432 lines
28 KiB
Plaintext
432 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 <ops/declarable/helpers/segment.h>
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#include <ops/declarable/helpers/segment_common.h>
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#include <array/NDArrayFactory.h>
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#include <helpers/ShapeUtils.h>
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#include <helpers/TAD.h>
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#include <exceptions/cuda_exception.h>
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#include <helpers/PointersManager.h>
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#include <helpers/ConstantTadHelper.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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// -------------------------------------------------------------------------------------------------------------- //
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// Segment ops linear kernels
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// -------------------------------------------------------------------------------------------------------------- //
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template<typename T, typename I>
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static __global__ void
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segmentMaxLinearKernel(void *input, Nd4jLong const* inputShape, int *starts, int *lengths, Nd4jLong numOfClasses,
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void *output, Nd4jLong const* outputShape) {
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__shared__ T *val;
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__shared__ Nd4jLong xLen, zLen, zIndex;
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__shared__ T *x;
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__shared__ T *z;
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__shared__ int threadsPerSegment, start, finish;
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auto segment = blockIdx.x;
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if (threadIdx.x == 0) {
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// threadsPerSegment = (gridDim.x + numOfClasses - 1) / numOfClasses;
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// segment = blockIdx.x / threadsPerSegment;
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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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if (segment < numOfClasses) {
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zIndex = shape::getIndexOffset(segment, outputShape);
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start = starts[segment];
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finish = start + lengths[segment];
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z[zIndex] = x[shape::getIndexOffset(start, inputShape)];
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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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auto xIndex = shape::getIndexOffset(e, inputShape);
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sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
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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 __global__ void
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unsortedSegmentMaxLinearKernel(void *input, Nd4jLong const* inputShape, void *indices, Nd4jLong const* indicesShape,
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int *starts, int *lengths, Nd4jLong numOfClasses, void *output,
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Nd4jLong const* outputShape) {
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__shared__ T *val;
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__shared__ Nd4jLong xLen, zLen, zIndex;
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__shared__ T *x;
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__shared__ T *z;
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__shared__ I *y; //int threadsPerSegment, start, finish;
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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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zIndex = shape::getIndexOffset(segment, outputShape);
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//start = starts[segment];
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//finish = start + lengths[segment];
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if (lengths[segment] > 0)
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z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)];
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else
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z[zIndex] = -DataTypeUtils::max<T>();
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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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auto xIndex = shape::getIndexOffset(e, inputShape);
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auto yIndex = shape::getIndexOffset(e, indicesShape);
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if (y[yIndex] == segment) {
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sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
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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 __global__ void segmentMaxTadKernel(void* inputBuf, Nd4jLong const* inputShape, Nd4jLong const* inputTads,
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Nd4jLong const* inputTadOffsets, I* indices, int* starts, int* lengths, Nd4jLong numOfClasses, void* outputBuf,
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Nd4jLong const* outputShape, Nd4jLong const* outputTads, Nd4jLong const* outputTadOffsets, T filler = 0) {
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__shared__ T* val;
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__shared__ Nd4jLong len, zIndex, total;
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__shared__ T* z;
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__shared__ int start, finish;
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__shared__ I segment;
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if (threadIdx.x == 0) {
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segment = indices[blockIdx.x]; // / threadsPerSegment;
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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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}
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__syncthreads();
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auto idx = blockIdx.x;
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if (idx <= 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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auto xIndex = shape::getIndexOffset(e, inputTads);
