/* ****************************************************************************** * * * 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. * * See the NOTICE file distributed with this work for additional * information regarding copyright ownership. * 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 // @author Yurii Shyrma (iuriish@yahoo.com) // #include #include #include #include #include #include "mkldnnUtils.h" using namespace dnnl; namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(maxpool2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D MKLDNN OP: input array should have rank of 4, but got %i instead", input->rankOf()); // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same // mode; const sd::LongType kH = INT_ARG(0); const sd::LongType kW = INT_ARG(1); const sd::LongType sH = INT_ARG(2); const sd::LongType sW = INT_ARG(3); sd::LongType pH = INT_ARG(4); sd::LongType pW = INT_ARG(5); const sd::LongType dH = INT_ARG(6); const sd::LongType dW = INT_ARG(7); const int paddingMode = INT_ARG(8); // const int extraParam0 = INT_ARG(9); const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 1-NHWC, 0-NCHW REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW); sd::LongType bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); if (paddingMode) ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); onednnUtils::poolingONEDNN(input, output, 0, kH, kW, 0, sH, sW, 0, pH, pW, isNCHW, algorithm::pooling_max); return sd::Status::OK; } ////////////////////////////////////////////////////////////////////////// PLATFORM_CHECK(maxpool2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); Requirements req("ONEDNN MAXPOOL2d OP"); req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) && req.expectTrue(sd::ONEDNNStream::isSupported({input, output}), ONEDNN_STREAM_NOT_SUPPORTED); if (req) onednnUtils::checkPoolingONEDNN(req, block, input, output); req.logTheSuccess(); return req; } ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(maxpool2d_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto gradO = INPUT_VARIABLE(1); // [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 sd::LongType kH = INT_ARG(0); // filter(kernel) height sd::LongType kW = INT_ARG(1); // filter(kernel) width sd::LongType sH = INT_ARG(2); // strides height sd::LongType sW = INT_ARG(3); // strides width sd::LongType pH = INT_ARG(4); // paddings height sd::LongType pW = INT_ARG(5); // paddings width sd::LongType dH = INT_ARG(6); // dilations height sd::LongType dW = INT_ARG(7); // dilations width int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME // int extraParam0 = INT_ARG(9); int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D_BP MKLDNN op: input should have rank of 4, but got %i instead", input->rankOf()); REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D_BP MKLDNN op: dilation must not be zero, but got instead {%i, %i}", dH, dW); sd::LongType bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oH, oW, 0, indIOioC, indIiH, indIiH + 1}); REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "MAXPOOL2D_BP MKLDNN op: wrong shape of output's gradients array (next epsilon), expected is %s, but " "got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); if (paddingMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); onednnUtils::poolingBpONEDNN(input, gradO, gradI, 0, kH, kW, 0, sH, sW, 0, pH, pW, isNCHW, algorithm::pooling_max); return sd::Status::OK; } PLATFORM_CHECK(maxpool2d_bp, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto gradO = INPUT_VARIABLE(1); auto output = OUTPUT_VARIABLE(0); Requirements req("ONEDNN MAXPOOL2d_BP OP"); req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) && req.expectTrue(sd::ONEDNNStream::isSupported({input, output}), ONEDNN_STREAM_NOT_SUPPORTED); if (req) onednnUtils::checkPoolingONEDNN(req, block, input, gradO); req.logTheSuccess(); return req; } } // namespace platforms } // namespace ops } // namespace sd