/* ****************************************************************************** * * * 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 raver119@gmail.com // @author Yurii Shyrma #include #if NOT_EXCLUDED(OP_conv1d) #include #include #include namespace sd { namespace ops { CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) { auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW) auto weights = INPUT_VARIABLE(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC] auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_NULLIFIED(0); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW)sa LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) width LongType sW = INT_ARG(1); // strides width LongType pW = INT_ARG(2); // paddings width LongType dW = INT_ARG(3); // dilations width LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME /** * TODO: fix java -> c++ NCW/NWC conversion. */ LongType isNCW = block.getIArguments()->size() > 5 ? INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW LongType originalNCW = isNCW; //normally nchw is 0 and 1 being passed in, we're using it as a boolean here //so we want it to be whether nchw is 0 or not. isNCW = isNCW == 0; LongType wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC] LongType bS = input->sizeAt(0); // batch size LongType iC = ConvolutionUtils::inChannels(weights->shapeInfo(), wFormat); LongType iW = ConvolutionUtils::inputWidth(input->shapeInfo(), isNCW); LongType oC = ConvolutionUtils::outChannels(weights->shapeInfo(), wFormat); LongType oW = ConvolutionUtils::calcOutDimConv(iW,kW,sW,pW,dW,paddingMode); // batch size, input channels, input height/width, output channels, output height/width; std::vector reshapeForInput, reshapeForOutput; if (!isNCW) { reshapeForInput = {bS, 1, iW, iC}; // [bS, iW, iC] -> [bS, 1, iW, iC] reshapeForOutput = {bS, 1, oW, oC}; // [bS, oW, oC] -> [bS, 1, oW, oC] } else { reshapeForInput = {bS, iC, 1, iW}; // [bS, iC, iW] -> [bS, iC, 1, iW] reshapeForOutput = {bS,oC, 1, oW}; // [bS, oC, oW] -> [bS, oC, 1, oW] } auto inputReshaped = input->reshape(input->ordering(), reshapeForInput,false); auto outputReshaped = output->reshape(output->ordering(), reshapeForOutput, false); std::vector weightsShape = {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}; auto weightsReshaped = weights->reshape( weights->ordering(), weightsShape,false); // [kW, iC, oC] -> [1, kW, iC, oC] conv2d conv2d; Status ret = Status::OK; if(bias == nullptr) { //note this might look strange but we get a segfault otherwise. //this problem was actually the source of a very strange JVM hang. ret = conv2d.execute({inputReshaped, weightsReshaped}, {outputReshaped}, {}, {1, kW, 1, sW, 0, pW, 1, dW, paddingMode, originalNCW}, {}); output->assign(outputReshaped); } else { ret = conv2d.execute({inputReshaped, weightsReshaped, bias}, {outputReshaped}, {}, {1, kW, 1, sW, 0, pW, 1, dW, paddingMode, originalNCW}, {}); output->assign(outputReshaped); } return ret; } DECLARE_SHAPE_FN(conv1d) { auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC] auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC] LongType wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC] LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(shape::sizeAt(weightsShapeInfo,0)); // filter(kernel) width LongType sW = INT_ARG(1); // strides width LongType pW = INT_ARG(2); // paddings width LongType dW = INT_ARG(3); // dilations width LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME LongType isNCW = block.getIArguments()->size() > 5 ? INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW //normally nchw is 0 and 1 being passed in, we're using it as a boolean here //so we want it to be whether nchw is 0 or not. isNCW = isNCW == 0; const LongType rank = 3; // 4 LongType bS = shape::sizeAt(inputShapeInfo, 0); // batch size LongType iC = ConvolutionUtils::inChannels(weightsShapeInfo, wFormat); LongType iW = ConvolutionUtils::inputWidth(inputShapeInfo, isNCW); LongType oC = ConvolutionUtils::outChannels(weightsShapeInfo, wFormat); LongType oW = ConvolutionUtils::calcOutDimConv(iW,kW,sW,pW,dW,paddingMode); // batch size, input channels, input height/width, output channels, output height/width; LongType* outputShapeInfo = nullptr; ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), sd::LongType); outputShapeInfo[0] = 3; outputShapeInfo[1] = bS; if (isNCW) { outputShapeInfo[2] = oC; outputShapeInfo[3] = oW; } else { outputShapeInfo[2] = oW; outputShapeInfo[3] = oC; } sd::LongType * second = shape::calcStridesFortran(outputShapeInfo,shape::rank(outputShapeInfo)); shape::setStride(outputShapeInfo,second); shape::setOrder(outputShapeInfo, 'f'); ArrayOptions::setDataType(outputShapeInfo, ArrayOptions::dataType(inputShapeInfo)); delete[] second; return SHAPELIST(CONSTANT(outputShapeInfo)); } DECLARE_TYPES(conv1d) { getOpDescriptor() ->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, QINT8, QINT16}) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedOutputTypes(0, {ALL_FLOATS}); } ////////////////////////////////////////////////////////////////////////// CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) { auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW) auto weights = INPUT_VARIABLE(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC] auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next auto gradI = OUTPUT_NULLIFIED(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW), epsilon auto gradW = OUTPUT_NULLIFIED(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC] auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC] LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) width LongType sW = INT_ARG(1); // strides width LongType pW = INT_ARG(2); // paddings width LongType dW = INT_ARG(3); // dilations width LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME, 2-CAUSAL LongType isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW LongType wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC] const LongType rank = 3; REQUIRE_TRUE(input->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, input->rankOf()); REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM CONV1D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weights->rankOf()); LongType indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0); if (!isNCW) { indIOioC = 2; indIiW = 1; } else { indIOioC = 1; indIiW = 2; } const LongType bS = input->sizeAt(0); // batch size const LongType iW = input->sizeAt(indIiW); // input width const LongType iC = input->sizeAt(indIOioC); // input channels const LongType oC = weights->sizeAt(indWoC); // output channels LongType trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, 1, kW, 1, sW, 0, pW, 1, dW, 1, iW, paddingMode); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoW, 0, indIOioC, indIiW}); std::vector expectedWeightsShape = 0 == wFormat ? std::vector({kW, iC, oC}) : (1 == wFormat ? std::vector({oC, iC, kW}) : std::vector({oC, kW, iC})); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV1D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, " "%i instead !", oC, bias->rankOf(), bias->lengthOf()); std::vector reshapeForInput, reshapeForGradO; if (!isNCW) { if(!gradO->isScalar()) { reshapeForGradO = {gradO->sizeAt(0), 1, gradO->sizeAt(1), gradO->sizeAt(2)}; // [bS, oW, oC] -> [bS, 1, oW, oC] reshapeForInput = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, iW, iC] -> [bS, 1, iW, iC] } else { reshapeForGradO = {input->sizeAt(0), input->sizeAt(1), input->sizeAt(2),1}; // [bS, oW, oC] -> [bS, 1, oW, oC] reshapeForInput = {input->sizeAt(0), input->sizeAt(1), input->sizeAt(2),1}; // [bS, iW, iC] -> [bS, 1, iW, iC] } } else { if (!gradO->isScalar()) { reshapeForGradO = {gradO->sizeAt(0), gradO->sizeAt(1), 1, gradO->sizeAt(2)}; // [bS, oC, oW] -> [bS, oC, 1, oW] reshapeForInput = {input->sizeAt(0), input->sizeAt(1), 1, input->sizeAt(2)}; // [bS, iC, iW] -> [bS, iC, 1, iW] } else { reshapeForGradO = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, oW, oC] -> [bS, 1, oW, oC] reshapeForInput = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, iW, iC] -> [bS, 1, iW, iC] } } auto inputReshaped = input->reshape(input->ordering(), reshapeForInput,false); auto gradIReshaped = !gradO->isScalar() ? gradI->reshape(gradI->ordering(), reshapeForInput, false) : gradI; auto gradOReshaped = !gradO->isScalar() ?gradO->reshape(gradO->ordering(), reshapeForGradO,false) : gradO; std::vector