Add Ascend NPU support for nf4 quant (bitsandbytes-foundation#1422)

* Add npu support for nf4 quant

Co-authored-by: Slightwind <slightwindsec@gmail.com>
Co-authored-by: Ginray <ginray0215@gmail.com>

* code format

* update

* pass lint check and fix typos

* add npu to supported devices

---------

Co-authored-by: Slightwind <slightwindsec@gmail.com>
Co-authored-by: Ginray <ginray0215@gmail.com>

CMakeLists.txt

@@ -3,7 +3,7 @@
 #   For  GCC: `cmake -B build . && cmake --build build`
 #   For MSVC: `cmake -B build . && cmake --build build --config Release`
 # You can also use the following options and variables
-#  - COMPUTE_BACKEND: Set to `cpu`, `cuda`, `hip` or `mps` to select the backend
+#  - COMPUTE_BACKEND: Set to `cpu`, `cuda`, `hip`, `mps` or `npu` to select the backend
 #  - NO_CUBLASLT: Default OFF, will skip building/linking CUBLASLT support
 #  - CUDA_VERSION: The expected CUDA version, for sanity checking. The actual version
 #                  is whatever CMake finds on your path.
@@ -29,11 +29,12 @@ set(CUDA_FILES csrc/ops.cu csrc/kernels.cu)
 set(HIP_FILES csrc/ops.hip csrc/kernels.hip)
 set(MPS_FILES csrc/mps_ops.mm)
 set(METAL_FILES csrc/mps_kernels.metal)
+set(NPU_FILES csrc/npu_ops.cpp)
 # C++ sources are always included
 list(APPEND SRC_FILES ${CPP_FILES})
 
-set(COMPUTE_BACKEND "cpu" CACHE STRING "The compute backend to use (cpu, cuda, hip, mps)")
-set_property(CACHE COMPUTE_BACKEND PROPERTY STRINGS cpu cuda hip mps)
+set(COMPUTE_BACKEND "cpu" CACHE STRING "The compute backend to use (cpu, cuda, hip, mps, npu)")
+set_property(CACHE COMPUTE_BACKEND PROPERTY STRINGS cpu cuda hip mps npu)
 option(PTXAS_VERBOSE "Pass through -v flag to PTX Assembler" OFF)
 
 if(APPLE)
@@ -69,6 +70,11 @@ elseif(${COMPUTE_BACKEND} STREQUAL "mps")
     set(BUILD_CUDA OFF)
     set(BUILD_HIP OFF)
     set(BUILD_MPS ON)
+elseif(${COMPUTE_BACKEND} STREQUAL "npu")
+    set(BUILD_CUDA OFF)
+    set(BUILD_HIP OFF)
+    set(BUILD_MPS OFF)
+    set(BUILD_NPU ON)
 else()
     set(BUILD_CUDA OFF)
     set(BUILD_HIP OFF)
@@ -232,6 +238,33 @@ elseif(BUILD_MPS)
                 COMMENT "Compiling Metal kernels"
                 VERBATIM)
     add_custom_target(metallib DEPENDS "bitsandbytes/bitsandbytes.metallib")
+elseif(BUILD_NPU)
+    list(APPEND SRC_FILES ${NPU_FILES})
+
+    set(SOC_VERSION "Ascend910B4" CACHE STRING "system on chip type")
+    set(ASCEND_CANN_PACKAGE_PATH $ENV{ASCEND_HOME_PATH} CACHE
+        STRING "ASCEND CAN package installation directory"
+    )
+
+    # ${KERNEL_FILES} are used to compile library, push files written by ascendc in ${KERNEL_FILES}.
+    # ref to cmake/npu.cmake ascendc_library, cmake/cpu.cmake add_library
+    # file(GLOB KERNEL_FILES ${CMAKE_CURRENT_SOURCE_DIR}/csrc/npu_kernels.cpp)
+    file(GLOB KERNEL_FILES csrc/npu_kernels.cpp)
+
+    if(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
+        set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
+    elseif(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/tools/tikcpp/ascendc_kernel_cmake)
+        set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/tools/tikcpp/ascendc_kernel_cmake)
+    else()
+        message(FATAL_ERROR "ascendc_kernel_cmake does not exist ,please check whether the can package is installed")
+    endif()
+    include(${ASCENDC_CMAKE_DIR}/ascendc.cmake)
+
+    # ascendc_library use to add kernel file to generate ascendc library
+    ascendc_library(ascendc_kernels_npu STATIC ${KERNEL_FILES})
+
+    string(APPEND BNB_OUTPUT_NAME "_npu")
+    add_compile_definitions(BUILD_NPU)
 else()
     string(APPEND BNB_OUTPUT_NAME "_cpu")
     set(GPU_SOURCES)
@@ -249,7 +282,11 @@ endif()
 
