538 lines
25 KiB
Plaintext
538 lines
25 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 <array/NDArrayFactory.h>
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#include <helpers/TAD.h>
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#include <exceptions/cuda_exception.h>
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#include <helpers/PointersManager.h>
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#include <helpers/ConstantTadHelper.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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//////////////////////////////////////////////////////////////////////////
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template <typename T, typename Z>
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static __global__ void mergeMaxIndexCudaLauncher(void** inArrs, void** inShapes, const int numArrays, void* voutput, Nd4jLong* outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<Z*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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Z mIdx(0);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
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auto val = x[shape::getIndexOffset(e, xShape)];;
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if (mVal < val) {
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mIdx = static_cast<Z>(i);
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mVal = val;
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}
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}
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output[shape::getIndexOffset(e, outputShape)] = mIdx;
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}
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}
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template <typename T, typename Z>
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static void mergeMaxIndex_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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int nArrSize = static_cast<int>(inArrs.size());
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std::vector<void*> inBuffers(nArrSize), inShapes(nArrSize);
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for (int e = 0; e < nArrSize; e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMaxIndex");
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auto pInBuffers = reinterpret_cast<void**>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void*)));
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auto pInShapes = reinterpret_cast<void**>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void*)));
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auto length = output.lengthOf();
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
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mergeMaxIndexCudaLauncher<T, Z> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, nArrSize, output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeMaxIndex(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({ &output }, inArrs);
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BUILD_DOUBLE_SELECTOR(inArrs[0]->dataType(), output.dataType(), mergeMaxIndex_, (context, inArrs, output), LIBND4J_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({ &output }, inArrs);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void mergeMaxCudaLauncher(void** inArrs, void** inShapes, const int numArrays, void* voutput, Nd4jLong* outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<const T*>(inArrs[i]);
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auto xShape = reinterpret_cast<const Nd4jLong*>(inShapes[i]);
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auto val = x[shape::getIndexOffset(e, xShape)];;
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if (mVal < val)
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mVal = val;
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}
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output[shape::getIndexOffset(e, outputShape)] = mVal;
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}
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}
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template<typename T>
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static void mergeMax_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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int nArrsSize = static_cast<int>(inArrs.size());
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std::vector<void*> inBuffers(nArrsSize), inShapes(nArrsSize);
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for (int e = 0; e < nArrsSize; e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMax");
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auto pInBuffers = reinterpret_cast<void**>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void*)));
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auto pInShapes = reinterpret_cast<void**>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void*)));
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auto length = output.lengthOf();
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
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mergeMaxCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, nArrsSize, output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeMax(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({ &output }, inArrs);
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (context, inArrs, output), LIBND4J_TYPES);
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NDArray::registerSpecialUse({ &output }, inArrs);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void mergeMaxBpCudaLauncher(void** inArrs, void** inShapes, void* vgradient, Nd4jLong* gradientShape, const int numArrays,
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void** outArrs, void** outShapes, Nd4jLong length, bool bSameOrderAndEws1) {
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auto grad = reinterpret_cast<T*>(vgradient);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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int coords[MAX_RANK];
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for (Nd4jLong e = tid; e < length; e += step) {
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T mVal = -DataTypeUtils::max<T>();
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int nMaxIndex = 0;
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auto xOffset = e, zOffset = e, gradOffset = e;
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if (!bSameOrderAndEws1) {
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shape::index2coords(e, gradientShape, coords);
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gradOffset = shape::getOffset(gradientShape, coords);
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}
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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if (!bSameOrderAndEws1) {
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auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
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xOffset = shape::getOffset(xShape, coords);
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}
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auto val = x[xOffset];
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if (mVal < val) {
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mVal = val;
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nMaxIndex = i;
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}
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}
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// outputs have to be pre-nullify
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if (!bSameOrderAndEws1) {
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auto outShape = reinterpret_cast<Nd4jLong*>(outShapes[nMaxIndex]);
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zOffset = shape::getOffset(outShape, coords);
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}
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auto output = reinterpret_cast<T*>(outArrs[nMaxIndex]);
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output[zOffset] = grad[gradOffset];
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}
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}
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template<typename T>
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static void mergeMaxBp_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs, int nArrSize, bool bSameOrderAndEws1) {
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std::vector<void*> inBuffers(nArrSize), inShapes(nArrSize), outBuffers(nArrSize), outShapes(nArrSize);
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for (int e = 0; e < nArrSize; e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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outBuffers[e] = outArrs[e]->getSpecialBuffer();
