366 lines
14 KiB
Plaintext
366 lines
14 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
|
|
******************************************************************************/
|
|
|
|
//
|
|
// Created by Yurii Shyrma on 02.01.2018
|
|
//
|
|
|
|
#include <ops/declarable/helpers/stack.h>
|
|
#include <helpers/ShapeUtils.h>
|
|
#include <array/ResultSet.h>
|
|
#include <exceptions/cuda_exception.h>
|
|
#include <helpers/TAD.h>
|
|
#include <helpers/PointersManager.h>
|
|
#include <helpers/ConstantTadHelper.h>
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace helpers {
|
|
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static __global__ void stackScalarsCuda(void* pVx, void* vz, const Nd4jLong* zShapeInfo) {
|
|
|
|
T* z = reinterpret_cast<T*>(vz);
|
|
|
|
__shared__ Nd4jLong zLen, totalThreads;
|
|
|
|
if (threadIdx.x == 0) {
|
|
zLen = shape::length(zShapeInfo);
|
|
totalThreads = gridDim.x * blockDim.x;
|
|
}
|
|
__syncthreads();
|
|
|
|
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
|
|
|
|
const T *x = reinterpret_cast<const T*>(reinterpret_cast<void**>(pVx)[i]);
|
|
z[shape::getIndexOffset(i, zShapeInfo)] = *x;
|
|
}
|
|
}
|
|
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
__host__ static void stackScalarsCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
|
|
void* pVx, void* vz, const Nd4jLong* zShapeInfo) {
|
|
|
|
stackScalarsCuda<T><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(pVx, vz, zShapeInfo);
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void stack_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output, const int dim) {
|
|
|
|
const int numOfSubArrs = inArrs.size();
|
|
|
|
NDArray::prepareSpecialUse({&output}, inArrs);
|
|
|
|
if(inArrs[0]->rankOf() == 0) {
|
|
|
|
std::vector<void const*> hInBuffers(numOfSubArrs);
|
|
|
|
for(int i = 0; i < numOfSubArrs; ++i)
|
|
hInBuffers[i] = inArrs[i]->specialBuffer();
|
|
|
|
PointersManager manager(context, "helpers::stack cuda");
|
|
|
|
void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*));
|
|
|
|
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
|
|
stackScalarsCudaLauncher<T>(blocksPerGrid, threadsPerBlock, context->getCudaStream(), dInBuffers, output.specialBuffer(), output.specialShapeInfo());
|
|
|
|
manager.synchronize();
|
|
}
|
|
else {
|
|
|
|
auto zTadPack = ConstantTadHelper::getInstance()->tadForDimensions(output.shapeInfo(), ShapeUtils::evalDimsToExclude(output.rankOf(), {dim}));
|
|
auto zTadShapeInfo = zTadPack.primaryShapeInfo();
|
|
|
|
for (uint i = 0; i < numOfSubArrs; ++i) {
|
|
|
|
void* zBuff = output.specialBufferWithOffset(zTadPack.primaryOffsets()[i]);
|
|
|
|
NativeOpExecutioner::execTransformAny(context, transform::Assign,
|
|
nullptr, inArrs[i]->shapeInfo(), inArrs[i]->specialBuffer(), inArrs[i]->specialShapeInfo(),
|
|
nullptr, zTadShapeInfo, zBuff, zTadPack.specialShapeInfo(),
|
|
nullptr, nullptr, nullptr, false/*allowParallelism*/);
|
|
}
|
|
}
|
|
|
|
NDArray::registerSpecialUse({&output}, inArrs);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void stack(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output, const int dim) {
|
|
BUILD_SINGLE_SELECTOR(output.dataType(), stack_, (context, inArrs, output, dim), LIBND4J_TYPES);
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template void stack_ , (sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output, const int dim), LIBND4J_TYPES);
|
|
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static __global__ void unstackScalarsCuda(const void* vx, const Nd4jLong* xShapeInfo, void* pVz) {
|
|
|
|
const T* x = reinterpret_cast<const T*>(vx);
|
|
|
|
__shared__ Nd4jLong xLen, totalThreads;
|
|
|
|
if (threadIdx.x == 0) {
|
|
xLen = shape::length(xShapeInfo);
|
|
totalThreads = gridDim.x * blockDim.x;
|
|
}
|
|
__syncthreads();
|
|
|
|
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
for (Nd4jLong i = tid; i < xLen; i += totalThreads) {
|
|
|
|
T* z = reinterpret_cast<T*>(reinterpret_cast<void**>(pVz)[i]);
|
|
*z = x[shape::getIndexOffset(i, xShapeInfo)];
|
|
}
|
|
}
|
|
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template<typename T>
|
|
__host__ static void unstackScalarsCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
|
