wehub-resource-sync
633 lines
29 KiB
Plaintext
633 lines
29 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 segmentMeanLinearKernel(void* input, LongType const* inputShape, LongType* indices,
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LongType* lengths, LongType numOfClasses, void* output,
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LongType const* outputShape) {
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__shared__ T* val;
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__shared__ LongType xLen, zLen, zIndex;
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__shared__ 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 (threadIdx.x == 0) {
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x = reinterpret_cast<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 outputCoords[SD_MAX_RANK];
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LongType inputCoords[SD_MAX_RANK];
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LongType xOffset;
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LongType zOffset;
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INDEX2COORDS(segment, outputRank, outputShapePtr, outputCoords);
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COORDS2INDEX(outputRank, outputStridePtr, outputCoords, zIndex);
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start = indices[segment];
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finish = start + lengths[segment];
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INDEX2COORDS(start, inputRank, inputShapePtr, inputCoords);
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COORDS2INDEX(inputRank, inputStridePtr, inputCoords, xOffset);
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if (lengths[segment] > 0)
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z[zIndex] = T(x[xOffset] / T(lengths[segment]));
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else
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z[zIndex] = 0;
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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 inputCoords[SD_MAX_RANK];
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LongType xOffset;
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INDEX2COORDS(e, inputRank, inputShapePtr, inputCoords);
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COORDS2INDEX(inputRank, inputStridePtr, inputCoords, xOffset);
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math::atomics::sd_atomicAdd(&z[zIndex], T(x[xOffset] / static_cast<T>(lengths[segment])));
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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 unsortedSegmentMeanLinearKernel(void* input, LongType const* inputShape, void* indices,
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LongType const* indicesShape, LongType* starts, LongType* lengths,
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LongType numOfClasses, void* output,
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LongType const* outputShape) {
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__shared__ LongType xLen, zLen, zIndex;
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__shared__ T* x;
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__shared__ T* z;
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__shared__ 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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auto segment = blockIdx.x;
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if (threadIdx.x == 0) {
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x = reinterpret_cast<T*>(input);
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z = reinterpret_cast<T*>(output);
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y = reinterpret_cast<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 outputCoords[SD_MAX_RANK];
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LongType inputCoords[SD_MAX_RANK];
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LongType xOffset;
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LongType zOffset;
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INDEX2COORDS(segment, outputRank, outputShapePtr, outputCoords);
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COORDS2INDEX(outputRank, outputStridePtr, outputCoords, zIndex);
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INDEX2COORDS(starts[segment], inputRank, inputShapePtr, inputCoords);
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COORDS2INDEX(inputRank, inputStridePtr, inputCoords, xOffset);
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if (lengths[segment] > 0)
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z[zIndex] = T(x[xOffset] / T(lengths[segment]));
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else
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z[zIndex] = 0;
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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; e < xLen; e += blockDim.x) {
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LongType inputCoords[SD_MAX_RANK];
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LongType xOffset;
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LongType yIndex;
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INDEX2COORDS(e, inputRank, inputShapePtr, inputCoords);
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COORDS2INDEX(inputRank, inputStridePtr, inputCoords, xOffset);
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INDEX2COORDS(e, indicesRank, indicesShapePtr, inputCoords);
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COORDS2INDEX(indicesRank, indicesStridePtr, inputCoords, yIndex);
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if (y[yIndex] == segment && e != starts[segment]) {
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math::atomics::sd_atomicAdd(&z[zIndex], T(x[xOffset] / T(lengths[segment])));
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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 segmentMeanTadKernel(void* inputBuf, LongType const* inputShape, LongType const* inputTads,
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LongType const* inputTadOffsets,
