add qwen3 and fun-asr-nano

src/models/qwen3/config.rs

@@ -0,0 +1,44 @@
+use candle_nn::Activation;
+use serde::Deserialize;
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Qwen3Config {
+    pub attention_bias: bool,
+    pub attention_dropout: f64,
+    pub bos_token_id: u32,
+    pub eos_token_id: u32,
+    pub head_dim: usize,
+    pub hidden_act: Activation,
+    pub hidden_size: usize,
+    pub initializer_range: f64,
+    pub intermediate_size: usize,
+    pub max_position_embeddings: usize,
+    pub max_window_layers: usize,
+    pub num_attention_heads: usize,
+    pub num_hidden_layers: usize,
+    pub num_key_value_heads: usize,
+    pub rms_norm_eps: f64,
+    pub rope_theta: f32,
+    pub tie_word_embeddings: bool,
+    pub torch_dtype: String,
+    pub use_cache: bool,
+    pub use_sliding_window: bool,
+    pub vocab_size: usize,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Qwen3GenerationConfig {
+    pub bos_token_id: usize,
+    pub pad_token_id: usize,
+    pub do_sample: bool,
+    pub eos_token_id: Vec<usize>,
+    pub top_p: f32,
+    pub top_k: usize,
+    pub temperature: f32,
+    #[serde(default = "default_repetition_penalty")]
+    pub repetition_penalty: f32,
+}
+
+fn default_repetition_penalty() -> f32 {
+    1.0
+}

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)))
+    }
+}

src/models/qwen3/mod.rs

@@ -0,0 +1,3 @@
+pub mod config;
+pub mod generate;
+pub mod model;