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outShapes[e] = outArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeMaxBp");
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auto pInBuffers = reinterpret_cast<void**>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void*)));
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auto pInShapes = reinterpret_cast<void**>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void*)));
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auto pOutBuffers = reinterpret_cast<void**>(manager.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void*)));
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auto pOutShapes = reinterpret_cast<void**>(manager.replicatePointer(outShapes.data(), outShapes.size() * sizeof(void*)));
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auto length = inArrs[nArrSize]->lengthOf();
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
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mergeMaxBpCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, inArrs[nArrSize]->getSpecialBuffer(),
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inArrs[nArrSize]->getSpecialShapeInfo(), nArrSize, pOutBuffers, pOutShapes,
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length, bSameOrderAndEws1);
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manager.synchronize();
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}
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void mergeMaxBp(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs) {
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// not use gradient
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int nArrSize = static_cast<int>(inArrs.size() - 1);
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const std::vector<const NDArray*>& out = reinterpret_cast<const std::vector<const NDArray*>&>(outArrs);
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NDArray::prepareSpecialUse(out, inArrs);
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bool bSameOrderAndEws1 = (1 == inArrs[nArrSize]->ews());
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auto ordering = inArrs[nArrSize]->ordering();
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for (int i = 0; i < nArrSize; ++i) {
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bSameOrderAndEws1 &= (ordering == inArrs[i]->ordering());
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bSameOrderAndEws1 &= (1 == inArrs[i]->ews());
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bSameOrderAndEws1 &= (ordering == outArrs[i]->ordering());
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bSameOrderAndEws1 &= (1 == outArrs[i]->ews());
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}
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BUILD_SINGLE_SELECTOR(inArrs[nArrSize]->dataType(), mergeMaxBp_, (context, inArrs, outArrs, nArrSize, bSameOrderAndEws1), LIBND4J_TYPES);
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NDArray::registerSpecialUse( out, inArrs );
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void mergeAvgCudaLauncher(void** inArrs, void** inShapes, const int numArrays, void* voutput, Nd4jLong* outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T sum(0.0f);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
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sum += x[shape::getIndexOffset(e, xShape)];
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}
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output[shape::getIndexOffset(e, outputShape)] = sum / numArrays;
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}
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}
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template<typename T>
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static void mergeAvg_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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std::vector<void*> inBuffers(inArrs.size()), inShapes(inArrs.size());
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for (int e = 0; e < inArrs.size(); e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeAvg");
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auto pInBuffers = reinterpret_cast<void**>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void*)));
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auto pInShapes = reinterpret_cast<void**>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void*)));
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auto length = output.lengthOf();
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
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mergeAvgCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, (int)inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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void mergeAvg(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({ &output }, inArrs);
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (context, inArrs, output), FLOAT_TYPES);
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NDArray::registerSpecialUse({ &output }, inArrs);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void mergeAvgBpCudaLauncher(void* vgradient, Nd4jLong* gradientShape, void** outArrs, void** outShapes,
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const int numArrays, Nd4jLong length, bool bSameOrderAndEws1) {
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auto grad = reinterpret_cast<T*>(vgradient);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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int coords[MAX_RANK];
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for (Nd4jLong e = tid; e < length; e += step) {
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auto zOffset = e, gradOffset = e;
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if (!bSameOrderAndEws1) {
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shape::index2coords(e, gradientShape, coords);
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gradOffset = shape::getOffset(gradientShape, coords);
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}
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for (int i = 0; i < numArrays; i++) {
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if (!bSameOrderAndEws1) {
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auto outShape = reinterpret_cast<Nd4jLong*>(outShapes[i]);
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zOffset = shape::getOffset(outShape, coords);
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}
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auto output = reinterpret_cast<T*>(outArrs[i]);
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output[zOffset] = grad[gradOffset] / numArrays;
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}
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}
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}
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template<typename T>
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static void mergeAvgBp_(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs, bool bSameOrderAndEws1) {
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int nArrSize = static_cast<int>(outArrs.size());
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std::vector<void*> outBuffers(nArrSize), outShapes(nArrSize);
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for (int e = 0; e < nArrSize; e++) {
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outBuffers[e] = outArrs[e]->getSpecialBuffer();
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outShapes[e] = outArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeAvgBp");
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auto pOutBuffers = reinterpret_cast<void**>(manager.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void*)));
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auto pOutShapes = reinterpret_cast<void**>(manager.replicatePointer(outShapes.data(), outShapes.size() * sizeof(void*)));
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auto length = gradient.lengthOf();
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
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mergeAvgBpCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (gradient.getSpecialBuffer(), gradient.getSpecialShapeInfo(),
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pOutBuffers, pOutShapes, nArrSize, length, bSameOrderAndEws1);
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manager.synchronize();
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}
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void mergeAvgBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
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const std::vector<const NDArray*>& out = reinterpret_cast<const std::vector<const NDArray*>&>(outArrs);
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NDArray::prepareSpecialUse( out, { &gradient });
