wehub-resource-sync
621 lines
28 KiB
Plaintext
621 lines
28 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 GS <sgazeos@gmail.com>
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//
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#include <array/NDArrayFactory.h>
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#include <exceptions/cuda_exception.h>
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#include <execution/cuda/LaunchDims.h>
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#include <helpers/ConstantTadHelper.h>
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#include <helpers/PointersManager.h>
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#include <helpers/ShapeUtils.h>
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#include <ops/declarable/helpers/segment.h>
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#include <ops/declarable/helpers/segment_common.h>
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#include "helpers/DebugHelper.h"
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#include <system/selective_rendering.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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// Segment ops linear kernels
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// -------------------------------------------------------------------------------------------------------------- //
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template <typename T, typename I>
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static SD_KERNEL void segmentMinLinearKernel(const void* input, const LongType* inputShape, LongType* starts,
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LongType* lengths, LongType numOfClasses, void* output,
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const LongType* outputShape) {
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__shared__ T* val;
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__shared__ LongType xLen, zLen, zIndex;
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__shared__ const T* x;
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__shared__ T* z;
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__shared__ LongType threadsPerSegment, start, finish;
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// Cache shape information
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__shared__ sd::LongType inputRank, outputRank;
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__shared__ const sd::LongType* inputShapePtr;
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__shared__ const sd::LongType* outputShapePtr;
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__shared__ const sd::LongType* inputStridePtr;
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__shared__ const sd::LongType* outputStridePtr;
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auto segment = blockIdx.x;
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if(blockIdx.x >= numOfClasses)
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return;
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if (threadIdx.x == 0) {
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x = reinterpret_cast<const T*>(input);
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z = reinterpret_cast<T*>(output);
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extern __shared__ unsigned char shmem[];
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val = reinterpret_cast<T*>(shmem);
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xLen = shape::length(inputShape);
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zLen = shape::length(outputShape);
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// Cache shape information
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inputRank = shape::rank(inputShape);
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outputRank = shape::rank(outputShape);
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inputShapePtr = shape::shapeOf(inputShape);
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outputShapePtr = shape::shapeOf(outputShape);
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inputStridePtr = shape::stride(inputShape);
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outputStridePtr = shape::stride(outputShape);
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if (segment < numOfClasses) {
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LongType zCoords[SD_MAX_RANK];
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INDEX2COORDS(segment, outputRank, outputShapePtr, zCoords);
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COORDS2INDEX(outputRank, outputStridePtr, zCoords, zIndex);
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if(zIndex >= zLen)
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return;
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start = starts[segment];
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finish = start + lengths[segment];
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LongType startCoords[SD_MAX_RANK];
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LongType startIndex;
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INDEX2COORDS(start, inputRank, inputShapePtr, startCoords);
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COORDS2INDEX(inputRank, inputStridePtr, startCoords, startIndex);
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z[zIndex] = x[startIndex];
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val[segment] = z[zIndex];
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}
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}
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__syncthreads();
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for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
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LongType eCoords[SD_MAX_RANK];
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LongType eIndex;
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INDEX2COORDS(e, inputRank, inputShapePtr, eCoords);
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COORDS2INDEX(inputRank, inputStridePtr, eCoords, eIndex);
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if (eIndex >= xLen) return;
