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Merged
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Jun 26, 2019
Merged

Enable an option to use the GPU for feature extraction #3420

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3 changes: 2 additions & 1 deletion src/cudadecoder/Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,8 @@ LIBNAME = kaldi-cudadecoder
ADDLIBS = ../cudamatrix/kaldi-cudamatrix.a ../base/kaldi-base.a ../matrix/kaldi-matrix.a \
../lat/kaldi-lat.a ../util/kaldi-util.a ../matrix/kaldi-matrix.a ../gmm/kaldi-gmm.a \
../fstext/kaldi-fstext.a ../hmm/kaldi-hmm.a ../gmm/kaldi-gmm.a ../transform/kaldi-transform.a \
../tree/kaldi-tree.a ../online2/kaldi-online2.a ../nnet3/kaldi-nnet3.a
../tree/kaldi-tree.a ../online2/kaldi-online2.a ../nnet3/kaldi-nnet3.a \
../cudafeat/kaldi-cudafeat.a

# Implicit rule for kernel compilation
%.o : %.cu
Expand Down
182 changes: 154 additions & 28 deletions src/cudadecoder/batched-threaded-nnet3-cuda-pipeline.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -248,9 +248,15 @@ void BatchedThreadedNnet3CudaPipeline::OpenDecodeHandle(
task->callback = std::move(callback);
task->Init(key, wave_data);

work_pool_->enqueue(THREAD_POOL_LOW_PRIORITY,
&BatchedThreadedNnet3CudaPipeline::ComputeOneFeature,
this, task);
if (config_.gpu_feature_extract) {
//Feature extraction done on device
AddTaskToPendingTaskQueue(task);
} else {
//Feature extraction done on host thread
work_pool_->enqueue(THREAD_POOL_LOW_PRIORITY,
&BatchedThreadedNnet3CudaPipeline::ComputeOneFeatureCPU,
this, task);
}
}

void BatchedThreadedNnet3CudaPipeline::OpenDecodeHandle(
Expand All @@ -262,9 +268,15 @@ void BatchedThreadedNnet3CudaPipeline::OpenDecodeHandle(
task->callback = std::move(callback);
task->Init(key, wave_data, sample_rate);

work_pool_->enqueue(THREAD_POOL_LOW_PRIORITY,
&BatchedThreadedNnet3CudaPipeline::ComputeOneFeature,
this, task);
if (config_.gpu_feature_extract) {
//Feature extraction done on device
AddTaskToPendingTaskQueue(task);
} else {
//Feature extraction done on host thread
work_pool_->enqueue(THREAD_POOL_LOW_PRIORITY,
&BatchedThreadedNnet3CudaPipeline::ComputeOneFeatureCPU,
this, task);
}
}

bool BatchedThreadedNnet3CudaPipeline::GetRawLattice(const std::string &key,
Expand Down Expand Up @@ -410,6 +422,8 @@ void BatchedThreadedNnet3CudaPipeline::AquireAdditionalTasks(
void BatchedThreadedNnet3CudaPipeline::ComputeBatchNnet(
nnet3::NnetBatchComputer &computer, int32 first,
std::vector<TaskState *> &tasks) {
nvtxRangePushA("ComputeBatchNnet");

bool output_to_cpu = false;
int32 online_ivector_period = 0;
int max_pending_minibatches =
Expand All @@ -421,17 +435,32 @@ void BatchedThreadedNnet3CudaPipeline::ComputeBatchNnet(
// for all new batches enqueue up nnet work.
for (int i = first; i < tasks.size(); i++) {
TaskState &task = *tasks[i];
Vector<BaseFloat> &ivector_features = task.task_data->ivector_features;
Matrix<BaseFloat> &input_features = task.task_data->input_features;
std::shared_ptr<TaskData> &task_data = task.task_data;
std::vector<nnet3::NnetInferenceTask> &ntasks = nnet_tasks[i];

if (config_.gpu_feature_extract) {
CuVector<BaseFloat> &ivector_features = task_data->ivector_features;
CuMatrix<BaseFloat> &input_features = task_data->input_features;

