remove nnpack integration from the codebase

Author: mshr-h

CMakeLists.txt

@@ -110,7 +110,6 @@ tvm_option(USE_MIOPEN "Build with ROCM:MIOpen" OFF)
 tvm_option(USE_ROCBLAS "Build with ROCM:RoCBLAS" OFF)
 tvm_option(USE_HIPBLAS "Build with ROCM:HIPBLAS" OFF)
 tvm_option(USE_SORT "Build with sort support" ON)
-tvm_option(USE_NNPACK "Build with nnpack support" OFF)
 tvm_option(USE_LIBTORCH "Build with libtorch support" OFF)
 tvm_option(USE_RANDOM "Build with random support" ON)
 tvm_option(USE_MICRO_STANDALONE_RUNTIME "Build with micro.standalone_runtime support" OFF)
@@ -591,7 +590,6 @@ include(cmake/modules/contrib/Posit.cmake)
 include(cmake/modules/contrib/MicroStandaloneRuntime.cmake)
 include(cmake/modules/contrib/MSCCLPP.cmake)
 include(cmake/modules/contrib/Sort.cmake)
-include(cmake/modules/contrib/NNPack.cmake)
 include(cmake/modules/contrib/LibTorch.cmake)
 include(cmake/modules/contrib/HybridDump.cmake)
 include(cmake/modules/contrib/TFLite.cmake)

cmake/modules/LibInfo.cmake

@@ -103,7 +103,6 @@ function(add_lib_info src_file)
     TVM_INFO_USE_MKL="${USE_MKL}"
     TVM_INFO_USE_MRVL="${USE_MRVL}"
     TVM_INFO_USE_MSVC_MT="${USE_MSVC_MT}"
-    TVM_INFO_USE_NNPACK="${USE_NNPACK}"
     TVM_INFO_USE_OPENCL="${USE_OPENCL}"
     TVM_INFO_USE_OPENCL_ENABLE_HOST_PTR="${USE_OPENCL_ENABLE_HOST_PTR}"
     TVM_INFO_USE_OPENCL_GTEST="${USE_OPENCL_GTEST}"

cmake/modules/contrib/NNPack.cmake

@@ -68,13 +68,6 @@ tvm.contrib.ndk
 .. automodule:: tvm.contrib.ndk
     :members:
 
-
-tvm.contrib.nnpack
-~~~~~~~~~~~~~~~~~~
-.. automodule:: tvm.contrib.nnpack
-    :members:
-
-
 tvm.contrib.nvcc
 ~~~~~~~~~~~~~~~~
 .. automodule:: tvm.contrib.nvcc

docker/install/ubuntu_install_nnpack.sh

@@ -282,25 +282,6 @@ struct Conv2DWinogradAttrs : public tvm::AttrsNode<Conv2DWinogradAttrs> {
   }
 };
 
-/*! \brief Attributes used in winograd weight transformation operators */
-struct Conv2DWinogradNNPACKWeightTransformAttrs
-    : public tvm::AttrsNode<Conv2DWinogradNNPACKWeightTransformAttrs> {
-  int convolution_algorithm;
-  DataType out_dtype;
-
-  TVM_DECLARE_ATTRS(Conv2DWinogradNNPACKWeightTransformAttrs,
-                    "relay.attrs.Conv2DWinogradNNPACKWeightTransformAttrs") {
-    TVM_ATTR_FIELD(convolution_algorithm)
-        .describe(
-            "The convolution algorithm for Winograd NNPACK. "
-            "E.g. tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8 for WT_8x8, "
-            "tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8_FP16 for WT_8x8_FP16");
-    TVM_ATTR_FIELD(out_dtype)
-        .set_default(NullValue<DataType>())
-        .describe("Output data type, set to explicit type under mixed precision setting");
-  }
-};
-
 /*! \brief Attributes used in convolution operators */
 struct Conv3DAttrs : public tvm::AttrsNode<Conv3DAttrs> {
   Array<IndexExpr> strides;

docs/reference/api/python/contrib.rst

@@ -820,22 +820,6 @@ def compute_contrib_conv2d_winograd_weight_transform(attrs, inputs, out_dtype):
 )
 
 
-@reg.register_compute("nn.contrib_conv2d_winograd_nnpack_weight_transform")
-def compute_contrib_conv2d_winograd_nnpack_weight_transform(attrs, inputs, out_dtype):
-    """Compute definition of contrib_conv2d_winograd_nnpack_weight_transform"""
-    convolution_algorithm = attrs.get_int("convolution_algorithm")
-    out = topi.nn.conv2d_winograd_nnpack_weight_transform(
-        inputs[0], convolution_algorithm, out_dtype
-    )
-    return [out]
-
-
-reg.register_schedule(
-    "nn.contrib_conv2d_winograd_nnpack_weight_transform",
-    strategy.schedule_conv2d_winograd_nnpack_weight_transform,
-)
-
-
 # conv2d_NCHWc
 reg.register_strategy("nn.contrib_conv2d_NCHWc", strategy.conv2d_NCHWc_strategy)