/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://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. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author saudet // @author raver119@gmail.com // #include #include #include #include #include "mkldnnUtils.h" #include using namespace dnnl; namespace nd4j { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////// static void conv2d_mkldnn(nd4j::graph::Context &block, const NDArray *input, const NDArray *weights, const NDArray *bias, NDArray *output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode, const int isNCHW) { int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode); dnnl_memory_desc_t empty; dnnl::memory::desc conv_src_md(empty), conv_weights_md(empty), conv_bias_md(empty), conv_dst_md( empty); dnnl::memory::desc user_src_md(empty), user_weights_md(empty), user_bias_md(empty), user_dst_md( empty); dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation; mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, bS, iC, iH, iW, oC, oH, oW, input, nullptr, weights, nullptr, bias, output, &conv_src_md, nullptr, &conv_weights_md, nullptr, &conv_bias_md, &conv_dst_md, &user_src_md, nullptr, &user_weights_md, nullptr, &user_bias_md, &user_dst_md, conv_strides, conv_padding, conv_padding_r, conv_dilation); auto conv_desc = bias != nullptr ? convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r) : convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); dnnl::stream stream(engine); auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine); auto user_src_memory = dnnl::memory(user_src_md, engine, const_cast(input)->buffer()); auto user_weights_memory = dnnl::memory(user_weights_md, engine, const_cast(weights)->buffer()); auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer()); auto conv_src_memory = user_src_memory; if (conv_prim_desc.src_desc() != user_src_memory.get_desc()) { conv_src_memory = dnnl::memory(conv_prim_desc.src_desc(), engine); reorder(user_src_memory, conv_src_memory).execute(stream, user_src_memory, conv_src_memory); } auto conv_weights_memory = user_weights_memory; if (conv_prim_desc.weights_desc() != user_weights_memory.get_desc()) { conv_weights_memory = dnnl::memory(conv_prim_desc.weights_desc(), engine); reorder(user_weights_memory, conv_weights_memory).execute(stream, user_weights_memory, conv_weights_memory); } auto conv_dst_memory = user_dst_memory; if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) { conv_dst_memory = dnnl::memory(conv_prim_desc.dst_desc(), engine); } if (bias != nullptr) { auto conv_bias_memory = dnnl::memory(conv_prim_desc.bias_desc(), engine, const_cast(bias)->buffer()); convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC, conv_src_memory}, {DNNL_ARG_WEIGHTS, conv_weights_memory}, {DNNL_ARG_BIAS, conv_bias_memory}, {DNNL_ARG_DST, conv_dst_memory}}); } else { convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC, conv_src_memory}, {DNNL_ARG_WEIGHTS, conv_weights_memory}, {DNNL_ARG_DST, conv_dst_memory}}); } if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) { reorder(conv_dst_memory, user_dst_memory).execute(stream, conv_dst_memory, user_dst_memory); } stream.wait(); } ////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(conv2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW) int sH = INT_ARG(2); // strides height int sW = INT_ARG(3); // strides width int pH = INT_ARG(4); // paddings height int pW = INT_ARG(5); // paddings width int dH = INT_ARG(6); // dilations height int dW = INT_ARG(7); // dilations width int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) height int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) width conv2d_mkldnn(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW); return Status::OK(); } PLATFORM_CHECK(conv2d, ENGINE_CPU) { // we don't want to use mkldnn if cpu doesn't support avx/avx2 if (::optimalLevel() < 2) return false; auto input = INPUT_VARIABLE(0); auto weights = INPUT_VARIABLE(1); // conv2d is only available for float32 dtype return block.isUseMKLDNN() && input->dataType() == nd4j::DataType::FLOAT32 && weights->dataType() == nd4j::DataType::FLOAT32; } ////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(conv2d_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC] int kH = INT_ARG(0); // filter(kernel) height int kW = INT_ARG(1); // filter(kernel) width int sH = INT_ARG(2); // strides height int sW = INT_ARG(3); // strides width int pH = INT_ARG(4); // paddings height int pW = INT_ARG(5); // paddings width int dH = INT_ARG(6); // dilations height int dW = INT_ARG(7); // dilations width int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf()); REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM CONV2D_BP OP: rank of output's gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf()); int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode); dnnl_memory_desc_t