wehub-resource-sync
530 lines
21 KiB
Plaintext
530 lines
21 KiB
Plaintext
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 raver119@gmail.com
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//
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#include <helpers/ConstantTadHelper.h>
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#include <helpers/PointersManager.h>
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#include <ops/declarable/helpers/dynamic.h>
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#include "execution/cuda/LaunchDims.h"
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#include "helpers/DebugHelper.h"
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namespace sd {
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namespace ops {
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namespace helpers {
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template <typename X, typename Y>
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static SD_KERNEL void dynamicPartitionScalarKernel(const void *vx, const LongType *xShapeInfo, const void *vi,
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const LongType *iShapeInfo, void **vz, LongType **zShapeInfos, const LongType numOutputs) {
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auto x = reinterpret_cast<const X *>(vx);
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auto i = reinterpret_cast<const Y *>(vi);
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__shared__ LongType xRank, iRank;
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__shared__ const LongType *xShape, *xStride;
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__shared__ const LongType *iShape, *iStride;
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// Shared variables for CUDA kernel
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if (threadIdx.x == 0) {
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xRank = shape::rank(xShapeInfo);
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iRank = shape::rank(iShapeInfo);
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xShape = shape::shapeOf(xShapeInfo);
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xStride = shape::stride(xShapeInfo);
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iShape = shape::shapeOf(iShapeInfo);
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iStride = shape::stride(iShapeInfo);
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}
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__syncthreads();
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auto xLength = shape::length(xShapeInfo);
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auto iLength = shape::length(iShapeInfo);
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extern __shared__ char shmem[];
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__shared__ Y *rawIndices;
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__shared__ Y *trueIndices;
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if (threadIdx.x == 0) {
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rawIndices = reinterpret_cast<Y *>(shmem);
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trueIndices = rawIndices + blockDim.x;
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}
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__syncthreads();
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// Process partitions
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for (LongType o = blockIdx.x; o < numOutputs; o += gridDim.x) {
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auto z = reinterpret_cast<X *>(vz[o]);
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auto zShapeInfo = zShapeInfos[o];
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__shared__ LongType zLength, zRank;
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__shared__ const LongType *zShape, *zStride;
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if (threadIdx.x == 0) {
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zLength = shape::length(zShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zShape = shape::shapeOf(zShapeInfo);
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zStride = shape::stride(zShapeInfo);
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}
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__syncthreads();
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// Ensure iLimit is a multiple of blockDim.x
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auto iLimit = (iLength <= blockDim.x) ? blockDim.x : (iLength + (blockDim.x - (iLength % blockDim.x)));
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int cnt = 0;
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for (LongType e = threadIdx.x; e < iLimit; e += blockDim.x) {
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if (e < iLength) {
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LongType iOffset, iCoords[SD_MAX_RANK];
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INDEX2COORDS(e, iRank, iShape, iCoords);
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COORDS2INDEX(iRank, iStride, iCoords, iOffset);
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rawIndices[threadIdx.x] = i[iOffset];
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}
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__syncthreads();
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// Map updates using prefix-like approach
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if (threadIdx.x == 0) {
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for (int f = 0; f < blockDim.x; f++) {
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if (rawIndices[f] == static_cast<Y>(o))
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trueIndices[f] = cnt++;
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else
