add Qwen3VL model

Cargo.lock

@@ -2445,9 +2445,8 @@ dependencies = [
 
 [[package]]
 name = "openai_dive"
-version = "1.3.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "bac82ff995cdbe4e120fa87fe4d76bd452866dd7b0c9bcbdb562ba4be0f1786d"
+version = "1.3.2"
+source = "git+https://github.com/jhqxxx/openai-client.git#83363a83fbd73273e2a1474ca7fdfb1a899e82d4"
 dependencies = [
  "bytes",
  "derive_builder",

Cargo.toml

@@ -21,7 +21,7 @@ base64 = "0.22.1"
 num = "0.4.3"
 minijinja = "2.12.0"
 tokenizers = "0.22.1"
-openai_dive = { version = "1.3.0", features = ["stream"]}
+openai_dive = { git = "https://github.com/jhqxxx/openai-client.git", version = "1.3.0", features = ["stream"]}
 uuid = { version = "1.18.1", features = ["v4"]}
 chrono = "0.4.42"
 rocket = "0.5.1"

README.md

@@ -15,6 +15,7 @@
 * Qwen2.5VL - 阿里通义千问 2.5 多模态大语言模型
 * MiniCPM4 - 面壁智能 MiniCPM 系列语言模型
 * VoxCPM - 面壁智能语音生成模型
+* Qwen3VL - 阿里通义千问 3 多模态大语言模型
 
 ## 计划支持
 我们持续扩展支持的模型列表，欢迎贡献！
@@ -39,8 +40,8 @@ aha = { git = "https://github.com/jhqxxx/aha.git", features = ["cuda", "flash-at
 git clone https://github.com/jhqxxx/aha.git
 cd aha
 # 修改测试用例中模型路径
-# 运行 Qwen2.5VL 示例
-cargo test -F cuda qwen2_5vl_generate -- --nocapture
+# 运行 Qwen3VL 示例
+cargo test -F cuda qwen3vl_generate -- --nocapture
 
 # 运行 MiniCPM4 示例  
 cargo test -F cuda minicpm_generate -- --nocapture
@@ -91,6 +92,7 @@ fn main() -> Result<()> {
 │   │   ├── common
 │   │   ├── minicpm4
 │   │   ├── qwen2_5vl
+│   │   ├── qwen3vl
 │   │   ├── voxcpm
 │   │   └── mod.rs
 │   ├── position_embed
@@ -121,6 +123,9 @@ fn main() -> Result<()> {
 2. 提交新的 Issue，包含详细描述和复现步骤
 
 ## 更新日志
+### v0.1.1
+* 添加 Qwen3VL 模型
+
 ### v0.1.0
 * 初始版本发布
 * 支持 Qwen2.5VL, MiniCPM4, VoxCPM 模型

src/lib.rs

@@ -3,28 +3,3 @@ pub mod models;
 pub mod position_embed;
 pub mod tokenizer;
 pub mod utils;
-
-// pub enum ModelType {
-//     Qwen2_5VL,
-//     MiniCPM4,
-// }
-
-// impl ModelType {
-//     pub fn init(
-//         model_type: ModelType,
-//         model_path: &str,
-//         device: Option<&Device>,
-//         dtype: Option<DType>,
-//     ) -> Result<Box<dyn GenerateModel>> {
-//         match model_type {
-//             ModelType::Qwen2_5VL => {
-//                 let model = Qwen2_5VLGenerateModel::init(model_path, device, dtype)?;
-//                 Ok(Box::new(model) as Box<dyn GenerateModel>)
-//             },
-//             ModelType::MiniCPM4 => {
-//                 let model = MiniCPMGenerateModel::init(model_path, device, dtype)?;
-//                 Ok(Box::new(model)as Box<dyn GenerateModel>)
-//            }
-//         }
-//     }
-// }

src/models/common/mod.rs

@@ -269,3 +269,54 @@ impl AttentionNobias {
         self.kv_cache = None
     }
 }
+
+pub fn eager_attention_forward(
+    query_states: &Tensor,
+    key_states: &Tensor,
+    value_states: &Tensor,
+    num_key_value_groups: Option<usize>,
+    attention_mask: Option<&Tensor>,
+    scaling: f64,
+) -> Result<Tensor> {
+    let key_states = match num_key_value_groups {
+        Some(g) => repeat_kv(key_states.clone(), g)?.contiguous()?,
+        None => key_states.clone()
+    };
+    let value_states = match num_key_value_groups {
+        Some(g) => repeat_kv(value_states.clone(), g)?.contiguous()?,
+        None => value_states.clone()
+    };
+    let attn_output = {
+        #[cfg(not(feature = "flash-attn"))]
+        {
+            let attn_weights = query_states.matmul(&key_states.transpose(D::Minus2, D::Minus1)?)?;
+            let attn_weights = (attn_weights * scaling)?;            
+            let attn_weights = match attention_mask {
+                None => attn_weights,
+                Some(mask) => attn_weights.broadcast_add(&mask.to_dtype(attn_weights.dtype())?)?,
+            };
+            let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
+            attn_weights.matmul(&value_states)?
+        }
+        #[cfg(feature = "flash-attn")]
+        {
+            // use flash-attn,
+            // flash-attn shape: (bs, seq_len, num_head, head_dim)
+            let query_states = query_states.transpose(1, 2)?;
+            let key_states = key_states.transpose(1, 2)?;
+            let value_states = value_states.transpose(1, 2)?;
+            let attn_output = candle_flash_attn::flash_attn(
+                &query_states,
+                &key_states,
+                &value_states,
+                scaling as f32,
+                attention_mask.is_some(),
+            )?
+            .transpose(1, 2)?;
+            attn_output
+        }
+    };
+    let attn_output = attn_output.transpose(1, 2)?.contiguous()?;
+    
+    Ok(attn_output)
+}

src/models/minicpm4/generate.rs

@@ -53,7 +53,7 @@ impl<'a> MiniCPMGenerateModel<'a> {
 
 impl<'a> GenerateModel for MiniCPMGenerateModel<'a> {
     fn generate(&mut self, mes: ChatCompletionParameters) -> Result<ChatCompletionResponse> {
-        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p);
+        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p, None);
         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)?;
@@ -81,7 +81,7 @@ impl<'a> GenerateModel for MiniCPMGenerateModel<'a> {
         &mut self,
         mes: ChatCompletionParameters,
     ) -> Result<impl Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>> {
-        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p);
+        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p, None);
         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)?;

src/models/mod.rs

@@ -2,6 +2,7 @@ pub mod common;
 pub mod minicpm4;
 pub mod qwen2_5vl;
 pub mod voxcpm;
+pub mod qwen3vl;
 
