cavis/libnd4j/include/helpers/cpu/ConstantTadHelper.cpp
Yurii Shyrma fe47f52896
Oleh tenzor mmul (#231)
* Libnd4j: TensorMMul backprop op #8174, raw implementation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 merge master and some corrections

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 algorithm update, need testing, sync with  master

* Libnd4j: TensorMMul backprop op #8174 fixed incorrect B axes calculation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 optimize axes identification and fix bug of indeces overlapping, added first test. need testing with different shapes

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 some fixes and improvements need more testing

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 fixed order of matrix multiply

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 fixed issue of incorrect axes definition, add tests based on TF, need additional testing for case dLdC not equal 1

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 fixed scalar case add test

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 fixed bp algorithm, axes definition, need some mode testing with different orders combination f,c; c,f f,f and add some checks for inputs

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 some checks and corrections added tests, exists the problem with different input orders support A-f B-c and A-f B-f

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: TensorMMul backprop op #8174 sync master

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* - correct bug in MmulHelper::tensorDot(a, b, c, axes_a, axes_b,permutForC)

Signed-off-by: Yurii <iuriish@yahoo.com>

* Libnd4j: TensorMMul backprop op #8174 code clean up and refactoring

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* - add check for linspase ordered permutations in ShapeUtils::evalShapeForTensorDot

Signed-off-by: Yurii <iuriish@yahoo.com>

* - provide additional code in shape::reshape stuff in order to reduce amount of allocation/copy operations during reshaping procedure

Signed-off-by: Yurii <iuriish@yahoo.com>

* - further work on problem of wrong shape evaluation during permute/reshape procedures

Signed-off-by: Yurii <iuriish@yahoo.com>

* - still looking for bug reason in reshape/permute stuff

Signed-off-by: Yurii <iuriish@yahoo.com>

* - correct bug in transform cuda native ops

Signed-off-by: Yurii <iuriish@yahoo.com>

* - correct bug in NDArray::assign

Signed-off-by: Yurii <iuriish@yahoo.com>

* - remove old shape::reshape stuff

Signed-off-by: Yurii <iuriish@yahoo.com>

* - add possibility to disable copy of old buffer to new buffer during reshape operation in NDArray class

Signed-off-by: Yurii <iuriish@yahoo.com>

* - correct bug in tensorDot which had to do with wrong pointers assigments

Signed-off-by: Yurii <iuriish@yahoo.com>

Co-authored-by: Oleh <oleg.semeniv@gmail.com>
2020-02-13 20:33:54 +03:00

118 lines
4.6 KiB
C++

/*******************************************************************************
* 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 "../ConstantTadHelper.h"
#include <TAD.h>
#include <ShapeUtils.h>
#ifndef __CUDABLAS__
namespace nd4j {
ConstantTadHelper::ConstantTadHelper() {
std::map<TadDescriptor, TadPack> pack;
_cache.emplace_back(pack);
}
ConstantTadHelper* ConstantTadHelper::getInstance() {
if (!_INSTANCE)
_INSTANCE = new ConstantTadHelper();
return _INSTANCE;
}
TadPack ConstantTadHelper::tadForDimensions(const Nd4jLong *originalShape, int dimension, const bool keepUnitiesInShape) {
return tadForDimensions(originalShape, &dimension, 1, keepUnitiesInShape);
}
TadPack ConstantTadHelper::tadForDimensions(const Nd4jLong *originalShape, const std::vector<int> &dimensions, const bool keepUnitiesInShape) {
return tadForDimensions(originalShape, const_cast<int *>(dimensions.data()), dimensions.size(), keepUnitiesInShape);
}
TadPack ConstantTadHelper::tadForDimensions(const Nd4jLong *originalShape, int* dimensions, int dimLength, const bool keepUnitiesInShape) {
TadDescriptor tadDescriptor(originalShape, dimensions, dimLength, keepUnitiesInShape);
return tadForDimensions(tadDescriptor);
}
TadPack ConstantTadHelper::tadForDimensions(ShapeDescriptor &descriptor, std::vector<int> &dimensions, const bool keepUnitiesInShape) {
TadDescriptor tadDescriptor(descriptor, dimensions, keepUnitiesInShape);
return tadForDimensions(tadDescriptor);
}
TadPack ConstantTadHelper::tadForDimensions(TadDescriptor &descriptor) {
const int deviceId = 0;
_mutex.lock();
if (_cache[deviceId].count(descriptor) == 0) {
const auto shapeInfo = descriptor.originalShape().toShapeInfo();
const int rank = shape::rank(shapeInfo);
const std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(rank, descriptor.axis());
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(shapeInfo, dimsToExclude);
const int subArrRank = (rank == dimsToExclude.size() || descriptor.areUnitiesinShape()) ? rank : rank - dimsToExclude.size();
auto sPtr = new Nd4jLong[shape::shapeInfoLength(subArrRank)]; // shape of sub-arrays (same for all for them)
auto oPtr = new Nd4jLong[numOfSubArrs];
if (numOfSubArrs > 0)
shape::calcSubArrShapeAndOffsets(shapeInfo, numOfSubArrs, dimsToExclude.size(), dimsToExclude.data(), sPtr, oPtr, descriptor.areUnitiesinShape());
ConstantDataBuffer shapesBuffer(sPtr, nullptr, shape::shapeInfoLength(subArrRank)*sizeof(Nd4jLong), DataType::INT64);
ConstantDataBuffer offsetsBuffer(oPtr, nullptr, numOfSubArrs*sizeof(Nd4jLong), DataType::INT64);
TadPack t(shapesBuffer, offsetsBuffer, numOfSubArrs);
// auto shapeInfo = descriptor.originalShape().toShapeInfo();
// shape::TAD tad;
// tad.init(shapeInfo, descriptor.axis().data(), descriptor.axis().size());
// tad.createTadOnlyShapeInfo();
// tad.createOffsets();
// auto sPtr = new Nd4jLong[shape::shapeInfoLength(tad.tadOnlyShapeInfo)];
// auto oPtr = new Nd4jLong[tad.numTads];
// memcpy(sPtr, tad.tadOnlyShapeInfo, shape::shapeInfoByteLength(tad.tadOnlyShapeInfo));
// memcpy(oPtr, tad.tadOffsets, tad.numTads * sizeof(Nd4jLong));
// TadPack t(shapesBuffer, offsetsBuffer, tad.numTads);
_cache[deviceId][descriptor] = t;
TadPack &r = _cache[deviceId][descriptor];
_mutex.unlock();
delete[] shapeInfo;
return r;
} else {
TadPack r = _cache[deviceId][descriptor];
_mutex.unlock();
return r;
}
}
nd4j::ConstantTadHelper* nd4j::ConstantTadHelper::_INSTANCE = 0;
}
#endif