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

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <ops/declarable/helpers/dropout.h>
#include <NativeOps.h>
#include <vector>
#include <memory>
#include <cuda_exception.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static __global__ void dropoutSimpleKernel(void const* inputBuf, Nd4jLong const* inputShape, void* outputBuf, Nd4jLong* outputShape, double probVal, int inLen, nd4j::graph::RandomGenerator* nodeRng) {
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
T const* input = reinterpret_cast<T const*>(inputBuf);
T* output = reinterpret_cast<T*>(outputBuf);
// trivial idea: loop through all elements, get independent probability for each element to be nullified
for (Nd4jLong e = 0; e < inLen; ++e) {
T val = nodeRng->relativeT(e, T(0.f), T(1.f));
// if probability is ok - we're saving scaled value
if (double(val) < probVal)
output[shape::getIndexOffset(e, outputShape)] = T(input[shape::getIndexOffset(e, inputShape)] / probVal);
}
}
template <typename T>
static void dropoutSimple(nd4j::LaunchContext* context, NDArray const* input, NDArray* output, double probValue, int seed) {
nd4j::graph::RandomGenerator nodeRng(3019L, seed);
int inLen = input->lengthOf();
nd4j::graph::RandomGenerator* dRandom;
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({output}, {input});
auto err = cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator));
if (err) {
throw cuda_exception::build("helpers::dropoutSimple: Cannot allocate device memory for random generator.", err);
}
err = cudaMemcpy(dRandom, &nodeRng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice);
if (err) {
throw cuda_exception::build("helpers::dropoutSimple: Cannot set up device memory for random generator.", err);
}
dropoutSimpleKernel<T><<<128, 256, 1024, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), probValue, inLen, dRandom);
err = cudaFree(dRandom);
if (err) {
throw cuda_exception::build("helpers::dropoutSimple: Cannot deallocate device memory for random generator.", err);
}
NDArray::registerSpecialUse({output}, {input});
}
template <typename T>
int _dropOutFunctor(graph::Context& context, NDArray* input, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
if (reduceShape == nullptr){
dropoutSimple<T>(context.launchContext(), 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());
reduceShape->syncToHost(); // to ensure that follows are actual
bool fit = true;
for( int 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(), context.launchContext()));
chunk->assign(1.f);
dropoutSimple<T>(context.launchContext(), 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;
// FIXME: we could do this in one step, aren't we?
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();
NDArray::prepareSpecialUse({output}, {input});
BUILD_SINGLE_SELECTOR(xType, return _dropOutFunctor, (context, input, output, reduceShape, seed, probValue), FLOAT_TYPES);
NDArray::registerSpecialUse({output}, {input});
}
/////////////////////////////////// backrpopagations ///////////////////////////////////////////////
template <typename T>
static __global__ void dropoutBPKernel(void* outputBuf, Nd4jLong* outputShape, void* gradOutBuf, Nd4jLong* gradOutShape, double probValue) {
__shared__ T* output;
__shared__ T* input;
__shared__ int len;
if (threadIdx.x == 0) {
len = shape::length(outputShape);
output = reinterpret_cast<T*>(outputBuf);
input = reinterpret_cast<T*>(gradOutBuf);
}
__syncthreads();
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (int e = tid; e < len; e += step) {
const auto zOffset = shape::getIndexOffset(e, outputShape);
// if probability was non-zero on FF step, we'll scale grads back
if (output[zOffset] != T(0.))
output[zOffset] = T(input[shape::getIndexOffset(e, gradOutShape)] / probValue);
}
}
template <typename T>
static int dropOutFunctorBP_(graph::Context& context, NDArray* input, NDArray* gradOut, NDArray* output, NDArray* reduceShape, int seed, double probValue) {
// we're making additional FF run to see how probabilities played out with given seeds
int res = dropOutFunctor(context, input, output, reduceShape, seed, probValue);
auto stream = context.launchContext()->getCudaStream();
NDArray::prepareSpecialUse({output}, {input, gradOut});
if (ND4J_STATUS_OK == res)
dropoutBPKernel<T><<<128, 256, 1024, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), gradOut->specialBuffer(), gradOut->specialShapeInfo(), probValue);
NDArray::registerSpecialUse({output}, {input, gradOut});
return res;
}
template <typename T>
static __global__ void alphaDropoutSimpleKernel(void const* inputBuf, Nd4jLong const* inputShape, void* outputBuf, Nd4jLong* outputShape, double probValue, double alpha, double alpha1, double beta, int inLen, nd4j::graph::RandomGenerator* nodeRng) {
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
T const* input = reinterpret_cast<T const*>(inputBuf);
T* output = reinterpret_cast<T*>(outputBuf);
for (auto e = tid; e < inLen; e += step) {
T val = nodeRng->relativeT(e, T(0.f), T(1.f));
T xVal = input[shape::getIndexOffset(e, inputShape)];
output[shape::getIndexOffset(e, outputShape)] = (val >= T(probValue) ? T(alpha * beta + alpha1) : T(alpha * (double)xVal + alpha1));
}
}
template <typename T>
static void alphaDropoutSimple(nd4j::LaunchContext* context, NDArray const* input, NDArray* output, int seed, double probValue, double alpha, double alpha1, double beta) {
nd4j::graph::RandomGenerator nodeRng(3019L, seed), *dRandom;
auto stream = context->getCudaStream();
auto err = cudaMalloc(&dRandom, sizeof(nd4j::graph::RandomGenerator));
NDArray::prepareSpecialUse({output}, {input});
if (err) {
throw cuda_exception::build("helpers::alphaDropoutSimple: Cannot allocate device memory for random generator.", err);
}
err = cudaMemcpy(dRandom, &nodeRng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice);
if (err) {
throw cuda_exception::build("helpers::alphaDropoutSimple: Cannot set up device memory for random generator.", err);
}
alphaDropoutSimpleKernel<T><<<128, 256, 1024, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), probValue, alpha, alpha1, beta, output->lengthOf(), dRandom);
err = cudaFree(dRandom);
if (err) {
throw cuda_exception::build("helpers::alphaDropoutSimple: Cannot deallocate device memory for random generator.", err);
}
NDArray::registerSpecialUse({output}, {input});
}
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) {
if (reduceShape == nullptr){
alphaDropoutSimple<T>(context.launchContext(), input, output, seed, probValue, alpha, alpha1, beta);
}
else {
REQUIRE_TRUE(reduceShape->lengthOf() <= input->rankOf(), 0, "dropout: Noise shape should be fittable to input");
std::vector<Nd4jLong> dims(reduceShape->lengthOf());
reduceShape->syncToHost(); // to ensure that follows are actual
bool fit = true;
for( int 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, "alpha_dropout: Noise shape should fit to input rank.");
std::unique_ptr<NDArray> chunk(new NDArray('c', dims, output->dataType(), context.launchContext()));
chunk->assign(1.f);
alphaDropoutSimple<T>(context.launchContext(), chunk.get(), chunk.get(), seed, probValue, alpha, alpha1, beta);
// 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();
}
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) {
// FIXME: can we make it single-loop?
(*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);
}
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);
}
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);
}
}
}
}
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