Backpropagation implementation of mergemax, mergeadd and mergeavg ops (#343)
* libnd4j: first step of merge_max implementation Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed typos Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j some corrections for mergeMaxBp Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j some minor corrections Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j test added for mergemax_bp Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed several problems tests added, check with gradCheck Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j remove duplicated tests Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j split implementation of transforms ops into separate file implementation Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j code clean up, added mergeavg_bp and mergeadd_bp, need testing Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j merge master, fixed typos and added tests Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j some minor fixes Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j added helper for mergeAddBp operation, this permits to skip nullify Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j file renaming changes and cuda some corrections, need some additional corrections Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j some additional corrections for merge ops Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j more corrections per request for cuda more proper usage Signed-off-by: Oleg <oleg.semeniv@gmail.com>master
Oleh
GitHub
parent
b1bc7df160
commit
e8cbf5255a
|
|
@ -33,7 +33,7 @@ OP_IMPL(mergeadd, -1, 1, false) {
|
|||
|
||||
auto output = OUTPUT_VARIABLE(0);
|
||||
|
||||
std::vector<NDArray*> inArrs(block.width());
|
||||
std::vector<const NDArray*> inArrs(block.width());
|
||||
|
||||
for(int i = 0; i < block.width(); ++i)
|
||||
inArrs[i] = INPUT_VARIABLE(i);
|
||||
|
|
@ -42,7 +42,6 @@ OP_IMPL(mergeadd, -1, 1, false) {
|
|||
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
DECLARE_SYN(mergesum, mergeadd);
|
||||
DECLARE_SYN(add_n, mergeadd);
|
||||
DECLARE_SYN(addn, mergeadd);
|
||||
|
|
@ -54,6 +53,45 @@ DECLARE_SYN(accumulate_n, mergeadd);
|
|||
->setAllowedInputTypes(sd::DataType::ANY)
|
||||
->setAllowedOutputTypes(sd::DataType::ANY);
|
||||
}
|
||||
|
||||
|
||||
CUSTOM_OP_IMPL(mergeadd_bp, 2, 1, false, 0, 0) {
|
||||
|
||||
auto inSize = block.width() - 1;
|
||||
|
||||
REQUIRE_OK(this->validateInputDimensionsMatch(block));
|
||||
|
||||
std::vector<NDArray*> outArrs(inSize);
|
||||
|
||||
const auto gradient = INPUT_VARIABLE(inSize);
|
||||
|
||||
for (int i = 0; i < inSize; ++i) {
|
||||
outArrs[i] = OUTPUT_VARIABLE(i);
|
||||
}
|
||||
helpers::mergeAddBp(block.launchContext(), *gradient, outArrs);
|
||||
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
DECLARE_TYPES(mergeadd_bp) {
|
||||
getOpDescriptor()
|
||||
->setAllowedInputTypes(sd::DataType::ANY)
|
||||
->setAllowedOutputTypes(sd::DataType::ANY);
|
||||
}
|
||||
DECLARE_SHAPE_FN(mergeadd_bp) {
|
||||
|
||||
const int numOfInArrs = block.width() - 1;
|
||||
|
||||
auto shapeList = SHAPELIST();
|
||||
|
||||
for (int e = 0; e < numOfInArrs; e++) {
|
||||
auto inShape = inputShape->at(e);
|
||||
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), shape::order(inShape), shape::shapeOf(inShape), shape::rank(inShape))));
|
||||
}
|
||||
|
||||
return shapeList;
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ OP_IMPL(mergeavg, -1, 1, false) {
|
|||
|
||||
auto output = OUTPUT_VARIABLE(0);
|
||||
|
||||
std::vector<NDArray*> inArrs(block.width());
|
||||
std::vector<const NDArray*> inArrs(block.width());
|
||||
|
||||
for(int i = 0; i < block.width(); ++i)
|
||||
inArrs[i] = INPUT_VARIABLE(i);
|
||||
|
|
@ -48,6 +48,44 @@ OP_IMPL(mergeavg, -1, 1, false) {
|
|||
->setAllowedInputTypes({ALL_FLOATS})
|
||||
->setAllowedOutputTypes({ALL_FLOATS});
|
||||
}
|
||||
|
||||
|
||||
CUSTOM_OP_IMPL(mergeavg_bp, 2, 1, false, 0, 0) {
|
||||
|
||||
auto inSize = block.width() - 1;
|
||||
|
||||
REQUIRE_OK(this->validateInputDimensionsMatch(block));
|
||||
|
||||
std::vector<NDArray*> outArrs(inSize);
|
||||
|
||||
const auto gradient = INPUT_VARIABLE(inSize);
|
||||
|
||||
for (int i = 0; i < inSize; ++i) {
|
||||
outArrs[i] = OUTPUT_VARIABLE(i);
|
||||
}
|
||||
helpers::mergeAvgBp(block.launchContext(), *gradient, outArrs);
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
DECLARE_TYPES(mergeavg_bp) {
|
||||
getOpDescriptor()
|
||||
->setAllowedInputTypes(sd::DataType::ANY)
|
||||
->setAllowedOutputTypes(sd::DataType::ANY);
|
||||
}
|
||||
DECLARE_SHAPE_FN(mergeavg_bp) {
|
||||
|
||||
const int numOfInArrs = block.width() - 1;
|
||||
|
||||
auto shapeList = SHAPELIST();
|
||||
|
||||
for (int e = 0; e < numOfInArrs; e++) {
|
||||
auto inShape = inputShape->at(e);
|
||||
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), shape::order(inShape), shape::shapeOf(inShape), shape::rank(inShape))));
|
||||
}
|
||||
|
||||
return shapeList;
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ OP_IMPL(mergemax, -1, 1, false) {
|
|||
|
||||
auto output = OUTPUT_VARIABLE(0);
|
||||
|
||||
std::vector<NDArray*> inArrs(block.width());
|
||||
std::vector<const NDArray*> inArrs(block.width());
|
||||
|
||||
for(int i = 0; i < block.width(); ++i)
|
||||
inArrs[i] = INPUT_VARIABLE(i);
|
||||
|
|
@ -42,7 +42,6 @@ OP_IMPL(mergemax, -1, 1, false) {
|
|||
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
DECLARE_SYN(MergeMax, mergemax);
|
||||
|
||||
DECLARE_TYPES(mergemax) {
|
||||
|
|
@ -51,6 +50,47 @@ DECLARE_SYN(MergeMax, mergemax);
|
|||
->setAllowedOutputTypes(sd::DataType::ANY);
|
||||
}
|
||||
|
||||
|
||||
CUSTOM_OP_IMPL(mergemax_bp, 2, 1, false, 0, 0) {
|
||||
|
||||
auto inSize = block.width();
|
||||
|
||||
REQUIRE_OK(this->validateInputDimensionsMatch(block));
|
||||
|
||||
std::vector<const NDArray*> inArrs(inSize);
|
||||
std::vector<NDArray*> outArrs(inSize - 1);
|
||||
|
||||
for (int i = 0; i < inSize; ++i)
|
||||
inArrs[i] = INPUT_VARIABLE(i);
|
||||
|
||||
for (int i = 0; i < (inSize - 1); ++i) {
|
||||
outArrs[i] = OUTPUT_NULLIFIED(i);
|
||||
}
|
||||
|
||||
helpers::mergeMaxBp(block.launchContext(), inArrs, outArrs);
|
||||
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
DECLARE_TYPES(mergemax_bp) {
|
||||
getOpDescriptor()
|
||||
->setAllowedInputTypes(sd::DataType::ANY)
|
||||
->setAllowedOutputTypes(sd::DataType::ANY);
|
||||
}
|
||||
DECLARE_SHAPE_FN(mergemax_bp) {
|
||||
|
||||
const int numOfInArrs = block.width() - 1;
|
||||
|
||||
auto shapeList = SHAPELIST();
|
||||
|
||||
for (int e = 0; e < numOfInArrs; e++) {
|
||||
auto inShape = inputShape->at(e);
|
||||
shapeList->push_back(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), shape::order(inShape), shape::shapeOf(inShape), shape::rank(inShape))));
|
||||
}
|
||||
|
||||
return shapeList;
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ CUSTOM_OP_IMPL(mergemaxindex, -1, 1, false, 0, 0) {
|
|||
REQUIRE_OK(this->validateInputDimensionsMatch(block));
|
||||
auto output = OUTPUT_VARIABLE(0);
|
||||
|
||||
std::vector<NDArray*> inArrs(block.width());
|
||||
std::vector<const NDArray*> inArrs(block.width());
|
||||
|
||||
for(int i = 0; i < block.width(); ++i)
|
||||
inArrs[i] = INPUT_VARIABLE(i);
|
||||
|
|
|
|||
|
|
@ -64,6 +64,7 @@ namespace sd {
|
|||
|
||||
#if NOT_EXCLUDED(OP_mergemax)
|
||||
DECLARE_OP(mergemax, -1, 1, false);
|
||||
DECLARE_CUSTOM_OP(mergemax_bp, 2, 1, false, 0, 0);
|
||||
#endif
|
||||
/*
|
||||
* Complete tensor with max indices merged from all input tensors list
|
||||
|
|
@ -78,10 +79,12 @@ namespace sd {
|
|||
|
||||
#if NOT_EXCLUDED(OP_mergeadd)
|
||||
DECLARE_OP(mergeadd, -1, 1, false);
|
||||
DECLARE_CUSTOM_OP(mergeadd_bp, 2, 1, false, 0, 0);
|
||||
#endif
|
||||
|
||||
#if NOT_EXCLUDED(OP_mergeavg)
|
||||
DECLARE_OP(mergeavg, -1, 1, false);
|
||||
DECLARE_CUSTOM_OP(mergeavg_bp, 2, 1, false, 0, 0);
|
||||
#endif
|
||||
|
||||
#if NOT_EXCLUDED(OP_scatter_update)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,274 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void clipByNorm_(NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
||||
|
||||
const int rank = input.rankOf();
|
||||
const auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
|
||||
|
||||
const T normActual = norm2.e<T>(0);
|
||||
const T normClip = clipNorm.e<T>(0);
|
||||
|
||||
if (isInplace) {
|
||||
|
||||
if(norm2.lengthOf() == 1) {
|
||||
|
||||
if(normActual > normClip)
|
||||
input *= (normClip / normActual);
|
||||
}
|
||||
else {
|
||||
|
||||
auto listOfInSubArrs = input.allTensorsAlongDimension(dimensions);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
const T iNormActual = norm2.e<T>(i);
|
||||
if (iNormActual > normClip)
|
||||
*listOfInSubArrs.at(i) *= normClip / iNormActual;
|
||||
}
|
||||
};
|
||||
samediff::Threads::parallel_tad(func, 0, listOfInSubArrs.size());
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
if(norm2.lengthOf() == 1) {
|
||||
|
||||
if(normActual > normClip)
|
||||
output.assign(input * (normClip / normActual));
|
||||
else
|
||||
output.assign(input);
|
||||
}
|
||||
else {
|
||||
|
||||
auto listOfInSubArrs = input.allTensorsAlongDimension(dimensions);
|
||||
auto listOfOutSubArrs = output.allTensorsAlongDimension(dimensions);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inputSubArr = listOfInSubArrs.at(i);
|
||||
auto outputSubArr = listOfOutSubArrs.at(i);
|
||||
outputSubArr->assign(inputSubArr);
|
||||
|
||||
const T iNormActual = norm2.e<T>(i);
|
||||
|
||||
if (iNormActual > clipNorm.e<T>(0))
|
||||
*outputSubArr *= clipNorm / iNormActual;
|
||||
}
|
||||
};
|
||||
samediff::Threads::parallel_tad(func, 0, listOfInSubArrs.size());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
void clipByNorm(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), clipByNorm_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
|
||||
template <typename T>
|
||||
static void clipByGlobalNorm_(std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
||||
T globalNorm = 0; //NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
|
||||
// PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm)
|
||||
for (size_t i = 0; i < inputs.size(); i++) {
|
||||
auto input = inputs[i];
|
||||
auto l2norm = input->reduceNumber(reduce::Norm2);
|
||||
globalNorm += l2norm.t<T>(0) * l2norm.t<T>(0);
|
||||
}
|
||||
|
||||
//globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = sd::math::nd4j_sqrt(globalNorm);
|
||||
auto normS = sd::math::nd4j_sqrt<T,T>(globalNorm);
|
||||
outputs[inputs.size()]->p(0, normS);
|
||||
|
||||
const T factor = clipNorm / normS;
|
||||
|
||||
// PRAGMA_OMP_PARALLEL_FOR
|
||||
for (size_t e = 0; e < inputs.size(); e++) {
|
||||
// all-reduce
|
||||
auto input = inputs[e];
|
||||
auto output = outputs[e];
|
||||
|
||||
if (normS <= clipNorm) {
|
||||
output->assign(input);
|
||||
}
|
||||
else {
|
||||
|
||||
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
|
||||
input->applyLambda<T>(lambda, *output);
|
||||
}
|
||||
}
|
||||
}
|
||||
void clipByGlobalNorm(sd::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
|
||||
BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void clipByNormBP_(const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
||||
|
||||
const int rank = input.rankOf();
|
||||
|
||||
auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
|
||||
|
||||
if(norm2.lengthOf() == 1) {
|
||||
|
||||
const T N = norm2.e<T>(0);
|
||||
|
||||
auto cn = clipNorm.e<T>(0);
|
||||
|
||||
if(N > cn) {
|
||||
|
||||
const T sumOfProd = (input * gradO).reduceNumber(reduce::Sum).e<T>(0); // reduce to scalar
|
||||
const T factor1 = static_cast<T>(1.f) / N;
|
||||
const T factor3 = factor1 / (N * N); // 1 / (N*N*N)
|
||||
|
||||
auto lambda = LAMBDA_TT(elem1, elem2, cn, sumOfProd, factor1, factor3) {
|
||||
return cn * (factor1 * elem2 - factor3 * elem1 * sumOfProd);
|
||||
};
|
||||
|
||||
(const_cast<NDArray&>(input)).applyPairwiseLambda<T>(const_cast<NDArray&>(gradO), lambda, gradI);
|
||||
}
|
||||
else
|
||||
gradI.assign(gradO);
|
||||
}
|
||||
else {
|
||||
|
||||
auto gradISubArrs = gradI.allTensorsAlongDimension({dimensions});
|
||||
auto gradOSubArrs = gradO.allTensorsAlongDimension({dimensions});
|
||||
auto inputSubArrs = input.allTensorsAlongDimension({dimensions});
|
||||
|
||||
auto cn = clipNorm.e<T>(0);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
T N = norm2.e<T>(i);
|
||||
|
||||
auto gradOSubArr = gradOSubArrs.at(i);
|
||||
auto gradISubArr = gradISubArrs.at(i);
|
||||
|
||||
if (N > cn) {
|
||||
auto inputSubArr = inputSubArrs.at(i);
|
||||
const T sumOfProd = (*inputSubArr * *gradOSubArr).reduceNumber(reduce::Sum).e<T>(0); // reduce to scalar
|
||||
const T factor1 = static_cast<T>(1.f) / N;
|
||||
const T factor3 = factor1 / (N * N); // 1 / (N*N*N)
|
||||
|
||||
auto lambda = LAMBDA_TT(elem1, elem2, cn, sumOfProd, factor1, factor3) {
|
||||
return cn * (factor1 * elem2 - factor3 * elem1 * sumOfProd);
|
||||
};
|
||||
|
||||
inputSubArr->applyPairwiseLambda<T>(*gradOSubArr, lambda, *gradISubArr);
|
||||
} else
|
||||
gradISubArr->assign(gradOSubArr);
|
||||
}
|
||||
};
|
||||
samediff::Threads::parallel_tad(func, 0, gradISubArrs.size());
|
||||
}
|
||||
}
|
||||
|
||||
void clipByNormBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm) {
|
||||
BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBP_, (input, gradO, gradI, dimensions, clipNorm), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void clipByNormBP_, (const NDArray& input, const NDArray& gradO, NDArray& gradI /*output*/, const std::vector<int>& dimensions, const NDArray& clipNorm), FLOAT_TYPES);
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void clipByAveraged_(NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
||||
|
||||
auto cn = clipNorm.e<T>(0);
|
||||
if (dimensions.size() == 0) {
|
||||
// all-reduce
|
||||
T n2 = input.reduceNumber(reduce::Norm2).e<T>(0) / input.lengthOf();
|
||||
if (n2 <= cn) {
|
||||
if (!isInplace)
|
||||
output.assign(input);
|
||||
}
|
||||
else {
|
||||
const T factor = cn / n2;
|
||||
auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
|
||||
input.applyLambda<T>(lambda, output);
|
||||
}
|
||||
}
|
||||
else {
|
||||
// along dimension
|
||||
auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions, false);
|
||||
if (!isInplace)
|
||||
output.assign(input);
|
||||
auto tads = output.allTensorsAlongDimension(dimensions);
|
||||
// TODO: make this CUDA-compliant somehow
|
||||
for (int e = 0; e < tads.size(); e++) {
|
||||
T n2 = norm2.e<T>(e) / tads.at(e)->lengthOf();
|
||||
const T factor = cn / n2;
|
||||
if (n2 > cn) {
|
||||
auto lambda = LAMBDA_T(_x, factor) {return _x * factor;};
|
||||
tads.at(e)->applyLambda<T>(lambda, output);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void clipByAveraged(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), clipByAveraged_, (input, output, dimensions, clipNorm, isInplace), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void clipByAveraged_, (NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace), FLOAT_TYPES);
|
||||
|
||||
/*
|
||||
if (d1 > params[1])
|
||||
return params[1];
|
||||
else if (d1 < params[0])
|
||||
return params[0];
|
||||
else return d1;
|
||||
*/
|
||||
|
||||
template <typename T>
|
||||
static void clipByValue_(NDArray& input, double leftBound, double rightBound, NDArray& output) {
|
||||
auto routine = LAMBDA_T(_x, leftBound, rightBound) {
|
||||
if (_x > rightBound) return rightBound;
|
||||
if (_x < leftBound) return leftBound;
|
||||
return _x;
|
||||
};
|
||||
|
||||
input.applyLambda<T>(routine, output);
|
||||
}
|
||||
|
||||
void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (input, leftBound, rightBound, output), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void clipByValue_, (NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
void eye(sd::LaunchContext * context, NDArray& output) {
|
||||
|
||||
const int rank = output.rankOf();
|
||||
auto arrs = output.allTensorsAlongDimension({rank-2, rank-1});
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++)
|
||||
arrs.at(i)->setIdentity();
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, arrs.size());
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,183 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/ShapeUtils.h>
|
||||
#include <numeric>
|
||||
#include <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
template<typename X, typename Y>
|
||||
static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
|
||||
|
||||
const X* x = reinterpret_cast<X*>(input.getBuffer());
|
||||
const Y* y = reinterpret_cast<Y*>(indices.getBuffer());
|
||||
X* z = reinterpret_cast<X*>(output.getBuffer());
|
||||
|
||||
const int xRank = input.rankOf();
|
||||
const int yRank = indices.rankOf();
|
||||
const int zRank = output.rankOf();
|
||||
const int maxRank = sd::math::nd4j_max<int>(yRank, sd::math::nd4j_max<int>(xRank, zRank));
|
||||
|
||||
const Nd4jLong zLen = output.lengthOf();
|
||||
|
||||
const uint yLastDim = indices.sizeAt(-1);
|
||||
|
||||
const int diff = zRank - xRank;
|
||||
const bool bEqual = yLastDim == xRank;
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
|
||||
int xCoords[MAX_RANK], zCoords[MAX_RANK], temp;
|
||||
|
||||
for (auto i = start; i < stop; i++) {
|
||||
|
||||
shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords);
|
||||
|
||||
const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords);
|
||||
|
||||
temp = zCoords[yRank - 1];
|
||||
zCoords[yRank - 1] = 0;
|
||||
const auto yOffset = shape::getOffset(indices.getShapeInfo(), zCoords);
|
||||
zCoords[yRank - 1] = temp;
|
||||
|
||||
if(bEqual)
|
||||
memcpy(xCoords, zCoords, zRank * sizeof(int));
|
||||
else if(diff >= 0)
|
||||
memcpy(xCoords, zCoords + diff, xRank * sizeof(int));
|
||||
else
|
||||
memcpy(xCoords - diff, zCoords, zRank * sizeof(int));
|
||||
|
||||
for (uint j = 0; j < yLastDim; ++j)
|
||||
xCoords[j] = y[yOffset + j * indices.stridesOf()[yRank - 1]]; // last stride
|
||||
|
||||
const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoords);
|
||||
|
||||
z[zOffset] = x[xOffset];
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, zLen);
|
||||
}
|
||||
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
void gatherND(sd::LaunchContext * context, NDArray& input, NDArray& indices, NDArray& output) {
|
||||
BUILD_DOUBLE_SELECTOR(input.dataType(), indices.dataType(), gatherND_, (input, indices, output), LIBND4J_TYPES, INDEXING_TYPES);
|
||||
}
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void gather_(NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
||||
|
||||
int axis = intArgs.size() > 0 ? intArgs[0] : 0;
|
||||
const int inputRank = input->rankOf();
|
||||
if(axis < 0)
|
||||
axis += inputRank;
|
||||
|
||||
const int numOfIntArgs = intArgs.size();
|
||||
|
||||
if (indices != nullptr) {
|
||||
|
||||
for(Nd4jLong i = 0; i < indices->lengthOf(); ++i)
|
||||
if(indices->e<Nd4jLong>(i) >= input->sizeAt(axis))
|
||||
throw std::runtime_error("helpers::gather function: indices array contains wrong elements, each element must be smaller than corresponding dimension of input array !");
|
||||
|
||||
// first case: indices consist of only one scalar
|
||||
if(indices->isScalar()) {
|
||||
if(input->rankOf() <= 1){
|
||||
//For scalar indices, rank 0 or 1 input: can't do tensor along dimension 0 as this is whole array... instead, we want to get a scalar
|
||||
auto idx = indices->e<Nd4jLong>(0);
|
||||
auto scalarNDArray = input->e(idx);
|
||||
output->assign(scalarNDArray);
|
||||
} else {
|
||||
auto dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {axis});
|
||||
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
|
||||
|
||||
auto tadArr = NDArray(reinterpret_cast<void *>(reinterpret_cast<T*>(input->getBuffer()) + tadPack.primaryOffsets()[indices->e<Nd4jLong>(0)]), tadPack.primaryShapeInfo(), output->getContext());
|
||||
output->assign(&tadArr);
|
||||
}
|
||||
}
|
||||
else if (input->rankOf() == 1 && indices->isVector()) {
|
||||
// special case
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto e = start; e < stop; e++)
|
||||
output->p(e, input->e<T>(indices->e<Nd4jLong>(e)));
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, indices->lengthOf());
|
||||
}
|
||||
else {
|
||||
|
||||
std::vector<int> dimsOut(indices->rankOf());
|
||||
std::iota(dimsOut.begin(), dimsOut.end(), axis); // fill with axis, axis+1, ... indices->rankOf()-1
|
||||
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->getShapeInfo(), dimsOut);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
NDArray subArrOut = (*output)(i, dimsOut);
|
||||
NDArray subArrIn = (*input)(indices->e<Nd4jLong>(i), {axis});
|
||||
subArrOut.assign(subArrIn);
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
for(int i = 1; i < numOfIntArgs; ++i)
|
||||
if(intArgs[i] >= input->sizeAt(axis))
|
||||
throw std::runtime_error("helpers::gather function: some of input indexes is larger than corresponding shape of input array !");
|
||||
|
||||
// we only allow scalar/vector case here
|
||||
if (numOfIntArgs == 2) { // scalar case
|
||||
output->assign((*input)(intArgs[1], {axis}));
|
||||
}
|
||||
else { // vector case
|
||||
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(output->getShapeInfo(), {axis});
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
NDArray subArrOut = (*output)(i, {axis});
|
||||
NDArray subArrIn = (*input)(intArgs[i + 1], {axis});
|
||||
subArrOut.assign(subArrIn);
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void gather(NDArray* input, const NDArray* indices, NDArray* output, const std::vector<int>& intArgs) {
|
||||
BUILD_SINGLE_SELECTOR(input->dataType(), gather_, (input, indices, output, intArgs), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
void invertPermutation(sd::LaunchContext * context, const NDArray& input, NDArray& output) {
|
||||
|
||||
std::set<int> uniqueElems;
|
||||
const int length = input.lengthOf();
|
||||
|
||||
for(int i = 0; i < length; ++i) {
|
||||
|
||||
int elem = input.e<int>(i);
|
||||
|
||||
if(!uniqueElems.insert(elem).second) // this operation forbids us to use #pragma omp
|
||||
throw std::runtime_error("helpers::invertPermutation function: input array contains duplicates !");
|
||||
|
||||
if(elem < 0 || elem > length - 1)
|
||||
throw std::runtime_error("helpers::invertPermutation function: element of input array is out of range (0, length-1) !");
|
||||
|
||||
output.p<int>(elem, i);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,277 @@
|
|||
/*******************************************************************************
|
||||
* Copyright (c) 2015-2018 Skymind, Inc.
