92 lines
4.2 KiB
Plaintext
92 lines
4.2 KiB
Plaintext
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/*******************************************************************************
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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 sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/nth_element.h>
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#include <TAD.h>
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#include <PointersManager.h>
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#include <NativeOps.h>
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#include <helpers/ConstantTadHelper.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static __global__ void fillUpElementKernel(void* outputBuffer, Nd4jLong* outputShapeInfo, void* inputBuffer, Nd4jLong* inputShapeInfo, Nd4jLong* pTadShape, Nd4jLong* pTadOffsets, Nd4jLong n) {
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__shared__ T *z, *x;
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__shared__ Nd4jLong bufferLength, arrLen;
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if (threadIdx.x == 0) {
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z = reinterpret_cast<T*>(outputBuffer);
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x = reinterpret_cast<T*>(inputBuffer);
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arrLen = shape::length(pTadShape);
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bufferLength = shape::length(outputShapeInfo);
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}
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__syncthreads();
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const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (int t = tid; t < bufferLength; t += step) {
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auto tX = x + pTadOffsets[t];
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z[shape::getIndexOffset(t, outputShapeInfo, bufferLength)] = tX[shape::getIndexOffset(n, pTadShape, arrLen)]; //tX];
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}
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}
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template <typename T>
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void nthElementFunctor_(nd4j::LaunchContext * context, NDArray* input, NDArray* nVal, NDArray* output, bool reverse) {
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Nd4jLong n = nVal->e<Nd4jLong>(0);
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NDArray sortedVals(*input);
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Nd4jPointer params[2];
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params[0] = context->getCudaStream();
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params[1] = *context->getCudaStream();
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if (input->isVector()) {
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NativeOps ops;
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ops.sort(params, sortedVals.buffer(), sortedVals.shapeInfo(), sortedVals.specialBuffer(), sortedVals.specialShapeInfo(), reverse);
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cudaMemcpy(reinterpret_cast<T*>(output->specialBuffer()), reinterpret_cast<T*>(sortedVals.specialBuffer()) + n, sizeof(T), cudaMemcpyDeviceToDevice);
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}
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else { // rank greater than 1
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std::vector<int> lastDims({input->rankOf() - 1});// = ShapeUtils::evalDimsToExclude(input->rankOf(), {input->rankOf() - 1});
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auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(sortedVals.getShapeInfo(), lastDims);
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//PointersManager manager(context, "helpers::nth_element");
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auto pTadShape = packX.specialShapeInfo();
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auto pTadOffsets = packX.specialOffsets();
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//auto pLastDimData = (int*) manager.replicatePointer(lastDims.data(), lastDims.size() * sizeof(int));
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NativeOps ops;
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ops.sortTad(params, sortedVals.buffer(), sortedVals.shapeInfo(), sortedVals.specialBuffer(), sortedVals.specialShapeInfo(), lastDims.data(), lastDims.size(), pTadShape, pTadOffsets, reverse);
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auto stream = context->getCudaStream();
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fillUpElementKernel<T><<<32, 64, 1024, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), sortedVals.specialBuffer(), sortedVals.specialShapeInfo(), pTadShape, pTadOffsets, n);
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//manager.synchronize();
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}
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}
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void nthElementFunctor(nd4j::LaunchContext * context, NDArray* input, NDArray* n, NDArray* output, bool reverse) {
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BUILD_SINGLE_SELECTOR(input->dataType(), nthElementFunctor_, (context, input, n, output, reverse), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void nthElementFunctor_, (nd4j::LaunchContext * context, NDArray* input, NDArray* n, NDArray* output, bool reverse), LIBND4J_TYPES);
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}
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}
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}
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