 set_source_files_properties(${CPP_FILES} PROPERTIES LANGUAGE CXX)
 add_library(bitsandbytes SHARED ${SRC_FILES})
-target_compile_features(bitsandbytes PUBLIC cxx_std_14)
+if(BUILD_NPU)
+    target_compile_features(bitsandbytes PUBLIC cxx_std_17)
+else()
+    target_compile_features(bitsandbytes PUBLIC cxx_std_14)
+endif()
 target_include_directories(bitsandbytes PUBLIC csrc include)
 
 
@@ -306,6 +343,10 @@ if(BUILD_MPS)
     add_dependencies(bitsandbytes metallib)
     target_link_libraries(bitsandbytes objc "-framework Foundation" "-framework Metal" "-framework MetalPerformanceShaders" "-framework MetalPerformanceShadersGraph")
 endif()
+if(BUILD_NPU)
+    target_compile_options(bitsandbytes PRIVATE -O2 -std=c++17)
+    target_link_libraries(bitsandbytes PRIVATE $<BUILD_INTERFACE:host_intf_pub> ascendc_kernels_npu)
+endif()
 
 if(WIN32)
     set_target_properties(bitsandbytes PROPERTIES PREFIX "lib")

_typos.toml

@@ -3,12 +3,15 @@
 [default]
 extend-ignore-re = [
     "@Ther-nul",  # valid Github user
+    "CANN",  # CANN (Compute Architecture for Neural Networks) is a heterogeneous computing architecture for Ascend NPU
 ]
 
 [default.extend-identifiers]
 
 [type.py.extend-words]
 "BA" = "BA"  # used as a commented-out variable in tests
+"cann" = "cann" # cann (Compute Architecture for Neural Networks) is a heterogeneous computing architecture for Ascend NPU
+
 
 [type.cuda.extend-words]
 "subtile" = "subtile"

bitsandbytes/__init__.py

@@ -25,6 +25,7 @@
 features = {"multi_backend"}
 supported_torch_devices = {
     "cuda",  # includes ROCm
+    "npu",  # Ascend NPU
     "xpu",  # Intel GPU
     "cpu",
     "hpu",

bitsandbytes/autograd/_functions.py

@@ -519,7 +519,12 @@ def forward(ctx, A, B, out=None, bias=None, quant_state: Optional[F.QuantState]
 
         # 1. Dequantize
         # 2. MatmulnN
-        output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
+        if A.device.type == "npu":
+            output = torch.matmul(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t())
+            if bias is not None:
+                output += bias
+        else:
+            output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
 
         # 3. Save state
         ctx.state = quant_state
@@ -550,7 +555,10 @@ def backward(ctx, grad_output):
         # not supported by PyTorch. TODO: create work-around
         # if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
         if req_gradA:
-            grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t())
+            if grad_output.device.type == "npu":
+                grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype))
+            else:
+                grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t())
 
         return grad_A, grad_B, None, grad_bias, None
 
@@ -586,7 +594,7 @@ def matmul_4bit(
             return out
         else:
             return MatMul4Bit.apply(A, B, out, bias, quant_state)
-    elif A.numel() == A.shape[-1] and A.requires_grad == False:
+    elif A.numel() == A.shape[-1] and A.requires_grad == False and A.device.type != "npu":
         if A.shape[-1] % quant_state.blocksize != 0:
             warn(
                 f"Some matrices hidden dimension is not a multiple of {quant_state.blocksize} and efficient inference kernels are not supported for these (slow). Matrix input size found: {A.shape}",

bitsandbytes/backends/cpu_xpu_common.py

@@ -24,7 +24,7 @@
 
 gxx_available = False
 try:
-    subprocess.run(["g++", "--version"])
+    subprocess.run(["g++", "--version"], capture_output=True)  # hide terminal output
     gxx_available = True
 except BaseException:
     warnings.warn("g++ not found, torch.compile disabled for CPU/XPU.")
@@ -445,22 +445,13 @@ def dequantize_4bit_impl(
         quant_state.ipex = False
 