src/models/qwen3/model.rs

@@ -0,0 +1,277 @@
+use anyhow::Result;
+use candle_core::Tensor;
+use candle_nn::{
+    Embedding, Linear, Module, RmsNorm, VarBuilder, embedding, linear, linear_no_bias, rms_norm,
+};
+
+use crate::{
+    models::{
+        common::{GateUpDownMLP, eager_attention_forward},
+        qwen3::config::Qwen3Config,
+    },
+    position_embed::rope::{RoPE, apply_rotary_pos_emb},
+    utils::tensor_utils::prepare_causal_attention_mask,
+};
+
+pub struct Qwen3Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    q_norm: RmsNorm,
+    k_norm: RmsNorm,
+    num_attention_heads: usize,
+    num_key_value_heads: usize,
+    num_kv_groups: usize,
+    head_dim: usize,
+    scaling: f64,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+impl Qwen3Attention {
+    pub fn new(config: &Qwen3Config, vb: VarBuilder) -> Result<Self> {
+        let hidden_size = config.hidden_size;
+        let num_attention_heads = config.num_attention_heads;
+        let head_dim = config.head_dim;
+        let num_key_value_heads = config.num_key_value_heads;
+        let num_kv_groups = num_attention_heads / num_key_value_heads;
+        let scaling = 1f64 / f64::sqrt(head_dim as f64);
+        let (q_proj, k_proj, v_proj, o_proj) = if config.attention_bias {
+            let q_proj = linear(hidden_size, num_attention_heads * head_dim, vb.pp("q_proj"))?;
+            let k_proj = linear(hidden_size, num_key_value_heads * head_dim, vb.pp("k_proj"))?;
+            let v_proj = linear(hidden_size, num_key_value_heads * head_dim, vb.pp("v_proj"))?;
+            let o_proj = linear(num_attention_heads * head_dim, hidden_size, vb.pp("o_proj"))?;
+            (q_proj, k_proj, v_proj, o_proj)
+        } else {
+            let q_proj =
+                linear_no_bias(hidden_size, num_attention_heads * head_dim, vb.pp("q_proj"))?;
+            let k_proj =
+                linear_no_bias(hidden_size, num_key_value_heads * head_dim, vb.pp("k_proj"))?;
+            let v_proj =
+                linear_no_bias(hidden_size, num_key_value_heads * head_dim, vb.pp("v_proj"))?;
+            let o_proj =
+                linear_no_bias(num_attention_heads * head_dim, hidden_size, vb.pp("o_proj"))?;
+            (q_proj, k_proj, v_proj, o_proj)
+        };
+        let q_norm = rms_norm(head_dim, config.rms_norm_eps, vb.pp("q_norm"))?;
+        let k_norm = rms_norm(head_dim, config.rms_norm_eps, vb.pp("k_norm"))?;
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            q_norm,
+            k_norm,
+            num_attention_heads,
+            num_key_value_heads,
+            num_kv_groups,
+            head_dim,
+            scaling,
+            kv_cache: None,
+        })
+    }
+
+    pub fn forward(
+        &mut self,
+        xs: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        attention_mask: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let (b_sz, q_len, _) = xs.dims3()?;
+        let query_states = self.q_proj.forward(xs)?.reshape((
+            b_sz,
+            q_len,
+            self.num_attention_heads,
+            self.head_dim,
+        ))?;
+        let query_states = self.q_norm.forward(&query_states)?.transpose(1, 2)?;
+        let key_states = self.k_proj.forward(xs)?.reshape((
+            b_sz,
+            q_len,
+            self.num_key_value_heads,
+            self.head_dim,
+        ))?;
+        let key_states = self.k_norm.forward(&key_states)?.transpose(1, 2)?;
+        let value_states = self.v_proj.forward(xs)?;
+        let value_states = value_states
+            .reshape((b_sz, q_len, self.num_key_value_heads, self.head_dim))?
+            .transpose(1, 2)?;
+        let (query_states, key_states) =
+            apply_rotary_pos_emb(&query_states, &key_states, cos, sin, false)?;
+        let (key_states, value_states) = match &self.kv_cache {
+            None => (key_states, value_states),
+            Some((prev_k, prev_v)) => {
+                let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
+                let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
+                (key_states, value_states)
+            }
+        };
+        self.kv_cache = Some((key_states.clone(), value_states.clone()));
+        let attn_output = eager_attention_forward(
+            &query_states,
+            &key_states,
+            &value_states,
+            Some(self.num_kv_groups),
+            attention_mask,
+            self.scaling,
+        )?;
+        let attn_output =
+            attn_output.reshape((b_sz, q_len, self.num_attention_heads * self.head_dim))?;
+        let attn_output = attn_output.apply(&self.o_proj)?;
+        Ok(attn_output)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
+}
+
+pub struct Qwen3DecoderLayer {
