wehub-resource-sync
530 lines
26 KiB
Plaintext
530 lines
26 KiB
Plaintext
/* ******************************************************************************
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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 (iuriish@yahoo.com)
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//
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#include <ops/declarable/helpers/convolutions.h>
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#include "cudnnUtils.h"
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namespace sd {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////////
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static void conv2dCUDNN(const LaunchContext* context, NDArray* input, NDArray* weights, NDArray* bias,
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NDArray* output, const int kH, const LongType kW, const LongType sH, const LongType sW, const LongType pH,
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const LongType pW, const LongType dH, const LongType dW, const int paddingMode, const bool isNCHW,
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const int wFormat) {
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// cudnn support only two formats for weights {oC,iC,kH,kW} and {oC,kH,kW,iC}
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LongType bS, iC, iH, iW, oC, oH,
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oW; // batch size, input channels, input height/width, output channels, output height/width;
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LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWiC, indWoC, indWkH, indOoH);
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auto handle = reinterpret_cast<cudnnHandle_t*>(context->getCuDnnHandle());
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CHECK_CUDNN_FAILURE_MSG(STRINGIZE(cudnnSetStream), cudnnSetStream(*handle, *context->getCudaStream()));
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cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
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cudnnTensorFormat_t formatW = 0 == wFormat ? format : (1 == wFormat ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC);
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// input descriptor
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CudnnTensor x;
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if (input->ordering() == 'c')
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x.set4D(format, cudnnDataType(input->dataType()), bS, iC, iH, iW);
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else
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x.set4DEx(cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC),
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input->strideAt(indIiH), input->strideAt(indIiH + 1));
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// weights descriptor
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FilterDesc w;
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w.set4D(cudnnDataType(weights->dataType()), formatW, oC, iC, kH, kW);
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// output descriptor
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CudnnTensor z;
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if (output->ordering() == 'c')
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z.set4D(format, cudnnDataType(output->dataType()), bS, oC, oH, oW);
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else
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z.set4DEx(cudnnDataType(output->dataType()), bS, oC, oH, oW, output->strideAt(0), output->strideAt(indIOioC),
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output->strideAt(indOoH), output->strideAt(indOoH + 1));
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// description of convolution
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ConvolutionDesc conv;
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conv.set2D(pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(output->dataType()));
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// algorithm description
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cudnnConvolutionFwdAlgo_t algo;
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cudnnConvolutionFwdAlgoPerf_t algoPerf;
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int count = 0;
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// err = cudnnGetConvolutionForwardAlgorithm(*handle, x, w, conv, z, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo);
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CHECK_CUDNN_FAILURE_MSG(STRINGIZE(cudnnFindConvolutionForwardAlgorithm),
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cudnnFindConvolutionForwardAlgorithm(*handle, x, w, conv, z, 1, &count, &algoPerf));
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if (count == 0)
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throw cuda_exception::build("conv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed as the count is 0", 0);
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algo = algoPerf.algo;
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PointersManager manager(context, __func__);
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// allocate auxiliary device memory, abbreviation ws means workspace
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size_t wsSize;
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CHECK_CUDNN_FAILURE_MSG(STRINGIZE(cudnnGetConvolutionForwardWorkspaceSize),
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cudnnGetConvolutionForwardWorkspaceSize(*handle, x, w, conv, z, algo, &wsSize));
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void* wsData = manager.allocateDevMem(wsSize);
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// provide scaling parameters
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const float alpha32(1), beta32(0);
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const double alpha64(1), beta64(0);
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const void* alpha =
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output->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&alpha32) : reinterpret_cast<const void*>(&alpha64);
