cast(batch_indices, infer_type(rois).type_annotation.dtype)
+        batch_indices = _op.cast(batch_indices, infer_type(rois).checked_type.dtype)
         rois = _op.concatenate([batch_indices, rois], 1)
 
         return _vision.roi_align(
-            x, rois, [output_height, output_width], spatial_scale, sampling_ratio
+            x, rois, [output_height, output_width], spatial_scale, sampling_ratio, mode=mode
         )
 
 
@@ -2084,6 +2113,10 @@ def convert_attributes(inputs, attr, params):
 
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
+        if "min" not in attr:
+            attr["min"] = -np.inf
+        if "max" not in attr:
+            attr["max"] = np.inf
         return Clip.convert_attributes(inputs, attr, params)
 
     @classmethod
@@ -2119,7 +2152,9 @@ def _impl_v11(cls, inputs, attr, params):
         cond = inputs[1]
         loop_deps = inputs[2:]
         num_deps = len(loop_deps)
-        body = attr["body"]
+        # Create a copy of the body function to prevent the original
+        # from being modified.
+        body = copy.copy(attr["body"])
         iter_dtype = infer_type(max_loop_count).checked_type.dtype
 
         # Determine what condition mode we're in.
@@ -2147,7 +2182,9 @@ def cond_fn(*loop_inputs):
 
         # Get the current graph proto and create a clone for the subgraph
         graph_scope = GraphProto.current
-        subgraph_scope = GraphProto(graph_scope._shape, graph_scope._dtype)
+        subgraph_scope = GraphProto(
+            graph_scope._shape, graph_scope._dtype, graph_scope._freeze_params
+        )
         # Load nodes from outer graph into inner graph.
         subgraph_scope._nodes = graph_scope._nodes.copy()
 
@@ -2156,6 +2193,8 @@ def get_var(name, val, scan=False):
             checked_type = infer_type(val)
             if hasattr(checked_type, "type_annotation"):
                 checked_type = checked_type.type_annotation
+            if hasattr(checked_type, "checked_type"):
+                checked_type = checked_type.checked_type
             shape = get_const_tuple(checked_type.shape)
             actual_shape = []
             for dim in shape:
@@ -2191,8 +2230,14 @@ def get_var(name, val, scan=False):
         scan_output_init = []
         for i in range(num_scan_outputs):
             name, shape, dtype, _ = get_info(body.output[i + 1 + num_deps])
-            scan_output_vars.append(_expr.var(name, shape=([_ty.Any()] + shape), dtype=dtype))
-            scan_output_init.append(_op.reshape(_expr.const([]), [0] + shape))
+            if dtype == "float":
+                dtype = "float32"
+            scan_output_vars.append(
+                _expr.var(name, shape=([_ty.Any()] * (len(shape) + 1)), dtype=dtype)
+            )
+            scan_output_init.append(
+                _op.reshape(_expr.const(np.array([]).astype(dtype)), [0] + [1] * len(shape))
+            )
 
         # Now we can remove loop iter variables from our inner loop's inputs.
         # This is kind of a hack since we have graph inputs that we don't
@@ -2225,24 +2270,33 @@ def body_fn(*loop_inputs):
             new_loop_vars = [loop_outputs[i] for i in range(1, 1 + num_deps)]
             new_scan_outputs = [loop_outputs[i] for i in range(1 + num_deps, len(loop_outputs))]
 
-            # Increment counter.
-            if max_loop_count is not None:
-                incr = _expr.const(1, dtype=iter_dtype)
-                loop_count = loop_count + incr
-
             # Add new scan outputs to tracking
             combined_scan_outputs = []
             for i, scan in enumerate(scan_outputs):
-                new_scan = _op.expand_dims(new_scan_outputs[i], axis=0)
-                combined_scan = _op.concatenate([scan, new_scan], axis=0)
+                rank = len(infer_shape(scan)) - 1
+                new_scan = new_scan_outputs[i]
+                expand_scan = _op.expand_dims(new_scan, axis=0)
+                # For non scalar outputs we need to broadcast the initial value.
+                if rank > 0:
+                    new_scan_shape = shape_of(new_scan, dtype=iter_dtype)
+                    scan_broadcast = _op.concatenate(
+                        [_op.reshape(loop_count, [1]), new_scan_shape], axis=0
+                    )
+                    scan = _op.broadcast_to(scan, scan_broadcast)
+                combined_scan = _op.concatenate([scan, expand_scan], axis=0)
                 combined_scan_outputs.append(combined_scan)
 
+            # Increment counter.
+            if max_loop_count is not None:
+                incr = _expr.const(1, dtype=iter_dtype)
+                loop_count = loop_count + incr
+
             # Pack loop outputs for next iteration
             # [iter_count, cond, loop_deps, loop_scans]
             return [loop_count, max_count, new_cond] + new_loop_vars + combined_scan_outputs
 
         # Create the loop function.
-        loop = _loops.while_loop(cond_fn, loop_vars + scan_output_vars, body_fn)
+        loop = fold_constant(_loops.while_loop(cond_fn, loop_vars + scan_output_vars, body_fn))
 
         # Now need to run initial values through the graph.
         init_count = _expr.const(0, dtype=iter_dtype)
@@ -2265,6 +2319,7 @@ def body_fn(*loop_inputs):
         # Update outer graph with constants found in the subgraph.
         free_vars = analysis.free_vars(loop)
         graph_scope._params.update(subgraph_scope._params)
+        graph_scope._nodes.update(subgraph_scope._nodes)
         for var in free_vars:
             graph_scope._nodes.update({var.name_hint: var})
         return outputs
@@ -2276,15 +2331,18 @@ class If(OnnxOpConverter):
     @classmethod
     def _impl_v1(cls, inputs, attr, params):
         cond = inputs[0]
+        # Convert array to bool if needed.
+        if len(infer_shape(cond)) > 0:
+            cond = _op.take(cond, _expr.const(0, dtype="int64"))
         then_branch = attr.get("then_branch", None)
         else_branch = attr.get("else_branch", None)
         assert then_branch is not None and else_branch is not None
 
         # Create graph converters for both branches.
         graph_scope = GraphProto.current
-        then_graph = GraphProto(graph_scope._shape, graph_scope._dtype)
+        then_graph = GraphProto(graph_scope._shape, graph_scope._dtype, graph_scope._freeze_params)
         then_graph._nodes = graph_scope._nodes.copy()
-        else_graph = GraphProto(graph_scope._shape, graph_scope._dtype)
+        else_graph = GraphProto(graph_scope._shape, graph_scope._dtype, graph_scope._freeze_params)
         else_graph._nodes = graph_scope._nodes.copy()
 
         # Convert each branch to a relay expression.
@@ -2295,10 +2353,12 @@ def _impl_v1(cls, inputs, attr, params):
 
         # Add constants from both branches to parent graph.
         graph_scope._params.update(then_graph._params)
+        graph_scope._nodes.update(then_graph._nodes)
         then_free_vars = analysis.free_vars(then_expr)
         for var in then_free_vars:
             graph_scope._nodes.update({var.name_hint: var})
         graph_scope._params.update(else_graph._params)
+        graph_scope._nodes.update(else_graph._nodes)
         else_free_vars = analysis.free_vars(else_expr)
         for var in else_free_vars:
             graph_scope._nodes.update({var.name_hint: var})
@@ -2393,7 +2453,7 @@ def _first_cond(
             nms_size_out,
         ):
             # Loop over classes, end when i == C
-            return _op.min(_op.less(i, C))
+            return _op.take(_op.less(i, C), _expr.const(0))
 
         def _first_body(
             i,
@@ -2443,9 +2503,9 @@ def _first_body(
             # partially prepare ONNX output format by labeling batch_num, class_id
             nms_padded_out = _op.expand_dims(nms_ret[0], -1, 1)
             batch_num = _op.expand_dims(_op.arange(_op.squeeze(B, [0]), dtype="int64"), -1, 1)
-            batch_num = _op.broadcast_to(batch_num, _op.shape_of(nms_ret[0], dtype="int64"))
+            batch_num = _op.broadcast_to(batch_num, shape_of(nms_ret[0], dtype="int64"))
             batch_num = _op.expand_dims(batch_num, -1, 1)
-            class_num = _op.broadcast_to(i, _op.shape_of(nms_padded_out, dtype="int64"))
+            class_num = _op.broadcast_to(i, shape_of(nms_padded_out, dtype="int64"))
             new_onnx_out = _op.concatenate(
                 [batch_num, class_num, _op.cast(nms_padded_out, "int64")], -1
             )
@@ -2501,7 +2561,7 @@ def _first_body(
 
         def _inner_cond(i, j, C, onnx_out, nms_size, out):
             # inner loop over number of classes
-            return _op.min(_op.less(j, C))
+            return _op.take(_op.less(j, C), _expr.const(0))
 
         def _inner_body(i, j, C, onnx_out, nms_size, out):
             # slice to get current batch and class for valid box indicator
@@ -2531,7 +2591,7 @@ def _inner_body(i, j, C, onnx_out, nms_size, out):
 
         def _outer_cond(i, B, C, onnx_out, nms_size_out, out):
             # Outer loop is over batch size
-            return _op.min(_op.less(i, B))
+            return _op.take(_op.less(i, B), _expr.const(0))
 
         def _outer_body(i, B, C, onnx_out, nms_size_out, out):
             # Outer loop just calls inner loop
@@ -2545,7 +2605,7 @@ def _outer_body(i, B, C, onnx_out, nms_size_out, out):
         )
 
         # Call the first loop, perform NMS
-        B, C, S = _op.split(_op.shape_of(scores, dtype="int64"), 3)
+        B, C, S = _op.split(shape_of(scores, dtype="int64"), 3)
         init_count = _op.const(np.array([0]), dtype="int64")
         init_onnx_out = _op.const([1], dtype="int64")
         init_onnx_out = _op.broadcast_to(init_onnx_out, _op.concatenate([B, one, S, three], 0))
@@ -2569,10 +2629,10 @@ def _outer_body(i, B, C, onnx_out, nms_size_out, out):
 
         # Call the second loop, rework outputs into correct form
         init_count = _op.const(np.array([0]).astype("int64"), dtype="int64")
-        init_out = _op.const(np.array([]).reshape([0, 3]).astype("int64"), dtype="int64")
+        init_out = _op.const(np.array([1, 1, 1]).reshape([1, 3]).astype("int64"), dtype="int64")
         loop_vals = outer_loop(init_count, B, C, onnx_output, nms_size_output, init_out)
-
-        return _expr.TupleGetItem(loop_vals, 5)
+        loop_out = _expr.TupleGetItem(loop_vals, 5)
+        return _op.strided_slice(loop_out, [1, 0], shape_of(loop_out), [1, 1])
 
 
 # compatible operators that do NOT require any conversion.
@@ -2592,6 +2652,7 @@ def _get_convert_map(opset):
         "ThresholdedRelu": ThresholdedRelu.get_converter(opset),
         "ScaledTanh": ScaledTanh.get_converter(opset),
         "ParametricSoftplus": ParametricSoftPlus.get_converter(opset),
+        "Constant": Constant.get_converter(opset),
         "ConstantOfShape": ConstantOfShape.get_converter(opset),
         # 'GivenTensorFill'
         "FC": AttrCvt("dense", ignores=["axis", "axis_w"]),
@@ -2633,12 +2694,12 @@ def _get_convert_map(opset):
         "Greater": Greater.get_converter(opset),
         "Less": Less.get_converter(opset),
         "Log": Renamer("log"),
-        "ACos": Renamer("acos"),
-        "ACosh": Renamer("acosh"),
-        "ASin": Renamer("asin"),
-        "ASinh": Renamer("asinh"),
-        "ATan": Renamer("atan"),
-        "ATanh": Renamer("atanh"),
+        "Acos": Renamer("acos"),
+        "Acosh": Renamer("acosh"),
+        "Asin": Renamer("asin"),
+        "Asinh": Renamer("asinh"),
+        "Atan": Renamer("atan"),
+        "Atanh": Renamer("atanh"),
         "Cos": Renamer("cos"),
         "Cosh": Renamer("cosh"),
         "Sin": Renamer("sin"),
@@ -2661,7 +2722,6 @@ def _get_convert_map(opset):
         "OneHot": OneHot.get_converter(opset),
         # 'Hardmax'
         "Softsign": Softsign.get_converter(opset),
-        "SoftPlus": SoftPlus.get_converter(opset),
         "Gemm": Gemm.get_converter(opset),
         "MatMul": MatMul.get_converter(opset),
         "Mod": Mod.get_converter(opset),
@@ -2734,6 +2794,7 @@ def _get_convert_map(opset):
         "Resize": Resize.get_converter(opset),
         "NonZero": NonZero.get_converter(opset),
         "Range": Range.get_converter(opset),
+        "CumSum": CumSum.get_converter(opset),
         # defs/control_flow
         "Loop": Loop.get_converter(opset),
         "If": If.get_converter(opset),
@@ -2751,11 +2812,19 @@ class GraphProto:
 
     dtype : str or dict of str to str
         The input types to the graph
+
+    freeze_params: bool
+        If this parameter is true, the importer will take any provided
+        onnx input values (weights, shapes, etc) and embed them into the relay model
+        as Constants instead of variables. This allows more aggressive optimizations
+        at compile time and helps in making models static if certain inputs represent
+        attributes relay would traditionally consider compile-time constants.
+
     """
 
     current = None
 
-    def __init__(self, shape, dtype):
+    def __init__(self, shape, dtype, freeze_params=False):
         self._nodes = {}
         self._params = {}
         self._inputs = {}
@@ -2765,6 +2834,7 @@ def __init__(self, shape, dtype):
         self._shape = shape if shape else {}
         self._dtype = dtype
         self.opset = None
+        self._freeze_params = freeze_params
 
     def __enter__(self):
         self._old_manager = GraphProto.current
@@ -2783,7 +2853,7 @@ def freeze(self, func, params):
         fn = _function.Function(analysis.free_vars(body), body)
         return fn, {}
 
-    def from_onnx(self, graph, opset, freeze_params=False, get_output_expr=False):
+    def from_onnx(self, graph, opset, get_output_expr=False):
         """Construct Relay expression from ONNX graph.
 
         Onnx graph is a python protobuf object.
@@ -2800,13 +2870,6 @@ def from_onnx(self, graph, opset, freeze_params=False, get_output_expr=False):
 
         opset : opset version
 
-        freeze_params: bool
-            If this parameter is true, the importer will take any provided
-            onnx input values (weights, shapes, etc) and embed them into the relay model
-            as Constants instead of variables. This allows more aggressive optimizations
-            at compile time and helps in making models static if certain inputs represent
-            attributes relay would traditionally consider compile-time constants.
-
         get_output_expr: bool
             If set to true, this conversion will return each output expression rather
             than a packaged module. This can be useful when converting subgraphs to
@@ -2825,12 +2888,16 @@ def from_onnx(self, graph, opset, freeze_params=False, get_output_expr=False):
         for init_tensor in graph.initializer:
             if not init_tensor.name.strip():
                 raise ValueError("Tensor's name is required.")
-            self._params[init_tensor.name] = self._parse_array(init_tensor)
-            self._nodes[init_tensor.name] = new_var(
-                init_tensor.name,
-                shape=self._params[init_tensor.name].shape,
-                dtype=self._params[init_tensor.name].dtype,
-            )
+            array = self._parse_array(init_tensor)
+            if self._freeze_params:
+                self._nodes[init_tensor.name] = _expr.const(array)
+            else:
+                self._params[init_tensor.name] = array
+                self._nodes[init_tensor.name] = new_var(
+                    init_tensor.name,
+                    shape=self._params[init_tensor.name].shape,
+                    dtype=self._params[init_tensor.name].dtype,
+                )
         for i in graph.input:
             # from onnx v0.2, GraphProto.input has type ValueInfoProto,
             #  and the name is 'i.name'
@@ -2842,6 +2909,8 @@ def from_onnx(self, graph, opset, freeze_params=False, get_output_expr=False):
                 self._nodes[i_name] = new_var(
                     i_name, shape=self._params[i_name].shape, dtype=self._params[i_name].dtype
                 )
+            elif i_name in self._nodes:
+                continue
             else:
                 self._num_input += 1
                 if i_name in self._shape:
@@ -2884,37 +2953,28 @@ def from_onnx(self, graph, opset, freeze_params=False, get_output_expr=False):
             for i in node.input:
                 if i != "":
                     inputs[i] = self._nodes[self._renames.get(i, i)]
-            if op_name == "Constant":
-                t_proto = self._parse_attr(node.attribute)["value"]
-                self._num_param += 1
-                # We should convert scalar integers to int32, to normalize.
-                array = self._parse_array(t_proto)
-                self._params[node.output[0]] = array
-                self._nodes[node.output[0]] = new_var(
-                    node.output[0], shape=list(t_proto.dims), dtype=array.dtype
-                )
+            i_name = self._parse_value_proto(node)
+            node_output = self._fix_outputs(op_name, node.output)
+            attr["tvm_custom"] = {}
+            attr["tvm_custom"]["name"] = i_name
+            attr["tvm_custom"]["num_outputs"] = len(node_output)
+
+            op = self._convert_operator(op_name, inputs, attr, opset)
+            if not isinstance(op, _expr.TupleWrapper):
+                outputs_num = 1
             else:
-                i_name = self._parse_value_proto(node)
-                node_output = self._fix_outputs(op_name, node.output)
-                attr["tvm_custom"] = {}
-                attr["tvm_custom"]["name"] = i_name
-                attr["tvm_custom"]["num_outputs"] = len(node_output)
-
-                op = self._convert_operator(op_name, inputs, attr, opset)
-                if not isinstance(op, _expr.TupleWrapper):
-                    outputs_num = 1
-                else:
-                    outputs_num = len(op)
-                assert (
-                    len(node_output) == outputs_num
-                ), "Number of output mismatch {} vs {} in {}.".format(
-                    len(node_output), outputs_num, op_name
-                )
-                if outputs_num == 1:
-                    self._nodes[node_output[0]] = op
-                else:
-                    for k, i in zip(list(node_output), range(len(node_output))):
-                        self._nodes[k] = op[i]
+                outputs_num = len(op)
+            assert (
+                len(node_output) == outputs_num
+            ), "Number of output mismatch {} vs {} in {}.".format(
+                len(node_output), outputs_num, op_name
+            )
+            if outputs_num == 1:
+                self._nodes[node_output[0]] = fold_constant(op)
+            else:
+                op = _expr.TupleWrapper(fold_constant(op.astuple()), len(op))
+                for k, i in zip(list(node_output), range(len(node_output))):
+                    self._nodes[k] = op[i]
 
         # now return the outputs
         outputs = [self._nodes[self._parse_value_proto(i)] for i in graph.output]
@@ -2932,9 +2992,6 @@ def from_onnx(self, graph, opset, freeze_params=False, get_output_expr=False):
                 self._inputs[i_name] = self._nodes[i_name]
         # Create a function from our output expression and all input variables.
         func = _function.Function([v for k, v in self._inputs.items()], outputs)
-        if freeze_params:
-            func, params = self.freeze(func, self._params)
-            return IRModule.from_expr(func), params
         return IRModule.from_expr(func), self._params
 
     def _parse_value_proto(self, value_proto):
@@ -3075,7 +3132,7 @@ def from_onnx(model, shape=None, dtype="float32", opset=None, freeze_params=Fals
                 warnings.warn(str(e))
     except ImportError:
         pass
-    g = GraphProto(shape, dtype)
+    g = GraphProto(shape, dtype, freeze_params)
     graph = model.graph
     if opset is None:
         try:
@@ -3084,5 +3141,5 @@ def from_onnx(model, shape=None, dtype="float32", opset=None, freeze_params=Fals
             opset = 1
     # Use the graph proto as a scope so that ops can access other nodes if needed.
     with g:
-        mod, params = g.from_onnx(graph, opset, freeze_params)
+        mod, params = g.from_onnx(graph, opset)
     return mod, params
diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 94ee9282e4fa..fd0a07e35c15 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -34,6 +34,7 @@
 from .. import expr as _expr
 from .. import function as _function
 from .. import op as _op
+from .. import qnn
 from ..ty import TupleType, TensorType, Any
 from ..loops import while_loop
 from .. import transform
@@ -385,26 +386,34 @@ def tensor_array_concat(lst, axis):
 
     def slice(self, inputs, input_types):
         axis_dtype = "int64"
-        index_size_limit = 2 ** 63 - 1
+        index_size_limit = sys.maxsize
         data = inputs[0]
         dshape = self.infer_shape(data)
         ndim = len(dshape)
-        end = []
-        for dim in dshape:
-            if isinstance(dim, tvm.tir.Any):
-                end = _op.shape_of(data)
-                break
-            end.append(int(dim))
-
-        begin = [0] * ndim
         dim = int(inputs[1])
-        stride = int(inputs[4])
-        if isinstance(inputs[2], _expr.Call):
-            begin[dim], _ = try_infer_value(inputs[2], lambda ret: np.asscalar(ret.astype(np.int)))
-        else:
-            begin[dim] = int(inputs[2])
+        stride = inputs[4]
+
+        target_begin, is_begin_const = try_infer_value(
+            inputs[2], lambda ret: np.asscalar(ret.astype(np.int))
+        )
+        target_end, is_end_const = try_infer_value(
+            inputs[3], lambda ret: np.asscalar(ret.astype(np.int))
+        )
+
+        # A fast path when slicing is nop.
+        if (
+            isinstance(target_begin, int)
+            and isinstance(target_end, int)
+            and target_begin == 0
+            and target_end >= index_size_limit
+            and stride == 1
+        ):
+            return data
 
         # Process begin
+        begin = [0] * ndim
+        begin[dim] = target_begin
+
         if not isinstance(begin[dim], int):
             tmp = []
             for b in begin:
@@ -417,27 +426,15 @@ def slice(self, inputs, input_types):
             if str(btype) != axis_dtype:
                 begin = _op.cast(begin, axis_dtype)
 
-        if isinstance(inputs[3], str) and inputs[3].isdigit():
-            target_end = int(inputs[3])
+        # Process end
+        if isinstance(target_end, int) and target_end >= index_size_limit:
+            target_end = dshape[dim]
+
+        if any([isinstance(d, tvm.tir.Any) for d in dshape]):
+            end = _op.shape_of(data)
         else:
-            if isinstance(inputs[3], _expr.Expr):
-                target_end, _ = try_infer_value(
-                    inputs[3], lambda ret: np.asscalar(ret.astype(np.int))
-                )
-            else:
-                target_end = inputs[3]
-
-            if isinstance(target_end, int) and target_end >= index_size_limit:
-                # Quick path for original data.
-                if (
-                    isinstance(begin, _expr.Constant)
-                    and begin.data.asnumpy().tolist()[dim] == 0
-                    and stride == 1
-                ):
-                    return data
-                target_end = dshape[dim]
+            end = dshape
 
-        # Process end
         if isinstance(target_end, int):
             if isinstance(end, list):
                 end[dim] = target_end
@@ -477,12 +474,25 @@ def slice(self, inputs, input_types):
                 end = _op.cast(end, axis_dtype)
 
         strides = [1] * ndim
-        strides[dim] = int(inputs[4])
+        strides[dim] = stride
 
         return _op.transform.strided_slice(
             data, begin=begin, end=end, strides=strides, slice_mode="end"
         )
 
+    def narrow(self, inputs, input_types):
+        # Inputs are:
+        # 0 - the tensor to narrow
+        # 1 - the dimension along which to narrow
+        # 2 - the starting dimension
+        # 3 - the distance to the ending dimension
+        # Lets find the ending dimension
+        end = self.add(inputs[2:4], input_types[2:4])
+        stride = 1
+        slice_input = inputs[:3] + [end, stride]
+        slice_types = input_types + ["int32"]
+        return self.slice(slice_input, slice_types)
+
     def split(self, inputs, input_types):
         data = inputs[0]
         split_size = int(inputs[1])
@@ -518,13 +528,13 @@ def select(self, inputs, input_types):
         data = inputs[0]
         dim = int(inputs[1])
         index = _wrap_const(inputs[2])
-        return _op.transform.take(data, index, axis=dim)
+        return _op.transform.take(data, index, axis=dim, mode="wrap")
 
     def take(self, inputs, input_types):
         data = inputs[0]
         indices = _op.cast(inputs[1], "int32")
 
-        return _op.transform.take(data, indices=indices)
+        return _op.transform.take(data, indices=indices, mode="wrap")
 
     def topk(self, inputs, input_types):
         data = inputs[0]
@@ -551,7 +561,13 @@ def reciprocal(self, inputs, input_types):
 
     def repeat(self, inputs, input_types):
         data = inputs[0]
-        reps = inputs[1]
+        reps = []
+        for r in inputs[1]:
+            if isinstance(r, int):
+                reps.append(r)
+            else:
+                reps.append(int(_infer_value(r, {}).asnumpy()))
+
         return _op.transform.tile(data, reps=reps)
 
     def repeat_interleave(self, inputs, input_types):
@@ -790,6 +806,36 @@ def log_sigmoid(self, inputs, input_types):
         data = inputs[0]
         return _op.log(_op.tensor.sigmoid(data))
 
+    def hard_sigmoid(self, inputs, input_types):
+        def _relu6(x):
+            return _op.tensor.clip(x, 0.0, 6.0)
+
+        def func(x):
+            return _relu6(x + _expr.const(3.0)) / _expr.const(6.0)
+
+        if self.is_quantized_tensor(inputs[0]):
+            input_scale = _expr.const(inputs[1])
+            input_zero_point = _expr.const(inputs[2])
+            # PyTorch seems to use the following output qparams, but accuracy
+            # is broken if we use this.
+            # TODO(masahi): Revisit this parameter choice
+            #
+            # Taken from src/ATen/native/quantized/cpu/kernels/QuantizedOpKernels.cpp
+            # output_scale = _expr.const(0.00390625)  # 1.0 / 2^8
+            # output_zero_point = _expr.const(-128)
+            output_scale = input_scale
+            output_zero_point = input_zero_point
+
+            data = qnn.op.dequantize(inputs[0], input_scale, input_zero_point, axis=1)
+            out = func(data)
+            return qnn.op.quantize(out, output_scale, output_zero_point, out_dtype="uint8")
+
+        return func(inputs[0])
+
+    def hard_swish(self, inputs, input_types):
+        data = inputs[0]
+        return data * self.hard_sigmoid(inputs, input_types)
+
     def adaptive_avg_pool_2d(self, inputs, input_types):
         data = inputs[0]
         output_size = inputs[1]
@@ -820,11 +866,19 @@ def adaptive_avg_pool_3d(self, inputs, input_types):
         output_size = inputs[1]
         return _op.nn.adaptive_avg_pool3d(data, output_size=output_size)
 
+    @staticmethod
+    def convert_const_list(data):
+        if isinstance(data, list):
+            for i, _ in enumerate(data):
+                if isinstance(data[i], _expr.Expr):
+                    data[i] = int(_infer_value_simulated(data[i], {}).asnumpy())
+        return data
+
     def maxpool_2d(self, inputs, input_types):
         data = inputs[0]
 
-        pool_size = inputs[1]
-        strides = inputs[2] if inputs[2] else pool_size
+        pool_size = self.convert_const_list(inputs[1])
+        strides = self.convert_const_list(inputs[2] if inputs[2] else pool_size)
         padding = inputs[3]
         dilation = inputs[4]
         ceil_mode = int(inputs[5])
@@ -978,8 +1032,7 @@ def threshold(self, inputs, input_types):
         return _op.nn.relu(data)
 
     def contiguous(self, inputs, input_types):
-        data = inputs[0]
-        return _op.tensor.copy(data)
+        return inputs[0]
 
     def batch_norm(self, inputs, input_types):
         data = inputs[0]
@@ -1041,8 +1094,7 @@ def instance_norm(self, inputs, input_types):
             data, gamma, beta, axis=1, epsilon=epsilon, center=center, scale=scale
         )
 
-    @staticmethod
-    def get_dims(data):
+    def get_dims(self, data):
         import torch
 
         if isinstance(data, _expr.Expr):
@@ -1304,8 +1356,8 @@ def softplus(self, inputs, input_types):
     def avg_pool2d(self, inputs, input_types):
         data = inputs[0]
 
-        pool_size = inputs[1]
-        strides = inputs[2] if inputs[2] else pool_size
+        pool_size = self.convert_const_list(inputs[1])
+        strides = self.convert_const_list(inputs[2] if inputs[2] else pool_size)
         padding = inputs[3]
         ceil_mode = int(inputs[4])
         count_include_pad = int(inputs[5])
@@ -1343,6 +1395,20 @@ def avg_pool3d(self, inputs, input_types):
             count_include_pad=count_include_pad,
         )
 
+    def linear(self, inputs, input_types):
+        # https://pytorch.org/docs/stable/nn.functional.html#linear
+        # 0 - input
+        # 1 - weight
+        bias = inputs[2]
+        mm_out = self.matmul(inputs[:2], input_types[:2])
+        if isinstance(bias, _expr.Expr):
+            bias_ndims = len(self.infer_shape_with_prelude(bias))
+            if bias_ndims == 1:
+                return _op.nn.bias_add(mm_out, bias)
+            mm_dtype = self.infer_type_with_prelude(mm_out).dtype
+            return self.add([mm_out, bias], [mm_dtype, input_types[2]])
+        return mm_out
+
     def dropout(self, inputs, input_types):
         data = inputs[0]
         rate = float(inputs[1])
@@ -1508,21 +1574,31 @@ def matmul(self, inputs, input_types):
 
         # When performing a batch matmul, we need to properly handle N-dim shapes.
         if len(a_shape) > 2 or len(b_shape) > 2:
-            # Convert a and b into 3 dimensional tensors.
-            a = _op.reshape(inputs_0, [-1, a_shape[-2], a_shape[-1]])
-            b = _op.reshape(inputs_1, [-1, b_shape[-2], b_shape[-1]])
-            # Broadcast b to match batch size of a
-            new_b_shape = list(self.infer_shape_with_prelude(b))
-            new_a_shape = self.infer_shape_with_prelude(a)
-            if new_a_shape[0] > new_b_shape[0]:
-                new_b_shape[0] = new_a_shape[0]
-                b = _op.broadcast_to(b, new_b_shape)
+            # Convert a into a 3 dimensional tensors.
+            need_reshape_output = False
+            if len(a_shape) != 3:
+                a = _op.reshape(inputs_0, [-1, a_shape[-2], a_shape[-1]])
+                need_reshape_output = True
+            else:
+                a = inputs_0
+
             # Transpose matrix dimensions of b.
-            b = _op.transpose(b, [0, 2, 1])
+            trans_axes = list(range(len(b_shape)))
+            trans_axes[-2], trans_axes[-1] = trans_axes[-1], trans_axes[-2]
+            b = _op.transpose(inputs_1, trans_axes)
+
+            # Convert b into a 3 dimensional tensor. Note that the last two dimensions
+            # are transposed.
+            if len(b_shape) != 3:
+                b = _op.reshape(b, [-1, b_shape[-1], b_shape[-2]])
+
             # Perform a batch matmul.
             output = _op.nn.batch_matmul(a, b)
+
             # Reshape output to original dimensions.
-            return _op.reshape(output, [*a_shape[:-2], a_shape[-2], b_shape[-1]])
+            if need_reshape_output:
+                return _op.reshape(output, [*a_shape[:-2], a_shape[-2], b_shape[-1]])
+            return output
 
         # Otherwise a simple dense op will get the job done.
         if len(b_shape) == 1:
@@ -1857,18 +1933,18 @@ def nms(self, inputs, input_types):
         scores = inputs[1]
         iou_threshold = inputs[2]
 
-        num_boxes = _op.shape_of(scores)
-
         # TVM NMS assumes score > 0
         scores = scores - _op.min(scores) + _op.const(1.0)
+
+        num_boxes = _op.shape_of(scores)
+        # PyTorch NMS doesn't have score_threshold, so no need to run get_valid_count
+        indices = _op.transform.arange(_op.squeeze(num_boxes), dtype="int32")
+        indices = _op.expand_dims(indices, 0, 1)
+
         # Generate data with shape (1, num_anchors, 5)
         scores = AttrCvt(op_name="expand_dims", extras={"axis": -1, "num_newaxis": 1})([scores], {})
         data = _op.concatenate([scores, boxes], -1)
         data = _op.expand_dims(data, 0, 1)
-        # PyTorch NMS doesn't have score_threshold, so no need to run get_valid_count
-        indices = _op.transform.arange(_op.squeeze(num_boxes), dtype="int32")
-        indices = _op.expand_dims(indices, 0, 1)
-        ct = num_boxes
 
         # Perform Non-Maximum Suppression,
         # PyTorch NMS doesn't have parameter top_k and max_output_size
@@ -1876,7 +1952,7 @@ def nms(self, inputs, input_types):
         top_k = max_out_size = -1
         nms_ret = get_relay_op("non_max_suppression")(
             data=data,
-            valid_count=ct,
+            valid_count=num_boxes,
             indices=indices,
             max_output_size=max_out_size,
             iou_threshold=iou_threshold,
@@ -1922,6 +1998,32 @@ def roi_align(self, inputs, input_types):
 
         return _op.vision.roi_align(data, boxes, output_size, spatial_scale, sample_ratio)
 
+    def deform_conv2d(self, inputs, input_types):
+        data = inputs[0]
+        weight = inputs[1]
+        offset = inputs[2]
+        strides = (inputs[4], inputs[5])
+        padding = (inputs[6], inputs[7])
+        dilation = (inputs[8], inputs[9])
+        groups = inputs[10]
+        deformable_groups = inputs[11]
+        weight_shape = self.infer_shape(weight)
+        output_channels = weight_shape[0]
+        kernel_size = (weight_shape[2], weight_shape[3])
+
+        return _op.nn.deformable_conv2d(
+            data,
+            offset,
+            weight,
+            strides,
+            padding,
+            dilation,
+            deformable_groups,
+            groups,
+            output_channels,
+            kernel_size,
+        )
+
     def unbind(self, inputs, input_types):
         data = inputs[0]
         dim = int(inputs[1])
@@ -1978,6 +2080,32 @@ def scatter(self, inputs, input_types):
         src = inputs[3]
         return _op.transform.scatter(data, index, src, axis)
 
+    def index_put(self, inputs, input_types):
+        in_tensor = inputs[0]
+        indices = inputs[1]
+        values = inputs[2]
+        accumulate = inputs[3]
+        # accumulate parameter is ignored.
+        # torch.index_put default is False but Relay.scatter_nd accumulates values.
+        # We assume there is no duplicate indices in torch.index_put input
+        if not accumulate:
+            logging.warning(
+                "torch.index_put accumulate parameter is False. "
+                "TVM uses tvm.relay.scatter_nd operator which accumulates values. "
+                "Make sure there is no duplicate indices in torch.index_put input."
+            )
+        # Relay scatter_nd does not support input tensor
+        # We assume that torch.index_put is used with empty zero-values input tensor
+        # scatter_nd will create empty zero-values tensor with a given shape
+        out_shape = self.infer_shape(in_tensor)
+        logging.warning(
+            "tvm.relay.scatter_nd operator does not support input tensor parameter. "
+            "TVM assumes that torch.index_put is used with empty zero-values input tensor"
+        )
+        # Combine array of index tensors into one index tensor with shape (N,_)
+        index_tensor = _op.stack(indices, axis=0)
+        return _op.transform.scatter_nd(values, index_tensor, out_shape)
+
     def scalar_tensor(self, inputs, input_types):
         data = inputs[0]
         cast_map = {
@@ -2061,6 +2189,40 @@ def scatter_add(self, inputs, input_types):
         src = inputs[3]
         return _op.scatter_add(data, index, src, axis=axis)
 
+    def cumsum(self, inputs, input_types):
+        data = inputs[0]
+        dim = inputs[1]
+        dtype = inputs[2]
+
+        if inputs[2] is not None:
+            dtype = _convert_dtype_value(inputs[2])
+
+        return _op.cumsum(data, axis=dim, dtype=dtype)
+
+    def masked_fill(self, inputs, input_types):
+        mask = inputs[1]
+        value = _op.cast(_wrap_const(inputs[2]), input_types[0])
+        return _op.where(mask, value, inputs[0])
+
+    def masked_select(self, inputs, input_types):
+        mask = inputs[1]
+        indices = self.nonzero([mask], input_types, is_numpy_style=True)
+        return _op.adv_index([inputs[0]] + [indices[i] for i in range(indices.size)])
+
+    def sort(self, inputs, input_types):
+        data = inputs[0]
+        dim = inputs[1]
+        is_descending = inputs[2]
+        # pytorch sort returns both sorted indices and values
+        indices = _op.argsort(data, dim, not is_descending)
+        return _op.gather(data, dim, indices), indices
+
+    def argsort(self, inputs, input_types):
+        data = inputs[0]
+        dim = inputs[1]
+        is_descending = inputs[2]
+        return _op.argsort(data, dim, not is_descending)
+
     def is_floating_point(self, inputs, input_types):
         assert len(inputs) == 1
 
@@ -2072,6 +2234,24 @@ def is_floating_point(self, inputs, input_types):
         is_float = input_type in ["float32", "float64", "float16", "bfloat16"]
         return _expr.const(is_float)
 
+    def unique(self, inputs, input_types):
+        assert len(inputs) == 4
+        [data, is_sorted, return_inverse, return_counts] = inputs
+        if not is_sorted:
+            logging.warning("TVM always assumes sorted=True for torch.unique")
+            is_sorted = True
+        if return_counts:
+            [unique, indices, num_uniq, counts] = _op.unique(
+                data, is_sorted=is_sorted, return_counts=True
+            )
+            unique_sliced = _op.strided_slice(unique, begin=[0], end=num_uniq, slice_mode="size")
+            counts_sliced = _op.strided_slice(counts, begin=[0], end=num_uniq, slice_mode="size")
+            return (unique_sliced, indices, counts_sliced)
+        else:
+            [unique, indices, num_uniq] = _op.unique(data, is_sorted=is_sorted, return_counts=False)
+            unique_sliced = _op.strided_slice(unique, begin=[0], end=num_uniq, slice_mode="size")
+            return (unique_sliced, indices)
+
     # Operator mappings
     def create_convert_map(self):
         self.convert_map = {
@@ -2108,8 +2288,10 @@ def create_convert_map(self):
             "aten::to": self.to,
             "aten::squeeze": self.squeeze,
             "aten::unsqueeze": self.unsqueeze,
+            "aten::unsqueeze_": self.unsqueeze,
             "aten::cat": self.concatenate,
             "aten::slice": self.slice,
+            "aten::narrow": self.narrow,
             "aten::split": self.split,
             "aten::split_with_sizes": self.split_with_sizes,
             "aten::select": self.select,
@@ -2158,6 +2340,7 @@ def create_convert_map(self):
             "aten::softplus": self.softplus,
             "aten::avg_pool2d": self.avg_pool2d,
             "aten::avg_pool3d": self.avg_pool3d,
+            "aten::linear": self.linear,
             "aten::dropout": self.dropout,
             "aten::dropout_": self.dropout,
             "aten::feature_dropout": self.dropout,
@@ -2251,12 +2434,16 @@ def create_convert_map(self):
             "torchvision::nms": self.nms,
             "aten::logsumexp": self.logsumexp,
             "torchvision::roi_align": self.roi_align,
+            "torchvision::deform_conv2d": self.deform_conv2d,
             "aten::unbind": self.unbind,
             "aten::__and__": self.logical_and,
+            "aten::logical_and": self.logical_and,
             "aten::_shape_as_tensor": self.shape_as_tensor,
             "aten::nonzero": self.nonzero,
             "aten::nonzero_numpy": self.nonzero_numpy,
             "aten::scatter": self.scatter,
+            "aten::index_put": self.index_put,
+            "aten::index_put_": self.index_put,
             "aten::scalar_tensor": self.scalar_tensor,
             "aten::__interpolate": self.interpolate,
             "aten::IntImplicit": self.identity,
@@ -2266,6 +2453,16 @@ def create_convert_map(self):
             "aten::bincount": self.bincount,
             "aten::scatter_add": self.scatter_add,
             "aten::__not__": self.logical_not,
+            "aten::hardswish_": self.hard_swish,
+            "aten::hardswish": self.hard_swish,
+            "aten::hardsigmoid_": self.hard_sigmoid,
+            "aten::hardsigmoid": self.hard_sigmoid,
+            "aten::cumsum": self.cumsum,
+            "aten::masked_fill": self.masked_fill,
+            "aten::masked_select": self.masked_select,
+            "aten::argsort": self.argsort,
+            "aten::sort": self.sort,
+            "aten::_unique2": self.unique,
         }
 
     def update_convert_map(self, custom_map):
@@ -3058,5 +3255,16 @@ def from_pytorch(script_module, input_infos, custom_convert_map=None, default_dt
         # ListConstruct kept original python list. Convert to tuple.
         ret = _expr.Tuple(ret)
 
-    mod["main"] = tvm.relay.Function(_analysis.free_vars(ret), ret)
+    # Separate data inputs and parameters to make sure data inputs are always in the beginning.
+    func_args = []
+    data_inputs = []
+    for arg in _analysis.free_vars(ret):
+        if arg.name_hint not in tvm_params.keys():
+            data_inputs.append(arg)
+        else:
+            func_args.append(arg)
+    func_args = data_inputs + func_args
+
+    mod["main"] = tvm.relay.Function(func_args, ret)
+
     return transform.RemoveUnusedFunctions()(mod), tvm_params
diff --git a/python/tvm/relay/frontend/pytorch_utils.py b/python/tvm/relay/frontend/pytorch_utils.py
index d0f0b9b4b019..02b2484d4fb7 100644
--- a/python/tvm/relay/frontend/pytorch_utils.py
+++ b/python/tvm/relay/frontend/pytorch_utils.py
@@ -14,8 +14,20 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=import-outside-toplevel
+# pylint: disable=import-outside-toplevel, unused-argument, invalid-name
 """ Common utilities used by PyTorch frontend """
+from .. import expr
+from .. import op
+from ..dataflow_pattern import (
+    wildcard,
+    is_constant,
+    is_op,
+    rewrite,
+    is_tuple,
+    is_tuple_get_item,
+    is_if,
+    DFPatternCallback,
+)
 
 
 def is_version_greater_than(ver):
@@ -25,3 +37,370 @@ def is_version_greater_than(ver):
     return "".join(re.findall(r"(\d+\.)(\d+\.)(\d)", torch.__version__)[0]) > "".join(
         re.findall(r"(\d+\.)(\d+\.)(\d)", ver)[0]
     )
+
+
+def dyn_strided_slice_pattern(inp, end):
+    """A pattern to detect dynamic strided slice op."""
+    zero = is_constant()
+    cast_like = is_op("cast_like")(zero, is_constant())
+    less = is_op("less")(is_constant(), cast_like)
+    shape_of = is_op("shape_of")(inp)
+    cast_like = is_op("cast_like")(shape_of, is_constant())
+    add = is_op("add")(is_constant(), cast_like)
+    where = is_op("where")(less, add, is_constant())
+
+    return is_op("dyn.strided_slice")(inp, where, end, is_constant())
+
+
+def batched_nms_pattern(boxes, scores, idxs, iou_threshold, num_boxes, indices):
+    """A pattern to detect batched_nms function in torchvision
+
+    The inputs to this function, boxes, scores, idxs, iou_threshold are wildcard
+    patterns which can be used later in the rewriting to extract matched Relay fragments.
+
+    We want to detect the following PyTorch code snippet:
+
+    def batched_nms(boxes, scores, idxs, iou_threshold):
+        max_coordinate = boxes.max()
+        offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
+        boxes_for_nms = boxes + offsets[:, None]
+        keep = nms(boxes_for_nms, scores, iou_threshold)
+        return keep
+
+    Here is how PyTorch frontend lowers above PyTorch code. For simplicity, Relay ops for
+    dealing with dynamic strided_slice are omitted. %num_boxes, %indices are complex
+    expressions, but since we can use the wildcard part for them, we do not need to construct
+    their patterns.
+
+    %2 = expand_dims(%scores, axis=-1);
+    %3 = cast(%idxs, dtype="float32");
+    %4 = max(%boxes);
+    %5 = add(%4, 1f);
+    %6 = multiply(%3, %5);
+    %7 = strided_slice(%6, begin=[0], end=[4507], strides=[1]);
+    %8 = expand_dims(%7, axis=1);
+    %9 = add(%boxes, %8);
+    %10 = (%2, %9);
+    %11 = concatenate(%10, axis=-1);
+    %12 = expand_dims(%11, axis=0);
+    ...
+    ...
+    %17 = vision.non_max_suppression(%12, %num_boxes, %indices, -1, 0.7f, ...);
+
+    """
+    one = is_constant()
+
+    # Equivelent PyTorch code from above snippet
+    # offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
+    cast = is_op("cast")(idxs)
+    mx = is_op("max")(boxes)
+    add = is_op("add")(mx, one)
+    mul = is_op("multiply")(cast, add)
+
+    shape_of = is_op("shape_of")(mul)
+    cast = is_op("cast")(shape_of)
+
+    # Add offsets to the boxes
+    expand_dims = is_op("expand_dims")(mul)
+    add = is_op("add")(boxes, expand_dims)
+
+    # The rest of patterns correspond to the PyTorch frontend conversion
+    # function for torchvision::nms
+    score_expand_dims = is_op("expand_dims")(scores)
+    tup = is_tuple([score_expand_dims, add])
+    concat = is_op("concatenate")(tup)
+    data = is_op("expand_dims")(concat)
+
+    return is_op("vision.non_max_suppression")(
+        data, num_boxes, indices, is_constant(), iou_threshold
+    )
+
+
+def topk_after_batch_nms_pattern(cond, true_branch, data, valid_count, indices, iou_threshold):
+    """
+    Detect the following pattern used in torchvision detection models.
+
+    def batched_nms(...):
+        if boxes.numel() == 0:
+            return torch.empty((0,), dtype=torch.int64, device=boxes.device)
+        else:
+            ...
+            return nms(boxes_for_nms, scores, iou_threshold)
+
+    keep = batched_nms(boxes, scores, lvl, self.nms_thresh)
+    keep = keep[:post_nms_top_k] # keep only topk scoring predictions
+
+    An equivalent Relay subgraph:
+
+    %1184 = if (%1117) {
+      ...
+    } else {
+      ...
+      %1172 = vision.non_max_suppression(%1167, %1168, %1171, -1, 0.7f, ...);
+      ...
+      %1183 = dyn.strided_slice(%1174, %1180, %1182, ...);
+      cast(%1183, dtype="int64")
+    };
+    %1185 = strided_slice(%1184, begin=[0], end=[1000], strides=[1]);
+
+    """
+    nms = is_op("vision.non_max_suppression")(
+        data, valid_count, indices, is_constant(), iou_threshold
+    )
+    indices = is_op("squeeze")(is_tuple_get_item(nms, 0))
+    size = is_op("squeeze")(is_tuple_get_item(nms, 1))
+    dyn_strided_slice = dyn_strided_slice_pattern(indices, size)
+    cast_i64 = is_op("cast")(dyn_strided_slice)
+
+    batched_nms_result = is_if(cond, true_branch, cast_i64)
+
+    return is_op("strided_slice")(batched_nms_result)
+
+
+class MulticlassNMSRewrite(DFPatternCallback):
+    """A callback to rewrite nms and restore batched nms."""
+
+    def __init__(self):
+        super().__init__()
+        # exprs to extract
+        self.boxes = wildcard()
+        self.scores = wildcard()
+        self.idxs = wildcard()
+        self.iou_threshold = wildcard()
+        self.num_boxes = wildcard()
+        self.indices = wildcard()
+
+        self.pattern = batched_nms_pattern(
+            self.boxes,
+            self.scores,
+            self.idxs,
+            self.iou_threshold,
+            self.num_boxes,
+            self.indices,
+        )
+
+    def convert_batched_nms(self, boxes, scores, idxs, iou_thres, num_boxes, indices):
+        """Restore class-aware NMS using extracted class indices"""
+        scores = op.expand_dims(scores, axis=-1, num_newaxis=1)
+        idxs = op.expand_dims(idxs, axis=-1, num_newaxis=1)
+        idxs = op.cast(idxs, "float32")
+        data = op.concatenate([idxs, scores, boxes], -1)
+        data = op.expand_dims(data, 0, 1)
+
+        top_k = max_out_size = -1
+        out = op.vision.non_max_suppression(
+            data=data,
+            valid_count=num_boxes,
+            indices=indices,
+            max_output_size=max_out_size,
+            iou_threshold=iou_thres,
+            force_suppress=False,
+            top_k=top_k,
+            coord_start=2,
+            score_index=1,
+            id_index=0,
+            return_indices=True,
+            invalid_to_bottom=False,
+        )
+        return out.tuple_value
+
+    def callback(self, pre, post, node_map):
+        boxes = node_map[self.boxes][0]
+        scores = node_map[self.scores][0]
+        idxs = node_map[self.idxs][0]
+        iou_thres = node_map[self.iou_threshold][0]
+        num_boxes = node_map[self.num_boxes][0]
+        indices = node_map[self.indices][0]
+        return self.convert_batched_nms(boxes, scores, idxs, iou_thres, num_boxes, indices)
+
+
+class PostNMSTopKRewrite(DFPatternCallback):
+    """A callback to rewrite nms to exploit max_out_size parameter."""
+
+    def __init__(self):
+        super().__init__()
+        self.cond = wildcard()
+        self.true_branch = wildcard()
+        self.data = wildcard()
+        self.valid_count = wildcard()
+        self.indices = wildcard()
+        self.iou_threshold = wildcard()
+
+        self.pattern = topk_after_batch_nms_pattern(
+            self.cond,
+            self.true_branch,
+            self.data,
+            self.valid_count,
+            self.indices,
+            self.iou_threshold,
+        )
+
+    def rewrite_batch_nms_with_max_out_size(
+        self, cond, true_branch, data, valid_count, indices, iou_threshold, post_nms_topk
+    ):
+        """Use the detected post NMS topk parameter in NMS op."""
+        nms_ret = op.vision.non_max_suppression(
+            data=data,
+            valid_count=valid_count,
+            indices=indices,
+            max_output_size=post_nms_topk,
+            iou_threshold=iou_threshold,
+            force_suppress=False,
+            top_k=-1,
+            coord_start=2,
+            score_index=1,
+            id_index=0,
+            return_indices=True,
+            invalid_to_bottom=False,
+        )
+
+        size = op.squeeze(nms_ret[1], axis=[1])
+        data_slice = op.squeeze(nms_ret[0], axis=[0])
+
+        ret = op.strided_slice(data_slice, begin=expr.const([0]), end=size, slice_mode="size")
+
+        nms_result = op.cast(ret, "int64")
+
+        return expr.If(cond, true_branch, nms_result)
+
+    def callback(self, pre, post, node_map):
+        post_nms_topk = post.attrs.end[0].value
+        return self.rewrite_batch_nms_with_max_out_size(
+            node_map[self.cond][0],
+            node_map[self.true_branch][0],
+            node_map[self.data][0],
+            node_map[self.valid_count][0],
+            node_map[self.indices][0],
+            node_map[self.iou_threshold][0],
+            post_nms_topk,
+        )
+
+
+def scatter_roi_align_result_pattern(levels, roi_align_results, num_scales):
+    """Detect the Relay subgraph corresponding to the following PyTorch code
+
+    first_result = roi_align_results[0]
+    dtype, device = first_result.dtype, first_result.device
+    res = torch.zeros((levels.size(0), first_result.size(1),
+                       first_result.size(2), first_result.size(3)),
+                      dtype=dtype, device=device)
+    for level in range(len(roi_align_results)):
+        index = torch.where(levels == level)[0].view(-1, 1, 1, 1)
+        index = index.expand(index.size(0),
+                             roi_align_results[level].size(1),
+                             roi_align_results[level].size(2),
+                             roi_align_results[level].size(3))
+        res = res.scatter(0, index, roi_align_results[level])
+    return res
+    """
+
+    def do_where(levels, _):
+        idx_in_level = is_op("argwhere")(is_op("equal")(levels, is_constant()))
+        idx_in_level = is_op("split")(idx_in_level)
+        idx_in_level = is_tuple_get_item(idx_in_level, 0)
+        idx_in_level = is_op("squeeze")(idx_in_level)
+        idx_in_level = is_tuple_get_item(is_tuple([idx_in_level]), 0)
+        return idx_in_level
+
+    scatter_res = wildcard()
+
+    for i in range(num_scales):
+        # index = torch.where(levels == level)[0].view(-1, 1, 1, 1)
+        scatter_indices = do_where(levels, i)
+        scatter_indices = is_op("reshape")(scatter_indices)
+
+        # index = index.expand(index.size(0),
+        #                      unmerged_results[level].size(1),
+        #                      unmerged_results[level].size(2),
+        #                      unmerged_results[level].size(3))
+        scatter_indices = is_op("repeat")(scatter_indices)
+        scatter_indices = is_op("repeat")(scatter_indices)
+        scatter_indices = is_op("repeat")(scatter_indices)
+
+        scatter_res = is_op("scatter")(scatter_res, scatter_indices, roi_align_results[i])
+
+    return is_op("reshape")(scatter_res)
+
+
+class ScatterRewrite(DFPatternCallback):
+    """A callback to rewrite repeated scatters with a batched gather."""
+
+    def __init__(self, num_scales):
+        super().__init__()
+        self.num_scales = num_scales
+        self.levels = wildcard()
+        self.roi_align_results = []
+        for _ in range(num_scales):
+            self.roi_align_results.append(wildcard())
+
+        self.pattern = scatter_roi_align_result_pattern(
+            self.levels, self.roi_align_results, num_scales
+        )
+
+    def convert_scatter_to_gather(self, levels, roi_align_results):
+        """Replace the detected scatter loop with the following PyTorch code
+
+        indices_per_level = []
+        for level in range(num_scales):
+            idx_in_level = torch.where(levels == level)[0]
+            indices_per_leve.append(idx_in_level)
+
+        stacked_features = torch.cat(roi_align_results, dim=0)
+        stacked_indices = torch.cat(indices_per_level, dim=0)
+        argsort_indices = torch.argort(stacked_indices)
+        return stacked_features[argsort_indices, :]
+        """
+
+        # Collect inidices and concat them
+        indices_per_level = []
+        for i in range(self.num_scales):
+            equal = op.equal(levels, expr.const(i, dtype="int64"))
+            argwhere = op.argwhere(equal)
+            split = op.split(argwhere, indices_or_sections=1, axis=1)
+            squeeze = op.squeeze(split[0], axis=[1])
+            indices = op.cast(squeeze, dtype="int64")
+            indices_per_level.append(indices)
+
+        indices_concat = op.concatenate(indices_per_level, 0)
+
+        # Concat roi align results per level, and argsort indices
+        # To prepare for a batched gather
+        roi_align_results_concat = op.concatenate(roi_align_results, 0)
+        argsort_indices = op.cast(op.argsort(indices_concat), dtype="int64")
+
+        # Permute rows by argsorted indices
+        permuted = op.take(roi_align_results_concat, argsort_indices, axis=0)
+
+        return op.reshape(permuted, [0, -1, 1, 1])
+
+    def callback(self, pre, post, node_map):
+        levels = node_map[self.levels][0]
+        roi_align_results = [node_map[feat][0] for feat in self.roi_align_results]
+        return self.convert_scatter_to_gather(levels, roi_align_results)
+
+
+def rewrite_nms_to_batched_nms(mod):
+    """Rewrite the input graph to replace non maximum surpression
+    in torchvision that does not take class id into account with the one
+    that avoids IOU tests between different classes.
+    """
+    mod["main"] = rewrite(MulticlassNMSRewrite(), mod["main"])
+    return mod
+
+
+def rewrite_batched_nms_with_max_out_size(mod):
+    """Rewrite the input graph to detect slicing after batched nms and
+    use the slicing size as the parameter max_out_size in NMS.
+    """
+    mod["main"] = rewrite(PostNMSTopKRewrite(), mod["main"])
+    return mod
+
+
+def rewrite_scatter_to_gather(mod, num_scales):
+    """Rewrite the input graph to replace a repeated scatter loop with
+    a batched gather. The scatter loop is used in torchvision MultiScaleRoIAlign
+    to merge roi_align results for all scales. The scatter is used to emulate
+    inplace updates.
+    """
+    mod["main"] = rewrite(ScatterRewrite(num_scales), mod["main"])
+    return mod
diff --git a/python/tvm/relay/frontend/qnn_torch.py b/python/tvm/relay/frontend/qnn_torch.py
index e3431043bc86..2dd84b650bd2 100644
--- a/python/tvm/relay/frontend/qnn_torch.py
+++ b/python/tvm/relay/frontend/qnn_torch.py
@@ -191,6 +191,7 @@ def _get_quant_param_for_input(input_value):
         "quantized::cat": (2, 3),
         "quantized::mul_scalar": (2, 3),
         "quantized::add_scalar": (2, 3),
+        "quantized::hardswish": (1, 2),
     }
 
     def dfs(current_node):
@@ -352,12 +353,15 @@ def add_input_quant_params_to_op_inputs(graph):
         "quantized::mul": 2,
         "aten::dequantize": 1,
         "aten::mean": 1,
+        "aten::upsample_nearest2d": 1,
         "aten::upsample_bilinear2d": 1,
         "aten::relu_": 1,
         "aten::relu": 1,
         "quantized::add_scalar": 1,
         "quantized::mul_scalar": 1,
         "quantized::relu6": 1,
+        "quantized::hardswish": 1,
+        "aten::hardsigmoid": 1,
     }
 
     need_input_quant_param = set(num_quantized_inputs.keys())
@@ -765,6 +769,7 @@ def _impl(inputs, _):
         out_zp = _expr.const(inputs[3])
 
         if q_min > z - c_q or q_max < z - c_q:
+            # TODO(masahi): Replace this with integer only compute
             dequant = relay.qnn.op.dequantize(inputs[0], _expr.const(s), _expr.const(z))
             dequantized_add = _op.tensor.add(dequant, _expr.const(c_q * s))
             return relay.qnn.op.quantize(
@@ -820,6 +825,35 @@ def _impl(inputs, _):
     return _impl
 
 
+def _hswish():
+    # refer to src/ATen/native/quantized/cpu/kernels/QuantizedOpKernels.cpp
+    # They fallback to fp32
+    def _impl(inputs, _):
+        assert len(inputs) == 5, "Input quant params not found in op inputs"
+        # TODO(masahi): Replace this with integer only compute.
+        # We do not have to strictly follow how PyTorch does it.
+
+        def relu6(x):
+            return _op.tensor.clip(x, 0.0, 6.0)
+
+        def hardsigmoid(x):
+            dtype = "float32"
+            return relu6(x + _expr.const(3.0, dtype=dtype)) / _expr.const(6.0, dtype=dtype)
+
+        output_scale = _expr.const(inputs[1])
+        output_zero_point = _expr.const(inputs[2])
+        input_scale = _expr.const(inputs[3])
+        input_zero_point = _expr.const(inputs[4])
+
+        dequant = relay.qnn.op.dequantize(inputs[0], input_scale, input_zero_point, axis=1)
+        dequantized_hswish = dequant * hardsigmoid(dequant)
+        return relay.qnn.op.quantize(
+            dequantized_hswish, output_scale, output_zero_point, out_dtype="uint8"
+        )
+
+    return _impl
+
+
 def _linear_dynamic():
     def _calculate_qparam(inp):
         # reference ATen/native/quantized/cpu/qlinear_dynamic.cpp
@@ -906,4 +940,5 @@ def _impl(inputs, _):
     "quantized::mul_scalar": _mul_scalar(),
     "quantized::relu6": _relu6(),
     "quantized::linear_dynamic": _linear_dynamic(),
+    "quantized::hardswish": _hswish(),
 }
diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
index d5746a38582c..1946223a50a4 100644
--- a/python/tvm/relay/frontend/tensorflow.py
+++ b/python/tvm/relay/frontend/tensorflow.py
@@ -44,6 +44,17 @@
 __all__ = ["from_tensorflow"]
 
 
+def check_symbolic_shape(shape):
+    return not all([isinstance(dim, (int, tvm.tir.IntImm)) for dim in shape])
+
+
+def list_shape_of(tensor, ndim):
+    shape_tensor = _op.shape_of(tensor)
+    return [
+        _op.strided_slice(shape_tensor, begin=[i], end=[i + 1], strides=[1]) for i in range(ndim)
+    ]
+
+
 def _get_pad_pair(input1d, kernel1d, stride1d):
     if input1d % stride1d == 0:
         pad = max(kernel1d - stride1d, 0)
@@ -268,6 +279,13 @@ def _impl(inputs, attr, params, mod):
             pad_h = _get_pad_pair(in_w, kernel_w, stride_w)
 
             attr["padding"] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
+        elif attr["padding"] == "EXPLICIT":
+            paddings = attr["explicit_paddings"]
+            assert len(paddings) == 8
+            if flip_layout or attr["data_format"] == "NHWC":
+                attr["padding"] = [paddings[2], paddings[4], paddings[3], paddings[5]]
+            else:
+                attr["padding"] = [paddings[4], paddings[6], paddings[5], paddings[7]]
         else:
             msg = 'Value {} in attribute "padding" of operator Pooling is ' "not valid."
             raise tvm.error.OpAttributeInvalid(msg.format(attr["padding"]))
@@ -278,7 +296,7 @@ def _impl(inputs, attr, params, mod):
         out = AttrCvt(
             op_name=_dimension_picker(name),
             transforms={"kernel_shape": "pool_size", "data_format": "layout"},
-            ignores=["ksize"],
+            ignores=["ksize", "explicit_paddings"],
             extras={"ceil_mode": False},
             custom_check=_dimension_constraint(),
         )(inputs, attr)
@@ -418,6 +436,13 @@ def _impl(inputs, attr, params, mod):
             pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
 
             attr["padding"] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
+        elif attr["padding"] == "EXPLICIT":
+            paddings = attr["explicit_paddings"]
+            assert len(paddings) == 8
+            if flip_layout or attr["data_format"] == "NHWC":
+                attr["padding"] = [paddings[2], paddings[4], paddings[3], paddings[5]]
+            else:
+                attr["padding"] = [paddings[4], paddings[6], paddings[5], paddings[7]]
         else:
             msg = 'Value {} in attribute "padding" of operator Conv is not ' "valid."
             raise tvm.error.OpAttributeInvalid(msg.format(attr["padding"]))
@@ -626,7 +651,27 @@ def _impl(inputs, attr, params, mod):
             pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
 
             attr["padding"] = [pad_d[0], pad_v[0], pad_h[0], pad_d[1], pad_v[1], pad_h[1]]
-
+        elif attr["padding"] == "EXPLICIT":
+            paddings = attr["explicit_paddings"]
+            assert len(paddings) == 10
+            if flip_layout or attr["data_format"] == "NDHWC":
+                attr["padding"] = [
+                    paddings[2],
+                    paddings[4],
+                    paddings[6],
+                    paddings[3],
+                    paddings[5],
+                    paddings[7],
+                ]
+            else:
+                attr["padding"] = [
+                    paddings[4],
+                    paddings[6],
+                    paddings[8],
+                    paddings[5],
+                    paddings[7],
+                    paddings[9],
+                ]
         else:
             msg = 'Value {} in attribute "padding" of operator Conv is not ' "valid."
             raise tvm.error.OpAttributeInvalid(msg.format(attr["padding"]))
@@ -739,6 +784,109 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _combined_nms():
+    def _impl(inputs, attr, params, mod):
+        # Get parameter values
+        boxes = inputs[0]
+        scores = inputs[1]
+        try:
+            max_output_size = int(np.atleast_1d(inputs[2].data.asnumpy().astype("int64"))[0])
+        except Exception:
+            try:
+                max_output_size = (
+                    _infer_value(inputs[2], params, mod).asnumpy().astype("int64").tolist()[0]
+                )
+            except Exception:
+                max_output_size = inputs[2]
+        max_total_size = inputs[3]
+        iou_threshold = np.atleast_1d(inputs[4].data.asnumpy())[0]
+        score_threshold = np.atleast_1d(inputs[5].data.asnumpy())[0]
+        if attr["pad_per_class"]:
+            raise tvm.error.OpAttributeUnImplemented(
+                "pad_per_class for CombinedNonMaxSuppression is not supported"
+            )
+        boxes_shape = _infer_shape(inputs[0], mod)
+        scores_shape = _infer_shape(inputs[1], mod)
+        batch_size = boxes_shape[0]
+        num_anchors = boxes_shape[1]
+        q = boxes_shape[2]
+        num_classes = scores_shape[2]
+
+        if q != num_classes:
+            # When q is 1, it means same box coords are used for all classes.
+            boxes = _op.broadcast_to(boxes, (batch_size, num_anchors, num_classes, 4))
+        boxes = _op.reshape(boxes, newshape=[batch_size, num_anchors * num_classes, 4])
+        scores = _op.reshape(scores, newshape=[batch_size, num_anchors * num_classes, 1])
+
+        # In TF, class is specified by memory layout only.
+        ids = _op.arange(_op.const(num_classes, dtype="float32"))
+        ids = _op.broadcast_to(ids, (batch_size, num_anchors, num_classes))
+        ids = _op.reshape(ids, newshape=[batch_size, num_anchors * num_classes, 1])
+
+        data = _op.concatenate([ids, scores, boxes], -1)
+        ct, data, indices = _op.vision.get_valid_counts(
+            data, score_threshold=score_threshold, id_index=0, score_index=1
+        )
+        nms_ret = _op.vision.non_max_suppression(
+            data=data,
+            valid_count=ct,
+            indices=indices,
+            max_output_size=max_output_size,
+            iou_threshold=iou_threshold,
+            force_suppress=False,
+            top_k=-1,
+            coord_start=2,
+            score_index=1,
+            id_index=0,
+            return_indices=False,
+            invalid_to_bottom=True,
+        )
+        # Dynamic slice to max_total_size
+        neg_one = _expr.const([-1])
+        slice_end = _op.concatenate(
+            [neg_one, _op.expand_dims(max_total_size, axis=0), neg_one], axis=0
+        )
+        nms_ret = _op.strided_slice(
+            nms_ret, begin=[0, 0, 0], end=slice_end, strides=[1, 1, 1], slice_mode="size"
+        )
+
+        # Slice output into boxes, scores, classes
+        nmsed_boxes = _op.strided_slice(
+            nms_ret, begin=[0, 0, 2], end=[-1, -1, 4], slice_mode="size"
+        )
+        if attr["clip_boxes"]:
+            nmsed_boxes = _op.maximum(nmsed_boxes, _expr.const(0, dtype="float32"))
+            nmsed_boxes = _op.minimum(nmsed_boxes, _expr.const(1, dtype="float32"))
+        nmsed_scores = _op.strided_slice(
+            nms_ret, begin=[0, 0, 1], end=[-1, -1, 1], slice_mode="size"
+        )
+        nmsed_scores = _op.squeeze(nmsed_scores, axis=[2])
+        nmsed_classes = _op.strided_slice(
+            nms_ret, begin=[0, 0, 0], end=[-1, -1, 1], slice_mode="size"
+        )
+        nmsed_classes = _op.squeeze(nmsed_classes, axis=[2])
+        # Get number of valid boxes
+        nms_count = _op.sum(
+            _op.cast(_op.greater(nmsed_scores, _expr.const(0, dtype="float32")), "int32"), axis=1
+        )
+
+        # TVM uses -1 for invalid outputs while TF uses 0
+        box_range = _op.arange(_expr.const(0, dtype="int32"), max_total_size, dtype="int32")
+        shape = _op.strided_slice(_op.shape_of(nmsed_boxes), begin=[0], end=[2])
+        box_range = _op.broadcast_to(box_range, shape)
+        valid_mask = _op.cast(_op.less(box_range, _op.expand_dims(nms_count, axis=1)), "float32")
+        nmsed_boxes = nmsed_boxes * _op.expand_dims(valid_mask, axis=2)
+        # Could instead use mask for scores, classes if negative values are possible.
+        nmsed_scores = _op.maximum(nmsed_scores, _expr.const(0, dtype="float32"))
+        nmsed_classes = _op.maximum(nmsed_classes, _expr.const(0, dtype="float32"))
+
+        return _expr.TupleWrapper(
+            _expr.Tuple([nmsed_boxes, nmsed_scores, nmsed_classes, nms_count]), 4
+        )
+
+    return _impl
+
+
 def _decode_image():
     def _impl(inputs, attr, params, mod):
         # Image decode wrapper: Expecting user to feed decoded input to next layer drop this layer.
@@ -885,13 +1033,31 @@ def _impl(inputs, attr, params, mod):
         input_y = inputs[1]
         orig_shape_x = _infer_shape(input_x, mod)
         orig_shape_y = _infer_shape(input_y, mod)
+        ndim = len(orig_shape_x)
+
+        is_static = not check_symbolic_shape(orig_shape_x)
+
+        if ndim > 3 and not is_static:
+            shape_of_x = list_shape_of(inputs[0], ndim)
+            shape_of_y = list_shape_of(inputs[1], ndim)
 
         # reshape n-dimensional batch matmul into 3d
-        if len(orig_shape_x) > 3:
+        if ndim > 3:
             outer_dims = [orig_shape_x[i] for i in range(0, len(orig_shape_x) - 2)]
-            num_outer_elts = np.prod(outer_dims)
-            new_shape_x = (num_outer_elts, orig_shape_x[-2], orig_shape_x[-1])
-            new_shape_y = (num_outer_elts, orig_shape_y[-2], orig_shape_y[-1])
+            if is_static:
+                num_outer_elts = np.prod(outer_dims)
+                new_shape_x = (num_outer_elts, orig_shape_x[-2], orig_shape_x[-1])
+                new_shape_y = (num_outer_elts, orig_shape_y[-2], orig_shape_y[-1])
+            else:  # handle dynamic shape (dyn.reshape op)
+                # new shape = [prod(shape[:-2]), -2, -1]
+                new_shape_x = [_op.const(1), shape_of_x[-2], shape_of_x[-1]]
+                new_shape_y = [_op.const(1), shape_of_y[-2], shape_of_y[-1]]
+                for i in range(ndim - 2):
+                    new_shape_x[0] *= shape_of_x[i]
+                    new_shape_y[0] *= shape_of_y[i]
+                new_shape_x = _op.concatenate(_op.Tuple(new_shape_x), axis=0)
+                new_shape_y = _op.concatenate(_op.Tuple(new_shape_y), axis=0)
+
             input_x = _op.reshape(input_x, newshape=new_shape_x)
             input_y = _op.reshape(input_y, newshape=new_shape_y)
 
@@ -902,22 +1068,30 @@ def _impl(inputs, attr, params, mod):
         ret = get_relay_op("batch_matmul")(input_x, input_y)
 
         # reshape result back to n-dimensional
-        if len(orig_shape_x) > 3:
-            final_shape = list(orig_shape_x)
-            final_shape[-2] = orig_shape_x[-1] if adj_x else orig_shape_x[-2]
-            final_shape[-1] = orig_shape_y[-2] if adj_y else orig_shape_y[-1]
-            ret = _op.reshape(ret, newshape=final_shape)
+        if ndim > 3:
+            if is_static:
+                final_shape = list(orig_shape_x)
+                final_shape[-2] = orig_shape_x[-1] if adj_x else orig_shape_x[-2]
+                final_shape[-1] = orig_shape_y[-2] if adj_y else orig_shape_y[-1]
+            else:
+                # calculate the resulting shape = [shape[:-2], 0, 0]
+                final_shape = list(shape_of_x)
+                final_shape[-2] = shape_of_x[-1] if adj_x else shape_of_x[-2]
+                final_shape[-1] = shape_of_y[-2] if adj_y else shape_of_y[-1]
+                final_shape = _op.concatenate(_op.Tuple(final_shape), axis=0)
 
+            ret = _op.reshape(ret, newshape=final_shape)
         return ret
 
     return _impl
 
 
 def _sparse_tensor_dense_matmul():
-    # Sparse utility from scipy
-    from scipy.sparse import csr_matrix
-
     def _impl(inputs, attr, params, mod):
+        # Loading this by default causes TVM to not be loadable from other languages.
+        # Sparse utility from scipy
+        from scipy.sparse import csr_matrix
+
         assert len(inputs) == 4, "There should be 4 input tensors"
 
         indices_tensor = _infer_value(inputs[0], params, mod).asnumpy()
@@ -926,13 +1100,6 @@ def _impl(inputs, attr, params, mod):
 
         data = inputs[3]
 
-        # By default, in tensorflow the first input ,i.e., data is sparse
-        sparse_lhs = True
-
-        # If both are true means First input was dense and second was sparse
-        if attr.get("adjoint_a") and attr.get("adjoint_b"):
-            sparse_lhs = False
-
         rows = [x[0] for x in indices_tensor]
         cols = [x[1] for x in indices_tensor]
 
@@ -941,9 +1108,53 @@ def _impl(inputs, attr, params, mod):
             (values_tensor, (rows, cols)), shape=tuple(dense_shape_tensor.tolist())
         )
 
-        if sparse_lhs:
+        # As per tensorflow implementation, we have 4 possible input combination
+        # and the first input(A) is always sparse and second input(B) is always dense.
+        # Case 1: A , B , adjoint_a=False, adjoint_b=False  --> A * B
+        # Case 2: A , B , adjoint_a=True,   adjoint_b=False  --> A.T * B
+        # Case 3: A , B , adjoint_a=False, adjoint_b=True    --> A * B.T
+        # Case 4: A , B , adjoint_a=True,   adjoint_b=True    --> A.T * B.T
+        #
+        # Topi implementation for sparse_dense(matmul) has 2 possible input
+        # combination where first input(A) is always dense
+        # and second input(B) is always sparse.
+        # Case 1: A , B, sparse_lhs = False  --> A * B.T
+        # Case 2: A , B, sparse_lhs = True    --> B * A.T
+        #
+        # The mapping would be as below:
+        # TF Case 1: A , B , adjoint_a=False, adjoint_b=False
+        #           --> In TF: A * B   --> In Topi: A * B.T.T
+        #           --> sparse_dense(transpose(B), A, sparse_lhs=True)
+        #
+        # TF Case 2: A , B , adjoint_a=True, adjoint_b=False
+        #           --> In TF: A.T * B   --> In Topi: A.T * B.T.T
+        #           --> sparse_dense(transpose(B), transpose(A), sparse_lhs=True)
+        #
+        # TF Case 3: A , B , adjoint_a=False, adjoint_b=True
+        #           --> In TF: A * B.T   --> In Topi: A * B
+        #           --> sparse_dense(B, A, sparse_lhs=True)
+        #
+        # TF Case 4: A , B , adjoint_a=True, adjoint_b=True
+        #           --> In TF: A.T * B.T   --> In Topi: (B * A.T).T
+        #           --> transpose(sparse_dense(B, transpose(A), sparse_lhs=False))
+
+        # By default, in tensorflow the first input ,i.e., data is sparse
+        sparse_lhs = True
+
+        # TF Case 1:
+        if not attr.get("adjoint_a") and not attr.get("adjoint_b"):
+            data = _op.transpose(data)
+        # TF Case 2:
+        elif attr.get("adjoint_a") and not attr.get("adjoint_b"):
             data = _op.transpose(data)
+            weight_sp = csr_matrix(weight_sp.transpose())
+        # TF Case 3:
+        elif not attr.get("adjoint_a") and attr.get("adjoint_b"):
+            pass
+        # TF Case 4:
+        # attr.get("adjoint_a") and attr.get("adjoint_b"):
         else:
+            sparse_lhs = False
             weight_sp = csr_matrix(weight_sp.transpose())
 
         weight_data = _expr.const(weight_sp.data, weight_sp.data.dtype)
@@ -953,22 +1164,192 @@ def _impl(inputs, attr, params, mod):
         ret = _op.nn.sparse_dense(data, [weight_data, weight_indices, weight_indptrs], sparse_lhs)
 
         if not sparse_lhs:
+            # TF Case 4
             ret = _op.transpose(ret)
 
-        # Case 1. If both are true means first input was dense and second was sparse
-        # Case 2. If both are false means first input was sparse and second was dense
-        # TODO(ANSHUMAN87): Support other adjoint option too
-        if not (
-            (attr.get("adjoint_a") and attr.get("adjoint_b"))
-            or ((not attr.get("adjoint_a")) and (not attr.get("adjoint_b")))
-        ):
-            raise tvm.error.OpAttributeUnImplemented(
-                "Only tf.sparse.sparse_dense_matmul() with adjoint_a=True and adjoint_b=True"
-                "or with adjoint_a=False and adjoint_b=False"
-                " is supported, but adjoint_a={} and adjoint_b={} was supplied.".format(
-                    attr.get("adjoint_a"), attr.get("adjoint_b")
-                )
-            )
+        return ret
+
+    return _impl
+
+
+def _sparse_fill_empty_rows():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 4, "There should be 4 input tensors"
+        sparse_indices = inputs[0]
+        sparse_values = inputs[1]
+        sparse_indices_num_cols = _infer_shape(sparse_indices, mod)[1]
+        first_column = _op.split(sparse_indices, sparse_indices_num_cols, axis=1)[0]
+        sorted_indices = _op.argsort(_op.squeeze(first_column))
+        sorted_sparse_indices = _op.take(sparse_indices, sorted_indices, axis=0)
+        sorted_sparse_values = _op.take(sparse_values, sorted_indices, axis=0)
+        new_sparse_indices, new_sparse_values, empty_row_indicator = _op.sparse_fill_empty_rows(
+            sorted_sparse_indices, sorted_sparse_values, inputs[2], inputs[3]
+        )
+
+        return _expr.TupleWrapper(
+            _expr.Tuple([new_sparse_indices, new_sparse_values, empty_row_indicator]),
+            3,
+        )
+
+    return _impl
+
+
+def _sparse_reshape():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 3, "There should be 3 input tensors"
+        new_indices, new_shape = get_relay_op("sparse_reshape")(inputs[0], inputs[1], inputs[2])
+        return _expr.TupleWrapper(_expr.Tuple([new_indices, new_shape]), 2)
+
+    return _impl
+
+
+def _math_segment_sum():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 2, "There should be 2 input tensors"
+        return get_relay_op("segment_sum")(inputs[0], inputs[1])
+
+    return _impl
+
+
+def _sparse_segment_sum():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 3, "There should be 3 input tensors"
+        data = _op.take(inputs[0], inputs[1], axis=0)
+        return _op.segment_sum(data, inputs[2])
+
+    return _impl
+
+
+def _sparse_segment_sum_with_num_segments():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 4, "There should be 4 input tensors"
+        data = _op.take(inputs[0], inputs[1], axis=0)
+        num_segments = int(inputs[3].data.asnumpy().item())
+        return _op.segment_sum(data, inputs[2], num_segments)
+
+    return _impl
+
+
+def row_wise_divide(multi_dim_tensor, one_dim_vector):
+    """
+    This function enables row-wise division of multi_dim_tensor and one_dim_vector.
+    To achieve this, it is first tiled to the appropriate shape and then elemwise_division
+    """
+    multi_dim_tensor_offrow_shape = _op.strided_slice(
+        _op.shape_of(multi_dim_tensor, "int32"), [1], [-1], slice_mode="size"
+    )
+    one_dim_vector_tiled_shape = _op.concatenate(
+        [_op.reverse(multi_dim_tensor_offrow_shape, 0), _expr.const([1])], axis=0
+    )
+    one_dim_vector_tiled = _op.transpose(_op.tile(one_dim_vector, one_dim_vector_tiled_shape))
+    return _op.divide(multi_dim_tensor, one_dim_vector_tiled)
+
+
+def count_all_indices(segment_ids, counts_dtype, num_segments=None):
+    """
+    This snippet calculates the sqrt count of each index among all valid indices
+    Valid indices are from 0 to max of [segment ids, num_segments]
+    """
+
+    max_segments = _op.reshape(_op.max(segment_ids), -1) + _expr.const([1])
+    if num_segments:
+        max_segments = _op.maximum(max_segments, _expr.const([num_segments]))
+    max_ones = _op.maximum(max_segments, _op.shape_of(segment_ids))
+    counts = _op.segment_sum(
+        _op.ones(max_ones, counts_dtype), segment_ids, num_segments=num_segments
+    )
+    real_counts = _op.clip(counts, 1, 2147483647)  # Clip max doesn't work over int32
+    return real_counts
+
+
+def _sparse_segment_sum_sqrtn():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 3, "There should be 3 input tensors"
+        data = _op.take(inputs[0], inputs[1], axis=0)
+        real_counts = count_all_indices(inputs[2], attr["T"].name)
+        real_sqrt_counts = _op.sqrt(_op.cast_like(real_counts, data))
+
+        # Calculate regular segment sum
+        segment_sum = _op.segment_sum(data, inputs[2])
+
+        return row_wise_divide(segment_sum, real_sqrt_counts)
+
+    return _impl
+
+
+def _sparse_segment_sum_sqrtn_with_num_segments():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 4, "There should be 4 input tensors"
+        data = _op.take(inputs[0], inputs[1], axis=0)
+        num_segments = int(inputs[3].data.asnumpy().item())
+        real_counts = count_all_indices(inputs[2], attr["T"].name, num_segments=num_segments)
+        real_sqrt_counts = _op.sqrt(_op.cast_like(real_counts, data))
+
+        # Calculate regular segment sum
+        segment_sum = _op.segment_sum(data, inputs[2], num_segments=num_segments)
+
+        return row_wise_divide(segment_sum, real_sqrt_counts)
+
+    return _impl
+
+
+def _sparse_segment_mean():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 3, "There should be 3 input tensors"
+        data = _op.take(inputs[0], inputs[1], axis=0)
+        real_counts = count_all_indices(inputs[2], attr["T"].name)
+
+        # Calculate regular segment sum
+        segment_sum = _op.segment_sum(data, inputs[2])
+
+        return row_wise_divide(segment_sum, real_counts)
+
+    return _impl
+
+
+def _sparse_segment_mean_with_num_segments():
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 4, "There should be 4 input tensors"
+        data = _op.take(inputs[0], inputs[1], axis=0)
+        num_segments = int(inputs[3].data.asnumpy().item())
+        real_counts = count_all_indices(inputs[2], attr["T"].name, num_segments=num_segments)
+
+        # Calculate regular segment sum
+        segment_sum = _op.segment_sum(data, inputs[2], num_segments=num_segments)
+
+        return row_wise_divide(segment_sum, real_counts)
+
+    return _impl
+
+
+def _sparse_tensor_dense_add():
+    # Sparse utility from scipy
+    from scipy.sparse import csr_matrix
+
+    def _impl(inputs, attr, params, mod):
+        assert (
+            len(inputs) == 4
+        ), "There should be 4 input tensors [sparse_indices, sparse_values, sparse_shape, dense]."
+
+        indices_tensor = _infer_value(inputs[0], params, mod).asnumpy()
+        values_tensor = _infer_value(inputs[1], params, mod).asnumpy()
+        dense_shape_tensor = _infer_value(inputs[2], params, mod).asnumpy()
+
+        data = inputs[3]
+
+        rows = [x[0] for x in indices_tensor]
+        cols = [x[1] for x in indices_tensor]
+
+        # Create scipy sparse Tensor(CSR)
+        weight_sp = csr_matrix(
+            (values_tensor, (rows, cols)), shape=tuple(dense_shape_tensor.tolist())
+        )
+
+        weight_data = _expr.const(weight_sp.data, weight_sp.data.dtype)
+        weight_indptrs = _expr.const(weight_sp.indptr, weight_sp.indptr.dtype)
+        weight_indices = _expr.const(weight_sp.indices, weight_sp.indices.dtype)
+
+        ret = _op.nn.sparse_add(data, [weight_data, weight_indices, weight_indptrs])
 
         return ret
 
@@ -982,6 +1363,13 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _identityn():
+    def _impl(inputs, attr, params, mod):
+        return inputs
+
+    return _impl
+
+
 def _concatV2():
     def _impl(inputs, attr, params, mod):
         pop_node = inputs.pop(len(inputs) - 1)
@@ -1393,9 +1781,9 @@ def _squeeze():
     def _impl(inputs, attr, params, mod):
         if len(attr["squeeze_dims"]) == 0:
             attr["squeeze_dims"] = None
-        return AttrCvt(op_name="squeeze", transforms={"squeeze_dims": "axis"}, ignores=["T"])(
-            inputs, attr
-        )
+        return AttrCvt(
+            op_name="squeeze", transforms={"squeeze_dims": "axis"}, ignores=["T", "_cloned"]
+        )(inputs, attr)
 
     return _impl
 
@@ -1890,6 +2278,16 @@ def _impl(inputs, attr, params, mod):
                 # Symbolic delta
                 delta = inputs[2]
 
+        # if all attributes are constant, evalute the range function and return relay.const
+        if all(
+            [
+                isinstance(start, (np.int32, np.int64, int, np.float32, np.float64, float)),
+                isinstance(limit, (np.int32, np.int64, int, np.float32, np.float64, float)),
+                isinstance(delta, (np.int32, np.int64, int, np.float32, np.float64, float)),
+            ]
+        ):
+            return tvm.relay.const(list(range(int(start), int(limit), int(delta))))
+
         dtype = attr["Tidx"].name if "Tidx" in attr else str(start.dtype)
         if isinstance(start, (np.int32, np.int64, int, np.float32, np.float64, float)):
             start = _expr.const(start, dtype=dtype)
@@ -2272,6 +2670,30 @@ def _impl(inputs, attr, params, mod):
     return _impl
 
 
+def _unique(return_counts=True):
+    def _impl(inputs, attr, params, mod):
+        assert len(inputs) == 1
+        data = inputs[0]
+        if return_counts:
+            [unique, indices, num_uniq, counts] = _op.unique(
+                data, is_sorted=False, return_counts=True
+            )
+            unique_sliced = _op.strided_slice(unique, begin=[0], end=num_uniq, slice_mode="size")
+            counts_sliced = _op.strided_slice(counts, begin=[0], end=num_uniq, slice_mode="size")
+            return _expr.TupleWrapper(
+                _expr.Tuple([unique_sliced, indices, counts_sliced]),
+                3,
+            )
+        [unique, indices, num_uniq] = _op.unique(data, is_sorted=False, return_counts=False)
+        unique_sliced = _op.strided_slice(unique, begin=[0], end=num_uniq, slice_mode="size")
+        return _expr.TupleWrapper(
+            _expr.Tuple([unique_sliced, indices]),
+            2,
+        )
+
+    return _impl
+
+
 # compatible operators that do NOT require any conversion.
 _identity_list = []
 
@@ -2355,8 +2777,10 @@ def _impl(inputs, attr, params, mod):
     "Greater": _broadcast("greater"),
     "GreaterEqual": _broadcast("greater_equal"),
     "Identity": _identity(),
+    "IdentityN": _identityn(),
     "IsFinite": AttrCvt("isfinite"),
     "IsInf": AttrCvt("isinf"),
+    "IsNan": AttrCvt("isnan"),
     "LeakyRelu": AttrCvt("leaky_relu"),
     "LeftShift": AttrCvt("left_shift"),
     "Less": _broadcast("less"),
@@ -2385,6 +2809,7 @@ def _impl(inputs, attr, params, mod):
     "NonMaxSuppressionV3": _nms(),
     "NonMaxSuppressionV4": _nms(),
     "NonMaxSuppressionV5": _nms(True),
+    "CombinedNonMaxSuppression": _combined_nms(),
     "NoOp": _no_op(),
     "NotEqual": _broadcast("not_equal"),
     "OneHot": _one_hot(),
@@ -2423,6 +2848,16 @@ def _impl(inputs, attr, params, mod):
     "SpaceToDepth": _space_to_depth(),
     "SparseToDense": _sparse_to_dense(),
     "SparseTensorDenseMatMul": _sparse_tensor_dense_matmul(),
+    "SparseFillEmptyRows": _sparse_fill_empty_rows(),
+    "SparseReshape": _sparse_reshape(),
+    "SegmentSum": _math_segment_sum(),
+    "SparseSegmentSum": _sparse_segment_sum(),
+    "SparseSegmentSumWithNumSegments": _sparse_segment_sum_with_num_segments(),
+    "SparseSegmentSqrtN": _sparse_segment_sum_sqrtn(),
+    "SparseSegmentSqrtNWithNumSegments": _sparse_segment_sum_sqrtn_with_num_segments(),
+    "SparseSegmentMean": _sparse_segment_mean(),
+    "SparseSegmentMeanWithNumSegments": _sparse_segment_mean_with_num_segments(),
+    "SparseTensorDenseAdd": _sparse_tensor_dense_add(),
     "Split": _split(False),
     "SplitV": _split(True),
     "Sqrt": AttrCvt("sqrt"),
@@ -2447,6 +2882,8 @@ def _impl(inputs, attr, params, mod):
     "TopKV2": _topk(),
     "Transpose": _transpose(),
     "TruncateMod": _elemwise("mod"),
+    "Unique": _unique(False),
+    "UniqueWithCounts": _unique(True),
     "Unpack": _unpack(),
     "UnravelIndex": _unravel_index(),
     "Where": _where(),
diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
index 7a9adf7b1126..d6f704703cae 100644
--- a/python/tvm/relay/frontend/tflite.py
+++ b/python/tvm/relay/frontend/tflite.py
@@ -65,6 +65,7 @@ def __init__(self, model, subgraph, exp_tab):
         self.builtin_op_code = build_str_map(BuiltinOperator())
         self.activation_fn_type = build_str_map(ActivationFunctionType())
         self.builtin_options = build_str_map(BuiltinOptions())
+        self.prefetched_nodes = {}
 
         # Add more operators
         self.convert_map = {
@@ -80,6 +81,7 @@ def __init__(self, model, subgraph, exp_tab):
             "CONCATENATION": self.convert_concatenation,
             "CONV_2D": self.convert_conv2d,
             "COS": self.convert_cos,
+            "DENSIFY": self.convert_densify,
             "DEPTH_TO_SPACE": self.convert_depth_to_space,
             "DEPTHWISE_CONV_2D": self.convert_depthwise_conv2d,
             "DEQUANTIZE": self.convert_dequantize,
@@ -174,17 +176,45 @@ def __init__(self, model, subgraph, exp_tab):
     def check_unsupported_ops(self):
         """Check unsupported TFLite ops in our converter."""
         unsupported_ops_set = set()
-
+        dynamic_range_ops_set = set()
         for op_idx in range(self.subgraph.OperatorsLength()):
             op = self.subgraph.Operators(op_idx)
             op_code_str = self.get_op_code_str(op)
             if op_code_str not in self.convert_map:
                 unsupported_ops_set.add(op_code_str)
+                continue
+
+            # Trying to exclude "dynamic range quantization" optimized ops as not supported in TVM
+            qnn_in_cnt = len(
+                [_.qnn_params for _ in self.get_input_tensors(op)[0:1] if _.qnn_params is not None]
+            )
+            qnn_weight_cnt = len(
+                [_.qnn_params for _ in self.get_input_tensors(op)[1:] if _.qnn_params is not None]
+            )
+            qnn_out_cnt = len(
+                [_.qnn_params for _ in self.get_output_tensors(op) if _.qnn_params is not None]
+            )
+
+            if qnn_in_cnt == 0 and qnn_out_cnt == 0 and qnn_weight_cnt > 0:
+                dynamic_range_ops_set.add(op_code_str)
+
+        raise_msg = ""
 
         if unsupported_ops_set:
-            msg = "The following operators are not supported in frontend " "TFLite: {}"
+            msg = "The following operators are not supported in frontend " "TFLite: {}\n"
             ops = str(list(unsupported_ops_set)).strip("[,]")
-            raise tvm.error.OpNotImplemented(msg.format(ops))
+            raise_msg += msg.format(ops)
+
+        if dynamic_range_ops_set:
+            msg = (
+                "The following operators are likely to have dynamic range quantization: {}. "
+                "If you are running an optimized graph, please turn off dynamic range quantization "
+                "or use full integer quantization"
+            )
+            raise_msg += msg.format(str(list(dynamic_range_ops_set)).strip("[,]"))
+
+        if len(raise_msg) > 0:
+            raise tvm.error.OpNotImplemented(raise_msg)
 
     def convert_op_to_relay(self):
         """Convert TFLite ops to relay ops"""
@@ -200,6 +230,10 @@ def convert_op_to_relay(self):
             assert isinstance(op, Operator)
             ret = self.convert_map[op_code_str](op)
 
+            # In case the Op can be prefetched, the output can be optimized out
+            if ret is None:
+                continue
+
             if len(output_tensors) == 1:
                 tensor_idx = output_tensors[0].tensor_idx
                 self.exp_tab.set_expr(get_tensor_name(self.subgraph, tensor_idx), ret)
@@ -338,7 +372,8 @@ def get_tensor_type_as_numpy(self, tensor_wrapper):
                 "Tensor type '{}' currently not supported".format(tensor_wrapper.tensor.Type())
             )
 
-    def get_tensor_value(self, tensor_wrapper):
+    # pylint: disable=no-else-return
+    def get_tensor_value(self, tensor_wrapper, is_sparse=False):
         """Get tensor buffer value from given tensor wrapper"""
         assert isinstance(tensor_wrapper, TensorWrapper)
 
@@ -346,11 +381,14 @@ def get_tensor_value(self, tensor_wrapper):
         data = tensor_wrapper.buffer.DataAsNumpy()
 
         if tensor_wrapper.tensor.ShapeLength() != 0:
-            shape = to_int_list(tensor_wrapper.tensor.ShapeAsNumpy())
+            shape = to_int_list(self.get_tensor_shape(tensor_wrapper))
         else:
             shape = []
 
-        return np.frombuffer(data, dtype=dtype).reshape(shape)
+        if is_sparse:
+            return np.frombuffer(data, dtype=dtype)
+        else:
+            return np.frombuffer(data, dtype=dtype).reshape(shape)
 
     def get_tensor_type_str(self, tensor_type):
         """Get tensor type string representation when given TFLite tensor type"""
@@ -511,13 +549,30 @@ def convert_reshape(self, op):
         in_expr = self.get_expr(input_tensor_idx)
 
         # If the tensors are quantized, ensure that input/output qnn params are same.
-        if input_tensor.qnn_params:
+
+        input_tensor_type_str = self.get_tensor_type_str(input_tensor.tensor.Type())
+        if input_tensor.qnn_params and input_tensor_type_str == "int8":
+            # TFLite 2.x quantization spec requires qnn params to be same and dtype to be int8.
+            # For TFLite 1.x, dtype can be uint8 and qnn params can be different
             output_tensor = output_tensors[0]
             assert self.has_same_qnn_params(
                 input_tensor, output_tensor
             ), "TFLite reshape requires input and output scale and zero points to be equal"
 
         out = _op.reshape(in_expr, newshape=target_shape)
+        if input_tensor.qnn_params and input_tensor_type_str == "uint8":
+            output_tensor = output_tensors[0]
+            if not self.has_same_qnn_params(input_tensor, output_tensor):
+                output_tensor_type_str = self.get_tensor_type_str(output_tensor.tensor.Type())
+                out = _qnn.op.requantize(
+                    out,
+                    input_scale=input_tensor.qnn_params["scale"],
+                    input_zero_point=input_tensor.qnn_params["zero_point"],
+                    output_scale=output_tensor.qnn_params["scale"],
+                    output_zero_point=output_tensor.qnn_params["zero_point"],
+                    out_dtype=output_tensor_type_str,
+                )
+
         return out
 
     def _convert_resize(self, method, op):
@@ -965,7 +1020,7 @@ def convert_concatenation(self, op):
 
         input_tensors = self.get_input_tensors(op)
         assert len(input_tensors) >= 1, "input tensors should greater than 1"
-        in_exprs = [self.get_expr(input_tensor.tensor_idx) for input_tensor in input_tensors]
+        in_exprs = [self.get_tensor_expr(_) for _ in input_tensors]
 
         output_tensors = self.get_output_tensors(op)
         assert len(output_tensors) == 1, "output tensors length should be 1"
@@ -1390,7 +1445,7 @@ def convert_gather(self, op):
         axis = gather_options.Axis()
 
         # Check the indices are with in bounds.
-        data_shape = to_int_list(input_tensors[0].tensor.ShapeAsNumpy())
+        data_shape = to_int_list(self.get_tensor_shape(input_tensors[0]))
         data_dim = len(data_shape)
 
         axis = data_dim + axis if axis < 0 else axis
@@ -1508,7 +1563,7 @@ def convert_strided_slice(self, op):
         new_axis_mask = options.NewAxisMask()
         shrink_axis_mask = options.ShrinkAxisMask()
 
-        data_shape = to_int_list(input_tensors[0].tensor.ShapeAsNumpy())
+        data_shape = to_int_list(self.get_tensor_shape(input_tensors[0]))
         data_dim = len(data_shape)
         stride_dim = len(stride)
 
@@ -1586,14 +1641,19 @@ def _transform_mask(stride_dim, ellipsis_mask):
 
         # Create final output shape.
         final_output = []
+        final_len = len(fshape_indices)
         for gather_index in fshape_indices:
             if gather_index == -1:
                 final_output.append(1)
+                final_len += 1
             elif gather_index == -2:
-                pass
+                final_len -= 1
             else:
                 final_output.append(out_shape[gather_index])
 
+        if final_len == 0:
+            return _op.squeeze(out, axis=tuple(range(len(fshape_indices))))
+
         if not final_output:
             return out
         return _op.reshape(out, newshape=tuple(final_output))
@@ -1645,11 +1705,15 @@ def _convert_reduce(self, relay_op, op):
         axis = tuple(axis_value) if len(axis_value.shape) > 0 else tuple((axis_value.item(),))
 
         # Options - keep_dims (bool)
-        assert op.BuiltinOptionsType() == BuiltinOptions.ReducerOptions
-        reduce_options = ReducerOptions()
-        op_options = op.BuiltinOptions()
-        reduce_options.Init(op_options.Bytes, op_options.Pos)
-        keep_dims = reduce_options.KeepDims()
+        # In case Options are not present, set keep_dims to False(default)
+        if op.BuiltinOptionsType():
+            assert op.BuiltinOptionsType() == BuiltinOptions.ReducerOptions
+            reduce_options = ReducerOptions()
+            op_options = op.BuiltinOptions()
+            reduce_options.Init(op_options.Bytes, op_options.Pos)
+            keep_dims = reduce_options.KeepDims()
+        else:
+            keep_dims = False
 
         if input_tensor.qnn_params:
             in_expr = _op.cast(in_expr, "int32")
@@ -1761,7 +1825,7 @@ def convert_fully_connected(self, op):
         output_tensor_type = output_tensor.tensor.Type()
         output_tensor_type_str = self.get_tensor_type_str(output_tensor_type)
 
-        weight_tensor_shape = to_int_list(weight_tensor.tensor.ShapeAsNumpy())
+        weight_tensor_shape = to_int_list(self.get_tensor_shape(weight_tensor))
 
         # Weight should have only 2 dimensions(TFLite convention)
         assert len(weight_tensor_shape) == 2, "Weight should be only 2-dim"
@@ -1813,14 +1877,15 @@ def convert_fully_connected(self, op):
         # if we have bias
         if len(input_tensors) == 3:
             bias_tensor = input_tensors[2]
-            bias_tensor_type = bias_tensor.tensor.Type()
-            # bias tensor type should be INT32 (quantization) or FLOAT32
-            assert bias_tensor_type in (TensorType.INT32, TensorType.FLOAT32)
-            bias_tensor_type_str = self.get_tensor_type_str(bias_tensor_type)
-            bias_expr = self.exp_tab.new_const(
-                self.get_tensor_value(bias_tensor), dtype=bias_tensor_type_str
-            )
-            out = _op.nn.bias_add(out, bias_expr)
+            if bias_tensor.tensor_idx != -1:
+                bias_tensor_type = bias_tensor.tensor.Type()
+                # bias tensor type should be INT32 (quantization) or FLOAT32
+                assert bias_tensor_type in (TensorType.INT32, TensorType.FLOAT32)
+                bias_tensor_type_str = self.get_tensor_type_str(bias_tensor_type)
+                bias_expr = self.exp_tab.new_const(
+                    self.get_tensor_value(bias_tensor), dtype=bias_tensor_type_str
+                )
+                out = _op.nn.bias_add(out, bias_expr)
 
         # Finally if the dense is quantized. Add a requantize at the end.
         if output_tensor.qnn_params:
@@ -1955,16 +2020,16 @@ def convert_conv(self, op, conv_type):
         padding = conv_options.Padding()
         fused_activation_fn = conv_options.FusedActivationFunction()
 
-        _, input_h, input_w, input_c = to_int_list(input_tensor.tensor.ShapeAsNumpy())
+        _, input_h, input_w, input_c = to_int_list(self.get_tensor_shape(input_tensor))
 
         if is_depthwise_conv:
             # TFLite depthwise convolution kernel layout is:
             # 1 KH KW C(input_c * depth_multiplier)
-            _, kernel_h, kernel_w, in_channels = to_int_list(weight_tensor.tensor.ShapeAsNumpy())
+            _, kernel_h, kernel_w, in_channels = to_int_list(self.get_tensor_shape(weight_tensor))
             assert in_channels == input_c * depth_multiplier
         else:
             output_channels, kernel_h, kernel_w, _ = to_int_list(
-                weight_tensor.tensor.ShapeAsNumpy()
+                self.get_tensor_shape(weight_tensor)
             )
 
         dilated_kernel_h = dilation_h * (kernel_h - 1) + 1
@@ -2008,7 +2073,11 @@ def convert_conv(self, op, conv_type):
             else:
                 weight_expr = _op.transpose(weight_expr, axes=(1, 2, 3, 0))
         else:
-            weight_value = self.get_tensor_value(weight_tensor)
+            if self.is_prefetched(weight_tensor.tensor_idx):
+                weight_value = self.get_prefetched_node(weight_tensor.tensor_idx)
+            else:
+                weight_value = self.get_tensor_value(weight_tensor)
+
             # TFLite kernel layout:
             # convolution:
             # OC KH KW IC, we require KH KW IC OC (HWIO)
@@ -2183,7 +2252,7 @@ def convert_slice(self, op):
         size = list(self.get_tensor_value(input_tensors[2]))
         # strided_slice(Relay) needs the slice's end indices, not the size
         end = size
-        input_tensor_shape = to_int_list(input_tensor.tensor.ShapeAsNumpy())
+        input_tensor_shape = to_int_list(self.get_tensor_shape(input_tensor))
         input_tensor_rank = len(input_tensor_shape)
         for i in range(input_tensor_rank):
             if size[i] == -1:
@@ -2345,7 +2414,8 @@ def convert_pool2d(self, op, pool_type):
 
         in_expr = self.get_expr(input_tensor_idx)
 
-        _, input_h, input_w, _ = to_int_list(input_tensor.tensor.ShapeAsNumpy())
+        _, input_h, input_w, _ = to_int_list(self.get_tensor_shape(input_tensor))
+
         if padding == Padding.VALID:
             pass
         elif padding == Padding.SAME:
@@ -2527,6 +2597,17 @@ def convert_pack(self, op):
         output_tensors = self.get_output_tensors(op)
         assert len(output_tensors) == 1, "output tensors length should be 1"
 
+        if input_tensors[0].qnn_params:
+            output_tensor = output_tensors[0]
+            assert self.has_same_qnn_params(
+                input_tensors[0], output_tensor
+            ), "TFLite pack requires input and output scale and zero points to be equal"
+
+            for input_tensor in input_tensors:
+                assert self.has_same_qnn_params(
+                    input_tensors[0], input_tensor
+                ), "TFLite pack requires all input tensors to have same scale and zero point"
+
         assert op.BuiltinOptionsType() == BuiltinOptions.PackOptions
         op_options = op.BuiltinOptions()
         pack_options = PackOptions()
@@ -2724,12 +2805,13 @@ def convert_transpose_conv(self, op):
 
         # Input (data) Tensor. NHWC layout
         input_tensor = input_tensors[2]
-        _, input_h, input_w, input_c = to_int_list(input_tensor.tensor.ShapeAsNumpy())
+        _, input_h, input_w, input_c = to_int_list(self.get_tensor_shape(input_tensor))
         # Weights tensor. TFLite uses OHWI layout
         weights_tensor = input_tensors[1]
         out_channels, kernel_h, kernel_w, in_channels = to_int_list(
-            weights_tensor.tensor.ShapeAsNumpy()
+            self.get_tensor_shape(weights_tensor)
         )
+
         assert (
             input_c == in_channels
         ), "Input channel in the filter should match to channel in the input"
@@ -3011,7 +3093,7 @@ def convert_detection_postprocess(self, op):
         valid_count = ret[0]
         # keep only the top 'max_detections' rows
         ret = _op.strided_slice(
-            ret[1], [0, 0, 0], [batch_size, custom_options["max_detections"], anchor_boxes]
+            ret[1], [0, 0, 0], [batch_size, custom_options["max_detections"], 6]
         )
         # the output needs some reshaping to match tflite
         ret = _op.split(ret, 6, axis=2)
@@ -3157,7 +3239,7 @@ def convert_matrix_diag(self, op):
             ), "TFLite MATRIX_DIAG requires diagonal and output tensors' \
                     scale and zero points to be equal"
 
-        shape = to_int_list(diagonal.tensor.ShapeAsNumpy())
+        shape = to_int_list(self.get_tensor_shape(diagonal))
         shape = np.append(shape, shape[-1])
         dtype = self.get_tensor_type_str(diagonal.tensor.Type())
 
@@ -3167,21 +3249,207 @@ def convert_matrix_diag(self, op):
         out = _op.matrix_set_diag(input_expr, diagonal_expr)
         return out
 
+    def convert_densify(self, op):
+        """Convert TFLite DENSIFY"""
+        input_tensors = self.get_input_tensors(op)
+        assert len(input_tensors) == 1, "input tensors length should be 1"
+
+        output_tensors = self.get_output_tensors(op)
+        assert len(output_tensors) == 1, "output tensors length should be 1"
+        output_tensor = output_tensors[0]
+
+        sparse_weight_tensor = input_tensors[0]
+        sparse_weight_tensor_type_str = self.get_tensor_type_str(sparse_weight_tensor.tensor.Type())
+
+        # NOTE: With current implementation in TFLite, Densify Op does not need to be present
+        # in runtime.
+        # TODO(ANSHUMAN87): we need to use the sparse_indices output
+        # from below function and use that in sparse_to_dense Op.
+        # Once the stack corruption issue is resolved in sparse_to_dense Op.
+        _, dense_weight = prepare_dense_matrix_from_sparse(
+            sparse_weight_tensor.tensor,
+            self.get_tensor_value(sparse_weight_tensor, is_sparse=True),
+            sparse_weight_tensor_type_str,
+        )
+
+        self.set_prefetched_node(output_tensor.tensor_idx, dense_weight)
+
     def get_expr(self, input_tensor_idx):
         return self.exp_tab.get_expr(get_tensor_name(self.subgraph, input_tensor_idx))
 
     def has_expr(self, input_tensor_idx):
         return self.exp_tab.has_expr(get_tensor_name(self.subgraph, input_tensor_idx))
 
-    def get_tensor_expr(self, tensor):
+    def is_prefetched(self, input_tensor_idx):
+        return (
+            self.prefetched_nodes.get(get_tensor_name(self.subgraph, input_tensor_idx)) is not None
+        )
+
+    def set_prefetched_node(self, input_tensor_idx, value):
+        self.prefetched_nodes[get_tensor_name(self.subgraph, input_tensor_idx)] = value
+
+    def get_prefetched_node(self, input_tensor_idx):
+        return self.prefetched_nodes[get_tensor_name(self.subgraph, input_tensor_idx)]
+
+    def get_tensor_expr(self, tensor, is_sparse=False):
         """ Return the Relay expr for tensor. """
         if self.has_expr(tensor.tensor_idx):
             expr = self.get_expr(tensor.tensor_idx)
         else:
             type_str = self.get_tensor_type_str(tensor.tensor.Type())
-            expr = self.exp_tab.new_const(self.get_tensor_value(tensor), dtype=type_str)
+            expr = self.exp_tab.new_const(self.get_tensor_value(tensor, is_sparse), dtype=type_str)
         return expr
 
+    def get_tensor_shape(self, tensor_wrapper):
+        """ Returns tensor shape. Infers shape if the shape is empty. """
+        assert isinstance(tensor_wrapper, TensorWrapper), "Expecting TensorWrapper here"
+        return (
+            tensor_wrapper.tensor.ShapeAsNumpy()
+            if tensor_wrapper.tensor.ShapeLength() > 0
+            else _infer_shape(self.get_tensor_expr(tensor_wrapper))
+        )
+
+
+# pylint: disable=no-else-return
+def prepare_dense_matrix_from_sparse(sparse_tensor, sparse_tensor_value, sparse_tensor_type):
+    """ Prepare sparse indices and dense matrix from TFLite sparse parameters. """
+    # The function is implemented based on TFLite sparse parameter specifications
+    # Please refer
+    # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs#L89
+    # for details about each parameters
+    sparsity = sparse_tensor.Sparsity()
+    dense_shape = sparse_tensor.ShapeAsNumpy()
+    orig_rank = len(dense_shape)
+
+    # The traversal order of the dimensions defined in the `shape` field of the to be dense tensor.
+    traversal_order = sparsity.TraversalOrderAsNumpy()
+
+    # For an n-dimensional tensor with a k-dimensional block (0 <= k <= n),
+    # stores how a block dimension in (dn, ..., dn+k-1) maps to the original
+    # tensor dimension in (d0, ..., dn). It's stored in the order of (dn, ..., dn+k-1).
+    # If not block-sparse, this field is NULL.
+    block_map = sparsity.BlockMapAsNumpy()
+
+    total_rank = sparsity.TraversalOrderLength()
+    dense_mat = np.full(shape=dense_shape, fill_value=0, dtype=sparse_tensor_type).flatten()
+
+    from enum import Enum
+
+    # NOTE: Here the Vector term is borrowed from TFLite spec.
+    class VectorType(Enum):
+        Empty = 0
+        Int32 = 1
+        Uint16 = 2
+        Uint8 = 3
+
+    def _get_vector_flag(v_type):
+        if VectorType(v_type) == VectorType.Int32:
+            return N.Int32Flags
+        elif VectorType(v_type) == VectorType.Uint16:
+            return N.Uint16Flags
+        elif VectorType(v_type) == VectorType.Uint8:
+            return N.Uint8Flags
+        else:
+            raise tvm.error.OpNotImplemented("The provided type {} is not supported".format(v_type))
+
+    def _get_flattened_index(indices, shape):
+        index = 0
+        sub_elements = 1
+        for i in reversed(range(0, len(dense_shape))):
+            index += indices[i] * sub_elements
+            sub_elements *= shape[i]
+        return index
+
+    # DimensionMetadata per dimension: the metadata needed for
+    #     each dimension to locate the non-zero values in the original dense tensor
+    #     inline with traversal order parameter.
+    #
+    # sp_format has 2 possible values: {DENSE = 0, SPARSE_CSR = 1}
+    # If format = DENSE{0} : DenseSize represents size of that dimension
+    # If format = SPARSE_CSR{1} : array_segments represents how to segment the indices array,
+    #      each segment corresponds to one element in the previous dimension. array_indices
+    #      represents the index of the non-zero elements within this dimension
+    #      (as those in the CSR matrix format, where the first array is row pointers
+    #       and the second array is column indices).
+    sp_format = np.zeros(sparsity.DimMetadataLength())
+    dim_metadata = [None] * (2 * sparsity.DimMetadataLength())
+
+    # Below loop will fetch all meta data per dimension based on format type
+    # Dense or Sparse and will put it in an agnostic array for easy access
+    # while preparing dense buffer or indices.
+    for i in range(sparsity.DimMetadataLength()):
+        sp_format[i] = sparsity.DimMetadata(i).Format()
+        if sp_format[i] == 0:
+            dim_metadata[2 * i] = [sparsity.DimMetadata(i).DenseSize()]
+        else:
+            from flatbuffers import number_types as N
+
+            dim_metadata[2 * i] = (
+                sparsity.DimMetadata(i)
+                .ArraySegments()
+                .GetVectorAsNumpy(
+                    flags=_get_vector_flag(sparsity.DimMetadata(i).ArraySegmentsType()), off=4
+                )
+            )
+            dim_metadata[2 * i + 1] = (
+                sparsity.DimMetadata(i)
+                .ArrayIndices()
+                .GetVectorAsNumpy(
+                    flags=_get_vector_flag(sparsity.DimMetadata(i).ArrayIndicesType()), off=4
+                )
+            )
+
+    block_dim = 0
+    block_size = np.zeros(sparsity.BlockMapLength())
+
+    # Block size parameter if encoded in BSR format
+    for i in range(orig_rank):
+        if block_dim < sparsity.BlockMapLength() and block_map[block_dim] == i:
+            orig_dim = traversal_order[orig_rank + block_dim]
+            block_size[block_dim] = sparsity.DimMetadata(orig_dim).DenseSize()
+            block_dim += 1
+
+    indices_list = []
+
+    # Below function iterates through each applicable indices per dimension
+    # based on format type specified and finaly produce the dense matrix and the NZ indices.
+    def _def_prepare_dense_matrix_from_sparse(indices, level, prev_idx):
+        if level == len(indices):
+            start_pos = 0
+            orig_idx = np.zeros(orig_rank, dtype="int32")
+            while start_pos < orig_rank:
+                orig_idx[traversal_order[start_pos]] = indices[start_pos]
+                start_pos += 1
+            while start_pos < len(indices):
+                block_idx = traversal_order[start_pos] - orig_rank
+                orig_dim = block_map[block_idx]
+                orig_idx[orig_dim] = orig_idx[orig_dim] * block_size[block_idx] + indices[start_pos]
+                start_pos += 1
+            indices_list.append(orig_idx)
+            nonlocal value_idx
+            dense_mat[_get_flattened_index(orig_idx, dense_shape)] = sparse_tensor_value[value_idx]
+            value_idx += 1
+        else:
+            metadata_idx = 2 * level
+            if sp_format[level] == 0:
+                shape_of_level = dim_metadata[metadata_idx][0]
+                for idx in range(shape_of_level):
+                    indices[level] = idx
+                    _def_prepare_dense_matrix_from_sparse(
+                        indices, level + 1, prev_idx * shape_of_level + idx
+                    )
+            else:
+                array_segments = dim_metadata[metadata_idx]
+                array_indices = dim_metadata[metadata_idx + 1]
+                for idx in range(array_segments[prev_idx], array_segments[prev_idx + 1]):
+                    indices[level] = array_indices[idx]
+                    _def_prepare_dense_matrix_from_sparse(indices, level + 1, idx)
+
+    indices = np.zeros(total_rank)
+    value_idx = 0
+    _def_prepare_dense_matrix_from_sparse(indices, 0, 0)
+    return np.array(indices_list, dtype="int32"), dense_mat.reshape(dense_shape)
+
 
 def get_scalar_from_constant(expr):
     """ Returns scalar value from Relay constant scalar. """
@@ -3271,7 +3539,45 @@ def get_tensor_name(subgraph, tensor_idx):
     return subgraph.Tensors(tensor_idx).Name().decode("utf-8")
 
 
-def from_tflite(model, shape_dict, dtype_dict):
+def _decode_type(n):
+    _tflite_m = {
+        0: "float32",
+        1: "float16",
+        2: "int32",
+        3: "uint8",
+        4: "int64",
+        5: "string",
+        6: "bool",
+        7: "int16",
+        8: "complex64",
+        9: "int8",
+    }
+    return _tflite_m[n]
+
+
+def _input_type(model):
+    subgraph_count = model.SubgraphsLength()
+    assert subgraph_count > 0
+    shape_dict = {}
+    dtype_dict = {}
+    for subgraph_index in range(subgraph_count):
+        subgraph = model.Subgraphs(subgraph_index)
+        inputs_count = subgraph.InputsLength()
+        assert inputs_count >= 1
+        for input_index in range(inputs_count):
+            input_ = subgraph.Inputs(input_index)
+            assert subgraph.TensorsLength() > input_
+            tensor = subgraph.Tensors(input_)
+            input_shape = tuple(tensor.ShapeAsNumpy())
+            tensor_type = tensor.Type()
+            input_name = tensor.Name().decode("utf8")
+            shape_dict[input_name] = input_shape
+            dtype_dict[input_name] = _decode_type(tensor_type)
+
+    return shape_dict, dtype_dict
+
+
+def from_tflite(model, shape_dict=None, dtype_dict=None):
     """Convert from tflite model into compatible relay Function.
 
     Parameters
@@ -3309,6 +3615,12 @@ def from_tflite(model, shape_dict, dtype_dict):
 
         assert isinstance(model, tflite.Model.Model)
 
+    _shape_dict, _dtype_dict = _input_type(model)
+    if shape_dict is not None:
+        _shape_dict.update(shape_dict)
+    if dtype_dict is not None:
+        _dtype_dict.update(dtype_dict)
+
     # keep the same as tflite
     assert model.SubgraphsLength() == 1, "only support one subgraph (main subgraph)"
     subgraph = model.Subgraphs(0)
@@ -3320,8 +3632,8 @@ def from_tflite(model, shape_dict, dtype_dict):
     exp_tab = ExprTable()
     for model_input in model_inputs:
         model_input_name = get_tensor_name(subgraph, model_input)
-        shape = shape_dict[model_input_name] if model_input_name in shape_dict else None
-        dtype = dtype_dict[model_input_name] if model_input_name in dtype_dict else "float32"
+        shape = _shape_dict[model_input_name] if model_input_name in _shape_dict else None
+        dtype = _dtype_dict[model_input_name] if model_input_name in _dtype_dict else "float32"
         exp_tab.set_expr(model_input_name, _expr.var(model_input_name, shape=shape, dtype=dtype))
 
     # op code in model
diff --git a/python/tvm/relay/op/__init__.py b/python/tvm/relay/op/__init__.py
index f6afa443d280..1f267abedc1a 100644
--- a/python/tvm/relay/op/__init__.py
+++ b/python/tvm/relay/op/__init__.py
@@ -43,6 +43,7 @@
 from . import image
 from . import vision
 from . import op_attrs
+from . import random
 
 
 # operator registry
diff --git a/python/tvm/relay/op/_tensor.py b/python/tvm/relay/op/_tensor.py
index 6fc423371325..5f68be84d46a 100644
--- a/python/tvm/relay/op/_tensor.py
+++ b/python/tvm/relay/op/_tensor.py
@@ -235,6 +235,7 @@ def elemwise_shape_func(attrs, inputs, _):
 
 register_shape_func("cast", False, elemwise_shape_func)
 register_shape_func("cast_like", False, elemwise_shape_func)
+register_shape_func("round", False, elemwise_shape_func)
 register_shape_func("zeros", False, no_data_full_shape_func)
 register_shape_func("zeros_like", False, elemwise_shape_func)
 register_shape_func("ones", False, no_data_full_shape_func)
@@ -280,3 +281,4 @@ def elemwise_shape_func(attrs, inputs, _):
 register_shape_func("clip", False, elemwise_shape_func)
 register_shape_func("log2", False, elemwise_shape_func)
 register_shape_func("sigmoid", False, elemwise_shape_func)
+register_shape_func("tanh", False, elemwise_shape_func)
diff --git a/python/tvm/relay/op/_tensor_grad.py b/python/tvm/relay/op/_tensor_grad.py
index 9c84411352f2..5836aebce393 100644
--- a/python/tvm/relay/op/_tensor_grad.py
+++ b/python/tvm/relay/op/_tensor_grad.py
@@ -198,7 +198,7 @@ def sigmoid_grad(orig, grad):
 @register_gradient("tanh")
 def tanh_grad(orig, grad):
     """Returns grad * (1 - tanh(x) * tanh(x))."""
-    return [grad * ones_like(orig) - orig * orig]
+    return [grad * (ones_like(orig) - orig * orig)]
 
 
 @register_gradient("nn.relu")
@@ -238,14 +238,28 @@ def divide_grad(orig, grad):
 
 @register_gradient("zeros")
 def zeros_grad(orig, grad):
-    """Returns [shape]"""
-    return [orig.args[0]]
+    """Returns []"""
+    return []
+
+
+@register_gradient("dyn.zeros")
+def dyn_zeros_grad(orig, grad):
+    """Returns the gradient of dyn.zeros which is just zero."""
+    assert len(orig.args) == 1
+    return [zeros_like(orig.args[0])]
 
 
 @register_gradient("ones")
 def ones_grad(orig, grad):
-    """Returns [shape]"""
-    return [orig.args[0]]
+    """Returns []"""
+    return []
+
+
+@register_gradient("dyn.ones")
+def dyn_ones_grad(orig, grad):
+    """Returns the gradient of dyn.ones which is just zero."""
+    assert len(orig.args) == 1
+    return [zeros_like(orig.args[0])]
 
 
 @register_gradient("zeros_like")
@@ -357,16 +371,24 @@ def global_avg_pool2d_grad(orig, grad):
     return [pool_grad]
 
 
-# not implemented, this is only for testing.
 @register_gradient("concatenate")
 def concatenate_grad(orig, grad):
+    """
+    Returns the gradient of concatenate, which is just the downstream gradient
+    split across the inputs.
+    """
     assert len(orig.args) == 1
     t = orig.args[0]
-    x = TupleGetItem(t, 0)
-    y = TupleGetItem(t, 1)
-    # Assume only two element in tuple rn.
-    # In the real implementation, concatenate_grad probably need to be implemented by an operator.
-    return [Tuple([zeros_like(x), zeros_like(y)])]
+
+    # calculate split indices. TODO(@altanh): support Any?
+    axis_dims = [ty.shape[orig.attrs.axis] for ty in t.checked_type.fields]
+    splits, cumsum = [], 0
+    for dim in axis_dims[:-1]:
+        cumsum += dim
+        splits.append(cumsum)
+
+    grads = split(grad, tuple(splits), axis=orig.attrs.axis).tuple_value
+    return [grads]
 
 
 @register_gradient("nn.conv2d")
@@ -808,5 +830,39 @@ def arange_grad(orig, grad):
 
 @register_gradient("gather_nd")
 def gather_nd_grad(orig, grad):
+    """
+    Returns the gradient of gather_nd, which is simply scatter_nd.
+    """
     data, indices = orig.args
     return [scatter_nd(grad, indices, data.checked_type.concrete_shape), zeros_like(indices)]
+
+
+@register_gradient("reshape_like")
+def reshape_like_grad(orig, grad):
+    """
+    Returns the gradient of reshape_like.
+    """
+    data, shape_like = orig.args
+    return [reshape_like(grad, data), zeros_like(shape_like)]
+
+
+@register_gradient("where")
+def where_grad(orig, grad):
+    """
+    Returns the gradient of where.
+    """
+    cond, x, y = orig.args
+    g_zeros = zeros_like(grad)
+
+    grad_x = collapse_sum_like(where(cond, grad, g_zeros), x)
+    grad_y = collapse_sum_like(where(cond, g_zeros, grad), y)
+
+    return [zeros_like(cond), grad_x, grad_y]
+
+
+@register_gradient("less_equal")
+def less_equal_grad(orig, grad):
+    """
+    Returns the gradient of less_equal.
+    """
+    return [zeros_like(orig.args[0]), zeros_like(orig.args[1])]
diff --git a/python/tvm/relay/op/_transform.py b/python/tvm/relay/op/_transform.py
index 05ca6d2e4bb9..e90263d794bc 100644
--- a/python/tvm/relay/op/_transform.py
+++ b/python/tvm/relay/op/_transform.py
@@ -15,7 +15,9 @@
 # specific language governing permissions and limitations
 # under the License.
 """Backend compiler related feature registration"""
-# pylint: disable=invalid-name,unused-argument, len-as-condition, too-many-nested-blocks, too-many-local-variables, too-many-arguments
+# pylint: disable=invalid-name,unused-argument, len-as-condition, too-many-nested-blocks,
+# pylint: disable=too-many-local-variables, too-many-arguments, no-else-return
+
 from __future__ import absolute_import
 import tvm
 from tvm import te
@@ -64,6 +66,7 @@
 _reg.register_injective_schedule("matrix_set_diag")
 _reg.register_injective_schedule("adv_index")
 
+
 # concatenate
 _reg.register_schedule("concatenate", strategy.schedule_concatenate)
 
@@ -94,6 +97,40 @@ def compute_scatter(attrs, inputs, output_type):
 
 _reg.register_strategy("scatter", strategy.scatter_strategy)
 
+# sparse_fill_empty_rows
+@_reg.register_compute("sparse_fill_empty_rows")
+def compute_sparse_fill_empty_rows(attrs, inputs, output_type):
+    """Compute definition of sparse_fill_empty_rows"""
+
+    return topi.sparse_fill_empty_rows(
+        inputs[0],
+        inputs[1],
+        inputs[2],
+        inputs[3],
+        output_type.fields[0].shape,
+        output_type.fields[1].shape,
+        output_type.fields[2].shape,
+    )
+
+
+_reg.register_strategy("sparse_fill_empty_rows", strategy.sparse_fill_empty_rows_strategy)
+
+# sparse_reshape
+@_reg.register_compute("sparse_reshape")
+def compute_reshape(attrs, inputs, output_type):
+    """Compute definition of sparse_reshape"""
+
+    return topi.sparse_reshape(
+        inputs[0],
+        inputs[1],
+        inputs[2],
+        output_type.fields[0].shape,
+        output_type.fields[1].shape,
+    )
+
+
+_reg.register_strategy("sparse_reshape", strategy.sparse_reshape_strategy)
+
 # scatter_add
 @_reg.register_compute("scatter_add")
 def compute_scatter_add(attrs, inputs, output_type):
@@ -103,7 +140,7 @@ def compute_scatter_add(attrs, inputs, output_type):
 
 _reg.register_strategy("scatter_add", strategy.scatter_add_strategy)
 
-# scatter
+# scatter_nd
 @_reg.register_compute("scatter_nd")
 def compute_scatter_nd(attrs, inputs, output_type):
     """Compute definition of scatter_nd"""
@@ -112,6 +149,25 @@ def compute_scatter_nd(attrs, inputs, output_type):
 
 _reg.register_strategy("scatter_nd", strategy.scatter_nd_strategy)
 
+# cumsum
+@_reg.register_compute("cumsum")
+def compute_cumsum(attrs, inputs, output_type):
+    """Compute definition of cumsum"""
+    return [topi.cumsum(inputs[0], attrs.axis, attrs.dtype, attrs.exclusive)]
+
+
+_reg.register_strategy("cumsum", strategy.cumsum_strategy)
+_reg.register_shape_func("cumsum", False, elemwise_shape_func)
+
+
+@_reg.register_compute("unique")
+def compute_unique(attrs, inputs, output_type):
+    """Compute definition of unique"""
+    return topi.unique(inputs[0], attrs.sorted, attrs.return_counts)
+
+
+_reg.register_strategy("unique", strategy.unique_strategy)
+
 #####################
 #  Shape functions  #
 #####################
@@ -191,6 +247,31 @@ def strided_slice_shape_func(attrs, inputs, _):
     ]
 
 
+@script
+def _one_hot_shape_func(indices_shape, depth, axis):
+    in_ndim = indices_shape.shape[0]
+    out_ndim = in_ndim + 1
+    true_axis = in_ndim if axis == -1 else axis
+    indices_i = 0
+    out = output_tensor((out_ndim,), "int64")
+    for i in range(out_ndim):
+        if i == true_axis:
+            out[i] = int64(depth)
+        else:
+            out[i] = int64(indices_shape[indices_i])
+            indices_i += 1
+    return out
+
+
+@_reg.register_shape_func("one_hot", False)
+def one_hot_shape_func(attrs, inputs, _):
+    """
+    Shape func for one_hot
+    """
+    shape_func = [_one_hot_shape_func(inputs[0], convert(attrs.depth), convert(attrs.axis))]
+    return shape_func
+
+
 @script
 def _concatenate_shape_func(inputs, axis):
     ndim = inputs[0].shape[0]
@@ -435,6 +516,65 @@ def argwhere_shape_func(attrs, inputs, out_ndims):
 _reg.register_shape_func("scatter_add", False, elemwise_shape_func)
 
 
+@script
+def _sparse_fill_empty_rows_shape_func(sparse_indices, dense_shape):
+
+    new_sparse_indices_shape = output_tensor((2,), "int64")
+    new_sparse_values_shape = output_tensor((1,), "int64")
+    empty_row_indicator_shape = output_tensor((1,), "int64")
+    num_dense_rows = int64(dense_shape[0])
+
+    if int64(sparse_indices.shape[0]) == int64(0):  # Handle Empty Case
+        #  Total rows will equal dense_shape[0]
+        new_sparse_indices_shape[0] = num_dense_rows
+        new_sparse_indices_shape[1] = int64(sparse_indices.shape[1])
+        new_sparse_values_shape[0] = num_dense_rows
+        empty_row_indicator_shape[0] = num_dense_rows
+        return (new_sparse_indices_shape, new_sparse_values_shape, empty_row_indicator_shape)
+
+    else:
+        count = int64(sparse_indices.shape[0])  # Add count of all rows already in sparse_indices
+        for i in range(1, int64(sparse_indices.shape[0])):
+            index = int64(sparse_indices[i, 0])
+            prev_index = int64(sparse_indices[i - 1, 0] + 1)
+
+            if index > prev_index:
+                count += index - prev_index  # Add count of all rows between two consecutive indices
+
+        count += int64(sparse_indices[0, 0])  # Add count from 0 to first row id in sparse_indices
+        count += int64(
+            num_dense_rows - 1 - sparse_indices[sparse_indices.shape[0] - 1, 0]
+        )  # Add count from last row id to dense_shape - 1
+        new_sparse_indices_shape[0] = int64(count)
+        new_sparse_indices_shape[1] = int64(sparse_indices.shape[1])
+        new_sparse_values_shape[0] = int64(count)
+        empty_row_indicator_shape[0] = num_dense_rows
+        return (new_sparse_indices_shape, new_sparse_values_shape, empty_row_indicator_shape)
+
+
+@_reg.register_shape_func("sparse_fill_empty_rows", True)
+def sparse_fill_empty_rows_func(attrs, inputs, _):
+    return _sparse_fill_empty_rows_shape_func(inputs[0], inputs[2])
+
+
+@script
+def _sparse_reshape_shape_func(sparse_indices_shape, prev_shape_shape, new_shape_shape):
+    indices_shape = output_tensor((2,), "int64")
+    indices_shape[0] = int64(sparse_indices_shape[0])
+    indices_shape[1] = int64(new_shape_shape[0])
+    shape_tensor = output_tensor((1,), "int64")
+    shape_tensor[0] = int64(new_shape_shape[0])
+    return (indices_shape, shape_tensor)
+
+
+@_reg.register_shape_func("sparse_reshape", False)
+def sparse_reshape_shape_func(attrs, inputs, _):
+    """
+    Shape func for sparse_reshape.
+    """
+    return _sparse_reshape_shape_func(inputs[0], inputs[1], inputs[2])
+
+
 @script
 def _layout_transform_shape_func(
     data_shape, out_layout_len, dst_equal_list, dst_mul_list, dst_div_list, dst_mix_list
@@ -875,3 +1015,38 @@ def where_shape_func(attrs, inputs, _):
     out_shape = _broadcast_shape_tensors(bcast_shape, cond_shape)
 
     return [out_shape]
+
+
+@script
+def _unique_shape(data_shape):
+    unique_shape = output_tensor((1,), "int64")
+    indices_shape = output_tensor((1,), "int64")
+    num_unique_shape = output_tensor((1,), "int64")
+    unique_shape[0] = data_shape[0]
+    indices_shape[0] = data_shape[0]
+    num_unique_shape[0] = int64(1)
+    return (unique_shape, indices_shape, num_unique_shape)
+
+
+@script
+def _unique_with_counts_shape(data_shape):
+    unique_shape = output_tensor((1,), "int64")
+    indices_shape = output_tensor((1,), "int64")
+    num_unique_shape = output_tensor((1,), "int64")
+    counts_shape = output_tensor((1,), "int64")
+    unique_shape[0] = data_shape[0]
+    indices_shape[0] = data_shape[0]
+    num_unique_shape[0] = int64(1)
+    counts_shape[0] = data_shape[0]
+    return (unique_shape, indices_shape, num_unique_shape, counts_shape)
+
+
+@_reg.register_shape_func("unique", False)
+def unique_shape_func(attrs, inputs, _):
+    """
+    Shape func for unique operator.
+    """
+    if attrs.return_counts:
+        return _unique_with_counts_shape(inputs[0])
+    else:
+        return _unique_shape(inputs[0])
diff --git a/python/tvm/relay/op/algorithm.py b/python/tvm/relay/op/algorithm.py
index 99140fcb3e11..6fd5c0645eed 100644
--- a/python/tvm/relay/op/algorithm.py
+++ b/python/tvm/relay/op/algorithm.py
@@ -17,9 +17,9 @@
 """Classic algorithm operation"""
 from __future__ import absolute_import as _abs
 
+from ..expr import Constant, Expr, TupleWrapper
 from . import _make
 from .dyn import _make as _dyn_make
-from ..expr import TupleWrapper, Expr, Constant
 
 
 def sort(data, axis=-1, is_ascend=1):
diff --git a/python/tvm/relay/op/contrib/__init__.py b/python/tvm/relay/op/contrib/__init__.py
index 49abf36134b4..30c2db0ddf0b 100644
--- a/python/tvm/relay/op/contrib/__init__.py
+++ b/python/tvm/relay/op/contrib/__init__.py
@@ -20,6 +20,7 @@
 
 from .arm_compute_lib import *
 from .dnnl import *
+from .bnns import *
 from .coreml import *
 from .ethosn import *
 from .tensorrt import *
diff --git a/python/tvm/relay/op/contrib/arm_compute_lib.py b/python/tvm/relay/op/contrib/arm_compute_lib.py
index a78ad294b770..fabb639845b6 100644
--- a/python/tvm/relay/op/contrib/arm_compute_lib.py
+++ b/python/tvm/relay/op/contrib/arm_compute_lib.py
@@ -16,15 +16,17 @@
 # under the License.
 # pylint: disable=invalid-name, unused-argument
 """Arm Compute Library supported operators."""
-import numpy as np
 import tvm
 
+from tvm import relay
+from tvm._ffi import register_func
 from tvm.relay.expr import const
 from tvm.relay import transform
 from tvm.relay.build_module import bind_params_by_name
 
 from ...dataflow_pattern import wildcard, is_op, is_constant, is_expr
 from .register import register_pattern_table
+from ..strategy.generic import is_depthwise_conv2d
 
 
 def is_arm_compute_runtime_enabled():
@@ -71,6 +73,61 @@ def partition_for_arm_compute_lib(mod, params=None):
     return seq(mod)
 
 
+@register_func("relay.ext.arm_compute_lib.optimize")
+def preprocess_module(mod):
+    """
+    Pre-process a module containing functions ready for ACL codegen. For now we enforce OHWI
+    kernel layout and fold the transforms away.
+
+    Parameters
+    ----------
+    mod : Module
+        The module to run passes on.
+
+    Returns
+    -------
+    preprocessed_mod : The processed module.
+    """
+
+    def convert_layout_conv2d(conv2d_function):
+        def convert_conv(attrs, inputs, tinfos, desired_layouts):
+            new_attrs = dict(attrs)
+            data_info = tinfos[0]
+            weight_info = tinfos[1]
+            desired_data_layout, desired_kernel_layout = map(str, desired_layouts)
+            new_attrs["data_layout"] = desired_data_layout
+            new_attrs["kernel_layout"] = desired_kernel_layout
+
+            if is_depthwise_conv2d(
+                data_info.shape,
+                attrs["data_layout"],
+                weight_info.shape,
+                attrs["kernel_layout"],
+                attrs["groups"],
+            ):
+                dkl = desired_kernel_layout
+                new_attrs["kernel_layout"] = dkl[3] + dkl[1:3] + dkl[0]
+            return conv2d_function(*inputs, **new_attrs)
+
+        return convert_conv
+
+    with OpAttrContext(
+        "nn.conv2d", "FTVMConvertOpLayout", convert_layout_conv2d(tvm.relay.nn.conv2d)
+    ), OpAttrContext(
+        "qnn.conv2d", "FTVMConvertOpLayout", convert_layout_conv2d(tvm.relay.qnn.op.conv2d)
+    ):
+        seq = tvm.transform.Sequential(
+            [
+                transform.ConvertLayout(
+                    {"nn.conv2d": ["NHWC", "OHWI"], "qnn.conv2d": ["NHWC", "OHWI"]}
+                ),
+                transform.FoldConstant(),
+            ]
+        )
+        preprocessed_mod = seq(mod)
+    return preprocessed_mod
+
+
 @register_pattern_table("arm_compute_lib")
 def arm_compute_lib_pattern_table():
     """Get the ACL pattern table."""
@@ -236,8 +293,6 @@ def _func_wrapper(expr):
 def conv2d(expr):
     """Check if the external ACL codegen for conv2d should be used."""
     attrs, args = expr.attrs, expr.args
-    if attrs.groups != 1:
-        return False
     if attrs.data_layout != "NHWC":
         return False
     if attrs.out_dtype != "float32" and attrs.out_dtype != "":
@@ -248,14 +303,25 @@ def conv2d(expr):
     kernel_typ = args[1].checked_type
     if len(kernel_typ.shape) != 4 or kernel_typ.dtype != "float32":
         return False
+    is_depthwise = is_depthwise_conv2d(
+        data_typ.shape,
+        attrs["data_layout"],
+        kernel_typ.shape,
+        attrs["kernel_layout"],
+        attrs["groups"],
+    )
+    if is_depthwise:
+        return depthwise_conv2d(attrs, args)
+    # ACL doesn't support grouped convolution
+    if attrs.groups != 1 and not is_depthwise:
+        return False
     return True
 
 
 def qnn_conv2d(expr):
     """Check if the external ACL codegen for qnn.conv2d should be used."""
     attrs, args = expr.attrs, expr.args
-    if attrs.groups != 1:
-        return False
+
     if attrs.data_layout != "NHWC":
         return False
     if attrs.out_dtype != "int32" and attrs.out_dtype != "":
@@ -266,6 +332,40 @@ def qnn_conv2d(expr):
     kernel_typ = args[1].checked_type
     if len(kernel_typ.shape) != 4 or kernel_typ.dtype != "uint8":
         return False
+    is_depthwise = is_depthwise_conv2d(
+        data_typ.shape,
+        attrs["data_layout"],
+        kernel_typ.shape,
+        attrs["kernel_layout"],
+        attrs["groups"],
+    )
+    if is_depthwise:
+        return depthwise_conv2d(attrs, args)
+    # ACL doesn't support grouped convolution
+    if attrs.groups != 1 and not is_depthwise:
+        return False
+    return True
+
+
+def depthwise_conv2d(attrs, args):
+    """Check if the external ACL codegen for depthwise convolution should be used.
+
+    Note
+    ----
+    Relay does not have a depthwise conv2d operator whilst ACL does. We simply
+    separate the checks for depthwise for clarity.
+    """
+    kernel_typ = args[1].checked_type
+    # Only supports 3x3, 5x5 depthwise
+    if (
+        kernel_typ.shape[0] not in [3, 5]
+        or kernel_typ.shape[1] not in [3, 5]
+        or kernel_typ.shape[0] != kernel_typ.shape[1]
+    ):
+        return False
+    # Stride must be (1, 1) or (2, 2)
+    if (attrs.strides[0], attrs.strides[1]) not in [(1, 1), (2, 2)]:
+        return False
     return True
 
 
@@ -281,7 +381,7 @@ def dense(expr):
         return False
     if attrs.out_dtype != "float32" and attrs.out_dtype != "":
         return False
-    return not require_padding([*args, expr.checked_type])
+    return True
 
 
 def qnn_dense(expr):
@@ -295,7 +395,7 @@ def qnn_dense(expr):
         return False
     if attrs.out_dtype != "int32":
         return False
-    return not require_padding([*args, expr.checked_type])
+    return True
 
 
 @tvm.ir.register_op_attr("nn.max_pool2d", "target.arm_compute_lib")
@@ -307,33 +407,7 @@ def max_pool2d(expr):
     typ = args[0].checked_type
     if typ.dtype not in ["float32", "uint8"]:
         return False
-    return not require_padding([*args, expr.checked_type])
-
-
-def require_padding(inputs):
-    """Checks whether supplied data will require padding.
-    Most of the operators ACL up to 20.11 uses padded data.
-    """
-
-    def _check(shape, dtype):
-        """NEON has 128bits/16bytes per vector"""
-        if len(shape) == 0:
-            return False
-        return (shape[-1] * np.dtype(dtype).itemsize) % 16 != 0
-
-    for i in inputs:
-        if isinstance(i, (tvm.relay.expr.Var, tvm.relay.expr.Call)):
-            if _check(i.checked_type.shape, i.checked_type.dtype):
-                return True
-        elif isinstance(i, tvm.relay.expr.Constant):
-            if _check(i.data.shape, i.data.dtype):
-                return True
-        elif isinstance(i, tvm.ir.tensor_type.TensorType):
-            if _check(i.shape, i.dtype):
-                return True
-        else:
-            raise RuntimeException("Not supported input type: %s" % type(i))
-    return False
+    return True
 
 
 @tvm.ir.register_op_attr("nn.avg_pool2d", "target.arm_compute_lib")
@@ -351,7 +425,7 @@ def avg_pool2d(expr, from_quantized_composite=False):
     if attrs.layout != "NHWC":
         return False
 
-    return not require_padding([*args, expr.checked_type])
+    return True
 
 
 @tvm.ir.register_op_attr("nn.global_max_pool2d", "target.arm_compute_lib")
@@ -363,7 +437,7 @@ def global_max_pool2d(expr):
         return False
     if attrs.layout != "NHWC":
         return False
-    return not require_padding([*args, expr.checked_type])
+    return True
 
 
 @tvm.ir.register_op_attr("nn.global_avg_pool2d", "target.arm_compute_lib")
@@ -375,7 +449,7 @@ def global_avg_pool2d(expr):
         return False
     if attrs.layout != "NHWC":
         return False
-    return not require_padding([*args, expr.checked_type])
+    return True
 
 
 @tvm.ir.register_op_attr("maximum", "target.arm_compute_lib")
@@ -407,3 +481,36 @@ def qnn_add(expr):
             return False
 
     return True
+
+
+class OpAttrContext(object):
+    """ Temporarily changes the attr of an op. """
+
+    def __init__(self, op_name, attr_key, attr_value):
+        """Saves the required info for RAII pattern usage.
+
+        Parameters
+        ----------
+        op_name : str
+            The op name.
+
+        attr_key : str
+            The attribute name.
+
+        attr_value : object
+            The attribute value.
+        """
+        self.op = relay.op.get(op_name)
+        self.attr_key = attr_key
+        self.attr_value = attr_value
+
+    def __enter__(self):
+        self.older_attr = self.op.get_attr(self.attr_key)
+        self.op.reset_attr(self.attr_key)
+        self.op.set_attr(self.attr_key, self.attr_value)
+        return self
+
+    def __exit__(self, ptype, value, trace):
+        self.op.reset_attr(self.attr_key)
+        if self.older_attr:
+            self.op.set_attr(self.attr_key, self.older_attr)
diff --git a/python/tvm/relay/op/contrib/bnns.py b/python/tvm/relay/op/contrib/bnns.py
new file mode 100644
index 000000000000..2ace502e6528
--- /dev/null
+++ b/python/tvm/relay/op/contrib/bnns.py
@@ -0,0 +1,327 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, unused-argument
+"""BNNS library supported operators.
+Is a part of Accelerate framework on macOS/iOS platforms. Apple provide several APIs
+to handle tensor processing. Particularly:
+ * BNNS (basic neural )
+ * vDSP (1D and 2D tensor processing)
+"""
+import math
+import tvm.ir
+
+from tvm.relay import transform
+from tvm.relay.expr import const
+from tvm.relay.build_module import bind_params_by_name
+
+from .register import register_pattern_table, get_pattern_table
+from ...dataflow_pattern import wildcard, is_op, is_expr
+
+
+def partition_for_bnns(mod, params=None):
+    """Partition the graph greedily offloading supported
+    operators to BNNS.
+
+    Parameters
+    ----------
+    mod : Module
+        The module to run passes on.
+    params : Optional[Dict[str, NDArray]]
+        Constant input parameters.
+
+    Returns
+    -------
+    ret : annotated and partitioned module.
+    """
+    if params:
+        mod["main"] = bind_params_by_name(mod["main"], params)
+
+    seq = tvm.transform.Sequential(
+        [
+            transform.InferType(),
+            transform.FoldConstant(),
+            transform.FoldScaleAxis(),
+            transform.DynamicToStatic(),
+            transform.AlterOpLayout(),
+            # TODO(apeskov): WA. AlterOpLayout call lead to constants shape transformation
+            #   Some expand_dims op may appears after constants. It breaks BNNS fusing.
+            #   So we have to call FoldConstant right before bnns composite passes.
+            transform.FoldConstant(),
+            transform.MergeComposite(get_pattern_table("bnns")),
+            transform.AnnotateTarget("bnns"),
+            #   If you no need in per layer performance statistic you can
+            #   uncomment next line
+            # transform.MergeCompilerRegions(),
+            transform.PartitionGraph(),
+        ]
+    )
+
+    return seq(mod)
+
+
+def _register_external_op_helper(op_name, supported=True):
+    """The helper function to indicate that a given operator can be supported
+    by BNNS.
+
+    Parameters
+    ----------
+    op_name : Str
+        The name of supported operator that will be registered.
+
+    Returns
+    -------
+    f : callable
+        A function that returns if the operator is supported by BNNS.
+    """
+
+    @tvm.ir.register_op_attr(op_name, "target.bnns")
+    def _func_wrapper(expr):
+        return supported
+
+    return _func_wrapper
+
+
+_register_external_op_helper("nn.batch_matmul")
+
+
+@tvm.ir.register_op_attr("nn.max_pool2d", "target.bnns")
+def max_pool2d_check(expr):
+    """Check if the nn.max_pool2d can be executed in BNNS"""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    rank = len(data_typ.shape)
+    if rank < 3 or rank > 4 or data_typ.dtype != "float32":
+        return False
+    if attrs.layout != "NCHW":
+        return False
+    return True
+
+
+@tvm.ir.register_op_attr("nn.avg_pool2d", "target.bnns")
+def avg_pool2d_check(expr):
+    """Check if the nn.avg_pool2d can be executed in BNNS"""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    rank = len(data_typ.shape)
+    if rank < 3 or rank > 4 or data_typ.dtype != "float32":
+        return False
+    if attrs.layout != "NCHW":
+        return False
+    return True
+
+
+@tvm.ir.register_op_attr("nn.global_max_pool2d", "target.bnns")
+def global_max_pool2d_check(expr):
+    """Check if the nn.global_max_pool2d can be executed in BNNS"""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    rank = len(data_typ.shape)
+    if rank < 3 or rank > 4 or data_typ.dtype != "float32":
+        return False
+    if attrs.layout != "NCHW":
+        return False
+    return True
+
+
+@tvm.ir.register_op_attr("nn.global_avg_pool2d", "target.bnns")
+def global_avg_pool2d_check(expr):
+    """Check if the nn.global_avg_pool2d can be executed in BNNS"""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    rank = len(data_typ.shape)
+    if rank < 3 or rank > 4 or data_typ.dtype != "float32":
+        return False
+    if attrs.layout != "NCHW":
+        return False
+    return True
+
+
+def dtype_is_supported(dtype):
+    """Check if data type is supported by BNNS backend"""
+    return dtype in ("", "float32")
+
+
+@tvm.ir.register_op_attr("nn.conv2d", "target.bnns")
+def conv2d_check(expr):
+    """Check if the conv2d can be executed in BNNS"""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    if len(data_typ.shape) != 4 or data_typ.dtype != "float32":
+        return False
+    if not isinstance(args[1], tvm.relay.expr.Constant):
+        return False
+    kernel_typ = args[1].checked_type
+    if len(kernel_typ.shape) != 4 or kernel_typ.dtype != "float32":
+        return False
+    if attrs.data_layout != "NCHW":
+        return False
+    if not dtype_is_supported(attrs.out_dtype):
+        return False
+    return True
+
+
+def bias_check(expr):
+    """Check is bias added through the correct dimension"""
+    attrs, args = expr.attrs, expr.args
+    if not isinstance(args[1], tvm.relay.expr.Constant):
+        return False
+    if expr.op.name == "nn.bias_add":
+        return attrs.axis == 1
+    if expr.op.name == "add":
+        b_shape = args[1].checked_type.shape
+        if len(b_shape) == 4:
+            return bool(b_shape[0] == 1 and b_shape[2] == 1 and b_shape[3] == 1)
+        if len(b_shape) == 3:
+            return bool(b_shape[1] == 1 and b_shape[2] == 1)
+
+    return False
+
+
+@tvm.ir.register_op_attr("nn.dense", "target.bnns")
+def dense(expr):
+    """Check if the dense can be used in BNNS."""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    if data_typ.dtype != "float32":
+        return False
+    if not isinstance(args[1], tvm.relay.expr.Constant):
+        return False
+    kernel_typ = args[1].checked_type
+    if len(kernel_typ.shape) != 2 or kernel_typ.dtype != "float32":
+        return False
+    if attrs.out_dtype != "float32" and attrs.out_dtype != "":
+        return False
+    return True
+
+
+def make_conv_pattern(with_bias=True, activation="none"):
+    """Make pattern for bnns.conv2d primitive"""
+    data = wildcard()
+    weight = wildcard()
+    bias = wildcard()
+    pat = is_op("nn.conv2d")(data, weight)
+    if with_bias:
+        pat = is_op("add")(pat, bias) | is_op("nn.bias_add")(pat, bias)
+    if activation == "relu":
+        pat = is_op("nn.relu")(pat)
+    elif activation == "sigmoid":
+        pat = is_op("sigmoid")(pat)
+    return pat
+
+
+def check_conv(extract):
+    """Check conv pattern is supported by BNNS."""
+    bias_is_ok = True
+    call = extract
+    while call.op.name != "nn.conv2d":
+        if call.op.name in ("nn.bias_add", "add"):
+            bias_is_ok &= bias_check(call)
+        call = call.args[0]
+    return conv2d_check(call) and bias_is_ok
+
+
+def make_dense_bias_pattern():
+    """Make pattern for bnns.dense primitive"""
+    data = wildcard()
+    weight = wildcard()
+    bias = wildcard()
+    d = is_op("nn.dense")(data, weight)
+    return is_op("add")(d, bias)
+
+
+def make_dense_bias_gelu_pattern():
+    """Make pattern for bnns.dense primitive with fused bias and gelu activation"""
+    dense_bias = make_dense_bias_pattern()
+    const1 = is_expr(const(0.044715))
+    const2 = is_expr(const(math.sqrt(2 / math.pi)))
+
+    gelu = is_op("power")(dense_bias, is_expr(const(3, dtype="float32")))
+    gelu = is_op("multiply")(gelu, const1)
+    gelu = is_op("add")(gelu, dense_bias)
+    gelu = is_op("multiply")(gelu, const2)
+    gelu = is_op("tanh")(gelu)
+    gelu = is_op("add")(gelu, is_expr(const(1, dtype="float32")))
+    gelu = is_op("multiply")(gelu, is_expr(const(0.5)))
+    gelu = is_op("multiply")(gelu, dense_bias)
+    return gelu
+
+
+def check_dense(extract):
+    """Check dense pattern is supported by BNNS."""
+    call = extract
+    while call.op.name != "nn.dense":
+        call = call.args[0]
+    return dense(call)
+
+
+@tvm.ir.register_op_attr("nn.instance_norm", "target.bnns")
+def instance_norm_check(expr):
+    """Check if the nn.instance_norm can be executed in BNNS"""
+    attrs, args = expr.attrs, expr.args
+    data_typ = args[0].checked_type
+    rank = len(data_typ.shape)
+    if rank < 3 or rank > 4 or data_typ.dtype != "float32":
+        return False
+    if not isinstance(args[1], tvm.relay.expr.Constant) or not isinstance(
+        args[2], tvm.relay.expr.Constant
+    ):
+        return False
+    if attrs.axis == 0 and rank == 3 or attrs.axis == 1 and rank == 4:
+        return True
+    return False
+
+
+@register_pattern_table("bnns")
+def pattern_table():
+    """Get BNNS specific fusing patterns collection"""
+    conv2d_bias_pat = (
+        "bnns.conv2d_bias",
+        make_conv_pattern(with_bias=True),
+        check_conv,
+    )
+    conv2d_bias_relu_pat = (
+        "bnns.conv2d_bias_relu",
+        make_conv_pattern(with_bias=True, activation="relu"),
+        check_conv,
+    )
+    conv2d_relu_pat = (
+        "bnns.conv2d_relu",
+        make_conv_pattern(with_bias=False, activation="relu"),
+        check_conv,
+    )
+    conv2d_bias_sigmoid_pat = (
+        "bnns.conv2d_bias_sigmoid",
+        make_conv_pattern(with_bias=True, activation="sigmoid"),
+        check_conv,
+    )
+    conv2d_sigmoid_pat = (
+        "bnns.conv2d_sigmoid",
+        make_conv_pattern(with_bias=False, activation="sigmoid"),
+        check_conv,
+    )
+    dense_bias_gelu = ("bnns.dense_bias_gelu", make_dense_bias_gelu_pattern(), check_dense)
+    dense_bias = ("bnns.dense_bias", make_dense_bias_pattern(), check_dense)
+    bnns_patterns = [
+        conv2d_bias_relu_pat,
+        conv2d_relu_pat,
+        conv2d_bias_sigmoid_pat,
+        conv2d_sigmoid_pat,
+        conv2d_bias_pat,
+        dense_bias_gelu,
+        dense_bias,
+    ]
+    return bnns_patterns
diff --git a/python/tvm/relay/op/contrib/ethosn.py b/python/tvm/relay/op/contrib/ethosn.py
index 3a05011242e7..478a1ec46f26 100644
--- a/python/tvm/relay/op/contrib/ethosn.py
+++ b/python/tvm/relay/op/contrib/ethosn.py
@@ -17,7 +17,11 @@
 # pylint: disable=invalid-name, unused-argument
 """Arm(R) Ethos(TM) -N NPU supported operators."""
 from enum import Enum
+
 import tvm.ir
+from tvm.relay import transform
+from tvm.relay.build_module import bind_params_by_name
+
 from ...dataflow_pattern import wildcard, is_op, is_constant
 from ... import qnn as _qnn
 from .register import register_pattern_table
@@ -42,6 +46,37 @@ def ethosn_available():
     return Available.SW_AND_HW if hw else Available.SW_ONLY
 
 
+def partition_for_ethosn(mod, params=None):
+    """Partition the graph greedily offloading supported
+    operators to Arm Ethos-N NPU.
+
+    Parameters
+    ----------
+    mod : Module
+        The module to run passes on.
+    params : Optional[Dict[str, NDArray]]
+        Constant input parameters.
+
+    Returns
+    -------
+    ret : annotated and partitioned module.
+    """
+    if params:
+        mod["main"] = bind_params_by_name(mod["main"], params)
+
+    seq = tvm.transform.Sequential(
+        [
+            transform.InferType(),
+            transform.MergeComposite(pattern_table()),
+            transform.AnnotateTarget("ethos-n"),
+            transform.MergeCompilerRegions(),
+            transform.PartitionGraph(),
+        ]
+    )
+
+    return seq(mod)
+
+
 @register_pattern_table("ethos-n")
 def pattern_table():
     """Get the Ethos-N compiler pattern table."""
diff --git a/python/tvm/relay/op/contrib/tensorrt.py b/python/tvm/relay/op/contrib/tensorrt.py
index bda71468d9e2..afdea9712342 100644
--- a/python/tvm/relay/op/contrib/tensorrt.py
+++ b/python/tvm/relay/op/contrib/tensorrt.py
@@ -140,7 +140,11 @@ def partition_for_tensorrt(
             RemoveDropoutPass(),
             transform.RemoveUnusedFunctions(),
             transform.ConvertLayout(
-                {"nn.conv2d": ["NCHW", "default"], "nn.conv3d": ["NCDHW", "default"]}
+                {
+                    "nn.conv2d": ["NCHW", "default"],
+                    "nn.conv3d": ["NCDHW", "default"],
+                    "nn.conv2d_transpose": ["NCHW", "default"],
+                }
             ),
             transform.FoldConstant(),
             transform.AnnotateTarget("tensorrt"),
@@ -611,7 +615,6 @@ def layout_transform_annotate_fn(expr):  # pylint: disable=unused-variable
 @_register_external_dynamic_check_func("reshape")
 def reshape_annotate_fn(expr):  # pylint: disable=unused-variable
     """Check if reshape is supported by TensorRT."""
-
     attrs, args = expr.attrs, expr.args
     if args[0].checked_type.dtype != "float32":
         logger.info("Only float32 inputs are supported for TensorRT.")
@@ -625,23 +628,23 @@ def reshape_annotate_fn(expr):  # pylint: disable=unused-variable
         if len(new_shape) == 0 or len(shape) == 0:
             logger.info("reshape: Can't reshape to or from scalar.")
             return False
-
         dynamic_reshape = any([isinstance(x, tvm.tir.expr.Any) for x in shape])
 
         if dynamic_reshape:
             # Make sure that the batch dim is unmodified.
             if int(new_shape[0]) < 0:
-                for shape_val, new_shape_val in enumerate(shape[1:], new_shape[1:]):
+                for shape_val, new_shape_val in zip(shape[1:], new_shape[1:]):
                     if not (
-                        isinstance(shape_val, int)
-                        and isinstance(new_shape_val, int)
+                        isinstance(shape_val, (int, tvm.tir.expr.IntImm))
+                        and isinstance(new_shape_val, (int, tvm.tir.expr.IntImm))
                         and int(shape_val) == int(new_shape_val)
                     ):
                         return False
             elif int(new_shape[0]) > 0:
+                # Currently we only allow dim[0] to be Any, so this branch will always be False
                 if not (
-                    isinstance(shape[0], int)
-                    and isinstance(new_shape[0], int)
+                    isinstance(shape[0], (int, tvm.tir.expr.IntImm))
+                    and isinstance(new_shape[0], (int, tvm.tir.expr.IntImm))
                     and int(shape[0]) == int(new_shape[0])
                 ):
                     return False
diff --git a/python/tvm/relay/op/contrib/vitis_ai.py b/python/tvm/relay/op/contrib/vitis_ai.py
index fa17c63fc00a..aaa9f99e61ed 100644
--- a/python/tvm/relay/op/contrib/vitis_ai.py
+++ b/python/tvm/relay/op/contrib/vitis_ai.py
@@ -85,6 +85,10 @@ def visit_call(self, call):
 
 def annotation(mod, params, target):
     """Annotate Relay expression for Vitis-AI DPU accelerators"""
+    # We need type information for supporting models that contain operations that don't
+    #   have a Relay to XLayer translation
+    mod = relay.transform.InferType()(mod)
+
     xgraph = pyxir.frontend.tvm.from_relay(mod, params, postprocessing=None)
     xgraph = pyxir.partition(xgraph, targets=[target])
 
diff --git a/python/tvm/relay/op/dyn/_transform.py b/python/tvm/relay/op/dyn/_transform.py
index b61d4f9655f6..a36b56214bc4 100644
--- a/python/tvm/relay/op/dyn/_transform.py
+++ b/python/tvm/relay/op/dyn/_transform.py
@@ -32,11 +32,8 @@
 
 
 @script
-def _reshape_shape_func_input_data(data, newshape, ndim):
+def _reshape_shape_func_input_data(data_shape, newshape, ndim):
     out = output_tensor((ndim,), "int64")
-    data_shape = allocate((len(data.shape),), "int64")
-    for x in const_range(len(data.shape)):
-        data_shape[x] = int64(data.shape[x])
     src_idx = 0
     dst_idx = 0
     infer_idx = -1
@@ -87,7 +84,7 @@ def _reshape_shape_func_input_data(data, newshape, ndim):
     return out
 
 
-@_reg.register_shape_func("dyn.reshape", True)
+@_reg.register_shape_func("dyn.reshape", [False, True])
 def dynamic_reshape_shape_func(attrs, inputs, out_ndims):
     return [_reshape_shape_func_input_data(*inputs, out_ndims[0])]
 
@@ -150,36 +147,36 @@ def one_hot_shape_func(attrs, inputs, _):
 
 
 @script
-def _strided_slice_shape_func_input_data(data, begin, end, strides, slice_mode):
-    ndim = len(data.shape)
+def _strided_slice_shape_func_input_data(data_shape, begin, end, strides, slice_mode):
+    ndim = len(data_shape)
     out = output_tensor((ndim,), "int64")
     for i in const_range(ndim):
         cbegin = int64(0)
-        cend = int64(data.shape[i])
+        cend = int64(data_shape[i])
         cstride = int64(1)
         if strides.shape[0] > i:
             cstride = int64(strides[i])
         if begin.shape[0] > i:
             cbegin = int64(begin[i])
             if cbegin < 0:
-                cbegin += int64(data.shape[i])
+                cbegin += int64(data_shape[i])
         if end.shape[0] <= i:
-            cend = int64(data.shape[i])
+            cend = int64(data_shape[i])
         elif slice_mode != 0:
             cstride = int64(1)
             if end[i] < 0:
-                cend = int64(data.shape[i])
+                cend = int64(data_shape[i])
             else:
                 cend = cbegin + int64(end[i])
         else:
-            if end[i] > data.shape[i]:
-                cend = int64(data.shape[i])
-            elif end[i] < -data.shape[i]:
+            if end[i] > data_shape[i]:
+                cend = int64(data_shape[i])
+            elif end[i] < -data_shape[i]:
                 cend = int64(-1)
             else:
                 cend = int64(end[i])
                 if cend < 0:
-                    cend += int64(data.shape[i])
+                    cend += int64(data_shape[i])
         assert cstride != 0, "Strides can't be zero."
         if cstride < 0:
             slice_range = cbegin - cend
@@ -192,7 +189,7 @@ def _strided_slice_shape_func_input_data(data, begin, end, strides, slice_mode):
     return out
 
 
-@_reg.register_shape_func("dyn.strided_slice", True)
+@_reg.register_shape_func("dyn.strided_slice", [False, True, True, True])
 def strided_slice_shape_func(attrs, inputs, _):
     """
     Shape func for strided_slice
diff --git a/python/tvm/relay/op/image/image.py b/python/tvm/relay/op/image/image.py
index a3f3a3e8cb92..153439b1e20c 100644
--- a/python/tvm/relay/op/image/image.py
+++ b/python/tvm/relay/op/image/image.py
@@ -17,7 +17,7 @@
 """Image operations."""
 from . import _make
 from ..dyn.image import _make as _dyn_make
-from ...expr import Expr
+from ...expr import Expr, Constant
 
 
 def resize(
@@ -66,6 +66,8 @@ def resize(
     result: relay.Expr
         The resized result.
     """
+    if isinstance(size, Constant):
+        size = list(size.data.asnumpy().astype("int32"))
     if isinstance(size, Expr):
         return _dyn_make.resize(
             data, size, layout, method, coordinate_transformation_mode, out_dtype
diff --git a/python/tvm/relay/op/nn/_nn.py b/python/tvm/relay/op/nn/_nn.py
index c5af5d83bd7d..af64873ee904 100644
--- a/python/tvm/relay/op/nn/_nn.py
+++ b/python/tvm/relay/op/nn/_nn.py
@@ -52,11 +52,43 @@
 reg.register_pattern("nn.log_softmax", OpPattern.OPAQUE)
 
 
+@reg.register_legalize("nn.dense")
+def legalize_dense(attrs, inputs, types):
+    """Legalize dense op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    return topi.nn.dense_legalize(attrs, inputs, types)
+
+
 # dense
 reg.register_strategy("nn.dense", strategy.dense_strategy)
 reg.register_pattern("nn.dense", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
 
 
+@reg.register_alter_op_layout("nn.dense")
+def alter_op_layout_dense(attrs, inputs, tinfos, out_type):
+    """Alternate the layout of dense"""
+    return topi.nn.dense_alter_layout(attrs, inputs, tinfos, out_type)
+
+
+# dense_pack
+reg.register_strategy("nn.contrib_dense_pack", strategy.dense_pack_strategy)
+reg.register_pattern("nn.contrib_dense_pack", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
+
+
 # fifo_buffer
 @reg.register_compute("nn.fifo_buffer")
 def compute_fifo_buffer(attrs, inputs, out_type):
@@ -67,6 +99,27 @@ def compute_fifo_buffer(attrs, inputs, out_type):
 reg.register_pattern("nn.fifo_buffer", OpPattern.OPAQUE)
 
 
+@reg.register_legalize("nn.batch_matmul")
+def legalize_batch_matmul(attrs, inputs, types):
+    """Legalize batch_matmul op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    return topi.nn.batch_matmul_legalize(attrs, inputs, types)
+
+
 # batch_matmul
 reg.register_strategy("nn.batch_matmul", strategy.batch_matmul_strategy)
 reg.register_pattern("nn.batch_matmul", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
@@ -89,6 +142,11 @@ def alter_op_layout_sparse_dense(attrs, inputs, tinfos, out_type):
     return topi.nn.sparse_dense_alter_layout(attrs, inputs, tinfos, out_type)
 
 
+# sparse_add
+reg.register_strategy("nn.sparse_add", strategy.sparse_add_strategy)
+reg.register_pattern("nn.sparse_add", reg.OpPattern.OPAQUE)
+
+
 @reg.register_compute("nn.internal.sparse_dense_padded")
 def compute_sparse_dense_padded(attrs, inputs, out_type):
     """Compute definition of sparse_dense_padded"""
@@ -1088,6 +1146,25 @@ def dense_shape_func(attrs, inputs, _):
     return ret
 
 
+@script
+def _dense_pack_shape_func(data_shape, weight_shape):
+    out = output_tensor((data_shape.shape[0],), "int64")
+    for i in const_range(out.shape[0] - 1):
+        out[i] = data_shape[i]
+    out[out.shape[0] - 1] = weight_shape[0] * weight_shape[2]
+
+    return out
+
+
+@reg.register_shape_func("nn.contrib_dense_pack", False)
+def dense_pack_shape_func(attrs, inputs, _):
+    """
+    Shape function for dense_pack op.
+    """
+    ret = [_dense_pack_shape_func(inputs[0], inputs[1])]
+    return ret
+
+
 @script
 def _batch_matmul_shape_func(data_shape, weight_shape):
     out = output_tensor((data_shape.shape[0],), "int64")
diff --git a/python/tvm/relay/op/nn/nn.py b/python/tvm/relay/op/nn/nn.py
index fef82e7c1fd3..a1147fec4d7e 100644
--- a/python/tvm/relay/op/nn/nn.py
+++ b/python/tvm/relay/op/nn/nn.py
@@ -21,7 +21,7 @@
 from . import _make
 from ..dyn.nn import _make as _dyn_make
 from .utils import get_pad_tuple1d, get_pad_tuple2d, get_pad_tuple3d
-from ...expr import const, Expr
+from ...expr import const, Expr, Constant
 
 
 def conv1d(
@@ -1279,6 +1279,10 @@ def upsampling(
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(scale_h, Constant):
+        scale_h = scale_h.data.asnumpy().item()
+    if isinstance(scale_w, Constant):
+        scale_w = scale_w.data.asnumpy().item()
     if isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
         if not isinstance(scale_h, Expr):
             scale_h = const(scale_h, "float64")
@@ -1338,6 +1342,12 @@ def upsampling3d(
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(scale_d, Constant):
+        scale_d = scale_d.data.asnumpy().item()
+    if isinstance(scale_h, Constant):
+        scale_h = scale_h.data.asnumpy().item()
+    if isinstance(scale_w, Constant):
+        scale_w = scale_w.data.asnumpy().item()
     if isinstance(scale_d, Expr) or isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
         if not isinstance(scale_d, Expr):
             scale_d = const(scale_d, "float64")
@@ -1435,6 +1445,39 @@ def dense(data, weight, units=None, out_dtype=""):
     return _make.dense(data, weight, units, out_dtype)
 
 
+def contrib_dense_pack(data, weight, units=None, out_dtype=""):
+    """Dense operator.
+    Applies a linear transformation
+
+    .. math::
+
+    `Y = X * W^T`
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator,
+        of shape `(d_1, d_2, ..., d_n, units_in)`.
+
+    weight : tvm.relay.Expr
+        The transformed weight expressions, 3-D matrix,
+        of shape `(units // pack_weight_tile, units_in, pack_weight_tile)`.
+
+    units : int, optional
+        Number of hidden units of the dense transformation.
+
+    out_dtype : str, optional
+        Specifies the output data type for mixed precision dense,
+        of shape `(d_1, d_2, ..., d_n, units)`.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    return _make.contrib_dense_pack(data, weight, units, out_dtype)
+
+
 def fifo_buffer(data, buffer, axis):
     """FIFO buffer to enable computation reuse in CNNs with sliding indow input
 
@@ -1488,7 +1531,7 @@ def relu(data):
     return _make.relu(data)
 
 
-def leaky_relu(data, alpha):
+def leaky_relu(data, alpha=0.01):
     """This operator takes data as input and does Leaky version
     of a Rectified Linear Unit.
 
@@ -1563,6 +1606,10 @@ def pad(data, pad_width, pad_value=0, pad_mode="constant"):
     result : tvm.relay.Expr
         The computed result.
     """
+    if isinstance(pad_value, Constant):
+        pad_value = pad_value.data.asnumpy().item()
+    if isinstance(pad_width, Constant):
+        pad_width = [list(i) for i in pad_width.data.asnumpy()]
     if isinstance(pad_width, Expr) or (isinstance(pad_value, Expr)):
         if not isinstance(pad_width, Expr):
             pad_width = const(list(pad_width))
@@ -2101,6 +2148,53 @@ def sparse_transpose(x):
     return expr.TupleWrapper(_make.sparse_transpose(x[0], x[1], x[2]), 3)
 
 
+# pylint: disable=no-else-return,inconsistent-return-statements
+def sparse_add(dense_mat, sparse_mat):
+    r"""
+    Computes the matrix addition of `dense_mat` and `sparse_mat`, where `dense_mat` is
+    a dense matrix and `sparse_mat` is a sparse (CSR) namedtuple with
+    fields `data`, `indices`, and `indptr`.
+
+    .. math::
+
+        \mbox{sparse_add}(dense_mat, sparse_mat)[m, n] = \mbox{add}(\mbox{as_dense}(S), (D))[m, n]
+
+    where `as_dense` returns dense equivalent of the given S(sparse matrix)
+    while performing addition with given D(dense matrix).
+
+    Parameters
+    ----------
+    dense_mat : tvm.relay.Expr
+        The input dense matrix for the matrix addition
+
+    sparse_mat : Union[namedtuple, Tuple[ndarray, ndarray, ndarray]].
+        The input sparse matrix(CSR) for the matrix addition.
+
+    Returns
+    -------
+    result: tvm.relay.Expr
+        The computed result.
+
+    Examples
+    -------
+    .. code-block:: python
+        dense_data = [[ 3.,   4.,   4. ]
+                      [ 4.,  2.,  5. ]]
+        sparse_data = [4., 8.]
+        sparse_indices =[0, 2]
+        sparse_indptr =[0, 1, 2]
+
+        output = relay.sparse_add(dense_data, sparse_data, sparse_indices, sparse_indptr)
+
+        output = [[ 7.,   4.,   4. ]
+                  [ 4.,  2.,  13. ]]
+    """
+    if hasattr(sparse_mat, "indices"):
+        return _make.sparse_add(dense_mat, sparse_mat.data, sparse_mat.indices, sparse_mat.indptr)
+    else:
+        return _make.sparse_add(dense_mat, sparse_mat[0], sparse_mat[1], sparse_mat[2])
+
+
 def contrib_conv2d_winograd_without_weight_transform(
     data,
     weight,
diff --git a/python/tvm/relay/op/op.py b/python/tvm/relay/op/op.py
index d4d20b3ebc4a..5882027fb1d8 100644
--- a/python/tvm/relay/op/op.py
+++ b/python/tvm/relay/op/op.py
@@ -356,7 +356,7 @@ def register_gradient(op_name, fgradient=None, level=10):
     return tvm.ir.register_op_attr(op_name, "FPrimalGradient", fgradient, level)
 
 
-def register_shape_func(op_name, data_dependant, shape_func=None, level=10):
+def register_shape_func(op_name, data_dependent, shape_func=None, level=10):
     """Register operator shape function for an op.
 
     Parameters
@@ -364,8 +364,10 @@ def register_shape_func(op_name, data_dependant, shape_func=None, level=10):
     op_name : str
         The name of the op.
 
-    data_dependant : bool
-        Whether the shape function depends on input data.
+    data_dependent : bool or list of bool
+        Whether the shape function depends on input data. If this is a list of bool,
+        the length of the list must be the same as the number of arguments of this op.
+        The list specifies per-input data dependence of the op.
 
     shape_func : function (attrs: Attrs, inputs: List[Tensor], out_ndims: List[IndexExpr])
                  -> shape_tensors: List<Tensor>
@@ -374,7 +376,9 @@ def register_shape_func(op_name, data_dependant, shape_func=None, level=10):
     level : int
         The priority level
     """
-    get(op_name).set_attr("TShapeDataDependant", data_dependant, level)
+    if not isinstance(data_dependent, list):
+        data_dependent = [data_dependent]
+    get(op_name).set_attr("TShapeDataDependent", data_dependent, level)
     return tvm.ir.register_op_attr(op_name, "FShapeFunc", shape_func, level)
 
 
diff --git a/python/tvm/relay/op/op_attrs.py b/python/tvm/relay/op/op_attrs.py
index cb837b192a6c..41076817b374 100644
--- a/python/tvm/relay/op/op_attrs.py
+++ b/python/tvm/relay/op/op_attrs.py
@@ -552,3 +552,8 @@ class SpaceToBatchNDAttrs(Attrs):
 @tvm._ffi.register_object("relay.attrs.BatchToSpaceNDAttrs")
 class BatchToSpaceNDAttrs(Attrs):
     """Attributes used in BatchToSpaceNDAttrs operators"""
+
+
+@tvm._ffi.register_object("relay.attrs.ThreefryGenerateAttrs")
+class ThreefryGenerateAttrs(Attrs):
+    """Attributes used in ThreefryGenerateAttrs operators"""
diff --git a/python/tvm/relay/op/random/__init__.py b/python/tvm/relay/op/random/__init__.py
new file mode 100644
index 000000000000..8366f4a06dac
--- /dev/null
+++ b/python/tvm/relay/op/random/__init__.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=wildcard-import
+"""PRNG related operators."""
+from .kernel import *
+from . import _kernel
diff --git a/python/tvm/relay/op/random/_kernel.py b/python/tvm/relay/op/random/_kernel.py
new file mode 100644
index 000000000000..8be3397008d5
--- /dev/null
+++ b/python/tvm/relay/op/random/_kernel.py
@@ -0,0 +1,29 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Splittable and parallelizable PRNG kernels."""
+# pylint: disable=invalid-name,unused-argument
+from __future__ import absolute_import
+
+from .. import strategy
+from ..op import register_strategy, register_pattern, OpPattern
+
+
+# Threefry
+register_strategy("random.threefry_generate", strategy.threefry_generate_strategy)
+register_pattern("random.threefry_generate", OpPattern.OPAQUE)
+register_strategy("random.threefry_split", strategy.threefry_split_strategy)
+register_pattern("random.threefry_split", OpPattern.OPAQUE)
diff --git a/python/tvm/relay/op/random/_make.py b/python/tvm/relay/op/random/_make.py
new file mode 100644
index 000000000000..51a8a6aa9339
--- /dev/null
+++ b/python/tvm/relay/op/random/_make.py
@@ -0,0 +1,20 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Constructor APIs"""
+import tvm._ffi
+
+tvm._ffi._init_api("relay.op.random._make", __name__)
diff --git a/python/tvm/relay/op/random/kernel.py b/python/tvm/relay/op/random/kernel.py
new file mode 100644
index 000000000000..96634943128d
--- /dev/null
+++ b/python/tvm/relay/op/random/kernel.py
@@ -0,0 +1,134 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Splittable and parallelizable PRNG kernels."""
+# pylint: disable=invalid-name,unused-argument
+from __future__ import absolute_import
+
+import sys
+import numpy as np
+
+from ...expr import Constant
+from .... import nd
+from . import _make
+
+
+def threefry_key(seed):
+    """Create a new Threefry random number generator key.
+
+    Example
+    -------
+
+    .. code-block:: python
+
+        gen = threefry_key(0)
+        _, random_number = threefry_generate(gen, (4,))
+
+    Parameters
+    ----------
+    seed : int
+        Starting seed for the key
+
+    Returns
+    -------
+    key : relay.Expr
+        New key to pass to future uses of :py:func:`threefry_split` or
+        :py:func:`threefry_generate`.
+    """
+    s = np.frombuffer(seed.to_bytes(32, sys.byteorder), dtype="uint64")
+    a = np.concatenate((s, np.array([0, 0, 0, 0, 1 << 63, 0], dtype="uint64")))
+    return Constant(nd.array(a))
+
+
+def threefry_generate(key, shape):
+    """Generate an array of random bits (`uint64`) using the Threefry algorithm
+
+    Example
+    -------
+
+    .. code-block:: python
+
+        key = threefry_key(0)
+        new_key, random1 = threefry_generate(key, (4,))
+        _, random2 = threefry_generate(new_key, (4,))
+        # random1 and random2 are different random numbers
+
+    Parameters
+    ----------
+    key : relay.Expr
+        key that uniquely determines the random values. Multiple uses with the
+        same key will generate the same random values. This key should be
+        treated as an opaque pointer. You can create one from calling
+        :py:func:`threefry_key`, :py:func:`threefry_split`, or
+        :py:func:`threefry_generate`. **Do not use this key again after calling
+        this function.**
+
+    shape : Sequence[int]
+        Desired outputs shape of random numbers. **Currently the total
+        number of elements must be a multiple of 4.**
+
+    Returns
+    -------
+    new_key : relay.Expr
+        New key to pass to future uses of :py:func:`threefry_split` or
+        :py:func:`threefry_generate`.
+
+    random_array : relay.Expr
+        Array of random numbers. Has shape `shape`.
+    """
+    return _make.threefry_generate(key, shape)
+
+
+def threefry_split(key):
+    """Split an existing Threefry key into two new ones.
+
+    This is useful if you have to subsequent calls which each need their own
+    independent random number generation.
+
+    Example
+    -------
+
+    .. code-block:: python
+
+        def foo(key):
+            new_key, num = threefry_generate(key, (4,))
+            return num
+
+        key = threefry_key(0)
+        key1, key2 = threefry_split(key)
+        assert foo(key1) != foo(key2)
+
+    Parameters
+    ----------
+    key : relay.Expr
+        key that uniquely determines the random values. Multiple uses with the
+        same generator will generate the same random values. This generator should be
+        treated as an opaque pointer. You can create one from calling
+        :py:func:`threefry_key`, :py:func:`threefry_split`, or
+        :py:func:`threefry_generate`. **Do not use this generator again after calling
+        this function.**
+
+    Returns
+    -------
+    new_key_1 : relay.Expr
+        New key to pass to future uses of :py:func:`threefry_split` or
+        :py:func:`threefry_generate`.
+
+    new_key_2 : relay.Expr
+        New key to pass to future uses of :py:func:`threefry_split` or
+        :py:func:`threefry_generate`.
+    """
+    return _make.threefry_split(key)
diff --git a/python/tvm/relay/op/strategy/cuda.py b/python/tvm/relay/op/strategy/cuda.py
index 9d8420c69610..e0d0f165219e 100644
--- a/python/tvm/relay/op/strategy/cuda.py
+++ b/python/tvm/relay/op/strategy/cuda.py
@@ -20,7 +20,7 @@
 from tvm.auto_scheduler import is_auto_scheduler_enabled
 from tvm.te import SpecializedCondition
 from tvm.contrib import nvcc
-from tvm._ffi import get_global_func
+from tvm.contrib.thrust import can_use_thrust
 from .generic import *
 from .. import op as _op
 
@@ -354,6 +354,8 @@ def judge_winograd(
     OH = (H + pt + pb - KH) // stride_h + 1
     OW = (W + pl + pr - KW) // stride_w + 1
     nH, nW = (OH + tile_size - 1) // tile_size, (OW + tile_size - 1) // tile_size
+    if not isinstance(N, int):
+        return False, False, False
     P = N * nH * nW
 
     judge_winograd_tensorcore = (
@@ -655,7 +657,7 @@ def dense_strategy_cuda(attrs, inputs, out_type, target):
     data, weights = inputs
     b, i = get_const_tuple(data.shape)
     o, _ = get_const_tuple(weights.shape)
-    if out_type.dtype == "int8":
+    if data.dtype == "int8" and weights.dtype == "int8" and out_type.dtype == "int32":
         strategy.add_implementation(
             wrap_compute_dense(topi.cuda.dense_int8),
             wrap_topi_schedule(topi.cuda.schedule_dense_int8),
@@ -678,9 +680,26 @@ def dense_strategy_cuda(attrs, inputs, out_type, target):
         if target.kind.name == "cuda":
             if nvcc.have_tensorcore(target=target):
                 if (
-                    (i % 16 == 0 and b % 16 == 0 and o % 16 == 0)
-                    or (i % 16 == 0 and b % 8 == 0 and o % 32 == 0)
-                    or (i % 16 == 0 and b % 32 == 0 and o % 8 == 0)
+                    (
+                        data.dtype in ["float16", "int8", "uint8"]
+                        and (
+                            (i % 16 == 0 and b % 16 == 0 and o % 16 == 0)
+                            or (i % 16 == 0 and b % 8 == 0 and o % 32 == 0)
+                            or (i % 16 == 0 and b % 32 == 0 and o % 8 == 0)
+                        )
+                    )
+                    or (
+                        data.dtype in ["int4", "uint4"]
+                        and i % 32 == 0
+                        and b % 8 == 0
+                        and o % 8 == 0
+                    )
+                    or (
+                        data.dtype in ["int1", "uint1"]
+                        and i % 128 == 0
+                        and b % 8 == 0
+                        and o % 8 == 0
+                    )
                 ):
                     strategy.add_implementation(
                         wrap_compute_dense(topi.cuda.dense_tensorcore),
@@ -715,6 +734,22 @@ def batch_matmul_strategy_cuda(attrs, inputs, out_type, target):
             name="batch_matmul_cublas.cuda",
             plevel=15,
         )
+    if target.kind.name == "cuda" and nvcc.have_tensorcore(target=target):
+        x, y = inputs
+        _, M, K = get_const_tuple(x.shape)
+        _, N, K = get_const_tuple(y.shape)
+        if x.dtype in ["float16", "int8", "uint8"] and (
+            (M % 8 == 0 and K % 16 == 0 and N % 32 == 0)
+            or (M % 16 == 0 and K % 16 == 0 and N % 16 == 0)
+            or (M % 32 == 0 and K % 16 == 0 and N % 8 == 0)
+        ):
+            strategy.add_implementation(
+                wrap_compute_batch_matmul(topi.cuda.batch_matmul_tensorcore),
+                wrap_topi_schedule(topi.cuda.schedule_batch_matmul_tensorcore),
+                name="batch_matmul_tensorcore.cuda",
+                plevel=20,
+            )
+
     return strategy
 
 
@@ -731,6 +766,17 @@ def sparse_dense_strategy_cuda(attrs, inputs, out_type, target):
     return strategy
 
 
+@sparse_reshape_strategy.register(["cuda", "gpu"])
+def sparse_reshape_strategy_cuda(attrs, inputs, out_type, target):
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_sparse_reshape(topi.cuda.sparse_reshape),
+        wrap_topi_schedule(topi.generic.schedule_extern),
+        name="sparse_reshape.cuda",
+    )
+    return strategy
+
+
 @sparse_dense_padded_strategy.register(["cuda", "gpu"])
 def sparse_dense_padded_strategy_cuda(attrs, inputs, out_type, target):
     """sparse dense cuda strategy"""
@@ -750,10 +796,21 @@ def scatter_cuda(attrs, inputs, out_type, target):
     strategy = _op.OpStrategy()
     strategy.add_implementation(
         wrap_compute_scatter(topi.cuda.scatter),
-        wrap_topi_schedule(topi.generic.schedule_extern),
+        wrap_topi_schedule(topi.cuda.schedule_scatter),
         name="scatter.cuda",
         plevel=10,
     )
+
+    rank = len(inputs[0].shape)
+
+    with SpecializedCondition(rank == 1):
+        if can_use_thrust(target, "tvm.contrib.thrust.stable_sort_by_key"):
+            strategy.add_implementation(
+                wrap_compute_scatter(topi.cuda.scatter_via_sort),
+                wrap_topi_schedule(topi.cuda.schedule_scatter_via_sort),
+                name="scatter_via_sort.cuda",
+                plevel=9,  # use the sequential version by default
+            )
     return strategy
 
 
@@ -780,6 +837,7 @@ def scatter_nd_cuda(attrs, inputs, out_type, target):
         name="scatter_nd.cuda",
         plevel=10,
     )
+    return strategy
 
 
 @sort_strategy.register(["cuda", "gpu"])
@@ -791,9 +849,7 @@ def sort_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_sort),
         name="sort.cuda",
     )
-    if target.kind.name == "cuda" and get_global_func(
-        "tvm.contrib.thrust.sort", allow_missing=True
-    ):
+    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_sort(topi.cuda.sort_thrust),
             wrap_topi_schedule(topi.cuda.schedule_sort),
@@ -812,9 +868,7 @@ def argsort_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_argsort),
         name="argsort.cuda",
     )
-    if target.kind.name == "cuda" and get_global_func(
-        "tvm.contrib.thrust.sort", allow_missing=True
-    ):
+    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_argsort(topi.cuda.argsort_thrust),
             wrap_topi_schedule(topi.cuda.schedule_argsort),
@@ -833,9 +887,7 @@ def topk_strategy_cuda(attrs, inputs, out_type, target):
         wrap_topi_schedule(topi.cuda.schedule_topk),
         name="topk.cuda",
     )
-    if target.kind.name == "cuda" and get_global_func(
-        "tvm.contrib.thrust.sort", allow_missing=True
-    ):
+    if can_use_thrust(target, "tvm.contrib.thrust.sort"):
         strategy.add_implementation(
             wrap_compute_topk(topi.cuda.topk_thrust),
             wrap_topi_schedule(topi.cuda.schedule_topk),
@@ -898,12 +950,20 @@ def roi_align_strategy_cuda(attrs, inputs, out_type, target):
     """roi_align cuda strategy"""
     strategy = _op.OpStrategy()
     layout = attrs.layout
-    assert layout == "NCHW", "only support nchw for now"
-    strategy.add_implementation(
-        wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
-        wrap_topi_schedule(topi.cuda.schedule_roi_align),
-        name="roi_align_nchw.cuda",
-    )
+
+    if layout == "NCHW":
+        strategy.add_implementation(
+            wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
+            wrap_topi_schedule(topi.cuda.schedule_roi_align),
+            name="roi_align_nchw.cuda",
+        )
+    else:
+        assert layout == "NHWC", "layout must be NCHW or NHWC."
+        strategy.add_implementation(
+            wrap_compute_roi_align(topi.vision.rcnn.roi_align_nhwc),
+            wrap_topi_schedule(topi.cuda.schedule_roi_align),
+            name="roi_align_nhwc.cuda",
+        )
     return strategy
 
 
@@ -950,3 +1010,27 @@ def argwhere_strategy_cuda(attrs, inputs, out_type, target):
         name="argwhere.cuda",
     )
     return strategy
+
+
+@cumsum_strategy.register(["cuda", "gpu"])
+def cumsum_strategy_cuda(attrs, inputs, out_type, target):
+    """cumsum cuda strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_cumsum(topi.cuda.cumsum),
+        wrap_topi_schedule(topi.cuda.schedule_scan),
+        name="cumsum.cuda",
+    )
+    return strategy
+
+
+@unique_strategy.register(["cuda", "gpu"])
+def unique_strategy_cuda(attrs, inputs, out_type, target):
+    """unique cuda strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_unique(topi.cuda.unique),
+        wrap_topi_schedule(topi.cuda.schedule_scan),
+        name="unique.cuda",
+    )
+    return strategy
diff --git a/python/tvm/relay/op/strategy/generic.py b/python/tvm/relay/op/strategy/generic.py
index ea572ba05cd1..04f25640574a 100644
--- a/python/tvm/relay/op/strategy/generic.py
+++ b/python/tvm/relay/op/strategy/generic.py
@@ -35,7 +35,7 @@ def naive_schedule(_, outs, target):
     if "gpu" in target.keys:
         # For GPU, we at least need thread binding to make a valid schedule.
         # So the naive schedule cannot be compiled.
-        raise RuntimeError(
+        logger.debug(
             "Cannot compile for GPU targets if no tuned schedule is found. "
             "Please see the warning messages above for more information about the failed workloads."
         )
@@ -731,6 +731,19 @@ def dense_strategy(attrs, inputs, out_type, target):
     return strategy
 
 
+@override_native_generic_func("dense_pack_strategy")
+def dense_pack_strategy(attrs, inputs, out_type, target):
+    """dense_pack generic strategy"""
+    logger.warning("dense_pack is not optimized for this platform.")
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_dense(topi.nn.dense_pack),
+        wrap_topi_schedule(topi.generic.schedule_dense),
+        name="dense_pack.generic",
+    )
+    return strategy
+
+
 # batch_matmul
 def wrap_compute_batch_matmul(topi_compute, need_auto_scheduler_layout=False):
     """wrap batch_matmul topi compute"""
@@ -786,6 +799,29 @@ def sparse_dense_padded_strategy(attrs, inputs, out_type, target):
     raise NotImplementedError("sparse_dense_padded is only implemented for cuda")
 
 
+# sparse_add
+def wrap_compute_sparse_add(topi_compute):
+    """wrap sparse add topi compute"""
+
+    def _compute_sparse_add(attrs, inputs, out_type):
+        return [topi_compute(inputs[0], inputs[1], inputs[2], inputs[3])]
+
+    return _compute_sparse_add
+
+
+@override_native_generic_func("sparse_add_strategy")
+def sparse_add_strategy(attrs, inputs, out_type, target):
+    """sparse add generic strategy"""
+    logger.warning("sparse add is not optimized for this platform.")
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_sparse_add(topi.nn.sparse_add),
+        wrap_topi_schedule(topi.generic.schedule_extern),
+        name="sparse_add.generic",
+    )
+    return strategy
+
+
 # sparse_transpose
 @generic_func
 def schedule_sparse_transpose(attrs, outs, target):
@@ -1026,8 +1062,8 @@ def wrap_compute_roi_align(topi_compute):
     """wrap roi_align topi compute"""
 
     def _compute_roi_align(attrs, inputs, out_type):
-        assert attrs.layout == "NCHW"
         pooled_size = get_const_tuple(attrs.pooled_size)
+        mode = bytes(attrs.mode, "utf-8")
         return [
             topi_compute(
                 inputs[0],
@@ -1035,6 +1071,7 @@ def _compute_roi_align(attrs, inputs, out_type):
                 pooled_size=pooled_size,
                 spatial_scale=attrs.spatial_scale,
                 sample_ratio=attrs.sample_ratio,
+                mode=mode,
             )
         ]
 
@@ -1046,15 +1083,78 @@ def roi_align_strategy(attrs, inputs, out_type, target):
     """roi_align generic strategy"""
     strategy = _op.OpStrategy()
     layout = attrs.layout
-    assert layout == "NCHW", "only support nchw for now"
+    if layout == "NCHW":
+        strategy.add_implementation(
+            wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
+            wrap_topi_schedule(topi.generic.schedule_roi_align),
+            name="roi_align.generic",
+        )
+    else:
+        assert layout == "NHWC", "layout must be NCHW or NHWC."
+        strategy.add_implementation(
+            wrap_compute_roi_align(topi.vision.rcnn.roi_align_nhwc),
+            wrap_topi_schedule(topi.generic.schedule_roi_align),
+            name="roi_align.generic",
+        )
+    return strategy
+
+
+# sparse_fill_empty_rows
+@override_native_generic_func("sparse_fill_empty_rows_strategy")
+def sparse_fill_empty_rows_strategy(attrs, outs, out_type, target):
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_sparse_fill_empty_rows(topi.sparse_fill_empty_rows),
+        wrap_topi_schedule(topi.generic.schedule_sparse_fill_empty_rows),
+        name="sparse_fill_empty_rows.generic",
+    )
+    return strategy
+
+
+def wrap_compute_sparse_fill_empty_rows(topi_compute):
+    """Wrap sparse_fill_empty_rows compute"""
+
+    def _compute_sparse_fill_empty_rows(attrs, inputs, output_type):
+        return topi_compute(
+            inputs[0],
+            inputs[1],
+            inputs[2],
+            inputs[3],
+            output_type.fields[0].shape,
+            output_type.fields[1].shape,
+            output_type.fields[2].shape,
+        )
+
+    return _compute_sparse_fill_empty_rows
+
+
+# sparse_reshape
+@override_native_generic_func("sparse_reshape_strategy")
+def sparse_reshape_strategy(attrs, outs, out_type, target):
+    strategy = _op.OpStrategy()
     strategy.add_implementation(
-        wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
-        wrap_topi_schedule(topi.generic.schedule_roi_align),
-        name="roi_align.generic",
+        wrap_compute_sparse_reshape(topi.sparse_reshape),
+        wrap_topi_schedule(topi.generic.schedule_extern),
+        name="sparse_reshape.generic",
     )
     return strategy
 
 
+def wrap_compute_sparse_reshape(topi_compute):
+    """Wrap sparse_reshape compute"""
+
+    def _compute_sparse_reshape(attrs, inputs, output_type):
+        return topi_compute(
+            inputs[0],
+            inputs[1],
+            inputs[2],
+            output_type.fields[0].shape,
+            output_type.fields[1].shape,
+        )
+
+    return _compute_sparse_reshape
+
+
 # roi_pool
 @generic_func
 def schedule_roi_pool(attrs, outs, target):
@@ -1123,7 +1223,7 @@ def wrap_compute_scatter(topi_compute):
     """Wrap scatter topi compute"""
 
     def _compute_scatter(attrs, inputs, _):
-        return [topi_compute(inputs[0], inputs[1], inputs[2], axis=attrs.axis)]
+        return [topi_compute(inputs[0], inputs[1], inputs[2], attrs.axis)]
 
     return _compute_scatter
 
@@ -1317,3 +1417,89 @@ def argwhere_strategy(attrs, inputs, out_type, target):
         name="argwhere.generic",
     )
     return strategy
+
+
+# threefry_generate
+def wrap_compute_threefry_generate(topi_compute):
+    """Wrap threefry_generate topi compute"""
+
+    def _compute_threefry_generate(attrs, inputs, _):
+        return topi_compute(inputs[0], attrs.out_shape)
+
+    return _compute_threefry_generate
+
+
+@override_native_generic_func("threefry_generate_strategy")
+def threefry_generate_strategy(attrs, inputs, out_type, target):
+    """threefry_generate generic strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_threefry_generate(topi.random.threefry_generate),
+        wrap_topi_schedule(topi.generic.schedule_extern),
+        name="threefry_generate.generic",
+    )
+    return strategy
+
+
+# threefry_split
+def wrap_compute_threefry_split(topi_compute):
+    """Wrap threefry_split topi compute"""
+
+    def _compute_threefry_split(attrs, inputs, _):
+        return topi_compute(inputs[0])
+
+    return _compute_threefry_split
+
+
+@override_native_generic_func("threefry_split_strategy")
+def threefry_split_strategy(attrs, inputs, out_type, target):
+    """threefry_split generic strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_threefry_split(topi.random.threefry_split),
+        wrap_topi_schedule(topi.generic.schedule_extern),
+        name="threefry_split.generic",
+    )
+    return strategy
+
+
+def wrap_compute_cumsum(topi_compute):
+    """Wrap cumsum topi compute"""
+
+    def _compute_cumsum(attrs, inputs, _):
+        return [topi_compute(inputs[0], attrs.axis, attrs.dtype, attrs.exclusive)]
+
+    return _compute_cumsum
+
+
+@override_native_generic_func("cumsum_strategy")
+def cumsum_strategy(attrs, inputs, out_type, target):
+    """cumsum generic strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_cumsum(topi.cumsum),
+        wrap_topi_schedule(topi.generic.schedule_extern),
+        name="cumsum.generic",
+    )
+    return strategy
+
+
+def wrap_compute_unique(topi_compute):
+    """Wrap unique topi compute"""
+
+    def _compute_unique(attrs, inputs, _):
+        return topi_compute(inputs[0], attrs.sorted, attrs.return_counts)
+
+    return _compute_unique
+
+
+@override_native_generic_func("unique_strategy")
+def unique_strategy(attrs, inputs, out_type, target):
+    """unique generic strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_unique(topi.unique),
+        wrap_topi_schedule(topi.generic.schedule_unique),
+        name="unique.generic",
+    )
+    return strategy
diff --git a/python/tvm/relay/op/strategy/mali.py b/python/tvm/relay/op/strategy/mali.py
index c4cb4a135e8e..fc47bd65a8f7 100644
--- a/python/tvm/relay/op/strategy/mali.py
+++ b/python/tvm/relay/op/strategy/mali.py
@@ -171,9 +171,16 @@ def conv2d_winograd_without_weight_transfrom_strategy_mali(attrs, inputs, out_ty
 def dense_strategy_mali(attrs, inputs, out_type, target):
     """dense mali strategy"""
     strategy = _op.OpStrategy()
-    strategy.add_implementation(
-        wrap_compute_dense(topi.mali.dense),
-        wrap_topi_schedule(topi.mali.schedule_dense),
-        name="dense.mali",
-    )
+    if not is_auto_scheduler_enabled():
+        strategy.add_implementation(
+            wrap_compute_dense(topi.mali.dense),
+            wrap_topi_schedule(topi.mali.schedule_dense),
+            name="dense.mali",
+        )
+    else:
+        strategy.add_implementation(
+            wrap_compute_dense(topi.nn.dense, need_auto_scheduler_layout=True),
+            naive_schedule,
+            name="dense.mali",
+        )
     return strategy
diff --git a/python/tvm/relay/op/strategy/rocm.py b/python/tvm/relay/op/strategy/rocm.py
index c52da541a8ab..f4538071e11e 100644
--- a/python/tvm/relay/op/strategy/rocm.py
+++ b/python/tvm/relay/op/strategy/rocm.py
@@ -18,6 +18,9 @@
 # pylint: disable=invalid-name,unused-argument,unused-wildcard-import,wildcard-import
 from tvm import topi
 from tvm.auto_scheduler import is_auto_scheduler_enabled
+from tvm.te import SpecializedCondition
+from tvm.contrib.thrust import can_use_rocthrust
+
 from .generic import *
 from .. import op as _op
 from .cuda import judge_winograd, naive_schedule
@@ -219,3 +222,85 @@ def batch_matmul_strategy_rocm(attrs, inputs, out_type, target):
             plevel=12,
         )
     return strategy
+
+
+@argsort_strategy.register(["rocm"])
+def argsort_strategy_cuda(attrs, inputs, out_type, target):
+    """argsort rocm strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_argsort(topi.cuda.argsort),
+        wrap_topi_schedule(topi.cuda.schedule_argsort),
+        name="argsort.rocm",
+    )
+    if can_use_rocthrust(target, "tvm.contrib.thrust.sort"):
+        strategy.add_implementation(
+            wrap_compute_argsort(topi.cuda.argsort_thrust),
+            wrap_topi_schedule(topi.cuda.schedule_argsort),
+            name="argsort_thrust.rocm",
+            plevel=15,
+        )
+    return strategy
+
+
+@scatter_strategy.register(["rocm"])
+def scatter_cuda(attrs, inputs, out_type, target):
+    """scatter rocm strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_scatter(topi.cuda.scatter),
+        wrap_topi_schedule(topi.cuda.schedule_scatter),
+        name="scatter.rocm",
+        plevel=10,
+    )
+
+    rank = len(inputs[0].shape)
+
+    with SpecializedCondition(rank == 1):
+        if can_use_rocthrust(target, "tvm.contrib.thrust.stable_sort_by_key"):
+            strategy.add_implementation(
+                wrap_compute_scatter(topi.cuda.scatter_via_sort),
+                wrap_topi_schedule(topi.cuda.schedule_scatter_via_sort),
+                name="scatter_via_sort.rocm",
+                plevel=9,  # use the sequential version by default
+            )
+    return strategy
+
+
+@sort_strategy.register(["rocm"])
+def sort_strategy_cuda(attrs, inputs, out_type, target):
+    """sort rocm strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_sort(topi.cuda.sort),
+        wrap_topi_schedule(topi.cuda.schedule_sort),
+        name="sort.rocm",
+    )
+    if can_use_rocthrust(target, "tvm.contrib.thrust.sort"):
+        strategy.add_implementation(
+            wrap_compute_sort(topi.cuda.sort_thrust),
+            wrap_topi_schedule(topi.cuda.schedule_sort),
+            name="sort_thrust.cuda",
+            plevel=15,
+        )
+    return strategy
+
+
+@topk_strategy.register(["rocm"])
+def topk_strategy_cuda(attrs, inputs, out_type, target):
+    """topk rocm strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_topk(topi.cuda.topk),
+        wrap_topi_schedule(topi.cuda.schedule_topk),
+        name="topk.rocm",
+    )
+
+    if can_use_rocthrust(target, "tvm.contrib.thrust.sort"):
+        strategy.add_implementation(
+            wrap_compute_topk(topi.cuda.topk_thrust),
+            wrap_topi_schedule(topi.cuda.schedule_topk),
+            name="topk_thrust.rocm",
+            plevel=15,
+        )
+    return strategy
diff --git a/python/tvm/relay/op/strategy/x86.py b/python/tvm/relay/op/strategy/x86.py
index 9e3e191b2f2b..1f37a4f8e98c 100644
--- a/python/tvm/relay/op/strategy/x86.py
+++ b/python/tvm/relay/op/strategy/x86.py
@@ -304,7 +304,7 @@ def conv3d_strategy_cpu(attrs, inputs, out_type, target):
         # or packed layouts.
         if layout == "NCDHW":
             strategy.add_implementation(
-                wrap_compute_conv3d(topi.nn.conv3d_ncdhw, need_auto_scheduler_layout=True),
+                wrap_compute_conv3d(topi.nn.conv3d_ncdhw),
                 naive_schedule,
                 name="conv3d_ncdhw.x86",
             )
@@ -364,7 +364,6 @@ def conv1d_strategy_cpu(attrs, inputs, out_type, target):
 def dense_strategy_cpu(attrs, inputs, out_type, target):
     """dense x86 strategy"""
     strategy = _op.OpStrategy()
-    m, _ = inputs[0].shape
     same_type = inputs[0].dtype == inputs[1].dtype == out_type.dtype
     dtype = inputs[0].dtype
     u8s8s32 = dtype == "uint8" and inputs[1].dtype == "int8" and out_type.dtype == "int32"
@@ -372,6 +371,13 @@ def dense_strategy_cpu(attrs, inputs, out_type, target):
         wrap_compute_dense(topi.x86.dense_nopack),
         wrap_topi_schedule(topi.x86.schedule_dense_nopack),
         name="dense_nopack.x86",
+        plevel=5,
+    )
+
+    strategy.add_implementation(
+        wrap_compute_dense(topi.x86.dense_pack),
+        wrap_topi_schedule(topi.x86.schedule_dense_pack),
+        name="dense_pack.x86",
         plevel=10,
     )
 
@@ -407,14 +413,18 @@ def dense_strategy_cpu(attrs, inputs, out_type, target):
                 name="dense_mkldnn.x86",
                 plevel=15,
             )
-    with SpecializedCondition(m >= 16):
-        # this implementation may not be well-optimized, so use plevel=5 for now.
-        strategy.add_implementation(
-            wrap_compute_dense(topi.x86.dense_pack),
-            wrap_topi_schedule(topi.x86.schedule_dense_pack),
-            name="dense_pack.x86",
-            plevel=5,
-        )
+    return strategy
+
+
+@dense_pack_strategy.register("cpu")
+def dense_pack_strategy_cpu(attrs, inputs, out_type, target):
+    """dense_pack x86 strategy"""
+    strategy = _op.OpStrategy()
+    strategy.add_implementation(
+        wrap_compute_dense(topi.x86.dense_pack),
+        wrap_topi_schedule(topi.x86.schedule_dense_pack),
+        name="dense_pack.x86",
+    )
     return strategy
 
 
@@ -471,12 +481,19 @@ def roi_align_strategy_cpu(attrs, inputs, out_type, target):
     """roi_align x86 strategy"""
     strategy = _op.OpStrategy()
     layout = attrs.layout
-    assert layout == "NCHW", "only support nchw for now"
-    strategy.add_implementation(
-        wrap_compute_roi_align(topi.x86.roi_align_nchw),
-        wrap_topi_schedule(topi.generic.schedule_roi_align),
-        name="roi_align.x86",
-    )
+    if layout == "NCHW":
+        strategy.add_implementation(
+            wrap_compute_roi_align(topi.x86.roi_align_nchw),
+            wrap_topi_schedule(topi.generic.schedule_roi_align),
+            name="roi_align.x86",
+        )
+    else:
+        assert layout == "NHWC", "layout must be NCHW or NHWC."
+        strategy.add_implementation(
+            wrap_compute_roi_align(topi.vision.rcnn.roi_align_nhwc),
+            wrap_topi_schedule(topi.generic.schedule_roi_align),
+            name="roi_align.x86",
+        )
     return strategy
 
 
diff --git a/python/tvm/relay/op/tensor.py b/python/tvm/relay/op/tensor.py
index 453a9b7a7759..5b011043f588 100644
--- a/python/tvm/relay/op/tensor.py
+++ b/python/tvm/relay/op/tensor.py
@@ -22,7 +22,7 @@
 
 from . import _make
 from .dyn import _make as _dyn_make
-from ..expr import Tuple, Expr
+from ..expr import Tuple, Expr, Constant
 from . import op as reg
 
 
@@ -960,6 +960,8 @@ def zeros(shape, dtype):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.zeros(shape, dtype)
     if isinstance(shape, int):
@@ -1001,6 +1003,8 @@ def ones(shape, dtype):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.ones(shape, dtype)
     if isinstance(shape, int):
@@ -1105,8 +1109,8 @@ def stack(data, axis):
 
     Parameters
     ----------
-    data : Union(List[relay.Expr], Tuple(relay.Expr))
-        A list of tensors.
+    data : Union(List[relay.Expr], relay.Expr)
+        A list of tensors or a Relay expression that evaluates to a tuple of tensors.
 
     axis : int
         The axis in the result array along which the input arrays are stacked.
@@ -1116,12 +1120,13 @@ def stack(data, axis):
     ret : relay.Expr
         The stacked tensor.
     """
-    data = list(data)
     if not data:
         raise ValueError("relay.stack requires data to be non-empty.")
     if not isinstance(axis, int):
         raise ValueError("For now, we only support integer axis")
-    return _make.stack(Tuple(data), axis)
+    if not isinstance(data, Expr):
+        data = Tuple(list(data))
+    return _make.stack(data, axis)
 
 
 def copy(data):
diff --git a/python/tvm/relay/op/transform.py b/python/tvm/relay/op/transform.py
index 7e7f9b299593..4129b610cb7c 100644
--- a/python/tvm/relay/op/transform.py
+++ b/python/tvm/relay/op/transform.py
@@ -21,7 +21,7 @@
 from . import _make
 from .dyn import _make as _dyn_make
 from .tensor import shape_of
-from ..expr import TupleWrapper, const, Expr, Tuple
+from ..expr import TupleWrapper, const, Constant, Expr, Tuple
 from ...tir import expr as _expr
 
 
@@ -86,7 +86,7 @@ def reinterpret(data, dtype):
 
 
 def expand_dims(data, axis, num_newaxis=1):
-    """Insert `num_newaxis` axises at the position given by `axis`.
+    """Insert `num_newaxis` axes at the position given by `axis`.
 
     Parameters
     ----------
@@ -216,6 +216,8 @@ def reshape(data, newshape):
     result : relay.Expr
         The reshaped result.
     """
+    if isinstance(newshape, Constant):
+        newshape = list(newshape.data.asnumpy())
     if isinstance(newshape, Expr):
         return _dyn_make.reshape(data, newshape)
     if isinstance(newshape, int):
@@ -321,7 +323,7 @@ def scatter_nd(data, indices, out_shape):
     indices : relay.Expr
         The index locations to update.
 
-    out_shape : relay.Expr
+    out_shape : Union[Tuple[int], List[int]]
         Output shape of the scatter.
 
     Returns
@@ -431,6 +433,8 @@ def full(fill_value, shape=(), dtype=""):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.full(fill_value, shape, dtype)
     if isinstance(shape, int):
@@ -614,6 +618,8 @@ def tile(data, reps):
     data is promoted to be d-dimensional by prepending new axes.
     If data.ndim >=  d, reps is promoted to a.ndim by pre-pending 1's to it.
     """
+    if isinstance(reps, Constant):
+        reps = list(reps.data.asnumpy())
     if isinstance(reps, Expr):
         return _dyn_make.tile(data, reps)
     return _make.tile(data, reps)
@@ -753,6 +759,8 @@ def broadcast_to(data, shape):
     result : relay.Expr
         The resulting tensor.
     """
+    if isinstance(shape, Constant):
+        shape = list(shape.data.asnumpy())
     if isinstance(shape, Expr):
         return _dyn_make.broadcast_to(data, shape)
     if isinstance(shape, int):
@@ -884,6 +892,12 @@ def strided_slice(data, begin, end, strides=None, slice_mode="end"):
         The computed result.
     """
     strides = strides or [1]
+    if isinstance(begin, Constant):
+        begin = list(begin.data.asnumpy())
+    if isinstance(end, Constant):
+        end = list(end.data.asnumpy())
+    if isinstance(strides, Constant):
+        strides = list(strides.data.asnumpy())
     if isinstance(begin, Expr) or isinstance(end, Expr) or isinstance(strides, Expr):
         if isinstance(begin, (tuple, list)):
             begin = const(list(begin))
@@ -1033,7 +1047,7 @@ def gather(data, axis, indices):
         The input data to the operator.
 
     axis: int
-        The axis along which to index.
+        The axis along which to index. negative axis is supported.
 
     indices: relay.Expr
         The indices of values to gather.
@@ -1170,6 +1184,8 @@ def one_hot(indices, on_value, off_value, depth, axis, dtype):
              [0, 1, 0],
              [0, 0, 1]]
     """
+    if isinstance(depth, Constant):
+        depth = depth.data.asnumpy().item()
     if isinstance(depth, Expr):
         return _dyn_make.one_hot(indices, on_value, off_value, depth, axis, dtype)
     return _make.one_hot(indices, on_value, off_value, depth, axis, dtype)
@@ -1320,3 +1336,293 @@ def adv_index(inputs):
         Output tensor.
     """
     return _make.adv_index(Tuple(inputs))
+
+
+def sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape, default_value):
+    """
+    Fill rows in a sparse matrix that do no contain any values. Values are placed in the first
+    column of empty rows. The sparse array is in COO format.
+    It returns a TupleWrapper with 3 outputs
+    Parameters
+    ----------
+    sparse_indices : relay.Expr
+        A 2-D tensor[N, ndims] of integers containing location of sparse values, where N is
+        the number of sparse values and n_dim is the number of dimensions of the dense_shape.
+        The first column of this relay parameter must be sorted in ascending order.
+    sparse_values : relay.Expr
+        A 1-D tensor[N] containing the sparse values for the sparse indices.
+    dense_shape : relay.Expr
+        A 1-D tensor[ndims] which contains shape of the dense output tensor.
+    default_value : relay.Expr
+        A 1-D tensor[1] containing the default value for the remaining locations.
+    Returns
+    -------
+    new_sparse_indices : relay.Expr
+        A 2-D tensor[?, ndims] of integers containing location of new sparse
+        indices. The first column outputs must be sorted in ascending order.
+    new_sparse_values : relay.Expr
+        A 1-D tensor[?] containing the sparse values for the sparse indices.
+    empty_row_indicator : relay.Expr
+        A 1-D tensor[dense_shape[0]] filled with zeros and ones
+        indicating whether the particular row is empty or full respectively
+
+    Note
+    ----
+    This op exactly follows the documentation here:
+    https://www.tensorflow.org/api_docs/python/tf/sparse/fill_empty_rows
+    There are two exceptions:
+    1. Input Sparse Indices are expected to be in row-major order.
+    2. Empty Row Indicator has int64 output type with 1(for True) and 0(for False).
+
+    Examples
+    -------
+    .. code-block:: python
+        sparse_indices = [[0, 1],
+                         [0, 3],
+                         [2, 0],
+                         [3, 1]]
+        sparse_values = [1, 2, 3, 4]
+        default_value = [10]
+        dense_shape = [5, 6]
+        new_sparse_indices, empty_row_indicator, new_sparse_values, slice_element_index =
+                            relay.sparse_fill_empty_rows(
+                            sparse_indices,
+                            sparse_values,
+                            default_value,
+                            dense_shape)
+        new_sparse_indices = [[0, 1],
+                             [0, 3],
+                             [1, 0],
+                             [2, 0],
+                             [3, 1],
+                             [4, 0]]
+        empty_row_indicator = [False, True, False, False, True]
+        new_sparse_values = [1, 2, 10, 3, 4, 10]
+
+    """
+    new_sparse_indices, new_sparse_values, empty_row_indicator = TupleWrapper(
+        _make.sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape, default_value), 3
+    )
+    new_sparse_indices = cast_like(new_sparse_indices, sparse_indices)
+    new_sparse_values = cast_like(new_sparse_values, sparse_values)
+    empty_row_indicator = cast(empty_row_indicator, "bool")
+
+    return Tuple((new_sparse_indices, new_sparse_values, empty_row_indicator))
+
+
+def sparse_reshape(sparse_indices, prev_shape, new_shape):
+    """
+    Reshape a Sparse Tensor. The sparse array is in COO format.
+
+    Parameters
+    ----------
+    sparse_indices : relay.Expr
+        A 2-D tensor[N, n_dim] of integers containing location of sparse values, where N is the
+        number of sparse values and n_dim is the number of dimensions of the dense_shape
+    prev_shape : relay.Expr
+        A 1-D tensor containing the previous shape of the dense tensor
+    new_shape : relay.Expr
+        A 1-D tensor containing the new shape of the dense tensor
+    Returns
+    -------
+    result: relay.Expr
+        Output tensor.
+    Examples
+    --------
+    .. code-block:: python
+        sparse_indices = [[0, 0, 0],
+                            [0, 0, 1],
+                            [0, 1, 0],
+                            [1, 0, 0],
+                            [1, 2, 3]]
+        prev_shape = [2, 3, 4]
+        new_shape = [9, -1]
+        new_sparse_indices, new_shape = relay.sparse_reshape(sparse_indices,
+                            prev_shape,
+                            new_shape)
+        new_sparse_indices = [[0, 0],
+                              [0, 1],
+                              [1, 2],
+                              [4, 2],
+                              [8, 1]]
+        new_shape = [9, 4]
+    """
+    return TupleWrapper(_make.sparse_reshape(sparse_indices, prev_shape, new_shape), 2)
+
+
+def segment_sum(data, segment_ids, num_segments=None):
+    """
+    Computes the sum along segment_ids along axis 0. If multiple segment_ids reference the same
+    location their contributions add up.
+    result[index, j, k, ...] = Σi... data[i, j, k,..] where index = segment_ids[i]
+    This op is much better understood with visualization articulated in the following links and
+    examples at the end of this docstring.
+
+    https://www.tensorflow.org/api_docs/python/tf/math/unsorted_segment_sum
+    https://caffe2.ai/docs/sparse-operations.html#null__unsorted-segment-reduction-ops
+
+    Parameters
+    ----------
+    data : relay.Expr
+        Input Tensor. It can be of any type and multi-dimensional
+    segment_ids : relay.Expr
+        A 1-D int32/int64 tensor containing the segment_ids of the rows to calculate the output
+        sum upon. It defines a mapping from the zeroth dimension of data onto segment_ids. The
+        segment_ids tensor should be the size of the first dimension, d0, with consecutive IDs
+        in the range 0 to k, where k<d0. In particular, a segmentation of a matrix tensor is a
+        mapping of rows to segments. This tensor doesn't need to be sorted
+    num_segments : Optional[int]
+        An integer describing the shape of the zeroth dimension. If unspecified, its calculated
+        equivalent to the number of unique segment_ids
+    Returns
+    -------
+    result: relay.Expr
+        Output tensor.
+    Examples
+    --------
+    .. code-block:: python
+        data = [[1, 2, 3, 4],
+                [4, -3, 2, -1],
+                [5, 6, 7, 8]]
+        segment_ids = [0, 0, 1]
+        result = segment_sum(data, segment_ids)
+        result = [[5, -1, 5, 3],[5, 6, 7, 8]]
+
+        data = [[1, 2, 3, 4],
+                [4, -3, 2, -1],
+                [5, 6, 7, 8]]
+        segment_ids = [2, 0, 0]
+        num_segments = 3
+        result = segment_sum(data, segment_ids, num_segments)
+        result = [[5, 6, 7, 8],[0, 0, 0, 0], [5, -1, 5, 3]]
+    """
+
+    one_tensor = cast_like(const([1]), segment_ids)
+    if num_segments:
+        if isinstance(num_segments, int):
+            max_segments = const([num_segments])
+            max_segments = cast_like(max_segments, segment_ids)
+        else:
+            max_segments = cast_like(num_segments, segment_ids)
+    else:
+        max_segments = _make.add(reshape(_make.max(segment_ids, [0], False, False), -1), one_tensor)
+
+    data_offrow_shape = strided_slice(_make.shape_of(data, "int32"), [1], [-1], slice_mode="size")
+    data_offrow_shape = cast_like(data_offrow_shape, max_segments)
+    new_shape = _make.concatenate(Tuple([max_segments, data_offrow_shape]), 0)
+    segment_ids_tiled_shape = _make.concatenate(
+        Tuple([reverse(data_offrow_shape, 0), one_tensor]), 0
+    )
+    expanded_segment_ids = tile(segment_ids, segment_ids_tiled_shape)
+    scatter_add_segment_ids = transpose(expanded_segment_ids)
+    src = cast_like(_dyn_make.zeros(new_shape, "float64"), data)
+    return scatter_add(src, scatter_add_segment_ids, data, axis=0)
+
+
+def cumsum(data, axis=None, dtype=None, exclusive=None):
+    """Numpy style cumsum op. Return the cumulative inclusive sum of the elements along
+    a given axis.
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The input data to the operator.
+
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default (None) is to compute
+        the cumsum over the flattened array.
+
+    dtype : string, optional
+        Type of the returned array and of the accumulator in which the elements are summed.
+        If dtype is not specified, it defaults to the dtype of data.
+
+    exclusive : int, optional
+        If set to 1 will return exclusive sum in which the first element is not
+        included. In other terms, if set to 1, the j-th output element would be
+        the sum of the first (j-1) elements. Otherwise, it would be the sum of
+        the first j elements.
+
+    Returns
+    -------
+    result : relay.Expr
+        The result has the same size as data, and the same shape as data if axis is not None.
+        If axis is None, the result is a 1-d array.
+
+    Examples
+    --------
+    .. code-block:: python
+        a = [[1,2,3], [4,5,6]]
+
+        cumsum(a)  # if axis is not provided, cumsum is done over the flattened input.
+        -> [ 1,  3,  6, 10, 15, 21]
+
+        cumsum(a, dtype="float32")
+        -> [  1.,   3.,   6.,  10.,  15.,  21.]
+
+        cumsum(a, axis=0)  # sum over rows for each of the 3 columns
+        -> [[1, 2, 3],
+            [5, 7, 9]]
+
+        cumsum(a, axis=1)
+        -> [[ 1,  3,  6],
+            [ 4,  9, 15]]
+
+        a = [1, 0, 1, 0, 1, 1, 0]  # a is a boolean array
+        cumsum(a, dtype=int32)  # dtype should be provided to get the expected results
+        -> [1, 1, 2, 2, 3, 4, 4]
+    """
+    return _make.cumsum(data, axis, dtype, exclusive)
+
+
+def unique(data, is_sorted=True, return_counts=False):
+    """
+    Find the unique elements of a 1-D tensor. Please note `output` and `counts` are all padded to
+    have the same length of `data` and element with index >= num_unique[0] has undefined value.
+
+    Parameters
+    ----------
+    data : relay.Expr
+        A 1-D tensor of integers.
+
+    sorted : bool
+        Whether to sort the unique elements in ascending order before returning as output.
+
+    return_counts : bool
+        Whether to return the count of each unique element.
+
+    Returns
+    -------
+    output : relay.Expr
+        A 1-D tensor containing the unique elements of the input data tensor.
+
+    indices : relay.Expr
+        A 1-D tensor containing the index of each data element in the output tensor.
+
+    num_unique : relay.Expr
+        A 1-D tensor with size=1 containing the number of unique elements in the input data tensor.
+
+    counts (optional) : relay.Expr
+        A 1-D tensor containing the count of each unique element in the output.
+
+    Examples
+    --------
+    .. code-block:: python
+        [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, False)
+        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
+        num_unique     =  [5]
+
+        [output, indices, num_unique, counts] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, True)
+        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
+        num_unique     =  [5]
+        counts         =  [2, 2, 1, 1, 2, ?, ?, ?]
+
+        [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], True)
+        output         =  [1, 2, 3, 4, 5, ?, ?, ?]
+        indices        =  [3, 4, 0, 1, 2, 2, 3, 4]
+        num_unique     =  [5]
+    """
+    if return_counts:
+        return TupleWrapper(_make.unique(data, is_sorted, return_counts), 4)
+    return TupleWrapper(_make.unique(data, is_sorted, return_counts), 3)
diff --git a/python/tvm/relay/op/vision/_vision.py b/python/tvm/relay/op/vision/_vision.py
index 04676e24adf6..9c8c853fa3d2 100644
--- a/python/tvm/relay/op/vision/_vision.py
+++ b/python/tvm/relay/op/vision/_vision.py
@@ -86,7 +86,7 @@ def nms_shape_func(attrs, inputs, _):
 
 
 @script
-def _roi_align_shape_func(data_shape, rois_shape, pooled_size):
+def _roi_align_shape_func_nchw(data_shape, rois_shape, pooled_size):
     out = output_tensor((4,), "int64")
     out[0] = rois_shape[0]
     out[1] = data_shape[1]
@@ -95,6 +95,19 @@ def _roi_align_shape_func(data_shape, rois_shape, pooled_size):
     return out
 
 
+@script
+def _roi_align_shape_func_nhwc(data_shape, rois_shape, pooled_size):
+    out = output_tensor((4,), "int64")
+    out[0] = rois_shape[0]
+    out[1] = int64(pooled_size[0])
+    out[2] = int64(pooled_size[1])
+    out[3] = data_shape[3]
+    return out
+
+
 @reg.register_shape_func("vision.roi_align", False)
 def roi_align_shape_func(attrs, inputs, _):
-    return [_roi_align_shape_func(inputs[0], inputs[1], convert(attrs.pooled_size))]
+    if attrs.layout == "NCHW":
+        return [_roi_align_shape_func_nchw(inputs[0], inputs[1], convert(attrs.pooled_size))]
+    assert attrs.layout == "NHWC", "layout must be NCHW or NHWC."
+    return [_roi_align_shape_func_nhwc(inputs[0], inputs[1], convert(attrs.pooled_size))]
diff --git a/python/tvm/relay/op/vision/rcnn.py b/python/tvm/relay/op/vision/rcnn.py
index b87eb07d7563..d25c5de89cee 100644
--- a/python/tvm/relay/op/vision/rcnn.py
+++ b/python/tvm/relay/op/vision/rcnn.py
@@ -18,7 +18,7 @@
 from . import _make
 
 
-def roi_align(data, rois, pooled_size, spatial_scale, sample_ratio=-1, layout="NCHW"):
+def roi_align(data, rois, pooled_size, spatial_scale, sample_ratio=-1, layout="NCHW", mode="avg"):
     """ROI align operator.
 
     Parameters
@@ -40,12 +40,15 @@ def roi_align(data, rois, pooled_size, spatial_scale, sample_ratio=-1, layout="N
     sample_ratio : int
         Optional sampling ratio of ROI align, using adaptive size by default.
 
+    mode : str, Optional
+        The pooling method. Relay supports two methods, 'avg' and 'max'. Default is 'avg'.
+
     Returns
     -------
     output : relay.Expr
         4-D tensor with shape [num_roi, channel, pooled_size, pooled_size]
     """
-    return _make.roi_align(data, rois, pooled_size, spatial_scale, sample_ratio, layout)
+    return _make.roi_align(data, rois, pooled_size, spatial_scale, sample_ratio, layout, mode)
 
 
 def roi_pool(data, rois, pooled_size, spatial_scale, layout="NCHW"):
diff --git a/python/tvm/relay/param_dict.py b/python/tvm/relay/param_dict.py
index 2d0398e20486..2714607947f3 100644
--- a/python/tvm/relay/param_dict.py
+++ b/python/tvm/relay/param_dict.py
@@ -16,12 +16,7 @@
 # under the License.
 # pylint: disable=invalid-name
 """Helper utility to save parameter dicts."""
-import tvm
-import tvm._ffi
-
-
-_save_param_dict = tvm._ffi.get_global_func("tvm.relay._save_param_dict")
-_load_param_dict = tvm._ffi.get_global_func("tvm.relay._load_param_dict")
+import tvm.runtime
 
 
 def save_param_dict(params):
@@ -30,6 +25,9 @@ def save_param_dict(params):
     The result binary bytes can be loaded by the
     GraphModule with API "load_params".
 
+    .. deprecated:: 0.9.0
+        Use :py:func:`tvm.runtime.save_param_dict` instead.
+
     Parameters
     ----------
     params : dict of str to NDArray
@@ -47,21 +45,20 @@ def save_param_dict(params):
        # set up the parameter dict
        params = {"param0": arr0, "param1": arr1}
        # save the parameters as byte array
-       param_bytes = tvm.relay.save_param_dict(params)
+       param_bytes = tvm.runtime.save_param_dict(params)
        # We can serialize the param_bytes and load it back later.
        # Pass in byte array to module to directly set parameters
-       graph_runtime_mod.load_params(param_bytes)
+       tvm.runtime.load_param_dict(param_bytes)
     """
-    args = []
-    for k, v in params.items():
-        args.append(k)
-        args.append(tvm.nd.array(v))
-    return _save_param_dict(*args)
+    return tvm.runtime.save_param_dict(params)
 
 
 def load_param_dict(param_bytes):
     """Load parameter dictionary to binary bytes.
 
+    .. deprecated:: 0.9.0
+        Use :py:func:`tvm.runtime.load_param_dict` instead.
+
     Parameters
     ----------
     param_bytes: bytearray
@@ -72,7 +69,4 @@ def load_param_dict(param_bytes):
     params : dict of str to NDArray
         The parameter dictionary.
     """
-    if isinstance(param_bytes, (bytes, str)):
-        param_bytes = bytearray(param_bytes)
-    load_arr = _load_param_dict(param_bytes)
-    return {v.name: v.array for v in load_arr}
+    return tvm.runtime.load_param_dict(param_bytes)
diff --git a/python/tvm/relay/qnn/op/__init__.py b/python/tvm/relay/qnn/op/__init__.py
index 6d66e12eeafc..848409360a9d 100644
--- a/python/tvm/relay/qnn/op/__init__.py
+++ b/python/tvm/relay/qnn/op/__init__.py
@@ -19,4 +19,4 @@
 from __future__ import absolute_import as _abs
 from .qnn import *
 from .op import register_qnn_legalize
-from . import legalizations, layout_conversions
+from . import _qnn, legalizations, layout_conversions
diff --git a/python/tvm/relay/qnn/op/_qnn.py b/python/tvm/relay/qnn/op/_qnn.py
new file mode 100644
index 000000000000..a059c293a0f8
--- /dev/null
+++ b/python/tvm/relay/qnn/op/_qnn.py
@@ -0,0 +1,52 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, unused-argument, len-as-condition
+"""QNN operator feature registration"""
+
+from tvm import topi
+
+from ...op.op import register_compute
+from ...op.op import register_injective_schedule
+from ...op.op import register_pattern, OpPattern
+
+
+@register_compute("qnn.simulated_quantize")
+def simulated_quantize_compute(attrs, inputs, output_type):
+    assert len(inputs) == 4
+    return [
+        topi.nn.simulated_quantize(
+            inputs[0], inputs[1], inputs[2], inputs[3], axis=attrs.get_int("axis")
+        )
+    ]
+
+
+register_injective_schedule("qnn.simulated_quantize")
+register_pattern("qnn.simulated_quantize", OpPattern.ELEMWISE)
+
+
+@register_compute("qnn.simulated_dequantize")
+def simulated_dequantize_compute(attrs, inputs, output_type):
+    assert len(inputs) == 4
+    return [
+        topi.nn.simulated_dequantize(
+            inputs[0], inputs[1], inputs[2], inputs[3], axis=attrs.get_int("axis")
+        )
+    ]
+
+
+register_injective_schedule("qnn.simulated_dequantize")
+register_pattern("qnn.simulated_dequantize", OpPattern.ELEMWISE)
diff --git a/python/tvm/relay/qnn/op/qnn.py b/python/tvm/relay/qnn/op/qnn.py
index a5892f331f06..f02f8227e14a 100644
--- a/python/tvm/relay/qnn/op/qnn.py
+++ b/python/tvm/relay/qnn/op/qnn.py
@@ -18,8 +18,10 @@
 """QNN dialect operators."""
 
 from __future__ import absolute_import as _abs
+from tvm import relay
 from tvm.relay.expr import Tuple, TupleWrapper
 from tvm.relay.op.nn.utils import get_pad_tuple2d
+from tvm.topi.nn.qnn import SQNN_DTYPE_TO_CODE
 from . import _make
 from ... import op as reg
 from ...op import OpPattern
@@ -118,6 +120,40 @@ def quantize(data, output_scale, output_zero_point, axis=-1, out_dtype="int8"):
     return _make.quantize(data, output_scale, output_zero_point, axis, out_dtype)
 
 
+def simulated_quantize(data, output_scale, output_zero_point, axis=-1, out_dtype="int8"):
+    r"""Simulated Quantize op
+    Mimics the quantize op but has more flexibility in valid inputs and always
+    outputs the same type as the input. This can be useful for
+    calibrating or training a quantized network.
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input tensor to be quantized. Can be of type float32.
+    output_zero_point : tvm.relay.Expr
+        The output zero_point.
+    output_scale : tvm.relay.Expr
+        The output scale.
+    axis : int
+        The channel axis for quantization. Default value is -1 which corresponds to the last axis.
+    out_dtype : string or tvm.relay.Expr
+        A string or tensor indicating which datatype to quantize to.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    # Convert string dtype to a constant if needed.
+    if isinstance(out_dtype, str):
+        type_code = SQNN_DTYPE_TO_CODE[out_dtype]
+        out_dtype = relay.const(type_code, dtype="int32")
+    # Wrap reshapes around qnn parameter tensors to guarantee shape compatibility.
+    output_scale = relay.op.reshape(output_scale, [-1])
+    output_zero_point = relay.op.reshape(output_zero_point, [-1])
+    return _make.simulated_quantize(data, out_dtype, output_scale, output_zero_point, axis)
+
+
 def dequantize(data, input_scale, input_zero_point, axis=-1):
     r"""Dequantize op
     This operator takes quantized int8 and unit8 as input and produces
@@ -127,7 +163,7 @@ def dequantize(data, input_scale, input_zero_point, axis=-1):
     Parameters
     ----------
     data : tvm.relay.Expr
-        The input tensor to be dequantized. Can be of type [int8, uint8].
+        The input tensor to be dequantized. Can be of type [int8, uint8, int32].
     input_zero_point : tvm.relay.Expr
         The input zero_point.
     input_scale : tvm.relay.Expr
@@ -143,6 +179,40 @@ def dequantize(data, input_scale, input_zero_point, axis=-1):
     return _make.dequantize(data, input_scale, input_zero_point, axis)
 
 
+def simulated_dequantize(data, input_scale, input_zero_point, axis=-1, in_dtype="int8"):
+    r"""Simulated Dequantize op
+    Mimics the dequantize op but has more flexibility in valid inputs and always
+    outputs the same type as the input. This can be useful for calibrating or
+    training a quantized network.
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input tensor to be dequantized.
+    input_zero_point : tvm.relay.Expr
+        The input zero_point.
+    input_scale : tvm.relay.Expr
+        The input scale.
+    axis : int
+        The channel axis for quantization. Default value is -1 which corresponds to the last axis.
+    in_dtype : string or tvm.relay.Expr
+        A string or tensor indicating which datatype to dequantize from.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+    # Convert string dtype to a constant if needed.
+    if isinstance(in_dtype, str):
+        type_code = SQNN_DTYPE_TO_CODE[in_dtype]
+        in_dtype = relay.const(type_code, dtype="int32")
+    # Wrap reshapes around qnn parameter tensors to guarantee shape compatibility.
+    input_scale = relay.op.reshape(input_scale, [-1])
+    input_zero_point = relay.op.reshape(input_zero_point, [-1])
+    return _make.simulated_dequantize(data, in_dtype, input_scale, input_zero_point, axis)
+
+
 def concatenate(data, input_scales, input_zero_points, output_scale, output_zero_point, axis):
     """Concatenate the quantized input tensors along the given axis.
 
diff --git a/python/tvm/relay/testing/__init__.py b/python/tvm/relay/testing/__init__.py
index 0b81cb9c7ec6..f0c79bed1218 100644
--- a/python/tvm/relay/testing/__init__.py
+++ b/python/tvm/relay/testing/__init__.py
@@ -22,9 +22,9 @@
 
 import tvm
 from tvm import te
-import tvm.relay as relay
-import tvm.relay.op as op
-from tvm.relay import Prelude
+from tvm import relay
+from tvm.relay import op
+from tvm.relay.prelude import Prelude
 from tvm.testing import enabled_targets
 
 from . import mlp
diff --git a/python/tvm/relay/testing/darknet.py b/python/tvm/relay/testing/darknet.py
index c0468b7ef692..e1345043c6bb 100644
--- a/python/tvm/relay/testing/darknet.py
+++ b/python/tvm/relay/testing/darknet.py
@@ -31,7 +31,7 @@
 def convert_image(image):
     """Convert the image with numpy."""
     imagex = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    imagex = np.array(image)
+    imagex = np.array(imagex)
     imagex = imagex.transpose((2, 0, 1))
     imagex = np.divide(imagex, 255.0)
     imagex = np.flip(imagex, 0)
diff --git a/python/tvm/relay/testing/resnet.py b/python/tvm/relay/testing/resnet.py
index bc5f5c4eed3e..b35e01f6779b 100644
--- a/python/tvm/relay/testing/resnet.py
+++ b/python/tvm/relay/testing/resnet.py
@@ -177,7 +177,7 @@ def resnet(
         Channel size of each stage
 
     num_classes : int
-        Ouput size of symbol
+        Output size of symbol
 
     data_shape : tuple of int.
         The shape of input data.
diff --git a/python/tvm/relay/testing/resnet_3d.py b/python/tvm/relay/testing/resnet_3d.py
index 484f51dcac9b..715e3951b856 100644
--- a/python/tvm/relay/testing/resnet_3d.py
+++ b/python/tvm/relay/testing/resnet_3d.py
@@ -174,7 +174,7 @@ def resnet(
         Channel size of each stage
 
     num_classes : int
-        Ouput size of symbol
+        Output size of symbol
 
     data_shape : tuple of int.
         The shape of input data.
diff --git a/python/tvm/relay/transform/__init__.py b/python/tvm/relay/transform/__init__.py
index 1d0ea176b16f..ca9996aeaaae 100644
--- a/python/tvm/relay/transform/__init__.py
+++ b/python/tvm/relay/transform/__init__.py
@@ -19,4 +19,3 @@
 # transformation passes
 from .transform import *
 from .recast import recast
-from . import memory_alloc
diff --git a/python/tvm/relay/transform/memory_alloc.py b/python/tvm/relay/transform/memory_alloc.py
deleted file mode 100644
index 66528c861788..000000000000
--- a/python/tvm/relay/transform/memory_alloc.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-# pylint: disable=no-else-return,invalid-name,len-as-condition,too-many-nested-blocks
-"""
-A pass for manifesting explicit memory allocations.
-"""
-import numpy as np
-
-from tvm.ir.transform import PassContext, module_pass
-from tvm.relay.transform import InferType
-from tvm import nd, container
-from ..function import Function
-from ..expr_functor import ExprVisitor, ExprMutator
-from ..scope_builder import ScopeBuilder
-from .. import op
-from ... import DataType, register_func
-from .. import ty, expr
-from ..backend import compile_engine
-from ..op.memory import flatten_tuple_type, from_tuple_type, to_tuple_type
-from ... import cpu
-from ..op.memory import alloc_storage
-from ..analysis import context_analysis
-from ..._ffi.runtime_ctypes import TVMContext
-
-
-def alloc_tensor(storage, shape, dtype="float32", assert_shape=None):
-    offset = expr.const(0, dtype="int64")
-    return op.memory.alloc_tensor(storage, offset, shape, dtype, assert_shape)
-
-
-def is_primitive(call):
-    return (
-        hasattr(call, "op")
-        and hasattr(call.op, "attrs")
-        and hasattr(call.op.attrs, "Primitive")
-        and int(call.op.attrs.Primitive) == 1
-    )
-
-
-def is_device_copy(func):
-    """
-    Check if the current relay expression is a device copy call. We can simply check
-    the body of it if it is a function becase the device_copy op is opaque.
-    """
-    if isinstance(func, Function):
-        body = func.body
-        return isinstance(body, expr.Call) and body.op == op.get("device_copy")
-    if isinstance(func, expr.Call):
-        return func.op == op.get("device_copy")
-    return False
-
-
-class CheckReshapeOnly(ExprVisitor):
-    """A pass to check if the fused op contains only reshape ops."""
-
-    def __init__(self):
-        super().__init__()
-        self._reshape_ops = [
-            op.get("reshape"),
-            op.get("contrib_reverse_reshape"),
-            op.get("dyn.reshape"),
-        ]
-        self.reshape_only = True
-
-    def visit_call(self, call):
-        if not self.reshape_only:
-            return
-        if call.op not in self._reshape_ops:
-            self.reshape_only = False
-        for arg in call.args:
-            self.visit(arg)
-
-    def visit_var(self, var):
-        var_type = var.checked_type
-        if not isinstance(var_type, ty.TensorType):
-            self.reshape_only = False
-
-
-def is_reshape_only(func):
-    """Check if the primitive function contains only reshape ops."""
-    check = CheckReshapeOnly()
-    check.visit(func)
-    return check.reshape_only
-
-
-class ManifestAllocPass(ExprMutator):
-    """A pass for explicitly manifesting all memory allocations in Relay."""
-
-    def __init__(self, target_host, context_analysis_map):
-        self.invoke_tvm = op.vm.invoke_tvm_op
-        self.shape_func = op.vm.shape_func
-        self.shape_of = op.vm.shape_of
-        self.reshape_tensor = op.vm.reshape_tensor
-        self.scopes = [ScopeBuilder()]
-        self.target_host = target_host
-        self.default_context = cpu(0)
-        self.compute_dtype = "int64"
-        self.context_analysis_map = context_analysis_map
-        super().__init__()
-
-    def get_context(self, exp):
-        """Get the context of a given expression"""
-        assert exp in self.context_analysis_map, exp.astext(False)
-        val = self.context_analysis_map[exp]
-        # val[0], val[1] are device_type and device_id, respectively.
-        # We don't need to unpack after porting this pass to C++.
-        assert len(val) == 2
-        return TVMContext(val[0].value, val[1].value)
-
-    def device_copy(self, inp, src_ctx, dst_ctx):
-        """Insert a device copy node."""
-        return self.visit(op.tensor.device_copy(inp, src_ctx, dst_ctx))
-
-    def current_scope(self):
-        return self.scopes[-1]
-
-    def visit_tuple(self, tup):
-        scope = self.current_scope()
-        new_fields = []
-        for field in tup.fields:
-            field = self.visit(field)
-            if isinstance(field, expr.Constant):
-                field = scope.let("const", field)
-            new_fields.append(field)
-        return expr.Tuple(new_fields)
-
-    def compute_alignment(self, dtype):
-        dtype = DataType(dtype)
-        align = (dtype.bits // 8) * dtype.lanes
-        # MAGIC CONSTANT FROM device_api.h
-        if align < 64:
-            align = 64
-
-        return expr.const(align, dtype="int64")
-
-    def compute_storage_in_relay(self, shape, dtype):
-        dtype = DataType(dtype)
-        els = op.prod(shape)
-        num = expr.const(dtype.bits * dtype.lanes, self.compute_dtype)
-        num = num + expr.const(7, self.compute_dtype)
-        div = expr.const(8, self.compute_dtype)
-        return els * (num / div)
-
-    def compute_storage(self, tensor_type):
-        dtype = DataType(tensor_type.dtype)
-        shape = [int(sh) for sh in tensor_type.shape]
-        size = 1
-        for sh in shape:
-            size *= sh
-        size *= (dtype.bits * dtype.lanes + 7) // 8
-        return expr.const(size, dtype=self.compute_dtype)
-
-    def make_static_allocation(self, scope, tensor_type, ctx, name_hint):
-        """Allocate a tensor with a statically known shape."""
-        shape = [int(sh) for sh in tensor_type.shape]
-        if len(shape) == 0:
-            shape = expr.const(np.empty((), dtype=self.compute_dtype), dtype=self.compute_dtype)
-        else:
-            shape = expr.const(np.array(shape), dtype=self.compute_dtype)
-        size = self.compute_storage(tensor_type)
-        alignment = self.compute_alignment(tensor_type.dtype)
-        dtype = tensor_type.dtype
-        sto = scope.let("storage_{0}".format(name_hint), alloc_storage(size, alignment, ctx, dtype))
-        # TODO(@jroesch): There is a bug with typing based on the constant shape.
-        tensor = alloc_tensor(sto, shape, dtype, tensor_type.shape)
-        return scope.let("tensor_{0}".format(name_hint), tensor)
-
-    def visit_let(self, let):
-        scope = ScopeBuilder()
-
-        self.scopes.append(scope)
-        while isinstance(let, expr.Let):
-            new_val = self.visit(let.value)
-            scope.let(let.var, new_val)
-            let = let.body
-
-        new_body = self.visit(let)
-        scope.ret(new_body)
-        self.scopes.pop()
-
-        return scope.get()
-
-    def emit_shape_func(self, scope, func, new_args):
-        """Insert the shape function given a primitive function."""
-        shape_func_ins = []
-        engine = compile_engine.get()
-        cfunc = engine.lower_shape_func(func, self.target_host)
-        input_states = cfunc.shape_func_param_states
-
-        is_inputs = []
-        input_pos = 0
-        cpu_ctx = nd.cpu(0)
-        for i, (arg, state) in enumerate(zip(new_args, input_states)):
-            state = int(state)
-            # Pass Shapes
-            if state == 2:
-                for j, subexp in enumerate(from_tuple_type(arg.type_annotation, arg)):
-                    sh_of = self.visit(self.shape_of(subexp))
-                    shape_func_ins.append(scope.let("in_shape_{0}".format(input_pos + j), sh_of))
-                    input_pos += 1
-                is_inputs.append(0)
-            # Pass Inputs
-            elif state == 1:
-                new_arg = self.visit(arg)
-                ctx = self.get_context(arg)
-                if ctx.device_type != cpu_ctx.device_type:
-                    new_arg = self.device_copy(new_arg, ctx, cpu_ctx)
-                shape_func_ins.append(scope.let("in_shape_{0}".format(input_pos), new_arg))
-                input_pos += 1
-                is_inputs.append(1)
-            else:
-                # TODO(@jroesch): handle 3rd case
-                raise Exception("unsupported shape function input state")
-
-        out_shapes = []
-        for i, out in enumerate(cfunc.outputs):
-            tt = ty.TensorType(out.shape, out.dtype)
-            # Put shape func on CPU. This also ensures that everything between
-            # shape_of and shape_func are on CPU.
-            alloc = self.make_static_allocation(scope, tt, cpu_ctx, i)
-            alloc = scope.let("shape_func_out_{0}".format(i), alloc)
-            out_shapes.append(alloc)
-
-        shape_call = self.shape_func(
-            func, expr.Tuple(shape_func_ins), expr.Tuple(out_shapes), is_inputs
-        )
-
-        scope.let("shape_func", shape_call)
-        return out_shapes
-
-    def dynamic_invoke(self, scope, func, ins, new_args, out_types, ret_type):
-        """Generate the code for invoking a TVM op with a dynamic shape."""
-        out_shapes = self.emit_shape_func(scope, func, new_args)
-
-        storages = []
-        func_ctx = self.get_context(func)
-        for i, (out_shape, out_type) in enumerate(zip(out_shapes, out_types)):
-            size = self.compute_storage_in_relay(out_shape, out_type.dtype)
-            alignment = self.compute_alignment(out_type.dtype)
-            sto = scope.let(
-                "storage_{i}".format(i=i), alloc_storage(size, alignment, func_ctx, out_type.dtype)
-            )
-            storages.append(sto)
-
-        outs = []
-        sh_ty_storage = zip(out_shapes, out_types, storages)
-        for i, (out_shape, out_type, storage) in enumerate(sh_ty_storage):
-            alloc = alloc_tensor(storage, out_shape, out_type.dtype, out_type.shape)
-            alloc = scope.let("out_{i}".format(i=i), alloc)
-            outs.append(alloc)
-
-        tuple_outs = expr.Tuple(outs)
-        invoke = self.invoke_tvm(func, ins, tuple_outs)
-        scope.let("", invoke)
-        return to_tuple_type(ret_type, tuple_outs.fields)
-
-    def emit_reshape_tensor(self, scope, func, new_args, ret_type):
-        if self.is_dynamic(ret_type):
-            out_shapes = self.emit_shape_func(scope, func, new_args)
-            shape_expr = out_shapes[0]
-        else:
-            # constant output shape
-            shape = [int(dim) for dim in ret_type.shape]
-            shape_expr = expr.const(shape, dtype=self.compute_dtype)
-        return self.reshape_tensor(new_args[0], shape_expr, ret_type.shape)
-
-    def is_dynamic(self, ret_type):
-        is_dynamic = ty.is_dynamic(ret_type)
-        # TODO(@jroesch): restore this code, more complex then it seems
-        # for arg in call.args:
-        #     is_dynamic = is_dynamic or arg.checked_type.is_dynamic()
-        return is_dynamic
-
-    def visit_call(self, call):
-        if is_primitive(call):
-            # Because we are in ANF we do not need to visit the arguments.
-            scope = self.current_scope()
-            new_args = [self.visit(arg) for arg in call.args]
-
-            ins = expr.Tuple(new_args)
-            ret_type = call.checked_type
-            out_types = flatten_tuple_type(ret_type)
-
-            if is_reshape_only(call.op):
-                # Handle fused op that only contains reshape op
-                return self.emit_reshape_tensor(scope, call.op, new_args, ret_type)
-
-            if is_device_copy(call.op):
-                # Handle device copy op
-                if isinstance(call.op, Function):
-                    attr = call.op.body.attrs
-                else:
-                    attr = call.attr
-                return self.device_copy(
-                    new_args[0], TVMContext(attr.src_dev_type, 0), TVMContext(attr.dst_dev_type, 0)
-                )
-
-            if self.is_dynamic(ret_type):
-                # Handle dynamic case.
-                return self.dynamic_invoke(scope, call.op, ins, new_args, out_types, ret_type)
-
-            # Handle static case.
-            outs = []
-            for i, out_ty in enumerate(out_types):
-                ctx = self.get_context(call)
-                assert isinstance(ctx, TVMContext)
-                out = self.make_static_allocation(scope, out_ty, ctx, i)
-                outs.append(out)
-
-            output = expr.Tuple(outs)
-            invoke = self.invoke_tvm(call.op, ins, output)
-            scope.let("", invoke)
-            return to_tuple_type(ret_type, output.fields)
-        return super().visit_call(call)
-
-
-def mk_analysis_annotator(results):
-    """Pretty print the annotated relay program with device info"""
-
-    def _annotator(exp):
-        if exp in results:
-            val = results[exp]
-            assert len(val) == 2
-            ctx = TVMContext(val[0].value, val[1].value)
-            return f"<{ctx}>"
-        else:
-            return ""
-
-    return _annotator
-
-
-@module_pass(opt_level=0)
-class ManifestAlloc:
-    """The explicit pass wrapper around ManifestAlloc."""
-
-    # TODO(zhiics, jroesch) Port this pass to C++.
-    def __init__(self, target_host, targets):
-        self.target_host = target_host
-        self.targets = targets
-
-    def transform_module(self, mod, _):
-        """Invokes the pass"""
-        # TODO(@jroesch): Is there a way to do one shot initialization?
-        # can we have def pass_init?
-        mod.import_from_std("core.rly")
-        mod = InferType()(mod)
-
-        assert isinstance(self.targets, (dict, container.Map))
-        if len(self.targets) > 1:
-            pass_ctx = PassContext.current()
-            if "relay.fallback_device_type" in pass_ctx.config:
-                fallback_ctx = nd.context(pass_ctx.config["relay.fallback_device_type"])
-            else:
-                fallback_ctx = cpu(0)
-            ca = context_analysis(mod, TVMContext(fallback_ctx.device_type, 0))
-        else:
-            if isinstance(self.targets, dict):
-                dev = list(self.targets.keys())[0]
-            else:
-                dev, _ = self.targets.items()[0]
-            ca = context_analysis(mod, nd.context(dev.value))
-
-        # The following code can be used for debugging the module after
-        # annotation.
-        # print(mod.astext(show_meta_data=False, annotate=mk_analysis_annotator(ca)))
-
-        gv_funcs = mod.functions
-        for gv, f in gv_funcs.items():
-            ea = ManifestAllocPass(self.target_host, ca)
-            f = ea.visit(f)
-            mod.update_func(gv, f)
-        return mod
-
-
-register_func("relay.transform.ManifestAlloc", ManifestAlloc)
diff --git a/python/tvm/relay/transform/transform.py b/python/tvm/relay/transform/transform.py
index c6df8c1e6ea2..5b0e480f5f28 100644
--- a/python/tvm/relay/transform/transform.py
+++ b/python/tvm/relay/transform/transform.py
@@ -240,6 +240,23 @@ def LazyGradientInit():
     return _ffi_api.LazyGradientInit()
 
 
+def FoldConstantExpr(expr, mod):
+    """Fold the constant expressions in a Relay program.
+    Parameters
+    ----------
+    expr: Expr
+        The expression to fold
+    mod: IRModule
+        The module the expr lives in (for global calls)
+
+    Returns
+    -------
+    new_expr: Expr
+        The expr after Constant Folding
+    """
+    return _ffi_api.FoldConstantExpr(expr, mod)
+
+
 def FoldConstant():
     """Fold the constant expressions in a Relay program.
 
@@ -783,12 +800,36 @@ def gradient(expr, mod=None, mode="higher_order"):
       The transformed expression.
     """
     if mode == "first_order":
-        return _ffi_api.first_order_gradient(expr, mod)
+        warnings.warn(
+            "using transform.gradient for first-order AD is deprecated, please use the"
+            "FirstOrderGradient module pass",
+            DeprecationWarning,
+        )
+        if mod is not None:
+            raise RuntimeError(
+                "to run first-order AD on a module, please use the FirstOrderGradient module pass."
+            )
+        return FirstOrderGradient()(tvm.IRModule.from_expr(expr))["main"]
     if mode == "higher_order":
         return _ffi_api.gradient(expr, mod)
     raise Exception("unknown mode")
 
 
+def FirstOrderGradient():
+    """
+    Transforms all global functions in the module to return the original result, paired with the
+    gradients of the inputs. This pass transforms each global function independently and does not
+    support interprocedural AD. Additionally, this pass does not support any control-flow or
+    references, and should only be used on pure data-flow graphs.
+
+    Returns
+    -------
+    ret : tvm.transform.Pass
+        The registered FirstOrderGradient pass.
+    """
+    return _ffi_api.FirstOrderGradient()
+
+
 def Defunctionalization(func, mod):
     """
     Performs defunctionalization on func,
@@ -968,7 +1009,7 @@ def transform(func, mod, ctx):
     """
 
     if opt_level is None:
-        raise ValueError("Please provide opt_level for the funtion pass.")
+        raise ValueError("Please provide opt_level for the function pass.")
 
     required = required if required else []
     if not isinstance(required, (list, tuple)):
@@ -1082,6 +1123,19 @@ def SimplifyExpr():
     return _ffi_api.SimplifyExpr()
 
 
+def FoldExplicitPadding():
+    """
+    FoldExplicitPadding finds explict padding before an op that can support
+    implicit padding and fuses them.
+
+    Returns
+    -------
+    ret : tvm.transform.Pass
+        The registered ImplicitPadding pass.
+    """
+    return _ffi_api.FoldExplicitPadding()
+
+
 def AnnotateSpans():
     """
     Annotate a program with span information by first generating its textual
diff --git a/python/tvm/rpc/tracker.py b/python/tvm/rpc/tracker.py
index 557c9ae24d40..e1c366e99b0d 100644
--- a/python/tvm/rpc/tracker.py
+++ b/python/tvm/rpc/tracker.py
@@ -42,9 +42,9 @@
 # pylint: disable=invalid-name
 
 import heapq
-import time
 import logging
 import socket
+import threading
 import multiprocessing
 import errno
 import struct
@@ -112,10 +112,12 @@ def summary(self):
 
 
 class PriorityScheduler(Scheduler):
-    """Priority based scheduler, FIFO based on time"""
+    """Priority based scheduler, FIFO based on request order"""
 
     def __init__(self, key):
         self._key = key
+        self._request_cnt = 0
+        self._lock = threading.Lock()
         self._values = []
         self._requests = []
 
@@ -134,7 +136,9 @@ def put(self, value):
         self._schedule()
 
     def request(self, user, priority, callback):
-        heapq.heappush(self._requests, (-priority, time.time(), callback))
+        with self._lock:
+            heapq.heappush(self._requests, (-priority, self._request_cnt, callback))
+            self._request_cnt += 1
         self._schedule()
 
     def remove(self, value):
diff --git a/python/tvm/runtime/__init__.py b/python/tvm/runtime/__init__.py
index 21c06c517bd7..7d58af70afe1 100644
--- a/python/tvm/runtime/__init__.py
+++ b/python/tvm/runtime/__init__.py
@@ -29,3 +29,4 @@
 from .ndarray import vpi, rocm, ext_dev, micro_dev
 from .module import load_module, enabled, system_lib
 from .container import String
+from .params import save_param_dict, load_param_dict
diff --git a/python/tvm/runtime/module.py b/python/tvm/runtime/module.py
index 63267969ab4e..09bef9ecbd6a 100644
--- a/python/tvm/runtime/module.py
+++ b/python/tvm/runtime/module.py
@@ -105,6 +105,9 @@ def __getitem__(self, name):
             raise ValueError("Can only take string as function name")
         return self.get_function(name)
 
+    def __eq__(self, other):
+        return self.handle.value == other.handle.value
+
     def __call__(self, *args):
         if self._entry:
             return self._entry(*args)
@@ -233,15 +236,27 @@ def evaluator(*args):
         except NameError:
             raise NameError("time_evaluate is only supported when RPC is enabled")
 
-    def _collect_dso_modules(self):
-        """Helper function to collect dso modules, then return it."""
+    def _collect_from_import_tree(self, filter_func):
+        """Helper function to collect modules from the tree matching a filter_func, then return it.
+
+        Parameters
+        ----------
+        filter_func : Callable[[Module], bool]
+            A function which is invoked for each Module discovered in the import tree (including
+            self).
+
+        Returns
+        -------
+        list[Module] :
+            A list of matching Module.
+        """
         visited, stack, dso_modules = set(), [], []
         # append root module
         visited.add(self)
         stack.append(self)
         while stack:
             module = stack.pop()
-            if module._dso_exportable():
+            if filter_func(module):
                 dso_modules.append(module)
             for m in module.imported_modules:
                 if m not in visited:
@@ -249,8 +264,9 @@ def _collect_dso_modules(self):
                     stack.append(m)
         return dso_modules
 
-    def _dso_exportable(self):
-        return self.type_key == "llvm" or self.type_key == "c"
+    def _collect_dso_modules(self):
+        is_dso_exportable = lambda m: (m.type_key == "llvm" or m.type_key == "c")
+        return self._collect_from_import_tree(is_dso_exportable)
 
     def export_library(self, file_name, fcompile=None, addons=None, workspace_dir=None, **kwargs):
         """Export the module and its imported device code one library.
@@ -323,6 +339,9 @@ def export_library(self, file_name, fcompile=None, addons=None, workspace_dir=No
                 else:
                     assert module.type_key == "c"
                     object_format = "c"
+                    if "cc" in kwargs:
+                        if kwargs["cc"] == "nvcc":
+                            object_format = "cu"
                     has_c_module = True
             path_obj = os.path.join(workspace_dir, f"lib{index}.{object_format}")
             module.save(path_obj)
diff --git a/python/tvm/runtime/ndarray.py b/python/tvm/runtime/ndarray.py
index 2f616ce879c9..5c60515e3448 100644
--- a/python/tvm/runtime/ndarray.py
+++ b/python/tvm/runtime/ndarray.py
@@ -23,6 +23,7 @@
 from tvm._ffi.base import _LIB, check_call, c_array, string_types, _FFI_MODE
 from tvm._ffi.runtime_ctypes import DataType, TVMContext, TVMArray, TVMArrayHandle
 from tvm._ffi.runtime_ctypes import DataTypeCode, tvm_shape_index_t
+from . import _ffi_api
 
 try:
     # pylint: disable=wrong-import-position
@@ -147,7 +148,9 @@ def copyfrom(self, source_array):
                     source_array.shape, shape
                 )
             )
-        source_array = np.ascontiguousarray(source_array, dtype=dtype)
+        source_array = np.ascontiguousarray(
+            source_array, dtype="uint16" if dtype == "bfloat16" else dtype
+        )
         assert source_array.flags["C_CONTIGUOUS"]
         data = source_array.ctypes.data_as(ctypes.c_void_p)
         nbytes = ctypes.c_size_t(source_array.size * source_array.dtype.itemsize)
@@ -253,42 +256,41 @@ def numpyasarray(np_data):
     return arr, shape
 
 
-def empty(shape, dtype="float32", ctx=context(1, 0)):
+def empty(shape, dtype="float32", ctx=context(1, 0), mem_scope=None):
     """Create an empty array given shape and device
 
     Parameters
     ----------
     shape : tuple of int
-        The shape of the array
+        The shape of the array.
 
     dtype : type or str
         The data type of the array.
 
     ctx : TVMContext
-        The context of the array
+        The context of the array.
+
+    mem_scope : Optional[str]
+        The memory scope of the array.
 
     Returns
     -------
     arr : tvm.nd.NDArray
         The array tvm supported.
     """
-    shape = c_array(tvm_shape_index_t, shape)
-    ndim = ctypes.c_int(len(shape))
-    handle = TVMArrayHandle()
+    shape_imm = []
+    for s in shape:
+        if isinstance(s, tvm.tir.IntImm):
+            shape_imm.append(s.value)
+        else:
+            shape_imm.append(int(s))
+    arr = np.array(shape_imm, "int64")
+    ptr = arr.ctypes.data_as(ctypes.POINTER(ctypes.c_int64))
+    shape_ptr = ctypes.cast(ptr, ctypes.c_void_p)
+    ndim = len(shape_imm)
     dtype = DataType(dtype)
-    check_call(
-        _LIB.TVMArrayAlloc(
-            shape,
-            ndim,
-            ctypes.c_int(dtype.type_code),
-            ctypes.c_int(dtype.bits),
-            ctypes.c_int(dtype.lanes),
-            ctx.device_type,
-            ctx.device_id,
-            ctypes.byref(handle),
-        )
-    )
-    return _make_array(handle, False, False)
+    arr = _ffi_api.TVMArrayAllocWithScope(shape_ptr, ndim, dtype, ctx, mem_scope)
+    return arr
 
 
 def from_dlpack(dltensor):
diff --git a/python/tvm/runtime/object.py b/python/tvm/runtime/object.py
index bfee7f544f9c..0c2abd296b42 100644
--- a/python/tvm/runtime/object.py
+++ b/python/tvm/runtime/object.py
@@ -56,6 +56,9 @@ def __dir__(self):
         return sorted([fnames(i) for i in range(size)] + class_names)
 
     def __getattr__(self, name):
+        if name in self.__slots__:
+            raise AttributeError(f"{name} is not set")
+
         try:
             return _ffi_node_api.NodeGetAttr(self, name)
         except AttributeError:
diff --git a/python/tvm/runtime/object_generic.py b/python/tvm/runtime/object_generic.py
index 4aa83c17d178..974523d1eb1a 100644
--- a/python/tvm/runtime/object_generic.py
+++ b/python/tvm/runtime/object_generic.py
@@ -64,7 +64,7 @@ def convert_to_object(value, span=None):
         return _ffi_api.String(value)
     if isinstance(value, (list, tuple)):
         value = [convert_to_object(x) for x in value]
-        return _ffi_node_api.Array(*value)
+        return _ffi_api.Array(*value)
     if isinstance(value, dict):
         vlist = []
         for item in value.items():
@@ -72,7 +72,7 @@ def convert_to_object(value, span=None):
                 raise ValueError("key of map must already been a container type")
             vlist.append(item[0])
             vlist.append(convert_to_object(item[1]))
-        return _ffi_node_api.Map(*vlist)
+        return _ffi_api.Map(*vlist)
     if isinstance(value, ObjectGeneric):
         return value.asobject()
     if value is None:
diff --git a/python/tvm/runtime/params.py b/python/tvm/runtime/params.py
new file mode 100644
index 000000000000..78e745686c95
--- /dev/null
+++ b/python/tvm/runtime/params.py
@@ -0,0 +1,69 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""Helper utility to save and load parameter dicts."""
+from . import _ffi_api, ndarray
+
+
+def save_param_dict(params):
+    """Save parameter dictionary to binary bytes.
+
+    The result binary bytes can be loaded by the
+    GraphModule with API "load_params".
+
+    Parameters
+    ----------
+    params : dict of str to NDArray
+        The parameter dictionary.
+
+    Returns
+    -------
+    param_bytes: bytearray
+        Serialized parameters.
+
+    Examples
+    --------
+    .. code-block:: python
+
+       # set up the parameter dict
+       params = {"param0": arr0, "param1": arr1}
+       # save the parameters as byte array
+       param_bytes = tvm.runtime.save_param_dict(params)
+       # We can serialize the param_bytes and load it back later.
+       # Pass in byte array to module to directly set parameters
+       tvm.runtime.load_param_dict(param_bytes)
+    """
+    transformed = {k: ndarray.array(v) for (k, v) in params.items()}
+    return _ffi_api.SaveParams(transformed)
+
+
+def load_param_dict(param_bytes):
+    """Load parameter dictionary to binary bytes.
+
+    Parameters
+    ----------
+    param_bytes: bytearray
+        Serialized parameters.
+
+    Returns
+    -------
+    params : dict of str to NDArray
+        The parameter dictionary.
+    """
+    if isinstance(param_bytes, (bytes, str)):
+        param_bytes = bytearray(param_bytes)
+    return _ffi_api.LoadParams(param_bytes)
diff --git a/python/tvm/runtime/vm.py b/python/tvm/runtime/vm.py
index 448cb137cc9b..d641e52d7184 100644
--- a/python/tvm/runtime/vm.py
+++ b/python/tvm/runtime/vm.py
@@ -113,7 +113,7 @@ def save(self):
             # define a simple network.
             x = relay.var('x', shape=(10, 10))
             f = relay.Function([x], x + x)
-            mod = relay.Module({"main": f})
+            mod = tvm.IRModule({"main": f})
             # create a Relay VM.
             ctx = tvm.cpu()
             target = "llvm"
@@ -128,7 +128,7 @@ def save(self):
             loaded_lib = tvm.runtime.load_module(path_lib)
             loaded_code = bytearray(open(tmp.relpath("code.ro"), "rb").read())
             # deserialize.
-            des_exec = tvm.runtime.vm.Executable.load_exec(loaded_code, loaded_code)
+            des_exec = tvm.runtime.vm.Executable.load_exec(loaded_code, loaded_lib)
             # execute the deserialized executable.
             x_data = np.random.rand(10, 10).astype('float32')
             des_vm = tvm.runtime.vm.VirtualMachine(des_exec, ctx)
diff --git a/python/tvm/script/parser.py b/python/tvm/script/parser.py
index db976d0ee677..33b0bab0d7e7 100644
--- a/python/tvm/script/parser.py
+++ b/python/tvm/script/parser.py
@@ -230,6 +230,19 @@ def parse_arg_list(self, func, node_call):
         """Match the arguments of a function call in the AST to the required
         arguments of the function. This handles positional arguments,
         positional arguments specified by name, keyword arguments, and varargs.
+
+        Parameters
+        ----------
+        func : Function
+            The function that provides the signature
+
+        node_call: ast.Call
+            The AST call node that calls into the function.
+
+        Returns
+        -------
+        arg_list : list
+            The parsed positional argument.
         """
         assert isinstance(node_call, ast.Call)
         # collect arguments
@@ -435,8 +448,8 @@ def transform_Assign(self, node):
                         node.rhs.span,
                     )
                 # Pattern 4
-                func.enter_scope(node, self.context)
                 arg_list = self.parse_arg_list(func, node.rhs)
+                func.enter_scope(node, self.context, arg_list, node.rhs.func_name.span)
                 func.body = self.parse_body(node)
                 return func.exit_scope(node, self.context, arg_list, node.rhs.func_name.span)
             elif isinstance(func, SpecialStmt):
@@ -532,9 +545,9 @@ def transform_For(self, node):
         self.current_col_offset = node.span.start_column
         self.context.new_scope(nodes=node.body.stmts)
         # for scope handler process the scope
-        func.enter_scope(node, self.context)
-        func.body = self.parse_body(node)
         arg_list = self.parse_arg_list(func, node.rhs)
+        func.enter_scope(node, self.context, arg_list, node.rhs.func_name.span)
+        func.body = self.parse_body(node)
         res = func.exit_scope(node, self.context, arg_list, node.rhs.func_name.span)
         # exit the scope
         self.context.pop_scope()
@@ -571,9 +584,9 @@ def transform_With(self, node):
         self.current_col_offset = node.body.span.start_column
         self.context.new_scope(nodes=node.body.stmts)
         # with scope handler process the scope
-        func.enter_scope(node, self.context)
-        func.body = self.parse_body(node)
         arg_list = self.parse_arg_list(func, node.rhs)
+        func.enter_scope(node, self.context, arg_list, node.rhs.func_name.span)
+        func.body = self.parse_body(node)
         res = func.exit_scope(node, self.context, arg_list, node.rhs.func_name.span)
         # exit the scope
         self.context.pop_scope()
@@ -689,7 +702,7 @@ def f():
         if isinstance(func, Intrin) and func.stmt:
             return func.handle(arg_list, node.call.func_name.span)
         elif isinstance(func, WithScopeHandler) and func.concise_scope and not func.def_symbol:
-            func.enter_scope(node, self.context)
+            func.enter_scope(node, self.context, arg_list, node.call.func_name.span)
             func.body = self.parse_body(node)
             return func.exit_scope(node, self.context, arg_list, node.call.func_name.span)
         elif isinstance(func, SpecialStmt) and not func.def_symbol:
diff --git a/python/tvm/script/scope_handler.py b/python/tvm/script/scope_handler.py
index 7f252e3e381d..9449cbdc156c 100644
--- a/python/tvm/script/scope_handler.py
+++ b/python/tvm/script/scope_handler.py
@@ -35,7 +35,7 @@ def __init__(self, func):
     def signature(self):
         return "tir." + self.func.__name__, get_param_list(self.func)
 
-    def enter_scope(self, node, context):
+    def enter_scope(self, node, context, arg_list, span):
         pass
 
     def exit_scope(self, node, context, arg_list, span):
@@ -86,7 +86,7 @@ def allocate(extents, dtype, scope, condition=True, span=None):
         super().__init__(allocate, concise_scope=True, def_symbol=True)
         self.buffer_var = None
 
-    def enter_scope(self, node, context):
+    def enter_scope(self, node, context, arg_list, span):
         # define buffer vars in symbol table
         if isinstance(node, ast.With):
             names = WithScopeHandler.get_optional_var_names(node, context)
@@ -98,7 +98,12 @@ def enter_scope(self, node, context):
         else:
             raise Exception("Internal Bug")
 
-        self.buffer_var = tvm.te.var(name, "handle", span=from_synr_span(node.lhs.id.span))
+        def setup_buffer_var(extents, dtype, scope, condition=True, span=None):
+            """Setup buffer var for a given type."""
+            buffer_ptr_type = tvm.ir.PointerType(tvm.ir.PrimType(dtype))
+            self.buffer_var = tvm.tir.Var(name, buffer_ptr_type, span)
+
+        setup_buffer_var(*arg_list, span=from_synr_span(node.lhs.id.span))
         context.update_symbol(name, self.buffer_var)
 
 
@@ -187,7 +192,7 @@ def __init__(self, func):
         super().__init__(func)
         self.loop_vars = None
 
-    def enter_scope(self, node, context):
+    def enter_scope(self, node, context, arg_list, span):
         assert isinstance(node, ast.For)
 
         loop_var_names = list()
@@ -221,7 +226,7 @@ def serial(begin, end, span):
                 self.context.report_error("Expect exact 1 loop var", span)
             ana = tvm.arith.Analyzer()
             extent = end if begin == 0 else ana.simplify(end - begin)
-            return tvm.tir.For(self.loop_vars[0], begin, extent, 0, 0, self.body, span=span)
+            return tvm.tir.For(self.loop_vars[0], begin, extent, 0, self.body, span=span)
 
         super().__init__(serial)
 
@@ -236,7 +241,7 @@ def parallel(begin, end, span):
                 self.context.report_error("Expect exact 1 loop var")
             ana = tvm.arith.Analyzer()
             extent = end if begin == 0 else ana.simplify(end - begin)
-            return tvm.tir.For(self.loop_vars[0], begin, extent, 1, 0, self.body, span=span)
+            return tvm.tir.For(self.loop_vars[0], begin, extent, 1, self.body, span=span)
 
         super().__init__(parallel)
 
@@ -251,7 +256,7 @@ def vectorized(begin, end, span):
                 self.context.report_error("Expect exact 1 loop var")
             ana = tvm.arith.Analyzer()
             extent = end if begin == 0 else ana.simplify(end - begin)
-            return tvm.tir.For(self.loop_vars[0], begin, extent, 2, 0, self.body, span=span)
+            return tvm.tir.For(self.loop_vars[0], begin, extent, 2, self.body, span=span)
 
         super().__init__(vectorized)
 
@@ -266,6 +271,6 @@ def unroll(begin, end, span):
                 self.context.report_error("Expect exact 1 loop var")
             ana = tvm.arith.Analyzer()
             extent = end if begin == 0 else ana.simplify(end - begin)
-            return tvm.tir.For(self.loop_vars[0], begin, extent, 3, 0, self.body, span=span)
+            return tvm.tir.For(self.loop_vars[0], begin, extent, 3, self.body, span=span)
 
         super().__init__(unroll)
diff --git a/python/tvm/support.py b/python/tvm/support.py
index e0d688abb9e8..800bfe4e2546 100644
--- a/python/tvm/support.py
+++ b/python/tvm/support.py
@@ -15,7 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 """Support infra of TVM."""
+import ctypes
 import tvm._ffi
+from .runtime.module import Module
+from . import get_global_func
 
 
 def libinfo():
@@ -29,4 +32,26 @@ def libinfo():
     return {k: v for k, v in GetLibInfo().items()}  # pylint: disable=unnecessary-comprehension
 
 
+class FrontendTestModule(Module):
+    """A tvm.runtime.Module whose member functions are PackedFunc."""
+
+    def __init__(self, entry_name=None):
+        underlying_mod = get_global_func("testing.FrontendTestModule")()
+        handle = underlying_mod.handle
+
+        # Set handle to NULL to avoid cleanup in c++ runtime, transferring ownership.
+        # Both cython and ctypes FFI use c_void_p, so this is safe to assign here.
+        underlying_mod.handle = ctypes.c_void_p(0)
+
+        super(FrontendTestModule, self).__init__(handle)
+        if entry_name is not None:
+            self.entry_name = entry_name
+
+    def add_function(self, name, func):
+        self.get_function("__add_function")(name, func)
+
+    def __setitem__(self, key, value):
+        self.add_function(key, value)
+
+
 tvm._ffi._init_api("support", __name__)
diff --git a/python/tvm/target/target.py b/python/tvm/target/target.py
index edbb0fa3792a..8c60260e640a 100644
--- a/python/tvm/target/target.py
+++ b/python/tvm/target/target.py
@@ -46,7 +46,7 @@ class Target(Object):
     - :py:func:`tvm.target.intel_graphics` create Intel Graphics target
     """
 
-    def __init__(self, tag_or_str_or_dict):
+    def __init__(self, tag_or_str_or_dict, host_tag_or_str_or_dict=None):
         """Construct a TVM target object from
         1) Raw target string
         2) Target config dict
@@ -86,10 +86,22 @@ def __init__(self, tag_or_str_or_dict):
             mfloat-abi : str (optional)
                 An llvm setting that is one of 'hard' or 'soft' indicating whether to use
                 hardware or software floating-point operations.
+            host : Union[str, Dict[str, Any]] (optional)
+                Description for target host. Can be recursive. Similar to tag_or_str_or_dict.
+        host_tag_or_str_or_dict : Optional[Union[str, Dict[str, Any]]]
+            Similar to tag_or_str_or_dict but for target host. Can be one of a literal
+            target host string, a json string describing a configuration, or a dictionary of
+            configuration options. When using a dictionary or json string to configure target,
+            the possible values are same as tag_or_str_or_dict.
         """
         if not isinstance(tag_or_str_or_dict, (dict, str, Target)):
             raise ValueError("target has to be a string or dictionary.")
-        self.__init_handle_by_constructor__(_ffi_api.Target, tag_or_str_or_dict)
+        if host_tag_or_str_or_dict is not None:
+            self.__init_handle_by_constructor__(
+                _ffi_api.Target, Target(tag_or_str_or_dict), Target(host_tag_or_str_or_dict)
+            )
+        else:
+            self.__init_handle_by_constructor__(_ffi_api.Target, tag_or_str_or_dict)
 
     def __enter__(self):
         _ffi_api.TargetEnterScope(self)
@@ -147,6 +159,11 @@ def mattr(self):
     def libs(self):
         return list(self.attrs.get("libs", []))
 
+    @staticmethod
+    def list_kinds():
+        """Returns the list of available target names."""
+        return list(_ffi_api.ListTargetKinds())
+
 
 # TODO(@tvm-team): Deprecate the helper functions below. Encourage the usage of config dict instead.
 
@@ -232,9 +249,12 @@ def micro(model="unknown", options=None):
         Additional options
     """
     trans_table = {
-        "host": ["-mcpu=native"],
+        "host": [],
         "stm32f746xx": ["-mcpu=cortex-m7", "-march=armv7e-m"],
+        "nrf5340dk": ["-mcpu=cortex-m33"],
     }
+    if model not in trans_table:
+        raise ValueError(f"Model {model} not supported by tvm.target.micro.")
     opts = _merge_opts(
         trans_table[model] + ["-runtime=c", "--system-lib", f"-model={model}"],
         options,
@@ -288,6 +308,7 @@ def arm_cpu(model="unknown", options=None):
             "-model=stm32mp1",
             "-mtriple=armv7a-linux-gnueabihf",
             "-mattr=+neon,+vfp4,+thumb2",
+            "-mcpu=cortex-a7",
         ],
         "thunderx": [
             "-model=thunderx",
diff --git a/python/tvm/te/hybrid/calls.py b/python/tvm/te/hybrid/calls.py
index 761189115050..462066106a9d 100644
--- a/python/tvm/te/hybrid/calls.py
+++ b/python/tvm/te/hybrid/calls.py
@@ -23,18 +23,18 @@
 from tvm.target import Target
 from tvm.tir import expr as _expr
 from tvm.tir import call_intrin
-from tvm.tir.stmt import For
+from tvm.tir.stmt import ForKind
 
 from .utils import _internal_assert
 
 # pylint: disable=redefined-builtin,invalid-name
 
 LOOP_INTRIN = {
-    "range": For.Serial,
-    "unroll": For.Unrolled,
-    "parallel": For.Parallel,
-    "vectorize": For.Vectorized,
-    "const_range": (For.Unrolled,),
+    "range": ForKind.SERIAL,
+    "unroll": ForKind.UNROLLED,
+    "parallel": ForKind.PARALLEL,
+    "vectorize": ForKind.VECTORIZED,
+    "const_range": (ForKind.UNROLLED,),
 }
 
 
@@ -48,9 +48,9 @@ def _range(annotation, args):
         low, ext = args[0], args[1]
     if not tvm.tir.analysis.expr_deep_equal(low, const(0, dtype="int32")):
         ext = ext - low
-    for_type = LOOP_INTRIN[annotation]
+    kind = LOOP_INTRIN[annotation]
     iter_var = None
-    return iter_var, low, ext, for_type
+    return iter_var, low, ext, kind
 
 
 range = unroll = vectorize = parallel = const_range = _range  # pylint: disable=invalid-name
@@ -63,8 +63,8 @@ def bind(func_id, args):
     _internal_assert(isinstance(args[0], str), "A loop bind's first argument should be a string!")
     low, ext = const(0, "int32"), args[1]
     iter_var = tvm.te.thread_axis((low, ext), args[0])
-    for_type = None
-    return iter_var, low, ext, for_type
+    kind = None
+    return iter_var, low, ext, kind
 
 
 def _math_intrin(func_id, args):
@@ -167,3 +167,17 @@ def max_num_threads(func_id, args):
         _internal_assert(isinstance(args[0], _expr.IntImm), "In tvm bool should be uint")
         res = Target.current(args[0].value).max_num_threads
     return convert(res)
+
+
+def inf(func_id, args):
+    """Infinity"""
+    _internal_assert(func_id == "inf", "This function cannot be directly invoked!")
+    _internal_assert(args.__len__() == 1, "One argument accepted!")
+    return tvm.tir.max_value(args[0])
+
+
+def ninf(func_id, args):
+    """Negative infinity"""
+    _internal_assert(func_id == "ninf", "This function cannot be directly invoked!")
+    _internal_assert(args.__len__() == 1, "One argument accepted!")
+    return tvm.tir.min_value(args[0])
diff --git a/python/tvm/te/hybrid/parser.py b/python/tvm/te/hybrid/parser.py
index d47b2ee879fc..7bb85e3da83c 100644
--- a/python/tvm/te/hybrid/parser.py
+++ b/python/tvm/te/hybrid/parser.py
@@ -480,14 +480,14 @@ def visit_Call(self, node):
         return op
 
     def visit_For(self, node):
-        iter_var, low, ext, for_type = self.visit(node.iter)
+        iter_var, low, ext, kind = self.visit(node.iter)
         _internal_assert(
             isinstance(node.target, ast.Name), "The loop iterator should be a variable!"
         )
 
         _name = node.target.id
 
-        if isinstance(for_type, tuple):
+        if isinstance(kind, tuple):
             low = self.analyzer.simplify(low)
             ext = self.analyzer.simplify(ext)
             _internal_assert(
@@ -511,14 +511,14 @@ def visit_For(self, node):
             return concat_list_to_block(bodies)
 
         if iter_var is None:
-            _internal_assert(for_type is not None, "The loop iterating function parse error!")
+            _internal_assert(kind is not None, "The loop iterating function parse error!")
             offset = iter_var = tvm.te.var(_name)
             if not tvm.tir.analysis.expr_deep_equal(low, tvm.runtime.const(0, "int32")):
                 offset = iter_var + low
             self.add_symbol(_name, Symbol.LoopVar, offset)
             _body = visit_list_to_block(self.visit, node.body)
         else:
-            _internal_assert(for_type is None, "The loop bind function parse error!")
+            _internal_assert(kind is None, "The loop bind function parse error!")
             self.add_symbol(_name, Symbol.ThreadBind, iter_var)
             self.device += 1
             _body = visit_list_to_block(self.visit, node.body)
@@ -526,13 +526,13 @@ def visit_For(self, node):
 
         _body = self.wrap_up_realize(node, _body)
 
-        if for_type is None:
+        if kind is None:
             res = _body
         else:
             _internal_assert(
-                not isinstance(for_type, tuple), "Micro expansion should be handled before!"
+                not isinstance(kind, tuple), "Micro expansion should be handled before!"
             )
-            res = tvm.tir.For(iter_var, tvm.runtime.const(0, "int32"), ext, for_type, 0, _body)
+            res = tvm.tir.For(iter_var, tvm.runtime.const(0, "int32"), ext, kind, _body)
 
         self.symbols.pop(_name)
         return res
diff --git a/python/tvm/te/hybrid/runtime.py b/python/tvm/te/hybrid/runtime.py
index 7b90f8729014..615bd7e43a7d 100644
--- a/python/tvm/te/hybrid/runtime.py
+++ b/python/tvm/te/hybrid/runtime.py
@@ -111,6 +111,14 @@ def max_num_threads(allow_none=True):
     return Target.current(allow_none).max_num_threads
 
 
+def inf(dtype):
+    return numpy.iinfo(dtype).max
+
+
+def ninf(dtype):
+    return numpy.iinfo(dtype).min
+
+
 HYBRID_GLOBALS = {
     "unroll": range,
     "vectorize": range,
@@ -142,6 +150,8 @@ def max_num_threads(allow_none=True):
     "float64": numpy.float64,
     "ceil_div": lambda a, b: (a + b - 1) // b,
     "max_num_threads": max_num_threads,
+    "inf": inf,
+    "ninf": inf,
 }
 
 
diff --git a/python/tvm/testing.py b/python/tvm/testing.py
index 8311a63d0749..1cb43b29c521 100644
--- a/python/tvm/testing.py
+++ b/python/tvm/testing.py
@@ -76,6 +76,9 @@ def assert_allclose(actual, desired, rtol=1e-7, atol=1e-7):
     compares the `abs(actual-desired)` with `atol+rtol*abs(desired)`.  Since we
     often allow `desired` to be close to zero, we generally want non-zero `atol`.
     """
+    actual = np.asanyarray(actual)
+    desired = np.asanyarray(desired)
+    np.testing.assert_allclose(actual.shape, desired.shape)
     np.testing.assert_allclose(actual, desired, rtol=rtol, atol=atol, verbose=True)
 
 
@@ -511,6 +514,25 @@ def requires_cuda(*args):
     return _compose(args, _requires_cuda)
 
 
+def requires_cudagraph(*args):
+    """Mark a test as requiring the CUDA Graph Feature
+
+    This also marks the test as requiring cuda
+
+    Parameters
+    ----------
+    f : function
+        Function to mark
+    """
+    _requires_cudagraph = [
+        pytest.mark.skipif(
+            not nvcc.have_cudagraph(), reason="CUDA Graph is not supported in this environment"
+        ),
+        *requires_cuda(),
+    ]
+    return _compose(args, _requires_cudagraph)
+
+
 def requires_opencl(*args):
     """Mark a test as requiring the OpenCL runtime.
 
diff --git a/python/tvm/tir/__init__.py b/python/tvm/tir/__init__.py
index 1aac55fa9920..ad91eab64b52 100644
--- a/python/tvm/tir/__init__.py
+++ b/python/tvm/tir/__init__.py
@@ -27,15 +27,16 @@
 from .expr import Select, BufferLoad, ProducerLoad, Load, Ramp, Broadcast, Shuffle
 from .expr import Call, CallEffectKind, Let, IterVar, Any
 
-from .stmt import Stmt, LetStmt, AssertStmt, For
+from .stmt import Stmt, LetStmt, AssertStmt, ForKind, For
 from .stmt import BufferStore, BufferRealize, Store, ProducerStore, Allocate, AttrStmt
 from .stmt import ProducerRealize, SeqStmt
 from .stmt import IfThenElse, Evaluate, Prefetch, stmt_seq, stmt_list
+from .stmt import BufferRegion, MatchBufferRegion, Block, BlockRealize
 
 from .function import PrimFunc
 
 from .op import call_packed, call_intrin, call_pure_extern, call_extern
-from .op import call_llvm_intrin, call_llvm_pure_intrin, all, any, min_value, max_value, trace
+from .op import call_llvm_intrin, call_llvm_pure_intrin, ret, all, any, min_value, max_value, trace
 from .op import exp, exp2, exp10, log, log2, log10, log1p, ldexp
 from .op import sin, sinh, asin, asinh
 from .op import cos, cosh, acos, acosh
diff --git a/python/tvm/tir/buffer.py b/python/tvm/tir/buffer.py
index 2f50aa8e50a1..95966a5050e1 100644
--- a/python/tvm/tir/buffer.py
+++ b/python/tvm/tir/buffer.py
@@ -247,7 +247,10 @@ def decl_buffer(
         shape_dtype = shape[0].dtype if hasattr(shape[0], "dtype") else "int32"
         elem_offset = Var("%s_elem_offset" % name, shape_dtype)
     if data is None:
-        data = Var(name, PointerType(PrimType(dtype)), span)
+        # Bool is represented as uint1 in the IR, but stored as int8
+        storage_type = PrimType(dtype)
+        storage_type = PrimType("int8") if storage_type.dtype == "bool" else storage_type
+        data = Var(name, PointerType(storage_type), span)
     return _ffi_api.Buffer(
         data,
         dtype,
diff --git a/python/tvm/tir/ir_builder.py b/python/tvm/tir/ir_builder.py
index 6dcc8580a221..2ecbdeda8371 100644
--- a/python/tvm/tir/ir_builder.py
+++ b/python/tvm/tir/ir_builder.py
@@ -206,7 +206,7 @@ def scope_attr(self, node, attr_key, value):
             value = op.max(1, value)
         self.emit(lambda x: _stmt.AttrStmt(node, attr_key, value, x))
 
-    def for_range(self, begin, end, name="i", dtype="int32", for_type="serial"):
+    def for_range(self, begin, end, name="i", dtype="int32", kind="serial"):
         """Create a for iteration scope.
 
         Parameters
@@ -224,7 +224,7 @@ def for_range(self, begin, end, name="i", dtype="int32", for_type="serial"):
         dtype : str, optional
             The data type of iteration variable.
 
-        for_type : str, optional
+        kind : str, optional
             The special tag on the for loop.
 
         Returns
@@ -249,20 +249,49 @@ def for_range(self, begin, end, name="i", dtype="int32", for_type="serial"):
         extent = end if begin == 0 else (end - begin)
 
         def _exit_cb():
-            if for_type == "serial":
-                for_type_id = 0
-            elif for_type == "parallel":
-                for_type_id = 1
-            elif for_type == "vectorize":
-                for_type_id = 2
-            elif for_type == "unroll":
-                for_type_id = 3
+            if kind == "serial":
+                kind_id = _stmt.ForKind.SERIAL
+            elif kind == "parallel":
+                kind_id = _stmt.ForKind.PARALLEL
+            elif kind == "vectorize":
+                kind_id = _stmt.ForKind.VECTORIZED
+            elif kind == "unroll":
+                kind_id = _stmt.ForKind.UNROLLED
             else:
-                raise ValueError("Unknown for_type")
-            self.emit(_stmt.For(loop_var, begin, extent, for_type_id, 0, self._pop_seq()))
+                raise ValueError("Unknown kind")
+            self.emit(_stmt.For(loop_var, begin, extent, kind_id, self._pop_seq()))
 
         return WithScope(loop_var, _exit_cb)
 
+    def while_loop(self, condition):
+        """Create a while loop scope.
+
+        Parameters
+        ----------
+        condition : Expr
+            The termination condition.
+
+        Returns
+        -------
+        loop_scope : With.Scope of Var
+            The while scope.
+
+        Examples
+        --------
+        .. code-block:: python
+
+            ib = tvm.tir.ir_builder.create()
+            iterations = ib.allocate("int32", (1,), name="iterations", scope="local")
+            with ib.while_loop(iterations[0] < 10):
+                iterations[0] += 1
+        """
+        self._seq_stack.append([])
+
+        def _exit_cb():
+            self.emit(_stmt.While(condition, self._pop_seq()))
+
+        return WithScope(None, _exit_cb)
+
     def if_scope(self, cond):
         """Create an if scope.
 
diff --git a/python/tvm/tir/op.py b/python/tvm/tir/op.py
index ca61be4fcd83..182264f0db92 100644
--- a/python/tvm/tir/op.py
+++ b/python/tvm/tir/op.py
@@ -221,6 +221,22 @@ def call_llvm_pure_intrin(dtype, name, *args, span=None):
     )
 
 
+def ret(val):
+    """Create a tir return expression
+
+    Parameters
+    ----------
+    val : Expr
+        The returned tir expression, whose data type is int, float or void pointer.
+
+    Returns
+    -------
+    ret : PrimExpr
+        The return expression
+    """
+    return call_intrin(val.dtype, "tir.ret", val)
+
+
 def any(*args, span=None):
     """Create a new experssion of the union of all conditions in the arguments
 
@@ -241,10 +257,10 @@ def any(*args, span=None):
         raise ValueError("Any must take at least 1 argument")
     if len(args) == 1:
         return args[0]
-    ret = _ffi_api._OpOr(args[0], args[1], span)
+    val = _ffi_api._OpOr(args[0], args[1], span)
     for i in range(2, len(args)):
-        ret = _ffi_api._OpOr(ret, args[i], span)
-    return ret
+        val = _ffi_api._OpOr(val, args[i], span)
+    return val
 
 
 def all(*args, span=None):
@@ -268,10 +284,10 @@ def all(*args, span=None):
         raise ValueError("Any must take at least 1 argument")
     if len(args) == 1:
         return args[0]
-    ret = _ffi_api._OpAnd(args[0], args[1], span)
+    val = _ffi_api._OpAnd(args[0], args[1], span)
     for i in range(2, len(args)):
-        ret = _ffi_api._OpAnd(ret, args[i], span)
-    return ret
+        val = _ffi_api._OpAnd(val, args[i], span)
+    return val
 
 
 @tvm._ffi.register_func("tvm.default_trace_action")
diff --git a/python/tvm/tir/stmt.py b/python/tvm/tir/stmt.py
index 6857b68c261d..47462066c364 100644
--- a/python/tvm/tir/stmt.py
+++ b/python/tvm/tir/stmt.py
@@ -26,10 +26,15 @@
     assert isinstance(st, tvm.tir.stmt.Store)
     assert(st.buffer_var == a)
 """
+from typing import List, Optional, Mapping
+from enum import IntEnum
 import tvm._ffi
 
 from tvm.runtime import Object
+from tvm.ir import Span, PrimExpr, Range
 from . import _ffi_api
+from .buffer import Buffer
+from .expr import IterVar
 
 
 class Stmt(Object):
@@ -82,6 +87,22 @@ def __init__(self, condition, message, body, span=None):
         self.__init_handle_by_constructor__(_ffi_api.AssertStmt, condition, message, body, span)
 
 
+class ForKind(IntEnum):
+    """The kind of the for loop.
+
+    note
+    ----
+    ForKind can change the control flow semantics
+    of the loop and need to be considered in all TIR passes.
+    """
+
+    SERIAL = 0
+    PARALLEL = 1
+    VECTORIZED = 2
+    UNROLLED = 3
+    THREAD_BINDING = 4
+
+
 @tvm._ffi.register_object("tir.For")
 class For(Stmt):
     """For node.
@@ -92,32 +113,74 @@ class For(Stmt):
         The loop variable.
 
     min_val : PrimExpr
-        The begining value.
+        The beginning value.
 
     extent : PrimExpr
         The length of the loop.
 
-    for_type : int
-        The for type.
-
-    device_api : int
-        The device api type.
+    kind : ForKind
+        The type of the for.
 
     body : Stmt
         The body statement.
 
+    thread_binding: Optional[tir.IterVar]
+        The thread this loop binds to. Only valid
+        if kind is ThreadBinding
+
+    annotations: tvm.ir.Map
+        Additional annotation hints.
+
     span : Optional[Span]
         The location of this itervar in the source code.
     """
 
-    Serial = 0
-    Parallel = 1
-    Vectorized = 2
-    Unrolled = 3
+    def __init__(
+        self,
+        loop_var,
+        min_val,
+        extent,
+        kind,
+        body,
+        thread_binding=None,
+        annotations=None,
+        span=None,
+    ):
+        self.__init_handle_by_constructor__(
+            _ffi_api.For,
+            loop_var,
+            min_val,
+            extent,
+            kind,
+            body,
+            thread_binding,
+            annotations,
+            span,
+        )
+
+
+@tvm._ffi.register_object("tir.While")
+class While(Stmt):
+    """While node.
+
+    Parameters
+    ----------
+    condition : PrimExpr
+        The termination condition.
+
+    body : Stmt
+        The body statement.
+
+    span : Optional[Span]
+        The location of this itervar in the source code.
+    """
 
-    def __init__(self, loop_var, min_val, extent, for_type, device_api, body, span=None):
+    def __init__(self, condition, body, span=None):
         self.__init_handle_by_constructor__(
-            _ffi_api.For, loop_var, min_val, extent, for_type, device_api, body, span
+            _ffi_api.While,
+            condition,
+            body,
+            span,
         )
 
 
@@ -395,6 +458,164 @@ def __init__(self, buffer, bounds, span=None):
         self.__init_handle_by_constructor__(_ffi_api.Prefetch, buffer, bounds, span)
 
 
+@tvm._ffi.register_object("tir.BufferRegion")
+class BufferRegion(Object):
+    """BufferRegion node.
+
+    Parameters
+    ----------
+    buffer : Buffer
+        The buffer of the buffer region
+
+    region : List[Range]
+        The region array of the buffer region
+    """
+
+    buffer: Buffer
+    region: List[Range]
+
+    def __init__(self, buffer: Buffer, region: List[Range]):
+        self.__init_handle_by_constructor__(_ffi_api.BufferRegion, buffer, region)
+
+
+@tvm._ffi.register_object("tir.MatchBufferRegion")
+class MatchBufferRegion(Object):
+    """MatchBufferRegion node.
+
+    Parameters
+    ----------
+    buffer : Buffer
+        The target buffer
+
+    source : BufferRegion
+        The region of source buffer
+    """
+
+    buffer: Buffer
+    source: BufferRegion
+
+    def __init__(self, buffer: Buffer, source: BufferRegion):
+        self.__init_handle_by_constructor__(_ffi_api.MatchBufferRegion, buffer, source)
+
+
+@tvm._ffi.register_object("tir.Block")
+class Block(Stmt):
+    """Block node.
+
+    Parameters
+    ----------
+    iter_vars : List[IterVar]
+        The block Variable.
+
+    reads : List[BufferRegion]
+        The read buffer regions of the block.
+
+    writes: List[BufferRegion]
+        The write buffer regions of the block.
+
+    name_hint: str
+        the name_hint of the block.
+
+    body: Stmt
+        The body of the block.
+
+    init: Optional[Stmt]
+        The init block of the reduction block
+
+    alloc_buffers: Optional[list[Buffer]]
+        The buffer allocations
+
+    match_buffers: Optional[List[MatchBufferRegion]]
+        The subregion buffer match
+
+    annotations: Optional[Mapping[str, Object]]
+        Additional annotation hints.
+
+    span : Optional[Span]
+        The location of this block in the source code.
+    """
+
+    iter_vars: List[IterVar]
+    reads: List[BufferRegion]
+    writes: List[BufferRegion]
+    name_hint: str
+    body: Stmt
+    init: Optional[Stmt]
+    alloc_buffers: Optional[List[Buffer]]
+    match_buffers: Optional[List[MatchBufferRegion]]
+    annotations: Optional[Mapping[str, Object]]
+    span: Optional[Span]
+
+    def __init__(
+        self,
+        iter_vars: List[IterVar],
+        reads: List[BufferRegion],
+        writes: List[BufferRegion],
+        name_hint: str,
+        body: Stmt,
+        init: Optional[Stmt] = None,
+        alloc_buffers: Optional[List[Buffer]] = None,
+        match_buffers: Optional[List[MatchBufferRegion]] = None,
+        annotations: Optional[Mapping[str, Object]] = None,
+        span: Optional[Span] = None,
+    ):
+        if alloc_buffers is None:
+            alloc_buffers = []
+        if match_buffers is None:
+            match_buffers = []
+        if annotations is None:
+            annotations = {}
+        self.__init_handle_by_constructor__(
+            _ffi_api.Block,
+            iter_vars,
+            reads,
+            writes,
+            name_hint,
+            body,
+            init,
+            alloc_buffers,
+            match_buffers,
+            annotations,
+            span,
+        )
+
+
+@tvm._ffi.register_object("tir.BlockRealize")
+class BlockRealize(Stmt):
+    """BlockRealize node.
+
+    Parameters
+    ----------
+    iter_values : List[PrimExpr]
+        The binding values of the block var.
+
+    predicate : PrimExpr
+        The predicate of the block.
+
+    block : Block
+        The block to realize
+
+    span : Optional[Span]
+        The location of this block_realize in the source code.
+    """
+
+    iter_values: List[PrimExpr]
+    predicate: PrimExpr
+    block: Block
+    span: Optional[Span]
+
+    def __init__(
+        self,
+        iter_values: List[PrimExpr],
+        predicate: PrimExpr,
+        block: Block,
+        span: Optional[Span] = None,
+    ):
+        self.__init_handle_by_constructor__(
+            _ffi_api.BlockRealize, iter_values, predicate, block, span
+        )
+
+
 def stmt_seq(*args):
     """Make sequence of statements
 
diff --git a/python/tvm/tir/transform/function_pass.py b/python/tvm/tir/transform/function_pass.py
index 59b3ecd6237d..7cff1f66a625 100644
--- a/python/tvm/tir/transform/function_pass.py
+++ b/python/tvm/tir/transform/function_pass.py
@@ -130,7 +130,7 @@ def transform(func, mod, ctx):
     """
 
     if opt_level is None:
-        raise ValueError("Please provide opt_level for the funtion pass.")
+        raise ValueError("Please provide opt_level for the function pass.")
 
     required = required if required else []
     if not isinstance(required, (list, tuple)):
diff --git a/python/tvm/topi/__init__.py b/python/tvm/topi/__init__.py
index 97951d941f64..c196b33cf880 100644
--- a/python/tvm/topi/__init__.py
+++ b/python/tvm/topi/__init__.py
@@ -38,8 +38,13 @@
 from .broadcast import *
 from .sort import *
 from .scatter import *
+from .sparse_fill_empty_rows import *
+from .sparse_reshape import *
 from .scatter_add import *
 from .argwhere import *
+from .cumsum import *
+from .einsum import *
+from .unique import *
 from . import generic
 from . import nn
 from . import x86
@@ -54,6 +59,7 @@
 from . import image
 from . import sparse
 from . import hls
+from . import random
 
 # error reporting
 from .utils import InvalidShapeError
diff --git a/python/tvm/topi/arm_cpu/conv2d_int8.py b/python/tvm/topi/arm_cpu/conv2d_int8.py
index 445b9ec0c113..fc7e4036341a 100644
--- a/python/tvm/topi/arm_cpu/conv2d_int8.py
+++ b/python/tvm/topi/arm_cpu/conv2d_int8.py
@@ -32,12 +32,12 @@
 from .arm_utils import get_tiling_B_interleaved_t
 
 
-def _get_default_config(cfg, data, kernel, strides, padding, out_dtype):
+def _get_default_config(cfg, data, kernel, strides, padding, dilation, out_dtype):
     """
     Get default int8 schedule config for the workload
     """
-    wkl = _get_conv2d_workload(data, kernel, strides, padding, out_dtype)
-    is_kernel_1x1 = wkl.hkernel == 1 and wkl.wkernel == 1
+    wkl = _get_conv2d_workload(data, kernel, strides, padding, dilation, out_dtype)
+    is_kernel_1x1 = wkl.kernel_h == 1 and wkl.kernel_w == 1
     if is_kernel_1x1:
         conv2d_generic.fallback_schedule_cpu_1x1_int8(cfg, wkl, int32_lanes=2, num_int8_elements=4)
     else:
@@ -65,6 +65,7 @@ def conv2d_NCHWc_int8(cfg, data, kernel, strides, padding, dilation, layout, out
             te.placeholder((num_filter, in_channel, kh, kw), dtype=kernel.dtype),
             strides,
             padding,
+            dilation,
             out_dtype,
         )
     return nn.conv2d_NCHWc_int8_compute(
diff --git a/python/tvm/topi/arm_cpu/depthwise_conv2d.py b/python/tvm/topi/arm_cpu/depthwise_conv2d.py
index 441b0a5a3688..c21480724ae4 100644
--- a/python/tvm/topi/arm_cpu/depthwise_conv2d.py
+++ b/python/tvm/topi/arm_cpu/depthwise_conv2d.py
@@ -692,7 +692,7 @@ def _schedule_spatial_pack(cfg, s, data_vec, kernel_vec, conv, output, last):
     if kernel_vec.op.name == "kernel_vec":
         co, _, _, _, _ = s[kernel_vec].op.axis
         if autotvm.GLOBAL_SCOPE.in_tuning:
-            # kernel packing will be pre-computed during compliation, so we skip
+            # kernel packing will be pre-computed during compilation, so we skip
             # this part to make tuning records correct
             s[kernel_vec].pragma(co, "debug_skip_region")
         else:
diff --git a/python/tvm/topi/cuda/__init__.py b/python/tvm/topi/cuda/__init__.py
index 23c625ae7ff7..c2f55668d2e2 100644
--- a/python/tvm/topi/cuda/__init__.py
+++ b/python/tvm/topi/cuda/__init__.py
@@ -17,8 +17,6 @@
 
 # pylint: disable=redefined-builtin, wildcard-import
 """CUDA specific declaration and schedules."""
-from __future__ import absolute_import as _abs
-
 from .conv1d import *
 from .conv1d_transpose_ncw import *
 from .conv2d import *
@@ -42,6 +40,7 @@
 from .pooling import *
 from .nn import schedule_lrn
 from .batch_matmul import *
+from .batch_matmul_tensorcore import *
 from .vision import *
 from .ssd import *
 from .nms import get_valid_counts, non_max_suppression
@@ -54,4 +53,8 @@
 from .conv2d_hwnc_tensorcore import *
 from .correlation import *
 from .sparse import *
+from . import tensorcore_alter_op
 from .argwhere import *
+from .scan import *
+from .sparse_reshape import *
+from .unique import *
diff --git a/python/tvm/topi/cuda/argwhere.py b/python/tvm/topi/cuda/argwhere.py
index e39004dc76a9..cc6c4c26eddb 100644
--- a/python/tvm/topi/cuda/argwhere.py
+++ b/python/tvm/topi/cuda/argwhere.py
@@ -21,169 +21,135 @@
 
 import tvm
 from tvm import te
-from tvm._ffi import get_global_func
 from .injective import schedule_injective_from_existing
-from .nms import atomic_add
-from .sort import topk, topk_thrust, argsort, argsort_thrust
+from .scan import exclusive_scan
 from .. import tag
-from ..transform import strided_slice, adv_index, squeeze
-
-logger = logging.getLogger("topi")
+from ..utils import ceil_div, prod
+from ..transform import reshape
+from ..broadcast import not_equal
+from ..math import cast
 
 
-def _get_sort_func(mode=0):
-    """Get sort function for argwhere. mode 0 for topk and others for argsort."""
-    if get_global_func("tvm.contrib.thrust.sort", allow_missing=True):
-        ret = topk_thrust if mode == 0 else argsort_thrust
-    else:
-        logger.warning(
-            "It's highly recommended to enable thrust library with set(USE_THRUST ON)"
-            " when compiling argwhere for cuda target. Otherwise, it can result in"
-            " significant performance degradation or incorrect result"
-        )
-        ret = topk if mode == 0 else argsort
+logger = logging.getLogger("topi")
 
-    return ret
+fdiv = tvm.tir.floordiv
+fmod = tvm.tir.floormod
 
 
-def argwhere_1d_ir(condition, out):
-    """Low level IR for argwhere 1D
+def compact_nonzero_indices_ir(condition, write_indices, out, do_write_func):
+    """Copy nonzero indices to the corresponding write locations.
 
     Parameters
     ----------
     condition : Buffer
-        The condition buffer.
+        The input condition.
+
+    write_indices : Buffer
+        The result of exclusive scan on a boolean array, where True indicates that
+        the condition is non zero at that position.
 
     out : Buffer
-        The output buffer.
+        The output buffer to copy indices to.
+
+    do_write_func : a function
+        A callback that accepts an output buffer, a dst index to write to, and a src index.
 
     Returns
     -------
     stmt : Stmt
         The result IR statement.
     """
+
     ib = tvm.tir.ir_builder.create()
-    a0 = condition.shape[0]
+    size_1d = prod(condition.shape)
 
     condition = ib.buffer_ptr(condition)
+    write_indices = ib.buffer_ptr(write_indices)
     out = ib.buffer_ptr(out)
 
-    valid_index = ib.allocate("int32", (1,), name="valid_index", scope="global")
-    tmp = ib.allocate("int32", (1,), name="tmp", scope="local")
-    one_count = tvm.tir.const(1, dtype="int32")
-
-    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
-    nthread_tx = max_threads
-    # Limit threads to a single block to make sure atomic_add works normally.
+    nthread_tx = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+    nthread_bx = ceil_div(size_1d, nthread_tx)
     tx = te.thread_axis("threadIdx.x")
+    bx = te.thread_axis("blockIdx.x")
     ib.scope_attr(tx, "thread_extent", nthread_tx)
-    len_inner_for = a0 // nthread_tx + 1
-    valid_index[0] = 0
+    ib.scope_attr(bx, "thread_extent", nthread_bx)
 
-    with ib.for_range(0, len_inner_for, name="i") as i:
-        idx = tx * len_inner_for + i
-        with ib.if_scope(idx < a0):
+    with ib.new_scope():
+        idx = bx * nthread_tx + tx
+        with ib.if_scope(idx < size_1d):
             with ib.if_scope(condition[idx] != 0):
-                tmp[0] = atomic_add(
-                    tvm.tir.call_intrin("handle", "tir.address_of", valid_index[0]),
-                    one_count,
-                )
-                out[tmp[0]] = idx
+                do_write_func(out, write_indices[idx], idx)
 
     return ib.get()
 
 
-def argwhere_1d(output_shape, condition):
-    """Compute for argwhere 1D
+def argwhere_common(output_shape, condition, do_write_func):
+    """A common compute used by argwhere of various ranks.
 
     Parameters
     ----------
-    condition : list of int or tvm.tir.Any
-        The output shape
+    output_shape : list of int or tvm.tir.Any
+        Tensor with output shape info.
 
-    out : tvm.te.Tensor
-        Tensor with boolean values.
+    condition : tvm.te.Tensor
+        The input condition.
+
+    do_write_func : a function
+        A callback that accepts an output buffer, a dst index to write to, and a src index.
 
     Returns
     -------
-    stmt : Stmt
-        The result IR statement.
+    out : tvm.te.Tensor
+        Indices of non-zero elements.
     """
+
+    flags = not_equal(condition, tvm.tir.const(0))
+    flags_1d = reshape(flags, (prod(flags.shape),))
+    write_indices = exclusive_scan(cast(flags_1d, dtype="int32"))
+
     condition_buf = tvm.tir.decl_buffer(
         condition.shape, condition.dtype, "data_buf", data_alignment=8
     )
+    write_indices_buf = tvm.tir.decl_buffer(
+        write_indices.shape, write_indices.dtype, "write_indices_buf", data_alignment=8
+    )
     out_buf = tvm.tir.decl_buffer(output_shape, "int32", "out_buf", data_alignment=8)
 
     out = te.extern(
         [output_shape],
-        [condition],
-        lambda ins, outs: argwhere_1d_ir(ins[0], outs[0]),
+        [condition, write_indices],
+        lambda ins, outs: compact_nonzero_indices_ir(ins[0], ins[1], outs[0], do_write_func),
         dtype=["int32"],
-        in_buffers=[condition_buf],
+        in_buffers=[condition_buf, write_indices_buf],
         out_buffers=[out_buf],
-        name="argwhere_1d",
-        tag="argwhere1d_gpu",
+        name="argwhere",
+        tag="argwhere_gpu",
     )
 
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)) and int(out.shape[0]) <= 1:
-        return out
-
-    sorted_out = _get_sort_func()(
-        out, k=0, axis=0, ret_type="values", is_ascend="True", dtype="int32"
-    )
-
-    return sorted_out
+    return out
 
 
-def argwhere_2d_ir(condition, out):
-    """Low level IR for argwhere 2D
+def argwhere_1d(output_shape, condition):
+    """Compute for argwhere 1D
 
     Parameters
     ----------
-    condition : Buffer
-        The condition buffer.
+    condition : list of int or tvm.tir.Any
+        The output shape
 
-    out : Buffer
-        The output buffer.
+    out : tvm.te.Tensor
+        Tensor with boolean values.
 
