wehub-resource-sync
129 lines
4.8 KiB
Plaintext
129 lines
4.8 KiB
Plaintext
/*
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* ******************************************************************************
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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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//
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <helpers/PointersManager.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include "execution/cuda/LaunchDims.h"
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#include "helpers/DebugHelper.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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SD_KERNEL static void upsampling3dBPCuda(const void* vx, const LongType* xShapeInfo, void* vz,
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const LongType* zShapeInfo, const bool isNCDHW) {
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// x (gradO) has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC)
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// z (gradI) has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH,
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// factorW*iW, iC] (NDHWC)
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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, dimID;
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__shared__ LongType factorD, factorH, factorW;
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__shared__ LongType zLen;
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__shared__ LongType *sharedMem;
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__shared__ LongType *xShape, *zShape;
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__shared__ LongType *xStride, *zStride;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<LongType*>(shmem);
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// Cache shape and stride pointers
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xShape = shape::shapeOf(xShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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xStride = shape::stride(xShapeInfo);
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zStride = shape::stride(zShapeInfo);
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dimID = isNCDHW ? 2 : 1;
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zLen = shape::length(zShapeInfo);
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rank = 5;
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factorD = xShape[dimID + 1] / zShape[dimID + 1];
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factorH = xShape[dimID + 2] / zShape[dimID + 2];
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factorW = xShape[dimID + 3] / zShape[dimID + 3];
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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) return;
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auto coords = sharedMem + threadIdx.x * rank;
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INDEX2COORDS(zInd, rank, zShape, coords);
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LongType zOffset;
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COORDS2INDEX(rank, zStride, coords, zOffset);
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z[zOffset] = 0;
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const LongType zCoord2 = coords[dimID] * factorD;
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const LongType zCoord3 = coords[dimID + 1] * factorH;
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const LongType zCoord4 = coords[dimID + 2] * factorW;
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for (coords[dimID] = zCoord2; coords[dimID] < zCoord2 + factorD; ++coords[dimID])
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for (coords[dimID + 1] = zCoord3; coords[dimID + 1] < zCoord3 + factorH; ++coords[dimID + 1])
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for (coords[dimID + 2] = zCoord4; coords[dimID + 2] < zCoord4 + factorW; ++coords[dimID + 2]) {
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LongType xOffset;
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COORDS2INDEX(rank, xStride, coords, xOffset);
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z[zOffset] += x[xOffset];
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static void upsampling3dBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem,
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const cudaStream_t* stream, const void* vx, const LongType* xShapeInfo,
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void* vz, const LongType* zShapeInfo, const bool isNCDHW) {
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upsampling3dBPCuda<T>
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<<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, isNCDHW);
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DebugHelper::checkErrorCode(const_cast<cudaStream_t*>(stream),"upsampling3dBPCudaLauncher failed");
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}
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//////////////////////////////////////////////////////////////////////////
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void ConvolutionUtils::upsampling3dBP(graph::Context& block, NDArray& gradO, NDArray& gradI,
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const bool isNCDHW) {
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PointersManager manager(block.launchContext(), "upsampling3d_bp");
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dim3 getUpSampling = getUpsamplingDims(gradI.lengthOf(),gradI.rankOf());
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NDArray::prepareSpecialUse({&gradI}, {&gradO});
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BUILD_SINGLE_SELECTOR(
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gradI.dataType(), upsampling3dBPCudaLauncher,
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(getUpSampling.x, getUpSampling.y, getUpSampling.z, block.launchContext()->getCudaStream(), gradO.specialBuffer(),
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gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), isNCDHW),
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SD_FLOAT_TYPES);
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NDArray::registerSpecialUse({&gradI}, {&gradO});
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manager.synchronize();
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}
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} // namespace ops
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} // namespace sd
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