|
||||
* Copyright (c) 2019-2020 Konduit K.K.
|
||||
*
|
||||
* 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
|
||||
// @author Oleh Semeniv (oleg.semeniv@gmail.com)
|
||||
//
|
||||
|
||||
#include <ops/declarable/helpers/transforms.h>
|
||||
#include <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeMaxIndex_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
const Nd4jLong numArgs = inArrs.size();
|
||||
auto x = inArrs[0];
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto e = start; e < stop; e++) {
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
Nd4jLong idx = 0;
|
||||
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
T v = inArrs[i]->e<T>(e);
|
||||
if (v > max) {
|
||||
max = v;
|
||||
idx = i;
|
||||
}
|
||||
}
|
||||
output.p(e, idx);
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
||||
}
|
||||
|
||||
void mergeMaxIndex(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
BUILD_SINGLE_SELECTOR(inArrs[0]->dataType(), mergeMaxIndex_, (inArrs, output), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeMax_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
const Nd4jLong numArgs = inArrs.size();
|
||||
auto x = inArrs[0];
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto e = start; e < stop; e++) {
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
T v = inArrs[i]->e<T>(e);
|
||||
if (v > max)
|
||||
max = v;
|
||||
}
|
||||
output.p(e, max);
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
||||
}
|
||||
|
||||
void mergeMax(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (inArrs, output), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeMaxBp_(const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs) {
|
||||
|
||||
// outArrs.size() == inArrs.size() - 1
|
||||
const Nd4jLong numArgs = outArrs.size();
|
||||
// last array is gradient
|
||||
const auto gradient = inArrs[numArgs]->bufferAsT<T>();
|
||||
auto length = inArrs[numArgs]->lengthOf();
|
||||
|
||||
bool bSameOrderAndEws1 = (1 == inArrs[numArgs]->ews());
|
||||
|
||||
if (bSameOrderAndEws1) {
|
||||
auto gradOrdering = inArrs[numArgs]->ordering();
|
||||
|
||||
for (int i = 0; i < numArgs; ++i) {
|
||||
bSameOrderAndEws1 &= (gradOrdering == inArrs[i]->ordering());
|
||||
bSameOrderAndEws1 &= (1 == inArrs[i]->ews());
|
||||
bSameOrderAndEws1 &= (gradOrdering == outArrs[i]->ordering());
|
||||
bSameOrderAndEws1 &= (1 == outArrs[i]->ews());
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if(bSameOrderAndEws1){
|
||||
auto func = PRAGMA_THREADS_FOR{
|
||||
for (auto e = start; e < stop; e++) {
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
Nd4jLong nMaxIndex = 0;
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
const T* v = inArrs[i]->bufferAsT<T>();
|
||||
if (v[e] > max) {
|
||||
max = v[e];
|
||||
nMaxIndex = i;
|
||||
}
|
||||
}
|
||||
T* z = outArrs[nMaxIndex]->bufferAsT<T>();
|
||||
z[e] = gradient[e];
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, length);
|
||||
return;
|
||||
}
|
||||
|
||||
auto gradShape = inArrs[numArgs]->getShapeInfo();
|
||||
std::vector<bool> vbSameShaepeAndStrides(numArgs);
|
||||
for (int i = 0; i < numArgs; ++i) {
|
||||
vbSameShaepeAndStrides[i] = shape::haveSameShapeAndStrides(gradShape, inArrs[i]->getShapeInfo());
|
||||
}
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR{
|
||||
|
||||
int coords[MAX_RANK];
|
||||
for (auto e = start; e < stop; e++) {
|
||||
|
||||
shape::index2coordsCPU(start, e, gradShape, coords);
|
||||
|
||||
const auto gradOffset = shape::getOffset(gradShape, coords);
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
Nd4jLong nMaxIndex = 0;
|
||||
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
|
||||
const auto xOffset = vbSameShaepeAndStrides[i] ? gradOffset : shape::getOffset(inArrs[i]->getShapeInfo(), coords);
|
||||
const T* v = inArrs[i]->bufferAsT<T>();
|
||||
if (v[xOffset] > max) {
|
||||
max = v[xOffset];
|
||||
nMaxIndex = i;
|
||||
}
|
||||
}
|
||||
|
||||
const auto zOffset = vbSameShaepeAndStrides[nMaxIndex] ? gradOffset : shape::getOffset(outArrs[nMaxIndex]->getShapeInfo(), coords);
|
||||
|
||||
T* z = outArrs[nMaxIndex]->bufferAsT<T>();
|
||||
z[zOffset] = gradient[gradOffset];
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, length);
|
||||
return;
|
||||
}
|
||||
|
||||
void mergeMaxBp(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs) {
|
||||
BUILD_SINGLE_SELECTOR(outArrs[0]->dataType(), mergeMaxBp_, (inArrs, outArrs), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeAvg_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
const Nd4jLong numArgs = inArrs.size();
|
||||
const T factor = 1.f / numArgs;
|
||||
auto x = inArrs[0];
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto e = start; e < stop; e++) {
|
||||
T sum = 0.;
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
T v = inArrs[i]->e<T>(e);
|
||||
sum += v;
|
||||
}
|
||||
output.p<T>(e, sum * factor);
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
||||
}
|
||||
|
||||
void mergeAvg(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (inArrs, output), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeAvgBp_(const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
||||
|
||||
const Nd4jLong numArgs = outArrs.size();
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR{
|
||||
for (auto e = start; e < stop; e++) {
|
||||
|
||||
T v = gradient.e<T>(e) / numArgs;
|
||||
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
outArrs[i]->p<T>(e, v);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, gradient.lengthOf());
|
||||
}
|
||||
|
||||
void mergeAvgBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
||||
BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAvgBp_, (gradient, outArrs), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeAdd_(const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
const Nd4jLong numArgs = inArrs.size();
|
||||
auto x = inArrs[0];
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto e = start; e < stop; e++) {
|
||||
T sum = (T) 0.f;
|
||||
for (Nd4jLong i = 0; i < numArgs; i++)
|
||||
sum += inArrs[i]->e<T>(e);
|
||||
|
||||
output.p(e, sum);
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, x->lengthOf());
|
||||
}
|
||||
void mergeAdd(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (inArrs, output), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mergeAddBp_(const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
||||
|
||||
const Nd4jLong numArgs = outArrs.size();
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR{
|
||||
for (auto e = start; e < stop; e++) {
|
||||
|
||||
T v = gradient.e<T>(e);
|
||||
|
||||
for (Nd4jLong i = 0; i < numArgs; i++) {
|
||||
outArrs[i]->p<T>(e, v);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, gradient.lengthOf());
|
||||
}
|
||||
|
||||
void mergeAddBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
||||
BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAddBp_, (gradient, outArrs), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,483 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, const NDArray& padValue) {
|
||||
|
||||
const T* x = input.bufferAsT<T>();
|
||||
T* z = output.bufferAsT<T>();
|
||||
|
||||
const Nd4jLong* xShape = input.shapeOf();
|
||||
const Nd4jLong* zShape = output.shapeOf();
|
||||
|
||||
const int rank = input.rankOf(); // both input and output have the same rank
|
||||
const int rankMinusOne = rank - 1;
|
||||
|
||||
const auto zLen = output.lengthOf();
|
||||
|
||||
if(mode == 0) { // CONSTANT case
|
||||
|
||||
const T padVal = padValue.e<T>(0);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
|
||||
int zCoords[MAX_RANK], xCoords[MAX_RANK];
|
||||
|
||||
for (auto i = start; i < stop; i++) {
|
||||
|
||||
shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords);
|
||||
const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords);
|
||||
|
||||
memcpy(xCoords, zCoords, rank * sizeof(int));
|
||||
|
||||
bool within = true;
|
||||
|
||||
for (int j = rankMinusOne; j >= 0; --j) {
|
||||
|
||||
if (xShape[j] == zShape[j])
|
||||
continue;
|
||||
|
||||
const auto left = paddings.e<Nd4jLong>(j, 0);
|
||||
|
||||
if (zCoords[j] < left || zCoords[j] >= left + xShape[j]) {
|
||||
within = false;
|
||||
break;
|
||||
}
|
||||
else
|
||||
xCoords[j] = zCoords[j] - left;
|
||||
}
|
||||
|
||||
if (within)
|
||||
z[zOffset] = x[shape::getOffset(input.getShapeInfo(), xCoords)];
|
||||
else
|
||||
z[zOffset] = padVal;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, zLen);
|
||||
}
|
||||
else { // REFLECT and SYMMETRIC cases
|
||||
|
||||
const Nd4jLong shift1 = mode == 1 ? 0 : 1; // REFLECT : SYMMETRIC
|
