zheng-da commented on a change in pull request #8302: Refactor operators URL: https://github.com/apache/incubator-mxnet/pull/8302#discussion_r144995243
########## File path: src/operator/nn/convolution.cc ########## @@ -0,0 +1,433 @@ +/* + * 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 convolution.cc + * \brief + * \author Bing Xu, Jun Wu, Da Zheng +*/ + +#include "./convolution-inl.h" +#include "../elemwise_op_common.h" +#if MXNET_USE_MKL2017 == 1 +#include <mkl_memory.h> +#include "./mkl/mkl_memory-inl.h" +#include "./mkl/mkl_convolution-inl.h" +#endif // MXNET_USE_MKL2017 +#if MXNET_USE_NNPACK == 1 +#include "./nnpack/nnpack_convolution-inl.h" +#endif // MXNET_USE_NNPACK + +namespace mxnet { +namespace op { +DMLC_REGISTER_PARAMETER(ConvolutionParam); + +static inline index_t AddPad(index_t dsize, index_t pad) { + return dsize + 2 * pad; +} + +static inline std::vector<std::string> ListArguments(const ConvolutionParam& param_) { + if (!param_.no_bias) { + return {"data", "weight", "bias"}; + } else { + return {"data", "weight"}; + } +} + +static bool ConvolutionShape(const nnvm::NodeAttrs& attrs, + std::vector<TShape> *in_shape, std::vector<TShape> *out_shape) { + using namespace mshadow; + const ConvolutionParam& param_ = nnvm::get<ConvolutionParam>(attrs.parsed); + if (!param_.no_bias) { + CHECK_EQ(in_shape->size(), 3U) << "Input:[data, weight, bias]"; + } else { + CHECK_EQ(in_shape->size(), 2U) << "Input:[data, weight]"; + } + // CHECK_EQ(out_shape->size(), 1) << "Output: [output]"; + out_shape->resize(1, TShape()); + const TShape &dshp = (*in_shape)[conv::kData]; + if (dshp.ndim() == 0) return false; + + if (param_.kernel.ndim() == 1) { + // 1d conv + CHECK_EQ(dshp.ndim(), 3U) << "Input data should be 3D in batch-num_filter-x"; + Shape<3> dshape = ConvertLayout(dshp.get<3>(), param_.layout.value(), kNCW); + Shape<3> wshape = Shape3(param_.num_filter / param_.num_group, dshape[1] / param_.num_group, + param_.kernel[0]); + wshape = ConvertLayout(wshape, kNCW, param_.layout.value()); + wshape[0] *= param_.num_group; + SHAPE_ASSIGN_CHECK(*in_shape, conv::kWeight, wshape); + if (!param_.no_bias) { + SHAPE_ASSIGN_CHECK(*in_shape, conv::kBias, Shape1(param_.num_filter)); + } + + const index_t dilated_ksize_x = param_.DilatedKernelSize(0); + CHECK_EQ(dshape[1] % param_.num_group, 0U) \ + << "input num_filter must divide group size"; + CHECK_EQ(param_.num_filter % param_.num_group, 0U) \ + << "output num_filter must divide group size"; + CHECK_GT(param_.kernel.Size(), 0U) \ + << "incorrect kernel size: " << param_.kernel; + CHECK_GT(param_.stride.Size(), 0U) \ + << "incorrect stride size: " << param_.stride; + CHECK_GT(param_.dilate.Size(), 0U) \ + << "incorrect dilate size: " << param_.dilate; + Shape<3> oshape; + oshape[0] = dshape[0]; + oshape[1] = param_.num_filter; + oshape[2] = dshape[2] ? + (AddPad(dshape[2], param_.pad[0]) - dilated_ksize_x) / param_.stride[0] + 1 : 0; + SHAPE_ASSIGN_CHECK(*out_shape, 0, ConvertLayout(oshape, kNCW, param_.layout.value())); + // Perform incomplete shape inference. Fill in the missing values in data shape. + // 1) We can always fill in the batch_size. + // 2) We can back-calculate the input height/width if the corresponding stride is 1. + oshape = ConvertLayout((*out_shape)[0].get<3>(), param_.layout.value(), kNCW); + dshape[0] = oshape[0]; + if (oshape[2] && param_.stride[0] == 1) { + dshape[2] = oshape[2] + dilated_ksize_x - 1 - 2 * param_.pad[0]; + } + SHAPE_ASSIGN_CHECK(*in_shape, conv::kData, + ConvertLayout(dshape, kNCW, param_.layout.value())); + // Check whether the kernel sizes are valid + if (dshape[2] != 0) { + CHECK_LE(dilated_ksize_x, AddPad(dshape[2], param_.pad[0])) << "kernel size exceed input"; + } + return true; + } else if (param_.kernel.ndim() == 2) { + // 2d conv + CHECK_EQ(dshp.ndim(), 4U) \ + << "Input data should be 4D in batch-num_filter-y-x"; + Shape<4> dshape = ConvertLayout(dshp.get<4>(), param_.layout.value(), kNCHW); + Shape<4> wshape = Shape4(param_.num_filter / param_.num_group, + dshape[1] / param_.num_group, + param_.kernel[0], param_.kernel[1]); + wshape = ConvertLayout(wshape, kNCHW, param_.layout.value()); + wshape[0] *= param_.num_group; + SHAPE_ASSIGN_CHECK(*in_shape, conv::kWeight, wshape); + if (!param_.no_bias) { + SHAPE_ASSIGN_CHECK(*in_shape, conv::kBias, Shape1(param_.num_filter)); + } + + const index_t dilated_ksize_y = param_.DilatedKernelSize(0); + const index_t dilated_ksize_x = param_.DilatedKernelSize(1); + CHECK_EQ(dshape[1] % param_.num_group, 0U) \ + << "input num_filter must divide group size"; + CHECK_EQ(param_.num_filter % param_.num_group, 0U) \ + << "output num_filter must divide group size"; + CHECK_GT(param_.kernel.Size(), 0U) \ + << "incorrect kernel size: " << param_.kernel; + CHECK_GT(param_.stride.Size(), 0U) \ + << "incorrect stride size: " << param_.stride; + CHECK_GT(param_.dilate.Size(), 0U) \ + << "incorrect dilate size: " << param_.dilate; + Shape<4> oshape; + oshape[0] = dshape[0]; + oshape[1] = param_.num_filter; + oshape[2] = dshape[2] ? + (AddPad(dshape[2], param_.pad[0]) - dilated_ksize_y) / param_.stride[0] + 1 : 0; + oshape[3] = dshape[3] ? + (AddPad(dshape[3], param_.pad[1]) - dilated_ksize_x) / param_.stride[1] + 1 : 0; + SHAPE_ASSIGN_CHECK(*out_shape, 0, ConvertLayout(oshape, kNCHW, param_.layout.value())); + // Perform incomplete shape inference. Fill in the missing values in data shape. + // 1) We can always fill in the batch_size. + // 2) We can back-calculate the input height/width if the corresponding stride is 1. + oshape = ConvertLayout((*out_shape)[0].get<4>(), param_.layout.value(), kNCHW); + dshape[0] = oshape[0]; + if (oshape[2] && param_.stride[0] == 1) { + dshape[2] = oshape[2] + dilated_ksize_y - 1 - 2 * param_.pad[0]; + } + if (oshape[3] && param_.stride[1] == 1) { + dshape[3] = oshape[3] + dilated_ksize_x - 1 - 2 * param_.pad[1]; + } + SHAPE_ASSIGN_CHECK(*in_shape, conv::kData, + ConvertLayout(dshape, kNCHW, param_.layout.value())); + // Check whether the kernel sizes are valid + if (dshape[2] != 0) { + CHECK_LE(dilated_ksize_y, AddPad(dshape[2], param_.pad[0])) << "kernel size exceed input"; + } + if (dshape[3] != 0) { + CHECK_LE(dilated_ksize_x, AddPad(dshape[3], param_.pad[1])) << "kernel size exceed input"; + } + return true; + } else if (param_.kernel.ndim() == 3) { + // 3d conv + CHECK_EQ(dshp.ndim(), 5U) \ + << "Input data should be 5D in batch-num_filter-depth-y-x"; + Shape<5> dshape = ConvertLayout(dshp.get<5>(), param_.layout.value(), kNCDHW); + Shape<5> wshape = Shape5(param_.num_filter / param_.num_group, dshape[1] / param_.num_group, + param_.kernel[0], param_.kernel[1], param_.kernel[2]); + wshape = ConvertLayout(wshape, kNCDHW, param_.layout.value()); + wshape[0] *= param_.num_group; + SHAPE_ASSIGN_CHECK(*in_shape, conv::kWeight, wshape); + if (!param_.no_bias) { + SHAPE_ASSIGN_CHECK(*in_shape, conv::kBias, Shape1(param_.num_filter)); + } + + // Note: 3D dilation currently not supported. + // Calculations below done to preserve symmetry with 1D/2D code. + const index_t dilated_ksize_d = param_.DilatedKernelSize(0); + const index_t dilated_ksize_y = param_.DilatedKernelSize(1); + const index_t dilated_ksize_x = param_.DilatedKernelSize(2); + CHECK_EQ(dshape[1] % param_.num_group, 0U) + << "input num_filter must divide group size"; + CHECK_EQ(param_.num_filter % param_.num_group, 0U) + << "output num_filter must divide group size"; + CHECK_GT(param_.kernel.Size(), 0U) \ + << "incorrect kernel size: " << param_.kernel; + CHECK_GT(param_.stride.Size(), 0U) \ + << "incorrect stride size: " << param_.stride; + CHECK_GT(param_.dilate.Size(), 0U) \ + << "incorrect dilate size: " << param_.dilate; + CHECK_EQ(param_.dilate.Size(), 1U) + << "Dilate is not supported in 3d convolution"; + Shape<5> oshape; + oshape[0] = dshape[0]; + oshape[1] = param_.num_filter; + oshape[2] = dshape[2] ? + (AddPad(dshape[2], param_.pad[0]) - dilated_ksize_d) / param_.stride[0] + 1 : 0; + oshape[3] = dshape[3] ? + (AddPad(dshape[3], param_.pad[1]) - dilated_ksize_y) / param_.stride[1] + 1 : 0; + oshape[4] = dshape[4] ? + (AddPad(dshape[4], param_.pad[2]) - dilated_ksize_x) / param_.stride[2] + 1 : 0; + SHAPE_ASSIGN_CHECK(*out_shape, 0, ConvertLayout(oshape, kNCDHW, param_.layout.value())); + // Perform incomplete shape inference. Fill in the missing values in data shape. + // 1) We can always fill in the batch_size. + // 2) We can back-calculate the input depth/height/width if the corresponding stride is 1. + oshape = ConvertLayout((*out_shape)[0].get<5>(), param_.layout.value(), kNCDHW); + dshape[0] = oshape[0]; + if (oshape[2] && param_.stride[0] == 1) { + dshape[2] = oshape[2] + dilated_ksize_d - 1 - 2 * param_.pad[0]; + } + if (oshape[3] && param_.stride[1] == 1) { + dshape[3] = oshape[3] + dilated_ksize_y - 1 - 2 * param_.pad[1]; + } + if (oshape[4] && param_.stride[2] == 1) { + dshape[4] = oshape[4] + dilated_ksize_x - 1 - 2 * param_.pad[2]; + } + SHAPE_ASSIGN_CHECK(*in_shape, conv::kData, + ConvertLayout(dshape, kNCDHW, param_.layout.value())); + // Check whether the kernel sizes are valid + if (dshape[2] != 0) { + CHECK_LE(dilated_ksize_d, AddPad(dshape[2], param_.pad[0])) << "kernel size exceed input"; + } + if (dshape[3] != 0) { + CHECK_LE(dilated_ksize_y, AddPad(dshape[3], param_.pad[1])) << "kernel size exceed input"; + } + if (dshape[4] != 0) { + CHECK_LE(dilated_ksize_x, AddPad(dshape[4], param_.pad[2])) << "kernel size exceed input"; + } + return true; + } else { + LOG(FATAL) << "Unknown convolution type"; + return false; + } +} + +static bool ConvolutionType(const nnvm::NodeAttrs& attrs, Review comment: I guess this is just personal preference. I usually try to expose as few symbols as possible to avoid symbol collision. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
