589401477d
* - profiling bias_add op - add some docementation Signed-off-by: Yurii <yurii@skymind.io> * - minor change Signed-off-by: Yurii <yurii@skymind.io> * - provide addBias cuda kernel Signed-off-by: Yurii <yurii@skymind.io> * - improve shape::getIndexOfffset and change its signature Signed-off-by: Yurii <yurii@skymind.io> * - same as previous Signed-off-by: Yurii <yurii@skymind.io> * - improve and change signature in some shape:: stuff which has to do with calculation of offsets for array elements Signed-off-by: Yurii <yurii@skymind.io> * - minor changes in flatten Signed-off-by: Yurii <shyrma@skymind.io> * - add function shape::getIndexOffsetOrdered Signed-off-by: Yurii <shyrma@skymind.io> * - correct shape::getIndexOffsetOrdered() Signed-off-by: Yurii <shyrma@skymind.io> * - move getIndexOffsetOrdered to flatten.h header in order to isolate this function Signed-off-by: Yurii <shyrma@skymind.io>
93 lines
3.6 KiB
Plaintext
93 lines
3.6 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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// @author Yurii Shyrma (iuriish@yahoo.com), created on 26.04.2019
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//
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#include<ops/declarable/helpers/polyGamma.h>
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#include<ops/declarable/helpers/zeta.h>
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#include <NDArrayFactory.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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///////////////////////////////////////////////////////////////////
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template<typename T>
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__global__ static void polyGammaCuda(const void *vn, const Nd4jLong *nShapeInfo,
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const void *vx, const Nd4jLong *xShapeInfo,
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void *vz, const Nd4jLong *zShapeInfo) {
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const auto n = reinterpret_cast<const T*>(vn);
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const auto x = reinterpret_cast<const T*>(vx);
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auto z = reinterpret_cast<T*>(vz);
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__shared__ Nd4jLong len;
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if (threadIdx.x == 0)
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len = shape::length(nShapeInfo);
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto totalThreads = gridDim.x * blockDim.x;
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for (int i = tid; i < len; i += totalThreads) {
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const auto nOffset = shape::getIndexOffset(i, nShapeInfo);
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const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
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const auto zOffset = shape::getIndexOffset(i, zShapeInfo);
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const T nVal = n[nOffset];
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int sign = (static_cast<int>(nVal) + 1) % 2 ? -1 : 1;
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T factorial = 1;
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if(nVal != 0 && nVal != 1)
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for(int i = 2; i <= nVal; ++i)
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factorial *= i;
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z[zOffset] = sign * factorial * zetaScalar<T>(nVal + 1, x[xOffset]);
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename T>
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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) {
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polyGammaCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vn, nShapeInfo, vx, xShapeInfo, vz, zShapeInfo);
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}
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///////////////////////////////////////////////////////////////////
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void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& z) {
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NDArray::prepareSpecialUse({&z}, {&n, &x});
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int threadsPerBlock = MAX_NUM_THREADS;
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int blocksPerGrid = (z.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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BUILD_SINGLE_SELECTOR(n.dataType(), polyGammaCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), n.getSpecialBuffer(), n.getSpecialShapeInfo(), x.getSpecialBuffer(), x.getSpecialShapeInfo(), z.getSpecialBuffer(), z.getSpecialShapeInfo()), FLOAT_TYPES);
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NDArray::registerSpecialUse({&z}, {&n, &x});
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}
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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);
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}
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}
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}
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