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const void* beta =
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output->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&beta32) : reinterpret_cast<const void*>(&beta64);
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NDArray::prepareSpecialUse({output}, {input, weights, bias});
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// run calculation
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnConvolutionForward),
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cudnnConvolutionForward(*handle, alpha, x, input->specialBuffer(), w, weights->specialBuffer(), conv, algo,
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wsData, wsSize, beta, z, output->specialBuffer()));
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// add bias if it is present
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if (bias != nullptr) {
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CudnnTensor b;
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b.set4D(CUDNN_TENSOR_NCHW, cudnnDataType(bias->dataType()), 1, oC, 1, 1);
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CHECK_CUDNN_FAILURE_MSG(STRINGIZE(cudnnAddTensor), cudnnAddTensor(*handle, alpha, b, bias->specialBuffer(), alpha,
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z, output->specialBuffer()));
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}
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NDArray::registerSpecialUse({output}, {input, weights, bias});
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}
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//////////////////////////////////////////////////////////////////////////
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static void conv2dBpCUDNN(const LaunchContext* context, NDArray* input, NDArray* weights,
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NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const LongType kH,
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const LongType kW, const LongType sH, const LongType sW, const LongType pH, const LongType pW, const LongType dH,
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const LongType dW, const LongType paddingMode, const bool isNCHW, const int wFormat) {
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LongType bS, iC, iH, iW, oC, oH,
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oW; // batch size, input channels, input height/width, output channels, output height/width;
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LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWiC, indWoC, indWkH, indOoH);
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auto handle = reinterpret_cast<cudnnHandle_t*>(context->getCuDnnHandle());
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CHECK_CUDNN_FAILURE_MSG(STRINGIZE(cudnnSetStream), cudnnSetStream(*handle, *context->getCudaStream()));
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cudnnTensorFormat_t format = isNCHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
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cudnnTensorFormat_t formatW = 0 == wFormat ? format : (1 == wFormat ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC);
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PointersManager manager(context, __func__);
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// input descriptor, gradO descriptor, gradI descriptor
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CudnnTensor x, dz, dx;
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if (input->ordering() == 'c')
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x.set4D(format, cudnnDataType(input->dataType()), bS, iC, iH, iW);
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else
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x.set4DEx(cudnnDataType(input->dataType()), bS, iC, iH, iW, input->strideAt(0), input->strideAt(indIOioC),
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input->strideAt(indIiH), input->strideAt(indIiH + 1));
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if (gradO->ordering() == 'c')
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dz.set4D(format, cudnnDataType(gradO->dataType()), bS, oC, oH, oW);
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else
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dz.set4DEx(cudnnDataType(gradO->dataType()), bS, oC, oH, oW, gradO->strideAt(0), gradO->strideAt(indIOioC),
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gradO->strideAt(indOoH), gradO->strideAt(indOoH + 1));
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if (gradI->ordering() == 'c')
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dx.set4D(format, cudnnDataType(gradI->dataType()), bS, iC, iH, iW);
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else
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dx.set4DEx(cudnnDataType(gradI->dataType()), bS, iC, iH, iW, gradI->strideAt(0), gradI->strideAt(indIOioC),
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gradI->strideAt(indIiH), gradI->strideAt(indIiH + 1));
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// gradW descriptor
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FilterDesc dw;
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dw.set4D(cudnnDataType(gradW->dataType()), formatW, oC, iC, kH, kW);
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// description of convolution
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ConvolutionDesc conv;
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conv.set2D(pH, pW, sH, sW, dH, dW, CUDNN_CROSS_CORRELATION, cudnnDataType(gradO->dataType()));
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// gradW algorithm description
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cudnnConvolutionBwdFilterAlgo_t algoGradW;
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cudnnConvolutionBwdFilterAlgoPerf_t algoGradWPerf;
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int count = 0;
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnFindConvolutionBackwardFilterAlgorithm),
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cudnnFindConvolutionBackwardFilterAlgorithm(*handle, x, dz, conv, dw, 1, &count, &algoGradWPerf));
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if (count == 0)
