cavis/libnd4j/include/ops/declarable/helpers/cuda/convolutions_upsampling3d.cu

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/*******************************************************************************
* 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 <ops/declarable/helpers/convolutions.h>
#include <helpers/PointersManager.h>
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
template <typename T>
__global__ static void upsampling3dCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int factorD, const int factorH, const int factorW, const bool isNCDHW) {
// x has shape [bS, iC, iD, iH, iW] (NCDHW) or [bS, iD, iH, iW, iC] (NDHWC)
// z has shape [bS, iC, factorD*iD, factorH*iH, factorW*iW ] (NCDHW) or [bS, factorD*iD, factorH*iH, factorW*iW, iC] (NDHWC)
const T* x = reinterpret_cast<const T*>(vx);
T* z = reinterpret_cast<T*>(vz);
__shared__ int rank, dimID;
__shared__ Nd4jLong zLen, *sharedMem;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
dimID = isNCDHW ? 2 : 1;
zLen = shape::length(zShapeInfo);
rank = 5;
}
__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);
coords[dimID] /= factorD;
coords[dimID + 1] /= factorH;
coords[dimID + 2] /= factorW;
const auto xOffset = shape::getOffset(xShapeInfo, coords);
z[zOffset] = x[xOffset];
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void upsampling3dCudaLauncher(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 int factorD, const int factorH, const int factorW, const bool isNCDHW) {
upsampling3dCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, factorD, factorH, factorW, isNCDHW);
}
//////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::upsampling3d(sd::graph::Context& block, const NDArray& input, NDArray& output, const int factorD, const int factorH, const int factorW, const bool isNCDHW) {
PointersManager manager(block.launchContext(), "upsampling3d");
const int threadsPerBlock = MAX_NUM_THREADS / 2;
const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = output.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
NDArray::prepareSpecialUse({&output}, {&input});
BUILD_SINGLE_SELECTOR(input.dataType(), upsampling3dCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), factorD, factorH, factorW, isNCDHW), FLOAT_TYPES);
NDArray::registerSpecialUse({&output}, {&input});
manager.synchronize();
}
}
}