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trueIndices[f] = -1;
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}
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}
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__syncthreads();
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// Perform actual update
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if (e < iLength && trueIndices[threadIdx.x] >= 0) {
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LongType xOffset, xCoords[SD_MAX_RANK];
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INDEX2COORDS(e, xRank, xShape, xCoords);
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COORDS2INDEX(xRank, xStride, xCoords, xOffset);
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z[trueIndices[threadIdx.x]] = x[xOffset];
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}
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__syncthreads();
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}
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}
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}
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template <typename X, typename Y>
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static SD_KERNEL void dynamicPartitionTadKernel(const void *vx, const LongType *xTadShapeInfo,
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const LongType *xTadOffsets, LongType xLength,
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const void *vindices, const LongType *iShapeInfo, LongType iLength, void **vz,
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LongType **zTadShapeInfos, LongType **zTadOffsets,
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LongType numOutputs) {
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auto x = reinterpret_cast<const X *>(vx);
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auto indices = reinterpret_cast<const Y *>(vindices);
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// we run things in blocks, 1 partition per block of threads
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for (int i = blockIdx.x; i < numOutputs; i += gridDim.x) {
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auto z = reinterpret_cast<X *>(vz[i]);
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// each thread has own counter for partitions
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int outCnt = 0;
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for (LongType e = 0; e < iLength; e++) {
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LongType iCoords[SD_MAX_RANK];
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LongType iOffset;
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INDEX2COORDS(e, shape::rank(iShapeInfo), shape::shapeOf(iShapeInfo), iCoords);
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COORDS2INDEX(shape::rank(iShapeInfo), shape::stride(iShapeInfo), iCoords, iOffset);
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if (indices[iOffset] == i) {
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auto dx = x + xTadOffsets[e];
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auto dz = z + zTadOffsets[i][outCnt++];
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for (int f = threadIdx.x; f < xLength; f += blockDim.x) {
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LongType fCoords[SD_MAX_RANK];
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LongType xOffset;
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LongType zOffset;
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INDEX2COORDS(f, shape::rank(xTadShapeInfo), shape::shapeOf(xTadShapeInfo), fCoords);
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COORDS2INDEX(shape::rank(xTadShapeInfo), shape::stride(xTadShapeInfo), fCoords, xOffset);
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INDEX2COORDS(f, shape::rank(zTadShapeInfos[i]), shape::shapeOf(zTadShapeInfos[i]), fCoords);
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COORDS2INDEX(shape::rank(zTadShapeInfos[i]), shape::stride(zTadShapeInfos[i]), fCoords, zOffset);
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dz[zOffset] = dx[xOffset];
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}
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}
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}
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}
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}
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template <typename X, typename Y>
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static void _dynamicPartitionFunctor(LaunchContext *context, NDArray *input, NDArray *indices,
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std::vector<NDArray *> &outputList) {
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std::vector<std::pair<NDArray *, int>> outputs(outputList.size());
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int sourceDimsLen = input->rankOf() - indices->rankOf();
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unsigned int outSize = outputList.size();
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PointersManager pm(context, "dynamicPartition");
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if (sourceDimsLen) { // non-linear case
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std::vector<LongType> sourceDims(sourceDimsLen);
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for (int i = sourceDimsLen; i > 0; i--) sourceDims[sourceDimsLen - i] = input->rankOf() - i;
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// compute tad array for given dimensions
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auto packX = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), &sourceDims);
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std::vector<void *> outBuffers(outSize);
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std::vector<const LongType *> tadShapes(outSize);