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throw cuda_exception::build(
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"conv2dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed as the count is 0", 0);
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algoGradW = algoGradWPerf.algo;
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// gradI algorithm description
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cudnnConvolutionBwdDataAlgo_t algoGradI;
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cudnnConvolutionBwdDataAlgoPerf_t algoGradIPerf;
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnFindConvolutionBackwardDataAlgorithm),
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cudnnFindConvolutionBackwardDataAlgorithm(*handle, dw, dz, conv, x, 1, &count, &algoGradIPerf));
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if (count == 0)
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throw cuda_exception::build("conv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed as the count is 0",
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0);
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algoGradI = algoGradIPerf.algo;
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// allocate auxiliary device memory for gradW calculation, abbreviation ws means workspace
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size_t wsGradWSize;
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnGetConvolutionBackwardFilterWorkspaceSize),
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cudnnGetConvolutionBackwardFilterWorkspaceSize(*handle, x, dz, conv, dw, algoGradW, &wsGradWSize));
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void* wsGradWData = manager.allocateDevMem(wsGradWSize);
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// allocate auxiliary device memory for gradI calculation, abbreviation ws means workspace
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size_t wsGradISize;
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnGetConvolutionBackwardDataWorkspaceSize),
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cudnnGetConvolutionBackwardDataWorkspaceSize(*handle, dw, dz, conv, dx, algoGradI, &wsGradISize));
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void* wsGradIData = manager.allocateDevMem(wsGradISize);
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// provide scaling parameters
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const float alpha32(1), beta32(0);
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const double alpha64(1), beta64(0);
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const void* alpha =
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gradO->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&alpha32) : reinterpret_cast<const void*>(&alpha64);
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const void* beta =
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gradO->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&beta32) : reinterpret_cast<const void*>(&beta64);
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NDArray::prepareSpecialUse({gradI, gradW, gradB}, {input, weights, gradO});
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// run calculation for gradB (if not nullptr)
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if (gradB != nullptr) {
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CudnnTensor db;
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db.set4D(CUDNN_TENSOR_NCHW, cudnnDataType(gradB->dataType()), 1, oC, 1, 1);
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnConvolutionBackwardBias),
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cudnnConvolutionBackwardBias(*handle, alpha, dz, gradO->specialBuffer(), beta, db, gradB->specialBuffer()));
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}
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// run calculation for gradW
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnConvolutionBackwardFilter),
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cudnnConvolutionBackwardFilter(*handle, alpha, x, input->specialBuffer(), dz, gradO->specialBuffer(), conv,
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algoGradW, wsGradWData, wsGradWSize, beta, dw, gradW->specialBuffer()));
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// run calculation for gradI
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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnConvolutionBackwardData),
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cudnnConvolutionBackwardData(*handle, alpha, dw, weights->specialBuffer(), dz, gradO->specialBuffer(), conv,
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algoGradI, wsGradIData, wsGradISize, beta, dx, gradI->specialBuffer()));
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NDArray::registerSpecialUse({gradI, gradW, gradB}, {input, weights, gradO});
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(conv2d, ENGINE_CUDA) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int wFormat = block.getIArguments()->size() > 10
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? INT_ARG(10)
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: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
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LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) height
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LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) width
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"CUSTOM CONV2D CUDNN OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 4, 0,
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"CUSTOM CONV2D CUDNN OP: rank of weights array must be equal to 4, but got %i instead !",
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weights->rankOf());
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LongType bS, iC, iH, iW, oC, oH,
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oW; // batch size, input channels, input height/width, output channels, output height/width;
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LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
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indIiH, indWiC, indWoC, indWkH, indOoH);