empty; dnnl::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty), conv_diff_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty); dnnl::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty), user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty); dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation; mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, bS, iC, iH, iW, oC, oH, oW, input, gradI, weights, gradW, gradB, gradO, &conv_src_md, &conv_diff_src_md, &conv_weights_md, &conv_diff_weights_md, &conv_bias_md, &conv_dst_md, &user_src_md, &user_diff_src_md, &user_weights_md, &user_diff_weights_md, &user_bias_md, &user_dst_md, conv_strides, conv_padding, conv_padding_r, conv_dilation); auto conv_desc = gradB != nullptr ? convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r) : convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r); auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, mkldnnUtils::getEngine( LaunchContext::defaultContext()->engine())); if (gradW != nullptr) { auto convW_desc = gradB != nullptr ? convolution_backward_weights::desc( algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r) : convolution_backward_weights::desc( algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); dnnl::stream stream(engine); auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine, conv_prim_desc); auto userW_src_memory = dnnl::memory(user_src_md, engine, const_cast(input)->buffer()); auto userW_weights_memory = dnnl::memory(user_diff_weights_md, engine, gradW->buffer()); auto userW_dst_memory = dnnl::memory(user_dst_md, engine, const_cast(gradO)->buffer()); auto convW_src_memory = userW_src_memory; if (convW_prim_desc.src_desc() != userW_src_memory.get_desc()) { convW_src_memory = dnnl::memory(convW_prim_desc.src_desc(), engine); reorder(userW_src_memory, convW_src_memory).execute(stream, userW_src_memory, convW_src_memory); } auto convW_weights_memory = userW_weights_memory; if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) { convW_weights_memory = dnnl::memory(convW_prim_desc.diff_weights_desc(), engine); } auto convW_dst_memory = userW_dst_memory; if (convW_prim_desc.diff_dst_desc() != userW_dst_memory.get_desc()) { convW_dst_memory = dnnl::memory(convW_prim_desc.diff_dst_desc(), engine); reorder(userW_dst_memory, convW_dst_memory).execute(stream, userW_dst_memory, convW_dst_memory); } if (gradB != nullptr) { auto convW_bias_memory = dnnl::memory(convW_prim_desc.diff_bias_desc(), engine, gradB->buffer()); convolution_backward_weights(convW_prim_desc).execute(stream, {{DNNL_ARG_SRC, convW_src_memory}, {DNNL_ARG_DIFF_DST, convW_dst_memory}, {DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory}, {DNNL_ARG_DIFF_BIAS, convW_bias_memory}}); } else { convolution_backward_weights(convW_prim_desc).execute(stream, {{DNNL_ARG_SRC, convW_src_memory}, {DNNL_ARG_DIFF_DST, convW_dst_memory}, {DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory}}); } if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) { reorder(convW_weights_memory, userW_weights_memory).execute(stream, convW_weights_memory, userW_weights_memory); } stream.wait(); } if (gradI != nullptr) { auto convI_desc = convolution_backward_data::desc(algorithm::convolution_auto, conv_diff_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r); auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine()); dnnl::stream stream(engine); auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine, conv_prim_desc); auto userI_src_memory = dnnl::memory(user_diff_src_md, engine, gradI->buffer()); auto userI_weights_memory = dnnl::memory(user_weights_md, engine, const_cast(weights)->buffer()); auto userI_dst_memory = dnnl::memory(user_dst_md, engine, const_cast(gradO)->buffer()); auto convI_src_memory = userI_src_memory; if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) { convI_src_memory = dnnl::memory(convI_prim_desc.diff_src_desc(), engine); } auto convI_weights_memory = userI_weights_memory; if (convI_prim_desc.weights_desc() != userI_weights_memory.get_desc()) { convI_weights_memory = dnnl::memory(convI_prim_desc.weights_desc(), engine); reorder(userI_weights_memory, convI_weights_memory).execute(stream, userI_weights_memory, convI_weights_memory); } auto convI_dst_memory = userI_dst_memory; if (convI_prim_desc.diff_dst_desc() != userI_dst_memory.get_desc()) { convI_dst_memory = dnnl::memory(convI_prim_desc.diff_dst_desc(), engine); reorder(userI_dst_memory, convI_dst_memory).execute(stream, userI_dst_memory, convI_dst_memory); } convolution_backward_data(convI_prim_desc).execute(stream, {{DNNL_ARG_DIFF_DST, convI_dst_memory}, {DNNL_ARG_WEIGHTS, convI_weights_memory}, {DNNL_ARG_DIFF_SRC, convI_src_memory}}); if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) { reorder(convI_src_memory, userI_src_memory).execute(stream, convI_src_memory, userI_src_memory); } stream.wait(); }; return Status::OK(); } PLATFORM_CHECK(conv2d_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC] return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}); } } } }