145 lines
6.2 KiB
Plaintext
145 lines
6.2 KiB
Plaintext
/*******************************************************************************
|
|
* Copyright (c) 2015-2018 Skymind, Inc.
|
|
*
|
|
* 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), created on 20.04.2018
|
|
//
|
|
|
|
|
|
#include<ops/declarable/helpers/transforms.h>
|
|
#include <array/ResultSet.h>
|
|
#include <helpers/ShapeUtils.h>
|
|
#include <numeric>
|
|
#include <array/NDArrayFactory.h>
|
|
#include <helpers/TAD.h>
|
|
#include <exceptions/cuda_exception.h>
|
|
#include <helpers/PointersManager.h>
|
|
#include <helpers/ConstantTadHelper.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace helpers {
|
|
///////////////////////////////////////////////////////////////////
|
|
// x - input, y - indices, z - output
|
|
template<typename X, typename Y>
|
|
__global__ static void gatherNDCuda(const void *vx, const Nd4jLong *xShapeInfo,
|
|
const void *vy, const Nd4jLong *yShapeInfo,
|
|
void *vz, const Nd4jLong *zShapeInfo) {
|
|
|
|
const auto x = reinterpret_cast<const X*>(vx);
|
|
const auto y = reinterpret_cast<const Y*>(vy);
|
|
auto z = reinterpret_cast<X*>(vz);
|
|
|
|
__shared__ int xRank, yRank, zRank, maxRank, yLastDim;
|
|
__shared__ Nd4jLong zLen, totalThreads, *sharedMem;
|
|
|
|
if (threadIdx.x == 0) {
|
|
extern __shared__ unsigned char shmem[];
|
|
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
|
|
|
|
xRank = shape::rank(xShapeInfo);
|
|
yRank = shape::rank(yShapeInfo);
|
|
zRank = shape::rank(zShapeInfo);
|
|
maxRank = sd::math::nd4j_max<int>(yRank, sd::math::nd4j_max<int>(xRank, zRank));
|
|
|
|
zLen = shape::length(zShapeInfo);
|
|
yLastDim = yShapeInfo[yRank];
|
|
|
|
totalThreads = gridDim.x * blockDim.x;
|
|
}
|
|
__syncthreads();
|
|
|
|
auto coord = sharedMem + threadIdx.x * maxRank;
|
|
|
|
Nd4jLong *zCoordStart, *xCoordStart;
|
|
|
|
if(yLastDim == xRank) {
|
|
zCoordStart = coord;
|
|
xCoordStart = coord;
|
|
}
|
|
if(zRank >= xRank) {
|
|
zCoordStart = coord;
|
|
xCoordStart = coord + zRank - xRank;
|
|
}
|
|
else {
|
|
zCoordStart = coord + xRank - zRank;
|
|
xCoordStart = coord;
|
|
}
|
|
|
|
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
|
|
|
|
shape::index2coords(i, zShapeInfo, zCoordStart);
|
|
|
|
const auto zOffset = shape::getOffset(zShapeInfo, zCoordStart);
|
|
|
|
// last y coordinate
|
|
int coordToRestore;
|
|
if(yLastDim != xRank)
|
|
coordToRestore = static_cast<int>(zCoordStart[yRank - 1]);
|
|
|
|
zCoordStart[yRank - 1] = 0; // last y coordinate
|
|
const auto yOffset = shape::getOffset(yShapeInfo, zCoordStart);
|
|
|
|
//restore z coordinate
|
|
if(yLastDim != xRank)
|
|
zCoordStart[yRank - 1] = coordToRestore;
|
|
|
|
// construct coordinates for x
|
|
for(uint j = 0; j < yLastDim; ++j)
|
|
xCoordStart[j] = y[yOffset + j * yShapeInfo[2 * yRank]]; // last stride
|
|
|
|
const auto xOffset = shape::getOffset(xShapeInfo, xCoordStart);
|
|
|
|
z[zOffset] = x[xOffset];
|
|
// printf("z[%lld] = x[%lld] = %f\n", zOffset, xOffset, (float) z[zOffset]);
|
|
}
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template<typename X, typename Y>
|
|
static void gatherNDCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
|
|
const void *vx, const Nd4jLong *xShapeInfo,
|
|
const void *vy, const Nd4jLong *yShapeInfo,
|
|
void *vz, const Nd4jLong *zShapeInfo) {
|
|
|
|
gatherNDCuda<X,Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo);
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
void gatherND(sd::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
|
|
|
|
const int maxRank = sd::math::nd4j_max<int>(indices.rankOf(), sd::math::nd4j_max<int>(input.rankOf(), output.rankOf()));
|
|
|
|
const int threadsPerBlock = 256;
|
|
const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
const int sharedMem = 8 * threadsPerBlock * maxRank + 128;
|
|
|
|
const auto xType = input.dataType();
|
|
const auto yType = indices.dataType();
|
|
|
|
PointersManager manager(context, "gatherND");
|
|
|
|
NDArray::prepareSpecialUse({&output}, {&input, &indices});
|
|
BUILD_DOUBLE_SELECTOR(xType, yType, gatherNDCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), indices.specialBuffer(), indices.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo()), LIBND4J_TYPES, INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({&output}, {&input, &indices});
|
|
|
|
manager.synchronize();
|
|
}
|
|
}
|
|
}
|
|
} |