     Returns
     -------
     stmt : Stmt
         The result IR statement.
     """
-    ib = tvm.tir.ir_builder.create()
-    a0 = condition.shape[0]
-    a1 = condition.shape[1]
 
-    condition = ib.buffer_ptr(condition)
-    out = ib.buffer_ptr(out)
+    def do_write(out, write_index, idx):
+        out[write_index] = idx
 
-    valid_index = ib.allocate("int32", (1,), name="valid_index", scope="local")
-    tmp = ib.allocate("int32", (1,), name="tmp", scope="local")
-    one_count = tvm.tir.const(1, dtype="int32")
-
-    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
-    nthread_tx = max_threads
-
-    # Limit threads to a single block to make sure atomic_add works normally.
-    tx = te.thread_axis("threadIdx.x")
-    ib.scope_attr(tx, "thread_extent", nthread_tx)
-    len_inner_for = (a0 * a1) // nthread_tx + 1
-
-    valid_index[0] = 0
-
-    with ib.for_range(0, len_inner_for, name="i") as i:
-        idx = tx * len_inner_for + i
-        with ib.if_scope(idx < (a0 * a1)):
-            with ib.if_scope(condition[idx] != 0):
-                tmp[0] = atomic_add(
-                    tvm.tir.call_intrin("handle", "tir.address_of", valid_index[0]),
-                    one_count,
-                )
-                out[tmp[0] * 2] = tvm.tir.floordiv(idx, a1)
-                out[tmp[0] * 2 + 1] = tvm.tir.floormod(idx, a1)
-
-    return ib.get()
+    return argwhere_common(output_shape, condition, do_write)
 
 
 def argwhere_2d(output_shape, condition):
@@ -202,109 +168,13 @@ def argwhere_2d(output_shape, condition):
     stmt : Stmt
         The result IR statement.
     """
-    condition_buf = tvm.tir.decl_buffer(
-        condition.shape, condition.dtype, "data_buf", data_alignment=8
-    )
-    out_buf = tvm.tir.decl_buffer(output_shape, "int32", "out_buf", data_alignment=8)
-
-    out = te.extern(
-        [output_shape],
-        [condition],
-        lambda ins, outs: argwhere_2d_ir(ins[0], outs[0]),
-        dtype=["int32"],
-        in_buffers=[condition_buf],
-        out_buffers=[out_buf],
-        name="argwhere_2d",
-        tag="argwhere2d_gpu",
-    )
-
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)) and int(out.shape[0]) <= 1:
-        return out
-
-    sort_func = _get_sort_func(1)
-
-    # sort the output from the least significant to the most significant
-    # column.
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)):
-        out1 = strided_slice(out, [0, 1], [out.shape[0], 2])
-        out2 = sort_func(out1, axis=0, dtype="int32")
-        out3 = squeeze(out2)
-        out = adv_index(out, [out3])
-
-        out1 = strided_slice(out, [0, 0], [out.shape[0], 1])
-        out2 = sort_func(out1, axis=0, dtype="int32")
-        out3 = squeeze(out2)
-
-        out = adv_index(out, [out3])
-    else:
-        out1 = strided_slice(out, [0, 1], [out.shape[0], 2], [1, 1])
-        out2 = sort_func(out1, axis=0, dtype="int32")
-        out3 = squeeze(out2)
-        out = adv_index(out, [out3])
-
-        out1 = strided_slice(out, [0, 0], [out.shape[0], 1], [1, 1])
-        out2 = sort_func(out1, axis=0, dtype="int32")
-        out3 = squeeze(out2)
-        out = adv_index(out, [out3])
-    return out
-
-
-def argwhere_3d_ir(condition, out):
-    """Low level IR for argwhere 3D
-
-    Parameters
-    ----------
-    condition : Buffer
-        The condition buffer.
-
-    out : Buffer
-        The output buffer.
-
-    Returns
-    -------
-    stmt : Stmt
-        The result IR statement.
-    """
-    ib = tvm.tir.ir_builder.create()
-    a0 = condition.shape[0]
-    a1 = condition.shape[1]
-    a2 = condition.shape[2]
-    s1 = a1 * a2
-    s0 = a0 * s1
-
-    condition = ib.buffer_ptr(condition)
-    out = ib.buffer_ptr(out)
-
-    valid_index = ib.allocate("int32", (1,), name="valid_index", scope="local")
-    tmp = ib.allocate("int32", (1,), name="tmp", scope="local")
-    one_count = tvm.tir.const(1, dtype="int32")
 
-    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
-    nthread_tx = max_threads
+    def do_write(out, write_index, idx):
+        a1 = condition.shape[1]
+        out[write_index * 2] = tvm.tir.floordiv(idx, a1)
+        out[write_index * 2 + 1] = tvm.tir.floormod(idx, a1)
 
-    # Limit threads to a single block to make sure atomic_add works normally.
-    tx = te.thread_axis("threadIdx.x")
-    ib.scope_attr(tx, "thread_extent", nthread_tx)
-    len_inner_for = s0 // nthread_tx + 1
-
-    fdiv = tvm.tir.floordiv
-    fmod = tvm.tir.floormod
-
-    valid_index[0] = 0
-
-    with ib.for_range(0, len_inner_for, name="i") as i:
-        idx = tx * len_inner_for + i
-        with ib.if_scope(idx < s0):
-            with ib.if_scope(condition[idx] != 0):
-                tmp[0] = atomic_add(
-                    tvm.tir.call_intrin("handle", "tir.address_of", valid_index[0]),
-                    one_count,
-                )
-                out[tmp[0] * 3] = fdiv(idx, s1)
-                out[tmp[0] * 3 + 1] = fdiv(fmod(idx, s1), a2)
-                out[tmp[0] * 3 + 2] = fmod(idx, a2)
-
-    return ib.get()
+    return argwhere_common(output_shape, condition, do_write)
 
 
 def argwhere_3d(output_shape, condition):
@@ -323,103 +193,15 @@ def argwhere_3d(output_shape, condition):
     stmt : Stmt
         The result IR statement.
     """
-    condition_buf = tvm.tir.decl_buffer(
-        condition.shape, condition.dtype, "data_buf", data_alignment=8
-    )
-    out_buf = tvm.tir.decl_buffer(output_shape, "int32", "out_buf", data_alignment=8)
-
-    out = te.extern(
-        [output_shape],
-        [condition],
-        lambda ins, outs: argwhere_3d_ir(ins[0], outs[0]),
-        dtype=["int32"],
-        in_buffers=[condition_buf],
-        out_buffers=[out_buf],
-        name="argwhere_3d",
-        tag="argwhere3d_gpu",
-    )
-
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)) and int(out.shape[0]) <= 1:
-        return out
-
-    # sort the output from the least significant to the most significant
-    # column.
-    sort_func = _get_sort_func(1)
-
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)):
-        for i in reversed(range(3)):
-            out1 = strided_slice(out, [0, i], [out.shape[0], i + 1])
-            out2 = sort_func(out1, axis=0, dtype="int32")
-            out3 = squeeze(out2)
-            out = adv_index(out, [out3])
-    else:
-        for i in reversed(range(3)):
-            out1 = strided_slice(out, [0, i], [out.shape[0], i + 1], [1, 1])
-            out2 = sort_func(out1, axis=0, dtype="int32")
-            out3 = squeeze(out2)
-            out = adv_index(out, [out3])
-    return out
-
-
-def argwhere_4d_ir(condition, out):
-    """Low level IR for argwhere 4D
-
-    Parameters
-    ----------
-    condition : Buffer
-        The condition buffer.
-
-    out : Buffer
-        The output buffer.
-
-    Returns
-    -------
-    stmt : Stmt
-        The result IR statement.
-    """
-    ib = tvm.tir.ir_builder.create()
-    a0 = condition.shape[0]
-    a1 = condition.shape[1]
-    a2 = condition.shape[2]
-    a3 = condition.shape[3]
-    s1 = a2 * a3
-    s2 = a1 * s1
-    s0 = a0 * s2
-
-    condition = ib.buffer_ptr(condition)
-    out = ib.buffer_ptr(out)
-
-    valid_index = ib.allocate("int32", (1,), name="valid_index", scope="local")
-    tmp = ib.allocate("int32", (1,), name="tmp", scope="local")
-    one_count = tvm.tir.const(1, dtype="int32")
-
-    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
-    nthread_tx = max_threads
-
-    # Limit threads to a single block to make sure atomic_add works normally.
-    tx = te.thread_axis("threadIdx.x")
-    ib.scope_attr(tx, "thread_extent", nthread_tx)
-    len_inner_for = s0 // nthread_tx + 1
-
-    fdiv = tvm.tir.floordiv
-    fmod = tvm.tir.floormod
 
-    valid_index[0] = 0
+    def do_write(out, write_index, idx):
+        _, a1, a2 = condition.shape
+        s1 = a1 * a2
+        out[write_index * 3] = fdiv(idx, s1)
+        out[write_index * 3 + 1] = fdiv(fmod(idx, s1), a2)
+        out[write_index * 3 + 2] = fmod(idx, a2)
 
-    with ib.for_range(0, len_inner_for, name="i") as i:
-        idx = tx * len_inner_for + i
-        with ib.if_scope(idx < s0):
-            with ib.if_scope(condition[idx] != 0):
-                tmp[0] = atomic_add(
-                    tvm.tir.call_intrin("handle", "tir.address_of", valid_index[0]),
-                    one_count,
-                )
-                out[tmp[0] * 4] = fdiv(idx, s2)
-                out[tmp[0] * 4 + 1] = fdiv(fmod(idx, s2), s1)
-                out[tmp[0] * 4 + 2] = fdiv(fmod(idx, s1), a3)
-                out[tmp[0] * 4 + 3] = fmod(idx, a3)
-
-    return ib.get()
+    return argwhere_common(output_shape, condition, do_write)
 
 
 def argwhere_4d(output_shape, condition):
@@ -438,106 +220,17 @@ def argwhere_4d(output_shape, condition):
     stmt : Stmt
         The result IR statement.
     """
-    condition_buf = tvm.tir.decl_buffer(
-        condition.shape, condition.dtype, "data_buf", data_alignment=8
-    )
-    out_buf = tvm.tir.decl_buffer(output_shape, "int32", "out_buf", data_alignment=8)
-
-    out = te.extern(
-        [output_shape],
-        [condition],
-        lambda ins, outs: argwhere_4d_ir(ins[0], outs[0]),
-        dtype=["int32"],
-        in_buffers=[condition_buf],
-        out_buffers=[out_buf],
-        name="argwhere_4d",
-        tag="argwhere4d_gpu",
-    )
-
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)) and int(out.shape[0]) <= 1:
-        return out
-
-    # sort the output from the least significant to the most significant
-    # column.
-    sort_func = _get_sort_func(1)
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)):
-        for i in reversed(range(4)):
-            out1 = strided_slice(out, [0, i], [out.shape[0], i + 1])
-            out2 = sort_func(out1, axis=0, dtype="int32")
-            out3 = squeeze(out2)
-            out = adv_index(out, [out3])
-    else:
-        for i in reversed(range(4)):
-            out1 = strided_slice(out, [0, i], [out.shape[0], i + 1], [1, 1])
-            out2 = sort_func(out1, axis=0, dtype="int32")
-            out3 = squeeze(out2)
-            out = adv_index(out, [out3])
-
-    return out
-
-
-def argwhere_5d_ir(condition, out):
-    """Low level IR for argwhere 5D
-
-    Parameters
-    ----------
-    condition : Buffer
-        The condition buffer.
-
-    out : Buffer
-        The output buffer.
-
-    Returns
-    -------
-    stmt : Stmt
-        The result IR statement.
-    """
-    ib = tvm.tir.ir_builder.create()
-    a0 = condition.shape[0]
-    a1 = condition.shape[1]
-    a2 = condition.shape[2]
-    a3 = condition.shape[3]
-    a4 = condition.shape[4]
-    s1 = a3 * a4
-    s2 = a2 * s1
-    s3 = a1 * s2
-    s0 = a0 * s3
-
-    condition = ib.buffer_ptr(condition)
-    out = ib.buffer_ptr(out)
-
-    valid_index = ib.allocate("int32", (1,), name="valid_index", scope="local")
-    tmp = ib.allocate("int32", (1,), name="tmp", scope="local")
-    one_count = tvm.tir.const(1, dtype="int32")
-
-    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
-    nthread_tx = max_threads
 
-    # Limit threads to a single block to make sure atomic_add works normally.
-    tx = te.thread_axis("threadIdx.x")
-    ib.scope_attr(tx, "thread_extent", nthread_tx)
-    len_inner_for = s0 // nthread_tx + 1
+    def do_write(out, write_index, idx):
+        _, a1, a2, a3 = condition.shape
+        s1 = a2 * a3
+        s2 = a1 * s1
+        out[write_index * 4] = fdiv(idx, s2)
+        out[write_index * 4 + 1] = fdiv(fmod(idx, s2), s1)
+        out[write_index * 4 + 2] = fdiv(fmod(idx, s1), a3)
+        out[write_index * 4 + 3] = fmod(idx, a3)
 
-    fdiv = tvm.tir.floordiv
-    fmod = tvm.tir.floormod
-
-    valid_index[0] = 0
-
-    with ib.for_range(0, len_inner_for, name="i") as i:
-        idx = tx * len_inner_for + i
-        with ib.if_scope(idx < s0):
-            with ib.if_scope(condition[idx] != 0):
-                tmp[0] = atomic_add(
-                    tvm.tir.call_intrin("handle", "tir.address_of", valid_index[0]),
-                    one_count,
-                )
-                out[tmp[0] * 5] = fdiv(idx, s3)
-                out[tmp[0] * 5 + 1] = fdiv(fmod(idx, s3), s2)
-                out[tmp[0] * 5 + 2] = fdiv(fmod(idx, s2), s1)
-                out[tmp[0] * 5 + 3] = fdiv(fmod(idx, s1), a4)
-                out[tmp[0] * 5 + 4] = fmod(idx, a4)
-
-    return ib.get()
+    return argwhere_common(output_shape, condition, do_write)
 
 
 def argwhere_5d(output_shape, condition):
@@ -556,42 +249,19 @@ def argwhere_5d(output_shape, condition):
     stmt : Stmt
         The result IR statement.
     """
-    condition_buf = tvm.tir.decl_buffer(
-        condition.shape, condition.dtype, "data_buf", data_alignment=8
-    )
-    out_buf = tvm.tir.decl_buffer(output_shape, "int32", "out_buf", data_alignment=8)
 
-    out = te.extern(
-        [output_shape],
-        [condition],
-        lambda ins, outs: argwhere_5d_ir(ins[0], outs[0]),
-        dtype=["int32"],
-        in_buffers=[condition_buf],
-        out_buffers=[out_buf],
-        name="argwhere_5d",
-        tag="argwhere5d_gpu",
-    )
-
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)) and int(out.shape[0]) <= 1:
-        return out
-
-    # sort the output from the least significant to the most significant
-    # column.
-    sort_func = _get_sort_func(1)
-    if isinstance(out.shape[0], (int, tvm.tir.expr.IntImm)):
-        for i in reversed(range(5)):
-            out1 = strided_slice(out, [0, i], [out.shape[0], i + 1])
-            out2 = sort_func(out1, axis=0, dtype="int32")
-            out3 = squeeze(out2)
-            out = adv_index(out, [out3])
-    else:
-        for i in reversed(range(5)):
-            out1 = strided_slice(out, [0, i], [out.shape[0], i + 1], [1, 1])
-            out2 = sort_func(out1, axis=0, dtype="int32")
-            out3 = squeeze(out2)
-            out = adv_index(out, [out3])
-
-    return out
+    def do_write(out, write_index, idx):
+        _, a1, a2, a3, a4 = condition.shape
+        s1 = a3 * a4
+        s2 = a2 * s1
+        s3 = a1 * s2
+        out[write_index * 5] = fdiv(idx, s3)
+        out[write_index * 5 + 1] = fdiv(fmod(idx, s3), s2)
+        out[write_index * 5 + 2] = fdiv(fmod(idx, s2), s1)
+        out[write_index * 5 + 3] = fdiv(fmod(idx, s1), a4)
+        out[write_index * 5 + 4] = fmod(idx, a4)
+
+    return argwhere_common(output_shape, condition, do_write)
 
 
 def argwhere(output_shape, condition):
diff --git a/python/tvm/topi/cuda/batch_matmul.py b/python/tvm/topi/cuda/batch_matmul.py
index 8d34b2996593..04e484f526d2 100644
--- a/python/tvm/topi/cuda/batch_matmul.py
+++ b/python/tvm/topi/cuda/batch_matmul.py
@@ -21,7 +21,7 @@
 from tvm import te
 from tvm.contrib import cublas
 from tvm.autotvm.task.space import SplitEntity, OtherOptionEntity
-from .. import nn
+from .. import nn, generic
 from ..utils import traverse_inline, get_const_tuple, get_max_power2_factor
 
 
@@ -138,7 +138,8 @@ def _callback(op):
     return s
 
 
-def batch_matmul_cublas(x, y, out_shape=None):
+@autotvm.register_topi_compute("batch_matmul_cublas.cuda")
+def batch_matmul_cublas(cfg, x, y, out_shape=None):
     """Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
     data in batch.
 
@@ -158,4 +159,14 @@ def batch_matmul_cublas(x, y, out_shape=None):
     output : tvm.te.Tensor
         3-D with shape [batch, M, N]
     """
+    b, m, k = get_const_tuple(x.shape)
+    b, n, k = get_const_tuple(y.shape)
+    if all([isinstance(s, int) for s in [b, m, n, k]]):
+        cfg.add_flop(b * m * k * n * 2)
     return cublas.batch_matmul(x, y, False, True)
+
+
+@autotvm.register_topi_schedule("batch_matmul_cublas.cuda")
+def schedule_batch_matmul_cublas(_, outs):
+    """Schedule batch_matmul operator using CUBLAS"""
+    return generic.schedule_extern(outs)
diff --git a/python/tvm/topi/cuda/batch_matmul_tensorcore.py b/python/tvm/topi/cuda/batch_matmul_tensorcore.py
new file mode 100644
index 000000000000..962a8af7853b
--- /dev/null
+++ b/python/tvm/topi/cuda/batch_matmul_tensorcore.py
@@ -0,0 +1,315 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,too-many-locals,unused-variable,unused-argument
+"""cuda batch_matmul operators"""
+import tvm
+from tvm import autotvm
+from tvm import te
+from ..utils import traverse_inline, get_const_tuple
+from .tensor_intrin import (
+    intrin_wmma_load_matrix_A,
+    intrin_wmma_load_matrix_W,
+    intrin_wmma_store_matrix,
+    intrin_wmma_gemm,
+)
+
+
+@autotvm.register_topi_compute("batch_matmul_tensorcore.cuda")
+def batch_matmul_tensorcore(cfg, x, y, out_shape=None):
+    """batch matmul tensorcore operator on cuda"""
+    # todo: deal with out_shape for broadcast, liuxin.ai
+    return batch_matmul_tensorcore_cuda(x, y)
+
+
+@autotvm.register_topi_schedule("batch_matmul_tensorcore.cuda")
+def schedule_batch_matmul_tensorcore(cfg, outs):
+    """Schedule for batch_matmul operator using Tensorcore
+
+    Parameters
+    ----------
+    outs: Array of Tensor
+          The computation graph description of batch_matmul
+          in the format of an array of tensors.
+
+    Returns
+    -------
+    s: Schedule
+        The computation schedule for the op.
+    """
+    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+    s = te.create_schedule([x.op for x in outs])
+
+    def _schedule(cfg, s, C):
+        A, B = s[C].op.input_tensors
+        batch, m_dim, k_dim = get_const_tuple(A.shape)
+        batch, n_dim, k_dim = get_const_tuple(B.shape)
+        out_dtype = C.dtype
+        # inline astype fp16
+        s[A].compute_inline()
+        s[B].compute_inline()
+
+        # Explicit memory access
+        AS = s.cache_read(A, "shared", [C])
+        BS = s.cache_read(B, "shared", [C])
+        AF = s.cache_read(AS, "wmma.matrix_a", [C])
+        BF = s.cache_read(BS, "wmma.matrix_b", [C])
+        CF = s.cache_write(C, "wmma.accumulator")
+        CS = s.cache_read(CF, "shared", [C])
+
+        # fallback support
+        target = tvm.target.Target.current()
+        if cfg.is_fallback:
+            ref_log = autotvm.tophub.load_reference_log(
+                target.kind.name, target.model, "batch_matmul_tensorcore.cuda"
+            )
+            cfg.fallback_with_reference_log(ref_log)
+
+        # Deal with op fusion, such as bias/relu and slice after padding
+        if C.op not in s.outputs and "injective" in s.outputs[0].tag:
+            s[C].compute_inline()
+            C = s.outputs[0].output(0)
+
+        # create tuning space
+        cfg.define_knob("block_row_warps", [1, 2, 4])
+        cfg.define_knob("block_col_warps", [1, 2, 4])
+        cfg.define_knob("warp_row_tiles", [1, 2, 4])
+        cfg.define_knob("warp_col_tiles", [1, 2, 4])
+        cfg.define_knob("chunk", [1, 2, 4, 8])
+        cfg.define_knob("offset", [0, 8])
+        cfg.define_knob("offsetCS", [0, 8])
+        cfg.define_knob("vec", [1, 2, 4, 8])
+
+        # Ensure that the default parameters are applicable when autotvm is not in use
+        if m_dim % 32 == 0 and n_dim % 8 == 0:
+            cfg.define_knob("wmma_m", [32, 16, 8])
+        elif m_dim % 16 == 0 and n_dim % 16 == 0:
+            cfg.define_knob("wmma_m", [16, 8, 32])
+        elif m_dim % 8 == 0 and n_dim % 32 == 0:
+            cfg.define_knob("wmma_m", [8, 16, 32])
+
+        warp_size = 32
+        wmma_k = 16
+        block_row_warps = cfg["block_row_warps"].val
+        block_col_warps = cfg["block_col_warps"].val
+        warp_row_tiles = cfg["warp_row_tiles"].val
+        warp_col_tiles = cfg["warp_col_tiles"].val
+        chunk = cfg["chunk"].val
+        offset = cfg["offset"].val
+        offsetCS = cfg["offsetCS"].val
+        wmma_m = cfg["wmma_m"].val
+        vec = cfg["vec"].val
+
+        if wmma_m == 16:
+            wmma_n = 16
+        elif wmma_m == 8:
+            wmma_n = 32
+        elif wmma_m == 32:
+            wmma_n = 8
+
+        # Define the stride of intrin functions
+        AS_align = chunk * wmma_k + offset
+        BS_align = chunk * wmma_k + offset
+        CS_align = warp_col_tiles * block_col_warps * wmma_n + offsetCS
+        AS_stride = [AS_align, 1]
+        BS_stride = [BS_align, 1]
+        AF_stride = [wmma_k, 1]
+        BF_stride = [wmma_k, 1]
+        CF_stride = [warp_col_tiles * wmma_n, 1]
+        CS_stride = [CS_align, 1]
+
+        block_x = te.thread_axis("blockIdx.x")
+        block_y = te.thread_axis("blockIdx.y")
+        block_z = te.thread_axis("blockIdx.z")
+        thread_x = te.thread_axis("threadIdx.x")
+        thread_y = te.thread_axis("threadIdx.y")
+        thread_z = te.thread_axis("threadIdx.z")
+
+        # Schedule for dense computation
+        block_factor_m = wmma_m * warp_row_tiles * block_row_warps
+        block_factor_n = wmma_n * warp_col_tiles * block_col_warps
+        b, m, n = C.op.axis
+        block_i, bc = s[C].split(m, factor=block_factor_m)
+        block_j, oc = s[C].split(n, factor=block_factor_n)
+        s[C].reorder(b, block_i, block_j, bc, oc)
+        t = s[C].fuse(bc, oc)
+        t, vi = s[C].split(t, factor=vec)
+        t, tx = s[C].split(t, factor=warp_size)
+        t, ty = s[C].split(t, factor=block_row_warps)
+        t, tz = s[C].split(t, factor=block_col_warps)
+        s[C].bind(block_i, block_x)
+        s[C].bind(block_j, block_y)
+        s[C].bind(b, block_z)
+        s[C].bind(tz, thread_z)
+        s[C].bind(ty, thread_y)
+        s[C].bind(tx, thread_x)
+        s[C].vectorize(vi)
+
+        # Schedule for wmma store
+        s[CS].compute_at(s[C], block_j)
+        bs, bb, oo = CS.op.axis
+        s[CS].storage_align(bb, CS_align - 1, CS_align)
+        bb, bbi = s[CS].split(bb, factor=wmma_m)
+        oo, ooi = s[CS].split(oo, factor=wmma_n)
+        bb, bbii = s[CS].split(bb, factor=warp_row_tiles)
+        oo, ooii = s[CS].split(oo, factor=warp_col_tiles)
+        s[CS].reorder(bs, bb, oo, bbii, ooii, bbi, ooi)
+
+        # Schedule for wmma computation
+        s[CF].compute_at(s[CS], oo)
+        bs, warp_i, warp_j = CF.op.axis
+        warp_i, _ii = s[CF].split(warp_i, factor=wmma_m)
+        warp_j, _jj = s[CF].split(warp_j, factor=wmma_n)
+        (k,) = CF.op.reduce_axis
+        k, _k = s[CF].split(k, factor=wmma_k)
+        ko, ki = s[CF].split(k, factor=chunk)
+        s[CF].reorder(bs, ko, ki, warp_i, warp_j, _ii, _jj, _k)
+
+        # Schedule for  wmma_matrix_a load
+        s[AF].compute_at(s[CF], ki)
+        bs, b, i = AF.op.axis
+        b, b_ii = s[AF].split(b, factor=wmma_m)
+        i, i_jj = s[AF].split(i, factor=wmma_k)
+        s[AF].reorder(bs, b, i, b_ii, i_jj)
+
+        # Schedule for  wmma_matrix_b load
+        s[BF].compute_at(s[CF], ki)
+        bs, o, i = BF.op.axis
+        o, o_ii = s[BF].split(o, factor=wmma_n)
+        i, i_ii = s[BF].split(i, factor=wmma_k)
+        s[BF].reorder(bs, o, i, o_ii, i_ii)
+
+        # Schedule for A's(B's) shared memory load
+        def shared_shedule(stage, strides):
+            s[stage].compute_at(s[CF], ko)
+            bs, xo, yo = stage.op.axis
+            s[stage].storage_align(xo, strides - 1, strides)
+            t = s[stage].fuse(xo, yo)
+            t, vi = s[stage].split(t, factor=vec)
+            t, tx = s[stage].split(t, factor=warp_size)
+            t, ty = s[stage].split(t, factor=block_row_warps)
+            _, tz = s[stage].split(t, factor=block_col_warps)
+            s[stage].bind(ty, thread_y)
+            s[stage].bind(tz, thread_z)
+            s[stage].bind(tx, thread_x)
+            s[stage].vectorize(vi)
+
+        shared_shedule(AS, AS_align)
+        shared_shedule(BS, BS_align)
+
+        shape = (wmma_m, wmma_n, wmma_k)
+        # TODO: add checking here, datatype casting may cause precision loss
+        in_dtype = "float16"
+        AL_gemm = te.placeholder((wmma_m, wmma_k), name="AL_gemm", dtype=in_dtype)
+        BL_gemm = te.placeholder((wmma_n, wmma_k), name="BL_gemm", dtype=in_dtype)
+        k_gemm = te.reduce_axis((0, wmma_k), name="k_gemm")
+        CL_compute = te.compute(
+            (wmma_m, wmma_n),
+            lambda ii, jj: te.sum(
+                AL_gemm[ii, k_gemm].astype(out_dtype) * BL_gemm[jj, k_gemm].astype(out_dtype),
+                axis=k_gemm,
+            ),
+            name="CL_compute",
+        )
+
+        # lower the computation loops down to TensorCore hardware intrinsics
+        # by mapping the dense tensorcore to tensor intrinsics
+        s[AF].tensorize(
+            b_ii,
+            intrin_wmma_load_matrix_A(
+                AF_stride,
+                AS_stride,
+                shape,
+                "row_major",
+                (wmma_m, wmma_k),
+                (wmma_m, wmma_k),
+                "float16",
+            ),
+        )
+        s[BF].tensorize(
+            o_ii,
+            intrin_wmma_load_matrix_W(
+                BF_stride,
+                BS_stride,
+                shape,
+                "col_major",
+                (wmma_n, wmma_k),
+                (wmma_n, wmma_k),
+                "float16",
+            ),
+        )
+        s[CF].tensorize(
+            _ii,
+            intrin_wmma_gemm(AL_gemm, BL_gemm, CL_compute, AF_stride, BF_stride, CF_stride, shape),
+        )
+        s[CS].tensorize(
+            bbi,
+            intrin_wmma_store_matrix(
+                CS_stride, CF_stride, shape, out_dtype, (wmma_m, wmma_n), (wmma_m, wmma_n)
+            ),
+        )
+
+    def _callback(op):
+        if "batch_matmul_tensorcore" in op.tag:
+            _schedule(cfg, s, op.output(0))
+
+    traverse_inline(s, outs[0].op, _callback)
+    return s
+
+
+def batch_matmul_tensorcore_cuda(x, y):
+    """Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
+    data in batch.
+
+    Parameters
+    ----------
+    x : tvm.te.Tensor
+        3-D with shape [batch, M, K]
+
+    y : tvm.te.Tensor
+        3-D with shape [batch, N, K]
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        3-D with shape [batch, M, N]
+    """
+    assert len(x.shape) == 3 and len(y.shape) == 3, "only support 3-dim batch_matmul"
+    x_shape = get_const_tuple(x.shape)
+    y_shape = get_const_tuple(y.shape)
+    assert x_shape[0] == y_shape[0], "batch dimension doesn't match"
+    assert x_shape[2] == y_shape[2], "shapes of x and y is inconsistent"
+    batch, M, K = x.shape
+    N = y.shape[1]
+    out_dtype = x.dtype
+
+    assert (
+        (M % 8 == 0 and K % 16 == 0 and N % 32 == 0)
+        or (M % 16 == 0 and K % 16 == 0 and N % 16 == 0)
+        or (M % 32 == 0 and K % 16 == 0 and N % 8 == 0)
+    ), "The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32)"
+
+    x_16 = te.compute((batch, M, K), lambda b, i, k: x[b, i, k].astype("float16"))
+    y_16 = te.compute((batch, N, K), lambda b, j, k: y[b, j, k].astype("float16"))
+
+    k = te.reduce_axis((0, K), name="k")
+    return te.compute(
+        (batch, M, N),
+        lambda b, i, j: te.sum(
+            x_16[b, i, k].astype(out_dtype) * y_16[b, j, k].astype(out_dtype), axis=k
+        ),
+        tag="batch_matmul_tensorcore",
+    )
diff --git a/python/tvm/topi/cuda/conv2d.py b/python/tvm/topi/cuda/conv2d.py
index ce9cebc3c963..63c7c9308284 100644
--- a/python/tvm/topi/cuda/conv2d.py
+++ b/python/tvm/topi/cuda/conv2d.py
@@ -96,17 +96,19 @@ def conv2d_cudnn(
     pt, pl, pb, pr = get_pad_tuple(padding, (KH, KW))
     OH = (H + pt + pb - KH) // stride_h + 1
     OW = (W + pl + pr - KW) // stride_w + 1
-    cfg.add_flop(
-        groups
-        * 2
-        * N
-        * OH
-        * OW
-        * CO
-        * CI
-        * ((KH - 1) * dilation_h + 1)
-        * ((KW - 1) * dilation_w + 1)
-    )
+
+    if isinstance(N, int):
+        cfg.add_flop(
+            groups
+            * 2
+            * N
+            * OH
+            * OW
+            * CO
+            * CI
+            * ((KH - 1) * dilation_h + 1)
+            * ((KW - 1) * dilation_w + 1)
+        )
 
     if data.dtype == "int8" or kernel.dtype == "int8":
         if layout == "NCHW":
diff --git a/python/tvm/topi/cuda/conv2d_alter_op.py b/python/tvm/topi/cuda/conv2d_alter_op.py
index 8cf0519ebe29..65bf9d1f178d 100644
--- a/python/tvm/topi/cuda/conv2d_alter_op.py
+++ b/python/tvm/topi/cuda/conv2d_alter_op.py
@@ -24,8 +24,10 @@
 from .. import nn
 from ..utils import get_const_tuple
 from .conv2d_winograd import _infer_tile_size
+from .tensorcore_alter_op import pad_to_tensorcore
 from ..nn import conv2d_legalize
 
+
 logger = logging.getLogger("topi")
 
 
@@ -345,4 +347,50 @@ def _conv2d_legalize(attrs, inputs, arg_types):
             else:
                 out = relay.nn.conv2d(data, kernel, **new_attrs)
             return out
+    elif data_dtype in ["float16"]:  # todo: support int8/int4
+        if data_layout == "NHWC" and kernel_layout == "HWIO":
+            batch = data_tensor.shape[0].value
+            in_channel = data_tensor.shape[3].value
+            out_channel = kernel_tensor.shape[3].value
+
+            if (
+                (batch % 8 == 0 and in_channel % 16 == 0 and out_channel % 32 == 0)
+                or (batch % 16 == 0 and in_channel % 16 == 0 and out_channel % 16 == 0)
+                or (batch % 32 == 0 and in_channel % 16 == 0 and out_channel % 8 == 0)
+            ):
+                # no need to pad
+                return None
+
+            (db, di, do), extra_flops = pad_to_tensorcore(batch, in_channel, out_channel)
+
+            if extra_flops > 2:
+                logger.info("conv2d pad_to_tensorcore skipped, extra_flops %s", extra_flops)
+                return None
+
+            logger.info("conv2d pad_to_tensorcore, extra_flops %s", extra_flops)
+
+            # Pad batch size
+            if db != 0:
+                data = relay.nn.pad(data, pad_width=((0, db), (0, 0), (0, 0), (0, 0)))
+
+            # Pad input channel
+            if di != 0:
+                data = relay.nn.pad(data, pad_width=((0, 0), (0, 0), (0, 0), (0, di)))
+                kernel = relay.nn.pad(kernel, pad_width=((0, 0), (0, 0), (0, di), (0, 0)))
+
+            # Pad output channel
+            if do != 0:
+                kernel = relay.nn.pad(kernel, pad_width=((0, 0), (0, 0), (0, 0), (0, do)))
+
+            if do != 0:
+                new_out_channel = out_channel + do
+                new_attrs["channels"] = new_out_channel
+
+            out = relay.nn.conv2d(data, kernel, **new_attrs)
+
+            if db != 0 or do != 0:
+                original_out_shape = [x.value for x in output_tensor.shape]
+                out = relay.strided_slice(out, begin=[0, 0, 0, 0], end=original_out_shape)
+
+            return out
     return None
diff --git a/python/tvm/topi/cuda/conv2d_int8.py b/python/tvm/topi/cuda/conv2d_int8.py
index 50a0e8b71661..001411d6e4c9 100644
--- a/python/tvm/topi/cuda/conv2d_int8.py
+++ b/python/tvm/topi/cuda/conv2d_int8.py
@@ -142,9 +142,10 @@ def conv2d_NCHWc_int8(cfg, data, kernel, stride, padding, dilation, layout, out_
     pad_data = pad(packed_data, pad_before, pad_after, name="pad_data")
 
     # compute the output shape
-    out_height = (in_height - (kernel_h - 1) * dilation_h - 1 + pad_top + pad_down) // stride_h + 1
-    out_width = (in_width - (kernel_w - 1) * dilation_w - 1 + pad_left + pad_right) // stride_w + 1
-
+    dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+    dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+    out_height = (in_height - dilated_kernel_h + pad_top + pad_down) // stride_h + 1
+    out_width = (in_width - dilated_kernel_w + pad_left + pad_right) // stride_w + 1
     oshape = (batch, oc_chunk, out_height, out_width, oc_block)
 
     icc = te.reduce_axis((0, ic_chunk), name="ic_chunk")
diff --git a/python/tvm/topi/cuda/conv2d_nhwc.py b/python/tvm/topi/cuda/conv2d_nhwc.py
index a08d217696e2..991585587bbf 100644
--- a/python/tvm/topi/cuda/conv2d_nhwc.py
+++ b/python/tvm/topi/cuda/conv2d_nhwc.py
@@ -129,4 +129,6 @@ def schedule_conv2d_nhwc_direct(cfg, s, Conv):
 
     N, OH, OW, CO = get_const_tuple(output.shape)
     KH, KW, CI, _ = get_const_tuple(kernel.shape)
-    cfg.add_flop(2 * N * OH * OW * CO * CI * KH * KW)
+
+    if isinstance(N, int):
+        cfg.add_flop(2 * N * OH * OW * CO * CI * KH * KW)
diff --git a/python/tvm/topi/cuda/conv2d_nhwc_tensorcore.py b/python/tvm/topi/cuda/conv2d_nhwc_tensorcore.py
index f665cc779dc5..76f082f07b44 100644
--- a/python/tvm/topi/cuda/conv2d_nhwc_tensorcore.py
+++ b/python/tvm/topi/cuda/conv2d_nhwc_tensorcore.py
@@ -72,6 +72,7 @@ def nhwc_tensorcore_cuda(cfg, Input, Filter, stride, padding, dilation, out_dtyp
     ry = te.reduce_axis((0, kernel_h), name="ry")
     rx = te.reduce_axis((0, kernel_w), name="rx")
     # convert data type of input feature maps and weights
+    # TODO: add checking here, datatype casting may cause precision loss
     TransPaddedInput = te.compute(
         PaddedInput.shape, lambda n, h, w, c: PaddedInput[n, h, w, c].astype("float16")
     )
diff --git a/python/tvm/topi/cuda/conv3d.py b/python/tvm/topi/cuda/conv3d.py
index e5a3a53a89ff..530df31ed3dc 100644
--- a/python/tvm/topi/cuda/conv3d.py
+++ b/python/tvm/topi/cuda/conv3d.py
@@ -206,18 +206,20 @@ def conv3d_cudnn(
     OD = (D + 2 * pad_d - KD) // stride_d + 1
     OH = (H + 2 * pad_h - KH) // stride_h + 1
     OW = (W + 2 * pad_w - KW) // stride_w + 1
-    cfg.add_flop(
-        2
-        * N
-        * OD
-        * OH
-        * OW
-        * CO
-        * CI
-        * ((KD - 1) * dilation_d + 1)
-        * ((KH - 1) * dilation_h + 1)
-        * ((KW - 1) * dilation_w + 1)
-    )
+
+    if isinstance(N, int):
+        cfg.add_flop(
+            2
+            * N
+            * OD
+            * OH
+            * OW
+            * CO
+            * CI
+            * ((KD - 1) * dilation_d + 1)
+            * ((KH - 1) * dilation_h + 1)
+            * ((KW - 1) * dilation_w + 1)
+        )
 
     return cudnn.conv_forward(
         data,
diff --git a/python/tvm/topi/cuda/conv3d_ndhwc_tensorcore.py b/python/tvm/topi/cuda/conv3d_ndhwc_tensorcore.py
index a5c4e81a4dc3..efb25744b802 100644
--- a/python/tvm/topi/cuda/conv3d_ndhwc_tensorcore.py
+++ b/python/tvm/topi/cuda/conv3d_ndhwc_tensorcore.py
@@ -75,6 +75,7 @@ def ndhwc_tensorcore_cuda(cfg, Input, Filter, stride, padding, dilation, out_dty
     ry = te.reduce_axis((0, kernel_h), name="ry")
     rx = te.reduce_axis((0, kernel_w), name="rx")
     # convert data type of input feature maps and weights
+    # TODO: add checking here, datatype casting may cause precision loss
     TransPaddedInput = te.compute(
         PaddedInput.shape, lambda n, d, h, w, c: PaddedInput[n, d, h, w, c].astype("float16")
     )
diff --git a/python/tvm/topi/cuda/dense.py b/python/tvm/topi/cuda/dense.py
index 47b9db4f390a..8adc38b84b1b 100644
--- a/python/tvm/topi/cuda/dense.py
+++ b/python/tvm/topi/cuda/dense.py
@@ -39,10 +39,11 @@ def dense_cublas(cfg, data, weight, bias=None, out_dtype=None):
     if out_dtype is None:
         out_dtype = data.dtype
     assert out_dtype == data.dtype, "Mixed precision not supported."
-    batch, in_dim = data.shape
-    out_dim, _ = weight.shape
+    batch, in_dim = get_const_tuple(data.shape)
+    out_dim, _ = get_const_tuple(weight.shape)
     matmul = cublas.matmul(data, weight, False, True)
-    cfg.add_flop(batch * in_dim * out_dim * 2)
+    if all(isinstance(d, int) for d in [batch, in_dim, out_dim]):
+        cfg.add_flop(batch * in_dim * out_dim * 2)
     if bias is not None:
         matmul = te.compute(
             (batch, out_dim), lambda i, j: matmul[i, j] + bias[j], tag=tag.BROADCAST
@@ -77,13 +78,26 @@ def _callback(op):
 
 
 def _schedule_dense_small_batch(cfg, s, C):
-    A, _ = C.op.input_tensors
-    _, in_dim = get_const_tuple(A.shape)
-    cfg.define_split("tile_k", in_dim, num_outputs=2)
-    if cfg.is_fallback:
-        cfg["tile_k"] = SplitEntity([-1, 64] if in_dim > 64 else [1, 64])
+    A, weights = C.op.input_tensors
+    _, in_dim_weights = get_const_tuple(weights.shape)
+    _, in_dim_A = get_const_tuple(A.shape)
+
+    if isinstance(in_dim_A, int):
+        in_dim = in_dim_A
+    elif isinstance(in_dim_weights, int):
+        in_dim = in_dim_weights
+    else:
+        in_dim = None
+
+    if in_dim is not None:
+        cfg.define_split("tile_k", in_dim, num_outputs=2)
+        if cfg.is_fallback:
+            cfg["tile_k"] = SplitEntity([-1, 64] if in_dim > 64 else [1, 64])
+        _, kf = cfg["tile_k"].apply(s, C, C.op.reduce_axis[0])
+    else:
+        tile_k = 64
+        _, kf = s[C].split(C.op.reduce_axis[0], tile_k)
 
-    _, kf = cfg["tile_k"].apply(s, C, C.op.reduce_axis[0])
     CF = s.rfactor(C, kf)
 
     if C.op in s.outputs:
diff --git a/python/tvm/topi/cuda/dense_tensorcore.py b/python/tvm/topi/cuda/dense_tensorcore.py
index a59ebd7347bb..430f8044528c 100644
--- a/python/tvm/topi/cuda/dense_tensorcore.py
+++ b/python/tvm/topi/cuda/dense_tensorcore.py
@@ -245,6 +245,7 @@ def shared_shedule(stage, strides):
     shared_shedule(BS, BS_align)
 
     shape = (wmma_m, wmma_n, wmma_k)
+    # TODO: add checking here, datatype casting may cause precision loss
     in_dtype = "float16"
     AL_gemm = te.placeholder((wmma_m, wmma_k), name="AL_gemm", dtype=in_dtype)
     BL_gemm = te.placeholder((wmma_n, wmma_k), name="BL_gemm", dtype=in_dtype)
diff --git a/python/tvm/topi/cuda/nms.py b/python/tvm/topi/cuda/nms.py
index 020cf9b5bc63..ccc2ec9d0c21 100644
--- a/python/tvm/topi/cuda/nms.py
+++ b/python/tvm/topi/cuda/nms.py
@@ -19,9 +19,12 @@
 """Non-maximum suppression operator"""
 import tvm
 from tvm import te
-
+from tvm.contrib import nvcc
+from tvm.contrib.thrust import can_use_thrust, can_use_rocthrust
 from tvm.tir import if_then_else
 from .sort import argsort, argsort_thrust
+from .scan import exclusive_scan
+from ..utils import ceil_div
 
 
 def cuda_atomic_add_rule(op):
@@ -51,10 +54,6 @@ def atomic_add(x, y):
     return tvm.tir.call_intrin(y.dtype, "tir.atomic_add", x, y)
 
 
-def ceil_div(a, b):
-    return tvm.tir.indexdiv(a + b - 1, b)
-
-
 def get_valid_boxes_ir(data, valid_boxes, score_threshold, id_index, score_index):
     """Low level IR to identify bounding boxes given a score threshold.
 
@@ -95,7 +94,7 @@ def get_valid_boxes_ir(data, valid_boxes, score_threshold, id_index, score_index
     max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
     with ib.new_scope():
         nthread_tx = max_threads
-        nthread_bx = num_anchors // max_threads + 1
+        nthread_bx = ceil_div(num_anchors, max_threads)
         nthread_by = batch_size
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
@@ -123,59 +122,7 @@ def get_valid_boxes_ir(data, valid_boxes, score_threshold, id_index, score_index
     return ib.get()
 
 
-def get_valid_indices_ir(valid_boxes, valid_count, valid_indices):
-    """Low level IR to get the ouput indices of valid boxes
-    and the count of valid boxes
-
-    Parameters
-    ----------
-    valid_boxes: Buffer
-        2D Buffer  indicating valid boxes with shape [batch_size, num_anchors].
-
-    Returns
-    -------
-    valid_count: Buffer
-        1D Buffer of number of valid boxes per batch [batch_size].
-
-    valid_indices: Buffer
-        2D Buffer indicating output sorted indcies of valid boxes [batch_size, num_anchors].
-    """
-    batch_size = valid_boxes.shape[0]
-    num_anchors = valid_boxes.shape[1]
-
-    ib = tvm.tir.ir_builder.create()
-
-    valid_boxes = ib.buffer_ptr(valid_boxes)
-
-    valid_count = ib.buffer_ptr(valid_count)
-    valid_indices = ib.buffer_ptr(valid_indices)
-
-    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
-    with ib.new_scope():
-        nthread_tx = max_threads
-        nthread_bx = batch_size // max_threads + 1
-        tx = te.thread_axis("threadIdx.x")
-        bx = te.thread_axis("blockIdx.x")
-        ib.scope_attr(tx, "thread_extent", nthread_tx)
-        ib.scope_attr(bx, "thread_extent", nthread_bx)
-        tid = bx * max_threads + tx
-        # TODO(mbrookhart): Parallelize the sum and cumsum here
-        current_index = ib.allocate("int32", (1,), name="current_index", scope="local")
-        with ib.if_scope(tid < batch_size):
-            current_index[0] = 0
-            valid_count[tid] = 0
-            with ib.for_range(0, num_anchors) as j:
-                idx = tid * num_anchors + j
-                valid_count[tid] = valid_count[tid] + valid_boxes[idx]
-                with ib.if_scope(valid_boxes[idx] == 1):
-                    valid_indices[idx] = current_index[0]
-                    current_index[0] = current_index[0] + 1
-                with ib.else_scope():
-                    valid_indices[idx] = -1
-    return ib.get()
-
-
-def get_valid_counts_ir(data, valid_indices, out, out_indices):
+def get_valid_counts_ir(data, valid_indices, valid_boxes, out, out_indices):
     """Low level IR to get valid count of bounding boxes
     given a score threshold. Also prepares to move valid boxes to the
     top of input data.
@@ -203,8 +150,9 @@ def get_valid_counts_ir(data, valid_indices, out, out_indices):
     ib = tvm.tir.ir_builder.create()
 
     data = ib.buffer_ptr(data)
-
     valid_indices = ib.buffer_ptr(valid_indices)
+    valid_boxes = ib.buffer_ptr(valid_boxes)
+
     out = ib.buffer_ptr(out)
     out_indices = ib.buffer_ptr(out_indices)
     one = tvm.tir.const(1, dtype=out.dtype)
@@ -213,41 +161,36 @@ def get_valid_counts_ir(data, valid_indices, out, out_indices):
     nthread_tx = max_threads
     nthread_bx = num_anchors // max_threads + 1
     nthread_by = batch_size
-    nthread_bz = elem_length
     with ib.new_scope():
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
         by = te.thread_axis("blockIdx.y")
-        bz = te.thread_axis("blockIdx.z")
         ib.scope_attr(tx, "thread_extent", nthread_tx)
         ib.scope_attr(bx, "thread_extent", nthread_bx)
         ib.scope_attr(by, "thread_extent", nthread_by)
-        ib.scope_attr(bz, "thread_extent", nthread_bz)
         tid = bx * max_threads + tx
         with ib.if_scope(tid < num_anchors):
             i = by
             j = tid
-            k = bz
-            out[(i * num_anchors + j) * elem_length + k] = -one
+            with ib.for_range(0, elem_length) as k:
+                out[(i * num_anchors + j) * elem_length + k] = -one
             out_indices[i * num_anchors + j] = -1
     with ib.new_scope():
         tx = te.thread_axis("threadIdx.x")
         bx = te.thread_axis("blockIdx.x")
         by = te.thread_axis("blockIdx.y")
-        bz = te.thread_axis("blockIdx.z")
         ib.scope_attr(tx, "thread_extent", nthread_tx)
         ib.scope_attr(bx, "thread_extent", nthread_bx)
         ib.scope_attr(by, "thread_extent", nthread_by)
-        ib.scope_attr(bz, "thread_extent", nthread_bz)
         tid = bx * max_threads + tx
         with ib.if_scope(tid < num_anchors):
             i = by
             j = tid
-            k = bz
-            with ib.if_scope(valid_indices[i, tid] >= 0):
-                out[(i * num_anchors + valid_indices[i, tid]) * elem_length + k] = data[
-                    (i * num_anchors + j) * elem_length + k
-                ]
+            with ib.if_scope(valid_boxes[i, tid] > 0):
+                with ib.for_range(0, elem_length) as k:
+                    out[(i * num_anchors + valid_indices[i, tid]) * elem_length + k] = data[
+                        (i * num_anchors + j) * elem_length + k
+                    ]
                 out_indices[i * num_anchors + valid_indices[i, tid]] = j
     return ib.get()
 
@@ -300,19 +243,8 @@ def get_valid_counts(data, score_threshold=0, id_index=0, score_index=1):
     valid_indices_buf = tvm.tir.decl_buffer(
         (batch_size, num_anchors), "int32", "valid_indices_buf", data_alignment=8
     )
-    valid_count_buf = tvm.tir.decl_buffer(
-        (batch_size,), "int32", "valid_count_buf", data_alignment=8
-    )
-    valid_count, valid_indices = te.extern(
-        [(batch_size,), (batch_size, num_anchors)],
-        [valid_boxes],
-        lambda ins, outs: get_valid_indices_ir(ins[0], outs[0], outs[1]),
-        dtype=["int32"],
-        in_buffers=[valid_boxes_buf],
-        out_buffers=[valid_count_buf, valid_indices_buf],
-        name="get_valid_indices",
-        tag="get_valid_indices_gpu",
-    )
+
+    valid_indices, valid_count = exclusive_scan(valid_boxes, axis=1, return_reduction=True)
 
     out_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "out_buf", data_alignment=8)
     out_indices_buf = tvm.tir.decl_buffer(
@@ -321,10 +253,10 @@ def get_valid_counts(data, score_threshold=0, id_index=0, score_index=1):
 
     out, out_indices = te.extern(
         [data.shape, (batch_size, num_anchors)],
-        [data, valid_indices],
-        lambda ins, outs: get_valid_counts_ir(ins[0], ins[1], outs[0], outs[1]),
+        [data, valid_indices, valid_boxes],
+        lambda ins, outs: get_valid_counts_ir(ins[0], ins[1], ins[2], outs[0], outs[1]),
         dtype=["int32", data.dtype],
-        in_buffers=[data_buf, valid_indices_buf],
+        in_buffers=[data_buf, valid_indices_buf, valid_boxes_buf],
         out_buffers=[out_buf, out_indices_buf],
         name="get_valid_counts",
         tag="get_valid_counts_gpu",
@@ -338,7 +270,10 @@ def nms_ir(
     sorted_index,
     valid_count,
     indices,
-    out,
+    out_bboxes,
+    out_scores,
+    out_class_ids,
+    out_features,
     box_indices,
     num_valid_boxes,
     max_output_size,
@@ -370,8 +305,14 @@ def nms_ir(
         dimension are like the output of arange(num_anchors) if get_valid_counts
         is not used before non_max_suppression.
 
-    out : Buffer
-        Output buffer, to be filled with sorted boxes.
+    out_bboxes : Buffer
+        Output buffer, to be filled with sorted box coordinates.
+
+    out_scores : Buffer
+        Output buffer, to be filled with sorted scores.
+
+    out_class_ids : Buffer
+        Output buffer, to be filled with sorted class ids.
 
     box_indices : Buffer
         A indices tensor mapping sorted indices to original indices
@@ -451,6 +392,7 @@ def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
     batch_size = data.shape[0]
     num_anchors = data.shape[1]
     box_data_length = data.shape[2]
+    num_features = out_features.shape[2]
 
     ib = tvm.tir.ir_builder.create()
 
@@ -458,9 +400,14 @@ def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
     sorted_index = ib.buffer_ptr(sorted_index)
     valid_count = ib.buffer_ptr(valid_count)
     indices = ib.buffer_ptr(indices)
-    num_valid_boxes = ib.buffer_ptr(num_valid_boxes)
-    out = ib.buffer_ptr(out)
+
+    # outputs
+    out_bboxes = ib.buffer_ptr(out_bboxes)
+    out_scores = ib.buffer_ptr(out_scores)
+    out_class_ids = ib.buffer_ptr(out_class_ids)
+    out_features = ib.buffer_ptr(out_features)
     box_indices = ib.buffer_ptr(box_indices)
+    num_valid_boxes = ib.buffer_ptr(num_valid_boxes)
 
     if isinstance(iou_threshold, float):
         iou_threshold = tvm.tir.FloatImm("float32", iou_threshold)
@@ -483,98 +430,160 @@ def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
         ib.scope_attr(tx, "thread_extent", nthread_tx)
         ib.scope_attr(bx, "thread_extent", nthread_bx)
         i = by
-        base_idx = i * num_anchors * box_data_length
+        base_src_idx = i * num_anchors * box_data_length
+        base_bbox_idx = i * num_anchors * 4
+        base_features_idx = i * num_anchors * num_features
+
         with ib.if_scope(tvm.tir.all(iou_threshold > 0, valid_count[i] > 0)):
             # Reorder output
             nkeep = if_then_else(
                 tvm.tir.all(top_k > 0, top_k < valid_count[i]), top_k, valid_count[i]
             )
             j = bx * max_threads + tx
-            with ib.if_scope(j < num_anchors):
-                box_indices[i * num_anchors + j] = -1
             with ib.if_scope(j < nkeep):
-                # Fill in out with sorted boxes
-                with ib.for_range(0, box_data_length) as k:
-                    out[(base_idx + j * box_data_length + k)] = data[
-                        (base_idx + sorted_index[i * num_anchors + j] * box_data_length + k)
+                src_idx = base_src_idx + sorted_index[i * num_anchors + j] * box_data_length
+                with ib.for_range(0, 4, kind="unroll") as k:
+                    out_bboxes[(base_bbox_idx + j * 4 + k)] = data[src_idx + coord_start + k]
+                with ib.for_range(0, num_features, kind="unroll") as k:
+                    out_features[(base_features_idx + j * num_features + k)] = data[
+                        src_idx + coord_start + 4 + k
                     ]
+
+                out_scores[i * num_anchors + j] = data[src_idx + score_index]
+
+                if id_index >= 0:
+                    out_class_ids[i * num_anchors + j] = data[src_idx + id_index]
+
             with ib.else_scope():
                 # Indices > nkeep are discarded
+                # Only needed for return_indices = False case
+                if return_indices is False:
+                    with ib.if_scope(j < num_anchors):
+                        with ib.for_range(0, 4, kind="unroll") as k:
+                            out_bboxes[(base_bbox_idx + j * 4 + k)] = -1.0
+                        with ib.for_range(0, num_features, kind="unroll") as k:
+                            out_features[(base_features_idx + j * num_features + k)] = -1.0
+
+                        out_scores[i, j] = -1.0
+
+                        if id_index >= 0:
+                            out_class_ids[i, j] = -1.0
+
+            if return_indices:
                 with ib.if_scope(j < num_anchors):
-                    with ib.for_range(0, box_data_length) as k:
-                        out[(base_idx + j * box_data_length + k)] = -1.0
+                    box_indices[i * num_anchors + j] = -1
+
         with ib.else_scope():
             with ib.if_scope(j < valid_count[i]):
-                with ib.for_range(0, box_data_length) as k:
-                    offset = base_idx + j * box_data_length + k
-                    out[offset] = data[offset]
+                src_offset = base_src_idx + j * box_data_length
+
+                with ib.for_range(0, 4, kind="unroll") as k:
+                    out_bboxes[base_bbox_idx + j * 4 + k] = data[src_offset + coord_start + k]
+                with ib.for_range(0, num_features, kind="unroll") as k:
+                    out_features[(base_features_idx + j * num_features + k)] = data[
+                        src_offset + coord_start + 4 + k
+                    ]
+                out_scores[i * num_anchors + j] = data[src_offset + score_index]
+
+                if id_index >= 0:
+                    out_class_ids[i * num_anchors + j] = data[src_offset + id_index]
+
                 box_indices[i * num_anchors + j] = j
 
     with ib.new_scope():
         nthread_by = batch_size
+        nthread_tx = max_threads
+
+        # Some cuda architectures have smaller limit of 32K for cudaDevAttrMaxRegistersPerBlock
+        # vs 64K for most GPUs. Since this kernel uses many registers (around 35), the limit will
+        # be exceeded with 1024 threads.
+        target = tvm.target.Target.current(allow_none=False)
+        if target.kind.name == "cuda":
+            if nvcc.get_target_compute_version(target) in ["3.2", "5.3", "6.2"]:
+                nthread_tx = 512
+
         by = te.thread_axis("blockIdx.y")
+        tx = te.thread_axis("threadIdx.x")
         ib.scope_attr(by, "thread_extent", nthread_by)
+        ib.scope_attr(tx, "thread_extent", nthread_tx)
+
         i = by
-        base_idx = i * num_anchors * box_data_length
+
+        base_bbox_idx = i * num_anchors * 4
         num_valid_boxes_local = ib.allocate(
             "int32", (1,), name="num_valid_boxes_local", scope="local"
         )
         num_valid_boxes_local[0] = 0
+        nkeep = if_then_else(tvm.tir.all(top_k > 0, top_k < valid_count[i]), top_k, valid_count[i])
 
         def nms_inner_loop(ib, j):
-            offset_j = j * box_data_length
+            # The box j is valid, invalidate other boxes that overlap with j above iou_threshold
+
+            # When return_indices is False, no need to populate box_indices
+            if return_indices:
+                with ib.if_scope(tx + 0 == 0):
+                    orig_idx = sorted_index[i * num_anchors + j]
+                    box_indices[i, num_valid_boxes_local[0]] = indices[i, orig_idx]
+
+            num_valid_boxes_local[0] += 1
+
+            offset_j = j * 4
+            num_iter_per_thread = ceil_div(nkeep - (j + 1), nthread_tx)
 
-            with ib.for_range(0, j) as k:
-                offset_k = k * box_data_length
+            with ib.for_range(0, num_iter_per_thread, name="_k") as _k:
+                k = j + 1 + _k * nthread_tx + tx
+                offset_k = k * 4
 
                 with ib.if_scope(
                     tvm.tir.all(
-                        out[base_idx + offset_j + score_index] > -1.0,  # if already surpressed
-                        out[base_idx + offset_k + score_index] > 0,
-                        tvm.tir.any(id_index < 0, out[base_idx + offset_k + id_index] >= 0),
+                        k < nkeep,
+                        out_scores[i, k] > 0,  # is the box k still valid?
                         tvm.tir.any(
                             force_suppress > 0,
                             id_index < 0,
-                            out[base_idx + offset_k + id_index]
-                            == out[base_idx + offset_j + id_index],
+                            out_class_ids[i, k] == out_class_ids[i, j],
                         ),
                     )
                 ):
                     iou = calculate_overlap(
-                        out,
-                        base_idx + offset_j + coord_start,
-                        base_idx + offset_k + coord_start,
+                        out_bboxes,
+                        base_bbox_idx + offset_j,
+                        base_bbox_idx + offset_k,
                     )
                     with ib.if_scope(iou >= iou_threshold):
-                        out[base_idx + offset_j + score_index] = -1.0
-                        with ib.if_scope(id_index >= 0):
-                            out[base_idx + offset_j + id_index] = -1.0
-
-            # Has the box j survived IOU tests?
-            with ib.if_scope(out[base_idx + offset_j + score_index] > -1.0):
-                # When return_indices is False, no need to populate box_indices
-                if return_indices:
-                    orig_idx = sorted_index[i * num_anchors + j]
-                    box_indices[i, num_valid_boxes_local[0]] = indices[i, orig_idx]
-                num_valid_boxes_local[0] += 1
+                        # invalidate the box k
+                        out_scores[i, k] = -1.0
+
+                        if return_indices is False and id_index >= 0:
+                            out_class_ids[i, k] = -1.0
+
+                ib.emit(tvm.tir.Call(None, "tir.tvm_storage_sync", tvm.runtime.convert(["shared"])))
 
         if isinstance(max_output_size, int):
             max_output_size = tvm.tir.const(max_output_size)
 
         with ib.if_scope(tvm.tir.all(iou_threshold > 0, valid_count[i] > 0)):
             # Apply nms
-            with ib.for_range(0, valid_count[i]) as j:
-                with ib.if_scope(
-                    tvm.tir.any(id_index < 0, out[base_idx + j * box_data_length + id_index] >= 0)
+            with ib.if_scope(max_output_size > 0):
+                # No need to do more iteration if we have already reached max_output_size boxes
+                box_idx = ib.allocate("int32", (1,), name="box_idx", scope="local")
+                box_idx[0] = 0
+                with ib.while_loop(
+                    tvm.tir.all(box_idx[0] < nkeep, num_valid_boxes_local[0] < max_output_size)
                 ):
-                    with ib.if_scope(max_output_size > 0):
-                        # No need to do more iteration if we already reach max_output_size boxes
-                        with ib.if_scope(num_valid_boxes_local[0] < max_output_size):
-                            nms_inner_loop(ib, j)
-                    with ib.else_scope():
+                    # Proceed to the inner loop if the box with id box_idx is still valid
+                    with ib.if_scope(out_scores[i, box_idx[0]] > -1.0):
+                        nms_inner_loop(ib, box_idx[0])
+                    box_idx[0] += 1
+
+            with ib.else_scope():
+                with ib.for_range(0, nkeep, name="j") as j:
+                    # Proceed to the inner loop if the box j is still valid
+                    with ib.if_scope(out_scores[i, j] > -1.0):
                         nms_inner_loop(ib, j)
 
-            num_valid_boxes[i] = num_valid_boxes_local[0]
+            with ib.if_scope(tx + 0 == 0):
+                num_valid_boxes[i] = num_valid_boxes_local[0]
 
         with ib.else_scope():
             num_valid_boxes[i] = 0
@@ -611,6 +620,170 @@ def _fetch_score_ir(data, score, axis):
     return ib.get()
 
 
+def _get_sorted_indices(data, data_buf, score_index, score_shape):
+    """Extract a 1D score tensor from the packed input and do argsort on it."""
+    score_buf = tvm.tir.decl_buffer(score_shape, data.dtype, "score_buf", data_alignment=8)
+    score_tensor = te.extern(
+        [score_shape],
+        [data],
+        lambda ins, outs: _fetch_score_ir(
+            ins[0],
+            outs[0],
+            score_index,
+        ),
+        dtype=[data.dtype],
+        in_buffers=[data_buf],
+        out_buffers=[score_buf],
+        name="fetch_score",
+        tag="fetch_score",
+    )
+
+    target = tvm.target.Target.current()
+    if target and (
+        can_use_thrust(target, "tvm.contrib.thrust.sort")
+        or can_use_rocthrust(target, "tvm.contrib.thrust.sort")
+    ):
+        sort_tensor = argsort_thrust(score_tensor, axis=1, is_ascend=False, dtype="int32")
+    else:
+        sort_tensor = argsort(score_tensor, axis=1, is_ascend=False, dtype="int32")
+
+    return sort_tensor
+
+
+def _run_nms(
+    data,
+    data_buf,
+    sort_tensor,
+    valid_count,
+    indices,
+    max_output_size,
+    iou_threshold,
+    force_suppress,
+    top_k,
+    coord_start,
+    id_index,
+    score_index,
+    return_indices,
+):
+    """Run NMS using sorted scores."""
+    sort_tensor_buf = tvm.tir.decl_buffer(
+        sort_tensor.shape, sort_tensor.dtype, "sort_tensor_buf", data_alignment=8
+    )
+
+    valid_count_dtype = "int32"
+    valid_count_buf = tvm.tir.decl_buffer(
+        valid_count.shape, valid_count_dtype, "valid_count_buf", data_alignment=4
+    )
+    indices_buf = tvm.tir.decl_buffer(indices.shape, indices.dtype, "indices_buf", data_alignment=8)
+
+    batch_size = data.shape[0]
+    num_anchors = data.shape[1]
+    # Number of extra features per box beyond coords, score, and id.
+    num_features = data.shape[2] - 6 if id_index >= 0 else data.shape[2] - 5
+
+    # output shapes
+    bbox_shape = (batch_size, num_anchors, 4)
+    score_shape = (batch_size, num_anchors)
+    class_id_shape = score_shape
+    out_features_shape = (batch_size, num_anchors, num_features)
+    box_indices_shape = score_shape
+    num_valid_boxes_shape = (batch_size, 1)
+
+    return te.extern(
+        [
+            bbox_shape,
+            score_shape,
+            class_id_shape,
+            out_features_shape,
+            box_indices_shape,
+            num_valid_boxes_shape,
+        ],
+        [data, sort_tensor, valid_count, indices],
+        lambda ins, outs: nms_ir(
+            ins[0],
+            ins[1],
+            ins[2],
+            ins[3],
+            outs[0],  # sorted bbox
+            outs[1],  # sorted scores
+            outs[2],  # sorted class ids
+            outs[3],  # sorted box feats
+            outs[4],  # box_indices
+            outs[5],  # num_valid_boxes
+            max_output_size,
+            iou_threshold,
+            force_suppress,
+            top_k,
+            coord_start,
+            id_index,
+            score_index,
+            return_indices,
+        ),
+        dtype=[data.dtype, "float32", "float32", "float32", "int32", "int32"],
+        in_buffers=[data_buf, sort_tensor_buf, valid_count_buf, indices_buf],
+        name="nms",
+        tag="nms",
+    )
+
+
+def _concatenate_outputs(
+    out_bboxes,
+    out_scores,
+    out_class_ids,
+    out_features,
+    out_shape,
+    coord_start,
+    score_index,
+    id_index,
+):
+    """Pack the results from NMS into a single 5D or 6D tensor."""
+    batch_size = out_bboxes.shape[0]
+    num_anchors = out_bboxes.shape[1]
+    num_features = out_features.shape[2]
+
+    def ir(out_bboxes, out_scores, out_class_ids, out):
+        ib = tvm.tir.ir_builder.create()
+
+        out_bboxes = ib.buffer_ptr(out_bboxes)
+        out_scores = ib.buffer_ptr(out_scores)
+        out_class_ids = ib.buffer_ptr(out_class_ids)
+        out = ib.buffer_ptr(out)
+
+        with ib.if_scope(num_anchors > 0):
+            max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+            nthread_tx = max_threads
+            nthread_bx = ceil_div(num_anchors, nthread_tx)
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            by = te.thread_axis("blockIdx.y")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+            ib.scope_attr(by, "thread_extent", batch_size)
+
+            tid = bx * nthread_tx + tx
+            i = by
+
+            with ib.if_scope(tid < num_anchors):
+                with ib.for_range(0, 4, kind="unroll") as j:
+                    out[i, tid, coord_start + j] = out_bboxes[i, tid, j]
+                with ib.for_range(0, num_features, kind="unroll") as j:
+                    out[i, tid, coord_start + 4 + j] = out_features[i, tid, j]
+                out[i, tid, score_index] = out_scores[i, tid]
+                if id_index >= 0:
+                    out[i, tid, id_index] = out_class_ids[i, tid]
+
+        return ib.get()
+
+    return te.extern(
+        [out_shape],
+        [out_bboxes, out_scores, out_class_ids],
+        lambda ins, outs: ir(ins[0], ins[1], ins[2], outs[0]),
+        dtype=["float32"],
+        name="nms_output_concat",
+        tag="nms_output_concat",
+    )
+
+
 def non_max_suppression(
     data,
     valid_count,
@@ -702,77 +875,36 @@ def non_max_suppression(
         tvm_out = tvm.nd.array(np.zeros(dshape, dtype=data.dtype), ctx)
         f(tvm_data, tvm_valid_count, tvm_out)
     """
-    batch_size = data.shape[0]
-    num_anchors = data.shape[1]
-
-    valid_count_dtype = "int32"
-    valid_count_buf = tvm.tir.decl_buffer(
-        valid_count.shape, valid_count_dtype, "valid_count_buf", data_alignment=4
-    )
-    score_axis = score_index
-    score_shape = (batch_size, num_anchors)
     data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "data_buf", data_alignment=8)
-    score_buf = tvm.tir.decl_buffer(score_shape, data.dtype, "score_buf", data_alignment=8)
-    score_tensor = te.extern(
-        [score_shape],
-        [data],
-        lambda ins, outs: _fetch_score_ir(
-            ins[0],
-            outs[0],
-            score_axis,
-        ),
-        dtype=[data.dtype],
-        in_buffers=[data_buf],
-        out_buffers=[score_buf],
-        name="fetch_score",
-        tag="fetch_score",
-    )
-    target = tvm.target.Target.current()
-    if (
-        target
-        and target.kind.name == "cuda"
-        and tvm.get_global_func("tvm.contrib.thrust.sort_nms", allow_missing=True)
-    ):
-        sort_tensor = argsort_thrust(
-            score_tensor, valid_count=None, axis=1, is_ascend=False, dtype=valid_count_dtype
-        )
-    else:
-        sort_tensor = argsort(score_tensor, axis=1, is_ascend=False, dtype=valid_count_dtype)
 
-    sort_tensor_buf = tvm.tir.decl_buffer(
-        sort_tensor.shape, sort_tensor.dtype, "sort_tensor_buf", data_alignment=8
-    )
-
-    data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "data_buf", data_alignment=8)
-    indices_buf = tvm.tir.decl_buffer(indices.shape, indices.dtype, "indices_buf", data_alignment=8)
-
-    out, box_indices, num_valid_boxes = te.extern(
-        [data.shape, score_shape, [batch_size, 1]],
-        [data, sort_tensor, valid_count, indices],
-        lambda ins, outs: nms_ir(
-            ins[0],
-            ins[1],
-            ins[2],
-            ins[3],
-            outs[0],
-            outs[1],
-            outs[2],
-            max_output_size,
-            iou_threshold,
-            force_suppress,
-            top_k,
-            coord_start,
-            id_index,
-            score_index,
-            return_indices,
-        ),
-        dtype=[data.dtype, "int32", "int32"],
-        in_buffers=[data_buf, sort_tensor_buf, valid_count_buf, indices_buf],
-        name="nms",
-        tag="nms",
+    sort_tensor = _get_sorted_indices(data, data_buf, score_index, (data.shape[0], data.shape[1]))
+
+    out_bboxes, out_scores, out_class_ids, out_features, box_indices, num_valid_boxes = _run_nms(
+        data,
+        data_buf,
+        sort_tensor,
+        valid_count,
+        indices,
+        max_output_size,
+        iou_threshold,
+        force_suppress,
+        top_k,
+        coord_start,
+        id_index,
+        score_index,
+        return_indices,
     )
 
     if return_indices:
         return [box_indices, num_valid_boxes]
 
-    return out
+    return _concatenate_outputs(
+        out_bboxes,
+        out_scores,
+        out_class_ids,
+        out_features,
+        data.shape,
+        coord_start,
+        score_index,
+        id_index,
+    )
diff --git a/python/tvm/topi/cuda/rcnn/proposal.py b/python/tvm/topi/cuda/rcnn/proposal.py
index 5b7884c7363b..12f7a23abe35 100644
--- a/python/tvm/topi/cuda/rcnn/proposal.py
+++ b/python/tvm/topi/cuda/rcnn/proposal.py
@@ -181,7 +181,7 @@ def argsort_ir(data_buf, out_index_buf):
 
     idxm = tvm.tir.indexmod
 
-    with ib.for_range(0, batch, for_type="unroll") as b:
+    with ib.for_range(0, batch, kind="unroll") as b:
         start = b * num_bbox
         for i in range(2):
             bbox_id = tid * 2 + i
@@ -203,7 +203,7 @@ def argsort_ir(data_buf, out_index_buf):
 
 
 def nms_ir(sorted_bbox_buf, out_buf, nms_threshold):
-    """Non-maximum supression.
+    """Non-maximum suppression.
 
     Parameters
     ----------
@@ -259,7 +259,7 @@ def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
     ib.scope_attr(tx, "thread_extent", nthread_tx)
     ib.scope_attr(bx, "thread_extent", nthread_bx)
     i = bx * max_threads + tx
-    with ib.for_range(0, batch, for_type="unroll", name="n") as b:
+    with ib.for_range(0, batch, kind="unroll", name="n") as b:
         base_idx = b * num_bbox
         with ib.if_scope(i < num_bbox):
             p_out[base_idx + i] = False
@@ -323,7 +323,7 @@ def prepare_output_ir(sorted_bbox_buf, remove_mask_buf, out_buf):
                     tvm.tir.all(i[0] < rpn_post_nms_top_n, p_remove[(b * num_bbox + j)] == False)
                 ):
                     p_out[offset_i] = tvm.tir.Cast("float32", b)
-                    with ib.for_range(0, 4, for_type="unroll") as k:
+                    with ib.for_range(0, 4, kind="unroll") as k:
                         p_out[offset_i + k + 1] = p_sorted_bbox[offset_j + k]
                     i[0] = i[0] + 1
 
diff --git a/python/tvm/topi/cuda/scan.py b/python/tvm/topi/cuda/scan.py
new file mode 100644
index 000000000000..84ab5dcf9756
--- /dev/null
+++ b/python/tvm/topi/cuda/scan.py
@@ -0,0 +1,523 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, too-many-locals, too-many-statements
+"Scan related operators"
+import tvm
+from tvm import te
+from tvm.contrib.thrust import can_use_thrust, can_use_rocthrust
+from ..transform import expand_dims, squeeze, transpose, reshape
+from ..utils import ceil_div, swap, prod, get_const_int
+from ..math import cast
+from .. import tag
+from .injective import schedule_injective_from_existing
+
+
+def _get_thrust_func_name(tvmop):
+    tvmop_to_thrust_func_name = {tvm.tir.generic.add: "tvm.contrib.thrust.sum_scan"}
+    assert tvmop in tvmop_to_thrust_func_name, "{} not supported by thrust".format(tvmop)
+    return tvmop_to_thrust_func_name[tvmop]
+
+
+def exclusive_scan_ir(data, output, reduction=None, binop=tvm.tir.generic.add):
+    """Low level IR to do exclusive sum scan along rows of 2D input.
+
+    Parameters
+    ----------
+    data : Buffer
+        Input N-D Buffer. Scan is done over the innermost axis.
+
+    output: Buffer
+        A buffer to store the output scan, of the same shape as data
+
+    reduction: Buffer, optional
+        (N-1)-D Buffer, to store the sum of each scan axis.
+
+    binop: function, optional
+        A binary associative op to use for scan. The function takes two TIR expressions
+        and produce a new TIR expression. By default it uses tvm.tir.generic.add to compute
+        prefix sum.
+    """
+
+    batch_size = prod(data.shape[:-1])
+    scan_axis_size = data.shape[-1]
+
+    ib = tvm.tir.ir_builder.create()
+
+    data = ib.buffer_ptr(data)
+    output = ib.buffer_ptr(output)
+
+    out_dtype = output.dtype
+
+    if reduction is not None:
+        reduction = ib.buffer_ptr(reduction)
+
+    max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+
+    with ib.if_scope(scan_axis_size == 0):
+        with ib.new_scope():
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(bx, "thread_extent", batch_size)
+            with ib.if_scope(bx < batch_size):
+                if reduction is not None:
+                    reduction[bx] = 0
+    with ib.else_scope():
+        with ib.new_scope():
+            nthread_tx = max_threads
+            nthread_bx = ceil_div(scan_axis_size, max_threads)
+            nthread_by = batch_size
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            by = te.thread_axis("blockIdx.y")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+            ib.scope_attr(by, "thread_extent", nthread_by)
+            tid = bx * nthread_tx + tx
+            with ib.if_scope(tid < scan_axis_size):
+                output[by * scan_axis_size + tid] = cast(data[by * scan_axis_size + tid], out_dtype)
+
+        nthread_tx = max_threads
+        nthread_bx = ceil_div(scan_axis_size, max_threads)
+        nthread_by = batch_size
+
+        # The following algorithm performs parallel exclusive scan
+        # Up Sweep of exclusive scan
+        lim = tvm.tir.generic.cast(
+            tvm.tir.ceil(tvm.tir.log2(tvm.tir.generic.cast(scan_axis_size, "float64"))), "int64"
+        )
+        with ib.for_range(0, lim, dtype="int64") as l2_width:
+            width = 2 << l2_width
+
+            with ib.new_scope():
+                tx = te.thread_axis("threadIdx.x")
+                bx = te.thread_axis("blockIdx.x")
+                ib.scope_attr(tx, "thread_extent", nthread_tx)
+                ib.scope_attr(
+                    bx,
+                    "thread_extent",
+                    tvm.tir.generic.cast(ceil_div(scan_axis_size, max_threads * width), "int32"),
+                )
+                tid = bx * nthread_tx + tx
+
+                by = te.thread_axis("blockIdx.y")
+                ib.scope_attr(by, "thread_extent", nthread_by)
+                start = ib.allocate("int64", (1,), name="start", scope="local")
+                middle = ib.allocate("int64", (1,), name="middle", scope="local")
+                end = ib.allocate("int64", (1,), name="end", scope="local")
+                start[0] = width * tid
+                with ib.if_scope(start[0] < scan_axis_size):
+                    middle[0] = start[0] + tvm.tir.indexdiv(width, 2)
+                    end[0] = tvm.te.min(start[0] + width, scan_axis_size)
+                    with ib.if_scope(middle[0] < scan_axis_size):
+                        output[by * scan_axis_size + end[0] - 1] = binop(
+                            output[by * scan_axis_size + end[0] - 1],
+                            output[by * scan_axis_size + middle[0] - 1],
+                        )
+
+        # Down Sweep of exclusive scan
+        with ib.new_scope():
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(bx, "thread_extent", batch_size)
+            with ib.if_scope(bx < batch_size):
+                if reduction is not None:
+                    reduction[bx] = output[(bx + 1) * scan_axis_size - 1]
+                output[(bx + 1) * scan_axis_size - 1] = cast(0, out_dtype)
+
+        with ib.for_range(0, lim, dtype="int64") as l2_width:
+            width = 2 << (lim - l2_width - 1)
+
+            with ib.new_scope():
+                tx = te.thread_axis("threadIdx.x")
+                bx = te.thread_axis("blockIdx.x")
+                ib.scope_attr(tx, "thread_extent", nthread_tx)
+                ib.scope_attr(
+                    bx,
+                    "thread_extent",
+                    tvm.tir.generic.cast(ceil_div(scan_axis_size, max_threads * width), "int32"),
+                )
+                tid = bx * nthread_tx + tx
+
+                by = te.thread_axis("blockIdx.y")
+                ib.scope_attr(by, "thread_extent", nthread_by)
+                start = ib.allocate("int64", (1,), name="start", scope="local")
+                middle = ib.allocate("int64", (1,), name="middle", scope="local")
+                end = ib.allocate("int64", (1,), name="end", scope="local")
+                tmp = ib.allocate(out_dtype, (1,), name="end", scope="local")
+                start[0] = width * tid
+                with ib.if_scope(tvm.tir.all(start[0] < scan_axis_size)):
+                    middle[0] = start[0] + tvm.tir.indexdiv(width, 2)
+                    end[0] = tvm.tir.min(start[0] + width, scan_axis_size)
+                    with ib.if_scope(middle[0] < scan_axis_size):
+                        tmp[0] = output[by * scan_axis_size + middle[0] - 1]
+                        output[by * scan_axis_size + middle[0] - 1] = output[
+                            by * scan_axis_size + end[0] - 1
+                        ]
+                        output[by * scan_axis_size + end[0] - 1] = binop(
+                            output[by * scan_axis_size + end[0] - 1], tmp[0]
+                        )
+    return ib.get()
+
+
+def get_reduction_from_exclusive_scan(data, ex_scan_output, binop=tvm.tir.generic.add):
+    """Return the sum of the last element of data and the exclusive scan output.
+    The is the reduction of data along each row (for 2-D case).
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        Input data of any shape
+
+    ex_scan_output : tvm.te.Tensor
+        The output of exclusive scan on data
+
+    binop: function, optional
+        A binary associative op to use for scan. The function takes two TIR expressions
+        and produce a new TIR expression. By default it uses tvm.tir.generic.add to compute
+        prefix sum.
+
+    Returns
+    -------
+    reduction : tvm.te.Tensor
+        (N-1)-D tensor storing the reduction of each scan axis.
+    """
+    ndim = len(data.shape)
+    if ndim == 1:
+        data = expand_dims(data, axis=0)
+        ex_scan_output = expand_dims(ex_scan_output, axis=0)
+
+    def ir(data, data_ex_scan, reduction):
+        batch_size = prod(data.shape[:-1])
+        scan_axis_size = data.shape[-1]
+
+        ib = tvm.tir.ir_builder.create()
+
+        data = ib.buffer_ptr(data)
+        data_ex_scan = ib.buffer_ptr(data_ex_scan)
+        reduction = ib.buffer_ptr(reduction)
+
+        max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+        with ib.new_scope():
+            nthread_tx = max_threads
+            nthread_bx = ceil_div(batch_size, max_threads)
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+            tid = bx * max_threads + tx
+            with ib.if_scope(tid < batch_size):
+                with ib.if_scope(scan_axis_size > 0):
+                    reduction[tid] = binop(
+                        data_ex_scan[tid * scan_axis_size + scan_axis_size - 1],
+                        data[tid * scan_axis_size + scan_axis_size - 1],
+                    )
+                with ib.else_scope():
+                    reduction[tid] = 0
+
+        return ib.get()
+
+    data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "valid_indices_buf", data_alignment=8)
+    ex_scan_output_buf = tvm.tir.decl_buffer(
+        ex_scan_output.shape, ex_scan_output.dtype, "ex_scan_output_buf", data_alignment=8
+    )
+
+    reduction = te.extern(
+        [data.shape[:-1]],
+        [data, ex_scan_output],
+        lambda ins, outs: ir(ins[0], ins[1], outs[0]),
+        dtype=[ex_scan_output.dtype],
+        in_buffers=[data_buf, ex_scan_output_buf],
+        name="ex_scan_reduction",
+        tag="ex_scan_reduction_gpu",
+    )
+
+    if ndim == 1:
+        return squeeze(reduction, 0)
+
+    return reduction
+
+
+def scan_thrust(
+    data, output_dtype, exclusive=True, return_reduction=False, binop=tvm.tir.generic.add
+):
+    """Do exclusive or inclusive scan on 1D or multidimensional input, using thrust.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        Input data of any shape. The scan is done over the innermost axis.
+
+    output_dtype: string
+        The dtype of the output scan tensor.
+
+    exclusive: bool, optional
+        Whether or not do exclusive or inclusive scan.
+
+    return_reduction: bool, optional
+        Whether or not return a (N-1)-D tensor storing the reduction of each scan axis.
+        Reductions are computed as part of the upsweep pass, so there is no extra cost.
+        If False, reductions are ignored. It must be False when exclusive is False.
+
+    binop: function, optional
+        A binary associative op to use for scan. Since we need to lookup the corresponding
+        thrust function, arbitrariy callables are not supported. Currently only
+        tvm.tir.generic.add can be passed in.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        A N-D tensor of the same rank N and shape as the input data.
+
+    reduction : tvm.te.Tensor, optional
+        (N-1)-D tensor storing the reduction of each scan axis.
+        Returned if return_reduction is True.
+    """
+    data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "data_buf", data_alignment=8)
+    output_buf = tvm.tir.decl_buffer(data.shape, output_dtype, "output_buf", data_alignment=8)
+
+    output = te.extern(
+        [data.shape],
+        [data],
+        lambda ins, outs: tvm.tir.call_packed(
+            _get_thrust_func_name(binop), ins[0], outs[0], exclusive
+        ),
+        dtype=[output_dtype],
+        in_buffers=[data_buf],
+        out_buffers=[output_buf],
+        name="exclusive_scan_thrust",
+        tag="exclusive_scan_thrust_gpu",
+    )
+
+    if return_reduction:
+        assert exclusive, "return_reduction should be False for inclusive scan"
+        reduction = get_reduction_from_exclusive_scan(data, output, binop)
+        return output, reduction
+
+    return output
+
+
+def exclusive_scan(
+    data, axis=-1, return_reduction=False, output_dtype=None, binop=tvm.tir.generic.add
+):
+    """Do exclusive scan on 1D or multidimensional input.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        Input data of any shape.
+
+    axis: int, optional
+        The axis to do scan on. By default, scan is done on the innermost axis.
+
+    return_reduction: bool, optional
+        Whether or not return a tensor storing the reduction over each scan axis.
+        If the input rank is N, this tensor is of rank N - 1.
+        Reductions are computed as part of the upsweep pass, so there is no extra cost.
+        If False, reductions are ignored.
+
+    output_dtype: string, optional
+        The dtype of the output scan tensor. If not provided, the dtype of the input is used.
+
+    binop: function, optional
+        A binary associative op to use for scan. The function takes two TIR expressions
+        and produce a new TIR expression. By default it uses tvm.tir.generic.add to compute
+        prefix sum.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        A N-D tensor of the same rank N and shape as the input data.
+
+    reduction : tvm.te.Tensor, optional
+        (N-1)-D tensor storing the reduction of each scan axis.
+        Returned if return_reduction is True.
+    """
+
+    def do_scan(data, output_dtype):
+        target = tvm.target.Target.current()
+        if target and (
+            can_use_thrust(target, "tvm.contrib.thrust.sum_scan")
+            or can_use_rocthrust(target, "tvm.contrib.thrust.sum_scan")
+        ):
+            return scan_thrust(
+                data, output_dtype, exclusive=True, return_reduction=return_reduction, binop=binop
+            )
+
+        if ndim == 1:
+            # TIR exclusive scan accepts only 2D or higher-rank inputs.
+            data = expand_dims(data, axis=0)
+
+        data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "data_buf", data_alignment=8)
+        output_buf = tvm.tir.decl_buffer(data.shape, output_dtype, "output_buf", data_alignment=8)
+
+        if return_reduction:
+            output, reduction = te.extern(
+                [data.shape, data.shape[:-1]],
+                [data],
+                lambda ins, outs: exclusive_scan_ir(ins[0], outs[0], outs[1], binop=binop),
+                dtype=[data.dtype, output_dtype],
+                in_buffers=[data_buf],
+                name="exclusive_scan",
+                tag="exclusive_scan_gpu",
+            )
+        else:
+            output = te.extern(
+                [data.shape],
+                [data],
+                lambda ins, outs: exclusive_scan_ir(ins[0], outs[0], binop=binop),
+                dtype=[output_dtype],
+                in_buffers=[data_buf],
+                out_buffers=[output_buf],
+                name="exclusive_scan",
+                tag="exclusive_scan_gpu",
+            )
+            reduction = None
+
+        if ndim == 1:
+            output = squeeze(output, 0)
+            if return_reduction:
+                reduction = squeeze(reduction, 0)
+
+        if return_reduction:
+            return output, reduction
+
+        return output
+
+    if output_dtype is None or output_dtype == "":
+        output_dtype = data.dtype
+
+    ndim = len(data.shape)
+    if axis < 0:
+        axis += ndim
+
+    # If scan axis is not the innermost one, swap the scan and the innermost axes
+    # Scan is always done on the innermost axis, for performance reason.
+    if axis != ndim - 1:
+        axes = swap(list(range(ndim)), axis)
+        data = transpose(data, axes)
+
+    if return_reduction:
+        output, reduction = do_scan(data, output_dtype)
+    else:
+        output = do_scan(data, output_dtype)
+
+    if axis != ndim - 1:
+        axes = swap(list(range(ndim)), axis)
+        output = transpose(output, axes)
+
+    if return_reduction:
+        return output, reduction
+
+    return output
+
+
+def inclusive_scan(data, axis=-1, output_dtype=None, binop=tvm.tir.generic.add):
+    """Do inclusive scan on 1D or multidimensional input.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        Input data of any shape.
+
+    axis: int, optional
+        The axis to do scan on. By default, scan is done on the innermost axis.
+
+    output_dtype: string, optional
+        The dtype of the output scan tensor. If not provided, the dtype of the input is used.
+
+    binop: function, optional
+        A binary associative op to use for scan. The function takes two TIR expressions
+        and produce a new TIR expression. By default it uses tvm.tir.generic.add to compute
+        prefix sum.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        A N-D tensor of the same rank N as the input data.
+    """
+    ex_scan = exclusive_scan(data, axis, output_dtype=output_dtype, binop=binop)
+
+    if output_dtype is not None and data.dtype != output_dtype and output_dtype != "":
+        data = cast(data, output_dtype)
+
+    return binop(data, ex_scan)
+
+
+def schedule_scan(outs):
+    """Schedule for scan operator.
+
+    Parameters
+    ----------
+    outs: Array of Tensor
+        The computation graph description of scan
+        in the format of an array of tensors.
+
+    Returns
+    -------
+    s: Schedule
+      The computation schedule for the op.
+    """
+    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+    s = te.create_schedule([x.op for x in outs])
+    scheduled_ops = []
+
+    def traverse(op):
+        if tag.is_injective(op.tag):
+            schedule_injective_from_existing(s, op.output(0))
+        for tensor in op.input_tensors:
+            if tensor.op.input_tensors and tensor.op not in scheduled_ops:
+                traverse(tensor.op)
+        scheduled_ops.append(op)
+
+    for out in outs:
+        traverse(out.op)
+    return s
+
+
+def cumsum(data, axis=None, dtype=None, exclusive=None):
+    """Numpy style cumsum op. Return the cumulative sum of the elements along a given axis.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        The input data to the operator.
+
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default (None) is to compute
+        the cumsum over the flattened array.
+
+    dtype : string, optional
+        Type of the returned array and of the accumulator in which the elements are summed.
+        If dtype is not specified, it defaults to the dtype of data.
+
+    exclusive : int, optional
+        If set to 1 will return exclusive sum in which the first element is not
+        included. In other terms, if set to 1, the j-th output element would be
+        the sum of the first (j-1) elements. Otherwise, it would be the sum of
+        the first j elements.
+
+    Returns
+    -------
+    result : tvm.te.Tensor
+        The result has the same size as data, and the same shape as data if axis is not None.
+        If axis is None, the result is a 1-d array.
+    """
+    if axis is None:
+        axis = 0
+        data = reshape(data, (prod(data.shape),))
+    axis = get_const_int(axis)
+    if exclusive is not None and exclusive != 0:
+        return exclusive_scan(data, axis, output_dtype=dtype, binop=tvm.tir.generic.add)
+    return inclusive_scan(data, axis, output_dtype=dtype, binop=tvm.tir.generic.add)
diff --git a/python/tvm/topi/cuda/scatter.py b/python/tvm/topi/cuda/scatter.py
index be602c8ab7a3..fd05904ba8e7 100644
--- a/python/tvm/topi/cuda/scatter.py
+++ b/python/tvm/topi/cuda/scatter.py
@@ -17,14 +17,27 @@
 # pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, too-many-statements, singleton-comparison, unused-argument
 """Scatter operator """
 import tvm
-from tvm import te
+from tvm import te, autotvm
 from ..scatter import _verify_scatter_nd_inputs
+from ..generic import schedule_extern
 from .nms import atomic_add
-from .sort import stable_sort_by_key_thrust, is_thrust_available
+from .sort import stable_sort_by_key_thrust
+from ..utils import prod, ceil_div
 
 
-def ceil_div(a, b):
-    return (a + b - 1) // b
+def _memcpy_ir(ib, out_ptr, data_ptr, shape):
+    fused = prod(shape)
+    with ib.new_scope():
+        num_thread = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+        num_blocks = ceil_div(fused, num_thread)
+        bx = te.thread_axis("blockIdx.x")
+        ib.scope_attr(bx, "thread_extent", num_blocks)
+        tx = te.thread_axis("threadIdx.x")
+        ib.scope_attr(tx, "thread_extent", num_thread)
+        tid = bx * num_thread + tx
+
+        with ib.if_scope(tid < fused):
+            out_ptr[tid] = data_ptr[tid]
 
 
 def gen_ir_1d(data, indices, updates, axis, out, update_func):
@@ -63,10 +76,7 @@ def gen_ir_1d(data, indices, updates, axis, out, update_func):
     out_ptr = ib.buffer_ptr(out)
     data_ptr = ib.buffer_ptr(data)
 
-    with ib.new_scope():
-        bx = te.thread_axis("blockIdx.x")
-        ib.scope_attr(bx, "thread_extent", n)
-        out_ptr[bx] = data_ptr[bx]
+    _memcpy_ir(ib, out_ptr, data_ptr, data.shape)
 
     indices_ptr = ib.buffer_ptr(indices)
     updates_ptr = ib.buffer_ptr(updates)
@@ -114,8 +124,6 @@ def gen_ir_2d(data, indices, updates, axis, out, update_func):
     ret : tir
         The computational ir.
     """
-    warp_size = tvm.target.Target.current(False).thread_warp_size
-
     n = data.shape[0]
     c = data.shape[1]
 
@@ -124,16 +132,7 @@ def gen_ir_2d(data, indices, updates, axis, out, update_func):
     out_ptr = ib.buffer_ptr(out)
     data_ptr = ib.buffer_ptr(data)
 
-    with ib.new_scope():
-        bx = te.thread_axis("blockIdx.x")
-        ib.scope_attr(bx, "thread_extent", n)
-        tx = te.thread_axis("threadIdx.x")
-        ib.scope_attr(tx, "thread_extent", warp_size)
-        with ib.for_range(0, ceil_div(c, warp_size), name="j") as j_:
-            j = j_ * warp_size + tx
-            with ib.if_scope(j < c):
-                idx = bx * c + j
-                out_ptr[idx] = data_ptr[idx]
+    _memcpy_ir(ib, out_ptr, data_ptr, data.shape)
 
     indices_ptr = ib.buffer_ptr(indices)
     updates_ptr = ib.buffer_ptr(updates)
@@ -205,18 +204,7 @@ def gen_ir_3d(data, indices, updates, axis, out, update_func):
     out_ptr = ib.buffer_ptr(out)
     data_ptr = ib.buffer_ptr(data)
 
-    with ib.new_scope():
-        bx = te.thread_axis("blockIdx.x")
-        ib.scope_attr(bx, "thread_extent", n)
-        by = te.thread_axis("blockIdx.y")
-        ib.scope_attr(by, "thread_extent", c)
-        tx = te.thread_axis("threadIdx.x")
-        ib.scope_attr(tx, "thread_extent", warp_size)
-        with ib.for_range(0, ceil_div(h, warp_size), name="k") as k_:
-            k = k_ * warp_size + tx
-            with ib.if_scope(k < h):
-                idx = (bx * c + by) * h + k
-                out_ptr[idx] = data_ptr[idx]
+    _memcpy_ir(ib, out_ptr, data_ptr, data.shape)
 
     indices_ptr = ib.buffer_ptr(indices)
     updates_ptr = ib.buffer_ptr(updates)
@@ -311,20 +299,7 @@ def gen_ir_4d(data, indices, updates, axis, out, update_func):
 
     out_ptr = ib.buffer_ptr(out)
     data_ptr = ib.buffer_ptr(data)
-    with ib.new_scope():
-        i = te.thread_axis("blockIdx.x")
-        ib.scope_attr(i, "thread_extent", n)
-        j = te.thread_axis("blockIdx.y")
-        ib.scope_attr(j, "thread_extent", c)
-        k = te.thread_axis("blockIdx.z")
-        ib.scope_attr(k, "thread_extent", h)
-        tx = te.thread_axis("threadIdx.x")
-        ib.scope_attr(tx, "thread_extent", warp_size)
-        with ib.for_range(0, ceil_div(w, warp_size), name="l") as l_:
-            l = l_ * warp_size + tx
-            with ib.if_scope(l < w):
-                idx = ((i * c + j) * h + k) * w + l
-                out_ptr[idx] = data_ptr[idx]
+    _memcpy_ir(ib, out_ptr, data_ptr, data.shape)
 
     indices_ptr = ib.buffer_ptr(indices)
     updates_ptr = ib.buffer_ptr(updates)
@@ -417,7 +392,71 @@ def gen_ir_4d(data, indices, updates, axis, out, update_func):
     return ib.get()
 
 
-def gen_scatter_1d_thrust(data, indices_sorted, updates_sorted, axis, out, _):
+@autotvm.register_topi_compute("scatter.cuda")
+def scatter(cfg, data, indices, updates, axis=0):
+    """Update data at positions defined by indices with values in updates
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The input data to the operator.
+
+    indices : relay.Expr
+        The index locations to update.
+
+    updates : relay.Expr
+        The values to update.
+
+    axis : int
+        The axis to scatter on
+
+    Returns
+    -------
+    ret : relay.Expr
+        The computed result.
+    """
+    if axis < 0:
+        axis += len(data.shape)
+    assert axis >= 0
+    assert axis < len(data.shape)
+
+    rank = len(data.shape)
+    assert 1 <= rank <= 4, "scatter only supports 1-4 dimensions"
+
+    ir_funcs = {
+        1: gen_ir_1d,
+        2: gen_ir_2d,
+        3: gen_ir_3d,
+        4: gen_ir_4d,
+    }
+
+    def update_func(dst_ptr, dst_index, update):
+        dst_ptr[dst_index] = update
+
+    out_shape = data.shape
+    out_buf = tvm.tir.decl_buffer(out_shape, data.dtype, "out_buf")
+
+    cfg.add_flop(1)  # A dummy value to satisfy AutoTVM
+
+    out = te.extern(
+        [out_shape],
+        [data, indices, updates],
+        lambda ins, outs: ir_funcs[rank](ins[0], ins[1], ins[2], axis, outs[0], update_func),
+        dtype=data.dtype,
+        out_buffers=[out_buf],
+        name="scatter_gpu",
+        tag="scatter_gpu",
+    )
+
+    return out
+
+
+@autotvm.register_topi_schedule("scatter.cuda")
+def schedule_scatter(_, outs):
+    return schedule_extern(outs)
+
+
+def gen_scatter_1d_thrust(data, indices_sorted, updates_sorted, out):
     """Generate scatter ir for 1d inputs, using a sorting based approach.
     By sorting indices and comparing neighboring two indices, we can tell which
     of elements in the indices tensor can scatter its update value into the output.
@@ -438,9 +477,6 @@ def gen_scatter_1d_thrust(data, indices_sorted, updates_sorted, axis, out, _):
     updates : tir.Tensor
         The values to update, sorted by indices.
 
-    axis : int
-        The axis to scatter on. It must be 0 for this function.
-
     out : tir.Tensor
         The output tensor.
 
@@ -449,7 +485,6 @@ def gen_scatter_1d_thrust(data, indices_sorted, updates_sorted, axis, out, _):
     ret : tir
         The computational ir.
     """
-    assert axis == 0
     n = data.shape[0]
 
     ib = tvm.tir.ir_builder.create()
@@ -504,7 +539,8 @@ def gen_scatter_1d_thrust(data, indices_sorted, updates_sorted, axis, out, _):
     return ib.get()
 
 
-def scatter(data, indices, updates, axis=0):
+@autotvm.register_topi_compute("scatter_via_sort.cuda")
+def scatter_via_sort(cfg, data, indices, updates, axis=0):
     """Update data at positions defined by indices with values in updates
 
     Parameters
@@ -528,49 +564,33 @@ def scatter(data, indices, updates, axis=0):
     """
     if axis < 0:
         axis += len(data.shape)
-    assert axis >= 0
-    assert axis < len(data.shape)
-
-    rank = len(data.shape)
-    assert 1 <= rank <= 4, "scatter only supports 1-4 dimensions"
-
-    ir_funcs = {
-        1: gen_ir_1d,
-        2: gen_ir_2d,
-        3: gen_ir_3d,
-        4: gen_ir_4d,
-    }
+    assert axis == 0 and len(data.shape) == 1, "sorting based scatter only supported for 1d input"
 
-    def update_func(dst_ptr, dst_index, update):
-        dst_ptr[dst_index] = update
+    cfg.add_flop(1)  # A dummy value to satisfy AutoTVM
 
     out_shape = data.shape
     out_buf = tvm.tir.decl_buffer(out_shape, data.dtype, "out_buf")
 
-    in_bufs = [data]
-
-    if rank == 1 and is_thrust_available():
-        ir_funcs[1] = gen_scatter_1d_thrust
-        indices_sorted, updates_sorted = stable_sort_by_key_thrust(
-            indices, updates, for_scatter=True
-        )
-        in_bufs += [indices_sorted, updates_sorted]
-    else:
-        in_bufs += [indices, updates]
+    indices_sorted, updates_sorted = stable_sort_by_key_thrust(indices, updates, for_scatter=True)
 
     out = te.extern(
         [out_shape],
-        in_bufs,
-        lambda ins, outs: ir_funcs[rank](ins[0], ins[1], ins[2], axis, outs[0], update_func),
+        [data, indices_sorted, updates_sorted],
+        lambda ins, outs: gen_scatter_1d_thrust(ins[0], ins[1], ins[2], outs[0]),
         dtype=data.dtype,
         out_buffers=[out_buf],
-        name="scatter_gpu",
-        tag="scatter_gpu",
+        name="scatter_via_sort_gpu",
+        tag="scatter_via_sort_gpu",
     )
 
     return out
 
 
+@autotvm.register_topi_schedule("scatter_via_sort.cuda")
+def schedule_scatter_via_sort(_, outs):
+    return schedule_extern(outs)
+
+
 def gen_scatter_add_1d_atomic(data, indices, updates, axis, out, _):
     """Generate scatter add ir for 1d inputs, using atomic_add instruction
 
diff --git a/python/tvm/topi/cuda/sort.py b/python/tvm/topi/cuda/sort.py
index 18872a242160..5ebd3060a6bb 100644
--- a/python/tvm/topi/cuda/sort.py
+++ b/python/tvm/topi/cuda/sort.py
@@ -18,16 +18,12 @@
 """Sort related operators """
 import tvm
 from tvm import te
-from tvm._ffi import get_global_func
 
 from .injective import schedule_injective_from_existing
 from ..transform import strided_slice, transpose
 from .. import tag
-
-
-def swap(arr, axis):
-    """ swap arr[axis] and arr[-1] """
-    return arr[:axis] + [arr[-1]] + arr[axis + 1 : -1] + [arr[axis]]
+from ..utils import ceil_div, swap
+from ..math import cast
 
 
 def _schedule_sort(outs):
@@ -61,8 +57,18 @@ def traverse(op):
     return s
 
 
-def ceil_div(a, b):
-    return tvm.tir.indexdiv(a + b - 1, b)
+def _get_threads(ib, nthread_tx, nthread_bx, nthread_by, nthread_bz):
+    tx = te.thread_axis("threadIdx.x")
+    bx = te.thread_axis("blockIdx.x")
+    ib.scope_attr(tx, "thread_extent", nthread_tx)
+    ib.scope_attr(bx, "thread_extent", nthread_bx)
+
+    by = te.thread_axis("blockIdx.y")
+    bz = te.thread_axis("blockIdx.z")
+    ib.scope_attr(by, "thread_extent", nthread_by)
+    ib.scope_attr(bz, "thread_extent", nthread_bz)
+
+    return tx, bx, by, bz
 
 
 def _sort_init(ib, shape, axis, keys_in, keys_out, values_out=None, value_init_func=None):
@@ -86,16 +92,8 @@ def _sort_init(ib, shape, axis, keys_in, keys_out, values_out=None, value_init_f
 
     # Copy the keys_in to initial output
     with ib.new_scope():
-        tx = te.thread_axis("threadIdx.x")
-        bx = te.thread_axis("blockIdx.x")
-        ib.scope_attr(tx, "thread_extent", nthread_tx)
-        ib.scope_attr(bx, "thread_extent", nthread_bx)
+        tx, bx, by, bz = _get_threads(ib, nthread_tx, nthread_bx, nthread_by, nthread_bz)
         tid = bx * nthread_tx + tx
-
-        by = te.thread_axis("blockIdx.y")
-        bz = te.thread_axis("blockIdx.z")
-        ib.scope_attr(by, "thread_extent", nthread_by)
-        ib.scope_attr(bz, "thread_extent", nthread_bz)
         idx = (by * shape[axis] + tid) * axis_mul_after + bz
         with ib.if_scope(tid < shape[axis]):
             keys_out[idx] = keys_in[idx]
@@ -105,6 +103,100 @@ def _sort_init(ib, shape, axis, keys_in, keys_out, values_out=None, value_init_f
     return axis_mul_before, axis_mul_after
 
 
+## TODO(mbrookhart): These are effective optimziation hyperparametrs
+## Perhaps we can autotune?
+block_size = 128
+thread_work = 4
+
+
+def _odd_even_sort(
+    ib,
+    size,
+    axis_mul_before,
+    axis_mul_after,
+    is_ascend,
+    keys,
+    keys_swap,
+    values=None,
+    values_swap=None,
+):
+
+    nthread_tx = block_size // 2
+    nthread_bx = ceil_div(size, block_size)
+    nthread_by = axis_mul_before
+    nthread_bz = axis_mul_after
+    with ib.new_scope():
+        ib.scope_attr(tvm.tir.const(0), "hand_threaded", 0)
+        tx, bx, by, bz = _get_threads(ib, nthread_tx, nthread_bx, nthread_by, nthread_bz)
+        tid = 2 * tx
+        start = bx * block_size
+
+        ## Create shared memory as syncable thread scratch space
+        tmp_keys_swap = ib.allocate(
+            keys_swap.dtype,
+            (block_size,),
+            name="temp_keys_swap",
+            scope="shared",
+        )
+        if values_swap is not None:
+            tmp_values_swap = ib.allocate(
+                values_swap.dtype,
+                (block_size,),
+                name="temp_values_swap",
+                scope="shared",
+            )
+
+        ## Create thread local data for swapping
+        temp_keys = ib.allocate(keys_swap.dtype, (1,), name="temp_keys", scope="local")
+        if values_swap is not None:
+            temp_values = ib.allocate(values_swap.dtype, (1,), name="temp_values", scope="local")
+
+        temp_cond1 = ib.allocate(keys_swap.dtype, (1,), name="temp_cond1", scope="local")
+        temp_cond2 = ib.allocate(keys_swap.dtype, (1,), name="temp_cond2", scope="local")
+        # Copy data to scratch space
+        base_idx = by * size * axis_mul_after + bz
+        with ib.for_range(0, 2) as n:
+            with ib.if_scope((tid + n + start) < size):
+                tmp_keys_swap[tid + n] = keys[base_idx + (tid + n + start) * axis_mul_after]
+                if values_swap is not None:
+                    tmp_values_swap[tid + n] = values[base_idx + (tid + n + start) * axis_mul_after]
+
+        ib.emit(tvm.tir.Call(None, "tir.tvm_storage_sync", tvm.runtime.convert(["shared"])))
+
+        idxm = tvm.tir.indexmod
+        # OddEvenTransposeSort
+        current_sort_num = tvm.tir.min(block_size, size - start)
+        with ib.for_range(0, current_sort_num) as k:
+            n = idxm(tid + k, 2)
+            with ib.if_scope(tid + n < current_sort_num - 1):
+                temp_cond1[0] = tmp_keys_swap[tid + n]
+                temp_cond2[0] = tmp_keys_swap[tid + n + 1]
+                if is_ascend:
+                    cond = temp_cond1[0] > temp_cond2[0]
+                else:
+                    cond = temp_cond1[0] < temp_cond2[0]
+                with ib.if_scope(cond):
+                    temp_keys[0] = tmp_keys_swap[tid + n]
+                    tmp_keys_swap[tid + n] = tmp_keys_swap[tid + n + 1]
+                    tmp_keys_swap[tid + n + 1] = temp_keys[0]
+                    if values_swap is not None:
+                        temp_values[0] = tmp_values_swap[tid + n]
+                        tmp_values_swap[tid + n] = tmp_values_swap[tid + n + 1]
+                        tmp_values_swap[tid + n + 1] = temp_values[0]
+            ib.emit(tvm.tir.Call(None, "tir.tvm_storage_sync", tvm.runtime.convert(["shared"])))
+
+        ## Copy sorted data to output
+        with ib.for_range(0, 2) as n:
+            with ib.if_scope(tid + n + start < size):
+                keys[base_idx + (tid + n + start) * axis_mul_after] = tmp_keys_swap[tid + n]
+                keys_swap[base_idx + (tid + n + start) * axis_mul_after] = tmp_keys_swap[tid + n]
+                if values_swap is not None:
+                    values[base_idx + (tid + n + start) * axis_mul_after] = tmp_values_swap[tid + n]
+                    values_swap[base_idx + (tid + n + start) * axis_mul_after] = tmp_values_swap[
+                        tid + n
+                    ]
+
+
 def _sort_common(
     ib,
     size,
@@ -118,22 +210,22 @@ def _sort_common(
 ):
     """Either sort only values or sort values by keys."""
 
-    ## we are looping over the array doing mergesort from the bottom up.
-    ## The outer loop runs on the host and launches a cuda kernel for each iteration
-    ## of the algorithm.
-    ## The basic idea is that at iteration 0, each thread does sort on 2 elements.
-    ## On iteration 1, each thread merges 2 sorted arrays of 2 elements,
-    ## to deal with 4 total elements.
-    ## On iteration 2, each thread merges 2 sorted arrays of 4 elements,
-    ## to deal with 8 total elements. On iteration 3, each thread deals with 16 elements, etc
-    ## On the final iteration of the algorithm, one thread will merge two sorted lists
-    ## to sort the entire array
+    ## This function performs a multi-level mergesort
+    ## For blocks of length <= block_size, it does odd-even transpose sort
+    ##    in GPU shared memory
+    ## For intermediate block sizes (>block_size, < max_threads * thread_work)
+    ##    it uses the mergpath algorthim https://arxiv.org/abs/1406.2628
+    ##    to merge blocks in parallel
+    ## At some point, the size of the blocks to be merged is too big for max_threads
+    ##    and we switch to using a dual-level mergepath where the outer mergepath
+    ##    finds the start/end locations of the inner mergepath so that we can split
+    ##    the merge into more blocks
 
     max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+    nthread_by = axis_mul_before * axis_mul_after
+    nthread_bz = 1
     nthread_tx = max_threads
-    nthread_bx = ceil_div(size, max_threads)
-    nthread_by = axis_mul_before
-    nthread_bz = axis_mul_after
+    nthread_bx = ceil_div(size, nthread_tx)
 
     def compare(a, b):
         """
@@ -145,93 +237,234 @@ def compare(a, b):
             out = b <= a
         return out
 
-    def bottom_up_merge(source, dest, source_idx, dest_idx, start, middle, end, even):
-        """
-        Merge the two sections of the array assigned to this thread
-        """
-        # pylint: disable=arguments-out-of-order
-        # initialize iterators
-        i[0] = start
-        j[0] = middle
-        # set up indexes
-        base_idx = by * size * axis_mul_after + bz
-        # iterate over the output loop
-        with ib.for_range(0, end - start) as k:
-            i_idx = base_idx + i[0] * axis_mul_after
-            j_idx = base_idx + j[0] * axis_mul_after
-            k_idx = base_idx + (k + start) * axis_mul_after
-
-            def swap_values(source, dest, source_idx, dest_idx):
-                def assign_i():
-                    """assign i value to current output"""
-                    dest[k_idx] = source[i_idx]
-                    if values is not None:
-                        dest_idx[k_idx] = source_idx[i_idx]
-                    i[0] += 1
-
-                def assign_j():
-                    """assign j value to current output"""
-                    dest[k_idx] = source[j_idx]
-                    if values is not None:
-                        dest_idx[k_idx] = source_idx[j_idx]
-                    j[0] += 1
-
-                ## if both of the iterators are in range
-                with ib.if_scope(tvm.tir.all(i[0] < middle, j[0] < end)):
-                    # compare them and insert whichever is next into the output
-                    with ib.if_scope(compare(source[i_idx], source[j_idx])):
-                        assign_i()
-                    with ib.else_scope():
-                        assign_j()
-                # otherwise, simply copy the remainder of the valid iterator to the output
-                with ib.else_scope():
-                    with ib.if_scope(i[0] < middle):
-                        assign_i()
-                    with ib.else_scope():
-                        assign_j()
+    # Sort the lower levels of the merge using odd-even sort, it's fast for small inputs
+    lower_lim = tvm.tir.generic.cast(
+        tvm.tir.ceil(tvm.tir.log2(tvm.tir.generic.cast(block_size, "float64"))), "int64"
+    )
 
-            # Switch which input is the source and which is the destination each iteration
-            with ib.if_scope(even):
-                swap_values(source, dest, source_idx, dest_idx)
-            with ib.else_scope():
-                swap_values(dest, source, dest_idx, source_idx)
-
-    def mergesort(source, dest, source_idx, dest_idx, size, width, even):
-        # calculate the start, mid, and end points of this section
-        start[0] = width * tid
-        with ib.if_scope(start[0] < size):
-            middle[0] = tvm.te.min(start[0] + tvm.tir.indexdiv(width, 2), size)
-            end[0] = tvm.te.min(start[0] + width, size)
-            ## merge the start->middle and middle->end arrays
-            bottom_up_merge(source, dest, source_idx, dest_idx, start[0], middle[0], end[0], even)
-
-    lim = tvm.tir.generic.cast(
+    _odd_even_sort(
+        ib,
+        size,
+        axis_mul_before * axis_mul_after,
+        1,
+        is_ascend,
+        keys,
+        keys_swap,
+        values,
+        values_swap,
+    )
+
+    upper_lim = tvm.tir.generic.cast(
         tvm.tir.ceil(tvm.tir.log2(tvm.tir.generic.cast(size, "float64"))), "int64"
     )
-    with ib.for_range(0, lim, dtype="int64") as l2_width:
-        width = 2 << l2_width
+
+    def get_merge_begin(source, base_idx, aCount, bCount, aStart, bStart, diag, step_count):
+        first = ib.allocate("int64", (1,), name="first", scope="local")
+        mid = ib.allocate("int64", (1,), name="mid", scope="local")
+        last = ib.allocate("int64", (1,), name="last", scope="local")
+        first[0] = tvm.te.max(0, diag - bCount)
+        last[0] = tvm.te.min(diag, aCount)
+        with ib.while_loop(first[0] < last[0]):
+            mid = (first[0] + last[0]) >> 1
+            a = source[base_idx + (aStart + mid)]
+            b = source[base_idx + (bStart + diag - 1 - mid)]
+            with ib.if_scope(compare(a, b)):
+                first[0] = mid + 1
+            with ib.else_scope():
+                last[0] = mid
+        return first[0], last[0]
+
+    def serial_merge(
+        source,
+        dest,
+        source_idx,
+        dest_idx,
+        base_idx,
+        aCount,
+        bCount,
+        aStart,
+        bStart,
+        kStart,
+        diag,
+        step_count,
+        first,
+        last,
+    ):
+        i = ib.allocate("int64", (1,), name="i", scope="local")
+        j = ib.allocate("int64", (1,), name="j", scope="local")
+        i[0] = aStart + first
+        j[0] = bStart + diag - last
+        with ib.for_range(0, tvm.te.min(aCount + bCount - diag, step_count)) as count:
+            i_idx = base_idx + i[0]
+            j_idx = base_idx + j[0]
+            k_idx = base_idx + (kStart + diag + count)
+
+            def assign_i():
+                """assign i value to current output"""
+                dest[k_idx] = source[i_idx]
+                if values is not None:
+                    dest_idx[k_idx] = source_idx[i_idx]
+                i[0] += 1
+
+            def assign_j():
+                """assign j value to current output"""
+                dest[k_idx] = source[j_idx]
+                if values is not None:
+                    dest_idx[k_idx] = source_idx[j_idx]
+                j[0] += 1
+
+            ## if both of the iterators are in range
+            with ib.if_scope(tvm.tir.all(i[0] < aStart + aCount, j[0] < bStart + bCount)):
+                # compare them and insert whichever is next into the output
+                with ib.if_scope(compare(source[i_idx], source[j_idx])):
+                    assign_i()
+                with ib.else_scope():
+                    assign_j()
+            # otherwise, simply copy the remainder of the valid iterator to the output
+            with ib.else_scope():
+                with ib.if_scope(i[0] < aStart + aCount):
+                    assign_i()
+                with ib.else_scope():
+                    assign_j()
+
+    with ib.for_range(0, upper_lim - lower_lim, dtype="int64") as l2_width:
+        width = 2 << (l2_width + lower_lim)
         # Define and launch the cuda kernel
         with ib.new_scope():
-            i = ib.allocate("int64", (1,), name="i", scope="local")
-            j = ib.allocate("int64", (1,), name="j", scope="local")
-            start = ib.allocate("int64", (1,), name="start", scope="local")
-            middle = ib.allocate("int64", (1,), name="middle", scope="local")
-            end = ib.allocate("int64", (1,), name="end", scope="local")
-            tx = te.thread_axis("threadIdx.x")
-            bx = te.thread_axis("blockIdx.x")
-            ib.scope_attr(tx, "thread_extent", nthread_tx)
-            # Reduce the number of blocks as the work per thread grows
-            ib.scope_attr(
-                bx,
-                "thread_extent",
-                tvm.tir.generic.cast(ceil_div(size, width * max_threads), "int32"),
-            )
-            tid = bx * nthread_tx + tx
-
-            by = te.thread_axis("blockIdx.y")
-            bz = te.thread_axis("blockIdx.z")
-            ib.scope_attr(by, "thread_extent", nthread_by)
-            ib.scope_attr(bz, "thread_extent", nthread_bz)
+            target = tvm.target.Target.current()
+            if "vulkan" in str(target):
+                # Vulkan can't handle dynamic nthread, so we thread slightly differently
+                # for vulkan. We don't do this generally because it causes a 15% perf
+                # regression on other platforms
+                ntx = max_threads
+                nbx = tvm.tir.generic.cast(ceil_div(width, max_threads * thread_work), "int32")
+                nbz = tvm.tir.generic.cast(ceil_div(size, width), "int32")
+                tx, bx, by, bz = _get_threads(ib, ntx, nbx, nthread_by, nbz)
+            else:
+                ntx = tvm.tir.generic.cast(tvm.te.min(max_threads, width), "int32")
+                nbx = tvm.tir.generic.cast(ceil_div(width, max_threads * thread_work), "int32")
+                nbz = tvm.tir.generic.cast(ceil_div(size, width), "int32")
+                tx, bx, by, bz = _get_threads(ib, ntx, nbx, nthread_by, nbz)
+
+            def mergepath(
+                source,
+                dest,
+                source_idx,
+                dest_idx,
+                aCount,
+                bCount,
+                aStart,
+                bStart,
+                kStart,
+                step_count,
+                even,
+            ):
+                # pylint: disable=arguments-out-of-order
+                def merge(source, dest, source_idx, dest_idx):
+                    diag = tx * step_count
+                    first, last = get_merge_begin(
+                        source,
+                        by * size,
+                        aCount,
+                        bCount,
+                        aStart,
+                        bStart,
+                        diag,
+                        step_count,
+                    )
+                    # iterate over the output loop
+                    serial_merge(
+                        source,
+                        dest,
+                        source_idx,
+                        dest_idx,
+                        by * size,
+                        aCount,
+                        bCount,
+                        aStart,
+                        bStart,
+                        kStart,
+                        diag,
+                        step_count,
+                        first,
+                        last,
+                    )
+
+                with ib.if_scope(even):
+                    merge(source, dest, source_idx, dest_idx)
+                with ib.else_scope():
+                    merge(dest, source, dest_idx, source_idx)
+
+            def mergesort(source, dest, source_idx, dest_idx, size, width, even):
+                # calculate the start, mid, and end points of this section
+                start = width * bz
+                middle = cast(tvm.te.min(start + tvm.tir.indexdiv(width, 2), size), "int64")
+                end = cast(tvm.te.min(start + width, size), "int64")
+                with ib.if_scope(start < size):
+                    with ib.if_scope(nbx == 1):
+                        ## merge the start->middle and middle->end arrays
+                        aCount = middle - start
+                        bCount = end - middle
+                        mergepath(
+                            source,
+                            dest,
+                            source_idx,
+                            dest_idx,
+                            aCount,
+                            bCount,
+                            start,
+                            middle,
+                            start,
+                            ceil_div(width, ntx),
+                            even,
+                        )
+                    with ib.else_scope():
+                        step_count = max_threads * thread_work
+                        diag = bx * step_count
+
+                        def do_merge(first, last):
+                            aStart = start + first
+                            bStart = middle + diag - last
+                            aCount = tvm.te.min(middle - aStart, step_count)
+                            bCount = tvm.te.min(end - bStart, step_count)
+                            mergepath(
+                                source,
+                                dest,
+                                source_idx,
+                                dest_idx,
+                                aCount,
+                                bCount,
+                                aStart,
+                                bStart,
+                                start + diag,
+                                thread_work,
+                                even,
+                            )
+
+                        with ib.if_scope(even):
+                            first, last = get_merge_begin(
+                                source,
+                                by * size,
+                                middle - start,
+                                end - middle,
+                                start,
+                                middle,
+                                diag,
+                                step_count,
+                            )
+                            do_merge(first, last)
+                        with ib.else_scope():
+                            first, last = get_merge_begin(
+                                dest,
+                                by * size,
+                                middle - start,
+                                end - middle,
+                                start,
+                                middle,
+                                diag,
+                                step_count,
+                            )
+                            do_merge(first, last)
 
             # Call the kernel
             mergesort(
@@ -243,29 +476,23 @@ def mergesort(source, dest, source_idx, dest_idx, size, width, even):
                 width,
                 tvm.tir.indexmod(l2_width, 2) == 0,
             )
-
+    nthread_by = axis_mul_before
+    nthread_bz = axis_mul_after
+    nthread_tx = max_threads
+    nthread_bx = ceil_div(size, nthread_tx)
     ## if the final sorted data ended up in the swap, copy it to the real output
-    with ib.if_scope(tvm.tir.indexmod(lim, 2) == 1):
+    with ib.if_scope(
+        tvm.tir.all(upper_lim > lower_lim, tvm.tir.indexmod(upper_lim - lower_lim, 2) == 1)
+    ):
         with ib.new_scope():
-            tx = te.thread_axis("threadIdx.x")
-            bx = te.thread_axis("blockIdx.x")
-            ib.scope_attr(tx, "thread_extent", nthread_tx)
-            ib.scope_attr(bx, "thread_extent", nthread_bx)
+            tx, bx, by, bz = _get_threads(ib, nthread_tx, nthread_bx, nthread_by, nthread_bz)
             tid = bx * nthread_tx + tx
-
-            by = te.thread_axis("blockIdx.y")
-            bz = te.thread_axis("blockIdx.z")
-            ib.scope_attr(by, "thread_extent", nthread_by)
-            ib.scope_attr(bz, "thread_extent", nthread_bz)
-            idx = (by * size + tid) * axis_mul_after + bz
+            idx = (by * axis_mul_after + bz) * size + tid
             with ib.if_scope(tid < size):
-                idx = (by * size + tid) * axis_mul_after + bz
                 keys[idx] = keys_swap[idx]
                 if values is not None:
                     values[idx] = values_swap[idx]
 
-    return ib.get()
-
 
 def sort_ir(
     data, values_out, values_out_swap, axis, is_ascend, indices_out=None, indices_out_swap=None
@@ -311,27 +538,30 @@ def sort_ir(
         assert indices_out_swap is not None
         indices_out_swap = ib.buffer_ptr(indices_out_swap)
 
-    axis_mul_before, axis_mul_after = _sort_init(
-        ib,
-        shape,
-        axis,
-        data,
-        values_out,
-        indices_out,
-        value_init_func=lambda _, tid: tvm.tir.generic.cast(tid, indices_out.dtype),
-    )
+    with ib.if_scope(shape[axis] > 0):
+        axis_mul_before, axis_mul_after = _sort_init(
+            ib,
+            shape,
+            axis,
+            data,
+            values_out,
+            indices_out,
+            value_init_func=lambda _, tid: tvm.tir.generic.cast(tid, indices_out.dtype),
+        )
+
+        _sort_common(
+            ib,
+            shape[axis],
+            axis_mul_before,
+            axis_mul_after,
+            is_ascend,
+            values_out,
+            values_out_swap,
+            values=indices_out,
+            values_swap=indices_out_swap,
+        )
 
-    return _sort_common(
-        ib,
-        shape[axis],
-        axis_mul_before,
-        axis_mul_after,
-        is_ascend,
-        values_out,
-        values_out_swap,
-        values=indices_out,
-        values_swap=indices_out_swap,
-    )
+    return ib.get()
 
 
 def sort_by_key_ir(
@@ -386,121 +616,74 @@ def sort_by_key_ir(
     values_out = ib.buffer_ptr(values_out)
     values_out_swap = ib.buffer_ptr(values_out_swap)
 
-    axis_mul_before, axis_mul_after = _sort_init(
-        ib,
-        shape,
-        axis,
-        keys_in,
-        keys_out,
-        values_out,
-        value_init_func=lambda idx, _: values_in[idx],
-    )
-
-    return _sort_common(
-        ib,
-        shape[axis],
-        axis_mul_before,
-        axis_mul_after,
-        is_ascend,
-        keys_out,
-        keys_out_swap,
-        values=values_out,
-        values_swap=values_out_swap,
-    )
+    with ib.if_scope(shape[axis] > 0):
+        axis_mul_before, axis_mul_after = _sort_init(
+            ib,
+            shape,
+            axis,
+            keys_in,
+            keys_out,
+            values_out,
+            value_init_func=lambda idx, _: values_in[idx],
+        )
+
+        _sort_common(
+            ib,
+            shape[axis],
+            axis_mul_before,
+            axis_mul_after,
+            is_ascend,
+            keys_out,
+            keys_out_swap,
+            values=values_out,
+            values_swap=values_out_swap,
+        )
+    return ib.get()
 
 
-def argsort_nms_thrust(data, valid_count, axis=-1, is_ascend=1, dtype="float32"):
-    """Performs sorting along the given axis and returns an array of indicies
-    having same shape as an input array that index data in sorted order.
+def sort(data, axis=-1, is_ascend=1):
+    """Performs sorting along the given axis and returns an array of
+    sorted values with the same shape as the input data.
 
     Parameters
     ----------
     data: tvm.te.Tensor
         The input array.
 
-    valid_count : tvm.te.Tensor, optional
-        The number of valid elements to be sorted.
-
     axis : int, optional
         Axis long which to sort the input tensor.
 
     is_ascend : boolean, optional
         Whether to sort in ascending or descending order.
 
-    dtype : string, optional
-        DType of the output indices.
-
     Returns
     -------
     out : tvm.te.Tensor
         The output of this function.
     """
     ndim = len(data.shape)
-    if axis < 0:
-        axis = ndim + axis
+    axis = ndim + axis if axis < 0 else axis
     if axis != ndim - 1:
         # Prepare for sorting along axis -1.
         axes = swap(list(range(ndim)), axis)
         data = transpose(data, axes)
 
-    data_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "data_buf", data_alignment=8)
-    valid_count_buf = tvm.tir.decl_buffer(
-        valid_count.shape, valid_count.dtype, "valid_count_buf", data_alignment=4
-    )
-    out_bufs = [
-        tvm.tir.decl_buffer(data.shape, data.dtype, "value_buf", data_alignment=8),
-        tvm.tir.decl_buffer(data.shape, "int32", "indices_buf", data_alignment=8),
-    ]
-    out = te.extern(
-        [data.shape, data.shape],
-        [data, valid_count],
-        lambda ins, outs: tvm.tir.call_packed(
-            "tvm.contrib.thrust.sort_nms", ins[0], ins[1], outs[0], outs[1], is_ascend
-        ),
-        in_buffers=[data_buf, valid_count_buf],
-        out_buffers=out_bufs,
-        dtype=[data.dtype, "int32"],
-        name="nms_argsort_gpu",
-        tag="nms_argsort_gpu",
-    )
-
-    if axis != ndim - 1:
-        axes = swap(list(range(ndim)), axis)
-        out = [transpose(o, axes) for o in out]
-
-    return out[1]
-
-
-def sort(data, axis=-1, is_ascend=1):
-    """Performs sorting along the given axis and returns an array of
-    sorted values with the same shape as the input data.
-
-    Parameters
-    ----------
-    data: tvm.te.Tensor
-        The input array.
-
-    axis : int, optional
-        Axis long which to sort the input tensor.
-
-    is_ascend : boolean, optional
-        Whether to sort in ascending or descending order.
-
-    Returns
-    -------
-    out : tvm.te.Tensor
-        The output of this function.
-    """
     value_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "value_buf", data_alignment=8)
     value_buf_swap = tvm.tir.decl_buffer(data.shape, data.dtype, "value_buf_swap", data_alignment=8)
+
     out = te.extern(
         [data.shape, data.shape],
         [data],
-        lambda ins, outs: sort_ir(ins[0], outs[0], outs[1], axis, is_ascend),
+        lambda ins, outs: sort_ir(ins[0], outs[0], outs[1], -1, is_ascend),
         out_buffers=[value_buf, value_buf_swap],
         name="sort_gpu",
         tag="sort_gpu",
     )[0]
+
+    if axis != ndim - 1:
+        axes = swap(list(range(ndim)), axis)
+        out = transpose(out, axes)
+
     return out
 
 
@@ -579,10 +762,18 @@ def argsort(data, axis=-1, is_ascend=1, dtype="float32"):
     out : tvm.te.Tensor
         The output of this function.
     """
+    ndim = len(data.shape)
+    axis = ndim + axis if axis < 0 else axis
+    if axis != ndim - 1:
+        # Prepare for sorting along axis -1.
+        axes = swap(list(range(ndim)), axis)
+        data = transpose(data, axes)
+
     value_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "value_buf", data_alignment=8)
     value_swap_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "value_swap_buf", data_alignment=8)
     indices_buf = tvm.tir.decl_buffer(data.shape, dtype, "out_buf", data_alignment=8)
     indices_swap_buf = tvm.tir.decl_buffer(data.shape, dtype, "out_swap_buf", data_alignment=8)
+
     out = te.extern(
         [data.shape, data.shape, data.shape, data.shape],
         [data],
@@ -590,7 +781,7 @@ def argsort(data, axis=-1, is_ascend=1, dtype="float32"):
             ins[0],
             outs[0],
             outs[2],
-            axis,
+            -1,
             is_ascend,
             indices_out=outs[1],
             indices_out_swap=outs[3],
@@ -599,10 +790,15 @@ def argsort(data, axis=-1, is_ascend=1, dtype="float32"):
         name="argsort_gpu",
         tag="argsort_gpu",
     )[1]
+
+    if axis != ndim - 1:
+        axes = swap(list(range(ndim)), axis)
+        out = transpose(out, axes)
+
     return out
 
 
-def argsort_thrust(data, valid_count=None, axis=-1, is_ascend=1, dtype="float32"):
+def argsort_thrust(data, axis=-1, is_ascend=1, dtype="float32"):
     """Performs sorting along the given axis and returns an array of indicies
     having same shape as an input array that index data in sorted order.
 
@@ -611,9 +807,6 @@ def argsort_thrust(data, valid_count=None, axis=-1, is_ascend=1, dtype="float32"
     data: tvm.te.Tensor
         The input array.
 
-    valid_count : tvm.te.Tensor, optional
-        The number of valid elements to be sorted.
-
     axis : int, optional
         Axis long which to sort the input tensor.
 
@@ -628,11 +821,7 @@ def argsort_thrust(data, valid_count=None, axis=-1, is_ascend=1, dtype="float32"
     out : tvm.te.Tensor
         The output of this function.
     """
-    if valid_count is not None:
-        out = argsort_nms_thrust(data, valid_count, axis, is_ascend, dtype)
-    else:
-        out = topk_thrust(data, 0, axis, "indices", is_ascend, dtype)
-    return out
+    return topk_thrust(data, 0, axis, "indices", is_ascend, dtype)
 
 
 def schedule_sort(outs):
@@ -704,21 +893,30 @@ def topk(data, k=1, axis=-1, ret_type="both", is_ascend=False, dtype="int64"):
     ndim = len(data.shape)
     axis = axis + ndim if axis < 0 else axis
     assert 0 <= axis < ndim
+    dshape = data.shape
+    if axis != ndim - 1:
+        axes = swap(list(range(ndim)), axis)
+        data = transpose(data, axes)
+
     values_buf = tvm.tir.decl_buffer(data.shape, data.dtype, "values_buf", data_alignment=8)
     values_swap_buf = tvm.tir.decl_buffer(
         data.shape, data.dtype, "values_swap_buf", data_alignment=8
     )
     indices_buf = tvm.tir.decl_buffer(data.shape, dtype, "indices_buf", data_alignment=8)
     indices_swap_buf = tvm.tir.decl_buffer(data.shape, dtype, "indies_swap_buf", data_alignment=8)
+
     if ret_type == "values":
         output = te.extern(
             [data.shape, data.shape],
             [data],
-            lambda ins, outs: sort_ir(ins[0], outs[0], outs[1], axis, is_ascend),
+            lambda ins, outs: sort_ir(ins[0], outs[0], outs[1], -1, is_ascend),
             out_buffers=[values_buf, values_swap_buf],
             name="topk_gpu",
             tag="topk_gpu",
         )[0]
+        if axis != ndim - 1:
+            axes = swap(list(range(ndim)), axis)
+            output = transpose(output, axes)
     else:
         output = te.extern(
             [data.shape, data.shape, data.shape, data.shape],
@@ -727,7 +925,7 @@ def topk(data, k=1, axis=-1, ret_type="both", is_ascend=False, dtype="int64"):
                 ins[0],
                 outs[0],
                 outs[2],
-                axis,
+                -1,
                 is_ascend,
                 indices_out=outs[1],
                 indices_out_swap=outs[3],
@@ -736,6 +934,11 @@ def topk(data, k=1, axis=-1, ret_type="both", is_ascend=False, dtype="int64"):
             name="topk_gpu",
             tag="topk_gpu",
         )[0:2]
+        if axis != ndim - 1:
+            axes = swap(list(range(ndim)), axis)
+            output[0] = transpose(output[0], axes)
+            output[1] = transpose(output[1], axes)
+
     if isinstance(k, int) and k < 1:
         if ret_type == "indices":
             return output[1]
@@ -747,7 +950,7 @@ def topk(data, k=1, axis=-1, ret_type="both", is_ascend=False, dtype="int64"):
         if i == axis:
             end.append(k if isinstance(k, int) else tvm.te.size_var("dim"))
         else:
-            end.append(data.shape[i])
+            end.append(dshape[i])
     if ret_type == "both":
         values_out, indices_out = output
         values_out = strided_slice(values_out, beg, end, strides)
@@ -956,10 +1159,3 @@ def stable_sort_by_key_thrust(keys, values, for_scatter=False):
         tag="stable_sort_by_key",
     )
     return out[0], out[1]
-
-
-def is_thrust_available():
-    """
-    Test if thrust based sorting ops are available.
-    """
-    return get_global_func("tvm.contrib.thrust.sort", allow_missing=True) is not None
diff --git a/python/tvm/topi/cuda/sparse.py b/python/tvm/topi/cuda/sparse.py
index c59e6887d47e..f68b31ec30ef 100644
--- a/python/tvm/topi/cuda/sparse.py
+++ b/python/tvm/topi/cuda/sparse.py
@@ -23,10 +23,10 @@
 from tvm import relay, te
 
 from .. import nn
-from ..utils import traverse_inline
+from ..utils import traverse_inline, get_const_tuple, prod, get_const_int, ceil_div
 
 
-def sparse_dense(data, weight_data, weight_indices, weight_indptr):
+def sparse_dense(data, weight_data, weight_indices, weight_indptr, sparse_lhs=False):
     """
     Computes sparse-dense matrix multiplication of `data` and
     `(weight_data, weight_indices, weight_indptr).T`
@@ -57,7 +57,7 @@ def sparse_dense(data, weight_data, weight_indices, weight_indptr):
         2-D with shape [M, N]
     """
     # pylint:disable=unused-argument
-    return nn.sparse_dense(data, weight_data, weight_indices, weight_indptr)
+    return nn.sparse_dense(data, weight_data, weight_indices, weight_indptr, sparse_lhs)
 
 
 def schedule_sparse_dense(outs):
@@ -65,11 +65,13 @@ def schedule_sparse_dense(outs):
     # pylint:disable=invalid-name
     s = te.create_schedule([x.op for x in outs])
 
-    # TODO(ANSHUMAN87): Add for sparse_dense_bsrmm_v1 also
     def _callback(op):
-        if op.tag == "sparse_dense_bsrmm_v2":
+        if op.tag == "sparse_dense_sp_rhs_bsrmm" or op.tag == "sparse_dense_sp_lhs_bsrmm":
             y_bsrmm = op.input_tensors[0]
-            assert y_bsrmm.op.tag == "sparse_dense_bsrmm_block_v2"
+            assert (
+                y_bsrmm.op.tag == "sparse_dense_sp_rhs_bsrmm_block"
+                or y_bsrmm.op.tag == "sparse_dense_sp_lhs_bsrmm_block"
+            )
             out = s.outputs[0].output(0)
 
             if op not in s.outputs:
@@ -91,6 +93,13 @@ def _callback(op):
             s[y_bsrmm_factored].compute_at(s[y_bsrmm], tx)
             s[y_bsrmm].set_store_predicate(thread_x.var.equal(0))
             s[out].set_store_predicate(thread_x.var.equal(0))
+        elif op.tag == "sparse_dense_sp_lhs_csrmm" or op.tag == "sparse_dense_sp_rhs_csrmm":
+            out = op.output(0)
+            const_size = get_const_int(prod(out.shape))
+            fused = s[out].fuse(*s[out].op.axis)
+            bx, tx = s[out].split(fused, factor=const_size)
+            s[out].bind(tx, te.thread_axis("threadIdx.x"))
+            s[out].bind(bx, te.thread_axis("blockIdx.x"))
 
     traverse_inline(s, outs[0].op, _callback)
     return s
@@ -153,9 +162,6 @@ def sparse_dense_tir(data, w_data, w_indices, w_indptr):
     default_function_kernel1 for the multiply.
     """
 
-    def ceil_div(a, b):
-        return (a + (b - 1)) // b
-
     def gen_ir(data, w_data, w_indices, w_indptr, out):
         # pylint: disable=invalid-name
         # TODO(tkonolige): use tensorcores for block multiply
@@ -219,8 +225,8 @@ def gen_ir(data, w_data, w_indices, w_indptr, out):
         )
 
         # zero block
-        with ib.for_range(0, bs_m, name="x", for_type="unroll") as x:
-            with ib.for_range(0, bs_n, name="y", for_type="unroll") as y:
+        with ib.for_range(0, bs_m, name="x", kind="unroll") as x:
+            with ib.for_range(0, bs_n, name="y", kind="unroll") as y:
                 block[x, y] = 0.0
         # compute into thread local storage using warp_size chunks
         with ib.for_range(0, rowlength_bo, name="bb") as bb:
@@ -231,26 +237,26 @@ def gen_ir(data, w_data, w_indices, w_indptr, out):
             # each thread has a row
             # TODO: ideally we could vectorize this
             with ib.for_range(0, rowlength_bi, name="bi") as bi:
-                with ib.for_range(0, bs_m, name="x", for_type="unroll") as x:
-                    with ib.for_range(0, bs_k, name="z", for_type="unroll") as z:
+                with ib.for_range(0, bs_m, name="x", kind="unroll") as x:
+                    with ib.for_range(0, bs_k, name="z", kind="unroll") as z:
                         # This memory acces should be out of bounds when
                         # m_index >= mb (which occurs when the dense matrix
                         # rows % 32 != 0), but it seems to work just fine...
                         data_cache[bi, x, z] = data_ptr[indices[bi] * bs_k + z, m_index * bs_m + x]
             # cache w_data
             elem_idx = bb * rowlength_bi + tx
-            with ib.for_range(0, bs_n, name="y", for_type="unroll") as y:
-                with ib.for_range(0, bs_k, name="z", for_type="unroll") as z:
+            with ib.for_range(0, bs_n, name="y", kind="unroll") as y:
+                with ib.for_range(0, bs_k, name="z", kind="unroll") as z:
                     w_data_cache[tx, y, z] = w_data_ptr[row_start + elem_idx, y, z]
             with ib.for_range(0, mi, name="i") as i:
                 # thread local block matmul
-                with ib.for_range(0, bs_m, name="x", for_type="unroll") as x:
-                    with ib.for_range(0, bs_n, name="y", for_type="unroll") as y:
-                        with ib.for_range(0, bs_k, name="z", for_type="unroll") as z:
+                with ib.for_range(0, bs_m, name="x", kind="unroll") as x:
+                    with ib.for_range(0, bs_n, name="y", kind="unroll") as y:
+                        with ib.for_range(0, bs_k, name="z", kind="unroll") as z:
                             block[x, y] += data_cache[i, x, z] * w_data_cache[i, y, z]
         # store results
-        with ib.for_range(0, bs_m, name="x", for_type="unroll") as x:
-            with ib.for_range(0, bs_n, name="y", for_type="unroll") as y:
+        with ib.for_range(0, bs_m, name="x", kind="unroll") as x:
+            with ib.for_range(0, bs_n, name="y", kind="unroll") as y:
                 with ib.if_scope(m_index < mb):
                     with ib.if_scope(n_index < nb):
                         # It doesn't seem like we would be getting coelesced
@@ -279,7 +285,33 @@ def gen_ir(data, w_data, w_indices, w_indptr, out):
     return out
 
 
-def sparse_dense_padded(data, weight_data, weight_indices, weight_indptr):
+def is_valid_for_sparse_dense_padded(data, weight_data):
+    """
+    Check whether input is applicable for sparse_dense_padded op.
+    If not we should fall back to default scheduling.
+    """
+    # pylint:disable=invalid-name
+    warp_size = int(tvm.target.Target.current(allow_none=False).thread_warp_size)
+    # If there are multiple alter_ops in a model, the first alteration does not
+    # run type inference for the subsequent ones. In this case, we don't have
+    # the shape information, so we run the inferencer manually.
+    try:
+        m = get_const_tuple(data.checked_type.shape)[1]
+    except ValueError:
+        data_infered = relay.transform.InferType()(tvm.IRModule.from_expr(data))["main"]
+        m = get_const_tuple(data_infered.ret_type.shape)[1]
+    if len(weight_data.shape) == 1:
+        bs_m = 1
+    else:
+        bs_m = weight_data.shape[1]
+
+    mb = m // bs_m
+    if mb >= warp_size:
+        return True
+    return False
+
+
+def sparse_dense_padded(data, weight_data, weight_indices, weight_indptr, sparse_lhs=False):
     """
     Computes sparse-dense matrix multiplication of `data` and
     `(weight_data, weight_indices, weight_indptr).T`
@@ -311,6 +343,8 @@ def sparse_dense_padded(data, weight_data, weight_indices, weight_indptr):
     output : tvm.te.Tensor
         2-D with shape [M, N]
     """
+    # TODO(ANSHUMAN87): Handle for sparse_lhs case too
+    assert not sparse_lhs, "Currently only sparse weight is supported."
     return sparse_dense_tir(data, weight_data, weight_indices, weight_indptr)
 
 
@@ -368,6 +402,7 @@ def _alter_sparse_dense_layout(_attrs, inputs, _tinfos, _out_type):
         isinstance(inputs[1], relay.Constant)
         and isinstance(inputs[2], relay.Constant)
         and isinstance(inputs[3], relay.Constant)
+        and is_valid_for_sparse_dense_padded(inputs[0], inputs[1].data.asnumpy())
     ):
         if len(inputs[1].data.asnumpy().shape) == 1:
             sparse_matrix = sp.csr_matrix(
diff --git a/python/tvm/topi/cuda/sparse_reshape.py b/python/tvm/topi/cuda/sparse_reshape.py
new file mode 100644
index 000000000000..4476648e0aa4
--- /dev/null
+++ b/python/tvm/topi/cuda/sparse_reshape.py
@@ -0,0 +1,209 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, too-many-arguments, too-many-nested-blocks
+"""Sparse_Reshape operator"""
+import tvm
+from tvm import te
+from ...tir import decl_buffer, ir_builder, Cast
+from ...te import extern, div, floordiv, floormod
+from ..utils import ceil_div
+
+
+def sparse_reshape(
+    sparse_indices,
+    prev_shape,
+    new_shape,
+    new_sparse_indices_shape,
+    new_shape_shape,
+):
+    """
+    Reshape a Sparse Tensor
+    Parameters
+    ----------
+    sparse_indices : relay.Expr
+        A 2-D tensor[N, n_dim] of integers containing location of sparse values, where N is the
+        number of sparse values and n_dim is the number of dimensions of the dense_shape
+    prev_shape : relay.Expr
+        A 1-D tensor containing the previous shape of the dense tensor
+    new_shape : relay.Expr
+        A 1-D tensor containing the new shape of the dense tensor
+    Returns
+    -------
+    result: relay.Expr
+        Output tensor.
+    Examples
+    --------
+    .. code-block:: python
+        sparse_indices = [[0, 0, 0],
+                            [0, 0, 1],
+                            [0, 1, 0],
+                            [1, 0, 0],
+                            [1, 2, 3]]
+        prev_shape = [2, 3, 4]
+        new_shape = [9, -1]
+        new_sparse_indices, new_shape = relay.sparse_reshape(sparse_indices,
+                            prev_shape,
+                            new_shape)
+        new_sparse_indices = [[0, 0],
+                              [0, 1],
+                              [1, 2],
+                              [4, 2],
+                              [8, 1]]
+        new_shape = [9, 4]
+    """
+
+    def gen_ir(
+        sparse_indices_ptr,
+        prev_shape_ptr,
+        new_shape_ptr,
+        new_sparse_indices_ptr,
+        out_new_shape_ptr,
+    ):
+        ib = ir_builder.create()
+
+        sparse_indices = ib.buffer_ptr(sparse_indices_ptr)
+        prev_shape = ib.buffer_ptr(prev_shape_ptr)
+
+        new_shape = ib.buffer_ptr(new_shape_ptr)
+        out_new_shape = ib.buffer_ptr(out_new_shape_ptr)
+        new_sparse_indices = ib.buffer_ptr(new_sparse_indices_ptr)
+        out_new_shape = ib.buffer_ptr(out_new_shape_ptr)
+
+        prev_shape_size = prev_shape_ptr.shape[0]
+        new_shape_size = new_shape_ptr.shape[0]
+
+        multipliers = ib.allocate(
+            new_shape_ptr.dtype, (prev_shape_size,), name="multipliers", scope="global"
+        )
+        dividers = ib.allocate(
+            new_shape_ptr.dtype, (new_shape_size,), name="dividers", scope="global"
+        )
+        flattened_indices = ib.allocate(
+            new_shape_ptr.dtype,
+            (sparse_indices_ptr.shape[0],),
+            name="flattened_indices",
+            scope="global",
+        )
+        total_ele = ib.allocate(new_shape_ptr.dtype, (1,), name="total_ele", scope="global")
+        division_total_ele = ib.allocate(
+            new_shape_ptr.dtype, (1,), name="division_total_ele", scope="global"
+        )
+        equal_shape = ib.allocate("bool", (1,), name="equal_shape", scope="global")
+        max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+        with ib.new_scope():
+            # The computation in this block is very very miniscule since we are just iterating over
+            # shape tensors which are very small (< 10) and there is no need of parallelization
+            nthread_tx = 1
+            nthread_bx = 1
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+
+            total_ele[0] = prev_shape[0]
+
+            # Cumulative Reverse Exclusive Multiply
+            multipliers[prev_shape_size - 1] = Cast(new_shape_ptr.dtype, 1)
+            with ib.for_range(0, prev_shape_size - 1) as i_:
+                i = i_ + 1
+                multipliers[prev_shape_size - 1 - i] = (
+                    prev_shape[prev_shape_size - i] * multipliers[prev_shape_size - i]
+                )
+                total_ele[0] *= prev_shape[prev_shape_size - i]
+
+            division_total_ele[0] = Cast(new_shape_ptr.dtype, 1)
+            with ib.for_range(0, new_shape_size) as i:
+                with ib.if_scope(new_shape[i] != -1):
+                    division_total_ele[0] *= new_shape[i]
+
+            # Compute true output shape (replace negative ones)
+            with ib.for_range(0, new_shape_size) as i:
+                with ib.if_scope(new_shape[i] == -1):
+                    out_new_shape[i] = Cast(
+                        new_shape_ptr.dtype, div(total_ele[0], division_total_ele[0])
+                    )
+                with ib.else_scope():
+                    out_new_shape[i] = new_shape[i]
+
+            # Check if prev_shape and new_shape are equal
+            equal_shape[0] = True
+            with ib.if_scope(prev_shape_size == new_shape_size):
+                with ib.for_range(0, prev_shape_size) as i:
+                    with ib.if_scope(prev_shape[i] != out_new_shape[i]):
+                        equal_shape[0] = False
+            with ib.else_scope():
+                equal_shape[0] = False
+
+        with ib.new_scope():
+            nthread_tx = max_threads
+            nthread_bx = ceil_div(sparse_indices_ptr.shape[0], max_threads)
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+
+            row_number = bx * max_threads + tx
+
+            # Return same inputs if shapes are equal
+            with ib.if_scope(equal_shape[0]):
+                with ib.if_scope(row_number < sparse_indices_ptr.shape[0]):
+                    with ib.for_range(0, sparse_indices_ptr.shape[1]) as j:
+                        new_sparse_indices[row_number, j] = sparse_indices[row_number, j]
+
+            # Else compute new_sparse_indices
+            with ib.else_scope():
+                dividers[new_shape_size - 1] = Cast(new_shape_ptr.dtype, 1)
+                with ib.for_range(0, new_shape_size - 1) as i_:
+                    i = i_ + 1
+                    dividers[new_shape_size - 1 - i] = (
+                        dividers[new_shape_size - i] * out_new_shape[new_shape_size - i]
+                    )
+
+                with ib.if_scope(row_number < sparse_indices_ptr.shape[0]):
+                    flattened_indices[row_number] = Cast(new_shape_ptr.dtype, 0)
+                    with ib.for_range(0, sparse_indices_ptr.shape[1]) as j:
+                        flattened_indices[row_number] += (
+                            sparse_indices[row_number, j] * multipliers[j]
+                        )
+
+                with ib.if_scope(row_number < sparse_indices_ptr.shape[0]):
+                    current_element = ib.allocate(
+                        new_shape_ptr.dtype, (1,), name="current_element", scope="local"
+                    )
+                    current_element[0] = flattened_indices[row_number]
+
+                    with ib.for_range(0, new_sparse_indices_ptr.shape[1]) as j:
+                        new_sparse_indices[row_number, j] = Cast(
+                            sparse_indices_ptr.dtype, floordiv(current_element[0], dividers[j])
+                        )
+                        current_element[0] = floormod(current_element[0], dividers[j])
+
+        return ib.get()
+
+    new_sparse_indices_buf = decl_buffer(
+        new_sparse_indices_shape, sparse_indices.dtype, "new_sparse_indices_buf"
+    )
+    new_shape_buf = decl_buffer(new_shape_shape, prev_shape.dtype, "new_shape_buf")
+
+    return extern(
+        [new_sparse_indices_shape, new_shape_shape],
+        [sparse_indices, prev_shape, new_shape],
+        lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0], outs[1]),
+        out_buffers=[new_sparse_indices_buf, new_shape_buf],
+        name="sparse_reshape_cuda",
+        tag="sparse_reshape_cuda",
+    )
diff --git a/python/tvm/topi/cuda/tensorcore_alter_op.py b/python/tvm/topi/cuda/tensorcore_alter_op.py
new file mode 100644
index 000000000000..aec7acbfde56
--- /dev/null
+++ b/python/tvm/topi/cuda/tensorcore_alter_op.py
@@ -0,0 +1,204 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Tensorcore alter op and legalize functions for cuda backend"""
+
+import logging
+import math
+from tvm import relay
+
+from .. import nn
+
+logger = logging.getLogger("topi")
+
+
+@nn.batch_matmul_legalize.register("cuda")
+def _batch_matmul_legalize(attrs, inputs, arg_types):
+    """Legalizes batch_matmul op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    arg_types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # Collect the input tensors.
+    x_tensor, y_tensor = arg_types[0], arg_types[1]
+    dtype = x_tensor.dtype
+
+    # Collect the output tensor.
+    output_tensor = arg_types[2]
+
+    # Collect the input exprs.
+    x, y = inputs
+
+    # Pad input and output channels to use tensorcore schedule.
+    if dtype in ["float16"]:  # todo: support int8/int4
+        B, M, K = x_tensor.shape
+        B, N, K = y_tensor.shape
+        M = M.value
+        K = K.value
+        N = N.value
+
+        # The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32)
+        if (
+            (M % 8 == 0 and K % 16 == 0 and N % 32 == 0)
+            or (M % 16 == 0 and K % 16 == 0 and N % 16 == 0)
+            or (M % 32 == 0 and K % 16 == 0 and N % 8 == 0)
+        ):
+            # no need to pad
+            return None
+
+        (dm, dk, dn), extra_flops = pad_to_tensorcore(M, K, N)
+
+        if extra_flops > 2:
+            logger.info("batch_matmul pad_to_tensorcore skipped, extra_flops %s", extra_flops)
+            return None
+
+        logger.info("batch_matmul pad_to_tensorcore, extra_flops %s", extra_flops)
+        if dm or dk:
+            x_ = relay.nn.pad(x, pad_width=((0, 0), (0, dm), (0, dk)))
+        else:
+            x_ = x
+        if dn or dk:
+            y_ = relay.nn.pad(y, pad_width=((0, 0), (0, dn), (0, dk)))
+        else:
+            y_ = y
+        out_ = relay.nn.batch_matmul(x_, y_)
+        if dm or dn:
+            original_out_shape = [x.value for x in output_tensor.shape]
+            out = relay.strided_slice(out_, begin=[0, 0, 0], end=original_out_shape)
+        else:
+            out = out_
+        return out
+    return None
+
+
+@nn.dense_legalize.register("cuda")
+def _dense_legalize(attrs, inputs, arg_types):
+    """Legalizes dense op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # Collect the input tensors.
+    x_tensor, y_tensor = arg_types[0], arg_types[1]
+    dtype = x_tensor.dtype
+
+    # Collect the output tensor.
+    output_tensor = arg_types[2]
+
+    # Collect the input exprs.
+    x, y = inputs
+
+    # Pad input and output channels to use tensorcore schedule.
+    if dtype in ["float16"]:  # todo: support int8/int4
+        M, K = x_tensor.shape
+        N, K = y_tensor.shape
+        try:
+            M = M.value
+            K = K.value
+            N = N.value
+        except AttributeError:
+            # todo: deal with unfixed shape when compiling wdl model
+            return None
+
+        # The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32)
+        if (
+            (M % 8 == 0 and K % 16 == 0 and N % 32 == 0)
+            or (M % 16 == 0 and K % 16 == 0 and N % 16 == 0)
+            or (M % 32 == 0 and K % 16 == 0 and N % 8 == 0)
+        ):
+            # no need to pad
+            return None
+
+        (dm, dk, dn), extra_flops_ratio = pad_to_tensorcore(M, K, N)
+
+        if extra_flops_ratio > 2:
+            logger.info("dense pad_to_tensorcore skipped, extra_flops_ratio %s", extra_flops_ratio)
+            return None
+
+        logger.info("dense pad_to_tensorcore, extra_flops_ratio %s", extra_flops_ratio)
+
+        if dm or dk:
+            x_ = relay.nn.pad(x, pad_width=((0, dm), (0, dk)))
+        else:
+            x_ = x
+        if dn or dk:
+            y_ = relay.nn.pad(y, pad_width=((0, dn), (0, dk)))
+        else:
+            y_ = y
+        out_ = relay.nn.dense(x_, y_)
+        if dm or dn:
+            original_out_shape = [x.value for x in output_tensor.shape]
+            out = relay.strided_slice(out_, begin=[0, 0], end=original_out_shape)
+        else:
+            out = out_
+        return out
+    return None
+
+
+def pad_to_tensorcore(M, K, N):
+    """pad shape to enable tensorcore"""
+    candidates = [(16, 16, 16), (32, 16, 8), (8, 16, 32)]
+
+    flops = M * K * N
+    extra_flops = math.inf
+    best_pad = (0, 0, 0)
+    for padding in candidates:
+        dm, dk, dn = _pad_to(M, K, N, padding)
+        e = (M + dm) * (N + dn) * (K + dk) - M * N * K
+        # print(dm, dk, dn, e, flops)
+        if e < extra_flops:
+            extra_flops = e
+            best_pad = (dm, dk, dn)
+    return best_pad, extra_flops / flops
+
+
+def _pad_to(M, K, N, PADDING):
+    dm, dk, dn = 0, 0, 0
+
+    if M % PADDING[0] != 0:
+        M_ = ((M + PADDING[0]) // PADDING[0]) * PADDING[0]
+        dm = M_ - M
+    if K % PADDING[1] != 0:
+        K_ = ((K + PADDING[1]) // PADDING[1]) * PADDING[1]
+        dk = K_ - K
+    if N % PADDING[2] != 0:
+        N_ = ((N + PADDING[2]) // PADDING[2]) * PADDING[2]
+        dn = N_ - N
+
+    return dm, dk, dn
diff --git a/python/tvm/topi/cuda/unique.py b/python/tvm/topi/cuda/unique.py
new file mode 100644
index 000000000000..02a5cf3bc592
--- /dev/null
+++ b/python/tvm/topi/cuda/unique.py
@@ -0,0 +1,396 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""Unique operator"""
+import tvm
+from tvm import te, tir
+from ...te import hybrid
+from .scan import cumsum
+from .sort import sort, argsort
+from ..utils import ceil_div
+
+
+def _calc_adjacent_diff_ir(data, output, binop=tir.Sub):
+    """Low level IR to calculate adjacent difference in an 1-D array.
+
+    Parameters
+    ----------
+    data : Buffer
+        Input 1-D Buffer.
+
+    output: Buffer
+        A buffer to store adjacent difference, of the same shape as data. The adjacent difference
+        is defined as: output[0] = 0, output[i] = binop(data[i], data[i-1])
+        where i > 0 and i < len(data).
+
+    binop: function, optional
+        A binary associative op to use for calculating adjacent difference. The function takes two
+        TIR expressions and produce a new TIR expression. By default it uses tvm.tir.Sub to
+        compute the adjacent difference.
+    """
+    ib = tir.ir_builder.create()
+    data_ptr = ib.buffer_ptr(data)
+    output_ptr = ib.buffer_ptr(output)
+    batch_size = data.shape[0]
+    max_threads = tir.min(batch_size, tvm.target.Target.current(allow_none=False).max_num_threads)
+    with ib.new_scope():
+        nthread_tx = max_threads
+        nthread_bx = ceil_div(batch_size, max_threads)
+        tx = te.thread_axis("threadIdx.x")
+        bx = te.thread_axis("blockIdx.x")
+        ib.scope_attr(tx, "thread_extent", nthread_tx)
+        ib.scope_attr(bx, "thread_extent", nthread_bx)
+        tid = bx * max_threads + tx
+        with ib.if_scope(tid < batch_size):
+            with ib.if_scope(tid == 0):
+                output_ptr[tid] = 0
+            with ib.else_scope():
+                output_ptr[tid] = tir.Cast(output.dtype, binop(data_ptr[tid], data_ptr[tid - 1]))
+    return ib.get()
+
+
+def _calc_adjacent_diff(data, out_dtype="int32", binop=tir.Sub):
+    """Function calculate adjacent difference in an 1-D array.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        Input 1-D tensor.
+
+    output_dtype : str
+        The output tensor data type.
+
+    binop: function, optional
+        A binary associative op to use for calculating difference. The function takes two
+        TIR expressions and produce a new TIR expression. By default it uses tvm.tir.Sub to
+        compute the adjacent difference.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        1-D tensor storing the adjacent difference of the input tensor. The adjacent difference
+        is defined as: output[0] = 0, output[i] = binop(data[i], data[i-1])
+        where i > 0 and i < len(data).
+    """
+    data_buf = tir.decl_buffer(data.shape, data.dtype, "sorted_data_buf", data_alignment=8)
+    output_buf = tir.decl_buffer(data.shape, out_dtype, "output_buf", data_alignment=8)
+    return te.extern(
+        [data.shape],
+        [data],
+        lambda ins, outs: _calc_adjacent_diff_ir(ins[0], outs[0], binop=binop),
+        dtype=[out_dtype],
+        in_buffers=[data_buf],
+        out_buffers=[output_buf],
+        name="_calc_adjacent_diff",
+        tag="_calc_adjacent_diff_gpu",
+    )
+
+
+@hybrid.script
+def _calc_num_unique(inc_scan):
+    """Helper function to get the number of unique elements fron inc_scan tensor"""
+    output = output_tensor((1,), "int32")
+    for i in bind("threadIdx.x", 1):
+        output[i] = inc_scan[inc_scan.shape[0] - 1] + int32(1)
+    return output
+
+
+def _calc_unique_ir(
+    data, argsorted_indices, inc_scan, index_converter, unique_elements, indices, counts
+):
+    """Low level IR to calculate unique elements, inverse indices, and counts (optional) of
+    unique elements of 1-D array.
+
+    Parameters
+    ----------
+    data : Buffer
+        Input 1-D Buffer.
+
+    argsorted_indices : Buffer
+        A buffer that stores the argsorted indices of the input data.
+
+    inc_scan : Buffer
+        A buffer that stores the inclusive scan of the binary tir.NE adjacent difference
+        of the sorted data.
+
+    index_converter (optional) : Buffer
+        An optional index converter that transforms the unique element index
+        such that new_idx = index_converter[old_idx].
+
+    unique_elements : Buffer
+        A buffer that stores the unique elements.
+
+    indices : Buffer
+        A buffer that stores the the index of each input data element in the unique element array.
+
+    counts (optional) : Buffer
+        A buffer that stores the count of each unique element.
+    """
+    ib = tir.ir_builder.create()
+    data_ptr = ib.buffer_ptr(data)
+    argsorted_indices_ptr = ib.buffer_ptr(argsorted_indices)
+    inc_scan_ptr = ib.buffer_ptr(inc_scan)
+    unique_elements_ptr = ib.buffer_ptr(unique_elements)
+    indices_ptr = ib.buffer_ptr(indices)
+
+    index_converter_ptr = None
+    if isinstance(index_converter, tir.Buffer):
+        index_converter_ptr = ib.buffer_ptr(index_converter)
+
+    if isinstance(counts, tir.Buffer):
+        counts_ptr = ib.buffer_ptr(counts)
+        # use indices_ptr as a tmp buffer to store tids with inc_scan[tid] != inc_scan[tid-1]
+        unique_seq_indices_ptr = ib.buffer_ptr(indices)
+
+    batch_size = data.shape[0]
+    max_threads = tir.min(batch_size, tvm.target.Target.current(allow_none=False).max_num_threads)
+
+    # if need to return counts
+    if isinstance(counts, tir.Buffer):
+        num_unique = inc_scan_ptr[inc_scan.shape[0] - 1] + 1
+        num_elements = data.shape[0]
+        with ib.new_scope():
+            nthread_tx = max_threads
+            nthread_bx = ceil_div(batch_size, max_threads)
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+            tid = bx * max_threads + tx
+            with ib.if_scope(tid < batch_size):
+                with ib.if_scope(tid == 0):
+                    unique_seq_indices_ptr[num_unique - 1] = num_elements
+                with ib.else_scope():
+                    with ib.if_scope(inc_scan_ptr[tid] != inc_scan_ptr[tid - 1]):
+                        unique_seq_indices_ptr[inc_scan_ptr[tid] - 1] = tid
+        with ib.new_scope():
+            nthread_tx = max_threads
+            nthread_bx = ceil_div(batch_size, max_threads)
+            tx = te.thread_axis("threadIdx.x")
+            bx = te.thread_axis("blockIdx.x")
+            ib.scope_attr(tx, "thread_extent", nthread_tx)
+            ib.scope_attr(bx, "thread_extent", nthread_bx)
+            tid = bx * max_threads + tx
+            with ib.if_scope(tid < num_unique):
+                unique_idx = tid if not index_converter_ptr else index_converter_ptr[tid]
+                with ib.if_scope(tid == 0):
+                    counts_ptr[unique_idx] = unique_seq_indices_ptr[tid]
+                with ib.else_scope():
+                    counts_ptr[unique_idx] = (
+                        unique_seq_indices_ptr[tid] - unique_seq_indices_ptr[tid - 1]
+                    )
+    # calculate unique elements and inverse indices
+    with ib.new_scope():
+        nthread_tx = max_threads
+        nthread_bx = ceil_div(batch_size, max_threads)
+        tx = te.thread_axis("threadIdx.x")
+        bx = te.thread_axis("blockIdx.x")
+        ib.scope_attr(tx, "thread_extent", nthread_tx)
+        ib.scope_attr(bx, "thread_extent", nthread_bx)
+        tid = bx * max_threads + tx
+        with ib.if_scope(tid < batch_size):
+            data_idx = argsorted_indices_ptr[tid]
+            unique_idx = (
+                inc_scan_ptr[tid]
+                if not index_converter_ptr
+                else index_converter_ptr[inc_scan_ptr[tid]]
+            )
+            indices_ptr[data_idx] = unique_idx
+            with ib.if_scope(tid == 0):
+                unique_elements_ptr[unique_idx] = data_ptr[data_idx]
+            with ib.else_scope():
+                with ib.if_scope(inc_scan_ptr[tid] != inc_scan_ptr[tid - 1]):
+                    unique_elements_ptr[unique_idx] = data_ptr[data_idx]
+    return ib.get()
+
+
+def _calc_first_occurence_ir(argsorted_indices, inc_scan, first_occurence):
+    """Low level IR to calculate the first occurence of each unique element in the input data.
+
+    Parameters
+    ----------
+    argsorted_indices : Buffer
+        A buffer that stores the argsorted indices of the input data.
+
+    inc_scan : Buffer
+        A buffer that stores the inclusive scan of the binary tir.NE adjacent difference
+        of the sorted data.
+
+    first_occurence : Buffer
+        A buffer that stores the first occurence of each unique element in the input data.
+    """
+    ib = tir.ir_builder.create()
+    argsorted_indices_ptr = ib.buffer_ptr(argsorted_indices)
+    inc_scan_ptr = ib.buffer_ptr(inc_scan)
+    first_occurence_ptr = ib.buffer_ptr(first_occurence)
+    batch_size = argsorted_indices.shape[0]
+    max_threads = tir.min(batch_size, tvm.target.Target.current(allow_none=False).max_num_threads)
+    with ib.new_scope():
+        nthread_tx = max_threads
+        nthread_bx = ceil_div(batch_size, max_threads)
+        tx = te.thread_axis("threadIdx.x")
+        bx = te.thread_axis("blockIdx.x")
+        ib.scope_attr(tx, "thread_extent", nthread_tx)
+        ib.scope_attr(bx, "thread_extent", nthread_bx)
+        tid = bx * max_threads + tx
+        with ib.if_scope(tid < batch_size):
+            first_occurence_ptr[tid] = batch_size
+    with ib.new_scope():
+        nthread_tx = max_threads
+        nthread_bx = ceil_div(batch_size, max_threads)
+        tx = te.thread_axis("threadIdx.x")
+        bx = te.thread_axis("blockIdx.x")
+        ib.scope_attr(tx, "thread_extent", nthread_tx)
+        ib.scope_attr(bx, "thread_extent", nthread_bx)
+        tid = bx * max_threads + tx
+        with ib.if_scope(tid < batch_size):
+            with ib.if_scope(tid == 0):
+                first_occurence_ptr[inc_scan_ptr[tid]] = argsorted_indices_ptr[tid]
+            with ib.else_scope():
+                with ib.if_scope(inc_scan_ptr[tid] != inc_scan_ptr[tid - 1]):
+                    first_occurence_ptr[inc_scan_ptr[tid]] = argsorted_indices_ptr[tid]
+    return ib.get()
+
+
+def unique(data, is_sorted=True, return_counts=False):
+    """
+    Find the unique elements of a 1-D tensor. Please note `output` and `counts` are all padded to
+    have the same length of `data` and element with index >= num_unique[0] has undefined value.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        A 1-D tensor of integers.
+
+    sorted : bool
+        Whether to sort the unique elements in ascending order before returning as output.
+
+    return_counts : bool
+        Whether to return the count of each unique element.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        A 1-D tensor containing the unique elements of the input data tensor.
+
+    indices : tvm.te.Tensor
+        A 1-D tensor containing the index of each data element in the output tensor.
+
+    num_unique : tvm.te.Tensor
+        A 1-D tensor with size=1 containing the number of unique elements in the input data tensor.
+
+    counts (optional) : tvm.te.Tensor
+        A 1-D tensor containing the count of each unique element in the output.
+
+    Examples
+    --------
+    .. code-block:: python
+        [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, False)
+        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
+        num_unique     =  [5]
+
+        [output, indices, num_unique, counts] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, True)
+        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
+        num_unique     =  [5]
+        counts         =  [2, 2, 1, 1, 2, ?, ?, ?]
+
+        [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], True)
+        output         =  [1, 2, 3, 4, 5, ?, ?, ?]
+        indices        =  [3, 4, 0, 1, 2, 2, 3, 4]
+        num_unique     =  [5]
+    """
+    sorted_data = sort(data)
+    argsorted_indices = argsort(data, dtype="int32")
+    # adjacent difference
+    adjacent_diff = _calc_adjacent_diff(sorted_data, out_dtype="int32", binop=tir.NE)
+    # inclusive scan
+    inc_scan = cumsum(adjacent_diff, dtype="int32", exclusive=0)
+    # total number of unique elements
+    num_unique_elements = _calc_num_unique(inc_scan)
+    # buffers
+    data_buf = tir.decl_buffer(data.shape, data.dtype, "data_buf", data_alignment=8)
+    argsorted_indices_buf = tir.decl_buffer(
+        data.shape, "int32", "argsorted_indices_buf", data_alignment=8
+    )
+    inc_scan_buf = tvm.tir.decl_buffer(data.shape, "int32", "inc_scan_buf", data_alignment=8)
+    unique_elements_buf = tir.decl_buffer(
+        data.shape, data.dtype, "unique_elements_buf", data_alignment=8
+    )
+    inverse_indices_buf = tvm.tir.decl_buffer(
+        data.shape, "int32", "inverse_indices_buf", data_alignment=8
+    )
+    # prepare outputs
+    if return_counts:
+        counts_buf = tir.decl_buffer(data.shape, "int32", "counts_buf", data_alignment=8)
+        out_data_shape = [data.shape] * 3
+        out_buffers = [unique_elements_buf, inverse_indices_buf, counts_buf]
+        out_dtypes = [data.dtype, "int32", "int32"]
+    else:
+        out_data_shape = [data.shape] * 2
+        out_buffers = [unique_elements_buf, inverse_indices_buf]
+        out_dtypes = [data.dtype, "int32"]
+    # prepare inputs and fcompute
+    if is_sorted:
+        in_data = [data, argsorted_indices, inc_scan]
+        in_buffers = [data_buf, argsorted_indices_buf, inc_scan_buf]
+        if return_counts:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, None, *outs)
+        else:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, None, *outs, None)
+    else:
+        # calculate the index converter if the unique elements should not be sorted
+        # calculate first occurence
+        first_occurence_buf = tir.decl_buffer(
+            data.shape, "int32", "first_occurence_buf", data_alignment=8
+        )
+        first_occurence = te.extern(
+            [data.shape],
+            [argsorted_indices, inc_scan],
+            lambda ins, outs: _calc_first_occurence_ir(ins[0], ins[1], outs[0]),
+            dtype=["int32"],
+            in_buffers=[argsorted_indices_buf, inc_scan_buf],
+            out_buffers=[first_occurence_buf],
+            name="_calc_first_occurence",
+            tag="_calc_first_occurence_gpu",
+        )
+        # calculate index converter by sorting unique elements by their first occurence
+        argsorted_first_occurence = argsort(first_occurence, dtype="int32")
+        index_converter = argsort(argsorted_first_occurence, dtype="int32")
+        index_converter_buf = tir.decl_buffer(
+            data.shape, "int32", "index_converter_buf", data_alignment=8
+        )
+        in_data = [data, argsorted_indices, inc_scan, index_converter]
+        in_buffers = [data_buf, argsorted_indices_buf, inc_scan_buf, index_converter_buf]
+        if return_counts:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, *outs)
+        else:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, *outs, None)
+    outs = te.extern(
+        out_data_shape,
+        in_data,
+        fcompute,
+        dtype=out_dtypes,
+        in_buffers=in_buffers,
+        out_buffers=out_buffers,
+        name="_calc_unique",
+        tag="_calc_unique_gpu",
+    )
+    if return_counts:
+        return [outs[0], outs[1], num_unique_elements, outs[2]]
+    return [*outs, num_unique_elements]
diff --git a/python/tvm/topi/cumsum.py b/python/tvm/topi/cumsum.py
new file mode 100644
index 000000000000..2013a352874d
--- /dev/null
+++ b/python/tvm/topi/cumsum.py
@@ -0,0 +1,121 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""Cumsum operator"""
+from ..tir import decl_buffer, ir_builder
+from ..te import extern
+from .utils import prod, get_const_int
+from .math import cast
+
+
+def cumsum(data, axis=None, dtype=None, exclusive=None):
+    """Numpy style cumsum op. Return the cumulative sum of the elements along a given axis.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        The input data to the operator.
+
+    axis : int, optional
+        Axis along which the cumulative sum is computed. The default (None) is to compute
+        the cumsum over the flattened array.
+
+    dtype : string, optional
+        Type of the returned array and of the accumulator in which the elements are summed.
+        If dtype is not specified, it defaults to the dtype of data.
+
+    exclusive : int, optional
+        If set to 1 will return exclusive sum in which the first element is not
+        included. In other terms, if set to 1, the j-th output element would be
+        the sum of the first (j-1) elements. Otherwise, it would be the sum of
+        the first j elements.
+
+    Returns
+    -------
+    result : tvm.te.Tensor
+        The result has the same size as data, and the same shape as data if axis is not None.
+        If axis is None, the result is a 1-d array.
+    """
+    if dtype is None or dtype == "":
+        dtype = data.dtype
+
+    def maybe_cast(x):
+        if dtype != data.dtype:
+            return cast(x, dtype)
+        return x
+
+    axis_mul_before = 1
+    axis_mul_after = 1
+
+    if axis is None:
+        axis = 0
+        cumsum_axis_len = prod(data.shape)
+        shape = (cumsum_axis_len,)
+    else:
+        if not isinstance(axis, int):
+            axis = get_const_int(axis)
+
+        shape = data.shape
+        cumsum_axis_len = shape[axis]
+
+        if axis < 0:
+            axis = len(shape) + axis
+
+        for i, value in enumerate(shape, 0):
+            if i < axis:
+                axis_mul_before *= value
+            elif i > axis:
+                axis_mul_after *= value
+
+    if exclusive is None:
+        exclusive = 0
+
+    def gen_ir(data_buf, out_buf):
+        ib = ir_builder.create()
+        data_buf = ib.buffer_ptr(data_buf)
+        out_buf = ib.buffer_ptr(out_buf)
+
+        with ib.for_range(0, axis_mul_before * axis_mul_after, "fused", kind="parallel") as fused:
+            i = fused // axis_mul_after
+            j = fused % axis_mul_after
+            base_idx = i * cumsum_axis_len * axis_mul_after + j
+            if exclusive == 0:
+                out_buf[base_idx] = maybe_cast(data_buf[base_idx])
+            else:
+                out_buf[base_idx] = cast(0, dtype)
+            with ib.for_range(0, cumsum_axis_len - 1, "_k") as _k:
+                k = _k + 1
+                cur_idx = base_idx + k * axis_mul_after
+                prev_idx = base_idx + (k - 1) * axis_mul_after
+                if exclusive == 0:
+                    out_buf[cur_idx] = out_buf[prev_idx] + maybe_cast(data_buf[cur_idx])
+                else:
+                    out_buf[cur_idx] = out_buf[prev_idx] + maybe_cast(data_buf[prev_idx])
+
+        return ib.get()
+
+    out_buf = decl_buffer(shape, dtype, "out_buf")
+
+    return extern(
+        [shape],
+        [data],
+        lambda ins, outs: gen_ir(ins[0], outs[0]),
+        dtype=dtype,
+        out_buffers=[out_buf],
+        name="cumsum_generic",
+        tag="cumsum_generic",
+    )
diff --git a/python/tvm/topi/einsum.py b/python/tvm/topi/einsum.py
new file mode 100644
index 000000000000..f1f426ec8173
--- /dev/null
+++ b/python/tvm/topi/einsum.py
@@ -0,0 +1,44 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,consider-using-enumerate,redefined-outer-name
+"""Einsum operator"""
+from . import cpp
+
+
+def einsum(subscripts, *operand):
+    """Evaluates the Einstein summation convention on the operands.
+
+    Parameters
+    ----------
+    subscripts : string
+        Specifies the subscripts for summation as comma separated list of subscript labels.
+        An implicit (classical Einstein summation) calculation is performed unless the
+        explicit indicator ‘->’ is included as well as subscript labels of the precise
+        output form.
+
+    a_tuple : tuple of tvm.te.Tensor
+        These are the Tensors for the operation.
+        The only difference of einsum between in tvm and numpy is it needs an extra brackets
+        for the tensors. For example, topi.einsum("ij, jk -> ik", (A, B)).
+
+    Returns
+    -------
+    out : tvm.te.Tensor
+        The calculation based on the Einstein summation convention.
+    """
+
+    return cpp.einsum(subscripts, operand)
diff --git a/python/tvm/topi/generic/conv2d.py b/python/tvm/topi/generic/conv2d.py
index 7dd9aed7545d..4daa84c29528 100644
--- a/python/tvm/topi/generic/conv2d.py
+++ b/python/tvm/topi/generic/conv2d.py
@@ -38,9 +38,10 @@ def fallback_schedule_cpu_common_int8(cfg, wkl, int32_lanes, num_int8_elements):
         How many numbers of input int32/uint32 will be multiplied and reduced.
         This is related to input channel.
     """
-    HPAD, WPAD = wkl.hpad, wkl.wpad
-    HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
+    pt, pl, pb, pr = wkl.padt, wkl.padl, wkl.padb, wkl.padr
+    HSTR, WSTR = wkl.stride_h, wkl.stride_w
+    dilated_kernel_w = (wkl.kernel_w - 1) * wkl.dilation_w + 1
+    out_width = (wkl.width + pl + pr - dilated_kernel_w) // WSTR + 1
 
     assert wkl.out_filter % int32_lanes == 0, "wkl.out_filter=%d, int32_lanes=%d" % (
         wkl.out_filter,
@@ -85,10 +86,10 @@ def fallback_schedule_cpu_1x1_int8(cfg, wkl, int32_lanes, num_int8_elements):
         How many numbers of input int32/uint32 will be multiplied and reduced.
         This is related to input channel.
     """
-    HPAD, WPAD = wkl.hpad, wkl.wpad
-    HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
-    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
+    pt, pl, pb, pr = wkl.padt, wkl.padl, wkl.padb, wkl.padr
+    HSTR, WSTR = wkl.stride_h, wkl.stride_w
+    out_height = (wkl.height + pt + pb - wkl.kernel_h) // HSTR + 1
+    out_width = (wkl.width + pl + pr - wkl.kernel_w) // WSTR + 1
 
     assert wkl.out_filter % int32_lanes == 0, "wkl.out_filter=%d, int32_lanes=%d" % (
         wkl.out_filter,
diff --git a/python/tvm/topi/generic/search.py b/python/tvm/topi/generic/search.py
index b3c8772046fd..f458ee7bc782 100644
--- a/python/tvm/topi/generic/search.py
+++ b/python/tvm/topi/generic/search.py
@@ -66,3 +66,23 @@ def schedule_scatter_add(outs):
       The computation schedule for the op.
     """
     return _default_schedule(outs, False)
+
+
+def schedule_sparse_fill_empty_rows(outs):
+    return _default_schedule(outs, False)
+
+
+def schedule_unique(outs):
+    """Schedule for unique operator.
+
+    Parameters
+    ----------
+    outs: Array of Tensor
+      The computation graph description of unique.
+
+    Returns
+    -------
+    s: Schedule
+      The computation schedule for the op.
+    """
+    return _default_schedule(outs, False)
diff --git a/python/tvm/topi/image/resize.py b/python/tvm/topi/image/resize.py
index 103850de4923..433a92008b6e 100644
--- a/python/tvm/topi/image/resize.py
+++ b/python/tvm/topi/image/resize.py
@@ -653,11 +653,7 @@ def resize(
         or 5-D with shape [batch, channel-major, in_height*scale, in_width*scale, channel-minor]
     """
     method = method.lower()
-    if method == "nearest_neighbor" and coordinate_transformation_mode != "asymmetric":
-        raise ValueError(
-            "Topi Resize does not support the combination of method %s "
-            "and coordinate_transformation_mode %s" % (method, coordinate_transformation_mode)
-        )
+
     if layout == "NHWC":
         in_n, in_h, in_w, in_c = data.shape
         if output_shape is None:
diff --git a/python/tvm/topi/nn/__init__.py b/python/tvm/topi/nn/__init__.py
index 2ebbd1d67bd1..94a5b30c9b76 100644
--- a/python/tvm/topi/nn/__init__.py
+++ b/python/tvm/topi/nn/__init__.py
@@ -36,6 +36,7 @@
 from .conv2d_transpose import *
 from .conv1d_transpose import *
 from .bnn import *
+from .qnn import *
 from .upsampling import *
 from .local_response_norm import *
 from .bitserial_conv2d import *
diff --git a/python/tvm/topi/nn/batch_matmul.py b/python/tvm/topi/nn/batch_matmul.py
index 9ca2df7c46e1..b6ed5a373e81 100644
--- a/python/tvm/topi/nn/batch_matmul.py
+++ b/python/tvm/topi/nn/batch_matmul.py
@@ -16,6 +16,7 @@
 # under the License.
 """Batch matrix multiplication"""
 # pylint: disable=invalid-name
+import tvm
 from tvm import te, auto_scheduler
 from ..utils import get_const_tuple
 
@@ -61,7 +62,7 @@ def batch_matmul(x, y, oshape=None, auto_scheduler_rewritten_layout=""):
     k = te.reduce_axis((0, K), name="k")
     if oshape is None:
         assert XB == YB or XB == 1 or YB == 1, "batch dimension doesn't match"
-        assert x_shape[2] == y_shape[2], "shapes of x and y is inconsistant"
+        assert x_shape[2] == y_shape[2], "shapes of x and y is inconsistent"
         batch = te.max(XB, YB)
         N = y.shape[1]
         oshape = (batch, M, N)
@@ -77,3 +78,26 @@ def batch_matmul(x, y, oshape=None, auto_scheduler_rewritten_layout=""):
         output = auto_scheduler.rewrite_compute_body(output, auto_scheduler_rewritten_layout)
 
     return output
+
+
+@tvm.target.generic_func
+def batch_matmul_legalize(attrs, inputs, types):
+    """Legalizes batch_matmul op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current batch_matmul
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # not to change by default
+    # pylint: disable=unused-argument
+    return None
diff --git a/python/tvm/topi/nn/conv2d.py b/python/tvm/topi/nn/conv2d.py
index 886470bb3b9d..80f87f86736c 100644
--- a/python/tvm/topi/nn/conv2d.py
+++ b/python/tvm/topi/nn/conv2d.py
@@ -38,12 +38,16 @@
         "in_filter",
         "groups",
         "out_filter",
-        "hkernel",
-        "wkernel",
-        "hpad",
-        "wpad",
-        "hstride",
-        "wstride",
+        "kernel_h",
+        "kernel_w",
+        "padt",
+        "padl",
+        "padb",
+        "padr",
+        "dilation_h",
+        "dilation_w",
+        "stride_h",
+        "stride_w",
     ],
 )
 
@@ -154,7 +158,7 @@ def conv2d_infer_layout(workload, cfg):
     raise ValueError("missing register for topi.nn.conv2d_infer_layout")
 
 
-def _get_workload(data, kernel, stride, padding, out_dtype, data_layout="NCHW"):
+def _get_workload(data, kernel, stride, padding, dilation, out_dtype, data_layout="NCHW"):
     """ Get the workload structure. """
     if data_layout == "NCHW":
         _, CI, IH, IW = get_const_tuple(data.shape)
@@ -170,7 +174,10 @@ def _get_workload(data, kernel, stride, padding, out_dtype, data_layout="NCHW"):
     else:
         KH, KW, CIG, CO = get_const_tuple(kernel.shape)
 
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, (get_const_int(KH), get_const_int(KW)))
+    pt, pl, pb, pr = get_pad_tuple(padding, (get_const_int(KH), get_const_int(KW)))
+    dilation_h, dilation_w = (
+        dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
+    )
     GRPS = CI // CIG
     if isinstance(stride, (tuple, list)):
         HSTR, WSTR = stride
@@ -182,7 +189,25 @@ def _get_workload(data, kernel, stride, padding, out_dtype, data_layout="NCHW"):
         '{} vs. {}".format(
         data.dtype, kernel.dtype
     )
-    return Workload(data.dtype, out_dtype, IH, IW, CI, GRPS, CO, KH, KW, HPAD, WPAD, HSTR, WSTR)
+    return Workload(
+        data.dtype,
+        out_dtype,
+        IH,
+        IW,
+        CI,
+        GRPS,
+        CO,
+        KH,
+        KW,
+        pt,
+        pl,
+        pb,
+        pr,
+        dilation_h,
+        dilation_w,
+        HSTR,
+        WSTR,
+    )
 
 
 def conv2d_nchw(Input, Filter, stride, padding, dilation, out_dtype=None):
diff --git a/python/tvm/topi/nn/dense.py b/python/tvm/topi/nn/dense.py
index 474fea42a7cb..e8ec476b86a5 100644
--- a/python/tvm/topi/nn/dense.py
+++ b/python/tvm/topi/nn/dense.py
@@ -14,7 +14,9 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+# pylint: disable=invalid-name,unused-argument
 """TVM operator fully connected compute."""
+import tvm
 from tvm import te, auto_scheduler
 from .. import tag
 
@@ -80,3 +82,95 @@ def dense(data, weight, bias=None, out_dtype=None, auto_scheduler_rewritten_layo
         matmul = auto_scheduler.rewrite_compute_body(matmul, auto_scheduler_rewritten_layout)
 
     return matmul
+
+
+@tvm.target.generic_func
+def dense_legalize(attrs, inputs, types):
+    """Legalizes dense op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current dense
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # not to change by default
+    # pylint: disable=unused-argument
+    return None
+
+
+def dense_pack(data, weight, bias=None, out_dtype=None):
+    """The default implementation of dense_pack in topi.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        2-D with shape [batch, in_dim]
+
+    weight : tvm.te.Tensor
+        2-D with shape [out_dim, in_dim]
+
+    bias : Optional[tvm.te.Tensor]
+        1-D with shape [out_dim]
+
+    out_dtype : Optional[str]
+        The output type. This is used for mixed precision.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        2-D with shape [batch, out_dim]
+    """
+    if out_dtype is None:
+        out_dtype = data.dtype
+    M, K = get_const_tuple(data.shape)  # batch, in_dim
+    N, _, packw_bn = get_const_tuple(weight.shape)  # out_dim
+    N = N * packw_bn
+
+    idxdiv = tvm.tir.indexdiv
+    idxmod = tvm.tir.indexmod
+    k = te.reduce_axis((0, K), name="k")
+    C = te.compute(
+        (M, N),
+        lambda y, x: te.sum(
+            data[y, k].astype(out_dtype)
+            * weight[idxdiv(x, packw_bn), k, idxmod(x, packw_bn)].astype(out_dtype),
+            axis=k,
+        ),
+        name="T_dense_pack",
+        tag="dense_pack",
+    )
+    if bias is not None:
+        C = te.compute((M, N), lambda i, j: C[i, j] + bias[j].astype(out_dtype), tag=tag.BROADCAST)
+    return C
+
+
+@tvm.target.generic_func
+def dense_alter_layout(attrs, inputs, tinfos, out_type):
+    """Change dense layout.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : tvm.relay.Expr
+        Grouped input symbols
+    tinfos : list
+        Input shape and dtype
+    out_type: type
+        The output type
+
+    Note
+    ----
+    Unlike other TOPI functions, this function operates on both graph level and operator level.
+    """
+    # not to change by default
+    return None
diff --git a/python/tvm/topi/nn/depthwise_conv2d.py b/python/tvm/topi/nn/depthwise_conv2d.py
index 72356821770d..052ab8b88d1c 100644
--- a/python/tvm/topi/nn/depthwise_conv2d.py
+++ b/python/tvm/topi/nn/depthwise_conv2d.py
@@ -36,22 +36,28 @@
         "width",
         "in_filter",
         "out_filter",
-        "hkernel",
-        "wkernel",
-        "hpad",
-        "wpad",
-        "hstride",
-        "wstride",
+        "kernel_h",
+        "kernel_w",
+        "padt",
+        "padl",
+        "padb",
+        "padr",
+        "dilation_h",
+        "dilation_w",
+        "stride_h",
+        "stride_w",
     ],
 )
 
 
-def _get_workload(data, kernel, stride, padding, out_dtype):
+def _get_workload(data, kernel, stride, padding, dilation, out_dtype):
     """ Get the workload structure. """
     _, in_channel, height, width = [x.value for x in data.shape]
     channel, channel_multiplier, kh, kw = [x.value for x in kernel.shape]
     out_channel = channel * channel_multiplier
-    HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
+    dilation_h, dilation_w = (
+        dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
+    )
     if isinstance(stride, (tuple, list)):
         HSTR, WSTR = stride
     else:
@@ -62,6 +68,9 @@ def _get_workload(data, kernel, stride, padding, out_dtype):
         '{} vs. {}".format(
         data.dtype, kernel.dtype
     )
+    dilated_kernel_h = (kh - 1) * dilation_h + 1
+    dilated_kernel_w = (kw - 1) * dilation_w + 1
+    pt, pl, pb, pr = get_pad_tuple(padding, (dilated_kernel_h, dilated_kernel_w))
     return Workload(
         data.dtype,
         out_dtype,
@@ -71,8 +80,12 @@ def _get_workload(data, kernel, stride, padding, out_dtype):
         out_channel,
         kh,
         kw,
-        HPAD,
-        WPAD,
+        pt,
+        pl,
+        pb,
+        pr,
+        dilation_h,
+        dilation_w,
         HSTR,
         WSTR,
     )
diff --git a/python/tvm/topi/nn/qnn.py b/python/tvm/topi/nn/qnn.py
new file mode 100644
index 000000000000..caed28580037
--- /dev/null
+++ b/python/tvm/topi/nn/qnn.py
@@ -0,0 +1,190 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Quantized Neural Network (QNN) Operators"""
+import tvm
+from tvm import te, tir, topi
+
+SQNN_DISABLE = 0
+SQNN_INT8 = 1
+SQNN_UINT8 = 2
+SQNN_INT32 = 3
+
+SQNN_DTYPE_TO_CODE = {
+    "disable": SQNN_DISABLE,
+    "int8": SQNN_INT8,
+    "uint8": SQNN_UINT8,
+    "int32": SQNN_INT32,
+}
+
+SQNN_CODE_TO_DTYPE = {v: k for k, v in SQNN_DTYPE_TO_CODE.items()}
+
+
+@tvm.te.tag_scope(tag=topi.tag.ELEMWISE)
+def simulated_quantize(data, out_dtype, output_scale=None, output_zero_point=None, axis=-1):
+    """Simulated QNN quantize operator that mimics QNN outputs without changing datatype.
+    The benefit of this operator over true QNN quantize is that this operator allows dynamic
+    datatype selection and can operate on both per-channel and scalar scales and zero points while
+    QNN quantize requires both of these to be fixed at compile time.
+
+    Parameters
+    ----------
+    data: tvm.te.Tensor
+        An N-D input tensor to the operator.
+
+    out_dtype: tvm.te.Tensor
+        A scalar variable that indicates which datatype to simulate quantization with. Use
+        SQNN_DTYPE_TO_CODE to convert a dtype string into the corresponding variable
+        value.
+
+    output_scale: tvm.te.Tensor, optional
+        A scalar tensor representing the scale to use when quantizing to integer datatypes.
+        When it contains more than a single value, N must match the number of channels in data.
+
+    output_zero_point: tvm.te.Tensor, optional
+        A 1-D tensor representing the zero point to use when quantizing to integer datatypes.
+        When it contains more than a single value, N must match the number of channels in data.
+
+    axis: int, optional
+        The channel axis for quantization. Default value is -1 which corresponds to the last axis.
+
+    """
+    # When disabled, just pass through the input values.
+    def _compute_pass_through(value, *indices):
+        return value[indices]
+
+    # Simulate quantization for arbitrary integer datatypes. The computation for all datatypes is:
+    # Q_output = clip((round(input_tensor/output_scale) + output_zero_point),
+    #                 out_dtype::min,
+    #                 out_dtype::max)
+    def _compute_intn(dtype, value, *indices):
+        assert output_scale is not None and output_zero_point is not None
+        const_min = tvm.tir.min_value(dtype)
+        const_max = tvm.tir.max_value(dtype)
+        # Use indexmod to handle both scalar and per-channel QNN parameters.
+        scale_idx = tir.indexmod(indices[axis], topi.shape(output_scale)[0])
+        zp_idx = tir.indexmod(indices[axis], topi.shape(output_zero_point)[0])
+        return te.max(
+            te.min(
+                te.round(value[indices] / output_scale[scale_idx]) + output_zero_point[zp_idx],
+                const_max,
+            ),
+            const_min,
+        )
+
+    # Use an if chain to dynamically return the proper quantization based on the input datatype.
+    # This allows the op to compile once but apply different quantization approaches
+    # using a variable datatype input.
+    def _dispatch_sim_quantize(value):
+        pass_through_value = te.compute(
+            data.shape, lambda *indices: _compute_pass_through(value, *indices)
+        )
+        int8_value = te.compute(
+            data.shape,
+            lambda *indices: tir.if_then_else(
+                out_dtype.equal(SQNN_DTYPE_TO_CODE["int8"]),
+                _compute_intn("int8", value, *indices),
+                pass_through_value[indices],
+            ),
+        )
+        uint8_value = te.compute(
+            data.shape,
+            lambda *indices: tir.if_then_else(
+                out_dtype.equal(SQNN_DTYPE_TO_CODE["uint8"]),
+                _compute_intn("uint8", value, *indices),
+                int8_value[indices],
+            ),
+        )
+        int32_value = te.compute(
+            data.shape,
+            lambda *indices: tir.if_then_else(
+                out_dtype.equal(SQNN_DTYPE_TO_CODE["int32"]),
+                _compute_intn("int32", value, *indices),
+                uint8_value[indices],
+            ),
+        )
+
+        return int32_value
+
+    return te.compute(data.shape, lambda *indices: _dispatch_sim_quantize(data)[indices])
+
+
+@tvm.te.tag_scope(tag=topi.tag.ELEMWISE)
+def simulated_dequantize(data, in_dtype, input_scale=None, input_zero_point=None, axis=-1):
+    """Simulated QNN dequantize operator that mimics QNN outputs without changing datatype.
+    The benefit of this operator over true QNN dequantize is that this operator allows dynamic
+    datatype selection and can operate on both per-channel and scalar scales and zero points while
+    QNN dequantize requires both of these to be fixed at compile time.
+
+    Parameters
+    ----------
+    data: tvm.te.Tensor
+        An N-D input tensor to the operator.
+
+    in_dtype: tvm.te.Tensor
+        A scalar variable that indicates which datatype to simulate dequantization with. Use
+        SQNN_DTYPE_TO_CODE to convert a dtype string into the corresponding variable
+        value.
+
+    input_scale: tvm.te.Tensor, optional
+        A scalar tensor representing the scale to use when dequantizing from integer datatypes.
+        When it contains more than a single value, N must match the number of channels in data.
+
+    input_zero_point: tvm.te.Tensor, optional
+        A 1-D tensor representing the zero point to use when dequantizing from integer datatypes.
+        When it contains more than a single value, N must match the number of channels in data.
+
+    axis: int, optional
+        The channel axis for quantization. Default value is -1 which corresponds to the last axis.
+
+    """
+    # When disabled simply return the input tensor.
+    def _compute_pass_through(value, *indices):
+        return value[indices]
+
+    # Simulate dequantization for arbitrary integer datatypes. The computation for all datatypes is:
+    # DQ_output = (input - zero_point) * scale
+    def _compute_intn(value, *indices):
+        assert input_scale is not None and input_zero_point is not None
+        # Use indexmod to handle both scalar and per-channel QNN parameters.
+        scale_idx = tir.indexmod(indices[axis], topi.shape(input_scale)[0])
+        zp_idx = tir.indexmod(indices[axis], topi.shape(input_zero_point)[0])
+        return (value[indices] - input_zero_point[zp_idx]) * input_scale[scale_idx]
+
+    # Use an if chain to dynamically return the proper dequantization based on the input datatype.
+    # This allows the op to compile once but apply different quantization approaches
+    # using a variable datatype input.
+    def _dispatch_sim_dequantize(value):
+        pass_through_value = te.compute(
+            data.shape, lambda *indices: _compute_pass_through(value, *indices)
+        )
+        intn_condition = tvm.te.any(
+            in_dtype.equal(SQNN_DTYPE_TO_CODE["int8"]),
+            in_dtype.equal(SQNN_DTYPE_TO_CODE["uint8"]),
+            in_dtype.equal(SQNN_DTYPE_TO_CODE["int32"]),
+        )
+        intn_value = te.compute(
+            data.shape,
+            lambda *indices: tir.if_then_else(
+                intn_condition,
+                _compute_intn(value, *indices),
+                pass_through_value[indices],
+            ),
+        )
+
+        return intn_value
+
+    return te.compute(data.shape, lambda *indices: _dispatch_sim_dequantize(data)[indices])
diff --git a/python/tvm/topi/nn/sparse.py b/python/tvm/topi/nn/sparse.py
index 94d6d9a16330..756110624aa1 100644
--- a/python/tvm/topi/nn/sparse.py
+++ b/python/tvm/topi/nn/sparse.py
@@ -18,12 +18,12 @@
 """Sparse operators"""
 from __future__ import absolute_import
 import tvm
-from tvm import te
+from tvm import te, auto_scheduler
 
 from ..utils import get_const_tuple
 
 
-def sparse_dense_v2(data, weight_data, weight_indices, weight_indptr):
+def sparse_dense_sp_rhs(data, weight_data, weight_indices, weight_indptr):
     """
     Computes sparse-dense matrix multiplication of `data` and
     `(weight_data, weight_indices, weight_indptr).T`
@@ -52,13 +52,13 @@ def sparse_dense_v2(data, weight_data, weight_indices, weight_indptr):
     """
     assert len(weight_data.shape) in (1, 3)
     if len(weight_data.shape) == 1:
-        func = _sparse_dense_csrmm_v2
+        func = _sparse_dense_sp_rhs_csrmm
     if len(weight_data.shape) == 3:
-        func = _sparse_dense_bsrmm_v2
+        func = _sparse_dense_sp_rhs_bsrmm
     return func(data, weight_data, weight_indices, weight_indptr)
 
 
-def sparse_dense_v1(data_data, data_indices, data_indptr, weight):
+def sparse_dense_sp_lhs(data_data, data_indices, data_indptr, weight):
     """
     Computes sparse-dense matrix multiplication of
     `(data_data, data_indices, data_indptr)` and `weight.T`
@@ -87,9 +87,9 @@ def sparse_dense_v1(data_data, data_indices, data_indptr, weight):
     """
     assert len(data_data.shape) in (1, 3)
     if len(data_data.shape) == 1:
-        func = _sparse_dense_csrmm_v1
+        func = _sparse_dense_sp_lhs_csrmm
     if len(data_data.shape) == 3:
-        func = _sparse_dense_bsrmm_v1
+        func = _sparse_dense_sp_lhs_bsrmm
     return func(data_data, data_indices, data_indptr, weight)
 
 
@@ -128,12 +128,12 @@ def sparse_dense(dense_data, sparse_data, sparse_indices, sparse_indptr, sparse_
         2-D with shape [M, N]
     """
     if sparse_lhs:
-        return sparse_dense_v1(sparse_data, sparse_indices, sparse_indptr, dense_data)
+        return sparse_dense_sp_lhs(sparse_data, sparse_indices, sparse_indptr, dense_data)
     else:
-        return sparse_dense_v2(dense_data, sparse_data, sparse_indices, sparse_indptr)
+        return sparse_dense_sp_rhs(dense_data, sparse_data, sparse_indices, sparse_indptr)
 
 
-def _sparse_dense_csrmm_v1(data_data, data_indices, data_indptr, weight):
+def _sparse_dense_sp_lhs_csrmm(data_data, data_indices, data_indptr, weight):
     oshape = (get_const_tuple(data_indptr.shape)[0] - 1, get_const_tuple(weight.shape)[0])
 
     def f(row, i):
@@ -146,10 +146,10 @@ def f(row, i):
         weight_val = weight[i, data_indices[elem]]
         return te.sum(a_val * weight_val, axis=elem_idx)
 
-    return te.compute(oshape, f, tag="sparse_dense_csrmm_v1")
+    return te.compute(oshape, f, tag="sparse_dense_sp_lhs_csrmm")
 
 
-def _sparse_dense_csrmm_v2(data, weight_data, weight_indices, weight_indptr):
+def _sparse_dense_sp_rhs_csrmm(data, weight_data, weight_indices, weight_indptr):
     oshape = (get_const_tuple(data.shape)[0], get_const_tuple(weight_indptr.shape)[0] - 1)
 
     def f(i, row):
@@ -162,10 +162,10 @@ def f(i, row):
         weight_val = data[i, weight_indices[elem]]
         return te.sum(a_val * weight_val, axis=elem_idx)
 
-    return te.compute(oshape, f, tag="sparse_dense_csrmm_v2")
+    return te.compute(oshape, f, tag="sparse_dense_sp_rhs_csrmm")
 
 
-def _sparse_dense_bsrmm_v1(data_data, data_indices, data_indptr, weight):
+def _sparse_dense_sp_lhs_bsrmm(data_data, data_indices, data_indptr, weight):
     (m, _) = get_const_tuple(weight.shape)
     (_, bs_r, bs_c) = get_const_tuple(data_data.shape)
     (num_blocks_plus_1,) = get_const_tuple(data_indptr.shape)
@@ -187,17 +187,17 @@ def _compute_block(nb_j, j, i):
     idxm = tvm.tir.indexmod
 
     bsrmm_block = te.compute(
-        (num_blocks, bs_r, m), _compute_block, tag="sparse_dense_bsrmm_block_v1"
+        (num_blocks, bs_r, m), _compute_block, tag="sparse_dense_sp_lhs_bsrmm_block"
     )
     return te.compute(
         (num_blocks * bs_r, m),
         lambda m, n: bsrmm_block[idxd(m, bs_r), idxm(m, bs_r), n],
-        tag="sparse_dense_bsrmm_v1",
+        tag="sparse_dense_sp_lhs_bsrmm",
     )
 
 
-def _sparse_dense_bsrmm_v2(data, weight_data, weight_indices, weight_indptr):
-    (m, _) = get_const_tuple(data.shape)
+def _sparse_dense_sp_rhs_bsrmm(data, weight_data, weight_indices, weight_indptr):
+    (m, k) = get_const_tuple(data.shape)
     (_, bs_r, bs_c) = get_const_tuple(weight_data.shape)
     (num_blocks_plus_1,) = get_const_tuple(weight_indptr.shape)
     num_blocks = num_blocks_plus_1 - 1
@@ -218,12 +218,15 @@ def _compute_block(i, nb_j, j):
     idxm = tvm.tir.indexmod
 
     bsrmm_block = te.compute(
-        (m, num_blocks, bs_r), _compute_block, tag="sparse_dense_bsrmm_block_v2"
+        (m, num_blocks, bs_r),
+        _compute_block,
+        tag="sparse_dense_sp_rhs_bsrmm_block",
+        attrs={"FLOP": 2 * m * num_blocks * bs_r * k},
     )
     return te.compute(
         (m, num_blocks * bs_r),
         lambda m, n: bsrmm_block[m, idxd(n, bs_r), idxm(n, bs_r)],
-        tag="sparse_dense_bsrmm_v2",
+        tag="sparse_dense_sp_rhs_bsrmm",
     )
 
 
@@ -294,26 +297,26 @@ def _csr_transpose_ir(data, indices, indptr, out_data, out_indices, out_indptr):
     n = get_const_tuple(indptr.shape)[0] - 1
     nnz = get_const_tuple(data.shape)[0]
 
-    with irb.for_range(0, n, for_type="parallel", name="col") as col:
+    with irb.for_range(0, n, kind="parallel", name="col") as col:
         out_indptr_ptr[col] = 0
 
-    with irb.for_range(0, nnz, for_type="serial", name="nz_idx") as nz_idx:
+    with irb.for_range(0, nnz, kind="serial", name="nz_idx") as nz_idx:
         out_indptr_ptr[indices_ptr[nz_idx]] += 1
 
     cumsum = irb.allocate("int32", (1,), name="cumsum", scope="local")
     temp = irb.allocate("int32", (1,), name="temp", scope="local")
     cumsum[0] = 0
-    with irb.for_range(0, n, for_type="serial", name="col") as col:
+    with irb.for_range(0, n, kind="serial", name="col") as col:
         temp[0] = out_indptr_ptr[col]
         out_indptr_ptr[col] = cumsum[0]
         cumsum[0] += temp[0]
 
     out_indptr_ptr[n] = nnz
 
-    with irb.for_range(0, n, for_type="serial", name="row") as row:
+    with irb.for_range(0, n, kind="serial", name="row") as row:
         offset = indptr_ptr[row]
         diff = indptr_ptr[row + 1] - indptr_ptr[row]
-        with irb.for_range(0, diff, for_type="serial", name="idx") as idx:
+        with irb.for_range(0, diff, kind="serial", name="idx") as idx:
             real_idx = offset + idx
             col = indices_ptr[real_idx]
             dest = out_indptr_ptr[col]
@@ -325,7 +328,7 @@ def _csr_transpose_ir(data, indices, indptr, out_data, out_indices, out_indptr):
     last = irb.allocate("int32", (1,), name="last", scope="local")
     temp2 = irb.allocate("int32", (1,), name="temp2", scope="local")
     last[0] = 0
-    with irb.for_range(0, n, for_type="serial", name="col") as col:
+    with irb.for_range(0, n, kind="serial", name="col") as col:
         temp2[0] = out_indptr_ptr[col]
         out_indptr_ptr[col] = last[0]
         last[0] = temp2[0]
@@ -356,3 +359,181 @@ def sparse_dense_alter_layout(_attrs, _inputs, _tinfos, _out_type):
     Unlike other TOPI functions, this function operates on both graph level and operator level.
     """
     return None
+
+
+@auto_scheduler.register_task_input_check_func
+def try_get_sparse_input(args):
+    """Analyze the input data from the given args.
+
+    Parameters
+    ----------
+    args : List[Tensor]
+        Input/output Tensor of a TVM subgraph.
+
+    Returns
+    -------
+    Dict[Tensor, str] :
+        Map from the input Tensor to its buffer name.
+
+    Notes
+    -----
+    The buffer name is specially designed, and these buffer should be provided in
+    `SearchTask(..., task_inputs={...})`.
+    """
+    sparse_prefix = sparse_data = sparse_indices = sparse_indptr = None
+
+    def _process_inputs(input_tensors, m, n, prefix_init):
+        nonlocal sparse_prefix
+        nonlocal sparse_data
+        nonlocal sparse_indices
+        nonlocal sparse_indptr
+
+        assert len(input_tensors) == 4
+        unsure_tensors = list(input_tensors)
+        # Get the Dense data
+        dense_data = None
+        for tensor in unsure_tensors:
+            if len(tensor.shape) == 2:
+                assert dense_data is None
+                dense_data = tensor
+                assert m == dense_data.shape[0]
+                k = dense_data.shape[1]
+        unsure_tensors.remove(dense_data)
+
+        # Get the Sparse data
+        sparse_data = None
+        for tensor in unsure_tensors:
+            if len(tensor.shape) == 3:
+                assert sparse_data is None
+                sparse_data = tensor
+                block_size, bs_r, bs_c = sparse_data.shape
+        unsure_tensors.remove(sparse_data)
+
+        # Get the Sparse indptr & indices
+        sparse_indices = None
+        for tensor in unsure_tensors:
+            assert len(tensor.shape) == 1
+            if tensor.shape[0] == block_size:
+                assert sparse_indices is None
+                sparse_indices = tensor
+        unsure_tensors.remove(sparse_indices)
+        assert len(unsure_tensors) == 1
+        sparse_indptr = unsure_tensors[0]
+
+        # Generate the sparse_prefix
+        density = 1.0
+        for i in sparse_data.shape:
+            density *= i
+        density /= k * n
+        density = density.value
+        sparse_prefix = "%s_%d_%d_%d_%d_%d_%.2f_" % (prefix_init, m, n, k, bs_r, bs_c, density)
+
+    visited = set()
+
+    def _traverse(t):
+        # We cannot directly add tensors to the set, because the comparison of
+        # two tensors with ndim=0 is ambiguous.
+        assert t.handle is not None
+        if t.handle.value in visited:
+            return
+
+        if isinstance(t.op, te.ComputeOp):
+            # TODO(jcf94): Currently only support to one sparse op, add more support here
+            if t.op.tag == "sparse_dense_sp_rhs_bsrmm":
+                m, n = t.shape
+                assert len(t.op.input_tensors) == 1
+                block_tensor = t.op.input_tensors[0]
+                _process_inputs(block_tensor.op.input_tensors, m, n, "sparse_dense_bsr")
+            if sparse_prefix is not None:
+                # Early stop if we find a sparse_prefix
+                # Notice: If any workload has more than one sparse input, this may get problem
+                return
+            for x in t.op.input_tensors:
+                _traverse(x)
+        visited.add(t.handle.value)
+
+    try:
+        for arg in args:
+            _traverse(arg)
+    # pylint: disable=broad-except
+    except Exception:
+        return {}
+
+    if sparse_data is None or sparse_indices is None or sparse_indptr is None:
+        return {}
+
+    sparse_input_map = {}
+    sparse_input_map[sparse_data] = sparse_prefix + "W_data"
+    sparse_input_map[sparse_indices] = sparse_prefix + "W_indices"
+    sparse_input_map[sparse_indptr] = sparse_prefix + "W_indptr"
+
+    return sparse_input_map
+
+
+def sparse_add(dense_data, sparse_data, sparse_indices, sparse_indptr):
+    """
+    Computes sparse-dense addition
+
+    Parameters
+    ----------
+    dense_data : tvm.te.Tensor
+        2-D with shape [M, N]
+
+    sparse_data : tvm.te.Tensor
+        1-D with shape [nnz] (CSR)
+
+    sparse_indices : tvm.te.Tensor
+        1-D with shape [nnz] (CSR)
+
+    sparse_indptr : tvm.te.Tensor
+        1-D with shape [M + 1] (CSR)
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        2-D with shape [M, N]
+    """
+    # TODO(ANSHUMAN87): support BSR format too
+    assert len(sparse_data.shape) == 1, "only CSR format is supported"
+    return _sparse_add_csr(dense_data, sparse_data, sparse_indices, sparse_indptr)
+
+
+def _sparse_add_csr(dense_data_inp, sparse_data_inp, sparse_indices_inp, sparse_indptr_inp):
+    oshape = get_const_tuple(dense_data_inp.shape)
+
+    def _csr_add_ir(dense_data, sparse_data, sparse_indices, sparse_indptr, out_data):
+        irb = tvm.tir.ir_builder.create()
+        dense_data_ptr = irb.buffer_ptr(dense_data)
+        sparse_data_ptr = irb.buffer_ptr(sparse_data)
+        sparse_indices_ptr = irb.buffer_ptr(sparse_indices)
+        sparse_indptr_ptr = irb.buffer_ptr(sparse_indptr)
+
+        out_data_ptr = irb.buffer_ptr(out_data)
+
+        with irb.for_range(0, oshape[0], kind="vectorize", name="row") as row:
+            with irb.for_range(0, oshape[1], kind="parallel", name="col") as col:
+                out_data_ptr[row, col] = dense_data_ptr[row, col]
+
+        with irb.for_range(0, oshape[0], kind="parallel", name="row") as row:
+            offset = sparse_indptr_ptr[row]
+            diff = sparse_indptr_ptr[row + 1] - sparse_indptr_ptr[row]
+            with irb.for_range(0, diff, kind="serial", name="idx") as idx:
+                real_idx = offset + idx
+                col = sparse_indices_ptr[real_idx]
+                out_data_ptr[row, col] = sparse_data_ptr[real_idx] + out_data_ptr[row, col]
+
+        return irb.get()
+
+    return te.extern(
+        shape=oshape,
+        inputs=[dense_data_inp, sparse_data_inp, sparse_indices_inp, sparse_indptr_inp],
+        fcompute=lambda ins, outs: _csr_add_ir(ins[0], ins[1], ins[2], ins[3], outs[0]),
+        tag="sparse_add_csr",
+        dtype=[
+            dense_data_inp.dtype,
+            sparse_data_inp.dtype,
+            sparse_indices_inp.dtype,
+            sparse_indptr_inp.dtype,
+        ],
+        name="sparse_add_csr_output",
+    )
diff --git a/python/tvm/topi/random/__init__.py b/python/tvm/topi/random/__init__.py
new file mode 100644
index 000000000000..ee8d1d6385b7
--- /dev/null
+++ b/python/tvm/topi/random/__init__.py
@@ -0,0 +1,22 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=wildcard-import
+"""Pseudorandom generator kernels and operators."""
+from __future__ import absolute_import
+
+from .kernel import *
diff --git a/python/tvm/topi/random/kernel.py b/python/tvm/topi/random/kernel.py
new file mode 100644
index 000000000000..728cd682fa42
--- /dev/null
+++ b/python/tvm/topi/random/kernel.py
@@ -0,0 +1,468 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Pseudorandom number kernels."""
+import tvm
+import tvm.topi
+import numpy as np
+from ... import tir
+from ...tir import ir_builder
+
+
+# Threefry PRNG with splitting based on
+# - J. K. Salmon, M. A. Moraes, R. O. Dror and D. E. Shaw, "Parallel random numbers: As easy as 1,
+#   2, 3," SC '11: Proceedings of 2011 International Conference for High Performance Computing,
+#   Networking, Storage and Analysis, Seattle, WA, 2011, pp. 1-12, doi: 10.1145/2063384.2063405.
+# - Claessen, K. ; Palka, M. (2013) "Splittable Pseudorandom Number Generators using Cryptographic
+#   Hashing". Proceedings of Haskell Symposium 2013 pp. 47-58.  MLA
+# - Ferguson, Niels, et al. "The Skein hash function family." Submission to NIST (round 3) 7.7.5
+#   (2010): 3.
+
+
+# Threefry is a counter based PRNG: given a unique input, it generates a unique random number. As
+# there is no state to maintain, we can apply it to a sequence of numbers (0..N) to generate a
+# sequence of random numbers in parallel. In order to make the PRNG splittable (that is we can
+# generate a sequence of random numbers in one place, and another sequence in another), we add a
+# path and key in addition to the counter. The path allows us to encode a sequence of splits (a 0 in
+# the path indicates the left result of a split, a 1 indicates the right). To avoid continuously
+# growing the path, we can compress an existing path into the key portion of the generator by
+# hashing the current key, path, and counter to create the new key (this same technique is used if
+# we run out of room for the counter). They key is initialized with a unique initial state.
+#
+# Random numbers are generated by applying the Threefry hash to the current key, path, and counter.
+
+# This module use encoding e4 from the appendix of "Splittable Pseudorandom Number Generators using
+# Cryptographic Hashing" (confusingly, the definition in the paper uses e3 to define the encoding
+# function). This encoding uses a 10 element uint64 tensor where each byte means the following:
+
+# .. code-block:
+
+#     gen:
+#     words: 0 1 2 3 | 4 5  | 6 7     | 8 9
+#     usage: key     | path | counter | position of next step in path encoded in binary
+#                                       ex: 0b00010 -> next path entry goes one from the right
+
+# Right now, counter only uses the rightmost word.
+
+# Threefry rotation constants from the Skein paper ("The Skein Hash Function Family"
+# https://www.schneier.com/wp-content/uploads/2015/01/skein.pdf)
+_ROTATIONS = {
+    4: [[14, 16], [52, 57], [23, 40], [5, 37], [25, 33], [46, 12], [58, 22], [32, 32]],
+    8: [
+        [46, 36, 19, 37],
+        [33, 27, 14, 42],
+        [17, 49, 36, 39],
+        [44, 9, 54, 56],
+        [39, 30, 34, 24],
+        [13, 50, 10, 17],
+        [25, 29, 39, 43],
+        [8, 35, 56, 22],
+    ],
+    16: [
+        [24, 13, 8, 47, 8, 17, 22, 37],
+        [38, 19, 10, 55, 49, 18, 23, 52],
+        [33, 4, 51, 13, 34, 41, 59, 17],
+        [5, 20, 48, 41, 47, 28, 16, 25],
+        [41, 9, 37, 31, 12, 47, 44, 30],
+        [16, 34, 56, 51, 4, 53, 42, 41],
+        [31, 44, 47, 46, 19, 42, 44, 25],
+        [9, 48, 35, 52, 23, 31, 37, 20],
+    ],
+}
+
+# Threefry permutation constants from the Skein paper ("The Skein Hash Function Family"
+# https://www.schneier.com/wp-content/uploads/2015/01/skein.pdf)
+_PERMUTATIONS = {
+    4: [0, 3, 2, 1],
+    8: [2, 1, 4, 7, 6, 5, 0, 3],
+    16: [0, 9, 2, 13, 6, 11, 4, 15, 10, 7, 12, 3, 14, 5, 8, 1],
+}
+
+
+def _threefry(
+    irb, key_buf, key_offset, counter_buf, counter_offset, out_buf, out_offset, out_shape
+):
+    """IRBuilder code for running Threefry
+
+    Parameters
+    ----------
+    irb: IRBuilder
+        IRBuilder that this code will be generated for.
+
+    key_buf: BufferVar
+        Buffer to read the key from.
+
+    key_offset: number
+        Threefry will write to :code:`key_buf[key_offset:key_offset+4]`
+
+    counter_buf: BufferVar
+        Buffer to read the counter from.
+
+    counter_offset: number
+        Threefry will write to :code:`counter_buf[counter_offset:counter_offset+4]`
+
+    out_buf: BufferVar
+        Buffer to read the counter from.
+
+    out_offset: number
+        Threefry will write to :code:`out_buf[out_offset:out_offset+4*product(out_shape)]`
+
+    out_shape: number
+        Determines the number of output states to generate. :code:`state[i]` will correspond to
+        counter+i.
+    """
+    nrounds = 20
+    nwords = 4
+    iwidth = 64
+    assert nrounds % 4 == 0
+    assert nwords in [4, 8, 16]
+
+    # The paper has constants for 32 bit threefry, but we keep the implementation simple by only
+    # using 64-bit words.
+    assert key_buf.dtype == "uint64", "threefry only supports 64-bit keys"
+    assert key_buf.dtype == counter_buf.dtype, "threefry key and counter must be the same dtype"
+
+    def mix(a, b, rotation):
+        x = a + b  # wrapping
+        y = x ^ ((b << rotation) | (b >> (iwidth - rotation)))
+        return [x, y]
+
+    # temporary buffer for holding the results of _PERMUTATIONS
+    tmp = irb.allocate(out_buf.dtype, out_shape, name="tmp", scope="global")
+    tmp_offset = 0
+
+    # Initialize entire key. It is composed of the original key with one
+    # element appended. The appended element is the xor of all key words plus a
+    # constant.
+    full_key = irb.allocate("uint64", nwords + 1, name="full_key", scope="global")
+    for i in range(nwords):
+        full_key[i] = key_buf[key_offset + i]
+    # initial key constant, full_key[nwords] is equivalent to k_{N_W} in the Skein paper.
+    full_key[nwords] = tvm.tir.const(0x1BD11BDAA9FC1A22, dtype="uint64")
+    for i in range(nwords):
+        full_key[nwords] ^= key_buf[key_offset + i]
+
+    with irb.for_range(0, out_shape, dtype="uint64", name="i") as i:
+        for j in range(nwords):
+            out_buf[out_offset + i * nwords + j] = counter_buf[counter_offset + j] + i
+
+    def key_schedule(s, i):
+        # Threefry uses no tweak, so the key schedule is simple
+        if i == nwords - 1:
+            return full_key[(s + i) % (nwords + 1)] + tvm.tir.const(s, dtype="uint64")
+        return full_key[(s + i) % (nwords + 1)]
+
+    with irb.for_range(0, out_shape, name="l") as l:  # pylint: disable=invalid-name
+        for i in range(nrounds // 4):
+            for j in range(nwords):
+                out_buf[out_offset + l * nwords + j] += key_schedule(i, j)  # wrapping
+            for k in range(4):
+                for j in range(nwords // 2):
+                    (
+                        out_buf[out_offset + l * nwords + j * 2 + 0],
+                        out_buf[out_offset + l * nwords + j * 2 + 1],
+                    ) = mix(
+                        out_buf[out_offset + l * nwords + j * 2 + 0],
+                        out_buf[out_offset + l * nwords + j * 2 + 1],
+                        _ROTATIONS[nwords][(i * 4 + k) % 8][j],
+                    )
+                for j in range(nwords):
+                    tmp[tmp_offset + l * nwords + j] = out_buf[
+                        out_offset + l * nwords + _PERMUTATIONS[nwords][j]
+                    ]
+                # number of rounds is even, so out always contains the result
+                (out_buf, tmp) = (tmp, out_buf)
+                (out_offset, tmp_offset) = (tmp_offset, out_offset)
+
+
+def threefry_generate(gen, out_shape):
+    """Generate a series of random values
+
+    Notes
+    -----
+    This function uses the counter portion of the generator state to generate a series of random
+    numbers in parallel. Random number `i` is generated by applying Threefry to the current
+    generator state with the counter portion incremented by `i`. This means that each random number
+    is generated independently from each other random number, so we can compute them in parallel.
+
+    If there is not enough room left in the counter to generate the desired shape of random values,
+    then a new generator is created by applying Threefry to the current key, path, and counter.
+    This new generator will have a reset counter.
+
+    Warning
+    -------
+    Threeyfry requires that unsigned integer arithmetic wraps on overflow. Currently TVM has no
+    guarantee of this, so threefry contains an internal assert to check wrapping behavior. This
+    assert may or may not run depending on your platform, so it is recommended you run
+    :py:func:`threefry_test_wrapping` to verify wrapping behavior.
+
+    Parameters
+    ----------
+    gen : Tensor[10, uint64]
+        Generator state. Can be create with :py:func:`tvm.relay.threefry_key`. This should not be
+        reused in another function, otherwise random numbers will be repeated.
+
+    out_shape : Sequence[int]
+        Output shape of the random numbers. Product of all dimensions must be a multiple of 4.
+
+    Returns
+    -------
+    new_gen : Tensor[10, uint64]
+        The new generator state to be used in subsequent calls.
+
+    rand : Tensor[out_shape, uint64]
+        Tensor of random numbers with shape `out_shape`.
+    """
+    out_len = tir.const(1)
+    for s in out_shape:
+        out_len *= s
+    assert (
+        out_len.value % 4 == 0
+    ), f"Threefry can only generate arrays who's size is a multiple of 4 ({out_len} was provided)."
+    assert (
+        out_len.value <= 2 ** 64 - 1
+    ), f"Can only generate up to 2^64 random numbers, but {out_len} were requested."
+
+    def gen_ir(gen_ptr, out_gen_ptr, out_array_ptr):
+        irb = ir_builder.create()
+        gen = irb.buffer_ptr(gen_ptr)
+        out_gen = irb.buffer_ptr(out_gen_ptr)
+        out_array = irb.buffer_ptr(out_array_ptr)
+
+        # Check that unsigned arithmetic wraps, as it is required to implement threefry correctly.
+        irb.emit(
+            tvm.tir.AssertStmt(
+                tvm.tir.const(0xFFFFFFFFFFFFFFFF, "uint64") + tvm.tir.const(1, "uint64")
+                == tvm.tir.const(0, "uint64"),
+                tvm.tir.StringImm(
+                    "Unsigned integer arithmetic is not wrapping, but threefry requires wrapping."
+                ),
+                tvm.tir.Evaluate(0),
+            )
+        )
+
+        # Create a temporary array to hold the generator state we will use to create the random
+        # numbers. We cannot use gen because we may need to update the key + path if there is not
+        # enough room in the counter.
+        tmp = irb.allocate(gen.dtype, 10, name="tmp", scope="global")
+
+        # TODO(tkonolige): for now we only use the last word of the counter for counting. It is too
+        # much work to figure out how to do 128 bit addition.
+
+        # Max value for counter should be 2**64-2 because we need to reserve a special value to
+        # indicate the counter is used up.
+        with irb.if_scope(gen[7] < tir.const(2 ** 64 - 1, dtype=gen.dtype) - out_len):
+            for i in range(10):
+                tmp[i] = gen[i]
+        with irb.else_scope():
+            # no room left in the counter, we have to change the path or key
+            with irb.if_scope(gen[8] == 0 and gen[9] == 0):
+                # out of room in the path, have to generate new key
+
+                # The paper says the counter that we will be hashing should be a special value of
+                # all ones. We need to allocate some space for it because we cannot overwrite gen.
+                tmp_counter = irb.allocate(gen.dtype, 2, name="tmp_counter", scope="global")
+                tmp_counter[0] = tir.const(0xFFFFFFFFFFFFFFFF, dtype=gen.dtype)
+                tmp_counter[1] = tir.const(0xFFFFFFFFFFFFFFFF, dtype=gen.dtype)
+                _threefry(irb, gen, 0, tmp_counter, 0, tmp, 0, 1)
+                tmp[4] = tir.const(0, dtype=gen.dtype)  # zero path, i.e. no path
+                tmp[5] = tir.const(0, dtype=gen.dtype)
+                tmp[6] = tir.const(0, dtype=gen.dtype)  # zero counter
+                tmp[7] = tir.const(0, dtype=gen.dtype)
+                tmp[8] = tir.const(1 << 63, dtype=gen.dtype)  # one in the leftmost position
+                tmp[9] = tir.const(0, dtype=gen.dtype)
+            with irb.else_scope():
+                tmp[0] = gen[0]
+                tmp[1] = gen[1]
+                tmp[2] = gen[2]
+                tmp[3] = gen[3]
+                tmp[4] = gen[4] | gen[8]  # add a 1 to the path
+                tmp[5] = gen[5] | gen[9]
+                tmp[6] = tir.const(0, dtype=gen.dtype)  # zero counter
+                tmp[7] = tir.const(0, dtype=gen.dtype)
+                _shift_right(irb, gen[8], gen[9], tmp, 8, tmp, 9)
+
+        # Compute random values
+        _threefry(irb, tmp, 0, tmp, 4, out_array, 0, out_len // 4)
+
+        # Update generator state
+        out_gen[0] = tmp[0]  # key stays the same
+        out_gen[1] = tmp[1]
+        out_gen[2] = tmp[2]
+        out_gen[3] = tmp[3]
+        out_gen[4] = tmp[4]  # path stays the same
+        out_gen[5] = tmp[5]
+        out_gen[6] = tir.const(0, dtype=gen.dtype)  # unused, leave it as 0
+        out_gen[7] = tmp[7] + tir.Cast(gen.dtype, out_len)  # increment counter
+        out_gen[8] = tmp[8]  # path unchanged, so no update here
+        out_gen[9] = tmp[9]
+
+        return irb.get()
+
+    out_gen = tvm.tir.decl_buffer((10,), name="out_gen", dtype="uint64")
+    out_array = tvm.tir.decl_buffer(out_shape, name="out_array", dtype="uint64")
+    return tvm.te.extern(
+        [out_gen.shape, out_array.shape],
+        [gen],
+        lambda ins, outs: gen_ir(ins[0], outs[0], outs[1]),
+        out_buffers=[out_gen, out_array],
+        name="threefry_generate",
+        tag="threefry_generate",
+    )
+
+
+def _shift_right(irb, a, b, out_a, a_off, out_b, b_off):
+    """Binary shift a 128bit number composed of two 64 bit words right by one."""
+    with irb.if_scope(a == 1):
+        out_a[a_off] = tir.const(0, dtype=a.dtype)
+        out_b[b_off] = tir.const(0x8000000000000000, dtype=a.dtype)
+    with irb.else_scope():
+        with irb.if_scope(a == 0):
+            out_a[a_off] = tir.const(0, dtype=a.dtype)
+            out_b[b_off] = b >> 1
+        with irb.else_scope():
+            out_a[a_off] = a >> 1
+            out_b[b_off] = tir.const(0, dtype=a.dtype)
+
+
+def threefry_split(gen):
+    """Split a single generator state into two new ones
+
+    Notes
+    -----
+    The new generator is created by appending a one (for the right output) or a zero (for the left
+    output) to the end of the path portion of the generator If there is no longer and room in the
+    path, then we create a new key portion of the generator by applying Threefry to the old state,
+    path, and counter. i.e. :code:`new_key = threefry(old_key, [old_path, old_counter])`. This
+    resets the path portion of the new generator.
+
+    Parameters
+    ----------
+    gen : Tensor[10, uint64]
+        Generator state. Can be create with :py:func:`tvm.relay.threefry_key`. This should not be
+        reused in another function, otherwise random numbers will be repeated.
+
+    Returns
+    -------
+    out_gen_left : Tensor[10, uint64]
+        New generator state that is distinct from `out_gen_right`.
+
+    out_gen_right : Tensor[10, uint64]
+        New generator state that is distinct from `out_gen_left`.
+    """
+
+    def gen_ir(gen_ptr, out_left_ptr, out_right_ptr):
+        irb = ir_builder.create()
+        gen = irb.buffer_ptr(gen_ptr)
+        out_left = irb.buffer_ptr(out_left_ptr)
+        out_right = irb.buffer_ptr(out_right_ptr)
+
+        with irb.if_scope(gen[8] == 0 and gen[9] == 0):
+            # Generate new key because we have run out of room to extend the path
+            _threefry(irb, gen, 0, gen, 4, out_left, 0, 1)
+            out_left[4] = tir.const(0, dtype=gen.dtype)
+            out_left[5] = tir.const(0, dtype=gen.dtype)
+            out_left[6] = tir.const(0, dtype=gen.dtype)  # counter gets zeroed
+            out_left[7] = tir.const(0, dtype=gen.dtype)  # counter gets zeroed
+            out_left[8] = tir.const(
+                1 << 62, dtype=gen.dtype
+            )  # one in the second from the leftmost position
+            out_left[9] = tir.const(0, dtype=gen.dtype)
+
+            out_right[0] = out_left[0]
+            out_right[1] = out_left[1]
+            out_right[2] = out_left[2]
+            out_right[3] = out_left[3]
+            out_right[4] = tir.const(1 << 63, dtype=gen.dtype)  # one in the leftmost position
+            out_right[5] = tir.const(0, dtype=gen.dtype)
+            out_right[6] = tir.const(0, dtype=gen.dtype)
+            out_right[7] = tir.const(0, dtype=gen.dtype)
+            out_right[8] = tir.const(
+                1 << 62, dtype=gen.dtype
+            )  # one in the second from the leftmost position
+            out_right[9] = tir.const(0, dtype=gen.dtype)
+        with irb.else_scope():
+            out_left[0] = gen[0]
+            out_left[1] = gen[1]
+            out_left[2] = gen[2]
+            out_left[3] = gen[3]
+            out_left[4] = gen[4]  # adding a zero here, but its already zero padded
+            out_left[5] = gen[5]
+            out_left[6] = gen[6]
+            out_left[7] = gen[7]
+            # move path position over one bit
+            _shift_right(irb, gen[8], gen[9], out_left, 8, out_left, 9)
+
+            out_right[0] = gen[0]
+            out_right[1] = gen[1]
+            out_right[2] = gen[2]
+            out_right[3] = gen[3]
+            out_right[4] = gen[4] | gen[8]  # add a one to the path
+            out_right[5] = gen[5] | gen[9]
+            out_right[6] = gen[6]
+            out_right[7] = gen[7]
+            _shift_right(irb, gen[8], gen[9], out_right, 8, out_right, 9)
+
+        return irb.get()
+
+    out_left = tvm.tir.decl_buffer((10,), name="out_left", dtype="uint64")
+    out_right = tvm.tir.decl_buffer((10,), name="out_right", dtype="uint64")
+    return tvm.te.extern(
+        [out_left.shape, out_right.shape],
+        [gen],
+        lambda ins, outs: gen_ir(ins[0], outs[0], outs[1]),
+        out_buffers=[out_left, out_right],
+        name="threefry_split",
+        tag="threefry_split",
+    )
+
+
+def threefry_test_wrapping(target, ctx):
+    """Test that unsigned arithmetic wraps on overflow.
+
+    Parameters
+    ----------
+    target : tvm.target.Target
+        Target to run against
+    ctx : tvm.runtime.TVMContext
+        Context to run the test on
+
+    Returns
+    -------
+    is_wrapping : bool
+        Whether or not unsigned integer arithmetic is wrapping for this target, context pair. True
+        indicates that threefry will work on this platform.
+    """
+    if isinstance(target, str):
+        target = tvm.target.Target(target)
+
+    def gen_ir(out_ptr):
+        irb = ir_builder.create()
+        out = irb.buffer_ptr(out_ptr)
+        if "gpu" in target.keys:
+            thread_x = tvm.te.thread_axis("threadIdx.x")
+            irb.scope_attr(thread_x, "thread_extent", 1)
+        out[0] = tvm.tir.const(0xFFFFFFFFFFFFFFFF, "uint64") + tvm.tir.const(1, "uint64")
+        return irb.get()
+
+    out = tvm.tir.decl_buffer((1,), dtype="uint64")
+    f = tvm.te.extern(
+        [out.shape], [], lambda ins, outs: gen_ir(outs[0]), dtype="uint64", out_buffers=[out]
+    )
+    s = tvm.te.create_schedule([f.op])
+    out_ary = tvm.nd.array(np.ones((1,), "uint64"), ctx)
+    tvm.build(s, [f], target=target)(out_ary)
+    return out_ary.asnumpy()[0] == 0
diff --git a/python/tvm/topi/scatter_add.py b/python/tvm/topi/scatter_add.py
index 4c77a0767785..6b04837b7766 100644
--- a/python/tvm/topi/scatter_add.py
+++ b/python/tvm/topi/scatter_add.py
@@ -32,8 +32,8 @@ def _scatter_add_1d(data, indices, updates):
 @hybrid.script
 def _scatter_add_2d(data, indices, updates, axis):
     out = output_tensor(data.shape, data.dtype)
-    for i in const_range(data.shape[0]):
-        for j in const_range(data.shape[1]):
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
             out[i, j] = data[i, j]
     if axis == 0:
         for i in range(indices.shape[0]):
@@ -54,14 +54,14 @@ def _scatter_add_2d(data, indices, updates, axis):
 @hybrid.script
 def _scatter_add_3d(data, indices, updates, axis):
     out = output_tensor(data.shape, data.dtype)
-    for i in const_range(data.shape[0]):
-        for j in const_range(data.shape[1]):
-            for k in const_range(data.shape[2]):
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            for k in range(data.shape[2]):
                 out[i, j, k] = data[i, j, k]
     if axis == 0:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
+                for k in range(indices.shape[2]):
                     out[
                         indices[i, j, k]
                         if indices[i, j, k] >= 0
@@ -72,7 +72,7 @@ def _scatter_add_3d(data, indices, updates, axis):
     elif axis == 1:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
+                for k in range(indices.shape[2]):
                     out[
                         i,
                         indices[i, j, k]
@@ -83,7 +83,7 @@ def _scatter_add_3d(data, indices, updates, axis):
     else:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
+                for k in range(indices.shape[2]):
                     out[
                         i,
                         j,
@@ -98,17 +98,17 @@ def _scatter_add_3d(data, indices, updates, axis):
 @hybrid.script
 def _scatter_add_4d(data, indices, updates, axis):
     out = output_tensor(data.shape, data.dtype)
-    for i in const_range(data.shape[0]):
-        for j in const_range(data.shape[1]):
-            for k in const_range(data.shape[2]):
-                for l in const_range(data.shape[3]):
+    for i in range(data.shape[0]):
+        for j in range(data.shape[1]):
+            for k in range(data.shape[2]):
+                for l in range(data.shape[3]):
                     out[i, j, k, l] = data[i, j, k, l]
 
     if axis == 0:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
-                    for l in const_range(indices.shape[3]):
+                for k in range(indices.shape[2]):
+                    for l in range(indices.shape[3]):
                         out[
                             indices[i, j, k, l]
                             if indices[i, j, k, l] >= 0
@@ -120,8 +120,8 @@ def _scatter_add_4d(data, indices, updates, axis):
     elif axis == 1:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
-                    for l in const_range(indices.shape[3]):
+                for k in range(indices.shape[2]):
+                    for l in range(indices.shape[3]):
                         out[
                             i,
                             indices[i, j, k, l]
@@ -133,8 +133,8 @@ def _scatter_add_4d(data, indices, updates, axis):
     elif axis == 2:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
-                    for l in const_range(indices.shape[3]):
+                for k in range(indices.shape[2]):
+                    for l in range(indices.shape[3]):
                         out[
                             i,
                             j,
@@ -146,8 +146,8 @@ def _scatter_add_4d(data, indices, updates, axis):
     else:
         for i in range(indices.shape[0]):
             for j in range(indices.shape[1]):
-                for k in const_range(indices.shape[2]):
-                    for l in const_range(indices.shape[3]):
+                for k in range(indices.shape[2]):
+                    for l in range(indices.shape[3]):
                         out[
                             i,
                             j,
diff --git a/python/tvm/topi/sparse/csrmm.py b/python/tvm/topi/sparse/csrmm.py
index f578e6001351..39ba3332fc72 100644
--- a/python/tvm/topi/sparse/csrmm.py
+++ b/python/tvm/topi/sparse/csrmm.py
@@ -72,8 +72,8 @@ def csrmm_default_ir(data, indices, indptr, weight, out):
         out_ptr = irb.buffer_ptr(out)
         M = simplify(indptr.shape[0] - 1)
         _, N = weight.shape
-        with irb.for_range(0, N, for_type="vectorize", name="n") as n:
-            with irb.for_range(0, M, for_type="parallel", name="row") as row:
+        with irb.for_range(0, N, kind="vectorize", name="n") as n:
+            with irb.for_range(0, M, kind="parallel", name="row") as row:
                 dot = irb.allocate("float32", (1,), name="dot", scope="local")
                 out_ptr[row * N + n] = 0.0
                 dot[0] = 0.0
diff --git a/python/tvm/topi/sparse/csrmv.py b/python/tvm/topi/sparse/csrmv.py
index afe3bc76d121..a2d22afe01e0 100644
--- a/python/tvm/topi/sparse/csrmv.py
+++ b/python/tvm/topi/sparse/csrmv.py
@@ -63,7 +63,7 @@ def csrmv_default_ir(data, indices, indptr, weight, out):
         weight_ptr = irb.buffer_ptr(weight)
         out_ptr = irb.buffer_ptr(out)
         num_rows = indptr.shape[0] - 1
-        with irb.for_range(0, num_rows, for_type="parallel", name="row") as row:
+        with irb.for_range(0, num_rows, kind="parallel", name="row") as row:
             dot = irb.allocate("float32", (1,), name="dot", scope="local")
             out_ptr[row] = 0.0
             dot[0] = 0.0
diff --git a/python/tvm/topi/sparse/dense.py b/python/tvm/topi/sparse/dense.py
index d1516d0c20fc..5c63e44f691a 100644
--- a/python/tvm/topi/sparse/dense.py
+++ b/python/tvm/topi/sparse/dense.py
@@ -74,8 +74,8 @@ def dense_default_ir(data, indices, indptr, weight, out):
         out_ptr = irb.buffer_ptr(out)
         M = simplify(indptr.shape[0] - 1)
         N, K = weight.shape
-        with irb.for_range(0, N, for_type="vectorize", name="n") as n:
-            with irb.for_range(0, M, for_type="parallel", name="m") as m:
+        with irb.for_range(0, N, kind="vectorize", name="n") as n:
+            with irb.for_range(0, M, kind="parallel", name="m") as m:
                 dot = irb.allocate(dtype, (1,), name="dot", scope="local")
                 out_ptr[m * N + n] = tvm.tir.const(0, dtype)
                 dot[0] = tvm.tir.const(0, dtype)
@@ -153,8 +153,8 @@ def dense_default_ir(data, w_data, w_indices, w_indptr, out):
         out_ptr = irb.buffer_ptr(out)
         M, K = data.shape
         N = simplify(w_indptr.shape[0] - 1)
-        with irb.for_range(0, M, for_type="vectorize", name="m") as m:
-            with irb.for_range(0, N, for_type="parallel", name="n") as n:
+        with irb.for_range(0, M, kind="vectorize", name="m") as m:
+            with irb.for_range(0, N, kind="parallel", name="n") as n:
                 dot = irb.allocate(dtype, (1,), name="dot", scope="local")
                 out_ptr[m * N + n] = tvm.tir.const(0, dtype)
                 dot[0] = tvm.tir.const(0, dtype)
diff --git a/python/tvm/topi/sparse_fill_empty_rows.py b/python/tvm/topi/sparse_fill_empty_rows.py
new file mode 100644
index 000000000000..10dc6ee3bfa3
--- /dev/null
+++ b/python/tvm/topi/sparse_fill_empty_rows.py
@@ -0,0 +1,109 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHnew_sparse_indices WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# pylint: disable=no-else-return, too-many-locals, too-many-arguments, too-many-branches
+# pylint: disable=undefined-variable, invalid-name
+"""SparseFillEmptyRows operator"""
+from ..te import hybrid
+
+
+@hybrid.script
+def _sparse_fill_empty_rows(
+    sparse_indices,
+    sparse_values,
+    dense_shape,
+    default_value,
+    new_sparse_indices_shape,
+    new_sparse_values_shape,
+    empty_row_indicator_shape,
+):
+    default_value_ = int64(default_value[0])
+    new_sparse_indices = output_tensor(new_sparse_indices_shape, "int64")
+    new_sparse_values = output_tensor(new_sparse_values_shape, "int64")
+    empty_row_indicator = output_tensor(empty_row_indicator_shape, "int64")
+    new_sparse_indices_row_id = 0
+
+    if int64(sparse_indices.shape[0]) == int64(0):  # Handle Empty Case
+        #  Fill all rows with default values
+        for i in range(0, new_sparse_indices_shape[0]):
+            new_sparse_indices[i, 0] = int64(i)
+            new_sparse_values[i] = default_value_
+            empty_row_indicator[i] = int64(1)
+            for k in range(1, int64(new_sparse_indices_shape[1])):
+                new_sparse_indices[i, k] = int64(0)
+
+        return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+    else:
+        # Iterate through sparse_indices and add rows if/when required
+        for i in range(0, int64(sparse_indices.shape[0])):
+            if i == 0:
+                prev_row_id = int64(0)
+            else:
+                prev_row_id = int64(sparse_indices[i - 1, 0] + 1)
+            row_id = int64(sparse_indices[i, 0])
+
+            # Since input is in row-major order, add rows between prev_row_id and row_id
+            for j in range(prev_row_id, row_id):
+                new_sparse_indices[new_sparse_indices_row_id, 0] = int64(j)
+                for k in range(1, int64(new_sparse_indices_shape[1])):
+                    new_sparse_indices[new_sparse_indices_row_id, k] = int64(0)
+                empty_row_indicator[prev_row_id] = int64(1)
+                new_sparse_values[new_sparse_indices_row_id] = default_value_
+                new_sparse_indices_row_id += 1
+
+            # Add current element to output
+            new_sparse_indices[new_sparse_indices_row_id, 0] = row_id
+            for k in range(1, int64(new_sparse_indices_shape[1])):
+                new_sparse_indices[new_sparse_indices_row_id, k] = int64(sparse_indices[i, k])
+            new_sparse_values[new_sparse_indices_row_id] = int64(sparse_values[i])
+            empty_row_indicator[row_id] = int64(0)
+            new_sparse_indices_row_id += 1
+
+        # Add rows with default value if last row id of sparse_indices is not dense_shape[0] - 1
+        for i in range(
+            int64(sparse_indices[sparse_indices.shape[0] - 1, 0] + 1), int64(dense_shape[0])
+        ):
+
+            new_sparse_indices[new_sparse_indices_row_id, 0] = int64(i)
+            for k in range(1, int64(new_sparse_indices_shape[1])):
+                new_sparse_indices[new_sparse_indices_row_id, k] = int64(0)
+            empty_row_indicator[i] = int64(1)
+            new_sparse_values[new_sparse_indices_row_id] = default_value_
+            new_sparse_indices_row_id += 1
+
+        return (new_sparse_indices, new_sparse_values, empty_row_indicator)
+
+
+def sparse_fill_empty_rows(
+    sparse_indices,
+    sparse_values,
+    dense_shape,
+    default_value,
+    new_sparse_indices_shape,
+    new_sparse_values_shape,
+    empty_row_indicator_shape,
+):
+    return _sparse_fill_empty_rows(
+        sparse_indices,
+        sparse_values,
+        dense_shape,
+        default_value,
+        new_sparse_indices_shape,
+        new_sparse_values_shape,
+        empty_row_indicator_shape,
+    )
diff --git a/python/tvm/topi/sparse_reshape.py b/python/tvm/topi/sparse_reshape.py
new file mode 100644
index 000000000000..5535477e17c8
--- /dev/null
+++ b/python/tvm/topi/sparse_reshape.py
@@ -0,0 +1,185 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, too-many-arguments, too-many-nested-blocks
+"""Sparse_Reshape operator"""
+from ..tir import decl_buffer, ir_builder, Cast
+from ..te import extern, div, floordiv, floormod
+
+
+def sparse_reshape(
+    sparse_indices,
+    prev_shape,
+    new_shape,
+    new_sparse_indices_shape,
+    new_shape_shape,
+):
+    """
+    Reshape a Sparse Tensor
+    Parameters
+    ----------
+    sparse_indices : relay.Expr
+        A 2-D tensor[N, n_dim] of integers containing location of sparse values, where N is the
+        number of sparse values and n_dim is the number of dimensions of the dense_shape
+    prev_shape : relay.Expr
+        A 1-D tensor containing the previous shape of the dense tensor
+    new_shape : relay.Expr
+        A 1-D tensor containing the new shape of the dense tensor
+    Returns
+    -------
+    result: relay.Expr
+        Output tensor.
+    Examples
+    --------
+    .. code-block:: python
+        sparse_indices = [[0, 0, 0],
+                            [0, 0, 1],
+                            [0, 1, 0],
+                            [1, 0, 0],
+                            [1, 2, 3]]
+        prev_shape = [2, 3, 4]
+        new_shape = [9, -1]
+        new_sparse_indices, new_shape = relay.sparse_reshape(sparse_indices,
+                            prev_shape,
+                            new_shape)
+        new_sparse_indices = [[0, 0],
+                              [0, 1],
+                              [1, 2],
+                              [4, 2],
+                              [8, 1]]
+        new_shape = [9, 4]
+    """
+
+    def gen_ir(
+        sparse_indices_ptr,
+        prev_shape_ptr,
+        new_shape_ptr,
+        new_sparse_indices_ptr,
+        out_new_shape_ptr,
+    ):
+        ib = ir_builder.create()
+
+        sparse_indices = ib.buffer_ptr(sparse_indices_ptr)
+        prev_shape = ib.buffer_ptr(prev_shape_ptr)
+
+        new_shape = ib.buffer_ptr(new_shape_ptr)
+        out_new_shape = ib.buffer_ptr(out_new_shape_ptr)
+        new_sparse_indices = ib.buffer_ptr(new_sparse_indices_ptr)
+        out_new_shape = ib.buffer_ptr(out_new_shape_ptr)
+
+        prev_shape_size = prev_shape_ptr.shape[0]
+        new_shape_size = new_shape_ptr.shape[0]
+
+        multipliers = ib.allocate(
+            new_shape_ptr.dtype, (prev_shape_size,), name="multipliers", scope="local"
+        )
+        dividers = ib.allocate(
+            new_shape_ptr.dtype, (new_shape_size,), name="dividers", scope="local"
+        )
+        flattened_indices = ib.allocate(
+            new_shape_ptr.dtype,
+            (sparse_indices_ptr.shape[0],),
+            name="flattened_indices",
+            scope="local",
+        )
+
+        total_ele = ib.allocate(new_shape_ptr.dtype, (1,), name="total_ele", scope="local")
+        total_ele[0] = prev_shape[0]
+
+        # Cumulative Reverse Exclusive Multiply
+        multipliers[prev_shape_size - 1] = Cast(new_shape_ptr.dtype, 1)
+        with ib.for_range(0, prev_shape_size - 1) as i_:
+            i = i_ + 1
+            multipliers[prev_shape_size - 1 - i] = (
+                prev_shape[prev_shape_size - i] * multipliers[prev_shape_size - i]
+            )
+            total_ele[0] *= prev_shape[prev_shape_size - i]
+
+        division_total_ele = ib.allocate(
+            new_shape_ptr.dtype, (1,), name="division_total_ele", scope="local"
+        )
+        division_total_ele[0] = Cast(new_shape_ptr.dtype, 1)
+        with ib.for_range(0, new_shape_size) as i:
+            with ib.if_scope(new_shape[i] != -1):
+                division_total_ele[0] *= new_shape[i]
+
+        # Compute true output shape (replace negative ones)
+        with ib.for_range(0, new_shape_size) as i:
+            with ib.if_scope(new_shape[i] == -1):
+                out_new_shape[i] = Cast(
+                    new_shape_ptr.dtype, div(total_ele[0], division_total_ele[0])
+                )
+            with ib.else_scope():
+                out_new_shape[i] = new_shape[i]
+
+        equal_shape = ib.allocate("bool", (1,), name="equal_shape", scope="local")
+
+        # Check if prev_shape and new_shape are equal
+        equal_shape[0] = True
+        with ib.if_scope(prev_shape_size == new_shape_size):
+            with ib.for_range(0, prev_shape_size) as i:
+                with ib.if_scope(prev_shape[i] != out_new_shape[i]):
+                    equal_shape[0] = False
+        with ib.else_scope():
+            equal_shape[0] = False
+
+        # Return same inputs if shapes are equal
+        with ib.if_scope(equal_shape[0]):
+            with ib.for_range(0, sparse_indices_ptr.shape[0], kind="parallel") as i:
+                with ib.for_range(0, sparse_indices_ptr.shape[1]) as j:
+                    new_sparse_indices[i, j] = sparse_indices[i, j]
+
+        # Else compute new_sparse_indices
+        with ib.else_scope():
+            dividers[new_shape_size - 1] = Cast(new_shape_ptr.dtype, 1)
+            with ib.for_range(0, new_shape_size - 1) as i_:
+                i = i_ + 1
+                dividers[new_shape_size - 1 - i] = (
+                    dividers[new_shape_size - i] * out_new_shape[new_shape_size - i]
+                )
+
+            with ib.for_range(0, sparse_indices_ptr.shape[0], kind="parallel") as i:
+                flattened_indices[i] = Cast(new_shape_ptr.dtype, 0)
+                with ib.for_range(0, sparse_indices_ptr.shape[1]) as j:
+                    flattened_indices[i] += sparse_indices[i, j] * multipliers[j]
+
+            with ib.for_range(0, new_sparse_indices_ptr.shape[0], kind="parallel") as i:
+                current_element = ib.allocate(
+                    new_shape_ptr.dtype, (1,), name="current_element", scope="local"
+                )
+                current_element[0] = flattened_indices[i]
+
+                with ib.for_range(0, new_sparse_indices_ptr.shape[1]) as j:
+                    new_sparse_indices[i, j] = Cast(
+                        sparse_indices_ptr.dtype, floordiv(current_element[0], dividers[j])
+                    )
+                    current_element[0] = floormod(current_element[0], dividers[j])
+
+        return ib.get()
+
+    new_sparse_indices_buf = decl_buffer(
+        new_sparse_indices_shape, sparse_indices.dtype, "new_sparse_indices_buf"
+    )
+    new_shape_buf = decl_buffer(new_shape_shape, prev_shape.dtype, "new_shape_buf")
+
+    return extern(
+        [new_sparse_indices_shape, new_shape_shape],
+        [sparse_indices, prev_shape, new_shape],
+        lambda ins, outs: gen_ir(ins[0], ins[1], ins[2], outs[0], outs[1]),
+        out_buffers=[new_sparse_indices_buf, new_shape_buf],
+        name="sparse_reshape_cpu",
+        tag="sparse_reshape_cpu",
+    )
diff --git a/python/tvm/topi/testing/__init__.py b/python/tvm/topi/testing/__init__.py
index 85f13a763c40..ef36b9e73446 100644
--- a/python/tvm/topi/testing/__init__.py
+++ b/python/tvm/topi/testing/__init__.py
@@ -39,7 +39,7 @@
 from .bilinear_resize_python import bilinear_resize_python
 from .trilinear_resize3d_python import trilinear_resize3d_python
 from .reorg_python import reorg_python
-from .roi_align_python import roi_align_nchw_python
+from .roi_align_python import roi_align_nchw_python, roi_align_nhwc_python
 from .roi_pool_python import roi_pool_nchw_python
 from .lrn_python import lrn_python
 from .l2_normalize_python import l2_normalize_python
diff --git a/python/tvm/topi/testing/deformable_conv2d_python.py b/python/tvm/topi/testing/deformable_conv2d_python.py
index 093084397ff1..758a70eb4cc1 100644
--- a/python/tvm/topi/testing/deformable_conv2d_python.py
+++ b/python/tvm/topi/testing/deformable_conv2d_python.py
@@ -17,6 +17,7 @@
 # pylint: disable=invalid-name, too-many-locals, too-many-arguments
 """Deformable convolution in python"""
 import itertools
+import math
 import numpy as np
 from tvm.topi.nn.utils import get_pad_tuple
 
@@ -80,15 +81,22 @@ def deformable_conv2d_nchw_python(
         dilation_h, dilation_w = dilation
 
     def _bilinear(n, c, h, w):
-        low_h, low_w = int(h), int(w)
-        high_h = min(low_h + 1, in_height - 1)
-        high_w = min(low_w + 1, in_width - 1)
-        y_lerp = h - low_h
-        x_lerp = w - low_w
-
-        bottom = (1 - x_lerp) * a_np[n, c, low_h, low_w] + x_lerp * a_np[n, c, low_h, high_w]
-        top = (1 - x_lerp) * a_np[n, c, high_h, low_w] + x_lerp * a_np[n, c, high_h, high_w]
-        return (1 - y_lerp) * bottom + y_lerp * top
+        y_low = int(math.floor(h))
+        x_low = int(math.floor(w))
+        y_high = y_low + 1
+        x_high = x_low + 1
+
+        wy_h = h - y_low
+        wx_h = w - x_low
+        wy_l = 1 - wy_h
+        wx_l = 1 - wx_h
+
+        val = 0
+        for wx, xp in zip((wx_l, wx_h), (x_low, x_high)):
+            for wy, yp in zip((wy_l, wy_h), (y_low, y_high)):
+                if 0 <= yp < in_height and 0 <= xp < in_width:
+                    val += wx * wy * a_np[n, c, yp, xp]
+        return val
 
     a_deform = np.zeros((batch, in_channel, out_height, out_width, kernel_h, kernel_w), dtype=dtype)
     for n, h, w in itertools.product(range(batch), range(out_height), range(out_width)):
diff --git a/python/tvm/topi/testing/depthwise_conv2d_python.py b/python/tvm/topi/testing/depthwise_conv2d_python.py
index 06f26ab3a2e4..2239c56134f5 100644
--- a/python/tvm/topi/testing/depthwise_conv2d_python.py
+++ b/python/tvm/topi/testing/depthwise_conv2d_python.py
@@ -65,7 +65,7 @@ def depthwise_conv2d_python_nchw(input_np, filter_np, stride, padding):
                     0 : (in_height - filter_height + 1) : stride_h,
                     0 : (in_width - filter_width + 1) : stride_w,
                 ]
-    if padding == "SAME":
+    elif padding == "SAME":
         out_channel = in_channel * channel_multiplier
         out_height = np.int(np.ceil(float(in_height) / float(stride_h)))
         out_width = np.int(np.ceil(float(in_width) / float(stride_w)))
diff --git a/python/tvm/topi/testing/roi_align_python.py b/python/tvm/topi/testing/roi_align_python.py
index 5bb292c46fbb..986123b6c9c6 100644
--- a/python/tvm/topi/testing/roi_align_python.py
+++ b/python/tvm/topi/testing/roi_align_python.py
@@ -20,36 +20,51 @@
 import numpy as np
 
 
-def roi_align_nchw_python(a_np, rois_np, pooled_size, spatial_scale, sample_ratio):
-    """Roi align in python"""
-    _, channel, height, width = a_np.shape
-    num_roi = rois_np.shape[0]
-    b_np = np.zeros((num_roi, channel, pooled_size, pooled_size), dtype=a_np.dtype)
+def _bilinear(a_np, n, c, y, x, height, width, layout):
+    if y < -1 or y > height or x < -1 or x > width:
+        return 0
 
-    if isinstance(pooled_size, int):
-        pooled_size_h = pooled_size_w = pooled_size
-    else:
-        pooled_size_h, pooled_size_w = pooled_size
+    y = min(max(y, 0), height - 1)
+    x = min(max(x, 0), width - 1)
+
+    y_low = int(math.floor(y))
+    x_low = int(math.floor(x))
+    y_high = y_low + 1
+    x_high = x_low + 1
+
+    wy_h = y - y_low
+    wx_h = x - x_low
+    wy_l = 1 - wy_h
+    wx_l = 1 - wx_h
 
-    def _bilinear(b, c, y, x):
-        if y < -1 or y > height or x < -1 or x > width:
-            return 0
-        y = max(y, 0.0)
-        x = max(x, 0.0)
-        y_low = int(y)
-        x_low = int(x)
-
-        y_high = min(y_low + 1, height - 1)
-        x_high = min(x_low + 1, width - 1)
-
-        ly = y - y_low
-        lx = x - x_low
-        return (
-            (1 - ly) * (1 - lx) * a_np[b, c, y_low, x_low]
-            + (1 - ly) * lx * a_np[b, c, y_low, x_high]
-            + ly * (1 - lx) * a_np[b, c, y_high, x_low]
-            + ly * lx * a_np[b, c, y_high, x_high]
-        )
+    val = 0
+    for wx, xp in zip((wx_l, wx_h), (x_low, x_high)):
+        for wy, yp in zip((wy_l, wy_h), (y_low, y_high)):
+            if 0 <= yp < height and 0 <= xp < width:
+                if layout == "NCHW":
+                    val += wx * wy * a_np[n, c, yp, xp]
+                else:
+                    val += wx * wy * a_np[n, yp, xp, c]
+    return val
+
+
+def roi_align_common(
+    a_np,
+    b_np,
+    rois_np,
+    channel,
+    pooled_size_h,
+    pooled_size_w,
+    spatial_scale,
+    sample_ratio,
+    avg_mode,
+    max_mode,
+    height,
+    width,
+    layout,
+):
+    """Common code used by roi align NCHW and NHWC"""
+    num_roi = rois_np.shape[0]
 
     for i in range(num_roi):
         roi = rois_np[i]
@@ -64,19 +79,97 @@ def _bilinear(b, c, y, x):
         if sample_ratio > 0:
             roi_bin_grid_h = roi_bin_grid_w = int(sample_ratio)
         else:
-            roi_bin_grid_h = int(math.ceil(roi_h / pooled_size))
-            roi_bin_grid_w = int(math.ceil(roi_w / pooled_size))
+            roi_bin_grid_h = int(math.ceil(roi_h / pooled_size_h))
+            roi_bin_grid_w = int(math.ceil(roi_w / pooled_size_w))
 
         count = roi_bin_grid_h * roi_bin_grid_w
 
         for c in range(channel):
             for ph in range(pooled_size_h):
                 for pw in range(pooled_size_w):
-                    total = 0.0
+                    if avg_mode:
+                        total = 0.0
+                    if max_mode:
+                        total = float("-inf")
                     for iy in range(roi_bin_grid_h):
                         for ix in range(roi_bin_grid_w):
                             y = roi_start_h + ph * bin_h + (iy + 0.5) * bin_h / roi_bin_grid_h
                             x = roi_start_w + pw * bin_w + (ix + 0.5) * bin_w / roi_bin_grid_w
-                            total += _bilinear(batch_index, c, y, x)
-                    b_np[i, c, ph, pw] = total / count
+                            if avg_mode:
+                                total += (
+                                    _bilinear(a_np, batch_index, c, y, x, height, width, layout)
+                                    / count
+                                )
+                            if max_mode:
+                                total = max(
+                                    total,
+                                    _bilinear(a_np, batch_index, c, y, x, height, width, layout),
+                                )
+
+                    if layout == "NCHW":
+                        b_np[i, c, ph, pw] = total
+                    else:
+                        b_np[i, ph, pw, c] = total
     return b_np
+
+
+def roi_align_nchw_python(a_np, rois_np, pooled_size, spatial_scale, sample_ratio, mode=b"avg"):
+    """Roi align NCHW in python"""
+    avg_mode = mode in (b"avg", "avg", 0)
+    max_mode = mode in (b"max", "max", 1)
+    assert avg_mode or max_mode, "Mode must be average or max. Please pass a valid mode."
+    _, channel, height, width = a_np.shape
+    if isinstance(pooled_size, int):
+        pooled_size_h = pooled_size_w = pooled_size
+    else:
+        pooled_size_h, pooled_size_w = pooled_size
+
+    b_np = np.zeros((rois_np.shape[0], channel, pooled_size_h, pooled_size_w), dtype=a_np.dtype)
+
+    return roi_align_common(
+        a_np,
+        b_np,
+        rois_np,
+        channel,
+        pooled_size_h,
+        pooled_size_w,
+        spatial_scale,
+        sample_ratio,
+        avg_mode,
+        max_mode,
+        height,
+        width,
+        "NCHW",
+    )
+
+
+def roi_align_nhwc_python(a_np, rois_np, pooled_size, spatial_scale, sample_ratio, mode=b"avg"):
+    """Roi align NHWC in python"""
+    avg_mode = mode in (b"avg", "avg", 0)
+    max_mode = mode in (b"max", "max", 1)
+    assert avg_mode or max_mode, "Mode must be average or max. Please pass a valid mode."
+    _, height, width, channel = a_np.shape
+    num_roi = rois_np.shape[0]
+
+    if isinstance(pooled_size, int):
+        pooled_size_h = pooled_size_w = pooled_size
+    else:
+        pooled_size_h, pooled_size_w = pooled_size
+
+    b_np = np.zeros((num_roi, pooled_size_h, pooled_size_w, channel), dtype=a_np.dtype)
+
+    return roi_align_common(
+        a_np,
+        b_np,
+        rois_np,
+        channel,
+        pooled_size_h,
+        pooled_size_w,
+        spatial_scale,
+        sample_ratio,
+        avg_mode,
+        max_mode,
+        height,
+        width,
+        "NHWC",
+    )
diff --git a/python/tvm/topi/testing/strided_slice_python.py b/python/tvm/topi/testing/strided_slice_python.py
index c5eb72396c4f..30466c785778 100644
--- a/python/tvm/topi/testing/strided_slice_python.py
+++ b/python/tvm/topi/testing/strided_slice_python.py
@@ -26,7 +26,7 @@ def strided_slice_python(data, begin, end, strides, slice_mode="end"):
         Input data
 
     begin : list
-        Begining of the slices.
+        Beginning of the slices.
 
     end : list
         End of the slices.
@@ -81,7 +81,7 @@ def strided_set_python(data, v, begin, end, strides):
         Value data
 
     begin : list
-        Begining of the slices.
+        Beginning of the slices.
 
     end : list
         End of the slices.
diff --git a/python/tvm/topi/unique.py b/python/tvm/topi/unique.py
new file mode 100644
index 000000000000..b4f27b38f65f
--- /dev/null
+++ b/python/tvm/topi/unique.py
@@ -0,0 +1,297 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""Unique operator"""
+from tvm import te, tir
+from ..te import hybrid
+from .cumsum import cumsum
+from .sort import sort, argsort
+
+
+def _calc_adjacent_diff_ir(data, output, binop=tir.Sub):
+    """Low level IR to calculate adjacent difference in an 1-D array.
+
+    Parameters
+    ----------
+    data : Buffer
+        Input 1-D Buffer.
+
+    output: Buffer
+        A buffer to store adjacent difference, of the same shape as data. The adjacent difference
+        is defined as: output[0] = 0, output[i] = binop(data[i], data[i-1])
+        where i > 0 and i < len(data).
+
+    binop: function, optional
+        A binary associative op to use for calculating adjacent difference. The function takes two
+        TIR expressions and produce a new TIR expression. By default it uses tvm.tir.Sub to
+        compute the adjacent difference.
+    """
+    ib = tir.ir_builder.create()
+    data_ptr = ib.buffer_ptr(data)
+    output_ptr = ib.buffer_ptr(output)
+    with ib.for_range(0, data.shape[0], kind="parallel") as i:
+        with ib.if_scope(i == 0):
+            output_ptr[0] = 0
+        with ib.else_scope():
+            output_ptr[i] = tir.Cast(output.dtype, binop(data_ptr[i], data_ptr[i - 1]))
+    return ib.get()
+
+
+def _calc_adjacent_diff(data, out_dtype="int32", binop=tir.Sub):
+    """Function calculate adjacent difference in an 1-D array.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        Input 1-D tensor.
+
+    output_dtype : str
+        The output tensor data type.
+
+    binop: function, optional
+        A binary associative op to use for calculating difference. The function takes two
+        TIR expressions and produce a new TIR expression. By default it uses tvm.tir.Sub to
+        compute the adjacent difference.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        1-D tensor storing the adjacent difference of the input tensor. The adjacent difference
+        is defined as: output[0] = 0, output[i] = binop(data[i], data[i-1])
+        where i > 0 and i < len(data).
+    """
+    return te.extern(
+        [data.shape],
+        [data],
+        lambda ins, outs: _calc_adjacent_diff_ir(ins[0], outs[0], binop=binop),
+        dtype=[out_dtype],
+        name="_calc_adjacent_diff",
+        tag="_calc_adjacent_diff_cpu",
+    )
+
+
+@hybrid.script
+def _calc_num_unique(inc_scan):
+    """Helper function to get the number of unique elements fron inc_scan tensor"""
+    output = output_tensor((1,), "int32")
+    output[0] = inc_scan[inc_scan.shape[0] - 1] + int32(1)
+    return output
+
+
+def _calc_unique_ir(
+    data, argsorted_indices, inc_scan, index_converter, unique_elements, indices, counts
+):
+    """Low level IR to calculate unique elements, inverse indices, and counts (optional) of
+    unique elements of 1-D array.
+
+    Parameters
+    ----------
+    data : Buffer
+        Input 1-D Buffer.
+
+    argsorted_indices : Buffer
+        A buffer that stores the argsorted indices of the input data.
+
+    inc_scan : Buffer
+        A buffer that stores the inclusive scan of the binary tir.NE adjacent difference
+        of the sorted data.
+
+    index_converter (optional) : Buffer
+        An optional index converter that transforms the unique element index
+        such that new_idx = index_converter[old_idx].
+
+    unique_elements : Buffer
+        A buffer that stores the unique elements.
+
+    indices : Buffer
+        A buffer that stores the the index of each input data element in the unique element array.
+
+    counts (optional) : Buffer
+        A buffer that stores the count of each unique element.
+    """
+    ib = tir.ir_builder.create()
+    data_ptr = ib.buffer_ptr(data)
+    argsorted_indices_ptr = ib.buffer_ptr(argsorted_indices)
+    inc_scan_ptr = ib.buffer_ptr(inc_scan)
+    unique_elements_ptr = ib.buffer_ptr(unique_elements)
+    indices_ptr = ib.buffer_ptr(indices)
+
+    index_converter_ptr = None
+    if isinstance(index_converter, tir.Buffer):
+        index_converter_ptr = ib.buffer_ptr(index_converter)
+
+    if isinstance(counts, tir.Buffer):
+        counts_ptr = ib.buffer_ptr(counts)
+        # use indices_ptr as a tmp buffer to store tids with inc_scan[tid] != inc_scan[tid-1]
+        unique_seq_indices_ptr = ib.buffer_ptr(indices)
+
+    data_length = data.shape[0]
+
+    # if need to return counts
+    if isinstance(counts, tir.Buffer):
+        num_unique = inc_scan_ptr[inc_scan.shape[0] - 1] + 1
+        num_elements = data.shape[0]
+        unique_seq_indices_ptr[num_unique - 1] = num_elements
+        with ib.new_scope():
+            with ib.for_range(0, data_length, kind="parallel") as i:
+                with ib.if_scope(i > 0):
+                    with ib.if_scope(inc_scan_ptr[i] != inc_scan_ptr[i - 1]):
+                        unique_seq_indices_ptr[inc_scan_ptr[i] - 1] = i
+        with ib.new_scope():
+            with ib.for_range(0, num_unique, kind="parallel") as i:
+                unique_idx = i if not index_converter_ptr else index_converter_ptr[i]
+                with ib.if_scope(i == 0):
+                    counts_ptr[unique_idx] = unique_seq_indices_ptr[i]
+                with ib.else_scope():
+                    counts_ptr[unique_idx] = (
+                        unique_seq_indices_ptr[i] - unique_seq_indices_ptr[i - 1]
+                    )
+    # calculate unique elements and inverse indices
+    with ib.new_scope():
+        with ib.for_range(0, data_length, kind="parallel") as i:
+            data_idx = argsorted_indices_ptr[i]
+            unique_idx = (
+                inc_scan_ptr[i] if not index_converter_ptr else index_converter_ptr[inc_scan_ptr[i]]
+            )
+            indices_ptr[data_idx] = unique_idx
+            with ib.if_scope(i == 0):
+                unique_elements_ptr[unique_idx] = data_ptr[data_idx]
+            with ib.else_scope():
+                with ib.if_scope(inc_scan_ptr[i] != inc_scan_ptr[i - 1]):
+                    unique_elements_ptr[unique_idx] = data_ptr[data_idx]
+    return ib.get()
+
+
+@hybrid.script
+def _calc_first_occurence(argsorted_indices, inc_scan):
+    """Hybrid script to calculate the first occurence of each unique element in the input data.
+
+    Parameters
+    ----------
+    argsorted_indices : tvm.te.Tensor
+        A tensor that stores the argsorted indices of the input data.
+
+    inc_scan : tvm.te.Tensor
+        A tensor that stores the inclusive scan of the binary tir.NE adjacent difference
+        of the sorted data.
+
+    first_occurence : tvm.te.Tensor
+        A tensor that stores the first occurence of each unique element in the input data.
+    """
+    first_occurence = output_tensor(argsorted_indices.shape, "int32")
+    for i in parallel(argsorted_indices.shape[0]):
+        first_occurence[i] = argsorted_indices.shape[0]
+    for i in parallel(argsorted_indices.shape[0]):
+        if i == 0 or inc_scan[i] != inc_scan[i - 1]:
+            first_occurence[inc_scan[i]] = argsorted_indices[i]
+    return first_occurence
+
+
+def unique(data, is_sorted=True, return_counts=False):
+    """
+    Find the unique elements of a 1-D tensor. Please note `output` and `counts` are all padded to
+    have the same length of `data` and element with index >= num_unique[0] has undefined value.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        A 1-D tensor of integers.
+
+    sorted : bool
+        Whether to sort the unique elements in ascending order before returning as output.
+
+    return_counts : bool
+        Whether to return the count of each unique element.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        A 1-D tensor containing the unique elements of the input data tensor.
+
+    indices : tvm.te.Tensor
+        A 1-D tensor containing the index of each data element in the output tensor.
+
+    num_unique : tvm.te.Tensor
+        A 1-D tensor with size=1 containing the number of unique elements in the input data tensor.
+
+    counts (optional) : tvm.te.Tensor
+        A 1-D tensor containing the count of each unique element in the output.
+
+    Examples
+    --------
+    .. code-block:: python
+        [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, False)
+        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
+        num_unique     =  [5]
+
+        [output, indices, num_unique, counts] = unique([4, 5, 1, 2, 3, 3, 4, 5], False, True)
+        output         =  [4, 5, 1, 2, 3, ?, ?, ?]
+        indices        =  [0, 1, 2, 3, 4, 4, 0, 1]
+        num_unique     =  [5]
+        counts         =  [2, 2, 1, 1, 2, ?, ?, ?]
+
+        [output, indices, num_unique] = unique([4, 5, 1, 2, 3, 3, 4, 5], True)
+        output         =  [1, 2, 3, 4, 5, ?, ?, ?]
+        indices        =  [3, 4, 0, 1, 2, 2, 3, 4]
+        num_unique     =  [5]
+    """
+    sorted_data = sort(data)
+    argsorted_indices = argsort(data, dtype="int32")
+    # adjacent difference
+    adjacent_diff = _calc_adjacent_diff(sorted_data, "int32", tir.NE)
+    # inclusive scan
+    inc_scan = cumsum(adjacent_diff, dtype="int32", exclusive=0)
+    # total number of unique elements
+    num_unique_elements = _calc_num_unique(inc_scan)
+    # prepare outputs
+    if return_counts:
+        out_data_shape = [data.shape] * 3
+        out_dtypes = [data.dtype, "int32", "int32"]
+    else:
+        out_data_shape = [data.shape] * 2
+        out_dtypes = [data.dtype, "int32"]
+    # prepare inputs and fcompute
+    if is_sorted:
+        in_data = [data, argsorted_indices, inc_scan]
+        if return_counts:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, None, *outs)
+        else:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, None, *outs, None)
+    else:
+        # calculate the index converter if the unique elements should not be sorted
+        # calculate first occurence
+        first_occurence = _calc_first_occurence(argsorted_indices, inc_scan)
+        # calculate index converter by sorting unique elements by their first occurence
+        argsorted_first_occurence = argsort(first_occurence, dtype="int32")
+        index_converter = argsort(argsorted_first_occurence, dtype="int32")
+        in_data = [data, argsorted_indices, inc_scan, index_converter]
+        if return_counts:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, *outs)
+        else:
+            fcompute = lambda ins, outs: _calc_unique_ir(*ins, *outs, None)
+    outs = te.extern(
+        out_data_shape,
+        in_data,
+        fcompute,
+        dtype=out_dtypes,
+        name="_calc_unique",
+        tag="_calc_unique_cpu",
+    )
+    if return_counts:
+        return [outs[0], outs[1], num_unique_elements, outs[2]]
+    return [*outs, num_unique_elements]
diff --git a/python/tvm/topi/utils.py b/python/tvm/topi/utils.py
index c3e14eff3919..2e8528c5e76c 100644
--- a/python/tvm/topi/utils.py
+++ b/python/tvm/topi/utils.py
@@ -460,7 +460,7 @@ def make_idx(b, e, s, z, i):
 
     Returns
     -------
-    postion: Expr
+    position: Expr
         int expression that corresponds to an array position in the selection.
     """
     bc = tvm.tir.Select(s < 0, i <= e, i < b)
@@ -487,3 +487,13 @@ def is_empty_shape(shape):
       Whether input shape is empty or has dimesion with size 0.
     """
     return cpp.utils.is_empty_shape(shape)
+
+
+def ceil_div(a, b):
+    """Return ceil division of a by b"""
+    return tvm.tir.indexdiv(a + (b - 1), b)
+
+
+def swap(arr, axis):
+    """ swap arr[axis] and arr[-1] """
+    return arr[:axis] + [arr[-1]] + arr[axis + 1 : -1] + [arr[axis]]
diff --git a/python/tvm/topi/vision/nms.py b/python/tvm/topi/vision/nms.py
index 035d19f25ec7..cbf136a5552c 100644
--- a/python/tvm/topi/vision/nms.py
+++ b/python/tvm/topi/vision/nms.py
@@ -213,7 +213,7 @@ def get_valid_counts(data, score_threshold=0, id_index=0, score_index=1):
     out_indices: tvm.te.Tensor or numpy NDArray
         Related index in input data.
     """
-    if isinstance(score_threshold, float):
+    if isinstance(score_threshold, (float, int)):
         score_threshold = tvm.tir.const(score_threshold, dtype=data.dtype)
     id_index_const = tvm.tir.const(id_index, "int32")
     score_index_const = tvm.tir.const(score_index, "int32")
diff --git a/python/tvm/topi/vision/rcnn/proposal.py b/python/tvm/topi/vision/rcnn/proposal.py
index 89726efd5d0e..12a0d6bcf0a0 100644
--- a/python/tvm/topi/vision/rcnn/proposal.py
+++ b/python/tvm/topi/vision/rcnn/proposal.py
@@ -208,7 +208,7 @@ def argsort_ir(data_buf, out_index_buf):
     temp_data = ib.allocate("float32", (1,), name="temp_data", scope="local")
     temp_index = ib.allocate("int32", (1,), name="temp_index", scope="local")
     idxm = tvm.tir.indexmod
-    with ib.for_range(0, batch, for_type="unroll") as b:
+    with ib.for_range(0, batch, kind="unroll") as b:
         start = b * num_bbox
         for i in range(2):
             with ib.for_range(0, (num_bbox + 1) // 2) as tid:
@@ -231,7 +231,7 @@ def argsort_ir(data_buf, out_index_buf):
 
 
 def nms_ir(sorted_bbox_buf, out_buf, nms_threshold):
-    """Non-maximum supression.
+    """Non-maximum suppression.
 
     Parameters
     ----------
@@ -279,7 +279,7 @@ def calculate_overlap(out_tensor, box_a_idx, box_b_idx):
     ib = tvm.tir.ir_builder.create()
     p_data = ib.buffer_ptr(sorted_bbox_buf)
     p_out = ib.buffer_ptr(out_buf)
-    with ib.for_range(0, batch, for_type="unroll", name="n") as b:
+    with ib.for_range(0, batch, kind="unroll", name="n") as b:
         base_idx = b * num_bbox
         for i in range(num_bbox):
             p_out[base_idx + i] = False
@@ -345,7 +345,7 @@ def prepare_output_ir(sorted_bbox_buf, remove_mask_buf, out_buf):
                         )
                     ):
                         p_out[offset_i] = tvm.tir.Cast("float32", b)
-                        with ib.for_range(0, 4, for_type="unroll") as k:
+                        with ib.for_range(0, 4, kind="unroll") as k:
                             p_out[offset_i + k + 1] = p_sorted_bbox[offset_j + k]
                         i[b] = i[b] + 1
 
diff --git a/python/tvm/topi/vision/rcnn/roi_align.py b/python/tvm/topi/vision/rcnn/roi_align.py
index a51ba33a6c45..655ba2637d84 100644
--- a/python/tvm/topi/vision/rcnn/roi_align.py
+++ b/python/tvm/topi/vision/rcnn/roi_align.py
@@ -19,10 +19,74 @@
 import tvm
 from tvm import te
 from ...utils import get_const_tuple
-from ...cpp.utils import bilinear_sample_nchw
+from ...cpp.utils import bilinear_sample_nchw, bilinear_sample_nhwc
+
+
+def _sample_common(
+    i,
+    c,
+    ph,
+    pw,
+    rois,
+    pooled_size_h,
+    pooled_size_w,
+    spatial_scale,
+    sample_ratio,
+    dtype,
+    avg_mode,
+    bilinear_func,
+):
+    roi = rois[i]
+    batch_index = roi[0].astype("int32")
+    roi_start_w, roi_start_h, roi_end_w, roi_end_h = roi[1], roi[2], roi[3], roi[4]
+    roi_start_h *= spatial_scale
+    roi_end_h *= spatial_scale
+    roi_start_w *= spatial_scale
+    roi_end_w *= spatial_scale
+
+    # force malformed ROIs to be 1x1
+    roi_h = tvm.te.max(roi_end_h - roi_start_h, tvm.tir.const(1.0, dtype))
+    roi_w = tvm.te.max(roi_end_w - roi_start_w, tvm.tir.const(1.0, dtype))
+
+    bin_h = roi_h / pooled_size_h
+    bin_w = roi_w / pooled_size_w
+
+    if sample_ratio > 0:
+        roi_bin_grid_h = roi_bin_grid_w = tvm.tir.const(sample_ratio, "int32")
+    else:
+        roi_bin_grid_h = te.ceil(roi_h / pooled_size_h).astype("int32")
+        roi_bin_grid_w = te.ceil(roi_w / pooled_size_w).astype("int32")
+
+    count = roi_bin_grid_h * roi_bin_grid_w
+    rh = te.reduce_axis((0, roi_bin_grid_h))
+    rw = te.reduce_axis((0, roi_bin_grid_w))
+    roi_start_h += ph * bin_h
+    roi_start_w += pw * bin_w
+
+    if avg_mode:
+        return te.sum(
+            bilinear_func(
+                batch_index,
+                c,
+                roi_start_h + (rh + 0.5) * bin_h / roi_bin_grid_h,
+                roi_start_w + (rw + 0.5) * bin_w / roi_bin_grid_w,
+            )
+            / count,
+            axis=[rh, rw],
+        )
+    # max mode
+    return te.max(
+        bilinear_func(
+            batch_index,
+            c,
+            roi_start_h + (rh + 0.5) * bin_h / roi_bin_grid_h,
+            roi_start_w + (rw + 0.5) * bin_w / roi_bin_grid_w,
+        ),
+        axis=[rh, rw],
+    )
 
 
-def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
+def roi_align_nchw(data, rois, pooled_size, spatial_scale, mode, sample_ratio=-1):
     """ROI align operator in NCHW layout.
 
     Parameters
@@ -41,6 +105,10 @@ def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
         Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal
         of total stride in convolutional layers, which should be in range (0.0, 1.0]
 
+    mode : int or str
+        There are two modes, average and max. For the average mode, you can pass b'avg' or 0, and
+        for the max mode, you can pass b'max' or 1.
+
     sample_ratio : int
         Optional sampling ratio of ROI align, using adaptive size by default.
 
@@ -49,6 +117,9 @@ def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
     output : tvm.te.Tensor
         4-D with shape [num_roi, channel, pooled_size, pooled_size]
     """
+    avg_mode = mode in (b"avg", 0)
+    max_mode = mode in (b"max", 1)
+    assert avg_mode or max_mode, "Mode must be avg or max. Please pass in a valid mode."
     dtype = rois.dtype
     _, channel, height, width = get_const_tuple(data.shape)
     num_roi, _ = get_const_tuple(rois.shape)
@@ -60,49 +131,98 @@ def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
 
     def _bilinear(i, c, y, x):
         outside = tvm.tir.any(y < -1.0, x < -1.0, y > height, x > width)
-        y = tvm.te.max(y, 0.0)
-        x = tvm.te.max(x, 0.0)
+        y = tvm.te.min(tvm.te.max(y, 0.0), height - 1)
+        x = tvm.te.min(tvm.te.max(x, 0.0), width - 1)
         val = bilinear_sample_nchw(data, (i, c, y, x), height - 1, width - 1)
         return tvm.tir.if_then_else(outside, 0.0, val)
 
     def _sample(i, c, ph, pw):
-        roi = rois[i]
-        batch_index = roi[0].astype("int32")
-        roi_start_w, roi_start_h, roi_end_w, roi_end_h = roi[1], roi[2], roi[3], roi[4]
-        roi_start_h *= spatial_scale
-        roi_end_h *= spatial_scale
-        roi_start_w *= spatial_scale
-        roi_end_w *= spatial_scale
-
-        # force malformed ROIs to be 1x1
-        roi_h = tvm.te.max(roi_end_h - roi_start_h, tvm.tir.const(1.0, dtype))
-        roi_w = tvm.te.max(roi_end_w - roi_start_w, tvm.tir.const(1.0, dtype))
-
-        bin_h = roi_h / pooled_size_h
-        bin_w = roi_w / pooled_size_w
-
-        if sample_ratio > 0:
-            roi_bin_grid_h = roi_bin_grid_w = tvm.tir.const(sample_ratio, "int32")
-        else:
-            roi_bin_grid_h = te.ceil(roi_h / pooled_size_h).astype("int32")
-            roi_bin_grid_w = te.ceil(roi_w / pooled_size_w).astype("int32")
-
-        count = roi_bin_grid_h * roi_bin_grid_w
-        rh = te.reduce_axis((0, roi_bin_grid_h))
-        rw = te.reduce_axis((0, roi_bin_grid_w))
-        roi_start_h += ph * bin_h
-        roi_start_w += pw * bin_w
-        return te.sum(
-            _bilinear(
-                batch_index,
-                c,
-                roi_start_h + (rh + 0.5) * bin_h / roi_bin_grid_h,
-                roi_start_w + (rw + 0.5) * bin_w / roi_bin_grid_w,
-            )
-            / count,
-            axis=[rh, rw],
+        return _sample_common(
+            i,
+            c,
+            ph,
+            pw,
+            rois,
+            pooled_size_h,
+            pooled_size_w,
+            spatial_scale,
+            sample_ratio,
+            dtype,
+            avg_mode,
+            _bilinear,
         )
 
     return te.compute(
         (num_roi, channel, pooled_size_h, pooled_size_w), _sample, tag="pool,roi_align_nchw"
     )
+
+
+def roi_align_nhwc(data, rois, pooled_size, spatial_scale, mode, sample_ratio=-1):
+    """ROI align operator in NHWC layout.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        4-D with shape [batch, height, width, channel]
+
+    rois : tvm.te.Tensor
+        2-D with shape [num_roi, 5]. The last dimension should be in format of
+        [batch_index, w_start, h_start, w_end, h_end]
+
+    pooled_size : int or list/tuple of two ints
+        output size, or [out_height, out_width]
+
+    spatial_scale : float
+        Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal
+        of total stride in convolutional layers, which should be in range (0.0, 1.0]
+
+    mode : int or str
+        There are two modes, average and max. For the average mode, you can pass b'avg' or 0, and
+        for the max mode, you can pass b'max' or 1.
+
+    sample_ratio : int
+        Optional sampling ratio of ROI align, using adaptive size by default.
+
+    Returns
+    -------
+    output : tvm.te.Tensor
+        4-D with shape [num_roi, pooled_size, pooled_size, channel]
+    """
+    avg_mode = mode in (b"avg", 0)
+    max_mode = mode in (b"max", 1)
+    assert avg_mode or max_mode, "Mode must be avg or max. Please pass in a valid mode."
+    dtype = rois.dtype
+    _, height, width, channel = get_const_tuple(data.shape)
+    num_roi, _ = get_const_tuple(rois.shape)
+
+    if isinstance(pooled_size, int):
+        pooled_size_h = pooled_size_w = pooled_size
+    else:
+        pooled_size_h, pooled_size_w = pooled_size
+
+    def _bilinear(i, c, y, x):
+        outside = tvm.tir.any(y < -1.0, x < -1.0, y > height, x > width)
+        y = tvm.te.min(tvm.te.max(y, 0.0), height - 1)
+        x = tvm.te.min(tvm.te.max(x, 0.0), width - 1)
+        val = bilinear_sample_nhwc(data, (i, y, x, c), height - 1, width - 1)
+        return tvm.tir.if_then_else(outside, 0.0, val)
+
+    def _sample(i, ph, pw, c):
+        return _sample_common(
+            i,
+            c,
+            ph,
+            pw,
+            rois,
+            pooled_size_h,
+            pooled_size_w,
+            spatial_scale,
+            sample_ratio,
+            dtype,
+            avg_mode,
+            _bilinear,
+        )
+
+    return te.compute(
+        (num_roi, pooled_size_h, pooled_size_w, channel), _sample, tag="pool,roi_align_nchw"
+    )
diff --git a/python/tvm/topi/x86/__init__.py b/python/tvm/topi/x86/__init__.py
index 154511010a1c..bb6a7cdd4122 100644
--- a/python/tvm/topi/x86/__init__.py
+++ b/python/tvm/topi/x86/__init__.py
@@ -39,4 +39,5 @@
 from .conv3d_transpose import *
 from .sparse import *
 from .conv2d_alter_op import *
+from .dense_alter_op import *
 from .scatter import *
diff --git a/python/tvm/topi/x86/batch_matmul.py b/python/tvm/topi/x86/batch_matmul.py
index 79b38de8cf93..df480123375d 100644
--- a/python/tvm/topi/x86/batch_matmul.py
+++ b/python/tvm/topi/x86/batch_matmul.py
@@ -49,7 +49,7 @@ def batch_matmul(cfg, x, y, out_shape=None):
     XB, M, XK = get_const_tuple(x.shape)
     YB, N, YK = get_const_tuple(y.shape)
     assert (XB == YB) or (YB == 1) or (XB == 1), "batch dimension doesn't match"
-    assert XK == YK, "shapes of x and y is inconsistant"
+    assert XK == YK, "shapes of x and y is inconsistent"
     B = te.max(XB, YB)
     K = XK
     if out_shape is not None:
@@ -151,7 +151,7 @@ def batch_matmul_blas_common(cfg, x, y, out_shape, lib):
         3-D with shape [batch, N, K]
     out_shape : tuple or None
         Shape of the output
-    lib : A contrib module which implements batch_matmul funtion
+    lib : A contrib module which implements batch_matmul function
         cblas and mkl are supported
 
     Returns
@@ -163,7 +163,7 @@ def batch_matmul_blas_common(cfg, x, y, out_shape, lib):
     XB, M, XK = get_const_tuple(x.shape)
     YB, N, YK = get_const_tuple(y.shape)
     assert XB == YB, "batch dimension doesn't match"
-    assert XK == YK, "shapes of x and y is inconsistant"
+    assert XK == YK, "shapes of x and y is inconsistent"
     if out_shape is not None:
         assert out_shape[0] == XB, "got invalid output shape"
         assert out_shape[1] == M, "got invalid output shape"
diff --git a/python/tvm/topi/x86/conv2d.py b/python/tvm/topi/x86/conv2d.py
index a3b7e473415e..182454acf3a6 100644
--- a/python/tvm/topi/x86/conv2d.py
+++ b/python/tvm/topi/x86/conv2d.py
@@ -35,7 +35,7 @@
 
 
 def _get_default_config(
-    cfg, data, kernel, strides, padding, out_dtype, is_depthwise=False, layout="NCHW"
+    cfg, data, kernel, strides, padding, dilation, out_dtype, is_depthwise=False, layout="NCHW"
 ):
     """
     Get default schedule config for the workload
@@ -48,13 +48,13 @@ def _get_default_config(
             static_data_shape.append(dim)
     data = te.placeholder(static_data_shape, dtype=data.dtype)
     if is_depthwise:
-        wkl = _get_depthwise_conv2d_workload(data, kernel, strides, padding, out_dtype)
+        wkl = _get_depthwise_conv2d_workload(data, kernel, strides, padding, dilation, out_dtype)
         from .depthwise_conv2d import _fallback_schedule
 
         _fallback_schedule(cfg, wkl)
     else:
-        wkl = _get_conv2d_workload(data, kernel, strides, padding, out_dtype, layout)
-        is_kernel_1x1 = wkl.hkernel == 1 and wkl.wkernel == 1
+        wkl = _get_conv2d_workload(data, kernel, strides, padding, dilation, out_dtype, layout)
+        is_kernel_1x1 = wkl.kernel_h == 1 and wkl.kernel_w == 1
         if is_kernel_1x1:
             conv2d_avx_1x1._fallback_schedule(cfg, wkl)
         else:
@@ -69,8 +69,11 @@ def _conv2d_infer_layout(workload, cfg):
     idxdiv = tvm.tir.indexdiv
 
     pt, pl, pb, pr = get_pad_tuple(padding, (k_height, k_width))
-    out_height = idxdiv(in_height + pt + pb - k_height, strides[0]) + 1
-    out_width = idxdiv(in_width + pl + pr - k_width, strides[1]) + 1
+    hdilation, wdilation = dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
+    dilated_kernel_h = (k_height - 1) * hdilation + 1
+    dilated_kernel_w = (k_width - 1) * wdilation + 1
+    out_height = idxdiv(in_height + pt + pb - dilated_kernel_h, strides[0]) + 1
+    out_width = idxdiv(in_width + pl + pr - dilated_kernel_w, strides[1]) + 1
     tile_ic, tile_oc = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
     in_shape = (batch_size, idxdiv(in_channel, tile_ic), in_height, in_width, tile_ic)
     in_layout = "NCHW%dc" % tile_ic
@@ -208,6 +211,7 @@ def conv2d_NCHWc(cfg, data, kernel, strides, padding, dilation, layout, out_layo
             ),
             strides,
             padding,
+            dilation,
             out_dtype,
         )
 
diff --git a/python/tvm/topi/x86/conv2d_alter_op.py b/python/tvm/topi/x86/conv2d_alter_op.py
index 979dc5ab5702..f05bac82ff0c 100644
--- a/python/tvm/topi/x86/conv2d_alter_op.py
+++ b/python/tvm/topi/x86/conv2d_alter_op.py
@@ -97,7 +97,15 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         if data_layout == "NCHW" and kernel_layout == "OIHW":
             if cfg.is_fallback:
                 _get_default_config(
-                    cfg, data_tensor, kernel_tensor, strides, padding, out_dtype, False, data_layout
+                    cfg,
+                    data_tensor,
+                    kernel_tensor,
+                    strides,
+                    padding,
+                    dilation,
+                    out_dtype,
+                    False,
+                    data_layout,
                 )
             batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape)
             out_channel, _, kh, kw = get_const_tuple(kernel_tensor.shape)
@@ -142,7 +150,15 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         assert data_layout == "NCHW" and kernel_layout == "OIHW"
         if cfg.is_fallback:
             _get_default_config_int8(
-                cfg, data_tensor, kernel_tensor, strides, padding, out_dtype, False, data_layout
+                cfg,
+                data_tensor,
+                kernel_tensor,
+                strides,
+                padding,
+                dilation,
+                out_dtype,
+                False,
+                data_layout,
             )
 
         batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape)
@@ -198,7 +214,15 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         if data_layout == "NCHW" and kernel_layout == "OIHW":
             if cfg.is_fallback:
                 _get_default_config(
-                    cfg, data_tensor, kernel_tensor, strides, padding, out_dtype, True, data_layout
+                    cfg,
+                    data_tensor,
+                    kernel_tensor,
+                    strides,
+                    padding,
+                    dilation,
+                    out_dtype,
+                    True,
+                    data_layout,
                 )
 
             batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape)
diff --git a/python/tvm/topi/x86/conv2d_avx_1x1.py b/python/tvm/topi/x86/conv2d_avx_1x1.py
index 3e5a12bc43b2..32b06725cdc2 100644
--- a/python/tvm/topi/x86/conv2d_avx_1x1.py
+++ b/python/tvm/topi/x86/conv2d_avx_1x1.py
@@ -31,10 +31,13 @@
 
 def _fallback_schedule(cfg, wkl):
     simd_width = get_fp32_len()
-    HPAD, WPAD = wkl.hpad, wkl.wpad
-    HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_height = (wkl.height + 2 * HPAD - wkl.hkernel) // HSTR + 1
-    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
+    pt, pl, pb, pr = wkl.padt, wkl.padl, wkl.padb, wkl.padr
+    HSTR, WSTR = wkl.stride_h, wkl.stride_w
+    dilated_kernel_h = (wkl.kernel_h - 1) * wkl.dilation_h + 1
+    dilated_kernel_w = (wkl.kernel_w - 1) * wkl.dilation_w + 1
+
+    out_height = (wkl.height + pt + pb - dilated_kernel_h) // HSTR + 1
+    out_width = (wkl.width + pl + pr - dilated_kernel_w) // WSTR + 1
 
     oc_bn = 1
     for bn in range(simd_width, 0, -1):
@@ -188,7 +191,7 @@ def _declaration_conv_nhwc_pack(cfg, Input, Filter, stride, padding, dilation, o
     pad_before = [0, pad_top, pad_left, 0]
     pad_after = [0, pad_down, pad_right, 0]
     PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
-    # todo: padding filter to accomodate the intrinsic
+    # todo: padding filter to accommodate the intrinsic
 
     # packing the Filter to let memory access be consecutive for AVX512 intrinsic
     # Done in pre-compute stage
diff --git a/python/tvm/topi/x86/conv2d_avx_common.py b/python/tvm/topi/x86/conv2d_avx_common.py
index 8d707445be05..5e63de329bba 100644
--- a/python/tvm/topi/x86/conv2d_avx_common.py
+++ b/python/tvm/topi/x86/conv2d_avx_common.py
@@ -27,9 +27,11 @@
 
 def _fallback_schedule(cfg, wkl):
     simd_width = get_fp32_len()
-    HPAD, WPAD = wkl.hpad, wkl.wpad
-    HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
+    pt, pl, pb, pr = wkl.padt, wkl.padl, wkl.padb, wkl.padr
+    HSTR, WSTR = wkl.stride_h, wkl.stride_w
+    dilated_kernel_w = (wkl.kernel_w - 1) * wkl.dilation_w + 1
+
+    out_width = (wkl.width + pl + pr - dilated_kernel_w) // WSTR + 1
 
     oc_bn = 1
     for bn in range(simd_width, 0, -1):
@@ -56,9 +58,9 @@ def _fallback_schedule(cfg, wkl):
 
 
 def _fallback_schedule_int8(cfg, wkl):
-    HPAD, WPAD = wkl.hpad, wkl.wpad
-    HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
+    pt, pl, pb, pr = wkl.padt, wkl.padl, wkl.padb, wkl.padr
+    HSTR, WSTR = wkl.stride_h, wkl.stride_w
+    out_width = (wkl.width + pl + pr - wkl.kernel_w) // WSTR + 1
 
     oc_bn = 16
     assert wkl.out_filter % oc_bn == 0
diff --git a/python/tvm/topi/x86/conv2d_int8.py b/python/tvm/topi/x86/conv2d_int8.py
index 905ada68f277..ca0d0b8b223c 100644
--- a/python/tvm/topi/x86/conv2d_int8.py
+++ b/python/tvm/topi/x86/conv2d_int8.py
@@ -33,7 +33,7 @@
 
 
 def _get_default_config_int8(
-    cfg, data, kernel, strides, padding, out_dtype, is_depthwise=False, layout="NCHW"
+    cfg, data, kernel, strides, padding, dilation, out_dtype, is_depthwise=False, layout="NCHW"
 ):
     """
     Get default schedule config for the workload
@@ -45,8 +45,8 @@ def _get_default_config_int8(
 
         _fallback_schedule(cfg, wkl)
     else:
-        wkl = _get_conv2d_workload(data, kernel, strides, padding, out_dtype, layout)
-        is_kernel_1x1 = wkl.hkernel == 1 and wkl.wkernel == 1
+        wkl = _get_conv2d_workload(data, kernel, strides, padding, dilation, out_dtype, layout)
+        is_kernel_1x1 = wkl.kernel_h == 1 and wkl.kernel_w == 1
         if is_kernel_1x1:
             conv2d_generic.fallback_schedule_cpu_1x1_int8(
                 cfg, wkl, int32_lanes=16, num_int8_elements=4
@@ -138,8 +138,11 @@ def conv2d_NCHWc_int8(cfg, data, kernel, strides, padding, dilation, layout, out
     is_kernel_1x1 = kernel_height == 1 and kernel_width == 1
     pt, pl, pb, pr = get_pad_tuple(padding, (kernel_height, kernel_width))
     sh, sw = strides if isinstance(strides, (tuple, list)) else (strides, strides)
-    oh = (ih - kernel_height + pt + pb) // sh + 1
-    ow = (iw - kernel_width + pl + pr) // sw + 1
+    dh, dw = dilation if isinstance(dilation, (tuple, list)) else (dilation, dilation)
+    dilated_kernel_h = (kernel_height - 1) * dh + 1
+    dilated_kernel_w = (kernel_width - 1) * dw + 1
+    oh = (ih - dilated_kernel_h + pt + pb) // sh + 1
+    ow = (iw - dilated_kernel_w + pl + pr) // sw + 1
 
     cfg.define_split("tile_ic", in_channel, num_outputs=2, filter=lambda y: y.size[-1] % 4 == 0)
     cfg.define_split("tile_oc", num_filter, num_outputs=2, filter=lambda y: y.size[-1] % 16 == 0)
@@ -159,6 +162,7 @@ def conv2d_NCHWc_int8(cfg, data, kernel, strides, padding, dilation, layout, out
             ),
             strides,
             padding,
+            dilation,
             out_dtype,
         )
 
diff --git a/python/tvm/topi/x86/dense.py b/python/tvm/topi/x86/dense.py
index 15d7a1a310d6..6011f01c2cb0 100644
--- a/python/tvm/topi/x86/dense.py
+++ b/python/tvm/topi/x86/dense.py
@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 # pylint: disable=invalid-name,too-many-locals,unused-variable
+# pylint: disable=no-value-for-parameter
 """x86 dense operators"""
 from __future__ import absolute_import as _abs
 import tvm
@@ -26,11 +27,12 @@
 from tvm.contrib import mkldnn
 
 from .utils import get_fp32_len
+from .injective import schedule_injective_from_existing
 from .. import generic, tag
 from ..utils import traverse_inline, get_const_tuple
 
 
-def _schedule_dense_pack_template(cfg, s, C):
+def _schedule_dense_pack_template(cfg, s, C, O):
     A, packedB = s[C].op.input_tensors
 
     CC = s.cache_write(C, "global")
@@ -39,9 +41,10 @@ def _schedule_dense_pack_template(cfg, s, C):
 
     yt, yo, yi = cfg["tile_y"].apply(s, C, y)
     xt, xo, xi = cfg["tile_x"].apply(s, C, x)
-    s[C].reorder(yt, xt, yo, xo, yi, xi)
-    xyt = s[C].fuse(yt, xt)
-    s[C].parallel(xyt)
+    s[C].reorder(xt, yt, yo, xo, yi, xi)
+    xyt = s[C].fuse(xt, yt)
+    if C == O:
+        s[C].parallel(xyt)
     xyo = s[C].fuse(yo, xo)
     s[C].unroll(yi)
     s[C].vectorize(xi)
@@ -51,12 +54,27 @@ def _schedule_dense_pack_template(cfg, s, C):
     ko, ki = cfg["tile_k"].apply(s, CC, k)
     s[CC].reorder(ko, ki, y, x)
     s[CC].vectorize(x)
-    s[CC].unroll(y)
-    s[CC].unroll(ki)
 
-    z, y, x = s[packedB].op.axis
-    s[packedB].reorder(z, x, y)
-    s[packedB].parallel(z)
+    tile_inner = cfg["tile_inner"].size[-1]
+    if tile_inner > 1:
+        yo, yi = s[CC].split(y, tile_inner)
+        s[CC].reorder(ko, yo, ki, yi, x)
+        s[CC].unroll(yo)
+        s[CC].unroll(ki)
+        s[CC].unroll(yi)
+    else:
+        s[CC].unroll(ki)
+        s[CC].unroll(y)
+
+    if C != O:
+        y, x = s[O].op.axis
+        yt, yo, yi = cfg["tile_y"].apply(s, O, y)
+        xt, xo, xi = cfg["tile_x"].apply(s, O, x)
+        s[O].reorder(xt, yt, yo, xo, yi, xi)
+        xyt = s[O].fuse(xt, yt)
+        s[C].compute_at(s[O], xyt)
+        s[O].vectorize(xi)
+        s[O].parallel(xyt)
     return s
 
 
@@ -83,11 +101,11 @@ def _schedule_dense_nopack_template(cfg, s, C):
 
 def _default_dense_pack_config(cfg, M, N, K):
     # Generate default schedule for dynamic shape.
-    if isinstance(M, tvm.tir.Var):
+    if isinstance(M, (tvm.tir.Var, tvm.tir.Any)):
         M = 16
-    if isinstance(N, tvm.tir.Var):
+    if isinstance(N, (tvm.tir.Var, tvm.tir.Any)):
         N = 16
-    if isinstance(K, tvm.tir.Var):
+    if isinstance(K, (tvm.tir.Var, tvm.tir.Any)):
         K = 16
 
     vec_width = get_fp32_len()
@@ -116,15 +134,16 @@ def _default_dense_pack_config(cfg, M, N, K):
     cfg["tile_y"] = SplitEntity([MM // tiley_oi, tiley_oi, tiley_ii])
     cfg["tile_x"] = SplitEntity([NN // tilex_oi, tilex_oi, tilex_ii])
     cfg["tile_k"] = SplitEntity([K, 1])
+    cfg["tile_inner"] = SplitEntity([M // tiley_ii, tiley_ii])
 
 
 def _default_dense_nopack_config(cfg, M, N, K):
     # Generate default schedule for dynamic shape.
-    if isinstance(M, tvm.tir.Var):
+    if isinstance(M, (tvm.tir.Var, tvm.tir.Any)):
         M = 16
-    if isinstance(N, tvm.tir.Var):
+    if isinstance(N, (tvm.tir.Var, tvm.tir.Any)):
         N = 16
-    if isinstance(K, tvm.tir.Var):
+    if isinstance(K, (tvm.tir.Var, tvm.tir.Any)):
         K = 16
 
     vec_width = get_fp32_len()
@@ -146,9 +165,15 @@ def dense_nopack(cfg, data, weight, bias=None, out_dtype=None):
     M, K = get_const_tuple(data.shape)
     N, _ = get_const_tuple(weight.shape)
     # create tuning space
-    cfg.define_split("tile_y", 32 if isinstance(M, tvm.tir.Var) else M, num_outputs=2)
-    cfg.define_split("tile_x", 32 if isinstance(N, tvm.tir.Var) else N, num_outputs=2)
-    cfg.define_split("tile_k", 32 if isinstance(K, tvm.tir.Var) else K, num_outputs=2)
+    cfg.define_split(
+        "tile_y", 32 if isinstance(M, (tvm.tir.Var, tvm.tir.Any)) else M, num_outputs=2
+    )
+    cfg.define_split(
+        "tile_x", 32 if isinstance(N, (tvm.tir.Var, tvm.tir.Any)) else N, num_outputs=2
+    )
+    cfg.define_split(
+        "tile_k", 32 if isinstance(K, (tvm.tir.Var, tvm.tir.Any)) else K, num_outputs=2
+    )
     if cfg.is_fallback:
         _default_dense_nopack_config(cfg, M, N, K)
 
@@ -184,23 +209,46 @@ def _callback(op):
 
 @autotvm.register_topi_compute("dense_pack.x86")
 def dense_pack(cfg, data, weight, bias=None, out_dtype=None):
-    """Compute dense with packing"""
+    """Compute dense with transformed weight."""
     if out_dtype is None:
         out_dtype = data.dtype
     M, K = get_const_tuple(data.shape)  # batch, in_dim
-    N, _ = get_const_tuple(weight.shape)  # out_dim
+    if len(weight.shape) == 3:
+        N, _, packw_bn = get_const_tuple(weight.shape)  # out_dim
+        N = N * packw_bn
+    else:
+        N, _ = get_const_tuple(weight.shape)  # out_dim
     # create tuning space
-    cfg.define_split("tile_y", M, num_outputs=3)
-    cfg.define_split("tile_x", N, num_outputs=3)
-    cfg.define_split("tile_k", K, num_outputs=2)
+    cfg.define_split(
+        "tile_y", 32 if isinstance(M, (tvm.tir.Var, tvm.tir.Any)) else M, num_outputs=3
+    )
+    cfg.define_split(
+        "tile_x", 32 if isinstance(N, (tvm.tir.Var, tvm.tir.Any)) else N, num_outputs=3
+    )
+    cfg.define_split(
+        "tile_k", 32 if isinstance(K, (tvm.tir.Var, tvm.tir.Any)) else K, num_outputs=2
+    )
+    cfg.define_split(
+        "tile_inner",
+        32 if isinstance(M, (tvm.tir.Var, tvm.tir.Any)) else M,
+        num_outputs=2,
+        filter=lambda y: y.size[-1] <= 16,
+    )
     if cfg.is_fallback:
         _default_dense_pack_config(cfg, M, N, K)
 
-    packw_bn = cfg["tile_x"].size[-1]
-    packw_shape = (N // packw_bn, K, packw_bn)
-    packw = te.compute(
-        packw_shape, lambda z, y, x: weight[z * packw_bn + x, y], name="packed_weight"
-    )
+    if len(weight.shape) == 2:
+        packw_bn = cfg["tile_x"].size[-1]
+        packw_shape = (N // packw_bn, K, packw_bn)
+        if autotvm.GLOBAL_SCOPE.in_tuning:
+            # Directly use modified data layout placeholder.
+            packw = tvm.te.placeholder(packw_shape, weight.dtype, name="packed_weight")
+        else:
+            packw = te.compute(
+                packw_shape, lambda z, y, x: weight[z * packw_bn + x, y], name="packed_weight"
+            )
+    else:
+        packw = weight
 
     idxdiv = tvm.tir.indexdiv
     idxmod = tvm.tir.indexmod
@@ -226,7 +274,7 @@ def schedule_dense_pack(cfg, outs):
 
     def _callback(op):
         if "dense_pack" in op.tag:
-            _schedule_dense_pack_template(cfg, s, op.output(0))
+            _schedule_dense_pack_template(cfg, s, op.output(0), outs[0])
 
     traverse_inline(s, outs[0].op, _callback)
     return s
@@ -276,7 +324,19 @@ def dense_mkl(cfg, data, weight, bias=None, out_dtype=None):
 @autotvm.register_topi_schedule("dense_mkl.x86")
 def schedule_dense_mkl(_, outs):
     """Create schedule for dense_mkl"""
-    return generic.schedule_extern(outs)
+    # return generic.schedule_extern(outs)
+    s = te.create_schedule([x.op for x in outs])
+    te.schedule.AutoInlineInjective(s)
+
+    def _callback(op):
+        if "broadcast" in op.tag or "injective" in op.tag or "elemwise" in op.tag:
+            schedule_injective_from_existing(s, op.output(0))
+
+    # traverse_inline(s, outs[0].op, _callback)
+    for out in outs:
+        if "dense" not in out.op.name:
+            schedule_injective_from_existing(s, out)
+    return s
 
 
 @autotvm.register_topi_compute("dense_mkldnn.x86")
diff --git a/python/tvm/topi/x86/dense_alter_op.py b/python/tvm/topi/x86/dense_alter_op.py
new file mode 100644
index 000000000000..5e15c8bf5368
--- /dev/null
+++ b/python/tvm/topi/x86/dense_alter_op.py
@@ -0,0 +1,68 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
+"""Dense alter op functions for x86"""
+
+import tvm
+from tvm import te
+from tvm import relay
+from tvm import autotvm
+from .dense import _default_dense_pack_config
+from ..utils import get_const_tuple
+from ..nn import dense_alter_layout
+
+
+@dense_alter_layout.register(["cpu", "arm_cpu"])
+def _alter_dense_layout(attrs, inputs, tinfos, out_type):
+    target = tvm.target.Target.current(allow_none=False)
+    dispatch_ctx = autotvm.task.DispatchContext.current
+    data_tensor, weight_tensor = tinfos
+    out_dtype = out_type.dtype
+    M, K = get_const_tuple(data_tensor.shape)
+    N, _ = get_const_tuple(weight_tensor.shape)
+
+    impl, outs = relay.backend.compile_engine.select_implementation(
+        relay.op.get("nn.dense"), attrs, tinfos, out_type, target
+    )
+    workload = autotvm.task.get_workload(outs)
+    if workload:
+        cfg = dispatch_ctx.query(target, workload)
+        topi_impl = workload[0]
+        if topi_impl == "dense_pack.x86":
+            if cfg.is_fallback:
+                _default_dense_pack_config(cfg, M, N, K)
+            packw_bn = cfg["tile_x"].size[-1]
+            weight_layout = "NK%dn" % packw_bn
+            new_weight = te.placeholder(
+                (N // packw_bn, K, packw_bn),
+                dtype=weight_tensor.dtype,
+            )
+            # Relay dense doesn't have bias.
+            new_workload = autotvm.task.args_to_workload(
+                [
+                    data_tensor,
+                    new_weight,
+                    None,
+                    out_dtype,
+                ],
+                topi_impl,
+            )
+            dispatch_ctx.update(target, new_workload, cfg)
+            weight_transform = relay.layout_transform(inputs[1], "NK", weight_layout)
+            return relay.nn.contrib_dense_pack(inputs[0], weight_transform, None, out_dtype)
+
+    return None
diff --git a/python/tvm/topi/x86/depthwise_conv2d.py b/python/tvm/topi/x86/depthwise_conv2d.py
index badba1a248e9..a0225ef9e147 100644
--- a/python/tvm/topi/x86/depthwise_conv2d.py
+++ b/python/tvm/topi/x86/depthwise_conv2d.py
@@ -42,9 +42,11 @@ def _fallback_schedule(cfg, wkl):
     """
     simd_width = get_fp32_len()
 
-    HPAD, WPAD = wkl.hpad, wkl.wpad
-    HSTR, WSTR = wkl.hstride, wkl.wstride
-    out_width = (wkl.width + 2 * WPAD - wkl.wkernel) // WSTR + 1
+    pt, pl, pb, pr = wkl.padt, wkl.padl, wkl.padb, wkl.padr
+    HSTR, WSTR = wkl.stride_h, wkl.stride_w
+    dilated_kernel_w = (wkl.kernel_w - 1) * wkl.dilation_w + 1
+
+    out_width = (wkl.width - dilated_kernel_w + pl + pr) // WSTR + 1
 
     oc_bn = 1
     for bn in range(simd_width, 0, -1):
@@ -165,6 +167,7 @@ def depthwise_conv2d_NCHWc(
         ),
         strides,
         (pad_top, pad_down),
+        dilation,
         out_dtype,
     )
     if cfg.is_fallback:
diff --git a/python/tvm/topi/x86/injective.py b/python/tvm/topi/x86/injective.py
index 29f903fd4e35..6492b78d6037 100644
--- a/python/tvm/topi/x86/injective.py
+++ b/python/tvm/topi/x86/injective.py
@@ -17,6 +17,7 @@
 # pylint: disable=invalid-name
 """x86 declaration and schedules."""
 from tvm import te
+from tvm.tir import IntImm
 from ..utils import is_empty_shape
 
 
@@ -100,18 +101,20 @@ def schedule_concatenate(outs):
     def vectorize(sch, tensor, vectorize_limit):
         """Internal vectorization function for concatenate."""
         inner_axis = s[tensor].op.axis[len(s[tensor].op.axis) - 1]
-        inner_length = tensor.shape[len(tensor.shape) - 1].value
-        if inner_length <= vectorize_limit:
-            sch[tensor].vectorize(inner_axis)
-        else:
-            split_factor = 1
-            for i in range(vectorize_limit, 1, -1):
-                if inner_length % i == 0:
-                    split_factor = i
-                    break
-            if split_factor > 1:
-                _, inner_i = sch[tensor].split(inner_axis, split_factor)
-                sch[tensor].vectorize(inner_i)
+        # Check that the tensor shape is static. Otherwise skip vectorization.
+        if isinstance(tensor.shape[len(tensor.shape) - 1], IntImm):
+            inner_length = tensor.shape[len(tensor.shape) - 1].value
+            if inner_length <= vectorize_limit:
+                sch[tensor].vectorize(inner_axis)
+            else:
+                split_factor = 1
+                for i in range(vectorize_limit, 1, -1):
+                    if inner_length % i == 0:
+                        split_factor = i
+                        break
+                if split_factor > 1:
+                    _, inner_i = sch[tensor].split(inner_axis, split_factor)
+                    sch[tensor].vectorize(inner_i)
 
     outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
     x = outs[0]
diff --git a/python/tvm/topi/x86/roi_align.py b/python/tvm/topi/x86/roi_align.py
index ac2146b558f9..336a336f50e5 100644
--- a/python/tvm/topi/x86/roi_align.py
+++ b/python/tvm/topi/x86/roi_align.py
@@ -17,15 +17,17 @@
 # pylint: disable=invalid-name, no-member, too-many-locals, too-many-arguments, undefined-variable, too-many-nested-blocks, too-many-branches, too-many-statements
 """Non-maximum suppression operator for intel cpu"""
 import math
-import tvm
 
+import tvm
 from tvm.te import hybrid
 from ..tensor import full
 from ..utils import get_const_tuple
 
 
 @hybrid.script
-def roi_align_nchw_ir(data, rois, num_rois, w_pc, pos_pc, pooled_size, spatial_scale, sample_ratio):
+def roi_align_nchw_ir(
+    data, rois, num_rois, w_pc, pos_pc, pooled_size, spatial_scale, sample_ratio, mode
+):
     """Hybrid routing fo ROI align operator in NCHW layout.
 
