95 lines
4.6 KiB
Plaintext
95 lines
4.6 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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 "ResultSet.h"
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#include <ops/declarable/helpers/matrix_diag.h>
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#include <Status.h>
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#include <ShapeUtils.h>
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#include <ShapeUtils.h>
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#include <TAD.h>
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#include <cuda_exception.h>
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#include <helpers/ConstantTadHelper.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static __global__ void matrixDiagKernel(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
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Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
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Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets) {
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int totalThreads = blockDim.x;
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for (Nd4jLong 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 (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) {
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Nd4jLong coords[2] = {j, j};
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Nd4jLong tadOffset = shape::getOffset(0, shape::shapeOf(tadOnlyOutputShapeInfo), shape::stride(tadOnlyOutputShapeInfo), coords, 2);
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//shape::getIndexOffset(j, tadOnlyOutputShapeInfo, inputLength)
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*(reinterpret_cast<T*>(outputBuffer) + xOffset + tadOffset) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + shape::getIndexOffset(j, tadOnlyInputShapeInfo, inputLength));
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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: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag
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template <typename T>
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static int _matrixDiag(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
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cudaStream_t* stream = context->getCudaStream();
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//auto listOut = output->allTensorsAlongDimension({output->rankOf() - 2, output->rankOf() - 1});
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//auto listDiag = input->allTensorsAlongDimension({input->rankOf() - 1});
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//auto repeatDelta = shape::prodLong(newShape.data(), rank) / this->lengthOf();
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std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input->rankOf(), {input->rankOf() - 1});
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const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(input->getShapeInfo(), dimsToExclude); //this->tensorsAlongDimension({dimension});
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//printf("Repeat delta %lld, numTads %lld\n", repeatDelta, numTads);
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//tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets;
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std::vector<int> inputDims({input->rankOf() - 1});
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std::vector<int> outputDims({output->rankOf() - 2, output->rankOf() - 1});
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), inputDims);
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), outputDims);
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if (!input->isActualOnDeviceSide())
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input->syncToDevice();
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if (!output->isActualOnDeviceSide())
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output->syncToDevice();
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// create cuda stream and LaunchContext
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cudaError_t cudaResult;
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dim3 launchDims(256, 512, 8192);
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matrixDiagKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(input->getSpecialBuffer(), output->getSpecialBuffer(), numTads, input->sizeAt(-1), packX.specialShapeInfo(), packX.specialOffsets(), packZ.specialShapeInfo(), packZ.specialOffsets());
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return Status::OK();
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}
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int matrixDiag(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), return _matrixDiag, (context, input, output), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template int _matrixDiag, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output), LIBND4J_TYPES);
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}
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}
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} |