||||
const Nd4jLong shift2 = mode == 1 ? 2 : 1; // REFLECT : SYMMETRIC
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
|
||||
int zCoords[MAX_RANK], xCoords[MAX_RANK];
|
||||
|
||||
for (auto i = start; i < stop; i++) {
|
||||
|
||||
shape::index2coordsCPU(start, i, output.getShapeInfo(), zCoords);
|
||||
const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoords);
|
||||
|
||||
memcpy(xCoords, zCoords, rank * sizeof(int));
|
||||
|
||||
for (int j = rankMinusOne; j >= 0; --j) {
|
||||
|
||||
if (xShape[j] == zShape[j])
|
||||
continue;
|
||||
|
||||
xCoords[j] = zCoords[j] - paddings.e<Nd4jLong>(j, 0); // are ready to fill middle (within input dimension range)
|
||||
|
||||
if (xCoords[j] < 0)
|
||||
xCoords[j] = -xCoords[j] - shift1; // means fill from left
|
||||
else if (xCoords[j] >= xShape[j])
|
||||
xCoords[j] = 2 * xShape[j] - xCoords[j] - shift2; // means fill from right
|
||||
}
|
||||
|
||||
const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoords);
|
||||
z[zOffset] = x[xOffset];
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, zLen);
|
||||
}
|
||||
}
|
||||
|
||||
// //////////////////////////////////////////////////////////////////////////
|
||||
// template<typename T>
|
||||
// void pad2_(const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
|
||||
|
||||
// const int rank = output.rankOf();
|
||||
// std::vector<int> dimsToExclude(rank);
|
||||
// std::iota(dimsToExclude.begin(), dimsToExclude.end(), 0); // fill with 0, 1, ... rank-1
|
||||
|
||||
// Nd4jLong numLeft = paddings.e<Nd4jLong>(rank-1,0);
|
||||
// Nd4jLong numRight = paddings.e<Nd4jLong>(rank-1,1);
|
||||
// Nd4jLong inDimSize = input.sizeAt(rank-1);
|
||||
// Nd4jLong outDimSize = output.sizeAt(rank-1);
|
||||
|
||||
// std::vector<std::vector<Nd4jLong>> outIdx = { std::vector<Nd4jLong>(2*rank), {numLeft, numLeft + inDimSize}, {0, numLeft}, {numLeft + inDimSize, outDimSize} };
|
||||
|
||||
// for(int i = 0; i < rank-1; ++i) {
|
||||
// outIdx[0][2*i] = paddings.e<Nd4jLong>(i, 0);
|
||||
// outIdx[0][2*i + 1] = outIdx[0][2*i] + input.sizeAt(i);
|
||||
// }
|
||||
// outIdx[0][2*rank-1] = outIdx[0][2*rank-2] = 0;
|
||||
|
||||
// // ***** populate innermost sub-arrays firstly ***** //
|
||||
// dimsToExclude.pop_back();
|
||||
|
||||
// Nd4jLong startL = mode == 1 ? 1 : 0; // REFLECT or SYMMETRIC
|
||||
// Nd4jLong startR = mode == 1 ? inDimSize-2 : inDimSize-1; // REFLECT or SYMMETRIC
|
||||
|
||||
// Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(input.getShapeInfo(), dimsToExclude);
|
||||
|
||||
// NDArray outSubArr0 = output(outIdx[0], true);
|
||||
|
||||
// PRAGMA_OMP_PARALLEL_FOR
|
||||
// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
|
||||
|
||||
// NDArray outSubArr1 = outSubArr0(j, dimsToExclude);
|
||||
// NDArray inSubArr = input(j, dimsToExclude);
|
||||
// NDArray outSubArrMid = outSubArr1(outIdx[1]);
|
||||
|
||||
// outSubArrMid.assign(inSubArr); // assign middle
|
||||
|
||||
// if(mode == 0) { // CONSTANT
|
||||
// if(numLeft != 0) {
|
||||
// NDArray temp = outSubArr1(outIdx[2]);
|
||||
// temp.assign(padValue); // assign left
|
||||
// }
|
||||
// if(numRight != 0) {
|
||||
// NDArray temp = outSubArr1(outIdx[3]);
|
||||
// temp.assign(padValue); // assign right
|
||||
// }
|
||||
// }
|
||||
// else { // REFLECT or SYMMETRIC
|
||||
|
||||
// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) // fill left side
|
||||
// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
|
||||
|
||||
// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) // fill right side
|
||||
// outSubArr1.t<T>(k) = inSubArr.t<T>(e);
|
||||
// }
|
||||
// }
|
||||
|
||||
// // ***** fill rest of outer sub-arrays ***** //
|
||||
// std::vector<Nd4jLong> outIdxInner(2, 0);
|
||||
// std::vector<Nd4jLong> outIdxOuter(2, 0);
|
||||
|
||||
// for(int i = rankBorder - 1; i >= 0; --i) {
|
||||
|
||||
// dimsToExclude.pop_back();
|
||||
|
||||
// outIdxInner.push_back(0), outIdxInner.push_back(0);
|
||||
// outIdxOuter.push_back(0), outIdxOuter.push_back(0);
|
||||
|
||||
// Nd4jLong numLeft = paddings.e<Nd4jLong>(i, 0);
|
||||
// Nd4jLong numRight = paddings.e<Nd4jLong>(i, 1);
|
||||
|
||||
// if(numLeft == 0 && numRight == 0)
|
||||
// continue;
|
||||
|
||||
// Nd4jLong inDimSize = input.sizeAt(i);
|
||||
// Nd4jLong outDimSize = output.sizeAt(i);
|
||||
|
||||
// if(mode == 0) {
|
||||
// outIdxOuter[0] = 0; outIdxOuter[1] = numLeft;
|
||||
// outIdxInner[0] = numLeft + inDimSize; outIdxInner[1] = outDimSize;
|
||||
// }
|
||||
|
||||
// startL = mode == 1 ? numLeft + 1 : numLeft; // REFLECT or SYMMETRIC
|
||||
// startR = mode == 1 ? numLeft + inDimSize - 2 : numLeft + inDimSize-1; // REFLECT or SYMMETRIC
|
||||
|
||||
// numOfSubArrs = ShapeUtils::getNumOfSubArrs(output.getShapeInfo(), dimsToExclude);
|
||||
|
||||
// PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(outIdxOuter, outIdxInner))
|
||||
// for(Nd4jLong j = 0; j < numOfSubArrs; ++j) {
|
||||
|
||||
// NDArray outSubArr = output(j, dimsToExclude);
|
||||
|
||||
// if(mode == 0) { // CONSTANT
|
||||
|
||||
// if(numLeft != 0) {
|
||||
// NDArray tempO = outSubArr(outIdxOuter);
|
||||
// tempO.assign(padValue); // assign left
|
||||
// }
|
||||
|
||||
// if(numRight != 0) {
|
||||
// NDArray tempI = outSubArr(outIdxInner);
|
||||
// tempI.assign(padValue); // assign right
|
||||
// }
|
||||
// }
|
||||
// else { // REFLECT or SYMMETRIC
|
||||
|
||||
// for(Nd4jLong k = numLeft-1, e = startL; k >= 0; --k, ++e) { // fill left side
|
||||
// outIdxOuter[0] = k;
|
||||
// outIdxOuter[1] = k+1;
|
||||
// outIdxInner[0] = e;
|
||||
// outIdxInner[1] = e+1;
|
||||
// NDArray outSubArrInner = outSubArr(outIdxInner);
|
||||
// NDArray outSubArrOuter = outSubArr(outIdxOuter);
|
||||
// outSubArrOuter.assign(outSubArrInner);
|
||||
// }
|
||||
|
||||
// for(Nd4jLong k = numLeft + inDimSize, e = startR; k < outDimSize; ++k, --e) { // fill right side
|
||||
// outIdxOuter[0] = k;
|
||||
// outIdxOuter[1] = k+1;
|
||||
// outIdxInner[0] = e;
|
||||
// outIdxInner[1] = e+1;
|
||||
// NDArray outSubArrInner = outSubArr(outIdxInner);
|
||||
// NDArray outSubArrOuter = outSubArr(outIdxOuter);
|
||||
// outSubArrOuter.assign(outSubArrInner);
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
|
||||
void pad(sd::LaunchContext * context, const int mode, const NDArray& input, const NDArray& paddings, NDArray& output, NDArray const& padValue) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), pad_, (mode, input, paddings, output, padValue), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template<typename T>
|
||||
static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
|
||||
|
||||
// mode: 0 - REFLECT, else - SYMMETRIC
|
||||
const int reflBorder = (bool)mode ? 1 : 0;
|
||||
const int rank = input.rankOf();
|
||||
const Nd4jLong outLen = output.lengthOf();
|
||||
|
||||
if(rank <= 1) {
|
||||
|
||||
const Nd4jLong inLen = input.lengthOf();
|
||||
const auto leftSide = paddings.e<Nd4jLong>(0);
|
||||
const auto leftSideCorrected = leftSide - reflBorder;
|
||||
const Nd4jLong len = 2*(inLen-1) + leftSide + reflBorder;
|
||||
|
||||
for(int i = 0; i < outLen; ++i) {
|
||||
|
||||
if (i < leftSide) // left side
|
||||
output.p(i, input.e<T>(leftSideCorrected - i));
|
||||
|
||||
else if(i >= leftSide && i < leftSide + inLen) // middle
|
||||
output.p(i, input.e<T>(i - leftSide));
|
||||
|
||||
else // right side
|
||||
output.p(i, input.e<T>(len - i));
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
|
||||
int inIdx[MAX_RANK], outIdx[MAX_RANK];
|
||||
|
||||
for (auto i = start; i < stop; i++) {
|
||||
|
||||
shape::index2coordsCPU(start, i, output.getShapeInfo(), outIdx);
|
||||
|
||||
for (int j = 0; j < rank; ++j) {
|
||||
const Nd4jLong inLen = input.sizeAt(j);
|
||||
const auto leftSide = paddings.e<T>(j, 0);
|
||||
const auto leftSideCorrected = leftSide - reflBorder;
|
||||
const Nd4jLong len = 2 * (inLen - 1) + leftSide + reflBorder;
|
||||
|
||||
if (outIdx[j] < leftSide) // left side
|
||||
inIdx[j] = leftSideCorrected - outIdx[j];
|
||||
|
||||
else if (outIdx[j] >= leftSide && outIdx[j] < leftSide + inLen) // middle
|
||||
inIdx[j] = outIdx[j] - leftSide;
|
||||
|
||||
else // right side
|
||||
inIdx[j] = len - outIdx[j];
|
||||
}
|
||||
|
||||
auto outOffset = shape::getOffset(output.getShapeInfo(), outIdx);
|
||||
auto inOffset = shape::getOffset(input.getShapeInfo(), inIdx);
|
||||
reinterpret_cast<T *>(output.buffer())[outOffset] = reinterpret_cast<T *>(input.getBuffer())[inOffset];
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, outLen);
|
||||
}
|
||||
}
|
||||
|
||||