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math::atomics::sd_atomicMin(&z[zIndex], x[eIndex]);
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}
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}
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template <typename T, typename I>
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static SD_KERNEL void unsortedSegmentMinLinearKernel(const void* input, const LongType* inputShape,
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const void* indices, const LongType* indicesShape, LongType* starts, LongType* lengths,
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LongType numOfClasses, void* output,
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const LongType* outputShape) {
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__shared__ T* val;
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__shared__ LongType xLen, zLen, segment, zIndex;
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__shared__ const T* x;
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__shared__ T* z;
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__shared__ const I* y;
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// Cache shape information
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__shared__ sd::LongType inputRank, outputRank, indicesRank;
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__shared__ const sd::LongType* inputShapePtr;
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__shared__ const sd::LongType* outputShapePtr;
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__shared__ const sd::LongType* indicesShapePtr;
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__shared__ const sd::LongType* inputStridePtr;
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__shared__ const sd::LongType* outputStridePtr;
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__shared__ const sd::LongType* indicesStridePtr;
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if (threadIdx.x == 0) {
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segment = blockIdx.x;
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x = reinterpret_cast<const T*>(input);
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z = reinterpret_cast<T*>(output);
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y = reinterpret_cast<const I*>(indices);
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xLen = shape::length(inputShape);
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zLen = shape::length(outputShape);
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// Cache shape information
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inputRank = shape::rank(inputShape);
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outputRank = shape::rank(outputShape);
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indicesRank = shape::rank(indicesShape);
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inputShapePtr = shape::shapeOf(inputShape);
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outputShapePtr = shape::shapeOf(outputShape);
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indicesShapePtr = shape::shapeOf(indicesShape);
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inputStridePtr = shape::stride(inputShape);
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outputStridePtr = shape::stride(outputShape);
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indicesStridePtr = shape::stride(indicesShape);
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LongType zCoords[SD_MAX_RANK];
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INDEX2COORDS(segment, outputRank, outputShapePtr, zCoords);
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COORDS2INDEX(outputRank, outputStridePtr, zCoords, zIndex);
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if (lengths[segment] > 0) {
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LongType startCoords[SD_MAX_RANK];
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LongType startIndex;
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INDEX2COORDS(starts[segment], inputRank, inputShapePtr, startCoords);
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COORDS2INDEX(inputRank, inputStridePtr, startCoords, startIndex);
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z[zIndex] = x[startIndex];
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} else {
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z[zIndex] = DataTypeUtils::max<T>();
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}
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}
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__syncthreads();
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if (lengths[segment] > 0) {
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for (auto e = threadIdx.x + 1; e < xLen; e += blockDim.x) {
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LongType eCoords[SD_MAX_RANK];
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LongType eIndex;
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INDEX2COORDS(e, inputRank, inputShapePtr, eCoords);
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COORDS2INDEX(inputRank, inputStridePtr, eCoords, eIndex);
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LongType yCoords[SD_MAX_RANK];
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LongType yIndex;
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INDEX2COORDS(e, indicesRank, indicesShapePtr, yCoords);
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COORDS2INDEX(indicesRank, indicesStridePtr, yCoords, yIndex);
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if (y[yIndex] == segment) {
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math::atomics::sd_atomicMin(&z[zIndex], x[eIndex]);
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}
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}
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}
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}
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template <typename T, typename I>
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static SD_KERNEL void segmentMinTadKernel(const void* inputBuf, const LongType* inputShape,