     # Map nf4 to [-1, 1]
-<<<<<<< HEAD
-    out_uint8 = torch.empty(A.size(0) * 2, dtype=torch.uint8, device=A.device)
-    out_uint8[::2] = A.bitwise_and(0xF)
-    out_uint8[1::2] = A.bitwise_right_shift(4)
-    out_dq = torch.empty(out_uint8.shape, dtype=quant_state.code.dtype, device= quant_state.code.device)
-    for i in range(len(quant_state.code)):
-        out_dq[out_uint8 == i] = quant_state.code[i]
-=======
     out_dq = torch.empty(A.size(0) * 2, dtype=torch.int32, device=A.device)
     n = out_dq.numel()
     out_dq[::2] = A & 0xF
     out_dq[1::2] = A >> 4
     # quant_state.code is fp32, cast to quant_state dtype to avoid the mismatch issue
     quant_state.code = quant_state.code.to(quant_state.dtype)
     out_dq = quant_state.code[out_dq]
->>>>>>> b2ac423 (Enable XPU and optimize cpu/xpu op (#1418))
 
     # Apply scales
     if out_dq.numel() != n:

bitsandbytes/backends/npu.py

@@ -1,17 +1,32 @@
+import ctypes as ct
 from typing import Literal, Optional, Tuple, Union
 
 import torch
 
-from bitsandbytes.utils import QuantState
-
-from .base import Backend
-
 try:
     # to support Ascend NPU backend
     import torch_npu  # noqa: F401
 except ImportError:
     pass
 
+from bitsandbytes.cextension import lib
+from bitsandbytes.functional import (
+    get_4bit_type,
+    get_ptr,
+)
+from bitsandbytes.utils import QuantState
+
+from .base import Backend
+
+
+def assert_on_npu(tensors):
+    if not all(t.device.type == "npu" for t in tensors if t is not None):
+        raise TypeError(
+            "All input tensors to be on NPU, but found some tensors not be on NPU:\n"
+            f"{[(t.shape, t.device) if isinstance(t, torch.Tensor) else None for t in tensors]}"
+        )
+    return True
+
 
 class NPUBackend(Backend):
     def double_quant(
@@ -75,23 +90,140 @@ def quantize_4bit(
         A: torch.Tensor,
         absmax: Optional[torch.Tensor] = None,
         out: Optional[torch.Tensor] = None,
-        blocksize=64,
+        blocksize: Optional[int] = None,
         compress_statistics=False,
-        quant_type: Literal["fp4", "nf4"] = "fp4",
+        quant_type: Literal["fp4", "nf4"] = "nf4",
         quant_storage=torch.uint8,
     ) -> Tuple[torch.Tensor, QuantState]:
-        raise NotImplementedError
+        if quant_type not in ["nf4"]:
+            raise NotImplementedError(f"4-bit quantization data type {quant_type} is not implemented.")
+        if compress_statistics:
+            raise NotImplementedError("compress_statistics is not implemented.")
+        if blocksize is None:
+            blocksize = 128
+
+        prev_device = torch.npu.current_device()
+        torch.npu.set_device(A.device)
+        if A.dtype in [torch.float32, torch.float16, torch.bfloat16]:
+            data = [
+                -1.0,
+                -0.6961928009986877,
+                -0.5250730514526367,
+                -0.39491748809814453,
+                -0.28444138169288635,
+                -0.18477343022823334,
+                -0.09105003625154495,
+                0.0,
+                0.07958029955625534,
+                0.16093020141124725,
+                0.24611230194568634,
+                0.33791524171829224,
+                0.44070982933044434,
+                0.5626170039176941,
+                0.7229568362236023,
+                1.0,
+            ]
+            data = torch.tensor(data, device="npu", dtype=torch.float32).view(1, -1)
+            absmax = A.view(-1, blocksize).abs().max(dim=1, keepdim=True).values
+            a = A.view(-1, blocksize) / absmax.float()
+            diff = torch.abs(a.unsqueeze(-1) - data)
+            out = (torch.argmin(diff, dim=-1) + 8) % 16
+            out = out.reshape(-1, 2)
+            out = (out[:, 0] + out[:, 1] * 16).to(torch.uint8)
+        else:
+            raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
+        assert_on_npu([A, absmax, out])
+        torch.npu.set_device(prev_device)
+
+        code = get_4bit_type(quant_type, device=A.device)
+        state = QuantState(
+            absmax=absmax,
+            shape=A.shape,
+            dtype=A.dtype,
+            blocksize=blocksize,
+            code=code,
+            quant_type=quant_type,
+        )
+
+        return out, state
 