CuVector<BaseFloat> *ifeat = NULL;
if (ivector_features.Dim() > 0) {
ifeat = &ivector_features;
}
// create task list
computer.SplitUtteranceIntoTasks(output_to_cpu, input_features, ifeat,
NULL, online_ivector_period, &ntasks);
} else {
Vector<BaseFloat> &ivector_features = task_data->ivector_features_cpu;
Matrix<BaseFloat> &input_features = task_data->input_features_cpu;

Vector<BaseFloat> *ifeat = NULL;
if (ivector_features.Dim() > 0) {
ifeat = &ivector_features;
Vector<BaseFloat> *ifeat = NULL;
if (ivector_features.Dim() > 0) {
ifeat = &ivector_features;
}
// create task list
computer.SplitUtteranceIntoTasks(output_to_cpu, input_features, ifeat,
NULL, online_ivector_period, &ntasks);
}
// create task list
computer.SplitUtteranceIntoTasks(output_to_cpu, input_features, ifeat, NULL,
online_ivector_period, &ntasks);

// Add tasks to computer
for (size_t j = 0; j < ntasks.size(); j++) {
Expand All @@ -448,33 +477,37 @@ void BatchedThreadedNnet3CudaPipeline::ComputeBatchNnet(
// Extract Posteriors
for (int i = first; i < tasks.size(); i++) {
TaskState &task = *tasks[i];
CuMatrix<BaseFloat> &posteriors = task.task_data->posteriors;
std::shared_ptr<TaskData> &task_data = task.task_data;
CuMatrix<BaseFloat> &posteriors = task_data->posteriors;
MergeTaskOutput(nnet_tasks[i], &posteriors);

// nnet output is no longer necessary as we have copied the output out
nnet_tasks[i].resize(0);

// featurs are no longer needed so free memory
task.task_data->ivector_features.Resize(0);
task.task_data->input_features.Resize(0, 0);
task_data->ivector_features.Resize(0);
task_data->input_features.Resize(0, 0);
}

nvtxRangePop();
}

// Computes Features for a single decode instance.
void BatchedThreadedNnet3CudaPipeline::ComputeOneFeature(TaskState *task_) {
nvtxRangePushA("ComputeOneFeature");
void BatchedThreadedNnet3CudaPipeline::ComputeOneFeatureCPU(TaskState *task_) {
nvtxRangePushA("ComputeOneFeatureCPU");
TaskState &task = *task_;
Vector<BaseFloat> &ivector_features = task.task_data->ivector_features;
Matrix<BaseFloat> &input_features = task.task_data->input_features;
std::shared_ptr<TaskData> &task_data = task.task_data;
Vector<BaseFloat> &ivector_features = task_data->ivector_features_cpu;
Matrix<BaseFloat> &input_features = task_data->input_features_cpu;

// create decoding state
OnlineNnet2FeaturePipeline feature(*feature_info_);

// Accept waveforms
feature.AcceptWaveform(
task.task_data->sample_frequency,
SubVector<BaseFloat>(*task.task_data->wave_samples, 0,
task.task_data->wave_samples->Dim()));
task_data->sample_frequency,
SubVector<BaseFloat>(*task_data->wave_samples, 0,
task_data->wave_samples->Dim()));
feature.InputFinished();
// All frames should be ready here
int32 numFrames = feature.NumFramesReady();
Expand All @@ -487,7 +520,8 @@ void BatchedThreadedNnet3CudaPipeline::ComputeOneFeature(TaskState *task_) {

std::vector<int> frames(numFrames);
// create list of frames
for (int j = 0; j < numFrames; j++) frames[j] = j;
for (int j = 0; j < numFrames; j++)
frames[j] = j;

// Copy Features
input_features.Resize(numFrames, input_dim);
Expand All @@ -501,18 +535,106 @@ void BatchedThreadedNnet3CudaPipeline::ComputeOneFeature(TaskState *task_) {
// Copy Features
feature.IvectorFeature()->GetFrame(numFrames - 1, &ivector_features);
}
nvtxRangePop();

