153 lines
9.1 KiB
C++
153 lines
9.1 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 dnnl;
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namespace nd4j {
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namespace ops {
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namespace platforms {
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PLATFORM_IMPL(avgpool2d_bp) {
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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 gradO = INPUT_VARIABLE(
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1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(
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0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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int kH = INT_ARG(0); // filter(kernel) height
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int kW = INT_ARG(1); // filter(kernel) width
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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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int extraParam0 = INT_ARG(9);
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int isNCHW =
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block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"AVGPOOL2D_BP op: input should have rank of 4, but got %i instead", input->rankOf());
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REQUIRE_TRUE(dH != 0 && dW != 0, 0,
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"AVGPOOL2D_BP op: dilation must not be zero, but got instead {%i, %i}", dH, dW);
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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, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWiC, indWoC, indWkH, indOoH);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oH, oW, 0, indIOioC, indIiH, indIiH + 1}));
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std::string expectedGradIShape = ShapeUtils::shapeAsString(
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, iH, iW, 0, indIOioC, indIiH, indIiH + 1}));
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0,
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"AVGPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !",
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expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(expectedGradIShape == ShapeUtils::shapeAsString(gradI), 0,
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"AVGPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !",
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expectedGradIShape.c_str(), ShapeUtils::shapeAsString(gradI).c_str());
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if (!isNCHW) {
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input = new NDArray(input->permute(
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{0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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gradI = new NDArray(gradI->permute(
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{0, 3, 1, 2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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gradO = new NDArray(gradO->permute(
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{0, 3, 1, 2})); // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
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}
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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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auto poolingMode = PoolingType::AVG_POOL;
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dnnl_memory_desc_t empty;
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dnnl::memory::desc pool_src_md(empty), pool_diff_src_md(empty), pool_dst_md(empty);
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dnnl::memory::desc user_src_md(empty), user_diff_src_md(empty), user_dst_md(empty);
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dnnl::memory::dims pool_strides, pool_kernel, pool_padding, pool_padding_r;
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dnnl::algorithm algorithm;
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mkldnnUtils::getMKLDNNMemoryDescPool2d(kH, kW, sH, sW, pH, pW, dH, dW, poolingMode, extraParam0,
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true,
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bS, iC, iH, iW, oC, oH, oW, input, gradI, gradO, algorithm,
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&pool_src_md, &pool_diff_src_md, &pool_dst_md, &user_src_md,
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&user_diff_src_md, &user_dst_md,
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pool_strides, pool_kernel, pool_padding, pool_padding_r);
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auto pool_desc = pooling_forward::desc(prop_kind::forward, algorithm,
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input->buffer() != nullptr ? pool_src_md : pool_diff_src_md,
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pool_dst_md, pool_strides, pool_kernel, pool_padding,
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pool_padding_r);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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auto pool_prim_desc = pooling_forward::primitive_desc(pool_desc, engine);
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auto poolB_desc = pooling_backward::desc(algorithm, pool_diff_src_md, pool_dst_md, pool_strides,
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pool_kernel, pool_padding, pool_padding_r);
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auto poolB_prim_desc = pooling_backward::primitive_desc(poolB_desc, engine, pool_prim_desc);
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auto userB_src_memory = dnnl::memory(user_src_md, engine, gradI->buffer());
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auto userB_dst_memory = dnnl::memory(user_dst_md, engine, gradO->buffer());
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auto poolB_src_memory = userB_src_memory;
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dnnl::stream stream(engine);
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if (poolB_prim_desc.diff_src_desc() != userB_src_memory.get_desc()) {
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poolB_src_memory = dnnl::memory(poolB_prim_desc.diff_src_desc(), engine);
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}
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auto poolB_dst_memory = userB_dst_memory;
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if (poolB_prim_desc.diff_dst_desc() != userB_dst_memory.get_desc()) {
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poolB_dst_memory = dnnl::memory(poolB_prim_desc.diff_dst_desc(), engine);
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reorder(userB_dst_memory, poolB_dst_memory).execute(stream, userB_dst_memory, poolB_dst_memory);
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}
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pooling_backward(poolB_prim_desc).execute(stream, {{DNNL_ARG_DIFF_DST, poolB_dst_memory},
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{DNNL_ARG_DIFF_SRC, poolB_src_memory}});
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if (poolB_prim_desc.diff_src_desc() != userB_src_memory.get_desc()) {
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reorder(poolB_src_memory, userB_src_memory).execute(stream, poolB_src_memory, userB_src_memory);
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}
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stream.wait();
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if (!isNCHW) {
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delete input;
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delete gradI;
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delete gradO;
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}
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return Status::OK();
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}
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PLATFORM_CHECK(avgpool2d_bp) {
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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 output = OUTPUT_VARIABLE(0);
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return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
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}
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}
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}
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} |