259 lines
14 KiB
Plaintext
259 lines
14 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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template <typename T, typename I>
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static __global__ void unsortedSegmentSqrtNLinearKernel(T* input, Nd4jLong const* inputShape, I* indices, Nd4jLong const* indicesShape, int* starts, int* lengths, Nd4jLong numOfClasses, T* output, Nd4jLong const* outputShape) {
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__shared__ Nd4jLong xLen, zLen;
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if (threadIdx.x == 0) {
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xLen = shape::length(inputShape);
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zLen = shape::length(outputShape);
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}
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__syncthreads();
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auto start = threadIdx.x + blockIdx.x * blockDim.x;
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auto step = blockDim.x * gridDim.x;
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for (auto idx = start; idx < xLen; idx += step) {
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auto yIndex = shape::getIndexOffset(idx, indicesShape);
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auto segment = indices[yIndex];
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auto zIndex = shape::getIndexOffset(segment, outputShape);
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if (lengths[segment] == 0) continue;
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auto xIndex = shape::getIndexOffset(idx, inputShape);
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sd::math::atomics::nd4j_atomicAdd(&output[zIndex], input[xIndex] / sd::math::nd4j_sqrt<int, T>(lengths[segment]));
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}
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}
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// -------------------------------------------------------------------------------------------------------------- //
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// SegmentSqrtN kernel
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template <typename T, typename I>
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static __global__ void segmentSqrtNTadKernel(T* inputBuf, Nd4jLong const* inputShape, Nd4jLong const* inputTads, Nd4jLong const* inputTadOffsets, I* indices, int* starts, int* lengths, Nd4jLong numOfClasses, void* outputBuf, Nd4jLong const* outputShape, Nd4jLong const* outputTads, Nd4jLong const* outputTadOffsets) {
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__shared__ Nd4jLong len, total;
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if (threadIdx.x == 0) {
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total = shape::sizeAt(inputShape, 0);
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len = shape::length(inputTads);
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}
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__syncthreads();
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for (auto idx = blockIdx.x; idx < total; idx += gridDim.x) {
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auto segment = indices[idx];
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auto x = inputBuf + inputTadOffsets[idx];
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auto z = reinterpret_cast<T *>(outputBuf) + outputTadOffsets[segment];
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auto start = starts[segment];
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auto finish = start + lengths[segment];
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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_atomicAdd(&z[zIndex], x[xIndex] / sd::math::nd4j_sqrt<int, 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 void unsortedSegmentSqrtNFunctor_(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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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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dim3 dims(128, 256, 256);
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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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output->nullify();
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if (input->isVector()) {
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unsortedSegmentSqrtNLinearKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(
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input->dataBuffer()->specialAsT<T>(), input->specialShapeInfo(),
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indices->dataBuffer()->specialAsT<I>(), indices->specialShapeInfo(), begins, lengths, numOfClasses,
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output->dataBuffer()->specialAsT<T>(), output->specialShapeInfo());
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}
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else {
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output->nullify();
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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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segmentSqrtNTadKernel<T,I><<<dims.x, dims.y, dims.z, *stream>>>(
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input->dataBuffer()->specialAsT<T>(), input->specialShapeInfo(), inputTads, inputTadOffsets, indices->dataBuffer()->specialAsT<I>(),
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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 unsortedSegmentSqrtNFunctor(sd::LaunchContext* context , NDArray* input, NDArray* indices, Nd4jLong numOfClasses, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices});
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BUILD_DOUBLE_SELECTOR(input->dataType(), indices->dataType(), unsortedSegmentSqrtNFunctor_, (context, input, indices, numOfClasses, output),
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FLOAT_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 __global__ void segmentSqrtNBPLinearKernel(void* inputBuf, Nd4jLong const* inputShape, void* eps, Nd4jLong const* epsShape, void* indicesBuf, Nd4jLong const* indicesShape,
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int* lengths, 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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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 gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
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z[zOffset] = T(gradOut[gradOffsetO] / math::nd4j_sqrt<int, float>(lengths[classIndex]));
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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 segmentSqrtNBPTadKernel(void* inputBuf, Nd4jLong const* inputShape, void* eps, Nd4jLong const* epsShape,
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void* indicesBuf, Nd4jLong const* indicesShape, int* lengths, void* outputBuf, Nd4jLong const* outputShape,Nd4jLong const* inputTad,
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Nd4jLong const* inputOffsets, Nd4jLong const* gradOutTad, Nd4jLong const* gradOutOffsets, Nd4jLong const* outTad, Nd4jLong const* outOffsets) {
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__shared__ T* x;
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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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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[i]; //yIndex];
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T* currentOut = z + outOffsets[i];
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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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auto zIndex = shape::getIndexOffset(e, outTad);
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auto gradIndex = shape::getIndexOffset(e, gradOutTad);
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if (lengths[segment] > 0)
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currentOut[zIndex] = T(outGrad[gradIndex] / math::nd4j_sqrt<int, float>(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 int unsortedSegmentSqrtNFunctorBP_(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
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auto stream = context->getCudaStream();
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NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
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auto numClasses = indices->e<int>(indices->lengthOf() - 1) + 1;
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NDArray classesRangesLens = NDArrayFactory::create<int>('c', {numClasses}, context);
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NDArray classesRangesBegs = NDArrayFactory::create<int>('c', {numClasses}, context);
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classesRangesBegs.assign(indices->lengthOf());
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classesRangesLens.assign(0);
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dim3 dims(numClasses, indices->lengthOf(), numClasses * 32 + 32);
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fillUpSegments(indices, numClasses, 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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Nd4jLong loop_size = input->lengthOf();
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auto numOfClasses = gradOut->lengthOf(); //indices->e<Nd4jLong>(loop_size - 1);
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segmentSqrtNBPLinearKernel<T,I><<<gradOut->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(),
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input->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(),
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indices->specialBuffer(), indices->specialShapeInfo(), lengths, 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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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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auto gradOutTads = packGradOut.specialShapeInfo();
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auto gradOutTadOffsets = packGradOut.specialOffsets();
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segmentSqrtNBPTadKernel<T,I><<<indices->lengthOf(), input->lengthOf(), 256, *stream>>>(input->specialBuffer(), input->specialShapeInfo(),
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gradOut->specialBuffer(), gradOut->specialShapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(), lengths,
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output->specialBuffer(), output->specialShapeInfo(), inputTads, inputTadOffsets, gradOutTads, gradOutTadOffsets,
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outputTads, outputTadOffsets);
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}
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NDArray::registerSpecialUse({output}, {input, indices, gradOut});
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return Status::OK();
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}
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// -------------------------------------------------------------------------------------------------------------- //
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int unsortedSegmentSqrtNFunctorBP(sd::LaunchContext* context , NDArray* input, NDArray* indices, NDArray* gradOut, Nd4jLong numOfClasses, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {input, indices, gradOut});
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BUILD_DOUBLE_SELECTOR(output->dataType(), indices->dataType(), return unsortedSegmentSqrtNFunctorBP_, (context, input, indices, gradOut, numOfClasses, 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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}
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} |