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Merge branch 'master' into ag_test_updates

master
Adam Gibson 2021-03-12 18:43:27 +09:00 committed by GitHub
parent 4f92a7789b c1eb8de81a
commit 91e8cf64ed
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2 changed files with 249 additions and 205 deletions
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229
libnd4j/include/ops/declarable/helpers/cuda/ctcLoss.cu
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@ -13,213 +13,32 @@
*
* SPDX-License-Identifier: Apache-2.0
*******************************************************************************/
//
// @author AbdelRauf
//
//
// @author AbdelRauf
//
#include <type_traits>
#include <cmath>
#include <stdexcept>
#include <memory>
#include <execution/Threads.h>
#include <execution/ThreadPool.h>
#include <helpers/LoopsCoordsHelper.h>
#include <ops/declarable/helpers/ctcLoss.h>
#include <ops/declarable/platform/cudnn/cudnnUtils.h>
#include <array/NDArrayFactory.h>
#include <vector>
namespace sd
{
namespace ops
{
namespace helpers
{
namespace sd {
namespace ops {
namespace platforms {
void ctcLoss(graph::Context& block, const NDArray &logits, const NDArray &targetLabels, const NDArray &logitsLengths, const NDArray &targetLabelLengths, NDArray &logLosses, NDArray &gradients, int blankIndex){
//not imeplemented
throw std::runtime_error("ctcLoss:: Not implemented yet");
}
template<typename Op, typename ...Args>
void callCudnnIfNoErr(cudnnStatus_t &err, Op op, Args&&... args){
if(err==CUDNN_STATUS_SUCCESS){
err = op(std::forward<Args>(args)...);
if(err){
nd4j_printf("Cudnn error code %s\n",cudnnGetErrorString(err));
}
}
}
template <typename T>
const T* bufferInHost( const NDArray &array) {
array.syncToHost();
return reinterpret_cast<const T*>(array.buffer());
}
std::vector<int> getConcatTargets(const NDArray &targetLabels, const NDArray &targetLabelLengths){
//concatenate target labels
const int32_t *tlabels = bufferInHost<int32_t>(targetLabels);
const int32_t *tlens =bufferInHost<int32_t>(targetLabelLengths);
int32_t nextOffset = targetLabels.strideAt(0);
int32_t elStride = targetLabels.strideAt(1);
int32_t batchCount = targetLabelLengths.lengthOf();
std::vector<int> labels;
labels.resize(targetLabels.lengthOf());
int j=0;
if(targetLabels.ews()){
for(int i=0; i<batchCount;i++){
int count = tlens[i];
for( int k=0;k<count;k++){
labels[j] = tlabels[k];
j++;
}
tlabels+=nextOffset;
}
}else{
for(int i=0; i<batchCount;i++){
int count = tlens[i];
for( int k=0;k<count;k++){
labels[j] = tlabels[k*elStride];
j++;
}
tlabels+=nextOffset;
}
}
return labels;
}
cudnnStatus_t cudnnCtcLoss(const LaunchContext &context, const NDArray &probs, const int32_t* targetLabelsPtr, const NDArray& probInputLengthes,
const NDArray &targetLabelLengths, NDArray &ctcLosses, NDArray &grads){
const int dims[] = {(int)probs.sizeAt(0), (int)probs.sizeAt(1), (int)probs.sizeAt(2)};
const int strides[] = {(int)probs.strideAt(0), (int)probs.strideAt(1), (int)probs.strideAt(2)};
auto handle = reinterpret_cast<cudnnHandle_t *>(context.getCuDnnHandle());
cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
callCudnnIfNoErr(err, cudnnSetStream, *handle, *context.getCudaStream());
cudnnCTCLossDescriptor_t ctcLossDesc;
cudnnTensorDescriptor_t probsDesc = nullptr;
cudnnTensorDescriptor_t gradsDesc = nullptr;
callCudnnIfNoErr(err, cudnnCreateCTCLossDescriptor, &ctcLossDesc);
callCudnnIfNoErr(err, cudnnSetCTCLossDescriptorEx, ctcLossDesc, CUDNN_DATA_FLOAT, CUDNN_LOSS_NORMALIZATION_SOFTMAX, CUDNN_PROPAGATE_NAN);
callCudnnIfNoErr(err, cudnnCreateTensorDescriptor, &probsDesc);
callCudnnIfNoErr(err, cudnnSetTensorNdDescriptor, probsDesc, cudnnDataType(probs.dataType()), probs.rankOf() , dims, strides);
if(!grads.isEmpty()){
const int gradStrides[] = {(int)grads.strideAt(0), (int)grads.strideAt(1), (int)grads.strideAt(2)};
callCudnnIfNoErr(err, cudnnCreateTensorDescriptor, &gradsDesc);
callCudnnIfNoErr(err, cudnnSetTensorNdDescriptor, gradsDesc, cudnnDataType(grads.dataType()), grads.rankOf() , dims, gradStrides);
}
size_t tempWorkSpaceSize=0;
callCudnnIfNoErr(err,cudnnGetCTCLossWorkspaceSize, *handle, probsDesc, gradsDesc,
targetLabelsPtr,
bufferInHost<int32_t>(targetLabelLengths),
bufferInHost<int32_t>(probInputLengthes),
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC,
ctcLossDesc, &tempWorkSpaceSize);
// Allocate temp tempWorkspace buffer
void *tempWorkSpace = nullptr;
cudaMalloc(&tempWorkSpace, tempWorkSpaceSize);
NDArray::prepareSpecialUse({&ctcLosses, &grads}, {&probs});
callCudnnIfNoErr(err, cudnnCTCLoss,*handle,
probsDesc,
probs.specialBuffer(),
targetLabelsPtr,
bufferInHost<int32_t>(targetLabelLengths),
bufferInHost<int32_t>(probInputLengthes),
ctcLosses.specialBuffer(),
gradsDesc,
grads.specialBuffer(),
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC,
ctcLossDesc,
tempWorkSpace,
tempWorkSpaceSize);
NDArray::registerSpecialUse({&ctcLosses, &grads}, {&probs});
cudaFree(tempWorkSpace);
callCudnnIfNoErr(err, cudnnDestroyTensorDescriptor,probsDesc);
if(gradsDesc) callCudnnIfNoErr(err, cudnnDestroyTensorDescriptor,gradsDesc);
callCudnnIfNoErr(err, cudnnDestroyCTCLossDescriptor,ctcLossDesc);
return err;
}
PLATFORM_IMPL(ctc_loss, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputLosses = OUTPUT_VARIABLE(0);
auto context = block.launchContext();
//in Cudnn Batch is in the middle dimension
logitInput->permutei({1,0,2});
//in Cudnn targets are concantenated instead of batched as matrix
auto labels = getConcatTargets(*targetLabels, *targetLabelLengths);
const int32_t *ldata= labels.data();
auto emptyGrads= NDArrayFactory::empty<float>();
auto err = cudnnCtcLoss(*context, *logitInput, ldata, *logitInputLengths, *targetLabelLengths, *outputLosses, emptyGrads);
if(err!=CUDNN_STATUS_SUCCESS) throw sd::cuda_exception::build("ctc_loss CUDNN call failure ", err);
return Status::OK();
}
template<typename T>
bool checkLabelLength(const NDArray &labelLengthArr){
//check label lengthes
auto lenBatch = labelLengthArr.lengthOf();
for(int i=0; i < lenBatch; i++){
// The labelLengths is greater than 256.
if(labelLengthArr.e<int32_t>(i)>256) return false;
}
return true;
}
PLATFORM_CHECK(ctc_loss, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputLosses = OUTPUT_VARIABLE(0);
int blankIndex = INT_ARG(0);
auto dTypeInput = logitInput->dataType();
auto intType = targetLabelLengths->dataType();
auto dTypeOutput = outputLosses->dataType();
bool is_supported = blankIndex==0 && intType == DataType::INT32 && dTypeInput == DataType::FLOAT32;
is_supported = is_supported && outputLosses->ews() && targetLabelLengths->ews() && targetLabels->ews() && logitInputLengths->ews();
is_supported = is_supported && checkLabelLength<int32_t>(*targetLabelLengths);
return is_supported;
}
PLATFORM_IMPL(ctc_loss_grad, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputGradients = OUTPUT_VARIABLE(0);
auto context = block.launchContext();
//in Cudnn Batch is in the middle dimension
logitInput->permutei({1,0,2});
outputGradients->permutei({1,0,2});
//in Cudnn targets are concantenated instead of batched as matrix
auto labels = getConcatTargets(*targetLabels, *targetLabelLengths);
const int32_t * ldata= labels.data();
auto tempLosses = NDArrayFactory::create<float>('c', {logitInputLengths->sizeAt(0)});
auto err = cudnnCtcLoss(*context, *logitInput, ldata, *logitInputLengths, *targetLabelLengths, tempLosses, *outputGradients);
if(err!=CUDNN_STATUS_SUCCESS) throw sd::cuda_exception::build("ctc_loss CUDNN call failure ", err);
//restore grads shape from {T, BATCH, C} -> {BATCHS, T, C}
outputGradients->permutei({1,0,2});
//tempLosses.printIndexedBuffer("tempLosses");
return Status::OK();
}
PLATFORM_CHECK(ctc_loss_grad, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputGrads = OUTPUT_VARIABLE(0);
int blankIndex = INT_ARG(0);
auto dTypeInput = logitInput->dataType();
auto intType = targetLabelLengths->dataType();
auto dTypeOutput = outputGrads->dataType();
bool is_supported = blankIndex==0 && intType == DataType::INT32 && dTypeInput == DataType::FLOAT32;
is_supported = is_supported && outputGrads->ews() && targetLabelLengths->ews() && targetLabels->ews() && logitInputLengths->ews();
is_supported = is_supported && checkLabelLength<int32_t>(*targetLabelLengths);
return is_supported;
}
}
}
}
} // namespace helpers
} // namespace ops
} // namespace sd

225
libnd4j/include/ops/declarable/platform/cudnn/ctcloss.cu Normal file
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@ -0,0 +1,225 @@
/*******************************************************************************
* Copyright (c) 2021 Deeplearning4j Contributors
*
* 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
*******************************************************************************/tt
//
// @author AbdelRauf
//
#include "cudnnUtils.h"
#include <array/NDArrayFactory.h>
#include <vector>
namespace sd {
namespace ops {
namespace platforms {
template<typename Op, typename ...Args>
void callCudnnIfNoErr(cudnnStatus_t &err, Op op, Args&&... args){
if(err==CUDNN_STATUS_SUCCESS){
err = op(std::forward<Args>(args)...);
if(err){
nd4j_printf("Cudnn error code %s\n",cudnnGetErrorString(err));
}
}
}
template <typename T>
const T* bufferInHost( const NDArray &array) {
array.syncToHost();
return reinterpret_cast<const T*>(array.buffer());
}
std::vector<int> getConcatTargets(const NDArray &targetLabels, const NDArray &targetLabelLengths){
//concatenate target labels
const int32_t *tlabels = bufferInHost<int32_t>(targetLabels);
const int32_t *tlens =bufferInHost<int32_t>(targetLabelLengths);
int32_t nextOffset = targetLabels.strideAt(0);
int32_t elStride = targetLabels.strideAt(1);
int32_t batchCount = targetLabelLengths.lengthOf();
std::vector<int> labels;
labels.resize(targetLabels.lengthOf());
int j=0;
if(targetLabels.ews()){
for(int i=0; i<batchCount;i++){