     Parameters
@@ -57,6 +59,10 @@ def roi_align_nchw_ir(data, rois, num_rois, w_pc, pos_pc, pooled_size, spatial_s
     sample_ratio : tvm.tir.const
         Sampling ratio of ROI align, using adaptive size by default.
 
+    mode : tvm.tir.const
+        Mode of RoiAlign. A value of 0 corrensponds to b'avg', while a value of 1 corresponds to
+        b'max'.
+
     Returns
     -------
     output : tvm.te.Tensor or numpy NDArray
@@ -160,10 +166,12 @@ def roi_align_nchw_ir(data, rois, num_rois, w_pc, pos_pc, pooled_size, spatial_s
             pre_calc_index = 0
             for ph in range(pooled_size_h):
                 for pw in range(pooled_size_w):
-                    output_val = 0.0
+                    output_val = 0.0  # Avg mode
+                    if mode == 1:  # Max mode
+                        output_val = ninf("float32")
                     for iy in range(roi_bin_grid_h):
                         for ix in range(roi_bin_grid_w):
-                            output_val += (
+                            bilinear_val = (
                                 w_pc[n, pre_calc_index, 0]
                                 * data[
                                     roi_batch_index,
@@ -194,14 +202,15 @@ def roi_align_nchw_ir(data, rois, num_rois, w_pc, pos_pc, pooled_size, spatial_s
                                 ]
                             )
                             pre_calc_index += 1
-
-                    output_val /= count
-                    output[n, c, ph, pw] = output_val
-
+                            if mode == 0:  # Avg mode
+                                output_val += bilinear_val / count
+                            if mode == 1:  # Max mode
+                                output_val = max(output_val, bilinear_val)
+                        output[n, c, ph, pw] = output_val
     return output
 
 
-def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
+def roi_align_nchw(data, rois, pooled_size, spatial_scale, mode, sample_ratio=-1):
     """ROI align operator in NCHW layout.
 
     Parameters
@@ -220,6 +229,9 @@ def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
         Ratio of input feature map height (or w) to raw image height (or w). Equals the reciprocal
         of total stride in convolutional layers, which should be in range (0.0, 1.0]
 
+    mode : str
+        Mode of RoiAlign. Should be b'max' or b'avg'.
+
     sample_ratio : int
         Optional sampling ratio of ROI align, using adaptive size by default.
 
@@ -250,6 +262,21 @@ def roi_align_nchw(data, rois, pooled_size, spatial_scale, sample_ratio=-1):
     pooled_size = tvm.runtime.convert(pooled_size)
     spatial_scale = tvm.tir.const(spatial_scale, "float32")
     sample_ratio = tvm.tir.const(sample_ratio, "int32")
+    if mode in (b"avg", 0):
+        mode = tvm.tir.const(0, dtype="float32")
+    elif mode in (b"max", 1):
+        mode = tvm.tir.const(1, dtype="float32")
+    else:
+        raise ValueError(mode, "Value %s passed in for mode not supported", mode)
+
     return roi_align_nchw_ir(
-        data, rois, num_rois, w_pc_buffer, pos_pc_buffer, pooled_size, spatial_scale, sample_ratio
+        data,
+        rois,
+        num_rois,
+        w_pc_buffer,
+        pos_pc_buffer,
+        pooled_size,
+        spatial_scale,
+        sample_ratio,
+        mode,
     )
diff --git a/python/tvm/topi/x86/scatter.py b/python/tvm/topi/x86/scatter.py
index 8147d3a00135..8bb3f57e82e4 100644
--- a/python/tvm/topi/x86/scatter.py
+++ b/python/tvm/topi/x86/scatter.py
@@ -84,7 +84,7 @@ def gen_ir(data_ptr, indices_ptr, out_ptr):
             out[i] = tvm.tir.Cast(data_ptr.dtype, 0)
 
         with ib.for_range(0, fused_indices_dimension) as i:
-            with ib.for_range(0, fused_data_dimension, for_type="parallel") as j:
+            with ib.for_range(0, fused_data_dimension, kind="parallel") as j:
                 offset = fused_data_dimension
                 index = j  # This is x_M, .. x_{N-1} part of the index into out.
                 # Build up the indices[0, y_0, .. y_{K-1}], .. indices[M-1, y_0, .. y_{K-1}] part
diff --git a/python/tvm/topi/x86/sparse.py b/python/tvm/topi/x86/sparse.py
index b6291083c8c1..c6300f6701e0 100644
--- a/python/tvm/topi/x86/sparse.py
+++ b/python/tvm/topi/x86/sparse.py
@@ -28,15 +28,17 @@ def schedule_sparse_dense(outs):
 
     def _callback(op):
         simd_width = get_fp32_len()
-        if op.tag == "sparse_dense_csrmm" and op != outs[0].op:
-            (_, v_i) = s[op].op.axis
-            s[op].vectorize(v_i)
-            (y_o, y_i) = s[outs[0].op].split(s[outs[0].op].op.axis[1], 2 * simd_width)
-            s[op].compute_at(s[outs[0]], y_o)
-            s[outs[0].op].vectorize(y_i)
-        if op.tag == "sparse_dense_bsrmm":
+        if op.tag == "sparse_dense_sp_lhs_csrmm" or op.tag == "sparse_dense_sp_lhs_csrmm":
+            (y_o, y_i) = s[op].split(s[op].op.axis[1], 2)
+            fused = s[op].fuse(s[op].op.axis[0], y_o)
+            s[op].parallel(fused)
+            s[op].vectorize(y_i)
+        elif op.tag == "sparse_dense_sp_rhs_bsrmm" or op.tag == "sparse_dense_sp_rhs_bsrmm":
             y_bsrmm = op.input_tensors[0]
-            assert y_bsrmm.op.tag == "sparse_dense_bsrmm_block"
+            assert (
+                y_bsrmm.op.tag == "sparse_dense_sp_rhs_bsrmm_block"
+                or y_bsrmm.op.tag == "sparse_dense_sp_lhs_bsrmm_block"
+            )
             y_reshape = op
             (m, num_blocks, b_r) = s[y_bsrmm].op.axis
             bs_r = get_const_int(b_r.dom.extent)
diff --git a/rust/tvm-graph-rt/src/graph.rs b/rust/tvm-graph-rt/src/graph.rs
index 646a20daaf5b..83fe37ea7970 100644
--- a/rust/tvm-graph-rt/src/graph.rs
+++ b/rust/tvm-graph-rt/src/graph.rs
@@ -483,7 +483,7 @@ named! {
     )
 }
 
-/// Loads a param dict saved using `relay.save_param_dict`.
+/// Loads a param dict saved using `runtime.save_param_dict`.
 pub fn load_param_dict(bytes: &[u8]) -> Result<HashMap<String, Tensor>, GraphFormatError> {
     match parse_param_dict(bytes) {
         Ok((remaining_bytes, param_dict)) => {
diff --git a/rust/tvm-graph-rt/tests/build_model.py b/rust/tvm-graph-rt/tests/build_model.py
index d34b4403c936..969075929a42 100755
--- a/rust/tvm-graph-rt/tests/build_model.py
+++ b/rust/tvm-graph-rt/tests/build_model.py
@@ -23,7 +23,7 @@
 import numpy as np
 import tvm
 from tvm import te
-from tvm import relay
+from tvm import relay, runtime
 from tvm.relay import testing
 
 CWD = osp.dirname(osp.abspath(osp.expanduser(__file__)))
@@ -47,7 +47,7 @@ def main():
     with open(osp.join(CWD, "graph.json"), "w") as f_resnet:
         f_resnet.write(graph)
     with open(osp.join(CWD, "graph.params"), "wb") as f_params:
-        f_params.write(relay.save_param_dict(params))
+        f_params.write(runtime.save_param_dict(params))
 
 
 if __name__ == "__main__":
diff --git a/rust/tvm-graph-rt/tests/test_nn/src/build_test_graph.py b/rust/tvm-graph-rt/tests/test_nn/src/build_test_graph.py
index e743e48b01f8..0045b3b0557d 100755
--- a/rust/tvm-graph-rt/tests/test_nn/src/build_test_graph.py
+++ b/rust/tvm-graph-rt/tests/test_nn/src/build_test_graph.py
@@ -23,7 +23,7 @@
 
 import numpy as np
 import tvm
-from tvm import te
+from tvm import te, runtime
 from tvm import relay
 from tvm.relay import testing
 
@@ -49,7 +49,7 @@ def main():
         f_resnet.write(graph)
 
     with open(osp.join(out_dir, "graph.params"), "wb") as f_params:
-        f_params.write(relay.save_param_dict(params))
+        f_params.write(runtime.save_param_dict(params))
 
 
 if __name__ == "__main__":
diff --git a/rust/tvm-rt/README.md b/rust/tvm-rt/README.md
index a99eeaa578dd..58b1f8a30a39 100644
--- a/rust/tvm-rt/README.md
+++ b/rust/tvm-rt/README.md
@@ -17,8 +17,8 @@
 
 # TVM Runtime Support
 
-This crate provides an idiomatic Rust API for [TVM](https://github.com/apache/tvm) runtime.
-Currently this is tested on `1.42.0` and above.
+This crate provides an idiomatic Rust API for [TVM](https://github.com/apache/tvm) runtime,
+see [here](https://github.com/apache/tvm/blob/main/rust/tvm/README.md) for more details.
 
 ## What Does This Crate Offer?
 
diff --git a/rust/tvm-rt/src/array.rs b/rust/tvm-rt/src/array.rs
index 5abf66708f45..e8902b54f6ef 100644
--- a/rust/tvm-rt/src/array.rs
+++ b/rust/tvm-rt/src/array.rs
@@ -39,9 +39,9 @@ pub struct Array<T: IsObjectRef> {
 // TODO(@jroesch): convert to use generics instead of casting inside
 // the implementation.
 external! {
-    #[name("node.ArrayGetItem")]
+    #[name("runtime.ArrayGetItem")]
     fn array_get_item(array: ObjectRef, index: isize) -> ObjectRef;
-    #[name("node.ArraySize")]
+    #[name("runtime.ArraySize")]
     fn array_size(array: ObjectRef) -> i64;
 }
 
@@ -69,8 +69,8 @@ impl<T: IsObjectRef> Array<T> {
     pub fn from_vec(data: Vec<T>) -> Result<Array<T>> {
         let iter = data.into_iter().map(T::into_arg_value).collect();
 
-        let func = Function::get("node.Array").expect(
-            "node.Array function is not registered, this is most likely a build or linking error",
+        let func = Function::get("runtime.Array").expect(
+            "runtime.Array function is not registered, this is most likely a build or linking error",
         );
 
         // let array_data = func.invoke(iter)?;
diff --git a/rust/tvm-rt/src/lib.rs b/rust/tvm-rt/src/lib.rs
index 4b163eff9c8f..5f9ab1617378 100644
--- a/rust/tvm-rt/src/lib.rs
+++ b/rust/tvm-rt/src/lib.rs
@@ -99,7 +99,6 @@ pub mod map;
 pub mod module;
 pub mod ndarray;
 mod to_function;
-pub mod value;
 
 /// Outputs the current TVM version.
 pub fn version() -> &'static str {
@@ -112,6 +111,8 @@ pub fn version() -> &'static str {
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::{ByteArray, Context, DataType};
+    use std::{convert::TryInto, str::FromStr};
 
     #[test]
     fn print_version() {
@@ -127,4 +128,29 @@ mod tests {
             errors::NDArrayError::EmptyArray.to_string()
         );
     }
+
+    #[test]
+    fn bytearray() {
+        let w = vec![1u8, 2, 3, 4, 5];
+        let v = ByteArray::from(w.as_slice());
+        let tvm: ByteArray = RetValue::from(v).try_into().unwrap();
+        assert_eq!(
+            tvm.data(),
+            w.iter().copied().collect::<Vec<u8>>().as_slice()
+        );
+    }
+
+    #[test]
+    fn ty() {
+        let t = DataType::from_str("int32").unwrap();
+        let tvm: DataType = RetValue::from(t).try_into().unwrap();
+        assert_eq!(tvm, t);
+    }
+
+    #[test]
+    fn ctx() {
+        let c = Context::from_str("gpu").unwrap();
+        let tvm: Context = RetValue::from(c).try_into().unwrap();
+        assert_eq!(tvm, c);
+    }
 }
diff --git a/rust/tvm-rt/src/map.rs b/rust/tvm-rt/src/map.rs
index b8bfb4e5e644..d6dfaf3641b8 100644
--- a/rust/tvm-rt/src/map.rs
+++ b/rust/tvm-rt/src/map.rs
@@ -48,13 +48,13 @@ where
 // TODO(@jroesch): convert to use generics instead of casting inside
 // the implementation.
 external! {
-   #[name("node.MapSize")]
+   #[name("runtime.MapSize")]
    fn map_size(map: ObjectRef) -> i64;
-   #[name("node.MapGetItem")]
+   #[name("runtime.MapGetItem")]
    fn map_get_item(map_object: ObjectRef, key: ObjectRef) -> ObjectRef;
-   #[name("node.MapCount")]
+   #[name("runtime.MapCount")]
    fn map_count(map: ObjectRef, key: ObjectRef) -> ObjectRef;
-   #[name("node.MapItems")]
+   #[name("runtime.MapItems")]
    fn map_items(map: ObjectRef) -> Array<ObjectRef>;
 }
 
@@ -81,8 +81,8 @@ where
     V: IsObjectRef,
 {
     pub fn from_data(data: Vec<ArgValue>) -> Result<Map<K, V>> {
-        let func = Function::get("node.Map").expect(
-            "node.Map function is not registered, this is most likely a build or linking error",
+        let func = Function::get("runtime.Map").expect(
+            "runtime.Map function is not registered, this is most likely a build or linking error",
         );
 
         let map_data: ObjectPtr<Object> = func.invoke(data)?.try_into()?;
@@ -107,6 +107,18 @@ where
         let oref: ObjectRef = map_get_item(self.object.clone(), key.upcast())?;
         oref.downcast()
     }
+
+    pub fn empty() -> Self {
+        Self::from_iter(vec![].into_iter())
+    }
+
+    //(@jroesch): I don't think this is a correct implementation.
+    pub fn null() -> Self {
+        Map {
+            object: ObjectRef::null(),
+            _data: PhantomData,
+        }
+    }
 }
 
 pub struct IntoIter<K, V> {
diff --git a/rust/tvm-rt/src/module.rs b/rust/tvm-rt/src/module.rs
index c0822a5045e6..6109819939af 100644
--- a/rust/tvm-rt/src/module.rs
+++ b/rust/tvm-rt/src/module.rs
@@ -26,21 +26,24 @@ use std::{
     ptr,
 };
 
+use crate::object::Object;
+use tvm_macros::Object;
 use tvm_sys::ffi;
 
 use crate::errors::Error;
+use crate::String as TString;
 use crate::{errors, function::Function};
 
-const ENTRY_FUNC: &str = "__tvm_main__";
-
 /// Wrapper around TVM module handle which contains an entry function.
 /// The entry function can be applied to an imported module through [`entry_func`].
 ///
 /// [`entry_func`]:struct.Module.html#method.entry_func
-#[derive(Debug, Clone)]
-pub struct Module {
-    pub(crate) handle: ffi::TVMModuleHandle,
-    entry_func: Option<Function>,
+#[repr(C)]
+#[derive(Object, Debug)]
+#[ref_name = "Module"]
+#[type_key = "runtime.Module"]
+pub struct ModuleNode {
+    base: Object,
 }
 
 crate::external! {
@@ -49,21 +52,18 @@ crate::external! {
 
     #[name("runtime.ModuleLoadFromFile")]
     fn load_from_file(file_name: CString, format: CString) -> Module;
+
+    #[name("runtime.ModuleSaveToFile")]
+    fn save_to_file(module: Module, name: TString, fmt: TString);
+
+    // TODO(@jroesch): we need to refactor this
+    #[name("tvm.relay.module_export_library")]
+    fn export_library(module: Module, file_name: TString);
 }
 
 impl Module {
-    pub(crate) fn new(handle: ffi::TVMModuleHandle) -> Self {
-        Self {
-            handle,
-            entry_func: None,
-        }
-    }
-
-    pub fn entry(&mut self) -> Option<Function> {
-        if self.entry_func.is_none() {
-            self.entry_func = self.get_function(ENTRY_FUNC, false).ok();
-        }
-        self.entry_func.clone()
+    pub fn default_fn(&mut self) -> Result<Function, Error> {
+        self.get_function("default", true)
     }
 
     /// Gets a function by name from a registered module.
@@ -72,7 +72,7 @@ impl Module {
         let mut fhandle = ptr::null_mut() as ffi::TVMFunctionHandle;
 
         check_call!(ffi::TVMModGetFunction(
-            self.handle,
+            self.handle(),
             name.as_ptr() as *const c_char,
             query_import as c_int,
             &mut fhandle as *mut _
@@ -87,7 +87,7 @@ impl Module {
 
     /// Imports a dependent module such as `.ptx` for gpu.
     pub fn import_module(&self, dependent_module: Module) {
-        check_call!(ffi::TVMModImport(self.handle, dependent_module.handle))
+        check_call!(ffi::TVMModImport(self.handle(), dependent_module.handle()))
     }
 
     /// Loads a module shared library from path.
@@ -110,6 +110,14 @@ impl Module {
         Ok(module)
     }
 
+    pub fn save_to_file(&self, name: String, fmt: String) -> Result<(), Error> {
+        save_to_file(self.clone(), name.into(), fmt.into())
+    }
+
+    pub fn export_library(&self, name: String) -> Result<(), Error> {
+        export_library(self.clone(), name.into())
+    }
+
     /// Checks if a target device is enabled for a module.
     pub fn enabled(&self, target: &str) -> bool {
         let target = CString::new(target).unwrap();
@@ -118,13 +126,7 @@ impl Module {
     }
 
     /// Returns the underlying module handle.
-    pub fn handle(&self) -> ffi::TVMModuleHandle {
-        self.handle
-    }
-}
-
-impl Drop for Module {
-    fn drop(&mut self) {
-        check_call!(ffi::TVMModFree(self.handle));
+    pub unsafe fn handle(&self) -> ffi::TVMModuleHandle {
+        self.0.clone().unwrap().into_raw() as *mut _
     }
 }
diff --git a/rust/tvm-rt/src/object/object_ptr.rs b/rust/tvm-rt/src/object/object_ptr.rs
index 8df6041956b8..264d5febd103 100644
--- a/rust/tvm-rt/src/object/object_ptr.rs
+++ b/rust/tvm-rt/src/object/object_ptr.rs
@@ -267,6 +267,10 @@ impl<T: IsObject> ObjectPtr<T> {
             Err(Error::downcast("TODOget_type_key".into(), U::TYPE_KEY))
         }
     }
+
+    pub unsafe fn into_raw(self) -> *mut T {
+        self.ptr.as_ptr()
+    }
 }
 
 impl<T: IsObject> std::ops::Deref for ObjectPtr<T> {
@@ -300,7 +304,7 @@ impl<'a, T: IsObject> TryFrom<RetValue> for ObjectPtr<T> {
         use crate::ndarray::NDArrayContainer;
 
         match ret_value {
-            RetValue::ObjectHandle(handle) => {
+            RetValue::ObjectHandle(handle) | RetValue::ModuleHandle(handle) => {
                 let optr = ObjectPtr::from_raw(handle as *mut Object).ok_or(Error::Null)?;
                 debug_assert!(optr.count() >= 1);
                 optr.downcast()
@@ -329,6 +333,11 @@ impl<'a, T: IsObject> From<ObjectPtr<T>> for ArgValue<'a> {
                 assert!(!raw_ptr.is_null());
                 ArgValue::NDArrayHandle(raw_ptr)
             }
+            "runtime.Module" => {
+                let raw_ptr = ObjectPtr::leak(object_ptr) as *mut Object as *mut std::ffi::c_void;
+                assert!(!raw_ptr.is_null());
+                ArgValue::ModuleHandle(raw_ptr)
+            }
             _ => {
                 let raw_ptr = ObjectPtr::leak(object_ptr) as *mut Object as *mut std::ffi::c_void;
                 assert!(!raw_ptr.is_null());
@@ -346,7 +355,7 @@ impl<'a, T: IsObject> TryFrom<ArgValue<'a>> for ObjectPtr<T> {
         use crate::ndarray::NDArrayContainer;
 
         match arg_value {
-            ArgValue::ObjectHandle(handle) => {
+            ArgValue::ObjectHandle(handle) | ArgValue::ModuleHandle(handle) => {
                 let optr = ObjectPtr::from_raw(handle as *mut Object).ok_or(Error::Null)?;
                 debug_assert!(optr.count() >= 1);
                 optr.downcast()
diff --git a/rust/tvm-rt/src/to_function.rs b/rust/tvm-rt/src/to_function.rs
index affd81b0e7ed..c5ede7d224ce 100644
--- a/rust/tvm-rt/src/to_function.rs
+++ b/rust/tvm-rt/src/to_function.rs
@@ -255,6 +255,7 @@ impl_typed_and_to_function!(2; A, B);
 impl_typed_and_to_function!(3; A, B, C);
 impl_typed_and_to_function!(4; A, B, C, D);
 impl_typed_and_to_function!(5; A, B, C, D, E);
+impl_typed_and_to_function!(6; A, B, C, D, E, G);
 
 #[cfg(test)]
 mod tests {
diff --git a/rust/tvm-rt/src/value.rs b/rust/tvm-rt/src/value.rs
deleted file mode 100644
index b8cd190176c4..000000000000
--- a/rust/tvm-rt/src/value.rs
+++ /dev/null
@@ -1,106 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-//! This module implements [`ArgValue`] and [`RetValue`] types
-//! and their conversions needed for the types used in frontend crate.
-//! `RetValue` is the owned version of `TVMPODValue`.
-
-use std::convert::TryFrom;
-
-use crate::{ArgValue, Module, RetValue};
-use tvm_sys::{errors::ValueDowncastError, ffi::TVMModuleHandle, try_downcast};
-
-macro_rules! impl_handle_val {
-    ($type:ty, $variant:ident, $inner_type:ty, $ctor:path) => {
-        impl<'a> From<&'a $type> for ArgValue<'a> {
-            fn from(arg: &'a $type) -> Self {
-                ArgValue::$variant(arg.handle() as $inner_type)
-            }
-        }
-
-        impl<'a> From<&'a mut $type> for ArgValue<'a> {
-            fn from(arg: &'a mut $type) -> Self {
-                ArgValue::$variant(arg.handle() as $inner_type)
-            }
-        }
-
-        impl<'a> TryFrom<ArgValue<'a>> for $type {
-            type Error = ValueDowncastError;
-            fn try_from(val: ArgValue<'a>) -> Result<$type, Self::Error> {
-                try_downcast!(val -> $type, |ArgValue::$variant(val)| { $ctor(val) })
-            }
-        }
-
-        impl<'a, 'v> TryFrom<&'a ArgValue<'v>> for $type {
-            type Error = ValueDowncastError;
-            fn try_from(val: &'a ArgValue<'v>) -> Result<$type, Self::Error> {
-                try_downcast!(val -> $type, |ArgValue::$variant(val)| { $ctor(*val) })
-            }
-        }
-
-        impl From<$type> for RetValue {
-            fn from(val: $type) -> RetValue {
-                RetValue::$variant(val.handle() as $inner_type)
-            }
-        }
-
-        impl TryFrom<RetValue> for $type {
-            type Error = ValueDowncastError;
-            fn try_from(val: RetValue) -> Result<$type, Self::Error> {
-                try_downcast!(val -> $type, |RetValue::$variant(val)| { $ctor(val) })
-            }
-        }
-    };
-}
-
-impl_handle_val!(Module, ModuleHandle, TVMModuleHandle, Module::new);
-
-#[cfg(test)]
-mod tests {
-    use std::{convert::TryInto, str::FromStr};
-
-    use crate::{ByteArray, Context, DataType};
-
-    use super::*;
-
-    #[test]
-    fn bytearray() {
-        let w = vec![1u8, 2, 3, 4, 5];
-        let v = ByteArray::from(w.as_slice());
-        let tvm: ByteArray = RetValue::from(v).try_into().unwrap();
-        assert_eq!(
-            tvm.data(),
-            w.iter().copied().collect::<Vec<u8>>().as_slice()
-        );
-    }
-
-    #[test]
-    fn ty() {
-        let t = DataType::from_str("int32").unwrap();
-        let tvm: DataType = RetValue::from(t).try_into().unwrap();
-        assert_eq!(tvm, t);
-    }
-
-    #[test]
-    fn ctx() {
-        let c = Context::from_str("gpu").unwrap();
-        let tvm: Context = RetValue::from(c).try_into().unwrap();
-        assert_eq!(tvm, c);
-    }
-}
diff --git a/rust/tvm/Cargo.toml b/rust/tvm/Cargo.toml
index 29d2003b5089..9438f340f78f 100644
--- a/rust/tvm/Cargo.toml
+++ b/rust/tvm/Cargo.toml
@@ -50,9 +50,10 @@ tvm-macros = { version = "*", path = "../tvm-macros/" }
 paste = "0.1"
 mashup = "0.1"
 once_cell = "^1.3.1"
-pyo3 = { version = "0.11.1", optional = true }
+pyo3 = { version = "^0.13", optional = true }
 codespan-reporting = "0.9.5"
 structopt = { version = "0.3" }
+tracing = "^0.1"
 
 [[bin]]
 name = "tyck"
diff --git a/rust/tvm/README.md b/rust/tvm/README.md
index 26f9f1fbedfd..75fabe7d9a1b 100644
--- a/rust/tvm/README.md
+++ b/rust/tvm/README.md
@@ -15,221 +15,40 @@
 <!--- specific language governing permissions and limitations -->
 <!--- under the License. -->
 
-# TVM Runtime Frontend Support
+# TVM
 
-This crate provides an idiomatic Rust API for [TVM](https://github.com/apache/tvm) runtime frontend. Currently this requires **Nightly Rust** and tested on `rustc 1.32.0-nightly`
+This crate provides an idiomatic Rust API for [TVM](https://github.com/apache/tvm).
+The code works on **Stable Rust** and is tested against `rustc 1.47`.
 
-## What Does This Crate Offer?
-
-Here is a major workflow
-
-1. Train your **Deep Learning** model using any major framework such as [PyTorch](https://pytorch.org/), [Apache MXNet](https://mxnet.apache.org/) or [TensorFlow](https://www.tensorflow.org/)
-2. Use **TVM** to build optimized model artifacts on a supported context such as CPU, GPU, OpenCL and specialized accelerators.
-3. Deploy your models using **Rust** :heart:
-
-### Example: Deploy Image Classification from Pretrained Resnet18 on ImageNet1k
-
-Please checkout [examples/resnet](examples/resnet) for the complete end-to-end example.
-
-Here's a Python snippet for downloading and building a pretrained Resnet18 via Apache MXNet and TVM
-
-```python
-block = get_model('resnet18_v1', pretrained=True)
-
-sym, params = relay.frontend.from_mxnet(block, shape_dict)
-# compile the model
-with relay.build_config(opt_level=opt_level):
-    graph, lib, params = relay.build(
-        net, target, params=params)
-# same the model artifacts
-lib.save(os.path.join(target_dir, "deploy_lib.o"))
-cc.create_shared(os.path.join(target_dir, "deploy_lib.so"),
-                [os.path.join(target_dir, "deploy_lib.o")])
-
-with open(os.path.join(target_dir, "deploy_graph.json"), "w") as fo:
-    fo.write(graph.json())
-with open(os.path.join(target_dir,"deploy_param.params"), "wb") as fo:
-    fo.write(relay.save_param_dict(params))
-```
+You can find the API Documentation [here](https://tvm.apache.org/docs/api/rust/tvm/index.html).
 
-Now, we need to input the artifacts to create and run the *Graph Runtime* to detect our input cat image
-
-![cat](https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true)
+## What Does This Crate Offer?
 
-as demostrated in the following Rust snippet
+The goal of this crate is to provide bindings to both the TVM compiler and runtime
+APIs. First train your **Deep Learning** model using any major framework such as
+[PyTorch](https://pytorch.org/), [Apache MXNet](https://mxnet.apache.org/) or [TensorFlow](https://www.tensorflow.org/).
+Then use **TVM** to build and deploy optimized model artifacts on a supported devices such as CPU, GPU, OpenCL and specialized accelerators.
 
-```rust
-    let graph = fs::read_to_string("deploy_graph.json")?;
-    // load the built module
-    let lib = Module::load(&Path::new("deploy_lib.so"))?;
-    // get the global TVM graph runtime function
-    let runtime_create_fn = Function::get("tvm.graph_runtime.create", true).unwrap();
-    let runtime_create_fn_ret = call_packed!(
-        runtime_create_fn,
-        &graph,
-        &lib,
-        &ctx.device_type,
-        &ctx.device_id
-    )?;
-    // get graph runtime module
-    let graph_runtime_module: Module = runtime_create_fn_ret.try_into()?;
-    // get the registered `load_params` from runtime module
-    let ref load_param_fn = graph_runtime_module
-        .get_function("load_params", false)
-        .unwrap();
-    // parse parameters and convert to TVMByteArray
-    let params: Vec<u8> = fs::read("deploy_param.params")?;
-    let barr = TVMByteArray::from(&params);
-    // load the parameters
-    call_packed!(load_param_fn, &barr)?;
-    // get the set_input function
-    let ref set_input_fn = graph_runtime_module
-        .get_function("set_input", false)
-        .unwrap();
+The Rust bindings are composed of a few crates:
+- The [tvm](https://tvm.apache.org/docs/api/rust/tvm/index.html) crate which exposes Rust bindings to
+  both the compiler and runtime.
+- The [tvm_macros](https://tvm.apache.org/docs/api/rust/tvm/index.html) crate which provides macros
+  which generate unsafe boilerplate for TVM's data structures.
+- The [tvm_rt](https://tvm.apache.org/docs/api/rust/tvm_rt/index.html) crate which exposes Rust
+  bindings to the TVM runtime APIs.
+- The [tvm_sys] crate which provides raw bindings and linkage to the TVM C++ library.
+- The [tvm_graph_rt] crate which implements a version of the TVM graph runtime in Rust vs. C++.
 
-    call_packed!(set_input_fn, "data", &input)?;
-    // get `run` function from runtime module
-    let ref run_fn = graph_runtime_module.get_function("run", false).unwrap();
-    // execute the run function. Note that it has no argument
-    call_packed!(run_fn,)?;
-    // prepare to get the output
-    let output_shape = &mut [1, 1000];
-    let output = empty(output_shape, TVMContext::cpu(0), TVMType::from("float32"));
-    // get the `get_output` function from runtime module
-    let ref get_output_fn = graph_runtime_module
-        .get_function("get_output", false)
-        .unwrap();
-    // execute the get output function
-    call_packed!(get_output_fn, &0, &output)?;
-    // flatten the output as Vec<f32>
-    let output = output.to_vec::<f32>()?;
-```
+These crates have been recently refactored and reflect a much different philosophy than
+previous bindings, as well as much increased support for more of the TVM API including
+exposing all of the compiler internals.
 
-and the model correctly predicts the input image as **tiger cat**.
+These are still very much in development and should not be considered stable, but contributions
+and usage is welcome and encouraged. If you want to discuss design issues check our Discourse
+[forum](https://discuss.tvm.ai) and for bug reports check our GitHub [repository](https://github.com/apache/tvm).
 
-## Installations
+## Install
 
-Please follow TVM [installations](https://tvm.apache.org/docs/install/index.html), `export TVM_HOME=/path/to/tvm` and add `libtvm_runtime` to your `LD_LIBRARY_PATH`.
+Please follow the TVM [install](https://tvm.apache.org/docs/install/index.html) instructions, `export TVM_HOME=/path/to/tvm` and add `libtvm_runtime` to your `LD_LIBRARY_PATH`.
 
 *Note:* To run the end-to-end examples and tests, `tvm` and `topi` need to be added to your `PYTHONPATH` or it's automatic via an Anaconda environment when it is installed individually.
-
-## Supported TVM Functionalities
-
-### Use TVM to Generate Shared Library
-
-One can use the following Python snippet to generate `add_gpu.so` which add two vectors on GPU.
-
-```python
-import os
-import tvm
-from tvm import te
-from tvm.contrib import cc
-
-def test_add(target_dir):
-    if not tvm.runtime.enabled("cuda"):
-        print("skip {__file__} because cuda is not enabled...".format(__file__=__file__))
-        return
-    n = te.var("n")
-    A = te.placeholder((n,), name='A')
-    B = te.placeholder((n,), name='B')
-    C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
-    s = te.create_schedule(C.op)
-    bx, tx = s[C].split(C.op.axis[0], factor=64)
-    s[C].bind(bx, tvm.thread_axis("blockIdx.x"))
-    s[C].bind(tx, tvm.thread_axis("threadIdx.x"))
-    fadd_cuda = tvm.build(s, [A, B, C], "cuda", target_host="llvm", name="myadd")
-
-    fadd_cuda.save(os.path.join(target_dir, "add_gpu.o"))
-    fadd_cuda.imported_modules[0].save(os.path.join(target_dir, "add_gpu.ptx"))
-    cc.create_shared(os.path.join(target_dir, "add_gpu.so"),
-            [os.path.join(target_dir, "add_gpu.o")])
-
-
-if __name__ == "__main__":
-    import sys
-    if len(sys.argv) != 2:
-        sys.exit(-1)
-    test_add(sys.argv[1])
-```
-
-### Run the Generated Shared Library
-
-The following code snippet demonstrates how to load and test the generated shared library (`add_gpu.so`) in Rust.
-
-```rust
-extern crate tvm_frontend as tvm;
-
-use tvm::*;
-
-fn main() {
-    let shape = &mut [2];
-    let mut data = vec![3f32, 4.0];
-    let mut arr = empty(shape, TVMContext::gpu(0), TVMType::from("float32"));
-    arr.copy_from_buffer(data.as_mut_slice());
-    let mut ret = empty(shape, TVMContext::gpu(0), TVMType::from("float32"));
-    let mut fadd = Module::load(&Path::new("add_gpu.so")).unwrap();
-    let fadd_dep = Module::load(&Path::new("add_gpu.ptx")).unwrap();
-    assert!(fadd.enabled("gpu"));
-    fadd.import_module(fadd_dep);
-    fadd.entry();
-    function::Builder::from(&mut fadd)
-        .arg(&arr)
-        .arg(&arr)
-        .set_output(&mut ret)?
-        .invoke()
-        .unwrap();
-
-    assert_eq!(ret.to_vec::<f32>().unwrap(), vec![6f32, 8.0]);
-}
-```
-
-**Note:** it is required to instruct the `rustc` to link to the generated `add_gpu.so` in runtime, for example by
-`cargo:rustc-link-search=native=add_gpu`.
-
-See the tests and examples custom `build.rs` for more details.
-
-### Convert and Register a Rust Function as a TVM Packed Function
-
-One can use `register_global_func!` macro to convert and register a Rust
-function of type `fn(&[TVMArgValue]) -> Result<TVMRetValue>` to a global TVM **packed function** as follows
-
-```rust
-#[macro_use]
-extern crate tvm_frontend as tvm;
-use std::convert::TryInto;
-use tvm::*;
-
-fn main() {
-    register_global_func! {
-        fn sum(args: &[TVMArgValue]) -> Result<TVMRetValue> {
-            let mut ret = 0f32;
-            let shape = &mut [2];
-            for arg in args.iter() {
-                let e = empty(shape, TVMContext::cpu(0), TVMType::from("float32"));
-                let arg: NDArray = arg.try_into()?;
-                let arr = arg.copy_to_ndarray(e).unwrap();
-                let rnd: ArrayD<f32> = ArrayD::try_from(&arr).unwrap();
-                ret += rnd.scalar_sum();
-            }
-            let ret_val = TVMRetValue::from(&ret);
-            Ok(ret_val)
-        }
-    }
-
-    let shape = &mut [2];
-    let mut data = vec![3f32, 4.0];
-    let mut arr = empty(shape, TVMContext::cpu(0), TVMType::from("float32"));
-    arr.copy_from_buffer(data.as_mut_slice());
-    let mut registered = function::Builder::default();
-    let ret: f64 = registered
-        .get_function("sum", true)
-        .arg(&arr)
-        .arg(&arr)
-        .invoke()
-        .unwrap()
-        .try_into()
-        .unwrap();
-
-    assert_eq!(ret, 14f64);
-}
-```
diff --git a/rust/tvm/examples/resnet/src/build_resnet.py b/rust/tvm/examples/resnet/src/build_resnet.py
index 03ac611a191a..fdacb5bb1fca 100644
--- a/rust/tvm/examples/resnet/src/build_resnet.py
+++ b/rust/tvm/examples/resnet/src/build_resnet.py
@@ -27,7 +27,7 @@
 
 import tvm
 from tvm import te
-from tvm import relay
+from tvm import relay, runtime
 from tvm.relay import testing
 from tvm.contrib import graph_runtime, cc
 from PIL import Image
@@ -88,7 +88,7 @@ def build(target_dir):
         fo.write(graph)
 
     with open(osp.join(target_dir, "deploy_param.params"), "wb") as fo:
-        fo.write(relay.save_param_dict(params))
+        fo.write(runtime.save_param_dict(params))
 
 
 def download_img_labels():
diff --git a/rust/tvm/src/compiler/graph_rt.rs b/rust/tvm/src/compiler/graph_rt.rs
new file mode 100644
index 000000000000..6b5873398cab
--- /dev/null
+++ b/rust/tvm/src/compiler/graph_rt.rs
@@ -0,0 +1,124 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+use std::convert::TryInto;
+use std::io::Read;
+use std::path::Path;
+
+use once_cell::sync::Lazy;
+use thiserror::Error;
+
+use crate::ir::IRModule;
+use crate::python;
+use crate::runtime::{map::Map, Function, Module as RtModule, NDArray, String};
+
+#[derive(Error, Debug)]
+pub enum Error {
+    #[error("{0}")]
+    IO(#[from] std::io::Error),
+    #[error("{0}")]
+    TVM(#[from] crate::errors::Error),
+}
+
+static TVM_BUILD: Lazy<Function> = Lazy::new(|| {
+    python::import("tvm").unwrap();
+    python::import("tvm.relay").unwrap();
+    Function::get("tvm.relay.build").unwrap()
+});
+
+fn _compile_module(
+    module: IRModule,
+    target: String,
+    target_host: String,
+    params: Map<String, NDArray>,
+    module_name: String,
+) -> Result<RtModule, Error> {
+    // The RAW API is Fn(IRModule, String, String, Map<String, NDArray>, String);
+    let module = TVM_BUILD.invoke(vec![
+        module.into(),
+        target.into(),
+        target_host.into(),
+        params.into(),
+        module_name.into(),
+    ])?;
+    let module: RtModule = module.try_into().unwrap();
+    Ok(module)
+}
+
+#[derive(Debug)]
+pub struct CompilerConfig {
+    target: Option<String>,
+    target_host: Option<String>,
+    params: Map<String, NDArray>,
+    module_name: Option<String>,
+}
+
+impl Default for CompilerConfig {
+    fn default() -> Self {
+        CompilerConfig {
+            target: None,
+            target_host: None,
+            params: Map::empty(),
+            module_name: None,
+        }
+    }
+}
+
+/// Compile a module from a configuration and IRModule.
+///
+/// # Arguments
+///
+/// * `config` - The configuration for the compiler.
+/// * `module` - The IRModule to compile.
+pub fn compile_module(config: CompilerConfig, module: IRModule) -> Result<RtModule, Error> {
+    let target = config.target.unwrap_or("llvm".into());
+    _compile_module(
+        module,
+        target,
+        "llvm".into(),
+        Map::<String, NDArray>::empty(),
+        "default".into(),
+    )
+}
+
+/// Compile an IRModule on disk and output a runtime module to disk.
+///
+/// # Arguments
+/// * `config` - The configuration for the compiler.
+/// * `ir_mod_path` - The path the serialized IRModule.
+//
+/// * `output_rt_mod_path` - The path to the output runtime module.
+pub fn compile_from_disk<P1, P2>(
+    config: CompilerConfig,
+    ir_mod_path: P1,
+    output_rt_mod_path: P2,
+) -> Result<(), Error>
+where
+    P1: AsRef<Path>,
+    P2: AsRef<Path>,
+{
+    let mut input_file = std::fs::File::open(ir_mod_path.as_ref())?;
+    let mut input_module_text = std::string::String::new();
+    input_file.read_to_string(&mut input_module_text)?;
+    let input_module = IRModule::parse("name", input_module_text)?;
+    let rt_module = compile_module(config, input_module)?;
+    let output_path_str = output_rt_mod_path.as_ref().display().to_string();
+    rt_module.export_library(output_path_str)?;
+    Ok(())
+}
diff --git a/rust/tvm/src/compiler/mod.rs b/rust/tvm/src/compiler/mod.rs
new file mode 100644
index 000000000000..ed8b47edbad4
--- /dev/null
+++ b/rust/tvm/src/compiler/mod.rs
@@ -0,0 +1,20 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+pub mod graph_rt;
diff --git a/rust/tvm/src/ir/diagnostics/mod.rs b/rust/tvm/src/ir/diagnostics/mod.rs
index 8bcdf8f51e60..182ffd4d9081 100644
--- a/rust/tvm/src/ir/diagnostics/mod.rs
+++ b/rust/tvm/src/ir/diagnostics/mod.rs
@@ -35,7 +35,7 @@ use tvm_macros::{external, Object};
 pub mod codespan;
 
 external! {
-    #[name("node.ArrayGetItem")]
+    #[name("runtime.ArrayGetItem")]
     fn get_renderer() -> DiagnosticRenderer;
 
     #[name("diagnostics.DiagnosticRenderer")]
diff --git a/rust/tvm/src/ir/expr.rs b/rust/tvm/src/ir/expr.rs
index 653169def3a4..03d8a4920718 100644
--- a/rust/tvm/src/ir/expr.rs
+++ b/rust/tvm/src/ir/expr.rs
@@ -32,12 +32,14 @@ use super::span::Span;
 #[type_key = "Expr"]
 pub struct BaseExprNode {
     pub base: Object,
+    pub span: Span,
 }
 
 impl BaseExprNode {
-    pub fn base<T: IsObject>() -> BaseExprNode {
+    pub fn base<T: IsObject>(span: Span) -> BaseExprNode {
         BaseExprNode {
             base: Object::base::<T>(),
+            span,
         }
     }
 }
@@ -52,9 +54,9 @@ pub struct PrimExprNode {
 }
 
 impl PrimExprNode {
-    pub fn base<T: IsObject>(datatype: DataType) -> PrimExprNode {
+    pub fn base<T: IsObject>(datatype: DataType, span: Span) -> PrimExprNode {
         PrimExprNode {
-            base: BaseExprNode::base::<T>(),
+            base: BaseExprNode::base::<T>(span),
             datatype,
         }
     }
@@ -70,9 +72,9 @@ pub struct GlobalVarNode {
 }
 
 impl GlobalVar {
-    pub fn new(name_hint: String, _span: Span) -> GlobalVar {
+    pub fn new(name_hint: String, span: Span) -> GlobalVar {
         let node = GlobalVarNode {
-            base: relay::ExprNode::base::<GlobalVarNode>(),
+            base: relay::ExprNode::base::<GlobalVarNode>(span),
             name_hint: name_hint.into(),
         };
         GlobalVar(Some(ObjectPtr::new(node)))
diff --git a/rust/tvm/src/ir/function.rs b/rust/tvm/src/ir/function.rs
index 14c00ea02bf6..43aca869f385 100644
--- a/rust/tvm/src/ir/function.rs
+++ b/rust/tvm/src/ir/function.rs
@@ -17,12 +17,12 @@
  * under the License.
  */
 
-use crate::ir::relay::ExprNode;
-use crate::runtime::{IsObject, IsObjectRef, ObjectRef};
-
 use tvm_macros::Object;
 
-// Define Calling Convention.
+use super::span::Span;
+
+use crate::ir::relay::ExprNode;
+use crate::runtime::{IsObject, IsObjectRef, ObjectRef};
 
 // TODO(@jroesch): define DictAttrs
 pub type DictAttrs = ObjectRef;
@@ -39,7 +39,7 @@ pub struct BaseFuncNode {
 impl BaseFuncNode {
     pub fn base<T: IsObject>() -> BaseFuncNode {
         BaseFuncNode {
-            base: ExprNode::base::<T>(),
+            base: ExprNode::base::<T>(Span::null()),
             attrs: <ObjectRef as IsObjectRef>::null(),
         }
     }
diff --git a/rust/tvm/src/ir/module.rs b/rust/tvm/src/ir/module.rs
index a09f70dc25b9..513a906f6db4 100644
--- a/rust/tvm/src/ir/module.rs
+++ b/rust/tvm/src/ir/module.rs
@@ -279,8 +279,8 @@ mod tests {
         let name = GlobalTypeVar::new("my_type", TypeKind::Type, Span::null());
         let type_data = TypeData::new(name.clone(), vec![], vec![], Span::null());
         module.add_def(name.clone(), type_data, true)?;
-        let by_gtv = module.lookup_def(name)?;
-        let by_gv = module.lookup_def_str("my_type")?;
+        let _by_gtv = module.lookup_def(name)?;
+        let _by_gv = module.lookup_def_str("my_type")?;
         Ok(())
     }
 
diff --git a/rust/tvm/src/ir/relay/mod.rs b/rust/tvm/src/ir/relay/mod.rs
index 9d2983237acb..f43967f28d60 100644
--- a/rust/tvm/src/ir/relay/mod.rs
+++ b/rust/tvm/src/ir/relay/mod.rs
@@ -23,7 +23,7 @@ use super::attrs::Attrs;
 use super::expr::BaseExprNode;
 use super::function::BaseFuncNode;
 use super::span::Span;
-use super::ty::{Type, TypeNode};
+use super::ty::Type;
 
 use tvm_macros::Object;
 use tvm_rt::NDArray;
@@ -39,19 +39,14 @@ pub mod attrs;
 #[type_key = "RelayExpr"]
 pub struct ExprNode {
     pub base: BaseExprNode,
-    pub span: ObjectRef,
     pub checked_type: Type,
 }
 
 impl ExprNode {
-    pub fn base<T: IsObject>() -> ExprNode {
+    pub fn base<T: IsObject>(span: Span) -> ExprNode {
         ExprNode {
-            base: BaseExprNode::base::<T>(),
-            span: ObjectRef::null(),
-            checked_type: Type::from(TypeNode {
-                base: Object::base::<TypeNode>(),
-                span: Span::null(),
-            }),
+            base: BaseExprNode::base::<T>(span.clone()),
+            checked_type: Type::null(),
         }
     }
 }
@@ -85,9 +80,9 @@ pub struct ConstantNode {
 }
 
 impl Constant {
-    pub fn new(data: NDArray, _span: ObjectRef) -> Constant {
+    pub fn new(data: NDArray, span: Span) -> Constant {
         let node = ConstantNode {
-            base: ExprNode::base::<ConstantNode>(),
+            base: ExprNode::base::<ConstantNode>(span),
             data: data,
         };
         Constant(Some(ObjectPtr::new(node)))
@@ -104,9 +99,9 @@ pub struct TupleNode {
 }
 
 impl Tuple {
-    pub fn new(fields: Array<Expr>, _span: ObjectRef) -> Tuple {
+    pub fn new(fields: Array<Expr>, span: Span) -> Tuple {
         let node = TupleNode {
-            base: ExprNode::base::<TupleNode>(),
+            base: ExprNode::base::<TupleNode>(span),
             fields,
         };
         Tuple(Some(ObjectPtr::new(node)))
@@ -124,9 +119,9 @@ pub struct VarNode {
 }
 
 impl Var {
-    pub fn new(name_hint: String, type_annotation: Type, _span: Span) -> Var {
+    pub fn new(name_hint: String, type_annotation: Type, span: Span) -> Var {
         let node = VarNode {
-            base: ExprNode::base::<VarNode>(),
+            base: ExprNode::base::<VarNode>(span),
             vid: Id::new(name_hint.into()),
             type_annotation: type_annotation,
         };
@@ -165,10 +160,10 @@ impl Call {
         args: Array<Expr>,
         attrs: Attrs,
         type_args: Array<Type>,
-        _span: ObjectRef,
+        span: Span,
     ) -> Call {
         let node = CallNode {
-            base: ExprNode::base::<VarNode>(),
+            base: ExprNode::base::<VarNode>(span),
             op: op,
             args: args,
             attrs: attrs,
@@ -190,9 +185,9 @@ pub struct LetNode {
 }
 
 impl Let {
-    pub fn new(var: Var, value: Expr, body: Expr, _span: ObjectRef) -> Let {
+    pub fn new(var: Var, value: Expr, body: Expr, span: Span) -> Let {
         let node = LetNode {
-            base: ExprNode::base::<LetNode>(),
+            base: ExprNode::base::<LetNode>(span),
             var,
             value,
             body,
@@ -213,9 +208,9 @@ pub struct IfNode {
 }
 
 impl If {
-    pub fn new(cond: Expr, true_branch: Expr, false_branch: Expr, _span: ObjectRef) -> If {
+    pub fn new(cond: Expr, true_branch: Expr, false_branch: Expr, span: Span) -> If {
         let node = IfNode {
-            base: ExprNode::base::<IfNode>(),
+            base: ExprNode::base::<IfNode>(span),
             cond,
             true_branch,
             false_branch,
@@ -235,9 +230,9 @@ pub struct TupleGetItemNode {
 }
 
 impl TupleGetItem {
-    pub fn new(tuple: Expr, index: i32, _span: ObjectRef) -> TupleGetItem {
+    pub fn new(tuple: Expr, index: i32, span: Span) -> TupleGetItem {
         let node = TupleGetItemNode {
-            base: ExprNode::base::<TupleGetItemNode>(),
+            base: ExprNode::base::<TupleGetItemNode>(span),
             tuple,
             index,
         };
@@ -255,9 +250,9 @@ pub struct RefCreateNode {
 }
 
 impl RefCreate {
-    pub fn new(value: Expr, _span: ObjectRef) -> RefCreate {
+    pub fn new(value: Expr, span: Span) -> RefCreate {
         let node = RefCreateNode {
-            base: ExprNode::base::<RefCreateNode>(),
+            base: ExprNode::base::<RefCreateNode>(span),
             value,
         };
         RefCreate(Some(ObjectPtr::new(node)))
@@ -274,9 +269,9 @@ pub struct RefReadNode {
 }
 
 impl RefRead {
-    pub fn new(ref_value: Expr, _span: ObjectRef) -> RefRead {
+    pub fn new(ref_value: Expr, span: Span) -> RefRead {
         let node = RefReadNode {
-            base: ExprNode::base::<RefReadNode>(),
+            base: ExprNode::base::<RefReadNode>(span),
             ref_value,
         };
         RefRead(Some(ObjectPtr::new(node)))
@@ -294,9 +289,9 @@ pub struct RefWriteNode {
 }
 
 impl RefWrite {
-    pub fn new(ref_value: Expr, value: Expr, _span: ObjectRef) -> RefWrite {
+    pub fn new(ref_value: Expr, value: Expr, span: Span) -> RefWrite {
         let node = RefWriteNode {
-            base: ExprNode::base::<RefWriteNode>(),
+            base: ExprNode::base::<RefWriteNode>(span),
             ref_value,
             value,
         };
@@ -316,9 +311,9 @@ pub struct ConstructorNode {
 }
 
 impl Constructor {
-    pub fn new(name_hint: String, inputs: Array<Type>, tag: i32, _span: ObjectRef) -> Constructor {
+    pub fn new(name_hint: String, inputs: Array<Type>, tag: i32, span: Span) -> Constructor {
         let node = ConstructorNode {
-            base: ExprNode::base::<ConstructorNode>(),
+            base: ExprNode::base::<ConstructorNode>(span),
             name_hint,
             inputs,
             tag,
@@ -335,14 +330,14 @@ impl Constructor {
 #[type_key = "relay.Pattern"]
 pub struct PatternNode {
     pub base: Object,
-    pub span: ObjectRef,
+    pub span: Span,
 }
 
 impl PatternNode {
-    pub fn base<T: IsObject>() -> PatternNode {
+    pub fn base<T: IsObject>(span: Span) -> PatternNode {
         PatternNode {
             base: Object::base::<T>(),
-            span: ObjectRef::null(),
+            span: span,
         }
     }
 }
@@ -356,9 +351,9 @@ pub struct PatternWildcardNode {
 }
 
 impl PatternWildcard {
-    pub fn new(_span: ObjectRef) -> PatternWildcard {
+    pub fn new(span: Span) -> PatternWildcard {
         let node = PatternWildcardNode {
-            base: PatternNode::base::<PatternWildcardNode>(),
+            base: PatternNode::base::<PatternWildcardNode>(span),
         };
         PatternWildcard(Some(ObjectPtr::new(node)))
     }
@@ -374,9 +369,9 @@ pub struct PatternVarNode {
 }
 
 impl PatternVar {
-    pub fn new(var: Var, _span: ObjectRef) -> PatternVar {
+    pub fn new(var: Var, span: Span) -> PatternVar {
         let node = PatternVarNode {
-            base: PatternNode::base::<PatternVarNode>(),
+            base: PatternNode::base::<PatternVarNode>(span),
             var: var,
         };
         PatternVar(Some(ObjectPtr::new(node)))
@@ -397,10 +392,10 @@ impl PatternConstructor {
     pub fn new(
         constructor: Constructor,
         patterns: Array<Pattern>,
-        _span: ObjectRef,
+        span: Span,
     ) -> PatternConstructor {
         let node = PatternConstructorNode {
-            base: PatternNode::base::<PatternConstructorNode>(),
+            base: PatternNode::base::<PatternConstructorNode>(span),
             constructor,
             patterns,
         };
@@ -418,9 +413,9 @@ pub struct PatternTupleNode {
 }
 
 impl PatternTuple {
-    pub fn new(patterns: Array<Pattern>, _span: ObjectRef) -> PatternTuple {
+    pub fn new(patterns: Array<Pattern>, span: Span) -> PatternTuple {
         let node = PatternTupleNode {
-            base: PatternNode::base::<PatternTupleNode>(),
+            base: PatternNode::base::<PatternTupleNode>(span),
             patterns,
         };
         PatternTuple(Some(ObjectPtr::new(node)))
@@ -438,7 +433,7 @@ pub struct ClauseNode {
 }
 
 impl Clause {
-    pub fn new(lhs: Pattern, rhs: Expr, _span: ObjectRef) -> Clause {
+    pub fn new(lhs: Pattern, rhs: Expr, _span: Span) -> Clause {
         let node = ClauseNode {
             base: Object::base::<ClauseNode>(),
             lhs,
@@ -460,9 +455,9 @@ pub struct MatchNode {
 }
 
 impl Match {
-    pub fn new(data: Expr, clauses: Array<Clause>, complete: bool, _span: ObjectRef) -> Match {
+    pub fn new(data: Expr, clauses: Array<Clause>, complete: bool, span: Span) -> Match {
         let node = MatchNode {
-            base: ExprNode::base::<MatchNode>(),
+            base: ExprNode::base::<MatchNode>(span),
             data,
             clauses,
             complete,
diff --git a/rust/tvm/src/ir/tir.rs b/rust/tvm/src/ir/tir.rs
index ccbe30c95820..dcbec520d3b6 100644
--- a/rust/tvm/src/ir/tir.rs
+++ b/rust/tvm/src/ir/tir.rs
@@ -18,7 +18,9 @@
  */
 
 use super::{PrimExpr, PrimExprNode};
-use crate::runtime::String as TVMString;
+
+use crate::ir::span::Span;
+use crate::runtime::{IsObjectRef, String as TVMString};
 use crate::DataType;
 
 use tvm_macros::Object;
@@ -36,7 +38,7 @@ macro_rules! define_node {
 
         impl $name {
             pub fn new(datatype: DataType, $($id : $t,)*) -> $name {
-                let base = PrimExprNode::base::<$node>(datatype);
+                let base = PrimExprNode::base::<$node>(datatype, Span::null());
                 let node = $node { base, $($id),* };
                 node.into()
             }
@@ -56,7 +58,6 @@ impl From<i32> for IntImm {
 
 impl From<i32> for PrimExpr {
     fn from(i: i32) -> PrimExpr {
-        use crate::runtime::IsObjectRef;
         IntImm::from(i).upcast()
     }
 }
diff --git a/rust/tvm/src/ir/ty.rs b/rust/tvm/src/ir/ty.rs
index f7c52b51f332..83fdbfeb66aa 100644
--- a/rust/tvm/src/ir/ty.rs
+++ b/rust/tvm/src/ir/ty.rs
@@ -23,7 +23,7 @@ use tvm_rt::{array::Array, DataType};
 use crate::ir::relay::Constructor;
 use crate::ir::span::Span;
 use crate::ir::PrimExpr;
-use crate::runtime::{string::String as TString, IsObject, Object, ObjectPtr};
+use crate::runtime::{string::String as TString, IsObject, IsObjectRef, Object, ObjectPtr};
 
 #[repr(C)]
 #[derive(Object, Debug)]
@@ -147,8 +147,17 @@ pub struct TupleTypeNode {
 }
 
 impl TupleType {
+    // todo add coercion
+    pub fn new(fields: Vec<Type>, span: Span) -> Self {
+        let node = TupleTypeNode {
+            base: TypeNode::base::<TupleTypeNode>(span),
+            fields: Array::from_vec(fields).unwrap(),
+        };
+        ObjectPtr::new(node).into()
+    }
+
     pub fn empty() -> TupleType {
-        todo!()
+        TupleType::new(vec![], Span::null())
     }
 }
 
@@ -236,7 +245,13 @@ impl TensorType {
         };
         ObjectPtr::new(node).into()
     }
+
+    pub fn static_sh(shape: Vec<i32>, dtype: DataType, span: Span) -> TensorType {
+        let sh = Array::from_vec(shape.into_iter().map(Into::into).collect()).unwrap();
+        Self::new(sh, dtype, span)
+    }
 }
+
 // TODO(@jroesch): implement these in future.
 //
 // using TypeCall = tvm::TypeCall;
diff --git a/rust/tvm/src/lib.rs b/rust/tvm/src/lib.rs
index e86420eb70c9..caae07775d21 100644
--- a/rust/tvm/src/lib.rs
+++ b/rust/tvm/src/lib.rs
@@ -39,7 +39,9 @@ pub use tvm_rt::errors;
 pub use tvm_rt::function;
 pub use tvm_rt::module;
 pub use tvm_rt::ndarray;
-pub use tvm_rt::value;
+
+#[cfg(feature = "python")]
+pub mod compiler;
 pub mod ir;
 #[cfg(feature = "python")]
 pub mod python;
diff --git a/rust/tvm/src/python.rs b/rust/tvm/src/python.rs
index 89558af733b3..c224fb4db372 100644
--- a/rust/tvm/src/python.rs
+++ b/rust/tvm/src/python.rs
@@ -29,6 +29,8 @@ use pyo3::prelude::*;
 pub fn load() -> Result<String, ()> {
     let gil = Python::acquire_gil();
     let py = gil.python();
+    // let main_mod = initialize();
+    //let main_mod = main_mod.as_ref(py);
     load_python_tvm_(py).map_err(|e| {
         // We can't display Python exceptions via std::fmt::Display,
         // so print the error here manually.
@@ -36,25 +38,33 @@ pub fn load() -> Result<String, ()> {
     })
 }
 
-// const TVMC_CODE: &'static str = include_str!("tvmc.py");
+pub fn import(mod_to_import: &str) -> PyResult<()> {
+    let gil = Python::acquire_gil();
+    let py = gil.python();
+    import_python(py, mod_to_import)?;
+    Ok(())
+}
+
+fn import_python<'p, 'b: 'p>(py: Python<'p>, to_import: &'b str) -> PyResult<&'p PyModule> {
+    let imported_mod = py.import(to_import)?;
+    Ok(imported_mod)
+}
 
 fn load_python_tvm_(py: Python) -> PyResult<String> {
-    let sys = py.import("tvm")?;
-    let version: String = sys.get("__version__")?.extract()?;
-    // py.run(TVMC_CODE, None, None)?;
+    let imported_mod = import_python(py, "tvm")?;
+    let version: String = imported_mod.get("__version__")?.extract()?;
     Ok(version)
 }
 
 #[cfg(test)]
 mod tests {
-    use super::load_python_tvm_;
+    use super::*;
     use anyhow::Result;
-    use pyo3::prelude::*;
 
     #[ignore]
     #[test]
     fn test_run() -> Result<()> {
-        load_python_tvm_(Python::acquire_gil().python()).unwrap();
+        load().unwrap();
         Ok(())
     }
 }
diff --git a/rust/tvm/src/runtime/graph_rt.rs b/rust/tvm/src/runtime/graph_rt.rs
index 8b26ebb4ca22..fcc41aca560f 100644
--- a/rust/tvm/src/runtime/graph_rt.rs
+++ b/rust/tvm/src/runtime/graph_rt.rs
@@ -34,13 +34,23 @@ pub struct GraphRt {
 }
 
 impl GraphRt {
+    /// Create a graph runtime directly from a runtime module.
+    pub fn from_module(module: Module, ctx: Context) -> Result<GraphRt> {
+        let default: Box<dyn Fn(Context) -> Result<Module>> =
+            module.get_function("default", false)?.into();
+
+        Ok(Self {
+            module: default(ctx)?,
+        })
+    }
+
     /// Create a graph runtime from the deprecated graph, lib, ctx triple.
     pub fn create_from_parts(graph: &str, lib: Module, ctx: Context) -> Result<Self> {
         let runtime_create_fn = Function::get("tvm.graph_runtime.create").unwrap();
 
         let runtime_create_fn_ret = runtime_create_fn.invoke(vec![
             graph.into(),
-            (&lib).into(),
+            lib.into(),
             (&ctx.device_type).into(),
             // NOTE you must pass the device id in as i32 because that's what TVM expects
             (ctx.device_id as i32).into(),
diff --git a/rust/tvm/tests/basics/src/main.rs b/rust/tvm/tests/basics/src/main.rs
index e4249a491746..450ab48dc1b2 100644
--- a/rust/tvm/tests/basics/src/main.rs
+++ b/rust/tvm/tests/basics/src/main.rs
@@ -30,6 +30,7 @@ fn main() {
     } else {
         (Context::gpu(0), "gpu")
     };
+
     let dtype = DataType::from_str("float32").unwrap();
     let mut arr = NDArray::empty(shape, ctx, dtype);
     arr.copy_from_buffer(data.as_mut_slice());
@@ -38,11 +39,13 @@ fn main() {
     if !fadd.enabled(ctx_name) {
         return;
     }
+
     if cfg!(feature = "gpu") {
         fadd.import_module(Module::load(&concat!(env!("OUT_DIR"), "/test_add.ptx")).unwrap());
     }
 
-    fadd.entry()
+    // todo(@jroesch): fix the entry_name
+    fadd.get_function("__tvm_main__", false)
         .expect("module must have entry point")
         .invoke(vec![(&arr).into(), (&arr).into(), (&ret).into()])
         .unwrap();
diff --git a/rust/tvm/tests/basics/src/tvm_add.py b/rust/tvm/tests/basics/src/tvm_add.py
index b9672fbf4aaf..3c1fc64d3e36 100755
--- a/rust/tvm/tests/basics/src/tvm_add.py
+++ b/rust/tvm/tests/basics/src/tvm_add.py
@@ -37,7 +37,6 @@ def main(target, out_dir):
         s[C].bind(tx, te.thread_axis("threadIdx.x"))
 
     fadd = tvm.build(s, [A, B, C], target, target_host="llvm", name="myadd")
-
     fadd.save(osp.join(out_dir, "test_add.o"))
     if target == "cuda":
         fadd.imported_modules[0].save(osp.join(out_dir, "test_add.ptx"))
diff --git a/src/arith/canonical_simplify.cc b/src/arith/canonical_simplify.cc
index d0a0702a0fb0..ba549959ac98 100644
--- a/src/arith/canonical_simplify.cc
+++ b/src/arith/canonical_simplify.cc
@@ -77,6 +77,27 @@ inline PrimExpr DivImpl(PrimExpr a, PrimExpr b, DivMode mode) {
   }
 }
 
+/*!
+ * \brief check if value fits in dtype
+ * \param value The value to be analyzed
+ * \param dtype The target dtype
+ * \param analyzer The analyzer
+ * \return whether value fits in dtype
+ */
+bool CastIsSafe(DataType dtype, PrimExpr value, Analyzer* analyzer) {
+  if (!IsIndexType(dtype)) {
+    return false;
+  }
+  ConstIntBound bound = analyzer->const_int_bound(value);
+  int64_t ubound = Downcast<IntImm>(max_value(dtype))->value;
+  int64_t lbound = Downcast<IntImm>(min_value(dtype))->value;
+  if (value.dtype().bits() <= dtype.bits() ||  // upcast is safe
+      (bound->max_value <= ubound && bound->min_value >= lbound)) {
+    return true;
+  }
+  return false;
+}
+
 /*!
  * \brief Internal "Split normal form" of expression.
  *
@@ -128,6 +149,58 @@ class SplitExprNode : public CanonicalExprNode {
 
   void MulToSelf(int64_t scale) { this->scale *= scale; }
 
+  /*!
+   * \brief check if cast can be pushed to sub-expressions
+   * \param dtype The target datatype
+   * \param analyzer The analyzer
+   * \return whether the cast can be safely pushed to children
+   */
+  bool CanPushCastToChildren(DataType dtype, Analyzer* analyzer) const {
+    // cast(dtype, index % upper_factor / lower_factor * scale) ==
+    // cast(dtype, index) % upper_factor / lower_factor * scale
+    // iff it is an upcast (dtype.bits >= self.dtype.bits) or all of
+    // its intermediate results fit in the range of dtype
+    if (dtype.bits() >= this->dtype.bits()) {
+      return true;  // upcast is safe
+    }
+    PrimExpr res = this->index;
+    if (this->scale == 0) {
+      return true;
+    }
+    if (!CastIsSafe(dtype, res, analyzer)) {
+      return false;
+    }
+    if (this->upper_factor != SplitExprNode::kPosInf) {
+      res = ModImpl(res, make_const(this->dtype, this->upper_factor), div_mode);
+      if (!CastIsSafe(dtype, res, analyzer)) {
+        return false;
+      }
+    }
+    if (this->lower_factor != 1) {
+      res = DivImpl(res, make_const(this->dtype, this->lower_factor), div_mode);
+      if (!CastIsSafe(dtype, res, analyzer)) {
+        return false;
+      }
+    }
+    if (this->scale != 1) {
+      ICHECK(!this->dtype.is_uint() || this->scale > 0);
+      res = res * make_const(this->dtype, this->scale);
+      if (!CastIsSafe(dtype, res, analyzer)) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  /*!
+   * \brief self = cast(dtype, self)
+   * \param dtype The target datatype
+   */
+  void PushCastToChildren(DataType dtype) {
+    this->index = cast(dtype, this->index);
+    this->dtype = dtype;
+  }
+
   inline bool IndexEqual(const SplitExpr& other) const;
   inline bool DivModeCompatibleTo(DivMode mode) const;
 
@@ -255,6 +328,69 @@ class SumExprNode : public CanonicalExprNode {
 
   void AddToSelf(const SumExpr& other, int64_t scale);
 
+  /*!
+   * \brief check if cast can be pushed to sub-expressions
+   * \param dtype The target datatype
+   * \param analyzer The analyzer
+   * \return whether the cast can be safely pushed to children
+   */
+  bool CanPushCastToChildren(DataType dtype, Analyzer* analyzer) const {
+    // cast(dtype, arg_1 + arg_2 + ... arg_n) ==
+    // cast(dtype, arg_1) + ... + cast(dtype, arg_n)
+    // iff it is an upcast (dtype.bits >= self.dtype.bits) or all of
+    // its intermediate results fit in the range of dtype
+    if (dtype.bits() >= this->dtype.bits()) {
+      return true;  // upcast is safe
+    }
+    PrimExpr res = make_const(dtype, 0);
+    for (size_t i = 0; i < args.size(); ++i) {
+      if (args[i]->scale > 0) {
+        res = res + args[i]->Normalize();
+        if (!CastIsSafe(dtype, res, analyzer)) {
+          return false;
+        }
+      }
+    }
+    if (base > 0) {
+      res = res + make_const(dtype, base);
+      if (!CastIsSafe(dtype, res, analyzer)) {
+        return false;
+      }
+    }
+    // negative scales follows using sub.
+    for (size_t i = 0; i < args.size(); ++i) {
+      if (args[i]->scale < 0) {
+        res = res - args[i]->NormalizeWithScale(-1);
+        if (!CastIsSafe(dtype, res, analyzer)) {
+          return false;
+        }
+      }
+    }
+    if (base < 0) {
+      res = res - make_const(dtype, -base);
+      if (!CastIsSafe(dtype, res, analyzer)) {
+        return false;
+      }
+    }
+    for (const auto& arg : args) {
+      if (!arg->CanPushCastToChildren(dtype, analyzer)) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  /*!
+   * \brief self = cast(dtype, self)
+   * \param dtype The target datatype
+   */
+  void PushCastToChildren(DataType dtype) {
+    for (auto& arg : args) {
+      arg.CopyOnWrite()->PushCastToChildren(dtype);
+    }
+    this->dtype = dtype;
+  }
+
   static constexpr const char* _type_key = "arith.SumExpr";
   TVM_DECLARE_FINAL_OBJECT_INFO(SumExprNode, CanonicalExprNode);
 
@@ -430,6 +566,7 @@ class CanonicalSimplifier::Impl : public RewriteSimplifier::Impl {
   PrimExpr VisitExpr_(const FloorDivNode* op) final;
   PrimExpr VisitExpr_(const FloorModNode* op) final;
   PrimExpr VisitExpr_(const ReduceNode* op) final;
+  PrimExpr VisitExpr_(const CastNode* op) final;
 
  private:
   /*!
@@ -1071,6 +1208,30 @@ PrimExpr CanonicalSimplifier::Impl::VisitExpr_(const ReduceNode* op) {
   return ret;
 }
 
+PrimExpr CanonicalSimplifier::Impl::VisitExpr_(const CastNode* op) {
+  if (!IsIndexType(op->dtype)) {
+    return Rewriter::VisitExpr_(op);
+  }
+  // normalize
+  PrimExpr value = this->CanonicalMutate(op->value);
+  // PushCastToChildren
+  if (value.as<SumExprNode>()) {
+    SumExpr se = Downcast<SumExpr>(value);
+    if (se->CanPushCastToChildren(op->dtype, analyzer_)) {
+      se.CopyOnWrite()->PushCastToChildren(op->dtype);
+      return std::move(se);
+    }
+  }
+  if (value.as<SplitExprNode>()) {
+    SplitExpr se = Downcast<SplitExpr>(value);
+    if (se->CanPushCastToChildren(op->dtype, analyzer_)) {
+      se.CopyOnWrite()->PushCastToChildren(op->dtype);
+      return std::move(se);
+    }
+  }
+  return Rewriter::VisitExpr_(op);
+}
+
 PrimExpr CanonicalSimplifier::operator()(const PrimExpr& expr) {
   return impl_->CanonicalSimplify(expr);
 }
diff --git a/src/arith/iter_affine_map.cc b/src/arith/iter_affine_map.cc
index 7896db73d10a..7efdd03fa11e 100644
--- a/src/arith/iter_affine_map.cc
+++ b/src/arith/iter_affine_map.cc
@@ -412,8 +412,8 @@ class IterMapRewriter : public ExprMutator {
     return analyzer_->CanProve(floormod(lhs, rhs) == 0);
   }
 
-  PrimExpr SplitFloorDivConst(IterSplitExpr lhs, PrimExpr rhs);
-  PrimExpr SplitFloorModConst(IterSplitExpr lhs, PrimExpr rhs);
+  PrimExpr SplitFloorDivConst(IterSplitExpr lhs, PrimExpr rhs, const PrimExpr& orig);
+  PrimExpr SplitFloorModConst(IterSplitExpr lhs, PrimExpr rhs, const PrimExpr& orig);
 
   static void AddToLhs(IterSumExprNode* lhs, IterSplitExpr rhs, int sign) {
     tir::ExprDeepEqual equal;
@@ -584,7 +584,7 @@ PrimExpr IterMapRewriter::VisitExpr_(const MulNode* op) {
   if (a->IsInstance<IterMapExprNode>() && b->IsInstance<IterMapExprNode>()) {
     // cannot multiply two iterators, mark as unresolved.
     ++unresolved_count_;
-    return Mul(a, b);
+    return GetRef<PrimExpr>(op);
   }
 
   if (!a->IsInstance<IterMapExprNode>()) {
@@ -603,7 +603,8 @@ PrimExpr IterMapRewriter::VisitExpr_(const MulNode* op) {
   }
 }
 
-PrimExpr IterMapRewriter::SplitFloorDivConst(IterSplitExpr lhs, PrimExpr rhs) {
+PrimExpr IterMapRewriter::SplitFloorDivConst(IterSplitExpr lhs, PrimExpr rhs,
+                                             const PrimExpr& orig) {
   // floordiv(x*scale, rhs)
   if (is_one(rhs)) return std::move(lhs);
   if (!is_one(lhs->scale)) {
@@ -619,7 +620,7 @@ PrimExpr IterMapRewriter::SplitFloorDivConst(IterSplitExpr lhs, PrimExpr rhs) {
       } else {
         // mark as unresolved.
         ++unresolved_count_;
-        return floordiv(lhs, rhs);
+        return orig;
       }
     }
   }
@@ -641,7 +642,7 @@ PrimExpr IterMapRewriter::SplitFloorDivConst(IterSplitExpr lhs, PrimExpr rhs) {
   } else {
     // mark as unresolved.
     ++unresolved_count_;
-    return floordiv(lhs, rhs);
+    return orig;
   }
 }
 
@@ -669,25 +670,26 @@ PrimExpr IterMapRewriter::VisitExpr_(const FloorDivNode* op) {
   if (b->IsInstance<IterMapExprNode>()) {
     // cannot divide an iterator, mark as unresolved.
     ++unresolved_count_;
-    return FloorDiv(a, b);
+    return GetRef<PrimExpr>(op);
   }
 
   if (a->IsInstance<IterSumExprNode>()) {
     IterSumExpr ret = Downcast<IterSumExpr>(a);
     if (auto opt = TryFuseIters(ret)) {
-      return SplitFloorDivConst(opt.value(), b);
+      return SplitFloorDivConst(opt.value(), b, GetRef<PrimExpr>(op));
     } else {
       ++unresolved_count_;
-      return FloorDiv(a, b);
+      return GetRef<PrimExpr>(op);
     }
   } else {
     ICHECK(a->IsInstance<IterSplitExprNode>());
     IterSplitExpr ret = Downcast<IterSplitExpr>(std::move(a));
-    return SplitFloorDivConst(ret, b);
+    return SplitFloorDivConst(ret, b, GetRef<PrimExpr>(op));
   }
 }
 
-PrimExpr IterMapRewriter::SplitFloorModConst(IterSplitExpr lhs, PrimExpr rhs) {
+PrimExpr IterMapRewriter::SplitFloorModConst(IterSplitExpr lhs, PrimExpr rhs,
+                                             const PrimExpr& orig) {
   // floormod(x*scale, rhs)
   if (is_one(rhs)) return make_zero(lhs->dtype);
   if (!is_one(lhs->scale)) {
@@ -701,7 +703,7 @@ PrimExpr IterMapRewriter::SplitFloorModConst(IterSplitExpr lhs, PrimExpr rhs) {
       } else {
         // mark as unresolved.
         ++unresolved_count_;
-        return floormod(lhs, rhs);
+        return orig;
       }
     }
   }
@@ -715,7 +717,7 @@ PrimExpr IterMapRewriter::SplitFloorModConst(IterSplitExpr lhs, PrimExpr rhs) {
   } else {
     // mark as unresolved.
     ++unresolved_count_;
-    return floormod(lhs, rhs);
+    return orig;
   }
 }
 
@@ -743,21 +745,21 @@ PrimExpr IterMapRewriter::VisitExpr_(const FloorModNode* op) {
   if (b->IsInstance<IterMapExprNode>()) {
     // cannot mod an iterator, mark as unresolved.
     ++unresolved_count_;
-    return FloorMod(a, b);
+    return GetRef<PrimExpr>(op);
   }
 
   if (a->IsInstance<IterSumExprNode>()) {
     IterSumExpr ret = Downcast<IterSumExpr>(a);
     if (auto opt = TryFuseIters(ret)) {
-      return SplitFloorModConst(opt.value(), b);
+      return SplitFloorModConst(opt.value(), b, GetRef<PrimExpr>(op));
     } else {
       ++unresolved_count_;
-      return FloorMod(a, b);
+      return GetRef<PrimExpr>(op);
     }
   } else {
     ICHECK(a->IsInstance<IterSplitExprNode>());
     IterSplitExpr ret = Downcast<IterSplitExpr>(std::move(a));
-    return SplitFloorModConst(ret, b);
+    return SplitFloorModConst(ret, b, GetRef<PrimExpr>(op));
   }
 }
 
diff --git a/src/arith/solve_linear_equation.cc b/src/arith/solve_linear_equation.cc
index 22bf7360563d..d66e75d9d361 100644
--- a/src/arith/solve_linear_equation.cc
+++ b/src/arith/solve_linear_equation.cc
@@ -427,11 +427,10 @@ IntConstraintsTransform SolveLinearEquations(const IntConstraints& system_to_sol
 
   // We have to transform ranges of the old variables into relations over new variables because
   // new ranges are not enough usually.
-  for (const auto& p : system_to_solve->ranges) {
-    const Var& old_var = p.first;
-    const Range& old_range = p.second;
-    if (old_to_new_map.count(old_var)) {
-      PrimExpr express_by_new_vars = old_to_new_map[old_var];
+  for (const auto& old_var : system_to_solve->variables) {
+    if (system_to_solve->ranges.find(old_var) != system_to_solve->ranges.end()) {
+      const Range& old_range = system_to_solve->ranges.at(old_var);
+      PrimExpr express_by_new_vars = old_to_new_map.at(old_var);
       PrimExpr lower_cond = analyzer_solution.Simplify(old_range->min <= express_by_new_vars);
       PrimExpr upper_cond =
           analyzer_solution.Simplify(express_by_new_vars < old_range->min + old_range->extent);
diff --git a/src/arith/solve_linear_inequality.cc b/src/arith/solve_linear_inequality.cc
index f4de9ffb197b..dd9044833546 100644
--- a/src/arith/solve_linear_inequality.cc
+++ b/src/arith/solve_linear_inequality.cc
@@ -94,11 +94,10 @@ struct ExprLess {
   }
 };
 
-void DebugPrint(
-    const std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>& current_ineq_set,
-    const std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>& next_ineq_set,
-    const std::vector<PrimExpr>& rest, const std::vector<std::pair<int64_t, PrimExpr>>& coef_pos,
-    const std::vector<std::pair<int64_t, PrimExpr>>& coef_neg) {
+void DebugPrint(const std::vector<PrimExpr>& current_ineq_set,
+                const std::vector<PrimExpr>& next_ineq_set, const std::vector<PrimExpr>& rest,
+                const std::vector<std::pair<int64_t, PrimExpr>>& coef_pos,
+                const std::vector<std::pair<int64_t, PrimExpr>>& coef_neg) {
   std::cout << "Current ineq set:\n[";
   for (auto& ineq : current_ineq_set) {
     std::cout << ineq << ", ";
@@ -148,9 +147,12 @@ class NormalizeComparisons : public ExprMutator {
   arith::Analyzer analyzer_;
 };
 
-void AddInequality(std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>* inequality_set,
-                   const PrimExpr& new_ineq, Analyzer* analyzer) {
-  if (analyzer->CanProve(new_ineq) || inequality_set->find(new_ineq) != inequality_set->end()) {
+void AddInequality(std::vector<PrimExpr>* inequality_set, const PrimExpr& new_ineq,
+                   Analyzer* analyzer) {
+  if (analyzer->CanProve(new_ineq) ||
+      std::find_if(inequality_set->begin(), inequality_set->end(), [&](const PrimExpr& e) {
+        return StructuralEqual()(e, new_ineq);
+      }) != inequality_set->end()) {
     // redundant: follows from the vranges
     // or has already been added
     return;
@@ -168,15 +170,13 @@ void AddInequality(std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>
     }
   }
 
-  inequality_set->insert(new_ineq);
+  inequality_set->push_back(new_ineq);
 }
 
-void ClassifyByPolarity(
-    const Var& var,
-    const std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>& current_ineq_set,
-    std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>* next_ineq_set,
-    std::vector<PrimExpr>* rest, std::vector<std::pair<int64_t, PrimExpr>>* coef_pos,
-    std::vector<std::pair<int64_t, PrimExpr>>* coef_neg, Analyzer* analyzer) {
+void ClassifyByPolarity(const Var& var, const std::vector<PrimExpr>& current_ineq_set,
+                        std::vector<PrimExpr>* next_ineq_set, std::vector<PrimExpr>* rest,
+                        std::vector<std::pair<int64_t, PrimExpr>>* coef_pos,
+                        std::vector<std::pair<int64_t, PrimExpr>>* coef_neg, Analyzer* analyzer) {
   // Take formulas from current_ineq_set and classify them according to polarity wrt var
   // and store to coef_pos and coef_neg respectively.
   for (const PrimExpr& ineq : current_ineq_set) {
@@ -218,14 +218,14 @@ void ClassifyByPolarity(
   }
 }
 
-void MoveEquality(std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>* upper_bounds,
-                  std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>* lower_bounds,
-                  std::unordered_set<PrimExpr, StructuralHash, StructuralEqual>* equalities) {
+void MoveEquality(std::vector<PrimExpr>* upper_bounds, std::vector<PrimExpr>* lower_bounds,
+                  std::vector<PrimExpr>* equalities) {
   // those exist in both upper & lower bounds will be moved to equalities
   for (auto ub = upper_bounds->begin(); ub != upper_bounds->end();) {
-    auto lb = lower_bounds->find(*ub);
+    auto lb = std::find_if(lower_bounds->begin(), lower_bounds->end(),
+                           [&](const PrimExpr& e) { return StructuralEqual()(e, *ub); });
     if (lb != lower_bounds->end()) {
-      equalities->insert(*lb);
+      equalities->push_back(*lb);
       lower_bounds->erase(lb);
       ub = upper_bounds->erase(ub);
     } else {
@@ -249,8 +249,8 @@ PartialSolvedInequalities SolveLinearInequalities(const IntConstraints& system_t
   //   and move to the next variable.
 
   // normalized inequality
-  std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> current_ineq_set_to_solve;
-  std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> next_ineq_set_to_solve;
+  std::vector<PrimExpr> current_ineq_set_to_solve;
+  std::vector<PrimExpr> next_ineq_set_to_solve;
   // A vector of pairs (c, e), c > 0, representing formulas of the form c*v + e <= 0
   std::vector<std::pair<int64_t, PrimExpr>> coef_pos;
   // A vector of pairs (c, e), c < 0, representing formulas of the form c*v + e <= 0
@@ -321,8 +321,8 @@ PartialSolvedInequalities SolveLinearInequalities(const IntConstraints& system_t
     }
 
     // The resulting lower and upper bounds
-    std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> upper_bounds;
-    std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> lower_bounds;
+    std::vector<PrimExpr> upper_bounds;
+    std::vector<PrimExpr> lower_bounds;
     upper_bounds.reserve(coef_pos.size());
     lower_bounds.reserve(coef_neg.size());
 
@@ -345,7 +345,7 @@ PartialSolvedInequalities SolveLinearInequalities(const IntConstraints& system_t
         }
       }
       // Add the upper bound
-      upper_bounds.insert(bound);
+      upper_bounds.push_back(bound);
     }
     for (const auto& neg : coef_neg) {
       PrimExpr bound = make_const(v.dtype(), -coef_lcm / neg.first) * neg.second;
@@ -366,10 +366,10 @@ PartialSolvedInequalities SolveLinearInequalities(const IntConstraints& system_t
         }
       }
       // Add the lower bound
-      lower_bounds.insert(bound);
+      lower_bounds.push_back(bound);
     }
 
-    std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> equal;
+    std::vector<PrimExpr> equal;
     equal.reserve(std::min(upper_bounds.size(), lower_bounds.size()));
     MoveEquality(&upper_bounds, &lower_bounds, &equal);
     std::vector<PrimExpr> equal_list(equal.begin(), equal.end());
diff --git a/src/auto_scheduler/compute_dag.cc b/src/auto_scheduler/compute_dag.cc
old mode 100755
new mode 100644
index b65878225f5a..abbcba234848
--- a/src/auto_scheduler/compute_dag.cc
+++ b/src/auto_scheduler/compute_dag.cc
@@ -873,7 +873,14 @@ std::string GetNewLayout(const State& state, const int stage_id, const Stage& st
       ori_iter_name = new_axis_names[i];
     }
     if (placeholder_axis_names.count(ori_iter_name)) {
-      os << iter->range->extent << ori_iter_name;
+      PrimExpr extent;
+      if (iter->range.defined()) {
+        extent = iter->range->extent;
+      } else {
+        // This iter is simplified by InferBound, so it must have a length of one.
+        extent = 1;
+      }
+      os << extent << ori_iter_name;
       new_names.push_back(ori_iter_name);
     }
   }
@@ -1236,6 +1243,62 @@ String ComputeDAG::PrintStepsAsPython(const Array<Step>& transform_steps) const
   return ss.str();
 }
 
+String ComputeDAG::PrintDAG(bool simple_mode) const {
+  std::stringstream ss;
+
+  for (const auto& op : operator->()->ops) {
+    if (op->IsInstance<te::PlaceholderOpNode>()) {
+      ss << op->name << " = PLACEHOLDER ";
+      if (!simple_mode) {
+        ss << op.output(0)->shape;
+      }
+      ss << "\n";
+    } else if (auto pop = op.as<te::ComputeOpNode>()) {
+      for (size_t k = 0; k < pop->body.size(); ++k) {
+        ss << op->name << "(";
+        for (size_t i = 0; i < pop->axis.size(); i++) {
+          ss << pop->axis[i]->var->name_hint;
+          if (i != pop->axis.size() - 1) {
+            ss << ", ";
+          }
+        }
+        ss << ")";
+        if (pop->body.size() > 1) {
+          ss << ".v" << k;
+        }
+        if (auto preduce = pop->body[k].as<ReduceNode>()) {
+          ICHECK_LT(k, preduce->combiner->result.size());
+          PrimExpr combiner = preduce->combiner->result[k];
+          if (combiner->IsInstance<AddNode>()) {
+            ss << " += " << preduce->source[0] << "\n";
+          } else if (combiner->IsInstance<MaxNode>()) {
+            ss << " max= " << preduce->source[0] << "\n";
+          } else if (combiner->IsInstance<MinNode>()) {
+            ss << " min= " << preduce->source[0] << "\n";
+          } else if (combiner->IsInstance<SelectNode>()) {
+            const auto& select = combiner.as<SelectNode>();
+            ss << " select(" << select->condition << ", " << select->true_value << ", "
+               << select->false_value << ")= " << '(' << preduce->source[0] << ','
+               << preduce->source[1] << ")\n";
+          } else {
+            ss << "reduce" << combiner << "\n";
+          }
+        } else {
+          auto call = pop->body[k].as<CallNode>();
+          if (simple_mode && call) {
+            ss << " = " << call->op << "\n";
+          } else {
+            ss << " = " << pop->body[k] << "\n";
+          }
+        }
+      }
+    } else {
+      LOG(FATAL) << "Invalid op";
+    }
+  }
+  return String(ss.str());
+}
+
 State ComputeDAG::InferBound(const State& state) const {
   ICHECK(state->concrete) << "Only concrete state can be processed to get bound info.";
 
@@ -1304,7 +1367,7 @@ Array<State> ComputeDAG::InferBound(const Array<State>& states) const {
   support::parallel_for(0, states.size(), [this, &states, &out_states](int i) {
     try {
       out_states.Set(i, (states[i].defined()) ? this->InferBound(states[i]) : states[i]);
-    } catch (dmlc::Error& e) {
+    } catch (Error& e) {
       LOG(WARNING) << "InferBound fails on the state:\n"
                    << states[i] << "\n"
                    << "with: " << e.what() << std::endl;
@@ -1376,51 +1439,9 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
 TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
     .set_dispatch<ComputeDAGNode>([](const ObjectRef& ref, ReprPrinter* p) {
       auto* node = static_cast<const ComputeDAGNode*>(ref.get());
-      std::stringstream ss;
-
-      for (const auto& op : node->ops) {
-        if (op->IsInstance<te::PlaceholderOpNode>()) {
-          ss << op->name << " = PLACEHOLDER " << op.output(0)->shape << "\n";
-        } else if (auto pop = op.as<te::ComputeOpNode>()) {
-          for (size_t k = 0; k < pop->body.size(); ++k) {
-            ss << op->name << "(";
-            for (size_t i = 0; i < pop->axis.size(); i++) {
-              ss << pop->axis[i]->var->name_hint;
-              if (i != pop->axis.size() - 1) {
-                ss << ", ";
-              }
-            }
-            ss << ")";
-            if (pop->body.size() > 1) {
-              ss << ".v" << k;
-            }
-            if (auto preduce = pop->body[k].as<ReduceNode>()) {
-              ICHECK_LT(k, preduce->combiner->result.size());
-              PrimExpr combiner = preduce->combiner->result[k];
-              if (combiner->IsInstance<AddNode>()) {
-                ss << " += " << preduce->source[0] << "\n";
-              } else if (combiner->IsInstance<MaxNode>()) {
-                ss << " max= " << preduce->source[0] << "\n";
-              } else if (combiner->IsInstance<MinNode>()) {
-                ss << " min= " << preduce->source[0] << "\n";
-              } else if (combiner->IsInstance<SelectNode>()) {
-                const auto& select = combiner.as<SelectNode>();
-                ss << " select(" << select->condition << ", " << select->true_value << ", "
-                   << select->false_value << ")= " << '(' << preduce->source[0] << ','
-                   << preduce->source[1] << ")\n";
-              } else {
-                LOG(FATAL) << "Unsupported reduction operator" << combiner;
-              }
-            } else {
-              ss << " = " << pop->body[k] << "\n";
-            }
-          }
-        } else {
-          LOG(FATAL) << "Invalid op";
-        }
-      }
-
-      p->stream << ss.str();
+      auto dag = GetRef<ComputeDAG>(node);
+      auto dag_str = dag.PrintDAG();
+      p->stream << dag_str;
     });
 
 Array<PrimExpr> GetShapeFromRewrittenLayout(String rewritten_layout, Array<String> axis_names) {
@@ -1469,6 +1490,11 @@ TVM_REGISTER_GLOBAL("auto_scheduler.ComputeDAGPrintPythonCodeFromState")
       return dag.PrintStepsAsPython(state->transform_steps);
     });
 
+TVM_REGISTER_GLOBAL("auto_scheduler.ComputeDAGPrintDAG")
+    .set_body_typed([](const ComputeDAG& dag, bool simple_mode) {
+      return dag.PrintDAG(simple_mode);
+    });
+
 TVM_REGISTER_GLOBAL("auto_scheduler.ComputeDAGInferBoundFromState")
     .set_body_typed([](const ComputeDAG& dag, const State& state) {
       return dag.InferBound(state);
diff --git a/src/auto_scheduler/feature.cc b/src/auto_scheduler/feature.cc
index 47b9fb60aab4..b3c62f01c7c8 100755
--- a/src/auto_scheduler/feature.cc
+++ b/src/auto_scheduler/feature.cc
@@ -618,7 +618,7 @@ class PerStoreFeatureExtractor : public StmtExprVisitor {
       is_gpu_ = true;
 
       // make a fake for node for blockIdx.x or threadIdx.x
-      Stmt fake_for_node = For(var, 0, extent, ForType::Parallel, DeviceAPI::None, node->body);
+      Stmt fake_for_node = For(var, 0, extent, ForKind::kParallel, node->body);
 
       outer_loop_prod_ *= extent;
       for_loop_stack_.push_back(fake_for_node.as<ForNode>());
@@ -642,11 +642,11 @@ class PerStoreFeatureExtractor : public StmtExprVisitor {
   void VisitStmt_(const ForNode* node) final {
     int64_t loop_extent = GetLoopExtent(node);
 
-    if (node->for_type == ForType::Vectorized) {
+    if (node->kind == ForKind::kVectorized) {
       vec_for_stack_.push_back(node);
-    } else if (node->for_type == ForType::Unrolled) {
+    } else if (node->kind == ForKind::kUnrolled) {
       unroll_for_stack_.push_back(node);
-    } else if (node->for_type == ForType::Parallel) {
+    } else if (node->kind == ForKind::kParallel) {
       parallel_for_stack_.push_back(node);
     }
 
@@ -656,11 +656,11 @@ class PerStoreFeatureExtractor : public StmtExprVisitor {
     for_loop_stack_.pop_back();
     outer_loop_prod_ /= loop_extent;
 
-    if (node->for_type == ForType::Vectorized) {
+    if (node->kind == ForKind::kVectorized) {
       vec_for_stack_.pop_back();
-    } else if (node->for_type == ForType::Unrolled) {
+    } else if (node->kind == ForKind::kUnrolled) {
       unroll_for_stack_.pop_back();
-    } else if (node->for_type == ForType::Parallel) {
+    } else if (node->kind == ForKind::kParallel) {
       parallel_for_stack_.pop_back();
     }
   }
@@ -1328,7 +1328,7 @@ void GetPerStoreFeaturesWorkerFunc(const SearchTask& task, const State& state, i
     const auto& prim_func = (*it).second.as<PrimFuncNode>();
     GetPerStoreFeature(prim_func->body, task->hardware_params->cache_line_bytes, max_n_bufs,
                        feature);
-  } catch (dmlc::Error& e) {
+  } catch (Error& e) {
     (*error_ct)++;
   }
 }
@@ -1399,7 +1399,7 @@ void GetPerStoreFeaturesFromFile(const std::string& filename, int max_lines, int
       Array<te::Tensor> tensors = (*workload_key_to_tensors)(workload_key);
       task = SearchTask(ComputeDAG(tensors), workload_key, cur_inp->task->target,
                         cur_inp->task->target_host, cur_inp->task->hardware_params,
-                        cur_inp->task->layout_rewrite_option);
+                        cur_inp->task->layout_rewrite_option, cur_inp->task->task_input_names);
       task_id = task_cache.size();
 
       // compute min cost for each task
@@ -1462,12 +1462,19 @@ void GetPerStoreFeaturesFromMeasurePairs(const Array<MeasureInput>& inputs,
     if (find_res == task_cache.end()) {
       if (inputs[i]->task->compute_dag.defined()) {  // the measure input is complete
         task = inputs[i]->task;
-      } else {  // the measure input is incomplete
-        // rebuild task for incomplete measure pairs read from file
-        Array<te::Tensor> tensors = (*workload_key_to_tensors)(workload_key);
-        task = SearchTask(ComputeDAG(tensors), workload_key, inputs[i]->task->target,
-                          inputs[i]->task->target_host, inputs[i]->task->hardware_params,
-                          inputs[i]->task->layout_rewrite_option);
+      } else {
+        // The measure input is incomplete, rebuild task for incomplete measure pairs read from file
+        try {
+          Array<te::Tensor> tensors = (*workload_key_to_tensors)(workload_key);
+          task =
+              SearchTask(ComputeDAG(tensors), workload_key, inputs[i]->task->target,
+                         inputs[i]->task->target_host, inputs[i]->task->hardware_params,
+                         inputs[i]->task->layout_rewrite_option, inputs[i]->task->task_input_names);
+        } catch (std::exception& e) {
+          // Cannot build ComputeDAG from workload key, the task may have not been registered in
+          // this search round
+          continue;
+        }
       }
       task_id = task_cache.size();
 
@@ -1512,7 +1519,7 @@ void GetPerStoreFeaturesFromMeasurePairs(const Array<MeasureInput>& inputs,
  *   ... // until i == n - 1
  *
  *   float throughputs[sizes[n]];  // The normalized throughputs for n records
- *   int   task_ids[size[n+1];   // The task ids for n records
+ *   int   task_ids[size[n+1]];   // The task ids for n records
  *
  * }
  * To implement this format, we also store int as float, so we can store all numbers
diff --git a/src/auto_scheduler/measure_record.cc b/src/auto_scheduler/measure_record.cc
index 1120f437b176..5dafa8d98702 100644
--- a/src/auto_scheduler/measure_record.cc
+++ b/src/auto_scheduler/measure_record.cc
@@ -169,6 +169,12 @@ struct Handler<::tvm::auto_scheduler::SearchTaskNode> {
       writer->WriteArrayItem(std::string(""));
     }
     writer->WriteArrayItem(static_cast<int>(data.layout_rewrite_option));
+    writer->WriteArraySeperator();
+    writer->BeginArray(false);
+    for (const auto& i : data.task_input_names) {
+      writer->WriteArrayItem(std::string(i));
+    }
+    writer->EndArray();
     writer->EndArray();
   }
   inline static void Read(dmlc::JSONReader* reader, ::tvm::auto_scheduler::SearchTaskNode* data) {
@@ -200,6 +206,17 @@ struct Handler<::tvm::auto_scheduler::SearchTaskNode> {
         reader->Read(&int_value);
         data->layout_rewrite_option = ::tvm::auto_scheduler::LayoutRewriteOption(int_value);
         s = reader->NextArrayItem();
+        if (s) {
+          reader->BeginArray();
+          s = reader->NextArrayItem();
+          while (s) {
+            reader->Read(&str_value);
+            data->task_input_names.push_back(str_value);
+            s = reader->NextArrayItem();
+          }
+          // Process the end of array
+          s = reader->NextArrayItem();
+        }
         ICHECK(!s);
       }
     }
@@ -444,5 +461,22 @@ TVM_REGISTER_GLOBAL("auto_scheduler.DeserializeMeasureInput").set_body_typed([](
   reader.Read(inp.get());
   return ObjectRef(inp);
 });
+
+TVM_REGISTER_GLOBAL("auto_scheduler.SerializeSearchTask")
+    .set_body_typed([](const SearchTask& search_task) {
+      std::ostringstream os;
+      dmlc::JSONWriter writer(&os);
+      writer.Write(*search_task.get());
+      return os.str();
+    });
+
+TVM_REGISTER_GLOBAL("auto_scheduler.DeserializeSearchTask").set_body_typed([](String json) {
+  std::istringstream ss(json);
+  dmlc::JSONReader reader(&ss);
+  auto search_task = make_object<SearchTaskNode>();
+  reader.Read(search_task.get());
+  return ObjectRef(search_task);
+});
+
 }  // namespace auto_scheduler
 }  // namespace tvm
diff --git a/src/auto_scheduler/search_policy/sketch_policy.cc b/src/auto_scheduler/search_policy/sketch_policy.cc
index e2678373ef8b..4a4ab18b5eed 100644
--- a/src/auto_scheduler/search_policy/sketch_policy.cc
+++ b/src/auto_scheduler/search_policy/sketch_policy.cc
@@ -78,6 +78,8 @@ SketchPolicy::SketchPolicy(SearchTask task, CostModel program_cost_model,
   node->rand_gen = std::mt19937(seed);
   node->params = std::move(params);
   node->verbose = verbose;
+  node->sample_init_min_pop_ =
+      GetIntParam(node->params, SketchParamKey::SampleInitPopulation::min_population);
 
   if (init_search_callbacks) {
     PrintTitle("Call init-search callbacks", verbose);
@@ -382,8 +384,6 @@ Array<State> SketchPolicyNode::GenerateSketches() {
 Array<State> SketchPolicyNode::SampleInitPopulation(const Array<State>& sketches) {
   // Use this population as the parallel degree to do sampling
   int population = GetIntParam(params, SketchParamKey::EvolutionarySearch::population);
-  // At least we should sample this number of valid programs
-  int min_population = GetIntParam(params, SketchParamKey::SampleInitPopulation::min_population);
 
   auto tic_begin = std::chrono::high_resolution_clock::now();
 
@@ -397,9 +397,8 @@ Array<State> SketchPolicyNode::SampleInitPopulation(const Array<State>& sketches
 
   std::unordered_set<std::string> explored_state_strs;
   size_t iter = 1;
-  size_t target_size = min_population;
   size_t unchange_cnt = 0;
-  while (out_states.size() < target_size) {
+  while (static_cast<int>(out_states.size()) < sample_init_min_pop_) {
     std::vector<State> temp_states(population);
 
     // Sample a batch of states randomly
@@ -458,7 +457,7 @@ Array<State> SketchPolicyNode::SampleInitPopulation(const Array<State>& sketches
                             std::chrono::high_resolution_clock::now() - tic_begin)
                             .count();
       StdCout(verbose) << "Sample Iter: " << iter << std::fixed << std::setprecision(4)
-                       << "\t#Pop: " << out_states.size() << "\t#Target: " << target_size
+                       << "\t#Pop: " << out_states.size() << "\t#Target: " << sample_init_min_pop_
                        << "\tfail_ct: " << fail_ct << "\tTime elapsed: " << std::fixed
                        << std::setprecision(2) << duration << std::endl;
     }
@@ -466,9 +465,9 @@ Array<State> SketchPolicyNode::SampleInitPopulation(const Array<State>& sketches
     if (unchange_cnt == 5) {
       // Reduce the target size to avoid too-long time in this phase if no valid state was found
       // in the past iterations
-      if (target_size > 1) {
-        target_size /= 2;
-        StdCout(verbose) << "#Target has been reduced to " << target_size
+      if (sample_init_min_pop_ > 1) {
+        sample_init_min_pop_ /= 2;
+        StdCout(verbose) << "#Target has been reduced to " << sample_init_min_pop_
                          << " due to too many failures or duplications" << std::endl;
       }
       unchange_cnt = 0;
@@ -520,7 +519,7 @@ Array<State> SketchPolicyNode::EvolutionarySearch(const Array<State>& init_popul
   // auxiliary global variables
   std::vector<float> pop_scores;
   std::vector<double> pop_selection_probs;
-  float max_score = -1e-10;
+  float max_score = -1e-10f;
   pop_scores.reserve(population);
   pop_selection_probs.reserve(population);
   std::uniform_real_distribution<> dis(0.0, 1.0);
@@ -672,6 +671,26 @@ Array<MeasureInput> SketchPolicyNode::PickStatesWithEpsGreedy(const Array<State>
   return inputs;
 }
 
+/********** PreloadCustomSketchRule **********/
+TVM_REGISTER_OBJECT_TYPE(PreloadCustomSketchRuleNode);
+
+PreloadCustomSketchRule::PreloadCustomSketchRule(PackedFunc meet_condition_func,
+                                                 PackedFunc apply_func, String rule_name) {
+  auto node = make_object<PreloadCustomSketchRuleNode>();
+  node->meet_condition_func = std::move(meet_condition_func);
+  node->apply_func = std::move(apply_func);
+  node->rule_name = std::move(rule_name);
+  data_ = std::move(node);
+}
+
+void PreloadCustomSketchRuleNode::Callback(SearchPolicyNode* policy) {
+  CHECK(policy->IsInstance<SketchPolicyNode>());
+  auto sketch_policy = dynamic_cast<SketchPolicyNode*>(policy);
+  sketch_policy->sketch_rules.push_back(
+      new RuleCustomSketch(meet_condition_func, apply_func, rule_name));
+  StdCout(policy->verbose) << "Custom sketch rule \"" << rule_name << "\" added." << std::endl;
+}
+
 TVM_REGISTER_GLOBAL("auto_scheduler.SketchPolicy")
     .set_body_typed([](SearchTask task, CostModel program_cost_model, Map<String, ObjectRef> params,
                        int seed, int verbose,
@@ -700,5 +719,10 @@ TVM_REGISTER_GLOBAL("auto_scheduler.PrintTitle").set_body_typed([](std::string t
   PrintTitle(title, 1);
 });
 
+TVM_REGISTER_GLOBAL("auto_scheduler.PreloadCustomSketchRule")
+    .set_body_typed([](PackedFunc meet_condition_func, PackedFunc apply_func, String rule_name) {
+      return PreloadCustomSketchRule(meet_condition_func, apply_func, rule_name);
+    });
+
 }  // namespace auto_scheduler
 }  // namespace tvm
diff --git a/src/auto_scheduler/search_policy/sketch_policy.h b/src/auto_scheduler/search_policy/sketch_policy.h
index 3d135d1bda94..faf058b45b19 100644
--- a/src/auto_scheduler/search_policy/sketch_policy.h
+++ b/src/auto_scheduler/search_policy/sketch_policy.h
@@ -87,6 +87,8 @@ struct SketchParamKey {
   static constexpr const char* disable_change_compute_location = "disable_change_compute_location";
 };
 
+class SketchPolicy;
+
 /*!
  * \brief The search policy that searches in a hierarchical search space defined by sketches.
  * The policy randomly samples programs from the space defined by sketches
@@ -166,6 +168,11 @@ class SketchPolicyNode : public SearchPolicyNode {
 
   /*! \brief The cached sketches */
   Array<State> sketch_cache_;
+
+  /*! \brief The minimul output population of SampleInitPopulation */
+  int sample_init_min_pop_;
+
+  friend class SketchPolicy;
 };
 
 /*!
@@ -190,6 +197,40 @@ class SketchPolicy : public SearchPolicy {
   TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(SketchPolicy, SearchPolicy, SketchPolicyNode);
 };
 
+/*! \brief Pre-search callback function to load custom rules for sketch generation */
+class PreloadCustomSketchRuleNode : public SearchCallbackNode {
+ public:
+  /*! \brief The condition check function of this rule. */
+  PackedFunc meet_condition_func;
+  /*! \brief The apply function of this rule. */
+  PackedFunc apply_func;
+  /*! \brief The name of this rule. */
+  String rule_name;
+
+  void Callback(SearchPolicyNode* policy) final;
+
+  static constexpr const char* _type_key = "auto_scheduler.PreloadCustomSketchRule";
+  TVM_DECLARE_FINAL_OBJECT_INFO(PreloadCustomSketchRuleNode, SearchCallbackNode);
+};
+
+/*!
+ * \brief Managed reference to PreloadCustomSketchRuleNode.
+ * \sa PreloadCustomSketchRuleNode
+ */
+class PreloadCustomSketchRule : public SearchCallback {
+ public:
+  /*!
+   * \brief The constructor.
+   * \param meet_condition_func The condition check function of this rule.
+   * \param apply_func The apply function of this rule.
+   * \param rule_name The name of this rule.
+   */
+  PreloadCustomSketchRule(PackedFunc meet_condition_func, PackedFunc apply_func, String rule_name);
+
+  TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(PreloadCustomSketchRule, SearchCallback,
+                                        PreloadCustomSketchRuleNode);
+};
+
 }  // namespace auto_scheduler
 }  // namespace tvm
 
diff --git a/src/auto_scheduler/search_policy/sketch_policy_rules.cc b/src/auto_scheduler/search_policy/sketch_policy_rules.cc
index f704fe9e82d5..8eaf80321456 100644
--- a/src/auto_scheduler/search_policy/sketch_policy_rules.cc
+++ b/src/auto_scheduler/search_policy/sketch_policy_rules.cc
@@ -461,6 +461,33 @@ std::vector<std::pair<State, int>> RuleSpecialComputeLocationGPU::Apply(
   return {std::make_pair(std::move(tmp_s), stage_id - 1)};
 }
 
+/********** RuleCustomSketch **********/
+
+SketchGenerationRule::ConditionKind RuleCustomSketch::MeetCondition(const SketchPolicyNode& policy,
+                                                                    const State& state,
+                                                                    int stage_id) const {
+  auto ret = meet_condition_func_(tvm::runtime::GetRef<SketchPolicy>(&policy), state, stage_id);
+  if (ret.type_code() == 0) {
+    return ConditionKind(static_cast<int>(ret));
+  } else {
+    LOG(WARNING) << "Wrong rule condition value. Apply the rule and skip the rest";
+    return ConditionKind::kApplyAndSkipRest;
+  }
+}
+
+std::vector<std::pair<State, int>> RuleCustomSketch::Apply(const SketchPolicyNode& policy,
+                                                           const State& state, int stage_id) const {
+  Array<Array<ObjectRef>> apply_ret =
+      apply_func_(tvm::runtime::GetRef<SketchPolicy>(&policy), state, stage_id);
+  std::vector<std::pair<State, int>> ret;
+  for (const auto& item : apply_ret) {
+    CHECK_EQ(item.size(), 2);
+    auto next = item[1].as<IntImmNode>();
+    ret.emplace_back(Downcast<State>(item[0]), next->value);
+  }
+  return ret;
+}
+
 /********** Init Population **********/
 
 PopulationGenerationRule::ResultKind InitFillTileSize::Apply(SketchPolicyNode* policy, State* state,
@@ -1079,7 +1106,7 @@ PopulationGenerationRule::ResultKind MutateComputeLocation::Apply(SketchPolicyNo
     }
     try {
       StepApplyToState(tmp_s->transform_steps.back(), &tmp_s, policy->search_task->compute_dag);
-    } catch (dmlc::Error& e) {
+    } catch (Error& e) {
       return ResultKind::kInvalid;
     }
   }
@@ -1201,7 +1228,7 @@ PopulationGenerationRule::ResultKind MutateParallel::Apply(SketchPolicyNode* pol
     tmp_s.CopyOnWrite()->transform_steps.push_back(step);
     try {
       StepApplyToState(tmp_s->transform_steps.back(), &tmp_s, policy->search_task->compute_dag);
-    } catch (dmlc::Error& e) {
+    } catch (Error& e) {
       return ResultKind::kInvalid;
     }
   }
diff --git a/src/auto_scheduler/search_policy/sketch_policy_rules.h b/src/auto_scheduler/search_policy/sketch_policy_rules.h
index 046f036d59d9..fc1916b8c67d 100644
--- a/src/auto_scheduler/search_policy/sketch_policy_rules.h
+++ b/src/auto_scheduler/search_policy/sketch_policy_rules.h
@@ -131,6 +131,29 @@ DEFINE_SKETCH_GENERATION_RULE(RuleCrossThreadReduction);
  * location of the producers of compute ops that perform "fake reduction" with const tensors. */
 DEFINE_SKETCH_GENERATION_RULE(RuleSpecialComputeLocationGPU);
 
+/*! \brief The rule that allows users to generate custom sketches. */
+class RuleCustomSketch : public SketchGenerationRule {
+ public:
+  RuleCustomSketch(PackedFunc meet_condition_func, PackedFunc apply_func,
+                   String rule_name = "CustomSketchRule")
+      : meet_condition_func_(std::move(meet_condition_func)),
+        apply_func_(std::move(apply_func)),
+        rule_name_(std::move(rule_name)) {}
+
+  ConditionKind MeetCondition(const SketchPolicyNode& policy, const State& state,
+                              int stage_id) const final;
+
+  std::vector<std::pair<State, int>> Apply(const SketchPolicyNode& policy, const State& state,
+                                           int stage_id) const final;
+
+  std::string GetRuleName() const final { return rule_name_; }
+
+ private:
+  PackedFunc meet_condition_func_;
+  PackedFunc apply_func_;
+  String rule_name_;
+};
+
 /********** Init Population **********/
 
 /*! \brief The base class for rules used to annotate the sketches to get the initial population. */
diff --git a/src/auto_scheduler/search_policy/utils.cc b/src/auto_scheduler/search_policy/utils.cc
index d59df6965776..ce8dc39922e0 100644
--- a/src/auto_scheduler/search_policy/utils.cc
+++ b/src/auto_scheduler/search_policy/utils.cc
@@ -465,6 +465,22 @@ const std::vector<int>& SplitFactorizationMemo::GetFactors(int n) {
 
 /********** Utils interface API for ffi **********/
 
+TVM_REGISTER_GLOBAL("auto_scheduler.SearchPolicyUtilsGetConsumers")
+    .set_body_typed([](const SearchTask& task, const State& state, int stage_id) {
+      const std::set<int>& consumers = GetConsumers(task, state, stage_id);
+      tvm::Map<IntImm, IntImm> ret;
+      for (const auto& i : consumers) {
+        ret.Set(Integer(i), Integer(i));
+      }
+      return ret;
+    });
+
+TVM_REGISTER_GLOBAL("auto_scheduler.SearchPolicyUtilsIsElementwiseMatch")
+    .set_body_typed([](const SearchTask& task, const State& state, int stage_id,
+                       int target_stage_id) {
+      return ElementwiseMatch(task, state, stage_id, target_stage_id);
+    });
+
 TVM_REGISTER_GLOBAL("auto_scheduler.SearchPolicyUtilsIsTiled")
     .set_body_typed([](const Stage& stage) { return IsTiled(stage); });
 
diff --git a/src/auto_scheduler/search_policy/utils.h b/src/auto_scheduler/search_policy/utils.h
index d59a6ca220ca..eb2cd69c9209 100644
--- a/src/auto_scheduler/search_policy/utils.h
+++ b/src/auto_scheduler/search_policy/utils.h
@@ -609,12 +609,11 @@ inline State FuseAllOuterSpaceIterators(const State& state, int stage_id, Iterat
     to_fuse.push_back(it);
   }
 
-  ICHECK(!to_fuse.empty());
   State tmp_s = state;
-  if (to_fuse.size() > 1) {
-    *fused_iter = tmp_s.fuse(stage_id, to_fuse);
-  } else {
+  if (to_fuse.size() == 1) {
     *fused_iter = to_fuse[0];
+  } else {
+    *fused_iter = tmp_s.fuse(stage_id, to_fuse);
   }
   return tmp_s;
 }
diff --git a/src/auto_scheduler/search_task.cc b/src/auto_scheduler/search_task.cc
index 0abee16fceab..f25e581dbf24 100755
--- a/src/auto_scheduler/search_task.cc
+++ b/src/auto_scheduler/search_task.cc
@@ -106,6 +106,29 @@ HardwareParams HardwareParamsNode::GetDefaultHardwareParams(const Target& target
       auto target_device = target->GetAttr<String>("device", "");
       LOG(FATAL) << "No default hardware parameters for opencl target device: " << target_device;
     }
+  } else if (device_type == kDLVulkan) {
+    auto ctx = TVMContext{static_cast<DLDeviceType>(device_type), 0};
+    auto device_name = "device_api.vulkan";
+    auto func = tvm::runtime::Registry::Get(device_name);
+    ICHECK(func != nullptr) << "Cannot find Vulkan device_api in registry";
+    auto device_api = static_cast<tvm::runtime::DeviceAPI*>(((*func)()).operator void*());
+
+    tvm::runtime::TVMRetValue ret;
+    device_api->GetAttr(ctx, tvm::runtime::DeviceAttrKind::kMaxSharedMemoryPerBlock, &ret);
+    int max_shared_memory_per_block = ret;
+
+    int max_local_memory_per_block = INT32_MAX;
+
+    device_api->GetAttr(ctx, tvm::runtime::DeviceAttrKind::kMaxThreadsPerBlock, &ret);
+    int max_threads_per_block = ret;
+
+    device_api->GetAttr(ctx, tvm::runtime::DeviceAttrKind::kWarpSize, &ret);
+    int warp_size = ret;
+
+    int max_vthread_extent = std::max(1, warp_size / 4);
+
+    return HardwareParams(-1, 16, 64, max_shared_memory_per_block, max_local_memory_per_block,
+                          max_threads_per_block, max_vthread_extent, warp_size);
   } else {
     LOG(FATAL) << "No default hardware parameters for target: " << target;
   }
@@ -114,7 +137,7 @@ HardwareParams HardwareParamsNode::GetDefaultHardwareParams(const Target& target
 
 SearchTask::SearchTask(ComputeDAG compute_dag, String workload_key, Target target,
                        Target target_host, Optional<HardwareParams> hardware_params,
-                       LayoutRewriteOption layout_rewrite_option) {
+                       LayoutRewriteOption layout_rewrite_option, Array<String> task_input_names) {
   auto node = make_object<SearchTaskNode>();
   node->compute_dag = std::move(compute_dag);
   node->workload_key = std::move(workload_key);
@@ -127,6 +150,7 @@ SearchTask::SearchTask(ComputeDAG compute_dag, String workload_key, Target targe
         HardwareParamsNode::GetDefaultHardwareParams(node->target, node->target_host);
   }
   node->layout_rewrite_option = layout_rewrite_option;
+  node->task_input_names = std::move(task_input_names);
   data_ = std::move(node);
 }
 
@@ -142,9 +166,9 @@ TVM_REGISTER_GLOBAL("auto_scheduler.HardwareParams")
 TVM_REGISTER_GLOBAL("auto_scheduler.SearchTask")
     .set_body_typed([](ComputeDAG compute_dag, String workload_key, Target target,
                        Target target_host, Optional<HardwareParams> hardware_params,
-                       int layout_rewrite_option) {
+                       int layout_rewrite_option, Array<String> task_input_names) {
       return SearchTask(compute_dag, workload_key, target, target_host, hardware_params,
-                        LayoutRewriteOption(layout_rewrite_option));
+                        LayoutRewriteOption(layout_rewrite_option), task_input_names);
     });
 
 }  // namespace auto_scheduler
diff --git a/src/auto_scheduler/transform_step.cc b/src/auto_scheduler/transform_step.cc
old mode 100755
new mode 100644
index 5560907dcffa..b67d5cdd7bd9
--- a/src/auto_scheduler/transform_step.cc
+++ b/src/auto_scheduler/transform_step.cc
@@ -26,8 +26,8 @@
 #include <tvm/auto_scheduler/compute_dag.h>
 #include <tvm/auto_scheduler/loop_state.h>
 #include <tvm/auto_scheduler/transform_step.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 #include <tvm/te/operation.h>
 
 #include <string>
@@ -538,15 +538,25 @@ Iterator FuseStepNode::ApplyToState(State* state) const {
   Iterator new_it =
       Iterator(new_name, range, new_iter_kind, IteratorAnnotation::kNone, &orig_iters);
   Array<Iterator> new_iters;
-  new_iters.insert(new_iters.end(), stage->iters.begin(), stage->iters.begin() + fused_ids.front());
-  new_iters.push_back(new_it);
-  new_iters.insert(new_iters.end(), stage->iters.begin() + fused_ids.back() + 1,
-                   stage->iters.end());
+
+  if (fused_ids.empty()) {
+    new_iters.push_back(new_it);
+  } else {
+    new_iters.insert(new_iters.end(), stage->iters.begin(),
+                     stage->iters.begin() + fused_ids.front());
+    new_iters.push_back(new_it);
+    new_iters.insert(new_iters.end(), stage->iters.begin() + fused_ids.back() + 1,
+                     stage->iters.end());
+  }
 
   StateNode* pstate = state->CopyOnWrite();
   pstate->stages.Set(stage_id,
                      Stage(stage->op, stage->op_type, new_iters, stage->compute_at, stage->attrs));
 
+  if (fused_ids.empty()) {
+    return new_it;
+  }
+
   // Two vectors are used to represent the iterator relation before and after fuse
   // The original iterators in AttachMap will be updated with the new iterators
   std::vector<IterKey> from_iters;
@@ -583,9 +593,13 @@ IterVar FuseStepNode::ApplyToSchedule(Array<te::Stage>* stages,
   stage.fuse(to_fuse, &fused_axis);
 
   Array<IterVar> new_axes;
-  new_axes.insert(new_axes.end(), axes.begin(), axes.begin() + fused_ids.front());
-  new_axes.push_back(fused_axis);
-  new_axes.insert(new_axes.end(), axes.begin() + fused_ids.back() + 1, axes.end());
+  if (fused_ids.empty()) {
+    new_axes.push_back(fused_axis);
+  } else {
+    new_axes.insert(new_axes.end(), axes.begin(), axes.begin() + fused_ids.front());
+    new_axes.push_back(fused_axis);
+    new_axes.insert(new_axes.end(), axes.begin() + fused_ids.back() + 1, axes.end());
+  }
 
   stage_to_axes->Set(stage, std::move(new_axes));
   stages->Set(stage_id, std::move(stage));
@@ -683,9 +697,12 @@ void PragmaStepNode::ApplyToSchedule(Array<te::Stage>* stages,
     }
     ICHECK_LT(pos, pragma_type.size()) << "max step value not found.";
     int value = atoi(pragma_type.c_str() + pos + 1);
-    stage.pragma(axes[iter_id], "auto_unroll_max_step", value);
-    stage.pragma(axes[iter_id], "unroll_explicit", true);
+    if (iter_id < static_cast<int>(axes.size())) {
+      stage.pragma(axes[iter_id], "auto_unroll_max_step", value);
+      stage.pragma(axes[iter_id], "unroll_explicit", true);
+    }
   } else {
+    ICHECK_LT(iter_id, axes.size());
     stage.pragma(axes[iter_id], pragma_type);
   }
   stages->Set(stage_id, std::move(stage));
diff --git a/src/autotvm/feature_visitor.cc b/src/autotvm/feature_visitor.cc
index 15e09755cee2..59cac9cc9827 100644
--- a/src/autotvm/feature_visitor.cc
+++ b/src/autotvm/feature_visitor.cc
@@ -34,19 +34,23 @@ void FeatureVisitor::VisitStmt_(const ForNode* op) {
   int64_t loop_extent = -1;
   if (extent != nullptr) loop_extent = extent->value;
   AnnotationType ann = kSerial;
-  switch (op->for_type) {
-    case ForType ::Parallel:
+  switch (op->kind) {
+    case ForKind ::kParallel:
       ann = kParallel;
       break;
-    case ForType::Unrolled:
+    case ForKind::kUnrolled:
       ann = kUnrolled;
       break;
-    case ForType::Vectorized:
+    case ForKind::kVectorized:
       ann = kVectorized;
       break;
-    case ForType::Serial:
+    case ForKind::kSerial:
       ann = kSerial;
       break;
+    case ForKind::kThreadBinding:
+      LOG(FATAL) << "Loop ThreadBinding is reserved for future used and "
+                 << "not yet supported in TIR";
+      break;
   }
 
   if (EnterItervar_(op->loop_var, loop_extent, ann)) {
diff --git a/src/driver/driver_api.cc b/src/driver/driver_api.cc
index f88b6215f927..bbbb7e3f9eb5 100644
--- a/src/driver/driver_api.cc
+++ b/src/driver/driver_api.cc
@@ -69,7 +69,8 @@ Target DefaultTargetHost(Target target) {
 
 tir::Buffer BufferWithOffsetAlignment(Array<PrimExpr> shape, DataType dtype, std::string name,
                                       int data_alignment, int offset_factor, bool compact) {
-  auto data = tir::Var(name, PointerType(PrimType(dtype)));
+  DataType storage_dtype = (dtype == DataType::Bool() ? DataType::Int(8) : dtype);
+  auto data = tir::Var(name, PointerType(PrimType(storage_dtype)));
   bool has_any = false;
   if (!compact) {
     for (const auto& it : shape) {
diff --git a/src/ir/error.cc b/src/ir/error.cc
index 5d3978dda4ff..0089f55a4da8 100644
--- a/src/ir/error.cc
+++ b/src/ir/error.cc
@@ -132,7 +132,8 @@ void ErrorReporter::RenderErrors(const IRModule& module, bool use_color) {
   LOG(FATAL) << annotated_prog.str() << std::endl;
 }
 
-void ErrorReporter::ReportAt(const GlobalVar& global, const ObjectRef& node, const Error& err) {
+void ErrorReporter::ReportAt(const GlobalVar& global, const ObjectRef& node,
+                             const CompileError& err) {
   size_t index_to_insert = this->errors_.size();
   this->errors_.push_back(err);
   auto it = this->node_to_error_.find(node);
diff --git a/src/ir/expr.cc b/src/ir/expr.cc
index 4cc2ac31a4a1..203520802091 100644
--- a/src/ir/expr.cc
+++ b/src/ir/expr.cc
@@ -49,9 +49,9 @@ PrimExpr PrimExpr::FromObject_(ObjectRef ref) {
   if (auto* ptr = ref.as<runtime::StringObj>()) {
     return tir::StringImm(GetRef<runtime::String>(ptr));
   }
-  ICHECK(ObjectTypeChecker<PrimExpr>::Check(ref.get()))
-      << "Expect type " << ObjectTypeChecker<PrimExpr>::TypeName() << " but get "
-      << ref->GetTypeKey();
+  Optional<String> actual_type = ObjectTypeChecker<PrimExpr>::CheckAndGetMismatch(ref.get());
+  ICHECK(!actual_type.defined()) << "Expected type " << ObjectTypeChecker<PrimExpr>::TypeName()
+                                 << " but got " << actual_type.value();
   return Downcast<PrimExpr>(ref);
 }
 
diff --git a/src/ir/transform.cc b/src/ir/transform.cc
index f4516d5e57c5..48f13bc81df4 100644
--- a/src/ir/transform.cc
+++ b/src/ir/transform.cc
@@ -28,6 +28,8 @@
 #include <tvm/runtime/device_api.h>
 #include <tvm/runtime/registry.h>
 
+#include <chrono>
+#include <iomanip>
 #include <stack>
 #include <unordered_set>
 
@@ -169,6 +171,161 @@ void PassContext::Trace(const IRModule& module, const PassInfo& info, bool is_be
 
 class ModulePass;
 
+/*! \brief PassProfile stores profiling information for a given pass and its sub-passes. */
+struct PassProfile {
+  // TODO(@altanh): expose PassProfile through TVM Object API
+  using Clock = std::chrono::steady_clock;
+  using Duration = std::chrono::duration<double, std::micro>;
+  using Time = std::chrono::time_point<Clock>;
+
+  /*! \brief The name of the pass being profiled. */
+  String name;
+  /*! \brief The time when the pass was entered. */
+  Time start;
+  /*! \brief The time when the pass completed. */
+  Time end;
+  /*! \brief The total duration of the pass, i.e. end - start. */
+  Duration duration;
+  /*! \brief PassProfiles for all sub-passes invoked during the execution of the pass. */
+  std::vector<PassProfile> children;
+
+  explicit PassProfile(String name)
+      : name(name), start(Clock::now()), end(Clock::now()), children() {}
+
+  /*! \brief Gets the PassProfile of the currently executing pass. */
+  static PassProfile* Current();
+  /*! \brief Pushes a new PassProfile with the given pass name. */
+  static void EnterPass(String name);
+  /*! \brief Pops the current PassProfile. */
+  static void ExitPass();
+};
+
+struct PassProfileThreadLocalEntry {
+  /*! \brief The placeholder top-level PassProfile. */
+  PassProfile root;
+  /*! \brief The stack of PassProfiles for nested passes currently running. */
+  std::stack<PassProfile*> profile_stack;
+  /*! \brief Whether or not pass profiling is active. */
+  bool active;
+
+  PassProfileThreadLocalEntry() : root("root"), active(false) {}
+};
+
+/*! \brief Thread local store to hold the pass profiling data. */
+typedef dmlc::ThreadLocalStore<PassProfileThreadLocalEntry> PassProfileThreadLocalStore;
+
+void PassProfile::EnterPass(String name) {
+  if (!PassProfileThreadLocalStore::Get()->active) return;
+  PassProfile* cur = PassProfile::Current();
+  cur->children.emplace_back(name);
+  PassProfileThreadLocalStore::Get()->profile_stack.push(&cur->children.back());
+}
+
+void PassProfile::ExitPass() {
+  if (!PassProfileThreadLocalStore::Get()->active) return;
+  PassProfile* cur = PassProfile::Current();
+  ICHECK_NE(cur->name, "root") << "mismatched enter/exit for pass profiling";
+  cur->end = std::move(PassProfile::Clock::now());
+  cur->duration = std::chrono::duration_cast<PassProfile::Duration>(cur->end - cur->start);
+  PassProfileThreadLocalStore::Get()->profile_stack.pop();
+}
+
+PassProfile* PassProfile::Current() {
+  PassProfileThreadLocalEntry* entry = PassProfileThreadLocalStore::Get();
+  if (!entry->profile_stack.empty()) {
+    return entry->profile_stack.top();
+  } else {
+    return &entry->root;
+  }
+}
+
+IRModule Pass::operator()(IRModule mod) const {
+  const PassNode* node = operator->();
+  ICHECK(node != nullptr);
+  PassProfile::EnterPass(node->Info()->name);
+  auto ret = node->operator()(std::move(mod));
+  PassProfile::ExitPass();
+  return std::move(ret);
+}
+
+IRModule Pass::operator()(IRModule mod, const PassContext& pass_ctx) const {
+  const PassNode* node = operator->();
+  ICHECK(node != nullptr);
+  PassProfile::EnterPass(node->Info()->name);
+  auto ret = node->operator()(std::move(mod), pass_ctx);
+  PassProfile::ExitPass();
+  return std::move(ret);
+}
+
+String RenderPassProfiles() {
+  PassProfileThreadLocalEntry* entry = PassProfileThreadLocalStore::Get();
+  CHECK(entry->profile_stack.empty()) << "cannot print pass profile while still in a pass!";
+
+  if (entry->root.children.empty()) {
+    LOG(WARNING) << "no passes have been profiled, did you enable pass profiling?";
+    return String();
+  }
+
+  // (depth, parent_duration, pass)
+  std::stack<std::tuple<size_t, PassProfile::Duration, PassProfile*>> profiles;
+
+  // push top level passes
+  PassProfile::Duration top_dur(0);
+  for (auto it = entry->root.children.begin(); it != entry->root.children.end(); ++it) {
+    top_dur += it->duration;
+  }
+  for (auto it = entry->root.children.rbegin(); it != entry->root.children.rend(); ++it) {
+    profiles.push(std::make_tuple(0, top_dur, &*it));
+  }
+
+  std::ostringstream os;
+  os << std::fixed;
+
+  while (profiles.size() > 0) {
+    size_t depth;
+    PassProfile::Duration parent_duration;
+    PassProfile* profile;
+    std::tie(depth, parent_duration, profile) = profiles.top();
+    profiles.pop();
+
+    // indent depth
+    for (size_t i = 0; i < depth; ++i) {
+      os << "\t";
+    }
+
+    // calculate time spent in pass itself (excluding sub-passes), and push children
+    PassProfile::Duration self_duration = profile->duration;
+    for (auto it = profile->children.rbegin(); it != profile->children.rend(); ++it) {
+      self_duration -= it->duration;
+      profiles.push(std::make_tuple(depth + 1, profile->duration, &*it));
+    }
+
+    double parent_pct = profile->duration.count() / parent_duration.count() * 100.0;
+    double total_pct = profile->duration.count() / top_dur.count() * 100.0;
+
+    os << profile->name << ": ";
+    os << std::setprecision(0);
+    os << profile->duration.count() << "us [" << self_duration.count() << "us] ";
+    os << std::setprecision(2) << "(" << total_pct << "%; " << parent_pct << "%)\n";
+  }
+
+  return os.str();
+}
+
+TVM_REGISTER_GLOBAL("transform.render_pass_profiles").set_body_typed(RenderPassProfiles);
+
+TVM_REGISTER_GLOBAL("transform.clear_pass_profiles").set_body_typed([]() {
+  PassProfileThreadLocalStore::Get()->root.children.clear();
+});
+
+TVM_REGISTER_GLOBAL("transform.enable_pass_profiling").set_body_typed([]() {
+  PassProfileThreadLocalStore::Get()->active = true;
+});
+
+TVM_REGISTER_GLOBAL("transform.disable_pass_profiling").set_body_typed([]() {
+  PassProfileThreadLocalStore::Get()->active = false;
+});
+
 /*!
  * \brief Module-level passes are designed to implement global
  * analysis/optimizations, i.e. interprocedural optimizations (IPO), etc. Passes
diff --git a/src/node/container.cc b/src/node/container.cc
deleted file mode 100644
index b72d5a4cd736..000000000000
--- a/src/node/container.cc
+++ /dev/null
@@ -1,363 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-/*!
- *  Expose container API to frontend.
- * \file src/node/container.cc
- */
-#include <tvm/node/container.h>
-#include <tvm/runtime/container.h>
-#include <tvm/runtime/registry.h>
-#include <tvm/tir/expr.h>
-
-#include "../support/str_escape.h"
-
-namespace tvm {
-
-// SEQualReduce traits for runtime containers.
-struct StringObjTrait {
-  static constexpr const std::nullptr_t VisitAttrs = nullptr;
-
-  static void SHashReduce(const runtime::StringObj* key, SHashReducer hash_reduce) {
-    hash_reduce->SHashReduceHashedValue(runtime::String::HashBytes(key->data, key->size));
-  }
-
-  static bool SEqualReduce(const runtime::StringObj* lhs, const runtime::StringObj* rhs,
-                           SEqualReducer equal) {
-    if (lhs == rhs) return true;
-    if (lhs->size != rhs->size) return false;
-    if (lhs->data == rhs->data) return true;
-    return std::memcmp(lhs->data, rhs->data, lhs->size) == 0;
-  }
-};
-
-struct RefToObjectPtr : public ObjectRef {
-  static ObjectPtr<Object> Get(const ObjectRef& ref) { return GetDataPtr<Object>(ref); }
-};
-
-TVM_REGISTER_REFLECTION_VTABLE(runtime::StringObj, StringObjTrait)
-    .set_creator([](const std::string& bytes) {
-      return RefToObjectPtr::Get(runtime::String(bytes));
-    })
-    .set_repr_bytes([](const Object* n) -> std::string {
-      return GetRef<runtime::String>(static_cast<const runtime::StringObj*>(n))
-          .
-          operator std::string();
-    });
-
-TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
-    .set_dispatch<runtime::StringObj>([](const ObjectRef& node, ReprPrinter* p) {
-      auto* op = static_cast<const runtime::StringObj*>(node.get());
-      p->stream << '"' << support::StrEscape(op->data, op->size) << '"';
-    });
-
-struct ADTObjTrait {
-  static constexpr const std::nullptr_t VisitAttrs = nullptr;
-
-  static void SHashReduce(const runtime::ADTObj* key, SHashReducer hash_reduce) {
-    hash_reduce(key->tag);
-    hash_reduce(static_cast<uint64_t>(key->size));
-    for (uint32_t i = 0; i < key->size; ++i) {
-      hash_reduce((*key)[i]);
-    }
-  }
-
-  static bool SEqualReduce(const runtime::ADTObj* lhs, const runtime::ADTObj* rhs,
-                           SEqualReducer equal) {
-    if (lhs == rhs) return true;
-    if (lhs->tag != rhs->tag) return false;
-    if (lhs->size != rhs->size) return false;
-
-    for (uint32_t i = 0; i < lhs->size; ++i) {
-      if (!equal((*lhs)[i], (*rhs)[i])) return false;
-    }
-    return true;
-  }
-};
-
-TVM_REGISTER_REFLECTION_VTABLE(runtime::ADTObj, ADTObjTrait);
-
-struct NDArrayContainerTrait {
-  static constexpr const std::nullptr_t VisitAttrs = nullptr;
-
-  static void SHashReduce(const runtime::NDArray::Container* key, SHashReducer hash_reduce) {
-    ICHECK_EQ(key->dl_tensor.ctx.device_type, kDLCPU) << "can only compare CPU tensor";
-    ICHECK(runtime::IsContiguous(key->dl_tensor)) << "Can only hash contiguous tensor";
-    hash_reduce(runtime::DataType(key->dl_tensor.dtype));
-    hash_reduce(key->dl_tensor.ndim);
-    for (int i = 0; i < key->dl_tensor.ndim; ++i) {
-      hash_reduce(key->dl_tensor.shape[i]);
-    }
-    hash_reduce->SHashReduceHashedValue(runtime::String::HashBytes(
-        static_cast<const char*>(key->dl_tensor.data), runtime::GetDataSize(key->dl_tensor)));
-  }
-
-  static bool SEqualReduce(const runtime::NDArray::Container* lhs,
-                           const runtime::NDArray::Container* rhs, SEqualReducer equal) {
-    if (lhs == rhs) return true;
-
-    auto ldt = lhs->dl_tensor.dtype;
-    auto rdt = rhs->dl_tensor.dtype;
-    ICHECK_EQ(lhs->dl_tensor.ctx.device_type, kDLCPU) << "can only compare CPU tensor";
-    ICHECK_EQ(rhs->dl_tensor.ctx.device_type, kDLCPU) << "can only compare CPU tensor";
-    ICHECK(runtime::IsContiguous(lhs->dl_tensor)) << "Can only compare contiguous tensor";
-    ICHECK(runtime::IsContiguous(rhs->dl_tensor)) << "Can only compare contiguous tensor";
-
-    if (lhs->dl_tensor.ndim != rhs->dl_tensor.ndim) return false;
-    for (int i = 0; i < lhs->dl_tensor.ndim; ++i) {
-      if (!equal(lhs->dl_tensor.shape[i], rhs->dl_tensor.shape[i])) return false;
-    }
-    if (ldt.code == rdt.code && ldt.lanes == rdt.lanes && ldt.bits == rdt.bits) {
-      size_t data_size = runtime::GetDataSize(lhs->dl_tensor);
-      return std::memcmp(lhs->dl_tensor.data, rhs->dl_tensor.data, data_size) == 0;
-    } else {
-      return false;
-    }
-  }
-};
-
-TVM_REGISTER_REFLECTION_VTABLE(runtime::NDArray::Container, NDArrayContainerTrait);
-
-struct ArrayNodeTrait {
-  static constexpr const std::nullptr_t VisitAttrs = nullptr;
-
-  static void SHashReduce(const ArrayNode* key, SHashReducer hash_reduce) {
-    hash_reduce(static_cast<uint64_t>(key->size()));
-    for (size_t i = 0; i < key->size(); ++i) {
-      hash_reduce(key->at(i));
-    }
-  }
-
-  static bool SEqualReduce(const ArrayNode* lhs, const ArrayNode* rhs, SEqualReducer equal) {
-    if (lhs->size() != rhs->size()) return false;
-    for (size_t i = 0; i < lhs->size(); ++i) {
-      if (!equal(lhs->at(i), rhs->at(i))) return false;
-    }
-    return true;
-  }
-};
-
-TVM_REGISTER_OBJECT_TYPE(ArrayNode);
-TVM_REGISTER_REFLECTION_VTABLE(ArrayNode, ArrayNodeTrait)
-    .set_creator([](const std::string&) -> ObjectPtr<Object> {
-      return ::tvm::runtime::make_object<ArrayNode>();
-    });
-
-TVM_REGISTER_GLOBAL("node.Array").set_body([](TVMArgs args, TVMRetValue* ret) {
-  std::vector<ObjectRef> data;
-  for (int i = 0; i < args.size(); ++i) {
-    if (args[i].type_code() != kTVMNullptr) {
-      data.push_back(args[i].operator ObjectRef());
-    } else {
-      data.push_back(ObjectRef(nullptr));
-    }
-  }
-  *ret = Array<ObjectRef>(data);
-});
-
-TVM_REGISTER_GLOBAL("node.ArrayGetItem").set_body([](TVMArgs args, TVMRetValue* ret) {
-  int64_t i = args[1];
-  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
-  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
-  ICHECK(ptr->IsInstance<ArrayNode>());
-  auto* n = static_cast<const ArrayNode*>(ptr);
-  ICHECK_LT(static_cast<size_t>(i), n->size()) << "out of bound of array";
-  *ret = n->at(i);
-});
-
-TVM_REGISTER_GLOBAL("node.ArraySize").set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
-  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
-  ICHECK(ptr->IsInstance<ArrayNode>());
-  *ret = static_cast<int64_t>(static_cast<const ArrayNode*>(ptr)->size());
-});
-
-struct MapNodeTrait {
-  static constexpr const std::nullptr_t VisitAttrs = nullptr;
-
-  static void SHashReduceForOMap(const MapNode* key, SHashReducer hash_reduce) {
-    // SHash's var handling depends on the determinism of traversal.
-    // NOTE: only book-keep the mapped hash keys.
-    // This resolves common use cases where we want to store
-    // Map<Var, Value> where Var is defined in the function
-    // parameters.
-    using KV = std::pair<size_t, ObjectRef>;
-    std::vector<KV> temp;
-    for (const auto& kv : *key) {
-      size_t hashed_value;
-      if (hash_reduce->LookupHashedValue(kv.first, &hashed_value)) {
-        temp.emplace_back(hashed_value, kv.second);
-      }
-    }
-    // sort by the hash key of the keys.
-    std::sort(temp.begin(), temp.end(),
-              [](const KV& lhs, const KV& rhs) { return lhs.first < rhs.first; });
-    // add size to the hash
-    hash_reduce(static_cast<uint64_t>(key->size()));
-    // hash the content
-    for (size_t i = 0; i < temp.size();) {
-      size_t k = i + 1;
-      for (; k < temp.size() && temp[k].first == temp[i].first; ++k) {
-      }
-      // ties are rare, but we need to skip them to make the hash determinsitic
-      if (k == i + 1) {
-        hash_reduce->SHashReduceHashedValue(temp[i].first);
-        hash_reduce(temp[i].second);
-      }
-      i = k;
-    }
-  }
-
-  static void SHashReduceForSMap(const MapNode* key, SHashReducer hash_reduce) {
-    // NOTE: only book-keep the mapped hash keys.
-    // This resolves common use cases where we want to store
-    // Map<Var, Value> where Var is defined in the function
-    // parameters.
-    using KV = std::pair<String, ObjectRef>;
-    std::vector<KV> temp;
-    for (const auto& kv : *key) {
-      temp.push_back(std::make_pair(Downcast<String>(kv.first), kv.second));
-    }
-    // sort by the hash key of the keys.
-    std::sort(temp.begin(), temp.end(),
-              [](const KV& lhs, const KV& rhs) { return lhs.first < rhs.first; });
-    // NOTE: we won't have ties
-    // add size to the hash after sorting.
-    hash_reduce(static_cast<uint64_t>(key->size()));
-    // hash the content
-    for (size_t i = 0; i < temp.size(); ++i) {
-      hash_reduce(temp[i].first);
-      hash_reduce(temp[i].second);
-    }
-  }
-
-  static void SHashReduce(const MapNode* key, SHashReducer hash_reduce) {
-    bool is_str_map = std::all_of(key->begin(), key->end(), [](const auto& v) {
-      return v.first->template IsInstance<StringObj>();
-    });
-    if (is_str_map) {
-      SHashReduceForSMap(key, hash_reduce);
-    } else {
-      SHashReduceForOMap(key, hash_reduce);
-    }
-  }
-
-  static bool SEqualReduceForOMap(const MapNode* lhs, const MapNode* rhs, SEqualReducer equal) {
-    for (const auto& kv : *lhs) {
-      // Only allow equal checking if the keys are already mapped
-      // This resolves common use cases where we want to store
-      // Map<Var, Value> where Var is defined in the function
-      // parameters.
-      ObjectRef rhs_key = equal->MapLhsToRhs(kv.first);
-      if (!rhs_key.defined()) return false;
-      auto it = rhs->find(rhs_key);
-      if (it == rhs->end()) return false;
-      if (!equal(kv.second, it->second)) return false;
-    }
-    return true;
-  }
-
-  static bool SEqualReduceForSMap(const MapNode* lhs, const MapNode* rhs, SEqualReducer equal) {
-    for (const auto& kv : *lhs) {
-      auto it = rhs->find(kv.first);
-      if (it == rhs->end()) return false;
-      if (!equal(kv.second, it->second)) return false;
-    }
-    return true;
-  }
-
-  static bool SEqualReduce(const MapNode* lhs, const MapNode* rhs, SEqualReducer equal) {
-    if (rhs->size() != lhs->size()) return false;
-    if (rhs->size() == 0) return true;
-    bool ls = std::all_of(lhs->begin(), lhs->end(),
-                          [](const auto& v) { return v.first->template IsInstance<StringObj>(); });
-    bool rs = std::all_of(rhs->begin(), rhs->end(),
-                          [](const auto& v) { return v.first->template IsInstance<StringObj>(); });
-    if (ls != rs) {
-      return false;
-    }
-    return (ls && rs) ? SEqualReduceForSMap(lhs, rhs, equal) : SEqualReduceForOMap(lhs, rhs, equal);
-  }
-};
-
-TVM_REGISTER_OBJECT_TYPE(MapNode);
-TVM_REGISTER_REFLECTION_VTABLE(MapNode, MapNodeTrait)
-    .set_creator([](const std::string&) -> ObjectPtr<Object> { return MapNode::Empty(); });
-
-TVM_REGISTER_GLOBAL("node.Map").set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_EQ(args.size() % 2, 0);
-  std::unordered_map<ObjectRef, ObjectRef, ObjectPtrHash, ObjectPtrEqual> data;
-  for (int i = 0; i < args.num_args; i += 2) {
-    ObjectRef k =
-        String::CanConvertFrom(args[i]) ? args[i].operator String() : args[i].operator ObjectRef();
-    ObjectRef v = args[i + 1];
-    data.emplace(std::move(k), std::move(v));
-  }
-  *ret = Map<ObjectRef, ObjectRef>(std::move(data));
-});
-
-TVM_REGISTER_GLOBAL("node.MapSize").set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
-  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
-  ICHECK(ptr->IsInstance<MapNode>());
-  auto* n = static_cast<const MapNode*>(ptr);
-  *ret = static_cast<int64_t>(n->size());
-});
-
-TVM_REGISTER_GLOBAL("node.MapGetItem").set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
-  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
-  ICHECK(ptr->IsInstance<MapNode>());
-
-  auto* n = static_cast<const MapNode*>(ptr);
-  auto it = n->find(String::CanConvertFrom(args[1]) ? args[1].operator String()
-                                                    : args[1].operator ObjectRef());
-  ICHECK(it != n->end()) << "cannot find the corresponding key in the Map";
-  *ret = (*it).second;
-});
-
-TVM_REGISTER_GLOBAL("node.MapCount").set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
-  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
-  ICHECK(ptr->IsInstance<MapNode>());
-  const MapNode* n = static_cast<const MapNode*>(ptr);
-  int64_t cnt = n->count(String::CanConvertFrom(args[1]) ? args[1].operator String()
-                                                         : args[1].operator ObjectRef());
-  *ret = cnt;
-});
-
-TVM_REGISTER_GLOBAL("node.MapItems").set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
-  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
-  auto* n = static_cast<const MapNode*>(ptr);
-  Array<ObjectRef> rkvs;
-  for (const auto& kv : *n) {
-    if (kv.first->IsInstance<StringObj>()) {
-      rkvs.push_back(Downcast<String>(kv.first));
-    } else {
-      rkvs.push_back(kv.first);
-    }
-    rkvs.push_back(kv.second);
-  }
-  *ret = std::move(rkvs);
-});
-
-#if (USE_FALLBACK_STL_MAP == 0)
-TVM_DLL constexpr uint64_t DenseMapNode::kNextProbeLocation[];
-#endif
-}  // namespace tvm
diff --git a/src/node/reflection.cc b/src/node/reflection.cc
index 9dc9d330bb77..79a53aa26440 100644
--- a/src/node/reflection.cc
+++ b/src/node/reflection.cc
@@ -22,9 +22,9 @@
  * \file node/reflection.cc
  */
 #include <tvm/ir/attrs.h>
-#include <tvm/node/container.h>
 #include <tvm/node/node.h>
 #include <tvm/node/reflection.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/registry.h>
 
 namespace tvm {
diff --git a/src/node/serialization.cc b/src/node/serialization.cc
index c7e4d27c8b2c..ad42799b55e5 100644
--- a/src/node/serialization.cc
+++ b/src/node/serialization.cc
@@ -24,9 +24,9 @@
 #include <dmlc/json.h>
 #include <dmlc/memory_io.h>
 #include <tvm/ir/attrs.h>
-#include <tvm/node/container.h>
 #include <tvm/node/reflection.h>
 #include <tvm/node/serialization.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
diff --git a/src/node/structural_hash.cc b/src/node/structural_hash.cc
index e0b729d3f103..efedd1b99d6d 100644
--- a/src/node/structural_hash.cc
+++ b/src/node/structural_hash.cc
@@ -28,6 +28,7 @@
 #include <algorithm>
 #include <unordered_map>
 
+#include "../support/str_escape.h"
 #include "../support/utils.h"
 
 namespace tvm {
@@ -260,4 +261,241 @@ size_t StructuralHash::operator()(const ObjectRef& object) const {
   return VarCountingSHashHandler().Hash(object, false);
 }
 
+// SEQualReduce traits for runtime containers.
+struct StringObjTrait {
+  static constexpr const std::nullptr_t VisitAttrs = nullptr;
+
+  static void SHashReduce(const runtime::StringObj* key, SHashReducer hash_reduce) {
+    hash_reduce->SHashReduceHashedValue(runtime::String::HashBytes(key->data, key->size));
+  }
+
+  static bool SEqualReduce(const runtime::StringObj* lhs, const runtime::StringObj* rhs,
+                           SEqualReducer equal) {
+    if (lhs == rhs) return true;
+    if (lhs->size != rhs->size) return false;
+    if (lhs->data == rhs->data) return true;
+    return std::memcmp(lhs->data, rhs->data, lhs->size) == 0;
+  }
+};
+
+struct RefToObjectPtr : public ObjectRef {
+  static ObjectPtr<Object> Get(const ObjectRef& ref) { return GetDataPtr<Object>(ref); }
+};
+
+TVM_REGISTER_REFLECTION_VTABLE(runtime::StringObj, StringObjTrait)
+    .set_creator([](const std::string& bytes) {
+      return RefToObjectPtr::Get(runtime::String(bytes));
+    })
+    .set_repr_bytes([](const Object* n) -> std::string {
+      return GetRef<runtime::String>(static_cast<const runtime::StringObj*>(n))
+          .
+          operator std::string();
+    });
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<runtime::StringObj>([](const ObjectRef& node, ReprPrinter* p) {
+      auto* op = static_cast<const runtime::StringObj*>(node.get());
+      p->stream << '"' << support::StrEscape(op->data, op->size) << '"';
+    });
+
+struct ADTObjTrait {
+  static constexpr const std::nullptr_t VisitAttrs = nullptr;
+
+  static void SHashReduce(const runtime::ADTObj* key, SHashReducer hash_reduce) {
+    hash_reduce(key->tag);
+    hash_reduce(static_cast<uint64_t>(key->size));
+    for (uint32_t i = 0; i < key->size; ++i) {
+      hash_reduce((*key)[i]);
+    }
+  }
+
+  static bool SEqualReduce(const runtime::ADTObj* lhs, const runtime::ADTObj* rhs,
+                           SEqualReducer equal) {
+    if (lhs == rhs) return true;
+    if (lhs->tag != rhs->tag) return false;
+    if (lhs->size != rhs->size) return false;
+
+    for (uint32_t i = 0; i < lhs->size; ++i) {
+      if (!equal((*lhs)[i], (*rhs)[i])) return false;
+    }
+    return true;
+  }
+};
+
+TVM_REGISTER_REFLECTION_VTABLE(runtime::ADTObj, ADTObjTrait);
+
+struct NDArrayContainerTrait {
+  static constexpr const std::nullptr_t VisitAttrs = nullptr;
+
+  static void SHashReduce(const runtime::NDArray::Container* key, SHashReducer hash_reduce) {
+    ICHECK_EQ(key->dl_tensor.ctx.device_type, kDLCPU) << "can only compare CPU tensor";
+    ICHECK(runtime::IsContiguous(key->dl_tensor)) << "Can only hash contiguous tensor";
+    hash_reduce(runtime::DataType(key->dl_tensor.dtype));
+    hash_reduce(key->dl_tensor.ndim);
+    for (int i = 0; i < key->dl_tensor.ndim; ++i) {
+      hash_reduce(key->dl_tensor.shape[i]);
+    }
+    hash_reduce->SHashReduceHashedValue(runtime::String::HashBytes(
+        static_cast<const char*>(key->dl_tensor.data), runtime::GetDataSize(key->dl_tensor)));
+  }
+
+  static bool SEqualReduce(const runtime::NDArray::Container* lhs,
+                           const runtime::NDArray::Container* rhs, SEqualReducer equal) {
+    if (lhs == rhs) return true;
+
+    auto ldt = lhs->dl_tensor.dtype;
+    auto rdt = rhs->dl_tensor.dtype;
+    ICHECK_EQ(lhs->dl_tensor.ctx.device_type, kDLCPU) << "can only compare CPU tensor";
+    ICHECK_EQ(rhs->dl_tensor.ctx.device_type, kDLCPU) << "can only compare CPU tensor";
+    ICHECK(runtime::IsContiguous(lhs->dl_tensor)) << "Can only compare contiguous tensor";
+    ICHECK(runtime::IsContiguous(rhs->dl_tensor)) << "Can only compare contiguous tensor";
+
+    if (lhs->dl_tensor.ndim != rhs->dl_tensor.ndim) return false;
+    for (int i = 0; i < lhs->dl_tensor.ndim; ++i) {
+      if (!equal(lhs->dl_tensor.shape[i], rhs->dl_tensor.shape[i])) return false;
+    }
+    if (ldt.code == rdt.code && ldt.lanes == rdt.lanes && ldt.bits == rdt.bits) {
+      size_t data_size = runtime::GetDataSize(lhs->dl_tensor);
+      return std::memcmp(lhs->dl_tensor.data, rhs->dl_tensor.data, data_size) == 0;
+    } else {
+      return false;
+    }
+  }
+};
+
+TVM_REGISTER_REFLECTION_VTABLE(runtime::NDArray::Container, NDArrayContainerTrait);
+
+struct ArrayNodeTrait {
+  static constexpr const std::nullptr_t VisitAttrs = nullptr;
+
+  static void SHashReduce(const ArrayNode* key, SHashReducer hash_reduce) {
+    hash_reduce(static_cast<uint64_t>(key->size()));
+    for (size_t i = 0; i < key->size(); ++i) {
+      hash_reduce(key->at(i));
+    }
+  }
+
+  static bool SEqualReduce(const ArrayNode* lhs, const ArrayNode* rhs, SEqualReducer equal) {
+    if (lhs->size() != rhs->size()) return false;
+    for (size_t i = 0; i < lhs->size(); ++i) {
+      if (!equal(lhs->at(i), rhs->at(i))) return false;
+    }
+    return true;
+  }
+};
+TVM_REGISTER_REFLECTION_VTABLE(ArrayNode, ArrayNodeTrait)
+    .set_creator([](const std::string&) -> ObjectPtr<Object> {
+      return ::tvm::runtime::make_object<ArrayNode>();
+    });
+
+struct MapNodeTrait {
+  static constexpr const std::nullptr_t VisitAttrs = nullptr;
+
+  static void SHashReduceForOMap(const MapNode* key, SHashReducer hash_reduce) {
+    // SHash's var handling depends on the determinism of traversal.
+    // NOTE: only book-keep the mapped hash keys.
+    // This resolves common use cases where we want to store
+    // Map<Var, Value> where Var is defined in the function
+    // parameters.
+    using KV = std::pair<size_t, ObjectRef>;
+    std::vector<KV> temp;
+    for (const auto& kv : *key) {
+      size_t hashed_value;
+      if (hash_reduce->LookupHashedValue(kv.first, &hashed_value)) {
+        temp.emplace_back(hashed_value, kv.second);
+      }
+    }
+    // sort by the hash key of the keys.
+    std::sort(temp.begin(), temp.end(),
+              [](const KV& lhs, const KV& rhs) { return lhs.first < rhs.first; });
+    // add size to the hash
+    hash_reduce(static_cast<uint64_t>(key->size()));
+    // hash the content
+    for (size_t i = 0; i < temp.size();) {
+      size_t k = i + 1;
+      for (; k < temp.size() && temp[k].first == temp[i].first; ++k) {
+      }
+      // ties are rare, but we need to skip them to make the hash determinsitic
+      if (k == i + 1) {
+        hash_reduce->SHashReduceHashedValue(temp[i].first);
+        hash_reduce(temp[i].second);
+      }
+      i = k;
+    }
+  }
+
+  static void SHashReduceForSMap(const MapNode* key, SHashReducer hash_reduce) {
+    // NOTE: only book-keep the mapped hash keys.
+    // This resolves common use cases where we want to store
+    // Map<Var, Value> where Var is defined in the function
+    // parameters.
+    using KV = std::pair<String, ObjectRef>;
+    std::vector<KV> temp;
+    for (const auto& kv : *key) {
+      temp.push_back(std::make_pair(Downcast<String>(kv.first), kv.second));
+    }
+    // sort by the hash key of the keys.
+    std::sort(temp.begin(), temp.end(),
+              [](const KV& lhs, const KV& rhs) { return lhs.first < rhs.first; });
+    // NOTE: we won't have ties
+    // add size to the hash after sorting.
+    hash_reduce(static_cast<uint64_t>(key->size()));
+    // hash the content
+    for (size_t i = 0; i < temp.size(); ++i) {
+      hash_reduce(temp[i].first);
+      hash_reduce(temp[i].second);
+    }
+  }
+
+  static void SHashReduce(const MapNode* key, SHashReducer hash_reduce) {
+    bool is_str_map = std::all_of(key->begin(), key->end(), [](const auto& v) {
+      return v.first->template IsInstance<StringObj>();
+    });
+    if (is_str_map) {
+      SHashReduceForSMap(key, hash_reduce);
+    } else {
+      SHashReduceForOMap(key, hash_reduce);
+    }
+  }
+
+  static bool SEqualReduceForOMap(const MapNode* lhs, const MapNode* rhs, SEqualReducer equal) {
+    for (const auto& kv : *lhs) {
+      // Only allow equal checking if the keys are already mapped
+      // This resolves common use cases where we want to store
+      // Map<Var, Value> where Var is defined in the function
+      // parameters.
+      ObjectRef rhs_key = equal->MapLhsToRhs(kv.first);
+      if (!rhs_key.defined()) return false;
+      auto it = rhs->find(rhs_key);
+      if (it == rhs->end()) return false;
+      if (!equal(kv.second, it->second)) return false;
+    }
+    return true;
+  }
+
+  static bool SEqualReduceForSMap(const MapNode* lhs, const MapNode* rhs, SEqualReducer equal) {
+    for (const auto& kv : *lhs) {
+      auto it = rhs->find(kv.first);
+      if (it == rhs->end()) return false;
+      if (!equal(kv.second, it->second)) return false;
+    }
+    return true;
+  }
+
+  static bool SEqualReduce(const MapNode* lhs, const MapNode* rhs, SEqualReducer equal) {
+    if (rhs->size() != lhs->size()) return false;
+    if (rhs->size() == 0) return true;
+    bool ls = std::all_of(lhs->begin(), lhs->end(),
+                          [](const auto& v) { return v.first->template IsInstance<StringObj>(); });
+    bool rs = std::all_of(rhs->begin(), rhs->end(),
+                          [](const auto& v) { return v.first->template IsInstance<StringObj>(); });
+    if (ls != rs) {
+      return false;
+    }
+    return (ls && rs) ? SEqualReduceForSMap(lhs, rhs, equal) : SEqualReduceForOMap(lhs, rhs, equal);
+  }
+};
+TVM_REGISTER_REFLECTION_VTABLE(MapNode, MapNodeTrait)
+    .set_creator([](const std::string&) -> ObjectPtr<Object> { return MapNode::Empty(); });
+
 }  // namespace tvm
diff --git a/src/parser/parser.cc b/src/parser/parser.cc
index afcf70737933..c7d8e025848a 100644
--- a/src/parser/parser.cc
+++ b/src/parser/parser.cc
@@ -28,9 +28,9 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/function.h>
 #include <tvm/relay/transform.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/object.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <fstream>
 
@@ -172,8 +172,8 @@ class ScopeStack {
   void PopStack() { this->scope_stack.pop_back(); }
 };
 
-struct DuplicateKeyError : public dmlc::Error {
-  explicit DuplicateKeyError(const std::string& msg) : dmlc::Error(msg) {}
+struct DuplicateKeyError : public Error {
+  explicit DuplicateKeyError(const std::string& msg) : Error(msg) {}
 };
 
 /*! \brief A table of interning strings as global function and type names. */
@@ -1334,6 +1334,8 @@ class Parser {
       case TokenType::kBoolean:
       case TokenType::kStringLiteral:
         return Match(next->token_type)->data;
+      case TokenType::kMetaReference:
+        return ParseMetaRef();
       case TokenType::kLSquare: {
         return ParseSequence<ObjectRef>(TokenType::kLSquare, TokenType::kComma, TokenType::kRSquare,
                                         [&]() { return ParseAttributeValue(); });
@@ -1408,7 +1410,7 @@ class Parser {
             auto last_meta = Lookahead(2)->token_type == TokenType::kCloseParen;
             auto is_meta_attrs = is_meta_next && last_meta;
 
-            if (is_op && (is_pretty_attrs || is_meta_attrs)) {
+            if (is_pretty_attrs || is_meta_attrs) {
               if (is_meta_attrs) {
                 auto meta_ref = ParseMetaRef();
                 if (meta_ref.as<BaseAttrsNode>()) {
@@ -1420,13 +1422,23 @@ class Parser {
                 }
               } else {
                 auto raw_attrs = ParseAttrs();
-                auto attr_obj = tvm::ReflectionVTable::Global()->CreateObject(op_key, raw_attrs);
-                ICHECK(attr_obj.defined());
-                attrs = Downcast<Attrs>(attr_obj);
+                if (is_op && op_key.size()) {
+                  auto attr_obj = tvm::ReflectionVTable::Global()->CreateObject(op_key, raw_attrs);
+                  ICHECK(attr_obj.defined());
+                  attrs = Downcast<Attrs>(attr_obj);
+                } else if (raw_attrs.count("attrs_type_key")) {
+                  String attr_key = Downcast<String>(raw_attrs["attrs_type_key"]);
+                  if (attr_key.size()) {
+                    raw_attrs.erase("attrs_type_key");
+                    auto tbl = tvm::ReflectionVTable::Global();
+                    auto attr_obj = tbl->CreateObject(attr_key, raw_attrs);
+                    ICHECK(attr_obj.defined());
+                    attrs = Downcast<Attrs>(attr_obj);
+                  }
+                }
               }
               return true;
             }
-
             return false;
           });
 
@@ -1480,7 +1492,7 @@ class Parser {
     DLOG(INFO) << "op_name=" << op_name << " span=" << span;
     try {
       return Op::Get(op_name);
-    } catch (const dmlc::Error& e) {
+    } catch (const Error& e) {
       // we can relax this, but probably need to relax checks or return non-null here.
       this->diag_ctx.EmitFatal(Diagnostic::Error(span)
                                << "operator `" << op_name
diff --git a/src/parser/span_check.h b/src/parser/span_check.h
index 9a887474fe67..ab71d30a54f5 100644
--- a/src/parser/span_check.h
+++ b/src/parser/span_check.h
@@ -30,8 +30,8 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/runtime/container.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/object.h>
-#include <tvm/support/logging.h>
 
 #include <fstream>
 #include <string>
diff --git a/src/parser/tokenizer.h b/src/parser/tokenizer.h
index c6fb3e09f4d1..5e71794cc7fb 100644
--- a/src/parser/tokenizer.h
+++ b/src/parser/tokenizer.h
@@ -212,6 +212,25 @@ struct Tokenizer {
     }
   }
 
+  Token ParseNumber(bool is_pos) {
+    std::stringstream ss;
+    while (More() && IsNumeric(Peek())) {
+      ss << Next();
+    }
+
+    bool is_float = false;
+
+    // Remove trailing floating point prefix.
+    if (More() && Peek() == 'f') {
+      ss << Next();
+      while (More() && IsNumeric(Peek())) {
+        ss << Next();
+      }
+      is_float = true;
+    }
+    return ParseNumber(is_pos, is_float, ss.str());
+  }
+
   bool MatchString(const std::string& string) {
     int start = this->pos;
 
@@ -340,38 +359,28 @@ struct Tokenizer {
       auto token = NewToken(TokenType::kWhitespace);
       Next();
       return token;
-    } else if (IsDigit(next) || next == '-') {
+    } else if (next == '-') {
       int negs = 0;
       while (More() && Peek() == '-') {
         Next();
         negs++;
       }
-      // If there isn't a number right after either,
-      // this is really slow for lexing, should replace
-      // with multi-token return or something.
-      if (negs && !IsDigit(Peek())) {
+      bool is_neg = negs % 2 == 1;
+      if (More() && IsDigit(Peek())) {
+        return ParseNumber(!is_neg);
+      } else if (More() && MatchString("inff")) {
+        return ParseNumber(!is_neg, true, "inff");
+      } else {
+        // If there isn't a number right after either,
+        // this is really slow for lexing, should replace
+        // with multi-token return or something.
         pos = pos - (negs - 1);
         return NewToken(TokenType::kMinus);
       }
-
-      bool is_neg = negs % 2 == 1;
-      std::stringstream ss;
-      while (More() && IsNumeric(Peek())) {
-        ss << Next();
-      }
-
-      bool is_float = false;
-
-      // Remove trailing floating point prefix.
-      if (More() && Peek() == 'f') {
-        ss << Next();
-        while (More() && IsNumeric(Peek())) {
-          ss << Next();
-        }
-        is_float = true;
-      }
-
-      return ParseNumber(!is_neg, is_float, ss.str());
+    } else if (IsDigit(next)) {
+      return ParseNumber(true);
+    } else if (MatchString("inff")) {
+      return ParseNumber(true, true, "inff");
     } else if (next == '.') {
       auto token = NewToken(TokenType::kPeriod);
       Next();
@@ -404,10 +413,6 @@ struct Tokenizer {
       auto token = NewToken(TokenType::kPlus);
       Next();
       return token;
-    } else if (next == '-') {
-      auto token = NewToken(TokenType::kMinus);
-      Next();
-      return token;
     } else if (next == '*') {
       auto token = NewToken(TokenType::kStar);
       Next();
diff --git a/src/printer/meta_data.h b/src/printer/meta_data.h
index 233da1baffd8..f76c32d353cf 100644
--- a/src/printer/meta_data.h
+++ b/src/printer/meta_data.h
@@ -24,8 +24,8 @@
 #ifndef TVM_PRINTER_META_DATA_H_
 #define TVM_PRINTER_META_DATA_H_
 
-#include <tvm/node/container.h>
 #include <tvm/node/serialization.h>
+#include <tvm/runtime/container.h>
 
 #include <string>
 #include <unordered_map>
diff --git a/src/printer/relay_text_printer.cc b/src/printer/relay_text_printer.cc
index da4f8cadfb3d..cbee04f96096 100644
--- a/src/printer/relay_text_printer.cc
+++ b/src/printer/relay_text_printer.cc
@@ -827,6 +827,11 @@ std::vector<Doc> RelayTextPrinter::PrintCallAttrs(const Attrs& attrs, const Expr
   } else {
     AttrPrinter printer(&docs, this);
     const_cast<BaseAttrsNode*>(attrs.operator->())->VisitNonDefaultAttrs(&printer);
+    if (!op_node) {
+      // print call attr type key to restore expr for relay parser
+      std::string s = std::string(attrs->GetTypeKey());
+      printer.Visit("attrs_type_key", &s);
+    }
     return docs;
   }
 }
diff --git a/src/printer/text_printer.h b/src/printer/text_printer.h
index 9a24fe65b4b1..6ec32a9e104c 100644
--- a/src/printer/text_printer.h
+++ b/src/printer/text_printer.h
@@ -308,6 +308,7 @@ class TIRTextPrinter : public StmtFunctor<Doc(const Stmt&)>,
   Doc VisitStmt_(const SeqStmtNode* op) override;
   Doc VisitStmt_(const EvaluateNode* op) override;
   Doc VisitStmt_(const ForNode* op) override;
+  Doc VisitStmt_(const WhileNode* op) override;
   Doc VisitStmt_(const PrefetchNode* op) override;
   Doc VisitStmtDefault_(const Object* op) override;
 
diff --git a/src/printer/tir_text_printer.cc b/src/printer/tir_text_printer.cc
index 107817db29b3..8d5bba5e5bb0 100644
--- a/src/printer/tir_text_printer.cc
+++ b/src/printer/tir_text_printer.cc
@@ -301,7 +301,7 @@ Doc TIRTextPrinter::VisitExpr_(const NotNode* op) {
 Doc TIRTextPrinter::VisitExpr_(const SelectNode* op) {
   Doc doc;
   doc << "select(" << Print(op->condition) << ", " << Print(op->true_value) << ", "
-      << Print(op->false_value);
+      << Print(op->false_value) << ")";
   return doc;
 }
 
@@ -465,18 +465,21 @@ Doc TIRTextPrinter::VisitStmt_(const EvaluateNode* op) {
   return doc;
 }
 
-inline const char* ForType2String(ForType t) {
+inline const char* ForKind2String(ForKind t) {
   switch (t) {
-    case ForType::Serial:
+    case ForKind::kSerial:
       return "serial";
-    case ForType::Parallel:
+    case ForKind::kParallel:
       return "parallel";
-    case ForType::Vectorized:
+    case ForKind::kVectorized:
       return "vectorized";
-    case ForType::Unrolled:
+    case ForKind::kUnrolled:
       return "unroll";
+    case ForKind::kThreadBinding:
+      LOG(FATAL) << "Loop ThreadBinding is reserved for future used and "
+                 << "not yet supported in TIR";
   }
-  LOG(FATAL) << "Unknown ForType";
+  LOG(FATAL) << "Unknown ForKind";
   return "Unknown";
 }
 
@@ -484,13 +487,20 @@ Doc TIRTextPrinter::VisitStmt_(const ForNode* op) {
   Doc doc;
   doc << "for (" << Print(op->loop_var) << ", " << Print(op->min) << ", "
       << Print(op->min + op->extent) << ")";
-  if (op->for_type != ForType::Serial) {
-    doc << " " << Doc::StrLiteral(ForType2String(op->for_type));
+  if (op->kind != ForKind::kSerial) {
+    doc << " " << Doc::StrLiteral(ForKind2String(op->kind));
   }
   doc << PrintBody(op->body);
   return doc;
 }
 
+Doc TIRTextPrinter::VisitStmt_(const WhileNode* op) {
+  Doc doc;
+  doc << "while (" << Print(op->condition) << ")";
+  doc << PrintBody(op->body);
+  return doc;
+}
+
 Doc TIRTextPrinter::VisitStmt_(const PrefetchNode* op) {
   Doc doc;
   doc << "prefetch(" << Print(op->buffer) << ", " << Print(op->bounds) << ")";
diff --git a/src/printer/tvmscript_printer.cc b/src/printer/tvmscript_printer.cc
index 09f95e44b6d8..86b175e1676c 100644
--- a/src/printer/tvmscript_printer.cc
+++ b/src/printer/tvmscript_printer.cc
@@ -649,27 +649,30 @@ Doc TVMScriptPrinter::VisitStmt_(const EvaluateNode* op) {
   return doc;
 }
 
-inline const char* ForType2String(ForType t) {
+inline const char* ForKind2String(ForKind t) {
   switch (t) {
-    case ForType::Serial:
+    case ForKind::kSerial:
       return "serial";
-    case ForType::Parallel:
+    case ForKind::kParallel:
       return "parallel";
-    case ForType::Vectorized:
+    case ForKind::kVectorized:
       return "vectorized";
-    case ForType::Unrolled:
+    case ForKind::kUnrolled:
       return "unroll";
+    case ForKind::kThreadBinding:
+      LOG(FATAL) << "Loop ThreadBinding is reserved for future used and "
+                 << "not yet supported in TIR";
+      return "threadbinding";
   }
-  LOG(FATAL) << "Unknown ForType";
+  LOG(FATAL) << "Unknown ForKind";
   return "Unknown";
 }
 
 Doc TVMScriptPrinter::VisitStmt_(const ForNode* op) {
   Doc doc;
   var_not_in_headers.insert(op->loop_var.get());
-  doc << "for " << Print(op->loop_var)
-      << " in tir." + std::string(ForType2String(op->for_type)) + "(" << Print(op->min) << ", "
-      << Print(op->min + op->extent)
+  doc << "for " << Print(op->loop_var) << " in tir." + std::string(ForKind2String(op->kind)) + "("
+      << Print(op->min) << ", " << Print(op->min + op->extent)
       << "):" << Doc::Indent(4, Doc::NewLine() << PrintBody(op->body));
   return doc;
 }
diff --git a/src/relay/analysis/annotated_region_set.cc b/src/relay/analysis/annotated_region_set.cc
index 04a18c4b7351..85a9c51a2fa8 100644
--- a/src/relay/analysis/annotated_region_set.cc
+++ b/src/relay/analysis/annotated_region_set.cc
@@ -157,8 +157,9 @@ class AnnotatedRegionSet::Creator : protected MixedModeVisitor {
       // Check if the argument already belongs to a region
       auto region = region_set_->GetRegion(call->args[0]);
       if (!region.defined()) {
-        throw Error(ErrorBuilder() << "Cannot find the corresponding region for end annotation:\n"
-                                   << AsText(GetRef<Call>(call), false));
+        throw CompileError(ErrorBuilder()
+                           << "Cannot find the corresponding region for end annotation:\n"
+                           << AsText(GetRef<Call>(call), false));
       } else {
         // If the argument is belonged to a region, it must have the same target.
         // Otherwise we should see a region_begin op.
diff --git a/src/relay/analysis/kind_check.cc b/src/relay/analysis/kind_check.cc
index c7c5a0a9f083..65b8516cb16c 100644
--- a/src/relay/analysis/kind_check.cc
+++ b/src/relay/analysis/kind_check.cc
@@ -139,7 +139,7 @@ struct KindChecker : TypeFunctor<Kind(const Type&)> {
                   << "Expected " << data->type_vars.size() << "arguments for " << tc << "; got "
                   << op->args.size());
       }
-    } catch (const dmlc::Error& err) {
+    } catch (const Error& err) {
       // TODO(@jroesch): can probably relax to just emit
       EmitFatal(Diagnostic::Error(op->span)
                 << "the type variable : `" << var->name_hint << "` is undefined");
diff --git a/src/relay/analysis/match_exhaustion.cc b/src/relay/analysis/match_exhaustion.cc
index bb6e8f14ca09..2a90b911b676 100644
--- a/src/relay/analysis/match_exhaustion.cc
+++ b/src/relay/analysis/match_exhaustion.cc
@@ -124,9 +124,14 @@ class CandidateChecker : public PatternFunctor<MatchResult(const Pattern&, const
   }
 };
 
-// Returns list of arrays corresponding to Cartesian product of input list
+// Returns list of arrays corresponding to Cartesian product of input list.
+// Note: CartesianProduct({}) = {{}}
 Array<Array<Pattern>> CartesianProduct(Array<Array<Pattern>> fields) {
-  ICHECK_NE(fields.size(), 0);
+  // the only combination of 0 fields is 0 fields
+  if (fields.size() == 0) {
+    return {{}};
+  }
+
   Array<Pattern> field_vals = fields[fields.size() - 1];
   Array<Array<Pattern>> ret;
 
@@ -197,7 +202,7 @@ Array<Pattern> ExpandWildcardsConstructor(const PatternConstructor& clause_ctor,
 
   auto ctor_cand = Downcast<PatternConstructor>(cand);
 
-  // for constructors, we will expand the wildcards in any field that is an ADT.
+  // expand all fields' wildcards
   Array<Array<Pattern>> values_by_field;
   for (size_t i = 0; i < ctor_cand->constructor->inputs.size(); i++) {
     values_by_field.push_back(
@@ -217,7 +222,7 @@ Array<Pattern> ExpandWildcardsConstructor(const PatternConstructor& clause_ctor,
 // Returns a list of all possible expansions.
 Array<Pattern> ExpandWildcardsTuple(const PatternTuple& clause_tuple, const Pattern& cand,
                                     const IRModule& mod) {
-  // for a wildcard node, create constructor nodes with wildcards for all args.
+  // for a wildcard node, create tuple with wildcards for all args.
   if (cand.as<PatternWildcardNode>()) {
     Array<Pattern> args;
     for (auto inp : clause_tuple->patterns) {
@@ -228,7 +233,7 @@ Array<Pattern> ExpandWildcardsTuple(const PatternTuple& clause_tuple, const Patt
 
   auto tuple_cand = Downcast<PatternTuple>(cand);
 
-  // for constructors, we will expand the wildcards in any field that is an ADT.
+  // expand all members' patterns
   Array<Array<Pattern>> values_by_field;
   for (size_t i = 0; i < tuple_cand->patterns.size(); i++) {
     values_by_field.push_back(
diff --git a/src/relay/analysis/type_solver.cc b/src/relay/analysis/type_solver.cc
index 64db13acbac0..22e2e9a71040 100644
--- a/src/relay/analysis/type_solver.cc
+++ b/src/relay/analysis/type_solver.cc
@@ -102,11 +102,12 @@ class TypeSolver::Unifier : public TypeFunctor<Type(const Type&, const Type&)> {
  public:
   explicit Unifier(TypeSolver* solver, const Span& span) : solver_(solver), span(span) {}
 
-  Type Unify(const Type& src, const Type& dst) {
+  Type Unify(const Type& lhs_type, const Type& rhs_type, bool assign_lhs = true,
+             bool assign_rhs = true) {
     // Known limitation
     // - handle shape pattern matching
-    TypeNode* lhs = solver_->GetTypeNode(dst);
-    TypeNode* rhs = solver_->GetTypeNode(src);
+    TypeNode* lhs = solver_->GetTypeNode(lhs_type);
+    TypeNode* rhs = solver_->GetTypeNode(rhs_type);
 
     // do occur check so we don't create self-referencing structure
     if (lhs->FindRoot() == rhs->FindRoot()) {
@@ -127,7 +128,7 @@ class TypeSolver::Unifier : public TypeFunctor<Type(const Type&, const Type&)> {
       solver_->MergeFromTo(rhs, lhs);
       return lhs->resolved_type;
     } else {
-      Type resolved = this->VisitType(lhs->resolved_type, rhs->resolved_type);
+      Type resolved = this->VisitType(rhs->resolved_type, lhs->resolved_type);
 
       if (!resolved.defined()) {
         solver_->diag_ctx_.Emit(
@@ -139,8 +140,8 @@ class TypeSolver::Unifier : public TypeFunctor<Type(const Type&, const Type&)> {
         return lhs->resolved_type;
       } else {
         TypeNode* top = solver_->GetTypeNode(resolved);
-        solver_->MergeFromTo(lhs, top);
-        solver_->MergeFromTo(rhs, top);
+        if (assign_lhs) solver_->MergeFromTo(lhs, top);
+        if (assign_rhs) solver_->MergeFromTo(rhs, top);
         return resolved;
       }
     }
@@ -549,9 +550,10 @@ void TypeSolver::MergeFromTo(TypeNode* src, TypeNode* dst) {
 }
 
 // Add equality constraint
-Type TypeSolver::Unify(const Type& dst, const Type& src, const Span& span) {
+Type TypeSolver::Unify(const Type& dst, const Type& src, const Span& span, bool assign_lhs,
+                       bool assign_rhs) {
   Unifier unifier(this, span);
-  return unifier.Unify(dst, src);
+  return unifier.Unify(dst, src, assign_lhs, assign_rhs);
 }
 
 // Add type constraint to the solver.
@@ -615,10 +617,10 @@ bool TypeSolver::Solve() {
       }
 
       rnode->resolved = resolved;
-    } catch (const Error& err) {
+    } catch (const CompileError& err) {
       this->diag_ctx_.Emit(Diagnostic::Error(rnode->span) << err.what());
       rnode->resolved = false;
-    } catch (const dmlc::Error& e) {
+    } catch (const Error& e) {
       ICHECK(false) << e.what();
     }
 
diff --git a/src/relay/analysis/type_solver.h b/src/relay/analysis/type_solver.h
index 4ae2e6a2b07b..56cea60ceeda 100644
--- a/src/relay/analysis/type_solver.h
+++ b/src/relay/analysis/type_solver.h
@@ -88,7 +88,8 @@ class TypeSolver {
    * \param rhs The right operand
    * \param location The location at which the unification problem arose.
    */
-  Type Unify(const Type& lhs, const Type& rhs, const Span& span);
+  Type Unify(const Type& lhs, const Type& rhs, const Span& span, bool assign_lhs = true,
+             bool assign_rhs = true);
   /*!
    * \brief Report a diagnostic.
    * \param diag The diagnostic to report.
diff --git a/src/relay/analysis/util.cc b/src/relay/analysis/util.cc
index bcfbc83da514..90750575b9d4 100644
--- a/src/relay/analysis/util.cc
+++ b/src/relay/analysis/util.cc
@@ -141,6 +141,18 @@ class TypeVarEVisitor : private MixedModeVisitor {
     ExprVisitor::VisitExpr_(f);
   }
 
+  void VisitExpr_(const LetNode* op) final {
+    auto pre_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->var);
+      this->VisitExpr(op->value);
+    };
+    auto post_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->body);
+      this->visit_counter_[op] += 1;
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
+  }
+
   void VisitExpr_(const ConstructorNode* cn) final {
     // for constructors, type vars will be bound in the module
     auto data = mod_->LookupTypeDef(cn->belong_to);
@@ -473,24 +485,27 @@ bool IsDynamic(const Type& ty) {
 
 TVM_REGISTER_GLOBAL("relay.ir.IsDynamic").set_body_typed(IsDynamic);
 
-bool IsDataDependant(const CallNode* call) {
-  static auto tshape_data_dependant = Op::GetAttrMap<TShapeDataDependant>("TShapeDataDependant");
+bool IsDataDependent(const CallNode* call) {
+  static auto tshape_data_dependent = Op::GetAttrMap<TShapeDataDependent>("TShapeDataDependent");
   Op op = Downcast<Op>(call->op);
 
-  if (!tshape_data_dependant.count(op)) {
+  if (!tshape_data_dependent.count(op)) {
     return false;
   }
 
   if (op->name == "strided_slice") {
     if (const auto* attrs = call->attrs.as<StridedSliceAttrs>()) {
       if (attrs->begin && attrs->end && attrs->strides) {
-        // not data dependant if begin, end and strides exist
+        // not data dependent if begin, end and strides exist
         return false;
       }
     }
   }
 
-  return tshape_data_dependant[op];
+  for (auto req : tshape_data_dependent[op]) {
+    if (req->value != 0) return true;
+  }
+  return false;
 }
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/analysis/well_formed.cc b/src/relay/analysis/well_formed.cc
index 856c5dc7aac1..acc1a9adc9f4 100644
--- a/src/relay/analysis/well_formed.cc
+++ b/src/relay/analysis/well_formed.cc
@@ -24,7 +24,7 @@
 #include <tvm/relay/analysis.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/pattern_functor.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <unordered_set>
 
diff --git a/src/relay/backend/build_module.cc b/src/relay/backend/build_module.cc
index e17d9c0e1ca6..08846925bede 100644
--- a/src/relay/backend/build_module.cc
+++ b/src/relay/backend/build_module.cc
@@ -278,10 +278,11 @@ class RelayBuildModule : public runtime::ModuleNode {
       pass_seqs.push_back(transform::Legalize());
     }
 
+    pass_seqs.push_back(transform::SimplifyInference());
+
     // Convert Dynamic ops to static versions
     pass_seqs.push_back(transform::DynamicToStatic());
 
-    pass_seqs.push_back(transform::SimplifyInference());
     PackedFunc fskip = PackedFunc([](TVMArgs args, TVMRetValue* rv) {
       Expr expr = args[0];
       *rv = false;
diff --git a/src/relay/backend/compile_engine.cc b/src/relay/backend/compile_engine.cc
index 98d913662953..ae975a5f3240 100644
--- a/src/relay/backend/compile_engine.cc
+++ b/src/relay/backend/compile_engine.cc
@@ -157,8 +157,7 @@ class ScheduleGetter : public backend::MemoizedExprTranslator<Array<te::Tensor>>
             runtime::Registry::Get("auto_scheduler.relay_integration.auto_schedule_topi_compute");
         ICHECK(fauto_schedule != nullptr)
             << "auto_scheduler.relay_integration.auto_schedule_topi_compute is not registered";
-        bool has_complex_op = anchor_op_pattern_ >= kCommReduce;
-        ObjectRef obj = (*fauto_schedule)(tensor_outs, has_complex_op);
+        ObjectRef obj = (*fauto_schedule)(tensor_outs);
         if (obj.defined()) {
           schedule = Downcast<te::Schedule>(obj);
         }
@@ -436,9 +435,9 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
       LOG(FATAL) << "Free variable " << var->name_hint();
       return {};
     } else {
-      ICHECK(data_dependants_.size());
-      bool data_dependant = data_dependants_.back();
-      if (data_dependant) {
+      ICHECK(data_dependents_per_input_.size());
+      auto data_dependent = data_dependents_per_input_.back();
+      if (data_dependent) {
         param_states_[var] |= kNeedInputData;
         return param_data_[var];
       } else {
@@ -450,12 +449,12 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
 
   Array<te::Tensor> VisitExpr_(const ConstantNode* op) final {
     using tir::make_const;
-    ICHECK(data_dependants_.size());
-    bool data_dependant = data_dependants_.back();
+    ICHECK(data_dependents_per_input_.size());
+    bool data_dependent = data_dependents_per_input_.back();
     if (!op->is_scalar()) {
       // This is a constant weight, extract the shape of the weight tensor.
       // This can not be data dependent.
-      CHECK(!data_dependant);
+      CHECK(!data_dependent);
       auto ttype = op->checked_type().as<TensorTypeNode>();
       int ndim = static_cast<int>(ttype->shape.size());
       Array<PrimExpr> out_shape{ndim};
@@ -473,7 +472,7 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
       scalars_.push_back(value);
       return {value};
     }
-    if (data_dependant) {
+    if (data_dependent) {
       void* data = op->data->data;
       DataType dtype = DataType(op->data->dtype);
       auto value = tvm::te::compute(
@@ -508,27 +507,38 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
 
   Array<te::Tensor> VisitExpr_(const CallNode* call_node) final {
     static auto fshape_func = Op::GetAttrMap<FShapeFunc>("FShapeFunc");
-    static auto tshape_data_dependant = Op::GetAttrMap<TShapeDataDependant>("TShapeDataDependant");
+    static auto tshape_data_dependent = Op::GetAttrMap<TShapeDataDependent>("TShapeDataDependent");
     ICHECK(call_node->op.as<OpNode>()) << "Primitive function only allows call into primitive ops";
     Op op = Downcast<Op>(call_node->op);
-    ICHECK(data_dependants_.empty() || !data_dependants_.back())
+    ICHECK(data_dependents_per_input_.empty() || !data_dependents_per_input_.back())
         << "Error in op fusion: output of the shape func is fed to a "
-        << "data-dependant shape func";
+        << "data-dependent shape func";
     ICHECK_GT(fshape_func.count(op), 0) << "Internal error, cannot find ShapeFunc for " << op->name;
-    ICHECK_GT(tshape_data_dependant.count(op), 0)
-        << "Internal error, cannot find TShapeDataDependant for " << op->name;
+    ICHECK_GT(tshape_data_dependent.count(op), 0)
+        << "Internal error, cannot find TShapeDataDependent for " << op->name;
+
+    Array<Integer> dep_spec = tshape_data_dependent[op];
+    if (dep_spec.size() == 1) {
+      // This is for cases when data dependence is specified per op
+      // Replicate 0 or 1 flag to all arguments
+      for (size_t i = 1; i < call_node->args.size(); ++i) {
+        dep_spec.push_back(dep_spec[0]);
+      }
+    }
 
-    data_dependants_.push_back(IsDataDependant(call_node));
     // Visit all inputs
     Array<te::Tensor> inputs;
     int count_tuple = 0;
-    for (Expr arg : call_node->args) {
+    for (size_t i = 0; i < call_node->args.size(); ++i) {
+      Expr arg = call_node->args[i];
       if (arg->checked_type().as<TupleTypeNode>()) {
         ++count_tuple;
       }
+      data_dependents_per_input_.push_back(dep_spec[i]->value != 0);
       for (te::Tensor tensor : VisitExpr(arg)) {
         inputs.push_back(tensor);
       }
+      data_dependents_per_input_.pop_back();
     }
     if (count_tuple) {
       ICHECK_EQ(call_node->args.size(), 1U) << "Only allow function with a single tuple input";
@@ -550,7 +560,6 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
     }
     // Call shape function
     auto outputs = fshape_func[op](call_node->attrs, inputs, out_ndims);
-    data_dependants_.pop_back();
     readable_name_stream_ << "_" << op->name;
     return outputs;
   }
@@ -594,8 +603,8 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
   std::unordered_map<Expr, Array<te::Tensor>, ObjectPtrHash, ObjectPtrEqual> param_data_;
   /*! \brief Map from parameter to list of shape placeholder */
   std::unordered_map<Expr, Array<te::Tensor>, ObjectPtrHash, ObjectPtrEqual> param_shapes_;
-  /*! \brief Stack of data dependencies for shape function */
-  std::vector<bool> data_dependants_;
+  /*! \brief Stack of data dependencies for shape function, specified per each op input */
+  std::vector<bool> data_dependents_per_input_;
   /*! \brief Scalars used in the shape function */
   Array<te::Tensor> scalars_;
 };
@@ -642,10 +651,10 @@ class CompileEngineImpl : public CompileEngineNode {
                                       << AsText(src_func, false);
 
         std::string sn = symbol_name.value();
-        if (cached_symbol.count(sn)) {
+        if (!cached_symbol.count(sn)) {
           cached_symbol[sn] = code_gen_name;
         } else {
-          ICHECK_NE(sn, code_gen_name)
+          ICHECK_NE(cached_symbol[sn], code_gen_name)
               << "Found duplicated symbol: " << sn << " for: " << code_gen_name;
         }
 
@@ -683,6 +692,17 @@ class CompileEngineImpl : public CompileEngineNode {
     return items;
   }
 
+  // List all items in the shape_func_cache.
+  Array<ObjectRef> ListShapeFuncItems() {
+    std::lock_guard<std::mutex> lock(mutex_);
+    Array<ObjectRef> items;
+    for (auto& kv : shape_func_cache_) {
+      items.push_back(kv.first);
+      items.push_back(kv.second);
+    }
+    return items;
+  }
+
   /*!
    * \brief Get the cache key of the function that is being lowered currently
    * \return the cache key
@@ -702,7 +722,9 @@ class CompileEngineImpl : public CompileEngineNode {
     } else {
       value = CCacheValue(make_object<CCacheValueNode>());
       value->use_count = 0;
-      cache_[key] = value;
+      if (!backend::IsCompileEngineCacheDisabled()) {
+        cache_[key] = value;
+      }
     }
     cur_ccache_key_ = key;
 
@@ -833,6 +855,7 @@ CompileEngine& CompileEngine::Global() {
 }
 
 TVM_REGISTER_PASS_CONFIG_OPTION("relay.backend.use_auto_scheduler", Bool);
+TVM_REGISTER_PASS_CONFIG_OPTION("relay.backend.disable_compile_engine_cache", Bool);
 
 TVM_REGISTER_GLOBAL("relay.backend._make_LoweredOutput")
     .set_body_typed([](tvm::Array<te::Tensor> outputs, OpImplementation impl) {
@@ -870,6 +893,13 @@ TVM_REGISTER_GLOBAL("relay.backend._CompileEngineListItems").set_body_typed([](C
   return ptr->ListItems();
 });
 
+TVM_REGISTER_GLOBAL("relay.backend._CompileEngineListShapeFuncItems")
+    .set_body_typed([](CompileEngine self) {
+      CompileEngineImpl* ptr = dynamic_cast<CompileEngineImpl*>(self.operator->());
+      ICHECK(ptr != nullptr);
+      return ptr->ListShapeFuncItems();
+    });
+
 TVM_REGISTER_GLOBAL("relay.backend._CompileEngineGetCurrentCCacheKey")
     .set_body_typed([](CompileEngine self) {
       CompileEngineImpl* ptr = dynamic_cast<CompileEngineImpl*>(self.operator->());
diff --git a/src/relay/backend/contrib/arm_compute_lib/codegen.cc b/src/relay/backend/contrib/arm_compute_lib/codegen.cc
index a963242f82d5..e0669ae64bdb 100644
--- a/src/relay/backend/contrib/arm_compute_lib/codegen.cc
+++ b/src/relay/backend/contrib/arm_compute_lib/codegen.cc
@@ -24,6 +24,7 @@
 #include <tvm/ir/module.h>
 #include <tvm/relay/attrs/nn.h>
 #include <tvm/relay/type.h>
+#include <tvm/tir/analysis.h>
 
 #include <memory>
 #include <string>
@@ -126,7 +127,7 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
       nodes.activation = current_call;
       current_call = current_call->args[0].as<CallNode>();
     }
-    if (backend::IsOp(current_call, "nn.bias_add")) {
+    if (backend::IsOp(current_call, "add")) {
       nodes.bias = current_call;
       current_call = current_call->args[0].as<CallNode>();
     }
@@ -154,19 +155,32 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
    */
   std::shared_ptr<JSONGraphNode> CreateCompositeConvJSONNode(const CallNode* cn) {
     CompositeConvNode nodes = UnpackCompositeConvolution(cn);
-    std::string name = "nn.conv2d";
 
     const auto* conv_attr = nodes.conv->attrs.as<Conv2DAttrs>();
     ICHECK(conv_attr);
-    ICHECK(conv_attr->kernel_layout == "OHWI")
-        << "Kernel layout must be OHWI, has the module been pre-processed correctly?";
+
+    std::string name;
+    std::string name_prefix = "nn";
+
+    // Distinguish between normal and depth-wise convolution
+    if (conv_attr->channels.defined() &&
+        tvm::tir::ExprDeepEqual()(conv_attr->channels, conv_attr->groups) &&
+        conv_attr->groups != 1) {
+      name = "depthwise_conv2d";
+      ICHECK(conv_attr->kernel_layout == "IHWO")
+          << "Kernel layout must be IHWO, has the module been pre-processed correctly?";
+    } else {
+      name = "conv2d";
+      ICHECK(conv_attr->kernel_layout == "OHWI")
+          << "Kernel layout must be OHWI, has the module been pre-processed correctly?";
+    }
 
     // Inputs must be added in the same order they appear in the relay graph.
     std::vector<JSONGraphNodeEntry> inputs;
     inputs.push_back(VisitExpr(cn->args[0])[0]);
     inputs.push_back(VisitExpr(nodes.conv->args[1])[0]);
     if (nodes.requantize) {
-      name = "qnn.conv2d";
+      name_prefix = "qnn";
       inputs.push_back(VisitExpr(nodes.conv->args[2])[0]);  // input zero-point
       inputs.push_back(VisitExpr(nodes.conv->args[3])[0]);  // kernel zero-point
       inputs.push_back(VisitExpr(nodes.conv->args[4])[0]);  // input scale
@@ -180,7 +194,7 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
       inputs.push_back(VisitExpr(nodes.requantize->args[4])[0]);  // output zero-point
     }
 
-    auto json_node = std::make_shared<JSONGraphNode>(name, "kernel", inputs, 1);
+    auto json_node = std::make_shared<JSONGraphNode>(name_prefix + "." + name, "kernel", inputs, 1);
     SetCallNodeAttribute(json_node, nodes.conv);
 
     // Override attributes
@@ -224,10 +238,11 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
       nodes.requantize = current_call;
       current_call = current_call->args[0].as<CallNode>();
     }
-    if (backend::IsOp(current_call, "nn.bias_add")) {
+    if (backend::IsOp(current_call, "add")) {
       nodes.bias = current_call;
       current_call = current_call->args[0].as<CallNode>();
     }
+
     // Enforce a dense node exists at this point during traversal
     if (nodes.requantize) {
       ICHECK(backend::IsOp(current_call, "qnn.dense"));
@@ -329,25 +344,6 @@ class ACLJSONSerializer : public backend::contrib::JSONSerializer {
   }
 };
 
-/*!
- * \brief Pre-process a module containing functions ready for ACL codegen.
- *
- * For now we enforce OHWI kernel layout and fold the transforms away.
- *
- * \param mod The module to be pre-processed.
- * \return The processed module.
- */
-IRModule PreProcessModule(const IRModule& mod) {
-  IRModule preprocessed_module;
-  tvm::Map<String, Array<String>> desired_layouts = {{"nn.conv2d", {"NHWC", "OHWI"}},
-                                                     {"qnn.conv2d", {"NHWC", "OHWI"}}};
-  preprocessed_module = transform::ConvertLayout(desired_layouts)(mod);
-  preprocessed_module = transform::FoldConstant()(preprocessed_module);
-  return preprocessed_module;
-}
-
-TVM_REGISTER_GLOBAL("relay.ext.arm_compute_lib.optimize").set_body_typed(PreProcessModule);
-
 /*!
  * \brief Create a runtime module for ACL.
  *
diff --git a/src/relay/backend/contrib/bnns/codegen.cc b/src/relay/backend/contrib/bnns/codegen.cc
new file mode 100644
index 000000000000..72c32fb5b19e
--- /dev/null
+++ b/src/relay/backend/contrib/bnns/codegen.cc
@@ -0,0 +1,215 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file
+ * \brief Implementation of BNNS codegen APIs.
+ */
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/runtime/module.h>
+#include <tvm/runtime/registry.h>
+
+#include <numeric>
+#include <sstream>
+
+#include "../../../../runtime/contrib/json/json_node.h"
+#include "../../utils.h"
+#include "../codegen_json/codegen_json.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+
+using namespace backend;
+
+/*!
+ * \brief Retrieve the expected "root" op nested inside a fused call, such as conv2d in
+ *        relu(add(conv2d))
+ * \param call A Relay call node. Typically nn.relu when called the first time.
+ * \param max_depth The maximum number of calls before the root op, counting from current_call.
+ * \param root_name The name of expected "root" op in this fused call.
+ * \return A CallNode corresponding to the root op
+ */
+inline const CallNode* FindCallWithName(const CallNode* current_call, int max_depth,
+                                        const std::string& root_name) {
+  ICHECK(current_call && max_depth >= 0);
+
+  if (max_depth == 0) {
+    ICHECK(current_call && IsOp(current_call, root_name));
+    return current_call;
+  }
+  if (IsOp(current_call, root_name)) {
+    return current_call;
+  }
+
+  ICHECK_GT(current_call->args.size(), 0);
+
+  const auto* next_call = current_call->args[0].as<CallNode>();
+  return FindCallWithName(next_call, max_depth - 1, root_name);
+}
+
+class BNNSJSONSerializer : public backend::contrib::JSONSerializer {
+  using JSONGraphNode = tvm::runtime::json::JSONGraphNode;
+  using JSONGraphNodeEntry = tvm::runtime::json::JSONGraphNodeEntry;
+
+ public:
+  BNNSJSONSerializer(const std::string& symbol, const Expr& expr) : JSONSerializer(symbol, expr) {}
+
+  std::vector<JSONGraphNodeEntry> VisitExpr_(const CallNode* cn) override {
+    Expr expr = GetRef<Expr>(cn);
+    std::string name;
+    const CallNode* call = cn;
+    if (const auto* op_node = cn->op.as<OpNode>()) {
+      name = op_node->name;
+    } else if (const auto* fn = cn->op.as<FunctionNode>()) {
+      auto comp = fn->GetAttr<String>(attr::kComposite);
+      ICHECK(comp.defined()) << "BNNS JSON runtime only supports composite functions.";
+      name = comp.value();
+
+      auto body = fn->body.as<CallNode>();
+      if (name == "bnns.conv2d_bias_relu") {
+        auto add_op_type = IsOp(body->args[0].as<CallNode>(), "add") ? "add" : "nn.bias_add";
+        call = GetRootCall(body, 2, {"nn.conv2d", add_op_type, "nn.relu"});
+      } else if (name == "bnns.conv2d_bias") {
+        auto add_op_type = IsOp(body, "add") ? "add" : "nn.bias_add";
+        call = GetRootCall(body, 1, {"nn.conv2d", add_op_type});
+      } else if (name == "bnns.conv2d_relu") {
+        call = GetRootCall(body, 1, {"nn.conv2d", "nn.relu"});
+        ICHECK(call->op.as<OpNode>()) << "Not op node";
+      } else if (name == "bnns.conv2d_bias_sigmoid") {
+        auto add_op_type = IsOp(body->args[0].as<CallNode>(), "add") ? "add" : "nn.bias_add";
+        call = GetRootCall(body, 2, {"nn.conv2d", add_op_type, "sigmoid"});
+        ICHECK(call->op.as<OpNode>()) << "Not op node";
+      } else if (name == "bnns.conv2d_sigmoid") {
+        call = GetRootCall(body, 1, {"nn.conv2d", "sigmoid"});
+        ICHECK(call->op.as<OpNode>()) << "Not op node";
+      } else if (name == "bnns.dense_bias") {
+        call = GetRootCall(fn->body.as<CallNode>(), 1, {"nn.dense", "add"});
+      } else if (name == "bnns.dense_bias_gelu") {
+        call = FindCallWithName(fn->body.as<CallNode>(), 10, "nn.dense");
+      } else {
+        LOG(FATAL) << "Unrecognized BNNS pattern: " << name;
+      }
+    } else {
+      LOG(FATAL) << "BNNS JSON runtime does not support calls to " << cn->op->GetTypeKey();
+    }
+
+    std::vector<JSONGraphNodeEntry> inputs;
+    for (const auto& arg : cn->args) {
+      auto res = VisitExpr(arg);
+      inputs.insert(inputs.end(), res.begin(), res.end());
+    }
+    auto node = std::make_shared<JSONGraphNode>(name,     /* name_ */
+                                                "kernel", /* op_type_ */
+                                                inputs, 1 /* num_outputs_ */);
+    SetCallNodeAttribute(node, call);
+    return AddNode(node, GetRef<Expr>(cn));
+  }
+};
+
+/*!
+ * \brief The external compiler/codegen tool. It takes a Relay expression/module and
+ * compile it into a runtime module.
+ */
+runtime::Module BNNSCompiler(const ObjectRef& ref) {
+  ICHECK(ref->IsInstance<FunctionNode>());
+  auto func = Downcast<Function>(ref);
+  auto func_name = GetExtSymbol(func);
+  BNNSJSONSerializer serializer(func_name, func);
+  serializer.serialize();
+  std::string graph_json = serializer.GetJSON();
+  auto params = serializer.GetParams();
+
+  const auto* pf = runtime::Registry::Get("runtime.BNNSJSONRuntimeCreate");
+  ICHECK(pf != nullptr) << "Cannot find JSON runtime module to create";
+  auto mod = (*pf)(func_name, graph_json, params);
+  return mod;
+}
+
+TVM_REGISTER_GLOBAL("relay.ext.bnns").set_body_typed(BNNSCompiler);
+
+/**
+ * \brief A helper to expand the params by adding ones which used by BNNS runtime
+ * for a given expression. Same as default ConstantUpdater but skip constant from
+ * essential BNNS composed function ops.
+ */
+struct BNNSConstantUpdater : public ConstantUpdater {
+ public:
+  BNNSConstantUpdater(const std::string& symbol,
+                      std::unordered_map<std::string, runtime::NDArray>* params,
+                      const std::vector<std::string>& skip_mask)
+      : ConstantUpdater(symbol, params), skip_mask_(skip_mask) {}
+  using ConstantUpdater::VisitExpr_;
+
+  /**!
+   * Like an original implementation but avoid visiting of body nodes
+   * for BNNS specific composite primitives.
+   */
+  void VisitExpr_(const FunctionNode* op) final {
+    this->VisitSpan(op->span);
+    for (auto param : op->params) {
+      this->VisitExpr(param);
+    }
+
+    if (!isBNNSSpecificCompositeFunc(op)) {
+      this->VisitExpr(op->body);
+    }
+  }
+
+ private:
+  bool isBNNSSpecificCompositeFunc(const FunctionNode* op) {
+    auto comp = op->GetAttr<String>(attr::kComposite);
+    if (!comp) return false;
+
+    auto comp_name = comp.value();
+
+    bool is_match = false;
+    for (const auto& mask : skip_mask_) {
+      if (std::string(comp_name).substr(0, mask.size()) == mask) {
+        is_match = true;
+        break;
+      }
+    }
+    return is_match;
+  }
+
+  std::vector<std::string> skip_mask_;
+};
+
+Map<String, runtime::NDArray> BNNSConstantUpdaterFunc(Expr expr, std::string symbol) {
+  std::vector<std::string> bnns_composite_filter = {"bnns."};
+
+  // Visit all suitable constant nodes
+  std::unordered_map<std::string, runtime::NDArray> res;
+  BNNSConstantUpdater const_updater(symbol, &res, bnns_composite_filter);
+  const_updater(expr);
+
+  // Convert to tvm::Map
+  Map<String, runtime::NDArray> ret;
+  for (const auto& kvp : res) ret.Set(kvp.first, kvp.second);
+  return ret;
+}
+
+TVM_REGISTER_GLOBAL("relay.ext.bnns.constant_updater").set_body_typed(BNNSConstantUpdaterFunc);
+
+}  // namespace contrib
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/backend/contrib/codegen_c/codegen.cc b/src/relay/backend/contrib/codegen_c/codegen.cc
index 998393d450c2..550afb3159fc 100644
--- a/src/relay/backend/contrib/codegen_c/codegen.cc
+++ b/src/relay/backend/contrib/codegen_c/codegen.cc
@@ -157,8 +157,7 @@ class CodegenC : public MemoizedExprTranslator<std::vector<Output>>, public Code
     for (size_t i = 0; i < out_shape.size(); ++i) {
       out_size *= out_shape[i];
     }
-    buf_stream << dtype << "* " << out << " = (" << dtype << "*)std::malloc(4 * " << out_size
-               << ");";
+    buf_stream << dtype << "* " << out << " = (" << dtype << "*)malloc(4 * " << out_size << ");";
     buf_decl_.push_back(buf_stream.str());
 
     decl_stream << ", " << out << ");";
@@ -229,25 +228,33 @@ class CSourceCodegen : public CSourceModuleCodegenBase {
     String func_name = std::get<1>(res);
 
     // Create headers
-    code_stream_ << "#include <cstring>\n";
-    code_stream_ << "#include <vector>\n";
+    code_stream_ << "#include <stdio.h>\n";
+    code_stream_ << "#include <stdlib.h>\n";
+    code_stream_ << "#include <string.h>\n";
     code_stream_ << "#include <tvm/runtime/c_runtime_api.h>\n";
-    code_stream_ << "#include <tvm/runtime/container.h>\n";
-    code_stream_ << "#include <tvm/runtime/packed_func.h>\n";
-    code_stream_ << "#include <dlpack/dlpack.h>\n";
-    code_stream_ << "using namespace tvm::runtime;\n";
+    code_stream_ << "#include <tvm/runtime/c_backend_api.h>\n";
+    if (!variables.empty()) {
+      // This segment would be generated in C++ because of the usage
+      // of tvm::runtime::Array. This is not ideal, but this to demonstrate
+      // constant copying process used packed imports in other external
+      // codegen. Moreover, in uTVM we dont expect this part to be generated.
+      code_stream_ << "#ifdef __cplusplus\n";
+      code_stream_ << "#include <tvm/runtime/ndarray.h>\n";
+      code_stream_ << "#include <tvm/runtime/packed_func.h>\n";
+      code_stream_ << "#endif\n";
+    }
 
     // Append some common macro for operator definition.
     const char* operator_macro = R"op_macro(
     #define CSOURCE_BINARY_OP_1D(p_ID_, p_OP_, p_DIM1_, p_DTYPE)       \
-      extern "C" void p_ID_(p_DTYPE* a, p_DTYPE* b, p_DTYPE* out) {    \
+      void p_ID_(p_DTYPE* a, p_DTYPE* b, p_DTYPE* out) {    \
         for (int64_t i = 0; i < p_DIM1_; ++i) {                        \
           out[i] = a[i] p_OP_ b[i];                                    \
         }                                                              \
       }
 
     #define CSOURCE_BINARY_OP_2D(p_ID_, p_OP_, p_DIM1_, p_DIM2_, p_DTYPE)  \
-      extern "C" void p_ID_(p_DTYPE* a, p_DTYPE* b, p_DTYPE* out) {        \
+      void p_ID_(p_DTYPE* a, p_DTYPE* b, p_DTYPE* out) {        \
         for (int64_t i = 0; i < p_DIM1_; ++i) {                            \
           for (int64_t j = 0; j < p_DIM2_; ++j) {                          \
             int64_t k = i * p_DIM2_ + j;                                   \
diff --git a/src/relay/backend/contrib/codegen_c/codegen_c.h b/src/relay/backend/contrib/codegen_c/codegen_c.h
index 9448b4d0738d..b81fd14b99c2 100644
--- a/src/relay/backend/contrib/codegen_c/codegen_c.h
+++ b/src/relay/backend/contrib/codegen_c/codegen_c.h
@@ -89,6 +89,40 @@ class CodegenCBase {
     indent_ -= 2;
   }
 
+  /*!
+   * \brief Creates a runtime function header
+   */
+  void PrintRuntimeFunctionHeader(std::string func_name) {
+    code_stream_ << "#ifdef __cplusplus\n";
+    code_stream_ << "extern \"C\" {\n";
+    code_stream_ << "#endif\n";
+    code_stream_ << "TVM_DLL int32_t ";
+    code_stream_ << func_name << "(";
+    code_stream_ << "TVMValue* args, ";
+    code_stream_ << "int* type_code, ";
+    code_stream_ << "int num_args, ";
+    code_stream_ << "TVMValue* out_value, ";
+    code_stream_ << "int* out_type_code) {\n";
+  }
+
+  /*!
+   * \brief Adds a line to convert TVMValue args to DLTensors
+   */
+  void PrintArgToData(int idx) {
+    PrintIndents();
+    code_stream_ << "DLTensor* arg" << idx << " = ";
+    code_stream_ << "(DLTensor*)(((TVMValue*)args)[" << idx << "].v_handle);\n";
+  }
+
+  /*!
+   * \brief Adds a line to convert TVMValue rets to DLTensors
+   */
+  void PrintRetToData(int idx) {
+    PrintIndents();
+    code_stream_ << "DLTensor* ret" << idx << " = ";
+    code_stream_ << "(DLTensor*)(((TVMValue*)args)[" << idx << "].v_handle);\n";
+  }
+
   /*!
    * \brief Gerenate C code for the external function.
    *
@@ -100,12 +134,12 @@ class CodegenCBase {
    * Array<NDArray> foo_consts;
    *
    * // An example code for the generated C function.
-   * extern "C" int foo_wrapper_(DLTensor* arg0,
+   * int foo_wrapper_(DLTensor* arg0,
    *                              DLTensor* arg1,
    *                              DLTensor* out) {
-   *   foo_(static_cast<float*>(arg0->data),
-   *        static_cast<float*>(arg1->data),
-   *        static_cast<float*>(out->data));
+   *   foo_((float*)(arg0->data),
+   *        (float*)(arg1->data),
+   *        (float*)(out->data));
    *   return 0;
    * }
    *
@@ -124,7 +158,8 @@ class CodegenCBase {
                             const std::string& const_arr_name, const std::vector<Output>& outs) {
     // Print signature
     code_stream_ << "\n";
-    code_stream_ << "extern \"C\" int " << func_name << "_wrapper_(";
+
+    code_stream_ << "int " << func_name << "_wrapper_(";
     for (size_t i = 0; i < args.size(); i++) {
       code_stream_ << "DLTensor* arg" << i << ",\n";
       code_stream_ << "\t";
@@ -142,26 +177,54 @@ class CodegenCBase {
     code_stream_ << func_name << "_(";
     for (size_t i = 0; i < args.size(); i++) {
       const auto& dtype_str = GetDtypeString(args[i]);
-      code_stream_ << "static_cast<" << dtype_str << "*>(arg" << i << "->data),\n";
+      code_stream_ << "(" << dtype_str << "*)(arg" << i << "->data),\n";
       PrintIndents();
     }
     for (size_t i = 0; i < outs.size() - 1; i++) {
-      code_stream_ << "static_cast<" << outs[i].dtype << "*>(out" << i << "->data),\n";
+      code_stream_ << "(" << outs[i].dtype << "*)(out" << i << "->data),\n";
       PrintIndents();
     }
-    code_stream_ << "static_cast<" << outs.back().dtype << "*>(out" << outs.size() - 1
-                 << "->data));\n";
+    code_stream_ << "(" << outs.back().dtype << "*)(out" << outs.size() - 1 << "->data));\n";
     PrintIndents();
     code_stream_ << "return 0;\n";
     ExitScope();
     code_stream_ << "}\n\n";
 
-    // Generate the macro
-    code_stream_ << "TVM_DLL_EXPORT_TYPED_FUNC(" << func_name << ", " << func_name
-                 << "_wrapper_);\n\n";
+    // Create the external function
+    PrintRuntimeFunctionHeader(func_name);
+    EnterScope();
+    for (size_t i = 0; i < args.size(); i++) {
+      PrintArgToData(i);
+    }
+    for (size_t i = 0; i < outs.size(); i++) {
+      PrintRetToData(args.size() + i);
+    }
+    PrintIndents();
+    code_stream_ << func_name << "_wrapper_(";
+    for (size_t i = 0; i < args.size(); i++) {
+      code_stream_ << "arg" << i << ",";
+    }
+    for (size_t i = 0; i < outs.size() - 1; i++) {
+      code_stream_ << "ret" << args.size() + i << ",";
+    }
+    code_stream_ << "ret" << args.size() + outs.size() - 1 << ");\n";
+    PrintIndents();
+    code_stream_ << "return 0;\n";
+    ExitScope();
+    code_stream_ << "}\n";
+    code_stream_ << "#ifdef __cplusplus\n";
+    code_stream_ << "}\n";
+    code_stream_ << "#endif\n";
 
     if (!const_arr_name.empty()) {
-      code_stream_ << "int " << func_name << "_init_wrapper_(Array<NDArray> arr) {\n";
+      // If there are constants, insert the __init_ and the wrapper
+      // This segment would be generated in C++ because of the usage
+      // of tvm::runtime::Array. This is not ideal, but this to demonstrate
+      // constant copying process used packed imports in other external
+      // codegen. Moreover, in uTVM we dont expect this part to be generated.
+      code_stream_ << "#ifdef __cplusplus\n";
+      code_stream_ << "int " << func_name
+                   << "_init_wrapper_(tvm::runtime::Array<tvm::runtime::NDArray> arr) {\n";
       EnterScope();
       PrintIndents();
       code_stream_ << func_name << "_consts = arr;\n";
@@ -170,6 +233,7 @@ class CodegenCBase {
       code_stream_ << "}\n\n";
       code_stream_ << "TVM_DLL_EXPORT_TYPED_FUNC(__init_" << func_name << ", " << func_name
                    << "_init_wrapper_);\n\n";
+      code_stream_ << "#endif\n";
     }
   }
 
@@ -202,11 +266,13 @@ class CodegenCBase {
                       const std::vector<Output>& outs) {
     // Create a declaration for global ndarrays that contain constant data.
     if (!const_arr_name.empty()) {
+      code_stream_ << "#ifdef __cplusplus\n";
       code_stream_ << const_arr_name << "\n\n";
+      code_stream_ << "#endif\n";
     }
     // Create the signature. For example, it could be:
-    // extern "C" void dnnl_0_(float* in0, float* in1, float* out0, float* out1) {}
-    code_stream_ << "extern \"C\" void " << ext_func_id << "_(";
+    // void dnnl_0_(float* in0, float* in1, float* out0, float* out1) {}
+    code_stream_ << "void " << ext_func_id << "_(";
 
     for (const auto& arg : args) {
       const auto& dtype_str = GetDtypeString(arg);
@@ -235,14 +301,14 @@ class CodegenCBase {
         continue;
       }
       this->PrintIndents();
-      code_stream_ << "std::memcpy(out" << i << ", " << outs[i].name << ", 4 * " << outs[i].size
+      code_stream_ << "memcpy(out" << i << ", " << outs[i].name << ", 4 * " << outs[i].size
                    << ");\n";
     }
 
     // Free buffers
     for (size_t i = 0; i < buf_decl.size(); i++) {
       this->PrintIndents();
-      code_stream_ << "std::free(buf_" << i << ");\n";
+      code_stream_ << "free(buf_" << i << ");\n";
     }
 
     this->ExitScope();
@@ -277,6 +343,8 @@ class CodegenCBase {
     std::string dtype;
     if (runtime::TypeMatch(ttype->dtype, kDLFloat, 32)) {
       dtype = "float";
+    } else if (runtime::TypeMatch(ttype->dtype, kDLFloat, 16)) {
+      dtype = "half";
     } else if (runtime::TypeMatch(ttype->dtype, kDLInt, 32)) {
       dtype = "int";
     } else if (runtime::TypeMatch(ttype->dtype, kDLInt, 64)) {
@@ -310,7 +378,7 @@ class CodegenCBase {
    * \return The created declaration
    */
   std::string CreateNDArrayPool(const std::string& symbol) const {
-    return "Array<NDArray> " + symbol + "_consts;";
+    return "tvm::runtime::Array<tvm::runtime::NDArray> " + symbol + "_consts;";
   }
 
   /*!
@@ -322,7 +390,7 @@ class CodegenCBase {
    * \return The created reference
    */
   std::string CreateDataReference(const std::string& symbol, int const_id) const {
-    return "static_cast<float*>(" + symbol + "_consts[" + std::to_string(const_id) + "]->data)";
+    return "(float*)(" + symbol + "_consts[" + std::to_string(const_id) + "]->data)";
   }
 
   /*!
diff --git a/src/relay/backend/contrib/codegen_json/codegen_json.h b/src/relay/backend/contrib/codegen_json/codegen_json.h
index 859ef8c9bdb2..192e09140375 100644
--- a/src/relay/backend/contrib/codegen_json/codegen_json.h
+++ b/src/relay/backend/contrib/codegen_json/codegen_json.h
@@ -26,7 +26,6 @@
 
 #include <dmlc/any.h>
 #include <dmlc/json.h>
-#include <tvm/node/container.h>
 #include <tvm/node/reflection.h>
 #include <tvm/runtime/container.h>
 #include <tvm/tir/op.h>
diff --git a/src/relay/backend/contrib/ethosn/capabilities.h b/src/relay/backend/contrib/ethosn/capabilities.h
index 77b2d911d38f..cc14ca101da6 100644
--- a/src/relay/backend/contrib/ethosn/capabilities.h
+++ b/src/relay/backend/contrib/ethosn/capabilities.h
@@ -20,7 +20,8 @@
 /*!
  * \file src/relay/backend/contrib/ethosn/capabilities.h
  * \brief The Ethos-N processor series has four variants, the Ethos-N37, Ethos-N57, Ethos-N77
- * and the Ethos-N78. This release of the integration supports the first three variants.
+ * and the Ethos-N78. This release of the integration supports the first three variants and
+ * the default configuration of the fourth variant.
  * Configuration information for each variant is stored as a blob in this file. These blobs
  * are passed into the Ethos-N support library, which in turn uses them to optimize the
  * generated command-stream appropriately for the specified variant.
@@ -38,13 +39,14 @@ namespace relay {
 namespace contrib {
 namespace ethosn {
 
-/* Ethos-N variants (N77, N57 and N37)
- * variant[0] - N77
- * variant[1] - N57
- * variant[2] - N37
+/* Ethos-N variants (Ethos-N77, Ethos-N57, Ethos-N37 and Ethos-N78)
+ * variant[0] - Ethos-N77
+ * variant[1] - Ethos-N57
+ * variant[2] - Ethos-N37
+ * variant[3] - Ethos-N78
  */
-#if _ETHOSN_API_VERSION_ == 2008
-static std::vector<char> variants[3] = {
+#if _ETHOSN_API_VERSION_ == 2011
+static std::vector<char> variants[4] = {
     {
         0x03, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x10, 0x00,
@@ -74,38 +76,58 @@ static std::vector<char> variants[3] = {
         0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00,
         0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x01,
         0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    },
+    {
+        0x03, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x00, 0x02, 0x00,
+        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00,
+        0x10, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
+        0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00,
+        0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00,
+        0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x02,
+        0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
     }};
 #else
-static std::vector<char> variants[3] = {
+static std::vector<char> variants[4] = {
     {
-        0x02, 0x00, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x03, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x10, 0x00,
         0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00,
         0x10, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
         0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00,
         0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x01,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     },
     {
-        0x02, 0x00, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x03, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x10, 0x00,
         0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00,
         0x10, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
         0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00,
         0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x01,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
     },
     {
-        0x02, 0x00, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x03, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x10, 0x00, 0x10, 0x00,
         0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00,
         0x10, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
         0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00,
         0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00,
         0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x01,
-        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    },
+    {
+        0x03, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+        0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x00, 0x02, 0x00,
+        0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00,
+        0x10, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
+        0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00,
+        0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00,
+        0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x01,
+        0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
     }};
 #endif
 }  // namespace ethosn
diff --git a/src/relay/backend/contrib/ethosn/codegen.cc b/src/relay/backend/contrib/ethosn/codegen.cc
index 3097a300a0d9..5e052b3e4fd6 100644
--- a/src/relay/backend/contrib/ethosn/codegen.cc
+++ b/src/relay/backend/contrib/ethosn/codegen.cc
@@ -198,8 +198,19 @@ sl::TensorsAndId MakeOps(const sl::TensorAndId<sl::Operand>& op) {
 
 NetworkWithIDs ConstructNetworkVisitor::Construct(const Function& func) {
   // Initialise everything
+#if _ETHOSN_API_VERSION_ == 2011
+  auto ctx = transform::PassContext::Current();
+  auto cfg = ctx->GetConfig<EthosnCompilerConfig>("relay.ext.ethos-n.options");
+  if (!cfg.defined()) {
+    cfg = AttrsWithDefaultValues<EthosnCompilerConfig>();
+  }
+#endif
   NetworkWithIDs network_with_ids;
+#if _ETHOSN_API_VERSION_ == 2011
+  network_ = sl::CreateNetwork(variants[cfg.value()->variant]);
+#else
   network_ = sl::CreateNetwork();
+#endif
   network_with_ids.network = network_;
   operand_table_.clear();
 
@@ -561,7 +572,11 @@ sl::CompilationOptions EthosnCompiler::CreateOptions() {
     cfg = AttrsWithDefaultValues<EthosnCompilerConfig>();
   }
 
+#if _ETHOSN_API_VERSION_ == 2011
+  sl::CompilationOptions options;
+#else
   sl::CompilationOptions options(variants[cfg.value()->variant]);
+#endif
   options.m_Strategy0 = cfg.value()->strategy0;
   options.m_Strategy1 = cfg.value()->strategy1;
   options.m_Strategy3 = cfg.value()->strategy3;
@@ -575,15 +590,13 @@ sl::CompilationOptions EthosnCompiler::CreateOptions() {
   options.m_BlockConfig8x32 = cfg.value()->block_config_8x32;
   options.m_BlockConfig8x8 = cfg.value()->block_config_8x8;
   options.m_EnableIntermediateCompression = cfg.value()->enable_intermediate_compression;
-  options.m_DisableWinograd = cfg.value()->disable_winograd;
+#if _ETHOSN_API_VERSION_ == 2008
   options.m_DebugInfo.m_DumpDebugFiles = cfg.value()->dump_debug_files;
+#endif
+  options.m_DisableWinograd = cfg.value()->disable_winograd;
   options.m_DebugInfo.m_DebugDir = cfg.value()->debug_dir;
-#if _ETHOSN_API_VERSION_ == 2008
   options.m_CompilerAlgorithm =
       sl::EthosNCompilerAlgorithmFromString(cfg.value()->compiler_algorithm.c_str());
-#else
-  options.m_EnableCascading = cfg.value()->enable_cascading;
-#endif
   return options;
 }
 
@@ -606,6 +619,175 @@ std::pair<std::vector<uint32_t>, std::vector<uint32_t>> EthosnCompiler::GetInput
   return std::make_pair(input_order, output_order);
 }
 
+#if _ETHOSN_API_VERSION_ == 2011
+auto ctx = transform::PassContext::Current();
+auto cfg = ctx -> GetConfig<EthosnCompilerConfig>("relay.ext.ethos-n.options").defined()
+               ? ctx -> GetConfig<EthosnCompilerConfig>("relay.ext.ethos-n.options")
+               : AttrsWithDefaultValues<EthosnCompilerConfig>();
+auto m_Queries = sl::SupportQueries(variants[cfg.value()->variant]);
+#endif
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.conv2d")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      ConvolutionParams params;
+      auto err = EthosnAPI::QnnConv2d(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      if (params.is_depthwise) {
+        *rv = !err &&
+              m_Queries.IsDepthwiseConvolutionSupported(params.bias_info, params.weights_info,
+                                                        params.conv_info, params.activation_info);
+      } else {
+        *rv = !err && m_Queries.IsConvolutionSupported(params.bias_info, params.weights_info,
+                                                       params.conv_info, params.activation_info);
+      }
+#else
+      if (params.is_depthwise) {
+        *rv = !err && sl::IsDepthwiseConvolutionSupported(params.bias_info, params.weights_info,
+                                                          params.conv_info, params.activation_info);
+      } else {
+        *rv = !err && sl::IsConvolutionSupported(params.bias_info, params.weights_info,
+                                                 params.conv_info, params.activation_info);
+      }
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.fc")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      FullyConnectedParams params;
+      auto err = EthosnAPI::QnnFullyConnected(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsFullyConnectedSupported(params.bias_info, params.weights_info,
+                                                        params.fc_info, params.input_info);
+#else
+      *rv = !err && sl::IsFullyConnectedSupported(params.bias_info, params.weights_info,
+                                                  params.fc_info, params.input_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.max_pool2d")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      MaxPool2DParams params;
+      auto err = EthosnAPI::MaxPool2D(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsPoolingSupported(params.pool_info, params.input_info);
+#else
+      *rv = !err && sl::IsPoolingSupported(params.pool_info, params.input_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.avg_pool2d")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      AvgPool2DParams params;
+      auto err = EthosnAPI::AvgPool2D(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsPoolingSupported(params.pool_info, params.input_info);
+#else
+      *rv = !err && sl::IsPoolingSupported(params.pool_info, params.input_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.reshape")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      ReshapeParams params;
+      auto err = EthosnAPI::Reshape(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsReshapeSupported(params.new_shape, params.input_info);
+#else
+      *rv = !err && sl::IsReshapeSupported(params.new_shape, params.input_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.addition")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      AdditionParams params;
+      auto err = EthosnAPI::Addition(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsAdditionSupported(params.lhs_info, params.rhs_info,
+                                                  params.output_quantization_info);
+#else
+      *rv = !err && sl::IsAdditionSupported(params.lhs_info, params.rhs_info,
+                                            params.output_quantization_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.sigmoid")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      SigmoidParams params;
+      auto err = EthosnAPI::Sigmoid(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsSigmoidSupported(params.input_info);
+#else
+      *rv = !err && sl::IsSigmoidSupported(params.input_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.concatenate")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      ConcatenateParams params;
+      auto err = EthosnAPI::Concatenate(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsConcatenationSupported(params.input_infos, params.concat_info);
+#else
+      *rv = !err && sl::IsConcatenationSupported(params.input_infos, params.concat_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.split")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      SplitParams params;
+      auto err = EthosnAPI::Split(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsSplitSupported(params.input_info, params.split_info);
+#else
+      *rv = !err && sl::IsSplitSupported(params.input_info, params.split_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.depth_to_space")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      DepthToSpaceParams params;
+      auto err = EthosnAPI::DepthToSpace(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsDepthToSpaceSupported(params.input_info, params.depth_info);
+#else
+      *rv = !err && sl::IsDepthToSpaceSupported(params.input_info, params.depth_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.relu")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      ReluParams params;
+      auto err = EthosnAPI::Relu(call, &params);
+#if _ETHOSN_API_VERSION_ == 2011
+      *rv = !err && m_Queries.IsReluSupported(params.relu_info, params.input_info);
+#else
+      *rv = !err && sl::IsReluSupported(params.relu_info, params.input_info);
+#endif
+    });
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.query").set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+#if defined ETHOSN_HW
+  *rv = true;
+#else
+  *rv = false;
+#endif
+});
+
+TVM_REGISTER_GLOBAL("relay.ethos-n.api.version").set_body_typed([]() -> int {
+  return _ETHOSN_API_VERSION_;
+});
+
 }  // namespace ethosn
 }  // namespace contrib
 }  // namespace relay
diff --git a/src/relay/backend/contrib/ethosn/codegen_ethosn.h b/src/relay/backend/contrib/ethosn/codegen_ethosn.h
index 4b3e1bc05367..e44aa31d6b13 100644
--- a/src/relay/backend/contrib/ethosn/codegen_ethosn.h
+++ b/src/relay/backend/contrib/ethosn/codegen_ethosn.h
@@ -240,18 +240,18 @@ struct EthosnCompilerConfigNode : public tvm::AttrsNode<EthosnCompilerConfigNode
   bool block_config_8x32;
   bool block_config_8x8;
   bool enable_intermediate_compression;
-  bool disable_winograd;
+#if _ETHOSN_API_VERSION_ == 2008
   bool dump_debug_files;
+#endif
+  bool disable_winograd;
   String debug_dir;
-#if _ETHOSN_API_VERSION_ == 2008
   String compiler_algorithm;
-#else
-  bool enable_cascading;
-#endif
 
   TVM_DECLARE_ATTRS(EthosnCompilerConfigNode, "ext.attrs.EthosnCompilerConfigNode") {
     TVM_ATTR_FIELD(variant)
-        .describe("0 for Ethos-N77, 1 for Ethos-N57, 2 for Ethos-N37. See Ethos-N documentation.")
+        .describe(
+            "0 for Ethos-N77, 1 for Ethos-N57, 2 for Ethos-N37,"
+            " 3 for Ethos-N78. See Ethos-N documentation.")
         .set_default(0);
     TVM_ATTR_FIELD(strategy0).set_default(true);
     TVM_ATTR_FIELD(strategy1).set_default(true);
@@ -266,14 +266,12 @@ struct EthosnCompilerConfigNode : public tvm::AttrsNode<EthosnCompilerConfigNode
     TVM_ATTR_FIELD(block_config_8x32).set_default(true);
     TVM_ATTR_FIELD(block_config_8x8).set_default(true);
     TVM_ATTR_FIELD(enable_intermediate_compression).set_default(true);
-    TVM_ATTR_FIELD(disable_winograd).set_default(false);
+#if _ETHOSN_API_VERSION_ == 2008
     TVM_ATTR_FIELD(dump_debug_files).set_default(false);
+#endif
+    TVM_ATTR_FIELD(disable_winograd).set_default(false);
     TVM_ATTR_FIELD(debug_dir).set_default(".");
-#if _ETHOSN_API_VERSION_ == 2008
     TVM_ATTR_FIELD(compiler_algorithm).set_default("NonCascadingOnly");
-#else
-    TVM_ATTR_FIELD(enable_cascading).set_default(false);
-#endif
   }
 };
 
diff --git a/src/relay/backend/contrib/ethosn/ethosn_api.cc b/src/relay/backend/contrib/ethosn/ethosn_api.cc
index 6cc03ac7b8f0..4587cdbfed5a 100644
--- a/src/relay/backend/contrib/ethosn/ethosn_api.cc
+++ b/src/relay/backend/contrib/ethosn/ethosn_api.cc
@@ -72,11 +72,7 @@ EthosnError EthosnAPI::QnnConv2d(const Expr& expr, ConvolutionParams* params) {
   sl::QuantizationInfo output_q_info;
   err += Tvm2Npu(input_zero_point, input_scale, &data_q_info);
   err += Tvm2Npu(kernel_zero_point, kernel_scale, &weights_q_info);
-#if _ETHOSN_API_VERSION_ == 2008
   err += Tvm2Npu(0, data_q_info.GetScale() * weights_q_info.GetScale(), &bias_q_info);
-#else
-  err += Tvm2Npu(0, data_q_info.m_Scale * weights_q_info.m_Scale, &bias_q_info);
-#endif
   err += Tvm2Npu(output_zero_point, output_scale, &output_q_info);
 
   // Convert convolution attributes
@@ -170,11 +166,7 @@ EthosnError EthosnAPI::QnnFullyConnected(const Expr& expr, FullyConnectedParams*
   sl::QuantizationInfo output_q_info;
   err += Tvm2Npu(input_zero_point, input_scale, &data_q_info);
   err += Tvm2Npu(kernel_zero_point, kernel_scale, &weights_q_info);
-#if _ETHOSN_API_VERSION_ == 2008
   err += Tvm2Npu(0, data_q_info.GetScale() * weights_q_info.GetScale(), &bias_q_info);
-#else
-  err += Tvm2Npu(0, data_q_info.m_Scale * weights_q_info.m_Scale, &bias_q_info);
-#endif
   err += Tvm2Npu(output_zero_point, output_scale, &output_q_info);
 
   // Create fc info
@@ -629,114 +621,6 @@ EthosnError EthosnAPI::AsConstant(const Expr& expr, T* out) {
   return EthosnError();
 }
 
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.conv2d")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      ConvolutionParams params;
-      auto err = EthosnAPI::QnnConv2d(call, &params);
-      if (params.is_depthwise) {
-        *rv = !err && sl::IsDepthwiseConvolutionSupported(params.bias_info, params.weights_info,
-                                                          params.conv_info, params.activation_info);
-      } else {
-        *rv = !err && sl::IsConvolutionSupported(params.bias_info, params.weights_info,
-                                                 params.conv_info, params.activation_info);
-      }
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.fc")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      FullyConnectedParams params;
-      auto err = EthosnAPI::QnnFullyConnected(call, &params);
-      *rv = !err && sl::IsFullyConnectedSupported(params.bias_info, params.weights_info,
-                                                  params.fc_info, params.input_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.max_pool2d")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      MaxPool2DParams params;
-      auto err = EthosnAPI::MaxPool2D(call, &params);
-      *rv = !err && sl::IsPoolingSupported(params.pool_info, params.input_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.avg_pool2d")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      AvgPool2DParams params;
-      auto err = EthosnAPI::AvgPool2D(call, &params);
-      *rv = !err && sl::IsPoolingSupported(params.pool_info, params.input_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.reshape")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      ReshapeParams params;
-      auto err = EthosnAPI::Reshape(call, &params);
-      *rv = !err && sl::IsReshapeSupported(params.new_shape, params.input_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.addition")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      AdditionParams params;
-      auto err = EthosnAPI::Addition(call, &params);
-      *rv = !err && sl::IsAdditionSupported(params.lhs_info, params.rhs_info,
-                                            params.output_quantization_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.sigmoid")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      SigmoidParams params;
-      auto err = EthosnAPI::Sigmoid(call, &params);
-      *rv = !err && sl::IsSigmoidSupported(params.input_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.concatenate")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      ConcatenateParams params;
-      auto err = EthosnAPI::Concatenate(call, &params);
-      *rv = !err && sl::IsConcatenationSupported(params.input_infos, params.concat_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.split")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      SplitParams params;
-      auto err = EthosnAPI::Split(call, &params);
-      *rv = !err && sl::IsSplitSupported(params.input_info, params.split_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.depth_to_space")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      DepthToSpaceParams params;
-      auto err = EthosnAPI::DepthToSpace(call, &params);
-      *rv = !err && sl::IsDepthToSpaceSupported(params.input_info, params.depth_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.support.relu")
-    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-      Call call = args[0];
-      ReluParams params;
-      auto err = EthosnAPI::Relu(call, &params);
-      *rv = !err && sl::IsReluSupported(params.relu_info, params.input_info);
-    });
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.query").set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
-#if defined ETHOSN_HW
-  *rv = true;
-#else
-  *rv = false;
-#endif
-});
-
-TVM_REGISTER_GLOBAL("relay.ethos-n.api.version").set_body_typed([]() -> int {
-  return _ETHOSN_API_VERSION_;
-});
-
 }  // namespace ethosn
 }  // namespace contrib
 }  // namespace relay
diff --git a/src/relay/backend/contrib/ethosn/ethosn_api_version.h b/src/relay/backend/contrib/ethosn/ethosn_api_version.h
index 618b702da333..78f08950bb48 100644
--- a/src/relay/backend/contrib/ethosn/ethosn_api_version.h
+++ b/src/relay/backend/contrib/ethosn/ethosn_api_version.h
@@ -29,10 +29,12 @@
  * along with associated compatibility measures when no
  * longer necessary.
  */
+#ifndef ETHOSN_API_VERSION
 #define _ETHOSN_API_VERSION_ 2008
-#ifndef COMPILER_ALGORITHM_MODE
-#undef _ETHOSN_API_VERSION_
-#define _ETHOSN_API_VERSION_ 2005
+#elif ~(~ETHOSN_API_VERSION + 0) == 0 && ~(~ETHOSN_API_VERSION + 1) == 1
+#define _ETHOSN_API_VERSION_ 2008
+#else
+#define _ETHOSN_API_VERSION_ ETHOSN_API_VERSION
 #endif
 
 #endif  // TVM_RELAY_BACKEND_CONTRIB_ETHOSN_ETHOSN_API_VERSION_H_
diff --git a/src/relay/backend/contrib/tensorrt/codegen.cc b/src/relay/backend/contrib/tensorrt/codegen.cc
index cb648333df8d..059dbc192a04 100644
--- a/src/relay/backend/contrib/tensorrt/codegen.cc
+++ b/src/relay/backend/contrib/tensorrt/codegen.cc
@@ -156,6 +156,9 @@ class TensorRTJSONSerializer : public backend::contrib::JSONSerializer {
         // with slice_mode = "size", attrs->end_value mean the size of the slice
         int end_value = attrs->end.value()[i].as<IntImmNode>()->value;
         size_value = (end_value == -1) ? ishape[i] - begin_value : end_value;
+      } else {
+        LOG(FATAL) << "Unexpected slice_mode " << attrs->slice_mode << ", expected end or size";
+        throw;
       }
       ICHECK_GT(size_value, 0);
       size.push_back(std::to_string(size_value));
diff --git a/src/relay/backend/contrib/verilator/codegen.cc b/src/relay/backend/contrib/verilator/codegen.cc
index 4124fa2459d6..b206288f7e96 100644
--- a/src/relay/backend/contrib/verilator/codegen.cc
+++ b/src/relay/backend/contrib/verilator/codegen.cc
@@ -34,6 +34,7 @@
 #include <sstream>
 
 #include "../../../../runtime/contrib/json/json_node.h"
+#include "../../../../runtime/contrib/verilator/verilator_runtime.h"
 #include "../../utils.h"
 #include "../codegen_json/codegen_json.h"
 
@@ -43,6 +44,7 @@ namespace contrib {
 
 using namespace backend;
 
+/*! \brief Verilator JSON serializer */
 class VerilatorJSONSerializer : public backend::contrib::JSONSerializer {
   using JSONGraphNode = tvm::runtime::json::JSONGraphNode;
   using JSONGraphNodeEntry = tvm::runtime::json::JSONGraphNodeEntry;
@@ -74,11 +76,34 @@ class VerilatorJSONSerializer : public backend::contrib::JSONSerializer {
   }
 };
 
+/*! \brief Attributes to store options for Verilator */
+struct VerilatorOptionsNode : public tvm::AttrsNode<VerilatorOptionsNode> {
+  String lib_path;
+  int reset_cycles;
+  bool profiler_enable;
+  int profiler_cycle_counter_id;
+
+  TVM_DECLARE_ATTRS(VerilatorOptionsNode, "ext.attrs.VerilatorOptionsNode") {
+    TVM_ATTR_FIELD(lib_path).describe("the design library path").set_default("libverilator.so");
+    TVM_ATTR_FIELD(reset_cycles).describe("the number of reset cycles").set_default(1);
+    TVM_ATTR_FIELD(profiler_enable).describe("enable profiler").set_default(false);
+    TVM_ATTR_FIELD(profiler_cycle_counter_id).describe("profiler cycle counter id").set_default(0);
+  }
+};
+
+class VerilatorOptions : public Attrs {
+ public:
+  TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS(VerilatorOptions, Attrs, VerilatorOptionsNode);
+};
+
+TVM_REGISTER_NODE_TYPE(VerilatorOptionsNode);
+TVM_REGISTER_PASS_CONFIG_OPTION("relay.ext.verilator.options", VerilatorOptions);
+
 /*!
- * \brief The external compiler/codegen tool. It takes a Relay expression/module and
- * compile it into a runtime module.
+ * \brief The Verilator codegen tool. It takes a Relay expression/module and
+ * compile it into a Verilator runtime module.
  */
-runtime::Module VerilatorCompiler(const ObjectRef& ref) {
+runtime::Module VerilatorBackend(const ObjectRef& ref) {
   CHECK(ref->IsInstance<FunctionNode>());
   auto func = Downcast<Function>(ref);
   auto func_name = GetExtSymbol(func);
@@ -87,13 +112,28 @@ runtime::Module VerilatorCompiler(const ObjectRef& ref) {
   std::string graph_json = serializer.GetJSON();
   auto params = serializer.GetParams();
 
-  const auto* pf = runtime::Registry::Get("runtime.VerilatorJSONRuntimeCreate");
-  CHECK(pf != nullptr) << "Cannot find JSON runtime module to create";
-  auto mod = (*pf)(func_name, graph_json, params);
-  return mod;
+  // Create runtime object
+  auto n = make_object<runtime::contrib::VerilatorRuntime>(func_name, graph_json, params);
+
+  // Get Verilator compiler options
+  auto ctx = transform::PassContext::Current();
+  auto cfg = ctx->GetConfig<VerilatorOptions>("relay.ext.verilator.options");
+  if (!cfg.defined()) {
+    cfg = AttrsWithDefaultValues<VerilatorOptions>();
+  }
+
+  n->SetLibrary(cfg.value()->lib_path);
+  n->SetResetCycles(cfg.value()->reset_cycles);
+
+  if (cfg.value()->profiler_enable) {
+    n->EnableProfiler();
+    n->SetProfilerCycleCounterId(cfg.value()->profiler_cycle_counter_id);
+  }
+
+  return runtime::Module(n);
 }
 
-TVM_REGISTER_GLOBAL("relay.ext.verilator").set_body_typed(VerilatorCompiler);
+TVM_REGISTER_GLOBAL("relay.ext.verilator").set_body_typed(VerilatorBackend);
 
 }  // namespace contrib
 }  // namespace relay
diff --git a/src/relay/backend/param_dict.cc b/src/relay/backend/param_dict.cc
index 1d7e08abcdde..bb0fad9142c1 100644
--- a/src/relay/backend/param_dict.cc
+++ b/src/relay/backend/param_dict.cc
@@ -31,70 +31,24 @@
 #include <utility>
 #include <vector>
 
+#include "../../runtime/file_utils.h"
+
 namespace tvm {
 namespace relay {
 
 using namespace runtime;
 
-TVM_REGISTER_GLOBAL("tvm.relay._save_param_dict").set_body([](TVMArgs args, TVMRetValue* rv) {
-  ICHECK_EQ(args.size() % 2, 0u);
-  // `args` is in the form "key, value, key, value, ..."
-  size_t num_params = args.size() / 2;
-  std::vector<std::string> names;
-  names.reserve(num_params);
-  std::vector<DLTensor*> arrays;
-  arrays.reserve(num_params);
-  for (size_t i = 0; i < num_params * 2; i += 2) {
-    names.emplace_back(args[i].operator String());
-    arrays.emplace_back(args[i + 1].operator DLTensor*());
-  }
-  std::string bytes;
-  dmlc::MemoryStringStream strm(&bytes);
-  dmlc::Stream* fo = &strm;
-  uint64_t header = kTVMNDArrayListMagic, reserved = 0;
-  fo->Write(header);
-  fo->Write(reserved);
-  fo->Write(names);
-  {
-    uint64_t sz = static_cast<uint64_t>(arrays.size());
-    fo->Write(sz);
-    for (size_t i = 0; i < sz; ++i) {
-      tvm::runtime::SaveDLTensor(fo, arrays[i]);
-    }
-  }
-  TVMByteArray arr;
-  arr.data = bytes.c_str();
-  arr.size = bytes.length();
-  *rv = arr;
-});
-
-TVM_REGISTER_GLOBAL("tvm.relay._load_param_dict").set_body([](TVMArgs args, TVMRetValue* rv) {
-  std::string bytes = args[0];
-  std::vector<std::string> names;
-  dmlc::MemoryStringStream memstrm(&bytes);
-  dmlc::Stream* strm = &memstrm;
-  uint64_t header, reserved;
-  ICHECK(strm->Read(&header)) << "Invalid parameters file format";
-  ICHECK(header == kTVMNDArrayListMagic) << "Invalid parameters file format";
-  ICHECK(strm->Read(&reserved)) << "Invalid parameters file format";
-  ICHECK(strm->Read(&names)) << "Invalid parameters file format";
-  uint64_t sz;
-  strm->Read(&sz, sizeof(sz));
-  size_t size = static_cast<size_t>(sz);
-  ICHECK(size == names.size()) << "Invalid parameters file format";
-  tvm::Array<NamedNDArray> ret;
-  for (size_t i = 0; i < size; ++i) {
-    tvm::runtime::NDArray temp;
-    temp.Load(strm);
-    auto n = tvm::make_object<NamedNDArrayNode>();
-    n->name = std::move(names[i]);
-    n->array = temp;
-    ret.push_back(NamedNDArray(n));
-  }
-  *rv = ret;
+TVM_REGISTER_GLOBAL("tvm.relay._save_param_dict")
+    .set_body_typed([](const Map<String, NDArray>& params) {
+      std::string s = ::tvm::runtime::SaveParams(params);
+      // copy return array so it is owned by the ret value
+      TVMRetValue rv;
+      rv = TVMByteArray{s.data(), s.size()};
+      return rv;
+    });
+TVM_REGISTER_GLOBAL("tvm.relay._load_param_dict").set_body_typed([](const String& s) {
+  return ::tvm::runtime::LoadParams(s);
 });
 
-TVM_REGISTER_NODE_TYPE(NamedNDArrayNode);
-
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/backend/param_dict.h b/src/relay/backend/param_dict.h
index 384201f94648..96e17a9da07b 100644
--- a/src/relay/backend/param_dict.h
+++ b/src/relay/backend/param_dict.h
@@ -32,32 +32,7 @@
 #include <string>
 
 namespace tvm {
-namespace relay {
-
-/*! \brief Magic number for NDArray list file  */
-constexpr uint64_t kTVMNDArrayListMagic = 0xF7E58D4F05049CB7;
-
-/*!
- * \brief Wrapper node for naming `NDArray`s.
- */
-struct NamedNDArrayNode : public ::tvm::Object {
-  std::string name;
-  tvm::runtime::NDArray array;
-
-  void VisitAttrs(tvm::AttrVisitor* v) {
-    v->Visit("name", &name);
-    v->Visit("array", &array);
-  }
-
-  static constexpr const char* _type_key = "NamedNDArray";
-  TVM_DECLARE_FINAL_OBJECT_INFO(NamedNDArrayNode, Object);
-};
-
-class NamedNDArray : public ObjectRef {
- public:
-  TVM_DEFINE_OBJECT_REF_METHODS(NamedNDArray, ObjectRef, NamedNDArrayNode);
-};
-}  // namespace relay
+namespace relay {}  // namespace relay
 }  // namespace tvm
 
 #endif  // TVM_RELAY_BACKEND_PARAM_DICT_H_
diff --git a/src/relay/backend/utils.h b/src/relay/backend/utils.h
index e1677205ffa1..6908ca85f582 100644
--- a/src/relay/backend/utils.h
+++ b/src/relay/backend/utils.h
@@ -303,6 +303,15 @@ inline bool IsAutoSchedulerEnabled() {
       .value();
 }
 
+/*!
+ * \brief Return whether the compile engine cache is disabled in the pass context.
+ */
+inline bool IsCompileEngineCacheDisabled() {
+  return transform::PassContext::Current()
+      ->GetConfig<Bool>("relay.backend.disable_compile_engine_cache", Bool(false))
+      .value();
+}
+
 }  // namespace backend
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/backend/vm/compiler.cc b/src/relay/backend/vm/compiler.cc
index 8fbe31edce3d..9d3ffc558aae 100644
--- a/src/relay/backend/vm/compiler.cc
+++ b/src/relay/backend/vm/compiler.cc
@@ -33,8 +33,8 @@
 #include <tvm/relay/interpreter.h>
 #include <tvm/relay/qnn/transform.h>
 #include <tvm/relay/transform.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/vm/vm.h>
-#include <tvm/support/logging.h>
 #include <tvm/te/operation.h>
 
 #include <iostream>
@@ -58,12 +58,6 @@ namespace transform {
 Pass LambdaLift();
 Pass InlinePrimitives();
 
-Pass ManifestAlloc(Target target_host, vm::TargetsMap targets) {
-  auto f = tvm::runtime::Registry::Get("relay.transform.ManifestAlloc");
-  ICHECK(f != nullptr) << "unable to load allocation manifestation pass";
-  return (*f)(target_host, targets);
-}
-
 Pass MemoryPlan() {
   auto f = tvm::runtime::Registry::Get("relay.transform.MemoryPlan");
   ICHECK(f != nullptr) << "unable to load the memory planning pass";
@@ -382,11 +376,16 @@ class VMFunctionCompiler : ExprFunctor<void(const Expr& expr)> {
     CompileMatch(match);
   }
 
-  void VisitExpr_(const LetNode* let_node) {
-    DLOG(INFO) << PrettyPrint(let_node->value);
-    this->VisitExpr(let_node->value);
-    var_register_map_.insert({let_node->var, this->last_register_});
-    this->VisitExpr(let_node->body);
+  void VisitExpr_(const LetNode* l) final {
+    Expr let_binding = GetRef<Expr>(l);
+    const LetNode* let;
+    while ((let = let_binding.as<LetNode>())) {
+      VisitExpr(let->value);
+      var_register_map_.insert({let->var, this->last_register_});
+      let_binding = let->body;
+    }
+
+    VisitExpr(let_binding);
   }
 
   void VisitExpr_(const TupleGetItemNode* get_node) {
@@ -898,15 +897,6 @@ void VMCompiler::SetParam(const std::string& name, runtime::NDArray data_in) {
 }
 
 void VMCompiler::Lower(IRModule mod, const TargetsMap& targets, const tvm::Target& target_host) {
-  if (params_.size()) {
-    BaseFunc base_func = mod->Lookup("main");
-    ICHECK(base_func->IsInstance<FunctionNode>())
-        << "VM compiler expects to compile relay::Function";
-    auto f = relay::backend::BindParamsByName(Downcast<Function>(base_func), params_);
-    auto gvar = mod->GetGlobalVar("main");
-    mod->Add(gvar, f);
-  }
-
   exec_ = make_object<Executable>();
   targets_ = targets;
   target_host_ = target_host;
@@ -985,8 +975,11 @@ transform::Sequential MemoryOpt(tvm::Target host_target, TargetsMap targets) {
   // Fuse the shape functions.
   pass_seqs.push_back(transform::FuseOps());
 
-  // Perform memory planning in order to coalesce/reduce allocations.
-  pass_seqs.push_back(transform::MemoryPlan());
+  // TODO(mbrookhart, jroesch, masahi): this pass is very slow, and is
+  // incomplete to provide memory resuse optimizations. Disable it until we can
+  // rewrite it in C++ and complete it.
+  // // Perform memory planning in order to coalesce/reduce allocations.
+  // pass_seqs.push_back(transform::MemoryPlan());
 
   // Compute away constant computation introduced by coalescing allocations.
   pass_seqs.push_back(transform::FoldConstant());
@@ -1008,8 +1001,17 @@ transform::Sequential MemoryOpt(tvm::Target host_target, TargetsMap targets) {
   return transform::Sequential(pass_seqs);
 }
 
-IRModule VMCompiler::OptimizeModule(const IRModule& mod, const TargetsMap& targets,
+IRModule VMCompiler::OptimizeModule(IRModule mod, const TargetsMap& targets,
                                     const Target& target_host) {
+  if (params_.size()) {
+    BaseFunc base_func = mod->Lookup("main");
+    ICHECK(base_func->IsInstance<FunctionNode>())
+        << "VM compiler expects to compile relay::Function";
+    auto f = relay::backend::BindParamsByName(Downcast<Function>(base_func), params_);
+    auto gvar = mod->GetGlobalVar("main");
+    mod->Add(gvar, f);
+  }
+
   Array<Pass> pass_seqs;
   Array<runtime::String> entry_functions{"main"};
   pass_seqs.push_back(transform::RemoveUnusedFunctions(entry_functions));
@@ -1069,6 +1071,23 @@ IRModule VMCompiler::OptimizeModule(const IRModule& mod, const TargetsMap& targe
   }
 
   pass_seqs.push_back(transform::FuseOps());
+  // Do layout rewrite for auto-scheduler.
+  transform::PassContext pass_ctx = PassContext::Current();
+  if (backend::IsAutoSchedulerEnabled() && targets.size() == 1) {
+    const auto& target = (*targets.begin()).second;
+    Pass major_pass = transform::AutoSchedulerLayoutRewrite();
+    bool enable_layout_rewrite_targets =
+        target->kind->device_type == kDLCPU || target->GetAttr<String>("device", "") == "mali";
+    if (enable_layout_rewrite_targets && pass_ctx.PassEnabled(major_pass->Info())) {
+      With<Target> tctx(target);
+      pass_seqs.push_back(major_pass);
+      // Defuse ops to fold constants, then fuse them again
+      pass_seqs.push_back(transform::DefuseOps());
+      pass_seqs.push_back(transform::FoldConstant());
+      pass_seqs.push_back(transform::FuseOps());
+    }
+  }
+
   pass_seqs.push_back(transform::ToANormalForm());
   pass_seqs.push_back(transform::InferType());
   pass_seqs.push_back(transform::LambdaLift());
@@ -1085,7 +1104,6 @@ IRModule VMCompiler::OptimizeModule(const IRModule& mod, const TargetsMap& targe
   pass_seqs.push_back(transform::InferType());
 
   transform::Sequential seq(pass_seqs);
-  transform::PassContext pass_ctx = PassContext::Current();
   tvm::With<relay::transform::PassContext> ctx(pass_ctx);
   if (targets.size() == 1) {
     const auto& it = targets.begin();
diff --git a/src/relay/backend/vm/compiler.h b/src/relay/backend/vm/compiler.h
index 56965c544701..9c813a4f561c 100644
--- a/src/relay/backend/vm/compiler.h
+++ b/src/relay/backend/vm/compiler.h
@@ -29,8 +29,8 @@
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/interpreter.h>
 #include <tvm/relay/transform.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/vm/vm.h>
-#include <tvm/support/logging.h>
 #include <tvm/tir/function.h>
 
 #include <iostream>
@@ -125,8 +125,7 @@ class VMCompiler : public runtime::ModuleNode {
    *
    * \return The optimized IRModule.
    */
-  IRModule OptimizeModule(const IRModule& mod, const TargetsMap& targets,
-                          const Target& target_host);
+  IRModule OptimizeModule(IRModule mod, const TargetsMap& targets, const Target& target_host);
 
   /*!
    * \brief Populate the global function names in a map where the value is used
diff --git a/src/relay/backend/vm/inline_primitives.cc b/src/relay/backend/vm/inline_primitives.cc
index 650df99645e7..05fb2a120620 100644
--- a/src/relay/backend/vm/inline_primitives.cc
+++ b/src/relay/backend/vm/inline_primitives.cc
@@ -25,7 +25,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <iostream>
 #include <vector>
@@ -58,8 +58,19 @@ struct PrimitiveInliner : ExprMutator {
   explicit PrimitiveInliner(const IRModule& module) : module_(module) {}
 
   Expr VisitExpr_(const LetNode* let_node) {
-    var_map.insert({let_node->var, VisitExpr(let_node->value)});
-    return ExprMutator::VisitExpr_(let_node);
+    auto pre_visit = [this](const LetNode* op) {
+      var_map.insert({op->var, this->VisitExpr(op->value)});
+    };
+    auto post_visit = [this](const LetNode* op) {
+      // Rely on the Memoizer to cache pre-visit values
+      Expr value = this->VisitExpr(op->value);
+      // Visit body and cache the op
+      Expr body = this->VisitExpr(op->body);
+      auto expr = GetRef<Expr>(op);
+      this->memo_[expr] = Let(op->var, value, body);
+    };
+    ExpandANormalForm(let_node, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(let_node)];
   }
 
   Expr VisitExpr_(const CallNode* call) {
diff --git a/src/relay/backend/vm/lambda_lift.cc b/src/relay/backend/vm/lambda_lift.cc
index 8e9cc625063b..c768a2c300ec 100644
--- a/src/relay/backend/vm/lambda_lift.cc
+++ b/src/relay/backend/vm/lambda_lift.cc
@@ -28,7 +28,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <iostream>
 #include <vector>
@@ -61,19 +61,30 @@ class LambdaLifter : public ExprMutator {
   explicit LambdaLifter(const IRModule& module) : module_(module) {}
 
   Expr VisitExpr_(const LetNode* let_node) final {
-    bool is_lambda = false;
-    if (auto func = let_node->value.as<FunctionNode>()) {
-      if (!func->HasNonzeroAttr(attr::kPrimitive)) {
-        is_lambda = true;
-        letrec_.push_back(let_node->var);
+    auto pre_visit = [this](const LetNode* op) {
+      bool is_lambda = false;
+      if (auto func = op->value.as<FunctionNode>()) {
+        if (!func->HasNonzeroAttr(attr::kPrimitive)) {
+          is_lambda = true;
+          this->letrec_.push_back(op->var);
+        }
       }
-    }
-    auto value = VisitExpr(let_node->value);
-    if (is_lambda) {
-      letrec_.pop_back();
-    }
-    auto body = VisitExpr(let_node->body);
-    return Let(let_node->var, value, body);
+      Expr value = this->VisitExpr(op->value);
+
+      if (is_lambda) {
+        this->letrec_.pop_back();
+      }
+    };
+    auto post_visit = [this](const LetNode* op) {
+      // Rely on the Memoizer to cache pre-visit values
+      Expr value = this->VisitExpr(op->value);
+      // Visit body and cache the op
+      Expr body = this->VisitExpr(op->body);
+      auto expr = GetRef<Expr>(op);
+      this->memo_[expr] = Let(op->var, value, body);
+    };
+    ExpandANormalForm(let_node, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(let_node)];
   }
 
   Expr VisitExpr_(const CallNode* call_node) final {
@@ -192,7 +203,6 @@ class LambdaLifter : public ExprMutator {
       global = module_->GetGlobalVar(name);
     } else {
       // Add the lifted function to the module.
-      std::cout << AsText(lifted_func) << std::endl;
       module_->Add(global, lifted_func);
     }
 
diff --git a/src/relay/backend/vm/removed_unused_funcs.cc b/src/relay/backend/vm/removed_unused_funcs.cc
index cdf898fca756..5e9b1b7978f9 100644
--- a/src/relay/backend/vm/removed_unused_funcs.cc
+++ b/src/relay/backend/vm/removed_unused_funcs.cc
@@ -26,7 +26,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <iostream>
 #include <unordered_set>
diff --git a/src/relay/ir/dataflow_matcher.cc b/src/relay/ir/dataflow_matcher.cc
index c5cc3dd17429..43a6473fb632 100644
--- a/src/relay/ir/dataflow_matcher.cc
+++ b/src/relay/ir/dataflow_matcher.cc
@@ -55,6 +55,8 @@ class DFPatternMatcher : public DFPatternFunctor<bool(const DFPattern&, const Ex
   bool VisitDFPattern_(const DominatorPatternNode* op, const Expr& expr) override;
   bool VisitDFPattern_(const ExprPatternNode* op, const Expr& expr) override;
   bool VisitDFPattern_(const FunctionPatternNode* op, const Expr& expr) override;
+  bool VisitDFPattern_(const IfPatternNode* op, const Expr& expr) override;
+  bool VisitDFPattern_(const LetPatternNode* op, const Expr& expr) override;
   bool VisitDFPattern_(const ShapePatternNode* op, const Expr& expr) override;
   bool VisitDFPattern_(const TupleGetItemPatternNode* op, const Expr& expr) override;
   bool VisitDFPattern_(const TuplePatternNode* op, const Expr& expr) override;
@@ -124,6 +126,13 @@ bool MatchRetValue(const ObjectRef& lhs, const TVMRetValue& rhs) {
         return val->data == rhs.operator std::string();
       }
       break;
+    case kTVMDataType:
+      if (auto* val = lhs.as<tir::StringImmNode>()) {
+        return rhs.operator std::string() == val->value;
+      } else if (auto* val = lhs.as<StringObj>()) {
+        return rhs.operator std::string() == val->data;
+      }
+      break;
     case kTVMObjectHandle:
       if (rhs.IsObjectRef<String>()) {
         if (auto* val = lhs.as<tir::StringImmNode>()) {
@@ -140,16 +149,25 @@ bool MatchRetValue(const ObjectRef& lhs, const TVMRetValue& rhs) {
 }
 
 bool DFPatternMatcher::VisitDFPattern_(const AttrPatternNode* attr_pattern, const Expr& expr) {
-  bool matches = false;
+  bool matches = VisitDFPattern(attr_pattern->pattern, expr);
+  if (!matches) {
+    return matches;
+  }
   auto attributes = attr_pattern->attrs.as<DictAttrsNode>()->dict;
   if (const auto* op_node = expr.as<OpNode>()) {
     Op op = GetRef<Op>(op_node);
     for (auto kv : attributes) {
       auto attr_name = kv.first;
       auto attr_value = kv.second;
-      auto op_map = Op::GetAttrMap<TVMRetValue>(attr_name);
-      if (op_map.count(op)) {
-        matches = MatchRetValue(attr_value, op_map[op]);
+      if (Op::HasAttrMap(attr_name)) {
+        auto op_map = Op::GetAttrMap<TVMRetValue>(attr_name);
+        if (op_map.count(op)) {
+          matches &= MatchRetValue(attr_value, op_map[op]);
+        } else {
+          matches = false;
+        }
+      } else {
+        matches = false;
       }
     }
   } else if (auto* op = expr.as<CallNode>()) {
@@ -158,7 +176,11 @@ bool DFPatternMatcher::VisitDFPattern_(const AttrPatternNode* attr_pattern, cons
     // and replace the whole thing with a Visitor-based approach
     ReflectionVTable* reflection = ReflectionVTable::Global();
     auto attrs_node = const_cast<BaseAttrsNode*>(op->attrs.get());
-    auto attr_names = reflection->ListAttrNames(attrs_node);
+    // attrs may be undefined on non-op calls so we check first
+    std::vector<std::string> attr_names;
+    if (attrs_node) {
+      attr_names = reflection->ListAttrNames(attrs_node);
+    }
     for (auto kv : attributes) {
       std::string attr = kv.first;
       if (matches && std::find(attr_names.begin(), attr_names.end(), attr) != attr_names.end()) {
@@ -178,8 +200,10 @@ bool DFPatternMatcher::VisitDFPattern_(const AttrPatternNode* attr_pattern, cons
         break;
       }
     }
+  } else {
+    matches = false;
   }
-  return matches && VisitDFPattern(attr_pattern->pattern, expr);
+  return matches;
 }
 
 Array<DFPattern> reverse(const Array<DFPattern>& args) {
@@ -397,6 +421,25 @@ bool DFPatternMatcher::VisitDFPattern_(const TuplePatternNode* op, const Expr& e
   return matches;
 }
 
+bool DFPatternMatcher::VisitDFPattern_(const IfPatternNode* op, const Expr& expr) {
+  if (const auto* if_node = expr.as<IfNode>()) {
+    auto cond = if_node->cond;
+    auto true_branch = if_node->true_branch;
+    auto false_branch = if_node->false_branch;
+    return VisitDFPattern(op->cond, cond) && VisitDFPattern(op->true_branch, true_branch) &&
+           VisitDFPattern(op->false_branch, false_branch);
+  }
+  return false;
+}
+
+bool DFPatternMatcher::VisitDFPattern_(const LetPatternNode* op, const Expr& expr) {
+  if (const auto* let_node = expr.as<LetNode>()) {
+    return VisitDFPattern(op->var, let_node->var) && VisitDFPattern(op->value, let_node->value) &&
+           VisitDFPattern(op->body, let_node->body);
+  }
+  return false;
+}
+
 Expr InferType(const Expr& expr) {
   auto mod = IRModule::FromExpr(expr);
   mod = transform::InferType()(mod);
@@ -691,11 +734,12 @@ class PatternGrouper {
           // Exit due to overlapping partitions
           return;
         } else if (kv.second != body) {
-          // if the node isn't the ouput of the group
+          // if the node isn't the output of the group
           auto node = matcher_->expr_graph_.node_map_.at(kv.first);
           for (auto* output : node->outputs_) {
             // and the node is used by nodes outside of the group
-            if (memo.count(output->ref_) == 0) {
+            if (memo.count(output->ref_) == 0 &&
+                !matcher_->expr_graph_.node_map_.at(expr)->Dominates(output)) {
               // Exit because nodes in this pattern's body are used outside the pattern
               // fusing it would be invalid
               return;
diff --git a/src/relay/ir/dataflow_pattern.cc b/src/relay/ir/dataflow_pattern.cc
index 46c53c8bd96c..9c65c490d855 100644
--- a/src/relay/ir/dataflow_pattern.cc
+++ b/src/relay/ir/dataflow_pattern.cc
@@ -22,6 +22,7 @@
  * \brief The dataflow pattern language for Relay.
  */
 #include <tvm/relay/dataflow_pattern.h>
+#include <tvm/runtime/data_type.h>
 
 namespace tvm {
 namespace relay {
@@ -44,29 +45,22 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
       p->Print(node->expr);
     });
 
-VarPattern::VarPattern(String name_hint, Type type_annotation) {
+VarPattern::VarPattern(String name_hint) {
   ObjectPtr<VarPatternNode> n = make_object<VarPatternNode>();
   n->name = std::move(name_hint);
-  n->type_annotation = std::move(type_annotation);
   data_ = std::move(n);
 }
 
 TVM_REGISTER_NODE_TYPE(VarPatternNode);
 
-TVM_REGISTER_GLOBAL("relay.dataflow_pattern.VarPattern")
-    .set_body_typed([](String name_hint, Type type_annotation) {
-      return VarPattern(name_hint, type_annotation);
-    });
+TVM_REGISTER_GLOBAL("relay.dataflow_pattern.VarPattern").set_body_typed([](String name_hint) {
+  return VarPattern(name_hint);
+});
 
 TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
     .set_dispatch<VarPatternNode>([](const ObjectRef& ref, ReprPrinter* p) {
       auto* node = static_cast<const VarPatternNode*>(ref.get());
-      p->stream << "VarPattern(" << node->name_hint();
-      if (node->type_annotation.defined()) {
-        p->stream << ", ty=";
-        p->Print(node->type_annotation);
-      }
-      p->stream << ")";
+      p->stream << "VarPattern(" << node->name_hint() << ")";
     });
 
 TVM_REGISTER_NODE_TYPE(ConstantPatternNode);
@@ -118,6 +112,50 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
       p->stream << "FunctionPatternNode(" << node->params << ", " << node->body << ")";
     });
 
+LetPattern::LetPattern(DFPattern var, DFPattern value, DFPattern body) {
+  ObjectPtr<LetPatternNode> n = make_object<LetPatternNode>();
+  n->var = std::move(var);
+  n->value = std::move(value);
+  n->body = std::move(body);
+  data_ = std::move(n);
+}
+
+TVM_REGISTER_NODE_TYPE(LetPatternNode);
+
+TVM_REGISTER_GLOBAL("relay.dataflow_pattern.LetPattern")
+    .set_body_typed([](DFPattern var, DFPattern value, DFPattern body) {
+      return LetPattern(var, value, body);
+    });
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<LetPatternNode>([](const ObjectRef& ref, ReprPrinter* p) {
+      auto* node = static_cast<const LetPatternNode*>(ref.get());
+      p->stream << "LetPatternNode(" << node->var << ", " << node->value << ", " << node->body
+                << ")";
+    });
+
+IfPattern::IfPattern(DFPattern cond, DFPattern true_branch, DFPattern false_branch) {
+  ObjectPtr<IfPatternNode> n = make_object<IfPatternNode>();
+  n->cond = std::move(cond);
+  n->true_branch = std::move(true_branch);
+  n->false_branch = std::move(false_branch);
+  data_ = std::move(n);
+}
+
+TVM_REGISTER_NODE_TYPE(IfPatternNode);
+
+TVM_REGISTER_GLOBAL("relay.dataflow_pattern.IfPattern")
+    .set_body_typed([](DFPattern cond, DFPattern true_branch, DFPattern false_branch) {
+      return IfPattern(cond, true_branch, false_branch);
+    });
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<IfPatternNode>([](const ObjectRef& ref, ReprPrinter* p) {
+      auto* node = static_cast<const IfPatternNode*>(ref.get());
+      p->stream << "IfPattern(" << node->cond << ", " << node->true_branch << ", "
+                << node->false_branch << ")";
+    });
+
 TuplePattern::TuplePattern(tvm::Array<DFPattern> fields) {
   ObjectPtr<TuplePatternNode> n = make_object<TuplePatternNode>();
   n->fields = std::move(fields);
@@ -241,7 +279,7 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
       p->stream << "TypePattern(" << node->pattern << " has dtype " << node->dtype << ")";
     });
 
-AttrPattern::AttrPattern(DFPattern pattern, Attrs attrs) {
+AttrPattern::AttrPattern(DFPattern pattern, DictAttrs attrs) {
   ObjectPtr<AttrPatternNode> n = make_object<AttrPatternNode>();
   n->pattern = std::move(pattern);
   n->attrs = std::move(attrs);
@@ -251,7 +289,7 @@ AttrPattern::AttrPattern(DFPattern pattern, Attrs attrs) {
 TVM_REGISTER_NODE_TYPE(AttrPatternNode);
 
 TVM_REGISTER_GLOBAL("relay.dataflow_pattern.AttrPattern")
-    .set_body_typed([](DFPattern pattern, Attrs attrs) { return AttrPattern(pattern, attrs); });
+    .set_body_typed([](DFPattern pattern, DictAttrs attrs) { return AttrPattern(pattern, attrs); });
 
 TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
     .set_dispatch<AttrPatternNode>([](const ObjectRef& ref, ReprPrinter* p) {
@@ -263,6 +301,7 @@ DominatorPattern::DominatorPattern(DFPattern parent, DFPattern path, DFPattern c
   ObjectPtr<DominatorPatternNode> n = make_object<DominatorPatternNode>();
   n->parent = std::move(parent);
   n->path = std::move(path);
+
   n->child = std::move(child);
   data_ = std::move(n);
 }
@@ -281,5 +320,50 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
                 << ")";
     });
 
+// Syntatic Sugar
+DFPattern DFPattern::operator()(const std::vector<DFPattern>& args) {
+  return CallPattern(GetRef<DFPattern>(this->get()), Array<DFPattern>(args));
+}
+DFPattern DFPattern::operator+(const DFPattern& other) {
+  return IsOp("add")({GetRef<DFPattern>(this->get()), other});
+}
+DFPattern DFPattern::operator-(const DFPattern& other) {
+  return IsOp("subtract")({GetRef<DFPattern>(this->get()), other});
+}
+DFPattern DFPattern::operator*(const DFPattern& other) {
+  return IsOp("multiply")({GetRef<DFPattern>(this->get()), other});
+}
+DFPattern DFPattern::operator/(const DFPattern& other) {
+  return IsOp("divide")({GetRef<DFPattern>(this->get()), other});
+}
+DFPattern DFPattern::operator||(const DFPattern& other) {
+  return AltPattern(GetRef<DFPattern>(this->get()), other);
+}
+
+DFPattern DFPattern::HasAttr(const Map<String, ObjectRef>& attrs) {
+  return AttrPattern(GetRef<DFPattern>(this->get()), DictAttrs(attrs));
+}
+DFPattern DFPattern::HasType(const Type& type) {
+  return TypePattern(GetRef<DFPattern>(this->get()), type);
+}
+DFPattern DFPattern::HasDtype(const DataType& dtype) {
+  return DataTypePattern(GetRef<DFPattern>(this->get()), dtype);
+}
+DFPattern DFPattern::HasDtype(const std::string& dtype) {
+  return HasDtype(DataType(runtime::String2DLDataType(dtype)));
+}
+DFPattern DFPattern::HasShape(const Array<PrimExpr> shape) {
+  return ShapePattern(GetRef<DFPattern>(this->get()), shape);
+}
+DFPattern IsVar(const String& name) { return VarPattern(name); }
+DFPattern IsConstant() { return ConstantPattern(make_object<ConstantPatternNode>()); }
+DFPattern IsWildcard() { return WildcardPattern(make_object<WildcardPatternNode>()); }
+DFPattern IsExpr(const Expr& expr) { return ExprPattern(expr); }
+DFPattern IsOp(const String& op_name) { return IsExpr(Op::Get(op_name)); }
+DFPattern IsTuple(const Array<DFPattern>& fields) { return TuplePattern(fields); }
+DFPattern IsTupleGetItem(const DFPattern tuple, int index) {
+  return TupleGetItemPattern(tuple, index);
+}
+
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/ir/dataflow_pattern_functor.cc b/src/relay/ir/dataflow_pattern_functor.cc
index aaa4f84b3254..828e867b332c 100644
--- a/src/relay/ir/dataflow_pattern_functor.cc
+++ b/src/relay/ir/dataflow_pattern_functor.cc
@@ -81,6 +81,18 @@ void DFPatternVisitor::VisitDFPattern_(const TuplePatternNode* op) {
   }
 }
 
+void DFPatternVisitor::VisitDFPattern_(const IfPatternNode* op) {
+  VisitDFPattern(op->cond);
+  VisitDFPattern(op->true_branch);
+  VisitDFPattern(op->false_branch);
+}
+
+void DFPatternVisitor::VisitDFPattern_(const LetPatternNode* op) {
+  VisitDFPattern(op->var);
+  VisitDFPattern(op->value);
+  VisitDFPattern(op->body);
+}
+
 void DFPatternVisitor::VisitDFPattern_(const TypePatternNode* op) { VisitDFPattern(op->pattern); }
 
 void DFPatternVisitor::VisitDFPattern_(const VarPatternNode* op) {}
diff --git a/src/relay/ir/expr_functor.cc b/src/relay/ir/expr_functor.cc
index 74095a753950..5984a208efe0 100644
--- a/src/relay/ir/expr_functor.cc
+++ b/src/relay/ir/expr_functor.cc
@@ -103,11 +103,41 @@ Expr MixedModeMutator::VisitExpr(const Expr& expr) {
 class PostOrderRewriter : public MixedModeMutator {
  public:
   explicit PostOrderRewriter(ExprRewriter* rewriter) : rewriter_(rewriter) {}
+
   Expr DispatchVisitExpr(const Expr& expr) final {
     auto post = ExprFunctor::VisitExpr(expr);
     return rewriter_->Rewrite(expr, post);
   }
 
+  using MixedModeMutator::VisitExpr_;
+
+  Expr VisitExpr_(const LetNode* node) final {
+    auto pre_visit = [this](const LetNode* op) {
+      Expr var = this->Mutate(op->var);
+      Expr value = this->Mutate(op->value);
+    };
+    auto post_visit = [this, node](const LetNode* op) {
+      Var var = Downcast<Var>(this->Mutate(op->var));
+      Expr value = this->Mutate(op->value);
+      Expr body = this->Mutate(op->body);
+      Expr expr = GetRef<Expr>(op);
+      Expr post;
+      if (var.same_as(op->var) && value.same_as(op->value) && body.same_as(op->body)) {
+        post = expr;
+      } else {
+        post = Let(var, value, body);
+      }
+      //  avoid rewriting the first LetNode twice
+      if (op == node) {
+        this->memo_[expr] = post;
+      } else {
+        this->memo_[expr] = this->rewriter_->Rewrite(expr, post);
+      }
+    };
+    ExpandANormalForm(node, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(node)];
+  }
+
  protected:
   ExprRewriter* rewriter_;
 };
@@ -532,5 +562,27 @@ TVM_REGISTER_GLOBAL("relay.ir.Bind").set_body([](TVMArgs args, TVMRetValue* ret)
     *ret = Bind(Downcast<Type>(input), args[1]);
   }
 });
+
+void ExpandANormalForm(const LetNode* op, std::function<void(const LetNode*)> pre_visit,
+                       std::function<void(const LetNode*)> post_visit) {
+  std::stack<const LetNode*> stack;
+  stack.push(op);
+  bool is_anormal = true;
+  while (is_anormal) {
+    const LetNode* current_op = stack.top();
+    pre_visit(current_op);
+    if (const LetNode* new_op = current_op->body.as<LetNode>()) {
+      stack.push(new_op);
+    } else {
+      is_anormal = false;
+    }
+  }
+  while (stack.size()) {
+    const LetNode* current_op = stack.top();
+    stack.pop();
+    post_visit(current_op);
+  }
+}
+
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/ir/indexed_graph.cc b/src/relay/ir/indexed_graph.cc
index 4ba053c429de..36789e6f808a 100644
--- a/src/relay/ir/indexed_graph.cc
+++ b/src/relay/ir/indexed_graph.cc
@@ -73,7 +73,7 @@ IndexedGraph<Expr> CreateIndexedGraph(const Expr& expr) {
       return std::move(graph_);
     }
 
-    /*! Default visitation pushes the parent to the child's ouputs and the child to the parent's
+    /*! Default visitation pushes the parent to the child's outputs and the child to the parent's
      * inputs*/
     void VisitExpr(const Expr& expr, NodePtr parent) override {
       auto current = graph_.node_map_[expr];
@@ -220,7 +220,7 @@ IndexedGraph<DFPattern> CreateIndexedGraph(const DFPattern& pattern) {
       return std::move(graph_);
     }
 
-    /*! Default visitation pushes the parent to the child's ouputs */
+    /*! Default visitation pushes the parent to the child's outputs */
     void VisitDFPattern(const DFPattern& pattern, NodePtr parent) override {
       auto current = graph_.node_map_[pattern];
       if (parent) {
@@ -282,6 +282,18 @@ IndexedGraph<DFPattern> CreateIndexedGraph(const DFPattern& pattern) {
       }
     }
 
+    void VisitDFPattern_(const IfPatternNode* op, NodePtr parent) override {
+      VisitDFPattern(op->cond, graph_.node_map_[GetRef<DFPattern>(op)]);
+      VisitDFPattern(op->true_branch, graph_.node_map_[GetRef<DFPattern>(op)]);
+      VisitDFPattern(op->false_branch, graph_.node_map_[GetRef<DFPattern>(op)]);
+    }
+
+    void VisitDFPattern_(const LetPatternNode* op, NodePtr parent) override {
+      VisitDFPattern(op->var, graph_.node_map_[GetRef<DFPattern>(op)]);
+      VisitDFPattern(op->value, graph_.node_map_[GetRef<DFPattern>(op)]);
+      VisitDFPattern(op->body, graph_.node_map_[GetRef<DFPattern>(op)]);
+    }
+
     void VisitDFPattern_(const TypePatternNode* op, NodePtr parent) override {
       VisitDFPattern(op->pattern, graph_.node_map_[GetRef<DFPattern>(op)]);
     }
diff --git a/src/relay/ir/indexed_graph.h b/src/relay/ir/indexed_graph.h
index 4bbb741b760d..d073bcaeea5c 100644
--- a/src/relay/ir/indexed_graph.h
+++ b/src/relay/ir/indexed_graph.h
@@ -27,6 +27,7 @@
 #include <tvm/relay/dataflow_pattern.h>
 
 #include <memory>
+#include <stack>
 #include <unordered_map>
 #include <unordered_set>
 #include <utility>
@@ -74,6 +75,27 @@ class IndexedGraph {
     Node* dominator_parent_;
     /*! \brief The nodes this node dominates */
     std::vector<Node*> dominator_children_;
+
+    bool Dominates(const Node* other) {
+      std::stack<const Node*> stack;
+      std::unordered_set<const Node*> visited;
+      stack.push(this);
+      while (!stack.empty()) {
+        const Node* current = stack.top();
+        stack.pop();
+        for (auto node : current->dominator_children_) {
+          if (visited.count(node) == 0) {
+            if (other == node) {
+              return true;
+            } else {
+              stack.push(node);
+            }
+            visited.insert(node);
+          }
+        }
+      }
+      return false;
+    }
   };
   /*! \brief Construct the domination tree inside IndexedGraph */
   void PostDom() {
diff --git a/src/relay/op/device_copy.cc b/src/relay/op/device_copy.cc
deleted file mode 100644
index 997eec5a333f..000000000000
--- a/src/relay/op/device_copy.cc
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- *
- * \file src/relay/op/device_copy.cc
- * \brief Crossing device data copy operator.
- *
- * The pattern of this operator is registered as kOpaque. Hence, it could be
- * used as "barrier" to avoid fusing operators belonging to differen devices.
- */
-
-#include <tvm/relay/attrs/device_copy.h>
-#include <tvm/relay/expr.h>
-#include <tvm/relay/op.h>
-#include <tvm/relay/op_attr_types.h>
-#include <tvm/tir/expr.h>
-#include <tvm/topi/elemwise.h>
-
-#include "../transforms/infer_layout_utils.h"
-#include "type_relations.h"
-
-namespace tvm {
-namespace relay {
-
-// relay.device_copy
-TVM_REGISTER_NODE_TYPE(DeviceCopyAttrs);
-
-TVM_REGISTER_GLOBAL("relay.op._make.device_copy")
-    .set_body_typed([](Expr data, int src_dev_type, int dst_dev_type) {
-      auto attrs = make_object<DeviceCopyAttrs>();
-      attrs->src_dev_type = src_dev_type;
-      attrs->dst_dev_type = dst_dev_type;
-      static const Op& op = Op::Get("device_copy");
-      return Call(op, {data}, Attrs(attrs), {});
-    });
-
-RELAY_REGISTER_OP("device_copy")
-    .describe(R"code(
-Copy data from one tensor to another. The source and destination might be
-on different devices.
-)code" TVM_ADD_FILELINE)
-    .set_num_inputs(1)
-    .add_argument("data", "Tensor", "The input data.")
-    .set_support_level(10)
-    .add_type_rel("Identity", IdentityRel)
-    .set_attr<TOpPattern>("TOpPattern", kOpaque)
-    .set_attr<TOpIsStateful>("TOpIsStateful", false)
-    .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout)
-    .set_attr<FTVMCompute>("FTVMCompute",
-                           [](const Attrs& attrs, const Array<te::Tensor>& inputs,
-                              const Type& out_dtype) -> Array<te::Tensor> {
-                             return {topi::identity(inputs[0])};
-                           });
-
-}  // namespace relay
-}  // namespace tvm
diff --git a/src/relay/op/dyn/tensor/transform.cc b/src/relay/op/dyn/tensor/transform.cc
index 815f24b6bda9..9724a92e8776 100644
--- a/src/relay/op/dyn/tensor/transform.cc
+++ b/src/relay/op/dyn/tensor/transform.cc
@@ -64,8 +64,9 @@ bool ReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     return false;
   }
 
-  // Doesn't support dynamic output rank
-  for (int i = 0; i < newshape->shape[0].as<IntImmNode>()->value; i++) {
+  const IntImmNode* rank = newshape->shape[0].as<IntImmNode>();
+  ICHECK(rank != nullptr) << "Dynamic Reshape doesn't support Dynamic Rank";
+  for (int i = 0; i < rank->value; i++) {
     oshape.push_back(Any());
   }
 
@@ -90,7 +91,6 @@ Array<te::Tensor> ReshapeCompute(const Attrs& attrs, const Array<te::Tensor>& in
 
 Expr MakeReshape(Expr data, Expr newshape) {
   auto attrs = make_object<ReshapeAttrs>();
-  attrs->reverse = false;
   static const Op& op = Op::Get("dyn.reshape");
   return Call(op, {data, newshape}, Attrs(attrs), {});
 }
@@ -401,6 +401,9 @@ bool FullRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   if (fill_value == nullptr) {
     return false;
   }
+  if (fill_shape == nullptr) {
+    return false;
+  }
 
   DataType out_dtype = param->dtype;
   if (out_dtype.bits() == 0) {
diff --git a/src/relay/op/make_op.h b/src/relay/op/make_op.h
index 2b05290b270c..36a5ec1c0e72 100644
--- a/src/relay/op/make_op.h
+++ b/src/relay/op/make_op.h
@@ -75,6 +75,8 @@ Expr MakeSqueeze(Expr data, Array<Integer> axis);
 
 Expr MakeStack(Expr data, int axis);
 
+Expr MakeTranspose(Expr data, Array<Integer> axes);
+
 Expr MakeStridedSlice(Expr data, Array<Integer> begin, Array<Integer> end, Array<Integer> strides,
                       String slice_mode);
 
@@ -100,6 +102,12 @@ Expr MakeResize(Expr data, Array<IndexExpr> size, String layout, String method,
 
 Expr MakeSparseToDense(Expr indices, Array<Integer> output_shape, Expr values, Expr default_value);
 
+Expr MakeArange(Expr start, Expr stop, Expr step, DataType dtype);
+
+Expr MakeShapeOf(Expr data, DataType dtype);
+
+Expr MakeTake(Expr data, Expr indices, Integer axis, String mode);
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_OP_MAKE_OP_H_
diff --git a/src/relay/op/memory/memory.cc b/src/relay/op/memory/memory.cc
index c0edf467815a..287564ba4f21 100644
--- a/src/relay/op/memory/memory.cc
+++ b/src/relay/op/memory/memory.cc
@@ -22,6 +22,9 @@
  * \brief Operators for manifest shape-aware memory allocation in Relay.
  */
 
+#include "memory.h"
+
+#include <tvm/node/node.h>
 #include <tvm/relay/attrs/memory.h>
 #include <tvm/relay/expr.h>
 #include <tvm/relay/op.h>
@@ -29,9 +32,12 @@
 #include <tvm/runtime/data_type.h>
 #include <tvm/topi/elemwise.h>
 
+#include <vector>
+
 #include "../../transforms/infer_layout_utils.h"
 #include "../op_common.h"
 #include "../type_relations.h"
+#include "tvm/relay/attrs/device_copy.h"
 
 namespace tvm {
 namespace relay {
@@ -42,15 +48,16 @@ TVM_REGISTER_NODE_TYPE(AllocTensorAttrs);
 // The passing value in attrs and args doesn't seem super great.
 // We should consider a better solution, i.e the type relation
 // being able to see the arguments as well?
-TVM_REGISTER_GLOBAL("relay.op.memory._make.alloc_storage")
-    .set_body_typed([](Expr size, Expr alignment, TVMContext ctx, DataType dtype_hint) {
-      auto attrs = make_object<AllocStorageAttrs>();
-      attrs->dtype = dtype_hint;
-      attrs->device_id = ctx.device_id;
-      attrs->device_type = ctx.device_type;
-      static const Op& op = Op::Get("memory.alloc_storage");
-      return Call(op, {size, alignment}, Attrs(attrs), {});
-    });
+Expr AllocStorage(Expr size, Expr alignment, TVMContext ctx, DataType dtype_hint) {
+  auto attrs = make_object<AllocStorageAttrs>();
+  attrs->dtype = dtype_hint;
+  attrs->device_id = ctx.device_id;
+  attrs->device_type = ctx.device_type;
+  static const Op& op = Op::Get("memory.alloc_storage");
+  return Call(op, {size, alignment}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.memory._make.alloc_storage").set_body_typed(AllocStorage);
 
 bool AllocStorageRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                      const TypeReporter& reporter) {
@@ -90,19 +97,20 @@ RELAY_REGISTER_OP("memory.alloc_storage")
                              return {topi::identity(inputs[0])};
                            });
 
-TVM_REGISTER_GLOBAL("relay.op.memory._make.alloc_tensor")
-    .set_body_typed([](Expr storage, Expr offset, tvm::relay::Expr shape, DataType dtype,
-                       Array<IndexExpr> assert_shape) {
-      auto attrs = make_object<AllocTensorAttrs>();
-      attrs->dtype = dtype;
-      if (assert_shape.defined()) {
-        attrs->assert_shape = assert_shape;
-      } else {
-        attrs->const_shape = Downcast<Constant>(shape);
-      }
-      static const Op& op = Op::Get("memory.alloc_tensor");
-      return Call(op, {storage, offset, shape}, Attrs(attrs), {});
-    });
+Expr AllocTensor(Expr storage, Expr offset, tvm::relay::Expr shape, DataType dtype,
+                 Array<IndexExpr> assert_shape) {
+  auto attrs = make_object<AllocTensorAttrs>();
+  attrs->dtype = dtype;
+  if (assert_shape.defined()) {
+    attrs->assert_shape = assert_shape;
+  } else {
+    attrs->const_shape = Downcast<Constant>(shape);
+  }
+  static const Op& op = Op::Get("memory.alloc_tensor");
+  return Call(op, {storage, offset, shape}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.memory._make.alloc_tensor").set_body_typed(AllocTensor);
 
 std::vector<int64_t> FromConstShape(Constant konst) {
   runtime::NDArray shape = konst->data;
@@ -299,5 +307,36 @@ TVM_REGISTER_GLOBAL("relay.op.memory._make.ToTupleType")
       return ToTupleType(t, std::vector<Expr>(array.begin(), array.end()));
     });
 
+// relay.device_copy
+TVM_REGISTER_NODE_TYPE(DeviceCopyAttrs);
+
+Expr DeviceCopy(Expr data, int src_dev_type, int dst_dev_type) {
+  auto attrs = make_object<DeviceCopyAttrs>();
+  attrs->src_dev_type = src_dev_type;
+  attrs->dst_dev_type = dst_dev_type;
+  static const Op& op = Op::Get("device_copy");
+  return Call(op, {data}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.device_copy").set_body_typed(DeviceCopy);
+
+RELAY_REGISTER_OP("device_copy")
+    .describe(R"code(
+Copy data from one tensor to another. The source and destination might be
+on different devices.
+)code" TVM_ADD_FILELINE)
+    .set_num_inputs(1)
+    .add_argument("data", "Tensor", "The input data.")
+    .set_support_level(10)
+    .add_type_rel("Identity", IdentityRel)
+    .set_attr<TOpPattern>("TOpPattern", kOpaque)
+    .set_attr<TOpIsStateful>("TOpIsStateful", false)
+    .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout)
+    .set_attr<FTVMCompute>("FTVMCompute",
+                           [](const Attrs& attrs, const Array<te::Tensor>& inputs,
+                              const Type& out_dtype) -> Array<te::Tensor> {
+                             return {topi::identity(inputs[0])};
+                           });
+
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/op/memory/memory.h b/src/relay/op/memory/memory.h
new file mode 100644
index 000000000000..6e184507bad5
--- /dev/null
+++ b/src/relay/op/memory/memory.h
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/op/memory/memory.h
+ * \brief Operators for memory related operations in Relay.
+ */
+
+#ifndef TVM_RELAY_OP_MEMORY_MEMORY_H_
+#define TVM_RELAY_OP_MEMORY_MEMORY_H_
+
+#include <vector>
+
+#include "tvm/relay/expr.h"
+
+namespace tvm {
+namespace relay {
+
+Expr AllocStorage(Expr size, Expr alignment, TVMContext ctx, DataType dtype_hint);
+Expr DeviceCopy(Expr data, int src_dev_type, int dst_dev_type);
+Expr AllocTensor(Expr storage, Expr offset, tvm::relay::Expr shape, DataType dtype,
+                 Array<IndexExpr> assert_shape);
+Expr ToTupleType(const Type& ty, const std::vector<Expr>& exprs);
+std::vector<Expr> FromTupleType(const Type& type, const Expr& expr);
+std::vector<TensorType> FlattenTupleType(const Type& type);
+
+}  // namespace relay
+}  // namespace tvm
+
+#endif  // TVM_RELAY_OP_MEMORY_MEMORY_H_
diff --git a/src/relay/op/nn/convolution.h b/src/relay/op/nn/convolution.h
index c08d3553e4cc..379fa3fa71d3 100644
--- a/src/relay/op/nn/convolution.h
+++ b/src/relay/op/nn/convolution.h
@@ -24,7 +24,8 @@
 #ifndef TVM_RELAY_OP_NN_CONVOLUTION_H_
 #define TVM_RELAY_OP_NN_CONVOLUTION_H_
 
-#include <tvm/support/logging.h>
+#include <tvm/auto_scheduler/compute_dag.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/tir/analysis.h>
 
 #include <string>
@@ -225,7 +226,18 @@ bool Conv2DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   } else {
     // use weight to infer the conv shape.
     if (weight == nullptr) return false;
-    auto wshape = trans_kernel_layout.ForwardShape(weight->shape);
+
+    Array<PrimExpr> wshape;
+    if (param->auto_scheduler_rewritten_layout.size() == 0) {
+      wshape = weight->shape;
+    } else {
+      // works for the default kernel layout "HWIO"
+      ICHECK_EQ(param->kernel_layout, "HWIO");
+      wshape = auto_scheduler::GetShapeFromRewrittenLayout(param->auto_scheduler_rewritten_layout,
+                                                           {"ry", "rx", "rc", "ff"});
+    }
+
+    wshape = trans_kernel_layout.ForwardShape(wshape);
     if (param->kernel_size.defined()) {
       ICHECK_EQ(param->kernel_size.size(), 2);
 
@@ -369,7 +381,18 @@ bool Conv3DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   } else {
     // use weight to infer the conv shape.
     if (weight == nullptr) return false;
-    auto wshape = trans_kernel_layout.ForwardShape(weight->shape);
+
+    Array<PrimExpr> wshape;
+    if (param->auto_scheduler_rewritten_layout.size() == 0) {
+      wshape = weight->shape;
+    } else {
+      // works for the default kernel layout "DHWIO"
+      ICHECK_EQ(param->kernel_layout, "DHWIO");
+      wshape = auto_scheduler::GetShapeFromRewrittenLayout(param->auto_scheduler_rewritten_layout,
+                                                           {"rd", "rh", "rw", "rc", "cc"});
+    }
+
+    wshape = trans_kernel_layout.ForwardShape(wshape);
     if (param->kernel_size.defined()) {
       ICHECK_EQ(param->kernel_size.size(), 3);
       // check the size
diff --git a/src/relay/op/nn/nn.cc b/src/relay/op/nn/nn.cc
index ce622429bdb9..b2404cc1954b 100644
--- a/src/relay/op/nn/nn.cc
+++ b/src/relay/op/nn/nn.cc
@@ -61,8 +61,13 @@ bool BiasAddRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   if (axis < 0) {
     axis = data->shape.size() + axis;
   }
-  ICHECK_LE(axis, static_cast<int>(data->shape.size()))
-      << "axis " << param->axis << " is out of range";
+  if (axis >= static_cast<int>(data->shape.size()) || axis < 0) {
+    reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
+                                     << "The axis in bias_add must be in range for the shape; "
+                                     << "attempted to access index " << param->axis << " of "
+                                     << PrettyPrint(data->shape));
+    return false;
+  }
 
   // assign output type
   reporter->Assign(types[1], TensorType({data->shape[axis]}, data->dtype));
@@ -186,6 +191,33 @@ RELAY_REGISTER_OP("nn.dense")
     .set_support_level(1)
     .add_type_rel("Dense", DenseRel<DenseAttrs>);
 
+// relay.nn.contrib_dense_pack
+// Positional relay function to create dense_pack operator used by frontend FFI.
+Expr MakeDensePack(Expr data, Expr weight, IndexExpr units, DataType out_dtype) {
+  auto attrs = make_object<DenseAttrs>();
+  attrs->units = units;
+  attrs->out_dtype = out_dtype;
+  static const Op& op = Op::Get("nn.contrib_dense_pack");
+  return Call(op, {data, weight}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.nn._make.contrib_dense_pack").set_body_typed(MakeDensePack);
+
+RELAY_REGISTER_OP("nn.contrib_dense_pack")
+    .describe(R"code(Applies a linear transformation: :math:`Y = XW^T`.
+
+- **data**: `(x1, x2, ..., xn, input_dim)`
+- **weight**: `(units // pack_weight_tile, input_dim, pack_weight_tile)`
+- **out**: `(x1, x2, ..., xn, units)`.
+
+)code" TVM_ADD_FILELINE)
+    .set_attrs_type<DenseAttrs>()
+    .set_num_inputs(2)
+    .add_argument("data", "nD Tensor", "Input data.")
+    .add_argument("weight", "3D Tensor", "Packed weight matrix.")
+    .set_support_level(10)
+    .add_type_rel("DensePack", DensePackRel<DenseAttrs>);
+
 // relay.leaky_relu
 TVM_REGISTER_NODE_TYPE(LeakyReluAttrs);
 
@@ -558,8 +590,10 @@ The whole array is rescaled by ``1/(1-p)`` to keep the expected sum of the input
     .set_num_inputs(1)
     .add_argument("data", "Tensor", "Input to which dropout will be applied.")
     .set_support_level(1)
+    .set_attr<TOpPattern>("TOpPattern", kOpaque)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout)
-    .add_type_rel("Dropout", DropoutRel);
+    .add_type_rel("Dropout", DropoutRel)
+    .set_attr<TOpIsStateful>("TOpIsStateful", true);
 
 // batch_norm
 TVM_REGISTER_NODE_TYPE(BatchNormAttrs);
@@ -718,10 +752,7 @@ Expr MakeInstanceNorm(Expr data, Expr gamma, Expr beta, int axis, double epsilon
   return Call(op, {data, gamma, beta}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op.nn._make.instance_norm")
-    .set_body([](const TVMArgs& args, TVMRetValue* rv) {
-      runtime::detail::unpack_call<Expr, 7>(MakeInstanceNorm, args, rv);
-    });
+TVM_REGISTER_GLOBAL("relay.op.nn._make.instance_norm").set_body_typed(MakeInstanceNorm);
 
 RELAY_REGISTER_OP("nn.instance_norm")
     .describe(R"code(Instance Normalization (Ulyanov and et al., 2016)
@@ -785,10 +816,7 @@ Expr MakeLayerNorm(Expr data, Expr gamma, Expr beta, int axis, double epsilon, b
   return Call(op, {data, gamma, beta}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op.nn._make.layer_norm")
-    .set_body([](const TVMArgs& args, TVMRetValue* rv) {
-      runtime::detail::unpack_call<Expr, 7>(MakeLayerNorm, args, rv);
-    });
+TVM_REGISTER_GLOBAL("relay.op.nn._make.layer_norm").set_body_typed(MakeLayerNorm);
 
 RELAY_REGISTER_OP("nn.layer_norm")
     .describe(R"code(
@@ -831,10 +859,7 @@ Expr MakeGroupNorm(Expr data, Expr gamma, Expr beta, int num_groups, int axis, d
   return Call(op, {data, gamma, beta}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op.nn._make.group_norm")
-    .set_body([](const TVMArgs& args, TVMRetValue* rv) {
-      runtime::detail::unpack_call<Expr, 8>(MakeGroupNorm, args, rv);
-    });
+TVM_REGISTER_GLOBAL("relay.op.nn._make.group_norm").set_body_typed(MakeGroupNorm);
 
 RELAY_REGISTER_OP("nn.group_norm")
     .describe(R"code(
diff --git a/src/relay/op/nn/nn.h b/src/relay/op/nn/nn.h
index 9b9cff2dba81..8802cd903b01 100644
--- a/src/relay/op/nn/nn.h
+++ b/src/relay/op/nn/nn.h
@@ -26,11 +26,13 @@
 
 #include <tvm/ir/attrs.h>
 #include <tvm/ir/expr.h>
-#include <tvm/node/container.h>
 #include <tvm/relay/type.h>
+#include <tvm/runtime/container.h>
 
 #include <utility>
 
+#include "../op_common.h"
+
 namespace tvm {
 namespace relay {
 
@@ -88,6 +90,29 @@ bool DenseRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   return true;
 }
 
+template <typename AttrType>
+bool DensePackRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                  const TypeReporter& reporter) {
+  ICHECK_EQ(types.size(), 3);
+  const auto* data = types[0].as<TensorTypeNode>();
+  const auto* weight = types[1].as<TensorTypeNode>();
+  if (data == nullptr || weight == nullptr) return false;
+
+  const AttrType* param = attrs.as<AttrType>();
+  ICHECK(param != nullptr);
+
+  Array<tvm::PrimExpr> oshape = data->shape;
+  oshape.Set((oshape.size() - 1), weight->shape[0] * weight->shape[2]);
+
+  DataType out_dtype = param->out_dtype;
+  if (out_dtype.bits() == 0) {
+    out_dtype = data->dtype;
+  }
+  // assign output type
+  reporter->Assign(types[2], TensorType(oshape, out_dtype));
+  return true;
+}
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_OP_NN_NN_H_
diff --git a/src/relay/op/nn/pad.cc b/src/relay/op/nn/pad.cc
index 5b9988b101eb..c6b987eb42aa 100644
--- a/src/relay/op/nn/pad.cc
+++ b/src/relay/op/nn/pad.cc
@@ -139,14 +139,13 @@ bool PadRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     ICHECK(width1 != nullptr);
     ICHECK(width2 != nullptr);
 
-    ICHECK(*width1 >= 0) << "Param width elements should be positive but first pad width at "
-                         << "index " << i << " is " << *width1 << ".";
-    ICHECK(*width2 >= 0) << "Param width elements should be positive but first pad width at "
-                         << "index " << i << " is " << *width2 << ".";
-
     if (!data->shape[i].as<tir::AnyNode>()) {
       auto padding = tir::make_const(data->shape[i].dtype(), *width1 + *width2);
       oshape.push_back(data->shape[i] + padding);
+      if (tir::as_const_int(data->shape[i])) {
+        ICHECK(topi::detail::GetConstInt(data->shape[i] + padding) >= 0)
+            << "Output shape post padding should be positive but got " << data->shape[i] + padding;
+      }
     } else {
       oshape.push_back(data->shape[i]);
     }
diff --git a/src/relay/op/nn/sparse.cc b/src/relay/op/nn/sparse.cc
index e9073730641d..b1a16f18b623 100644
--- a/src/relay/op/nn/sparse.cc
+++ b/src/relay/op/nn/sparse.cc
@@ -101,10 +101,7 @@ Expr MakeSparseDense(Expr data, Expr weight_data, Expr weight_indices, Expr weig
   return Call(op, {data, weight_data, weight_indices, weight_indptr}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_dense")
-    .set_body([](const TVMArgs& args, TVMRetValue* rv) {
-      runtime::detail::unpack_call<Expr, 5>(MakeSparseDense, args, rv);
-    });
+TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_dense").set_body_typed(MakeSparseDense);
 
 RELAY_REGISTER_OP("nn.sparse_dense")
     .describe(
@@ -130,10 +127,7 @@ Expr MakeSparseDensePadded(Expr data, Expr weight_data, Expr weight_indices, Exp
   return Call(op, {data, weight_data, weight_indices, weight_indptr}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_dense_padded")
-    .set_body([](const TVMArgs& args, TVMRetValue* rv) {
-      runtime::detail::unpack_call<Expr, 4>(MakeSparseDensePadded, args, rv);
-    });
+TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_dense_padded").set_body_typed(MakeSparseDensePadded);
 
 RELAY_REGISTER_OP("nn.internal.sparse_dense_padded")
     .describe(
@@ -202,5 +196,46 @@ RELAY_REGISTER_OP("nn.sparse_transpose")
     .set_support_level(1)
     .add_type_rel("SparseTranspose", SparseTransposeRel);
 
+// relay.nn.sparse_add
+bool SparseAddRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                  const TypeReporter& reporter) {
+  ICHECK_EQ(types.size(), 5) << "expecting 4 inputs and 1 output.";
+  const auto* dense_data = types[0].as<TensorTypeNode>();
+  const auto* sparse_data = types[1].as<TensorTypeNode>();
+  ICHECK(reporter->Assert(sparse_data->dtype == dense_data->dtype))
+      << "sparse tensor and dense tensor datatype should match.";
+  ICHECK(reporter->Assert(sparse_data->shape.size() == 1)) << "sparse data tensor should be 1D.";
+  const auto* sparse_indices = types[2].as<TensorTypeNode>();
+  ICHECK(reporter->Assert(sparse_indices->shape.size() == 1))
+      << "sparse indices tensor should be 1D.";
+
+  reporter->Assign(types[4], TensorType(dense_data->shape, dense_data->dtype));
+  return true;
+}
+
+Expr MakeSparseAdd(Expr dense_data, Expr sparse_data, Expr sparse_indices, Expr sparse_indptr) {
+  static const Op& op = Op::Get("nn.sparse_add");
+  return Call(op, {dense_data, sparse_data, sparse_indices, sparse_indptr}, Attrs(), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.nn._make.sparse_add").set_body_typed(MakeSparseAdd);
+
+RELAY_REGISTER_OP("nn.sparse_add")
+    .describe(R"code(Add a dense matrix X with sparse matrix Y.
+
+- **dense**: `(M, N)`
+- **sparse**: `(M, N)`
+
+- **out**: `(M, N)`.
+
+)code" TVM_ADD_FILELINE)
+    .set_num_inputs(4)
+    .add_argument("dense_data", "2D Tensor", "Dense data matrix.")
+    .add_argument("sparse_data", "1D Tensor", "Sparse data vector.")
+    .add_argument("sparse_indices", "1D Tensor", "Sparse indices vector.")
+    .add_argument("sparse_indptr", "1D Tensor", "Sparse index pointer vector.")
+    .set_support_level(1)
+    .add_type_rel("SparseAdd", SparseAddRel);
+
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/op/random/kernel.cc b/src/relay/op/random/kernel.cc
new file mode 100644
index 000000000000..ec092a7e05f2
--- /dev/null
+++ b/src/relay/op/random/kernel.cc
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <tvm/relay/attrs/random.h>
+#include <tvm/relay/op.h>
+
+namespace tvm {
+namespace relay {
+
+TVM_REGISTER_NODE_TYPE(ThreefryGenerateAttrs);
+
+static TensorType ThreefryKeyType() { return TensorType({10}, tvm::DataType::UInt(64)); }
+
+bool ThreefryGenerateRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                         const TypeReporter& reporter) {
+  const ThreefryGenerateAttrs* param = attrs.as<ThreefryGenerateAttrs>();
+  ICHECK_EQ(types.size(), 2) << "ThreefryGenerate should have one input and one output";
+
+  reporter->Assign(types[0], ThreefryKeyType());
+
+  std::vector<IndexExpr> oshape;
+  for (auto& x : param->out_shape) {
+    oshape.push_back(x);
+  }
+  // generate returns the next key and an array of random values
+  // TODO(@tkonolige, @altanh): support other output dtypes?
+  reporter->Assign(types[1],
+                   TupleType({ThreefryKeyType(), TensorType(oshape, tvm::DataType::UInt(64))}));
+  return true;
+}
+
+Expr MakeThreefryGenerate(Expr key, Array<Integer> out_shape) {
+  auto attrs = make_object<ThreefryGenerateAttrs>();
+  attrs->out_shape = out_shape;
+  static const Op& op = Op::Get("random.threefry_generate");
+  return Call(op, {key}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.random._make.threefry_generate").set_body_typed(MakeThreefryGenerate);
+
+RELAY_REGISTER_OP("random.threefry_generate")
+    .describe(
+        R"doc(Generate an array of random numbers using the Threefry algorithm.)doc" TVM_ADD_FILELINE)
+    .set_num_inputs(1)
+    .set_attrs_type<ThreefryGenerateAttrs>()
+    .add_argument("key", "Tensor", "Input Threefry key")
+    .add_type_rel("ThreefryGenerate", ThreefryGenerateRel);
+
+bool ThreefrySplitRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                      const TypeReporter& reporter) {
+  ICHECK_EQ(types.size(), 2) << "ThreefrySplit should have one input and one output";
+
+  reporter->Assign(types[0], ThreefryKeyType());
+  reporter->Assign(types[1], TupleType({ThreefryKeyType(), ThreefryKeyType()}));
+
+  return true;
+}
+
+Expr MakeThreefrySplit(Expr key) {
+  static const Op& op = Op::Get("random.threefry_split");
+  return Call(op, {key}, Attrs(), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.random._make.threefry_split").set_body_typed(MakeThreefrySplit);
+
+RELAY_REGISTER_OP("random.threefry_split")
+    .describe(R"doc(Split the input Threefry key into two new ones.)doc" TVM_ADD_FILELINE)
+    .set_num_inputs(1)
+    .add_argument("key", "Tensor", "Input Threefry key")
+    .add_type_rel("ThreefrySplit", ThreefrySplitRel);
+
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/op/tensor/reduce.cc b/src/relay/op/tensor/reduce.cc
index f611dc2eefd2..4fa8aca4f3a9 100644
--- a/src/relay/op/tensor/reduce.cc
+++ b/src/relay/op/tensor/reduce.cc
@@ -475,7 +475,11 @@ Array<te::Tensor> ProdCompute(const Attrs& attrs, const Array<te::Tensor>& input
   return ReduceCompute(attrs, inputs, out_type, topi::prod);
 }
 
-RELAY_REGISTER_REDUCE_OP("prod")
+TVM_REGISTER_GLOBAL("relay.op._make.prod").set_body_typed(Prod);
+
+RELAY_REGISTER_OP("prod")
+    .set_num_inputs(1)
+    .add_argument("data", "Tensor", "The input tensor.")
     .describe(R"code(Computes the products of array elements over given axes.
 
