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

77 lines
3.7 KiB
Plaintext

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