cavis/libnd4j/include/ops/declarable/helpers/cuda/convolutions_vol2col.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 {
//////////////////////////////////////////////////////////////////////////
// vol [bS, iC, iD, iH, iW] is convoluted to col [bS, iC, kD, kH, kW, oD, oH, oW]
template <typename T>
static __global__ void vol2colCuda(const void* volume, const Nd4jLong* volShapeInfo, void* columns, const Nd4jLong* colShapeInfo, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
const T* vol = reinterpret_cast<const T*>(volume);
T* col = reinterpret_cast<T*>(columns);
__shared__ int colRank, volRank;
__shared__ Nd4jLong colLen, iD, iH, iW, *sharedMem;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
volRank = 5;
colRank = 8;
colLen = shape::length(colShapeInfo);
iD = volShapeInfo[3];
iH = volShapeInfo[4];
iW = volShapeInfo[5];
}
__syncthreads();
const auto colInd = threadIdx.x + blockIdx.x * blockDim.x;
if(colInd >= colLen)
return;
auto coords = sharedMem + threadIdx.x * colRank;
shape::index2coords(colInd, colShapeInfo, coords);
// const auto colW = coords[7];
// const auto colH = coords[6];
// const auto colD = coords[5];
// const auto kCol = coords[4];
// const auto kRow = coords[3];
// const auto kDep = coords[2];
// const auto c = coords[1];
// const auto b = coords[0];
const auto colOffset = shape::getOffset(colShapeInfo, coords);
coords[2] = -pD + coords[2] * dD + coords[5] * sD; // const auto volDep = (-pD + kDep * dD) + colD * sD;
coords[3] = -pH + coords[3] * dH + coords[6] * sH; // const auto volRow = (-pH + kRow * dH) + colH * sH;
coords[4] = -pW + coords[4] * dW + coords[7] * sW; // const auto volCol = (-pW + kCol * dW) + colW * sW;
if (static_cast<unsigned>(coords[2]) >= static_cast<unsigned>(iD) || static_cast<unsigned>(coords[3]) >= static_cast<unsigned>(iH) || static_cast<unsigned>(coords[4]) >= static_cast<unsigned>(iW))
col[colOffset] = static_cast<T>(0.);
else
col[colOffset] = vol[shape::getOffset(volShapeInfo, coords)];
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void vol2colCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
const void* volume, const Nd4jLong* volShapeInfo,
void* columns, const Nd4jLong* colShapeInfo,
const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
vol2colCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(volume, volShapeInfo, columns, colShapeInfo, sD, sH, sW, pD, pH, pW, dD, dH, dW);
}
//////////////////////////////////////////////////////////////////////////
void ConvolutionUtils::vol2col(sd::graph::Context& block, const NDArray& vol, NDArray& col, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW) {
PointersManager manager(block.launchContext(), "vol2col");
const int threadsPerBlock = MAX_NUM_THREADS / 4;
const int blocksPerGrid = (col.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
const int sharedMem = col.rankOf() * sizeof(Nd4jLong) * threadsPerBlock + 128;
NDArray::prepareSpecialUse({&col}, {&vol});
BUILD_SINGLE_SELECTOR(vol.dataType(), vol2colCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), vol.getSpecialBuffer(), vol.getSpecialShapeInfo(), col.specialBuffer(), col.specialShapeInfo(), sD, sH, sW, pD, pH, pW, dD, dH, dW), FLOAT_TYPES);
NDArray::registerSpecialUse({&col}, {&vol});
manager.synchronize();
}
}
}