wehub-resource-sync
157 lines
7.7 KiB
C++
157 lines
7.7 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <helpers/MKLDNNStream.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <system/platform_boilerplate.h>
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#include "mkldnnUtils.h"
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using namespace dnnl;
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namespace sd {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(maxpool3dnew, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW)
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sd::LongType kD = INT_ARG(0); // filter(kernel) depth
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sd::LongType kH = INT_ARG(1); // filter(kernel) height
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sd::LongType kW = INT_ARG(2); // filter(kernel) width
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sd::LongType sD = INT_ARG(3); // strides depth
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sd::LongType sH = INT_ARG(4); // strides height
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sd::LongType sW = INT_ARG(5); // strides width
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sd::LongType pD = INT_ARG(6); // paddings depth
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sd::LongType pH = INT_ARG(7); // paddings height
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sd::LongType pW = INT_ARG(8); // paddings width
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sd::LongType dD = INT_ARG(9); // dilations depth
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sd::LongType dH = INT_ARG(10); // dilations height
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sd::LongType dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
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// int extraParam0 = INT_ARG(13); // unnecessary for max case, required only
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// for avg and pnorm cases
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW
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REQUIRE_TRUE(input->rankOf() == 5, 0,
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"MAXPOOL3DNEW MKLDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
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"MAXPOOL3DNEW MKLDNN op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
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sd::LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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sd::LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC,
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indIOioD, indWiC, indWoC, indWkD);
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if (paddingMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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onednnUtils::poolingONEDNN(input, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, isNCDHW, algorithm::pooling_max);
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return sd::Status::OK;
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(maxpool3dnew, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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Requirements req("ONEDNN MAXPOOL3d OP");
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req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
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req.expectTrue(sd::ONEDNNStream::isSupported({input, output}), ONEDNN_STREAM_NOT_SUPPORTED);
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if (req) onednnUtils::checkPoolingONEDNN(req, block, input, output);
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req.logTheSuccess();
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return req;
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(maxpool3dnew_bp, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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const sd::LongType kD = INT_ARG(0); // filter(kernel) depth
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const sd::LongType kH = INT_ARG(1); // filter(kernel) height
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const sd::LongType kW = INT_ARG(2); // filter(kernel) width
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const sd::LongType sD = INT_ARG(3); // strides depth
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const sd::LongType sH = INT_ARG(4); // strides height
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const sd::LongType sW = INT_ARG(5); // strides width
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sd::LongType pD = INT_ARG(6); // paddings depth
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sd::LongType pH = INT_ARG(7); // paddings height
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sd::LongType pW = INT_ARG(8); // paddings width
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const sd::LongType dD = INT_ARG(9); // dilations depth
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const sd::LongType dH = INT_ARG(10); // dilations height
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const sd::LongType dW = INT_ARG(11); // dilations width
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const int paddngMode = INT_ARG(12); // 1-SAME, 0-VALID
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// int extraParam0 = INT_ARG(13); // unnecessary for max case, required only
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// for avg and pnorm cases
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int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 1-NDHWC, 0-NCDHW
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REQUIRE_TRUE(input->rankOf() == 5, 0, "MAXPOOL3DNEW_BP MKLDNN op: input should have rank of 5, but got %i instead",
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input->rankOf());
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REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0,
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"MAXPOOL3DNEW_BP MKLDNN op: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW);
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sd::LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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sd::LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC,
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indIOioD, indWiC, indWoC, indWkD);
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std::vector<sd::LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oD, oH, oW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
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"MAXPOOL3DNEW_BP MKLDNN op: wrong shape of output's gradients array (next epsilon), expected is %s, but "
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"got %s instead !",
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ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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if (paddngMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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onednnUtils::poolingBpONEDNN(input, gradO, gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, isNCDHW,
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algorithm::pooling_max);
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return sd::Status::OK;
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(maxpool3dnew_bp, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto gradO = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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Requirements req("ONEDNN MAXPOOL3d_BP OP");
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req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
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req.expectTrue(sd::ONEDNNStream::isSupported({input, output}), ONEDNN_STREAM_NOT_SUPPORTED);
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if (req) onednnUtils::checkPoolingONEDNN(req, block, input, gradO);
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req.logTheSuccess();
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return req;
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}
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} // namespace platforms
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} // namespace ops
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} // namespace sd
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