cavis/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by GS <sgazeos@gmail.com> on 3/21/2018.
//
#include "ResultSet.h"
#include <ops/declarable/helpers/matrix_diag_part.h>
#include <Status.h>
#include <ShapeUtils.h>
#include <ShapeUtils.h>
#include <TAD.h>
#include <cuda_exception.h>
#include <helpers/ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// put diagonals from input batched matricies to output batched vectors
template <typename T>
static __global__ void matrixDiagPartKernel(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
Nd4jLong* tadOnlyInputShapeInfo, Nd4jLong *tadInputOffsets,
Nd4jLong* tadOnlyOutputShapeInfo, Nd4jLong *tadOutputOffsets) {
int totalThreads = blockDim.x;
for (Nd4jLong i = blockIdx.x; i < numTads; i += gridDim.x) {
auto yOffset = tadInputOffsets[i];
auto xOffset = tadOutputOffsets[i];
for (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) {
Nd4jLong coords[2] = {j, j};
Nd4jLong tadOffset = shape::getOffset(tadOnlyInputShapeInfo, coords);
*(reinterpret_cast<T*>(outputBuffer) + xOffset + shape::getIndexOffset(j, tadOnlyOutputShapeInfo)) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + tadOffset);
}
}
}
//////////////////////////////////////////////////////////////////////////
// Returns a batched matrix tensor with new batched diagonal values.
// for detailed explanations please take a look on web page: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag
//
template <typename T>
int _matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
auto stream = context->getCudaStream();
auto listOut = output->allTensorsAlongDimension({output->rankOf() - 1});
auto listDiag = input->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1});
if (listOut.size() != listDiag.size()) {
nd4j_printf("matrix_diag_part: Input matrix has wrong shape.", "");
return ND4J_STATUS_VALIDATION;
}
Nd4jLong lastDimension = nd4j::math::nd4j_min(input->sizeAt(-2), input->sizeAt(-1));
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(output->rankOf(), {output->rankOf() - 1});
const Nd4jLong numTads = ShapeUtils::getNumOfSubArrs(input->getShapeInfo(), dimsToExclude); //this->tensorsAlongDimension({dimension});
//printf("Repeat delta %lld, numTads %lld\n", repeatDelta, numTads);
//tadOnlyInputShapeInfo, tadInputOffsets, tadOnlyOutputShapeInfo, tadOutputOffsets;
std::vector<int> outputDims({output->rankOf() - 1});
std::vector<int> inputDims({input->rankOf() - 2, input->rankOf() - 1});
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), inputDims);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), outputDims);
if (!output->isActualOnDeviceSide())
input->syncToDevice();
if (!input->isActualOnDeviceSide())
input->syncToDevice();
dim3 launchDims(256, 512, 8192);
matrixDiagPartKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(input->getSpecialBuffer(), output->getSpecialBuffer(), numTads, lastDimension, packX.specialShapeInfo(), packX.specialOffsets(), packZ.specialShapeInfo(), packZ.specialOffsets());
return Status::OK();
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// caller for _matrixDiagPart
//
int matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), return _matrixDiagPart, (context, input, output), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template int _matrixDiagPart, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output), LIBND4J_TYPES);
}
}
}