 Example::
@@ -595,9 +599,7 @@ Expr MakeVariance(Expr data, Expr mean, Array<Integer> axis, bool keepdims, bool
   return Call(op, {data, mean}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op._make._variance").set_body([](const TVMArgs& args, TVMRetValue* rv) {
-  runtime::detail::unpack_call<Expr, 6>(MakeVariance, args, rv);
-});
+TVM_REGISTER_GLOBAL("relay.op._make._variance").set_body_typed(MakeVariance);
 
 RELAY_REGISTER_OP("variance")
     .describe(R"code(Computes the variance of array elements over given axes.
diff --git a/src/relay/op/tensor/transform.cc b/src/relay/op/tensor/transform.cc
index 6819ea93f249..b65068bd0506 100644
--- a/src/relay/op/tensor/transform.cc
+++ b/src/relay/op/tensor/transform.cc
@@ -157,9 +157,7 @@ Expr MakeReinterpret(Expr data, DataType dtype) {
   return Call(op, {data}, Attrs(attrs), {});
 }
 
-TVM_REGISTER_GLOBAL("relay._make.reinterpret").set_body([](const TVMArgs& args, TVMRetValue* rv) {
-  runtime::detail::unpack_call<Expr, 2>(MakeReinterpret, args, rv);
-});
+TVM_REGISTER_GLOBAL("relay._make.reinterpret").set_body_typed(MakeReinterpret);
 
 RELAY_REGISTER_OP("reinterpret")
     .describe(R"code(Reinterpret the data into a new data type.
@@ -229,7 +227,7 @@ Expr MakeExpandDims(Expr data, int axis, int num_newaxis) {
 TVM_REGISTER_GLOBAL("relay.op._make.expand_dims").set_body_typed(MakeExpandDims);
 
 RELAY_REGISTER_OP("expand_dims")
-    .describe(R"code(Insert `num_newaxis` axises at the position given by `axis`
+    .describe(R"code(Insert `num_newaxis` axes at the position given by `axis`
 
 - **data**: The input data to the operator.
 
@@ -314,7 +312,7 @@ bool StackRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
       if (first->shape[j].as<AnyNode>() || e->shape[j].as<AnyNode>() ||
           reporter->AssertEQ(first->shape[j], e->shape[j]))
         continue;
-      throw Error(
+      throw CompileError(
           "relay.stack requires all tensors have the same shape "
           "on non-stacking axes");
     }
@@ -419,6 +417,80 @@ bool TransposeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   return true;
 }
 
+Array<Array<Layout>> TransposeInferCorrectLayout(const Attrs& attrs,
+                                                 const Array<Layout>& new_in_layouts,
+                                                 const Array<Layout>& old_in_layouts,
+                                                 const Array<tvm::relay::Type>& old_in_types) {
+  // Discard "const" qualifier.
+  auto* params = const_cast<TransposeAttrs*>(attrs.as<TransposeAttrs>());
+  ICHECK(params != nullptr);
+
+  std::string in_layout_str = "";
+  std::string out_layout_str = "";
+
+  // Infer the input layout string and update the axes.
+  if (old_in_layouts.defined() && old_in_layouts[0].defined()) {
+    ICHECK_EQ(old_in_layouts.size(), 1);
+    auto old_layout = old_in_layouts[0];
+    Array<Integer> old_axes = params->axes;
+
+    // Deal with default axes and negative axes.
+    if (!old_axes.defined() || old_axes.size() == 0) {
+      for (int i = old_layout.ndim() - 1; i >= 0; --i) {
+        old_axes.push_back(i);
+      }
+    }
+    for (size_t i = 0; i < old_axes.size(); ++i) {
+      int axis = static_cast<int>(old_axes[i]->value);
+      if (axis < 0) {
+        int pos_axis = static_cast<int>(old_layout.ndim()) + axis;
+        old_axes.Set(i, pos_axis);
+      }
+    }
+
+    if (new_in_layouts.defined() && new_in_layouts[0].defined()) {
+      ICHECK_EQ(new_in_layouts.size(), 1);
+      auto new_layout = new_in_layouts[0];
+
+      // Update the axes based on the new layout.
+      Array<Integer> new_axes = Array<Integer>();
+      for (auto axis : old_axes) {
+        auto new_axis = new_layout.IndexOf(old_layout[axis->value]);
+        if (new_axis == -1) {  // Cannot find the target axis in the new layout.
+          new_axes.clear();
+          break;
+        }
+        new_axes.push_back(new_axis);
+      }
+      if (new_axes.defined() && new_axes.size() == new_layout.ndim()) {
+        params->axes = std::move(new_axes);
+        in_layout_str = new_layout.name();
+      }
+    }
+
+    // If the input layout string cannot be determined, propagate the old layout.
+    if (in_layout_str == "") {
+      params->axes = std::move(old_axes);
+      in_layout_str = old_layout.name();
+    }
+  }
+
+  // Infer the output layout string based on the input layout and the axes.
+  if (in_layout_str != "") {
+    for (auto axis : params->axes) {
+      ICHECK_LT(axis->value, in_layout_str.length());
+      out_layout_str += in_layout_str[axis->value];
+    }
+    try {
+      return Array<Array<Layout>>({{Layout(in_layout_str)}, {Layout(out_layout_str)}});
+    } catch (const tvm::Error& e) {
+      // If the layout string is invalid for any reason, give up.
+      return Array<Array<Layout>>({{Layout::Undef()}, {Layout::Undef()}});
+    }
+  }
+  return Array<Array<Layout>>({{Layout::Undef()}, {Layout::Undef()}});
+}
+
 Array<te::Tensor> TransposeCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
                                    const Type& out_type) {
   const auto* param = attrs.as<TransposeAttrs>();
@@ -449,19 +521,21 @@ RELAY_REGISTER_OP("transpose")
     .set_support_level(3)
     .add_type_rel("Transpose", TransposeRel)
     .set_attr<FTVMCompute>("FTVMCompute", TransposeCompute)
+    .set_attr<FInferCorrectLayout>("FInferCorrectLayout", TransposeInferCorrectLayout)
     .set_attr<TOpPattern>("TOpPattern", kInjective);
 
 /* relay.reshape */
 TVM_REGISTER_NODE_TYPE(ReshapeAttrs);
 TVM_REGISTER_NODE_TYPE(ReshapeLikeAttrs);
 
-Array<IndexExpr> infer_newshape(const Array<IndexExpr>& data_shape, const Attrs& attrs) {
+Array<IndexExpr> InferNewShape(const Array<IndexExpr>& data_shape, const Attrs& attrs,
+                               bool reverse) {
   const auto* param = attrs.as<ReshapeAttrs>();
   Array<IndexExpr> oshape;
   Array<IndexExpr> ishape;
   Array<Integer> newshape;
 
-  if (param->reverse) {
+  if (reverse) {
     ishape.Assign(data_shape.rbegin(), data_shape.rend());
     newshape.Assign(param->newshape.rbegin(), param->newshape.rend());
   } else {
@@ -584,7 +658,6 @@ Array<IndexExpr> infer_newshape(const Array<IndexExpr>& data_shape, const Attrs&
 
 bool ReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                 const TypeReporter& reporter) {
-  const auto* param = attrs.as<ReshapeAttrs>();
   // types: [data, result]
   ICHECK_EQ(types.size(), 2);
   const auto* data = types[0].as<TensorTypeNode>();
@@ -594,16 +667,12 @@ bool ReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     return false;
   }
 
-  const auto& oshape = infer_newshape(data->shape, attrs);
+  const auto& oshape = InferNewShape(data->shape, attrs, false);
 
   // Verify that the sum of dimensions in the output shape is the sum of
   // dimensions in the input shape
   Array<IndexExpr> data_shape;
-  if (param->reverse) {
-    data_shape.Assign(data->shape.rbegin(), data->shape.rend());
-  } else {
-    data_shape = data->shape;
-  }
+  data_shape = data->shape;
 
   bool found_dynamic = false;
   int64_t oshape_sum = 1;
@@ -633,12 +702,58 @@ bool ReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
         << "Input tensor shape and reshaped shape are not compatible";
   }
 
-  if (param->reverse) {
-    reporter->Assign(types[1],
-                     TensorType(Array<IndexExpr>(oshape.rbegin(), oshape.rend()), data->dtype));
-  } else {
-    reporter->Assign(types[1], TensorType(oshape, data->dtype));
+  reporter->Assign(types[1], TensorType(oshape, data->dtype));
+  return true;
+}
+
+bool ReverseReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                       const TypeReporter& reporter) {
+  // types: [data, result]
+  ICHECK_EQ(types.size(), 2);
+  const auto* data = types[0].as<TensorTypeNode>();
+  if (data == nullptr) {
+    ICHECK(types[0].as<IncompleteTypeNode>())
+        << "reshape: expect input type to be TensorType but get " << types[0];
+    return false;
   }
+
+  const auto& oshape = InferNewShape(data->shape, attrs, true);
+
+  // Verify that the sum of dimensions in the output shape is the sum of
+  // dimensions in the input shape
+  Array<IndexExpr> data_shape;
+  data_shape.Assign(data->shape.rbegin(), data->shape.rend());
+
+  bool found_dynamic = false;
+  int64_t oshape_sum = 1;
+  for (auto& x : oshape) {
+    // Check if we have a dynamic shape. If we do, we can't verify if the
+    // reshape is valid. Dynamic shapes are marker by using Any, but can also
+    // occur from SizeVar's. In the case of SizeVar, the shape expression can
+    // be an AST. We can't easily check if we have an AST because of a ShapeVar
+    // or some other reason, so our check for dynamic shape is just if we can
+    // convert the shape to in integer or not.
+    if (!x->IsInstance<tvm::Integer::ContainerType>()) {
+      found_dynamic = true;
+      break;
+    }
+    oshape_sum *= Downcast<tvm::Integer>(x)->value;
+  }
+  int64_t data_shape_sum = 1;
+  for (auto& x : data_shape) {
+    if (!x->IsInstance<tvm::Integer::ContainerType>()) {
+      found_dynamic = true;
+      break;
+    }
+    data_shape_sum *= Downcast<tvm::Integer>(x)->value;
+  }
+  if (!found_dynamic) {
+    ICHECK_EQ(oshape_sum, data_shape_sum)
+        << "Input tensor shape and reshaped shape are not compatible";
+  }
+
+  reporter->Assign(types[1],
+                   TensorType(Array<IndexExpr>(oshape.rbegin(), oshape.rend()), data->dtype));
   return true;
 }
 
@@ -701,7 +816,7 @@ Array<te::Tensor> ReshapeCompute(const Attrs& attrs, const Array<te::Tensor>& in
   }
 
   if (newshape_has_any) {
-    newshape = infer_newshape(inputs[0]->shape, attrs);
+    newshape = InferNewShape(inputs[0]->shape, attrs, false);
   }
   return {topi::reshape(inputs[0], newshape)};
 }
@@ -709,7 +824,6 @@ Array<te::Tensor> ReshapeCompute(const Attrs& attrs, const Array<te::Tensor>& in
 Expr MakeReshape(Expr data, Array<Integer> newshape) {
   auto attrs = make_object<ReshapeAttrs>();
   attrs->newshape = std::move(newshape);
-  attrs->reverse = false;
   static const Op& op = Op::Get("reshape");
   return Call(op, {data}, Attrs(attrs), {});
 }
@@ -1032,6 +1146,9 @@ Expr MakeScatterND(Expr data, Expr indices, const Array<Integer> out_shape) {
 
 TVM_REGISTER_GLOBAL("relay.op._make.scatter_nd").set_body_typed(MakeScatterND);
 
+// scatter_nd operator has extern schedules for CPU and GPU devices.
+// Fusing extern schedules with Injective schedules leads to errors.
+// So, converting the scatter_nd to Opaque to prevent compilation failures
 RELAY_REGISTER_OP("scatter_nd")
     .describe(R"code(Scatter elements or slices from data and store to a tensor
 whose shape is defined by indices.
@@ -1044,7 +1161,7 @@ Given data with shape (Y_0, ..., Y_{K-1}, X_M, ..., X_{N-1}) and indices with sh
     .add_argument("indices", "Tensor", "The indices tensor.")
     .set_support_level(3)
     .add_type_rel("ScatterND", ScatterNDRel)
-    .set_attr<TOpPattern>("TOpPattern", kInjective);
+    .set_attr<TOpPattern>("TOpPattern", kOpaque);
 
 // Take
 TVM_REGISTER_NODE_TYPE(TakeAttrs);
@@ -1470,6 +1587,100 @@ RELAY_REGISTER_OP("repeat")
     .set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
     .set_attr<TOpPattern>("TOpPattern", kBroadcast);
 
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                            const TypeReporter& reporter) {
+  // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+  ICHECK_EQ(types.size(), 5) << "SparseFillEmptyRowsRel expects 5 inputs but " << types.size()
+                             << "provided";
+  std::vector<Type> fields;
+  auto sparse_indices = types[0].as<TensorTypeNode>();
+  auto ndims = sparse_indices->shape[1];
+  fields.push_back(TensorType(Array<PrimExpr>{Any(), ndims}, tvm::DataType::Int(64)));
+  fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+  fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+  reporter->Assign(types[types.size() - 1], TupleType(Array<Type>(fields)));
+  return true;
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr dense_shape,
+                             Expr default_value) {
+  static const Op& op = Op::Get("sparse_fill_empty_rows");
+  return Call(op, {sparse_indices, sparse_values, dense_shape, default_value}, Attrs(), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparse_fill_empty_rows")
+    .set_body_typed(MakeSparseFillEmptyRows);
+
+RELAY_REGISTER_OP("sparse_fill_empty_rows")
+    .describe(
+        R"code(Fill empty rows of a sparse tensor with a default value.)code" TVM_ADD_FILELINE)
+    .set_num_inputs(4)
+    .add_argument("sparse_indices", "Tensor",
+                  "A 2-D int64 tensor of shape [N, ndims], which specifies the indices of the"
+                  "elements in the sparse tensor that contain nonzero values. COO Format")
+    .add_argument(
+        "sparse_values", "Tensor",
+        "A 1-D tensor[N] which supplies the values for each element in indices. COO Format")
+    .add_argument("dense_shape", "Tensor",
+                  "A 1-D int64 tensor of shape [ndims], which specifies the dense_shape of the"
+                  "sparse tensor. Takes a list indicating the number of elements in each "
+                  "dimension")
+    .add_argument("default_value", "Tensor",
+                  "The value to fill for empty rows, with the same type as sparse_values")
+    .add_type_rel("sparse_fill_empty_rows", SparseFillEmptyRowsRel)
+    .set_support_level(3)
+    .set_attr<TOpPattern>("TOpPattern", kOpaque);
+
+bool SparseReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                      const TypeReporter& reporter) {
+  // types: [sparse_indices, prev_shape, new_shape, result]
+  ICHECK_EQ(types.size(), 4) << "SparseReshapeRel expects 4 types but " << types.size()
+                             << " provided";
+  ICHECK_EQ(num_inputs, 3) << "SparseReshapeRel expects 4 inputs but " << num_inputs << " provided";
+  auto sparse_indices = types[0].as<TensorTypeNode>();
+  auto prev_shape = types[1].as<TensorTypeNode>();
+  auto new_shape = types[2].as<TensorTypeNode>();
+  if (sparse_indices == nullptr || prev_shape == nullptr || new_shape == nullptr) {
+    return false;
+  }
+  CHECK(sparse_indices->dtype.is_int()) << "sparse_indices must be tensor of integers";
+  CHECK(prev_shape->dtype.is_int()) << "prev_shape must be tensor of integers";
+  CHECK(new_shape->dtype.is_int()) << "new_shape must be tensor of integers";
+  ICHECK_EQ(sparse_indices->shape.size(), 2) << "sparse_indices must be 2-D tensor";
+  ICHECK_EQ(prev_shape->shape.size(), 1) << "prev_shape must be 1-D tensor";
+  ICHECK_EQ(new_shape->shape.size(), 1) << "new_shape must be 1-D tensor";
+  std::vector<Type> fields;
+  Array<PrimExpr> new_sparse_indices_shape{sparse_indices->shape[0], new_shape->shape[0]};
+  fields.push_back(TensorType(new_sparse_indices_shape, sparse_indices->dtype));
+  fields.push_back(TensorType(new_shape->shape, new_shape->dtype));
+  reporter->Assign(types[3], TupleType(Array<Type>(fields)));
+  return true;
+}
+
+Expr MakeSparseReshape(Expr sparse_indices, Expr prev_shape, Expr new_shape) {
+  static const Op& op = Op::Get("sparse_reshape");
+  return Call(op, {sparse_indices, prev_shape, new_shape}, Attrs(), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparse_reshape").set_body_typed(MakeSparseReshape);
+
+RELAY_REGISTER_OP("sparse_reshape")
+    .describe(R"code(Return new sparse indices of the reshaped tensor
+)code" TVM_ADD_FILELINE)
+    .set_num_inputs(3)
+    .add_argument("sparse_indices", "Tensor",
+                  "A 2-D tensor of shape [N, ndims], which specifies the indices of the"
+                  "elements in the sparse tensor that contain nonzero values.  COO Format")
+    .add_argument("prev_shape", "Tensor",
+                  "A 1-D tensor of shape [ndims], which specifies the previous dense shape of the"
+                  "sparse tensor")
+    .add_argument("new_shape", "Tensor",
+                  "A 1-D tensor of shape [ndims], which specifies the desired dense shape of the"
+                  "sparse tensor")
+    .add_type_rel("sparse_reshape", SparseReshapeRel)
+    .set_attr<TOpPattern>("TOpPattern", kInjective)
+    .set_support_level(3);
+
 // meshgrid operator
 TVM_REGISTER_NODE_TYPE(MeshgridAttrs);
 
@@ -1480,8 +1691,8 @@ bool MeshgridRel(const Array<Type>& types, int num_inputs, const Attrs& raw_attr
   const MeshgridAttrs* attrs = raw_attrs.as<MeshgridAttrs>();
   const auto* tensor_tuple = types[0].as<TupleTypeNode>();
   if (tensor_tuple == nullptr) {
-    throw Error(
-        ErrorBuilder() << "meshgrid requires a tuple of tensors as the first argument, found "
+    throw CompileError(ErrorBuilder()
+                       << "meshgrid requires a tuple of tensors as the first argument, found "
                        << PrettyPrint(types[0]));
   } else if (types[0].as<IncompleteTypeNode>() != nullptr) {
     return false;
@@ -1503,14 +1714,14 @@ bool MeshgridRel(const Array<Type>& types, int num_inputs, const Attrs& raw_attr
     int e_ndim = static_cast<int>(e->shape.size());
     const DataType& e_dtype = e->dtype;
     if (e_dtype != dtype) {
-      throw Error("relay.meshgrid requires all tensors have the same dtype");
+      throw CompileError("relay.meshgrid requires all tensors have the same dtype");
     }
     if (e_ndim == 0) {
       grid_shape.emplace_back(1);
     } else if (e_ndim == 1) {
       grid_shape.emplace_back(e->shape[0]);
     } else {
-      throw Error("relay.meshgrid requires all tensors be either scalars or 1-D vectors.");
+      throw CompileError("relay.meshgrid requires all tensors be either scalars or 1-D vectors.");
     }
   }
 
@@ -2711,6 +2922,46 @@ Expr MakeSliceLike(Expr data, Expr shape_like, Array<Integer> axes) {
   return Call(op, {data, shape_like}, Attrs(attrs), {});
 }
 
+Array<Array<Layout>> SliceLikeInferCorrectLayout(const Attrs& attrs,
+                                                 const Array<Layout>& new_in_layouts,
+                                                 const Array<Layout>& old_in_layouts,
+                                                 const Array<tvm::relay::Type>& old_in_types) {
+  Array<Integer> new_axes;
+  if (old_in_layouts.defined() && new_in_layouts.defined()) {
+    ICHECK_EQ(new_in_layouts.size(), 2);
+    ICHECK_EQ(new_in_layouts[0]->name, new_in_layouts[1]->name);
+    ICHECK_EQ(old_in_layouts.size(), 2);
+    ICHECK_EQ(old_in_layouts[0]->name, old_in_layouts[1]->name);
+
+    auto old_layout = old_in_layouts[0];
+    auto new_layout = new_in_layouts[0];
+
+    // Discard "const" qualifier.
+    auto* params = const_cast<SliceLikeAttrs*>(attrs.as<SliceLikeAttrs>());
+    ICHECK(params != nullptr);
+
+    for (auto axis : params->axes) {
+      auto new_axis = new_layout.IndexOf(old_layout[axis->value]);
+      // Cannot find the target axis in the new layout.
+      if (new_axis == -1) {
+        new_axes.clear();
+        break;
+      }
+      new_axes.push_back(new_axis);
+    }
+    if (!new_axes.empty()) {
+      params->axes = std::move(new_axes);
+      return Array<Array<Layout>>({{new_layout, new_layout}, {new_layout}});
+    }
+  }
+
+  if (old_in_layouts.defined()) {
+    ICHECK_EQ(old_in_layouts.size(), 2);
+    return {{old_in_layouts[0], old_in_layouts[1]}, {old_in_layouts[1]}};
+  }
+  return Array<Array<Layout>>({{Layout::Undef(), Layout::Undef()}, {Layout::Undef()}});
+}
+
 Array<te::Tensor> SliceLikeCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
                                    const Type& out_type) {
   const auto* param = attrs.as<SliceLikeAttrs>();
@@ -2760,6 +3011,7 @@ RELAY_REGISTER_OP("slice_like")
     .set_support_level(10)
     .add_type_rel("SliceLike", SliceLikeRel)
     .set_attr<FTVMCompute>("FTVMCompute", SliceLikeCompute)
+    .set_attr<FInferCorrectLayout>("FInferCorrectLayout", SliceLikeInferCorrectLayout)
     .set_attr<TOpPattern>("TOpPattern", kInjective);
 
 // relay.layout_transform
@@ -2871,7 +3123,6 @@ RELAY_REGISTER_OP("auto_scheduler_layout_transform")
 Expr MakeReverseReshape(Expr data, Array<Integer> newshape) {
   auto attrs = make_object<ReshapeAttrs>();
   attrs->newshape = std::move(newshape);
-  attrs->reverse = true;
   static const Op& op = Op::Get("contrib_reverse_reshape");
   return Call(op, {data}, Attrs(attrs), {});
 }
@@ -2896,7 +3147,7 @@ example below::
     .set_attrs_type<ReshapeAttrs>()
     .add_argument("data", "Tensor", "The input tensor.")
     .set_support_level(10)
-    .add_type_rel("Reshape", ReshapeRel)
+    .add_type_rel("ReverseReshape", ReverseReshapeRel)
     .set_attr<FTVMCompute>("FTVMCompute", ReshapeCompute)
     .set_attr<TOpPattern>("TOpPattern", kInjective);
 
@@ -2928,6 +3179,9 @@ bool GatherRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   const auto ndim_indices = indices->shape.size();
   int axis = param->axis->value;
   ICHECK_EQ(ndim_data, ndim_indices);
+  if (axis < 0) {
+    axis += ndim_data;
+  }
   ICHECK_GE(axis, 0);
   ICHECK_LT(axis, ndim_data);
 
@@ -3518,5 +3772,105 @@ RELAY_REGISTER_OP("adv_index")
     .set_attr<TOpPattern>("TOpPattern", kInjective)
     .set_attr<FTVMCompute>("FTVMCompute", AdvIndexCompute);
 
+TVM_REGISTER_NODE_TYPE(CumsumAttrs);
+
+bool CumsumRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+               const TypeReporter& reporter) {
+  // types: [data, output]
+  ICHECK_EQ(types.size(), 2) << "Expects two types, one for the input and another for the output";
+  const auto* data = types[0].as<TensorTypeNode>();
+  if (data == nullptr) {
+    ICHECK(types[0].as<IncompleteTypeNode>())
+        << "cumsum: expect input type to be TensorType but get " << types[0];
+    return false;
+  }
+
+  const auto* param = attrs.as<CumsumAttrs>();
+
+  auto dtype = param->dtype;
+  if (dtype.is_void()) {
+    dtype = data->dtype;
+  }
+
+  if (param->axis.defined()) {
+    reporter->Assign(types[1], TensorType(data->shape, dtype));
+  } else {
+    auto prod = data->shape[0];
+    for (size_t i = 1; i < data->shape.size(); ++i) {
+      prod = prod * data->shape[i];
+    }
+    reporter->Assign(types[1], TensorType({prod}, dtype));
+  }
+
+  return true;
+}
+
+Expr MakeCumsum(Expr data, Integer axis, DataType dtype, Integer exclusive) {
+  auto attrs = make_object<CumsumAttrs>();
+  attrs->dtype = dtype;
+  attrs->axis = axis;
+  attrs->exclusive = exclusive;
+  static const Op& op = Op::Get("cumsum");
+  return Call(op, {data}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.cumsum").set_body_typed(MakeCumsum);
+
+RELAY_REGISTER_OP("cumsum")
+    .describe(
+        R"doc(Return the cumulative sum of the elements along a given axis.)doc" TVM_ADD_FILELINE)
+    .set_num_inputs(1)
+    .add_argument("data", "Tensor", "The input tensor.")
+    .set_support_level(3)
+    .add_type_rel("Cumsum", CumsumRel)
+    .set_attr<TOpPattern>("TOpPattern", kOpaque);
+
+TVM_REGISTER_NODE_TYPE(UniqueAttrs);
+
+bool UniqueRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+               const TypeReporter& reporter) {
+  // types: [data, result]
+  ICHECK_EQ(types.size(), 2) << "Unique: expect 2 types but " << types.size() << " provided";
+  ICHECK_EQ(num_inputs, 1) << "Unique: expect 1 inputs but " << num_inputs << " provided";
+  auto data = types[0].as<TensorTypeNode>();
+  if (data == nullptr) {
+    ICHECK(types[0].as<IncompleteTypeNode>())
+        << "Unique: expect input type to be TensorType but get " << types[0];
+    return false;
+  }
+  const int ndim = static_cast<int>(data->shape.size());
+  ICHECK_EQ(ndim, 1) << "Unique: input must be 1-D tensor";
+  ICHECK_EQ(data->dtype.is_int(), true) << "Unique: input must have int32 or int64 dtype";
+  std::vector<Type> fields;
+  fields.push_back(TensorType(data->shape, data->dtype));               // unique
+  fields.push_back(TensorType(data->shape, DataType::Int(32)));         // indices
+  fields.push_back(TensorType(Array<PrimExpr>{1}, DataType::Int(32)));  // num_unique
+  const auto* param = attrs.as<UniqueAttrs>();
+  if (param->return_counts) {
+    fields.push_back(TensorType(data->shape, DataType::Int(32)));  // counts
+  }
+  reporter->Assign(types[1], TupleType(Array<Type>(fields)));
+  return true;
+}
+
+Expr MakeUnique(Expr data, bool sorted, bool return_counts) {
+  auto attrs = make_object<UniqueAttrs>();
+  attrs->sorted = sorted;
+  attrs->return_counts = return_counts;
+  static const Op& op = Op::Get("unique");
+  return Call(op, {data}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.unique").set_body_typed(MakeUnique);
+
+RELAY_REGISTER_OP("unique")
+    .describe(
+        R"code(This operation returns the unique elements and the new index of each item in a given 1-D array.
+    )code" TVM_ADD_FILELINE)
+    .set_num_inputs(1)
+    .add_argument("data", "Tensor", "The input tensor")
+    .add_type_rel("unique", UniqueRel)
+    .set_support_level(3)
+    .set_attr<TOpPattern>("TOpPattern", kOpaque);
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/op/tensor/transform.h b/src/relay/op/tensor/transform.h
index 34aaf4689a59..3c670bcaaa51 100644
--- a/src/relay/op/tensor/transform.h
+++ b/src/relay/op/tensor/transform.h
@@ -78,8 +78,8 @@ bool ConcatenateRel(const Array<Type>& types, int num_inputs, const Attrs& attrs
   // Sanity check: axis
   int axis = param->axis;
   if (!(-ndim <= axis && axis < ndim)) {
-    throw Error(ErrorBuilder() << "concatenate only accepts `axis` in [-ndim, ndim)"
-                               << ", but got axis = " << axis << ", and ndim = " << ndim);
+    throw CompileError(ErrorBuilder() << "concatenate only accepts `axis` in [-ndim, ndim)"
+                                      << ", but got axis = " << axis << ", and ndim = " << ndim);
   }
   axis = axis < 0 ? ndim + axis : axis;
 
@@ -101,29 +101,64 @@ bool ConcatenateRel(const Array<Type>& types, int num_inputs, const Attrs& attrs
   }
 
   // Calculate shape
-  std::vector<IndexExpr> oshape(first->shape.begin(), first->shape.end());
-  int data_length = static_cast<int>(tensor_tuple->fields.size());
+  std::vector<IndexExpr> oshape(ndim);
+  const size_t data_length = tensor_tuple->fields.size();
+
+  // Accumulate the concat axis output dim or decide if this is dynamic concat
+  bool is_dynamic_concat = false;
+  std::vector<TensorType> input_tensors;
+  IndexExpr concat_output_dim = first->shape[axis];
+  for (size_t i = 0; i < data_length; ++i) {
+    const auto& e = Downcast<TensorType>(tensor_tuple->fields[i]);
+    input_tensors.push_back(e);
+    if (e->shape[axis].as<AnyNode>()) {
+      is_dynamic_concat = true;
+      concat_output_dim = Any();
+    } else if (i > 0 && !is_dynamic_concat) {
+      // accumulate axis dimension
+      concat_output_dim += e->shape[axis];
+    }
+  }
+
+  oshape[axis] = concat_output_dim;
+
   for (int i = 0; i < ndim; ++i) {
+    if (i == axis) {
+      // The concat axis is already handled above.
+      // The rest of the body sets the output shape for non-concat axes
+      continue;
+    }
     std::vector<IndexExpr> non_any;
-    for (int j = 0; j < data_length; ++j) {
-      const auto& e = Downcast<TensorType>(tensor_tuple->fields[j]);
+    for (size_t j = 0; j < data_length; ++j) {
+      const auto& e = input_tensors[j];
       if (!e->shape[i].as<AnyNode>()) {
         non_any.push_back(e->shape[i]);
-        // accumulate axis dimension
-        if (j > 0 && i == axis && !oshape[i].as<AnyNode>()) {
-          oshape[i] += e->shape[i];
-        }
       }
     }
-    int non_any_size = static_cast<int>(non_any.size());
-    if (non_any_size != data_length) oshape[i] = Any();
-    if (i != axis) {
-      for (int k = 1; k < non_any_size; k++) {
-        if (reporter->AssertEQ(non_any[0], non_any[k])) continue;
-        throw Error(
-            "relay.concatenate requires all tensors have the same shape "
-            "on non-concatenating axes");
-      }
+    size_t non_any_size = non_any.size();
+    for (size_t k = 1; k < non_any_size; k++) {
+      if (reporter->AssertEQ(non_any[0], non_any[k])) continue;
+      throw Error(
+          "relay.concatenate requires all tensors have the same shape "
+          "on non-concatenating axes");
+    }
+
+    if (non_any_size == data_length) {
+      // All static case
+      oshape[i] = non_any[0];
+    } else if (non_any_size > 0 && is_dynamic_concat) {
+      // For non-concat axes, we want to enforce static shape constraint.
+      // However, if the concat axis is static, the output shape would become static while
+      // the input could be partially static/dynamic. To prevent runtime segfaults due to the lack
+      // of runtime input shape checking for such cases, static shape constraint is only enforced
+      // when the output concat axis is dynamic.
+      //
+      // Examples (both concat on the first axis):
+      // * [(?, 3), (?, ?)] -> (?, 3)
+      // * [(1, 3), (1, ?)] -> (2, ?)
+      oshape[i] = non_any[0];
+    } else {
+      oshape[i] = Any();
     }
   }
 
@@ -193,9 +228,11 @@ static inline Array<Array<Layout>> ConcatenateLayout(const Attrs& attrs,
  *
  * \param data_shape The input data shape.
  * \param attrs The attributes.
+ * \param reverse Whether to reverse the indices.
  * \return Output shape.
  */
-Array<IndexExpr> infer_newshape(const Array<IndexExpr>& data_shape, const Attrs& attrs);
+Array<IndexExpr> InferNewShape(const Array<IndexExpr>& data_shape, const Attrs& attrs,
+                               bool reverse);
 
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/op/tensor/unary.cc b/src/relay/op/tensor/unary.cc
index e17bdc0e0906..3e82b92a5f03 100644
--- a/src/relay/op/tensor/unary.cc
+++ b/src/relay/op/tensor/unary.cc
@@ -430,12 +430,14 @@ Array<te::Tensor> ShapeOfCompute(const Attrs& attrs, const Array<te::Tensor>& in
   return {topi::shape(inputs[0], param->dtype)};
 }
 
-TVM_REGISTER_GLOBAL("relay.op._make.shape_of").set_body_typed([](Expr data, DataType dtype) {
+Expr MakeShapeOf(Expr data, DataType dtype) {
   auto attrs = make_object<ShapeOfAttrs>();
   attrs->dtype = dtype;
   static const Op& op = Op::Get("shape_of");
   return Call(op, {data}, Attrs(attrs), {});
-});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.shape_of").set_body_typed(MakeShapeOf);
 
 RELAY_REGISTER_OP("shape_of")
     .describe(R"code(Returns a tensor representing the shape of a tensor.
diff --git a/src/relay/op/type_relations.cc b/src/relay/op/type_relations.cc
index 7a3bfcb21ce6..6e30ad9624c4 100644
--- a/src/relay/op/type_relations.cc
+++ b/src/relay/op/type_relations.cc
@@ -85,7 +85,7 @@ TensorType ConcreteBroadcast(const TensorType& t1, const TensorType& t2, DataTyp
     } else if (EqualCheck(s1, s2)) {
       oshape.push_back(s1);
     } else {
-      throw Error(ErrorBuilder() << "Incompatible broadcast type " << t1 << " and " << t2);
+      throw CompileError(ErrorBuilder() << "Incompatible broadcast type " << t1 << " and " << t2);
     }
   }
 
@@ -104,7 +104,11 @@ bool BroadcastRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
   //                 << ",Out:" << types[2] << std::endl;
   if (auto* t0 = types[0].as<TensorTypeNode>()) {
     if (auto* t1 = types[1].as<TensorTypeNode>()) {
-      ICHECK_EQ(t0->dtype, t1->dtype);
+      if (t0->dtype != t1->dtype) {
+        reporter->GetDiagCtx().Emit(Diagnostic::Error(t0->span)
+                                    << "data types " << t0->dtype << " and " << t1->dtype
+                                    << "do not match in BroadcastRel");
+      }
       reporter->Assign(
           types[2], ConcreteBroadcast(GetRef<TensorType>(t0), GetRef<TensorType>(t1), t0->dtype));
       return true;
@@ -120,7 +124,11 @@ bool BroadcastCompRel(const Array<Type>& types, int num_inputs, const Attrs& att
   //                 << ",Out:" << types[2] << std::endl;
   if (auto* t0 = types[0].as<TensorTypeNode>()) {
     if (auto* t1 = types[1].as<TensorTypeNode>()) {
-      ICHECK_EQ(t0->dtype, t1->dtype);
+      if (t0->dtype != t1->dtype) {
+        reporter->GetDiagCtx().Emit(Diagnostic::Error(t0->span)
+                                    << "data types " << t0->dtype << " and " << t1->dtype
+                                    << "do not match in BroadcastCompRel");
+      }
       reporter->Assign(types[2], ConcreteBroadcast(GetRef<TensorType>(t0), GetRef<TensorType>(t1),
                                                    DataType::Bool()));
       return true;
diff --git a/src/relay/op/vision/rcnn_op.cc b/src/relay/op/vision/rcnn_op.cc
index f7bbf378d09c..c899681733f8 100644
--- a/src/relay/op/vision/rcnn_op.cc
+++ b/src/relay/op/vision/rcnn_op.cc
@@ -76,12 +76,13 @@ Array<Array<Layout> > ROIAlignInferCorrectLayout(const Attrs& attrs,
 }
 
 Expr MakeROIAlign(Expr data, Expr rois, Array<IndexExpr> pooled_size, double spatial_scale,
-                  int sample_ratio, String layout) {
+                  int sample_ratio, String layout, String mode) {
   auto attrs = make_object<ROIAlignAttrs>();
   attrs->pooled_size = pooled_size;
   attrs->spatial_scale = spatial_scale;
   attrs->sample_ratio = sample_ratio;
   attrs->layout = layout;
+  attrs->mode = mode;
   static const Op& op = Op::Get("vision.roi_align");
   return Call(op, {data, rois}, Attrs(attrs), {});
 }
diff --git a/src/relay/op/vm/vm.cc b/src/relay/op/vm/vm.cc
index 0fb79206d71d..a74a259a114f 100644
--- a/src/relay/op/vm/vm.cc
+++ b/src/relay/op/vm/vm.cc
@@ -22,6 +22,8 @@
  * \brief Dialect operators for Relay VM.
  */
 
+#include "vm.h"
+
 #include <tvm/relay/attrs/memory.h>
 #include <tvm/relay/attrs/vm.h>
 #include <tvm/relay/expr.h>
@@ -30,6 +32,8 @@
 #include <tvm/runtime/data_type.h>
 #include <tvm/topi/elemwise.h>
 
+#include <utility>
+
 #include "../../transforms/infer_layout_utils.h"
 #include "../op_common.h"
 #include "../type_relations.h"
@@ -52,20 +56,23 @@ RELAY_REGISTER_OP("vm.shape_of")
     .set_attr<TNonComputational>("TNonComputational", true)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout);
 
-TVM_REGISTER_GLOBAL("relay.op.vm.shape_of").set_body_typed([](Expr expr) {
+Expr ShapeOf(Expr expr) {
   auto attrs = make_object<ShapeOfAttrs>();
   attrs->dtype = DataType::Int(64);
   static const Op& op = Op::Get("vm.shape_of");
   return Call(op, {expr}, Attrs(attrs), {});
-});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.vm.shape_of").set_body_typed(ShapeOf);
+
+Expr ShapeFunc(Expr func, Expr inputs, Expr outputs, Array<tvm::Integer> is_input) {
+  static const Op& op = Op::Get("vm.shape_func");
+  auto attrs = make_object<ShapeFuncAttrs>();
+  attrs->is_input = is_input;
+  return Call(op, {func, inputs, outputs}, Attrs(attrs), {});
+}
 
-TVM_REGISTER_GLOBAL("relay.op.vm.shape_func")
-    .set_body_typed([](Expr func, Expr inputs, Expr outputs, Array<tvm::Integer> is_input) {
-      static const Op& op = Op::Get("vm.shape_func");
-      auto attrs = make_object<ShapeFuncAttrs>();
-      attrs->is_input = is_input;
-      return Call(op, {func, inputs, outputs}, Attrs(attrs), {});
-    });
+TVM_REGISTER_GLOBAL("relay.op.vm.shape_func").set_body_typed(ShapeFunc);
 
 bool ShapeFuncRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                   const TypeReporter& reporter) {
@@ -162,10 +169,11 @@ bool InvokeTVMOpRel(const Array<Type>& types, int num_inputs, const Attrs& attrs
   return true;
 }
 
-TVM_REGISTER_GLOBAL("relay.op.vm.invoke_tvm_op")
-    .set_body_typed([](Expr func, Expr inputs, Expr outputs) {
-      return Call(Op::Get("vm.invoke_tvm_op"), {func, inputs, outputs}, Attrs());
-    });
+Expr InvokeTVMOp(Expr func, Expr inputs, Expr outputs) {
+  return Call(Op::Get("vm.invoke_tvm_op"), {func, inputs, outputs}, Attrs());
+}
+
+TVM_REGISTER_GLOBAL("relay.op.vm.invoke_tvm_op").set_body_typed(InvokeTVMOp);
 
 RELAY_REGISTER_OP("vm.invoke_tvm_op")
     .describe(R"code(Invoke an operation compiled by TVM.)code" TVM_ADD_FILELINE)
@@ -212,13 +220,14 @@ RELAY_REGISTER_OP("vm.reshape_tensor")
     .set_attr<TNonComputational>("TNonComputational", true)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout);
 
-TVM_REGISTER_GLOBAL("relay.op.vm.reshape_tensor")
-    .set_body_typed([](Expr data, Expr shape, Array<PrimExpr> newshape) {
-      static const Op& op = Op::Get("vm.reshape_tensor");
-      auto attrs = make_object<ReshapeTensorAttrs>();
-      attrs->newshape = std::move(newshape);
-      return Call(op, {data, shape}, Attrs(attrs), {});
-    });
+Expr ReshapeTensor(Expr data, Expr shape, Array<PrimExpr> newshape) {
+  static const Op& op = Op::Get("vm.reshape_tensor");
+  auto attrs = make_object<ReshapeTensorAttrs>();
+  attrs->newshape = std::move(newshape);
+  return Call(op, {data, shape}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.vm.reshape_tensor").set_body_typed(ReshapeTensor);
 
 }  // namespace relay
 }  // namespace tvm
diff --git a/src/relay/op/vm/vm.h b/src/relay/op/vm/vm.h
new file mode 100644
index 000000000000..802c8100125a
--- /dev/null
+++ b/src/relay/op/vm/vm.h
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/op/vm/vm.h
+ * \brief Dialect operators for Relay VM.
+ */
+#ifndef TVM_RELAY_OP_VM_VM_H_
+#define TVM_RELAY_OP_VM_VM_H_
+
+#include "tvm/relay/expr.h"
+
+namespace tvm {
+namespace relay {
+
+Expr InvokeTVMOp(Expr func, Expr inputs, Expr outputs);
+Expr ShapeFunc(Expr func, Expr inputs, Expr outputs, Array<tvm::Integer> is_input);
+Expr ShapeOf(Expr expr);
+Expr ReshapeTensor(Expr data, Expr shape, Array<PrimExpr> newshape);
+
+}  // namespace relay
+}  // namespace tvm
+
+#endif  // TVM_RELAY_OP_VM_VM_H_
diff --git a/src/relay/qnn/op/concatenate.cc b/src/relay/qnn/op/concatenate.cc
index 59a519d66436..eb0f83836a54 100644
--- a/src/relay/qnn/op/concatenate.cc
+++ b/src/relay/qnn/op/concatenate.cc
@@ -51,9 +51,10 @@ bool QnnConcatenateRel(const Array<Type>& types, int num_inputs, const Attrs& at
     if (types[1].as<IncompleteTypeNode>()) {
       return false;
     } else {
-      throw Error(ErrorBuilder()
-                  << "qnn concatenate requires a tuple of scales as the second argument, found "
-                  << PrettyPrint(types[1]));
+      throw CompileError(
+          ErrorBuilder()
+          << "qnn concatenate requires a tuple of scales as the second argument, found "
+          << PrettyPrint(types[1]));
     }
   }
   for (const auto& input_scale : input_scales_tuple->fields) {
@@ -68,9 +69,10 @@ bool QnnConcatenateRel(const Array<Type>& types, int num_inputs, const Attrs& at
     if (types[2].as<IncompleteTypeNode>()) {
       return false;
     } else {
-      throw Error(ErrorBuilder()
-                  << "qnn concatenate requires a tuple of zero_points as the third argument, found "
-                  << PrettyPrint(types[2]));
+      throw CompileError(
+          ErrorBuilder()
+          << "qnn concatenate requires a tuple of zero_points as the third argument, found "
+          << PrettyPrint(types[2]));
     }
   }
   for (const auto& input_zero_point : input_zero_points_tuple->fields) {
diff --git a/src/relay/qnn/op/dequantize.cc b/src/relay/qnn/op/dequantize.cc
index 724441e0c523..b0fe9356a758 100644
--- a/src/relay/qnn/op/dequantize.cc
+++ b/src/relay/qnn/op/dequantize.cc
@@ -53,7 +53,7 @@ bool DequantizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
 
   const auto* dequantize_attrs = attrs.as<DequantizeAttrs>();
   int axis = dequantize_attrs->axis;
-  axis = (axis == -1) ? data->shape.size() - 1 : axis;
+  axis = (axis < 0) ? data->shape.size() + axis : axis;
   ICHECK_LT(axis, static_cast<int>(data->shape.size()))
       << "axis " << dequantize_attrs->axis << " is out of range";
   ICHECK_GE(axis, 0) << "axis " << dequantize_attrs->axis << " is out of range";
@@ -81,7 +81,7 @@ Expr MakeDequantize(Expr data, Expr input_scale, Expr input_zero_point, int axis
 Expr DequantizeLower(const Expr& input_tensor, const Expr& input_scale,
                      const Expr& input_zero_point, const Array<tvm::relay::Type>& types,
                      const DequantizeAttrs* attrs) {
-  const auto axis = attrs->axis;
+  auto axis = attrs->axis;
 
   ICHECK_EQ(types.size(), 4);
   auto in_type = types[0];
@@ -92,6 +92,11 @@ Expr DequantizeLower(const Expr& input_tensor, const Expr& input_scale,
 
   size_t n_dim = input_shape.size();
 
+  // Wrap axis from negative to positive if needed.
+  if (axis < 0) {
+    axis = static_cast<int>(n_dim) + axis;
+  }
+
   // Expand scale and zero point if the input tensor is channel quantized
   auto expanded_input_scale = input_scale;
   if (!IsConstScalar(input_scale) && !IsScalarType(types[1])) {
diff --git a/src/relay/qnn/op/quantize.cc b/src/relay/qnn/op/quantize.cc
index 9829834f43a3..751abfc5ca81 100644
--- a/src/relay/qnn/op/quantize.cc
+++ b/src/relay/qnn/op/quantize.cc
@@ -19,8 +19,8 @@
 
 /*!
  * \file src/relay/qnn/op/quantize.cc
- * \brief QNN dequantize operator. Dequantize operator converts from quantized
- * domain to unquantized domain.
+ * \brief QNN quantize operator. Quantize operator converts from unquantized
+ * domain to quantized domain.
  */
 
 #include <tvm/relay/analysis.h>
@@ -51,7 +51,7 @@ bool QuantizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
 
   const auto* quantize_attrs = attrs.as<QuantizeAttrs>();
   int axis = quantize_attrs->axis;
-  axis = (axis == -1) ? data->shape.size() - 1 : axis;
+  axis = (axis < 0) ? data->shape.size() + axis : axis;
   ICHECK_LT(axis, static_cast<int>(data->shape.size()))
       << "axis " << quantize_attrs->axis << " is out of range";
   ICHECK_GE(axis, 0) << "axis " << quantize_attrs->axis << " is out of range";
@@ -93,10 +93,15 @@ Expr QuantizeLower(const Expr& input_tensor, const Expr& output_scale,
   Array<IndexExpr> input_shape = in_tensor_type->shape;
 
   const auto out_dtype = attrs->out_dtype;
-  const auto axis = attrs->axis;
+  auto axis = attrs->axis;
 
   size_t n_dim = input_shape.size();
 
+  // Wrap axis from negative to positive if needed.
+  if (axis < 0) {
+    axis = static_cast<int>(n_dim) + axis;
+  }
+
   auto expanded_output_scale = output_scale;
   if (!IsConstScalar(output_scale) && !IsScalarType(types[1])) {
     expanded_output_scale = ExpandBiasToMatchAxis(output_scale, n_dim, {axis});
diff --git a/src/relay/qnn/op/simulated_dequantize.cc b/src/relay/qnn/op/simulated_dequantize.cc
new file mode 100644
index 000000000000..e1fc47d700c9
--- /dev/null
+++ b/src/relay/qnn/op/simulated_dequantize.cc
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/qnn/op/simulated_dequantize.cc
+ * \brief QNN simulated dequantize operator. Mimics the behavior
+ * of QNN dequantize in floating point with added flexibility.
+ */
+
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/op_attr_types.h>
+#include <tvm/relay/qnn/attrs.h>
+
+#include "../../transforms/pattern_utils.h"
+#include "../utils.h"
+
+namespace tvm {
+namespace relay {
+namespace qnn {
+
+bool SimulatedDequantizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                            const TypeReporter& reporter) {
+  // types = [data_type, datatype_type, scale_type, zp_type, ret_type]
+  ICHECK_EQ(types.size(), 5);
+  const auto* data = types[0].as<TensorTypeNode>();
+  const auto* dtype = types[1].as<TensorTypeNode>();
+
+  if ((data == nullptr) || (dtype == nullptr)) {
+    return false;
+  }
+
+  // assign output type
+  reporter->Assign(types[4], TensorType(data->shape, data->dtype));
+  return true;
+}
+
+Expr MakeSimulatedDequantize(Expr data, Expr in_dtype, Expr input_scale, Expr input_zero_point,
+                             int axis) {
+  auto attrs = make_object<DequantizeAttrs>();
+  attrs->axis = axis;
+  static const Op& op = Op::Get("qnn.simulated_dequantize");
+  return Call(op, {data, in_dtype, input_scale, input_zero_point}, Attrs(attrs), {});
+}
+
+RELAY_REGISTER_OP("qnn.simulated_dequantize")
+    .describe(R"code(Simulates the functionality of qnn.dequantize but allows more flexible
+    dynamic input type conversion and always operates on float values.
+)code" TVM_ADD_FILELINE)
+    .set_attrs_type<DequantizeAttrs>()
+    .set_num_inputs(4)
+    .add_argument("data", "Tensor", "The tensor to dequantize.")
+    .add_argument("in_dtype", "Tensor",
+                  "A code corresponding to the type of quantization to convert from.")
+    .add_argument("input_scale", "Tensor", "The quantization scale of the input tensor.")
+    .add_argument("input_zero_point", "Tensor", "The quantization zero_point of the input tensor.")
+    .set_support_level(11)
+    .add_type_rel("QNNSimulatedDequantize", SimulatedDequantizeRel);
+
+TVM_REGISTER_GLOBAL("relay.qnn.op._make.simulated_dequantize")
+    .set_body_typed(MakeSimulatedDequantize);
+
+}  // namespace qnn
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/qnn/op/simulated_quantize.cc b/src/relay/qnn/op/simulated_quantize.cc
new file mode 100644
index 000000000000..089762a6ade0
--- /dev/null
+++ b/src/relay/qnn/op/simulated_quantize.cc
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/qnn/op/simulated_quantize.cc
+ * \brief QNN simulated quantize operator. Mimics the behavior
+ * of QNN quantize in floating point with added flexibility.
+ */
+
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/op_attr_types.h>
+#include <tvm/relay/qnn/attrs.h>
+
+#include "../../transforms/pattern_utils.h"
+#include "../utils.h"
+
+namespace tvm {
+namespace relay {
+namespace qnn {
+
+TVM_REGISTER_NODE_TYPE(SimulatedQuantizeAttrs);
+
+bool SimulatedQuantizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+                          const TypeReporter& reporter) {
+  // types = [data_type, datatype_type, scale_type, zp_type, ret_type]
+  ICHECK_EQ(types.size(), 5);
+  const auto* data = types[0].as<TensorTypeNode>();
+  const auto* dtype = types[1].as<TensorTypeNode>();
+
+  if ((data == nullptr) || (dtype == nullptr)) {
+    return false;
+  }
+
+  // assign output type
+  reporter->Assign(types[4], TensorType(data->shape, data->dtype));
+  return true;
+}
+
+Expr MakeSimulatedQuantize(Expr data, Expr out_dtype, Expr output_scale, Expr output_zero_point,
+                           int axis) {
+  auto attrs = make_object<SimulatedQuantizeAttrs>();
+  attrs->axis = axis;
+  static const Op& op = Op::Get("qnn.simulated_quantize");
+  return Call(op, {data, out_dtype, output_scale, output_zero_point}, Attrs(attrs), {});
+}
+
+RELAY_REGISTER_OP("qnn.simulated_quantize")
+    .describe(R"code(Simulates the functionality of qnn.quantize but allows more flexible
+    dynamic input type conversion and always outputs float values.
+)code" TVM_ADD_FILELINE)
+    .set_attrs_type<SimulatedQuantizeAttrs>()
+    .set_num_inputs(4)
+    .add_argument("data", "Tensor", "The tensor to quantize.")
+    .add_argument("out_dtype", "Tensor",
+                  "A code corresponding to the type of quantization to apply.")
+    .add_argument("output_scale", "Tensor", "The quantization scale of the output tensor.")
+    .add_argument("output_zero_point", "Tensor",
+                  "The quantization zero_point of the output tensor.")
+    .set_support_level(11)
+    .add_type_rel("QNNSimulatedQuantize", SimulatedQuantizeRel);
+
+TVM_REGISTER_GLOBAL("relay.qnn.op._make.simulated_quantize").set_body_typed(MakeSimulatedQuantize);
+
+}  // namespace qnn
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/quantize/realize.cc b/src/relay/quantize/realize.cc
index 2716c6e65f65..d77ede3acbf9 100644
--- a/src/relay/quantize/realize.cc
+++ b/src/relay/quantize/realize.cc
@@ -165,7 +165,7 @@ Expr QuantizeRealize(const Call& ref_call, const Array<Expr>& new_args, const Ob
                           MakeConstantScalar(cfg->dtype_activation, static_cast<int>(shift_nbit)));
       } else {
         data = LeftShift(data,
-                         MakeConstantScalar(cfg->dtype_activation, static_cast<int>(shift_nbit)));
+                         MakeConstantScalar(cfg->dtype_activation, static_cast<int>(-shift_nbit)));
       }
       data = Clip(data, clip_min_imm, clip_max_imm);
       return QRealizeIntExpr(data, dom_scale, n->dtype);
diff --git a/src/relay/transforms/alter_op_layout.cc b/src/relay/transforms/alter_op_layout.cc
index 924e61ad0d16..d7ffff68c1f5 100644
--- a/src/relay/transforms/alter_op_layout.cc
+++ b/src/relay/transforms/alter_op_layout.cc
@@ -110,6 +110,7 @@ class AlterTransformMemorizer : public TransformMemorizer {
  * 2. Do not support nested tuple arguments.
  */
 Expr AlterOpLayout(const Expr& expr) {
+  // TODO(@icemelon9): need to rerun type inference after applying an alter op.
   AlterTransformMemorizer alterMemorizer(make_object<AlterTransformMemorizerNode>());
   auto fcontext = [&](const Call& call) -> ObjectRef { return alterMemorizer; };
 
diff --git a/src/relay/transforms/annotate_target.cc b/src/relay/transforms/annotate_target.cc
index 76585cf1272f..e365dca3860f 100644
--- a/src/relay/transforms/annotate_target.cc
+++ b/src/relay/transforms/annotate_target.cc
@@ -144,11 +144,12 @@ class AnnotateTargetRewriter : public ExprRewriter {
      */
     Expr new_expr = expr;
     const CallNode* call = expr.as<CallNode>();
+    const TupleNode* tup = expr.as<TupleNode>();
     if (op_expr_to_target_.find(expr) != op_expr_to_target_.end()) {
       // Check whether expr has args, if not - do not insert compiler_end.
       if (expr->IsInstance<RefWriteNode>() || expr->IsInstance<RefCreateNode>() ||
-          expr->IsInstance<RefReadNode>() || expr->IsInstance<TupleNode>() ||
-          expr->IsInstance<TupleGetItemNode>() || (call && !call->args.empty())) {
+          expr->IsInstance<RefReadNode>() || expr->IsInstance<TupleGetItemNode>() ||
+          (call && !call->args.empty()) || (tup && !tup->fields.empty())) {
         std::string target = op_expr_to_target_[new_expr];
         new_expr = InsertAnnotation(new_expr, target, make_end_op);
         op_expr_to_target_[new_expr] = target;
diff --git a/src/relay/transforms/de_duplicate.cc b/src/relay/transforms/de_duplicate.cc
index 43b71f6f10cc..2fd88736bf31 100644
--- a/src/relay/transforms/de_duplicate.cc
+++ b/src/relay/transforms/de_duplicate.cc
@@ -27,6 +27,8 @@
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/pattern_functor.h>
 
+#include <stack>
+
 namespace tvm {
 namespace relay {
 
@@ -61,8 +63,20 @@ Expr DeDup(const Expr& e) {
     }
 
     Expr VisitExpr_(const LetNode* op) final {
-      Var v = Fresh(op->var);
-      return Let(v, VisitExpr(op->value), VisitExpr(op->body));
+      std::unordered_map<Expr, Var, ObjectPtrHash, ObjectPtrEqual> new_vars;
+      auto pre_visit = [this, &new_vars](const LetNode* op) {
+        Expr expr = GetRef<Expr>(op);
+        new_vars[expr] = this->Fresh(op->var);
+        // Rely on the Memoizer to cache pre-visit values
+        this->VisitExpr(op->value);
+      };
+      auto post_visit = [this, &new_vars](const LetNode* op) {
+        Expr expr = GetRef<Expr>(op);
+        this->memo_[expr] =
+            Let(new_vars[expr], this->VisitExpr(op->value), this->VisitExpr(op->body));
+      };
+      ExpandANormalForm(op, pre_visit, post_visit);
+      return memo_[GetRef<Expr>(op)];
     }
 
     Type VisitType(const Type& t) final { return t.defined() ? TypeMutator::VisitType(t) : t; }
@@ -99,7 +113,7 @@ Expr DeDup(const Expr& e) {
   ICHECK(WellFormed(ret));
   ICHECK_EQ(FreeVars(e).size(), FreeVars(ret).size());
   return ret;
-}
+}  // namespace relay
 
 TVM_REGISTER_GLOBAL("relay._transform.dedup").set_body_typed(DeDup);
 
diff --git a/src/relay/transforms/dead_code.cc b/src/relay/transforms/dead_code.cc
index 2e7c08a684dc..26624e438b8a 100644
--- a/src/relay/transforms/dead_code.cc
+++ b/src/relay/transforms/dead_code.cc
@@ -46,10 +46,16 @@ class FindDef : private ExprVisitor {
   VarMap<Expr> expr_map_;
 
   void VisitExpr_(const LetNode* l) final {
-    ICHECK_EQ(expr_map_.count(l->var), 0);
-    expr_map_[l->var] = l->value;
-    VisitExpr(l->value);
-    VisitExpr(l->body);
+    auto pre_visit = [this](const LetNode* op) {
+      ICHECK_EQ(expr_map_.count(op->var), 0);
+      expr_map_[op->var] = op->value;
+      this->VisitExpr(op->value);
+    };
+    auto post_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->body);
+      this->visit_counter_[op] += 1;
+    };
+    ExpandANormalForm(l, pre_visit, post_visit);
   }
 
   friend CalcDep;
@@ -81,12 +87,24 @@ class Eliminator : private ExprMutator {
   }
 
   Expr VisitExpr_(const LetNode* op) final {
-    Var v = op->var;
-    if (HasLet(v)) {
-      return Let(v, VisitExpr(op->value), VisitExpr(op->body));
-    } else {
-      return VisitExpr(op->body);
-    }
+    auto pre_visit = [this](const LetNode* op) {
+      if (HasLet(op->var)) {
+        Expr value = this->VisitExpr(op->value);
+      }
+    };
+    auto post_visit = [this](const LetNode* op) {
+      Expr body = this->VisitExpr(op->body);
+      auto expr = GetRef<Expr>(op);
+      Var v = op->var;
+      if (HasLet(v)) {
+        Expr value = this->VisitExpr(op->value);
+        this->memo_[expr] = Let(v, value, body);
+      } else {
+        this->memo_[expr] = body;
+      }
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(op)];
   }
 };
 
@@ -121,7 +139,15 @@ class CalcDep : protected MixedModeVisitor {
     }
   }
 
-  void VisitExpr_(const LetNode* l) final { VisitExpr(l->body); }
+  void VisitExpr_(const LetNode* l) final {
+    Expr let_binding = GetRef<Expr>(l);
+    const LetNode* let;
+    while ((let = let_binding.as<LetNode>())) {
+      let_binding = let->body;
+      visit_counter_[l] += 1;
+    }
+    VisitExpr(let_binding);
+  }
 
   void VisitExpr_(const VarNode* v) final {
     Var var = GetRef<Var>(v);
diff --git a/src/relay/transforms/dynamic_to_static.cc b/src/relay/transforms/dynamic_to_static.cc
index f78d05bd9d2c..815e4d224cc5 100644
--- a/src/relay/transforms/dynamic_to_static.cc
+++ b/src/relay/transforms/dynamic_to_static.cc
@@ -34,27 +34,30 @@ namespace relay {
 
 class DynamicToStaticMutator : public MixedModeMutator {
  public:
-  DynamicToStaticMutator() {
+  DynamicToStaticMutator(IRModule mod, Function func) : mod_(mod), func_(func) {
     op_map_ = {
         {Op::Get("dyn.reshape"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* shape = call_node->args[1].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* shape = args[1].as<ConstantNode>()) {
              ICHECK_EQ(shape->data->ndim, 1);
              return MakeReshape(call_node->args[0], ToVector(shape->data));
            }
            return Expr(nullptr);
          }},
         {Op::Get("dyn.tile"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* reps = call_node->args[1].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* reps = args[1].as<ConstantNode>()) {
              ICHECK_EQ(reps->data->ndim, 1);
              return MakeTile(call_node->args[0], ToVector(reps->data));
            }
            return Expr(nullptr);
          }},
         {Op::Get("dyn.topk"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* k = call_node->args[1].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* k = args[1].as<ConstantNode>()) {
              const TopKAttrs* param = call_node->attrs.as<TopKAttrs>();
              ICHECK(param);
              return MakeTopK(call_node->args[0], static_cast<int>(ToScalar(k->data, 0)),
@@ -63,16 +66,18 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.broadcast_to"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* shape = call_node->args[1].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* shape = args[1].as<ConstantNode>()) {
              ICHECK_EQ(shape->data->ndim, 1);
              return MakeBroadCastTo(call_node->args[0], ToVector(shape->data));
            }
            return Expr(nullptr);
          }},
         {Op::Get("dyn.zeros"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* shape = call_node->args[0].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* shape = args[0].as<ConstantNode>()) {
              const InitOpAttrs* param = call_node->attrs.as<InitOpAttrs>();
              ICHECK(param);
              return MakeZeros(ToVector(shape->data), param->dtype);
@@ -80,8 +85,9 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.ones"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* shape = call_node->args[0].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* shape = args[0].as<ConstantNode>()) {
              const InitOpAttrs* param = call_node->attrs.as<InitOpAttrs>();
              ICHECK(param);
              return MakeOnes(ToVector(shape->data), param->dtype);
@@ -89,8 +95,9 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.one_hot"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* depth = call_node->args[3].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* depth = args[3].as<ConstantNode>()) {
              const OneHotAttrs* param = call_node->attrs.as<OneHotAttrs>();
              ICHECK(param);
              return MakeOneHot(call_node->args[0], call_node->args[1], call_node->args[2],
@@ -100,8 +107,9 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.image.resize"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* size = call_node->args[1].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* size = args[1].as<ConstantNode>()) {
              const ResizeAttrs* param = call_node->attrs.as<ResizeAttrs>();
              ICHECK(param);
              auto size_int = ToVector(size->data);
@@ -115,8 +123,9 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.full"),
-         [](const CallNode* call_node) {
-           if (const ConstantNode* shape = call_node->args[1].as<ConstantNode>()) {
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           if (const ConstantNode* shape = args[1].as<ConstantNode>()) {
              ICHECK_EQ(shape->data->ndim, 1);
              const InitOpAttrs* param = call_node->attrs.as<InitOpAttrs>();
              ICHECK(param);
@@ -125,9 +134,10 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.nn.upsampling"),
-         [](const CallNode* call_node) {
-           const ConstantNode* scale_h = call_node->args[1].as<ConstantNode>();
-           const ConstantNode* scale_w = call_node->args[2].as<ConstantNode>();
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           const ConstantNode* scale_h = args[1].as<ConstantNode>();
+           const ConstantNode* scale_w = args[2].as<ConstantNode>();
            if (scale_h && scale_w) {
              ICHECK_EQ(scale_h->data->ndim, 0);
              ICHECK_EQ(scale_w->data->ndim, 0);
@@ -140,10 +150,11 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.nn.upsampling3d"),
-         [](const CallNode* call_node) {
-           const ConstantNode* scale_d = call_node->args[1].as<ConstantNode>();
-           const ConstantNode* scale_h = call_node->args[2].as<ConstantNode>();
-           const ConstantNode* scale_w = call_node->args[3].as<ConstantNode>();
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           const ConstantNode* scale_d = args[1].as<ConstantNode>();
+           const ConstantNode* scale_h = args[2].as<ConstantNode>();
+           const ConstantNode* scale_w = args[3].as<ConstantNode>();
            if (scale_d && scale_h && scale_w) {
              ICHECK_EQ(scale_d->data->ndim, 0);
              ICHECK_EQ(scale_h->data->ndim, 0);
@@ -159,9 +170,10 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.nn.pad"),
-         [](const CallNode* call_node) {
-           const ConstantNode* pad_width = call_node->args[1].as<ConstantNode>();
-           const ConstantNode* pad_fill = call_node->args[2].as<ConstantNode>();
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           const ConstantNode* pad_width = args[1].as<ConstantNode>();
+           const ConstantNode* pad_fill = args[2].as<ConstantNode>();
            if (pad_width && pad_fill) {
              ICHECK_EQ(pad_fill->data->ndim, 0);   // pad_val is 1d
              ICHECK_EQ(pad_width->data->ndim, 2);  // pad_width is 2d
@@ -174,10 +186,11 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.strided_slice"),
-         [](const CallNode* call_node) {
-           const ConstantNode* begin = call_node->args[1].as<ConstantNode>();
-           const ConstantNode* end = call_node->args[2].as<ConstantNode>();
-           const ConstantNode* stride = call_node->args[3].as<ConstantNode>();
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           const ConstantNode* begin = args[1].as<ConstantNode>();
+           const ConstantNode* end = args[2].as<ConstantNode>();
+           const ConstantNode* stride = args[3].as<ConstantNode>();
            if (begin && end && stride) {
              ICHECK_EQ(begin->data->ndim, 1);
              ICHECK_EQ(end->data->ndim, 1);
@@ -190,8 +203,9 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
         {Op::Get("dyn.sparse_to_dense"),
-         [](const CallNode* call_node) {
-           const ConstantNode* output_shape = call_node->args[3].as<ConstantNode>();
+         [this](const CallNode* call_node) {
+           auto args = PrepareArgs(call_node);
+           const ConstantNode* output_shape = args[3].as<ConstantNode>();
            if (output_shape) {
              ICHECK_EQ(output_shape->data->ndim, 1);
              return MakeSparseToDense(call_node->args[0], ToVector(output_shape->data),
@@ -200,6 +214,45 @@ class DynamicToStaticMutator : public MixedModeMutator {
            return Expr(nullptr);
          }},
     };
+    Map<BaseFunc, GlobalVar> vars;
+    for (auto kv : mod_->functions) {
+      vars.Set(kv.second, kv.first);
+    }
+    gv_ = vars[func_];
+  }
+
+  Expr PrepareInput(const Expr& expr) {
+    BaseFunc func;
+    if (auto* func_node = expr.as<BaseFuncNode>()) {
+      func = GetRef<BaseFunc>(func_node);
+    } else {
+      func =
+          relay::Function(relay::FreeVars(expr), expr, Type(), relay::FreeTypeVars(expr, mod_), {});
+    }
+    mod_->Update(gv_, func);
+    mod_ = transform::FoldConstant()(mod_);
+    mod_ = transform::InferType()(mod_);
+    mod_ = transform::FoldConstant()(mod_);
+    mod_ = transform::InferType()(mod_);
+    Expr out;
+    if (expr.as<FunctionNode>()) {
+      out = mod_->Lookup(gv_);
+    } else {
+      out = mod_->Lookup(gv_).as<FunctionNode>()->body;
+    }
+    return out;
+  }
+
+  std::vector<Expr> PrepareArgs(const CallNode* call_node) {
+    std::vector<Expr> args;
+    for (auto arg : call_node->args) {
+      if (arg.as<ConstantNode>()) {
+        args.emplace_back(arg);
+      } else {
+        args.emplace_back(PrepareInput(arg));
+      }
+    }
+    return args;
   }
 
  private:
@@ -222,35 +275,19 @@ class DynamicToStaticMutator : public MixedModeMutator {
     }
     return post;
   }
+
   std::unordered_map<Expr, std::function<Expr(const CallNode*)>, ObjectPtrHash, ObjectPtrEqual>
       op_map_;
+  IRModule mod_;
+  Function func_;
+  GlobalVar gv_;
 };
 
 Expr DynamicToStatic(Function f, IRModule m) {
-  Expr pre = f;
-  Expr expr = f;
-  auto fold_const = transform::FoldConstant();
-  auto infer_type = transform::InferType();
-  DynamicToStaticMutator mutator;
-  Map<BaseFunc, GlobalVar> vars;
-  for (auto kv : m->functions) {
-    vars.Set(kv.second, kv.first);
-  }
-  const auto gv = vars[f];
-  // Put a limit on the while loop
-  // Primarily used to prevent accidental infinite lops in development
-  const int loop_limit = 1000;
-  int i = 0;
-  do {
-    pre = expr;
-    // TODO(mbrookhart): Is it possible to run these passes JUST on the current function?
-    m = infer_type(m);
-    m = fold_const(m);
-    expr = mutator.Mutate(m->functions[gv]);
-    m->Update(gv, Downcast<BaseFunc>(expr));
-    i += 1;
-  } while (!StructuralEqual()(pre, expr) && i < loop_limit);
-  return expr;
+  DynamicToStaticMutator mutator(m, f);
+  Expr expr = mutator.Mutate(f);
+  Expr out = mutator.PrepareInput(expr);
+  return out;
 }
 
 namespace transform {
@@ -260,7 +297,7 @@ Pass DynamicToStatic() {
       [=](Function f, IRModule m, PassContext pc) {
         return Downcast<Function>(DynamicToStatic(f, m));
       };
-  return CreateFunctionPass(pass_func, 3, "DynamicToStatic", {});
+  return CreateFunctionPass(pass_func, 2, "DynamicToStatic", {});
 }
 
 TVM_REGISTER_GLOBAL("relay._transform.DynamicToStatic").set_body_typed([]() {
diff --git a/src/relay/transforms/first_order_gradient.cc b/src/relay/transforms/first_order_gradient.cc
new file mode 100644
index 000000000000..55714592ded7
--- /dev/null
+++ b/src/relay/transforms/first_order_gradient.cc
@@ -0,0 +1,309 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file first_order_gradient.cc
+ * \brief First-order Automatic Differentiation in Relay for pure dataflow graphs.
+ */
+#include <tvm/ir/type_functor.h>
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/feature.h>
+#include <tvm/relay/transform.h>
+#include <tvm/te/operation.h>
+
+#include "gradient.h"
+#include "let_list.h"
+#include "pass_utils.h"
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+template <typename F>
+Expr MultiFactory(const Type& t, F factory, DiagnosticContext diag_ctx) {
+  if (auto* tt = t.as<TensorTypeNode>()) {
+    return factory(tt->shape, tt->dtype);
+  } else if (auto* tt = t.as<TupleTypeNode>()) {
+    std::vector<Expr> res;
+    for (size_t i = 0; i < tt->fields.size(); i++) {
+      res.push_back(MultiFactory(tt->fields[i], factory, diag_ctx));
+    }
+    return Tuple(res);
+  } else {
+    diag_ctx.EmitFatal(Diagnostic::Error(t->span)
+                       << "could not build tensors using factory for type " << PrettyPrint(t));
+    throw;
+  }
+}
+
+template <typename F, typename F2>
+Expr MultiFactoryLike(const Expr& e, const Type& t, F factory, F2 factory_like,
+                      DiagnosticContext diag_ctx) {
+  if (t.as<TensorTypeNode>()) {
+    return factory_like(e);
+  } else if (auto* tt = t.as<TupleTypeNode>()) {
+    return MultiFactory(t, factory, diag_ctx);
+  } else {
+    diag_ctx.EmitFatal(Diagnostic::Error(t->span)
+                       << "could not build tensors using factory for type " << PrettyPrint(t));
+    throw;
+  }
+}
+
+/*! \brief A fragment of the program being built by the automatic differentation
+ *  pass.
+ */
+struct ADValueNode {
+  virtual ~ADValueNode() {}
+  template <typename T>
+  T& get() {
+    auto ret = dynamic_cast<T*>(this);
+    ICHECK(ret) << "cannot downcast";
+    return *ret;
+  }
+};
+
+using ADValue = std::shared_ptr<ADValueNode>;
+
+/*! \brief AD over a program which generates a tensor output. */
+struct ADTensor : ADValueNode {
+  Expr forward;
+  mutable Expr reverse;  // must be a variable to avoid duplication
+  ADTensor(LetList* ll, const Expr& forward, DiagnosticContext diag_ctx)
+      : forward(ll->Push(forward)),
+        reverse(ll->Push(
+            MultiFactoryLike(this->forward, forward->checked_type(), Zeros, ZerosLike, diag_ctx))) {
+    this->forward->checked_type_ = forward->checked_type();
+  }
+};
+
+/*! \brief A staged representation of the program, we reflect
+ * Relay functions into a function over fragments of AD. We
+ * can compute away this function to obtain a reverse mode program.
+ */
+struct ADFunction : ADValueNode {
+  // (ad_args, orig) -> ad_ret
+  using ADFunctionType = ADValue(const std::vector<ADValue>&, const Call&);
+  std::function<ADFunctionType> func;
+  explicit ADFunction(const std::function<ADFunctionType>& func) : func(func) {}
+};
+
+struct FirstOrderReverseAD : ExprFunctor<ADValue(const Expr&)> {
+  const OpAttrMap<FPrimalGradient> rev_map = Op::GetAttrMap<FPrimalGradient>("FPrimalGradient");
+  std::vector<std::function<void(LetList* ll)>> backprop_actions;
+  // we assume no closure so no need for lexical scoping
+  std::unordered_map<Expr, ADValue, ObjectPtrHash, ObjectPtrEqual> env;
+  LetList* ll;
+  DiagnosticContext diag_ctx;
+
+  FirstOrderReverseAD(LetList* ll, DiagnosticContext diag_ctx) : ll(ll), diag_ctx(diag_ctx) {}
+
+  ADValue VisitExpr(const Expr& n) final {
+    if (env.count(n)) {
+      return env.at(n);
+    }
+    auto ret = ExprFunctor::VisitExpr(n);
+    env[n] = ret;
+    return ret;
+  }
+
+  static Expr LiftedAdd(const Type& t, const Expr& x, const Expr& y, LetList* ll) {
+    if (t.as<TensorTypeNode>()) {
+      return ll->Push(Add(x, y));
+    } else if (auto* tt = t.as<TupleTypeNode>()) {
+      Array<Expr> fields;
+      for (size_t i = 0; i < tt->fields.size(); ++i) {
+        fields.push_back(
+            LiftedAdd(tt->fields[i], ll->Push(GetField(x, i)), ll->Push(GetField(y, i)), ll));
+      }
+      return ll->Push(Tuple(fields));
+    } else {
+      LOG(FATAL) << "cannot lift addition for type " << PrettyPrint(t);
+      throw;
+    }
+  }
+
+  ADValue VisitExpr_(const OpNode* op) final {
+    Op op_ref = GetRef<Op>(op);
+    if (!rev_map.count(op_ref)) {
+      diag_ctx.EmitFatal(Diagnostic::Error(op->span)
+                         << "the operator " << op->name << " does not have a registered gradient.");
+    }
+    return std::make_shared<ADFunction>([this, op_ref](const std::vector<ADValue>& ad_args,
+                                                       const Call& orig) {
+      std::vector<Expr> orig_args;
+      for (const ADValue& adval : ad_args) {
+        orig_args.push_back(adval->get<ADTensor>().forward);
+      }
+      auto orig_new = Call(op_ref, orig_args, orig->attrs, orig->type_args);
+      orig_new->checked_type_ = orig->checked_type();
+      auto ret = std::make_shared<ADTensor>(ll, orig_new, diag_ctx);
+      backprop_actions.push_back([this, ad_args, orig_new, ret, op_ref](LetList* ll) {
+        tvm::Array<Expr> rev = rev_map[op_ref](orig_new, ret->reverse);
+        if (ad_args.size() != rev.size()) {
+          diag_ctx.EmitFatal(Diagnostic::Error(op_ref->span)
+                             << "arity mismatch for operator " << op_ref->name
+                             << " and its registered gradient: expected " << ad_args.size()
+                             << " but got " << rev.size() << " gradients.");
+        }
+        for (size_t i = 0; i < ad_args.size(); ++i) {
+          auto& ad_arg = ad_args[i]->get<ADTensor>();
+          ad_arg.reverse = LiftedAdd(ad_arg.forward->checked_type(), ad_arg.reverse, rev[i], ll);
+        }
+      });
+      return ret;
+    });
+  }
+
+  ADValue VisitExpr_(const TupleGetItemNode* op) final {
+    Expr e = GetRef<Expr>(op);
+    ADValue tup = VisitExpr(op->tuple);
+    auto tt = op->tuple->checked_type().as<TupleTypeNode>();
+    size_t idx = op->index;
+    auto ret = std::make_shared<ADTensor>(ll, e, diag_ctx);
+    backprop_actions.push_back([tup, tt, idx, ret](LetList* ll) {
+      auto& ad_tup = tup->get<ADTensor>();
+      std::vector<Expr> updated_grads;
+      for (size_t i = 0; i < tt->fields.size(); ++i) {
+        Expr grad_pre = GetField(ad_tup.reverse, i);
+        updated_grads.push_back(i != idx ? grad_pre
+                                         : LiftedAdd(tt->fields[i], grad_pre, ret->reverse, ll));
+      }
+      ad_tup.reverse = ll->Push(Tuple(updated_grads));
+    });
+    return ret;
+  }
+
+  ADValue VisitExpr_(const TupleNode* op) final {
+    Expr e = GetRef<Expr>(op);
+    std::vector<ADValue> fields;
+    for (const auto& f : op->fields) {
+      fields.push_back(VisitExpr(f));
+    }
+    auto tt = op->checked_type().as<TupleTypeNode>();
+    auto ret = std::make_shared<ADTensor>(ll, e, diag_ctx);
+    backprop_actions.push_back([fields, tt, ret](LetList* ll) {
+      for (size_t i = 0; i < fields.size(); ++i) {
+        auto& ad_field = fields[i]->get<ADTensor>();
+        ad_field.reverse =
+            LiftedAdd(tt->fields[i], ad_field.reverse, GetField(ret->reverse, i), ll);
+      }
+    });
+    return ret;
+  }
+
+  ADValue VisitExpr_(const ConstantNode* op) final {
+    Expr e = GetRef<Expr>(op);
+    return std::make_shared<ADTensor>(ll, e, diag_ctx);
+  }
+
+  ADValue VisitExpr_(const CallNode* op) final {
+    ADValue f = VisitExpr(op->op);
+    std::vector<ADValue> args;
+    for (const auto& arg : op->args) {
+      args.push_back(VisitExpr(arg));
+    }
+    return f->get<ADFunction>().func(args, GetRef<Call>(op));
+  }
+
+  ADValue VisitExpr_(const FunctionNode* op) final {
+    Function f = GetRef<Function>(op);
+    // todo: assert no closure
+    return std::make_shared<ADFunction>(
+        [this, f](const std::vector<ADValue>& ad_args, const Call& orig) {
+          ICHECK_EQ(f->params.size(), ad_args.size());
+          for (size_t i = 0; i < f->params.size(); ++i) {
+            env[f->params[i]] = ad_args[i];
+          }
+          return VisitExpr(f->body);
+        });
+  }
+
+  // Var will always be in env, handled in VisitExpr (without _), so we don't need
+  // to implement its VisitExpr_.
+};
+
+namespace transform {
+
+Pass FirstOrderGradient() {
+  runtime::TypedPackedFunc<IRModule(IRModule, PassContext)> f = [](IRModule mod, PassContext ctx) {
+    CheckFeature(
+        mod, FeatureSet({fVar, fConstant, fTuple, fTupleGetItem, fFunction, fOp, fCall, fGraph}));
+    IRModule ad_mod = GetRef<IRModule>(mod.CopyOnWrite());
+    DiagnosticContext diag_ctx = DiagnosticContext::Default(ad_mod);
+
+    if (mod->functions.size() > 1) {
+      LOG(WARNING) << "IRModule contains multiple global functions: first-order AD will transform "
+                      "them indepedently!";
+    }
+
+    for (const auto& pr : mod->functions) {
+      const FunctionNode* func = pr.second.as<FunctionNode>();
+      if (!func) {
+        diag_ctx.Emit(Diagnostic::Warning(pr.second->span)
+                      << "AD can only be performed on Relay functions, skipping "
+                      << PrettyPrint(pr.first));
+      }
+      if (func->type_params.size() > 0) {
+        diag_ctx.EmitFatal(Diagnostic::Error(pr.second->span)
+                           << "first-order AD does not support polymorphism yet.");
+      }
+      Expr body = LetList::With([&](LetList* ll) {
+        FirstOrderReverseAD reverse_ad(ll, diag_ctx);
+        ADValue rev = reverse_ad(pr.second);
+        std::vector<ADValue> args;
+        for (const auto& p : func->params) {
+          args.push_back(std::make_shared<ADTensor>(ll, p, diag_ctx));
+        }
+        Call placeholder = Call(GetRef<Function>(func), {});
+        placeholder->checked_type_ = func->checked_type().as<FuncTypeNode>()->ret_type;
+        auto grad_call = rev->get<ADFunction>().func(args, placeholder);
+        auto& res = grad_call->get<ADTensor>();
+        Expr grad_tuple = LetList::With([&](LetList* ll) {
+          res.reverse =
+              MultiFactoryLike(res.forward, res.forward->checked_type(), Ones, OnesLike, diag_ctx);
+          for (auto it = reverse_ad.backprop_actions.rbegin();
+               it != reverse_ad.backprop_actions.rend(); ++it) {
+            (*it)(ll);
+          }
+          std::vector<Expr> grads;
+          for (const auto& a : args) {
+            grads.push_back(a->get<ADTensor>().reverse);
+          }
+          return Tuple(grads);
+        });
+        return Pair(res.forward, grad_tuple);
+      });
+      ad_mod->Update(pr.first,
+                     Function(func->params, body, GradRetType(GetRef<Function>(func)), {}));
+    }
+
+    return ad_mod;
+  };
+  return CreateModulePass(f, 0, "FirstOrderGradient", {});
+}
+
+TVM_REGISTER_GLOBAL("relay._transform.FirstOrderGradient").set_body_typed(FirstOrderGradient);
+
+}  // namespace transform
+
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/transforms/fold_constant.cc b/src/relay/transforms/fold_constant.cc
index 48af31f9a11f..9416b0ec4580 100644
--- a/src/relay/transforms/fold_constant.cc
+++ b/src/relay/transforms/fold_constant.cc
@@ -82,29 +82,39 @@ class ConstantFolder : public MixedModeMutator {
         device_copy_op_(Op::Get("device_copy")),
         shape_of_op_(Op::Get("shape_of")),
         vm_shape_of_op_(Op::Get("vm.shape_of")),
-        invoke_tvm_op_(Op::Get("vm.invoke_tvm_op")),
-        shape_func_op_(Op::Get("vm.shape_func")),
-        alloc_tensor_op_(Op::Get("memory.alloc_tensor")),
-        alloc_storage_op_(Op::Get("memory.alloc_storage")),
         cast_op_(Op::Get("cast")),
         ndarray_size_op_(Op::Get("ndarray_size")) {}
 
   using MixedModeMutator::VisitExpr_;
 
   Expr VisitExpr_(const LetNode* op) final {
-    Expr value = this->Mutate(op->value);
-    if (value.as<ConstantNode>()) {
-      memo_[op->var] = value;
-      return this->Mutate(op->body);
-    } else {
-      Var var = Downcast<Var>(this->Mutate(op->var));
-      Expr body = this->Mutate(op->body);
-      if (var.same_as(op->var) && value.same_as(op->value) && body.same_as(op->body)) {
-        return GetRef<Expr>(op);
+    auto pre_visit = [this](const LetNode* op) {
+      // Rely on the Memoizer to cache pre-visit values
+      Expr value = this->Mutate(op->value);
+      if (value.as<ConstantNode>()) {
+        this->memo_[op->var] = value;
       } else {
-        return Let(var, value, body);
+        this->Mutate(op->var);
       }
-    }
+    };
+    auto post_visit = [this](const LetNode* op) {
+      Expr expr = GetRef<Expr>(op);
+      // Rely on the Memoizer to cache pre-visit values
+      Expr value = this->Mutate(op->value);
+      if (value.as<ConstantNode>()) {
+        this->memo_[expr] = this->Mutate(op->body);
+      } else {
+        Var var = Downcast<Var>(this->Mutate(op->var));
+        Expr body = this->Mutate(op->body);
+        if (var.same_as(op->var) && value.same_as(op->value) && body.same_as(op->body)) {
+          this->memo_[expr] = expr;
+        } else {
+          this->memo_[expr] = Let(var, value, body);
+        }
+      }
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(op)];
   }
 
   bool inside_primitive = false;
@@ -120,14 +130,24 @@ class ConstantFolder : public MixedModeMutator {
     }
   }
 
+  Expr VisitExpr_(const IfNode* op) final {
+    auto new_cond = ExprMutator::VisitExpr(op->cond);
+    if (auto const_cond = new_cond.as<ConstantNode>()) {
+      if (reinterpret_cast<uint8_t*>(const_cond->data->data)[0]) {
+        return ExprMutator::VisitExpr(op->true_branch);
+      } else {
+        return ExprMutator::VisitExpr(op->false_branch);
+      }
+    }
+    return ExprMutator::VisitExpr_(op);
+  }
+
   Expr Rewrite_(const CallNode* call, const Expr& post) final {
     if (inside_primitive) {
       return GetRef<Expr>(call);
     }
     static auto op_stateful = Op::GetAttrMap<TOpIsStateful>("TOpIsStateful");
 
-    std::unordered_set<std::string> skip_list{"zeros_like", "ones_like", "full_like", "full"};
-
     auto origin_args = call->args;
     call = post.as<CallNode>();
     // We don't constant fold function with zero arguments.
@@ -136,9 +156,6 @@ class ConstantFolder : public MixedModeMutator {
     if (call->args.size() == 0) return post;
     const OpNode* op = call->op.as<OpNode>();
     if (op == nullptr) return post;
-    if (skip_list.count(op->name)) {
-      return post;
-    }
     // skip stateful ops.
     if (op_stateful.get(GetRef<Op>(op), false)) return post;
     // Try to evaluate shape_of op
@@ -191,10 +208,6 @@ class ConstantFolder : public MixedModeMutator {
   const Op& device_copy_op_;
   const Op& shape_of_op_;
   const Op& vm_shape_of_op_;
-  const Op& invoke_tvm_op_;
-  const Op& shape_func_op_;
-  const Op& alloc_tensor_op_;
-  const Op& alloc_storage_op_;
   const Op& cast_op_;
   const Op& ndarray_size_op_;
 
@@ -361,6 +374,8 @@ Expr FoldConstant(const Expr& expr, const IRModule& mod) {
   return ConstantFolder(mod).Mutate(expr);
 }
 
+TVM_REGISTER_GLOBAL("relay._transform.FoldConstantExpr").set_body_typed(FoldConstant);
+
 namespace transform {
 
 Pass FoldConstant() {
diff --git a/src/relay/transforms/fold_explicit_padding.cc b/src/relay/transforms/fold_explicit_padding.cc
new file mode 100644
index 000000000000..d959e5b75e40
--- /dev/null
+++ b/src/relay/transforms/fold_explicit_padding.cc
@@ -0,0 +1,207 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/transforms/fold_explicit_padding.cc
+ * \brief A pass for folding explicit pads into other ops.
+ */
+
+#include <tvm/relay/dataflow_matcher.h>
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/transform.h>
+#include <tvm/runtime/logging.h>
+
+#include "../op/tensor/transform.h"
+#include "pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+
+/*!
+ * \brief SimplifyConvPad matches a pad followed by a conv/convtranspose/pool/etc
+ * with a pad attribute and merges the padding into the kernel.
+ */
+class SimplifyConvPad {
+ public:
+  DFPattern pattern() const { return pattern_; }
+
+  SimplifyConvPad() {
+    x_ = IsWildcard();
+    w_ = IsWildcard();
+    pad_ = IsOp("nn.pad")({x_});
+    conv1d_ = IsOp("nn.conv1d");
+    conv2d_ = IsOp("nn.conv2d");
+    conv3d_ = IsOp("nn.conv3d");
+    conv_ = (conv1d_ || conv2d_ || conv3d_)({pad_, w_});
+    pattern_ = conv_;
+  }
+
+  template <typename T>
+  Attrs MakeConvAttrs(const T* old_attrs, const Array<PrimExpr> padding) const {
+    ICHECK(old_attrs);
+    ICHECK(padding.size() == old_attrs->padding.size())
+        << "Number of dimensions to pad and convolution padding attributes should have the same "
+           "extent";
+
+    auto new_attrs = make_object<T>();
+    Array<PrimExpr> combined_padding;
+    for (size_t i = 0; i < padding.size(); ++i) {
+      combined_padding.push_back(padding[i] + old_attrs->padding[i]);
+    }
+    new_attrs->strides = old_attrs->strides;
+    new_attrs->padding = combined_padding;
+    new_attrs->dilation = old_attrs->dilation;
+    new_attrs->groups = old_attrs->groups;
+    new_attrs->channels = old_attrs->channels;
+    new_attrs->kernel_size = old_attrs->kernel_size;
+    new_attrs->data_layout = old_attrs->data_layout;
+    new_attrs->kernel_layout = old_attrs->kernel_layout;
+    new_attrs->out_layout = old_attrs->out_layout;
+    new_attrs->out_dtype = old_attrs->out_dtype;
+    return Attrs(new_attrs);
+  }
+
+  template <typename T>
+  Attrs GetAttrs(const PadAttrs* param, const T* attrs) const {
+    ICHECK(param);
+    ICHECK(attrs);
+    ICHECK(attrs->data_layout.size() == param->pad_width.size())
+        << "Data Layout and padding attributes should have the same extent";
+
+    std::string data_layout = attrs->data_layout;
+    std::set<char> image_dims({'H', 'W', 'D'});
+    Array<PrimExpr> padding;
+    // If we're padding a non-spatial dimension, don't simplify
+    // Convolution can only pad on spatial axes
+    for (size_t i = 0; i < param->pad_width.size(); ++i) {
+      if (!image_dims.count(data_layout[i])) {
+        for (size_t j = 0; j < param->pad_width[i].size(); ++j) {
+          if (param->pad_width[i][j] != 0) {
+            return Attrs();
+          }
+        }
+      }
+    }
+    for (size_t j = 0; j < param->pad_width[0].size(); ++j) {
+      for (size_t i = 0; i < param->pad_width.size(); ++i) {
+        if (image_dims.count(data_layout[i])) {
+          padding.push_back(param->pad_width[i][j]);
+        }
+      }
+    }
+
+    return MakeConvAttrs(attrs, padding);
+  }
+
+  Expr callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const {
+    const CallNode* call_node = post.as<CallNode>();
+    ICHECK(call_node);
+    auto pad = node_map[pad_][0];
+    const CallNode* pad_node = pad.as<CallNode>();
+    ICHECK(pad_node);
+    const PadAttrs* param = pad_node->attrs.as<PadAttrs>();
+    ICHECK(param);
+    if (param->pad_mode == "constant" && param->pad_value == 0.0) {
+      Attrs attrs;
+      if (node_map.count(conv1d_)) {
+        attrs = GetAttrs(param, call_node->attrs.as<Conv1DAttrs>());
+      } else if (node_map.count(conv2d_)) {
+        attrs = GetAttrs(param, call_node->attrs.as<Conv2DAttrs>());
+      } else if (node_map.count(conv3d_)) {
+        attrs = GetAttrs(param, call_node->attrs.as<Conv3DAttrs>());
+      } else {
+        return post;
+      }
+      if (!attrs.defined()) {
+        return post;
+      }
+      auto x = node_map[x_][0];
+      auto w = node_map[w_][0];
+      return Call(call_node->op, {x, w}, attrs, call_node->type_args, call_node->span);
+    }
+    return post;
+  }
+
+ private:
+  /*! \brief Pattern for rewriting */
+  DFPattern pattern_;
+  /*! \brief Pattern input */
+  DFPattern x_;
+  /*! \brief Pattern input weight */
+  DFPattern w_;
+  /*! \brief Pattern pad */
+  DFPattern pad_;
+  /*! \brief Pattern conv */
+  DFPattern conv_;
+  DFPattern conv1d_;
+  DFPattern conv2d_;
+  DFPattern conv3d_;
+};
+
+class SimplifyExplicitPadding {
+ public:
+  explicit SimplifyExplicitPadding(IRModule mod) : mod_(mod) {
+    CreateCallback(SimplifyConvPad());
+    // TODO(mbrookhart): ConvTranspose(Pad(x)), Pool(Pad(x))
+  }
+  template <typename T>
+  void CreateCallback(const T& pattern) {
+    auto func = [pattern](TVMArgs args, TVMRetValue* rv) {
+      Expr pre = args[0];
+      Expr post = args[1];
+      Map<DFPattern, Array<Expr>> node_map = args[2];
+      *rv = pattern.callback(pre, post, node_map);
+    };
+    callbacks_.push_back(DFPatternCallback(pattern.pattern(), PackedFunc(func), true));
+  }
+
+  Expr Simplify(const Expr& expr) { return RewritePatterns(callbacks_, expr, mod_); }
+
+ private:
+  IRModule mod_;
+  /*! \brief Callbacks for expr simplification */
+  Array<DFPatternCallback> callbacks_;
+};
+
+/*!
+ * \brief FoldExplicitPadding finds explict padding before an op that can
+ * support implicit padding and fuses them.
+ */
+Expr FoldExplicitPadding(const Expr& expr, const IRModule& mod) {
+  return SimplifyExplicitPadding(mod).Simplify(expr);
+}
+
+namespace transform {
+
+Pass FoldExplicitPadding() {
+  runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)> pass_func =
+      [=](Function f, IRModule m, PassContext pc) {
+        return Downcast<Function>(FoldExplicitPadding(f, m));
+      };
+  return CreateFunctionPass(pass_func, 0, " FoldExplicitPadding", {"InferType"});
+}
+
+TVM_REGISTER_GLOBAL("relay._transform.FoldExplicitPadding").set_body_typed(FoldExplicitPadding);
+
+}  // namespace transform
+
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/transforms/fuse_ops.cc b/src/relay/transforms/fuse_ops.cc
index 29f3bfa0a17e..eaef0b905079 100644
--- a/src/relay/transforms/fuse_ops.cc
+++ b/src/relay/transforms/fuse_ops.cc
@@ -241,7 +241,7 @@ class IndexedForwardGraph::Creator : private ExprVisitor {
     OpPatternKind op_pattern = kOpaque;
     if (const OpNode* opnode = call->op.as<OpNode>()) {
       auto op = GetRef<Op>(opnode);
-      if (IsDynamic(call->checked_type()) && IsDataDependant(call)) {
+      if (IsDynamic(call->checked_type()) && IsDataDependent(call)) {
         // output of a shape func can't be fed to a data-dependent shape func
         op_pattern = kOpaque;
       } else {
@@ -315,11 +315,20 @@ class IndexedForwardGraph::Creator : private ExprVisitor {
 
   void VisitExpr_(const LetNode* op) final {
     // do not fuse through let.
-    this->Update(op->var, nullptr, kOpaque);
-    this->Update(op->value, nullptr, kOpaque);
-    this->Update(op->body, nullptr, kOpaque);
-    ExprVisitor::VisitExpr_(op);
-    this->AddNode(op);
+    auto pre_visit = [this](const LetNode* op) {
+      // Rely on the Memoizer to cache pre-visit values
+      this->Update(op->var, nullptr, kOpaque);
+      this->Update(op->value, nullptr, kOpaque);
+      this->Update(op->body, nullptr, kOpaque);
+      this->VisitExpr(op->var);
+      this->VisitExpr(op->value);
+    };
+    auto post_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->body);
+      this->visit_counter_[op] += 1;
+      this->AddNode(op);
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
   }
 
   void VisitExpr_(const IfNode* op) final {
@@ -797,7 +806,7 @@ std::vector<GraphPartitioner::Group*> GraphPartitioner::Partition(
   return std::move(groups_);
 }
 
-class FuseMutator : private ExprMutator {
+class FuseMutator : private MixedModeMutator {
  public:
   // Run the transform
   Expr Transform(const Expr& body, int fuse_opt_level, size_t max_fuse_depth) {
@@ -814,6 +823,8 @@ class FuseMutator : private ExprMutator {
   }
 
  private:
+  using MixedModeMutator::VisitExpr_;
+
   /*! \brief Temporary information from each group. */
   struct GroupInfo {
    public:
@@ -853,7 +864,7 @@ class FuseMutator : private ExprMutator {
   }
 
   // Transform calls.
-  Expr VisitExpr_(const CallNode* call) {
+  Expr Rewrite_(const CallNode* call, const Expr& post) {
     if (call->op.as<OpNode>()) {
       static auto fnoncomputational = Op::GetAttrMap<TNonComputational>("TNonComputational");
 
@@ -886,7 +897,7 @@ class FuseMutator : private ExprMutator {
     }
   }
 
-  Expr VisitExpr_(const TupleNode* tuple) {
+  Expr Rewrite_(const TupleNode* tuple, const Expr& post) {
     auto* ret_group = gmap_.at(tuple)->FindRoot();
     if (ret_group->root_ref == tuple) {
       return ExprMutator::VisitExpr_(tuple);
@@ -896,7 +907,7 @@ class FuseMutator : private ExprMutator {
     return Tuple(new_fields);
   }
 
-  Expr VisitExpr_(const TupleGetItemNode* tuple_get) {
+  Expr Rewrite_(const TupleGetItemNode* tuple_get, const Expr& post) {
     auto* ret_group = gmap_.at(tuple_get)->FindRoot();
     auto new_tuple = GetNewArguments({tuple_get->tuple}, ret_group)[0];
     auto new_node = TupleGetItem(new_tuple, tuple_get->index);
@@ -913,6 +924,29 @@ class FuseMutator : private ExprMutator {
     return std::move(new_node);
   }
 
+  Expr VisitExpr_(const LetNode* op) final {
+    auto pre_visit = [this](const LetNode* op) {
+      // Rely on the Memoizer to cache pre-visit values
+      this->VisitExpr(op->var);
+      this->VisitExpr(op->value);
+    };
+    auto post_visit = [this](const LetNode* op) {
+      // Rely on the Memoizer to cache pre-visit values
+      Var var = Downcast<Var>(this->VisitExpr(op->var));
+      Expr value = this->VisitExpr(op->value);
+      // Visit body and cache the op
+      Expr body = this->VisitExpr(op->body);
+      auto expr = GetRef<Expr>(op);
+      if (var.same_as(op->var) && value.same_as(op->value) && body.same_as(op->body)) {
+        this->memo_[expr] = expr;
+      } else {
+        this->memo_[expr] = Let(var, value, body);
+      }
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(op)];
+  }
+
   Expr MakeNewFunction(GraphPartitioner::Group* group, Type ret_type, Expr body) {
     // If the function has no call, it is not a primitive function.
     struct HasCallVisitor : ExprVisitor {
diff --git a/src/relay/transforms/gradient.h b/src/relay/transforms/gradient.h
new file mode 100644
index 000000000000..2e6ffbcc7c9e
--- /dev/null
+++ b/src/relay/transforms/gradient.h
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file gradient.h
+ * \brief Utility functions for Automatic Differentiation in Relay.
+ */
+#ifndef TVM_RELAY_TRANSFORMS_GRADIENT_H_
+#define TVM_RELAY_TRANSFORMS_GRADIENT_H_
+
+#include <tvm/relay/expr.h>
+#include <tvm/relay/function.h>
+
+#include <vector>
+
+namespace tvm {
+namespace relay {
+
+inline Type GradRetType(const Function& f) {
+  // if type annotations are provided, we will construct a ret type;
+  // otherwise, leave it to be inferred
+  if (!f->ret_type.defined()) {
+    return Type();
+  }
+  std::vector<Type> vt;
+  for (const auto& p : f->params) {
+    if (!p->type_annotation.defined()) {
+      return Type();
+    }
+    vt.push_back(p->type_annotation);
+  }
+
+  return TupleType({f->ret_type, TupleType(vt)});
+}
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_TRANSFORMS_GRADIENT_H_
diff --git a/src/relay/transforms/gradient.cc b/src/relay/transforms/higher_order_gradient.cc
similarity index 64%
rename from src/relay/transforms/gradient.cc
rename to src/relay/transforms/higher_order_gradient.cc
index cd3a99655341..202275626d5d 100644
--- a/src/relay/transforms/gradient.cc
+++ b/src/relay/transforms/higher_order_gradient.cc
@@ -18,8 +18,8 @@
  */
 
 /*!
- * \file gradient.cc
- * \brief API for Automatic Differentiation for the Relay IR.
+ * \file higher_order_gradient.cc
+ * \brief Higher-order Automatic Differentiation in Relay IR, for non-graph programs.
  */
 #include <tvm/ir/type_functor.h>
 #include <tvm/relay/analysis.h>
@@ -28,6 +28,7 @@
 #include <tvm/relay/transform.h>
 #include <tvm/te/operation.h>
 
+#include "gradient.h"
 #include "let_list.h"
 #include "pass_utils.h"
 #include "pattern_utils.h"
@@ -64,13 +65,6 @@ using namespace tvm::runtime;
  * output. There are multiple implementation of AD in relay, with different characteristic. However,
  * they all transform the input expr according to WithGradientType.
  */
-Type WithGradientType(const Type&);
-
-/*! return an expression that represent differentiation of e (according to WithGradientType).
- *  This version only work on first order code without control flow.
- */
-Expr FirstOrderGradient(const Expr& e, const Optional<IRModule>& mod);
-
 Type WithGradientType(const Type& t) {
   // TODO(@M.K.): stricter checking
   auto ty = t.as<FuncTypeNode>();
@@ -94,268 +88,6 @@ Expr DeGlobal(const Optional<IRModule>& mod, const Expr& e) {
   }
 }
 
-/*! \brief A fragment of the program being built by the automatic differentation
- *  pass.
- */
-struct ADValueNode {
-  virtual ~ADValueNode() {}
-  template <typename T>
-  T& get() {
-    auto ret = dynamic_cast<T*>(this);
-    ICHECK(ret) << "cannot downcast";
-    return *ret;
-  }
-};
-
-template <typename F>
-Expr MultiFactory(const Type& t, F factory) {
-  if (auto* tt = t.as<TensorTypeNode>()) {
-    return factory(tt->shape, tt->dtype);
-  } else if (auto* tt = t.as<TupleTypeNode>()) {
-    std::vector<Expr> res;
-    for (size_t i = 0; i < tt->fields.size(); i++) {
-      res.push_back(MultiFactory(tt->fields[i], factory));
-    }
-    return Tuple(res);
-  } else {
-    LOG(FATAL) << "unsupported type to create tensors of: " << tt;
-    throw;
-  }
-}
-
-template <typename F, typename F2>
-Expr MultiFactoryLike(const Expr& e, const Type& t, F factory, F2 factory_like) {
-  if (t.as<TensorTypeNode>()) {
-    return factory_like(e);
-  } else if (auto* tt = t.as<TupleTypeNode>()) {
-    return MultiFactory(t, factory);
-  } else {
-    LOG(FATAL) << "unsupported type to tensors of: " << tt;
-    throw;
-  }
-}
-
-using ADValue = std::shared_ptr<ADValueNode>;
-
-/*! \brief AD over a program which generates a tensor output. */
-struct ADTensor : ADValueNode {
-  Expr forward;
-  mutable Expr reverse;  // must be a variable to avoid duplication
-  ADTensor(LetList* ll, const Expr& forward)
-      : forward(ll->Push(forward)),
-        reverse(
-            ll->Push(MultiFactoryLike(this->forward, forward->checked_type(), Zeros, ZerosLike))) {
-    this->forward->checked_type_ = forward->checked_type();
-  }
-};
-
-/*! \brief A staged representation of the program, we reflect
- * Relay functions into a function over fragments of AD. We
- * can compute away this function to obtain a reverse mode program.
- */
-struct ADFunction : ADValueNode {
-  std::function<ADValue(const Type&, const std::vector<ADValue>&, const Attrs&,
-                        const tvm::Array<Type>&)>
-      func;
-  explicit ADFunction(const std::function<ADValue(const Type&, const std::vector<ADValue>&,
-                                                  const Attrs&, const tvm::Array<Type>&)>& func)
-      : func(func) {}
-};
-
-struct FirstOrderReverseAD : ExprFunctor<ADValue(const Expr&)> {
-  using TBase = ExprFunctor<ADValue(const Expr&)>;
-  const OpAttrMap<FPrimalGradient> rev_map = Op::GetAttrMap<FPrimalGradient>("FPrimalGradient");
-  std::vector<std::function<void(LetList* ll)>> backprop_actions;
-  // we assume no closure so no need for lexical scoping
-  std::unordered_map<Expr, ADValue, ObjectPtrHash, ObjectPtrEqual> env;
-  LetList* ll;
-
-  FirstOrderReverseAD(LetList* ll) : ll(ll) {}
-
-  ADValue VisitExpr(const Expr& n) final {
-    if (env.count(n)) {
-      return env.at(n);
-    }
-    auto ret = TBase::VisitExpr(n);
-    env[n] = ret;
-    return ret;
-  }
-
-  Expr UpdateGrad(const Type& t, const Expr& arg, const Expr& grad, LetList* ll) {
-    if (t.as<TensorTypeNode>()) {
-      return ll->Push(Add(arg, grad));
-    } else if (auto* tt = t.as<TupleTypeNode>()) {
-      Array<Expr> updates;
-      for (size_t i = 0; i < tt->fields.size(); ++i) {
-        updates.push_back(this->UpdateGrad(tt->fields[i], ll->Push(GetField(arg, i)),
-                                           ll->Push(GetField(grad, i)), ll));
-      }
-      return ll->Push(Tuple(updates));
-    } else {
-      LOG(FATAL) << "unsupported arg type of operator: " << t;
-      throw;
-    }
-  }
-
-  ADValue VisitExpr_(const OpNode* op) final {
-    Op op_ref = GetRef<Op>(op);
-    ICHECK(rev_map.count(op_ref)) << op->name << " does not have reverse mode defined";
-    return std::make_shared<ADFunction>(
-        [this, op_ref](const Type& orig_type, const std::vector<ADValue>& args, const Attrs& attrs,
-                       const tvm::Array<Type>& type_args) {
-          std::vector<Expr> call_args;
-          for (const ADValue& adval : args) {
-            call_args.push_back(adval->get<ADTensor>().forward);
-          }
-          auto orig = Call(op_ref, call_args, attrs, type_args);
-          orig->checked_type_ = orig_type;
-          auto ret = std::make_shared<ADTensor>(ll, orig);
-          backprop_actions.push_back([this, args, orig, ret, op_ref](LetList* ll) {
-            tvm::Array<Expr> rev = rev_map[op_ref](orig, ret->reverse);
-            ICHECK(args.size() == rev.size());
-            for (size_t i = 0; i < args.size(); ++i) {
-              auto ad_arg = args[i]->get<ADTensor>();
-              auto ad_arg_type = ad_arg.forward->checked_type();
-              args[i]->get<ADTensor>().reverse =
-                  this->UpdateGrad(ad_arg_type, ad_arg.reverse, rev[i], ll);
-            }
-          });
-          return ret;
-        });
-  }
-
-  ADValue VisitExpr_(const TupleGetItemNode* op) final {
-    Expr e = GetRef<Expr>(op);
-    ADValue tup = VisitExpr(op->tuple);
-    auto tt = op->tuple->checked_type().as<TupleTypeNode>();
-    size_t size = tt->fields.size();
-    size_t idx = op->index;
-    auto ret = std::make_shared<ADTensor>(ll, e);
-    backprop_actions.push_back([tup, idx, size, ret](LetList* ll) {
-      auto rev = tup->get<ADTensor>().reverse;
-      // special-case Tuple, to avoid long chains of GetItem/Tuple,
-      // but we might have functions using tuples, so we don't know
-      // that the reverse node is always a tuple
-      std::vector<Expr> grfields;
-      if (auto tup_node = rev.as<TupleNode>()) {
-        for (size_t i = 0; i < size; ++i) {
-          grfields.push_back(i != idx ? tup_node->fields[i]
-                                      : Add(tup_node->fields[i], ret->reverse));
-        }
-      } else {
-        for (size_t i = 0; i < size; ++i) {
-          grfields.push_back(i != idx ? TupleGetItem(rev, i)
-                                      : Add(TupleGetItem(rev, i), ret->reverse));
-        }
-      }
-      tup->get<ADTensor>().reverse = ll->Push(Tuple(grfields));
-    });
-    return ret;
-  }
-
-  ADValue VisitExpr_(const TupleNode* op) final {
-    Expr e = GetRef<Expr>(op);
-    std::vector<ADValue> fields;
-    for (const auto& f : op->fields) {
-      fields.push_back(VisitExpr(f));
-    }
-    auto ret = std::make_shared<ADTensor>(ll, e);
-    backprop_actions.push_back([fields, ret](LetList* ll) {
-      for (size_t i = 0; i < fields.size(); ++i) {
-        fields[i]->get<ADTensor>().reverse =
-            ll->Push(Add(fields[i]->get<ADTensor>().reverse, TupleGetItem(ret->reverse, i)));
-      }
-    });
-    return ret;
-  }
-
-  ADValue VisitExpr_(const ConstantNode* op) final {
-    Expr e = GetRef<Expr>(op);
-    return std::make_shared<ADTensor>(ll, e);
-  }
-
-  ADValue VisitExpr_(const CallNode* op) final {
-    ADValue f = VisitExpr(op->op);
-    std::vector<ADValue> args;
-    for (const auto& arg : op->args) {
-      args.push_back(VisitExpr(arg));
-    }
-    return f->get<ADFunction>().func(op->checked_type(), args, op->attrs, op->type_args);
-  }
-
-  ADValue VisitExpr_(const FunctionNode* op) final {
-    Function f = GetRef<Function>(op);
-    // todo: assert no closure
-    return std::make_shared<ADFunction>(
-        [this, f](const Type& orig_type, const std::vector<ADValue>& args, const Attrs& attrs,
-                  const tvm::Array<Type>& type_args) {
-          ICHECK_EQ(f->params.size(), args.size());
-          for (size_t i = 0; i < f->params.size(); ++i) {
-            env[f->params[i]] = args[i];
-          }
-          return VisitExpr(f->body);
-        });
-  }
-
-  // Var will always be in env, handled in VisitExpr (without _), so we don't need
-  // to implement its VisitExpr_.
-};
-
-Type GradRetType(const Function& f) {
-  // if type annotations are provided, we will construct a ret type;
-  // otherwise, leave it to be inferred
-  if (!f->ret_type.defined()) {
-    return Type();
-  }
-  std::vector<Type> vt;
-  for (const auto& p : f->params) {
-    if (!p->type_annotation.defined()) {
-      return Type();
-    }
-    vt.push_back(p->type_annotation);
-  }
-
-  return TupleType({f->ret_type, TupleType(vt)});
-}
-
-Expr FirstOrderGradient(const Expr& re, const Optional<IRModule>& mod) {
-  // Currently we first remove any global functions for the first
-  // order case.
-  auto e = DeGlobal(mod, re);
-  auto f = e.as<FunctionNode>();
-  ICHECK(f) << "FOWithGradient expects its argument to be a function: " << f;
-  ICHECK(f->type_params.size() == 0) << "no polymorphism supported for now";
-
-  // We will then build a sequence of lets which implement reverse mode.
-  Expr body = LetList::With([&](LetList* ll) {
-    FirstOrderReverseAD reverse_ad(ll);
-    ADValue rev = reverse_ad(e);
-    std::vector<ADValue> args;
-    for (const auto& p : f->params) {
-      args.push_back(std::make_shared<ADTensor>(ll, p));
-    }
-    auto c = rev->get<ADFunction>().func(f->checked_type(), args, Attrs(), {});
-    const auto& res = c->get<ADTensor>();
-    Expr grad = LetList::With([&](LetList* ll) {
-      res.reverse = MultiFactoryLike(res.forward, res.forward->checked_type(), Ones, OnesLike);
-      for (auto it = reverse_ad.backprop_actions.rbegin(); it != reverse_ad.backprop_actions.rend();
-           ++it) {
-        (*it)(ll);
-      }
-      std::vector<Expr> grad_res;
-      for (const auto& a : args) {
-        grad_res.push_back(a->get<ADTensor>().reverse);
-      }
-      return Tuple(grad_res);
-    });
-    return Pair(res.forward, grad);
-  });
-
-  return Function(f->params, body, GradRetType(GetRef<Function>(f)), {});
-}
-
-TVM_REGISTER_GLOBAL("relay._transform.first_order_gradient").set_body_typed(FirstOrderGradient);
-
 static Type bpt = RelayRefType(FuncType({}, TupleType(Array<Type>()), {}, {}));
 
 struct ReverseADType : TypeMutator {
diff --git a/src/relay/transforms/inline.cc b/src/relay/transforms/inline.cc
index dae34674de77..6e6505b28dc6 100644
--- a/src/relay/transforms/inline.cc
+++ b/src/relay/transforms/inline.cc
@@ -36,7 +36,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <string>
 #include <unordered_set>
diff --git a/src/relay/transforms/memory_alloc.cc b/src/relay/transforms/memory_alloc.cc
new file mode 100644
index 000000000000..f75b7ba1fc75
--- /dev/null
+++ b/src/relay/transforms/memory_alloc.cc
@@ -0,0 +1,467 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/relay/transforms/memory_alloc.cc
+ * \brief A pass for manifesting explicit memory allocations.
+ */
+
+#include <tvm/node/structural_equal.h>
+#include <tvm/node/structural_hash.h>
+#include <tvm/relay/analysis.h>
+#include <tvm/relay/attrs/device_copy.h>
+#include <tvm/relay/attrs/memory.h>
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/op.h>
+#include <tvm/relay/transform.h>
+#include <tvm/runtime/logging.h>
+#include <tvm/target/target.h>
+
+#include <cstdint>
+#include <cstdio>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+#include "../backend/compile_engine.h"
+#include "../op/memory/memory.h"
+#include "../op/vm/vm.h"
+#include "let_list.h"
+#include "pattern_utils.h"
+
+using namespace tvm::runtime;
+
+namespace tvm {
+namespace relay {
+
+using AnalysisResultMap =
+    std::unordered_map<Expr, TVMContext, runtime::ObjectPtrHash, runtime::ObjectPtrEqual>;
+
+inline Constant MakeConstant(const std::vector<int64_t>& value) {
+  return MakeConstantTensor(DataType::Int(64), {static_cast<int64_t>(value.size())}, value);
+}
+
+inline Expr AllocTensor(const Expr& storage, tvm::relay::Expr shape, DataType dtype,
+                        Array<IndexExpr> assert_shape) {
+  auto offset = MakeConstantScalar(DataType::Int(64), 0);
+  return AllocTensor(storage, offset, shape, dtype, assert_shape);
+}
+
+// A pass to check if the fused op contains only reshape ops.
+class CheckReshapeOnly : public ExprVisitor {
+ public:
+  CheckReshapeOnly()
+      : reshape_(Op::Get("reshape")),
+        contr_reshape_(Op::Get("contrib_reverse_reshape")),
+        dyn_reshape_(Op::Get("dyn.reshape")) {}
+
+  void VisitExpr_(const CallNode* cn) final {
+    if (!reshape_only) return;
+    if (cn->op != reshape_ && cn->op != contr_reshape_ && cn->op != dyn_reshape_) {
+      reshape_only = false;
+    }
+    for (auto arg : cn->args) ExprVisitor::VisitExpr(arg);
+  }
+
+  void VisitExpr_(const VarNode* vn) final {
+    if (!vn->checked_type_->IsInstance<TensorTypeNode>()) {
+      reshape_only = false;
+    }
+  }
+
+  const Op& reshape_;
+  const Op& contr_reshape_;
+  const Op& dyn_reshape_;
+  bool reshape_only{true};
+};
+
+// Check if the primitive function contains only reshape ops.
+bool IsReshapeOnly(const Expr& expr) {
+  auto check = CheckReshapeOnly();
+  check.VisitExpr(expr);
+  return check.reshape_only;
+}
+
+class DialectRewriter : public ExprMutator {
+ public:
+  DialectRewriter(const Target& target_host, const AnalysisResultMap& context_analysis_map)
+      : target_host_(target_host), context_analysis_map_(context_analysis_map) {}
+
+  // Get the context of an expression.
+  TVMContext GetContext(const Expr& expr) const {
+    auto it = context_analysis_map_.find(expr);
+    CHECK(it != context_analysis_map_.end()) << "Cannot find expr in the context analysis map:\n"
+                                             << AsText(expr, false);
+    return it->second;
+  }
+
+  Function Rewrite(const Function& expr) {
+    auto ret = ExprMutator::Mutate(expr);
+    return Downcast<Function>(ret);
+  }
+
+  Expr VisitExpr_(const TupleNode* tn) final {
+    LetList& scope = scopes_.back();
+    Array<Expr> new_fields;
+    for (auto field : tn->fields) {
+      auto new_field = ExprMutator::Mutate(field);
+      if (new_field->IsInstance<ConstantNode>()) {
+        Var const_var("const", Type(nullptr));
+        new_field = scope.Push(const_var, new_field);
+      }
+      new_fields.push_back(new_field);
+    }
+    return Tuple(new_fields);
+  }
+
+  Expr VisitExpr_(const LetNode* ln) final {
+    scopes_.emplace_back();
+
+    const LetNode* let = ln;
+    Expr body;
+    while (let) {
+      auto new_value = ExprMutator::Mutate(let->value);
+      scopes_.back().Push(let->var, new_value);
+      body = let->body;
+      let = body.as<LetNode>();
+    }
+
+    CHECK(body.defined());
+    auto new_body = ExprMutator::Mutate(body);
+    auto ret = scopes_.back().Get(new_body);
+    scopes_.pop_back();
+    return ret;
+  }
+
+  Expr VisitExpr_(const CallNode* cn) final {
+    if (IsPrimitive(cn)) {
+      // Because we are in ANF we do not need to visit the arguments.
+      LetList& scope = scopes_.back();
+      std::vector<Expr> new_args;
+      for (const auto& it : cn->args) {
+        new_args.push_back(ExprMutator::Mutate(it));
+      }
+
+      Tuple ins(new_args);
+      Type ret_type = cn->checked_type_;
+      std::vector<TensorType> out_types = FlattenTupleType(ret_type);
+
+      // Handle fused op that only contains reshape op
+      if (IsReshapeOnly(cn->op)) {
+        Function func = Downcast<Function>(cn->op);
+        return EmitReshapeTensor(&scope, func, new_args, ret_type);
+      }
+
+      // Handle device copy op
+      if (IsDeviceCopy(cn->op)) {
+        Attrs attr;
+        if (const auto* fn = cn->op.as<FunctionNode>()) {
+          const auto* copy_call = fn->body.as<CallNode>();
+          CHECK(copy_call);
+          attr = copy_call->attrs;
+        } else {
+          attr = cn->attrs;
+        }
+        const DeviceCopyAttrs* copy_attr = attr.as<DeviceCopyAttrs>();
+        CHECK(copy_attr);
+        return DeviceCopy(new_args[0], copy_attr->src_dev_type, copy_attr->dst_dev_type);
+      } else if (IsDynamic(ret_type)) {
+        Function func = Downcast<Function>(cn->op);
+        return DynamicInvoke(&scope, func, ins, new_args, out_types, ret_type);
+      } else {
+        // Handle the static case
+        Array<Expr> outs;
+        for (size_t i = 0; i < out_types.size(); ++i) {
+          TVMContext ctx = GetContext(GetRef<Call>(cn));
+          auto out = MakeStaticAllocation(&scope, out_types[i], ctx, std::to_string(i));
+          outs.push_back(out);
+        }
+        Tuple output(outs);
+        Expr invoke = InvokeTVMOp(cn->op, ins, output);
+        scope.Push(invoke);
+        return ToTupleType(ret_type,
+                           std::vector<Expr>(output->fields.begin(), output->fields.end()));
+      }
+    } else {
+      return ExprMutator::VisitExpr_(cn);
+    }
+  }
+
+ private:
+  // Insert a device copy node.
+  Expr DeviceCopy(const Expr& inp, int src_ctx, int dst_ctx) {
+    return ExprMutator::Mutate(relay::DeviceCopy(inp, src_ctx, dst_ctx));
+  }
+
+  // Check if a call invokes a primitive function.
+  bool IsPrimitive(const CallNode* call) const {
+    if (const auto* fn = call->op.as<FunctionNode>()) {
+      return fn->HasNonzeroAttr(attr::kPrimitive);
+    }
+    return false;
+  }
+
+  // Check if the current relay expression is a device copy call. We can simply
+  // check the body of it if it is a function because the device_copy op is opaque.
+  bool IsDeviceCopy(const Expr& expr) const {
+    if (const auto* fn = expr.as<FunctionNode>()) {
+      auto body = fn->body;
+      const CallNode* call = body.as<CallNode>();
+      return call && call->op == Op::Get("device_copy");
+    } else if (const CallNode* cn = expr.as<CallNode>()) {
+      return cn->op == Op::Get("device_copy");
+    } else {
+      return false;
+    }
+  }
+
+  Expr ComputeAlignment(const DataType& dtype) const {
+    int64_t align = dtype.bits() / 8 * dtype.lanes();
+    if (align < 64) {
+      align = 64;
+    }
+    return MakeConstantScalar(DataType::Int(64), align);
+  }
+
+  Expr ComputeStorageInRelay(const Expr& shape, const TensorType& type) const {
+    auto dtype = DataType(type->dtype);
+    Expr els = Prod(shape, Array<Integer>(nullptr), false, false);
+    Expr num = MakeConstantScalar(DataType::Int(64), dtype.bits() * dtype.lanes());
+    Expr add = Add(num, MakeConstantScalar(DataType::Int(64), 7));
+    Expr div = MakeConstantScalar(DataType::Int(64), 8);
+    Expr ret = Multiply(els, Divide(add, div));
+    return std::move(ret);
+  }
+
+  Expr ComputeStorage(const TensorType& type) {
+    int64_t size = 1;
+    for (auto it : type->shape) {
+      auto val = it.as<IntImmNode>();
+      CHECK(val);
+      size *= val->value;
+    }
+    size *= (type->dtype.bits() * type->dtype.lanes() + 7) / 8;
+    return std::move(MakeConstantScalar(DataType::Int(64), size));
+  }
+
+  // Allocate a tensor with a statically known shape.
+  Var MakeStaticAllocation(LetList* scope, const TensorType& type, TVMContext ctx,
+                           String name_hint) {
+    std::vector<int64_t> int_shape;
+    for (auto it : type->shape) {
+      const auto* imm = it.as<IntImmNode>();
+      CHECK(imm) << "expect static int shape";
+      int_shape.push_back(imm->value);
+    }
+    Expr shape = MakeConstant(int_shape);
+    Expr size = ComputeStorage(type);
+    Expr alignment = ComputeAlignment(type->dtype);
+    // Run type inference later to get the correct type.
+    Var var("storage_" + name_hint, Type(nullptr));
+    Expr value = AllocStorage(size, alignment, ctx, type->dtype);
+    auto sto = scope->Push(var, value);
+
+    // TODO(@jroesch): There is a bug with typing based on the constant shape.
+    auto tensor = AllocTensor(sto, shape, type->dtype, type->shape);
+    Var tensor_var("tensor_" + name_hint, Type(nullptr));
+    return scope->Push(tensor_var, tensor);
+  }
+
+  // Insert the shape function given a primitive function.
+  Array<Expr> EmitShapeFunc(LetList* scope, const Function& func,
+                            const std::vector<Expr>& new_args) {
+    Array<Expr> shape_func_ins;
+    auto engine = CompileEngine::Global();
+    CCacheKey key(func, target_host_);
+    auto cfunc = engine->LowerShapeFunc(key);
+    auto input_states = cfunc->shape_func_param_states;
+
+    Array<Integer> is_inputs;
+    int input_pos = 0;
+    TVMContext cpu_ctx = default_context_;
+    CHECK_EQ(new_args.size(), input_states.size());
+    for (size_t i = 0; i < new_args.size(); ++i) {
+      Expr arg = new_args[i];
+      Type ty;
+      if (const auto* vn = arg.as<VarNode>()) {
+        ty = vn->type_annotation;
+      } else {
+        ty = arg->checked_type();
+      }
+      int state = input_states[i]->value;
+      // Pass Shapes
+      if (state == 2) {
+        std::vector<Expr> exprs = FromTupleType(ty, arg);
+        for (size_t j = 0; j < exprs.size(); ++j) {
+          Expr sh_of = ExprMutator::Mutate(ShapeOf(exprs[j]));
+          Var in_shape_var("in_shape_" + std::to_string(input_pos + j), Type(nullptr));
+          shape_func_ins.push_back(scope->Push(in_shape_var, sh_of));
+          input_pos++;
+        }
+        is_inputs.push_back(0);
+      } else if (state == 1) {
+        auto new_arg = ExprMutator::Mutate(arg);
+        auto ctx = GetContext(arg);
+        if (ctx.device_type != cpu_ctx.device_type) {
+          new_arg = DeviceCopy(new_arg, ctx.device_type, cpu_ctx.device_type);
+        }
+        Var in_shape_var("in_shape_" + std::to_string(input_pos), Type(nullptr));
+        shape_func_ins.push_back(scope->Push(in_shape_var, new_arg));
+        input_pos++;
+        is_inputs.push_back(1);
+      } else {
+        // TODO(@jroesch): handle 3rd case
+        LOG(FATAL) << "unsupported shape function input state";
+      }
+    }
+
+    Array<Expr> out_shapes;
+    for (size_t i = 0; i < cfunc->outputs.size(); ++i) {
+      auto out = cfunc->outputs[i];
+      auto tt = TensorType(out->shape, out->dtype);
+      // Put shape func on CPU. This also ensures that everything between
+      // shape_of and shape_func are on CPU.
+      auto alloc = MakeStaticAllocation(scope, tt, cpu_ctx, std::to_string(i));
+      Var shape_func_out_var("shape_func_out_" + std::to_string(i), Type(nullptr));
+      alloc = scope->Push(shape_func_out_var, alloc);
+      out_shapes.push_back(alloc);
+    }
+    auto shape_call = ShapeFunc(func, Tuple(shape_func_ins), Tuple(out_shapes), is_inputs);
+    Var shape_func_var("shape_func", Type(nullptr));
+    scope->Push(shape_func_var, shape_call);
+    return out_shapes;
+  }
+
+  // Generate the code for invoking a TVM op with a dynamic shape.
+  Expr DynamicInvoke(LetList* scope, const Function& func, const Tuple& ins,
+                     const std::vector<Expr>& new_args, const std::vector<TensorType>& out_types,
+                     const Type& ret_type) {
+    auto out_shapes = EmitShapeFunc(scope, func, new_args);
+    std::vector<Var> storages;
+    auto func_ctx = GetContext(func);
+    CHECK_EQ(out_shapes.size(), out_types.size());
+    for (size_t i = 0; i < out_shapes.size(); ++i) {
+      auto out_shape = out_shapes[i];
+      auto out_type = out_types[i];
+      auto size = ComputeStorageInRelay(out_shape, out_type);
+      auto alignment = ComputeAlignment(out_type->dtype);
+      Var sto_var("storage_" + std::to_string(i), Type(nullptr));
+      auto val = AllocStorage(size, alignment, func_ctx, out_type->dtype);
+      storages.push_back(scope->Push(sto_var, val));
+    }
+
+    Array<Expr> outs;
+    for (size_t i = 0; i < storages.size(); ++i) {
+      auto out_shape = out_shapes[i];
+      auto out_type = out_types[i];
+      auto storage = storages[i];
+      auto alloc = AllocTensor(storage, out_shape, out_type->dtype, out_type->shape);
+      Var out_var("out_" + std::to_string(i), Type(nullptr));
+      outs.push_back(scope->Push(out_var, alloc));
+    }
+
+    Tuple tuple_outs(outs);
+    auto invoke = InvokeTVMOp(func, ins, tuple_outs);
+    scope->Push(invoke);
+    return ToTupleType(ret_type,
+                       std::vector<Expr>(tuple_outs->fields.begin(), tuple_outs->fields.end()));
+  }
+
+  Expr EmitReshapeTensor(LetList* scope, const Function& func, const std::vector<Expr>& new_args,
+                         const Type& ret_type) {
+    TensorType ret_ty = Downcast<TensorType>(ret_type);
+    Expr shape_expr;
+    if (IsDynamic(ret_type)) {
+      auto out_shapes = EmitShapeFunc(scope, func, new_args);
+      shape_expr = out_shapes[0];
+    } else {
+      std::vector<int64_t> shape;
+      for (const auto& it : ret_ty->shape) {
+        const auto* imm = it.as<IntImmNode>();
+        CHECK(imm) << "expect static int shape";
+        shape.push_back(imm->value);
+      }
+      shape_expr = MakeConstant(shape);
+    }
+    return ReshapeTensor(new_args[0], shape_expr, ret_ty->shape);
+  }
+
+ private:
+  Target target_host_;
+  AnalysisResultMap context_analysis_map_;
+  std::vector<LetList> scopes_;
+
+  runtime::DataType compute_dtype_ = runtime::DataType::Int(64);
+  TVMContext default_context_{kDLCPU, 0};
+};
+
+namespace transform {
+
+Pass ManifestAlloc(Target target_host, Map<tvm::Integer, tvm::Target> targets) {
+  return tvm::transform::CreateModulePass(
+      [=](IRModule mod, const PassContext& pass_ctx) {
+        DLOG(INFO) << "tvm::relay::transform::ManifestAlloc";
+        // We need to mutate module, therefore making a copy of it.
+        mod.CopyOnWrite();
+        mod->ImportFromStd("core.rly");
+        mod = relay::transform::InferType()(mod);
+
+        TVMContext fallback_ctx;
+        if (targets.size() > 1) {
+          auto pass_ctx = PassContext::Current();
+          Optional<Integer> opt_fallback_dev =
+              pass_ctx->GetConfig("relay.fallback_device_type", Integer(static_cast<int>(kDLCPU)));
+          auto fallback_dev = opt_fallback_dev.value();
+          CHECK_GT(fallback_dev->value, 0U);
+          fallback_ctx.device_type = static_cast<DLDeviceType>(fallback_dev->value);
+          fallback_ctx.device_id = 0;
+        } else {
+          const auto& it = targets.begin();
+          fallback_ctx.device_type = static_cast<DLDeviceType>((*it).first->value);
+          fallback_ctx.device_id = 0;
+        }
+        auto ca = ContextAnalysis(mod, fallback_ctx);
+
+        auto glob_funcs = mod->functions;
+        for (const auto& it : glob_funcs) {
+          if (auto* func_node = it.second.as<FunctionNode>()) {
+            auto func = GetRef<Function>(func_node);
+            auto rewriter = DialectRewriter(target_host, ca);
+            auto updated_func = rewriter.Rewrite(func);
+
+            mod->Update(it.first, updated_func);
+          }
+        }
+
+        mod = relay::transform::InferType()(mod);
+        return mod;
+      },
+      0, "ManifestAlloc", {});
+}
+
+TVM_REGISTER_GLOBAL("relay.transform.ManifestAlloc")
+    .set_body_typed([](Target target_host, Map<tvm::Integer, tvm::Target> targets) {
+      return ManifestAlloc(target_host, targets);
+    });
+
+}  // namespace transform
+
+}  // namespace relay
+}  // namespace tvm
diff --git a/src/relay/transforms/partial_eval.cc b/src/relay/transforms/partial_eval.cc
index fa080a7ff22c..3a87aa8ed498 100644
--- a/src/relay/transforms/partial_eval.cc
+++ b/src/relay/transforms/partial_eval.cc
@@ -861,8 +861,8 @@ class PartialEvaluator : public ExprFunctor<PStatic(const Expr& e, LetList* ll)>
     return VisitFunc(GetRef<Function>(op), ll);
   }
 
-  struct ReflectError : dmlc::Error {
-    ReflectError() : dmlc::Error("static value not found") {}
+  struct ReflectError : Error {
+    ReflectError() : Error("static value not found") {}
   };
 
   Expr Reflect(const PStatic& st) {
diff --git a/src/relay/transforms/partition_graph.cc b/src/relay/transforms/partition_graph.cc
index 7508d4437c18..404c7efb10b0 100644
--- a/src/relay/transforms/partition_graph.cc
+++ b/src/relay/transforms/partition_graph.cc
@@ -177,7 +177,7 @@ class Partitioner : public MixedModeMutator {
       AnnotatedRegion region = GetRegion(GetRef<Call>(call));
 
       // TODO(@manupa-arm) : need to use the parent function (to which region
-      // belongs to) name/key for the funtions that are created
+      // belongs to) name/key for the functions that are created
       BaseFunc f = GetFunc(GetRef<Call>(call));
 
       // Traverse subgraph inputs.
diff --git a/src/relay/transforms/pass_utils.h b/src/relay/transforms/pass_utils.h
index a2f22cbbf106..bb2f268a23d7 100644
--- a/src/relay/transforms/pass_utils.h
+++ b/src/relay/transforms/pass_utils.h
@@ -90,11 +90,11 @@ Expr TypeSubst(const Expr& expr, const tvm::Map<TypeVar, Type>& subst_map);
 bool IsDynamic(const Type& ty);
 
 /*!
- * \brief Check if call is data dependant.
+ * \brief Check if call is data dependent.
  * \param call The call to be checked.
- * \return Whether the call is data dependant.
+ * \return Whether the call is data dependent.
  */
-bool IsDataDependant(const CallNode* call);
+bool IsDataDependent(const CallNode* call);
 
 /*!
  * \brief Make arbitrary transformation preserve the out most function.
diff --git a/src/relay/transforms/pattern_utils.h b/src/relay/transforms/pattern_utils.h
index 8ef86e088193..c1eebde15fba 100644
--- a/src/relay/transforms/pattern_utils.h
+++ b/src/relay/transforms/pattern_utils.h
@@ -86,6 +86,9 @@ namespace relay {
   } else if (type == DataType::UInt(8)) {                                             \
     typedef uint8_t DType;                                                            \
     { __VA_ARGS__ }                                                                   \
+  } else if (type == DataType::Bool()) {                                              \
+    typedef bool DType;                                                               \
+    { __VA_ARGS__ }                                                                   \
   } else if ((*tvm::runtime::Registry::Get("runtime._datatype_get_type_registered"))( \
                  static_cast<uint8_t>(type.code()))) {                                \
     typedef double DType;                                                             \
@@ -644,6 +647,10 @@ static inline Expr Sum(Expr data, Array<Integer> axis, bool keepdims, bool exclu
   return MakeReduce(data, axis, keepdims, exclude, "sum");
 }
 
+static inline Expr Prod(Expr data, Array<Integer> axis, bool keepdims, bool exclude) {
+  return MakeReduce(data, axis, keepdims, exclude, "prod");
+}
+
 static inline Expr Reshape(Expr data, Array<Integer> newshape) {
   return MakeReshape(data, newshape);
 }
diff --git a/src/relay/transforms/simplify_expr.cc b/src/relay/transforms/simplify_expr.cc
index cb42ab09aae4..b4f4cc16e9df 100644
--- a/src/relay/transforms/simplify_expr.cc
+++ b/src/relay/transforms/simplify_expr.cc
@@ -26,30 +26,41 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "../op/tensor/transform.h"
+#include "pattern_utils.h"
 
 namespace tvm {
 namespace relay {
 
-static Op reshape_op = Op::Get("reshape");
-static Op reverse_reshape_op = Op::Get("contrib_reverse_reshape");
+class SimplifyPattern {
+ public:
+  virtual Expr callback(const Expr& pre, const Expr& post,
+                        const Map<DFPattern, Array<Expr>>& node_map) const = 0;
+
+  DFPattern pattern() const { return pattern_; }
+
+ protected:
+  /*! \brief Pattern for rewriting */
+  DFPattern pattern_;
+};
 
 /*!
  * \brief SimplifyReshape matches the pattern of consecutive reshape or reverse_reshape ops,
  *   and merges into one reshape op.
  */
-class SimplifyReshape {
+class SimplifyReshape : public SimplifyPattern {
  public:
   SimplifyReshape() {
-    x_ = WildcardPattern(make_object<WildcardPatternNode>());
-    auto reshape1 = AltPattern(ExprPattern(reshape_op), ExprPattern(reverse_reshape_op));
-    auto reshape2 = AltPattern(ExprPattern(reshape_op), ExprPattern(reverse_reshape_op));
-    pattern_ = CallPattern(reshape1, {CallPattern(reshape2, {x_})});
+    x_ = IsWildcard();
+    auto reshape1 = IsOp("reshape") || IsOp("contrib_reverse_reshape");
+    auto reshape2 = IsOp("reshape") || IsOp("contrib_reverse_reshape");
+    pattern_ = reshape1({reshape2({x_})});
   }
 
-  Expr callback(const Expr& pre, const Expr& post, const Map<DFPattern, Array<Expr>>& node_map) {
+  Expr callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const override {
     auto x = node_map[x_][0];
     bool const_shape = true;
     Array<Integer> newshape;
@@ -66,13 +77,175 @@ class SimplifyReshape {
     return post;
   }
 
-  DFPattern pattern() const { return pattern_; }
+ private:
+  /*! \brief Pattern input */
+  DFPattern x_;
+};
+
+/*!
+ * \brief SimplifyTranspose matches the pattern of consecutive transpose op,
+ *   and merges or cancels them.
+ */
+class SimplifyTranspose : public SimplifyPattern {
+ public:
+  SimplifyTranspose() {
+    x_ = IsWildcard();
+    auto trans1 = IsOp("transpose") || IsOp("layout_transform");
+    auto trans2 = IsOp("transpose") || IsOp("layout_transform");
+    pattern_ = trans1({trans2({x_})});
+  }
+
+  Expr callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const override {
+    // Helper function to get the axes from call node attribute
+    auto get_axes_from_call = [](const Call trans_call, int ndim) {
+      std::vector<int> attr_axes;
+      if (auto attr = trans_call->attrs.as<TransposeAttrs>()) {
+        if (attr->axes.defined()) {
+          for (int i = 0; i < ndim; ++i) {
+            int64_t axis = attr->axes[i];
+            axis += (axis < 0) ? ndim : 0;
+            attr_axes.push_back(axis);
+          }
+        } else {
+          // Empty axes means reverse
+          for (int i = ndim - 1; i >= 0; --i) {
+            attr_axes.push_back(i);
+          }
+        }
+      } else if (auto attr = trans_call->attrs.as<LayoutTransformAttrs>()) {
+        Layout src_layout(attr->src_layout);
+        Layout dst_layout(attr->dst_layout);
+        for (int i = 0; i < ndim; ++i) {
+          attr_axes.push_back(src_layout.IndexOf(dst_layout[i]));
+        }
+      } else {
+        CHECK(false) << "Expected transpose or layout_transform, but got "
+                     << Downcast<Op>(trans_call->op)->name;
+      }
+      return std::move(attr_axes);
+    };
+
+    auto x = node_map[x_][0];
+
+    // Initialize axes
+    int ndim = Downcast<TensorType>(pre->checked_type())->shape.size();
+    Array<Integer> axes;
+    for (int i = 0; i < ndim; ++i) {
+      axes.push_back(i);
+    }
+
+    // Collect axes changes from the matched pattern, including two consecutive transposes.
+    std::vector<std::vector<int>> interm_axes;
+    Call trans_call = Downcast<Call>(post);
+    interm_axes.push_back(get_axes_from_call(trans_call, ndim));
+    trans_call = Downcast<Call>(trans_call->args[0]);
+    interm_axes.push_back(get_axes_from_call(trans_call, ndim));
+
+    // Calculate the final axes in reverse order (from root to output)
+    auto it = interm_axes.rbegin();
+    while (it != interm_axes.rend()) {
+      auto interm = *it;
+
+      Array<Integer> new_axes;
+      for (int i = 0; i < ndim; ++i) {
+        new_axes.push_back(axes[interm[i]]);
+      }
+      axes = new_axes;
+      it++;
+    }
+
+    // Check if the transpose is still required
+    bool need_transpose = false;
+    for (int i = 0; i < ndim; ++i) {
+      if (axes[i] != i) {
+        need_transpose = true;
+        break;
+      }
+    }
+
+    if (need_transpose) {
+      return MakeTranspose(x, axes);
+    }
+    return x;
+  }
 
  private:
   /*! \brief Pattern input */
   DFPattern x_;
-  /*! \brief Pattern for consecutive reshape or reverse_reshape ops */
-  DFPattern pattern_;
+};
+
+/*!
+ * \brief FullArgwhere finds full followed by argwhere and turns it into an Arange op
+ */
+class FullElementwise : public SimplifyPattern {
+ public:
+  FullElementwise() {
+    x_ = IsWildcard();
+    data_ = IsWildcard();
+    value_ = IsConstant();
+
+    full_ = IsOp("full")({value_}) || IsOp("full_like")({data_, value_});
+    ones_ = IsOp("ones")({}) || IsOp("ones_like")({data_});
+    zeros_ = IsOp("zeros")({}) || IsOp("zeros_like")({data_});
+
+    Map<String, ObjectRef> attrs;
+    attrs.Set("TOpPattern", Integer(static_cast<int>(kBroadcast)));
+    DFPattern op = IsWildcard().HasAttr(attrs);
+    DFPattern full = full_ || ones_ || zeros_;
+    pattern_ = op({full, x_}) || op({x_, full});
+  }
+
+  Expr callback(const Expr& pre, const Expr& post,
+                const Map<DFPattern, Array<Expr>>& node_map) const override {
+    const CallNode* call = pre.as<CallNode>();
+    ICHECK(call);
+    Type pre_type = pre->checked_type_;
+    ICHECK(pre_type.as<TensorTypeNode>());
+    auto dtype = pre_type.as<TensorTypeNode>()->dtype;
+    auto x = node_map[x_][0];
+    bool is_left = post.as<CallNode>()->args[1] == x;
+    Type x_type;
+    if (is_left) {
+      x_type = call->args[1]->checked_type_;
+    } else {
+      x_type = call->args[0]->checked_type_;
+    }
+
+    if (StructuralEqual()(x_type, pre_type)) {
+      Expr value;
+      if (node_map.count(full_)) {
+        value = node_map[value_][0];
+        ICHECK(IsConstScalar(value));
+      } else if (node_map.count(ones_)) {
+        value = MakeConstantScalar(dtype, 1);
+      } else if (node_map.count(zeros_)) {
+        value = MakeConstantScalar(dtype, 0);
+      } else {
+        ICHECK(false) << "Didn't find a full op while matching full + elementwise";
+      }
+      if (is_left) {
+        return Call(call->op, {value, x}, call->attrs, call->type_args, call->span);
+      } else {
+        return Call(call->op, {x, value}, call->attrs, call->type_args, call->span);
+      }
+    }
+    return post;
+  }
+
+ private:
+  /*! \brief binary argument */
+  DFPattern x_;
+  /*! \brief data ops get shape from */
+  DFPattern data_;
+  /*! \brief constant input */
+  DFPattern value_;
+  /*! \brief full op */
+  DFPattern full_;
+  /*! \brief ones op */
+  DFPattern ones_;
+  /*! \brief zeros op */
+  DFPattern zeros_;
 };
 
 /*!
@@ -81,22 +254,25 @@ class SimplifyReshape {
 class ExprSimplifier {
  public:
   explicit ExprSimplifier(IRModule mod) : mod_(mod) {
-    auto reshape_func = [this](TVMArgs args, TVMRetValue* rv) {
+    CreateCallback(SimplifyReshape());
+    CreateCallback(SimplifyTranspose());
+    CreateCallback(FullElementwise());
+  }
+  template <typename T>
+  void CreateCallback(const T& pattern) {
+    auto func = [pattern](TVMArgs args, TVMRetValue* rv) {
       Expr pre = args[0];
       Expr post = args[1];
       Map<DFPattern, Array<Expr>> node_map = args[2];
-      *rv = simplify_reshape_.callback(pre, post, node_map);
+      *rv = pattern.callback(pre, post, node_map);
     };
-    callbacks_.push_back(
-        DFPatternCallback(simplify_reshape_.pattern(), PackedFunc(reshape_func), true));
+    callbacks_.push_back(DFPatternCallback(pattern.pattern(), PackedFunc(func), true));
   }
 
   Expr Simplify(const Expr& expr) { return RewritePatterns(callbacks_, expr, mod_); }
 
  private:
   IRModule mod_;
-  /*! \brief Simplify reshape pattern */
-  SimplifyReshape simplify_reshape_;
   /*! \brief Callbacks for expr simplification */
   Array<DFPatternCallback> callbacks_;
 };
diff --git a/src/relay/transforms/to_a_normal_form.cc b/src/relay/transforms/to_a_normal_form.cc
index 05844477cc5b..91e8d90c1232 100644
--- a/src/relay/transforms/to_a_normal_form.cc
+++ b/src/relay/transforms/to_a_normal_form.cc
@@ -26,7 +26,7 @@
 #include <tvm/relay/analysis.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "../../support/arena.h"
 #include "../analysis/dependency_graph.h"
diff --git a/src/relay/transforms/to_basic_block_normal_form.cc b/src/relay/transforms/to_basic_block_normal_form.cc
index 1aab367cf22a..79157bba1918 100644
--- a/src/relay/transforms/to_basic_block_normal_form.cc
+++ b/src/relay/transforms/to_basic_block_normal_form.cc
@@ -26,7 +26,7 @@
 #include <tvm/relay/analysis.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "../../support/arena.h"
 #include "../analysis/dependency_graph.h"
diff --git a/src/relay/transforms/type_infer.cc b/src/relay/transforms/type_infer.cc
index 327b5d1e260a..4c6013792426 100644
--- a/src/relay/transforms/type_infer.cc
+++ b/src/relay/transforms/type_infer.cc
@@ -162,10 +162,11 @@ class TypeInferencer : private ExprFunctor<Type(const Expr&)>,
 
   // Perform unification on two types and report the error at the expression
   // or the span of the expression.
-  Type Unify(const Type& t1, const Type& t2, const Span& span) {
+  Type Unify(const Type& t1, const Type& t2, const Span& span, bool assign_lhs = true,
+             bool assign_rhs = true) {
     try {
-      return solver_.Unify(t1, t2, span);
-    } catch (const dmlc::Error& e) {
+      return solver_.Unify(t1, t2, span, assign_lhs, assign_rhs);
+    } catch (const Error& e) {
       this->EmitFatal(Diagnostic::Error(span)
                       << "Error unifying `" << t1 << "` and `" << t2 << "`: " << e.what());
       return Type();
@@ -340,26 +341,34 @@ class TypeInferencer : private ExprFunctor<Type(const Expr&)>,
   Type VisitExpr_(const OpNode* op) final { return op->op_type; }
 
   Type VisitExpr_(const LetNode* let) final {
-    // if the definition is a function literal, permit recursion
-    bool is_functional_literal = let->value.as<FunctionNode>() != nullptr;
-    Type let_type = IncompleteType(Kind::kType);
-
-    if (is_functional_literal) {
-      let_type = GetType(let->var);
-      type_map_[let->var].checked_type = let_type;
-    }
+    auto pre_visit = [this](const LetNode* op) {
+      // if the definition is a function literal, permit recursion
+      bool is_functional_literal = op->value.as<FunctionNode>() != nullptr;
+      Type let_type = IncompleteType(Kind::kType);
+
+      if (is_functional_literal) {
+        let_type = this->GetType(op->var);
+        this->type_map_[op->var].checked_type = let_type;
+      }
 
-    if (let->var->type_annotation.defined()) {
-      let_type = Unify(let_type, let->var->type_annotation, let->span);
-    }
+      if (op->var->type_annotation.defined()) {
+        let_type = this->Unify(let_type, op->var->type_annotation, op->span);
+      }
 
-    Type vtype = GetType(let->value);
-    let_type = Unify(let_type, vtype, let->span);
+      Type vtype = this->GetType(op->value);
+      let_type = this->Unify(let_type, vtype, op->span);
 
-    ICHECK(is_functional_literal || !type_map_.count(let->var));
-    // NOTE: no scoping is necessary because var are unique in program
-    type_map_[let->var].checked_type = let_type;
-    return GetType(let->body);
+      ICHECK(is_functional_literal || !this->type_map_.count(op->var));
+      // NOTE: no scoping is necessary because var are unique in program
+      this->type_map_[op->var].checked_type = let_type;
+    };
+    auto post_visit = [this](const LetNode* op) {
+      Expr expr = GetRef<Expr>(op);
+      this->memo_[expr] = this->GetType(op->body);
+      this->type_map_[expr].checked_type = this->memo_[expr];
+    };
+    ExpandANormalForm(let, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(let)];
   }
 
   Type VisitExpr_(const IfNode* ite) final {
@@ -495,7 +504,7 @@ class TypeInferencer : private ExprFunctor<Type(const Expr&)>,
     }
 
     for (size_t i = 0; i < fn_ty->arg_types.size(); i++) {
-      this->Unify(fn_ty->arg_types[i], arg_types[i], call->span);
+      this->Unify(fn_ty->arg_types[i], arg_types[i], call->span, true, false);
     }
 
     for (auto cs : fn_ty->type_constraints) {
@@ -526,6 +535,7 @@ class TypeInferencer : private ExprFunctor<Type(const Expr&)>,
       }
     }
 
+    solver_.Solve();
     return GeneralCall(call, arg_types);
   }
 
@@ -572,9 +582,7 @@ class TypeInferencer : private ExprFunctor<Type(const Expr&)>,
     return FuncType(c->inputs, TypeCall(c->belong_to, types), td->type_vars, {});
   }
 
-  void Solve() {
-    solver_.Solve();
-  }
+  void Solve() { solver_.Solve(); }
 };
 
 class TypeInferencer::Resolver : public MixedModeMutator, PatternMutator {
@@ -603,7 +611,21 @@ class TypeInferencer::Resolver : public MixedModeMutator, PatternMutator {
 
   Expr Rewrite_(const CallNode* op, const Expr& post) final { return AttachCheckedType(op, post); }
 
-  Expr VisitExpr_(const LetNode* op) final { return AttachCheckedType(op); }
+  Expr VisitExpr_(const LetNode* op) final {
+    auto pre_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->var);
+      this->VisitExpr(op->value);
+    };
+    auto post_visit = [this](const LetNode* op) {
+      Expr expr = GetRef<Expr>(op);
+      Var var = Downcast<Var>(this->VisitExpr(op->var));
+      Expr value = this->VisitExpr(op->value);
+      Expr body = this->VisitExpr(op->body);
+      this->memo_[expr] = this->AttachCheckedType(op, Let(var, value, body));
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
+    return memo_[GetRef<Expr>(op)];
+  }
 
   Expr VisitExpr_(const IfNode* op) final { return AttachCheckedType(op); }
 
@@ -738,6 +760,7 @@ Expr TypeInferencer::Infer(GlobalVar var, Function function) {
 }
 
 struct AllCheckTypePopulated : MixedModeVisitor {
+  using MixedModeVisitor::VisitExpr_;
   void DispatchExprVisit(const Expr& e) {
     if (e.as<OpNode>()) {
       return;
@@ -751,6 +774,17 @@ struct AllCheckTypePopulated : MixedModeVisitor {
     ICHECK(e->checked_type_.defined()) << "Expression: " << e;
     return ExprVisitor::VisitExpr(e);
   }
+  void VisitExpr_(const LetNode* op) final {
+    auto pre_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->var);
+      this->VisitExpr(op->value);
+    };
+    auto post_visit = [this](const LetNode* op) {
+      this->VisitExpr(op->body);
+      this->visit_counter_[op] += 1;
+    };
+    ExpandANormalForm(op, pre_visit, post_visit);
+  }
 };
 
 void EnsureCheckedType(const Expr& e) { AllCheckTypePopulated().VisitExpr(e); }
diff --git a/src/runtime/c_runtime_api.cc b/src/runtime/c_runtime_api.cc
index 6ecc60a93dec..150d7f215da5 100644
--- a/src/runtime/c_runtime_api.cc
+++ b/src/runtime/c_runtime_api.cc
@@ -144,6 +144,50 @@ void* DeviceAPI::AllocWorkspace(TVMContext ctx, size_t size, DLDataType type_hin
   return AllocDataSpace(ctx, size, kTempAllocaAlignment, type_hint);
 }
 
+static size_t GetDataAlignment(const DLDataType dtype) {
+  size_t align = (dtype.bits / 8) * dtype.lanes;
+  if (align < kAllocAlignment) return kAllocAlignment;
+  return align;
+}
+
+void* DeviceAPI::AllocDataSpace(TVMContext ctx, int ndim, const int64_t* shape, DLDataType dtype,
+                                Optional<String> mem_scope) {
+  if (!mem_scope.defined() || mem_scope.value() == "global") {
+    // by default, we can always redirect to the flat memory allocations
+    DLTensor temp;
+    temp.data = nullptr;
+    temp.ctx = ctx;
+    temp.ndim = ndim;
+    temp.dtype = dtype;
+    temp.shape = const_cast<int64_t*>(shape);
+    temp.strides = nullptr;
+    temp.byte_offset = 0;
+    size_t size = GetDataSize(temp);
+    size_t alignment = GetDataAlignment(temp.dtype);
+    return AllocDataSpace(ctx, size, alignment, dtype);
+  }
+  LOG(FATAL) << "Device does not support allocate data space with "
+             << "specified memory scope: " << mem_scope.value();
+  return nullptr;
+}
+
+void DeviceAPI::CopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream) {
+  // by default, we can always redirect to the flat memory copy operation.
+  size_t nbytes = GetDataSize(*from);
+  ICHECK_EQ(nbytes, GetDataSize(*to));
+
+  ICHECK(IsContiguous(*from) && IsContiguous(*to))
+      << "CopyDataFromTo only support contiguous array for now";
+  CopyDataFromTo(from->data, from->byte_offset, to->data, to->byte_offset, nbytes, from->ctx,
+                 to->ctx, from->dtype, stream);
+}
+
+void DeviceAPI::CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
+                               size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
+                               DLDataType type_hint, TVMStreamHandle stream) {
+  LOG(FATAL) << "Device does not support CopyDataFromTo.";
+}
+
 void DeviceAPI::FreeWorkspace(TVMContext ctx, void* ptr) { FreeDataSpace(ctx, ptr); }
 
 TVMStreamHandle DeviceAPI::CreateStream(TVMContext ctx) {
@@ -169,7 +213,7 @@ void DeviceAPI::SyncStreamFromTo(TVMContext ctx, TVMStreamHandle event_src,
 // {message1}
 // {message2}
 // {Stack trace:}    // stack traces follow by this line
-//   {trace 0}       // two spaces in the begining.
+//   {trace 0}       // two spaces in the beginning.
 //   {trace 1}
 //   {trace 2}
 //--------------------------------------------------------
@@ -340,7 +384,7 @@ typedef dmlc::ThreadLocalStore<TVMRuntimeEntry> TVMAPIRuntimeStore;
 
 const char* TVMGetLastError() { return TVMAPIRuntimeStore::Get()->last_error.c_str(); }
 
-int TVMAPIHandleException(const std::runtime_error& e) {
+int TVMAPIHandleException(const std::exception& e) {
   TVMAPISetLastError(NormalizeError(e.what()).c_str());
   return -1;
 }
@@ -474,7 +518,7 @@ int TVMFuncCreateFromCFunc(TVMPackedCFunc func, void* resource_handle, TVMPacked
       int ret = func(const_cast<TVMValue*>(args.values), const_cast<int*>(args.type_codes),
                      args.num_args, rv, resource_handle);
       if (ret != 0) {
-        throw dmlc::Error(TVMGetLastError() + ::dmlc::StackTrace());
+        throw tvm::Error(TVMGetLastError() + tvm::runtime::Backtrace());
       }
     });
   } else {
@@ -485,7 +529,7 @@ int TVMFuncCreateFromCFunc(TVMPackedCFunc func, void* resource_handle, TVMPacked
       int ret = func(const_cast<TVMValue*>(args.values), const_cast<int*>(args.type_codes),
                      args.num_args, rv, rpack.get());
       if (ret != 0) {
-        throw dmlc::Error(TVMGetLastError() + ::dmlc::StackTrace());
+        throw tvm::Error(TVMGetLastError() + tvm::runtime::Backtrace());
       }
     });
   }
@@ -553,19 +597,29 @@ int TVMDeviceAllocDataSpace(DLContext ctx, size_t nbytes, size_t alignment, DLDa
   API_END();
 }
 
+int TVMDeviceAllocDataSpaceWithScope(DLContext ctx, int ndim, const int64_t* shape,
+                                     DLDataType dtype, const char* mem_scope, void** out_data) {
+  API_BEGIN();
+  Optional<String> scope;
+  if (mem_scope != nullptr) {
+    scope = String(std::string(mem_scope));
+  }
+  out_data[0] = DeviceAPIManager::Get(ctx)->AllocDataSpace(ctx, ndim, shape, dtype, scope);
+  API_END();
+}
+
 int TVMDeviceFreeDataSpace(DLContext ctx, void* ptr) {
   API_BEGIN();
   DeviceAPIManager::Get(ctx)->FreeDataSpace(ctx, ptr);
   API_END();
 }
 
-int TVMDeviceCopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
-                            size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
-                            DLDataType type_hint, TVMStreamHandle stream) {
+int TVMDeviceCopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream) {
   API_BEGIN();
+  TVMContext ctx_from = from->ctx;
+  TVMContext ctx_to = to->ctx;
   TVMContext ctx = ctx_from.device_type != kDLCPU ? ctx_from : ctx_to;
-  DeviceAPIManager::Get(ctx)->CopyDataFromTo(from, from_offset, to, to_offset, num_bytes, ctx_from,
-                                             ctx_to, type_hint, stream);
+  DeviceAPIManager::Get(ctx)->CopyDataFromTo(from, to, stream);
   API_END();
 }
 
diff --git a/src/runtime/container.cc b/src/runtime/container.cc
index 916a912b3c5e..3d9b1481f6e6 100644
--- a/src/runtime/container.cc
+++ b/src/runtime/container.cc
@@ -79,5 +79,100 @@ TVM_REGISTER_OBJECT_TYPE(ADTObj);
 TVM_REGISTER_OBJECT_TYPE(StringObj);
 TVM_REGISTER_OBJECT_TYPE(ClosureObj);
 
+TVM_REGISTER_OBJECT_TYPE(ArrayNode);
+
+TVM_REGISTER_GLOBAL("runtime.Array").set_body([](TVMArgs args, TVMRetValue* ret) {
+  std::vector<ObjectRef> data;
+  for (int i = 0; i < args.size(); ++i) {
+    if (args[i].type_code() != kTVMNullptr) {
+      data.push_back(args[i].operator ObjectRef());
+    } else {
+      data.push_back(ObjectRef(nullptr));
+    }
+  }
+  *ret = Array<ObjectRef>(data);
+});
+
+TVM_REGISTER_GLOBAL("runtime.ArrayGetItem").set_body([](TVMArgs args, TVMRetValue* ret) {
+  int64_t i = args[1];
+  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
+  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
+  ICHECK(ptr->IsInstance<ArrayNode>());
+  auto* n = static_cast<const ArrayNode*>(ptr);
+  ICHECK_LT(static_cast<size_t>(i), n->size()) << "out of bound of array";
+  *ret = n->at(i);
+});
+
+TVM_REGISTER_GLOBAL("runtime.ArraySize").set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
+  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
+  ICHECK(ptr->IsInstance<ArrayNode>());
+  *ret = static_cast<int64_t>(static_cast<const ArrayNode*>(ptr)->size());
+});
+
+TVM_REGISTER_OBJECT_TYPE(MapNode);
+
+TVM_REGISTER_GLOBAL("runtime.Map").set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args.size() % 2, 0);
+  std::unordered_map<ObjectRef, ObjectRef, ObjectPtrHash, ObjectPtrEqual> data;
+  for (int i = 0; i < args.num_args; i += 2) {
+    ObjectRef k =
+        String::CanConvertFrom(args[i]) ? args[i].operator String() : args[i].operator ObjectRef();
+    ObjectRef v = args[i + 1];
+    data.emplace(std::move(k), std::move(v));
+  }
+  *ret = Map<ObjectRef, ObjectRef>(std::move(data));
+});
+
+TVM_REGISTER_GLOBAL("runtime.MapSize").set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
+  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
+  ICHECK(ptr->IsInstance<MapNode>());
+  auto* n = static_cast<const MapNode*>(ptr);
+  *ret = static_cast<int64_t>(n->size());
+});
+
+TVM_REGISTER_GLOBAL("runtime.MapGetItem").set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
+  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
+  ICHECK(ptr->IsInstance<MapNode>());
+
+  auto* n = static_cast<const MapNode*>(ptr);
+  auto it = n->find(String::CanConvertFrom(args[1]) ? args[1].operator String()
+                                                    : args[1].operator ObjectRef());
+  ICHECK(it != n->end()) << "cannot find the corresponding key in the Map";
+  *ret = (*it).second;
+});
+
+TVM_REGISTER_GLOBAL("runtime.MapCount").set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
+  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
+  ICHECK(ptr->IsInstance<MapNode>());
+  const MapNode* n = static_cast<const MapNode*>(ptr);
+  int64_t cnt = n->count(String::CanConvertFrom(args[1]) ? args[1].operator String()
+                                                         : args[1].operator ObjectRef());
+  *ret = cnt;
+});
+
+TVM_REGISTER_GLOBAL("runtime.MapItems").set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args[0].type_code(), kTVMObjectHandle);
+  Object* ptr = static_cast<Object*>(args[0].value().v_handle);
+  auto* n = static_cast<const MapNode*>(ptr);
+  Array<ObjectRef> rkvs;
+  for (const auto& kv : *n) {
+    if (kv.first->IsInstance<StringObj>()) {
+      rkvs.push_back(Downcast<String>(kv.first));
+    } else {
+      rkvs.push_back(kv.first);
+    }
+    rkvs.push_back(kv.second);
+  }
+  *ret = std::move(rkvs);
+});
+
+#if (USE_FALLBACK_STL_MAP == 0)
+TVM_DLL constexpr uint64_t DenseMapNode::kNextProbeLocation[];
+#endif
+
 }  // namespace runtime
 }  // namespace tvm
diff --git a/src/runtime/contrib/arm_compute_lib/acl_runtime.cc b/src/runtime/contrib/arm_compute_lib/acl_runtime.cc
index 09879bdc6e95..ed8f6adbd083 100644
--- a/src/runtime/contrib/arm_compute_lib/acl_runtime.cc
+++ b/src/runtime/contrib/arm_compute_lib/acl_runtime.cc
@@ -32,6 +32,7 @@
 #include <arm_compute/core/Types.h>
 #include <arm_compute/runtime/NEON/functions/NEArithmeticAddition.h>
 #include <arm_compute/runtime/NEON/functions/NEConvolutionLayer.h>
+#include <arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h>
 #include <arm_compute/runtime/NEON/functions/NEElementwiseOperations.h>
 #include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
 #include <arm_compute/runtime/NEON/functions/NEPoolingLayer.h>
@@ -131,6 +132,9 @@ class ACLRuntime : public JSONRuntimeBase {
         if ("nn.conv2d" == op_name || "qnn.conv2d" == op_name) {
           CreateConvolution2DLayer(&layer_, node, mm);
           num_pools++;
+        } else if ("nn.depthwise_conv2d" == op_name || "qnn.depthwise_conv2d" == op_name) {
+          CreateDepthwiseConvolution2DLayer(&layer_, node, mm);
+          num_pools++;
         } else if ("nn.dense" == op_name || "qnn.dense" == op_name) {
           CreateFullyConnectedLayer(&layer_, node, mm);
           num_pools++;
@@ -227,12 +231,7 @@ class ACLRuntime : public JSONRuntimeBase {
     arm_compute::ActivationLayerInfo act_info;
     if (node.HasAttr("activation_type")) {
       std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
-      if (activation_type == "relu") {
-        act_info = arm_compute::ActivationLayerInfo(
-            arm_compute::ActivationLayerInfo::ActivationFunction::RELU);
-      } else {
-        LOG(FATAL) << "Unsupported activation function";
-      }
+      act_info = MakeACLActivationInfo(activation_type);
     }
 
     arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
@@ -269,6 +268,64 @@ class ACLRuntime : public JSONRuntimeBase {
     layer->function = function;
   }
 
+  /*!
+   * \brief Create a 2D depthwise convolution layer.
+   *
+   * \param layer The ACL layer to build. Containing inputs, outputs and the ACL function.
+   * \param node The JSON representation of the operator.
+   * \param mm The ACL conv2d layer can request auxiliary memory from TVM.
+   */
+  void CreateDepthwiseConvolution2DLayer(
+      CachedLayer* layer, const JSONGraphNode& node,
+      const std::shared_ptr<arm_compute::MemoryManagerOnDemand>& mm) {
+    std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+    std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+    std::vector<std::string> dilation = node.GetAttr<std::vector<std::string>>("dilation");
+    arm_compute::PadStrideInfo pad_stride_info = MakeACLPadStride(padding, strides);
+
+    arm_compute::ActivationLayerInfo act_info;
+    if (node.HasAttr("activation_type")) {
+      std::string activation_type = node.GetAttr<std::vector<std::string>>("activation_type")[0];
+      act_info = MakeACLActivationInfo(activation_type);
+    }
+
+    arm_compute::Size2D dilation_2d(std::stoi(dilation[0]), std::stoi(dilation[1]));
+
+    // Collect inputs and outputs, handling both nn.conv2d and qnn.conv2d cases.
+    std::vector<JSONGraphNodeEntry> inputs = node.GetInputs();
+    size_t num_inputs = inputs.size();
+    bool has_bias;
+    if (node.GetOpName() == "qnn.depthwise_conv2d") {
+      ICHECK(num_inputs >= 8U && num_inputs <= 9U)
+          << "Quantized convolution requires 9 inputs with a bias, 8 inputs without.";
+      has_bias = num_inputs == 9;
+      layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[0], &inputs[4], &inputs[2]));
+      layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[1], &inputs[5], &inputs[3]));
+      if (has_bias) {
+        layer->inputs.push_back(MakeACLTensorFromJSONEntry(inputs[6]));
+      }
+      layer->outputs.push_back(
+          MakeACLTensorFromJSONNode(node, &inputs[6 + has_bias], &inputs[7 + has_bias]));
+    } else {
+      ICHECK(num_inputs >= 2U && num_inputs <= 3U)
+          << "Convolution requires 3 inputs with a bias, 2 inputs without.";
+      has_bias = num_inputs == 3;
+      for (const auto& i : inputs) {
+        layer->inputs.push_back(MakeACLTensorFromJSONEntry(i));
+      }
+      layer->outputs.push_back(MakeACLTensorFromJSONNode(node));
+    }
+
+    // Depth multiplier is the final dimension in acl weights tensor (IWH*M*)
+    int depth_multiplier = layer->inputs[1].info()->tensor_shape()[3];
+
+    auto function = std::make_shared<arm_compute::NEDepthwiseConvolutionLayer>(mm);
+    function->configure(&layer->inputs[0], &layer->inputs[1],
+                        has_bias ? &layer->inputs[2] : nullptr, &layer->outputs[0], pad_stride_info,
+                        depth_multiplier, act_info, dilation_2d);
+    layer->function = function;
+  }
+
   /*!
    * \brief Create a fully connected (dense) layer.
    *
diff --git a/src/runtime/contrib/arm_compute_lib/acl_utils.cc b/src/runtime/contrib/arm_compute_lib/acl_utils.cc
index 604c619bf49c..3b2620987ab0 100644
--- a/src/runtime/contrib/arm_compute_lib/acl_utils.cc
+++ b/src/runtime/contrib/arm_compute_lib/acl_utils.cc
@@ -134,6 +134,16 @@ arm_compute::DataType MakeACLDataType(const DLDataType& data_type) {
   }
 }
 
+arm_compute::ActivationLayerInfo MakeACLActivationInfo(const std::string& activation_type) {
+  auto act_func = arm_compute::ActivationLayerInfo::ActivationFunction::IDENTITY;
+  if (activation_type == "relu") {
+    act_func = arm_compute::ActivationLayerInfo::ActivationFunction::RELU;
+  } else {
+    LOG(FATAL) << "Activation " << activation_type << " unsupported by ACL runtime";
+  }
+  return {act_func};
+}
+
 template <typename T>
 std::vector<T> GetVectorFromDLTensor(const DLTensor* tensor) {
   ICHECK(tensor) << "Cannot convert a nullptr";
diff --git a/src/runtime/contrib/arm_compute_lib/acl_utils.h b/src/runtime/contrib/arm_compute_lib/acl_utils.h
index 576ed916ff60..dbb006fbb347 100644
--- a/src/runtime/contrib/arm_compute_lib/acl_utils.h
+++ b/src/runtime/contrib/arm_compute_lib/acl_utils.h
@@ -108,6 +108,15 @@ arm_compute::PadStrideInfo MakeACLPadStride(const std::vector<std::string>& pad,
  */
 arm_compute::DataType MakeACLDataType(const DLDataType& data_type);
 
+/*!
+ * \brief Convert string to arm_compute::ActivationLayerInfo
+ *
+ * \param activation_type A string representing activation function.
+ * Currently supports the following options: "relu".
+ * \return arm_compute::ActivationLayerInfo.
+ */
+arm_compute::ActivationLayerInfo MakeACLActivationInfo(const std::string& activation_type);
+
 /*!
  * \brief Get a vector from DLTensor data.
  * \note Performs a copy of data.
diff --git a/src/runtime/contrib/bnns/bnns_json_runtime.cc b/src/runtime/contrib/bnns/bnns_json_runtime.cc
new file mode 100644
index 000000000000..87b01567cd30
--- /dev/null
+++ b/src/runtime/contrib/bnns/bnns_json_runtime.cc
@@ -0,0 +1,573 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/**
+ * \file
+ * \brief Simple JSON runtime for Apple BNNS primitives
+ */
+
+#include <tvm/runtime/c_backend_api.h>
+#include <tvm/runtime/ndarray.h>
+#include <tvm/runtime/registry.h>
+
+#include <cstddef>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "../json/json_node.h"
+#include "../json/json_runtime.h"
+#include "bnns_wrp.h"
+
+namespace tvm {
+namespace runtime {
+namespace contrib {
+
+using namespace ::tvm::runtime;
+using namespace ::tvm::runtime::json;
+using namespace ::tvm::runtime::contrib::BNNS;
+
+struct ThreadingConfig {
+  /**
+   * Internal parallelism level ov BNNS primitive specified via BNNSFilterParameters
+   * struct. BNNS doesn't provide real control of internal threading, so it may be
+   * ignored by BNNS implementation.
+   *
+   * Valid values:
+   *   0  use default num of threads suggested by BNNS implementation
+   *  >0  suggests to use this num of internal BNNS threads
+   */
+  size_t internalConcurrency = 0;
+
+  /**
+   * TVM level parallelism for BNNS runtime.
+   * BNNS runtime will split primitive into set of independent sub primitives which
+   * can be executed in parallel. As a rule the splitting are performed through output
+   * channels, so the effective shape of executed primitive is changed.
+   *
+   * Valid values:
+   *   0  do not use graph level treading
+   *  >0  split into this num of primitives
+   */
+  size_t externalConcurrency = 0;
+};
+
+/**
+ * Depends on platform hardware the optimal ThreadingConfig may differ.
+ * This function contains a priori knowledge about some Apple platforms
+ * and their specific.
+ *
+ * @return default ThreadingConfig suggested for this platform
+ */
+ThreadingConfig getDefaultThreadingConfig() {
+  // TODO(apeskov): have to implement CPU/iOS version check.
+  //  meanwhile will use {0, 2} stub to utilize big cores of A13/A14 CPU.
+  return {0, 2};
+}
+
+/**
+ * Main entry point to BNNS runtime
+ */
+class BNNSJSONRuntime : public JSONRuntimeBase {
+ public:
+  BNNSJSONRuntime(const std::string& symbol_name, const std::string& graph_json,
+                  const Array<String> const_names)
+      : JSONRuntimeBase(symbol_name, graph_json, const_names) {}
+
+  const char* type_key() const override { return "bnns_json"; }
+
+  void Init(const Array<NDArray>& consts) override {
+    ICHECK_EQ(consts.size(), const_idx_.size())
+        << "The number of input constants must match the number of required.";
+
+    SetupConstants(consts);
+    BindInputsAndOutputs();
+    AllocateIntermediateTensors();
+    BuildEngine();
+  }
+
+  void Run() override {
+    // Wrap external handler into BNNS tensor representation
+    auto bind_ext_hdl_to_tensor = [this](uint32_t eid) {
+      const auto& ext_dlt = *data_entry_[eid];
+      auto& bnns_tensor = tensors_eid_[eid];
+      bnns_tensor->set_data_hdl(ext_dlt.data);
+    };
+
+    // Bind all input/output external data object into internal abstractions
+    for (const auto& eid : input_var_eid_) bind_ext_hdl_to_tensor(eid);
+    for (const auto& out_entity : outputs_) bind_ext_hdl_to_tensor(EntryID(out_entity));
+
+    // Invoke primitives in topological order
+    for (const auto& prim : primitives_) prim->execute();
+  }
+
+ private:
+  /** Make corresponding input/output tensor stubs */
+  void BindInputsAndOutputs() {
+    tensors_eid_.resize(data_entry_.size());
+    auto createTensor = [&](JSONGraphNodeEntry entry) {
+      auto node = nodes_[entry.id_];
+      auto dlshape = node.GetOpShape()[entry.index_];
+      auto dltype = node.GetOpDataType()[entry.index_];
+      void* data = nullptr;
+      if (data_entry_[entry.id_] != nullptr) data = data_entry_[entry.id_]->data;
+      tensors_eid_[entry.id_] = std::make_shared<BNNS::Tensor>(
+          BNNS::Shape{dlshape.begin(), dlshape.end()}, convertToBNNS(dltype), data);
+    };
+
+    for (auto& id : input_nodes_) {
+      auto eid = JSONGraphNodeEntry(id, 0);
+      createTensor(eid);
+    }
+
+    for (auto entry : outputs_) {
+      createTensor(entry);
+    }
+  }
+
+  /** Allocate intermediate tensors */
+  void AllocateIntermediateTensors() {
+    for (int i = 0; i < nodes_.size(); ++i) {
+      auto eid = JSONGraphNodeEntry(i, 0);
+      if (tensors_eid_[eid.id_] != nullptr) continue;
+      auto node = nodes_[i];
+      auto dlshape = node.GetOpShape()[0];
+      auto dltype = node.GetOpDataType()[0];
+      tensors_eid_[eid.id_] = std::make_shared<BNNS::Tensor>(
+          BNNS::Shape{dlshape.begin(), dlshape.end()}, convertToBNNS(dltype), nullptr);
+      tensors_eid_[eid.id_]->allocate_memory();
+    }
+  }
+
+  // Build up the engine based on the input graph.
+  void BuildEngine() {
+    // Build subgraph engine.
+    for (size_t nid = 0; nid < nodes_.size(); ++nid) {
+      const auto& node = nodes_[nid];
+      if (node.GetOpType() == "kernel") {
+        ICHECK_EQ(node.GetOpType(), "kernel");
+        auto op_name = node.GetOpName();
+        if ("nn.conv2d" == op_name) {
+          Conv2d(nid);
+        } else if ("bnns.conv2d_relu" == op_name) {
+          Conv2d(nid, false, "relu");
+        } else if ("bnns.conv2d_bias_relu" == op_name) {
+          Conv2d(nid, true, "relu");
+        } else if ("bnns.conv2d_sigmoid" == op_name) {
+          Conv2d(nid, false, "sigmoid");
+        } else if ("bnns.conv2d_bias_sigmoid" == op_name) {
+          Conv2d(nid, true, "sigmoid");
+        } else if ("bnns.conv2d_bias" == op_name) {
+          Conv2d(nid, true);
+        } else if ("nn.dense" == op_name) {
+          Dense(nid);
+        } else if ("bnns.dense_bias" == op_name) {
+          Dense(nid, true);
+        } else if ("bnns.dense_bias_gelu" == op_name) {
+          Dense(nid, true, true);
+        } else if ("nn.batch_matmul" == op_name) {
+          MatMul(nid);
+        } else if ("nn.instance_norm" == op_name) {
+          InstanceNormalization(nid);
+        } else if ("nn.max_pool2d" == op_name) {
+          Pooling(nid, false);
+        } else if ("nn.avg_pool2d" == op_name) {
+          Pooling(nid, true);
+        } else if ("nn.global_max_pool2d" == op_name) {
+          Pooling(nid, false, true);
+        } else if ("nn.global_avg_pool2d" == op_name) {
+          Pooling(nid, true, true);
+        } else {
+          LOG(FATAL) << "Unsupported op: " << op_name;
+        }
+      }
+    }
+  }
+
+  // Get BNNS tensor.
+  std::shared_ptr<BNNS::Tensor> GetBNNSTensor(const JSONGraphNodeEntry& entry) {
+    auto eid = EntryID(entry);
+    ICHECK(eid < tensors_eid_.size());
+    return tensors_eid_[eid];
+  }
+
+  void Conv2d(const size_t& nid, const bool has_bias = false,
+              const std::string activation_type = "none") {
+    auto node = nodes_[nid];
+
+    // Setup attributes.
+    auto src_entry = node.GetInputs()[0];
+    auto wgh_entry = node.GetInputs()[1];
+    auto dst_entry = JSONGraphNodeEntry(nid, 0);
+
+    auto dl_input_shape = nodes_[src_entry.id_].GetOpShape()[src_entry.index_];
+    auto dl_weight_shape = nodes_[wgh_entry.id_].GetOpShape()[wgh_entry.index_];
+    BNNS::Shape input_shape{dl_input_shape.begin(), dl_input_shape.end()};
+    BNNS::Shape weight_shape{dl_weight_shape.begin(), dl_weight_shape.end()};
+    std::vector<std::string> str_strides = node.GetAttr<std::vector<std::string>>("strides");
+    std::vector<std::string> str_dilation = node.GetAttr<std::vector<std::string>>("dilation");
+    std::vector<std::string> str_padding = node.GetAttr<std::vector<std::string>>("padding");
+    BNNS::Dim groups = std::stoi(node.GetAttr<std::vector<std::string>>("groups")[0]);
+
+    BNNS::Dim PH_L = std::stoi(str_padding[0]),  // height padding: left
+        PH_R = std::stoi(str_padding[2]),        // height padding: right
+        PW_L = std::stoi(str_padding[1]),        // width padding: left
+        PW_R = std::stoi(str_padding[3]),        // width padding: right
+        SH = std::stoi(str_strides[0]),          // height-wise stride
+        SW = std::stoi(str_strides[1]),          // weight-wise stride
+        DH = std::stoi(str_dilation[0]),         // height kernel dilation
+        DW = std::stoi(str_dilation[1]);         // width kernel dilation
+
+    // Memory descriptions.
+    const auto& src_t = GetBNNSTensor(src_entry);
+    const auto& wgh_t = GetBNNSTensor(wgh_entry);
+    const auto& dst_t = GetBNNSTensor(dst_entry);
+
+    auto src_view = TView::as_is(src_t).extract_outer_dim().with_layout(BNNSDataLayoutImageCHW);
+    auto wgh_view = TView::as_is(wgh_t).with_layout(BNNSDataLayoutConvolutionWeightsOIHW);
+    auto dst_view = TView::as_is(dst_t).extract_outer_dim().with_layout(BNNSDataLayoutImageCHW);
+    TView bias_view;
+
+    if (has_bias) {
+      auto bias_entry = node.GetInputs()[2];
+
+      auto bias_t = GetBNNSTensor(bias_entry);
+      bias_view = TView::as_is(bias_t).squeeze().with_layout(BNNSDataLayoutVector);
+    }
+
+    BNNSActivation activation = {BNNSActivationFunctionIdentity};
+    if (activation_type == "relu")
+      activation = {BNNSActivationFunctionRectifiedLinear};
+    else if (activation_type == "sigmoid")
+      activation = {BNNSActivationFunctionSigmoid};
+
+    BNNSLayerParametersConvolution conv_param = {
+        src_view.get_bnns_view(),
+        wgh_view.get_bnns_view(),
+        dst_view.get_bnns_view(),
+        bias_view.get_bnns_view(),
+        activation,
+        SW,                      /* x_stride */
+        SH,                      /* y_stride */
+        DW,                      /* x_dilation_stride */
+        DH,                      /* y_dilation_stride */
+        0,                       /* x_padding, explicit pads will be used */
+        0,                       /* y_padding, explicit pads will be used */
+        groups,                  /* groups */
+        {PW_L, PW_R, PH_L, PH_R} /* explicit pad values */
+    };
+
+    size_t num_sub_prim = default_thread_config.externalConcurrency;
+    std::vector<BNNSLayerParametersConvolution> params;
+    std::tie(params, src_view, dst_view) =
+        split_to_n(num_sub_prim, conv_param, src_view, wgh_view, bias_view, dst_view);
+
+    std::vector<BNNSFilter> filters(params.size(), nullptr);
+    for (int i = 0; i < params.size(); i++) {
+      auto common_filter_param = getCommonFilterParams();
+      filters[i] = BNNSFilterCreateLayerConvolution(&params[i], &common_filter_param);
+      ICHECK(filters[i]) << "BNNS primitive was not created. Unsupported attributes configuration";
+    }
+
+    primitives_.emplace_back(std::make_shared<BNNS::Primitive>(filters, src_view, dst_view));
+  }
+
+  void Dense(const size_t& nid, const bool has_bias = false, const bool has_gelu = false) {
+    auto node = nodes_[nid];
+
+    // Setup attributes.
+    auto src_entry = node.GetInputs()[0];
+    auto weight_entry = node.GetInputs()[1];
+    auto dst_entry = JSONGraphNodeEntry(nid, 0);
+
+    // Memory descriptions.
+    auto src_t = GetBNNSTensor(src_entry);
+    auto wgh_t = GetBNNSTensor(weight_entry);
+    auto dst_t = GetBNNSTensor(dst_entry);
+
+    auto src_view = TView::as_is(src_t).extract_outer_dim().with_layout(BNNSDataLayoutVector);
+    auto wgh_view = TView::as_is(wgh_t).with_layout(BNNSDataLayoutRowMajorMatrix);
+    auto dst_view = TView::as_is(dst_t).extract_outer_dim().with_layout(BNNSDataLayoutVector);
+
+    TView bias_view;
+    if (has_bias) {
+      auto bias_entry = node.GetInputs()[2];
+      auto bias_md = GetBNNSTensor(bias_entry);
+      bias_view = TView::as_is(bias_md).with_layout(BNNSDataLayoutVector);
+    }
+
+    BNNSActivation activation = {BNNSActivationFunctionIdentity};
+    if (has_gelu) {
+      activation = {BNNSActivationFunctionGELUApproximation};
+      activation.alpha = std::sqrt(2.0 / M_PI);
+      activation.beta = 0.044715;
+    }
+
+    BNNSLayerParametersFullyConnected layerParameters = {
+        src_view.get_bnns_view(),
+        wgh_view.get_bnns_view(),
+        dst_view.get_bnns_view(),
+        bias_view.get_bnns_view(),
+        activation,
+    };
+
+    auto common_filter_param = getCommonFilterParams();
+    auto filter = BNNSFilterCreateLayerFullyConnected(&layerParameters, &common_filter_param);
+    ICHECK(filter) << "BNNS primitive was not created. Unsupported attributes configuration";
+    std::vector<BNNSFilter> filters = {filter};
+    primitives_.emplace_back(std::make_shared<BNNS::Primitive>(filters, src_view, dst_view));
+  }
+
+  void MatMul(const size_t& nid) {
+    auto node = nodes_[nid];
+
+    // Setup attributes.
+    auto a_entry = node.GetInputs()[0];
+    auto b_entry = node.GetInputs()[1];
+    auto dst_entry = JSONGraphNodeEntry(nid, 0);
+    bool a_is_weighted = data_entry_[EntryID(a_entry)] != nullptr;
+    bool b_is_weighted = data_entry_[EntryID(b_entry)] != nullptr;
+
+    // Memory descriptions.
+    auto a_t = GetBNNSTensor(a_entry);
+    auto b_t = GetBNNSTensor(b_entry);
+    auto dst_t = GetBNNSTensor(dst_entry);
+
+    auto a_view = TView::as_is(a_t);
+    auto b_view = TView::as_is(b_t);
+    auto dst_view = TView::as_is(dst_t);
+
+    BNNSLayerParametersBroadcastMatMul layerParameters = {1,      // alpha
+                                                          0,      // beta
+                                                          false,  // transA
+                                                          true,   // transB
+                                                          false,  // quadratic
+                                                          a_is_weighted,
+                                                          b_is_weighted,
+                                                          a_view.get_bnns_view(),
+                                                          b_view.get_bnns_view(),
+                                                          dst_view.get_bnns_view()};
+
+    // BNNS limitation: MatMul use reverse dims values. However strides are calculated correctly
+    //    based on BNNSNDArrayDescriptor::layout value.
+    std::reverse(layerParameters.iA_desc.size, layerParameters.iA_desc.size + 3);
+    std::reverse(layerParameters.iB_desc.size, layerParameters.iB_desc.size + 3);
+    std::reverse(layerParameters.o_desc.size, layerParameters.o_desc.size + 3);
+
+    auto common_filter_param = getCommonFilterParams();
+    auto filter = BNNSFilterCreateLayerBroadcastMatMul(&layerParameters, &common_filter_param);
+    ICHECK(filter) << "BNNS primitive was not created. Unsupported attributes configuration";
+
+    std::vector<BNNSFilter> filters{filter};
+    if (a_is_weighted || b_is_weighted) {
+      auto src_view = a_is_weighted ? b_view : a_view;
+      primitives_.emplace_back(std::make_shared<BNNS::Primitive>(filters, src_view, dst_view));
+    } else {
+      primitives_.emplace_back(
+          std::make_shared<BNNS::TwoInputPrimitive>(filters, a_view, b_view, dst_view));
+    }
+  }
+
+  void InstanceNormalization(const size_t& nid) {
+    auto node = nodes_[nid];
+    size_t axis = std::stoi(node.GetAttr<std::vector<std::string>>("axis")[0]);
+    float epsilon = std::stof(node.GetAttr<std::vector<std::string>>("epsilon")[0]);
+    bool center = std::stoi(node.GetAttr<std::vector<std::string>>("center")[0]);
+    bool scale = std::stoi(node.GetAttr<std::vector<std::string>>("scale")[0]);
+
+    // Setup attributes.
+    auto src_entry = node.GetInputs()[0];
+    auto scale_entry = node.GetInputs()[1];
+    auto bias_entry = node.GetInputs()[2];
+    auto dst_entry = JSONGraphNodeEntry(nid, 0);
+
+    // Memory descriptions.
+    auto src_t = GetBNNSTensor(src_entry);
+    auto scale_t = GetBNNSTensor(scale_entry);
+    auto bias_t = GetBNNSTensor(bias_entry);
+    auto dst_t = GetBNNSTensor(dst_entry);
+
+    auto src_view = TView::as_is(src_t);
+    auto dst_view = TView::as_is(dst_t);
+    size_t src_rank = Tensor::getRank(src_view.get_bnns_view());
+    size_t dst_rank = Tensor::getRank(dst_view.get_bnns_view());
+    ICHECK_EQ(src_rank, dst_rank);
+    ICHECK_LE(src_rank, 4);
+    if (src_rank < 4) {
+      src_view = src_view.unsqueeze(4);
+      dst_view = dst_view.unsqueeze(4);
+    }
+    src_view = src_view.extract_outer_dim().with_layout(BNNSDataLayoutImageCHW);
+    dst_view = dst_view.extract_outer_dim().with_layout(BNNSDataLayoutImageCHW);
+    auto scale_view = TView::as_is(scale_t).with_layout(BNNSDataLayoutVector);
+    auto bias_view = TView::as_is(bias_t).with_layout(BNNSDataLayoutVector);
+    BNNSActivation activation = {BNNSActivationFunctionIdentity};
+
+    auto b_desc = bias_view.get_bnns_view();
+    if (!center) b_desc = {};
+    auto s_desc = scale_view.get_bnns_view();
+    if (!scale) s_desc = {};
+
+    // NOTE: Axis option is ignored in BNNS. The result doesn't depends on value of axis.
+    BNNSLayerParametersNormalization layerParameters = {src_view.get_bnns_view(),  // i_desc
+                                                        dst_view.get_bnns_view(),  // o_desc
+                                                        b_desc,                    // beta_desc
+                                                        s_desc,                    // gamma_desc
+                                                        {},          // moving_mean_desc
+                                                        {},          // moving_variance_desc
+                                                        1.f,         // momentum
+                                                        epsilon,     // epsilon
+                                                        activation,  // activation
+                                                        1,           // num_groups
+                                                        axis};       // normalization_axis
+
+    BNNSFilterType filter_type = BNNSInstanceNorm;
+    auto common_filter_param = getCommonFilterParams();
+    auto filter =
+        BNNSFilterCreateLayerNormalization(filter_type, &layerParameters, &common_filter_param);
+    ICHECK(filter) << "BNNS primitive was not created. Unsupported attributes configuration";
+
+    std::vector<BNNSFilter> filters{filter};
+    primitives_.emplace_back(std::make_shared<BNNS::NormPrimitive>(filters, src_view, dst_view));
+  }
+
+  void Pooling(const size_t& nid, bool avg_pooling, bool global = false) {
+    auto node = nodes_[nid];
+
+    auto src_entry = node.GetInputs()[0];
+    auto dst_entry = JSONGraphNodeEntry(nid, 0);
+
+    // Memory descriptions.
+    auto src_t = GetBNNSTensor(src_entry);
+    auto dst_t = GetBNNSTensor(dst_entry);
+
+    auto src_view = TView::as_is(src_t);
+    auto dst_view = TView::as_is(dst_t);
+    size_t src_rank = Tensor::getRank(src_view.get_bnns_view());
+    size_t dst_rank = Tensor::getRank(dst_view.get_bnns_view());
+    ICHECK_EQ(src_rank, dst_rank);
+    ICHECK_LE(src_rank, 4);
+    if (src_rank < 4) {
+      src_view = src_view.unsqueeze(4);
+      dst_view = dst_view.unsqueeze(4);
+    }
+    src_view = src_view.extract_outer_dim().with_layout(BNNSDataLayoutImageCHW);
+    dst_view = dst_view.extract_outer_dim().with_layout(BNNSDataLayoutImageCHW);
+    BNNSActivation activation = {BNNSActivationFunctionIdentity};
+    BNNSPoolingFunction pf = {BNNSPoolingFunctionMax};
+    if (avg_pooling) pf = {BNNSPoolingFunctionAverageCountExcludePadding};
+
+    // Setup attributes.
+    size_t k_height = 0;
+    size_t k_width = 0;
+    size_t y_padding = 0;
+    size_t x_padding = 0;
+    size_t y_stride = 1;
+    size_t x_stride = 1;
+    if (!global) {
+      std::vector<std::string> pool_size = node.GetAttr<std::vector<std::string>>("pool_size");
+      std::vector<std::string> padding = node.GetAttr<std::vector<std::string>>("padding");
+      std::vector<std::string> strides = node.GetAttr<std::vector<std::string>>("strides");
+      k_height = std::stoi(pool_size[0]);
+      k_width = std::stoi(pool_size[1]);
+      y_padding = std::stoi(padding[0]);
+      x_padding = std::stoi(padding[1]);
+      y_stride = std::stoi(strides[0]);
+      x_stride = std::stoi(strides[1]);
+    } else {
+      auto sv = src_view.get_bnns_view();
+      k_height = sv.size[1];
+      k_width = sv.size[0];
+    }
+
+    BNNSLayerParametersPooling layerParameters = {src_view.get_bnns_view(),  // i_desc
+                                                  dst_view.get_bnns_view(),  // o_desc
+                                                  {},                        // bias
+                                                  activation,                // activation
+                                                  pf,                        // pooling_function
+                                                  k_width,                   // k_width
+                                                  k_height,                  // k_height
+                                                  x_stride,                  // x_stride
+                                                  y_stride,                  // y_stride
+                                                  0,                         // x_dilation_stride
+                                                  0,                         // y_dilation_stride
+                                                  x_padding,                 // x_padding
+                                                  y_padding,                 // y_padding
+                                                  {}};  // pad left, right, up, down padding
+
+    auto common_filter_param = getCommonFilterParams();
+    auto filter = BNNSFilterCreateLayerPooling(&layerParameters, &common_filter_param);
+    ICHECK(filter) << "BNNS primitive was not created. Unsupported attributes configuration";
+
+    std::vector<BNNSFilter> filters{filter};
+    primitives_.emplace_back(std::make_shared<BNNS::PoolingPrimitive>(filters, src_view, dst_view));
+  }
+
+  BNNS::Dtype convertToBNNS(const DLDataType& dl_dtype) {
+    if (dl_dtype.code == DLDataTypeCode::kDLFloat) {
+      if (dl_dtype.bits == 32) return BNNSDataTypeFloat32;
+      if (dl_dtype.bits == 16) return BNNSDataTypeFloat16;
+    }
+    if (dl_dtype.code == DLDataTypeCode::kDLInt) {
+      if (dl_dtype.bits == 32) return BNNSDataTypeInt32;
+      if (dl_dtype.bits == 16) return BNNSDataTypeInt16;
+      if (dl_dtype.bits == 8) return BNNSDataTypeInt8;
+    }
+    if (dl_dtype.code == DLDataTypeCode::kDLUInt) {
+      if (dl_dtype.bits == 32) return BNNSDataTypeUInt32;
+      if (dl_dtype.bits == 16) return BNNSDataTypeUInt16;
+      if (dl_dtype.bits == 8) return BNNSDataTypeUInt8;
+    }
+    LOG(FATAL) << "Unsupported data type for BNNS runtime";
+    return BNNS::Dtype(0);
+  }
+
+  BNNSFilterParameters getCommonFilterParams() {
+    // NOTE: To force weights tensor copy on stage of filter create
+    //       just change : BNNSFlagsUseClientPtr -> 0
+    return {BNNSFlagsUseClientPtr, default_thread_config.internalConcurrency};
+  }
+
+  /** Default threading config. Should be used if there are
+   *  no other threading specificator. */
+  const ThreadingConfig default_thread_config = getDefaultThreadingConfig();
+
+  /** Collection of all primitives in topological order */
+  std::vector<std::shared_ptr<BNNS::Primitive>> primitives_;
+
+  /** Vector with BNNS tensors. Index of tensor matched with
+   *  corresponding EntryID from base JSONRuntimeBase. */
+  std::vector<TensorPtr> tensors_eid_;
+};
+
+runtime::Module BNNSJSONRuntimeCreate(String symbol_name, String graph_json,
+                                      const Array<String>& const_names) {
+  auto n = make_object<BNNSJSONRuntime>(symbol_name, graph_json, const_names);
+  return runtime::Module(n);
+}
+
+TVM_REGISTER_GLOBAL("runtime.BNNSJSONRuntimeCreate").set_body_typed(BNNSJSONRuntimeCreate);
+
+TVM_REGISTER_GLOBAL("runtime.module.loadbinary_bnns_json")
+    .set_body_typed(BNNSJSONRuntime::LoadFromBinary<BNNSJSONRuntime>);
+
+}  // namespace contrib
+}  // namespace runtime
+}  // namespace tvm
diff --git a/src/runtime/contrib/bnns/bnns_wrp.h b/src/runtime/contrib/bnns/bnns_wrp.h
new file mode 100644
index 000000000000..b31e97e554da
--- /dev/null
+++ b/src/runtime/contrib/bnns/bnns_wrp.h
@@ -0,0 +1,495 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/**
+ * \file
+ * \brief C++ wrappers and helpers to handle BNNS objects
+ */
+
+#ifndef TVM_RUNTIME_CONTRIB_BNNS_BNNS_WRP_H_
+#define TVM_RUNTIME_CONTRIB_BNNS_BNNS_WRP_H_
+
+#include <Accelerate/Accelerate.h>
+
+#include <algorithm>
+#include <functional>
+#include <memory>
+#include <numeric>
+#include <tuple>
+#include <vector>
+
+namespace tvm {
+namespace runtime {
+namespace contrib {
+namespace BNNS {
+
+using Dim = size_t;
+using Shape = std::vector<Dim>;
+using Dtype = BNNSDataType;
+using HDL = void*;
+
+void* default_alloc(size_t size) { return malloc(size); }
+
+void default_free(void* ptr) { free(ptr); }
+
+/**
+ * Main abstraction for tensor representation
+ *
+ * Contains buffer handler and common attributes like shape and dtype.
+ */
+class Tensor {
+ public:
+  Tensor() = delete;
+  Tensor(Tensor&) = delete;
+
+  Tensor(Shape shape, Dtype dtype, void* hdl) {
+    auto rank = shape.size();
+    ICHECK(rank < BNNS_MAX_TENSOR_DIMENSION);
+
+    desc_ = {BNNSNDArrayFlags(0),
+             getPlainLayout(rank),
+             {},       // shape
+             {},       // strides
+             hdl,      // data handler
+             dtype,    // data type
+             nullptr,  // table_data (clustering case), is not used
+             dtype,
+             1.f,
+             0.f};
+    std::copy(shape.rbegin(), shape.rend(), std::begin(desc_.size));
+
+    desc_.data = hdl;
+    is_external_data = true;
+  }
+
+  ~Tensor() {
+    if (desc_.data && !is_external_data) {
+      default_free(desc_.data);
+      desc_.data = nullptr;
+    }
+  }
+
+  void allocate_memory() {
+    if (desc_.data && !is_external_data) {
+      default_free(desc_.data);
+    }
+    const size_t buff_size = getSize(desc_) * getElementSize(desc_);
+    desc_.data = default_alloc(buff_size);
+    ICHECK(desc_.data);
+    is_external_data = false;
+  }
+
+  void* get_data_hdl() const { return desc_.data; }
+
+  void set_data_hdl(void* hdl) {
+    if (desc_.data && !is_external_data) {
+      default_free(desc_.data);
+      desc_.data = nullptr;
+    }
+
+    desc_.data = hdl;
+    is_external_data = true;
+  }
+
+  const BNNSNDArrayDescriptor& get_desc() const { return desc_; }
+
+  static BNNSDataLayout getPlainLayout(size_t rank) {
+    ICHECK(rank <= BNNS_MAX_TENSOR_DIMENSION);
+    return static_cast<BNNSDataLayout>((rank << 16) | 0x8001);
+  }
+
+  static size_t getRank(BNNSDataLayout layout) { return (layout & 0xF0000) >> 16; }
+
+  static size_t getRank(BNNSNDArrayDescriptor desc) { return getRank(desc.layout); }
+
+  static size_t getSize(BNNSNDArrayDescriptor desc) {
+    auto rank = getRank(desc);
+    return std::accumulate(desc.size, desc.size + rank, 1, std::multiplies<int>());
+  }
+
+  /** return size of element in bytes */
+  static size_t getElementSize(Dtype dtype) { return (dtype & 0xFFFF) / 8; }
+
+  /** return size of element in bytes */
+  static size_t getElementSize(const BNNSNDArrayDescriptor& desc) {
+    return getElementSize(desc.data_type);
+  }
+
+ private:
+  bool is_external_data = false;
+  BNNSNDArrayDescriptor desc_;
+};
+
+using TensorPtr = std::shared_ptr<Tensor>;
+
+/**
+ * Tensor View object which represent how provided BNNS::Tensor will be considered
+ *
+ * The single BNNS::Tensor can be treated in different form depend on particular primitive
+ * expectation. More other some primitive supports only external form of batching. So we have
+ * some abstraction to describe how primitive will handle provided tensor.
+ *
+ * Batched View
+ *   View with extracted dimension as external batch value
+ *   example: Tensor [2, 3, 224, 224] -> View [3, 224, 224] with ext batch 2
+ *
+ * Party View
+ *   The collection of view on the same tensor, can be the same view or with some stride
+ *   example: Tensor [6, 5, 3, 3] -> 3 x View [2, 5, 3, 3] with stride 45
+ */
+class TView {
+ public:
+  /** Make view on provided tensor as is */
+  static TView as_is(const TensorPtr& origin) {
+    TView res;
+    res.origin_ = origin;
+    res.view_desc_ = origin->get_desc();
+    return res;
+  }
+
+  /** Extract outer dimension to separate batch field. TView will became batched view */
+  TView extract_outer_dim() const {
+    auto rank = Tensor::getRank(view_desc_);
+    TView res = *this;
+    res.batch_size_ = view_desc_.size[rank - 1];
+    res.batch_stride_ =
+        std::accumulate(view_desc_.size, view_desc_.size + rank - 1, 1, std::multiplies<>());
+    res.view_desc_.size[rank - 1] = 0;
+    res.view_desc_.layout = Tensor::getPlainLayout(rank - 1);
+    return res;
+  }
+
+  /** Squeeze all dims equal 1 */
+  TView squeeze(size_t min_rank = 1) const {
+    auto rank = Tensor::getRank(view_desc_);
+    size_t squeezed_shape[BNNS_MAX_TENSOR_DIMENSION] = {};
+    size_t squeezed_rank = 0;
+    for (int i = 0; i < rank; i++)
+      if (view_desc_.size[i] != 1) squeezed_shape[squeezed_rank++] = view_desc_.size[i];
+
+    if (min_rank > squeezed_rank) {
+      std::fill(squeezed_shape + squeezed_rank, squeezed_shape + min_rank, 1);
+      squeezed_rank = min_rank;
+    }
+
+    TView res = *this;
+    std::copy(squeezed_shape, squeezed_shape + squeezed_rank, res.view_desc_.size);
+    std::fill(res.view_desc_.size + squeezed_rank, res.view_desc_.size + rank, 0);
+    res.view_desc_.layout = Tensor::getPlainLayout(squeezed_rank);
+    return res;
+  }
+
+  /** Expand the shape of an array */
+  TView expand_dims(std::vector<size_t> axes) const {
+    auto rank = Tensor::getRank(view_desc_);
+    TView res = *this;
+    size_t unsqueezed_shape[BNNS_MAX_TENSOR_DIMENSION] = {};
+    size_t unsqueezed_rank = axes.size() + rank;
+    ICHECK_LE(unsqueezed_rank, BNNS_MAX_TENSOR_DIMENSION);
+    for (const auto& axis : axes) {
+      ICHECK_LT(axis, unsqueezed_rank);
+      unsqueezed_shape[axis] = 1;
+    }
+    for (int i = 0, orig_idx = 0; i < unsqueezed_rank; ++i) {
+      if (unsqueezed_shape[i] == 1) continue;
+      unsqueezed_shape[i] = view_desc_.size[orig_idx++];
+    }
+    std::copy(unsqueezed_shape, unsqueezed_shape + unsqueezed_rank, res.view_desc_.size);
+    res.view_desc_.layout = Tensor::getPlainLayout(unsqueezed_rank);
+    return res;
+  }
+
+  /** Unsqueeze tensor to a new rank */
+  TView unsqueeze(size_t new_rank) const {
+    ICHECK_LE(new_rank, BNNS_MAX_TENSOR_DIMENSION);
+    auto rank = Tensor::getRank(view_desc_);
+    ICHECK_GT(new_rank, rank);
+    std::vector<size_t> axes(new_rank - rank);
+    std::iota(axes.begin(), axes.end(), rank);
+    return expand_dims(axes);
+  }
+
+  /** Construct new TView with specified layout if it applicable */
+  TView with_layout(BNNSDataLayout layout) const {
+    ICHECK_EQ(Tensor::getRank(view_desc_), Tensor::getRank(layout));
+
+    TView res = *this;
+    res.view_desc_.layout = layout;
+    return res;
+  }
+
+  /** Construct party TView by splitting original TView into num parts */
+  TView party_split_n(size_t num) const {
+    ICHECK_EQ(party_size_, 1);
+
+    TView res = *this;
+    size_t rank = Tensor::getRank(view_desc_);
+    size_t size = Tensor::getSize(view_desc_);
+    res.party_size_ = num;
+    res.party_stride_ = size / num;
+
+    if (res.batch_size_ != 1) {
+      res.batch_size_ /= num;
+    } else {
+      res.view_desc_.size[rank - 1] /= num;
+      res.batch_stride_ /= num;
+    }
+    return res;
+  }
+
+  /** Construct party TView by duplicating original TView num times */
+  TView party_duplicate_n(size_t num) const {
+    ICHECK_EQ(party_size_, 1);
+
+    TView res = *this;
+    res.party_size_ = num;
+    res.party_stride_ = 0;
+
+    return res;
+  }
+
+  /** Return data buffer handler */
+  HDL get_data_hdl() const { return view_desc_.data; }
+
+  /** Return external batch dimension value */
+  size_t get_batch_size() const { return batch_size_; }
+
+  /** Return external batch dimension stride */
+  size_t get_stride() const { return batch_stride_; }
+
+  /** Return party element by index */
+  TView operator[](size_t i) const {
+    ICHECK_LT(i, party_size_);
+
+    TView res = *this;
+    res.party_size_ = 1;
+    if (origin_) {
+      auto hdl = reinterpret_cast<uint8_t*>(origin_->get_data_hdl());
+      hdl += i * party_stride_ * Tensor::getElementSize(view_desc_.data_type);
+      res.view_desc_.data = hdl;
+    }
+    return res;
+  }
+
+  /** Check if view is empty and doesn't relay to any tensor */
+  operator bool() const { return origin_ != nullptr; }
+
+  /** Get BNNS descriptor for particular View. Batch and Party attributed are ignored. */
+  const BNNSNDArrayDescriptor& get_bnns_view() const { return view_desc_; }
+
+ private:
+  /** Original tensor object to view on */
+  TensorPtr origin_;
+
+  /** Batched view parameters */
+  BNNSNDArrayDescriptor view_desc_ = {};
+  size_t batch_size_ = 1;
+  size_t batch_stride_ = 0;
+
+  /** Party representation parameters */
+  size_t party_size_ = 1;
+  size_t party_stride_ = 0;
+};
+
+/**
+ * Wrapper on top of BNNSFilter and src/dst TensorView.
+ *
+ * Support decomposed representation of filter and can execute sub primitives in parallel.
+ */
+class Primitive {
+ public:
+  Primitive(const std::vector<BNNSFilter> fs, const TView& src, const TView& dst)
+      : filters(fs), src_view(src), dst_view(dst) {}
+
+  virtual ~Primitive() {
+    for (auto& filter : filters)
+      if (filter) {
+        BNNSFilterDestroy(filter);
+        filter = nullptr;
+      }
+  }
+
+  /** Execute primitive with using specified src/dst */
+  void execute() {
+    auto res = TVMBackendParallelLaunch(run_task, this, filters.size());
+    ICHECK_EQ(res, 0) << "BNNS runtime. Primitive was not executed properly";
+  }
+
+ private:
+  virtual int execute_impl(int part_idx) {
+    const auto filter = this->filters[part_idx];
+    const auto src_view = this->src_view[part_idx];
+    const auto dst_view = this->dst_view[part_idx];
+
+    size_t mb = src_view.get_batch_size();
+
+    // NB! BNNS limitations
+    //   * Do not use simple BNNSFilterApply. There is a bug inside BNNS,
+    //     BNNSFilterApply doesn't work for grouped convolution.
+    //   * Group convolution doesn't support arbitrary stride for Batch dim.
+    //     The tensor should be dense.
+    return BNNSFilterApplyBatch(filter, mb, src_view.get_data_hdl(), src_view.get_stride(),
+                                dst_view.get_data_hdl(), dst_view.get_stride());
+  }
+
+  static int run_task(int task_id, TVMParallelGroupEnv* penv, void* cdata) {
+    auto prim = reinterpret_cast<Primitive*>(cdata);
+    return prim->execute_impl(task_id);
+  }
+
+ protected:
+  /** BNNS kernels/filters collect which will execute primitive */
+  std::vector<BNNSFilter> filters = {};
+  const TView src_view;
+  const TView dst_view;
+};
+
+/**
+ * Wrapper on top of BNNS::Primitive
+ *
+ * This primitive should be used for executing primitive with two inputs.
+ */
+class TwoInputPrimitive : public Primitive {
+ public:
+  TwoInputPrimitive(const std::vector<BNNSFilter> fs, const TView& src, const TView& src2,
+                    const TView& dst)
+      : Primitive(fs, src, dst), src2_view(src2) {}
+
+ private:
+  int execute_impl(int task_id) override {
+    const auto filter = this->filters[task_id];
+    const auto src_view = this->src_view[task_id];
+    const auto src2_view = this->src2_view[task_id];
+    const auto dst_view = this->dst_view[task_id];
+
+    size_t mb = src_view.get_batch_size();
+
+    return BNNSFilterApplyTwoInputBatch(filter, mb, src_view.get_data_hdl(), src_view.get_stride(),
+                                        src2_view.get_data_hdl(), src2_view.get_stride(),
+                                        dst_view.get_data_hdl(), dst_view.get_stride());
+  }
+
+ protected:
+  const TView src2_view;
+};
+
+/**
+ * Wrapper on top of BNNS::Primitive
+ *
+ * This primitive should be used for executing normalization filter
+ */
+class NormPrimitive : public Primitive {
+ public:
+  using Primitive::Primitive;
+
+ private:
+  int execute_impl(int task_id) override {
+    const auto filter = this->filters[task_id];
+    const auto src_view = this->src_view[task_id];
+    const auto dst_view = this->dst_view[task_id];
+
+    size_t mb = src_view.get_batch_size();
+    return BNNSNormalizationFilterApplyBatch(filter, mb, src_view.get_data_hdl(),
+                                             src_view.get_stride(), dst_view.get_data_hdl(),
+                                             dst_view.get_stride(), false);
+  }
+};
+
+/**
+ * Wrapper on top of BNNS::Primitive
+ *
+ * This primitive should be used for executing pooling filter
+ */
+class PoolingPrimitive : public Primitive {
+ public:
+  using Primitive::Primitive;
+
+ private:
+  int execute_impl(int task_id) override {
+    const auto filter = this->filters[task_id];
+    const auto src_view = this->src_view[task_id];
+    const auto dst_view = this->dst_view[task_id];
+
+    size_t mb = src_view.get_batch_size();
+    return BNNSPoolingFilterApplyBatch(filter, mb, src_view.get_data_hdl(), src_view.get_stride(),
+                                       dst_view.get_data_hdl(), dst_view.get_stride(), nullptr, 0);
+  }
+};
+
+/**
+ * Function which split primitive into sub primitives to parallel execution
+ *
+ * @param num requested num of sub primitives
+ * @param orig_conv_param original convolution descriptor
+ * @param src_view source tensor view
+ * @param wgh_view weight tensor view
+ * @param b_view bias tensor view
+ * @param dst_view destination tensor view
+ * @param num number of part to split into
+ * @return collection of Convolution descriptors plus corresponding src/dst tensors view
+ */
+static std::tuple<std::vector<BNNSLayerParametersConvolution>, TView, TView> split_to_n(
+    size_t num, const BNNSLayerParametersConvolution& orig_conv_param, const TView& src_view,
+    const TView& wgh_view, const TView& b_view, const TView& dst_view) {
+  size_t batch = src_view.get_batch_size();
+  size_t oc = dst_view.get_bnns_view().size[2];
+  size_t groups = orig_conv_param.groups;
+
+  BNNS::TView src_view_new;
+  BNNS::TView wgh_view_new;
+  BNNS::TView b_view_new;
+  BNNS::TView dst_view_new;
+
+  // TODO(apeskov): Add split by batch dim. Meanwhile we just disable it...
+  if (batch > 1 || oc % num != 0 || (groups > 1 && groups % num != 0)) {
+    return {{orig_conv_param}, src_view, dst_view};
+  }
+
+  // if groups > 1 split only by groups
+  // otherwise split inside one convolution by output channels
+  if (groups > 1) {
+    src_view_new = src_view.party_split_n(num);
+    groups = groups / num;
+  } else {
+    src_view_new = src_view.party_duplicate_n(num);
+  }
+
+  wgh_view_new = wgh_view.party_split_n(num);
+  b_view_new = b_view.party_split_n(num);
+  dst_view_new = dst_view.party_split_n(num);
+
+  std::vector<BNNSLayerParametersConvolution> res(num);
+  for (size_t i = 0; i < num; i++) {
+    auto& cur = res[i];
+    cur = orig_conv_param;
+
+    cur.i_desc = src_view_new[i].get_bnns_view();
+    cur.o_desc = dst_view_new[i].get_bnns_view();
+    cur.w_desc = wgh_view_new[i].get_bnns_view();
+    cur.bias = b_view_new[i].get_bnns_view();
+    cur.groups = groups;
+  }
+  return {res, src_view_new, dst_view_new};
+}
+
+}  // namespace BNNS
+}  // namespace contrib
+}  // namespace runtime
+}  // namespace tvm
+#endif  // TVM_RUNTIME_CONTRIB_BNNS_BNNS_WRP_H_
diff --git a/src/runtime/contrib/cblas/cblas.cc b/src/runtime/contrib/cblas/cblas.cc
index 16496e06aae3..fbac6222488d 100644
--- a/src/runtime/contrib/cblas/cblas.cc
+++ b/src/runtime/contrib/cblas/cblas.cc
@@ -21,8 +21,8 @@
  * \file Use external cblas library call.
  */
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 extern "C" {
 #include <cblas.h>
diff --git a/src/runtime/contrib/cblas/gemm_common.h b/src/runtime/contrib/cblas/gemm_common.h
index 6c31fbdd06a3..9ccfa5183cd6 100644
--- a/src/runtime/contrib/cblas/gemm_common.h
+++ b/src/runtime/contrib/cblas/gemm_common.h
@@ -21,7 +21,9 @@
  * \file tvm/contrib/gemm.h
  * \brief Shared implementation of gemm
  */
-#pragma once
+
+#ifndef TVM_RUNTIME_CONTRIB_CBLAS_GEMM_COMMON_H_
+#define TVM_RUNTIME_CONTRIB_CBLAS_GEMM_COMMON_H_
 
 #include <tvm/runtime/data_type.h>
 #include <tvm/runtime/registry.h>
@@ -215,3 +217,4 @@ inline void CallBatchGemm(TVMArgs args, TVMRetValue* ret, TBatchGemmOp op) {
 
 }  // namespace contrib
 }  // namespace tvm
+#endif  // TVM_RUNTIME_CONTRIB_CBLAS_GEMM_COMMON_H_
diff --git a/src/runtime/contrib/cblas/mkl.cc b/src/runtime/contrib/cblas/mkl.cc
index 273aa45367dd..4323878db276 100644
--- a/src/runtime/contrib/cblas/mkl.cc
+++ b/src/runtime/contrib/cblas/mkl.cc
@@ -21,8 +21,8 @@
  * \file Use external mkl library call.
  */
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 extern "C" {
 #include <mkl_cblas.h>
diff --git a/src/runtime/contrib/cblas/mkldnn.cc b/src/runtime/contrib/cblas/mkldnn.cc
index 1c3fa023dcc7..31abd317c6a4 100644
--- a/src/runtime/contrib/cblas/mkldnn.cc
+++ b/src/runtime/contrib/cblas/mkldnn.cc
@@ -21,8 +21,8 @@
  * \file Use external cblas library call.
  */
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 extern "C" {
 #include <dnnl.h>
diff --git a/src/runtime/contrib/cublas/cublas.cc b/src/runtime/contrib/cublas/cublas.cc
index ce69d4ca7bde..9af1602cf3c0 100644
--- a/src/runtime/contrib/cublas/cublas.cc
+++ b/src/runtime/contrib/cublas/cublas.cc
@@ -21,8 +21,8 @@
  * \file Use external cblas library call.
  */
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include "../cblas/gemm_common.h"
 #include "cublas_utils.h"
@@ -167,7 +167,7 @@ inline void CallLtIgemm(TVMArgs args, TVMRetValue* ret, cublasLtHandle_t hdl) {
   ICHECK(CheckMixPrecisionType(A->dtype, C->dtype)) << "Unsupported data type";
   int32_t alpha = args.size() > 5 ? args[5] : 1;
   int32_t beta = args.size() > 6 ? args[6] : 0;
-  cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
+  cublasLtMatrixLayout_t Adesc = nullptr, Bdesc = nullptr, Cdesc = nullptr;
   auto A_data = reinterpret_cast<void*>(static_cast<char*>(A->data) + A->byte_offset);
   auto B_data = reinterpret_cast<void*>(static_cast<char*>(B->data) + B->byte_offset);
   auto C_data = reinterpret_cast<void*>(static_cast<char*>(C->data) + C->byte_offset);
@@ -204,7 +204,7 @@ inline void CallLtIgemm(TVMArgs args, TVMRetValue* ret, cublasLtHandle_t hdl) {
                                                       &order_COL32, sizeof(order_COL32)));
 
   CHECK_CUBLAS_ERROR(cublasLtMatmul(hdl, operationDesc, &alpha, B_data, Adesc, A_data, Bdesc, &beta,
-                                    C_data, Cdesc, C_data, Cdesc, NULL, NULL, 0, 0));
+                                    C_data, Cdesc, C_data, Cdesc, nullptr, nullptr, 0, nullptr));
 }
 #endif
 
diff --git a/src/runtime/contrib/cublas/cublas_utils.cc b/src/runtime/contrib/cublas/cublas_utils.cc
index d4ec08770723..4b4a1b755e66 100644
--- a/src/runtime/contrib/cublas/cublas_utils.cc
+++ b/src/runtime/contrib/cublas/cublas_utils.cc
@@ -35,7 +35,7 @@ CuBlasThreadEntry::CuBlasThreadEntry() { CHECK_CUBLAS_ERROR(cublasCreate(&handle
 CuBlasThreadEntry::~CuBlasThreadEntry() {
   if (handle) {
     cublasDestroy(handle);
-    handle = 0;
+    handle = nullptr;
   }
 }
 
diff --git a/src/runtime/contrib/cublas/cublas_utils.h b/src/runtime/contrib/cublas/cublas_utils.h
index 32c3b03ddbb0..3edb8300be88 100644
--- a/src/runtime/contrib/cublas/cublas_utils.h
+++ b/src/runtime/contrib/cublas/cublas_utils.h
@@ -28,7 +28,7 @@
 #include <cuda_runtime.h>
 #include <cuda_runtime_api.h>
 #include <dlpack/dlpack.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <cstdint>
 #if CUDART_VERSION >= 10010
diff --git a/src/runtime/contrib/cudnn/cudnn_utils.h b/src/runtime/contrib/cudnn/cudnn_utils.h
index 528298b75187..9b8e9fb33f98 100644
--- a/src/runtime/contrib/cudnn/cudnn_utils.h
+++ b/src/runtime/contrib/cudnn/cudnn_utils.h
@@ -26,7 +26,7 @@
 
 #include <cudnn.h>
 #include <tvm/runtime/device_api.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "../../cuda/cuda_common.h"
 
diff --git a/src/runtime/contrib/json/json_runtime.h b/src/runtime/contrib/json/json_runtime.h
index 3ae652ccaf24..55f16635b9e6 100644
--- a/src/runtime/contrib/json/json_runtime.h
+++ b/src/runtime/contrib/json/json_runtime.h
@@ -55,7 +55,7 @@ class JSONRuntimeBase : public ModuleNode {
     LoadGraph(graph_json_);
   }
 
-  const char* type_key() const { return "json"; }
+  const char* type_key() const override { return "json"; }
 
   /*! \brief Initialize a specific json runtime. */
   virtual void Init(const Array<NDArray>& consts) = 0;
@@ -69,7 +69,7 @@ class JSONRuntimeBase : public ModuleNode {
    * \param sptr_to_self The pointer to the module node.
    * \return The packed function.
    */
-  virtual PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self) {
+  PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self) override {
     if (name == "get_symbol") {
       return PackedFunc(
           [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { *rv = this->symbol_name_; });
@@ -98,7 +98,7 @@ class JSONRuntimeBase : public ModuleNode {
     }
   }
 
-  virtual void SaveToBinary(dmlc::Stream* stream) {
+  void SaveToBinary(dmlc::Stream* stream) override {
     // Save the symbol
     stream->Write(symbol_name_);
     // Save the graph
diff --git a/src/runtime/contrib/miopen/miopen_utils.h b/src/runtime/contrib/miopen/miopen_utils.h
index 9982f0914f6b..e5a769a974f0 100644
--- a/src/runtime/contrib/miopen/miopen_utils.h
+++ b/src/runtime/contrib/miopen/miopen_utils.h
@@ -26,7 +26,7 @@
 
 #include <miopen/miopen.h>
 #include <tvm/runtime/device_api.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <string>
 
diff --git a/src/runtime/contrib/mps/mps_utils.h b/src/runtime/contrib/mps/mps_utils.h
index d1c49732318a..c2b7e3c7aa99 100644
--- a/src/runtime/contrib/mps/mps_utils.h
+++ b/src/runtime/contrib/mps/mps_utils.h
@@ -28,8 +28,8 @@
 #include <dmlc/thread_local.h>
 #include <tvm/runtime/data_type.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <vector>
 
diff --git a/src/runtime/contrib/nnpack/convolution.cc b/src/runtime/contrib/nnpack/convolution.cc
index b3ea6c891d43..0d6359495902 100644
--- a/src/runtime/contrib/nnpack/convolution.cc
+++ b/src/runtime/contrib/nnpack/convolution.cc
@@ -23,8 +23,8 @@
 #include <nnpack.h>
 #include <tvm/runtime/data_type.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include "nnpack_utils.h"
 
diff --git a/src/runtime/contrib/nnpack/fully_connected.cc b/src/runtime/contrib/nnpack/fully_connected.cc
index 8b72eb38e08c..28570026ada3 100644
--- a/src/runtime/contrib/nnpack/fully_connected.cc
+++ b/src/runtime/contrib/nnpack/fully_connected.cc
@@ -22,8 +22,8 @@
  */
 #include <nnpack.h>
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include "nnpack_utils.h"
 
diff --git a/src/runtime/contrib/nnpack/nnpack_utils.h b/src/runtime/contrib/nnpack/nnpack_utils.h
index 231309baaa8e..4396ea0bcde6 100644
--- a/src/runtime/contrib/nnpack/nnpack_utils.h
+++ b/src/runtime/contrib/nnpack/nnpack_utils.h
@@ -25,8 +25,8 @@
 #include <dmlc/thread_local.h>
 #include <nnpack.h>
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 namespace tvm {
 namespace contrib {
diff --git a/src/runtime/contrib/random/mt_random_engine.cc b/src/runtime/contrib/random/mt_random_engine.cc
index 49bc056dcafb..699f6bbcf376 100644
--- a/src/runtime/contrib/random/mt_random_engine.cc
+++ b/src/runtime/contrib/random/mt_random_engine.cc
@@ -22,8 +22,8 @@
  * \brief mt19937 random engine
  */
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/ndarray.h>
-#include <tvm/support/logging.h>
 
 #include <algorithm>
 #include <ctime>
diff --git a/src/runtime/contrib/random/random.cc b/src/runtime/contrib/random/random.cc
index edcd20883369..2d111bc322ab 100644
--- a/src/runtime/contrib/random/random.cc
+++ b/src/runtime/contrib/random/random.cc
@@ -22,8 +22,8 @@
  */
 #include <dmlc/thread_local.h>
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <algorithm>
 
diff --git a/src/runtime/contrib/rocblas/rocblas.cc b/src/runtime/contrib/rocblas/rocblas.cc
index dca1ebc6ed83..d977b1a211b0 100644
--- a/src/runtime/contrib/rocblas/rocblas.cc
+++ b/src/runtime/contrib/rocblas/rocblas.cc
@@ -23,8 +23,8 @@
 #include "rocblas.h"
 
 #include <tvm/runtime/data_type.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 namespace tvm {
 namespace contrib {
diff --git a/src/runtime/contrib/sort/sort.cc b/src/runtime/contrib/sort/sort.cc
index fba57d923b38..66f36ffa50d6 100644
--- a/src/runtime/contrib/sort/sort.cc
+++ b/src/runtime/contrib/sort/sort.cc
@@ -289,7 +289,7 @@ TVM_REGISTER_GLOBAL("tvm.contrib.sort.sort").set_body([](TVMArgs args, TVMRetVal
     sort<double>(input, output, axis, is_ascend);
 #if (__ARM_FEATURE_FP16_SCALAR_ARITHMETIC == 1)
   } else if (data_dtype == "float16") {
-    sort<__fp16, __fp16>(input, output, axis, is_ascend);
+    sort<__fp16>(input, output, axis, is_ascend);
 #endif
   } else if (data_dtype == "int32") {
     sort<int32_t>(input, output, axis, is_ascend);
diff --git a/src/runtime/contrib/tensorrt/tensorrt_builder.cc b/src/runtime/contrib/tensorrt/tensorrt_builder.cc
index 4060b240cf8e..09b36d720877 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_builder.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_builder.cc
@@ -91,10 +91,6 @@ void TensorRTBuilder::AddInput(int nid, uint32_t entry_id, const JSONGraphNode&
 void TensorRTBuilder::AddConstant(int nid, const DLTensor* data) {
   nvinfer1::Weights weight = GetDLTensorAsWeights(data, kDLCPU);
   std::vector<int> shape(data->shape, data->shape + data->ndim);
-  // Remove batch dim when not in explicit batch mode.
-  if (use_implicit_batch_ && shape.size() > 1 && shape[0] == 1) {
-    shape.erase(shape.begin());
-  }
   node_output_map_[nid] = {TensorRTOpInput(weight, shape)};
 }
 
@@ -103,6 +99,14 @@ void TensorRTBuilder::AddOutput(const JSONGraphNodeEntry& node, uint32_t entry_i
   ICHECK(it != node_output_map_.end()) << "Output was not found.";
   auto out_tensor = it->second[node.index_].tensor;
   std::string name = "tensorrt_output_" + std::to_string(network_output_names_.size());
+  // If the network is already marked as an input or output, make a copy to avoid TRT crash.
+  if (out_tensor->isNetworkOutput()) {
+    LOG(WARNING) << name << " is a duplicate output.";
+    out_tensor = network_->addIdentity(*out_tensor)->getOutput(0);
+  } else if (out_tensor->isNetworkInput()) {
+    LOG(WARNING) << name << " is both an input and an output.";
+    out_tensor = network_->addIdentity(*out_tensor)->getOutput(0);
+  }
   out_tensor->setName(name.c_str());
   network_->markOutput(*out_tensor);
   network_output_names_.push_back(name);
@@ -212,8 +216,18 @@ nvinfer1::Weights TensorRTBuilder::GetDLTensorAsWeights(const DLTensor* dptr,
 
 nvinfer1::ITensor* TensorRTBuilder::GetInputAsTensor(const TensorRTOpInput& input) {
   if (input.type == kTensor) return input.tensor;
-  auto dims = VectorToTrtDims(input.weight_shape);
-  return network_->addConstant(dims, input.weight)->getOutput(0);
+  auto shape = input.weight_shape;
+  // Remove batch dim when not in explicit batch mode.
+  // Example:
+  // x = Relay dims (1, 32, 224, 224) which becomes TRT Dims (32, 224, 224)
+  // y = Relay dims (1, 32)
+  // z = add(x, y)
+  // y needs to have TRT dims (32,), otherwise broadcasting will result in z having
+  // TRT Dims(1, 32, 224, 224) when it should be (32, 224, 224).
+  if (use_implicit_batch_ && shape.size() > 1 && shape[0] == 1) {
+    shape.erase(shape.begin());
+  }
+  return network_->addConstant(VectorToTrtDims(shape), input.weight)->getOutput(0);
 }
 
 void TensorRTBuilder::CleanUp() {
diff --git a/src/runtime/contrib/tensorrt/tensorrt_logger.h b/src/runtime/contrib/tensorrt/tensorrt_logger.h
index 087cb010189c..eb0164210dbb 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_logger.h
+++ b/src/runtime/contrib/tensorrt/tensorrt_logger.h
@@ -25,7 +25,7 @@
 #ifndef TVM_RUNTIME_CONTRIB_TENSORRT_TENSORRT_LOGGER_H_
 #define TVM_RUNTIME_CONTRIB_TENSORRT_TENSORRT_LOGGER_H_
 
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "NvInfer.h"
 #include "tensorrt_utils.h"
diff --git a/src/runtime/contrib/tensorrt/tensorrt_ops.cc b/src/runtime/contrib/tensorrt/tensorrt_ops.cc
index 1e6867b83cff..04b1e838ee8e 100644
--- a/src/runtime/contrib/tensorrt/tensorrt_ops.cc
+++ b/src/runtime/contrib/tensorrt/tensorrt_ops.cc
@@ -309,8 +309,8 @@ class Conv3DOpConverter : public TensorRTOpConverter {
     bool use_asymmetric_padding;
     GetPadding3D(str_padding, &use_asymmetric_padding, &prepadding, &postpadding);
 
-    // Could use attrs->channels.as<IntImmNode>()->value
-    const int num_outputs = weight_shape[0];
+    const int num_outputs =
+        std::stoi(params->node.GetAttr<std::vector<std::string>>("channels")[0]);
     const auto kernel_size = nvinfer1::Dims3(weight_shape[2], weight_shape[3], weight_shape[4]);
     nvinfer1::Weights bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
     auto conv_layer = params->network->addConvolutionNd(*input_tensor, num_outputs, kernel_size,
@@ -447,7 +447,7 @@ class BatchNormOpConverter : public TensorRTOpConverter {
     nvinfer1::IScaleLayer* scale_layer = params->network->addScaleNd(
         *input, nvinfer1::ScaleMode::kCHANNEL, weight_shift, weight_scale, power, channel_dim);
 #else
-    ICHECK_EQ(input->getDimensions().nbDims(), 3);
+    ICHECK_EQ(input->getDimensions().nbDims, 3);
     nvinfer1::IScaleLayer* scale_layer = params->network->addScale(
         *input, nvinfer1::ScaleMode::kCHANNEL, weight_shift, weight_scale, power);
 #endif
@@ -788,8 +788,8 @@ class Conv2DTransposeOpConverter : public TensorRTOpConverter {
     }
 #endif
 
-    // Could use conv2d_attr->channels.as<IntImmNode>()->value
-    const int num_outputs = weight_shape[1];
+    const int num_outputs =
+        std::stoi(params->node.GetAttr<std::vector<std::string>>("channels")[0]);
     const auto kernel_size = nvinfer1::DimsHW(weight_shape[2], weight_shape[3]);
     nvinfer1::Weights bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
     auto deconv_layer = params->network->addDeconvolution(*input_tensor, num_outputs, kernel_size,
@@ -846,8 +846,8 @@ class Conv3DTransposeOpConverter : public TensorRTOpConverter {
     bool use_asymmetric_padding;
     GetPadding3D(str_padding, &use_asymmetric_padding, &prepadding, &postpadding);
 
-    // Could use attrs->channels.as<IntImmNode>()->value
-    const int num_outputs = weight_shape[1];
+    const int num_outputs =
+        std::stoi(params->node.GetAttr<std::vector<std::string>>("channels")[0]);
     const auto kernel_size = nvinfer1::Dims3(weight_shape[2], weight_shape[3], weight_shape[4]);
     nvinfer1::Weights bias{nvinfer1::DataType::kFLOAT, nullptr, 0};
     auto deconv_layer = params->network->addDeconvolutionNd(*input_tensor, num_outputs, kernel_size,
@@ -921,7 +921,6 @@ class ReshapeOpConverter : public TensorRTOpConverter {
 
   void Convert(TensorRTOpConverterParams* params) const {
     auto input = params->inputs.at(0).tensor;
-    ICHECK_EQ(std::stoi(params->node.GetAttr<std::vector<std::string>>("reverse")[0]), false);
     auto str_newshape = params->node.GetAttr<std::vector<std::string>>("newshape");
     std::vector<int> new_shape;
     const int start_index = TRT_HAS_IMPLICIT_BATCH(params) ? 1 : 0;
diff --git a/src/runtime/contrib/thrust/thrust.cu b/src/runtime/contrib/thrust/thrust.cu
index dddbb043fddc..df83b57847a0 100644
--- a/src/runtime/contrib/thrust/thrust.cu
+++ b/src/runtime/contrib/thrust/thrust.cu
@@ -22,7 +22,11 @@
  */
 
 #include <thrust/device_ptr.h>
+#include <thrust/device_vector.h>
 #include <thrust/sort.h>
+#include <thrust/gather.h>
+#include <thrust/scan.h>
+#include <thrust/sequence.h>
 
 #include <tvm/runtime/registry.h>
 #include <dlpack/dlpack.h>
@@ -41,21 +45,19 @@ void thrust_sort(DLTensor* input,
                  DLTensor* out_values,
                  DLTensor* out_indices,
                  bool is_ascend,
-                 const std::function<int(int)> &get_sort_len) {
+                 int n_values) {
   thrust::device_ptr<DataType> data_ptr(static_cast<DataType *>(input->data));
   thrust::device_ptr<DataType> values_ptr(static_cast<DataType *>(out_values->data));
   thrust::device_ptr<IndicesType> indices_ptr(static_cast<IndicesType *>(out_indices->data));
 
-  int n_values = input->shape[input->ndim - 1];
-  int n_iter = 1;
-  for (int i = 0; i < input->ndim - 1; ++i) {
-    n_iter *= input->shape[i];
+  size_t size = 1;
+  for (int i = 0; i < input->ndim; ++i) {
+    size *= input->shape[i];
   }
+  thrust::copy(data_ptr, data_ptr + size, values_ptr);
 
-  thrust::copy(data_ptr, data_ptr + n_iter * n_values, values_ptr);
-
-  for (int i = 0 ; i < n_iter; ++i) {
-    n_values = get_sort_len(i);
+  if (size == static_cast<size_t>(input->shape[input->ndim - 1])) {
+    // A fast path for single segment case
     thrust::sequence(indices_ptr, indices_ptr + n_values);
     if (is_ascend) {
       thrust::sort_by_key(values_ptr, values_ptr + n_values, indices_ptr);
@@ -63,8 +65,47 @@ void thrust_sort(DLTensor* input,
       thrust::sort_by_key(values_ptr, values_ptr + n_values, indices_ptr,
                           thrust::greater<DataType>());
     }
-    values_ptr += n_values;
-    indices_ptr += n_values;
+  } else {
+    // segmented sort by key
+    // Follow the back-to-back stable_sort_by_key strategy explained below
+    // https://groups.google.com/g/thrust-users/c/BoLsxO6b4FY
+    thrust::device_vector<int64_t> argsort_order(size);
+    thrust::sequence(argsort_order.begin(), argsort_order.end());
+
+    // First, sort values and store the sorted order in argsort_order.
+    if (is_ascend) {
+      thrust::stable_sort_by_key(values_ptr, values_ptr + size, argsort_order.begin());
+    } else {
+      thrust::stable_sort_by_key(values_ptr, values_ptr + size, argsort_order.begin(),
+                                 thrust::greater<DataType>());
+    }
+
+    // The following is to create the indices array 0, 1, 2, 0, 1, 2 ... 0, 1, 2
+    // without materializing it
+    auto counting_iter = thrust::counting_iterator<int64_t>(0);
+    auto linear_index_to_sort_axis_index = [n_values] __host__ __device__(int64_t i) {
+      return i % n_values;
+    }; // NOLINT(*)
+    auto init_indices_iter = thrust::make_transform_iterator(counting_iter,
+                                                             linear_index_to_sort_axis_index);
+
+    // This will reorder indices 0, 1, 2 ... in the sorted order of values_ptr
+    thrust::gather(argsort_order.begin(), argsort_order.end(), init_indices_iter, indices_ptr);
+
+    thrust::device_vector<int> segment_ids(size);
+    auto linear_index_to_segment_id = [n_values] __host__ __device__(int64_t i) {
+      return i / n_values;
+    }; // NOLINT(*)
+    // We also reorder segment indices 0, 0, 0, 1, 1, 1 ... in the order of values_ptr
+    thrust::transform(argsort_order.begin(), argsort_order.end(), segment_ids.begin(),
+                      linear_index_to_segment_id);
+
+    // The second sort key-ed by segment_ids would bring segment_ids back to 0, 0, 0, 1, 1, 1 ...
+    // values_ptr and indices_ptr will also be sorted in the order of segmend_ids above
+    // Since sorting has been done in a stable way, relative orderings of values and indices
+    // in the segment do not change and hence they remain sorted.
+    auto key_val_zip = thrust::make_zip_iterator(thrust::make_tuple(values_ptr, indices_ptr));
+    thrust::stable_sort_by_key(segment_ids.begin(), segment_ids.end(), key_val_zip);
   }
 }
 
@@ -72,54 +113,54 @@ void thrust_sort_common(DLTensor* input,
                         DLTensor* values_out,
                         DLTensor* indices_out,
                         bool is_ascend,
-                        const std::function<int(int)> &get_sort_len,
+                        int sort_len,
                         std::string data_dtype,
                         std::string out_dtype) {
   if (data_dtype == "float32") {
     if (out_dtype == "int32") {
-      thrust_sort<float, int32_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<float, int32_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "int64") {
-      thrust_sort<float, int64_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<float, int64_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float32") {
-      thrust_sort<float, float>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<float, float>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float64") {
-      thrust_sort<float, double>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<float, double>(input, values_out, indices_out, is_ascend, sort_len);
     } else {
       LOG(FATAL) << "Unsupported output dtype: " << out_dtype;
     }
   } else if (data_dtype == "float64") {
     if (out_dtype == "int32") {
-      thrust_sort<double, int32_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<double, int32_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "int64") {
-      thrust_sort<double, int64_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<double, int64_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float32") {
-      thrust_sort<double, float>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<double, float>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float64") {
-      thrust_sort<double, double>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<double, double>(input, values_out, indices_out, is_ascend, sort_len);
     } else {
       LOG(FATAL) << "Unsupported output dtype: " << out_dtype;
     }
   } else if (data_dtype == "int32") {
     if (out_dtype == "int32") {
-      thrust_sort<int32_t, int32_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int32_t, int32_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "int64") {
-      thrust_sort<int32_t, int64_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int32_t, int64_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float32") {
-      thrust_sort<int32_t, float>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int32_t, float>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float64") {
-      thrust_sort<int32_t, double>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int32_t, double>(input, values_out, indices_out, is_ascend, sort_len);
     } else {
       LOG(FATAL) << "Unsupported output dtype: " << out_dtype;
     }
   }  else if (data_dtype == "int64") {
     if (out_dtype == "int32") {
-      thrust_sort<int64_t, int32_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int64_t, int32_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "int64") {
-      thrust_sort<int64_t, int64_t>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int64_t, int64_t>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float32") {
-      thrust_sort<int64_t, float>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int64_t, float>(input, values_out, indices_out, is_ascend, sort_len);
     } else if (out_dtype == "float64") {
-      thrust_sort<int64_t, double>(input, values_out, indices_out, is_ascend, get_sort_len);
+      thrust_sort<int64_t, double>(input, values_out, indices_out, is_ascend, sort_len);
     } else {
       LOG(FATAL) << "Unsupported output dtype: " << out_dtype;
     }
@@ -128,25 +169,6 @@ void thrust_sort_common(DLTensor* input,
   }
 }
 
-TVM_REGISTER_GLOBAL("tvm.contrib.thrust.sort_nms")
-.set_body([](TVMArgs args, TVMRetValue* ret) {
-  ICHECK_GE(args.num_args, 5);
-  DLTensor* input = args[0];
-  DLTensor* valid_count = args[1];
-  DLTensor* values_out = args[2];
-  DLTensor* indices_out = args[3];
-  bool is_ascend = args[4];
-
-  auto data_dtype = DLDataType2String(input->dtype);
-  auto out_dtype = DLDataType2String(indices_out->dtype);
-
-  thrust::device_ptr<int> valid_count_ptr(static_cast<int *>(valid_count->data));
-  auto get_sort_len = [&valid_count_ptr](int i) { return valid_count_ptr[i]; };
-  thrust_sort_common(input, values_out, indices_out, is_ascend, get_sort_len,
-                     data_dtype, out_dtype);
-});
-
-
 TVM_REGISTER_GLOBAL("tvm.contrib.thrust.sort")
 .set_body([](TVMArgs args, TVMRetValue* ret) {
   ICHECK_GE(args.num_args, 4);
@@ -159,8 +181,7 @@ TVM_REGISTER_GLOBAL("tvm.contrib.thrust.sort")
   auto out_dtype = DLDataType2String(indices_out->dtype);
 
   int n_values = input->shape[input->ndim - 1];
-  auto get_sort_len = [=](int i) { return n_values; };
-  thrust_sort_common(input, values_out, indices_out, is_ascend, get_sort_len,
+  thrust_sort_common(input, values_out, indices_out, is_ascend, n_values,
                      data_dtype, out_dtype);
 });
 
@@ -245,5 +266,129 @@ TVM_REGISTER_GLOBAL("tvm.contrib.thrust.stable_sort_by_key")
   }
 });
 
+template<typename InType, typename OutType>
+void thrust_scan(DLTensor* data,
+                 DLTensor* output,
+                 bool exclusive) {
+  thrust::device_ptr<InType> data_ptr(static_cast<InType *>(data->data));
+  thrust::device_ptr<OutType> output_ptr(static_cast<OutType *>(output->data));
+  const auto scan_size = data->shape[data->ndim - 1];
+
+  if (scan_size == 0) return;
+
+  size_t size = 1;
+  for (int i = 0; i < data->ndim; ++i) size *= data->shape[i];
+
+  const bool need_cast = std::is_same<InType, OutType>::value == false;
+
+  auto data_cast_ptr = thrust::make_transform_iterator(data_ptr, [] __host__ __device__(InType v) {
+    return static_cast<OutType>(v);
+  }); // NOLINT(*)
+
+  if (size == static_cast<size_t>(data->shape[data->ndim - 1])) {
+    if (exclusive && need_cast) {
+      thrust::exclusive_scan(data_cast_ptr, data_cast_ptr + scan_size, output_ptr);
+    } else if (exclusive && !need_cast) {
+      thrust::exclusive_scan(data_ptr, data_ptr + scan_size, output_ptr);
+    } else if (!exclusive && need_cast) {
+      thrust::inclusive_scan(data_cast_ptr, data_cast_ptr + scan_size, output_ptr);
+    } else {
+      thrust::inclusive_scan(data_ptr, data_ptr + scan_size, output_ptr);
+    }
+  } else {
+    // Use thrust segmented scan to compute scan on the inner most axis
+    // data->shape[0] * data->shape[1] * ... * data->shape[ndim - 2] scans are
+    // computed in parallel
+
+    // This is for constructing a sequence 0, 0, 0,...,1, 1, 1,...,2, 2, 2,...,
+    // without materializing the sequence vector
+    auto counting_iter = thrust::counting_iterator<size_t>(0);
+    // Without __host__ annotation, cub crashes
+    auto linear_index_to_scan_key = [scan_size] __host__ __device__(size_t i) {
+        return i / scan_size;
+    }; // NOLINT(*)
+    auto key_iter = thrust::make_transform_iterator(counting_iter, linear_index_to_scan_key);
+
+    if (exclusive && need_cast) {
+      thrust::exclusive_scan_by_key(key_iter, key_iter + size, data_cast_ptr, output_ptr);
+    } else if (exclusive && !need_cast) {
+      thrust::exclusive_scan_by_key(key_iter, key_iter + size, data_ptr, output_ptr);
+    } else if (!exclusive && need_cast) {
+      thrust::inclusive_scan_by_key(key_iter, key_iter + size, data_cast_ptr, output_ptr);
+    } else {
+      thrust::inclusive_scan_by_key(key_iter, key_iter + size, data_ptr, output_ptr);
+    }
+  }
+}
+
+TVM_REGISTER_GLOBAL("tvm.contrib.thrust.sum_scan")
+.set_body([](TVMArgs args, TVMRetValue* ret) {
+  ICHECK_EQ(args.num_args, 3);
+  DLTensor* data = args[0];
+  DLTensor* output = args[1];
+  bool exclusive = args[2];
+
+  auto in_dtype = DLDataType2String(data->dtype);
+  auto out_dtype = DLDataType2String(output->dtype);
+
+  if (in_dtype == "bool") {
+    if (out_dtype == "int32") {
+      thrust_scan<bool, int>(data, output, exclusive);
+    } else if (out_dtype == "int64") {
+      thrust_scan<bool, int64_t>(data, output, exclusive);
+    } else if (out_dtype == "float32") {
+      thrust_scan<bool, float>(data, output, exclusive);
+    } else if (out_dtype == "float64") {
+      thrust_scan<bool, double>(data, output, exclusive);
+    } else {
+      LOG(FATAL) << "Unsupported output dtype: " << out_dtype
+                 << ". Supported output dtypes are int32, int64, float32, and float64";
+    }
+  } else if (in_dtype == "int32") {
+    if (out_dtype == "int32") {
+      thrust_scan<int, int>(data, output, exclusive);
+    } else if (out_dtype == "int64") {
+      thrust_scan<int, int64_t>(data, output, exclusive);
+    } else if (out_dtype == "float32") {
+      thrust_scan<int, float>(data, output, exclusive);
+    } else if (out_dtype == "float64") {
+      thrust_scan<int, double>(data, output, exclusive);
+    } else {
+      LOG(FATAL) << "Unsupported output dtype: " << out_dtype
+                 << ". Supported output dtypes are int32, int64, float32, and float64";
+    }
+  } else if (in_dtype == "int64") {
+    if (out_dtype == "int64") {
+      thrust_scan<int64_t, int64_t>(data, output, exclusive);
+    } else if (out_dtype == "float32") {
+      thrust_scan<int64_t, float>(data, output, exclusive);
+    } else if (out_dtype == "float64") {
+      thrust_scan<int64_t, double>(data, output, exclusive);
+    } else {
+      LOG(FATAL) << "Unsupported output dtype: " << out_dtype
+                 << ". Supported output dtypes are int64, float32, and float64";
+    }
+  } else if (in_dtype == "float32") {
+    if (out_dtype == "float32") {
+      thrust_scan<float, float>(data, output, exclusive);
+    } else if (out_dtype == "float64") {
+      thrust_scan<float, double>(data, output, exclusive);
+    } else {
+      LOG(FATAL) << "Unsupported output dtype: " << out_dtype
+                 << ". Supported output dtypes are float32, and float64";
+    }
+  } else if (in_dtype == "float64") {
+    if (out_dtype == "float64") {
+      thrust_scan<double, double>(data, output, exclusive);
+    } else {
+      LOG(FATAL) << "Unsupported output dtype: " << out_dtype
+                 << ". Supported output dtype is float64";
+    }
+  } else {
+    LOG(FATAL) << "Unsupported input dtype: " << in_dtype
+               << ". Supported input dtypes are bool, int32, int64, float32, and float64";
+  }
+});
+
 }  // namespace contrib
 }  // namespace tvm
diff --git a/src/runtime/contrib/verilator/verilator_device.h b/src/runtime/contrib/verilator/verilator_device.h
index acd91a53bcff..298e41c06daf 100644
--- a/src/runtime/contrib/verilator/verilator_device.h
+++ b/src/runtime/contrib/verilator/verilator_device.h
@@ -31,24 +31,51 @@ namespace tvm {
 namespace runtime {
 namespace contrib {
 
+/*! \brief Verilator device resource context  */
 typedef void* VerilatorHandle;
 
-/* allocate Verilator object */
+/*!
+ * \brief Allocate a verilator device resource handle
+ * \return The verilator device handle.
+ */
 extern "C" TVM_DLL VerilatorHandle VerilatorAlloc();
 
-/* deallocate Verilator object */
+/*!
+ * \brief Free a verilator device handle
+ * \param handle The verilator device handle to be freed.
+ */
 extern "C" TVM_DLL void VerilatorDealloc(VerilatorHandle handle);
 
-/* read Verilator register or memory */
+/*!
+ * \brief Read verilator register or memory
+ * \param handle The verilator device handle.
+ * \param id The register or memory identifier.
+ * \param addr The register or memory address (word-level).
+ * \return The value of register or memory.
+ */
 extern "C" TVM_DLL int VerilatorRead(VerilatorHandle handle, int id, int addr);
 
-/* write Verilator register or memory */
+/*!
+ * \brief Write verilator register or memory
+ * \param handle The verilator device handle.
+ * \param id The register or memory identifier.
+ * \param addr The register or memory address (word-level).
+ * \param value The value of register or memory.
+ */
 extern "C" TVM_DLL void VerilatorWrite(VerilatorHandle handle, int id, int addr, int value);
 
-/* reset Verilator for n clock cycles */
+/*!
+ * \brief Reset Verilator for n clock cycles
+ * \param handle The verilator device handle.
+ * \param n The number of reset cycles.
+ */
 extern "C" TVM_DLL void VerilatorReset(VerilatorHandle handle, int n);
 
-/* run Verilator for n clock cycles */
+/*!
+ * \brief Run Verilator for n clock cycles
+ * \param handle The verilator device handle.
+ * \param n The number of run cycles.
+ */
 extern "C" TVM_DLL void VerilatorRun(VerilatorHandle handle, int n);
 
 }  // namespace contrib
diff --git a/src/runtime/contrib/verilator/verilator_runtime.cc b/src/runtime/contrib/verilator/verilator_runtime.cc
index a44faf6d3274..5dfb8441c864 100644
--- a/src/runtime/contrib/verilator/verilator_runtime.cc
+++ b/src/runtime/contrib/verilator/verilator_runtime.cc
@@ -19,9 +19,12 @@
 
 /*!
  * \file src/runtime/contrib/verilator/verilator_runtime.cc
- * \brief A simple JSON runtime for Verilator.
+ * \brief A runtime for Verilator.
  */
 
+#include "verilator_runtime.h"
+
+#include <dlfcn.h>
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/registry.h>
 
@@ -29,6 +32,7 @@
 #include <string>
 #include <vector>
 
+#include "../../library_module.h"
 #include "../json/json_node.h"
 #include "../json/json_runtime.h"
 #include "verilator_device.h"
@@ -39,77 +43,122 @@ namespace runtime {
 namespace contrib {
 
 using namespace tvm::runtime;
+using namespace tvm::runtime::contrib;
 using namespace tvm::runtime::json;
 
-class VerilatorJSONRuntime : public JSONRuntimeBase {
- public:
-  VerilatorJSONRuntime(const std::string& symbol_name, const std::string& graph_json,
-                       const Array<String> const_names)
-      : JSONRuntimeBase(symbol_name, graph_json, const_names) {}
+VerilatorLibrary::~VerilatorLibrary() {
+  if (lib_handle_) {
+    dlclose(lib_handle_);
+    lib_handle_ = nullptr;
+  }
+}
 
-  const char* type_key() const { return "verilator_json"; }
+void VerilatorLibrary::Load(const std::string& name) {
+  lib_handle_ = dlopen(name.c_str(), RTLD_LAZY | RTLD_LOCAL);
+  ICHECK(lib_handle_ != nullptr) << "Failed to load dynamic shared library " << name << " "
+                                 << dlerror();
+}
 
-  void Init(const Array<NDArray>& consts) override {
-    BuildEngine();
+void* VerilatorLibrary::GetSymbol(const char* name) { return dlsym(lib_handle_, name); }
 
-    CHECK_EQ(consts.size(), const_idx_.size())
-        << "The number of input constants must match the number of required.";
+void VerilatorProfiler::Clear() { cycle_counter = 0; }
 
-    // Setup constants entries for weights.
-    SetupConstants(consts);
-  }
+std::string VerilatorProfiler::AsJSON() {
+  std::ostringstream os;
+  os << "{\n"
+     << " \"cycle_counter\":" << cycle_counter << "\n"
+     << "}\n";
+  return os.str();
+}
 
-  void Run() override {
-    std::vector<int*> in_ptr;
-    std::vector<int*> out_ptr;
-    for (size_t i = 0; i < input_nodes_.size(); ++i) {
-      uint32_t eid = EntryID(input_nodes_[i], 0);
-      int* data = static_cast<int*>(data_entry_[eid]->data);
-      in_ptr.push_back(data);
-    }
-    for (size_t i = 0; i < outputs_.size(); ++i) {
-      uint32_t eid = EntryID(outputs_[i]);
-      int* data = static_cast<int*>(data_entry_[eid]->data);
-      out_ptr.push_back(data);
-    }
-    for (size_t nid = 0; nid < nodes_.size(); ++nid) {
-      const auto& node = nodes_[nid];
-      if (node.GetOpType() == "kernel") {
-        CHECK_EQ(node.GetOpType(), "kernel");
-        auto op_name = node.GetOpName();
-        if ("add" == op_name) {
-          auto entry = node.GetInputs()[0];
-          auto shape = nodes_[entry.id_].GetOpShape()[entry.index_];
-          verilator_add(device_, in_ptr[0], in_ptr[1], out_ptr[0], shape[0], shape[1]);
-        } else {
-          LOG(FATAL) << "Unsupported op: " << op_name;
-        }
+VerilatorProfiler* VerilatorProfiler::ThreadLocal() {
+  static thread_local VerilatorProfiler inst;
+  return &inst;
+}
+
+VerilatorRuntime::~VerilatorRuntime() {
+  auto dealloc = reinterpret_cast<VerilatorDeallocFunc>(lib_->GetSymbol("VerilatorDealloc"));
+  ICHECK(dealloc != nullptr);
+  dealloc(device_);
+  delete lib_;
+}
+
+void VerilatorRuntime::SetLibrary(const std::string& lib_path) { lib_path_ = lib_path; }
+
+void VerilatorRuntime::SetResetCycles(const int cycles) { reset_cycles_ = cycles; }
+
+void VerilatorRuntime::EnableProfiler() { prof_enable_ = true; }
+
+void VerilatorRuntime::SetProfilerCycleCounterId(const int id) { prof_cycle_counter_id_ = id; }
+
+void VerilatorRuntime::Init(const Array<NDArray>& consts) {
+  lib_ = new VerilatorLibrary();
+  lib_->Load(lib_path_);
+  auto alloc = reinterpret_cast<VerilatorAllocFunc>(lib_->GetSymbol("VerilatorAlloc"));
+  ICHECK(alloc != nullptr);
+  auto reset = reinterpret_cast<VerilatorResetFunc>(lib_->GetSymbol("VerilatorReset"));
+  ICHECK(reset != nullptr);
+  read_ = reinterpret_cast<VerilatorReadFunc>(lib_->GetSymbol("VerilatorRead"));
+  ICHECK(read_ != nullptr);
+  add_op_ = reinterpret_cast<VerilatorAddFunc>(lib_->GetSymbol("verilator_add"));
+
+  // alloc verilator device
+  device_ = alloc();
+
+  // enable profiler
+  if (prof_enable_) prof_ = VerilatorProfiler::ThreadLocal();
+
+  // reset verilator device.
+  reset(device_, reset_cycles_);
+
+  CHECK_EQ(consts.size(), const_idx_.size())
+      << "The number of input constants must match the number of required.";
+
+  // Setup constants entries for weights.
+  SetupConstants(consts);
+}
+
+void VerilatorRuntime::Run() {
+  std::vector<int*> in_ptr;
+  std::vector<int*> out_ptr;
+  for (size_t i = 0; i < input_nodes_.size(); ++i) {
+    uint32_t eid = EntryID(input_nodes_[i], 0);
+    int* data = static_cast<int*>(data_entry_[eid]->data);
+    in_ptr.push_back(data);
+  }
+  for (size_t i = 0; i < outputs_.size(); ++i) {
+    uint32_t eid = EntryID(outputs_[i]);
+    int* data = static_cast<int*>(data_entry_[eid]->data);
+    out_ptr.push_back(data);
+  }
+  for (size_t nid = 0; nid < nodes_.size(); ++nid) {
+    const auto& node = nodes_[nid];
+    if (node.GetOpType() == "kernel") {
+      CHECK_EQ(node.GetOpType(), "kernel");
+      auto op_name = node.GetOpName();
+      if ("add" == op_name) {
+        auto entry = node.GetInputs()[0];
+        auto shape = nodes_[entry.id_].GetOpShape()[entry.index_];
+        ICHECK(add_op_ != nullptr);
+        add_op_(device_, in_ptr[0], in_ptr[1], out_ptr[0], shape[0], shape[1]);
+      } else {
+        LOG(FATAL) << "Unsupported op: " << op_name;
       }
     }
   }
-
- private:
-  void BuildEngine() {
-    device_ = VerilatorAlloc();
-    // reset for 10 cycles
-    VerilatorReset(device_, 10);
+  if (prof_enable_) {
+    int cycles = read_(device_, prof_cycle_counter_id_, 0);
+    prof_->cycle_counter += cycles;
   }
-
-  /* The verilator handle. */
-  VerilatorHandle device_{nullptr};
-};
-
-runtime::Module VerilatorJSONRuntimeCreate(String symbol_name, String graph_json,
-                                           const Array<String>& const_names) {
-  auto n = make_object<VerilatorJSONRuntime>(symbol_name, graph_json, const_names);
-  return runtime::Module(n);
 }
 
-TVM_REGISTER_GLOBAL("runtime.VerilatorJSONRuntimeCreate")
-    .set_body_typed(VerilatorJSONRuntimeCreate);
+TVM_REGISTER_GLOBAL("verilator.profiler_clear").set_body([](TVMArgs args, TVMRetValue* rv) {
+  VerilatorProfiler::ThreadLocal()->Clear();
+});
 
-TVM_REGISTER_GLOBAL("runtime.module.loadbinary_verilator_json")
-    .set_body_typed(JSONRuntimeBase::LoadFromBinary<VerilatorJSONRuntime>);
+TVM_REGISTER_GLOBAL("verilator.profiler_status").set_body([](TVMArgs args, TVMRetValue* rv) {
+  *rv = VerilatorProfiler::ThreadLocal()->AsJSON();
+});
 
 }  // namespace contrib
 }  // namespace runtime
diff --git a/src/runtime/contrib/verilator/verilator_runtime.h b/src/runtime/contrib/verilator/verilator_runtime.h
new file mode 100644
index 000000000000..acdaa3b03ce2
--- /dev/null
+++ b/src/runtime/contrib/verilator/verilator_runtime.h
@@ -0,0 +1,138 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/runtime/contrib/verilator/verilator_runtime.h
+ * \brief A runtime for Verilator.
+ */
+
+#ifndef TVM_RUNTIME_CONTRIB_VERILATOR_VERILATOR_RUNTIME_H_
+#define TVM_RUNTIME_CONTRIB_VERILATOR_VERILATOR_RUNTIME_H_
+
+#include <dlfcn.h>
+#include <tvm/runtime/ndarray.h>
+#include <tvm/runtime/registry.h>
+
+#include <cstddef>
+#include <string>
+#include <vector>
+
+#include "../../library_module.h"
+#include "../json/json_node.h"
+#include "../json/json_runtime.h"
+#include "verilator_device.h"
+#include "verilator_kernel.h"
+
+namespace tvm {
+namespace runtime {
+namespace contrib {
+
+using namespace tvm::runtime;
+using namespace tvm::runtime::contrib;
+using namespace tvm::runtime::json;
+
+typedef VerilatorHandle (*VerilatorAllocFunc)();
+typedef void (*VerilatorDeallocFunc)(VerilatorHandle);
+typedef void (*VerilatorResetFunc)(VerilatorHandle, int);
+typedef void (*VerilatorAddFunc)(VerilatorHandle, int*, int*, int*, int, int);
+typedef int (*VerilatorReadFunc)(VerilatorHandle, int, int);
+
+class VerilatorLibrary : public Library {
+ public:
+  ~VerilatorLibrary();
+
+  /*! \brief load library */
+  void Load(const std::string& name);
+
+  /*! \brief get symbol from libray */
+  void* GetSymbol(const char* name) final;
+
+ private:
+  /*! \brief the library handle */
+  void* lib_handle_{nullptr};
+};
+
+class VerilatorProfiler {
+ public:
+  /*! \brief the number of cycle counter */
+  uint32_t cycle_counter{0};
+
+  /*! \brief clear the profiler */
+  void Clear();
+
+  /*! \brief get profiler data */
+  std::string AsJSON();
+
+  /*! \brief profiler constructor */
+  static VerilatorProfiler* ThreadLocal();
+};
+
+class VerilatorRuntime : public JSONRuntimeBase {
+ public:
+  VerilatorRuntime(const std::string& symbol_name, const std::string& graph_json,
+                   const Array<String> const_names)
+      : JSONRuntimeBase(symbol_name, graph_json, const_names) {}
+
+  ~VerilatorRuntime();
+
+  const char* type_key() const { return "verilator"; }
+
+  /*! \brief set verilator library */
+  void SetLibrary(const std::string& lib_name);
+
+  /*! \brief set the number of reset cycles */
+  void SetResetCycles(const int cycles);
+
+  /*! \brief enable profiler */
+  void EnableProfiler();
+
+  /*! \brief set cycle counter register id */
+  void SetProfilerCycleCounterId(const int id);
+
+  /*! \brief init verilator runtime */
+  void Init(const Array<NDArray>& consts) override;
+
+  /*! \brief run verilator runtime */
+  void Run() override;
+
+ private:
+  /*! \brief the verilator library path */
+  String lib_path_;
+  /*! \brief the verilator device */
+  VerilatorHandle device_{nullptr};
+  /*! \brief the verilator library */
+  VerilatorLibrary* lib_{nullptr};
+  /*! \brief the verilator profiler */
+  VerilatorProfiler* prof_{nullptr};
+  /*! \brief the verilator read function */
+  VerilatorReadFunc read_{nullptr};
+  /*! \brief the verilator add op function */
+  VerilatorAddFunc add_op_{nullptr};
+  /*! \brief the verilator reset cycles */
+  int reset_cycles_{1};
+  /*! \brief the verilator profiler status */
+  bool prof_enable_{false};
+  /*! \brief the verilator profiler cycle counter id */
+  int prof_cycle_counter_id_{0};
+};
+
+}  // namespace contrib
+}  // namespace runtime
+}  // namespace tvm
+#endif  // TVM_RUNTIME_CONTRIB_VERILATOR_VERILATOR_RUNTIME_H_
diff --git a/src/runtime/contrib/vitis_ai/vitis_ai_runtime.cc b/src/runtime/contrib/vitis_ai/vitis_ai_runtime.cc
index 37dc767d31af..0e5e2ce4c4fa 100755
--- a/src/runtime/contrib/vitis_ai/vitis_ai_runtime.cc
+++ b/src/runtime/contrib/vitis_ai/vitis_ai_runtime.cc
@@ -25,6 +25,7 @@
 
 #include <tvm/runtime/registry.h>
 
+#include <cassert>
 #include <fstream>
 #include <streambuf>
 #include <string>
diff --git a/src/runtime/cpu_device_api.cc b/src/runtime/cpu_device_api.cc
index 146bfa804785..133bb01d7d13 100644
--- a/src/runtime/cpu_device_api.cc
+++ b/src/runtime/cpu_device_api.cc
@@ -22,8 +22,8 @@
  */
 #include <dmlc/thread_local.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <cstdlib>
 #include <cstring>
@@ -69,12 +69,6 @@ class CPUDeviceAPI final : public DeviceAPI {
 #endif
   }
 
-  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
-                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
-                      TVMStreamHandle stream) final {
-    memcpy(static_cast<char*>(to) + to_offset, static_cast<const char*>(from) + from_offset, size);
-  }
-
   void StreamSync(TVMContext ctx, TVMStreamHandle stream) final {}
 
   void* AllocWorkspace(TVMContext ctx, size_t size, DLDataType type_hint) final;
@@ -86,6 +80,13 @@ class CPUDeviceAPI final : public DeviceAPI {
     static auto* inst = new CPUDeviceAPI();
     return inst;
   }
+
+ protected:
+  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
+                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
+                      TVMStreamHandle stream) final {
+    memcpy(static_cast<char*>(to) + to_offset, static_cast<const char*>(from) + from_offset, size);
+  }
 };
 
 struct CPUWorkspacePool : public WorkspacePool {
diff --git a/src/runtime/crt/Makefile b/src/runtime/crt/Makefile
index 0f3e3096e319..d707d0c63b81 100644
--- a/src/runtime/crt/Makefile
+++ b/src/runtime/crt/Makefile
@@ -45,8 +45,8 @@ QUIET ?= @
 CRT_PREFIX = $(wildcard src/crt)
 
 INCLUDES ?= -isystem include -iquote $(dir ${CRT_CONFIG})
-CFLAGS += ${INCLUDES} -Werror -g $(EXTRA_CFLAGS)
-CXXFLAGS += ${INCLUDES} -std=c++11 -Werror -g $(EXTRA_CXXFLAGS)
+CFLAGS += ${INCLUDES} -Werror -g $(EXTRA_CFLAGS) -DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>
+CXXFLAGS += ${INCLUDES} -std=c++11 -Werror -g $(EXTRA_CXXFLAGS) -DDMLC_USE_LOGGING_LIBRARY=\<tvm/runtime/logging.h\>
 LDFLAGS += -Werror -g $(EXTRA_LDFLAGS)
 
 ${BUILD_DIR}/%.o: src/%.c $(CRT_CONFIG)
diff --git a/src/runtime/crt/common/crt_runtime_api.c b/src/runtime/crt/common/crt_runtime_api.c
index fcfb51f9ef4c..c2eb1ff903e3 100644
--- a/src/runtime/crt/common/crt_runtime_api.c
+++ b/src/runtime/crt/common/crt_runtime_api.c
@@ -22,6 +22,7 @@
 #include <assert.h>
 #include <inttypes.h>
 #include <stdarg.h>
+#include <stdbool.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
@@ -38,7 +39,10 @@
 
 static char g_last_error[1024];
 
-void TVMAPISetLastError(const char* msg) { strncpy(g_last_error, msg, sizeof(g_last_error)); }
+void TVMAPISetLastError(const char* msg) {
+  strncpy(g_last_error, msg, sizeof(g_last_error) - 1);
+  g_last_error[sizeof(g_last_error) - 1] = 0;
+}
 
 __attribute__((format(printf, 1, 2))) int TVMAPIErrorf(const char* msg, ...) {
   va_list args;
@@ -84,16 +88,44 @@ int TVMDeviceAllocDataSpace(DLContext ctx, size_t nbytes, size_t alignment, DLDa
   if (alignment != 1) {
     nbytes = (nbytes + alignment - 1) / alignment * alignment;
   }
-
   return TVMPlatformMemoryAllocate(nbytes, ctx, out_data);
 }
 
+int TVMDeviceAllocDataSpaceWithScope(DLContext ctx, int ndim, const int64_t* shape,
+                                     DLDataType dtype, const char* mem_scope, void** out_data) {
+  size_t nbytes = 1;
+  for (int i = 0; i < ndim; ++i) {
+    nbytes *= shape[i];
+  }
+  nbytes *= (dtype.bits * dtype.lanes + 7) / 8;
+
+  int kAllocAlignment = 128;
+  size_t align = (dtype.bits / 8) * dtype.lanes;
+  if (align < kAllocAlignment) align = kAllocAlignment;
+  return TVMDeviceAllocDataSpace(ctx, nbytes, align, dtype, out_data);
+}
+
 int TVMDeviceFreeDataSpace(TVMContext ctx, void* ptr) { return TVMPlatformMemoryFree(ptr, ctx); }
 
-int TVMDeviceCopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
-                            size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
-                            DLDataType type_hint, TVMStreamHandle stream) {
-  memcpy(((uint8_t*)to) + to_offset, ((uint8_t*)from) + from_offset, num_bytes);
+static bool IsContiguous(const DLTensor* arr) {
+  if (arr->strides == NULL) return true;
+  int64_t expected_stride = 1;
+  for (int32_t i = arr->ndim; i != 0; --i) {
+    int32_t k = i - 1;
+    if (arr->strides[k] != expected_stride) return false;
+    expected_stride *= arr->shape[k];
+  }
+  return true;
+}
+
+int TVMDeviceCopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream) {
+  assert(IsContiguous(from) && IsContiguous(to));
+  size_t size = 1;
+  for (int i = 0; i < from->ndim; ++i) {
+    size *= from->shape[i];
+  }
+  size *= (from->dtype.bits * from->dtype.lanes + 7) / 8;
+  memcpy(((uint8_t*)to->data) + to->byte_offset, ((uint8_t*)from->data) + from->byte_offset, size);
   return 0;
 }
 
@@ -506,3 +538,8 @@ release_and_return : {
 }
   return err;
 }
+
+// Default implementation, overridden by the platform runtime.
+__attribute__((weak)) tvm_crt_error_t TVMPlatformGenerateRandom(uint8_t* buffer, size_t num_bytes) {
+  return kTvmErrorFunctionCallNotImplemented;
+}
diff --git a/src/runtime/crt/common/ndarray.c b/src/runtime/crt/common/ndarray.c
index 33dcaab0e77b..c90a4667903c 100644
--- a/src/runtime/crt/common/ndarray.c
+++ b/src/runtime/crt/common/ndarray.c
@@ -68,22 +68,22 @@ int TVMNDArray_Empty(int32_t ndim, const tvm_index_t* shape, DLDataType dtype, D
 int TVMNDArray_Load(TVMNDArray* ret, const char** strm) {
   int32_t status = 0;
   uint64_t header, reserved;
-  header = ((uint64_t*)*strm)[0];  // NOLINT(*)
+  memcpy(&header, *strm, sizeof(header));
   *strm += sizeof(header);
   if (header != kTVMNDArrayMagic) {
     fprintf(stderr, "Invalid DLTensor file format\n");
     status = -1;
   }
-  reserved = ((uint64_t*)*strm)[0];  // NOLINT(*)
+  memcpy(&reserved, *strm, sizeof(reserved));
   *strm += sizeof(reserved);
   DLContext ctx;
   int ndim;  // sizeof ndim should match dlpack
   DLDataType dtype;
-  ctx = ((DLContext*)*strm)[0];  // NOLINT(*)
+  memcpy(&ctx, *strm, sizeof(ctx));
   *strm += sizeof(ctx);
-  ndim = ((int*)*strm)[0];  // NOLINT(*)
+  memcpy(&ndim, *strm, sizeof(ndim));
   *strm += sizeof(ndim);
-  dtype = ((DLDataType*)*strm)[0];  // NOLINT(*)
+  memcpy(&dtype, *strm, sizeof(dtype));
   *strm += sizeof(dtype);
   if ((ndim < 0) || (ndim > TVM_CRT_MAX_NDIM)) {
     fprintf(stderr, "Invalid ndim=%d: expected to be 0 ~ %d.\n", ndim, TVM_CRT_MAX_NDIM);
@@ -97,7 +97,7 @@ int TVMNDArray_Load(TVMNDArray* ret, const char** strm) {
   int32_t idx;
   if (ndim != 0) {
     for (idx = 0; idx < ndim; idx++) {
-      shape[idx] = ((int64_t*)*strm)[0];  // NOLINT(*)
+      memcpy(&shape[idx], *strm, sizeof(int64_t));
       *strm += sizeof(shape[idx]);
     }
   }
@@ -111,7 +111,7 @@ int TVMNDArray_Load(TVMNDArray* ret, const char** strm) {
     num_elems *= ret->dl_tensor.shape[idx];
   }
   int64_t data_byte_size;
-  data_byte_size = ((int64_t*)*strm)[0];  // NOLINT(*)
+  memcpy(&data_byte_size, *strm, sizeof(data_byte_size));
   *strm += sizeof(data_byte_size);
   if (!(data_byte_size == num_elems * elem_bytes)) {
     fprintf(stderr,
diff --git a/src/runtime/crt/graph_runtime/graph_runtime.c b/src/runtime/crt/graph_runtime/graph_runtime.c
index 9f7b53c997f8..21b72f0e400c 100644
--- a/src/runtime/crt/graph_runtime/graph_runtime.c
+++ b/src/runtime/crt/graph_runtime/graph_runtime.c
@@ -777,13 +777,13 @@ int TVMGraphRuntime_LoadParams(TVMGraphRuntime* runtime, const char* param_blob,
   int status = 0;
   const char* bptr = param_blob;
   uint64_t header, reserved;
-  header = ((uint64_t*)bptr)[0];  // NOLINT(*)
+  memcpy(&header, bptr, sizeof(header));
   bptr += sizeof(header);
   if (header != kTVMNDArrayListMagic) {
     fprintf(stderr, "Invalid parameters file format");
     status = -1;
   }
-  reserved = ((uint64_t*)bptr)[0];  // NOLINT(*)
+  memcpy(&reserved, bptr, sizeof(reserved));
   bptr += sizeof(reserved);
 
   // read names
@@ -799,11 +799,11 @@ int TVMGraphRuntime_LoadParams(TVMGraphRuntime* runtime, const char* param_blob,
   memset(names, 0, TVM_CRT_STRLEN_NAME * runtime->nodes_count);
   uint64_t names_count;
   int idx;
-  names_count = ((uint64_t*)bptr)[0];  // NOLINT(*)
+  memcpy(&names_count, bptr, sizeof(names_count));
   bptr += sizeof(names_count);
   for (idx = 0; idx < names_count; idx++) {
     uint64_t name_length;
-    name_length = ((uint64_t*)bptr)[0];  // NOLINT(*)
+    memcpy(&name_length, bptr, sizeof(name_length));
     bptr += sizeof(name_length);
     if (name_length >= TVM_CRT_STRLEN_NAME) {
       fprintf(stderr, "Error: function name longer than expected.\n");
@@ -815,7 +815,7 @@ int TVMGraphRuntime_LoadParams(TVMGraphRuntime* runtime, const char* param_blob,
 
   // read sizes
   uint64_t sz;
-  sz = ((uint64_t*)bptr)[0];  // NOLINT(*)
+  memcpy(&sz, bptr, sizeof(sz));
   bptr += sizeof(sz);
   uint32_t size = sz;
   if (size != names_count) {
diff --git a/src/runtime/crt/graph_runtime/load_json.c b/src/runtime/crt/graph_runtime/load_json.c
index 6de49a3f9789..3d1fb601a355 100644
--- a/src/runtime/crt/graph_runtime/load_json.c
+++ b/src/runtime/crt/graph_runtime/load_json.c
@@ -173,7 +173,7 @@ char JSONReader_PeekNextNonSpace(JSONReader* reader) {
  * \param out_str the output string. NULL to merely consume input and discard it.
  * \param out_str_size Number of bytes available to write starting from out_str. Includes
  *      terminating \0.
- * \throw dmlc::Error when next token is not string
+ * \throw tvm::Error when next token is not string
  */
 int JSONReader_ReadString(JSONReader* reader, char* out_str, size_t out_str_size) {
   int status = 0;
diff --git a/src/runtime/crt/host/main.cc b/src/runtime/crt/host/main.cc
index 7db17f50ccbf..bf36deacb938 100644
--- a/src/runtime/crt/host/main.cc
+++ b/src/runtime/crt/host/main.cc
@@ -22,6 +22,7 @@
  * \brief main entry point for host subprocess-based CRT
  */
 #include <inttypes.h>
+#include <time.h>
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/crt/logging.h>
 #include <tvm/runtime/crt/memory.h>
@@ -93,6 +94,20 @@ tvm_crt_error_t TVMPlatformTimerStop(double* elapsed_time_seconds) {
   g_utvm_timer_running = 0;
   return kTvmErrorNoError;
 }
+
+static_assert(RAND_MAX >= (1 << 8), "RAND_MAX is smaller than acceptable");
+unsigned int random_seed = 0;
+tvm_crt_error_t TVMPlatformGenerateRandom(uint8_t* buffer, size_t num_bytes) {
+  if (random_seed == 0) {
+    random_seed = (unsigned int)time(NULL);
+  }
+  for (size_t i = 0; i < num_bytes; ++i) {
+    int random = rand_r(&random_seed);
+    buffer[i] = (uint8_t)random;
+  }
+
+  return kTvmErrorNoError;
+}
 }
 
 uint8_t memory[512 * 1024];
diff --git a/src/runtime/crt/utvm_rpc_common/session.cc b/src/runtime/crt/utvm_rpc_common/session.cc
index 5930863da37a..e1e338e42825 100644
--- a/src/runtime/crt/utvm_rpc_common/session.cc
+++ b/src/runtime/crt/utvm_rpc_common/session.cc
@@ -95,7 +95,10 @@ tvm_crt_error_t Session::StartSession() {
   return to_return;
 }
 
-tvm_crt_error_t Session::Initialize() { return TerminateSession(); }
+tvm_crt_error_t Session::Initialize(uint8_t initial_session_nonce) {
+  local_nonce_ = initial_session_nonce;
+  return TerminateSession();
+}
 
 tvm_crt_error_t Session::TerminateSession() {
   SetSessionId(0, 0);
diff --git a/src/runtime/crt/utvm_rpc_server/rpc_server.cc b/src/runtime/crt/utvm_rpc_server/rpc_server.cc
index 074799c44b1d..0b9e96cd660f 100644
--- a/src/runtime/crt/utvm_rpc_server/rpc_server.cc
+++ b/src/runtime/crt/utvm_rpc_server/rpc_server.cc
@@ -112,7 +112,7 @@ class MicroRPCServer {
                  utvm_rpc_channel_write_t write_func, void* write_func_ctx)
       : receive_buffer_{receive_storage, receive_storage_size_bytes},
         framer_{&send_stream_},
-        session_{0xa5, &framer_, &receive_buffer_, &HandleCompleteMessageCb, this},
+        session_{&framer_, &receive_buffer_, &HandleCompleteMessageCb, this},
         io_{&session_, &receive_buffer_},
         unframer_{session_.Receiver()},
         rpc_server_{&io_},
@@ -120,7 +120,13 @@ class MicroRPCServer {
 
   void* operator new(size_t count, void* ptr) { return ptr; }
 
-  void Initialize() { CHECK_EQ(kTvmErrorNoError, session_.Initialize(), "rpc server init"); }
+  void Initialize() {
+    uint8_t initial_session_nonce = Session::kInvalidNonce;
+    tvm_crt_error_t error =
+        TVMPlatformGenerateRandom(&initial_session_nonce, sizeof(initial_session_nonce));
+    CHECK_EQ(kTvmErrorNoError, error, "generating random session id");
+    CHECK_EQ(kTvmErrorNoError, session_.Initialize(initial_session_nonce), "rpc server init");
+  }
 
   /*! \brief Process one message from the receive buffer, if possible.
    *
@@ -242,7 +248,7 @@ void TVMLogf(const char* format, ...) {
   } else {
     tvm::runtime::micro_rpc::SerialWriteStream write_stream;
     tvm::runtime::micro_rpc::Framer framer{&write_stream};
-    tvm::runtime::micro_rpc::Session session{0xa5, &framer, nullptr, nullptr, nullptr};
+    tvm::runtime::micro_rpc::Session session{&framer, nullptr, nullptr, nullptr};
     tvm_crt_error_t err =
         session.SendMessage(tvm::runtime::micro_rpc::MessageType::kLog,
                             reinterpret_cast<uint8_t*>(log_buffer), num_bytes_logged);
diff --git a/src/runtime/cuda/cuda_device_api.cc b/src/runtime/cuda/cuda_device_api.cc
index 30abfc8dc559..f156d68d283e 100644
--- a/src/runtime/cuda/cuda_device_api.cc
+++ b/src/runtime/cuda/cuda_device_api.cc
@@ -25,6 +25,7 @@
 #include <cuda_runtime.h>
 #include <dmlc/thread_local.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/profiling.h>
 #include <tvm/runtime/registry.h>
 
 #include <cstring>
@@ -127,6 +128,7 @@ class CUDADeviceAPI final : public DeviceAPI {
     }
   }
 
+ protected:
   void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
                       TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
                       TVMStreamHandle stream) final {
@@ -166,6 +168,7 @@ class CUDADeviceAPI final : public DeviceAPI {
     }
   }
 
+ public:
   TVMStreamHandle CreateStream(TVMContext ctx) {
     CUDA_CALL(cudaSetDevice(ctx.device_id));
     cudaStream_t retval;
@@ -241,5 +244,40 @@ TVM_REGISTER_GLOBAL("device_api.cpu_pinned").set_body([](TVMArgs args, TVMRetVal
   *rv = static_cast<void*>(ptr);
 });
 
+class GPUTimerNode : public TimerNode {
+ public:
+  virtual void Start() {
+    CUDA_CALL(cudaEventRecord(start_, CUDAThreadEntry::ThreadLocal()->stream));
+  }
+  virtual void Stop() { CUDA_CALL(cudaEventRecord(stop_, CUDAThreadEntry::ThreadLocal()->stream)); }
+  virtual int64_t SyncAndGetElapsedNanos() {
+    CUDA_CALL(cudaEventSynchronize(stop_));
+    float milliseconds = 0;
+    CUDA_CALL(cudaEventElapsedTime(&milliseconds, start_, stop_));
+    return milliseconds * 1e6;
+  }
+  virtual ~GPUTimerNode() {
+    CUDA_CALL(cudaEventDestroy(start_));
+    CUDA_CALL(cudaEventDestroy(stop_));
+  }
+  GPUTimerNode() {
+    CUDA_CALL(cudaEventCreate(&start_));
+    CUDA_CALL(cudaEventCreate(&stop_));
+  }
+
+  static constexpr const char* _type_key = "GPUTimerNode";
+  TVM_DECLARE_FINAL_OBJECT_INFO(GPUTimerNode, TimerNode);
+
+ private:
+  cudaEvent_t start_;
+  cudaEvent_t stop_;
+};
+
+TVM_REGISTER_OBJECT_TYPE(GPUTimerNode);
+
+TVM_REGISTER_GLOBAL("profiling.timer.gpu").set_body_typed([](TVMContext ctx) {
+  return Timer(make_object<GPUTimerNode>());
+});
+
 }  // namespace runtime
 }  // namespace tvm
diff --git a/src/runtime/file_utils.cc b/src/runtime/file_utils.cc
index 42cbfdc3b1ed..32dd1d8020c9 100644
--- a/src/runtime/file_utils.cc
+++ b/src/runtime/file_utils.cc
@@ -23,8 +23,10 @@
 #include "file_utils.h"
 
 #include <dmlc/json.h>
+#include <dmlc/memory_io.h>
+#include <tvm/runtime/logging.h>
+#include <tvm/runtime/registry.h>
 #include <tvm/runtime/serializer.h>
-#include <tvm/support/logging.h>
 
 #include <fstream>
 #include <unordered_map>
@@ -157,5 +159,71 @@ void LoadMetaDataFromFile(const std::string& file_name,
 
 void RemoveFile(const std::string& file_name) { std::remove(file_name.c_str()); }
 
+Map<String, NDArray> LoadParams(const std::string& param_blob) {
+  dmlc::MemoryStringStream strm(const_cast<std::string*>(&param_blob));
+  return LoadParams(&strm);
+}
+Map<String, NDArray> LoadParams(dmlc::Stream* strm) {
+  Map<String, NDArray> params;
+  uint64_t header, reserved;
+  ICHECK(strm->Read(&header)) << "Invalid parameters file format";
+  ICHECK(header == kTVMNDArrayListMagic) << "Invalid parameters file format";
+  ICHECK(strm->Read(&reserved)) << "Invalid parameters file format";
+
+  std::vector<std::string> names;
+  ICHECK(strm->Read(&names)) << "Invalid parameters file format";
+  uint64_t sz;
+  strm->Read(&sz);
+  size_t size = static_cast<size_t>(sz);
+  ICHECK(size == names.size()) << "Invalid parameters file format";
+  for (size_t i = 0; i < size; ++i) {
+    // The data_entry is allocated on device, NDArray.load always load the array into CPU.
+    NDArray temp;
+    temp.Load(strm);
+    params.Set(names[i], temp);
+  }
+  return params;
+}
+
+void SaveParams(dmlc::Stream* strm, const Map<String, NDArray>& params) {
+  std::vector<std::string> names;
+  std::vector<const DLTensor*> arrays;
+  for (auto& p : params) {
+    names.push_back(p.first);
+    arrays.push_back(p.second.operator->());
+  }
+
+  uint64_t header = kTVMNDArrayListMagic, reserved = 0;
+  strm->Write(header);
+  strm->Write(reserved);
+  strm->Write(names);
+  {
+    uint64_t sz = static_cast<uint64_t>(arrays.size());
+    strm->Write(sz);
+    for (size_t i = 0; i < sz; ++i) {
+      tvm::runtime::SaveDLTensor(strm, arrays[i]);
+    }
+  }
+}
+
+std::string SaveParams(const Map<String, NDArray>& params) {
+  std::string bytes;
+  dmlc::MemoryStringStream strm(&bytes);
+  dmlc::Stream* fo = &strm;
+  SaveParams(fo, params);
+  return bytes;
+}
+
+TVM_REGISTER_GLOBAL("runtime.SaveParams").set_body_typed([](const Map<String, NDArray>& params) {
+  std::string s = ::tvm::runtime::SaveParams(params);
+  // copy return array so it is owned by the ret value
+  TVMRetValue rv;
+  rv = TVMByteArray{s.data(), s.size()};
+  return rv;
+});
+TVM_REGISTER_GLOBAL("runtime.LoadParams").set_body_typed([](const String& s) {
+  return ::tvm::runtime::LoadParams(s);
+});
+
 }  // namespace runtime
 }  // namespace tvm
diff --git a/src/runtime/file_utils.h b/src/runtime/file_utils.h
index 696a9760c2e1..718d10d5df70 100644
--- a/src/runtime/file_utils.h
+++ b/src/runtime/file_utils.h
@@ -24,6 +24,8 @@
 #ifndef TVM_RUNTIME_FILE_UTILS_H_
 #define TVM_RUNTIME_FILE_UTILS_H_
 
+#include <tvm/runtime/container.h>
+
 #include <string>
 #include <unordered_map>
 
@@ -92,6 +94,32 @@ void LoadMetaDataFromFile(const std::string& file_name,
  * \param file_name The file name.
  */
 void RemoveFile(const std::string& file_name);
+
+constexpr uint64_t kTVMNDArrayListMagic = 0xF7E58D4F05049CB7;
+/*!
+ * \brief Load parameters from a string.
+ * \param param_blob Serialized string of parameters.
+ * \return Map of parameter name to parameter value.
+ */
+Map<String, NDArray> LoadParams(const std::string& param_blob);
+/*!
+ * \brief Load parameters from a stream.
+ * \param strm Stream to load parameters from.
+ * \return Map of parameter name to parameter value.
+ */
+Map<String, NDArray> LoadParams(dmlc::Stream* strm);
+/*!
+ * \brief Serialize parameters to a byte array.
+ * \param params Parameters to save.
+ * \return String containing binary parameter data.
+ */
+std::string SaveParams(const Map<String, NDArray>& params);
+/*!
+ * \brief Serialize parameters to a stream.
+ * \param strm Stream to write to.
+ * \param params Parameters to save.
+ */
+void SaveParams(dmlc::Stream* strm, const Map<String, NDArray>& params);
 }  // namespace runtime
 }  // namespace tvm
 #endif  // TVM_RUNTIME_FILE_UTILS_H_
diff --git a/src/runtime/graph/cuda_graph/graph_runtime_cuda_graph.cc b/src/runtime/graph/cuda_graph/graph_runtime_cuda_graph.cc
new file mode 100644
index 000000000000..ee5e50a3b9d4
--- /dev/null
+++ b/src/runtime/graph/cuda_graph/graph_runtime_cuda_graph.cc
@@ -0,0 +1,135 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file graph_runtime_cuda_graph.cc
+ */
+
+#include <tvm/runtime/registry.h>
+
+#include "../../cuda/cuda_common.h"
+#include "../graph_runtime.h"
+
+namespace tvm {
+namespace runtime {
+
+/*!
+ * \brief Graph runtime with CUDA Graph Support.
+ *
+ *  This is the extension of GraphRuntime class used for CUDA graph launch
+ *  instead of CUDA kernel launch. CUDA graph launch requires CUDA 10.0 or
+ *  above, currently there are two ways of constructing CUDA graphs:
+ *  (1) Using CUDA stream capture API to capture a series of operations on
+ *  CUDA stream, and automatically generates a graph (2) Building a graph
+ *  using CUDA graph API manually. This implementation uses stream capture.
+ */
+class GraphRuntimeCudaGraph : public GraphRuntime {
+ public:
+  /*!
+   * \brief Begin CUDA graph capture on stream, the stream enters capture mode.
+   */
+  void StartCapture() {
+    const TVMContext& ctx = data_entry_[entry_id(0, 0)]->ctx;
+
+    TVMStreamCreate(ctx.device_type, ctx.device_id, &capture_stream_);
+    TVMSetStream(ctx.device_type, ctx.device_id, capture_stream_);
+
+    CUDA_CALL(cudaStreamBeginCapture(static_cast<cudaStream_t>(capture_stream_),
+                                     cudaStreamCaptureModeGlobal));
+  }
+
+  /*!
+   * \brief Launch the instantiated graph on stream
+   */
+  void RunCudaGraph() {
+    cudaStream_t cuStream = static_cast<cudaStream_t>(capture_stream_);
+    CUDA_CALL(cudaGraphLaunch(cuda_graph_exec_, cuStream));
+    CUDA_CALL(cudaStreamSynchronize(cuStream));
+  }
+
+  /*!
+   * \brief End CUDA graph capture on stream, a graph will be created and
+   * instantiated.
+   */
+  void EndCapture() {
+    cudaGraph_t graph;
+    CUDA_CALL(cudaStreamEndCapture(static_cast<cudaStream_t>(capture_stream_), &graph));
+
+    cudaGraphNode_t* nodes = NULL;
+    size_t numNodes = 0;
+    CUDA_CALL(cudaGraphGetNodes(graph, nodes, &numNodes));
+    LOG(INFO) << "Num of nodes in the cuda graph created using stream capture API = " << numNodes;
+
+    CUDA_CALL(cudaGraphInstantiate(&cuda_graph_exec_, graph, NULL, NULL, 0));
+  }
+
+  /*!
+   * \brief GetFunction Get the function based on input.
+   * \param name The function which needs to be invoked.
+   * \param sptr_to_self Packed function pointer.
+   */
+  PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self);
+
+ private:
+  /*! \brief The Cuda stream on which to capture a CUDA graph. */
+  TVMStreamHandle capture_stream_;
+  /*! \brief The captured CUDA graph will be instantiated to this. */
+  cudaGraphExec_t cuda_graph_exec_;
+};
+
+PackedFunc GraphRuntimeCudaGraph::GetFunction(const std::string& name,
+                                              const ObjectPtr<Object>& sptr_to_self) {
+  if (name == "run_cuda_graph") {
+    return PackedFunc(
+        [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { this->RunCudaGraph(); });
+  } else if (name == "start_capture") {
+    return PackedFunc(
+        [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { this->StartCapture(); });
+  } else if (name == "end_capture") {
+    return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { this->EndCapture(); });
+  } else {
+    return GraphRuntime::GetFunction(name, sptr_to_self);
+  }
+}
+
+Module GraphRuntimeCudaGraphCreate(const std::string& sym_json, const tvm::runtime::Module& m,
+                                   const std::vector<TVMContext>& ctxs,
+                                   PackedFunc lookup_linked_param_func) {
+  auto exec = make_object<GraphRuntimeCudaGraph>();
+  exec->Init(sym_json, m, ctxs, lookup_linked_param_func);
+  return Module(exec);
+}
+
+TVM_REGISTER_GLOBAL("tvm.graph_runtime_cuda_graph.create")
+    .set_body([](TVMArgs args, TVMRetValue* rv) {
+      ICHECK_GE(args.num_args, 4) << "The expected number of arguments for graph_runtime.create is "
+                                     "at least 4, but it has "
+                                  << args.num_args;
+      PackedFunc lookup_linked_param_func;
+      int ctx_start_arg = 2;
+      if (args[2].type_code() == kTVMPackedFuncHandle) {
+        lookup_linked_param_func = args[2];
+        ctx_start_arg++;
+      }
+
+      *rv = GraphRuntimeCudaGraphCreate(args[0], args[1], GetAllContext(args, ctx_start_arg),
+                                        lookup_linked_param_func);
+    });
+}  // namespace runtime
+}  // namespace tvm
diff --git a/src/runtime/graph/debug/graph_runtime_debug.cc b/src/runtime/graph/debug/graph_runtime_debug.cc
index 3353c117318b..0e3003aa42c3 100644
--- a/src/runtime/graph/debug/graph_runtime_debug.cc
+++ b/src/runtime/graph/debug/graph_runtime_debug.cc
@@ -23,6 +23,7 @@
 #include <tvm/runtime/container.h>
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/profiling.h>
 #include <tvm/runtime/registry.h>
 
 #include <chrono>
@@ -59,11 +60,11 @@ class GraphRuntimeDebug : public GraphRuntime {
     // warmup run
     GraphRuntime::Run();
     std::string tkey = module_->type_key();
-    std::vector<double> time_per_op(op_execs_.size(), 0);
+    std::vector<double> time_sec_per_op(op_execs_.size(), 0);
     if (tkey == "rpc") {
       // RPC modules rely on remote timing which implements the logic from the else branch.
       for (size_t index = 0; index < op_execs_.size(); ++index) {
-        time_per_op[index] += RunOpRPC(index, number, repeat, min_repeat_ms);
+        time_sec_per_op[index] += RunOpRPC(index, number, repeat, min_repeat_ms);
       }
     } else {
       for (int i = 0; i < repeat; ++i) {
@@ -71,45 +72,67 @@ class GraphRuntimeDebug : public GraphRuntime {
             tbegin, tend;
         double duration_ms = 0.0;
         do {
-          std::fill(time_per_op.begin(), time_per_op.end(), 0);
+          std::fill(time_sec_per_op.begin(), time_sec_per_op.end(), 0);
           if (duration_ms > 0.0) {
             number = static_cast<int>(std::max((min_repeat_ms / (duration_ms / number) + 1),
                                                number * 1.618));  // 1.618 is chosen by random
           }
           tbegin = std::chrono::high_resolution_clock::now();
+          std::vector<std::vector<Timer>> op_timers;
+          for (size_t index = 0; index < op_execs_.size(); index++) {
+            op_timers.push_back({});
+          }
           for (int k = 0; k < number; k++) {
             for (size_t index = 0; index < op_execs_.size(); ++index) {
               if (op_execs_[index]) {
-                time_per_op[index] += RunOpHost(index);
+                op_timers[index].push_back(RunOpHost(index));
               }
             }
           }
+          for (size_t index = 0; index < op_execs_.size(); ++index) {
+            for (auto t : op_timers[index]) {
+              time_sec_per_op[index] += t->SyncAndGetElapsedNanos() / 1e9;
+            }
+          }
           tend = std::chrono::high_resolution_clock::now();
           duration_ms =
-              std::chrono::duration_cast<std::chrono::duration<double> >(tend - tbegin).count() *
+              std::chrono::duration_cast<std::chrono::duration<double>>(tend - tbegin).count() *
               1000;
         } while (duration_ms < min_repeat_ms);
 
         LOG(INFO) << "Iteration: " << i;
         int op = 0;
-        for (size_t index = 0; index < time_per_op.size(); index++) {
+        for (size_t index = 0; index < time_sec_per_op.size(); index++) {
           if (op_execs_[index]) {
-            time_per_op[index] /= number;
-            LOG(INFO) << "Op #" << op++ << " " << GetNodeName(index) << ": " << time_per_op[index]
-                      << " us/iter";
+            time_sec_per_op[index] /= number;
+            LOG(INFO) << "Op #" << op++ << " " << GetNodeName(index) << ": "
+                      << time_sec_per_op[index] * 1e6 << " us/iter";
           }
         }
       }
     }
 
     std::ostringstream os;
-    for (size_t index = 0; index < time_per_op.size(); index++) {
-      os << time_per_op[index] << ",";
+    for (size_t index = 0; index < time_sec_per_op.size(); index++) {
+      os << time_sec_per_op[index] << ",";
     }
     return os.str();
   }
 
   double RunOpRPC(int index, int number, int repeat, int min_repeat_ms) {
+    // Right now we expect either "tvm_op" for nodes which run PackedFunc or "null" for nodes which
+    // represent inputs/parameters to the graph. Other types may be supported in the future, but
+    // consideration would be needed as to how to do that over RPC before we support it here.
+    if (nodes_[index].op_type != "tvm_op") {
+      CHECK_EQ(nodes_[index].op_type, "null")
+          << "Don't know how to run op type " << nodes_[index].op_type
+          << " remotely over RPC right now";
+
+      // NOTE: GraphRuntimeDebug expects graph nodes to have an "op" attribute of "tvm_op" or "null"
+      // and "null" is a placeholder node for a parameter or input.
+      return 0;
+    }
+
     const TVMContext& ctx = data_entry_[entry_id(index, 0)]->ctx;
     TVMOpParam param = nodes_[index].param;
     std::string name = param.func_name;
@@ -147,15 +170,12 @@ class GraphRuntimeDebug : public GraphRuntime {
     return results_arr[0];
   }
 
-  double RunOpHost(int index) {
-    auto op_tbegin = std::chrono::high_resolution_clock::now();
-    op_execs_[index]();
+  Timer RunOpHost(int index) {
     const TVMContext& ctx = data_entry_[entry_id(index, 0)]->ctx;
-    TVMSynchronize(ctx.device_type, ctx.device_id, nullptr);
-    auto op_tend = std::chrono::high_resolution_clock::now();
-    double op_duration =
-        std::chrono::duration_cast<std::chrono::duration<double> >(op_tend - op_tbegin).count();
-    return op_duration;
+    Timer t = Timer::Start(ctx);
+    op_execs_[index]();
+    t->Stop();
+    return t;
   }
 
   /*!
diff --git a/src/runtime/graph/graph_runtime.cc b/src/runtime/graph/graph_runtime.cc
index 6d586cfdd042..5c7b75696168 100644
--- a/src/runtime/graph/graph_runtime.cc
+++ b/src/runtime/graph/graph_runtime.cc
@@ -38,6 +38,8 @@
 #include <utility>
 #include <vector>
 
+#include "../file_utils.h"
+
 namespace tvm {
 namespace runtime {
 namespace details {
@@ -64,10 +66,11 @@ void GraphRuntime::Run() {
  * processor.
  * \param ctxs The context of the host and devices where graph nodes will be
  * executed on.
- * \param lookup_linked_param_func Linked parameter lookup function.
+ * \param lookup_linked_param_func Linked parameter lookup function. Default is nullptr.
  */
 void GraphRuntime::Init(const std::string& graph_json, tvm::runtime::Module module,
-                        const std::vector<TVMContext>& ctxs, PackedFunc lookup_linked_param_func) {
+                        const std::vector<TVMContext>& ctxs,
+                        const PackedFunc lookup_linked_param_func) {
   std::istringstream is(graph_json);
   dmlc::JSONReader reader(&is);
   this->Load(&reader);
@@ -196,31 +199,10 @@ void GraphRuntime::LoadParams(const std::string& param_blob) {
 }
 
 void GraphRuntime::LoadParams(dmlc::Stream* strm) {
-  uint64_t header, reserved;
-  ICHECK(strm->Read(&header)) << "Invalid parameters file format";
-  ICHECK(header == kTVMNDArrayListMagic) << "Invalid parameters file format";
-  ICHECK(strm->Read(&reserved)) << "Invalid parameters file format";
-
-  std::vector<std::string> names;
-  ICHECK(strm->Read(&names)) << "Invalid parameters file format";
-  uint64_t sz;
-  strm->Read(&sz);
-  size_t size = static_cast<size_t>(sz);
-  ICHECK(size == names.size()) << "Invalid parameters file format";
-  for (size_t i = 0; i < size; ++i) {
-    int in_idx = GetInputIndex(names[i]);
-    if (in_idx < 0) {
-      NDArray temp;
-      temp.Load(strm);
-      continue;
-    }
-    uint32_t eid = this->entry_id(input_nodes_[in_idx], 0);
-    ICHECK_LT(eid, data_entry_.size());
-
-    // The data_entry is allocated on device, NDArray.load always load the array into CPU.
-    NDArray temp;
-    temp.Load(strm);
-    data_entry_[eid].CopyFrom(temp);
+  Map<String, NDArray> params = ::tvm::runtime::LoadParams(strm);
+  for (auto& p : params) {
+    uint32_t eid = this->entry_id(input_nodes_[GetInputIndex(p.first)], 0);
+    data_entry_[eid].CopyFrom(p.second);
   }
 }
 
@@ -510,7 +492,7 @@ PackedFunc GraphRuntime::GetFunction(const std::string& name,
   } else if (name == "share_params") {
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
       const auto& module = args[0].operator Module();
-      ICHECK_EQ(module.operator->()->type_key(), "GraphRuntime");
+      ICHECK_EQ(module.operator->()->type_key(), std::string("GraphRuntime"));
       const auto& param_blob = args[1].operator std::string();
       dmlc::MemoryStringStream strm(const_cast<std::string*>(&param_blob));
       this->ShareParams(dynamic_cast<const GraphRuntime&>(*module.operator->()), &strm);
diff --git a/src/runtime/graph/graph_runtime.h b/src/runtime/graph/graph_runtime.h
index 627911883dfb..e417d2aa4bfc 100644
--- a/src/runtime/graph/graph_runtime.h
+++ b/src/runtime/graph/graph_runtime.h
@@ -47,9 +47,6 @@ namespace runtime {
     ICHECK_EQ(ret, 0) << TVMGetLastError(); \
   }
 