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
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std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM CONV2D CUDNN 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 CONV2D CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got "
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"%i, %i instead !",
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oC, bias->rankOf(), bias->lengthOf());
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REQUIRE_TRUE((bias->rankOf() == 1 && bias->strideAt(0) == 1) ||
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(bias->rankOf() == 2 && bias->sizeAt(0) == 1 && bias->strideAt(1) == 1) ||
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(bias->rankOf() == 2 && bias->sizeAt(1) == 1 && bias->strideAt(0) == 1),
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0, "CUSTOM CONV2D CUDNN OP: bias array should be contiguous in memory !");
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}
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std::unique_ptr<NDArray> tmpWeight = {}, tmpInput = {};
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NDArray* newWeights = weights; // cudnn support only two formats {oC,iC,kH,kW} and {oC,kH,kW,iC}
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if (0 == wFormat) {
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// Create named vectors as lvalues
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std::vector<LongType> nchwShape = {oC, iC, kH, kW};
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std::vector<LongType> nhwcShape = {oC, kH, kW, iC};
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// Use the appropriate one for the weight reset
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tmpWeight.reset(
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new NDArray(weights->ordering(),
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isNCHW ? nchwShape : nhwcShape,
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weights->dataType(), weights->getContext()));
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newWeights = tmpWeight.get();
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// Create named vectors as lvalues
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std::vector<LongType> nchwDims = {3, 2, 0, 1};
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std::vector<LongType> nhwcDims = {3, 0, 1, 2};
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// Use the appropriate one in the call
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NDArray assign = weights->permute(
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isNCHW ? nchwDims : nhwcDims,
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true, // copyToNewBuff
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true); // resetStrides
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newWeights->assign(&assign); // (kH, kW, iC, oC --> oC, iC, kH, kW) or (kH, kW, iC, oC --> oC, kH, kW, iC)
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}
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if (paddingMode == 1) { // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
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auto ret = checkConv2dCUDNNPadAsymmetric(input, nullptr, iH, iW, oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW);
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tmpInput = std::move(std::get<0>(ret)); // prolong life
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if (tmpInput) input = tmpInput.get();
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}
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conv2dCUDNN(block.launchContext(), input, newWeights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode,
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isNCHW, wFormat);
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return Status::OK;
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(conv2d, ENGINE_CUDA) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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const int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME, 2-CAUSAL
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const bool badInputType = input->dataType() != DOUBLE && input->dataType() != FLOAT32 &&
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input->dataType() != HALF;
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const bool badWeightsType = weights->dataType() != DOUBLE && weights->dataType() != FLOAT32 &&
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weights->dataType() != HALF;
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const bool badBiasType = bias == nullptr
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? false
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: (bias->dataType() != DOUBLE && bias->dataType() != FLOAT32 &&
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bias->dataType() != HALF);
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return paddingMode != 2 && !badInputType && !badWeightsType && !badBiasType;
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Requirements req("CUDNN CONV2d OP");
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req.expectNotEq(makeInfoVariable(paddingMode, "paddingMode"), 2) &&
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req.expectIn(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0),
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{HALF, FLOAT32, DOUBLE}) &&
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req.expectIn(makeInfoVariable(weights->dataType(), TYPE_MSG_INPUT1),
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{HALF, FLOAT32, DOUBLE});
|
|
if (bias) {
|
|
req.expectIn(makeInfoVariable(bias->dataType(), TYPE_MSG_INPUT_ "#bias"),
|
|
{HALF, FLOAT32, DOUBLE});
|
|
}
|
|
req.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(conv2d_bp, ENGINE_CUDA) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, 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
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? INPUT_VARIABLE(3)
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: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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|
