112 lines
4.5 KiB
Plaintext
112 lines
4.5 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 30.05.2019
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//
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#include <ops/declarable/helpers/one_hot.h>
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#include <array/ResultSet.h>
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#include <helpers/ShapeUtils.h>
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#include <numeric>
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#include <NDArrayFactory.h>
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#include <helpers/TAD.h>
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#include <exceptions/cuda_exception.h>
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#include <PointersManager.h>
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#include <ConstantTadHelper.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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// x - indices, z - output
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template<typename X, typename Z>
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__global__ static void onehotCuda(const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const uint axis, const uint depth, const Z on, const Z off) {
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const auto x = reinterpret_cast<const X*>(vx);
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auto z = reinterpret_cast<Z*>(vz);
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__shared__ int xRank, zRank;
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__shared__ Nd4jLong zLen, totalThreads, *sharedMem;
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if (threadIdx.x == 0) {
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extern __shared__ unsigned char shmem[];
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sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
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xRank = shape::rank(xShapeInfo);
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zRank = shape::rank(zShapeInfo);
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zLen = shape::length(zShapeInfo);
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totalThreads = gridDim.x * blockDim.x;
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}
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auto coord = sharedMem + threadIdx.x * zRank;
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
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shape::index2coords(zRank, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), i, zLen, coord);
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const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coord, zRank);
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const auto depthCoord = coord[axis];
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for (uint j = axis; j < zRank - 1; ++j)
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coord[j] = coord[j + 1];
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const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coord, xRank);
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const Nd4jLong idx = x[xOffset];
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z[zOffset] = depthCoord == idx ? on : off;
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}
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}
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///////////////////////////////////////////////////////////////////
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template<typename X, typename Y>
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static void onehotCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
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const void *vx, const Nd4jLong *xShapeInfo,
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void *vz, const Nd4jLong *zShapeInfo,
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const uint axis, const uint depth,
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const double on, const double off) {
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onehotCuda<X,Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, axis, depth, static_cast<Y>(on), static_cast<Y>(off));
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}
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///////////////////////////////////////////////////////////////////
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void onehot(const nd4j::LaunchContext* context, const NDArray *indices, NDArray *output, const uint axis, const uint depth, const double on, const double off) {
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const auto xType = indices->dataType();
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const auto zType = output->dataType();
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const int threadsPerBlock = MAX_NUM_THREADS / 4;
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const int blocksPerGrid = (output->lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
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const int sharedMem = threadsPerBlock * sizeof(decltype(*output->getShapeInfo())) * output->rankOf() + 128;
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PointersManager manager(context, "onehot");
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NDArray::prepareSpecialUse({output}, {indices});
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BUILD_DOUBLE_SELECTOR(xType, zType, onehotCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), indices->getSpecialBuffer(), indices->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), axis, depth, on, off), LIBND4J_TYPES, LIBND4J_TYPES);
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NDArray::registerSpecialUse({output}, {indices});
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manager.synchronize();
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}
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}
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}
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} |