-/*! \brief Magic number for NDArray list file  */
-constexpr uint64_t kTVMNDArrayListMagic = 0xF7E58D4F05049CB7;
-
 /*! \brief operator attributes about tvm op */
 struct TVMOpParam {
   std::string func_name;
@@ -96,11 +93,12 @@ class TVM_DLL GraphRuntime : public ModuleNode {
    *  executed on.
    * \param lookup_linked_param_func If given, a PackedFunc invoked to lookup linked parameters
    *  by storage_id. If not given, linked parameters are looked-up using an internal implementation,
-   *  which is not compatible with RPCModules.
+   *  which is not compatible with RPCModules. Default is nullptr.
    */
 
   void Init(const std::string& graph_json, tvm::runtime::Module module,
-            const std::vector<TVMContext>& ctxs, const PackedFunc lookup_linked_param_func);
+            const std::vector<TVMContext>& ctxs,
+            const PackedFunc lookup_linked_param_func = nullptr);
 
   /*!
    * \brief Get the input index given the name of input.
diff --git a/src/runtime/graph/graph_runtime_factory.cc b/src/runtime/graph/graph_runtime_factory.cc
index 2c055e16cc9f..1682afa8464a 100644
--- a/src/runtime/graph/graph_runtime_factory.cc
+++ b/src/runtime/graph/graph_runtime_factory.cc
@@ -24,7 +24,7 @@
 
 #include "./graph_runtime_factory.h"
 
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/device_api.h>
 #include <tvm/runtime/registry.h>
 
@@ -72,6 +72,14 @@ PackedFunc GraphRuntimeFactory::GetFunction(
       exec->Import(this->imports_[0]);
       *rv = Module(exec);
     });
+  } else if (name == "cuda_graph_create") {
+    return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
+      std::vector<TVMContext> contexts;
+      for (int i = 0; i < args.num_args; ++i) {
+        contexts.emplace_back(args[i].operator TVMContext());
+      }
+      *rv = this->CudaGraphRuntimeCreate(contexts);
+    });
   } else {
     return PackedFunc();
   }
@@ -130,6 +138,31 @@ Module GraphRuntimeFactory::DebugRuntimeCreate(const std::vector<TVMContext>& ct
   return mod;
 }
 
+Module GraphRuntimeFactory::CudaGraphRuntimeCreate(const std::vector<TVMContext>& ctxs) {
+  const PackedFunc* pf = tvm::runtime::Registry::Get("tvm.graph_runtime_cuda_graph.create");
+  ICHECK(pf != nullptr) << "Cannot find function tvm.graph_runtime_cuda_graph.create in registry. "
+                           "Did you set(USE_GRAPH_RUNTIME_CUGRAPH=ON)?";
+  std::vector<int> unpacked_ctxs;
+  for (const auto& ctx : ctxs) {
+    unpacked_ctxs.emplace_back(ctx.device_type);
+    unpacked_ctxs.emplace_back(ctx.device_id);
+  }
+  size_t args_size = unpacked_ctxs.size() + 2;
+  std::vector<TVMValue> values(args_size);
+  std::vector<int> codes(args_size);
+  runtime::TVMArgsSetter setter(values.data(), codes.data());
+  setter(0, this->graph_json_);
+  setter(1, this->imports_[0]);
+  for (size_t i = 0; i < unpacked_ctxs.size(); ++i) {
+    setter(i + 2, unpacked_ctxs[i]);
+  }
+  TVMRetValue rv;
+  pf->CallPacked(TVMArgs(values.data(), codes.data(), args_size), &rv);
+  Module mod = rv.operator Module();
+  SetParams(const_cast<GraphRuntime*>(mod.as<GraphRuntime>()), this->params_);
+  return mod;
+}
+
 Module GraphRuntimeFactoryModuleLoadBinary(void* strm) {
   dmlc::Stream* stream = static_cast<dmlc::Stream*>(strm);
   std::string graph_json;
@@ -156,7 +189,8 @@ TVM_REGISTER_GLOBAL("tvm.graph_runtime_factory.create").set_body([](TVMArgs args
                                  "graph_runtime_factory.create needs at least 3, "
                                  "but it has "
                               << args.num_args;
-  // The argument order is graph_json, module, module_name, params.
+  // The argument order is graph_json, module, module_name, param0_name, param0_tensor,
+  // [param1_name, param1_tensor], ...
   ICHECK_EQ((args.size() - 3) % 2, 0);
   std::unordered_map<std::string, tvm::runtime::NDArray> params;
   for (size_t i = 3; i < static_cast<size_t>(args.size()); i += 2) {
diff --git a/src/runtime/graph/graph_runtime_factory.h b/src/runtime/graph/graph_runtime_factory.h
index 98fb27c43ea2..f2f11ee66802 100644
--- a/src/runtime/graph/graph_runtime_factory.h
+++ b/src/runtime/graph/graph_runtime_factory.h
@@ -89,6 +89,14 @@ class TVM_DLL GraphRuntimeFactory : public runtime::ModuleNode {
    */
   Module DebugRuntimeCreate(const std::vector<TVMContext>& ctxs);
 
+  /*!
+   * \brief Create a specific cuda graph runtime module
+   * \param ctxs The context of the host and devices where graph nodes will be
+   *  executed on.
+   * \return created cuda graph runtime module
+   */
+  Module CudaGraphRuntimeCreate(const std::vector<TVMContext>& ctx);
+
   /*!
    * \brief Set params.
    * \param graph_runtime The graph runtime we want to set the params into.
diff --git a/src/runtime/hexagon/hexagon_device_api.cc b/src/runtime/hexagon/hexagon_device_api.cc
index 605c55eb89b9..a01c9def5d5d 100644
--- a/src/runtime/hexagon/hexagon_device_api.cc
+++ b/src/runtime/hexagon/hexagon_device_api.cc
@@ -18,8 +18,8 @@
  */
 
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <algorithm>
 #include <cstring>
@@ -35,9 +35,6 @@ class HexagonDeviceAPI : public DeviceAPI {
   void GetAttr(TVMContext ctx, DeviceAttrKind kind, TVMRetValue* rv) final;
   void* AllocDataSpace(TVMContext ctx, size_t nbytes, size_t alignment, DLDataType type_hint) final;
   void FreeDataSpace(TVMContext ctx, void* ptr) final;
-  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
-                      size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
-                      DLDataType type_hint, TVMStreamHandle stream) final;
   void StreamSync(TVMContext ctx, TVMStreamHandle stream) final;
   void* AllocWorkspace(TVMContext ctx, size_t nbytes, DLDataType type_hint = {}) final;
   void FreeWorkspace(TVMContext ctx, void* ptr) final;
@@ -48,6 +45,11 @@ class HexagonDeviceAPI : public DeviceAPI {
     static HexagonDeviceAPI* inst = new HexagonDeviceAPI();
     return inst;
   }
+
+ protected:
+  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
+                      size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
+                      DLDataType type_hint, TVMStreamHandle stream) final;
 };
 
 // HexagonDeviceAPI.
diff --git a/src/runtime/hexagon/hexagon_module.cc b/src/runtime/hexagon/hexagon_module.cc
index 994e24b99084..f6a57ff55355 100644
--- a/src/runtime/hexagon/hexagon_module.cc
+++ b/src/runtime/hexagon/hexagon_module.cc
@@ -22,8 +22,8 @@
 #ifdef __ANDROID__
 #include <android/log.h>
 #endif
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <memory>
 #include <set>
diff --git a/src/runtime/hexagon/hexagon_module.h b/src/runtime/hexagon/hexagon_module.h
index e558997b7a4c..02ed7d2541c2 100644
--- a/src/runtime/hexagon/hexagon_module.h
+++ b/src/runtime/hexagon/hexagon_module.h
@@ -20,8 +20,8 @@
 #ifndef TVM_RUNTIME_HEXAGON_HEXAGON_MODULE_H_
 #define TVM_RUNTIME_HEXAGON_HEXAGON_MODULE_H_
 
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/module.h>
-#include <tvm/support/logging.h>
 
 #include <array>
 #include <memory>
diff --git a/src/runtime/hexagon/sim/hexagon_device_sim.cc b/src/runtime/hexagon/sim/hexagon_device_sim.cc
index 6cc7dcf3209f..1d3f0fd1006f 100644
--- a/src/runtime/hexagon/sim/hexagon_device_sim.cc
+++ b/src/runtime/hexagon/sim/hexagon_device_sim.cc
@@ -22,7 +22,7 @@
 #include <llvm/ADT/StringRef.h>
 #include <llvm/Support/FileSystem.h>
 #include <llvm/Support/Process.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <algorithm>
 #include <deque>
diff --git a/src/runtime/hexagon/target/hexagon_dsprpcapi.cc b/src/runtime/hexagon/target/hexagon_dsprpcapi.cc
index d494db82e2c7..a089684c4188 100644
--- a/src/runtime/hexagon/target/hexagon_dsprpcapi.cc
+++ b/src/runtime/hexagon/target/hexagon_dsprpcapi.cc
@@ -22,7 +22,7 @@
 
 #include <dlfcn.h>
 #include <stdint.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "hexagon_target_log.h"
 
diff --git a/src/runtime/hexagon/target/hexagon_dsprpcapi.h b/src/runtime/hexagon/target/hexagon_dsprpcapi.h
index c0e40805ecbf..e4711e3da584 100644
--- a/src/runtime/hexagon/target/hexagon_dsprpcapi.h
+++ b/src/runtime/hexagon/target/hexagon_dsprpcapi.h
@@ -22,7 +22,7 @@
 
 #ifdef __ANDROID__
 #include <stdint.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "remote.h"
 #include "remote64.h"
diff --git a/src/runtime/hexagon/target/hexagon_stubapi.cc b/src/runtime/hexagon/target/hexagon_stubapi.cc
index 5428ae7c1cff..1fb7d942e968 100644
--- a/src/runtime/hexagon/target/hexagon_stubapi.cc
+++ b/src/runtime/hexagon/target/hexagon_stubapi.cc
@@ -23,7 +23,7 @@
 #include <dlfcn.h>
 #include <stdint.h>
 #include <sys/stat.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include "hexagon_target_log.h"
 
diff --git a/src/runtime/hexagon/target/hexagon_stubapi.h b/src/runtime/hexagon/target/hexagon_stubapi.h
index cc5b7b7413ca..fba22b10247c 100644
--- a/src/runtime/hexagon/target/hexagon_stubapi.h
+++ b/src/runtime/hexagon/target/hexagon_stubapi.h
@@ -24,7 +24,7 @@
 #include <AEEStdErr.h>
 #include <rpcmem.h>
 #include <stdint.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <tuple>
 
diff --git a/src/runtime/logging.cc b/src/runtime/logging.cc
new file mode 100644
index 000000000000..8a44ec04532c
--- /dev/null
+++ b/src/runtime/logging.cc
@@ -0,0 +1,151 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#ifdef TVM_BACKTRACE_DISABLED
+#include <string>
+
+// TODO(bkimball,tkonolige) This inline function is to work around a linking error I am having when
+// using MSVC If the function definition is in logging.cc then the linker can't find it no matter
+// what kind of attributes (dllexport) I decorate it with. This is temporary and will be addressed
+// when we get backtrace working on Windows.
+namespace tvm {
+namespace runtime {
+__declspec(dllexport) std::string Backtrace() { return ""; }
+}  // namespace runtime
+}  // namespace tvm
+#else
+
+#include <backtrace.h>
+#include <cxxabi.h>
+#include <tvm/runtime/logging.h>
+
+#include <iomanip>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+namespace tvm {
+namespace runtime {
+namespace {
+
+struct BacktraceInfo {
+  std::vector<std::string> lines;
+  size_t max_size;
+  std::string error_message;
+};
+
+void BacktraceCreateErrorCallback(void* data, const char* msg, int errnum) {
+  std::cerr << "Could not initialize backtrace state: " << msg << std::endl;
+}
+
+backtrace_state* BacktraceCreate() {
+  return backtrace_create_state(nullptr, 1, BacktraceCreateErrorCallback, nullptr);
+}
+
+static backtrace_state* _bt_state = BacktraceCreate();
+
+std::string DemangleName(std::string name) {
+  int status = 0;
+  size_t length = name.size();
+  std::unique_ptr<char, void (*)(void* __ptr)> demangled_name = {
+      abi::__cxa_demangle(name.c_str(), nullptr, &length, &status), &std::free};
+  if (demangled_name && status == 0 && length > 0) {
+    return demangled_name.get();
+  } else {
+    return name;
+  }
+}
+
+void BacktraceErrorCallback(void* data, const char* msg, int errnum) {
+  // do nothing
+}
+
+void BacktraceSyminfoCallback(void* data, uintptr_t pc, const char* symname, uintptr_t symval,
+                              uintptr_t symsize) {
+  auto str = reinterpret_cast<std::string*>(data);
+
+  if (symname != nullptr) {
+    std::string tmp(symname, symsize);
+    *str = DemangleName(tmp.c_str());
+  } else {
+    std::ostringstream s;
+    s << "0x" << std::setfill('0') << std::setw(sizeof(uintptr_t) * 2) << std::hex << pc;
+    *str = s.str();
+  }
+}
+
+int BacktraceFullCallback(void* data, uintptr_t pc, const char* filename, int lineno,
+                          const char* symbol) {
+  auto stack_trace = reinterpret_cast<BacktraceInfo*>(data);
+  std::stringstream s;
+
+  std::unique_ptr<std::string> symbol_str = std::make_unique<std::string>("<unknown>");
+  if (symbol != nullptr) {
+    *symbol_str = DemangleName(symbol);
+  } else {
+    // see if syminfo gives anything
+    backtrace_syminfo(_bt_state, pc, BacktraceSyminfoCallback, BacktraceErrorCallback,
+                      symbol_str.get());
+  }
+  s << *symbol_str;
+
+  if (filename != nullptr) {
+    s << std::endl << "        at " << filename;
+    if (lineno != 0) {
+      s << ":" << lineno;
+    }
+  }
+  // Skip tvm::backtrace and tvm::LogFatal::~LogFatal at the beginning of the trace as they don't
+  // add anything useful to the backtrace.
+  if (!(stack_trace->lines.size() == 0 &&
+        (symbol_str->find("tvm::runtime::Backtrace", 0) == 0 ||
+         symbol_str->find("tvm::runtime::detail::LogFatal", 0) == 0))) {
+    stack_trace->lines.push_back(s.str());
+  }
+  // TVMFuncCall denotes the API boundary so we stop there. Exceptions should be caught there.
+  if (*symbol_str == "TVMFuncCall" || stack_trace->lines.size() >= stack_trace->max_size) {
+    return 1;
+  }
+  return 0;
+}
+}  // namespace
+
+std::string Backtrace() {
+  BacktraceInfo bt;
+  bt.max_size = 100;
+  if (_bt_state == nullptr) {
+    return "";
+  }
+  // libbacktrace eats memory if run on multiple threads at the same time, so we guard against it
+  static std::mutex m;
+  std::lock_guard<std::mutex> lock(m);
+  backtrace_full(_bt_state, 0, BacktraceFullCallback, BacktraceErrorCallback, &bt);
+
+  std::ostringstream s;
+  s << "Stack trace:\n";
+  for (size_t i = 0; i < bt.lines.size(); i++) {
+    s << "  " << i << ": " << bt.lines[i] << "\n";
+  }
+
+  return s.str();
+}
+}  // namespace runtime
+}  // namespace tvm
+#endif
diff --git a/src/runtime/metadata_module.cc b/src/runtime/metadata_module.cc
index acef9d4736fd..665c72cc5e0d 100644
--- a/src/runtime/metadata_module.cc
+++ b/src/runtime/metadata_module.cc
@@ -27,7 +27,7 @@
  * code and metadata significantly reduces the efforts for handling external
  * codegen and runtimes.
  */
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
diff --git a/src/runtime/metal/metal_common.h b/src/runtime/metal/metal_common.h
index d13ac7e78982..b5d06192396b 100644
--- a/src/runtime/metal/metal_common.h
+++ b/src/runtime/metal/metal_common.h
@@ -32,8 +32,8 @@
 #import <Metal/MTLLibrary.h>
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/packed_func.h>
-#include <tvm/support/logging.h>
 
 #include <memory>
 #include <mutex>
@@ -84,14 +84,16 @@ class MetalWorkspace final : public DeviceAPI {
   void GetAttr(TVMContext ctx, DeviceAttrKind kind, TVMRetValue* rv) final;
   void* AllocDataSpace(TVMContext ctx, size_t nbytes, size_t alignment, DLDataType type_hint) final;
   void FreeDataSpace(TVMContext ctx, void* ptr) final;
-  void CopyDataFromTo(const void* from, size_t from_size, void* to, size_t to_size, size_t size,
-                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
-                      TVMStreamHandle stream) final;
   void StreamSync(TVMContext ctx, TVMStreamHandle stream) final;
   void* AllocWorkspace(TVMContext ctx, size_t size, DLDataType type_hint) final;
   void FreeWorkspace(TVMContext ctx, void* data) final;
   // get the global workspace
   static MetalWorkspace* Global();
+
+ protected:
+  void CopyDataFromTo(const void* from, size_t from_size, void* to, size_t to_size, size_t size,
+                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
+                      TVMStreamHandle stream) final;
 };
 
 /*! \brief Thread local workspace */
diff --git a/src/runtime/metal/metal_module.mm b/src/runtime/metal/metal_module.mm
index 981dd6129f9e..8f1fde86f074 100644
--- a/src/runtime/metal/metal_module.mm
+++ b/src/runtime/metal/metal_module.mm
@@ -180,7 +180,7 @@ void Init(MetalModuleNode* m, ObjectPtr<Object> sptr, const std::string& func_na
     scache_[dev_id] = m->GetPipelineState(dev_id, func_name);
   }
   // invoke the function with void arguments
-  void operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion* pack_args) const {
+  void operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion64* pack_args) const {
     metal::MetalThreadEntry* t = metal::MetalThreadEntry::ThreadLocal();
     int device_id = t->context.device_id;
     if (scache_[device_id] == nil) {
@@ -197,7 +197,7 @@ void operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion* pack_args) const
     }
     if (num_pack_args_ != 0) {
       [encoder setBytes:pack_args
-                 length:num_pack_args_ * sizeof(ArgUnion)
+                 length:num_pack_args_ * sizeof(ArgUnion64)
                 atIndex:num_buffer_args_];
     }
     // launch
diff --git a/src/runtime/micro/micro_session.cc b/src/runtime/micro/micro_session.cc
index ceaa5dd6245b..cd916d46971d 100644
--- a/src/runtime/micro/micro_session.cc
+++ b/src/runtime/micro/micro_session.cc
@@ -25,8 +25,8 @@
 
 #include <tvm/runtime/crt/rpc_common/framing.h>
 #include <tvm/runtime/crt/rpc_common/session.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <algorithm>
 #include <chrono>
@@ -105,7 +105,7 @@ class MicroTransportChannel : public RPCChannel {
         write_stream_{fsend, session_start_timeout},
         framer_{&write_stream_},
         receive_buffer_{new uint8_t[TVM_CRT_MAX_PACKET_SIZE_BYTES], TVM_CRT_MAX_PACKET_SIZE_BYTES},
-        session_{0x5c, &framer_, &receive_buffer_, &HandleMessageReceivedCb, this},
+        session_{&framer_, &receive_buffer_, &HandleMessageReceivedCb, this},
         unframer_{session_.Receiver()},
         did_receive_message_{false},
         frecv_{frecv},
@@ -161,13 +161,35 @@ class MicroTransportChannel : public RPCChannel {
     }
   }
 
+  static constexpr const int kNumRandRetries = 10;
+  static std::atomic<unsigned int> random_seed;
+
+  inline uint8_t GenerateRandomNonce() {
+    // NOTE: this is bad concurrent programming but in practice we don't really expect race
+    // conditions here, and even if they occur we don't particularly care whether a competing
+    // process computes a different random seed. This value is just chosen pseudo-randomly to
+    // form an initial distinct session id. Here we just want to protect against bad loads causing
+    // confusion.
+    unsigned int seed = random_seed.load();
+    if (seed == 0) {
+      seed = (unsigned int)time(nullptr);
+    }
+    uint8_t initial_nonce = 0;
+    for (int i = 0; i < kNumRandRetries && initial_nonce == 0; ++i) {
+      initial_nonce = rand_r(&seed);
+    }
+    random_seed.store(seed);
+    ICHECK_NE(initial_nonce, 0) << "rand() does not seem to be producing random values";
+    return initial_nonce;
+  }
+
   bool StartSessionInternal() {
     using ::std::chrono::duration_cast;
     using ::std::chrono::microseconds;
     using ::std::chrono::steady_clock;
 
     steady_clock::time_point start_time = steady_clock::now();
-    ICHECK_EQ(kTvmErrorNoError, session_.Initialize());
+    ICHECK_EQ(kTvmErrorNoError, session_.Initialize(GenerateRandomNonce()));
     ICHECK_EQ(kTvmErrorNoError, session_.StartSession());
 
     if (session_start_timeout_ == microseconds::zero() &&
@@ -198,7 +220,7 @@ class MicroTransportChannel : public RPCChannel {
       }
       end_time += session_start_retry_timeout_;
 
-      ICHECK_EQ(kTvmErrorNoError, session_.Initialize());
+      ICHECK_EQ(kTvmErrorNoError, session_.Initialize(GenerateRandomNonce()));
       ICHECK_EQ(kTvmErrorNoError, session_.StartSession());
     }
 
@@ -365,6 +387,8 @@ class MicroTransportChannel : public RPCChannel {
   std::string pending_chunk_;
 };
 
+std::atomic<unsigned int> MicroTransportChannel::random_seed{0};
+
 TVM_REGISTER_GLOBAL("micro._rpc_connect").set_body([](TVMArgs args, TVMRetValue* rv) {
   MicroTransportChannel* micro_channel =
       new MicroTransportChannel(args[1], args[2], ::std::chrono::microseconds(uint64_t(args[3])),
diff --git a/src/runtime/minrpc/minrpc_server.h b/src/runtime/minrpc/minrpc_server.h
index d28e0c396e36..3b9772f2fb60 100644
--- a/src/runtime/minrpc/minrpc_server.h
+++ b/src/runtime/minrpc/minrpc_server.h
@@ -46,7 +46,7 @@
 #endif
 
 #if TVM_MINRPC_ENABLE_LOGGING
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 #endif
 
 namespace tvm {
@@ -169,28 +169,39 @@ class MinRPCServer {
   }
 
   void HandleCopyFromRemote() {
-    uint64_t handle, offset, num_bytes;
-    TVMContext ctx;
-    DLDataType type_hint;
-
-    this->Read(&handle);
-    this->Read(&offset);
+    DLTensor* arr = this->ArenaAlloc<DLTensor>(1);
+    uint64_t data_handle;
+    this->Read(&data_handle);
+    arr->data = reinterpret_cast<void*>(data_handle);
+    this->Read(&(arr->ctx));
+    this->Read(&(arr->ndim));
+    this->Read(&(arr->dtype));
+    arr->shape = this->ArenaAlloc<int64_t>(arr->ndim);
+    this->ReadArray(arr->shape, arr->ndim);
+    arr->strides = nullptr;
+    this->Read(&(arr->byte_offset));
+
+    uint64_t num_bytes;
     this->Read(&num_bytes);
-    this->Read(&ctx);
-    this->Read(&type_hint);
 
     uint8_t* data_ptr;
     int call_ecode = 0;
-    if (ctx.device_type == kDLCPU) {
-      data_ptr = reinterpret_cast<uint8_t*>(handle) + offset;
+    if (arr->ctx.device_type == kDLCPU) {
+      data_ptr = reinterpret_cast<uint8_t*>(data_handle) + arr->byte_offset;
     } else {
       data_ptr = this->ArenaAlloc<uint8_t>(num_bytes);
-      call_ecode =
-          TVMDeviceCopyDataFromTo(reinterpret_cast<void*>(handle), offset, data_ptr, 0, num_bytes,
-                                  ctx, DLContext{kDLCPU, 0}, type_hint, nullptr);
+      DLTensor temp;
+      temp.data = reinterpret_cast<void*>(data_ptr);
+      temp.ctx = arr->ctx;
+      temp.ndim = arr->ndim;
+      temp.dtype = arr->dtype;
+      temp.shape = arr->shape;
+      temp.strides = nullptr;
+      temp.byte_offset = 0;
+      call_ecode = TVMDeviceCopyDataFromTo(arr, &temp, nullptr);
       // need sync to make sure that the copy is completed.
       if (call_ecode == 0) {
-        call_ecode = TVMSynchronize(ctx.device_type, ctx.device_id, nullptr);
+        call_ecode = TVMSynchronize(arr->ctx.device_type, arr->ctx.device_id, nullptr);
       }
     }
 
@@ -209,30 +220,39 @@ class MinRPCServer {
   }
 
   void HandleCopyToRemote() {
-    uint64_t handle, offset, num_bytes;
-    TVMContext ctx;
-    DLDataType type_hint;
-
-    this->Read(&handle);
-    this->Read(&offset);
+    DLTensor* arr = this->ArenaAlloc<DLTensor>(1);
+    uint64_t data_handle;
+    this->Read(&data_handle);
+    arr->data = reinterpret_cast<void*>(data_handle);
+    this->Read(&(arr->ctx));
+    this->Read(&(arr->ndim));
+    this->Read(&(arr->dtype));
+    arr->shape = this->ArenaAlloc<int64_t>(arr->ndim);
+    this->ReadArray(arr->shape, arr->ndim);
+    arr->strides = nullptr;
+    this->Read(&(arr->byte_offset));
+    uint64_t num_bytes;
     this->Read(&num_bytes);
-    this->Read(&ctx);
-    this->Read(&type_hint);
-    int call_ecode = 0;
 
-    if (ctx.device_type == kDLCPU) {
-      uint8_t* dptr = reinterpret_cast<uint8_t*>(handle) + offset;
+    int call_ecode = 0;
+    if (arr->ctx.device_type == kDLCPU) {
+      uint8_t* dptr = reinterpret_cast<uint8_t*>(data_handle) + arr->byte_offset;
       this->ReadArray(dptr, num_bytes);
     } else {
       uint8_t* temp_data = this->ArenaAlloc<uint8_t>(num_bytes);
       this->ReadArray(temp_data, num_bytes);
-
-      call_ecode =
-          TVMDeviceCopyDataFromTo(temp_data, 0, reinterpret_cast<void*>(handle), offset, num_bytes,
-                                  DLContext{kDLCPU, 0}, ctx, type_hint, nullptr);
+      DLTensor temp;
+      temp.data = temp_data;
+      temp.ctx = DLContext{kDLCPU, 0};
+      temp.ndim = arr->ndim;
+      temp.dtype = arr->dtype;
+      temp.shape = arr->shape;
+      temp.strides = nullptr;
+      temp.byte_offset = 0;
+      call_ecode = TVMDeviceCopyDataFromTo(&temp, arr, nullptr);
       // need sync to make sure that the copy is completed.
       if (call_ecode == 0) {
-        call_ecode = TVMSynchronize(ctx.device_type, ctx.device_id, nullptr);
+        call_ecode = TVMSynchronize(arr->ctx.device_type, arr->ctx.device_id, nullptr);
       }
     }
 
@@ -269,6 +289,10 @@ class MinRPCServer {
         this->SyscallDevAllocData(values, tcodes, num_args);
         break;
       }
+      case RPCCode::kDevAllocDataWithScope: {
+        this->SyscallDevAllocDataWithScope(values, tcodes, num_args);
+        break;
+      }
       case RPCCode::kDevFreeData: {
         this->SyscallDevFreeData(values, tcodes, num_args);
         break;
@@ -342,34 +366,20 @@ class MinRPCServer {
   }
 
   void SyscallCopyAmongRemote(TVMValue* values, int* tcodes, int num_args) {
-    MINRPC_CHECK(num_args == 9);
-    // from, from_offset
-    MINRPC_CHECK(tcodes[0] == kTVMOpaqueHandle);
-    MINRPC_CHECK(tcodes[1] == kDLInt);
-    // to, to_offset
+    MINRPC_CHECK(num_args == 3);
+    // from dltensor
+    MINRPC_CHECK(tcodes[0] == kTVMDLTensorHandle);
+    // to dltensor
+    MINRPC_CHECK(tcodes[1] == kTVMDLTensorHandle);
+    // stream
     MINRPC_CHECK(tcodes[2] == kTVMOpaqueHandle);
-    MINRPC_CHECK(tcodes[3] == kDLInt);
-    // size
-    MINRPC_CHECK(tcodes[4] == kDLInt);
-    // ctx_from, ctx_to
-    MINRPC_CHECK(tcodes[5] == kTVMContext);
-    MINRPC_CHECK(tcodes[6] == kTVMContext);
-    // type_hint, stream
-    MINRPC_CHECK(tcodes[7] == kTVMDataType);
-    MINRPC_CHECK(tcodes[8] == kTVMOpaqueHandle);
 
     void* from = values[0].v_handle;
-    int64_t from_offset = values[1].v_int64;
-    void* to = values[2].v_handle;
-    int64_t to_offset = values[3].v_int64;
-    int64_t size = values[4].v_int64;
-    TVMContext ctx_from = values[5].v_ctx;
-    TVMContext ctx_to = values[6].v_ctx;
-    DLDataType type_hint = values[7].v_type;
-    TVMStreamHandle stream = values[8].v_handle;
-
-    int call_ecode = TVMDeviceCopyDataFromTo(from, from_offset, to, to_offset, size, ctx_from,
-                                             ctx_to, type_hint, stream);
+    void* to = values[1].v_handle;
+    TVMStreamHandle stream = values[2].v_handle;
+
+    int call_ecode = TVMDeviceCopyDataFromTo(reinterpret_cast<DLTensor*>(from),
+                                             reinterpret_cast<DLTensor*>(to), stream);
 
     if (call_ecode == 0) {
       this->ReturnVoid();
@@ -400,6 +410,23 @@ class MinRPCServer {
     }
   }
 
+  void SyscallDevAllocDataWithScope(TVMValue* values, int* tcodes, int num_args) {
+    MINRPC_CHECK(num_args == 2);
+    MINRPC_CHECK(tcodes[0] == kTVMDLTensorHandle);
+    MINRPC_CHECK(tcodes[1] == kTVMNullptr || tcodes[1] == kTVMStr);
+
+    DLTensor* arr = reinterpret_cast<DLTensor*>(values[0].v_handle);
+    const char* mem_scope = (tcodes[1] == kTVMNullptr ? nullptr : values[1].v_str);
+    void* handle;
+    int call_ecode = TVMDeviceAllocDataSpaceWithScope(arr->ctx, arr->ndim, arr->shape, arr->dtype,
+                                                      mem_scope, &handle);
+    if (call_ecode == 0) {
+      this->ReturnHandle(handle);
+    } else {
+      this->ReturnLastTVMError();
+    }
+  }
+
   void SyscallDevFreeData(TVMValue* values, int* tcodes, int num_args) {
     MINRPC_CHECK(num_args == 2);
     MINRPC_CHECK(tcodes[0] == kTVMContext);
diff --git a/src/runtime/minrpc/rpc_reference.h b/src/runtime/minrpc/rpc_reference.h
index e195b9ca9e89..07d13a7ff67b 100644
--- a/src/runtime/minrpc/rpc_reference.h
+++ b/src/runtime/minrpc/rpc_reference.h
@@ -28,7 +28,7 @@ namespace tvm {
 namespace runtime {
 
 /*! \brief The current RPC procotol version. */
-constexpr const char* kRPCProtocolVer = "0.7.0";
+constexpr const char* kRPCProtocolVer = "0.8.0";
 
 /*! \brief The RPC code */
 enum class RPCCode : int {
@@ -51,6 +51,7 @@ enum class RPCCode : int {
   kDevFreeData,
   kDevStreamSync,
   kCopyAmongRemote,
+  kDevAllocDataWithScope,
 };
 
 /*!
@@ -107,6 +108,8 @@ inline const char* RPCCodeToString(RPCCode code) {
       return "kDevStreamSync";
     case RPCCode::kCopyAmongRemote:
       return "kCopyAmongRemote";
+    case RPCCode::kDevAllocDataWithScope:
+      return "kDevAllocDataWithScope";
     default:
       return "";
   }
@@ -218,6 +221,44 @@ struct RPCReference {
     return getter.num_bytes();
   }
 
+  template <typename TChannelPtr>
+  static void SendDLTensor(TChannelPtr channel, DLTensor* arr) {
+    TVMContext ctx;
+    uint64_t data;
+    // When we return NDArray, we directly return
+    // the space and the context
+    // The client will be further wrapping
+    ctx = arr->ctx;
+    data = reinterpret_cast<uint64_t>(arr->data);
+    channel->Write(data);
+    channel->Write(ctx);
+    channel->Write(arr->ndim);
+    channel->Write(arr->dtype);
+    channel->WriteArray(arr->shape, arr->ndim);
+    if (arr->strides != nullptr) {
+      channel->ThrowError(RPCServerStatus::kInvalidDLTensorFieldStride);
+    }
+    channel->Write(arr->byte_offset);
+    return;
+  }
+
+  template <typename TChannelPtr>
+  static DLTensor* ReceiveDLTensor(TChannelPtr channel) {
+    uint64_t handle;
+    channel->Read(&handle);
+    DLTensor* arr = channel->template ArenaAlloc<DLTensor>(1);
+    DLTensor& tensor = *arr;
+    tensor.data = reinterpret_cast<void*>(handle);
+    channel->Read(&(tensor.ctx));
+    channel->Read(&(tensor.ndim));
+    channel->Read(&(tensor.dtype));
+    tensor.shape = channel->template ArenaAlloc<int64_t>(tensor.ndim);
+    channel->ReadArray(tensor.shape, tensor.ndim);
+    tensor.strides = nullptr;
+    channel->Read(&(tensor.byte_offset));
+    return arr;
+  }
+
   /*!
    * \brief Send packed argument sequnce to the other peer.
    *
@@ -292,24 +333,7 @@ struct RPCReference {
         }
         case kTVMDLTensorHandle: {
           DLTensor* arr = static_cast<DLTensor*>(value.v_handle);
-          TVMContext ctx;
-          uint64_t data;
-          // When we return NDArray, we directly return
-          // the space and the context
-          // The client will be further wrapping
-          ctx = arr->ctx;
-          data = reinterpret_cast<uint64_t>(arr->data);
-          channel->Write(data);
-          channel->Write(ctx);
-          channel->Write(arr->ndim);
-          channel->Write(arr->dtype);
-          channel->WriteArray(arr->shape, arr->ndim);
-          if (arr->strides != nullptr) {
-            channel->ThrowError(RPCServerStatus::kInvalidDLTensorFieldStride);
-          }
-          if (arr->byte_offset != 0) {
-            channel->ThrowError(RPCServerStatus::kInvalidDLTensorFieldByteOffset);
-          }
+          SendDLTensor(channel, arr);
           break;
         }
         case kTVMNullptr:
@@ -422,19 +446,7 @@ struct RPCReference {
           break;
         }
         case kTVMDLTensorHandle: {
-          uint64_t handle;
-          channel->Read(&handle);
-          DLTensor* arr = channel->template ArenaAlloc<DLTensor>(1);
-          DLTensor& tensor = *arr;
-          tensor.data = reinterpret_cast<void*>(handle);
-          channel->Read(&(tensor.ctx));
-          channel->Read(&(tensor.ndim));
-          channel->Read(&(tensor.dtype));
-          tensor.shape = channel->template ArenaAlloc<int64_t>(tensor.ndim);
-          channel->ReadArray(tensor.shape, tensor.ndim);
-          tensor.strides = nullptr;
-          tensor.byte_offset = 0;
-          value.v_handle = arr;
+          value.v_handle = ReceiveDLTensor(channel);
           break;
         }
         default: {
diff --git a/src/runtime/module.cc b/src/runtime/module.cc
index 4cec5e3643c1..d84a8215421f 100644
--- a/src/runtime/module.cc
+++ b/src/runtime/module.cc
@@ -178,7 +178,7 @@ TVM_REGISTER_GLOBAL("runtime.ModuleGetTypeKey").set_body_typed([](Module mod) {
 TVM_REGISTER_GLOBAL("runtime.ModuleLoadFromFile").set_body_typed(Module::LoadFromFile);
 
 TVM_REGISTER_GLOBAL("runtime.ModuleSaveToFile")
-    .set_body_typed([](Module mod, std::string name, std::string fmt) {
+    .set_body_typed([](Module mod, tvm::String name, tvm::String fmt) {
       mod->SaveToFile(name, fmt);
     });
 
diff --git a/src/runtime/ndarray.cc b/src/runtime/ndarray.cc
index dae775606a7e..d46f0868a2ea 100644
--- a/src/runtime/ndarray.cc
+++ b/src/runtime/ndarray.cc
@@ -23,8 +23,9 @@
  */
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/ndarray.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/registry.h>
 
 #include "runtime_base.h"
 
@@ -58,36 +59,39 @@ inline void VerifyDataType(DLDataType dtype) {
   ICHECK_EQ(dtype.bits & (dtype.bits - 1), 0);
 }
 
-inline size_t GetDataAlignment(const DLTensor& arr) {
-  size_t align = (arr.dtype.bits / 8) * arr.dtype.lanes;
-  if (align < kAllocAlignment) return kAllocAlignment;
-  return align;
-}
-
 void ArrayCopyFromBytes(DLTensor* handle, const void* data, size_t nbytes) {
-  TVMContext cpu_ctx;
-  cpu_ctx.device_type = kDLCPU;
-  cpu_ctx.device_id = 0;
   size_t arr_size = GetDataSize(*handle);
   ICHECK_EQ(arr_size, nbytes) << "ArrayCopyFromBytes: size mismatch";
   ICHECK(IsContiguous(*handle)) << "ArrayCopyFromBytes only support contiguous array for now";
-  DeviceAPI::Get(handle->ctx)
-      ->CopyDataFromTo(data, 0, handle->data, static_cast<size_t>(handle->byte_offset), nbytes,
-                       cpu_ctx, handle->ctx, handle->dtype, nullptr);
+
+  DLTensor from;
+  from.data = const_cast<void*>(data);
+  from.ctx = DLContext{kDLCPU, 0};
+  from.ndim = handle->ndim;
+  from.dtype = handle->dtype;
+  from.shape = handle->shape;
+  from.strides = nullptr;
+  from.byte_offset = 0;
+  DeviceAPI::Get(handle->ctx)->CopyDataFromTo(&from, handle, nullptr);
   // Synchronize in case data become unavailable later.
   DeviceAPI::Get(handle->ctx)->StreamSync(handle->ctx, nullptr);
 }
 
 void ArrayCopyToBytes(const DLTensor* handle, void* data, size_t nbytes) {
-  TVMContext cpu_ctx;
-  cpu_ctx.device_type = kDLCPU;
-  cpu_ctx.device_id = 0;
   size_t arr_size = GetDataSize(*handle);
   ICHECK_EQ(arr_size, nbytes) << "ArrayCopyToBytes: size mismatch";
   ICHECK(IsContiguous(*handle)) << "ArrayCopyToBytes only support contiguous array for now";
-  DeviceAPI::Get(handle->ctx)
-      ->CopyDataFromTo(handle->data, static_cast<size_t>(handle->byte_offset), data, 0, nbytes,
-                       handle->ctx, cpu_ctx, handle->dtype, nullptr);
+
+  DLTensor to;
+  to.data = const_cast<void*>(data);
+  to.ctx = DLContext{kDLCPU, 0};
+  to.ndim = handle->ndim;
+  to.dtype = handle->dtype;
+  to.shape = handle->shape;
+  to.strides = nullptr;
+  to.byte_offset = 0;
+
+  DeviceAPI::Get(handle->ctx)->CopyDataFromTo(const_cast<DLTensor*>(handle), &to, nullptr);
   // Synchronize in case data become unavailable later.
   DeviceAPI::Get(handle->ctx)->StreamSync(handle->ctx, nullptr);
 }
@@ -186,13 +190,11 @@ NDArray NDArray::CreateView(std::vector<int64_t> shape, DLDataType dtype) {
 
 DLManagedTensor* NDArray::ToDLPack() const { return Internal::ToDLPack(get_mutable()); }
 
-NDArray NDArray::Empty(std::vector<int64_t> shape, DLDataType dtype, DLContext ctx) {
+NDArray NDArray::Empty(std::vector<int64_t> shape, DLDataType dtype, DLContext ctx,
+                       Optional<String> mem_scope) {
   NDArray ret = Internal::Create(shape, dtype, ctx);
-  // setup memory content
-  size_t size = GetDataSize(ret.get_mutable()->dl_tensor);
-  size_t alignment = GetDataAlignment(ret.get_mutable()->dl_tensor);
-  ret.get_mutable()->dl_tensor.data =
-      DeviceAPI::Get(ret->ctx)->AllocDataSpace(ret->ctx, size, alignment, ret->dtype);
+  ret.get_mutable()->dl_tensor.data = DeviceAPI::Get(ret->ctx)->AllocDataSpace(
+      ret->ctx, shape.size(), shape.data(), ret->dtype, mem_scope);
   return ret;
 }
 
@@ -236,9 +238,7 @@ void NDArray::CopyFromTo(const DLTensor* from, DLTensor* to, TVMStreamHandle str
   // api manager.
   TVMContext ctx = from->ctx.device_type != kDLCPU ? from->ctx : to->ctx;
 
-  DeviceAPI::Get(ctx)->CopyDataFromTo(from->data, static_cast<size_t>(from->byte_offset), to->data,
-                                      static_cast<size_t>(to->byte_offset), from_size, from->ctx,
-                                      to->ctx, from->dtype, stream);
+  DeviceAPI::Get(ctx)->CopyDataFromTo(const_cast<DLTensor*>(from), to, stream);
 }
 
 std::vector<int64_t> NDArray::Shape() const { return get_mutable()->shape_; }
@@ -279,6 +279,17 @@ int TVMArrayAlloc(const tvm_index_t* shape, int ndim, int dtype_code, int dtype_
   API_END();
 }
 
+TVM_REGISTER_GLOBAL("runtime.TVMArrayAllocWithScope").set_body([](TVMArgs args, TVMRetValue* ret) {
+  int64_t* shape_ptr = static_cast<int64_t*>(static_cast<void*>(args[0]));
+  int ndim = args[1];
+  std::vector<int64_t> shape(shape_ptr, shape_ptr + ndim);
+  DataType dtype = args[2];
+  TVMContext ctx = args[3];
+  Optional<String> mem_scope = args[4];
+  auto ndarray = NDArray::Empty(shape, dtype, ctx, mem_scope);
+  *ret = ndarray;
+});
+
 int TVMArrayFree(TVMArrayHandle handle) {
   API_BEGIN();
   NDArray::Internal::FFIDecRef(handle);
diff --git a/src/runtime/object.cc b/src/runtime/object.cc
index ad68c70698ea..c9a9669671e6 100644
--- a/src/runtime/object.cc
+++ b/src/runtime/object.cc
@@ -20,9 +20,9 @@
  * \file src/runtime/object.cc
  * \brief Object type management system.
  */
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/object.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <iostream>
 #include <mutex>
diff --git a/src/runtime/opencl/opencl_common.h b/src/runtime/opencl/opencl_common.h
index fa118ed9525b..3fca368c758b 100644
--- a/src/runtime/opencl/opencl_common.h
+++ b/src/runtime/opencl/opencl_common.h
@@ -26,8 +26,8 @@
 
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/packed_func.h>
-#include <tvm/support/logging.h>
 
 /* There are many OpenCL platforms that do not yet support OpenCL 2.0,
  * hence we use 1.2 APIs, some of which are now deprecated.  In order
@@ -232,9 +232,6 @@ class OpenCLWorkspace : public DeviceAPI {
   void GetAttr(TVMContext ctx, DeviceAttrKind kind, TVMRetValue* rv) final;
   void* AllocDataSpace(TVMContext ctx, size_t size, size_t alignment, DLDataType type_hint) final;
   void FreeDataSpace(TVMContext ctx, void* ptr) final;
-  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
-                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
-                      TVMStreamHandle stream) final;
   void StreamSync(TVMContext ctx, TVMStreamHandle stream) final;
   void* AllocWorkspace(TVMContext ctx, size_t size, DLDataType type_hint) final;
   void FreeWorkspace(TVMContext ctx, void* data) final;
@@ -246,6 +243,11 @@ class OpenCLWorkspace : public DeviceAPI {
 
   // get the global workspace
   static OpenCLWorkspace* Global();
+
+ protected:
+  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
+                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
+                      TVMStreamHandle stream) final;
 };
 
 /*! \brief Thread local workspace */
diff --git a/src/runtime/pack_args.h b/src/runtime/pack_args.h
index 45cde22bda08..7c852da77df6 100644
--- a/src/runtime/pack_args.h
+++ b/src/runtime/pack_args.h
@@ -40,13 +40,24 @@ namespace tvm {
 namespace runtime {
 /*!
  * \brief argument union type of 32bit.
- * Choose 32 bit because most GPU API do not work well with 64 bit.
  */
-union ArgUnion {
+union ArgUnion32 {
   int32_t v_int32;
   uint32_t v_uint32;
   float v_float32;
 };
+
+/*!
+ * \brief argument union type of 64 bit, for use by Vulkan and Metal runtime.
+ */
+union ArgUnion64 {
+  int32_t v_int32[2];
+  uint32_t v_uint32[2];
+  float v_float32[2];
+  int64_t v_int64;
+  uint64_t v_uint64;
+  double v_float64;
+};
 /*!
  * \brief Create a packed function from void addr types.
  *
@@ -140,9 +151,9 @@ inline PackedFunc PackFuncVoidAddr_(F f, const std::vector<ArgConvertCode>& code
   int num_args = static_cast<int>(codes.size());
   auto ret = [f, codes, num_args](TVMArgs args, TVMRetValue* ret) {
     TempArray<void*, N> addr_(num_args);
-    TempArray<ArgUnion, N> holder_(num_args);
+    TempArray<ArgUnion32, N> holder_(num_args);
     void** addr = addr_.data();
-    ArgUnion* holder = holder_.data();
+    ArgUnion32* holder = holder_.data();
     for (int i = 0; i < num_args; ++i) {
       switch (codes[i]) {
         case INT64_TO_INT64:
@@ -177,25 +188,28 @@ template <int N, typename F>
 inline PackedFunc PackFuncNonBufferArg_(F f, int base, const std::vector<ArgConvertCode>& codes) {
   int num_args = static_cast<int>(codes.size());
   auto ret = [f, codes, base, num_args](TVMArgs args, TVMRetValue* ret) {
-    TempArray<ArgUnion, N> holder_(num_args);
-    ArgUnion* holder = holder_.data();
+    TempArray<ArgUnion64, N> holder_(num_args);
+    ArgUnion64* holder = holder_.data();
     for (int i = 0; i < num_args; ++i) {
       switch (codes[i]) {
-        case INT64_TO_INT64:
+        case INT64_TO_INT64: {
+          holder[i].v_int64 = args.values[base + i].v_int64;
+          break;
+        }
         case FLOAT64_TO_FLOAT64: {
-          LOG(FATAL) << "Do not support 64bit argument to device function";
+          holder[i].v_float64 = args.values[base + i].v_float64;
           break;
         }
         case INT64_TO_INT32: {
-          holder[i].v_int32 = static_cast<int32_t>(args.values[base + i].v_int64);
+          holder[i].v_int32[0] = static_cast<int32_t>(args.values[base + i].v_int64);
           break;
         }
         case INT64_TO_UINT32: {
-          holder[i].v_uint32 = static_cast<uint32_t>(args.values[base + i].v_int64);
+          holder[i].v_uint32[0] = static_cast<uint32_t>(args.values[base + i].v_int64);
           break;
         }
         case FLOAT64_TO_FLOAT32: {
-          holder[i].v_float32 = static_cast<float>(args.values[base + i].v_float64);
+          holder[i].v_float32[0] = static_cast<float>(args.values[base + i].v_float64);
           break;
         }
         case HANDLE_TO_HANDLE: {
diff --git a/src/runtime/profiling.cc b/src/runtime/profiling.cc
new file mode 100644
index 000000000000..3d204166986d
--- /dev/null
+++ b/src/runtime/profiling.cc
@@ -0,0 +1,97 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file src/runtime/profiling.cc
+ * \brief Runtime profiling including timers.
+ */
+
+#include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/profiling.h>
+
+#include <chrono>
+#include <map>
+
+namespace tvm {
+namespace runtime {
+
+class DefaultTimerNode : public TimerNode {
+ public:
+  virtual void Start() {
+    TVMSynchronize(ctx_.device_type, ctx_.device_id, nullptr);
+    start_ = std::chrono::high_resolution_clock::now();
+  }
+  virtual void Stop() {
+    TVMSynchronize(ctx_.device_type, ctx_.device_id, nullptr);
+    duration_ = std::chrono::high_resolution_clock::now() - start_;
+  }
+  virtual int64_t SyncAndGetElapsedNanos() { return duration_.count(); }
+  virtual ~DefaultTimerNode() {}
+
+  explicit DefaultTimerNode(TVMContext ctx) : ctx_(ctx) {}
+  static constexpr const char* _type_key = "DefaultTimerNode";
+  TVM_DECLARE_FINAL_OBJECT_INFO(DefaultTimerNode, TimerNode);
+
+ private:
+  std::chrono::high_resolution_clock::time_point start_;
+  std::chrono::duration<int64_t, std::nano> duration_;
+  TVMContext ctx_;
+};
+
+TVM_REGISTER_OBJECT_TYPE(DefaultTimerNode);
+TVM_REGISTER_OBJECT_TYPE(TimerNode);
+
+Timer DefaultTimer(TVMContext ctx) { return Timer(make_object<DefaultTimerNode>(ctx)); }
+
+class CPUTimerNode : public TimerNode {
+ public:
+  virtual void Start() { start_ = std::chrono::high_resolution_clock::now(); }
+  virtual void Stop() { duration_ = std::chrono::high_resolution_clock::now() - start_; }
+  virtual int64_t SyncAndGetElapsedNanos() { return duration_.count(); }
+  virtual ~CPUTimerNode() {}
+
+  static constexpr const char* _type_key = "CPUTimerNode";
+  TVM_DECLARE_FINAL_OBJECT_INFO(CPUTimerNode, TimerNode);
+
+ private:
+  std::chrono::high_resolution_clock::time_point start_;
+  std::chrono::duration<int64_t, std::nano> duration_;
+};
+TVM_REGISTER_OBJECT_TYPE(CPUTimerNode);
+
+TVM_REGISTER_GLOBAL("profiling.timer.cpu").set_body_typed([](TVMContext ctx) {
+  return Timer(make_object<CPUTimerNode>());
+});
+
+Timer Timer::Start(TVMContext ctx) {
+  auto f = Registry::Get(std::string("profiling.timer.") + DeviceName(ctx.device_type));
+  if (f == nullptr) {
+    Timer t = DefaultTimer(ctx);
+    t->Start();
+    return t;
+  } else {
+    Timer t = f->operator()(ctx);
+    t->Start();
+    return t;
+  }
+}
+
+TVM_REGISTER_GLOBAL("profiling.start_timer").set_body_typed(Timer::Start);
+}  // namespace runtime
+}  // namespace tvm
diff --git a/src/runtime/registry.cc b/src/runtime/registry.cc
index a65235090bfd..bb5a794a030b 100644
--- a/src/runtime/registry.cc
+++ b/src/runtime/registry.cc
@@ -22,8 +22,8 @@
  * \brief The global registry of packed function.
  */
 #include <dmlc/thread_local.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <array>
 #include <memory>
diff --git a/src/runtime/rocm/rocm_device_api.cc b/src/runtime/rocm/rocm_device_api.cc
index 26e44eca0d12..5d03374a4571 100644
--- a/src/runtime/rocm/rocm_device_api.cc
+++ b/src/runtime/rocm/rocm_device_api.cc
@@ -25,8 +25,9 @@
 #include <hip/hip_runtime_api.h>
 #include <hsa/hsa.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
+#include <tvm/runtime/profiling.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include "rocm_common.h"
 
@@ -200,5 +201,41 @@ TVM_REGISTER_GLOBAL("device_api.rocm").set_body([](TVMArgs args, TVMRetValue* rv
   DeviceAPI* ptr = ROCMDeviceAPI::Global();
   *rv = static_cast<void*>(ptr);
 });
+
+class ROCMTimerNode : public TimerNode {
+ public:
+  virtual void Start() {
+    ROCM_CALL(hipEventRecord(start_, ROCMThreadEntry::ThreadLocal()->stream));
+  }
+  virtual void Stop() { ROCM_CALL(hipEventRecord(stop_, ROCMThreadEntry::ThreadLocal()->stream)); }
+  virtual int64_t SyncAndGetElapsedNanos() {
+    ROCM_CALL(hipEventSynchronize(stop_));
+    float milliseconds = 0;
+    ROCM_CALL(hipEventElapsedTime(&milliseconds, start_, stop_));
+    return milliseconds * 1e6;
+  }
+  virtual ~ROCMTimerNode() {
+    ROCM_CALL(hipEventDestroy(start_));
+    ROCM_CALL(hipEventDestroy(stop_));
+  }
+  ROCMTimerNode() {
+    ROCM_CALL(hipEventCreate(&start_));
+    ROCM_CALL(hipEventCreate(&stop_));
+  }
+
+  static constexpr const char* _type_key = "ROCMTimerNode";
+  TVM_DECLARE_FINAL_OBJECT_INFO(ROCMTimerNode, TimerNode);
+
+ private:
+  hipEvent_t start_;
+  hipEvent_t stop_;
+};
+
+TVM_REGISTER_OBJECT_TYPE(ROCMTimerNode);
+
+TVM_REGISTER_GLOBAL("profiling.timer.rocm").set_body_typed([](TVMContext ctx) {
+  return Timer(make_object<ROCMTimerNode>());
+});
+
 }  // namespace runtime
 }  // namespace tvm
diff --git a/src/runtime/rpc/rpc_device_api.cc b/src/runtime/rpc/rpc_device_api.cc
index a1e96e92b4e0..cdeeb368f5a2 100644
--- a/src/runtime/rpc/rpc_device_api.cc
+++ b/src/runtime/rpc/rpc_device_api.cc
@@ -21,8 +21,8 @@
  * \file rpc_device_api.cc
  */
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/registry.h>
-#include <tvm/support/logging.h>
 
 #include <utility>
 
@@ -43,6 +43,18 @@ class RPCDeviceAPI final : public DeviceAPI {
     GetSess(ctx)->GetDeviceAPI(remote_ctx)->GetAttr(remote_ctx, kind, rv);
   }
 
+  void* AllocDataSpace(TVMContext ctx, int ndim, const int64_t* shape, DLDataType dtype,
+                       Optional<String> mem_scope) final {
+    auto sess = GetSess(ctx);
+    auto remote_ctx = RemoveRPCSessionMask(ctx);
+    void* data =
+        sess->GetDeviceAPI(remote_ctx)->AllocDataSpace(remote_ctx, ndim, shape, dtype, mem_scope);
+    RemoteSpace* space = new RemoteSpace();
+    space->data = data;
+    space->sess = std::move(sess);
+    return space;
+  }
+
   void* AllocDataSpace(TVMContext ctx, size_t nbytes, size_t alignment,
                        DLDataType type_hint) final {
     auto sess = GetSess(ctx);
@@ -60,35 +72,41 @@ class RPCDeviceAPI final : public DeviceAPI {
     auto remote_ctx = RemoveRPCSessionMask(ctx);
     try {
       GetSess(ctx)->GetDeviceAPI(remote_ctx)->FreeDataSpace(remote_ctx, space->data);
-    } catch (const dmlc::Error& e) {
+    } catch (const Error& e) {
       // fault tolerance to remote close.
     }
     delete space;
   }
-  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
-                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
-                      TVMStreamHandle stream) final {
+
+  void CopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream) final {
+    DLContext ctx_from = from->ctx;
+    DLContext ctx_to = to->ctx;
     if (IsRPCSessionContext(ctx_from) && IsRPCSessionContext(ctx_to)) {
       ICHECK(ctx_from.device_type == ctx_to.device_type)
           << "Cannot copy across two different remote session";
-      auto remote_ctx_from = RemoveRPCSessionMask(ctx_from);
-      auto remote_ctx_to = RemoveRPCSessionMask(ctx_to);
-      auto remote_ctx = remote_ctx_from;
-      if (remote_ctx.device_type == kDLCPU) remote_ctx = remote_ctx_to;
-      GetSess(ctx_from)
-          ->GetDeviceAPI(remote_ctx)
-          ->CopyDataFromTo(static_cast<const RemoteSpace*>(from)->data, from_offset,
-                           static_cast<const RemoteSpace*>(to)->data, to_offset, size,
-                           remote_ctx_from, remote_ctx_to, type_hint, stream);
+      DLTensor from_tensor = *from;
+      from_tensor.ctx = RemoveRPCSessionMask(ctx_from);
+      from_tensor.data = static_cast<const RemoteSpace*>(from->data)->data;
+      DLTensor to_tensor = *to;
+      to_tensor.ctx = RemoveRPCSessionMask(ctx_to);
+      to_tensor.data = static_cast<const RemoteSpace*>(to->data)->data;
+      auto remote_ctx = from_tensor.ctx;
+      if (remote_ctx.device_type == kDLCPU) remote_ctx = to_tensor.ctx;
+      GetSess(ctx_from)->GetDeviceAPI(remote_ctx)->CopyDataFromTo(&from_tensor, &to_tensor, stream);
     } else if (IsRPCSessionContext(ctx_from) && ctx_to.device_type == kDLCPU) {
-      auto remote_ctx_from = RemoveRPCSessionMask(ctx_from);
-      GetSess(ctx_from)->CopyFromRemote(static_cast<const RemoteSpace*>(from)->data, from_offset,
-                                        to, to_offset, size, remote_ctx_from, type_hint);
+      DLTensor from_tensor = *from;
+      from_tensor.ctx = RemoveRPCSessionMask(ctx_from);
+      from_tensor.data = static_cast<const RemoteSpace*>(from->data)->data;
+      void* to_bytes = static_cast<char*>(to->data) + to->byte_offset;
+      size_t nbytes = GetDataSize(*to);
+      GetSess(ctx_from)->CopyFromRemote(&from_tensor, to_bytes, nbytes);
     } else if (ctx_from.device_type == kDLCPU && IsRPCSessionContext(ctx_to)) {
-      auto remote_ctx_to = RemoveRPCSessionMask(ctx_to);
-      GetSess(ctx_to)->CopyToRemote(const_cast<void*>(from), from_offset,
-                                    static_cast<const RemoteSpace*>(to)->data, to_offset, size,
-                                    remote_ctx_to, type_hint);
+      DLTensor to_tensor = *to;
+      to_tensor.ctx = RemoveRPCSessionMask(ctx_to);
+      to_tensor.data = static_cast<const RemoteSpace*>(to->data)->data;
+      void* from_bytes = static_cast<char*>(from->data) + from->byte_offset;
+      size_t nbytes = GetDataSize(*from);
+      GetSess(ctx_to)->CopyToRemote(from_bytes, &to_tensor, nbytes);
     } else {
       LOG(FATAL) << "expect copy from/to remote or between remote";
     }
@@ -99,6 +117,13 @@ class RPCDeviceAPI final : public DeviceAPI {
     GetSess(ctx)->GetDeviceAPI(remote_ctx)->StreamSync(remote_ctx, stream);
   }
 
+ protected:
+  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset,
+                      size_t num_bytes, TVMContext ctx_from, TVMContext ctx_to,
+                      DLDataType type_hint, TVMStreamHandle stream) final {
+    LOG(FATAL) << "Not implemented.";
+  }
+
  private:
   std::shared_ptr<RPCSession> GetSess(TVMContext ctx) {
     int tbl_index = GetRPCSessionIndex(ctx);
diff --git a/src/runtime/rpc/rpc_endpoint.cc b/src/runtime/rpc/rpc_endpoint.cc
index fbdd93fb4f62..5e2bba88921e 100644
--- a/src/runtime/rpc/rpc_endpoint.cc
+++ b/src/runtime/rpc/rpc_endpoint.cc
@@ -387,88 +387,72 @@ class RPCEndpoint::EventHandler : public dmlc::Stream {
   void HandleSyscall(RPCCode code);
 
   void HandleCopyFromRemote() {
-    uint64_t handle, offset, num_bytes;
-    TVMContext ctx;
-    DLDataType type_hint;
-    this->Read(&handle);
-    this->Read(&offset);
-    this->Read(&num_bytes);
-    this->Read(&ctx);
-    this->Read(&type_hint);
-    size_t elem_bytes = (type_hint.bits * type_hint.lanes + 7) / 8;
-
+    DLTensor* arr = RPCReference::ReceiveDLTensor(this);
+    uint64_t data_bytes;
+    this->Read(&data_bytes);
+    size_t elem_bytes = (arr->dtype.bits * arr->dtype.lanes + 7) / 8;
     auto* sess = GetServingSession();
-
     // Return Copy Ack with the given data
-    auto fcopyack = [this](char* data_ptr, size_t num_bytes) {
+    auto fcopyack = [this](char* dptr, size_t num_bytes) {
       RPCCode code = RPCCode::kCopyAck;
       uint64_t packet_nbytes = sizeof(code) + num_bytes;
 
       this->Write(packet_nbytes);
       this->Write(code);
-      this->WriteArray(data_ptr, num_bytes);
+      this->WriteArray(dptr, num_bytes);
       this->SwitchToState(kRecvPacketNumBytes);
     };
 
     // When session is local, we can directly treat handle
     // as the cpu pointer without allocating a temp space.
-    if (ctx.device_type == kDLCPU && sess->IsLocalSession() && DMLC_IO_NO_ENDIAN_SWAP) {
-      char* data_ptr = reinterpret_cast<char*>(handle) + offset;
-      fcopyack(data_ptr, num_bytes);
+    if (arr->ctx.device_type == kDLCPU && sess->IsLocalSession() && DMLC_IO_NO_ENDIAN_SWAP) {
+      char* data_ptr = reinterpret_cast<char*>(arr->data) + arr->byte_offset;
+      fcopyack(data_ptr, data_bytes);
     } else {
-      char* data_ptr = this->ArenaAlloc<char>(num_bytes);
-
-      auto on_copy_complete = [this, elem_bytes, num_bytes, data_ptr, fcopyack](RPCCode status,
-                                                                                TVMArgs args) {
+      char* temp_data = this->ArenaAlloc<char>(data_bytes);
+      auto on_copy_complete = [this, elem_bytes, data_bytes, temp_data, fcopyack](RPCCode status,
+                                                                                  TVMArgs args) {
         if (status == RPCCode::kException) {
           this->ReturnException(args.values[0].v_str);
           this->SwitchToState(kRecvPacketNumBytes);
         } else {
           // endian aware handling
           if (!DMLC_IO_NO_ENDIAN_SWAP) {
-            dmlc::ByteSwap(data_ptr, elem_bytes, num_bytes / elem_bytes);
+            dmlc::ByteSwap(temp_data, elem_bytes, data_bytes / elem_bytes);
           }
-          fcopyack(data_ptr, num_bytes);
+          fcopyack(temp_data, data_bytes);
         }
       };
 
       this->SwitchToState(kWaitForAsyncCallback);
-      sess->AsyncCopyFromRemote(reinterpret_cast<void*>(handle), offset, data_ptr, 0, num_bytes,
-                                ctx, type_hint, on_copy_complete);
+      sess->AsyncCopyFromRemote(arr, static_cast<void*>(temp_data), data_bytes, on_copy_complete);
     }
   }
 
   void HandleCopyToRemote() {
-    uint64_t handle, offset, num_bytes;
-    TVMContext ctx;
-    DLDataType type_hint;
-
-    this->Read(&handle);
-    this->Read(&offset);
-    this->Read(&num_bytes);
-    this->Read(&ctx);
-    this->Read(&type_hint);
-
-    size_t elem_bytes = (type_hint.bits * type_hint.lanes + 7) / 8;
+    DLTensor* arr = RPCReference::ReceiveDLTensor(this);
+    uint64_t data_bytes;
+    this->Read(&data_bytes);
+    size_t elem_bytes = (arr->dtype.bits * arr->dtype.lanes + 7) / 8;
     auto* sess = GetServingSession();
 
     // When session is local, we can directly treat handle
     // as the cpu pointer without allocating a temp space.
-    if (ctx.device_type == kDLCPU && sess->IsLocalSession()) {
-      char* dptr = reinterpret_cast<char*>(handle) + offset;
-      this->ReadArray(dptr, num_bytes);
+    if (arr->ctx.device_type == kDLCPU && sess->IsLocalSession()) {
+      char* dptr = reinterpret_cast<char*>(arr->data) + arr->byte_offset;
+      this->ReadArray(dptr, data_bytes);
 
       if (!DMLC_IO_NO_ENDIAN_SWAP) {
-        dmlc::ByteSwap(dptr, elem_bytes, num_bytes / elem_bytes);
+        dmlc::ByteSwap(dptr, elem_bytes, data_bytes / elem_bytes);
       }
       this->ReturnVoid();
       this->SwitchToState(kRecvPacketNumBytes);
     } else {
-      char* temp_data = this->ArenaAlloc<char>(num_bytes);
-      this->ReadArray(temp_data, num_bytes);
+      char* temp_data = this->ArenaAlloc<char>(data_bytes);
+      this->ReadArray(temp_data, data_bytes);
 
       if (!DMLC_IO_NO_ENDIAN_SWAP) {
-        dmlc::ByteSwap(temp_data, elem_bytes, num_bytes / elem_bytes);
+        dmlc::ByteSwap(temp_data, elem_bytes, data_bytes / elem_bytes);
       }
 
       auto on_copy_complete = [this](RPCCode status, TVMArgs args) {
@@ -482,8 +466,7 @@ class RPCEndpoint::EventHandler : public dmlc::Stream {
       };
 
       this->SwitchToState(kWaitForAsyncCallback);
-      sess->AsyncCopyToRemote(temp_data, 0, reinterpret_cast<void*>(handle), offset, num_bytes, ctx,
-                              type_hint, on_copy_complete);
+      sess->AsyncCopyToRemote(static_cast<void*>(temp_data), arr, data_bytes, on_copy_complete);
     }
   }
 
@@ -543,7 +526,7 @@ class RPCEndpoint::EventHandler : public dmlc::Stream {
 
       try {
         fconstructor->CallPacked(constructor_args, &con_ret);
-      } catch (const dmlc::Error& e) {
+      } catch (const Error& e) {
         LOG(FATAL) << "Server[" << name_ << "]:"
                    << " Error caught from session constructor " << constructor_name << ":\n"
                    << e.what();
@@ -557,7 +540,7 @@ class RPCEndpoint::EventHandler : public dmlc::Stream {
       ICHECK_EQ(tkey, "rpc") << "Constructor " << constructor_name << " to return an RPCModule";
       serving_session_ = RPCModuleGetSession(mod);
       this->ReturnVoid();
-    } catch (const std::runtime_error& e) {
+    } catch (const std::exception& e) {
       this->ReturnException(e.what());
     }
 
@@ -579,7 +562,7 @@ class RPCEndpoint::EventHandler : public dmlc::Stream {
         }
         this->SwitchToState(kRecvPacketNumBytes);
       });
-    } catch (const std::runtime_error& e) {
+    } catch (const std::exception& e) {
       this->ReturnException(e.what());
       this->SwitchToState(kRecvPacketNumBytes);
     }
@@ -598,7 +581,7 @@ class RPCEndpoint::EventHandler : public dmlc::Stream {
       setter(0, rv);
 
       this->ReturnPackedSeq(TVMArgs(&ret_value, &ret_tcode, 1));
-    } catch (const std::runtime_error& e) {
+    } catch (const std::exception& e) {
       this->ReturnException(e.what());
     }
     this->SwitchToState(kRecvPacketNumBytes);
@@ -736,7 +719,7 @@ void RPCEndpoint::Shutdown() {
             writer_.bytes_available());
         if (n == 0) break;
       }
-    } catch (const dmlc::Error& e) {
+    } catch (const Error& e) {
     }
     channel_.reset(nullptr);
   }
@@ -815,51 +798,47 @@ void RPCEndpoint::CallFunc(RPCSession::PackedFuncHandle h, const TVMValue* arg_v
   ICHECK(code == RPCCode::kReturn) << "code=" << static_cast<int>(code);
 }
 
-void RPCEndpoint::CopyToRemote(void* from, size_t from_offset, void* to, size_t to_offset,
-                               size_t data_size, TVMContext ctx_to, DLDataType type_hint) {
+void RPCEndpoint::CopyToRemote(void* from_bytes, DLTensor* to, uint64_t nbytes) {
   std::lock_guard<std::mutex> lock(mutex_);
   RPCCode code = RPCCode::kCopyToRemote;
-  uint64_t handle = reinterpret_cast<uint64_t>(to);
-  uint64_t offset = static_cast<uint64_t>(to_offset);
-  uint64_t size = static_cast<uint64_t>(data_size);
 
-  uint64_t packet_nbytes = sizeof(code) + sizeof(handle) + sizeof(offset) + sizeof(size) +
-                           sizeof(ctx_to) + sizeof(type_hint) + data_size;
+  uint64_t num_data_bytes = static_cast<uint64_t>(GetDataSize(*to));
+  ICHECK_EQ(nbytes, num_data_bytes);
+
+  uint64_t to_data = reinterpret_cast<uint64_t>(to->data);
+  uint64_t shape_bytes = to->ndim * sizeof(int64_t);
+  uint64_t packet_nbytes = sizeof(code) + sizeof(to_data) + sizeof(to->ctx) + sizeof(to->ndim) +
+                           sizeof(to->dtype) + sizeof(to->byte_offset) + shape_bytes +
+                           sizeof(nbytes) + num_data_bytes;
 
   handler_->Write(packet_nbytes);
   handler_->Write(code);
-  handler_->Write(handle);
-  handler_->Write(offset);
-  handler_->Write(size);
-  handler_->Write(ctx_to);
-  handler_->Write(type_hint);
-  handler_->WriteArray(reinterpret_cast<char*>(from) + from_offset, data_size);
-
+  RPCReference::SendDLTensor(handler_, to);
+  handler_->Write(nbytes);
+  handler_->WriteArray(reinterpret_cast<char*>(from_bytes), nbytes);
   ICHECK(HandleUntilReturnEvent(true, [](TVMArgs) {}) == RPCCode::kReturn);
 }
 
-void RPCEndpoint::CopyFromRemote(void* from, size_t from_offset, void* to, size_t to_offset,
-                                 size_t data_size, TVMContext ctx_from, DLDataType type_hint) {
+void RPCEndpoint::CopyFromRemote(DLTensor* from, void* to_bytes, uint64_t nbytes) {
   std::lock_guard<std::mutex> lock(mutex_);
   RPCCode code = RPCCode::kCopyFromRemote;
-  uint64_t handle = reinterpret_cast<uint64_t>(from);
-  uint64_t offset = static_cast<uint64_t>(from_offset);
-  uint64_t size = static_cast<uint64_t>(data_size);
 
-  uint64_t packet_nbytes = sizeof(code) + sizeof(handle) + sizeof(offset) + sizeof(size) +
-                           sizeof(ctx_from) + sizeof(type_hint);
+  uint64_t num_data_bytes = static_cast<uint64_t>(GetDataSize(*from));
+  CHECK_EQ(nbytes, num_data_bytes);
+
+  uint64_t from_data = reinterpret_cast<uint64_t>(from->data);
+  uint64_t shape_bytes = from->ndim * sizeof(int64_t);
+  uint64_t packet_nbytes = sizeof(code) + sizeof(from_data) + sizeof(from->ctx) +
+                           sizeof(from->ndim) + sizeof(from->dtype) + sizeof(from->byte_offset) +
+                           shape_bytes + sizeof(nbytes);
 
   handler_->Write(packet_nbytes);
   handler_->Write(code);
-  handler_->Write(handle);
-  handler_->Write(offset);
-  handler_->Write(size);
-  handler_->Write(ctx_from);
-  handler_->Write(type_hint);
-
-  TVMRetValue rv;
+  RPCReference::SendDLTensor(handler_, from);
+  handler_->Write(nbytes);
   ICHECK(HandleUntilReturnEvent(true, [](TVMArgs) {}) == RPCCode::kCopyAck);
-  handler_->ReadArray(reinterpret_cast<char*>(to) + to_offset, data_size);
+
+  handler_->ReadArray(reinterpret_cast<char*>(to_bytes), nbytes);
   handler_->FinishCopyAck();
 }
 
@@ -904,6 +883,23 @@ void RPCDevAllocData(RPCSession* handler, TVMArgs args, TVMRetValue* rv) {
   *rv = data;
 }
 
+void RPCDevAllocDataWithScope(RPCSession* handler, TVMArgs args, TVMRetValue* rv) {
+  DLTensor* arr = args[0];
+  TVMContext ctx = arr->ctx;
+  int ndim = arr->ndim;
+  int64_t* shape = arr->shape;
+  DLDataType dtype = arr->dtype;
+  int tcode = args[1].type_code();
+  Optional<String> mem_scope = NullOpt;
+  if (tcode == kTVMStr) {
+    mem_scope = args[1].operator String();
+  } else {
+    ICHECK_EQ(tcode, kTVMNullptr);
+  }
+  void* data = handler->GetDeviceAPI(ctx)->AllocDataSpace(ctx, ndim, shape, dtype, mem_scope);
+  *rv = data;
+}
+
 void RPCDevFreeData(RPCSession* handler, TVMArgs args, TVMRetValue* rv) {
   TVMContext ctx = args[0];
   void* ptr = args[1];
@@ -911,25 +907,18 @@ void RPCDevFreeData(RPCSession* handler, TVMArgs args, TVMRetValue* rv) {
 }
 
 void RPCCopyAmongRemote(RPCSession* handler, TVMArgs args, TVMRetValue* rv) {
-  void* from = args[0];
-  uint64_t from_offset = args[1];
-  void* to = args[2];
-  uint64_t to_offset = args[3];
-  uint64_t size = args[4];
-  TVMContext ctx_from = args[5];
-  TVMContext ctx_to = args[6];
-  DLDataType type_hint = args[7];
-  TVMStreamHandle stream = args[8];
-  TVMContext ctx = ctx_from;
+  DLTensor* from = args[0];
+  DLTensor* to = args[1];
+  TVMStreamHandle stream = args[2];
 
+  TVMContext ctx = from->ctx;
   if (ctx.device_type == kDLCPU) {
-    ctx = ctx_to;
+    ctx = to->ctx;
   } else {
-    ICHECK(ctx_to.device_type == kDLCPU || ctx_to.device_type == ctx_from.device_type)
+    ICHECK(to->ctx.device_type == kDLCPU || to->ctx.device_type == from->ctx.device_type)
         << "Can not copy across different ctx types directly";
   }
-  handler->GetDeviceAPI(ctx)->CopyDataFromTo(from, from_offset, to, to_offset, size, ctx_from,
-                                             ctx_to, type_hint, stream);
+  handler->GetDeviceAPI(ctx)->CopyDataFromTo(from, to, stream);
 }
 
 void RPCEndpoint::EventHandler::HandleSyscall(RPCCode code) {
@@ -951,6 +940,9 @@ void RPCEndpoint::EventHandler::HandleSyscall(RPCCode code) {
     case RPCCode::kDevAllocData:
       SysCallHandler(RPCDevAllocData);
       break;
+    case RPCCode::kDevAllocDataWithScope:
+      SysCallHandler(RPCDevAllocDataWithScope);
+      break;
     case RPCCode::kDevFreeData:
       SysCallHandler(RPCDevFreeData);
       break;
@@ -989,14 +981,12 @@ class RPCClientSession : public RPCSession, public DeviceAPI {
     endpoint_->CallFunc(func, arg_values, arg_type_codes, num_args, fencode_return);
   }
 
-  void CopyToRemote(void* from, size_t from_offset, void* to, size_t to_offset, size_t nbytes,
-                    TVMContext ctx_to, DLDataType type_hint) final {
-    endpoint_->CopyToRemote(from, from_offset, to, to_offset, nbytes, ctx_to, type_hint);
+  void CopyToRemote(void* local_from_bytes, DLTensor* remote_to, uint64_t nbytes) final {
+    endpoint_->CopyToRemote(local_from_bytes, remote_to, nbytes);
   }
 
-  void CopyFromRemote(void* from, size_t from_offset, void* to, size_t to_offset, size_t nbytes,
-                      TVMContext ctx_from, DLDataType type_hint) final {
-    endpoint_->CopyFromRemote(from, from_offset, to, to_offset, nbytes, ctx_from, type_hint);
+  void CopyFromRemote(DLTensor* remote_from, void* local_to_bytes, uint64_t nbytes) final {
+    endpoint_->CopyFromRemote(remote_from, local_to_bytes, nbytes);
   }
 
   void FreeHandle(void* handle, int type_code) final {
@@ -1019,15 +1009,30 @@ class RPCClientSession : public RPCSession, public DeviceAPI {
     return endpoint_->SysCallRemote(RPCCode::kDevAllocData, ctx, nbytes, alignment, type_hint);
   }
 
+  void* AllocDataSpace(TVMContext ctx, int ndim, const int64_t* shape, DLDataType dtype,
+                       Optional<String> mem_scope) final {
+    DLTensor temp;
+    temp.data = nullptr;
+    temp.ctx = ctx;
+    temp.ndim = ndim;
+    temp.dtype = dtype;
+    temp.shape = const_cast<int64_t*>(shape);
+    temp.strides = nullptr;
+    temp.byte_offset = 0;
+    if (mem_scope.defined()) {
+      return endpoint_->SysCallRemote(RPCCode::kDevAllocDataWithScope, &temp,
+                                      static_cast<std::string>(mem_scope.value()));
+    } else {
+      return endpoint_->SysCallRemote(RPCCode::kDevAllocDataWithScope, &temp, nullptr);
+    }
+  }
+
   void FreeDataSpace(TVMContext ctx, void* ptr) final {
     endpoint_->SysCallRemote(RPCCode::kDevFreeData, ctx, ptr);
   }
 
-  void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
-                      TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
-                      TVMStreamHandle stream) final {
-    endpoint_->SysCallRemote(RPCCode::kCopyAmongRemote, const_cast<void*>(from), from_offset, to,
-                             to_offset, size, ctx_from, ctx_to, type_hint, stream);
+  void CopyDataFromTo(DLTensor* from, DLTensor* to, TVMStreamHandle stream) final {
+    endpoint_->SysCallRemote(RPCCode::kCopyAmongRemote, from, to, stream);
   }
 
   void StreamSync(TVMContext ctx, TVMStreamHandle stream) final {
diff --git a/src/runtime/rpc/rpc_endpoint.h b/src/runtime/rpc/rpc_endpoint.h
index 031435fc8ef9..8e08bfa75623 100644
--- a/src/runtime/rpc/rpc_endpoint.h
+++ b/src/runtime/rpc/rpc_endpoint.h
@@ -135,8 +135,7 @@ class RPCEndpoint {
    * \param ctx_to The target context.
    * \param type_hint Hint of content data type.
    */
-  void CopyToRemote(void* from, size_t from_offset, void* to, size_t to_offset, size_t nbytes,
-                    TVMContext ctx_to, DLDataType type_hint);
+  void CopyToRemote(void* from_bytes, DLTensor* to, uint64_t nbytes);
   /*!
    * \brief Copy bytes from remote array content.
    * \param from The source host data.
@@ -147,8 +146,7 @@ class RPCEndpoint {
    * \param ctx_from The source context.
    * \param type_hint Hint of content data type.
    */
-  void CopyFromRemote(void* from, size_t from_offset, void* to, size_t to_offset, size_t nbytes,
-                      TVMContext ctx_from, DLDataType type_hint);
+  void CopyFromRemote(DLTensor* from, void* to_bytes, uint64_t nbytes);
 
   /*!
    * \brief Call a remote defined system function with arguments.
diff --git a/src/runtime/rpc/rpc_local_session.cc b/src/runtime/rpc/rpc_local_session.cc
index b35c62d255fc..0650b55d0d7c 100644
--- a/src/runtime/rpc/rpc_local_session.cc
+++ b/src/runtime/rpc/rpc_local_session.cc
@@ -87,26 +87,36 @@ void LocalSession::CallFunc(RPCSession::PackedFuncHandle func, const TVMValue* a
   this->EncodeReturn(std::move(rv), encode_return);
 }
 
-void LocalSession::CopyToRemote(void* from, size_t from_offset, void* to, size_t to_offset,
-                                size_t nbytes, TVMContext ctx_to, DLDataType type_hint) {
-  TVMContext cpu_ctx;
-  cpu_ctx.device_type = kDLCPU;
-  cpu_ctx.device_id = 0;
-  this->GetDeviceAPI(ctx_to)->CopyDataFromTo(from, from_offset, to, to_offset, nbytes, cpu_ctx,
-                                             ctx_to, type_hint, nullptr);
+void LocalSession::CopyToRemote(void* from_bytes, DLTensor* to, uint64_t nbytes) {
+  ICHECK_EQ(nbytes, GetDataSize(*to));
+  DLTensor from;
+  from.data = from_bytes;
+  from.ctx = {kDLCPU, 0};
+  from.ndim = to->ndim;
+  from.shape = to->shape;
+  from.dtype = to->dtype;
+  from.strides = nullptr;
+  from.byte_offset = 0;
+  TVMContext ctx_to = to->ctx;
+  this->GetDeviceAPI(ctx_to)->CopyDataFromTo(&from, to, nullptr);
   // Copy can happen asynchrously
   // synchronize to make sure that copy is completed
   this->GetDeviceAPI(ctx_to)->StreamSync(ctx_to, nullptr);
 }
 
-void LocalSession::CopyFromRemote(void* from, size_t from_offset, void* to, size_t to_offset,
-                                  size_t nbytes, TVMContext ctx_from, DLDataType type_hint) {
-  TVMContext cpu_ctx;
-  cpu_ctx.device_type = kDLCPU;
-  cpu_ctx.device_id = 0;
-
-  this->GetDeviceAPI(ctx_from)->CopyDataFromTo(from, from_offset, to, to_offset, nbytes, ctx_from,
-                                               cpu_ctx, type_hint, nullptr);
+void LocalSession::CopyFromRemote(DLTensor* from, void* to_bytes, uint64_t nbytes) {
+  ICHECK_EQ(nbytes, GetDataSize(*from));
+  DLTensor to;
+  to.data = to_bytes;
+  to.ctx = {kDLCPU, 0};
+  to.ndim = from->ndim;
+  to.shape = from->shape;
+  to.dtype = from->dtype;
+  to.strides = nullptr;
+  to.byte_offset = 0;
+
+  TVMContext ctx_from = from->ctx;
+  this->GetDeviceAPI(ctx_from)->CopyDataFromTo(from, &to, nullptr);
   // Copy can happen asynchrously
   // synchronize to make sure that copy is completed
   this->GetDeviceAPI(ctx_from)->StreamSync(ctx_from, nullptr);
diff --git a/src/runtime/rpc/rpc_local_session.h b/src/runtime/rpc/rpc_local_session.h
index 7a67ce86bf80..ea070e34bd35 100644
--- a/src/runtime/rpc/rpc_local_session.h
+++ b/src/runtime/rpc/rpc_local_session.h
@@ -48,11 +48,9 @@ class LocalSession : public RPCSession {
   void CallFunc(PackedFuncHandle func, const TVMValue* arg_values, const int* arg_type_codes,
                 int num_args, const FEncodeReturn& fencode_return) override;
 
-  void CopyToRemote(void* from, size_t from_offset, void* to, size_t to_offset, size_t nbytes,
-                    TVMContext ctx_to, DLDataType type_hint) override;
+  void CopyToRemote(void* from_bytes, DLTensor* to, uint64_t nbytes) override;
 
-  void CopyFromRemote(void* from, size_t from_offset, void* to, size_t to_offset, size_t nbytes,
-                      TVMContext ctx_from, DLDataType type_hint) override;
+  void CopyFromRemote(DLTensor* from, void* to_bytes, uint64_t nbytes) override;
 
   void FreeHandle(void* handle, int type_code) override;
 
diff --git a/src/runtime/rpc/rpc_module.cc b/src/runtime/rpc/rpc_module.cc
index 4f721e122a4c..46e1be794520 100644
--- a/src/runtime/rpc/rpc_module.cc
+++ b/src/runtime/rpc/rpc_module.cc
@@ -23,6 +23,7 @@
  */
 #include <tvm/runtime/container.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/profiling.h>
 #include <tvm/runtime/registry.h>
 
 #include <cstring>
@@ -129,7 +130,7 @@ class RPCWrappedFunc : public Object {
   ~RPCWrappedFunc() {
     try {
       sess_->FreeHandle(handle_, kTVMPackedFuncHandle);
-    } catch (const dmlc::Error& e) {
+    } catch (const Error& e) {
       // fault tolerance to remote close
     }
   }
@@ -164,7 +165,7 @@ class RPCModuleNode final : public ModuleNode {
     if (module_handle_ != nullptr) {
       try {
         sess_->FreeHandle(module_handle_, kTVMModuleHandle);
-      } catch (const dmlc::Error& e) {
+      } catch (const Error& e) {
         // fault tolerance to remote close
       }
       module_handle_ = nullptr;
@@ -364,8 +365,6 @@ PackedFunc WrapTimeEvaluator(PackedFunc pf, TVMContext ctx, int number, int repe
       if (f_preproc != nullptr) {
         f_preproc.CallPacked(args, &temp);
       }
-      std::chrono::time_point<std::chrono::high_resolution_clock, std::chrono::nanoseconds> tbegin,
-          tend;
       double duration_ms = 0.0;
 
       do {
@@ -374,20 +373,17 @@ PackedFunc WrapTimeEvaluator(PackedFunc pf, TVMContext ctx, int number, int repe
                                              number * 1.618));  // 1.618 is chosen by random
         }
 
-        tbegin = std::chrono::high_resolution_clock::now();
+        Timer t = Timer::Start(ctx);
         // start timing
         for (int i = 0; i < number; ++i) {
           pf.CallPacked(args, &temp);
         }
-        DeviceAPI::Get(ctx)->StreamSync(ctx, nullptr);
-        tend = std::chrono::high_resolution_clock::now();
-
-        duration_ms =
-            std::chrono::duration_cast<std::chrono::duration<double>>(tend - tbegin).count() * 1000;
+        t->Stop();
+        int64_t t_nanos = t->SyncAndGetElapsedNanos();
+        duration_ms = t_nanos / 1e6;
       } while (duration_ms < min_repeat_ms);
 
-      double speed =
-          std::chrono::duration_cast<std::chrono::duration<double>>(tend - tbegin).count() / number;
+      double speed = duration_ms / 1e3 / number;
       os.write(reinterpret_cast<char*>(&speed), sizeof(speed));
     }
 
diff --git a/src/runtime/rpc/rpc_session.cc b/src/runtime/rpc/rpc_session.cc
index f5405f0c2fa0..2b75018099d5 100644
--- a/src/runtime/rpc/rpc_session.cc
+++ b/src/runtime/rpc/rpc_session.cc
@@ -46,40 +46,35 @@ void RPCSession::AsyncCallFunc(PackedFuncHandle func, const TVMValue* arg_values
   try {
     this->CallFunc(func, arg_values, arg_type_codes, num_args,
                    [&callback](TVMArgs args) { callback(RPCCode::kReturn, args); });
-  } catch (const std::runtime_error& e) {
+  } catch (const std::exception& e) {
     this->SendException(callback, e.what());
   }
 }
 
-void RPCSession::AsyncCopyToRemote(void* local_from, size_t local_from_offset, void* remote_to,
-                                   size_t remote_to_offset, size_t nbytes, TVMContext remote_ctx_to,
-                                   DLDataType type_hint, RPCSession::FAsyncCallback callback) {
+void RPCSession::AsyncCopyToRemote(void* local_from_bytes, DLTensor* remote_to, uint64_t nbytes,
+                                   RPCSession::FAsyncCallback callback) {
   TVMValue value;
   int32_t tcode = kTVMNullptr;
   value.v_handle = nullptr;
 
   try {
-    this->CopyToRemote(local_from, local_from_offset, remote_to, remote_to_offset, nbytes,
-                       remote_ctx_to, type_hint);
+    this->CopyToRemote(local_from_bytes, remote_to, nbytes);
     callback(RPCCode::kReturn, TVMArgs(&value, &tcode, 1));
-  } catch (const std::runtime_error& e) {
+  } catch (const std::exception& e) {
     this->SendException(callback, e.what());
   }
 }
 
-void RPCSession::AsyncCopyFromRemote(void* remote_from, size_t remote_from_offset, void* local_to,
-                                     size_t local_to_offset, size_t nbytes,
-                                     TVMContext remote_ctx_from, DLDataType type_hint,
+void RPCSession::AsyncCopyFromRemote(DLTensor* remote_from, void* local_to_bytes, uint64_t nbytes,
                                      RPCSession::FAsyncCallback callback) {
   TVMValue value;
   int32_t tcode = kTVMNullptr;
   value.v_handle = nullptr;
 
   try {
-    this->CopyFromRemote(remote_from, remote_from_offset, local_to, local_to_offset, nbytes,
-                         remote_ctx_from, type_hint);
+    this->CopyFromRemote(remote_from, local_to_bytes, nbytes);
     callback(RPCCode::kReturn, TVMArgs(&value, &tcode, 1));
-  } catch (const std::runtime_error& e) {
+  } catch (const std::exception& e) {
     this->SendException(callback, e.what());
   }
 }
@@ -93,7 +88,7 @@ void RPCSession::AsyncStreamWait(TVMContext ctx, TVMStreamHandle stream,
   try {
     this->GetDeviceAPI(ctx)->StreamSync(ctx, stream);
     callback(RPCCode::kReturn, TVMArgs(&value, &tcode, 1));
-  } catch (const std::runtime_error& e) {
+  } catch (const std::exception& e) {
     this->SendException(callback, e.what());
   }
 }
diff --git a/src/runtime/rpc/rpc_session.h b/src/runtime/rpc/rpc_session.h
index 4ea937acc6ef..4b942f2230ba 100644
--- a/src/runtime/rpc/rpc_session.h
+++ b/src/runtime/rpc/rpc_session.h
@@ -127,30 +127,18 @@ class RPCSession {
 
   /*!
    * \brief Copy bytes into remote array content.
-   * \param local_from The source host data.
-   * \param local_from_offset The byte offeset in the from.
+   * \param local_from_bytes The source host data.
    * \param remote_to The target array.
-   * \param remote_to_offset The byte offset in the to.
    * \param nbytes The size of the memory in bytes.
-   * \param remote_ctx_to The target context.
-   * \param type_hint Hint of content data type.
    */
-  virtual void CopyToRemote(void* local_from, size_t local_from_offset, void* remote_to,
-                            size_t remote_to_offset, size_t nbytes, TVMContext remote_ctx_to,
-                            DLDataType type_hint) = 0;
+  virtual void CopyToRemote(void* local_from_bytes, DLTensor* remote_to, uint64_t nbytes) = 0;
   /*!
    * \brief Copy bytes from remote array content.
    * \param remote_from The source host data.
-   * \param remote_from_offset The byte offeset in the from.
-   * \param to The target array.
-   * \param to_offset The byte offset in the to.
+   * \param local_to_bytes The target array.
    * \param nbytes The size of the memory in bytes.
-   * \param remote_ctx_from The source context in the remote.
-   * \param type_hint Hint of content data type.
    */
-  virtual void CopyFromRemote(void* remote_from, size_t remote_from_offset, void* local_to,
-                              size_t local_to_offset, size_t nbytes, TVMContext remote_ctx_from,
-                              DLDataType type_hint) = 0;
+  virtual void CopyFromRemote(DLTensor* remote_from, void* local_to_bytes, uint64_t nbytes) = 0;
 
   /*!
    * \brief Free a remote function.
@@ -223,40 +211,27 @@ class RPCSession {
   /*!
    * \brief Asynchrous version of CopyToRemote.
    *
-   * \param local_from The source host data.
-   * \param local_from_offset The byte offeset in the from.
+   * \param local_from_bytes The source host data.
    * \param remote_to The target array.
-   * \param remote_to_offset The byte offset in the to.
    * \param nbytes The size of the memory in bytes.
-   * \param remote_ctx_to The target context.
-   * \param type_hint Hint of content data type.
-   *
    * \param on_complete The callback to signal copy complete.
    * \note All the allocated memory in local_from, and remote_to
    *       must stay alive until on_compelete is called.
    */
-  virtual void AsyncCopyToRemote(void* local_from, size_t local_from_offset, void* remote_to,
-                                 size_t remote_to_offset, size_t nbytes, TVMContext remote_ctx_to,
-                                 DLDataType type_hint, FAsyncCallback on_complete);
+  virtual void AsyncCopyToRemote(void* local_from_bytes, DLTensor* remote_to, uint64_t nbytes,
+                                 FAsyncCallback on_complete);
 
   /*!
    * \brief Asynchrous version of CopyFromRemote.
    *
    * \param remote_from The source host data.
-   * \param remote_from_offset The byte offeset in the from.
-   * \param to The target array.
-   * \param to_offset The byte offset in the to.
+   * \param local_to_bytes The target array.
    * \param nbytes The size of the memory in bytes.
-   * \param remote_ctx_from The source context in the remote.
-   * \param type_hint Hint of content data type.
-   *
    * \param on_complete The callback to signal copy complete.
    * \note All the allocated memory in remote_from, and local_to
    *       must stay alive until on_compelete is called.
    */
-  virtual void AsyncCopyFromRemote(void* remote_from, size_t remote_from_offset, void* local_to,
-                                   size_t local_to_offset, size_t nbytes,
-                                   TVMContext remote_ctx_from, DLDataType type_hint,
+  virtual void AsyncCopyFromRemote(DLTensor* remote_from, void* local_to_bytes, uint64_t nbytes,
                                    FAsyncCallback on_complete);
   /*!
    * \brief Asynchrously wait for all events in ctx, stream compeletes.
diff --git a/src/runtime/runtime_base.h b/src/runtime/runtime_base.h
index 21601df1ad39..7abb32935a2b 100644
--- a/src/runtime/runtime_base.h
+++ b/src/runtime/runtime_base.h
@@ -34,7 +34,7 @@
      and finishes with API_END() or API_END_HANDLE_ERROR */
 #define API_END()                           \
   }                                         \
-  catch (std::runtime_error & _except_) {   \
+  catch (std::exception & _except_) {       \
     return TVMAPIHandleException(_except_); \
   }                                         \
   return 0;  // NOLINT(*)
@@ -45,7 +45,7 @@
  */
 #define API_END_HANDLE_ERROR(Finalize)      \
   }                                         \
-  catch (std::runtime_error & _except_) {   \
+  catch (std::exception & _except_) {       \
     Finalize;                               \
     return TVMAPIHandleException(_except_); \
   }                                         \
@@ -56,6 +56,6 @@
  * \param e the exception
  * \return the return value of API after exception is handled
  */
-int TVMAPIHandleException(const std::runtime_error& e);
+int TVMAPIHandleException(const std::exception& e);
 
 #endif  // TVM_RUNTIME_RUNTIME_BASE_H_
diff --git a/src/runtime/thread_pool.cc b/src/runtime/thread_pool.cc
index ba14c733176e..cab04ec0db4a 100644
--- a/src/runtime/thread_pool.cc
+++ b/src/runtime/thread_pool.cc
@@ -24,10 +24,10 @@
 #include <dmlc/thread_local.h>
 #include <tvm/runtime/c_backend_api.h>
 #include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
 #include <tvm/runtime/threading_backend.h>
-#include <tvm/support/logging.h>
 #if TVM_THREADPOOL_USE_OPENMP
 #include <omp.h>
 #endif
@@ -363,21 +363,30 @@ TVM_REGISTER_GLOBAL("runtime.config_threadpool").set_body([](TVMArgs args, TVMRe
 }  // namespace tvm
 
 int TVMBackendParallelLaunch(FTVMParallelLambda flambda, void* cdata, int num_task) {
+  int num_workers = tvm::runtime::threading::MaxConcurrency();
+  if (num_workers == 1) {
+    std::atomic<int32_t> sync_counter{0};
+    TVMParallelGroupEnv env;
+    env.num_task = 1;
+    env.sync_handle = &sync_counter;
+    (*flambda)(0, &env, cdata);
+    return 0;
+  } else {
 #if !TVM_THREADPOOL_USE_OPENMP
-  int res = tvm::runtime::ThreadPool::ThreadLocal()->Launch(flambda, cdata, num_task, 1);
-  return res;
+    int res = tvm::runtime::ThreadPool::ThreadLocal()->Launch(flambda, cdata, num_task, 1);
+    return res;
 #else
-  int num_workers = tvm::runtime::threading::MaxConcurrency();
-  if (num_task == 0) num_task = num_workers;
-  omp_set_num_threads(num_task);
+    if (num_task == 0) num_task = num_workers;
+    omp_set_num_threads(num_task);
 #pragma omp parallel num_threads(num_task)
-  {
-    TVMParallelGroupEnv env;
-    env.num_task = num_task;
-    (*flambda)(omp_get_thread_num(), &env, cdata);
-  }
-  return 0;
+    {
+      TVMParallelGroupEnv env;
+      env.num_task = num_task;
+      (*flambda)(omp_get_thread_num(), &env, cdata);
+    }
+    return 0;
 #endif
+  }
 }
 
 int TVMBackendParallelBarrier(int task_id, TVMParallelGroupEnv* penv) {
diff --git a/src/runtime/thread_storage_scope.h b/src/runtime/thread_storage_scope.h
index 1917096bb24c..c0393600b60c 100644
--- a/src/runtime/thread_storage_scope.h
+++ b/src/runtime/thread_storage_scope.h
@@ -215,7 +215,11 @@ class ThreadAxisConfig {
     ThreadWorkLoad w;
     std::fill(w.work_size, w.work_size + 6, 1);
     for (size_t i = 0; i < arg_index_map_.size(); ++i) {
-      w.work_size[arg_index_map_[i]] = static_cast<size_t>(x.values[base_ + i].v_int64);
+      // Dynamic shapes can result in 0 dim size. Guard to ensure that the dim size is atleast 1.
+      size_t size = static_cast<size_t>(x.values[base_ + i].v_int64);
+      if (size > 0) {
+        w.work_size[arg_index_map_[i]] = size;
+      }
     }
     return w;
   }
diff --git a/src/runtime/threading_backend.cc b/src/runtime/threading_backend.cc
index 2527f4799086..7f9cfaa8730c 100644
--- a/src/runtime/threading_backend.cc
+++ b/src/runtime/threading_backend.cc
@@ -21,8 +21,8 @@
  * \file threading_backend.cc
  * \brief Native threading backend
  */
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/threading_backend.h>
-#include <tvm/support/logging.h>
 
 #include <algorithm>
 #include <thread>
diff --git a/src/runtime/vm/bytecode.cc b/src/runtime/vm/bytecode.cc
index f82d708468f7..09b928fa1e39 100644
--- a/src/runtime/vm/bytecode.cc
+++ b/src/runtime/vm/bytecode.cc
@@ -22,8 +22,8 @@
  * \brief The bytecode for Relay virtual machine.
  */
 
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/vm/bytecode.h>
-#include <tvm/support/logging.h>
 
 #include <sstream>
 
diff --git a/src/runtime/vm/executable.cc b/src/runtime/vm/executable.cc
index eb1707b25aa3..6992097e8d69 100644
--- a/src/runtime/vm/executable.cc
+++ b/src/runtime/vm/executable.cc
@@ -252,11 +252,7 @@ void Executable::SaveConstantSection(dmlc::Stream* strm) {
   }
 
   // Save the const to device mapping.
-  std::vector<size_t> const_device_type;
-  for (auto dev_type : this->const_device_type) {
-    const_device_type.push_back(static_cast<size_t>(dev_type));
-  }
-  strm->Write(const_device_type);
+  strm->Write(this->const_device_type);
 }
 
 void Executable::SavePrimitiveOpNames(dmlc::Stream* strm) {
@@ -525,12 +521,10 @@ void Executable::LoadConstantSection(dmlc::Stream* strm) {
   }
 
   // Load the const to device mapping.
-  std::vector<size_t> const_device_type;
+  std::vector<Index> const_device_type;
   STREAM_CHECK(strm->Read(&const_device_type), "constant");
   ICHECK_EQ(size, const_device_type.size());
-  for (auto dev : const_device_type) {
-    this->const_device_type.push_back(static_cast<Index>(dev));
-  }
+  this->const_device_type = const_device_type;
 }
 
 void Executable::LoadPrimitiveOpNames(dmlc::Stream* strm) {
diff --git a/src/runtime/vm/profiler/vm.cc b/src/runtime/vm/profiler/vm.cc
index 94d827893b92..fc01a754ca50 100644
--- a/src/runtime/vm/profiler/vm.cc
+++ b/src/runtime/vm/profiler/vm.cc
@@ -45,7 +45,15 @@ PackedFunc VirtualMachineDebug::GetFunction(const std::string& name,
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
       ICHECK_EQ(args.size(), 1U);
       std::vector<std::pair<Index, double>> op_acc_time;
-      for (auto kv : op_durations_) {
+      std::unordered_map<Index, std::vector<double>> op_durations;
+      for (auto kv : op_timers_) {
+        std::vector<double> durations_us;
+        for (auto t : kv.second) {
+          durations_us.push_back(t->SyncAndGetElapsedNanos() / 1e3);
+        }
+        op_durations[kv.first] = durations_us;
+      }
+      for (auto kv : op_durations) {
         auto val =
             std::make_pair(kv.first, std::accumulate(kv.second.begin(), kv.second.end(), 0.0));
         op_acc_time.push_back(val);
@@ -66,7 +74,7 @@ PackedFunc VirtualMachineDebug::GetFunction(const std::string& name,
          << "#Duration(us): Sum/Mean/Min/Max" << std::endl;
 
       for (auto kv : op_acc_time) {
-        auto vals = op_durations_[kv.first];
+        auto vals = op_durations[kv.first];
         auto sum = kv.second;
         auto mean = sum / static_cast<double>(vals.size());
         auto min_value = *std::min_element(vals.begin(), vals.end());
@@ -85,7 +93,7 @@ PackedFunc VirtualMachineDebug::GetFunction(const std::string& name,
     });
   } else if (name == "reset") {
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
-      op_durations_.clear();
+      op_timers_.clear();
       op_invokes_.clear();
     });
   } else {
@@ -118,16 +126,11 @@ void VirtualMachineDebug::InvokePacked(Index packed_index, const PackedFunc& fun
   auto nd_array = Downcast<NDArray>(arg);
   auto ctx = nd_array->ctx;
 
-  TVMSynchronize(ctx.device_type, ctx.device_id, nullptr);
-
-  auto op_begin = std::chrono::high_resolution_clock::now();
+  Timer t = Timer::Start(ctx);
   VirtualMachine::InvokePacked(packed_index, func, arg_count, output_size, args);
-  TVMSynchronize(ctx.device_type, ctx.device_id, nullptr);
-  auto op_end = std::chrono::high_resolution_clock::now();
-  double op_duration =
-      std::chrono::duration_cast<std::chrono::duration<double>>(op_end - op_begin).count();
+  t->Stop();
 
-  op_durations_[packed_index].push_back(op_duration * 1e6);
+  op_timers_[packed_index].push_back(t);
   op_invokes_[packed_index] += 1;
 }
 
diff --git a/src/runtime/vm/profiler/vm.h b/src/runtime/vm/profiler/vm.h
index 797d414fe8f3..9f5ce87bcf47 100644
--- a/src/runtime/vm/profiler/vm.h
+++ b/src/runtime/vm/profiler/vm.h
@@ -25,6 +25,7 @@
 #ifndef TVM_RUNTIME_VM_PROFILER_VM_H_
 #define TVM_RUNTIME_VM_PROFILER_VM_H_
 
+#include <tvm/runtime/profiling.h>
 #include <tvm/runtime/vm/vm.h>
 
 #include <memory>
@@ -51,7 +52,7 @@ class VirtualMachineDebug : public VirtualMachine {
                     const std::vector<ObjectRef>& args) final;
 
   std::unordered_map<Index, std::string> packed_index_map_;
-  std::unordered_map<Index, std::vector<double>> op_durations_;
+  std::unordered_map<Index, std::vector<Timer>> op_timers_;
   std::unordered_map<Index, int> op_invokes_;
 };
 
diff --git a/src/runtime/vm/serialize_utils.h b/src/runtime/vm/serialize_utils.h
index 990da31750d4..b4a10806caaf 100644
--- a/src/runtime/vm/serialize_utils.h
+++ b/src/runtime/vm/serialize_utils.h
@@ -24,7 +24,6 @@
 #ifndef TVM_RUNTIME_VM_SERIALIZE_UTILS_H_
 #define TVM_RUNTIME_VM_SERIALIZE_UTILS_H_
 
-#include <dmlc/common.h>
 #include <dmlc/memory_io.h>
 #include <tvm/runtime/vm/executable.h>
 
@@ -32,6 +31,8 @@
 #include <string>
 #include <vector>
 
+#include "../../support/utils.h"
+
 namespace tvm {
 namespace runtime {
 namespace vm {
@@ -40,9 +41,9 @@ namespace vm {
 constexpr uint64_t kTVMVMBytecodeMagic = 0xD225DE2F4214151D;
 
 template <typename T>
-static inline size_t VectorHash(size_t key, const std::vector<T>& values) {
+static inline uint64_t VectorHash(uint64_t key, const std::vector<T>& values) {
   for (const auto& it : values) {
-    key = dmlc::HashCombine(key, it);
+    key = support::HashCombine(key, it);
   }
   return key;
 }
@@ -122,7 +123,7 @@ struct VMInstructionSerializer {
    * instruction.
    */
   Index Hash() const {
-    size_t key = static_cast<size_t>(opcode);
+    uint64_t key = static_cast<uint64_t>(opcode);
     key = VectorHash(key, fields);
     return key;
   }
diff --git a/src/runtime/vm/vm.cc b/src/runtime/vm/vm.cc
index 3f890baf52c0..4683398b01d4 100644
--- a/src/runtime/vm/vm.cc
+++ b/src/runtime/vm/vm.cc
@@ -24,10 +24,10 @@
 
 #include <dmlc/memory_io.h>
 #include <tvm/runtime/container.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/memory.h>
 #include <tvm/runtime/object.h>
 #include <tvm/runtime/vm/vm.h>
-#include <tvm/support/logging.h>
 
 #include <algorithm>
 #include <chrono>
@@ -35,6 +35,8 @@
 #include <stdexcept>
 #include <vector>
 
+#include "../file_utils.h"
+
 using namespace tvm::runtime;
 
 namespace tvm {
diff --git a/src/runtime/vulkan/vulkan.cc b/src/runtime/vulkan/vulkan.cc
index cbf1974ee3c7..ff1b82f930d7 100644
--- a/src/runtime/vulkan/vulkan.cc
+++ b/src/runtime/vulkan/vulkan.cc
@@ -199,6 +199,7 @@ class VulkanDeviceAPI final : public DeviceAPI {
     delete pbuf;
   }
 
+ protected:
   void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
                       TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
                       TVMStreamHandle stream) final {
@@ -307,6 +308,7 @@ class VulkanDeviceAPI final : public DeviceAPI {
     }
   }
 
+ public:
   // Always use the default stream
   TVMStreamHandle CreateStream(TVMContext ctx) {
     LOG(FATAL) << "Not implemented";
@@ -365,28 +367,37 @@ void VulkanDeviceAPI::GetAttr(TVMContext ctx, DeviceAttrKind kind, TVMRetValue*
   }
   ICHECK_LT(index, context_.size()) << "Invalid device id " << index;
   const auto& vctx = context(index);
+  VkPhysicalDeviceProperties phy_prop;
+  vkGetPhysicalDeviceProperties(vctx.phy_device, &phy_prop);
+
   switch (kind) {
     case kMaxThreadsPerBlock: {
-      VkPhysicalDeviceProperties phy_prop;
-      vkGetPhysicalDeviceProperties(vctx.phy_device, &phy_prop);
       int64_t value = phy_prop.limits.maxComputeWorkGroupInvocations;
       *rv = value;
       break;
     }
     case kMaxSharedMemoryPerBlock: {
-      VkPhysicalDeviceProperties phy_prop;
-      vkGetPhysicalDeviceProperties(vctx.phy_device, &phy_prop);
       int64_t value = phy_prop.limits.maxComputeSharedMemorySize;
       *rv = value;
       break;
     }
     case kWarpSize: {
-      *rv = 1;
+      VkPhysicalDeviceSubgroupProperties subgroup_prop;
+      subgroup_prop.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_PROPERTIES;
+      subgroup_prop.pNext = NULL;
+
+      VkPhysicalDeviceProperties2 phy_prop2;
+      phy_prop2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PROPERTIES_2;
+      phy_prop2.pNext = &subgroup_prop;
+
+      vkGetPhysicalDeviceProperties2(vctx.phy_device, &phy_prop2);
+      int64_t subgroup_size = subgroup_prop.subgroupSize;
+      ICHECK(subgroup_size >= 1);
+
+      *rv = subgroup_size;
       break;
     }
     case kComputeVersion: {
-      VkPhysicalDeviceProperties phy_prop;
-      vkGetPhysicalDeviceProperties(vctx.phy_device, &phy_prop);
       int64_t value = phy_prop.apiVersion;
       std::ostringstream os;
       os << VK_VERSION_MAJOR(value) << "." << VK_VERSION_MINOR(value) << "."
@@ -403,8 +414,6 @@ void VulkanDeviceAPI::GetAttr(TVMContext ctx, DeviceAttrKind kind, TVMRetValue*
     case kExist:
       break;
     case kMaxThreadDimensions: {
-      VkPhysicalDeviceProperties phy_prop;
-      vkGetPhysicalDeviceProperties(vctx.phy_device, &phy_prop);
       int64_t dims[3];
       dims[0] = phy_prop.limits.maxComputeWorkGroupSize[0];
       dims[1] = phy_prop.limits.maxComputeWorkGroupSize[1];
@@ -709,7 +718,7 @@ class VulkanWrappedFunc {
     thread_axis_cfg_.Init(num_buffer_args + num_pack_args, thread_axis_tags);
   }
 
-  void operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion* pack_args) const;
+  void operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion64* pack_args) const;
 
  private:
   // internal module
@@ -873,7 +882,7 @@ class VulkanModuleNode final : public runtime::ModuleNode {
     VkPushConstantRange crange;
     crange.stageFlags = VK_SHADER_STAGE_COMPUTE_BIT;
     crange.offset = 0;
-    crange.size = sizeof(ArgUnion) * num_pack_args;
+    crange.size = sizeof(ArgUnion64) * num_pack_args;
 
     VkPipelineLayoutCreateInfo playout_cinfo;
     playout_cinfo.sType = VK_STRUCTURE_TYPE_PIPELINE_LAYOUT_CREATE_INFO;
@@ -1044,7 +1053,8 @@ VulkanStream* VulkanThreadEntry::Stream(size_t device_id) {
   return streams_[device_id].get();
 }
 
-void VulkanWrappedFunc::operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion* pack_args) const {
+void VulkanWrappedFunc::operator()(TVMArgs args, TVMRetValue* rv,
+                                   const ArgUnion64* pack_args) const {
   int device_id = VulkanThreadEntry::ThreadLocal()->ctx.device_id;
   ICHECK_LT(device_id, kVulkanMaxNumDevice);
   const auto& vctx = VulkanDeviceAPI::Global()->context(device_id);
@@ -1073,7 +1083,7 @@ void VulkanWrappedFunc::operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion
           descriptor_buffers.data());
       if (num_pack_args_ != 0) {
         vkCmdPushConstants(state->cmd_buffer_, pipeline->pipeline_layout,
-                           VK_SHADER_STAGE_COMPUTE_BIT, 0, num_pack_args_ * sizeof(ArgUnion),
+                           VK_SHADER_STAGE_COMPUTE_BIT, 0, num_pack_args_ * sizeof(ArgUnion64),
                            pack_args);
       }
       vkCmdDispatch(state->cmd_buffer_, wl.grid_dim(0), wl.grid_dim(1), wl.grid_dim(2));
@@ -1091,7 +1101,7 @@ void VulkanWrappedFunc::operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion
   }
 
   // Otherwise, the more expensive deferred path.
-  std::vector<ArgUnion> pack_args_storage(pack_args, pack_args + num_pack_args_);
+  std::vector<ArgUnion64> pack_args_storage(pack_args, pack_args + num_pack_args_);
   const auto& deferred_initializer = [&vctx, pipeline, descriptor_buffers]() {
     std::vector<VkWriteDescriptorSet> write_descriptor_sets;
     write_descriptor_sets.resize(descriptor_buffers.size());
@@ -1117,7 +1127,8 @@ void VulkanWrappedFunc::operator()(TVMArgs args, TVMRetValue* rv, const ArgUnion
                             nullptr);
     if (pack_args_storage.size() != 0) {
       vkCmdPushConstants(state->cmd_buffer_, pipeline->pipeline_layout, VK_SHADER_STAGE_COMPUTE_BIT,
-                         0, pack_args_storage.size() * sizeof(ArgUnion), pack_args_storage.data());
+                         0, pack_args_storage.size() * sizeof(ArgUnion64),
+                         pack_args_storage.data());
     }
     vkCmdDispatch(state->cmd_buffer_, wl.grid_dim(0), wl.grid_dim(1), wl.grid_dim(2));
     VkMemoryBarrier barrier_info;
diff --git a/src/runtime/vulkan/vulkan_common.h b/src/runtime/vulkan/vulkan_common.h
index da604f6fa792..3083ba6f9ce4 100644
--- a/src/runtime/vulkan/vulkan_common.h
+++ b/src/runtime/vulkan/vulkan_common.h
@@ -16,12 +16,14 @@
  * specific language governing permissions and limitations
  * under the License.
  */
-#pragma once
+
+#ifndef TVM_RUNTIME_VULKAN_VULKAN_COMMON_H_
+#define TVM_RUNTIME_VULKAN_VULKAN_COMMON_H_
 
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/packed_func.h>
-#include <tvm/support/logging.h>
 #include <vulkan/vulkan.h>
 
 #include <memory>
@@ -143,3 +145,4 @@ struct VulkanContext {
 }  // namespace vulkan
 }  // namespace runtime
 }  // namespace tvm
+#endif  // TVM_RUNTIME_VULKAN_VULKAN_COMMON_H_
diff --git a/src/runtime/vulkan/vulkan_module.h b/src/runtime/vulkan/vulkan_module.h
index 15c9ec313d63..c75a077a361d 100644
--- a/src/runtime/vulkan/vulkan_module.h
+++ b/src/runtime/vulkan/vulkan_module.h
@@ -16,7 +16,9 @@
  * specific language governing permissions and limitations
  * under the License.
  */
-#pragma once
+
+#ifndef TVM_RUNTIME_VULKAN_VULKAN_MODULE_H_
+#define TVM_RUNTIME_VULKAN_VULKAN_MODULE_H_
 
 #include <string>
 #include <unordered_map>
@@ -35,3 +37,4 @@ Module VulkanModuleCreate(std::unordered_map<std::string, VulkanShader> smap,
 using vulkan::VulkanModuleCreate;
 }  // namespace runtime
 }  // namespace tvm
+#endif  // TVM_RUNTIME_VULKAN_VULKAN_MODULE_H_
diff --git a/src/runtime/vulkan/vulkan_shader.h b/src/runtime/vulkan/vulkan_shader.h
index 7558a95ee45e..513e3bccc36e 100644
--- a/src/runtime/vulkan/vulkan_shader.h
+++ b/src/runtime/vulkan/vulkan_shader.h
@@ -16,12 +16,14 @@
  * specific language governing permissions and limitations
  * under the License.
  */
-#pragma once
+
+#ifndef TVM_RUNTIME_VULKAN_VULKAN_SHADER_H_
+#define TVM_RUNTIME_VULKAN_VULKAN_SHADER_H_
 
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/runtime/packed_func.h>
-#include <tvm/support/logging.h>
 
 #include <vector>
 
@@ -55,3 +57,4 @@ using vulkan::VulkanShader;
 namespace dmlc {
 DMLC_DECLARE_TRAITS(has_saveload, ::tvm::runtime::vulkan::VulkanShader, true);
 }  // namespace dmlc
+#endif  // TVM_RUNTIME_VULKAN_VULKAN_SHADER_H_
diff --git a/src/runtime/vulkan/vulkan_stream.h b/src/runtime/vulkan/vulkan_stream.h
index c5094bdf28db..d096a644a1f0 100644
--- a/src/runtime/vulkan/vulkan_stream.h
+++ b/src/runtime/vulkan/vulkan_stream.h
@@ -16,7 +16,9 @@
  * specific language governing permissions and limitations
  * under the License.
  */
-#pragma once
+
+#ifndef TVM_RUNTIME_VULKAN_VULKAN_STREAM_H_
+#define TVM_RUNTIME_VULKAN_VULKAN_STREAM_H_
 
 #include <functional>
 #include <memory>
@@ -184,3 +186,4 @@ class VulkanStream {
 }  // namespace vulkan
 }  // namespace runtime
 }  // namespace tvm
+#endif  // TVM_RUNTIME_VULKAN_VULKAN_STREAM_H_
diff --git a/src/support/base64.h b/src/support/base64.h
index 901922db8edc..3aac9920a075 100644
--- a/src/support/base64.h
+++ b/src/support/base64.h
@@ -26,7 +26,7 @@
 #ifndef TVM_SUPPORT_BASE64_H_
 #define TVM_SUPPORT_BASE64_H_
 
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <cctype>
 #include <cstdio>
diff --git a/src/support/ffi_testing.cc b/src/support/ffi_testing.cc
index 839f52968b82..b06a8bb461be 100644
--- a/src/support/ffi_testing.cc
+++ b/src/support/ffi_testing.cc
@@ -23,6 +23,7 @@
  */
 #include <tvm/ir/attrs.h>
 #include <tvm/ir/env_func.h>
+#include <tvm/runtime/module.h>
 #include <tvm/runtime/registry.h>
 #include <tvm/te/tensor.h>
 #include <tvm/tir/expr.h>
@@ -99,4 +100,45 @@ TVM_REGISTER_GLOBAL("testing.object_use_count").set_body([](TVMArgs args, TVMRet
   // and get another value.
   *ret = (obj.use_count() - 1);
 });
+
+class FrontendTestModuleNode : public runtime::ModuleNode {
+ public:
+  virtual const char* type_key() const { return "frontend_test"; }
+
+  static constexpr const char* kAddFunctionName = "__add_function";
+
+  virtual PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self);
+
+ private:
+  std::unordered_map<std::string, PackedFunc> functions_;
+};
+
+constexpr const char* FrontendTestModuleNode::kAddFunctionName;
+
+PackedFunc FrontendTestModuleNode::GetFunction(const std::string& name,
+                                               const ObjectPtr<Object>& sptr_to_self) {
+  if (name == kAddFunctionName) {
+    return TypedPackedFunc<void(std::string, PackedFunc)>(
+        [this, sptr_to_self](std::string func_name, PackedFunc pf) {
+          CHECK_NE(func_name, kAddFunctionName)
+              << "func_name: cannot be special function " << kAddFunctionName;
+          functions_[func_name] = pf;
+        });
+  }
+
+  auto it = functions_.find(name);
+  if (it == functions_.end()) {
+    return PackedFunc();
+  }
+
+  return it->second;
+}
+
+runtime::Module NewFrontendTestModule() {
+  auto n = make_object<FrontendTestModuleNode>();
+  return runtime::Module(n);
+}
+
+TVM_REGISTER_GLOBAL("testing.FrontendTestModule").set_body_typed(NewFrontendTestModule);
+
 }  // namespace tvm
diff --git a/src/support/libinfo.cc b/src/support/libinfo.cc
index c8aa76b9d1f5..d6c8f1799596 100644
--- a/src/support/libinfo.cc
+++ b/src/support/libinfo.cc
@@ -16,7 +16,7 @@
  * specific language governing permissions and limitations
  * under the License.
  */
-#include <tvm/node/container.h>
+#include <tvm/runtime/container.h>
 #include <tvm/runtime/object.h>
 #include <tvm/runtime/registry.h>
 
@@ -208,6 +208,10 @@
 #define TVM_INFO_INDEX_DEFAULT_I64 "NOT-FOUND"
 #endif
 
+#ifndef TVM_CXX_COMPILER_PATH
+#define TVM_CXX_COMPILER_PATH ""
+#endif
+
 namespace tvm {
 
 /*!
@@ -262,7 +266,8 @@ TVM_DLL Map<String, String> GetLibInfo() {
       {"USE_TARGET_ONNX", TVM_INFO_USE_TARGET_ONNX},
       {"USE_ARM_COMPUTE_LIB", TVM_INFO_USE_ARM_COMPUTE_LIB},
       {"USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME", TVM_INFO_USE_ARM_COMPUTE_LIB_GRAPH_RUNTIME},
-      {"INDEX_DEFAULT_I64", TVM_INFO_INDEX_DEFAULT_I64}};
+      {"INDEX_DEFAULT_I64", TVM_INFO_INDEX_DEFAULT_I64},
+      {"TVM_CXX_COMPILER_PATH", TVM_CXX_COMPILER_PATH}};
   return result;
 }
 
diff --git a/src/support/parallel_for.cc b/src/support/parallel_for.cc
index f4756c29adeb..4ced0df6ddf3 100644
--- a/src/support/parallel_for.cc
+++ b/src/support/parallel_for.cc
@@ -21,7 +21,7 @@
  * \file parallel_for.cc
  * \brief An implementation to run loop in parallel.
  */
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/support/parallel_for.h>
 
 #include <future>
diff --git a/src/support/pipe.h b/src/support/pipe.h
index 3c1356ba174c..a2803638e1f3 100644
--- a/src/support/pipe.h
+++ b/src/support/pipe.h
@@ -25,7 +25,7 @@
 #define TVM_SUPPORT_PIPE_H_
 
 #include <dmlc/io.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #ifdef _WIN32
 #include <windows.h>
diff --git a/src/support/socket.h b/src/support/socket.h
index 16fba6b58e3d..11060ae8aae1 100644
--- a/src/support/socket.h
+++ b/src/support/socket.h
@@ -49,7 +49,7 @@ using ssize_t = int;
 #include <sys/socket.h>
 #include <unistd.h>
 #endif
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 
 #include <cstring>
 #include <string>
diff --git a/src/support/utils.h b/src/support/utils.h
index ce1f2bed43f9..c51b7b966478 100644
--- a/src/support/utils.h
+++ b/src/support/utils.h
@@ -162,6 +162,15 @@ inline size_t HashCombine(size_t key, size_t value) {
   return key ^ (value + 0x9e3779b9 + (key << 6) + (key >> 2));
 }
 
+/*!
+ * \brief hash an object and combines uint64_t key with previous keys
+ */
+template <typename T>
+inline uint64_t HashCombine(uint64_t key, const T& value) {
+  std::hash<T> hash_func;
+  return key ^ (hash_func(value) + 0x9e3779b9 + (key << 6) + (key >> 2));
+}
+
 }  // namespace support
 }  // namespace tvm
 #endif  // TVM_SUPPORT_UTILS_H_
diff --git a/src/target/generic_func.cc b/src/target/generic_func.cc
index 16e5a5f9cdc6..5dbceec32ed7 100644
--- a/src/target/generic_func.cc
+++ b/src/target/generic_func.cc
@@ -51,7 +51,7 @@ struct GenericFunc::Manager {
 
 GenericFunc GenericFunc::Get(const std::string& name) {
   Manager* m = Manager::Global();
-  std::lock_guard<std::mutex>(m->mutex);
+  std::lock_guard<std::mutex> lock(m->mutex);
   auto it = m->fmap.find(name);
   if (it == m->fmap.end()) {
     auto f = make_object<GenericFuncNode>();
@@ -66,7 +66,7 @@ GenericFunc GenericFunc::Get(const std::string& name) {
 
 void GenericFunc::RegisterGenericFunc(GenericFunc func, const std::string& name) {
   Manager* m = Manager::Global();
-  std::lock_guard<std::mutex>(m->mutex);
+  std::lock_guard<std::mutex> lock(m->mutex);
   auto it = m->fmap.find(name);
   ICHECK(it == m->fmap.end()) << "GenericFunc already registered " << name;
   func->name_ = name;
diff --git a/src/target/intrin_rule.cc b/src/target/intrin_rule.cc
index f8f4d0ef5414..1a7214476188 100644
--- a/src/target/intrin_rule.cc
+++ b/src/target/intrin_rule.cc
@@ -77,6 +77,12 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.ldexp").set_body(DispatchPureExtern
 
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.sqrt").set_body(DispatchPureExtern<FloatSuffix>);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.floor").set_body(DispatchPureExtern<FloatSuffix>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.ceil").set_body(DispatchPureExtern<FloatSuffix>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.round").set_body(DispatchPureExtern<FloatSuffix>);
+
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.rsqrt")
     .set_body([](const TVMArgs& args, TVMRetValue* rv) {
       PrimExpr e = args[0];
diff --git a/src/target/llvm/codegen_amdgpu.cc b/src/target/llvm/codegen_amdgpu.cc
index 605870f48c52..ca21892ccc5f 100644
--- a/src/target/llvm/codegen_amdgpu.cc
+++ b/src/target/llvm/codegen_amdgpu.cc
@@ -190,14 +190,26 @@ class CodeGenAMDGPU : public CodeGenLLVM {
       llvm::Value* v1 = MakeValue(op->args[1]);
       if (op->args[1]->dtype.is_float()) {
 #if TVM_LLVM_VERSION >= 90
+#if TVM_LLVM_VERSION >= 130
         return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::FAdd, v0, v1,
+                                         llvm::MaybeAlign::MaybeAlign(),
                                          llvm::AtomicOrdering::Monotonic);
+#else
+        return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::FAdd, v0, v1,
+                                         llvm::AtomicOrdering::Monotonic);
+#endif
 #else
         LOG(FATAL) << "Floating point atomic requires LLVM 9 or newer";
 #endif
       }
+#if TVM_LLVM_VERSION >= 130
       return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::Add, v0, v1,
+                                       llvm::MaybeAlign::MaybeAlign(),
                                        llvm::AtomicOrdering::Monotonic);
+#else
+      return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::Add, v0, v1,
+                                       llvm::AtomicOrdering::Monotonic);
+#endif
     }
     return CodeGenLLVM::CreateIntrinsic(op);
   }
diff --git a/src/target/llvm/codegen_cpu.cc b/src/target/llvm/codegen_cpu.cc
index 6143e7050495..b49f850b2d90 100644
--- a/src/target/llvm/codegen_cpu.cc
+++ b/src/target/llvm/codegen_cpu.cc
@@ -123,12 +123,6 @@ void CodeGenCPU::AddFunction(const PrimFunc& f) {
         << "CodeGenLLVM: Expect PrimFunc to have the global_symbol attribute";
     export_system_symbols_.emplace_back(
         std::make_pair(global_symbol.value().operator std::string(), function_));
-  } else if (target_c_runtime_) {
-    auto global_symbol = f->GetAttr<String>(tvm::attr::kGlobalSymbol);
-    ICHECK(global_symbol.defined())
-        << "CodeGenLLVM: Expect PrimFunc to have the global_symbol attribute";
-    registry_functions_.emplace_back(
-        std::make_pair(global_symbol.value().operator std::string(), function_));
   }
   AddDebugInformation(function_);
 }
@@ -443,11 +437,14 @@ void CodeGenCPU::CreateComputeScope(const AttrStmtNode* op) {
     arg_types.push_back(value->getType());
   }
   llvm::FunctionType* ftype = llvm::FunctionType::get(t_int_, arg_types, false);
+  // $xxx_compute_ functions are not global. They should be marked as static (via InternalLinkage)
+  // to call them correctly on MIPS platform (CALL16 issue)
+  // Linkage ld Error: CALL16 reloc at 0x290 not against global symbol
   llvm::Function* fcompute = llvm::Function::Create(
-      ftype, llvm::Function::PrivateLinkage,
+      ftype, llvm::Function::InternalLinkage,
       op->value.as<StringImmNode>()->value.operator llvm::StringRef(), module_.get());
   BasicBlock* compute_call_end = CheckCallSuccess(builder_->CreateCall(fcompute, arg_values));
-  // setup compute fuinction.
+  // setup compute function.
   std::unordered_map<const VarNode*, llvm::Value*> new_vmap;
   size_t idx = 0;
   for (auto it = fcompute->arg_begin(); it != fcompute->arg_end(); ++it, ++idx) {
@@ -791,47 +788,50 @@ llvm::Value* CodeGenCPU::RuntimeTVMParallelBarrier() {
   return GetContextPtr(gv_tvm_parallel_barrier_);
 }
 
-void CodeGenCPU::AddStartupFunction() {
-  if (registry_functions_.size() != 0) {
-    ICHECK(is_system_lib_) << "Loading of --system-lib modules is yet to be defined for C runtime";
-    Array<String> symbols;
-    std::vector<llvm::Constant*> funcs;
-    for (auto sym : registry_functions_) {
-      symbols.push_back(sym.first);
-      funcs.emplace_back(llvm::ConstantExpr::getBitCast(
-          sym.second, ftype_tvm_backend_packed_c_func_->getPointerTo()));
-    }
-    llvm::DataLayout layout(module_.get());
-    llvm::ArrayType* t_tvm_crt_func_ptrs =
-        llvm::ArrayType::get(ftype_tvm_backend_packed_c_func_->getPointerTo(), funcs.size());
-    llvm::GlobalVariable* func_registry_ptrs = new llvm::GlobalVariable(
-        *module_, t_tvm_crt_func_ptrs, true, llvm::GlobalValue::InternalLinkage,
-        llvm::ConstantArray::get(t_tvm_crt_func_ptrs, funcs), "_tvm_func_registry_ptrs");
-    uint64_t align = layout.getTypeAllocSize(ftype_tvm_backend_packed_c_func_->getPointerTo());
+void CodeGenCPU::DefineFunctionRegistry(Array<String> func_names) {
+  ICHECK(is_system_lib_) << "Loading of --system-lib modules is yet to be defined for C runtime";
+  Array<String> symbols;
+  std::vector<llvm::Constant*> funcs;
+  for (auto sym : func_names) {
+    symbols.push_back(sym);
+    llvm::GlobalVariable* sym_func = new llvm::GlobalVariable(
+        *module_, ftype_tvm_backend_packed_c_func_, true, llvm::GlobalValue::ExternalLinkage,
+        nullptr, sym.operator std::string());
+    funcs.emplace_back(sym_func);
+  }
+  llvm::DataLayout layout(module_.get());
+  llvm::ArrayType* t_tvm_crt_func_ptrs =
+      llvm::ArrayType::get(ftype_tvm_backend_packed_c_func_->getPointerTo(), funcs.size());
+  llvm::GlobalVariable* func_registry_ptrs = new llvm::GlobalVariable(
+      *module_, t_tvm_crt_func_ptrs, true, llvm::GlobalValue::InternalLinkage,
+      llvm::ConstantArray::get(t_tvm_crt_func_ptrs, funcs), "_tvm_func_registry_ptrs");
+  uint64_t align = layout.getTypeAllocSize(ftype_tvm_backend_packed_c_func_->getPointerTo());
 #if TVM_LLVM_VERSION >= 100
-    func_registry_ptrs->setAlignment(llvm::Align(align));
+  func_registry_ptrs->setAlignment(llvm::Align(align));
 #else
-    func_registry_ptrs->setAlignment(align);
+  func_registry_ptrs->setAlignment(align);
 #endif
-    llvm::GlobalVariable* func_registry = new llvm::GlobalVariable(
-        *module_, t_tvm_crt_func_registry_, true, llvm::GlobalVariable::InternalLinkage,
-        llvm::ConstantStruct::get(
-            t_tvm_crt_func_registry_,
-            {GetConstString(::tvm::target::GenerateFuncRegistryNames(symbols)),
-             func_registry_ptrs}),
-        "_tvm_crt_func_registry");
-    llvm::GlobalVariable* module = new llvm::GlobalVariable(
-        *module_, t_tvm_crt_module_, true, llvm::GlobalValue::InternalLinkage,
-        llvm::ConstantStruct::get(t_tvm_crt_module_, {func_registry}), "_tvm_crt_module");
-
-    // Now build TVMSystemLibEntryPoint.
-    llvm::FunctionType* ftype = llvm::FunctionType::get(t_void_p_, {}, false);
-    function_ = llvm::Function::Create(ftype, llvm::Function::ExternalLinkage,
-                                       "TVMSystemLibEntryPoint", module_.get());
-    llvm::BasicBlock* entry_point_entry = llvm::BasicBlock::Create(*ctx_, "entry", function_);
-    builder_->SetInsertPoint(entry_point_entry);
-    builder_->CreateRet(builder_->CreateBitCast(module, t_void_p_));
-  } else {
+  llvm::GlobalVariable* func_registry = new llvm::GlobalVariable(
+      *module_, t_tvm_crt_func_registry_, true, llvm::GlobalVariable::InternalLinkage,
+      llvm::ConstantStruct::get(
+          t_tvm_crt_func_registry_,
+          {GetConstString(::tvm::target::GenerateFuncRegistryNames(symbols)), func_registry_ptrs}),
+      "_tvm_crt_func_registry");
+  llvm::GlobalVariable* module = new llvm::GlobalVariable(
+      *module_, t_tvm_crt_module_, true, llvm::GlobalValue::InternalLinkage,
+      llvm::ConstantStruct::get(t_tvm_crt_module_, {func_registry}), "_tvm_crt_module");
+
+  // Now build TVMSystemLibEntryPoint.
+  llvm::FunctionType* ftype = llvm::FunctionType::get(t_void_p_, {}, false);
+  function_ = llvm::Function::Create(ftype, llvm::Function::ExternalLinkage,
+                                     "TVMSystemLibEntryPoint", module_.get());
+  llvm::BasicBlock* entry_point_entry = llvm::BasicBlock::Create(*ctx_, "entry", function_);
+  builder_->SetInsertPoint(entry_point_entry);
+  builder_->CreateRet(builder_->CreateBitCast(module, t_void_p_));
+}
+
+void CodeGenCPU::AddStartupFunction() {
+  if (!target_c_runtime_) {
     llvm::FunctionType* ftype = llvm::FunctionType::get(t_void_, {}, false);
     function_ = llvm::Function::Create(ftype, llvm::Function::InternalLinkage,
                                        "__tvm_module_startup", module_.get());
@@ -976,12 +976,13 @@ void CodeGenCPU::VisitStmt_(const AttrStmtNode* op) {
 
 void CodeGenCPU::VisitStmt_(const ForNode* op) {
   ICHECK(is_zero(op->min));
-  if (op->for_type == ForType::Serial || op->for_type == ForType::Unrolled) {
+  if (op->kind == ForKind::kSerial || op->kind == ForKind::kUnrolled) {
     CodeGenLLVM::VisitStmt_(op);
-  } else if (op->for_type == ForType::Parallel) {
+  } else if (op->kind == ForKind::kParallel) {
     if (parallel_env_.penv == nullptr) {
-      CreateParallelLaunch(
-          For(op->loop_var, op->min, op->extent, op->for_type, op->device_api, op->body), 0);
+      CreateParallelLaunch(For(op->loop_var, op->min, op->extent, op->kind, op->body,
+                               op->thread_binding, op->annotations),
+                           0);
     } else {
       // already in parallel env.
       ICHECK(parallel_env_.task_id.defined());
@@ -1007,7 +1008,7 @@ void CodeGenCPU::VisitStmt_(const ForNode* op) {
       ++parallel_env_.parallel_loop_count;
     }
   } else {
-    LOG(FATAL) << "cannot handle for type " << op->for_type;
+    LOG(FATAL) << "cannot handle for type " << op->kind;
   }
 }
 
diff --git a/src/target/llvm/codegen_cpu.h b/src/target/llvm/codegen_cpu.h
index fc46dc53ce15..d08bd639e131 100644
--- a/src/target/llvm/codegen_cpu.h
+++ b/src/target/llvm/codegen_cpu.h
@@ -50,6 +50,12 @@ class CodeGenCPU : public CodeGenLLVM {
   llvm::Value* CreateCallExtern(Type ret_type, String global_symbol, const Array<PrimExpr>& args,
                                 bool skip_first_arg) override;
 
+  /*!
+   * \brief A CPU-specific function to create the FuncRegistry.
+   * \param func_names List of functions to be included, in order.
+   */
+  void DefineFunctionRegistry(Array<String> func_names);
+
  protected:
   void AddStartupFunction() final;
   // meta data
diff --git a/src/target/llvm/codegen_llvm.cc b/src/target/llvm/codegen_llvm.cc
index 70f094a186e7..d5140677d45a 100644
--- a/src/target/llvm/codegen_llvm.cc
+++ b/src/target/llvm/codegen_llvm.cc
@@ -927,6 +927,18 @@ llvm::Value* CodeGenLLVM::CreateIntrinsic(const CallNode* op) {
     value->addIncoming(then_value, then_value_block);
     value->addIncoming(else_value, else_value_block);
     return value;
+  } else if (op->op.same_as(builtin::ret())) {
+    auto const* val = op->args[0].as<IntImmNode>();
+    ICHECK(val) << "the tir.ret should be transformed to return zero "
+                << "before the llvm code generation.";
+    ICHECK_EQ(val->value, 0) << "the tir.ret should be transformed to "
+                             << "return zero before the llvm code generation.";
+    builder_->CreateRet(ConstInt32(0));
+    // LLVM allows exactly one terminator in a single basic block
+    // append a new dummy basic block to avoid error.
+    llvm::BasicBlock* ret_dummy = llvm::BasicBlock::Create(*ctx_, "ret_dummy", function_);
+    builder_->SetInsertPoint(ret_dummy);
+    return ret_dummy;
   } else if (op->op.same_as(builtin::reinterpret())) {
     llvm::Type* target = DTypeToLLVMType(op->dtype);
     return builder_->CreateBitCast(MakeValue(op->args[0]), target);
@@ -1306,16 +1318,30 @@ void CodeGenLLVM::VisitStmt_(const StoreNode* op) {
 void CodeGenLLVM::VisitStmt_(const ForNode* op) {
   ICHECK(is_zero(op->min));
   analyzer_->Bind(op->loop_var, Range::FromMinExtent(op->min, op->extent));
-  if (op->for_type == ForType::Unrolled) {
+  if (op->kind == ForKind::kUnrolled) {
     LOG(WARNING) << "Unroll hint get ignore at CodeGenLLVM backend, "
                  << " consider set unroll_explicit=True";
   } else {
-    ICHECK(op->for_type == ForType::Serial);
+    ICHECK(op->kind == ForKind::kSerial);
   }
   CreateSerialFor(MakeValue(op->min), MakeValue(op->extent),
                   llvm::ConstantInt::getSigned(GetLLVMType(op->extent), 1), op->loop_var, op->body);
 }
 
+void CodeGenLLVM::VisitStmt_(const WhileNode* op) {
+  using llvm::BasicBlock;
+  BasicBlock* while_cond = BasicBlock::Create(*ctx_, "while_cond", function_);
+  BasicBlock* while_body = BasicBlock::Create(*ctx_, "while_body", function_);
+  BasicBlock* while_merge = BasicBlock::Create(*ctx_, "while_merge", function_);
+  builder_->CreateBr(while_cond);
+  builder_->SetInsertPoint(while_cond);
+  builder_->CreateCondBr(MakeValue(op->condition), while_body, while_merge);
+  builder_->SetInsertPoint(while_body);
+  this->VisitStmt(op->body);
+  builder_->CreateBr(while_cond);
+  builder_->SetInsertPoint(while_merge);
+}
+
 void CodeGenLLVM::VisitStmt_(const IfThenElseNode* op) {
   using llvm::BasicBlock;
   llvm::Value* cond = MakeValue(op->condition);
diff --git a/src/target/llvm/codegen_llvm.h b/src/target/llvm/codegen_llvm.h
index 71583708da2c..e56a6de6d914 100644
--- a/src/target/llvm/codegen_llvm.h
+++ b/src/target/llvm/codegen_llvm.h
@@ -152,6 +152,7 @@ class CodeGenLLVM : public ExprFunctor<llvm::Value*(const PrimExpr&)>,
   // stmt
   void VisitStmt_(const StoreNode* op) override;
   void VisitStmt_(const ForNode* op) override;
+  void VisitStmt_(const WhileNode* op) override;
   void VisitStmt_(const IfThenElseNode* op) override;
   void VisitStmt_(const AllocateNode* op) override;
   void VisitStmt_(const AttrStmtNode* op) override;
diff --git a/src/target/llvm/codegen_nvptx.cc b/src/target/llvm/codegen_nvptx.cc
index d8002a2b58a6..05d017862516 100644
--- a/src/target/llvm/codegen_nvptx.cc
+++ b/src/target/llvm/codegen_nvptx.cc
@@ -238,14 +238,26 @@ llvm::Value* CodeGenNVPTX::CreateIntrinsic(const CallNode* op) {
     llvm::Value* v1 = MakeValue(op->args[1]);
     if (op->args[1]->dtype.is_float()) {
 #if TVM_LLVM_VERSION >= 90
+#if TVM_LLVM_VERSION >= 130
       return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::FAdd, v0, v1,
+                                       llvm::MaybeAlign::MaybeAlign(),
                                        llvm::AtomicOrdering::Monotonic);
+#else
+      return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::FAdd, v0, v1,
+                                       llvm::AtomicOrdering::Monotonic);
+#endif
 #else
       LOG(FATAL) << "Floating point atomic requires LLVM 9 or newer";
 #endif
     }
+#if TVM_LLVM_VERSION >= 130
     return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::Add, v0, v1,
+                                     llvm::MaybeAlign::MaybeAlign(),
                                      llvm::AtomicOrdering::Monotonic);
+#else
+    return builder_->CreateAtomicRMW(llvm::AtomicRMWInst::Add, v0, v1,
+                                     llvm::AtomicOrdering::Monotonic);
+#endif
   }
   return CodeGenLLVM::CreateIntrinsic(op);
 }
diff --git a/src/target/llvm/llvm_common.cc b/src/target/llvm/llvm_common.cc
index 35bfc8dc2e5b..61dd7024ff05 100644
--- a/src/target/llvm/llvm_common.cc
+++ b/src/target/llvm/llvm_common.cc
@@ -24,7 +24,7 @@
 
 #include "llvm_common.h"
 
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/target/target.h>
 
 #include <atomic>
diff --git a/src/target/llvm/llvm_module.cc b/src/target/llvm/llvm_module.cc
index 43d20971404e..24fb3dc95819 100644
--- a/src/target/llvm/llvm_module.cc
+++ b/src/target/llvm/llvm_module.cc
@@ -34,6 +34,7 @@
 #include "../../runtime/library_module.h"
 #include "../func_registry_generator.h"
 #include "codegen_blob.h"
+#include "codegen_cpu.h"
 #include "codegen_llvm.h"
 #include "llvm_common.h"
 
@@ -445,6 +446,58 @@ TVM_REGISTER_GLOBAL("codegen.codegen_blob")
       return runtime::Module(n);
     });
 
+runtime::Module CreateLLVMCrtMetadataModule(const Array<runtime::Module>& modules, Target target) {
+  Array<String> func_names;
+  for (runtime::Module mod : modules) {
+    auto pf_funcs = mod.GetFunction("get_func_names");
+    if (pf_funcs != nullptr) {
+      Array<String> func_names_ = pf_funcs();
+      for (const auto& fname : func_names_) {
+        func_names.push_back(fname);
+      }
+    }
+  }
+
+  InitializeLLVM();
+  auto tm = GetLLVMTargetMachine(target);
+  bool system_lib = target->GetAttr<Bool>("system-lib").value_or(Bool(false));
+  bool target_c_runtime = (target->GetAttr<String>("runtime").value_or("") == kTvmRuntimeCrt);
+  ICHECK(system_lib && target_c_runtime)
+      << "For LLVM C-runtime metadata module, must include --system-lib and --runtime=c; "
+      << "got target: " << target->str();
+  auto ctx = std::make_shared<llvm::LLVMContext>();
+  std::unique_ptr<CodeGenCPU> cg{new CodeGenCPU()};
+  cg->Init("TVMMetadataMod", tm.get(), ctx.get(), system_lib, system_lib, target_c_runtime);
+
+  cg->DefineFunctionRegistry(func_names);
+  auto mod = cg->Finish();
+  mod->addModuleFlag(llvm::Module::Warning, "tvm_target",
+                     llvm::MDString::get(*ctx, LLVMTargetToString(target)));
+  mod->addModuleFlag(llvm::Module::Override, "Debug Info Version", llvm::DEBUG_METADATA_VERSION);
+
+  if (tm->getTargetTriple().isOSDarwin()) {
+    mod->addModuleFlag(llvm::Module::Override, "Dwarf Version", 2);
+  }
+
+  std::string verify_errors_storage;
+  llvm::raw_string_ostream verify_errors(verify_errors_storage);
+  LOG_IF(FATAL, llvm::verifyModule(*mod, &verify_errors))
+      << "LLVM module verification failed with the following errors: \n"
+      << verify_errors.str();
+
+  auto n = make_object<LLVMModuleNode>();
+  n->Init(std::move(mod), ctx);
+  for (auto m : modules) {
+    n->Import(m);
+  }
+  return runtime::Module(n);
+}
+
+TVM_REGISTER_GLOBAL("runtime.CreateLLVMCrtMetadataModule")
+    .set_body_typed([](const Array<runtime::Module>& modules, Target target) {
+      return CreateLLVMCrtMetadataModule(modules, target);
+    });
+
 }  // namespace codegen
 }  // namespace tvm
 #endif  // TVM_LLVM_VERSION
diff --git a/src/target/llvm/llvm_module.h b/src/target/llvm/llvm_module.h
new file mode 100644
index 000000000000..3eab00c643e5
--- /dev/null
+++ b/src/target/llvm/llvm_module.h
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file llvm_module.h
+ * \brief Declares top-level shared functions related to the LLVM codegen.
+ */
+
+#ifndef TVM_TARGET_LLVM_LLVM_MODULE_H_
+#define TVM_TARGET_LLVM_LLVM_MODULE_H_
+
+#include <tvm/runtime/container.h>
+#include <tvm/runtime/module.h>
+#include <tvm/target/target.h>
+
+#ifdef TVM_LLVM_VERSION
+
+namespace tvm {
+namespace codegen {
+
+runtime::Module CreateLLVMCrtMetadataModule(const Array<runtime::Module>& modules, Target target);
+
+}  // namespace codegen
+}  // namespace tvm
+
+#endif  // TVM_LLVM_VERSION
+
+#endif  // TVM_TARGET_LLVM_LLVM_MODULE_H_
diff --git a/src/target/metadata_module.cc b/src/target/metadata_module.cc
new file mode 100644
index 000000000000..0b30d42c876c
--- /dev/null
+++ b/src/target/metadata_module.cc
@@ -0,0 +1,140 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file metadata_module.cc
+ * \brief Defines functions that build MetadataModules for C++ and C runtimes.
+ */
+
+#include "metadata_module.h"
+
+#include <vector>
+
+#include "../runtime/meta_data.h"
+#include "llvm/llvm_module.h"
+#include "source/source_module.h"
+
+namespace tvm {
+namespace codegen {
+
+/*!
+ * \brief Create a metadata module wrapper. The helper is used by different
+ *        codegens, such as graph runtime codegen and the vm compiler.
+ *
+ * \param params The metadata for initialization of all modules.
+ * \param target_module the internal module that is compiled by tvm.
+ * \param ext_modules The external modules that needs to be imported inside the metadata
+ * module(s).
+ * \param target The target that all the modules are compiled for
+ * \return The created metadata module that manages initialization of metadata.
+ */
+runtime::Module CreateMetadataModule(
+    const std::unordered_map<std::string, runtime::NDArray>& params,
+    tvm::runtime::Module target_module, const Array<runtime::Module>& ext_modules, Target target) {
+  // Here we split modules into two groups:
+  //  1. Those modules which can be exported to C-runtime. These are DSO-exportable
+  //     (i.e. llvm or c) modules which return nothing from get_const_vars().
+  //  2. Other modules.
+  Array<runtime::Module> crt_exportable_modules;
+  Array<runtime::Module> non_crt_exportable_modules;
+
+  auto DSOExportable = [](tvm::runtime::Module& mod) {
+    return !std::strcmp(mod->type_key(), "llvm") || !std::strcmp(mod->type_key(), "c");
+  };
+
+  bool is_targeting_crt =
+      target.defined() && target->GetAttr<String>("runtime").value_or(String("")) == kTvmRuntimeCrt;
+
+  // Wrap all submodules in the initialization wrapper.
+  std::unordered_map<std::string, std::vector<std::string>> sym_metadata;
+  for (tvm::runtime::Module mod : ext_modules) {
+    auto pf_sym = mod.GetFunction("get_symbol");
+    auto pf_var = mod.GetFunction("get_const_vars");
+    std::vector<std::string> arrays;
+    if (pf_sym != nullptr && pf_var != nullptr) {
+      String symbol = pf_sym();
+      Array<String> variables = pf_var();
+      for (size_t i = 0; i < variables.size(); i++) {
+        arrays.push_back(variables[i].operator std::string());
+      }
+      ICHECK_EQ(sym_metadata.count(symbol), 0U) << "Found duplicated symbol: " << symbol;
+      sym_metadata[symbol] = arrays;
+    }
+    // We only need loading of serialized constant data
+    // if there are constants present and required by the
+    // runtime module to be initialized by the binary
+    // metadata module. If not rest of the modules are
+    // wrapped in c-source metadata module.
+
+    // TODO(@manupa-arm) : we should be able to use csource_metadata
+    // if the variables are empty when all the runtime modules implement get_func_names
+    if (arrays.empty() && is_targeting_crt && DSOExportable(mod) &&
+        (target->kind->name == "c" || target->kind->name == "llvm")) {
+      crt_exportable_modules.push_back(mod);
+    } else {
+      non_crt_exportable_modules.push_back(mod);
+    }
+  }
+
+  if (is_targeting_crt) {
+    if (!non_crt_exportable_modules.empty()) {
+      std::string non_exportable_modules;
+      for (unsigned int i = 0; i < non_crt_exportable_modules.size(); i++) {
+        if (i > 0) {
+          non_exportable_modules += ", ";
+        }
+        auto mod = non_crt_exportable_modules[i];
+        auto pf_sym = mod.GetFunction("get_symbol");
+        if (pf_sym != nullptr) {
+          non_exportable_modules += pf_sym().operator std::string();
+        } else {
+          non_exportable_modules +=
+              std::string{"(module type_key="} + mod->type_key() + std::string{")"};
+        }
+      }
+      CHECK(false) << "These " << non_crt_exportable_modules.size()
+                   << " modules are not exportable to C-runtime: " << non_exportable_modules;
+    }
+
+    if (target->kind->name == "c") {
+      crt_exportable_modules.push_back(target_module);
+      target_module = CreateCSourceCrtMetadataModule(crt_exportable_modules, target);
+    } else if (target->kind->name == "llvm") {
+#ifdef TVM_LLVM_VERSION
+      crt_exportable_modules.push_back(target_module);
+      target_module = CreateLLVMCrtMetadataModule(crt_exportable_modules, target);
+#else   // TVM_LLVM_VERSION
+      LOG(FATAL) << "TVM was not built with LLVM enabled.";
+#endif  // TVM_LLVM_VERSION
+    }
+  } else {
+    if (!non_crt_exportable_modules.empty()) {
+      runtime::Module binary_meta_mod = runtime::MetadataModuleCreate(params, sym_metadata);
+      binary_meta_mod.Import(target_module);
+      for (const auto& it : non_crt_exportable_modules) {
+        binary_meta_mod.Import(it);
+      }
+      return binary_meta_mod;
+    }
+  }
+  return target_module;
+}
+
+}  // namespace codegen
+}  // namespace tvm
diff --git a/src/target/metadata_module.h b/src/target/metadata_module.h
new file mode 100644
index 000000000000..83cb29dd5a46
--- /dev/null
+++ b/src/target/metadata_module.h
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file metadata_module.h
+ * \brief Declares functions that build MetadataModules for C++ and C runtimes.
+ */
+
+#ifndef TVM_TARGET_METADATA_MODULE_H_
+#define TVM_TARGET_METADATA_MODULE_H_
+
+#include <tvm/runtime/container.h>
+#include <tvm/runtime/module.h>
+#include <tvm/runtime/ndarray.h>
+#include <tvm/target/target.h>
+
+#include <string>
+#include <unordered_map>
+
+namespace tvm {
+namespace codegen {
+
+runtime::Module CreateMetadataModule(
+    const std::unordered_map<std::string, runtime::NDArray>& params,
+    tvm::runtime::Module target_module, const Array<runtime::Module>& ext_modules, Target target);
+
+}  // namespace codegen
+}  // namespace tvm
+
+#endif  // TVM_TARGET_METADATA_MODULE_H_
diff --git a/src/target/source/codegen_c.cc b/src/target/source/codegen_c.cc
index af175c7f2208..55db59f8d842 100644
--- a/src/target/source/codegen_c.cc
+++ b/src/target/source/codegen_c.cc
@@ -728,7 +728,6 @@ void CodeGenC::VisitStmt_(const StoreNode* op) {
     ICHECK(is_one(op->predicate)) << "Predicated store is not supported";
     arith::PVar<PrimExpr> base;
 
-
     if (arith::ramp(base, 1, t.lanes()).Match(op->index)) {
       std::string value = this->PrintExpr(op->value);
       this->PrintVecStore(op->buffer_var.get(), t, base.Eval(), value);
@@ -899,6 +898,16 @@ void CodeGenC::VisitStmt_(const ForNode* op) {
   stream << "}\n";
 }
 
+void CodeGenC::VisitStmt_(const WhileNode* op) {
+  PrintIndent();
+  stream << "while (" << PrintExpr(op->condition) << ") {\n";
+  int while_scope = BeginScope();
+  PrintStmt(op->body);
+  this->EndScope(while_scope);
+  PrintIndent();
+  stream << "}\n";
+}
+
 void CodeGenC::VisitStmt_(const IfThenElseNode* op) {
   std::string cond = PrintExpr(op->condition);
   PrintIndent();
diff --git a/src/target/source/codegen_c.h b/src/target/source/codegen_c.h
index c1b566c064a4..76e6a9bc7197 100644
--- a/src/target/source/codegen_c.h
+++ b/src/target/source/codegen_c.h
@@ -150,6 +150,7 @@ class CodeGenC : public ExprFunctor<void(const PrimExpr&, std::ostream&)>,
   void VisitStmt_(const LetStmtNode* op) override;
   void VisitStmt_(const StoreNode* op) override;
   void VisitStmt_(const ForNode* op) override;
+  void VisitStmt_(const WhileNode* op) override;
   void VisitStmt_(const IfThenElseNode* op) override;
   void VisitStmt_(const AllocateNode* op) override;
   void VisitStmt_(const AttrStmtNode* op) override;
diff --git a/src/target/source/codegen_c_host.cc b/src/target/source/codegen_c_host.cc
index bee5441649c5..3ec64ed2ace9 100644
--- a/src/target/source/codegen_c_host.cc
+++ b/src/target/source/codegen_c_host.cc
@@ -44,6 +44,7 @@ void CodeGenCHost::Init(bool output_ssa, bool emit_asserts, std::string target_s
   emit_asserts_ = emit_asserts;
   declared_globals_.clear();
   decl_stream << "// tvm target: " << target_str << "\n";
+  decl_stream << "#define TVM_EXPORTS\n";
   decl_stream << "#include \"tvm/runtime/c_runtime_api.h\"\n";
   decl_stream << "#include \"tvm/runtime/c_backend_api.h\"\n";
   decl_stream << "#include <math.h>\n";
diff --git a/src/target/source/codegen_cuda.cc b/src/target/source/codegen_cuda.cc
index c0fb39f0a4f6..e54acd2221d1 100644
--- a/src/target/source/codegen_cuda.cc
+++ b/src/target/source/codegen_cuda.cc
@@ -61,6 +61,18 @@ std::string CodeGenCUDA::Finish() {
     decl_stream << _cuda_half_util;
   }
 
+  if (enable_bf16_) {
+    decl_stream << "#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 800)\n";
+    decl_stream << "#include <cuda_bf16.h>\n";
+    decl_stream << "__device__ nv_bfloat16 max"
+                << "(nv_bfloat16 a, nv_bfloat16 b)\n"
+                << "{\n  return __hgt(a, b) ? a : b;\n}\n";
+    decl_stream << "__device__ nv_bfloat16 min(nv_bfloat16 a, nv_bfloat16 b)\n"
+                << "{\n  return __hlt(a, b) ? a : b;\n}\n";
+    decl_stream << "#endif\n\n";
+    decl_stream << _cuda_bfloat16_util;
+  }
+
   if (enable_warp_shuffle_) {
     decl_stream << _cuda_warp_intrinsic_util;
   }
@@ -79,12 +91,26 @@ std::string CodeGenCUDA::Finish() {
     decl_stream << "#include <mma.h>\n";
   }
 
+  decl_stream << "\n#ifdef _WIN32\n";
+  decl_stream << "  using uint = unsigned int;\n";
+  decl_stream << "  using uchar = unsigned char;\n";
+  decl_stream << "  using ushort = unsigned short;\n";
+  decl_stream << "  using int64_t = long long;\n";
+  decl_stream << "  using uint64_t = unsigned long long;\n";
+  decl_stream << "#else\n";
+  decl_stream << "  #define uint unsigned int\n";
+  decl_stream << "  #define uchar unsigned char\n";
+  decl_stream << "  #define ushort unsigned short\n";
+  decl_stream << "  #define int64_t long long\n";
+  decl_stream << "  #define uint64_t unsigned long long\n";
+  decl_stream << "#endif\n";
+
   return CodeGenC::Finish();
 }
 
 void CodeGenCUDA::VisitStmt_(const tir::ForNode* op) {
   ICHECK(is_const_int(op->min, 0));
-  if (op->for_type == tir::ForType::Unrolled) {
+  if (op->kind == tir::ForKind::kUnrolled) {
     PrintIndent();
     stream << "#pragma unroll\n";
   }
@@ -99,7 +125,7 @@ void CodeGenCUDA::BindThreadIndex(const IterVar& iv) {
 void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
   int lanes = t.lanes();
   if (t.is_handle()) {
-    ICHECK_EQ(lanes, 1) << "do not yet support vector types";
+    ICHECK(t.is_scalar()) << "do not yet support vector types";
     os << "void*";
     return;
   }
@@ -108,7 +134,7 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
     switch (t.bits()) {
       case 16:
         enable_fp16_ = true;
-        if (lanes == 1) {
+        if (t.is_scalar()) {
           os << "half";
         } else if (lanes <= 8) {
           // Emit CUDA code to access fp16 vector elements.
@@ -127,7 +153,21 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
         }
         break;
       case 32:
-        os << "float";
+        if (lanes <= 4) {
+          os << "float";
+        } else if (lanes <= 8) {
+          // Emit CUDA code to access fp32 vector elements for 4 < lanes <= 8.
+          //
+          // float8 is stored as ulonglong4
+          //
+          // f8.v1 is emitted as *(float2*)(&(ul4.x)).x
+          // f8.v2 is emitted as *(float2*)(&(ul4.x)).y
+          //
+          ICHECK_EQ(lanes % 2, 0) << "only support even lane for float type with lanes > 4";
+          os << "ulonglong" << lanes / 2;
+        } else {
+          fail = true;
+        }
         break;
       case 64:
         os << "double";
@@ -136,11 +176,23 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
         fail = true;
         break;
     }
-    if (!fail && (lanes == 1 || t.bits() == 16)) return;
+    if (!fail && (t.is_scalar() || t.bits() == 16)) return;
+    if (!fail && (lanes > 4 && lanes <= 8 && t.bits() == 32)) return;
     if (!fail && (lanes >= 2 && lanes <= 4)) {
       os << lanes;
       return;
     }
+  } else if (t.is_bfloat16()) {
+    enable_bf16_ = true;
+    if (t.is_scalar()) {
+      os << "nv_bfloat16";
+    } else if (lanes <= 8) {
+      ICHECK_EQ(lanes % 2, 0) << "only support even lane for half type";
+      os << "uint" << lanes / 2;
+    } else {
+      fail = true;
+    }
+    if (!fail) return;
   } else if (t == DataType::Bool()) {
     os << "bool";
     return;
@@ -154,15 +206,11 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
     }
   } else if (t.is_uint() || t.is_int()) {
     if (t.is_uint()) {
-      if (t.lanes() != 1) {
-        os << "u";
-      } else {
-        os << "unsigned ";
-      }
+      os << "u";
     }
     switch (t.bits()) {
       case 1: {
-        if (t.lanes() == 1) {
+        if (t.is_scalar()) {
           os << "int";
           return;
         } else if (t.lanes() == 8) {
@@ -179,7 +227,7 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
         }
       }
       case 4: {
-        if (t.lanes() == 1) {
+        if (t.is_scalar()) {
           os << "int";
           return;
         } else if (t.lanes() == 4) {
@@ -220,7 +268,7 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
           enable_int8_ = true;
           os << "int4";
           return;
-        } else if (!t.is_uint() && t.lanes() == 1) {
+        } else if (!t.is_uint() && t.is_scalar()) {
           os << "signed char";
           break;
         } else {
@@ -228,29 +276,65 @@ void CodeGenCUDA::PrintType(DataType t, std::ostream& os) {  // NOLINT(*)
           break;
         }
       }
-      case 16:
-        os << "short";
+      case 16: {
+        if (t.is_scalar()) {
+          os << "short";
+        } else if (t.lanes() <= 4) {
+          os << "short" << lanes;
+        } else if (t.lanes() <= 8) {
+          // Emit CUDA code to access int16 vector elements.
+          //
+          // short4 is stored as int2
+          //
+          // s4.x is emitted as *(short2*)(&(i2.x)).x
+          // s4.y is emitted as *(short2*)(&(i2.x)).y
+          // s4.z is emitted as *(short2*)(&(i2.y)).x
+          // s4.w is emitted as *(short2*)(&(i2.y)).y
+          //
+          ICHECK_EQ(t.lanes() % 2, 0) << "only support even lane for shorT type with lanes > 4";
+          os << "int" << t.lanes() / 2;
+        } else {
+          fail = true;
+        }
+        if (!fail) {
+          return;
+        }
         break;
-      case 32:
-        os << "int";
+      }
+      case 32: {
+        if (t.is_scalar()) {
+          os << "int";
+        } else if (t.lanes() <= 4) {
+          os << "int" << t.lanes();
+        } else if (t.lanes() <= 8) {
+          // Emit CUDA code to access int32 vector elements for 4 < lanes <= 8.
+          //
+          // int8 is stored as longlong4
+          //
+          // i8.v1 is emitted as *(int2*)(&(l4.x)).x
+          // i8.v2 is emitted as *(int2*)(&(l4.x)).y
+          //
+          ICHECK_EQ(lanes % 2, 0) << "only support even lane for int32 type with lanes > 4";
+          os << "longlong" << lanes / 2;
+        } else {
+          fail = true;
+        }
+        if (!fail) {
+          return;
+        }
         break;
+      }
       case 64: {
-        if (sizeof(long) != 8) {  // NOLINT(*)
-          if (t.lanes() == 1) {
-            os << "long long";
-            break;
-          } else if (t.lanes() == 2) {
-            os << "longlong";
-            break;
-          } else {
-            // No longlong3, longlong4
-            LOG(FATAL) << "Cannot convert type " << t << " to CUDA type on a L32 platform";
-            break;
-          }
-        } else {
-          os << "long";
-          break;
+        if (t.is_scalar()) {
+          os << "int64_t";
+        } else if (t.lanes() == 2) {
+          os << "longlong2";
+        } else if (t.lanes() == 3) {
+          os << "longlong3";
+        } else if (t.lanes() == 4) {
+          os << "longlong4";
         }
+        return;
       }
       default:
         fail = true;
@@ -310,21 +394,38 @@ void CodeGenCUDA::PrintVecElemLoad(const std::string& vec, DataType t, int i,
   }
 
   static const char access[] = {'x', 'y', 'z', 'w'};
-  ICHECK(i >= 0 && i < (t.is_float16() ? 8 : 4));
-  if ((t.is_int()) && t.bits() == 8) {
-    if (t.lanes() == 2 || t.lanes() == 3) {
-      os << vec << "." << access[i % t.lanes()];
-    } else {
-      os << "((char)(" << vec << " >> " << i * 8 << "))";
-    }
-  } else if ((t.is_uint()) && t.bits() == 8) {
+  ICHECK(i >= 0 && i < (t.bits() == 8 ? 16 : (t.bits() == 16 || t.bits() == 32) ? 8 : 4));
+  if (t.bits() == 8 && (t.is_int() || t.is_uint())) {
+    std::string type_name = t.is_int() ? "char" : "unsigned char";
     if (t.lanes() == 2 || t.lanes() == 3) {
       os << vec << "." << access[i % t.lanes()];
     } else {
-      os << "((unsigned char)(" << vec << " >> " << i * 8 << "))";
+      std::string ac = t.lanes() == 4 ? vec : (vec + "." + access[i / 4]);
+      os << "((" << type_name << ")(" << ac << " >> " << i % 4 * 8 << "))";
     }
   } else if (t.is_float16()) {
     os << "((half2*)(&(" << vec << "." << access[i / 2] << ")))->" << access[i % 2];
+  } else if (t.is_bfloat16()) {
+    os << "((nv_bfloat162*)(&(" << vec << "." << access[i / 2] << ")))->" << access[i % 2];
+  } else if (t.lanes() > 4 && t.lanes() <= 8) {
+    std::string type_name;
+    if (t.bits() == 16) {
+      if (t.is_int()) {
+        type_name = "short";
+      } else if (t.is_uint()) {
+        type_name = "ushort";
+      }
+    } else if (t.bits() == 32) {
+      if (t.is_int()) {
+        type_name = "int";
+      } else if (t.is_uint()) {
+        type_name = "uint";
+      } else if (t.is_float()) {
+        type_name = "float";
+      }
+    }
+    ICHECK(!type_name.empty());
+    os << "((" << type_name << "2*)(&(" << vec << "." << access[i / 2] << ")))->" << access[i % 2];
   } else {
     os << vec << "." << access[i];
   }
@@ -334,22 +435,46 @@ void CodeGenCUDA::PrintVecElemStore(const std::string& vec, DataType t, int i,
                                     const std::string& value) {
   this->PrintIndent();
   static const char access[] = {'x', 'y', 'z', 'w'};
-  ICHECK(i >= 0 && i < (t.is_float16() ? 8 : 4));
+  ICHECK(i >= 0 && i < (t.bits() == 8 ? 16 : (t.bits() == 16 || t.bits() == 32) ? 8 : 4));
   if (t.bits() == 8 && (t.is_int() || t.is_uint())) {
     if (t.lanes() == 2 || t.lanes() == 3) {
       stream << vec << '.' << access[i % t.lanes()] << "="
              << "(" << value << ");\n";
     } else {
-      stream << vec << "=";
+      std::string ac = t.lanes() == 4 ? vec : (vec + "." + access[i / 4]);
+      stream << ac << "=";
       // Do not read the first undef lane.
       if (i != 0) {
-        stream << vec << " & ~(0x000000ff << " << i * 8 << ") |";
+        stream << ac << " & ~(0x000000ff << " << i % 4 * 8 << ") |";
       }
-      stream << "(" << value << " << " << i * 8 << ");\n";
+      stream << "(" << value << " << " << i % 4 * 8 << ");\n";
     }
   } else if (t.is_float16()) {
     stream << "((half2*)(&(" << vec << "." << access[i / 2] << ")))->" << access[i % 2] << " = "
            << value << ";\n";
+  } else if (t.is_bfloat16()) {
+    stream << "((nv_bfloat162*)(&(" << vec << "." << access[i / 2] << ")))->" << access[i % 2]
+           << " = " << value << ";\n";
+  } else if (t.lanes() > 4 && t.lanes() <= 8) {
+    std::string type_name;
+    if (t.bits() == 16) {
+      if (t.is_int()) {
+        type_name = "short";
+      } else if (t.is_uint()) {
+        type_name = "ushort";
+      }
+    } else if (t.bits() == 32) {
+      if (t.is_int()) {
+        type_name = "int";
+      } else if (t.is_uint()) {
+        type_name = "uint";
+      } else if (t.is_float()) {
+        type_name = "float";
+      }
+    }
+    ICHECK(!type_name.empty());
+    stream << "((" << type_name << "2*)(&(" << vec << "." << access[i / 2] << ")))->"
+           << access[i % 2] << " = " << value << ";\n";
   } else {
     stream << vec << "." << access[i] << " = " << value << ";\n";
   }
@@ -581,12 +706,17 @@ void CodeGenCUDA::VisitStmt_(const AllocateNode* op) {
   int32_t constant_size = op->constant_allocation_size();
   ICHECK_GT(constant_size, 0) << "Can only handle constant size stack allocation for now";
   const VarNode* buffer = op->buffer_var.as<VarNode>();
-  std::string scope = alloc_storage_scope_.at(buffer);
+  auto it = alloc_storage_scope_.find(buffer);
+  ICHECK(it != alloc_storage_scope_.end())
+      << "Buffer " << op->buffer_var << " is missing an AttrStmt with a \"storage_scope\" key";
+
+  std::string scope = it->second;
   if (scope.find("wmma.") == 0) {
     if (scope == "wmma.matrix_a" || scope == "wmma.matrix_b") {
       ICHECK(op->dtype == DataType::Float(16) || op->dtype == DataType::Int(8) ||
              op->dtype == DataType::UInt(8) || op->dtype == DataType::Int(4) ||
-             op->dtype == DataType::UInt(4) || op->dtype == DataType::Int(1))
+             op->dtype == DataType::UInt(4) || op->dtype == DataType::Int(1) ||
+             op->dtype == DataType::BFloat(16))
           << "Matrix_a and matrix_b only support half or char or unsigned char "
           << "or uint4 or int4 or int1 type for now";
     } else {
@@ -666,6 +796,19 @@ void CodeGenCUDA::VisitExpr_(const BroadcastNode* op, std::ostream& os) {  // NO
     return;
   }
 
+  if (op->dtype.is_bfloat16()) {
+    std::string v = PrintExpr(op->value);
+    os << "make_";
+    PrintType(op->dtype, os);
+    os << '(';
+    for (int i = 0; i < op->lanes / 2; ++i) {
+      if (i != 0) os << ", ";
+      os << "__pack_nv_bfloat162(" << v << ", " << v << ")";
+    }
+    os << ')';
+    return;
+  }
+
   std::string v = PrintExpr(op->value);
   os << "make_";
   PrintType(op->dtype, os);
@@ -735,6 +878,13 @@ void CodeGenCUDA::VisitExpr_(const SelectNode* op, std::ostream& os) {
 }
 
 inline void PrintConst(const FloatImmNode* op, std::ostream& os, CodeGenCUDA* p) {  // NOLINT(*)
+  // Type code is kBFloat
+  if (op->dtype.is_bfloat16()) {
+    os << "__float2bfloat16_rn";
+    os << '(' << std::scientific << op->value << 'f' << ')';
+    return;
+  }
+  // Type code is kFloat
   switch (op->dtype.bits()) {
     case 64:
     case 32: {
@@ -837,7 +987,7 @@ void CodeGenCUDA::HandleVolatileLoads(const std::string& value, const LoadNode*
   // Cast away volatile qualifier for fp16 types. That is, only loads and
   // stores are volatile. The loaded objects are not marked as volatile.
   //
-  if (op->dtype.is_float16() && IsVolatile(op->buffer_var.get())) {
+  if ((op->dtype.is_float16() || op->dtype.is_bfloat16()) && IsVolatile(op->buffer_var.get())) {
     os << "(";
     PrintType(op->dtype, os);
     os << ")(" << value << ")";
@@ -878,6 +1028,25 @@ void CodeGenCUDA::PrintVecElemLoadExpr(DataType t, int i, const std::string& val
     return;
   }
 
+  if (t.is_bfloat16()) {
+    if (i == 0) {
+      os << "make_";
+      PrintType(t, os);
+      os << '(';
+    }
+    if (i % 2 == 0) {
+      os << "__pack_bfloat162(" << value;
+    } else {
+      os << "," << value << ")";
+      if (i != t.lanes() - 1) {
+        os << ",";
+      } else {
+        os << ")";
+      }
+    }
+    return;
+  }
+
   if (i == 0) {
     os << "make_";
     PrintType(t, os);
diff --git a/src/target/source/codegen_cuda.h b/src/target/source/codegen_cuda.h
index 3cde8e379eb4..2098b8ac8344 100644
--- a/src/target/source/codegen_cuda.h
+++ b/src/target/source/codegen_cuda.h
@@ -42,7 +42,7 @@ class CodeGenCUDA final : public CodeGenC {
   void Init(bool output_ssa);
   std::string Finish();
   bool need_include_path() {
-    return (enable_fp16_ || enable_int8_ || need_math_constants_h_ || need_mma_h_);
+    return (enable_fp16_ || enable_bf16_ || enable_int8_ || need_math_constants_h_ || need_mma_h_);
   }
   // override behavior
   void PrintFuncPrefix() final;
@@ -88,6 +88,8 @@ class CodeGenCUDA final : public CodeGenC {
   std::string vid_global_barrier_expect_;
   // whether enable fp16
   bool enable_fp16_{false};
+  // whether enable bf16
+  bool enable_bf16_{false};
   // whether enable int8
   bool enable_int8_{false};
   // whether enable warp shuffle intrinsics
diff --git a/src/target/source/codegen_metal.cc b/src/target/source/codegen_metal.cc
index baa30065a7f9..c95d578df686 100644
--- a/src/target/source/codegen_metal.cc
+++ b/src/target/source/codegen_metal.cc
@@ -47,7 +47,7 @@ CodeGenMetal::CodeGenMetal() {
   decl_stream << "#include <metal_stdlib>\n";
   decl_stream << "using namespace metal;\n\n";
   decl_stream << "union __TVMArgUnion {\n"
-              << " int v_int;\n"
+              << " int v_int[2];\n"
               << "};\n\n";
 }
 
@@ -102,6 +102,11 @@ void CodeGenMetal::AddFunction(const PrimFunc& f) {
       std::string vid = AllocVarID(v.get());
       std::ostringstream vref;
       if (v.dtype().bits() == 32) {
+        decl_stream << "  ";
+        PrintType(v.dtype(), decl_stream);
+        decl_stream << " " << vid << "[2];\n";
+        vref << varg << "." << vid << "[0]";
+      } else if (v.dtype().bits() == 64) {
         decl_stream << "  ";
         PrintType(v.dtype(), decl_stream);
         decl_stream << " " << vid << ";\n";
diff --git a/src/target/source/codegen_source_base.h b/src/target/source/codegen_source_base.h
index ed838f825812..3baa44eb639f 100644
--- a/src/target/source/codegen_source_base.h
+++ b/src/target/source/codegen_source_base.h
@@ -170,12 +170,13 @@ runtime::Module DeviceSourceModuleCreate(
     std::string type_key, std::function<std::string(const std::string&)> fget_source = nullptr);
 
 /*!
- * \brief Wrap the submodules that are to be wrapped in a c-source metadata module.
+ * \brief Wrap the submodules that are to be wrapped in a c-source metadata module for C runtime.
  * \param modules The modules to be wrapped.
  * \param target the target the modules are compiled for.
  * \return The wrapped module.
  */
-runtime::Module CreateCSourceMetadataModule(const Array<runtime::Module>& modules, Target target);
+runtime::Module CreateCSourceCrtMetadataModule(const Array<runtime::Module>& modules,
+                                               Target target);
 
 }  // namespace codegen
 }  // namespace tvm
diff --git a/src/target/source/intrin_rule_cuda.cc b/src/target/source/intrin_rule_cuda.cc
index 5c562f7b1643..965b86c24d9e 100644
--- a/src/target/source/intrin_rule_cuda.cc
+++ b/src/target/source/intrin_rule_cuda.cc
@@ -43,6 +43,8 @@ struct CUDAMath {
         default:
           return "";
       }
+    } else if (t.is_bfloat16()) {
+      return 'h' + name;
     }
     return "";
   }
diff --git a/src/target/source/literal/cuda_half_t.h b/src/target/source/literal/cuda_half_t.h
index f8e92d508d88..3888f3a4fb07 100644
--- a/src/target/source/literal/cuda_half_t.h
+++ b/src/target/source/literal/cuda_half_t.h
@@ -311,6 +311,30 @@ static inline __device__ __host__ half htanh(half x) {
 #endif
 )";
 
+static constexpr const char* _cuda_bfloat16_util = R"(
+// Pack two bfloat16 values.
+static inline __device__ __host__ unsigned
+__pack_nv_bfloat162(const nv_bfloat16 x, const nv_bfloat16 y) {
+  unsigned v0 = *((unsigned short *)&x);
+  unsigned v1 = *((unsigned short *)&y);
+  return (v1 << 16) | v0;
+}
+
+// fix undefined fp16 match function
+static inline __device__ __host__ nv_bfloat16 hpow(nv_bfloat16 x, nv_bfloat16 y) {
+  float tmp_x = __bfloat162float(x);
+  float tmp_y = __bfloat162float(y);
+  float result = powf(tmp_x, tmp_y);
+  return __float2bfloat16(result);
+}
+
+static inline __device__ __host__ nv_bfloat16 htanh(nv_bfloat16 x) {
+  float tmp_x = __bfloat162float(x);
+  float result = tanhf(tmp_x);
+  return __float2bfloat16(result);
+}
+)";
+
 static constexpr const char* _cuda_warp_intrinsic_util = R"(
 #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 700)
 #define __shfl_sync(mask, var, lane, width) \
diff --git a/src/target/source/source_module.cc b/src/target/source/source_module.cc
index 4b4770a79816..26f1850c0e47 100644
--- a/src/target/source/source_module.cc
+++ b/src/target/source/source_module.cc
@@ -21,12 +21,17 @@
  * \file source_module.cc
  * \brief Source code module, only for viewing
  */
+#include "source_module.h"
+
 #include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
 
+#include <string>
+#include <unordered_map>
+#include <utility>
+
 #include "../../runtime/file_utils.h"
-#include "../../runtime/meta_data.h"
 #include "../../support/str_escape.h"
 #include "../func_registry_generator.h"
 #include "codegen_source_base.h"
@@ -43,73 +48,6 @@ using runtime::GetFileFormat;
 using runtime::GetMetaFilePath;
 using runtime::SaveBinaryToFile;
 
-/*!
- * \brief Create a metadata module wrapper. The helper is used by different
- *        codegens, such as graph runtime codegen and the vm compiler.
- *
- * \param params The metadata for initialization of all modules.
- * \param target_module the internal module that is compiled by tvm.
- * \param ext_modules The external modules that needs to be imported inside the metadata
- * module(s).
- * \param target The target that all the modules are compiled for
- * \return The created metadata module that manages initialization of metadata.
- */
-runtime::Module CreateMetadataModule(
-    const std::unordered_map<std::string, runtime::NDArray>& params,
-    tvm::runtime::Module target_module, const Array<runtime::Module>& ext_modules, Target target) {
-  Array<tvm::runtime::Module> csource_modules;
-  Array<tvm::runtime::Module> binary_modules;
-
-  auto DSOExportable = [](tvm::runtime::Module& mod) {
-    return !std::strcmp(mod->type_key(), "llvm") || !std::strcmp(mod->type_key(), "c");
-  };
-
-  // Wrap all submodules in the initialization wrapper.
-  std::unordered_map<std::string, std::vector<std::string>> sym_metadata;
-  for (tvm::runtime::Module mod : ext_modules) {
-    auto pf_sym = mod.GetFunction("get_symbol");
-    auto pf_var = mod.GetFunction("get_const_vars");
-    std::vector<std::string> arrays;
-    if (pf_sym != nullptr && pf_var != nullptr) {
-      String symbol = pf_sym();
-      Array<String> variables = pf_var();
-      for (size_t i = 0; i < variables.size(); i++) {
-        arrays.push_back(variables[i].operator std::string());
-      }
-      ICHECK_EQ(sym_metadata.count(symbol), 0U) << "Found duplicated symbol: " << symbol;
-      sym_metadata[symbol] = arrays;
-    }
-    // We only need loading of serialized constant data
-    // if there are constants present and required by the
-    // runtime module to be initialized by the binary
-    // metadata module. If not rest of the modules are
-    // wrapped in c-source metadata module.
-
-    // TODO(@manupa-arm) : we should be able to use csource_metadata
-    // if the variables are empty when all the runtime modules implement get_func_names
-    if (arrays.empty() && DSOExportable(mod) && target->kind->name == "c") {
-      csource_modules.push_back(mod);
-    } else {
-      binary_modules.push_back(mod);
-    }
-  }
-
-  if (target.defined() && target->kind->name == "c") {
-    csource_modules.push_back(target_module);
-    target_module = CreateCSourceMetadataModule(csource_modules, target);
-  }
-
-  if (!binary_modules.empty()) {
-    runtime::Module binary_meta_mod = runtime::MetadataModuleCreate(params, sym_metadata);
-    binary_meta_mod.Import(target_module);
-    for (const auto& it : binary_modules) {
-      binary_meta_mod.Import(it);
-    }
-    return binary_meta_mod;
-  }
-  return target_module;
-}
-
 // Simulator function
 class SourceModuleNode : public runtime::ModuleNode {
  public:
@@ -166,7 +104,7 @@ class CSourceModuleNode : public runtime::ModuleNode {
   void SaveToFile(const std::string& file_name, const std::string& format) final {
     std::string fmt = GetFileFormat(file_name, format);
     std::string meta_file = GetMetaFilePath(file_name);
-    if (fmt == "c") {
+    if (fmt == "c" || fmt == "cu") {
       ICHECK_NE(code_.length(), 0);
       SaveBinaryToFile(file_name, code_);
     } else {
@@ -189,9 +127,10 @@ runtime::Module CSourceModuleCreate(const String& code, const String& fmt,
   return runtime::Module(n);
 }
 
-class CSourceMetadataModuleNode : public runtime::ModuleNode {
+class CSourceCrtMetadataModuleNode : public runtime::ModuleNode {
  public:
-  CSourceMetadataModuleNode(const Array<String>& func_names, const std::string& fmt, Target target)
+  CSourceCrtMetadataModuleNode(const Array<String>& func_names, const std::string& fmt,
+                               Target target)
       : fmt_(fmt), func_names_(func_names), target_(target) {
     CreateSource();
   }
@@ -261,7 +200,8 @@ class CSourceMetadataModuleNode : public runtime::ModuleNode {
   }
 };
 
-runtime::Module CreateCSourceMetadataModule(const Array<runtime::Module>& modules, Target target) {
+runtime::Module CreateCSourceCrtMetadataModule(const Array<runtime::Module>& modules,
+                                               Target target) {
   Array<String> func_names;
   for (runtime::Module mod : modules) {
     auto pf_funcs = mod.GetFunction("get_func_names");
@@ -272,7 +212,7 @@ runtime::Module CreateCSourceMetadataModule(const Array<runtime::Module>& module
       }
     }
   }
-  auto n = make_object<CSourceMetadataModuleNode>(func_names, "cc", target);
+  auto n = make_object<CSourceCrtMetadataModuleNode>(func_names, "cc", target);
   auto csrc_metadata_module = runtime::Module(n);
   for (const auto& mod : modules) {
     csrc_metadata_module.Import(mod);
@@ -341,9 +281,9 @@ TVM_REGISTER_GLOBAL("runtime.CSourceModuleCreate")
       return CSourceModuleCreate(code, fmt, func_names, const_vars);
     });
 
-TVM_REGISTER_GLOBAL("runtime.CreateCSourceMetadataModule")
+TVM_REGISTER_GLOBAL("runtime.CreateCSourceCrtMetadataModule")
     .set_body_typed([](const Array<runtime::Module>& modules, Target target) {
-      return CreateCSourceMetadataModule(modules, target);
+      return CreateCSourceCrtMetadataModule(modules, target);
     });
 
 }  // namespace codegen
diff --git a/src/target/source/source_module.h b/src/target/source/source_module.h
new file mode 100644
index 000000000000..45858b9f4ef2
--- /dev/null
+++ b/src/target/source/source_module.h
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file source_module.h
+ * \brief Source code module
+ */
+
+#ifndef TVM_TARGET_SOURCE_SOURCE_MODULE_H_
+#define TVM_TARGET_SOURCE_SOURCE_MODULE_H_
+
+#include <tvm/runtime/container.h>
+#include <tvm/runtime/module.h>
+#include <tvm/target/target.h>
+
+namespace tvm {
+namespace codegen {
+
+/*!
+ * \brief Create C-runtime targeted metadata module for "c" backend.
+ * \param modules Array of modules included in the compilation output.
+ * \param target TVM target.
+ */
+runtime::Module CreateCSourceCrtMetadataModule(const Array<runtime::Module>& modules,
+                                               tvm::Target target);
+
+}  // namespace codegen
+}  // namespace tvm
+
+#endif  // TVM_TARGET_SOURCE_SOURCE_MODULE_H_
diff --git a/src/target/spirv/codegen_spirv.cc b/src/target/spirv/codegen_spirv.cc
index c3b12ab943c6..24608ebc93f4 100644
--- a/src/target/spirv/codegen_spirv.cc
+++ b/src/target/spirv/codegen_spirv.cc
@@ -45,10 +45,15 @@ std::vector<uint32_t> CodeGenSPIRV::BuildFunction(const PrimFunc& f, const std::
       if (auto* ptr = arg->type_annotation.as<PointerTypeNode>()) {
         auto* prim = ptr->element_type.as<PrimTypeNode>();
         ICHECK(prim);
-        DataType value_type = prim->dtype;
+        DataType value_storage_type = prim->dtype;
+        if (value_storage_type == DataType::UInt(1)) {
+          // We need a physically addressable buffer type to support boolean tensors.
+          // The loaded byte is cast to bool inside the LoadNode visitor below.
+          value_storage_type = DataType::UInt(8);
+        }
         spirv::Value arg_value =
-            builder_->BufferArgument(builder_->GetSType(value_type), 0, num_buffer);
-        storage_info_[arg.get()].UpdateContentType(value_type);
+            builder_->BufferArgument(builder_->GetSType(value_storage_type), 0, num_buffer);
+        storage_info_[arg.get()].UpdateContentType(value_storage_type);
         var_map_[arg.get()] = arg_value;
       } else {
         LOG(FATAL) << "require all handles to be typed";
@@ -369,11 +374,18 @@ spirv::Value CodeGenSPIRV::VisitExpr_(const LoadNode* op) {
     mask |= spv::MemoryAccessVolatileMask;
   }
   if (op->dtype.lanes() == 1) {
-    ICHECK_EQ(info.content_type, op->dtype)
-        << "Vulkan only allow one type access to the same buffer";
     spirv::Value index = MakeValue(op->index);
     spirv::Value ptr = builder_->StructArrayAccess(ptr_type, buffer, index);
-    return builder_->MakeValue(spv::OpLoad, content_type, ptr, mask);
+    spirv::Value loaded = builder_->MakeValue(spv::OpLoad, content_type, ptr, mask);
+    if (op->dtype == DataType::UInt(1)) {
+      // A bool tensor is backed by a byte buffer, we cast to bool here.
+      auto bool_ty = builder_->GetSType(DataType::UInt(1));
+      return builder_->Cast(bool_ty, loaded);
+    } else {
+      ICHECK_EQ(info.content_type, op->dtype)
+          << "Vulkan only allow one type access to the same buffer";
+      return loaded;
+    }
   } else {
     if (op->dtype.element_of() == info.content_type) {
       // because content type is element type, we can only do scalarize load.
@@ -492,7 +504,7 @@ void CodeGenSPIRV::VisitStmt_(const ForNode* op) {
   loop_var.SetIncoming(0, init_value, init_label);
   spirv::Value loop_cond = builder_->LT(loop_var, extent_value);
   uint32_t control =
-      (op->for_type == ForType::Unrolled ? spv::LoopControlUnrollMask : spv::LoopControlMaskNone);
+      (op->kind == ForKind::kUnrolled ? spv::LoopControlUnrollMask : spv::LoopControlMaskNone);
   builder_->MakeInst(spv::OpLoopMerge, merge_label, continue_label, control);
   builder_->MakeInst(spv::OpBranchConditional, loop_cond, body_label, merge_label,
                      weight_likely_branch_, 1);
@@ -514,6 +526,34 @@ void CodeGenSPIRV::VisitStmt_(const ForNode* op) {
   builder_->StartLabel(merge_label);
 }
 
+void CodeGenSPIRV::VisitStmt_(const WhileNode* op) {
+  spirv::Label head_label = builder_->NewLabel();
+  spirv::Label body_label = builder_->NewLabel();
+  spirv::Label continue_label = builder_->NewLabel();
+  spirv::Label merge_label = builder_->NewLabel();
+  builder_->MakeInst(spv::OpBranch, head_label);
+
+  // Loop head
+  builder_->StartLabel(head_label);
+  spirv::Value loop_cond = MakeValue(op->condition);
+  uint32_t control = spv::LoopControlMaskNone;
+  builder_->MakeInst(spv::OpLoopMerge, merge_label, continue_label, control);
+  builder_->MakeInst(spv::OpBranchConditional, loop_cond, body_label, merge_label,
+                     weight_likely_branch_, 1);
+
+  // loop body
+  builder_->StartLabel(body_label);
+  this->VisitStmt(op->body);
+  builder_->MakeInst(spv::OpBranch, continue_label);
+
+  // loop continue
+  builder_->StartLabel(continue_label);
+  builder_->MakeInst(spv::OpBranch, head_label);
+
+  // loop merge
+  builder_->StartLabel(merge_label);
+}
+
 void CodeGenSPIRV::VisitStmt_(const IfThenElseNode* op) {
   spirv::Value cond = MakeValue(op->condition);
   spirv::Label then_label = builder_->NewLabel();
diff --git a/src/target/spirv/codegen_spirv.h b/src/target/spirv/codegen_spirv.h
index be755641c8a5..1e80fcc4a931 100644
--- a/src/target/spirv/codegen_spirv.h
+++ b/src/target/spirv/codegen_spirv.h
@@ -93,6 +93,7 @@ class CodeGenSPIRV : public ExprFunctor<spirv::Value(const PrimExpr&)>,
   // stmt
   void VisitStmt_(const StoreNode* op) override;
   void VisitStmt_(const ForNode* op) override;
+  void VisitStmt_(const WhileNode* op) override;
   void VisitStmt_(const IfThenElseNode* op) override;
   void VisitStmt_(const AllocateNode* op) override;
   void VisitStmt_(const AttrStmtNode* op) override;
diff --git a/src/target/spirv/intrin_rule_spirv.cc b/src/target/spirv/intrin_rule_spirv.cc
index 90b2eb2a671f..b75fb53b150d 100644
--- a/src/target/spirv/intrin_rule_spirv.cc
+++ b/src/target/spirv/intrin_rule_spirv.cc
@@ -62,8 +62,14 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.fabs").set_body(DispatchGLSLPureIntr
 
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.exp").set_body(DispatchGLSLPureIntrin<GLSLstd450Exp>);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.sin").set_body(DispatchGLSLPureIntrin<GLSLstd450Sin>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.cos").set_body(DispatchGLSLPureIntrin<GLSLstd450Cos>);
+
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.log").set_body(DispatchGLSLPureIntrin<GLSLstd450Log>);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.log2").set_body(DispatchGLSLPureIntrin<GLSLstd450Log2>);
+
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.sqrt").set_body(DispatchGLSLPureIntrin<GLSLstd450Sqrt>);
 
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.pow").set_body(DispatchGLSLPureIntrin<GLSLstd450Pow>);
diff --git a/src/target/spirv/ir_builder.cc b/src/target/spirv/ir_builder.cc
index 273fc48c3e30..5a1457387ae5 100644
--- a/src/target/spirv/ir_builder.cc
+++ b/src/target/spirv/ir_builder.cc
@@ -48,6 +48,8 @@ void IRBuilder::InitHeader() {
   header_.push_back(0U);
   // shader
   ib_.Begin(spv::OpCapability).Add(spv::CapabilityShader).Commit(&header_);
+  // Declare int64 capability by default
+  ib_.Begin(spv::OpCapability).Add(spv::CapabilityInt64).Commit(&header_);
   // memory model
   ib_.Begin(spv::OpMemoryModel)
       .AddSeq(spv::AddressingModelLogical, spv::MemoryModelGLSL450)
@@ -222,7 +224,14 @@ Value IRBuilder::DeclarePushConstant(const std::vector<SType>& value_types) {
     DataType t = value_types[i].type;
     uint32_t nbits = t.bits() * t.lanes();
     ICHECK_EQ(nbits % 8, 0);
-    offset += nbits / 8;
+    uint32_t bytes = (nbits / 8);
+    if (t.bits() == 32) {
+      // In our Vulkan runtime, each push constant always occupies 64 bit.
+      offset += bytes * 2;
+    } else {
+      ICHECK_EQ(t.bits(), 64);
+      offset += bytes;
+    }
   }
   // Decorate push constants as UBO
   this->Decorate(spv::OpDecorate, struct_type, spv::DecorationBlock);
diff --git a/src/target/tag.cc b/src/target/tag.cc
index 8198435a9494..a931a288924e 100644
--- a/src/target/tag.cc
+++ b/src/target/tag.cc
@@ -21,6 +21,8 @@
  * \file src/target/target_tag.cc
  * \brief Target tag registry
  */
+
+#include <tvm/ir/expr.h>
 #include <tvm/runtime/registry.h>
 #include <tvm/target/tag.h>
 #include <tvm/target/target.h>
@@ -68,10 +70,259 @@ Target TargetTag::AddTag(String name, Map<String, ObjectRef> config, bool overri
 
 /**********  Register Target tags  **********/
 
-TVM_REGISTER_TARGET_TAG("nvidia/rtx2080ti")
-    .set_config({
-        {"kind", String("cuda")},
-        {"arch", String("sm_75")},
-    });
+#define TVM_REGISTER_CUDA_TAG(Name, Arch, SharedMem, RegPerBlock) \
+  TVM_REGISTER_TARGET_TAG(Name).set_config({                      \
+      {"kind", String("cuda")},                                   \
+      {"arch", String(Arch)},                                     \
+      {"shared_memory_per_block", Integer(SharedMem)},            \
+      {"registers_per_block", Integer(RegPerBlock)},              \
+      {"max_threads_per_block", Integer(1024)},                   \
+      {"thread_warp_size", Integer(32)},                          \
+  });
+
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k80", "sm_37", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k40", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k20", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-c2075", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-c2050", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-c2070", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-a100", "sm_80", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-t4", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-v100", "sm_70", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-p100", "sm_60", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-p40", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-p4", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-m60", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-m40", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k80", "sm_37", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k40", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k20", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/tesla-k10", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-rtx-8000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-rtx-6000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-rtx-5000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-rtx-4000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-gv100", "sm_70", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-gp100", "sm_60", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p6000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p5000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p4000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p2200", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p2000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p1000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p620", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p600", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p400", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m6000-24gb", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m6000", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k6000", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m5000", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k5200", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k5000", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m4000", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k4200", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k4000", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m2000", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k2200", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k2000", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k2000d", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k1200", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k620", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k600", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k420", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-410", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-plex-7000", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/rtx-5000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/rtx-4000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/rtx-3000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/t2000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/t1000", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/p620", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/p520", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p5200", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p4200", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p3200", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p5000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p4000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p3000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p2000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p1000", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p600", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-p500", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m5500m", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m2200", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m1200", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m620", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m520", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k6000m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k5200m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k5100m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m5000m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k500m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k4200m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k4100m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m4000m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k3100m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m3000m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k2200m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k2100m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m2000m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k1100m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m1000m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k620m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k610m", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m600m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-k510m", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/quadro-m500m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-nvs-810", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-nvs-510", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-nvs-315", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-nvs-310", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/nvs-5400m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/nvs-5200m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/nvs-4200m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-3090", "sm_86", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-3080", "sm_86", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-3070", "sm_86", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-titan-rtx", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2080-ti", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2080", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2070", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2060", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-titan-v", "sm_70", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-titan-xp", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/nvidia-titan-x", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1080-ti", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1080", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1070-ti", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1070", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1060", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1050", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-titan-x", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-titan-z", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-titan-black", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-titan", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-980-ti", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-980", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-970", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-960", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-950", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-780-ti", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-780", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-770", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-760", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-750-ti", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-750", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-690", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-680", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-670", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-660-ti", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-660", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-650-ti-boost", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-650-ti", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-650", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-560-ti", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-550-ti", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-460", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gts-450", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-590", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-580", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-570", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-480", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-470", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-465", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-740", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-730", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-730-ddr3,128bit", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-720", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-705", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-640-gddr5", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-640-gddr3", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-630", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-620", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-610", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-520", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-440", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-430", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2080", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2070", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-rtx-2060", "sm_75", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1080", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1070", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-1060", "sm_61", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-980", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-980m", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-970m", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-965m", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-960m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-950m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-940m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-930m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-920m", "sm_35", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-910m", "sm_52", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-880m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-870m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-860m-sm-30", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-860m-sm-50", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-850m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-840m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-830m", "sm_50", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-820m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-800m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-780m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-770m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-765m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-760m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-680mx", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-680m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-675mx", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-675m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-670mx", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-670m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-660m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-755m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-750m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-650m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-745m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-645m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-740m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-730m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-640m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-640m-le", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-735m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-635m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-730m", "sm_30", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-630m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-625m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-720m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-620m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-710m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-705m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-610m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-580m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-570m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-560m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-555m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-550m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-540m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-525m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-520mx", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-520m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-485m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-470m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-460m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-445m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-435m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-420m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gt-415m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-gtx-480m", "sm_20", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-710m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/geforce-410m", "sm_21", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/jetson-agx-xavier", "sm_72", 49152, 65536);
+TVM_REGISTER_CUDA_TAG("nvidia/jetson-nano", "sm_53", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/jetson-tx2", "sm_62", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/jetson-tx1", "sm_53", 49152, 32768);
+TVM_REGISTER_CUDA_TAG("nvidia/tegra-x1", "sm_53", 49152, 32768);
 
+#undef TVM_REGISTER_CUDA_TAG
 }  // namespace tvm
diff --git a/src/target/target.cc b/src/target/target.cc
index e44a15c3ff59..55ef5f1a4e24 100644
--- a/src/target/target.cc
+++ b/src/target/target.cc
@@ -79,7 +79,7 @@ static const TObj* ObjTypeCheck(const ObjectRef& obj, const std::string& expecte
     std::ostringstream os;
     os << ": Expects type \"" << expected_type << "\", but gets \"" << obj->GetTypeKey()
        << "\" for object: " << obj;
-    throw dmlc::Error(os.str());
+    throw Error(os.str());
   }
   return ptr;
 }
@@ -87,7 +87,7 @@ static const TObj* ObjTypeCheck(const ObjectRef& obj, const std::string& expecte
 static TargetKind GetTargetKind(const String& name) {
   Optional<TargetKind> kind = TargetKind::Get(name);
   if (!kind.defined()) {
-    throw dmlc::Error(": Target kind \"" + name + "\" is not defined");
+    throw Error(": Target kind \"" + name + "\" is not defined");
   }
   return kind.value();
 }
@@ -98,10 +98,10 @@ static std::string RemovePrefixDashes(const std::string& s) {
   for (; n_dashes < len && s[n_dashes] == '-'; ++n_dashes) {
   }
   if (n_dashes == 0) {
-    throw dmlc::Error(": Attribute keys should start with '-', not an attribute key: " + s);
+    throw Error(": Attribute keys should start with '-', not an attribute key: " + s);
   }
   if (n_dashes >= len) {
-    throw dmlc::Error(": Not an attribute key: " + s);
+    throw Error(": Not an attribute key: " + s);
   }
   return s.substr(n_dashes);
 }
@@ -133,7 +133,7 @@ static int ParseKVPair(const std::string& s, const std::string& s_next, std::str
     result_k = s.substr(0, pos);
     result_v = s.substr(pos + 1);
     if (result_k.empty() || result_v.empty()) {
-      throw dmlc::Error(": Empty attribute key or value in \"" + s + "\"");
+      throw Error(": Empty attribute key or value in \"" + s + "\"");
     }
     return 1;
   } else if (!s_next.empty() && s_next[0] != '-') {
@@ -163,7 +163,7 @@ const TargetKindNode::ValueTypeInfo& TargetInternal::FindTypeInfo(const TargetKi
       }
       os << kv.first;
     }
-    throw dmlc::Error(os.str());
+    throw Error(os.str());
   }
   return it->second;
 }
@@ -177,14 +177,14 @@ ObjectRef TargetInternal::ParseType(const std::string& str,
     // Parsing integer
     int v;
     if (!(is >> v)) {
-      throw dmlc::Error(": Cannot parse into type \"Integer\" from string: " + str);
+      throw Error(": Cannot parse into type \"Integer\" from string: " + str);
     }
     return Integer(v);
   } else if (info.type_index == String::ContainerType::_GetOrAllocRuntimeTypeIndex()) {
     // Parsing string
     std::string v;
     if (!(is >> v)) {
-      throw dmlc::Error(": Cannot parse into type \"String\" from string: " + str);
+      throw Error(": Cannot parse into type \"String\" from string: " + str);
     }
     return String(v);
   } else if (info.type_index == Target::ContainerType::_GetOrAllocRuntimeTypeIndex()) {
@@ -197,14 +197,14 @@ ObjectRef TargetInternal::ParseType(const std::string& str,
       try {
         ObjectRef parsed = TargetInternal::ParseType(substr, *info.key);
         result.push_back(parsed);
-      } catch (const dmlc::Error& e) {
+      } catch (const Error& e) {
         std::string index = "[" + std::to_string(result.size()) + "]";
-        throw dmlc::Error(index + e.what());
+        throw Error(index + e.what());
       }
     }
     return Array<ObjectRef>(result);
   }
-  throw dmlc::Error(": Unsupported type \"" + info.type_key + "\" for parsing from string: " + str);
+  throw Error(": Unsupported type \"" + info.type_key + "\" for parsing from string: " + str);
 }
 
 ObjectRef TargetInternal::ParseType(const ObjectRef& obj,
@@ -224,15 +224,14 @@ ObjectRef TargetInternal::ParseType(const ObjectRef& obj,
     } else if (const auto* ptr = obj.as<MapNode>()) {
       for (const auto& kv : *ptr) {
         if (!kv.first->IsInstance<StringObj>()) {
-          throw dmlc::Error(": Target object requires key of dict to be str, but get: " +
-                            kv.first->GetTypeKey());
+          throw Error(": Target object requires key of dict to be str, but get: " +
+                      kv.first->GetTypeKey());
         }
       }
       Map<String, ObjectRef> config = GetRef<Map<String, ObjectRef>>(ptr);
       return Target(TargetInternal::FromConfig({config.begin(), config.end()}));
     }
-    throw dmlc::Error(": Expect type 'dict' or 'str' to construct Target, but get: " +
-                      obj->GetTypeKey());
+    throw Error(": Expect type 'dict' or 'str' to construct Target, but get: " + obj->GetTypeKey());
   } else if (info.type_index == ArrayNode::_GetOrAllocRuntimeTypeIndex()) {
     // Parsing array
     const auto* array = ObjTypeCheck<ArrayNode>(obj, "Array");
@@ -240,9 +239,9 @@ ObjectRef TargetInternal::ParseType(const ObjectRef& obj,
     for (const ObjectRef& e : *array) {
       try {
         result.push_back(TargetInternal::ParseType(e, *info.key));
-      } catch (const dmlc::Error& e) {
+      } catch (const Error& e) {
         std::string index = '[' + std::to_string(result.size()) + ']';
-        throw dmlc::Error(index + e.what());
+        throw Error(index + e.what());
       }
     }
     return Array<ObjectRef>(result);
@@ -254,17 +253,17 @@ ObjectRef TargetInternal::ParseType(const ObjectRef& obj,
       ObjectRef key, val;
       try {
         key = TargetInternal::ParseType(kv.first, *info.key);
-      } catch (const dmlc::Error& e) {
+      } catch (const Error& e) {
         std::ostringstream os;
         os << "'s key \"" << key << "\"" << e.what();
-        throw dmlc::Error(os.str());
+        throw Error(os.str());
       }
       try {
         val = TargetInternal::ParseType(kv.second, *info.val);
-      } catch (const dmlc::Error& e) {
+      } catch (const Error& e) {
         std::ostringstream os;
         os << "[\"" << key << "\"]" << e.what();
-        throw dmlc::Error(os.str());
+        throw Error(os.str());
       }
       result[key] = val;
     }
@@ -275,7 +274,7 @@ ObjectRef TargetInternal::ParseType(const ObjectRef& obj,
     os << ": Parsing type \"" << info.type_key
        << "\" is not supported for the given object of type \"" << obj->GetTypeKey()
        << "\". The object is: " << obj;
-    throw dmlc::Error(os.str());
+    throw Error(os.str());
   }
   return obj;
 }
@@ -355,7 +354,7 @@ Target::Target(const String& tag_or_config_or_target_str) {
   ObjectPtr<Object> target;
   try {
     target = TargetInternal::FromString(tag_or_config_or_target_str);
-  } catch (const dmlc::Error& e) {
+  } catch (const Error& e) {
     LOG(FATAL) << "ValueError" << e.what()
                << ". Target creation from string failed: " << tag_or_config_or_target_str;
   }
@@ -366,13 +365,22 @@ Target::Target(const Map<String, ObjectRef>& config) {
   ObjectPtr<Object> target;
   try {
     target = TargetInternal::FromConfig({config.begin(), config.end()});
-  } catch (const dmlc::Error& e) {
+  } catch (const Error& e) {
     LOG(FATAL) << "ValueError" << e.what()
                << ". Target creation from config dict failed: " << config;
   }
   data_ = std::move(target);
 }
 
+Target::Target(Target target, Target host) {
+  ObjectPtr<TargetNode> n = make_object<TargetNode>(*target.get());
+  CHECK(!n->host.defined())
+      << "ValueError: Adding a host to a target whose host field has been defined";
+  // add target host into host field
+  n->host = std::move(host);
+  data_ = std::move(n);
+}
+
 std::vector<std::string> TargetNode::GetKeys() const {
   std::vector<std::string> result;
   for (auto& expr : keys) {
@@ -456,8 +464,18 @@ void TargetInternal::ConstructorDispatcher(TVMArgs args, TVMRetValue* rv) {
                  << runtime::ArgTypeCode2Str(arg.type_code());
     }
     return;
+  } else if (args.num_args == 2) {
+    if (args[0].IsObjectRef<Target>() && args[1].IsObjectRef<Target>()) {
+      Target target = args[0];
+      Target host = args[1];
+      *rv = Target(target, host);
+    } else {
+      LOG(FATAL) << "ValueError: Invalid type of arguments. Expect 2 Target arguments.";
+    }
+    return;
   }
-  LOG(FATAL) << "ValueError: Invalid number of arguments. Expect 1, but gets: " << args.num_args;
+  LOG(FATAL) << "ValueError: Invalid number of arguments. Expect 1 or 2, but gets: "
+             << args.num_args;
 }
 
 ObjectPtr<Object> TargetInternal::FromString(const String& tag_or_config_or_target_str) {
@@ -477,7 +495,7 @@ ObjectPtr<Object> TargetInternal::FromConfigString(const String& config_str) {
                     "if the python module is properly loaded";
   Optional<Map<String, ObjectRef>> config = (*loader)(config_str);
   if (!config.defined()) {
-    throw dmlc::Error(": Cannot load config dict with python JSON loader");
+    throw Error(": Cannot load config dict with python JSON loader");
   }
   return TargetInternal::FromConfig({config.value().begin(), config.value().end()});
 }
@@ -495,7 +513,7 @@ ObjectPtr<Object> TargetInternal::FromRawString(const String& target_str) {
     }
   }
   if (name.empty()) {
-    throw dmlc::Error(": Cannot parse empty target string");
+    throw Error(": Cannot parse empty target string");
   }
   // Create the target config
   std::unordered_map<String, ObjectRef> config = {{"kind", String(name)}};
@@ -506,17 +524,17 @@ ObjectPtr<Object> TargetInternal::FromRawString(const String& target_str) {
       // Parse key-value pair
       std::string s_next = (iter + 1 < options.size()) ? options[iter + 1] : "";
       iter += ParseKVPair(RemovePrefixDashes(options[iter]), s_next, &key, &value);
-    } catch (const dmlc::Error& e) {
-      throw dmlc::Error(": Error when parsing target" + std::string(e.what()));
+    } catch (const Error& e) {
+      throw Error(": Error when parsing target" + std::string(e.what()));
     }
     try {
       // check if `key` has been used
       if (config.count(key)) {
-        throw dmlc::Error(": The key \"" + key + "\" appears more than once");
+        throw Error(": The key \"" + key + "\" appears more than once");
       }
       config[key] = TargetInternal::ParseType(value, TargetInternal::FindTypeInfo(kind, key));
-    } catch (const dmlc::Error& e) {
-      throw dmlc::Error(": Error when parsing target[\"" + key + "\"]" + e.what());
+    } catch (const Error& e) {
+      throw Error(": Error when parsing target[\"" + key + "\"]" + e.what());
     }
   }
   return TargetInternal::FromConfig(config);
@@ -527,6 +545,7 @@ ObjectPtr<Object> TargetInternal::FromConfig(std::unordered_map<String, ObjectRe
   const String kTag = "tag";
   const String kKeys = "keys";
   const String kDeviceName = "device";
+  const String kHost = "host";
   ObjectPtr<TargetNode> target = make_object<TargetNode>();
   // parse 'kind'
   if (config.count(kKind)) {
@@ -534,11 +553,11 @@ ObjectPtr<Object> TargetInternal::FromConfig(std::unordered_map<String, ObjectRe
       target->kind = GetTargetKind(GetRef<String>(kind));
       config.erase(kKind);
     } else {
-      throw dmlc::Error(": Expect type of field \"kind\" is String, but get type: " +
-                        config[kKind]->GetTypeKey());
+      throw Error(": Expect type of field \"kind\" is String, but get type: " +
+                  config[kKind]->GetTypeKey());
     }
   } else {
-    throw dmlc::Error(": Field \"kind\" is not found");
+    throw Error(": Field \"kind\" is not found");
   }
   // parse "tag"
   if (config.count(kTag)) {
@@ -546,8 +565,8 @@ ObjectPtr<Object> TargetInternal::FromConfig(std::unordered_map<String, ObjectRe
       target->tag = GetRef<String>(tag);
       config.erase(kTag);
     } else {
-      throw dmlc::Error(": Expect type of field \"tag\" is String, but get type: " +
-                        config[kTag]->GetTypeKey());
+      throw Error(": Expect type of field \"tag\" is String, but get type: " +
+                  config[kTag]->GetTypeKey());
     }
   } else {
     target->tag = "";
@@ -562,15 +581,15 @@ ObjectPtr<Object> TargetInternal::FromConfig(std::unordered_map<String, ObjectRe
           if (const auto* key = e.as<StringObj>()) {
             keys.push_back(GetRef<String>(key));
           } else {
-            throw dmlc::Error(
+            throw Error(
                 ": Expect 'keys' to be an array of strings, but it "
                 "contains an element of type: " +
                 e->GetTypeKey());
           }
         }
       } else {
-        throw dmlc::Error(": Expect type of field \"keys\" is Array, but get type: " +
-                          config[kKeys]->GetTypeKey());
+        throw Error(": Expect type of field \"keys\" is Array, but get type: " +
+                    config[kKeys]->GetTypeKey());
       }
     }
     // add device name
@@ -595,10 +614,17 @@ ObjectPtr<Object> TargetInternal::FromConfig(std::unordered_map<String, ObjectRe
     try {
       const TargetKindNode::ValueTypeInfo& info = TargetInternal::FindTypeInfo(target->kind, key);
       attrs[key] = TargetInternal::ParseType(value, info);
-    } catch (const dmlc::Error& e) {
-      throw dmlc::Error(": Error when parsing target[\"" + key + "\"]" + e.what());
+    } catch (const Error& e) {
+      throw Error(": Error when parsing target[\"" + key + "\"]" + e.what());
     }
   }
+  // parse host
+  if (config.count(kHost)) {
+    target->host = PackedFunc(ConstructorDispatcher)(config[kHost]).AsObjectRef<Target>();
+    config.erase(kHost);
+  } else {
+    target->host = NullOpt;
+  }
   // set default attribute values if they do not exist
   for (const auto& kv : target->kind->key2default_) {
     if (!attrs.count(kv.first)) {
diff --git a/src/target/target_kind.cc b/src/target/target_kind.cc
index 903c3dcfefb5..863d99993f4a 100644
--- a/src/target/target_kind.cc
+++ b/src/target/target_kind.cc
@@ -23,6 +23,7 @@
  */
 #include <tvm/ir/expr.h>
 #include <tvm/runtime/device_api.h>
+#include <tvm/runtime/registry.h>
 #include <tvm/target/target.h>
 #include <tvm/target/target_kind.h>
 
@@ -44,6 +45,10 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
 
 using TargetKindRegistry = AttrRegistry<TargetKindRegEntry, TargetKind>;
 
+Array<String> TargetKindRegEntry::ListTargetKinds() {
+  return TargetKindRegistry::Global()->ListAllNames();
+}
+
 TargetKindRegEntry& TargetKindRegEntry::RegisterOrGet(const String& target_kind_name) {
   return TargetKindRegistry::Global()->RegisterOrGet(target_kind_name);
 }
@@ -230,6 +235,9 @@ TVM_REGISTER_TARGET_KIND("cuda", kDLGPU)
     .add_attr_option<Bool>("system-lib")
     .add_attr_option<Integer>("max_num_threads", Integer(1024))
     .add_attr_option<Integer>("thread_warp_size", Integer(32))
+    .add_attr_option<Integer>("shared_memory_per_block")
+    .add_attr_option<Integer>("registers_per_block")
+    .add_attr_option<Integer>("max_threads_per_block")
     .set_default_keys({"cuda", "gpu"});
 
 TVM_REGISTER_TARGET_KIND("nvptx", kDLGPU)
@@ -301,7 +309,10 @@ TVM_REGISTER_TARGET_KIND("hybrid", kDLCPU)  // line break
     .add_attr_option<Bool>("system-lib");
 
 TVM_REGISTER_TARGET_KIND("composite", kDLCPU)
-    .add_attr_option<Target>("target_host")
     .add_attr_option<Array<Target>>("devices");
 
+/**********  Registry  **********/
+
+TVM_REGISTER_GLOBAL("target.ListTargetKinds").set_body_typed(TargetKindRegEntry::ListTargetKinds);
+
 }  // namespace tvm
diff --git a/src/te/autodiff/ad_simplify.cc b/src/te/autodiff/ad_simplify.cc
index cc0e82066171..96f278e63be7 100644
--- a/src/te/autodiff/ad_simplify.cc
+++ b/src/te/autodiff/ad_simplify.cc
@@ -413,15 +413,17 @@ class FactorOutAtomicFormulasFunctor
     auto res_b = VisitExpr(op->b);
 
     // For the And case we return the union of the sets of atomic formulas
-    std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> res_set;
-    res_set.reserve(res_a.atomic_formulas.size() + res_b.atomic_formulas.size());
+    std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> res_a_set;
+    res_a_set.reserve(res_a.atomic_formulas.size());
     std::copy(res_a.atomic_formulas.begin(), res_a.atomic_formulas.end(),
-              std::inserter(res_set, res_set.end()));
-    std::copy(res_b.atomic_formulas.begin(), res_b.atomic_formulas.end(),
-              std::inserter(res_set, res_set.end()));
-
-    std::vector<PrimExpr> res{res_set.begin(), res_set.end()};
+              std::inserter(res_a_set, res_a_set.end()));
 
+    std::vector<PrimExpr> res = res_a.atomic_formulas;
+    for (const auto& e : res_b.atomic_formulas) {
+      if (res_a_set.find(e) == res_a_set.end()) {
+        res.emplace_back(e);
+      }
+    }
     // And the residuals are combined with &&
     return {res, res_a.rest && res_b.rest};
   }
@@ -443,10 +445,13 @@ class FactorOutAtomicFormulasFunctor
 
     // For the Or case we intersect the sets of atomic formulas
     std::unordered_set<PrimExpr, StructuralHash, StructuralEqual> res_set;
+    std::vector<PrimExpr> res;
     res_set.reserve(std::min(res_a.atomic_formulas.size(), res_b.atomic_formulas.size()));
-    for (const auto& res_b_formula : res_b_set) {
+    res.reserve(std::min(res_a.atomic_formulas.size(), res_b.atomic_formulas.size()));
+    for (const auto& res_b_formula : res_b.atomic_formulas) {
       if (res_a_set.count(res_b_formula)) {
         res_set.insert(res_b_formula);
+        res.push_back(res_b_formula);
       }
     }
 
@@ -454,13 +459,13 @@ class FactorOutAtomicFormulasFunctor
     // which are left behind, and then combine them with the residuals into the new residual.
     std::vector<PrimExpr> new_cond_a;
     new_cond_a.reserve(res_a.atomic_formulas.size() - res_set.size());
-    for (const auto& formula : res_a_set) {
+    for (const auto& formula : res_a.atomic_formulas) {
       if (!res_set.count(formula)) new_cond_a.emplace_back(formula);
     }
 
     std::vector<PrimExpr> new_cond_b;
     new_cond_b.reserve(res_b.atomic_formulas.size() - res_set.size());
-    for (const auto& formula : res_b_set) {
+    for (const auto& formula : res_b.atomic_formulas) {
       if (!res_set.count(formula)) new_cond_b.emplace_back(formula);
     }
 
@@ -468,7 +473,6 @@ class FactorOutAtomicFormulasFunctor
     res_b.atomic_formulas = std::move(new_cond_b);
 
     PrimExpr new_rest = res_a.to_expr() || res_b.to_expr();
-    std::vector<PrimExpr> res{res_set.begin(), res_set.end()};
 
     return {res, new_rest};
   }
diff --git a/src/te/operation/cross_thread_reduction.cc b/src/te/operation/cross_thread_reduction.cc
index b0fb9b667558..da20dd875ba5 100644
--- a/src/te/operation/cross_thread_reduction.cc
+++ b/src/te/operation/cross_thread_reduction.cc
@@ -145,7 +145,8 @@ Stmt MakeCrossThreadReduction(const ComputeOpNode* self, const Stage& stage,
     Array<PrimExpr> lhs;
     for (size_t i = 0; i < size; ++i) {
       DataType t = reduces[i]->dtype;
-      normal_res_handles.emplace_back("normal_reduce_temp" + std::to_string(i), DataType::Handle());
+      normal_res_handles.emplace_back("normal_reduce_temp" + std::to_string(i),
+                                      PointerType(PrimType(t)));
       lhs.push_back(Load(t, normal_res_handles[i], 0, const_true(t.lanes())));
     }
     Array<PrimExpr> init_value = combiner->identity_element;
@@ -175,7 +176,8 @@ Stmt MakeCrossThreadReduction(const ComputeOpNode* self, const Stage& stage,
   freduce_args.push_back(const_true(1));
   std::vector<Var> res_handles(size);
   for (size_t idx = 0; idx < size; ++idx) {
-    res_handles[idx] = Var("reduce_temp" + std::to_string(idx), DataType::Handle());
+    DataType dtype = reduces[idx]->dtype;
+    res_handles[idx] = Var("reduce_temp" + std::to_string(idx), PointerType(PrimType(dtype)));
     freduce_args.push_back(res_handles[idx]);
   }
 
diff --git a/src/te/operation/hybrid_op.cc b/src/te/operation/hybrid_op.cc
index 94e06d206ddb..65b8660ca1fb 100644
--- a/src/te/operation/hybrid_op.cc
+++ b/src/te/operation/hybrid_op.cc
@@ -234,9 +234,9 @@ Stmt ApplyLoopShapes(const Stage& stage, const std::unordered_map<IterVar, Range
         PrimExpr cond = likely(outer * factor < (op->extent - inner));
         ret = IfThenElse(cond, ret);
         ret = For(inner->var, PrimExpr(0), inner->dom->extent,
-                  IterVarTypeToForType(inner->iter_type), op->device_api, ret);
+                  IterVarTypeToForKind(inner->iter_type), ret);
         ret = For(outer->var, PrimExpr(0), outer->dom->extent,
-                  IterVarTypeToForType(outer->iter_type), op->device_api, ret);
+                  IterVarTypeToForKind(outer->iter_type), ret);
         splitted = true;
         return ret;
       }
@@ -277,8 +277,8 @@ Stmt ApplyLoopShapes(const Stage& stage, const std::unordered_map<IterVar, Range
         rmap[op->loop_var.get()] = indexdiv(parent, extent);
         body = tir::Substitute(body, rmap);
         under_outer = false;
-        return For(parent->var, PrimExpr(0), extent * op->extent, op->for_type, op->device_api,
-                   body);
+        return For(parent->var, PrimExpr(0), extent * op->extent, op->kind, body,
+                   op->thread_binding, op->annotations);
       } else if (under_outer) {
         Stmt body = this->VisitStmt(op->body);
         std::unordered_map<const VarNode*, PrimExpr> rmap;
@@ -331,8 +331,8 @@ Stmt ApplyLoopAnnotations(const Stage& stage, const std::unordered_map<IterVar,
           Stmt body = tir::Substitute(op->body, rmap);
           return AttrStmt(iter_var, "thread_extent", op->extent, body);
         } else {
-          return For(op->loop_var, op->min, op->extent, IterVarTypeToForType(attr->iter_type),
-                     op->device_api, op->body);
+          return For(op->loop_var, op->min, op->extent, IterVarTypeToForKind(attr->iter_type),
+                     op->body, op->thread_binding, op->annotations);
         }
       }
       return StmtMutator::VisitStmt_(op);
@@ -345,18 +345,18 @@ Stmt ApplyLoopAnnotations(const Stage& stage, const std::unordered_map<IterVar,
 
     const IterVar& actual = rebased.count(iter_var) ? rebased.find(iter_var)->second : iter_var;
     const VarNode* var = actual->var.get();
-    ForType expected = IterVarTypeToForType(iter_var->iter_type);
+    ForKind expected = IterVarTypeToForKind(iter_var->iter_type);
     IterVarAttr attr;
     if (stage->iter_var_attrs.count(iter_var)) {
       attr = stage->iter_var_attrs[iter_var];
-      expected = IterVarTypeToForType(attr->iter_type);
+      expected = IterVarTypeToForKind(attr->iter_type);
     }
 
     PostOrderVisit(stmt, [&found, &var, &attr, &expected, &need_change](const ObjectRef& node) {
       if (const ForNode* op = node.as<ForNode>()) {
         if (op->loop_var.get() == var) {
           ++found;
-          need_change = expected != op->for_type || (attr.defined() && attr->bind_thread.defined());
+          need_change = expected != op->kind || (attr.defined() && attr->bind_thread.defined());
         }
       }
     });
@@ -409,12 +409,13 @@ Stmt ApplyLoopOrder(const Stage& stage, const std::unordered_map<IterVar, Range>
       if (body_.same_as(op->body) && op->loop_var.get() == target->var.get())
         return GetRef<Stmt>(op);
       const Stmt& body = op->body.same_as(body_) ? op->body : body_;
-      ForType for_type = IterVarTypeToForType(target->iter_type);
+      ForKind kind = IterVarTypeToForKind(target->iter_type);
       if (stage->iter_var_attrs.count(target)) {
-        for_type = IterVarTypeToForType(stage->iter_var_attrs[target]->iter_type);
+        kind = IterVarTypeToForKind(stage->iter_var_attrs[target]->iter_type);
       }
       const Range& range = target->dom.defined() ? target->dom : dom_map.find(target)->second;
-      return For(target->var, range->min, range->extent, for_type, DeviceAPI::None, body);
+      return For(target->var, range->min, range->extent, kind, body, op->thread_binding,
+                 op->annotations);
     }
   };
 
@@ -448,7 +449,7 @@ std::vector<IterVar> GatherLoopVars(Stmt stmt) {
     if (const ForNode* op = node.as<ForNode>()) {
       Var loop_var(op->loop_var);
       Range dom = Range::FromMinExtent(op->min, op->extent);
-      res_.push_back(IterVar(dom, loop_var, ForTypeToIterVarType(op->for_type)));
+      res_.push_back(IterVar(dom, loop_var, ForKindToIterVarType(op->kind)));
     }
   });
   std::reverse(res_.begin(), res_.end());
diff --git a/src/te/operation/op_utils.cc b/src/te/operation/op_utils.cc
index f1991c181e67..b3897e142545 100644
--- a/src/te/operation/op_utils.cc
+++ b/src/te/operation/op_utils.cc
@@ -77,7 +77,7 @@ std::vector<std::vector<Stmt> > MakeLoopNest(const Stage& stage,
         var = Var(iv->var->name_hint + ".init", bind_iv->var.dtype());
       }
 
-      ForType for_type = ForType::Serial;
+      ForKind kind = ForKind::kSerial;
       IterVarAttr it_attr;
       if (stage->iter_var_attrs.count(iv)) {
         it_attr = stage->iter_var_attrs[iv];
@@ -85,13 +85,13 @@ std::vector<std::vector<Stmt> > MakeLoopNest(const Stage& stage,
       if (it_attr.defined()) {
         switch (it_attr->iter_type) {
           case kUnrolled:
-            for_type = ForType::Unrolled;
+            kind = ForKind::kUnrolled;
             break;
           case kVectorized:
-            for_type = ForType::Vectorized;
+            kind = ForKind::kVectorized;
             break;
           case kParallelized:
-            for_type = ForType::Parallel;
+            kind = ForKind::kParallel;
             break;
           case kDataPar:
             break;
@@ -115,11 +115,11 @@ std::vector<std::vector<Stmt> > MakeLoopNest(const Stage& stage,
         nest[i + 1].emplace_back(LetStmt(var, cast(var.dtype(), dom->min), no_op));
         value_map[iv] = cast(var.dtype(), dom->min);
       } else if (is_zero(dom->min)) {
-        nest[i + 1].emplace_back(For(var, 0, dom->extent, for_type, DeviceAPI::None, no_op));
+        nest[i + 1].emplace_back(For(var, 0, dom->extent, kind, no_op));
         value_map[iv] = var;
       } else {
         Var idx(bind_iv->var->name_hint + ".idx", bind_iv->var.dtype());
-        nest[i + 1].emplace_back(For(idx, 0, dom->extent, for_type, DeviceAPI::None, no_op));
+        nest[i + 1].emplace_back(For(idx, 0, dom->extent, kind, no_op));
         PrimExpr new_value = dom->min + idx;
         value_map[iv] = new_value;
         nest[i + 1].emplace_back(LetStmt(var, new_value, no_op));
@@ -243,33 +243,41 @@ Stmt Substitute(Stmt s, const std::unordered_map<IterVar, PrimExpr>& value_map)
   return tir::Substitute(s, init);
 }
 
-IterVarType ForTypeToIterVarType(tir::ForType for_type) {
-  switch (for_type) {
-    case ForType::Serial:
+PrimExpr Substitute(PrimExpr s, const std::unordered_map<IterVar, PrimExpr>& value_map) {
+  std::unordered_map<const VarNode*, PrimExpr> init;
+  for (const auto& kv : value_map) {
+    init[kv.first->var.get()] = kv.second;
+  }
+  return tir::Substitute(s, init);
+}
+
+IterVarType ForKindToIterVarType(tir::ForKind kind) {
+  switch (kind) {
+    case ForKind::kSerial:
       return kDataPar;
-    case ForType::Parallel:
+    case ForKind::kParallel:
       return kParallelized;
-    case ForType::Vectorized:
+    case ForKind::kVectorized:
       return kVectorized;
-    case ForType::Unrolled:
+    case ForKind::kUnrolled:
       return kUnrolled;
     default:
       return kDataPar;
   }
 }
 
-tir::ForType IterVarTypeToForType(IterVarType iter_type) {
+tir::ForKind IterVarTypeToForKind(IterVarType iter_type) {
   switch (iter_type) {
     case kDataPar:
-      return ForType::Serial;
+      return ForKind::kSerial;
     case kParallelized:
-      return ForType::Parallel;
+      return ForKind::kParallel;
     case kVectorized:
-      return ForType::Vectorized;
+      return ForKind::kVectorized;
     case kUnrolled:
-      return ForType::Unrolled;
+      return ForKind::kUnrolled;
     default:
-      return ForType::Serial;
+      return ForKind::kSerial;
   }
 }
 
diff --git a/src/te/operation/op_utils.h b/src/te/operation/op_utils.h
index 16f7d96cfa77..02f4a860a01d 100644
--- a/src/te/operation/op_utils.h
+++ b/src/te/operation/op_utils.h
@@ -73,7 +73,7 @@ std::vector<Stmt> MakeIfNest(const std::vector<PrimExpr>& predicates);
  */
 Stmt ReplaceTensor(Stmt stmt, const std::unordered_map<Tensor, Tensor>& replace);
 /*!
- * \brief Replace the tensor reference (especially in Call's) in stmt by the replace map.
+ * \brief Replace the tensor reference (especially in Call's) in primExpr by the replace map.
  * \param expr The expression to be processed.
  * \param replace The replacement rule.
  */
@@ -88,16 +88,24 @@ PrimExpr ReplaceTensor(PrimExpr expr, const std::unordered_map<Tensor, Tensor>&
 Stmt Substitute(Stmt stmt, const std::unordered_map<IterVar, PrimExpr>& value_map);
 
 /*!
- * \brief Converts Halide ForType to its corresponding IterVarType
- * \param for_type The ForType to be converted
+ * \brief Substitute the variables of primExpr by value map.
+ * \param expr the expression to be processed.
+ * \param value_map The value map.
+ * \return Substituted result.
+ */
+PrimExpr Substitute(PrimExpr expr, const std::unordered_map<IterVar, PrimExpr>& value_map);
+
+/*!
+ * \brief Converts Halide ForKind to its corresponding IterVarType
+ * \param kind The ForKind to be converted
  */
-IterVarType ForTypeToIterVarType(tir::ForType for_type);
+IterVarType ForKindToIterVarType(tir::ForKind kind);
 
 /*!
- * \brief Converts IterVarType to its corresponding Halide ForType
+ * \brief Converts IterVarType to its corresponding Halide ForKind
  * \param iter_type The IterVarType to be converted
  */
-tir::ForType IterVarTypeToForType(IterVarType iter_type);
+tir::ForKind IterVarTypeToForKind(IterVarType iter_type);
 
 }  // namespace te
 }  // namespace tvm
diff --git a/src/te/operation/tensorize.cc b/src/te/operation/tensorize.cc
index bfd1ec579818..ea713220eddd 100644
--- a/src/te/operation/tensorize.cc
+++ b/src/te/operation/tensorize.cc
@@ -311,6 +311,7 @@ Array<PrimExpr> MatchTensorizeBody(const ComputeOpNode* self, const Stage& stage
 }
 
 void VerifyTensorizeBody(const ComputeOpNode* self, const Stage& stage,
+                         const std::unordered_map<IterVar, PrimExpr>& value_map,
                          const std::unordered_map<IterVar, Range>& dom_map,
                          const std::unordered_map<IterVar, Range>& out_dom,
                          const std::unordered_map<Tensor, Array<Range> >& in_region,
@@ -327,7 +328,8 @@ void VerifyTensorizeBody(const ComputeOpNode* self, const Stage& stage,
 
   for (size_t i = 0; i < body.size(); ++i) {
     PrimExpr lhs = ana.Simplify(body[i]);
-    PrimExpr rhs = ana.Simplify(intrin_compute->body[i]);
+    // run substitution because the intrin body could depend on outer loop vars.
+    PrimExpr rhs = ana.Simplify(Substitute(intrin_compute->body[i], value_map));
     if (lhs.dtype() != rhs.dtype()) {
       LOG(FATAL) << "Failed to match the data type with TensorIntrin " << intrin->name
                  << "'s declaration "
@@ -349,7 +351,7 @@ Stmt MakeTensorize(const ComputeOpNode* self, const Stage& stage,
   ICHECK(intrin.defined());
   ComputeLoopNest n = ComputeLoopNest::Create(self, stage, dom_map, debug_keep_trivial_loop);
   VerifyTensorizeLoopNest(self, stage, n, tloc);
-  VerifyTensorizeBody(self, stage, dom_map, out_dom, in_region, intrin);
+  VerifyTensorizeBody(self, stage, n.main_vmap, dom_map, out_dom, in_region, intrin);
   // Start bind data.
   Stmt nop = Evaluate(0);
   std::vector<Stmt> input_bind_nest, output_bind_nest;
diff --git a/src/te/schedule/auto_inline_elem_wise.cc b/src/te/schedule/auto_inline_elem_wise.cc
index e2b7215158b2..bf584df25825 100644
--- a/src/te/schedule/auto_inline_elem_wise.cc
+++ b/src/te/schedule/auto_inline_elem_wise.cc
@@ -39,15 +39,15 @@ class ElemWiseDetector : public tir::ExprVisitor {
     ExprVisitor::VisitExpr(e);
   }
 
-  void VisitExpr_(const CallNode* op) final {
-    Array<PrimExpr> axis = op->args;
-    if (axis_.size() != axis.size()) {
+  void VisitExpr_(const ProducerLoadNode* op) final {
+    Array<PrimExpr> indices = op->indices;
+    if (axis_.size() != indices.size()) {
       is_elem_wise_ = false;
       return;
     }
 
     for (size_t i = 0; i < axis_.size(); ++i) {
-      if (!axis[i].same_as(axis_[i]->var)) {
+      if (!indices[i].same_as(axis_[i]->var)) {
         is_elem_wise_ = false;
         return;
       }
@@ -83,7 +83,11 @@ bool IsBroadcast(const Operation& op) {
     if (compute->reduce_axis.size()) {
       return false;
     }
-    // TODO(nicolasvasilache): Implement Me
+    constexpr auto kBroadcast = "broadcast";
+    // broadcast op in topi has tag `broadcast`
+    if (op->tag == kBroadcast) {
+      return true;
+    }
   }
   return false;
 }
@@ -113,6 +117,8 @@ void AutoInlineInjective(Schedule sch) {
 
 TVM_REGISTER_GLOBAL("schedule.AutoInlineElemWise").set_body_typed(AutoInlineElemWise);
 
+TVM_REGISTER_GLOBAL("schedule.AutoInlineBroadcast").set_body_typed(AutoInlineBroadcast);
+
 TVM_REGISTER_GLOBAL("schedule.AutoInlineInjective").set_body_typed(AutoInlineInjective);
 
 }  // namespace te
diff --git a/src/te/schedule/schedule_postproc_rewrite_for_tensor_core.cc b/src/te/schedule/schedule_postproc_rewrite_for_tensor_core.cc
index f81d72e0fe02..74d1a19d2cfe 100644
--- a/src/te/schedule/schedule_postproc_rewrite_for_tensor_core.cc
+++ b/src/te/schedule/schedule_postproc_rewrite_for_tensor_core.cc
@@ -968,7 +968,8 @@ class TensorCoreIRMutator : public StmtExprMutator {
           scaled_extent_value = ori_extent_value / scale_factor;
         }
         PrimExpr scaled_extent = make_const(op->extent.dtype(), scaled_extent_value);
-        stmt = For(op->loop_var, op->min, scaled_extent, op->for_type, op->device_api, op->body);
+        stmt = For(op->loop_var, op->min, scaled_extent, op->kind, op->body, op->thread_binding,
+                   op->annotations);
       }
     }
     return stmt;
diff --git a/src/te/tensor.cc b/src/te/tensor.cc
index 18d4947cdddc..b48f39a38627 100644
--- a/src/te/tensor.cc
+++ b/src/te/tensor.cc
@@ -46,7 +46,7 @@ PrimExpr Tensor::operator()(Array<Var> indices) const {
 
 PrimExpr Tensor::operator()(Array<PrimExpr> indices) const {
   if (ndim() != 0) {
-    ICHECK_EQ(ndim(), indices.size()) << "Tensor dimension mismatch in read"
+    ICHECK_EQ(ndim(), indices.size()) << "Tensor dimension mismatch in read "
                                       << "ndim = " << ndim() << ", indices.size=" << indices.size();
   }
 
diff --git a/src/tir/ir/buffer.cc b/src/tir/ir/buffer.cc
index 23a2b3a3b3c7..1667eb7d1fbd 100644
--- a/src/tir/ir/buffer.cc
+++ b/src/tir/ir/buffer.cc
@@ -46,8 +46,9 @@ Array<PrimExpr> SimplifyArray(arith::Analyzer* ana, Array<PrimExpr> array) {
 }
 
 Buffer decl_buffer(Array<PrimExpr> shape, DataType dtype, String name, Span span) {
-  return Buffer(Var(name, PointerType(PrimType(dtype)), span), dtype, shape, Array<PrimExpr>(),
-                PrimExpr(), name, "", 0, 0, kDefault, span);
+  DataType storage_dtype = (dtype == DataType::Bool() ? DataType::Int(8) : dtype);
+  return Buffer(Var(name, PointerType(PrimType(storage_dtype)), span), dtype, shape,
+                Array<PrimExpr>(), PrimExpr(), name, "", 0, 0, kDefault, span);
 }
 
 // Split the given expression w.r.t the add operator
@@ -384,9 +385,14 @@ PrimExpr Buffer::access_ptr(int access_mask, DataType ptr_type, int content_lane
 Buffer::Buffer(Var data, DataType dtype, Array<PrimExpr> shape, Array<PrimExpr> strides,
                PrimExpr elem_offset, String name, String scope, int data_alignment,
                int offset_factor, BufferType buffer_type, Span span) {
-  ICHECK(IsPointerType(data->type_annotation, dtype))
+  DataType storage_dtype = dtype;
+  // specially handle bool
+  if (storage_dtype == DataType::Bool()) {
+    storage_dtype = DataType::Int(8);
+  }
+  ICHECK(IsPointerType(data->type_annotation, storage_dtype))
       << "Buffer data field expect to have the right pointer type annotation"
-      << " annotation=" << data->type_annotation << ", dtype=" << dtype;
+      << " annotation=" << data->type_annotation << ", storage_dtype=" << storage_dtype;
 
   auto n = make_object<BufferNode>();
   n->data = std::move(data);
diff --git a/src/tir/ir/functor_common.h b/src/tir/ir/functor_common.h
index f63dcfe003c6..9ed911f6b782 100644
--- a/src/tir/ir/functor_common.h
+++ b/src/tir/ir/functor_common.h
@@ -34,19 +34,10 @@ inline void VisitArray(const Array<T>& arr, F fvisit) {
   }
 }
 
-// Implementation of mutators
 template <typename T, typename F>
-inline Array<T> MutateArray(const Array<T>& arr, F fmutate, bool allow_copy_on_write = false) {
-  if (allow_copy_on_write) {
-    // if we allow copy on write, we can directly
-    // call the inplace mutate function.
-    const_cast<Array<T>&>(arr).MutateByApply(fmutate);
-    return arr;
-  } else {
-    Array<T> copy = arr;
-    copy.MutateByApply(fmutate);
-    return copy;
-  }
+inline Array<T> MutateArray(Array<T> arr, F fmutate) {
+  arr.MutateByApply(fmutate);
+  return arr;
 }
 
 }  // namespace tir
diff --git a/src/tir/ir/stmt.cc b/src/tir/ir/stmt.cc
index 86960d9bd999..2aeaae3eb592 100644
--- a/src/tir/ir/stmt.cc
+++ b/src/tir/ir/stmt.cc
@@ -128,8 +128,8 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
     });
 
 // For
-For::For(Var loop_var, PrimExpr min, PrimExpr extent, ForType for_type, DeviceAPI device_api,
-         Stmt body, Span span) {
+For::For(Var loop_var, PrimExpr min, PrimExpr extent, ForKind kind, Stmt body,
+         Optional<IterVar> thread_binding, Map<String, ObjectRef> annotations, Span span) {
   ICHECK(min.defined());
   ICHECK(extent.defined());
   ICHECK(min.dtype().is_scalar());
@@ -141,36 +141,40 @@ For::For(Var loop_var, PrimExpr min, PrimExpr extent, ForType for_type, DeviceAP
   node->loop_var = std::move(loop_var);
   node->min = std::move(min);
   node->extent = std::move(extent);
-  node->for_type = for_type;
-  node->device_api = device_api;
+  node->kind = kind;
   node->body = std::move(body);
+  node->thread_binding = std::move(thread_binding);
+  node->annotations = std::move(annotations);
   node->span = std::move(span);
   data_ = std::move(node);
 }
 
-TVM_REGISTER_GLOBAL("tir.For").set_body_typed([](Var loop_var, PrimExpr min, PrimExpr extent,
-                                                 int for_type, int device_api, Stmt body,
-                                                 Span span) {
-  return For(loop_var, min, extent, static_cast<ForType>(for_type),
-             static_cast<DeviceAPI>(device_api), body, span);
-});
+TVM_REGISTER_GLOBAL("tir.For").set_body_typed(
+    [](Var loop_var, PrimExpr min, PrimExpr extent, int kind, Stmt body,
+       Optional<IterVar> thread_binding, Optional<Map<String, ObjectRef>> annotations, Span span) {
+      return For(loop_var, min, extent, static_cast<ForKind>(kind), body, thread_binding,
+                 annotations.value_or(Map<String, ObjectRef>()), span);
+    });
 
 TVM_REGISTER_NODE_TYPE(ForNode);
 
-std::ostream& operator<<(std::ostream& out, ForType type) {  // NOLINT(*)
+std::ostream& operator<<(std::ostream& out, ForKind type) {  // NOLINT(*)
   switch (type) {
-    case ForType::Serial:
+    case ForKind::kSerial:
       out << "for";
       break;
-    case ForType::Parallel:
+    case ForKind::kParallel:
       out << "parallel";
       break;
-    case ForType::Unrolled:
+    case ForKind::kUnrolled:
       out << "unrolled";
       break;
-    case ForType::Vectorized:
+    case ForKind::kVectorized:
       out << "vectorized";
       break;
+    case ForKind::kThreadBinding:
+      out << "launch_thread";
+      break;
   }
   return out;
 }
@@ -179,7 +183,7 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
     .set_dispatch<ForNode>([](const ObjectRef& node, ReprPrinter* p) {
       auto* op = static_cast<const ForNode*>(node.get());
       p->PrintIndent();
-      p->stream << op->for_type << " (" << op->loop_var << ", ";
+      p->stream << op->kind << " (" << op->loop_var << ", ";
       p->Print(op->min);
       p->stream << ", ";
       p->Print(op->extent);
@@ -193,6 +197,38 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
       p->stream << "}\n";
     });
 
+// While
+While::While(PrimExpr condition, Stmt body, Span span) {
+  ICHECK(condition.defined());
+  ICHECK(condition.dtype().is_scalar());
+  ICHECK(condition.as<tir::IntImmNode>() == nullptr) << "The condition should not be trivial.";
+  ICHECK(body.defined());
+
+  ObjectPtr<WhileNode> node = make_object<WhileNode>();
+  node->condition = std::move(condition);
+  node->body = std::move(body);
+  node->span = std::move(span);
+  data_ = std::move(node);
+}
+
+TVM_REGISTER_GLOBAL("tir.While").set_body_typed([](PrimExpr condition, Stmt body, Span span) {
+  return While(condition, body, span);
+});
+
+TVM_REGISTER_NODE_TYPE(WhileNode);
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<WhileNode>([](const ObjectRef& node, ReprPrinter* p) {
+      auto* op = static_cast<const WhileNode*>(node.get());
+      p->PrintIndent();
+      p->stream << "while(" << op->condition << "){\n";
+      p->indent += 2;
+      p->Print(op->body);
+      p->indent -= 2;
+      p->PrintIndent();
+      p->stream << "}\n";
+    });
+
 // Store
 Store::Store(Var buffer_var, PrimExpr value, PrimExpr index, PrimExpr predicate, Span span) {
   ICHECK(value.defined());
@@ -274,9 +310,12 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
 // Allocate
 Allocate::Allocate(Var buffer_var, DataType dtype, Array<PrimExpr> extents, PrimExpr condition,
                    Stmt body, Span span) {
-  // TODO(tvm-team): Add invariant check to make sure
-  // IsPointerPType(buffer_var->type_annotation, dtype)
-  // once we fix the allocate tvm script printing.
+  CHECK(IsPointerType(buffer_var->type_annotation, dtype))
+      << "The allocated data type (" << dtype
+      << ") does not match the type annotation of the buffer " << buffer_var << " ("
+      << buffer_var->type_annotation
+      << "). The data type should be an element of the pointer type.";
+
   for (size_t i = 0; i < extents.size(); ++i) {
     ICHECK(extents[i].defined());
     ICHECK(extents[i].dtype().is_scalar());
@@ -591,6 +630,225 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
       p->stream << "}\n";
     });
 
+// BufferRegion
+BufferRegion::BufferRegion(Buffer buffer, Array<Range> region) {
+  ObjectPtr<BufferRegionNode> node = make_object<BufferRegionNode>();
+  node->buffer = std::move(buffer);
+  node->region = std::move(region);
+  data_ = std::move(node);
+}
+
+BufferRegion BufferRegion::FullRegion(Buffer buffer) {
+  Array<Range> region;
+  for (PrimExpr extent : buffer->shape) {
+    region.push_back(Range::FromMinExtent(0, extent));
+  }
+  return BufferRegion(buffer, region);
+}
+
+TVM_REGISTER_GLOBAL("tir.BufferRegion").set_body_typed([](Buffer buffer, Array<Range> region) {
+  return BufferRegion(buffer, region);
+});
+
+TVM_REGISTER_NODE_TYPE(BufferRegionNode);
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<BufferRegionNode>([](const ObjectRef& node, ReprPrinter* p) {
+      auto* op = static_cast<const BufferRegionNode*>(node.get());
+      p->stream << op->buffer->name;
+      p->stream << "[";
+      for (size_t i = 0; i < op->region.size(); ++i) {
+        const auto& range = op->region[i];
+        p->Print(range->min);
+        if (!is_one(range->extent)) {
+          p->stream << ":";
+          p->Print(range->min + range->extent);
+        }
+        if (i != op->region.size() - 1) p->stream << ", ";
+      }
+      p->stream << "]";
+    });
+
+// MatchBufferRegion
+MatchBufferRegion::MatchBufferRegion(Buffer buffer, BufferRegion source) {
+  ObjectPtr<MatchBufferRegionNode> node = make_object<MatchBufferRegionNode>();
+  node->buffer = std::move(buffer);
+  node->source = std::move(source);
+  data_ = std::move(node);
+}
+
+TVM_REGISTER_GLOBAL("tir.MatchBufferRegion").set_body_typed([](Buffer buffer, BufferRegion source) {
+  return MatchBufferRegion(buffer, source);
+});
+
+TVM_REGISTER_NODE_TYPE(MatchBufferRegionNode);
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<MatchBufferRegionNode>([](const ObjectRef& node, ReprPrinter* p) {
+      auto* op = static_cast<const MatchBufferRegionNode*>(node.get());
+      p->PrintIndent();
+      p->stream << op->buffer->name << " = match_buffer_region(";
+      p->Print(op->source);
+      p->stream << ")\n";
+    });
+
+// Block
+Block::Block(Array<IterVar> iter_vars, Array<BufferRegion> reads, Array<BufferRegion> writes,
+             String name_hint, Stmt body, Optional<Stmt> init, Array<Buffer> alloc_buffers,
+             Array<MatchBufferRegion> match_buffers, Map<String, ObjectRef> annotations,
+             Span span) {
+  ObjectPtr<BlockNode> node = make_object<BlockNode>();
+  node->iter_vars = std::move(iter_vars);
+  node->reads = std::move(reads);
+  node->writes = std::move(writes);
+  node->name_hint = std::move(name_hint);
+  node->body = std::move(body);
+  node->init = std::move(init);
+  node->alloc_buffers = std::move(alloc_buffers);
+  node->match_buffers = std::move(match_buffers);
+  node->annotations = std::move(annotations);
+  node->span = std::move(span);
+  data_ = std::move(node);
+}
+
+TVM_REGISTER_GLOBAL("tir.Block")
+    .set_body_typed([](Array<IterVar> iter_vars, Array<BufferRegion> reads,
+                       Array<BufferRegion> writes, String name_hint, Stmt body, Optional<Stmt> init,
+                       Array<Buffer> alloc_buffers, Array<MatchBufferRegion> match_buffers,
+                       Map<String, ObjectRef> annotations, Span span) {
+      return Block(iter_vars, reads, writes, name_hint, body, init, alloc_buffers, match_buffers,
+                   annotations, span);
+    });
+
+TVM_REGISTER_NODE_TYPE(BlockNode);
+
+void PrintBlockTitle(const BlockNode* op, ReprPrinter* p) {
+  p->stream << "block " << op->name_hint << "(";
+  for (size_t i = 0; i < op->iter_vars.size(); i++) {
+    p->Print(op->iter_vars[i]);
+    if (i < op->iter_vars.size() - 1) p->stream << ", ";
+  }
+  p->stream << ")";
+}
+
+void PrintBlockSignature(const BlockNode* op, ReprPrinter* p) {
+  // print read/write regions
+  p->PrintIndent();
+  p->stream << "reads(";
+  p->Print(op->reads);
+  p->stream << ")\n";
+  p->PrintIndent();
+  p->stream << "writes(";
+  p->Print(op->writes);
+  p->stream << ")\n";
+  // Print alloc_buffers
+  for (const auto& alloc_buf : op->alloc_buffers) {
+    p->PrintIndent();
+    p->stream << alloc_buf->name << " = alloc_buffer(" << alloc_buf->dtype << "[";
+    for (size_t i = 0; i < alloc_buf->shape.size(); ++i) {
+      if (i > 0) p->stream << ", ";
+      p->Print(alloc_buf->shape[i]);
+    }
+    p->stream << "])\n";
+  }
+  // Print match_buffer_regions
+  for (const auto& match_buf : op->match_buffers) {
+    p->Print(match_buf);
+  }
+  if (!op->annotations.empty()) {
+    p->PrintIndent();
+    p->stream << "annotations(" << op->annotations << ")\n";
+  }
+}
+
+void PrintBlockBody(const BlockNode* op, ReprPrinter* p) {
+  // Print init
+  if (op->init.defined()) {
+    p->PrintIndent();
+    p->stream << "with init() {\n";
+    p->indent += 2;
+    p->Print(op->init.value());
+    p->indent -= 2;
+    p->PrintIndent();
+    p->stream << "}\n";
+  }
+  // Print body
+  p->Print(op->body);
+}
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<BlockNode>([](const ObjectRef& node, ReprPrinter* p) {
+      auto* op = static_cast<const BlockNode*>(node.get());
+      p->PrintIndent();
+      PrintBlockTitle(op, p);
+      p->stream << "{\n";
+      p->indent += 2;
+
+      // Print block elements (e.g. reads/writes, etc)
+      PrintBlockSignature(op, p);
+      // Print block init and body
+      PrintBlockBody(op, p);
+
+      p->indent -= 2;
+      p->PrintIndent();
+      p->stream << "}\n";
+    });
+
+// BlockRealize
+BlockRealize::BlockRealize(Array<PrimExpr> values, PrimExpr predicate, Block block, Span span) {
+  CHECK_EQ(block->iter_vars.size(), values.size())
+      << "ValueError: BlockRealize needs to have the same number of iter_vars and binding values";
+  CHECK(predicate.dtype().is_bool()) << "TypeError: Expect Block.predicate to be a bool expression";
+  ObjectPtr<BlockRealizeNode> node = make_object<BlockRealizeNode>();
+  node->iter_values = std::move(values);
+  node->predicate = std::move(predicate);
+  node->block = std::move(block);
+  node->span = std::move(span);
+  data_ = std::move(node);
+}
+
+TVM_REGISTER_GLOBAL("tir.BlockRealize")
+    .set_body_typed([](Array<PrimExpr> iter_values, PrimExpr predicate, Block block, Span span) {
+      return BlockRealize(iter_values, predicate, block, span);
+    });
+
+TVM_REGISTER_NODE_TYPE(BlockRealizeNode);
+
+TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
+    .set_dispatch<BlockRealizeNode>([](const ObjectRef& node, ReprPrinter* p) {
+      auto* op = static_cast<const BlockRealizeNode*>(node.get());
+      auto* block_op = op->block.get();
+      p->PrintIndent();
+      PrintBlockTitle(block_op, p);
+      p->stream << "{\n";
+      p->indent += 2;
+
+      // Print binding iter_values
+      for (size_t i = 0; i < block_op->iter_vars.size(); ++i) {
+        p->PrintIndent();
+        p->stream << "bind(";
+        p->Print(block_op->iter_vars[i]->var);
+        p->stream << ", ";
+        p->Print(op->iter_values[i]);
+        p->stream << ")\n";
+      }
+      // Print predicate
+      if (!is_one(op->predicate)) {
+        p->PrintIndent();
+        p->stream << "where(";
+        p->Print(op->predicate);
+        p->stream << ")\n";
+      }
+      // Print block elements (e.g. reads/writes, etc)
+      PrintBlockSignature(block_op, p);
+      // Print block init and body
+      PrintBlockBody(block_op, p);
+
+      p->indent -= 2;
+      p->PrintIndent();
+      p->stream << "}\n";
+    });
+
 PrimExpr TypeAnnotation(DataType dtype, Span span) {
   static auto op = Op::Get("tir.type_annotation");
   return tir::Call(dtype, op, {}, span);
diff --git a/src/tir/ir/stmt_functor.cc b/src/tir/ir/stmt_functor.cc
index 529380bf9d59..07574e4fb2f1 100644
--- a/src/tir/ir/stmt_functor.cc
+++ b/src/tir/ir/stmt_functor.cc
@@ -19,12 +19,14 @@
 /*!
  * \file stmt_functor.cc
  */
+#include <tvm/ir/module.h>
 #include <tvm/runtime/registry.h>
+#include <tvm/tir/function.h>
 #include <tvm/tir/stmt_functor.h>
 
 #include <functional>
 
-#include "functor_common.h"
+#include "./functor_common.h"
 
 namespace tvm {
 namespace tir {
@@ -45,6 +47,11 @@ void StmtVisitor::VisitStmt_(const ForNode* op) {
   this->VisitStmt(op->body);
 }
 
+void StmtVisitor::VisitStmt_(const WhileNode* op) {
+  this->VisitExpr(op->condition);
+  this->VisitStmt(op->body);
+}
+
 void StmtVisitor::VisitStmt_(const AllocateNode* op) {
   VisitArray(op->extents, [this](const PrimExpr& e) { this->VisitExpr(e); });
   this->VisitStmt(op->body);
@@ -112,16 +119,95 @@ void StmtVisitor::VisitStmt_(const SeqStmtNode* op) {
 
 void StmtVisitor::VisitStmt_(const EvaluateNode* op) { this->VisitExpr(op->value); }
 
+void StmtVisitor::VisitStmt_(const BlockNode* op) {
+  auto fvisit_buffer_region = [this](const BufferRegion& s) {
+    for (const auto& range : s->region) {
+      this->VisitExpr(range->min);
+      this->VisitExpr(range->extent);
+    }
+  };
+  VisitArray(op->iter_vars, [this](const IterVar& iter_var) {
+    this->VisitExpr(iter_var->dom->min);
+    this->VisitExpr(iter_var->dom->extent);
+  });
+  VisitArray(op->reads, fvisit_buffer_region);
+  VisitArray(op->writes, fvisit_buffer_region);
+  VisitArray(op->match_buffers,
+             [fvisit_buffer_region](const MatchBufferRegion& match_buffer_region) {
+               fvisit_buffer_region(match_buffer_region->source);
+             });
+  if (op->init.defined()) {
+    this->VisitStmt(op->init.value());
+  }
+  this->VisitStmt(op->body);
+}
+
+void StmtVisitor::VisitStmt_(const BlockRealizeNode* op) {
+  VisitArray(op->iter_values, [this](const PrimExpr& e) { this->VisitExpr(e); });
+  this->VisitExpr(op->predicate);
+  this->VisitStmt(op->block);
+}
+
 class StmtMutator::Internal {
  public:
+  /*!
+   * \brief Mutate array's element by fmutate function.
+   *
+   * \note Use extra care for copy on write setting.
+   *
+   * In particular, consider the following case of two reference chains:
+   * - strongref0 -> loop0 -> loop1 -> loop2
+   * - strongref1 -> loop3 -> loop1 -> loop2
+   *
+   * Think of the case of calling MutateArray on loop1->loop2(as const reference).
+   * When both strongref0 and strongref1 exists, the context does not allow copy
+   * on write, even though loop1 uniquely refers to loop2.
+   *
+   * \param self The pointer to the mutator.
+   * \param arr Array to be mutated, const reference is used to allow copy on write
+   *            mutation in a recursive visitor.
+   * \param fmutate The mutator function.
+   * \return The mutated array, a new copy can be created.
+   */
+  template <typename T, typename F>
+  static Array<T> MutateArray(StmtMutator* self, const Array<T>& arr, F fmutate) {
+    if (self->allow_copy_on_write_ && arr.unique()) {
+      // if we allow copy on write, we can directly
+      // call the inplace mutate function.
+      const_cast<Array<T>&>(arr).MutateByApply(fmutate);
+      return arr;
+    } else {
+      bool allow_cow = false;
+      Array<T> copy = arr;
+      std::swap(allow_cow, self->allow_copy_on_write_);
+      copy.MutateByApply(fmutate);
+      std::swap(allow_cow, self->allow_copy_on_write_);
+      return copy;
+    }
+  }
+
+  static Array<IterVar> Mutate(StmtMutator* self, const Array<IterVar>& arr) {
+    auto fmutate = [self](const IterVar& iter_var) {
+      PrimExpr min = self->VisitExpr(iter_var->dom->min);
+      PrimExpr extent = self->VisitExpr(iter_var->dom->extent);
+      if (min.same_as(iter_var->dom->min) && extent.same_as(iter_var->dom->extent)) {
+        return iter_var;
+      } else {
+        return IterVar(Range(min, extent), iter_var->var, iter_var->iter_type,
+                       iter_var->thread_tag);
+      }
+    };
+    return MutateArray(self, arr, fmutate);
+  }
+
   static Array<PrimExpr> Mutate(StmtMutator* self, const Array<PrimExpr>& arr) {
     auto fmutate = [self](const PrimExpr& e) { return self->VisitExpr(e); };
-    return MutateArray(arr, fmutate, self->allow_copy_on_write_);
+    return MutateArray(self, arr, fmutate);
   }
 
   static Array<Stmt> Mutate(StmtMutator* self, const Array<Stmt>& arr) {
     auto fmutate = [self](const Stmt& s) { return self->VisitStmt(s); };
-    return MutateArray(arr, fmutate, self->allow_copy_on_write_);
+    return MutateArray(self, arr, fmutate);
   }
 
   static Array<Range> Mutate(StmtMutator* self, const Array<Range>& arr) {
@@ -134,7 +220,32 @@ class StmtMutator::Internal {
         return Range::FromMinExtent(min, extent);
       }
     };
-    return MutateArray(arr, fmutate, self->allow_copy_on_write_);
+    return MutateArray(self, arr, fmutate);
+  }
+
+  static Array<BufferRegion> Mutate(StmtMutator* self, const Array<BufferRegion>& arr) {
+    auto fmutate = [self](const BufferRegion& buffer_region) {
+      Array<Range> region = Mutate(self, buffer_region->region);
+      if (region.same_as(buffer_region->region)) {
+        return buffer_region;
+      } else {
+        return BufferRegion(buffer_region->buffer, region);
+      }
+    };
+    return MutateArray(self, arr, fmutate);
+  }
+
+  static Array<MatchBufferRegion> Mutate(StmtMutator* self, const Array<MatchBufferRegion>& arr) {
+    auto fmutate = [self](const MatchBufferRegion& match_buffer_region) {
+      Array<Range> region = Mutate(self, match_buffer_region->source->region);
+      if (region.same_as(match_buffer_region->source->region)) {
+        return match_buffer_region;
+      } else {
+        return MatchBufferRegion(match_buffer_region->buffer,
+                                 BufferRegion(match_buffer_region->source->buffer, region));
+      }
+    };
+    return MutateArray(self, arr, fmutate);
   }
 };
 
@@ -179,6 +290,19 @@ Stmt StmtMutator::VisitStmt_(const ForNode* op) {
   }
 }
 
+Stmt StmtMutator::VisitStmt_(const WhileNode* op) {
+  PrimExpr condition = this->VisitExpr(op->condition);
+  Stmt body = this->VisitStmt(op->body);
+  if (condition.same_as(op->condition) && body.same_as(op->body)) {
+    return GetRef<Stmt>(op);
+  } else {
+    auto n = CopyOnWrite(op);
+    n->condition = std::move(condition);
+    n->body = std::move(body);
+    return Stmt(n);
+  }
+}
+
 Stmt StmtMutator::VisitStmt_(const AllocateNode* op) {
   Array<PrimExpr> extents = Internal::Mutate(this, op->extents);
   Stmt body = this->VisitStmt(op->body);
@@ -323,7 +447,7 @@ Stmt StmtMutator::VisitSeqStmt_(const SeqStmtNode* op, bool flatten_before_visit
   }
   // function to run the visit.
   auto frunvisit = [&](const SeqStmtNode* op) {
-    Array<Stmt> seq = fmutate != nullptr ? MutateArray(op->seq, fmutate, allow_copy_on_write_)
+    Array<Stmt> seq = fmutate != nullptr ? Internal::MutateArray(this, op->seq, fmutate)
                                          : Internal::Mutate(this, op->seq);
     if (seq.same_as(op->seq)) {
       return GetRef<Stmt>(op);
@@ -379,6 +503,47 @@ Stmt StmtMutator::VisitStmt_(const EvaluateNode* op) {
   }
 }
 
+Stmt StmtMutator::VisitStmt_(const BlockNode* op) {
+  Array<IterVar> iter_vars = Internal::Mutate(this, op->iter_vars);
+  Array<BufferRegion> reads = Internal::Mutate(this, op->reads);
+  Array<BufferRegion> writes = Internal::Mutate(this, op->writes);
+  Array<MatchBufferRegion> match_buffers = Internal::Mutate(this, op->match_buffers);
+  Optional<Stmt> init = NullOpt;
+  if (op->init.defined()) {
+    init = VisitStmt(op->init.value());
+  }
+  Stmt body = VisitStmt(op->body);
+  if (iter_vars.same_as(op->iter_vars) && reads.same_as(op->reads) && writes.same_as(op->writes) &&
+      body.same_as(op->body) && init.same_as(op->init) &&
+      match_buffers.same_as(op->match_buffers)) {
+    return GetRef<Block>(op);
+  } else {
+    auto n = CopyOnWrite(op);
+    n->iter_vars = std::move(iter_vars);
+    n->reads = std::move(reads);
+    n->writes = std::move(writes);
+    n->body = std::move(body);
+    n->init = std::move(init);
+    n->match_buffers = std::move(match_buffers);
+    return Stmt(n);
+  }
+}
+
+Stmt StmtMutator::VisitStmt_(const BlockRealizeNode* op) {
+  Array<PrimExpr> v = Internal::Mutate(this, op->iter_values);
+  PrimExpr pred = this->VisitExpr(op->predicate);
+  Stmt block = this->VisitStmt(op->block);
+  if (v.same_as(op->iter_values) && pred.same_as(op->predicate) && block.same_as(op->block)) {
+    return GetRef<Stmt>(op);
+  } else {
+    auto n = CopyOnWrite(op);
+    n->iter_values = std::move(v);
+    n->predicate = std::move(pred);
+    n->block = Downcast<Block>(block);
+    return Stmt(n);
+  }
+}
+
 // Implementations of IRTransform, PostOrderVisit and Substitute
 class IRApplyVisit : public StmtExprVisitor {
  public:
@@ -468,9 +633,9 @@ Stmt IRTransform(Stmt ir_node, const runtime::PackedFunc& f_preorder,
   return transform(std::move(ir_node));
 }
 
-class IRSubstitue : public StmtExprMutator {
+class IRSubstitute : public StmtExprMutator {
  public:
-  explicit IRSubstitue(std::function<Optional<PrimExpr>(const Var&)> vmap) : vmap_(vmap) {}
+  explicit IRSubstitute(std::function<Optional<PrimExpr>(const Var&)> vmap) : vmap_(vmap) {}
 
   PrimExpr VisitExpr_(const VarNode* op) final {
     Var var = GetRef<Var>(op);
@@ -480,7 +645,6 @@ class IRSubstitue : public StmtExprMutator {
   }
 
   PrimExpr VisitExpr_(const LoadNode* op) final {
-    // NOTE: we do not explicit recursivly mutate op->buffer_var
     PrimExpr ret = StmtExprMutator::VisitExpr_(op);
     op = ret.as<LoadNode>();
     if (auto mapped_var = vmap_(op->buffer_var)) {
@@ -491,7 +655,6 @@ class IRSubstitue : public StmtExprMutator {
   }
 
   Stmt VisitStmt_(const StoreNode* op) final {
-    // NOTE: we do not explicit recursivly mutate op->buffer_var
     Stmt ret = StmtExprMutator::VisitStmt_(op);
     op = ret.as<StoreNode>();
     if (auto mapped_var = vmap_(op->buffer_var)) {
@@ -501,16 +664,70 @@ class IRSubstitue : public StmtExprMutator {
     }
   }
 
+  Stmt VisitStmt_(const AttrStmtNode* op) final {
+    Stmt ret = StmtExprMutator::VisitStmt_(op);
+    op = ret.as<AttrStmtNode>();
+    // remap var node in attr
+    if (const auto* var_node = op->node.as<VarNode>()) {
+      if (auto mapped_var = vmap_(GetRef<Var>(var_node))) {
+        return AttrStmt(mapped_var, op->attr_key, op->value, op->body);
+      }
+    }
+    return ret;
+  }
+
  private:
   std::function<Optional<PrimExpr>(const Var&)> vmap_;
 };
 
 Stmt Substitute(Stmt stmt, std::function<Optional<PrimExpr>(const Var&)> vmap) {
-  return IRSubstitue(vmap)(std::move(stmt));
+  return IRSubstitute(vmap)(std::move(stmt));
 }
 
 PrimExpr Substitute(PrimExpr expr, std::function<Optional<PrimExpr>(const Var&)> vmap) {
-  return IRSubstitue(vmap)(std::move(expr));
+  return IRSubstitute(vmap)(std::move(expr));
+}
+
+void PreOrderVisit(const ObjectRef& stmt_or_expr,
+                   const std::function<bool(const ObjectRef&)>& fvisit) {
+  class PreOrderVisitor : public StmtExprVisitor {
+   public:
+    explicit PreOrderVisitor(const std::function<bool(const ObjectRef&)>& f) : f_(f) {}
+
+   private:
+    void VisitExpr(const PrimExpr& expr) final {
+      const PrimExprNode* p_expr = expr.get();
+      if (visited_.count(p_expr) == 0) {
+        visited_.insert(p_expr);
+        if (f_(expr)) {
+          ExprVisitor::VisitExpr(expr);
+        }
+      }
+    }
+
+    void VisitStmt(const Stmt& stmt) final {
+      const StmtNode* p_stmt = stmt.get();
+      if (visited_.count(p_stmt) == 0) {
+        visited_.insert(p_stmt);
+        if (f_(stmt)) {
+          StmtVisitor::VisitStmt(stmt);
+        }
+      }
+    }
+
+    const std::function<bool(const ObjectRef&)>& f_;
+    std::unordered_set<const Object*> visited_;
+  };
+
+  PreOrderVisitor visitor(fvisit);
+  if (const auto* stmt = stmt_or_expr.as<StmtNode>()) {
+    visitor(GetRef<Stmt>(stmt));
+  } else if (const auto* expr = stmt_or_expr.as<PrimExprNode>()) {
+    visitor(GetRef<PrimExpr>(expr));
+  } else {
+    LOG(FATAL) << "InternalError: PreOrderVisit does not accept object with type: "
+               << stmt_or_expr->GetTypeKey();
+  }
 }
 
 TVM_REGISTER_GLOBAL("tir.IRTransform").set_body_typed(IRTransform);
diff --git a/src/tir/op/builtin.cc b/src/tir/op/builtin.cc
index 796b113a4054..1117571c8b75 100644
--- a/src/tir/op/builtin.cc
+++ b/src/tir/op/builtin.cc
@@ -42,6 +42,10 @@ TIR_DEFINE_BUILTIN_FUNC(reinterpret)
     .set_attr<TCallEffectKind>("TCallEffectKind", Integer(CallEffectKind::kPure))
     .set_num_inputs(1);
 
+TIR_DEFINE_BUILTIN_FUNC(ret)
+    .set_attr<TCallEffectKind>("TCallEffectKind", Integer(CallEffectKind::kControlJump))
+    .set_num_inputs(1);
+
 TIR_DEFINE_BUILTIN_FUNC(likely)
     .set_num_inputs(1)
     .set_attr<TCallEffectKind>("TCallEffectKind", Integer(CallEffectKind::kExprAnnotation))
diff --git a/src/tir/op/op.cc b/src/tir/op/op.cc
index b576fe4faee8..9fcb07149d19 100644
--- a/src/tir/op/op.cc
+++ b/src/tir/op/op.cc
@@ -145,6 +145,10 @@ void BinaryOpMatchTypes(PrimExpr& lhs, PrimExpr& rhs, Span span) {  // NOLINT(*)
   }
 }
 
+PrimExpr ret(PrimExpr value, Span span) {
+  return tir::Call(value.dtype(), tir::builtin::ret(), {value}, span);
+}
+
 // maximum and min limits
 PrimExpr max_value(const DataType& dtype, Span span) {
   using namespace tir;
diff --git a/src/tir/transforms/combine_context_call.cc b/src/tir/transforms/combine_context_call.cc
index 03a0d5e751cf..4a3986460b15 100644
--- a/src/tir/transforms/combine_context_call.cc
+++ b/src/tir/transforms/combine_context_call.cc
@@ -72,7 +72,7 @@ class ContextCallCombiner final : public StmtExprMutator {
   }
 
   Stmt VisitStmt_(const ForNode* op) final {
-    if (op->for_type == ForType::Parallel) {
+    if (op->kind == ForKind::kParallel) {
       // Map of comparison expression to variable
       std::unordered_map<PrimExpr, Var, StructuralHash, StructuralEqual> temp;
       std::swap(temp, ctx_map_);
diff --git a/src/tir/transforms/coproc_sync.cc b/src/tir/transforms/coproc_sync.cc
index f9245442d268..424a1bbb0ae6 100644
--- a/src/tir/transforms/coproc_sync.cc
+++ b/src/tir/transforms/coproc_sync.cc
@@ -429,6 +429,11 @@ class CoProcInstDepDetector : public StmtVisitor {
     }
   }
 
+  void VisitStmt_(const WhileNode* op) final {
+    // TODO(masahi): Do we need a special handling for While nodes?
+    LOG(FATAL) << "WhileNode not supported in CoProcSync.";
+  }
+
   // insert before is stored in reverse order
   // the first element is closest to the node.
   std::unordered_map<const Object*, std::vector<Stmt> > insert_before_;
diff --git a/src/tir/transforms/hoist_if_then_else.cc b/src/tir/transforms/hoist_if_then_else.cc
index 7bae0ce8ca75..4a11a7e90e30 100644
--- a/src/tir/transforms/hoist_if_then_else.cc
+++ b/src/tir/transforms/hoist_if_then_else.cc
@@ -168,7 +168,7 @@ class HoistCandidateSelector final : public StmtExprVisitor {
     // To stop hoisting if any of the block variables are used.
     //
     // In case we want to use hoisting in between certain passes
-    // which have interdependencies of the postioning of if nodes with scope var
+    // which have interdependencies of the positioning of if nodes with scope var
     // it is better to disable this section
     if (support_block_scope_hosting_) {
       if (IsRecordingOn()) {
diff --git a/src/tir/transforms/inject_double_buffer.cc b/src/tir/transforms/inject_double_buffer.cc
index 22a6ca23c24c..7a16c06d8058 100644
--- a/src/tir/transforms/inject_double_buffer.cc
+++ b/src/tir/transforms/inject_double_buffer.cc
@@ -158,8 +158,7 @@ class DoubleBufferInjector : public StmtExprMutator {
           vmap[old_loop->loop_var.get()] = outer_var * factor + make_const(factor.dtype(), i);
           loop_seq.emplace_back(Substitute(old_loop->body, vmap));
         }
-        Stmt loop = For(outer_var, zero, outer_ext, old_loop->for_type, old_loop->device_api,
-                        SeqStmt::Flatten(loop_seq));
+        Stmt loop = For(outer_var, zero, outer_ext, old_loop->kind, SeqStmt::Flatten(loop_seq));
         // tail
         std::vector<Stmt> tail_seq;
         Stmt tail_body = StripDoubleBufferWrite()(old_loop->body);
diff --git a/src/tir/transforms/inject_prefetch.cc b/src/tir/transforms/inject_prefetch.cc
index b5c4cf5ec582..4ce9c7639b77 100644
--- a/src/tir/transforms/inject_prefetch.cc
+++ b/src/tir/transforms/inject_prefetch.cc
@@ -71,11 +71,11 @@ class PrefetchInjector : public StmtMutator {
   Stmt VisitStmt_(const ForNode* op) final {
     auto& var = op->loop_var;
     loop_nest_.push_back(var);
-    if (op->for_type == ForType::Vectorized) {
+    if (op->kind == ForKind::kVectorized) {
       vectorized_[var.get()] = IntSet::Interval(op->min, (op->min + op->extent) - 1);
     }
     Stmt ret = StmtMutator::VisitStmt_(op);
-    if (op->for_type == ForType::Vectorized) {
+    if (op->kind == ForKind::kVectorized) {
       vectorized_.erase(var.get());
     }
     loop_nest_.pop_back();
diff --git a/src/tir/transforms/inject_virtual_thread.cc b/src/tir/transforms/inject_virtual_thread.cc
index 5622d140a625..4ef10f326bb0 100644
--- a/src/tir/transforms/inject_virtual_thread.cc
+++ b/src/tir/transforms/inject_virtual_thread.cc
@@ -303,7 +303,10 @@ class VTInjector : public StmtExprMutator {
     if (extent.same_as(op->extent) && body.same_as(op->body)) {
       return GetRef<Stmt>(op);
     } else {
-      return For(op->loop_var, op->min, extent, op->for_type, op->device_api, body);
+      auto n = CopyOnWrite(op);
+      n->extent = std::move(extent);
+      n->body = std::move(body);
+      return Stmt(n);
     }
   }
   // IfThenElse
@@ -330,6 +333,13 @@ class VTInjector : public StmtExprMutator {
     }
   }
 
+  // While
+  Stmt VisitStmt_(const WhileNode* op) final {
+    // TODO(masahi): What should we do for While nodes?
+    LOG(FATAL) << "WhileNode in InjectVirtualThread not supported yet";
+    return Stmt();
+  }
+
   // Seq
   Stmt VisitStmt_(const SeqStmtNode* op) final {
     ICHECK_EQ(max_loop_depth_, 0);
@@ -417,7 +427,7 @@ class VTInjector : public StmtExprMutator {
       Map<Var, PrimExpr> values{{var_, idx}};
       stmt = Substitute(stmt, values);
       return For(idx, make_zero(idx.dtype()), make_const(idx.dtype(), num_threads_),
-                 ForType::Serial, DeviceAPI::None, stmt);
+                 ForKind::kSerial, stmt);
     }
   }
 
diff --git a/src/tir/transforms/ir_utils.cc b/src/tir/transforms/ir_utils.cc
index 033a2e093a2a..cbae3f95ec68 100644
--- a/src/tir/transforms/ir_utils.cc
+++ b/src/tir/transforms/ir_utils.cc
@@ -149,7 +149,8 @@ class IRConvertSSA final : public StmtExprMutator {
       Stmt stmt = StmtExprMutator::VisitStmt_(op);
       scope_[v.get()].pop_back();
       op = stmt.as<ForNode>();
-      return For(new_var, op->min, op->extent, op->for_type, op->device_api, op->body);
+      return For(new_var, op->min, op->extent, op->kind, op->body, op->thread_binding,
+                 op->annotations);
     } else {
       defined_.insert(v.get());
       return StmtExprMutator::VisitStmt_(op);
diff --git a/src/tir/transforms/lift_attr_scope.cc b/src/tir/transforms/lift_attr_scope.cc
index 27dd583b8b42..40d152b3b3b6 100644
--- a/src/tir/transforms/lift_attr_scope.cc
+++ b/src/tir/transforms/lift_attr_scope.cc
@@ -157,6 +157,12 @@ class AttrScopeLifter : public StmtMutator {
     }
   }
 
+  Stmt VisitStmt_(const WhileNode* op) final {
+    // TODO(masahi): Do we need a special handling for While nodes?
+    LOG(FATAL) << "WhileNode not supported in LiftAttrScope.";
+    return Stmt();
+  }
+
  private:
   // value comparison that also compares content of int constant
   static bool ValueSame(const PrimExpr& a, const PrimExpr& b) {
diff --git a/src/tir/transforms/loop_partition.cc b/src/tir/transforms/loop_partition.cc
index a104dbb029eb..f1d816f0baef 100644
--- a/src/tir/transforms/loop_partition.cc
+++ b/src/tir/transforms/loop_partition.cc
@@ -607,8 +607,8 @@ inline Stmt LoopPartitioner::MakeFor(const Object* node, PrimExpr extent, Stmt b
     // If the loop extent is 1, do not create the loop anymore
     return Substitute(body, {{Var{for_node->loop_var}, make_const(DataType::Int(32), 0)}});
   } else {
-    return For(for_node->loop_var, IntImm(for_node->min.dtype(), 0), extent, for_node->for_type,
-               for_node->device_api, body);
+    ICHECK(for_node->kind != ForKind::kThreadBinding);
+    return For(for_node->loop_var, IntImm(for_node->min.dtype(), 0), extent, for_node->kind, body);
   }
 }
 
diff --git a/src/tir/transforms/lower_custom_datatypes.cc b/src/tir/transforms/lower_custom_datatypes.cc
index a3e5a920a0b2..21f1b18d523b 100644
--- a/src/tir/transforms/lower_custom_datatypes.cc
+++ b/src/tir/transforms/lower_custom_datatypes.cc
@@ -44,14 +44,14 @@ class CustomDatatypesLowerer : public StmtExprMutator {
  public:
   explicit CustomDatatypesLowerer(const std::string& target) : target_(target) {}
 
-  inline PrimExpr VisitExpr_(const CastNode* op) final {
+  PrimExpr VisitExpr_(const CastNode* op) final {
     auto type_code = op->dtype.code();
     auto src_type_code = op->value.dtype().code();
     // If either datatype is a registered custom datatype, we must lower.
-    bool toBeLowered = datatype::Registry::Global()->GetTypeRegistered(type_code) ||
-                       datatype::Registry::Global()->GetTypeRegistered(src_type_code);
+    bool to_be_lowered = datatype::Registry::Global()->GetTypeRegistered(type_code) ||
+                         datatype::Registry::Global()->GetTypeRegistered(src_type_code);
     PrimExpr expr = StmtExprMutator::VisitExpr_(op);
-    if (toBeLowered) {
+    if (to_be_lowered) {
       auto lower = datatype::GetCastLowerFunc(target_, type_code, src_type_code);
       ICHECK(lower) << "Cast lowering function for target " << target_ << " destination type "
                     << static_cast<unsigned>(type_code) << " source type "
@@ -61,7 +61,7 @@ class CustomDatatypesLowerer : public StmtExprMutator {
     return expr;
   }
 
-  inline PrimExpr VisitExpr_(const FloatImmNode* imm) final {
+  PrimExpr VisitExpr_(const FloatImmNode* imm) final {
     auto type_code = imm->dtype.code();
     auto e = GetRef<PrimExpr>(imm);
     if (datatype::Registry::Global()->GetTypeRegistered(type_code)) {
@@ -73,35 +73,86 @@ class CustomDatatypesLowerer : public StmtExprMutator {
     return e;
   }
 
-  inline Stmt VisitStmt_(const AllocateNode* allocate) final {
-    bool toBeLowered = datatype::Registry::Global()->GetTypeRegistered(allocate->dtype.code());
-    Stmt stmt = StmtExprMutator::VisitStmt_(allocate);
-    allocate = stmt.as<AllocateNode>();
+  PrimExpr VisitExpr_(const VarNode* op) final {
+    Var var = GetRef<Var>(op);
 
-    if (toBeLowered) {
+    auto itr = var_remap_.find(var);
+    if (itr != var_remap_.end()) {
+      return itr->second;
+    } else {
+      return std::move(var);
+    }
+  }
+
+  Stmt VisitStmt_(const AllocateNode* allocate) final {
+    bool to_be_lowered = datatype::Registry::Global()->GetTypeRegistered(allocate->dtype.code());
+
+    if (to_be_lowered) {
       auto new_allocate_type = DataType::UInt(allocate->dtype.bits(), allocate->dtype.lanes());
-      return Allocate(allocate->buffer_var, new_allocate_type, allocate->extents,
-                      allocate->condition, allocate->body);
+      auto new_buffer_var =
+          Var(allocate->buffer_var->name_hint, PointerType(PrimType(new_allocate_type)));
+      var_remap_[allocate->buffer_var] = new_buffer_var;
+
+      Stmt stmt = StmtExprMutator::VisitStmt_(allocate);
+      allocate = stmt.as<AllocateNode>();
+
+      return Allocate(new_buffer_var, new_allocate_type, allocate->extents, allocate->condition,
+                      allocate->body);
+    } else {
+      return StmtExprMutator::VisitStmt_(allocate);
     }
-    return stmt;
   }
 
-  inline PrimExpr VisitExpr_(const LoadNode* load) final {
-    bool toBeLowered = datatype::Registry::Global()->GetTypeRegistered(load->dtype.code());
+  PrimExpr VisitExpr_(const LoadNode* load) final {
+    bool to_be_lowered = datatype::Registry::Global()->GetTypeRegistered(load->dtype.code());
     PrimExpr expr = StmtExprMutator::VisitExpr_(load);
     load = expr.as<LoadNode>();
-    if (toBeLowered) {
+    if (to_be_lowered) {
       auto new_load_type = DataType::UInt(load->dtype.bits());
-      return Load(new_load_type, load->buffer_var, load->index, load->predicate);
+      auto buffer_var = load->buffer_var;
+      auto it = var_remap_.find(buffer_var);
+      if (it != var_remap_.end()) {
+        buffer_var = it->second;
+      }
+      return Load(new_load_type, buffer_var, load->index, load->predicate);
     }
     return expr;
   }
 
-  inline PrimExpr VisitExpr_(const CallNode* call) final {
-    bool toBeLowered = datatype::Registry::Global()->GetTypeRegistered(call->dtype.code());
+  Stmt VisitStmt_(const StoreNode* op) final {
+    Stmt ret = StmtExprMutator::VisitStmt_(op);
+    op = ret.as<StoreNode>();
+
+    auto it = var_remap_.find(op->buffer_var);
+    if (it != var_remap_.end()) {
+      return Store(it->second, op->value, op->index, op->predicate);
+    } else {
+      return ret;
+    }
+  }
+
+  Stmt VisitStmt_(const AttrStmtNode* op) final {
+    Stmt ret = StmtExprMutator::VisitStmt_(op);
+    op = ret.as<AttrStmtNode>();
+    // Due to legacy reasons, some attr node can contain
+    // information(e.g. alignment) of buffer variables.
+    // remap these vars when needed
+    // TODO(tvm-team): remove the rewriting once the buffer var
+    // attrs are being refactored into the corresponding definition node
+    if (const auto* var_node = op->node.as<VarNode>()) {
+      auto it = var_remap_.find(GetRef<Var>(var_node));
+      if (it != var_remap_.end()) {
+        return AttrStmt(it->second, op->attr_key, op->value, op->body);
+      }
+    }
+    return ret;
+  }
+
+  PrimExpr VisitExpr_(const CallNode* call) final {
+    bool to_be_lowered = datatype::Registry::Global()->GetTypeRegistered(call->dtype.code());
     PrimExpr expr = StmtExprMutator::VisitExpr_(call);
     call = expr.as<CallNode>();
-    if (toBeLowered) {
+    if (to_be_lowered) {
       auto op = call->op.as<OpNode>();
       ICHECK(op != nullptr) << "Lowering non-intrinsic Calls not implemented";
       auto lower = datatype::GetIntrinLowerFunc(target_, op->name, call->dtype.code());
@@ -113,38 +164,42 @@ class CustomDatatypesLowerer : public StmtExprMutator {
     return expr;
   }
 
-#define DEFINE_MUTATE(OP, NodeName)                                                \
-  inline PrimExpr VisitExpr_(const NodeName* op) final {                           \
-    auto type_code = op->dtype.code();                                             \
-    bool toBeLowered = datatype::Registry::Global()->GetTypeRegistered(type_code); \
-    PrimExpr expr = StmtExprMutator::VisitExpr_(op);                               \
-    op = expr.as<NodeName>();                                                      \
-    if (toBeLowered) {                                                             \
-      auto lower = datatype::Get##OP##LowerFunc(target_, type_code);               \
-      ICHECK(lower) << #OP " lowering function for target " << target_ << " type " \
-                    << static_cast<unsigned>(type_code) << " not found";           \
-      return (*lower)(expr);                                                       \
-    }                                                                              \
-    return expr;                                                                   \
+#define TVM_DEFINE_MUTATE_CUSTOM_DTYPE(OP, NodeName)                                 \
+  PrimExpr VisitExpr_(const NodeName* op) final {                                    \
+    auto type_code = op->dtype.code();                                               \
+    bool to_be_lowered = datatype::Registry::Global()->GetTypeRegistered(type_code); \
+    PrimExpr expr = StmtExprMutator::VisitExpr_(op);                                 \
+    op = expr.as<NodeName>();                                                        \
+    if (to_be_lowered) {                                                             \
+      auto lower = datatype::Get##OP##LowerFunc(target_, type_code);                 \
+      ICHECK(lower) << #OP " lowering function for target " << target_ << " type "   \
+                    << static_cast<unsigned>(type_code) << " not found";             \
+      return (*lower)(expr);                                                         \
+    }                                                                                \
+    return expr;                                                                     \
   }
 
-  DEFINE_MUTATE(Add, AddNode);
-  DEFINE_MUTATE(Sub, SubNode);
-  DEFINE_MUTATE(Mul, MulNode);
-  DEFINE_MUTATE(Div, DivNode);
-  DEFINE_MUTATE(Mod, ModNode);
-  DEFINE_MUTATE(Min, MinNode);
-  DEFINE_MUTATE(Max, MaxNode);
-  DEFINE_MUTATE(EQ, EQNode);
-  DEFINE_MUTATE(NE, NENode);
-  DEFINE_MUTATE(LT, LTNode);
-  DEFINE_MUTATE(LE, LENode);
-  DEFINE_MUTATE(GT, GTNode);
-  DEFINE_MUTATE(GE, GENode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Add, AddNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Sub, SubNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Mul, MulNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Div, DivNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Mod, ModNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Min, MinNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(Max, MaxNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(EQ, EQNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(NE, NENode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(LT, LTNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(LE, LENode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(GT, GTNode);
+  TVM_DEFINE_MUTATE_CUSTOM_DTYPE(GE, GENode);
   // Later changes may need to add more mutate functions as we support workloads with more ops.
 
