454 lines
29 KiB
Plaintext
454 lines
29 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2019 Konduit K.K.
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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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* 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 "cudnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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namespace nd4j {
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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,
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const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output,
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const int kD, const int kH, const int kW,
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const int sD, const int sH, const int sW,
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const int pD, const int pH, const int pW,
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const int dD, const int dH, const int dW,
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const int paddingMode, const bool isNCDHW) {
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const int numDims = 5;
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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auto handle = reinterpret_cast<cudnnHandle_t *>(context->getCuDnnHandle());
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cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: can't set stream for cuDNN", err);
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const std::vector<int> pads = {pD, pH, pW};
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const std::vector<int> filtStrides = {sD, sH, sW};
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const std::vector<int> dilations = {dD, dH, dW};
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const std::vector<int> xShape = {bS, iC, iD, iH, iW};
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const std::vector<int> zShape = {bS, oC, oD, oH, oW};
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const std::vector<int> wShape = {oC, iC, kD, kH, kW};
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const std::vector<int> bShape = {1, (isNCDHW ? oC : 1), 1, 1, (isNCDHW ? 1 : oC)};
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const std::vector<int> xStrides = {(int)input->strideAt(0), (int)input->strideAt(1), (int)input->strideAt(2), (int)input->strideAt(3), (int)input->strideAt(4)};
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const std::vector<int> zStrides = {(int)output->strideAt(0), (int)output->strideAt(1), (int)output->strideAt(2), (int)output->strideAt(3), (int)output->strideAt(4)};
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cudnnTensorFormat_t format = isNCDHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
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// input descriptor
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cudnnTensorDescriptor_t x;
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cudnnCreateTensorDescriptor(&x);
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if(input->ews() == 1)
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err = cudnnSetTensorNdDescriptorEx(x, format, cudnnDataType(input->dataType()), numDims, xShape.data());
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else
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err = cudnnSetTensorNdDescriptor(x, cudnnDataType(input->dataType()), numDims, xShape.data(), xStrides.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err);
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// weights descriptor
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cudnnFilterDescriptor_t w;
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cudnnCreateFilterDescriptor(&w);
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err = cudnnSetFilterNdDescriptor(w, cudnnDataType(weights->dataType()), CUDNN_TENSOR_NCHW, numDims, wShape.data());
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if(err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetFilterNdDescriptor failed", err);
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// output descriptor
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cudnnTensorDescriptor_t z;
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cudnnCreateTensorDescriptor(&z);
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if(output->ews() == 1)
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err = cudnnSetTensorNdDescriptorEx(z, format, cudnnDataType(output->dataType()), numDims, zShape.data());
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else
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err = cudnnSetTensorNdDescriptor(z, cudnnDataType(output->dataType()), numDims, zShape.data(), zStrides.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for output failed", err);
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// description of convolution
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cudnnConvolutionDescriptor_t conv;
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cudnnCreateConvolutionDescriptor(&conv);
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err = cudnnSetConvolutionNdDescriptor(conv, numDims-2, pads.data(), filtStrides.data(), dilations.data(), CUDNN_CROSS_CORRELATION, cudnnDataType(output->dataType()));
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetConvolutionNdDescriptor failed", err);
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// algorithm description
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cudnnConvolutionFwdAlgo_t algo;
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err = cudnnGetConvolutionForwardAlgorithm(*handle, x, w, conv, z, CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, &algo);
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnGetConvolutionForwardAlgorithm failed", err);
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// allocate auxiliary device memory, abbreviation ws means workspace
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size_t wsSize;
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err = cudnnGetConvolutionForwardWorkspaceSize(*handle, x, w, conv, z, algo, &wsSize);
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnGetConvolutionForwardWorkspaceSize failed", err);
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void* wsData;
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auto cudaErr = cudaMalloc(&wsData, wsSize);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudaMalloc for auxiliary workspace memory failed", cudaErr);
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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 = output->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&alpha32) : reinterpret_cast<const void*>(&alpha64);
