[microTVM] Update tutorials (apache#13845)

This PR updates microTVM tutorials to use updated APIs.
It also adds an ordering to the tutorials that are useful for first time users.
RVM tutorial is also removed as it is not supported anymore.

docs/conf.py

@@ -511,15 +511,15 @@ def jupyter_notebook(script_blocks, gallery_conf, target_dir, real_func):
         "use_pass_instrument.py",
         "bring_your_own_datatypes.py",
     ],
-    "micro": [
-        "micro_train.py",
-        "micro_autotune.py",
-        "micro_reference_vm.py",
-        "micro_tflite.py",
-        "micro_ethosu.py",
+    "work_with_microtvm": [
         "micro_tvmc.py",
+        "micro_tflite.py",
         "micro_aot.py",
         "micro_pytorch.py",
+        "micro_train.py",
+        "micro_autotune.py",
+        "micro_ethosu.py",
+        "micro_mlperftiny.py",
     ],
 }
 

docs/topic/microtvm/index.rst

@@ -50,13 +50,12 @@ 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:
+tutorials to get started with microTVM. Tutorials are in the order that could help developers to learn
+more as they follow through them. Here is a list of tutorials that you can start with:
 
-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 :ref:`microTVM with TFLite Tutorial <microTVM-with-TFLite>`.
-3. Try running a more complex `CIFAR10-CNN model <https://github.com/areusch/microtvm-blogpost-eval>`_.
+1. Try :ref:`microTVM CLI Tool <tutorial-micro-cli-tool>`.
+2. Try the :ref:`microTVM TFLite Tutorial <tutorial_micro_tflite>`.
+3. Try running a more complex tutorial: :ref:`Creating Your MLPerfTiny Submission with microTVM <tutorial-micro-mlperftiny>`.
 
 
 How microTVM Works

gallery/how_to/work_with_microtvm/micro_aot.py

@@ -15,10 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-.. _tutorial-micro-AoT:
+.. _tutorial-micro-aot:
 
-microTVM Host-Driven AoT
-===========================
+3. microTVM Ahead-of-Time (AOT) Compilation
+===========================================
 **Authors**:
 `Mehrdad Hessar <https://github.com/mehrdadh>`_,
 `Alan MacDonald <https://github.com/alanmacd>`_
@@ -59,6 +59,7 @@
 
 import tvm
 from tvm import relay
+import tvm.micro.testing
 from tvm.relay.backend import Executor, Runtime
 from tvm.contrib.download import download_testdata
 
@@ -102,27 +103,23 @@
 # using AOT host driven executor. We use the host micro target which is for running a model
 # on x86 CPU using CRT runtime or running a model with Zephyr platform on qemu_x86 simulator
 # board. In the case of a physical microcontroller, we get the target model for the physical
-# board (E.g. nucleo_l4r5zi) and pass it to `tvm.target.target.micro` to create a full
-# micro target.
+# board (E.g. nucleo_l4r5zi) and change `BOARD` to supported Zephyr board.
 #
 
 # Use the C runtime (crt) and enable static linking by setting system-lib to True
 RUNTIME = Runtime("crt", {"system-lib": True})
 
 # Simulate a microcontroller on the host machine. Uses the main() from `src/runtime/crt/host/main.cc`.
 # To use physical hardware, replace "host" with something matching your hardware.
-TARGET = tvm.target.target.micro("host")
+TARGET = tvm.micro.testing.get_target("crt")
 
 # Use the AOT executor rather than graph or vm executors. Don't use unpacked API or C calling style.
 EXECUTOR = Executor("aot")
 
 if use_physical_hw:
-    boards_file = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")) / "boards.json"
-    with open(boards_file) as f:
-        boards = json.load(f)
     BOARD = os.getenv("TVM_MICRO_BOARD", default="nucleo_l4r5zi")
     SERIAL = os.getenv("TVM_MICRO_SERIAL", default=None)
-    TARGET = tvm.target.target.micro(boards[BOARD]["model"])
+    TARGET = tvm.micro.testing.get_target("zephyr", BOARD)
 
 ######################################################################
 # Compile the model

gallery/how_to/work_with_microtvm/micro_autotune.py

@@ -18,8 +18,8 @@
 """
 .. _tutorial-micro-autotune:
 