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auto zIndex = shape::getIndexOffset(e, outputTads);
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sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
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//z[zIndex] = x[xIndex];
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}
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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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auto xIndex = shape::getIndexOffset(e, inputTads);
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auto zIndex = shape::getIndexOffset(e, outputTads);
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if (lengths[segment])
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sd::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
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}
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}
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}
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}
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// -------------------------------------------------------------------------------------------------------------- //
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template <typename T, typename I>
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static void segmentMaxFunctor_(LaunchContext* context, NDArray* input, NDArray* indices, NDArray* output) {
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//int numClasses = output->sizeAt(0);
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// if input is a vector: (as if in doc sample)
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//Nd4jLong idx = indices->e<Nd4jLong>(0);
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output->assign(-DataTypeUtils::infOrMax<T>());
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auto stream = context->getCudaStream();
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indices->syncToHost();
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Nd4jLong numOfClasses = indices->e<Nd4jLong>(indices->lengthOf() - 1) + 1;
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NDArray classesRangesLens = NDArrayFactory::create<int>('c', {numOfClasses}, context);
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NDArray classesRangesBegs = NDArrayFactory::create<int>('c', {numOfClasses}, context);
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classesRangesBegs.assign(indices->lengthOf());
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classesRangesLens.assign(0);
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dim3 dims(256, 512, 256);
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int* begins = reinterpret_cast<int*>(classesRangesBegs.specialBuffer());
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int* lengths = reinterpret_cast<int*>(classesRangesLens.specialBuffer());
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fillUpSegments(indices, numOfClasses, classesRangesBegs, classesRangesLens);
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NDArray::prepareSpecialUse({output}, {input, indices, &classesRangesBegs, &classesRangesLens});
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if (input->isVector()) {
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segmentMaxLinearKernel<T,I><<<numOfClasses, input->lengthOf(), numOfClasses * 32 + 32, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo());
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}
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else {
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std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
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auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
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auto packZ = sd::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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segmentMaxTadKernel<T,I><<<packX.numberOfTads(), 512, 2048, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), inputTads, inputTadOffsets, reinterpret_cast<I*>(indices->specialBuffer()), begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo(), outputTads, outputTadOffsets);
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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 segmentMaxFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices});
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BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), segmentMaxFunctor_, (context, input, indices, output), NUMERIC_TYPES, 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 unsortedSegmentMaxFunctor_(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) {
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auto stream = context->getCudaStream();
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// NDArray classes = NDArrayFactory::create<int>('c', {numOfClasses, 2});
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output->assign(DataTypeUtils::infOrMax<T>());
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NDArray classesRangesBegs = NDArrayFactory::create<int>('c', {numOfClasses}, context);
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NDArray classesRangesLens = NDArrayFactory::create<int>('c', {numOfClasses}, context);
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// NDArray row = NDArrayFactory::create<int>('c', {1, 2}, {(int)indices->lengthOf(), (int)0});
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// classes.applyTrueBroadcast(sd::BroadcastOpsTuple::Assign(), row, classes);
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classesRangesBegs.assign(indices->lengthOf());
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classesRangesLens.assign(0);
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dim3 dims(numOfClasses, indices->lengthOf(), numOfClasses * 32 + 32);
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// int* classesBuf = reinterpret_cast<int*>(classes.specialBuffer());
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fillUpSegments(indices, numOfClasses, classesRangesBegs, classesRangesLens);
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int* begins = reinterpret_cast<int*>(classesRangesBegs.specialBuffer());
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int* lengths = reinterpret_cast<int*>(classesRangesLens.specialBuffer());
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if (input->isVector()) {
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unsortedSegmentMaxLinearKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(), begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo());
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}
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else {
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std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
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auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
