/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * Copyright (c) 2019 Konduit K.K. * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * License for the specific language governing permissions and limitations * under the License. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author Yurii Shyrma (iuriish@yahoo.com) // #include #include namespace sd { namespace ops { ////////////////////////////////////////////////////////////////////////// template __global__ static void upsampling2dBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const bool isNCHW) { // x (gradO) has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC) // z (gradI) has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC) const T* x = reinterpret_cast(vx); T* z = reinterpret_cast(vz); __shared__ int rank, dimIH; __shared__ uint factorH, factorW; __shared__ Nd4jLong zLen, *sharedMem; if (threadIdx.x == 0) { extern __shared__ unsigned char shmem[]; sharedMem = reinterpret_cast(shmem); dimIH = isNCHW ? 2 : 1; zLen = shape::length(zShapeInfo); rank = 4; factorH = xShapeInfo[dimIH + 1] / zShapeInfo[dimIH + 1]; factorW = xShapeInfo[dimIH + 2] / zShapeInfo[dimIH + 2]; } __syncthreads(); const auto zInd = threadIdx.x + blockIdx.x * blockDim.x; if(zInd >= zLen) return; auto coords = sharedMem + threadIdx.x * rank; shape::index2coords(zInd, zShapeInfo, coords); const auto zOffset = shape::getOffset(zShapeInfo, coords); z[zOffset] = 0; const Nd4jLong zCoord2 = coords[dimIH] * factorH; const Nd4jLong zCoord3 = coords[dimIH + 1] * factorW; for(coords[dimIH] = zCoord2; coords[dimIH] < zCoord2 + factorH; ++coords[dimIH]) for(coords[dimIH + 1] = zCoord3; coords[dimIH + 1] < zCoord3 + factorW; ++coords[dimIH + 1]) z[zOffset] += x[shape::getOffset(xShapeInfo, coords)]; } ////////////////////////////////////////////////////////////////////////// template static void upsampling2dBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const bool isNCHW) { upsampling2dBPCuda<<>>(vx, xShapeInfo, vz, zShapeInfo, isNCHW); } ////////////////////////////////////////////////////////////////////////// void ConvolutionUtils::upsampling2dBP(sd::graph::Context& block, const NDArray& gradO, NDArray& gradI, const bool isNCHW) { PointersManager manager(block.launchContext(), "upsampling2d_bp"); const int threadsPerBlock = MAX_NUM_THREADS / 2; const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock; const int sharedMem = gradI.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128; NDArray::prepareSpecialUse({&gradI}, {&gradO}); BUILD_SINGLE_SELECTOR(gradI.dataType(), upsampling2dBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), isNCHW), FLOAT_TYPES); NDArray::registerSpecialUse({&gradI}, {&gradO}); manager.synchronize(); } } }