wehub-resource-sync
395 lines
18 KiB
C++
395 lines
18 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
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv1d)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/DeclarableOp.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(conv1d, 2, 1, false, 0, 5) {
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auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
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auto weights = INPUT_VARIABLE(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_NULLIFIED(0); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW)sa
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LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) width
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LongType sW = INT_ARG(1); // strides width
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LongType pW = INT_ARG(2); // paddings width
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LongType dW = INT_ARG(3); // dilations width
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LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
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/**
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* TODO: fix java -> c++ NCW/NWC conversion.
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*/
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LongType isNCW = block.getIArguments()->size() > 5 ? INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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LongType originalNCW = isNCW;
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//normally nchw is 0 and 1 being passed in, we're using it as a boolean here
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//so we want it to be whether nchw is 0 or not.
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isNCW = isNCW == 0;
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LongType wFormat =
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block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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LongType bS = input->sizeAt(0); // batch size
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LongType iC = ConvolutionUtils::inChannels(weights->shapeInfo(), wFormat);
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LongType iW = ConvolutionUtils::inputWidth(input->shapeInfo(), isNCW);
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LongType oC = ConvolutionUtils::outChannels(weights->shapeInfo(), wFormat);
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LongType oW = ConvolutionUtils::calcOutDimConv(iW,kW,sW,pW,dW,paddingMode); // batch size, input channels, input height/width, output channels, output height/width;
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std::vector<LongType> reshapeForInput, reshapeForOutput;
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if (!isNCW) {
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reshapeForInput = {bS, 1, iW, iC}; // [bS, iW, iC] -> [bS, 1, iW, iC]
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reshapeForOutput = {bS, 1, oW, oC}; // [bS, oW, oC] -> [bS, 1, oW, oC]
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} else {
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reshapeForInput = {bS, iC, 1, iW}; // [bS, iC, iW] -> [bS, iC, 1, iW]
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reshapeForOutput = {bS,oC, 1, oW}; // [bS, oC, oW] -> [bS, oC, 1, oW]
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}
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auto inputReshaped = input->reshape(input->ordering(), reshapeForInput,false);
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auto outputReshaped = output->reshape(output->ordering(), reshapeForOutput, false);
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std::vector<LongType> weightsShape = {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)};
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auto weightsReshaped = weights->reshape(
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weights->ordering(),
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weightsShape,false); // [kW, iC, oC] -> [1, kW, iC, oC]
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conv2d conv2d;
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Status ret = Status::OK;
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if(bias == nullptr) {
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//note this might look strange but we get a segfault otherwise.
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//this problem was actually the source of a very strange JVM hang.
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ret = conv2d.execute({inputReshaped, weightsReshaped}, {outputReshaped}, {},
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{1, kW, 1, sW, 0, pW, 1, dW, paddingMode, originalNCW}, {});
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output->assign(outputReshaped);
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} else {
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ret = conv2d.execute({inputReshaped, weightsReshaped, bias}, {outputReshaped}, {},
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{1, kW, 1, sW, 0, pW, 1, dW, paddingMode, originalNCW}, {});
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output->assign(outputReshaped);
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}
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return ret;
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}
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DECLARE_SHAPE_FN(conv1d) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weightsShapeInfo = inputShape->at(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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LongType wFormat =
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block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo,0)); // filter(kernel) width
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LongType sW = INT_ARG(1); // strides width
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LongType pW = INT_ARG(2); // paddings width
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LongType dW = INT_ARG(3); // dilations width
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LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
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LongType isNCW = block.getIArguments()->size() > 5 ? INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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//normally nchw is 0 and 1 being passed in, we're using it as a boolean here
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//so we want it to be whether nchw is 0 or not.
