aha/src/models/qwen3/generate.rs

use crate::params::chat::{
    ChatCompletionChunkResponse, ChatCompletionParameters, ChatCompletionResponse,
};
use anyhow::{Result, anyhow};
use candle_core::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use rocket::async_stream::stream;
use rocket::futures::Stream;

use crate::models::qwen3::config::{Qwen3Config, Qwen3GenerationConfig};
use crate::models::qwen3::model::Qwen3Model;
// use crate::models::GenerateStream;
use crate::utils::{
    build_completion_chunk_response, build_completion_response, find_type_files, get_device,
    get_dtype, get_logit_processor,
};
use crate::{chat_template::ChatTemplate, models::GenerateModel, tokenizer::TokenizerModel};

pub struct Qwen3GenerateModel<'a> {
    chat_template: ChatTemplate<'a>,
    tokenizer: TokenizerModel,
    qwen3: Qwen3Model,
    device: Device,
    eos_token_id1: u32,
    eos_token_id2: u32,
    generation_config: Qwen3GenerationConfig,
    model_name: String,
}

impl<'a> Qwen3GenerateModel<'a> {
    pub fn init(path: &str, device: Option<&Device>, dtype: Option<DType>) -> Result<Self> {
        let chat_template = ChatTemplate::init(path)?;
        let tokenizer = TokenizerModel::init(path)?;
        let config_path = path.to_string() + "/config.json";
        let cfg: Qwen3Config = serde_json::from_slice(&std::fs::read(config_path)?)?;
        let device = &get_device(device);
        let cfg_dtype = cfg.torch_dtype.as_str();
        let dtype = get_dtype(dtype, cfg_dtype);
        let model_list = find_type_files(path, "safetensors")?;
        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
        let qwen3 = Qwen3Model::new(&cfg, vb)?;
        let generation_config_path = path.to_string() + "/generation_config.json";
        let generation_config: Qwen3GenerationConfig =
            serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
        let model_name = std::path::Path::new(path)
            .file_name()
            .and_then(|s| s.to_str())
            .unwrap_or("qwen3")
            .to_string();
        Ok(Qwen3GenerateModel {
            chat_template,
            tokenizer,
            qwen3,
            device: device.clone(),
            eos_token_id1: generation_config.eos_token_id[0] as u32,
            eos_token_id2: generation_config.eos_token_id[1] as u32,
            generation_config,
            model_name,
        })
    }
}

impl<'a> GenerateModel for Qwen3GenerateModel<'a> {
    fn generate(&mut self, mes: ChatCompletionParameters) -> Result<ChatCompletionResponse> {
        let temperature = mes
            .temperature
            .unwrap_or(self.generation_config.temperature);
        let top_p = mes.top_p.unwrap_or(self.generation_config.top_p);
        let top_k = self.generation_config.top_k;
        let seed = mes.seed.unwrap_or(34562) as u64;
        let mut logit_processor =
            get_logit_processor(Some(temperature), Some(top_p), Some(top_k), seed);

        let mes_render = self.chat_template.apply_chat_template(&mes)?;
        // let enable_thinking = extract_metadata_value::<bool>(&mes.metadata, "enable_thinking");
        // let mes_render = self
        //     .chat_template
        //     .apply_chat_temp_think(&mes, enable_thinking)?;
        let mut input_ids = self.tokenizer.text_encode(mes_render, &self.device)?;
        let mut seq_len = input_ids.dim(1)?;
        let prompt_tokens = seq_len as u32;
        let mut seqlen_offset = 0;
        let mut generate = Vec::new();
        let sample_len = mes.max_tokens.unwrap_or(2048);
        for _ in 0..sample_len {
            let logits = self.qwen3.forward(Some(&input_ids), None, seqlen_offset)?;
            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
            let next_token = logit_processor.sample(&logits)?;
            generate.push(next_token);
            if next_token == self.eos_token_id1 || next_token == self.eos_token_id2 {
                break;
            }
            seqlen_offset += seq_len;
            seq_len = 1;
            input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
        }
        let num_token = generate.len() as u32;
        let res = self.tokenizer.token_decode(generate)?;
        self.qwen3.clear_kv_cache();
        let response =
            build_completion_response(res, &self.model_name, Some(num_token), Some(prompt_tokens));
        Ok(response)
    }
    fn generate_stream(
        &mut self,
        mes: ChatCompletionParameters,
    ) -> Result<
        Box<
            dyn Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>
                + Send
                + Unpin
                + '_,
        >,
    > {
        let temperature = mes
            .temperature
            .unwrap_or(self.generation_config.temperature);
        let top_p = mes.top_p.unwrap_or(self.generation_config.top_p);
        let top_k = self.generation_config.top_k;
        let seed = mes.seed.unwrap_or(34562) as u64;
        let mut logit_processor =
            get_logit_processor(Some(temperature), Some(top_p), Some(top_k), seed);
        let mes_render = self.chat_template.apply_chat_template(&mes)?;
        // let enable_thinking = extract_metadata_value::<bool>(&mes.metadata, "enable_thinking");
        // let mes_render = self
        //     .chat_template
        //     .apply_chat_temp_think(&mes, enable_thinking)?;
        let mut input_ids = self.tokenizer.text_encode(mes_render, &self.device)?;
        let mut seq_len = input_ids.dim(1)?;
        let mut seqlen_offset = 0;
        let sample_len = mes.max_tokens.unwrap_or(512);
        let stream = stream! {
            let mut error_tokens = Vec::new();
            for _ in 0..sample_len {
                let logits = self.qwen3.forward(
                    Some(&input_ids),
                    None,
                    seqlen_offset,
                )?;
                let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
                let next_token = logit_processor.sample(&logits)?;
                let mut decode_ids = Vec::new();
                if !error_tokens.is_empty(){
                    decode_ids.extend_from_slice(&error_tokens);
                }
                decode_ids.push(next_token);
                let decoded_token = self.tokenizer.token_decode(decode_ids).map_err(|e| anyhow!(format!("stream decode error{e}")))?;
                if decoded_token.contains("�") {
                    error_tokens.push(next_token);
                    if error_tokens.len() > 3 {
                        error_tokens.clear();
                    }
                    seqlen_offset += seq_len;
                    seq_len = 1;
                    input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
                    continue;
                }
                error_tokens.clear();
                let chunk = build_completion_chunk_response(decoded_token, &self.model_name, None, None);
                yield Ok(chunk);
                if next_token == self.eos_token_id1 || next_token == self.eos_token_id2 {
                    break;
                }
                seqlen_offset += seq_len;
                seq_len = 1;
                input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;

            }
            self.qwen3.clear_kv_cache();
        };
        Ok(Box::new(Box::pin(stream)))
    }
}