online-rnn-lm.cc
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// sherpa-onnx/csrc/on-rnn-lm.cc
//
// Copyright (c) 2023 Pingfeng Luo
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/online-rnn-lm.h"
#include <string>
#include <utility>
#include <vector>
#include "onnxruntime_cxx_api.h" // NOLINT
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
#include "sherpa-onnx/csrc/text-utils.h"
#include "sherpa-onnx/csrc/session.h"
namespace sherpa_onnx {
class OnlineRnnLM::Impl {
public:
explicit Impl(const OnlineLMConfig &config)
: config_(config),
env_(ORT_LOGGING_LEVEL_ERROR),
sess_opts_{GetSessionOptions(config)},
allocator_{} {
Init(config);
}
void ComputeLMScore(float scale, Hypothesis *hyp) {
if (hyp->nn_lm_states.empty()) {
auto init_states = GetInitStates();
hyp->nn_lm_scores.value = std::move(init_states.first);
hyp->nn_lm_states = Convert(std::move(init_states.second));
}
// get lm score for cur token given the hyp->ys[:-1] and save to lm_log_prob
const float *nn_lm_scores = hyp->nn_lm_scores.value.GetTensorData<float>();
hyp->lm_log_prob += nn_lm_scores[hyp->ys.back()] * scale;
// get lm scores for next tokens given the hyp->ys[:] and save to
// nn_lm_scores
std::array<int64_t, 2> x_shape{1, 1};
Ort::Value x = Ort::Value::CreateTensor<int64_t>(allocator_, x_shape.data(),
x_shape.size());
*x.GetTensorMutableData<int64_t>() = hyp->ys.back();
auto lm_out =
ScoreToken(std::move(x), Convert(hyp->nn_lm_states));
hyp->nn_lm_scores.value = std::move(lm_out.first);
hyp->nn_lm_states = Convert(std::move(lm_out.second));
}
std::pair<Ort::Value, std::vector<Ort::Value>> ScoreToken(
Ort::Value x, std::vector<Ort::Value> states) {
std::array<Ort::Value, 3> inputs = {std::move(x), std::move(states[0]),
std::move(states[1])};
auto out =
sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
output_names_ptr_.data(), output_names_ptr_.size());
std::vector<Ort::Value> next_states;
next_states.reserve(2);
next_states.push_back(std::move(out[1]));
next_states.push_back(std::move(out[2]));
return {std::move(out[0]), std::move(next_states)};
}
std::pair<Ort::Value, std::vector<Ort::Value>> GetInitStates() const {
std::vector<Ort::Value> ans;
ans.reserve(init_states_.size());
for (const auto &s : init_states_) {
ans.emplace_back(Clone(allocator_, &s));
}
return {std::move(Clone(allocator_, &init_scores_.value)), std::move(ans)};
}
private:
void Init(const OnlineLMConfig &config) {
auto buf = ReadFile(config_.model);
sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
sess_opts_);
GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
Ort::ModelMetadata meta_data = sess_->GetModelMetadata();
Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
SHERPA_ONNX_READ_META_DATA(rnn_num_layers_, "num_layers");
SHERPA_ONNX_READ_META_DATA(rnn_hidden_size_, "hidden_size");
SHERPA_ONNX_READ_META_DATA(sos_id_, "sos_id");
ComputeInitStates();
}
void ComputeInitStates() {
constexpr int32_t kBatchSize = 1;
std::array<int64_t, 3> h_shape{rnn_num_layers_, kBatchSize,
rnn_hidden_size_};
std::array<int64_t, 3> c_shape{rnn_num_layers_, kBatchSize,
rnn_hidden_size_};
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());
Fill<float>(&h, 0);
Fill<float>(&c, 0);
std::array<int64_t, 2> x_shape{1, 1};
Ort::Value x = Ort::Value::CreateTensor<int64_t>(allocator_, x_shape.data(),
x_shape.size());
*x.GetTensorMutableData<int64_t>() = sos_id_;
std::vector<Ort::Value> states;
states.push_back(std::move(h));
states.push_back(std::move(c));
auto pair = ScoreToken(std::move(x), std::move(states));
init_scores_.value = std::move(pair.first);
init_states_ = std::move(pair.second);
}
private:
OnlineLMConfig config_;
Ort::Env env_;
Ort::SessionOptions sess_opts_;
Ort::AllocatorWithDefaultOptions allocator_;
std::unique_ptr<Ort::Session> sess_;
std::vector<std::string> input_names_;
std::vector<const char *> input_names_ptr_;
std::vector<std::string> output_names_;
std::vector<const char *> output_names_ptr_;
CopyableOrtValue init_scores_;
std::vector<Ort::Value> init_states_;
int32_t rnn_num_layers_ = 2;
int32_t rnn_hidden_size_ = 512;
int32_t sos_id_ = 1;
};
OnlineRnnLM::OnlineRnnLM(const OnlineLMConfig &config)
: impl_(std::make_unique<Impl>(config)) {}
OnlineRnnLM::~OnlineRnnLM() = default;
std::pair<Ort::Value, std::vector<Ort::Value>> OnlineRnnLM::GetInitStates() {
return impl_->GetInitStates();
}
std::pair<Ort::Value, std::vector<Ort::Value>> OnlineRnnLM::ScoreToken(
Ort::Value x, std::vector<Ort::Value> states) {
return impl_->ScoreToken(std::move(x), std::move(states));
}
void OnlineRnnLM::ComputeLMScore(float scale, Hypothesis *hyp) {
return impl_->ComputeLMScore(scale, hyp);
}
} // namespace sherpa_onnx