mirror of
https://github.com/StarCitizenToolBox/app.git
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feat: [rust] add Opus-MT
This commit is contained in:
xkeyC
546
rust/Cargo.lock
generated
546
rust/Cargo.lock
generated
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|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ea2f10b9bb0928dfb1b42b65e1f9e36f7f54dbdf08457afefb38afcdec4fa2bb"
|
||||
|
||||
[[package]]
|
||||
name = "xattr"
|
||||
version = "1.6.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "32e45ad4206f6d2479085147f02bc2ef834ac85886624a23575ae137c8aa8156"
|
||||
dependencies = [
|
||||
"libc",
|
||||
"rustix",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "yoke"
|
||||
version = "0.8.0"
|
||||
|
||||
@@ -23,6 +23,10 @@ scopeguard = "1.2"
|
||||
notify-rust = "4"
|
||||
asar = "0.3.0"
|
||||
walkdir = "2.5.0"
|
||||
ort = { version = "2.0.0-rc.10", features = ["xnnpack", "download-binaries", "ndarray"] }
|
||||
tokenizers = { version = "0.20", default-features = false, features = ["onig"] }
|
||||
ndarray = "0.16"
|
||||
serde_json = "1.0"
|
||||
|
||||
[target.'cfg(windows)'.dependencies]
|
||||
windows = { version = "0.62.2", features = ["Win32_UI_WindowsAndMessaging"] }
|
||||
|
||||
@@ -5,3 +5,4 @@ pub mod http_api;
|
||||
pub mod rs_process;
|
||||
pub mod win32_api;
|
||||
pub mod asar_api;
|
||||
pub mod ort_api;
|
||||
|
||||
107
rust/src/api/ort_api.rs
Normal file
107
rust/src/api/ort_api.rs
Normal file
@@ -0,0 +1,107 @@
|
||||
use anyhow::Result;
|
||||
use once_cell::sync::Lazy;
|
||||
use std::collections::HashMap;
|
||||
use std::sync::Mutex;
|
||||
|
||||
use crate::ort_models::opus_mt::OpusMtModel;
|
||||
|
||||
/// 全局模型缓存
|
||||
static MODEL_CACHE: Lazy<Mutex<HashMap<String, OpusMtModel>>> =
|
||||
Lazy::new(|| Mutex::new(HashMap::new()));
|
||||
|
||||
/// 加载 ONNX 翻译模型
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `model_path` - 模型文件夹路径
|
||||
/// * `model_key` - 模型缓存键（用于标识模型，如 "zh-en"）
|
||||
/// * `quantization_suffix` - 量化后缀（如 "_q4", "_q8"，空字符串表示使用默认模型）
|
||||
///
|
||||
pub fn load_translation_model(
|
||||
model_path: String,
|
||||
model_key: String,
|
||||
quantization_suffix: String,
|
||||
) -> Result<()> {
|
||||
let model = OpusMtModel::new(&model_path, &quantization_suffix)?;
|
||||
|
||||
let mut cache = MODEL_CACHE
|
||||
.lock()
|
||||
.map_err(|e| anyhow::anyhow!("Failed to lock model cache: {}", e))?;
|
||||
|
||||
cache.insert(model_key, model);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 翻译文本
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `model_key` - 模型缓存键（如 "zh-en"）
|
||||
/// * `text` - 要翻译的文本
|
||||
///
|
||||
/// # Returns
|
||||
/// * `Result<String>` - 翻译后的文本
|
||||
pub fn translate_text(model_key: String, text: String) -> Result<String> {
|
||||
let cache = MODEL_CACHE
|
||||
.lock()
|
||||
.map_err(|e| anyhow::anyhow!("Failed to lock model cache: {}", e))?;
|
||||
|
||||
let model = cache.get(&model_key).ok_or_else(|| {
|
||||
anyhow::anyhow!(
|
||||
"Model not found: {}. Please load the model first.",
|
||||
model_key
|
||||
)
|
||||
})?;
|
||||
|
||||
model.translate(&text)
|
||||
}
|
||||
|
||||
/// 批量翻译文本
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `model_key` - 模型缓存键（如 "zh-en"）
