cavis/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.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
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 26.04.2019
//
#include<ops/declarable/helpers/gammaMathFunc.h>
#include<ops/declarable/helpers/zeta.h>
#include <NDArrayFactory.h>
namespace nd4j {
namespace ops {
namespace helpers {
///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void polyGammaCuda(const void *vn, const Nd4jLong *nShapeInfo,
const void *vx, const Nd4jLong *xShapeInfo,
void *vz, const Nd4jLong *zShapeInfo) {
const auto n = reinterpret_cast<const T*>(vn);
const auto x = reinterpret_cast<const T*>(vx);
auto z = reinterpret_cast<T*>(vz);
__shared__ Nd4jLong len;
__shared__ bool sameOffsetNX, sameOffsetNZ;
if (threadIdx.x == 0) {
len = shape::length(nShapeInfo);
sameOffsetNX = shape::haveSameShapeAndStrides(xShapeInfo, nShapeInfo);
sameOffsetNZ = shape::haveSameShapeAndStrides(zShapeInfo, nShapeInfo);
}
__syncthreads();
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto totalThreads = gridDim.x * blockDim.x;
for (int i = tid; i < len; i += totalThreads) {
const auto nOffset = shape::getIndexOffset(i, nShapeInfo);
const auto xOffset = sameOffsetNX ? nOffset : shape::getIndexOffset(i, xShapeInfo);
const auto zOffset = sameOffsetNZ ? nOffset : shape::getIndexOffset(i, zShapeInfo);
const T order = n[nOffset];
int sign = (static_cast<int>(order) + 1) % 2 ? -1 : 1;
if(order != static_cast<int>(order)) {
z[zOffset] = DataTypeUtils::nanOrZero<T>();
}
else if(order == 0) {
z[zOffset] = diGammaScalar<T>(x[xOffset]);
}
else {
T factorial = 1;
for(int i = 2; i <= order; ++i)
factorial *= i;
z[zOffset] = sign * factorial * zetaScalar<T>(order + 1, x[xOffset]);
}
}
}
///////////////////////////////////////////////////////////////////
template<typename T>
static void polyGammaCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void *vn, const Nd4jLong *nShapeInfo, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo) {
polyGammaCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vn, nShapeInfo, vx, xShapeInfo, vz, zShapeInfo);
}
///////////////////////////////////////////////////////////////////
void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& z) {
NDArray::prepareSpecialUse({&z}, {&n, &x});
int threadsPerBlock = MAX_NUM_THREADS / 2;
int blocksPerGrid = (z.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
BUILD_SINGLE_SELECTOR(n.dataType(), polyGammaCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), n.getSpecialBuffer(), n.getSpecialShapeInfo(), x.getSpecialBuffer(), x.getSpecialShapeInfo(), z.getSpecialBuffer(), z.getSpecialShapeInfo()), FLOAT_TYPES);
NDArray::registerSpecialUse({&z}, {&n, &x});
}
BUILD_SINGLE_TEMPLATE(template void polyGammaCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream, const void *vn, const Nd4jLong *nShapeInfo, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo), FLOAT_TYPES);
}
}
}