-Autotuning with microTVM
-=========================
+6. Model Tuning with microTVM
+=============================
 **Authors**:
 `Andrew Reusch <https://github.com/areusch>`_,
 `Mehrdad Hessar <https://github.com/mehrdadh>`_
@@ -55,6 +55,7 @@
 
 import tvm
 from tvm.relay.backend import Runtime
+import tvm.micro.testing
 
 ####################
 # Defining the model
@@ -102,20 +103,16 @@
 #
 
 RUNTIME = Runtime("crt", {"system-lib": True})
-TARGET = tvm.target.target.micro("host")
+TARGET = tvm.micro.testing.get_target("crt")
 
 # Compiling for physical hardware
 # --------------------------------------------------------------------------
 #  When running on physical hardware, choose a TARGET and a BOARD that describe the hardware. The
 #  STM32L4R5ZI Nucleo target and board is chosen in the example below.
 if use_physical_hw:
-    boards_file = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr")) / "boards.json"
-    with open(boards_file) as f:
-        boards = json.load(f)
-
     BOARD = os.getenv("TVM_MICRO_BOARD", default="nucleo_l4r5zi")
     SERIAL = os.getenv("TVM_MICRO_SERIAL", default=None)
-    TARGET = tvm.target.target.micro(boards[BOARD]["model"])
+    TARGET = tvm.micro.testing.get_target("zephyr", BOARD)
 
 
 #########################

gallery/how_to/work_with_microtvm/micro_ethosu.py

@@ -15,8 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN
-======================================================================================
+.. _tutorial-micro-ethosu:
+
+7. Running TVM on bare metal Arm(R) Cortex(R)-M55 CPU and Ethos(TM)-U55 NPU with CMSIS-NN
+=========================================================================================
 **Author**:
 `Grant Watson <https://github.com/grant-arm>`_
 

gallery/how_to/work_with_microtvm/micro_mlperftiny.py

@@ -15,10 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-.. _tutorial-micro-MLPerfTiny:
+.. _tutorial-micro-mlperftiny:
 
-Creating Your MLPerfTiny Submission with microTVM
-=================================================
+8. Creating Your MLPerfTiny Submission with microTVM
+====================================================
 **Authors**:
 `Mehrdad Hessar <https://github.com/mehrdadh>`_
 
@@ -69,6 +69,7 @@
 from tvm.contrib.download import download_testdata
 from tvm.micro import export_model_library_format
 from tvm.micro.model_library_format import generate_c_interface_header
+import tvm.micro.testing
 from tvm.micro.testing.utils import (
     create_header_file,
     mlf_extract_workspace_size_bytes,

gallery/how_to/work_with_microtvm/micro_pytorch.py

@@ -15,10 +15,10 @@
 # specific language governing permissions and limitations
 # under the License.
 """
-.. _tutorial-micro-Pytorch:
+.. _tutorial-micro-pytorch:
 
-microTVM PyTorch Tutorial
-===========================
+4. microTVM PyTorch Tutorial
+============================
 **Authors**:
 `Mehrdad Hessar <https://github.com/mehrdadh>`_
 
@@ -46,6 +46,7 @@
 from tvm import relay
 from tvm.contrib.download import download_testdata
 from tvm.relay.backend import Executor
+import tvm.micro.testing
 
 ##################################
 # Load a pre-trained PyTorch model
@@ -91,13 +92,14 @@
 # and we use `host` micro target. Using this setup, TVM compiles the model
 # for C runtime which can run on a x86 CPU machine with the same flow that
 # would run on a physical microcontroller.
+# CRT Uses the main() from `src/runtime/crt/host/main.cc`
+# To use physical hardware, replace `board` with another physical micro target, e.g. `nrf5340dk_nrf5340_cpuapp`
+# or `mps2_an521` and change the platform type to Zephyr.
+# See more target examples in :ref:`Training Vision Models for microTVM on Arduino <tutorial-micro-train-arduino>`
+# and :ref:`microTVM TFLite Tutorial<tutorial_micro_tflite>`.
 #
 
-
-# Simulate a microcontroller on the host machine. Uses the main() from `src/runtime/crt/host/main.cc`
-# To use physical hardware, replace "host" with another physical micro target, e.g. `nrf52840`
-# or `mps2_an521`. See more more target examples in micro_train.py and micro_tflite.py tutorials.
-target = tvm.target.target.micro("host")
+target = tvm.micro.testing.get_target(platform="crt", board=None)
 
 # Use the C runtime (crt) and enable static linking by setting system-lib to True
 runtime = tvm.relay.backend.Runtime("crt", {"system-lib": True})