 use anyhow::Result;
 use openai_dive::v1::resources::chat::{

src/models/qwen2_5vl/generate.rs

@@ -63,7 +63,7 @@ impl<'a> Qwen2_5VLGenerateModel<'a> {
 
 impl<'a> GenerateModel for Qwen2_5VLGenerateModel<'a> {
     fn generate(&mut self, mes: ChatCompletionParameters) -> Result<ChatCompletionResponse> {
-        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p);
+        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p, None);
         let mes_render = self.chat_template.apply_chat_template(&mes)?;
         let input = self.pre_processor.process_info(&mes, &mes_render)?;
         let mut input_ids = self
@@ -118,11 +118,12 @@ impl<'a> GenerateModel for Qwen2_5VLGenerateModel<'a> {
         let response = build_completion_response(res, "qwen2.5vl");
         Ok(response)
     }
+    
     fn generate_stream(
         &mut self,
         mes: ChatCompletionParameters,
     ) -> Result<impl Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>> {
-        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p);
+        let mut logit_processor = get_logit_processor(mes.temperature, mes.top_p, None);
         let mes_render = self.chat_template.apply_chat_template(&mes)?;
         let input = self.pre_processor.process_info(&mes, &mes_render)?;
         let mut input_ids = self

src/models/qwen2_5vl/model.rs

@@ -841,7 +841,7 @@ impl Qwen2_5VLTextModel {
             if seq_len <= 1 {
                 None
             } else {
-                Some(&self.prepare_causal_attention_mask(b_size, seq_len, seqlen_offset)?)
+                Some(&self.prepare_causal_attention_mask(b_size, seq_len, 0)?)
             }
         };
         for layer in self.layers.iter_mut() {

src/models/qwen3vl/config.rs

@@ -0,0 +1,89 @@
+use candle_nn::Activation;
+
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Size {
+    pub longest_edge: usize,
+    pub shortest_edge: usize,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct PreprocessorConfig {
+    pub size: Size,
+    pub patch_size: usize,
+    pub temporal_patch_size: usize,
+    pub merge_size: usize,
+    pub image_mean: Vec<f32>,
+    pub image_std: Vec<f32>,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct RopeScaling {
+    pub rope_type: String,
+    pub mrope_section: Vec<usize>,
+    pub mrope_interleaved: bool,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Qwen3VLTextConfig {
+    pub attention_bias: bool,
+    pub attention_dropout: f32,
+    pub bos_token_id: usize,
+    pub dtype: String,
+    pub eos_token_id: usize,
+    pub head_dim: usize,
+    pub hidden_act: Activation,
+    pub hidden_size: usize,
+    pub initializer_range: f32,
+    pub intermediate_size: usize,
+    pub max_position_embeddings: usize,
+    pub num_attention_heads: usize,
+    pub num_hidden_layers: usize,
+    pub num_key_value_heads: usize,
+    pub rms_norm_eps: f64,
+    pub rope_scaling: RopeScaling,
+    pub rope_theta: f32,
+    pub tie_word_embeddings: bool,
+    pub use_cache: bool,
+    pub vocab_size: usize,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Qwen3VLVisionConfig {
+    pub deepstack_visual_indexes: Vec<usize>,
+    pub depth: usize,
+    pub hidden_act: String,
+    pub hidden_size: usize,
+    pub in_channels: usize,
+    pub initializer_range: f32,
+    pub intermediate_size: usize,
+    pub num_heads: usize,
+    pub num_position_embeddings: usize,
+    pub out_hidden_size: usize,
+    pub patch_size: usize,
+    pub spatial_merge_size: usize,
+    pub temporal_patch_size: usize,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Qwen3VLConfig {
+    pub image_token_id: usize,
+    pub text_config: Qwen3VLTextConfig,
+    pub tie_word_embeddings: bool,
+    pub video_token_id: usize,
+    pub vision_config: Qwen3VLVisionConfig,
+    pub vision_end_token_id: usize,
+    pub vision_start_token_id: usize,
+}
+
+#[derive(Debug, Clone, PartialEq, serde::Deserialize)]
+pub struct Qwen3VLGenerationConfig {
+    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,
+    pub repetition_penalty: f32,
+}

src/models/qwen3vl/generate.rs

@@ -0,0 +1,199 @@
+use anyhow::{Result, anyhow};
+use candle_core::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
+use openai_dive::v1::resources::chat::{ChatCompletionChunkResponse, ChatCompletionParameters, ChatCompletionResponse};
+use rocket::futures::Stream;
+use rocket::async_stream::stream;
+
+use crate::{
+    chat_template::ChatTemplate,
+    models::{GenerateModel, qwen3vl::{
+        config::{Qwen3VLConfig, Qwen3VLGenerationConfig},
+        model::Qwen3VLModel,
+        processor::Qwen3VLProcessor,
+    }},
+    tokenizer::TokenizerModel,
+    utils::{
+        build_completion_chunk_response, build_completion_response, find_type_files, get_device, get_dtype, get_logit_processor
+    },
+};
+
+pub struct Qwen3VLGenerateModel<'a> {
+    chat_template: ChatTemplate<'a>,
+    tokenizer: TokenizerModel,
+    pre_processor: Qwen3VLProcessor,
+    qwen3_vl: Qwen3VLModel,
+    device: Device,
+    eos_token_id1: u32,
+    eos_token_id2: u32,
+    generation_config: Qwen3VLGenerationConfig,
+}
+
+impl<'a> Qwen3VLGenerateModel<'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: Qwen3VLConfig = serde_json::from_slice(&std::fs::read(config_path)?)?;
+        let device = get_device(device);
+        let cfg_dtype = cfg.text_config.dtype.as_str();
+        let dtype = get_dtype(dtype, cfg_dtype);
+        let pre_processor = Qwen3VLProcessor::new(path, &device, dtype)?;
+        let model_list = find_type_files(path, "safetensors")?;
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, &device)? };
+        let vb = vb.pp("model");
+        let qwen3_vl = Qwen3VLModel::new(cfg, vb)?;
+        let generation_config_path = path.to_string() + "/generation_config.json";
+        let generation_config: Qwen3VLGenerationConfig =
+            serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
+        Ok(Self {
+            chat_template,
+            tokenizer,
+            pre_processor,
+            qwen3_vl,
+            device,
+            eos_token_id1: generation_config.eos_token_id[0] as u32,
+            eos_token_id2: generation_config.eos_token_id[1] as u32,
+            generation_config,
+        })
+    }
+
+}
+
+impl<'a> GenerateModel for Qwen3VLGenerateModel<'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 mut logit_processor = get_logit_processor(Some(temperature), Some(top_p), Some(top_k));
+        let mes_render = self.chat_template.apply_chat_template(&mes)?;
+        let input = self.pre_processor.process_info(&mes, &mes_render)?;
+        let mut input_ids = self
+            .tokenizer
+            .text_encode(input.replace_text.clone(), &self.device)?;
+        let mut seq_len = input_ids.dim(1)?;
+        let mut seqlen_offset = 0;
+        let mut pixel_values = input.pixel_values.as_ref();
+        let image_grid_thw = input.image_grid_thw.as_ref();
+        let mut pixel_values_video = input.pixel_values_video.as_ref();
+        let video_grid_thw = input.video_grid_thw.as_ref();
+        let mut cache_position = Tensor::arange(0u32, seq_len as u32, &self.device)?;
+        let mut generate = Vec::new();
+        let sample_len = mes.max_tokens.unwrap_or(1024);
+        for _ in 0..sample_len {
+            let logits = self.qwen3_vl.forward(
+                &input_ids,
+                pixel_values,
+                image_grid_thw,
+                pixel_values_video,
+                video_grid_thw,
+                Some(&cache_position),
+                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)?;
+            cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
+            pixel_values = None;
+            pixel_values_video = None;
+        }
+        let res = self.tokenizer.token_decode(generate)?;
+        self.qwen3_vl.clear_kv_cache();
+        let response = build_completion_response(res, "qwen3vl");
+        Ok(response)
+    }
+
+    fn generate_stream(
+        &mut self,
+        mes: ChatCompletionParameters,
+    ) -> Result<impl Stream<Item = Result<ChatCompletionChunkResponse, anyhow::Error>>> {
+        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 mut logit_processor = get_logit_processor(Some(temperature), Some(top_p), Some(top_k));
+        let mes_render = self.chat_template.apply_chat_template(&mes)?;
+        let input = self.pre_processor.process_info(&mes, &mes_render)?;
+        let mut input_ids = self
+            .tokenizer
+            .text_encode(input.replace_text.clone(), &self.device)?;
+        let mut seq_len = input_ids.dim(1)?;
+        let mut seqlen_offset = 0;
+        let pixel_values = input.pixel_values.clone();
+        let image_grid_thw = input.image_grid_thw.clone();
+        let pixel_values_video = input.pixel_values_video.clone();
+        let video_grid_thw = input.video_grid_thw.clone();
+        let mut cache_position = Tensor::arange(0u32, seq_len as u32, &self.device)?;
+        let sample_len = mes.max_tokens.unwrap_or(1024);
+        let stream = stream! {
+            let mut error_tokens = Vec::new();
+            let mut pixel_values = pixel_values.as_ref();
+            let image_grid_thw = image_grid_thw.as_ref();
+            let mut pixel_values_video = pixel_values_video.as_ref();
+            let video_grid_thw = video_grid_thw.as_ref();
+            for _ in 0..sample_len {
+            let logits = self.qwen3_vl.forward(
+                &input_ids,
+                pixel_values,
+                image_grid_thw,
+                pixel_values_video,
+                video_grid_thw,
+                Some(&cache_position),
+                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)?;
+                    cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
+                    pixel_values = None;
+                    pixel_values_video = None;
+                    continue;
+                }
+                error_tokens.clear();
+                let chunk = build_completion_chunk_response(decoded_token, "qwen3vl", 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)?;
+                cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
+                pixel_values = None;
+                pixel_values_video = None;
+            }
+            self.qwen3_vl.clear_kv_cache();
+        };
+        Ok(stream)
+    }
+}