void mirrorPad(sd::LaunchContext * context, const NDArray& input, const NDArray& paddings, NDArray& output, const int mode) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), mirrorPad_, (input, paddings, output, mode), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void mirrorPad_, (const NDArray& input, const NDArray& paddings, NDArray& output, const int mode), LIBND4J_TYPES);
|
||||
|
||||
|
||||
////////////////////////////////////////////////////////////////////////
|
||||
/*// initial values of inIdx, outIdx, dim must be equal to zero
|
||||
template<typename T>
|
||||
static void recursiveLoopForPad_(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
|
||||
|
||||
int leftOffset;
|
||||
// dimensions are array of input dimensions, it is sorted in increasing order
|
||||
// every time at the beginning we erase first element from it (not good idea to use vector for this purpose, but luckily it is small enough)
|
||||
// then we use this array for tads building, every time while recursion the number of built tads becomes bigger
|
||||
dimensions.erase(dimensions.begin());
|
||||
// build tad basing on output array, also create auxiliary arrays pointing on required output array ranges
|
||||
shape::TAD tadOut(output.getShapeInfo(), dimensions.data(), dimensions.size());
|
||||
tadOut.createTadOnlyShapeInfo();
|
||||
tadOut.createOffsets();
|
||||
auto subArrOut = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
|
||||
auto subArr = NDArray(output.getBuffer(), tadOut.tadOnlyShapeInfo, output.getContext());
|
||||
// build tad basing on input array, also create auxiliary array pointing on required input array range
|
||||
shape::TAD tadIn(input.getShapeInfo(), dimensions.data(), dimensions.size());
|
||||
tadIn.createTadOnlyShapeInfo();
|
||||
tadIn.createOffsets();
|
||||
auto subArrIn = NDArray(input.getBuffer(), tadIn.tadOnlyShapeInfo, output.getContext());
|
||||
// these indices take into account recursion and always point to actual tads numbers
|
||||
if (input.rankOf() > 1 && output.rankOf() > 1) {// only for non-vector cases
|
||||
outIdx = outIdx * output.sizeAt(dim + 1);
|
||||
inIdx = inIdx * input.sizeAt(dim + 1);
|
||||
}
|
||||
// current input tad number, we add to it unity in a loop
|
||||
int k = -1;
|
||||
// loop through current dimension
|
||||
for(int i = 0; i < output.sizeAt(dim); ++i) {
|
||||
// corresponds to outer range (relevant indices are absent in input)
|
||||
leftOffset = paddings.e<int>(dim, 0);
|
||||
if(i < leftOffset || i >= (input.sizeAt(dim) + leftOffset))
|
||||
continue;
|
||||
|
||||
// increase input tads number
|
||||
++k;
|
||||
// recursion condition allows for the fact that tad can't reduce to scalar
|
||||
if(dim < input.rankOf() - 2)
|
||||
recursiveLoopForPad(mode, input, paddings, output, dimensions, dim + 1, inIdx + k, outIdx + i, padValue);
|
||||
else if (paddings.sizeAt(0) > dim + 1){
|
||||
leftOffset = paddings.e<int>(dim + 1, 0);
|
||||
// shift buffers pointers to actual element position
|
||||
if (output.rankOf() > 1) {
|
||||
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + i]);
|
||||
subArrIn.setBuffer(reinterpret_cast<T*>(input.getBuffer()) + tadIn.tadOffsets[inIdx + i - paddings.e<int>(dim, 0)]);
|
||||
}
|
||||
else {
|
||||
subArrOut.p(i, subArrIn.e<T>(i - leftOffset));
|
||||
}
|
||||
// most inner loop, corresponds to last dim = rank-1
|
||||
switch (mode) {
|
||||
case 0: // CONSTANT mode
|
||||
for(int j = 0; j < subArrOut.lengthOf(); ++j)
|
||||
if(j < leftOffset || j >= (subArrIn.lengthOf() + leftOffset) ) // firstly fill with zeros outer ranges
|
||||
subArrOut.p(j, (T)0.f);
|
||||
else
|
||||
subArrOut.p(j, subArrIn.e<T>(j - leftOffset)); // fill middle with elements of input array
|
||||
break;
|
||||
|
||||
case 1: // REFLECT mode
|
||||
for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
|
||||
subArrOut.p(leftOffset - j, subArrIn.e<T>(j));
|
||||
for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
|
||||
subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
|
||||
for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
|
||||
subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j - 1));
|
||||
break;
|
||||
|
||||
case 2: // SYMMETRIC mode
|
||||
for(int j = 1; j <= leftOffset; ++j) // fill firstly left side
|
||||
subArrOut.p(leftOffset - j, subArrIn.e<T>(j-1));
|
||||
for(int j = 0; j < subArrIn.lengthOf(); ++j) // fill middle
|
||||
subArrOut.p(leftOffset + j, subArrIn.e<T>(j));
|
||||
for(int j = (subArrOut.lengthOf() - leftOffset); j < subArrOut.lengthOf(); ++j) // fill right side
|
||||
subArrOut.p(j, subArrIn.e<T>(subArrOut.lengthOf() - j));
|
||||
break;
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
if (mode == 0 && input.rankOf() < 2)
|
||||
subArrOut.p(i, subArrIn.e<T>(i - leftOffset)); // fill middle with elements of input array
|
||||
}
|
||||
}
|
||||
// populate sub-array formed previously
|
||||
leftOffset = paddings.e<int>(dim,0);
|
||||
switch (mode) {
|
||||
case 0: // CONSTANT mode
|
||||
for(int j = 1; j <= leftOffset; ++j) {
|
||||
// fill left side with padValue
|
||||
if (output.rankOf() > 1) {
|
||||
subArrOut.setBuffer(
|
||||
reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
||||
subArrOut.assign(padValue);
|
||||
}
|
||||
else {
|
||||
subArrOut.p(j - 1, padValue);
|
||||
}
|
||||
}
|
||||
// output.printIndexedBuffer("Output at");
|
||||
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill left side with zeros
|
||||
if (output.rankOf() > 1) {
|
||||
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
||||
subArrOut.assign(padValue);
|
||||
}
|
||||
else {
|
||||
subArrOut.p(j, padValue);
|
||||
}
|
||||
}
|
||||
break;
|
||||
|
||||
case 1: // REFLECT mode
|
||||
for(int j = 1; j <= leftOffset; ++j) { // fill left side
|
||||
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j]);
|
||||
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
||||
subArrOut.assign(&subArr);
|
||||
}
|
||||
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
|
||||
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - 1 - j]);
|
||||
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
||||
subArrOut.assign(&subArr);
|
||||
}
|
||||
break;
|
||||
|
||||
case 2: // SYMMETRIC mode
|
||||
for(int j = 1; j <= leftOffset; ++j) { // fill left side
|
||||
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset + j - 1]);
|
||||
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + leftOffset - j]);
|
||||
subArrOut.assign(&subArr);
|
||||
}
|
||||
for(int j = (output.sizeAt(dim) - leftOffset); j < output.sizeAt(dim); ++j) { // fill right side
|
||||
subArr.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + output.sizeAt(dim) + leftOffset - j]);
|
||||
subArrOut.setBuffer(reinterpret_cast<T*>(output.getBuffer()) + tadOut.tadOffsets[outIdx + j]);
|
||||
subArrOut.assign(&subArr);
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
*/
|
||||
/*
|
||||
void recursiveLoopForPad(const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue ) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), recursiveLoopForPad_, (mode, input, paddings, output, dimensions, dim, inIdx, outIdx, padValue), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void recursiveLoopForPad_, (const int mode, NDArray& input, const NDArray& paddings, NDArray& output, std::vector<int> dimensions, int dim, int inIdx, int outIdx, NDArray& padValue), LIBND4J_TYPES);
|
||||
|
||||
*/
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,126 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
#include <graph/RandomGenerator.h>
|
||||
#include <numeric>
|
||||
#include <helpers/ShapeUtils.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
void randomShuffle_(NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) {
|
||||
|
||||
// check edge cases first
|
||||
int temp;
|
||||
const int firstDim = input.sizeAt(0);
|
||||
if(input.lengthOf() == 1 || firstDim == 1) {
|
||||
|
||||
if(!isInplace)
|
||||
output.assign(input);
|
||||
}
|
||||
else if (input.isVector() || shape::isLikeVector(input.getShapeInfo(), temp)) {
|
||||
|
||||
// apply Fisher-Yates shuffle
|
||||
if(isInplace) {
|
||||
//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
|
||||
for(int i = firstDim-1; i > 0; --i) {
|
||||
int r = rng.relativeInt(i) % i;
|
||||
if(i == r)
|
||||
continue;
|
||||
T t0 = input.t<T>(i);
|
||||
T t1 = input.t<T>(r);
|
||||
//math::nd4j_swap<T>(input(i), input(r));
|
||||
input.t<T>(i) = t1;
|
||||
input.t<T>(r) = t0;
|
||||
}
|
||||
}
|
||||
else {
|
||||
std::vector<int> indices(firstDim);
|
||||
std::iota(indices.begin(), indices.end(), 0);
|
||||
output.p<T>(Nd4jLong(0), input.e<T>(0));
|
||||
|
||||
// FIXME: parallelism!!