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const LongType* inputTads, const LongType* inputTadOffsets,
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I* indices, LongType* starts,
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LongType* lengths, LongType numOfClasses, void* outputBuf, const LongType* outputShape,
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const LongType* outputTads, const LongType* outputTadOffsets, LongType indicesLen) {
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__shared__ T* val;
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__shared__ LongType len, zIndex, total;
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__shared__ T* z;
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__shared__ int threadsPerSegment, start, finish;
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// Cache shape information
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__shared__ sd::LongType inputTadRank, outputTadRank;
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__shared__ const sd::LongType* inputTadShapePtr;
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__shared__ const sd::LongType* outputTadShapePtr;
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__shared__ const sd::LongType* inputTadStridePtr;
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__shared__ const sd::LongType* outputTadStridePtr;
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if(blockIdx.x >= indicesLen)
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return;
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auto segment = indices[blockIdx.x];
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if (threadIdx.x == 0) {
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z = reinterpret_cast<T*>(outputBuf) + outputTadOffsets[segment];
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len = shape::length(inputTads);
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start = starts[segment];
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finish = start + lengths[segment];
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total = shape::sizeAt(inputShape, 0);
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// Cache TAD shape information
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inputTadRank = shape::rank(inputTads);
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outputTadRank = shape::rank(outputTads);
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inputTadShapePtr = shape::shapeOf(inputTads);
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outputTadShapePtr = shape::shapeOf(outputTads);
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inputTadStridePtr = shape::stride(inputTads);
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outputTadStridePtr = shape::stride(outputTads);
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}
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__syncthreads();
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auto idx = blockIdx.x;
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if (blockIdx.x <= total) {
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auto x = reinterpret_cast<const T*>(inputBuf) + inputTadOffsets[idx];
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LongType xCoords[SD_MAX_RANK];
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LongType zCoords[SD_MAX_RANK];
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LongType xOffset;
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LongType zOffset;
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for (auto e = threadIdx.x; e < len; e += blockDim.x) {
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INDEX2COORDS(e, inputTadRank, inputTadShapePtr, xCoords);
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COORDS2INDEX(inputTadRank, inputTadStridePtr, xCoords, xOffset);
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INDEX2COORDS(e, outputTadRank, outputTadShapePtr, zCoords);
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COORDS2INDEX(outputTadRank, outputTadStridePtr, zCoords, zOffset);
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math::atomics::sd_atomicMin(&z[zOffset], x[xOffset]);
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}
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}
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}
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// -------------------------------------------------------------------------------------------------------------- //
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// segmen min
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template <typename T, typename I>
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static void segmentMinFunctor_(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) {
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auto stream = context->getCudaStream();
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LongType numClasses = indices->e<LongType>(indices->lengthOf() - 1) + 1;
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auto classesRangesLens = NDArrayFactory::create<LongType>('c', {numClasses}, context);
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auto classesRangesBegs = NDArrayFactory::create<LongType>('c', {numClasses}, context);
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T val = DataTypeUtils::infOrMax<T>();
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output->assign(val);
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sd::LongType zero2 = 0;
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sd::LongType len = indices->lengthOf();
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classesRangesBegs.assign(zero2);
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classesRangesLens.assign(len);
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fillUpSegments(indices, numClasses, classesRangesBegs, classesRangesLens);
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NDArray::prepareSpecialUse({output}, {input, indices, &classesRangesBegs, &classesRangesLens});
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LongType* begins = reinterpret_cast<LongType*>(classesRangesBegs.specialBuffer());