+#undef TVM_DEFINE_MUTATE_CUSTOM_DTYPE
+
  private:
   std::string target_;
+  // remap buffer vars
+  std::unordered_map<Var, Var, ObjectPtrHash, ObjectPtrEqual> var_remap_;
 };
 
 namespace transform {
diff --git a/src/tir/transforms/lower_thread_allreduce.cc b/src/tir/transforms/lower_thread_allreduce.cc
index c24e26b58db0..f6cb096720da 100644
--- a/src/tir/transforms/lower_thread_allreduce.cc
+++ b/src/tir/transforms/lower_thread_allreduce.cc
@@ -224,14 +224,15 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
       PrimExpr index(0);
 
       for (size_t idx = 0; idx < size; ++idx) {
-        shared_bufs[idx] = Var("red_buf" + std::to_string(idx), DataType::Handle());
+        Type ptr_type = PointerType(PrimType(types[idx]));
+        shared_bufs[idx] = Var("red_buf" + std::to_string(idx), ptr_type);
         PrimExpr pred = const_true(types[idx].lanes());
         seq.emplace_back(Store(shared_bufs[idx], values[idx], index, pred));
 
         // Uses a local variable to store the shuffled data.
         // Later on, this allocation will be properly attached to this statement.
-        Var var("t" + std::to_string(idx), types[idx]);
-        Stmt s = Allocate(var, var.dtype(), {PrimExpr(1)}, pred, Evaluate(0));
+        Var var("t" + std::to_string(idx), ptr_type);
+        Stmt s = Allocate(var, types[idx], {PrimExpr(1)}, pred, Evaluate(0));
         local_vars.push_back(s);
       }
 
@@ -239,14 +240,15 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
       // a divergent control flow. Here it uses a variable to cache the current
       // active channels.
       //
-      Var mask_var("mask", DataType::UInt(32));
+      DataType mask_dtype = DataType::UInt(32);
+      Var mask_var("mask", PointerType(PrimType(mask_dtype)));
       {
         PrimExpr pred = const_true(1);
-        PrimExpr mask = Call(DataType::UInt(32), builtin::tvm_warp_activemask(), {});
+        PrimExpr mask = Call(mask_dtype, builtin::tvm_warp_activemask(), {});
         seq.emplace_back(Store(mask_var, mask, index, pred));
         // Push allocation with an empty body. Later this will be fixed
         // when the entire body is ready.
-        auto stmt = Allocate(mask_var, mask_var->dtype, {PrimExpr(1)}, pred, Evaluate(0));
+        auto stmt = Allocate(mask_var, mask_dtype, {PrimExpr(1)}, pred, Evaluate(0));
         local_vars.push_back(stmt);
       }
 
@@ -338,7 +340,7 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
       // previous iteration on the same buffer.
       seq.emplace_back(SyncThread("shared"));
       for (size_t idx = 0; idx < size; ++idx) {
-        shared_bufs[idx] = Var("red_buf" + std::to_string(idx), DataType::Handle());
+        shared_bufs[idx] = Var("red_buf" + std::to_string(idx), PointerType(PrimType(types[idx])));
         PrimExpr pred = const_true(types[idx].lanes());
         seq.emplace_back(Store(shared_bufs[idx], values[idx],
                                BufIndex(reduce_index, group_index, reduce_extent), pred));
diff --git a/src/tir/transforms/make_packed_api.cc b/src/tir/transforms/make_packed_api.cc
index 7c4a8ef92724..3842f3e9a8ee 100644
--- a/src/tir/transforms/make_packed_api.cc
+++ b/src/tir/transforms/make_packed_api.cc
@@ -41,6 +41,67 @@
 namespace tvm {
 namespace tir {
 
+class ReturnRewriter : public StmtMutator {
+ public:
+  explicit ReturnRewriter(Var ret_var, Var ret_tcode) : ret_var_(ret_var), ret_tcode_(ret_tcode) {}
+
+  Stmt VisitStmt_(const ForNode* node) override {
+    if (node->kind == ForKind::kParallel) in_parallel_ += 1;
+    Stmt ret = StmtMutator::VisitStmt_(node);
+    if (node->kind == ForKind::kParallel) in_parallel_ -= 1;
+    return ret;
+  }
+
+  Stmt VisitStmt_(const EvaluateNode* node) override {
+    Stmt ret = StmtMutator::VisitStmt_(node);
+    const EvaluateNode* eval = ret.as<EvaluateNode>();
+    ICHECK(eval);
+    if (const CallNode* call = eval->value.as<CallNode>()) {
+      if (call->op.same_as(builtin::ret())) {
+        ICHECK_EQ(in_parallel_, 0) << "tir.ret cannot be used in parallel scope.";
+        ICHECK_EQ(call->args.size(), 1) << "tir.ret expect a single argument.";
+        ret = WriteToOut(call->args[0], ret_var_, ret_tcode_);
+      }
+    }
+    return ret;
+  }
+
+ private:
+  std::pair<int, PrimExpr> ConvertForFFI(PrimExpr val) {
+    // convert val's data type to FFI data type, return type code
+    DataType dtype = val.dtype();
+    if (dtype.is_int() || dtype.is_uint()) {
+      return {kTVMArgInt, Cast(DataType::Int(64), val)};
+    } else if (dtype.is_float()) {
+      return {kTVMArgFloat, Cast(DataType::Float(64), val)};
+    } else if (dtype.is_void()) {
+      return {kTVMNullptr, val};
+    } else {
+      LOG(FATAL) << "data type " << dtype << " not supported yet";
+    }
+    return {kTVMNullptr, val};
+  }
+
+  Stmt WriteToOut(PrimExpr val, Var ret_var, Var ret_tcode) {
+    auto p = ConvertForFFI(val);
+    int tcode = p.first;
+    val = p.second;
+    Stmt store_val = Store(ret_var_, val, 0, const_true());
+    Stmt store_tcode = Store(ret_tcode_, tcode, 0, const_true());
+    Stmt ret_zero = Evaluate(tvm::ret(0));
+    return SeqStmt({store_val, store_tcode, ret_zero});
+  }
+
+  Var ret_var_;
+  Var ret_tcode_;
+  int in_parallel_{0};
+};
+
+Stmt RewriteReturn(Stmt body, Var ret_var, Var ret_tcode) {
+  ReturnRewriter rewriter(ret_var, ret_tcode);
+  return rewriter(body);
+}
+
 inline Stmt MakeAssertEQ(PrimExpr lhs, PrimExpr rhs, std::string msg) {
   return AssertStmt(lhs == rhs, tvm::tir::StringImm(msg), Evaluate(0));
 }
@@ -168,7 +229,7 @@ PrimFunc MakePackedAPI(PrimFunc&& func, int num_unpacked_args) {
   //
   // For example, for auto broadcasting, checks are required to guarantee that
   // either 0 or the original stride will be correctly used. Checks here have
-  // to use the args that may have no let bining yet. Therefore, hoisting let
+  // to use the args that may have no let binding yet. Therefore, hoisting let
   // binding for args before buffer declaration is needed.
   for (const auto& kv : var_def) {
     binder.Bind(kv.second, kv.first, kv.first->name_hint, true);
@@ -182,8 +243,9 @@ PrimFunc MakePackedAPI(PrimFunc&& func, int num_unpacked_args) {
     func = WithAttr(std::move(func), tvm::attr::kCallingConv, Integer(CallingConv::kCPackedFunc));
   }
 
-  Stmt body = AttrStmt(make_zero(DataType::Int(32)), attr::compute_scope,
-                       StringImm(name_hint + "_compute_"), func_ptr->body);
+  Stmt body = RewriteReturn(func_ptr->body, v_out_ret_value, v_out_ret_tcode);
+  body = AttrStmt(make_zero(DataType::Int(32)), attr::compute_scope,
+                  StringImm(name_hint + "_compute_"), body);
   // Set device context
   if (vmap.count(device_id.get())) {
     PrimExpr node = StringImm("default");
diff --git a/src/tir/transforms/narrow_datatype.cc b/src/tir/transforms/narrow_datatype.cc
index 0b248959ec6e..dc34626205a1 100644
--- a/src/tir/transforms/narrow_datatype.cc
+++ b/src/tir/transforms/narrow_datatype.cc
@@ -220,8 +220,8 @@ class DataTypeRewriter : public StmtExprMutator {
                           << ", but get " << s->GetTypeKey();
     PrimExpr e = VisitExpr(op->loop_var);
     Var var = Downcast<Var>(e);
-    return For(var, cast(var.dtype(), op->min), cast(var.dtype(), op->extent), op->for_type,
-               op->device_api, op->body);
+    return For(var, cast(var.dtype(), op->min), cast(var.dtype(), op->extent), op->kind, op->body,
+               op->thread_binding, op->annotations);
   }
 
   Stmt VisitStmt_(const AttrStmtNode* op) final {
diff --git a/src/tir/transforms/storage_access.cc b/src/tir/transforms/storage_access.cc
index be20724ae207..00002d3587db 100644
--- a/src/tir/transforms/storage_access.cc
+++ b/src/tir/transforms/storage_access.cc
@@ -132,6 +132,10 @@ void StorageAccessVisitor::VisitStmt_(const AttrStmtNode* op) {
       StmtExprVisitor::VisitStmt_(op);
     }
     env_threads_.pop_back();
+  } else if (op->attr_key == attr::hand_threaded) {
+    // skip this pass on blocks that were hand_threaded
+    // this avoids control flow and read/write conflicts
+    // between hand-threaded kernels and automatic threading
   } else {
     StmtExprVisitor::VisitStmt_(op);
   }
@@ -180,6 +184,19 @@ void StorageAccessVisitor::VisitStmt_(const IfThenElseNode* op) {
   --condition_counter_;
 }
 
+void StorageAccessVisitor::VisitStmt_(const WhileNode* op) {
+  ++condition_counter_;
+  this->VisitExpr(op->condition);
+  scope_.push_back(std::vector<StmtEntry>());
+  this->VisitStmt(op->body);
+  StmtEntry s;
+  s.stmt = op;
+  s.access = Summarize(std::move(scope_.back()), nullptr);
+  scope_.pop_back();
+  scope_.back().emplace_back(std::move(s));
+  --condition_counter_;
+}
+
 void StorageAccessVisitor::VisitExpr_(const CallNode* op) {
   if (op->op.same_as(builtin::address_of())) {
     const LoadNode* l = op->args[0].as<LoadNode>();
diff --git a/src/tir/transforms/storage_access.h b/src/tir/transforms/storage_access.h
index 80bbff4c1fe4..663c570fd15c 100644
--- a/src/tir/transforms/storage_access.h
+++ b/src/tir/transforms/storage_access.h
@@ -84,6 +84,7 @@ class StorageAccessVisitor : public StmtExprVisitor {
   void VisitStmt_(const AttrStmtNode* op) final;
   void VisitStmt_(const ForNode* op) final;
   void VisitStmt_(const IfThenElseNode* op) final;
+  void VisitStmt_(const WhileNode* op) final;
   void VisitExpr_(const CallNode* op) final;
 
  protected:
diff --git a/src/tir/transforms/storage_flatten.cc b/src/tir/transforms/storage_flatten.cc
index d392866b3694..43fc1f1ec53f 100644
--- a/src/tir/transforms/storage_flatten.cc
+++ b/src/tir/transforms/storage_flatten.cc
@@ -318,14 +318,14 @@ class StorageFlattener : public StmtExprMutator {
     }
     for (int i = starts; i >= 0; --i) {
       if (i < starts) {
-        stmt = For(vars[i], 0, op->bounds[i]->extent, ForType::Serial, DeviceAPI::None, stmt);
+        stmt = For(vars[i], 0, op->bounds[i]->extent, ForKind::kSerial, stmt);
       } else {
         PrimExpr load = e.buffer.vload(e.RelIndex(args), e.buffer->dtype);
         PrimExpr address = Call(DataType::Handle(), builtin::address_of(), {load});
         PrimExpr prefetch = Call(op->buffer->dtype, builtin::prefetch(), {address, 0, 3, 1});
         stmt = Evaluate(prefetch);
         PrimExpr extent = (op->bounds[i]->extent - 1) / stride + 1;
-        stmt = For(vars[i], 0, extent, ForType::Serial, DeviceAPI::None, stmt);
+        stmt = For(vars[i], 0, extent, ForKind::kSerial, stmt);
       }
     }
     return stmt;
diff --git a/src/tir/transforms/storage_rewrite.cc b/src/tir/transforms/storage_rewrite.cc
index 78c5ca7460ad..36eeddb17d89 100644
--- a/src/tir/transforms/storage_rewrite.cc
+++ b/src/tir/transforms/storage_rewrite.cc
@@ -23,6 +23,7 @@
  *  Re-write data access to enable memory sharing when possible.
  */
 #include <tvm/arith/analyzer.h>
+#include <tvm/ir/type.h>
 #include <tvm/runtime/registry.h>
 #include <tvm/target/target_info.h>
 #include <tvm/tir/analysis.h>
@@ -191,6 +192,8 @@ class LinearAccessPatternFinder final : public StmtExprVisitor {
 
   void VisitStmt_(const ForNode* op) final { VisitNewScope(op); }
 
+  void VisitStmt_(const WhileNode* op) final { VisitNewScope(op); }
+
   void VisitStmt_(const AssertStmtNode* op) final { VisitNewScope(op); }
 
   // linearized access sequence.
@@ -243,6 +246,8 @@ class InplaceOpVerifier : public StmtExprVisitor {
       VisitStmt_(static_cast<const ForNode*>(stmt));
     } else if (stmt->IsInstance<IfThenElseNode>()) {
       VisitStmt_(static_cast<const IfThenElseNode*>(stmt));
+    } else if (stmt->IsInstance<WhileNode>()) {
+      VisitStmt_(static_cast<const WhileNode*>(stmt));
     } else if (stmt->IsInstance<StoreNode>()) {
       VisitStmt_(static_cast<const StoreNode*>(stmt));
     } else {
@@ -349,16 +354,7 @@ class StoragePlanRewriter : public StmtExprMutator {
     // start rewrite
     stmt = operator()(std::move(stmt));
     if (attach_map_.count(nullptr)) {
-      std::vector<Stmt> nest;
-      for (StorageEntry* e : attach_map_.at(nullptr)) {
-        // ICHECK_EQ(e->scope.rank, 0);
-        if (e->new_alloc.defined()) {
-          nest.emplace_back(AttrStmt(e->alloc_var, attr::storage_scope,
-                                     StringImm(e->scope.to_string()), Evaluate(0)));
-          nest.push_back(e->new_alloc);
-        }
-      }
-      stmt = MergeNest(nest, stmt);
+      return MakeAttach(attach_map_.at(nullptr), stmt);
     }
     return stmt;
   }
@@ -436,15 +432,16 @@ class StoragePlanRewriter : public StmtExprMutator {
       return StmtExprMutator::VisitStmt_(op);
     }
   }
+
   Stmt VisitStmt_(const ForNode* op) final {
-    ICHECK(op->for_type != ForType::Vectorized) << "VectorizeLoop before LiftStorageAlloc";
+    ICHECK(op->kind != ForKind::kVectorized) << "VectorizeLoop before LiftStorageAlloc";
     // remake all the allocation at the attach scope.
     if (attach_map_.count(op)) {
       auto& svec = attach_map_[op];
       Stmt stmt = StmtExprMutator::VisitStmt_(op);
       op = stmt.as<ForNode>();
-      return For(op->loop_var, op->min, op->extent, op->for_type, op->device_api,
-                 MakeAttach(svec, op->body));
+      return For(op->loop_var, op->min, op->extent, op->kind, MakeAttach(svec, op->body),
+                 op->thread_binding, op->annotations);
     } else {
       return StmtExprMutator::VisitStmt_(op);
     }
@@ -764,7 +761,7 @@ class StoragePlanRewriter : public StmtExprMutator {
         }
       } else if (s.stmt->IsInstance<ForNode>()) {
         const auto* op = static_cast<const ForNode*>(s.stmt);
-        if (op->for_type == ForType::Parallel) {
+        if (op->kind == ForKind::kParallel) {
           if (thread_scope_ == nullptr || thread_scope_ == op) {
             PlanNewScope(op);
           }
@@ -934,7 +931,12 @@ class VectorAllocRewriter : public StmtExprMutator {
       if (me->base % factor == 0 && me->coeff % factor == 0) {
         extents.Set(extents.size() - 1,
                     extents[extents.size() - 1] / make_const(extents[0].dtype(), factor));
-        return Allocate(op->buffer_var, tvec[0], extents, op->condition, op->body);
+        // create a new buffer var
+        DataType new_dtype = tvec[0];
+        Var new_buffer_var(op->buffer_var->name_hint, PointerType(PrimType(new_dtype)));
+        // update the remap req.
+        var_remap_.Set(op->buffer_var, new_buffer_var);
+        return Allocate(new_buffer_var, new_dtype, extents, op->condition, op->body);
       }
     }
     return stmt;
@@ -949,23 +951,21 @@ class VectorAllocRewriter : public StmtExprMutator {
 
   // Internal access map
   std::unordered_map<const VarNode*, std::vector<DataType> > acc_map_;
+  // Variables to remap
+  Map<tir::Var, PrimExpr> var_remap_;
   // internal analyzer
   arith::Analyzer analyzer_;
 };
 
-Stmt StorageRewrite(Stmt stmt) {
-  stmt = StoragePlanRewriter().Rewrite(std::move(stmt), true);
-  return VectorAllocRewriter()(std::move(stmt));
-}
-
 PrimFunc PointerValueTypeRewrite(PrimFunc f) {
   auto* n = f.CopyOnWrite();
   VectorAllocRewriter rewriter;
-  n->body = rewriter(n->body);
+  n->body = rewriter(std::move(n->body));
 
+  Map<tir::Var, PrimExpr> var_remap = std::move(rewriter.var_remap_);
   Array<tir::Var> args;
-  Map<tir::Var, PrimExpr> remap_vars;
 
+  // rewrite paramters if needed.
   for (Var var : f->params) {
     if (var.dtype().is_handle()) {
       const auto& tvec = rewriter.acc_map_[var.get()];
@@ -973,15 +973,14 @@ PrimFunc PointerValueTypeRewrite(PrimFunc f) {
       if (tvec.size() == 1) {
         tir::Var new_var(var->name_hint, PointerType(PrimType(tvec[0])));
         args.push_back(new_var);
-        remap_vars.Set(var, new_var);
-
+        var_remap.Set(var, new_var);
       } else {
         // always set data type to be non vectorized so
         // load/store can still work via scalarization
         if (tvec.size() != 0 && !var->type_annotation.defined()) {
           tir::Var new_var(var->name_hint, PointerType(PrimType(tvec[0].with_lanes(1))));
           args.push_back(new_var);
-          remap_vars.Set(var, new_var);
+          var_remap.Set(var, new_var);
         } else {
           args.push_back(var);
         }
@@ -991,9 +990,13 @@ PrimFunc PointerValueTypeRewrite(PrimFunc f) {
     }
   }
 
+  // no variable remap is needed.
+  if (var_remap.size() == 0) return f;
+
+  // remap the variables.
   ICHECK_EQ(args.size(), n->params.size());
   n->params = args;
-  n->body = Substitute(n->body, remap_vars);
+  n->body = Substitute(n->body, var_remap);
   return f;
 }
 
@@ -1003,8 +1006,7 @@ Pass StorageRewrite() {
   auto pass_func = [](PrimFunc f, IRModule m, PassContext ctx) {
     auto* n = f.CopyOnWrite();
     n->body = StoragePlanRewriter().Rewrite(std::move(n->body), true);
-    n->body = VectorAllocRewriter()(std::move(n->body));
-    return f;
+    return PointerValueTypeRewrite(std::move(f));
   };
   return CreatePrimFuncPass(pass_func, 0, "tir.StorageRewrite", {});
 }
diff --git a/src/tir/transforms/unroll_loop.cc b/src/tir/transforms/unroll_loop.cc
index 71ad899273a6..c6e0b5c5f41e 100644
--- a/src/tir/transforms/unroll_loop.cc
+++ b/src/tir/transforms/unroll_loop.cc
@@ -100,13 +100,13 @@ class LoopUnroller : public StmtExprMutator {
     op = stmt.as<ForNode>();
     int value = GetExtent(op);
     // condition for auto unroll
-    bool auto_unroll = (op->for_type == ForType::Serial && value >= 0 && normal_loop_depth_ == 0 &&
+    bool auto_unroll = (op->kind == ForKind::kSerial && value >= 0 && normal_loop_depth_ == 0 &&
                         unroll_depth_ <= auto_max_depth_);
 
     auto_unroll =
         auto_unroll && (value * step_count_ <= auto_max_step_ || value <= auto_max_extent_);
 
-    if (op->for_type == ForType::Unrolled) {
+    if (op->kind == ForKind::kUnrolled) {
       ICHECK_GE(value, 0) << "Cannot unroll non-constant loop";
       auto_unroll = true;
     }
@@ -124,9 +124,9 @@ class LoopUnroller : public StmtExprMutator {
       return Unroll(op);
     } else {
       if (auto_unroll) {
-        if (op->for_type != ForType::Unrolled) {
-          return For(op->loop_var, op->min, op->extent, ForType::Unrolled, op->device_api,
-                     op->body);
+        if (op->kind != ForKind::kUnrolled) {
+          return For(op->loop_var, op->min, op->extent, ForKind::kUnrolled, op->body,
+                     op->thread_binding, op->annotations);
         }
       }
       return stmt;
diff --git a/src/tir/transforms/vectorize_loop.cc b/src/tir/transforms/vectorize_loop.cc
index 239f42266b83..64956bc8ee54 100644
--- a/src/tir/transforms/vectorize_loop.cc
+++ b/src/tir/transforms/vectorize_loop.cc
@@ -352,7 +352,7 @@ class Vectorizer : public StmtMutator, public ExprFunctor<PrimExpr(const PrimExp
   }
   // For
   Stmt VisitStmt_(const ForNode* op) final {
-    if (op->for_type == ForType::Vectorized) {
+    if (op->kind == ForKind::kVectorized) {
       LOG(WARNING) << "Detect vectorize inside vectorized loop, ignoring...";
     }
     ICHECK(is_zero(op->min));
@@ -365,7 +365,8 @@ class Vectorizer : public StmtMutator, public ExprFunctor<PrimExpr(const PrimExp
     if (extent.same_as(op->extent) && body.same_as(op->body)) {
       return GetRef<Stmt>(op);
     } else {
-      return For(op->loop_var, op->min, extent, op->for_type, op->device_api, body);
+      return For(op->loop_var, op->min, extent, op->kind, body, op->thread_binding,
+                 op->annotations);
     }
   }
   // IfThenElse
@@ -387,6 +388,11 @@ class Vectorizer : public StmtMutator, public ExprFunctor<PrimExpr(const PrimExp
       return IfThenElse(condition, then_case, else_case);
     }
   }
+  // While
+  Stmt VisitStmt_(const WhileNode* op) final {
+    LOG(FATAL) << "A while loop inside a vectorized loop not supported.";
+    return Stmt();
+  }
   // LetStmt
   Stmt VisitStmt_(const LetStmtNode* op) final {
     PrimExpr value = this->VisitExpr(op->value);
@@ -436,11 +442,11 @@ class Vectorizer : public StmtMutator, public ExprFunctor<PrimExpr(const PrimExp
     Var idx(var_->name_hint + ".s", var_->dtype);
     Map<Var, PrimExpr> values{{var_, idx}};
     stmt = Substitute(stmt, values);
-    return For(idx, 0, var_lanes_, ForType::Serial, DeviceAPI::None, stmt);
+    return For(idx, 0, var_lanes_, ForKind::kSerial, stmt);
   }
   // ProducerStore
   Stmt VisitStmt_(const ProducerStoreNode* op) final {
-    LOG(FATAL) << "ProducerProvide is cannot appear in a TIR PrimFunc";
+    LOG(FATAL) << "ProducerProvide cannot appear in a TIR PrimFunc";
     return Stmt();
   }
 
@@ -525,7 +531,7 @@ class Vectorizer : public StmtMutator, public ExprFunctor<PrimExpr(const PrimExp
 class LoopVectorizer : public StmtMutator {
  public:
   Stmt VisitStmt_(const ForNode* op) final {
-    if (op->for_type == ForType::Vectorized) {
+    if (op->kind == ForKind::kVectorized) {
       ICHECK(is_zero(op->min));
       auto* extent_as_int = op->extent.as<IntImmNode>();
       if (!extent_as_int || extent_as_int->value < 1) {
@@ -545,8 +551,8 @@ class VectorizeSkipper : public StmtMutator {
   Stmt VisitStmt_(const ForNode* op) final {
     Stmt stmt = StmtMutator::VisitStmt_(op);
     op = stmt.as<ForNode>();
-    if (op->for_type == ForType::Vectorized) {
-      return For(op->loop_var, op->min, op->extent, ForType::Serial, op->device_api, op->body);
+    if (op->kind == ForKind::kVectorized) {
+      return For(op->loop_var, op->min, op->extent, ForKind::kSerial, op->body);
     } else {
       return stmt;
     }
diff --git a/src/topi/transform.cc b/src/topi/transform.cc
index e1e3988f6400..f71fae3c5aaa 100644
--- a/src/topi/transform.cc
+++ b/src/topi/transform.cc
@@ -23,6 +23,7 @@
  */
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
+#include <tvm/topi/einsum.h>
 #include <tvm/topi/transform.h>
 #include <tvm/topi/utils.h>
 
@@ -165,6 +166,10 @@ TVM_REGISTER_GLOBAL("topi.tensordot").set_body([](TVMArgs args, TVMRetValue* rv)
   }
 });
 
+TVM_REGISTER_GLOBAL("topi.einsum").set_body([](TVMArgs args, TVMRetValue* rv) {
+  *rv = einsum(args[0], args[1]);
+});
+
 TVM_REGISTER_GLOBAL("topi.strided_slice").set_body([](TVMArgs args, TVMRetValue* rv) {
   *rv = strided_slice(args[0], args[1], args[2], args[3], args[4]);
 });
diff --git a/tests/cpp/contrib/bnns.cc b/tests/cpp/contrib/bnns.cc
new file mode 100644
index 000000000000..1efd487caff9
--- /dev/null
+++ b/tests/cpp/contrib/bnns.cc
@@ -0,0 +1,307 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+TEST(PackedFunc, Basic) {
+  using namespace tvm;
+  using namespace tvm::tir;
+  using namespace tvm::runtime;
+  int x = 0;
+  void* handle = &x;
+  DLTensor a;
+
+  Var v = PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    ICHECK(args.num_args == 3);
+    ICHECK(args.values[0].v_float64 == 1.0);
+    ICHECK(args.type_codes[0] == kDLFloat);
+    ICHECK(args.values[1].v_handle == &a);
+    ICHECK(args.type_codes[1] == kTVMDLTensorHandle);
+    ICHECK(args.values[2].v_handle == &x);
+    ICHECK(args.type_codes[2] == kTVMOpaqueHandle);
+    *rv = Var("a");
+  })(1.0, &a, handle);
+  ICHECK(v->name_hint == "a");
+}
+
+TEST(PackedFunc, Node) {
+  using namespace tvm;
+  using namespace tvm::tir;
+  using namespace tvm::runtime;
+  Var x;
+  Var t = PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    ICHECK(args.num_args == 1);
+    ICHECK(args[0].IsObjectRef<ObjectRef>());
+    Var b = args[0];
+    ICHECK(x.same_as(b));
+    *rv = b;
+  })(x);
+  ICHECK(t.same_as(x));
+}
+
+TEST(PackedFunc, NDArray) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  auto x = NDArray::Empty({}, String2DLDataType("float32"), TVMContext{kDLCPU, 0});
+  reinterpret_cast<float*>(x->data)[0] = 10.0f;
+  ICHECK(x.use_count() == 1);
+
+  PackedFunc forward([&](TVMArgs args, TVMRetValue* rv) { *rv = args[0]; });
+
+  NDArray ret = PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    NDArray y = args[0];
+    DLTensor* ptr = args[0];
+    ICHECK(ptr == x.operator->());
+    ICHECK(x.same_as(y));
+    ICHECK(x.use_count() == 2);
+    *rv = forward(y);
+  })(x);
+  ICHECK(ret.use_count() == 2);
+  ICHECK(ret.same_as(x));
+}
+
+TEST(PackedFunc, str) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    ICHECK(args.num_args == 1);
+    std::string x = args[0];
+    ICHECK(x == "hello");
+    String y = args[0];
+    ICHECK(y == "hello");
+    *rv = x;
+  })("hello");
+
+  PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    ICHECK(args.num_args == 1);
+    runtime::String s = args[0];
+    ICHECK(s == "hello");
+  })(runtime::String("hello"));
+}
+
+TEST(PackedFunc, func) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  PackedFunc addone([&](TVMArgs args, TVMRetValue* rv) { *rv = args[0].operator int() + 1; });
+  // function as arguments
+  int r0 = PackedFunc([](TVMArgs args, TVMRetValue* rv) {
+    PackedFunc f = args[0];
+    // TVMArgValue -> Arguments as function
+    *rv = f(args[1]).operator int();
+  })(addone, 1);
+  ICHECK_EQ(r0, 2);
+
+  int r1 = PackedFunc([](TVMArgs args, TVMRetValue* rv) {
+    // TVMArgValue -> TVMRetValue
+    *rv = args[1];
+  })(2, 100);
+  ICHECK_EQ(r1, 100);
+
+  int r2 = PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    // re-assignment
+    *rv = args[0];
+    // TVMRetValue -> Function argument
+    *rv = addone(args[0].operator PackedFunc()(args[1], 1));
+  })(addone, 100);
+  ICHECK_EQ(r2, 102);
+}
+
+TEST(PackedFunc, Expr) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  // automatic conversion of int to expr
+  PackedFunc addone([](TVMArgs args, TVMRetValue* rv) {
+    PrimExpr x = args[0];
+    *rv = x.as<tvm::tir::IntImmNode>()->value + 1;
+  });
+  int r0 = PackedFunc([](TVMArgs args, TVMRetValue* rv) {
+    PackedFunc f = args[0];
+    // TVMArgValue -> Arguments as function
+    *rv = f(args[1]).operator int();
+  })(addone, 1);
+  ICHECK_EQ(r0, 2);
+}
+
+TEST(PackedFunc, Type) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  auto get_type = PackedFunc([](TVMArgs args, TVMRetValue* rv) {
+    DataType x = args[0];
+    *rv = x;
+  });
+  auto get_type2 = PackedFunc([](TVMArgs args, TVMRetValue* rv) { *rv = args[0]; });
+  ICHECK(get_type("int32").operator DataType() == DataType::Int(32));
+  ICHECK(get_type("float").operator DataType() == DataType::Float(32));
+  ICHECK(get_type2("float32x2").operator DataType() == DataType::Float(32, 2));
+}
+
+TEST(TypedPackedFunc, HighOrder) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  using Int1Func = TypedPackedFunc<int(int)>;
+  using Int2Func = TypedPackedFunc<int(int, int)>;
+  using BindFunc = TypedPackedFunc<Int1Func(Int2Func, int value)>;
+  BindFunc ftyped;
+  ftyped = [](Int2Func f1, int value) -> Int1Func {
+    auto binded = [f1, value](int x) { return f1(value, x); };
+    Int1Func x(binded);
+    return x;
+  };
+  auto add = [](int x, int y) { return x + y; };
+  ICHECK_EQ(ftyped(Int2Func(add), 1)(2), 3);
+  PackedFunc f = ftyped(Int2Func(add), 1);
+  ICHECK_EQ(f(3).operator int(), 4);
+  // call the type erased version.
+  Int1Func f1 = ftyped.packed()(Int2Func(add), 1);
+  ICHECK_EQ(f1(3), 4);
+}
+
+TEST(TypedPackedFunc, Deduce) {
+  using namespace tvm::runtime;
+  using tvm::runtime::detail::function_signature;
+
+  TypedPackedFunc<int(float)> x;
+  auto f = [](int x) -> int { return x + 1; };
+  std::function<void(float)> y;
+
+  static_assert(std::is_same<function_signature<decltype(x)>::FType, int(float)>::value,
+                "invariant1");
+  static_assert(std::is_same<function_signature<decltype(f)>::FType, int(int)>::value,
+                "invariant2");
+  static_assert(std::is_same<function_signature<decltype(y)>::FType, void(float)>::value,
+                "invariant3");
+}
+
+TEST(PackedFunc, ObjectConversion) {
+  using namespace tvm;
+  using namespace tvm::tir;
+  using namespace tvm::runtime;
+  TVMRetValue rv;
+  auto x = NDArray::Empty({}, String2DLDataType("float32"), TVMContext{kDLCPU, 0});
+  // assign null
+  rv = ObjectRef();
+  ICHECK_EQ(rv.type_code(), kTVMNullptr);
+
+  // Can assign NDArray to ret type
+  rv = x;
+  ICHECK_EQ(rv.type_code(), kTVMNDArrayHandle);
+  // Even if we assign base type it still shows as NDArray
+  rv = ObjectRef(x);
+  ICHECK_EQ(rv.type_code(), kTVMNDArrayHandle);
+  // Check convert back
+  ICHECK(rv.operator NDArray().same_as(x));
+  ICHECK(rv.operator ObjectRef().same_as(x));
+  ICHECK(!rv.IsObjectRef<PrimExpr>());
+
+  auto pf1 = PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    ICHECK_EQ(args[0].type_code(), kTVMNDArrayHandle);
+    ICHECK(args[0].operator NDArray().same_as(x));
+    ICHECK(args[0].operator ObjectRef().same_as(x));
+    ICHECK(args[1].operator ObjectRef().get() == nullptr);
+    ICHECK(args[1].operator NDArray().get() == nullptr);
+    ICHECK(args[1].operator Module().get() == nullptr);
+    ICHECK(args[1].operator Array<NDArray>().get() == nullptr);
+    ICHECK(!args[0].IsObjectRef<PrimExpr>());
+  });
+  pf1(x, ObjectRef());
+  pf1(ObjectRef(x), NDArray());
+
+  // testcases for modules
+  auto* pf = tvm::runtime::Registry::Get("runtime.SourceModuleCreate");
+  ICHECK(pf != nullptr);
+  Module m = (*pf)("", "xyz");
+  rv = m;
+  ICHECK_EQ(rv.type_code(), kTVMModuleHandle);
+  // Even if we assign base type it still shows as NDArray
+  rv = ObjectRef(m);
+  ICHECK_EQ(rv.type_code(), kTVMModuleHandle);
+  // Check convert back
+  ICHECK(rv.operator Module().same_as(m));
+  ICHECK(rv.operator ObjectRef().same_as(m));
+  ICHECK(!rv.IsObjectRef<NDArray>());
+
+  auto pf2 = PackedFunc([&](TVMArgs args, TVMRetValue* rv) {
+    ICHECK_EQ(args[0].type_code(), kTVMModuleHandle);
+    ICHECK(args[0].operator Module().same_as(m));
+    ICHECK(args[0].operator ObjectRef().same_as(m));
+    ICHECK(args[1].operator ObjectRef().get() == nullptr);
+    ICHECK(args[1].operator NDArray().get() == nullptr);
+    ICHECK(args[1].operator Module().get() == nullptr);
+    ICHECK(!args[0].IsObjectRef<PrimExpr>());
+  });
+  pf2(m, ObjectRef());
+  pf2(ObjectRef(m), Module());
+}
+
+TEST(TypedPackedFunc, RValue) {
+  using namespace tvm;
+  using namespace tvm::runtime;
+  {
+    auto inspect = [](TVMArgs args, TVMRetValue* rv) {
+      for (int i = 0; i < args.size(); ++i) {
+        ICHECK_EQ(args[0].type_code(), kTVMObjectRValueRefArg);
+      }
+    };
+    PackedFunc finspect(inspect);
+    finspect(tir::Var("x"));
+  }
+  {
+    auto f = [](tir::Var x, bool move) {
+      if (move) {
+        ICHECK(x.unique());
+      } else {
+        ICHECK(!x.unique());
+      }
+      ICHECK(x->name_hint == "x");
+      return x;
+    };
+    TypedPackedFunc<tir::Var(tir::Var, bool)> tf(f);
+
+    tir::Var var("x");
+    ICHECK(var.unique());
+    tf(var, false);
+    // move the result to the function.
+    tir::Var ret = tf(std::move(var), true);
+    ICHECK(!var.defined());
+  }
+
+  {
+    // pass child class.
+    auto f = [](PrimExpr x, bool move) {
+      if (move) {
+        ICHECK(x.unique());
+      } else {
+        ICHECK(!x.unique());
+      }
+      return x;
+    };
+    TypedPackedFunc<PrimExpr(PrimExpr, bool)> tf(f);
+
+    tir::Var var("x");
+    ICHECK(var.unique());
+    tf(var, false);
+    tf(std::move(var), true);
+    // auto conversion.
+    tf(1, true);
+  }
+}
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  testing::FLAGS_gtest_death_test_style = "threadsafe";
+  return RUN_ALL_TESTS();
+}
diff --git a/tests/cpp/dataflow_pattern_test.cc b/tests/cpp/dataflow_pattern_test.cc
new file mode 100644
index 000000000000..bdccaaa2e6ba
--- /dev/null
+++ b/tests/cpp/dataflow_pattern_test.cc
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <gtest/gtest.h>
+#include <tvm/relay/dataflow_pattern.h>
+#include <tvm/tir/analysis.h>
+
+TEST(DFPattern, IsVar) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto pattern = IsVar("add");
+  auto* node = pattern.as<VarPatternNode>();
+  ICHECK(node);
+  ICHECK(node->name == String("add"));
+}
+
+TEST(DFPattern, IsConstant) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto pattern = IsConstant();
+  auto* node = pattern.as<ConstantPatternNode>();
+  ICHECK(node);
+}
+
+TEST(DFPattern, IsOp) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto pattern = IsOp("add");
+  auto* node = pattern.as<ExprPatternNode>();
+  ICHECK(node);
+  ICHECK(node->expr == Op::Get("add"));
+}
+
+TEST(DFPattern, IsTuple) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto pattern = IsTuple({a, b});
+  auto* node = pattern.as<TuplePatternNode>();
+  ICHECK(node);
+  ICHECK(node->fields[0] == a);
+  ICHECK(node->fields[1] == b);
+}
+
+TEST(DFPattern, IsTupleGetItem) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto tuple = IsTuple({a, b});
+  auto pattern = IsTupleGetItem(tuple, 1);
+  auto* node = pattern.as<TupleGetItemPatternNode>();
+  ICHECK(node);
+  ICHECK(node->tuple == tuple);
+  ICHECK(node->index == 1);
+}
+
+TEST(DFPattern, ADD) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto pattern = a + b;
+  auto* node = pattern.as<CallPatternNode>();
+  ICHECK(node);
+  ICHECK(node->args[0] == a);
+  ICHECK(node->args[1] == b);
+  auto* expr_pattern = node->op.as<ExprPatternNode>();
+  ICHECK(expr_pattern);
+  ICHECK(expr_pattern->expr == Op::Get("add"));
+}
+
+TEST(DFPattern, SUB) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto pattern = a - b;
+  auto* node = pattern.as<CallPatternNode>();
+  ICHECK(node);
+  ICHECK(node->args[0] == a);
+  ICHECK(node->args[1] == b);
+  auto* expr_pattern = node->op.as<ExprPatternNode>();
+  ICHECK(expr_pattern);
+  ICHECK(expr_pattern->expr == Op::Get("subtract"));
+}
+
+TEST(DFPattern, MUL) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto pattern = a * b;
+  auto* node = pattern.as<CallPatternNode>();
+  ICHECK(node);
+  ICHECK(node->args[0] == a);
+  ICHECK(node->args[1] == b);
+  auto* expr_pattern = node->op.as<ExprPatternNode>();
+  ICHECK(expr_pattern);
+  ICHECK(expr_pattern->expr == Op::Get("multiply"));
+}
+
+TEST(DFPattern, DIV) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto pattern = a / b;
+  auto* node = pattern.as<CallPatternNode>();
+  ICHECK(node);
+  ICHECK(node->args[0] == a);
+  ICHECK(node->args[1] == b);
+  auto* expr_pattern = node->op.as<ExprPatternNode>();
+  ICHECK(expr_pattern);
+  ICHECK(expr_pattern->expr == Op::Get("divide"));
+}
+
+TEST(DFPattern, OR) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto b = WildcardPattern();
+  auto pattern = a || b;
+  auto* node = pattern.as<AltPatternNode>();
+  ICHECK(node);
+  ICHECK(node->left == a);
+  ICHECK(node->right == b);
+}
+
+TEST(DFPattern, HasAttr) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  Map<String, ObjectRef> attrs;
+  auto b = String("b");
+  attrs.Set("a", b);
+  auto pattern = a.HasAttr(attrs);
+  auto* node = pattern.as<AttrPatternNode>();
+  ICHECK(node);
+  ICHECK(node->pattern == a);
+  ICHECK(node->attrs->dict.at("a") == b);
+}
+
+TEST(DFPattern, HasType) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  TensorType type({1, 2, 3}, DataType(runtime::String2DLDataType("float32")));
+  auto pattern = a.HasType(type);
+  auto* node = pattern.as<TypePatternNode>();
+  ICHECK(node);
+  ICHECK(node->pattern == a);
+  ICHECK(node->type == type);
+}
+
+TEST(DFPattern, HasDtype) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  auto pattern = a.HasDtype("float32");
+  auto* node = pattern.as<DataTypePatternNode>();
+  ICHECK(node);
+  ICHECK(node->pattern == a);
+  ICHECK(runtime::DLDataType2String(node->dtype.operator DLDataType()) == "float32");
+}
+
+TEST(DFPattern, HasShape) {
+  using namespace tvm;
+  using namespace tvm::relay;
+  auto a = WildcardPattern();
+  Array<PrimExpr> shape{1, 2, 3};
+  auto pattern = a.HasShape(shape);
+  auto* node = pattern.as<ShapePatternNode>();
+  ICHECK(node);
+  ICHECK(node->pattern == a);
+  ICHECK(node->shape == shape);
+}
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  testing::FLAGS_gtest_death_test_style = "threadsafe";
+  return RUN_ALL_TESTS();
+}
diff --git a/tests/cpp/ir_functor_test.cc b/tests/cpp/ir_functor_test.cc
index 683caaa7c5de..9e8595d6809c 100644
--- a/tests/cpp/ir_functor_test.cc
+++ b/tests/cpp/ir_functor_test.cc
@@ -19,10 +19,14 @@
 
 #include <dmlc/logging.h>
 #include <gtest/gtest.h>
+#include <tvm/ir/module.h>
 #include <tvm/node/functor.h>
+#include <tvm/relay/function.h>
+#include <tvm/tir/analysis.h>
 #include <tvm/tir/builtin.h>
 #include <tvm/tir/expr.h>
 #include <tvm/tir/expr_functor.h>
+#include <tvm/tir/function.h>
 #include <tvm/tir/op.h>
 #include <tvm/tir/stmt_functor.h>
 
@@ -52,6 +56,55 @@ TEST(IRF, CountVar) {
   ICHECK_EQ(n_var, 2);
 }
 
+TEST(IRF, VisitPrimFuncs) {
+  using namespace tvm;
+  using namespace tvm::tir;
+  PrimFunc prim_func(/*params=*/{}, /*body=*/Evaluate(Integer(0)));
+  relay::Function relay_func(/*params=*/{}, /*body=*/relay::Expr(nullptr),
+                             /*ret_type=*/relay::Type{nullptr}, /*ty_params=*/{});
+  IRModule mod({
+      {GlobalVar("main"), prim_func},
+      {GlobalVar("main2"), relay_func},
+  });
+  int n_visited = 0;
+  VisitPrimFuncs(mod, [&](const PrimFuncNode* func) { ++n_visited; });
+  ASSERT_EQ(n_visited, 1);
+}
+
+TEST(IRF, PreOrderVisit) {
+  using namespace tvm;
+  using namespace tvm::tir;
+  Stmt init = IfThenElse(const_true(), Evaluate(Integer(0)), Evaluate(Integer(0)));
+  Stmt body = Evaluate(Integer(1));
+  Block block(/*iter_vars=*/{}, /*reads=*/{},
+              /*writes=*/{}, /*name_hint=*/"block", /*body=*/body,
+              /*init=*/init);
+  bool init_visited = false;
+  bool stopped_at_if = true;
+  bool body_visited = false;
+  PreOrderVisit(block, [&](const ObjectRef& n) -> bool {
+    if (n->IsInstance<IfThenElseNode>()) {
+      init_visited = true;
+      return false;
+    }
+    if (const auto* eval = n.as<EvaluateNode>()) {
+      if (const auto* int_imm = eval->value.as<IntImmNode>()) {
+        if (int_imm->value == 0) {
+          stopped_at_if = false;
+        } else if (int_imm->value == 1) {
+          body_visited = true;
+        } else {
+          LOG(FATAL) << "Unreachable";
+        }
+      }
+    }
+    return true;
+  });
+  ASSERT_EQ(init_visited, true);
+  ASSERT_EQ(stopped_at_if, true);
+  ASSERT_EQ(body_visited, true);
+}
+
 TEST(IRF, ExprTransform) {
   using namespace tvm;
   using namespace tvm::tir;
@@ -72,7 +125,7 @@ TEST(IRF, ExprTransform) {
   try {
     f(z - 1, 2);
     LOG(FATAL) << "should fail";
-  } catch (dmlc::Error) {
+  } catch (Error&) {
   }
 }
 
@@ -114,11 +167,31 @@ TEST(IRF, StmtVisitor) {
   auto fmaketest = [&]() {
     auto z = x + 1;
     Stmt body = Evaluate(z);
-    Var buffer("b", DataType::Handle());
-    return Allocate(buffer, DataType::Float(32), {z, z}, const_true(), body);
+    DataType dtype = DataType::Float(32);
+    Var buffer("b", PointerType(PrimType(dtype)));
+    return Allocate(buffer, dtype, {z, z}, const_true(), body);
   };
   v(fmaketest());
   ICHECK_EQ(v.count, 3);
+
+  {
+    // tests for block and block_realize
+    Stmt body = fmaketest();
+    DataType dtype = DataType::Float(32);
+    Var buf_var("b", PointerType(PrimType(dtype)));
+    Buffer buffer = decl_buffer({16});
+    BufferRegion buffer_region(buffer, {Range::FromMinExtent(x + 1, 1)});
+    MatchBufferRegion match_buffer_region(decl_buffer({1}), buffer_region);
+
+    // construct block and block_realize
+    Block block =
+        Block({}, {buffer_region}, {buffer_region}, "block", body, body, {}, {match_buffer_region});
+    Stmt block_realize = BlockRealize({}, const_true(), block);
+
+    v.count = 0;
+    v(block_realize);
+    ICHECK_EQ(v.count, 9);
+  }
 }
 
 TEST(IRF, StmtMutator) {
@@ -140,8 +213,9 @@ TEST(IRF, StmtMutator) {
   auto fmakealloc = [&]() {
     auto z = x + 1;
     Stmt body = Evaluate(z);
-    Var buffer("b", DataType::Handle());
-    return Allocate(buffer, DataType::Float(32), {1, z}, const_true(), body);
+    DataType dtype = DataType::Float(32);
+    Var buffer("b", PointerType(PrimType(dtype)));
+    return Allocate(buffer, dtype, {1, z}, const_true(), body);
   };
 
   auto fmakeif = [&]() {
@@ -227,6 +301,28 @@ TEST(IRF, StmtMutator) {
     // the seq get flattened
     ICHECK(body.as<SeqStmtNode>()->seq[0].as<AllocateNode>()->extents.get() != extentptr);
   }
+
+  {
+    // tests for block and block_realize
+    Stmt body = fmakealloc();
+    DataType dtype = DataType::Float(32);
+    Var buf_var("b", PointerType(PrimType(dtype)));
+    Buffer buffer = decl_buffer({16});
+    BufferRegion buffer_region(buffer, {Range::FromMinExtent(x + 1, 1)});
+    MatchBufferRegion match_buffer_region(decl_buffer({1}), buffer_region);
+    // construct block and block_realize
+    Block block =
+        Block({}, {buffer_region}, {buffer_region}, "block", body, body, {}, {match_buffer_region});
+    Stmt block_realize = BlockRealize({}, const_true(), block);
+    body = v(std::move(block_realize));
+    // the body should be changed
+    Block new_block = body.as<BlockRealizeNode>()->block;
+    ICHECK(new_block->body.as<AllocateNode>()->extents[1].same_as(x));
+    ICHECK(new_block->init.as<AllocateNode>()->extents[1].same_as(x));
+    ICHECK(new_block->reads[0]->region[0]->min.same_as(x));
+    ICHECK(new_block->writes[0]->region[0]->min.same_as(x));
+    ICHECK(new_block->match_buffers[0]->source->region[0]->min.same_as(x));
+  }
 }
 
 int main(int argc, char** argv) {
diff --git a/tests/cpp/parallel_for_test.cc b/tests/cpp/parallel_for_test.cc
index bf5fe94b83ff..a4549344bd11 100644
--- a/tests/cpp/parallel_for_test.cc
+++ b/tests/cpp/parallel_for_test.cc
@@ -19,7 +19,7 @@
 
 #include <dmlc/logging.h>
 #include <gtest/gtest.h>
-#include <tvm/support/logging.h>
+#include <tvm/runtime/logging.h>
 #include <tvm/support/parallel_for.h>
 
 #include <vector>
diff --git a/tests/cpp/profiling.cc b/tests/cpp/profiling.cc
new file mode 100644
index 000000000000..6ec2fc060f9f
--- /dev/null
+++ b/tests/cpp/profiling.cc
@@ -0,0 +1,47 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <gtest/gtest.h>
+#include <tvm/runtime/profiling.h>
+
+#include <chrono>
+#include <thread>
+
+namespace tvm {
+namespace runtime {
+TEST(DefaultTimer, Basic) {
+  using namespace tvm::runtime;
+  DLContext ctx;
+  ctx.device_type = kDLCPU;
+  ctx.device_id = 0;
+
+  Timer t = Timer::Start(ctx);
+  std::this_thread::sleep_for(std::chrono::milliseconds(10));
+  t->Stop();
+  int64_t elapsed = t->SyncAndGetElapsedNanos();
+  CHECK_GT(elapsed, 9 * 1e6);
+}
+}  // namespace runtime
+}  // namespace tvm
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  testing::FLAGS_gtest_death_test_style = "threadsafe";
+  return RUN_ALL_TESTS();
+}
diff --git a/tests/cpp/relay_build_module_test.cc b/tests/cpp/relay_build_module_test.cc
index 3212f9079619..a15cdcd3926b 100644
--- a/tests/cpp/relay_build_module_test.cc
+++ b/tests/cpp/relay_build_module_test.cc
@@ -105,7 +105,9 @@ TEST(Relay, BuildModule) {
   }
   auto fgeneric = GenericFunc::Get("test.strategy_generic").set_default(*fs);
   (*reg)("add", "FTVMStrategy", fgeneric, 10);
-  (*reg)("add", "TShapeDataDependant", false, 10);
+  Array<Integer> dep;
+  dep.push_back(0);
+  (*reg)("add", "TShapeDataDependent", dep, 10);
   // build
   auto pfb = tvm::runtime::Registry::Get("relay.build_module._BuildModule");
   tvm::runtime::Module build_mod = (*pfb)();
diff --git a/tests/cpp/target_test.cc b/tests/cpp/target_test.cc
index a422f12b04d7..8dba462132ac 100644
--- a/tests/cpp/target_test.cc
+++ b/tests/cpp/target_test.cc
@@ -152,6 +152,12 @@ TEST(TargetCreation, DeduplicateKeys) {
   ICHECK_EQ(target->GetAttr<Bool>("link-params"), false);
 }
 
+TEST(TargetKindRegistryListTargetKinds, Basic) {
+  Array<String> names = TargetKindRegEntry::ListTargetKinds();
+  ICHECK_EQ(names.empty(), false);
+  ICHECK_EQ(std::count(std::begin(names), std::end(names), "llvm"), 1);
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   testing::FLAGS_gtest_death_test_style = "threadsafe";
diff --git a/tests/crt/session_test.cc b/tests/crt/session_test.cc
index a1d57fcb5436..60686be25060 100644
--- a/tests/crt/session_test.cc
+++ b/tests/crt/session_test.cc
@@ -55,8 +55,9 @@ class TestSession {
   TestSession(uint8_t initial_nonce)
       : framer{&framer_write_stream},
         receive_buffer{receive_buffer_array, sizeof(receive_buffer_array)},
-        sess{initial_nonce, &framer, &receive_buffer, TestSessionMessageReceivedThunk, this},
-        unframer{sess.Receiver()} {}
+        sess{&framer, &receive_buffer, TestSessionMessageReceivedThunk, this},
+        unframer{sess.Receiver()},
+        initial_nonce{initial_nonce} {}
 
   void WriteTo(TestSession* other) {
     auto framer_buffer = framer_write_stream.BufferContents();
@@ -84,6 +85,7 @@ class TestSession {
   FrameBuffer receive_buffer;
   Session sess;
   Unframer unframer;
+  uint8_t initial_nonce;
 };
 
 #define EXPECT_FRAMED_PACKET(session, expected)          \
@@ -126,14 +128,14 @@ class SessionTest : public ::testing::Test {
 
 TEST_F(SessionTest, NormalExchange) {
   tvm_crt_error_t err;
-  err = alice_.sess.Initialize();
+  err = alice_.sess.Initialize(alice_.initial_nonce);
   EXPECT_EQ(kTvmErrorNoError, err);
   EXPECT_FRAMED_PACKET(alice_,
                        "\xfe\xff\xfd\x03\0\0\0\0\0\x02"
                        "fw");
   alice_.WriteTo(&bob_);
 
-  err = bob_.sess.Initialize();
+  err = bob_.sess.Initialize(bob_.initial_nonce);
   EXPECT_EQ(kTvmErrorNoError, err);
   EXPECT_FRAMED_PACKET(bob_,
                        "\xfe\xff\xfd\x03\0\0\0\0\0\x02"
@@ -212,14 +214,14 @@ static constexpr const char kBobStartPacket[] = "\xff\xfd\x04\0\0\0f\0\0\x01`\xa
 
 TEST_F(SessionTest, DoubleStart) {
   tvm_crt_error_t err;
-  err = alice_.sess.Initialize();
+  err = alice_.sess.Initialize(alice_.initial_nonce);
   EXPECT_EQ(kTvmErrorNoError, err);
   EXPECT_FRAMED_PACKET(alice_,
                        "\xfe\xff\xfd\x03\0\0\0\0\0\x02"
                        "fw");
   alice_.WriteTo(&bob_);
 
-  err = bob_.sess.Initialize();
+  err = bob_.sess.Initialize(bob_.initial_nonce);
   EXPECT_EQ(kTvmErrorNoError, err);
   EXPECT_FRAMED_PACKET(bob_,
                        "\xfe\xff\xfd\x03\0\0\0\0\0\x02"
diff --git a/tests/lint/check_file_type.py b/tests/lint/check_file_type.py
index ab51b6c79c83..f5c0de0a50b0 100644
--- a/tests/lint/check_file_type.py
+++ b/tests/lint/check_file_type.py
@@ -131,6 +131,8 @@
     # microTVM Virtual Machines
     "apps/microtvm/reference-vm/zephyr/Vagrantfile",
     "apps/microtvm/reference-vm/zephyr/base-box/Vagrantfile.packer-template",
+    # patch file for libbacktrace
+    "cmake/modules/libbacktrace_macos.patch",
 }
 
 
diff --git a/tests/micro/qemu/conftest.py b/tests/micro/qemu/conftest.py
index e6cd9f2ffb1a..3fc54df02063 100644
--- a/tests/micro/qemu/conftest.py
+++ b/tests/micro/qemu/conftest.py
@@ -14,6 +14,7 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+import pytest
 
 
 def pytest_addoption(parser):
@@ -25,8 +26,16 @@ def pytest_addoption(parser):
             "for microTVM tests."
         ),
     )
+    parser.addoption(
+        "--west-cmd", default="west", help="Path to `west` command for flashing device."
+    )
 
 
 def pytest_generate_tests(metafunc):
     if "platform" in metafunc.fixturenames:
         metafunc.parametrize("platform", metafunc.config.getoption("microtvm_platforms").split(","))
+
+
+@pytest.fixture
+def west_cmd(request):
+    return request.config.getoption("--west-cmd")
diff --git a/tests/micro/qemu/test_zephyr.py b/tests/micro/qemu/test_zephyr.py
index 1c38c2dcd187..4c8bd5f5dae8 100644
--- a/tests/micro/qemu/test_zephyr.py
+++ b/tests/micro/qemu/test_zephyr.py
@@ -33,6 +33,8 @@
 
 from tvm.micro.contrib import zephyr
 from tvm.contrib import utils
+from tvm.relay.expr_functor import ExprMutator
+from tvm.relay.op.annotation import compiler_begin, compiler_end
 
 BUILD = True
 DEBUG = False
@@ -41,15 +43,15 @@
 TARGET = None
 
 
-def _make_sess_from_op(model, zephyr_board, op_name, sched, arg_bufs):
+def _make_sess_from_op(model, zephyr_board, west_cmd, op_name, sched, arg_bufs):
     target = tvm.target.target.micro(model)
     with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
         mod = tvm.build(sched, arg_bufs, target, target_host=target, name=op_name)
 
-    return _make_session(model, target, zephyr_board, mod)
+    return _make_session(model, target, zephyr_board, west_cmd, mod)
 
 
-def _make_session(model, target, zephyr_board, mod):
+def _make_session(model, target, zephyr_board, west_cmd, mod):
     test_name = f"{os.path.splitext(os.path.abspath(__file__))[0]}-{model}"
     prev_build = f"{test_name}-last-build.micro-binary"
     workspace_root = (
@@ -63,8 +65,9 @@ def _make_session(model, target, zephyr_board, mod):
     project_dir = os.path.join(os.path.dirname(__file__) or ".", "zephyr-runtime")
     compiler = zephyr.ZephyrCompiler(
         project_dir=project_dir,
-        board="nucleo_f746zg" if "stm32f746" in str(target) else "qemu_x86",
+        board=zephyr_board,
         zephyr_toolchain_variant="zephyr",
+        west_cmd=west_cmd,
     )
 
     opts = tvm.micro.default_options(f"{project_dir}/crt")
@@ -89,8 +92,7 @@ def _make_session(model, target, zephyr_board, mod):
             workspace,
             compiler,
             mod,
-            lib_opts=opts["lib_opts"],
-            bin_opts=opts["bin_opts"],
+            opts,
         )
         if os.path.exists(prev_build):
             os.unlink(prev_build)
@@ -104,12 +106,12 @@ def _make_session(model, target, zephyr_board, mod):
     return tvm.micro.Session(**session_kw)
 
 
-def _make_add_sess(model, zephyr_board):
+def _make_add_sess(model, zephyr_board, west_cmd):
     A = tvm.te.placeholder((2,), dtype="int8")
     B = tvm.te.placeholder((1,), dtype="int8")
     C = tvm.te.compute(A.shape, lambda i: A[i] + B[0], name="C")
     sched = tvm.te.create_schedule(C.op)
-    return _make_sess_from_op(model, zephyr_board, "add", sched, [A, B, C])
+    return _make_sess_from_op(model, zephyr_board, west_cmd, "add", sched, [A, B, C])
 
 
 # The models that should pass this configuration. Maps a short, identifying platform string to
@@ -117,11 +119,12 @@ def _make_add_sess(model, zephyr_board):
 PLATFORMS = {
     "host": ("host", "qemu_x86"),
     "stm32f746xx": ("stm32f746xx", "nucleo_f746zg"),
+    "nrf5340dk": ("nrf5340dk", "nrf5340dk_nrf5340_cpuapp"),
 }
 
 
 # The same test code can be executed on both the QEMU simulation and on real hardware.
-def test_compile_runtime(platform):
+def test_compile_runtime(platform, west_cmd):
     """Test compiling the on-device runtime."""
 
     model, zephyr_board = PLATFORMS[platform]
@@ -139,11 +142,11 @@ def test_basic_add(sess):
         system_lib.get_function("add")(A_data, B_data, C_data)
         assert (C_data.asnumpy() == np.array([6, 7])).all()
 
-    with _make_add_sess(model, zephyr_board) as sess:
+    with _make_add_sess(model, zephyr_board, west_cmd) as sess:
         test_basic_add(sess)
 
 
-def test_platform_timer(platform):
+def test_platform_timer(platform, west_cmd):
     """Test compiling the on-device runtime."""
 
     model, zephyr_board = PLATFORMS[platform]
@@ -166,11 +169,11 @@ def test_basic_add(sess):
         assert result.mean > 0
         assert len(result.results) == 3
 
-    with _make_add_sess(model, zephyr_board) as sess:
+    with _make_add_sess(model, zephyr_board, west_cmd) as sess:
         test_basic_add(sess)
 
 
-def test_relay(platform):
+def test_relay(platform, west_cmd):
     """Testing a simple relay graph"""
     model, zephyr_board = PLATFORMS[platform]
     shape = (10,)
@@ -186,7 +189,7 @@ def test_relay(platform):
     with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
         graph, mod, params = tvm.relay.build(func, target=target)
 
-    with _make_session(model, target, zephyr_board, mod) as session:
+    with _make_session(model, target, zephyr_board, west_cmd, mod) as session:
         graph_mod = tvm.micro.create_local_graph_runtime(
             graph, session.get_system_lib(), session.context
         )
@@ -198,5 +201,144 @@ def test_relay(platform):
         tvm.testing.assert_allclose(result, x_in * x_in + 1)
 
 
+class CcompilerAnnotator(ExprMutator):
+    """
+    This is used to create external functions for ccompiler.
+    A simple annotator that creates the following program:
+           |
+      -- begin --
+           |
+          add
+           |
+        subtract
+           |
+        multiply
+           |
+       -- end --
+           |
+    """
+
+    def __init__(self):
+        super(CcompilerAnnotator, self).__init__()
+        self.in_compiler = 0
+
+    def visit_call(self, call):
+        if call.op.name == "add":  # Annotate begin at args
+            if self.in_compiler == 1:
+                lhs = compiler_begin(super().visit(call.args[0]), "ccompiler")
+                rhs = compiler_begin(super().visit(call.args[1]), "ccompiler")
+                op = relay.add(lhs, rhs)
+                self.in_compiler = 2
+                return op
+        elif call.op.name == "subtract":
+            if self.in_compiler == 1:
+                lhs = super().visit(call.args[0])
+                rhs = super().visit(call.args[1])
+                if isinstance(lhs, relay.expr.Var):
+                    lhs = compiler_begin(lhs, "ccompiler")
+                if isinstance(rhs, relay.expr.Var):
+                    rhs = compiler_begin(rhs, "ccompiler")
+                return relay.subtract(lhs, rhs)
+        elif call.op.name == "multiply":  # Annotate end at output
+            self.in_compiler = 1
+            lhs = super().visit(call.args[0])
+            rhs = super().visit(call.args[1])
+            if isinstance(lhs, relay.expr.Var):
+                lhs = compiler_begin(lhs, "ccompiler")
+            if isinstance(rhs, relay.expr.Var):
+                rhs = compiler_begin(rhs, "ccompiler")
+            op = relay.multiply(lhs, rhs)
+            if self.in_compiler == 2:
+                op = compiler_end(op, "ccompiler")
+            self.in_compiler = 0
+            return op
+        return super().visit_call(call)
+
+
+def check_result(relay_mod, model, zephyr_board, west_cmd, map_inputs, out_shape, result):
+    """Helper function to verify results"""
+    TOL = 1e-5
+    target = tvm.target.target.micro(model)
+    with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
+        graph, mod, params = tvm.relay.build(relay_mod, target=target)
+
+    with _make_session(model, target, zephyr_board, west_cmd, mod) as session:
+        rt_mod = tvm.micro.create_local_graph_runtime(
+            graph, session.get_system_lib(), session.context
+        )
+        rt_mod.set_input(**params)
+        for name, data in map_inputs.items():
+            rt_mod.set_input(name, data)
+        rt_mod.set_input(**params)
+        rt_mod.run()
+
+        out_shapes = out_shape if isinstance(out_shape, list) else [out_shape]
+        results = result if isinstance(result, list) else [result]
+
+        for idx, shape in enumerate(out_shapes):
+            out = tvm.nd.empty(shape, ctx=session.context)
+            out = rt_mod.get_output(idx, out)
+            tvm.testing.assert_allclose(out.asnumpy(), results[idx], rtol=TOL, atol=TOL)
+
+
+def test_byoc_utvm(platform, west_cmd):
+    """This is a simple test case to check BYOC capabilities of uTVM"""
+    model, zephyr_board = PLATFORMS[platform]
+    x = relay.var("x", shape=(10, 10))
+    w0 = relay.var("w0", shape=(10, 10))
+    w1 = relay.var("w1", shape=(10, 10))
+    w2 = relay.var("w2", shape=(10, 10))
+    w3 = relay.var("w3", shape=(10, 10))
+    w4 = relay.var("w4", shape=(10, 10))
+    w5 = relay.var("w5", shape=(10, 10))
+    w6 = relay.var("w6", shape=(10, 10))
+    w7 = relay.var("w7", shape=(10, 10))
+
+    # C compiler
+    z0 = relay.add(x, w0)
+    p0 = relay.subtract(z0, w1)
+    q0 = relay.multiply(p0, w2)
+
+    z1 = relay.add(x, w3)
+    p1 = relay.subtract(z1, w4)
+    q1 = relay.multiply(p1, w5)
+
+    # Other parts on TVM
+    z2 = relay.add(x, w6)
+    q2 = relay.subtract(z2, w7)
+
+    r = relay.concatenate((q0, q1, q2), axis=0)
+    f = relay.Function([x, w0, w1, w2, w3, w4, w5, w6, w7], r)
+    mod = tvm.IRModule()
+    ann = CcompilerAnnotator()
+    mod["main"] = ann.visit(f)
+    mod = tvm.relay.transform.PartitionGraph()(mod)
+    mod = tvm.relay.transform.InferType()(mod)
+
+    x_data = np.random.rand(10, 10).astype("float32")
+    w_data = []
+    for _ in range(8):
+        w_data.append(np.random.rand(10, 10).astype("float32"))
+
+    map_inputs = {"w{}".format(i): w_data[i] for i in range(8)}
+    map_inputs["x"] = x_data
+    check_result(
+        relay_mod=mod,
+        map_inputs=map_inputs,
+        out_shape=(30, 10),
+        result=np.concatenate(
+            (
+                ((x_data + w_data[0]) - w_data[1]) * w_data[2],
+                ((x_data + w_data[3]) - w_data[4]) * w_data[5],
+                x_data + w_data[6] - w_data[7],
+            ),
+            axis=0,
+        ),
+        model=model,
+        zephyr_board=zephyr_board,
+        west_cmd=west_cmd,
+    )
+
+
 if __name__ == "__main__":
     sys.exit(pytest.main([os.path.dirname(__file__)] + sys.argv[1:]))
diff --git a/tests/micro/qemu/zephyr-runtime/prj.conf b/tests/micro/qemu/zephyr-runtime/prj.conf
index cebb55756e8c..7be42b260bbb 100644
--- a/tests/micro/qemu/zephyr-runtime/prj.conf
+++ b/tests/micro/qemu/zephyr-runtime/prj.conf
@@ -29,3 +29,7 @@ CONFIG_FPU=y
 
 # For TVMPlatformAbort().
 CONFIG_REBOOT=y
+
+# For TVMPlatformGenerateRandom(). Remember, these values do not need to be truly random.
+CONFIG_TEST_RANDOM_GENERATOR=y
+CONFIG_TIMER_RANDOM_GENERATOR=y
diff --git a/tests/micro/qemu/zephyr-runtime/src/main.c b/tests/micro/qemu/zephyr-runtime/src/main.c
index 9d10504dcbed..e04fc20508b4 100644
--- a/tests/micro/qemu/zephyr-runtime/src/main.c
+++ b/tests/micro/qemu/zephyr-runtime/src/main.c
@@ -26,6 +26,7 @@
 #include <drivers/uart.h>
 #include <kernel.h>
 #include <power/reboot.h>
+#include <random/rand32.h>
 #include <stdio.h>
 #include <sys/printk.h>
 #include <sys/ring_buffer.h>
@@ -161,6 +162,26 @@ tvm_crt_error_t TVMPlatformTimerStop(double* elapsed_time_seconds) {
   return kTvmErrorNoError;
 }
 
+tvm_crt_error_t TVMPlatformGenerateRandom(uint8_t* buffer, size_t num_bytes) {
+  uint32_t random;  // one unit of random data.
+
+  // Fill parts of `buffer` which are as large as `random`.
+  size_t num_full_blocks = num_bytes / sizeof(random);
+  for (int i = 0; i < num_full_blocks; ++i) {
+    random = sys_rand32_get();
+    memcpy(&buffer[i * sizeof(random)], &random, sizeof(random));
+  }
+
+  // Fill any leftover tail which is smaller than `random`.
+  size_t num_tail_bytes = num_bytes % sizeof(random);
+  if (num_tail_bytes > 0) {
+    random = sys_rand32_get();
+    memcpy(&buffer[num_bytes - num_tail_bytes], &random, num_tail_bytes);
+  }
+
+  return kTvmErrorNoError;
+}
+
 #define RING_BUF_SIZE 512
 struct uart_rx_buf_t {
   struct ring_buf buf;
diff --git a/tests/python/conftest.py b/tests/python/conftest.py
new file mode 100644
index 000000000000..e8042c8f5095
--- /dev/null
+++ b/tests/python/conftest.py
@@ -0,0 +1,42 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import sys
+import tvm
+
+collect_ignore = []
+if sys.platform.startswith("win"):
+    collect_ignore.append("frontend/caffe")
+    collect_ignore.append("frontend/caffe2")
+    collect_ignore.append("frontend/coreml")
+    collect_ignore.append("frontend/darknet")
+    collect_ignore.append("frontend/keras")
+    collect_ignore.append("frontend/mxnet")
+    collect_ignore.append("frontend/pytorch")
+    collect_ignore.append("frontend/tensorflow")
+    collect_ignore.append("frontend/tflite")
+    collect_ignore.append("frontend/onnx")
+    collect_ignore.append("driver/tvmc/test_autoscheduler.py")
+    collect_ignore.append("unittest/test_auto_scheduler_cost_model.py")  # stack overflow
+    # collect_ignore.append("unittest/test_auto_scheduler_measure.py") # exception ignored
+    collect_ignore.append("unittest/test_auto_scheduler_search_policy.py")  # stack overflow
+    # collect_ignore.append("unittest/test_auto_scheduler_measure.py") # exception ignored
+
+    collect_ignore.append("unittest/test_tir_intrin.py")
+
+if tvm.support.libinfo().get("USE_MICRO", "OFF") != "ON":
+    collect_ignore.append("unittest/test_micro_transport.py")
diff --git a/tests/python/contrib/test_arm_compute_lib/infrastructure.py b/tests/python/contrib/test_arm_compute_lib/infrastructure.py
index c5d711d7afa3..9a9bf69958f5 100644
--- a/tests/python/contrib/test_arm_compute_lib/infrastructure.py
+++ b/tests/python/contrib/test_arm_compute_lib/infrastructure.py
@@ -275,7 +275,7 @@ def extract_acl_modules(module):
 def verify_codegen(
     module,
     known_good_codegen,
-    num_acl_modules,
+    num_acl_modules=1,
     tvm_ops=0,
     target="llvm -mtriple=aarch64-linux-gnu -mattr=+neon",
 ):
@@ -303,45 +303,3 @@ def verify_codegen(
             f"Actual={codegen_str} \n"
             f"Expected={known_good_codegen_str}"
         )
-
-
-def generate_trials(space, r_factor=3):
-    """Generates a series of trials.
-
-    This algorithm generates a series of non-deterministic trials given a
-    space of options to test. A trial is generated by pulling a value from
-    each option in the space. On some occasions the values are shuffled to
-    ensure a different trial on each r_factor iteration. The algorithm ensures
-    that each value from an option is used at least once. The total number of
-    trials is determined by the r_factor * the option with the largest number
-    of values.
-
-    Parameters
-    ----------
-    space: List[List[Any]]
-        A list of different options with varying values to test.
-    r_factor: (optional) int
-        The repeat factor.
-
-    Returns
-    -------
-    A list of trials specifying values for each option.
-
-    """
-    np.random.seed(0)
-    max_len = 1
-    for option in space:
-        max_len = max(max_len, len(option))
-
-    num_trials = r_factor * max_len
-    trials = []
-    for i in range(num_trials):
-        trial = []
-        for option in space:
-            if i % len(option) == 0:
-                np.random.shuffle(option)
-            trial.append(option[i % len(option)])
-
-        trials.append(trial)
-
-    return trials
diff --git a/tests/python/contrib/test_arm_compute_lib/test_conv2d.py b/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
index 4496a2a1afa9..cc5bbfec7c69 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_conv2d.py
@@ -21,15 +21,14 @@
 import tvm
 from tvm import relay
 
-from .infrastructure import (
+from test_arm_compute_lib.infrastructure import (
     skip_runtime_test,
     skip_codegen_test,
     build_and_run,
     verify,
     verify_codegen,
-    generate_trials,
 )
-from .infrastructure import Device
+from test_arm_compute_lib.infrastructure import Device
 
 
 def _get_model(
@@ -57,7 +56,12 @@ def _get_model(
         if len(padding) == 2:
             padding = (padding[0], padding[1], padding[0], padding[1])
         shape = (shape[0], shape[1] + padding[0] * 2, shape[2] + padding[1] * 2, shape[3])
-    weight_shape = (kernel_h, kernel_w, shape[3] // groups, channels)
+    is_depthwise = shape[3] == channels == groups
+    weight_format = "HWOI" if is_depthwise else "HWIO"
+    if weight_format == "HWIO":
+        weight_shape = (kernel_h, kernel_w, shape[3] // groups, channels)
+    else:
+        weight_shape = (kernel_h, kernel_w, channels, shape[3] // groups)
     w = tvm.nd.array(np.random.uniform(-128, 127, weight_shape).astype(dtype))
     weights = relay.const(w, dtype)
     out = relay.nn.conv2d(
@@ -65,7 +69,7 @@ def _get_model(
         weights,
         kernel_size=(kernel_h, kernel_w),
         data_layout="NHWC",
-        kernel_layout="HWIO",
+        kernel_layout=weight_format,
         dilation=dilation,
         strides=strides,
         padding=padding,
@@ -75,7 +79,8 @@ def _get_model(
     )
     params = {"w": w}
     if has_bias:
-        b = tvm.nd.array(np.random.uniform(-128, 127, weight_shape[3]).astype(dtype))
+        bias_shape = weight_shape[2] if is_depthwise else weight_shape[3]
+        b = tvm.nd.array(np.random.uniform(-128, 127, bias_shape).astype(dtype))
         biasc = relay.const(b, dtype)
         out = relay.nn.bias_add(out, biasc, axis=3)
         params["b"] = b
@@ -134,7 +139,12 @@ def _get_qnn_model(
         if len(padding) == 2:
             padding = (padding[0], padding[1], padding[0], padding[1])
         shape = (shape[0], shape[1] + padding[0] * 2, shape[2] + padding[1] * 2, shape[3])
-    weight_shape = (kernel_h, kernel_w, shape[3] // groups, channels)
+    is_depthwise = shape[3] == channels == groups
+    weight_format = "HWOI" if is_depthwise else "HWIO"
+    if weight_format == "HWIO":
+        weight_shape = (kernel_h, kernel_w, shape[3] // groups, channels)
+    else:
+        weight_shape = (kernel_h, kernel_w, channels, shape[3] // groups)
     w = tvm.nd.array(np.random.uniform(0, 255, weight_shape).astype(dtype))
     weights = relay.const(w, dtype)
     out = relay.qnn.op.conv2d(
@@ -146,7 +156,7 @@ def _get_qnn_model(
         kernel_scale=relay.const(kernel_sc, "float32"),
         kernel_size=(kernel_h, kernel_w),
         data_layout="NHWC",
-        kernel_layout="HWIO",
+        kernel_layout=weight_format,
         dilation=dilation,
         strides=strides,
         padding=padding,
@@ -156,7 +166,8 @@ def _get_qnn_model(
     )
     params = {"w": w}
     if has_bias:
-        b = tvm.nd.array(np.random.uniform(0, 255, weight_shape[3]).astype("int32"))
+        bias_shape = weight_shape[2] if is_depthwise else weight_shape[3]
+        b = tvm.nd.array(np.random.uniform(-128, 127, bias_shape).astype("int32"))
         biasc = relay.const(b, "int32")
         out = relay.nn.bias_add(out, biasc, axis=3)
         params["b"] = b
@@ -188,21 +199,30 @@ def _get_expected_codegen(
 ):
     if len(padding) == 2:
         padding = (padding[0], padding[1], padding[0], padding[1])
-    weight_shape = (channels, kernel_h, kernel_w, shape[3] // groups)
     output_height = ((shape[1] - kernel_h + padding[0] + padding[2]) / strides[0]) + 1
     output_width = ((shape[2] - kernel_w + padding[1] + padding[3]) / strides[1]) + 1
     output_shape = (1, int(output_height), int(output_width), channels)
     out_dtype = "int32" if dtype == "uint8" else "float32"
+    is_depthwise = shape[3] == channels == groups
+    weight_format = "IHWO" if is_depthwise else "OHWI"
+    if weight_format == "IHWO":
+        weight_shape = (shape[3] // groups, kernel_h, kernel_w, channels)
+    else:
+        weight_shape = (channels, kernel_h, kernel_w, shape[3] // groups)
+    if is_depthwise:
+        name = "nn.depthwise_conv2d"
+    else:
+        name = "nn.conv2d"
 
     node = {
         "op": "kernel",
-        "name": "nn.conv2d",
+        "name": name,
         "inputs": [],
         "attrs": {
-            "groups": [["1"]],
+            "groups": [[str(groups)]],
             "num_outputs": "1",
             "data_layout": [["NHWC"]],
-            "kernel_layout": [["OHWI"]],
+            "kernel_layout": [[weight_format]],
             "channels": [[str(channels)]],
             "dilation": [[str(dilation[0]), str(dilation[1])]],
             "out_layout": [[""]],
@@ -229,7 +249,7 @@ def _get_expected_codegen(
 
     # qnn.conv2d params, input and kernel
     if dtype == "uint8":
-        node["name"] = "qnn.conv2d"
+        node["name"] = "qnn." + node["name"].split(".")[1]
         for param_dtype in ["int32", "float32"]:
             for _ in range(2):
                 inputs.append(
@@ -246,7 +266,10 @@ def _get_expected_codegen(
             {
                 "op": "const",
                 "name": "",
-                "attrs": {"shape": [[[weight_shape[0]]]], "dtype": [[bias_dtype]]},
+                "attrs": {
+                    "shape": [[[1, 1, 1, weight_shape[3] if is_depthwise else weight_shape[0]]]],
+                    "dtype": [[bias_dtype]],
+                },
             }
         )
 
@@ -275,29 +298,43 @@ def test_conv2d():
     device = Device()
     np.random.seed(0)
 
-    kernel_hs = [1, 2, 3, 5]
-    kernel_ws = [1, 2, 3, 5]
-    pad = [(1, 1), (2, 2), (2, 1)]
-    strides = [(1, 1), (2, 2)]
-    dilation = [(1, 1)]
-    out_channels = [4, 7, 16]
-    input_shapes = [(10, 10, 14), (12, 15, 16), (20, 20, 20)]
-    # composite operator (pad, bias, activation)
-    composite = [
-        (False, False, False),
-        (False, True, False),
-        (False, False, True),
-        (False, True, True),
-        (True, False, False),
-    ]
     dtype = "float32"
-    trials = generate_trials(
-        [kernel_hs, kernel_ws, pad, strides, dilation, out_channels, input_shapes, composite], 3
-    )
+    trials = [
+        # Normal convolution
+        [2, 2, (1, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, False, False), False],
+        [2, 1, (2, 2), (1, 1), (1, 1), 7, (12, 15, 16), (False, False, True), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, True, False), False],
+        [3, 3, (1, 1), (1, 1), (1, 1), 16, (12, 15, 16), (False, False, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [1, 3, (1, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, True), False],
+        [2, 2, (2, 2), (1, 1), (1, 1), 4, (20, 20, 20), (False, True, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, False), False],
+        [3, 3, (1, 1), (2, 2), (1, 1), 16, (10, 10, 14), (False, True, True), False],
+        # Depth-wise convolution
+        [3, 3, (1, 1), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, True), True],
+        [5, 5, (2, 2), (1, 1), (1, 1), 20, (20, 20, 20), (False, True, False), True],
+        [3, 3, (2, 2), (2, 2), (1, 1), 14, (10, 10, 14), (True, False, False), True],
+        [5, 5, (0, 0), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, False), True],
+        [3, 3, (1, 1), (2, 2), (1, 1), 14, (10, 10, 14), (False, True, True), True],
+    ]
 
-    for kernel_h, kernel_w, pad, stride, dilation, out_channels, input_shapes, composite in trials:
-        groups = 1
-        shape = (1, *input_shapes)
+    for (
+        kernel_h,
+        kernel_w,
+        pad,
+        stride,
+        dilation,
+        out_channels,
+        shape,
+        composite,
+        is_depthwise,
+    ) in trials:
+        shape = (1, *shape)
+        if is_depthwise:
+            groups = shape[3]
+        else:
+            groups = 1
         outputs = []
         inputs = {
             "a": tvm.nd.array(np.random.uniform(-128, 127, shape).astype(dtype)),
@@ -338,31 +375,43 @@ def test_codegen_conv2d():
     if skip_codegen_test():
         return
 
-    np.random.seed(0)
-
-    kernel_hs = [1, 2, 3, 5]
-    kernel_ws = [1, 2, 3, 5]
-    pad = [(1, 1), (2, 2), (2, 1)]
-    strides = [(1, 1), (2, 2)]
-    dilation = [(1, 1)]
-    out_channels = [4, 7, 16]
-    input_shapes = [(10, 10, 14), (12, 15, 16), (20, 20, 20)]
-    # composite operator (pad, bias, activation)
-    composite = [
-        (False, False, False),
-        (False, True, False),
-        (False, False, True),
-        (False, True, True),
-        (True, False, False),
-    ]
     dtype = "float32"
-    trials = generate_trials(
-        [kernel_hs, kernel_ws, pad, strides, dilation, out_channels, input_shapes, composite], 3
-    )
+    trials = [
+        # Normal convolution
+        [2, 2, (1, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, False, False), False],
+        [2, 1, (2, 2), (1, 1), (1, 1), 7, (12, 15, 16), (False, False, True), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, True, False), False],
+        [3, 3, (1, 1), (1, 1), (1, 1), 16, (12, 15, 16), (False, False, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [1, 3, (1, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, True), False],
+        [2, 2, (2, 2), (1, 1), (1, 1), 4, (20, 20, 20), (False, True, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, False), False],
+        [3, 3, (1, 1), (2, 2), (1, 1), 16, (10, 10, 14), (False, True, True), False],
+        # Depth-wise convolution
+        [3, 3, (1, 1), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, True), True],
+        [5, 5, (2, 2), (1, 1), (1, 1), 20, (20, 20, 20), (False, True, False), True],
+        [3, 3, (2, 2), (2, 2), (1, 1), 14, (10, 10, 14), (True, False, False), True],
+        [5, 5, (0, 0), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, False), True],
+        [3, 3, (1, 1), (2, 2), (1, 1), 14, (10, 10, 14), (False, True, True), True],
+    ]
 
-    for kernel_h, kernel_w, pad, stride, dilation, out_channels, input_shapes, composite in trials:
-        groups = 1
-        shape = (1, *input_shapes)
+    for (
+        kernel_h,
+        kernel_w,
+        pad,
+        stride,
+        dilation,
+        out_channels,
+        shape,
+        composite,
+        is_depthwise,
+    ) in trials:
+        shape = (1, *shape)
+        if is_depthwise:
+            groups = shape[3]
+        else:
+            groups = 1
         inputs = {"a"}
 
         args = (shape, kernel_h, kernel_w, pad, stride, dilation, groups, dtype, out_channels)
@@ -389,29 +438,43 @@ def test_qnn_conv2d():
     device = Device()
     np.random.seed(0)
 
-    kernel_hs = [1, 2, 3, 5]
-    kernel_ws = [1, 2, 3, 5]
-    pad = [(1, 1), (2, 2)]
-    strides = [(1, 1), (2, 2)]
-    dilation = [(1, 1)]
-    out_channels = [4, 7, 16]
-    input_shapes = [(10, 10, 14), (12, 15, 16), (20, 20, 20)]
-    # composite operator (pad, bias, activation)
-    composite = [
-        (False, False, False),
-        (False, True, False),
-        (False, False, True),
-        (False, True, True),
-        (True, False, False),
-    ]
     dtype = "uint8"
-    trials = generate_trials(
-        [kernel_hs, kernel_ws, pad, strides, dilation, out_channels, input_shapes, composite], 3
-    )
+    trials = [
+        # Normal convolution
+        [2, 2, (1, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, False, False), False],
+        [2, 1, (2, 2), (1, 1), (1, 1), 7, (12, 15, 16), (False, False, True), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, True, False), False],
+        [3, 3, (1, 1), (1, 1), (1, 1), 16, (12, 15, 16), (False, False, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [1, 3, (1, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, True), False],
+        [2, 2, (2, 2), (1, 1), (1, 1), 4, (20, 20, 20), (False, True, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, False), False],
+        [3, 3, (1, 1), (2, 2), (1, 1), 16, (10, 10, 14), (False, True, True), False],
+        # Depth-wise convolution
+        [3, 3, (1, 1), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, True), True],
+        [5, 5, (2, 2), (1, 1), (1, 1), 20, (20, 20, 20), (False, True, False), True],
+        [3, 3, (2, 2), (2, 2), (1, 1), 14, (10, 10, 14), (True, False, False), True],
+        [5, 5, (0, 0), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, False), True],
+        [3, 3, (1, 1), (2, 2), (1, 1), 14, (10, 10, 14), (False, True, True), True],
+    ]
 
-    for kernel_h, kernel_w, pad, stride, dilation, out_channels, input_shapes, composite in trials:
-        groups = 1
-        shape = (1, *input_shapes)
+    for (
+        kernel_h,
+        kernel_w,
+        pad,
+        stride,
+        dilation,
+        out_channels,
+        shape,
+        composite,
+        is_depthwise,
+    ) in trials:
+        shape = (1, *shape)
+        if is_depthwise:
+            groups = shape[3]
+        else:
+            groups = 1
         outputs = []
         inputs = {"a": tvm.nd.array(np.random.uniform(0, 255, shape).astype(dtype))}
 
@@ -463,36 +526,52 @@ def test_qnn_conv2d():
             "output scale": output_sc,
             "output zero point": output_zp,
         }
-        verify(outputs, atol=1, rtol=0, config=config, verify_saturation=True)
+
+        atol = 2 if is_depthwise else 1
+        verify(outputs, atol=atol, rtol=0, config=config, verify_saturation=True)
 
 
 def test_codegen_qnn_conv2d():
     if skip_codegen_test():
         return
 
-    kernel_hs = [1, 2, 3, 5]
-    kernel_ws = [1, 2, 3, 5]
-    pad = [(1, 1), (2, 2), (2, 1)]
-    strides = [(1, 1), (2, 2)]
-    dilation = [(1, 1)]
-    out_channels = [4, 7, 16]
-    input_shapes = [(10, 10, 14), (12, 15, 16), (20, 20, 20)]
-    # composite operator (pad, bias, activation)
-    composite = [
-        (False, False, False),
-        (False, True, False),
-        (False, False, True),
-        (False, True, True),
-        (True, False, False),
-    ]
     dtype = "uint8"
-    trials = generate_trials(
-        [kernel_hs, kernel_ws, pad, strides, dilation, out_channels, input_shapes, composite], 3
-    )
+    trials = [
+        # Normal convolution
+        [2, 2, (1, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, False, False), False],
+        [2, 1, (2, 2), (1, 1), (1, 1), 7, (12, 15, 16), (False, False, True), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, True, False), False],
+        [3, 3, (1, 1), (1, 1), (1, 1), 16, (12, 15, 16), (False, False, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [1, 3, (1, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, True), False],
+        [2, 2, (2, 2), (1, 1), (1, 1), 4, (20, 20, 20), (False, True, False), False],
+        [5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
+        [3, 3, (2, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, False), False],
+        [3, 3, (1, 1), (2, 2), (1, 1), 16, (10, 10, 14), (False, True, True), False],
+        # Depth-wise convolution
+        [3, 3, (1, 1), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, True), True],
+        [5, 5, (2, 2), (1, 1), (1, 1), 20, (20, 20, 20), (False, True, False), True],
+        [3, 3, (2, 2), (2, 2), (1, 1), 14, (10, 10, 14), (True, False, False), True],
+        [5, 5, (0, 0), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, False), True],
+        [3, 3, (1, 1), (2, 2), (1, 1), 14, (10, 10, 14), (False, True, True), True],
+    ]
 
-    for kernel_h, kernel_w, pad, stride, dilation, out_channels, input_shapes, composite in trials:
-        groups = 1
-        shape = (1, *input_shapes)
+    for (
+        kernel_h,
+        kernel_w,
+        pad,
+        stride,
+        dilation,
+        out_channels,
+        shape,
+        composite,
+        is_depthwise,
+    ) in trials:
+        shape = (1, *shape)
+        if is_depthwise:
+            groups = shape[3]
+        else:
+            groups = 1
         inputs = {"a"}
 
         input_zp = 100
diff --git a/tests/python/contrib/test_arm_compute_lib/test_dense.py b/tests/python/contrib/test_arm_compute_lib/test_dense.py
index 0279aa72eaf7..e6620a4bc1cb 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_dense.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_dense.py
@@ -28,7 +28,6 @@
     build_and_run,
     verify,
     verify_codegen,
-    generate_trials,
 )
 
 
@@ -102,7 +101,7 @@ def _get_qnn_model(
     out = relay.qnn.op.requantize(
         out,
         relay.const(input_sc * kernel_sc, "float32"),  # input scale
-        relay.const(input_zp * kernel_zp, "int32"),  # input zero point
+        relay.const(0, "int32"),  # input zero point
         relay.const(output_sc, "float32"),  # output scale
         relay.const(output_zp, "int32"),  # output zero point
         out_dtype="uint8",
@@ -183,18 +182,18 @@ def test_dense():
 
     device = Device()
     np.random.seed(0)
-
-    dtype = ["float32"]
-    shape = [
-        (1, (1, 128), (16, 128), 16),
-        (1, (32, 32), (32, 32), 32),
-        (0, (1, 64), (1, 64), 1),
-        (0, (11, 2), (2, 2), 2),
+    dtype = "float32"
+    trials = [
+        [(1, 128), (16, 128), 16, True],
+        [(1, 128), (16, 128), 16, False],
+        [(32, 32), (32, 32), 32, True],
+        [(32, 32), (32, 32), 32, False],
+        [(1, 64), (1, 64), 1, True],
+        [(1, 64), (1, 64), 1, False],
+        [(11, 2), (2, 2), 2, True],
+        [(11, 2), (2, 2), 2, False],
     ]
-    composite = [False, True]
-    trials = generate_trials([dtype, shape, composite], 3)
-
-    for dtype, (acl_partitions, shape, weight_shape, units), composite in trials:
+    for shape, weight_shape, units, composite in trials:
         outputs = []
         inputs = {"a": tvm.nd.array(np.random.uniform(-128, 127, shape).astype(dtype))}
         func, params = _get_model(
@@ -209,11 +208,8 @@ def test_dense():
                     params,
                     device,
                     enable_acl=acl,
-                    tvm_ops=(1 - acl_partitions) * (2 - int(not composite)),
-                    acl_partitions=acl_partitions,
                 )[0]
             )
-
         config = {
             "shape": shape,
             "weight_shape": weight_shape,
@@ -229,20 +225,25 @@ def test_codegen_dense():
         return
 
     np.random.seed(0)
-
-    dtype = ["float32"]
-    shape = [(1, (1, 128), (16, 128), 16), (1, (32, 32), (32, 32), 32), (0, (1, 64), (1, 64), 1)]
-    composite = [False, True]
-    trials = generate_trials([dtype, shape, composite], 3)
-
-    for dtype, (acl_partitions, shape, weight_shape, units), composite in trials:
+    dtype = "float32"
+    trials = [
+        [(1, 128), (16, 128), 16, True],
+        [(1, 128), (16, 128), 16, False],
+        [(32, 32), (32, 32), 32, True],
+        [(32, 32), (32, 32), 32, False],
+        [(1, 64), (1, 64), 1, True],
+        [(1, 64), (1, 64), 1, False],
+        [(11, 2), (2, 2), 2, True],
+        [(11, 2), (2, 2), 2, False],
+    ]
+    for shape, weight_shape, units, composite in trials:
         inputs = {"a"}
 
         args = (shape, weight_shape, units, dtype)
 
         func, params = _get_model(*args, var_names=iter(inputs), has_bias=composite)
         exp_codegen = _get_expected_codegen(*args, has_bias=composite)
-        verify_codegen(func, exp_codegen, acl_partitions, 1 - acl_partitions)
+        verify_codegen(func, exp_codegen)
 
 
 def test_qnn_dense():
@@ -254,19 +255,22 @@ def test_qnn_dense():
     device = Device()
     np.random.seed(0)
 
-    dtype = ["uint8"]
-    shape = [
-        (0, (4, 4), (4, 4), 4),
-        (1, (16, 16), (4, 16), 4),
-        (1, (1, 128), (16, 128), 16),
-        (1, (32, 32), (32, 32), 32),
-        (0, (1, 64), (1, 64), 1),
+    dtype = "uint8"
+    trials = [
+        [(1, 2), (2, 2), 2, True],
+        [(1, 2), (2, 2), 2, False],
+        [(4, 4), (4, 4), 4, True],
+        [(4, 4), (4, 4), 4, False],
+        [(16, 16), (4, 16), 4, True],
+        [(16, 16), (4, 16), 4, False],
+        [(1, 128), (16, 128), 16, True],
+        [(1, 128), (16, 128), 16, False],
+        [(32, 32), (32, 32), 32, True],
+        [(32, 32), (32, 32), 32, False],
+        [(1, 64), (1, 64), 1, True],
+        [(1, 64), (1, 64), 1, False],
     ]
-
-    composite = [False, True]
-    trials = generate_trials([dtype, shape, composite], 3)
-
-    for dtype, (acl_partitions, shape, weight_shape, units), composite in trials:
+    for shape, weight_shape, units, composite in trials:
         outputs = []
         inputs = {"a": tvm.nd.array(np.random.uniform(0, 255, shape).astype(dtype))}
         input_zp = 100
@@ -300,8 +304,6 @@ def test_qnn_dense():
                     1,
                     params,
                     device,
-                    tvm_ops=(1 - acl_partitions) * (3 - int(not composite)),
-                    acl_partitions=acl_partitions,
                     enable_acl=acl,
                 )[0]
             )
@@ -328,12 +330,22 @@ def test_codegen_qnn_dense():
 
     np.random.seed(0)
 
-    dtype = ["uint8"]
-    shape = [(1, (1, 128), (16, 128), 16), (1, (32, 32), (32, 32), 32), (0, (1, 64), (1, 64), 1)]
-    composite = [False, True]
-    trials = generate_trials([dtype, shape, composite], 3)
-
-    for dtype, (acl_partitions, shape, weight_shape, units), composite in trials:
+    dtype = "uint8"
+    trials = [
+        [(1, 2), (2, 2), 2, True],
+        [(1, 2), (2, 2), 2, False],
+        [(4, 4), (4, 4), 4, True],
+        [(4, 4), (4, 4), 4, False],
+        [(16, 16), (4, 16), 4, True],
+        [(16, 16), (4, 16), 4, False],
+        [(1, 128), (16, 128), 16, True],
+        [(1, 128), (16, 128), 16, False],
+        [(32, 32), (32, 32), 32, True],
+        [(32, 32), (32, 32), 32, False],
+        [(1, 64), (1, 64), 1, True],
+        [(1, 64), (1, 64), 1, False],
+    ]
+    for shape, weight_shape, units, composite in trials:
         inputs = {"a"}
         args = (shape, weight_shape, units, dtype)
 
@@ -357,7 +369,7 @@ def test_codegen_qnn_dense():
             has_bias=composite,
         )
         exp_codegen = _get_expected_codegen(*args, has_bias=composite)
-        verify_codegen(func, exp_codegen, acl_partitions, 2 - 2 * acl_partitions)
+        verify_codegen(func, exp_codegen)
 
 
 if __name__ == "__main__":
diff --git a/tests/python/contrib/test_arm_compute_lib/test_network.py b/tests/python/contrib/test_arm_compute_lib/test_network.py
index 898446b32ed9..bb44b79078dd 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_network.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_network.py
@@ -123,7 +123,7 @@ def get_model():
         return mod, params, inputs
 
     _build_and_run_network(
-        *get_model(), device=device, tvm_ops=73, acl_partitions=18, atol=0.002, rtol=0.01
+        *get_model(), device=device, tvm_ops=56, acl_partitions=31, atol=0.002, rtol=0.01
     )
 
 
@@ -148,7 +148,7 @@ def get_model():
         return mod, params, inputs
 
     _build_and_run_network(
-        *get_model(), device=device, tvm_ops=42, acl_partitions=17, atol=8, rtol=0
+        *get_model(), device=device, tvm_ops=3, acl_partitions=30, atol=9, rtol=0
     )
 
 
@@ -172,7 +172,7 @@ def get_model():
         return mod, params, inputs
 
     _build_and_run_network(
-        *get_model(), device=device, tvm_ops=10, acl_partitions=30, atol=8, rtol=0
+        *get_model(), device=device, tvm_ops=9, acl_partitions=31, atol=8, rtol=0
     )
 
 
diff --git a/tests/python/contrib/test_arm_compute_lib/test_reshape.py b/tests/python/contrib/test_arm_compute_lib/test_reshape.py
index 9364c6b1a478..94942727416a 100644
--- a/tests/python/contrib/test_arm_compute_lib/test_reshape.py
+++ b/tests/python/contrib/test_arm_compute_lib/test_reshape.py
@@ -50,7 +50,6 @@ def _get_expected_codegen(input_shape, output_shape, dtype):
             "newshape": [[str(s) for s in output_shape]],
             "shape": [[list(output_shape)]],
             "dtype": [[dtype]],
-            "reverse": [["0"]],
         },
     }
 
diff --git a/tests/python/contrib/test_bnns/__init__.py b/tests/python/contrib/test_bnns/__init__.py
new file mode 100644
index 000000000000..724b23f1378b
--- /dev/null
+++ b/tests/python/contrib/test_bnns/__init__.py
@@ -0,0 +1,17 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Infrastructure and tests for BNNS"""
diff --git a/tests/python/contrib/test_bnns/infrastructure.py b/tests/python/contrib/test_bnns/infrastructure.py
new file mode 100644
index 000000000000..0107de54a04f
--- /dev/null
+++ b/tests/python/contrib/test_bnns/infrastructure.py
@@ -0,0 +1,330 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+from itertools import zip_longest, combinations
+import json
+import os
+import warnings
+
+import numpy as np
+
+import tvm
+from tvm import relay
+from tvm import rpc
+from tvm.contrib import graph_runtime
+from tvm.relay.op.contrib.bnns import partition_for_bnns
+from tvm.contrib import utils
+from tvm.autotvm.measure import request_remote
+from tvm.relay.analysis import analysis
+
+
+class Device:
+    """
+    Common device configuration for python tests.
+
+    Check tests/python/contrib/arm_compute_lib/ for the presence of an test_config.json file.
+    This file can be used to override the default configuration here which will attempt to run the BNNS
+    runtime tests locally if the runtime is available. Changing the configuration will allow these
+    runtime tests to be offloaded to a remote device with BNNS via a tracker for example.
+
+    Notes
+    -----
+        The test configuration will be loaded once when the the class is created. If the configuration
+        changes between tests, any changes will not be picked up.
+
+
+    Attributes
+    ----------
+    connection_type : str
+        Details the type of RPC connection to use. Options:
+        local - Use the local device,
+        tracker - Connect to a tracker to request a remote device,
+        remote - Connect to a remote device directly.
+    host : str
+        Specify IP address or hostname of remote target.
+    port : int
+        Specify port number of remote target.
+    target : str
+        The compilation target.
+    device_key : str
+        The device key of the remote target. Use when connecting to a remote device via a tracker.
+    cross_compile : str
+        Specify path to cross compiler to use when connecting a remote device from a non-arm platform.
+    """
+
+    connection_type = "local"
+    host = "localhost"
+    port = 9090
+    target = "llvm"
+    device_key = ""
+    cross_compile = ""
+
+    def __init__(self):
+        """Keep remote device for lifetime of object."""
+        self.device = self._get_remote()
+
+    @classmethod
+    def _get_remote(cls):
+        """Get a remote (or local) device to use for testing."""
+        if cls.connection_type == "tracker":
+            device = request_remote(cls.device_key, cls.host, cls.port, timeout=1000)
+        elif cls.connection_type == "remote":
+            device = rpc.connect(cls.host, cls.port)
+        elif cls.connection_type == "local":
+            device = rpc.LocalSession()
+        else:
+            raise ValueError(
+                "connection_type in test_config.json should be one of: " "local, tracker, remote."
+            )
+
+        return device
+
+    @classmethod
+    def load(cls, file_name):
+        """Load test config
+
+        Load the test configuration by looking for file_name relative
+        to the test_bnns directory.
+        """
+        location = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
+        config_file = os.path.join(location, file_name)
+        if not os.path.exists(config_file):
+            warnings.warn("Config file doesn't exist, resuming tests with default config.")
+            return
+        with open(config_file, mode="r") as config:
+            test_config = json.load(config)
+
+        cls.connection_type = test_config["connection_type"]
+        cls.host = test_config["host"]
+        cls.port = test_config["port"]
+        cls.target = test_config["target"]
+        cls.device_key = test_config.get("device_key") or ""
+        cls.cross_compile = test_config.get("cross_compile") or ""
+
+
+Device.target = "llvm"
+
+
+def skip_runtime_test():
+    """Skip test if it requires the runtime and it's not present."""
+    # BNNS codegen not present.
+    if not tvm.get_global_func("relay.ext.bnns", True):
+        print("Skip because BNNS codegen is not available.")
+        return True
+    return False
+
+
+def skip_codegen_test():
+    """Skip test if it requires the BNNS codegen and it's not present."""
+    if not tvm.get_global_func("relay.ext.bnns", True):
+        print("Skip because BNNS codegen is not available.")
+        return True
+
+
+def build_module(mod, target, params=None, enable_bnns=True, tvm_ops=0):
+    """Build module with option to build for BNNS."""
+    if isinstance(mod, tvm.relay.expr.Call):
+        mod = tvm.IRModule.from_expr(mod)
+    with tvm.transform.PassContext(opt_level=3):
+        if enable_bnns:
+            mod = partition_for_bnns(mod)
+        relay.backend.compile_engine.get().clear()
+        return relay.build(mod, target=target, target_host=target, params=params)
+
+
+def build_and_run(
+    mod,
+    inputs,
+    outputs,
+    params,
+    device,
+    enable_bnns=True,
+    no_runs=1,
+    tvm_ops=0,
+    config=None,
+):
+    """Build and run the relay module."""
+    if config is None:
+        config = {}
+
+    try:
+        lib = build_module(mod, device.target, params, enable_bnns, tvm_ops)
+    except Exception as e:
+        err_msg = "The module could not be built.\n"
+        if config:
+            err_msg += f"The test failed with the following parameters: {config}\n"
+        err_msg += str(e)
+        raise Exception(err_msg)
+
+    lib = update_lib(lib, device.device, device.cross_compile)
+    gen_module = graph_runtime.GraphModule(lib["default"](device.device.cpu(0)))
+    gen_module.set_input(**inputs)
+    out = []
+    for _ in range(no_runs):
+        gen_module.run()
+        out.append([gen_module.get_output(i) for i in range(outputs)])
+    return out
+
+
+def update_lib(lib, device, cross_compile):
+    """Export the library to the remote/local device."""
+    lib_name = "mod.so"
+    temp = utils.tempdir()
+    lib_path = temp.relpath(lib_name)
+    if cross_compile:
+        lib.export_library(lib_path, cc=cross_compile)
+    else:
+        lib.export_library(lib_path)
+    device.upload(lib_path)
+    lib = device.load_module(lib_name)
+    return lib
+
+
+def extract_bnns_modules(module):
+    """Get the BNNS module(s) from llvm module."""
+    return list(filter(lambda mod: mod.type_key == "bnns_json", module.get_lib().imported_modules))
+
+
+def verify(answers, atol, rtol, verify_saturation=False, config=None):
+    """Compare the array of answers. Each entry is a list of outputs."""
+    if config is None:
+        config = {}
+
+    if len(answers) < 2:
+        raise RuntimeError(f"No results to compare: expected at least two, found {len(answers)}")
+    for answer in zip_longest(*answers):
+        for outs in combinations(answer, 2):
+            try:
+                if verify_saturation:
+                    assert (
+                        np.count_nonzero(outs[0].asnumpy() == 255) < 0.25 * outs[0].asnumpy().size
+                    ), "Output is saturated: {}".format(outs[0])
+                    assert (
+                        np.count_nonzero(outs[0].asnumpy() == 0) < 0.25 * outs[0].asnumpy().size
+                    ), "Output is saturated: {}".format(outs[0])
+                tvm.testing.assert_allclose(
+                    outs[0].asnumpy(), outs[1].asnumpy(), rtol=rtol, atol=atol
+                )
+            except AssertionError as e:
+                err_msg = "Results not within the acceptable tolerance.\n"
+                if config:
+                    err_msg += f"The test failed with the following parameters: {config}\n"
+                err_msg += str(e)
+                raise AssertionError(err_msg)
+
+
+def verify_codegen(
+    module,
+    known_good_codegen,
+    num_bnns_modules,
+    tvm_ops=0,
+    target=Device.target,
+):
+    """Check BNNS codegen against a known good output."""
+    module = build_module(module, target, tvm_ops=tvm_ops)
+    bnns_modules = extract_bnns_modules(module)
+
+    assert len(bnns_modules) == num_bnns_modules, (
+        f"The number of BNNS modules produced ({len(bnns_modules)}) does not "
+        f"match the expected value ({num_bnns_modules})."
+    )
+
+    for mod in bnns_modules:
+        source = mod.get_source("json")
+        codegen = json.loads(source)["nodes"]
+        # remove input and const names as these cannot be predetermined
+        for node in range(len(codegen)):
+            if codegen[node]["op"] == "input" or codegen[node]["op"] == "const":
+                codegen[node]["name"] = ""
+        codegen_str = json.dumps(codegen, sort_keys=True, indent=2)
+        known_good_codegen_str = json.dumps(known_good_codegen, sort_keys=True, indent=2)
+
+        assert codegen_str == known_good_codegen_str, (
+            f"The JSON produced by codegen does not match the expected result. \n"
+            f"Actual={codegen_str} \n"
+            f"Expected={known_good_codegen_str}"
+        )
+
+
+def compare_inference_with_ref(func, params, atol=0.002, rtol=0.007):
+    """Compare scoring results for compilation with and without BNNS.
+
+    Provided function will be compiled two times with and without BNNS.
+    The scoring results for both type of compilation will be compared
+    with provided atol and rtol. The input data will be automatically
+    generated based of shape and dtype info provided for var nodes.
+
+    """
+    # Generate input tensor values
+    inputs = {}
+    for free_param in analysis.free_vars(func):
+        name = free_param.name_hint
+        dtype = free_param.type_annotation.dtype
+        shape = [s.value for s in free_param.type_annotation.shape]
+        inputs[name] = tvm.nd.array(np.random.uniform(0, 127, shape).astype(dtype))
+
+    # Run for both type of compilation
+    device = Device()
+    outputs = []
+    for bnns in [False, True]:
+        outputs.append(build_and_run(func, inputs, 1, params, device, enable_bnns=bnns)[0])
+
+    # Compare result tensors
+    verify(outputs, atol=atol, rtol=rtol)
+
+
+def generate_trials(space, r_factor=3):
+    """Generates a series of trials.
+
+    This algorithm generates a series of non-deterministic trials given a
+    space of options to test. A trial is generated by pulling a value from
+    each option in the space. On some occasions the values are shuffled to
+    ensure a different trial on each r_factor iteration. The algorithm ensures
+    that each value from an option is used at least once. The total number of
+    trials is determined by the r_factor * the option with the largest number
+    of values.
+
+    Parameters
+    ----------
+    space: List[List[Any]]
+        A list of different options with varying values to test.
+    r_factor: Optional[int]
+        The repeat factor.
+
+    Returns
+    -------
+    result: List[Tuple]
+        A list of trials specifying values for each option.
+
+    """
+    np.random.seed(0)
+    max_len = 1
+    for option in space:
+        max_len = max(max_len, len(option))
+
+    num_trials = r_factor * max_len
+    trials = []
+    for i in range(num_trials):
+        trial = []
+        for option in space:
+            if i % len(option) == 0:
+                np.random.shuffle(option)
+            trial.append(option[i % len(option)])
+
+        trials.append(trial)
+
+    return trials
diff --git a/tests/python/contrib/test_bnns/test_conv2d.py b/tests/python/contrib/test_bnns/test_conv2d.py
new file mode 100644
index 000000000000..886958cf3076
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_conv2d.py
@@ -0,0 +1,177 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS integration conv2d tests."""
+
+import numpy as np
+import pytest
+import tvm
+from tvm import relay
+
+from .infrastructure import skip_runtime_test, compare_inference_with_ref, generate_trials
+
+# TODO: Missed cases
+#   1. Bias as add with 3d const tensor. Lead to additional unsqueeze op between
+#   2. Check unsupported cases of fusion. Like bias add with axis != 1, add with broadcast by spatial dims
+#   3. Check if bias/weights is not constants. Should fallback into LLVM or decompose it
+#   4. Check if bias/weights is constants expr. Should works somehow.
+
+
+def _get_model(
+    shape,
+    kernel=(3, 3),
+    padding=(1, 1),
+    strides=(1, 1),
+    dilation=(1, 1),
+    groups=1,
+    dtype="float32",
+    channels=-1,  # -1 means same as input channels
+    bias_type="none",
+    activation_type="none",
+):
+    """Return a model and any parameters it may have"""
+    if channels == -1:
+        channels = shape[1]
+
+    a = relay.var("a", shape=shape, dtype=dtype)
+    weight_shape = (channels, shape[1] // groups, *kernel)
+    w = tvm.nd.array(np.random.uniform(-128, 127, weight_shape).astype(dtype))
+    weights = relay.const(w, dtype)
+    out = relay.nn.conv2d(
+        a,
+        weights,
+        kernel_size=kernel,
+        dilation=dilation,
+        strides=strides,
+        padding=padding,
+        groups=groups,
+        channels=channels,
+        out_dtype=dtype,
+    )
+    params = {"w": w}
+    if bias_type == "bias_add":
+        b = tvm.nd.array(np.random.uniform(-10, 10, weight_shape[0]).astype(dtype))
+        biasc = relay.const(b, dtype)
+        out = relay.nn.bias_add(out, biasc, axis=1)
+        params["b"] = b
+    elif bias_type == "add_3d" or bias_type == "add_4d":
+        bias_shape = (
+            (weight_shape[0], 1, 1) if bias_type == "add_3d" else (1, weight_shape[0], 1, 1)
+        )
+        b = tvm.nd.array(np.random.uniform(-10, 10, bias_shape).astype(dtype))
+        biasc = relay.const(b, dtype)
+        out = relay.add(out, biasc)
+        params["b"] = b
+
+    if activation_type == "relu":
+        out = relay.nn.relu(out)
+    elif activation_type == "sigmoid":
+        out = relay.op.sigmoid(out)
+    return out, params
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_conv2d():
+    np.random.seed(0)
+
+    kernel_hs = [1, 2, 3, 5]
+    kernel_ws = [1, 2, 3, 5]
+    pad = [(1, 1), (2, 2), (2, 1)]
+    strides = [(1, 1), (2, 2)]
+    dilation = [(1, 1)]
+    out_channels = [1, 4, 8, 16]
+    input_shapes = [(10, 10, 14), (12, 15, 16), (20, 20, 20)]
+    batches = [1, 2]
+    groups = [1, 2]
+    bias_kind = ["none", "add_3d", "add_4d", "bias.add"]
+    activation_kind = ["none", "relu", "sigmoid"]
+    trials = generate_trials(
+        [
+            kernel_hs,
+            kernel_ws,
+            pad,
+            strides,
+            dilation,
+            out_channels,
+            input_shapes,
+            groups,
+            batches,
+            bias_kind,
+            activation_kind,
+        ],
+        3,
+    )
+
+    for (
+        kernel_h,
+        kernel_w,
+        pad,
+        stride,
+        dilation,
+        out_channels,
+        input_shapes,
+        group,
+        batch,
+        bias,
+        activation,
+    ) in trials:
+        if out_channels % group != 0:
+            continue
+        func, params = _get_model(
+            shape=(batch, *input_shapes),
+            kernel=(kernel_h, kernel_w),
+            padding=pad,
+            strides=stride,
+            dilation=dilation,
+            groups=group,
+            channels=out_channels,
+            bias_type=bias,
+            activation_type=activation,
+        )
+        compare_inference_with_ref(func, params)
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_conv2d_dw():
+    if skip_runtime_test():
+        return
+
+    np.random.seed(0)
+    shape = [4, 5, 5]
+
+    for batch in [1, 2]:
+        mod, params = _get_model(shape=(batch, *shape), groups=shape[0])
+        compare_inference_with_ref(mod, params)
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_conv2d_with_oc1():
+    if skip_runtime_test():
+        return
+
+    np.random.seed(0)
+    shape = [3, 5, 5]
+
+    for batch in [1, 2]:
+        for bias in ["none", "add_4d"]:
+            mod, params = _get_model(shape=(batch, *shape), channels=1, bias_type=bias)
+            compare_inference_with_ref(mod, params)
+
+
+if __name__ == "__main__":
+    test_conv2d()
+    test_conv2d_dw()
+    test_conv2d_with_oc1()
diff --git a/tests/python/contrib/test_bnns/test_conv2d_patterns.py b/tests/python/contrib/test_bnns/test_conv2d_patterns.py
new file mode 100644
index 000000000000..b10504bbc961
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_conv2d_patterns.py
@@ -0,0 +1,107 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS pattern detection check"""
+
+import tvm
+from tvm import relay
+import numpy as np
+
+from tvm.relay.op.contrib.bnns import partition_for_bnns
+
+fp32 = "float32"
+
+
+def partition(exp):
+    """Apply BNNS specific partitioning transformation"""
+    mod = tvm.IRModule.from_expr(exp)
+    with tvm.transform.PassContext(opt_level=3):
+        mod = partition_for_bnns(mod)
+    return mod
+
+
+def is_op_fused(func, op_name):
+    is_fused = False
+
+    def visit(op):
+        if (
+            isinstance(op, tvm.relay.function.Function)
+            and op_name in op.attrs["PartitionedFromPattern"]
+        ):
+            nonlocal is_fused
+            is_fused = True
+
+    tvm.relay.analysis.post_order_visit(func.body, visit)
+    return is_fused
+
+
+def test_pattern_conv2d_with_bias_add():
+    for axis in (1, 2):
+        a = relay.var("a", shape=(2, 7, 8, 8), dtype=fp32)
+        w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype(fp32))
+        res = relay.nn.conv2d(a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype=fp32)
+        b = relay.const(np.random.uniform(-10, 10, 8).astype(fp32))
+        res = relay.nn.bias_add(res, b, axis=axis)
+
+        mod = partition(res)
+        bias_is_fused = is_op_fused(mod["bnns_0"], "nn.bias_add")
+
+        assert bias_is_fused if axis == 1 else not bias_is_fused
+
+
+def test_pattern_conv2d_with_add():
+    workloads = {8: False, (8, 1): False, (8, 1, 1): True, (1, 8, 1, 1): True}
+
+    for b_shape, should_be_fused in workloads.items():
+        a = relay.var("a", shape=(2, 7, 8, 8), dtype=fp32)
+        w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype(fp32))
+        res = relay.nn.conv2d(a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype=fp32)
+        b = relay.const(np.random.uniform(-10, 10, b_shape).astype(fp32))
+        res = relay.add(res, b)
+
+        mod = partition(res)
+        bias_is_fused = is_op_fused(mod["bnns_0"], "add")
+
+        assert bias_is_fused == should_be_fused
+
+
+def test_pattern_conv2d_with_non_cons_weights():
+    for const_weights in (True, False):
+        a = relay.var("a", shape=(2, 7, 8, 8), dtype=fp32)
+        if const_weights:
+            w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype(fp32))
+        else:
+            w = relay.var("w", shape=(8, 7, 3, 3), dtype=fp32)
+
+        res = relay.nn.conv2d(a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype=fp32)
+
+        mod = partition(res)
+        use_bnns = len(mod.get_global_vars()) == 2  # GlobalVar: "main" and "bnns_0"
+
+        assert use_bnns == const_weights
+
+
+def test_pattern_conv2d_with_non_cons_bias():
+    a = relay.var("a", shape=[2, 7, 8, 8], dtype=fp32)
+    w = relay.const(np.random.uniform(-10, 10, (8, 7, 3, 3)).astype(fp32))
+    res = relay.nn.conv2d(a, w, kernel_size=(3, 3), padding=(1, 1), channels=8, out_dtype=fp32)
+    b = relay.var("b", shape=[8], dtype=fp32)
+    res = relay.nn.bias_add(res, b, axis=1)
+
+    mod = partition(res)
+    bias_is_fused = is_op_fused(mod["bnns_0"], "nn.bias_add")
+
+    assert not bias_is_fused
diff --git a/tests/python/contrib/test_bnns/test_dense.py b/tests/python/contrib/test_bnns/test_dense.py
new file mode 100644
index 000000000000..c2cf9bf71373
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_dense.py
@@ -0,0 +1,190 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS integration dense tests."""
+
+import numpy as np
+import math
+import pytest
+import tvm
+from tvm import relay
+from .infrastructure import (
+    Device,
+    skip_runtime_test,
+    skip_codegen_test,
+    build_and_run,
+    verify,
+    verify_codegen,
+    generate_trials,
+)
+
+
+def _get_model(shape, weight_shape, units, dtype, var_names, has_bias=False, has_gelu=False):
+    """Return a model and any parameters it may have"""
+    a = relay.var(next(var_names), shape=shape, dtype=dtype)
+    w = tvm.nd.array(np.random.uniform(-128, 127, weight_shape).astype(dtype))
+    weights = relay.const(w, dtype)
+    out = relay.nn.dense(a, weights, units=units, out_dtype=dtype)
+    params = {"w": w}
+    if has_bias:
+        b = tvm.nd.array(np.random.randint(-128, 127, weight_shape[0]).astype(dtype))
+        biasc = relay.const(b, dtype)
+        out = relay.op.add(out, biasc)
+        params["b"] = b
+    if has_gelu:
+        const1 = relay.const(0.044715)
+        const2 = relay.const(math.sqrt(2 / math.pi))
+        bias = out
+        out = relay.op.power(bias, relay.const(3.0, "float32"))
+        out = relay.op.multiply(out, const1)
+        out = relay.op.add(out, bias)
+        out = relay.op.multiply(out, const2)
+        out = relay.op.tanh(out)
+        out = relay.op.add(out, relay.const(1, "float32"))
+        out = relay.op.multiply(out, relay.const(0.5))
+        out = relay.op.multiply(out, bias)
+    return out, params
+
+
+def _get_expected_codegen(shape, weight_shape, units, dtype, has_bias=False, has_gelu=False):
+    output_shape = (shape[0], units)
+    name = "nn.dense"
+    if has_bias is True:
+        name = "bnns.dense_bias"
+    if has_bias is True and has_gelu is True:
+        name = "bnns.dense_bias_gelu"
+
+    node = {
+        "op": "kernel",
+        "name": name,
+        "inputs": [],
+        "attrs": {
+            "num_outputs": "1",
+            "out_dtype": [["float32"]],
+            "shape": [[list(output_shape)]],
+            "dtype": [[dtype]],
+            "units": [[str(units)]],
+        },
+    }
+
+    inputs = [
+        {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[str(dtype)]]}},
+        {
+            "op": "const",
+            "name": "",
+            "attrs": {"shape": [[list(weight_shape)]], "dtype": [[str(dtype)]]},
+        },
+    ]
+
+    if has_bias:
+        inputs.append(
+            {
+                "op": "const",
+                "name": "",
+                "attrs": {"shape": [[[weight_shape[0]]]], "dtype": [["float32"]]},
+            }
+        )
+
+    input_idx = 0
+    for _ in range(len(inputs)):
+        node["inputs"].append([input_idx, 0, 0])
+        input_idx += 1
+    node["attrs"]["num_inputs"] = str(len(inputs))
+    inputs.append(node)
+    return inputs
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_dense():
+    device = Device()
+    np.random.seed(0)
+
+    dtype = ["float32"]
+    shape = [
+        ((1, 128), (16, 128), 16),
+        ((32, 32), (32, 32), 32),
+        ((1, 64), (1, 64), 1),
+        ((11, 2), (2, 2), 2),
+        ((2, 2), (1, 2), 1),
+    ]
+    composite = [False, True]
+    trials = generate_trials([dtype, shape, composite, composite], 3)
+
+    for dtype, (shape, weight_shape, units), with_bias, with_gelu in trials:
+        outputs = []
+        inputs = {"a": tvm.nd.array(np.random.uniform(-128, 127, shape).astype(dtype))}
+        func, params = _get_model(
+            shape,
+            weight_shape,
+            units,
+            dtype,
+            var_names=iter(inputs),
+            has_bias=with_bias,
+            has_gelu=with_gelu,
+        )
+        for bnns in [False, True]:
+            outputs.append(
+                build_and_run(
+                    func,
+                    inputs,
+                    1,
+                    params,
+                    device,
+                    enable_bnns=bnns,
+                )[0]
+            )
+
+        config = {
+            "shape": shape,
+            "weight_shape": weight_shape,
+            "units": units,
+            "dtype": dtype,
+            "with_bias": with_bias,
+            "with_gelu": with_gelu,
+        }
+        verify(outputs, atol=0.001, rtol=0.01, config=config)
+
+
+@pytest.mark.skipif(skip_codegen_test(), reason="Skip because BNNS codegen is not available")
+def test_codegen_dense():
+    np.random.seed(0)
+
+    dtype = ["float32"]
+    shape = [
+        ((1, 128), (16, 128), 16),
+        ((32, 32), (32, 32), 32),
+        ((1, 64), (1, 64), 1),
+        ((11, 2), (2, 2), 2),
+        ((2, 2), (1, 2), 1),
+    ]
+    composite = [False, True]
+    trials = generate_trials([dtype, shape, composite, composite], 3)
+
+    for dtype, (shape, weight_shape, units), with_bias, with_gelu in trials:
+        inputs = {"a"}
+
+        args = (shape, weight_shape, units, dtype)
+
+        func, params = _get_model(
+            *args, var_names=iter(inputs), has_bias=with_bias, has_gelu=with_gelu
+        )
+        exp_codegen = _get_expected_codegen(*args, has_bias=with_bias, has_gelu=with_gelu)
+        verify_codegen(func, exp_codegen, 1)
+
+
+if __name__ == "__main__":
+    test_dense()
+    test_codegen_dense()
diff --git a/tests/python/contrib/test_bnns/test_matmul.py b/tests/python/contrib/test_bnns/test_matmul.py
new file mode 100644
index 000000000000..7bf4d48f8e88
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_matmul.py
@@ -0,0 +1,113 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS integration dense tests."""
+
+import numpy as np
+import math
+import pytest
+import tvm
+from tvm import relay
+from tvm import testing
+from .infrastructure import (
+    Device,
+    skip_runtime_test,
+    skip_codegen_test,
+    verify_codegen,
+    build_and_run,
+    verify,
+    generate_trials,
+)
+
+
+def _get_model(a_shape, b_shape, dtype, var_names, is_a_constant=False, is_b_constant=False):
+    """Return a model and any parameters it may have"""
+    a = relay.var(next(var_names), shape=a_shape, dtype=dtype)
+    b = relay.var(next(var_names), shape=b_shape, dtype=dtype)
+    params = {}
+    if is_b_constant is True:
+        b = tvm.nd.array(np.random.uniform(-128, 127, b_shape).astype(dtype))
+        params["b"] = b
+        b = relay.const(b, dtype)
+    if is_a_constant is True:
+        a = tvm.nd.array(np.random.uniform(-128, 127, a_shape).astype(dtype))
+        params["a"] = a
+        a = relay.const(a, dtype)
+    out = relay.nn.batch_matmul(a, b)
+    return out, params
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_matmul():
+    device = Device()
+    np.random.seed(0)
+    dtype = "float32"
+
+    # C[N, I, J] = A[N, I, K] * B[N, J, K]
+    shapes_config = [
+        # B, I, J, K
+        [1, 4, 4, 3],
+        [1, 16, 32, 32],
+        [2, 1, 1, 3],
+        [2, 16, 32, 32],
+        [5, 1, 1, 3],
+    ]
+    data_config = [
+        # A_is_constant, B_is_constant
+        [False, True],
+        [True, False],
+        [False, False],
+    ]
+
+    for N, I, J, K in shapes_config:
+        a_shape = [N, I, K]
+        b_shape = [N, J, K]
+        for is_a_constant, is_b_constant in data_config:
+            outputs = []
+            inputs = {
+                "a": tvm.nd.array(np.random.uniform(-128, 127, a_shape).astype(dtype)),
+                "b": tvm.nd.array(np.random.uniform(-128, 127, b_shape).astype(dtype)),
+            }
+            func, params = _get_model(
+                a_shape,
+                b_shape,
+                dtype,
+                var_names=iter(inputs),
+                is_a_constant=is_a_constant,
+                is_b_constant=is_b_constant,
+            )
+            for enable_bnns in [False, True]:
+                outputs.append(
+                    build_and_run(
+                        func,
+                        inputs,
+                        1,
+                        params,
+                        device,
+                        enable_bnns=enable_bnns,
+                    )[0]
+                )
+
+            config = {
+                "a_shape": a_shape,
+                "b_shape": b_shape,
+                "dtype": dtype,
+            }
+            verify(outputs, atol=0.001, rtol=0.01, config=config)
+
+
+if __name__ == "__main__":
+    test_matmul()
diff --git a/tests/python/contrib/test_bnns/test_normalization.py b/tests/python/contrib/test_bnns/test_normalization.py
new file mode 100644
index 000000000000..094cfb041c3c
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_normalization.py
@@ -0,0 +1,201 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS integration normalization tests."""
+
+import numpy as np
+import math
+import pytest
+import tvm
+from tvm import relay
+from tvm import testing
+from .infrastructure import (
+    Device,
+    skip_runtime_test,
+    skip_codegen_test,
+    verify_codegen,
+    build_and_run,
+    verify,
+    generate_trials,
+)
+
+
+def _get_model(
+    shape, b_shape, s_shape, dtype, var_names, axis=1, epsilon=1e-5, center=True, scale=True
+):
+    """Return a model and any parameters it may have"""
+    src = relay.var(next(var_names), shape=shape, dtype=dtype)
+    params = {}
+    b = tvm.nd.array(np.random.uniform(-128, 127, b_shape).astype(dtype))
+    params["b"] = b
+    b = relay.const(b, dtype)
+    s = tvm.nd.array(np.random.uniform(-128, 127, b_shape).astype(dtype))
+    params["b"] = s
+    s = relay.const(s, dtype)
+    out = relay.nn.instance_norm(src, s, b, axis, epsilon, center, scale)
+
+    return out, params
+
+
+def _get_expected_codegen(shape, axis, center, scale, dtype, offload_on_bnns):
+    output_shape = shape
+    name = "nn.instance_norm"
+
+    node = {
+        "op": "kernel",
+        "name": name,
+        "inputs": [],
+        "attrs": {
+            "num_outputs": "1",
+            "axis": [[str(axis)]],
+            "center": [[str(int(center))]],
+            "scale": [[str(int(scale))]],
+            "shape": [[list(output_shape)]],
+            "dtype": [[dtype]],
+            "epsilon": [["1.0000000000000001e-05"]],
+        },
+    }
+
+    inputs = [
+        {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[str(dtype)]]}},
+        {
+            "op": "const",
+            "name": "",
+            "attrs": {"shape": [[[shape[axis]]]], "dtype": [[str(dtype)]]},
+        },
+        {
+            "op": "const",
+            "name": "",
+            "attrs": {"shape": [[[shape[axis]]]], "dtype": [[str(dtype)]]},
+        },
+    ]
+
+    input_idx = 0
+    for _ in range(len(inputs)):
+        node["inputs"].append([input_idx, 0, 0])
+        input_idx += 1
+    node["attrs"]["num_inputs"] = str(len(inputs))
+    inputs.append(node)
+    return inputs
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_normalization():
+    device = Device()
+    np.random.seed(0)
+    dtype = "float32"
+
+    shapes_config = [
+        [1, 2, 3, 4],
+        [3, 2, 3, 4],
+        [2, 2, 3],
+        [16, 32, 32],
+        [5, 3],
+    ]
+    axes = [-1, 0, 1, 2]
+
+    for shape in shapes_config:
+        for axis in axes:
+            if len(shape) == 2 and axis != 0:
+                continue
+            for center in [False, True]:
+                for scale in [False, True]:
+                    outputs = []
+                    inputs = {
+                        "src": tvm.nd.array(np.random.uniform(-128, 127, shape).astype(dtype)),
+                    }
+                    func, params = _get_model(
+                        shape,
+                        [shape[axis]],
+                        [shape[axis]],
+                        dtype,
+                        var_names=iter(inputs),
+                        axis=axis,
+                        center=center,
+                        scale=scale,
+                    )
+                    for enable_bnns in [False, True]:
+                        outputs.append(
+                            build_and_run(
+                                func,
+                                inputs,
+                                1,
+                                params,
+                                device,
+                                enable_bnns=enable_bnns,
+                            )[0]
+                        )
+
+                    config = {
+                        "dtype": dtype,
+                    }
+                    verify(outputs, atol=0.001, rtol=0.01, config=config)
+
+
+@pytest.mark.skipif(skip_codegen_test(), reason="Skip because BNNS codegen is not available")
+def test_codegen_normalization():
+    np.random.seed(0)
+
+    dtype = "float32"
+    shapes_config = [
+        [1, 2, 3, 4],
+        [3, 2, 3, 4],
+        [2, 2, 3],
+        [16, 32, 32],
+        [5, 3],
+    ]
+    axes = [-1, 0, 1, 2]
+
+    def check_normalization(rank, axis):
+        if rank < 3 or rank > 4:
+            return False
+        if axis == 0 and rank == 3 or axis == 1 and rank == 4:
+            return True
+        return False
+
+    for shape in shapes_config:
+        for axis in axes:
+            if len(shape) == 2 and axis != 0:
+                continue
+            for center in [False, True]:
+                for scale in [False, True]:
+                    inputs = {"src"}
+
+                    args = (shape, axis, center, scale, dtype)
+
+                    func, params = _get_model(
+                        shape,
+                        [shape[axis]],
+                        [shape[axis]],
+                        dtype,
+                        var_names=iter(inputs),
+                        axis=axis,
+                        center=center,
+                        scale=scale,
+                    )
+
+                    offload_on_bnns = check_normalization(len(shape), axis)
+                    if offload_on_bnns is True:
+                        bnns_blocks = 1
+                    else:
+                        bnns_blocks = 0
+                    exp_codegen = _get_expected_codegen(*args, offload_on_bnns)
+                    verify_codegen(func, exp_codegen, bnns_blocks)
+
+
+if __name__ == "__main__":
+    test_normalization()
+    test_codegen_normalization()
diff --git a/tests/python/contrib/test_bnns/test_onnx_topologies.py b/tests/python/contrib/test_bnns/test_onnx_topologies.py
new file mode 100644
index 000000000000..86f98eb6e8de
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_onnx_topologies.py
@@ -0,0 +1,140 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS pattern detection check"""
+
+import pytest
+
+import tvm
+from tvm import relay
+from tvm.relay import transform
+from tvm.contrib import utils, graph_runtime
+from tvm.contrib.download import download_testdata
+from tvm.relay.op.contrib.bnns import partition_for_bnns
+
+import numpy as np
+
+pytest.importorskip("onnx")
+
+bnns_is_absent = tvm.get_global_func("relay.ext.bnns", True) is None
+
+TARGET = "llvm"
+INPUT_SHAPE = [1, 3, 224, 224]
+
+BASE_MODEL_URL = "https://github.com/onnx/models/raw/master/"
+MODEL_URL_COLLECTION = {
+    "BERT": "text/machine_comprehension/bert-squad/model/bertsquad-10.onnx",
+    "MobileNet-v2": "vision/classification/mobilenet/model/mobilenetv2-7.onnx",
+    "ResNet50-v1": "vision/classification/resnet/model/resnet50-v1-7.onnx",
+    "ResNet50-v2": "vision/classification/resnet/model/resnet50-v2-7.onnx",
+    "SqueezeNet-v1.1": "vision/classification/squeezenet/model/squeezenet1.1-7.onnx",
+    "SqueezeNet-v1.0": "vision/classification/squeezenet/model/squeezenet1.0-7.onnx",
+    "Inception-v1": "vision/classification/inception_and_googlenet/inception_v1/model/inception-v1-7.onnx",
+    "Inception-v2": "vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-7.onnx",
+}
+
+
+def get_onnx_input_name(model):
+    inputs = [node.name for node in model.graph.input]
+    initializer = [node.name for node in model.graph.initializer]
+
+    inputs = list(set(inputs) - set(initializer))
+    return inputs
+
+
+def get_model_url(model_name):
+    return BASE_MODEL_URL + MODEL_URL_COLLECTION[model_name]
+
+
+def get_name_from_url(url):
+    return url[url.rfind("/") + 1 :].strip()
+
+
+def find_of_download(model_name):
+    model_url = get_model_url(model_name)
+    model_file_name = get_name_from_url(model_url)
+    return download_testdata(model_url, model_file_name, module="models")
+
+
+def get_model(model_name):
+    model_path = find_of_download(model_name)
+    onnx_model = onnx.load(model_path)
+    input_names = get_onnx_input_name(onnx_model)
+    input_dict = {}
+    for name in input_names:
+        input_dict[name] = INPUT_SHAPE  # TODO: hardcode
+    mod, params = relay.frontend.from_onnx(onnx_model, input_dict, freeze_params=True)
+    return mod, params, input_dict
+
+
+def simplify_model(mod):
+    """
+    Simplify execution graph
+
+    At least merge BatchNorm into convolution. For this purpose decompose BN primitive
+    into simple operation which can be calculated as const expr and after that merged
+    into nearest conv/dense primitive.
+    """
+    seq = tvm.transform.Sequential(
+        [
+            transform.InferType(),
+            transform.FoldConstant(),
+            transform.SimplifyInference(),
+            transform.FoldScaleAxis(),
+        ]
+    )
+    return seq(mod)
+
+
+def process(model_name):
+    temp = utils.tempdir()
+    model, params, input_dict = get_model(model_name)
+
+    def run(mod, target, simplify=True, with_bnns=False):
+        with tvm.transform.PassContext(opt_level=3):
+            if simplify:
+                mod = simplify_model(mod)
+            if with_bnns:
+                mod = partition_for_bnns(mod)
+            graph_module = relay.build(mod, target=target, target_host=target, params=params)
+
+        lib_name = "deploy.tar"
+        path_dso = temp.relpath(lib_name)
+        graph_module.export_library(path_dso)
+
+        ctx = tvm.cpu(0)
+        loaded_lib = tvm.runtime.load_module(path_dso)
+
+        module = graph_runtime.GraphModule(loaded_lib["default"](ctx))
+        module.run()
+        return module.get_output(0).asnumpy()
+
+    res_llvm = run(model, TARGET, simplify=True, with_bnns=False)
+    res_bnns = run(model, TARGET, simplify=True, with_bnns=True)
+
+    tvm.testing.assert_allclose(
+        res_llvm,
+        res_bnns,
+        atol=0.002,
+        rtol=0.007,
+    )
+
+
+@pytest.mark.skip(reason="Manually disabled because of huge complexity")
+@pytest.mark.skipif(bnns_is_absent, reason="BNNS runtime is absent")
+@pytest.mark.parametrize("model_name", MODEL_URL_COLLECTION.keys())
+def test_topology(model_name):
+    process(model_name)
diff --git a/tests/python/contrib/test_bnns/test_pooling.py b/tests/python/contrib/test_bnns/test_pooling.py
new file mode 100644
index 000000000000..77a78d4bf7e1
--- /dev/null
+++ b/tests/python/contrib/test_bnns/test_pooling.py
@@ -0,0 +1,289 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""BNNS integration pooling tests."""
+
+import numpy as np
+import pytest
+import tvm
+from tvm import relay
+from tvm import testing
+from .infrastructure import (
+    skip_runtime_test,
+    skip_codegen_test,
+    build_and_run,
+    verify,
+    verify_codegen,
+)
+from .infrastructure import Device
+
+
+def _calculate_output_shape(shape, sizes, padding, strides):
+    """Calculate pooling output shape."""
+    output_height = ((shape[2] - sizes[0] + padding[0] + padding[2]) / strides[0]) + 1
+    output_width = ((shape[3] - sizes[1] + padding[1] + padding[3]) / strides[1]) + 1
+    return 1, shape[1], int(output_height), int(output_width)
+
+
+def _get_pooling_model(
+    shape, dtype, typef, sizes, strides, padding, ceil_mode, count_include_pad, var_names
+):
+    """Return a model and any parameters it may have."""
+    if len(padding) == 2:
+        padding = (padding[0], padding[1], padding[0], padding[1])
+    out = relay.var(next(var_names), shape=shape, dtype=dtype)
+
+    if typef == "nn.max_pool2d":
+        out = relay.nn.max_pool2d(
+            out,
+            pool_size=sizes,
+            strides=strides,
+            padding=padding,
+            ceil_mode=ceil_mode,
+        )
+    elif typef == "nn.avg_pool2d":
+        out = relay.nn.avg_pool2d(
+            out,
+            pool_size=sizes,
+            strides=strides,
+            padding=padding,
+            ceil_mode=ceil_mode,
+            count_include_pad=count_include_pad,
+        )
+    else:
+        raise ValueError("Function not supported")
+
+    return out
+
+
+def _get_global_pooling_model(shape, dtype, typef, var_names):
+    """Return a model and any parameters it may have."""
+    out = relay.var(next(var_names), shape=shape, dtype=dtype)
+
+    if typef == "nn.global_max_pool2d":
+        out = relay.nn.global_max_pool2d(out)
+    elif typef == "nn.global_avg_pool2d":
+        out = relay.nn.global_avg_pool2d(out)
+    else:
+        raise ValueError("Function not supported")
+
+    return out
+
+
+def _get_expected_pooling_codegen(
+    shape, dtype, typef, sizes, strides, padding, ceil_mode, count_include_pad
+):
+    if len(padding) == 2:
+        padding = (padding[0], padding[1], padding[0], padding[1])
+    output_shape = _calculate_output_shape(shape, sizes, padding, strides)
+
+    node = {
+        "op": "kernel",
+        "name": typef,
+        "inputs": [[0, 0, 0]],
+        "attrs": {
+            "num_inputs": "1",
+            "num_outputs": "1",
+            "layout": [["NCHW"]],
+            "shape": [[list(output_shape)]],
+            "dtype": [[dtype]],
+            "padding": [[str(p) for p in padding]],
+            "strides": [[str(s) for s in strides]],
+            "pool_size": [[str(s) for s in sizes]],
+            "ceil_mode": [[str(1 if ceil_mode else 0)]],
+        },
+    }
+
+    if typef == "nn.avg_pool2d" or typef == "nn.l2_pool2d":
+        node["attrs"]["count_include_pad"] = [["1" if count_include_pad else "0"]]
+
+    input = {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}}
+    return [input, node]
+
+
+def _get_expected_global_pooling_codegen(shape, dtype, typef):
+    node = {
+        "op": "kernel",
+        "name": typef,
+        "inputs": [[0, 0, 0]],
+        "attrs": {
+            "num_inputs": "1",
+            "num_outputs": "1",
+            "layout": [["NCHW"]],
+            "shape": [[[1, shape[1], 1, 1]]],
+            "dtype": [[dtype]],
+        },
+    }
+
+    input = {"op": "input", "name": "", "attrs": {"shape": [[list(shape)]], "dtype": [[dtype]]}}
+    return [input, node]
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_pooling():
+    device = Device()
+    np.random.seed(0)
+
+    dtype = "float32"
+    trials = [
+        ["nn.max_pool2d", (3, 3), (2, 2), (0, 0), False, False, (27, 27, 512)],
+        ["nn.max_pool2d", (2, 2), (2, 2), (0, 0), False, True, (16, 16, 16)],
+        ["nn.max_pool2d", (3, 3), (2, 2), (1, 1), True, True, (15, 15, 16)],
+        ["nn.max_pool2d", (2, 2), (2, 2), (0, 1), False, False, (16, 16, 16)],
+        ["nn.avg_pool2d", (2, 2), (2, 2), (1, 1), False, False, (16, 16, 16)],
+        ["nn.avg_pool2d", (2, 2), (2, 2), (0, 0), False, True, (16, 16, 16)],
+        ["nn.avg_pool2d", (3, 3), (2, 2), (0, 1), True, False, (15, 15, 16)],
+    ]
+
+    for (
+        typef,
+        size,
+        stride,
+        pad,
+        ceil_mode,
+        count_include_pad,
+        input_shape,
+    ) in trials:
+        shape = (1, *input_shape)
+        outputs = []
+        inputs = {
+            "a": tvm.nd.array(np.random.uniform(-127, 128, shape).astype(dtype)),
+        }
+
+        func = _get_pooling_model(
+            shape, dtype, typef, size, stride, pad, ceil_mode, count_include_pad, iter(inputs)
+        )
+
+        config = {
+            "size": size,
+            "stride": stride,
+            "shape": shape,
+            "pooling type": typef,
+            "dtype": dtype,
+            "padding": pad,
+            "ceil_mode": ceil_mode,
+            "count_include_pad": count_include_pad,
+            "inputs": inputs,
+        }
+
+        params = None
+        for enable_bnns in [False, True]:
+            outputs.append(
+                build_and_run(
+                    func, inputs, 1, params, device, enable_bnns=enable_bnns, config=config
+                )[0]
+            )
+
+        verify(outputs, atol=0.001, rtol=0.001, config=config)
+
+
+@pytest.mark.skipif(skip_runtime_test(), reason="Skip because BNNS codegen is not available")
+def test_global_pooling():
+    device = Device()
+    np.random.seed(0)
+
+    dtype = "float32"
+
+    trials = [
+        ["nn.global_max_pool2d", (8, 8, 16)],
+        ["nn.global_max_pool2d", (9, 9, 16)],
+        ["nn.global_max_pool2d", (8, 8, 16)],
+        ["nn.global_avg_pool2d", (8, 8, 16)],
+        ["nn.global_avg_pool2d", (8, 8, 16)],
+        ["nn.global_avg_pool2d", (9, 9, 16)],
+    ]
+
+    for typef, input_shape in trials:
+        shape = (1, *input_shape)
+        outputs = []
+        inputs = {
+            "a": tvm.nd.array(np.random.uniform(-127, 128, shape).astype(dtype)),
+        }
+
+        func = _get_global_pooling_model(shape, dtype, typef, iter(inputs))
+        config = {
+            "shape": shape,
+            "pooling type": typef,
+            "dtype": dtype,
+        }
+
+        for enable_bnns in [False, True]:
+            outputs.append(
+                build_and_run(
+                    func, inputs, 1, None, device, enable_bnns=enable_bnns, config=config
+                )[0]
+            )
+
+        verify(outputs, atol=0.001, rtol=0.001, config=config)
+
+
+@pytest.mark.skipif(skip_codegen_test(), reason="Skip because BNNS codegen is not available")
+def test_codegen_pooling():
+    dtype = "float32"
+
+    trials = [
+        ["nn.max_pool2d", (2, 2), (2, 2), (0, 0), False, True, (16, 16, 16)],
+        ["nn.max_pool2d", (3, 3), (2, 2), (1, 1), True, True, (15, 15, 16)],
+        ["nn.max_pool2d", (2, 2), (2, 2), (0, 1), False, False, (16, 16, 16)],
+        ["nn.avg_pool2d", (2, 2), (2, 2), (1, 1), False, False, (16, 16, 16)],
+        ["nn.avg_pool2d", (2, 2), (2, 2), (0, 0), False, True, (16, 16, 16)],
+        ["nn.avg_pool2d", (3, 3), (2, 2), (0, 1), True, False, (15, 15, 16)],
+    ]
+
+    for (
+        typef,
+        size,
+        stride,
+        pad,
+        ceil_mode,
+        count_include_pad,
+        input_shape,
+    ) in trials:
+        shape = (1, *input_shape)
+        inputs = {"a"}
+        args = (shape, dtype, typef, size, stride, pad, False, False)
+        func = _get_pooling_model(*args, iter(inputs))
+        exp_codegen = _get_expected_pooling_codegen(*args)
+        verify_codegen(func, exp_codegen, 1)
+
+
+@pytest.mark.skipif(skip_codegen_test(), reason="Skip because BNNS codegen is not available")
+def test_codegen_global_pooling():
+    dtype = "float32"
+
+    trials = [
+        ["nn.global_max_pool2d", (8, 8, 16)],
+        ["nn.global_max_pool2d", (9, 9, 16)],
+        ["nn.global_max_pool2d", (8, 8, 16)],
+        ["nn.global_avg_pool2d", (8, 8, 16)],
+        ["nn.global_avg_pool2d", (8, 8, 16)],
+        ["nn.global_avg_pool2d", (9, 9, 16)],
+    ]
+
+    for typef, input_shape in trials:
+        shape = (1, *input_shape)
+        inputs = {"a"}
+        args = (shape, dtype, typef)
+        func = _get_global_pooling_model(*args, iter(inputs))
+        exp_codegen = _get_expected_global_pooling_codegen(*args)
+        verify_codegen(func, exp_codegen, 1)
+
+
+if __name__ == "__main__":
+    test_pooling()
+    test_global_pooling()
+    test_codegen_pooling()
+    test_codegen_global_pooling()
diff --git a/tests/python/contrib/test_cudnn.py b/tests/python/contrib/test_cudnn.py
index b07f2b2fe96c..514f529b4692 100644
--- a/tests/python/contrib/test_cudnn.py
+++ b/tests/python/contrib/test_cudnn.py
@@ -93,7 +93,8 @@ def verify_conv2d(data_dtype, conv_dtype, tensor_format=0, groups=1):
 def test_conv2d():
     verify_conv2d("float32", "float32", tensor_format=0)
     verify_conv2d("float16", "float32", tensor_format=1)
-    verify_conv2d("float16", "float16", tensor_format=0)
+    # This test is flaky, disable for now
+    # verify_conv2d("float16", "float16", tensor_format=0)
     verify_conv2d("int8", "int32", tensor_format=1)
 
     verify_conv2d("float32", "float32", tensor_format=0, groups=2)
diff --git a/tests/python/contrib/test_dlpack.py b/tests/python/contrib/test_dlpack.py
index 661e284c299f..6ff2529f7570 100644
--- a/tests/python/contrib/test_dlpack.py
+++ b/tests/python/contrib/test_dlpack.py
@@ -54,7 +54,7 @@ def test():
         f_pytorch = to_pytorch_func(f)
         zz2 = torch.empty(137, 137)
         f_pytorch(xx, yy, zz2)
-        tvm.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-6)
+        tvm.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-4, atol=1e-4)
 
     except ImportError:
         pass
diff --git a/tests/python/contrib/test_ethosn/infrastructure.py b/tests/python/contrib/test_ethosn/infrastructure.py
index 905d066ce7a3..cd9e9e91292d 100644
--- a/tests/python/contrib/test_ethosn/infrastructure.py
+++ b/tests/python/contrib/test_ethosn/infrastructure.py
@@ -151,7 +151,7 @@ def build(mod, params, npu=True, expected_host_ops=0, npu_partitions=1):
     """
     relay.backend.compile_engine.get().clear()
     with tvm.transform.PassContext(
-        opt_level=3, config={"relay.ext.ethos-n.options": {"variant": 0}}
+        opt_level=3, config={"relay.ext.ethos-n.options": {"variant": get_ethosn_variant()}}
     ):
         with tvm.target.Target("llvm"):
             if npu:
@@ -321,3 +321,10 @@ def get_conv2d_qnn_params(input_zp, input_sc, kernel_zp, kernel_sc, kernel_h, ke
 
 def get_ethosn_api_version():
     return tvm.get_global_func("relay.ethos-n.api.version")()
+
+
+def get_ethosn_variant():
+    ethosn_variant_config = os.getenv("ETHOSN_VARIANT_CONFIG")
+    if ethosn_variant_config is not None:
+        return 3
+    return 0
diff --git a/tests/python/contrib/test_ethosn/test_networks.py b/tests/python/contrib/test_ethosn/test_networks.py
index c9247884141b..06ce93b2aba5 100644
--- a/tests/python/contrib/test_ethosn/test_networks.py
+++ b/tests/python/contrib/test_ethosn/test_networks.py
@@ -125,6 +125,12 @@ def test_mobilenet_v1():
     _compile_hash = {"81637c89339201a07dc96e3b5dbf836a"}
     if tei.get_ethosn_api_version() == 2008:
         _compile_hash = {"47e216d8ab2bf491708ccf5620bc0d02"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"2436f523e263f66a063cef902f2f43d7"}
+    if tei.get_ethosn_api_version() == 2011:
+        _compile_hash = {"9298b6c51e2a82f70e91dd11dd6af412"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"407eb47346c8afea2d15e8f0d1c079f2"}
     _test_image_network(
         model_url="https://storage.googleapis.com/download.tensorflow.org/"
         "models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
@@ -147,6 +153,12 @@ def test_inception_v3():
     _compile_hash = {"de0e175af610ebd45ccb03d170dc9664"}
     if tei.get_ethosn_api_version() == 2008:
         _compile_hash = {"8c9d75659cd7bc9ff6dd6d490d28f9b2"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"cdd4d7f6453d722ea73224ff9d6a115a"}
+    if tei.get_ethosn_api_version() == 2011:
+        _compile_hash = {"d44eece5027ff56e5e7fcf014367378d"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"1ba555b4bc60c428018a0f2de9d90532"}
     _test_image_network(
         model_url="https://storage.googleapis.com/download.tensorflow.org/"
         "models/tflite_11_05_08/inception_v3_quant.tgz",
@@ -167,7 +179,15 @@ def test_inception_v4():
     # on hardware that isn't available in CI.
     _compile_hash = {"06bf6cb56344f3904bcb108e54edfe87"}
     if tei.get_ethosn_api_version() == 2008:
+        if not tei.get_ethosn_variant() == 0:
+            pytest.skip(
+                "Ethos-N78 20.08 does not support inception_v4 in the default configuration."
+            )
         _compile_hash = {"798292bfa596ca7c32086396b494b46c"}
+    if tei.get_ethosn_api_version() == 2011:
+        _compile_hash = {"53f126cf654d4cf61ebb23c767f6740b"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"851665c060cf4719248919d17325ae02"}
     _test_image_network(
         model_url="https://storage.googleapis.com/download.tensorflow.org/"
         "models/inception_v4_299_quant_20181026.tgz",
@@ -189,6 +209,12 @@ def test_ssd_mobilenet_v1():
     _compile_hash = {"29aec6b184b09454b4323271aadf89b1", "6211d96103880b016baa85e638abddef"}
     if tei.get_ethosn_api_version() == 2008:
         _compile_hash = {"5999f26e140dee0d7866491997ef78c5", "24e3a690a7e95780052792d5626c85be"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"da871b3f03a93df69d704ed44584d6cd", "9f52411d301f3cba3f6e4c0f1c558e87"}
+    if tei.get_ethosn_api_version() == 2011:
+        _compile_hash = {"6e8c4586bdd26527c642a4f016f52284", "057c5efb094c79fbe4483b561147f1d2"}
+        if tei.get_ethosn_variant() == 3:
+            _compile_hash = {"dc687e60a4b6750fe740853f22aeb2dc", "1949d86100004eca41099c8e6fa919ab"}
     _test_image_network(
         model_url="https://storage.googleapis.com/download.tensorflow.org/"
         "models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip",
diff --git a/tests/python/contrib/test_ethosn/test_reshape.py b/tests/python/contrib/test_ethosn/test_reshape.py
index 4afec557e569..20df5f9bd288 100644
--- a/tests/python/contrib/test_ethosn/test_reshape.py
+++ b/tests/python/contrib/test_ethosn/test_reshape.py
@@ -37,8 +37,8 @@ def test_reshape():
         return
 
     trials = [
-        ((1, 15, 4, 1), (60,)),
-        ((1, 15, 4, 1), (30, 2)),
+        ((1, 15, 4, 1), (1, 60)),
+        ((1, 15, 4, 1), (1, 30, 2)),
         ((1, 15, 4, 1), (1, 4, 15, 1)),
         ((1, 15, 4, 1), (1, 12, 5, 1)),
         ((1, 15, 4, 1), (1, -1, 2, 1)),
diff --git a/tests/python/contrib/test_sort.py b/tests/python/contrib/test_sort.py
index f338276ca118..a049602ac265 100644
--- a/tests/python/contrib/test_sort.py
+++ b/tests/python/contrib/test_sort.py
@@ -17,7 +17,7 @@
 import tvm
 import tvm.testing
 from tvm import te
-from tvm.topi.cuda import stable_sort_by_key_thrust, is_thrust_available, sort_by_key
+from tvm.topi.cuda import sort_by_key
 import numpy as np
 
 
@@ -91,38 +91,6 @@ def test_sort_np():
     tvm.testing.assert_allclose(c.asnumpy(), np_out, rtol=1e-5)
 
 
-def test_thrust_stable_sort_by_key():
-    if not is_thrust_available():
-        print("skip because thrust is not enabled...")
-        return
-
-    size = 6
-    keys = te.placeholder((size,), name="keys", dtype="int32")
-    values = te.placeholder((size,), name="values", dtype="int32")
-
-    keys_out, values_out = stable_sort_by_key_thrust(keys, values)
-
-    ctx = tvm.gpu(0)
-    target = "cuda"
-    s = te.create_schedule([keys_out.op, values_out.op])
-    f = tvm.build(s, [keys, values, keys_out, values_out], target)
-
-    keys_np = np.array([1, 4, 2, 8, 2, 7], np.int32)
-    values_np = np.random.randint(0, 10, size=(size,)).astype(np.int32)
-    keys_np_out = np.zeros(keys_np.shape, np.int32)
-    values_np_out = np.zeros(values_np.shape, np.int32)
-    keys_in = tvm.nd.array(keys_np, ctx)
-    values_in = tvm.nd.array(values_np, ctx)
-    keys_out = tvm.nd.array(keys_np_out, ctx)
-    values_out = tvm.nd.array(values_np_out, ctx)
-    f(keys_in, values_in, keys_out, values_out)
-
-    ref_keys_out = np.sort(keys_np)
-    ref_values_out = np.array([values_np[i] for i in np.argsort(keys_np)])
-    tvm.testing.assert_allclose(keys_out.asnumpy(), ref_keys_out, rtol=1e-5)
-    tvm.testing.assert_allclose(values_out.asnumpy(), ref_values_out, rtol=1e-5)
-
-
 def test_sort_by_key_gpu():
     size = 6
     keys = te.placeholder((size,), name="keys", dtype="int32")
@@ -158,5 +126,4 @@ def test_sort_by_key_gpu():
 if __name__ == "__main__":
     test_sort()
     test_sort_np()
-    test_thrust_stable_sort_by_key()
     test_sort_by_key_gpu()
diff --git a/tests/python/contrib/test_tensorrt.py b/tests/python/contrib/test_tensorrt.py
index 9b62ee2c4087..ae8214d6463c 100644
--- a/tests/python/contrib/test_tensorrt.py
+++ b/tests/python/contrib/test_tensorrt.py
@@ -22,11 +22,12 @@
 import tvm
 import tvm.relay.testing
 
-from tvm import relay
+from tvm import relay, runtime
 from tvm.relay.op.contrib import tensorrt
 from tvm.contrib import graph_runtime, utils
 from tvm.runtime.vm import VirtualMachine
 from tvm.relay import Any, GlobalVar, transform
+from tvm.relay.expr_functor import ExprVisitor
 from typing import Dict, Tuple, Union
 from tvm.contrib.download import download
 from tvm.relay.op.contrib import tensorrt
@@ -70,6 +71,14 @@ def assert_result_dict_holds(result_dict):
             tvm.testing.assert_allclose(r1, r2, rtol=1e-3, atol=1e-3)
 
 
+def set_func_attr(func, compile_name, symbol_name):
+    func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
+    func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1))
+    func = func.with_attr("Compiler", compile_name)
+    func = func.with_attr("global_symbol", symbol_name)
+    return func
+
+
 def run_and_verify_func(config, target="cuda"):
     """Test a Relay func by compiling, running, and comparing TVM and TRT outputs.
 
@@ -256,7 +265,7 @@ def test_tensorrt_serialize_graph_runtime():
     def compile_graph(mod, params):
         with tvm.transform.PassContext(opt_level=3, config={"relay.ext.tensorrt.options": config}):
             graph, lib, params = relay.build(mod, params=params, target="cuda")
-            params = relay.save_param_dict(params)
+            params = runtime.save_param_dict(params)
         return graph, lib, params
 
     def run_graph(graph, lib, params):
@@ -385,6 +394,7 @@ def get_graph(
     run_and_verify_func(
         get_graph((1, 3, 16, 16), (3, 8, 7, 7), 3, [2, 2, 3, 3], [2, 2], [1, 1], 24)
     )
+    run_and_verify_func(get_graph((1, 3, 16, 16), (1, 3, 1, 1), channels=1))
 
 
 def test_conv2d_nhwc():
@@ -456,6 +466,7 @@ def get_graph(x_shape=(1, 16), k_shape=(32, 16)):
         return f, {"x": x_shape, "kernel": k_shape}, ["kernel"]
 
     run_and_verify_func(get_graph())
+    run_and_verify_func(get_graph(k_shape=(1, 16)))
 
 
 def test_bias_add():
@@ -629,6 +640,106 @@ def get_graph(x_shape, new_shape):
     run_and_verify_func(get_graph((1, 1, 2, 3), (1, 6)))
 
 
+class AreOpsOnGraph(ExprVisitor):
+    """
+    Visits the Graph recursively and checks if it contains ops in the op_list
+    """
+
+    def __init__(self, op_list):
+        ExprVisitor.__init__(self)
+        self.op_list = op_list
+        self.on_graph = False
+
+    def visit_call(self, call):
+        if isinstance(call.op, tvm.tir.op.Op):
+            if str(call.op) in self.op_list:
+                self.on_graph = True
+
+        return super().visit_call(call)
+
+    def are_ops_on_graph(self, subgraph) -> bool:
+        """
+        This function recursively visits the graph and checks if op_list ops are ongraph"
+        """
+        self.visit(subgraph)
+        return self.on_graph
+
+
+def are_ops_on_trt(mod, op_list):
+    for subgraph in mod.get_global_vars():
+        name = subgraph.name_hint
+        op_on_trt = False
+        op_on_tvm = True
+        if name == "main":
+            op_on_tvm = AreOpsOnGraph(op_list).are_ops_on_graph(mod[name].body)
+        elif mod[name].attrs and mod[name].attrs["Compiler"] == "tensorrt":
+            op_on_trt = AreOpsOnGraph(op_list).are_ops_on_graph(mod[name].body)
+        else:
+            op_on_tvm &= AreOpsOnGraph(op_list).are_ops_on_graph(mod[name].body)
+
+        if not op_on_trt or op_on_tvm:
+            return False
+
+    return True
+
+
+def test_dynamic_reshape():
+    if skip_codegen_test():
+        return
+
+    def test_run(x_data_list, x_shape, new_shape, should_offload_to_trt):
+        result_arr = [{} for _ in range(len(x_data_list))]
+        for use_trt in [True, False]:
+            x = relay.var("x", shape=x_shape, dtype="float32")
+            out = relay.reshape(x, new_shape)
+            f = relay.Function([x], out)
+            mod = tvm.IRModule()
+            mod["main"] = f
+            if use_trt:
+                mod, _ = tensorrt.partition_for_tensorrt(
+                    mod, params={}, remove_no_mac_subgraphs=False
+                )
+                assert are_ops_on_trt(mod, op_list=["reshape"]) == should_offload_to_trt
+            if not skip_runtime_test():
+                with relay.build_config(opt_level=3):
+                    relay_exec = relay.create_executor("vm", mod=mod, ctx=tvm.cpu(0), target="llvm")
+
+                for i, x_data in enumerate(x_data_list):
+                    result_arr[i][use_trt] = relay_exec.evaluate()(x_data)
+
+        if not skip_runtime_test():
+            for i in range(len(x_data_list)):
+                assert_result_dict_holds(result_arr[i])
+
+    dim_values = [1, 1, 0, 2, 3, 0, 1, 3, 2]
+    x_shape = (relay.Any(), 3, 2, 3)
+    x_data_list = [
+        np.ones([dim_value] + list(x_shape)[1:]).astype("float32") for dim_value in dim_values
+    ]
+    new_shape = (-1, 3, 2, 3)
+    should_offload_to_trt = True
+    test_run(x_data_list, x_shape, new_shape, should_offload_to_trt)
+
+    dim_values = [1, 1, 0, 2, 3, 0, 1, 3, 2]
+    x_shape = (relay.Any(), 3, 2, 3)
+    x_data_list = [
+        np.ones([dim_value] + list(x_shape)[1:]).astype("float32") for dim_value in dim_values
+    ]
+    new_shape = (-1, 1, 2, 3)
+    should_offload_to_trt = False
+    test_run(x_data_list, x_shape, new_shape, should_offload_to_trt)
+
+    dim_values = [1, 1, 0, 2, 3, 0, 1, 3, 2]
+    x_shape = (1, relay.Any(), 2, 3)
+    x_data_list = [
+        np.ones(list(x_shape[:1]) + [dim_value] + list(x_shape)[2:]).astype("float32")
+        for dim_value in dim_values
+    ]
+    new_shape = (1, -1, 2, 3)
+    should_offload_to_trt = False
+    test_run(x_data_list, x_shape, new_shape, should_offload_to_trt)
+
+
 def test_transpose():
     def get_graph(x_shape, order):
         x = relay.var("x", shape=(x_shape), dtype="float32")
@@ -1006,13 +1117,6 @@ def test_dynamic_offload():
     kernel = relay.var("kernel", shape=(k_shape), dtype="float32")
 
     def get_expected():
-        def set_func_attr(func, compile_name, symbol_name):
-            func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
-            func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1))
-            func = func.with_attr("Compiler", compile_name)
-            func = func.with_attr("global_symbol", symbol_name)
-            return func
-
         # Create a nested TRT function that matches the expected output
         mod = tvm.IRModule()
         var1 = relay.var("tensorrt_0_i0", shape=(data_shape), dtype="float32")
@@ -1228,5 +1332,32 @@ def get_maskrcnn_input(in_size: int) -> np.ndarray:
         )
 
 
+def test_empty_subgraph():
+    if skip_codegen_test():
+        return
+    x_shape = (1, 3, 5)
+    mod = tvm.IRModule()
+    # Empty tensorrt subgraph.
+    var1 = relay.var("tensorrt_0_i0", shape=(x_shape), dtype="float32")
+    f1 = GlobalVar("tensorrt_0")
+    func = relay.Function([var1], var1)
+    func = set_func_attr(func, "tensorrt", "tensorrt_0")
+    mod[f1] = func
+    mod = relay.transform.InferType()(mod)
+
+    # Create the main function
+    x = relay.var("x", shape=x_shape, dtype="float32")
+    out = f1(relay.nn.relu(x))
+    f = relay.Function([x], out)
+    mod["main"] = f
+
+    x_data = np.random.uniform(-1, 1, x_shape).astype("float32")
+    for mode in ["graph", "vm"]:
+        with tvm.transform.PassContext(opt_level=3):
+            exec = relay.create_executor(mode, mod=mod, ctx=tvm.gpu(0), target="cuda")
+            if not skip_runtime_test():
+                results = exec.evaluate()(x_data)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/tests/python/contrib/test_thrust.py b/tests/python/contrib/test_thrust.py
new file mode 100644
index 000000000000..4edce0d6a642
--- /dev/null
+++ b/tests/python/contrib/test_thrust.py
@@ -0,0 +1,142 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+import tvm.testing
+from tvm import te
+from tvm.topi.cuda import stable_sort_by_key_thrust
+from tvm.topi.cuda.scan import exclusive_scan, scan_thrust, schedule_scan
+from tvm.contrib.thrust import can_use_thrust, can_use_rocthrust
+import numpy as np
+
+
+thrust_check_func = {"cuda": can_use_thrust, "rocm": can_use_rocthrust}
+
+
+def test_stable_sort_by_key():
+    size = 6
+    keys = te.placeholder((size,), name="keys", dtype="int32")
+    values = te.placeholder((size,), name="values", dtype="int32")
+
+    keys_out, values_out = stable_sort_by_key_thrust(keys, values)
+
+    for target in ["cuda", "rocm"]:
+        if not tvm.testing.device_enabled(target):
+            print("Skip because %s is not enabled" % target)
+            continue
+
+        with tvm.target.Target(target + " -libs=thrust") as tgt:
+            if not thrust_check_func[target](tgt, "tvm.contrib.thrust.stable_sort_by_key"):
+                print("skip because thrust is not enabled...")
+                return
+
+            ctx = tvm.context(target, 0)
+            s = te.create_schedule([keys_out.op, values_out.op])
+            f = tvm.build(s, [keys, values, keys_out, values_out], target)
+
+            keys_np = np.array([1, 4, 2, 8, 2, 7], np.int32)
+            values_np = np.random.randint(0, 10, size=(size,)).astype(np.int32)
+            keys_np_out = np.zeros(keys_np.shape, np.int32)
+            values_np_out = np.zeros(values_np.shape, np.int32)
+            keys_in = tvm.nd.array(keys_np, ctx)
+            values_in = tvm.nd.array(values_np, ctx)
+            keys_out = tvm.nd.array(keys_np_out, ctx)
+            values_out = tvm.nd.array(values_np_out, ctx)
+            f(keys_in, values_in, keys_out, values_out)
+
+            ref_keys_out = np.sort(keys_np)
+            ref_values_out = np.array([values_np[i] for i in np.argsort(keys_np)])
+            tvm.testing.assert_allclose(keys_out.asnumpy(), ref_keys_out, rtol=1e-5)
+            tvm.testing.assert_allclose(values_out.asnumpy(), ref_values_out, rtol=1e-5)
+
+
+def test_exclusive_scan():
+    for target in ["cuda", "rocm"]:
+        if not tvm.testing.device_enabled(target):
+            print("Skip because %s is not enabled" % target)
+            continue
+
+        with tvm.target.Target(target + " -libs=thrust") as tgt:
+            if not thrust_check_func[target](tgt, "tvm.contrib.thrust.sum_scan"):
+                print("skip because thrust is not enabled...")
+                return
+
+            for ishape in [(10,), (10, 10), (10, 10, 10)]:
+                values = te.placeholder(ishape, name="values", dtype="int32")
+
+                scan, reduction = exclusive_scan(values, return_reduction=True)
+                s = schedule_scan([scan, reduction])
+
+                ctx = tvm.context(target, 0)
+                f = tvm.build(s, [values, scan, reduction], target)
+
+                values_np = np.random.randint(0, 10, size=ishape).astype(np.int32)
+                values_np_out = np.zeros(values_np.shape, np.int32)
+
+                if len(ishape) == 1:
+                    reduction_shape = ()
+                else:
+                    reduction_shape = ishape[:-1]
+
+                reduction_np_out = np.zeros(reduction_shape, np.int32)
+
+                values_in = tvm.nd.array(values_np, ctx)
+                values_out = tvm.nd.array(values_np_out, ctx)
+                reduction_out = tvm.nd.array(reduction_np_out, ctx)
+                f(values_in, values_out, reduction_out)
+
+                ref_values_out = np.cumsum(values_np, axis=-1, dtype="int32") - values_np
+                tvm.testing.assert_allclose(values_out.asnumpy(), ref_values_out, rtol=1e-5)
+                ref_reduction_out = np.sum(values_np, axis=-1)
+                tvm.testing.assert_allclose(reduction_out.asnumpy(), ref_reduction_out, rtol=1e-5)
+
+
+def test_inclusive_scan():
+    out_dtype = "int64"
+
+    for target in ["cuda", "rocm"]:
+        if not tvm.testing.device_enabled(target):
+            print("Skip because %s is not enabled" % target)
+            continue
+
+        with tvm.target.Target(target + " -libs=thrust") as tgt:
+            if not thrust_check_func[target](tgt, "tvm.contrib.thrust.sum_scan"):
+                print("skip because thrust is not enabled...")
+                return
+
+            for ishape in [(10,), (10, 10)]:
+                values = te.placeholder(ishape, name="values", dtype="int32")
+
+                scan = scan_thrust(values, out_dtype, exclusive=False)
+                s = tvm.te.create_schedule([scan.op])
+
+                ctx = tvm.context(target, 0)
+                f = tvm.build(s, [values, scan], target)
+
+                values_np = np.random.randint(0, 10, size=ishape).astype(np.int32)
+                values_np_out = np.zeros(values_np.shape, out_dtype)
+                values_in = tvm.nd.array(values_np, ctx)
+                values_out = tvm.nd.array(values_np_out, ctx)
+                f(values_in, values_out)
+
+                ref_values_out = np.cumsum(values_np, axis=-1, dtype=out_dtype)
+                tvm.testing.assert_allclose(values_out.asnumpy(), ref_values_out, rtol=1e-5)
+
+
+if __name__ == "__main__":
+    test_stable_sort_by_key()
+    test_exclusive_scan()
+    test_inclusive_scan()
diff --git a/tests/python/contrib/test_verilator/infrastructure.py b/tests/python/contrib/test_verilator/infrastructure.py
index 1333f484aec9..7e4c297853d5 100644
--- a/tests/python/contrib/test_verilator/infrastructure.py
+++ b/tests/python/contrib/test_verilator/infrastructure.py
@@ -16,7 +16,9 @@
 # under the License.
 """Verilator utility functions"""
 
+import os
 import sys
+import subprocess as sp
 
 import tvm
 from tvm import relay
@@ -66,10 +68,43 @@ def offload(mod):
     return mod
 
 
+def verilator_app_path():
+    """Find verilator hardware app path"""
+
+    cur_dir = os.path.dirname(os.path.realpath(__file__))
+    return os.path.join(
+        cur_dir,
+        "..",
+        "..",
+        "..",
+        "..",
+        "3rdparty",
+        "vta-hw",
+        "apps",
+        "verilator",
+    )
+
+
+def compile_hardware():
+    """Compile hardware into shared library"""
+
+    cmd = []
+    cmd.append("make")
+    cmd.append("--directory")
+    cmd.append(verilator_app_path())
+    sp.run(cmd, check=True)
+
+
 def compile_module(mod):
-    """Compile Relay module"""
+    """Compile Relay module and hardware library"""
+
+    lib = os.path.join(verilator_app_path(), "libverilator.so")
+    if not os.path.isfile(lib):
+        compile_hardware()
+
+    opts = {"lib_path": lib}
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3, config={"relay.ext.verilator.options": opts}):
         exe = relay.vm.compile(mod, target="llvm", params=None)
         code, lib = exe.save()
         return runtime.vm.Executable.load_exec(code, lib)
diff --git a/tests/python/driver/tvmc/conftest.py b/tests/python/driver/tvmc/conftest.py
index 882d793ccebd..534953deecbc 100644
--- a/tests/python/driver/tvmc/conftest.py
+++ b/tests/python/driver/tvmc/conftest.py
@@ -99,6 +99,23 @@ def keras_resnet50(tmpdir_factory):
     return model_file_name
 
 
+@pytest.fixture(scope="session")
+def pytorch_resnet18(tmpdir_factory):
+    try:
+        import torch
+        import torchvision.models as models
+    except ImportError:
+        # Not all environments provide Pytorch, so skip if that's the case.
+        return ""
+    model = models.resnet18()
+    model_file_name = "{}/{}".format(tmpdir_factory.mktemp("data"), "resnet18.pth")
+    # Trace model into torchscript.
+    traced_cpu = torch.jit.trace(model, torch.randn(1, 3, 224, 224))
+    torch.jit.save(traced_cpu, model_file_name)
+
+    return model_file_name
+
+
 @pytest.fixture(scope="session")
 def onnx_resnet50():
     base_url = "https://github.com/onnx/models/raw/master/vision/classification/resnet/model"
diff --git a/tests/python/driver/tvmc/test_common.py b/tests/python/driver/tvmc/test_common.py
deleted file mode 100644
index 5ffbc6fe37dd..000000000000
--- a/tests/python/driver/tvmc/test_common.py
+++ /dev/null
@@ -1,151 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-import argparse
-import os
-from os import path
-
-import pytest
-
-import tvm
-from tvm.driver import tvmc
-
-
-def test_compile_tflite_module_nhwc_to_nchw(tflite_mobilenet_v1_1_quant):
-    # some CI environments wont offer TFLite, so skip in case it is not present
-    pytest.importorskip("tflite")
-
-    before, _ = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
-
-    expected_layout = "NCHW"
-    after = tvmc.common.convert_graph_layout(before, expected_layout)
-
-    layout_transform_calls = []
-
-    def _is_layout_transform(node):
-        if isinstance(node, tvm.relay.expr.Call):
-            layout_transform_calls.append(
-                node.op.name == "layout_transform"
-                and node.attrs.src_layout == "NHWC"
-                and node.attrs.dst_layout == "NCHW"
-            )
-
-    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
-
-    assert any(layout_transform_calls), "Expected 'layout_transform NHWC->NCHW' not found"
-
-
-def test_compile_onnx_module_nchw_to_nhwc(onnx_resnet50):
-    # some CI environments wont offer ONNX, so skip in case it is not present
-    pytest.importorskip("onnx")
-
-    before, _ = tvmc.frontends.load_model(onnx_resnet50)
-
-    expected_layout = "NHWC"
-    after = tvmc.common.convert_graph_layout(before, expected_layout)
-
-    layout_transform_calls = []
-
-    def _is_layout_transform(node):
-        if isinstance(node, tvm.relay.expr.Call):
-            layout_transform_calls.append(
-                node.op.name == "layout_transform"
-                and node.attrs.src_layout == "NCHW"
-                and node.attrs.dst_layout == "NHWC"
-            )
-
-    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
-
-    assert any(layout_transform_calls), "Expected 'layout_transform NCWH->NHWC' not found"
-
-
-def test_compile_tflite_module__same_layout__nhwc_to_nhwc(tflite_mobilenet_v1_1_quant):
-    # some CI environments wont offer TFLite, so skip in case it is not present
-    pytest.importorskip("tflite")
-
-    before, _ = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
-
-    expected_layout = "NHWC"
-    after = tvmc.common.convert_graph_layout(before, expected_layout)
-
-    layout_transform_calls = []
-
-    def _is_layout_transform(node):
-        if isinstance(node, tvm.relay.expr.Call):
-            layout_transform_calls.append(
-                node.op.name == "layout_transform"
-                and node.attrs.src_layout == "NHWC"
-                and node.attrs.dst_layout == "NHWC"
-            )
-
-    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
-
-    assert not any(layout_transform_calls), "Unexpected 'layout_transform' call"
-
-
-def test_compile_onnx_module__same_layout__nchw_to_nchw(onnx_resnet50):
-    # some CI environments wont offer ONNX, so skip in case it is not present
-    pytest.importorskip("onnx")
-
-    before, _ = tvmc.frontends.load_model(onnx_resnet50)
-
-    expected_layout = "NCHW"
-    after = tvmc.common.convert_graph_layout(before, expected_layout)
-
-    layout_transform_calls = []
-
-    def _is_layout_transform(node):
-        if isinstance(node, tvm.relay.expr.Call):
-            layout_transform_calls.append(
-                node.op.name == "layout_transform"
-                and node.attrs.src_layout == "NCHW"
-                and node.attrs.dst_layout == "NCHW"
-            )
-
-    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
-
-    assert not any(layout_transform_calls), "Unexpected 'layout_transform' call"
-
-
-def test_tracker_host_port_from_cli__hostname_port():
-    input_str = "1.2.3.4:9090"
-    expected_host = "1.2.3.4"
-    expected_port = 9090
-
-    actual_host, actual_port = tvmc.common.tracker_host_port_from_cli(input_str)
-
-    assert expected_host == actual_host
-    assert expected_port == actual_port
-
-
-def test_tracker_host_port_from_cli__hostname_port__empty():
-    input_str = ""
-
-    actual_host, actual_port = tvmc.common.tracker_host_port_from_cli(input_str)
-
-    assert actual_host is None
-    assert actual_port is None
-
-
-def test_tracker_host_port_from_cli__only_hostname__default_port_is_9090():
-    input_str = "1.2.3.4"
-    expected_host = "1.2.3.4"
-    expected_port = 9090
-
-    actual_host, actual_port = tvmc.common.tracker_host_port_from_cli(input_str)
-
-    assert expected_host == actual_host
-    assert expected_port == actual_port
diff --git a/tests/python/driver/tvmc/test_compiler.py b/tests/python/driver/tvmc/test_compiler.py
index 4bbb6fbf2cf8..ae859298facd 100644
--- a/tests/python/driver/tvmc/test_compiler.py
+++ b/tests/python/driver/tvmc/test_compiler.py
@@ -19,10 +19,13 @@
 import shutil
 from os import path
 
+from unittest import mock
 import pytest
 
 import tvm
 
+from tvm.relay.op.contrib.ethosn import ethosn_available
+
 from tvm.driver import tvmc
 
 
@@ -39,14 +42,11 @@ def test_save_dumps(tmpdir_factory):
 # End to end tests for compilation
 
 
-def test_compile_tflite_module(tflite_mobilenet_v1_1_quant):
+def verify_compile_tflite_module(model, shape_dict=None):
     pytest.importorskip("tflite")
 
     graph, lib, params, dumps = tvmc.compiler.compile_model(
-        tflite_mobilenet_v1_1_quant,
-        target="llvm",
-        dump_code="ll",
-        alter_layout="NCHW",
+        model, target="llvm", dump_code="ll", alter_layout="NCHW", shape_dict=shape_dict
     )
 
     # check for output types
@@ -56,6 +56,17 @@ def test_compile_tflite_module(tflite_mobilenet_v1_1_quant):
     assert type(dumps) is dict
 
 
+def test_compile_tflite_module(tflite_mobilenet_v1_1_quant):
+    # some CI environments wont offer tflite, so skip in case it is not present
+    pytest.importorskip("tflite")
+    # Check default compilation.
+    verify_compile_tflite_module(tflite_mobilenet_v1_1_quant)
+    # Check with manual shape override
+    shape_string = "input:[1,224,224,3]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    verify_compile_tflite_module(tflite_mobilenet_v1_1_quant, shape_dict)
+
+
 # This test will be skipped if the AArch64 cross-compilation toolchain is not installed.
 @pytest.mark.skipif(
     not shutil.which("aarch64-linux-gnu-gcc"), reason="cross-compilation toolchain not installed"
@@ -65,7 +76,7 @@ def test_cross_compile_aarch64_tflite_module(tflite_mobilenet_v1_1_quant):
 
     graph, lib, params, dumps = tvmc.compiler.compile_model(
         tflite_mobilenet_v1_1_quant,
-        target="llvm -device=arm_cpu -mtriple=aarch64-linux-gnu -mattr=+neon",
+        target="llvm -device=arm_cpu -mtriple=aarch64-linux-gnu -mattr='+neon'",
         dump_code="asm",
     )
 
@@ -102,7 +113,7 @@ def test_cross_compile_aarch64_keras_module(keras_resnet50):
 
     graph, lib, params, dumps = tvmc.compiler.compile_model(
         keras_resnet50,
-        target="llvm -device=arm_cpu -mtriple=aarch64-linux-gnu -mattr=+neon",
+        target="llvm -device=arm_cpu -mtriple=aarch64-linux-gnu -mattr='+neon'",
         dump_code="asm",
     )
 
@@ -114,12 +125,12 @@ def test_cross_compile_aarch64_keras_module(keras_resnet50):
     assert "asm" in dumps.keys()
 
 
-def test_compile_onnx_module(onnx_resnet50):
+def verify_compile_onnx_module(model, shape_dict=None):
     # some CI environments wont offer onnx, so skip in case it is not present
     pytest.importorskip("onnx")
 
     graph, lib, params, dumps = tvmc.compiler.compile_model(
-        onnx_resnet50, target="llvm", dump_code="ll"
+        model, target="llvm", dump_code="ll", shape_dict=shape_dict
     )
 
     # check for output types
@@ -130,6 +141,15 @@ def test_compile_onnx_module(onnx_resnet50):
     assert "ll" in dumps.keys()
 
 
+def test_compile_onnx_module(onnx_resnet50):
+    # Test default compilation
+    verify_compile_onnx_module(onnx_resnet50)
+    # Test with manual shape dict
+    shape_string = "data:[1,3,200,200]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    verify_compile_onnx_module(onnx_resnet50, shape_dict)
+
+
 # This test will be skipped if the AArch64 cross-compilation toolchain is not installed.
 @pytest.mark.skipif(
     not shutil.which("aarch64-linux-gnu-gcc"), reason="cross-compilation toolchain not installed"
@@ -168,3 +188,43 @@ def test_compile_opencl(tflite_mobilenet_v1_0_25_128):
     assert type(lib) is tvm.runtime.module.Module
     assert type(params) is dict
     assert type(dumps) is dict
+
+
+@pytest.mark.skipif(
+    not ethosn_available(),
+    reason="--target=ethos-n77 is not available. TVM built with 'USE_ETHOSN OFF'",
+)
+def test_compile_tflite_module_with_external_codegen(tflite_mobilenet_v1_1_quant):
+    pytest.importorskip("tflite")
+
+    graph, lib, params, dumps = tvmc.compiler.compile_model(
+        tflite_mobilenet_v1_1_quant, target="ethos-n77, llvm", dump_code="relay"
+    )
+
+    # check for output types
+    assert type(graph) is str
+    assert type(lib) is tvm.runtime.module.Module
+    assert type(params) is dict
+    assert type(dumps) is dict
+
+
+@mock.patch("tvm.relay.build")
+@mock.patch("tvm.driver.tvmc.composite_target.get_codegen_by_target")
+@mock.patch("tvm.driver.tvmc.frontends.load_model")
+@mock.patch("tvm.transform.PassContext")
+def test_compile_check_configs_composite_target(mock_pc, mock_fe, mock_ct, mock_relay):
+    mock_codegen = {}
+    mock_codegen["config_key"] = "relay.ext.mock.options"
+    mock_codegen["pass_pipeline"] = lambda *args: None
+
+    mock_fe.return_value = (None, None)
+    mock_ct.return_value = mock_codegen
+    mock_relay.return_value = mock.MagicMock()
+
+    graph, lib, params, dumps = tvmc.compiler.compile_model(
+        "no_file_needed", target="mockcodegen -testopt=value, llvm"
+    )
+
+    mock_pc.assert_called_once_with(
+        opt_level=3, config={"relay.ext.mock.options": {"testopt": "value"}}
+    )
diff --git a/tests/python/driver/tvmc/test_composite_target.py b/tests/python/driver/tvmc/test_composite_target.py
new file mode 100644
index 000000000000..cef8b117d989
--- /dev/null
+++ b/tests/python/driver/tvmc/test_composite_target.py
@@ -0,0 +1,62 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import argparse
+import os
+import shutil
+
+from inspect import isfunction
+from os import path
+
+import pytest
+
+import tvm
+
+from tvm.driver import tvmc
+
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_get_codegen_names():
+    names = tvmc.composite_target.get_codegen_names()
+
+    assert "ethos-n77" in names
+    assert len(names) > 0
+
+
+def test_valid_codegen():
+    codegen = tvmc.composite_target.get_codegen_by_target("compute-library")
+
+    assert codegen is not None
+    assert codegen["pass_pipeline"] is not None
+
+
+def test_invalid_codegen():
+    with pytest.raises(TVMCException):
+        _ = tvmc.composite_target.get_codegen_by_target("invalid")
+
+
+def test_all_codegens_contain_pass_pipeline():
+    for name in tvmc.composite_target.get_codegen_names():
+        codegen = tvmc.composite_target.get_codegen_by_target(name)
+        assert "pass_pipeline" in codegen, f"{name} does not contain a pass_pipeline"
+        assert isfunction(codegen["pass_pipeline"])
+
+
+def test_all_pass_pipelines_are_functions():
+    for name in tvmc.composite_target.get_codegen_names():
+        codegen = tvmc.composite_target.get_codegen_by_target(name)
+        assert isfunction(codegen["pass_pipeline"]), f"pass_pipeline for {name} is not a function"
diff --git a/tests/python/driver/tvmc/test_frontends.py b/tests/python/driver/tvmc/test_frontends.py
index d77a17addabf..5a63c5c47933 100644
--- a/tests/python/driver/tvmc/test_frontends.py
+++ b/tests/python/driver/tvmc/test_frontends.py
@@ -115,26 +115,34 @@ def test_load_model__tflite(tflite_mobilenet_v1_1_quant):
     assert "_param_1" in params.keys()
 
 
-def test_load_model__keras(keras_resnet50):
+@pytest.mark.parametrize("load_model_kwargs", [{}, {"layout": "NCHW"}])
+def test_load_model__keras(keras_resnet50, load_model_kwargs):
     # some CI environments wont offer TensorFlow/Keras, so skip in case it is not present
     pytest.importorskip("tensorflow")
 
-    mod, params = tvmc.frontends.load_model(keras_resnet50)
+    mod, params = tvmc.frontends.load_model(keras_resnet50, **load_model_kwargs)
     assert type(mod) is IRModule
     assert type(params) is dict
     ## check whether one known value is part of the params dict
     assert "_param_1" in params.keys()
 
 
+def verify_load_model__onnx(model, **kwargs):
+    mod, params = tvmc.frontends.load_model(model, **kwargs)
+    assert type(mod) is IRModule
+    assert type(params) is dict
+    return mod, params
+
+
 def test_load_model__onnx(onnx_resnet50):
     # some CI environments wont offer onnx, so skip in case it is not present
     pytest.importorskip("onnx")
-
-    mod, params = tvmc.frontends.load_model(onnx_resnet50)
-    assert type(mod) is IRModule
-    assert type(params) is dict
-    ## check whether one known value is part of the params dict
+    mod, params = verify_load_model__onnx(onnx_resnet50)
+    # check whether one known value is part of the params dict
     assert "resnetv24_batchnorm0_gamma" in params.keys()
+    mod, params = verify_load_model__onnx(onnx_resnet50, freeze_params=True)
+    # check that the parameter dict is empty, implying that they have been folded into constants
+    assert params == {}
 
 
 def test_load_model__pb(pb_mobilenet_v1_1_quant):
@@ -174,9 +182,28 @@ def test_load_model___wrong_language__to_onnx(tflite_mobilenet_v1_1_quant):
         tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant, model_format="onnx")
 
 
+@pytest.mark.skip(reason="https://github.com/apache/tvm/issues/7455")
+def test_load_model__pth(pytorch_resnet18):
+    # some CI environments wont offer torch, so skip in case it is not present
+    pytest.importorskip("torch")
+    pytest.importorskip("torchvision")
+
+    mod, params = tvmc.frontends.load_model(
+        pytorch_resnet18, shape_dict={"input": [1, 3, 224, 224]}
+    )
+    assert type(mod) is IRModule
+    assert type(params) is dict
+    # check whether one known value is part of the params dict
+    assert "layer1.0.conv1.weight" in params.keys()
+
+
 def test_load_model___wrong_language__to_pytorch(tflite_mobilenet_v1_1_quant):
     # some CI environments wont offer pytorch, so skip in case it is not present
     pytest.importorskip("torch")
 
     with pytest.raises(RuntimeError) as e:
-        tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant, model_format="pytorch")
+        tvmc.frontends.load_model(
+            tflite_mobilenet_v1_1_quant,
+            model_format="pytorch",
+            shape_dict={"input": [1, 3, 224, 224]},
+        )
diff --git a/tests/python/driver/tvmc/test_tvmc_common.py b/tests/python/driver/tvmc/test_tvmc_common.py
new file mode 100644
index 000000000000..474649d8b1b3
--- /dev/null
+++ b/tests/python/driver/tvmc/test_tvmc_common.py
@@ -0,0 +1,290 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import argparse
+import os
+from os import path
+
+import pytest
+
+import tvm
+from tvm import relay
+from tvm.driver import tvmc
+
+from tvm.driver.tvmc.common import TVMCException
+
+
+def test_compile_tflite_module_nhwc_to_nchw(tflite_mobilenet_v1_1_quant):
+    # some CI environments wont offer TFLite, so skip in case it is not present
+    pytest.importorskip("tflite")
+
+    before, _ = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
+
+    expected_layout = "NCHW"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NHWC"
+                and node.attrs.dst_layout == "NCHW"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert any(layout_transform_calls), "Expected 'layout_transform NHWC->NCHW' not found"
+
+
+def test_compile_onnx_module_nchw_to_nhwc(onnx_resnet50):
+    # some CI environments wont offer ONNX, so skip in case it is not present
+    pytest.importorskip("onnx")
+
+    before, _ = tvmc.frontends.load_model(onnx_resnet50)
+
+    expected_layout = "NHWC"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NCHW"
+                and node.attrs.dst_layout == "NHWC"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert any(layout_transform_calls), "Expected 'layout_transform NCWH->NHWC' not found"
+
+
+def test_compile_tflite_module__same_layout__nhwc_to_nhwc(tflite_mobilenet_v1_1_quant):
+    # some CI environments wont offer TFLite, so skip in case it is not present
+    pytest.importorskip("tflite")
+
+    before, _ = tvmc.frontends.load_model(tflite_mobilenet_v1_1_quant)
+
+    expected_layout = "NHWC"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NHWC"
+                and node.attrs.dst_layout == "NHWC"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert not any(layout_transform_calls), "Unexpected 'layout_transform' call"
+
+
+def test_compile_onnx_module__same_layout__nchw_to_nchw(onnx_resnet50):
+    # some CI environments wont offer ONNX, so skip in case it is not present
+    pytest.importorskip("onnx")
+
+    before, _ = tvmc.frontends.load_model(onnx_resnet50)
+
+    expected_layout = "NCHW"
+    after = tvmc.common.convert_graph_layout(before, expected_layout)
+
+    layout_transform_calls = []
+
+    def _is_layout_transform(node):
+        if isinstance(node, tvm.relay.expr.Call):
+            layout_transform_calls.append(
+                node.op.name == "layout_transform"
+                and node.attrs.src_layout == "NCHW"
+                and node.attrs.dst_layout == "NCHW"
+            )
+
+    tvm.relay.analysis.post_order_visit(after["main"], _is_layout_transform)
+
+    assert not any(layout_transform_calls), "Unexpected 'layout_transform' call"
+
+
+def test_tracker_host_port_from_cli__hostname_port():
+    input_str = "1.2.3.4:9090"
+    expected_host = "1.2.3.4"
+    expected_port = 9090
+
+    actual_host, actual_port = tvmc.common.tracker_host_port_from_cli(input_str)
+
+    assert expected_host == actual_host
+    assert expected_port == actual_port
+
+
+def test_tracker_host_port_from_cli__hostname_port__empty():
+    input_str = ""
+
+    actual_host, actual_port = tvmc.common.tracker_host_port_from_cli(input_str)
+
+    assert actual_host is None
+    assert actual_port is None
+
+
+def test_tracker_host_port_from_cli__only_hostname__default_port_is_9090():
+    input_str = "1.2.3.4"
+    expected_host = "1.2.3.4"
+    expected_port = 9090
+
+    actual_host, actual_port = tvmc.common.tracker_host_port_from_cli(input_str)
+
+    assert expected_host == actual_host
+    assert expected_port == actual_port
+
+
+def test_shape_parser():
+    # Check that a valid input is parsed correctly
+    shape_string = "input:[10,10,10]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input": [10, 10, 10]}
+    # Check that multiple valid input shapes are parse correctly
+    shape_string = "input:[10,10,10] input2:[20,20,20,20]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input": [10, 10, 10], "input2": [20, 20, 20, 20]}
+    # Check that alternate syntax parses correctly
+    shape_string = "input: [10, 10, 10] input2: [20, 20, 20, 20]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input": [10, 10, 10], "input2": [20, 20, 20, 20]}
+    shape_string = "input:[10,10,10],input2:[20,20,20,20]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    assert shape_dict == {"input": [10, 10, 10], "input2": [20, 20, 20, 20]}
+    # Check that negative dimensions parse to Any correctly.
+    shape_string = "input:[-1,3,224,224]"
+    shape_dict = tvmc.common.parse_shape_string(shape_string)
+    # Convert to strings to allow comparison with Any.
+    assert str(shape_dict) == "{'input': [?, 3, 224, 224]}"
+
+    # Check that invalid pattern raises expected error.
+    shape_string = "input:[a,10]"
+    with pytest.raises(argparse.ArgumentTypeError):
+        tvmc.common.parse_shape_string(shape_string)
+    # Check that input with invalid separators raises error.
+    shape_string = "input:5,10 input2:10,10"
+    with pytest.raises(argparse.ArgumentTypeError):
+        tvmc.common.parse_shape_string(shape_string)
+
+
+def test_target_from_cli__error_duplicate():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.target_from_cli("llvm, llvm")
+
+
+def test_target_from_cli__error_target_not_found():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.target_from_cli("invalidtarget")
+
+
+def test_target_from_cli__error_no_tvm_target():
+    with pytest.raises(TVMCException):
+        _ = tvmc.common.target_from_cli("ethos-n77")
+
+
+def test_tokenize_target_with_opts():
+    tokens = tvmc.common.tokenize_target("foo -opt1=value1 --flag, bar -opt2=value2")
+    expected_tokens = ["foo", "-opt1=value1", "--flag", ",", "bar", "-opt2=value2"]
+
+    assert len(tokens) == len(expected_tokens)
+    assert tokens == expected_tokens
+
+
+def test_tokenize_target_with_plus_sign():
+    tokens = tvmc.common.tokenize_target("foo -opt1=+value1 --flag, bar -opt2=test,+v")
+    expected_tokens = ["foo", "-opt1=+value1", "--flag", ",", "bar", "-opt2=test,+v"]
+
+    assert len(tokens) == len(expected_tokens)
+    assert tokens == expected_tokens
+
+
+def test_tokenize_target_with_commas():
+    tokens = tvmc.common.tokenize_target("foo -opt1=v,a,l,u,e,1 --flag")
+    expected_tokens = ["foo", "-opt1=v,a,l,u,e,1", "--flag"]
+
+    assert len(tokens) == len(expected_tokens)
+    assert tokens == expected_tokens
+
+
+def test_tokenize_target_with_commas_and_single_quotes():
+    tokens = tvmc.common.tokenize_target("foo -opt1='v, a, l, u, e', bar")
+    expected_tokens = ["foo", "-opt1='v, a, l, u, e'", ",", "bar"]
+
+    assert len(tokens) == len(expected_tokens)
+    assert tokens == expected_tokens
+
+
+def test_tokenize_target_with_commas_and_double_quotes():
+    tokens = tvmc.common.tokenize_target('foo -opt1="v, a, l, u, e", bar')
+    expected_tokens = ["foo", '-opt1="v, a, l, u, e"', ",", "bar"]
+
+    assert len(tokens) == len(expected_tokens)
+    assert tokens == expected_tokens
+
+
+def test_tokenize_target_with_dashes():
+    tokens = tvmc.common.tokenize_target("foo-bar1 -opt-1=t-e-s-t, baz")
+    expected_tokens = ["foo-bar1", "-opt-1=t-e-s-t", ",", "baz"]
+
+    assert len(tokens) == len(expected_tokens)
+    assert tokens == expected_tokens
+
+
+def test_parse_single_target_with_opts():
+    targets = tvmc.common.parse_target("llvm -device=arm_cpu --system-lib")
+
+    assert len(targets) == 1
+    assert "device" in targets[0]["opts"]
+    assert "system-lib" in targets[0]["opts"]
+
+
+def test_parse_multiple_target():
+    targets = tvmc.common.parse_target("compute-library, llvm -device=arm_cpu --system-lib")
+
+    assert len(targets) == 2
+    assert "compute-library" == targets[0]["name"]
+    assert "llvm" == targets[1]["name"]
+
+
+def test_parse_multiple_target_with_opts():
+    targets = tvmc.common.parse_target("ethos-n77 -myopt=value, llvm -device=arm_cpu --system-lib")
+
+    assert len(targets) == 2
+    assert "ethos-n77" == targets[0]["name"]
+    assert "myopt" in targets[0]["opts"]
+    assert "value" == targets[0]["opts"]["myopt"]
+    assert "llvm" == targets[1]["name"]
+
+
+def test_parse_quotes_and_separators_on_options():
+    targets_no_quote = tvmc.common.parse_target("foo -option1=+v1.0x,+value,+bar")
+    targets_single_quote = tvmc.common.parse_target("foo -option1='+v1.0x,+value'")
+    targets_double_quote = tvmc.common.parse_target('foo -option1="+v1.0x,+value"')
+
+    assert len(targets_no_quote) == 1
+    assert "+v1.0x,+value,+bar" == targets_no_quote[0]["opts"]["option1"]
+
+    assert len(targets_single_quote) == 1
+    assert "+v1.0x,+value" == targets_single_quote[0]["opts"]["option1"]
+
+    assert len(targets_double_quote) == 1
+    assert "+v1.0x,+value" == targets_double_quote[0]["opts"]["option1"]
diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py
index 05d890419aa4..561e444f077f 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -350,6 +350,16 @@ def test_forward_reshape(self, keras):
         x = keras.layers.Reshape(target_shape=(4, 4))(data)
         keras_model = keras.models.Model(data, x)
         verify_keras_frontend(keras_model, need_transpose=False)
+        # "non-square" target shape
+        data = keras.layers.Input(shape=(15,))
+        x = keras.layers.Reshape(target_shape=(5, 3))(data)
+        keras_model = keras.models.Model(data, x)
+        verify_keras_frontend(keras_model, need_transpose=False)
+        # modify channel dim
+        data = keras.layers.Input(shape=(3, 2, 4))
+        x = keras.layers.Reshape(target_shape=(3, 8))(data)
+        keras_model = keras.models.Model(data, x)
+        verify_keras_frontend(keras_model)
 
     def test_forward_crop(self, keras):
         data = keras.layers.Input(shape=(32, 32, 3))
diff --git a/tests/python/frontend/mxnet/model_zoo/resnet.py b/tests/python/frontend/mxnet/model_zoo/resnet.py
index 98cdce6b4ea7..00e68958b462 100644
--- a/tests/python/frontend/mxnet/model_zoo/resnet.py
+++ b/tests/python/frontend/mxnet/model_zoo/resnet.py
@@ -182,7 +182,7 @@ def resnet(
     filter_list : list
         Channel size of each stage
     num_classes : int
-        Ouput size of symbol
+        Output size of symbol
     dataset : str
         Dataset type, only cifar10 and imagenet supports
     workspace : int
diff --git a/tests/python/frontend/mxnet/test_forward.py b/tests/python/frontend/mxnet/test_forward.py
index f076a27755ad..4eb7f6139e8f 100644
--- a/tests/python/frontend/mxnet/test_forward.py
+++ b/tests/python/frontend/mxnet/test_forward.py
@@ -1064,14 +1064,23 @@ def verify(shape, axis, is_ascend, dtype="float32"):
 
 @tvm.testing.uses_gpu
 def test_forward_topk():
-    def verify(shape, k, axis, ret_type, is_ascend=False, dtype="float32"):
+    def verify(shape, k, axis, ret_type, is_ascend=None, dtype="float32"):
         x_np = np.random.uniform(size=shape).astype("float32")
-        ref_res = mx.nd.topk(
-            mx.nd.array(x_np), k=k, axis=axis, ret_typ=ret_type, is_ascend=is_ascend, dtype=dtype
-        )
-        mx_sym = mx.sym.topk(
-            mx.sym.var("x"), k=k, axis=axis, ret_typ=ret_type, is_ascend=is_ascend, dtype=dtype
-        )
+        if is_ascend is None:
+            ref_res = mx.nd.topk(mx.nd.array(x_np), k=k, axis=axis, ret_typ=ret_type, dtype=dtype)
+            mx_sym = mx.sym.topk(mx.sym.var("x"), k=k, axis=axis, ret_typ=ret_type, dtype=dtype)
+        else:
+            ref_res = mx.nd.topk(
+                mx.nd.array(x_np),
+                k=k,
+                axis=axis,
+                ret_typ=ret_type,
+                is_ascend=is_ascend,
+                dtype=dtype,
+            )
+            mx_sym = mx.sym.topk(
+                mx.sym.var("x"), k=k, axis=axis, ret_typ=ret_type, is_ascend=is_ascend, dtype=dtype
+            )
         mod, _ = relay.frontend.from_mxnet(mx_sym, {"x": shape})
         for target, ctx in tvm.testing.enabled_targets():
             for kind in ["graph", "debug"]:
@@ -1086,7 +1095,7 @@ def verify(shape, k, axis, ret_type, is_ascend=False, dtype="float32"):
 
     verify((3, 4), k=1, axis=0, ret_type="both")
     verify((3, 4), k=1, axis=-1, ret_type="indices")
-    verify((3, 5, 6), k=2, axis=2, ret_type="value")
+    verify((3, 5, 6), k=2, axis=2, ret_type="value", is_ascend=False)
     verify((3, 5, 6), k=2, axis=1, ret_type="value", is_ascend=True)
     verify((3, 5, 6), k=0, axis=2, ret_type="both", dtype="int32")
 
@@ -1263,6 +1272,38 @@ def verify(shape, axis=-1):
     verify((2, 5, 6))
 
 
+@tvm.testing.uses_gpu
+def test_forward_group_norm():
+    def verify(shape, num_groups=1):
+        x = np.random.uniform(size=shape).astype("float32")
+        gamma = np.random.uniform(size=(shape[1])).astype("float32")
+        beta = np.random.uniform(size=(shape[1])).astype("float32")
+        ref_res = mx.nd.GroupNorm(
+            data=mx.nd.array(x),
+            gamma=mx.nd.array(gamma),
+            beta=mx.nd.array(beta),
+            num_groups=num_groups,
+        )
+        mx_sym = mx.sym.GroupNorm(
+            mx.sym.var("x"), mx.sym.var("gamma"), mx.sym.var("beta"), num_groups=num_groups
+        )
+        shape_dict = {"x": x.shape, "gamma": gamma.shape, "beta": beta.shape}
+        mod, _ = relay.frontend.from_mxnet(mx_sym, shape_dict)
+        for target, ctx in tvm.testing.enabled_targets():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                op_res = intrp.evaluate()(x, gamma, beta)
+                tvm.testing.assert_allclose(
+                    op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-3, atol=1e-5
+                )
+
+    verify((1, 4, 2), num_groups=4)
+    # TODO(trevmorr): MXNet GroupNorm implementation is bugged for cases when num_groups != num_channels
+    # https://github.com/apache/incubator-mxnet/pull/18199
+    # verify((1, 4, 2, 3), num_groups=2)
+    # verify((1, 4, 2, 3))
+
+
 @tvm.testing.uses_gpu
 def test_forward_one_hot():
     def verify(indices_shape, depth, on_value, off_value, dtype):
@@ -2012,6 +2053,34 @@ def test_forward_npi_concatenate(data_shape1, data_shape2, axis, dtype, target,
     tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)
 
 
+@pytest.mark.parametrize(
+    "data_shape1, data_shape2, axis",
+    [
+        ((3,), (3,), 0),
+        ((3,), (3,), -1),
+        ((1, 3, 2), (1, 3, 2), 2),
+        ((1, 3, 3), (1, 3, 3), 1),
+        ((1, 3), (1, 3), 0),
+    ],
+)
+@pytest.mark.parametrize("dtype", ["float64", "float32", "int64", "int32"])
+@tvm.testing.parametrize_targets
+@pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
+def test_forward_npi_stack(data_shape1, data_shape2, axis, dtype, target, ctx, kind):
+    data_np1 = np.random.uniform(size=data_shape1).astype(dtype)
+    data_np2 = np.random.uniform(size=data_shape2).astype(dtype)
+    data1 = mx.sym.var("data1")
+    data2 = mx.sym.var("data2")
+    ref_res = mx.np.stack([mx.np.array(data_np1), mx.np.array(data_np2)], axis=axis)
+    mx_sym = mx.sym.np.stack([data1.as_np_ndarray(), data2.as_np_ndarray()], axis=axis)
+    mod, _ = relay.frontend.from_mxnet(
+        mx_sym, shape={"data1": data_shape1, "data2": data_shape2}, dtype=dtype
+    )
+    intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+    op_res = intrp.evaluate()(data_np1, data_np2)
+    tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)
+
+
 @pytest.mark.parametrize("data_shape", [(2, 2, 2), (2, 7, 2), (2, 2, 2, 1, 2, 3, 1), (1, 8)])
 @pytest.mark.parametrize("dtype", ["float64", "float32", "int64", "int32", "bool"])
 @tvm.testing.parametrize_targets
@@ -2062,8 +2131,14 @@ def test_forward_npx_reshape(data_shape, out_shape, dtype, target, reverse, ctx,
 @tvm.testing.parametrize_targets
 @pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
 def test_forward_npi_binary(data_shape, dtype, target, ctx, kind):
-    ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.less]
-    mx_ops = [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add, mx.sym.np.less]
+    ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.subtract, mx.np.less]
+    mx_ops = [
+        mx.sym.np.power,
+        mx.sym.np.multiply,
+        mx.sym.np.add,
+        mx.sym.np.subtract,
+        mx.sym.np.less,
+    ]
     for i in range(len(ref_ops)):
         ref_op = ref_ops[i]
         mx_op = mx_ops[i]
@@ -2092,8 +2167,14 @@ def test_forward_npi_binary(data_shape, dtype, target, ctx, kind):
 @pytest.mark.parametrize("scalar", [1.0, 2.0, 3.0, 4.0])
 @pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
 def test_forward_npi_binary_scalar(data_shape, dtype, scalar, target, ctx, kind):
-    ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.true_divide]
-    mx_ops = [mx.sym.np.power, mx.sym.np.multiply, mx.sym.np.add, mx.sym.np.true_divide]
+    ref_ops = [mx.np.power, mx.np.multiply, mx.np.add, mx.np.subtract, mx.np.true_divide]
+    mx_ops = [
+        mx.sym.np.power,
+        mx.sym.np.multiply,
+        mx.sym.np.add,
+        mx.sym.np.subtract,
+        mx.sym.np.true_divide,
+    ]
     for i in range(len(ref_ops)):
         ref_op = ref_ops[i]
         mx_op = mx_ops[i]
diff --git a/tests/python/frontend/onnx/test_forward.py b/tests/python/frontend/onnx/test_forward.py
index 33dd048896b6..177bed66f466 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -15,7 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 import numpy as np
-import math
 import onnx
 from onnx import helper, TensorProto, mapping, numpy_helper
 import torch
@@ -94,7 +93,7 @@ def get_tvm_output(
     # execute
     m.run()
     # get outputs
-    if isinstance(output_shape, list) and isinstance(output_dtype, list):
+    if isinstance(output_shape, list):
         tvm_output_list = []
         for i, _ in enumerate(output_shape):
             tvm_output = m.get_output(i)
@@ -105,17 +104,19 @@ def get_tvm_output(
         return tvm_output.asnumpy()
 
 
-def get_onnxruntime_output(model, inputs, dtype="float32"):
+def get_onnxruntime_output(model, inputs):
     import onnxruntime.backend
 
     rep = onnxruntime.backend.prepare(model, "CPU")
-    if isinstance(inputs, list) and len(inputs) > 1:
-        return rep.run(inputs)
-    elif isinstance(inputs, list) and len(inputs) == 1:
+    if isinstance(inputs, list) and len(inputs) == 1:
         inp = inputs[0]
     else:
         inp = inputs
-    return rep.run(inp.astype(dtype))[0]
+    output = rep.run(inp)
+    # Unpack output if there's only a single value.
+    if len(output) == 1:
+        output = output[0]
+    return output
 
 
 def verify_with_ort_with_inputs(
@@ -130,15 +131,11 @@ def verify_with_ort_with_inputs(
     dtype="float32",
     rtol=1e-5,
     atol=1e-5,
+    apply_softmax=False,
 ):
-    def flatten(out):
-        if isinstance(out, list) and len(out) == 1:
-            out = out[0]
-        if isinstance(out, np.ndarray):
-            return out.flatten()
-        return out
-
-    ort_out = get_onnxruntime_output(model, inputs, dtype)
+    if opset is not None:
+        model.opset_import[0].version = opset
+    ort_out = get_onnxruntime_output(model, inputs)
 
     if targets is None:
         targets = [tgt for (tgt, _) in tvm.testing.enabled_targets()]
@@ -157,8 +154,16 @@ def flatten(out):
             )
         else:
             tvm_out = get_tvm_output(model, inputs, target, ctx, out_shape, dtype, opset=opset)
-
-        tvm.testing.assert_allclose(flatten(ort_out), flatten(tvm_out), rtol=rtol, atol=atol)
+        if not isinstance(tvm_out, list):
+            tvm_out = [tvm_out]
+        if not isinstance(ort_out, list):
+            ort_out = [ort_out]
+        for tvm_val, ort_val in zip(tvm_out, ort_out):
+            if apply_softmax:
+                ort_val = scipy.special.softmax(ort_val)
+                tvm_val = scipy.special.softmax(tvm_val)
+            tvm.testing.assert_allclose(ort_val, tvm_val, rtol=rtol, atol=atol)
+            assert ort_val.dtype == tvm_val.dtype
 
 
 def verify_with_ort(
@@ -342,7 +347,7 @@ def verify_depth_to_space(inshape, outshape, mode, blockSize):
 
     model = helper.make_model(graph, producer_name="depth_to_space_test")
 
-    verify_with_ort(model, [inshape], outshape)
+    verify_with_ort(model, [inshape], [outshape])
 
 
 @tvm.testing.uses_gpu
@@ -365,7 +370,7 @@ def verify_space_to_depth(inshape, outshape, blockSize):
 
     model = helper.make_model(graph, producer_name="space_to_depth_test")
 
-    verify_with_ort(model, [inshape], outshape)
+    verify_with_ort(model, [inshape], [outshape])
 
 
 @tvm.testing.uses_gpu
@@ -494,11 +499,8 @@ def test_squeeze():
     )
 
     model = helper.make_model(graph, producer_name="squeeze_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        x = np.random.uniform(size=in_shape).astype("float32")
-        tvm_out = get_tvm_output(model, x, target, ctx, out_shape, "float32")
-        tvm.testing.assert_allclose(out_shape, tvm_out.shape)
+    x = np.random.uniform(size=in_shape).astype("float32")
+    verify_with_ort_with_inputs(model, [x], [out_shape])
 
 
 @tvm.testing.uses_gpu
@@ -518,11 +520,7 @@ def test_flatten():
     )
 
     model = helper.make_model(graph, producer_name="flatten_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        x = np.random.uniform(size=in_shape).astype("int32")
-        tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, "float32")
-        tvm.testing.assert_allclose(ref_shape, tvm_out.shape)
+    verify_with_ort(model, [in_shape])
 
 
 @tvm.testing.uses_gpu
@@ -540,16 +538,12 @@ def test_unsqueeze():
     )
 
     model = helper.make_model(graph, producer_name="squeeze_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        x = np.random.uniform(size=in_shape).astype("float32")
-        tvm_out = get_tvm_output(model, x, target, ctx, out_shape, "float32")
-        tvm.testing.assert_allclose(out_shape, tvm_out.shape)
+    verify_with_ort(model, [in_shape])
 
 
 def verify_gather(in_shape, indices, axis, dtype):
     x = np.random.uniform(size=in_shape).astype(dtype)
-    indices = np.array(indices, dtype="int32")
+    indices = np.array(indices, dtype="int64")
     out_np = np.take(x, indices, axis=axis)
 
     y = helper.make_node("Gather", ["in", "indices"], ["out"], axis=axis)
@@ -558,16 +552,19 @@ def verify_gather(in_shape, indices, axis, dtype):
         [y],
         "gather_test",
         inputs=[
-            helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape)),
-            helper.make_tensor_value_info("indices", TensorProto.INT32, list(indices.shape)),
+            helper.make_tensor_value_info(
+                "in", mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)], list(in_shape)
+            ),
+            helper.make_tensor_value_info("indices", TensorProto.INT64, list(indices.shape)),
+        ],
+        outputs=[
+            helper.make_tensor_value_info(
+                "out", mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)], list(out_np.shape)
+            )
         ],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_np.shape))],
     )
     model = helper.make_model(graph, producer_name="gather_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x, indices], target, ctx, out_np.shape)
-        tvm.testing.assert_allclose(out_np, tvm_out)
+    verify_with_ort_with_inputs(model, [x, indices], dtype=dtype)
 
 
 @tvm.testing.uses_gpu
@@ -660,10 +657,7 @@ def _test_slice_iteration_v1(indata, outdata, starts, ends, axes=None):
     )
 
     model = helper.make_model(graph, producer_name="slice_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, "float32", opset=1)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [indata], [outdata.shape], opset=1)
 
 
 def _test_slice_iteration_v10(indata, outdata, **attrs):
@@ -738,14 +732,14 @@ def add_noop_to_input_attr(attr_name, attr):
 
     if axes:
         axes = np.asarray(axes)
-        inputs.append(helper.make_tensor_value_info("axes", TensorProto.INT32, list(axes.shape)))
-        initializer.append(helper.make_tensor("axes", TensorProto.INT32, list(axes.shape), axes))
+        inputs.append(helper.make_tensor_value_info("axes", TensorProto.INT64, list(axes.shape)))
+        initializer.append(helper.make_tensor("axes", TensorProto.INT64, list(axes.shape), axes))
 
     if steps:
         assert axes is not None and len(axes) == len(steps)
         steps = np.asarray(steps)
-        inputs.append(helper.make_tensor_value_info("steps", TensorProto.INT32, list(axes.shape)))
-        initializer.append(helper.make_tensor("steps", TensorProto.INT32, list(steps.shape), steps))
+        inputs.append(helper.make_tensor_value_info("steps", TensorProto.INT64, list(axes.shape)))
+        initializer.append(helper.make_tensor("steps", TensorProto.INT64, list(steps.shape), steps))
 
     y = helper.make_node("Slice", ["data", *slice_inputs], ["out"])
 
@@ -758,10 +752,7 @@ def add_noop_to_input_attr(attr_name, attr):
         initializer=initializer,
     )
     model = helper.make_model(graph, producer_name="slice_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output_with_vm(model, indata, target, ctx, opset=10, freeze_params=True)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [indata], opset=10, freeze_params=True, use_vm=True)
 
 
 # TODO(mbrookhart): enable once VM supports heterogenous execution
@@ -840,7 +831,7 @@ def test_slice():
     )
 
 
-def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
+def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs, opset=None):
     indata = np.random.uniform(-1, 1, size=inshape).astype(dtype)
     outdata = outfunc(indata, **npargs)
 
@@ -854,10 +845,7 @@ def _test_onnx_op_elementwise(inshape, outfunc, npargs, dtype, opname, kwargs):
     )
 
     model = helper.make_model(graph, producer_name=opname + "_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, dtype)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [indata], [outdata.shape], opset=opset, dtype=dtype)
 
 
 @tvm.testing.uses_gpu
@@ -879,6 +867,27 @@ def test_clip():
         "float32",
         "Clip",
         {"min": -1.0, "max": 1.0},
+        opset=6,
+    )
+
+    _test_onnx_op_elementwise(
+        (2, 4, 5, 6),
+        np.clip,
+        {"a_min": -np.inf, "a_max": 1.0},
+        "float32",
+        "Clip",
+        {"max": 1.0},
+        opset=6,
+    )
+
+    _test_onnx_op_elementwise(
+        (2, 4, 5, 6),
+        np.clip,
+        {"a_min": -1.0, "a_max": np.inf},
+        "float32",
+        "Clip",
+        {"min": -1.0},
+        opset=6,
     )
 
 
@@ -899,7 +908,7 @@ def test_clip_min_max_as_inputs():
     )
     model = helper.make_model(graph, producer_name="clip_test")
 
-    verify_with_ort(model, [input_shape], input_shape)
+    verify_with_ort(model, [input_shape], out_shape=[input_shape])
 
 
 @tvm.testing.uses_gpu
@@ -921,10 +930,7 @@ def _test_finite_ops(inshape, outfunc, npargs, dtype, opname, kwargs):
     )
 
     model = helper.make_model(graph, producer_name=opname + "_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, dtype)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [indata], [outdata.shape], dtype=dtype)
 
 
 @tvm.testing.uses_gpu
@@ -937,10 +943,9 @@ def test_isnan():
     _test_finite_ops((2, 4, 5, 6), np.isnan, {}, "float32", "IsNaN", {})
 
 
-def verify_gather_nd(in_shape, indices, dtype):
+def verify_gather_nd(in_shape, indices, out_shape, dtype="float32"):
     x = np.random.uniform(size=in_shape).astype(dtype)
-    indices = np.array(indices, dtype="int32")
-    out_np = tvm.topi.testing.gather_nd_python(x, indices)
+    indices = np.array(indices, dtype="int64")
 
     y = helper.make_node("GatherND", ["in", "indices"], ["out"])
 
@@ -948,23 +953,27 @@ def verify_gather_nd(in_shape, indices, dtype):
         [y],
         "gather_test",
         inputs=[
-            helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape)),
-            helper.make_tensor_value_info("indices", TensorProto.INT32, list(indices.shape)),
+            helper.make_tensor_value_info(
+                "in", mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)], list(in_shape)
+            ),
+            helper.make_tensor_value_info("indices", TensorProto.INT64, list(indices.shape)),
+        ],
+        outputs=[
+            helper.make_tensor_value_info(
+                "out", mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)], list(out_shape)
+            )
         ],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_np.shape))],
     )
     model = helper.make_model(graph, producer_name="gather_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x, indices], target, ctx, out_np.shape)
-        tvm.testing.assert_allclose(out_np, tvm_out)
+    verify_with_ort_with_inputs(model, [x, indices], [out_shape])
 
 
 @tvm.testing.uses_gpu
 def test_gather_nd():
-    verify_gather_nd((2, 2), [[0, 0], [1, 1]], "int32")
-    verify_gather_nd((3, 3, 3), [[0, 1], [1, 0]], "float32")
-    verify_gather_nd((4, 3, 5, 6), [[2, 1, 0, 0]], "float32")
+    verify_gather_nd([2, 2], [[0, 0], [1, 1]], [2], "int32")
+    verify_gather_nd([2, 2], [[1], [0]], [2, 2])
+    verify_gather_nd([2, 2, 2], [[0, 1], [1, 0]], [2, 2])
+    verify_gather_nd([2, 2, 2], [[[0, 1]], [[1, 0]]], [2, 1, 2])
 
 
 # TODO(mbrookhart): enable once VM supports heterogenous execution
@@ -991,6 +1000,7 @@ def test_onehot():
 
     model = helper.make_model(graph, producer_name="onehot_test")
 
+    # TODO(jwfromm): Replace test against np with test against onnxrt once we update versions.
     for target, ctx in tvm.testing.enabled_targets():
         tvm_out = get_tvm_output_with_vm(
             model, [indices_array, np.array([depth]).astype("int32"), values], target, ctx
@@ -998,14 +1008,50 @@ def test_onehot():
         tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
 
 
+def verify_gemm(a_shape, b_shape, c_shape=None, freeze_params=False):
+    out_shape = [a_shape[0], b_shape[1]]
+    a_array = np.random.uniform(size=a_shape).astype("float32")
+    b_array = np.random.uniform(size=b_shape).astype("float32")
+    input_names = ["a", "b"]
+    input_nodes = [
+        helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape)),
+        helper.make_tensor_value_info("b", TensorProto.FLOAT, list(b_shape)),
+    ]
+    input_values = [a_array, b_array]
+    if c_shape is not None:
+        c_array = np.random.uniform(size=c_shape).astype("float32")
+        input_names.append("c")
+        input_nodes.append(helper.make_tensor_value_info("c", TensorProto.FLOAT, list(c_shape)))
+        input_values.append(c_array)
+
+    gemm_node = helper.make_node("Gemm", input_names, ["out"])
+
+    graph = helper.make_graph(
+        [gemm_node],
+        "gemm_test",
+        inputs=input_nodes,
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
+    )
+
+    model = helper.make_model(graph, producer_name="gemm_test")
+    verify_with_ort_with_inputs(model, input_values, freeze_params=freeze_params)
+
+
+@tvm.testing.uses_gpu
+def test_gemm():
+    verify_gemm(a_shape=(4, 3), b_shape=(3, 4))
+    verify_gemm(a_shape=(4, 3), b_shape=(3, 4), c_shape=(4,))
+    verify_gemm(a_shape=(4, 3), b_shape=(3, 4), c_shape=(4,), freeze_params=True)
+
+
 @tvm.testing.uses_gpu
 def test_matmul():
     a_shape = (4, 3)
     b_shape = (3, 4)
+    out_shape = [a_shape[0], b_shape[1]]
 
     a_array = np.random.uniform(size=a_shape).astype("float32")
     b_array = np.random.uniform(size=b_shape).astype("float32")
-    out_np = np.matmul(a_array, b_array)
 
     mul_node = helper.make_node("MatMul", ["a", "b"], ["out"])
 
@@ -1016,14 +1062,11 @@ def test_matmul():
             helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape)),
             helper.make_tensor_value_info("b", TensorProto.FLOAT, list(b_shape)),
         ],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_np.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
     )
 
     model = helper.make_model(graph, producer_name="matmul_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_array, b_array], target, ctx, out_np.shape)
-        tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [a_array, b_array])
 
 
 def verify_batch_matmul(a_shape, b_shape, out_shape, target, ctx):
@@ -1043,10 +1086,7 @@ def verify_batch_matmul(a_shape, b_shape, out_shape, target, ctx):
     )
 
     model = helper.make_model(graph, producer_name="matmul_test")
-    onnx_out = get_onnxruntime_output(model, [a_array, b_array], "float32")[0]
-
-    tvm_out = get_tvm_output_with_vm(model, [a_array, b_array], target, ctx)
-    tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [a_array, b_array], use_vm=True, targets=[target])
 
 
 # TODO(mbrookhart): enable cuda once VM supports heterogenous execution
@@ -1132,29 +1172,7 @@ def verify_lrn(shape, nsize, dtype, alpha=None, beta=None, bias=None):
         outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(shape))],
     )
     model = helper.make_model(graph, producer_name="lrn_test")
-
-    def _get_python_lrn():
-        square_sum = np.zeros(shape).astype(dtype)
-        for n, c, h, w in np.ndindex(in_array.shape):
-            square_sum[n, c, h, w] = sum(
-                in_array[
-                    n,
-                    max(0, c - int(math.floor((nsize - 1) / 2))) : min(
-                        5, c + int(math.ceil((nsize - 1) / 2)) + 1
-                    ),
-                    h,
-                    w,
-                ]
-                ** 2
-            )
-        py_out = in_array / ((bias + (alpha / nsize) * square_sum) ** beta)
-        return py_out
-
-    for target, ctx in tvm.testing.enabled_targets():
-        input_name = model.graph.input[0].name
-        py_out = _get_python_lrn()
-        tvm_out = get_tvm_output(model, in_array, target, ctx, py_out.shape, "float32")
-        tvm.testing.assert_allclose(py_out, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [in_array])
 
 
 @tvm.testing.uses_gpu
@@ -1164,21 +1182,10 @@ def test_lrn():
 
 
 def verify_instance_norm(shape, axis=1):
-    def _get_python_instance_norm(x, gamma, beta, epsilon=1e-5):
-        dims_x = len(x.shape)
-        axis = tuple(range(2, dims_x))
-        mean = np.mean(x, axis=axis, keepdims=True)
-        var = np.var(x, axis=axis, keepdims=True)
-        dim_ones = (1,) * (dims_x - 2)
-        gamma = gamma.reshape(-1, *dim_ones)
-        beta = beta.reshape(-1, *dim_ones)
-        return gamma * (x - mean) / np.sqrt(var + epsilon) + beta
-
     x = np.random.randn(*shape).astype(np.float32)
     gamma = np.random.randn(shape[1]).astype(np.float32)
     beta = np.random.randn(shape[1]).astype(np.float32)
     epsilon = 1e-5
-    y = _get_python_instance_norm(x, gamma, beta, epsilon).astype(np.float32)
 
     node = onnx.helper.make_node(
         "InstanceNormalization",
@@ -1197,9 +1204,7 @@ def _get_python_instance_norm(x, gamma, beta, epsilon=1e-5):
         outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(shape))],
     )
     model = helper.make_model(graph, producer_name="instance_norm_test")
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x, gamma, beta], target, ctx, shape, "float32")
-        tvm.testing.assert_allclose(y, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [x, gamma, beta], out_shape=[shape])
 
 
 @tvm.testing.uses_gpu
@@ -1210,14 +1215,13 @@ def test_instance_norm():
     verify_instance_norm((8, 7, 6, 5, 4))
 
 
-def _test_upsample_nearest():
+def verify_upsample_nearest():
     scale = 2
     in_shape = (1, 1, 3, 3)
     out_shape = (1, 1, 3 * scale, 3 * scale)
     y = helper.make_node("Upsample", ["in"], ["out"], mode="nearest", scales=[1.0, 1.0, 2.0, 2.0])
 
     in_array = np.random.uniform(size=in_shape).astype(np.float32)
-    out_array = tvm.topi.testing.upsampling_python(in_array, (scale, scale), "NCHW")
 
     graph = helper.make_graph(
         [y],
@@ -1227,13 +1231,10 @@ def _test_upsample_nearest():
     )
 
     model = helper.make_model(graph, producer_name="upsample_nearest_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, "float32")
-        tvm.testing.assert_allclose(out_array, tvm_out)
+    verify_with_ort_with_inputs(model, [in_array], [out_shape], opset=7)
 
 
-def _test_upsample3d_nearest():
+def verify_upsample3d_nearest():
     scale = 2
     in_shape = (1, 1, 3, 3, 3)
     out_shape = (1, 1, 3 * scale, 3 * scale, 3 * scale)
@@ -1242,7 +1243,6 @@ def _test_upsample3d_nearest():
     )
 
     in_array = np.random.uniform(size=in_shape).astype(np.float32)
-    out_array = tvm.topi.testing.upsampling3d_python(in_array, (scale, scale, scale), "NCDHW")
 
     graph = helper.make_graph(
         [y],
@@ -1252,20 +1252,17 @@ def _test_upsample3d_nearest():
     )
 
     model = helper.make_model(graph, producer_name="upsample_nearest_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, "float32")
-        tvm.testing.assert_allclose(out_array, tvm_out)
+    # Upsample is deprecated after opset 9
+    verify_with_ort_with_inputs(model, [in_array], [out_shape], opset=7)
 
 
-def _test_upsample_bilinear():
+def verify_upsample_bilinear():
     scale = 2
     in_shape = (1, 1, 3, 3)
     out_shape = (1, 1, 3 * scale, 3 * scale)
     y = helper.make_node("Upsample", ["in"], ["out"], mode="linear", scales=[1.0, 1.0, 2.0, 2.0])
 
     in_array = np.random.uniform(size=in_shape).astype(np.float32)
-    out_array = tvm.topi.testing.bilinear_resize_python(in_array, (3 * scale, 3 * scale), "NCHW")
 
     graph = helper.make_graph(
         [y],
@@ -1275,51 +1272,10 @@ def _test_upsample_bilinear():
     )
 
     model = helper.make_model(graph, producer_name="upsample_bilinear_test")
+    verify_with_ort_with_inputs(model, [in_array], [out_shape], opset=7)
 
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, "float32")
-        tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)
-
-
-def _test_upsample_bilinear_opset9():
-    scale = 2
-    in_shape = (1, 1, 3, 3)
-    out_shape = (1, 1, 3 * scale, 3 * scale)
-    y = helper.make_node("Upsample", ["in", "scales"], ["out"], mode="linear")
-    scales = [1, 1, 2, 2]
-    in_array = np.random.uniform(size=in_shape).astype(np.float32)
-    out_array = tvm.topi.testing.bilinear_resize_python(in_array, (3 * scale, 3 * scale), "NCHW")
-
-    ref_node = helper.make_node(
-        "Constant",
-        inputs=[],
-        outputs=["const"],
-        value=onnx.helper.make_tensor(
-            name="const_tensor",
-            data_type=TensorProto.FLOAT,
-            dims=scales,
-            vals=np.random.random(scales).flatten().astype(float),
-        ),
-    )
-
-    shape_node = helper.make_node("Shape", ["const"], ["scales"])
 
-    graph = helper.make_graph(
-        [ref_node, shape_node, y],
-        "upsample_bilinear_opset9_test",
-        inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
-    )
-
-    model = helper.make_model(graph, producer_name="upsample_bilinear_opset9_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output_with_vm(
-            model, [in_array], target, ctx, opset=9, freeze_params=True
-        )
-
-
-def _test_upsample3d_trilinear():
+def verify_upsample3d_trilinear():
     scale = 2
     in_shape = (1, 1, 3, 3, 3)
     out_shape = (1, 1, 3 * scale, 3 * scale, 3 * scale)
@@ -1354,7 +1310,8 @@ def _test_upsample3d_trilinear():
     )
 
     model = helper.make_model(graph, producer_name="upsample_trilinear_test")
-
+    # TODO(jwfromm): Trilinear upsampling not supported in 1.0.0 onnxruntime.
+    # Replace topi comparison with verify_with_ort once we update.
     for target, ctx in tvm.testing.enabled_targets():
         tvm_out = get_tvm_output(model, in_array, target, ctx, out_shape, "float32")
         tvm.testing.assert_allclose(out_array, tvm_out, rtol=1e-5, atol=1e-5)
@@ -1363,41 +1320,36 @@ def _test_upsample3d_trilinear():
 # TODO(mbrookhart): enable once VM supports heterogenous execution
 # @tvm.testing.uses_gpu
 def test_upsample():
-    _test_upsample_nearest()
-    _test_upsample_bilinear()
-    _test_upsample_bilinear_opset9()
-    _test_upsample3d_nearest()
-    _test_upsample3d_trilinear()
+    verify_upsample_nearest()
+    verify_upsample_bilinear()
+    verify_upsample3d_nearest()
+    verify_upsample3d_trilinear()
 
 
-def _test_softmax(inshape, axis):
+def verify_softmax(inshape, axis):
     opname = "Softmax"
     indata = np.random.uniform(size=inshape).astype(np.float32)
     outshape = inshape
-    outdata = tvm.topi.testing.softmax_python(indata)
-    if isinstance(axis, int):
-        y = helper.make_node(opname, ["in"], ["out"], axis=axis)
-    elif axis is None:
-        y = helper.make_node(opname, ["in"], ["out"])
+    y = helper.make_node(opname, ["in"], ["out"])
+    if axis is not None:
+        axis_attr = helper.make_attribute("axis", axis)
+        y.attribute.append(axis_attr)
 
     graph = helper.make_graph(
         [y],
         opname + "_test",
         inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(indata.shape))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outdata.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outshape))],
     )
 
     model = helper.make_model(graph, producer_name=opname + "_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, indata, target, ctx, outshape, "float32")
-        tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [indata])
 
 
 @tvm.testing.uses_gpu
 def test_softmax():
-    _test_softmax((1, 10), None)
-    _test_softmax((1, 10), 1)
+    verify_softmax((1, 10), None)
+    verify_softmax((1, 10), 1)
 
 
 def verify_min(input_dim):
@@ -1407,8 +1359,6 @@ def verify_min(input_dim):
     a_np2 = np.random.uniform(size=input_dim).astype(dtype)
     a_np3 = np.random.uniform(size=input_dim).astype(dtype)
 
-    b_np = np.min((a_np1, a_np2, a_np3), axis=0)
-
     min_node = helper.make_node("Min", ["a_np1", "a_np2", "a_np3"], ["out"])
 
     graph = helper.make_graph(
@@ -1419,14 +1369,11 @@ def verify_min(input_dim):
             helper.make_tensor_value_info("a_np2", TensorProto.FLOAT, list(input_dim)),
             helper.make_tensor_value_info("a_np3", TensorProto.FLOAT, list(input_dim)),
         ],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(b_np.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(input_dim))],
     )
 
     model = helper.make_model(graph, producer_name="Min_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
-        tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [a_np1, a_np2, a_np3])
 
 
 @tvm.testing.uses_gpu
@@ -1442,8 +1389,6 @@ def verify_max(input_dim):
     a_np2 = np.random.uniform(size=input_dim).astype(dtype)
     a_np3 = np.random.uniform(size=input_dim).astype(dtype)
 
-    b_np = np.max((a_np1, a_np2, a_np3), axis=0)
-
     max_node = helper.make_node("Max", ["a_np1", "a_np2", "a_np3"], ["out"])
 
     graph = helper.make_graph(
@@ -1454,14 +1399,11 @@ def verify_max(input_dim):
             helper.make_tensor_value_info("a_np2", TensorProto.FLOAT, list(input_dim)),
             helper.make_tensor_value_info("a_np3", TensorProto.FLOAT, list(input_dim)),
         ],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(b_np.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(input_dim))],
     )
 
     model = helper.make_model(graph, producer_name="Max_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
-        tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [a_np1, a_np2, a_np3])
 
 
 @tvm.testing.uses_gpu
@@ -1477,8 +1419,6 @@ def verify_mean(input_dim):
     a_np2 = np.random.uniform(size=input_dim).astype(dtype)
     a_np3 = np.random.uniform(size=input_dim).astype(dtype)
 
-    b_np = np.mean((a_np1, a_np2, a_np3), axis=0)
-
     mean_node = helper.make_node("Mean", ["a_np1", "a_np2", "a_np3"], ["out"])
 
     graph = helper.make_graph(
@@ -1489,14 +1429,11 @@ def verify_mean(input_dim):
             helper.make_tensor_value_info("a_np2", TensorProto.FLOAT, list(input_dim)),
             helper.make_tensor_value_info("a_np3", TensorProto.FLOAT, list(input_dim)),
         ],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(b_np.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(input_dim))],
     )
 
     model = helper.make_model(graph, producer_name="Mean_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_np1, a_np2, a_np3], target, ctx, b_np.shape)
-        tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [a_np1, a_np2, a_np3])
 
 
 @tvm.testing.uses_gpu
@@ -1510,22 +1447,17 @@ def verify_hardsigmoid(input_dim, alpha, beta):
 
     a_np1 = np.random.uniform(size=input_dim).astype(dtype)
 
-    b_np = np.clip(a_np1 * alpha + beta, 0, 1)
-
     hardsigmoid_node = helper.make_node("HardSigmoid", ["a_np1"], ["out"], alpha=alpha, beta=beta)
 
     graph = helper.make_graph(
         [hardsigmoid_node],
         "HardSigmoid_test",
         inputs=[helper.make_tensor_value_info("a_np1", TensorProto.FLOAT, list(input_dim))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(b_np.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(input_dim))],
     )
 
     model = helper.make_model(graph, producer_name="HardSigmoid_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape)
-        tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [a_np1])
 
 
 @tvm.testing.uses_gpu
@@ -1534,98 +1466,51 @@ def test_forward_hardsigmoid():
     verify_hardsigmoid((20, 20), 0.3, 0.4)
 
 
-def verify_argmin(input_dim, axis=None, keepdims=None):
-    def _argmin_numpy(data, axis=0, keepdims=True):
-        result = np.argmin(data, axis=axis)
-        if keepdims == 1:
-            result = np.expand_dims(result, axis)
-        return result.astype(data.dtype)
-
+def verify_argreduce(input_dim, op_name, axis=None, keepdims=None):
     a_np1 = np.random.uniform(-10, 10, input_dim).astype(np.int32)
-    if keepdims is None and axis is None:
-        b_np = _argmin_numpy(a_np1)
-        node = onnx.helper.make_node("ArgMin", inputs=["a_np1"], outputs=["out"])
-    elif axis is None:
-        b_np = _argmin_numpy(a_np1, keepdims=keepdims)
-        node = onnx.helper.make_node("ArgMin", inputs=["a_np1"], outputs=["out"], keepdims=keepdims)
-    elif keepdims is None:
-        b_np = _argmin_numpy(a_np1, axis=axis)
-        node = onnx.helper.make_node("ArgMin", inputs=["a_np1"], outputs=["out"], axis=axis)
+    out_shape = list(a_np1.shape)
+    def_axis = axis if axis is not None else 0
+    if keepdims == 1 or keepdims == None:
+        out_shape[def_axis] = 1
     else:
-        b_np = _argmin_numpy(a_np1, axis=axis, keepdims=keepdims)
-        node = onnx.helper.make_node(
-            "ArgMin", inputs=["a_np1"], outputs=["out"], axis=axis, keepdims=keepdims
-        )
-    graph = helper.make_graph(
-        [node],
-        "argmin_test",
-        inputs=[helper.make_tensor_value_info("a_np1", TensorProto.INT32, list(a_np1.shape))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.INT32, list(b_np.shape))],
-    )
-
-    model = helper.make_model(graph, producer_name="argmin_test")
+        out_shape.pop(def_axis)
 
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
-        tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
-
-
-def verify_argmax(input_dim, axis=None, keepdims=None):
-    def _argmax_numpy(data, axis=0, keepdims=True):
-        result = np.argmax(data, axis=axis)
-        if keepdims == 1:
-            result = np.expand_dims(result, axis)
-        return result.astype(data.dtype)
+    node = onnx.helper.make_node(op_name, inputs=["a_np1"], outputs=["out"])
 
-    a_np1 = np.random.uniform(-10, 10, input_dim).astype(np.int32)
-    if keepdims is None and axis is None:
-        b_np = _argmax_numpy(a_np1)
-        node = onnx.helper.make_node("ArgMax", inputs=["a_np1"], outputs=["out"])
-    elif axis is None:
-        b_np = _argmax_numpy(a_np1, keepdims=keepdims)
-        node = onnx.helper.make_node("ArgMax", inputs=["a_np1"], outputs=["out"], keepdims=keepdims)
-    elif keepdims is None:
-        b_np = _argmax_numpy(a_np1, axis=axis)
-        node = onnx.helper.make_node("ArgMax", inputs=["a_np1"], outputs=["out"], axis=axis)
-    else:
-        b_np = _argmax_numpy(a_np1, axis=axis, keepdims=keepdims)
-        node = onnx.helper.make_node(
-            "ArgMax", inputs=["a_np1"], outputs=["out"], axis=axis, keepdims=keepdims
-        )
+    if keepdims is not None:
+        keepdims_attr = helper.make_attribute("keepdims", keepdims)
+        node.attribute.append(keepdims_attr)
+    if axis is not None:
+        axis_attr = helper.make_attribute("axis", axis)
+        node.attribute.append(axis_attr)
 
     graph = helper.make_graph(
         [node],
-        "argmax_test",
+        "argreduce_test",
         inputs=[helper.make_tensor_value_info("a_np1", TensorProto.INT32, list(a_np1.shape))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.INT32, list(b_np.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.INT64, list(out_shape))],
     )
 
-    model = helper.make_model(graph, producer_name="argmax_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [a_np1], target, ctx, b_np.shape, b_np.dtype)
-        tvm.testing.assert_allclose(b_np, tvm_out, rtol=1e-5, atol=1e-5)
+    model = helper.make_model(graph, producer_name="argreduce_test")
+    verify_with_ort_with_inputs(model, [a_np1])
 
 
 @tvm.testing.uses_gpu
 def test_forward_arg_min_max():
     """Verify argmin and argmax"""
-    verify_argmin([3, 4, 4])
-    verify_argmax([3, 4, 4])
-    verify_argmin([3, 4, 4], axis=1)
-    verify_argmax([3, 4, 4], axis=0)
-    verify_argmin([3, 4, 4], keepdims=0)
-    verify_argmax([3, 4, 4], keepdims=1)
+    verify_argreduce([3, 4, 4], "ArgMin")
+    verify_argreduce([3, 4, 4], "ArgMax")
+    verify_argreduce([3, 4, 4], "ArgMin", axis=1)
+    verify_argreduce([3, 4, 4], "ArgMax", axis=0)
+    verify_argreduce([3, 4, 4], "ArgMin", keepdims=0)
+    verify_argreduce([3, 4, 4], "ArgMax", keepdims=1)
     for axis in [None, 0, 1, 2]:
         for keepdims in [None, True, False]:
-            verify_argmin([3, 4, 4], axis, keepdims)
-            verify_argmax([3, 4, 4], axis, keepdims)
+            verify_argreduce([3, 4, 4], "ArgMin", axis, keepdims)
+            verify_argreduce([3, 4, 4], "ArgMax", axis, keepdims)
 
 
 def verify_constantofshape(input_dim, value, dtype):
-    out = np.empty(shape=input_dim, dtype=dtype)
-    out.fill(value)
-
     fill_node = helper.make_node(
         "ConstantOfShape",
         ["input"],
@@ -1635,22 +1520,22 @@ def verify_constantofshape(input_dim, value, dtype):
         ),
     )
 
-    inputs = [helper.make_tensor_value_info("input", TensorProto.FLOAT, input_dim)]
+    inputs = [helper.make_tensor_value_info("input", TensorProto.INT64, [len(input_dim)])]
 
     graph = helper.make_graph(
         [fill_node],
         "fill_test",
         inputs,
-        outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, list(out.shape))],
+        outputs=[
+            helper.make_tensor_value_info(
+                "output", mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)], input_dim
+            )
+        ],
     )
 
     model = helper.make_model(graph, producer_name="fill_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        input_np = np.array(input_dim).astype("float32")
-        tvm_out = get_tvm_output_with_vm(model, [input_np], target, ctx)
-
-        tvm.testing.assert_allclose(out, tvm_out, rtol=1e-5, atol=1e-5)
+    input_np = np.array(input_dim).astype("int64")
+    verify_with_ort_with_inputs(model, [input_np], use_vm=True)
 
 
 # TODO(mbrookhart): enable once VM supports heterogenous execution
@@ -1688,10 +1573,7 @@ def verify_pad(indata, pads, mode="constant", value=0.0):
         outputs=[helper.make_tensor_value_info("output", TensorProto.FLOAT, list(outdata.shape))],
     )
     model = helper.make_model(graph, producer_name="pad_test")
-    #  tvm result
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, indata, target, ctx, outdata.shape, "float32", opset=2)
-    tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [indata], [outdata.shape], dtype="float32", opset=2)
 
 
 def verify_pad_v11(indata, pads, mode="constant", value=0.0):
@@ -1740,10 +1622,7 @@ def verify_pad_v11(indata, pads, mode="constant", value=0.0):
             ],
         )
     model = helper.make_model(graph, producer_name="pad_test")
-    #  tvm result
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output_with_vm(model, inputs, target, ctx, opset=11, freeze_params=False)
-    tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, inputs, opset=11, use_vm=True)
 
 
 # TODO(mbrookhart): enable once VM supports heterogenous execution
@@ -1784,7 +1663,7 @@ def verify_reduce_func(func, data, axis, keepdims):
 
     model = helper.make_model(graph, producer_name="reduce_test")
 
-    verify_with_ort_with_inputs(model, [data], outshape)
+    verify_with_ort_with_inputs(model, [data], [outshape])
 
 
 @tvm.testing.uses_gpu
@@ -1829,32 +1708,45 @@ def test_all_reduce_funcs():
             )
 
 
-def verify_split(indata, outdatas, split, axis=0, pass_split=True):
+def verify_split(indata, outdatas, split, axis=0, pass_split=True, opset=11):
     indata = np.array(indata).astype(np.float32)
     outdatas = [np.array(o).astype(np.float32) for o in outdatas]
+    inputs = [helper.make_tensor_value_info("input", TensorProto.FLOAT, list(indata.shape))]
+    input_names = ["input"]
+    initializer = []
+
     if split:
         split_index = range(len(split))
     else:
         split_index = range(len(outdatas))
+
     if pass_split:
-        node = helper.make_node(
-            "Split",
-            inputs=["input"],
-            outputs=["output_{}".format(i) for i in range(len(split_index))],
-            axis=axis,
-            split=split,
-        )
-    else:
-        node = helper.make_node(
-            "Split",
-            inputs=["input"],
-            outputs=["output_{}".format(i) for i in range(len(split_index))],
-            axis=axis,
-        )
+        if opset >= 13:
+            input_names.append("split")
+            np_split = np.array(split).astype(np.int64)
+            inputs.append(
+                helper.make_tensor_value_info("split", TensorProto.INT64, list(np_split.shape))
+            )
+            indata = [indata, np_split]
+            initializer.append(
+                helper.make_tensor("split", TensorProto.INT64, list(np_split.shape), np_split)
+            )
+    node = helper.make_node(
+        "Split",
+        inputs=input_names,
+        outputs=["output_{}".format(i) for i in range(len(split_index))],
+        axis=axis,
+    )
+
+    if pass_split and opset < 13:
+        split_attr = helper.make_attribute("split", split)
+        node.attribute.append(split_attr)
+
     graph = helper.make_graph(
         [node],
         "split_test",
-        inputs=[helper.make_tensor_value_info("input", TensorProto.FLOAT, list(indata.shape))],
+        inputs=inputs,
+        initializer=initializer,
         outputs=[
             helper.make_tensor_value_info(
                 "output_{}".format(i), TensorProto.FLOAT, list(outdatas[i].shape)
@@ -1863,18 +1755,7 @@ def verify_split(indata, outdatas, split, axis=0, pass_split=True):
         ],
     )
     model = helper.make_model(graph, producer_name="split_test")
-
-    import onnxruntime.backend
-
-    rep = onnxruntime.backend.prepare(model, "CPU")
-    onnx_out = rep.run(indata)
-
-    for target, ctx in tvm.testing.enabled_targets():
-        output_shape = [o.shape for o in outdatas]
-        output_type = ["float32", "float32", "float32"]
-        tvm_out = get_tvm_output(model, indata, target, ctx, output_shape, output_type)
-        for o, t in zip(onnx_out, tvm_out):
-            tvm.testing.assert_allclose(o, t)
+    verify_with_ort_with_inputs(model, indata, out_shape=list(range(len(split_index))), opset=opset)
 
 
 @tvm.testing.uses_gpu
@@ -1894,6 +1775,8 @@ def test_split():
     )
     # Split evenly (unstack)
     verify_split([1, 2, 3], [[1], [2], [3]], False, 0, False)
+    # Split a single value to a single value
+    verify_split([1], [[1]], [1], pass_split=True)
 
 
 @tvm.testing.uses_gpu
@@ -1902,88 +1785,90 @@ def test_binary_ops():
     dtype = "float32"
     out_shape = in_shape
 
-    def verify_binary_ops(op, x, y, out_np, x_name="in1", y_name="in2", broadcast=None):
-        if broadcast is None:
-            z = helper.make_node(op, [x_name, y_name], ["out"])
-        else:
-            z = helper.make_node(op, [x_name, y_name], ["out"], broadcast=1)
+    def verify_binary_ops(op, x, y, out_type="float32"):
+        z = helper.make_node(op, ["in1", "in2"], ["out"])
         graph = helper.make_graph(
             [z],
             "_test",
             inputs=[
-                helper.make_tensor_value_info(x_name, TensorProto.FLOAT, list(in_shape)),
-                helper.make_tensor_value_info(y_name, TensorProto.FLOAT, list(in_shape)),
+                helper.make_tensor_value_info("in1", TensorProto.FLOAT, x.shape),
+                helper.make_tensor_value_info("in2", TensorProto.FLOAT, y.shape),
+            ],
+            outputs=[
+                helper.make_tensor_value_info(
+                    "out", mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(out_type)], list(out_shape)
+                )
             ],
-            outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
         )
         model = helper.make_model(graph, producer_name="_test")
-        for target, ctx in tvm.testing.enabled_targets():
-            tvm_out = get_tvm_output(model, [x, y], target, ctx)
-            tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)
+        verify_with_ort_with_inputs(model, [x, y])
 
     x = np.random.uniform(size=in_shape).astype(dtype)
     y = np.random.uniform(size=in_shape).astype(dtype)
     z = np.random.uniform(size=(3,)).astype(dtype)
-    verify_binary_ops("Add", x, y, x + y, broadcast=None)
-    verify_binary_ops("Add", x, z, x + z, broadcast=True)
-    verify_binary_ops("Sub", x, y, x - y, broadcast=None)
-    verify_binary_ops("Sub", x, z, x - z, broadcast=True)
-    verify_binary_ops("Mul", x, y, x * y, broadcast=None)
-    verify_binary_ops("Mul", x, z, x * z, broadcast=True)
-    verify_binary_ops("Mul", x, x, x * x, x_name="in1", y_name="in1", broadcast=None)
-    verify_binary_ops("Div", x, y, x / y, broadcast=None)
-    verify_binary_ops("Div", x, z, x / z, broadcast=True)
-    verify_binary_ops("Sum", x, y, x + y, broadcast=None)
-    verify_binary_ops("Greater", x, y, x > y, broadcast=True)
-    verify_binary_ops("Less", x, y, x < y, broadcast=True)
-    verify_binary_ops("Equal", x, y, x == y, broadcast=True)
+    verify_binary_ops("Add", x, y)
+    verify_binary_ops("Add", x, z)
+    verify_binary_ops("Sub", x, y)
+    verify_binary_ops("Sub", x, z)
+    verify_binary_ops("Mul", x, y)
+    verify_binary_ops("Mul", x, z)
+    verify_binary_ops("Div", x, y)
+    verify_binary_ops("Div", x, z)
+    verify_binary_ops("Sum", x, y)
+    verify_binary_ops("Sum", x, z)
+    verify_binary_ops("Greater", x, y, "bool")
+    verify_binary_ops("Greater", x, z, "bool")
+    verify_binary_ops("Less", x, y, "bool")
+    verify_binary_ops("Less", x, z, "bool")
+    verify_binary_ops("Equal", x, y, "bool")
+    verify_binary_ops("Equal", x, z, "bool")
 
 
 @tvm.testing.uses_gpu
-def test_single_ops():
+def test_unary_ops():
     in_shape = (1, 2, 3, 3)
     dtype = "float32"
     out_shape = in_shape
 
-    def verify_single_ops(op, x, out_np, rtol=1e-5, atol=1e-5):
+    def verify_unary_ops(op, x, rtol=1e-5, atol=1e-5, dtype="float32"):
+        x = x.astype(dtype)
+        ONNX_DTYPE = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]
         z = helper.make_node(op, ["in1"], ["out"])
         graph = helper.make_graph(
             [z],
             "_test",
             inputs=[
-                helper.make_tensor_value_info("in1", TensorProto.FLOAT, list(in_shape)),
+                helper.make_tensor_value_info("in1", ONNX_DTYPE, list(in_shape)),
             ],
-            outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
+            outputs=[helper.make_tensor_value_info("out", ONNX_DTYPE, list(out_shape))],
         )
         model = helper.make_model(graph, producer_name="_test")
-        for target, ctx in tvm.testing.enabled_targets():
-            tvm_out = get_tvm_output(model, [x], target, ctx)
-            tvm.testing.assert_allclose(out_np, tvm_out, rtol=rtol, atol=atol)
-
-    x = np.random.uniform(size=in_shape).astype(dtype)
-    verify_single_ops("Neg", x, -x)
-    verify_single_ops("Abs", x, np.abs(x))
-    verify_single_ops("Reciprocal", x, 1 / x)
-    verify_single_ops("Sqrt", x, np.sqrt(x))
-    verify_single_ops("Relu", x, np.maximum(x, 0))
-    verify_single_ops("Exp", x, np.exp(x))
-    verify_single_ops("Log", x, np.log(x))
-    verify_single_ops("Log", x, np.log(x))
-    verify_single_ops("ACos", x, np.arccos(x))
-    verify_single_ops("ACosh", x, np.arccosh(x))
-    verify_single_ops("ASin", x, np.arcsin(x))
-    verify_single_ops("ASinh", x, np.arcsinh(x))
-    verify_single_ops("ATan", x, np.arctan(x))
-    verify_single_ops("ATanh", x, np.arctanh(x))
-    verify_single_ops("Cos", x, np.cos(x))
-    verify_single_ops("Cosh", x, np.cosh(x))
-    verify_single_ops("Sin", x, np.sin(x))
-    verify_single_ops("Sinh", x, np.sinh(x))
-    verify_single_ops("Tan", x, np.tan(x))
-    verify_single_ops("Tanh", x, np.tanh(x))
-    verify_single_ops("Sigmoid", x, 1 / (1 + np.exp(-x)))
-    verify_single_ops("Softsign", x, x / (1 + np.abs(x)))
-    verify_single_ops("SoftPlus", x, np.log(1 + np.exp(x)))
+        verify_with_ort_with_inputs(model, [x], rtol=rtol, atol=atol)
+
+    x = np.random.uniform(size=in_shape)
+    verify_unary_ops("Neg", x)
+    verify_unary_ops("Abs", x)
+    verify_unary_ops("Reciprocal", x)
+    verify_unary_ops("Reciprocal", x, dtype="float16")
+    verify_unary_ops("Sqrt", x)
+    verify_unary_ops("Relu", x)
+    verify_unary_ops("Exp", x)
+    verify_unary_ops("Log", x)
+    verify_unary_ops("Log", x)
+    verify_unary_ops("Acos", x)
+    verify_unary_ops("Acosh", x)
+    verify_unary_ops("Asin", x)
+    verify_unary_ops("Asinh", x)
+    verify_unary_ops("Atan", x)
+    verify_unary_ops("Atanh", x)
+    verify_unary_ops("Cos", x)
+    verify_unary_ops("Cosh", x)
+    verify_unary_ops("Sin", x)
+    verify_unary_ops("Sinh", x)
+    verify_unary_ops("Tan", x)
+    verify_unary_ops("Tanh", x)
+    verify_unary_ops("Sigmoid", x)
+    verify_unary_ops("Softsign", x)
 
 
 @tvm.testing.uses_gpu
@@ -2038,12 +1923,19 @@ def verify_prelu(x_shape, a_shape):
 
         model = helper.make_model(graph, producer_name="prelu_test")
 
-        verify_with_ort(model, [x_shape, a_shape], list(x_shape))
+        verify_with_ort(
+            model,
+            [x_shape, a_shape],
+            out_shape=[list(x_shape)],
+            use_vm=True,
+            convert_to_static=True,
+        )
 
     verify_prelu([3, 4, 5, 6], [1, 4, 1, 1])
     verify_prelu([1, 8, 5, 6], [1, 8, 1, 1])
     verify_prelu([2, 12, 16, 16], [1, 12, 1, 1])
     verify_prelu([2, 12, 16, 16], [1])  # Test alpha broadcasting.
+    verify_prelu([3, 1], [3, 1])  # Test non NCHW workload.
 
 
 @tvm.testing.uses_gpu
@@ -2063,46 +1955,6 @@ def ThresholdedRelu_x(x, alpha):
     )
 
 
-@tvm.testing.uses_gpu
-def test_ScaledTanh():
-    def ScaledTanh_x(x, alpha, beta):
-        return alpha * np.tanh(beta * x)
-
-    _test_onnx_op_elementwise(
-        (2, 4, 5, 6),
-        ScaledTanh_x,
-        {"alpha": 0.25, "beta": 0.3},
-        "float32",
-        "ScaledTanh",
-        {"alpha": 0.25, "beta": 0.3},
-    )
-
-
-@tvm.testing.uses_gpu
-def test_ParametricSoftplus():
-    def ParametricSoftplus_x(x, alpha, beta):
-        return alpha * np.log(np.exp(beta * x) + 1)
-
-    _test_onnx_op_elementwise(
-        (2, 4, 5, 6),
-        ParametricSoftplus_x,
-        {"alpha": 0.25, "beta": 0.3},
-        "float32",
-        "ParametricSoftplus",
-        {"alpha": 0.25, "beta": 0.3},
-    )
-
-
-@tvm.testing.uses_gpu
-def test_Scale():
-    def Scale_x(x, scale):
-        return scale * x
-
-    _test_onnx_op_elementwise(
-        (2, 4, 5, 6), Scale_x, {"scale": 0.25}, "float32", "Scale", {"scale": 0.25}
-    )
-
-
 @tvm.testing.uses_gpu
 def test_LogSoftmax():
     _test_onnx_op_elementwise(
@@ -2116,8 +1968,8 @@ def check_torch_conversion(model, input_size):
     # Set verbose=True for more output
     torch.onnx.export(model(), dummy_input, file_name, export_params=True, verbose=False)
     onnx_model = onnx.load(file_name)
-    input_data = np.random.uniform(size=input_size).astype("int32")
-    verify_with_ort_with_inputs(onnx_model, [input_data])
+    input_data = np.random.uniform(size=input_size).astype("float32")
+    verify_with_ort_with_inputs(onnx_model, [input_data], apply_softmax=True)
 
 
 @tvm.testing.uses_gpu
@@ -2169,7 +2021,6 @@ def Sign_x(x):
 
 def verify_not(indata, dtype):
     x = indata.astype(dtype)
-    outdata = np.logical_not(x)
 
     node = helper.make_node(
         "Not",
@@ -2181,14 +2032,11 @@ def verify_not(indata, dtype):
         [node],
         "not_test",
         inputs=[helper.make_tensor_value_info("in", TensorProto.BOOL, list(x.shape))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.BOOL, list(outdata.shape))],
+        outputs=[helper.make_tensor_value_info("out", TensorProto.BOOL, list(x.shape))],
     )
 
     model = helper.make_model(graph, producer_name="not_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x], target, ctx, outdata.shape)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [x])
 
 
 @tvm.testing.uses_gpu
@@ -2223,10 +2071,7 @@ def verify_and(indata, dtype):
     )
 
     model = helper.make_model(graph, producer_name="and_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x, y], target, ctx, outdata.shape)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [x, y], [outdata.shape])
 
 
 @tvm.testing.uses_gpu
@@ -2257,22 +2102,6 @@ def test_and():
     verify_and(indata=[x, y], dtype=bool)
 
 
-def verify_tile_v1(indata, outdata, **kwargs):
-    node = helper.make_node("Tile", inputs=["in"], outputs=["out"], **kwargs)
-    graph = helper.make_graph(
-        [node],
-        "tile_test",
-        inputs=[helper.make_tensor_value_info("in", TensorProto.FLOAT, list(indata.shape))],
-        outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outdata.shape))],
-    )
-
-    model = helper.make_model(graph, producer_name="tile_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [indata], target, ctx, outdata.shape, opset=1)
-        tvm.testing.assert_allclose(outdata, tvm_out)
-
-
 def verify_tile_v6(indata, repeats, outdata):
     node = helper.make_node("Tile", inputs=["input", "repeats"], outputs=["out"])
     graph = helper.make_graph(
@@ -2286,10 +2115,7 @@ def verify_tile_v6(indata, repeats, outdata):
     )
 
     model = helper.make_model(graph, producer_name="tile_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output_with_vm(model, [indata, repeats], target, ctx, opset=6)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [indata, repeats], use_vm=True, opset=6)
 
 
 # TODO(mbrookhart): enable once VM supports heterogenous execution
@@ -2298,7 +2124,6 @@ def test_tile():
     x = np.random.rand(2, 3, 4, 5).astype(np.float32)
     repeats = np.random.randint(low=1, high=10, size=(np.ndim(x),)).astype(np.int64)
     z = np.tile(x, repeats)
-    verify_tile_v1(x, z, repeats=repeats)
     verify_tile_v6(x, repeats, z)
 
 
@@ -2311,10 +2136,7 @@ def verify_erf(indata, outdata):
         outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(outdata.shape))],
     )
     model = helper.make_model(graph, producer_name="erf_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [indata], target, ctx, outdata.shape)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [indata], [outdata.shape])
 
 
 @tvm.testing.uses_gpu
@@ -2324,10 +2146,18 @@ def test_erf():
     verify_erf(x, z)
 
 
-def verify_where(condition, x, y, dtype, outdata):
-    node = helper.make_node("Where", inputs=["condition", "x", "y"], outputs=["out"])
+def verify_where(condition, x, y, dtype, outdata, dynamic=False):
+    node_list = []
+    where_inputs = ["condition", "x", "y"]
+    if dynamic:
+        shape_node = helper.make_node("Shape", ["x"], ["shape"])
+        reshape_node = helper.make_node("Reshape", ["x", "shape"], ["X"])
+        where_inputs[1] = "X"
+        node_list += [shape_node, reshape_node]
+    node = helper.make_node("Where", inputs=where_inputs, outputs=["out"])
+    node_list.append(node)
     graph = helper.make_graph(
-        [node],
+        node_list,
         "where_test",
         inputs=[
             helper.make_tensor_value_info("condition", TensorProto.BOOL, list(condition.shape)),
@@ -2337,10 +2167,7 @@ def verify_where(condition, x, y, dtype, outdata):
         outputs=[helper.make_tensor_value_info("out", dtype, list(outdata.shape))],
     )
     model = helper.make_model(graph, producer_name="where_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [condition, x, y], target, ctx, outdata.shape)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [condition, x, y], [outdata.shape], use_vm=True)
 
 
 @tvm.testing.uses_gpu
@@ -2376,6 +2203,7 @@ def test_where():
     y = np.array([[1], [7]], dtype=np.float32)
     outdata = np.where(condition, x, y)
     verify_where(condition, x, y, TensorProto.FLOAT, outdata)
+    verify_where(condition, x, y, TensorProto.FLOAT, outdata, dynamic=True)
 
 
 def verify_or(indata, dtype):
@@ -2400,10 +2228,7 @@ def verify_or(indata, dtype):
     )
 
     model = helper.make_model(graph, producer_name="or_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x, y], target, ctx, outdata.shape)
-        tvm.testing.assert_allclose(outdata, tvm_out)
+    verify_with_ort_with_inputs(model, [x, y], [outdata.shape])
 
 
 @tvm.testing.uses_gpu
@@ -2457,7 +2282,7 @@ def verify_batch_norm(in_shape):
         model = helper.make_model(graph, producer_name="batchnorm_test")
         # X, scale, b, mean, var
         inshapes = [in_shape, in_shape[1], in_shape[1], in_shape[1], in_shape[1]]
-        verify_with_ort(model, inshapes, in_shape)
+        verify_with_ort(model, inshapes, out_shape=[in_shape])
 
     verify_batch_norm([1, 3, 224, 224])
     verify_batch_norm([1, 3, 24, 24])
@@ -2495,7 +2320,7 @@ def verify_batch_norm_dynamic_subgraph(in_shape, o_shape):
 
         # X, inp, scale, b, mean, var
         inshapes = [in_shape, o_shape, in_shape[1], in_shape[1], in_shape[1], in_shape[1]]
-        verify_with_ort(model, inshapes, in_shape, use_vm=True)
+        verify_with_ort(model, inshapes, out_shape=[in_shape], use_vm=True)
 
     verify_batch_norm_dynamic_subgraph([16, 16, 10, 10], [160, 160])
 
@@ -2559,7 +2384,7 @@ def verify_conv(
 
     model = helper.make_model(graph, producer_name="conv_test")
 
-    verify_with_ort(model, [x_shape, w_shape], y_shape, use_vm=True, convert_to_static=True)
+    verify_with_ort(model, [x_shape, w_shape], [y_shape], use_vm=True, convert_to_static=True)
 
 
 @tvm.testing.uses_gpu
@@ -2664,42 +2489,27 @@ def verify_convtranspose_with_padding(
     dilations,
     auto_pad="NOTSET",
     unset_pad=False,
+    group=1,
 ):
-    if unset_pad:
-        node = helper.make_node(
-            "ConvTranspose",
-            inputs=["x", "W"],
-            outputs=["y"],
-            kernel_shape=kernel_shape,
-            # Default values for other attributes:
-            strides=strides,
-            dilations=dilations,
-            group=1,
-        )
-    elif padding is None:
-        node = helper.make_node(
-            "ConvTranspose",
-            inputs=["x", "W"],
-            outputs=["y"],
-            kernel_shape=kernel_shape,
-            # Default values for other attributes:
-            strides=strides,
-            dilations=dilations,
-            group=1,
-            auto_pad=auto_pad,
-        )
-    else:
-        node = helper.make_node(
-            "ConvTranspose",
-            inputs=["x", "W"],
-            outputs=["y"],
-            kernel_shape=kernel_shape,
-            # Default values for other attributes:
-            strides=strides,
-            dilations=dilations,
-            group=1,
-            pads=padding,
-        )
+    node = helper.make_node(
+        "ConvTranspose",
+        inputs=["x", "W"],
+        outputs=["y"],
+        kernel_shape=kernel_shape,
+        # Default values for other attributes:
+        strides=strides,
+        dilations=dilations,
+    )
+    if not unset_pad:
+        if padding is None:
+            pad_attr = helper.make_attribute("auto_pad", auto_pad)
+        else:
+            pad_attr = helper.make_attribute("pads", padding)
+        node.attribute.append(pad_attr)
+
+    if group is not None:
+        group_attr = helper.make_attribute("group", group)
+        node.attribute.append(group_attr)
 
     graph = helper.make_graph(
         [node],
@@ -2711,22 +2521,25 @@ def verify_convtranspose_with_padding(
         outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(y_shape))],
     )
 
-    model = helper.make_model(graph, producer_name="conv_test")
+    model = helper.make_model(graph, producer_name="convtranspose_pad_test")
 
-    verify_with_ort(model, [x_shape, w_shape], y_shape, use_vm=True, convert_to_static=True)
+    verify_with_ort(model, [x_shape, w_shape], [y_shape], use_vm=True, convert_to_static=True)
 
 
-def verify_convtranspose(x_shape, w_shape, y_shape, p):
+def verify_convtranspose(x_shape, w_shape, y_shape, p, group=1):
     node = onnx.helper.make_node(
         "ConvTranspose",
         inputs=["x", "W"],
         outputs=["y"],
         strides=[3, 2],
-        group=1,
         kernel_shape=[3, 3],
         pads=p,
     )
 
+    if group is not None:
+        group_attr = helper.make_attribute("group", group)
+        node.attribute.append(group_attr)
+
     graph = helper.make_graph(
         [node],
         "verify_convtranspose_test",
@@ -2737,7 +2550,7 @@ def verify_convtranspose(x_shape, w_shape, y_shape, p):
         outputs=[helper.make_tensor_value_info("y", TensorProto.FLOAT, list(y_shape))],
     )
 
-    model = helper.make_model(graph, producer_name="convtranspose_trest")
+    model = helper.make_model(graph, producer_name="convtranspose_test")
     verify_with_ort(model, [x_shape, w_shape], y_shape)
 
 
@@ -2749,6 +2562,8 @@ def test_convtranspose():
     # (1, 2, 7, 3) output tensor
     # [1, 2, 1, 2] list for pads
     verify_convtranspose((1, 1, 3, 3), (1, 2, 3, 3), (1, 2, 7, 3), [1, 2, 1, 2])
+    # Test undefined groups.
+    verify_convtranspose((1, 1, 3, 3), (1, 2, 3, 3), (1, 2, 7, 3), [1, 2, 1, 2], group=None)
 
     def repeat(N, D):
         return tuple([N for _ in range(D)])
@@ -2886,7 +2701,7 @@ def verify_pooling(x_shape, kernel_shape, strides, pads, out_shape, mode, auto_p
     )
 
     model = helper.make_model(graph, producer_name="pooling_test")
-    verify_with_ort(model, [x_shape], out_shape, use_vm=True, convert_to_static=True)
+    verify_with_ort(model, [x_shape], [out_shape], use_vm=True, convert_to_static=True)
 
 
 @tvm.testing.uses_gpu
@@ -2991,7 +2806,7 @@ def verify_mod(x_shape, y_shape, fmod, out_shape, dtype="float32"):
         outputs=[helper.make_tensor_value_info("z", onnx_dtype, list(out_shape))],
     )
     model = helper.make_model(graph, producer_name="mod_test")
-    verify_with_ort_with_inputs(model, [x_np, y_np], out_shape)
+    verify_with_ort_with_inputs(model, [x_np, y_np], [out_shape])
 
 
 @tvm.testing.uses_gpu
@@ -3044,10 +2859,7 @@ def verify_xor(x_shape, y_shape):
         outputs=[helper.make_tensor_value_info("z", onnx_dtype, list(out_shape))],
     )
     model = helper.make_model(graph, producer_name="xor_test")
-
-    for target, ctx in tvm.testing.enabled_targets():
-        tvm_out = get_tvm_output(model, [x_np, y_np], target, ctx, out_shape)
-        tvm.testing.assert_allclose(np_out, tvm_out, rtol=1e-5, atol=1e-5)
+    verify_with_ort_with_inputs(model, [x_np, y_np], [out_shape])
 
 
 @tvm.testing.uses_gpu
@@ -3084,7 +2896,7 @@ def verify_max_roi_pool(x_shape, rois_shape, pooled_shape, spatial_scale, out_sh
     )
 
     model = helper.make_model(graph, producer_name="pool_test")
-    verify_with_ort(model, [x_shape, rois_shape], out_shape)
+    verify_with_ort(model, [x_shape, rois_shape], [out_shape])
 
 
 @tvm.testing.uses_gpu
@@ -3136,7 +2948,7 @@ def verify_lppool(x_shape, kernel_shape, p, strides, pads, out_shape, auto_pad="
     )
 
     model = helper.make_model(graph, producer_name="lppool_test")
-    verify_with_ort(model, [x_shape], out_shape, use_vm=True, convert_to_static=True)
+    verify_with_ort(model, [x_shape], [out_shape], use_vm=True, convert_to_static=True)
 
 
 @tvm.testing.uses_gpu
@@ -3328,18 +3140,7 @@ def verify_rnn(
 
     model = helper.make_model(graph, producer_name="rnn_test")
 
-    for target, ctx in tvm.testing.enabled_targets():
-        onnx_out = get_onnxruntime_output(model, input_values, "float32")
-        tvm_out = get_tvm_output(
-            model,
-            input_values,
-            target,
-            ctx,
-            output_shapes,
-            output_dtype=["float32"] * len(output_shapes),
-        )
-        for o_out, t_out in zip(onnx_out, tvm_out):
-            tvm.testing.assert_allclose(o_out, t_out, rtol=5e-3, atol=5e-3)
+    verify_with_ort_with_inputs(model, input_values, output_shapes, atol=1e-2, rtol=1e-2)
 
 
 @tvm.testing.uses_gpu
@@ -3544,19 +3345,31 @@ def verify(ishape, oshape, scales, mode, coord_trans):
 
         model = helper.make_model(graph, producer_name="resize_test")
 
-        verify_with_ort(model, [ishape], oshape, use_vm=True, opset=11, freeze_params=True)
+        verify_with_ort(model, [ishape], [oshape], use_vm=True, opset=11, freeze_params=True)
 
     # upsampling
     verify([1, 16, 32, 32], [1, 16, 64, 64], [], "nearest", "asymmetric")
+    verify([1, 16, 32, 32], [1, 16, 64, 64], [], "linear", "asymmetric")
+    verify([1, 16, 32, 32], [1, 16, 64, 64], [], "nearest", "align_corners")
     verify([1, 16, 32, 32], [1, 16, 64, 64], [], "linear", "align_corners")
+    verify([1, 16, 32, 32], [1, 16, 64, 64], [], "nearest", "half_pixel")
     verify([1, 16, 32, 32], [1, 16, 64, 64], [], "linear", "half_pixel")
+
     # downsampling
     verify([1, 16, 32, 32], [1, 16, 16, 16], [], "nearest", "asymmetric")
+    verify([1, 16, 32, 32], [1, 16, 16, 16], [], "linear", "asymmetric")
+    verify([1, 16, 32, 32], [1, 16, 16, 16], [], "nearest", "align_corners")
     verify([1, 16, 32, 32], [1, 16, 16, 16], [], "linear", "align_corners")
+    verify([1, 16, 32, 32], [1, 16, 16, 16], [], "nearest", "half_pixel")
     verify([1, 16, 32, 32], [1, 16, 16, 16], [], "linear", "half_pixel")
+
     # scales are specified instead of sizes
     verify([1, 16, 32, 32], [], [1, 1, 2, 2], "nearest", "asymmetric")
+    verify([1, 16, 32, 32], [], [1, 1, 2, 2], "linear", "asymmetric")
+    verify([1, 16, 32, 32], [], [1, 1, 2, 2], "nearest", "align_corners")
+    verify([1, 16, 32, 32], [], [1, 1, 2, 2], "linear", "align_corners")
     verify([1, 16, 32, 32], [], [1, 1, 0.5, 0.5], "linear", "half_pixel")
+    verify([1, 16, 32, 32], [], [1, 1, 0.5, 0.5], "nearest", "half_pixel")
 
     def verify_opset_10(ishape, scales, mode):
         nodes = [
@@ -3581,9 +3394,7 @@ def verify_opset_10(ishape, scales, mode):
         )
 
         model = helper.make_model(graph, producer_name="resize_test")
-        model.opset_import[0].version = 10
-
-        verify_with_ort(model, [ishape], oshape, use_vm=True, freeze_params=True)
+        verify_with_ort(model, [ishape], [oshape], use_vm=True, freeze_params=True, opset=10)
 
     verify_opset_10([1, 16, 32, 32], [1, 1, 2, 2], "nearest")
     verify_opset_10([1, 16, 32, 32], [1, 1, 0.5, 0.5], "linear")
@@ -3652,11 +3463,7 @@ def verify_topk(input_dims, K, axis=-1):
         model = helper.make_model(graph, producer_name="topk_test")
 
         indata = np.random.uniform(-10, 10, input_dims).astype(np.float32)
-        onnx_out = get_onnxruntime_output(model, [indata, np.array([K])])
-
-        for target, ctx in [("llvm", tvm.cpu())]:
-            tvm_out = get_tvm_output_with_vm(model, [indata, np.array(K)], target, ctx)
-            tvm.testing.assert_allclose(onnx_out, tvm_out, rtol=1e-05, atol=1e-05)
+        verify_with_ort_with_inputs(model, [indata, np.array([K])], use_vm=True)
 
     for n in [12, 32]:
         for shape in [[n], [n, n], [n, n, n]]:
@@ -3671,7 +3478,13 @@ def verify_topk(input_dims, K, axis=-1):
 @tvm.testing.uses_gpu
 def test_roi_align():
     def verify_roi_align(
-        input_dims, num_roi, output_height, output_width, sampling_ratio=0, spatial_scale=1.0
+        input_dims,
+        num_roi,
+        output_height,
+        output_width,
+        sampling_ratio=0,
+        spatial_scale=1.0,
+        mode="avg",
     ):
         output_dims = [num_roi, input_dims[1], output_height, output_width]
 
@@ -3679,7 +3492,7 @@ def verify_roi_align(
             "RoiAlign",
             inputs=["X", "rois", "batch_indicies"],
             outputs=["Y"],
-            mode="avg",
+            mode=mode,
             output_height=output_height,
             output_width=output_width,
             sampling_ratio=sampling_ratio,
@@ -3709,7 +3522,9 @@ def verify_roi_align(
         np_rois = np.random.uniform(size=[num_roi, 4]).astype("float32") * input_dims[2]
         np_batch_indicies = np.random.randint(low=0, high=input_dims[0], size=num_roi)
 
-        verify_with_ort_with_inputs(model, [np_data, np_rois, np_batch_indicies], output_dims)
+        verify_with_ort_with_inputs(
+            model, [np_data, np_rois, np_batch_indicies], out_shape=[output_dims]
+        )
 
     verify_roi_align((1, 4, 16, 16), 32, 7, 7, sampling_ratio=0, spatial_scale=1.0)
     verify_roi_align((4, 4, 16, 32), 32, 7, 7, sampling_ratio=0, spatial_scale=1.0)
@@ -3722,11 +3537,13 @@ def verify_roi_align(
     verify_roi_align((5, 4, 16, 14), 32, 7, 7, sampling_ratio=1, spatial_scale=1.0)
     verify_roi_align((1, 4, 16, 16), 32, 7, 7, sampling_ratio=2, spatial_scale=1.0)
 
+    # ONNX implementation of roi_align with max mode is incorrect, so we don't compare outputs here.
+
 
 # @tvm.testing.uses_gpu
 def test_non_max_suppression():
     def verify_nms(
-        boxes, scores, max_ouput_boxes_per_class, iou_threshold, score_threshold, output_dims
+        boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, output_dims
     ):
         input_names = ["boxes", "scores", "max_output_boxes_per_class", "iou_threshold"]
         input_nodes = [
@@ -3892,23 +3709,18 @@ def verify_cond_loop():
     trip_count = np.array(40).astype(np.int64)
     cond = np.array(1).astype(np.bool)
     input_vals = [trip_count, cond, y]
-    onnx_out = get_onnxruntime_output(loop_model, input_vals)
-
-    for target, ctx in [("llvm", tvm.cpu())]:
-        tvm_out = get_tvm_output_with_vm(loop_model, input_vals, target, ctx, freeze_params=True)
-        for i in range(len(tvm_out)):
-            tvm.testing.assert_allclose(onnx_out[i], tvm_out[i], rtol=1e-05, atol=1e-05)
+    verify_with_ort_with_inputs(loop_model, input_vals, use_vm=True, freeze_params=True)
 
 
 def verify_count_loop():
-    y_in = helper.make_tensor_value_info("y_in", TensorProto.FLOAT, [1])
-    y_out = helper.make_tensor_value_info("y_out", TensorProto.FLOAT, [1])
-    scan_out = helper.make_tensor_value_info("scan_out", TensorProto.FLOAT, [1])
+    y_in = helper.make_tensor_value_info("y_in", TensorProto.FLOAT, [])
+    y_out = helper.make_tensor_value_info("y_out", TensorProto.FLOAT, [])
+    scan_out = helper.make_tensor_value_info("scan_out", TensorProto.FLOAT, [])
     cond_in = helper.make_tensor_value_info("cond_in", TensorProto.BOOL, [])
     cond_out = helper.make_tensor_value_info("cond_out", TensorProto.BOOL, [])
     iter_count = helper.make_tensor_value_info("iter_count", TensorProto.INT64, [])
 
-    y = np.array([-2]).astype(np.float32)
+    y = np.array(-2).astype(np.float32)
 
     iter_cast_node = helper.make_node(
         "Cast", inputs=["iter_count"], outputs=["iter_cast"], to=onnx.TensorProto.FLOAT
@@ -3940,11 +3752,11 @@ def verify_count_loop():
         inputs=[
             onnx.helper.make_tensor_value_info("trip_count", onnx.TensorProto.INT64, []),
             onnx.helper.make_tensor_value_info("cond", onnx.TensorProto.BOOL, []),
-            onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [1]),
+            onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, []),
         ],
         outputs=[
-            onnx.helper.make_tensor_value_info("res_y", onnx.TensorProto.FLOAT, [1]),
-            onnx.helper.make_tensor_value_info("res_scan", onnx.TensorProto.FLOAT, [5, 1]),
+            onnx.helper.make_tensor_value_info("res_y", onnx.TensorProto.FLOAT, []),
+            onnx.helper.make_tensor_value_info("res_scan", onnx.TensorProto.FLOAT, [5]),
         ],
     )
     loop_model = onnx.helper.make_model(loop_graph)
@@ -3952,23 +3764,75 @@ def verify_count_loop():
     trip_count = np.array(5).astype(np.int64)
     cond = np.array(1).astype(np.bool)
     input_vals = [trip_count, cond, y]
-    onnx_out = get_onnxruntime_output(loop_model, input_vals)
+    verify_with_ort_with_inputs(loop_model, input_vals, use_vm=True, freeze_params=True)
 
-    for target, ctx in [("llvm", tvm.cpu())]:
-        tvm_out = get_tvm_output_with_vm(loop_model, input_vals, target, ctx, freeze_params=True)
-        for i in range(len(tvm_out)):
-            tvm.testing.assert_allclose(onnx_out[i], tvm_out[i], rtol=1e-05, atol=1e-05)
+
+def verify_tensor_loop():
+    y_in = helper.make_tensor_value_info("y_in", TensorProto.FLOAT, [3, 3, 3, 3])
+    y_out = helper.make_tensor_value_info("y_out", TensorProto.FLOAT, [3, 3, 3, 3])
+    scan_out = helper.make_tensor_value_info("scan_out", TensorProto.FLOAT, [3, 3, 3, 3])
+    cond_in = helper.make_tensor_value_info("cond_in", TensorProto.BOOL, [])
+    cond_out = helper.make_tensor_value_info("cond_out", TensorProto.BOOL, [])
+    iter_count = helper.make_tensor_value_info("iter_count", TensorProto.INT64, [])
+
+    y = np.random.normal(size=[3, 3, 3, 3]).astype(np.float32)
+
+    iter_cast_node = helper.make_node(
+        "Cast", inputs=["iter_count"], outputs=["iter_cast"], to=onnx.TensorProto.FLOAT
+    )
+
+    y_add_node = helper.make_node("Add", inputs=["y_in", "iter_cast"], outputs=["y_out"])
+
+    identity_node = helper.make_node("Identity", inputs=["cond_in"], outputs=["cond_out"])
+
+    scan_identity_node = helper.make_node("Identity", inputs=["y_out"], outputs=["scan_out"])
+
+    loop_body = helper.make_graph(
+        [identity_node, iter_cast_node, y_add_node, scan_identity_node],
+        "loop_body",
+        [iter_count, cond_in, y_in],
+        [cond_out, y_out, scan_out],
+    )
+
+    loop_node = helper.make_node(
+        "Loop", inputs=["trip_count", "cond", "y"], outputs=["res_y", "res_scan"], body=loop_body
+    )
+
+    trip_count = np.array(5).astype(np.int64)
+    cond = np.array(1).astype(np.bool)
+    loop_graph = onnx.helper.make_graph(
+        [loop_node],
+        "loop_outer",
+        inputs=[
+            onnx.helper.make_tensor_value_info("trip_count", onnx.TensorProto.INT64, []),
+            onnx.helper.make_tensor_value_info("cond", onnx.TensorProto.BOOL, []),
+            onnx.helper.make_tensor_value_info("y", onnx.TensorProto.FLOAT, [3, 3, 3, 3]),
+        ],
+        outputs=[
+            onnx.helper.make_tensor_value_info("res_y", onnx.TensorProto.FLOAT, [3, 3, 3, 3]),
+            onnx.helper.make_tensor_value_info("res_scan", onnx.TensorProto.FLOAT, [5, 3, 3, 3, 3]),
+        ],
+    )
+    loop_model = onnx.helper.make_model(loop_graph)
+
+    trip_count = np.array(5).astype(np.int64)
+    cond = np.array(1).astype(np.bool)
+    input_vals = [trip_count, cond, y]
+    verify_with_ort_with_inputs(
+        loop_model, input_vals, use_vm=True, freeze_params=True, convert_to_static=True
+    )
 
 
 def test_loop():
     # Test a loop that exits once a condition is met.
     verify_cond_loop()
-    # Test a loop that exits after a fixed number of iterations.
+    # Test a loop that exits after a fixed number of iterations with scalar outputs.
     verify_count_loop()
+    # Test a loop that uses an array output.
+    verify_tensor_loop()
 
 
-@tvm.testing.uses_gpu
-def test_if():
+def verify_if(cond_array):
     # Given a bool scalar input cond.
     # return constant tensor x if cond is True, otherwise return constant tensor y.
     then_out = onnx.helper.make_tensor_value_info("then_out", onnx.TensorProto.FLOAT, [5])
@@ -3978,11 +3842,11 @@ def test_if():
     y = np.array([5, 4, 3, 2, 1]).astype(np.float32)
 
     then_const_node = onnx.helper.make_node(
-        "Constant", inputs=[], outputs=["then_out"], value=onnx.numpy_helper.from_array(x)
+        "Constant", inputs=[], outputs=["then_out"], value=numpy_helper.from_array(x)
     )
 
     else_const_node = onnx.helper.make_node(
-        "Constant", inputs=[], outputs=["else_out"], value=onnx.numpy_helper.from_array(y)
+        "Constant", inputs=[], outputs=["else_out"], value=numpy_helper.from_array(y)
     )
 
     then_body = onnx.helper.make_graph([then_const_node], "then_body", [], [then_out])
@@ -4005,15 +3869,27 @@ def test_if():
     )
 
     if_model = onnx.helper.make_model(if_graph)
-    cond = np.array(1).astype("bool")
+    if cond_array:
+        cond = np.array([1]).astype("bool")
+    else:
+        cond = np.array(1).astype("bool")
     correct_out = x if cond else y
 
+    # TODO(jwfromm): Onnxruntime 1.0.0 is buggy with If statements. Replace this with
+    # verify_with_ort once we update versions.
     for target, ctx in tvm.testing.enabled_targets():
         tvm_out = get_tvm_output_with_vm(if_model, [cond], target, ctx, freeze_params=True)
         for i in range(len(tvm_out)):
             tvm.testing.assert_allclose(correct_out[i], tvm_out[i], rtol=1e-05, atol=1e-05)
 
 
+@tvm.testing.uses_gpu
+def test_if():
+    # Confirm that if works with cond as an array or scalar.
+    verify_if(cond_array=False)
+    verify_if(cond_array=True)
+
+
 @tvm.testing.uses_gpu
 def test_size():
     def verify_size(indata):
@@ -4137,6 +4013,82 @@ def verify_softplus(indata):
     verify_softplus(input_data)
 
 
+def test_cumsum():
+    def verify_cumsum(indata, axis, exclusive=0, reverse=0, type="float32"):
+        cumsum_node = onnx.helper.make_node(
+            "CumSum",
+            inputs=["X", "axis"],
+            outputs=["Y"],
+        )
+        if exclusive != 0:
+            exclusive_attr = helper.make_attribute("exclusive", exclusive)
+            cumsum_node.attribute.append(exclusive_attr)
+        if reverse != 0:
+            reverse_attr = helper.make_attribute("reverse", reverse)
+            cumsum_node.attribute.append(reverse_attr)
+        nodes = [
+            make_constant_node("axis", onnx.TensorProto.INT32, [1], [axis]),
+            cumsum_node,
+        ]
+        if type == "float32":
+            tensor_type = TensorProto.FLOAT
+        else:
+            tensor_type = TensorProto.INT32
+            type = "int32"
+
+        graph = helper.make_graph(
+            nodes,
+            "cumsum_test",
+            inputs=[
+                helper.make_tensor_value_info("X", tensor_type, list(indata.shape)),
+            ],
+            outputs=[helper.make_tensor_value_info("Y", tensor_type, list(indata.shape))],
+        )
+
+        model = helper.make_model(graph, producer_name="cumsum_test")
+
+        verify_with_ort_with_inputs(model, [indata], dtype=type, use_vm=True, opset=11)
+
+    data = (
+        np.array(
+            [
+                1.0,
+                2.0,
+                3.0,
+                4.0,
+                5.0,
+                6.0,
+                7.0,
+                8.0,
+                9.0,
+                10.0,
+                11.0,
+                12.0,
+            ]
+        )
+        .astype(np.float32)
+        .reshape((3, 4))
+    )
+
+    verify_cumsum(data, 0)
+    verify_cumsum(data, 1)
+    verify_cumsum(data, 0, 1, 0)
+    verify_cumsum(data, 1, 1, 0)
+    verify_cumsum(data, 0, 0, 1)
+    verify_cumsum(data, 1, 0, 1)
+    verify_cumsum(data, 1, 1, 1)
+    data = np.random.randn(1, 32, 32, 3).astype("float32")
+    verify_cumsum(data, 1)
+    data = np.random.randn(1, 32, 32, 3).astype("int32")
+    verify_cumsum(data, 0, type="int32")
+    verify_cumsum(data, 1, type="int32")
+    verify_cumsum(data, 0, 1, 0, type="int32")
+    verify_cumsum(data, 1, 1, 0, type="int32")
+    verify_cumsum(data, 0, 0, 1, type="int32")
+    verify_cumsum(data, 1, 0, 1, type="int32")
+    verify_cumsum(data, 1, 1, 1, type="int32")
+
+
 if __name__ == "__main__":
     test_flatten()
     test_reshape()
@@ -4154,6 +4106,7 @@ def verify_softplus(indata):
     test_clip()
     test_clip_min_max_as_inputs()
     test_onehot()
+    test_gemm()
     test_matmul()
     test_gather()
     test_gatherelements()
@@ -4173,15 +4126,12 @@ def verify_softplus(indata):
     test_pad()
     test_split()
     test_binary_ops()
-    test_single_ops()
+    test_unary_ops()
     test_leaky_relu()
     test_elu()
     test_selu()
     test_prelu()
     test_ThresholdedRelu()
-    test_ScaledTanh()
-    test_ParametricSoftplus()
-    test_Scale()
     test_LogSoftmax()
     test_resnet()
     test_inception()
@@ -4216,3 +4166,4 @@ def verify_softplus(indata):
     test_size()
     test_maxunpool()
     test_softplus()
+    test_cumsum()
diff --git a/tests/python/frontend/pytorch/qnn_test.py b/tests/python/frontend/pytorch/qnn_test.py
index 07e52b7079e8..29c69abba542 100644
--- a/tests/python/frontend/pytorch/qnn_test.py
+++ b/tests/python/frontend/pytorch/qnn_test.py
@@ -41,7 +41,6 @@ def torch_version_check():
 
 
 def get_tvm_runtime(script_module, input_name, ishape):
-
     input_shapes = [(input_name, ishape)]
     mod, params = relay.frontend.from_pytorch(script_module, input_shapes)
 
@@ -125,43 +124,40 @@ def fuse_model(self):
 
 # Mobilenet V3 related modules
 class Hsigmoid(nn.Module):
-    def __init__(self, inplace=True, add_stub=False):
+    def __init__(self, add_stub=False):
         super().__init__()
-        self.float_op = nn.quantized.FloatFunctional()
-        self.relu6 = nn.ReLU6(inplace=inplace)
         self.quant = QuantStub()
         self.dequant = DeQuantStub()
         self.add_stub = add_stub
+        self.hsigmoid = nn.Hardsigmoid()
 
     def forward(self, x):
         if self.add_stub:
             x = self.quant(x)
-        relu6 = self.relu6(self.float_op.add_scalar(x, 3.0))
-        mul = self.float_op.mul_scalar(relu6, 1 / 6.0)
+        x = self.hsigmoid(x)
         if self.add_stub:
-            mul = self.dequant(mul)
-        return mul
+            x = self.dequant(x)
+        return x
 
     def fuse_model(self):
         pass
 
 
 class Hswish(nn.Module):
-    def __init__(self, inplace=True, add_stub=False):
-        super(Hswish, self).__init__()
-        self.float_op = nn.quantized.FloatFunctional()
-        self.hsigmoid = Hsigmoid(inplace, add_stub=False)
+    def __init__(self, add_stub=False):
+        super().__init__()
         self.quant = QuantStub()
         self.dequant = DeQuantStub()
         self.add_stub = add_stub
+        self.hswish = nn.Hardswish()
 
     def forward(self, x):
         if self.add_stub:
             x = self.quant(x)
-        mul = self.float_op.mul(x, self.hsigmoid(x))
+        x = self.hswish(x)
         if self.add_stub:
-            mul = self.dequant(mul)
-        return mul
+            x = self.dequant(x)
+        return x
 
     def fuse_model(self):
         pass
@@ -274,18 +270,12 @@ def test_quantized_modules():
             ("conv_bn_relu" + postfix, imagenet_ishape, ConvBn(with_relu=True), per_channel),
             ("linear" + postfix, (16, 16), Linear(), per_channel),
             ("linear_relu" + postfix, (16, 16), Linear(with_relu=True), per_channel),
-        ]
-
-    if torch_version_check():
-        qmodules += [
             ("hsigmoid", imagenet_ishape, Hsigmoid(add_stub=True), False),
             ("hswish", imagenet_ishape, Hswish(add_stub=True), False),
             ("semodule", (1, 16, 64, 64), SqueezeExcite(16, add_stub=True), False),
             ("semodule, per_channel", (1, 16, 64, 64), SqueezeExcite(16, add_stub=True), True),
             ("mul_scalar negative", imagenet_ishape, MulScalarNegative(), False),
         ]
-    else:
-        print("Skipping tests that require torch > 1.4")
 
     for (module_name, ishape, raw_module, per_channel) in qmodules:
         raw_module.eval()
@@ -372,6 +362,13 @@ def get_imagenet_input():
             # ("googlenet", qgooglenet(pretrained=True), per_channel),
         ]
 
+    if is_version_greater_than("1.7.1"):
+        from torchvision.models.quantization import mobilenet_v3_large as qmobilenet_v3_large
+
+        qmodels.append(
+            ("mobilenet_v3_large", qmobilenet_v3_large(pretrained=True, quantize=True).eval(), True)
+        )
+
     results = []
 
     for (model_name, raw_model, per_channel) in qmodels:
@@ -385,7 +382,10 @@ def get_imagenet_input():
         inp = get_imagenet_input()
         pt_inp = torch.from_numpy(inp)
 
-        quantize_model(raw_model, pt_inp, per_channel=per_channel)
+        if "mobilenet_v3_large" not in model_name:
+            # mv3 was qat-ed, quantize=True option above makes it already quantized
+            quantize_model(raw_model, pt_inp, per_channel=per_channel)
+
         script_module = torch.jit.trace(raw_model, pt_inp).eval()
 
         with torch.no_grad():
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 04f08b903bf1..83c1698799c7 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -24,6 +24,7 @@
 import torch
 import torchvision
 from torch.nn import Module
+from torch.nn import functional as F
 import tvm
 from tvm import relay
 from tvm.contrib import graph_runtime
@@ -181,14 +182,14 @@ def verify_model(model_name, input_data=[], custom_convert_map={}, rtol=1e-5, at
         baseline_input = [inp.cuda() for inp in baseline_input]
 
     with torch.no_grad():
-        baseline_outputs = baseline_model(*baseline_input)
+        baseline_outputs = baseline_model(*[input.clone() for input in baseline_input])
 
     if isinstance(baseline_outputs, tuple):
         baseline_outputs = tuple(out.cpu().numpy() for out in baseline_outputs)
     else:
         baseline_outputs = (baseline_outputs.cpu().numpy(),)
 
-    trace = torch.jit.trace(baseline_model, baseline_input)
+    trace = torch.jit.trace(baseline_model, [input.clone() for input in baseline_input])
     if isinstance(baseline_model, torch.nn.Module):
         trace = trace.float().eval()
 
@@ -200,7 +201,9 @@ def verify_model(model_name, input_data=[], custom_convert_map={}, rtol=1e-5, at
     input_names = ["input{}".format(idx) for idx, inp in enumerate(baseline_input)]
     input_shapes = list(zip(input_names, [inp.shape for inp in baseline_input]))
     mod, params = relay.frontend.from_pytorch(trace, input_shapes, custom_convert_map)
-    compiled_input = dict(zip(input_names, [inp.cpu().numpy() for inp in baseline_input]))
+    for arg in mod["main"].params[: len(input_names)]:
+        assert arg.name_hint in input_names
+    compiled_input = dict(zip(input_names, [inp.clone().cpu().numpy() for inp in baseline_input]))
 
     with tvm.transform.PassContext(opt_level=3):
         for target, ctx in tvm.testing.enabled_targets():
@@ -216,7 +219,6 @@ def verify_model(model_name, input_data=[], custom_convert_map={}, rtol=1e-5, at
 
                 assert_shapes_match(baseline_output, compiled_output)
                 tvm.testing.assert_allclose(baseline_output, compiled_output, rtol=rtol, atol=atol)
-
     del model_name
     del baseline_model
     torch.cuda.empty_cache()
@@ -447,8 +449,16 @@ class Unsqueeze1(Module):
         def forward(self, *args):
             return args[0].unsqueeze(2)
 
+    class Unsqueeze2(Module):
+        def forward(self, *args):
+            _ = args[0].unsqueeze_(2)
+            # Check whether operations after inplace unsqueeze works as expected
+            y = args[0].squeeze(2)
+            return torch.add(y, y)
+
     input_data = torch.rand(input_shape).float()
     verify_model(Unsqueeze1().float().eval(), input_data=input_data)
+    verify_model(Unsqueeze2().float().eval(), input_data=input_data)
 
 
 @tvm.testing.uses_gpu
@@ -729,7 +739,16 @@ def forward(self, *args):
             output, indices = self.pool(args[0])
             return output
 
+    class MaxPool2DWithIntStrides(Module):
+        def forward(self, *args):
+            # Makes kernel_size and strides a Relay expr to test converting back to int
+            x_shape = args[0].shape
+            kernel_size = [torch.tensor(x_shape[1]).int(), torch.tensor(x_shape[1]).int()]
+            strides = [torch.tensor(x_shape[0]).int(), torch.tensor(x_shape[0]).int()]
+            return torch.nn.functional.max_pool2d(args[0], kernel_size=[4, 4], stride=strides)
+
     verify_model(MaxPool2DWithIndices().float().eval(), input_data=input_data)
+    verify_model(MaxPool2DWithIntStrides().float().eval(), input_data=input_data)
 
 
 @tvm.testing.uses_gpu
@@ -916,6 +935,85 @@ def test_forward_conv_transpose():
     verify_model(torch.nn.ConvTranspose1d(3, 12, 3, bias=False), input_data=conv1d_input_data)
 
 
+def test_forward_deform_conv():
+    torch.set_grad_enabled(False)
+
+    def test_run(
+        batch_size,
+        in_channels,
+        out_channels,
+        in_height,
+        in_width,
+        out_height,
+        out_width,
+        offset_groups,
+        kh,
+        kw,
+        groups,
+    ):
+        input_shape = [batch_size, in_channels, in_height, in_width]
+        offset_shape = [batch_size, 2 * offset_groups * kh * kw, out_height, out_width]
+        weight_shape = [out_channels, in_channels // groups, kh, kw]
+        input_data = torch.rand(input_shape)
+        offset_data = torch.rand(offset_shape)
+        weight_data = torch.rand(weight_shape)
+
+        class DeformConv2D(Module):
+            def forward(self, *args):
+                return torchvision.ops.deform_conv2d(args[0], args[1], args[2])
+
+        verify_model(
+            DeformConv2D().float().eval(),
+            input_data=[input_data, offset_data, weight_data],
+            rtol=1e-4,
+            atol=1e-4,
+        )
+
+    batch_size = 4
+    in_channels, out_channels = 4, 6
+    in_height, in_width = 10, 10
+    out_height, out_width = 8, 8
+    offset_groups = 2
+    kh, kw = 3, 3
+    groups = 1
+
+    test_run(
+        batch_size,
+        in_channels,
+        out_channels,
+        in_height,
+        in_width,
+        out_height,
+        out_width,
+        offset_groups,
+        kh,
+        kw,
+        groups,
+    )
+
+    batch_size = 5
+    in_channels, out_channels = 4, 6
+    in_height, in_width = 10, 10
+    out_height, out_width = 8, 8
+    offset_groups = 1
+    kh, kw = 3, 3
+    groups = 1
+
+    test_run(
+        batch_size,
+        in_channels,
+        out_channels,
+        in_height,
+        in_width,
+        out_height,
+        out_width,
+        offset_groups,
+        kh,
+        kw,
+        groups,
+    )
+
+
 @tvm.testing.uses_gpu
 def test_forward_threshold():
     torch.set_grad_enabled(False)
@@ -1139,7 +1237,7 @@ def forward(self, *args):
 @tvm.testing.uses_gpu
 def test_forward_select():
     torch.set_grad_enabled(False)
-    input_shape = [1, 3, 10, 10]
+    input_shape = [5, 3, 10, 10]
 
     class Select1(Module):
         def forward(self, *args):
@@ -1159,6 +1257,9 @@ def forward(self, index):
     input_data = torch.rand(input_shape).float()
     verify_model(Select1().float().eval(), input_data=input_data)
 
+    # test negative indexing
+    verify_model(lambda x: x[-1], input_data=input_data)
+
     x = torch.randn(3, 4)
     indices = torch.tensor([0, 2])
     verify_model(IndexedSelect(x, 0).eval(), input_data=indices)
@@ -1361,6 +1462,39 @@ def forward(self, *args):
     assert not any([op.name == "multiply" for op in list_ops(mod["main"])])
 
 
+@tvm.testing.uses_gpu
+def test_forward_linear():
+    torch.set_grad_enabled(False)
+
+    class Linear(Module):
+        def forward(self, input, weight, bias):
+            return F.linear(input, weight, bias)
+
+    class LinearNoBias(Module):
+        def forward(self, input, weight):
+            return F.linear(input, weight)
+
+    input2d = torch.rand([2, 2]).float()
+    weight1d = torch.rand([2]).float()
+    weight2d = torch.rand([2, 2]).float()
+    bias1d = torch.rand([2]).float()
+    bias2d = torch.rand([2, 2]).float()
+    # 2D input, 2D weight, 1D bias
+    verify_model(Linear(), input_data=[input2d, weight2d, bias1d])
+    # 2D input, 2D weight, 2D bias
+    verify_model(Linear(), input_data=[input2d, weight2d, bias2d])
+    # 2D input, 2D weight, no bias
+    verify_model(LinearNoBias(), input_data=[input2d, weight2d])
+    # 2D input, 1D weight, 1D bias is not supported by torch.linear()
+    # 2D input, 1D weight, no bias
+    verify_model(LinearNoBias(), input_data=[input2d, weight1d])
+    # TODO: Add the following cases when matmul(1D, _) is supported by TVM
+    # 1D input, 2D weight, 1D bias
+    # 1D input, 2D weight, no bias
+    # 1D input, 1D weight, scalar bias
+    # 1D input, 1D weight, no bias
+
+
 @tvm.testing.uses_gpu
 def test_forward_dropout():
     torch.set_grad_enabled(False)
@@ -1399,6 +1533,10 @@ class SliceWithStride2(torch.nn.Module):
         def forward(self, x):
             return x[0::2, 0::2] + x[1::2, 1::2]
 
+    class DynamicLengthSlice(torch.nn.Module):
+        def forward(self, values, length):
+            return values[0:length]
+
     input_data = torch.rand(input_shape).float()
     verify_model(Slice1(), input_data=input_data)
     verify_model(Slice2(), input_data=input_data)
@@ -1406,6 +1544,36 @@ def forward(self, x):
     verify_model(SliceWithStride(), input_data=torch.randn(1, 4))
     verify_model(SliceWithStride2(), input_data=torch.randn(4, 4))
 
+    inp = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+    slice_len = torch.tensor(2)
+    targets = ["llvm", "cuda"]
+    verify_trace_model(DynamicLengthSlice(), [inp, slice_len], targets)
+
+
+@tvm.testing.uses_gpu
+def test_forward_narrow():
+    torch.set_grad_enabled(False)
+    input_shape = [3, 3]
+
+    class Narrow1(Module):
+        def forward(self, *args):
+            return torch.narrow(args[0], 0, 0, 2)
+
+    class Narrow2(Module):
+        def forward(self, *args):
+            return torch.narrow(args[0], 1, 1, 2)
+
+    class Narrow3(Module):
+        def forward(self, *args):
+            begin = torch.tensor(2) - torch.tensor(1)
+            length = torch.tensor(1) * torch.tensor(2)
+            return torch.narrow(args[0], 1, begin, length)
+
+    input_data = torch.rand(input_shape).float()
+    verify_model(Narrow1(), input_data=input_data)
+    verify_model(Narrow2(), input_data=input_data)
+    verify_model(Narrow3(), input_data=input_data)
+
 
 @tvm.testing.uses_gpu
 def test_forward_mean():
@@ -1689,7 +1857,7 @@ def test_forward_roi_align():
     """ROI align"""
     torch.set_grad_enabled(False)
 
-    class ROIAlgin(Module):
+    class ROIAlign(Module):
         def __init__(self, output_sizes, spatial_scale=1.0, sampling_ratio=-1):
             super().__init__()
             self.spatial_scale = spatial_scale
@@ -1710,9 +1878,9 @@ def forward(self, *args):
     in_batch = torch.zeros((35, 1), dtype=torch.float)
     in_boxes = torch.cat([in_batch, in_boxes], dim=1)
 
-    verify_model(ROIAlgin(7), [in_data, in_boxes])
-    verify_model(ROIAlgin((10, 10), 0.7, 5), [in_data, in_boxes])
-    verify_model(ROIAlgin(15, 0.9, 3), [in_data, in_boxes])
+    verify_model(ROIAlign(7), [in_data, in_boxes])
+    verify_model(ROIAlign((10, 10), 0.7, 5), [in_data, in_boxes])
+    verify_model(ROIAlign(15, 0.9, 3), [in_data, in_boxes])
 
 
 @tvm.testing.uses_gpu
@@ -1859,7 +2027,7 @@ def _impl(inputs, input_types):
 
 
 @tvm.testing.uses_gpu
-def test_segmentaton_models():
+def test_segmentation_models():
     class SegmentationModelWrapper(Module):
         def __init__(self, model):
             super().__init__()
@@ -1975,7 +2143,12 @@ def verify_model_vm(input_model, ishapes, idtype=None, idata=None, targets=["llv
             pt_result = input_model(*input_data)
 
         # Verify the accuracy
-        if not isinstance(pt_result, torch.Tensor):
+        if isinstance(pt_result, tuple):
+            # handle multiple outputs
+            for i in range(len(pt_result)):
+                tvm_res = vm_res[i].asnumpy()
+                tvm.testing.assert_allclose(tvm_res, pt_result[i].numpy(), rtol=1e-5, atol=1e-5)
+        elif not isinstance(pt_result, torch.Tensor):
             tvm_res = vm_res.asnumpy().item()
             assert pt_result == tvm_res
         else:
@@ -2645,6 +2818,8 @@ def forward(self, *args):
     verify_model(Take1().float().eval(), input_data=input_data)
     indices = torch.tensor([[0, 0], [1, 0]])
     verify_model(Take2().float().eval(), input_data=[input_data, indices])
+    indices = torch.tensor([0, -1])
+    verify_model(Take2().float().eval(), input_data=[input_data, indices])
 
 
 @tvm.testing.uses_gpu
@@ -3236,6 +3411,38 @@ def test_fn_scatter_add(dim):
     verify_trace_model(test_fn_scatter_add(1), [in_data, in_index, in_src], targets)
 
 
+def test_forward_index_put():
+    # torch.index_put for 2D tensor and default accumulate (False)
+    def test_fn_index_put2():
+        return lambda data, xidx, yidx, values: torch.index_put(
+            data, indices=[xidx, yidx], values=values
+        )
+
+    # torch.index_put for 3D tensor and accumulate=True
+    def test_fn_index_put3a():
+        return lambda data, xidx, yidx, zidx, values: torch.index_put(
+            data, indices=[xidx, yidx, zidx], values=values, accumulate=True
+        )
+
+    shape = (3, 5)
+    in_data = torch.zeros(shape)
+    xidx = torch.tensor([0, 1, 2, 2])
+    yidx = torch.tensor([0, 1, 3, 4])
+    values = torch.tensor([2.0, 4.0, 7.0, 9.0])
+
+    targets = ["llvm", "cuda"]
+    verify_trace_model(test_fn_index_put2(), [in_data, xidx, yidx, values], targets)
+
+    shape = (3, 5, 3)
+    in_data = torch.zeros(shape)
+    xidx = torch.tensor([0, 1, 2, 2, 0])
+    yidx = torch.tensor([0, 1, 3, 4, 0])
+    zidx = torch.tensor([0, 1, 1, 2, 0])
+    values = torch.tensor([2.0, 4.0, 7.0, 9.0, 1.0])
+
+    verify_trace_model(test_fn_index_put3a(), [in_data, xidx, yidx, zidx, values], targets)
+
+
 def test_numel():
     class Numel(Module):
         def forward(self, data):
@@ -3437,6 +3644,124 @@ def test_fn(x, weights=None):
     verify_trace_model(test_fn, [inp, weights], targets)
 
 
+def test_hard_swish():
+    examples = [torch.rand(8).float(), torch.rand(8, 10).float(), torch.rand(1, 1, 10).float()]
+    for input in examples:
+        verify_model(torch.nn.Hardswish().eval(), input_data=input)
+        verify_model(torch.nn.Hardswish(inplace=True).eval(), input_data=input)
+
+
+def test_hard_sigmoid():
+    examples = [torch.rand(8).float(), torch.rand(8, 10).float(), torch.rand(1, 1, 10).float()]
+    for input in examples:
+        verify_model(torch.nn.Hardsigmoid().eval(), input_data=input)
+        verify_model(torch.nn.Hardsigmoid(inplace=True).eval(), input_data=input)
+
+
+def test_cumsum():
+    def test_fn(dim, dtype=None):
+        return lambda x: torch.cumsum(x, dim=dim, dtype=dtype)
+
+    inp = torch.randint(0, 100, (10000,), dtype=torch.int32)
+    verify_model(test_fn(0), [inp])
+    verify_model(test_fn(0), [inp.to(torch.int64)])
+    verify_model(test_fn(0, dtype=torch.int64), [inp.to(torch.int64)])
+
+    inp = torch.randn((100, 100), dtype=torch.float32)
+    verify_model(test_fn(dim=0, dtype=torch.float64), [inp])
+    verify_model(test_fn(dim=1), [inp])
+
+    inp = torch.randn((100, 100), dtype=torch.float32) > 0.5
+    verify_model(test_fn(dim=0, dtype=torch.int32), [inp])
+
+
+def test_masked_fill():
+    def test_fn(x, mask):
+        return torch.masked_fill(x, mask, 0.0)
+
+    inp = torch.randn(100, 100)
+    verify_model(test_fn, [inp, inp > 0.5])
+    verify_model(test_fn, [inp.to(torch.float64), inp > 0.5])
+
+
+def test_transformer():
+    model = torch.nn.Transformer(d_model=256, nhead=8, num_encoder_layers=6, num_decoder_layers=6)
+    model = model.eval()
+    src = torch.rand((10, 32, 256))
+    tgt = torch.rand((20, 32, 256))
+    verify_model(model.eval(), input_data=[src, tgt])
+
+
+def test_argsort():
+    def test_fn(dim, descending):
+        return lambda x: torch.argsort(x, dim=dim, descending=descending)
+
+    inp = torch.randn(100)
+    verify_model(test_fn(0, True), [inp])
+    verify_model(test_fn(0, False), [inp])
+
+    inp = torch.randn(100, 100)
+    verify_model(test_fn(0, True), [inp])
+    verify_model(test_fn(0, False), [inp])
+    verify_model(test_fn(1, True), [inp])
+    verify_model(test_fn(1, False), [inp])
+
+
+def test_sort():
+    def test_fn(dim, descending):
+        return lambda x: torch.sort(x, dim=dim, descending=descending)
+
+    inp = torch.randn(100)
+    verify_model(test_fn(0, True), [inp])
+    verify_model(test_fn(-1, False), [inp])
+
+    inp = torch.randn(100, 100)
+    verify_model(test_fn(0, True), [inp])
+    verify_model(test_fn(-2, False), [inp])
+    verify_model(test_fn(1, True), [inp])
+    verify_model(test_fn(-1, False), [inp])
+
+
+def test_logical_and():
+    def test_fn(x, y):
+        return torch.logical_and(x, y)
+
+    a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
+    b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
+    verify_model(test_fn, [a, b])
+
+    a = torch.tensor([True, False, True])
+    b = torch.tensor([True, False, False])
+    verify_model(test_fn, [a, b])
+
+
+def test_masked_select():
+    def test_fn(x, mask):
+        return torch.masked_select(x, mask)
+
+    for shape in [(10,), (3, 4), (16, 32, 64)]:
+        x = torch.randn(*shape)
+        mask = x.ge(0.5)
+        verify_trace_model(test_fn, [x, mask], ["llvm", "cuda", "nvptx"])
+
+
+def test_unique():
+    def test_fn(is_sorted, return_inverse, return_counts):
+        return lambda x: torch.unique(x, is_sorted, return_inverse, return_counts)
+
+    in_data = torch.randint(0, 20, (10,), dtype=torch.int32)
+    targets = ["llvm", "cuda", "nvptx"]
+    verify_trace_model(test_fn(True, True, True), [in_data], targets)
+    verify_trace_model(test_fn(True, False, True), [in_data], targets)
+    verify_trace_model(test_fn(True, True, False), [in_data], targets)
+    verify_trace_model(test_fn(True, False, True), [in_data], targets)
+    in_data = torch.randint(0, 20, (20,), dtype=torch.int64)
+    verify_trace_model(test_fn(True, True, True), [in_data], targets)
+    verify_trace_model(test_fn(True, False, True), [in_data], targets)
+    verify_trace_model(test_fn(True, True, False), [in_data], targets)
+    verify_trace_model(test_fn(True, False, True), [in_data], targets)
+
+
 if __name__ == "__main__":
     # some structural tests
     test_forward_traced_function()
@@ -3510,6 +3835,7 @@ def test_fn(x, weights=None):
     test_forward_avgpool3d()
     test_forward_dropout()
     test_forward_slice()
+    test_forward_narrow()
     test_forward_mean()
     test_forward_expand()
     test_forward_pow()
@@ -3563,8 +3889,19 @@ def test_fn(x, weights=None):
     test_forward_unbind()
     test_forward_nonzero()
     test_forward_scatter()
+    test_forward_index_put()
     test_numel()
     test_bincount()
+    test_cumsum()
+    test_masked_fill()
+    test_transformer()
+    test_sort()
+    test_argsort()
+    test_logical_and()
+    test_masked_select()
+    test_unique()
+    test_hard_swish()
+    test_hard_sigmoid()
 
     # Model tests
     test_resnet18()
@@ -3580,7 +3917,7 @@ def test_fn(x, weights=None):
 
     test_custom_conversion_map()
 
-    test_segmentaton_models()
+    test_segmentation_models()
     test_3d_models()
 
     # Quantization test
diff --git a/tests/python/frontend/pytorch/test_object_detection.py b/tests/python/frontend/pytorch/test_object_detection.py
index e4545ec4ef5e..a404a88393bc 100644
--- a/tests/python/frontend/pytorch/test_object_detection.py
+++ b/tests/python/frontend/pytorch/test_object_detection.py
@@ -17,8 +17,6 @@
 # pylint: disable=import-self, invalid-name, unused-argument
 """Test torch vision fasterrcnn and maskrcnn models"""
 import numpy as np
-import torch
-import torchvision
 import cv2
 
 import tvm
@@ -26,8 +24,15 @@
 import tvm.testing
 from tvm import relay
 from tvm.runtime.vm import VirtualMachine
+from tvm.relay.frontend.pytorch_utils import (
+    rewrite_nms_to_batched_nms,
+    rewrite_batched_nms_with_max_out_size,
+    rewrite_scatter_to_gather,
+)
 from tvm.contrib.download import download
 
+import torch
+import torchvision
 
 in_size = 300
 
@@ -71,7 +76,7 @@ def generate_jit_model(index):
     ]
 
     model_func = model_funcs[index]
-    model = TraceWrapper(model_func(pretrained=True, rpn_pre_nms_top_n_test=200))
+    model = TraceWrapper(model_func(pretrained=True, rpn_pre_nms_top_n_test=1000))
 
     model.eval()
     inp = torch.Tensor(np.random.uniform(0.0, 250.0, size=(1, 3, in_size, in_size)))
@@ -108,15 +113,17 @@ def test_detection_models():
     with torch.no_grad():
         pt_res = scripted_model(data)
 
-    for target in ["llvm", "cuda"]:
+    def compile_and_run_vm(mod, params, data_np, target):
         with tvm.transform.PassContext(opt_level=3):
             vm_exec = relay.vm.compile(mod, target=target, params=params)
 
         ctx = tvm.context(target, 0)
         vm = VirtualMachine(vm_exec, ctx)
-
         vm.set_input("main", **{input_name: data_np})
-        tvm_res = vm.run()
+        return vm.run()
+
+    for target in ["llvm"]:
+        tvm_res = compile_and_run_vm(mod, params, data_np, target)
 
         # Bounding boxes
         tvm.testing.assert_allclose(
@@ -132,3 +139,26 @@ def test_detection_models():
         score_threshold = 0.9
         print("Num boxes:", pt_res[0].cpu().numpy().shape[0])
         print("Num valid boxes:", np.sum(pt_res[1].cpu().numpy() >= score_threshold))
+
+    before = mod["main"]
+    mod = rewrite_nms_to_batched_nms(mod)
+    after = mod["main"]
+    assert not tvm.ir.structural_equal(after, before)
+
+    # TODO(masahi): It seems this rewrite causes flaky segfaults on CI
+    # See https://github.com/apache/tvm/issues/7363
+    # before = mod["main"]
+    # mod = rewrite_batched_nms_with_max_out_size(mod)
+    # after = mod["main"]
+    # assert not tvm.ir.structural_equal(after, before)
+
+    before = mod["main"]
+    mod = rewrite_scatter_to_gather(mod, 4)  # num_scales is 4 for maskrcnn_resnet50_fpn
+    after = mod["main"]
+    assert not tvm.ir.structural_equal(after, before)
+
+    tvm_res_after_rewrite = compile_and_run_vm(mod, params, data_np, "llvm")
+
+    # Results should be equivalent after rewriting
+    for res1, res2 in zip(tvm_res, tvm_res_after_rewrite):
+        tvm.testing.assert_allclose(res1.asnumpy(), res2.asnumpy())
diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
index 22ed6c5b2edf..22afe8f88f66 100644
--- a/tests/python/frontend/tensorflow/test_forward.py
+++ b/tests/python/frontend/tensorflow/test_forward.py
@@ -210,6 +210,7 @@ def compare_tf_with_tvm(
     mode="graph_runtime",
     cuda_layout="NCHW",
     add_shapes_to_graph_def=True,
+    targets=None,
 ):
     """Generic function to generate and compare tensorflow and TVM output"""
 
@@ -233,13 +234,18 @@ def name_without_num(name):
 
         tf_output = run_tf_graph(sess, in_data, in_name, out_name)
 
-        for device in ["llvm", "cuda"]:
+        devices = targets if targets else ["llvm", "cuda"]
+
+        for device in devices:
             ctx = tvm.context(device, 0)
             if not tvm.testing.device_enabled(device):
                 print("Skip because %s is not enabled" % device)
                 continue
             if no_gpu and device == "cuda":
                 continue
+            if "cublas" in device and not tvm.get_global_func("tvm.contrib.cublas.matmul", True):
+                print("Skip because cublas is not enabled: %s" % device)
+                continue
 
             tvm_output = run_tvm_graph(
                 final_graph_def,
@@ -414,6 +420,16 @@ def test_forward_pooling():
             pooling_type=pool_type,
             dilation_rate=[2],
         )
+    # Explicit padding
+    if package_version.parse(tf.VERSION) >= package_version.parse("2.4.1"):
+        _test_pooling(
+            input_shape=[2, 9, 10, 2],
+            window_shape=[4, 4],
+            padding=[[0, 0], [0, 1], [2, 3], [0, 0]],
+            pooling_type="MAX",
+            dilation_rate=[1, 1],
+            strides=[1, 1],
+        )
 
 
 #######################################################################
@@ -830,6 +846,36 @@ def test_forward_convolution():
         [4, 8, 8, 176],
         add_shapes_to_graph_def=False,
     )
+    # Explicit padding
+    if package_version.parse(tf.VERSION) >= package_version.parse("2.4.1"):
+        _test_convolution(
+            "conv",
+            [4, 8, 8, 16],
+            [1, 1, 16, 32],
+            [1, 1],
+            [1, 1],
+            [[0, 0], [2, 3], [0, 1], [0, 0]],
+            "NHWC",
+        )
+        _test_convolution(
+            "depthwise",
+            [4, 8, 8, 16],
+            [1, 1, 16, 1],
+            [1, 1],
+            [1, 1],
+            [[0, 0], [2, 3], [0, 1], [0, 0]],
+            "NHWC",
+        )
+        _test_convolution(
+            "conv_transpose",
+            [4, 8, 8, 32],
+            [3, 3, 176, 32],
+            [1, 1],
+            [2, 2],
+            [[0, 0], [1, 0], [1, 0], [0, 0]],
+            "NHWC",
+            [4, 16, 16, 176],
+        )
 
 
 #######################################################################
@@ -1741,6 +1787,23 @@ def _test_batch_matmul(A_shape, B_shape, dtype, adjoint_a=False, adjoint_b=False
         compare_tf_with_tvm([A_np, B_np], [A.name, B.name], result.name)
 
 
+def _test_batch_matmul_dynamic(
+    A_shape, B_shape, A_np_shape, B_np_shape, dtype, adjoint_a=False, adjoint_b=False
+):
+    with tf.Graph().as_default():
+        A = tf.placeholder(shape=A_shape, dtype=dtype, name="A")
+        B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
+        result = tf.matmul(A, B, adjoint_a=adjoint_a, adjoint_b=adjoint_b, name="batchmatmul")
+
+        A_np = np.random.uniform(high=5.0, size=A_np_shape).astype(dtype)
+        B_np = np.random.uniform(high=5.0, size=B_np_shape).astype(dtype)
+        # for now, in TOPI, only cublas's implementation support dynamic shape
+        # TODO add more backends support in TOPI
+        compare_tf_with_tvm(
+            [A_np, B_np], [A.name, B.name], result.name, mode="vm", targets=["cuda -libs=cublas"]
+        )
+
+
 def test_forward_batch_matmul():
     """ TF op BatchMatMul, BatchMatMulV2 test"""
     _test_batch_matmul((3, 5, 4), (3, 4, 5), "int32")
@@ -1753,24 +1816,53 @@ def test_forward_batch_matmul():
     _test_batch_matmul((2, 3, 4, 2, 3, 4, 5, 6), (2, 3, 4, 2, 3, 4, 5, 6), "float32", False, True)
 
 
+@tvm.testing.requires_cuda
+def test_forward_batch_matmul_dynamic():
+    _test_batch_matmul_dynamic((None, 5, 4), (None, 4, 5), (3, 5, 4), (3, 4, 5), "int32")
+    _test_batch_matmul_dynamic(
+        (None, 5, 4), (None, 4, 5), (3, 5, 4), (3, 4, 5), "float32", True, True
+    )
+    _test_batch_matmul_dynamic(
+        (None, 5, 4), (None, 5, 4), (3, 5, 4), (3, 5, 4), "int32", True, False
+    )
+    _test_batch_matmul_dynamic(
+        (None, 5, 4), (None, 5, 4), (3, 5, 4), (3, 5, 4), "float32", False, True
+    )
+    _test_batch_matmul_dynamic(
+        (None, 4, 5, 6), (None, 4, 6, 5), (3, 4, 5, 6), (3, 4, 6, 5), "float32"
+    )
+    _test_batch_matmul_dynamic(
+        (None, None, 5, 6), (None, None, 6, 5), (3, 4, 5, 6), (3, 4, 6, 5), "float32"
+    )
+    _test_batch_matmul_dynamic(
+        (None, None, None, 5, 6),
+        (None, None, None, 6, 5),
+        (2, 3, 4, 5, 6),
+        (2, 3, 4, 6, 5),
+        "float32",
+    )
+
+
 #######################################################################
 # SparseTensorDenseMatMul
 # ----------------------------------
 
 
-def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype, flip=False):
+def _test_sparse_dense_matmul(indices, values, A_inp_shape, B_inp_shape, dtype, flip=False):
     """ One iteration of sparse_dense_matmul """
 
-    # TODO(ANSHUMAN87): Support adjoint options too
-    for adjoint_a in [False]:
-        for adjoint_b in [False]:
+    for adjoint_a in [False, True]:
+        for adjoint_b in [False, True]:
+            A_shape = A_inp_shape[::-1] if adjoint_a else A_inp_shape
+            B_shape = B_inp_shape[::-1] if adjoint_b else B_inp_shape
+
             with tf.Graph().as_default():
                 A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
                 B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
 
                 if flip:
                     result = tf.sparse.sparse_dense_matmul(
-                        B, A_sp, adjoint_a=adjoint_a, adjoint_b=adjoint_b
+                        B, A_sp, adjoint_a=adjoint_b, adjoint_b=adjoint_a
                     )
                 else:
                     result = tf.sparse.sparse_dense_matmul(
@@ -1779,8 +1871,7 @@ def _test_sparse_dense_matmul(indices, values, A_shape, B_shape, dtype, flip=Fal
 
                 B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
 
-                # TODO(ANSHUMAN87): There is an issue in cuda scheduling for csr, work in progress
-                compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
+                compare_tf_with_tvm([B_np], [B.name], result.name)
 
 
 def test_forward_sparse_dense_matmul():
@@ -1811,6 +1902,554 @@ def test_forward_sparse_dense_matmul():
     )
 