int count = tlens[i];
for( int k=0;k<count;k++){
labels[j] = tlabels[k];
j++;
}
tlabels+=nextOffset;
}
}else{
for(int i=0; i<batchCount;i++){
int count = tlens[i];
for( int k=0;k<count;k++){
labels[j] = tlabels[k*elStride];
j++;
}
tlabels+=nextOffset;
}
}
return labels;
}
cudnnStatus_t cudnnCtcLoss(const LaunchContext &context, const NDArray &probs, const int32_t* targetLabelsPtr, const NDArray& probInputLengthes,
const NDArray &targetLabelLengths, NDArray &ctcLosses, NDArray &grads){
const int dims[] = {(int)probs.sizeAt(0), (int)probs.sizeAt(1), (int)probs.sizeAt(2)};
const int strides[] = {(int)probs.strideAt(0), (int)probs.strideAt(1), (int)probs.strideAt(2)};
auto handle = reinterpret_cast<cudnnHandle_t *>(context.getCuDnnHandle());
cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
callCudnnIfNoErr(err, cudnnSetStream, *handle, *context.getCudaStream());
cudnnCTCLossDescriptor_t ctcLossDesc;
cudnnTensorDescriptor_t probsDesc = nullptr;
cudnnTensorDescriptor_t gradsDesc = nullptr;
callCudnnIfNoErr(err, cudnnCreateCTCLossDescriptor, &ctcLossDesc);
callCudnnIfNoErr(err, cudnnSetCTCLossDescriptorEx, ctcLossDesc, CUDNN_DATA_FLOAT, CUDNN_LOSS_NORMALIZATION_SOFTMAX, CUDNN_PROPAGATE_NAN);
callCudnnIfNoErr(err, cudnnCreateTensorDescriptor, &probsDesc);
callCudnnIfNoErr(err, cudnnSetTensorNdDescriptor, probsDesc, cudnnDataType(probs.dataType()), probs.rankOf() , dims, strides);
if(!grads.isEmpty()){
const int gradStrides[] = {(int)grads.strideAt(0), (int)grads.strideAt(1), (int)grads.strideAt(2)};
callCudnnIfNoErr(err, cudnnCreateTensorDescriptor, &gradsDesc);
callCudnnIfNoErr(err, cudnnSetTensorNdDescriptor, gradsDesc, cudnnDataType(grads.dataType()), grads.rankOf() , dims, gradStrides);
}
size_t tempWorkSpaceSize=0;
callCudnnIfNoErr(err,cudnnGetCTCLossWorkspaceSize, *handle, probsDesc, gradsDesc,
targetLabelsPtr,
bufferInHost<int32_t>(targetLabelLengths),
bufferInHost<int32_t>(probInputLengthes),
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC,
ctcLossDesc, &tempWorkSpaceSize);
// Allocate temp tempWorkspace buffer
void *tempWorkSpace = nullptr;
cudaMalloc(&tempWorkSpace, tempWorkSpaceSize);
NDArray::prepareSpecialUse({&ctcLosses, &grads}, {&probs});
callCudnnIfNoErr(err, cudnnCTCLoss,*handle,
probsDesc,
probs.specialBuffer(),
targetLabelsPtr,
bufferInHost<int32_t>(targetLabelLengths),
bufferInHost<int32_t>(probInputLengthes),
ctcLosses.specialBuffer(),
gradsDesc,
grads.specialBuffer(),
CUDNN_CTC_LOSS_ALGO_DETERMINISTIC,
ctcLossDesc,
tempWorkSpace,
tempWorkSpaceSize);
NDArray::registerSpecialUse({&ctcLosses, &grads}, {&probs});
cudaFree(tempWorkSpace);
callCudnnIfNoErr(err, cudnnDestroyTensorDescriptor,probsDesc);
if(gradsDesc) callCudnnIfNoErr(err, cudnnDestroyTensorDescriptor,gradsDesc);
callCudnnIfNoErr(err, cudnnDestroyCTCLossDescriptor,ctcLossDesc);
return err;
}
PLATFORM_IMPL(ctc_loss, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputLosses = OUTPUT_VARIABLE(0);
auto context = block.launchContext();
//in Cudnn Batch is in the middle dimension
logitInput->permutei({1,0,2});
//in Cudnn targets are concantenated instead of batched as matrix
auto labels = getConcatTargets(*targetLabels, *targetLabelLengths);
const int32_t *ldata= labels.data();
auto emptyGrads= NDArrayFactory::empty<float>();
auto err = cudnnCtcLoss(*context, *logitInput, ldata, *logitInputLengths, *targetLabelLengths, *outputLosses, emptyGrads);
if(err!=CUDNN_STATUS_SUCCESS) throw sd::cuda_exception::build("ctc_loss CUDNN call failure ", err);
return Status::OK();
}
template<typename T>
bool checkLabelLength(const NDArray &labelLengthArr){
//check label lengthes
auto lenBatch = labelLengthArr.lengthOf();
for(int i=0; i < lenBatch; i++){
// The labelLengths is greater than 256.