|
||||
for(int i = firstDim-1; i > 0; --i) {
|
||||
int r = rng.relativeInt(i) % i;
|
||||
output.t<T>(i) = input.t<T>(indices[r]);
|
||||
if(i == r)
|
||||
continue;
|
||||
|
||||
output.t<T>(r) = input.t<T>(indices[i]);
|
||||
math::nd4j_swap<int>(indices[i], indices[r]);
|
||||
}
|
||||
rng.rewindH(firstDim-1);
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
// evaluate sub-arrays list of input array through all dimensions excluding first one
|
||||
std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input.rankOf(), {0});
|
||||
auto subArrsListIn = input.allTensorsAlongDimension(dimensions);
|
||||
|
||||
// apply Fisher-Yates shuffle
|
||||
if(isInplace) {
|
||||
//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->elementwiseThreshold())
|
||||
for(int i = firstDim - 1; i > 0; --i) {
|
||||
int r = rng.relativeInt(i) % i;
|
||||
|
||||
if(i == r)
|
||||
continue;
|
||||
subArrsListIn.at(i)->swapUnsafe(*subArrsListIn.at(r));
|
||||
}
|
||||
}
|
||||
else {
|
||||
// evaluate sub-arrays list of output array through all dimensions excluding first one
|
||||
auto subArrsListOut = output.allTensorsAlongDimension(dimensions);
|
||||
std::vector<int> indices(firstDim);
|
||||
std::iota(indices.begin(), indices.end(), 0);
|
||||
bool isZeroShuffled = false;
|
||||
//PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
|
||||
for(int i = firstDim - 1; i > 0; --i) {
|
||||
int r = rng.relativeInt(i) % i;
|
||||
subArrsListOut.at(i)->assign(subArrsListIn.at(indices[r]));
|
||||
if(r == 0)
|
||||
isZeroShuffled = true;
|
||||
if(i == r)
|
||||
continue;
|
||||
subArrsListOut.at(r)->assign(subArrsListIn.at(indices[i]));
|
||||
math::nd4j_swap<int>(indices[i], indices[r]);
|
||||
}
|
||||
if(!isZeroShuffled)
|
||||
subArrsListOut.at(0)->assign(subArrsListIn.at(0));
|
||||
}
|
||||
rng.rewindH(firstDim-1);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
void randomShuffle(sd::LaunchContext * context, NDArray& input, NDArray& output, sd::graph::RandomGenerator& rng, const bool isInplace) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), randomShuffle_, (input, output, rng, isInplace), LIBND4J_TYPES);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,115 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/ShapeUtils.h>
|
||||
#include <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
void scatterUpdate(sd::LaunchContext * context, NDArray& input, NDArray& updates, const std::vector<int>* intArgs) {
|
||||
|
||||
int opCode = (*intArgs)[0];
|
||||
int dimSize = (*intArgs)[1];
|
||||
Nd4jLong e;
|
||||
Nd4jLong limg = 2 + dimSize;
|
||||
std::vector<int> tadDimensions(dimSize);
|
||||
for (e = 2; e < limg; e++)
|
||||
tadDimensions[e-2] = (*intArgs)[e];
|
||||
|
||||
std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(input.rankOf(), tadDimensions);
|
||||
|
||||
// increasing counter to skip numIndices
|
||||
e++;
|
||||
std::vector<int> indices;
|
||||
for (; e < static_cast<Nd4jLong>(intArgs->size()); e++)
|
||||
indices.push_back((*intArgs)[e]);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inSubArr = input(indices[i], dimsToExclude, true);
|
||||
auto updSubArr = updates(i, dimsToExclude, true);
|
||||
|
||||
if (inSubArr.lengthOf() != updSubArr.lengthOf())
|
||||
continue;
|
||||
|
||||
switch (opCode) {
|
||||
case 0:
|
||||
inSubArr.applyPairwiseTransform(pairwise::Add, updSubArr, inSubArr);
|
||||
break;
|
||||
case 1:
|
||||
inSubArr.applyPairwiseTransform(pairwise::Subtract, updSubArr, inSubArr);
|
||||
break;
|
||||
case 2:
|
||||
inSubArr.applyPairwiseTransform(pairwise::Multiply, updSubArr, inSubArr);
|
||||
break;
|
||||
case 3:
|
||||
inSubArr.applyPairwiseTransform(pairwise::Divide, updSubArr, inSubArr);
|
||||
break;
|
||||
case 4:
|
||||
inSubArr.applyPairwiseTransform(pairwise::ReverseSubtract, updSubArr, inSubArr);
|
||||
break;
|
||||
case 5:
|
||||
inSubArr.applyPairwiseTransform(pairwise::ReverseDivide, updSubArr, inSubArr);
|
||||
break;
|
||||
case 6:
|
||||
inSubArr.applyPairwiseTransform(pairwise::CopyPws, updSubArr, inSubArr);
|
||||
break;
|
||||
default:
|
||||
continue;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, indices.size());
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
void scatterSimple(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions) {
|
||||
|
||||
// updates and indices have same length
|
||||
const Nd4jLong len = indices.lengthOf();
|
||||
|
||||
switch (opId) {
|
||||
|
||||
case 6: { // copy
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inSubArr = input(i, dimensions);
|
||||
inSubArr.p(indices.t<Nd4jLong>(i), updates.e(i));
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_for(func, 0, len);
|
||||
}
|
||||
break;
|
||||
|
||||
default:
|
||||
throw std::invalid_argument("helpers::scatterSimple: operation is not implemented for given id !");
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,91 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/ShapeUtils.h>
|
||||
#include <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
|
||||
|
||||
T* gradIBuff = reinterpret_cast<T*>(gradI.getBuffer());
|
||||
const T* gradOBuff = reinterpret_cast<T*>(gradO.getBuffer());
|
||||
const Nd4jLong gradILen = gradI.lengthOf();
|
||||
const Nd4jLong gradOLen = gradO.lengthOf(); // gradOLen >= gradILen
|
||||
const Nd4jLong gradIEWS = sd::math::nd4j_abs<Nd4jLong>(gradI.ews());
|
||||
const Nd4jLong gradOEWS = gradO.ews();
|
||||
|
||||
// initial zeroing of gradI content
|
||||
if(gradIEWS == 1)
|
||||
memset(gradIBuff, 0, gradILen * sizeof(T));
|
||||
else {
|
||||
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
||||
for (Nd4jLong i = 0; i < gradILen * gradIEWS; i += gradIEWS)
|
||||
gradIBuff[i] = static_cast<T>(0.f);
|
||||
}
|
||||
|
||||
|
||||
if(gradO.ordering() == 'c' && gradOEWS == 1) {
|
||||
|
||||
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
||||
for(Nd4jLong i=0; i<gradOLen; ++i) {
|
||||
auto idx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
|
||||
gradI.p(idx, gradI.e<T>(idx) + gradOBuff[i]);
|
||||
}
|
||||
}
|
||||
else if(gradO.ordering() == 'c' && gradOEWS > 1) {
|
||||
|
||||
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
||||
for(Nd4jLong i=0; i<gradOLen; ++i) {
|
||||
auto idx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
|
||||
gradI.p(idx, gradI.e<T>(idx) + gradOBuff[i * gradOEWS]);
|
||||
}
|
||||
}
|
||||
else {
|
||||
|
||||
//PRAGMA_OMP_PARALLEL_FOR_SIMD
|
||||
for(Nd4jLong i=0; i<gradOLen; ++i) {
|
||||
|
||||
auto fidx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
|
||||
gradI.p(fidx, gradI.e<T>(fidx) + gradOBuff[shape::getIndexOffset(i, gradO.getShapeInfo())]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {
|
||||
BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBP_, (gradO, gradI, reps), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void tileBP_, (const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps), FLOAT_TYPES);
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,47 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static void trace_(const NDArray& input, NDArray& output) {
|
||||
const int inRank = input.rankOf();
|
||||
auto setOfSubArrs = input.allTensorsAlongDimension({inRank-2, inRank-1});
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++)
|
||||
output.p(i, setOfSubArrs.at(i)->getTrace());
|
||||
};
|
||||
samediff::Threads::parallel_for(func, 0, setOfSubArrs.size());
|
||||
}
|
||||
|
||||
void trace(sd::LaunchContext * context, const NDArray& input, NDArray& output) {
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), trace_, (input, output), LIBND4J_TYPES);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,56 @@
|
|||
/*******************************************************************************
|
||||
* 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 <helpers/Loops.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static void triuBP_(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
|
||||
|
||||
auto dOdI = NDArray(&gradO); // dO/dI
|
||||
const_cast<NDArray&>(input).fillAsTriangular<T>(0, diagonal, dOdI.sizeAt(-1), dOdI, 'b');
|
||||
int dLen = dOdI.lengthOf();
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
if (dOdI.t<T>(i) != static_cast<T>(0.f))
|
||||
dOdI.t<T>(i) = static_cast<T>(1.f);
|
||||
}
|
||||
};
|
||||
samediff::Threads::parallel_for(func, 0, dLen);
|
||||
|
||||
// FIXME: !!!
|
||||
gradI.assign(dOdI * gradO); // chain rule: dLoss/dI = dO/dI * dLoss/dO
|
||||
}
|
||||
|
||||
void triuBP(sd::LaunchContext * context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {
|
||||
BUILD_SINGLE_SELECTOR(gradO.dataType(), triuBP_, (context, input, gradO, gradI, diagonal), LIBND4J_TYPES);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -14,9 +14,9 @@
|
|||
* SPDX-License-Identifier: Apache-2.0
|
||||
******************************************************************************/
|
||||
|
||||
//
|
||||
// @author Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
|
||||
//
|
||||
//
|
||||
// @author Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
|
||||
//
|
||||
|
||||
|
||||
#include<ops/declarable/helpers/transforms.h>
|
||||
|
|
@ -34,7 +34,7 @@ namespace sd {
|
|||
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) {
|
||||
static __global__ void mergeMaxIndexCudaLauncher(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;
|
||||
|
|
@ -46,54 +46,56 @@ namespace sd {
|
|||
|
||||
for (int i = 0; i < numArrays; i++) {
|
||||
auto x = reinterpret_cast<T*>(inArrs[i]);
|
||||
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[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_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
std::vector<void *> inBuffers(inArrs.size());
|
||||
std::vector<void *> inShapes(inArrs.size());
|
||||
static void mergeMaxIndex_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
int nArrSize = static_cast<int>(inArrs.size());
|
||||
std::vector<void*> inBuffers(nArrSize), inShapes(nArrSize);
|
||||
|
||||
for (int e = 0; e < inArrs.size(); e++) {
|
||||
for (int e = 0; e < nArrSize; 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 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);
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeMaxIndexCudaLauncher<T, Z> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, nArrSize, output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
|
||||
void mergeMaxIndex(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
NDArray::prepareSpecialUse({&output}, {});
|
||||
for (auto v:inArrs)
|
||||
v->syncToDevice();
|
||||
|
||||
void mergeMaxIndex(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
NDArray::prepareSpecialUse({ &output }, inArrs);
|
||||
|
||||
BUILD_DOUBLE_SELECTOR(inArrs[0]->dataType(), output.dataType(), mergeMaxIndex_, (context, inArrs, output), LIBND4J_TYPES, INDEXING_TYPES);
|
||||
|
||||
NDArray::registerSpecialUse({&output}, {});
|
||||
NDArray::registerSpecialUse({ &output }, inArrs);
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static __global__ void global_mergeMax_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
|
||||
static __global__ void mergeMaxCudaLauncher(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;
|
||||
|
|
@ -103,51 +105,163 @@ namespace sd {
|
|||
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 x = reinterpret_cast<const T*>(inArrs[i]);
|
||||
auto xShape = reinterpret_cast<const 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_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