AddTaskToPendingTaskQueue(task_);

nvtxRangePop();
}

// Computes features across the tasks[first,tasks.size()
void BatchedThreadedNnet3CudaPipeline::ComputeBatchFeatures(
int32 first, std::vector<TaskState *> &tasks,
OnlineCudaFeaturePipeline &feature_pipeline) {
KALDI_ASSERT(config_.gpu_feature_extract==true);
nvtxRangePushA("CopyBatchWaves");
// below we will pack waves into a single buffer for efficient transfer across device

// first count the total number of elements and create a single large vector
int count=0;
for (int i = first; i < tasks.size(); i++) {
count+=tasks[i]->task_data->wave_samples->Dim();
}

// creating a thread local vector of pinned memory.
// wave data will be stagged through this memory to get
// more efficient non-blocking transfers to the device.
thread_local Vector<BaseFloat> pinned_vector;

if (pinned_vector.Dim() < count ) {
if ( pinned_vector.Dim()!=0) {
cudaHostUnregister(pinned_vector.Data());
}
// allocated array 2x size
pinned_vector.Resize(count*2,kUndefined);
cudaHostRegister(pinned_vector.Data(), pinned_vector.Dim()*sizeof(BaseFloat),0);
}

// We will launch a thread for each task in order to get better host memory bandwidth
std::vector<std::future<void> > futures; //for syncing

//vector copy function for threading below.
auto copy_vec = [](SubVector<BaseFloat> &dst, const SubVector<BaseFloat> &src) {
nvtxRangePushA("CopyVec");
dst.CopyFromVec(src);
nvtxRangePop();
};

// next launch threads to copy all waves for each task in parallel
count=0;
for (int i = first; i < tasks.size(); i++) {
std::shared_ptr<TaskData> &task_data = tasks[i]->task_data;
SubVector<BaseFloat> wave(pinned_vector,count,task_data->wave_samples->Dim());
count+=task_data->wave_samples->Dim();
futures.push_back(
work_pool_->enqueue(copy_vec, wave, *(task_data->wave_samples))
);
}

// wait for waves to be copied into place
for (int i = 0; i < futures.size(); i++) {
futures[i].get();
}

CuVector<BaseFloat> cu_waves(count, kUndefined);
// copy memory down asynchronously. Vector copy functions are synchronous so we do it manually.
// It is important for this to happen asynchrously to help hide launch latency of smaller kernels
// that come in the future.
cudaMemcpyAsync(cu_waves.Data(), pinned_vector.Data(), cu_waves.Dim()*sizeof(BaseFloat),
cudaMemcpyHostToDevice, cudaStreamPerThread);
nvtxRangePop();

nvtxRangePushA("ComputeBatchFeatures");
// extract features for each wave
count=0;
for (int i = first; i < tasks.size(); i++) {
TaskState &task = *tasks[i];
std::shared_ptr<TaskData> &task_data = task.task_data;

CuSubVector<BaseFloat> cu_wave(cu_waves,count,task_data->wave_samples->Dim());
count+=task_data->wave_samples->Dim();
feature_pipeline.ComputeFeatures(cu_wave, task_data->sample_frequency,
&task_data->input_features, &task_data->ivector_features);

int32 numFrames = task_data->input_features.NumRows();

if (numFrames == 0) {
//Make this a warning for now. Need to check how this is handled
KALDI_WARN << "Warning empty audio file";
}
}
nvtxRangePop();
}



// Allocates decodables for tasks in the range of tasks[first,tasks.size())
void BatchedThreadedNnet3CudaPipeline::AllocateDecodables(
int32 first, std::vector<TaskState *> &tasks,
std::vector<CudaDecodableInterface *> &decodables) {
// Create mapped decodable here
for (int i = first; i < tasks.size(); i++) {
CuMatrix<BaseFloat> &posteriors = tasks[i]->task_data->posteriors;
std::shared_ptr<TaskData> &task_data = tasks[i]->task_data;
CuMatrix<BaseFloat> &posteriors = task_data->posteriors;
decodables.push_back(
new DecodableCuMatrixMapped(*trans_model_, posteriors, 0));
}
Expand Down Expand Up @@ -666,6 +788,8 @@ void BatchedThreadedNnet3CudaPipeline::ExecuteWorker(int threadId) {
nnet3::NnetBatchComputer computer(config_.compute_opts, am_nnet_->GetNnet(),
am_nnet_->Priors());