|
const void* vx, const Nd4jLong* xShapeInfo, void* pVz) {
|
|
|
|
unstackScalarsCuda<T><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(vx, xShapeInfo, pVz);
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
template <typename T>
|
|
static void unstack_(sd::LaunchContext* context, const NDArray& input, const std::vector<NDArray*>& outArrs, const int dim) {
|
|
|
|
const int numOfSubArrs = outArrs.size();
|
|
|
|
// NDArray::prepareSpecialUse(outArrs, {&input});
|
|
input.syncToDevice();
|
|
for (const auto a : outArrs)
|
|
a->getDataBuffer()->allocateSpecial();
|
|
|
|
|
|
if(outArrs[0]->rankOf() == 0) {
|
|
|
|
std::vector<void*> hOutBuffers(numOfSubArrs);
|
|
|
|
for(int i = 0; i < numOfSubArrs; ++i)
|
|
hOutBuffers[i] = outArrs[i]->specialBuffer();
|
|
|
|
PointersManager manager(context, "helpers::unstack cuda");
|
|
|
|
void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*));
|
|
|
|
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
|
|
unstackScalarsCudaLauncher<T>(blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), dOutBuffers);
|
|
|
|
manager.synchronize();
|
|
}
|
|
else {
|
|
|
|
auto xTadPack = ConstantTadHelper::getInstance()->tadForDimensions(input.shapeInfo(), ShapeUtils::evalDimsToExclude(input.rankOf(), {dim}));
|
|
auto xTadShapeInfo = xTadPack.primaryShapeInfo();
|
|
|
|
for (uint i = 0; i < numOfSubArrs; ++i) {
|
|
|
|
auto xBuff = input.specialBufferWithOffset(xTadPack.primaryOffsets()[i]);
|
|
|
|
NativeOpExecutioner::execTransformAny(input.getContext(), transform::Assign,
|
|
nullptr, xTadShapeInfo, xBuff, xTadPack.specialShapeInfo(),
|
|
nullptr, outArrs[i]->shapeInfo(), outArrs[i]->specialBuffer(), outArrs[i]->specialShapeInfo(),
|
|
nullptr, nullptr, nullptr, false/*allowParallelism*/);
|
|
}
|
|
}
|
|
|
|
// NDArray::registerSpecialUse(outArrs, {&input});
|
|
input.tickReadDevice();
|
|
for (const auto p : outArrs)
|
|
p->tickWriteDevice();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
void unstack(sd::LaunchContext* context, const NDArray& input, const std::vector<NDArray*>& outArrs, const int dim) {
|
|
BUILD_SINGLE_SELECTOR(input.dataType(), unstack_, (context, input, outArrs, dim), LIBND4J_TYPES);
|
|
}
|
|
BUILD_SINGLE_TEMPLATE(template void unstack_, (sd::LaunchContext* context, const NDArray& input, const std::vector<NDArray*>& outArrs, const int dim), LIBND4J_TYPES);
|
|
|
|
///////////////////////////////////////////////////////////////////
|
|
// template <typename T>
|
|
// static __global__ void unstackCuda(const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis) {
|
|
|
|
// const T* x = reinterpret_cast<const T*>(vx);
|
|
// __shared__ Nd4jLong xLen, totalThreads;
|
|
// __shared__ int xRank;
|
|
|
|
// if (threadIdx.x == 0) {
|
|
// xLen = shape::length(xShapeInfo);
|
|
// xRank = shape::rank(xShapeInfo);
|
|
// totalThreads = gridDim.x * blockDim.x;
|
|
// }
|
|
// __syncthreads();
|
|
|
|
// const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
// Nd4jLong coords[MAX_RANK];
|
|
|
|
// for (uint64_t i = tid; i < xLen; i += totalThreads) {
|
|
|
|
// shape::index2coords(i, xShapeInfo, coords);
|
|
|
|
// const auto xOffset = shape::getOffset(xShapeInfo, coords);
|
|
|
|
// T *z = reinterpret_cast<T*>(reinterpret_cast<void **>(pVz)[coords[axis]]);
|
|
|
|
// for (uint j = axis; j < xRank - 1; ++j) // shift coords staring from axis position
|
|
// coords[j] = coords[j + 1];
|
|
|
|
// const auto zOffset = shape::getOffset(zTadShapeInfo, coords);
|
|
|
|
// z[zOffset] = x[xOffset];
|
|
// }
|
|
// }
|
|
|
|
// ///////////////////////////////////////////////////////////////////
|
|
// template<typename T>
|
|
// __host__ static void unstackCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
|
|
// const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis) {
|
|
|
|
// unstackCuda<T><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(vx, xShapeInfo, pVz, zTadShapeInfo, axis);
|
|
// }
|
|
// BUILD_SINGLE_TEMPLATE(template void unstackCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void* vx, const Nd4jLong* xShapeInfo, void* pVz, const Nd4jLong* zTadShapeInfo, const int axis), LIBND4J_TYPES);
