141 lines
4.4 KiB
C++
141 lines
4.4 KiB
C++
// sherpa-onnx/csrc/on-rnn-lm.cc
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//
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// Copyright (c) 2023 Pingfeng Luo
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/online-rnn-lm.h"
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#include <string>
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#include <utility>
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#include <vector>
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#include "onnxruntime_cxx_api.h" // NOLINT
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#include "sherpa-onnx/csrc/macros.h"
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#include "sherpa-onnx/csrc/onnx-utils.h"
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#include "sherpa-onnx/csrc/text-utils.h"
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namespace sherpa_onnx {
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class OnlineRnnLM::Impl {
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public:
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explicit Impl(const OnlineLMConfig &config)
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: config_(config),
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env_(ORT_LOGGING_LEVEL_ERROR),
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sess_opts_{},
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allocator_{} {
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Init(config);
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}
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std::pair<Ort::Value, std::vector<Ort::Value>> Rescore(
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Ort::Value x, Ort::Value y, std::vector<Ort::Value> states) {
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std::array<Ort::Value, 4> inputs = {
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std::move(x), std::move(y), std::move(states[0]), std::move(states[1])};
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auto out =
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sess_->Run({}, input_names_ptr_.data(), inputs.data(), inputs.size(),
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output_names_ptr_.data(), output_names_ptr_.size());
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std::vector<Ort::Value> next_states;
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next_states.reserve(2);
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next_states.push_back(std::move(out[1]));
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next_states.push_back(std::move(out[2]));
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return {std::move(out[0]), std::move(next_states)};
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}
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std::vector<Ort::Value> GetInitStates() const {
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std::vector<Ort::Value> ans;
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ans.reserve(init_states_.size());
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for (const auto &s : init_states_) {
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ans.emplace_back(Clone(allocator_, &s));
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}
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return ans;
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}
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private:
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void Init(const OnlineLMConfig &config) {
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auto buf = ReadFile(config_.model);
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sess_ = std::make_unique<Ort::Session>(env_, buf.data(), buf.size(),
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sess_opts_);
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GetInputNames(sess_.get(), &input_names_, &input_names_ptr_);
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GetOutputNames(sess_.get(), &output_names_, &output_names_ptr_);
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Ort::ModelMetadata meta_data = sess_->GetModelMetadata();
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Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
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SHERPA_ONNX_READ_META_DATA(rnn_num_layers_, "num_layers");
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SHERPA_ONNX_READ_META_DATA(rnn_hidden_size_, "hidden_size");
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SHERPA_ONNX_READ_META_DATA(sos_id_, "sos_id");
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ComputeInitStates();
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}
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void ComputeInitStates() {
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constexpr int32_t kBatchSize = 1;
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std::array<int64_t, 3> h_shape{rnn_num_layers_, kBatchSize,
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rnn_hidden_size_};
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std::array<int64_t, 3> c_shape{rnn_num_layers_, kBatchSize,
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rnn_hidden_size_};
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Ort::Value h = Ort::Value::CreateTensor<float>(allocator_, h_shape.data(),
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h_shape.size());
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Ort::Value c = Ort::Value::CreateTensor<float>(allocator_, c_shape.data(),
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c_shape.size());
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Fill<float>(&h, 0);
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Fill<float>(&c, 0);
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std::array<int64_t, 2> x_shape{1, 1};
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// shape of x and y are same
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Ort::Value x = Ort::Value::CreateTensor<int64_t>(allocator_, x_shape.data(),
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x_shape.size());
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Ort::Value y = Ort::Value::CreateTensor<int64_t>(allocator_, x_shape.data(),
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x_shape.size());
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*x.GetTensorMutableData<int64_t>() = sos_id_;
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*y.GetTensorMutableData<int64_t>() = sos_id_;
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std::vector<Ort::Value> states;
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states.push_back(std::move(h));
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states.push_back(std::move(c));
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auto pair = Rescore(std::move(x), std::move(y), std::move(states));
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init_states_ = std::move(pair.second);
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}
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private:
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OnlineLMConfig config_;
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Ort::Env env_;
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Ort::SessionOptions sess_opts_;
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Ort::AllocatorWithDefaultOptions allocator_;
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std::unique_ptr<Ort::Session> sess_;
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std::vector<std::string> input_names_;
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std::vector<const char *> input_names_ptr_;
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std::vector<std::string> output_names_;
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std::vector<const char *> output_names_ptr_;
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std::vector<Ort::Value> init_states_;
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int32_t rnn_num_layers_ = 2;
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int32_t rnn_hidden_size_ = 512;
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int32_t sos_id_ = 1;
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};
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OnlineRnnLM::OnlineRnnLM(const OnlineLMConfig &config)
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: impl_(std::make_unique<Impl>(config)) {}
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OnlineRnnLM::~OnlineRnnLM() = default;
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std::vector<Ort::Value> OnlineRnnLM::GetInitStates() {
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return impl_->GetInitStates();
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}
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std::pair<Ort::Value, std::vector<Ort::Value>> OnlineRnnLM::Rescore(
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Ort::Value x, Ort::Value y, std::vector<Ort::Value> states) {
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return impl_->Rescore(std::move(x), std::move(y), std::move(states));
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}
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} // namespace sherpa_onnx
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