85 lines
3.7 KiB
Plaintext
85 lines
3.7 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 raver119@gmail.com
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//
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#include <ops/declarable/helpers/flatten.h>
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#include <helpers/PointersManager.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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void _CUDA_G flattenKernel(void **xBuffers, Nd4jLong **xShapeInfos, Nd4jLong *offsets, Nd4jLong numInputs, void *zBuffer, Nd4jLong *zShapeInfo, char order) {
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Nd4jLong xCoord[MAX_RANK];
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// each block of threads works on 1 input array
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for (Nd4jLong e = blockIdx.x; e < numInputs; e += gridDim.x) {
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auto z = reinterpret_cast<T*>(zBuffer) + offsets[e];
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auto xBuffer = reinterpret_cast<T*>(xBuffers[e]);
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auto xShapeInfo = xShapeInfos[e];
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auto xLength = shape::length(xShapeInfo);
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// each element of this input array has own place within common output array
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for (uint i = threadIdx.x; i < xLength; i += blockDim.x)
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z[i] = xBuffer[getIndexOffsetOrdered(i, xShapeInfo, order)];
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}
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}
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template <typename T>
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void flatten_(nd4j::LaunchContext *context, std::vector<NDArray*> &inputs, NDArray *output, char order) {
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PointersManager pm(context, "flatten");
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std::vector<void*> hdBuffers(inputs.size());
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std::vector<Nd4jLong> hOffsets(inputs.size());
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std::vector<Nd4jLong *> hdShapes(inputs.size());
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Nd4jLong cOffset = 0;
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// calculating offsets in output
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for (int e = 0; e < inputs.size(); e++) {
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hOffsets[e] = cOffset;
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cOffset += inputs[e]->lengthOf();
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hdBuffers[e] = inputs[e]->specialBuffer();
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hdShapes[e] = inputs[e]->specialShapeInfo();
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}
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// copying pointers to device
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auto dBuffers = (void **) pm.replicatePointer(hdBuffers.data(), inputs.size() * sizeof(void*));
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auto dShapes = (Nd4jLong **)pm.replicatePointer(hdShapes.data(), inputs.size() * sizeof(Nd4jLong*));
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auto dOffsets = (Nd4jLong *) pm.replicatePointer(hOffsets.data(), inputs.size() * sizeof(Nd4jLong));
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flattenKernel<T><<<256, 512, 8192, *context->getCudaStream()>>>(dBuffers, dShapes, dOffsets, inputs.size(), output->getSpecialBuffer(), output->getSpecialShapeInfo(), order);
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pm.synchronize();
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}
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void flatten(nd4j::LaunchContext *context, std::vector<NDArray*> &inputs, NDArray *output, char order) {
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// FIXME: we want NDArrayFactory::prepareSpecialUse here eventually
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for (auto v:inputs)
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v->syncToDevice();
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BUILD_SINGLE_SELECTOR(output->dataType(), flatten_, (context, inputs, output, order), LIBND4J_TYPES);
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NDArray::registerSpecialUse({output}, {});
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}
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}
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}
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} |