cavis/libnd4j/include/ops/declarable/helpers/cuda/merge.cu

256 lines
12 KiB
Plaintext

/*******************************************************************************
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
//
#include<ops/declarable/helpers/transforms.h>
#include <array/ResultSet.h>
#include <helpers/ShapeUtils.h>
#include <numeric>
#include <NDArrayFactory.h>
#include <helpers/TAD.h>
#include <exceptions/cuda_exception.h>
#include <PointersManager.h>
#include <ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
//////////////////////////////////////////////////////////////////////////
template <typename T, typename Z>
static __global__ void global_mergeMaxIndex_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<Z*>(voutput);
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T mVal = -DataTypeUtils::max<T>();
Z mIdx(0);
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
auto val = x[shape::getIndexOffset(e, xShape)];;
if (mVal < val) {
mIdx = static_cast<Z>(i);
mVal = val;
}
}
__syncthreads();
output[shape::getIndexOffset(e, outputShape)] = mIdx;
}
}
template <typename T, typename Z>
static void mergeMaxIndex_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeMaxIndex");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeMaxIndex_<T,Z><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
void mergeMaxIndex(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
NDArray::prepareSpecialUse({&output}, {});
for (auto v:inArrs)
v->syncToDevice();
BUILD_DOUBLE_SELECTOR(inArrs[0]->dataType(), output.dataType(), mergeMaxIndex_, (context, inArrs, output), LIBND4J_TYPES, INDEXING_TYPES);
NDArray::registerSpecialUse({&output}, {});
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void global_mergeMax_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<T*>(voutput);
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T mVal = -DataTypeUtils::max<T>();
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
auto val = x[shape::getIndexOffset(e, xShape)];;
if (mVal < val)
mVal = val;
}
__syncthreads();
output[shape::getIndexOffset(e, outputShape)] = mVal;
}
}
template<typename T>
static void mergeMax_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeMax");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeMax_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
void mergeMax(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
NDArray::prepareSpecialUse({&output}, {});
for (auto v:inArrs)
v->syncToDevice();
BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (context, inArrs, output), LIBND4J_TYPES);
NDArray::registerSpecialUse({&output}, {});
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void global_mergeAvg_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<T*>(voutput);
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T sum(0.0f);
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
sum += x[shape::getIndexOffset(e, xShape)];
}
output[shape::getIndexOffset(e, outputShape)] = sum / numArrays;
}
}
template<typename T>
static void mergeAvg_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeAvg");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeAvg_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
void mergeAvg(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
NDArray::prepareSpecialUse({&output}, {});
for (auto v:inArrs)
v->syncToDevice();
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (context, inArrs, output), FLOAT_TYPES);
NDArray::registerSpecialUse({&output}, {});
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static __global__ void global_mergeAdd_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
auto output = reinterpret_cast<T*>(voutput);
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
const auto step = gridDim.x * blockDim.x;
for (Nd4jLong e = tid; e < length; e += step) {
T sum(0.0f);
for (int i = 0; i < numArrays; i++) {
auto x = reinterpret_cast<T*>(inArrs[i]);
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
sum += x[shape::getIndexOffset(e, xShape)];
}
output[shape::getIndexOffset(e, outputShape)] = sum;
}
}
template<typename T>
static void mergeAdd_(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
std::vector<void *> inBuffers(inArrs.size());
std::vector<void *> inShapes(inArrs.size());
for (int e = 0; e < inArrs.size(); e++) {
inBuffers[e] = inArrs[e]->getSpecialBuffer();
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
}
PointersManager manager(context, "mergeAdd");
auto pInBuffers = reinterpret_cast<void **>(manager.replicatePointer(inBuffers.data(), inBuffers.size() * sizeof(void *)));
auto pInShapes = reinterpret_cast<void **>(manager.replicatePointer(inShapes.data(), inShapes.size() * sizeof(void *)));
auto length = output.lengthOf();
global_mergeAdd_<T><<<512, 512, 512, *context->getCudaStream()>>>(pInBuffers, pInShapes, (int) inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
manager.synchronize();
}
BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), NUMERIC_TYPES);
void mergeAdd(nd4j::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
NDArray::prepareSpecialUse({&output}, {});
for (auto v:inArrs)
v->syncToDevice();
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (context, inArrs, output), NUMERIC_TYPES);
NDArray::registerSpecialUse({&output}, {});
}
}
}
}