154 lines
9.6 KiB
C++
154 lines
9.6 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author saudet
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// @author raver119@gmail.com
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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using namespace mkldnn;
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namespace nd4j {
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namespace ops {
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namespace platforms {
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static void conv2d_mkldnn(nd4j::graph::Context &block, const NDArray *input, const NDArray *weights,
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const NDArray *bias, NDArray *output, const int kH, const int kW, const int sH,
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const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode,
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const int isNCHW) {
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW,
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indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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mkldnn_memory_desc_t empty;
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mkldnn::memory::desc conv_src_md(empty), conv_weights_md(empty), conv_bias_md(empty), conv_dst_md(
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empty);
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mkldnn::memory::desc user_src_md(empty), user_weights_md(empty), user_bias_md(empty), user_dst_md(
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empty);
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mkldnn::memory::dims conv_strides, conv_padding, conv_padding_r;
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mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW,
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bS, iC, iH, iW, oC, oH, oW, input, nullptr, weights, nullptr,
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bias, output,
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&conv_src_md, nullptr, &conv_weights_md, nullptr,
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&conv_bias_md, &conv_dst_md,
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&user_src_md, nullptr, &user_weights_md, nullptr,
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&user_bias_md, &user_dst_md,
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conv_strides, conv_padding, conv_padding_r);
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auto conv_desc = bias != nullptr
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? convolution_forward::desc(prop_kind::forward,
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algorithm::convolution_auto, conv_src_md,
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conv_weights_md, conv_bias_md,
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conv_dst_md, conv_strides, conv_padding,
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conv_padding_r)
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: convolution_forward::desc(prop_kind::forward,
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algorithm::convolution_auto, conv_src_md,
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conv_weights_md,
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conv_dst_md, conv_strides, conv_padding,
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conv_padding_r);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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mkldnn::stream stream(engine);
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auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine);
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auto user_src_memory = mkldnn::memory(user_src_md, engine, const_cast<NDArray *>(input)->buffer());
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auto user_weights_memory = mkldnn::memory(user_weights_md, engine,
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const_cast<NDArray *>(weights)->buffer());
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auto user_dst_memory = mkldnn::memory(user_dst_md, engine, output->buffer());
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auto conv_src_memory = user_src_memory;
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if (conv_prim_desc.src_desc() != user_src_memory.get_desc()) {
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conv_src_memory = mkldnn::memory(conv_prim_desc.src_desc(), engine);
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reorder(user_src_memory, conv_src_memory).execute(stream, user_src_memory, conv_src_memory);
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}
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auto conv_weights_memory = user_weights_memory;
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if (conv_prim_desc.weights_desc() != user_weights_memory.get_desc()) {
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conv_weights_memory = mkldnn::memory(conv_prim_desc.weights_desc(), engine);
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reorder(user_weights_memory, conv_weights_memory).execute(stream, user_weights_memory,
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conv_weights_memory);
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}
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auto conv_dst_memory = user_dst_memory;
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if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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conv_dst_memory = mkldnn::memory(conv_prim_desc.dst_desc(), engine);
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}
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if (bias != nullptr) {
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auto conv_bias_memory = mkldnn::memory(conv_prim_desc.bias_desc(), engine,
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const_cast<NDArray *>(bias)->buffer());
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convolution_forward(conv_prim_desc).execute(stream, {{MKLDNN_ARG_SRC, conv_src_memory},
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{MKLDNN_ARG_WEIGHTS, conv_weights_memory},
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{MKLDNN_ARG_BIAS, conv_bias_memory},
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{MKLDNN_ARG_DST, conv_dst_memory}});
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} else {
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convolution_forward(conv_prim_desc).execute(stream, {{MKLDNN_ARG_SRC, conv_src_memory},
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{MKLDNN_ARG_WEIGHTS, conv_weights_memory},
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{MKLDNN_ARG_DST, conv_dst_memory}});
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}
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if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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reorder(conv_dst_memory, user_dst_memory).execute(stream, conv_dst_memory, user_dst_memory);
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}
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stream.wait();
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}
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PLATFORM_IMPL(conv2d) {
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auto input = INPUT_VARIABLE(
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0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(
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0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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int sH = INT_ARG(2); // strides height
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int sW = INT_ARG(3); // strides width
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int pH = INT_ARG(4); // paddings height
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int pW = INT_ARG(5); // paddings width
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int dH = INT_ARG(6); // dilations height
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int dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
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int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
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conv2d_mkldnn(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW);
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return Status::OK();
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}
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PLATFORM_CHECK(conv2d) {
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// we don't want to use mkldnn if cpu doesn't support avx/avx2
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if (::optimalLevel() < 2)
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return false;
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auto input = INPUT_VARIABLE(0);
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auto weights = INPUT_VARIABLE(1);
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// conv2d is only available for float32 dtype
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return block.isUseMKLDNN() && input->dataType() == nd4j::DataType::FLOAT32 &&
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weights->dataType() == nd4j::DataType::FLOAT32;
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}
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}
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}
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}
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