chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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/* ******************************************************************************
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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 Yurii Shyrma (iuriish@yahoo.com), created on 18.09.2018
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//
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#include <array/NDArrayFactory.h>
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#include <execution/Threads.h>
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#include <helpers/MmulHelper.h>
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#include <ops/declarable/helpers/addBias.h>
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#include <ops/declarable/helpers/col2im.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <ops/declarable/helpers/im2col.h>
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#include "helpers/ShapeUtils.h"
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#if NOT_EXCLUDED(OP_col2im) && NOT_EXCLUDED(OP_im2col)
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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template <typename X, typename Y>
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static void conv2dBP_(sd::graph::Context& block, NDArray* input, NDArray* weights, NDArray* bias,
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NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const LongType kH, const LongType kW,
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const LongType sH, const LongType sW, LongType pH, LongType pW, const LongType dH, const LongType dW,
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const int paddingMode, const int isNCHW, const int wFormat) {
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// input [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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// weights [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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// bias [oC]
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// gradO [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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// gradI [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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// gradW [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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// gradB [oC]
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const LongType bS = input->sizeAt(0); // batch size
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const LongType iC = isNCHW ? input->sizeAt(1) : input->sizeAt(3); // input channels
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const LongType iH = isNCHW ? input->sizeAt(2) : input->sizeAt(1); // input height
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const LongType iW = isNCHW ? input->sizeAt(3) : input->sizeAt(2); // input width
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const LongType oC = isNCHW ? gradO->sizeAt(1) : gradO->sizeAt(3); // output channels
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const LongType oH = isNCHW ? gradO->sizeAt(2) : gradO->sizeAt(1); // output height
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const LongType oW = isNCHW ? gradO->sizeAt(3) : gradO->sizeAt(2); // output width
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NDArray *inputPermuted, *gradOPermuted, *gradIPermuted;
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if (!isNCHW) {
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std::vector<sd::LongType> permute = {0, 3, 1, 2};
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inputPermuted = input->permute(permute, false, false); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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gradOPermuted = gradO->permute(permute, false, false); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
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gradIPermuted = gradI->permute(permute, false, false); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
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} else {
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inputPermuted = input;
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gradOPermuted = gradO;
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gradIPermuted = gradI;
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}
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std::vector<sd::LongType> gradOShape = {oC, bS * oH * oW};
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// Reshape gradO to 2D: [oC, bS * oH * oW]
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NDArray *gradO2d = gradOPermuted->reshape(gradOPermuted->ordering(), gradOShape,false);
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// Perform im2col
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NDArray* columns;
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if (block.hasIntermediateResults()) {
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columns = block.intermediateResult(0);
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if (columns->rankOf() < 6) {
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columns->reshapei({bS, iC, kH, kW, oH, oW});
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}
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} else {
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std::vector<sd::LongType> colShape = {bS, iC, kH, kW, oH, oW};
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columns = new NDArray(inputPermuted->ordering(), colShape, inputPermuted->dataType(), inputPermuted->getContext());
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auto ctx = block.launchContext();
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NDArray *zeroVal = NDArrayFactory::create<double>(0., inputPermuted->getContext());
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helpers::im2col(*ctx, *inputPermuted, *columns, kH, kW, sH, sW, pH, pW, dH, dW,
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*zeroVal);
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delete zeroVal;
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}
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// Calculate gradW
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if (gradW) {
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std::vector<sd::LongType> colShape = {bS * oH * oW, iC * kH * kW};
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std::vector<sd::LongType> wShape = {oC, iC * kH * kW};
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NDArray *columns2d = columns->reshape('c',colShape,false);
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std::vector<sd::LongType> permute = {1,0};
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NDArray *gradW2d = gradW->reshape('f', wShape, false)->permute(permute, false, false);
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MmulHelper::matmul( columns2d,gradO2d, gradW2d, true, true, 1.0, 0.0, gradW2d);
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gradW->assign(gradW2d);
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delete columns2d;
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}
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// Calculate gradB
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if (gradB) {
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std::vector<LongType> axes = {1}; // Sum over bS, oH, oW
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gradO2d->reduceAlongDimension(reduce::Sum, gradB, &axes);
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}
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// Calculate gradI
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NDArray *weights2d;
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if (wFormat == 0) {
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std::vector<sd::LongType> perm = {3,2,1,0};
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std::vector<sd::LongType> wShape = {iC * kH * kW,oC};
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weights2d = weights->permute(perm, false, false)->reshape('f', wShape);
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} else if (wFormat == 1) {
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std::vector<sd::LongType> wShape2 = {iC * kH * kW,oC};
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weights2d = weights->reshape('f', wShape2);
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} else {
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std::vector<sd::LongType> wPermute = {0,2,3,1};
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std::vector<sd::LongType> weights2dShape = {iC * kH * kW,oC};
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weights2d = weights->permute(wPermute, false, false)->reshape('f', weights2dShape);
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}
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std::vector<sd::LongType> columns2dShape = {iC * kH * kW, bS * oH * oW};
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NDArray columns2d('c', columns2dShape, columns->dataType(), columns->getContext());
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MmulHelper::matmul(weights2d, gradO2d, &columns2d, false, false, 1.0, 0.0);
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delete weights2d;
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//Calculate epsilonNext by doing im2col reduction.
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//Current col2im implementation expects input with order: [miniBatch,channels,kH,kW,outH,outW]
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//currently have [kH,kW,inDepth,outW,outH,miniBatch] -> permute first
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auto eps6d = columns2d.newShapeNoCopy({kH, kW,iC, oW, oH, bS }, 'f');
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std::vector<sd::LongType> epsPermute = {5,2,1,0,4,3};
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auto permuted = eps6d->permute(epsPermute, false, false);
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// Perform col2im
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auto ctx = block.launchContext();
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helpers::col2im(*ctx, permuted, gradIPermuted, sH, sW, pH, pW, iH, iW, dH, dW);
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// Handle NHWC format if necessary
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if (!isNCHW) {
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std::vector<sd::LongType> perm = {0,2,3,1};
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gradI->assign(gradIPermuted->permute(perm, false, false)); // [bS, iC, iH, iW] -> [bS, iH, iW, iC]
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}
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delete gradO2d;
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// Clean up
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if (!isNCHW) {
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delete inputPermuted;
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delete gradOPermuted;
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delete gradIPermuted;
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}
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if (!block.hasIntermediateResults()) {
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delete columns;
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}
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}
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void ConvolutionUtils::conv2dBP(sd::graph::Context& block, NDArray* input, NDArray* weights,
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NDArray* bias, NDArray* gradO, NDArray* gradI, NDArray* gradW,
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NDArray* gradB, const LongType kH, const LongType kW, const LongType sH, const LongType sW, LongType pH, LongType pW,
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const LongType dH, const LongType dW, const int paddingMode, const int isNCHW,
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const int wFormat) {
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BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_,
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(block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW,
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paddingMode, isNCHW, wFormat),
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SD_FLOAT_TYPES);
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}
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} // namespace ops
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} // namespace sd
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#endif
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