     def dequantize_4bit(
         self,
         A: torch.Tensor,
         quant_state: Optional[QuantState] = None,
         absmax: Optional[torch.Tensor] = None,
         out: Optional[torch.Tensor] = None,
-        blocksize: int = 64,
-        quant_type: Literal["fp4", "nf4"] = "fp4",
+        blocksize: Optional[int] = None,
+        quant_type: Literal["fp4", "nf4"] = "nf4",
     ) -> torch.Tensor:
-        raise NotImplementedError
+        if blocksize is None:
+            blocksize = 128
+        supported_blocksizes = [2048, 4096, 1024, 512, 256, 128, 64]
+        if blocksize not in supported_blocksizes:
+            raise ValueError(
+                f"The blockwise of {blocksize} is not supported. Supported values: {supported_blocksizes}"
+            )
+
+        if quant_state is None:
+            assert absmax is not None and out is not None
+            quant_state = QuantState(
+                absmax=absmax, shape=out.shape, dtype=out.dtype, blocksize=blocksize, quant_type=quant_type
+            )
+        else:
+            absmax = quant_state.absmax
+
+        if out is None:
+            out = torch.empty(quant_state.shape, dtype=quant_state.dtype, device=A.device)
+
+        n = out.numel()
+
+        prev_device = torch.npu.current_device()
+        torch.npu.set_device(A.device)
+        assert_on_npu([A, absmax, out])
+
+        if quant_state.quant_type not in ["nf4"]:
+            raise NotImplementedError(f"4-bit quantization data type {quant_type} is not implemented.")
+
+        if out.dtype == torch.float32:
+            lib.cdequantize_blockwise_fp32_nf4(
+                get_ptr(A),
+                get_ptr(absmax),
+                get_ptr(out),
+                ct.c_int(quant_state.blocksize),
+                ct.c_int(n),
+                torch.npu.current_stream(),
+            )
+        elif out.dtype == torch.float16:
+            lib.cdequantize_blockwise_fp16_nf4(
+                get_ptr(A),
+                get_ptr(absmax),
+                get_ptr(out),
+                ct.c_int(quant_state.blocksize),
+                ct.c_int(n),
+                torch.npu.current_stream(),
+            )
+        elif out.dtype == torch.bfloat16:
+            # bf16: bf16 -> fp32 -> op -> fp32 -> bf16
+            absmax = absmax.to(torch.float32)
+            out = out.to(torch.float32)
+            lib.cdequantize_blockwise_fp32_nf4(
+                get_ptr(A),
+                get_ptr(absmax),
+                get_ptr(out),
+                ct.c_int(quant_state.blocksize),
+                ct.c_int(n),
+                torch.npu.current_stream(),
+            )
+            out = out.to(torch.bfloat16)
+        else:
+            raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
+        torch.npu.set_device(prev_device)
+        is_transposed = True if A.shape[0] == 1 else False
+
+        if is_transposed:
+            return out.t()
+        else:
+            return out
 
     def gemv_4bit(
         self,

bitsandbytes/cextension.py

@@ -25,6 +25,7 @@
 
 from bitsandbytes.consts import DYNAMIC_LIBRARY_SUFFIX, PACKAGE_DIR
 from bitsandbytes.cuda_specs import CUDASpecs, get_cuda_specs, get_rocm_gpu_arch
+from bitsandbytes.npu_specs import get_npu_specs
 
 logger = logging.getLogger(__name__)
 
@@ -100,6 +101,10 @@ def get_native_library() -> BNBNativeLibrary:
             binary_path = cuda_binary_path
         else:
             logger.warning("Could not find the bitsandbytes %s binary at %r", BNB_BACKEND, cuda_binary_path)
+    npu_specs = get_npu_specs()
+    if npu_specs:
+        binary_path = PACKAGE_DIR / f"libbitsandbytes_npu{DYNAMIC_LIBRARY_SUFFIX}"
+
     logger.debug(f"Loading bitsandbytes native library from: {binary_path}")
     dll = ct.cdll.LoadLibrary(str(binary_path))