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isNCW = isNCW == 0;
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const LongType rank = 3; // 4
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LongType bS = shape::sizeAt(inputShapeInfo, 0); // batch size
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LongType iC = ConvolutionUtils::inChannels(weightsShapeInfo, wFormat);
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LongType iW = ConvolutionUtils::inputWidth(inputShapeInfo, isNCW);
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LongType oC = ConvolutionUtils::outChannels(weightsShapeInfo, wFormat);
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LongType oW = ConvolutionUtils::calcOutDimConv(iW,kW,sW,pW,dW,paddingMode); // batch size, input channels, input height/width, output channels, output height/width;
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LongType* outputShapeInfo = nullptr;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(rank), sd::LongType);
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outputShapeInfo[0] = 3;
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outputShapeInfo[1] = bS;
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if (isNCW) {
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outputShapeInfo[2] = oC;
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outputShapeInfo[3] = oW;
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} else {
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outputShapeInfo[2] = oW;
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outputShapeInfo[3] = oC;
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}
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sd::LongType * second = shape::calcStridesFortran(outputShapeInfo,shape::rank(outputShapeInfo));
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shape::setStride(outputShapeInfo,second);
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shape::setOrder(outputShapeInfo, 'f');
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ArrayOptions::setDataType(outputShapeInfo, ArrayOptions::dataType(inputShapeInfo));
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delete[] second;
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return SHAPELIST(CONSTANT(outputShapeInfo));
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}
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DECLARE_TYPES(conv1d) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS, QINT8, QINT16})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedOutputTypes(0, {ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv1d_bp, 3, 2, false, 0, 5) {
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auto input = INPUT_VARIABLE(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
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auto weights = INPUT_VARIABLE(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto gradO = block.width() > 3 ? INPUT_VARIABLE(3)
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: INPUT_VARIABLE(2); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next
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auto gradI = OUTPUT_NULLIFIED(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW), epsilon
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auto gradW = OUTPUT_NULLIFIED(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC]
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LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) width
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LongType sW = INT_ARG(1); // strides width
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LongType pW = INT_ARG(2); // paddings width
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LongType dW = INT_ARG(3); // dilations width
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LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME, 2-CAUSAL
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LongType isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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LongType wFormat =
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block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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const LongType rank = 3;
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REQUIRE_TRUE(input->rankOf() == rank, 0,
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"CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
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input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == rank, 0,
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"CUSTOM CONV1D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
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weights->rankOf());
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LongType indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
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if (!isNCW) {
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indIOioC = 2;
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indIiW = 1;
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} else {
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indIOioC = 1;
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indIiW = 2;
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}
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const LongType bS = input->sizeAt(0); // batch size
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const LongType iW = input->sizeAt(indIiW); // input width
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const LongType iC = input->sizeAt(indIOioC); // input channels
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const LongType oC = weights->sizeAt(indWoC); // output channels
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LongType trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, 1, kW, 1, sW, 0, pW, 1, dW, 1, iW, paddingMode);
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std::vector<LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoW, 0, indIOioC, indIiW});
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std::vector<LongType> expectedWeightsShape =
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0 == wFormat ? std::vector<LongType>({kW, iC, oC})
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: (1 == wFormat ? std::vector<LongType>({oC, iC, kW}) : std::vector<LongType>({oC, kW, iC}));
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if (bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"CUSTOM CONV1D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
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"%i instead !",
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oC, bias->rankOf(), bias->lengthOf());
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std::vector<LongType> reshapeForInput, reshapeForGradO;
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if (!isNCW) {
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if(!gradO->isScalar()) {
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reshapeForGradO = {gradO->sizeAt(0), 1, gradO->sizeAt(1), gradO->sizeAt(2)}; // [bS, oW, oC] -> [bS, 1, oW, oC]
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reshapeForInput = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, iW, iC] -> [bS, 1, iW, iC]
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} else {
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reshapeForGradO = {input->sizeAt(0), input->sizeAt(1), input->sizeAt(2),1}; // [bS, oW, oC] -> [bS, 1, oW, oC]
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reshapeForInput = {input->sizeAt(0), input->sizeAt(1), input->sizeAt(2),1}; // [bS, iW, iC] -> [bS, 1, iW, iC]
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}
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} else {
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if (!gradO->isScalar()) {
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reshapeForGradO = {gradO->sizeAt(0), gradO->sizeAt(1), 1, gradO->sizeAt(2)}; // [bS, oC, oW] -> [bS, oC, 1, oW]
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reshapeForInput = {input->sizeAt(0), input->sizeAt(1), 1, input->sizeAt(2)}; // [bS, iC, iW] -> [bS, iC, 1, iW]
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} else {
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reshapeForGradO = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, oW, oC] -> [bS, 1, oW, oC]
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reshapeForInput = {input->sizeAt(0), 1, input->sizeAt(1), input->sizeAt(2)}; // [bS, iW, iC] -> [bS, 1, iW, iC]
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}
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}
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auto inputReshaped = input->reshape(input->ordering(), reshapeForInput,false);
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auto gradIReshaped = !gradO->isScalar() ? gradI->reshape(gradI->ordering(), reshapeForInput, false) : gradI;
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auto gradOReshaped = !gradO->isScalar() ?gradO->reshape(gradO->ordering(), reshapeForGradO,false) : gradO;
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std::vector<LongType> weightsShape = {1, weights->sizeAt(0), weights->sizeAt(1), weights->sizeAt(2)};
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auto weightsReshaped = weights->reshape(
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weights->ordering(),
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weightsShape,false); // [kW, iC, oC] -> [1, kW, iC, oC]
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auto gradWReshaped =
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!gradO->isScalar() ?gradW->reshape(gradW->ordering(), weightsShape,
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false) : gradW; // [kW, iC, oC] -> [1, kW, iC, oC]
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Status ret = Status::OK;
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conv2d_bp conv2dBP;
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if(bias == nullptr) {
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if(gradO->isScalar()) {
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gradIReshaped->assign(gradO);
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gradWReshaped->assign(gradO);
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} else {
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std::vector<NDArray *> inputs = {inputReshaped, weightsReshaped, gradOReshaped};
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std::vector<NDArray *> outputs = {gradIReshaped, gradWReshaped};
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//note this might look strange but we get a segfault otherwise.