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I* indices, LongType* starts,
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LongType* lengths, LongType numOfClasses, void* outputBuf,
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LongType const* outputShape, LongType const* outputTads,
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LongType const* 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<T*>(inputBuf) + inputTadOffsets[idx];
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if (blockIdx.x == start) {
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for (auto e = threadIdx.x; e < len; e += blockDim.x) {
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LongType xCoords[SD_MAX_RANK];
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LongType zCoords[SD_MAX_RANK];
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LongType xIndex;
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LongType zIndex;
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INDEX2COORDS(e, inputTadRank, inputTadShapePtr, xCoords);
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COORDS2INDEX(inputTadRank, inputTadStridePtr, xCoords, xIndex);
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INDEX2COORDS(e, outputTadRank, outputTadShapePtr, zCoords);
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COORDS2INDEX(outputTadRank, outputTadStridePtr, zCoords, zIndex);
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math::atomics::sd_atomicAdd(&z[zIndex], T(x[xIndex] / lengths[segment]));
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}
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} else {
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for (auto e = threadIdx.x; e < len; e += blockDim.x) {
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LongType xCoords[SD_MAX_RANK];
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LongType zCoords[SD_MAX_RANK];
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LongType xIndex;
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LongType zIndex;
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INDEX2COORDS(e, inputTadRank, inputTadShapePtr, xCoords);
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COORDS2INDEX(inputTadRank, inputTadStridePtr, xCoords, xIndex);
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INDEX2COORDS(e, outputTadRank, outputTadShapePtr, zCoords);
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COORDS2INDEX(outputTadRank, outputTadStridePtr, zCoords, zIndex);
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if (lengths[segment]) math::atomics::sd_atomicAdd(&z[zIndex], T(x[xIndex] / lengths[segment]));
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}
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}
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}
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}
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// -------------------------------------------------------------------------------------------------------------- //
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// segment mean
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template <typename T, typename I>
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static void segmentMeanFunctor_(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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NDArray classesRangesLens = NDArrayFactory::create<LongType>('c', {numClasses}, context);
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NDArray classesRangesBegs = NDArrayFactory::create<LongType>('c', {numClasses}, context);
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int zero2 = 0;
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sd::LongType len = indices->lengthOf();
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classesRangesBegs.assign(len);
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classesRangesLens.assign(zero2);
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NDArray::prepareSpecialUse({output}, {input, indices});
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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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fillUpSegments(indices, numClasses, classesRangesBegs, classesRangesLens);
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if (input->isVector() || input->isScalar()) {
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dim3 launchDims = segmentDims(numClasses,input->lengthOf());
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segmentMeanLinearKernel<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, "segmentMeanLinearKernel 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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segmentMeanTadKernel<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, "segmentMeanTadKernel 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 segmentMeanFunctor(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices});
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auto indicesDType = indices->dataType();
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auto outputDType = output->dataType();
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UILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), segmentMeanFunctor_, (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 unsortedSegmentMeanFunctor_(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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int zero2 = 0;
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sd::LongType len = indices->lengthOf();
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classesRangesBegs.assign(len);
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classesRangesLens.assign(zero2);
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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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if (input->isVector() || input->isScalar()) {
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unsortedSegmentMeanLinearKernel<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, "unsortedSegmentMeanLinearKernel failed");
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} else {
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LongType zero = 0;