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std::vector<const LongType *> tadOffsets(outSize);
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std::vector<LongType> numTads(outSize);
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// fill up dimensions array for before kernel
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for (unsigned int i = 0; i < outSize; i++) {
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outputs[i].first = outputList[i];
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std::vector<LongType> outDims(outputs[i].first->rankOf() - 1);
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int r = outputs[i].first->rankOf();
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for (int k = 1; k < r; k++) outDims[k - 1] = k;
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auto packZ = ConstantTadHelper::getInstance().tadForDimensions(outputList.at(i)->shapeInfo(), &outDims);
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outBuffers[i] = outputList.at(i)->specialBuffer();
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tadShapes[i] = packZ->platformShapeInfo();
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tadOffsets[i] = packZ->platformOffsets();
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}
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// we copy pointers to device
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auto dOutBuffers =
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reinterpret_cast<void **>(pm.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void *)));
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auto dOutTadShapes = reinterpret_cast<LongType **>(
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pm.replicatePointer(tadShapes.data(), tadShapes.size() * sizeof(LongType *)));
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auto dOutTadOffsets = reinterpret_cast<LongType **>(
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pm.replicatePointer(tadOffsets.data(), tadOffsets.size() * sizeof(LongType *)));
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// run kernel on device
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dim3 launchDims = getDynamicPartitionDims(256,sizeof(Y));
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dynamicPartitionTadKernel<X, Y><<<launchDims.y,launchDims.x, launchDims.z, *context->getCudaStream()>>>(
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input->specialBuffer(), packX->platformShapeInfo(), packX->platformOffsets(),
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shape::length(packX->primaryShapeInfo()), indices->specialBuffer(), indices->specialShapeInfo(),
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indices->lengthOf(), dOutBuffers, dOutTadShapes, dOutTadOffsets, outSize);
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DebugHelper::checkErrorCode(context->getCudaStream(),"dynamicPartitionTadKernel failed");
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} else { // linear case
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dim3 launchDims = getDynamicPartitionDims(256,sizeof(Y));
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std::vector<void *> outBuffers;
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std::vector<const LongType *> outShapes;
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for (auto v : outputList) {
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outBuffers.emplace_back(v->specialBuffer());
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outShapes.emplace_back(v->specialShapeInfo());
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}
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auto dOutBuffers =
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reinterpret_cast<void **>(pm.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void *)));
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auto dOutShapes = reinterpret_cast<LongType **>(
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pm.replicatePointer(outShapes.data(), outShapes.size() * sizeof(LongType *)));
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dynamicPartitionScalarKernel<X, Y><<<launchDims.y,launchDims.x, launchDims.z, *context->getCudaStream()>>>(
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input->specialBuffer(), input->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(),
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dOutBuffers, dOutShapes, outSize);
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DebugHelper::checkErrorCode(context->getCudaStream(),"dynamicPartitionScalarKernel failed");
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}
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pm.synchronize();
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}
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template <typename X, typename Y>
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static SD_KERNEL void dynamicStitchScalarKernel(void **vx, LongType **xShapeInfos, void **vindices,
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LongType **iShapeInfos, int inputSize, void *vz,
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const LongType *zShapeInfo, LongType zLength) {
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__shared__ LongType zRank;
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__shared__ const LongType *zShapePtr, *zStridePtr;
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if (threadIdx.x == 0) {
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zRank = shape::rank(zShapeInfo);
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zShapePtr = shape::shapeOf(zShapeInfo);
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zStridePtr = shape::stride(zShapeInfo);
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}
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__syncthreads();
