online-lstm-transducer-model.cc
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// sherpa-onnx/csrc/online-lstm-transducer-model.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/online-lstm-transducer-model.h"
#include <algorithm>
#include <memory>
#include <sstream>
#include <string>
#include <utility>
#include <vector>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/online-transducer-decoder.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#define SHERPA_ONNX_READ_META_DATA(dst, src_key) \
do { \
auto value = \
meta_data.LookupCustomMetadataMapAllocated(src_key, allocator); \
if (!value) { \
fprintf(stderr, "%s does not exist in the metadata\n", src_key); \
exit(-1); \
} \
dst = atoi(value.get()); \
if (dst <= 0) { \
fprintf(stderr, "Invalud value %d for %s\n", dst, src_key); \
exit(-1); \
} \
} while (0)
namespace sherpa_onnx {
OnlineLstmTransducerModel::OnlineLstmTransducerModel(
const OnlineTransducerModelConfig &config)
: env_(ORT_LOGGING_LEVEL_WARNING),
config_(config),
sess_opts_{},
allocator_{} {
sess_opts_.SetIntraOpNumThreads(config.num_threads);
sess_opts_.SetInterOpNumThreads(config.num_threads);
InitEncoder(config.encoder_filename);
InitDecoder(config.decoder_filename);
InitJoiner(config.joiner_filename);
}
void OnlineLstmTransducerModel::InitEncoder(const std::string &filename) {
encoder_sess_ = std::make_unique<Ort::Session>(
env_, SHERPA_MAYBE_WIDE(filename).c_str(), sess_opts_);
GetInputNames(encoder_sess_.get(), &encoder_input_names_,
&encoder_input_names_ptr_);
GetOutputNames(encoder_sess_.get(), &encoder_output_names_,
&encoder_output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = encoder_sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
os << "---encoder---\n";
PrintModelMetadata(os, meta_data);
fprintf(stderr, "%s\n", os.str().c_str());
}
Ort::AllocatorWithDefaultOptions allocator;
SHERPA_ONNX_READ_META_DATA(num_encoder_layers_, "num_encoder_layers");
SHERPA_ONNX_READ_META_DATA(T_, "T");
SHERPA_ONNX_READ_META_DATA(decode_chunk_len_, "decode_chunk_len");
SHERPA_ONNX_READ_META_DATA(rnn_hidden_size_, "rnn_hidden_size");
SHERPA_ONNX_READ_META_DATA(d_model_, "d_model");
}
void OnlineLstmTransducerModel::InitDecoder(const std::string &filename) {
decoder_sess_ = std::make_unique<Ort::Session>(
env_, SHERPA_MAYBE_WIDE(filename).c_str(), sess_opts_);
GetInputNames(decoder_sess_.get(), &decoder_input_names_,
&decoder_input_names_ptr_);
GetOutputNames(decoder_sess_.get(), &decoder_output_names_,
&decoder_output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = decoder_sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
os << "---decoder---\n";
PrintModelMetadata(os, meta_data);
fprintf(stderr, "%s\n", os.str().c_str());
}
Ort::AllocatorWithDefaultOptions allocator;
SHERPA_ONNX_READ_META_DATA(vocab_size_, "vocab_size");
SHERPA_ONNX_READ_META_DATA(context_size_, "context_size");
}
void OnlineLstmTransducerModel::InitJoiner(const std::string &filename) {
joiner_sess_ = std::make_unique<Ort::Session>(
env_, SHERPA_MAYBE_WIDE(filename).c_str(), sess_opts_);
GetInputNames(joiner_sess_.get(), &joiner_input_names_,
&joiner_input_names_ptr_);
GetOutputNames(joiner_sess_.get(), &joiner_output_names_,
&joiner_output_names_ptr_);
// get meta data
Ort::ModelMetadata meta_data = joiner_sess_->GetModelMetadata();
if (config_.debug) {
std::ostringstream os;
os << "---joiner---\n";
PrintModelMetadata(os, meta_data);
fprintf(stderr, "%s\n", os.str().c_str());
}
}
std::vector<Ort::Value> OnlineLstmTransducerModel::StackStates(
const std::vector<std::vector<Ort::Value>> &states) const {
int32_t batch_size = static_cast<int32_t>(states.size());
std::array<int64_t, 3> h_shape{num_encoder_layers_, batch_size, d_model_};
Ort::Value h = Ort::Value::CreateTensor<float>(allocator_, h_shape.data(),
h_shape.size());
std::array<int64_t, 3> c_shape{num_encoder_layers_, batch_size,
rnn_hidden_size_};
Ort::Value c = Ort::Value::CreateTensor<float>(allocator_, c_shape.data(),
c_shape.size());
float *dst_h = h.GetTensorMutableData<float>();
float *dst_c = c.GetTensorMutableData<float>();
for (int32_t layer = 0; layer != num_encoder_layers_; ++layer) {
for (int32_t i = 0; i != batch_size; ++i) {
const float *src_h =
states[i][0].GetTensorData<float>() + layer * d_model_;
const float *src_c =
states[i][1].GetTensorData<float>() + layer * rnn_hidden_size_;
std::copy(src_h, src_h + d_model_, dst_h);
std::copy(src_c, src_c + rnn_hidden_size_, dst_c);