|
||||
/// * `texts` - 要翻译的文本列表
|
||||
///
|
||||
/// # Returns
|
||||
/// * `Result<Vec<String>>` - 翻译后的文本列表
|
||||
pub fn translate_text_batch(model_key: String, texts: Vec<String>) -> Result<Vec<String>> {
|
||||
let cache = MODEL_CACHE
|
||||
.lock()
|
||||
.map_err(|e| anyhow::anyhow!("Failed to lock model cache: {}", e))?;
|
||||
|
||||
let model = cache.get(&model_key).ok_or_else(|| {
|
||||
anyhow::anyhow!(
|
||||
"Model not found: {}. Please load the model first.",
|
||||
model_key
|
||||
)
|
||||
})?;
|
||||
|
||||
model.translate_batch(&texts)
|
||||
}
|
||||
|
||||
/// 卸载模型
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `model_key` - 模型缓存键（如 "zh-en"）
|
||||
///
|
||||
pub fn unload_translation_model(model_key: String) -> Result<()> {
|
||||
let mut cache = MODEL_CACHE
|
||||
.lock()
|
||||
.map_err(|e| anyhow::anyhow!("Failed to lock model cache: {}", e))?;
|
||||
|
||||
cache.remove(&model_key);
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// 清空所有已加载的模型
|
||||
///
|
||||
/// # Returns
|
||||
pub fn clear_all_models() -> Result<()> {
|
||||
let mut cache = MODEL_CACHE
|
||||
.lock()
|
||||
.map_err(|e| anyhow::anyhow!("Failed to lock model cache: {}", e))?;
|
||||
|
||||
cache.clear();
|
||||
|
||||
Ok(())
|
||||
}
|
||||
@@ -37,7 +37,7 @@ flutter_rust_bridge::frb_generated_boilerplate!(
|
||||
default_rust_auto_opaque = RustAutoOpaqueNom,
|
||||
);
|
||||
pub(crate) const FLUTTER_RUST_BRIDGE_CODEGEN_VERSION: &str = "2.11.1";
|
||||
pub(crate) const FLUTTER_RUST_BRIDGE_CODEGEN_CONTENT_HASH: i32 = 1832496273;
|
||||
pub(crate) const FLUTTER_RUST_BRIDGE_CODEGEN_CONTENT_HASH: i32 = -706588047;
|
||||
|
||||
// Section: executor
|
||||
|
||||
@@ -45,6 +45,27 @@ flutter_rust_bridge::frb_generated_default_handler!();
|
||||
|
||||
// Section: wire_funcs
|
||||
|
||||
fn wire__crate__api__ort_api__clear_all_models_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
) {
|
||||
FLUTTER_RUST_BRIDGE_HANDLER.wrap_normal::<flutter_rust_bridge::for_generated::DcoCodec, _, _>(
|
||||
flutter_rust_bridge::for_generated::TaskInfo {
|
||||
debug_name: "clear_all_models",
|
||||
port: Some(port_),
|
||||
mode: flutter_rust_bridge::for_generated::FfiCallMode::Normal,
|
||||
},
|
||||
move || {
|
||||
move |context| {
|
||||
transform_result_dco::<_, _, flutter_rust_bridge::for_generated::anyhow::Error>(
|
||||
(move || {
|
||||
let output_ok = crate::api::ort_api::clear_all_models()?;
|
||||
Ok(output_ok)
|
||||
})(),
|
||||
)
|
||||
}
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__http_api__dns_lookup_ips_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
host: impl CstDecode<String>,
|
||||
@@ -161,6 +182,37 @@ fn wire__crate__api__asar_api__get_rsi_launcher_asar_data_impl(
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__ort_api__load_translation_model_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
model_path: impl CstDecode<String>,
|
||||
model_key: impl CstDecode<String>,
|
||||
quantization_suffix: impl CstDecode<String>,
|
||||
) {
|
||||
FLUTTER_RUST_BRIDGE_HANDLER.wrap_normal::<flutter_rust_bridge::for_generated::DcoCodec, _, _>(
|
||||
flutter_rust_bridge::for_generated::TaskInfo {
|
||||
debug_name: "load_translation_model",