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auto z = reinterpret_cast<X *>(vz);
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// Process each input array
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for (int e = blockIdx.x; e < inputSize; e += gridDim.x) {
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auto x = reinterpret_cast<X *>(vx[e]);
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auto indices = reinterpret_cast<Y *>(vindices[e]);
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auto xShapeInfo = xShapeInfos[e];
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auto iShapeInfo = iShapeInfos[e];
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auto iLength = shape::length(iShapeInfo);
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// Loop over indices in parallel
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for (int i = threadIdx.x; i < iLength; i += blockDim.x) {
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LongType iCoords[SD_MAX_RANK], xCoords[SD_MAX_RANK], zCoords[SD_MAX_RANK];
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LongType iOffset, xOffset, zOffset;
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// Compute index for indices array
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INDEX2COORDS(i, shape::rank(iShapeInfo), shape::shapeOf(iShapeInfo), iCoords);
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COORDS2INDEX(shape::rank(iShapeInfo), shape::stride(iShapeInfo), iCoords, iOffset);
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auto idx = indices[iOffset];
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if (idx >= 0 && idx < zLength) {
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// Compute z offset
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INDEX2COORDS(idx, zRank, zShapePtr, zCoords);
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COORDS2INDEX(zRank, zStridePtr, zCoords, zOffset);
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// Compute x offset
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INDEX2COORDS(i, shape::rank(xShapeInfo), shape::shapeOf(xShapeInfo), xCoords);
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COORDS2INDEX(shape::rank(xShapeInfo), shape::stride(xShapeInfo), xCoords, xOffset);
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// Assign value to z
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z[zOffset] = x[xOffset];
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}
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}
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}
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}
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template <typename X, typename Y>
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static SD_KERNEL void dynamicStitchTadKernel(void **vx, LongType **xTadShapeInfos, LongType **xTadOffsets,
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void **vindices, LongType **iShapeInfos, int inputSize, void *vz,
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const LongType *zTadShapeInfo, const LongType *zTadOffsets,
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LongType *numTadsPerInput, LongType numOutputsTad) {
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__shared__ LongType zRank, zLength, zTadLength;
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__shared__ const LongType *zShapePtr, *zStridePtr;
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if (threadIdx.x == 0) {
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zRank = shape::rank(zTadShapeInfo);
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zLength = shape::length(zTadShapeInfo);
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zTadLength = shape::length(zTadShapeInfo);
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zShapePtr = shape::shapeOf(zTadShapeInfo);
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zStridePtr = shape::stride(zTadShapeInfo);
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}
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__syncthreads();
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auto bz = reinterpret_cast<X *>(vz);
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// Process each input array
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for (int e = threadIdx.x; e < inputSize; e += blockDim.x) {
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auto indices = reinterpret_cast<Y *>(vindices[e]);
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auto iShapeInfo = iShapeInfos[e];
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auto numTads = numTadsPerInput[e];
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if (shape::isEmptyConst(iShapeInfo)) continue;
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auto iLength = shape::length(iShapeInfo);
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auto xTadShapeInfo = xTadShapeInfos[e];
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auto xTadLength = shape::length(xTadShapeInfo);
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auto xShapePtr = shape::shapeOf(xTadShapeInfo);
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auto xStridePtr = shape::stride(xTadShapeInfo);
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// Process each index in the input
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for (int i = 0; i < iLength; i++) {
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LongType iCoords[SD_MAX_RANK], iOffset;
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INDEX2COORDS(i, shape::rank(iShapeInfo), shape::shapeOf(iShapeInfo), iCoords);
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COORDS2INDEX(shape::rank(iShapeInfo), shape::stride(iShapeInfo), iCoords, iOffset);
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auto idx = indices[iOffset];
