/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <ops/declarable/helpers/dropout.h>
#include <legacy/NativeOps.h>
#include <vector>
#include <memory>
#include <execution/Threads.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename T>
static void dropoutSimple(NDArray const* input, NDArray* output, double probValue, int seed) {
sd::graph::RandomGenerator nodeRng(3019L, seed);
int inLen = input->lengthOf();
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++) {
float val = nodeRng.relativeT<T>(e, T(0.f), T(1.f));
if (val < probValue)
output->p<T>(e, input->e<T>(e) / probValue);
}
};
samediff::Threads::parallel_for(func, 0, inLen);
}
BUILD_SINGLE_TEMPLATE(template void dropoutSimple, (NDArray const* input, NDArray* output, double probValue, int seed), FLOAT_TYPES);
template <typename T>
int dropOutFunctor_(graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
//NativeOps native;
//sd::graph::RandomGenerator nodeRng(seed); //static int dropOutFunctor_(sd::random::RandomBuffer* rng, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
//NativeOps native;
//native.reSeedBuffer(nullptr, (long)seed, rng);
//if (newRng )
if (reduceShape == nullptr){
dropoutSimple<T>(input, output, probValue, seed);
}
else {
REQUIRE_TRUE(reduceShape->lengthOf() <= input->rankOf(), 0, "dropout: Noise shape should be fittable to input");
std::vector<Nd4jLong> dims(reduceShape->lengthOf());
bool fit = true;
for(auto i = 0; i < dims.size(); i++ ) {
if (fit) {
dims[i] = reduceShape->e<Nd4jLong>(i);
for (int e = 0; e < input->rankOf(); ++e)
if (fit)
if (input->sizeAt(e) % dims[i]) {
fit = false;
}
}
}
// check dims to fit input
REQUIRE_TRUE(fit, 0, "dropout: Noise shape should fit to input rank.");
std::unique_ptr<NDArray> chunk(new NDArray('c', dims, output->dataType(), output->getContext()));
chunk->assign(1.f);
//chunk->applyRandom<randomOps::DropOutInverted<T>>(rng, nullptr, chunk.get(), &probValue);
//NativeOpExecutioner::execRandom(random::DropOutInverted, rng, chunk->buffer(), chunk->shapeInfo(), chunk->buffer(), chunk->shapeInfo(), &prob);
dropoutSimple<T>(chunk.get(), chunk.get(), probValue, seed);
// broadcast chunk to full matrix
std::unique_ptr<NDArray> dropOutMultiplier(new NDArray(*input));
dropOutMultiplier->assign(1.f);
*dropOutMultiplier += *chunk;
output->assign(*input * *dropOutMultiplier); //input->applyPairwiseTransform(pairwise::Multiply, dropOutMultiplier.get(), output, nullptr);
}
return Status::OK();
}
int dropOutFunctor(graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
auto xType = input->dataType();
BUILD_SINGLE_SELECTOR(xType, return dropOutFunctor_, (context, input, output, reduceShape, seed, probValue), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template int dropOutFunctor_, (graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue);, FLOAT_TYPES);
/////////////////////////////////// backrpopagations ///////////////////////////////////////////////
template <typename T>
static int dropOutFunctorBP_(graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
int res = dropOutFunctor(context, input, output, reduceShape, seed, probValue);
if (ND4J_STATUS_OK == res)
for (Nd4jLong e = 0; e < output->lengthOf(); e++) {
if (output->e<float>(e) != 0.f) output->p<T>(e, gradOut->e<double>(e) / probValue);
// else (*output)(e) = T(0.f);
}
return res;
}
template <typename T>
static int alphaDropOutFunctor_(graph::Context& context, NDArray* input, NDArray* output,
NDArray* reduceShape, int seed, double probValue, double alpha, double alpha1, double beta) {
//NativeOps native;
//auto rng = context.getRNG();
//native.reSeedBuffer(nullptr, (long)seed, rng);
//if (rng == nullptr)
// return ND4J_STATUS_BAD_RNG;
//T probValueArr[] = {probValue, alpha, alpha1, beta};
//input->template applyRandom<randomOps::AlphaDropOut<T>>(rng, nullptr, output, probValueArr);
sd::graph::RandomGenerator nodeRng(3019L, seed);
auto func = PRAGMA_THREADS_FOR {
for (auto e = start; e < stop; e++) {
float randVal = nodeRng.relativeT(e, T(0.f), T(1.f));
float xVal = input->e<float>(e);
output->p<float>(e, randVal >= probValue ? alpha * beta + alpha1 : alpha * xVal + alpha1);
}
};
samediff::Threads::parallel_for(func, 0, input->lengthOf());
return Status::OK();
}
template <typename T>
int alphaDropOutFunctorBP_(graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output,
NDArray* reduceShape, int seed, double probValue, double alpha, double alpha1, double beta) {
int res = alphaDropOutFunctor(context, input, output, reduceShape, seed, probValue, alpha, alpha1, beta);
if (res == ND4J_STATUS_OK) {
(*output) *= alpha;
(*output) *= (*gradOut); //->applyPairwiseTransform<transform::Multiply>(gradOut, output, nullptr);
}
return res;
}
int dropOutFunctorBP(graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
BUILD_SINGLE_SELECTOR(context.dataType(), return dropOutFunctorBP_, (context, input, gradOut, output, reduceShape, seed, probValue), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template int dropOutFunctorBP_, (graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output, NDArray* reduceShape, int seed, double probValue), FLOAT_TYPES);
int alphaDropOutFunctor(graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue, double alpha, double alpha1, double beta) {
BUILD_SINGLE_SELECTOR(context.dataType(), return alphaDropOutFunctor_, (context, input, output, reduceShape, seed, probValue, alpha, alpha1, beta), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template int alphaDropOutFunctor_, (graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue, double alpha, double alpha1, double beta), FLOAT_TYPES);
int alphaDropOutFunctorBP(graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output, NDArray* reduceShape, int seed, double probValue, double alpha, double alpha1, double beta) {
BUILD_SINGLE_SELECTOR(context.dataType(), return alphaDropOutFunctorBP_, (context, input, gradOut, output, reduceShape, seed, probValue, alpha, alpha1, beta), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template int alphaDropOutFunctorBP_, (graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output, NDArray* reduceShape, int seed, double probValue, double alpha, double alpha1, double beta), FLOAT_TYPES);
}
}
}