+    self_attn: Qwen3Attention,
+    mlp: GateUpDownMLP,
+    input_layernorm: RmsNorm,
+    post_attention_layernorm: RmsNorm,
+}
+
+impl Qwen3DecoderLayer {
+    pub fn new(config: &Qwen3Config, vb: VarBuilder) -> Result<Self> {
+        let self_attn = Qwen3Attention::new(config, vb.pp("self_attn"))?;
+        let mlp = GateUpDownMLP::new(
+            vb.pp("mlp"),
+            config.hidden_size,
+            config.intermediate_size,
+            config.hidden_act,
+            false,
+        )?;
+        let input_layernorm = rms_norm(
+            config.hidden_size,
+            config.rms_norm_eps,
+            vb.pp("input_layernorm"),
+        )?;
+        let post_attention_layernorm = rms_norm(
+            config.hidden_size,
+            config.rms_norm_eps,
+            vb.pp("post_attention_layernorm"),
+        )?;
+        Ok(Self {
+            self_attn,
+            mlp,
+            input_layernorm,
+            post_attention_layernorm,
+        })
+    }
+
+    pub fn forward(
+        &mut self,
+        xs: &Tensor,
+        cos: &Tensor,
+        sin: &Tensor,
+        attention_mask: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let residual = xs.clone();
+        let xs = self.input_layernorm.forward(xs)?;
+        let xs = self.self_attn.forward(&xs, cos, sin, attention_mask)?;
+        let xs = residual.add(&xs)?;
+        let residual = xs.clone();
+        let xs = self.post_attention_layernorm.forward(&xs)?;
+        let xs = self.mlp.forward(&xs)?;
+        let xs = residual.add(&xs)?;
+        Ok(xs)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.self_attn.clear_kv_cache();
+    }
+}
+
+pub struct Qwen3Model {
+    embed_tokens: Embedding,
+    layers: Vec<Qwen3DecoderLayer>,
+    norm: RmsNorm,
+    rotary_emb: RoPE,
+    lm_head: Linear,
+}
+
+impl Qwen3Model {
+    pub fn new(config: &Qwen3Config, vb: VarBuilder) -> Result<Self> {
+        let vb = vb.pp("model");
+        let vocab_size = config.vocab_size;
+        let embed_tokens = embedding(vocab_size, config.hidden_size, vb.pp("embed_tokens"))?;
+        let mut layers = vec![];
+        let vb_l = vb.pp("layers");
+        for layer_idx in 0..config.num_hidden_layers {
+            let layer = Qwen3DecoderLayer::new(config, vb_l.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let norm = rms_norm(config.hidden_size, config.rms_norm_eps, vb.pp("norm"))?;
+        let head_dim = config.head_dim;
+        let rotary_emb = RoPE::new(head_dim, config.rope_theta, vb.device())?;
+        let lm_head = if config.tie_word_embeddings {
+            Linear::new(embed_tokens.embeddings().clone(), None)
+        } else {
+            linear_no_bias(config.hidden_size, config.vocab_size, vb.pp("lm_head"))?
+        };
+        Ok(Self {
+            embed_tokens,
+            layers,
+            norm,
+            rotary_emb,
+            lm_head,
+        })
+    }
+    pub fn forward(
+        &mut self,
+        input_ids: Option<&Tensor>,
+        inputs_embeds: Option<&Tensor>,
+        seqlen_offset: usize,
+    ) -> Result<Tensor> {
+        if input_ids.is_none() && inputs_embeds.is_none() {
+            return Err(anyhow::anyhow!(
+                "You must specify exactly one of input_ids or inputs_embeds"
+            ));
+        }
+        let inputs_embeds = if let Some(inputs_embeds) = inputs_embeds {
+            inputs_embeds.clone()
+        } else {
+            let input_ids = input_ids.unwrap();
+            self.embedding_token_id(input_ids)?
+        };
+        let (bs, seq_len, _) = inputs_embeds.dims3()?;
+        let attention_mask: Option<Tensor> = {
+            if seq_len <= 1 {
+                None
+            } else {
+                Some(prepare_causal_attention_mask(
+                    bs,
+                    seq_len,
+                    0,
+                    inputs_embeds.device(),
+                )?)
+            }
+        };
+
+        let (cos, sin) = self
+            .rotary_emb
+            .forward(seqlen_offset, seq_len, inputs_embeds.device())?;
+
+        let mut hidden_states = inputs_embeds;
+        for decode_layer in &mut self.layers {
+            hidden_states =
+                decode_layer.forward(&hidden_states, &cos, &sin, attention_mask.as_ref())?;
+        }
+        hidden_states = self.norm.forward(&hidden_states)?;
+        let hidden_state = hidden_states.narrow(1, seq_len - 1, 1)?;
+        let logits = self.lm_head.forward(&hidden_state)?;
+        Ok(logits)
+    }
+    pub fn embedding_token_id(&self, input_ids: &Tensor) -> Result<Tensor> {
+        Ok(self.embed_tokens.forward(input_ids)?)
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        for layer in self.layers.iter_mut() {
+            layer.clear_kv_cache()
+        }
+    }
+}