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// Input array offset for current TAD
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auto x = reinterpret_cast<X *>(vx[e]) + xTadOffsets[e][i];
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auto zTad = bz + zTadOffsets[idx];
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// Copy data from input to output
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for (int j = threadIdx.x; j < xTadLength; j += blockDim.x) {
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LongType xCoords[SD_MAX_RANK], zCoords[SD_MAX_RANK];
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LongType xIdx, zIdx;
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INDEX2COORDS(j, shape::rank(xTadShapeInfo), xShapePtr, xCoords);
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COORDS2INDEX(shape::rank(xTadShapeInfo), xStridePtr, xCoords, xIdx);
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INDEX2COORDS(j, zRank, zShapePtr, zCoords);
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COORDS2INDEX(zRank, zStridePtr, zCoords, zIdx);
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if (xIdx < xTadLength && zIdx < zLength) {
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zTad[zIdx] = x[xIdx];
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}
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}
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}
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}
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__syncthreads();
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}
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template <typename X, typename Y>
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static Status _dynamicStitchFunctor(LaunchContext *context, std::vector<NDArray *> const &inputs,
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std::vector<NDArray *> const &indices, NDArray *output) {
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LongType inputSize = inputs.size();
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PointersManager pm(context, "dynamicStitch");
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if (output->isVector()) {
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std::vector<const void *> inputBuffers(inputSize);
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std::vector<const LongType *> inputShapes(inputSize);
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std::vector<const void *> indicesBuffers(inputSize);
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std::vector<const LongType *> indicesShapes(inputSize);
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for (LongType e = 0; e < inputSize; e++) {
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inputBuffers[e] = inputs.at(e)->specialBuffer();
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indicesBuffers[e] = indices.at(e)->specialBuffer();
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inputShapes[e] = inputs.at(e)->specialShapeInfo();
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indicesShapes[e] = indices.at(e)->specialShapeInfo();
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}
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// copying pointers to buffers to device
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auto dInputBuffers =
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reinterpret_cast<void **>(pm.replicatePointer(inputBuffers.data(), inputSize * sizeof(void *)));
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auto dIndicesBuffers =
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reinterpret_cast<void **>(pm.replicatePointer(indicesBuffers.data(), inputSize * sizeof(void *)));
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auto dInputShapes =
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reinterpret_cast<LongType **>(pm.replicatePointer(inputShapes.data(), inputSize * sizeof(LongType *)));
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auto dIndicesShapes = reinterpret_cast<LongType **>(
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pm.replicatePointer(indicesShapes.data(), inputSize * sizeof(LongType *)));
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dim3 launchDims = getLaunchDims("dynamic_stitch_tad");
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dynamicStitchScalarKernel<X, Y><<<launchDims.y, launchDims.x, launchDims.z, *context->getCudaStream()>>>(
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dInputBuffers, dInputShapes, dIndicesBuffers, dIndicesShapes, inputSize, output->specialBuffer(),
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output->specialShapeInfo(), output->lengthOf());
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DebugHelper::checkErrorCode(context->getCudaStream(),"dynamicStitchScalarKernel failed");
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} else {
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std::vector<LongType> restDims(output->rankOf() - 1);
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for (int i = restDims.size(); i > 0; i--) restDims[restDims.size() - i] = output->rankOf() - i;
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auto packZ = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), &restDims);
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std::vector<const void *> inputBuffers(inputSize);
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std::vector<const LongType *> inputTadShapes(inputSize);
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std::vector<const LongType *> inputTadOffsets(inputSize);
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std::vector<const void *> indicesBuffers(inputSize);
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std::vector<const LongType *> indicesShapes(inputSize);
