/* ****************************************************************************** * * * 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 Yurii Shyrma (iuriish@yahoo.com) // #include "cudnnUtils.h" #include namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(avgpool3dnew, ENGINE_CUDA) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, iC] (NDHWC) or [bS, iC, oD, oH, oW] (NCDHW) int kD = INT_ARG(0); // filter(kernel) depth int kH = INT_ARG(1); // filter(kernel) height int kW = INT_ARG(2); // filter(kernel) width int sD = INT_ARG(3); // strides depth int sH = INT_ARG(4); // strides height int sW = INT_ARG(5); // strides width int pD = INT_ARG(6); // paddings depth int pH = INT_ARG(7); // paddings height int pW = INT_ARG(8); // paddings width int dD = INT_ARG(9); // dilations depth int dH = INT_ARG(10); // dilations height int dW = INT_ARG(11); // dilations width int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID int extraParam0 = INT_ARG(13); int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW CUDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "AVGPOOL3DNEW CUDNN OP: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW); int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); std::vector expectedOutputShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); REQUIRE_TRUE(output->isSameShape(expectedOutputShape), 0, "AVGPOOL3DNEW CUDNN OP: wrong shape of output array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedOutputShape).c_str(), ShapeUtils::shapeAsString(output).c_str()); if(paddingMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); const cudnnPoolingMode_t mode = (extraParam0 == 0) ? CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING : CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; pooling3dCUDNN(block.launchContext(), input, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isNCDHW, mode); return Status::OK(); } ////////////////////////////////////////////////////////////////////////// PLATFORM_CHECK(avgpool3dnew, ENGINE_CUDA) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); const auto goodType = input->dataType() == DataType::DOUBLE || input->dataType() == DataType::FLOAT32 || input->dataType() == DataType::HALF || input->dataType() == DataType::INT32; return goodType && input->dataType() == output->dataType(); } ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(avgpool3dnew_bp, ENGINE_CUDA) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon const int kD = INT_ARG(0); // filter(kernel) depth const int kH = INT_ARG(1); // filter(kernel) height const int kW = INT_ARG(2); // filter(kernel) width const int sD = INT_ARG(3); // strides depth const int sH = INT_ARG(4); // strides height const int sW = INT_ARG(5); // strides width int pD = INT_ARG(6); // paddings depth int pH = INT_ARG(7); // paddings height int pW = INT_ARG(8); // paddings width const int dD = INT_ARG(9); // dilations depth const int dH = INT_ARG(10); // dilations height const int dW = INT_ARG(11); // dilations width const int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID const int extraParam0 = INT_ARG(13); // define what divisor to use while averaging const int isNCDHW = block.getIArguments()->size() > 14 ? !INT_ARG(14) : 1; // 0-NCDHW, 1-NDHWC REQUIRE_TRUE(input->rankOf() == 5, 0, "AVGPOOL3DNEW_BP CUDNN OP: input should have rank of 5, but got %i instead", input->rankOf()); REQUIRE_TRUE(dD != 0 && dH != 0 && dW != 0, 0, "AVGPOOL3DNEW_BP CUDNN OP: dilation must not be zero, but got instead {%i, %i, %i}", dD, dH, dW); int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width; int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, 0, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oD,oH,oW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); std::vector expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iD,iH,iW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}); REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL3DNEW_BP CUDNN: 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()); REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "AVGPOOL3DNEW_BP CUDNN: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str()); if(isSameMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); const cudnnPoolingMode_t mode = (extraParam0 == 0) ? CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING : CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING; pooling3dBpCUDNN(block.launchContext(), input, gradO, gradI, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isNCDHW, mode); return Status::OK(); } PLATFORM_CHECK(avgpool3dnew_bp, ENGINE_CUDA) { auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) auto gradO = INPUT_VARIABLE(1); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon const auto goodType = input->dataType() == DataType::DOUBLE || input->dataType() == DataType::FLOAT32 || input->dataType() == DataType::HALF || input->dataType() == DataType::INT32; return goodType && (input->dataType() == gradO->dataType()) && (input->dataType() == gradI->dataType()) && shape::haveSameShapeAndStrides(input->shapeInfo(), gradI->shapeInfo()); } } } }