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auto packZ = sd::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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output->assign(-DataTypeUtils::max<T>());
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segmentMaxTadKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), inputTads, inputTadOffsets, reinterpret_cast<I*>(indices->specialBuffer()), begins, lengths, numOfClasses, output->specialBuffer(), output->specialShapeInfo(), outputTads, outputTadOffsets);
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}
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}
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// -------------------------------------------------------------------------------------------------------------- //
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void unsortedSegmentMaxFunctor(sd::LaunchContext* context, NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices});
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output->nullify();
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BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentMaxFunctor_, (context, input, indices, numOfClasses, output), NUMERIC_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({output}, {input, indices});
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}
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// -------------------------------------------------------------------------------------------------------------- //
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// segment max
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// -------------------------------------------------------------------------------------------------------------- //
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template <typename T, typename I>
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static __global__ void segmentMaxBPLinearKernel(void* inputBuf, Nd4jLong const* inputShape, void* forwardOutput,
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Nd4jLong const* forwardShape, void* eps, Nd4jLong const* epsShape, void* indicesBuf, Nd4jLong const* indicesShape,
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void* outputBuf, Nd4jLong 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__ Nd4jLong xLen, gradLen;
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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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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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}
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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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auto zOffset = shape::getIndexOffset(e, outputShape);
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auto xOffset = shape::getIndexOffset(e, inputShape);
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auto yOffset = shape::getIndexOffset(e, indicesShape);
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auto classIndex = y[yOffset];
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auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape);
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auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
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if (sd::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) {
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z[zOffset] = gradOut[gradOffsetO];
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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 __global__ void segmentMaxBPTadKernel(void* inputBuf, Nd4jLong const* inputShape, void* forwardOutput,
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Nd4jLong const* forwardShape, void* eps, Nd4jLong const* epsShape, void* indicesBuf, Nd4jLong const* indicesShape,
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void* outputBuf, Nd4jLong const* outputShape,Nd4jLong const* inputTad,
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Nd4jLong const* inputOffsets, Nd4jLong const* gradInTad, Nd4jLong const* gradInOffsets,
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Nd4jLong const* gradOutTad, Nd4jLong const* gradOutOffsets, Nd4jLong const* outTad,
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Nd4jLong const* outOffsets) {
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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__ Nd4jLong xLen, yLen, gradLen, currentLen;
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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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yLen = shape::length(indicesShape);
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gradOut = reinterpret_cast<T*>(eps);
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gradIn = reinterpret_cast<T*>(forwardOutput);
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gradLen = shape::length(epsShape);
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currentLen = shape::length(outTad);
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}
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__syncthreads();
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for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
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auto yIndex = shape::getIndexOffset(i, indicesShape);
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auto segment = y[yIndex];
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T* current = x + inputOffsets[i];
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T* currentOut = z + outOffsets[i];
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T* in = gradIn + gradInOffsets[segment];
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T* outGrad = gradOut + gradOutOffsets[segment];
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for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
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if (sd::math::nd4j_abs(in[e] - current[e]) <= T(1.e-6))
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currentOut[e] = outGrad[e];
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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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int segmentMaxFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) {
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//int numOfClasses = gradOut->sizeAt(0);
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// if input is a vector: (as if in doc sample)
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auto stream = context->getCudaStream();
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NDArray tempRes(gradOut->ordering(), gradOut->getShapeAsVector(), DataTypeUtils::fromT<T>(), context);//->shapeInfo(), context);
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segmentMaxFunctor_<T, I>(context, input, indices, &tempRes);
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NDArray::prepareSpecialUse({output}, {input, indices, gradOut, &tempRes});
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if (input->isVector()) {
