103 lines
4.1 KiB
Plaintext
103 lines
4.1 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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 Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <ops/declarable/helpers/convolutions.h>
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#include <helpers/PointersManager.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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__global__ static void upsampling2dBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const bool isNCHW) {
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// x (gradO) has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
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// z (gradI) has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
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const T* x = reinterpret_cast<const T*>(vx);
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T* z = reinterpret_cast<T*>(vz);
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__shared__ int rank, dimIH;
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__shared__ uint factorH, factorW;
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__shared__ Nd4jLong zLen, *sharedMem;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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dimIH = isNCHW ? 2 : 1;
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zLen = shape::length(zShapeInfo);
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rank = 4;
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factorH = xShapeInfo[dimIH + 1] / zShapeInfo[dimIH + 1];
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factorW = xShapeInfo[dimIH + 2] / zShapeInfo[dimIH + 2];
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}
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__syncthreads();
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const auto zInd = threadIdx.x + blockIdx.x * blockDim.x;
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if(zInd >= zLen)
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return;
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auto coords = sharedMem + threadIdx.x * rank;
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shape::index2coords(zInd, zShapeInfo, coords);
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const auto zOffset = shape::getOffset(zShapeInfo, coords);
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z[zOffset] = 0;
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const Nd4jLong zCoord2 = coords[dimIH] * factorH;
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const Nd4jLong zCoord3 = coords[dimIH + 1] * factorW;
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for(coords[dimIH] = zCoord2; coords[dimIH] < zCoord2 + factorH; ++coords[dimIH])
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for(coords[dimIH + 1] = zCoord3; coords[dimIH + 1] < zCoord3 + factorW; ++coords[dimIH + 1])
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z[zOffset] += x[shape::getOffset(xShapeInfo, coords)];
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void upsampling2dBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
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const void* vx, const Nd4jLong* xShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo,
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const bool isNCHW) {
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upsampling2dBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, isNCHW);
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}
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//////////////////////////////////////////////////////////////////////////
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void ConvolutionUtils::upsampling2dBP(sd::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) {
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PointersManager manager(block.launchContext(), "upsampling2d_bp");
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = gradI.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
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NDArray::prepareSpecialUse({&gradI}, {&gradO});
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BUILD_SINGLE_SELECTOR(gradI.dataType(), upsampling2dBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), isNCHW), FLOAT_TYPES);
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NDArray::registerSpecialUse({&gradI}, {&gradO});
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manager.synchronize();
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}
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}
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} |