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LongType* lengths = reinterpret_cast<LongType*>(classesRangesLens.specialBuffer());
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if (input->isVector() || input->isScalar()) {
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dim3 launchDims = segmentDims(numClasses,input->lengthOf());
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segmentMinLinearKernel<T, I><<<launchDims.y,launchDims.x, launchDims.z, *stream>>>(
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input->specialBuffer(), input->specialShapeInfo(), begins, lengths, numClasses, output->specialBuffer(),
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output->specialShapeInfo());
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sd::DebugHelper::checkErrorCode(stream, "segmentMinLinearKernel failed");
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} else {
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LongType zero = 0;
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std::vector<LongType> *dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(),1,&zero);
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auto packX = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
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auto packZ = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimensions);
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auto inputTads = packX->specialShapeInfo();
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auto inputTadOffsets = packX->specialOffsets();
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auto outputTads = packZ->specialShapeInfo();
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auto outputTadOffsets = packZ->specialOffsets();
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dim3 launchDims = segmentTad(input->sizeAt(0));
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segmentMinTadKernel<T, I><<<launchDims.y, launchDims.x, launchDims.z, *stream>>>(
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input->specialBuffer(), input->specialShapeInfo(), inputTads, inputTadOffsets,
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reinterpret_cast<I*>(indices->specialBuffer()), begins, lengths, numClasses, output->specialBuffer(),
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output->specialShapeInfo(), outputTads, outputTadOffsets, indices->lengthOf());
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sd::DebugHelper::checkErrorCode(stream, "segmentMinTadKernel failed");
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delete dimensions;
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}
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NDArray::registerSpecialUse({output}, {input, indices, &classesRangesBegs, &classesRangesLens});
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}
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// -------------------------------------------------------------------------------------------------------------- //
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void segmentMinFunctor(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices});
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output->nullify();
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auto indicesDType = indices->dataType();
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auto outputDType = output->dataType();
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BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), segmentMinFunctor_, (context, input, indices, output),
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SD_NUMERIC_TYPES, SD_INDEXING_TYPES);
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NDArray::registerSpecialUse({output}, {input, indices});
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}
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// -------------------------------------------------------------------------------------------------------------- //
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template <typename T, typename I>
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static void unsortedSegmentMinFunctor_(LaunchContext* context, NDArray* input, NDArray* indices, LongType numOfClasses, NDArray* output) {
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auto stream = context->getCudaStream();
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NDArray classesRangesBegs = NDArrayFactory::create<LongType>('c', {numOfClasses}, context);
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NDArray classesRangesLens = NDArrayFactory::create<LongType>('c', {numOfClasses}, context);
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T val = DataTypeUtils::infOrMax<T>();
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sd::LongType len = indices->lengthOf();
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output->assign(val);
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sd::LongType zero = 0;
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classesRangesBegs.assign(len);
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classesRangesLens.assign(zero);
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dim3 dims = getFillUpSegmentsDims(numOfClasses, indices->lengthOf());
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fillUpSegments(indices, numOfClasses, classesRangesBegs, classesRangesLens);
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LongType* begins = reinterpret_cast<LongType*>(classesRangesBegs.specialBuffer());
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LongType* lengths = reinterpret_cast<LongType*>(classesRangesLens.specialBuffer());
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NDArray::prepareSpecialUse({output}, {input, indices});
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if (input->isVector() || input->isScalar()) {
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unsortedSegmentMinLinearKernel<T, I><<<dims.x, dims.y, dims.z, *stream>>>(
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input->specialBuffer(), input->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(),
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begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo());