src/models/qwen3vl/mod.rs

@@ -0,0 +1,4 @@
+pub mod processor;
+pub mod config;
+pub mod model;
+pub mod generate;

src/models/qwen3vl/processor.rs

@@ -0,0 +1,625 @@
+use std::collections::HashMap;
+
+use anyhow::{Result, anyhow};
+use candle_core::{DType, Device, IndexOp, Shape, Tensor};
+use ffmpeg_next as ffmpeg;
+use image::DynamicImage;
+use num::integer::lcm;
+use openai_dive::v1::resources::chat::{
+    ChatCompletionParameters, ChatMessage, ChatMessageContent, ChatMessageContentPart,
+};
+
+use crate::{
+    models::qwen3vl::config::PreprocessorConfig,
+    utils::{ceil_by_factor, floor_by_factor, img_utils::get_image, round_by_factor},
+};
+
+#[derive(Clone)]
+pub struct VisionInput {
+    pub data: Tensor,
+    pub grid_thw: Tensor,
+}
+
+#[derive(Clone)]
+pub struct GeneralInput {
+    pub replace_text: String,
+    pub pixel_values: Option<Tensor>,
+    pub image_grid_thw: Option<Tensor>,
+    pub pixel_values_video: Option<Tensor>,
+    pub video_grid_thw: Option<Tensor>,
+}
+
+#[allow(unused)]
+#[derive(Debug, Clone)]
+pub struct VideoMetadata {
+    total_num_frames: u32,
+    fps: f32,
+    width: u32,
+    height: u32,
+    duration: f32,
+    frame_indices: Vec<u32>,
+}
+
+pub struct Qwen3VLProcessor {
+    img_process_cfg: PreprocessorConfig,
+    video_process_cfg: PreprocessorConfig,
+    device: Device,
+    dtype: DType,
+    image_token: String,
+    video_token: String,
+    vision_start_token: String,
+    vision_end_token: String,
+    fps: u32,
+    min_frames: u32,
+    max_frames: u32,
+}
+
+impl Qwen3VLProcessor {
+    pub fn new(path: &str, device: &Device, dtype: DType) -> Result<Self> {
+        let path = path.to_string();
+        assert!(
+            std::path::Path::new(&path).exists(),
+            "model path file not exists"
+        );
+        let img_process_cfg_file = path.clone() + "/preprocessor_config.json";
+        assert!(
+            std::path::Path::new(&img_process_cfg_file).exists(),
+            "preprocessor_config.json not exists in model path"
+        );
+        let img_process_cfg: PreprocessorConfig =
+            serde_json::from_slice(&std::fs::read(img_process_cfg_file)?)?;
+
+        let video_process_cfg_file = path.clone() + "/video_preprocessor_config.json";
+        assert!(
+            std::path::Path::new(&video_process_cfg_file).exists(),
+            "video_preprocessor_config.json not exists in model path"
+        );
+        let video_process_cfg: PreprocessorConfig =
+            serde_json::from_slice(&std::fs::read(video_process_cfg_file)?)?;
+
+        let image_token = "<|image_pad|>".to_string();
+        let video_token = "<|video_pad|>".to_string();
+        let vision_start_token = "<|vision_start|>".to_string();
+        let vision_end_token = "<|vision_end|>".to_string();
+        Ok(Self {
+            img_process_cfg,
+            video_process_cfg,
+            device: device.clone(),
+            dtype,
+            image_token,
+            video_token,
+            vision_start_token,
+            vision_end_token,
+            fps: 2,
+            min_frames: 4,
+            max_frames: 768,
+        })
+    }
+
+    pub fn extract_vision_info(
+        &self,
+        mes: &ChatCompletionParameters,
+    ) -> Result<HashMap<String, Vec<String>>> {
+        let mut vision_map = HashMap::new();
+        vision_map.insert("image".to_string(), Vec::new());
+        vision_map.insert("video".to_string(), Vec::new());
+        for chat_mes in mes.messages.clone() {
+            if let ChatMessage::User { content, .. } = chat_mes
+                && let ChatMessageContent::ContentPart(part_vec) = content
+            {
+                for part in part_vec {
+                    if let ChatMessageContentPart::Image(img_part) = part {
+                        let img_url = img_part.image_url;
+                        vision_map.get_mut("image").unwrap().push(img_url.url);
+                    } else if let ChatMessageContentPart::Video(video_part) = part {
+                        let video_url = video_part.video_url;
+                        vision_map.get_mut("video").unwrap().push(video_url.url);
+                    }
+                }
+            }
+        }
+        Ok(vision_map)
+    }
+
+    pub fn process_img(
+        &self,
+        img: &DynamicImage,
+        img_mean: &Tensor,
+        img_std: &Tensor,
+    ) -> Result<Tensor> {
+        let img_h = img.height();
+        let img_w = img.width();
+        //  h,w resize成 28的倍数
+        let (resize_h, resize_w) = img_smart_resize(
+            img_h,
+            img_w,
+            (self.img_process_cfg.patch_size * self.img_process_cfg.merge_size) as u32,
+            self.img_process_cfg.size.shortest_edge as u32,
+            self.img_process_cfg.size.longest_edge as u32,
+            None,
+        )?;
+        let img = img.resize_exact(resize_w, resize_h, image::imageops::FilterType::CatmullRom);
+        let img_vec = img.to_rgb8().into_raw();
+        // (h, w, c) => (c, h, w)
+        let img_tensor = Tensor::from_slice(
+            &img_vec,
+            (resize_h as usize, resize_w as usize, 3),
+            &self.device,
+        )?
+        .permute((2, 0, 1))?
+        .to_dtype(self.dtype)?;
+        // 0-255 rescale to 0-1
+        let img_tensor = img_tensor.affine(1.0 / 255.0, 0.)?;
+        // normalize
+        let img_tensor = img_tensor.broadcast_sub(img_mean)?.broadcast_div(img_std)?;
+        // (c, h, w) => (1, c, h, w)
+        let img_tensor = img_tensor.unsqueeze(0)?;
+        Ok(img_tensor)
+    }
+
+    pub fn process_vision_tensor(&self, img_tensor: &Tensor) -> Result<(Tensor, Tensor)> {
+        let channel = img_tensor.dim(1)?;
+        let grid_t = img_tensor.dim(0)? / self.img_process_cfg.temporal_patch_size;
+        let grid_h = img_tensor.dim(2)? / self.img_process_cfg.patch_size;
+        let grid_w = img_tensor.dim(3)? / self.img_process_cfg.patch_size;
+        let shape = Shape::from(vec![
+            grid_t,
+            self.img_process_cfg.temporal_patch_size,
+            channel,
