173 lines
6.1 KiB
Rust
173 lines
6.1 KiB
Rust
use std::collections::HashMap;
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use crate::{
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models::common::{
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MultiModalData,
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generate::{GenerationContext, generate_generic, generate_stream_generic},
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},
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params::chat::{ChatCompletionChunkResponse, ChatCompletionParameters, ChatCompletionResponse},
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};
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use anyhow::{Result, anyhow};
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use candle_core::{DType, Device, pickle::read_all_with_key};
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use candle_nn::VarBuilder;
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use rocket::futures::Stream;
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use crate::{
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models::{
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GenerateModel,
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fun_asr_nano::{
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config::FunASRNanoConfig, model::FunAsrNanoModel, processor::FunAsrNanoProcessor,
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},
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qwen3::config::{Qwen3Config, Qwen3GenerationConfig},
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},
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tokenizer::TokenizerModel,
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utils::{find_type_files, get_device, get_dtype},
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};
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pub struct FunAsrNanoGenerateModel {
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tokenizer: TokenizerModel,
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processor: FunAsrNanoProcessor,
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fun_asr_nano: FunAsrNanoModel,
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device: Device,
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dtype: DType,
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generation_config: Qwen3GenerationConfig,
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model_name: String,
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}
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impl FunAsrNanoGenerateModel {
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pub fn init(path: &str, device: Option<&Device>, dtype: Option<DType>) -> Result<Self> {
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let llm_config_path = path.to_string() + "/Qwen3-0.6B";
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let tokenizer = TokenizerModel::init(&llm_config_path)?;
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let generation_config_path = llm_config_path.clone() + "/generation_config.json";
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let generation_config: Qwen3GenerationConfig =
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serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
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let config_path = llm_config_path + "/config.json";
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let llm_cfg: Qwen3Config = serde_json::from_slice(&std::fs::read(config_path)?)?;
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let device = get_device(device);
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let config_path = path.to_string() + "/config.yaml";
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let cfg: FunASRNanoConfig = serde_yaml::from_slice(&std::fs::read(config_path)?)?;
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let cfg_dtype = cfg.llm_conf.llm_dtype.as_str();
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let dtype = get_dtype(dtype, cfg_dtype);
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let processor = FunAsrNanoProcessor::new(&cfg.frontend_conf, &device)?;
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let model_list = find_type_files(path, "pt")?;
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let mut dict_to_hashmap = HashMap::new();
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for m in model_list {
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let dict = match read_all_with_key(m.clone(), Some("state_dict")) {
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Ok(dict) => dict,
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Err(e) => {
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println!(
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"model read_all_with_key {} get state_dict err: {}, use None try again",
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&m, e
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);
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match read_all_with_key(m.clone(), None) {
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Ok(dict) => dict,
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Err(e) => {
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return Err(anyhow!(format!(
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"model read_all_with_key({}, None): e: {}",
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&m, e
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)));
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}
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}
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}
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};
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for (k, v) in dict {
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dict_to_hashmap.insert(k, v);
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}
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}
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let vb = VarBuilder::from_tensors(dict_to_hashmap, dtype, &device);
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let fun_asr_nano =
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FunAsrNanoModel::new(vb, &cfg, &llm_cfg, generation_config.eos_token_id.clone())?;
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let model_name = std::path::Path::new(path)
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.file_name()
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.and_then(|s| s.to_str())
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.unwrap_or("fun-asr-nano")
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.to_string();
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Ok(Self {
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tokenizer,
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processor,
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fun_asr_nano,
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device,
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dtype,
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generation_config,
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model_name,
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})
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}
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}
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impl GenerateModel for FunAsrNanoGenerateModel {
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fn generate(&mut self, mes: ChatCompletionParameters) -> Result<ChatCompletionResponse> {
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let temperature = mes
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.temperature
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.unwrap_or(self.generation_config.temperature);
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let top_p = mes.top_p.unwrap_or(self.generation_config.top_p);
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let top_k = self.generation_config.top_k;
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let seed = mes.seed.unwrap_or(34562) as u64;
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let max_tokens = mes.max_tokens.unwrap_or(1024);
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let (speech, fbank_mask, input_ids) = self.processor.process_info(&mes, &self.tokenizer)?;
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let speech = speech.to_dtype(self.dtype)?;
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let mut ctx = GenerationContext::new(
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temperature.into(),
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top_p.into(),
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top_k.into(),
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mes.repeat_penalty,
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mes.repeat_last_n,
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seed,
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input_ids.dim(1)?,
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max_tokens,
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self.device.clone(),
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);
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let data_vec = vec![speech.into(), fbank_mask.into()];
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let data = MultiModalData::new(data_vec);
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generate_generic(
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&mut self.fun_asr_nano,
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&self.tokenizer,
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input_ids,
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data,
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&mut ctx,
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&self.model_name,
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)
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}
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fn generate_stream(
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&mut self,
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mes: ChatCompletionParameters,
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) -> Result<
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Box<
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dyn Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>
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+ Send
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+ Unpin
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+ '_,
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>,
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> {
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let temperature = mes
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.temperature
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.unwrap_or(self.generation_config.temperature);
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let top_p = mes.top_p.unwrap_or(self.generation_config.top_p);
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let top_k = self.generation_config.top_k;
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let seed = mes.seed.unwrap_or(34562) as u64;
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let max_tokens = mes.max_tokens.unwrap_or(1024);
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let (speech, fbank_mask, input_ids) = self.processor.process_info(&mes, &self.tokenizer)?;
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let speech = speech.to_dtype(self.dtype)?;
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let data_vec = vec![speech.into(), fbank_mask.into()];
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let data = MultiModalData::new(data_vec);
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let stream = generate_stream_generic(
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&mut self.fun_asr_nano,
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&self.tokenizer,
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input_ids,
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data,
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temperature.into(),
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top_p.into(),
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top_k.into(),
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mes.repeat_penalty,
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mes.repeat_last_n,
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seed,
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max_tokens,
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false,
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&self.device,
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&self.model_name,
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)?;
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Ok(Box::new(Box::pin(stream)))
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}
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}
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