if(labelLengthArr.e<int32_t>(i)>256) return false;
}
return true;
}
PLATFORM_CHECK(ctc_loss, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputLosses = OUTPUT_VARIABLE(0);
int blankIndex = INT_ARG(0);
auto dTypeInput = logitInput->dataType();
auto intType = targetLabelLengths->dataType();
auto dTypeOutput = outputLosses->dataType();
bool is_supported = blankIndex==0 && intType == DataType::INT32 && dTypeInput == DataType::FLOAT32;
is_supported = is_supported && outputLosses->ews() && targetLabelLengths->ews() && targetLabels->ews() && logitInputLengths->ews();
is_supported = is_supported && checkLabelLength<int32_t>(*targetLabelLengths);
return is_supported;
}
PLATFORM_IMPL(ctc_loss_grad, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputGradients = OUTPUT_VARIABLE(0);
auto context = block.launchContext();
//in Cudnn Batch is in the middle dimension
logitInput->permutei({1,0,2});
outputGradients->permutei({1,0,2});
//in Cudnn targets are concantenated instead of batched as matrix
auto labels = getConcatTargets(*targetLabels, *targetLabelLengths);
const int32_t * ldata= labels.data();
auto tempLosses = NDArrayFactory::create<float>('c', {logitInputLengths->sizeAt(0)});
auto err = cudnnCtcLoss(*context, *logitInput, ldata, *logitInputLengths, *targetLabelLengths, tempLosses, *outputGradients);
if(err!=CUDNN_STATUS_SUCCESS) throw sd::cuda_exception::build("ctc_loss CUDNN call failure ", err);
//restore grads shape from {T, BATCH, C} -> {BATCHS, T, C}
outputGradients->permutei({1,0,2});
//tempLosses.printIndexedBuffer("tempLosses");
return Status::OK();
}
PLATFORM_CHECK(ctc_loss_grad, ENGINE_CUDA) {
auto targetLabels = INPUT_VARIABLE(0);
auto logitInput = INPUT_VARIABLE(1);
auto targetLabelLengths = INPUT_VARIABLE(2);
auto logitInputLengths = INPUT_VARIABLE(3);
auto outputGrads = OUTPUT_VARIABLE(0);
int blankIndex = INT_ARG(0);
auto dTypeInput = logitInput->dataType();
auto intType = targetLabelLengths->dataType();
auto dTypeOutput = outputGrads->dataType();
bool is_supported = blankIndex==0 && intType == DataType::INT32 && dTypeInput == DataType::FLOAT32;
is_supported = is_supported && outputGrads->ews() && targetLabelLengths->ews() && targetLabels->ews() && logitInputLengths->ews();
is_supported = is_supported && checkLabelLength<int32_t>(*targetLabelLengths);
return is_supported;
}
}
}
}