|
|
|
|
|
|
// ///////////////////////////////////////////////////////////////////
|
|
// void unstack(sd::LaunchContext* context, const NDArray& input, const std::vector<const NDArray*>& outArrs, const int axis) {
|
|
|
|
// const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
// const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
|
|
// const int numOfSubArrs = outArrs.size();
|
|
|
|
// std::vector<void*> hOutBuffers(numOfSubArrs);
|
|
|
|
// for(int i = 0; i < numOfSubArrs; ++i)
|
|
// hOutBuffers[i] = outArrs[i]->specialBuffer();
|
|
|
|
// PointersManager manager(context, "helpers::unstack");
|
|
|
|
// void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*));
|
|
|
|
// for(uint i = 0; i < numOfSubArrs; ++i)
|
|
// outArrs[i]->syncToDevice();
|
|
// input.syncToDevice();
|
|
|
|
// BUILD_SINGLE_SELECTOR(input.dataType(), unstackCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), dOutBuffers, outArrs[0]->specialShapeInfo(), axis), LIBND4J_TYPES);
|
|
|
|
// manager.synchronize();
|
|
|
|
// for(uint i = 0; i < numOfSubArrs; ++i)
|
|
// outArrs[i]->tickReadDevice();
|
|
// input.tickWriteDevice();
|
|
// }
|
|
|
|
|
|
// ///////////////////////////////////////////////////////////////////
|
|
// template <typename T>
|
|
// static __global__ void stackCuda(void* pVx, const Nd4jLong* xTadShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int axis) {
|
|
|
|
// T* z = reinterpret_cast<T*>(vz);
|
|
|
|
// __shared__ Nd4jLong zLen, totalThreads;
|
|
// __shared__ int zRank;
|
|
|
|
// if (threadIdx.x == 0) {
|
|
// zLen = shape::length(zShapeInfo);
|
|
// zRank = shape::rank(zShapeInfo);
|
|
// totalThreads = gridDim.x * blockDim.x;
|
|
// }
|
|
// __syncthreads();
|
|
|
|
// const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
|
|
|
// Nd4jLong coords[MAX_RANK];
|
|
|
|
// for (uint64_t i = tid; i < zLen; i += totalThreads) {
|
|
|
|
// shape::index2coords(i, zShapeInfo, coords);
|
|
|
|
// const auto zOffset = shape::getOffset(zShapeInfo, coords);
|
|
|
|
// const T *x = reinterpret_cast<const T*>(reinterpret_cast<void**>(pVx)[coords[axis]]);
|
|
|
|
// for (uint j = axis; j < zRank - 1; ++j) // shift coords staring from axis position
|
|
// coords[j] = coords[j + 1];
|
|
|
|
// const auto xOffset = shape::getOffset(xTadShapeInfo, coords);
|
|
|
|
// z[zOffset] = x[xOffset];
|
|
// }
|
|
// }
|
|
|
|
// ///////////////////////////////////////////////////////////////////
|
|
// template<typename T>
|
|
// __host__ static void stackCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
|
|
// void* pVx, const Nd4jLong* xTadShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int axis) {
|
|
|
|
// stackCuda<T><<<blocksPerGrid, threadsPerBlock, 256, *stream>>>(pVx, xTadShapeInfo, vz, zShapeInfo, axis);
|
|
// }
|
|
// BUILD_SINGLE_TEMPLATE(template void stackCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, void* pVx, const Nd4jLong* xTadShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int axis), LIBND4J_TYPES);
|
|
|
|
|
|
// ///////////////////////////////////////////////////////////////////
|
|
// void stack(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output, const int axis) {
|
|
|
|
// const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
// const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
|
|
|
|
// const int numOfSubArrs = inArrs.size();
|
|
|
|
// std::vector<void*> hInBuffers(numOfSubArrs);
|
|
|
|
// for(int i = 0; i < numOfSubArrs; ++i)
|
|
// hInBuffers[i] = inArrs[i]->specialBuffer();
|
|
|
|
// PointersManager manager(context, "helpers::stack");
|
|
|
|
// void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*));
|
|
|
|
// for(uint i = 0; i < numOfSubArrs; ++i)
|
|
// inArrs[i]->syncToDevice();
|
|
// output.syncToDevice();
|
|
|
|
// BUILD_SINGLE_SELECTOR(output.dataType(), stackCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), dInBuffers, inArrs[0]->specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), axis), LIBND4J_TYPES);
|
|
|
|
// manager.synchronize();
|
|
|
|
// for(uint i = 0; i < numOfSubArrs; ++i)
|
|
// inArrs[i]->tickReadDevice();
|
|
// output.tickWriteDevice();
|
|
// }
|
|
|
|
}
|
|
}
|
|
}
|
|
|