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std::vector<LongType> inputsNumTads(inputSize);
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for (LongType e = 0; e < inputSize; e++) {
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std::vector<LongType> sourceDims(inputs[e]->rankOf() - indices[e]->rankOf());
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for (LongType i = sourceDims.size(); i > 0; i--) sourceDims[sourceDims.size() - i] = inputs[e]->rankOf() - i;
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auto packX = ConstantTadHelper::getInstance().tadForDimensions(inputs[e]->shapeInfo(), &sourceDims);
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indicesBuffers[e] = indices[e]->specialBuffer();
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indicesShapes[e] = indices[e]->specialShapeInfo();
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inputsNumTads[e] = packX->numberOfTads();
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inputBuffers[e] = inputs[e]->specialBuffer();
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inputTadShapes[e] = packX->platformShapeInfo();
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inputTadOffsets[e] = packX->platformOffsets();
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}
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// copying pointers to buffers to device
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auto dInputBuffers =
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reinterpret_cast<void **>(pm.replicatePointer(inputBuffers.data(), inputSize * sizeof(void *)));
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auto dInputTadShapes = reinterpret_cast<LongType **>(
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pm.replicatePointer(inputTadShapes.data(), inputSize * sizeof(LongType *)));
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auto dInputTadOffsets = reinterpret_cast<LongType **>(
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pm.replicatePointer(inputTadOffsets.data(), inputSize * sizeof(LongType *)));
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|
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auto dIndicesBuffers =
|
|
reinterpret_cast<void **>(pm.replicatePointer(indicesBuffers.data(), inputSize * sizeof(void *)));
|
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auto dIndicesShapes = reinterpret_cast<LongType **>(
|
|
pm.replicatePointer(indicesShapes.data(), inputSize * sizeof(LongType *)));
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|
|
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auto dNumTadsInputs = reinterpret_cast<LongType *>(
|
|
pm.replicatePointer(inputsNumTads.data(), inputSize * sizeof(LongType *)));
|
|
|
|
|
|
dim3 launchDims = getLaunchDims("dynamic_stitch_tad");
|
|
dynamicStitchTadKernel<X, Y><<<launchDims.x, launchDims.y, launchDims.z, *context->getCudaStream()>>>(
|
|
dInputBuffers, dInputTadShapes, dInputTadOffsets, dIndicesBuffers, dIndicesShapes, inputSize,
|
|
output->specialBuffer(), packZ->platformShapeInfo(), packZ->platformOffsets(),dNumTadsInputs, packZ->numberOfTads());
|
|
DebugHelper::checkErrorCode(context->getCudaStream(),"dynamicStitchTadKernel failed");
|
|
|
|
}
|
|
|
|
pm.synchronize();
|
|
|
|
return Status::OK;
|
|
}
|
|
|
|
template <typename T>
|
|
static void _dynamicPartitionFunctorBP(NDArray *input, NDArray *indices,
|
|
std::vector<NDArray *> const &inputGradientList,
|
|
std::vector<NDArray *> &outputList) {}
|
|
|
|
void dynamicPartitionFunctor(LaunchContext *context, NDArray *input, NDArray *indices,
|
|
std::vector<NDArray *> &outputList) {
|
|
auto xType = input->dataType();
|
|
auto yType = indices->dataType();
|
|
|
|
NDArray::prepareSpecialUse({}, {indices, input});
|
|
|
|
BUILD_DOUBLE_SELECTOR(xType, yType, _dynamicPartitionFunctor, (context, input, indices, outputList), SD_NUMERIC_TYPES,
|
|
SD_INDEXING_TYPES);
|
|
|
|
NDArray::registerSpecialUse({}, {indices, input});
|
|
|
|
// TODO: it would be nice to have NDArray::registerSpecialUse signature that accepts something else beyond
|
|
// initializer_list
|
|
for (auto v : outputList) {
|
|
v->tickWriteDevice();
|
|
}
|
|
}
|
|
|
|
template <typename T>
|
|
static Status _dynamicStitchFunctorBP(std::vector<NDArray *> const &inputs, std::vector<NDArray *> const &indices,
|
|
NDArray *gradInput, std::vector<NDArray *> &outputList) {
|
|
THROW_EXCEPTION("Not implemented yet");
|
|
}
|
|
|
|
Status dynamicStitchFunctor(LaunchContext *context, std::vector<NDArray *> const &inputs,
|
|
std::vector<NDArray *> const &indices, NDArray *output) {
|
|
auto xType = inputs.at(0)->dataType();
|
|
auto yType = indices.at(0)->dataType();
|
|
|
|
for (auto v : indices) {
|
|
v->syncToDevice();
|
|
v->tickReadDevice();
|
|
}
|
|
|
|
for (auto v : inputs) {
|
|
v->syncToDevice();
|
|
v->tickReadDevice();
|
|
}
|
|
|
|
NDArray::prepareSpecialUse({output}, {});
|
|
|
|
BUILD_DOUBLE_SELECTOR(xType, yType, _dynamicStitchFunctor, (context, inputs, indices, output), SD_NUMERIC_TYPES,
|
|
SD_INDEXING_TYPES);
|
|
|
|
NDArray::registerSpecialUse({output}, {});
|
|
|
|
return Status::OK;
|
|
}
|
|
|
|
Status dynamicStitchFunctorBP(LaunchContext *context, std::vector<NDArray *> const &inputs,
|
|
std::vector<NDArray *> const &indices, NDArray *gradInput,
|
|
std::vector<NDArray *> &outputList) {
|
|
auto xType = inputs.at(0)->dataType();
|
|
|
|
BUILD_SINGLE_SELECTOR(xType, return _dynamicStitchFunctorBP, (inputs, indices, gradInput, outputList),
|
|
SD_NUMERIC_TYPES);
|
|
}
|
|
|
|
void dynamicPartitionFunctorBP(LaunchContext *context, NDArray *input, NDArray *indices,
|
|
std::vector<NDArray *> const &inputGradientList, std::vector<NDArray *> &outputList) {
|
|
auto xType = input->dataType();
|
|
|
|
BUILD_SINGLE_SELECTOR(xType, _dynamicPartitionFunctorBP, (input, indices, inputGradientList, outputList),
|
|
SD_NUMERIC_TYPES);
|
|
}
|
|
|
|
} // namespace helpers
|
|
} // namespace ops
|
|
} // namespace sd
|