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bool bSameOrderAndEws1 = (1 == gradient.ews());
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auto ordering = gradient.ordering();
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for (const auto& v : outArrs) {
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bSameOrderAndEws1 &= (ordering == v->ordering());
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bSameOrderAndEws1 &= (1 == v->ews());
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}
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BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAvgBp_, (context, gradient, outArrs, bSameOrderAndEws1), LIBND4J_TYPES);
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NDArray::prepareSpecialUse(out, { &gradient });
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void mergeAddCudaLauncher(void** inArrs, void** inShapes, const int numArrays, void* voutput, Nd4jLong* outputShape, Nd4jLong length) {
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auto output = reinterpret_cast<T*>(voutput);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (Nd4jLong e = tid; e < length; e += step) {
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T sum(0.0f);
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for (int i = 0; i < numArrays; i++) {
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auto x = reinterpret_cast<T*>(inArrs[i]);
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auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
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sum += x[shape::getIndexOffset(e, xShape)];
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}
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output[shape::getIndexOffset(e, outputShape)] = sum;
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}
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}
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template<typename T>
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static void mergeAdd_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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int nArrSize = static_cast<int>(inArrs.size());
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std::vector<void*> inBuffers(nArrSize), inShapes(nArrSize);
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for (int e = 0; e < nArrSize; e++) {
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inBuffers[e] = inArrs[e]->getSpecialBuffer();
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inShapes[e] = inArrs[e]->getSpecialShapeInfo();
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}
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PointersManager manager(context, "mergeAdd");
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auto pInBuffers = reinterpret_cast<void**>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void*)));
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auto pInShapes = reinterpret_cast<void**>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void*)));
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auto length = output.lengthOf();
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
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mergeAddCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, nArrSize, output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
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manager.synchronize();
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}
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BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output), NUMERIC_TYPES);
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void mergeAdd(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
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NDArray::prepareSpecialUse({ &output }, inArrs);
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BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (context, inArrs, output), NUMERIC_TYPES);
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NDArray::registerSpecialUse({ &output }, inArrs);
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}
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//////////////////////////////////////////////////////////////////////////
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template <typename T>
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static __global__ void mergeAddBpCudaLauncher(void* vgradient, Nd4jLong* gradientShape, void** outArrs, void** outShapes,
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const int numArrays, Nd4jLong length, bool bSameOrderAndEws1) {
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auto grad = reinterpret_cast<T*>(vgradient);
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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int coords[MAX_RANK];
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for (Nd4jLong e = tid; e < length; e += step) {
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auto zOffset = e, gradOffset = e;
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if (!bSameOrderAndEws1) {
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shape::index2coords(e, gradientShape, coords);
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gradOffset = shape::getOffset(gradientShape, coords);
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}
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|
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for (int i = 0; i < numArrays; i++) {
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|
|
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if (!bSameOrderAndEws1) {
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auto outShape = reinterpret_cast<Nd4jLong*>(outShapes[i]);
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zOffset = shape::getOffset(outShape, coords);
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}
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|
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auto output = reinterpret_cast<T*>(outArrs[i]);
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output[zOffset] = grad[gradOffset];
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}
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|
}
|
|
}
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|
|
|
template<typename T>
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static void mergeAddBp_(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs, bool bSameOrderAndEws1) {
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|
|
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int nArrSize = static_cast<int>(outArrs.size());
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|
|
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std::vector<void*> outBuffers(nArrSize), outShapes(nArrSize);
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|
|
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for (int e = 0; e < nArrSize; e++) {
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outBuffers[e] = outArrs[e]->getSpecialBuffer();
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|
outShapes[e] = outArrs[e]->getSpecialShapeInfo();
|
|
}
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|
|
|
PointersManager manager(context, "mergeAddBp");
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|
|
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auto pOutBuffers = reinterpret_cast<void**>(manager.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void*)));
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|
auto pOutShapes = reinterpret_cast<void**>(manager.replicatePointer(outShapes.data(), outShapes.size() * sizeof(void*)));
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|
|
|
auto length = gradient.lengthOf();
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|
|
|
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
|
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
|
|
|
mergeAddBpCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (gradient.getSpecialBuffer(), gradient.getSpecialShapeInfo(),
|
|
pOutBuffers, pOutShapes, nArrSize, length, bSameOrderAndEws1);
|
|
|
|
manager.synchronize();
|
|
}
|
|
|
|
void mergeAddBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
|
|
|
const std::vector<const NDArray*>& out = reinterpret_cast<const std::vector<const NDArray*>& >(outArrs);
|
|
NDArray::prepareSpecialUse( out, { &gradient });
|
|
|
|
bool bSameOrderAndEws1 = (1 == gradient.ews());
|
|
auto ordering = gradient.ordering();
|
|
|
|
for (const auto& v : outArrs) {
|
|
bSameOrderAndEws1 &= (ordering == v->ordering());
|
|
bSameOrderAndEws1 &= (1 == v->ews());
|
|
}
|
|
|
|
BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAddBp_, (context, gradient, outArrs, bSameOrderAndEws1), LIBND4J_TYPES);
|
|
|
|
NDArray::prepareSpecialUse( out, { &gradient });
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|