 
+#######################################################################
+# SparseFillEmptyRows
+# ------------
+
+
+def _test_sparse_fill_empty_rows(indices_np, values_np, dense_shape_np, default_value_int, use_dyn):
+    with tf.Graph().as_default():
+        if use_dyn:
+            indices = tf.placeholder(shape=(None, None), dtype=indices_np.dtype, name="indices")
+            values = tf.placeholder(shape=(None), dtype=values_np.dtype, name="values")
+            dense_shape = tf.placeholder(
+                shape=(None), dtype=dense_shape_np.dtype, name="dense_shape"
+            )
+        else:
+            indices = tf.placeholder(shape=indices_np.shape, dtype=indices_np.dtype, name="indices")
+            values = tf.placeholder(shape=values_np.shape, dtype=values_np.dtype, name="values")
+            dense_shape = tf.placeholder(
+                shape=dense_shape_np.shape, dtype=dense_shape_np.dtype, name="dense_shape"
+            )
+
+        default_value = tf.placeholder(shape=(), dtype=values_np.dtype, name="default_value")
+        sp_input = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=dense_shape)
+        _ = tf.sparse.fill_empty_rows(sp_input, default_value, name="sparse_fill_empty_rows")
+        compare_tf_with_tvm(
+            [indices_np, values_np, dense_shape_np, default_value_int],
+            [indices.name, values.name, dense_shape.name, default_value.name],
+            [
+                "sparse_fill_empty_rows/SparseFillEmptyRows:0",
+                "sparse_fill_empty_rows/SparseFillEmptyRows:1",
+                "sparse_fill_empty_rows/SparseFillEmptyRows:2",
+            ],
+            mode="vm",
+        )
+
+
+@pytest.mark.parametrize(
+    "sparse_indices_np, sparse_values_np, dense_shape_np, default_value_int",
+    [
+        (
+            np.array([[1, 1], [0, 3], [0, 1], [2, 0], [3, 1]], dtype=np.int64),
+            np.array([1, 2, 3, 4, 5], dtype=np.int64),
+            np.array([5, 6], dtype=np.int64),
+            10,
+        ),
+        (
+            np.array([[1, 1], [0, 3], [2, 0], [3, 1]], dtype=np.int64),
+            np.array([1, 2, 3, 4], dtype=np.int64),
+            np.array([5, 6], dtype=np.int64),
+            10,
+        ),
+        (
+            np.array([[0, 1], [0, 3], [2, 0], [3, 1]], dtype=np.int64),
+            np.array([1, 2, 3, 4], dtype=np.int64),
+            np.array([5, 6], dtype=np.int64),
+            10,
+        ),
+        (
+            np.array([[1, 1, 1], [1, 3, 1], [2, 0, 5], [3, 1, 6]], dtype=np.int64),
+            np.array([1, 2, 3, 4], dtype=np.int64),
+            np.array([7, 7, 7], dtype=np.int64),
+            5,
+        ),
+        (
+            np.array([[1], [2]], dtype=np.int64),
+            np.array([7, 8], dtype=np.int64),
+            np.array([5], dtype=np.int64),
+            4,
+        ),
+        (
+            np.ones((0, 1), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([5], dtype=np.int64),
+            4,
+        ),
+        (
+            np.ones((0, 3), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([9, 3, 7], dtype=np.int64),
+            100,
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_dyn", [True, False])
+def test_forward_sparse_fill_empty_rows(
+    sparse_indices_np, sparse_values_np, dense_shape_np, default_value_int, use_dyn
+):
+    """ sparse_fill_empty_rows op test"""
+    ###################################################################
+    #
+    # In order to create a SparseTensor, it requires 3 input as below:
+    #    SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
+    #
+    # Above Sparse can be represented in Dense as below :
+    #    [[1, 0, 0, 0]
+    #     [0, 0, 2, 0]
+    #     [0, 0, 0, 0]]
+    #
+    # ------------------------------------------------------------------
+    _test_sparse_fill_empty_rows(
+        sparse_indices_np, sparse_values_np, dense_shape_np, default_value_int, use_dyn
+    )
+
+
+#######################################################################
+# SparseReshape
+# ------------
+
+
+def _test_sparse_reshape(indices_np, values_np, prev_shape_np, new_shape_np, use_dyn=False):
+    with tf.Graph().as_default():
+        if use_dyn:
+            indices = tf.placeholder(shape=(None, None), dtype=indices_np.dtype, name="indices")
+            values = tf.placeholder(shape=(None), dtype=values_np.dtype, name="values")
+            prev_shape = tf.placeholder(shape=(None), dtype=prev_shape_np.dtype, name="prev_shape")
+            new_shape = tf.placeholder(shape=(None), dtype=new_shape_np.dtype, name="new_shape")
+        else:
+            indices = tf.placeholder(shape=indices_np.shape, dtype=indices_np.dtype, name="indices")
+            values = tf.placeholder(shape=values_np.shape, dtype=values_np.dtype, name="values")
+            prev_shape = tf.placeholder(
+                shape=prev_shape_np.shape, dtype=prev_shape_np.dtype, name="prev_shape"
+            )
+            new_shape = tf.placeholder(
+                shape=new_shape_np.shape, dtype=new_shape_np.dtype, name="new_shape"
+            )
+        sp_input = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=prev_shape)
+
+        _ = tf.sparse.reshape(sp_input, new_shape, name="sparse_reshape")
+        compare_tf_with_tvm(
+            [indices_np, values_np, prev_shape_np, new_shape_np],
+            [indices.name, values.name, prev_shape.name, new_shape.name],
+            ["sparse_reshape:0", "sparse_reshape:1", "sparse_reshape/Identity:0"],
+            mode="vm",
+        )
+
+
+@pytest.mark.parametrize(
+    "sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np",
+    [
+        (
+            np.ones((0, 1), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([4], dtype=np.int64),
+            np.array([2, -1], dtype=np.int64),
+        ),
+        (
+            np.ones((0, 1), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([4], dtype=np.int64),
+            np.array([2, 2], dtype=np.int64),
+        ),
+        (
+            np.ones((0, 2), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([3, 6], dtype=np.int64),
+            np.array([-1, 2], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([2, 3, 6], dtype=np.int64),
+            np.array([-1, 9], dtype=np.int64),
+        ),
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0],
+                    [0, 0, 1, 2, 3],
+                    [0, 1, 0, 3, 5],
+                    [1, 0, 0, 4, 6],
+                    [1, 2, 3, 6, 8],
+                ],
+                dtype=np.int64,
+            ),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([2, 3, 6, 7, 9], dtype=np.int64),
+            np.array([9, -1, 7], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([9, 4], dtype=np.int64),
+            np.array([-1], dtype=np.int64),
+        ),
+        (
+            np.array([[0], [5], [10], [20], [24]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([25], dtype=np.int64),
+            np.array([5, 5], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+            np.array([500, -1], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+            np.array([250, 40], dtype=np.int64),
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_dyn", [True, False])
+def test_forward_sparse_reshape(
+    sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np, use_dyn
+):
+    """ sparse_reshape op test"""
+    ###################################################################
+    #
+    # In order to create a SparseTensor, it requires 3 input as below:
+    #    SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
+    #
+    # Above Sparse can be represented in Dense as below :
+    #    [[1, 0, 0, 0]
+    #     [0, 0, 2, 0]
+    #     [0, 0, 0, 0]]
+    #
+    # ------------------------------------------------------------------
+    _test_sparse_reshape(sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np, use_dyn)
+
+
+#######################################################################
+# Sparse Segment Variants
+# ------------
+
+
+def _test_sparse_segment_variant(
+    tf_op, data_np, indices_np, segment_ids_np, num_segments, use_dyn=False
+):
+    with tf.Graph().as_default():
+        if use_dyn:
+            data = tf.placeholder(
+                shape=[None for _ in data_np.shape], dtype=data_np.dtype, name="data"
+            )
+            indices = tf.placeholder(shape=[None], dtype=indices_np.dtype, name="indices")
+            segment_ids = tf.placeholder(
+                shape=(None), dtype=segment_ids_np.dtype, name="segment_ids"
+            )
+        else:
+            data = tf.placeholder(shape=data_np.shape, dtype=data_np.dtype, name="data")
+            indices = tf.placeholder(shape=indices_np.shape, dtype=indices_np.dtype, name="indices")
+            segment_ids = tf.placeholder(
+                shape=segment_ids_np.shape, dtype=segment_ids_np.dtype, name="segment_ids"
+            )
+
+        _ = tf_op(
+            data, indices, segment_ids, num_segments=num_segments, name="sparse_segment_variant"
+        )
+        compare_tf_with_tvm(
+            [data_np, indices_np, segment_ids_np],
+            [data.name, indices.name, segment_ids.name],
+            ["sparse_segment_variant:0"],
+            mode="vm",
+        )
+
+
+@pytest.mark.parametrize(
+    "data_np, indices_np, segment_ids_np, num_segments",
+    [
+        (
+            np.array([5, 1, 7, 2, 3, 4], dtype=np.float32),
+            np.array([0, 3, 4], dtype=np.int32),
+            np.array([0, 1, 1], dtype=np.int32),
+            None,
+        ),
+        (
+            np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float64),
+            np.array([0, 1], dtype=np.int32),
+            np.array([0, 2], dtype=np.int32),
+            4,
+        ),
+        (
+            np.random.random((6, 4, 5)),
+            np.array([0, 2, 4, 3, 1], dtype=np.int32),
+            np.array([0, 0, 1, 5, 5], dtype=np.int32),
+            100,
+        ),
+        (
+            np.random.random((6, 4, 5)),
+            np.array([0, 2, 4, 3, 1], dtype=np.int32),
+            np.array([0, 0, 1, 5, 5], dtype=np.int32),
+            None,
+        ),
+        (
+            np.array([[[1, 7]], [[3, 8]], [[2, 9]]], dtype=np.float64),
+            np.array([0, 1, 2], dtype=np.int32),
+            np.array([0, 0, 1], dtype=np.int32),
+            None,
+        ),
+        (
+            np.random.random((9, 4, 5, 7)),
+            np.array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=np.int32),
+            np.array([0, 0, 1, 3, 5, 6, 7, 7, 8], dtype=np.int32),
+            9,
+        ),
+        (
+            np.random.random((9, 4, 5, 7)),
+            np.array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=np.int32),
+            np.array([0, 0, 1, 3, 5, 6, 7, 7, 8], dtype=np.int32),
+            None,
+        ),
+        (
+            np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float64),
+            np.array([0, 1], dtype=np.int32),
+            np.array([0, 2], dtype=np.int32),
+            None,
+        ),
+        (
+            np.random.random((9, 4, 5, 7)),
+            np.array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=np.int32),
+            np.array([0, 0, 1, 3, 5, 5, 5, 5, 5], dtype=np.int32),
+            6,
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_dyn", [True, False])
+@pytest.mark.parametrize(
+    "tf_op",
+    [
+        tf.sparse.segment_sum,
+        tf.sparse.segment_sqrt_n,
+        tf.sparse.segment_mean,
+    ],
+)
+def test_forward_sparse_segment_sum_variants(
+    tf_op,
+    data_np,
+    indices_np,
+    segment_ids_np,
+    num_segments,
+    use_dyn,
+):
+    """sparse segment sum variants tests"""
+    _test_sparse_segment_variant(tf_op, data_np, indices_np, segment_ids_np, num_segments, use_dyn)
+
+
+#######################################################################
+# Math SegmentSum
+# ------------
+
+
+def _test_math_segment_sum(data_np, segment_ids_np, use_dyn=False):
+    with tf.Graph().as_default():
+        if use_dyn:
+            data = tf.placeholder(
+                shape=[None for _ in data_np.shape], dtype=data_np.dtype, name="data"
+            )
+            segment_ids = tf.placeholder(
+                shape=(None), dtype=segment_ids_np.dtype, name="segment_ids"
+            )
+        else:
+            data = tf.placeholder(shape=data_np.shape, dtype=data_np.dtype, name="data")
+            segment_ids = tf.placeholder(
+                shape=segment_ids_np.shape, dtype=segment_ids_np.dtype, name="segment_ids"
+            )
+
+        _ = tf.math.segment_sum(data, segment_ids, name="segment_sum")
+        compare_tf_with_tvm(
+            [data_np, segment_ids_np],
+            [data.name, segment_ids.name],
+            ["segment_sum:0"],
+            mode="vm",
+        )
+
+
+@pytest.mark.parametrize(
+    "data_np, segment_ids_np",
+    [
+        (
+            np.array([5, 1, 7, 2, 3, 4], dtype=np.float32),
+            np.array([0, 0, 0, 1, 1, 1], dtype=np.int32),
+        ),
+        (
+            np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float64),
+            np.array([0, 0, 1], dtype=np.int32),
+        ),
+        (
+            np.random.random((6, 4, 5)),
+            np.array([0, 0, 1, 2, 2, 3], dtype=np.int64),
+        ),
+        (
+            np.array([[[1, 7]], [[3, 8]], [[2, 9]]], dtype=np.float32),
+            np.array([0, 0, 1], dtype=np.int32),
+        ),
+        (
+            np.random.random((9, 4, 5, 7)),
+            np.array([0, 0, 0, 1, 2, 3, 4, 4, 5], dtype=np.int64),
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_dyn", [True, False])
+def test_forward_math_segment_sum(data_np, segment_ids_np, use_dyn):
+    """math segment sum test"""
+    _test_math_segment_sum(data_np, segment_ids_np, use_dyn)
+
+
+# tensorflow.compat.v1.sparse_to_dense
+# ---------------
+def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
+    with tf.Graph().as_default():
+        indices = tf.placeholder(
+            shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
+        )
+        values = tf.placeholder(
+            shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
+        )
+        oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))
+
+        if default_value == None:
+            output = tf.sparse_to_dense(indices, oshape, values)
+            compare_tf_with_tvm(
+                [sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
+            )
+        else:
+            dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
+            output = tf.sparse_to_dense(indices, oshape, values, dv)
+            compare_tf_with_tvm(
+                [sparse_indices, sparse_values, default_value],
+                ["indices:0", "values:0", "default_value:0"],
+                output.name,
+            )
+
+
+def test_forward_sparse_to_dense():
+    # scalar
+    _test_sparse_to_dense(
+        sparse_indices=np.int32(1),
+        sparse_values=np.int32(3),
+        default_value=np.int32(0),
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+    # vector
+    _test_sparse_to_dense(
+        sparse_indices=np.array([0, 1, 4]).astype("int32"),
+        sparse_values=np.array([3, 3, 3]).astype("int32"),
+        default_value=np.int32(0),
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+    # vector nXd
+    _test_sparse_to_dense(
+        sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
+        sparse_values=np.array([1, 2]).astype("int32"),
+        default_value=np.int32(0),
+        output_shape=np.array([3, 4]).astype("int32"),
+    )
+
+    _test_sparse_to_dense(
+        sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
+        sparse_values=np.array([1, 2]).astype("int32"),
+        default_value=np.int32(4),
+        output_shape=np.array([2, 3, 4]).astype("int32"),
+    )
+
+    # floats
+    _test_sparse_to_dense(
+        sparse_indices=np.array([0, 1, 4]).astype("int32"),
+        sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+        default_value=np.float32(3.5),
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+    # default value not specified
+    _test_sparse_to_dense(
+        sparse_indices=np.array([0, 1, 4]).astype("int32"),
+        sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
+        default_value=None,
+        output_shape=np.array([5]).astype("int32"),
+    )
+
+
+#######################################################################
+# tensorflow.sparse.to_dense
+# ---------------
+def _test_sparse_to_dense_v2(indices, values, A_shape, dtype, default_value=None):
+    with tf.Graph().as_default():
+        A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
+
+        result = tf.sparse.to_dense(A_sp, default_value=default_value)
+
+        compare_tf_with_tvm([], [], result.name)
+
+
+def test_forward_sparse_to_dense_v2():
+    _test_sparse_to_dense_v2([[1]], [3.0], [5], "float32")
+    _test_sparse_to_dense_v2([[1]], [3.0], [5], "float32", 0.3)
+    _test_sparse_to_dense_v2([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], "float32")
+    _test_sparse_to_dense_v2([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], "float32", 1.3)
+    _test_sparse_to_dense_v2([[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], "float32")
+    _test_sparse_to_dense_v2([[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], "float32", 1.9)
+
+
+#######################################################################
+# tensorflow.sparse.add
+# ----------------------------------
+
+
+def _test_sparse_add(indices, values, A_shape, B_shape, dtype, flip=False):
+    """ One iteration of tf.sparse.add """
+
+    # TODO(ANSHUMAN87): support cuda
+    # TODO(ANSHUMAN87): support both sparse input case
+
+    with tf.Graph().as_default():
+        A_sp = tf.sparse.SparseTensor(
+            indices=indices, values=np.array(values).astype(dtype), dense_shape=A_shape
+        )
+        B = tf.placeholder(shape=B_shape, dtype=dtype, name="B")
+
+        # TODO(ANSHUMAN87): support user input threashold values
+        if flip:
+            result = tf.sparse.add(B, A_sp, threshold=0)
+        else:
+            result = tf.sparse.add(A_sp, B, threshold=0)
+
+        B_np = np.random.uniform(high=5.0, size=B_shape).astype(dtype)
+
+        compare_tf_with_tvm([B_np], [B.name], result.name, no_gpu=True)
+
+
+def test_sparse_add():
+    """ sparse.add op test"""
+    ###################################################################
+    #
+    # In order to create a SparseTensor, it requires 3 input as below:
+    #    SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
+    #
+    # Above Sparse can be represented in Dense as below :
+    #    [[1, 0, 0, 0]
+    #     [0, 0, 2, 0]
+    #     [0, 0, 0, 0]]
+    #
+    # ------------------------------------------------------------------
+    for dtype_inp in ["float32", "float64", "int32"]:
+        _test_sparse_add([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], [3, 4], dtype_inp)
+        _test_sparse_add([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], [3, 4], dtype_inp, True)
+        _test_sparse_add([[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], [5, 5], dtype_inp)
+        _test_sparse_add([[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], [5, 5], dtype_inp, True)
+
+
 #######################################################################
 # StridedSlice
 # ------------
@@ -2693,6 +3332,55 @@ def test_forward_nms():
         _test_forward_nms((2000, 4), (2000,), 0.4, 0.6, 7)
 
 
+def _test_forward_combined_nms(
+    bx_shape,
+    score_shape,
+    iou_threshold,
+    score_threshold,
+    out_size,
+    total_size,
+    clip_boxes=False,
+    dtype="float32",
+):
+    boxes = np.random.uniform(-1, 2, size=bx_shape).astype(dtype)
+    scores = np.random.uniform(size=score_shape).astype(dtype)
+    max_output_size = np.int32(out_size)
+    tf.reset_default_graph()
+    in_data_1 = tf.placeholder(dtype, boxes.shape, name="in_data_1")
+    in_data_2 = tf.placeholder(dtype, scores.shape, name="in_data_2")
+    in_data_3 = tf.placeholder(tf.int32, name="in_data_3")
+    tf.image.combined_non_max_suppression(
+        boxes=in_data_1,
+        scores=in_data_2,
+        max_output_size_per_class=in_data_3,
+        max_total_size=total_size,
+        iou_threshold=iou_threshold,
+        score_threshold=score_threshold,
+        pad_per_class=False,
+        clip_boxes=clip_boxes,
+        name="nms",
+    )
+    compare_tf_with_tvm(
+        [boxes, scores, max_output_size],
+        ["in_data_1:0", "in_data_2:0", "in_data_3:0"],
+        [
+            "nms/CombinedNonMaxSuppression:0",
+            "nms/CombinedNonMaxSuppression:1",
+            "nms/CombinedNonMaxSuppression:2",
+            "nms/CombinedNonMaxSuppression:3",
+        ],
+        mode="vm",
+    )
+
+
+def test_forward_combined_nms():
+    """ CombinedNonMaxSuppression """
+    _test_forward_combined_nms((1, 64, 1, 4), (1, 64, 1), 0.7, 0.5, 64, 64)
+    _test_forward_combined_nms((1, 64, 1, 4), (1, 64, 20), 0.7, 0.5, 64, 10)
+    _test_forward_combined_nms((1, 64, 20, 4), (1, 64, 20), 0.7, 0.5, 64, 64, clip_boxes=True)
+    _test_forward_combined_nms((2, 200, 1, 4), (2, 200, 1), 0.4, 0.6, 100, 100)
+
+
 #######################################################################
 # LSTM
 # ----
@@ -3804,6 +4492,45 @@ def _test_math_op(op, dtypes=["int32", "float32"]):
         _test_math_op(tf.math.reduce_euclidean_norm)
 
 
+#######################################################################
+# All, Max, Min
+# ------------------------------------------------------------------
+
+
+def test_forward_raw_reduce():
+    def _check_op(tf_op, ishape, axis, keepdims, range_axis=False, dtype="float32"):
+        tf.reset_default_graph()
+        if dtype == "bool":
+            np_data = np.random.choice([True, False], size=ishape)
+        else:
+            np_data = np.random.uniform(size=ishape).astype(dtype)
+        if tf_op == tf.math.reduce_prod:
+            axis = 1
+            np_data = np_data.reshape(1, -1)
+        with tf.Graph().as_default():
+            if range_axis:
+                axis = tf.range(axis[0], axis[1], axis[2], name="range", dtype="int32")
+            in_data = tf.placeholder(dtype, name="in_data")
+            reduce_op = tf_op(input=in_data, axis=axis, keep_dims=keepdims, name="reduce_std")
+            compare_tf_with_tvm([np_data], ["in_data:0"], reduce_op.name)
+
+    def _test_raw_reduce_op(op, dtypes=["int32", "float32"]):
+        for dtype in dtypes:
+            _check_op(op, (3, 10), axis=(-1), keepdims=False, dtype=dtype)
+            _check_op(op, (8, 16, 32), axis=(-1), keepdims=False, dtype=dtype)
+            _check_op(op, (1, 8, 8, 3), axis=(2, 3), keepdims=True, dtype=dtype)
+            _check_op(op, (2, 3, 10, 10), axis=(1, 2), keepdims=True, dtype=dtype)
+            _check_op(op, (1, 8, 8, 3), axis=(2, 4, 1), keepdims=True, range_axis=True, dtype=dtype)
+            _check_op(
+                op, (2, 3, 10, 10), axis=(1, 3, 1), keepdims=True, range_axis=True, dtype=dtype
+            )
+
+    if package_version.parse(tf.VERSION) >= package_version.parse("2.4.1"):
+        _test_raw_reduce_op(tf.raw_ops.All, dtypes=["bool"])
+        _test_raw_reduce_op(tf.raw_ops.Max)
+        _test_raw_reduce_op(tf.raw_ops.Min)
+
+
 #######################################################################
 # Relational operators
 # --------------------
@@ -4073,81 +4800,54 @@ def test_forward_dilation():
     _test_dilation2d([1, 3, 3, 1], [2, 2, 1], [1, 1, 1, 1], [1, 1, 2, 1], "VALID")
 
 
-#######################################################################
-# Sparse To Dense
-# ---------------
-def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
+def _test_identityn(data_np_list):
     with tf.Graph().as_default():
-        indices = tf.placeholder(
-            shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
-        )
-        values = tf.placeholder(
-            shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
-        )
-        oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))
-
-        if default_value == None:
-            output = tf.sparse_to_dense(indices, oshape, values)
-            compare_tf_with_tvm(
-                [sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
+        data_tensors = []
+        data_tensors_name = []
+        for index, data_np in enumerate(data_np_list):
+            tensor_name = f"data_{index}"
+            data_tensors_name.append(tensor_name + ":0")
+            data_tensors.append(
+                tf.placeholder(shape=data_np.shape, dtype=str(data_np.dtype), name=tensor_name)
             )
-        else:
-            dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
-            output = tf.sparse_to_dense(indices, oshape, values, dv)
-            compare_tf_with_tvm(
-                [sparse_indices, sparse_values, default_value],
-                ["indices:0", "values:0", "default_value:0"],
-                output.name,
-            )
-
-
-def test_forward_sparse_to_dense():
-    # scalar
-    _test_sparse_to_dense(
-        sparse_indices=np.int32(1),
-        sparse_values=np.int32(3),
-        default_value=np.int32(0),
-        output_shape=np.array([5]).astype("int32"),
-    )
 
-    # vector
-    _test_sparse_to_dense(
-        sparse_indices=np.array([0, 1, 4]).astype("int32"),
-        sparse_values=np.array([3, 3, 3]).astype("int32"),
-        default_value=np.int32(0),
-        output_shape=np.array([5]).astype("int32"),
-    )
-
-    # vector nXd
-    _test_sparse_to_dense(
-        sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
-        sparse_values=np.array([1, 2]).astype("int32"),
-        default_value=np.int32(0),
-        output_shape=np.array([3, 4]).astype("int32"),
-    )
-
-    _test_sparse_to_dense(
-        sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
-        sparse_values=np.array([1, 2]).astype("int32"),
-        default_value=np.int32(4),
-        output_shape=np.array([2, 3, 4]).astype("int32"),
-    )
+        output = tf.identity_n(data_tensors)
+        output_names = [out.name for out in output]
+        compare_tf_with_tvm(
+            data_np_list,
+            data_tensors_name,
+            output_names,
+        )
 
-    # floats
-    _test_sparse_to_dense(
-        sparse_indices=np.array([0, 1, 4]).astype("int32"),
-        sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
-        default_value=np.float32(3.5),
-        output_shape=np.array([5]).astype("int32"),
-    )
 
-    # default value not specified
-    _test_sparse_to_dense(
-        sparse_indices=np.array([0, 1, 4]).astype("int32"),
-        sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
-        default_value=None,
-        output_shape=np.array([5]).astype("int32"),
-    )
+@pytest.mark.parametrize(
+    "data_np_list",
+    [
+        (
+            [
+                np.array([[1, 1], [0, 3], [0, 1], [2, 0], [3, 1]], dtype=np.int64),
+                np.array([1, 2, 3, 4, 5], dtype=np.int64),
+                np.array([5, 6], dtype=np.int64),
+            ]
+        ),
+        (
+            [
+                np.array([[1, 1], [0, 3], [2, 0], [3, 1]], dtype=np.int64),
+                np.array([1, 2, 3, 4], dtype=np.int64),
+                np.array([5, 6], dtype=np.int64),
+                np.array([True, False, True]),
+            ]
+        ),
+        (
+            [
+                np.array([]),
+                np.array([[]]),
+            ]
+        ),
+    ],
+)
+def test_forward_identityn(data_np_list):
+    _test_identityn(data_np_list)
 
 
 #######################################################################
@@ -4179,6 +4879,10 @@ def test_forward_isfinite():
     _verify_infiniteness_ops(tf.is_finite, "isfinite")
 
 
+def test_forward_isnan():
+    _verify_infiniteness_ops(tf.is_nan, "isnan")
+
+
 def _test_spop_placeholder_without_shape_info():
     with tf.Graph().as_default():
 
@@ -4681,5 +5385,70 @@ def lstm_cell():
             tvm.testing.assert_allclose(tf_output[i], tvm_output[i], atol=1e-5, rtol=1e-5)
 
 
+#######################################################################
+# Unique
+# ------------
+
+
+def _test_unique(n, dtype, is_dyn):
+    tf.reset_default_graph()
+    np_data = np.random.randint(100, size=n).astype(dtype)
+    with tf.Graph().as_default():
+        if is_dyn:
+            in_data = tf.placeholder(dtype, [n], name="in_data")
+        else:
+            in_data = tf.constant(np_data, dtype, name="in_data")
+        tf.unique(in_data)
+        if is_dyn:
+            compare_tf_with_tvm(np_data, "in_data:0", ["Unique:0", "Unique:1"], mode="vm")
+        else:
+            compare_tf_with_tvm(None, "", ["Unique:0", "Unique:1"])
+
+
+def test_forward_unique():
+    """test Unique"""
+
+    for dtype in ["int32", "int64"]:
+        for is_dyn in [False, True]:
+            _test_unique(50, dtype, is_dyn)
+            _test_unique(100, dtype, is_dyn)
+
+
+#######################################################################
+# Unique with counts
+# ------------
+
+
+def _test_unique_with_counts(n, dtype, is_dyn):
+    tf.reset_default_graph()
+    np_data = np.random.randint(100, size=n).astype(dtype)
+    with tf.Graph().as_default():
+        if is_dyn:
+            in_data = tf.placeholder(dtype, [n], name="in_data")
+        else:
+            in_data = tf.constant(np_data, dtype, name="in_data")
+        tf.unique_with_counts(in_data)
+        if is_dyn:
+            compare_tf_with_tvm(
+                np_data,
+                "in_data:0",
+                ["UniqueWithCounts:0", "UniqueWithCounts:1", "UniqueWithCounts:2"],
+                mode="vm",
+            )
+        else:
+            compare_tf_with_tvm(
+                None, "", ["UniqueWithCounts:0", "UniqueWithCounts:1", "UniqueWithCounts:2"]
+            )
+
+
+def test_forward_unique_with_counts():
+    """test UniqueWithCounts"""
+
+    for dtype in ["int32", "int64"]:
+        for is_dyn in [False, True]:
+            _test_unique_with_counts(10, dtype, is_dyn)
+            _test_unique_with_counts(20, dtype, is_dyn)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py
index 6cedc65678c5..0d02c15f2eb8 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -583,6 +583,24 @@ def _test_stridedslice(
 def test_forward_stridedslice():
     """test StridedSlice"""
     for quantized in [False, True]:
+        _test_stridedslice(
+            (1, 3, 3),
+            [0, 0, 0],
+            [3, 3, 3],
+            [1, 1, 1],
+            "float32",
+            shrink_axis_mask=7,
+            quantized=quantized,
+        )
+        _test_stridedslice(
+            (1, 3, 3),
+            [0, 0, 0],
+            [3, 3, 3],
+            [1, 1, 1],
+            "float32",
+            shrink_axis_mask=5,
+            quantized=quantized,
+        )
         _test_stridedslice((2), [1], [1], [1], "float32", shrink_axis_mask=1, quantized=quantized)
         _test_stridedslice(
             (3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1], "float32", quantized=quantized
@@ -1251,30 +1269,61 @@ def test_forward_transpose_conv():
 # -------
 
 
-def _test_reshape(data, out_shape, wrap_shape):
+def _test_reshape(data, out_shape, wrap_shape, quantized=False):
     """ One iteration of reshape operation with given data and out shape """
-    with tf.Graph().as_default():
-        in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
+    if quantized:
+        with tf.Graph().as_default():
+            in_data = array_ops.placeholder(shape=data.shape, dtype="float32", name="in")
+            inq_data = tf.quantization.fake_quant_with_min_max_args(
+                in_data, min=-100, max=100, name="inq_0"
+            )
 
-        out_shape = out_shape if not wrap_shape else np.array(out_shape, dtype=np.int32)
+            input_range = {"inq_0": (-100, 100)}
+            out_shape = out_shape if not wrap_shape else np.array(out_shape, dtype=np.int32)
 
-        in_shape = (
-            out_shape
-            if not wrap_shape
-            else array_ops.placeholder(
-                shape=out_shape.shape, dtype=out_shape.dtype, name="Newshape"
+            in_shape = (
+                out_shape
+                if not wrap_shape
+                else array_ops.placeholder(
+                    shape=out_shape.shape, dtype=out_shape.dtype, name="Newshape"
+                )
             )
-        )
 
-        out = array_ops.reshape(in_data, in_shape)
+            out = array_ops.reshape(inq_data, in_shape)
+            out = tf.quantization.fake_quant_with_min_max_args(out, min=-200, max=200, name="out")
+            compare_tflite_with_tvm(
+                [data, out_shape] if wrap_shape else [data],
+                ["inq_0:0", "Newshape:0"] if wrap_shape else ["inq_0:0"],
+                [inq_data, in_shape] if wrap_shape else [inq_data],
+                [out],
+                quantized=True,
+                input_range=input_range,
+                mode="vm",
+            )
+    else:
+        # Test with tensor and constant
+        with tf.Graph().as_default():
+            in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype)
 
-        compare_tflite_with_tvm(
-            [data, out_shape] if wrap_shape else [data],
-            ["Placeholder:0", "Newshape:0"] if wrap_shape else ["Placeholder:0"],
-            [in_data, in_shape] if wrap_shape else [in_data],
-            [out],
-            mode="vm",
-        )
+            out_shape = out_shape if not wrap_shape else np.array(out_shape, dtype=np.int32)
+
+            in_shape = (
+                out_shape
+                if not wrap_shape
+                else array_ops.placeholder(
+                    shape=out_shape.shape, dtype=out_shape.dtype, name="Newshape"
+                )
+            )
+
+            out = array_ops.reshape(in_data, in_shape)
+
+            compare_tflite_with_tvm(
+                [data, out_shape] if wrap_shape else [data],
+                ["Placeholder:0", "Newshape:0"] if wrap_shape else ["Placeholder:0"],
+                [in_data, in_shape] if wrap_shape else [in_data],
+                [out],
+                mode="vm",
+            )
 
 
 def test_forward_reshape():
@@ -1284,6 +1333,9 @@ def test_forward_reshape():
         _test_reshape(np.arange(6), [3, -1], wrap)
         _test_reshape(np.arange(6), [-1], wrap)
 
+    _test_reshape(np.arange(6, dtype=np.uint8), [2, 3], False, True)
+    _test_reshape(np.arange(6, dtype=np.uint8), [-1, 2], False, True)
+
 
 #######################################################################
 # Resize
@@ -2750,25 +2802,51 @@ def test_forward_one_hot():
 # ----
 
 
-def _test_pack(data, is_var, axis):
+def _test_pack(data, is_var, axis, quantized=False):
     """ One iteration of pack """
 
     assert len(data) >= 1
     assert len(data) == len(is_var)
+    if quantized:
+        with tf.Graph().as_default():
+            in_data = [
+                array_ops.placeholder(shape=d.shape, dtype="float32", name="in_" + str(idx))
+                if is_var[idx]
+                else constant_op.constant(
+                    d, shape=d.shape, dtype="float32", name="in_constant_" + str(idx)
+                )
+                for idx, d in enumerate(data)
+            ]
+            inq_data = [
+                tf.quantization.fake_quant_with_min_max_args(
+                    i_data, min=-100, max=100, name="inq_{}".format(idx)
+                )
+                for idx, i_data in enumerate(in_data)
+            ]
+            input_range = {}
+            for i in range(len(data)):
+                input_range["inq_{}".format(i)] = (-100, 100)
 
-    with tf.Graph().as_default():
-        in_data = [
-            array_ops.placeholder(shape=d.shape, dtype=d.dtype, name="in_" + str(idx))
-            if is_var[idx]
-            else constant_op.constant(
-                d, shape=d.shape, dtype=d.dtype, name="in_constant_" + str(idx)
+            out = array_ops.pack(inq_data, axis=axis)
+            out = tf.quantization.fake_quant_with_min_max_args(out, min=-100, max=100, name="out")
+            name = ["inq_{}:0".format(idx) for idx in range(len(data))]
+            compare_tflite_with_tvm(
+                data, name, inq_data, [out], quantized=True, input_range=input_range
             )
-            for idx, d in enumerate(data)
-        ]
+    else:
+        with tf.Graph().as_default():
+            in_data = [
+                array_ops.placeholder(shape=d.shape, dtype=d.dtype, name="in_" + str(idx))
+                if is_var[idx]
+                else constant_op.constant(
+                    d, shape=d.shape, dtype=d.dtype, name="in_constant_" + str(idx)
+                )
+                for idx, d in enumerate(data)
+            ]
 
-        out = array_ops.pack(in_data, axis=axis)
-        name = [_.name for _ in in_data]
-        compare_tflite_with_tvm(data, name, in_data, [out], experimental_new_converter=True)
+            out = array_ops.pack(in_data, axis=axis)
+            name = [_.name for _ in in_data]
+            compare_tflite_with_tvm(data, name, in_data, [out], experimental_new_converter=True)
 
 
 def test_forward_pack():
@@ -2791,6 +2869,17 @@ def test_forward_pack():
         1,
     )
 
+    _test_pack(
+        [
+            np.arange(6, dtype=np.uint8).reshape((2, 1, 1, 3)),
+            np.arange(6, dtype=np.uint8).reshape((2, 1, 1, 3)),
+            np.arange(6, dtype=np.uint8).reshape((2, 1, 1, 3)),
+        ],
+        [True, True, True],
+        1,
+        quantized=True,
+    )
+
 
 #######################################################################
 # Unpack
@@ -3271,9 +3360,9 @@ def test_forward_sparse_to_dense():
 #######################################################################
 # Fully Connected
 # ---------------
-
-
-def _test_fully_connected(tensor_in_sizes, const_input, filter_in_sizes, bias_in_size=None):
+def _test_fully_connected(
+    tensor_in_sizes, const_input, filter_in_sizes, bias_in_size=None, quantized=False
+):
     """ One iteration of fully connected """
 
     total_size_1 = np.prod(tensor_in_sizes)
@@ -3285,11 +3374,11 @@ def _test_fully_connected(tensor_in_sizes, const_input, filter_in_sizes, bias_in
 
     # Initializes the input tensor with array containing incrementing
     # numbers from 1.
-    data_array = np.arange(1, total_size_1 + 1, dtype=np.float32)
-    filter_array = np.arange(1, total_size_2 + 1, dtype=np.float32)
+    data_array = np.arange(1, total_size_1 + 1, dtype=np.uint8 if quantized else np.float32)
+    filter_array = np.arange(1, total_size_2 + 1, dtype=np.uint8 if quantized else np.float32)
+    in_name = "input"
 
     with tf.Graph().as_default():
-        in_name = "input"
         in_data = (
             constant_op.constant(data_array, shape=tensor_in_sizes, dtype=np.float32, name=in_name)
             if const_input
@@ -3297,30 +3386,73 @@ def _test_fully_connected(tensor_in_sizes, const_input, filter_in_sizes, bias_in
         )
 
         in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype=np.float32)
-
-        # reshape N H W C into N H*W*C
-        in_data_reshape = array_ops.reshape(in_data, [tensor_in_sizes[0], -1])
-
-        out = math_ops.mat_mul(in_data_reshape, in_filter)
+        data_array = np.reshape(data_array, tensor_in_sizes)
 
         # if we have bias
         if bias_in_size:
             assert bias_in_size[0] == filter_in_sizes[1], "bias and filter size are mismatched"
-            bias_array = np.arange(1, bias_in_size[0] + 1, dtype=np.float32)
+            bias_array = np.arange(
+                1, bias_in_size[0] + 1, dtype=np.uint8 if quantized else np.float32
+            )
             in_bias = constant_op.constant(bias_array, shape=bias_in_size, dtype=np.float32)
-            out = nn_ops.bias_add(out, in_bias)
 
-        data_array = np.reshape(data_array, tensor_in_sizes).astype(np.float32)
-        compare_tflite_with_tvm(data_array, [] if const_input else in_data.name, [in_data], [out])
+        if quantized:
+            inq_data = tf.quantization.fake_quant_with_min_max_args(
+                in_data, min=-100, max=100, name="inq_0"
+            )
+            input_range = {"inq_0": (-100, 100)}
+            inq_filter = tf.quantization.fake_quant_with_min_max_args(
+                in_filter, min=-100, max=100, name="inq_1"
+            )
+            input_range = {"inq_0": (-100, 100), "inq_1": (-100, 100)}
+            # reshape N H W C into N H*W*C
+            inq_data_reshape = array_ops.reshape(inq_data, [tensor_in_sizes[0], -1])
+            out = math_ops.mat_mul(inq_data_reshape, inq_filter)
+            out = tf.quantization.fake_quant_with_min_max_args(out, min=-100, max=100, name="out")
+
+            # if we have bias
+            if bias_in_size:
+                out = nn_ops.bias_add(out, in_bias)
+
+            compare_tflite_with_tvm(
+                data_array,
+                inq_data.name,
+                [inq_data],
+                [out],
+                quantized=True,
+                input_range=input_range,
+                experimental_new_converter=True,
+            )
+        else:
+            # reshape N H W C into N H*W*C
+            in_data_reshape = array_ops.reshape(in_data, [tensor_in_sizes[0], -1])
+            out = math_ops.mat_mul(in_data_reshape, in_filter)
+
+            # if we have bias
+            if bias_in_size:
+                out = nn_ops.bias_add(out, in_bias)
+
+            compare_tflite_with_tvm(
+                data_array, in_data.name, [in_data], [out], experimental_new_converter=True
+            )
 
 
 def test_forward_fully_connected():
     """ Fully Connected """
-    for const_input in [False, True]:
-        _test_fully_connected([1, 1, 1, 150], const_input, [150, 100])
-        _test_fully_connected([1, 1, 1, 150], const_input, [150, 100], [100])
-        _test_fully_connected([5, 1, 1, 150], const_input, [150, 100])
-        _test_fully_connected([5, 1, 1, 150], const_input, [150, 100], [100])
+    for input_shape, weight_shape, bias_shape in [
+        ([1, 4], [4, 4], None),
+        ([1, 4], [4, 4], [4]),
+        ([1, 1, 1, 5], [5, 5], None),
+        ([1, 1, 10], [10, 103], None),
+        ([1, 1, 1, 150], [150, 100], None),
+        ([1, 1, 1, 150], [150, 100], None),
+        ([1, 1, 1, 150], [150, 100], [100]),
+        ([5, 1, 1, 150], [150, 100], None),
+        ([5, 1, 1, 150], [150, 100], [100]),
+    ]:
+        for const_input in [False, True]:
+            for quantized in [False, True]:
+                _test_fully_connected(input_shape, const_input, weight_shape, bias_shape, quantized)
 
 
 #######################################################################
@@ -3577,6 +3709,50 @@ def test_forward_mobilenet_v3():
     )
 
 
+#######################################################################
+# Mobilenet V1 Sparse
+# -----------------
+
+
+def test_forward_sparse_mobilenet_v1():
+    """Test the Sparse version of Mobilenet V1 TF Lite model."""
+    # MobilenetV1
+    tflite_model_file = download_testdata(
+        "https://storage.googleapis.com/fast-convnets/tflite-models/mbv1_140_90_12b4_720.tflite",
+        "mbv1_140_90_12b4_720.tflite",
+    )
+    with open(tflite_model_file, "rb") as f:
+        tflite_model_buf = f.read()
+    data = np.random.uniform(size=(1, 224, 224, 3)).astype("float32")
+    tflite_output = run_tflite_graph(tflite_model_buf, data)
+    tvm_output = run_tvm_graph(tflite_model_buf, data, "float_image_input")
+    tvm.testing.assert_allclose(
+        np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-5
+    )
+
+
+#######################################################################
+# Mobilenet V2 Sparse
+# -----------------
+
+
+def test_forward_sparse_mobilenet_v2():
+    """Test the Sparse version of Mobilenet V2 TF Lite model."""
+    # MobilenetV1
+    tflite_model_file = download_testdata(
+        "https://storage.googleapis.com/fast-convnets/tflite-models/mbv2_200_85_11-16b2_744.tflite",
+        "mbv2_200_85_11-16b2_744.tflite",
+    )
+    with open(tflite_model_file, "rb") as f:
+        tflite_model_buf = f.read()
+    data = np.random.uniform(size=(1, 224, 224, 3)).astype("float32")
+    tflite_output = run_tflite_graph(tflite_model_buf, data)
+    tvm_output = run_tvm_graph(tflite_model_buf, data, "float_image_input")
+    tvm.testing.assert_allclose(
+        np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-5
+    )
+
+
 #######################################################################
 # Inception
 # ---------
@@ -3980,6 +4156,27 @@ def test_forward_mediapipe_hand_landmark():
         )
 
 
+#######################################################################
+# Test check for Tensorflow "dynamic range quantization" optimization
+# --------------
+def test_prevent_tensorflow_dynamic_range():
+    """
+    Should prevent runnung "dynamic range quantization" optimized TFLite graph
+    """
+    data_array = np.random.randint(0, 2, (1, 1024, 1024)).astype(dtype=np.float32)
+    filter_array = np.random.randint(0, 2, (1024, 1024)).astype(dtype=np.float32)
+    data_in = tf.keras.layers.Input(shape=data_array.shape[1:])
+    dense = tf.keras.layers.Dense(units=filter_array.shape[-1], use_bias=False)(data_in)
+    keras_model = tf.keras.models.Model(data_in, dense)
+    keras_model.layers[1].set_weights([filter_array])
+
+    converter = interpreter_wrapper.TFLiteConverter.from_keras_model(keras_model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    tflite_model = converter.convert()
+    with pytest.raises(tvm.error.OpNotImplemented):
+        tvm_output = run_tvm_graph(tflite_model, data_array, data_in.name.replace(":0", ""))
+
+
 #######################################################################
 # Main
 # ----
@@ -4083,6 +4280,10 @@ def test_forward_mediapipe_hand_landmark():
     test_forward_coco_ssd_mobilenet_v1()
     test_forward_mediapipe_hand_landmark()
 
+    # End to End Sparse models
+    test_forward_sparse_mobilenet_v1()
+    test_forward_sparse_mobilenet_v2()
+
     # End to End quantized
     test_forward_qnn_inception_v1_net()
     test_forward_qnn_mobilenet_v1_net()
diff --git a/tests/python/integration/test_dot.py b/tests/python/integration/test_dot.py
index d4364c88dc9a..609b6dedfb3a 100644
--- a/tests/python/integration/test_dot.py
+++ b/tests/python/integration/test_dot.py
@@ -27,7 +27,7 @@ def test_dot():
     A = te.placeholder((n,), name="A")
     B = te.placeholder((n,), name="B")
     k = te.reduce_axis((0, n), "k")
-    C = te.compute((1,), lambda _: te.sum(A[k] * B[k], axis=k), name="C")
+    C = te.compute((), lambda: te.sum(A[k] * B[k], axis=k), name="C")
     s = te.create_schedule(C.op)
 
     def verify(target):
@@ -36,7 +36,7 @@ def verify(target):
         ctx = tvm.cpu(0)
         a = tvm.nd.array(np.random.uniform(size=(nn,)).astype(A.dtype), ctx)
         b = tvm.nd.array(np.random.uniform(size=(nn,)).astype(B.dtype), ctx)
-        c = tvm.nd.array(np.zeros((1,), dtype=C.dtype), ctx)
+        c = tvm.nd.array(np.zeros((), dtype=C.dtype), ctx)
         f(a, b, c)
         tvm.testing.assert_allclose(c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-4)
 
diff --git a/tests/python/integration/test_reduce.py b/tests/python/integration/test_reduce.py
index b02b7980f37a..e978b83aabd6 100644
--- a/tests/python/integration/test_reduce.py
+++ b/tests/python/integration/test_reduce.py
@@ -73,7 +73,7 @@ def test_init_imm():
     n = tvm.runtime.convert(1027)
     A = te.placeholder((n,), name="A")
     k = te.reduce_axis((0, n))
-    B = te.compute((1,), lambda i: te.sum(A[k], axis=k, init=10.0), name="B")
+    B = te.compute((), lambda: te.sum(A[k], axis=k, init=10.0), name="B")
     # schedule
     s = te.create_schedule(B.op)
     # one line to build the function.
@@ -86,7 +86,7 @@ def check_target(target="llvm"):
         # launch the kernel.
         n = 1027
         a = tvm.nd.array(np.random.uniform(size=(n,)).astype(A.dtype), ctx)
-        b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx)
+        b = tvm.nd.array(np.zeros((), dtype=B.dtype), ctx)
         fsum(a, b)
         res = 10.0 + np.sum(a.asnumpy(), axis=0)
         tvm.testing.assert_allclose(b.asnumpy(), res, rtol=1e-4)
@@ -129,7 +129,7 @@ def test_rfactor():
     n = tvm.runtime.convert(1027)
     A = te.placeholder((n,), name="A")
     k = te.reduce_axis((0, n))
-    B = te.compute((1,), lambda i: te.sum(A[k], axis=k), name="B")
+    B = te.compute((), lambda: te.sum(A[k], axis=k), name="B")
     # schedule
     s = te.create_schedule(B.op)
     kf, ki = s[B].split(k, nparts=4)
@@ -145,7 +145,7 @@ def check_target(target="llvm"):
         # launch the kernel.
         n = 1027
         a = tvm.nd.array(np.random.uniform(size=(n,)).astype(A.dtype), ctx)
-        b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx)
+        b = tvm.nd.array(np.zeros((), dtype=B.dtype), ctx)
         fsum(a, b)
         res = np.sum(a.asnumpy(), axis=0)
         tvm.testing.assert_allclose(b.asnumpy(), res, rtol=1e-4)
@@ -191,11 +191,11 @@ def test_rfactor_factor_axis():
     n = tvm.runtime.convert(1027)
     A = te.placeholder((n,), name="A")
     k = te.reduce_axis((0, n))
-    B = te.compute((1,), lambda i: te.sum(A[k], axis=k), name="B")
+    B = te.compute((), lambda: te.sum(A[k], axis=k), name="B")
     # schedule
     s = te.create_schedule(B.op)
     kf, ki = s[B].split(k, nparts=4)
-    BF = s.rfactor(B, kf, 1)
+    BF = s.rfactor(B, kf, 0)
     s[BF].parallel(BF.op.axis[0])
     # one line to build the function.
     def check_target(target="llvm"):
@@ -207,7 +207,7 @@ def check_target(target="llvm"):
         # launch the kernel.
         n = 1027
         a = tvm.nd.array(np.random.uniform(size=(n,)).astype(A.dtype), ctx)
-        b = tvm.nd.array(np.zeros(1, dtype=B.dtype), ctx)
+        b = tvm.nd.array(np.zeros((), dtype=B.dtype), ctx)
         fsum(a, b)
         res = np.sum(a.asnumpy(), axis=0)
         tvm.testing.assert_allclose(b.asnumpy(), res, rtol=1e-4)
diff --git a/tests/python/integration/test_tuning.py b/tests/python/integration/test_tuning.py
index 64b2c16e155e..813352c52096 100644
--- a/tests/python/integration/test_tuning.py
+++ b/tests/python/integration/test_tuning.py
@@ -18,9 +18,14 @@
 Test the tuner
 """
 import logging
+import sys
+import textwrap
 import time
 
+import pytest
+
 import tvm
+import tvm.relay
 from tvm import te
 
 from tvm import autotvm
@@ -29,94 +34,100 @@
 import tvm.testing
 
 
-@autotvm.template("testing/conv2d_no_batching")
-def conv2d_no_batching(N, H, W, CI, CO, KH, KW):
-    """An example template for testing"""
-    assert N == 1, "Only consider batch_size = 1 in this template"
-
-    data = te.placeholder((N, CI, H, W), name="data")
-    kernel = te.placeholder((CO, CI, KH, KW), name="kernel")
-
-    rc = te.reduce_axis((0, CI), name="rc")
-    ry = te.reduce_axis((0, KH), name="ry")
-    rx = te.reduce_axis((0, KW), name="rx")
-
-    conv = te.compute(
-        (N, CO, H - KH + 1, W - KW + 1),
-        lambda nn, ff, yy, xx: te.sum(
-            data[nn, rc, yy + ry, xx + rx] * kernel[ff, rc, ry, rx], axis=[rc, ry, rx]
-        ),
-        tag="conv2d_nchw",
-    )
-
-    s = te.create_schedule([conv.op])
-
-    output = conv
-    OL = s.cache_write(conv, "local")
-
-    # create cache stage
-    AA = s.cache_read(data, "shared", [OL])
-    WW = s.cache_read(kernel, "shared", [OL])
-    AL = s.cache_read(AA, "local", [OL])
-    WL = s.cache_read(WW, "local", [OL])
-
-    # tile and bind spatial axes
-    n, f, y, x = s[output].op.axis
-    cfg = autotvm.get_config()
-    cfg.define_split("tile_f", cfg.axis(f), num_outputs=4)
-    cfg.define_split("tile_y", cfg.axis(y), num_outputs=4)
-    cfg.define_split("tile_x", cfg.axis(x), num_outputs=4)
-    bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f)
-    by, vy, ty, yi = cfg["tile_y"].apply(s, output, y)
-    bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x)
-    kernel_scope = n  # this is the scope to attach global config inside this kernel
-
-    s[output].bind(bf, te.thread_axis("blockIdx.z"))
-    s[output].bind(by, te.thread_axis("blockIdx.y"))
-    s[output].bind(bx, te.thread_axis("blockIdx.x"))
-    s[output].bind(vf, te.thread_axis("vthread"))
-    s[output].bind(vy, te.thread_axis("vthread"))
-    s[output].bind(vx, te.thread_axis("vthread"))
-    s[output].bind(tf, te.thread_axis("threadIdx.z"))
-    s[output].bind(ty, te.thread_axis("threadIdx.y"))
-    s[output].bind(tx, te.thread_axis("threadIdx.x"))
-    s[output].reorder(n, bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi)
-    s[OL].compute_at(s[output], tx)
-
-    # tile and bind reduction axes
-    n, f, y, x = s[OL].op.axis
-    rc, ry, rx = s[OL].op.reduce_axis
-    cfg.define_split("tile_rc", cfg.axis(rc), num_outputs=3)
-    cfg.define_split("tile_ry", cfg.axis(ry), num_outputs=3)
-    cfg.define_split("tile_rx", cfg.axis(rx), num_outputs=3)
-    rco, rcm, rci = cfg["tile_rc"].apply(s, OL, rc)
-    ryo, rym, ryi = cfg["tile_rx"].apply(s, OL, ry)
-    rxo, rxm, rxi = cfg["tile_ry"].apply(s, OL, rx)
-    s[OL].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, n, f, y, x)
-
-    s[AA].compute_at(s[OL], rxo)
-    s[WW].compute_at(s[OL], rxo)
-    s[AL].compute_at(s[OL], rxm)
-    s[WL].compute_at(s[OL], rxm)
-
-    # cooperative fetching
-    for load in [AA, WW]:
-        n, f, y, x = s[load].op.axis
-        fused = s[load].fuse(n, f, y, x)
-        tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2])
-        ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2])
-        tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2])
-        s[load].bind(tz, te.thread_axis("threadIdx.z"))
-        s[load].bind(ty, te.thread_axis("threadIdx.y"))
-        s[load].bind(tx, te.thread_axis("threadIdx.x"))
-
-    # tune unroll
-    cfg.define_knob("auto_unroll_max_step", [0, 512, 1500])
-    cfg.define_knob("unroll_explicit", [0, 1])
-    s[output].pragma(kernel_scope, "auto_unroll_max_step", cfg["auto_unroll_max_step"].val)
-    s[output].pragma(kernel_scope, "unroll_explicit", cfg["unroll_explicit"].val)
-
-    return s, [data, kernel, conv]
+def setup_module():
+    @autotvm.template("testing/conv2d_no_batching")
+    def conv2d_no_batching(N, H, W, CI, CO, KH, KW):
+        """An example template for testing"""
+        assert N == 1, "Only consider batch_size = 1 in this template"
+
+        data = te.placeholder((N, CI, H, W), name="data")
+        kernel = te.placeholder((CO, CI, KH, KW), name="kernel")
+
+        rc = te.reduce_axis((0, CI), name="rc")
+        ry = te.reduce_axis((0, KH), name="ry")
+        rx = te.reduce_axis((0, KW), name="rx")
+
+        conv = te.compute(
+            (N, CO, H - KH + 1, W - KW + 1),
+            lambda nn, ff, yy, xx: te.sum(
+                data[nn, rc, yy + ry, xx + rx] * kernel[ff, rc, ry, rx], axis=[rc, ry, rx]
+            ),
+            tag="conv2d_nchw",
+        )
+
+        s = te.create_schedule([conv.op])
+
+        output = conv
+        OL = s.cache_write(conv, "local")
+
+        # create cache stage
+        AA = s.cache_read(data, "shared", [OL])
+        WW = s.cache_read(kernel, "shared", [OL])
+        AL = s.cache_read(AA, "local", [OL])
+        WL = s.cache_read(WW, "local", [OL])
+
+        # tile and bind spatial axes
+        n, f, y, x = s[output].op.axis
+        cfg = autotvm.get_config()
+        cfg.define_split("tile_f", cfg.axis(f), num_outputs=4)
+        cfg.define_split("tile_y", cfg.axis(y), num_outputs=4)
+        cfg.define_split("tile_x", cfg.axis(x), num_outputs=4)
+        bf, vf, tf, fi = cfg["tile_f"].apply(s, output, f)
+        by, vy, ty, yi = cfg["tile_y"].apply(s, output, y)
+        bx, vx, tx, xi = cfg["tile_x"].apply(s, output, x)
+        kernel_scope = n  # this is the scope to attach global config inside this kernel
+
+        s[output].bind(bf, te.thread_axis("blockIdx.z"))
+        s[output].bind(by, te.thread_axis("blockIdx.y"))
+        s[output].bind(bx, te.thread_axis("blockIdx.x"))
+        s[output].bind(vf, te.thread_axis("vthread"))
+        s[output].bind(vy, te.thread_axis("vthread"))
+        s[output].bind(vx, te.thread_axis("vthread"))
+        s[output].bind(tf, te.thread_axis("threadIdx.z"))
+        s[output].bind(ty, te.thread_axis("threadIdx.y"))
+        s[output].bind(tx, te.thread_axis("threadIdx.x"))
+        s[output].reorder(n, bf, by, bx, vf, vy, vx, tf, ty, tx, fi, yi, xi)
+        s[OL].compute_at(s[output], tx)
+
+        # tile and bind reduction axes
+        n, f, y, x = s[OL].op.axis
+        rc, ry, rx = s[OL].op.reduce_axis
+        cfg.define_split("tile_rc", cfg.axis(rc), num_outputs=3)
+        cfg.define_split("tile_ry", cfg.axis(ry), num_outputs=3)
+        cfg.define_split("tile_rx", cfg.axis(rx), num_outputs=3)
+        rco, rcm, rci = cfg["tile_rc"].apply(s, OL, rc)
+        ryo, rym, ryi = cfg["tile_rx"].apply(s, OL, ry)
+        rxo, rxm, rxi = cfg["tile_ry"].apply(s, OL, rx)
+        s[OL].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, n, f, y, x)
+
+        s[AA].compute_at(s[OL], rxo)
+        s[WW].compute_at(s[OL], rxo)
+        s[AL].compute_at(s[OL], rxm)
+        s[WL].compute_at(s[OL], rxm)
+
+        # cooperative fetching
+        for load in [AA, WW]:
+            n, f, y, x = s[load].op.axis
+            fused = s[load].fuse(n, f, y, x)
+            tz, fused = s[load].split(fused, nparts=cfg["tile_f"].size[2])
+            ty, fused = s[load].split(fused, nparts=cfg["tile_y"].size[2])
+            tx, fused = s[load].split(fused, nparts=cfg["tile_x"].size[2])
+            s[load].bind(tz, te.thread_axis("threadIdx.z"))
+            s[load].bind(ty, te.thread_axis("threadIdx.y"))
+            s[load].bind(tx, te.thread_axis("threadIdx.x"))
+
+        # tune unroll
+        cfg.define_knob("auto_unroll_max_step", [0, 512, 1500])
+        cfg.define_knob("unroll_explicit", [0, 1])
+        s[output].pragma(kernel_scope, "auto_unroll_max_step", cfg["auto_unroll_max_step"].val)
+        s[output].pragma(kernel_scope, "unroll_explicit", cfg["unroll_explicit"].val)
+
+        return s, [data, kernel, conv]
+
+
+def teardown_module():
+    # TODO(areusch): Tasks should not be registered into a global.
+    del autotvm.task.task.TASK_TABLE["testing/conv2d_no_batching"]
 
 
 def get_sample_task(target=tvm.target.cuda(), target_host=None):
@@ -131,19 +142,62 @@ def get_sample_task(target=tvm.target.cuda(), target_host=None):
 
 
 @tvm.testing.parametrize_targets("cuda", "opencl")
-def test_tuning(target, ctx):
+def test_tuning_gpu(target, ctx):
     # init task
     task, target = get_sample_task(target, None)
-    logging.info("%s", task.config_space)
+    logging.info("task config space: %s", task.config_space)
 
     measure_option = autotvm.measure_option(autotvm.LocalBuilder(), autotvm.LocalRunner())
 
+    results = []
+
     tuner = RandomTuner(task)
-    tuner.tune(n_trial=20, measure_option=measure_option)
+    tuner.tune(
+        n_trial=20,
+        measure_option=measure_option,
+        callbacks=(lambda _tuner, _inputs, rs: results.extend(rs),),
+    )
 
+    assert len(results) == 20
 
-if __name__ == "__main__":
-    # only print log when invoked from main
-    logging.basicConfig(level=logging.DEBUG)
+    successful_results = [r for r in results if r.error_no == autotvm.MeasureErrorNo.NO_ERROR]
+    assert len(successful_results) > 0, f"No successful tuning runs: {results!r}"
+
+
+def test_tuning_cpu():
+    ir_mod = tvm.parser.fromtext(
+        textwrap.dedent(
+            """
+        #[version = "0.0.5"]
+        def @main(%a : Tensor[(1, 3, 32, 32), float32], %b : Tensor[(3, 3, 5, 5), float32]) {
+               nn.conv2d(%a, %b, data_layout="NCHW", kernel_layout="OIHW")
+        }
+        """
+        )
+    )
+    tasks = autotvm.task.relay_integration.extract_from_program(
+        ir_mod, {}, tvm.target.create("llvm")
+    )
+    assert len(tasks) == 1, f"Extracted != 1 task from program: {tasks!r}"
+
+    task = tasks[0]
+
+    measure_option = autotvm.measure_option(autotvm.LocalBuilder(), autotvm.LocalRunner())
+
+    results = []
+
+    tuner = RandomTuner(task)
+    tuner.tune(
+        n_trial=20,
+        measure_option=measure_option,
+        callbacks=(lambda _tuner, _inputs, rs: results.extend(rs),),
+    )
+
+    assert len(results) == 20
 
-    test_tuning()
+    successful_results = [r for r in results if r.error_no == autotvm.MeasureErrorNo.NO_ERROR]
+    assert len(successful_results) > 0, f"No successful tuning runs: {results!r}"
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))
diff --git a/tests/python/relay/dyn/test_dynamic_op_level3.py b/tests/python/relay/dyn/test_dynamic_op_level3.py
index dd73b9a96a52..d5f81e84e39d 100644
--- a/tests/python/relay/dyn/test_dynamic_op_level3.py
+++ b/tests/python/relay/dyn/test_dynamic_op_level3.py
@@ -26,14 +26,21 @@
 import tvm.testing
 
 
-def verify_func(func, data, ref_res):
+def verify_func(func, data, ref_res, target_ctx=tvm.testing.enabled_targets()):
     assert isinstance(data, list)
-    for target, ctx in tvm.testing.enabled_targets():
+    for target, ctx in target_ctx:
         for kind in ["vm", "debug"]:
             mod = tvm.ir.IRModule.from_expr(func)
             intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
             op_res = intrp.evaluate()(*data)
-            tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+            if isinstance(op_res, tvm.runtime.container.ADT):
+                assert len(op_res) == len(
+                    ref_res
+                ), "Outputs from TVM and Python implementation must be equal "
+                for op_result, ref_result in zip(op_res, ref_res):
+                    tvm.testing.assert_allclose(op_result.asnumpy(), ref_result, rtol=1e-5)
+            else:
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
             relay.backend.compile_engine.get().clear()
 
 
@@ -202,5 +209,160 @@ def verify_sparse_to_dense(sparse_indices, sparse_values, default_value, output_
     verify_sparse_to_dense(1, 3, None, [5], [0, 3, 0, 0, 0])  # default value not specified
 
 
+@pytest.mark.parametrize(
+    "sparse_indices, sparse_values, dense_shape, default_value",
+    [
+        (
+            np.array([[0, 1], [0, 3], [2, 0], [3, 1]], dtype=np.int64),
+            np.array([1, 2, 3, 4], dtype=np.int64),
+            np.array([5, 6], dtype=np.int64),
+            np.array([10], dtype=np.int64),
+        ),
+        (
+            np.array([[1, 1, 1], [1, 3, 1], [2, 0, 5], [3, 1, 6]], dtype=np.int64),
+            np.array([1, 2, 3, 4], dtype=np.int64),
+            np.array([7, 7, 7], dtype=np.int64),
+            np.array([5], dtype=np.int64),
+        ),
+        (
+            np.array([[1], [2]], dtype=np.int64),
+            np.array([7, 8], dtype=np.int64),
+            np.array([5], dtype=np.int64),
+            np.array([4], dtype=np.int64),
+        ),
+        (
+            np.ones((0, 1), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([5], dtype=np.int64),
+            np.array([4], dtype=np.int64),
+        ),
+        (
+            np.ones((0, 3), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([9, 3, 7], dtype=np.int64),
+            np.array([100], dtype=np.int64),
+        ),
+    ],
+)
+@pytest.mark.parametrize("dtype", [np.int64, np.int32])
+@pytest.mark.parametrize("use_dyn", [True, False])
+def test_sparse_fill_empty_rows(
+    sparse_indices, sparse_values, dense_shape, default_value, dtype, use_dyn
+):
+    def ref_sparse_fill_empty_rows(
+        sparse_indices: np.ndarray,
+        sparse_values: np.ndarray,
+        dense_shape: np.ndarray,
+        default_value: np.ndarray,
+    ) -> None:
+        """
+        This function calculates the expected output of sparse_fill_empty_rows operator given the
+        inputs.
+        """
+
+        def check_add_rows(current_idx, limit_idx):
+            while current_idx < limit_idx:
+                new_sparse_indices.append([current_idx] + [0] * (num_cols - 1))
+                new_sparse_values.append(default_value[0])
+                empty_row_indicator[current_idx] = True
+                current_idx += 1
+
+            return current_idx
+
+        current_idx = 0
+        new_sparse_indices = []
+        new_sparse_values = []
+        empty_row_indicator = [False for _ in range(dense_shape[0])]
+        num_cols = sparse_indices.shape[1]
+        for sparse_row, sparse_value in zip(sparse_indices, sparse_values):
+            limit_idx = sparse_row[0]
+            current_idx = check_add_rows(current_idx, limit_idx)
+            new_sparse_indices.append(list(sparse_row))
+            new_sparse_values.append(sparse_value)
+            current_idx = limit_idx + 1
+
+        check_add_rows(current_idx, dense_shape[0])
+        return new_sparse_indices, new_sparse_values, empty_row_indicator
+
+    def verify_sparse_fill_empty_rows(
+        sparse_indices_np: np.ndarray,
+        sparse_values_np: np.ndarray,
+        dense_shape_np: np.ndarray,
+        default_value_np: np.ndarray,
+    ) -> None:
+        """
+        This function verifies the relay output of sparse_fill_empty_rows with its expected output.
+        """
+        if use_dyn:
+            sparse_indices = relay.var(
+                "sparse_indices",
+                shape=[relay.Any(), relay.Any()],
+                dtype=str(sparse_indices_np.dtype),
+            )
+            sparse_values = relay.var(
+                "sparse_values",
+                shape=[relay.Any()],
+                dtype=str(sparse_values_np.dtype),
+            )
+            dense_shape = relay.var(
+                "dense_shape",
+                shape=[relay.Any()],
+                dtype=str(dense_shape_np.dtype),
+            )
+            default_value = relay.var(
+                "default_value",
+                shape=[relay.Any()],
+                dtype=str(default_value_np.dtype),
+            )
+        else:
+            sparse_indices = relay.var(
+                "sparse_indices",
+                relay.TensorType(sparse_indices_np.shape, str(sparse_indices_np.dtype)),
+            )
+            sparse_values = relay.var(
+                "sparse_values",
+                relay.TensorType(sparse_values_np.shape, str(sparse_values_np.dtype)),
+            )
+            dense_shape = relay.var(
+                "dense_shape",
+                relay.TensorType(dense_shape_np.shape, str(dense_shape_np.dtype)),
+            )
+            default_value = relay.var(
+                "default_value",
+                relay.TensorType(default_value_np.shape, str(default_value_np.dtype)),
+            )
+        z = relay.sparse_fill_empty_rows(sparse_indices, sparse_values, dense_shape, default_value)
+        func = relay.Function([sparse_indices, sparse_values, dense_shape, default_value], z)
+        ref_res = ref_sparse_fill_empty_rows(
+            sparse_indices_np,
+            sparse_values_np,
+            dense_shape_np,
+            default_value_np,
+        )
+        (
+            new_sparse_indices_infer_type,
+            new_sparse_values_infer_type,
+            empty_row_indicator_infer_type,
+        ) = run_infer_type(z)
+
+        assert new_sparse_indices_infer_type.checked_type.dtype == sparse_indices_np.dtype
+        assert new_sparse_values_infer_type.checked_type.dtype == sparse_indices_np.dtype
+        assert empty_row_indicator_infer_type.checked_type.dtype == "bool"
+
+        verify_func(
+            func,
+            [sparse_indices_np, sparse_values_np, dense_shape_np, default_value_np],
+            ref_res,
+            [("llvm", tvm.cpu())],
+        )
+
+    verify_sparse_fill_empty_rows(
+        sparse_indices.astype(dtype),
+        sparse_values.astype(dtype),
+        dense_shape.astype(dtype),
+        default_value.astype(dtype),
+    )
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/tests/python/relay/test_any.py b/tests/python/relay/test_any.py
index e6812aa3bbfa..32292de4c8ea 100644
--- a/tests/python/relay/test_any.py
+++ b/tests/python/relay/test_any.py
@@ -54,7 +54,6 @@ def check_result(
     for kind in ["debug", "vm"]:
         targets = targets or tvm.testing.enabled_targets()
         for tgt, ctx in targets:
-            print(tgt)
             if disable_targets and tgt in disable_targets:
                 continue
             if kind == "debug" and (only_vm or ctx.device_type != tvm.cpu().device_type):
@@ -72,12 +71,11 @@ def check_result(
                         str(e),
                         str(r),
                     )
-                    return
-
-                if flatten:
-                    r = r.flatten()
-                    e = e.flatten()
-                tvm.testing.assert_allclose(r, e, atol=2e-6)
+                else:
+                    if flatten:
+                        r = r.flatten()
+                        e = e.flatten()
+                    tvm.testing.assert_allclose(r, e, atol=2e-6)
 
 
 def verify_any_broadcast(x_shape, y_shape, x_np_shape, y_np_shape, op, np_op):
@@ -121,6 +119,7 @@ def test_any_elemwise():
     verify_any_elemwise((relay.Any(),), (3,), relay.sqrt, np.sqrt)
     verify_any_elemwise((relay.Any(), 2), (5, 2), relay.negative, np.negative)
     verify_any_elemwise((relay.Any(), relay.Any()), (5, 4), relay.exp, np.exp)
+    verify_any_elemwise((relay.Any(),), (3,), relay.round, np.round)
 
 
 @tvm.testing.uses_gpu
@@ -209,6 +208,27 @@ def test_any_concat():
     ref = np.concatenate(x_np, axis=0)
     check_result(x_np, mod, ref)
 
+    def test_oshape(in_vars, axis, oshape):
+        z = relay.op.concatenate(in_vars, axis=axis)
+        mod = tvm.IRModule()
+        mod["main"] = relay.Function(in_vars, z)
+        typed_mod = relay.transform.InferType()(mod)
+        assert typed_mod["main"].body.checked_type == relay.TensorType(oshape, dtype="float32")
+
+    x = [relay.var("x", shape=(relay.Any(), 3), dtype="float32") for _ in range(3)]
+    x.append(relay.var("x", shape=(relay.Any(), relay.Any()), dtype="float32"))
+
+    test_oshape(x, 0, (relay.Any(), 3))
+    test_oshape(x, 1, (relay.Any(), relay.Any()))
+
+    # [(1, 3), (1, ?)] -> (2, ?)
+    x = [
+        relay.var("x", shape=(1, 3), dtype="float32"),
+        relay.var("x", shape=(1, relay.Any()), dtype="float32"),
+    ]
+    test_oshape(x, 0, (2, relay.Any()))
+    test_oshape(x, 1, (1, relay.Any()))
+
 
 def verify_any_reshape(x_shape, newshape, x_np_shape, out_shape, variable_newshape=False):
     x = relay.var("x", shape=x_shape, dtype="float32")
@@ -240,6 +260,28 @@ def test_any_reshape():
     verify_any_reshape(any_dims(3), (-4, 2, -1, -2), (6, 3, 4), (2, 3, 3, 4))
 
 
+def verify_any_one_hot(indices_shape, indices_np_shape, depth, on_value, off_value, axis, dtype):
+    indices = relay.var("indices", shape=indices_shape, dtype="int32")
+    on_value_const = relay.const(on_value, dtype)
+    off_value_const = relay.const(off_value, dtype)
+    y = relay.one_hot(indices, on_value_const, off_value_const, depth, axis=axis, dtype=dtype)
+    params = [indices]
+    mod = tvm.IRModule()
+    mod["main"] = relay.Function(params, y)
+
+    indices_npy = np.random.randint(0, depth, size=indices_np_shape).astype("int32")
+    out_npy = tvm.topi.testing.one_hot(indices_npy, on_value, off_value, depth, axis, dtype)
+    args = [indices_npy]
+    check_result(args, mod, out_npy)
+
+
+@tvm.testing.uses_gpu
+def test_any_one_hot():
+    verify_any_one_hot(any_dims(1), (3,), 3, 1, 0, -1, "int32")
+    verify_any_one_hot(any_dims(2), (2, 2), 5, 0.5, -0.5, 1, "float32")
+    verify_any_one_hot(any_dims(4), (3, 2, 4, 5), 6, 1.0, 0.0, 0, "float32")
+
+
 def verify_any_argwhere(x_shape, x_np_shape, dtype="bool"):
     x = relay.var("x", shape=x_shape, dtype=dtype)
     y = relay.argwhere(x)
@@ -454,6 +496,7 @@ def verify_any_conv2d(
     dilation,
     static_data_shape,
     ref_out_shape,
+    use_cudnn=False,
 ):
     mod = tvm.IRModule()
     dtype = "float32"
@@ -463,7 +506,12 @@ def verify_any_conv2d(
     mod["main"] = relay.Function([data, kernel], y)
     data_np = np.random.uniform(size=static_data_shape).astype(dtype)
     kernel_np = np.random.uniform(size=kernel_shape).astype(dtype)
-    check_result([data_np, kernel_np], mod, ref_out_shape, assert_shape=True)
+
+    targets = None
+    if use_cudnn and tvm.get_global_func("tvm.contrib.cudnn.conv.output_shape", True):
+        targets = [("cuda -libs=cudnn", tvm.gpu(0))]
+
+    check_result([data_np, kernel_np], mod, ref_out_shape, assert_shape=True, targets=targets)
 
 
 # TODO(@kevinthesun): Support dynamic input height and width.
@@ -487,6 +535,16 @@ def test_any_conv2d():
         (2, 64, 224, 224),
         (2, 64, 222, 222),
     )
+    verify_any_conv2d(
+        (relay.Any(), 64, 224, 224),
+        (64, 64, 3, 3),
+        (1, 1),
+        (1, 1),
+        (1, 1),
+        (1, 64, 224, 224),
+        (1, 64, 224, 224),
+        use_cudnn=True,
+    )
 
 
 def verify_any_conv2d_NCHWc(
@@ -724,7 +782,13 @@ def test_any_batch_flatten():
 
 
 def verify_any_dense(
-    data_shape, weight_shape, units, static_data_shape, static_weight_shape, ref_out_shape
+    data_shape,
+    weight_shape,
+    units,
+    static_data_shape,
+    static_weight_shape,
+    ref_out_shape,
+    use_cublas=False,
 ):
     mod = tvm.IRModule()
     dtype = "float32"
@@ -734,7 +798,12 @@ def verify_any_dense(
     mod["main"] = relay.Function([data, weight], y)
     data_np = np.random.uniform(size=static_data_shape).astype(dtype)
     weight_np = np.random.uniform(size=static_weight_shape).astype(dtype)
-    check_result([data_np, weight_np], mod, ref_out_shape, assert_shape=True)
+
+    targets = None
+    if use_cublas and tvm.get_global_func("tvm.contrib.cublas.matmul", True):
+        targets = [("cuda -libs=cublas", tvm.gpu(0))]
+
+    check_result([data_np, weight_np], mod, ref_out_shape, assert_shape=True, targets=targets)
 
 
 # TODO(tvm-team) Fix dense schedule
@@ -744,6 +813,12 @@ def test_any_dense():
     verify_any_dense(any_dims(2), (50, relay.Any()), 50, (4, 40), (50, 40), (4, 50))
 
 
+@tvm.testing.uses_gpu
+def test_any_dense_dynamic_batch():
+    verify_any_dense((relay.Any(), 40), (50, 40), 50, (4, 40), (50, 40), (4, 50))
+    verify_any_dense((relay.Any(), 40), (50, 40), 50, (4, 40), (50, 40), (4, 50), use_cublas=True)
+
+
 @tvm.testing.uses_gpu
 def verify_any_pad(data_shape, pad_width, static_data_shape):
     mod = tvm.IRModule()
@@ -813,7 +888,7 @@ def test_any_softmax():
     verify_any_softmax(any_dims(4), 2, (13, 11, 3, 1), (13, 11, 3, 1))
 
 
-def verify_any_topk(data_shape, kval, np_dshape, dtype, const_k=False):
+def verify_any_topk(data_shape, kval, np_dshape, dtype, ret_type="indices", const_k=False):
     mod = tvm.IRModule()
     data = relay.var("data", shape=data_shape, dtype=dtype)
     np_data = np.random.uniform(size=np_dshape).astype(dtype)
@@ -825,7 +900,9 @@ def verify_any_topk(data_shape, kval, np_dshape, dtype, const_k=False):
         k = relay.var("k", shape=(), dtype="int32")
         args = [data, k]
         in_vals = [np_data, kval]
-    out = relay.topk(data, k, ret_type="indices")
+    out = relay.topk(data, k, ret_type=ret_type)
+    if ret_type == "both":
+        out = out[0]
     mod["main"] = relay.Function(args, out)
 
     sorted = np.argsort(-np_data)
@@ -841,7 +918,56 @@ def verify_any_topk(data_shape, kval, np_dshape, dtype, const_k=False):
 def test_any_topk():
     verify_any_topk(any_dims(1), 5, (10,), "float32")
     verify_any_topk(any_dims(2), 2, (6, 3), "int32")
-    verify_any_topk(any_dims(2), 3, (6, 3), "float32", True)
+    verify_any_topk(any_dims(2), 3, (6, 3), "float32", const_k=True)
+    verify_any_topk(any_dims(1), 0, (0,), "float32", ret_type="both")
+
+
+def verify_any_get_valid_counts(num_anchor_real, dtype, targets=None):
+    mod = tvm.IRModule()
+    batch_size = 1
+    num_anchor = relay.Any()
+    data = relay.var("data", shape=(batch_size, num_anchor, 5), dtype=dtype)
+    np_data = np.random.uniform(size=(batch_size, num_anchor_real, 5)).astype(dtype)
+
+    np_out1 = np.zeros(shape=(batch_size,))
+    np_out2 = np.zeros(shape=np_data.shape).astype(dtype)
+    np_out3 = np.zeros(shape=(batch_size, num_anchor_real))
+    score_threshold = 0.95
+
+    for i in range(batch_size):
+        np_out1[i] = 0
+        inter_idx = 0
+        for j in range(num_anchor_real):
+            score = np_data[i, j, 0]
+            if score > score_threshold:
+                for k in range(5):
+                    np_out2[i, inter_idx, k] = np_data[i, j, k]
+                np_out1[i] += 1
+                np_out3[i, inter_idx] = j
+                inter_idx += 1
+            if j >= np_out1[i]:
+                for k in range(5):
+                    np_out2[i, j, k] = -1.0
+                np_out3[i, j] = -1
+
+    z = relay.vision.get_valid_counts(data, score_threshold, 0, score_index=0)
+
+    mod["main"] = relay.Function([data], z.astuple())
+
+    check_result([np_data], mod, [np_out1, np_out2, np_out3], targets=targets)
+
+
+@tvm.testing.uses_gpu
+def test_any_get_valid_counts():
+    verify_any_get_valid_counts(10, "float32")
+    # opencl seems to have issues with empty size buffer
+    # Check failed: err_code == CL_SUCCESS == false: OpenCL Error,
+    # code=-61: CL_INVALID_BUFFER_SIZE
+    targets = []
+    for tgt, ctx in tvm.testing.enabled_targets():
+        if "opencl" not in tgt:
+            targets.append((tgt, ctx))
+    verify_any_get_valid_counts(0, "float32", targets=targets)
 
 
 @tvm.testing.uses_gpu
diff --git a/tests/python/relay/test_auto_scheduler_layout_rewrite.py b/tests/python/relay/test_auto_scheduler_layout_rewrite_networks.py
similarity index 100%
rename from tests/python/relay/test_auto_scheduler_layout_rewrite.py
rename to tests/python/relay/test_auto_scheduler_layout_rewrite_networks.py
diff --git a/tests/python/relay/test_auto_scheduler_task_extraction.py b/tests/python/relay/test_auto_scheduler_task_extraction.py
index 531d0412c97d..cfbca40cf379 100644
--- a/tests/python/relay/test_auto_scheduler_task_extraction.py
+++ b/tests/python/relay/test_auto_scheduler_task_extraction.py
@@ -132,6 +132,15 @@ def test_task_extraction():
     dtype = "float32"
     target = tvm.target.Target("llvm")
 
+    def verify_task_extraction(func, expected_task, include_simple_tasks=False):
+        mod = tvm.IRModule.from_expr(func)
+        tasks, task_weights = auto_scheduler.extract_tasks(
+            mod["main"], None, target, include_simple_tasks=include_simple_tasks
+        )
+
+        assert len(tasks) == expected_task
+        assert len(task_weights) == expected_task
+
     def get_func():
         data = relay.var("data", shape=(ishape), dtype=dtype)
         weight1 = relay.var("weight1", shape=(w1shape), dtype=dtype)
@@ -161,6 +170,29 @@ def get_simple_func():
         out = relay.image.affine_grid(data, (150, 150))
         return relay.Function([data], out)
 
+    def get_shape_of_func():
+        data = relay.var("data", shape=(relay.Any(), 28, 28), dtype="float32")
+        out = relay.shape_of(data)
+        return relay.Function([data], out)
+
+    def get_func_with_dynamic_shape():
+        data = relay.var("data", shape=(relay.Any(), 32), dtype="float32")
+        out = relay.max(data)
+        return relay.Function(relay.analysis.free_vars(out), out)
+
+    def get_func_with_control_flow():
+        data = relay.var("data", shape=(1, 3, 224, 224))
+        weight = relay.var("weight", shape=(32, 3, 3, 3))
+        eq1 = relay.var("e1", shape=[], dtype="float32")
+        eq2 = relay.var("e2", shape=[], dtype="float32")
+        eq = relay.equal(eq1, eq2)
+
+        true_branch = relay.zeros(shape=(1, 32, 222, 222), dtype="float32")
+        false_branch = relay.nn.conv2d(data, weight, kernel_size=(3, 3), channels=32)
+        ife = relay.If(eq, true_branch, false_branch)
+        out = relay.erf(ife)
+        return relay.Function([data, weight, eq1, eq2], out)
+
     def get_func_with_unsupported_op():
         def get_postproc_func():
             data = relay.var("data", shape=((1, 3, 6)), dtype=dtype)
@@ -180,48 +212,30 @@ def get_postproc_func():
         out = relay.Call(get_postproc_func(), [nms])
         return relay.Function([cls_prob, loc_pred, anchors], out)
 
-    func = get_func()
-    mod = tvm.IRModule.from_expr(func)
-    tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], None, target)
-
     # Relay FuseOps puts two conv2ds to separate functions and results in two tasks.
-    assert len(tasks) == 2
-    assert len(task_weights) == 2
-
-    func = get_fused_func()
-    mod = tvm.IRModule.from_expr(func)
-    tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], None, target)
+    verify_task_extraction(get_func(), 2)
 
     # By setting the function to primitive, Relay FuseOps will not break it and result in one task.
-    assert len(tasks) == 1
-    assert len(task_weights) == 1
-
-    func = get_simple_func()
-    mod = tvm.IRModule.from_expr(func)
-    tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], None, target)
+    verify_task_extraction(get_fused_func(), 1)
 
     # The Relay function without complex ops will not form a task by default.
-    assert len(tasks) == 0
-    assert len(task_weights) == 0
-
-    tasks, task_weights = auto_scheduler.extract_tasks(
-        mod["main"], None, target, include_simple_tasks=True
-    )
+    verify_task_extraction(get_simple_func(), 0)
 
     # Every Relay function becomes a task regardless what ops in its body.
-    assert len(tasks) == 1
-    assert len(task_weights) == 1
+    verify_task_extraction(get_simple_func(), 1, True)
 
-    # Func1 (with NMS) -> Func2 (injective).
-    func = get_func_with_unsupported_op()
-    mod = tvm.IRModule.from_expr(func)
-    tasks, task_weights = auto_scheduler.extract_tasks(
-        mod["main"], None, target, include_simple_tasks=True
-    )
+    # The Relay function without any reduce op is considered as a simple task.
+    verify_task_extraction(get_shape_of_func(), 0)
+    verify_task_extraction(get_shape_of_func(), 1, True)
 
-    # The function with NMS should fail, but the other function with ReLU should be a task.
-    assert len(tasks) == 1
-    assert len(task_weights) == 1
+    # The Relay function with dynamic shape inputs/outputs will not be extracted.
+    verify_task_extraction(get_func_with_dynamic_shape(), 0)
+
+    # The Conv2D in the Relay function with control flow could still be a task.
+    verify_task_extraction(get_func_with_control_flow(), 1)
+
+    # Func1 (with NMS) -> Func2 (injective).
+    verify_task_extraction(get_func_with_unsupported_op(), 1, True)
 
 
 if __name__ == "__main__":
diff --git a/tests/python/relay/test_auto_scheduler_tuning.py b/tests/python/relay/test_auto_scheduler_tuning.py
index 4ae434d72a20..1ec0e305311a 100644
--- a/tests/python/relay/test_auto_scheduler_tuning.py
+++ b/tests/python/relay/test_auto_scheduler_tuning.py
@@ -56,9 +56,16 @@ def tune_network(network, target):
             ):
                 lib = relay.build(mod, target=target, params=params)
 
+        # Sample a schedule when missing
+        with auto_scheduler.ApplyHistoryBestOrSample(None, num_measure=2):
+            with tvm.transform.PassContext(
+                opt_level=3, config={"relay.backend.use_auto_scheduler": True}
+            ):
+                lib2 = relay.build(mod, target=target, params=params)
+
         # Compile without auto-scheduler and any other optimization for correctness check
         with tvm.transform.PassContext(opt_level=0):
-            lib2 = relay.build(mod, target=target, params=params)
+            ref_lib = relay.build(mod, target=target, params=params)
 
         # Check the correctness
         def get_output(data, lib):
@@ -76,10 +83,12 @@ def get_output(data, lib):
         else:
             raise ValueError("Unknown network: " + network)
 
-        actual_output = get_output(data, lib)
-        expected_output = get_output(data, lib2)
+        actual_output1 = get_output(data, lib)
+        actual_output2 = get_output(data, lib2)
+        expected_output = get_output(data, ref_lib)
 
-        tvm.testing.assert_allclose(actual_output, expected_output, rtol=1e-4, atol=1e-4)
+        tvm.testing.assert_allclose(actual_output1, expected_output, rtol=1e-4, atol=1e-4)
+        tvm.testing.assert_allclose(actual_output2, expected_output, rtol=1e-4, atol=1e-4)
 
 
 @tvm.testing.requires_cuda
diff --git a/tests/python/relay/test_autotvm_task_extraction.py b/tests/python/relay/test_autotvm_task_extraction.py
index da71ac37f695..b3f1868969cc 100644
--- a/tests/python/relay/test_autotvm_task_extraction.py
+++ b/tests/python/relay/test_autotvm_task_extraction.py
@@ -60,9 +60,9 @@ def test_task_extraction():
     tasks = autotvm.task.extract_from_program(
         mod["main"], target=target, params=params, ops=(dense,)
     )
-    assert len(tasks) == 1
+    assert len(tasks) == 2
     tasks = autotvm.task.extract_from_program(mod, target=target, params=params, ops=(dense,))
-    assert len(tasks) == 1
+    assert len(tasks) == 2
 
     mod, params, _ = get_network("resnet-18", batch_size=1)
     mod_list.append(mod)
@@ -70,13 +70,13 @@ def test_task_extraction():
     tasks = autotvm.task.extract_from_program(
         mod["main"], target=target, params=params, ops=(conv2d, dense)
     )
-    assert len(tasks) == 13
+    assert len(tasks) == 14
     tasks = autotvm.task.extract_from_program(
         mod, target=target, params=params, ops=(conv2d, dense)
     )
-    assert len(tasks) == 13
+    assert len(tasks) == 14
     tasks = autotvm.task.extract_from_program(mod, target=target, params=params)
-    assert len(tasks) == 13
+    assert len(tasks) == 14
 
     mod, params, _ = get_network("resnet3d-18", batch_size=1)
     tasks = autotvm.task.extract_from_program(mod, target=target, params=params, ops=(conv3d,))
@@ -88,7 +88,7 @@ def test_task_extraction():
     tasks = autotvm.task.extract_from_program(
         mod, target=target, params=params, ops=(conv2d, dense)
     )
-    assert len(tasks) == 20
+    assert len(tasks) == 21
 
     mod, params, _ = get_network("dcgan", batch_size=1)
     tasks = autotvm.task.extract_from_program(
@@ -102,5 +102,26 @@ def test_task_extraction():
     assert len(tasks) == 31
 
 
+def test_task_extraction_for_dense_int8_cuda():
+    target = "cuda"
+    dense = relay.op.get("nn.dense")
+
+    def get_net(batch, in_dim, out_dim, dtype, out_dtype):
+        data = tvm.relay.var("data", shape=[batch, in_dim], dtype=dtype)
+        weight = tvm.relay.var("weight", shape=[out_dim, in_dim], dtype=dtype)
+        out = relay.nn.dense(data, weight, out_dtype=out_dtype)
+        mod, params = relay.testing.create_workload(out)
+        return mod, params
+
+    mod, params = get_net(1, 16, 32, "float32", "float32")
+    tasks = autotvm.task.extract_from_program(mod, target=target, params=params, ops=(dense,))
+    assert len(tasks) == 1 and tasks[0].name == "dense_small_batch.cuda"
+
+    mod, params = get_net(1, 16, 32, "int8", "int32")
+    tasks = autotvm.task.extract_from_program(mod, target=target, params=params, ops=(dense,))
+    assert len(tasks) == 1 and tasks[0].name == "dense_int8.cuda"
+
+
 if __name__ == "__main__":
     test_task_extraction()
+    test_task_extraction_for_dense_int8_cuda()
diff --git a/tests/python/relay/test_backend_graph_runtime.py b/tests/python/relay/test_backend_graph_runtime.py
index 3c42b7b4196f..68708aaeb413 100644
--- a/tests/python/relay/test_backend_graph_runtime.py
+++ b/tests/python/relay/test_backend_graph_runtime.py
@@ -209,6 +209,27 @@ def test_compile_nested_tuples():
         ref = ref + 1
 
 
+def test_graph_executor_nested_tuples():
+    x, y, z, w = [relay.var(c, shape=(2, 3), dtype="float32") for c in "xyzw"]
+    out = relay.Tuple([x, relay.Tuple([y, relay.Tuple([z, w])])])
+    func = relay.Function([x, y, z, w], out)
+
+    exe = relay.create_executor(
+        kind="graph", mod=tvm.IRModule.from_expr(func), ctx=tvm.cpu(0), target="llvm"
+    )
+    f = exe.evaluate()
+
+    data = [np.random.uniform(size=(2, 3)).astype("float32") for _ in "xyzw"]
+    out = f(*data)
+    assert len(out) == 2
+    tvm.testing.assert_allclose(out[0].asnumpy(), data[0])
+    assert len(out[1]) == 2
+    tvm.testing.assert_allclose(out[1][0].asnumpy(), data[1])
+    assert len(out[1][1]) == 2
+    tvm.testing.assert_allclose(out[1][1][0].asnumpy(), data[2])
+    tvm.testing.assert_allclose(out[1][1][1].asnumpy(), data[3])
+
+
 if __name__ == "__main__":
     test_plan_memory()
     test_with_params()
diff --git a/tests/python/relay/test_const.py b/tests/python/relay/test_const.py
new file mode 100644
index 000000000000..14fff0f7e65e
--- /dev/null
+++ b/tests/python/relay/test_const.py
@@ -0,0 +1,44 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import tvm
+import numpy as np
+from tvm import relay
+from tvm.relay.frontend.common import infer_type
+from tvm.relay import op as _op
+
+
+def test_const_dtype():
+    strides = (1, 1)
+    np_array = np.array(strides).astype("int32")
+    strides = _op.const(np_array, dtype="int64")
+
+    # strides needs to be autoconverted to int64 on Windows
+    assert infer_type(strides).checked_type.dtype == np.dtype(np.int64)
+
+    a = tvm.nd.array(np.random.randint(0, high=255, size=(2, 3), dtype="uint8"))
+    a = _op.const(a, dtype="uint8")
+    aa = a.data.asnumpy()
+    assert aa.dtype == np.dtype(np.uint8)
+
+    b = _op.const(1, dtype="int8")
+    bb = b.data.asnumpy()
+    assert bb.dtype == np.dtype(np.int8)
+
+    kshape = (3, 10, 3, 3)
+    w = relay.const(np.zeros(kshape, dtype="float32"))
+    assert w.data.asnumpy().dtype == np.dtype(np.float32)
diff --git a/tests/python/relay/test_cpp_build_module.py b/tests/python/relay/test_cpp_build_module.py
index 67f0621ef273..60f3dfa76e38 100644
--- a/tests/python/relay/test_cpp_build_module.py
+++ b/tests/python/relay/test_cpp_build_module.py
@@ -18,7 +18,7 @@
 
 import tvm
 from tvm import te
-from tvm import relay
+from tvm import relay, runtime
 from tvm.contrib.nvcc import have_fp16
 import tvm.testing
 
@@ -86,7 +86,7 @@ def test_fp16_build():
 
     # test
     rt = tvm.contrib.graph_runtime.create(g_json, mmod, ctx)
-    rt.load_params(relay.save_param_dict(params))
+    rt.load_params(runtime.save_param_dict(params))
     rt.run()
     out = rt.get_output(0)
 
diff --git a/tests/python/relay/test_dataflow_pattern.py b/tests/python/relay/test_dataflow_pattern.py
index d99e55b7c33f..a8e4b65f1bc6 100644
--- a/tests/python/relay/test_dataflow_pattern.py
+++ b/tests/python/relay/test_dataflow_pattern.py
@@ -16,6 +16,7 @@
 # under the License.
 # pylint: disable=unused-wildcard-import
 import numpy as np
+import pytest
 
 import tvm
 from tvm import relay
@@ -127,6 +128,29 @@ def test_AttrPattern():
     assert op.attrs["TOpPattern"] == K_ELEMWISE
 
 
+def test_IfPattern():
+    x = is_var("x")
+    y = is_var("y")
+    pat = is_if(is_op("less")(x, y), x, y)
+
+    assert isinstance(pat, IfPattern)
+    assert isinstance(pat.cond, CallPattern)
+    assert isinstance(pat.true_branch, VarPattern)
+    assert isinstance(pat.false_branch, VarPattern)
+
+
+def test_LetPattern():
+    x = is_var("x")
+    y = is_var("y")
+    let_var = is_var("let")
+    pat = is_let(let_var, is_op("less")(x, y), let_var)
+
+    assert isinstance(pat, LetPattern)
+    assert isinstance(pat.var, VarPattern)
+    assert isinstance(pat.value, CallPattern)
+    assert isinstance(pat.body, VarPattern)
+
+
 ## MATCHER TESTS
 
 
@@ -198,6 +222,57 @@ def test_no_match_func():
     assert not func_pattern.match(relay.Function([x, y], x - y))
 
 
+def test_match_if():
+    x = is_var("x")
+    y = is_var("y")
+    pat = is_if(is_op("less")(x, y), x, y)
+
+    x = relay.var("x")
+    y = relay.var("y")
+    cond = x < y
+
+    assert pat.match(relay.expr.If(cond, x, y))
+
+
+def test_no_match_if():
+    x = is_var("x")
+    y = is_var("y")
+    pat = is_if(is_op("less")(x, y), x, y)
+
+    x = relay.var("x")
+    y = relay.var("y")
+
+    assert not pat.match(relay.expr.If(x > y, x, y))
+    assert not pat.match(relay.expr.If(x < y, y, x))
+
+
+def test_match_let():
+    x = is_var("x")
+    y = is_var("y")
+    let_var = is_var("let")
+    pat = is_let(let_var, is_op("less")(x, y), let_var)
+
+    x = relay.var("x")
+    y = relay.var("y")
+    lv = relay.var("let")
+    cond = x < y
+    assert pat.match(relay.expr.Let(lv, cond, lv))
+
+
+def test_no_match_let():
+    x = is_var("x")
+    y = is_var("y")
+    let_var = is_var("let")
+    pat = is_let(let_var, is_op("less")(x, y), let_var)
+
+    x = relay.var("x")
+    y = relay.var("y")
+    lv = relay.var("let")
+
+    assert not pat.match(relay.expr.Let(lv, x > y, lv))
+    assert not pat.match(relay.expr.Let(lv, x < y, lv * x))
+
+
 def test_match_option():
     x = relay.var("x")
     w = relay.var("w")
@@ -362,6 +437,8 @@ def test_no_match_op_attr():
     x = relay.var("x")
     y = relay.var("y")
     assert not op_pat.match(x - y)
+    z = relay.var("z")
+    assert not op_pat.match(relay.Let(z, x + y, z))
 
 
 def test_match_func_attr():
@@ -389,6 +466,20 @@ def test_match_call_attr():
     y = relay.var("y")
     assert is_conv2d.match(relay.op.nn.conv2d(x, y))
 
+    # non-operator call
+    attr_dict = {"call_attr": "attr"}
+    call_has_attr = wildcard()(wildcard()).has_attr(attr_dict)
+    call_attr = tvm.ir.make_node("DictAttrs", **attr_dict)
+    a = relay.Var("a")
+    b = relay.Var("b")
+    assert call_has_attr.match(relay.Call(a, [b], attrs=call_attr))
+
+    # empty attrs should match anything
+    empty_attrs = tvm.ir.make_node("DictAttrs", **{})
+    call_has_empty_attrs = wildcard()(wildcard()).has_attr({})
+    assert call_has_empty_attrs.match(relay.Call(a, [b], attrs=empty_attrs))
+    assert call_has_empty_attrs.match(relay.Call(a, [b], attrs=call_attr))
+
 
 def test_no_match_call_attr():
     x = relay.var("x")
@@ -400,6 +491,27 @@ def test_no_match_call_attr():
     is_conv2d = is_op("nn.conv2d")(wildcard(), wildcard()).has_attr({"RandomAttr": "NCHW"})
     assert not is_conv2d.match(relay.op.nn.conv2d(x, y))
 
+    # non-operator calls
+    call_has_attr = wildcard()(wildcard()).has_attr({"call_attr": "attr"})
+    wrong_key = tvm.ir.make_node("DictAttrs", **{"wrong": "attr"})
+    wrong_value = tvm.ir.make_node("DictAttrs", **{"call_attr": "wrong"})
+    empty_attrs = tvm.ir.make_node("DictAttrs", **{})
+
+    a = relay.Var("a")
+    b = relay.Var("b")
+    # attrs left undefined
+    assert not call_has_attr.match(relay.Call(a, [b]))
+    # wrong attrs
+    assert not call_has_attr.match(relay.Call(a, [b], attrs=wrong_key))
+    assert not call_has_attr.match(relay.Call(a, [b], attrs=wrong_value))
+    assert not call_has_attr.match(relay.Call(a, [b], attrs=empty_attrs))
+
+
+def test_match_call_attr_dtype():
+    is_cast = is_op("cast")(wildcard()).has_attr({"dtype": "float32"})
+    x = relay.var("x")
+    assert is_cast.match(relay.op.cast(x, "float32"))
+
 
 def test_match_diamond():
     # Pattern
@@ -676,6 +788,29 @@ def callback(self, pre, post, node_map):
     assert sub_pattern.match(out)
 
 
+def test_rewrite_func_with_attr():
+    x = relay.var("x")
+    y = relay.var("y")
+    f = relay.Function([x, y], x + y).with_attr("Composite", "add")
+
+    a = relay.var("a")
+    b = relay.var("b")
+    c = relay.Call(f, [a, b])
+    c_abs = relay.abs(c)
+
+    class TestRewrite(DFPatternCallback):
+        def __init__(self):
+            super(TestRewrite, self).__init__()
+            self.pattern = wildcard().has_attr({"Composite": "add"})(wildcard(), wildcard())
+
+        def callback(self, pre, post, node_map):
+            return post.args[0] + post.args[1]
+
+    out = rewrite(TestRewrite(), c_abs)
+    inlined_add_pattern = is_op("abs")(is_op("add")(wildcard(), wildcard()))
+    assert inlined_add_pattern.match(out)
+
+
 def test_nested_rewrite():
     class PatternCallback(DFPatternCallback):
         def __init__(self, pattern):
@@ -1361,6 +1496,76 @@ def test_partition_function():
     assert tvm.ir.structural_equal(pattern.partition(expr), expr2)
 
 
+def test_rewrite_function_with_fuzzy_body():
+    """Allow Rewriting a function with a fuzzy body via dominator analysis"""
+    x = relay.var("x")
+    w = relay.var("w")
+    b = relay.var("b")
+
+    x1 = relay.var("x1")
+    w1 = relay.var("w1")
+
+    wc_x = wildcard()
+    wc_w = wildcard()
+    wc_b = wildcard()
+    wc_x1 = wildcard()
+    wc_w1 = wildcard()
+
+    func_pattern = FunctionPattern([wc_x1, wc_w1], wildcard())
+    pattern = func_pattern(wc_x, wc_w) + wc_b
+
+    func = relay.Function([x1, w1], relay.nn.conv2d(x1, w1))
+    expr = func(x, w) + b + b
+
+    class TestRewrite(DFPatternCallback):
+        def __init__(self):
+            super(TestRewrite, self).__init__()
+            self.pattern = pattern
+
+        def callback(self, pre, post, node_map):
+            return x + w
+
+    out = rewrite(TestRewrite(), expr)
+    assert tvm.ir.structural_equal(x + w, x + w)
+
+
+@pytest.mark.skip(
+    """TODO(mbrookhart): The current partitioner can't properly handle 
+                       the partitioned inputs on the fuzzy body"""
+)
+def test_partition_function_with_fuzzy_body():
+    """
+    Allow Rewriting a function with a fuzzy body via dominator analysis
+    """
+    x = relay.var("x")
+    w = relay.var("w")
+    b = relay.var("b")
+
+    x1 = relay.var("x1")
+    w1 = relay.var("w1")
+
+    wc_x = wildcard()
+    wc_w = wildcard()
+    wc_b = wildcard()
+    wc_x1 = wildcard()
+    wc_w1 = wildcard()
+
+    func_pattern = FunctionPattern([wc_x1, wc_w1], wildcard())
+    pattern = func_pattern(wc_x, wc_w) + wc_b
+
+    func = relay.Function([x1, w1], relay.nn.conv2d(x1, w1))
+    expr = func(x, w) + b + b
+
+    x2 = relay.var("x2")
+    w2 = relay.var("w2")
+    b2 = relay.var("b2")
+    func2 = relay.Function([x2, w2, b2], func(x2, w2) + b2).with_attr(
+        "PartitionedFromPattern", "FunctionCall_add_"
+    )
+    expr2 = func2(x, w, b) + b
+    assert tvm.ir.structural_equal(pattern.partition(expr), expr2)
+
+
 def test_match_match():
     add_pattern = is_op("add")(wildcard(), wildcard())
 
@@ -1506,3 +1711,6 @@ def test_partition_constant_embedding():
     test_partition_option()
     test_match_match()
     test_partition_constant_embedding()
+    test_IfPattern()
+    test_match_if()
+    test_no_match_if()
diff --git a/tests/python/relay/test_ir_parser.py b/tests/python/relay/test_ir_parser.py
index 162271756557..8b6b39e3df15 100644
--- a/tests/python/relay/test_ir_parser.py
+++ b/tests/python/relay/test_ir_parser.py
@@ -14,14 +14,14 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+import numpy as np
+
 import tvm
-from tvm import te
 from tvm import relay
 import tvm.relay.testing
 import pytest
 from numpy import isclose
 from typing import Union
-from functools import wraps
 
 
 SEMVER = '#[version = "0.0.5"]\n'
@@ -827,8 +827,8 @@ def test_import_grad():
     mod.import_from_std("gradient.rly")
 
 
-def test_resnet():
-    mod, _ = relay.testing.resnet.get_workload()
+def test_mlp():
+    mod, _ = relay.testing.mlp.get_workload(1)
     text = mod.astext()
     parsed_mod = tvm.parser.parse(text)
     tvm.ir.assert_structural_equal(mod, parsed_mod)
@@ -850,8 +850,8 @@ def inline_params(mod, params):
     return mod
 
 
-def test_resnet_inlined_params():
-    mod, params = relay.testing.resnet.get_workload()
+def test_mlp_inlined_params():
+    mod, params = relay.testing.mlp.get_workload(1)
     mod = inline_params(mod, params)
     mod = relay.transform.InferType()(mod)
     text = mod.astext()
@@ -910,6 +910,55 @@ def test_load_prelude():
     tvm.parser.parse(mod.astext())
 
 
+def test_call_attrs():
+    def get_func(shape, dtype):
+        x0 = relay.var("data", shape=shape, dtype=dtype)
+        w0 = relay.var("weight", shape=shape, dtype=dtype)
+        a = relay.nn.dense(x0, w0)
+        b = relay.nn.relu(a)
+        d = relay.add(b, relay.const(1.0, dtype=dtype))
+        return relay.Function([x0, w0], d)
+
+    # build relay graph
+    shape = (2, 4)
+    dtype = "float32"
+    sub_func = get_func(shape, dtype)
+    p0 = relay.var("p0", shape=shape, dtype=dtype)
+    p1 = relay.var("p1", shape=shape, dtype=dtype)
+    attr = tvm.ir.make_node("attrs.TestAttrs", name="func_call_attrs")
+    call = relay.Call(sub_func, [p0, p1], attrs=attr)
+    func = relay.Function([p0, p1], call)
+
+    # build relay module
+    mod = tvm.IRModule()
+    mod["main"] = func
+    mod = tvm.relay.transform.InferType()(mod)
+
+    # assert equal
+    program = """
+    def @main(%p0: Tensor[(2, 4), float32], %p1: Tensor[(2, 4), float32]) {
+    %2 = fn (%data: Tensor[(2, 4), float32], %weight: Tensor[(2, 4), float32]) {
+        %0 = nn.dense(%data, %weight, units=None);
+        %1 = nn.relu(%0);
+        add(%1, 1f)
+    };
+    %2(%p0, %p1, name="func_call_attrs", attrs_type_key="attrs.TestAttrs")
+    }
+    """
+    parsed = parse_module(program)
+    assert_graph_equal(parsed, mod)
+
+
+def test_tokenize_inf():
+    x = relay.var("x", shape=(3, 4), dtype="float32")
+    y = relay.clip(x, -np.inf, np.inf)
+
+    f = relay.Function([x], y)
+    mod = tvm.IRModule.from_expr(f)
+
+    mod = relay.transform.AnnotateSpans()(mod)
+
+
 if __name__ == "__main__":
     import sys
 
diff --git a/tests/python/relay/test_ir_text_printer.py b/tests/python/relay/test_ir_text_printer.py
index 72a243dbbb67..b2ae28649e6a 100644
--- a/tests/python/relay/test_ir_text_printer.py
+++ b/tests/python/relay/test_ir_text_printer.py
@@ -181,11 +181,6 @@ def test_squeezenet():
         astext(net)
 
 
-def test_vgg():
-    net, _ = tvm.relay.testing.vgg.get_workload(batch_size=1)
-    astext(net)
-
-
 def test_densenet():
     net, _ = tvm.relay.testing.densenet.get_workload(batch_size=1)
     astext(net)
diff --git a/tests/python/relay/test_memory_passes.py b/tests/python/relay/test_memory_passes.py
index c960d1f90c37..546aaf51f734 100644
--- a/tests/python/relay/test_memory_passes.py
+++ b/tests/python/relay/test_memory_passes.py
@@ -18,7 +18,6 @@
 from tvm import te
 import numpy as np
 from tvm import relay
-from tvm.relay import memory_alloc
 
 
 def check_memory_plan(func, check_fn):
diff --git a/tests/python/relay/test_op_grad_level1.py b/tests/python/relay/test_op_grad_level1.py
index cac07c437a42..0ac604c6bca1 100644
--- a/tests/python/relay/test_op_grad_level1.py
+++ b/tests/python/relay/test_op_grad_level1.py
@@ -42,42 +42,44 @@ def check_single_op(opfunc, ref, dtype):
         shape = (10, 4)
         tp = relay.TensorType(shape, dtype)
         x = relay.var("x", tp)
-        y = opfunc(x)
+        g = relay.var("g", tp)
+        y = opfunc(x) * g
 
         if ref is not None:
             data = np.random.rand(*shape).astype(dtype)
-            ref_grad = ref(data)
-            fwd_func = relay.Function([x], y)
+            grad_in = np.random.rand(*shape).astype(dtype)
+            ref_grad = ref(data, grad_in)
+            fwd_func = relay.Function([x, g], y)
             fwd_func = run_infer_type(fwd_func)
             bwd_func = run_infer_type(gradient(fwd_func))
 
             for target, ctx in tvm.testing.enabled_targets():
                 intrp = relay.create_executor(ctx=ctx, target=target)
-                op_res, (op_grad,) = intrp.evaluate(bwd_func)(data)
+                op_res, (op_grad, _) = intrp.evaluate(bwd_func)(data, grad_in)
                 np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
 
     for opfunc, ref in [
-        (tvm.relay.log, lambda x: 1 / x),
-        (tvm.relay.exp, np.exp),
-        (tvm.relay.sigmoid, lambda x: sigmoid(x) * (1 - sigmoid(x))),
-        (tvm.relay.tanh, lambda x: 1 - np.tanh(x) * np.tanh(x)),
-        (tvm.relay.sqrt, lambda x: 0.5 * np.power(x, -0.5)),
-        (tvm.relay.abs, lambda x: np.where(x < 0, -np.ones_like(x), np.ones_like(x))),
-        (relay.nn.relu, lambda x: np.where(x < 0, np.zeros_like(x), np.ones_like(x))),
-        (tvm.relay.erf, lambda x: 2.0 / (np.pi ** (0.5)) * np.exp(-x * x)),
-        (tvm.relay.cos, lambda x: -1.0 * np.sin(x)),
-        (tvm.relay.sin, lambda x: np.cos(x)),
-        (tvm.relay.tan, lambda x: 1.0 / (np.cos(x) ** 2)),
-        (tvm.relay.atan, lambda x: 1 / (1 + np.power(x, 2.0))),
-        (tvm.relay.log2, lambda x: 1 / (np.log(2) * x)),
-        (tvm.relay.log10, lambda x: 1 / (np.log(10) * x)),
-        (tvm.relay.cosh, lambda x: np.sinh(x)),
-        (tvm.relay.sinh, lambda x: np.cosh(x)),
-        (tvm.relay.asin, lambda x: 1.0 / (1.0 - x ** 2) ** (1.0 / 2.0)),
-        (tvm.relay.acos, lambda x: -1.0 / (1.0 - x ** 2.0) ** (1.0 / 2.0)),
-        (tvm.relay.acosh, lambda x: 1.0 / (x ** 2 - 1.0) ** (1.0 / 2.0)),
-        (tvm.relay.asinh, lambda x: 1.0 / (x ** 2 + 1.0) ** (1.0 / 2.0)),
-        (tvm.relay.atanh, lambda x: -1.0 / (x ** 2 - 1.0)),
+        (tvm.relay.log, lambda x, g: g * (1 / x)),
+        (tvm.relay.exp, lambda x, g: g * np.exp(x)),
+        (tvm.relay.sigmoid, lambda x, g: g * sigmoid(x) * (1 - sigmoid(x))),
+        (tvm.relay.tanh, lambda x, g: g * (1 - np.tanh(x) * np.tanh(x))),
+        (tvm.relay.sqrt, lambda x, g: g * 0.5 * np.power(x, -0.5)),
+        (tvm.relay.abs, lambda x, g: np.where(x < 0, -g, g)),
+        (relay.nn.relu, lambda x, g: np.where(x < 0, np.zeros_like(x), g)),
+        (tvm.relay.erf, lambda x, g: g * (2.0 / (np.pi ** (0.5)) * np.exp(-x * x))),
+        (tvm.relay.cos, lambda x, g: g * -1.0 * np.sin(x)),
+        (tvm.relay.sin, lambda x, g: g * np.cos(x)),
+        (tvm.relay.tan, lambda x, g: g * (1.0 / (np.cos(x) ** 2))),
+        (tvm.relay.atan, lambda x, g: g * (1 / (1 + np.power(x, 2.0)))),
+        (tvm.relay.log2, lambda x, g: g * (1 / (np.log(2) * x))),
+        (tvm.relay.log10, lambda x, g: g * (1 / (np.log(10) * x))),
+        (tvm.relay.cosh, lambda x, g: g * (np.sinh(x))),
+        (tvm.relay.sinh, lambda x, g: g * (np.cosh(x))),
+        (tvm.relay.asin, lambda x, g: g * (1.0 / (1.0 - x ** 2) ** (1.0 / 2.0))),
+        (tvm.relay.acos, lambda x, g: g * (-1.0 / (1.0 - x ** 2.0) ** (1.0 / 2.0))),
+        (tvm.relay.acosh, lambda x, g: g * (1.0 / (x ** 2 - 1.0) ** (1.0 / 2.0))),
+        (tvm.relay.asinh, lambda x, g: g * (1.0 / (x ** 2 + 1.0) ** (1.0 / 2.0))),
+        (tvm.relay.atanh, lambda x, g: g * (-1.0 / (x ** 2 - 1.0))),
     ]:
         for dtype in ("float32", "float64"):
             check_single_op(opfunc, ref, dtype)
@@ -150,5 +152,13 @@ def test_expand_dims_grad():
     check_grad(fwd_func)
 
 
+def test_concatenate_grad():
+    x = relay.var("x", shape=(2, 2, 5))
+    y = relay.var("y", shape=(2, 1, 5))
+    z = relay.var("z", shape=(2, 4, 5))
+    fwd_func = relay.Function([x, y, z], relay.concatenate([x, y, z], axis=1))
+    check_grad(fwd_func)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/tests/python/relay/test_op_grad_level3.py b/tests/python/relay/test_op_grad_level3.py
index 98ff62ed75d4..d43744b38e3e 100644
--- a/tests/python/relay/test_op_grad_level3.py
+++ b/tests/python/relay/test_op_grad_level3.py
@@ -20,7 +20,7 @@
 import tvm
 from tvm import te
 from tvm import relay
-from tvm.relay.testing import check_grad, run_infer_type, _np_randn_from_type
+from tvm.relay.testing import check_grad, run_infer_type, run_opt_pass, _np_randn_from_type
 from tvm.relay.transform import gradient
 import tvm.testing
 
@@ -126,5 +126,59 @@ def test_gather_nd_grad():
     check_grad(fwd, inputs=[data_np, indices_np], test_inputs=[data_np])
 
 
+def test_reshape_like_grad():
+    data = relay.var("data", shape=(2, 3, 4), dtype="float32")
+    shape_like = relay.var("shape_like", shape=(6, 2, 2), dtype="float32")
+    fwd_func = relay.Function([data, shape_like], relay.reshape_like(data, shape_like))
+    check_grad(fwd_func)
+
+
+def test_zeros_ones_grad_const_ints():
+    # when shape is static (i.e. not an input), there is no gradient at all
+    static_ty = relay.TensorType([2, 3, 4], dtype="float32")
+    expected_ty = relay.TupleType([static_ty, relay.TupleType([])])
+
+    for op in [relay.zeros, relay.ones]:
+        fwd_func = relay.Function([], op(static_ty.concrete_shape, static_ty.dtype))
+        bwd_func = run_infer_type(gradient(run_infer_type(fwd_func)))
+        tvm.ir.assert_structural_equal(bwd_func.ret_type, expected_ty)
+
+
+def test_zeros_ones_grad_const_expr():
+    # when shape is static (i.e. not an input), there is no gradient at all
+    shape_const = relay.const(np.array([2, 3, 4]), dtype="int32") * relay.const(1, dtype="int32")
+    static_ty = relay.TensorType([2, 3, 4], dtype="float32")
+    dyn_ty = relay.TensorType([relay.Any(), relay.Any(), relay.Any()], dtype="float32")
+    expected_ty_static = relay.TupleType([static_ty, relay.TupleType([])])
+    expected_ty_dyn = relay.TupleType([dyn_ty, relay.TupleType([])])
+
+    for op in [relay.zeros, relay.ones]:
+        # with DynamicToStatic, the shape should be concretized
+        fwd_func = relay.Function([], op(shape_const, static_ty.dtype))
+        fwd_func = run_opt_pass(fwd_func, relay.transform.DynamicToStatic())
+        bwd_func = run_infer_type(gradient(run_infer_type(fwd_func)))
+        tvm.ir.assert_structural_equal(bwd_func.ret_type, expected_ty_static)
+
+        fwd_func = relay.Function([], op(shape_const, static_ty.dtype))
+        bwd_func = run_infer_type(gradient(run_infer_type(fwd_func)))
+        tvm.ir.assert_structural_equal(bwd_func.ret_type, expected_ty_dyn)
+
+
+def test_zeros_ones_grad_dynamic():
+    rank = np.random.randint(low=1, high=5, dtype="int32")
+    dyn_shape = np.random.randint(low=1, high=4, size=(rank,), dtype="int32")
+    shape_data = relay.var("shape_data", shape=(rank,), dtype="int32")
+
+    for op, op_ref in [(relay.zeros, np.zeros), (relay.ones, np.ones)]:
+        fwd_func = relay.Function([shape_data], op(shape_data, dtype="float32"))
+        bwd_func = run_infer_type(gradient(run_infer_type(fwd_func)))
+
+        for target, ctx in tvm.testing.enabled_targets():
+            intrp = relay.create_executor(ctx=ctx, target=target)
+            res, (grad,) = intrp.evaluate(bwd_func)(dyn_shape)
+            tvm.testing.assert_allclose(res.asnumpy(), op_ref(dyn_shape, dtype="float32"))
+            tvm.testing.assert_allclose(grad.asnumpy(), np.zeros((rank,), dtype="int32"))
+
+
 if __name__ == "__main__":
     pytest.main()
diff --git a/tests/python/relay/test_op_grad_level4.py b/tests/python/relay/test_op_grad_level4.py
index d4792219816a..0f73e89c94ad 100644
--- a/tests/python/relay/test_op_grad_level4.py
+++ b/tests/python/relay/test_op_grad_level4.py
@@ -15,8 +15,9 @@
 # specific language governing permissions and limitations
 # under the License.
 import pytest
+import numpy as np
 from tvm import relay
-from tvm.relay.testing import check_grad
+from tvm.relay.testing import check_grad, _np_randn_from_type
 
 
 def verify_reduction_grad(red_fn, d_shape, axis=None, keepdims=False, exclude=False):
@@ -51,5 +52,39 @@ def test_max_grad():
     verify_max_grad((5, 4, 3), axis=(0, 2), exclude=True)
 
 
+def test_where_grad():
+    cond_type = relay.TensorType((2, 3, 4), "int32")
+    lhs_type = relay.TensorType((1, 3, 4), "float32")
+    rhs_type = relay.TensorType((2, 1, 4), "float32")
+    inputs = [
+        np.random.randint(2, size=cond_type.concrete_shape, dtype=cond_type.dtype),
+        _np_randn_from_type(lhs_type, scale=1e-5),
+        _np_randn_from_type(rhs_type, scale=1e-5),
+    ]
+
+    cond = relay.var("cond", type_annotation=cond_type)
+    lhs = relay.var("lhs", type_annotation=lhs_type)
+    rhs = relay.var("rhs", type_annotation=rhs_type)
+    fwd_func = relay.Function([cond, lhs, rhs], relay.where(cond, lhs, rhs))
+    check_grad(fwd_func, inputs=inputs, test_inputs=inputs[1:])
+
+
+def test_less_equal_grad():
+    x_type = relay.TensorType((2, 3, 4), "float32")
+    y_type = relay.TensorType((3, 1), "float32")
+    # We need to generate inputs far apart to get correct numerical gradients
+    # (otherwise adding epsilon may change comparison result). The gradient
+    # should always be zero for both inputs.
+    inputs = [
+        np.random.choice([-1, 1], size=x_type.concrete_shape).astype(x_type.dtype),
+        np.random.choice([-2, 2], size=y_type.concrete_shape).astype(y_type.dtype),
+    ]
+
+    x = relay.var("x", type_annotation=x_type)
+    y = relay.var("y", type_annotation=y_type)
+    fwd_func = relay.Function([x, y], relay.less_equal(x, y))
+    check_grad(fwd_func, inputs=inputs, test_inputs=inputs, eps=1e-6)
+
+
 if __name__ == "__main__":
     pytest.main()
diff --git a/tests/python/relay/test_op_level1.py b/tests/python/relay/test_op_level1.py
index 37a59c30f410..dfd350486c3b 100644
--- a/tests/python/relay/test_op_level1.py
+++ b/tests/python/relay/test_op_level1.py
@@ -201,6 +201,19 @@ def test_bias_add():
             np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=rtol)
 
 
+def test_bias_add_type_failure():
+    def assert_failure(expr):
+        try:
+            run_infer_type(expr)
+        except tvm._ffi.base.TVMError:
+            return
+        else:
+            assert False
+
+    for axis in (0, -1, -3, 1):
+        assert_failure(relay.nn.bias_add(relay.const(1), relay.const(2), axis=axis))
+
+
 def test_expand_dims_infer_type():
     for dtype in ["float16", "float32"]:
         n, t, d = te.size_var("n"), te.size_var("t"), 100
@@ -322,6 +335,16 @@ def test_dropout():
         yy = run_infer_type(y)
         assert yy.checked_type == input_ty
 
+    in_np = np.random.random([4, 5, 6]).astype("float32")
+    x = relay.const(in_np)
+    y = relay.nn.dropout(x, rate=0.5)
+    func = relay.Function([], y)
+    for target, ctx in tvm.testing.enabled_targets():
+        for backend in ["debug", "graph"]:
+            intrp = relay.create_executor("debug", ctx=ctx, target=target)
+            op_res = intrp.evaluate(func)()
+            tvm.testing.assert_allclose(op_res.asnumpy(), in_np, rtol=0.01)
+
 
 def test_batch_norm():
     for dtype in ["float16", "float32"]:
@@ -474,6 +497,7 @@ def test_bitserial_dense():
 if __name__ == "__main__":
     test_concatenate()
     test_bias_add()
+    test_bias_add_type_failure()
     test_unary_op()
     test_binary_op()
     test_expand_dims_infer_type()
diff --git a/tests/python/relay/test_op_level2.py b/tests/python/relay/test_op_level2.py
index 06bd01b4189a..1a1f451f4c74 100644
--- a/tests/python/relay/test_op_level2.py
+++ b/tests/python/relay/test_op_level2.py
@@ -1171,14 +1171,19 @@ def test_flatten_infer_type():
 
 @tvm.testing.uses_gpu
 def test_pad_infer_type():
-    # entirely concrete case
+    # entirely concrete cases
     n, c, h, w = 1, 2, 3, 4
     t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
     y = relay.nn.pad(t, ((1, 1), (2, 2), (3, 3), (4, 4)))
-    "pad_width=" in y.astext()
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((3, 6, 9, 12), "float32")
 
+    n, c, h, w = 4, 6, 3, 5
+    t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
+    y = relay.nn.pad(t, ((-1, -1), (2, -2), (0, -3), (4, 4)), pad_mode="reflect")
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((2, 6, 0, 13), "float32")
+
     # some symbolic values
     n, c, h, w = te.size_var("n"), 2, 3, te.size_var("w")
     t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
@@ -1186,20 +1191,42 @@ def test_pad_infer_type():
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((n + 2, 6, 9, w + 8), "float32")
 
+    n, c, h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), te.size_var("w")
+    t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
+    y = relay.nn.pad(t, ((-1, -1), (-2, -2), (1, -3), (4, 4)))
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((n + (-2), c + (-4), h + (-2), w + 8), "float32")
+
 
 @tvm.testing.uses_gpu
 def test_pad_run():
     def _test_run(dtype):
-        dshape = (4, 10, 7, 7)
-        x = relay.var("x", shape=dshape)
-        y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
-        func = relay.Function([x], y)
-        data = np.random.uniform(size=dshape).astype(dtype)
-        ref_res = np.pad(data, ((1, 1), (2, 2), (3, 3), (4, 4)), "constant")
-        for target, ctx in tvm.testing.enabled_targets():
-            intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
-            op_res1 = intrp1.evaluate(func)(data)
-            tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
+        dshape_list = [(4, 10, 7, 7), (4, 6, 3, 5)]
+        pad_list = [((1, 1), (2, 2), (3, 3), (4, 4)), ((-1, -1), (2, -2), (0, -2), (4, 4))]
+
+        for dshape, pad in zip(dshape_list, pad_list):
+            x = relay.var("x", shape=dshape)
+            y = relay.nn.pad(x, pad)
+            func = relay.Function([x], y)
+            data = np.random.uniform(size=dshape).astype(dtype)
+            mod_pad = []
+            mod_data = data
+            for axis, (pad_x, pad_y) in enumerate(pad):
+                indices = range(dshape[axis])
+                if pad_x < 0:
+                    indices = indices[abs(pad_x) :]
+                    pad_x = 0
+                if pad_y < 0:
+                    indices = indices[:pad_y]
+                    pad_y = 0
+                mod_data = np.take(mod_data, indices, axis)
+                mod_pad.append((pad_x, pad_y))
+
+            ref_res = np.pad(mod_data, tuple(mod_pad), "constant")
+            for target, ctx in tvm.testing.enabled_targets():
+                intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
+                op_res1 = intrp1.evaluate(func)(data)
+                tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
 
     _test_run("float32")
     _test_run("int32")
diff --git a/tests/python/relay/test_op_level3.py b/tests/python/relay/test_op_level3.py
index 668285dfb882..d2a5090943c3 100644
--- a/tests/python/relay/test_op_level3.py
+++ b/tests/python/relay/test_op_level3.py
@@ -24,6 +24,7 @@
 from tvm.error import TVMError
 from tvm.relay import create_executor, transform
 from tvm.relay.testing import check_grad, run_infer_type
+from typing import Optional
 import tvm.testing
 
 
@@ -787,28 +788,58 @@ def verify_repeat(dshape, repeats, axis):
 
 @tvm.testing.uses_gpu
 def test_stack():
-    def verify_stack(dshapes, axis):
-        y = []
-        for shape in dshapes:
-            y.append(relay.var("input", relay.TensorType(shape, "float32")))
-        x = relay.Tuple(y)
-        z = relay.stack(x, axis=axis)
+    def produce_input_tuple(dshapes):
+        y = [relay.var("input", relay.TensorType(shape, "float32")) for shape in dshapes]
+        return relay.Tuple(y)
 
-        func = relay.Function(y, z)
-        x_data = [np.random.normal(size=shape).astype("float32") for shape in dshapes]
-        ref_res = np.stack(x_data, axis=axis)
+    def ref_stack(inputs, axis):
+        return np.stack(inputs, axis=axis)
+
+    def verify_stack(input_expr, relay_args, ref_res, axis):
+        z = relay.stack(input_expr, axis=axis)
+        inp_vars = relay.analysis.free_vars(z)
+        func = relay.Function(inp_vars, z)
 
         for target, ctx in tvm.testing.enabled_targets():
             for kind in ["graph", "debug"]:
                 intrp = relay.create_executor(kind, ctx=ctx, target=target)
-                op_res = intrp.evaluate(func)(*x_data)
+                op_res = intrp.evaluate(func)(*relay_args)
                 tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
 
-    verify_stack([(2,), (2,), (2,)], -1)
-    verify_stack([(2,), (2,), (2,)], 0)
-    verify_stack([(2, 2, 4), (2, 2, 4), (2, 2, 4)], 1)
-    verify_stack([(2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4)], -1)
-    verify_stack([(2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4)], 4)
+    def verify_tup_lit_stack(dshapes, axis):
+        input_tuple = produce_input_tuple(dshapes)
+        input_data = [np.random.normal(size=shape).astype("float32") for shape in dshapes]
+        ref_res = ref_stack(input_data, axis)
+        verify_stack(input_tuple, input_data, ref_res, axis)
+
+    def verify_list_lit_stack(dshapes, axis):
+        input_list = produce_input_tuple(dshapes).fields
+        input_data = [np.random.normal(size=shape).astype("float32") for shape in dshapes]
+        ref_res = ref_stack(input_data, axis)
+        verify_stack(input_list, input_data, ref_res, axis)
+
+    def verify_tup_expr_stack(dshapes, axis):
+        input_data = [np.random.normal(size=shape).astype("float32") for shape in dshapes]
+        ref_res = ref_stack(input_data, axis)
+
+        # expression that evaluates to a tuple
+        # but is not a tuple literal
+        x = relay.Var("x")
+        input_expr = relay.Let(x, relay.Tuple([relay.const(inp) for inp in input_data]), x)
+        verify_stack(input_expr, [], ref_res, axis)
+
+    dshape_axis_combos = [
+        ([(2,), (2,), (2,)], -1),
+        ([(2,), (2,), (2,)], 0),
+        ([(2, 2, 4), (2, 2, 4), (2, 2, 4)], 1),
+        ([(2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4)], -1),
+        ([(2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4), (2, 2, 3, 4)], 4),
+    ]
+
+    for dshapes, axis in dshape_axis_combos:
+        verify_tup_lit_stack(dshapes, axis)
+        verify_list_lit_stack(dshapes, axis)
+        verify_tup_expr_stack(dshapes, axis)
 
 
 @tvm.testing.uses_gpu
@@ -993,7 +1024,25 @@ def verify_dynamic_scatter(dshape, ishape, axis=0):
 
 
 @tvm.testing.uses_gpu
-def test_scatter_add():
+@pytest.mark.parametrize(
+    "dshape, ishape, axis, dtype",
+    [
+        ((10,), (10,), 0, "int32"),
+        ((1000,), (1000,), 0, "int32"),
+        ((10, 5), (10, 5), -2, "float32"),
+        ((10, 5), (10, 5), -1, "float32"),
+        ((10, 5), (3, 5), 0, "float32"),
+        ((12, 4), (7, 2), 1, "float32"),
+        ((2, 3, 4), (1, 3, 4), 0, "float32"),
+        ((2, 3, 4), (2, 1, 4), 1, "float32"),
+        ((2, 3, 4), (2, 3, 1), 2, "float32"),
+        ((2, 3, 4, 5), (1, 3, 4, 5), 0, "float32"),
+        ((6, 3, 4, 5), (2, 3, 4, 5), 1, "float32"),
+        ((2, 3, 8, 5), (2, 3, 1, 1), 2, "float32"),
+        ((16, 16, 4, 5), (16, 16, 4, 5), 3, "float32"),
+    ],
+)
+def test_scatter_add(dshape, ishape, axis, dtype):
     def ref_scatter_add(data, indices, updates, axis=0):
         output = np.copy(data)
         for index in np.ndindex(*indices.shape):
@@ -1003,9 +1052,9 @@ def ref_scatter_add(data, indices, updates, axis=0):
         return output
 
     def verify_scatter_add(dshape, ishape, axis=0, dtype="float32"):
-        d = relay.var("d", relay.TensorType(dshape, dtype))
-        i = relay.var("i", relay.TensorType(ishape, "int64"))
-        u = relay.var("u", relay.TensorType(ishape, dtype))
+        d = relay.var("d", relay.TensorType(shape=[relay.Any() for _ in dshape], dtype=dtype))
+        i = relay.var("i", relay.TensorType(shape=[relay.Any() for _ in ishape], dtype="int64"))
+        u = relay.var("u", relay.TensorType(shape=[relay.Any() for _ in ishape], dtype=dtype))
         z = relay.op.scatter_add(d, i, u, axis)
 
         func = relay.Function([d, i, u], z)
@@ -1015,40 +1064,177 @@ def verify_scatter_add(dshape, ishape, axis=0, dtype="float32"):
         indices_np = np.random.randint(-dshape[axis], dshape[axis] - 1, ishape).astype("int64")
 
         ref_res = ref_scatter_add(data_np, indices_np, updates_np, axis)
-        for target, ctx in tvm.testing.enabled_targets():
-            for kind in ["graph", "debug"]:
-                if target == "nvptx" and dtype == "float32" and len(dshape) == 1:
-                    # scatter_add 1D on GPU is implemented via atomic.
-                    # Floating point atomic requires LLVM 9 or newer for nvptx backend.
-                    # But LLVM on CI is LLVM 8.
-                    continue
-                intrp = relay.create_executor(kind, ctx=ctx, target=target)
-                op_res = intrp.evaluate(func)(data_np, indices_np, updates_np)
-                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
 
-    verify_scatter_add((10,), (10,), 0, dtype="int32")
-    verify_scatter_add((1000,), (1000,))
-    verify_scatter_add((1000,), (1000,), 0, dtype="int32")
-    verify_scatter_add((10, 5), (10, 5), -2)
-    verify_scatter_add((10, 5), (10, 5), -1)
-    verify_scatter_add((10, 5), (3, 5), 0)
-    verify_scatter_add((12, 4), (7, 2), 1)
-    verify_scatter_add((2, 3, 4), (1, 3, 4), 0)
-    verify_scatter_add((2, 3, 4), (2, 1, 4), 1)
-    verify_scatter_add((2, 3, 4), (2, 3, 1), 2)
-    verify_scatter_add((2, 3, 4, 5), (1, 3, 4, 5), 0)
-    verify_scatter_add((6, 3, 4, 5), (2, 3, 4, 5), 1)
-    verify_scatter_add((2, 3, 8, 5), (2, 3, 1, 1), 2)
-    verify_scatter_add((16, 16, 4, 5), (16, 16, 4, 5), 3)
+        verify_func(
+            func,
+            [data_np, indices_np, updates_np],
+            ref_res,
+        )
+
+    verify_scatter_add(dshape, ishape, axis, dtype)
 
 
 @tvm.testing.uses_gpu
-def test_gather():
+@pytest.mark.parametrize(
+    "data, axis, indices, ref_res",
+    [
+        ([[1, 2], [3, 4]], 1, [[0, 0], [1, 0]], [[1, 1], [4, 3]]),
+        ([[1, 2], [3, 4]], -1, [[0, 0], [1, 0]], [[1, 1], [4, 3]]),
+        (
+            [[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]],
+            0,
+            [[[1, 0, 1], [1, 1, 0]]],
+            [[[6, 1, 8], [9, 10, 5]]],
+        ),
+        (
+            [[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]],
+            -3,
+            [[[1, 0, 1], [1, 1, 0]]],
+            [[[6, 1, 8], [9, 10, 5]]],
+        ),
+        (
+            [
+                [
+                    [-0.2321, -0.2024, -1.7624],
+                    [-0.3829, -0.4246, 0.2448],
+                    [0.1822, 0.2360, -0.8965],
+                    [0.4497, -0.2224, 0.6103],
+                ],
+                [
+                    [0.0408, -0.7667, -0.4303],
+                    [-0.3216, 0.7489, -0.1502],
+                    [0.0144, -0.4699, -0.0064],
+                    [-0.0768, -1.6064, 1.3390],
+                ],
+            ],
+            1,
+            [[[2, 2, 0], [1, 0, 3]], [[3, 2, 0], [1, 0, 0]]],
+            [
+                [[0.1822, 0.2360, -1.7624], [-0.3829, -0.2024, 0.6103]],
+                [[-0.0768, -0.4699, -0.4303], [-0.3216, -0.7667, -0.4303]],
+            ],
+        ),
+        (
+            [
+                [
+                    [-0.2321, -0.2024, -1.7624],
+                    [-0.3829, -0.4246, 0.2448],
+                    [0.1822, 0.2360, -0.8965],
+                    [0.4497, -0.2224, 0.6103],
+                ],
+                [
+                    [0.0408, -0.7667, -0.4303],
+                    [-0.3216, 0.7489, -0.1502],
+                    [0.0144, -0.4699, -0.0064],
+                    [-0.0768, -1.6064, 1.3390],
+                ],
+            ],
+            -2,
+            [[[2, 2, 0], [1, 0, 3]], [[3, 2, 0], [1, 0, 0]]],
+            [
+                [[0.1822, 0.2360, -1.7624], [-0.3829, -0.2024, 0.6103]],
+                [[-0.0768, -0.4699, -0.4303], [-0.3216, -0.7667, -0.4303]],
+            ],
+        ),
+        (
+            [
+                [
+                    [-0.2321, -0.2024, -1.7624],
+                    [-0.3829, -0.4246, 0.2448],
+                    [0.1822, 0.2360, -0.8965],
+                    [0.4497, -0.2224, 0.6103],
+                ],
+                [
+                    [0.0408, -0.7667, -0.4303],
+                    [-0.3216, 0.7489, -0.1502],
+                    [0.0144, -0.4699, -0.0064],
+                    [-0.0768, -1.6064, 1.3390],
+                ],
+            ],
+            -2,
+            [[[2, 2, 0], [1, 0, 3]], [[3, 2, 0], [1, 0, 0]]],
+            [
+                [[0.1822, 0.2360, -1.7624], [-0.3829, -0.2024, 0.6103]],
+                [[-0.0768, -0.4699, -0.4303], [-0.3216, -0.7667, -0.4303]],
+            ],
+        ),
+        (
+            [
+                [
+                    [0.3050, 1.6986, 1.1034],
+                    [0.7020, -0.6960, -2.1818],
+                    [0.3116, -0.5773, -0.9912],
+                    [0.0835, -1.3915, -1.0720],
+                ],
+                [
+                    [0.1694, -0.6091, -0.6539],
+                    [-0.5234, -0.1218, 0.5084],
+                    [0.2374, -1.9537, -2.0078],
+                    [-0.5700, -1.0302, 0.1558],
+                ],
+            ],
+            2,
+            [
+                [[1, 1, 0, 1], [0, 0, 2, 2], [1, 2, 1, 2], [2, 2, 1, 0]],
+                [[0, 0, 1, 2], [2, 2, 1, 0], [1, 2, 0, 0], [0, 2, 0, 2]],
+            ],
+            [
+                [
+                    [1.6986, 1.6986, 0.3050, 1.6986],
+                    [0.7020, 0.7020, -2.1818, -2.1818],
+                    [-0.5773, -0.9912, -0.5773, -0.9912],
+                    [-1.0720, -1.0720, -1.3915, 0.0835],
+                ],
+                [
+                    [0.1694, 0.1694, -0.6091, -0.6539],
+                    [0.5084, 0.5084, -0.1218, -0.5234],
+                    [-1.9537, -2.0078, 0.2374, 0.2374],
+                    [-0.5700, 0.1558, -0.5700, 0.1558],
+                ],
+            ],
+        ),
+        (
+            [
+                [
+                    [0.3050, 1.6986, 1.1034],
+                    [0.7020, -0.6960, -2.1818],
+                    [0.3116, -0.5773, -0.9912],
+                    [0.0835, -1.3915, -1.0720],
+                ],
+                [
+                    [0.1694, -0.6091, -0.6539],
+                    [-0.5234, -0.1218, 0.5084],
+                    [0.2374, -1.9537, -2.0078],
+                    [-0.5700, -1.0302, 0.1558],
+                ],
+            ],
+            -1,
+            [
+                [[1, 1, 0, 1], [0, 0, 2, 2], [1, 2, 1, 2], [2, 2, 1, 0]],
+                [[0, 0, 1, 2], [2, 2, 1, 0], [1, 2, 0, 0], [0, 2, 0, 2]],
+            ],
+            [
+                [
+                    [1.6986, 1.6986, 0.3050, 1.6986],
+                    [0.7020, 0.7020, -2.1818, -2.1818],
+                    [-0.5773, -0.9912, -0.5773, -0.9912],
+                    [-1.0720, -1.0720, -1.3915, 0.0835],
+                ],
+                [
+                    [0.1694, 0.1694, -0.6091, -0.6539],
+                    [0.5084, 0.5084, -0.1218, -0.5234],
+                    [-1.9537, -2.0078, 0.2374, 0.2374],
+                    [-0.5700, 0.1558, -0.5700, 0.1558],
+                ],
+            ],
+        ),
+    ],
+)
+def test_gather(data, axis, indices, ref_res):
     def verify_gather(data, axis, indices, ref_res):
         data = np.asarray(data, dtype="float32")
         indices = np.asarray(indices, dtype="int32")
         ref_res = np.asarray(ref_res)
-
         d = relay.var("x", relay.TensorType(data.shape, "float32"))
         i = relay.var("y", relay.TensorType(indices.shape, "int32"))
         z = relay.gather(d, axis, i)
@@ -1061,70 +1247,7 @@ def verify_gather(data, axis, indices, ref_res):
                 op_res = intrp.evaluate(func)(data, indices)
                 tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
 
-    verify_gather([[1, 2], [3, 4]], 1, [[0, 0], [1, 0]], [[1, 1], [4, 3]])
-    verify_gather(
-        [[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]],
-        0,
-        [[[1, 0, 1], [1, 1, 0]]],
-        [[[6, 1, 8], [9, 10, 5]]],
-    )
-    verify_gather(
-        [
-            [
-                [-0.2321, -0.2024, -1.7624],
-                [-0.3829, -0.4246, 0.2448],
-                [0.1822, 0.2360, -0.8965],
-                [0.4497, -0.2224, 0.6103],
-            ],
-            [
-                [0.0408, -0.7667, -0.4303],
-                [-0.3216, 0.7489, -0.1502],
-                [0.0144, -0.4699, -0.0064],
-                [-0.0768, -1.6064, 1.3390],
-            ],
-        ],
-        1,
-        [[[2, 2, 0], [1, 0, 3]], [[3, 2, 0], [1, 0, 0]]],
-        [
-            [[0.1822, 0.2360, -1.7624], [-0.3829, -0.2024, 0.6103]],
-            [[-0.0768, -0.4699, -0.4303], [-0.3216, -0.7667, -0.4303]],
-        ],
-    )
-    verify_gather(
-        [
-            [
-                [0.3050, 1.6986, 1.1034],
-                [0.7020, -0.6960, -2.1818],
-                [0.3116, -0.5773, -0.9912],
-                [0.0835, -1.3915, -1.0720],
-            ],
-            [
-                [0.1694, -0.6091, -0.6539],
-                [-0.5234, -0.1218, 0.5084],
-                [0.2374, -1.9537, -2.0078],
-                [-0.5700, -1.0302, 0.1558],
-            ],
-        ],
-        2,
-        [
-            [[1, 1, 0, 1], [0, 0, 2, 2], [1, 2, 1, 2], [2, 2, 1, 0]],
-            [[0, 0, 1, 2], [2, 2, 1, 0], [1, 2, 0, 0], [0, 2, 0, 2]],
-        ],
-        [
-            [
-                [1.6986, 1.6986, 0.3050, 1.6986],
-                [0.7020, 0.7020, -2.1818, -2.1818],
-                [-0.5773, -0.9912, -0.5773, -0.9912],
-                [-1.0720, -1.0720, -1.3915, 0.0835],
-            ],
-            [
-                [0.1694, 0.1694, -0.6091, -0.6539],
-                [0.5084, 0.5084, -0.1218, -0.5234],
-                [-1.9537, -2.0078, 0.2374, 0.2374],
-                [-0.5700, 0.1558, -0.5700, 0.1558],
-            ],
-        ],
-    )
+    verify_gather(data, axis, indices, ref_res)
 
 
 @tvm.testing.uses_gpu
@@ -1281,6 +1404,329 @@ def verify_sparse_to_dense(sparse_indices, sparse_values, default_value, output_
     # verify_sparse_to_dense([[[[0, 1, 4], [0, 2, 4]]]], [[[[3.1, 3.1, 3.1]]]], 3.5, [5], [3.1, 3.1, 3.5, 3.5, 3.1])
 
 
+@tvm.testing.uses_gpu
+@pytest.mark.parametrize(
+    "sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np",
+    [
+        (
+            np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]], dtype=np.int32),
+            np.array([7, 5, 6, 3, 9], dtype=np.int32),
+            np.array([2, 3, 6], dtype=np.int32),
+            np.array([9, -1], dtype=np.int32),
+        ),
+        (
+            np.array(
+                [[0, 0, 0, 0], [0, 0, 1, 2], [0, 1, 0, 3], [1, 0, 0, 4], [1, 2, 3, 6]],
+                dtype=np.int64,
+            ),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([2, 3, 6, 7], dtype=np.int64),
+            np.array([9, -1, 7], dtype=np.int64),
+        ),
+        (
+            np.array(
+                [
+                    [0, 0, 0, 0, 0],
+                    [0, 0, 1, 2, 3],
+                    [0, 1, 0, 3, 5],
+                    [1, 0, 0, 4, 6],
+                    [1, 2, 3, 6, 8],
+                ],
+                dtype=np.int64,
+            ),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([2, 3, 6, 7, 9], dtype=np.int64),
+            np.array([9, -1, 7], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int32),
+            np.array([7, 5, 6, 3, 9], dtype=np.int32),
+            np.array([9, 4], dtype=np.int32),
+            np.array([2, -1, 6], dtype=np.int32),
+        ),
+        (
+            np.array([[0, 0], [0, 1], [3, 4], [4, 3], [7, 3]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([9, 4], dtype=np.int64),
+            np.array([-1], dtype=np.int64),
+        ),
+        (
+            np.array([[0], [5], [10], [20], [24]], dtype=np.int32),
+            np.array([7, 5, 6, 3, 9], dtype=np.int32),
+            np.array([25], dtype=np.int32),
+            np.array([5, 5], dtype=np.int32),
+        ),
+        (
+            np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+        ),
+        (
+            np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]], dtype=np.int32),
+            np.array([7, 5, 6, 3, 9], dtype=np.int32),
+            np.array([500, 20], dtype=np.int32),
+            np.array([500, -1], dtype=np.int32),
+        ),
+        (
+            np.array([[0, 100], [200, 100], [300, 400], [50, 20], [400, 50]], dtype=np.int64),
+            np.array([7, 5, 6, 3, 9], dtype=np.int64),
+            np.array([500, 20], dtype=np.int64),
+            np.array([250, 40], dtype=np.int64),
+        ),
+        (
+            np.ones((0, 1), dtype=np.int32),
+            np.array([], dtype=np.int32),
+            np.array([4], dtype=np.int32),
+            np.array([2, -1], dtype=np.int32),
+        ),
+        (
+            np.ones((0, 1), dtype=np.int64),
+            np.array([], dtype=np.int64),
+            np.array([4], dtype=np.int64),
+            np.array([2, 2], dtype=np.int64),
+        ),
+        (
+            np.ones((0, 2), dtype=np.int32),
+            np.array([], dtype=np.int32),
+            np.array([3, 6], dtype=np.int32),
+            np.array([-1, 2], dtype=np.int32),
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_dyn", [True, False])
+def test_sparse_reshape(sparse_indices_np, sparse_values_np, prev_shape_np, new_shape_np, use_dyn):
+    def ref_sparse_reshape(
+        sparse_indices: np.ndarray,
+        prev_shape: np.ndarray,
+        new_shape: np.ndarray,
+    ):
+        """
+        This function calculates the expected output of sparseshape operator given the inputs.
+        """
+
+        new_sparse_indices = np.ones(
+            (sparse_indices.shape[0], new_shape.shape[0]), dtype=sparse_indices.dtype
+        )
+        multipliers = np.ones(prev_shape.shape[0])
+        dividers = np.ones(new_shape.shape[0])
+        total_ele = np.prod(prev_shape)
+        division_total_ele = 1
+        for i in range(new_shape.shape[0]):
+            if new_shape[i] == -1:
+                continue
+            division_total_ele *= new_shape[i]
+        for i in range(prev_shape.shape[0] - 2, -1, -1):
+            multipliers[i] = prev_shape[i + 1] * multipliers[i + 1]
+
+        for i in range(len(new_shape)):
+            if new_shape[i] == -1:
+                new_shape[i] = total_ele // division_total_ele
+
+        if np.array_equal(prev_shape, new_shape):
+            return sparse_indices, prev_shape
+
+        for i in range(new_shape.shape[0] - 2, -1, -1):
+            dividers[i] = new_shape[i + 1] * dividers[i + 1]
+
+        for row_num, sparse_row in enumerate(sparse_indices):
+            flat_idx = 0
+            if len(sparse_indices.shape) != 1:
+                for i, ele in enumerate(sparse_row):
+                    flat_idx += sparse_row[i] * multipliers[i]
+            else:
+                flat_idx += sparse_row
+            if len(new_sparse_indices.shape) != 1:
+                for i in range(new_sparse_indices.shape[1]):
+                    new_sparse_indices[row_num][i] = flat_idx // dividers[i]
+                    flat_idx = flat_idx % dividers[i]
+            else:
+                new_sparse_indices[row_num] = flat_idx
+
+        return new_sparse_indices, new_shape
+
+    def verify_sparse_reshape(
+        sparse_indices_np: np.ndarray,
+        sparse_values_np: np.ndarray,
+        prev_shape_np: np.ndarray,
+        new_shape_np: np.ndarray,
+    ):
+        """
+        This function verifies the relay output of sparse_reshape with its expected output.
+        """
+        if use_dyn:
+            sparse_indices = relay.var(
+                "sparse_indices",
+                shape=[relay.Any(), relay.Any()],
+                dtype=str(sparse_indices_np.dtype),
+            )
+            prev_shape = relay.var(
+                "prev_shape",
+                shape=[relay.Any()],
+                dtype=str(prev_shape_np.dtype),
+            )
+            new_shape = relay.var(
+                "new_shape",
+                shape=[relay.Any()],
+                dtype=str(new_shape_np.dtype),
+            )
+        else:
+            sparse_indices = relay.var(
+                "sparse_indices",
+                relay.TensorType(sparse_indices_np.shape, str(sparse_indices_np.dtype)),
+            )
+            prev_shape = relay.var(
+                "prev_shape", relay.TensorType(prev_shape_np.shape, str(prev_shape_np.dtype))
+            )
+            new_shape = relay.var(
+                "new_shape", relay.TensorType(new_shape_np.shape, str(new_shape_np.dtype))
+            )
+        z = relay.op.sparse_reshape(sparse_indices, prev_shape, new_shape).astuple()
+
+        func = relay.Function([sparse_indices, prev_shape, new_shape], z)
+
+        ref_res = ref_sparse_reshape(sparse_indices_np, prev_shape_np, new_shape_np)
+        outputs = run_infer_type(z)
+        new_sparse_indices_infer_type, new_shape_infer_type = (
+            outputs.checked_type.fields[0].dtype,
+            outputs.checked_type.fields[1].dtype,
+        )
+
+        assert new_sparse_indices_infer_type == sparse_indices_np.dtype
+        assert new_shape_infer_type == new_shape_np.dtype
+        verify_func(
+            func,
+            [sparse_indices_np, prev_shape_np, new_shape_np],
+            ref_res,
+        )
+
+    verify_sparse_reshape(
+        sparse_indices_np,
+        sparse_values_np,
+        prev_shape_np,
+        new_shape_np,
+    )
+
+
+@tvm.testing.uses_gpu
+@pytest.mark.parametrize(
+    "data_np, segment_ids_np, num_segments",
+    [
+        (
+            np.array([5, 1, 7, 2, 3, 4], dtype=np.float32),
+            np.array([0, 0, 1, 1, 0, 1], dtype=np.int32),
+            None,
+        ),
+        (
+            np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float64),
+            np.array([0, 0, 1], dtype=np.int32),
+            None,
+        ),
+        (
+            np.random.random((6, 4, 5)),
+            np.array([2, 0, 1, 0, 3, 2], dtype=np.int64),
+            None,
+        ),
+        (
+            np.array([[[1, 7]], [[3, 8]], [[2, 9]]], dtype=np.float32),
+            np.array([0, 0, 1], dtype=np.int32),
+            None,
+        ),
+        (
+            np.random.random((9, 4, 5, 7)),
+            np.array([5, 0, 1, 0, 3, 6, 8, 7, 7], dtype=np.int64),
+            9,
+        ),
+        (
+            np.array([[1, 2, 3, 4], [-1, -2, -3, -4], [5, 6, 7, 8]], dtype=np.float64),
+            np.array([0, 2], dtype=np.int32),
+            4,
+        ),
+        (
+            np.random.random((6, 4, 5)),
+            np.array([0, 0, 1, 5, 5], dtype=np.int32),
+            100,
+        ),
+    ],
+)
+@pytest.mark.parametrize("use_dyn", [True, False])
+def test_segment_sum(data_np, segment_ids_np, num_segments, use_dyn):
+    def ref_segment_sum(
+        data: np.ndarray,
+        segment_ids: np.ndarray,
+        num_segments: Optional[int] = None,
+    ):
+        """
+        This function calculates the expected output of segment_sum operator given the inputs.
+        """
+        if not num_segments:
+            num_segments = np.unique(segment_ids).shape[0]
+
+        result = np.zeros((num_segments,) + data.shape[1:], data.dtype)
+        for i, index in enumerate(segment_ids):
+            result[index] += data[i]
+        return result
+
+    def verify_segment_sum(
+        data_np: np.ndarray, segment_ids_np: np.ndarray, num_segments: Optional[int]
+    ):
+        """
+        This function verifies the relay output of segment_sum with its expected output.
+        """
+        if use_dyn:
+            data = relay.var(
+                "data",
+                shape=[relay.Any() for _ in data_np.shape],
+                dtype=str(data_np.dtype),
+            )
+            segment_ids = relay.var(
+                "segment_ids",
+                shape=[relay.Any()],
+                dtype=str(segment_ids_np.dtype),
+            )
+        else:
+            data = relay.var(
+                "data",
+                relay.TensorType(data_np.shape, str(data_np.dtype)),
+            )
+            segment_ids = relay.var(
+                "segment_ids", relay.TensorType(segment_ids_np.shape, str(segment_ids_np.dtype))
+            )
+        z = relay.op.segment_sum(data, segment_ids, num_segments)
+
+        func = relay.Function([data, segment_ids], z)
+        ref_res = ref_segment_sum(data_np, segment_ids_np, num_segments=num_segments)
+        segment_sum_result = run_infer_type(z)
+        assert segment_sum_result.checked_type.dtype == data_np.dtype
+        verify_func(
+            func,
+            [data_np, segment_ids_np],
+            ref_res,
+        )
+
+    verify_segment_sum(data_np, segment_ids_np, num_segments)
+
+
+def verify_func(func, data, ref_res, target_ctx=tvm.testing.enabled_targets()):
+    assert isinstance(data, list)
+    for target, ctx in target_ctx:
+        for kind in ["vm"]:
+            mod = tvm.ir.IRModule.from_expr(func)
+            intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+            op_res = intrp.evaluate()(*data)
+            if isinstance(op_res, tvm.runtime.container.ADT):
+                assert len(op_res) == len(
+                    ref_res
+                ), "Outputs from TVM and Python implementation must be equal "
+
+                for op_result, ref_result in zip(op_res, ref_res):
+                    tvm.testing.assert_allclose(op_result.asnumpy(), ref_result, rtol=1e-5)
+            else:
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+            relay.backend.compile_engine.get().clear()
+
+
+@tvm.testing.uses_gpu
 def test_adv_index():
     def verify_adv_index(data_shape, index_shapes):
         dtype = "float32"
@@ -1312,40 +1758,168 @@ def verify_adv_index(data_shape, index_shapes):
     verify_adv_index((10, 5, 15), [(1, 2, 1), (1, 2, 7)])
 
 
+@tvm.testing.parametrize_targets
+def test_cumsum(target, ctx):
+    def verify_cumsum(data_np, np_out, axis=None, out_dtype=None, rtol=1e-5, atol=1e-5):
+        inp = relay.var("data", relay.TensorType(data_np.shape, str(data_np.dtype)))
+
+        out = relay.op.cumsum(inp, axis, out_dtype)
+        func = relay.Function([inp], out)
+
+        for kind in ["graph", "debug"]:
+            intrp = relay.create_executor(kind, ctx=ctx, target=target)
+            op_res = intrp.evaluate(func)(data_np)
+            tvm.testing.assert_allclose(op_res.asnumpy(), np_out, rtol=rtol, atol=atol)
+
+    data = np.array([2, 3, 0])
+    verify_cumsum(data, np.cumsum(data))
+    verify_cumsum(data, np.cumsum(data), out_dtype="int64")
+
+    data = np.random.randn(10, 10)
+    verify_cumsum(data, np.cumsum(data))
+    verify_cumsum(data, np.cumsum(data, axis=0), axis=0)
+    verify_cumsum(data, np.cumsum(data, axis=1), axis=1)
+
+    data = np.random.randn(10, 5, 10).astype("float32")
+    verify_cumsum(data, np.cumsum(data), rtol=1e-4, atol=1e-4)
+    verify_cumsum(data, np.cumsum(data, axis=0), axis=0, rtol=1e-4, atol=1e-4)
+    verify_cumsum(data, np.cumsum(data, axis=1), axis=1, rtol=1e-4, atol=1e-4)
+    verify_cumsum(data, np.cumsum(data, axis=-1), axis=-1, rtol=1e-4, atol=1e-4)
+
+    data = np.random.rand(10) > 0.5
+    data = data.astype(np.int32)
+    verify_cumsum(data, np.cumsum(data, dtype=np.int32))
+    verify_cumsum(data, np.cumsum(data, dtype="int64"), out_dtype="int64")
+
+
+@tvm.testing.parametrize_targets
+def test_scatter_nd(target, ctx):
+    def verify_scatter_nd(data_np, indices_np, shape, ref_res, rtol=1e-5, atol=1e-5):
+        data = relay.var("data", shape=data_np.shape, dtype=str(data_np.dtype))
+        indices = relay.var("indices", shape=indices_np.shape, dtype=str(indices_np.dtype))
+
+        out = relay.op.scatter_nd(data, indices, shape)
+        func = relay.Function([data, indices], out)
+
+        for kind in ["graph", "debug"]:
+            intrp = relay.create_executor(kind, ctx=ctx, target=target)
+            op_res = intrp.evaluate(func)(data_np, indices_np)
+            tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=rtol, atol=atol)
+
+    def verify_scatter_nd_with_stack(data_np, indices_np, shape, ref_res, rtol=1e-5, atol=1e-5):
+        data = relay.var("data", shape=data_np.shape, dtype=str(data_np.dtype))
+        indices_vars = [
+            relay.var("ind{i}", shape=v.shape, dtype=str(v.dtype)) for i, v in enumerate(indices_np)
+        ]
+
+        # test if scatter_nd works in case indices are prepared by another Relay operator
+        indices = relay.op.stack(indices_vars, axis=0)
+        out = relay.op.scatter_nd(data, indices, shape)
+        func = relay.Function(
+            [
+                data,
+            ]
+            + indices_vars,
+            out,
+        )
+
+        fargs = [
+            data_np,
+        ]
+        for a in indices_np:
+            fargs.append(a)
+        for kind in ["graph", "debug"]:
+            intrp = relay.create_executor(kind, ctx=ctx, target=target)
+            op_res = intrp.evaluate(func)(*fargs)
+            tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=rtol, atol=atol)
+
+    data = np.array([2, 3, 0])
+    indices = np.array([[1, 1, 0], [0, 1, 0]])
+    shape = (2, 2)
+    out = np.array([[0, 0], [2, 3]])
+    verify_scatter_nd(data, indices, shape, out)
+    verify_scatter_nd_with_stack(data, indices, shape, out)
+
+    data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
+    indices = np.array([[0, 1], [1, 1]])
+    shape = (2, 2, 2, 2)
+    out = np.array([[[[0, 0], [0, 0]], [[1, 2], [3, 4]]], [[[0, 0], [0, 0]], [[5, 6], [7, 8]]]])
+    verify_scatter_nd(data, indices, shape, out)
+    verify_scatter_nd_with_stack(data, indices, shape, out)
+
+    data = np.reshape(np.arange(1560 * 3), (3, 1560)).astype("float32")
+    indices = np.array([[1, 0, 0]])
+    shape = (2, 1560)
+    out = np.zeros(shape).astype("float32")
+    out[1, :] += data[0, :]
+    out[0, :] += data[1, :]
+    out[0, :] += data[2, :]
+    verify_scatter_nd(data, indices, shape, out)
+    verify_scatter_nd_with_stack(data, indices, shape, out)
+
+    data = np.ones((5, 3)).astype("float64")
+    indices = np.stack((np.random.randint(2, size=5), np.random.randint(7, size=5))).astype("int64")
+    shape = (2, 7, 3)
+    out = np.zeros(shape).astype("float64")
+    for i in range(indices.shape[1]):
+        for j in range(data.shape[1]):
+            out[indices[0, i], indices[1, i], j] += data[i, j]
+    verify_scatter_nd(data, indices, shape, out)
+    verify_scatter_nd_with_stack(data, indices, shape, out)
+
+
+def test_unique():
+    def calc_numpy_unique(data, is_sorted=False):
+        uniq, index, inverse, counts = np.unique(
+            data, return_index=True, return_inverse=True, return_counts=True
+        )
+        num_uniq = np.array([len(uniq)]).astype("int32")
+        if not is_sorted:
+            order = np.argsort(index)
+            reverse_order = np.argsort(order)
+            uniq = uniq[order].astype(data.dtype)
+            inverse = np.array([reverse_order[i] for i in inverse]).astype("int32")
+            counts = counts[order].astype("int32")
+        return [uniq.astype(data.dtype), inverse.astype("int32"), counts, num_uniq]
+
+    def verify_unique(n, dtype, is_dyn=False, is_sorted=False, return_counts=False):
+        if is_dyn:
+            x = relay.var("x", relay.TensorType([relay.Any()], dtype))
+        else:
+            x = relay.var("x", relay.TensorType([n], dtype))
+        outs = relay.unique(x, is_sorted, return_counts)
+        outs = outs.astuple()
+        func = relay.Function([x], outs)
+        x_data = np.random.randint(50, size=n).astype(dtype)
+
+        if is_dyn:
+            backends = ["vm", "debug"]
+        else:
+            backends = ["graph", "debug"]
+
+        for target, ctx in tvm.testing.enabled_targets():
+            for kind in backends:
+                mod = tvm.ir.IRModule.from_expr(func)
+                intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
+                tvm_res = intrp.evaluate()(x_data)
+                np_res = calc_numpy_unique(x_data, is_sorted)
+                num_unique = np_res[3][0]
+                assert num_unique == tvm_res[2].asnumpy()[0]
+                # unique
+                tvm.testing.assert_allclose(tvm_res[0].asnumpy()[:num_unique], np_res[0], rtol=1e-5)
+                # inverse_indices
+                tvm.testing.assert_allclose(tvm_res[1].asnumpy(), np_res[1], rtol=1e-5)
+                # counts
+                if return_counts:
+                    tvm.testing.assert_allclose(
+                        tvm_res[3].asnumpy()[:num_unique], np_res[2], rtol=1e-5
+                    )
+
+    for dtype in ["int32", "int64"]:
+        for i in range(8):
+            is_dyn, is_sorted, return_counts = bool(i & 1), bool(i & 2), bool(i & 4)
+            verify_unique(10, dtype, is_dyn, is_sorted, return_counts)
+
+
 if __name__ == "__main__":
-    test_cast()
-    test_zeros_ones()
-    test_unary_identity()
-    test_clip()
-    test_transpose_infer_type()
-    test_transpose()
-    test_reshape_infer_type()
-    test_reshape()
-    test_reshape_fail()
-    test_reshape_like_infer_type()
-    test_reshape_like()
-    test_take_infer_type()
-    test_take()
-    test_full_infer_type()
-    test_full()
-    test_full_like_infer_type()
-    test_full_like()
-    test_infer_type_leaky_relu()
-    test_infer_type_prelu()
-    test_squeeze()
-    test_squeeze_infer_type()
-    test_squeeze_bad_axes_infer_type()
-    test_split_infer_type()
-    test_arange()
-    test_meshgrid()
-    test_reverse()
-    test_stack()
-    test_tile()
-    test_repeat()
-    test_gather_nd()
-    test_isfinite()
-    test_isinf()
-    test_unravel_index()
-    test_sparse_to_dense()
-    test_fixed_point_multiply()
-    test_adv_index()
+    pytest.main([__file__])
diff --git a/tests/python/relay/test_op_level5.py b/tests/python/relay/test_op_level5.py
index 1ce8a182f034..929764b6e40a 100644
--- a/tests/python/relay/test_op_level5.py
+++ b/tests/python/relay/test_op_level5.py
@@ -67,13 +67,19 @@ def verify_resize(dshape, scale, method, layout, coord_trans):
             for kind in ["graph", "debug"]:
                 intrp = relay.create_executor(kind, ctx=ctx, target=target)
                 op_res = intrp.evaluate(func)(x_data)
-                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-4, atol=1e-5)
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-3, atol=1e-4)
 
-    for layout in ["NHWC", "NCHW"]:
-        verify_resize((1, 4, 4, 4), 2, "bilinear", layout, "align_corners")
-        verify_resize((2, 8, 17, 20), 3, "bilinear", layout, "half_pixel")
-        verify_resize((2, 8, 17, 20), 3, "bilinear", layout, "asymmetric")
-        verify_resize((3, 4, 5, 6), 5, "nearest_neighbor", layout, "asymmetric")
+    for method in ["nearest_neighbor", "bilinear"]:
+        for coord_trans in ["asymmetric", "half_pixel", "align_corners"]:
+            for layout in ["NHWC", "NCHW"]:
+                # TODO: Topi test does not have a function to produce numpy output for resize with
+                # nearest_neighbors and align_corners. Enable when topi test has this option
+                if coord_trans == "align_corners" and method == "nearest_neighbor":
+                    continue
+                verify_resize((1, 4, 4, 4), 2, method, layout, coord_trans)
+                verify_resize((2, 8, 17, 20), 3, method, layout, coord_trans)
+                verify_resize((2, 8, 17, 20), 3, method, layout, coord_trans)
+                verify_resize((3, 4, 5, 6), 5, method, layout, coord_trans)
 
 
 def test_resize3d_infer_type():
@@ -313,10 +319,8 @@ def verify_get_valid_counts(dshape, score_threshold, id_index, score_index):
         for target, ctx in tvm.testing.enabled_targets():
             intrp = relay.create_executor("debug", ctx=ctx, target=target)
             out = intrp.evaluate(func)(np_data)
+
             tvm.testing.assert_allclose(out[0].asnumpy(), np_out1, rtol=1e-3, atol=1e-04)
-            # get_valid_count for opencl doesn't do data rearrangement
-            if target in ["opencl"]:
-                return
             tvm.testing.assert_allclose(out[1].asnumpy(), np_out2, rtol=1e-3, atol=1e-04)
             tvm.testing.assert_allclose(out[2].asnumpy(), np_out3, rtol=1e-3, atol=1e-04)
 
@@ -490,6 +494,42 @@ def verify_nms(
         top_k=2,
     )
 
+    np_data = np.array(
+        [
+            [
+                [0, 0.8, 1, 20, 25, 45, 1, 2, 3, 4],
+                [1, 0.7, 30, 60, 50, 80, 5, 6, 7, 8],
+                [0, 0.4, 4, 21, 19, 40, 9, 10, 11, 12],
+                [2, 0.9, 35, 61, 52, 79, 13, 14, 15, 16],
+                [1, 0.5, 100, 60, 70, 110, 17, 18, 19, 20],
+            ]
+        ]
+    ).astype("float32")
+    np_result = np.array(
+        [
+            [
+                [2, 0.9, 35, 61, 52, 79, 13, 14, 15, 16],
+                [0, 0.8, 1, 20, 25, 45, 1, 2, 3, 4],
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+                [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
+            ]
+        ]
+    )
+    dshape = (1, 5, 10)
+    verify_nms(
+        np_data,
+        np_valid_count,
+        np_indices,
+        np_max_output_size,
+        dshape,
+        np_result,
+        np_indices_result,
+        force_suppress=True,
+        top_k=2,
+        check_type_only=False,
+    )
+
 
 @tvm.testing.uses_gpu
 def test_multibox_transform_loc():
@@ -585,7 +625,18 @@ def test_threshold():
 
 @tvm.testing.uses_gpu
 def test_roi_align():
-    def verify_roi_align(data_shape, rois_shape, pooled_size, spatial_scale, sample_ratio):
+    def verify_roi_align(
+        data_shape,
+        rois_shape,
+        channel,
+        in_size,
+        pooled_size,
+        spatial_scale,
+        sample_ratio,
+        mode,
+        layout,
+        ref_func,
+    ):
         data = relay.var("data", relay.ty.TensorType(data_shape, "float32"))
         rois = relay.var("rois", relay.ty.TensorType(rois_shape, "float32"))
         z = relay.vision.roi_align(
@@ -594,28 +645,37 @@ def verify_roi_align(data_shape, rois_shape, pooled_size, spatial_scale, sample_
             pooled_size=(pooled_size, pooled_size),
             spatial_scale=spatial_scale,
             sample_ratio=sample_ratio,
-            layout="NCHW",
+            mode=mode,
+            layout=layout,
         )
         zz = run_infer_type(z)
-        batch, channel, in_size, _ = data_shape
+
         num_roi = rois_shape[0]
-        assert zz.checked_type == relay.ty.TensorType(
-            (num_roi, channel, pooled_size, pooled_size), "float32"
-        )
+
+        if layout == "NCHW":
+            assert zz.checked_type == relay.ty.TensorType(
+                (num_roi, channel, pooled_size, pooled_size), "float32"
+            )
+        else:
+            assert zz.checked_type == relay.ty.TensorType(
+                (num_roi, pooled_size, pooled_size, channel), "float32"
+            )
 
         func = relay.Function([data, rois], z)
         func = run_infer_type(func)
         np_data = np.random.uniform(size=data_shape).astype("float32")
         np_rois = np.random.uniform(size=rois_shape).astype("float32") * in_size
-        np_rois[:, 0] = np.random.randint(low=0, high=batch, size=num_roi)
-        ref_res = tvm.topi.testing.roi_align_nchw_python(
+        np_rois[:, 0] = np.random.randint(low=0, high=data_shape[0], size=num_roi)
+        ref_res = ref_func(
             np_data,
             np_rois,
             pooled_size=pooled_size,
             spatial_scale=spatial_scale,
             sample_ratio=sample_ratio,
+            mode=mode,
         )
         for target, ctx in tvm.testing.enabled_targets():
+            print("test on", target)
             intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
             op_res1 = intrp1.evaluate(func)(np_data, np_rois)
             tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-4)
@@ -623,8 +683,64 @@ def verify_roi_align(data_shape, rois_shape, pooled_size, spatial_scale, sample_
             op_res2 = intrp2.evaluate(func)(np_data, np_rois)
             tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=1e-4)
 
-    verify_roi_align((1, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=1.0, sample_ratio=-1)
-    verify_roi_align((4, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2)
+    def verify_roi_align_nchw(
+        data_shape, rois_shape, pooled_size, spatial_scale, sample_ratio, mode
+    ):
+        _, channel, in_size, _ = data_shape
+        return verify_roi_align(
+            data_shape,
+            rois_shape,
+            channel,
+            in_size,
+            pooled_size,
+            spatial_scale,
+            sample_ratio,
+            mode,
+            "NCHW",
+            tvm.topi.testing.roi_align_nchw_python,
+        )
+
+    def verify_roi_align_nhwc(
+        data_shape, rois_shape, pooled_size, spatial_scale, sample_ratio, mode
+    ):
+        _, in_size, _, channel = data_shape
+        return verify_roi_align(
+            data_shape,
+            rois_shape,
+            channel,
+            in_size,
+            pooled_size,
+            spatial_scale,
+            sample_ratio,
+            mode,
+            "NHWC",
+            tvm.topi.testing.roi_align_nhwc_python,
+        )
+
+    verify_roi_align_nchw(
+        (1, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=1.0, sample_ratio=-1, mode="avg"
+    )
+    verify_roi_align_nchw(
+        (4, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2, mode="avg"
+    )
+    verify_roi_align_nchw(
+        (1, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=1.0, sample_ratio=-1, mode="max"
+    )
+    verify_roi_align_nchw(
+        (4, 4, 16, 16), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2, mode="max"
+    )
+    verify_roi_align_nhwc(
+        (1, 16, 16, 4), (32, 5), pooled_size=7, spatial_scale=1.0, sample_ratio=-1, mode="avg"
+    )
+    verify_roi_align_nhwc(
+        (4, 16, 16, 4), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2, mode="avg"
+    )
+    verify_roi_align_nhwc(
+        (1, 16, 16, 4), (32, 5), pooled_size=7, spatial_scale=1.0, sample_ratio=-1, mode="max"
+    )
+    verify_roi_align_nhwc(
+        (4, 16, 16, 4), (32, 5), pooled_size=7, spatial_scale=0.5, sample_ratio=2, mode="max"
+    )
 
 
 @tvm.testing.uses_gpu
@@ -839,11 +955,31 @@ def test_infer_type(batch, in_channel, size, out_channel, deformable_groups, gro
     test_infer_type(1, 4, 16, 4, 4, 1, "NHWC")
     test_infer_type(2, 4, 16, 4, 1, 2, "NHWC")
 
-    def test_run(batch, in_channel, size, out_channel, deformable_groups, groups):
+    def test_run(batch, in_channel, size, out_channel, deformable_groups, groups, layout):
         kernel_size = (3, 3)
-        data_shape = (batch, in_channel, size, size)
-        offset_shape = (batch, 2 * kernel_size[0] * kernel_size[1] * deformable_groups, size, size)
-        kernel_shape = (out_channel, in_channel // groups, kernel_size[0], kernel_size[1])
+        if layout == "NCHW":
+            kernel_layout = "OIHW"
+            data_shape = (batch, in_channel, size, size)
+            kernel_shape = (out_channel, in_channel // groups, kernel_size[0], kernel_size[1])
+            out_shape = (batch, out_channel, size, size)
+            offset_shape = (
+                batch,
+                2 * kernel_size[0] * kernel_size[1] * deformable_groups,
+                out_shape[2],
+                out_shape[3],
+            )
+        else:
+            kernel_layout = "HWIO"
+            data_shape = (batch, size, size, in_channel)
+            kernel_shape = (kernel_size[0], kernel_size[1], in_channel // groups, out_channel)
+            out_shape = (batch, size, size, out_channel)
+            offset_shape = (
+                batch,
+                out_shape[1],
+                out_shape[2],
+                2 * kernel_size[0] * kernel_size[1] * deformable_groups,
+            )
+
         dtype = "float32"
         data = relay.var("data", shape=data_shape, dtype=dtype)
         offset = relay.var("offset")
@@ -855,6 +991,8 @@ def test_run(batch, in_channel, size, out_channel, deformable_groups, groups):
             strides=(1, 1),
             padding=(1, 1),
             dilation=(1, 1),
+            data_layout=layout,
+            kernel_layout=kernel_layout,
             kernel_size=kernel_size,
             deformable_groups=deformable_groups,
             groups=groups,
@@ -864,25 +1002,40 @@ def test_run(batch, in_channel, size, out_channel, deformable_groups, groups):
         data = np.random.uniform(size=data_shape).astype(dtype)
         offset = np.random.uniform(size=offset_shape).astype(dtype)
         kernel = np.random.uniform(size=kernel_shape).astype(dtype)
-        ref_res = tvm.topi.testing.deformable_conv2d_nchw_python(
-            data,
-            offset,
-            kernel,
-            stride=(1, 1),
-            padding=(1, 1),
-            dilation=(1, 1),
-            deformable_groups=deformable_groups,
-            groups=groups,
-        )
-
+        if layout == "NCHW":
+            ref_res = tvm.topi.testing.deformable_conv2d_nchw_python(
+                data,
+                offset,
+                kernel,
+                stride=(1, 1),
+                padding=(1, 1),
+                dilation=(1, 1),
+                deformable_groups=deformable_groups,
+                groups=groups,
+            )
+        else:
+            ref_res = tvm.topi.testing.deformable_conv2d_nhwc_python(
+                data,
+                offset,
+                kernel,
+                stride=(1, 1),
+                padding=(1, 1),
+                dilation=(1, 1),
+                deformable_groups=deformable_groups,
+                groups=groups,
+            )
         for target, ctx in tvm.testing.enabled_targets():
+            if target == "cuda" and layout == "NHWC":
+                continue  # Cannot run NHWC layout on cuda target, only on llvm
             for kind in ["graph", "debug"]:
                 intrp1 = relay.create_executor(kind, ctx=ctx, target=target)
                 op_res1 = intrp1.evaluate(func)(data, offset, kernel)
                 tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
 
-    test_run(1, 4, 16, 4, 1, 1)
-    test_run(2, 4, 16, 4, 4, 1)
+    test_run(1, 4, 16, 4, 1, 1, "NCHW")
+    test_run(1, 4, 16, 4, 1, 1, "NHWC")
+    test_run(2, 4, 16, 4, 4, 1, "NCHW")
+    test_run(2, 4, 16, 4, 4, 1, "NHWC")
 
 
 @tvm.testing.uses_gpu
@@ -1215,7 +1368,6 @@ def verify_batch_to_space_nd(dshape, block_shape, crops):
     test_resize_infer_type()
     test_resize()
     test_resize3d_infer_type()
-    test_resize3d()
     test_crop_and_resize()
     test_multibox_prior()
     test_multibox_transform_loc()
diff --git a/tests/python/relay/test_op_level6.py b/tests/python/relay/test_op_level6.py
index 0dac69e36025..f4b785f59df8 100644
--- a/tests/python/relay/test_op_level6.py
+++ b/tests/python/relay/test_op_level6.py
@@ -26,6 +26,7 @@
 @tvm.testing.uses_gpu
 def test_sort():
     def verify_sort(shape, axis, is_ascend, is_dyn=False):
+
         if is_dyn:
             x = relay.var("x", relay.TensorType([relay.Any()] * len(shape), "float32"))
         else:
@@ -87,9 +88,11 @@ def verify_argsort(shape, axis, is_ascend, dtype, is_dyn=False):
         for dtype in ["int32", "int64", "float32", "float64"]:
             verify_argsort((2, 3, 4), axis=0, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
             verify_argsort((1, 4, 6), axis=1, is_ascend=True, dtype=dtype, is_dyn=is_dyn)
-            verify_argsort((3, 5, 6), axis=-1, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
-            verify_argsort((3, 2000, 6), axis=1, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
-            verify_argsort((1, 122640), axis=1, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
+        dtype = "int32"
+        verify_argsort((3, 5, 6), axis=-1, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
+        verify_argsort((3, 6000, 6), axis=1, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
+        verify_argsort((1000, 1, 1), axis=0, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
+        verify_argsort((1, 122640), axis=1, is_ascend=False, dtype=dtype, is_dyn=is_dyn)
 
 
 @tvm.testing.uses_gpu
diff --git a/tests/python/relay/test_op_qnn_dequantize.py b/tests/python/relay/test_op_qnn_dequantize.py
index e7fb161a13cb..1833458fdb75 100644
--- a/tests/python/relay/test_op_qnn_dequantize.py
+++ b/tests/python/relay/test_op_qnn_dequantize.py
@@ -98,7 +98,7 @@ def test_channelwise_axis_1():
     }
 
     dequantize_test_driver(
-        in_dtype="uint8", quant_args=quant_args, in_data=data, verify_output_data=output, axis=1
+        in_dtype="uint8", quant_args=quant_args, in_data=data, verify_output_data=output, axis=-1
     )
 
 
diff --git a/tests/python/relay/test_op_qnn_quantize.py b/tests/python/relay/test_op_qnn_quantize.py
index 2ef298679904..b300c5612174 100644
--- a/tests/python/relay/test_op_qnn_quantize.py
+++ b/tests/python/relay/test_op_qnn_quantize.py
@@ -127,7 +127,7 @@ def test_channelwise_axis_1():
     quantize_test_driver(
         in_dtype="float32",
         quant_args=quant_args,
-        axis=1,
+        axis=-1,
         out_dtype="uint8",
         in_data=data,
         verify_output_data=output,
diff --git a/tests/python/relay/test_op_qnn_simulated_dequantize.py b/tests/python/relay/test_op_qnn_simulated_dequantize.py
new file mode 100644
index 000000000000..a9333c916561
--- /dev/null
+++ b/tests/python/relay/test_op_qnn_simulated_dequantize.py
@@ -0,0 +1,177 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import tvm
+from tvm import te
+import numpy as np
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.runtime.vm import VirtualMachine
+from tvm.topi.nn.qnn import SQNN_DTYPE_TO_CODE
+
+
+def dequantize_test_driver(in_dtype, quant_args, axis, in_data):
+    shape = in_data.shape
+    input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
+    input_zero_point = relay.const(quant_args["in_zero_point"])
+    input_scale = relay.const(quant_args["in_scale"])
+    dequantized_output = relay.qnn.op.dequantize(
+        input_data,
+        input_scale=input_scale,
+        input_zero_point=input_zero_point,
+        axis=axis,
+    )
+    mod = relay.Function(relay.analysis.free_vars(dequantized_output), dequantized_output)
+    mod = tvm.IRModule.from_expr(mod)
+    with tvm.transform.PassContext(opt_level=3):
+        graph, lib, params = relay.build(mod, "llvm", params=None)
+    rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
+    rt_mod.set_input(input_data=in_data)
+    rt_mod.set_input(**params)
+    rt_mod.run()
+    res = rt_mod.get_output(0).asnumpy()
+    return res
+
+
+def build_simulated_dequantize(input_data, scale, zp, dtype, axis=-1):
+    sim_q = relay.qnn.op.simulated_dequantize(
+        input_data,
+        scale,
+        zp,
+        axis=axis,
+        in_dtype=dtype,
+    )
+    mod = tvm.IRModule.from_expr(sim_q)
+    with tvm.transform.PassContext(opt_level=3):
+        vm_exec = relay.vm.compile(mod, "llvm", params=None)
+    vm = VirtualMachine(vm_exec, tvm.cpu(0))
+    return vm
+
+
+def verify_simulated_dequantize_simple(dtype):
+    data = np.random.uniform(low=-128, high=127, size=[2, 5]).astype(dtype)
+    data_fp = data.astype("float32")
+    scale_np = np.float32(0.5)
+    zp_np = np.int32(127)
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE[dtype])
+    quant_args = {"in_zero_point": zp_np, "in_scale": scale_np}
+    dq_out = dequantize_test_driver(
+        in_dtype=dtype,
+        quant_args=quant_args,
+        axis=-1,
+        in_data=data,
+    )
+    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
+    scale = relay.var("scale", shape=[])
+    zp = relay.var("zp", shape=[])
+    dtype = relay.var("dtype", shape=[])
+    vm = build_simulated_dequantize(input_data, scale, zp, dtype)
+    sim_dq_out = vm.invoke("main", input_data=data_fp, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    np.testing.assert_allclose(sim_dq_out.asnumpy(), dq_out, rtol=1e-5)
+
+
+def test_simulated_dequantize():
+    verify_simulated_dequantize_simple("uint8")
+    verify_simulated_dequantize_simple("int8")
+    verify_simulated_dequantize_simple("int32")
+
+
+def test_dynamic_channels():
+    # Compile simulated quantize once but support either per-channel or scalar params.
+    data = np.random.uniform(low=-64, high=64, size=[2, 5]).astype("int8")
+    data_fp = data.astype("float32")
+    # Test scalar qnn params.
+    scale_np = np.asarray([0.5]).astype("float32")
+    zp_np = np.asarray([0]).astype("int32")
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["int8"])
+    quant_args = {"in_zero_point": zp_np[0], "in_scale": scale_np[0]}
+    dq_out = dequantize_test_driver(
+        in_dtype="int8",
+        quant_args=quant_args,
+        axis=0,
+        in_data=data,
+    )
+    # Create variables with undefined shape and run with scalar inputs.
+    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
+    scale = relay.var("scale", shape=[relay.Any()], dtype="float32")
+    zp = relay.var("zp", shape=[relay.Any()], dtype="int32")
+    dtype = relay.var("dtype", shape=[])
+    vm = build_simulated_dequantize(input_data, scale, zp, dtype, axis=0)
+    sim_dq_out = vm.invoke("main", input_data=data_fp, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    np.testing.assert_allclose(sim_dq_out.asnumpy(), dq_out, rtol=1e-5)
+
+    # Now get the perchannel quantize output and compare without recompiling.
+    scale_np = np.array([0.5, 0.25]).astype("float32")
+    zp_np = np.array([127, 123]).astype("int32")
+
+    # Get the reference quantize output.
+    quant_args = {"in_zero_point": zp_np, "in_scale": scale_np}
+    dq_out = dequantize_test_driver(
+        in_dtype="int8",
+        quant_args=quant_args,
+        axis=0,
+        in_data=data,
+    )
+    # Run the simulated quantize without recompiling and confirm results match.
+    sim_dq_out = vm.invoke("main", input_data=data_fp, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    np.testing.assert_allclose(sim_dq_out.asnumpy(), dq_out, rtol=1e-5)
+
+
+def test_dynamic_dtype():
+    # Compile simulated quantize once but support any type of quantization.
+    data = np.random.uniform(low=0, high=255, size=[2, 5]).astype("uint8")
+    data_fp = data.astype("float32")
+    # Test scalar uint8 to fp32.
+    scale_np = np.asarray([0.5]).astype("float32")
+    zp_np = np.asarray([127]).astype("int32")
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["uint8"])
+    quant_args = {"in_zero_point": zp_np[0], "in_scale": scale_np[0]}
+    dq_out = dequantize_test_driver(
+        in_dtype="uint8",
+        quant_args=quant_args,
+        axis=-1,
+        in_data=data,
+    )
+    # Create variables with undefined shape and run with scalar inputs.
+    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
+    scale = relay.var("scale", shape=[relay.Any()], dtype="float32")
+    zp = relay.var("zp", shape=[relay.Any()], dtype="int32")
+    dtype = relay.var("dtype", shape=[])
+    vm = build_simulated_dequantize(input_data, scale, zp, dtype)
+    sim_dq_out = vm.invoke("main", input_data=data_fp, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    np.testing.assert_allclose(sim_dq_out.asnumpy(), dq_out, rtol=1e-5)
+
+    # Now test int8 to float32 compilation.
+    data = np.random.uniform(low=0, high=255, size=[2, 5]).astype("int8")
+    data_fp = data.astype("float32")
+    # Get the reference quantize output.
+    dq_out = dequantize_test_driver(
+        in_dtype="int8",
+        quant_args=quant_args,
+        axis=-1,
+        in_data=data,
+    )
+    # Run the simulated quantize without recompiling and confirm results match.
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["int8"])
+    sim_dq_out = vm.invoke("main", input_data=data_fp, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    np.testing.assert_allclose(sim_dq_out.asnumpy(), dq_out, rtol=1e-5)
+
+
+if __name__ == "__main__":
+    test_simulated_dequantize()
+    test_dynamic_channels()
+    test_dynamic_dtype()
diff --git a/tests/python/relay/test_op_qnn_simulated_quantize.py b/tests/python/relay/test_op_qnn_simulated_quantize.py
new file mode 100644
index 000000000000..c0fa0648d879
--- /dev/null
+++ b/tests/python/relay/test_op_qnn_simulated_quantize.py
@@ -0,0 +1,185 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import tvm
+from tvm import te
+import numpy as np
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.runtime.vm import VirtualMachine
+from tvm.topi.nn.qnn import SQNN_DTYPE_TO_CODE
+
+
+def allclose_with_rounding(a, b):
+    # Find number of mismatches in inputs.
+    mismatch = a != b
+    # Allow some rounding errors due to GPU fp32 arithmetic.
+    assert np.sum(mismatch) <= 3
+
+
+def quantize_test_driver(in_dtype, quant_args, axis, out_dtype, in_data):
+    shape = in_data.shape
+    input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
+    output_zero_point = relay.const(quant_args["out_zero_point"])
+    output_scale = relay.const(quant_args["out_scale"])
+    quantized_output = relay.qnn.op.quantize(
+        input_data,
+        output_scale=output_scale,
+        output_zero_point=output_zero_point,
+        axis=axis,
+        out_dtype=out_dtype,
+    )
+    mod = relay.Function(relay.analysis.free_vars(quantized_output), quantized_output)
+    mod = tvm.IRModule.from_expr(mod)
+    with tvm.transform.PassContext(opt_level=3):
+        graph, lib, params = relay.build(mod, "llvm", params=None)
+    rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
+    rt_mod.set_input(input_data=in_data)
+    rt_mod.set_input(**params)
+    rt_mod.run()
+    res = rt_mod.get_output(0).asnumpy()
+    return res
+
+
+def build_simulated_quantize(input_data, scale, zp, dtype, axis=-1):
+    sim_q = relay.qnn.op.simulated_quantize(
+        input_data,
+        scale,
+        zp,
+        axis=axis,
+        out_dtype=dtype,
+    )
+    mod = tvm.IRModule.from_expr(sim_q)
+    with tvm.transform.PassContext(opt_level=3):
+        vm_exec = relay.vm.compile(mod, "llvm", params=None)
+    vm = VirtualMachine(vm_exec, tvm.cpu(0))
+    return vm
+
+
+def verify_simulated_quantize_simple(dtype):
+    data = np.random.uniform(low=-128, high=127, size=[2, 5]).astype("float32")
+    scale_np = np.float32(0.5)
+    zp_np = np.int32(127)
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE[dtype])
+    quant_args = {"out_zero_point": zp_np, "out_scale": scale_np}
+    q_out = quantize_test_driver(
+        in_dtype="float32",
+        quant_args=quant_args,
+        axis=-1,
+        out_dtype=dtype,
+        in_data=data,
+    )
+    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
+    scale = relay.var("scale", shape=[])
+    zp = relay.var("zp", shape=[])
+    dtype = relay.var("dtype", shape=[])
+    vm = build_simulated_quantize(input_data, scale, zp, dtype)
+    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    allclose_with_rounding(sim_q_out.asnumpy(), q_out)
+
+
+def test_simulated_quantize():
+    verify_simulated_quantize_simple("uint8")
+    verify_simulated_quantize_simple("int8")
+    verify_simulated_quantize_simple("int32")
+
+
+def test_dynamic_channels():
+    # Compile simulated quantize once but support either per-channel or scalar params.
+    data = np.random.uniform(low=-64, high=64, size=[2, 5]).astype("float32")
+    # Test scalar qnn params.
+    scale_np = np.asarray([0.5]).astype("float32")
+    zp_np = np.asarray([127]).astype("int32")
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["uint8"])
+    quant_args = {"out_zero_point": zp_np[0], "out_scale": scale_np[0]}
+    q_out = quantize_test_driver(
+        in_dtype="float32",
+        quant_args=quant_args,
+        axis=0,
+        out_dtype="uint8",
+        in_data=data,
+    )
+    # Create variables with undefined shape and run with scalar inputs.
+    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
+    scale = relay.var("scale", shape=[relay.Any()], dtype="float32")
+    zp = relay.var("zp", shape=[relay.Any()], dtype="int32")
+    dtype = relay.var("dtype", shape=[])
+    vm = build_simulated_quantize(input_data, scale, zp, dtype, axis=0)
+    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    allclose_with_rounding(sim_q_out.asnumpy(), q_out)
+
+    # Now get the perchannel quantize output and compare without recompiling.
+    scale_np = np.array([0.5, 0.25]).astype("float32")
+    zp_np = np.array([127, 123]).astype("int32")
+
+    # Get the reference quantize output.
+    quant_args = {"out_zero_point": zp_np, "out_scale": scale_np}
+    q_out = quantize_test_driver(
+        in_dtype="float32",
+        quant_args=quant_args,
+        axis=0,
+        out_dtype="uint8",
+        in_data=data,
+    )
+    # Run the simulated quantize without recompiling and confirm results match.
+    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    allclose_with_rounding(sim_q_out.asnumpy(), q_out)
+
+
+def test_dynamic_dtype():
+    # Compile simulated quantize once but support any type of quantization.
+    data = np.random.uniform(low=-64, high=64, size=[2, 5]).astype("float32")
+    # Test scalar float32 to uint8.
+    scale_np = np.asarray([0.5]).astype("float32")
+    zp_np = np.asarray([127]).astype("int32")
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["uint8"])
+    quant_args = {"out_zero_point": zp_np[0], "out_scale": scale_np[0]}
+    q_out = quantize_test_driver(
+        in_dtype="float32",
+        quant_args=quant_args,
+        axis=-1,
+        out_dtype="uint8",
+        in_data=data,
+    )
+    # Create variables with undefined shape and run with scalar inputs.
+    input_data = relay.var("input_data", shape=data.shape, dtype="float32")
+    scale = relay.var("scale", shape=[relay.Any()], dtype="float32")
+    zp = relay.var("zp", shape=[relay.Any()], dtype="int32")
+    dtype = relay.var("dtype", shape=[])
+    vm = build_simulated_quantize(input_data, scale, zp, dtype)
+    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    allclose_with_rounding(sim_q_out.asnumpy(), q_out)
+
+    # Now test float32 to int32 compilation.
+    # Get the reference quantize output.
+    q_out = quantize_test_driver(
+        in_dtype="float32",
+        quant_args=quant_args,
+        axis=-1,
+        out_dtype="int32",
+        in_data=data,
+    )
+    # Run the simulated quantize without recompiling and confirm results match.
+    dtype_np = np.int32(SQNN_DTYPE_TO_CODE["int32"])
+    sim_q_out = vm.invoke("main", input_data=data, scale=scale_np, zp=zp_np, dtype=dtype_np)
+    allclose_with_rounding(sim_q_out.asnumpy(), q_out)
+
+
+if __name__ == "__main__":
+    test_simulated_quantize()
+    test_dynamic_channels()
+    test_dynamic_dtype()
diff --git a/tests/python/relay/test_param_dict.py b/tests/python/relay/test_param_dict.py
index 74c9ebcaa355..29e0b5c0463b 100644
--- a/tests/python/relay/test_param_dict.py
+++ b/tests/python/relay/test_param_dict.py
@@ -17,7 +17,7 @@
 import os
 import numpy as np
 import tvm
-from tvm import te
+from tvm import te, runtime
 import json
 import base64
 from tvm._ffi.base import py_str
@@ -31,7 +31,7 @@ def test_save_load():
     x = np.ones((10, 2)).astype("float32")
     y = np.ones((1, 2, 3)).astype("float32")
     params = {"x": x, "y": y}
-    param_bytes = relay.save_param_dict(params)
+    param_bytes = runtime.save_param_dict(params)
     assert isinstance(param_bytes, bytearray)
     param2 = relay.load_param_dict(param_bytes)
     assert len(param2) == 2
@@ -46,7 +46,7 @@ def test_ndarray_reflection():
     param_dict = {"x": tvm_array, "y": tvm_array}
     assert param_dict["x"].same_as(param_dict["y"])
     # Serialize then deserialize `param_dict`.
-    deser_param_dict = relay.load_param_dict(relay.save_param_dict(param_dict))
+    deser_param_dict = relay.load_param_dict(runtime.save_param_dict(param_dict))
     # Make sure the data matches the original data and `x` and `y` contain the same data.
     np.testing.assert_equal(deser_param_dict["x"].asnumpy(), tvm_array.asnumpy())
     # Make sure `x` and `y` contain the same data.
@@ -77,7 +77,7 @@ def verify_graph_runtime(remote, target, shape, dtype):
         lib = remote.load_module("dev_lib.o")
         ctx = remote.cpu(0)
         mod = graph_runtime.create(graph, lib, ctx)
-        mod.load_params(relay.save_param_dict(params))
+        mod.load_params(runtime.save_param_dict(params))
         mod.run()
         out = mod.get_output(0, tvm.nd.empty(shape, dtype=dtype, ctx=ctx))
         tvm.testing.assert_allclose(x_in + 1, out.asnumpy())
diff --git a/tests/python/relay/test_pass_alter_op_layout.py b/tests/python/relay/test_pass_alter_op_layout.py
index 58c279d750ec..41186884bdb2 100644
--- a/tests/python/relay/test_pass_alter_op_layout.py
+++ b/tests/python/relay/test_pass_alter_op_layout.py
@@ -18,7 +18,7 @@
 import pytest
 
 import tvm
-from tvm import relay
+from tvm import relay, topi
 from tvm.relay import transform, analysis
 from tvm.relay.testing.temp_op_attr import TempOpAttr
 from tvm.relay.testing import run_infer_type
@@ -1248,6 +1248,34 @@ def expected():
     assert tvm.ir.structural_equal(a, b, map_free_vars=True), "Actual = \n" + str(a)
 
 
+def test_alter_op_dense():
+    def before():
+        x = relay.var("x", shape=(32, 64))
+        weight = relay.var("weight", shape=(48, 64))
+        y = relay.nn.dense(x, weight)
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    def expected():
+        x = relay.var("x", shape=(32, 64))
+        weight = relay.var("weight", shape=(48, 64))
+        target_layout = "NK16n"
+        weight_transform = relay.layout_transform(weight, "NK", target_layout)
+        y = relay.nn.contrib_dense_pack(x, weight_transform, units=None, out_dtype="float32")
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    for target, _ in tvm.testing.enabled_targets():
+        with tvm.target.Target(target):
+            with TempOpAttr(
+                "nn.dense", "FTVMAlterOpLayout", topi.x86.dense_alter_op._alter_dense_layout
+            ):
+                a = before()
+                a = run_opt_pass(a, transform.AlterOpLayout())
+                b = run_opt_pass(expected(), transform.InferType())
+                assert tvm.ir.structural_equal(a, b)
+
+
 if __name__ == "__main__":
     test_alter_op()
     test_alter_return_none()
@@ -1269,3 +1297,4 @@ def expected():
     test_alter_layout_nhwc_arm()
     test_alter_layout_nhwc_int8_aarch64()
     test_alter_op_with_global_var()
+    test_alter_op_dense()
diff --git a/tests/python/relay/test_pass_annotate_target.py b/tests/python/relay/test_pass_annotate_target.py
index 4f35066a8384..ce86cc603d6d 100644
--- a/tests/python/relay/test_pass_annotate_target.py
+++ b/tests/python/relay/test_pass_annotate_target.py
@@ -738,8 +738,8 @@ def after():
         mod = tvm.IRModule.from_expr(func)
         return mod
 
-    for annotate_non_call_ops in [True, False, True]:
-        result = transform.AnnotateTarget(target)(before())
+    for annotate_non_call_ops in [True, False]:
+        result = transform.AnnotateTarget(target, annotate_non_call_ops)(before())
         expected = transform.InferType()(after())
         assert tvm.ir.structural_equal(expected, result)
 
@@ -764,6 +764,27 @@ def after():
     assert tvm.ir.structural_equal(expected, result)
 
 
+def test_empty_tuple():
+    target = "test_empty_tuple"
+
+    """An empty tuple should behave just like a call with no args (see above test)."""
+
+    def before():
+        func = relay.Function([], relay.Tuple([]))
+        mod = tvm.IRModule.from_expr(func)
+        return mod
+
+    def after():
+        func = relay.Function([], relay.Tuple([]))
+        mod = tvm.IRModule.from_expr(func)
+        return mod
+
+    for annotate_non_call_ops in [True, False]:
+        result = transform.AnnotateTarget(target, annotate_non_call_ops)(before())
+        expected = transform.InferType()(after())
+        assert tvm.ir.structural_equal(expected, result)
+
+
 if __name__ == "__main__":
     test_extern_dnnl()
     test_composite_function()
@@ -780,3 +801,4 @@ def after():
     test_double_target()
     test_ends_with_tuple()
     test_ref_create_read_write()
+    test_empty_tuple()
diff --git a/tests/python/relay/test_pass_auto_quantize.py b/tests/python/relay/test_pass_auto_quantize.py
index 8a7c4cbfbbd6..31f5ac6e71b1 100644
--- a/tests/python/relay/test_pass_auto_quantize.py
+++ b/tests/python/relay/test_pass_auto_quantize.py
@@ -307,6 +307,39 @@ def @main(
     verify_partition_fails(mod, params)
 
 
+def test_left_shift_negative():
+    data = relay.var("data", shape=(1, 16, 64, 64))
+    weight = relay.const(np.full((16, 16, 3, 3), 256.0))
+    conv2d = relay.nn.conv2d(data, weight, kernel_size=(3, 3), padding=(1, 1), channels=16)
+    relu = relay.nn.relu(conv2d)
+
+    mod = tvm.IRModule.from_expr(relu)
+
+    with tvm.transform.PassContext(opt_level=3):
+        with relay.quantize.qconfig(
+            calibrate_mode="global_scale", global_scale=8.0, skip_conv_layers=None
+        ):
+            qnn_mod = relay.quantize.quantize(mod)
+
+    class OpFinder(relay.ExprVisitor):
+        def __init__(self, op_name):
+            super(OpFinder, self).__init__()
+            self._op_name = op_name
+            self.ops = list()
+
+        def visit_call(self, call):
+            super().visit_call(call)
+            if call.op.name == self._op_name:
+                self.ops.append(call)
+
+    opf = OpFinder("left_shift")
+    opf.visit(qnn_mod["main"])
+    assert len(opf.ops) > 0, 'Broken case, can\'t find any "left_shift" operators.'
+    for left_shift_op in opf.ops:
+        shift_amount = left_shift_op.args[1].data.asnumpy()
+        assert shift_amount >= 0, "Shift amount must be non-negative."
+
+
 if __name__ == "__main__":
     test_mul_rewrite()
     test_batch_flatten_rewrite()
@@ -320,3 +353,4 @@ def @main(
     test_unquantizable_prefix_partition()
     test_unquantizable_core_partition()
     test_unquantizable_suffix_partition()
+    test_left_shift_negative()
diff --git a/tests/python/relay/test_pass_convert_op_layout.py b/tests/python/relay/test_pass_convert_op_layout.py
index 6765d1f69b00..ca2469ea0a4c 100644
--- a/tests/python/relay/test_pass_convert_op_layout.py
+++ b/tests/python/relay/test_pass_convert_op_layout.py
@@ -499,6 +499,159 @@ def before():
     assert len(has_lt) == 1
 
 
+def test_slice_like_convert_layout():
+    def verify_slice_like(after, expected_axes):
+        # Verify if the slice_like after the convert layout has the expected axes.
+        has_expected = list()
+        checker = lambda x: has_expected.append(
+            isinstance(x, tvm.relay.expr.Call)
+            and x.op.name == "slice_like"
+            and str(x.attrs.axes) == str(expected_axes)
+        )
+        relay.analysis.post_order_visit(after, checker)
+        assert any(has_expected)
+
+    def func_nhwc():
+        x = relay.var("x", shape=(1, 56, 56, 64))
+        weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
+        y = relay.nn.conv2d(
+            x,
+            weight1,
+            channels=32,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NHWC",
+            kernel_layout="HWIO",
+        )
+        out = relay.slice_like(y, y, axes=[1, 2])
+        return relay.Function(analysis.free_vars(out), out)
+
+    after = run_opt_pass(func_nhwc(), transform.ConvertLayout({"nn.conv2d": ["NCHW", "default"]}))
+    verify_slice_like(after, [2, 3])
+
+    def func_nchw():
+        x = relay.var("x", shape=(1, 64, 56, 56))
+        weight1 = relay.var("weight1", shape=(32, 64, 3, 3))
+        y = relay.nn.conv2d(
+            x,
+            weight1,
+            channels=32,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NCHW",
+            kernel_layout="OIHW",
+        )
+        out = relay.slice_like(y, y, axes=[2, 3])
+        return relay.Function(analysis.free_vars(out), out)
+
+    after = run_opt_pass(func_nchw(), transform.ConvertLayout({"nn.conv2d": ["NHWC", "default"]}))
+    verify_slice_like(after, [1, 2])
+
+    def func_vars():
+        x = relay.var("x", shape=(1, 56, 56, 64))
+        weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
+        y = relay.nn.conv2d(
+            x,
+            weight1,
+            channels=32,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NHWC",
+            kernel_layout="HWIO",
+        )
+        # z has no layout information so convert layout won't happen.
+        z = relay.var("y", shape=(1, 56, 56, 32))
+        out = relay.slice_like(y, z, axes=[1, 2])
+        return relay.Function(analysis.free_vars(out), out)
+
+    after = run_opt_pass(func_vars(), transform.ConvertLayout({"nn.conv2d": ["NCHW", "default"]}))
+    verify_slice_like(after, [1, 2])
+
+
+def test_transpose_convert_layout():
+    def verify_transpose(after, expected_axes, expected_transform_cnt):
+        # Verify if the transpose after the convert layout has the expected axes.
+        has_expected = list()
+        checker = lambda x: has_expected.append(
+            isinstance(x, tvm.relay.expr.Call)
+            and x.op.name == "transpose"
+            and str(x.attrs.axes) == str(expected_axes)
+        )
+        relay.analysis.post_order_visit(after, checker)
+        assert any(has_expected), after
+
+        is_transform = list()
+        checker = lambda x: is_transform.append(
+            1 if isinstance(x, tvm.relay.expr.Call) and x.op.name == "layout_transform" else 0
+        )
+        relay.analysis.post_order_visit(after, checker)
+        assert (
+            sum(is_transform) == expected_transform_cnt
+        ), "Expected %s layout_transform, but get\n%s" % (expected_transform_cnt, after)
+
+    def nhwc_to_nchw():
+        x = relay.var("x", shape=(1, 56, 56, 64))
+        weight1 = relay.var("weight1", shape=(3, 3, 64, 32))
+        y = relay.nn.conv2d(
+            x,
+            weight1,
+            channels=32,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NHWC",
+            kernel_layout="HWIO",
+        )
+        z = relay.var("z", shape=(56, 56, 32))
+        out = relay.add(y, z)
+        out = relay.transpose(out, axes=[0, 3, 1, 2])
+        out = relay.nn.batch_flatten(out)
+        func = relay.Function(analysis.free_vars(out), out)
+        return run_opt_pass(func, transform.ConvertLayout({"nn.conv2d": ["NCHW", "default"]}))
+
+    verify_transpose(nhwc_to_nchw(), [0, 1, 2, 3], 3)
+
+    def nchw_to_nhwc():
+        x = relay.var("x", shape=(1, 64, 56, 56))
+        weight1 = relay.var("weight1", shape=(32, 64, 3, 3))
+        y = relay.nn.conv2d(
+            x,
+            weight1,
+            channels=32,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NCHW",
+            kernel_layout="OIHW",
+        )
+        z = relay.var("z", shape=(32, 56, 56))
+        out = relay.add(y, z)
+        out = relay.transpose(out, axes=[0, 2, -1, 1])  # Also test a negative axis.
+        out = relay.nn.batch_flatten(out)
+        func = relay.Function(analysis.free_vars(out), out)
+        return run_opt_pass(func, transform.ConvertLayout({"nn.conv2d": ["NHWC", "default"]}))
+
+    verify_transpose(nchw_to_nhwc(), [0, 1, 2, 3], 3)
+
+    def default_axes():
+        x = relay.var("x", shape=(1, 64, 56, 56))
+        weight1 = relay.var("weight1", shape=(32, 64, 3, 3))
+        y = relay.nn.conv2d(
+            x,
+            weight1,
+            channels=32,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NCHW",
+            kernel_layout="OIHW",
+        )
+        z = relay.var("z", shape=(32, 56, 56))
+        out = relay.add(y, z)
+        out = relay.transpose(out)  # No axes provided, will use the reversed axes.
+        func = relay.Function(analysis.free_vars(out), out)
+        return run_opt_pass(func, transform.ConvertLayout({"nn.conv2d": ["NHWC", "default"]}))
+
+    verify_transpose(default_axes(), [2, 1, 3, 0], 3)
+
+
 def test_resnet_convert_layout():
     def before():
         x = relay.var("x", shape=(1, 56, 56, 64))
@@ -1412,6 +1565,8 @@ def expected():
     test_conv_concat_convert_layout()
     test_dual_path_convert_layout()
     test_bn_convert_layout()
+    test_slice_like_convert_layout()
+    test_transpose_convert_layout()
     test_resnet_convert_layout()
     test_scalar_convert_layout()
     test_conv_bn_convert_layout()
diff --git a/tests/python/relay/test_pass_dynamic_to_static.py b/tests/python/relay/test_pass_dynamic_to_static.py
index 141023d77019..c9e047a38540 100644
--- a/tests/python/relay/test_pass_dynamic_to_static.py
+++ b/tests/python/relay/test_pass_dynamic_to_static.py
@@ -232,11 +232,11 @@ def verify_ones_zeros(shape, dtype):
 
             func = run_infer_type(relay.Function([x], y))
             func2 = run_opt_pass(
-                run_opt_pass(func, transform.DynamicToStatic()), transform.InferType()
+                run_opt_pass(func, transform.DynamicToStatic()),
+                transform.InferType(),
             )
 
             zz = func2.body
-            assert isinstance(zz, relay.Constant)
             assert zz.checked_type == relay.ty.TensorType(shape, dtype)
 
             x_data = np.random.uniform(low=1, high=1, size=shape)
@@ -518,5 +518,45 @@ def verify_sparse_to_dense(sparse_indices, sparse_values, default_value, output_
     verify_sparse_to_dense(1, 3, None, [5], [0, 3, 0, 0, 0])  # default value not specified
 
 
+@tvm.testing.uses_gpu
+def test_dynamic_to_static_dynamic_rank():
+    def verify_full(fill_value, fill_shape, dtype):
+        x = relay.var("x", relay.scalar_type(dtype))
+        y = relay.var("y", relay.TensorType(fill_shape, "int64"))
+        shape = relay.shape_of(y)
+        shape = relay.strided_slice(shape, [0], relay.shape_of(shape))
+        z = relay.full(x, shape, dtype)
+
+        func = relay.Function([x, y], z)
+        func2 = run_opt_pass(run_opt_pass(func, transform.DynamicToStatic()), transform.InferType())
+
+        zz = func2.body
+        assert isinstance(zz, relay.Call)
+        assert zz.op == relay.op.get("full")
+
+        ref_res = np.full(fill_shape, fill_value).astype(dtype)
+        y_data = np.random.uniform(low=-1, high=1, size=fill_shape).astype("int64")
+        verify_func(func2, [fill_value, y_data], ref_res)
+
+    verify_full(4, (1, 2, 3, 4), "int32")
+    verify_full(4.0, (1, 2, 8, 10), "float32")
+
+
+@tvm.testing.uses_gpu
+def test_dynamic_to_static_dynamic_if():
+    x = relay.var("x", relay.TensorType((2, 2), "int64"))
+    cond = relay.const(1)
+    iff = relay.If(cond, relay.reshape(x, [1, 4]), relay.reshape(x, (4, 1)))
+
+    func = relay.Function([x], iff)
+    func2 = run_opt_pass(run_opt_pass(func, transform.DynamicToStatic()), transform.InferType())
+
+    zz = func2.body
+    assert isinstance(zz, relay.Call)
+    assert zz.op == relay.op.get("reshape")
+    x_data = np.random.uniform(low=-1, high=1, size=(2, 2)).astype("int64")
+    verify_func(func2, [x_data], x_data.reshape(1, 4))
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/tests/python/relay/test_pass_fold_constant.py b/tests/python/relay/test_pass_fold_constant.py
index 549596d61693..7b4eb5231a2c 100644
--- a/tests/python/relay/test_pass_fold_constant.py
+++ b/tests/python/relay/test_pass_fold_constant.py
@@ -16,7 +16,6 @@
 # under the License.
 import numpy as np
 import tvm
-from tvm import te
 from tvm import relay
 from tvm.relay import transform
 from tvm.relay.build_module import bind_params_by_name
@@ -147,6 +146,45 @@ def expected():
     assert tvm.ir.structural_equal(zz, zexpected)
 
 
+def test_fold_if():
+    cond_data = np.array(1).astype("bool")
+    x_data = np.array([[1, 2, 3]]).astype("float32")
+
+    def before():
+        a = relay.const(cond_data)
+        x = relay.const(x_data)
+        y = relay.const(x_data)
+        iff = relay.If(a, x + y, x - y)
+        return relay.Function([], iff)
+
+    def expected():
+        y_data = x_data + x_data
+        y = relay.const(y_data)
+        return relay.Function([], y)
+
+    zz = run_opt_pass(before(), transform.FoldConstant())
+    zexpected = run_opt_pass(expected(), transform.InferType())
+    assert tvm.ir.structural_equal(zz, zexpected)
+
+    cond_data = np.array(0).astype("bool")
+
+    def before():
+        a = relay.const(cond_data)
+        x = relay.const(x_data)
+        y = relay.const(x_data)
+        iff = relay.If(a, x + y, x - y)
+        return relay.Function([], iff)
+
+    def expected():
+        y_data = x_data - x_data
+        y = relay.const(y_data)
+        return relay.Function([], y)
+
+    zz = run_opt_pass(before(), transform.FoldConstant())
+    zexpected = run_opt_pass(expected(), transform.InferType())
+    assert tvm.ir.structural_equal(zz, zexpected)
+
+
 def test_fold_shape_of():
     c_shape = (8, 9, 10)
 
@@ -192,22 +230,6 @@ def expected(dtype):
         assert tvm.ir.structural_equal(zz, zexpected)
 
 
-def test_fold_full():
-    c_shape = (8, 9, 10)
-
-    def before():
-        dtype = "float32"
-        return relay.full(relay.const(1.0, dtype), c_shape, dtype=dtype)
-
-    def expected():
-        # expect no changes
-        return before()
-
-    zz = run_opt_pass(before(), transform.FoldConstant())
-    zexpected = run_opt_pass(expected(), transform.InferType())
-    assert tvm.ir.structural_equal(zz, zexpected)
-
-
 def test_fold_batch_norm():
     def expected():
         data = relay.var("data", relay.TensorType((1, 3, 224, 224), "float32"))
@@ -253,12 +275,35 @@ def initializer(_, param):
     assert tvm.ir.structural_equal(mod["main"], expect)
 
 
+def test_fold_dropout():
+    def before():
+        # A constant graph to fire fold constant
+        data = relay.const(np.arange(10).astype(np.float32))
+        dropout = relay.nn.dropout(data)
+        add = dropout + relay.const(1.0)
+        return relay.Function(relay.analysis.free_vars(add), add)
+
+    passes = tvm.transform.Sequential(
+        [
+            relay.transform.InferType(),
+            relay.transform.FoldConstant(),
+        ]
+    )
+
+    before_mod = tvm.IRModule.from_expr(before())
+
+    with tvm.transform.PassContext(opt_level=3):
+        after_mod = passes(before_mod)
+
+    assert tvm.ir.structural_equal(run_infer_type(before_mod["main"]), after_mod["main"])
+
+
 if __name__ == "__main__":
     test_fold_const()
     test_fold_let()
     test_fold_tuple()
     test_fold_concat()
     test_fold_shape_of()
-    test_fold_full()
     test_fold_batch_norm()
     test_fold_ndarray_size()
+    test_fold_dropout()
diff --git a/tests/python/relay/test_pass_fold_explicit_padding.py b/tests/python/relay/test_pass_fold_explicit_padding.py
new file mode 100644
index 000000000000..302a2b91bb8f
--- /dev/null
+++ b/tests/python/relay/test_pass_fold_explicit_padding.py
@@ -0,0 +1,102 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+from tvm import relay
+from tvm.relay import transform
+from tvm.relay.testing import run_opt_pass
+
+import numpy as np
+
+
+def test_simplify_conv_pad():
+    convs = [relay.nn.conv1d, relay.nn.conv2d, relay.nn.conv3d]
+
+    def validate(ndim, pad_width, pad_value, pad_mode, orig_padding, layout):
+        if layout[1] == "C":
+            shape = [1, 3] + [10] * ndim
+            wshape = [8, 3] + [3] * ndim
+        elif layout[-1] == "C":
+            shape = [1] + [10] * ndim + [3]
+            wshape = [8] + [3] * ndim + [3]
+        else:
+            raise ValueError("This test only supports NC* and N*C")
+
+        x = relay.var("x", shape=shape, dtype="float32")
+        w = relay.var("w", shape=wshape, dtype="float32")
+        pad = relay.nn.pad(x, pad_width, pad_value, pad_mode)
+        if layout[1] == "C":
+            conv = convs[ndim - 1](pad, w, padding=orig_padding)
+        else:
+            conv = convs[ndim - 1](
+                pad, w, padding=orig_padding, data_layout=layout, kernel_layout="DHWIO"[3 - ndim :]
+            )
+
+        if pad_mode == "constant" and pad_value == 0:
+            new_padding = []
+            for j in range(2):
+                for i in range(len(pad_width)):
+                    if layout[i] in ["D", "H", "W"]:
+                        new_padding.append(pad_width[i][j])
+            for i in range(len(new_padding)):
+                new_padding[i] += orig_padding[i]
+            if layout[1] == "C":
+                after = convs[ndim - 1](x, w, padding=new_padding)
+            else:
+                after = convs[ndim - 1](
+                    x, w, padding=new_padding, data_layout=layout, kernel_layout="DHWIO"[3 - ndim :]
+                )
+        else:
+            after = conv
+
+        zz = run_opt_pass(conv, transform.FoldExplicitPadding())
+        expected = run_opt_pass(after, transform.InferType())
+        assert tvm.ir.structural_equal(zz, expected)
+
+        mod1 = tvm.IRModule.from_expr(conv)
+        mod2 = tvm.IRModule.from_expr(zz)
+
+        with tvm.transform.PassContext():
+            ex1 = relay.create_executor("vm", mod=mod1, ctx=tvm.cpu(), target="llvm")
+        ex2 = relay.create_executor("vm", mod=mod2, ctx=tvm.cpu(), target="llvm")
+        x_np = np.random.rand(*shape).astype("float32")
+        w_np = np.random.rand(*wshape).astype("float32")
+        result1 = ex1.evaluate()(x_np, w_np)
+        result2 = ex2.evaluate()(x_np, w_np)
+
+        tvm.testing.assert_allclose(result1.asnumpy(), result2.asnumpy(), rtol=1e-5, atol=1e-5)
+
+    for orig_pad in [[0, 0], [2, 0], [0, 2]]:
+        for i_pad in [[0, 0], [1, 1], [1, 0]]:
+            for ndim in [1, 2, 3]:
+                for channels_last in [0, 1]:
+                    if channels_last:
+                        layout = "NDHWC"
+                        layout = layout[0:1] + layout[4 - ndim : 4] + layout[-1:]
+                        padding = [[0, 0]] + [i_pad] * ndim + [[0, 0]]
+                    else:
+                        layout = "NCDHW"
+                        layout = layout[0:2] + layout[5 - ndim :]
+                        padding = [[0, 0]] * 2 + [i_pad] * ndim
+
+                    validate(ndim, padding, 0, "constant", orig_pad * ndim, layout)
+    ndim = 2
+    validate(ndim, [[0, 0]] * 2 + [i_pad] * ndim, 1, "constant", orig_pad * ndim, "NCHW")
+    validate(ndim, [[0, 0]] * 2 + [i_pad] * ndim, 0, "edge", orig_pad * ndim, "NCHW")
+
+
+if __name__ == "__main__":
+    test_simplify_conv_pad()
diff --git a/tests/python/relay/test_pass_legalize_tensorcore.py b/tests/python/relay/test_pass_legalize_tensorcore.py
new file mode 100644
index 000000000000..5ecda4ba07a8
--- /dev/null
+++ b/tests/python/relay/test_pass_legalize_tensorcore.py
@@ -0,0 +1,239 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Test legalize pass"""
+import numpy as np
+import tvm
+from tvm import te
+from tvm import topi
+from tvm import relay
+from tvm.contrib import graph_runtime
+from tvm.relay import transform, analysis
+from tvm.relay.testing.temp_op_attr import TempOpAttr
+
+
+def run_opt_pass(expr, passes):
+    passes = passes if isinstance(passes, list) else [passes]
+    mod = tvm.IRModule.from_expr(expr)
+    seq = tvm.transform.Sequential(passes)
+    with tvm.transform.PassContext(opt_level=3):
+        mod = seq(mod)
+    entry = mod["main"]
+    return entry if isinstance(expr, relay.Function) else entry.body
+
+
+@tvm.testing.uses_gpu
+def test_legalize_conv2d():
+    """test legalize conv2d to enable tensorcore"""
+
+    def _test_legalize_conv2d(data_shape, kernel_shape, pad_shape, do_pad=True):
+        out_channel = kernel_shape[3]
+        out_shape = list(data_shape)
+        out_shape[3] = out_channel
+        db, di, do = pad_shape
+
+        def before():
+            x = relay.var("x", shape=data_shape, dtype="float16")
+            weight = relay.var("weight", shape=kernel_shape, dtype="float16")
+            y = relay.nn.conv2d(
+                x,
+                weight,
+                channels=out_channel,
+                kernel_size=(3, 3),
+                padding=(1, 1),
+                data_layout="NHWC",
+                kernel_layout="HWIO",
+            )
+            y = relay.Function([x, weight], y)
+            return y
+
+        def legalize_conv2d(attrs, inputs, types):
+            with tvm.target.Target("cuda"):
+                return topi.nn.conv2d_legalize(attrs, inputs, types)
+
+        def expected():
+            if not do_pad:
+                return before()
+            x = relay.var("x", shape=data_shape, dtype="float16")
+            if db or di:
+                x_pad = relay.nn.pad(x, pad_width=((0, db), (0, 0), (0, 0), (0, di)))
+            else:
+                x_pad = x
+            weight = relay.var("weight", shape=(kernel_shape), dtype="float16")
+            if di or do:
+                weight_pad = relay.nn.pad(weight, pad_width=((0, 0), (0, 0), (0, di), (0, do)))
+            else:
+                weight_pad = weight
+            y_pad = relay.nn.conv2d(
+                x_pad,
+                weight=weight_pad,
+                channels=out_channel + do,
+                kernel_size=(3, 3),
+                padding=(1, 1),
+                data_layout="NHWC",
+                kernel_layout="HWIO",
+            )
+            if db or do:
+                y = relay.strided_slice(y_pad, begin=[0, 0, 0, 0], end=out_shape)
+            else:
+                y = y_pad
+            y = relay.Function([x, weight], y)
+            return y
+
+        with TempOpAttr("nn.conv2d", "FTVMLegalize", legalize_conv2d):
+            a = before()
+            a = run_opt_pass(a, transform.Legalize())
+            b = run_opt_pass(expected(), transform.InferType())
+        assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a) + "Expected = \n" + str(b)
+
+    # conv2d pad batch
+    _test_legalize_conv2d((7, 16, 16, 64), (3, 3, 64, 64), (1, 0, 0))
+    _test_legalize_conv2d((3, 16, 16, 64), (3, 3, 64, 64), (5, 0, 0))
+    _test_legalize_conv2d((2, 16, 16, 64), (3, 3, 64, 64), (0, 0, 0), False)
+    # conv2d pad in_channel
+    _test_legalize_conv2d((8, 16, 16, 63), (3, 3, 63, 64), (0, 1, 0))
+    _test_legalize_conv2d((8, 16, 16, 33), (3, 3, 33, 64), (0, 15, 0))
+    _test_legalize_conv2d((8, 16, 16, 13), (3, 3, 13, 64), (0, 3, 0))
+    _test_legalize_conv2d((8, 16, 16, 1), (3, 3, 1, 64), (0, 0, 0), False)
+    # conv2d pad out_channel
+    _test_legalize_conv2d((8, 16, 16, 64), (3, 3, 64, 63), (0, 0, 1))
+    _test_legalize_conv2d((8, 16, 16, 64), (3, 3, 64, 33), (0, 0, 31))
+    _test_legalize_conv2d((8, 16, 16, 64), (3, 3, 64, 1), (0, 0, 0), False)
+
+
+@tvm.testing.uses_gpu
+def test_legalize_dense():
+    def _test_legalize_dense(data_shape, kernel_shape, pad_shape, do_pad=True):
+        """test legalize dense to enable tensorcore"""
+        M, K = data_shape
+        N, _ = kernel_shape
+        out_shape = (M, N)
+        dm, dk, dn = pad_shape
+
+        def before():
+            x = relay.var("x", shape=data_shape, dtype="float16")
+            weight = relay.var("weight", shape=kernel_shape, dtype="float16")
+            y = relay.nn.dense(x, weight)
+            y = relay.Function([x, weight], y)
+            return y
+
+        def legalize_dense(attrs, inputs, types):
+            with tvm.target.Target("cuda"):
+                return topi.nn.dense_legalize(attrs, inputs, types)
+
+        def expected():
+            if not do_pad:
+                return before()
+            x = relay.var("x", shape=data_shape, dtype="float16")
+            if dm or dk:
+                x_pad = relay.nn.pad(x, pad_width=((0, dm), (0, dk)))
+            else:
+                x_pad = x
+            weight = relay.var("weight", shape=(kernel_shape), dtype="float16")
+            if dn or dk:
+                weight_pad = relay.nn.pad(weight, pad_width=((0, dn), (0, dk)))
+            else:
+                weight_pad = weight
+            y_pad = relay.nn.dense(
+                x_pad,
+                weight_pad,
+            )
+            if dm or dn:
+                y = relay.strided_slice(y_pad, begin=[0, 0], end=out_shape)
+            else:
+                y = y_pad
+            y = relay.Function([x, weight], y)
+            return y
+
+        with TempOpAttr("nn.dense", "FTVMLegalize", legalize_dense):
+            a = before()
+            a = run_opt_pass(a, transform.Legalize())
+            b = run_opt_pass(expected(), transform.InferType())
+        assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a) + "Expected = \n" + str(b)
+
+    # dense
+    _test_legalize_dense((8, 16), (32, 16), (0, 0, 0), False)
+    _test_legalize_dense((7, 16), (32, 16), (1, 0, 0))
+    _test_legalize_dense((8, 15), (32, 15), (0, 1, 0))
+    _test_legalize_dense((8, 16), (31, 16), (0, 0, 1))
+    _test_legalize_dense((7, 15), (31, 15), (1, 1, 1))
+    _test_legalize_dense((3, 16), (32, 16), (5, 0, 0))
+    _test_legalize_dense((2, 16), (32, 16), (0, 0, 0), False)
+
+
+@tvm.testing.uses_gpu
+def test_legalize_batch_matmul():
+    def _test_legalize_batch_matmul(data_shape, kernel_shape, pad_shape, do_pad=True):
+        """test legalize dense to enable tensorcore"""
+        B, M, _ = data_shape
+        _, N, _ = kernel_shape
+        out_shape = (B, M, N)
+        dm, dk, dn = pad_shape
+
+        def before():
+            x = relay.var("x", shape=data_shape, dtype="float16")
+            weight = relay.var("weight", shape=kernel_shape, dtype="float16")
+            y = relay.nn.batch_matmul(x, weight)
+            y = relay.Function([x, weight], y)
+            return y
+
+        def legalize_batch_matmul(attrs, inputs, types):
+            with tvm.target.Target("cuda"):
+                return topi.nn.batch_matmul_legalize(attrs, inputs, types)
+
+        def expected():
+            if not do_pad:
+                return before()
+            x = relay.var("x", shape=data_shape, dtype="float16")
+            if dm or dk:
+                x_pad = relay.nn.pad(x, pad_width=((0, 0), (0, dm), (0, dk)))
+            else:
+                x_pad = x
+            weight = relay.var("weight", shape=(kernel_shape), dtype="float16")
+            if dn or dk:
+                weight_pad = relay.nn.pad(weight, pad_width=((0, 0), (0, dn), (0, dk)))
+            else:
+                weight_pad = weight
+            y_pad = relay.nn.batch_matmul(
+                x_pad,
+                weight_pad,
+            )
+            if dm or dn:
+                y = relay.strided_slice(y_pad, begin=[0, 0, 0], end=out_shape)
+            else:
+                y = y_pad
+            y = relay.Function([x, weight], y)
+            return y
+
+        with TempOpAttr("nn.batch_matmul", "FTVMLegalize", legalize_batch_matmul):
+            a = before()
+            a = run_opt_pass(a, transform.Legalize())
+            b = run_opt_pass(expected(), transform.InferType())
+        assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a) + "Expected = \n" + str(b)
+
+    _test_legalize_batch_matmul((16, 8, 16), (16, 32, 16), (0, 0, 0), False)
+    _test_legalize_batch_matmul((16, 7, 16), (16, 32, 16), (1, 0, 0))
+    _test_legalize_batch_matmul((16, 8, 15), (16, 32, 15), (0, 1, 0))
+    _test_legalize_batch_matmul((16, 8, 16), (16, 31, 16), (0, 0, 1))
+    _test_legalize_batch_matmul((16, 7, 15), (16, 31, 15), (1, 1, 1))
+    _test_legalize_batch_matmul((16, 3, 16), (16, 32, 16), (5, 0, 0))
+    _test_legalize_batch_matmul((16, 2, 16), (16, 32, 16), (0, 0, 0), False)
+
+
+if __name__ == "__main__":
+    test_legalize_conv2d()
+    test_legalize_dense()
+    test_legalize_batch_matmul()
diff --git a/tests/python/relay/test_pass_profiler.py b/tests/python/relay/test_pass_profiler.py
new file mode 100644
index 000000000000..acf6c8c50aff
--- /dev/null
+++ b/tests/python/relay/test_pass_profiler.py
@@ -0,0 +1,41 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+import tvm.relay
+from tvm.relay import op
+
+
+def test_pass_profiler():
+    x, y, z = [tvm.relay.var(c, shape=(3, 4), dtype="float32") for c in "xyz"]
+    e1 = op.add(x, y)
+    e2 = op.subtract(x, z)
+    e3 = op.multiply(e1, e1 / e2)
+    mod = tvm.IRModule.from_expr(e3 + e2)
+
+    tvm.transform.enable_pass_profiling()
+
+    mod = tvm.relay.transform.AnnotateSpans()(mod)
+    mod = tvm.relay.transform.ToANormalForm()(mod)
+    mod = tvm.relay.transform.InferType()(mod)
+
+    profiles = tvm.transform.render_pass_profiles()
+    assert "AnnotateSpans" in profiles
+    assert "ToANormalForm" in profiles
+    assert "InferType" in profiles
+
+    tvm.transform.clear_pass_profiles()
+    tvm.transform.disable_pass_profiling()
diff --git a/tests/python/relay/test_pass_simplify_expr.py b/tests/python/relay/test_pass_simplify_expr.py
index b57abc6942d7..897f90b9ee2a 100644
--- a/tests/python/relay/test_pass_simplify_expr.py
+++ b/tests/python/relay/test_pass_simplify_expr.py
@@ -19,6 +19,8 @@
 from tvm.relay import transform
 from tvm.relay.testing import run_opt_pass
 
+import numpy as np
+
 
 def test_simplify_reshape():
     def before():
@@ -58,5 +60,128 @@ def symbolic():
     assert tvm.ir.structural_equal(zz, after)
 
 
+def test_simplify_transpose():
+    # Test a series of transpose and layout_transform ops
+    def before1():
+        x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32")  # NCHW
+        y = relay.transpose(x, axes=[0, 2, 3, 1])  # To NHWC
+        y = relay.layout_transform(y, "NHWC", "HWCN")  # To HWCN
+        y = relay.transpose(y, axes=[3, 0, 1, 2])  # To NHWC
+        return relay.Function([x], y)
+
+    def expected1():
+        x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32")  # NCHW
+        y = relay.transpose(x, axes=[0, 2, 3, 1])  # To NHWC
+        return relay.Function([x], y)
+
+    # Test that all transpose ops can be cancelled
+    def before2():
+        x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32")  # NCHW
+        y = relay.nn.relu(x)
+        y = relay.transpose(y, axes=[0, 2, 3, 1])  # To NHWC
+        y = relay.transpose(y, axes=[1, 2, 3, 0])  # To HWCN
+        y = relay.transpose(y, axes=[3, 2, 0, 1])  # To NCHW
+        return relay.Function([x], y)
+
+    def expected2():
+        x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32")  # NCHW
+        y = relay.nn.relu(x)
+        return relay.Function([x], y)
+
+    # Test default axis (reverse) and negative axis
+    def before3():
+        x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32")  # NCHW
+        y = relay.nn.relu(x)
+        y = relay.transpose(y)  # Reverse
+        y = relay.transpose(y)  # Reverse
+        y = relay.transpose(y, axes=[0, 2, -1, 1])
+        y = relay.transpose(y)  # Reverse
+        y = relay.transpose(y)  # Reverse
+        return relay.Function([x], y)
+
+    def expected3():
+        x = relay.var("x", shape=(1, 3, 224, 224), dtype="float32")  # NCHW
+        y = relay.nn.relu(x)
+        y = relay.transpose(y, axes=[0, 2, 3, 1])
+        return relay.Function([x], y)
+
+    for before, expected in [
+        [before1(), expected1()],
+        [before2(), expected2()],
+        [before3(), expected3()],
+    ]:
+        after = run_opt_pass(before, transform.SimplifyExpr())
+        expected = run_opt_pass(expected, transform.InferType())
+        assert tvm.ir.structural_equal(after, expected), "\nafter: {} \nexpected: {}".format(
+            after, expected
+        )
+
+
+def test_simplify_full_elementwise():
+    def validate(shape, value, dtype):
+        def before_left(x, elem_op, full):
+            return elem_op(full, x)
+
+        def after_left(x, elem_op, value):
+            return elem_op(relay.const(value, dtype), x)
+
+        def before_right(x, elem_op, full):
+            return elem_op(x, full)
+
+        def after_right(x, elem_op, value):
+            return elem_op(x, relay.const(value, dtype))
+
+        x = relay.var("x", shape=shape, dtype=dtype)
+        elem_ops = [relay.add, relay.multiply, relay.subtract, relay.divide]
+        full_ops = []
+        if value == 0:
+            full_ops.append(relay.zeros(shape, dtype))
+            full_ops.append(relay.zeros_like(x))
+        if value == 1:
+            full_ops.append(relay.ones(shape, dtype))
+            full_ops.append(relay.ones_like(x))
+        else:
+            full_ops.append(relay.full(relay.const(value, dtype), shape))
+            full_ops.append(relay.full_like(x, relay.const(value, dtype)))
+        for op in elem_ops:
+            for full in full_ops:
+                z = before_left(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(after_left(x, op, value), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+                z = before_right(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(after_right(x, op, value), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+        # Test the case in which x is broadcast to full's shape
+        full_ops = []
+        if value == 0:
+            full_ops.append(relay.zeros(shape * 2, dtype))
+        if value == 1:
+            full_ops.append(relay.ones(shape * 2, dtype))
+        else:
+            full_ops.append(relay.full(relay.const(value, dtype), shape * 2))
+        for op in elem_ops:
+            for full in full_ops:
+                z = before_left(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(before_left(x, op, full), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+                z = before_right(x, op, full)
+                zz = run_opt_pass(z, transform.SimplifyExpr())
+                after = run_opt_pass(before_right(x, op, full), transform.InferType())
+                assert tvm.ir.structural_equal(zz, after)
+
+    for shape in [[10], [10, 10], [10, 10, 10]]:
+        for dtype in ["float32", "int32", "bool"]:
+            for value in [0, 1, 2]:
+                validate(shape, value, dtype)
+
+
 if __name__ == "__main__":
     test_simplify_reshape()
+    test_simplify_transpose()
+    test_simplify_full_elementwise()
diff --git a/tests/python/relay/test_pass_unmatched_cases.py b/tests/python/relay/test_pass_unmatched_cases.py
index c6b4deb0b2c2..255cecf76f2e 100644
--- a/tests/python/relay/test_pass_unmatched_cases.py
+++ b/tests/python/relay/test_pass_unmatched_cases.py
@@ -420,5 +420,51 @@ def @shallow_opt[A](%a: Arith[A]) -> Arith[A] {
     # fromtext parse the module, then checked it (which include strictness checking).
 
 
+def test_expanding_ctor_with_no_args():
+    code = """
+#[version = "0.0.5"]
+type List[A] {
+    Cons(A, List[A]),
+    Nil,
+}
+
+def @expand_on_nil_match(%a: List[(List[()],)]) -> int {
+    match (%a) {
+        Cons((Nil), Nil) => 1,
+        _ => 2,
+    }
+}
+"""
+    # exhausion checks:
+    # * hits Cons((Nil), Nil), expands to Cons(*, *), Nil()
+    # Nil() fails Cons((Nil), Nil), passes _
+    # Cons(*, *) hits Cons((Nil), Nil), expands to Cons((*), Cons(*, *)), Cons((*), Nil())
+    # Cons((*), Cons(*, *)) fails Cons((Nil), Nil), passes _
+    # Cons((*), Nil()) hits Cons((Nil), Nil), expands to Cons((Nil), Nil), Cons((Cons(*, *)), Nil)
+    # Cons((Nil), Nil) passes the first pattern
+    # Cons((Cons(*, *)), Nil) fails the first pattern, passes _
+    # Note Nil() is passed to ExpandWildcardsConstructor many times in the above!
+    tvm.parser.fromtext(code)
+
+
+def test_expanding_empty_tuple():
+    # same principle as above, but with empty tuple
+    code = """
+#[version = "0.0.5"]
+type List[A] {
+    Cons(A, List[A]),
+    Nil,
+}
+
+def @expand_on_empty_tuple_match(%a: (List[()], ())) -> int {
+    match (%a) {
+        (Cons((), Nil), ()) => 1,
+        _ => 2,
+    }
+}
+"""
+    tvm.parser.fromtext(code)
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
diff --git a/tests/python/relay/test_prng.py b/tests/python/relay/test_prng.py
new file mode 100644
index 000000000000..2109d3b30a82
--- /dev/null
+++ b/tests/python/relay/test_prng.py
@@ -0,0 +1,142 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import pytest
+import tvm
+import tvm.relay
+import tvm.testing
+from tvm.relay.testing import run_infer_type
+
+
+@tvm.testing.parametrize_targets
+def test_threefry_repeatability(target, ctx):
+    target, ctx = "llvm", tvm.cpu(0)
+    key1 = tvm.relay.random.threefry_key(1)
+    rand1 = tvm.relay.random.threefry_generate(key1, (12,))
+    out_key1, out1 = tvm.relay.create_executor(
+        "vm", tvm.IRModule.from_expr(tvm.relay.Function([], rand1)), target=target, ctx=ctx
+    ).evaluate()()
+
+    key2 = tvm.relay.random.threefry_key(1)
+    rand2 = tvm.relay.random.threefry_generate(key2, (12,))
+    out_key2, out2 = tvm.relay.create_executor(
+        "vm", tvm.IRModule.from_expr(tvm.relay.Function([], rand2)), target=target, ctx=ctx
+    ).evaluate()()
+
+    assert (
+        out1.asnumpy() == out2.asnumpy()
+    ).all(), "Generate on same seed should have the same output random numbers"
+
+    assert (
+        out_key1.asnumpy() == out_key2.asnumpy()
+    ).all(), "Generate on same seed should have the same next keys"
+
+
+@tvm.testing.parametrize_targets
+def test_threefry_split(target, ctx):
+    key = tvm.relay.random.threefry_key(1)
+    left, right = tvm.relay.TupleWrapper(tvm.relay.random.threefry_split(key), 2)
+    _, rand1 = tvm.relay.TupleWrapper(tvm.relay.random.threefry_generate(left, (16,)), 2)
+    _, rand2 = tvm.relay.TupleWrapper(tvm.relay.random.threefry_generate(right, (16,)), 2)
+    out1, out2 = tvm.relay.create_executor(
+        "vm",
+        tvm.IRModule.from_expr(tvm.relay.Function([], tvm.relay.Tuple((rand1, rand2)))),
+        target=target,
+        ctx=ctx,
+    ).evaluate()()
+
+    assert (
+        out1.asnumpy() != out2.asnumpy()
+    ).any(), "Generate after split should not have the same output"
+
+
+@tvm.testing.parametrize_targets
+def test_threefry_sequential_generate(target, ctx):
+    key = tvm.relay.random.threefry_key(1)
+    key, rand1 = tvm.relay.TupleWrapper(tvm.relay.random.threefry_generate(key, (4,)), 2)
+    _, rand2 = tvm.relay.TupleWrapper(tvm.relay.random.threefry_generate(key, (4,)), 2)
+    out1, out2 = tvm.relay.create_executor(
+        "vm",
+        tvm.IRModule.from_expr(tvm.relay.Function([], tvm.relay.Tuple((rand1, rand2)))),
+        target=target,
+        ctx=ctx,
+    ).evaluate()()
+
+    assert (
+        out1.asnumpy() != out2.asnumpy()
+    ).any(), "Sequential generates should not have the same output"
+
+
+def test_threefry_generate_infer():
+    oshape = (12,)
+    key_type = tvm.relay.TensorType([10], dtype="uint64")
+    gen_type = tvm.relay.TensorType(oshape, dtype="uint64")
+    expected_type = tvm.relay.TupleType([key_type, gen_type])
+
+    key = tvm.relay.random.threefry_key(1)
+    rand1 = tvm.relay.random.threefry_generate(key, oshape)
+    f = tvm.relay.Function([], rand1)
+    f = run_infer_type(f)
+    assert tvm.ir.structural_equal(f.ret_type, expected_type)
+
+
+def test_threefry_split_infer():
+    key_type = tvm.relay.TensorType([10], dtype="uint64")
+    expected_type = tvm.relay.TupleType([key_type, key_type])
+
+    key = tvm.relay.random.threefry_key(1)
+    out_keys = tvm.relay.random.threefry_split(key)
+    f = tvm.relay.Function([], out_keys)
+    f = run_infer_type(f)
+    assert tvm.ir.structural_equal(f.ret_type, expected_type)
+
+
+@pytest.mark.xfail(raises=tvm.error.TVMError)
+def test_threefry_generate_infer_fail():
+    # xfail: key size should be 10
+    fake_key = tvm.relay.const([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype="uint64")
+    rand1 = tvm.relay.random.threefry_generate(fake_key, (12,))
+    f = tvm.relay.Function([], rand1)
+    f = run_infer_type(f)
+
+
+@pytest.mark.xfail(raises=tvm.error.TVMError)
+def test_threefry_split_infer_fail():
+    # xfail: key size should be 10
+    fake_key = tvm.relay.const([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype="uint64")
+    out_keys = tvm.relay.random.threefry_split(fake_key)
+    f = tvm.relay.Function([], out_keys)
+    f = run_infer_type(f)
+
+
+@tvm.testing.requires_llvm
+@pytest.mark.xfail(raises=tvm.error.TVMError)
+def test_threefry_generate_incorrect_out_size():
+    key = tvm.relay.random.threefry_key(1)
+    # xfail: output size should be multiple of 4
+    key, rand1 = tvm.relay.TupleWrapper(tvm.relay.random.threefry_generate(key, (5,)), 2)
+    out1, out2 = tvm.relay.create_executor(
+        "vm",
+        tvm.IRModule.from_expr(tvm.relay.Function([], rand1)),
+        target=tvm.target.Target("llvm"),
+        ctx=tvm.context("cpu"),
+    ).evaluate()()
+
+
+if __name__ == "__main__":
+    test_threefry_repeatability(tvm.target.Target("llvm"), tvm.context("cpu"))
+    test_threefry_split(tvm.target.Target("llvm"), tvm.context("cpu"))
+    test_threefry_sequential_generate(tvm.target.Target("llvm"), tvm.context("cpu"))
diff --git a/tests/python/relay/test_type_infer.py b/tests/python/relay/test_type_infer.py
index b518c31d3e62..e8179a37756c 100644
--- a/tests/python/relay/test_type_infer.py
+++ b/tests/python/relay/test_type_infer.py
@@ -402,6 +402,20 @@ def @main(%f: float32) -> float32 {
     tvm.ir.assert_structural_equal(mod["main"].body.type_args, [relay.TensorType((), "float32")])
 
 
+def test_dynamic_function():
+    dy_tt = relay.TensorType([relay.Any()], "float32")
+    s_tt = relay.TensorType([10], "float32")
+    x = relay.Var("x", dy_tt)
+    f = relay.Function([x], x + x)
+    y = relay.Var("y", s_tt)
+    c = f(y)
+
+    mod = tvm.IRModule()
+    mod["main"] = relay.Function([y], c)
+    mod = transform.InferType()(mod)
+    assert mod["main"].params[0].checked_type == s_tt
+
+
 if __name__ == "__main__":
     import sys
 
diff --git a/tests/python/relay/test_vm.py b/tests/python/relay/test_vm.py
index 6958010176e3..975070ad1aaa 100644
--- a/tests/python/relay/test_vm.py
+++ b/tests/python/relay/test_vm.py
@@ -678,6 +678,10 @@ def test_vm_optimize():
     comp = relay.vm.VMCompiler()
     opt_mod, _ = comp.optimize(mod, target="llvm", params=params)
 
+    free_vars = relay.analysis.free_vars(opt_mod["main"].body)
+    # Paremeters should all be bound, so the only free var is data
+    assert len(free_vars) == 1
+
 
 @tvm.testing.uses_gpu
 def test_loop_free_var():
diff --git a/tests/python/topi/python/test_topi_batch_matmul_tensorcore.py b/tests/python/topi/python/test_topi_batch_matmul_tensorcore.py
new file mode 100644
index 000000000000..77df5be0a491
--- /dev/null
+++ b/tests/python/topi/python/test_topi_batch_matmul_tensorcore.py
@@ -0,0 +1,75 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Test code for batch_matmul operator"""
+import numpy as np
+import tvm
+from tvm import te
+from tvm import topi
+import tvm.topi.testing
+from tvm.topi.utils import get_const_tuple
+from tvm.contrib.pickle_memoize import memoize
+
+import tvm.testing
+
+_batch_matmul_implement = {
+    "gpu": (topi.cuda.batch_matmul_tensorcore, topi.cuda.schedule_batch_matmul_tensorcore),
+}
+
+
+def verify_batch_matmul(x_batch, y_batch, M, N, K):
+    x = te.placeholder((x_batch, M, K), name="x")
+    y = te.placeholder((y_batch, N, K), name="y")
+    dtype = x.dtype
+
+    # use memoize to pickle the test data for next time use
+    @memoize("topi.tests.test_topi_batch_matmul_tensorcore")
+    def get_ref_data():
+        a_np = np.random.uniform(size=(x_batch, M, K)).astype(dtype)
+        b_np = np.random.uniform(size=(y_batch, N, K)).astype(dtype)
+        c_np = tvm.topi.testing.batch_matmul(a_np, b_np)
+        return (a_np, b_np, c_np)
+
+    # get the test data
+    a_np, b_np, c_np = get_ref_data()
+
+    def check_device(device):
+        ctx = tvm.context(device, 0)
+        print("Running on target: %s" % device)
+        with tvm.target.Target(device):
+            fcompute, fschedule = tvm.topi.testing.dispatch(device, _batch_matmul_implement)
+            out = fcompute(x, y)
+            s = fschedule([out])
+        a = tvm.nd.array(a_np, ctx)
+        b = tvm.nd.array(b_np, ctx)
+        c = tvm.nd.array(np.zeros(get_const_tuple(out.shape), dtype=dtype), ctx)
+        f = tvm.build(s, [x, y, out], device, name="dense")
+        f(a, b, c)
+        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-3)
+
+    check_device("cuda")
+
+
+@tvm.testing.requires_tensorcore
+def test_batch_matmul():
+    verify_batch_matmul(1, 1, 16, 16, 32)
+    verify_batch_matmul(5, 5, 16, 16, 32)
+    verify_batch_matmul(5, 5, 16, 32, 32)
+    verify_batch_matmul(30, 30, 16, 32, 32)
+
+
+if __name__ == "__main__":
+    test_batch_matmul()
diff --git a/tests/python/topi/python/test_topi_broadcast.py b/tests/python/topi/python/test_topi_broadcast.py
index 44be28c318e4..ada03ea5377b 100644
--- a/tests/python/topi/python/test_topi_broadcast.py
+++ b/tests/python/topi/python/test_topi_broadcast.py
@@ -284,7 +284,7 @@ def test_shift():
     )
 
     verify_broadcast_binary_ele(
-        (1, 2, 2), (2,), topi.left_shift, np.left_shift, dtype="int8", rhs_min=0, rhs_max=32
+        (1, 2, 2), (2,), topi.left_shift, np.left_shift, dtype="int32", rhs_min=0, rhs_max=32
     )
 
 
diff --git a/tests/python/topi/python/test_topi_conv2d_int8.py b/tests/python/topi/python/test_topi_conv2d_int8.py
index 1bf83eba53ac..a934e3ef2fd2 100644
--- a/tests/python/topi/python/test_topi_conv2d_int8.py
+++ b/tests/python/topi/python/test_topi_conv2d_int8.py
@@ -27,6 +27,8 @@
 from tvm.topi.nn.utils import get_pad_tuple
 from tvm.topi.utils import get_const_tuple
 from tvm.topi.arm_cpu.conv2d_gemm import is_aarch64_arm
+from tvm.topi.nn.conv2d import _get_workload
+from tvm.topi.generic.conv2d import fallback_schedule_cpu_common_int8
 
 from common import Int8Fallback
 import tvm.testing
@@ -112,7 +114,7 @@ def compile_conv2d_NHWC_gemm_int8_arm(
                 s,
                 [A, W, bias, C],
                 device,
-                name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
+                name="relu_%dnnn_%d_%d_%d_%d_%d_%d_%d"
                 % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
             )
         else:
@@ -385,6 +387,22 @@ def get_ref_data():
 
     a_np, w_np, b_np, c_np = get_ref_data()
 
+    def verify_workload_padding():
+        _, _, out_height, out_width = get_const_tuple(c_np.shape)
+        wkl = _get_workload(A, W, (stride, stride), padding, dilation, dtype)
+
+        # for testing functionality,
+        # we choose arbitrary int32_lanes and num_int8_elements can divide the channel,
+        # regardless of the performance.
+        int32_lanes, num_int8_elements = num_filter, in_channel
+
+        # check if tile_ow candidates are the factors of the right output weight.
+        cfg = autotvm.get_config()
+        fallback_schedule_cpu_common_int8(cfg, wkl, int32_lanes, num_int8_elements)
+        ow_tile = np.prod(cfg["tile_ow"].size)
+
+        tvm.testing.assert_allclose(ow_tile, out_width)
+
     def check_device(device):
         ctx = tvm.context(device, 0)
         if not tvm.testing.device_enabled(device):
@@ -436,6 +454,8 @@ def check_device(device):
             func(a, w, c)
         tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
 
+    verify_workload_padding()
+
     for device in ["cuda"]:
         check_device(device)
 
@@ -547,6 +567,7 @@ def test_conv2d_nchw():
         verify_conv2d_nchw_int8(1, 32, 149, 32, 3, 1, 0)
         verify_conv2d_nchw_int8(7, 32, 149, 32, 3, 1, 0)
         verify_conv2d_nchw_int8(1, 32, 35, 64, 7, 2, (0, 0, 1, 1))
+        verify_conv2d_nchw_int8(1, 32, 35, 64, 7, 2, (0, 0, 2, 2))
 
 
 def test_conv2d_nhwc():
diff --git a/tests/python/topi/python/test_topi_conv2d_nchw.py b/tests/python/topi/python/test_topi_conv2d_nchw.py
index 1b7575211dac..07ad45c971df 100644
--- a/tests/python/topi/python/test_topi_conv2d_nchw.py
+++ b/tests/python/topi/python/test_topi_conv2d_nchw.py
@@ -25,6 +25,8 @@
 from tvm.contrib.pickle_memoize import memoize
 from tvm.topi.nn.utils import get_pad_tuple
 from tvm.topi.utils import get_const_tuple
+from tvm.topi.nn.conv2d import _get_workload
+from tvm.topi.x86.conv2d_avx_common import _fallback_schedule
 
 import tvm.testing
 
@@ -76,6 +78,17 @@ def get_ref_data():
 
     a_np, w_np, b_np, c_np = get_ref_data()
 
+    def verify_workload_padding():
+        _, _, out_height, out_width = get_const_tuple(c_np.shape)
+        wkl = _get_workload(A, W, (stride, stride), padding, dilation, dtype)
+
+        # check if tile_ow candidates are the factors of the right output weight.
+        cfg = autotvm.get_config()
+        _fallback_schedule(cfg, wkl)
+        ow_tile = np.prod(cfg["tile_ow"].size)
+
+        tvm.testing.assert_allclose(ow_tile, out_width)
+
     def check_device(device):
         ctx = tvm.context(device, 0)
         if not tvm.testing.device_enabled(device):
@@ -101,6 +114,9 @@ def check_device(device):
                 C = topi.nn.relu(C)
             s = fschedule([C])
 
+            if "llvm" in device:
+                verify_workload_padding()
+
         a = tvm.nd.array(a_np, ctx)
         w = tvm.nd.array(w_np, ctx)
         b = tvm.nd.array(b_np, ctx)
@@ -242,6 +258,7 @@ def test_conv2d_nchw():
     verify_conv2d_nchw(1, 64, 8, 64, 5, 2, (1, 3), add_bias=True)
     verify_conv2d_nchw(1, 64, 8, 64, 3, 1, "VALID", add_bias=True, add_relu=True)
     verify_conv2d_nchw(1, 64, 8, 64, 24, 1, "SAME", add_bias=True, add_relu=True)
+    verify_conv2d_nchw(1, 32, 35, 64, 7, 2, (0, 0, 2, 2))
 
 
 if __name__ == "__main__":
diff --git a/tests/python/topi/python/test_topi_cumsum.py b/tests/python/topi/python/test_topi_cumsum.py
new file mode 100644
index 000000000000..cfe5130643c5
--- /dev/null
+++ b/tests/python/topi/python/test_topi_cumsum.py
@@ -0,0 +1,79 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import numpy as np
+import tvm
+import tvm.testing
+from tvm import topi
+import tvm.topi.testing
+
+
+@tvm.testing.parametrize_targets
+def test_cumsum(ctx, target):
+    def check_cumsum(np_ref, data, axis=None, dtype=None):
+        implementations = {
+            "generic": (lambda x: topi.cumsum(x, axis, dtype), topi.generic.schedule_extern),
+            "cuda": (lambda x: topi.cuda.cumsum(x, axis, dtype), topi.cuda.schedule_scan),
+            "nvptx": (lambda x: topi.cuda.cumsum(x, axis, dtype), topi.cuda.schedule_scan),
+            "vulkan": (lambda x: topi.cuda.cumsum(x, axis, dtype), topi.cuda.schedule_scan),
+            "metal": (lambda x: topi.cuda.cumsum(x, axis, dtype), topi.cuda.schedule_scan),
+        }
+        fcompute, fschedule = tvm.topi.testing.dispatch(target, implementations)
+        tvm.topi.testing.compare_numpy_tvm([data], np_ref, target, ctx, fcompute, fschedule)
+
+    data = np.array([2, 3, 0])
+    check_cumsum(np.cumsum(data), data)
+
+    data = np.random.rand(10) > 0.5
+    data = data.astype(np.int32)
+    check_cumsum(np.cumsum(data, dtype=np.int32), data)
+    check_cumsum(np.cumsum(data), data, dtype="int64")
+
+    data = np.random.rand(10) > 0.5
+    check_cumsum(np.cumsum(data, dtype=np.int32), data, dtype="int32")
+
+    for in_dtype in ["float32", "float64"]:
+        if target == "metal" and in_dtype == "float64":
+            # float64 is not supported in metal
+            continue
+        data = np.random.randn(10, 10).astype(in_dtype)
+        check_cumsum(np.cumsum(data), data)
+        check_cumsum(np.cumsum(data, axis=0), data, axis=0)
+        check_cumsum(np.cumsum(data, axis=1), data, axis=1)
+
+        data = np.random.randn(10, 5, 10).astype(in_dtype)
+        check_cumsum(np.cumsum(data), data)
+        check_cumsum(np.cumsum(data, axis=0), data, axis=0)
+        check_cumsum(np.cumsum(data, axis=1), data, axis=1)
+        check_cumsum(np.cumsum(data, axis=-1), data, axis=-1)
+
+    for in_dtype in ["int32", "int64"]:
+        data = np.random.randint(-100, 100, size=(100, 100)).astype(in_dtype)
+        check_cumsum(np.cumsum(data, dtype=in_dtype), data)
+        check_cumsum(np.cumsum(data), data, dtype="int64")
+        check_cumsum(np.cumsum(data, axis=0, dtype=in_dtype), data, axis=0)
+        check_cumsum(np.cumsum(data, axis=1, dtype=in_dtype), data, axis=1)
+
+        data = np.random.randint(1 << 30, (1 << 31) - 1, size=(100)).astype(in_dtype)
+        check_cumsum(np.cumsum(data), data, dtype="int64")
+
+
+if __name__ == "__main__":
+    test_cumsum(tvm.context("cpu"), tvm.target.Target("llvm"))
+    test_cumsum(tvm.context("cuda"), tvm.target.Target("cuda"))
+    test_cumsum(tvm.context("nvptx"), tvm.target.Target("nvptx"))
+    test_cumsum(tvm.context("vulkan"), tvm.target.Target("vulkan"))
+    test_cumsum(tvm.context("metal"), tvm.target.Target("metal"))
diff --git a/tests/python/topi/python/test_topi_depthwise_conv2d.py b/tests/python/topi/python/test_topi_depthwise_conv2d.py
index 55d2fe0c4e52..804c486d27d7 100644
--- a/tests/python/topi/python/test_topi_depthwise_conv2d.py
+++ b/tests/python/topi/python/test_topi_depthwise_conv2d.py
@@ -23,6 +23,8 @@
 from tvm.topi.utils import get_const_tuple
 from tvm.topi.nn.utils import get_pad_tuple
 from tvm.contrib.pickle_memoize import memoize
+from tvm.topi.nn.depthwise_conv2d import _get_workload
+from tvm.topi.x86.depthwise_conv2d import _fallback_schedule
 
 import tvm.testing
 
@@ -116,8 +118,8 @@ def depthwise_conv2d_with_workload_nchw(
     if dilation == 1:
         # here we transform the padding argument from 'str' to  'tuple' ,
         # because we need this to match the "workload" tuple to the records in TopHub
-        pad_h, pad_w, _, _ = get_pad_tuple(padding, (filter_height, filter_width))
-        padding_args = (pad_h, pad_w)
+        padt, padl, padb, padr = get_pad_tuple(padding, (filter_height, filter_width))
+        padding_args = (padt, padl, padb, padr)
     else:
         padding_args = padding
 
@@ -205,6 +207,23 @@ def get_ref_data():
                 relu_scipy,
             ) = get_ref_data()
 
+            def verify_workload_padding():
+                _, _, out_height, out_width = get_const_tuple(depthwise_conv2d_scipy.shape)
+                wkl = _get_workload(
+                    Input, Filter, (stride_h, stride_w), padding_args, dilation, dtype
+                )
+
+                # check if tile_ow candidates are the factors of the right output weight.
+                with tvm.target.Target(device):
+                    cfg = autotvm.get_config()
+                    _fallback_schedule(cfg, wkl)
+                    ow_tile = np.prod(cfg["tile_ow"].size)
+
+                    tvm.testing.assert_allclose(ow_tile, out_width)
+
+            if "llvm" in device:
+                verify_workload_padding()
+
             input_tvm = tvm.nd.array(input_np, ctx)
             filter_tvm = tvm.nd.array(filter_np, ctx)
             scale_tvm = tvm.nd.array(scale_np, ctx)
diff --git a/tests/python/topi/python/test_topi_einsum.py b/tests/python/topi/python/test_topi_einsum.py
new file mode 100644
index 000000000000..49e951398f40
--- /dev/null
+++ b/tests/python/topi/python/test_topi_einsum.py
@@ -0,0 +1,78 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import numpy as np
+import tvm
+import tvm.testing
+from tvm import te
+from tvm import topi
+from tvm.topi.utils import get_const_tuple
+
+
+def with_tvm(lam, *args):
+    """Take numpy arrays as args, convert them to TVM tensors and call `lam`.
+    Result of lambda is converted back to numpy array and returned.
+    """
+    ctx = tvm.cpu(0)
+    pls = []  # placeholders
+    vals_nd = []  # initial values
+    for i, arg in enumerate(args):
+        pls.append(te.placeholder(arg.shape, name="pl" + str(i)))
+        vals_nd.append(tvm.nd.array(arg, ctx))
+
+    out = lam(*pls)
+    out_nd = tvm.nd.array(np.zeros(get_const_tuple(out.shape), dtype=out.dtype), ctx)
+    s = te.create_schedule([out.op])
+    m = tvm.build(s, pls + [out], "llvm")
+    m(*(vals_nd + [out_nd]))
+    return out_nd.asnumpy()
+
+
+def verify_einsum(subscripts, shapes):
+    ops = []
+    for shape in shapes:
+        tmp = np.random.uniform(low=-1.0, high=1.0, size=shape).astype(np.float32)
+        ops.append(tmp)
+
+    c1 = np.einsum(subscripts, *ops)
+
+    if len(ops) == 1:
+        c2 = with_tvm(lambda A: topi.einsum(subscripts, A), *ops)
+    elif len(ops) == 2:
+        c2 = with_tvm(lambda A, B: topi.einsum(subscripts, A, B), *ops)
+    elif len(ops) == 3:
+        c2 = with_tvm(lambda A, B, C: topi.einsum(subscripts, A, B, C), *ops)
+
+    tvm.testing.assert_allclose(c1, c2, rtol=1e-5, atol=1e-5)
+
+
+def test_einsum():
+    verify_einsum("ii", [(5, 5)])
+    verify_einsum("ii->i", [(5, 5)])
+    verify_einsum("ij->i", [(5, 5)])
+    verify_einsum("...j->...", [(5, 5)])
+    verify_einsum("...j, j", [(5, 5), (5,)])
+    verify_einsum("..., ...", [(), (2, 3)])
+    verify_einsum("ijk, jil->kl", [(3, 4, 5), (4, 3, 2)])
+    verify_einsum("ij, ij -> i", [(1, 4), (2, 4)])
+    verify_einsum("...ij, ...jk -> ...ik", [(1, 4), (4, 2)])
+    verify_einsum("...ij, ...ik -> ...jk", [(1, 1, 1, 4), (1, 1, 1, 3)])
+    verify_einsum("ij,jk->ik", [(2, 3), (3, 4)])
+    verify_einsum("ij,jk,km->im", [(2, 3), (3, 4), (4, 5)])
+
+
+if __name__ == "__main__":
+    test_einsum()
diff --git a/tests/python/topi/python/test_topi_image.py b/tests/python/topi/python/test_topi_image.py
index 518ee1f32676..c605df7037e4 100644
--- a/tests/python/topi/python/test_topi_image.py
+++ b/tests/python/topi/python/test_topi_image.py
@@ -59,6 +59,9 @@ def verify_resize(
             a_np, (out_height, out_width), layout, coord_trans
         )
     else:
+        # TODO: Nearest neighbor case doesn't do anything with coordinate transform mode, and also
+        # nearest_neighbors and align_corners combination in topi doesn't match the output of this
+        # function.
         scale_h = out_height / in_height
         scale_w = out_width / in_width
         b_np = tvm.topi.testing.upsampling_python(a_np, (scale_h, scale_w), layout)
@@ -88,15 +91,14 @@ def test_resize():
     verify_resize(4, 16, 32, 32, 50, 50, "NHWC")
     # Scale NHWC + Align Corners
     verify_resize(6, 32, 64, 64, 20, 20, "NHWC")
-    # Nearest + Fractional
-    verify_resize(4, 16, 32, 32, 50, 50, "NCHW", "asymmetric", method="nearest_neighbor")
-    verify_resize(4, 16, 32, 32, 50, 50, "NHWC", "asymmetric", method="nearest_neighbor")
-    # half_pixel
-    verify_resize(4, 16, 16, 16, 32, 32, "NCHW", "half_pixel", method="bilinear")
-    verify_resize(4, 16, 16, 16, 32, 32, "NHWC", "half_pixel", method="bilinear")
-    # Bilinear + Fractional
-    verify_resize(4, 16, 32, 32, 50, 50, "NCHW", "asymmetric", method="bilinear")
-    verify_resize(4, 16, 32, 32, 50, 50, "NHWC", "asymmetric", method="bilinear")
+    for method in ["nearest_neighbor", "bilinear"]:
+        for coord_trans in ["asymmetric", "half_pixel", "align_corners"]:
+            for layout in ["NCHW", "NHWC"]:
+                # TODO: When topi test has an option for align corners and nearest neighbor that
+                # produces correct results, re-enable it.
+                if coord_trans == "align_corners" and method == "nearest_neighbor":
+                    continue
+                verify_resize(4, 16, 32, 32, 50, 50, layout, coord_trans, method=method)
 
 
 def verify_resize3d(
diff --git a/tests/python/topi/python/test_topi_prng.py b/tests/python/topi/python/test_topi_prng.py
new file mode 100644
index 000000000000..649e5410c147
--- /dev/null
+++ b/tests/python/topi/python/test_topi_prng.py
@@ -0,0 +1,124 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+import tvm.relay
+import tvm.testing
+import tvm.topi
+import numpy as np
+
+
+def threefry_split(target, ctx, gen):
+    gen_placeholder = tvm.te.placeholder(gen.shape, name="gen", dtype="uint64")
+    left_placeholder, right_placeholder = tvm.topi.random.threefry_split(gen_placeholder)
+    s = tvm.topi.generic.schedule_extern([left_placeholder, right_placeholder])
+    f = tvm.build(s, [gen_placeholder, left_placeholder, right_placeholder])
+    left = tvm.nd.array(np.zeros(gen.shape, dtype="uint64"))
+    right = tvm.nd.array(np.zeros(gen.shape, dtype="uint64"))
+    f(tvm.nd.array(gen), left, right)
+    return left.asnumpy(), right.asnumpy()
+
+
+def threefry_generate(target, ctx, gen, size):
+    gen_placeholder = tvm.te.placeholder(gen.shape, name="gen", dtype="uint64")
+    left_placeholder, right_placeholder = tvm.topi.random.threefry_generate(gen_placeholder, size)
+    s = tvm.topi.generic.schedule_extern([left_placeholder, right_placeholder])
+    f = tvm.build(s, [gen_placeholder, left_placeholder, right_placeholder])
+    out_gen = tvm.nd.array(np.zeros(gen.shape, dtype="uint64"))
+    rands = tvm.nd.array(np.zeros(size, dtype="uint64"))
+    f(tvm.nd.array(gen), out_gen, rands)
+    return out_gen.asnumpy(), rands.asnumpy()
+
+
+@tvm.testing.parametrize_targets
+def test_threefry_split(target, ctx):
+    # test that results of split do not equal eachother or the input
+    gen = tvm.relay.random.threefry_key(0).data.asnumpy()
+    a, b = threefry_split(target, ctx, gen)
+    assert (a != b).any() and (
+        a != gen
+    ).any(), "Splitting a gen should result in different output gens"
+    # unittest some split inputs
+    assert (a == np.array([0, 0, 0, 0, 0, 0, 0, 0, 1 << 62, 0], dtype="uint64")).all()
+    assert (b == np.array([0, 0, 0, 0, 1 << 63, 0, 0, 0, 1 << 62, 0], dtype="uint64")).all()
+
+    # test enough splits to go over path length
+    for i in range(129):
+        a, b = threefry_split(target, ctx, b)
+    assert (a[0:4] == b[0:4]).all(), "State part of split should be the same"
+    assert (b[0:4] != np.zeros(4, dtype="uint64")).any()
+
+    # check that split then generate does not generate the same for both sides
+    a, a_rands = threefry_generate(target, ctx, a, (100,))
+    b, b_rands = threefry_generate(target, ctx, b, (100,))
+    assert (
+        a_rands != b_rands
+    ).all(), "Numbers generated from different initial states should be different"
+
+    # check repeatability
+    _, rands1 = threefry_generate(target, ctx, a, (100,))
+    _, rands2 = threefry_generate(target, ctx, a, (100,))
+    assert (
+        rands1 == rands2
+    ).all(), "Numbers generated from the same initial state should be the same"
+
+    a1, b1 = threefry_split(target, ctx, a)
+    a2, b2 = threefry_split(target, ctx, a)
+    assert (a1 == a2).all() and (
+        b1 == b2
+    ).all(), "Split called on the same input should return the same result"
+
+
+@tvm.testing.parametrize_targets
+def test_threefry_generate(target, ctx):
+    gen = tvm.relay.random.threefry_key(0).data.asnumpy()
+
+    # check that we can generate some data
+    a, rands = threefry_generate(target, ctx, gen, (100,))
+    assert (
+        rands.shape[0] == 100 and len(rands.shape) == 1
+    ), "Output shape should match requested shape"
+
+    # check that gen out does not equal input
+    assert (a != gen).any(), "Output generator should be different from input generator"
+
+    # test enough generates to go over generate limit
+    gen = np.array(
+        [0, 0, 0, 0, 0, 0, 0, 2 ** 64 - 2, 1 << 63, 0], dtype="uint64"
+    )  # make counter large
+    a, rands = threefry_generate(target, ctx, gen, (100,))
+    assert gen[4] != a[4], "Overflow of counter should trigger path change"
+    assert a[7] == 100, "Overflow of counter should still update counter"
+
+    # check generate with path at length limit
+    gen = np.array([0, 0, 0, 0, 0, 0, 0, 2 ** 64 - 2, 0, 0], dtype="uint64")  # make counter large
+    a, rands = threefry_generate(target, ctx, gen, (100,))
+    assert (
+        gen[0:4] != a[0:4]
+    ).any(), "Overflowing counter with no space left in path should change state"
+
+
+@tvm.testing.parametrize_targets
+def test_threefry_wrapping(target, ctx):
+    assert tvm.topi.random.threefry_test_wrapping(
+        target, ctx
+    ), f"{target} does not suppport wrapping unsigned integer arithmetic"
+
+
+if __name__ == "__main__":
+    test_threefry_split(tvm.target.Target("llvm"), tvm.context("cpu"))
+    test_threefry_generate(tvm.target.Target("llvm"), tvm.context("cpu"))
+    test_threefry_wrapping(tvm.target.Target("llvm"), tvm.context("cpu"))
diff --git a/tests/python/topi/python/test_topi_qnn.py b/tests/python/topi/python/test_topi_qnn.py
new file mode 100644
index 000000000000..386f77335f1a
--- /dev/null
+++ b/tests/python/topi/python/test_topi_qnn.py
@@ -0,0 +1,161 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Test code for QNN operators."""
+import numpy as np
+import tvm
+from tvm import topi, relay, te
+from tvm.contrib import graph_runtime
+import tvm.topi.testing
+
+
+def verify_simulated_quantize(data_shape, out_dtype, channels, axis):
+    # Create placeholder variables for all qnn inputs.
+    A = te.placeholder(data_shape, name="value", dtype="float32")
+    D = te.placeholder([], name="dtype", dtype="int32")
+    S = te.placeholder([te.size_var("scale_dim")], name="scale", dtype="float32")
+    Z = te.placeholder([te.size_var("zp_dim")], name="zp", dtype="int32")
+    SIM_Q = topi.nn.simulated_quantize(A, D, output_scale=S, output_zero_point=Z, axis=axis)
+
+    # Create random numpy values to assign to inputs.
+    a_np = np.random.uniform(size=data_shape).astype("float32")
+    d_np = np.int32(topi.nn.SQNN_DTYPE_TO_CODE[out_dtype])
+    s_np = np.random.uniform(low=1e-4, high=0.1, size=channels).astype("float32")
+    z_np = np.random.uniform(low=-10, high=10, size=channels).astype("int32")
+    q_np = np.zeros(shape=data_shape, dtype="float32")
+
+    def check_device(device, ctx):
+        # Wrap the numpy arrays in nd arrays.
+        a = tvm.nd.array(a_np, ctx)
+        d = tvm.nd.array(d_np, ctx)
+        s = tvm.nd.array(s_np, ctx)
+        z = tvm.nd.array(z_np, ctx)
+        q = tvm.nd.array(q_np, ctx)
+
+        # Construct equivalent relay graph.
+        per_channel = channels[0] != 1
+        a_var = relay.var("a", shape=data_shape, dtype="float32")
+        if per_channel:
+            s_var = relay.const(s_np)
+            z_var = relay.const(z_np)
+        else:
+            s_var = relay.const(s_np[0])
+            z_var = relay.const(z_np[0])
+        real_q_op = relay.qnn.op.quantize(a_var, s_var, z_var, axis=axis, out_dtype=out_dtype)
+        with tvm.transform.PassContext(opt_level=3):
+            lib = relay.build(tvm.IRModule.from_expr(real_q_op), target=device)
+
+        # Get real qnn quantize output.
+        m = graph_runtime.GraphModule(lib["default"](ctx))
+        m.set_input("a", a_np)
+
+        m.run()
+        real_q_out = m.get_output(0)
+
+        # Compile the simulated quantize function.
+        with tvm.target.Target(device):
+            sched = tvm.topi.testing.get_injective_schedule(device)(SIM_Q)
+        func = tvm.build(sched, [A, D, S, Z, SIM_Q], device, name="sim_quantize")
+        func(a, d, s, z, q)
+
+        # Check correctness against the true qnn output.
+        mismatch = q.asnumpy() != real_q_out.asnumpy().astype("float32")
+        # Allow some rounding errors due to GPU fp32 arithmetic.
+        assert np.sum(mismatch) <= 3
+
+    for target, ctx in tvm.testing.enabled_targets():
+        check_device(target, ctx)
+
+
+def test_simulated_quantize():
+    verify_simulated_quantize([1], "int8", [1], -1)
+    verify_simulated_quantize([2, 5], "int8", [5], 1)
+    verify_simulated_quantize([1, 32, 32, 32], "int8", [32], -1)
+    verify_simulated_quantize([1, 32, 32, 32], "uint8", [32], -2)
+    verify_simulated_quantize([2, 5], "int32", [5], 1)
+
+
+def verify_simulated_dequantize(data_shape, in_dtype, channels, axis):
+    # Create placeholder variables for all qnn inputs.
+    A = te.placeholder(data_shape, name="value", dtype="float32")
+    D = te.placeholder([], name="dtype", dtype="int32")
+    S = te.placeholder([te.size_var("scale_dim")], name="scale", dtype="float32")
+    Z = te.placeholder([te.size_var("zp_dim")], name="zp", dtype="int32")
+    SIM_DQ = topi.nn.simulated_dequantize(A, D, input_scale=S, input_zero_point=Z, axis=axis)
+
+    # Create random numpy values to assign to inputs.
+    a_np = np.random.uniform(low=-128, high=127, size=data_shape).astype(in_dtype)
+    a_np_f = a_np.astype("float32")
+    d_np = np.int32(topi.nn.SQNN_DTYPE_TO_CODE[in_dtype])
+    s_np = np.random.uniform(low=1e-4, high=0.1, size=channels).astype("float32")
+    z_np = np.random.uniform(low=-10, high=10, size=channels).astype("int32")
+    dq_np = np.zeros(shape=data_shape, dtype="float32")
+
+    def check_device(device, ctx):
+        # Wrap the numpy arrays in nd arrays.
+        a = tvm.nd.array(a_np_f, ctx)
+        d = tvm.nd.array(d_np, ctx)
+        s = tvm.nd.array(s_np, ctx)
+        z = tvm.nd.array(z_np, ctx)
+        dq = tvm.nd.array(dq_np, ctx)
+
+        # Construct equivalent relay graph.
+        per_channel = channels[0] != 1
+        a_var = relay.var("a", shape=data_shape, dtype=in_dtype)
+        if per_channel:
+            s_var = relay.const(s_np)
+            z_var = relay.const(z_np)
+        else:
+            s_var = relay.const(s_np[0])
+            z_var = relay.const(z_np[0])
+        real_dq_op = relay.qnn.op.dequantize(a_var, s_var, z_var, axis=axis)
+        with tvm.transform.PassContext(opt_level=3):
+            lib = relay.build(tvm.IRModule.from_expr(real_dq_op), target=device)
+
+        # Get real qnn quantize output.
+        m = graph_runtime.GraphModule(lib["default"](ctx))
+        m.set_input("a", a_np)
+
+        m.run()
+        real_dq_out = m.get_output(0)
+
+        # Compile the simulated quantize function.
+        with tvm.target.Target(device):
+            sched = tvm.topi.testing.get_injective_schedule(device)(SIM_DQ)
+        func = tvm.build(sched, [A, D, S, Z, SIM_DQ], device, name="sim_quantize")
+        func(a, d, s, z, dq)
+
+        # Check correctness against the true qnn output.
+        tvm.testing.assert_allclose(
+            dq.asnumpy(), real_dq_out.asnumpy().astype("float32"), rtol=1e-5
+        )
+
+    for target, ctx in tvm.testing.enabled_targets():
+        check_device(target, ctx)
+
+
+def test_simulated_dequantize():
+    verify_simulated_dequantize([1], "int8", [1], -1)
+    verify_simulated_dequantize([2, 5], "int8", [5], 1)
+    verify_simulated_dequantize([2, 5], "int8", [2], 0)
+    verify_simulated_dequantize([1, 32, 32, 32], "int8", [32], -1)
+    verify_simulated_dequantize([1, 32, 32, 32], "uint8", [32], -2)
+    verify_simulated_dequantize([2, 5], "int32", [5], 1)
+
+
+if __name__ == "__main__":
+    test_simulated_quantize()
+    test_simulated_dequantize()
diff --git a/tests/python/topi/python/test_topi_sort.py b/tests/python/topi/python/test_topi_sort.py
index 626218f30144..85a35488ab22 100644
--- a/tests/python/topi/python/test_topi_sort.py
+++ b/tests/python/topi/python/test_topi_sort.py
@@ -75,7 +75,7 @@ def check_device(device):
         f(tvm_data, tvm_out)
         tvm.testing.assert_allclose(tvm_out.asnumpy(), np_sort, rtol=1e0)
 
-    for device in ["llvm", "cuda", "opencl"]:
+    for device in ["llvm", "cuda", "opencl", "vulkan", "nvptx"]:
         check_device(device)
 
 
@@ -115,7 +115,7 @@ def check_device(device):
         f(tvm_data, tvm_out)
         tvm.testing.assert_allclose(tvm_out.asnumpy(), np_indices.astype(data_dtype), rtol=1e0)
 
-    for device in ["llvm", "cuda", "opencl"]:
+    for device in ["llvm", "cuda", "opencl", "vulkan", "nvptx"]:
         check_device(device)
 
 
@@ -167,7 +167,7 @@ def check_device(device):
         else:
             tvm.testing.assert_allclose(tvm_res[0].asnumpy(), np_indices)
 
-    for device in ["llvm", "cuda", "opencl"]:
+    for device in ["llvm", "cuda", "opencl", "vulkan", "nvptx"]:
         check_device(device)
 
 
diff --git a/tests/python/topi/python/test_topi_sparse.py b/tests/python/topi/python/test_topi_sparse.py
index e47bfddbf7fc..d84bd1530587 100644
--- a/tests/python/topi/python/test_topi_sparse.py
+++ b/tests/python/topi/python/test_topi_sparse.py
@@ -507,19 +507,51 @@ def test_sparse_dense_padded_alter_op():
         K = 128
         X_np = np.random.randn(M, K).astype("float32")
         W_sp_np = random_bsr_matrix(N, K, 2, 2, density=0.01, dtype="float32")
+        x = relay.var("x", relay.TensorType(X_np.shape, "float32"))
         mult = relay.op.nn.sparse_dense(
-            relay.Constant(tvm.nd.array(X_np)),
+            x,
             (
                 relay.Constant(tvm.nd.array(W_sp_np.data)),
                 relay.Constant(tvm.nd.array(W_sp_np.indices)),
                 relay.Constant(tvm.nd.array(W_sp_np.indptr)),
             ),
         )
-        f = relay.Function([], mult)
-        f = relay.transform.InferType()(tvm.IRModule.from_expr(f))
-        f_ = relay.transform.AlterOpLayout()(f)
+        f = relay.Function([x], mult)
+        f_ = relay.transform.InferType()(tvm.IRModule.from_expr(f))
+        f_ = relay.transform.AlterOpLayout()(f_)
         assert f_["main"].body.op.name == "nn.internal.sparse_dense_padded"
 
+        # build with cuda and AlterOpLayout to ensure that sparse_dense_padded is in action
+        with tvm.transform.PassContext(opt_level=3, required_pass="AlterOpLayout"):
+            x = relay.build(tvm.IRModule.from_expr(f), target=tvm.target.Target("cuda"))
+
+
+def test_sparse_add_csr():
+    for indices_dtype in ["int32", "int64"]:
+        for data_dtype in ["float32", "float64"]:
+            M, K, density = 3, 49, 0.2
+            X_np = np.random.randn(M, K).astype(data_dtype)
+            Y_sp_np = sp.random(M, K, density=density, format="csr", dtype=data_dtype)
+            Y_np = Y_sp_np.todense()
+            Z_np = X_np + Y_np
+
+            Y_data = te.placeholder(shape=Y_sp_np.data.shape, dtype=data_dtype)
+            Y_indices = te.placeholder(shape=Y_sp_np.indices.shape, dtype=indices_dtype)
+            Y_indptr = te.placeholder(shape=Y_sp_np.indptr.shape, dtype=indices_dtype)
+            X = te.placeholder(shape=X_np.shape, dtype=data_dtype)
+            Z = topi.nn.sparse_add(X, Y_data, Y_indices, Y_indptr)
+            s = te.create_schedule(Z.op)
+            func = tvm.build(s, [X, Y_data, Y_indices, Y_indptr, Z])
+            Z_tvm = tvm.nd.array(np.zeros(Z_np.shape, dtype=Z_np.dtype))
+            func(
+                tvm.nd.array(X_np.astype(data_dtype)),
+                tvm.nd.array(Y_sp_np.data.astype(data_dtype)),
+                tvm.nd.array(Y_sp_np.indices.astype(indices_dtype)),
+                tvm.nd.array(Y_sp_np.indptr.astype(indices_dtype)),
+                Z_tvm,
+            )
+            tvm.testing.assert_allclose(Z_tvm.asnumpy(), Z_np, atol=1e-4, rtol=1e-4)
+
 
 if __name__ == "__main__":
     test_csrmv()
@@ -532,3 +564,4 @@ def test_sparse_dense_padded_alter_op():
     test_sparse_dense_padded_alter_op()
     test_sparse_dense_csr_reverse()
     test_sparse_dense_bsr_reverse()
+    test_sparse_add_csr()
diff --git a/tests/python/topi/python/test_topi_transform.py b/tests/python/topi/python/test_topi_transform.py
index 30434f6fd266..e0018ba0c0d3 100644
--- a/tests/python/topi/python/test_topi_transform.py
+++ b/tests/python/topi/python/test_topi_transform.py
@@ -817,6 +817,7 @@ def test_strided_slice():
     verify_strided_slice((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1])
     verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3])
     verify_strided_slice((3, 4, 3), [0, 2, 0], [1, 2, 3])
+    verify_strided_slice((3, 4, 3), [0, 0, 0], [None, None, None])
 
 
 @tvm.testing.uses_gpu
diff --git a/tests/python/topi/python/test_topi_unique.py b/tests/python/topi/python/test_topi_unique.py
new file mode 100644
index 000000000000..d7ee74282922
--- /dev/null
+++ b/tests/python/topi/python/test_topi_unique.py
@@ -0,0 +1,111 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import numpy as np
+import tvm
+import tvm.testing
+from tvm import topi
+import tvm.topi.testing
+
+
+@tvm.testing.parametrize_targets
+def test_unique(ctx, target):
+    def calc_numpy_unique(data, is_sorted=False):
+        uniq, index, inverse, counts = np.unique(
+            data, return_index=True, return_inverse=True, return_counts=True
+        )
+        num_uniq = np.array([len(uniq)]).astype("int32")
+        if not is_sorted:
+            order = np.argsort(index)
+            reverse_order = np.argsort(order)
+            uniq = uniq[order].astype(data.dtype)
+            inverse = np.array([reverse_order[i] for i in inverse]).astype("int32")
+            counts = counts[order].astype("int32")
+        return [uniq.astype(data.dtype), inverse.astype("int32"), counts, num_uniq]
+
+    def check_unique(data, is_sorted=False):
+        # numpy reference
+        np_unique, np_indices, np_counts, np_num_unique = calc_numpy_unique(data, is_sorted)
+        num_unique = np_num_unique[0]
+
+        implementations = {
+            "generic": (
+                lambda x, return_counts: topi.unique(x, is_sorted, return_counts),
+                topi.generic.schedule_unique,
+            ),
+            "cuda": (
+                lambda x, return_counts: topi.cuda.unique(x, is_sorted, return_counts),
+                topi.cuda.schedule_scan,
+            ),
+            "nvptx": (
+                lambda x, return_counts: topi.cuda.unique(x, is_sorted, return_counts),
+                topi.cuda.schedule_scan,
+            ),
+        }
+        fcompute, fschedule = tvm.topi.testing.dispatch(target, implementations)
+        tvm_data = tvm.nd.array(data, ctx=ctx)
+        tvm_unique = tvm.nd.array(np.zeros(data.shape).astype(data.dtype), ctx=ctx)
+        tvm_indices = tvm.nd.array(np.zeros(data.shape).astype("int32"), ctx=ctx)
+        tvm_num_unique = tvm.nd.array(np.zeros([1]).astype("int32"), ctx=ctx)
+
+        # without counts
+        with tvm.target.Target(target):
+            te_input = tvm.te.placeholder(shape=data.shape, dtype=str(data.dtype))
+            outs = fcompute(te_input, False)
+            s = fschedule(outs)
+            func = tvm.build(s, [te_input, *outs])
+            func(tvm_data, tvm_unique, tvm_indices, tvm_num_unique)
+
+        assert tvm_num_unique.asnumpy()[0] == np_num_unique
+        np.testing.assert_allclose(
+            tvm_unique.asnumpy()[:num_unique], np_unique, atol=1e-5, rtol=1e-5
+        )
+        np.testing.assert_allclose(tvm_indices.asnumpy(), np_indices, atol=1e-5, rtol=1e-5)
+
+        # with counts
+        tvm_counts = tvm.nd.array(np.zeros(data.shape).astype("int32"), ctx=ctx)
+        with tvm.target.Target(target):
+            te_input = tvm.te.placeholder(shape=data.shape, dtype=str(data.dtype))
+            outs = fcompute(te_input, True)
+            s = fschedule(outs)
+            func = tvm.build(s, [te_input, *outs])
+            func(tvm_data, tvm_unique, tvm_indices, tvm_num_unique, tvm_counts)
+
+        np_unique, np_indices, _, np_num_unique = calc_numpy_unique(data, is_sorted)
+        num_unique = np_num_unique[0]
+        assert tvm_num_unique.asnumpy()[0] == np_num_unique
+        np.testing.assert_allclose(
+            tvm_unique.asnumpy()[:num_unique], np_unique, atol=1e-5, rtol=1e-5
+        )
+        np.testing.assert_allclose(tvm_indices.asnumpy(), np_indices, atol=1e-5, rtol=1e-5)
+        np.testing.assert_allclose(
+            tvm_counts.asnumpy()[:num_unique], np_counts, atol=1e-5, rtol=1e-5
+        )
+
+    for in_dtype in ["int32", "int64"]:
+        for is_sorted in [True, False]:
+            data = np.random.randint(0, 100, size=(1)).astype(in_dtype)
+            check_unique(data, is_sorted)
+            data = np.random.randint(0, 10, size=(10)).astype(in_dtype)
+            check_unique(data, is_sorted)
+            data = np.random.randint(0, 100, size=(10000)).astype(in_dtype)
+            check_unique(data, is_sorted)
+
+
+if __name__ == "__main__":
+    test_unique(tvm.context("cpu"), tvm.target.Target("llvm"))
+    test_unique(tvm.context("cuda"), tvm.target.Target("cuda"))
+    test_unique(tvm.context("nvptx"), tvm.target.Target("nvptx"))
diff --git a/tests/python/topi/python/test_topi_vision.py b/tests/python/topi/python/test_topi_vision.py
index 778843be37de..2fdf3cf4b170 100644
--- a/tests/python/topi/python/test_topi_vision.py
+++ b/tests/python/topi/python/test_topi_vision.py
@@ -105,27 +105,18 @@ def check_device(device):
         tvm_out1 = tvm.nd.array(np.zeros(np_out1.shape, dtype="int32"), ctx)
         tvm_out2 = tvm.nd.array(np.zeros(np_out2.shape, dtype=dtype), ctx)
         tvm_out3 = tvm.nd.array(np.zeros(np_out3.shape, dtype="int32"), ctx)
-        if device == "llvm":
-            f = tvm.build(s, [data, outs[0], outs[1], outs[2]], device)
-            f(tvm_input_data, tvm_out1, tvm_out2, tvm_out3)
-            tvm.testing.assert_allclose(tvm_out1.asnumpy(), np_out1, rtol=1e-3)
-            tvm.testing.assert_allclose(tvm_out2.asnumpy(), np_out2, rtol=1e-3)
-            tvm.testing.assert_allclose(tvm_out3.asnumpy(), np_out3, rtol=1e-3)
-        else:
-            f = tvm.build(s, [data, outs[0], outs[1]], device)
-            f(tvm_input_data, tvm_out1, tvm_out2)
-            tvm.testing.assert_allclose(tvm_out1.asnumpy(), np_out1, rtol=1e-3)
-            tvm.testing.assert_allclose(tvm_out2.asnumpy(), np_out2, rtol=1e-3)
 
-    for device in ["llvm", "cuda", "opencl"]:
+        f = tvm.build(s, [data, outs[0], outs[1], outs[2]], device)
+        f(tvm_input_data, tvm_out1, tvm_out2, tvm_out3)
+        tvm.testing.assert_allclose(tvm_out1.asnumpy(), np_out1, rtol=1e-3)
+        tvm.testing.assert_allclose(tvm_out2.asnumpy(), np_out2, rtol=1e-3)
+        tvm.testing.assert_allclose(tvm_out3.asnumpy(), np_out3, rtol=1e-3)
+
+    for device in ["llvm", "cuda", "opencl", "vulkan"]:
         check_device(device)
 
 
 @tvm.testing.uses_gpu
-@pytest.mark.skip(
-    "Skip this test as it is intermittent."
-    "See https://github.com/apache/tvm/pull/4901#issuecomment-595040094"
-)
 def test_get_valid_counts():
     verify_get_valid_counts((1, 1000, 5), 0.5, -1, 0)
     verify_get_valid_counts((1, 2500, 6), 0, 0, 1)
@@ -427,7 +418,9 @@ def check_device(device):
         check_device(device)
 
 
-def verify_roi_align(batch, in_channel, in_size, num_roi, pooled_size, spatial_scale, sample_ratio):
+def verify_roi_align(
+    batch, in_channel, in_size, num_roi, pooled_size, spatial_scale, sample_ratio, mode
+):  # For mode, 0 = avg, 1 = max
     a_shape = (batch, in_channel, in_size, in_size)
     rois_shape = (num_roi, 5)
 
@@ -436,8 +429,8 @@ def verify_roi_align(batch, in_channel, in_size, num_roi, pooled_size, spatial_s
 
     @memoize("topi.tests.test_topi_vision.verify_roi_align")
     def get_ref_data():
-        a_np = np.random.uniform(size=a_shape).astype("float32")
-        rois_np = np.random.uniform(size=rois_shape).astype("float32") * in_size
+        a_np = np.random.uniform(-1, 1, size=a_shape).astype("float32")
+        rois_np = np.random.uniform(-1, 1, size=rois_shape).astype("float32") * in_size
         rois_np[:, 0] = np.random.randint(low=0, high=batch, size=num_roi)
         b_np = tvm.topi.testing.roi_align_nchw_python(
             a_np,
@@ -445,6 +438,7 @@ def get_ref_data():
             pooled_size=pooled_size,
             spatial_scale=spatial_scale,
             sample_ratio=sample_ratio,
+            mode=mode,
         )
 
         return a_np, rois_np, b_np
@@ -456,8 +450,6 @@ def check_device(device):
         if not tvm.testing.device_enabled(device):
             print("Skip because %s is not enabled" % device)
             return
-        print("Running on target: %s" % device)
-
         with tvm.target.Target(device):
             fcompute, fschedule = tvm.topi.testing.dispatch(device, _roi_align_implement)
             b = fcompute(
@@ -466,6 +458,7 @@ def check_device(device):
                 pooled_size=pooled_size,
                 spatial_scale=spatial_scale,
                 sample_ratio=sample_ratio,
+                mode=mode,
             )
             s = fschedule(b)
 
@@ -474,7 +467,8 @@ def check_device(device):
         tvm_b = tvm.nd.array(np.zeros(get_const_tuple(b.shape), dtype=b.dtype), ctx=ctx)
         f = tvm.build(s, [a, rois, b], device)
         f(tvm_a, tvm_rois, tvm_b)
-        tvm.testing.assert_allclose(tvm_b.asnumpy(), b_np, rtol=1e-3)
+        tvm_val = tvm_b.asnumpy()
+        tvm.testing.assert_allclose(tvm_val, b_np, rtol=1e-3, atol=1e-4)
 
     for device in ["llvm", "cuda", "opencl"]:
         check_device(device)
@@ -482,10 +476,14 @@ def check_device(device):
 
 @tvm.testing.uses_gpu
 def test_roi_align():
-    verify_roi_align(1, 16, 32, 64, 7, 1.0, -1)
-    verify_roi_align(4, 16, 32, 64, 7, 0.5, 2)
-    verify_roi_align(1, 32, 32, 80, 8, 0.0625, 2)
-    verify_roi_align(1, 32, 500, 80, 8, 0.0625, 2)
+    verify_roi_align(1, 16, 32, 64, 7, 1.0, -1, 0)
+    verify_roi_align(4, 16, 32, 64, 7, 0.5, 2, 0)
+    verify_roi_align(1, 32, 32, 80, 8, 0.0625, 2, 0)
+    verify_roi_align(1, 32, 500, 80, 8, 0.0625, 2, 0)
+    verify_roi_align(1, 16, 32, 64, 7, 1.0, -1, 1)
+    verify_roi_align(4, 16, 32, 64, 7, 0.5, 2, 1)
+    verify_roi_align(1, 32, 32, 80, 8, 0.0625, 2, 1)
+    verify_roi_align(1, 32, 500, 80, 8, 0.0625, 2, 1)
 
 
 def verify_roi_pool(batch, in_channel, in_size, num_roi, pooled_size, spatial_scale):
diff --git a/tests/python/unittest/test_arith_canonical_simplify.py b/tests/python/unittest/test_arith_canonical_simplify.py
index 65c8ec3dfe02..c241b81da986 100644
--- a/tests/python/unittest/test_arith_canonical_simplify.py
+++ b/tests/python/unittest/test_arith_canonical_simplify.py
@@ -310,6 +310,46 @@ def test_complex_cases():
     ck.verify(res3, tdiv((x * 1024) + y, 256) - tdiv(y, 256) - (x * 4))
 
 
+def test_simplify_cast():
+    ck = CanonicalChecker()
+    tcast = tvm.tir.Cast
+    fld = tvm.te.floordiv
+    flm = tvm.te.floormod
+    # cast(i64, i + j + 1) - cast(i64, i)
+    i = te.var("i", dtype="int32")
+    j = te.var("j", dtype="int32")
+    res = tcast("int64", i + j + 1) - tcast("int64", i)
+    ck.verify(res, tcast("int64", j) + tvm.tir.const(1, "int64"))
+    # cast(i32, i + j + 1) - cast(i32, i)
+    i = te.var("i", dtype="int64")
+    j = te.var("j", dtype="int64")
+    ck.analyzer.update(i, tvm.arith.ConstIntBound(0, 10))
+    ck.analyzer.update(j, tvm.arith.ConstIntBound(0, 10))
+    res = tcast("int32", i + j + 1) - tcast("int32", i)
+    ck.verify(res, tcast("int32", j) + 1)
+    # cast(i32, i + j - 100)
+    i = te.var("i", dtype="int64")
+    j = te.var("j", dtype="int64")
+    ck.analyzer.update(i, tvm.arith.ConstIntBound(0, 2 ** 31 - 1))
+    ck.analyzer.update(j, tvm.arith.ConstIntBound(0, 10))
+    res = tcast("int32", i + j - 100)
+    ck.verify(res, res)
+    # cast(i32, flm(axis, 7i64) * 2i64 + 1i64) + 1i32
+    # - cast(i32, flm(axis, 7i64) * 2i64)
+    axis = te.var("axis", dtype="int64")
+    ck.analyzer.update(axis, tvm.arith.ConstIntBound(0, 42))
+    res = (
+        tcast(
+            "int32",
+            flm(axis, tvm.tir.const(7, "int64")) * tvm.tir.const(2, "int64")
+            + tvm.tir.const(1, "int64"),
+        )
+        + tvm.tir.const(1, "int32")
+        - tcast("int32", flm(axis, tvm.tir.const(7, "int64")) * tvm.tir.const(2, "int64"))
+    )
+    ck.verify(res, 2)
+
+
 if __name__ == "__main__":
     test_floormod_simplify()
     test_mul_sum_simplify()
@@ -321,3 +361,4 @@ def test_complex_cases():
     test_split_index_simplify()
     test_canonical_mixed()
     test_complex_cases()
+    test_simplify_cast()
diff --git a/tests/python/unittest/test_arith_domain_touched.py b/tests/python/unittest/test_arith_domain_touched.py
index ca5df4af6a71..af06a038e1f7 100644
--- a/tests/python/unittest/test_arith_domain_touched.py
+++ b/tests/python/unittest/test_arith_domain_touched.py
@@ -31,14 +31,12 @@ def test_domain_touched():
         i,
         0,
         n,
-        0,
-        0,
+        tvm.tir.ForKind.SERIAL,
         tvm.tir.For(
             j,
             0,
             m,
-            0,
-            0,
+            tvm.tir.ForKind.SERIAL,
             tvm.tir.BufferStore(
                 a,
                 tvm.tir.BufferLoad(b, [i - 1, j + 1]) + tvm.tir.BufferLoad(a, [i - 1, j - 1]),
diff --git a/tests/python/unittest/test_arith_iter_affine_map.py b/tests/python/unittest/test_arith_iter_affine_map.py
index 620540cc9841..6ab61fdd9592 100644
--- a/tests/python/unittest/test_arith_iter_affine_map.py
+++ b/tests/python/unittest/test_arith_iter_affine_map.py
@@ -161,6 +161,9 @@ def test_split():
     assert len(res) == 1
     assert_iter_sum_pattern(res[0], 8, 0, scale=2)
 
+    res = tvm.arith.detect_iter_map([fld(x, flm(flm(y, 8), 6))], var_dom([(x, 24), (y, 8)]))
+    assert len(res) == 0
+
 
 def test_compound():
     x = tvm.tir.Var("x", "int32"), 10
diff --git a/tests/python/unittest/test_auto_scheduler_common.py b/tests/python/unittest/test_auto_scheduler_common.py
index a037b680e2e1..2f9423104a68 100644
--- a/tests/python/unittest/test_auto_scheduler_common.py
+++ b/tests/python/unittest/test_auto_scheduler_common.py
@@ -145,6 +145,23 @@ def invalid_compute_definition():
     return [A, B]
 
 
+@auto_scheduler.register_workload
+def zero_rank_reduce_auto_scheduler_test(N):
+    A = tvm.te.placeholder((N,), name="A")
+    k = tvm.te.reduce_axis((0, N), name="k")
+    B = tvm.te.compute((), lambda: tvm.te.sum(A[k], k), name="B")
+
+    return [A, B]
+
+
+@auto_scheduler.register_workload
+def zero_rank_compute_auto_scheduler_test(N):
+    A = tvm.te.placeholder((N,), name="A")
+    B = tvm.te.compute((), lambda: A[0], name="B")
+
+    return [A, B]
+
+
 @auto_scheduler.register_workload
 def conv2d_winograd_nhwc_auto_scheduler_test(
     N, H, W, CI, CO, kernel_size=3, stride=1, padding=0, dilation=1
diff --git a/tests/python/unittest/test_auto_scheduler_compute_dag.py b/tests/python/unittest/test_auto_scheduler_compute_dag.py
index 60b986ec37b2..b303ef56c1d2 100644
--- a/tests/python/unittest/test_auto_scheduler_compute_dag.py
+++ b/tests/python/unittest/test_auto_scheduler_compute_dag.py
@@ -121,7 +121,7 @@ def test_stage_order():
     )
 
     task2 = pickle.loads(pickle.dumps(task))
-    assert "test-key" in auto_scheduler.workload_registry.WORKLOAD_FUNC_REGISTRY
+    assert '["test-key"]' in auto_scheduler.workload_registry.WORKLOAD_FUNC_REGISTRY
     assert str(task.compute_dag.get_init_state()) == str(task2.compute_dag.get_init_state())
     assert len(task.compute_dag.get_init_state().stage_ops) == len(
         task2.compute_dag.get_init_state().stage_ops
diff --git a/tests/python/unittest/test_auto_scheduler_cost_model.py b/tests/python/unittest/test_auto_scheduler_cost_model.py
index 36360da45c8d..0b34615583db 100644
--- a/tests/python/unittest/test_auto_scheduler_cost_model.py
+++ b/tests/python/unittest/test_auto_scheduler_cost_model.py
@@ -68,14 +68,15 @@ def test_xgb_model():
     assert rmse <= 0.3
 
     # test loading a record file
-    with tempfile.NamedTemporaryFile() as fp:
-        auto_scheduler.save_records(fp.name, inputs, results)
-        model.update_from_file(fp.name)
+    tmpdir = tvm.contrib.utils.tempdir()
+    tmpfile = tmpdir.relpath("test1")
+    auto_scheduler.save_records(tmpfile, inputs, results)
+    model.update_from_file(tmpfile)
 
     # test model serialization
-    with tempfile.NamedTemporaryFile() as fp:
-        model.save(fp.name)
-        model.load(fp.name)
+    tmpfile = tmpdir.relpath("test2")
+    model.save(tmpfile)
+    model.load(tmpfile)
 
 
 if __name__ == "__main__":
diff --git a/tests/python/unittest/test_auto_scheduler_layout_rewrite.py b/tests/python/unittest/test_auto_scheduler_layout_rewrite.py
index 6ca56bde7c60..795c3cb3b0a2 100644
--- a/tests/python/unittest/test_auto_scheduler_layout_rewrite.py
+++ b/tests/python/unittest/test_auto_scheduler_layout_rewrite.py
@@ -49,9 +49,24 @@ def test_apply_steps_with_layout_rewrite():
     assert bufs[1].shape[1] == 512
 
 
+def test_apply_steps_with_layout_rewrite_corner_case():
+    A, B, C = matmul_auto_scheduler_test(1, 1, 1)
+    dag = auto_scheduler.ComputeDAG([A, B, C])
+
+    s = dag.get_init_state()
+
+    s.compute_root(C)
+    i_j_fused = s.fuse(C, [s[C].iters[0], s[C].iters[1]])
+    s.parallel(C, i_j_fused)
+
+    _, bufs = dag.apply_steps_from_state(
+        s, layout_rewrite=auto_scheduler.LayoutRewriteOption.REWRITE_FOR_PRE_TRANSFORMED
+    )
+
+
 @tvm.testing.requires_llvm
 def test_correctness_layout_rewrite_rewrite_for_preTransformed():
-    N = 128
+    N = 16
     target = tvm.target.Target("llvm")
     task = auto_scheduler.SearchTask(func=matmul_auto_scheduler_test, args=(N, N, N), target=target)
     dag = task.compute_dag
@@ -63,9 +78,10 @@ def test_correctness_layout_rewrite_rewrite_for_preTransformed():
 
         measure_ctx = auto_scheduler.LocalRPCMeasureContext()
         tuning_options = auto_scheduler.TuningOptions(
-            num_measure_trials=2,
+            num_measure_trials=100,
             runner=measure_ctx.runner,
             verbose=2,
+            early_stopping=1,
             measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
         )
         task.tune(tuning_options, search_policy=search_policy)
@@ -169,5 +185,6 @@ def test_correctness_layout_rewrite_insert_transform_stage():
 
 if __name__ == "__main__":
     test_apply_steps_with_layout_rewrite()
+    test_apply_steps_with_layout_rewrite_corner_case()
     test_correctness_layout_rewrite_rewrite_for_preTransformed()
     test_correctness_layout_rewrite_insert_transform_stage()
diff --git a/tests/python/unittest/test_auto_scheduler_measure.py b/tests/python/unittest/test_auto_scheduler_measure.py
index e9f1fa40c8b3..7605b70be6f4 100644
--- a/tests/python/unittest/test_auto_scheduler_measure.py
+++ b/tests/python/unittest/test_auto_scheduler_measure.py
@@ -16,15 +16,19 @@
 # under the License.
 
