132 lines
6.3 KiB
Plaintext
132 lines
6.3 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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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// @author Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
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//
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#include<ops/declarable/helpers/transforms.h>
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#include <array/ResultSet.h>
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#include <helpers/ShapeUtils.h>
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#include <numeric>
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#include <NDArrayFactory.h>
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#include <helpers/TAD.h>
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#include <exceptions/cuda_exception.h>
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#include <PointersManager.h>
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#include <ConstantTadHelper.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ static void concatCuda(const int numOfArrs, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo) {
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__shared__ int arrIdx, blocksPerArr;
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if (threadIdx.x == 0) {
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blocksPerArr = (gridDim.x + numOfArrs - 1) / numOfArrs; // ceil
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arrIdx = blockIdx.x / blocksPerArr;
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}
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__syncthreads();
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for(int j = arrIdx; j < numOfArrs; j += gridDim.x) {
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const auto* x = reinterpret_cast<T*>(reinterpret_cast<void**>(pVx)[j]);
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auto* z = reinterpret_cast<T*>(reinterpret_cast<void**>(pVz)[j]);
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const auto* xShapeInfo = reinterpret_cast<Nd4jLong**>(pxShapeInfo)[j];
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const auto* zShapeInfo = reinterpret_cast<Nd4jLong**>(pzShapeInfo)[j];
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const auto arrLen = shape::length(xShapeInfo);
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const auto arrLenPerBlock = (arrLen + blocksPerArr - 1) / blocksPerArr; // ceil
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const auto start = (blockIdx.x % blocksPerArr) * arrLenPerBlock;
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const auto end = (start + arrLenPerBlock) > arrLen ? arrLen : (start + arrLenPerBlock);
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for (Nd4jLong i = start + threadIdx.x; i < end; i += blockDim.x)
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z[shape::getIndexOffset(i, zShapeInfo, arrLen)] = x[shape::getIndexOffset(i, xShapeInfo, arrLen)];
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__host__ static void concatCudaLauncher(const int numOfArrs, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo) {
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concatCuda<T><<<512, 512, 512, *stream>>>(numOfArrs, pVx, pxShapeInfo, pVz, pzShapeInfo);
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}
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BUILD_SINGLE_TEMPLATE(template void concatCudaLauncher, (const int numOfArrs, const cudaStream_t *stream, void* pVx, void* pxShapeInfo, void* pVz, void* pzShapeInfo), LIBND4J_TYPES);
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//////////////////////////////////////////////////////////////////////////
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void concat(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output, const int axis) {
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const int numOfArrs = inArrs.size();
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for(int i = 0; i < numOfArrs; ++i)
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if(!inArrs[i]->isActualOnDeviceSide()) inArrs[i]->syncToDevice();
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const int rank = inArrs[0]->rankOf();
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const int rank2 = 2*rank;
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std::vector<std::vector<Nd4jLong>> indices(numOfArrs, std::vector<Nd4jLong>(rank2,0));
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// take into account indices for first array
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indices[0][2 * axis + 1] = inArrs[0]->sizeAt(axis);
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// loop through the rest of input arrays
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for(int i = 1; i < numOfArrs; ++i) {
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indices[i][2 * axis] = indices[i-1][2 * axis + 1]; // index start from
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indices[i][2 * axis + 1] = indices[i-1][2 * axis + 1] + inArrs[i]->sizeAt(axis); // index end with (excluding)
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}
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std::vector<NDArray*> outSubArrs(numOfArrs);
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for(int i = 0; i < numOfArrs; ++i)
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outSubArrs[i] = new NDArray(output(indices[i], true));
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// prepare arrays of pointers on buffers and shapes
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std::vector<void*> hOutBuffers(numOfArrs), hInBuffers(numOfArrs);
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std::vector<Nd4jLong*> hOutShapeInfo(numOfArrs), hInShapeInfo(numOfArrs);
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for(int i = 0; i < numOfArrs; ++i) {
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hOutBuffers[i] = outSubArrs[i]->getSpecialBuffer();
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hInBuffers[i] = inArrs[i]->getSpecialBuffer();
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hOutShapeInfo[i] = outSubArrs[i]->getSpecialShapeInfo();
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hInShapeInfo[i] = inArrs[i]->getSpecialShapeInfo();
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}
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// allocate and copy all buffers and shapes arrays to global memory
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PointersManager manager(context, "helpers::concat");
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void* dOutBuffers = manager.replicatePointer(hOutBuffers.data(), hOutBuffers.size() * sizeof(void*));
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void* dInBuffers = manager.replicatePointer(hInBuffers.data(), hInBuffers.size() * sizeof(void*));
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void* dInShapeInfo = manager.replicatePointer(hInShapeInfo.data(), hInShapeInfo.size() * sizeof(Nd4jLong*));
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void* dOutShapeInfo = manager.replicatePointer(hOutShapeInfo.data(), hOutShapeInfo.size() * sizeof(Nd4jLong*));
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BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), concatCudaLauncher, (numOfArrs, context->getCudaStream(), dInBuffers, dInShapeInfo, dOutBuffers, dOutShapeInfo), LIBND4J_TYPES);
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manager.synchronize();
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for(int i = 0; i < numOfArrs; ++i)
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delete outSubArrs[i];
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for(int i = 0; i < numOfArrs; ++i)
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inArrs[i]->tickReadHost();
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output.tickWriteDevice();
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}
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}
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}
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} |