OnlineCudaFeaturePipeline feature_pipeline(config_.feature_opts);

ChannelState channel_state;

std::vector<TaskState *> tasks; // The state for each decode
Expand Down Expand Up @@ -713,10 +837,12 @@ void BatchedThreadedNnet3CudaPipeline::ExecuteWorker(int threadId) {

// New tasks are now in the in tasks[start,tasks.size())
if (start != tasks.size()) { // if there are new tasks
if (config_.gpu_feature_extract)
ComputeBatchFeatures(start, tasks, feature_pipeline);
ComputeBatchNnet(computer, start, tasks);
AllocateDecodables(start, tasks, decodables);
}
} // end if(tasks_front_!=tasks_back_)
} // end if (tasks_front_!=tasks_back_)

// check if there is no active work on this thread.
// This can happen if another thread was assigned the work.
Expand Down
23 changes: 19 additions & 4 deletions src/cudadecoder/batched-threaded-nnet3-cuda-pipeline.h
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@
#include "lat/determinize-lattice-pruned.h"
#include "nnet3/nnet-batch-compute.h"
#include "online2/online-nnet2-feature-pipeline.h"
#include "cudafeat/online-cuda-feature-pipeline.h"
#include "thread-pool.h"

// If num_channels sets to automatic,
Expand All @@ -53,7 +54,8 @@ struct BatchedThreadedNnet3CudaPipelineConfig {
num_worker_threads(20),
determinize_lattice(true),
max_pending_tasks(4000),
num_decoder_copy_threads(2){};
num_decoder_copy_threads(2),
gpu_feature_extract(true) {};
void Register(OptionsItf *po) {
po->Register("max-batch-size", &max_batch_size,
"The maximum batch size to be used by the decoder. "
Expand Down Expand Up @@ -88,6 +90,11 @@ struct BatchedThreadedNnet3CudaPipelineConfig {
po->Register("cuda-decoder-copy-threads", &num_decoder_copy_threads,
"Advanced - Number of worker threads used in the decoder for "
"the host to host copies.");
po->Register("gpu-feature-extract", &gpu_feature_extract,
"Extract features on the GPU. This reduces CPU overhead "
"leading to better scalability but may reduce overall "
"performance for a single GPU.");

feature_opts.Register(po);
decoder_opts.Register(po);
det_opts.Register(po);
Expand All @@ -101,6 +108,7 @@ struct BatchedThreadedNnet3CudaPipelineConfig {
bool determinize_lattice;
int max_pending_tasks;
int num_decoder_copy_threads;
bool gpu_feature_extract;

void ComputeConfig() {
if (num_channels == -1)
Expand Down Expand Up @@ -203,8 +211,10 @@ class BatchedThreadedNnet3CudaPipeline {
wave_samples; // Used as a pointer to either the raw
// data or the samples passed
float sample_frequency;
Vector<BaseFloat> ivector_features;
Matrix<BaseFloat> input_features;
Vector<BaseFloat> ivector_features_cpu;
Matrix<BaseFloat> input_features_cpu;
CuVector<BaseFloat> ivector_features;
CuMatrix<BaseFloat> input_features;
CuMatrix<BaseFloat> posteriors;

TaskData(const WaveData &wave_data_in)
Expand Down Expand Up @@ -289,7 +299,12 @@ class BatchedThreadedNnet3CudaPipeline {
std::vector<TaskState *> &tasks);

// Computes Features for a single decode instance.
void ComputeOneFeature(TaskState *task);
void ComputeOneFeatureCPU(TaskState *task);

// Computes features across the tasks[first,tasks.size()
void ComputeBatchFeatures(int32 first,
std::vector<TaskState *> &tasks,
OnlineCudaFeaturePipeline &feature_pipeline);

// Computes Nnet across the current decode batch
void ComputeBatchNnet(nnet3::NnetBatchComputer &computer, int32 first,
Expand Down