|
||||
port: Some(port_),
|
||||
mode: flutter_rust_bridge::for_generated::FfiCallMode::Normal,
|
||||
},
|
||||
move || {
|
||||
let api_model_path = model_path.cst_decode();
|
||||
let api_model_key = model_key.cst_decode();
|
||||
let api_quantization_suffix = quantization_suffix.cst_decode();
|
||||
move |context| {
|
||||
transform_result_dco::<_, _, flutter_rust_bridge::for_generated::anyhow::Error>(
|
||||
(move || {
|
||||
let output_ok = crate::api::ort_api::load_translation_model(
|
||||
api_model_path,
|
||||
api_model_key,
|
||||
api_quantization_suffix,
|
||||
)?;
|
||||
Ok(output_ok)
|
||||
})(),
|
||||
)
|
||||
}
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__asar_api__rsi_launcher_asar_data_write_main_js_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
that: impl CstDecode<crate::api::asar_api::RsiLauncherAsarData>,
|
||||
@@ -315,6 +367,82 @@ fn wire__crate__api__rs_process__start_impl(
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__ort_api__translate_text_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
model_key: impl CstDecode<String>,
|
||||
text: impl CstDecode<String>,
|
||||
) {
|
||||
FLUTTER_RUST_BRIDGE_HANDLER.wrap_normal::<flutter_rust_bridge::for_generated::DcoCodec, _, _>(
|
||||
flutter_rust_bridge::for_generated::TaskInfo {
|
||||
debug_name: "translate_text",
|
||||
port: Some(port_),
|
||||
mode: flutter_rust_bridge::for_generated::FfiCallMode::Normal,
|
||||
},
|
||||
move || {
|
||||
let api_model_key = model_key.cst_decode();
|
||||
let api_text = text.cst_decode();
|
||||
move |context| {
|
||||
transform_result_dco::<_, _, flutter_rust_bridge::for_generated::anyhow::Error>(
|
||||
(move || {
|
||||
let output_ok =
|
||||
crate::api::ort_api::translate_text(api_model_key, api_text)?;
|
||||
Ok(output_ok)
|
||||
})(),
|
||||
)
|
||||
}
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__ort_api__translate_text_batch_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
model_key: impl CstDecode<String>,
|
||||
texts: impl CstDecode<Vec<String>>,
|
||||
) {
|
||||
FLUTTER_RUST_BRIDGE_HANDLER.wrap_normal::<flutter_rust_bridge::for_generated::DcoCodec, _, _>(
|
||||
flutter_rust_bridge::for_generated::TaskInfo {
|
||||
debug_name: "translate_text_batch",
|
||||
port: Some(port_),
|
||||
mode: flutter_rust_bridge::for_generated::FfiCallMode::Normal,
|
||||
},
|
||||
move || {
|
||||
let api_model_key = model_key.cst_decode();
|
||||
let api_texts = texts.cst_decode();
|
||||
move |context| {
|
||||
transform_result_dco::<_, _, flutter_rust_bridge::for_generated::anyhow::Error>(
|
||||
(move || {
|
||||
let output_ok =
|
||||
crate::api::ort_api::translate_text_batch(api_model_key, api_texts)?;
|
||||
Ok(output_ok)
|
||||
})(),
|
||||
)
|
||||
}
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__ort_api__unload_translation_model_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
model_key: impl CstDecode<String>,
|
||||
) {
|
||||
FLUTTER_RUST_BRIDGE_HANDLER.wrap_normal::<flutter_rust_bridge::for_generated::DcoCodec, _, _>(
|
||||
flutter_rust_bridge::for_generated::TaskInfo {
|
||||
debug_name: "unload_translation_model",
|
||||
port: Some(port_),
|
||||
mode: flutter_rust_bridge::for_generated::FfiCallMode::Normal,