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const void* beta = 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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err = cudnnConvolutionForward(*handle, alpha, x, input->getSpecialBuffer(), w, weights->getSpecialBuffer(), conv, algo, wsData, wsSize, beta, z, output->specialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnConvolutionForward failed", err);
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// add bias if it is present
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if (bias != nullptr) {
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cudnnTensorDescriptor_t b;
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cudnnCreateTensorDescriptor(&b);
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err = cudnnSetTensorNdDescriptorEx(b, format, cudnnDataType(bias->dataType()), numDims, bShape.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnSetTensorNdDescriptor for bias failed", err);
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err = cudnnAddTensor(*handle, alpha, b, bias->getSpecialBuffer(), alpha, z, output->specialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudnnAddTensor bias failed", err);
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}
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// cudaErr = cudaStreamSynchronize(*context->getCudaStream());
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// if (cudaErr != 0)
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// throw cuda_exception::build("conv3dCUDNN: cudaStreamSynchronize failed !", cudaErr);
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cudaErr = cudaFree(wsData);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dCUDNN: cudaFree for auxiliary workspace memory failed", cudaErr);
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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,
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const NDArray* input, const NDArray* weights, const NDArray* gradO,
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NDArray* gradI, NDArray* gradW, NDArray* gradB,
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const int kD, const int kH, const int kW,
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const int sD, const int sH, const int sW,
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const int pD, const int pH, const int pW,
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const int dD, const int dH, const int dW,
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const int paddingMode, const bool isNCDHW) {
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const int numDims = 5;
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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auto handle = reinterpret_cast<cudnnHandle_t *>(context->getCuDnnHandle());
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cudnnStatus_t err = cudnnSetStream(*handle, *context->getCudaStream());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: can't set stream for cuDNN", err);
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const std::vector<int> pads = {pD, pH, pW};
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const std::vector<int> filtStrides = {sD, sH, sW};
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const std::vector<int> dilations = {dD, dH, dW};
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const std::vector<int> xShape = {bS, iC, iD, iH, iW};
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const std::vector<int> dzShape = {bS, oC, oD, oH, oW};
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const std::vector<int> wShape = {oC, iC, kD, kH, kW};
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const std::vector<int> dbShape = {1, (int)(isNCDHW ? oC : 1), 1, 1, (int)(isNCDHW ? 1 : oC)};
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const std::vector<int> xStrides = {(int)input->strideAt(0), (int)input->strideAt(1), (int)input->strideAt(2), (int)input->strideAt(3), (int)input->strideAt(4)};
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const std::vector<int> dxStrides = {(int)gradI->strideAt(0), (int)gradI->strideAt(1), (int)gradI->strideAt(2), (int)gradI->strideAt(3), (int)gradI->strideAt(4)};
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const std::vector<int> dzStrides = {(int)gradO->strideAt(0), (int)gradO->strideAt(1), (int)gradO->strideAt(2), (int)gradO->strideAt(3), (int)gradO->strideAt(4)};
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cudnnTensorFormat_t format = isNCDHW ? CUDNN_TENSOR_NCHW : CUDNN_TENSOR_NHWC;
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// input descriptor
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cudnnTensorDescriptor_t x;
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cudnnCreateTensorDescriptor(&x);
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if(input->ews() == 1)
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err = cudnnSetTensorNdDescriptorEx(x, format, cudnnDataType(input->dataType()), numDims, xShape.data());
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else
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err = cudnnSetTensorNdDescriptor(x, cudnnDataType(input->dataType()), numDims, xShape.data(), xStrides.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for input failed", err);
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// gradO descriptor
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cudnnTensorDescriptor_t dz;
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cudnnCreateTensorDescriptor(&dz);
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if(gradO->ews() == 1)
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err = cudnnSetTensorNdDescriptorEx(dz, format, cudnnDataType(gradO->dataType()), numDims, dzShape.data());
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else
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err = cudnnSetTensorNdDescriptor(dz, cudnnDataType(gradO->dataType()), numDims, dzShape.data(), dzStrides.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradO failed", err);
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// gradI descriptor
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cudnnTensorDescriptor_t dx;
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cudnnCreateTensorDescriptor(&dx);
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if(gradI->ews() == 1)
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err = cudnnSetTensorNdDescriptorEx(dx, format, cudnnDataType(gradI->dataType()), numDims, xShape.data());
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else
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err = cudnnSetTensorNdDescriptor(dx, cudnnDataType(gradI->dataType()), numDims, xShape.data(), dxStrides.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor/cudnnSetTensorNdDescriptorEx for gradI failed", err);
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// gradW descriptor
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cudnnFilterDescriptor_t dw;
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cudnnCreateFilterDescriptor(&dw);
