wehub-resource-sync
534 lines
27 KiB
Plaintext
534 lines
27 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 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 depthwiseConv2dCUDNN(const LaunchContext* context, NDArray* input, NDArray* weights,
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NDArray* bias, NDArray* output, const LongType kH, const LongType kW, const LongType sH,
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const LongType sW, const LongType pH, const LongType pW, const LongType dH, const LongType dW,
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const LongType paddingMode, const bool isNCHW) {
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// cudnn supports only following case: mC = 1, oC = iC (groupCount == iC)
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// input [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc
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// weights [iC, mC, kH, kW]
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// bias [oC], may be nullptr
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// output [bS, oC, oH, oW] nchw or [bS, oH, oW, oC] nhwc
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// oC = iC*mC
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LongType bS, iC, iH, iW, mC, 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, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH,
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indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(1);
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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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PointersManager manager(context, __func__);
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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()), CUDNN_TENSOR_NCHW, iC, mC, 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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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnSetConvolutionGroupCount),
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cudnnSetConvolutionGroupCount(
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conv, iC)); // set number of groups (depthwise mode) in description of convolution, groupCount == iC
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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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// CHECK_CUDNN_FAILURE_MSG(STRINGIZE(cudnnGetConvolutionForwardAlgorithm), cudnnGetConvolutionForwardAlgorithm(
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// *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("depthwiseConv2dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", 0);
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algo = algoPerf.algo;
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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.set( format, cudnnDataType(bias->dataType()), 1, isNCHW ? bias->lengthOf() : 1, 1, isNCHW ? 1:
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// bias->lengthOf());
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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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}
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//////////////////////////////////////////////////////////////////////////
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static void depthwiseConv2dBpCUDNN(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) {
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// cudnn supports only following case: mC = 1, oC = iC (groupCount == iC)
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// input, gradI [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc
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// weights, gradW [iC, mC, kH, kW]
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// gradB [oC], may be nullptr
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// gradO [bS, oC, oH, oW] nchw or [bS, oH, oW, oC] nhwc
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// oC = iC*mC
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LongType bS, iC, iH, iW, mC, 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, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH,
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indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(1);
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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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PointersManager manager(context, __func__);
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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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// gradO descriptor
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CudnnTensor dz;
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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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// gradI descriptor
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CudnnTensor dx;
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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()), CUDNN_TENSOR_NCHW, iC, mC, 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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CHECK_CUDNN_FAILURE_MSG(
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STRINGIZE(cudnnSetConvolutionGroupCount),
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cudnnSetConvolutionGroupCount(
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conv, iC)); // set number of groups (depthwise mode) in description of convolution, groupCount == iC
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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(STRINGIZE(cudnnGetConvolutionBackwardFilterAlgorithm),
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// cudnnGetConvolutionBackwardFilterAlgorithm( *handle, x, dz, conv, dw, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
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// 0, &algoGradW));
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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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"depthwiseConv2dBpCUDNN: 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(
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"depthwiseConv2dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed as the count is 0 ", 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.set( format, cudnnDataType(gradB->dataType()), 1, isNCHW ? gradB->lengthOf() : 1, 1, isNCHW ? 1:
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// gradB->lengthOf());
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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(depthwise_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, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] = iC*mC
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auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
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REQUIRE_TRUE(input->rankOf() == 4, 0,
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"DEPTHWISECONV2D CUDNN OP: rank of input array must be equal to 4, but got %i instead !",
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input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 4, 0,
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"DEPTHWISECONV2D 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 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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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, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
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LongType bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC =
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// iC*mC), output channels, output height/width
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LongType indIOioC, indIiH, indWmC, 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, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(indWmC); // channels multiplier
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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, mC);
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"DEPTHWISECONV2D 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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REQUIRE_TRUE(
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output->sizeAt(indIOioC) == iC * mC, 0,
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"DEPTHWISECONV2D CUDNN OP: the output_channels must be equal to input_channels * channels_multiplier = %i !",
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iC * mC);
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if (bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"DEPTHWISECONV2D 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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std::vector<LongType> wPermut; // cudnn support format {oC, iC/groupCount, kH, kW} only, mC = 1, oC = iC (groupCount ==
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// iC) that is {iC, mC, kH, kW} in our case
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if (0 == wFormat)
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wPermut = {2, 3, 0, 1}; // kH, kW, iC, mC -> iC, mC, kH, kW
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else if (1 == wFormat)
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wPermut = {1, 0, 2, 3}; // mC, iC, kH, kW -> iC, mC, kH, kW
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else
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wPermut = {3, 0, 1, 2}; // mC, kH, kW, iC -> iC, mC, kH, kW
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std::vector<sd::LongType > perm = {iC, mC, kH, kW};
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NDArray * uNewWeights = new NDArray(weights->ordering(),perm, weights->dataType(), weights->getContext());
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NDArray assign = weights->permute(wPermut,false,false);
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uNewWeights->assign(&assign);
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std::unique_ptr<NDArray> tmpInput = {};
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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));
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if (tmpInput) input = tmpInput.get();
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}
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depthwiseConv2dCUDNN(block.launchContext(), input, uNewWeights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW,
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paddingMode, isNCHW);
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return Status::OK;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_CHECK(depthwise_conv2d, 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, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] = iC*mC
|
|
|
|
const int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME, 2-CAUSAL
|
|
const int wFormat = block.getIArguments()->size() > 10
|
|
? INT_ARG(10)
|
|
: 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
|
|
|
|
Requirements req("CUDNN DEPTHWISE_CONV2d OP");
|
|
req.expectNotEq(makeInfoVariable(paddingMode, "paddingMode"), 2) &&
|
|
req.expectEq(makeInfoVariable(weights->sizeAt(0 == wFormat ? 3 : 0), "weights#mC"), 1) &&
|
|
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.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(depthwise_conv2d_bp, ENGINE_CUDA) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0,
|
|
"DEPTHWISECONV2D_BP CUDNN OP: rank of input array must be equal to 4, but got %i instead !",
|
|
input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 4, 0,
|
|
"DEPTHWISECONV2D_BP CUDNN OP: rank of weights array must be equal to 4, but got %i instead !",
|
|
weights->rankOf());
|
|
REQUIRE_TRUE(gradO->rankOf() == 4, 0,
|
|
"DEPTHWISECONV2D_BP CUDNN OP: rank of output gradients (next epsilon) array must be equal to 4, but got "
|
|
"%i instead !",
|
|
gradO->rankOf());
|
|
|
|
LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) height
|
|
LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) width
|
|
LongType sH = INT_ARG(2); // strides height
|
|
LongType sW = INT_ARG(3); // strides width
|
|
LongType pH = INT_ARG(4); // paddings height
|
|
LongType pW = INT_ARG(5); // paddings width
|
|
LongType dH = INT_ARG(6); // dilations height
|
|
LongType dW = INT_ARG(7); // dilations width
|
|
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
|
|
int wFormat = block.getIArguments()->size() > 10
|
|
? INT_ARG(10)
|
|
: 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
|
|
|
|
LongType bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC =
|
|
// iC*mC), output channels, output height/width
|
|
LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWmC, indWkH, indOoH);
|
|
mC = weights->sizeAt(indWmC); // channels multiplier
|
|
|
|
LongType trueoH, trueoW; // correct 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, mC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
|
|
"DEPTHWISECONV2D_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,
|
|
"DEPTHWISECONV2D_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,
|
|
"DEPTHWISECONV2D_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::vector<LongType> wPermut, gradWPermut; // cudnn support format {oC, iC/groupCount, kH, kW} only, mC = 1, oC = iC
|
|
// (groupCount == iC) that is {iC, mC, kH, kW}
|
|
if (0 == wFormat) {
|
|
wPermut = {2, 3, 0, 1}; // kH, kW, iC, mC -> iC, mC, kH, kW
|
|
gradWPermut = {2, 3, 0, 1}; // iC, mC, kH, kW -> kH, kW, iC, mC
|
|
} else if (1 == wFormat) {
|
|
wPermut = {1, 0, 2, 3}; // mC, iC, kH, kW -> iC, mC, kH, kW
|
|
gradWPermut = {1, 0, 2, 3}; // iC, mC, kH, kW -> mC, iC, kH, kW
|
|
} else {
|
|
wPermut = {3, 0, 1, 2}; // mC, kH, kW, iC -> iC, mC, kH, kW
|
|
gradWPermut = {1, 2, 3, 0}; // iC, mC, kH, kW -> mC, kH, kW, iC
|
|
}
|
|
|
|
std::unique_ptr<NDArray> tmpGradI = {}, tmpInput = {};
|
|
std::vector<sd::LongType> shape = {iC, mC, kH, kW};
|
|
NDArray * uNewGradW =
|
|
new NDArray(gradW->ordering(),shape, gradW->dataType(), gradW->getContext());
|
|
NDArray * uNewWeights =
|
|
new NDArray(weights->ordering(),shape, weights->dataType(), weights->getContext());
|
|
|
|
NDArray assign = weights->permute(wPermut,false,false);
|
|
uNewWeights->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();
|
|
}
|
|
depthwiseConv2dBpCUDNN(block.launchContext(), newInput, uNewWeights, gradO, newGradI, uNewGradW, gradB,
|
|
kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
|
|
|
|
uNewGradW->permutei(gradWPermut,false,false);
|
|
gradW->assign(uNewGradW);
|
|
|
|
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(depthwise_conv2d_bp, ENGINE_CUDA) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), 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
|
|
const int wFormat = block.getIArguments()->size() > 10
|
|
? INT_ARG(10)
|
|
: 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
|
|
|
|
Requirements req("CUDNN DEPTHWISE_CONV2d_BP OP");
|
|
const auto inType = input->dataType();
|
|
const auto wType = weights->dataType();
|
|
const auto gType = gradO->dataType();
|
|
req.expectNotEq(makeInfoVariable(paddingMode, "paddingMode"), 2) &&
|
|
req.expectTrue(makeInfoVariable(isNCHW, "isNCHW")) &&
|
|
req.expectEq(makeInfoVariable(weights->sizeAt(0 == wFormat ? 3 : 0), "weights#mC"), 1) &&
|
|
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
|