std::vector<void *> inBuffers(inArrs.size());
|
||||
std::vector<void *> inShapes(inArrs.size());
|
||||
static void mergeMax_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
int nArrsSize = static_cast<int>(inArrs.size());
|
||||
|
||||
for (int e = 0; e < inArrs.size(); e++) {
|
||||
std::vector<void*> inBuffers(nArrsSize), inShapes(nArrsSize);
|
||||
|
||||
for (int e = 0; e < nArrsSize; 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 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);
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeMaxCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, nArrsSize, output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
|
||||
void mergeMax(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
NDArray::prepareSpecialUse({&output}, {});
|
||||
for (auto v:inArrs)
|
||||
v->syncToDevice();
|
||||
|
||||
void mergeMax(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
NDArray::prepareSpecialUse({ &output }, inArrs);
|
||||
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), mergeMax_, (context, inArrs, output), LIBND4J_TYPES);
|
||||
NDArray::registerSpecialUse({&output}, {});
|
||||
|
||||
NDArray::registerSpecialUse({ &output }, inArrs);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static __global__ void global_mergeAvg_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
|
||||
static __global__ void mergeMaxBpCudaLauncher(void** inArrs, void** inShapes, void* vgradient, Nd4jLong* gradientShape, const int numArrays,
|
||||
void** outArrs, void** outShapes, Nd4jLong length, bool bSameOrderAndEws1) {
|
||||
|
||||
auto grad = reinterpret_cast<T*>(vgradient);
|
||||
|
||||
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const auto step = gridDim.x * blockDim.x;
|
||||
|
||||
int coords[MAX_RANK];
|
||||
|
||||
for (Nd4jLong e = tid; e < length; e += step) {
|
||||
|
||||
T mVal = -DataTypeUtils::max<T>();
|
||||
int nMaxIndex = 0;
|
||||
auto xOffset = e, zOffset = e, gradOffset = e;
|
||||
|
||||
if (!bSameOrderAndEws1) {
|
||||
shape::index2coords(e, gradientShape, coords);
|
||||
gradOffset = shape::getOffset(gradientShape, coords);
|
||||
}
|
||||
|
||||
for (int i = 0; i < numArrays; i++) {
|
||||
auto x = reinterpret_cast<T*>(inArrs[i]);
|
||||
|
||||
if (!bSameOrderAndEws1) {
|
||||
auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
|
||||
xOffset = shape::getOffset(xShape, coords);
|
||||
}
|
||||
|
||||
auto val = x[xOffset];
|
||||
if (mVal < val) {
|
||||
mVal = val;
|
||||
nMaxIndex = i;
|
||||
}
|
||||
}
|
||||
|
||||
// outputs have to be pre-nullify
|
||||
if (!bSameOrderAndEws1) {
|
||||
auto outShape = reinterpret_cast<Nd4jLong*>(outShapes[nMaxIndex]);
|
||||
zOffset = shape::getOffset(outShape, coords);
|
||||
}
|
||||
|
||||
auto output = reinterpret_cast<T*>(outArrs[nMaxIndex]);
|
||||
|
||||
output[zOffset] = grad[gradOffset];
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void mergeMaxBp_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs, int nArrSize, bool bSameOrderAndEws1) {
|
||||
|
||||
std::vector<void*> inBuffers(nArrSize), inShapes(nArrSize), outBuffers(nArrSize), outShapes(nArrSize);
|
||||
|
||||
for (int e = 0; e < nArrSize; e++) {
|
||||
inBuffers[e] = inArrs[e]->getSpecialBuffer();
|
||||
inShapes[e] = inArrs[e]->getSpecialShapeInfo();
|
||||
outBuffers[e] = outArrs[e]->getSpecialBuffer();
|
||||
outShapes[e] = outArrs[e]->getSpecialShapeInfo();
|
||||
}
|
||||
|
||||
PointersManager manager(context, "mergeMaxBp");
|
||||
|
||||
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 pOutBuffers = reinterpret_cast<void**>(manager.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void*)));
|
||||
auto pOutShapes = reinterpret_cast<void**>(manager.replicatePointer(outShapes.data(), outShapes.size() * sizeof(void*)));
|
||||
|
||||
auto length = inArrs[nArrSize]->lengthOf();
|
||||
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeMaxBpCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, inArrs[nArrSize]->getSpecialBuffer(),
|
||||
inArrs[nArrSize]->getSpecialShapeInfo(), nArrSize, pOutBuffers, pOutShapes,
|
||||
length, bSameOrderAndEws1);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
|
||||
void mergeMaxBp(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs) {
|
||||
|
||||
// not use gradient
|
||||
int nArrSize = static_cast<int>(inArrs.size() - 1);
|
||||
|
||||
const std::vector<const NDArray*>& out = reinterpret_cast<const std::vector<const NDArray*>&>(outArrs);
|
||||
|
||||
NDArray::prepareSpecialUse(out, inArrs);
|
||||
|
||||
bool bSameOrderAndEws1 = (1 == inArrs[nArrSize]->ews());
|
||||
auto ordering = inArrs[nArrSize]->ordering();
|
||||
|
||||
for (int i = 0; i < nArrSize; ++i) {
|
||||
bSameOrderAndEws1 &= (ordering == inArrs[i]->ordering());
|
||||
bSameOrderAndEws1 &= (1 == inArrs[i]->ews());
|
||||
|
||||
bSameOrderAndEws1 &= (ordering == outArrs[i]->ordering());
|
||||
bSameOrderAndEws1 &= (1 == outArrs[i]->ews());
|
||||
}
|
||||
|
||||
BUILD_SINGLE_SELECTOR(inArrs[nArrSize]->dataType(), mergeMaxBp_, (context, inArrs, outArrs, nArrSize, bSameOrderAndEws1), LIBND4J_TYPES);
|
||||
|
||||
NDArray::registerSpecialUse( out, inArrs );
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static __global__ void mergeAvgCudaLauncher(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;
|
||||
|
|
@ -158,7 +272,7 @@ namespace sd {
|
|||
|
||||
for (int i = 0; i < numArrays; i++) {
|
||||
auto x = reinterpret_cast<T*>(inArrs[i]);
|
||||
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
|
||||
auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
|
||||
|
||||
sum += x[shape::getIndexOffset(e, xShape)];
|
||||
}
|
||||
|
|
@ -168,9 +282,9 @@ namespace sd {
|
|||
}
|
||||
|
||||
template<typename T>
|
||||
static void mergeAvg_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
std::vector<void *> inBuffers(inArrs.size());
|
||||
std::vector<void *> inShapes(inArrs.size());
|
||||
static void mergeAvg_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
std::vector<void*> inBuffers(inArrs.size()), inShapes(inArrs.size());
|
||||
|
||||
for (int e = 0; e < inArrs.size(); e++) {
|
||||
inBuffers[e] = inArrs[e]->getSpecialBuffer();
|
||||
|
|
@ -179,28 +293,111 @@ namespace sd {
|
|||
|
||||
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 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);
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeAvgCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, (int)inArrs.size(), output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
|
||||
void mergeAvg(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
NDArray::prepareSpecialUse({&output}, {});
|
||||
for (auto v:inArrs)
|
||||
v->syncToDevice();
|
||||
void mergeAvg(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
NDArray::prepareSpecialUse({ &output }, inArrs);
|
||||
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAvg_, (context, inArrs, output), FLOAT_TYPES);
|
||||
|
||||
NDArray::registerSpecialUse({&output}, {});
|
||||
NDArray::registerSpecialUse({ &output }, inArrs);
|
||||
}
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static __global__ void mergeAvgBpCudaLauncher(void* vgradient, Nd4jLong* gradientShape, void** outArrs, void** outShapes,
|
||||
const int numArrays, Nd4jLong length, bool bSameOrderAndEws1) {
|
||||
|
||||
auto grad = reinterpret_cast<T*>(vgradient);
|
||||
|
||||
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const auto step = gridDim.x * blockDim.x;
|
||||
|
||||
int coords[MAX_RANK];
|
||||
|
||||
for (Nd4jLong e = tid; e < length; e += step) {
|
||||
|
||||
auto zOffset = e, gradOffset = e;
|
||||
if (!bSameOrderAndEws1) {
|
||||
shape::index2coords(e, gradientShape, coords);
|
||||
gradOffset = shape::getOffset(gradientShape, coords);
|
||||
}
|
||||
|
||||
for (int i = 0; i < numArrays; i++) {
|
||||
|
||||
if (!bSameOrderAndEws1) {
|
||||
auto outShape = reinterpret_cast<Nd4jLong*>(outShapes[i]);
|
||||
zOffset = shape::getOffset(outShape, coords);
|
||||
}
|
||||
|
||||
auto output = reinterpret_cast<T*>(outArrs[i]);
|
||||
|
||||
output[zOffset] = grad[gradOffset] / numArrays;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void mergeAvgBp_(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs, bool bSameOrderAndEws1) {
|
||||
|
||||
int nArrSize = static_cast<int>(outArrs.size());
|
||||
|
||||
std::vector<void*> outBuffers(nArrSize), outShapes(nArrSize);
|
||||
|
||||
for (int e = 0; e < nArrSize; e++) {
|
||||
outBuffers[e] = outArrs[e]->getSpecialBuffer();
|
||||
outShapes[e] = outArrs[e]->getSpecialShapeInfo();
|
||||
}
|
||||
|
||||
PointersManager manager(context, "mergeAvgBp");
|
||||
|
||||
auto pOutBuffers = reinterpret_cast<void**>(manager.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void*)));
|
||||
auto pOutShapes = reinterpret_cast<void**>(manager.replicatePointer(outShapes.data(), outShapes.size() * sizeof(void*)));
|
||||
|
||||
auto length = gradient.lengthOf();
|
||||
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeAvgBpCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (gradient.getSpecialBuffer(), gradient.getSpecialShapeInfo(),
|
||||
pOutBuffers, pOutShapes, nArrSize, length, bSameOrderAndEws1);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
|
||||
void mergeAvgBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
||||
|
||||
const std::vector<const NDArray*>& out = reinterpret_cast<const std::vector<const NDArray*>&>(outArrs);
|
||||
|
||||
NDArray::prepareSpecialUse( out, { &gradient });
|
||||
|
||||
bool bSameOrderAndEws1 = (1 == gradient.ews());
|
||||
auto ordering = gradient.ordering();
|
||||
|
||||
for (const auto& v : outArrs) {
|
||||
bSameOrderAndEws1 &= (ordering == v->ordering());
|
||||
bSameOrderAndEws1 &= (1 == v->ews());
|
||||
}
|
||||
|
||||
BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAvgBp_, (context, gradient, outArrs, bSameOrderAndEws1), LIBND4J_TYPES);
|
||||
|
||||
NDArray::prepareSpecialUse(out, { &gradient });
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static __global__ void global_mergeAdd_(void **inArrs, void **inShapes, const int numArrays, void *voutput, Nd4jLong *outputShape, Nd4jLong length) {
|
||||
static __global__ void mergeAddCudaLauncher(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;
|
||||
|
|
@ -211,7 +408,7 @@ namespace sd {
|
|||
|
||||
for (int i = 0; i < numArrays; i++) {
|
||||
auto x = reinterpret_cast<T*>(inArrs[i]);
|
||||
auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
|
||||
auto xShape = reinterpret_cast<Nd4jLong*>(inShapes[i]);
|
||||
|
||||
sum += x[shape::getIndexOffset(e, xShape)];
|
||||
}
|
||||
|
|
@ -221,36 +418,120 @@ namespace sd {
|
|||
}
|
||||
|
||||
template<typename T>
|
||||
static void mergeAdd_(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
std::vector<void *> inBuffers(inArrs.size());
|
||||
std::vector<void *> inShapes(inArrs.size());
|
||||
static void mergeAdd_(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
int nArrSize = static_cast<int>(inArrs.size());
|
||||
std::vector<void*> inBuffers(nArrSize), inShapes(nArrSize);
|
||||
|
||||
for (int e = 0; e < inArrs.size(); e++) {
|
||||
for (int e = 0; e < nArrSize; 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 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);