|
||||
},
|
||||
move || {
|
||||
let api_model_key = model_key.cst_decode();
|
||||
move |context| {
|
||||
transform_result_dco::<_, _, flutter_rust_bridge::for_generated::anyhow::Error>(
|
||||
(move || {
|
||||
let output_ok =
|
||||
crate::api::ort_api::unload_translation_model(api_model_key)?;
|
||||
Ok(output_ok)
|
||||
})(),
|
||||
)
|
||||
}
|
||||
},
|
||||
)
|
||||
}
|
||||
fn wire__crate__api__rs_process__write_impl(
|
||||
port_: flutter_rust_bridge::for_generated::MessagePort,
|
||||
rs_pid: impl CstDecode<u32>,
|
||||
@@ -1361,6 +1489,13 @@ mod io {
|
||||
}
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__ort_api__clear_all_models(
|
||||
port_: i64,
|
||||
) {
|
||||
wire__crate__api__ort_api__clear_all_models_impl(port_)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__http_api__dns_lookup_ips(
|
||||
port_: i64,
|
||||
@@ -1406,6 +1541,21 @@ mod io {
|
||||
wire__crate__api__asar_api__get_rsi_launcher_asar_data_impl(port_, asar_path)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__ort_api__load_translation_model(
|
||||
port_: i64,
|
||||
model_path: *mut wire_cst_list_prim_u_8_strict,
|
||||
model_key: *mut wire_cst_list_prim_u_8_strict,
|
||||
quantization_suffix: *mut wire_cst_list_prim_u_8_strict,
|
||||
) {
|
||||
wire__crate__api__ort_api__load_translation_model_impl(
|
||||
port_,
|
||||
model_path,
|
||||
model_key,
|
||||
quantization_suffix,
|
||||
)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__asar_api__rsi_launcher_asar_data_write_main_js(
|
||||
port_: i64,
|
||||
@@ -1459,6 +1609,32 @@ mod io {
|
||||
)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__ort_api__translate_text(
|
||||
port_: i64,
|
||||
model_key: *mut wire_cst_list_prim_u_8_strict,
|
||||
text: *mut wire_cst_list_prim_u_8_strict,
|
||||
) {
|
||||
wire__crate__api__ort_api__translate_text_impl(port_, model_key, text)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__ort_api__translate_text_batch(
|
||||
port_: i64,
|
||||
model_key: *mut wire_cst_list_prim_u_8_strict,
|
||||
texts: *mut wire_cst_list_String,
|
||||
) {
|
||||
wire__crate__api__ort_api__translate_text_batch_impl(port_, model_key, texts)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__ort_api__unload_translation_model(
|
||||
port_: i64,
|
||||
model_key: *mut wire_cst_list_prim_u_8_strict,
|
||||
) {
|
||||
wire__crate__api__ort_api__unload_translation_model_impl(port_, model_key)
|
||||
}
|
||||
|
||||
#[unsafe(no_mangle)]
|
||||
pub extern "C" fn frbgen_starcitizen_doctor_wire__crate__api__rs_process__write(
|
||||
port_: i64,
|
||||
|
||||
@@ -1,3 +1,4 @@
|
||||
pub mod api;
|
||||
mod frb_generated;
|
||||
pub mod http_package;
|
||||
pub mod ort_models;
|
||||
|
||||
1
rust/src/ort_models/mod.rs
Normal file
1
rust/src/ort_models/mod.rs
Normal file
@@ -0,0 +1 @@
|
||||
pub mod opus_mt;
|
||||
388
rust/src/ort_models/opus_mt.rs
Normal file
388
rust/src/ort_models/opus_mt.rs
Normal file
@@ -0,0 +1,388 @@
|
||||
use anyhow::{anyhow, Context, Result};
|
||||
use ndarray::{Array2, ArrayD};
|
||||
use ort::{
|
||||
execution_providers::XNNPACKExecutionProvider, session::builder::GraphOptimizationLevel,
|
||||
session::Session, value::Value,
|
||||
};