dst_h += d_model_;
dst_c += rnn_hidden_size_;
}
}
std::vector<Ort::Value> ans;
ans.reserve(2);
ans.push_back(std::move(h));
ans.push_back(std::move(c));
return ans;
}
std::vector<std::vector<Ort::Value>> OnlineLstmTransducerModel::UnStackStates(
const std::vector<Ort::Value> &states) const {
int32_t batch_size = states[0].GetTensorTypeAndShapeInfo().GetShape()[1];
std::vector<std::vector<Ort::Value>> ans(batch_size);
// allocate space
std::array<int64_t, 3> h_shape{num_encoder_layers_, 1, d_model_};
std::array<int64_t, 3> c_shape{num_encoder_layers_, 1, rnn_hidden_size_};
for (int32_t i = 0; i != batch_size; ++i) {
Ort::Value h = Ort::Value::CreateTensor<float>(allocator_, h_shape.data(),
h_shape.size());
Ort::Value c = Ort::Value::CreateTensor<float>(allocator_, c_shape.data(),
c_shape.size());
ans[i].push_back(std::move(h));
ans[i].push_back(std::move(c));
}
for (int32_t layer = 0; layer != num_encoder_layers_; ++layer) {
for (int32_t i = 0; i != batch_size; ++i) {
const float *src_h = states[0].GetTensorData<float>() +
layer * batch_size * d_model_ + i * d_model_;
const float *src_c = states[1].GetTensorData<float>() +
layer * batch_size * rnn_hidden_size_ +
i * rnn_hidden_size_;
float *dst_h = ans[i][0].GetTensorMutableData<float>() + layer * d_model_;
float *dst_c =
ans[i][1].GetTensorMutableData<float>() + layer * rnn_hidden_size_;
std::copy(src_h, src_h + d_model_, dst_h);
std::copy(src_c, src_c + rnn_hidden_size_, dst_c);
}
}
return ans;
}
std::vector<Ort::Value> OnlineLstmTransducerModel::GetEncoderInitStates() {
// Please see
// https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx.py#L185
// for details
constexpr int32_t kBatchSize = 1;
std::array<int64_t, 3> h_shape{num_encoder_layers_, kBatchSize, d_model_};
Ort::Value h = Ort::Value::CreateTensor<float>(allocator_, h_shape.data(),
h_shape.size());
std::fill(h.GetTensorMutableData<float>(),
h.GetTensorMutableData<float>() +
num_encoder_layers_ * kBatchSize * d_model_,
0);
std::array<int64_t, 3> c_shape{num_encoder_layers_, kBatchSize,
rnn_hidden_size_};
Ort::Value c = Ort::Value::CreateTensor<float>(allocator_, c_shape.data(),
c_shape.size());
std::fill(c.GetTensorMutableData<float>(),
c.GetTensorMutableData<float>() +
num_encoder_layers_ * kBatchSize * rnn_hidden_size_,
0);
std::vector<Ort::Value> states;
states.reserve(2);
states.push_back(std::move(h));
states.push_back(std::move(c));
return states;
}
std::pair<Ort::Value, std::vector<Ort::Value>>
OnlineLstmTransducerModel::RunEncoder(Ort::Value features,
std::vector<Ort::Value> states) {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::array<Ort::Value, 3> encoder_inputs = {
std::move(features), std::move(states[0]), std::move(states[1])};
auto encoder_out = encoder_sess_->Run(
{}, encoder_input_names_ptr_.data(), encoder_inputs.data(),
encoder_inputs.size(), encoder_output_names_ptr_.data(),
encoder_output_names_ptr_.size());
std::vector<Ort::Value> next_states;
next_states.reserve(2);
next_states.push_back(std::move(encoder_out[1]));
next_states.push_back(std::move(encoder_out[2]));
return {std::move(encoder_out[0]), std::move(next_states)};
}
Ort::Value OnlineLstmTransducerModel::BuildDecoderInput(
const std::vector<OnlineTransducerDecoderResult> &results) {
int32_t batch_size = static_cast<int32_t>(results.size());
std::array<int64_t, 2> shape{batch_size, context_size_};
Ort::Value decoder_input =
Ort::Value::CreateTensor<int64_t>(allocator_, shape.data(), shape.size());
int64_t *p = decoder_input.GetTensorMutableData<int64_t>();
for (const auto &r : results) {
const int64_t *begin = r.tokens.data() + r.tokens.size() - context_size_;
const int64_t *end = r.tokens.data() + r.tokens.size();
std::copy(begin, end, p);
p += context_size_;
}
return decoder_input;
}
Ort::Value OnlineLstmTransducerModel::RunDecoder(Ort::Value decoder_input) {
auto decoder_out = decoder_sess_->Run(
{}, decoder_input_names_ptr_.data(), &decoder_input, 1,
decoder_output_names_ptr_.data(), decoder_output_names_ptr_.size());
return std::move(decoder_out[0]);
}
Ort::Value OnlineLstmTransducerModel::RunJoiner(Ort::Value encoder_out,
Ort::Value decoder_out) {
std::array<Ort::Value, 2> joiner_input = {std::move(encoder_out),
std::move(decoder_out)};
auto logit =
joiner_sess_->Run({}, joiner_input_names_ptr_.data(), joiner_input.data(),
joiner_input.size(), joiner_output_names_ptr_.data(),
joiner_output_names_ptr_.size());
return std::move(logit[0]);
}
} // namespace sherpa_onnx