wehub-resource-sync
544 lines
30 KiB
Plaintext
544 lines
30 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 conv3dCUDNN(const LaunchContext* context, NDArray* input, NDArray* weights, NDArray* bias,
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NDArray* output, const LongType kD, const LongType kH, const LongType kW, const LongType sD, const LongType sH,
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const LongType sW, const LongType pD, const LongType pH, const LongType pW, const LongType dD, const LongType dH,
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const LongType dW, const int paddingMode, const bool isNCDHW, const int wFormat) {
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// cudnn support only one format for weights {oC,iC,kD,kH,kW}
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const int numDims = 5;
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LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWiC, indWoC, indWkD);
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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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const std::vector<int> pads = {static_cast<int>(pD), static_cast<int>(pH), static_cast<int>(pW)};
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const std::vector<int> filtStrides = {static_cast<int>(sD), static_cast<int>(sH), static_cast<int>(sW)};
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const std::vector<int> dilations = {static_cast<int>(dD), static_cast<int>(dH), static_cast<int>(dW)};
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const std::vector<int> xShape = {static_cast<int>(bS), static_cast<int>(iC), static_cast<int>(iD), static_cast<int>(iH), static_cast<int>(iW)};
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const std::vector<int> zShape = {static_cast<int>(bS), static_cast<int>(oC), static_cast<int>(oD), static_cast<int>(oH), static_cast<int>(oW)};
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const std::vector<int> wShape = {static_cast<int>(oC), static_cast<int>(iC), static_cast<int>(kD), static_cast<int>(kH), static_cast<int>(kW)};
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const std::vector<int> bShape = {1, static_cast<int>(oC), 1, 1, 1};
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const std::vector<int> xStrides = {static_cast<int>(input->strideAt(0)), static_cast<int>(input->strideAt(1)), static_cast<int>(input->strideAt(2)),
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static_cast<int>(input->strideAt(3)), static_cast<int>(input->strideAt(4))};
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const std::vector<int> zStrides = {static_cast<int>(output->strideAt(0)), static_cast<int>(output->strideAt(1)), static_cast<int>(output->strideAt(2)),
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static_cast<int>(output->strideAt(3)), static_cast<int>(output->strideAt(4))};
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cudnnTensorFormat_t format = isNCDHW ? 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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x.set(cudnnDataType(input->dataType()), numDims, xShape.data(), xStrides.data());
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// weights descriptor
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FilterDesc w;
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w.set(cudnnDataType(weights->dataType()), CUDNN_TENSOR_NCHW, numDims, wShape.data());
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// output descriptor
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CudnnTensor z;
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z.set(cudnnDataType(output->dataType()), numDims, zShape.data(), zStrides.data());
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// description of convolution
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ConvolutionDesc conv;
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conv.set(numDims - 2, pads.data(), filtStrides.data(), dilations.data(), CUDNN_CROSS_CORRELATION,
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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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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("conv3dCUDNN: cudnnGetConvolutionForwardAlgorithm failed as the count is 0", 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.setEx(/*format*/ CUDNN_TENSOR_NCHW, cudnnDataType(bias->dataType()), numDims, bShape.data());
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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 conv3dBpCUDNN(const LaunchContext* context, NDArray* input, NDArray* weights,
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NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kD,
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const LongType kH, const LongType kW, const LongType sD, const LongType sH, const LongType sW, const LongType pD,
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const LongType pH, const LongType pW, const LongType dD, const LongType dH, const LongType dW, const int paddingMode,
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const bool isNCDHW, const int wFormat) {
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// cudnn supports only two formats {oC,iC,kD,kH,kW} and {oC,kD,kH,kW,iC} for weights/gradW