|
||||
use std::path::Path;
|
||||
use std::sync::Mutex;
|
||||
use tokenizers::Tokenizer;
|
||||
|
||||
/// Opus-MT 翻译模型的推理结构
|
||||
pub struct OpusMtModel {
|
||||
encoder_session: Mutex<Session>,
|
||||
decoder_session: Mutex<Session>,
|
||||
tokenizer: Tokenizer,
|
||||
config: ModelConfig,
|
||||
}
|
||||
|
||||
/// 模型配置
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct ModelConfig {
|
||||
pub max_length: usize,
|
||||
pub num_beams: usize,
|
||||
pub decoder_start_token_id: i64,
|
||||
pub eos_token_id: i64,
|
||||
pub pad_token_id: i64,
|
||||
}
|
||||
|
||||
impl Default for ModelConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
max_length: 512,
|
||||
num_beams: 1,
|
||||
decoder_start_token_id: 0,
|
||||
eos_token_id: 0,
|
||||
pad_token_id: 0,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl OpusMtModel {
|
||||
/// 从模型路径创建新的 OpusMT 模型实例
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `model_path` - 模型文件夹路径（应包含 onnx 子文件夹）
|
||||
/// * `quantization_suffix` - 量化后缀，如 "_q4", "_q8"，为空字符串则使用默认模型
|
||||
///
|
||||
/// # Returns
|
||||
/// * `Result<Self>` - 成功返回模型实例，失败返回错误
|
||||
pub fn new<P: AsRef<Path>>(model_path: P, quantization_suffix: &str) -> Result<Self> {
|
||||
let model_path = model_path.as_ref();
|
||||
|
||||
// onnx-community 标准：模型在 onnx 子文件夹中
|
||||
let onnx_dir = model_path.join("onnx");
|
||||
|
||||
// 加载 tokenizer（在根目录）
|
||||
let tokenizer_path = model_path.join("tokenizer.json");
|
||||
|
||||
// 动态加载并修复 tokenizer
|
||||
let tokenizer =
|
||||
Self::load_tokenizer(&tokenizer_path).context("Failed to load tokenizer")?;
|
||||
|
||||
// 构建模型文件名
|
||||
let encoder_filename = if quantization_suffix.is_empty() {
|
||||
"encoder_model.onnx".to_string()
|
||||
} else {
|
||||
format!("encoder_model{}.onnx", quantization_suffix)
|
||||
};
|
||||
|
||||
let decoder_filename = if quantization_suffix.is_empty() {
|
||||
"decoder_model.onnx".to_string()
|
||||
} else {
|
||||
format!("decoder_model{}.onnx", quantization_suffix)
|
||||
};
|
||||
|
||||
// 加载 encoder 模型（在 onnx 子目录）
|
||||
let encoder_path = onnx_dir.join(&encoder_filename);
|
||||
if !encoder_path.exists() {
|
||||
return Err(anyhow!(
|
||||
"Encoder model not found: {}",
|
||||
encoder_path.display()
|
||||
));
|
||||
}
|
||||
|
||||
let encoder_session = Session::builder()
|
||||
.context("Failed to create encoder session builder")?
|
||||
.with_optimization_level(GraphOptimizationLevel::Level3)
|
||||
.context("Failed to set optimization level")?
|
||||
.with_intra_threads(4)
|
||||
.context("Failed to set intra threads")?
|
||||
.with_execution_providers([XNNPACKExecutionProvider::default().build()])
|
||||
.context("Failed to register XNNPACK execution provider")?
|
||||
.commit_from_file(&encoder_path)
|
||||
.context(format!(
|
||||
"Failed to load encoder model: {}",
|
||||
encoder_filename
|
||||
))?;
|
||||
|
||||
// 加载 decoder 模型（在 onnx 子目录）
|
||||
let decoder_path = onnx_dir.join(&decoder_filename);
|
||||
if !decoder_path.exists() {
|
||||
return Err(anyhow!(
|
||||
"Decoder model not found: {}",
|
||||
decoder_path.display()
|
||||
));
|
||||
}
|
||||
|
||||
let decoder_session = Session::builder()
|
||||
.context("Failed to create decoder session builder")?