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sd::DebugHelper::checkErrorCode(stream, "unsortedSegmentMinLinearKernel failed");
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} else {
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T val = DataTypeUtils::max<T>();
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output->assign(val);
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LongType zero = 0;
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std::vector<LongType> *dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(),1,&zero);
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auto packX = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
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auto packZ = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimensions);
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auto inputTads = packX->specialShapeInfo();
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auto inputTadOffsets = packX->specialOffsets();
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auto outputTads = packZ->specialShapeInfo();
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auto outputTadOffsets = packZ->specialOffsets();
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dims.x = input->sizeAt(0);
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segmentMinTadKernel<T, I><<<dims.x, dims.y, dims.z, *stream>>>(
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input->specialBuffer(), input->specialShapeInfo(), inputTads, inputTadOffsets,
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reinterpret_cast<I*>(indices->specialBuffer()), begins, lengths, numOfClasses, output->specialBuffer(),
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output->specialShapeInfo(), outputTads, outputTadOffsets, indices->lengthOf());
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sd::DebugHelper::checkErrorCode(stream, "segmentMinTadKernel failed");
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delete dimensions;
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}
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NDArray::registerSpecialUse({output}, {input, indices});
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}
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// -------------------------------------------------------------------------------------------------------------- //
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void unsortedSegmentMinFunctor(LaunchContext* context, NDArray* input, NDArray* indices, LongType numOfClasses,
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NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices});
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output->nullify();
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auto indicesDType = indices->dataType();
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auto outputDType = output->dataType();
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BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentMinFunctor_,
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(context, input, indices, numOfClasses, output), SD_NUMERIC_TYPES, SD_INDEXING_TYPES);
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NDArray::registerSpecialUse({output}, {input, indices});
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}
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template <typename T, typename I>
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static SD_KERNEL void segmentMinBPLinearKernel(const void* inputBuf, const LongType* inputShape,
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void* forwardOutput, const LongType* forwardShape, void* eps,
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const LongType* epsShape, const void* indicesBuf,
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const LongType* indicesShape, void* outputBuf,
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const LongType* outputShape) {
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__shared__ const T* x;
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__shared__ T* gradIn;
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__shared__ T* gradOut;
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__shared__ const I* y;
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__shared__ T* z;
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__shared__ LongType xLen, gradLen;
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// Cache shape information
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__shared__ sd::LongType inputRank, outputRank, indicesRank, forwardRank, epsRank;
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__shared__ const sd::LongType* inputShapePtr;
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__shared__ const sd::LongType* outputShapePtr;
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__shared__ const sd::LongType* indicesShapePtr;
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__shared__ const sd::LongType* forwardShapePtr;
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__shared__ const sd::LongType* epsShapePtr;
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__shared__ const sd::LongType* inputStridePtr;
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__shared__ const sd::LongType* outputStridePtr;
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__shared__ const sd::LongType* indicesStridePtr;
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__shared__ const sd::LongType* forwardStridePtr;
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__shared__ const sd::LongType* epsStridePtr;
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if (threadIdx.x == 0) {
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xLen = shape::length(inputShape);
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x = reinterpret_cast<const T*>(inputBuf);
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y = reinterpret_cast<const I*>(indicesBuf);
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z = reinterpret_cast<T*>(outputBuf);
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gradIn = reinterpret_cast<T*>(forwardOutput);
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gradOut = reinterpret_cast<T*>(eps);