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const int numDims = 5;
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LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWiC, indWoC, indWkD);
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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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const std::vector<int> pads = {static_cast<int>(pD), static_cast<int>(pH), static_cast<int>(pW)};
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const std::vector<int> filtStrides = {static_cast<int>(sD), static_cast<int>(sH), static_cast<int>(sW)};
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const std::vector<int> dilations = {static_cast<int>(dD), static_cast<int>(dH), static_cast<int>(dW)};
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const std::vector<int> xShape = {static_cast<int>(bS), static_cast<int>(iC), static_cast<int>(iD), static_cast<int>(iH), static_cast<int>(iW)};
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const std::vector<int> dzShape = {static_cast<int>(bS), static_cast<int>(oC), static_cast<int>(oD), static_cast<int>(oH), static_cast<int>(oW)};
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const std::vector<int> wShape = {static_cast<int>(oC), static_cast<int>(iC), static_cast<int>(kD), static_cast<int>(kH), static_cast<int>(kW)};
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const std::vector<int> dbShape = {1, static_cast<int>(isNCDHW ? oC : 1), 1, 1, (int)(isNCDHW ? 1 : oC)};
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const std::vector<int> xStrides = {static_cast<int>(input->strideAt(0)), static_cast<int>(input->strideAt(1)), static_cast<int>(input->strideAt(2)),
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static_cast<int>(input->strideAt(3)), static_cast<int>(input->strideAt(4))};
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const std::vector<int> dxStrides = {static_cast<int>(gradI->strideAt(0)), static_cast<int>(gradI->strideAt(1)), static_cast<int>(gradI->strideAt(2)),
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static_cast<int>(gradI->strideAt(3)), static_cast<int>(gradI->strideAt(4))};
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const std::vector<int> dzStrides = {static_cast<int>(gradO->strideAt(0)), static_cast<int>(gradO->strideAt(1)), static_cast<int>(gradO->strideAt(2)),
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static_cast<int>(gradO->strideAt(3)), static_cast<int>(gradO->strideAt(4))};
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cudnnTensorFormat_t format = isNCDHW ? 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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x.set(cudnnDataType(input->dataType()), numDims, xShape.data(), xStrides.data());
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dz.set(cudnnDataType(gradO->dataType()), numDims, dzShape.data(), dzStrides.data());
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dx.set(cudnnDataType(gradI->dataType()), numDims, xShape.data(), dxStrides.data());
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// gradW descriptor
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FilterDesc dw;
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dw.set(cudnnDataType(gradW->dataType()), formatW, numDims, wShape.data());
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// description of convolution
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ConvolutionDesc conv;
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conv.set(numDims - 2, pads.data(), filtStrides.data(), dilations.data(), CUDNN_CROSS_CORRELATION,
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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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"conv3dBpCUDNN: 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(STRINGIZE(cudnnGetConvolutionBackwardDataAlgorithm),
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// cudnnGetConvolutionBackwardDataAlgorithm( *handle, dw, dz, conv, x, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0,
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// &algoGradI));
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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("conv3dBpCUDNN: 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.setEx(format, cudnnDataType(gradB->dataType()), numDims, dbShape.data());
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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(conv3dnew, ENGINE_CUDA) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, 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, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
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REQUIRE_TRUE(input->rankOf() == 5, 0, "CONV3D CUDNN OP: rank of input array must be equal to 5, but got %i instead !",
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input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0,
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"CONV3D CUDNN OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
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LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) depth
