cavis/libnd4j/include/ops/declarable/helpers/cuda/flatten.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 raver119@gmail.com
//
#include <ops/declarable/helpers/flatten.h>
#include <helpers/PointersManager.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename T>
void _CUDA_G flattenKernel(void **xBuffers, Nd4jLong **xShapeInfos, Nd4jLong *offsets, Nd4jLong numInputs, void *zBuffer, Nd4jLong *zShapeInfo, char order) {
Nd4jLong xCoord[MAX_RANK];
// each block of threads works on 1 input array
for (Nd4jLong e = blockIdx.x; e < numInputs; e += gridDim.x) {
auto z = reinterpret_cast<T*>(zBuffer) + offsets[e];
auto xBuffer = reinterpret_cast<T*>(xBuffers[e]);
auto xShapeInfo = xShapeInfos[e];
auto xLength = shape::length(xShapeInfo);
// each element of this input array has own place within common output array
for (uint i = threadIdx.x; i < xLength; i += blockDim.x)
z[i] = xBuffer[getIndexOffsetOrdered(i, xShapeInfo, order)];
}
}
template <typename T>
void flatten_(sd::LaunchContext *context, std::vector<NDArray*> &inputs, NDArray *output, char order) {
PointersManager pm(context, "flatten");
std::vector<void*> hdBuffers(inputs.size());
std::vector<Nd4jLong> hOffsets(inputs.size());
std::vector<Nd4jLong *> hdShapes(inputs.size());
Nd4jLong cOffset = 0;
// calculating offsets in output
for (int e = 0; e < inputs.size(); e++) {
hOffsets[e] = cOffset;
cOffset += inputs[e]->lengthOf();
hdBuffers[e] = inputs[e]->specialBuffer();
hdShapes[e] = inputs[e]->specialShapeInfo();
}
// copying pointers to device
auto dBuffers = (void **) pm.replicatePointer(hdBuffers.data(), inputs.size() * sizeof(void*));
auto dShapes = (Nd4jLong **)pm.replicatePointer(hdShapes.data(), inputs.size() * sizeof(Nd4jLong*));
auto dOffsets = (Nd4jLong *) pm.replicatePointer(hOffsets.data(), inputs.size() * sizeof(Nd4jLong));
flattenKernel<T><<<256, 512, 8192, *context->getCudaStream()>>>(dBuffers, dShapes, dOffsets, inputs.size(), output->getSpecialBuffer(), output->getSpecialShapeInfo(), order);
pm.synchronize();
}
void flatten(sd::LaunchContext *context, std::vector<NDArray*> &inputs, NDArray *output, char order) {
// FIXME: we want NDArrayFactory::prepareSpecialUse here eventually
for (auto v:inputs)
v->syncToDevice();
BUILD_SINGLE_SELECTOR(output->dataType(), flatten_, (context, inputs, output, order), LIBND4J_TYPES);
NDArray::registerSpecialUse({output}, {});
}
}
}
}