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output->assign(zero);
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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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LongType const* inputTads = packX->specialShapeInfo();
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LongType const* inputTadOffsets = packX->specialOffsets();
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LongType const* outputTads = packZ->specialShapeInfo();
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LongType const* outputTadOffsets = packZ->specialOffsets();
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dims.x = input->sizeAt(0);
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segmentMeanTadKernel<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, "segmentMeanTadKernel failed");
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delete dimensions;
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}
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}
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// -------------------------------------------------------------------------------------------------------------- //
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void unsortedSegmentMeanFunctor(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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auto indicesDType = indices->dataType();
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auto inputDType = input->dataType();
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BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentMeanFunctor_,
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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 segmentMeanBPLinearKernel(void* inputBuf, LongType const* inputShape, void* eps,
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LongType const* epsShape, void* indicesBuf,
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LongType const* indicesShape, LongType* lengths, void* outputBuf,
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LongType const* outputShape) {
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__shared__ T* x;
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__shared__ T* gradIn;
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__shared__ T* gradOut;
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__shared__ 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, 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* 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* epsStridePtr;
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if (threadIdx.x == 0) {
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xLen = shape::length(inputShape);
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x = reinterpret_cast<T*>(inputBuf);
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y = reinterpret_cast<I*>(indicesBuf);
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z = reinterpret_cast<T*>(outputBuf);
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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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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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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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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 zOffset, xOffset, yOffset, gradOffsetO;
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sd::LongType zCoords[SD_MAX_RANK], xCoords[SD_MAX_RANK], yCoords[SD_MAX_RANK], gradCoords[SD_MAX_RANK];
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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];
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INDEX2COORDS(classIndex, epsRank, epsShapePtr, gradCoords);
|
|
COORDS2INDEX(epsRank, epsStridePtr, gradCoords, gradOffsetO);
|
|
|
|
z[zOffset] = T(gradOut[gradOffsetO] / float(lengths[classIndex]));
|
|
}
|
|
}
|
|
|
|
template <typename T, typename I>
|
|
static SD_KERNEL void segmentMeanBPTadKernel(void* inputBuf, LongType const* inputShape, void* eps,
|
|
LongType const* epsShape, void* indicesBuf, LongType const* indicesShape,
|
|
LongType* lengths, void* outputBuf, LongType const* outputShape,
|
|
LongType const* inputTad, LongType const* inputOffsets,
|
|
LongType const* gradOutTad, LongType const* gradOutOffsets,
|
|
LongType const* outTad, LongType const* outOffsets) {
|
|
__shared__ T* x;
|
|
__shared__ T* gradOut;
|
|
__shared__ I* y;
|
|
__shared__ T* z;
|
|
__shared__ LongType xLen, yLen, gradLen, currentLen;
|
|
|
|
// Cache shape information
|
|
__shared__ sd::LongType outTadRank, gradOutTadRank;
|
|
__shared__ const sd::LongType* outTadShapePtr;
|
|
__shared__ const sd::LongType* gradOutTadShapePtr;
|
|
__shared__ const sd::LongType* outTadStridePtr;
|
|
__shared__ const sd::LongType* gradOutTadStridePtr;
|
|
|
|
if (threadIdx.x == 0) {
|
|
xLen = shape::length(inputShape);
|
|
x = reinterpret_cast<T*>(inputBuf);
|
|
y = reinterpret_cast<I*>(indicesBuf);
|
|
z = reinterpret_cast<T*>(outputBuf);
|
|
yLen = shape::length(indicesShape);
|
|
gradOut = reinterpret_cast<T*>(eps);
|
|
gradLen = shape::length(epsShape);
|
|
currentLen = shape::length(outTad);
|
|
|
|
// Cache TAD shape information
|
|
outTadRank = shape::rank(outTad);
|
|
gradOutTadRank = shape::rank(gradOutTad);
|
|
outTadShapePtr = shape::shapeOf(outTad);
|
|
gradOutTadShapePtr = shape::shapeOf(gradOutTad);
|
|
outTadStridePtr = shape::stride(outTad);
|
|
gradOutTadStridePtr = shape::stride(gradOutTad);
|
|
}
|
|
__syncthreads();
|
|
|
|
for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
|
|
auto segment = y[i];
|
|
T* currentOut = z + outOffsets[i];
|
|
T* outGrad = gradOut + gradOutOffsets[segment];
|
|
|
|
for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
|
|
sd::LongType zCoords[SD_MAX_RANK];
|
|
sd::LongType gradCoords[SD_MAX_RANK];
|
|
sd::LongType zIndex;
|
|
sd::LongType gradIndex;
|
|
|
|
INDEX2COORDS(e, outTadRank, outTadShapePtr, zCoords);
|
|
COORDS2INDEX(outTadRank, outTadStridePtr, zCoords, zIndex);
|
|
INDEX2COORDS(e, gradOutTadRank, gradOutTadShapePtr, gradCoords);
|
|