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LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) height
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LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(2)); // filter(kernel) width
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LongType sD = INT_ARG(3); // strides depth
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LongType sH = INT_ARG(4); // strides height
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LongType sW = INT_ARG(5); // strides width
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LongType pD = INT_ARG(6); // paddings depth
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LongType pH = INT_ARG(7); // paddings height
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LongType pW = INT_ARG(8); // paddings width
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LongType dD = INT_ARG(9); // dilations depth
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LongType dH = INT_ARG(10); // dilations height
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LongType dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 0-SAME, 1-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int wFormat = block.getIArguments()->size() > 14
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? INT_ARG(14)
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: 0; // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]
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REQUIRE_TRUE(paddingMode < 2, 0,
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"CONV3D CUDNN OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWiC, indWoC, indWkD);
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
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std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CONV3D 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(
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bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
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"CONV3D CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
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oC, bias->rankOf(), bias->lengthOf());
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std::unique_ptr<NDArray> tmpWeight = {}, tmpInput = {};
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NDArray* newWeights = weights; // cudnn support only one format {oC,iC,kD,kH,kW}
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if (1 != wFormat) {
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// Create the tmpWeight object - this syntax is already valid
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std::vector<LongType> weightShape = {oC, iC, kD, kH, kW};
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// Use the vector for the NDArray constructor
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tmpWeight.reset(new NDArray(weights->ordering(), weightShape, weights->dataType(), weights->getContext()));
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newWeights = tmpWeight.get();
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// Create named vectors as lvalues for the permute call
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std::vector<LongType> format0Permute = {4, 3, 0, 1, 2};
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std::vector<LongType> format1Permute = {0, 4, 1, 2, 3};
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NDArray assign = weights->permute(
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0 == wFormat ? format0Permute : format1Permute,
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true, // copyToNewBuff
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true);
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// Use the appropriate one in the permute call
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newWeights->assign(&assign); // resetStrides
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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 = checkConv3dCUDNNPadAsymmetric(input, nullptr, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW,
|
|
dD, dH, dW, isNCDHW);
|
|
tmpInput = std::move(std::get<0>(ret)); // prolong life
|
|
if (tmpInput) input = tmpInput.get();
|
|
}
|
|
conv3dCUDNN(block.launchContext(), input, newWeights, bias, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW,
|
|
paddingMode, isNCDHW, wFormat);
|
|
|
|
return Status::OK;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_CHECK(conv3dnew, ENGINE_CUDA) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
int paddingMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
|
|
Requirements req("CUDNN CONV3d OP");
|
|
req.expectNotEq(makeInfoVariable(paddingMode, "paddingMode"), 2) &&
|
|
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(conv3dnew_bp, ENGINE_CUDA) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 5, 0,
|
|
"CONV3D_BP CUDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 5, 0,
|
|
"CONV3D_BP CUDNN OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
|
|
REQUIRE_TRUE(
|
|
gradO->rankOf() == 5, 0,
|
|
"CONV3D_BP CUDNN OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !",
|
|
gradO->rankOf());
|
|
|
|
LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(weights->sizeAt(0)); // filter(kernel) depth
|
|
LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(weights->sizeAt(1)); // filter(kernel) height
|
|
LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<LongType>(weights->sizeAt(2)); // filter(kernel) width
|
|
LongType sD = INT_ARG(3); // strides depth
|
|
LongType sH = INT_ARG(4); // strides height
|
|
LongType sW = INT_ARG(5); // strides width
|
|
LongType pD = INT_ARG(6); // paddings depth
|
|
LongType pH = INT_ARG(7); // paddings height
|
|
LongType pW = INT_ARG(8); // paddings width
|
|
LongType dD = INT_ARG(9); // dilations depth
|
|
LongType dH = INT_ARG(10); // dilations height
|
|
LongType dW = INT_ARG(11); // dilations width
|
|
int paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0-[kD, kH, kW, iC, oC], 1-[oC, iC, kD, kH, kW], 2-[oC, kD, kH, kW, iC]
|
|
|
|
LongType bS, iC, iD, iH, iW, oC, oD, oH,
|
|
oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
|
|
indIOioC, indIOioD, indWiC, indWoC, indWkD);
|
|
|
|
LongType trueoD, trueoH, trueoW; // true output depth/height/width
|
|
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
|
|
iW, paddingMode);
|
|
|
|
REQUIRE_TRUE(paddingMode < 2, 0,
|
|
"CONV3D_BP CUDNN OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
|
|
|
|
std::vector<LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
|
|
{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
|
|
std::vector<LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(
|
|
gradO->isSameShape(expectedGradOShape), 0,
|
|
"CONV3D_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(gradW->isSameShape(expectedWeightsShape), 0,
|
|
"CONV3D_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,
|
|
"CONV3D_BP CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i "
|
|
"instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW, paddingMode);
|
|
|
|
std::unique_ptr<NDArray> tmpGradI = {}, tmpInput = {}, tmpWeights = {}, tmpGradW = {};
|
|
NDArray *newWeights = weights,
|
|
*newGradW = gradW; // cudnn support only two formats {oC,iC,kD,kH,kW} and {oC,kD,kH,kW,iC}
|
|
if (0 == wFormat) {
|
|
// Create named vectors as lvalues for the NDArray constructor
|
|
std::vector<LongType> ncdhwGradShape = {oC, iC, kD, kH, kW};
|
|
std::vector<LongType> ndhwcGradShape = {oC, kD, kH, kW, iC};
|
|
|
|
// Use the appropriate one for the gradW reset
|
|
tmpGradW.reset(new NDArray(
|
|
gradW->ordering(),
|
|
isNCDHW ? ncdhwGradShape : ndhwcGradShape,
|
|
gradW->dataType(), gradW->getContext()));
|
|
// Create named vectors as lvalues for the NDArray constructor
|
|
std::vector<LongType> ncdhwShape = {oC, iC, kD, kH, kW};
|
|
std::vector<LongType> ndhwcShape = {oC, kD, kH, kW, iC};
|
|
|
|
// Use the appropriate one for the weights reset
|
|
tmpWeights.reset(new NDArray(
|
|
weights->ordering(),
|
|
isNCDHW ? ncdhwShape : ndhwcShape,
|
|
weights->dataType(), weights->getContext()));
|
|
|
|
// Create named vectors as lvalues for the permute call
|
|
std::vector<LongType> ncdhwPermute = {4, 3, 0, 1, 2};
|
|
std::vector<LongType> ndhwcPermute = {4, 0, 1, 2, 3};
|
|
|
|
// Set the pointer variables
|
|
newGradW = tmpGradW.get();
|
|
newWeights = tmpWeights.get();
|
|
|
|
NDArray assign = weights->permute(
|
|
isNCDHW ? ncdhwPermute : ndhwcPermute,
|
|
true, // copyToNewBuff
|
|
true);
|
|
// Use the appropriate one in the permute call
|
|
newWeights->assign(&assign); // resetStrides (kD, kH, kW, iC, oC --> oC, iC, kD, kH, kW) or (kD, kH, kW,
|
|
// iC, oC --> oC, kD, kH, kW, iC)
|
|
}
|
|
|
|
NDArray* newInput = input;
|
|
NDArray* newGradI = gradI;
|
|
if (paddingMode == 1) { // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
|
|
auto ret = checkConv3dCUDNNPadAsymmetric(input, gradI, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW,
|
|
dD, dH, dW, isNCDHW);
|
|
tmpInput = std::move(std::get<0>(ret));
|
|
tmpGradI = std::move(std::get<1>(ret));
|
|
if (tmpInput) newInput = tmpInput.get();
|
|
if (tmpGradI) newGradI = tmpGradI.get();
|
|
}
|
|
conv3dBpCUDNN(block.launchContext(), newInput, newWeights, gradO, newGradI, newGradW, gradB, kD, kH, kW, sD, sH, sW,
|
|
pD, pH, pW, dD, dH, dW, paddingMode, isNCDHW, wFormat);
|
|
|
|
if (0 == wFormat) {
|
|
// Create named vectors as lvalues for the permutei call
|
|
std::vector<LongType> ncdhwPermutei = {2, 3, 4, 1, 0};
|
|
std::vector<LongType> ndhwcPermutei = {1, 2, 3, 4, 0};
|
|
|
|
// Use the appropriate one in the permutei call
|
|
newGradW->permutei(
|
|
isNCDHW ? ncdhwPermutei : ndhwcPermutei,false,false); // (oC, iC, kD, kH, kW --> kD, kH, kW, iC, oC) or (oC, kD, kH, kW, iC --> kD, kH, kW, iC, oC) gradW->assign(newGradW);
|
|
}
|
|
|
|
if (newInput != input) {
|
|
if (isNCDHW) {
|
|
NDArray assign = (*newGradI)({0, 0, 0, 0, 0, gradI->sizeAt(2), 0, gradI->sizeAt(3), 0, gradI->sizeAt(4)});
|
|
gradI->assign(&assign);
|
|
} else {
|
|
NDArray assign = (*newGradI)({0, 0, 0, gradI->sizeAt(1), 0, gradI->sizeAt(2), 0, gradI->sizeAt(3), 0, 0});
|
|
gradI->assign(&assign);
|
|
}
|
|
}
|
|
return Status::OK;
|
|
}
|
|
|
|
PLATFORM_CHECK(conv3dnew_bp, ENGINE_CUDA) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
LongType paddingMode = INT_ARG(12); // 1-SAME, 0-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
|
|
Requirements req("CUDNN CONV3d_BP OP");
|
|
req.expectNotEq(makeInfoVariable(paddingMode, "paddingMode"), 2) &&
|
|
req.expectTrue(makeInfoVariable(isNCDHW, "isNCDHW")) &&
|
|
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
|