|
||||
.with_optimization_level(GraphOptimizationLevel::Level3)
|
||||
.context("Failed to set optimization level")?
|
||||
.with_intra_threads(4)
|
||||
.context("Failed to set intra threads")?
|
||||
.with_execution_providers([XNNPACKExecutionProvider::default().build()])
|
||||
.context("Failed to register XNNPACK execution provider")?
|
||||
.commit_from_file(&decoder_path)
|
||||
.context(format!(
|
||||
"Failed to load decoder model: {}",
|
||||
decoder_filename
|
||||
))?;
|
||||
|
||||
// 加载配置（如果存在，在根目录）
|
||||
let config = Self::load_config(model_path)?;
|
||||
|
||||
Ok(Self {
|
||||
encoder_session: Mutex::new(encoder_session),
|
||||
decoder_session: Mutex::new(decoder_session),
|
||||
tokenizer,
|
||||
config,
|
||||
})
|
||||
}
|
||||
|
||||
/// 动态加载 tokenizer，自动修复常见问题
|
||||
fn load_tokenizer(tokenizer_path: &Path) -> Result<Tokenizer> {
|
||||
use std::fs;
|
||||
|
||||
// 读取原始文件
|
||||
let content =
|
||||
fs::read_to_string(tokenizer_path).context("Failed to read tokenizer.json")?;
|
||||
|
||||
// 解析为 JSON
|
||||
let mut json: serde_json::Value =
|
||||
serde_json::from_str(&content).context("Failed to parse tokenizer.json")?;
|
||||
|
||||
let mut needs_fix = false;
|
||||
|
||||
// 修复 normalizer 中的问题
|
||||
if let Some(obj) = json.as_object_mut() {
|
||||
if let Some(normalizer) = obj.get("normalizer") {
|
||||
let mut should_remove_normalizer = false;
|
||||
|
||||
if normalizer.is_null() {
|
||||
// normalizer 是 null，需要移除
|
||||
should_remove_normalizer = true;
|
||||
} else if let Some(norm_obj) = normalizer.as_object() {
|
||||
// 检查是否是有问题的 Precompiled 类型
|
||||
if let Some(type_val) = norm_obj.get("type") {
|
||||
if type_val.as_str() == Some("Precompiled") {
|
||||
// 检查 precompiled_charsmap 字段
|
||||
if let Some(precompiled) = norm_obj.get("precompiled_charsmap") {
|
||||
if precompiled.is_null() {
|
||||
// precompiled_charsmap 是 null，移除整个 normalizer
|
||||
should_remove_normalizer = true;
|
||||
}
|
||||
} else {
|
||||
// 缺少 precompiled_charsmap 字段，移除整个 normalizer
|
||||
should_remove_normalizer = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if should_remove_normalizer {
|
||||
obj.remove("normalizer");
|
||||
needs_fix = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 从修复后的 JSON 字符串加载 tokenizer
|
||||
let json_str = if needs_fix {
|
||||
serde_json::to_string(&json).context("Failed to serialize fixed tokenizer")?