COORDS2INDEX(gradOutTadRank, gradOutTadStridePtr, gradCoords, gradIndex);
|
|
|
|
if (lengths[segment] > 0) currentOut[zIndex] = T(outGrad[gradIndex] / float(lengths[segment]));
|
|
}
|
|
}
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
// backrop for mean
|
|
template <typename T, typename I>
|
|
Status segmentMeanFunctorBP_(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut,
|
|
NDArray* output) {
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
|
|
auto numClasses = indices->e<LongType>(indices->lengthOf() - 1) + 1;
|
|
NDArray classesRangesLens = NDArrayFactory::create<LongType>('c', {numClasses}, context);
|
|
NDArray classesRangesBegs = NDArrayFactory::create<LongType>('c', {numClasses}, context);
|
|
sd::LongType zero2 = 0;
|
|
sd::LongType len = indices->lengthOf();
|
|
classesRangesBegs.assign(zero2);
|
|
classesRangesLens.assign(len);
|
|
fillUpSegments(indices, numClasses, classesRangesBegs, classesRangesLens);
|
|
LongType* begins = reinterpret_cast<LongType*>(classesRangesBegs.specialBuffer());
|
|
LongType* lengths = reinterpret_cast<LongType*>(classesRangesLens.specialBuffer());
|
|
|
|
if (input->isVector() || input->isScalar()) {
|
|
LongType loop_size = input->lengthOf();
|
|
auto numOfClasses = gradOut->lengthOf(); // indices->e<sd::LongType>(loop_size - 1);
|
|
dim3 segmentBpDims2 = segmentBpDims(gradOut->lengthOf(),input->lengthOf());
|
|
segmentMeanBPLinearKernel<T, I><<<segmentBpDims2.y, segmentBpDims2.x, segmentBpDims2.z, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
|
|
indices->specialBuffer(), indices->specialShapeInfo(), lengths, output->specialBuffer(),
|
|
output->specialShapeInfo());
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMeanBPLinearKernel 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 packGradOut = ConstantTadHelper::getInstance().tadForDimensions(gradOut->shapeInfo(), dimensions);
|
|
LongType const* inputTads = packX->specialShapeInfo();
|
|
LongType const* inputTadOffsets = packX->specialOffsets();
|
|
LongType const* outputTads = packZ->specialShapeInfo();
|
|
LongType const* outputTadOffsets = packZ->specialOffsets();
|
|
LongType const* gradOutTads = packGradOut->specialShapeInfo();
|
|
LongType const* gradOutTadOffsets = packGradOut->specialOffsets();
|
|
dim3 segmentBpTad2 = segmentBpTad(indices->lengthOf(),input->lengthOf());
|
|
|
|
segmentMeanBPTadKernel<T, I><<<segmentBpTad2.y, segmentBpTad2.x, segmentBpTad2.z, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
|
|
indices->specialBuffer(), indices->specialShapeInfo(), lengths, output->specialBuffer(),
|
|
output->specialShapeInfo(), inputTads, inputTadOffsets, gradOutTads, gradOutTadOffsets, outputTads,
|
|
outputTadOffsets);
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMeanBPTadKernel failed");
|
|
|
|
delete dimensions;
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
return Status::OK;
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
// segmen mean bp main
|
|
Status segmentMeanFunctorBP(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 segmentMeanFunctorBP_,
|
|
(context, input, indices, gradOut, output), SD_FLOAT_TYPES, SD_INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
|
|
template <typename T, typename I>
|
|
static Status unsortedSegmentMeanFunctorBP_(LaunchContext* context, NDArray* input, NDArray* indices,
|
|
NDArray* gradOut,
|
|
LongType numOfClasses, NDArray* output) {
|
|
auto stream = context->getCudaStream();
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
|
|
auto numClasses = indices->e<LongType>(indices->lengthOf() - 1) + 1;
|
|
NDArray classesRangesLens = NDArrayFactory::create<LongType>('c', {numClasses}, context);
|
|
NDArray classesRangesBegs = NDArrayFactory::create<LongType>('c', {numClasses}, context);
|
|
|
|
sd::LongType zero2 = 0;
|
|
sd::LongType len = indices->lengthOf();
|
|
classesRangesBegs.assign(zero2);
|
|
classesRangesLens.assign(len);
|
|
fillUpSegments(indices, numClasses, classesRangesBegs, classesRangesLens);
|
|
LongType* begins = reinterpret_cast<LongType*>(classesRangesBegs.specialBuffer());
|
|
LongType* lengths = reinterpret_cast<LongType*>(classesRangesLens.specialBuffer());
|
|
|
|
if (input->isVector() || input->isScalar()) {
|
|
LongType loop_size = input->lengthOf();
|
|
auto numOfClasses = gradOut->lengthOf();
|
|
dim3 segmentBpDims2 = segmentBpDims(gradOut->lengthOf(),input->lengthOf());
|
|
segmentMeanBPLinearKernel<T, I><<<segmentBpDims2.y,segmentBpDims2.x,segmentBpDims2.z, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
|
|
indices->specialBuffer(), indices->specialShapeInfo(), lengths, output->specialBuffer(),
|
|
output->specialShapeInfo());
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMeanBPLinearKernel 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 packGradOut = ConstantTadHelper::getInstance().tadForDimensions(gradOut->shapeInfo(), dimensions);
|
|
LongType const* inputTads = packX->specialShapeInfo();
|
|
LongType const* inputTadOffsets = packX->specialOffsets();
|
|
LongType const* outputTads = packZ->specialShapeInfo();
|
|
LongType const* outputTadOffsets = packZ->specialOffsets();
|
|
LongType const* gradOutTads = packGradOut->specialShapeInfo();
|
|
LongType const* gradOutTadOffsets = packGradOut->specialOffsets();
|
|
dim3 segmentBpTad2 = segmentBpTad(indices->lengthOf(),input->lengthOf());
|
|
|
|
segmentMeanBPTadKernel<T, I><<<segmentBpTad2.y,segmentBpTad2.x, segmentBpTad2.z, *stream>>>(
|
|
input->specialBuffer(), input->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
|
|
indices->specialBuffer(), indices->specialShapeInfo(), lengths, output->specialBuffer(),
|
|
output->specialShapeInfo(), inputTads, inputTadOffsets, gradOutTads, gradOutTadOffsets, outputTads,
|
|
outputTadOffsets);
|
|
sd::DebugHelper::checkErrorCode(stream, "segmentMeanBPTadKernel failed");
|
|
|
|
delete dimensions;
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
return Status::OK;
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
Status unsortedSegmentMeanFunctorBP(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 unsortedSegmentMeanFunctorBP_,
|
|
(context, input, indices, gradOut, numOfClasses, output), SD_FLOAT_TYPES, SD_INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
}
|
|
|
|
} // namespace helpers
|
|
} // namespace ops
|
|
} // namespace sd
|