weightsShape = {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)}; auto weightsReshaped = weights->reshape( weights->ordering(), weightsShape,false); // [kW, iC, oC] -> [1, kW, iC, oC] auto gradWReshaped = !gradO->isScalar() ?gradW->reshape(gradW->ordering(), weightsShape, false) : gradW; // [kW, iC, oC] -> [1, kW, iC, oC] Status ret = Status::OK; conv2d_bp conv2dBP; if(bias == nullptr) { if(gradO->isScalar()) { gradIReshaped->assign(gradO); gradWReshaped->assign(gradO); } else { std::vector inputs = {inputReshaped, weightsReshaped, gradOReshaped}; std::vector outputs = {gradIReshaped, gradWReshaped}; //note this might look strange but we get a segfault otherwise. //this problem was actually the source of a very strange JVM hang. ret = conv2dBP.execute(inputs, outputs, {}, {1, kW, 1, sW, 0, pW, 1, dW, paddingMode, !isNCW, wFormat}, {}); } } else { if(gradO->isScalar()) { gradIReshaped->assign(gradO); gradWReshaped->assign(gradO); gradB->assign(gradO); } else { std::vector inputs = {inputReshaped, weightsReshaped,bias, gradOReshaped}; std::vector outputs = {gradIReshaped, gradWReshaped, gradB}; ret = conv2dBP.execute(inputs, outputs, {}, {1, kW, 1, sW, 0, pW, 1, dW, paddingMode, !isNCW, wFormat}, {}); } } if(gradIReshaped->buffer() != gradI->buffer()) { gradI->assign(gradIReshaped); } if(gradWReshaped->buffer() != gradW->buffer()) { gradW->assign(gradWReshaped); } if(bias != nullptr) { if(gradB->buffer() != gradB->buffer()) { gradB->assign(gradB); } } return ret; } DECLARE_SHAPE_FN(conv1d_bp) { auto inputShapeInfo = inputShape->at(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW) auto weightsShapeInfo = inputShape->at(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC] LongType const* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC] LongType const* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next const LongType rank = 3; REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo[0]); REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV1D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo[0]); LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(shape::sizeAt(weightsShapeInfo, static_cast(0))); // filter(kernel) width LongType sW = INT_ARG(1); // strides width LongType pW = INT_ARG(2); // paddings width LongType dW = INT_ARG(3); // dilations width LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME LongType isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW LongType wFormat = block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC] LongType indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0); if (!isNCW) { indIOioC = 2; indIiW = 1; } else { indIOioC = 1; indIiW = 2; } const LongType bS = inputShapeInfo[1]; // batch size const LongType iW = inputShapeInfo[indIiW + 1]; // input width const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels LongType trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, 1, kW, 1, sW, 0, pW, 1, dW, 1, iW, paddingMode); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoW, 0, indIOioC, indIiW}); std::vector expectedWeightsShape = 0 == wFormat ? std::vector({kW, iC, oC}) : (1 == wFormat ? std::vector({oC, iC, kW}) : std::vector({oC, kW, iC})); REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0, "CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str()); if (biasShapeInfo) REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV1D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, " "%i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo)); auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace()); auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace()); if (biasShapeInfo) { auto gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace()); return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo)); } return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo)); } DECLARE_TYPES(conv1d_bp) { getOpDescriptor() ->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, QINT8, QINT16}) ->setAllowedInputTypes(1, {ALL_FLOATS}) ->setAllowedInputTypes(2, {ALL_FLOATS}) ->setAllowedInputTypes(3, {ALL_FLOATS}) ->setAllowedOutputTypes(0, {ALL_FLOATS}) ->setAllowedOutputTypes(1, {ALL_FLOATS}); } } // namespace ops } // namespace sd #endif