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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|
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LongType kH = INT_ARG(0); // filter(kernel) height
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LongType kW = INT_ARG(1); // filter(kernel) width
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LongType sH = INT_ARG(2); // strides height
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LongType sW = INT_ARG(3); // strides width
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LongType pH = INT_ARG(4); // paddings height
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LongType pW = INT_ARG(5); // paddings width
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LongType dH = INT_ARG(6); // dilations height
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LongType dW = INT_ARG(7); // dilations width
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int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
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int wFormat = block.getIArguments()->size() > 10
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? INT_ARG(10)
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: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0,
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|
"CUSTOM CONV2D_BP CUDNN OP: rank of input array must be equal to 4, but got %i instead !",
|
|
input->rankOf());
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|
REQUIRE_TRUE(weights->rankOf() == 4, 0,
|
|
"CUSTOM CONV2D_BP CUDNN OP: rank of weights array must be equal to 4, but got %i instead !",
|
|
weights->rankOf());
|
|
REQUIRE_TRUE(gradO->rankOf() == 4, 0,
|
|
"CUSTOM CONV2D_BP CUDNN OP: rank of output's gradients (next epsilon) array must be equal to 4, but got "
|
|
"%i instead !",
|
|
gradO->rankOf());
|
|
|
|
LongType bS, iC, iH, iW, oC, oH,
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|
oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
LongType trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
|
|
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
std::vector<LongType> expectedGradOShape =
|
|
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
|
|
std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
|
|
"CUSTOM CONV2D_BP CUDNN OP: wrong shape of output gradients (next epsilon) array, expected is %s, but "
|
|
"got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CUSTOM CONV2D_BP CUDNN 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 CONV2D_BP CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got "
|
|
"%i, %i instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
std::unique_ptr<NDArray> tmpGradI = {}, tmpInput = {}, tmpWeights = {}, tmpGradW = {};
|
|
NDArray *newWeights = weights, *newGradW = gradW; // cudnn support only two formats {oC,iC,kH,kW} and {oC,kH,kW,iC}
|
|
if (0 == wFormat) {
|
|
// Create named vectors as lvalues
|
|
std::vector<LongType> nchwGradShape = {oC, iC, kH, kW};
|
|
std::vector<LongType> nhwcGradShape = {oC, kH, kW, iC};
|
|
|
|
// Use the appropriate one for the gradW reset
|
|
tmpGradW.reset(
|
|
new NDArray(gradW->ordering(),
|
|
isNCHW ? nchwGradShape : nhwcGradShape,
|
|
gradW->dataType(), gradW->getContext()));
|
|
|
|
// Use the same vectors for the weights reset
|
|
tmpWeights.reset(
|
|
new NDArray(weights->ordering(),
|
|
isNCHW ? nchwGradShape : nhwcGradShape,
|
|
weights->dataType(), weights->getContext()));
|
|
newGradW = tmpGradW.get();
|
|
newWeights = tmpWeights.get();
|
|
// Create named vectors as lvalues
|
|
std::vector<LongType> nchwDims = {3, 2, 0, 1};
|
|
std::vector<LongType> nhwcDims = {3, 0, 1, 2};
|
|
NDArray assign = weights->permute(
|
|
isNCHW ? nchwDims : nhwcDims,
|
|
true, // copyToNewBuff
|
|
true);
|
|
// Use the appropriate one in the call
|
|
newWeights->assign(&assign);
|
|
}
|
|
|
|
NDArray* newInput = input;
|
|
NDArray* newGradI = gradI;
|
|
|
|
if (paddingMode == 1) { // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
|
|
auto ret = checkConv2dCUDNNPadAsymmetric(input, gradI, iH, iW, oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW);
|
|
tmpInput = std::move(std::get<0>(ret));
|
|
tmpGradI = std::move(std::get<1>(ret));
|
|
if (tmpInput) newInput = tmpInput.get();
|
|
if (tmpGradI) newGradI = tmpGradI.get();
|
|
}
|
|
conv2dBpCUDNN(block.launchContext(), newInput, newWeights, gradO, newGradI, newGradW, gradB, kH, kW, sH, sW, pH, pW,
|
|
dH, dW, paddingMode, isNCHW, wFormat);
|
|
|
|
if (0 == wFormat) {
|
|
// Create named vectors as lvalues
|
|
std::vector<LongType> nchwPermute = {2, 3, 1, 0};
|
|
std::vector<LongType> nhwcPermute = {1, 2, 3, 0};
|
|
|
|
// Use the appropriate one in the permutei call
|
|
newGradW->permutei(
|
|
isNCHW ? nchwPermute : nhwcPermute,false,false); // (oC, iC, kH, kW --> kH, kW, iC, oC) or (oC, kH, kW, iC --> kH, kW, iC, oC) iC, oC)
|
|
gradW->assign(newGradW);
|
|
}
|
|
|
|
if (newInput != input) {
|
|
if (isNCHW) {
|
|
NDArray assign = (*newGradI)({0, 0, 0, 0, 0, gradI->sizeAt(2), 0, gradI->sizeAt(3)});
|
|
gradI->assign(&assign);
|
|
} else {
|
|
NDArray assign = (*newGradI)({0, 0, 0, gradI->sizeAt(1), 0, gradI->sizeAt(2), 0, 0});
|
|
gradI->assign(&assign);
|
|
}
|
|
}
|
|
|
|
return Status::OK;
|
|
}
|
|
|
|
PLATFORM_CHECK(conv2d_bp, ENGINE_CUDA) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
|
|
|
const int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME, 2-CAUSAL
|
|
const int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
|
|
|
Requirements req("CUDNN CONV2d_BP OP");
|
|
req.expectNotEq(makeInfoVariable(paddingMode, "paddingMode"), 2) &&
|
|
req.expectTrue(makeInfoVariable(isNCHW, "isNCHW")) &&
|
|
req.expectIn(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0),
|
|
{HALF, FLOAT32, DOUBLE}) &&
|
|
req.expectIn(makeInfoVariable(weights->dataType(), TYPE_MSG_INPUT1),
|
|
{HALF, FLOAT32, DOUBLE});
|
|
if (bias) {
|
|
req.expectIn(makeInfoVariable(bias->dataType(), TYPE_MSG_INPUT_ "#bias"),
|
|
{HALF, FLOAT32, DOUBLE}) &&
|
|
req.expectIn(makeInfoVariable(gradO->dataType(), TYPE_MSG_INPUT3),
|
|
{HALF, FLOAT32, DOUBLE});
|
|
} else {
|
|
req.expectIn(makeInfoVariable(gradO->dataType(), TYPE_MSG_INPUT2),
|
|
{HALF, FLOAT32, DOUBLE});
|
|
}
|
|
req.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
} // namespace platforms
|
|
} // namespace ops
|
|
} // namespace sd
|