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err = cudnnSetFilterNdDescriptor(dw, cudnnDataType(gradW->dataType()), CUDNN_TENSOR_NCHW, numDims, wShape.data());
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if(err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetFilterNdDescriptor failed", err);
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// description of convolution
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cudnnConvolutionDescriptor_t conv;
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cudnnCreateConvolutionDescriptor(&conv);
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err = cudnnSetConvolutionNdDescriptor(conv, numDims-2, pads.data(), filtStrides.data(), dilations.data(), CUDNN_CROSS_CORRELATION, cudnnDataType(gradO->dataType()));
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetConvolutionNdDescriptor failed", err);
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// gradW algorithm description
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cudnnConvolutionBwdFilterAlgo_t algoGradW;
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err = cudnnGetConvolutionBackwardFilterAlgorithm(*handle, x, dz, conv, dw, CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, &algoGradW);
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardFilterAlgorithm failed", err);
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// gradI algorithm description
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cudnnConvolutionBwdDataAlgo_t algoGradI;
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err = cudnnGetConvolutionBackwardDataAlgorithm(*handle, dw, dz, conv, x, CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, &algoGradI);
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardDataAlgorithm failed", err);
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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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err = cudnnGetConvolutionBackwardFilterWorkspaceSize(*handle, x, dz, conv, dw, algoGradW, &wsGradWSize);
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardFilterWorkspaceSize failed", err);
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void* wsGradWData;
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auto cudaErr = cudaMalloc(&wsGradWData, wsGradWSize);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradWData failed", cudaErr);
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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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err = cudnnGetConvolutionBackwardDataWorkspaceSize(*handle, dw, dz, conv, dx, algoGradI, &wsGradISize);
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnGetConvolutionBackwardDataWorkspaceSize failed", err);
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void* wsGradIData;
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cudaErr = cudaMalloc(&wsGradIData, wsGradISize);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaMalloc for auxiliary workspace memory wsGradIData failed", cudaErr);
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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 = gradO->sizeOfT() <= 4 ? reinterpret_cast<const void*>(&alpha32) : reinterpret_cast<const void*>(&alpha64);
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const void* beta = 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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cudnnTensorDescriptor_t db;
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cudnnCreateTensorDescriptor(&db);
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err = cudnnSetTensorNdDescriptorEx(db, format, cudnnDataType(gradB->dataType()), numDims, dbShape.data());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnSetTensorNdDescriptor for gradB failed", err);
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err = cudnnConvolutionBackwardBias(*handle, alpha, dz, gradO->getSpecialBuffer(), beta, db, gradB->getSpecialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardBias failed", err);
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}
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// run calculation for gradW
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err = cudnnConvolutionBackwardFilter(*handle, alpha, x, input->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradW, wsGradWData, wsGradWSize, beta, dw, gradW->getSpecialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardFilter failed", err);
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// run calculation for gradI
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err = cudnnConvolutionBackwardData(*handle, alpha, dw, weights->getSpecialBuffer(), dz, gradO->getSpecialBuffer(), conv, algoGradI, wsGradIData, wsGradISize, beta, dx, gradI->getSpecialBuffer());
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if (err != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudnnConvolutionBackwardData failed", err);
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// cudaErr = cudaStreamSynchronize(*context->getCudaStream());
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// if (cudaErr != 0)
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// throw cuda_exception::build("conv3dBpCUDNN: cudaStreamSynchronize failed !", cudaErr);
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cudaErr = cudaFree(wsGradWData);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaFree for auxiliary workspace memory wsGradWData failed", cudaErr);
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cudaErr = cudaFree(wsGradIData);
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if (cudaErr != 0) throw nd4j::cuda_exception::build("conv3dBpCUDNN: cudaFree for auxiliary workspace memory wsGradIData failed", cudaErr);
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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] always
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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 !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0, "CONV3D CUDNN OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int 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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REQUIRE_TRUE(paddingMode < 2, 0, "CONV3D CUDNN OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, 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<Nd4jLong> expectedWeightsShape = {kD, kH, kW, iC, oC};
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CONV3D CUDNN OP: wrong shape of weights array, expected is %s, but got %s instead !", 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, "CONV3D CUDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
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NDArray* newWeights = new NDArray(weights->ordering(), {oC, iC, kD, kH, kW}, weights->dataType(), weights->getContext()); // cudnn support only two formats {oC,iC,kH,kW} and {oC,kH,kW,iC}
