cavis/libnd4j/include/ops/declarable/helpers/cuda/diag.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 4/6/2018.
//
#include "ResultSet.h"
#include <ops/declarable/helpers/diag.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputShape, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong inputLength) {
__shared__ T *z;
__shared__ T const* x;
__shared__ Nd4jLong outputLength;
if (threadIdx.x == 0) {
z = reinterpret_cast<T*>(outputBuffer);
x = reinterpret_cast<T const*>(inputBuffer);
outputLength = shape::length(outputShape);
}
__syncthreads();
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (int t = tid; t < inputLength; t += step) {
z[shape::getIndexOffset(t * (inputLength + 1), outputShape, outputLength)] = x[shape::getIndexOffset(t, inputShape, inputLength)]; //tX];
}
}
template <typename T>
static __global__ void diagPartFunctorKernel(void* outputBuffer, Nd4jLong* outputShape, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong outputLength, Nd4jLong inputLength) {
__shared__ T *z;
__shared__ T const* x;
if (threadIdx.x == 0) {
z = reinterpret_cast<T*>(outputBuffer);
x = reinterpret_cast<T const*>(inputBuffer);
}
__syncthreads();
const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
Nd4jLong i = threadIdx.x * (outputLength + 1);
for (int t = tid; t < outputLength && i < inputLength; t += step) {
z[shape::getIndexOffset(t, outputShape, outputLength)] = x[shape::getIndexOffset(i, inputShape, inputLength)]; //tX];
i += outputLength + 1;
}
}
//////////////////////////////////////////////////////////////////////////
// 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 _diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
auto stream = context->getCudaStream();
auto inputLength = input->lengthOf();
dim3 launchDims(256, 512, 8192);
if (!input->isActualOnDeviceSide())
input->syncToDevice();
diagFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), inputLength);
}
void diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
auto xType = input->dataType();
BUILD_SINGLE_SELECTOR(xType, _diagFunctor, (context, input, output), LIBND4J_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output);, LIBND4J_TYPES);
template <typename T>
void _diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) {
const int outLen = output->lengthOf();
const int inLen = input->lengthOf();
auto stream = context->getCudaStream();
dim3 launchDims(256, 512, 8192);
if (!input->isActualOnDeviceSide())
input->syncToDevice();
diagPartFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), outLen, inLen);
// int i(0), j;
// for (j = 0;j < outLen; j++) {
// output->p(j, input->e(i));
// i += outLen + 1;
// }
}
BUILD_SINGLE_TEMPLATE(template void _diagPartFunctor, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output);, LIBND4J_TYPES);
void diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) {
auto zType = output->dataType();
BUILD_SINGLE_SELECTOR(zType, _diagPartFunctor, (context, input, output), LIBND4J_TYPES);
}
}
}
}