|
||||
} else {
|
||||
content
|
||||
};
|
||||
|
||||
// 从字节数组加载 tokenizer
|
||||
Tokenizer::from_bytes(json_str.as_bytes())
|
||||
.map_err(|e| anyhow!("Failed to load tokenizer: {}", e))
|
||||
}
|
||||
|
||||
/// 从配置文件加载模型配置
|
||||
fn load_config(model_path: &Path) -> Result<ModelConfig> {
|
||||
let config_path = model_path.join("config.json");
|
||||
|
||||
if config_path.exists() {
|
||||
let config_str =
|
||||
std::fs::read_to_string(config_path).context("Failed to read config.json")?;
|
||||
let config_json: serde_json::Value =
|
||||
serde_json::from_str(&config_str).context("Failed to parse config.json")?;
|
||||
|
||||
Ok(ModelConfig {
|
||||
max_length: config_json["max_length"].as_u64().unwrap_or(512) as usize,
|
||||
num_beams: config_json["num_beams"].as_u64().unwrap_or(1) as usize,
|
||||
decoder_start_token_id: config_json["decoder_start_token_id"].as_i64().unwrap_or(0),
|
||||
eos_token_id: config_json["eos_token_id"].as_i64().unwrap_or(0),
|
||||
pad_token_id: config_json["pad_token_id"].as_i64().unwrap_or(0),
|
||||
})
|
||||
} else {
|
||||
Ok(ModelConfig::default())
|
||||
}
|
||||
}
|
||||
|
||||
/// 翻译文本
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `text` - 要翻译的文本
|
||||
///
|
||||
/// # Returns
|
||||
/// * `Result<String>` - 翻译后的文本
|
||||
pub fn translate(&self, text: &str) -> Result<String> {
|
||||
// 1. Tokenize 输入文本
|
||||
let encoding = self
|
||||
.tokenizer
|
||||
.encode(text, true)
|
||||
.map_err(|e| anyhow!("Failed to encode text: {}", e))?;
|
||||
|
||||
let input_ids = encoding.get_ids();
|
||||
let attention_mask = encoding.get_attention_mask();
|
||||
|
||||
// 2. 准备 encoder 输入
|
||||
let batch_size = 1;
|
||||
let seq_len = input_ids.len();
|
||||
|
||||
let input_ids_array: Array2<i64> = Array2::from_shape_vec(
|
||||
(batch_size, seq_len),
|
||||
input_ids.iter().map(|&id| id as i64).collect(),
|
||||
)
|
||||
.context("Failed to create input_ids array")?;
|
||||
|
||||
let attention_mask_array: Array2<i64> = Array2::from_shape_vec(
|
||||
(batch_size, seq_len),
|
||||
attention_mask.iter().map(|&mask| mask as i64).collect(),
|
||||
)
|
||||
.context("Failed to create attention_mask array")?;
|
||||
|
||||
// 3. 运行 encoder
|
||||
let input_ids_value =
|
||||
Value::from_array(input_ids_array).context("Failed to create input_ids value")?;
|
||||
let attention_mask_value = Value::from_array(attention_mask_array.clone())
|
||||
.context("Failed to create attention_mask value")?;
|
||||
|
||||
let encoder_inputs = ort::inputs![
|
||||
"input_ids" => input_ids_value,
|
||||
"attention_mask" => attention_mask_value,
|
||||
];
|
||||
|
||||
let mut encoder_session = self
|
||||
.encoder_session
|
||||
.lock()
|
||||
.map_err(|e| anyhow!("Failed to lock encoder session: {}", e))?;
|
||||
let encoder_outputs = encoder_session
|
||||
.run(encoder_inputs)
|
||||
.context("Failed to run encoder")?;
|
||||
|
||||
let encoder_hidden_states = encoder_outputs["last_hidden_state"]
|
||||
.try_extract_tensor::<f32>()
|
||||
.context("Failed to extract encoder hidden states")?;
|
||||
|
||||
// 将 tensor 转换为 ArrayD
|
||||
let (shape, data) = encoder_hidden_states;
|
||||
let shape_vec: Vec<usize> = shape.iter().map(|&x| x as usize).collect();
|
||||
let encoder_array = ArrayD::from_shape_vec(shape_vec, data.to_vec())
|
||||
.context("Failed to create encoder array")?;
|
||||
|
||||
// 4. 贪婪解码生成输出
|
||||
let output_ids = self.greedy_decode(encoder_array, &attention_mask_array)?;
|
||||
|
||||
// 5. Decode 输出 token IDs
|
||||
