wehub-resource-sync
125 lines
5.3 KiB
Plaintext
125 lines
5.3 KiB
Plaintext
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/* ******************************************************************************
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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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// Created by GS <sgazeos@gmail.com> on 3/21/2018.
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//
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#include <array/ResultSet.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/ShapeUtils.h>
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#include <ops/declarable/helpers/matrix_diag_part.h>
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#include "helpers/DebugHelper.h"
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namespace sd {
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namespace ops {
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namespace helpers {
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// put diagonals from input batched matrices to output batched vectors
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template <typename T>
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static SD_KERNEL void matrixDiagPartKernel(void* inputBuffer, void* outputBuffer, LongType numTads,
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LongType inputLength, LongType* tadOnlyInputShapeInfo,
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LongType* tadInputOffsets,
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LongType* tadOnlyOutputShapeInfo,
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LongType* tadOutputOffsets) {
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if(blockIdx.x >= numTads)
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return;
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auto outputBuffer2 = reinterpret_cast<T*>(outputBuffer);
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auto inputBuffer2 = reinterpret_cast<T const*>(inputBuffer);
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int totalThreads = blockDim.x;
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for (LongType i = blockIdx.x; i < numTads; i += gridDim.x) {
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auto yOffset = tadInputOffsets[i];
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auto xOffset = tadOutputOffsets[i];
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for (LongType j = threadIdx.x; j < inputLength; j += totalThreads) {
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LongType coords[2] = {j, j};
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LongType tadOffset, indexOffset;
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COORDS2INDEX(shape::rank(tadOnlyInputShapeInfo), shape::stride(tadOnlyInputShapeInfo), coords, tadOffset);
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COORDS2INDEX(shape::rank(tadOnlyOutputShapeInfo), shape::stride(tadOnlyOutputShapeInfo), coords, indexOffset);
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*(reinterpret_cast<T*>(outputBuffer) + xOffset + indexOffset) =
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*(reinterpret_cast<T const*>(inputBuffer) + yOffset + tadOffset);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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// Returns a batched matrix tensor with new batched diagonal values.
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// for detailed explanations please take a look on web page:
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// https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag
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//
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template <typename T>
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static Status _matrixDiagPart(LaunchContext* context, NDArray* input, NDArray* output) {
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auto stream = context->getCudaStream();
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auto listOut = output->allTensorsAlongDimension({output->rankOf() - 1});
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auto listDiag = input->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1});
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if (listOut.size() != listDiag.size()) {
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sd_printf("matrix_diag_part: Input matrix has wrong shape.", "");
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return Status::VALIDATION;
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}
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LongType lastDimension = math::sd_min(input->sizeAt(-2), input->sizeAt(-1));
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LongType dims = output->rankOf() - 1;
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std::vector<LongType> *dimsToExclude = ShapeUtils::evalDimsToExclude(output->rankOf(), 1,&dims);
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const LongType numTads =
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ShapeUtils::getNumOfSubArrs(input->shapeInfo(),*dimsToExclude);
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std::vector<LongType> outputDims({output->rankOf() - 1});
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std::vector<LongType> inputDims({input->rankOf() - 2, input->rankOf() - 1});
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auto packX = ConstantTadHelper::getInstance().tadForDimensions(input->shapeInfo(), &inputDims);
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auto packZ = ConstantTadHelper::getInstance().tadForDimensions(output->shapeInfo(), &outputDims);
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if (!output->isActualOnDeviceSide()) input->syncToDevice();
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if (!input->isActualOnDeviceSide()) input->syncToDevice();
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dim3 launchDims = getLaunchDims("matrixDiag");
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matrixDiagPartKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(
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input->specialBuffer(),
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output->specialBuffer(),numTads, lastDimension, const_cast<sd::LongType *>(packX->specialShapeInfo()),
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const_cast<sd::LongType *>(packX->specialOffsets()),
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const_cast<sd::LongType *>(packZ->specialShapeInfo()), const_cast<sd::LongType *>(packZ->specialOffsets()));
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sd::DebugHelper::checkErrorCode(stream, "matrixDiagPartKernel failed");
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delete dimsToExclude;
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return Status::OK;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// caller for _matrixDiagPart
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//
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Status matrixDiagPart(LaunchContext* context, NDArray* input, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), return _matrixDiagPart, (context, input, output), SD_COMMON_TYPES);
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}
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BUILD_SINGLE_TEMPLATE( sd::Status _matrixDiagPart,
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(sd::LaunchContext * context, NDArray* input, NDArray* output), SD_COMMON_TYPES);
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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