+            grid_h / self.img_process_cfg.merge_size,
+            self.img_process_cfg.merge_size,
+            self.img_process_cfg.patch_size,
+            grid_w / self.img_process_cfg.merge_size,
+            self.img_process_cfg.merge_size,
+            self.img_process_cfg.patch_size,
+        ]);
+        let img_tensor = img_tensor.reshape(shape)?;
+        // shape to // grid_t,
+        // grid_h / merge_size,
+        // grid_w / merge_size,
+        // merge_size,
+        // merge_size,
+        // channel,
+        // temporal_patch_size,
+        // patch_size,
+        // patch_size,
+        let img_tensor = img_tensor.permute(vec![0, 3, 6, 4, 7, 2, 1, 5, 8])?;
+        let img_tensor = img_tensor
+            .reshape((
+                grid_t * grid_h * grid_w,
+                channel
+                    * self.img_process_cfg.temporal_patch_size
+                    * self.img_process_cfg.patch_size
+                    * self.img_process_cfg.patch_size,
+            ))?
+            .contiguous()?;
+        let grid_thw = Tensor::from_vec(
+            vec![grid_t as u32, grid_h as u32, grid_w as u32],
+            (1, 3),
+            &self.device,
+        )?;
+        Ok((img_tensor, grid_thw))
+    }
+
+    pub fn process_images(
+        &self,
+        imgs: Vec<DynamicImage>,
+        img_mean: &Tensor,
+        img_std: &Tensor,
+    ) -> Result<VisionInput> {
+        let mut pixel_values_vec = Vec::new();
+        let mut vision_grid_thws_vec = Vec::new();
+
+        for img in imgs {
+            let img_tensor = self.process_img(&img, img_mean, img_std)?;
+            let img_tensor = Tensor::cat(&[&img_tensor, &img_tensor], 0)?.contiguous()?;
+            let (img_tensor, grid_thw) = self.process_vision_tensor(&img_tensor)?;
+            pixel_values_vec.push(img_tensor);
+            vision_grid_thws_vec.push(grid_thw);
+        }
+        let pixel_values = Tensor::cat(&pixel_values_vec, 0)?;
+        let vision_grid_thws = Tensor::cat(&vision_grid_thws_vec, 0)?;
+        Ok(VisionInput {
+            data: pixel_values,
+            grid_thw: vision_grid_thws,
+        })
+    }
+
+    pub fn process_videos(
+        &self,
+        data: Vec<Tensor>,
+        img_mean: &Tensor,
+        img_std: &Tensor,
+    ) -> Result<VisionInput> {
+        let mut pixel_values_vec = Vec::new();
+        let mut vision_grid_thws_vec = Vec::new();
+        for single_video in data {
+            // 0-255 rescale to 0-1
+            let video_tensor = single_video.to_dtype(self.dtype)?.affine(1.0 / 255.0, 0.)?;
+            // normalize
+            let video_tensor = video_tensor
+                .broadcast_sub(img_mean)?
+                .broadcast_div(img_std)?
+                .contiguous()?;
+            let (video_tensor, video_grid_thw) = self.process_vision_tensor(&video_tensor)?;
+            pixel_values_vec.push(video_tensor);
+            vision_grid_thws_vec.push(video_grid_thw);
+        }
+        let pixel_values = Tensor::cat(&pixel_values_vec, 0)?.contiguous()?;
+        let vision_grid_thws = Tensor::cat(&vision_grid_thws_vec, 0)?.contiguous()?;
+        Ok(VisionInput {
+            data: pixel_values,
+            grid_thw: vision_grid_thws,
+        })
+    }
+
+    fn calculate_timestamps(
+        &self,
+        frames_indices: Vec<u32>,
+        fps: f32,
+        t_merge_size: usize,
+    ) -> Result<Vec<f32>> {
+        let indices = if frames_indices.len() % t_merge_size != 0 {
+            let mut frames_indices = frames_indices.clone();
+            let last = frames_indices[frames_indices.len() - 1];
+            let pad_len = t_merge_size - frames_indices.len() % t_merge_size;
+            for _ in 0..pad_len {
+                frames_indices.push(last);
+            }
+            frames_indices
+        } else {
+            frames_indices.clone()
+        };
+        let timestamps: Vec<f32> = indices.iter().map(|&x| x as f32 / fps).collect();
+        let mut stamps = Vec::new();
+        for i in (0..timestamps.len()).step_by(t_merge_size) {
+            let stamp = (timestamps[i] + timestamps[i + t_merge_size - 1]) / 2.0;
+            stamps.push(stamp);
+        }
+        Ok(stamps)
+    }
+
+    pub fn process_info(
+        &self,
+        messages: &ChatCompletionParameters,
+        text: &str,
+    ) -> Result<GeneralInput> {
+        let mut pixel_values = None;
+        let mut image_grid_thw = None;
+        let mut pixel_values_video = None;
+        let mut video_grid_thw: Option<Tensor> = None;
+        let mut video_metadata = None;
+        let vision_map = self.extract_vision_info(messages)?;
+        let img_mean =
+            Tensor::from_slice(&self.img_process_cfg.image_mean, (3, 1, 1), &self.device)?
+                .to_dtype(self.dtype)?;
+        let img_std = Tensor::from_slice(&self.img_process_cfg.image_std, (3, 1, 1), &self.device)?
+            .to_dtype(self.dtype)?;
+        for (key, vec) in vision_map {
+            // println!("key: {}, \nvalue: {:?}", key, vec);
+            if key.eq("image") {
+                let mut file_vec = Vec::new();
+                for file in &vec {
+                    let image = get_image(file);
+                    match image {
+                        Ok(img) => file_vec.push(img),
+                        Err(e) => println!("get_image err: {:?}", e),
+                    };
+                }
+                if !file_vec.is_empty() {
+                    let vision_input = self.process_images(file_vec, &img_mean, &img_std);
+                    match vision_input {
+                        Ok(img_input) => {
+                            pixel_values = Some(img_input.data);
+                            image_grid_thw = Some(img_input.grid_thw);
+                        }
+                        Err(e) => println!("img process_images err: {:?}", e),
+                    };
+                }
+            }
+            if key.eq("video") {
+                let mut file_vec = Vec::new();
+                let mut video_infos = Vec::new();
+                for file in &vec {