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newWeights->assign(weights->permute({4,3,0,1,2})); // permute weights (kD, kH, kW, iC, oC --> oC, iC, kD, kH, kW)
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NDArray* newInput = input;
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NDArray* newGradI = nullptr;
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if(paddingMode == 1) // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
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checkConv3dCUDNNPadAsymmetric(newInput, newGradI, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isNCDHW);
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conv3dCUDNN(block.launchContext(), newInput, newWeights, bias, output, kD,kH,kW,sD,sH,sW,pD,pH,pW,dD,dH,dW, paddingMode, isNCDHW);
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if(newInput != input)
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delete newInput;
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delete newWeights;
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_CHECK(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] always
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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int paddingMode = INT_ARG(12); // 0-SAME, 1-VALID
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const bool badInputType = input->dataType() != DataType::DOUBLE && input->dataType() != DataType::FLOAT32 && input->dataType() != DataType::HALF;
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const bool badWeightsType = weights->dataType() != DataType::DOUBLE && weights->dataType() != DataType::FLOAT32 && weights->dataType() != DataType::HALF;
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const bool badBiasType = bias == nullptr ? false : (bias->dataType() != DataType::DOUBLE && bias->dataType() != DataType::FLOAT32 && bias->dataType() != DataType::HALF);
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return paddingMode != 2 && !badInputType && !badWeightsType && !badBiasType;
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(conv3dnew_bp, 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] always
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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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());
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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());
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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());
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int paddingMode = INT_ARG(12); // 1-SAME, 0-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 bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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int trueoD, trueoH, trueoW; // true output depth/height/width
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ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, paddingMode);
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REQUIRE_TRUE(paddingMode < 2, 0, "CONV3D_BP CUDNN OP: causal padding mode (paddingMode = 2) is not allowed for this operation !");
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std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2});
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std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, iC, oC};
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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());
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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());
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if(bias)
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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());
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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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NDArray* newGradW = new NDArray(gradW->ordering(), {oC, iC, kD, kH, kW}, gradW->dataType(), gradW->getContext()); // cudnn support only two formats for weights {oC,iC,kH,kW} and {oC,kH,kW,iC}
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NDArray* newWeights = new NDArray(weights->ordering(), {oC, iC, kD, kH, kW}, weights->dataType(), weights->getContext());
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newWeights->assign(weights->permute({4,3,0,1,2})); // permute weights (kD, kH, kW, iC, oC --> oC, iC, kD, kH, kW)
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NDArray* newInput = input;
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NDArray* newGradI = gradI;
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if(paddingMode == 1) // in same paddingMode cudnn doesn't support asymmetric left/right top/bottopm paddings
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checkConv3dCUDNNPadAsymmetric(newInput, newGradI, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isNCDHW);
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conv3dBpCUDNN(block.launchContext(), newInput, newWeights, gradO, newGradI, newGradW, gradB, kD,kH,kW,sD,sH,sW,pD,pH,pW,dD,dH,dW,paddingMode,isNCDHW);
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newGradW->permutei({2,3,4,1,0}); // [oC, iC, kD, kH, kW] -> [kD, kH, kW, iC, oC]
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gradW->assign(newGradW);
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|
|
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if(newInput != input) {
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|
|
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if(isNCDHW)
|
|
gradI->assign((*newGradI)({0,0, 0,0, 0,gradI->sizeAt(2), 0,gradI->sizeAt(3), 0,gradI->sizeAt(4)}));
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else
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gradI->assign((*newGradI)({0,0, 0,gradI->sizeAt(1), 0,gradI->sizeAt(2), 0,gradI->sizeAt(3), 0,0}));
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|
|
|
delete newInput;
|
|
delete newGradI;
|
|
}
|
|
|
|
delete newWeights;
|
|
delete newGradW;
|
|
|
|
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] always
|
|
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
|
|
|
|
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
|
|
|
|
const bool badInputType = input->dataType() != DataType::DOUBLE && input->dataType() != DataType::FLOAT32 && input->dataType() != DataType::HALF;
|
|
const bool badWeightsType = weights->dataType() != DataType::DOUBLE && weights->dataType() != DataType::FLOAT32 && weights->dataType() != DataType::HALF;
|
|
const bool badGradOType = gradO->dataType() != DataType::DOUBLE && gradO->dataType() != DataType::FLOAT32 && gradO->dataType() != DataType::HALF;
|
|
const bool badBiasType = bias == nullptr ? false : (bias->dataType() != DataType::DOUBLE && bias->dataType() != DataType::FLOAT32 && bias->dataType() != DataType::HALF);
|
|
|
|
return isNCDHW && paddingMode != 2 && !badInputType && !badWeightsType && !badGradOType && !badBiasType;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|