add qwen3 and fun-asr-nano

src/models/qwen3/generate.rs

@@ -0,0 +1,172 @@
+use aha_openai_dive::v1::resources::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)?)?;
+
+        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: "qwen3".to_string(),
+        })
+    }
+}
+
+impl<'a> GenerateModel for Qwen3GenerateModel<'a> {
+    fn generate(&mut self, mes: ChatCompletionParameters) -> Result<ChatCompletionResponse> {
+        let temperature = match mes.temperature {
+            None => self.generation_config.temperature,
+            Some(tem) => tem,
+        };
+        let top_p = match mes.top_p {
+            None => self.generation_config.top_p,
+            Some(top_p) => top_p,
+        };
+        let top_k = self.generation_config.top_k;
+        let seed = match mes.seed {
+            None => 34562u64,
+            Some(s) => s 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 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 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));
+        Ok(response)
+    }
+    fn generate_stream(
+        &mut self,
+        mes: ChatCompletionParameters,
+    ) -> Result<
+        Box<
+            dyn Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>
+                + Send
+                + Unpin
+                + '_,
+        >,
+    > {
+        let temperature = match mes.temperature {
+            None => self.generation_config.temperature,
+            Some(tem) => tem,
+        };
+        let top_p = match mes.top_p {
+            None => self.generation_config.top_p,
+            Some(top_p) => top_p,
+        };
+        let top_k = self.generation_config.top_k;
+        let seed = match mes.seed {
+            None => 34562u64,
+            Some(s) => s 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 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)))
+    }
+}