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//this problem was actually the source of a very strange JVM hang.
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ret = conv2dBP.execute(inputs,
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outputs, {},
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{1, kW, 1, sW, 0, pW, 1, dW, paddingMode, !isNCW, wFormat}, {});
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}
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} else {
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if(gradO->isScalar()) {
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gradIReshaped->assign(gradO);
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gradWReshaped->assign(gradO);
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gradB->assign(gradO);
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} else {
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std::vector<NDArray *> inputs = {inputReshaped, weightsReshaped,bias, gradOReshaped};
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std::vector<NDArray *> outputs = {gradIReshaped, gradWReshaped, gradB};
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ret = conv2dBP.execute(inputs,
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outputs, {},
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{1, kW, 1, sW, 0, pW, 1, dW, paddingMode, !isNCW, wFormat}, {});
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}
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}
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if(gradIReshaped->buffer() != gradI->buffer()) {
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gradI->assign(gradIReshaped);
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}
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if(gradWReshaped->buffer() != gradW->buffer()) {
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gradW->assign(gradWReshaped);
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}
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if(bias != nullptr) {
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if(gradB->buffer() != gradB->buffer()) {
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gradB->assign(gradB);
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}
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}
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return ret;
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}
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DECLARE_SHAPE_FN(conv1d_bp) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iW, iC] (NWC) or [bS, iC, iW] (NCW)
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auto weightsShapeInfo = inputShape->at(1); // [kW, iC, oC], [oC, iC, kW], [oC, kW, iC]
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LongType const* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
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LongType const* gradOShapeInfo =
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block.width() > 3 ? inputShape->at(3)
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: inputShape->at(2); // [bS, oW, oC] (NWC) or [bS, oC, oW] (NCW), epsilon_next
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const LongType rank = 3;
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0,
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"CUSTOM CONV1D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
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inputShapeInfo[0]);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0,
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"CUSTOM CONV1D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
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weightsShapeInfo[0]);
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LongType kW = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<LongType>(0))); // filter(kernel) width
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LongType sW = INT_ARG(1); // strides width
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LongType pW = INT_ARG(2); // paddings width
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LongType dW = INT_ARG(3); // dilations width
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LongType paddingMode = INT_ARG(4); // 0-VALID, 1-SAME
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LongType isNCW = block.getIArguments()->size() > 5 ? !INT_ARG(5) : 1; // INT_ARG(4): 1-NWC, 0-NCW
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LongType wFormat =
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block.getIArguments()->size() > 6 ? INT_ARG(6) : 0; // 0 - [kW, iC, oC], 1 - [oC, iC, kW], 2 - [oC, kW, iC]
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LongType indIOioC, indIiW, indWoC(0 == wFormat ? 2 : 0);
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if (!isNCW) {
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indIOioC = 2;
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indIiW = 1;
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} else {
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indIOioC = 1;
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indIiW = 2;
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}
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const LongType bS = inputShapeInfo[1]; // batch size
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const LongType iW = inputShapeInfo[indIiW + 1]; // input width
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const LongType iC = inputShapeInfo[indIOioC + 1]; // input channels
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const LongType oC = weightsShapeInfo[indWoC + 1]; // output channels
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LongType trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, 1, kW, 1, sW, 0, pW, 1, dW, 1, iW, paddingMode);
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std::vector<LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoW, 0, indIOioC, indIiW});
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std::vector<LongType> expectedWeightsShape =
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0 == wFormat ? std::vector<LongType>({kW, iC, oC})
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: (1 == wFormat ? std::vector<LongType>({oC, iC, kW}) : std::vector<LongType>({oC, kW, iC}));
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REQUIRE_TRUE(ShapeUtils::areShapesEqual(weightsShapeInfo, expectedWeightsShape), 0,
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"CUSTOM CONV1D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
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ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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if (biasShapeInfo)
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|
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0,
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|
"CUSTOM CONV1D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
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|
"%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
|