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gradLen = shape::length(epsShape);
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// Cache all shape information
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inputRank = shape::rank(inputShape);
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outputRank = shape::rank(outputShape);
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indicesRank = shape::rank(indicesShape);
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forwardRank = shape::rank(forwardShape);
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epsRank = shape::rank(epsShape);
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inputShapePtr = shape::shapeOf(inputShape);
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outputShapePtr = shape::shapeOf(outputShape);
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indicesShapePtr = shape::shapeOf(indicesShape);
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forwardShapePtr = shape::shapeOf(forwardShape);
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epsShapePtr = shape::shapeOf(epsShape);
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inputStridePtr = shape::stride(inputShape);
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outputStridePtr = shape::stride(outputShape);
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indicesStridePtr = shape::stride(indicesShape);
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forwardStridePtr = shape::stride(forwardShape);
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epsStridePtr = shape::stride(epsShape);
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}
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__syncthreads();
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = gridDim.x * blockDim.x;
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for (auto e = start; e < xLen; e += step) {
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LongType zCoords[SD_MAX_RANK];
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LongType xCoords[SD_MAX_RANK];
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LongType yCoords[SD_MAX_RANK];
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LongType gradICoords[SD_MAX_RANK];
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LongType gradOCoords[SD_MAX_RANK];
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LongType zOffset;
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LongType xOffset;
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LongType yOffset;
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LongType gradOffsetI;
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LongType gradOffsetO;
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INDEX2COORDS(e, outputRank, outputShapePtr, zCoords);
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COORDS2INDEX(outputRank, outputStridePtr, zCoords, zOffset);
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INDEX2COORDS(e, inputRank, inputShapePtr, xCoords);
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COORDS2INDEX(inputRank, inputStridePtr, xCoords, xOffset);
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INDEX2COORDS(e, indicesRank, indicesShapePtr, yCoords);
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COORDS2INDEX(indicesRank, indicesStridePtr, yCoords, yOffset);
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|
auto classIndex = y[yOffset];
|
|
INDEX2COORDS(classIndex, forwardRank, forwardShapePtr, gradICoords);
|
|
COORDS2INDEX(forwardRank, forwardStridePtr, gradICoords, gradOffsetI);
|
|
INDEX2COORDS(classIndex, epsRank, epsShapePtr, gradOCoords);
|
|
COORDS2INDEX(epsRank, epsStridePtr, gradOCoords, gradOffsetO);
|
|
|
|
if (math::sd_abs<T, T>(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) {
|
|
z[zOffset] = gradOut[gradOffsetO];
|
|
}
|
|
}
|
|
}
|
|
|
|
template <typename T, typename I>
|
|
static SD_KERNEL void segmentMinBPTadKernel(const void* inputBuf, const LongType* inputShape, void* forwardOutput,
|
|
const LongType* forwardShape, void* eps, const LongType* epsShape,
|
|
const void* indicesBuf, const LongType* indicesShape, void* outputBuf,
|
|
const LongType* outputShape, const LongType* inputTad,
|
|
const LongType* inputOffsets, const LongType* gradInTad,
|
|
const LongType* gradInOffsets, const LongType* gradOutTad,
|
|
const LongType* gradOutOffsets, const LongType* outTad,
|
|
const LongType* outOffsets) {
|
|
__shared__ const T* x;
|
|
__shared__ T* gradIn;
|
|
__shared__ T* gradOut;
|
|
__shared__ const I* y;
|
|
__shared__ T* z;
|
|
__shared__ LongType xLen, yLen, gradLen, currentLen;
|
|
|
|
// Cache shape information
|
|
__shared__ sd::LongType indicesRank;
|
|
__shared__ const sd::LongType* indicesShapePtr;
|
|
__shared__ const sd::LongType* indicesStridePtr;
|
|
|
|
if (threadIdx.x == 0) {
|
|
xLen = shape::length(inputShape);
|
|
x = reinterpret_cast<const T*>(inputBuf);
|
|
y = reinterpret_cast<const I*>(indicesBuf);
|
|
z = reinterpret_cast<T*>(outputBuf);
|
|
yLen = shape::length(indicesShape);
|
|
gradOut = reinterpret_cast<T*>(eps);
|
|
gradIn = reinterpret_cast<T*>(forwardOutput);
|
|
gradLen = shape::length(epsShape);
|
|
currentLen = shape::length(outTad);
|
|
|
|
// Cache indices shape information (only needed for segment calculation)
|
|
indicesRank = shape::rank(indicesShape);
|
|
indicesShapePtr = shape::shapeOf(indicesShape);
|
|
indicesStridePtr = shape::stride(indicesShape);
|
|
}
|
|
__syncthreads();
|
|
|
|
for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
|
|
LongType yCoords[SD_MAX_RANK];
|
|
LongType yIndex;
|
|
INDEX2COORDS(i, indicesRank, indicesShapePtr, yCoords);
|
|
COORDS2INDEX(indicesRank, indicesStridePtr, yCoords, yIndex);
|
|
auto segment = y[yIndex];
|
|
auto current = x + inputOffsets[i];
|
|
auto currentOut = z + outOffsets[i];
|
|
auto in = gradIn + gradInOffsets[segment];
|
|
auto outGrad = gradOut + gradOutOffsets[segment];
|
|
|
|
for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
|
|
if (math::sd_abs<T,T>(in[e] - current[e]) <= T(1.e-6)) currentOut[e] = outGrad[e];
|
|
}
|
|
}
|
|
}
|
|
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
template <typename T, typename I>
|
|
Status segmentMinFunctorBP_(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut,