+                    let video_data = get_video_data(
+                        file,
+                        self.video_process_cfg.patch_size as u32,
+                        self.video_process_cfg.temporal_patch_size as u32,
+                        self.video_process_cfg.merge_size as u32,
+                        self.fps,
+                        self.min_frames,
+                        self.max_frames,
+                        self.video_process_cfg.size.shortest_edge as u32,
+                        self.video_process_cfg.size.longest_edge as u32,
+                        &self.device,
+                    );
+                    match video_data {
+                        Ok((tensor, video_info)) => {
+                            file_vec.push(tensor);
+                            video_infos.push(video_info);
+                        }
+                        Err(e) => println!("get_video_data err: {:?}", e),
+                    };
+                }
+                if !file_vec.is_empty() {
+                    let vision_input = self.process_videos(file_vec, &img_mean, &img_std);
+                    match vision_input {
+                        Ok(video_input) => {
+                            pixel_values_video = Some(video_input.data);
+                            video_grid_thw = Some(video_input.grid_thw);
+                            video_metadata = Some(video_infos);
+                        }
+                        Err(e) => println!("video process_videos err: {:?}", e),
+                    };
+                }
+            }
+        }
+        let merge_length = self.img_process_cfg.merge_size.pow(2);
+        let mut text = text.to_string();
+        if let Some(ref image_grid_thw) = image_grid_thw {
+            let mut index = 0;
+            while text.contains(&self.image_token) {
+                let grid_i = image_grid_thw.i(index)?;
+                let repeat_num =
+                    grid_i.to_vec1::<u32>()?.iter().product::<u32>() as usize / merge_length;
+                let replace = "<|placeholder|>".repeat(repeat_num);
+                text = text.replacen(&self.image_token, &replace, 1);
+                index += 1;
+            }
+            text = text.replace("<|placeholder|>", &self.image_token);
+        }
+        if let Some(ref video_grid_thw) = video_grid_thw {
+            let mut index = 0;
+            while text.contains(&self.video_token) {
+                let grid_i = video_grid_thw.i(index)?;
+                let video_info = &video_metadata.as_ref().unwrap()[index];
+                let curr_timestamp = self.calculate_timestamps(
+                    video_info.frame_indices.clone(),
+                    video_info.fps,
+                    self.img_process_cfg.merge_size,
+                )?;
+                let mut video_placeholder = "".to_string();
+                let [t, h, w] = grid_i.to_vec1::<u32>()?[..] else {
+                    return Err(anyhow!(format!("grid_thw Expected exactly 3 elements")));
+                };
+                let frame_seqlen = h * w / merge_length as u32;
+                for frame_idx in 0..t {
+                    let curr_time = curr_timestamp[frame_idx as usize];
+                    video_placeholder = video_placeholder + format!("<{:.1} seconds>", curr_time).as_str();
+                    video_placeholder = video_placeholder + self.vision_start_token.as_str();
+                    video_placeholder = video_placeholder + "<|placeholder|>".repeat(frame_seqlen as usize).as_str();
+                    video_placeholder = video_placeholder + self.vision_end_token.as_str();
+                }
+                let three_token = format!("{}{}{}", self.vision_start_token, self.video_token, self.vision_end_token);
+                if text.contains(&three_token) {
+                    text = text.replacen(&three_token, &video_placeholder, 1);
+                } else {
+                    text = text.replacen(&self.video_token, &video_placeholder, 1);
+                }
+                index += 1;
+            }
+            text = text.replace("<|placeholder|>", &self.video_token);
+        }
+        let input = GeneralInput {
+            replace_text: text,
+            pixel_values,
+            image_grid_thw,
+            pixel_values_video,
+            video_grid_thw,
+        };
+        Ok(input)
+    }
+}
+
+pub fn img_smart_resize(
+    img_h: u32,
+    img_w: u32,
+    factor: u32,
+    min_pixels: u32,
+    max_pixels: u32,
+    video_ratio: Option<u32>,
+) -> Result<(u32, u32)> {
+    if std::cmp::max(img_h, img_w) / std::cmp::min(img_h, img_w) > 200 {
+        return Err(anyhow!(format!(
+            "absolute aspect ratio mush be smaller than {}, got {}",
+            200,
+            std::cmp::max(img_h, img_w) / std::cmp::min(img_h, img_w)
+        )));
+    }
+    let mut image_factor = factor;
+    if let Some(ratio) = video_ratio {
+        image_factor = lcm(image_factor, ratio);
+    }
+    let mut h_bar = std::cmp::max(image_factor, round_by_factor(img_h, image_factor));
+    let mut w_bar = std::cmp::max(image_factor, round_by_factor(img_w, image_factor));
+
+    if h_bar * w_bar > max_pixels {
+        let beta = ((img_h * img_w) as f32 / max_pixels as f32).sqrt();
+        h_bar = floor_by_factor(img_h as f32 / beta, image_factor);
+        w_bar = floor_by_factor(img_w as f32 / beta, image_factor);
+    } else if h_bar * w_bar < min_pixels {
+        let beta = (min_pixels as f32 / (img_h * img_w) as f32).sqrt();
+        h_bar = ceil_by_factor(img_h as f32 * beta, image_factor);
+        w_bar = ceil_by_factor(img_w as f32 * beta, image_factor);
+    }
+    Ok((h_bar, w_bar))
+}
+
+pub fn video_smart_resize(
+    num_frames: u32,
+    height: u32,
+    width: u32,
+    temporal_factor: u32,
+    factor: u32,
+    min_pixels: u32,
+    max_pixels: u32,
+    video_ratio: Option<u32>,
+) -> Result<(u32, u32)> {
+    if num_frames < temporal_factor {
+        return Err(anyhow!(format!(
+            "{} must be larger than temporal_factor {}",
+            num_frames, temporal_factor
+        )));
+    }
+    if height < factor || width < factor {
+        return Err(anyhow!(format!(
+            "height:{} or width:{} must be larger than factor:{}",
+            height, width, factor
+        )));
+    }
+    if std::cmp::max(height, width) / std::cmp::min(height, width) > 200 {
+        return Err(anyhow!(format!(
+            "absolute aspect ratio mush be smaller than {}, got {}",
+            200,
+            std::cmp::max(height, width) / std::cmp::min(height, width)
+        )));
+    }
+    let mut image_factor = factor;
+    if let Some(ratio) = video_ratio {
+        image_factor = lcm(image_factor, ratio);
+    }
+    let mut h_bar = round_by_factor(height, image_factor);
+    let mut w_bar = round_by_factor(width, image_factor);
+    let t_bar = round_by_factor(num_frames, temporal_factor);
+    if t_bar * h_bar * w_bar > max_pixels {
+        let beta = ((num_frames * height * width) as f32 / max_pixels as f32).sqrt();
+        h_bar = std::cmp::max(
+            image_factor,
+            floor_by_factor(height as f32 / beta, image_factor),
+        );
+        w_bar = std::cmp::max(
+            image_factor,
+            floor_by_factor(width as f32 / beta, image_factor),
+        );
+    } else if t_bar * h_bar * w_bar < min_pixels {
+        let beta = (min_pixels as f32 / (num_frames * height * width) as f32).sqrt();
+        h_bar = ceil_by_factor(height as f32 * beta, image_factor);
+        w_bar = ceil_by_factor(width as f32 * beta, image_factor);
+    }
+    Ok((h_bar, w_bar))
+}
+
+pub fn get_video_data(
+    file: &String,
+    patch_size: u32,
+    temporal_patch_size: u32,
+    merge_size: u32,
+    fps: u32,
+    min_frames: u32,
+    max_frames: u32,
+    min_pixels: u32,
+    max_pixels: u32,
+    device: &Device,
+) -> Result<(Tensor, VideoMetadata)> {
+    ffmpeg::init().map_err(|e| anyhow!(format!("Failed to initialize ffmpeg: {}", e)))?;
+
+    let mut ictx = ffmpeg::format::input(&file)
+        .map_err(|e| anyhow!(format!("Failed to open video file: {}", e)))?;
+    let input = ictx
+        .streams()
+        .best(ffmpeg::media::Type::Video)
+        .ok_or_else(|| anyhow!(format!("No video stream found")))?;
+    let video_stream_index = input.index();
+    let context_decoder = ffmpeg::codec::context::Context::from_parameters(input.parameters())
+        .map_err(|e| anyhow!(format!("Failed to crate decoder context: {}", e)))?;
+    let mut decoder = context_decoder
+        .decoder()
+        .video()
+        .map_err(|e| anyhow!(format!("Failed to decoder video: {}", e)))?;
+
+    let video_h = decoder.height();
+    let video_w = decoder.width();
+    let format = decoder.format();
+
+    let frames = input.frames();
+    let rate = input.rate().0 as f32 / input.rate().1 as f32;
+    let duration = frames as f32 * 1.0 / rate;
+    // 1s取两帧
+    let nframes = (frames as f32 / rate * fps as f32).round() as u32;
+    let nframes = std::cmp::min(
+        std::cmp::min(std::cmp::max(nframes, min_frames), max_frames),
+        frames as u32,
+    );
+    let sample_interval = (frames as f32 / nframes as f32).round() as u32;
+    let mut frame_indices = Vec::new();
+    let mut frame_id = 0_u32;
+
+    // 图片帧使用scaler reshape的时候需要保证宽高是16的倍数,不然reshape出来的是损坏的图片
+    // 所以计算resize的目标宽高时,需要用16和image_factor的最小公倍数
+    let (resize_h, resize_w) = video_smart_resize(
+        nframes,
+        video_h,
+        video_w,
+        temporal_patch_size,
+        patch_size * merge_size,
+        min_pixels,
+        max_pixels,
+        Some(16),
+    )?;
+    let mut scaler = ffmpeg::software::scaling::context::Context::get(
+        format,
+        video_w,
+        video_h,
+        ffmpeg::format::Pixel::RGB24,
+        resize_w,
+        resize_h,
+        ffmpeg::software::scaling::flag::Flags::BILINEAR
+            | ffmpeg::software::scaling::flag::Flags::ACCURATE_RND,
+    )
+    .map_err(|e| anyhow!(format!("Failed to crate scaler: {}", e)))?;
+
+    let mut frames_vec = Vec::new();
+    let mut receive_and_process_decoded_frames =
+        |decoder: &mut ffmpeg::decoder::Video| -> Result<()> {
+            let mut decoded = ffmpeg::frame::Video::empty();
+            while decoder.receive_frame(&mut decoded).is_ok() {
+                if frame_id.is_multiple_of(sample_interval) {
+                    frame_indices.push(frame_id);
+                    let mut rgb_frame = ffmpeg::frame::Video::empty();
+                    scaler
+                        .run(&decoded, &mut rgb_frame)
+                        .map_err(|e| anyhow!(format!("Failed to scaler run decoded: {}", e)))?;
+
+                    // save_file(&rgb_frame, frame_id as usize);
+                    let frame_data = rgb_frame.data(0);
+                    let frame_tensor = Tensor::from_slice(
+                        frame_data,
+                        (resize_h as usize, resize_w as usize, 3),
+                        device,
+                    )?
+                    .permute((2, 0, 1))?;
+                    frames_vec.push(frame_tensor);
+                }
+                frame_id += 1;
+            }
+            Ok(())
+        };
+
+    for (stream, packet) in ictx.packets() {
+        if stream.index() == video_stream_index {
+            decoder
+                .send_packet(&packet)
+                .map_err(|e| anyhow!(format!("Failed to send packet: {}", e)))?;
+            receive_and_process_decoded_frames(&mut decoder)?;
+        }
+    }
+    decoder
+        .send_eof()
+        .map_err(|e| anyhow!(format!("Failed to decoder.send_eof(): {}", e)))?;
+    receive_and_process_decoded_frames(&mut decoder)?;
+
+    if frames_vec.is_empty() {
+        return Err(anyhow!("No frames extracted from video".to_string()));
+    }
+    // (t, c, h, w)
+    let frames_tensor = Tensor::stack(&frames_vec, 0)?.contiguous()?;
+    let video_info = VideoMetadata {
+        total_num_frames: frames as u32,
+        fps: rate,
+        width: video_w,
+        height: video_h,
+        duration,
+        frame_indices,
+    };
+    Ok((frames_tensor, video_info))
+}

src/position_embed/rope.rs

@@ -64,7 +64,8 @@ pub fn apply_rotary_pos_emb_vision(
     // cos, sin -> (seq_len, head_dim) -> (seq_len, 1, head_dim)
     let cos = cos.unsqueeze(D::Minus2)?;
     let sin = sin.unsqueeze(D::Minus2)?;
-
+    let cos = cos.to_dtype(q.dtype())?;
+    let sin = sin.to_dtype(q.dtype())?;
     let q_embed = q
         .broadcast_mul(&cos)?
         .add(&rotate_half(q)?.broadcast_mul(&sin)?)?;
@@ -197,3 +198,75 @@ impl Qwen2_5VisionRotaryEmbedding {
         Ok(freqs)
     }
 }
+
+#[derive(Debug, Clone)]
+pub struct Qwen3VLTextRotaryEmbedding {
+    inv_freq: Vec<f32>,
+}
+
+impl Qwen3VLTextRotaryEmbedding {
+    pub fn new(dim: usize, theta_base: f32) -> Self {
+        let inv_freq = compute_default_rope_parameters(dim, theta_base);
+        Self { inv_freq }
+    }
+
+    pub fn apply_interleaved_mrope(
+        &self,
+        freqs: &Tensor,
+        mrope_section: Vec<usize>,
+    ) -> Result<Tensor> {
+        let mut freqs_t = freqs.i(0)?.contiguous()?; //(3, bs, seq_len, head_dim //2) -> (bs, seq_len, head_dim //2)
+
+        for dim in 1..3 {
+            let length = mrope_section[dim] * 3;
+            let idx = Tensor::arange_step(dim as u32, length as u32, 3, freqs.device())?;
+            let src = freqs.i(dim)?.contiguous()?; // (bs, seq_len, head_dim //2)
+            let src = src.index_select(&idx, D::Minus1)?.contiguous()?;
+            let idx = idx
+                .unsqueeze(0)?
+                .unsqueeze(0)?
+                .broadcast_as(src.shape())?
+                .contiguous()?;
+            freqs_t = freqs_t.scatter(&idx, &src, D::Minus1)?;
+        }
+        Ok(freqs_t)
+    }
+    pub fn forward(
+        &self,
+        position_ids: &Tensor,
+        dtype: DType,
+        mrope_section: Vec<usize>,
+    ) -> Result<(Tensor, Tensor)> {
+        // position_ids shape: (3, bs, position) -> (3, bs, 1, position)
+        let position_ids = if position_ids.rank() == 2 {
+            let (bs, len) = position_ids.dims2()?;
+            position_ids.unsqueeze(0)?.expand((3, bs, len))?
+        } else {
+            position_ids.clone()
+        };
+        let position_ids_expanded = position_ids
+            .unsqueeze(D::Minus2)?
+            .to_dtype(DType::F32)?
+            .contiguous()?;
+        // inv_freq Vec<f32> -> Tensor(1, 1, head_dim / 2, 1) -> (3, bs, head_dim / 2, 1)
+        let inv_freq_expanded = Tensor::from_vec(
+            self.inv_freq.clone(),
+            (1, 1, self.inv_freq.len(), 1),
+            position_ids.device(),
+        )?
+        .broadcast_as((3, position_ids.dim(1)?, self.inv_freq.len(), 1))?
+        .to_dtype(DType::F32)?
+        .contiguous()?;
+
+        // (3, bs, head_dim / 2, 1) matmul (3, bs, 1, position)
+        //    -> (3, bs, head_dim / 2, seq_len) -> (3, bs, seq_len, head_dim / 2)
+        let freqs = inv_freq_expanded
+            .matmul(&position_ids_expanded)?
+            .transpose(2, 3)?;
+        let freqs = self.apply_interleaved_mrope(&freqs, mrope_section)?;
+        let emb = Tensor::cat(&[&freqs, &freqs], D::Minus1)?.contiguous()?;
+        let cos = emb.cos()?;
+        let sin = emb.sin()?;
+        Ok((cos.to_dtype(dtype)?, sin.to_dtype(dtype)?))
+    }
+}

src/utils/mod.rs

@@ -5,7 +5,7 @@ pub mod video_utils;
 
 use anyhow::Result;
 use candle_core::{DType, Device};
-use candle_transformers::generation::LogitsProcessor;
+use candle_transformers::generation::{LogitsProcessor, Sampling};
 use openai_dive::v1::resources::{
     chat::{
         ChatCompletionChoice, ChatCompletionChunkChoice, ChatCompletionChunkResponse,
@@ -251,10 +251,33 @@ pub fn build_completion_chunk_response(
     response
 }
 
-pub fn get_logit_processor(temperature: Option<f32>, top_p: Option<f32>) -> LogitsProcessor {
-    LogitsProcessor::new(
-        34562,
-        temperature.map(|temp| temp as f64),
-        top_p.map(|tp| tp as f64),
-    )
+pub fn get_logit_processor(
+    temperature: Option<f32>,
+    top_p: Option<f32>,
+    top_k: Option<usize>,
+) -> LogitsProcessor {
+    match top_k {
+        None => LogitsProcessor::new(
+            34562,
+            temperature.map(|temp| temp as f64),
+            top_p.map(|tp| tp as f64),
+        ),
+        Some(k) => {
+            let sampling = match temperature {
+                None => Sampling::ArgMax,
+                Some(temperature) => match top_p {
+                    None => Sampling::TopK {
+                        k,
+                        temperature: temperature as f64,
+                    },
+                    Some(p) => Sampling::TopKThenTopP {
+                        k,
+                        p: p as f64,
+                        temperature: temperature as f64,
+                    },
+                },
+            };
+            LogitsProcessor::from_sampling(34562, sampling)
+        }
+    }
 }

src/utils/tensor_utils.rs

@@ -42,7 +42,7 @@ pub fn repeat_kv(xs: Tensor, n_rep: usize) -> Result<Tensor> {
     }
 }
 
