78 lines
3.7 KiB
Plaintext
78 lines
3.7 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 Yurii Shyrma on 07.12.2017.
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//
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#include "ResultSet.h"
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#include <ops/declarable/helpers/matrixSetDiag.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 matrixSetDiagKernel(void* outputBuffer, Nd4jLong* outputShape, void const* diagonalBuffer, Nd4jLong* diagonalShape, Nd4jLong lastDimSize, Nd4jLong last2DimSize, Nd4jLong lastSmallDim, Nd4jLong batchSize) {
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__shared__ T* z;
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__shared__ T const* x;
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__shared__ Nd4jLong outLength, diagonalLen;
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if (threadIdx.x == 0) {
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z = reinterpret_cast<T*>(outputBuffer);
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x = reinterpret_cast<T const*>(diagonalBuffer);
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outLength = shape::length(outputShape);
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diagonalLen = shape::length(diagonalShape);
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}
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__syncthreads();
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for(int i = blockIdx.x; i < batchSize; i+= gridDim.x )
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for(int j = threadIdx.x; j < lastSmallDim; j += blockDim.x) {
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// z[i * last2DimSize + j * (lastDimSize + 1)] = x[i * lastSmallDim + j];
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z[shape::getIndexOffset(i * last2DimSize + j * (lastDimSize + 1), outputShape, outLength)] = x[shape::getIndexOffset(i * lastSmallDim + j, diagonalShape, diagonalLen)];
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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 void _matrixSetDiag(nd4j::LaunchContext * context, const NDArray* input, const NDArray* diagonal, NDArray* output) {
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*output = *input;
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const int lastDimSize = input->sizeAt(-1);
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const int last2DimSize = input->sizeAt(-1) * input->sizeAt(-2);
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const int lastSmallDim = diagonal->sizeAt(-1);
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const int batchSize = input->lengthOf()/last2DimSize;
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auto stream = context->getCudaStream();
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dim3 launchDims(256, 512, 8192);
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matrixSetDiagKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), diagonal->getSpecialBuffer(), diagonal->getSpecialShapeInfo(), lastDimSize, last2DimSize, lastSmallDim, batchSize);
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//// #pragma omp parallel for if(batchSize > Environment::getInstance()->elementwiseThreshold()) schedule(static)
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// for(int i = 0; i < batchSize; ++i )
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// for(int j = 0; j < lastSmallDim; ++j) {
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// output->p(i*last2DimSize + j*(lastDimSize + 1), diagonal->e<T>(i*lastSmallDim + j));
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// }
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}
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void matrixSetDiag(nd4j::LaunchContext * context, const NDArray* input, const NDArray* diagonal, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), _matrixSetDiag, (context, input, diagonal, output), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void _matrixSetDiag, (nd4j::LaunchContext * context, const NDArray* input, const NDArray* diagonal, NDArray* output), LIBND4J_TYPES);
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}
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}
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} |