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Nd4jLong loop_size = input->lengthOf();
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auto numOfClasses = gradOut->lengthOf(); //indices->e<Nd4jLong>(loop_size - 1);
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segmentMaxBPLinearKernel<T,I><<<1 + gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
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tempRes.specialBuffer(), tempRes.specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
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indices->specialBuffer(), indices->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo());
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}
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else {
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std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
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auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
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auto packZ = sd::ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimensions);
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auto packGradIn = sd::ConstantTadHelper::getInstance().tadForDimensions(tempRes.shapeInfo(), dimensions);
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auto packGradOut = sd::ConstantTadHelper::getInstance().tadForDimensions(gradOut->shapeInfo(), dimensions);
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Nd4jLong const* inputTads = packX.specialShapeInfo();
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Nd4jLong const* inputTadOffsets = packX.specialOffsets();
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Nd4jLong const* outputTads = packZ.specialShapeInfo();
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Nd4jLong const* outputTadOffsets = packZ.specialOffsets();
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Nd4jLong const* gradInTads = packGradIn.specialShapeInfo();
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Nd4jLong const* gradInTadOffsets = packGradIn.specialOffsets();
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Nd4jLong const* gradOutTads = packGradOut.specialShapeInfo();
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Nd4jLong const* gradOutTadOffsets = packGradOut.specialOffsets();
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segmentMaxBPTadKernel<T,I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
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tempRes.specialBuffer(), tempRes.specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
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indices->specialBuffer(), indices->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(),
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inputTads, inputTadOffsets, gradInTads, gradInTadOffsets, gradOutTads, gradOutTadOffsets,
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outputTads, outputTadOffsets);
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}
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NDArray::registerSpecialUse({output}, {input, indices, gradOut, &tempRes});
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return Status::OK();
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}
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// -------------------------------------------------------------------------------------------------------------- //
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int segmentMaxFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
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BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return segmentMaxFunctorBP_, (context, input,
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indices, gradOut, output), FLOAT_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({output}, {input, indices, gradOut});
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}
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// -------------------------------------------------------------------------------------------------------------- //
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template <typename T, typename I>
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static int unsortedSegmentMaxFunctorBP_(sd::LaunchContext* context, NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
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//int numOfClasses = gradOut->sizeAt(0);
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// if input is a vector: (as if in doc sample)
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auto stream = context->getCudaStream();
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NDArray tempRes(gradOut->ordering(), gradOut->getShapeAsVector(), DataTypeUtils::fromT<T>(), context);//->shapeInfo(), context);
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unsortedSegmentMaxFunctor_<T, I>(context, input, indices, numOfClasses, &tempRes);
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NDArray::prepareSpecialUse({output}, {input, indices, gradOut, &tempRes});
|
|
if (input->isVector()) {
|
|
Nd4jLong loop_size = input->lengthOf();
|
|
auto numOfClasses = gradOut->lengthOf(); //indices->e<Nd4jLong>(loop_size - 1);
|
|
segmentMaxBPLinearKernel<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());
|
|
}
|
|
else {
|
|
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {0});
|
|
auto packX = sd::ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), dimensions);
|
|
auto packZ = sd::ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), dimensions);
|
|
auto packGradIn = sd::ConstantTadHelper::getInstance().tadForDimensions(tempRes.shapeInfo(), dimensions);
|
|
auto packGradOut = sd::ConstantTadHelper::getInstance().tadForDimensions(gradOut->shapeInfo(), dimensions);
|
|
Nd4jLong const* inputTads = packX.specialShapeInfo();
|
|
Nd4jLong const* inputTadOffsets = packX.specialOffsets();
|
|
Nd4jLong const* outputTads = packZ.specialShapeInfo();
|
|
Nd4jLong const* outputTadOffsets = packZ.specialOffsets();
|
|
Nd4jLong const* gradInTads = packGradIn.specialShapeInfo();
|
|
Nd4jLong const* gradInTadOffsets = packGradIn.specialOffsets();
|
|
Nd4jLong const* gradOutTads = packGradOut.specialShapeInfo();
|
|
Nd4jLong const* gradOutTadOffsets = packGradOut.specialOffsets();
|
|
|
|
segmentMaxBPTadKernel<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);
|
|
}
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut, &tempRes});
|
|
return Status::OK();
|
|
}
|
|
// -------------------------------------------------------------------------------------------------------------- //
|
|
int unsortedSegmentMaxFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
|
|
NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
|
|
BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentMaxFunctorBP_, (context, input, indices, gradOut, numOfClasses, output), FLOAT_TYPES, INDEXING_TYPES);
|
|
NDArray::registerSpecialUse({output}, {input, indices, gradOut});
|
|
}
|
|
}
|
|
}
|
|
} |