let output_tokens: Vec<u32> = output_ids.iter().map(|&id| id as u32).collect();
|
||||
let decoded = self
|
||||
.tokenizer
|
||||
.decode(&output_tokens, true)
|
||||
.map_err(|e| anyhow!("Failed to decode output: {}", e))?;
|
||||
|
||||
Ok(decoded)
|
||||
}
|
||||
|
||||
/// 贪婪解码
|
||||
fn greedy_decode(
|
||||
&self,
|
||||
encoder_hidden_states: ArrayD<f32>,
|
||||
encoder_attention_mask: &Array2<i64>,
|
||||
) -> Result<Vec<i64>> {
|
||||
let batch_size = 1;
|
||||
let mut generated_ids = vec![self.config.decoder_start_token_id];
|
||||
|
||||
for _ in 0..self.config.max_length {
|
||||
// 准备 decoder 输入
|
||||
let decoder_input_len = generated_ids.len();
|
||||
let decoder_input_ids: Array2<i64> =
|
||||
Array2::from_shape_vec((batch_size, decoder_input_len), generated_ids.clone())
|
||||
.context("Failed to create decoder input_ids")?;
|
||||
|
||||
// 创建 ORT Value
|
||||
let decoder_input_value = Value::from_array(decoder_input_ids)
|
||||
.context("Failed to create decoder input value")?;
|
||||
let encoder_hidden_value = Value::from_array(encoder_hidden_states.clone())
|
||||
.context("Failed to create encoder hidden value")?;
|
||||
let encoder_mask_value = Value::from_array(encoder_attention_mask.clone())
|
||||
.context("Failed to create encoder mask value")?;
|
||||
|
||||
// 运行 decoder
|
||||
let decoder_inputs = ort::inputs![
|
||||
"input_ids" => decoder_input_value,
|
||||
"encoder_hidden_states" => encoder_hidden_value,
|
||||
"encoder_attention_mask" => encoder_mask_value,
|
||||
];
|
||||
|
||||
let mut decoder_session = self
|
||||
.decoder_session
|
||||
.lock()
|
||||
.map_err(|e| anyhow!("Failed to lock decoder session: {}", e))?;
|
||||
let decoder_outputs = decoder_session
|
||||
.run(decoder_inputs)
|
||||
.context("Failed to run decoder")?;
|
||||
|
||||
// 获取 logits
|
||||
let logits_tensor = decoder_outputs["logits"]
|
||||
.try_extract_tensor::<f32>()
|
||||
.context("Failed to extract logits")?;
|
||||
|
||||
let (logits_shape, logits_data) = logits_tensor;
|
||||
let vocab_size = logits_shape[2] as usize;
|
||||
|
||||
// 获取最后一个 token 的 logits
|
||||
let last_token_idx = decoder_input_len - 1;
|
||||
let last_logits_start = last_token_idx * vocab_size;
|
||||
let last_logits_end = last_logits_start + vocab_size;
|
||||
|
||||
let last_logits_slice = &logits_data[last_logits_start..last_logits_end];
|
||||
|
||||
// 找到最大概率的 token
|
||||
let next_token_id = last_logits_slice
|
||||
.iter()
|
||||
.enumerate()
|
||||
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())
|
||||
.map(|(idx, _)| idx as i64)
|
||||
.context("Failed to find max token")?;
|
||||
|
||||
// 检查是否到达结束 token
|
||||
if next_token_id == self.config.eos_token_id {
|
||||
break;
|
||||
}
|
||||
|
||||
generated_ids.push(next_token_id);
|
||||
}
|
||||
|
||||
Ok(generated_ids)
|
||||
}
|
||||
|
||||
/// 批量翻译文本
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `texts` - 要翻译的文本列表
|
||||
///
|
||||
/// # Returns
|
||||
/// * `Result<Vec<String>>` - 翻译后的文本列表
|
||||
pub fn translate_batch(&self, texts: &[String]) -> Result<Vec<String>> {
|
||||
texts.iter().map(|text| self.translate(text)).collect()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_translation() {
|
||||
let model = OpusMtModel::new(
|
||||
"C:\\Users\\xkeyc\\Downloads\\onnx_models\\opus-mt-zh-en",
|
||||
"_q4f16",
|
||||
)
|
||||
.unwrap();
|
||||
let result = model.translate("你好世界").unwrap();
|
||||
println!("Translation: {}", result);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user