 """ Test measurement and log serialization. """
+import json
 
 import multiprocessing
+import numpy as np
 import tvm
 from tvm import topi
 from tvm import te, auto_scheduler
 import tempfile
 import tvm.testing
+import pickle
 
-from test_auto_scheduler_common import matmul_auto_scheduler_test, get_tiled_matmul
+from test_auto_scheduler_common import matmul_auto_scheduler_test
+from tvm.auto_scheduler import workload_registry
 
 
 def record_common(dag, s):
@@ -200,6 +204,39 @@ def test_recover_measure_input():
         assert str(correct_inp.state) == str(inp.state)
 
 
+def test_workload_dis_factor():
+    calc = auto_scheduler.utils.calc_workload_dis_factor
+    decode = auto_scheduler.utils.decode_workload_key
+
+    # Identical
+    target_wkl_key = json.dumps(
+        ["func1", [8, 3, 224, 224], [32, 3, 3, 3], [0, 0], [1, 1], "float32"]
+    )
+    assert calc(decode(target_wkl_key), decode(target_wkl_key)) == 1
+
+    # Compatible with a factor
+    wkl_key = json.dumps(["func1", [1, 3, 112, 112], [32, 3, 3, 3], [0, 0], [1, 1], "float32"])
+    assert calc(decode(target_wkl_key), decode(wkl_key)) == 8 * 2 * 2
+
+    # Incompatible argument with zeros
+    wkl_key = json.dumps(["func1", [8, 3, 224, 224], [32, 3, 3, 3], [1, 1], [1, 1], "float32"])
+    assert calc(decode(target_wkl_key), decode(wkl_key)) == float("inf")
+    wkl_key = json.dumps(["func1", [8, 3, 224, 224], [32, 3, 3, 3], [0, 0], [0, 0], "float32"])
+    assert calc(decode(target_wkl_key), decode(wkl_key)) == float("inf")
+
+    # Incompatible non-integter argument
+    wkl_key = json.dumps(["func1", [8, 3, 224, 224], [32, 3, 3, 3], [0, 0], [1, 1], "int8"])
+    assert calc(decode(target_wkl_key), decode(wkl_key)) == float("inf")
+
+    # Incompatible function
+    wkl_key = json.dumps(["func2", [8, 3, 224, 224], [32, 3, 3, 3], [0, 0], [1, 1], "float32"])
+    assert calc(decode(target_wkl_key), decode(wkl_key)) == float("inf")
+
+    # Incompatible due to non-dividable factor
+    wkl_key = json.dumps(["func1", [8, 3, 223, 223], [32, 3, 3, 3], [0, 0], [1, 1], "float32"])
+    assert calc(decode(target_wkl_key), decode(wkl_key)) == float("inf")
+
+
 def test_measure_local_builder_runner():
     if not tvm.testing.device_enabled("llvm"):
         return
@@ -221,6 +258,42 @@ def test_measure_local_builder_runner():
         assert mress[0].error_no == 0
 
 
+def test_dag_measure_local_builder_runner():
+    if not tvm.testing.device_enabled("llvm"):
+        return
+
+    A = te.placeholder((512, 512), name="A")
+    B = te.placeholder((512, 512), name="B")
+    k = te.reduce_axis((0, 512), name="k")
+    C = te.compute((512, 512), lambda i, j: te.sum(A[i][k] * B[k][j], axis=[k]), name="C")
+    D = topi.nn.relu(C)
+    E = topi.nn.relu(D)
+
+    tensors = [A, B, E]
+    dag = auto_scheduler.ComputeDAG(tensors)
+    key = workload_registry.register_workload_tensors(dag.workload_key(), tensors)
+    transfer_data = workload_registry.serialize_workload_registry_entry(key)
+    f_data = pickle.dumps(transfer_data)
+    f_new = pickle.loads(f_data)
+    del workload_registry.WORKLOAD_FUNC_REGISTRY[key]
+    workload_registry.deserialize_workload_registry_entry(f_new)
+
+    target = tvm.target.Target("llvm")
+    task = auto_scheduler.SearchTask(compute_dag=dag, workload_key=key, target=target)
+
+    for enable_cpu_cache_flush in [True, False]:
+        minp = auto_scheduler.MeasureInput(task, task.compute_dag.init_state)
+        local_builder = auto_scheduler.LocalBuilder()
+        local_runner = auto_scheduler.LocalRunner(
+            timeout=60, enable_cpu_cache_flush=enable_cpu_cache_flush
+        )
+
+        bress = local_builder.build([minp])
+        assert bress[0].error_no == 0
+        mress = local_runner.run([minp], bress)
+        assert mress[0].error_no == 0
+
+
 def test_measure_local_builder_rpc_runner():
     if not tvm.testing.device_enabled("llvm"):
         return
@@ -283,12 +356,76 @@ def test_measure_target_host():
         assert str(recovered_inp.task.target_host) == str(inp.task.target_host)
 
 
+@tvm.testing.requires_llvm
+def test_measure_special_inputs_map_by_name_local_runner():
+    @auto_scheduler.register_workload
+    def foo():
+        X = te.placeholder(shape=[10], dtype="int32")
+        Index = te.placeholder(shape=[1], dtype="int32", name="Index")
+        Y = te.compute((1,), lambda i: X[Index[i]])
+        return [X, Index, Y]
+
+    # This workload cannot use random input for the `Index` input
+    task = auto_scheduler.SearchTask(
+        func=foo,
+        target="llvm",
+        task_inputs={
+            "Index": tvm.nd.array(np.array([5], dtype="int32")),
+        },
+    )
+
+    minp = auto_scheduler.MeasureInput(task, task.compute_dag.init_state)
+    local_builder = auto_scheduler.LocalBuilder()
+    local_runner = auto_scheduler.LocalRunner(timeout=10)
+
+    bress = local_builder.build([minp])
+    assert bress[0].error_no == 0
+    mress = local_runner.run([minp], bress)
+    assert mress[0].error_no == 0
+
+
+@tvm.testing.requires_llvm
+def test_measure_special_inputs_map_by_name_rpc_runner():
+    @auto_scheduler.register_workload
+    def foo():
+        X = te.placeholder(shape=[10], dtype="int32")
+        Index = te.placeholder(shape=[1], dtype="int32", name="Index")
+        Y = te.compute((1,), lambda i: X[Index[i]])
+        return [X, Index, Y]
+
+    # This workload cannot use random input for the `Index` input
+    task = auto_scheduler.SearchTask(
+        func=foo,
+        target="llvm",
+        task_inputs={
+            "Index": tvm.nd.array(np.array([5], dtype="int32")),
+        },
+    )
+
+    for enable_cpu_cache_flush in [True, False]:
+        minp = auto_scheduler.MeasureInput(task, task.compute_dag.init_state)
+        local_builder = auto_scheduler.LocalBuilder()
+        measure_ctx = auto_scheduler.LocalRPCMeasureContext(
+            timeout=60, enable_cpu_cache_flush=enable_cpu_cache_flush
+        )
+        rpc_runner = measure_ctx.runner
+
+        bress = local_builder.build([minp])
+        assert bress[0].error_no == 0
+        mress = rpc_runner.run([minp], bress)
+        assert mress[0].error_no == 0
+
+
 if __name__ == "__main__":
     test_record_split_reorder_fuse_annotation()
     test_record_compute_at_root_inline_cache_read_write()
     test_record_follow_split_follow_fused_split()
     test_record_pragma_storage_align_rfactor()
     test_recover_measure_input()
+    test_workload_dis_factor()
     test_measure_local_builder_runner()
+    test_dag_measure_local_builder_runner()
     test_measure_local_builder_rpc_runner()
     test_measure_target_host()
+    test_measure_special_inputs_map_by_name_local_runner()
+    test_measure_special_inputs_map_by_name_rpc_runner()
diff --git a/tests/python/unittest/test_auto_scheduler_search_policy.py b/tests/python/unittest/test_auto_scheduler_search_policy.py
index 73ce0a1685bf..30aafbd22390 100644
--- a/tests/python/unittest/test_auto_scheduler_search_policy.py
+++ b/tests/python/unittest/test_auto_scheduler_search_policy.py
@@ -25,8 +25,13 @@
 import tvm
 import tvm.testing
 from tvm import auto_scheduler
+from tvm.auto_scheduler.utils import get_const_tuple
 
-from test_auto_scheduler_common import matmul_auto_scheduler_test
+from test_auto_scheduler_common import (
+    matmul_auto_scheduler_test,
+    zero_rank_compute_auto_scheduler_test,
+    zero_rank_reduce_auto_scheduler_test,
+)
 import multiprocessing
 
 
@@ -41,21 +46,21 @@ def callback(self, policy, inputs, results):
 
 
 def search_common(
-    workload=matmul_auto_scheduler_test,
+    task=None,
     target="llvm",
     search_policy="sketch",
-    seed=0,
     runner="local",
     num_measure_trials=100,
     cost_model=auto_scheduler.RandomModel(),
     init_search_callbacks=None,
 ):
-    print("Test search policy '%s' for '%s'" % (search_policy, target))
+    if task is None:
+        task = auto_scheduler.SearchTask(
+            func=matmul_auto_scheduler_test, args=(64, 64, 64), target=target
+        )
+    target = task.target
 
-    random.seed(seed)
-    N = 128
-    target = tvm.target.Target(target)
-    task = auto_scheduler.SearchTask(func=workload, args=(N, N, N), target=target)
+    print("Test search policy '%s' for '%s'" % (search_policy, target))
 
     with tempfile.NamedTemporaryFile() as fp:
         log_file = fp.name
@@ -72,6 +77,7 @@ def search_common(
         else:
             raise ValueError("Invalid policy: " + search_policy)
 
+        # Tune
         tuning_options = auto_scheduler.TuningOptions(
             num_measure_trials=num_measure_trials,
             num_measures_per_round=2,
@@ -80,33 +86,47 @@ def search_common(
             measure_callbacks=[auto_scheduler.RecordToFile(log_file), CustomMeasureCallback()],
         )
         task.tune(tuning_options=tuning_options, search_policy=search_policy)
+
+        # Compile with the best schedule
         sch, args = task.apply_best(log_file)
+        mod = tvm.build(sch, args, target)
+
+        # Compile with naive schedule for correctness check
+        sch, args = task.compute_dag.apply_steps_from_state(task.compute_dag.init_state)
+        mod_ref = tvm.build(sch, args, "llvm")
+
+        ctx = tvm.context(str(target), 0)
+        np_arrays = [np.random.uniform(size=get_const_tuple(x.shape)).astype(x.dtype) for x in args]
 
-        try:
-            mod = tvm.build(sch, args, target)
+        tvm_arrays = [tvm.nd.array(x, ctx) for x in np_arrays]
+        mod(*tvm_arrays)
+        actual = [x.asnumpy() for x in tvm_arrays]
 
-            ctx = tvm.context(str(target), 0)
-            dtype = task.compute_dag.tensors[0].dtype
-            a = tvm.nd.array(np.random.uniform(size=(N, N)).astype(dtype), ctx)
-            b = tvm.nd.array(np.random.uniform(size=(N, N)).astype(dtype), ctx)
-            c = tvm.nd.array(np.zeros((N, N), dtype=dtype), ctx)
-            mod(a, b, c)
-            tvm.testing.assert_allclose(c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5)
-        except Exception:
-            raise Exception("Error encountered with seed: %d" % (seed))
+        tvm_arrays = [tvm.nd.array(x) for x in np_arrays]
+        mod_ref(*tvm_arrays)
+        expected = [x.asnumpy() for x in tvm_arrays]
+
+        for x, y in zip(actual, expected):
+            tvm.testing.assert_allclose(x, y, rtol=1e-5)
 
 
 @tvm.testing.requires_llvm
-def test_workload_registry_search_basic():
+def test_workload_registry_empty_policy():
     search_common(search_policy="empty", num_measure_trials=2)
 
+    N = 64
+    target = "llvm"
     search_common(
-        workload="matmul_auto_scheduler_test",
+        task=auto_scheduler.SearchTask(
+            func="matmul_auto_scheduler_test", args=(N, N, N), target=target
+        ),
         num_measure_trials=2,
         search_policy="empty",
     )
     search_common(
-        workload="matmul_auto_scheduler_test_rename_1",
+        task=auto_scheduler.SearchTask(
+            func="matmul_auto_scheduler_test_rename_1", args=(N, N, N), target=target
+        ),
         num_measure_trials=2,
         search_policy="empty",
     )
@@ -147,10 +167,54 @@ def test_sketch_search_policy_cuda_xgbmodel_rpc_runner():
     search_common(target="cuda", runner=measure_ctx.runner, cost_model=auto_scheduler.XGBModel())
 
 
+@tvm.testing.requires_llvm
+@tvm.testing.requires_cuda
+def test_sketch_search_policy_zero_rank():
+    measure_ctx = auto_scheduler.LocalRPCMeasureContext()
+    for target in ["llvm", "cuda"]:
+        task = auto_scheduler.SearchTask(
+            func=zero_rank_compute_auto_scheduler_test, args=(10,), target=target
+        )
+        search_common(task, runner=measure_ctx.runner)
+
+        task = auto_scheduler.SearchTask(
+            func=zero_rank_reduce_auto_scheduler_test, args=(10,), target=target
+        )
+        search_common(task, runner=measure_ctx.runner)
+
+
+@tvm.testing.requires_llvm
+def test_sketch_search_policy_custom_sketch():
+    def meet_condition_func(search_policy, state, stage_id):
+        return auto_scheduler.PreloadCustomSketchRule.APPLY_AND_SKIP_REST
+
+    def apply_func(search_policy, state, stage_id):
+        ret = []
+        state = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+        C = state.stage_ops[2]
+
+        ret.append([state.state_object, -1])
+
+        s1 = state.copy()
+        i, _, _ = s1[C].iters
+        s1.split(C, i, [8])
+        ret.append([s1.state_object, -1])
+        return ret
+
+    search_common(
+        cost_model=auto_scheduler.XGBModel(),
+        init_search_callbacks=[
+            auto_scheduler.PreloadCustomSketchRule(meet_condition_func, apply_func)
+        ],
+    )
+
+
 if __name__ == "__main__":
-    test_workload_registry_search_basic()
+    test_workload_registry_empty_policy()
     test_sketch_search_policy_basic()
     test_sketch_search_policy_basic_spawn()
     test_sketch_search_policy_xgbmodel()
     test_sketch_search_policy_cuda_rpc_runner()
     test_sketch_search_policy_cuda_xgbmodel_rpc_runner()
+    test_sketch_search_policy_zero_rank()
+    test_sketch_search_policy_custom_sketch()
diff --git a/tests/python/unittest/test_auto_scheduler_search_task.py b/tests/python/unittest/test_auto_scheduler_search_task.py
new file mode 100644
index 000000000000..78e85dc213e0
--- /dev/null
+++ b/tests/python/unittest/test_auto_scheduler_search_task.py
@@ -0,0 +1,207 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Test search policy"""
+
+import numpy as np
+import tempfile
+
+import tvm
+import tvm.testing
+from tvm import auto_scheduler
+from tvm.auto_scheduler.utils import get_const_tuple
+from test_auto_scheduler_common import (
+    matmul_auto_scheduler_test,
+    zero_rank_compute_auto_scheduler_test,
+    zero_rank_reduce_auto_scheduler_test,
+)
+
+
+def test_search_task_add_task_input():
+    auto_scheduler.search_task.TASK_INPUT_BUFFER_TABLE.clear()
+    N = 64
+    target = "llvm"
+    test_input_0 = tvm.runtime.ndarray.empty((64, 64))
+    test_input_1 = tvm.runtime.ndarray.empty((10, 20))
+    test_input_2 = tvm.runtime.ndarray.empty((30, 40, 50))
+    task = auto_scheduler.SearchTask(
+        func="matmul_auto_scheduler_test",
+        args=(N, N, N),
+        target=target,
+        task_inputs={
+            "test_input_0": test_input_0,
+            "test_input_1": test_input_1,
+            "test_input_2": test_input_2,
+        },
+        task_inputs_overwrite=True,
+    )
+
+    assert len(task.task_input_names) == 3
+    assert task.task_input_names[0] == "test_input_0"
+    assert task.task_input_names[1] == "test_input_1"
+    assert task.task_input_names[2] == "test_input_2"
+
+
+def test_search_task_record():
+    auto_scheduler.search_task.TASK_INPUT_BUFFER_TABLE.clear()
+    N = 64
+    target = "llvm"
+
+    # Log with no task input
+    task = auto_scheduler.SearchTask(
+        func="matmul_auto_scheduler_test", args=(N, N, N), target=target
+    )
+    task_record = auto_scheduler._ffi_api.SerializeSearchTask(task)
+    new_task = auto_scheduler._ffi_api.DeserializeSearchTask(task_record)
+    # TODO(jcf94): Check the compute dag & hardware parameter
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+
+    # Log with 1 task input
+    test_input_0 = tvm.runtime.ndarray.empty((64, 64))
+    task = auto_scheduler.SearchTask(
+        func="matmul_auto_scheduler_test",
+        args=(N, N, N),
+        target=target,
+        task_inputs={"test_input_0": test_input_0},
+        task_inputs_overwrite=True,
+    )
+    task_record = auto_scheduler._ffi_api.SerializeSearchTask(task)
+    new_task = auto_scheduler._ffi_api.DeserializeSearchTask(task_record)
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+    assert len(new_task.task_input_names) == 1
+    assert new_task.task_input_names[0] == "test_input_0"
+
+    # Log with multiple task inputs
+    test_input_1 = tvm.runtime.ndarray.empty((64, 64))
+    task = auto_scheduler.SearchTask(
+        func="matmul_auto_scheduler_test",
+        args=(N, N, N),
+        target=target,
+        task_inputs={
+            "test_input_0": test_input_0,
+            "test_input_1": test_input_1,
+        },
+        task_inputs_overwrite=True,
+    )
+    task_record = auto_scheduler._ffi_api.SerializeSearchTask(task)
+    new_task = auto_scheduler._ffi_api.DeserializeSearchTask(task_record)
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+    assert len(new_task.task_input_names) == 2
+    assert new_task.task_input_names[0] == "test_input_0"
+    assert new_task.task_input_names[1] == "test_input_1"
+
+    # Log with version 0.5
+    v5_log = """["[\\\"matmul_auto_scheduler_test\\\", 64, 64, 64]", "llvm -keys=cpu -link-params=0", [6, 64, 64, 0, 0, 0, 0, 0], "", 1]"""
+    new_task = auto_scheduler._ffi_api.DeserializeSearchTask(v5_log)
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+    assert len(new_task.task_input_names) == 0
+
+
+def test_recover_measure_input_with_task_input():
+    auto_scheduler.search_task.TASK_INPUT_BUFFER_TABLE.clear()
+
+    # Since this file is tests for search_task, we only check the search_task here
+
+    # Log with no task input
+    task = auto_scheduler.SearchTask(
+        func=matmul_auto_scheduler_test, args=(512, 512, 512), target="llvm"
+    )
+    inp = auto_scheduler.measure.MeasureInput(task, task.compute_dag.init_state)
+    res = auto_scheduler.measure.MeasureResult([0.1], 0, "", 0.2, 1)
+    measure_record = auto_scheduler.measure_record.dump_record_to_string(inp, res)
+    measure_log = auto_scheduler.measure_record.load_record_from_string(measure_record)
+    new_task = measure_log[0].task
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+
+    # Log with 1 task input
+    test_input_0 = tvm.runtime.ndarray.empty((64, 64))
+    task = auto_scheduler.SearchTask(
+        func=matmul_auto_scheduler_test,
+        args=(512, 512, 512),
+        target="llvm",
+        task_inputs={
+            "test_input_0": test_input_0,
+        },
+        task_inputs_overwrite=True,
+    )
+    inp = auto_scheduler.measure.MeasureInput(task, task.compute_dag.init_state)
+    res = auto_scheduler.measure.MeasureResult([0.1], 0, "", 0.2, 1)
+    measure_record = auto_scheduler.measure_record.dump_record_to_string(inp, res)
+    measure_log = auto_scheduler.measure_record.load_record_from_string(measure_record)
+    new_task = measure_log[0].task
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+    assert len(new_task.task_input_names) == 1
+    assert new_task.task_input_names[0] == "test_input_0"
+
+    # Log with multiple task inputs
+    test_input_1 = tvm.runtime.ndarray.empty((64, 64))
+    task = auto_scheduler.SearchTask(
+        func=matmul_auto_scheduler_test,
+        args=(512, 512, 512),
+        target="llvm",
+        task_inputs={
+            "test_input_0": test_input_0,
+            "test_input_1": test_input_1,
+        },
+        task_inputs_overwrite=True,
+    )
+    inp = auto_scheduler.measure.MeasureInput(task, task.compute_dag.init_state)
+    res = auto_scheduler.measure.MeasureResult([0.1], 0, "", 0.2, 1)
+    measure_record = auto_scheduler.measure_record.dump_record_to_string(inp, res)
+    measure_log = auto_scheduler.measure_record.load_record_from_string(measure_record)
+    new_task = measure_log[0].task
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+    assert len(new_task.task_input_names) == 2
+    assert new_task.task_input_names[0] == "test_input_0"
+    assert new_task.task_input_names[1] == "test_input_1"
+
+    # Log with version 0.5
+    v5_log = """{"i": [["[\\\"matmul_auto_scheduler_test\\\", 512, 512, 512]", "llvm -keys=cpu -link-params=0", [6, 64, 64, 0, 0, 0, 0, 0], "", 1], [[], []]], "r": [[0.1], 0, 0.2, 1], "v": "v0.6"}"""
+    measure_log = auto_scheduler.measure_record.load_record_from_string(v5_log)
+    new_task = measure_log[0].task
+    assert task.workload_key == new_task.workload_key
+    assert str(task.target) == str(new_task.target)
+    assert str(task.target_host) == str(new_task.target_host)
+    assert task.layout_rewrite_option == new_task.layout_rewrite_option
+    assert len(new_task.task_input_names) == 0
+
+
+if __name__ == "__main__":
+    test_search_task_add_task_input()
+    test_search_task_record()
+    test_recover_measure_input_with_task_input()
diff --git a/tests/python/unittest/test_auto_scheduler_sketch_generation.py b/tests/python/unittest/test_auto_scheduler_sketch_generation.py
index 74d5729e4887..f3be6c0bc518 100644
--- a/tests/python/unittest/test_auto_scheduler_sketch_generation.py
+++ b/tests/python/unittest/test_auto_scheduler_sketch_generation.py
@@ -32,12 +32,17 @@
     softmax_nm_auto_scheduler_test,
     softmax_abcd_auto_scheduler_test,
     conv2d_winograd_nhwc_auto_scheduler_test,
+    zero_rank_reduce_auto_scheduler_test,
 )
 
 
-def generate_sketches(workload_func, args, target, print_for_debug=False):
+def generate_sketches(
+    workload_func, args, target, print_for_debug=False, init_search_callbacks=None
+):
     task = auto_scheduler.SearchTask(func=workload_func, args=args, target=target)
-    policy = auto_scheduler.SketchPolicy(task, verbose=0)
+    policy = auto_scheduler.SketchPolicy(
+        task, verbose=0, init_search_callbacks=init_search_callbacks
+    )
     return policy.generate_sketches(print_for_debug)
 
 
@@ -252,6 +257,48 @@ def test_cpu_conv2d_winograd_sketch():
     assert sketches[1] != sketches[2]
 
 
+def test_cpu_zero_rank_sketch():
+    sketches = generate_sketches(zero_rank_reduce_auto_scheduler_test, (128,), "llvm")
+    """ 2 rfactor sketches + 1 multi-level tiling sketches """
+    assert len(sketches) == 3
+
+
+def test_cpu_custom_sketch():
+    def meet_condition_func(search_policy, state, stage_id):
+        return auto_scheduler.PreloadCustomSketchRule.APPLY_AND_SKIP_REST
+
+    def apply_func(search_policy, state, stage_id):
+        ret = []
+        state = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+        C = state.stage_ops[2]
+
+        ret.append([state.state_object, -1])
+
+        s1 = state.copy()
+        i, _, _ = s1[C].iters
+        s1.split(C, i, [8, 2])
+        ret.append([s1.state_object, -1])
+        return ret
+
+    sketches = generate_sketches(
+        matmul_auto_scheduler_test,
+        (512, 512, 512),
+        "llvm",
+        init_search_callbacks=[
+            auto_scheduler.PreloadCustomSketchRule(meet_condition_func, apply_func)
+        ],
+    )
+    assert len(sketches) == 2
+    assert sketches[0].stages[2].iters[0].range.extent == 512
+    assert sketches[0].stages[2].iters[1].range.extent == 512
+    assert sketches[0].stages[2].iters[2].range.extent == 512
+    assert sketches[1].stages[2].iters[0].range.extent == 32
+    assert sketches[1].stages[2].iters[1].range.extent == 8
+    assert sketches[1].stages[2].iters[2].range.extent == 2
+    assert sketches[1].stages[2].iters[3].range.extent == 512
+    assert sketches[1].stages[2].iters[4].range.extent == 512
+
+
 @tvm.testing.requires_cuda
 def test_cuda_matmul_sketch():
     sketches = generate_sketches(matmul_auto_scheduler_test, (512, 512, 512), "cuda")
@@ -385,6 +432,13 @@ def test_cuda_conv2d_winograd_sketch():
     assert_is_not_tiled(sketches[0].stages[12])
 
 
+@tvm.testing.requires_cuda
+def test_cuda_zero_rank_sketch():
+    sketches = generate_sketches(zero_rank_reduce_auto_scheduler_test, (128,), "cuda")
+    """ 1 cross thread reuction sketch + 1 multi-level tiling sketch """
+    assert len(sketches) == 2
+
+
 if __name__ == "__main__":
     test_cpu_matmul_sketch()
     test_cpu_conv2d_bn_relu_sketch()
@@ -392,9 +446,12 @@ def test_cuda_conv2d_winograd_sketch():
     test_cpu_min_sketch()
     test_cpu_softmax_sketch()
     test_cpu_conv2d_winograd_sketch()
+    test_cpu_zero_rank_sketch()
+    test_cpu_custom_sketch()
     test_cuda_matmul_sketch()
     test_cuda_conv2d_bn_relu_sketch()
     test_cuda_max_pool2d_sketch()
     test_cuda_min_sketch()
     test_cuda_softmax_sketch()
     test_cuda_conv2d_winograd_sketch()
+    test_cuda_zero_rank_sketch()
diff --git a/tests/python/unittest/test_autotvm_common.py b/tests/python/unittest/test_autotvm_common.py
index 917036fc24a1..60f7d8bafb1b 100644
--- a/tests/python/unittest/test_autotvm_common.py
+++ b/tests/python/unittest/test_autotvm_common.py
@@ -101,6 +101,6 @@ def get_sample_records(n):
 
     inps, ress = [], []
     for i in range(n):
-        inps.append(MeasureInput(target, tsk, tsk.config_space.get(i)))
+        inps.append(MeasureInput(target, tsk, tsk.config_space.get(i % len(tsk.config_space))))
         ress.append(MeasureResult((i + 1,), 0, i, time.time()))
     return list(zip(inps, ress))
diff --git a/tests/python/unittest/test_autotvm_measure.py b/tests/python/unittest/test_autotvm_measure.py
index 1a18d6122bf0..9db9f18fa377 100644
--- a/tests/python/unittest/test_autotvm_measure.py
+++ b/tests/python/unittest/test_autotvm_measure.py
@@ -60,36 +60,8 @@ def test_task_tuner_without_measurement_spawn():
     p.join()
 
 
-def test_check_correctness():
-    task, target = get_sample_task()
-
-    measure_option = autotvm.measure_option(
-        builder=autotvm.LocalBuilder(), runner=autotvm.LocalRunner(check_correctness=True)
-    )
-
-    def _callback_correct(tuner, measure_inputs, measure_results):
-        for _, res in zip(measure_inputs, measure_results):
-            assert res.error_no == 0
-
-    tuner = autotvm.tuner.RandomTuner(task)
-    tuner.tune(n_trial=2, measure_option=measure_option, callbacks=[_callback_correct])
-
-    # a bad template
-    n = 128
-    target = tvm.target.Target("llvm -device=bad_device")
-    task = autotvm.task.create("testing/bad_matmul", args=(n, n, n, "float32"), target=target)
-
-    def _callback_wrong(tuner, measure_inputs, measure_results):
-        for _, res in zip(measure_inputs, measure_results):
-            assert res.error_no == MeasureErrorNo.WRONG_ANSWER
-
-    tuner = autotvm.tuner.RandomTuner(task)
-    tuner.tune(n_trial=2, measure_option=measure_option, callbacks=[_callback_wrong])
-
-
 if __name__ == "__main__":
     logging.basicConfig(level=logging.INFO)
 
     test_task_tuner_without_measurement()
     test_task_tuner_without_measurement_spawn()
-    test_check_correctness()
diff --git a/tests/python/unittest/test_crt.py b/tests/python/unittest/test_crt.py
index 659d1908096b..1bd24c931b72 100644
--- a/tests/python/unittest/test_crt.py
+++ b/tests/python/unittest/test_crt.py
@@ -19,7 +19,9 @@
 import copy
 import glob
 import os
-import pty
+import pytest
+
+pytest.importorskip("pty")
 import sys
 import subprocess
 import textwrap
@@ -28,7 +30,6 @@
 import pytest
 
 import tvm
-import tvm.testing
 import tvm.relay
 import tvm.testing
 
@@ -50,18 +51,15 @@ def _make_sess_from_op(workspace, op_name, sched, arg_bufs):
 
 def _make_session(workspace, mod):
     compiler = tvm.micro.DefaultCompiler(target=TARGET)
-    opts = tvm.micro.default_options(os.path.join(tvm.micro.CRT_ROOT_DIR, "host"))
+    opts = tvm.micro.default_options(
+        os.path.join(tvm.micro.get_standalone_crt_dir(), "template", "host")
+    )
     micro_binary = tvm.micro.build_static_runtime(
-        # the x86 compiler *expects* you to give the exact same dictionary for both
-        # lib_opts and bin_opts. so the library compiler is mutating lib_opts and
-        # the binary compiler is expecting those mutations to be in bin_opts.
-        # TODO(weberlo) fix this very bizarre behavior
         workspace,
         compiler,
         mod,
-        lib_opts=opts["bin_opts"],
-        bin_opts=opts["bin_opts"],
-        extra_libs=[os.path.join(tvm.micro.build.CRT_ROOT_DIR, "memory")],
+        opts,
+        extra_libs=[tvm.micro.get_standalone_crt_lib("memory")],
     )
 
     flasher_kw = {
@@ -106,6 +104,23 @@ def test_compile_runtime():
         assert (C_data.asnumpy() == np.array([6, 7])).all()
 
 
+@tvm.testing.requires_micro
+def test_compile_runtime_llvm():
+    """Test targeting the on-device runtime with the llvm backend."""
+    global TARGET
+    old_target = TARGET
+    try:
+        # NOTE: test_compile_runtime uses the "c" backend--re run it using the llvm backend.
+        target_str = str(TARGET)
+        assert target_str.startswith("c ")
+        TARGET = tvm.target.Target("llvm " + str(TARGET)[len("c ") :])
+
+        test_compile_runtime()
+
+    finally:
+        TARGET = old_target
+
+
 @tvm.testing.requires_micro
 def test_reset():
     """Test when the remote end resets during a session."""
@@ -127,7 +142,7 @@ def test_graph_runtime():
     """Test use of the graph runtime with microTVM."""
     import tvm.micro
 
-    workspace = tvm.micro.Workspace()
+    workspace = tvm.micro.Workspace(debug=True)
     relay_mod = tvm.parser.fromtext(
         """
       #[version = "0.0.5"]
@@ -160,6 +175,19 @@ def test_std_math_functions():
     """Verify that standard math functions can be used."""
     import tvm.micro
 
+    workspace = tvm.micro.Workspace()
+
+    with _make_add_sess(workspace) as sess:
+        A_data = tvm.nd.array(np.array([2, 3], dtype="int8"), ctx=sess.context)
+        assert (A_data.asnumpy() == np.array([2, 3])).all()
+        B_data = tvm.nd.array(np.array([4], dtype="int8"), ctx=sess.context)
+        assert (B_data.asnumpy() == np.array([4])).all()
+        C_data = tvm.nd.array(np.array([0, 0], dtype="int8"), ctx=sess.context)
+        assert (C_data.asnumpy() == np.array([0, 0])).all()
+
+        system_lib = sess.get_system_lib()
+        system_lib.get_function("add")(A_data, B_data, C_data)
+
     workspace = tvm.micro.Workspace()
     A = tvm.te.placeholder((2,), dtype="float32", name="A")
     B = tvm.te.compute(A.shape, lambda i: tvm.te.exp(A[i]), name="B")
diff --git a/tests/python/unittest/test_custom_datatypes.py b/tests/python/unittest/test_custom_datatypes.py
index 6aad93abd510..75e807456981 100644
--- a/tests/python/unittest/test_custom_datatypes.py
+++ b/tests/python/unittest/test_custom_datatypes.py
@@ -21,7 +21,6 @@
 import tvm.topi.testing
 import numpy as np
 import pytest
-from numpy.random import MT19937, RandomState, SeedSequence
 from tvm import relay
 from tvm.relay.testing.layers import batch_norm_infer
 from tvm.target.datatype import (
@@ -66,7 +65,7 @@ def get_cat_image(dimensions):
 
 # we use a random seed to generate input_data
 # to guarantee stable tests
-rs = RandomState(MT19937(SeedSequence(123456789)))
+np.random.seed(0)
 
 
 def convert_ndarray(dst_dtype, array):
@@ -341,7 +340,7 @@ def check_unary_op(op, src_dtype, dst_dtype, shape):
         t1 = relay.TensorType(shape, src_dtype)
         x = relay.var("x", t1)
         z = op(x)
-        x_data = rs.rand(*shape).astype(t1.dtype)
+        x_data = np.random.rand(*shape).astype(t1.dtype)
 
         module = tvm.IRModule.from_expr(relay.Function([x], z))
 
@@ -372,8 +371,8 @@ def check_binary_op(opfunc, src_dtype, dst_dtype):
         x = relay.var("x", t1)
         y = relay.var("y", t2)
         z = opfunc(x, y)
-        x_data = rs.rand(*shape1).astype(t1.dtype)
-        y_data = rs.rand(*shape2).astype(t2.dtype)
+        x_data = np.random.rand(*shape1).astype(t1.dtype)
+        y_data = np.random.rand(*shape2).astype(t2.dtype)
         module = tvm.IRModule.from_expr(relay.Function([x, y], z))
 
         compare(module, (x_data, y_data), src_dtype, dst_dtype, rtol, atol)
@@ -416,8 +415,8 @@ def run_test_conv2d(
         w = relay.var("w", shape=kshape, dtype=src_dtype)
         y = relay.nn.conv2d(x, w, padding=padding, dilation=dilation, groups=groups, **attrs)
         module = tvm.IRModule.from_expr(relay.Function([x, w], y))
-        data = rs.uniform(-scale, scale, size=dshape).astype(src_dtype)
-        kernel = rs.uniform(-scale, scale, size=kshape).astype(src_dtype)
+        data = np.random.uniform(-scale, scale, size=dshape).astype(src_dtype)
+        kernel = np.random.uniform(-scale, scale, size=kshape).astype(src_dtype)
 
         compare(module, (data, kernel), src_dtype, dst_dtype, rtol, atol)
 
@@ -497,7 +496,7 @@ def run_batchnorm(src_dtype, dst_dtype, rtol=1e-6, atol=1e-6):
     bn = batch_norm_infer(data=x, epsilon=2e-5, scale=False, name="bn_x")
     f = relay.Function(relay.analysis.free_vars(bn), bn)
 
-    x_data = rs.rand(*shape).astype(t.dtype)
+    x_data = np.random.rand(*shape).astype(t.dtype)
     module = tvm.IRModule.from_expr(f)
 
     zero_data = np.zeros((32), "float32")
diff --git a/tests/python/unittest/test_gen_requirements.py b/tests/python/unittest/test_gen_requirements.py
new file mode 100644
index 000000000000..1f6388ba3c76
--- /dev/null
+++ b/tests/python/unittest/test_gen_requirements.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python3
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Tests for gen_requirements, found in python/."""
+
+import collections
+import contextlib
+import os
+import sys
+
+import tvm
+
+import pytest
+
+# Insert the parent dir to python/tvm into the import path, so that gen_requirements may be
+# imported.
+sys.path.insert(0, os.path.dirname(tvm.__file__))
+try:
+    import gen_requirements
+finally:
+    sys.path.pop(0)
+
+
+@contextlib.contextmanager
+def patch(obj, **kw):
+    old = {}
+    for prop_name, new in kw.items():
+        old[prop_name] = getattr(obj, prop_name)
+        setattr(obj, prop_name, new)
+    yield
+    for prop_name, value in old.items():
+        setattr(obj, prop_name, value)
+
+
+PROBLEM_REQUIREMENTS = [
+    ("extras-pre-core", ("", ["foo", 123])),  # entry before core
+    (456, ("", ["foo", "bar"])),  # invalid extras name, deps should not be processed
+    ("core", ("", ["foo"])),  # ordinary core entry.
+    ("wrong-description-type", (None, ["foo"])),  # wrong description type
+    ("bad-value", None),  # value field is not a 2-tuple
+    ("bad-value-2", ("", ["foo"], 34)),  # value field is not a 2-tuple
+    ("invalid", ("", ["qux"])),  # duplicate invalid entry, all items valid.
+    ("extras-foo", ("", ["bar", "baz"])),  # ordinary extras entry.
+    ("invalid", ("", ["baz", None, 123])),  # valid extra name, invalid deps.
+    ("unsorted", ("", ["qux", "bar", "foo"])),  # deps out of order
+    ("versioned_dep", ("", ["baz==1.2", "foo==^2.0", "buz<3", "bar>4"])),
+    ("duplicate_dep", ("", ["buz", "buz", "foo"])),  # duplicate listed dependency
+    ("dev", ("", ["baz", "qux"])),  # ordinary dev entry.
+    ("extras-post-dev", ("", ["bar", "buzz"])),  # entry after dev
+]
+
+
+def test_validate_requirements():
+    with patch(gen_requirements, REQUIREMENTS_BY_PIECE=None):
+        assert gen_requirements.validate_requirements_by_piece() == [
+            "must be list or tuple, see None"
+        ]
+
+    with patch(gen_requirements, REQUIREMENTS_BY_PIECE=PROBLEM_REQUIREMENTS):
+        problems = gen_requirements.validate_requirements_by_piece()
+        assert problems == [
+            'piece extras-pre-core: must list after "core" (core must be first)',
+            "piece extras-pre-core: deps should be a list of strings, got ['foo', 123]",
+            "piece 456: must be str",
+            "piece wrong-description-type: description should be a string, got None",
+            (
+                'piece bad-value: should be formatted like ("bad-value", ("<requirements.txt '
+                'comment>", ["dep1", "dep2", ...])). got: None'
+            ),
+            (
+                'piece bad-value-2: should be formatted like ("bad-value-2", '
+                '("<requirements.txt comment>", ["dep1", "dep2", ...])). got: (\'\', '
+                "['foo'], 34)"
+            ),
+            "piece invalid: listed twice",
+            "piece invalid: deps should be a list of strings, got ['baz', None, 123]",
+            "piece unsorted: deps must be sorted. Correct order:\n  ['bar', 'foo', 'qux']",
+            "piece versioned_dep: deps must be sorted. Correct order:\n  ['bar>4', 'baz==1.2', 'buz<3', 'foo==^2.0']",
+            "piece versioned_dep: dependency baz==1.2 should not specify a version. Add it to CONSTRAINTS instead.",
+            "piece versioned_dep: dependency foo==^2.0 should not specify a version. Add it to CONSTRAINTS instead.",
+            "piece versioned_dep: dependency buz<3 should not specify a version. Add it to CONSTRAINTS instead.",
+            "piece versioned_dep: dependency bar>4 should not specify a version. Add it to CONSTRAINTS instead.",
+            "piece duplicate_dep: dependency buz listed twice",
+            'piece extras-post-dev: must list before "dev" (dev must be last)',
+            'pieces other than "core" and "dev" must appear in alphabetical order: '
+            "['bad-value', 'bad-value-2', 'duplicate_dep', 'extras-foo', 'extras-post-dev', "
+            "'extras-pre-core', 'invalid', 'invalid', 'unsorted', 'versioned_dep', "
+            "'wrong-description-type']",
+        ]
+
+
+TEST_REQUIREMENTS_BY_PIECE = (
+    ("core", ("core tvm requirements", ("bar", "foo", "non-constrained"))),
+    ("extra-one", ("requirements for one feature", ("baz", "qux"))),
+    ("extra-two", ("requirements for two feature", ("buz", "qux", "semver-minor", "semver-patch"))),
+    ("dev", ("requirements for dev", ("buz", "oof", "rab"))),
+)
+
+
+def test_validate_constraints():
+    with patch(
+        gen_requirements,
+        REQUIREMENTS_BY_PIECE=TEST_REQUIREMENTS_BY_PIECE,
+        CONSTRAINTS=(
+            ("unlisted", "~=3"),
+            ("double-specified", "<2"),
+            (
+                "double-specified",
+                "==3",
+            ),
+            ("bad-constraint", "1.2.0"),
+            ("bad-semver-constraint", "i don't match the regex :P"),
+            ("alpha-semver-constraint", "^foo.bar.23"),
+        ),
+    ):
+        problems = gen_requirements.validate_constraints()
+        assert problems == [
+            "unlisted: not specified in REQUIREMENTS_BY_PIECE",
+            "double-specified: not specified in REQUIREMENTS_BY_PIECE",
+            "double-specified: specified twice",
+            "double-specified: not specified in REQUIREMENTS_BY_PIECE",
+            "bad-constraint: not specified in REQUIREMENTS_BY_PIECE",
+            'bad-constraint: constraint "1.2.0" does not look like a valid constraint',
+            "bad-semver-constraint: not specified in REQUIREMENTS_BY_PIECE",
+            'bad-semver-constraint: constraint "i don\'t match the regex :P" does not look like a valid constraint',
+            "alpha-semver-constraint: not specified in REQUIREMENTS_BY_PIECE",
+            "alpha-semver-constraint: invalid semver constraint ^foo.bar.23",
+            "CONSTRAINTS entries should be in this sorted order: ['alpha-semver-constraint', 'bad-constraint', 'bad-semver-constraint', 'double-specified', 'double-specified', 'unlisted']",
+        ]
+
+
+TEST_CONSTRAINTS = (
+    ("bar", "==1.0"),
+    ("baz", ">2.3"),
+    ("buz", "^1.3.0"),
+    ("non-constrained", None),  # Support a comment.
+    ("oof", "==0.3.4"),
+    ("qux", "~=1.2.4"),
+    ("semver-minor", "^0.2.2-patch2.post3+buildmeta"),  # Ensure prerelease and buildmeta preserved.
+    ("semver-patch", "^0.0.2+bm"),  # Ensure postrelease preserved.
+)
+
+
+def test_join_requirements():
+    with patch(
+        gen_requirements,
+        REQUIREMENTS_BY_PIECE=TEST_REQUIREMENTS_BY_PIECE,
+        CONSTRAINTS=TEST_CONSTRAINTS,
+    ):
+        requirements = gen_requirements.join_requirements()
+        assert requirements == collections.OrderedDict(
+            [
+                ("core", ("core tvm requirements", ["bar==1.0", "foo", "non-constrained"])),
+                ("extra-one", ("requirements for one feature", ["baz>2.3", "qux~=1.2.4"])),
+                (
+                    "extra-two",
+                    (
+                        "requirements for two feature",
+                        [
+                            "buz>=1.3.0,<2.0.0",
+                            "qux~=1.2.4",
+                            "semver-minor>=0.2.2-patch2.post3+buildmeta,<0.3.0",
+                            "semver-patch>=0.0.2+bm,<0.0.3",
+                        ],
+                    ),
+                ),
+                ("dev", ("requirements for dev", ["buz>=1.3.0,<2.0.0", "oof==0.3.4", "rab"])),
+                (
+                    "all-prod",
+                    (
+                        "Combined dependencies for all TVM pieces, excluding dev",
+                        [
+                            "bar==1.0",
+                            "baz>2.3",
+                            "buz>=1.3.0,<2.0.0",
+                            "foo",
+                            "non-constrained",
+                            "qux~=1.2.4",
+                            "semver-minor>=0.2.2-patch2.post3+buildmeta,<0.3.0",
+                            "semver-patch>=0.0.2+bm,<0.0.3",
+                        ],
+                    ),
+                ),
+            ]
+        )
+
+
+def test_semver():
+    problems = []
+
+    assert gen_requirements.parse_semver("C", "^1.2.0", problems) == (["1", "2", "0"], 0, 1)
+    assert problems == []
+
+    assert gen_requirements.parse_semver("C", "^0.2.0", problems) == (["0", "2", "0"], 1, 2)
+    assert problems == []
+
+    assert gen_requirements.parse_semver("C", "^0.0.0", problems) == (["0", "0", "0"], 0, 0)
+    assert problems == []
+
+    assert gen_requirements.parse_semver("C", "^0.a.0", problems) == ([], 0, 0)
+    assert problems == ["C: invalid semver constraint ^0.a.0"]
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))
diff --git a/tests/python/unittest/test_link_params.py b/tests/python/unittest/test_link_params.py
index da87a3177c7c..ffe859927ad7 100644
--- a/tests/python/unittest/test_link_params.py
+++ b/tests/python/unittest/test_link_params.py
@@ -21,6 +21,7 @@
 import re
 import struct
 import sys
+import tempfile
 
 import numpy as np
 import pytest
@@ -182,31 +183,38 @@ def _add_decl(name, dtype):
 @tvm.testing.requires_llvm
 def test_llvm_link_params():
     for dtype in LINKABLE_DTYPES:
-        mod, param_init = _make_mod_and_params(dtype)
+        ir_mod, param_init = _make_mod_and_params(dtype)
         rand_input = _make_random_tensor(dtype, INPUT_SHAPE)
-        main_func = mod["main"]
+        main_func = ir_mod["main"]
         target = "llvm --runtime=c --system-lib --link-params"
         with tvm.transform.PassContext(opt_level=3):
-            lib = tvm.relay.build(mod, target, params=param_init)
+            lib = tvm.relay.build(ir_mod, target, params=param_init)
+
+            # NOTE: Need to export_library() and load_library() to link all the Module(llvm, ...)
+            # against one another.
+            temp_dir = tempfile.mkdtemp()
+            export_file = os.path.join(temp_dir, "lib.so")
+            lib.lib.export_library(export_file)
+            mod = tvm.runtime.load_module(export_file)
             assert set(lib.params.keys()) == {"p0", "p1"}  # NOTE: op folded
+            assert mod.get_function("TVMSystemLibEntryPoint") != None
 
-            print("graph", lib.graph_json)
             graph = json.loads(lib.graph_json)
             for p in lib.params:
-                _verify_linked_param(dtype, lib, lib.lib, graph, p) or found_one
+                _verify_linked_param(dtype, lib, mod, graph, p) or found_one
 
             # Wrap in function to explicitly deallocate the runtime.
-            def _run_linked(lib):
-                graph_json, mod, _ = lib
+            def _run_linked(lib, mod):
+                graph_json, _, _ = lib
                 graph_rt = tvm.contrib.graph_runtime.create(graph_json, mod, tvm.cpu(0))
                 graph_rt.set_input("rand_input", rand_input)  # NOTE: params not required.
                 graph_rt.run()
                 return graph_rt.get_output(0)
 
-            linked_output = _run_linked(lib)
+            linked_output = _run_linked(lib, mod)
 
         with tvm.transform.PassContext(opt_level=3):
-            lib = tvm.relay.build(mod, "llvm --system-lib", params=param_init)
+            lib = tvm.relay.build(ir_mod, "llvm --system-lib", params=param_init)
 
             def _run_unlinked(lib):
                 graph_json, mod, lowered_params = lib
@@ -266,8 +274,8 @@ def test_c_link_params():
             lib = tvm.relay.build(mod, target, params=param_init)
             assert set(lib.params.keys()) == {"p0", "p1"}  # NOTE: op folded
 
-            src = lib.lib.imported_modules[0].get_source()
-            lib.lib.save("test.c", "cc")
+            src = lib.lib.get_source()
+            lib.lib.save("test.c", "c")
             c_dtype = _get_c_datatype(dtype)
             src_lines = src.split("\n")
             param = lib.params["p0"].asnumpy().reshape(np.prod(KERNEL_SHAPE))
@@ -347,28 +355,25 @@ def test_crt_link_params():
         mod, param_init = _make_mod_and_params(dtype)
         rand_input = _make_random_tensor(dtype, INPUT_SHAPE)
         main_func = mod["main"]
-        target = "c -mcpu=native --system-lib --runtime=c --link-params"
+        target = "c --system-lib --runtime=c --link-params"
         with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
             graph_json, lib, params = tvm.relay.build(mod, target, params=param_init)
             assert set(params.keys()) == {"p0", "p1"}  # NOTE: op folded
 
             workspace = tvm.micro.Workspace()
             compiler = tvm.micro.DefaultCompiler(target=target)
-            opts = tvm.micro.default_options(os.path.join(tvm.micro.CRT_ROOT_DIR, "host"))
+            opts = tvm.micro.default_options(
+                os.path.join(tvm.micro.get_standalone_crt_dir(), "template", "host")
+            )
             opts["bin_opts"]["ldflags"].append("-DTVM_HOST_USE_GRAPH_RUNTIME_MODULE")
 
             micro_binary = tvm.micro.build_static_runtime(
-                # the x86 compiler *expects* you to give the exact same dictionary for both
-                # lib_opts and bin_opts. so the library compiler is mutating lib_opts and
-                # the binary compiler is expecting those mutations to be in bin_opts.
-                # TODO(weberlo) fix this very bizarre behavior
                 workspace,
                 compiler,
                 lib,
-                lib_opts=opts["bin_opts"],
-                bin_opts=opts["bin_opts"],
+                compiler_options=opts,
                 extra_libs=[
-                    os.path.join(tvm.micro.CRT_ROOT_DIR, m)
+                    tvm.micro.get_standalone_crt_lib(m)
                     for m in ("memory", "graph_runtime_module", "graph_runtime")
                 ],
             )
diff --git a/tests/python/unittest/test_micro_artifact.py b/tests/python/unittest/test_micro_artifact.py
index d757f0956b81..fc180200720d 100644
--- a/tests/python/unittest/test_micro_artifact.py
+++ b/tests/python/unittest/test_micro_artifact.py
@@ -17,6 +17,7 @@
 
 """Unit tests for the artifact module."""
 
+import pytest
 import json
 import os
 import shutil
@@ -24,6 +25,8 @@
 
 from tvm.contrib import utils
 
+pytest.importorskip("tvm.micro")
+from tvm.micro import artifact
 
 FILE_LIST = ["label1", "label2", "label12", "unlabelled"]
 
diff --git a/tests/python/unittest/test_micro_model_library_format.py b/tests/python/unittest/test_micro_model_library_format.py
new file mode 100644
index 000000000000..c999091cc3cc
--- /dev/null
+++ b/tests/python/unittest/test_micro_model_library_format.py
@@ -0,0 +1,190 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import datetime
+import json
+import os
+import sys
+import tarfile
+
+import numpy
+import pytest
+
+import tvm
+import tvm.relay
+from tvm.relay.backend import graph_runtime_factory
+import tvm.runtime.module
+import tvm.testing
+from tvm.contrib import utils
+
+
+def validate_graph_json(extract_dir, factory):
+    with open(os.path.join(extract_dir, "runtime-config", "graph", "graph.json")) as graph_f:
+        graph_json = graph_f.read()
+        assert graph_json == factory.graph_json
+
+        # Just check it parses and looks roughly right.
+        graph = json.loads(graph_json)
+        assert "nodes" in graph
+        assert len(graph["nodes"]) == 4
+        assert "attrs" in graph
+
+
+@tvm.testing.requires_micro
+def test_export_model_library_format_c():
+    with utils.TempDirectory.set_keep_for_debug(True):
+        target = tvm.target.target.micro("host")
+        with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
+            relay_mod = tvm.parser.fromtext(
+                """
+            #[version = "0.0.5"]
+            def @main(%a : Tensor[(1, 2), uint8], %b : Tensor[(1, 2), float32], %c : Tensor[(1, 2), float32]) {
+            %0 = cast(%a, dtype="float32") + %b * %c;
+            %0
+            }"""
+            )
+            factory = tvm.relay.build(
+                relay_mod,
+                target,
+                target_host=target,
+                mod_name="add",
+                params={"c": numpy.array([[2.0, 4.0]], dtype="float32")},
+            )
+
+        temp_dir = utils.tempdir()
+        mlf_tar_path = temp_dir.relpath("lib.tar")
+        import tvm.micro as micro
+
+        micro.export_model_library_format(factory, mlf_tar_path)
+        tf = tarfile.open(mlf_tar_path)
+
+        extract_dir = temp_dir.relpath("extract")
+        os.mkdir(extract_dir)
+        tf.extractall(extract_dir)
+
+        with open(os.path.join(extract_dir, "metadata.json")) as json_f:
+            metadata = json.load(json_f)
+            assert metadata["version"] == 1
+            assert metadata["model_name"] == "add"
+            export_datetime = datetime.datetime.strptime(
+                metadata["export_datetime"], "%Y-%m-%d %H:%M:%SZ"
+            )
+            assert (datetime.datetime.now() - export_datetime) < datetime.timedelta(seconds=60 * 5)
+            assert metadata["target"] == {"1": str(target)}
+            assert metadata["memory"] == [
+                {"storage_id": 0, "size_bytes": 2, "input_binding": "a"},
+                {"storage_id": 1, "size_bytes": 8, "input_binding": "b"},
+                {"storage_id": 2, "size_bytes": 8, "input_binding": "p0"},
+                {"storage_id": 3, "size_bytes": 8},
+            ]
+
+        assert os.path.exists(os.path.join(extract_dir, "codegen", "host", "src", "lib0.c"))
+        assert os.path.exists(os.path.join(extract_dir, "codegen", "host", "src", "lib1.c"))
+
+        validate_graph_json(extract_dir, factory)
+
+        with open(os.path.join(extract_dir, "relay.txt")) as relay_f:
+            assert relay_f.read() == str(relay_mod)
+
+        with open(os.path.join(extract_dir, "parameters", "add.params"), "rb") as params_f:
+            params = tvm.relay.load_param_dict(params_f.read())
+            assert "p0" in params
+
+
+@tvm.testing.requires_micro
+def test_export_model_library_format_llvm():
+    with utils.TempDirectory.set_keep_for_debug(True):
+        target = tvm.target.target.micro("host")
+        assert str(target)[:2] == "c "
+        target = tvm.target.Target("llvm " + str(target)[2:])
+        with tvm.transform.PassContext(opt_level=3):
+            relay_mod = tvm.parser.fromtext(
+                """
+            #[version = "0.0.5"]
+            def @main(%a : Tensor[(1, 2), uint8], %b : Tensor[(1, 2), float32], %c : Tensor[(1, 2), float32]) {
+            %0 = cast(%a, dtype="float32") + %b * %c;
+            %0
+            }"""
+            )
+            factory = tvm.relay.build(
+                relay_mod,
+                target,
+                target_host=target,
+                mod_name="add",
+                params={"c": numpy.array([[2.0, 4.0]], dtype="float32")},
+            )
+
+        temp_dir = utils.tempdir()
+        mlf_tar_path = temp_dir.relpath("lib.tar")
+        import tvm.micro as micro
+
+        micro.export_model_library_format(factory, mlf_tar_path)
+        tf = tarfile.open(mlf_tar_path)
+
+        extract_dir = temp_dir.relpath("extract")
+        os.mkdir(extract_dir)
+        tf.extractall(extract_dir)
+
+        with open(os.path.join(extract_dir, "metadata.json")) as json_f:
+            metadata = json.load(json_f)
+            assert metadata["version"] == 1
+            assert metadata["model_name"] == "add"
+            export_datetime = datetime.datetime.strptime(
+                metadata["export_datetime"], "%Y-%m-%d %H:%M:%SZ"
+            )
+            assert (datetime.datetime.now() - export_datetime) < datetime.timedelta(seconds=60 * 5)
+            assert metadata["target"] == {"1": str(target)}
+            assert metadata["memory"] == [
+                {"storage_id": 0, "size_bytes": 2, "input_binding": "a"},
+                {"storage_id": 1, "size_bytes": 8, "input_binding": "b"},
+                {"storage_id": 2, "size_bytes": 8, "input_binding": "p0"},
+                {"storage_id": 3, "size_bytes": 8},
+            ]
+
+        assert os.path.exists(os.path.join(extract_dir, "codegen", "host", "lib", "lib0.o"))
+
+        validate_graph_json(extract_dir, factory)
+
+        with open(os.path.join(extract_dir, "relay.txt")) as relay_f:
+            assert relay_f.read() == str(relay_mod)
+
+        with open(os.path.join(extract_dir, "parameters", "add.params"), "rb") as params_f:
+            params = tvm.relay.load_param_dict(params_f.read())
+            assert "p0" in params
+
+
+@tvm.testing.requires_micro
+def test_export_model():
+    module = tvm.support.FrontendTestModule()
+    factory = graph_runtime_factory.GraphRuntimeFactoryModule(
+        None, tvm.target.target.micro("host"), '"graph_json"', module, "test_module", {}
+    )
+
+    temp_dir = utils.tempdir()
+    import tvm.micro as micro
+    import tvm.micro.model_library_format as model_library_format
+
+    with pytest.raises(micro.UnsupportedInModelLibraryFormatError) as exc:
+        model_library_format._populate_codegen_dir(module, temp_dir.relpath("codegen"))
+
+        assert str(exc.exception) == (
+            "Don't know how to export non-c or non-llvm modules; found: ffi_testing"
+        )
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))
diff --git a/tests/python/unittest/test_runtime_graph.py b/tests/python/unittest/test_runtime_graph.py
index c43a35924420..16e9db42cba3 100644
--- a/tests/python/unittest/test_runtime_graph.py
+++ b/tests/python/unittest/test_runtime_graph.py
@@ -16,7 +16,7 @@
 # under the License.
 import tvm
 import tvm.testing
-from tvm import te
+from tvm import te, runtime
 import numpy as np
 import json
 from tvm import rpc
@@ -94,12 +94,12 @@ def check_sharing():
         graph, lib, params = relay.build(func, target="llvm", params=params)
 
         mod_shared = graph_runtime.create(graph, lib, tvm.cpu(0))
-        mod_shared.load_params(relay.save_param_dict(params))
+        mod_shared.load_params(runtime.save_param_dict(params))
         num_mods = 10
         mods = [graph_runtime.create(graph, lib, tvm.cpu(0)) for _ in range(num_mods)]
 
         for mod in mods:
-            mod.share_params(mod_shared, relay.save_param_dict(params))
+            mod.share_params(mod_shared, runtime.save_param_dict(params))
 
         a = np.random.uniform(size=(1, 10)).astype("float32")
         for mod in mods:
diff --git a/tests/python/unittest/test_runtime_graph_cuda_graph.py b/tests/python/unittest/test_runtime_graph_cuda_graph.py
new file mode 100644
index 000000000000..4a31873cb93c
--- /dev/null
+++ b/tests/python/unittest/test_runtime_graph_cuda_graph.py
@@ -0,0 +1,100 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import json
+import os
+import re
+import sys
+import time
+
+import pytest
+
+import tvm
+import tvm.testing
+from tvm import te
+import numpy as np
+
+from tvm.contrib import utils, graph_runtime
+from tvm.contrib.cuda_graph import cuda_graph_runtime
+
+
+bx = te.thread_axis("blockIdx.x")
+tx = te.thread_axis("threadIdx.x")
+
+
+@tvm.testing.requires_cudagraph
+def test_graph_simple():
+    n = 32
+    A = te.placeholder((n,), name="A")
+    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
+    s = te.create_schedule(B.op)
+    xo, xi = s[B].split(B.op.axis[0], factor=8)
+    s[B].bind(xo, bx)
+    s[B].bind(xi, tx)
+
+    node0 = {"op": "null", "name": "x", "inputs": []}
+    node1 = {
+        "op": "tvm_op",
+        "name": "add",
+        "inputs": [[0, 0, 0]],
+        "attrs": {"func_name": "myadd", "flatten_data": "1", "num_inputs": "1", "num_outputs": "1"},
+    }
+    nodes = [node0, node1]
+    arg_nodes = [0]
+    node_row_ptr = [0, 1, 2]
+    outputs = [[1, 0, 0]]
+    shape = (n,)
+    attrs = {
+        "shape": ["list_shape", [shape, shape]],
+        "dltype": ["list_str", ["float32", "float32"]],
+        "storage_id": ["list_int", [0, 1]],
+    }
+    graph = {
+        "nodes": nodes,
+        "arg_nodes": arg_nodes,
+        "node_row_ptr": node_row_ptr,
+        "heads": outputs,
+        "attrs": attrs,
+    }
+    graph = json.dumps(graph)
+
+    def check_verify():
+        mlib = tvm.build(s, [A, B], "cuda", name="myadd")
+        ctx = tvm.gpu(0)
+        try:
+            mod = cuda_graph_runtime.create(graph, mlib, ctx)
+        except ValueError:
+            return
+
+        for i in range(3):
+            a = np.random.uniform(size=(n,)).astype(A.dtype)
+            mod.run(x=a)  # The first run captured a CUDA graph
+            out = mod.get_output(0, tvm.nd.empty((n,)))
+            np.testing.assert_equal(out.asnumpy(), a + 1)
+
+        # capture / run CUDA graph manually
+        mod.capture_cuda_graph()
+        a = np.random.uniform(size=(n,)).astype(A.dtype)
+        mod.set_input(x=a)
+        mod.run_cuda_graph()
+        out = mod.get_output(0, tvm.nd.empty((n,)))
+        np.testing.assert_equal(out.asnumpy(), a + 1)
+
+    check_verify()
+
+
+if __name__ == "__main__":
+    test_graph_simple()
diff --git a/tests/python/unittest/test_runtime_graph_debug.py b/tests/python/unittest/test_runtime_graph_debug.py
index 8aeaf1a1a23b..996d426efaa9 100644
--- a/tests/python/unittest/test_runtime_graph_debug.py
+++ b/tests/python/unittest/test_runtime_graph_debug.py
@@ -16,13 +16,19 @@
 # under the License.
 import json
 import os
+import re
+import sys
+import time
+
+import pytest
+
 import tvm
 import tvm.testing
 from tvm import te
 import numpy as np
 from tvm import rpc
 from tvm.contrib import utils
-from tvm.contrib.debugger import debug_runtime as graph_runtime
+from tvm.contrib.debugger import debug_runtime
 
 
 @tvm.testing.requires_llvm
@@ -60,8 +66,16 @@ def test_graph_simple():
 
     def check_verify():
         mlib = tvm.build(s, [A, B], "llvm", name="myadd")
+
+        def myadd(*args):
+            to_return = mlib["myadd"](*args)
+            time.sleep(0.25)
+            return to_return
+
+        mlib_proxy = tvm.support.FrontendTestModule()
+        mlib_proxy["myadd"] = myadd
         try:
-            mod = graph_runtime.create(graph, mlib, tvm.cpu(0))
+            mod = debug_runtime.create(graph, mlib_proxy, tvm.cpu(0))
         except ValueError:
             return
 
@@ -92,6 +106,36 @@ def check_verify():
         # Verify the tensors are dumped
         assert len(os.listdir(directory)) > 1
 
+        debug_lines = mod.debug_datum.get_debug_result().split("\n")
+
+        def split_debug_line(i):
+            to_return = re.split(r"  [ ]*", debug_lines[i])
+            assert to_return[-1] == ""
+            to_return = to_return[:-1]  # strip empty trailing part
+            return to_return
+
+        assert split_debug_line(0) == [
+            "Node Name",
+            "Ops",
+            "Time(us)",
+            "Time(%)",
+            "Shape",
+            "Inputs",
+            "Outputs",
+        ]
+        myadd_lines = split_debug_line(2)
+        assert myadd_lines[0] == "add"
+        assert myadd_lines[1] == "myadd"
+        runtime_sec = float(myadd_lines[2]) / 1e6  # printed in us
+
+        # Ensure runtime is at least the sleep time and less than a unit prefix order of magnitude.
+        # Here we just care that the prefix is correct.
+        assert runtime_sec > 0.25 and runtime_sec < 0.25 * 1000
+
+        total_lines = split_debug_line(3)
+        assert total_lines[0] == "Total_time"
+        assert total_lines[2] == myadd_lines[2]
+
         CHROME_TRACE_FILE_NAME = "_tvmdbg_execution_trace.json"
         assert os.path.exists(os.path.join(directory, CHROME_TRACE_FILE_NAME))
 
@@ -127,9 +171,9 @@ def check_remote():
         remote.upload(path_dso)
         mlib = remote.load_module("dev_lib.so")
         try:
-            mod = graph_runtime.create(graph, mlib, remote.cpu(0))
+            mod = debug_runtime.create(graph, mlib, remote.cpu(0))
         except ValueError:
-            print("Skip because debug graph_runtime not enabled")
+            print("Skip because debug runtime not enabled")
             return
         a = np.random.uniform(size=(n,)).astype(A.dtype)
         mod.run(x=tvm.nd.array(a, ctx))
@@ -142,4 +186,4 @@ def check_remote():
 
 
 if __name__ == "__main__":
-    test_graph_simple()
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))
diff --git a/tests/python/unittest/test_runtime_module_based_interface.py b/tests/python/unittest/test_runtime_module_based_interface.py
index 64f87fb3c561..930011d4fd33 100644
--- a/tests/python/unittest/test_runtime_module_based_interface.py
+++ b/tests/python/unittest/test_runtime_module_based_interface.py
@@ -15,11 +15,12 @@
 # specific language governing permissions and limitations
 # under the License.
 import numpy as np
-from tvm import relay
+from tvm import relay, runtime
 from tvm.relay import testing
 import tvm
 from tvm.contrib import graph_runtime
 from tvm.contrib.debugger import debug_runtime
+from tvm.contrib.cuda_graph import cuda_graph_runtime
 import tvm.testing
 
 
@@ -314,7 +315,7 @@ def verify_cpu_remove_package_params(obj_format):
         complied_graph_lib_no_params = complied_graph_lib["remove_params"]()
         complied_graph_lib_no_params.export_library(path_lib)
         with open(temp.relpath("deploy_param.params"), "wb") as fo:
-            fo.write(relay.save_param_dict(complied_graph_lib.get_params()))
+            fo.write(runtime.save_param_dict(complied_graph_lib.get_params()))
         loaded_lib = tvm.runtime.load_module(path_lib)
         data = np.random.uniform(-1, 1, size=input_shape(mod)).astype("float32")
         ctx = tvm.cpu(0)
@@ -361,7 +362,7 @@ def verify_gpu_remove_package_params(obj_format):
         complied_graph_lib_no_params = complied_graph_lib["remove_params"]()
         complied_graph_lib_no_params.export_library(path_lib)
         with open(temp.relpath("deploy_param.params"), "wb") as fo:
-            fo.write(relay.save_param_dict(complied_graph_lib.get_params()))
+            fo.write(runtime.save_param_dict(complied_graph_lib.get_params()))
         loaded_lib = tvm.runtime.load_module(path_lib)
         data = np.random.uniform(-1, 1, size=input_shape(mod)).astype("float32")
         ctx = tvm.gpu(0)
@@ -409,7 +410,7 @@ def verify_rpc_cpu_remove_package_params(obj_format):
         complied_graph_lib_no_params.export_library(path_lib)
         path_params = temp.relpath("deploy_param.params")
         with open(path_params, "wb") as fo:
-            fo.write(relay.save_param_dict(complied_graph_lib.get_params()))
+            fo.write(runtime.save_param_dict(complied_graph_lib.get_params()))
 
         from tvm import rpc
 
@@ -462,7 +463,7 @@ def verify_rpc_gpu_remove_package_params(obj_format):
         complied_graph_lib_no_params.export_library(path_lib)
         path_params = temp.relpath("deploy_param.params")
         with open(path_params, "wb") as fo:
-            fo.write(relay.save_param_dict(complied_graph_lib.get_params()))
+            fo.write(runtime.save_param_dict(complied_graph_lib.get_params()))
 
         from tvm import rpc
 
@@ -538,6 +539,35 @@ def test_debug_graph_runtime():
     tvm.testing.assert_allclose(out, verify(data), atol=1e-5)
 
 
+@tvm.testing.requires_cudagraph
+def test_cuda_graph_runtime():
+    mod, params = relay.testing.synthetic.get_workload()
+    with tvm.transform.PassContext(opt_level=3):
+        complied_graph_lib = relay.build_module.build(mod, "cuda", params=params)
+    data = np.random.uniform(-1, 1, size=input_shape(mod)).astype("float32")
+
+    ctx = tvm.gpu()
+    try:
+        gmod = complied_graph_lib["cuda_graph_create"](ctx)
+    except:
+        print("Skip because cuda_graph not enabled")
+        return
+    set_input = gmod["set_input"]
+    run = gmod["run"]
+    get_output = gmod["get_output"]
+    set_input("data", tvm.nd.array(data))
+    run()
+    out = get_output(0).asnumpy()
+    tvm.testing.assert_allclose(out, verify(data), atol=1e-5)
+
+    # cuda graph runtime wrapper
+    cu_gmod = cuda_graph_runtime.GraphModuleCudaGraph(gmod)
+    cu_gmod.set_input("data", data)
+    cu_gmod.run()
+    out = cu_gmod.get_output(0).asnumpy()
+    tvm.testing.assert_allclose(out, verify(data), atol=1e-5)
+
+
 def test_multiple_imported_modules():
     def make_func(symbol):
         n = tvm.te.size_var("n")
@@ -547,8 +577,7 @@ def make_func(symbol):
             i,
             0,
             n - 1,
-            0,
-            0,
+            tvm.tir.ForKind.SERIAL,
             tvm.tir.Store(Ab.data, tvm.tir.Load("float32", Ab.data, i) + 1, i + 1),
         )
         return tvm.tir.PrimFunc([Ab], stmt).with_attr("global_symbol", symbol)
diff --git a/tests/python/unittest/test_runtime_module_load.py b/tests/python/unittest/test_runtime_module_load.py
index 7befed3bbcdd..38800e8de6ad 100644
--- a/tests/python/unittest/test_runtime_module_load.py
+++ b/tests/python/unittest/test_runtime_module_load.py
@@ -55,7 +55,11 @@ def save_object(names):
         i = te.var("i")
         # for i in 0 to n-1:
         stmt = tvm.tir.For(
-            i, 0, n - 1, 0, 0, tvm.tir.Store(Ab.data, tvm.tir.Load(dtype, Ab.data, i) + 1, i + 1)
+            i,
+            0,
+            n - 1,
+            tvm.tir.ForKind.SERIAL,
+            tvm.tir.Store(Ab.data, tvm.tir.Load(dtype, Ab.data, i) + 1, i + 1),
         )
         mod = tvm.IRModule.from_expr(
             tvm.tir.PrimFunc([Ab], stmt).with_attr("global_symbol", "main")
diff --git a/tests/python/unittest/test_runtime_rpc.py b/tests/python/unittest/test_runtime_rpc.py
index e975a1699341..11c109810fbb 100644
--- a/tests/python/unittest/test_runtime_rpc.py
+++ b/tests/python/unittest/test_runtime_rpc.py
@@ -17,11 +17,12 @@
 import tvm
 from tvm import te
 import tvm.testing
+import logging
+import multiprocessing
 import os
 import stat
-import logging
+import sys
 import time
-import multiprocessing
 
 import pytest
 import numpy as np
@@ -29,6 +30,12 @@
 from tvm.contrib import utils, cc
 from tvm.rpc.tracker import Tracker
 
+
+if __name__ == "__main__":
+    # NOTE: must live here to avoid registering PackedFunc with libtvm.so twice.
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))
+
+
 # tkonolige: The issue as I understand it is this: multiprocessing's spawn
 # method launches a new process and then imports the relevant modules. This
 # means that all registered functions must exist at the top level scope. In
@@ -526,20 +533,3 @@ def test_rpc_tracker_request():
     proc2.join()
     server.terminate()
     tracker.terminate()
-
-
-if __name__ == "__main__":
-    logging.basicConfig(level=logging.INFO)
-    test_rpc_echo()
-    test_rpc_session_constructor_args()
-    test_rpc_return_ndarray()
-    test_rpc_return_func()
-    test_bigendian_rpc()
-    test_rpc_remote_module()
-    test_rpc_file_exchange()
-    test_rpc_array()
-    test_rpc_simple()
-    test_local_func()
-    test_rpc_tracker_register()
-    test_rpc_tracker_request()
-    test_rpc_large_array()
diff --git a/tests/python/unittest/test_target_codegen_c_host.py b/tests/python/unittest/test_target_codegen_c_host.py
index 3178d6dad0e4..d1ca8b1450f0 100644
--- a/tests/python/unittest/test_target_codegen_c_host.py
+++ b/tests/python/unittest/test_target_codegen_c_host.py
@@ -30,12 +30,12 @@ def test_add():
     s = te.create_schedule(C.op)
 
     def check_c():
-        mhost = tvm.build(s, [A, B, C], "c", name="fadd")
+        mhost = tvm.build(s, [A, B, C], "c", name="test_fadd")
         temp = utils.tempdir()
         path_dso = temp.relpath("temp.so")
         mhost.export_library(path_dso)
         m = tvm.runtime.load_module(path_dso)
-        fadd = m["fadd"]
+        fadd = m["test_fadd"]
         ctx = tvm.cpu(0)
         # launch the kernel.
         n = nn
@@ -73,14 +73,14 @@ def check_c():
         )
         binds = {A: Ab}
         # BUILD and invoke the kernel.
-        f1 = tvm.lower(s, [A, B, C], name="fadd_pipeline")
+        f1 = tvm.lower(s, [A, B, C], name="test_fadd_pipeline")
         mhost = tvm.build(f1, target="c")
 
         temp = utils.tempdir()
         path_dso = temp.relpath("temp.so")
         mhost.export_library(path_dso)
         m = tvm.runtime.load_module(path_dso)
-        fadd = m["fadd_pipeline"]
+        fadd = m["test_fadd_pipeline"]
         ctx = tvm.cpu(0)
         # launch the kernel.
         n = nn
@@ -103,12 +103,12 @@ def test_reinterpret():
     s = te.create_schedule(B.op)
 
     def check_c():
-        mhost = tvm.build(s, [A, B], "c", name="reinterpret")
+        mhost = tvm.build(s, [A, B], "c", name="test_reinterpret")
         temp = utils.tempdir()
         path_dso = temp.relpath("temp.so")
         mhost.export_library(path_dso)
         m = tvm.runtime.load_module(path_dso)
-        fadd = m["reinterpret"]
+        fadd = m["test_reinterpret"]
         ctx = tvm.cpu(0)
         n = nn
         a = tvm.nd.array(np.random.randint(-(2 ** 30), 2 ** 30, size=n).astype(A.dtype), ctx)
@@ -119,7 +119,82 @@ def check_c():
     check_c()
 
 
+def test_ceil():
+    nn = 1024
+    n = tvm.runtime.convert(nn)
+    A = te.placeholder((n,), name="A", dtype="float32")
+    B = te.compute(A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.ceil", A(*i)), name="B")
+    s = te.create_schedule(B.op)
+
+    def check_c():
+        mhost = tvm.build(s, [A, B], "c", name="test_ceil")
+        temp = utils.tempdir()
+        path_dso = temp.relpath("temp.so")
+        mhost.export_library(path_dso)
+        m = tvm.runtime.load_module(path_dso)
+        fceil = m["test_ceil"]
+        ctx = tvm.cpu(0)
+        n = nn
+        a = tvm.nd.array(np.random.rand(n).astype(A.dtype), ctx)
+        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
+        fceil(a, b)
+        tvm.testing.assert_allclose(b.asnumpy(), (np.ceil(a.asnumpy()).view("float32")))
+
+    check_c()
+
+
+def test_floor():
+    nn = 1024
+    n = tvm.runtime.convert(nn)
+    A = te.placeholder((n,), name="A", dtype="float32")
+    B = te.compute(A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.floor", A(*i)), name="B")
+    s = te.create_schedule(B.op)
+
+    def check_c():
+        mhost = tvm.build(s, [A, B], "c", name="test_floor")
+        temp = utils.tempdir()
+        path_dso = temp.relpath("temp.so")
+        mhost.export_library(path_dso)
+        m = tvm.runtime.load_module(path_dso)
+        ffloor = m["test_floor"]
+        ctx = tvm.cpu(0)
+        n = nn
+        a = tvm.nd.array(np.random.rand(n).astype(A.dtype), ctx)
+        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
+        ffloor(a, b)
+        tvm.testing.assert_allclose(b.asnumpy(), (np.floor(a.asnumpy()).view("float32")))
+
+    check_c()
+
+
+def test_round():
+    nn = 1024
+    n = tvm.runtime.convert(nn)
+    A = te.placeholder((n,), name="A", dtype="float32")
+    B = te.compute(A.shape, lambda *i: tvm.tir.call_intrin("float32", "tir.round", A(*i)), name="B")
+    s = te.create_schedule(B.op)
+
+    def check_c():
+        mhost = tvm.build(s, [A, B], "c", name="test_round")
+        temp = utils.tempdir()
+        path_dso = temp.relpath("temp.so")
+        mhost.export_library(path_dso)
+        m = tvm.runtime.load_module(path_dso)
+        fround = m["test_round"]
+        ctx = tvm.cpu(0)
+        n = nn
+        a = tvm.nd.array(np.random.rand(n).astype(A.dtype), ctx)
+        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
+        fround(a, b)
+        tvm.testing.assert_allclose(b.asnumpy(), (np.round(a.asnumpy()).view("float32")))
+
+    check_c()
+
+
 if __name__ == "__main__":
     test_add()
     test_add_pipeline()
     test_reinterpret()
+    test_ceil()
+    test_floor()
+    test_round()
diff --git a/tests/python/unittest/test_target_codegen_cuda.py b/tests/python/unittest/test_target_codegen_cuda.py
index e87767475ab2..06d7cb4bb7bb 100644
--- a/tests/python/unittest/test_target_codegen_cuda.py
+++ b/tests/python/unittest/test_target_codegen_cuda.py
@@ -19,7 +19,7 @@
 import numpy as np
 from tvm import topi
 import unittest
-from tvm.contrib.nvcc import have_fp16, have_int8
+from tvm.contrib.nvcc import have_fp16, have_int8, have_bf16
 from tvm.contrib import nvcc
 import tvm.testing
 
@@ -67,6 +67,53 @@ def check_cuda(dtype, n, lanes):
     check_cuda("float16", 64, 8)
 
 
+@tvm.testing.requires_gpu
+@tvm.testing.requires_cuda
+def test_cuda_bf16_vectorize_add():
+    if not have_bf16(tvm.gpu(0).compute_version):
+        print("skip because gpu does not support bf16")
+        return
+    num_thread = 8
+
+    def np_float2np_bf16(arr):
+        """Convert a numpy array of float to a numpy array
+        of bf16 in uint16"""
+        orig = arr.view("<u4")
+        bias = np.bitwise_and(np.right_shift(orig, 16), 1) + 0x7FFF
+        return np.right_shift(orig + bias, 16).astype("uint16")
+
+    def np_bf162np_float(arr):
+        """Convert a numpy array of bf16 (uint16) to a numpy array
+        of float"""
+        u32 = np.left_shift(arr.astype("uint32"), 16)
+        return u32.view("<f4")
+
+    def check_cuda(n, lanes):
+        A = te.placeholder((n,), name="A", dtype="bfloat16x%d" % lanes)
+        B = te.compute((n,), lambda i: A[i] + tvm.tir.const(1, A.dtype), name="B")
+        s = te.create_schedule(B.op)
+        xo, xi = s[B].split(B.op.axis[0], factor=num_thread)
+        s[B].bind(xo, bx)
+        s[B].bind(xi, tx)
+        with tvm.transform.PassContext(
+            disabled_pass=["tir.BF16Promote", "tir.BF16CastElimination", "tir.BF16TypeLowering"]
+        ):
+            fun = tvm.build(s, [A, B], "cuda")
+        ctx = tvm.gpu(0)
+        np_a = np.random.uniform(size=(n, lanes)).astype("float32")
+        np_a = np_bf162np_float(np_float2np_bf16(np_a))
+        a = tvm.nd.empty((n,), A.dtype, ctx).copyfrom(np_float2np_bf16(np_a))
+        c = tvm.nd.empty((n,), B.dtype, ctx)
+        fun(a, c)
+        c = tvm.nd.empty((n, lanes), "uint16", ctx).copyfrom(c)
+        tvm.testing.assert_allclose(c.asnumpy(), np_float2np_bf16(np_a + 1))
+
+    check_cuda(64, 2)
+    check_cuda(64, 4)
+    check_cuda(64, 6)
+    check_cuda(64, 8)
+
+
 @tvm.testing.requires_gpu
 @tvm.testing.requires_cuda
 def test_cuda_multiply_add():
@@ -200,7 +247,7 @@ def test_cuda_shuffle():
 
     def MyVectorize():
         def vectorizer(op):
-            if op.for_type == tvm.tir.For.Vectorized:
+            if op.kind == tvm.tir.ForKind.VECTORIZED:
                 four = tvm.tir.const(4, "int32")
                 idx = tvm.tir.Ramp(thrx.var * four, tvm.tir.const(1, "int32"), 4)
                 all_ones = tvm.tir.const(1, "int32x4")
@@ -498,7 +545,7 @@ def test_cuda_floormod_with_vectorization():
 @tvm.testing.requires_gpu
 @tvm.testing.requires_cuda
 def test_vectorized_casts():
-    def check(t0, t1):
+    def check(t0, t1, factor):
         if (t0 == "float16" or t1 == "float16") and not have_fp16(tvm.gpu(0).compute_version):
             print("Skip because gpu does not have fp16 support")
             return
@@ -511,9 +558,8 @@ def check(t0, t1):
 
         # schedule
         s = tvm.te.create_schedule(C.op)
-        ob, ib = s[C].split(s[C].op.axis[0], nparts=32)
-        _, iib = s[C].split(ib, factor=4)
-        s[C].vectorize(iib)
+        ob, ib = s[C].split(s[C].op.axis[0], factor=factor)
+        s[C].vectorize(ib)
         s[C].bind(ob, tx)
         func = tvm.build(s, [A, B, C], "cuda")
 
@@ -538,9 +584,26 @@ def skip(t0, t1):
             return True
         return False
 
-    types = ["float16", "float32", "int8", "uint8", "int16", "uint16", "int32", "uint32"]
-    for t0, t1 in [(x, y) for x in types for y in types if not skip(x, y)]:
-        check(t0, t1)
+    types_4 = [
+        "float16",
+        "float32",
+        "int8",
+        "uint8",
+        "int16",
+        "uint16",
+        "int32",
+        "uint32",
+        "float64",
+        "int64",
+        "uint64",
+    ]
+    types_8 = ["float16", "float32", "int8", "uint8", "int16", "uint16", "int32", "uint32"]
+    for t0, t1 in [(x, y) for x in types_4 for y in types_4 if not skip(x, y)]:
+        check(t0, t1, 4)
+    for t0, t1 in [(x, y) for x in types_8 for y in types_8 if not skip(x, y)]:
+        check(t0, t1, 8)
+    check("int8", "uint8", 16)
+    check("uint8", "int8", 16)
 
 
 def sched(B):
@@ -906,6 +969,7 @@ def test_unrolled_vectorization():
 
 if __name__ == "__main__":
     test_cuda_vectorize_add()
+    test_cuda_bf16_vectorize_add()
     test_cuda_multiply_add()
     test_cuda_vectorize_load()
     test_cuda_make_int8()
diff --git a/tests/python/unittest/test_target_codegen_llvm.py b/tests/python/unittest/test_target_codegen_llvm.py
index 4b67752367db..ec7c5aea333f 100644
--- a/tests/python/unittest/test_target_codegen_llvm.py
+++ b/tests/python/unittest/test_target_codegen_llvm.py
@@ -17,6 +17,8 @@
 import collections
 import ctypes
 import json
+import sys
+
 import tvm
 import tvm.testing
 from tvm import te
@@ -26,6 +28,7 @@
 import ctypes
 import math
 import re
+import pytest
 
 
 @tvm.testing.requires_llvm
@@ -761,7 +764,7 @@ def do_atomic_add(A):
         atomic_add_return = ib.allocate(A.dtype, (1,), name="atomic_add_return", scope="local")
         one = tvm.tir.const(1, A.dtype)
         A_ptr = ib.buffer_ptr(A)
-        with ib.for_range(0, n, name="i", for_type="parallel") as i:
+        with ib.for_range(0, n, name="i", kind="parallel") as i:
             atomic_add_return[0] = atomic_add(
                 tvm.tir.call_intrin("handle", "tir.address_of", A_ptr[0]), one
             )
@@ -816,27 +819,4 @@ def do_atomic_add(A):
 
 
 if __name__ == "__main__":
-    test_multiple_func()
-    test_llvm_large_uintimm()
-    test_llvm_import()
-    test_alignment()
-    test_rank_zero()
-    test_rank_zero_bound_checkers()
-    test_llvm_bool()
-    test_llvm_persist_parallel()
-    test_llvm_condition()
-    test_llvm_vadd_pipeline()
-    test_llvm_add_pipeline()
-    test_llvm_intrin()
-    test_llvm_overloaded_intrin()
-    test_llvm_flip_pipeline()
-    test_llvm_madd_pipeline()
-    test_llvm_temp_space()
-    test_llvm_lookup_intrin()
-    test_llvm_div()
-    test_llvm_fp_math()
-    test_dwarf_debug_information()
-    test_llvm_shuffle()
-    test_llvm_bf16()
-    test_llvm_crt_static_lib()
-    test_llvm_gpu_lower_atomic()
+    sys.exit(pytest.main([__file__] + sys.argv[1:]))
diff --git a/tests/python/unittest/test_target_codegen_spirv.py b/tests/python/unittest/test_target_codegen_spirv.py
new file mode 100644
index 000000000000..68be5c480358
--- /dev/null
+++ b/tests/python/unittest/test_target_codegen_spirv.py
@@ -0,0 +1,109 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+import tvm.testing
+from tvm import te
+from tvm import relay
+from tvm.topi.math import cast
+import numpy as np
+
+
+def test_bool_load():
+    def do_copy(A, B, n):
+        ib = tvm.tir.ir_builder.create()
+        A = ib.buffer_ptr(A)
+        B = ib.buffer_ptr(B)
+
+        tx = te.thread_axis("threadIdx.x")
+        bx = te.thread_axis("blockIdx.x")
+
+        max_threads = 32
+        ib.scope_attr(bx, "thread_extent", tvm.tir.indexdiv(n + max_threads - 1, max_threads))
+        ib.scope_attr(tx, "thread_extent", max_threads)
+        tid = bx * max_threads + tx
+
+        with ib.if_scope(tid < n):
+            B[tid] = cast(A[tid], "int32")
+
+        return ib.get()
+
+    n = 1024
+    A = te.placeholder((n,), name="A", dtype="bool")
+    B = te.placeholder((n,), name="B", dtype="int32")
+
+    target = "vulkan"
+
+    if not tvm.testing.device_enabled(target):
+        return
+
+    B = te.extern(
+        A.shape,
+        [A],
+        lambda ins, outs: do_copy(ins[0], outs[0], n),
+        name="bool_copy_ir",
+        dtype="int32",
+    )
+    s = te.create_schedule(B.op)
+
+    with tvm.transform.PassContext(opt_level=3):
+        func = tvm.build(s, [A, B], target)
+
+    ctx = tvm.context(target, 0)
+    a_np = np.random.uniform(size=n) > 0.5
+    b_np = np.zeros((n,), dtype="int32")
+    a = tvm.nd.array(a_np, ctx)
+    b = tvm.nd.array(b_np, ctx)
+    func(a, b)
+    ref = a_np.astype(np.int32)
+    tvm.testing.assert_allclose(b.asnumpy(), ref)
+
+
+def test_pushconstants():
+    if not tvm.testing.device_enabled("vulkan"):
+        return
+
+    def check_mod(mod, x_np, res_np):
+        target = "vulkan"
+        ctx = tvm.context(target, 0)
+        ex = relay.create_executor("vm", mod=mod, ctx=ctx, target=target)
+        res = ex.evaluate()(x_np).asnumpy()
+        tvm.testing.assert_allclose(res, res_np, atol=1e-5)
+
+    # Three 32 bit pushconstants: any_dim, stride, stride
+    dtype = "float32"
+    x = relay.var("x", shape=(relay.Any(),), dtype=dtype)
+    mod = tvm.IRModule()
+    mod["main"] = relay.Function([x], relay.sqrt(x))
+    x_np = np.random.uniform(size=(10,)).astype(dtype)
+    res_np = np.sqrt(x_np)
+
+    check_mod(mod, x_np, res_np)
+
+    # One 64 bit and one 32 bit constants
+    dtype = "int32"
+    x = relay.var("x", shape=(relay.Any(),), dtype=dtype)
+    mod = tvm.IRModule()
+    mod["main"] = relay.Function([x], relay.argsort(x))
+    x_np = np.random.randint(0, high=10, size=(10,)).astype(dtype)
+    res_np = np.argsort(x_np)
+
+    check_mod(mod, x_np, res_np)
+
+
+if __name__ == "__main__":
+    test_bool_load()
+    test_pushconstants()
diff --git a/tests/python/unittest/test_target_codegen_static_init.py b/tests/python/unittest/test_target_codegen_static_init.py
index 179e302984cc..b0c19dfcffeb 100644
--- a/tests/python/unittest/test_target_codegen_static_init.py
+++ b/tests/python/unittest/test_target_codegen_static_init.py
@@ -30,7 +30,7 @@ def test_static_callback():
     cp = te.thread_axis((0, 1), "cop")
     finit = tvm.tir.StringImm("TVMBackendRunOnce")
     ib.scope_attr(cp, "coproc_uop_scope", finit)
-    with ib.for_range(0, n, "i", for_type="parallel") as i:
+    with ib.for_range(0, n, "i", kind="parallel") as i:
         A[i] = A[i] + 1
     stmt = ib.get()
 
diff --git a/tests/python/unittest/test_target_codegen_vm_basic.py b/tests/python/unittest/test_target_codegen_vm_basic.py
index 26f1493c4ec1..9bbee76e2736 100644
--- a/tests/python/unittest/test_target_codegen_vm_basic.py
+++ b/tests/python/unittest/test_target_codegen_vm_basic.py
@@ -109,7 +109,7 @@ def test_vm_parallel():
     i = te.size_var("i")
     ib = tvm.tir.ir_builder.create()
     A = ib.buffer_ptr(Ab)
-    with ib.for_range(0, n, "i", for_type="parallel") as i:
+    with ib.for_range(0, n, "i", kind="parallel") as i:
         A[i] = A[i] + 1
     stmt = ib.get()
     mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([Ab], stmt).with_attr("global_symbol", "test"))
diff --git a/tests/python/unittest/test_target_codegen_x86.py b/tests/python/unittest/test_target_codegen_x86.py
index b581f72ec763..ec42e0a4d749 100644
--- a/tests/python/unittest/test_target_codegen_x86.py
+++ b/tests/python/unittest/test_target_codegen_x86.py
@@ -52,21 +52,14 @@ def fp16_to_fp32(target, width, match=None, not_match=None):
             not_matches = [l for l in assembly if re.search(not_match, l)]
             assert not not_matches
 
-    fp16_to_fp32(
-        "llvm -mcpu=skylake-avx512", 15, match="vcvtph2ps.*ymm", not_match="vcvtph2ps.*zmm"
-    )
-    fp16_to_fp32("llvm -mcpu=skylake-avx512", 16, match="vcvtph2ps.*zmm")
-    fp16_to_fp32("llvm -mcpu=skylake-avx512", 17, match="vcvtph2ps.*zmm")
-    fp16_to_fp32("llvm -mcpu=skylake-avx512", 49, match="vcvtph2ps.*zmm")
-    fp16_to_fp32(
-        "llvm -mcpu=skylake-avx512 -mattr=-avx512f",
-        49,
-        match="vcvtph2ps.*ymm",
-        not_match="vcvtph2ps.*zmm",
-    )
+    fp16_to_fp32("llvm -mcpu=skylake-avx512", 15, match="vcvtph2ps.*mm")
+    fp16_to_fp32("llvm -mcpu=skylake-avx512", 16, match="vcvtph2ps.*mm")
+    fp16_to_fp32("llvm -mcpu=skylake-avx512", 17, match="vcvtph2ps.*mm")
+    fp16_to_fp32("llvm -mcpu=skylake-avx512", 49, match="vcvtph2ps.*mm")
+    fp16_to_fp32("llvm -mcpu=skylake-avx512 -mattr=-avx512f", 49, match="vcvtph2ps.*mm")
     fp16_to_fp32("llvm -mcpu=skylake-avx512 -mattr=-f16c,-avx512f", 49, not_match="vcvtph2ps")
-    fp16_to_fp32("llvm -mcpu=core-avx2", 8, match="vcvtph2ps.*ymm")
-    fp16_to_fp32("llvm -mcpu=core-avx2", 9, match="vcvtph2ps.*ymm")
+    fp16_to_fp32("llvm -mcpu=core-avx2", 8, match="vcvtph2ps.*mm")
+    fp16_to_fp32("llvm -mcpu=core-avx2", 9, match="vcvtph2ps.*mm")
     fp16_to_fp32("llvm", 9, not_match="vcvtph2ps")
 
 
diff --git a/tests/python/unittest/test_target_target.py b/tests/python/unittest/test_target_target.py
index 643043f13663..7b998bef34a5 100644
--- a/tests/python/unittest/test_target_target.py
+++ b/tests/python/unittest/test_target_target.py
@@ -16,6 +16,7 @@
 # under the License.
 import json
 import tvm
+import pytest
 from tvm import te
 from tvm.target import cuda, rocm, mali, intel_graphics, arm_cpu, vta, bifrost, hexagon
 
@@ -113,24 +114,111 @@ def test_config_map():
     attributes fails as expected.
     """
     target_config = {"kind": "llvm", "libs": {"a": "b", "c": "d"}}
-    failed = False
-    try:
+    with pytest.raises(ValueError):
         tvm.target.Target(target_config)
-    except ValueError:
-        failed = True
-    assert failed
 
 
 def test_composite_target():
-    tgt = tvm.target.Target("composite --target_host=llvm --devices=cuda,opencl")
+    tgt = tvm.target.Target("composite --host=llvm --devices=cuda,opencl")
     assert tgt.kind.name == "composite"
-    assert tgt.attrs["target_host"].kind.name == "llvm"
+    assert tgt.attrs["host"].kind.name == "llvm"
     assert len(tgt.attrs["devices"]) == 2
     cuda_device, opencl_device = tgt.attrs["devices"]
     assert cuda_device.kind.name == "cuda"
     assert opencl_device.kind.name == "opencl"
 
 
+def test_target_tag_0():
+    tgt = tvm.target.Target("nvidia/geforce-rtx-2080-ti")
+    assert tgt.kind.name == "cuda"
+    assert tgt.attrs["arch"] == "sm_75"
+    assert tgt.attrs["shared_memory_per_block"] == 49152
+    assert tgt.attrs["max_threads_per_block"] == 1024
+    assert tgt.attrs["thread_warp_size"] == 32
+    assert tgt.attrs["registers_per_block"] == 65536
+
+
+def test_target_tag_1():
+    tgt = tvm.target.Target("nvidia/jetson-nano")
+    assert tgt.kind.name == "cuda"
+    assert tgt.attrs["arch"] == "sm_53"
+    assert tgt.attrs["shared_memory_per_block"] == 49152
+    assert tgt.attrs["max_threads_per_block"] == 1024
+    assert tgt.attrs["thread_warp_size"] == 32
+    assert tgt.attrs["registers_per_block"] == 32768
+
+
+def test_list_kinds():
+    targets = tvm.target.Target.list_kinds()
+    assert len(targets) != 0
+    assert "llvm" in targets
+    assert all(isinstance(target_name, str) for target_name in targets)
+
+
+def test_target_host_tags():
+    tgt = tvm.target.Target("nvidia/jetson-nano", "nvidia/geforce-rtx-2080-ti")
+    assert tgt.kind.name == "cuda"
+    assert tgt.attrs["arch"] == "sm_53"
+    assert tgt.attrs["shared_memory_per_block"] == 49152
+    assert tgt.attrs["max_threads_per_block"] == 1024
+    assert tgt.attrs["thread_warp_size"] == 32
+    assert tgt.attrs["registers_per_block"] == 32768
+    assert tgt.host.kind.name == "cuda"
+    assert tgt.host.attrs["arch"] == "sm_75"
+    assert tgt.host.attrs["shared_memory_per_block"] == 49152
+    assert tgt.host.attrs["max_threads_per_block"] == 1024
+    assert tgt.host.attrs["thread_warp_size"] == 32
+    assert tgt.host.attrs["registers_per_block"] == 65536
+
+
+def test_target_host_tag_dict():
+    tgt = tvm.target.Target("nvidia/jetson-nano", {"kind": "llvm"})
+    assert tgt.kind.name == "cuda"
+    assert tgt.attrs["arch"] == "sm_53"
+    assert tgt.attrs["shared_memory_per_block"] == 49152
+    assert tgt.attrs["max_threads_per_block"] == 1024
+    assert tgt.attrs["thread_warp_size"] == 32
+    assert tgt.attrs["registers_per_block"] == 32768
+    assert tgt.host.kind.name == "llvm"
+
+
+def test_target_host_single_dict():
+    tgt = tvm.target.Target({"kind": "llvm", "host": "nvidia/jetson-nano"})
+    assert tgt.kind.name == "llvm"
+    assert tgt.host.kind.name == "cuda"
+    assert tgt.host.attrs["arch"] == "sm_53"
+    assert tgt.host.attrs["shared_memory_per_block"] == 49152
+    assert tgt.host.attrs["max_threads_per_block"] == 1024
+    assert tgt.host.attrs["thread_warp_size"] == 32
+    assert tgt.host.attrs["registers_per_block"] == 32768
+
+
+def test_target_host_single_string():
+    tgt = tvm.target.Target("cuda --host llvm")
+    assert tgt.kind.name == "cuda"
+    assert tgt.host.kind.name == "llvm"
+
+
+def test_target_host_single_string_with_tag():
+    tgt = tvm.target.Target("cuda --host nvidia/jetson-nano")
+    assert tgt.kind.name == "cuda"
+    assert tgt.host.kind.name == "cuda"
+    assert tgt.host.attrs["arch"] == "sm_53"
+    assert tgt.host.attrs["shared_memory_per_block"] == 49152
+    assert tgt.host.attrs["max_threads_per_block"] == 1024
+    assert tgt.host.attrs["thread_warp_size"] == 32
+    assert tgt.host.attrs["registers_per_block"] == 32768
+
+
+def test_target_host_warning():
+    """
+    Confirm that constructing a target with invalid
+    attributes fails as expected.
+    """
+    with pytest.raises(ValueError):
+        tgt = tvm.target.Target("cuda --host nvidia/jetson-nano", "llvm")
+
+
 if __name__ == "__main__":
     test_target_dispatch()
     test_target_string_parse()
@@ -138,3 +226,4 @@ def test_composite_target():
     test_target_config()
     test_config_map()
     test_composite_target()
+    test_list_kinds()
diff --git a/tests/python/unittest/test_te_autodiff.py b/tests/python/unittest/test_te_autodiff.py
index 6031182091fe..b2f26471d267 100644
--- a/tests/python/unittest/test_te_autodiff.py
+++ b/tests/python/unittest/test_te_autodiff.py
@@ -170,6 +170,10 @@ def fidentity(t0):
     Y = topi.tensordot(A, B, 1)
     check_grad(Y, X)
 
+    X = te.placeholder((3, 3), name="X")
+    Y = topi.einsum("ii->i", (X))
+    check_grad(Y, X)
+
 
 def test_topi():
     X = te.placeholder((1, 2, 4, 4), name="X")
diff --git a/tests/python/unittest/test_te_hybrid_script.py b/tests/python/unittest/test_te_hybrid_script.py
index 06d409933f1f..be9956529dcc 100644
--- a/tests/python/unittest/test_te_hybrid_script.py
+++ b/tests/python/unittest/test_te_hybrid_script.py
@@ -267,9 +267,9 @@ def looptype(a, b, c):
     iloop = ir[0]
     jloop = ir[1]
     kloop = ir[2]
-    assert iloop.for_type == tvm.tir.For.Parallel
-    assert jloop.for_type == tvm.tir.For.Vectorized
-    assert kloop.for_type == tvm.tir.For.Unrolled
+    assert iloop.kind == tvm.tir.ForKind.PARALLEL
+    assert jloop.kind == tvm.tir.ForKind.VECTORIZED
+    assert kloop.kind == tvm.tir.ForKind.UNROLLED
 
     func, ins, outs = run_and_check(looptype, [a, b, c])
     run_and_check(func, ins, outs=outs)
diff --git a/tests/python/unittest/test_te_schedule_ops.py b/tests/python/unittest/test_te_schedule_ops.py
index 1555974169fc..255e0cdb1f21 100644
--- a/tests/python/unittest/test_te_schedule_ops.py
+++ b/tests/python/unittest/test_te_schedule_ops.py
@@ -110,19 +110,53 @@ def argmax_init(idx_typ, val_typ):
 
 
 def test_auto_inline():
-    m = te.var("m")
-    n = te.var("n")
-    A = te.placeholder((m, n), name="A")
-    B = te.placeholder((m, n), name="B")
-    C = te.placeholder((m, n), name="C")
-    T1 = te.compute((m, n), lambda i, j: A(i, j) * B(i, j), name="T1")
-    T2 = te.compute((m, n), lambda i, j: T1(i, j) + C(i, j), name="T2")
-
-    s = te.create_schedule(T2.op)
-    tvm.te.schedule.AutoInlineElemWise(s)
-    s = s.normalize()
-    bounds = tvm.te.schedule.InferBound(s)
-    stmt = tvm.te.schedule.ScheduleOps(s, bounds)
+    def elemwise():
+        m = te.var("m")
+        n = te.var("n")
+        A = te.placeholder((m, n), name="A")
+        B = te.placeholder((m, n), name="B")
+        C = te.placeholder((m, n), name="C")
+        T1 = te.compute((m, n), lambda i, j: A(i, j) * B(i, j), name="T1")
+        T2 = te.compute((m, n), lambda i, j: T1(i, j) + C(i, j), name="T2")
+
+        return te.create_schedule(T2.op), T1
+
+    def broadcast():
+        m = te.var("m")
+        n = te.var("n")
+        A = te.placeholder((1,), name="A")
+        B = te.placeholder((m, n), name="B")
+        C = te.placeholder((m, n), name="C")
+        T1 = te.compute((m, n), lambda i, j: A(0) * B(i, j), name="T1", tag="broadcast")
+        T2 = te.compute((m, n), lambda i, j: T1(i, j) + C(i, j), name="T2")
+
+        return te.create_schedule(T2.op), T1
+
+    def injective():
+        m = te.var("m")
+        n = te.var("n")
+        A = te.placeholder((m,), name="A")
+        B = te.placeholder((m, n), name="B")
+        C = te.placeholder((m, n), name="C")
+        T1 = te.compute((m, n), lambda i, j: A(i) * B(i, j), name="T1")
+        T2 = te.compute((m, n), lambda i, j: T1(i, j) + C(i, j), name="T2")
+
+        return te.create_schedule(T2.op), T1
+
+    def check_auto_inline(schedule_func, auto_inline_func):
+        s, T1 = schedule_func()
+        # before auto inline the attach type is AttachType.kGroupRoot
+        assert s[T1].attach_type == 1
+        auto_inline_func(s)
+        # after auto inline the attach type is AttachType.kInline
+        assert s[T1].attach_type == 2
+        s = s.normalize()
+        bounds = tvm.te.schedule.InferBound(s)
+        stmt = tvm.te.schedule.ScheduleOps(s, bounds)
+
+    check_auto_inline(elemwise, tvm.te.schedule.AutoInlineElemWise)
+    check_auto_inline(broadcast, tvm.te.schedule.AutoInlineBroadcast)
+    check_auto_inline(injective, tvm.te.schedule.AutoInlineInjective)
 
 
 def test_schedule_const_bound():
diff --git a/tests/python/unittest/test_te_schedule_tensorize.py b/tests/python/unittest/test_te_schedule_tensorize.py
index 83a5d30bb90d..fdafdb74fc0b 100644
--- a/tests/python/unittest/test_te_schedule_tensorize.py
+++ b/tests/python/unittest/test_te_schedule_tensorize.py
@@ -18,14 +18,22 @@
 from tvm import te
 
 
-def intrin_vadd(n):
+def intrin_vadd(xo, m, n):
     x = te.placeholder((n,), name="vx")
     y = te.placeholder((n,), name="vy")
-    z = te.compute(x.shape, lambda i: x[i] + y[i], name="z")
+    if m % n == 0:
+        body = lambda i: x[i] + y[i]
+    else:
+        body = lambda i: tvm.tir.Select(
+            xo * n + i < m, x[i] + y[i], tvm.tir.const(0, dtype=x.dtype)
+        )
+    z = te.compute(x.shape, body, name="z")
 
     def intrin_func(ins, outs):
         xx, yy = ins
         zz = outs[0]
+        # special handle needed to tackle tail loop part when m % n != 0
+        # here is tvm.min(n, m - xo * n)
         return tvm.tir.call_packed("vadd", xx, yy, zz)
 
     buffer_params = {"offset_factor": 16}
@@ -84,15 +92,17 @@ def intrin_func(ins, outs):
 
 
 def test_tensorize_vadd():
-    m = 128
-    x = te.placeholder((m,), name="x")
-    y = te.placeholder((m,), name="y")
-    z = te.compute(x.shape, lambda i: x[i] + y[i], name="z")
+    def add(m):
+        x = te.placeholder((m,), name="x")
+        y = te.placeholder((m,), name="y")
+        z = te.compute(x.shape, lambda i: x[i] + y[i], name="z")
+        return x, y, z
 
-    def check(factor):
+    def check(m, factor):
+        x, y, z = add(m)
         s = te.create_schedule(z.op)
         xo, xi = s[z].split(z.op.axis[0], factor=factor)
-        vadd = intrin_vadd(factor)
+        vadd = intrin_vadd(xo, m, factor)
         s[z].tensorize(xi, vadd)
         s = s.normalize()
         dom_map = tvm.te.schedule.InferBound(s)
@@ -108,7 +118,36 @@ def check(factor):
         stmt = tvm.te.schedule.ScheduleOps(s, dom_map)
         tvm.lower(s, [x, y, z])
 
-    check(16)
+    def check_cache_write(m, factor):
+        x, y, z = add(m)
+        s = te.create_schedule(z.op)
+        _, _ = s[z].split(z.op.axis[0], factor=factor)
+
+        z_global = s.cache_write(z, "global")
+        xo, xi = z_global.op.axis
+
+        vadd = intrin_vadd(xo, m, factor)
+        s[z_global].tensorize(xi, vadd)
+        s = s.normalize()
+        dom_map = tvm.te.schedule.InferBound(s)
+        finfer = tvm.get_global_func("test.op.InferTensorizeRegion")
+        out_dom, in_dom = finfer(s[z_global], dom_map)
+        # outer loop var will be rebased, so min value is the new loop var and extent is 1
+        assert tvm.ir.structural_equal(out_dom[xo].extent, 1)
+        assert isinstance(out_dom[xo].min, tvm.tir.Var)
+        assert xo.var.name == out_dom[xo].min.name
+
+        fmatch = tvm.get_global_func("test.op.MatchTensorizeBody")
+        body = fmatch(s[z_global], out_dom, in_dom, vadd)[0]
+        ana = tvm.arith.Analyzer()
+        vars = tvm.runtime.convert({xo.var: out_dom[xo].min})
+        vadd_body = tvm.tir.stmt_functor.substitute(vadd.op.body[0], vars)
+        assert tvm.ir.structural_equal(ana.simplify(body), ana.simplify(vadd_body))
+        stmt = tvm.te.schedule.ScheduleOps(s, dom_map)
+        tvm.lower(s, [x, y, z])
+
+    check(128, 16)
+    check_cache_write(129, 16)
 
 
 def test_tensorize_matmul():
diff --git a/tests/python/unittest/test_tir_base.py b/tests/python/unittest/test_tir_base.py
new file mode 100644
index 000000000000..6e081a179059
--- /dev/null
+++ b/tests/python/unittest/test_tir_base.py
@@ -0,0 +1,60 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+from tvm import tir
+from tvm.ir.transform import PassContext
+
+
+def build_tir_func(func):
+    func = func.with_attr("global_symbol", "main")
+    pass_ctx = PassContext.current()
+    if pass_ctx.config.get("tir.noalias", True):
+        func = func.with_attr("tir.noalias", True)
+    mod = tvm.IRModule({"main": func})
+    func = tvm.build(mod)
+    return func
+
+
+def test_scalar_add():
+    a = tir.Var("a", "float32")
+    b = tir.Var("b", "float32")
+    c = a + b
+    c = tir.ret(c)
+    c = tir.Evaluate(c)
+    func = tir.PrimFunc([a, b], c)
+    func = build_tir_func(func)
+    out = func(1.0, 2.0)
+    assert out == 3.0
+
+
+def test_control_flow_jump():
+    ib = tvm.tir.ir_builder.create()
+    a = tir.Var("a", "float32")
+    b = tir.Var("b", "float32")
+    with ib.if_scope(True):
+        ib.emit(tir.Evaluate(tir.ret(a)))
+    ib.emit(tir.Evaluate(tir.ret(b)))
+    stmt = ib.get()
+    func = tir.PrimFunc([a, b], stmt)
+    func = build_tir_func(func)
+    out = func(1.0, 2.0)
+    assert out == 1.0
+
+
+if __name__ == "__main__":
+    test_scalar_add()
+    test_control_flow_jump()
diff --git a/tests/python/unittest/test_tir_constructor.py b/tests/python/unittest/test_tir_constructor.py
index 3cde5d7ad650..2cc21dbce91d 100644
--- a/tests/python/unittest/test_tir_constructor.py
+++ b/tests/python/unittest/test_tir_constructor.py
@@ -142,7 +142,7 @@ def test_stmt_constructor():
     assert isinstance(x, tvm.tir.AssertStmt)
     assert x.body == nop
 
-    x = tvm.tir.For(te.var("x"), 0, 10, 0, 0, nop)
+    x = tvm.tir.For(te.var("x"), 0, 10, tvm.tir.ForKind.SERIAL, nop)
     assert isinstance(x, tvm.tir.For)
     assert x.min.value == 0
     assert x.extent.value == 10
@@ -154,6 +154,7 @@ def test_stmt_constructor():
     assert x.index.value == 10
     assert x.value.value == 1
 
+    buffer_var = tvm.tir.Var("buf", tvm.ir.PointerType(tvm.ir.PrimType("float32")))
     x = tvm.tir.Allocate(buffer_var, "float32", [10], tvm.tir.const(1, "uint1"), nop)
     assert isinstance(x, tvm.tir.Allocate)
     assert x.dtype == "float32"
diff --git a/tests/python/unittest/test_tir_ir_builder.py b/tests/python/unittest/test_tir_ir_builder.py
index b84ee09b9fd9..8ad5cb63924e 100644
--- a/tests/python/unittest/test_tir_ir_builder.py
+++ b/tests/python/unittest/test_tir_ir_builder.py
@@ -173,9 +173,337 @@ def check_target(target):
     check_target("cuda")
 
 
+def test_while_vectorize():
+    """Test while loop + vectorized inner loop"""
+
+    n = 64
+    num_iter = 10
+
+    def test_ir(A, B, C):
+        ib = tvm.tir.ir_builder.create()
+        n = C.shape[0]
+        A = ib.buffer_ptr(A)
+        B = ib.buffer_ptr(B)
+        C = ib.buffer_ptr(C)
+        i = ib.allocate("int32", (1,), name="i", scope="local")
+        i[0] = 0
+
+        with ib.for_range(0, n) as j:
+            C[j] = 0.0
+
+        with ib.while_loop(i[0] < num_iter):
+            with ib.for_range(0, n, kind="vectorize") as j:
+                C[j] += A[j] + B[j]
+            i[0] += 1
+
+        return ib.get()
+
+    def check_target(target, ir):
+        dtype = "float32"
+        A = te.placeholder((n,), name="A", dtype=dtype)
+        B = te.placeholder((n,), name="B", dtype=dtype)
+
+        C = te.extern(
+            (n,),
+            [A, B],
+            lambda ins, outs: ir(ins[0], ins[1], outs[0]),
+            name="while_vectorize",
+            dtype=dtype,
+        )
+        s = te.create_schedule(C.op)
+
+        with tvm.transform.PassContext(opt_level=3):
+            func = tvm.build(s, [A, B, C], target)
+
+        ctx = tvm.context(target, 0)
+        a_np = np.random.uniform(size=n).astype(A.dtype)
+        b_np = np.random.uniform(size=n).astype(B.dtype)
+        a = tvm.nd.array(a_np, ctx)
+        b = tvm.nd.array(b_np, ctx)
+        c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
+        func(a, b, c)
+        ref = num_iter * (a_np + b_np)
+        tvm.testing.assert_allclose(c.asnumpy(), ref, rtol=1e-5, atol=1e-5)
+
+    check_target("llvm", test_ir)
+
+
+def test_while_collatz():
+    """Test while loop + if"""
+
+    def collatz_ref(n):
+        a = n
+        i = 0
+        while a > 1:
+            if a % 2 == 1:
+                a = 3 * a + 1
+            else:
+                a = a >> 1
+            i += 1
+        return i
+
+    def collatz(ib, n, C):
+        i = ib.allocate("int32", (1,), name="i", scope="local")
+        a = ib.allocate("int32", (1,), name="a", scope="local")
+        i[0] = 0
+        a[0] = n
+        with ib.while_loop(a[0] > 1):
+            with ib.if_scope(tvm.tir.floormod(a[0], 2) == 1):
+                a[0] = 3 * a[0] + 1
+            with ib.else_scope():
+                a[0] = a[0] >> 1
+            i[0] += 1
+
+        C[n] = i[0]
+
+    def collatz_ir_cpu(C):
+        ib = tvm.tir.ir_builder.create()
+        n = C.shape[0]
+        C = ib.buffer_ptr(C)
+
+        with ib.for_range(0, n, name="i", kind="parallel") as i:
+            collatz(ib, i, C)
+
+        body = ib.get()
+
+        return body
+
+    n = 30
+
+    def check_target(target, ir):
+        C = te.extern(
+            (n,),
+            [],
+            lambda ins, outs: ir(outs[0]),
+            name="collatz",
+            dtype="int32",
+        )
+        s = te.create_schedule(C.op)
+
+        with tvm.transform.PassContext(opt_level=3):
+            func = tvm.build(s, [C], target)
+
+        ctx = tvm.context(target, 0)
+        c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
+        func(c)
+        ref = np.array([collatz_ref(i) for i in range(n)])
+        tvm.testing.assert_allclose(c.asnumpy(), ref)
+
+    check_target("llvm", collatz_ir_cpu)
+
+
+def test_while_mandel():
+    n = 160
+    shape = (n * 2, n)
+    t = 300
+
+    def mandel_ref():
+        def complex_sqr(z):
+            return np.array([z[0] ** 2 - z[1] ** 2, z[1] * z[0] * 2])
+
+        pixels = np.zeros(shape)
+
+        for i in range(pixels.shape[0]):
+            for j in range(pixels.shape[1]):
+                c = np.array([-0.8, np.cos(t) * 0.2])
+                z = np.array([i / n - 1, j / n - 0.5]) * 2
+                iterations = 0
+
+                while np.linalg.norm(z) < 20 and iterations < 50:
+                    z = complex_sqr(z) + c
+                    iterations += 1
+
+                pixels[i, j] = 1 - iterations * 0.02
+
+        return pixels
+
+    def mandel(ib, i, j, pixels):
+        z = ib.allocate("float32", (2,), name="z", scope="local")
+        tmp = ib.allocate("float32", (1,), name="tmp", scope="local")
+        iterations = ib.allocate("int32", (1,), name="iterations", scope="local")
+
+        z[0] = (i / float(n) - 1) * 2
+        z[1] = (j / float(n) - 0.5) * 2
+        iterations[0] = 0
+        c = [-0.8, float(np.cos(t)) * 0.2]
+
+        def norm(z):
+            return tvm.tir.sqrt(z[0] * z[0] + z[1] * z[1])
+
+        with ib.while_loop(tvm.tir.all(norm(z) < 20, iterations[0] < 50)):
+            tmp[0] = z[0]
+            z[0] = z[0] * z[0] - z[1] * z[1] + c[0]
+            z[1] = z[1] * tmp[0] * 2 + c[1]
+            iterations[0] += 1
+
+        pixels[i, j] = 1 - iterations[0] * 0.02
+
+    def mandel_ir_cpu(C):
+        ib = tvm.tir.ir_builder.create()
+        ny = C.shape[0]
+        nx = C.shape[1]
+        C = ib.buffer_ptr(C)
+
+        with ib.for_range(0, ny, name="i", kind="parallel") as i:
+            with ib.for_range(0, nx, name="j") as j:
+                mandel(ib, i, j, C)
+
+        body = ib.get()
+
+        return body
+
+    def mandel_ir_gpu(C):
+        ib = tvm.tir.ir_builder.create()
+        ny = C.shape[0]
+        nx = C.shape[1]
+        C = ib.buffer_ptr(C)
+
+        bx = te.thread_axis("blockIdx.x")
+        tx = te.thread_axis("threadIdx.x")
+        by = te.thread_axis("blockIdx.y")
+        ty = te.thread_axis("threadIdx.y")
+
+        max_threads = 16
+        ib.scope_attr(bx, "thread_extent", tvm.tir.indexdiv(nx + max_threads - 1, max_threads))
+        ib.scope_attr(tx, "thread_extent", max_threads)
+        ib.scope_attr(by, "thread_extent", tvm.tir.indexdiv(ny + max_threads - 1, max_threads))
+        ib.scope_attr(ty, "thread_extent", max_threads)
+
+        tidx = bx * max_threads + tx
+        tidy = by * max_threads + ty
+
+        with ib.if_scope(tvm.tir.all(tidx < nx, tidy < ny)):
+            mandel(ib, tidy, tidx, C)
+
+        body = ib.get()
+
+        return body
+
+    ref = mandel_ref()
+
+    def check_target(target, ir):
+        if not tvm.testing.device_enabled(target):
+            return
+
+        C = te.extern(
+            shape,
+            [],
+            lambda ins, outs: ir(outs[0]),
+            name="mandel_ir",
+            dtype="float32",
+        )
+        s = te.create_schedule(C.op)
+
+        with tvm.transform.PassContext(opt_level=3):
+            func = tvm.build(s, [C], target)
+
+        ctx = tvm.context(target, 0)
+        c = tvm.nd.array(np.zeros(shape, dtype=C.dtype), ctx)
+        func(c)
+        tvm.testing.assert_allclose(c.asnumpy(), ref, rtol=1e-5, atol=1e-5)
+
+    check_target("llvm", mandel_ir_cpu)
+    check_target("npvtx", mandel_ir_gpu)
+    check_target("cuda", mandel_ir_gpu)
+    check_target("vulkan", mandel_ir_gpu)
+
+
+def test_while_binary_search():
+    def binary_search(ib, n, i, Aptr, Bptr, Cptr):
+        lo = ib.allocate("int32", (1,), name="lo", scope="local")
+        hi = ib.allocate("int32", (1,), name="hi", scope="local")
+
+        lo[0] = 0
+        hi[0] = n
+        v = Bptr[i]
+
+        with ib.while_loop(lo[0] < hi[0]):
+            mid = lo[0] + (hi[0] - lo[0] >> 1)
+            with ib.if_scope(Aptr[mid] < v):
+                lo[0] = mid + 1
+            with ib.else_scope():
+                hi[0] = mid
+
+        Cptr[i] = lo[0]
+
+    def searchsorted_ir_cpu(A, B, C, n):
+        ib = tvm.tir.ir_builder.create()
+        Aptr = ib.buffer_ptr(A)
+        Bptr = ib.buffer_ptr(B)
+        Cptr = ib.buffer_ptr(C)
+
+        with ib.for_range(0, n, name="i", kind="parallel") as i:
+            binary_search(ib, n, i, Aptr, Bptr, Cptr)
+
+        body = ib.get()
+
+        return body
+
+    def searchsorted_ir_gpu(A, B, C, n):
+        ib = tvm.tir.ir_builder.create()
+        Aptr = ib.buffer_ptr(A)
+        Bptr = ib.buffer_ptr(B)
+        Cptr = ib.buffer_ptr(C)
+
+        bx = te.thread_axis("blockIdx.x")
+        tx = te.thread_axis("threadIdx.x")
+        max_threads = 32
+        ib.scope_attr(bx, "thread_extent", tvm.tir.indexdiv(n + max_threads - 1, max_threads))
+        ib.scope_attr(tx, "thread_extent", max_threads)
+        tid = bx * max_threads + tx
+
+        with ib.if_scope(tid < n):
+            binary_search(ib, n, tid, Aptr, Bptr, Cptr)
+
+        body = ib.get()
+
+        return body
+
+    n = 1024
+    dtype = "float32"
+    A = te.placeholder((n,), name="A", dtype=dtype)
+    B = te.placeholder((n,), name="B", dtype=dtype)
+
+    def check_target(target, ir):
+        if not tvm.testing.device_enabled(target):
+            return
+
+        C = te.extern(
+            A.shape,
+            [A, B],
+            lambda ins, outs: ir(ins[0], ins[1], outs[0], n),
+            name="searchsorted_ir",
+            dtype="int32",
+        )
+        s = te.create_schedule(C.op)
+
+        with tvm.transform.PassContext(opt_level=3):
+            func = tvm.build(s, [A, B, C], target)
+
+        ctx = tvm.context(target, 0)
+        a_np = np.random.uniform(size=n).astype(A.dtype)
+        b_np = np.random.uniform(size=n).astype(B.dtype)
+        a_np = np.sort(a_np)
+        a = tvm.nd.array(a_np, ctx)
+        b = tvm.nd.array(b_np, ctx)
+        c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
+        func(a, b, c)
+        ref = np.searchsorted(a_np, b_np)
+        tvm.testing.assert_allclose(c.asnumpy(), ref)
+
+    check_target("llvm", searchsorted_ir_cpu)
+    check_target("cuda", searchsorted_ir_gpu)
+    check_target("nvptx", searchsorted_ir_gpu)
+    check_target("vulkan", searchsorted_ir_gpu)
+
+
 if __name__ == "__main__":
     test_prefetch()
     test_if()
     test_for()
     test_cpu()
     test_gpu()
+    test_while_vectorize()
+    test_while_collatz()
+    test_while_mandel()
+    test_while_binary_search()
diff --git a/tests/python/unittest/test_tir_nodes.py b/tests/python/unittest/test_tir_nodes.py
index 4d57ed8ec366..6e338d64a61c 100644
--- a/tests/python/unittest/test_tir_nodes.py
+++ b/tests/python/unittest/test_tir_nodes.py
@@ -129,7 +129,7 @@ def test_basic():
 
 def test_stmt():
     x = tvm.tir.Evaluate(0)
-    tvm.tir.For(te.var("i"), 0, 1, tvm.tir.For.Serial, 0, x)
+    tvm.tir.For(te.var("i"), 0, 1, tvm.tir.ForKind.SERIAL, x)
 
 
 def test_dir():
@@ -364,6 +364,87 @@ def test_intimm_cond():
     assert x == 1
 
 
+def test_block_blockrealize():
+    x = tvm.tir.Var("x", "int32")
+    y = tvm.tir.Var("y", "int32")
+    vx = tvm.tir.IterVar((16, 16), "vx", 0)
+    vx_var = vx.var
+    vy = tvm.tir.IterVar((16, 16), "vy", 2)
+    vy_var = vy.var
+    A = tvm.tir.decl_buffer((16), "float32")
+    B = tvm.tir.decl_buffer((16, 16), "float32")
+    alloc_buffer = tvm.tir.decl_buffer((16, 16), "float32")
+    match_buffer = tvm.tir.decl_buffer((16, 16), "float32")
+    init_body = tvm.tir.BufferStore(A, 0.0, [vx_var])
+    body = tvm.tir.BufferStore(
+        A,
+        tvm.tir.BufferLoad(A, [vx_var]) + tvm.tir.BufferLoad(B, [vx_var, vy_var]),
+        [vx_var],
+    )
+    reads = [
+        tvm.tir.BufferRegion(
+            B, [tvm.ir.Range.from_min_extent(vx_var, 1), tvm.ir.Range.from_min_extent(vy_var, 1)]
+        )
+    ]
+    writes = [tvm.tir.BufferRegion(A, [tvm.ir.Range.from_min_extent(vx_var, 1)])]
+    match_buffer_region = tvm.tir.MatchBufferRegion(
+        match_buffer, tvm.tir.BufferRegion(B, [tvm.ir.Range(0, 16), tvm.ir.Range(0, 16)])
+    )
+
+    block = tvm.tir.Block(
+        [vx, vy],
+        reads,
+        writes,
+        "block",
+        body,
+        init=init_body,
+        alloc_buffers=[alloc_buffer],
+        match_buffers=[match_buffer_region],
+        annotations={"attr_key": "attr_value"},
+    )
+
+    # Checking Block
+    assert isinstance(block, tvm.tir.Block)
+    # Checking iter_vars
+    assert block.iter_vars[0] == vx
+    assert block.iter_vars[1] == vy
+    # Checking reads/writes region
+    assert isinstance(block.reads[0], tvm.tir.BufferRegion)
+    assert block.reads[0].buffer == B
+    assert block.reads[0].region[0].min == vx_var
+    assert block.reads[0].region[1].min == vy_var
+    assert isinstance(block.writes[0], tvm.tir.BufferRegion)
+    assert block.writes[0].buffer == A
+    assert block.writes[0].region[0].min == vx_var
+    assert block.writes[0].region[0].extent == 1
+    # Checking name_hint
+    assert block.name_hint == "block"
+    # Checking body
+    assert block.body == body
+    # Checking init
+    assert block.init == init_body
+    # Checking alloc_buffers
+    assert block.alloc_buffers[0] == alloc_buffer
+    # Checking match_buffers
+    assert block.match_buffers[0].buffer == match_buffer
+    assert isinstance(block.match_buffers[0].source, tvm.tir.BufferRegion)
+    assert block.match_buffers[0].source.buffer == B
+    assert block.match_buffers[0].source.region[0].min == 0
+    assert block.match_buffers[0].source.region[0].extent == 16
+
+    # Checking BlockRealize
+    block_realize = tvm.tir.BlockRealize([x, y], tvm.tir.const(True, "bool"), block)
+    assert isinstance(block_realize, tvm.tir.BlockRealize)
+    assert block_realize.iter_values[0] == x
+    assert block_realize.iter_values[1] == y
+    assert block_realize.predicate == tvm.tir.const(True, "bool")
+    assert block_realize.block == block
+
+    # make sure we can print
+    str(block)
+    str(block_realize)
+
+
 if __name__ == "__main__":
     test_intimm_cond()
     test_buffer_load_store()
@@ -389,3 +470,4 @@ def test_intimm_cond():
     test_isnan()
     test_equality()
     test_equality_string_imm()
+    test_block_blockrealize()
diff --git a/tests/python/unittest/test_tir_transform_remove_no_op.py b/tests/python/unittest/test_tir_transform_remove_no_op.py
index 2edb8cf980c2..8b7a16952af9 100644
--- a/tests/python/unittest/test_tir_transform_remove_no_op.py
+++ b/tests/python/unittest/test_tir_transform_remove_no_op.py
@@ -34,20 +34,17 @@ def test_remove_no_op():
         i,
         0,
         4,
-        0,
-        0,
+        tvm.tir.ForKind.SERIAL,
         tvm.tir.For(
             j,
             0,
             n,
-            0,
-            0,
+            tvm.tir.ForKind.SERIAL,
             tvm.tir.For(
                 k,
                 0,
                 m,
-                0,
-                0,
+                tvm.tir.ForKind.SERIAL,
                 tvm.tir.IfThenElse((i * m + j + k < n), tvm.tir.Evaluate(m), tvm.tir.Evaluate(n)),
             ),
         ),
@@ -65,7 +62,7 @@ def test_remove_no_op():
     assert ret == store
 
     # remove zero extent loop
-    stmt3 = tvm.tir.For(i, 0, 0, 0, 0, store)
+    stmt3 = tvm.tir.For(i, 0, 0, tvm.tir.ForKind.SERIAL, store)
     mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([Ab], stmt3))
     ret = tvm.tir.transform.RemoveNoOp()(mod)["main"].body
     assert isinstance(ret, tvm.tir.Evaluate)
diff --git a/tests/python/unittest/test_tir_transform_storage_rewrite.py b/tests/python/unittest/test_tir_transform_storage_rewrite.py
index cc2b4273a5e3..dbe7e04700d9 100644
--- a/tests/python/unittest/test_tir_transform_storage_rewrite.py
+++ b/tests/python/unittest/test_tir_transform_storage_rewrite.py
@@ -269,7 +269,7 @@ def verify(n):
 def test_parallel_alloc():
     ib = tvm.tir.ir_builder.create()
     n = te.var("n")
-    with ib.for_range(0, n, name="i", for_type="parallel") as i:
+    with ib.for_range(0, n, name="i", kind="parallel") as i:
         with ib.for_range(0, 10, name="j") as j:
             A = ib.allocate("float32", n, name="A", scope="global")
             A[j] = A[j] + 2
@@ -286,7 +286,7 @@ def test_parallel_alloc():
         ib.scope_attr(
             tvm.tir.const(1, "int32"), "pragma_scope", tvm.tir.StringImm("parallel_launch_point")
         )
-        with ib.for_range(0, n, name="i", for_type="parallel") as i:
+        with ib.for_range(0, n, name="i", kind="parallel") as i:
             with ib.for_range(0, 10, name="j") as j:
                 A = ib.allocate("float32", n, name="A", scope="global")
                 A[j] = A[j] + 2
@@ -298,6 +298,76 @@ def test_parallel_alloc():
     assert isinstance(body.body.body.body.body, tvm.tir.Allocate)
 
 
+def test_while_alloc():
+    def get_mod(kind="serial"):
+        ib = tvm.tir.ir_builder.create()
+        n = te.var("n")
+        with ib.for_range(0, n, name="i", kind=kind) as i:
+            j = ib.allocate("int32", 1, name="j", scope="global")
+            j[0] = 0
+            with ib.while_loop(j[0] < 10):
+                A = ib.allocate("float32", n, name="A", scope="global")
+                A[j[0]] = A[j[0]] + 2
+                j[0] += j[0] + 1
+
+        body = ib.get()
+        return tvm.IRModule.from_expr(tvm.tir.PrimFunc([n], body))
+
+    mod = get_mod(kind="parallel")
+    # parallel (i, 0, n) {
+    #   // attr [j] storage_scope = "global"
+    #   allocate j[int32 * 1]
+    #   j[0] = 0
+    #   while((j[0] < 10)){
+    #     // attr [A] storage_scope = "global"
+    #     allocate A[float32 * n]
+    #     A[j[0]] = (A[j[0]] + 2f)
+    #     j[0] = (j[0] + (j[0] + 1))
+    #   }
+    # }
+    body = tvm.tir.transform.StorageRewrite()(mod)["main"].body
+    # parallel (i, 0, n) {
+    #   // attr [j] storage_scope = "global"
+    #   allocate j[int32 * 1]
+    #   // attr [A] storage_scope = "global"
+    #   allocate A[float32 * n]
+    #   j[0] = 0
+    #   while((j[0] < 10)){
+    #     A[j[0]] = (A[j[0]] + 2f)
+    #     j[0] = (j[0] + (j[0] + 1))
+    #   }
+    # }
+    assert isinstance(body.body.body, tvm.tir.Allocate)  # j
+    assert isinstance(body.body.body.body.body, tvm.tir.Allocate)  # A
+
+    mod = get_mod(kind="serial")
+    # for (i, 0, n) {
+    #   // attr [j] storage_scope = "global"
+    #   allocate j[int32 * 1]
+    #   j[0] = 0
+    #   while((j[0] < 10)){
+    #     // attr [A] storage_scope = "global"
+    #     allocate A[float32 * n]
+    #     A[j[0]] = (A[j[0]] + 2f)
+    #     j[0] = (j[0] + (j[0] + 1))
+    #   }
+    # }
+    body = tvm.tir.transform.StorageRewrite()(mod)["main"].body
+    # // attr [j] storage_scope = "global"
+    # allocate j[int32 * 1]
+    # // attr [A] storage_scope = "global"
+    # allocate A[float32 * n]
+    # for (i, 0, n) {
+    #   j[0] = 0
+    #   while((j[0] < 10)){
+    #     A[j[0]] = (A[j[0]] + 2f)
+    #     j[0] = (j[0] + (j[0] + 1))
+    #   }
+    # }
+    assert isinstance(body.body, tvm.tir.Allocate)  # j
+    assert isinstance(body.body.body.body, tvm.tir.Allocate)  # A
+
+
 def test_inplace_rule2(scope_tb="local_TB2", max_bits=1024 * 1024 * 1024):
     # Test Buffer
     register_mem(scope_tb, max_bits)
@@ -576,6 +646,7 @@ def verify(n):
     test_alloc_different_dtypes()
     test_inplace_rule()
     test_parallel_alloc()
+    test_while_alloc()
     test_storage_combine()
     test_storage_share_gpu()
     test_inplace_rule2()
diff --git a/tests/python/unittest/test_tir_transform_unroll_loop.py b/tests/python/unittest/test_tir_transform_unroll_loop.py
index 57b7810198c0..b511118f8b52 100644
--- a/tests/python/unittest/test_tir_transform_unroll_loop.py
+++ b/tests/python/unittest/test_tir_transform_unroll_loop.py
@@ -27,7 +27,7 @@ def test_unroll_loop():
     Aptr = ib.buffer_ptr(Ab)
     # for i in 0 to n-1:
     with ib.for_range(n, n + 2, name="i") as i:
-        with ib.for_range(0, 8, name="i", for_type="unroll") as j:
+        with ib.for_range(0, 8, name="i", kind="unroll") as j:
             Aptr[j + 1] = Aptr[i] + 1
 
     stmt = ib.get()
@@ -48,7 +48,7 @@ def test_unroll_loop():
     ):
         ret = tvm.tir.transform.UnrollLoop()(mod)["main"].body
         assert isinstance(ret, tvm.tir.For)
-        assert ret.for_type == tvm.tir.For.Unrolled
+        assert ret.kind == tvm.tir.ForKind.UNROLLED
 
     ib = tvm.tir.ir_builder.create()
     ib.scope_attr(tvm.tir.const(0, "int32"), "pragma_auto_unroll_max_step", 16)
@@ -63,9 +63,9 @@ def test_unroll_loop():
     ):
         ret = tvm.tir.transform.UnrollLoop()(mod)["main"].body
         assert isinstance(ret[0], tvm.tir.For)
-        assert ret[0].for_type == tvm.tir.For.Unrolled
+        assert ret[0].kind == tvm.tir.ForKind.UNROLLED
         assert isinstance(ret[1], tvm.tir.For)
-        assert ret[1].for_type != tvm.tir.For.Unrolled
+        assert ret[1].kind != tvm.tir.ForKind.UNROLLED
 
 
 def test_unroll_fake_loop():
diff --git a/tests/python/unittest/test_tir_transform_vectorize.py b/tests/python/unittest/test_tir_transform_vectorize.py
index 204e26feb6a9..b1e580957b24 100644
--- a/tests/python/unittest/test_tir_transform_vectorize.py
+++ b/tests/python/unittest/test_tir_transform_vectorize.py
@@ -24,7 +24,7 @@ def test_vectorize_loop():
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
     with ib.for_range(0, n) as i:
-        with ib.for_range(0, 4, for_type="vectorize") as j:
+        with ib.for_range(0, 4, kind="vectorize") as j:
             A[j] = tvm.tir.const(1, A.dtype)
     stmt = ib.get()
 
@@ -45,7 +45,7 @@ def test_vectorize_vector():
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32x4", name="A")
     with ib.for_range(0, n) as i:
-        with ib.for_range(0, 4, for_type="vectorize") as j:
+        with ib.for_range(0, 4, kind="vectorize") as j:
             A[j] = tvm.tir.const(1, A.dtype)
     stmt = ib.get()
     assert isinstance(stmt.body, tvm.tir.For)
@@ -64,7 +64,7 @@ def test_vectorize_with_if():
     x = te.var("x")
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
-    with ib.for_range(0, 4, for_type="vectorize") as i:
+    with ib.for_range(0, 4, kind="vectorize") as i:
         with ib.if_scope(x < n):
             A[i] = A[i] + 1
         with ib.else_scope():
@@ -86,7 +86,7 @@ def test_vectorize_let():
     v = tvm.tir.Var("v", "float32")
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
-    with ib.for_range(0, 4, for_type="vectorize") as i:
+    with ib.for_range(0, 4, kind="vectorize") as i:
         ib.emit(lambda body: tvm.tir.LetStmt(v, A[i] + 1, body))
         A[i] = v + 2
 
@@ -100,7 +100,7 @@ def test_vectorize_with_le_cond():
     n = te.var("n")
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
-    with ib.for_range(0, 4, for_type="vectorize") as i:
+    with ib.for_range(0, 4, kind="vectorize") as i:
         with ib.if_scope(i <= n):
             A[i] = A[i] + 1
     stmt = ib.get()
@@ -115,7 +115,7 @@ def test_vectorize_with_ge_cond():
     n = te.var("n")
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
-    with ib.for_range(0, 4, for_type="vectorize") as i:
+    with ib.for_range(0, 4, kind="vectorize") as i:
         with ib.if_scope(i >= n):
             A[i] = A[i] + 1
     stmt = ib.get()
@@ -131,7 +131,7 @@ def test_vectorize_if_then_else():
     x = te.var("x")
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
-    with ib.for_range(0, 4, for_type="vectorize") as i:
+    with ib.for_range(0, 4, kind="vectorize") as i:
         A[i] = tvm.tir.call_intrin("float32", "tir.if_then_else", i > 0, A[i] + 1, A[i])
     stmt = ib.get()
 
@@ -143,7 +143,7 @@ def test_vectorize_if_then_else():
     ib = tvm.tir.ir_builder.create()
     A = ib.pointer("float32", name="A")
     with ib.for_range(0, n) as k:
-        with ib.for_range(0, 4, for_type="vectorize") as i:
+        with ib.for_range(0, 4, kind="vectorize") as i:
             A[k * 4 + i] = tvm.tir.call_intrin(
                 "float32", "tir.if_then_else", k > 0, A[k * 4 + i], 0
             )
@@ -158,6 +158,53 @@ def test_vectorize_if_then_else():
     assert isinstance(stmt.body.value.args[2], tvm.tir.Broadcast)
 
 
+def test_vectorize_while_fail():
+    """A while loop inside a vectorized loop should fail."""
+
+    n = 64
+    num_iter = 10
+
+    def test_ir(A, B, C):
+        ib = tvm.tir.ir_builder.create()
+        n = C.shape[0]
+        A = ib.buffer_ptr(A)
+        B = ib.buffer_ptr(B)
+        C = ib.buffer_ptr(C)
+        i = ib.allocate("int32", (1,), name="i", scope="local")
+        i[0] = 0
+
+        with ib.for_range(0, n) as j:
+            C[j] = 0.0
+
+        with ib.for_range(0, n, kind="vectorize") as j:
+            with ib.while_loop(i[0] < num_iter):
+                C[j] += A[j] + B[j]
+                i[0] += 1
+
+        return ib.get()
+
+    dtype = "float32"
+    A = te.placeholder((n,), name="A", dtype=dtype)
+    B = te.placeholder((n,), name="B", dtype=dtype)
+
+    C = te.extern(
+        (n,),
+        [A, B],
+        lambda ins, outs: test_ir(ins[0], ins[1], outs[0]),
+        name="while_vectorize",
+        dtype=dtype,
+    )
+    s = te.create_schedule(C.op)
+
+    try:
+        tvm.lower(s, [A, B, C], "llvm")
+        assert False
+    except tvm.error.TVMError as e:
+        error_msg = str(e).split("\n")[-1]
+        expected = "A while loop inside a vectorized loop not supported"
+        assert expected in error_msg
+
+
 if __name__ == "__main__":
     test_vectorize_vector()
     test_vectorize_with_if()
@@ -166,3 +213,4 @@ def test_vectorize_if_then_else():
     test_vectorize_with_le_cond()
     test_vectorize_with_ge_cond()
     test_vectorize_let()
+    test_vectorize_while_fail()
diff --git a/tests/scripts/setup-pytest-env.sh b/tests/scripts/setup-pytest-env.sh
index 475ce1ce1c53..bcd27a16f659 100755
--- a/tests/scripts/setup-pytest-env.sh
+++ b/tests/scripts/setup-pytest-env.sh
@@ -20,11 +20,31 @@
 set +u
 
 if [[ ! -z $CI_PYTEST_ADD_OPTIONS ]]; then
-    export PYTEST_ADDOPTS="-v $CI_PYTEST_ADD_OPTIONS $PYTEST_ADDOPTS"
+    export PYTEST_ADDOPTS="-s -v $CI_PYTEST_ADD_OPTIONS $PYTEST_ADDOPTS"
 else
-    export PYTEST_ADDOPTS="-v $PYTEST_ADDOPTS"
+    export PYTEST_ADDOPTS="-s -v $PYTEST_ADDOPTS"
 fi
 set -u
 
 export TVM_PATH=`pwd`
-export PYTHONPATH=${TVM_PATH}/python
+export PYTHONPATH="${TVM_PATH}/python"
+
+export TVM_PYTEST_RESULT_DIR="${TVM_PATH}/build/pytest-results"
+mkdir -p "${TVM_PYTEST_RESULT_DIR}"
+
+function run_pytest() {
+    local ffi_type="$1"
+    shift
+    local test_suite_name="$1"
+    shift
+    if [ -z "${ffi_type}" -o -z "${test_suite_name}" ]; then
+        echo "error: run_pytest called incorrectly: run_pytest ${ffi_type} ${test_suite_name} $@"
+        echo "usage: run_pytest <FFI_TYPE> <TEST_SUITE_NAME> [pytest args...]"
+        exit 2
+    fi
+    TVM_FFI=${ffi_type} python3 -m pytest \
+           -o "junit_suite_name=${test_suite_name}-${ffi_type}" \
+           "--junit-xml=${TVM_PYTEST_RESULT_DIR}/${test_suite_name}-${ffi_type}.xml" \
+           "--junit-prefix=${ffi_type}" \
+           "$@"
+}
diff --git a/tests/scripts/task_build.sh b/tests/scripts/task_build.sh
index d8e35ebd4de3..845b7153ae20 100755
--- a/tests/scripts/task_build.sh
+++ b/tests/scripts/task_build.sh
@@ -16,4 +16,4 @@
 # specific language governing permissions and limitations
 # under the License.
 export VTA_HW_PATH=`pwd`/3rdparty/vta-hw
-cd $1 && cmake .. && make $2 && cd ..
+cd $1 && cmake .. -DCMAKE_BUILD_TYPE=RelWithDebInfo && make $2 && cd ..
diff --git a/tests/scripts/task_ci_python_setup.sh b/tests/scripts/task_ci_python_setup.sh
index 5ae1478fadc6..f48ed49a2266 100755
--- a/tests/scripts/task_ci_python_setup.sh
+++ b/tests/scripts/task_ci_python_setup.sh
@@ -30,4 +30,4 @@ set -o pipefail
 #
 echo "Addtiional setup in" ${CI_IMAGE_NAME}
 
-python3 -m pip install --user tlcpack-sphinx-addon==0.1.3 synr==0.2.1
+python3 -m pip install --user tlcpack-sphinx-addon==0.1.4 synr==0.2.1
diff --git a/tests/scripts/task_ci_setup.sh b/tests/scripts/task_ci_setup.sh
new file mode 100755
index 000000000000..17838c58a83c
--- /dev/null
+++ b/tests/scripts/task_ci_setup.sh
@@ -0,0 +1,38 @@
+#!/bin/bash
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+set -e
+set -u
+set -o pipefail
+
+# Script to setup additional python env.
+#
+# Use the following command to install the
+# package to /workspace/.local, these additional
+# packages will have precedence over the system packages.
+#
+# command: python3 -m pip install --user <package>==<version>
+#
+echo "Addtiional setup in" ${CI_IMAGE_NAME}
+
+python3 -m pip install --user tlcpack-sphinx-addon==0.1.4 synr==0.2.1
+
+# Rebuild standalone_crt in build/ tree. This file is not currently archived by pack_lib() in
+# Jenkinsfile. We expect config.cmake to be present from pack_lib().
+# TODO(areusch): Make pack_lib() pack all the data dependencies of TVM.
+(cd build && cmake .. && make standalone_crt)
diff --git a/tests/scripts/task_config_build_arm.sh b/tests/scripts/task_config_build_arm.sh
index 80527466c71e..b3a084aef371 100755
--- a/tests/scripts/task_config_build_arm.sh
+++ b/tests/scripts/task_config_build_arm.sh
@@ -25,10 +25,9 @@ cp ../cmake/config.cmake .
 
 echo set\(USE_SORT ON\) >> config.cmake
 echo set\(USE_RPC ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME_DEBUG ON\) >> config.cmake
 echo set\(USE_MICRO ON\) >> config.cmake
 echo set\(USE_MICRO_STANDALONE_RUNTIME ON\) >> config.cmake
-echo set\(USE_VM_PROFILER ON\) >> config.cmake
+echo set\(USE_PROFILER ON\) >> config.cmake
 echo set\(USE_LLVM llvm-config-8\) >> config.cmake
 echo set\(CMAKE_CXX_COMPILER g++\) >> config.cmake
 echo set\(CMAKE_CXX_FLAGS -Werror\) >> config.cmake
diff --git a/tests/scripts/task_config_build_cpu.sh b/tests/scripts/task_config_build_cpu.sh
index 9a009b6a4a78..aa5581b0e11a 100755
--- a/tests/scripts/task_config_build_cpu.sh
+++ b/tests/scripts/task_config_build_cpu.sh
@@ -26,11 +26,10 @@ cp ../cmake/config.cmake .
 echo set\(USE_SORT ON\) >> config.cmake
 echo set\(USE_MICRO ON\) >> config.cmake
 echo set\(USE_MICRO_STANDALONE_RUNTIME ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME_DEBUG ON\) >> config.cmake
-echo set\(USE_VM_PROFILER ON\) >> config.cmake
+echo set\(USE_PROFILER ON\) >> config.cmake
 echo set\(USE_DNNL_CODEGEN ON\) >> config.cmake
 echo set\(USE_ARM_COMPUTE_LIB ON\) >> config.cmake
-echo set\(USE_LLVM llvm-config-10\) >> config.cmake
+echo set\(USE_LLVM llvm-config-11\) >> config.cmake
 echo set\(USE_NNPACK ON\) >> config.cmake
 echo set\(NNPACK_PATH /NNPACK/build/\) >> config.cmake
 echo set\(USE_ANTLR ON\) >> config.cmake
@@ -45,3 +44,4 @@ echo set\(USE_FLATBUFFERS_PATH \"/flatbuffers\"\) >> config.cmake
 echo set\(USE_ETHOSN /opt/arm/ethosn-driver\) >> config.cmake
 echo set\(USE_ETHOSN_HW OFF\) >> config.cmake
 echo set\(USE_VITIS_AI ON\) >> config.cmake
+echo set\(USE_VERILATOR ON\) >> config.cmake
diff --git a/tests/scripts/task_config_build_gpu.sh b/tests/scripts/task_config_build_gpu.sh
index 155bac80533f..13dfb4136547 100755
--- a/tests/scripts/task_config_build_gpu.sh
+++ b/tests/scripts/task_config_build_gpu.sh
@@ -36,8 +36,7 @@ echo set\(USE_RPC ON\) >> config.cmake
 echo set\(USE_SORT ON\) >> config.cmake
 echo set\(USE_GRAPH_RUNTIME ON\) >> config.cmake
 echo set\(USE_STACKVM_RUNTIME ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME_DEBUG ON\) >> config.cmake
-echo set\(USE_VM_PROFILER ON\) >> config.cmake
+echo set\(USE_PROFILER ON\) >> config.cmake
 echo set\(USE_ANTLR ON\) >> config.cmake
 echo set\(USE_VTA_TSIM ON\) >> config.cmake
 echo set\(USE_VTA_FSIM ON\) >> config.cmake
diff --git a/tests/scripts/task_config_build_gpu_vulkan.sh b/tests/scripts/task_config_build_gpu_vulkan.sh
index 74096b1a9760..5865dc969958 100755
--- a/tests/scripts/task_config_build_gpu_vulkan.sh
+++ b/tests/scripts/task_config_build_gpu_vulkan.sh
@@ -27,7 +27,6 @@ echo set\(USE_OPENCL ON\) >> config.cmake
 echo set\(USE_ROCM ON\) >> config.cmake
 echo set\(USE_VULKAN ON\) >> config.cmake
 echo set\(USE_MICRO ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME_DEBUG ON\) >> config.cmake
-echo set\(USE_VM_PROFILER ON\) >> config.cmake
+echo set\(USE_PROFILER ON\) >> config.cmake
 echo set\(CMAKE_CXX_COMPILER clang-7\) >> config.cmake
 echo set\(CMAKE_CXX_FLAGS -Werror\) >> config.cmake
diff --git a/tests/scripts/task_config_build_i386.sh b/tests/scripts/task_config_build_i386.sh
index 8ed5f94e30dc..05acbb022124 100755
--- a/tests/scripts/task_config_build_i386.sh
+++ b/tests/scripts/task_config_build_i386.sh
@@ -25,12 +25,12 @@ cp ../cmake/config.cmake .
 
 echo set\(USE_SORT ON\) >> config.cmake
 echo set\(USE_RPC ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME_DEBUG ON\) >> config.cmake
 echo set\(USE_MICRO ON\) >> config.cmake
 echo set\(USE_MICRO_STANDALONE_RUNTIME ON\) >> config.cmake
-echo set\(USE_VM_PROFILER ON\) >> config.cmake
+echo set\(USE_PROFILER ON\) >> config.cmake
 echo set\(USE_LLVM llvm-config-4.0\) >> config.cmake
 echo set\(CMAKE_CXX_COMPILER g++\) >> config.cmake
 echo set\(CMAKE_CXX_FLAGS -Werror\) >> config.cmake
 echo set\(USE_VTA_TSIM ON\) >> config.cmake
 echo set\(USE_VTA_FSIM ON\) >> config.cmake
+echo set\(USE_VERILATOR ON\) >> config.cmake
diff --git a/tests/scripts/task_config_build_wasm.sh b/tests/scripts/task_config_build_wasm.sh
index c37a119b0590..78dc7550028b 100755
--- a/tests/scripts/task_config_build_wasm.sh
+++ b/tests/scripts/task_config_build_wasm.sh
@@ -26,8 +26,7 @@ cp ../cmake/config.cmake .
 echo set\(USE_SORT ON\) >> config.cmake
 echo set\(USE_MICRO ON\) >> config.cmake
 echo set\(USE_MICRO_STANDALONE_RUNTIME ON\) >> config.cmake
-echo set\(USE_GRAPH_RUNTIME_DEBUG ON\) >> config.cmake
-echo set\(USE_VM_PROFILER ON\) >> config.cmake
+echo set\(USE_PROFILER ON\) >> config.cmake
 echo set\(USE_LLVM llvm-config-11\) >> config.cmake
 echo set\(USE_ANTLR ON\) >> config.cmake
 echo set\(CMAKE_CXX_COMPILER g++\) >> config.cmake
diff --git a/tests/scripts/task_python_arm_compute_library.sh b/tests/scripts/task_python_arm_compute_library.sh
index e36d042676d6..7df894d93399 100755
--- a/tests/scripts/task_python_arm_compute_library.sh
+++ b/tests/scripts/task_python_arm_compute_library.sh
@@ -22,9 +22,9 @@ source tests/scripts/setup-pytest-env.sh
 
 
 # Rebuild cython
+# TODO(u99127): Enable cython tests.
 
 find . -type f -path "*.pyc" | xargs rm -f
 make cython3
 
-TVM_FFI=ctypes python3 -m pytest tests/python/contrib/test_arm_compute_lib
-
+run_pytest ctypes python-arm_compute_lib tests/python/contrib/test_arm_compute_lib
diff --git a/tests/scripts/task_python_ethosn_tests.sh b/tests/scripts/task_python_ethosn_tests.sh
index 36a3d0919650..ae9b82b679ef 100755
--- a/tests/scripts/task_python_ethosn_tests.sh
+++ b/tests/scripts/task_python_ethosn_tests.sh
@@ -22,9 +22,13 @@ source tests/scripts/setup-pytest-env.sh
 
 
 # Rebuild cython
+# TODO(u99127): Enable cython tests.
 
 find . -type f -path "*.pyc" | xargs rm -f
 make cython3
 
-TVM_FFI=ctypes python3 -m pytest tests/python/contrib/test_ethosn
-
+# Note: Default behaviour is to assume the test target is Ethos-N77
+# but setting ETHOSN_VARIANT_CONFIG appropriately
+# (e.g. ETHOSN_VARIANT_CONFIG=ETHOSN78_1TOPS_4PLE_448KSRAM)
+# switches the target to an Ethos-N78 configuration.
+run_pytest ctypes python-ethosn tests/python/contrib/test_ethosn
diff --git a/tests/scripts/task_python_frontend.sh b/tests/scripts/task_python_frontend.sh
index 3c5839bc7e1c..62a0fa1e7fc8 100755
--- a/tests/scripts/task_python_frontend.sh
+++ b/tests/scripts/task_python_frontend.sh
@@ -32,22 +32,22 @@ find . -type f -path "*.pyc" | xargs rm -f
 make cython3
 
 echo "Running relay MXNet frontend test..."
-python3 -m pytest tests/python/frontend/mxnet
+run_pytest cython python-frontend-mxnet tests/python/frontend/mxnet
 
 echo "Running relay ONNX frontend test..."
-python3 -m pytest tests/python/frontend/onnx
+run_pytest cython python-frontend-onnx tests/python/frontend/onnx
 
 echo "Running relay CoreML frontend test..."
-python3 -m pytest tests/python/frontend/coreml
+run_pytest cython python-frontend-coreml tests/python/frontend/coreml
 
 echo "Running relay Tensorflow frontend test..."
-python3 -m pytest tests/python/frontend/tensorflow
+run_pytest cython python-frontend-tensorflow tests/python/frontend/tensorflow
 
 echo "Running relay caffe2 frontend test..."
-python3 -m pytest tests/python/frontend/caffe2
+run_pytest cython python-frontend-caffe2 tests/python/frontend/caffe2
 
 echo "Running relay DarkNet frontend test..."
-python3 -m pytest tests/python/frontend/darknet
+run_pytest cython python-frontend-darknet tests/python/frontend/darknet
 
 echo "Running relay PyTorch frontend test..."
-python3 -m pytest tests/python/frontend/pytorch
+run_pytest cython python-frontend-pytorch tests/python/frontend/pytorch
diff --git a/tests/scripts/task_python_frontend_cpu.sh b/tests/scripts/task_python_frontend_cpu.sh
index 6dfcabc2cd37..208714c64988 100755
--- a/tests/scripts/task_python_frontend_cpu.sh
+++ b/tests/scripts/task_python_frontend_cpu.sh
@@ -33,10 +33,10 @@ find . -type f -path "*.pyc" | xargs rm -f
 make cython3
 
 echo "Running relay TFLite frontend test..."
-python3 -m pytest tests/python/frontend/tflite
+run_pytest cython python-frontend-tflite tests/python/frontend/tflite
 
 echo "Running relay Keras frontend test..."
-python3 -m pytest tests/python/frontend/keras
+run_pytest cython python-frontend-keras tests/python/frontend/keras
 
 echo "Running relay Caffe frontend test..."
-python3 -m pytest tests/python/frontend/caffe
+run_pytest cython python-frontend-caffe tests/python/frontend/caffe
diff --git a/tests/scripts/task_python_integration.sh b/tests/scripts/task_python_integration.sh
index ef86d6917424..613c7cbdf34f 100755
--- a/tests/scripts/task_python_integration.sh
+++ b/tests/scripts/task_python_integration.sh
@@ -27,6 +27,11 @@ export LD_LIBRARY_PATH="build:${LD_LIBRARY_PATH:-}"
 export TVM_BIND_THREADS=0
 export TVM_NUM_THREADS=2
 
+# NOTE: also set by task_python_integration_gpuonly.sh.
+if [ -z "${TVM_INTEGRATION_TESTSUITE_NAME:-}" ]; then
+    TVM_INTEGRATION_TESTSUITE_NAME=python-integration
+fi
+
 # cleanup pycache
 find . -type f -path "*.pyc" | xargs rm -f
 
@@ -39,29 +44,32 @@ rm -rf lib
 make
 cd ../..
 
-TVM_FFI=cython python3 -m pytest apps/extension/tests
-TVM_FFI=ctypes python3 -m pytest apps/extension/tests
+run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-extensions apps/extension/tests
+run_pytest cython ${TVM_INTEGRATION_TESTSUITE_NAME}-extensions apps/extension/tests
 
 # Test dso plugin
 cd apps/dso_plugin_module
 rm -rf lib
 make
 cd ../..
-TVM_FFI=cython python3 -m pytest apps/dso_plugin_module
-TVM_FFI=ctypes python3 -m pytest apps/dso_plugin_module
+run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-dso_plugin_module apps/dso_plugin_module
+run_pytest cython ${TVM_INTEGRATION_TESTSUITE_NAME}-dso_plugin_module apps/dso_plugin_module
 
 # Do not enable TensorFlow op
 # TVM_FFI=cython sh prepare_and_test_tfop_module.sh
 # TVM_FFI=ctypes sh prepare_and_test_tfop_module.sh
 
-TVM_FFI=ctypes python3 -m pytest tests/python/integration
-TVM_FFI=ctypes python3 -m pytest tests/python/contrib
+run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME} tests/python/integration
+if python -c "import tvm; from tvm.relay.op.contrib.ethosn import ethosn_available; print(ethosn_available().name)" -eq "SW_ONLY"; then
+  ETHOSN_VARIANT_CONFIG=ETHOSN78_1TOPS_4PLE_448KSRAM run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-contrib-test_ethosn tests/python/contrib/test_ethosn
+fi
+run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-contrib tests/python/contrib
 
-TVM_TEST_TARGETS="${TVM_RELAY_TEST_TARGETS:-llvm;cuda}" TVM_FFI=ctypes python3 -m pytest tests/python/relay
+TVM_TEST_TARGETS="${TVM_RELAY_TEST_TARGETS:-llvm;cuda}" \
+    run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-relay tests/python/relay
 
 # Command line driver test
-TVM_FFI=ctypes python3 -m pytest tests/python/driver
+run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-driver tests/python/driver
 
 # Do not enable OpenGL
-# TVM_FFI=cython python -m pytest tests/webgl
-# TVM_FFI=ctypes python3 -m pytest tests/webgl
+# run_pytest ctypes ${TVM_INTEGRATION_TESTSUITE_NAME}-webgl tests/webgl
diff --git a/tests/scripts/task_python_integration_gpuonly.sh b/tests/scripts/task_python_integration_gpuonly.sh
index c2a9e0c15abe..ac09cb5a14a3 100755
--- a/tests/scripts/task_python_integration_gpuonly.sh
+++ b/tests/scripts/task_python_integration_gpuonly.sh
@@ -19,5 +19,6 @@
 export TVM_TEST_TARGETS="cuda;opencl;metal;rocm;vulkan;nvptx;opencl -device=mali,aocl_sw_emu"
 export PYTEST_ADDOPTS="-m gpu $PYTEST_ADDOPTS"
 export TVM_RELAY_TEST_TARGETS="cuda"
+export TVM_INTEGRATION_TESTSUITE_NAME=python-integration-gpu
 
 ./tests/scripts/task_python_integration.sh
diff --git a/tests/scripts/task_python_microtvm.sh b/tests/scripts/task_python_microtvm.sh
index 7fb8d471a53a..2e06932ba536 100755
--- a/tests/scripts/task_python_microtvm.sh
+++ b/tests/scripts/task_python_microtvm.sh
@@ -18,12 +18,12 @@
 
 set -e
 set -u
+set -x  # NOTE(areusch): Adding to diagnose flaky timeouts
 
 source tests/scripts/setup-pytest-env.sh
 
 # cleanup pycache
 find . -type f -path "*.pyc" | xargs rm -f
 
-TVM_FFI=ctypes python3 -m pytest tests/micro/qemu
 make cython3
-TVM_FFI=cython python3 -m pytest tests/micro/qemu
+run_pytest ctypes python-microtvm-qemu tests/micro/qemu
diff --git a/tests/scripts/task_python_nightly.sh b/tests/scripts/task_python_nightly.sh
index 36a620541997..16c94dfdad31 100755
--- a/tests/scripts/task_python_nightly.sh
+++ b/tests/scripts/task_python_nightly.sh
@@ -27,4 +27,4 @@ make cython3
 # cleanup pycache
 find . -type f -path "*.pyc" | xargs rm -f
 
-python3 -m pytest tests/python/topi/nightly
+run_pytest cython python-topi-nightly tests/python/topi/nightly
diff --git a/tests/scripts/task_python_topi.sh b/tests/scripts/task_python_topi.sh
index 3bc3caf825cf..9a5991e6a766 100755
--- a/tests/scripts/task_python_topi.sh
+++ b/tests/scripts/task_python_topi.sh
@@ -31,4 +31,4 @@ make cython3
 # cleanup pycache
 find . -type f -path "*.pyc" | xargs rm -f
 
-python3 -m pytest tests/python/topi/
+run_pytest cython python-topi tests/python/topi/
diff --git a/tests/scripts/task_python_unittest.sh b/tests/scripts/task_python_unittest.sh
index 0aaf9fc86664..54a36f6dcfd4 100755
--- a/tests/scripts/task_python_unittest.sh
+++ b/tests/scripts/task_python_unittest.sh
@@ -25,7 +25,15 @@ source tests/scripts/setup-pytest-env.sh
 find . -type f -path "*.pyc" | xargs rm -f
 make cython3
 
-TVM_FFI=ctypes python3 -m pytest tests/python/all-platform-minimal-test
-TVM_FFI=cython python3 -m pytest tests/python/all-platform-minimal-test
-TVM_FFI=ctypes python3 -m pytest tests/python/unittest
-TVM_FFI=cython python3 -m pytest tests/python/unittest
+# NOTE: also set by task_python_unittest_gpuonly.sh.
+if [ -z "${TVM_UNITTEST_TESTSUITE_NAME:-}" ]; then
+    TVM_UNITTEST_TESTSUITE_NAME=python-unittest
+fi
+
+# First run minimal test on both ctypes and cython.
+run_pytest ctypes ${TVM_UNITTEST_TESTSUITE_NAME}-platform-minimal-test tests/python/all-platform-minimal-test
+run_pytest cython ${TVM_UNITTEST_TESTSUITE_NAME}-platform-minimal-test tests/python/all-platform-minimal-test
+
+# Then run all unittests on both ctypes and cython.
+run_pytest ctypes ${TVM_UNITTEST_TESTSUITE_NAME} tests/python/unittest
+run_pytest cython ${TVM_UNITTEST_TESTSUITE_NAME} tests/python/unittest
diff --git a/tests/scripts/task_python_unittest_gpuonly.sh b/tests/scripts/task_python_unittest_gpuonly.sh
index 56722b16a364..22f79bc70ec9 100755
--- a/tests/scripts/task_python_unittest_gpuonly.sh
+++ b/tests/scripts/task_python_unittest_gpuonly.sh
@@ -18,5 +18,6 @@
 
 export TVM_TEST_TARGETS="cuda;opencl;metal;rocm;vulkan;nvptx;opencl -device=mali,aocl_sw_emu"
 export PYTEST_ADDOPTS="-m gpu $PYTEST_ADDOPTS"
+export TVM_UNITTEST_TESTSUITE_NAME=python-unittest-gpu
 
 ./tests/scripts/task_python_unittest.sh
diff --git a/tests/scripts/task_python_vta_fsim.sh b/tests/scripts/task_python_vta_fsim.sh
index 8080bbe756c7..4074fb888351 100755
--- a/tests/scripts/task_python_vta_fsim.sh
+++ b/tests/scripts/task_python_vta_fsim.sh
@@ -40,8 +40,8 @@ cp ${VTA_HW_PATH}/config/fsim_sample.json ${VTA_HW_PATH}/config/vta_config.json
 
 # Run unit tests in functional/fast simulator
 echo "Running unittest in fsim..."
-python3 -m pytest ${TVM_PATH}/vta/tests/python/unittest
+run_pytest cython python-vta-fsim-unittest ${TVM_PATH}/vta/tests/python/unittest
 
 # Run unit tests in functional/fast simulator
 echo "Running integration test in fsim..."
-python3 -m pytest ${TVM_PATH}/vta/tests/python/integration
+run_pytest cython python-vta-fsim-integration ${TVM_PATH}/vta/tests/python/integration
diff --git a/tests/scripts/task_python_vta_tsim.sh b/tests/scripts/task_python_vta_tsim.sh
index c87d5483b8a5..3a6a35e5a06f 100755
--- a/tests/scripts/task_python_vta_tsim.sh
+++ b/tests/scripts/task_python_vta_tsim.sh
@@ -55,11 +55,11 @@ make -C ${VTA_HW_PATH}/hardware/chisel USE_THREADS=0 lib
 
 # Run unit tests in cycle accurate simulator
 echo "Running unittest in tsim..."
-python3 -m pytest ${TVM_PATH}/vta/tests/python/unittest
+run_pytest cython python-vta-tsim-unittest ${TVM_PATH}/vta/tests/python/unittest
 
 # Run unit tests in cycle accurate simulator
 echo "Running integration test in tsim..."
-python3 -m pytest ${TVM_PATH}/vta/tests/python/integration
+run_pytest cython python-vta-tsim-integration ${TVM_PATH}/vta/tests/python/integration
 
 # Reset default fsim simulation
 cp ${VTA_HW_PATH}/config/fsim_sample.json ${VTA_HW_PATH}/config/vta_config.json
diff --git a/tests/scripts/task_sphinx_precheck.sh b/tests/scripts/task_sphinx_precheck.sh
index fd67b0ab539b..894f7471bde4 100755
--- a/tests/scripts/task_sphinx_precheck.sh
+++ b/tests/scripts/task_sphinx_precheck.sh
@@ -36,7 +36,7 @@ make cython3
 echo "PreCheck sphinx doc generation WARNINGS.."
 cd docs
 make clean
-TVM_TUTORIAL_EXEC_PATTERN=none make html |& tee /tmp/$$.log.txt
+TVM_TUTORIAL_EXEC_PATTERN=none make html 2>1 | tee /tmp/$$.log.txt
 
 grep -v -E "__mro__|UserWarning|FutureWarning|tensorflow|Keras|pytorch|TensorFlow|403" < /tmp/$$.log.txt > /tmp/$$.logclean.txt || true
 echo "---------Sphinx Log----------"
diff --git a/tutorials/auto_scheduler/ci_logs/resnet-18-NHWC-B1-cuda.json b/tutorials/auto_scheduler/ci_logs/resnet-18-NHWC-B1-cuda.json
index 8d0a6ae980c4..7cb3a67067b0 100644
--- a/tutorials/auto_scheduler/ci_logs/resnet-18-NHWC-B1-cuda.json
+++ b/tutorials/auto_scheduler/ci_logs/resnet-18-NHWC-B1-cuda.json
@@ -1,26 +1,26 @@
 # Provide valid schedules for resnet-18 on GPU.
 # This is used to run the tutorial on the documentation web server.
-{"i": [["[\"b32ed43fb351136894c322ee49097a1a\"]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 65536, 1024, 8, 32]], [[], [["SP", 4, 1, 1000, [40], 1], ["AN", 4, 2, 6], ["FSP", 3, 1, 0, 1], ["AN", 3, 2, 6], ["CA", 3, 4, 0], ["CI", 2], ["FSP", 1, 1, 0, 1], ["AN", 1, 2, 6], ["CA", 1, 4, 0], ["AN", 4, 0, 5], ["PR", 1, 0, "auto_unroll_max_step$512"], ["PR", 3, 0, "auto_unroll_max_step$512"]]]], "r": [[4.87396e-06], 0, 1.30575, 1606984701], "v": "v0.3"}
-{"i": [["[\"d09dc1a6bb90d59c91b68989ad3492ff\"]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 65536, 1024, 8, 32]], [[], [["SP", 2, 0, 1, [1, 1, 1, 1], 1], ["SP", 2, 5, 1000, [1, 50, 1, 1], 1], ["SP", 2, 10, 512, [1, 16], 1], ["RE", 2, [0, 5, 1, 6, 2, 7, 10, 11, 3, 8, 12, 4, 9]], ["FSP", 4, 0, 0, 3], ["FSP", 4, 4, 1, 3], ["RE", 4, [0, 4, 1, 5, 2, 6, 3, 7]], ["CA", 2, 4, 5], ["CHR", 1, "shared", [2]], ["CA", 2, 3, 6], ["CHR", 0, "shared", [3]], ["CA", 1, 4, 6], ["FU", 6, [0, 1]], ["AN", 6, 0, 5], ["FU", 6, [1, 2]], ["AN", 6, 1, 4], ["FU", 6, [2, 3]], ["AN", 6, 2, 6], ["FU", 3, [0, 1]], ["SP", 3, 0, 32, [1], 1], ["AN", 3, 1, 2], ["FFSP", 3, 0, [1, 0], 1, 1], ["AN", 3, 1, 6], ["FU", 1, [0, 1]], ["SP", 1, 0, 32, [1], 1], ["AN", 1, 1, 2], ["FFSP", 1, 0, [1, 0], 1, 1], ["AN", 1, 1, 6], ["PR", 4, 0, "auto_unroll_max_step$0"]]]], "r": [[2.25155e-05], 0, 1.5128, 1606984719], "v": "v0.3"}
-{"i": [["[\"7de313da0ca29a8c63f647791692430d\"]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 65536, 1024, 8, 32]], [[], [["FU", 2, [0, 1, 2, 3]], ["SP", 2, 0, 512, [2], 1], ["AN", 2, 0, 5], ["AN", 2, 1, 6], ["FU", 1, [0, 1, 2, 3]], ["SP", 1, 0, 512, [32], 1], ["AN", 1, 0, 5], ["AN", 1, 1, 6], ["PR", 1, 0, "auto_unroll_max_step$64"]]]], "r": [[3.91068e-06], 0, 1.63708, 1606984742], "v": "v0.3"}
-{"i": [["[\"8d5a93959138dc7b2ee1f1b3219dfa14\"]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 65536, 1024, 8, 32]], [[], [["CI", 15], ["CI", 13], ["CI", 11], ["CI", 9], ["AN", 8, 0, 1], ["AN", 8, 1, 1], ["SP", 8, 2, 16, [2], 1], ["SP", 8, 4, 512, [16], 1], ["AN", 8, 6, 1], ["AN", 8, 7, 1], ["RE", 8, [2, 4, 3, 5, 0, 1, 6, 7]], ["CI", 7], ["CHW", 6, "local"], ["SP", 6, 0, 4, [1, 2, 1, 1], 1], ["SP", 6, 5, 4, [1, 4, 1, 1], 1], ["SP", 6, 10, 16, [4, 1, 4, 1], 1], ["SP", 6, 15, 512, [2, 8, 1, 1], 1], ["SP", 6, 20, 512, [2, 1], 1], ["RE", 6, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 21, 3, 8, 13, 18, 22, 4, 9, 14, 19]], ["FSP", 7, 0, 13, 3], ["FSP", 7, 4, 14, 3], ["FSP", 7, 8, 15, 3], ["FSP", 7, 12, 16, 3], ["RE", 7, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 6, 7, 11], ["CHR", 5, "shared", [6]], ["CA", 6, 7, 12], ["CHR", 4, "shared", [7]], ["CA", 5, 8, 12], ["AN", 4, 0, 1], ["AN", 4, 1, 1], ["SP", 4, 2, 16, [1], 1], ["SP", 4, 4, 512, [8], 1], ["AN", 4, 6, 1], ["AN", 4, 7, 1], ["RE", 4, [2, 4, 3, 5, 0, 1, 6, 7]], ["CI", 3], ["CA", 2, 4, 3], ["CI", 1], ["FU", 19, [0, 1, 2, 3]], ["SP", 19, 0, 25088, [32], 1], ["AN", 19, 0, 5], ["AN", 19, 1, 6], ["FU", 11, [0, 1, 2, 3]], ["SP", 11, 0, 8192, [64], 1], ["AN", 11, 0, 5], ["AN", 11, 1, 6], ["FU", 9, [0, 1, 2, 3]], ["AN", 9, 0, 5], ["FU", 9, [1, 2, 3, 4]], ["AN", 9, 1, 4], ["FU", 9, [2, 3, 4, 5]], ["AN", 9, 2, 6], ["FU", 7, [0, 1, 2, 3]], ["SP", 7, 0, 2, [2], 1], ["AN", 7, 1, 2], ["FFSP", 7, 0, [16, 15, 14, 13], 1, 1], ["AN", 7, 1, 6], ["FU", 5, [0, 1, 2, 3]], ["SP", 5, 0, 8, [1], 1], ["AN", 5, 1, 2], ["FFSP", 5, 0, [16, 15, 14, 13], 1, 1], ["AN", 5, 1, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 8192, [32], 1], ["AN", 4, 0, 5], ["AN", 4, 1, 6], ["PR", 4, 0, "auto_unroll_max_step$512"], ["PR", 8, 0, "auto_unroll_max_step$512"], ["PR", 11, 0, "auto_unroll_max_step$16"]]]], "r": [[0.000190231], 0, 1.95863, 1606984773], "v": "v0.3"}
-{"i": [["[\"ac6920940de3797cc3f9f9c260675e5d\"]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 65536, 1024, 8, 32]], [[], [["CI", 11], ["CI", 9], ["AN", 8, 0, 1], ["AN", 8, 1, 1], ["SP", 8, 2, 16, [4], 1], ["SP", 8, 4, 512, [8], 1], ["AN", 8, 6, 1], ["AN", 8, 7, 1], ["RE", 8, [2, 4, 3, 5, 0, 1, 6, 7]], ["CI", 7], ["CHW", 6, "local"], ["SP", 6, 0, 4, [1, 1, 1, 2], 1], ["SP", 6, 5, 4, [1, 1, 1, 2], 1], ["SP", 6, 10, 16, [4, 2, 2, 1], 1], ["SP", 6, 15, 512, [1, 16, 2, 1], 1], ["SP", 6, 20, 512, [2, 1], 1], ["RE", 6, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 21, 3, 8, 13, 18, 22, 4, 9, 14, 19]], ["FSP", 7, 0, 11, 3], ["FSP", 7, 4, 12, 3], ["FSP", 7, 8, 13, 3], ["FSP", 7, 12, 14, 3], ["RE", 7, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 6, 7, 11], ["CHR", 5, "shared", [6]], ["CA", 6, 7, 12], ["CHR", 4, "shared", [7]], ["CA", 5, 8, 12], ["AN", 4, 0, 1], ["AN", 4, 1, 1], ["SP", 4, 2, 16, [16], 1], ["SP", 4, 4, 512, [2], 1], ["AN", 4, 6, 1], ["AN", 4, 7, 1], ["RE", 4, [2, 4, 3, 5, 0, 1, 6, 7]], ["CI", 3], ["CA", 2, 4, 3], ["CI", 1], ["FU", 15, [0, 1, 2, 3]], ["SP", 15, 0, 25088, [32], 1], ["AN", 15, 0, 5], ["AN", 15, 1, 6], ["FU", 11, [0, 1, 2, 3]], ["SP", 11, 0, 8192, [32], 1], ["AN", 11, 0, 5], ["AN", 11, 1, 6], ["FU", 9, [0, 1, 2, 3]], ["AN", 9, 0, 5], ["FU", 9, [1, 2, 3, 4]], ["AN", 9, 1, 4], ["FU", 9, [2, 3, 4, 5]], ["AN", 9, 2, 6], ["FU", 7, [0, 1, 2, 3]], ["SP", 7, 0, 16, [1], 1], ["AN", 7, 1, 2], ["FFSP", 7, 0, [14, 13, 12, 11], 1, 1], ["AN", 7, 1, 6], ["FU", 5, [0, 1, 2, 3]], ["SP", 5, 0, 16, [1], 1], ["AN", 5, 1, 2], ["FFSP", 5, 0, [14, 13, 12, 11], 1, 1], ["AN", 5, 1, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 8192, [32], 1], ["AN", 4, 0, 5], ["AN", 4, 1, 6], ["PR", 4, 0, "auto_unroll_max_step$512"], ["PR", 8, 0, "auto_unroll_max_step$64"], ["PR", 11, 0, "auto_unroll_max_step$1024"]]]], "r": [[0.000218188], 0, 2.05807, 1606984806], "v": "v0.3"}
-{"i": [["[\"7e83a2ee5cd5d50282ed19310700046a\"]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 65536, 1024, 8, 32]], [[], [["CI", 9], ["AN", 8, 0, 1], ["AN", 8, 1, 1], ["SP", 8, 2, 16, [1], 1], ["SP", 8, 4, 512, [8], 1], ["AN", 8, 6, 1], ["AN", 8, 7, 1], ["RE", 8, [2, 4, 3, 5, 0, 1, 6, 7]], ["CI", 7], ["CHW", 6, "local"], ["SP", 6, 0, 4, [1, 1, 1, 2], 1], ["SP", 6, 5, 4, [1, 1, 1, 1], 1], ["SP", 6, 10, 16, [2, 1, 1, 8], 1], ["SP", 6, 15, 512, [1, 16, 1, 2], 1], ["SP", 6, 20, 512, [2, 8], 1], ["RE", 6, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 21, 3, 8, 13, 18, 22, 4, 9, 14, 19]], ["FSP", 7, 0, 10, 3], ["FSP", 7, 4, 11, 3], ["FSP", 7, 8, 12, 3], ["FSP", 7, 12, 13, 3], ["RE", 7, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 6, 7, 11], ["CHR", 5, "shared", [6]], ["CA", 6, 7, 12], ["CHR", 4, "shared", [7]], ["CA", 5, 8, 12], ["AN", 4, 0, 1], ["AN", 4, 1, 1], ["SP", 4, 2, 16, [2], 1], ["SP", 4, 4, 512, [16], 1], ["AN", 4, 6, 1], ["AN", 4, 7, 1], ["RE", 4, [2, 4, 3, 5, 0, 1, 6, 7]], ["CI", 3], ["CA", 2, 4, 3], ["CI", 1], ["FU", 14, [0, 1, 2, 3]], ["SP", 14, 0, 25088, [32], 1], ["AN", 14, 0, 5], ["AN", 14, 1, 6], ["FU", 11, [0, 1, 2, 3]], ["SP", 11, 0, 8192, [32], 1], ["AN", 11, 0, 5], ["AN", 11, 1, 6], ["FU", 9, [0, 1, 2, 3]], ["AN", 9, 0, 5], ["FU", 9, [1, 2, 3, 4]], ["AN", 9, 1, 4], ["FU", 9, [2, 3, 4, 5]], ["AN", 9, 2, 6], ["FU", 7, [0, 1, 2, 3]], ["SP", 7, 0, 64, [1], 1], ["AN", 7, 1, 2], ["FFSP", 7, 0, [13, 12, 11, 10], 1, 1], ["AN", 7, 1, 6], ["FU", 5, [0, 1, 2, 3]], ["SP", 5, 0, 256, [1], 1], ["AN", 5, 1, 2], ["FFSP", 5, 0, [13, 12, 11, 10], 1, 1], ["AN", 5, 1, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 8192, [32], 1], ["AN", 4, 0, 5], ["AN", 4, 1, 6], ["PR", 4, 0, "auto_unroll_max_step$1024"], ["PR", 8, 0, "auto_unroll_max_step$512"], ["PR", 11, 0, "auto_unroll_max_step$512"]]]], "r": [[0.000165484], 0, 2.76154, 1606984831], "v": "v0.3"}
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+{"i": [["[\"f4380bb1dc62422a69ad4a1a9771f927\", 1, 56, 56, 64, 1, 1, 64, 128, 1, 28, 28, 128]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 2147483647, 1024, 8, 32], "", 0], [[], [["CHW", 3, "local"], ["SP", 3, 0, 1, [1, 1, 1, 1], 1], ["SP", 3, 5, 28, [1, 7, 1, 1], 1], ["SP", 3, 10, 28, [1, 2, 1, 7], 1], ["SP", 3, 15, 128, [8, 8, 1, 1], 1], ["SP", 3, 20, 1, [1, 1], 1], ["SP", 3, 23, 1, [1, 1], 1], ["SP", 3, 26, 64, [2, 2], 1], ["RE", 3, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 23, 26, 21, 24, 27, 3, 8, 13, 18, 22, 25, 28, 4, 9, 14, 19]], ["FSP", 4, 0, 1, 3], ["FSP", 4, 4, 2, 3], ["FSP", 4, 8, 3, 3], ["FSP", 4, 12, 4, 3], ["RE", 4, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 3, 4, 11], ["CHR", 2, "shared", [3]], ["CA", 3, 4, 14], ["CHR", 1, "shared", [4]], ["CA", 2, 5, 14], ["CI", 1], ["FU", 6, [0, 1, 2, 3]], ["AN", 6, 0, 5], ["FU", 6, [1, 2, 3, 4]], ["AN", 6, 1, 4], ["FU", 6, [2, 3, 4, 5]], ["AN", 6, 2, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 16, [2], 1], ["AN", 4, 1, 2], ["FFSP", 4, 0, [4, 3, 2, 1], 1, 1], ["AN", 4, 1, 6], ["FU", 2, [0, 1, 2, 3]], ["SP", 2, 0, 208, [2], 1], ["AN", 2, 1, 2], ["FFSP", 2, 0, [4, 3, 2, 1], 1, 1], ["AN", 2, 1, 6], ["PR", 5, 0, "auto_unroll_max_step$16"]]]], "r": [[1.76965e-05], 0, 1.63284, 1606985253], "v": "v0.5"}
+{"i": [["[\"f4380bb1dc62422a69ad4a1a9771f927\", 1, 28, 28, 128, 1, 1, 128, 256, 1, 14, 14, 256]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 2147483647, 1024, 8, 32], "", 0], [[], [["CHW", 3, "local"], ["SP", 3, 0, 1, [1, 1, 1, 1], 1], ["SP", 3, 5, 14, [1, 1, 1, 1], 1], ["SP", 3, 10, 14, [2, 1, 7, 1], 1], ["SP", 3, 15, 256, [2, 64, 1, 2], 1], ["SP", 3, 20, 1, [1, 1], 1], ["SP", 3, 23, 1, [1, 1], 1], ["SP", 3, 26, 128, [1, 2], 1], ["RE", 3, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 23, 26, 21, 24, 27, 3, 8, 13, 18, 22, 25, 28, 4, 9, 14, 19]], ["FSP", 4, 0, 1, 3], ["FSP", 4, 4, 2, 3], ["FSP", 4, 8, 3, 3], ["FSP", 4, 12, 4, 3], ["RE", 4, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 3, 4, 11], ["CHR", 2, "shared", [3]], ["CA", 3, 4, 14], ["CHR", 1, "shared", [4]], ["CA", 2, 5, 14], ["CI", 1], ["FU", 6, [0, 1, 2, 3]], ["AN", 6, 0, 5], ["FU", 6, [1, 2, 3, 4]], ["AN", 6, 1, 4], ["FU", 6, [2, 3, 4, 5]], ["AN", 6, 2, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 8, [2], 1], ["AN", 4, 1, 2], ["FFSP", 4, 0, [4, 3, 2, 1], 1, 1], ["AN", 4, 1, 6], ["FU", 2, [0, 1, 2, 3]], ["SP", 2, 0, 52, [1], 1], ["AN", 2, 1, 2], ["FFSP", 2, 0, [4, 3, 2, 1], 1, 1], ["AN", 2, 1, 6], ["PR", 5, 0, "auto_unroll_max_step$0"]]]], "r": [[3.05015e-05], 0, 1.59532, 1606985280], "v": "v0.5"}
+{"i": [["[\"f4380bb1dc62422a69ad4a1a9771f927\", 1, 14, 14, 256, 1, 1, 256, 512, 1, 7, 7, 512]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 2147483647, 1024, 8, 32], "", 0], [[], [["CHW", 3, "local"], ["SP", 3, 0, 1, [1, 1, 1, 1], 1], ["SP", 3, 5, 7, [1, 1, 7, 1], 1], ["SP", 3, 10, 7, [1, 1, 1, 1], 1], ["SP", 3, 15, 512, [4, 128, 1, 1], 1], ["SP", 3, 20, 1, [1, 1], 1], ["SP", 3, 23, 1, [1, 1], 1], ["SP", 3, 26, 256, [1, 16], 1], ["RE", 3, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 23, 26, 21, 24, 27, 3, 8, 13, 18, 22, 25, 28, 4, 9, 14, 19]], ["FSP", 4, 0, 1, 3], ["FSP", 4, 4, 2, 3], ["FSP", 4, 8, 3, 3], ["FSP", 4, 12, 4, 3], ["RE", 4, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 3, 4, 11], ["CHR", 2, "shared", [3]], ["CA", 3, 4, 14], ["CHR", 1, "shared", [4]], ["CA", 2, 5, 14], ["CI", 1], ["FU", 6, [0, 1, 2, 3]], ["AN", 6, 0, 5], ["FU", 6, [1, 2, 3, 4]], ["AN", 6, 1, 4], ["FU", 6, [2, 3, 4, 5]], ["AN", 6, 2, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 16, [1], 1], ["AN", 4, 1, 2], ["FFSP", 4, 0, [4, 3, 2, 1], 1, 1], ["AN", 4, 1, 6], ["FU", 2, [0, 1, 2, 3]], ["SP", 2, 0, 2704, [1], 1], ["AN", 2, 1, 2], ["FFSP", 2, 0, [4, 3, 2, 1], 1, 1], ["AN", 2, 1, 6], ["PR", 5, 0, "auto_unroll_max_step$512"]]]], "r": [[2.18808e-05], 0, 1.88033, 1606985298], "v": "v0.5"}
+{"i": [["[\"12b88bedece6984af589a28b43e0f3c4\", 1, 14, 14, 256, 3, 3, 256, 512, 1, 1, 1, 512, 1, 7, 7, 512]", "cuda -keys=cuda,gpu -max_num_threads=1024 -thread_warp_size=32", [-1, 16, 64, 49152, 2147483647, 1024, 8, 32], "", 0], [[], [["CI", 5], ["SP", 3, 0, 1, [1, 1, 1, 1], 1], ["SP", 3, 5, 7, [7, 1, 1, 1], 1], ["SP", 3, 10, 7, [1, 7, 1, 1], 1], ["SP", 3, 15, 512, [1, 4, 1, 1], 1], ["SP", 3, 20, 3, [1, 3], 1], ["SP", 3, 23, 3, [1, 3], 1], ["SP", 3, 26, 256, [8, 2], 1], ["RE", 3, [0, 5, 10, 15, 1, 6, 11, 16, 2, 7, 12, 17, 20, 23, 26, 21, 24, 27, 3, 8, 13, 18, 22, 25, 28, 4, 9, 14, 19]], ["FSP", 6, 0, 1, 3], ["FSP", 6, 4, 2, 3], ["FSP", 6, 8, 3, 3], ["FSP", 6, 12, 4, 3], ["RE", 6, [0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15]], ["CA", 3, 6, 11], ["CHR", 2, "shared", [3]], ["CA", 3, 4, 14], ["CHR", 1, "shared", [4]], ["CA", 2, 5, 14], ["CI", 1], ["FU", 8, [0, 1, 2, 3]], ["AN", 8, 0, 5], ["FU", 8, [1, 2, 3, 4]], ["AN", 8, 1, 4], ["FU", 8, [2, 3, 4, 5]], ["AN", 8, 2, 6], ["FU", 4, [0, 1, 2, 3]], ["SP", 4, 0, 144, [1], 1], ["AN", 4, 1, 2], ["FFSP", 4, 0, [4, 3, 2, 1], 1, 1], ["AN", 4, 1, 6], ["FU", 2, [0, 1, 2, 3]], ["SP", 2, 0, 144, [1], 1], ["AN", 2, 1, 2], ["FFSP", 2, 0, [4, 3, 2, 1], 1, 1], ["AN", 2, 1, 6], ["PR", 5, 0, "auto_unroll_max_step$512"]]]], "r": [[0.000190239], 0, 2.28266, 1606985323], "v": "v0.5"}
diff --git a/tutorials/auto_scheduler/ci_logs/resnet-50-NHWC-B1-llvm.json b/tutorials/auto_scheduler/ci_logs/resnet-50-NHWC-B1-llvm.json
index 611f7765f584..3dd4541fd33a 100644
--- a/tutorials/auto_scheduler/ci_logs/resnet-50-NHWC-B1-llvm.json
+++ b/tutorials/auto_scheduler/ci_logs/resnet-50-NHWC-B1-llvm.json
@@ -1,31 +1,28 @@
 # Provide valid schedules for resnet-50 for CPU.
 # This is used to run the tutorial on the documentation web server.
-{"i": [["[\"b32ed43fb351136894c322ee49097a1a\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["SP", 3, 1, 1000, [50], 1], ["RF", 3, 2, 1], ["RE", 3, [0, 2, 1]], ["SP", 1, 1, 1000, [20], 1], ["RF", 1, 2, 1], ["RE", 1, [0, 2, 1]], ["CR", 6], ["CA", 5, 6, 1], ["CR", 4], ["CA", 2, 3, 1], ["AN", 1, 0, 3], ["FU", 3, [0, 1]], ["AN", 3, 0, 3], ["AN", 4, 0, 3], ["FU", 6, [0, 1]], ["AN", 6, 0, 3], ["PR", 1, 0, "auto_unroll_max_step$16"], ["PR", 2, 0, "auto_unroll_max_step$16"], ["PR", 4, 0, "auto_unroll_max_step$16"], ["PR", 5, 0, "auto_unroll_max_step$64"]]]], "r": [[8.75e-06, 1.0781e-05, 9.875e-06, 9.836e-06, 1.0357e-05, 1.0238e-05, 1.0341e-05, 9.75e-06, 9.561e-06, 1.0122e-05], 0, 0.17921, 1606960872], "v": "v0.3"}
-{"i": [["[\"6129df1a3d5f6326c8393a8d17160199\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["SP", 2, 0, 1, [1, 1, 1], 1], ["SP", 2, 4, 1000, [1, 1, 1], 1], ["SP", 2, 8, 16, [2, 2, 4], 1], ["SP", 2, 12, 128, [32], 1], ["RE", 2, [0, 4, 8, 1, 5, 9, 12, 2, 6, 10, 13, 3, 7, 11]], ["CR", 5], ["CA", 3, 5, 1], ["FU", 2, [0, 1]], ["AN", 2, 0, 3], ["FU", 5, [0, 1]], ["AN", 5, 0, 3], ["PR", 2, 0, "auto_unroll_max_step$16"], ["PR", 3, 0, "auto_unroll_max_step$512"], ["AN", 2, 12, 2]]]], "r": [[8.7769e-05, 8.6467e-05, 8.6989e-05, 9.3901e-05, 8.6221e-05, 8.4351e-05, 8.4747e-05, 8.8687e-05, 8.8928e-05, 8.3574e-05], 0, 0.33759, 1606960890], "v": "v0.3"}
-{"i": [["[\"36ee2798ed60bae3bcd1bb89a0285fe8\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["CA", 1, 2, 3], ["FU", 2, [0, 1, 2, 3]], ["AN", 2, 0, 3], ["PR", 1, 0, "auto_unroll_max_step$16"]]]], "r": [[6.28e-06, 8.176e-06, 8.048e-06, 7.942e-06, 7.977e-06, 8.002e-06, 8.093e-06, 7.924e-06, 7.943e-06, 7.924e-06], 0, 0.130759, 1606960900], "v": "v0.3"}
-{"i": [["[\"dcf6fcf5f56fa614bf9aef0c82382caf\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["CI", 9], ["CI", 7], ["CI", 5], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 7, [1, 7, 1], 1], ["SP", 3, 8, 7, [1, 1, 1], 1], ["SP", 3, 12, 2048, [8, 2, 8], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 512, [2], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["FSP", 10, 0, 3, 2], ["FSP", 10, 3, 4, 2], ["FSP", 10, 6, 5, 2], ["FSP", 10, 9, 6, 2], ["RE", 10, [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11]], ["CA", 3, 10, 7], ["CI", 1], ["FU", 10, [0, 1, 2, 3, 4, 5, 6, 7]], ["AN", 10, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$512"], ["AN", 3, 21, 2], ["AN", 10, 4, 2]]]], "r": [[0.000175984, 0.000171372, 0.00018538, 0.000178085, 0.00017879, 0.000179878, 0.000179221, 0.000178598, 0.000176714, 0.000168318], 0, 0.277929, 1606960917], "v": "v0.3"}
-{"i": [["[\"7657f886f5e9d8b5f19a5fd2c5b90d8d\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["CI", 5], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 7, [1, 1, 7], 1], ["SP", 3, 8, 7, [1, 1, 1], 1], ["SP", 3, 12, 512, [32, 1, 8], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 1024, [8], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["CI", 1], ["FU", 3, [0, 1, 2, 3, 4, 5, 6, 7]], ["AN", 3, 0, 3], ["FU", 6, [0, 1, 2, 3]], ["AN", 6, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$512"], ["AN", 3, 14, 2]]]], "r": [[0.00012651, 0.00012801, 0.000128605, 0.00013267, 0.00012596, 0.000126418, 0.000121995, 0.000127242, 0.000128152, 0.000129989], 0, 0.310011, 1606960986], "v": "v0.3"}
-{"i": [["[\"7e09b626cf077cd419190fee02091dd6\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["CI", 7], ["CI", 5], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 14, [7, 1, 2], 1], ["SP", 3, 8, 14, [2, 1, 1], 1], ["SP", 3, 12, 1024, [1, 1, 32], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 256, [8], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["CI", 1], ["FU", 3, [0, 1, 2, 3, 4, 5, 6]], ["AN", 3, 0, 3], ["FU", 8, [0, 1, 2, 3]], ["AN", 8, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$64"]]]], "r": [[0.000183629, 0.000188334, 0.000195553, 0.000187308, 0.000196409, 0.000190496, 0.000190344, 0.000188567, 0.000186319, 0.000187136], 0, 0.384722, 1606961002], "v": "v0.3"}
-{"i": [["[\"1dce2c5e4269b8a12dfc50cd4dd23ff1\"]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["CI", 5], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 14, [2, 1, 7], 1], ["SP", 3, 8, 14, [2, 1, 1], 1], ["SP", 3, 12, 256, [16, 4, 4], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 512, [64], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["CI", 1], ["CR", 6], ["FU", 3, [0, 1, 2, 3, 4, 5, 6, 7]], ["AN", 3, 0, 3], ["FU", 6, [0, 1, 2, 3]], ["AN", 6, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$64"], ["AN", 3, 14, 2]]]], "r": [[0.000118033, 0.000116806, 0.000134047, 0.000116701, 0.000116219, 0.000116834, 0.000117132, 0.000117029, 0.000116393, 0.000116778], 0, 0.31025, 1606961069], "v": "v0.3"}
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+{"i": [["[\"7006235cfc29b73be524cf390ed5a977\", 1, 56, 56, 64, 1, 1, 64, 256, 1, 56, 56, 256]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [8, 64, 64, 0, 0, 0, 0, 0], "", 2], [[], [["CHW", 3, "local"], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 56, [56, 1, 1], 1], ["SP", 3, 8, 56, [14, 1, 2], 1], ["SP", 3, 12, 256, [1, 2, 32], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 64, [64], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["FSP", 4, 0, 1, 2], ["FSP", 4, 3, 2, 2], ["FSP", 4, 6, 3, 2], ["FSP", 4, 9, 4, 2], ["RE", 4, [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11]], ["CA", 3, 4, 7], ["CI", 1], ["FU", 4, [0, 1, 2, 3, 4, 5, 6, 7]], ["AN", 4, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$64"], ["AN", 3, 21, 2], ["AN", 4, 4, 2]]]], "r": [[0.000159044, 0.000157356, 0.000158889, 0.000160304, 0.000158648, 0.000159749, 0.000143679, 0.000156393, 0.000164916, 0.000155957], 0, 0.240777, 1606961918], "v": "v0.5"}
+{"i": [["[\"1cc666833c122282e3fcf3595901b12b\", 1, 28, 28, 128, 1, 1, 128, 512, 1, 28, 28, 512, 1, 28, 28, 512]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [8, 64, 64, 0, 0, 0, 0, 0], "", 2], [[], [["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 28, [1, 1, 2], 1], ["SP", 3, 8, 28, [1, 1, 1], 1], ["SP", 3, 12, 512, [4, 1, 32], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 128, [4], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["FSP", 5, 0, 0, 2], ["FSP", 5, 3, 1, 2], ["FSP", 5, 6, 2, 2], ["FSP", 5, 9, 3, 2], ["RE", 5, [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11]], ["CA", 3, 5, 7], ["CI", 1], ["FU", 5, [0, 1, 2, 3, 4, 5, 6, 7]], ["AN", 5, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$64"], ["AN", 3, 21, 2], ["AN", 5, 4, 2]]]], "r": [[0.000168259, 0.000157338, 0.0001551, 0.000156552, 0.000160492, 0.000164505, 0.000144937, 0.000138397, 0.000153011, 0.000153186], 0, 0.231498, 1606961965], "v": "v0.5"}
+{"i": [["[\"1b524af89dd867d26059e1f621cf987c\", 1, 56, 56, 64, 1, 1, 64, 256, 1, 56, 56, 256, 1, 1, 1, 256, 1, 56, 56, 256]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [8, 64, 64, 0, 0, 0, 0, 0], "", 2], [[], [["CI", 7], ["CI", 5], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 56, [7, 1, 4], 1], ["SP", 3, 8, 56, [4, 2, 1], 1], ["SP", 3, 12, 256, [32, 1, 8], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 64, [2], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["FSP", 8, 0, 2, 2], ["FSP", 8, 3, 3, 2], ["FSP", 8, 6, 4, 2], ["FSP", 8, 9, 5, 2], ["RE", 8, [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11]], ["CA", 3, 8, 7], ["CI", 1], ["FU", 8, [0, 1, 2, 3, 4, 5, 6]], ["AN", 8, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$64"], ["AN", 3, 21, 2], ["AN", 8, 5, 2]]]], "r": [[0.000185957, 0.000180964, 0.000179419, 0.000168205, 0.000176155, 0.000178243, 0.000180175, 0.00017753, 0.000174475, 0.000158878], 0, 0.316404, 1606961979], "v": "v0.5"}
+{"i": [["[\"6b7583cf23c7c37d3212cad9d06e58c1\", 1, 28, 28, 512, 1, 1, 512, 128, 1, 1, 1, 128, 1, 28, 28, 128]", "llvm -keys=cpu -link-params=0 -mcpu=core-avx2", [8, 64, 64, 0, 0, 0, 0, 0], "", 2], [[], [["CI", 5], ["SP", 3, 0, 1, [1, 1, 1], 1], ["SP", 3, 4, 28, [7, 1, 4], 1], ["SP", 3, 8, 28, [14, 1, 1], 1], ["SP", 3, 12, 128, [1, 1, 16], 1], ["SP", 3, 16, 1, [1], 1], ["SP", 3, 18, 1, [1], 1], ["SP", 3, 20, 512, [1], 1], ["RE", 3, [0, 4, 8, 12, 1, 5, 9, 13, 16, 18, 20, 2, 6, 10, 14, 17, 19, 21, 3, 7, 11, 15]], ["FSP", 6, 0, 1, 2], ["FSP", 6, 3, 2, 2], ["FSP", 6, 6, 3, 2], ["FSP", 6, 9, 4, 2], ["RE", 6, [0, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 11]], ["CA", 3, 6, 7], ["CI", 1], ["FU", 6, [0, 1, 2, 3, 4, 5, 6, 7]], ["AN", 6, 0, 3], ["PR", 3, 0, "auto_unroll_max_step$512"], ["AN", 3, 21, 2], ["AN", 6, 4, 2]]]], "r": [[0.000150378, 0.000154444, 0.000156051, 0.000130306, 0.000156154, 0.000131167, 0.000142357, 0.000152532, 0.000131899, 0.000157696], 0, 0.18509, 1606962002], "v": "v0.5"}
diff --git a/tutorials/auto_scheduler/ci_logs/sparse_dense.json b/tutorials/auto_scheduler/ci_logs/sparse_dense.json
new file mode 100644
index 000000000000..7c1c100124dc
--- /dev/null
+++ b/tutorials/auto_scheduler/ci_logs/sparse_dense.json
@@ -0,0 +1,2 @@
+# Keep a valid schedule for demonstraction. This is used to prevent flasky errors in CI.
+{"i": [["[\"sparse_dense\", 512, 512, 512, [9831, 16, 1], [9831], [33], \"float32\"]", "llvm -keys=cpu -link-params=0", [6, 64, 64, 0, 0, 0, 0, 0], "", 1, ["sparse_dense_bsr_512_512_512_16_1_0.60_W_data", "sparse_dense_bsr_512_512_512_16_1_0.60_W_indices", "sparse_dense_bsr_512_512_512_16_1_0.60_W_indptr"]], [[], [["CI", 8], ["CI", 6], ["SP", 5, 0, 512, [1, 8], 1], ["FSP", 9, 0, 2, 1], ["SP", 5, 3, 32, [32], 1], ["FSP", 9, 2, 4, 1], ["RE", 5, [0, 3, 1, 4, 6, 2, 5, 7]], ["RE", 9, [0, 2, 1, 3]], ["CA", 5, 9, 1], ["CI", 4], ["FU", 9, [0, 1]], ["AN", 9, 0, 3], ["PR", 5, 0, "auto_unroll_max_step$0"], ["AN", 9, 2, 2]]]], "r": [[0.000957008], 0, 0.605709, 1614689820], "v": "v0.6"}
diff --git a/tutorials/auto_scheduler/tune_network_arm.py b/tutorials/auto_scheduler/tune_network_arm.py
new file mode 100644
index 000000000000..c4add79450e9
--- /dev/null
+++ b/tutorials/auto_scheduler/tune_network_arm.py
@@ -0,0 +1,421 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-scheduling a Neural Network for ARM CPU
+=============================================
+**Author**: `Thierry Moreau <https://github.com/tmoreau89, Lianmin Zheng <https://github.com/merrymercy>>`_
+
+Auto-tuning for specific devices and workloads is critical for getting the
+best performance. This is a tutorial on how to tune a whole neural
+network for ARM CPU with the auto-scheduler via RPC.
+
+To auto-tune a neural network, we partition the network into small subgraphs and 
+tune them independently. Each subgraph is treated as one search task.
+A task scheduler slices the time and dynamically allocates time resources to
+these tasks. The task scheduler predicts the impact of each task on the end-to-end
+execution time and prioritizes the one that can reduce the execution time the most.
+
+For each subgraph, we use the compute declaration in :code:`tvm/python/topi` to
+get the computational DAG in the tensor expression form.
+We then use the auto-scheduler to construct a search space of this DAG and search
+for good schedules (low-level optimizations).
+
+Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on
+manual templates to define the search space, the auto-scheduler does not require any
+schedule templates. In other words, the auto-scheduler only uses the compute declarations
+in :code:`tvm/python/topi` and does not use existing schedule templates.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+import numpy as np
+
+import tvm
+from tvm import relay, auto_scheduler
+import tvm.relay.testing
+from tvm.contrib import graph_runtime
+from tvm.contrib.utils import tempdir
+
+#################################################################
+# Define a Network
+# ----------------
+# First, we need to define the network with relay frontend API.
+# We can load some pre-defined network from :code:`tvm.relay.testing`.
+# We can also load models from MXNet, ONNX, PyTorch, and TensorFlow
+# (see :ref:`front end tutorials<tutorial-frontend>`).
+#
+# For convolutional neural networks, although auto-scheduler can work correctly
+# with any layout, we found the best performance is typically achieved with NHWC layout.
+# We also implemented more optimizations for NHWC layout with the auto-scheduler.
+# So it is recommended to convert your models to NHWC layout to use the auto-scheduler.
+# You can use :ref:`ConvertLayout <convert-layout-usage>` pass to do the layout conversion in TVM.
+
+
+def get_network(name, batch_size, layout="NHWC", dtype="float32"):
+    """Get the symbol definition and random weight of a network"""
+
+    # auto-scheduler prefers NHWC layout
+    if layout == "NHWC":
+        image_shape = (224, 224, 3)
+    elif layout == "NCHW":
+        image_shape = (3, 224, 224)
+    else:
+        raise ValueError("Invalid layout: " + layout)
+
+    input_shape = (batch_size,) + image_shape
+    output_shape = (batch_size, 1000)
+
+    if name.startswith("resnet-"):
+        n_layer = int(name.split("-")[1])
+        mod, params = relay.testing.resnet.get_workload(
+            num_layers=n_layer,
+            batch_size=batch_size,
+            layout=layout,
+            dtype=dtype,
+            image_shape=image_shape,
+        )
+    elif name.startswith("resnet3d-"):
+        n_layer = int(name.split("-")[1])
+        mod, params = relay.testing.resnet.get_workload(
+            num_layers=n_layer,
+            batch_size=batch_size,
+            layout=layout,
+            dtype=dtype,
+            image_shape=image_shape,
+        )
+    elif name == "mobilenet":
+        mod, params = relay.testing.mobilenet.get_workload(
+            batch_size=batch_size, layout=layout, dtype=dtype, image_shape=image_shape
+        )
+    elif name == "squeezenet_v1.1":
+        assert layout == "NCHW", "squeezenet_v1.1 only supports NCHW layout"
+        mod, params = relay.testing.squeezenet.get_workload(
+            version="1.1",
+            batch_size=batch_size,
+            dtype=dtype,
+            image_shape=image_shape,
+        )
+    elif name == "inception_v3":
+        input_shape = (batch_size, 3, 299, 299) if layout == "NCHW" else (batch_size, 299, 299, 3)
+        mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
+    elif name == "mxnet":
+        # an example for mxnet model
+        from mxnet.gluon.model_zoo.vision import get_model
+
+        assert layout == "NCHW"
+
+        block = get_model("resnet50_v1", pretrained=True)
+        mod, params = relay.frontend.from_mxnet(block, shape={"data": input_shape}, dtype=dtype)
+        net = mod["main"]
+        net = relay.Function(
+            net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs
+        )
+        mod = tvm.IRModule.from_expr(net)
+
+    return mod, params, input_shape, output_shape
+
+
+#################################################################
+# Start RPC Tracker
+# -----------------
+# TVM uses RPC session to communicate with ARM boards.
+# During tuning, the tuner will send the generated code to the board and
+# measure the speed of code on the board.
+#
+# To scale up the tuning, TVM uses RPC Tracker to manage distributed devices.
+# The RPC Tracker is a centralized controller node. We can register all devices to
+# the tracker. For example, if we have 10 phones, we can register all of them
+# to the tracker, and run 10 measurements in parallel, accelerating the tuning process.
+#
+# To start an RPC tracker, run this command on the host machine. The tracker is
+# required during the whole tuning process, so we need to open a new terminal for
+# this command:
+#
+# .. code-block:: bash
+#
+#   python -m tvm.exec.rpc_tracker --host=0.0.0.0 --port=9190
+#
+# The expected output is
+#
+# .. code-block:: bash
+#
+#   INFO:RPCTracker:bind to 0.0.0.0:9190
+
+#################################################################
+# Register Devices to RPC Tracker
+# -----------------------------------
+# Now we can register our devices to the tracker. The first step is to
+# build the TVM runtime for the ARM devices.
+#
+# * For Linux:
+#   Follow this section :ref:`build-tvm-runtime-on-device` to build
+#   the TVM runtime on the device. Then register the device to tracker by
+#
+#   .. code-block:: bash
+#
+#     python -m tvm.exec.rpc_server --tracker=[HOST_IP]:9190 --key=rasp4b-64
+#
+#   (replace :code:`[HOST_IP]` with the IP address of your host machine)
+#
+# * For Android:
+#   Follow this `readme page <https://github.com/apache/tvm/tree/main/apps/android_rpc>`_ to
+#   install the TVM RPC APK on the android device. Make sure you can pass the android rpc test.
+#   Then you have already registered your device. During tuning, you have to go to developer option
+#   and enable "Keep screen awake during changing" and charge your phone to make it stable.
+#
+# After registering devices, we can confirm it by querying rpc_tracker
+#
+# .. code-block:: bash
+#
+#   python -m tvm.exec.query_rpc_tracker --host=0.0.0.0 --port=9190
+#
+# For example, if we have 2 Huawei mate10 pro, 11 Raspberry Pi 4B with 64bit OS, and 2 rk3399,
+# the output can be
+#
+# .. code-block:: bash
+#
+#    Queue Status
+#    ----------------------------------
+#    key          total  free  pending
+#    ----------------------------------
+#    mate10pro    2      2     0
+#    rk3399       2      2     0
+#    rasp4b-64    11     11    0
+#    ----------------------------------
+#
+# You can register multiple devices to the tracker to accelerate the measurement in tuning.
+
+###########################################
+# Set Tuning Options
+# ------------------
+# Before tuning, we should apply some configurations. Here I use a Raspberry Pi 4b 4GB board
+# as example with a 64bit OS (Ubuntu 20.04). In your setting, you should modify the target
+# and device_key accordingly.
+# set :code:`use_ndk` to True if you use android phone.
+
+#### DEVICE CONFIG ####
+
+# Replace "aarch64-linux-gnu" with the correct target of your board.
+# This target is used for cross compilation. You can query it by :code:`gcc -v` on your device.
+# FIXME(tmoreau89, merrymercy): We leave '-device=arm_cpu' out of the target string
+#                               because we're sharing x86 op strategy.
+target = tvm.target.Target("llvm -mtriple=aarch64-linux-gnu -mattr=+neon")
+
+# Also replace this with the device key in your tracker
+device_key = "rasp4b-64"
+
+# Set this to True if you use ndk tools for cross compiling
+# And also set the environment variable below to point to the cross compiler
+use_ndk = False
+# os.environ["TVM_NDK_CC"] = "/usr/bin/aarch64-linux-gnu-g++"
+
+#### TUNING OPTION ####
+network = "mobilenet"
+batch_size = 1
+layout = "NHWC"
+dtype = "float32"
+log_file = "%s-%s-B%d-%s.json" % (network, layout, batch_size, target.kind.name)
+
+#################################################################
+# Extract Search Tasks
+# --------------------
+# Next, we extract the search tasks and their weights from a network.
+# The weight of a task is the number of appearances of the task's subgraph
+# in the whole network.
+# By using the weight, we can approximate the end-to-end latency of the network
+# as :code:`sum(latency[t] * weight[t])`, where :code:`latency[t]` is the
+# latency of a task and :code:`weight[t]` is the weight of the task.
+# The task scheduler will just optimize this objective.
+
+# Extract tasks from the network
+print("Extract tasks...")
+mod, params, input_shape, output_shape = get_network(network, batch_size, layout, dtype=dtype)
+tasks, task_weights = auto_scheduler.extract_tasks(mod["main"], params, target)
+
+for idx, task in enumerate(tasks):
+    print("========== Task %d  (workload key: %s) ==========" % (idx, task.workload_key))
+    print(task.compute_dag)
+
+
+#################################################################
+# Tuning and Evaluation
+# ---------------------
+# Now, we set some options for tuning and launch the search tasks
+#
+# * :code:`num_measure_trials` is the number of measurement trials we can use during the tuning.
+#   You can set it to a small number (e.g., 200) for a fast demonstrative run.
+#   In practice, we recommend setting it around :code:`800 * len(tasks)`,
+#   which is typically enough for the search to converge.
+#   For example, there are 29 tasks in resnet-50, so we can set it as 20000.
+#   You can adjust this parameter according to your time budget.
+# * In addition, we use :code:`RecordToFile` to dump measurement records into a log file,
+#   The measurement records can be used to query the history best, resume the search,
+#   and do more analyses later.
+# * see :any:`auto_scheduler.TuningOptions`,
+#   :any:`auto_scheduler.LocalRunner` for more parameters.
+#
+# After auto-tuning, we can compile the network with the best schedules we found.
+# All measurement records are dumped into the log file during auto-tuning,
+# so we can read the log file and load the best schedules.
+
+
+def tune_and_evaluate():
+    print("Begin tuning...")
+    tuner = auto_scheduler.TaskScheduler(tasks, task_weights)
+    tune_option = auto_scheduler.TuningOptions(
+        num_measure_trials=200,  # change this to 20000 to achieve the best performance
+        runner=auto_scheduler.RPCRunner(
+            device_key,
+            host="0.0.0.0",
+            port=9191,
+            timeout=30,
+            repeat=1,
+            min_repeat_ms=200,
+            enable_cpu_cache_flush=True,
+        ),
+        measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+    )
+
+    tuner.tune(tune_option)
+
+    # Compile with the history best
+    print("Compile...")
+    with auto_scheduler.ApplyHistoryBest(log_file):
+        with tvm.transform.PassContext(
+            opt_level=3, config={"relay.backend.use_auto_scheduler": True}
+        ):
+            lib = relay.build(mod, target=target, params=params)
+
+    # Export library
+    tmp = tempdir()
+    if use_ndk:
+        from tvm.contrib import ndk
+
+        filename = "net.so"
+        lib.export_library(tmp.relpath(filename), ndk.create_shared)
+    else:
+        filename = "net.tar"
+        lib.export_library(tmp.relpath(filename))
+
+    # Upload module to device
+    print("Upload...")
+    remote = auto_scheduler.utils.request_remote(device_key, "0.0.0.0", 9191, timeout=10000)
+    remote.upload(tmp.relpath(filename))
+    rlib = remote.load_module(filename)
+
+    # Create graph runtime
+    ctx = remote.cpu()
+    module = graph_runtime.GraphModule(rlib["default"](ctx))
+    data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
+    module.set_input("data", data_tvm)
+
+    # Evaluate
+    print("Evaluate inference time cost...")
+    ftimer = module.module.time_evaluator("run", ctx, repeat=3, min_repeat_ms=500)
+    prof_res = np.array(ftimer().results) * 1e3  # convert to millisecond
+    print(
+        "Mean inference time (std dev): %.2f ms (%.2f ms)" % (np.mean(prof_res), np.std(prof_res))
+    )
+
+
+# We do not run the tuning in our webpage server since the server doesn't have a Raspberry Pi,
+# or device tracker running.
+# Uncomment the following line to run it by yourself.
+
+# tune_and_evaluate()
+
+
+######################################################################
+# .. note:: Explaining the printed information during tuning
+#
+#   During the tuning, a lot of information will be printed on the console.
+#   They are used for debugging purposes. The most important info is the output
+#   of the task scheduler. The following table is a sample output.
+#
+#   .. code-block:: c
+#
+#    ----------------------------------------------------------------------
+#    ------------------------------  [ Task Scheduler ]
+#    ----------------------------------------------------------------------
+#    |  ID  | Latency (ms) | Speed (GFLOPS) | Trials |
+#    -------------------------------------------------
+#    |    0 |        0.013 |           0.31 |     64 |
+#    |    1 |        0.845 |           2.43 |    448 |
+#    |    2 |        0.046 |          -0.00 |     64 |
+#    |    3 |        4.194 |          24.53 |   2112 |
+#    |    4 |        0.109 |           9.21 |     64 |
+#    |    5 |        1.759 |          29.27 |    896 |
+#    |    6 |        0.083 |           6.01 |     64 |
+#    |    7 |        3.084 |          33.38 |   7680 |
+#    |    8 |        0.136 |          14.78 |    384 |
+#    |    9 |        1.349 |          38.23 |    768 |
+#    |   10 |        0.133 |           7.55 |    128 |
+#    |   11 |        2.747 |          37.56 |   1536 |
+#    |   12 |        0.338 |          11.87 |    192 |
+#    |   13 |        1.295 |          40.00 |    704 |
+#    |   14 |        0.482 |           4.16 |    256 |
+#    |   15 |        2.686 |          38.56 |   1344 |
+#    |   16 |        0.884 |           9.08 |    448 |
+#    |   17 |        1.332 |          39.18 |    704 |
+#    |   18 |        1.045 |           3.84 |    576 |
+#    |   19 |        1.391 |          38.09 |    704 |
+#    |   20 |        0.777 |          10.34 |    448 |
+#    |   21 |        0.739 |          30.97 |    448 |
+#    -------------------------------------------------
+#     Estimated total latency: 38.347 ms      Trials: 19992   Used time : 19260 s     Next ID: 3
+#
+#   This table lists the latency and (estimated) speed of all tasks.
+#   It also lists the allocation of measurement trials for all tasks.
+#   The last line prints the total weighted latency of these tasks,
+#   which can be a rough estimation of the end-to-end execution time
+#   of the network.
+#   The last line also prints the total number of measurement trials,
+#   total time spent on auto-tuning and the id of the next task to tune.
+#
+#   There will also be some "dmlc::Error"s errors, because the
+#   auto-scheduler will try some invalid schedules.
+#   You can safely ignore them if the tuning can continue, because these
+#   errors are isolated from the main process.
+#
+
+######################################################################
+# .. note:: Terminate the tuning earlier
+#
+#   You can terminate the tuning earlier by forcibly killing this process.
+#   As long as you get at least one valid schedule for each task in the log file,
+#   you should be able to do the compilation (the secion below).
+#
+
+#################################################################
+# Other Tips
+# ----------
+# 1. During the tuning, the auto-scheduler needs to compile many programs and
+#    extract feature from them. This part is CPU-intensive,
+#    so a high-performance CPU with many cores is recommended for faster search.
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`
+#    to distill the large log file and only save the best useful records.
+# 3. You can resume a search from the previous log file. You just need to
+#    add a new argument :code:`load_log_file` when creating the task scheduler
+#    in function :code:`run_tuning`. Say,
+#    :code:`tuner = auto_scheduler.TaskScheduler(tasks, task_weights, load_log_file=log_file)`
+# 4. If you have multiple target CPUs, you can use all of them for measurements to
+#    parallelize the measurements. Check this :ref:`section <tutorials-autotvm-rpc-tracker>`
+#    to learn how to use the RPC Tracker and RPC Server.
+#    To use the RPC Tracker in auto-scheduler, replace the runner in :code:`TuningOptions`
+#    with :any:`auto_scheduler.RPCRunner`.
diff --git a/tutorials/auto_scheduler/tune_network_cuda.py b/tutorials/auto_scheduler/tune_network_cuda.py
index b09886941c74..bc88457f94f9 100644
--- a/tutorials/auto_scheduler/tune_network_cuda.py
+++ b/tutorials/auto_scheduler/tune_network_cuda.py
@@ -252,7 +252,7 @@ def run_tuning():
 #   The last line also prints the total number of measurement trials,
 #   total time spent on auto-tuning and the id of the next task to tune.
 #
-#   There will also be some "dmlc::Error"s and CUDA errors, because the
+#   There will also be some "tvm::Error"s and CUDA errors, because the
 #   auto-scheduler will try some invalid schedules.
 #   You can safely ignore them if the tuning can continue, because these
 #   errors are isolated from the main process.
@@ -299,7 +299,7 @@ def run_tuning():
 # 1. During the tuning, the auto-scheduler needs to compile many programs and
 #    extract feature from them. This part is CPU-intensive,
 #    so a high-performance CPU with many cores is recommended for faster search.
-# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`
 #    to distill the large log file and only save the best useful records.
 # 3. You can resume a search from the previous log file. You just need to
 #    add a new argument :code:`load_log_file` when creating the task scheduler
diff --git a/tutorials/auto_scheduler/tune_network_mali.py b/tutorials/auto_scheduler/tune_network_mali.py
index d3fefa725d4c..2bce968771e3 100644
--- a/tutorials/auto_scheduler/tune_network_mali.py
+++ b/tutorials/auto_scheduler/tune_network_mali.py
@@ -329,7 +329,7 @@ def tune_and_evaluate():
 #   The last line also prints the total number of measurement trials,
 #   total time spent on auto-tuning and the id of the next task to tune.
 #
-#   There will also be some "dmlc::Error"s errors, because the
+#   There will also be some "tvm::Error"s errors, because the
 #   auto-scheduler will try some invalid schedules.
 #   You can safely ignore them if the tuning can continue, because these
 #   errors are isolated from the main process.
@@ -349,7 +349,7 @@ def tune_and_evaluate():
 # 1. During the tuning, the auto-scheduler needs to compile many programs and
 #    extract feature from them. This part is CPU-intensive,
 #    so a high-performance CPU with many cores is recommended for faster search.
-# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`
 #    to distill the large log file and only save the best useful records.
 # 3. You can resume a search from the previous log file. You just need to
 #    add a new argument :code:`load_log_file` when creating the task scheduler
diff --git a/tutorials/auto_scheduler/tune_network_x86.py b/tutorials/auto_scheduler/tune_network_x86.py
index 7f96254b2f49..2b47c64729e0 100644
--- a/tutorials/auto_scheduler/tune_network_x86.py
+++ b/tutorials/auto_scheduler/tune_network_x86.py
@@ -251,7 +251,7 @@ def run_tuning():
 #   The last line also prints the total number of measurement trials,
 #   total time spent on auto-tuning and the id of the next task to tune.
 #
-#   There will also be some "dmlc::Error"s errors, because the
+#   There will also be some "tvm::Error"s errors, because the
 #   auto-scheduler will try some invalid schedules.
 #   You can safely ignore them if the tuning can continue, because these
 #   errors are isolated from the main process.
@@ -298,7 +298,7 @@ def run_tuning():
 # 1. During the tuning, the auto-scheduler needs to compile many programs and
 #    extract feature from them. This part is CPU-intensive,
 #    so a high-performance CPU with many cores is recommended for faster search.
-# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill --i log.json`
+# 2. You can use :code:`python3 -m tvm.auto_scheduler.measure_record --mode distill -i log.json`
 #    to distill the large log file and only save the best useful records.
 # 3. You can resume a search from the previous log file. You just need to
 #    add a new argument :code:`load_log_file` when creating the task scheduler
diff --git a/tutorials/auto_scheduler/tune_sparse_x86.py b/tutorials/auto_scheduler/tune_sparse_x86.py
new file mode 100644
index 000000000000..ced416f6c500
--- /dev/null
+++ b/tutorials/auto_scheduler/tune_sparse_x86.py
@@ -0,0 +1,339 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule
+===========================================================================
+**Author**: `Chengfan Jia <https://github.com/jcf94/>`_
+
+This is a tutorial on how to use the auto-scheduler to tune a sparse matrix multiplication for
+CPUs.
+
+Auto-scheduler is designed to explore the schedule with best performance for a given computation
+declaration automatically. While sometimes, we may have a demand to try some special ops which may
+not been well-supported by auto-scheduler's default sketch rules and result in poor performance.
+Fortunately, auto-scheduler currently allows user to provide a CustomSketch to cover these cases.
+
+We use sparse matrix multiplication as an example in this tutorial to demonstrate how to implement
+and plug a custom sketch rule to the auto-scheduler's search policy.
+
+Note that this tutorial will not run on Windows or recent versions of macOS. To
+get it to run, you will need to wrap the body of this tutorial in a :code:`if
+__name__ == "__main__":` block.
+"""
+
+import os
+import itertools
+
+import numpy as np
+import tvm
+from tvm import te, auto_scheduler, runtime, topi
+from tvm.auto_scheduler import _ffi_api
+from tvm.topi.utils import get_const_tuple
+
+import scipy.sparse as sp
+
+######################################################################
+# Define the computation
+# ^^^^^^^^^^^^^^^^^^^^^^
+# To begin with, let us define the computation of a sparse matmul with several relu and bias add.
+# The function should return the list of input/output tensors.
+# From these tensors, the auto-scheduler can get the whole computational graph.
+
+# We use this function to generate a random bsr matrix
+def random_bsr_matrix(M, N, BS_R, BS_C, density, dtype):
+    import itertools
+
+    Y = np.zeros((M, N), dtype=dtype)
+    assert M % BS_R == 0
+    assert N % BS_C == 0
+    nnz = int(density * M * N)
+    num_blocks = int(nnz / (BS_R * BS_C)) + 1
+    candidate_blocks = np.asarray(list(itertools.product(range(0, M, BS_R), range(0, N, BS_C))))
+    assert candidate_blocks.shape[0] == M // BS_R * N // BS_C
+    chosen_blocks = candidate_blocks[
+        np.random.choice(candidate_blocks.shape[0], size=num_blocks, replace=False)
+    ]
+    for i in range(len(chosen_blocks)):
+        r, c = chosen_blocks[i]
+        Y[r : r + BS_R, c : c + BS_C] = np.random.randn(BS_R, BS_C)
+    s = sp.bsr_matrix(Y, blocksize=(BS_R, BS_C))
+    assert s.data.shape == (num_blocks, BS_R, BS_C)
+    assert s.indices.shape == (num_blocks,)
+    assert s.indptr.shape == (M // BS_R + 1,)
+    return s
+
+
+@auto_scheduler.register_workload
+def sparse_dense(M, N, K, w_data_shape, w_indices_shape, w_indptr_shape, dtype):
+    X = te.placeholder(shape=(M, K), dtype=dtype)
+    W_data = te.placeholder(shape=w_data_shape, dtype=dtype)
+    W_indices = te.placeholder(shape=w_indices_shape, dtype="int32")
+    W_indptr = te.placeholder(shape=w_indptr_shape, dtype="int32")
+    B = te.placeholder(shape=(M, N), dtype=dtype)
+
+    out = topi.nn.sparse_dense(topi.nn.relu(X), W_data, W_indices, W_indptr)
+    out = te.compute((M, N), lambda i, j: out[i, j] + B[i, j], name="BiasAdd")
+    out = topi.nn.relu(out)
+
+    return [X, W_data, W_indices, W_indptr, B, out]
+
+
+######################################################################
+# Special step for sparse workload
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# During schedule tuning, auto-scheduler will use random inputs to measure the performance of a
+# generated schedule. While we cannot directly use a random array as the input of a sparse op, for
+# the "indices" and "indptr" array are meaningful for the computation.
+#
+# To solve this problem, we register these as special buffers, and load them when process program
+# measuring.
+# See the `tvm.auto_scheduler.measure.py` for more details.
+
+# Define the basic shapes of this sparse computation
+M = K = N = 512
+BS_R = 16
+BS_C = 1
+density = 0.6
+
+# Generate the test data with numpy
+X_np = np.random.randn(M, K).astype("float32")
+X_np = np.maximum(np.zeros((M, K), dtype="float32"), X_np)  # Relu
+W_sp_np = random_bsr_matrix(N, K, BS_R, BS_C, density=density, dtype="float32")
+W_np = W_sp_np.todense()
+Y_np = X_np @ W_np.T  # Process the matrix multiplication
+B_np = np.random.randn(M, N).astype("float32")
+Y_np = Y_np + B_np  # Bias add
+Y_np = np.maximum(np.zeros((M, N), dtype="float32"), Y_np)  # Relu
+
+######################################################################
+# Create the search task
+# ^^^^^^^^^^^^^^^^^^^^^^
+# We then create a search task with M=N=K=512 and dtype="float32"
+# If your machine supports avx instructions, you can
+#
+#   - replace "llvm" below with "llvm -mcpu=core-avx2" to enable AVX2
+#   - replace "llvm" below with "llvm -mcpu=skylake-avx512" to enable AVX-512
+
+target = tvm.target.Target("llvm")
+
+# Register the sparse data to task inputs
+prefix = "sparse_dense_bsr_%d_%d_%d_%d_%d_%.2f_" % (M, N, K, BS_R, BS_C, density)
+task = tvm.auto_scheduler.SearchTask(
+    func=sparse_dense,
+    args=(M, N, K, W_sp_np.data.shape, W_sp_np.indices.shape, W_sp_np.indptr.shape, "float32"),
+    target=target,
+    task_inputs={
+        prefix + "W_data": runtime.ndarray.array(W_sp_np.data),
+        prefix + "W_indices": runtime.ndarray.array(W_sp_np.indices),
+        prefix + "W_indptr": runtime.ndarray.array(W_sp_np.indptr),
+    },
+    task_inputs_save_to_file=True,
+)
+
+# Inspect the computational graph
+print("Computational DAG:")
+print(task.compute_dag)
+
+######################################################################
+# Write the custom sketch for sparse dense op
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Before tuning, we will need to define the CustomSketchRule for the sparse dense op.
+#
+# CustomSketchRule consists of two parts: the condition function and the apply function.
+#
+#   - condition function: describe when to apply this sketch rule. For example, we can only apply
+#     the rule to the sparse ops by matching their name and tag.
+#   - apply function: describe how to generate the initial sketch. You can implement it using
+#     auto-scheduler provided loop state APIs.
+
+
+def meet_condition_func(search_policy, state, stage_id):
+    state = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+    if state.stages[stage_id].op.tag in [
+        "sparse_dense_sp_rhs_bsrmm",
+        "sparse_dense_sp_rhs_bsrmm_block",
+    ]:
+        return auto_scheduler.PreloadCustomSketchRule.APPLY_AND_SKIP_REST
+    else:
+        return auto_scheduler.PreloadCustomSketchRule.PASS
+
+
+def apply_func(search_policy, state, stage_id):
+    ret = []
+    s0 = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag)
+    if s0.stages[stage_id].op.tag == "sparse_dense_sp_rhs_bsrmm_block":
+        return [s0.state_object, stage_id - 1]
+
+    sparse_dense = s0.stages[stage_id].op
+    sparse_dense_block = s0.stages[stage_id - 1].op
+    assert sparse_dense.tag == "sparse_dense_sp_rhs_bsrmm"
+    assert sparse_dense_block.tag == "sparse_dense_sp_rhs_bsrmm_block"
+
+    # Set the default consumer of compute block
+    consumer = sparse_dense
+
+    # If sparse dense has a single elementwise consumer
+    # We can compute inline the sparse_dense output stage
+    consumers = _ffi_api.SearchPolicyUtilsGetConsumers(
+        search_policy.search_task, s0.state_object, stage_id
+    )
+    if len(consumers) == 1:
+        consumer_id = int(consumers.items()[0][0])
+        if _ffi_api.SearchPolicyUtilsIsElementwiseMatch(
+            search_policy.search_task, s0.state_object, stage_id, consumer_id
+        ):
+            consumer = s0.stages[consumer_id].op
+            s0.compute_inline(sparse_dense)
+
+    i, nb_j, j, row_offset, c = s0[sparse_dense_block].iters
+    m, n = s0[consumer].iters
+    i0, i1, i2 = s0.split(sparse_dense_block, i, [None, None])
+    m0, m1 = s0.follow_split(consumer, m, len(s0.transform_steps) - 1, 1)
+    j0, j1 = s0.split(sparse_dense_block, nb_j, [None])
+    n0, n1 = s0.follow_split(consumer, n, len(s0.transform_steps) - 1, 1)
+    s0.reorder(sparse_dense_block, [i0, j0, i1, j1, row_offset, i2, j, c])
+    s0.reorder(consumer, [m0, n0, m1, n1])
+    s0.compute_at(sparse_dense_block, consumer, n0)
+
+    ret.append([s0.state_object, stage_id - 2])
+
+    return ret
+
+
+######################################################################
+# Next, we set parameters for the auto-scheduler with the custom sketch plugged in.
+#
+# * :code:`num_measure_trials` is the number of measurement trials we can use during the search.
+#   We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
+#   good value for the search to converge. You can do more trials according to your time budget.
+# * In addition, we use :code:`RecordToFile` to dump measurement records into a file
+#   `sparse_dense.json`.
+#   The measurement records can be used to query the history best, resume the search,
+#   and do more analyses later.
+# * see :any:`auto_scheduler.TuningOptions` for more parameters
+# * Here, we need to create a :code:`auto_scheduler.SketchPolicy` object, and add the custom sketch
+#   rule as a `init_search_callbacks`.
+
+log_file = "sparse_dense.json"
+tune_option = auto_scheduler.TuningOptions(
+    num_measure_trials=10,
+    measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
+    verbose=2,
+)
+
+search_policy = auto_scheduler.SketchPolicy(
+    task,
+    program_cost_model=auto_scheduler.XGBModel(),
+    init_search_callbacks=[
+        auto_scheduler.PreloadCustomSketchRule(meet_condition_func, apply_func, "SparseDense")
+    ],
+)
+
+######################################################################
+# Run the search
+# ^^^^^^^^^^^^^^
+# Now we get all inputs ready.
+# We can kick off the search and let the auto-scheduler do its magic.
+# After some measurement trials, we can load the best schedule from the log
+# file and apply it.
+
+# Run auto-tuning (search)
+# Notice: We do not run the tuning in our webpage server since it takes too long.
+# Uncomment the following line to run it by yourself.
+task.tune(tune_option, search_policy)
+
+# Apply the best schedule
+sch, args = task.apply_best(log_file)
+
+######################################################################
+# We can lower the schedule to see the IR after auto-scheduling.
+# The auto-scheduler correctly performs optimizations including multi-level tiling,
+# layout transformation, parallelization, vectorization, unrolling, and operator fusion.
+
+print("Lowered TIR:")
+print(tvm.lower(sch, args, simple_mode=True))
+
+######################################################################
+# Check correctness and evaluate performance
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# We build the binary and check its correctness and performance.
+
+func = tvm.build(sch, args, target)
+
+ctx = tvm.cpu()
+
+X_tvm = tvm.nd.array(X_np, ctx=ctx)
+W_data_tvm = tvm.nd.array(W_sp_np.data, ctx=ctx)
+W_indices_tvm = tvm.nd.array(W_sp_np.indices, ctx=ctx)
+W_indptr_tvm = tvm.nd.array(W_sp_np.indptr, ctx=ctx)
+B_tvm = tvm.nd.array(B_np, ctx=ctx)
+Y_tvm = tvm.nd.empty(Y_np.shape, ctx=ctx)
+
+func(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm)
+
+# Check results
+tvm.testing.assert_allclose(Y_np, Y_tvm.asnumpy(), atol=1e-4, rtol=1e-4)
+
+# Evaluate execution time.
+evaluator = func.time_evaluator(func.entry_name, ctx, min_repeat_ms=500)
+print(
+    "Execution time of this operator: %.3f ms"
+    % (
+        np.median(evaluator(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm).results)
+        * 1000
+    )
+)
+
+######################################################################
+# .. note:: Tuning result example
+#
+#   .. code-block:: c
+#
+#    ----------------------------------------------------------------------
+#    Lowered TIR:
+#    primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> ()
+#      attr = {"global_symbol": "main", "tir.noalias": True}
+#      buffers = {placeholder_2: Buffer(placeholder_10: Pointer(float32), float32, [9831, 16, 1], []),
+#                 placeholder_4: Buffer(placeholder_11: Pointer(int32), int32, [33], []),
+#                 placeholder_3: Buffer(placeholder_12: Pointer(float32), float32, [512, 512], []),
+#                 compute: Buffer(compute_2: Pointer(float32), float32, [512, 512], []),
+#                 placeholder_1: Buffer(placeholder_13: Pointer(float32), float32, [512, 512], []),
+#                 placeholder: Buffer(placeholder_14: Pointer(int32), int32, [9831], [])}
+#      buffer_map = {placeholder_7: placeholder, placeholder_9: placeholder_1, placeholder_6: placeholder_2, compute_1: compute, placeholder_5: placeholder_3, placeholder_8: placeholder_4} {
+#      for (i0.outer.i1.outer.fused: int32, 0, 1024) "parallel" {
+#        attr [compute_3: Pointer(float32)] "storage_scope" = "global";
+#        allocate(compute_3, float32, [256]) {
+#          for (nb_j.inner: int32, 0, 2) {
+#            for (i.inner.init: int32, 0, 8) {
+#              for (j.init: int32, 0, 16) {
+#                compute_3[(((i.inner.init*32) + (nb_j.inner*16)) + j.init)] = 0f32
+#              }
+#            }
+#            for (elem_idx: int32, 0, ((int32*)placeholder_11[(((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) + 1)] - (int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)])) {
+#              for (i.inner: int32, 0, 8) {
+#                for (j: int32, 0, 16) {
+#                  compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] = ((float32*)compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] + ((float32*)placeholder_10[((((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_12[(((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i.inner*512)) + (int32*)placeholder_14[((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)] + elem_idx)])], 0f32)))
+#                }
+#              }
+#            }
+#          }
+#          for (i0.inner: int32, 0, 8) {
+#            compute_2[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)] = max(((float32x32*)compute_3[ramp((i0.inner*32), 1, 32)] + (float32x32*)placeholder_13[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)]), broadcast(0f32, 32))
+#          }
+#        }
+#      }
+#    }
diff --git a/tutorials/autotvm/tune_conv2d_cuda.py b/tutorials/autotvm/tune_conv2d_cuda.py
index c32049567679..dc8e6e522249 100644
--- a/tutorials/autotvm/tune_conv2d_cuda.py
+++ b/tutorials/autotvm/tune_conv2d_cuda.py
@@ -55,6 +55,7 @@
 import tvm
 from tvm import te, topi, testing
 from tvm.topi.testing import conv2d_nchw_python
+import tvm.testing
 
 from tvm import autotvm
 
diff --git a/tutorials/autotvm/tune_simple_template.py b/tutorials/autotvm/tune_simple_template.py
index d7d43c794cda..bd2dcf3cfd1e 100644
--- a/tutorials/autotvm/tune_simple_template.py
+++ b/tutorials/autotvm/tune_simple_template.py
@@ -59,7 +59,8 @@
 
 import numpy as np
 import tvm
-from tvm import te, testing
+from tvm import te
+import tvm.testing
 
 # the module is called `autotvm`
 from tvm import autotvm
diff --git a/tutorials/dev/low_level_custom_pass.py b/tutorials/dev/low_level_custom_pass.py
index 44fe59f99201..0bd656dd81dd 100644
--- a/tutorials/dev/low_level_custom_pass.py
+++ b/tutorials/dev/low_level_custom_pass.py
@@ -116,8 +116,8 @@ def vectorize8(op):
         name = op.loop_var.name
         lo, li = te.var(name + ".outer"), te.var(name + ".inner")
         body = tvm.tir.stmt_functor.substitute(op.body, {op.loop_var: lo * 8 + li})
-        body = tvm.tir.For(li, 0, 8, tvm.tir.For.Vectorized, 0, body)
-        body = tvm.tir.For(lo, 0, extent // 8, tvm.tir.For.Serial, 0, body)
+        body = tvm.tir.For(li, 0, 8, tvm.tir.ForKind.VECTORIZED, body)
+        body = tvm.tir.For(lo, 0, extent // 8, tvm.tir.ForKind.SERIAL, body)
         return body
     return None
 
diff --git a/tutorials/frontend/deploy_sparse.py b/tutorials/frontend/deploy_sparse.py
index dcf2fc4fe31d..98004a93c74f 100644
--- a/tutorials/frontend/deploy_sparse.py
+++ b/tutorials/frontend/deploy_sparse.py
@@ -81,7 +81,7 @@
 import itertools
 import numpy as np
 import tensorflow as tf
-from tvm import relay
+from tvm import relay, runtime
 from tvm.contrib import graph_runtime
 from tvm.relay import data_dep_optimization as ddo
 from tensorflow.python.framework.convert_to_constants import (
@@ -102,10 +102,8 @@
 batch_size = 1
 # The length of each input sequence.
 seq_len = 128
-# TVM platform identifier. Although cuda is also supported, it requires
-# tuning that is outside the scope of this tutorial. Note that best
-# cpu performance can be achieved by setting -mcpu appropriately for
-# your specific machine.
+# TVM platform identifier. Note that best cpu performance can be achieved by setting -mcpu
+# appropriately for your specific machine. CUDA and ROCm are also supported.
 target = "llvm"
 # Which device to run on. Should be one of tvm.cpu() or tvm.gpu().
 ctx = tvm.cpu()
@@ -198,7 +196,7 @@ def import_graphdef(
             with open(os.path.join(abs_path, relay_file), "w") as fo:
                 fo.write(tvm.ir.save_json(mod))
             with open(os.path.join(abs_path, relay_params), "wb") as fo:
-                fo.write(relay.save_param_dict(params))
+                fo.write(runtime.save_param_dict(params))
 
     return mod, params, shape_dict
 
@@ -339,3 +337,17 @@ def benchmark():
 # Runtime:             165.26 ms           (12.83 ms)
 # Block Sparse Model with 1x1 blocks:
 # Runtime:             67.75 ms            (8.83 ms)
+
+# Here is the output of this script on a GPU (GTX 1070) with the target "cuda -libs=cublas".
+#
+# Dense Model Benchmark:
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('dense_cublas.cuda', ('TENSOR', (1, 768), 'float32'), ('TENSOR', (2, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('dense_cublas.cuda', ('TENSOR', (1, 768), 'float32'), ('TENSOR', (768, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('dense_cublas.cuda', ('TENSOR', (128, 3072), 'float32'), ('TENSOR', (768, 3072), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('dense_cublas.cuda', ('TENSOR', (128, 768), 'float32'), ('TENSOR', (3072, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('dense_cublas.cuda', ('TENSOR', (128, 768), 'float32'), ('TENSOR', (768, 768), 'float32'), None, 'float32'). A fallback configuration is used, which may bring great performance regression.
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('batch_matmul_cublas.cuda', ('TENSOR', (12, 128, 128), 'float32'), ('TENSOR', (12, 64, 128), 'float32'), (12, 128, 64)). A fallback configuration is used, which may bring great performance regression.
+# Cannot find config for target=cuda -keys=cuda,gpu -libs=cublas -max_num_threads=1024 -thread_warp_size=32, workload=('batch_matmul_cublas.cuda', ('TENSOR', (12, 128, 64), 'float32'), ('TENSOR', (12, 128, 64), 'float32'), (12, 128, 128)). A fallback configuration is used, which may bring great performance regression.
+# Runtime:             10.64 ms            (0.29 ms)
+# Block Sparse Model with 1x1 blocks:
+# Runtime:             6.46 ms             (0.05 ms)
diff --git a/tutorials/frontend/deploy_ssd_gluoncv.py b/tutorials/frontend/deploy_ssd_gluoncv.py
index f1f1bbb7057e..478aff255e0c 100644
--- a/tutorials/frontend/deploy_ssd_gluoncv.py
+++ b/tutorials/frontend/deploy_ssd_gluoncv.py
@@ -94,6 +94,10 @@ def build(target):
 
 ######################################################################
 # Create TVM runtime and do inference
+# .. note::
+#
+#   Use target = "cuda -libs" to enable thrust based sort, if you
+#   enabled thrust during cmake by -DUSE_THRUST=ON.
 
 
 def run(lib, ctx):
diff --git a/tutorials/frontend/from_tflite.py b/tutorials/frontend/from_tflite.py
index a3014f9d2ea8..f7e8422c37b6 100644
--- a/tutorials/frontend/from_tflite.py
+++ b/tutorials/frontend/from_tflite.py
@@ -26,7 +26,7 @@
 .. code-block:: bash
 
     # install tflite
-    pip install tflite=2.1.0 --user
+    pip install tflite==2.1.0 --user
 
 
 or you could generate TFLite package yourself. The steps are the following:
diff --git a/tutorials/frontend/using_external_lib.py b/tutorials/frontend/using_external_lib.py
index a150b683a531..8e7fcd70e3e9 100644
--- a/tutorials/frontend/using_external_lib.py
+++ b/tutorials/frontend/using_external_lib.py
@@ -37,6 +37,7 @@
 from tvm.contrib import graph_runtime as runtime
 from tvm import relay
 from tvm.relay import testing
+import tvm.testing
 
 ######################################################################
 # Create a simple network
diff --git a/tutorials/get_started/relay_quick_start.py b/tutorials/get_started/relay_quick_start.py
index 6da62f5ced4b..444b915ca7c8 100644
--- a/tutorials/get_started/relay_quick_start.py
+++ b/tutorials/get_started/relay_quick_start.py
@@ -44,6 +44,7 @@
 import tvm
 from tvm import te
 from tvm.contrib import graph_runtime
+import tvm.testing
 
 ######################################################################
 # Define Neural Network in Relay
diff --git a/tutorials/auto_scheduler/tune_matmul_x86.py b/tutorials/get_started/tune_matmul_x86.py
similarity index 55%
rename from tutorials/auto_scheduler/tune_matmul_x86.py
rename to tutorials/get_started/tune_matmul_x86.py
index 084f5ae67518..a51f01115f31 100644
--- a/tutorials/auto_scheduler/tune_matmul_x86.py
+++ b/tutorials/get_started/tune_matmul_x86.py
@@ -15,24 +15,27 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-Auto-scheduling Matrix Multiplication for CPU
-=============================================
+Optimizing Operators with Auto-scheduling
+=========================================
 **Author**: `Lianmin Zheng <https://github.com/merrymercy>`_, \
             `Chengfan Jia <https://github.com/jcf94/>`_
 
-This is a tutorial on how to use the auto-scheduler for CPUs.
+In this tutorial, we will show how TVM's Auto Scheduling feature can find
+optimal schedules without the need for writing a custom template.
 
-Different from the template-based :ref:`autotvm <tutorials-autotvm-sec>` which relies on
-manual templates to define the search space, the auto-scheduler does not require any templates.
-Users only need to write the computation declaration without any schedule commands or templates.
-The auto-scheduler can automatically generate a large search space and
-find a good schedule in the space.
+Different from the template-based :ref:`<autotvm_matmul>` which relies on
+manual templates to define the search space, the auto-scheduler does not
+require any templates.  Users only need to write the computation declaration
+without any schedule commands or templates.  The auto-scheduler can
+automatically generate a large search space and find a good schedule in the
+space.
 
 We use matrix multiplication as an example in this tutorial.
 
-Note that this tutorial will not run on Windows or recent versions of macOS. To
-get it to run, you will need to wrap the body of this tutorial in a :code:`if
-__name__ == "__main__":` block.
+.. note::
+  Note that this tutorial will not run on Windows or recent versions of macOS. To
+  get it to run, you will need to wrap the body of this tutorial in a :code:`if
+  __name__ == "__main__":` block.
 """
 
 import os
@@ -41,15 +44,18 @@
 import tvm
 from tvm import te, auto_scheduler
 
-######################################################################
-# Define the computation
-# ^^^^^^^^^^^^^^^^^^^^^^
-# To begin with, let us define the computation of a matmul with bias add.
-# The function should return the list of input/output tensors.
-# From these tensors, the auto-scheduler can get the whole computational graph.
+################################################################################
+# Defining the Matrix Multiplication
+# ----------------------------------
+# To start, we define a matrix multiplication with a bias addition.  Note that
+# this uses standard operations available in TVMs Tensor Expression language.
+# The major difference is the use of the `auto_sceduler` decorator at the top
+# of the function definition.  The function should return a list of
+# input/output tensors.  From these tensors, the auto-scheduler can get the
+# whole computational graph.
 
 
-@auto_scheduler.register_workload
+@auto_scheduler.register_workload  # Note the auto_scheduler decorator
 def matmul_add(N, L, M, dtype):
     A = te.placeholder((N, L), name="A", dtype=dtype)
     B = te.placeholder((L, M), name="B", dtype=dtype)
@@ -67,12 +73,17 @@ def matmul_add(N, L, M, dtype):
     return [A, B, C, out]
 
 
-######################################################################
+################################################################################
 # Create the search task
-# ^^^^^^^^^^^^^^^^^^^^^^
-# We then create a search task with N=L=M=1024 and dtype="float32"
-# If your machine supports avx instructions, you can
+# ----------------------
+# With the function defined, we can now create the task for the auto_scheduler
+# to search against. We specify the particular parameters for this matrix
+# multiplication, in this case a multiplication of to square matricies of size
+# 1024x1024. We then create a search task with N=L=M=1024 and dtype="float32"
 #
+# .. note:: Improve performance with custom targets
+#   In order for TVM to take full advantage of specific hardware platforms,
+#   you will want to manuall specify your CPU capabilities. For example:
 #   - replace "llvm" below with "llvm -mcpu=core-avx2" to enable AVX2
 #   - replace "llvm" below with "llvm -mcpu=skylake-avx512" to enable AVX-512
 
@@ -84,15 +95,18 @@ def matmul_add(N, L, M, dtype):
 print("Computational DAG:")
 print(task.compute_dag)
 
-######################################################################
+################################################################################
+# Set Parameters for Auto-Scheduler
+# ---------------------------------
 # Next, we set parameters for the auto-scheduler.
 #
-# * :code:`num_measure_trials` is the number of measurement trials we can use during the search.
-#   We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a
-#   good value for the search to converge. You can do more trials according to your time budget.
-# * In addition, we use :code:`RecordToFile` to dump measurement records into a file `matmul.json`.
-#   The measurement records can be used to query the history best, resume the search,
-#   and do more analyses later.
+# * :code:`num_measure_trials` is the number of measurement trials we can use
+#   during the search.  We only make 10 trials in this tutorial for a fast
+#   demonstration. In practice, 1000 is a good value for the search to converge.
+#   You can do more trials according to your time budget.
+# * In addition, we use :code:`RecordToFile` to log measurement records into a
+#   file `matmul.json`.  The measurement records can be used to query the history
+#   best, resume the search, and do more analyses later.
 # * see :any:`auto_scheduler.TuningOptions` for more parameters
 
 log_file = "matmul.json"
@@ -102,30 +116,32 @@ def matmul_add(N, L, M, dtype):
     verbose=2,
 )
 
-######################################################################
+################################################################################
 # Run the search
-# ^^^^^^^^^^^^^^
-# Now we get all inputs ready. Pretty simple, isn't it?
-# We can kick off the search and let the auto-scheduler do its magic.
-# After some measurement trials, we can load the best schedule from the log
-# file and apply it.
+# --------------
+# Now we get all inputs ready. Pretty simple, isn't it?  We can kick off the
+# search and let the auto-scheduler do its magic.  After some measurement
+# trials, we can load the best schedule from the log file and apply it.
 
 # Run auto-tuning (search)
 task.tune(tune_option)
 # Apply the best schedule
 sch, args = task.apply_best(log_file)
 
-######################################################################
-# We can lower the schedule to see the IR after auto-scheduling.
-# The auto-scheduler correctly performs optimizations including multi-level tiling,
-# layout transformation, parallelization, vectorization, unrolling, and operator fusion.
+################################################################################
+# Inspecting the Optimized Schedule
+# ---------------------------------
+# We can lower the schedule to see the IR after auto-scheduling.  The
+# auto-scheduler correctly performs optimizations including multi-level tiling,
+# layout transformation, parallelization, vectorization, unrolling, and
+# operator fusion.
 
 print("Lowered TIR:")
 print(tvm.lower(sch, args, simple_mode=True))
 
-######################################################################
+################################################################################
 # Check correctness and evaluate performance
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# ------------------------------------------
 # We build the binary and check its correctness and performance.
 
 func = tvm.build(sch, args, target)
@@ -152,26 +168,25 @@ def matmul_add(N, L, M, dtype):
 )
 
 
-######################################################################
+################################################################################
 # Using the record file
-# ^^^^^^^^^^^^^^^^^^^^^
-# During the search, all measurement records are dumped into the record
-# file "matmul.json". The measurement records can be used to re-apply search results,
-# resume the search, and perform other analyses.
-
-######################################################################
-# Here is an example where we load the best schedule from a file,
-# and print the equivalent python schedule API. This can be used for
-# debugging and learning the behavior of the auto-scheduler.
+# ---------------------
+# During the search, all measurement records are logged into the record file
+# "matmul.json". The measurement records can be used to re-apply search
+# results, resume the search, and perform other analyses.
+#
+# Here is an example where we load the best schedule from a file, and print the
+# equivalent python schedule API. This can be used for debugging and learning
+# the behavior of the auto-scheduler.
 
 print("Equivalent python schedule:")
 print(task.print_best(log_file))
 
-######################################################################
-# A more complicated example is to resume the search.
-# In this case, we need to create the search policy and cost model by ourselves
-# and resume the status of search policy and cost model with the log file.
-# In the example below we resume the status and do more 5 trials.
+################################################################################
+# A more complicated example is to resume the search.  In this case, we need to
+# create the search policy and cost model by ourselves and resume the status of
+# search policy and cost model with the log file.  In the example below we
+# resume the status and do more 5 trials.
 
 
 def resume_search(task, log_file):
@@ -188,3 +203,12 @@ def resume_search(task, log_file):
 
 
 resume_search(task, log_file)
+
+################################################################################
+# Final Notes and Summary
+# -----------------------
+# In this tutorial, we have shown how to use the TVM Auto-Scheduler to
+# automatically optimize a matrix multiplication, without the need to specify a
+# search template.  It ends a series of examples that starts from the Tensor
+# Expression (TE) language that demonstrates how TVM can optimize computational
+# operations.
diff --git a/tutorials/get_started/tvmc_command_line_driver.py b/tutorials/get_started/tvmc_command_line_driver.py
index bcdf03e56875..fffbfbf0356f 100644
--- a/tutorials/get_started/tvmc_command_line_driver.py
+++ b/tutorials/get_started/tvmc_command_line_driver.py
@@ -15,31 +15,33 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-Getting Started with TVM command line driver - TVMC
-===================================================
+Compiling and Optimizing a Model with TVMC
+==========================================
 **Authors**:
 `Leandro Nunes <https://github.com/leandron>`_,
-`Matthew Barrett <https://github.com/mbaret>`_
-
-This tutorial is an introduction to working with TVMC, the TVM command
-line driver. TVMC is a tool that exposes TVM features such as
-auto-tuning, compiling, profiling and execution of models, via a
-command line interface.
-
-In this tutorial we are going to use TVMC to compile, run and tune a
-ResNet-50 on a x86 CPU.
-
-We are going to start by downloading ResNet 50 V2. Then, we are going
-to use TVMC to compile this model into a TVM module, and use the
-compiled module to generate predictions. Finally, we are going to experiment
-with the auto-tuning options, that can be used to help the compiler to
-improve network performance.
-
-The final goal is to give an overview of TVMC's capabilities and also
-some guidance on where to look for more information.
+`Matthew Barrett <https://github.com/mbaret>`_,
+`Chris Hoge <https://github.com/hogepodge>`_
+
+In this section, we will work with TVMC, the TVM command line driver. TVMC is a
+tool that exposes TVM features such as auto-tuning, compiling, profiling and
+execution of models through a command line interface.
+
+Upon completion of this section, we will have used TVMC to accomplish the
+following tasks:
+
+* Compile a pre-trained ResNet 50 v2 model for the TVM runtime.
+* Run a real image through the compiled model, and interpret the output and
+  model performance.
+* Tune the model on a CPU using TVM.
+* Re-compile an optimized model using the tuning data collected by TVM.
+* Run the image through the optimized model, and compare the output and model
+  performance.
+
+The goal of this section is to give you an overview of TVM and TVMC's
+capabilities, and set the stage for understanding how TVM works.
 """
 
-######################################################################
+################################################################################
 # Using TVMC
 # ----------
 #
@@ -61,32 +63,35 @@
 #
 #   tvmc --help
 #
-#
-# As you can see in the help page, the main features are
-# accessible via the subcommands ``tune``, ``compile`` and ``run``.
-# To read about specific options under a given subcommand, use
-# ``tvmc <subcommand> --help``.
-#
-# In the following sections we will use TVMC to tune, compile and
-# run a model. But first, we need a model.
+# The main features of TVM available to ``tvmc`` are from subcommands
+# ``compile``, and ``run``, and ``tune``.  To read about specific options under
+# a given subcommand, use ``tvmc <subcommand> --help``. We will cover each of
+# these commands in this tutorial, but first we need to download a pre-trained
+# model to work with.
 #
 
 
-######################################################################
-# Obtaining the model
+################################################################################
+# Obtaining the Model
 # -------------------
 #
-# We are going to use ResNet-50 V2 as an example to experiment with TVMC.
-# The version below is in ONNX format. To download the file, you can use
-# the command below:
+# For this tutorial, we will be working with ResNet-50 v2. ResNet-50 is a
+# convolutional neural network that is 50-layers deep and designed to classify
+# images. The model we will be using has been pre-trained on more than a
+# million images with 1000 different classifications. The network has an input
+# image size of 224x224. If you are interested exploring more of how the
+# ResNet-50 model is structured, we recommend downloading `Netron
+# <https://netron.app>`, a freely available ML model viewer.
+#
+# For this tutorial we will be using the model in ONNX format.
 #
 # .. code-block:: bash
 #
 #   wget https://github.com/onnx/models/raw/master/vision/classification/resnet/model/resnet50-v2-7.onnx
 #
-#
 
-######################################################################
+
+################################################################################
 # .. note:: Supported model formats
 #
 #   TVMC supports models created with Keras, ONNX, TensorFlow, TFLite
@@ -96,241 +101,398 @@
 #
 
 
-######################################################################
-# Compiling the model
-# -------------------
+################################################################################
+# Compiling an ONNX Model to the TVM Runtime
+# ------------------------------------------
 #
-# The next step once we've downloaded ResNet-50, is to compile it,
-# To accomplish that, we are going to use ``tvmc compile``. The
-# output we get from the compilation process is a TAR package,
-# that can be used to run our model on the target device.
+# Once we've downloaded the ResNet-50 model, the next step is to compile it. To
+# accomplish that, we are going to use ``tvmc compile``. The output we get from
+# the compilation process is a TAR package of the model compiled to a dynamic
+# library for our target platform. We can run that model on our target device
+# using the TVM runtime.
 #
 # .. code-block:: bash
 #
 #   tvmc compile \
-#     --target "llvm" \
-#     --output compiled_module.tar \
-#     resnet50-v2-7.onnx
+#   --target "llvm" \
+#   --output resnet50-v2-7-tvm.tar \
+#   resnet50-v2-7.onnx
 #
-# Once compilation finishes, the output ``compiled_module.tar`` will be created. This
-# can be directly loaded by your application and run via the TVM runtime APIs.
+# Let's take a look at the files that ``tvmc compile`` creates in the module:
 #
+# .. code-block:: bash
+#
+# 	mkdir model
+# 	tar -xvf resnet50-v2-7-tvm.tar -C model
+# 	ls model
+#
+# You will see three files listed.
+#
+# * ``mod.so`` is the model, represented as a C++ library, that can be loaded
+#   by the TVM runtime.
+# * ``mod.json`` is a text representation of the TVM Relay computation graph.
+# * ``mod.params`` is a file containing the parameters for the pre-trained
+#   model.
+#
+# This module can be directly loaded by your application, and the model can be
+# run via the TVM runtime APIs.
 
 
-######################################################################
-# .. note:: Defining the correct target
+################################################################################
+# .. note:: Defining the Correct Target
 #
 #   Specifying the correct target (option ``--target``) can have a huge
 #   impact on the performance of the compiled module, as it can take
 #   advantage of hardware features available on the target. For more
 #   information, please refer to `Auto-tuning a convolutional network
 #   for x86 CPU <https://tvm.apache.org/docs/tutorials/autotvm/tune_relay_x86.html#define-network>`_.
+#   We recommend identifying which CPU you are running, along with optional features,
+#   and set the target appropriately.
 #
 
-
-######################################################################
-#
-# In the next step, we are going to use the compiled module, providing it
-# with some inputs, to generate some predictions.
-#
-
-
-######################################################################
-# Input pre-processing
-# --------------------
+################################################################################
+# Running the Model from The Compiled Module with TVMC
+# ----------------------------------------------------
 #
-# In order to generate predictions, we will need two things:
+# Now that we've compiled the model to this module, we can use the TVM runtime
+# to make predictions with it. TVMC has the TVM runtime built in to it,
+# allowing you to run compiled TVM models. To use TVMC to run the model and
+# make predictions, we need two things:
 #
-# - the compiled module, which we just produced;
-# - a valid input to the model
+# - The compiled module, which we just produced.
+# - Valid input to the model to make predictions on.
 #
-# Each model is particular when it comes to expected tensor shapes, formats and data
-# types. For this reason, most models require some pre and
-# post processing, to ensure the input(s) is valid and to interpret the output(s).
+# Each model is particular when it comes to expected tensor shapes, formats and
+# data types. For this reason, most models require some pre and
+# post-processing, to ensure the input is valid and to interpret the output.
+# TVMC has adopted NumPy's ``.npz`` format for both input and output data. This
+# is a well-supported NumPy format to serialize multiple arrays into a file
 #
-# In TVMC, we adopted NumPy's ``.npz`` format for both input and output data.
-# This is a well-supported NumPy format to serialize multiple arrays into a file.
-#
-# We will use the usual cat image, similar to other TVM tutorials:
+# As input for this tutorial, we will use the image of a cat, but you can feel
+# free to substitute image for any of your choosing.
 #
 # .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg
 #    :height: 224px
 #    :width: 224px
 #    :align: center
+
+
+################################################################################
+# Input pre-processing
+# ~~~~~~~~~~~~~~~~~~~~
 #
 # For our ResNet 50 V2 model, the input is expected to be in ImageNet format.
 # Here is an example of a script to pre-process an image for ResNet 50 V2.
 #
-from tvm.contrib.download import download_testdata
-from PIL import Image
-import numpy as np
-
-img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
-img_path = download_testdata(img_url, "imagenet_cat.png", module="data")
-
-# Resize it to 224x224
-resized_image = Image.open(img_path).resize((224, 224))
-img_data = np.asarray(resized_image).astype("float32")
-
-# ONNX expects NCHW input, so convert the array
-img_data = np.transpose(img_data, (2, 0, 1))
-
-# Normalize according to ImageNet
-imagenet_mean = np.array([0.485, 0.456, 0.406])
-imagenet_stddev = np.array([0.229, 0.224, 0.225])
-norm_img_data = np.zeros(img_data.shape).astype("float32")
-for i in range(img_data.shape[0]):
-    norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i]
-
-# Add batch dimension
-img_data = np.expand_dims(norm_img_data, axis=0)
-
-# Save to .npz (outputs imagenet_cat.npz)
-np.savez("imagenet_cat", data=img_data)
-
+# .. code-block:: python
+#    :caption: preprocess.py
+#    :name: preprocess.py
+#
+#     #!python ./preprocess.py
+#     from tvm.contrib.download import download_testdata
+#     from PIL import Image
+#     import numpy as np
+#
+#     img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
+#     img_path = download_testdata(img_url, "imagenet_cat.png", module="data")
+#
+#     # Resize it to 224x224
+#     resized_image = Image.open(img_path).resize((224, 224))
+#     img_data = np.asarray(resized_image).astype("float32")
+#
+#     # ONNX expects NCHW input, so convert the array
+#     img_data = np.transpose(img_data, (2, 0, 1))
+#
+#     # Normalize according to ImageNet
+#     imagenet_mean = np.array([0.485, 0.456, 0.406])
+#     imagenet_stddev = np.array([0.229, 0.224, 0.225])
+#     norm_img_data = np.zeros(img_data.shape).astype("float32")
+#     for i in range(img_data.shape[0]):
+#    	    norm_img_data[i, :, :] = (img_data[i, :, :] / 255 - imagenet_mean[i]) / imagenet_stddev[i]
+#
+#     # Add batch dimension
+#     img_data = np.expand_dims(norm_img_data, axis=0)
+#
+#     # Save to .npz (outputs imagenet_cat.npz)
+#     np.savez("imagenet_cat", data=img_data)
+#
 
-######################################################################
-# Running the compiled module
-# ---------------------------
+################################################################################
+# Running the Compiled Module
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# With both the compiled module and input file in hand, we can run it by
-# invoking ``tvmc run``.
+# With both the model and input data in hand, we can now run TVMC to make a
+# prediction:
 #
 # .. code-block:: bash
 #
-#    tvmc run \
-#      --inputs imagenet_cat.npz \
-#      --output predictions.npz \
-#      compiled_module.tar
+#     tvmc run \
+#     --inputs imagenet_cat.npz \
+#     --output predictions.npz \
+#     resnet50-v2-7-tvm.tar
 #
-# When running the above command, a new file ``predictions.npz`` should
-# be produced. It contains the output tensors.
+# Recall that the `.tar` model file includes a C++ library, a description of
+# the Relay model, and the parameters for the model. TVMC includes the TVM
+# runtime, which can load the model and make predictions against input. When
+# running the above command, TVMC outputs a new file, ``predictions.npz``, that
+# contains the model output tensors in NumPy format.
 #
 # In this example, we are running the model on the same machine that we used
-# for compilation. In some cases we might want to run it remotely via
-# an RPC Tracker. To read more about these options please check ``tvmc
-# run --help``.
-#
+# for compilation. In some cases we might want to run it remotely via an RPC
+# Tracker. To read more about these options please check ``tvmc run --help``.
 
-######################################################################
-# Output post-processing
-# ----------------------
+################################################################################
+# Output Post-Processing
+# ~~~~~~~~~~~~~~~~~~~~~~
 #
-# As previously mentioned, each model will have its own particular way
-# of providing output tensors.
+# As previously mentioned, each model will have its own particular way of
+# providing output tensors.
 #
-# In our case, we need to run some post-processing to render the
-# outputs from ResNet 50 V2 into a more human-readable form.
+# In our case, we need to run some post-processing to render the outputs from
+# ResNet 50 V2 into a more human-readable form, using the lookup-table provided
+# for the model.
 #
-# The script below shows an example of the post-processing to extract
-# labels from the output of our compiled module.
+# The script below shows an example of the post-processing to extract labels
+# from the output of our compiled module.
 #
-import os.path
-import numpy as np
-
-from scipy.special import softmax
-
-from tvm.contrib.download import download_testdata
-
-# Download a list of labels
-labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
-labels_path = download_testdata(labels_url, "synset.txt", module="data")
-
-with open(labels_path, "r") as f:
-    labels = [l.rstrip() for l in f]
-
-output_file = "predictions.npz"
-
-# Open the output and read the output tensor
-if os.path.exists(output_file):
-    with np.load(output_file) as data:
-        scores = softmax(data["output_0"])
-        scores = np.squeeze(scores)
-        ranks = np.argsort(scores)[::-1]
-
-        for rank in ranks[0:5]:
-            print("class='%s' with probability=%f" % (labels[rank], scores[rank]))
-
-
-########################################################################
-# When running the script, a list of predictions should be printed similar
-# the the example below.
+# .. code-block:: python
+#     :caption: postprocess.py
+#     :name: postprocess.py
+#
+#     #!python ./postprocess.py
+#     import os.path
+#     import numpy as np
+#
+#     from scipy.special import softmax
+#
+#     from tvm.contrib.download import download_testdata
+#
+#     # Download a list of labels
+#     labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
+#     labels_path = download_testdata(labels_url, "synset.txt", module="data")
+#
+#     with open(labels_path, "r") as f:
+#         labels = [l.rstrip() for l in f]
+#
+#     output_file = "predictions.npz"
+#
+#     # Open the output and read the output tensor
+#     if os.path.exists(output_file):
+#         with np.load(output_file) as data:
+#             scores = softmax(data["output_0"])
+#             scores = np.squeeze(scores)
+#             ranks = np.argsort(scores)[::-1]
+#
+#             for rank in ranks[0:5]:
+#                 print("class='%s' with probability=%f" % (labels[rank], scores[rank]))
+#
+# Running this script should produce the following output:
 #
 # .. code-block:: bash
 #
-#   $ python post_processing.py
-#   class=n02123045 tabby, tabby cat ; probability=446.000000
-#   class=n02123159 tiger cat ; probability=675.000000
-#   class=n02124075 Egyptian cat ; probability=836.000000
-#   class=n02129604 tiger, Panthera tigris ; probability=917.000000
-#   class=n04040759 radiator ; probability=213.000000
+#     python postprocess.py
 #
+#     # class='n02123045 tabby, tabby cat' with probability=0.610553
+#     # class='n02123159 tiger cat' with probability=0.367179
+#     # class='n02124075 Egyptian cat' with probability=0.019365
+#     # class='n02129604 tiger, Panthera tigris' with probability=0.001273
+#     # class='n04040759 radiator' with probability=0.000261
+#
+# Try replacing the cat image with other images, and see what sort of
+# predictions the ResNet model makes.
 
-
-######################################################################
-# Tuning the model
-# ----------------
+################################################################################
+# Automatically Tuning the ResNet Model
+# -------------------------------------
+#
+# The previous model was compiled to work on the TVM runtime, but did not
+# include any platform specific optimization. In this section, we will show you
+# how to build an optimized model using TVMC to target your working platform.
 #
 # In some cases, we might not get the expected performance when running
-# inferences using our compiled module. In cases like this, we can make use
-# of the auto-tuner, to find a better configuration for our model and
-# get a boost in performance.
-#
-# Tuning in TVM refers to the process by which a model is optimized
-# to run faster on a given target. This differs from training or
-# fine-tuning in that it does not affect the accuracy of the model,
-# but only the runtime performance.
-#
-# As part of the tuning process, TVM will try running many different
-# operator implementation variants to see which perform best. The
-# results of these runs are stored in a tuning records file, which is
+# inferences using our compiled module.  In cases like this, we can make use of
+# the auto-tuner, to find a better configuration for our model and get a boost
+# in performance. Tuning in TVM refers to the process by which a model is
+# optimized to run faster on a given target. This differs from training or
+# fine-tuning in that it does not affect the accuracy of the model, but only
+# the runtime performance. As part of the tuning process, TVM will try running
+# many different operator implementation variants to see which perform best.
+# The results of these runs are stored in a tuning records file, which is
 # ultimately the output of the ``tune`` subcommand.
 #
 # In the simplest form, tuning requires you to provide three things:
 #
-# - the target specification of the device you intend to run this model on;
-# - the path to an output file in which the tuning records will be stored, and finally,
+# - the target specification of the device you intend to run this model on
+# - the path to an output file in which the tuning records will be stored, and
+#   finally
 # - a path to the model to be tuned.
 #
-#
 # The example below demonstrates how that works in practice:
 #
 # .. code-block:: bash
 #
-#   tvmc tune \
+#     tvmc tune \
 #     --target "llvm" \
-#     --output autotuner_records.json \
+#     --output resnet50-v2-7-autotuner_records.json \
 #     resnet50-v2-7.onnx
 #
+# In this example, you will see better results if you indicate a more specific
+# target for the `--target` flag.  For example, on an Intel i7 processor you
+# could use `--target llvm -mcpu=skylake`. For this tuning example, we are
+# tuning locally on the CPU using LLVM as the compiler for the specified
+# achitecture.
+#
+# TVMC will perform a search against the parameter space for the model, trying
+# out different configurations for operators and choosing the one that runs
+# fastest on your platform. Although this is a guided search based on the CPU
+# and model operations, it can still take several hours to complete the search.
+# The output of this search will be saved to the
+# `resnet50-v2-7-autotuner_records.json` file, which will later be used to
+# compile an optimized model.
+#
+# .. note:: Defining the Tuning Search Algorithm
+#
+#   By default this search is guided using an `XGBoost Grid` algorithm.
+#   Depending on your model complexity and amount of time avilable, you might
+#   want to choose a different algorithm. A full list is available by
+#   consulting ``tvmc tune --help``.
+#
+# The output will look something like this for a consumer-level Skylake CPU:
+#
+# .. code-block:: bash
+#
+#   tvmc tune   --target "llvm -mcpu=broadwell"   --output resnet50-v2-7-autotuner_records.json   resnet50-v2-7.onnx
+#   # [Task  1/24]  Current/Best:    9.65/  23.16 GFLOPS | Progress: (60/1000) | 130.74 s Done.
+#   # [Task  1/24]  Current/Best:    3.56/  23.16 GFLOPS | Progress: (192/1000) | 381.32 s Done.
+#   # [Task  2/24]  Current/Best:   13.13/  58.61 GFLOPS | Progress: (960/1000) | 1190.59 s Done.
+#   # [Task  3/24]  Current/Best:   31.93/  59.52 GFLOPS | Progress: (800/1000) | 727.85 s Done.
+#   # [Task  4/24]  Current/Best:   16.42/  57.80 GFLOPS | Progress: (960/1000) | 559.74 s Done.
+#   # [Task  5/24]  Current/Best:   12.42/  57.92 GFLOPS | Progress: (800/1000) | 766.63 s Done.
+#   # [Task  6/24]  Current/Best:   20.66/  59.25 GFLOPS | Progress: (1000/1000) | 673.61 s Done.
+#   # [Task  7/24]  Current/Best:   15.48/  59.60 GFLOPS | Progress: (1000/1000) | 953.04 s Done.
+#   # [Task  8/24]  Current/Best:   31.97/  59.33 GFLOPS | Progress: (972/1000) | 559.57 s Done.
+#   # [Task  9/24]  Current/Best:   34.14/  60.09 GFLOPS | Progress: (1000/1000) | 479.32 s Done.
+#   # [Task 10/24]  Current/Best:   12.53/  58.97 GFLOPS | Progress: (972/1000) | 642.34 s Done.
+#   # [Task 11/24]  Current/Best:   30.94/  58.47 GFLOPS | Progress: (1000/1000) | 648.26 s Done.
+#   # [Task 12/24]  Current/Best:   23.66/  58.63 GFLOPS | Progress: (1000/1000) | 851.59 s Done.
+#   # [Task 13/24]  Current/Best:   25.44/  59.76 GFLOPS | Progress: (1000/1000) | 534.58 s Done.
+#   # [Task 14/24]  Current/Best:   26.83/  58.51 GFLOPS | Progress: (1000/1000) | 491.67 s Done.
+#   # [Task 15/24]  Current/Best:   33.64/  58.55 GFLOPS | Progress: (1000/1000) | 529.85 s Done.
+#   # [Task 16/24]  Current/Best:   14.93/  57.94 GFLOPS | Progress: (1000/1000) | 645.55 s Done.
+#   # [Task 17/24]  Current/Best:   28.70/  58.19 GFLOPS | Progress: (1000/1000) | 756.88 s Done.
+#   # [Task 18/24]  Current/Best:   19.01/  60.43 GFLOPS | Progress: (980/1000) | 514.69 s Done.
+#   # [Task 19/24]  Current/Best:   14.61/  57.30 GFLOPS | Progress: (1000/1000) | 614.44 s Done.
+#   # [Task 20/24]  Current/Best:   10.47/  57.68 GFLOPS | Progress: (980/1000) | 479.80 s Done.
+#   # [Task 21/24]  Current/Best:   34.37/  58.28 GFLOPS | Progress: (308/1000) | 225.37 s Done.
+#   # [Task 22/24]  Current/Best:   15.75/  57.71 GFLOPS | Progress: (1000/1000) | 1024.05 s Done.
+#   # [Task 23/24]  Current/Best:   23.23/  58.92 GFLOPS | Progress: (1000/1000) | 999.34 s Done.
+#   # [Task 24/24]  Current/Best:   17.27/  55.25 GFLOPS | Progress: (1000/1000) | 1428.74 s Done.
+#
+# Tuning sessions can take a long time, so ``tvmc tune`` offers many options to customize your tuning
+# process, in terms of number of repetitions (``--repeat`` and ``--number``, for example), the tuning
+# algorithm to be used, and so on. Check ``tvmc tune --help`` for more information.
+#
+
+################################################################################
+# Compiling an Optimized Model with Tuning Data
+# ----------------------------------------------
+#
+# As an output of the tuning process above, we obtained the tuning records
+# stored in ``resnet50-v2-7-autotuner_records.json``. This file can be used in
+# two ways:
+#
+# - As input to further tuning (via ``tvmc tune --tuning-records``).
+# - As input to the compiler
+#
+# The compiler will use the results to generate high performance code for the
+# model on your specified target. To do that we can use ``tvmc compile
+# --tuning-records``. Check ``tvmc compile --help`` for more information.
+#
+# Now that tuning data for the model has been collected, we can re-compile the
+# model using optimized operators to speed up our computations.
+#
+# .. code-block:: bash
+#
+#   tvmc compile \
+#   --target "llvm" \
+#   --tuning-records resnet50-v2-7-autotuner_records.json  \
+#   --output resnet50-v2-7-tvm_autotuned.tar \
+#   resnet50-v2-7.onnx
+#
+# Verify that the optimized model runs and produces the same results:
+#
+# .. code-block:: bash
+#
+#   tvmc run \
+#   --inputs imagenet_cat.npz \
+#   --output predictions.npz \
+#   resnet50-v2-7-tvm_autotuned.tar
+#
+#   python postproccess.py
+#
+# Verifying that the predictions are the same:
+#
+# .. code-block:: bash
+#
+#   # class='n02123045 tabby, tabby cat' with probability=0.610550
+#   # class='n02123159 tiger cat' with probability=0.367181
+#   # class='n02124075 Egyptian cat' with probability=0.019365
+#   # class='n02129604 tiger, Panthera tigris' with probability=0.001273
+#   # class='n04040759 radiator' with probability=0.000261
+
+################################################################################
+# Comparing the Tuned and Untuned Models
+# --------------------------------------
+#
+# TVMC gives you tools for basic performance benchmarking between the models.
+# You can specify a number of repetitions and that TVMC report on the model run
+# time (independent of runtime startup). We can get a rough idea of how much
+# tuning has improved the model performance. For example, on a test Intel i7
+# system, we see that the tuned model runs 47% faster than the untuned model:
+#
+# .. code-block:: bash
 #
-# Tuning sessions can take a long time, so ``tvmc tune`` offers many options to
-# customize your tuning process, in terms of number of repetitions (``--repeat`` and
-# ``--number``, for example), the tuning algorithm to be use, and so on.
-# Check ``tvmc tune --help`` for more information.
+#   tvmc run \
+#   --inputs imagenet_cat.npz \
+#   --output predictions.npz  \
+#   --print-time \
+#   --repeat 100 \
+#   resnet50-v2-7-tvm_autotuned.tar
 #
-# As an output of the tuning process above, we obtained the tuning records stored
-# in ``autotuner_records.json``. This file can be used in two ways:
+#   # Execution time summary:
+#   # mean (s)   max (s)    min (s)    std (s)
+#   # 0.09219    0.11573    0.08985    0.00315
 #
-# - as an input to further tuning (via ``tvmc tune --tuning-records``), or
-# - as an input to the compiler
+#   tvmc run \
+#   --inputs imagenet_cat.npz \
+#   --output predictions.npz  \
+#   --print-time \
+#   --repeat 100 \
+#   resnet50-v2-7-tvm.tar
 #
-# The compiler will use the results to generate high performance code for the model
-# on your specified target. To do that we can use ``tvmc compile --tuning-records``.
-# Check ``tvmc compile --help`` for more information.
+#   # Execution time summary:
+#   # mean (s)   max (s)    min (s)    std (s)
+#   # 0.19332    0.21997    0.18504    0.00711
 #
 
 
-######################################################################
+################################################################################
 # Final Remarks
 # -------------
 #
-# In this tutorial, we presented TVMC, a command line driver for TVM.
-# We demonstrated how to compile, run and tune a model, as well
-# as discussed the need for pre and post processing of inputs and outputs.
+# In this tutorial, we presented TVMC, a command line driver for TVM. We
+# demonstrated how to compile, run, and tune a model. We also discussed the
+# need for pre and post-processing of inputs and outputs. After the tuning
+# process, we demonstrated how to compare the performance of the unoptimized
+# and optimize models.
 #
 # Here we presented a simple example using ResNet 50 V2 locally. However, TVMC
 # supports many more features including cross-compilation, remote execution and
 # profiling/benchmarking.
 #
-# To see what other options are available, please have a look at ``tvmc --help``.
+# To see what other options are available, please have a look at ``tvmc
+# --help``.
 #
+# In the next tutorial, `Compiling and Optimizing a Model with the Python
+# AutoScheduler <auto_tuning_with_pyton>`_, we will cover the same compilation
+# and optimization steps using the Python interface.
diff --git a/tutorials/language/extern_op.py b/tutorials/language/extern_op.py
index 454237a33783..794101a4fb56 100644
--- a/tutorials/language/extern_op.py
+++ b/tutorials/language/extern_op.py
@@ -35,6 +35,7 @@
 from tvm import te
 import numpy as np
 from tvm.contrib import cblas
+import tvm.testing
 
 if not tvm.get_global_func("tvm.contrib.cblas.matmul", allow_missing=True):
     raise Exception("Not compiled with cblas support; can't build this tutorial")
diff --git a/tutorials/language/schedule_primitives.py b/tutorials/language/schedule_primitives.py
index eb48dc218cdd..ade79f69707f 100644
--- a/tutorials/language/schedule_primitives.py
+++ b/tutorials/language/schedule_primitives.py
@@ -69,7 +69,7 @@
 ######################################################################
 # split
 # -----
-# :code:`split` can split a specified axis into two axises by
+# :code:`split` can split a specified axis into two axes by
 # :code:`factor`.
 A = te.placeholder((m,), name="A")
 B = te.compute((m,), lambda i: A[i] * 2, name="B")
@@ -92,7 +92,7 @@
 # tile
 # ----
 # :code:`tile` help you execute the computation tile by tile over two
-# axises.
+# axes.
 A = te.placeholder((m, n), name="A")
 B = te.compute((m, n), lambda i, j: A[i, j], name="B")
 
@@ -103,12 +103,12 @@
 ######################################################################
 # fuse
 # ----
-# :code:`fuse` can fuse two consecutive axises of one computation.
+# :code:`fuse` can fuse two consecutive axes of one computation.
 A = te.placeholder((m, n), name="A")
 B = te.compute((m, n), lambda i, j: A[i, j], name="B")
 
 s = te.create_schedule(B.op)
-# tile to four axises first: (i.outer, j.outer, i.inner, j.inner)
+# tile to four axes first: (i.outer, j.outer, i.inner, j.inner)
 xo, yo, xi, yi = s[B].tile(B.op.axis[0], B.op.axis[1], x_factor=10, y_factor=5)
 # then fuse (i.inner, j.inner) into one axis: (i.inner.j.inner.fused)
 fused = s[B].fuse(xi, yi)
@@ -117,14 +117,14 @@
 ######################################################################
 # reorder
 # -------
-# :code:`reorder` can reorder the axises in the specified order.
+# :code:`reorder` can reorder the axes in the specified order.
 A = te.placeholder((m, n), name="A")
 B = te.compute((m, n), lambda i, j: A[i, j], name="B")
 
 s = te.create_schedule(B.op)
-# tile to four axises first: (i.outer, j.outer, i.inner, j.inner)
+# tile to four axes first: (i.outer, j.outer, i.inner, j.inner)
 xo, yo, xi, yi = s[B].tile(B.op.axis[0], B.op.axis[1], x_factor=10, y_factor=5)
-# then reorder the axises: (i.inner, j.outer, i.outer, j.inner)
+# then reorder the axes: (i.inner, j.outer, i.outer, j.inner)
 s[B].reorder(xi, yo, xo, yi)
 print(tvm.lower(s, [A, B], simple_mode=True))
 
diff --git a/tutorials/language/tensorize.py b/tutorials/language/tensorize.py
index e91cfe43ab46..a75b78b65ca4 100644
--- a/tutorials/language/tensorize.py
+++ b/tutorials/language/tensorize.py
@@ -36,6 +36,7 @@
 
 import tvm
 from tvm import te
+import tvm.testing
 import numpy as np
 
 ######################################################################
diff --git a/tutorials/micro/README.txt b/tutorials/micro/README.txt
index 0654353e3426..70a5e580ecd1 100644
--- a/tutorials/micro/README.txt
+++ b/tutorials/micro/README.txt
@@ -1,4 +1,4 @@
 .. _tutorial-micro:
 
-Micro TVM 
----------
+microTVM
+--------
diff --git a/tutorials/micro/micro_reference_vm.py b/tutorials/micro/micro_reference_vm.py
index 4b449a0e7e14..93395a44c8ae 100644
--- a/tutorials/micro/micro_reference_vm.py
+++ b/tutorials/micro/micro_reference_vm.py
@@ -15,6 +15,8 @@
 # specific language governing permissions and limitations
 # under the License.
 """
+.. _tutorial-micro-reference-vm:
+
 ===================================
 microTVM Reference Virtual Machines
 ===================================
@@ -57,15 +59,17 @@
 
 A minimal set of prerequisites are needed:
 
-
 1. `Vagrant <https://vagrantup.com>`__
-2. A supported Virtual Machine hypervisor.
-   `VirtualBox <https://www.virtualbox.org>`__ is one suggested free hypervisor, but please note
+2. A supported Virtual Machine hypervisor (**VirtualBox**, **Parallels**, or **VMWare Fusion/Workstation**).
+   `VirtualBox <https://www.virtualbox.org>`__ is a suggested free hypervisor, but please note
    that the `VirtualBox Extension Pack`_ is required for proper USB forwarding. If using VirtualBox,
    also consider installing the `vbguest <https://github.com/dotless-de/vagrant-vbguest>`_ plugin.
 
 .. _VirtualBox Extension Pack: https://www.virtualbox.org/wiki/Downloads#VirtualBox6.1.16OracleVMVirtualBoxExtensionPack
 
+3. If required for your hypervisor, the
+   `Vagrant provider plugin <https://github.com/hashicorp/vagrant/wiki/Available-Vagrant-Plugins#providers>`__ (or see `here <https://www.vagrantup.com/vmware>`__ for VMWare).
+
 First boot
 ----------
 
@@ -73,9 +77,9 @@
 
 .. code-block:: bash
 
-    # Replace zepyhr with the name of a different platform, if you are not using Zephyr.
+    # Replace zephyr with the name of a different platform, if you are not using Zephyr.
     ~/.../tvm $ cd apps/microtvm/reference-vm/zephyr
-    # Replace <provider_name> with the name of the hypervisor you wish to use (i.e. virtualbox).
+    # Replace <provider_name> with the name of the hypervisor you wish to use (i.e. virtualbox, parallels, vmware_desktop).
     ~/.../tvm/apps/microtvm/reference-vm/zephyr $ vagrant up --provider=<provider_name>
 
 
@@ -90,6 +94,8 @@
 
 .. _microTVM base box: https://app.vagrantup.com/tlcpack/boxes/microtvm
 
+Connect Hardware to the VM
+--------------------------
 
 Next, you need to configure USB passthrough to attach your physical development board to the virtual
 machine (rather than directly to your laptop's host OS).
@@ -102,8 +108,8 @@
  * `Parallels <https://kb.parallels.com/122993>`__
  * `VMWare Workstation <https://docs.vmware.com/en/VMware-Workstation-Pro/15.0/com.vmware.ws.using.doc/GUID-E003456F-EB94-4B53-9082-293D9617CB5A.html>`__
 
-Future use
-----------
+Rebuilding TVM inside the Reference VM
+--------------------------------------
 
 After the first boot, you'll need to ensure you keep the build, in ``$TVM_HOME/build-microtvm``,
 up-to-date when you modify the C++ runtime or checkout a different revision. You can either
@@ -136,6 +142,19 @@
 
 .. code-block:: bash
 
-    $ poetry run python3 tests/micro/qemu/test_zephyr.py --microtvm-platforms=stm32f746xx
+    $ cd apps/microtvm/reference-vm/zephyr
+    $ poetry run python3 ../../../../tests/micro/qemu/test_zephyr.py --microtvm-platforms=stm32f746xx
+
+If you do not have physical hardware attached, but wish to run the tests using the
+local QEMU emulator running within the VM, run the following commands instead:
+
+.. code-block:: bash
+
+    $ cd /Users/yourusername/path/to/tvm
+    $ sudo ./docker/install/ubuntu_install_qemu.sh
+    $ cd apps/microtvm/reference-vm/zephyr/
+    $ poetry run pytest ../../../../tests/micro/qemu/test_zephyr.py --microtvm-platforms=host
+
+
 
 """
diff --git a/tutorials/micro/micro_tflite.py b/tutorials/micro/micro_tflite.py
index 7ec5506aa9b5..6ad0da5aecba 100644
--- a/tutorials/micro/micro_tflite.py
+++ b/tutorials/micro/micro_tflite.py
@@ -15,99 +15,121 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-Micro TVM with TFLite Models
-============================
+microTVM with TFLite Models
+===========================
 **Author**: `Tom Gall <https://github.com/tom-gall>`_
 
-This tutorial is an introduction to working with MicroTVM and a TFLite
+This tutorial is an introduction to working with microTVM and a TFLite
 model with Relay.
 """
 
-# %%
+######################################################################
+# .. note::
+#     If you want to run this tutorial on the microTVM Reference VM, download the Jupyter
+#     notebook using the link at the bottom of this page and save it into the TVM directory. Then:
+#
+#     #. Login to the reference VM with a modified ``vagrant ssh`` command:
+#
+#         ``$ vagrant ssh -- -L8888:localhost:8888``
+#
+#     #. Install jupyter:  ``pip install jupyterlab``
+#     #. ``cd`` to the TVM directory.
+#     #. Install tflite: poetry install -E importer-tflite
+#     #. Launch Jupyter Notebook: ``jupyter notebook``
+#     #. Copy the localhost URL displayed, and paste it into your browser.
+#     #. Navigate to saved Jupyter Notebook (``.ipynb`` file).
+#
+#
 # Setup
 # -----
 #
-# To get started, TFLite package needs to be installed as prerequisite.
+# Install TFLite
+# ^^^^^^^^^^^^^^
+#
+# To get started, TFLite package needs to be installed as prerequisite. You can do this in two ways:
 #
-# install tflite
+# 1. Install tflite with ``pip``
 #
-# .. code-block:: bash
+#     .. code-block:: bash
 #
-#   pip install tflite=2.1.0 --user
+#       pip install tflite=2.1.0 --user
 #
-# or you could generate TFLite package yourself. The steps are the following:
+# 2. Generate the TFLite package yourself. The steps are the following:
 #
-#   Get the flatc compiler.
-#   Please refer to https://github.com/google/flatbuffers for details
-#   and make sure it is properly installed.
+#     Get the flatc compiler.
+#     Please refer to https://github.com/google/flatbuffers for details
+#     and make sure it is properly installed.
 #
-# .. code-block:: bash
+#     .. code-block:: bash
 #
-#   flatc --version
+#       flatc --version
 #
-# Get the TFLite schema.
+#     Get the TFLite schema.
 #
-# .. code-block:: bash
+#     .. code-block:: bash
 #
-#   wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
+#       wget https://raw.githubusercontent.com/tensorflow/tensorflow/r1.13/tensorflow/lite/schema/schema.fbs
 #
-# Generate TFLite package.
+#     Generate TFLite package.
 #
-# .. code-block:: bash
+#     .. code-block:: bash
 #
-#   flatc --python schema.fbs
+#       flatc --python schema.fbs
 #
-# Add the current folder (which contains generated tflite module) to PYTHONPATH.
+#     Add the current folder (which contains generated tflite module) to PYTHONPATH.
 #
-# .. code-block:: bash
+#     .. code-block:: bash
 #
-#   export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
+#       export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$(pwd)
 #
 # To validate that the TFLite package was installed successfully, ``python -c "import tflite"``
 #
-# CMSIS needs to be downloaded and the CMSIS_ST_PATH environment variable setup
-# This tutorial only supports the STM32F7xx series of boards.
-# Download from : https://www.st.com/en/embedded-software/stm32cubef7.html
-# After you've expanded the zip file
+# Install Zephyr (physical hardware only)
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #
-# .. code-block:: bash
+# When running this tutorial with a host simulation (the default), you can use the host ``gcc`` to
+# build a firmware image that simulates the device. When compiling to run on physical hardware, you
+# need to install a *toolchain* plus some target-specific dependencies. microTVM allows you to
+# supply any compiler and runtime that can launch the TVM RPC server, but to get started, this
+# tutorial relies on the Zephyr RTOS to provide these pieces.
 #
-#   export CMSIS_ST_PATH=/path/to/STM32Cube_FW_F7_V1.16.0/Drivers/CMSIS
-
-# %%
-# Recreating your own Pre-Trained TFLite model
-# --------------------------------------------
+# You can install Zephyr by following the
+# `Installation Instructions <https://docs.zephyrproject.org/latest/getting_started/index.html>`_.
+#
+# Aside: Recreating your own Pre-Trained TFLite model
+#  The tutorial downloads a pretrained TFLite model. When working with microcontrollers
+#  you need to be mindful these are highly resource constrained devices as such standard
+#  models like MobileNet may not fit into their modest memory.
+#
+#  For this tutorial, we'll make use of one of the TF Micro example models.
 #
-# The tutorial downloads a pretrained TFLite model. When working with microcontrollers
-# you need to be mindful these are highly resource constrained devices as such standard
-# models like MobileNet may not fit into their modest memory.
+#  If you wish to replicate the training steps see:
+#  https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
 #
-# For this tutorial, we'll make use of one of the TF Micro example models.
+#    .. note::
 #
-# If you wish to replicate the training steps see:
-# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/micro/examples/hello_world/train
+#      If you accidentally download the example pretrained model from:
 #
-#   .. note::
+#      ``wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip``
 #
-#     If you accidentally download the example pretrained model from:
-#     wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/micro/hello_world_2020_04_13.zip
-#     this will fail due to an unimplemented opcode (114)
+#      this will fail due to an unimplemented opcode (114)
+#
+# Load and prepare the Pre-Trained Model
+# --------------------------------------
+#
+# Load the pretrained TFLite model from a file in your current
+# directory into a buffer
 
 import os
 import numpy as np
+import logging
+
 import tvm
 import tvm.micro as micro
 from tvm.contrib.download import download_testdata
 from tvm.contrib import graph_runtime, utils
 from tvm import relay
 
-# %%
-# Load and prepare the Pre-Trained Model
-# --------------------------------------
-#
-# Load the pretrained TFLite model from a file in your current
-# directory into a buffer
-
 model_url = "https://people.linaro.org/~tom.gall/sine_model.tflite"
 model_file = "sine_model.tflite"
 model_path = download_testdata(model_url, model_file, module="data")
@@ -137,8 +159,8 @@
 # is contained in the model.
 #
 # If you are unsure what that might be, this can be discovered by using
-# the visualize.py script within the Tensorflow project.
-# See : How do I inspect a .tflite file? `<https://www.tensorflow.org/lite/guide/faq>`_
+# the ``visualize.py`` script within the Tensorflow project.
+# See `How do I inspect a .tflite file? <https://www.tensorflow.org/lite/guide/faq>`_
 
 input_tensor = "dense_4_input"
 input_shape = (1,)
@@ -149,44 +171,80 @@
 )
 
 ######################################################################
+# Defining the target
+# -------------------
+#
 # Now we create a build config for relay. turning off two options
 # and then calling relay.build which will result in a C source
-# file.
-#
-# .. code-block:: python
-#
+# file. When running on a simulated target, choose "host" below:
 TARGET = tvm.target.target.micro("host")
 
+# %%
+# Compiling for physical hardware
+#  When running on physical hardware, choose a target and a board that
+#  describe the hardware. The STM32F746 Nucleo target and board is chosen in
+#  this commented code. Another option would be to choose the same target but
+#  the STM32F746 Discovery board instead. The disco board has the same
+#  microcontroller as the Nucleo board but a couple of wirings and configs
+#  differ, so it's necessary to select the "stm32f746g_disco" board below.
+#
+#  .. code-block:: python
+#
+#     TARGET = tvm.target.target.micro("stm32f746xx")
+#     BOARD = "nucleo_f746zg" # or "stm32f746g_disco"
+
+######################################################################
+# Now, compile the model for the target:
+
 with tvm.transform.PassContext(
-    opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["FuseOps"]
+    opt_level=3, config={"tir.disable_vectorize": True}, disabled_pass=["FuseOps", "AlterOpLayout"]
 ):
     graph, c_mod, c_params = relay.build(mod, target=TARGET, params=params)
 
 
 # %%
-# Running on simulated device
-# ----------------------------------------------
+# Compiling for a simulated device
+# --------------------------------
 #
 # First, compile a static microTVM runtime for the targeted device. In this case, the host simulated
 # device is used.
-workspace = tvm.micro.Workspace()
-
 compiler = tvm.micro.DefaultCompiler(target=TARGET)
-opts = tvm.micro.default_options(os.path.join(tvm.micro.CRT_ROOT_DIR, "host"))
+opts = tvm.micro.default_options(
+    os.path.join(tvm.micro.get_standalone_crt_dir(), "template", "host")
+)
 
+# %%
+# Compiling for physical hardware
+#  For physical hardware, comment out the previous section and use this compiler definition instead.
+#
+#  .. code-block:: python
+#
+#     import subprocess
+#     from tvm.micro.contrib import zephyr
+#
+#     repo_root = subprocess.check_output(["git", "rev-parse", "--show-toplevel"], encoding='utf-8').strip()
+#     project_dir = f"{repo_root}/tests/micro/qemu/zephyr-runtime"
+#     compiler = zephyr.ZephyrCompiler(
+#         project_dir=project_dir,
+#         board=BOARD if "stm32f746" in str(TARGET) else "qemu_x86",
+#         zephyr_toolchain_variant="zephyr",
+#     )
+#
+#     opts = tvm.micro.default_options(f"{project_dir}/crt")
+#
+# enable printing memory usage statistics of the runtime image
+# generated by Zephyr compiler for the physical hardware
+# logging.basicConfig(level="INFO")
+
+workspace = tvm.micro.Workspace()
 micro_binary = tvm.micro.build_static_runtime(
-    # the x86 compiler *expects* you to give the exact same dictionary for both
-    # lib_opts and bin_opts. so the library compiler is mutating lib_opts and
-    # the binary compiler is expecting those mutations to be in bin_opts.
-    # TODO(weberlo) fix this very bizarre behavior
     workspace,
     compiler,
     c_mod,
-    lib_opts=opts["bin_opts"],
-    bin_opts=opts["bin_opts"],
+    opts,
     # Use the microTVM memory manager. If, in your main.cc, you change TVMPlatformMemoryAllocate and
     # TVMPlatformMemoryFree to use e.g. malloc() and free(), you can omit this extra library.
-    extra_libs=[os.path.join(tvm.micro.build.CRT_ROOT_DIR, "memory")],
+    extra_libs=[tvm.micro.get_standalone_crt_lib("memory")],
 )
 
 
@@ -195,9 +253,7 @@
 # computation. The `with session` line would typically flash an attached
 # microcontroller, but in this tutorial, it simply launches a subprocess
 # to stand in for an attached microcontroller.
-#
-# .. code-block:: python
-#
+
 flasher = compiler.flasher()
 with tvm.micro.Session(binary=micro_binary, flasher=flasher) as session:
     graph_mod = tvm.micro.create_local_graph_runtime(
diff --git a/tutorials/optimize/opt_matmul_auto_tensorcore.py b/tutorials/optimize/opt_matmul_auto_tensorcore.py
index d81eca56210e..f5450b9524c6 100644
--- a/tutorials/optimize/opt_matmul_auto_tensorcore.py
+++ b/tutorials/optimize/opt_matmul_auto_tensorcore.py
@@ -50,6 +50,7 @@
 
 from tvm import autotvm
 from tvm.contrib import nvcc
+import tvm.testing
 
 
 def matmul_nn(A, B, L, dtype="float16", layout="NN"):
diff --git a/vta/python/vta/__init__.py b/vta/python/vta/__init__.py
index d143c4db6884..5fce76808c45 100644
--- a/vta/python/vta/__init__.py
+++ b/vta/python/vta/__init__.py
@@ -22,6 +22,7 @@
 """
 import sys
 
+from .autotvm import module_loader
 from .bitstream import get_bitstream_path, download_bitstream
 from .environment import get_env, Environment
 from .rpc_client import reconfig_runtime, program_fpga
diff --git a/vta/python/vta/autotvm.py b/vta/python/vta/autotvm.py
new file mode 100644
index 000000000000..9aa7390f238f
--- /dev/null
+++ b/vta/python/vta/autotvm.py
@@ -0,0 +1,52 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Defines AutoTVM components used with VTA."""
+
+from tvm.autotvm.measure import default_module_loader
+from . import rpc_client
+
+
+def module_loader(bitstream=None):
+    """Construct a ModuleLoader implementation specialized for VTA.
+
+    Parameters
+    ----------
+    bitsream : Optional[str]
+        Path to the bitstream to write prior to uploading code.
+
+    Returns
+    -------
+    ModuleLoader :
+        The ModuleLoader instance.
+    """
+
+    def reprogram_fpga(remote, _build_result):
+        """default_module_loader callback which reprograms the FPGA.
+
+        Parameters
+        ----------
+        remote : tvm.rpc.RPCSession
+            RPC session established to the remote device.
+
+        _build_result : tvm.autotvm.measure.measure_methods.BuildResult
+            Artifact from the build phase, unused here.
+        """
+        rpc_client.program_bitstream(remote, bitstream)
+        rpc_client.reconfig_runtime(remote)
+
+    return default_module_loader(reprogram_fpga)
diff --git a/vta/python/vta/transform.py b/vta/python/vta/transform.py
index a485d2cfb7b8..9770857fb0b9 100644
--- a/vta/python/vta/transform.py
+++ b/vta/python/vta/transform.py
@@ -231,7 +231,13 @@ def _merge_block(slist, body):
                     body = tvm.tir.AttrStmt(op.node, op.attr_key, op.value, body)
                 elif isinstance(op, tvm.tir.For):
                     body = tvm.tir.For(
-                        op.loop_var, op.min, op.extent, op.for_type, op.device_api, body
+                        op.loop_var,
+                        op.min,
+                        op.extent,
+                        op.kind,
+                        body,
+                        op.thread_binding,
+                        op.annotations,
                     )
                 else:
                     raise RuntimeError("unexpected op")
@@ -314,7 +320,9 @@ def _do_fold(stmt):
             if _match_pragma(stmt, "trim_loop"):
                 op = stmt.body
                 assert isinstance(op, tvm.tir.For)
-                return tvm.tir.For(op.loop_var, op.min, 2, op.for_type, op.device_api, op.body)
+                return tvm.tir.For(
+                    op.loop_var, op.min, 2, op.kind, op.body, op.thread_binding, op.annotations
+                )
             return None
 
         return f.with_body(
diff --git a/vta/scripts/tune_conv2d.py b/vta/scripts/tune_conv2d.py
index 2a1331f9f94b..6333ac245a95 100644
--- a/vta/scripts/tune_conv2d.py
+++ b/vta/scripts/tune_conv2d.py
@@ -159,7 +159,7 @@ def conv2d(N, CI, H, W, CO, KH, KW, strides, padding, dilation):
                 port=int(tracker_port),
                 number=5,
                 timeout=60,
-                check_correctness=True,
+                # check_correctness=True, # TODO: re-enable when check_correctness works again.
             ),
         )
 
diff --git a/vta/scripts/tune_conv2d_transpose.py b/vta/scripts/tune_conv2d_transpose.py
index ebfe7eb54e5c..e8721539ec77 100644
--- a/vta/scripts/tune_conv2d_transpose.py
+++ b/vta/scripts/tune_conv2d_transpose.py
@@ -151,7 +151,7 @@ def conv2d_transpose(N, CI, H, W, CO, KH, KW, strides, padding, opadding):
                 port=int(tracker_port),
                 number=5,
                 timeout=60,
-                check_correctness=True,
+                # check_correctness=True, # TODO: re-enable when check_correctness works again.
             ),
         )
 
diff --git a/vta/scripts/tune_dense.py b/vta/scripts/tune_dense.py
index 7e3aec86094b..6d600c4c322f 100644
--- a/vta/scripts/tune_dense.py
+++ b/vta/scripts/tune_dense.py
@@ -116,7 +116,7 @@ def dense(N, CI, CO):
                 port=int(tracket_port),
                 number=5,
                 timeout=60,
-                check_correctness=True,
+                # check_correctness=True, # TODO: re-enable when check_correctness works again.
             ),
         )
 
diff --git a/vta/scripts/tune_group_conv2d.py b/vta/scripts/tune_group_conv2d.py
index bfac4996e6ef..ebb7db88845f 100644
--- a/vta/scripts/tune_group_conv2d.py
+++ b/vta/scripts/tune_group_conv2d.py
@@ -154,7 +154,7 @@ def group_conv2d(N, CI, H, W, CO, KH, KW, strides, padding, dilation, group):
                 port=int(tracker_port),
                 number=5,
                 timeout=60,
-                check_correctness=True,
+                # check_correctness=True, # TODO: re-enable when check_correctness works again.
             ),
         )
 
diff --git a/vta/scripts/tune_resnet.py b/vta/scripts/tune_resnet.py
index 04f430ef8624..a10d1de8c46b 100644
--- a/vta/scripts/tune_resnet.py
+++ b/vta/scripts/tune_resnet.py
@@ -295,7 +295,7 @@ def tune_tasks(
                 min_repeat_ms=150,
                 repeat=opt.measurements,
                 timeout=60,
-                check_correctness=True,
+                # check_correctness=True, # TODO: re-enable when check_correctness works again.
             ),
         ),
     }
diff --git a/vta/tests/python/integration/test_benchmark_gemm.py b/vta/tests/python/integration/test_benchmark_gemm.py
index 3ce2d9c9e4a9..824aed6efa02 100644
--- a/vta/tests/python/integration/test_benchmark_gemm.py
+++ b/vta/tests/python/integration/test_benchmark_gemm.py
@@ -59,7 +59,7 @@ def run_gemm_packed(env, remote, batch_size, channel, block):
         )  # relu
         res = te.compute(res_shape, lambda *i: res_min(*i).astype(env.inp_dtype), name="res")
 
-        def verify(s, check_correctness=True):
+        def verify(s):
             mod = vta.build(s, [data, weight, res], "ext_dev", env.target_host, name="gemm")
             temp = utils.tempdir()
             mod.save(temp.relpath("gemm.o"))
@@ -102,11 +102,9 @@ def verify(s, check_correctness=True):
             res_unpack = res_arr.asnumpy().reshape(
                 batch_size // env.BATCH, channel // env.BLOCK_OUT, env.BATCH, env.BLOCK_OUT
             )
-            if check_correctness:
-                tvm.testing.assert_allclose(res_unpack, res_ref)
             return cost
 
-        def run_schedule(load_inp, load_wgt, gemm, alu, store_out, print_ir, check_correctness):
+        def run_schedule(load_inp, load_wgt, gemm, alu, store_out, print_ir):
             s = te.create_schedule(res.op)
             s[data_buf].set_scope(env.inp_scope)
             s[weight_buf].set_scope(env.wgt_scope)
@@ -156,13 +154,13 @@ def run_schedule(load_inp, load_wgt, gemm, alu, store_out, print_ir, check_corre
 
             if print_ir:
                 print(tvm.lower(s, [data, weight, res], simple_mode=True))
-            return verify(s, check_correctness)
+            return verify(s)
 
         def gemm_normal(print_ir):
             mock = env.mock
             print("----- GEMM GOPS End-to-End Test-------")
 
-            def run_test(header, print_ir, check_correctness):
+            def run_test(header, print_ir):
                 cost = run_schedule(
                     env.dma_copy,
                     env.dma_copy,
@@ -170,14 +168,13 @@ def run_test(header, print_ir, check_correctness):
                     env.alu,
                     env.dma_copy,
                     print_ir,
-                    check_correctness,
                 )
                 gops = (num_ops / cost.mean) / float(10 ** 9)
                 print(header)
                 print("\tTime cost = %g sec/op, %g GOPS" % (cost.mean, gops))
 
             with vta.build_config():
-                run_test("NORMAL", print_ir, True)
+                run_test("NORMAL", print_ir)
 
         def gemm_unittest(print_ir):
             mock = env.mock
@@ -185,7 +182,7 @@ def gemm_unittest(print_ir):
 
             def run_test(header, print_ir):
                 cost = run_schedule(
-                    mock.dma_copy, mock.dma_copy, env.gemm, mock.alu, mock.dma_copy, print_ir, False
+                    mock.dma_copy, mock.dma_copy, env.gemm, mock.alu, mock.dma_copy, print_ir
                 )
                 gops = (num_ops / cost.mean) / float(10 ** 9)
                 print(header)
@@ -200,7 +197,7 @@ def alu_unittest(print_ir):
 
             def run_test(header, print_ir):
                 cost = run_schedule(
-                    mock.dma_copy, mock.dma_copy, mock.gemm, env.alu, mock.dma_copy, print_ir, False
+                    mock.dma_copy, mock.dma_copy, mock.gemm, env.alu, mock.dma_copy, print_ir
                 )
                 gops = (num_ops / cost.mean) / float(10 ** 9)
                 print(header)
@@ -216,7 +213,7 @@ def load_inp_unittest(print_ir):
 
             def run_test(header, print_ir):
                 cost = run_schedule(
-                    env.dma_copy, mock.dma_copy, mock.gemm, mock.alu, mock.dma_copy, print_ir, False
+                    env.dma_copy, mock.dma_copy, mock.gemm, mock.alu, mock.dma_copy, print_ir
                 )
                 gops = (num_ops / cost.mean) / float(10 ** 9)
                 bandwith = (batch_size * channel * env.INP_WIDTH / cost.mean) / float(10 ** 9)
@@ -236,7 +233,7 @@ def load_wgt_unittest(print_ir):
 
             def run_test(header, print_ir):
                 cost = run_schedule(
-                    mock.dma_copy, env.dma_copy, mock.gemm, mock.alu, mock.dma_copy, print_ir, False
+                    mock.dma_copy, env.dma_copy, mock.gemm, mock.alu, mock.dma_copy, print_ir
                 )
                 gops = (num_ops / cost.mean) / float(10 ** 9)
                 bandwith = (channel * channel * env.WGT_WIDTH / cost.mean) / float(10 ** 9)
@@ -256,7 +253,7 @@ def store_out_unittest(print_ir):
 
             def run_test(header, print_ir):
                 cost = run_schedule(
-                    mock.dma_copy, mock.dma_copy, mock.gemm, mock.alu, env.dma_copy, print_ir, False
+                    mock.dma_copy, mock.dma_copy, mock.gemm, mock.alu, env.dma_copy, print_ir
                 )
                 gops = (num_ops / cost.mean) / float(10 ** 9)
                 bandwith = (batch_size * channel * env.OUT_WIDTH / cost.mean) / float(10 ** 9)
diff --git a/vta/tutorials/autotvm/tune_relay_vta.py b/vta/tutorials/autotvm/tune_relay_vta.py
index 273f0af4af03..ed2671c75ae8 100644
--- a/vta/tutorials/autotvm/tune_relay_vta.py
+++ b/vta/tutorials/autotvm/tune_relay_vta.py
@@ -215,7 +215,8 @@ def compile_network(env, target, model, start_pack, stop_pack):
             port=tracker_port,
             number=5,
             timeout=60,
-            check_correctness=True,
+            module_loader=vta.module_loader(),
+            # check_correctness=True, # TODO: re-enable when check_correctness works again.
         ),
     ),
 }
diff --git a/web/emcc/tvmjs_support.cc b/web/emcc/tvmjs_support.cc
index 6abd12252d1d..12f930f491a5 100644
--- a/web/emcc/tvmjs_support.cc
+++ b/web/emcc/tvmjs_support.cc
@@ -25,11 +25,9 @@
  */
 
 // configurations for the dmlc log.
-#define DMLC_LOG_CUSTOMIZE 0
-#define DMLC_LOG_STACK_TRACE 0
-#define DMLC_LOG_DEBUG 0
-#define DMLC_LOG_NODATE 1
-#define DMLC_LOG_FATAL_THROW 0
+#define TVM_LOG_DEBUG 0
+#define DMLC_USE_LOGGING_LIBRARY <tvm/runtime/logging.h>
+#define TVM_BACKTRACE_DISABLED 1
 
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/container.h>
@@ -177,33 +175,37 @@ class AsyncLocalSession : public LocalSession {
     }
   }
 
-  void AsyncCopyToRemote(void* local_from, size_t local_from_offset, void* remote_to,
-                         size_t remote_to_offset, size_t nbytes, TVMContext remote_ctx_to,
-                         DLDataType type_hint, FAsyncCallback on_complete) final {
-    TVMContext cpu_ctx;
-    cpu_ctx.device_type = kDLCPU;
-    cpu_ctx.device_id = 0;
+  void AsyncCopyToRemote(void* local_from_bytes, DLTensor* remote_to, uint64_t nbytes,
+                         FAsyncCallback on_complete) final {
     try {
-      this->GetDeviceAPI(remote_ctx_to)
-          ->CopyDataFromTo(local_from, local_from_offset, remote_to, remote_to_offset, nbytes,
-                           cpu_ctx, remote_ctx_to, type_hint, nullptr);
-      this->AsyncStreamWait(remote_ctx_to, nullptr, on_complete);
+      DLTensor local_from;
+      local_from.data = local_from_bytes;
+      local_from.ctx = TVMContext{kDLCPU, 0};
+      local_from.ndim = remote_to->ndim;
+      local_from.shape = remote_to->shape;
+      local_from.dtype = remote_to->dtype;
+      local_from.strides = nullptr;
+      local_from.byte_offset = 0;
+      this->GetDeviceAPI(remote_to->ctx)->CopyDataFromTo(&local_from, remote_to, nullptr);
+      this->AsyncStreamWait(remote_to->ctx, nullptr, on_complete);
     } catch (const std::runtime_error& e) {
       this->SendException(on_complete, e.what());
     }
   }
 
-  void AsyncCopyFromRemote(void* remote_from, size_t remote_from_offset, void* local_to,
-                           size_t local_to_offset, size_t nbytes, TVMContext remote_ctx_from,
-                           DLDataType type_hint, FAsyncCallback on_complete) final {
-    TVMContext cpu_ctx;
-    cpu_ctx.device_type = kDLCPU;
-    cpu_ctx.device_id = 0;
+  void AsyncCopyFromRemote(DLTensor* remote_from, void* local_to_bytes, uint64_t nbytes,
+                           FAsyncCallback on_complete) final {
     try {
-      this->GetDeviceAPI(remote_ctx_from)
-          ->CopyDataFromTo(remote_from, remote_from_offset, local_to, local_to_offset, nbytes,
-                           remote_ctx_from, cpu_ctx, type_hint, nullptr);
-      this->AsyncStreamWait(remote_ctx_from, nullptr, on_complete);
+      DLTensor local_to;
+      local_to.data = local_to_bytes;
+      local_to.ctx = TVMContext{kDLCPU, 0};
+      local_to.ndim = remote_from->ndim;
+      local_to.shape = remote_from->shape;
+      local_to.dtype = remote_from->dtype;
+      local_to.strides = nullptr;
+      local_to.byte_offset = 0;
+      this->GetDeviceAPI(remote_from->ctx)->CopyDataFromTo(&local_to, remote_from, nullptr);
+      this->AsyncStreamWait(remote_from->ctx, nullptr, on_complete);
     } catch (const std::runtime_error& e) {
       this->SendException(on_complete, e.what());
     }
diff --git a/web/emcc/wasm_runtime.cc b/web/emcc/wasm_runtime.cc
index 214c1883f874..0b14ef6476d2 100644
--- a/web/emcc/wasm_runtime.cc
+++ b/web/emcc/wasm_runtime.cc
@@ -23,14 +23,12 @@
  */
 
 // configurations for the dmlc log.
-#define DMLC_LOG_CUSTOMIZE 0
-#define DMLC_LOG_STACK_TRACE 0
-#define DMLC_LOG_DEBUG 0
-#define DMLC_LOG_NODATE 1
-#define DMLC_LOG_FATAL_THROW 0
+#define TVM_LOG_DEBUG 0
+#define DMLC_USE_LOGGING_LIBRARY <tvm/runtime/logging.h>
+#define TVM_BACKTRACE_DISABLED 1
 
-#include <dmlc/logging.h>
 #include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/logging.h>
 
 #include "src/runtime/c_runtime_api.cc"
 #include "src/runtime/cpu_device_api.cc"
diff --git a/web/emcc/webgpu_runtime.cc b/web/emcc/webgpu_runtime.cc
index 54601e37d037..01e42ef3faa8 100644
--- a/web/emcc/webgpu_runtime.cc
+++ b/web/emcc/webgpu_runtime.cc
@@ -22,12 +22,10 @@
  * \brief WebGPU runtime based on the TVM JS.
  */
 
-// configurations for the dmlc log.
-#define DMLC_LOG_CUSTOMIZE 0
-#define DMLC_LOG_STACK_TRACE 0
-#define DMLC_LOG_DEBUG 0
-#define DMLC_LOG_NODATE 1
-#define DMLC_LOG_FATAL_THROW 0
+// configurations for tvm logging.
+#define TVM_LOG_DEBUG 0
+#define DMLC_USE_LOGGING_LIBRARY <tvm/runtime/logging.h>
+#define TVM_BACKTRACE_DISABLED 1
 
 #include <dmlc/thread_local.h>
 #include <tvm/runtime/c_runtime_api.h>
@@ -35,12 +33,27 @@
 #include <tvm/runtime/packed_func.h>
 #include <tvm/runtime/registry.h>
 
+#include <iostream>
+#include <string>
+
 #include "../../src/runtime/meta_data.h"
 #include "../../src/runtime/vulkan/vulkan_shader.h"
 #include "../../src/runtime/workspace_pool.h"
 
 namespace tvm {
 namespace runtime {
+namespace detail {
+// Override logging mechanism
+void LogFatalImpl(const std::string& file, int lineno, const std::string& message) {
+  std::cerr << file << ":" << lineno << ": " << message << std::endl;
+  abort();
+}
+
+void LogMessageImpl(const std::string& file, int lineno, const std::string& message) {
+  std::cerr << file << ":" << lineno << ": " << message << std::endl;
+}
+
+}  // namespace detail
 
 /*! \brief Thread local workspace */
 class WebGPUThreadEntry {
@@ -82,6 +95,7 @@ class WebGPUDeviceAPI : public DeviceAPI {
 
   void FreeDataSpace(TVMContext ctx, void* ptr) final { return free_space_(ptr); }
 
+ protected:
   void CopyDataFromTo(const void* from, size_t from_offset, void* to, size_t to_offset, size_t size,
                       TVMContext ctx_from, TVMContext ctx_to, DLDataType type_hint,
                       TVMStreamHandle stream) final {
@@ -102,6 +116,7 @@ class WebGPUDeviceAPI : public DeviceAPI {
     }
   }
 
+ public:
   TVMStreamHandle CreateStream(TVMContext ctx) final {
     LOG(FATAL) << "Not implemented";
     return nullptr;