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeAddCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (pInBuffers, pInShapes, nArrSize, output.getSpecialBuffer(), output.getSpecialShapeInfo(), length);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output), NUMERIC_TYPES);
|
||||
|
||||
void mergeAdd(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output) {
|
||||
NDArray::prepareSpecialUse({&output}, {});
|
||||
for (auto v:inArrs)
|
||||
v->syncToDevice();
|
||||
BUILD_SINGLE_TEMPLATE(template void mergeAdd_, (sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output), NUMERIC_TYPES);
|
||||
|
||||
void mergeAdd(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, NDArray& output) {
|
||||
|
||||
NDArray::prepareSpecialUse({ &output }, inArrs);
|
||||
|
||||
BUILD_SINGLE_SELECTOR(output.dataType(), mergeAdd_, (context, inArrs, output), NUMERIC_TYPES);
|
||||
|
||||
NDArray::registerSpecialUse({&output}, {});
|
||||
NDArray::registerSpecialUse({ &output }, inArrs);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static __global__ void mergeAddBpCudaLauncher(void* vgradient, Nd4jLong* gradientShape, void** outArrs, void** outShapes,
|
||||
const int numArrays, Nd4jLong length, bool bSameOrderAndEws1) {
|
||||
|
||||
auto grad = reinterpret_cast<T*>(vgradient);
|
||||
|
||||
const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
const auto step = gridDim.x * blockDim.x;
|
||||
|
||||
int coords[MAX_RANK];
|
||||
|
||||
for (Nd4jLong e = tid; e < length; e += step) {
|
||||
|
||||
auto zOffset = e, gradOffset = e;
|
||||
if (!bSameOrderAndEws1) {
|
||||
shape::index2coords(e, gradientShape, coords);
|
||||
gradOffset = shape::getOffset(gradientShape, coords);
|
||||
}
|
||||
|
||||
for (int i = 0; i < numArrays; i++) {
|
||||
|
||||
if (!bSameOrderAndEws1) {
|
||||
auto outShape = reinterpret_cast<Nd4jLong*>(outShapes[i]);
|
||||
zOffset = shape::getOffset(outShape, coords);
|
||||
}
|
||||
|
||||
auto output = reinterpret_cast<T*>(outArrs[i]);
|
||||
|
||||
output[zOffset] = grad[gradOffset];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void mergeAddBp_(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs, bool bSameOrderAndEws1) {
|
||||
|
||||
int nArrSize = static_cast<int>(outArrs.size());
|
||||
|
||||
std::vector<void*> outBuffers(nArrSize), outShapes(nArrSize);
|
||||
|
||||
for (int e = 0; e < nArrSize; e++) {
|
||||
outBuffers[e] = outArrs[e]->getSpecialBuffer();
|
||||
outShapes[e] = outArrs[e]->getSpecialShapeInfo();
|
||||
}
|
||||
|
||||
PointersManager manager(context, "mergeAddBp");
|
||||
|
||||
auto pOutBuffers = reinterpret_cast<void**>(manager.replicatePointer(outBuffers.data(), outBuffers.size() * sizeof(void*)));
|
||||
auto pOutShapes = reinterpret_cast<void**>(manager.replicatePointer(outShapes.data(), outShapes.size() * sizeof(void*)));
|
||||
|
||||
auto length = gradient.lengthOf();
|
||||
|
||||
const int threadsPerBlock = MAX_NUM_THREADS / 2;
|
||||
const int blocksPerGrid = (length + threadsPerBlock - 1) / threadsPerBlock;
|
||||
|
||||
mergeAddBpCudaLauncher<T> << <blocksPerGrid, threadsPerBlock, 512, *context->getCudaStream() >> > (gradient.getSpecialBuffer(), gradient.getSpecialShapeInfo(),
|
||||
pOutBuffers, pOutShapes, nArrSize, length, bSameOrderAndEws1);
|
||||
|
||||
manager.synchronize();
|
||||
}
|
||||
|
||||
void mergeAddBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs) {
|
||||
|
||||
const std::vector<const NDArray*>& out = reinterpret_cast<const std::vector<const NDArray*>& >(outArrs);
|
||||
NDArray::prepareSpecialUse( out, { &gradient });
|
||||
|
||||
bool bSameOrderAndEws1 = (1 == gradient.ews());
|
||||
auto ordering = gradient.ordering();
|
||||
|
||||
for (const auto& v : outArrs) {
|
||||
bSameOrderAndEws1 &= (ordering == v->ordering());
|
||||
bSameOrderAndEws1 &= (1 == v->ews());
|
||||
}
|
||||
|
||||
BUILD_SINGLE_SELECTOR(gradient.dataType(), mergeAddBp_, (context, gradient, outArrs, bSameOrderAndEws1), LIBND4J_TYPES);
|
||||
|
||||
NDArray::prepareSpecialUse( out, { &gradient });
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -52,13 +52,16 @@ namespace helpers {
|
|||
|
||||
void scatterSimple(sd::LaunchContext * context, const int opId, NDArray& input, const NDArray& updates, const NDArray& indices, const std::vector<int>& dimensions);
|
||||
|
||||
void mergeMaxIndex(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output);
|
||||
void mergeMaxIndex(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output);
|
||||
|
||||
void mergeMax(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output);
|
||||
void mergeMax(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output);
|
||||
void mergeMaxBp(sd::LaunchContext* context, const std::vector<const NDArray*>& inArrs, std::vector<NDArray*>& outArrs);
|
||||
|
||||
void mergeAvg(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output);
|
||||
void mergeAvg(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output);
|
||||
void mergeAvgBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs);
|
||||
|
||||
void mergeAdd(sd::LaunchContext * context, const std::vector<NDArray*>& inArrs, NDArray& output);
|
||||
void mergeAdd(sd::LaunchContext * context, const std::vector<const NDArray*>& inArrs, NDArray& output);
|
||||
void mergeAddBp(sd::LaunchContext* context, const NDArray& gradient, std::vector<NDArray*>& outArrs);
|
||||
|
||||
void clipByNorm(sd::LaunchContext * context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace);
|
||||
void clipByGlobalNorm(sd::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace);
|
||||
|
|
|
|||
|
|
@ -955,7 +955,160 @@ TEST_F(DeclarableOpsTests13, mergemax_2) {
|
|||
|
||||
ASSERT_EQ(20, status);
|
||||
}
|
||||
/////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests13, mergemax_bp_1) {
|
||||
|
||||
NDArray x1('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x2('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x3('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray grad('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
x1.assign(3);
|
||||
x2.assign(1);
|
||||
x3.assign(2);
|
||||
grad.linspace(.1, .1);
|
||||
|
||||
|
||||
sd::ops::mergemax_bp op;
|
||||
auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
|
||||
ASSERT_EQ(Status::OK(), result.status());
|
||||
ASSERT_EQ(3, result.size());
|
||||
|
||||
auto z = result.at(0);
|
||||
|
||||
ASSERT_TRUE(grad.isSameShape(z));
|
||||
ASSERT_TRUE(grad.equalsTo(z));
|
||||
|
||||
}
|
||||
/////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests13, mergemax_bp_2) {
|
||||
|
||||
NDArray x1('c', { 2, 5 }, { 1,2,3,4,5,4,3,2,1,0 }, sd::DataType::FLOAT32);
|
||||
NDArray x2('c', { 2, 5 }, { 0,1,2,3,4,5,6,7,8,9 }, sd::DataType::FLOAT32);
|
||||
NDArray x3('c', { 2, 5 }, { 0,1,1,2,3,4,7,5,8,10 }, sd::DataType::FLOAT32);
|
||||
NDArray grad('c', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
grad.linspace(.1, .1);
|
||||
|
||||
NDArray exp1('c', { 2, 5 }, { 0.1, 0.2, 0.3, 0.4, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0 }, sd::DataType::FLOAT32);
|
||||
NDArray exp2('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.8, 0.9, 0.0 }, sd::DataType::FLOAT32);
|
||||
NDArray exp3('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7, 0.0, 0.0, 1.0 }, sd::DataType::FLOAT32);
|
||||
|
||||
sd::ops::mergemax_bp op;
|
||||
auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
|
||||
ASSERT_EQ(Status::OK(), result.status());
|
||||
ASSERT_EQ(3, result.size());
|
||||
|
||||
auto z1 = result.at(0);
|
||||
auto z2 = result.at(1);
|
||||
auto z3 = result.at(2);
|
||||
|
||||
ASSERT_TRUE(exp1.isSameShape(z1));
|
||||
ASSERT_TRUE(exp1.equalsTo(z1));
|
||||
ASSERT_TRUE(exp2.isSameShape(z2));
|
||||
ASSERT_TRUE(exp2.equalsTo(z2));
|
||||
ASSERT_TRUE(exp3.isSameShape(z3));
|
||||
ASSERT_TRUE(exp3.equalsTo(z3));
|
||||
|
||||
}
|
||||
/////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests13, mergemax_bp_3) {
|
||||
|
||||
NDArray x1C('c', { 2, 5 }, { 1,2,3,4,5,4,3,2,1,0 }, sd::DataType::FLOAT32);
|
||||
NDArray x2C('c', { 2, 5 }, { 0,1,2,3,4,5,6,7,8,9 }, sd::DataType::FLOAT32);
|
||||
NDArray x3C('c', { 2, 5 }, { 0,1,1,2,3,4,7,5,8,10 }, sd::DataType::FLOAT32);
|
||||
NDArray grad('c', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
grad.linspace(.1, .1);
|
||||
|
||||
NDArray x1('f', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x2('f', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x3('f', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
NDArray exp1C('c', { 2, 5 }, { 0.1, 0.2, 0.3, 0.4, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0 }, sd::DataType::FLOAT32);
|
||||
NDArray exp2C('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0, 0.8, 0.9, 0.0 }, sd::DataType::FLOAT32);
|
||||
NDArray exp3C('c', { 2, 5 }, { 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7, 0.0, 0.0, 1.0 }, sd::DataType::FLOAT32);
|
||||
|
||||
NDArray exp1('f', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray exp2('f', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray exp3('f', { 2, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
x1.assign(x1C);
|
||||
x2.assign(x2C);
|
||||
x3.assign(x3C);
|
||||
|
||||
exp1.assign(exp1C);
|
||||
exp2.assign(exp2C);
|
||||
exp3.assign(exp3C);
|
||||
|
||||
sd::ops::mergemax_bp op;
|
||||
auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
|
||||
ASSERT_EQ(Status::OK(), result.status());
|
||||
ASSERT_EQ(3, result.size());
|
||||
|
||||
auto z1 = result.at(0);
|
||||
auto z2 = result.at(1);
|
||||
auto z3 = result.at(2);
|
||||
|
||||
ASSERT_TRUE(exp1.isSameShape(z1));
|
||||
ASSERT_TRUE(exp1.equalsTo(z1));
|
||||
ASSERT_TRUE(exp2.isSameShape(z2));
|
||||
ASSERT_TRUE(exp2.equalsTo(z2));
|
||||
ASSERT_TRUE(exp3.isSameShape(z3));
|
||||
ASSERT_TRUE(exp3.equalsTo(z3));
|
||||
|
||||
}
|
||||
/////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests13, mergeadd_bp_1) {
|
||||
|
||||
NDArray x1('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x2('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x3('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray grad('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
x1.assign(3);
|
||||
x2.assign(1);
|
||||
x3.assign(2);
|
||||
grad.linspace(.1, .1);
|
||||
|
||||
sd::ops::mergeadd_bp op;
|
||||
auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
|
||||
ASSERT_EQ(Status::OK(), result.status());
|
||||
ASSERT_EQ(3, result.size());
|
||||
|
||||
for (int i = 0; i < 3; i++) {
|
||||
auto z = result.at(0);
|
||||
ASSERT_TRUE(grad.isSameShape(z));
|
||||
ASSERT_TRUE(grad.equalsTo(z));
|
||||
}
|
||||
}
|
||||
/////////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests13, mergeavg_bp_1) {
|
||||
|
||||
NDArray x1('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x2('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray x3('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
NDArray grad('c', { 5, 5 }, sd::DataType::FLOAT32);
|
||||
|
||||
x1.assign(3);
|
||||
x2.assign(1);
|
||||
x3.assign(2);
|
||||
grad.linspace(.1, .1);
|
||||
|
||||
sd::ops::mergeavg_bp op;
|
||||
auto result = op.evaluate({ &x1, &x2, &x3, &grad }, {}, {});
|
||||
ASSERT_EQ(Status::OK(), result.status());
|
||||
ASSERT_EQ(3, result.size());
|
||||
|
||||
grad.applyScalar(sd::scalar::Divide, 3, grad);
|
||||
|
||||
for (int i = 0; i < 3; i++) {
|
||||
auto z = result.at(i);
|
||||
ASSERT_TRUE(grad.isSameShape(z));
|
||||
ASSERT_TRUE(grad.equalsTo(z));
|
||||
}
|
||||
|
||||
}
|
||||
///////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests13, lstmLayer_1) {
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue