265 lines
8.0 KiB
C++
265 lines
8.0 KiB
C++
// sherpa-onnx/csrc/offline-canary-model.cc
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//
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// Copyright (c) 2025 Xiaomi Corporation
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#include "sherpa-onnx/csrc/offline-canary-model.h"
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#include <algorithm>
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#include <cmath>
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#include <string>
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#include <tuple>
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#include <unordered_map>
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#include <utility>
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#include "sherpa-onnx/csrc/offline-canary-model-meta-data.h"
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#if __ANDROID_API__ >= 9
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#include "android/asset_manager.h"
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#include "android/asset_manager_jni.h"
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#endif
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#if __OHOS__
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#include "rawfile/raw_file_manager.h"
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#endif
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#include "sherpa-onnx/csrc/file-utils.h"
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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/session.h"
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#include "sherpa-onnx/csrc/text-utils.h"
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namespace sherpa_onnx {
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class OfflineCanaryModel::Impl {
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public:
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explicit Impl(const OfflineModelConfig &config)
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: config_(config),
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env_(ORT_LOGGING_LEVEL_ERROR),
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sess_opts_(GetSessionOptions(config)),
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allocator_{} {
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{
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auto buf = ReadFile(config.canary.encoder);
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InitEncoder(buf.data(), buf.size());
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}
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{
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auto buf = ReadFile(config.canary.decoder);
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InitDecoder(buf.data(), buf.size());
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}
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}
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template <typename Manager>
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Impl(Manager *mgr, const OfflineModelConfig &config)
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: config_(config),
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env_(ORT_LOGGING_LEVEL_ERROR),
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sess_opts_(GetSessionOptions(config)),
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allocator_{} {
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{
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auto buf = ReadFile(mgr, config.canary.encoder);
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InitEncoder(buf.data(), buf.size());
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}
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{
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auto buf = ReadFile(mgr, config.canary.decoder);
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InitDecoder(buf.data(), buf.size());
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}
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}
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std::vector<Ort::Value> ForwardEncoder(Ort::Value features,
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Ort::Value features_length) {
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std::array<Ort::Value, 2> encoder_inputs = {std::move(features),
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std::move(features_length)};
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auto encoder_out = encoder_sess_->Run(
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{}, encoder_input_names_ptr_.data(), encoder_inputs.data(),
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encoder_inputs.size(), encoder_output_names_ptr_.data(),
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encoder_output_names_ptr_.size());
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return encoder_out;
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}
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std::pair<Ort::Value, std::vector<Ort::Value>> ForwardDecoder(
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Ort::Value tokens, std::vector<Ort::Value> decoder_states,
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Ort::Value encoder_states, Ort::Value enc_mask) {
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std::vector<Ort::Value> decoder_inputs;
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decoder_inputs.reserve(3 + decoder_states.size());
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decoder_inputs.push_back(std::move(tokens));
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for (auto &s : decoder_states) {
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decoder_inputs.push_back(std::move(s));
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}
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decoder_inputs.push_back(std::move(encoder_states));
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decoder_inputs.push_back(std::move(enc_mask));
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auto decoder_outputs = decoder_sess_->Run(
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{}, decoder_input_names_ptr_.data(), decoder_inputs.data(),
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decoder_inputs.size(), decoder_output_names_ptr_.data(),
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decoder_output_names_ptr_.size());
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Ort::Value logits = std::move(decoder_outputs[0]);
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std::vector<Ort::Value> output_decoder_states;
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output_decoder_states.reserve(decoder_states.size());
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int32_t i = 0;
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for (auto &s : decoder_outputs) {
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i += 1;
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if (i == 1) {
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continue;
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}
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output_decoder_states.push_back(std::move(s));
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}
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return {std::move(logits), std::move(output_decoder_states)};
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}
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std::vector<Ort::Value> GetInitialDecoderStates() {
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std::array<int64_t, 3> shape{1, 0, 1024};
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std::vector<Ort::Value> ans;
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ans.reserve(6);
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for (int32_t i = 0; i < 6; ++i) {
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Ort::Value state = Ort::Value::CreateTensor<float>(
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Allocator(), shape.data(), shape.size());
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ans.push_back(std::move(state));
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}
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return ans;
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}
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OrtAllocator *Allocator() { return allocator_; }
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const OfflineCanaryModelMetaData &GetModelMetadata() const { return meta_; }
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OfflineCanaryModelMetaData &GetModelMetadata() { return meta_; }
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private:
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void InitEncoder(void *model_data, size_t model_data_length) {
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encoder_sess_ = std::make_unique<Ort::Session>(
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env_, model_data, model_data_length, sess_opts_);
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GetInputNames(encoder_sess_.get(), &encoder_input_names_,
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&encoder_input_names_ptr_);
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GetOutputNames(encoder_sess_.get(), &encoder_output_names_,
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&encoder_output_names_ptr_);
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// get meta data
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Ort::ModelMetadata meta_data = encoder_sess_->GetModelMetadata();
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if (config_.debug) {
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std::ostringstream os;
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os << "---encoder---\n";
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PrintModelMetadata(os, meta_data);
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#if __OHOS__
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SHERPA_ONNX_LOGE("%{public}s\n", os.str().c_str());
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#else
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SHERPA_ONNX_LOGE("%s\n", os.str().c_str());
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#endif
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}
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Ort::AllocatorWithDefaultOptions allocator; // used in the macro below
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std::string model_type;
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SHERPA_ONNX_READ_META_DATA_STR(model_type, "model_type");
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if (model_type != "EncDecMultiTaskModel") {
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SHERPA_ONNX_LOGE(
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"Expected model type 'EncDecMultiTaskModel'. Given: '%s'",
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model_type.c_str());
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SHERPA_ONNX_EXIT(-1);
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}
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SHERPA_ONNX_READ_META_DATA(meta_.vocab_size, "vocab_size");
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SHERPA_ONNX_READ_META_DATA_STR_ALLOW_EMPTY(meta_.normalize_type,
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"normalize_type");
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SHERPA_ONNX_READ_META_DATA(meta_.subsampling_factor, "subsampling_factor");
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SHERPA_ONNX_READ_META_DATA(meta_.feat_dim, "feat_dim");
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}
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void InitDecoder(void *model_data, size_t model_data_length) {
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decoder_sess_ = std::make_unique<Ort::Session>(
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env_, model_data, model_data_length, sess_opts_);
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GetInputNames(decoder_sess_.get(), &decoder_input_names_,
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&decoder_input_names_ptr_);
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GetOutputNames(decoder_sess_.get(), &decoder_output_names_,
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&decoder_output_names_ptr_);
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}
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private:
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OfflineCanaryModelMetaData meta_;
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OfflineModelConfig 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> encoder_sess_;
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std::unique_ptr<Ort::Session> decoder_sess_;
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std::vector<std::string> encoder_input_names_;
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std::vector<const char *> encoder_input_names_ptr_;
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std::vector<std::string> encoder_output_names_;
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std::vector<const char *> encoder_output_names_ptr_;
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std::vector<std::string> decoder_input_names_;
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std::vector<const char *> decoder_input_names_ptr_;
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std::vector<std::string> decoder_output_names_;
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std::vector<const char *> decoder_output_names_ptr_;
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};
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OfflineCanaryModel::OfflineCanaryModel(const OfflineModelConfig &config)
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: impl_(std::make_unique<Impl>(config)) {}
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template <typename Manager>
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OfflineCanaryModel::OfflineCanaryModel(Manager *mgr,
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const OfflineModelConfig &config)
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: impl_(std::make_unique<Impl>(mgr, config)) {}
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OfflineCanaryModel::~OfflineCanaryModel() = default;
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std::vector<Ort::Value> OfflineCanaryModel::ForwardEncoder(
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Ort::Value features, Ort::Value features_length) const {
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return impl_->ForwardEncoder(std::move(features), std::move(features_length));
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}
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std::pair<Ort::Value, std::vector<Ort::Value>>
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OfflineCanaryModel::ForwardDecoder(Ort::Value tokens,
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std::vector<Ort::Value> decoder_states,
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Ort::Value encoder_states,
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Ort::Value enc_mask) const {
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return impl_->ForwardDecoder(std::move(tokens), std::move(decoder_states),
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std::move(encoder_states), std::move(enc_mask));
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}
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std::vector<Ort::Value> OfflineCanaryModel::GetInitialDecoderStates() const {
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return impl_->GetInitialDecoderStates();
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}
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OrtAllocator *OfflineCanaryModel::Allocator() const {
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return impl_->Allocator();
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}
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const OfflineCanaryModelMetaData &OfflineCanaryModel::GetModelMetadata() const {
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return impl_->GetModelMetadata();
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}
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OfflineCanaryModelMetaData &OfflineCanaryModel::GetModelMetadata() {
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return impl_->GetModelMetadata();
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}
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#if __ANDROID_API__ >= 9
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template OfflineCanaryModel::OfflineCanaryModel(
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AAssetManager *mgr, const OfflineModelConfig &config);
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#endif
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#if __OHOS__
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template OfflineCanaryModel::OfflineCanaryModel(
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NativeResourceManager *mgr, const OfflineModelConfig &config);
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#endif
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} // namespace sherpa_onnx
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