|
|
NDArray* output) {
|
|
|
|
// if input is a vector: (as if in doc sample)
|
|
auto stream = context->getCudaStream();
|
|
auto outShape = gradOut->getShapeAsVector();
|
|
NDArray tempRes(gradOut->ordering(), outShape, DataTypeUtils::fromT<T>(),
|
|
context);
|
|
segmentMinFunctor_<T, I>(context, input, indices, &tempRes);
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut, &tempRes});
|
|
if (input->isVector() || input->isScalar()) {
|
|
LongType loop_size = input->lengthOf();
|
|
auto numOfClasses = gradOut->lengthOf();
|
|
|
|
segmentMinBPLinearKernel<T, I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), tempRes.specialBuffer(), tempRes.specialShapeInfo(),
|
|
gradOut->specialBuffer(), gradOut->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(),
|
|
output->specialBuffer(), output->specialShapeInfo());
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMinBPLinearKernel failed");
|
|
|
|
|
|
} else {
|
|
LongType zero = 0;
|
|
std::vector<LongType> *dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(),1,&zero);
|
|
auto packX = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
|
|
auto packZ = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimensions);
|
|
auto packGradIn = ConstantTadHelper::getInstance().tadForDimensions(tempRes.shapeInfo(), dimensions);
|
|
auto packGradOut = ConstantTadHelper::getInstance().tadForDimensions(gradOut->shapeInfo(), dimensions);
|
|
auto inputTads = packX->specialShapeInfo();
|
|
auto inputTadOffsets = packX->specialOffsets();
|
|
auto outputTads = packZ->specialShapeInfo();
|
|
auto outputTadOffsets = packZ->specialOffsets();
|
|
auto gradInTads = packGradIn->specialShapeInfo();
|
|
auto gradInTadOffsets = packGradIn->specialOffsets();
|
|
auto gradOutTads = packGradOut->specialShapeInfo();
|
|
auto gradOutTadOffsets = packGradOut->specialOffsets();
|
|
|
|
segmentMinBPTadKernel<T, I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), tempRes.specialBuffer(), tempRes.specialShapeInfo(),
|
|
gradOut->specialBuffer(), gradOut->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(),
|
|
output->specialBuffer(), output->specialShapeInfo(), inputTads, inputTadOffsets, gradInTads, gradInTadOffsets,
|
|
gradOutTads, gradOutTadOffsets, outputTads, outputTadOffsets);
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMinBPTadKernel failed");
|
|
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut, &tempRes});
|
|
return Status::OK;
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
// segment min
|
|
Status segmentMinFunctorBP(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut,
|
|
NDArray* output) {
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
|
|
auto indicesDType = indices->dataType();
|
|
auto outputDType = output->dataType();
|
|
BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentMinFunctorBP_,
|
|
(context, input, indices, gradOut, output), SD_FLOAT_TYPES, SD_INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
}
|
|
|
|
template <typename T, typename I>
|
|
static Status unsortedSegmentMinFunctorBP_(LaunchContext* context, NDArray* input, NDArray* indices,
|
|
NDArray* gradOut,
|
|
LongType numOfClasses, NDArray* output) {
|
|
// if input is a vector: (as if in doc sample)
|
|
auto stream = context->getCudaStream();
|
|
auto outShape = gradOut->getShapeAsVector();
|
|
|
|
NDArray tempRes(gradOut->ordering(), outShape, DataTypeUtils::fromT<T>(),
|
|
context);
|
|
unsortedSegmentMinFunctor_<T, I>(context, input, indices, numOfClasses, &tempRes);
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut, &tempRes});
|
|
if (input->isVector() || input->isScalar()) {
|
|
LongType loop_size = input->lengthOf();
|
|
auto numOfClasses = gradOut->lengthOf();
|
|
segmentMinBPLinearKernel<T, I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), tempRes.specialBuffer(), tempRes.specialShapeInfo(),
|
|
gradOut->specialBuffer(), gradOut->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(),
|
|
output->specialBuffer(), output->specialShapeInfo());
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMinBPLinearKernel failed");
|
|
|
|
} else {
|
|
LongType zero = 0;
|
|
|
|
std::vector<LongType> *dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), 1,&zero);
|
|
auto packX = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
|
|
auto packZ = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimensions);
|
|
auto packGradIn = ConstantTadHelper::getInstance().tadForDimensions(tempRes.shapeInfo(), dimensions);
|
|
auto packGradOut = ConstantTadHelper::getInstance().tadForDimensions(gradOut->shapeInfo(), dimensions);
|
|
auto inputTads = packX->specialShapeInfo();
|
|
auto inputTadOffsets = packX->specialOffsets();
|
|
auto outputTads = packZ->specialShapeInfo();
|
|
auto outputTadOffsets = packZ->specialOffsets();
|
|
auto gradInTads = packGradIn->specialShapeInfo();
|
|
auto gradInTadOffsets = packGradIn->specialOffsets();
|
|
auto gradOutTads = packGradOut->specialShapeInfo();
|
|
auto gradOutTadOffsets = packGradOut->specialOffsets();
|
|
|
|
segmentMinBPTadKernel<T, I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), tempRes.specialBuffer(), tempRes.specialShapeInfo(),
|
|
gradOut->specialBuffer(), gradOut->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(),
|
|
output->specialBuffer(), output->specialShapeInfo(), inputTads, inputTadOffsets, gradInTads, gradInTadOffsets,
|
|
gradOutTads, gradOutTadOffsets, outputTads, outputTadOffsets);
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMinBPTadKernel failed");
|
|
|
|
delete dimensions;
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut, &tempRes});
|
|
return Status::OK;
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
Status unsortedSegmentMinFunctorBP(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut,
|
|
LongType numOfClasses, NDArray* output) {
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
|
|
auto indicesDType = indices->dataType();
|
|
auto outputDType = output->dataType();
|
|
BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentMinFunctorBP_,
|
|
(context, input, indices, gradOut, numOfClasses, output), SD_FLOAT_TYPES, SD_INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
}
|
|
} // namespace helpers
|
|
} // namespace ops
|
|
} // namespace sd
|