-pub fn split(t: &Tensor, splits: &[usize], dim: D) -> Result<Vec<Tensor>> {
+pub fn split_tensor<D: Dim>(t: &Tensor, splits: &[usize], dim: D) -> Result<Vec<Tensor>> {
     let dim = dim.to_index(t.shape(), "split")?;
     let mut split_res = Vec::new();
     let mut index = 0;
@@ -241,3 +241,114 @@ pub fn linspace(start: f32, end: f32, steps: usize, device: &Device) -> Result<T
     let t = Tensor::from_slice(&data, steps, device)?;
     Ok(t)
 }
+
+pub fn bitor_tensor(mask1: &Tensor, mask2: &Tensor) -> Result<Tensor> {
+    assert!(
+        mask1.shape() == mask2.shape(),
+        " bitor_tensor two tensor shape mask be equal"
+    );
+    let bitor = mask1.add(&mask2)?.ne(&Tensor::zeros_like(&mask1)?)?;
+    Ok(bitor)
+}
+
+pub fn prod_tensor_last_dim(t: &Tensor) -> Result<Tensor> {
+    let prod = match t.rank() {
+        0 => t.clone(),
+        1 => {
+            let data_type = t.dtype();
+            let t_prod = match data_type {
+                DType::U8 => {
+                    let t_vec = t.to_vec1::<u8>()?;
+                    let prod = t_vec.iter().product::<u8>();
+                    Tensor::from_slice(&vec![prod], 1, t.device())?
+                }
+                DType::U32 => {
+                    let t_vec = t.to_vec1::<u32>()?;
+                    let prod = t_vec.iter().product::<u32>();
+                    Tensor::from_slice(&vec![prod], 1, t.device())?
+                }
+                DType::I64 => {
+                    let t_vec = t.to_vec1::<i64>()?;
+                    let prod = t_vec.iter().product::<i64>();
+                    Tensor::from_slice(&vec![prod], 1, t.device())?
+                }
+                DType::F64 => {
+                    let t_vec = t.to_vec1::<f64>()?;
+                    let prod = t_vec.iter().product::<f64>();
+                    Tensor::from_slice(&vec![prod], 1, t.device())?
+                }
+                _ => {
+                    let t_vec = t.to_vec1::<f32>()?;
+                    let prod = t_vec.iter().product::<f32>();
+                    Tensor::from_slice(&vec![prod], 1, t.device())?
+                }
+            };
+            t_prod
+        }
+        2 => {
+            let data_type = t.dtype();
+            let t_prod = match data_type {
+                DType::U8 => {
+                    let t_vec = t.to_vec2::<u8>()?;
+                    let mut prod_vec = vec![];
+                    for v in t_vec.iter() {
+                        let prod = v.iter().product::<u8>();
+                        prod_vec.push(prod);
+                    }
+                    Tensor::new(prod_vec, t.device())?
+                }
+                DType::U32 => {
+                    let t_vec = t.to_vec2::<u32>()?;
+                    let mut prod_vec = vec![];
+                    for v in t_vec.iter() {
+                        let prod = v.iter().product::<u32>();
+                        prod_vec.push(prod);
+                    }
+                    Tensor::new(prod_vec, t.device())?
+                }
+                DType::I64 => {
+                    let t_vec = t.to_vec2::<i64>()?;
+                    let mut prod_vec = vec![];
+                    for v in t_vec.iter() {
+                        let prod = v.iter().product::<i64>();
+                        prod_vec.push(prod);
+                    }
+                    Tensor::new(prod_vec, t.device())?
+                }
+                DType::F64 => {
+                    let t_vec = t.to_vec2::<f64>()?;
+                    let mut prod_vec = vec![];
+                    for v in t_vec.iter() {
+                        let prod = v.iter().product::<f64>();
+                        prod_vec.push(prod);
+                    }
+                    Tensor::new(prod_vec, t.device())?
+                }
+                _ => {
+                    let t_vec = t.to_vec2::<f32>()?;
+                    let mut prod_vec = vec![];
+                    for v in t_vec.iter() {
+                        let prod = v.iter().product::<f32>();
+                        prod_vec.push(prod);
+                    }
+                    Tensor::new(prod_vec, t.device())?
+                }
+            };
+            t_prod
+        }
+        _ => {
+            return Err(anyhow!(format!("can not action this dim")));
+        }
+    };
+    Ok(prod)
+}
+
+pub fn mask_index_add(
+    original: &Tensor,
+    mask: &Tensor,
+    add: &Tensor,
+) -> Result<Tensor> {    
+    let visual_nonzero_index = nonzero_index(&mask)?;
+    let xs = original.index_add(&visual_nonzero_index, add, 0)?;
+    Ok(xs)
+}

tests/config_tests.rs

@@ -1,6 +1,5 @@
 use aha::models::{
-    minicpm4::config::MiniCPM4Config, qwen2_5vl::config::Qwen2_5VLConfig,
-    voxcpm::config::VoxCPMConfig,
+    minicpm4::config::MiniCPM4Config, qwen2_5vl::config::Qwen2_5VLConfig, qwen3vl::config::Qwen3VLConfig, voxcpm::config::VoxCPMConfig
 };
 use anyhow::Result;
 
@@ -34,3 +33,13 @@ fn voxcpm_config() -> Result<()> {
     println!("{:?}", config);
     Ok(())
 }
+
+#[test]
+fn qwen3vl_config() -> Result<()> {
+    // cargo test -F cuda qwen3vl_config -- --nocapture
+    let model_path = "/home/jhq/huggingface_model/Qwen/Qwen3-VL-4B-Instruct/";
+    let config_path = model_path.to_string() + "/config.json";
+    let config: Qwen3VLConfig = serde_json::from_slice(&std::fs::read(config_path)?)?;
+    println!("{:?}", config);
+    Ok(())
+}

tests/messy_test.rs

@@ -1,13 +1,25 @@
-use aha::utils::audio_utils::load_audio_with_resample;
+use aha::utils::{tensor_utils::bitor_tensor};
 use anyhow::Result;
+use candle_core::Tensor;
 
 #[test]
 fn messy_test() -> Result<()> {
-    let device = candle_core::Device::Cpu;
-    let wav_path = "./assets/audio/voice_01.wav";
-    let audio_tensor = load_audio_with_resample(wav_path, device, Some(16000))?;
-
-    println!("audio_tensor: {}", audio_tensor);
+    let device = &candle_core::Device::Cpu;
+    let image_mask = Tensor::new(vec![0u32, 0, 0, 1, 0, 1], device)?;
+    let video_mask = Tensor::new(vec![0u32, 1, 0, 1, 0, 1], device)?;
+    let visual_mask = bitor_tensor(&image_mask, &video_mask)?;
+    println!("visual_mask: {}", visual_mask);
+    // let x = Tensor::arange_step(0.0_f32, 5., 0.5, &device)?;
+    // let x_int = x.to_dtype(candle_core::DType::U32)?;
+    // println!("x: {}", x);
+    // println!("x_int: {}", x_int);
+    // let x_affine = x_int.affine(1.0, 1.0)?;
+    // println!("x_affine: {}", x_affine);
+    // let x_clamp = x_affine.clamp(0u32, 3u32)?;
+    // println!("x_clamp: {}", x_clamp);
+    // let wav_path = "./assets/audio/voice_01.wav";
+    // let audio_tensor = load_audio_with_resample(wav_path, device, Some(16000))?;
+    // println!("audio_tensor: {}", audio_tensor);
     // let string = "你好啊".to_string();
     // let vec_str: Vec<String>= string.chars().map(|c| c.to_string()).collect();
     // println!("vec_str: {:?}", vec_str);

tests/test_qwen3vl.rs

@@ -0,0 +1,94 @@
+use std::{pin::pin, time::Instant};
+
+use aha::models::{GenerateModel, qwen3vl::generate::Qwen3VLGenerateModel};
+use anyhow::Result;
+use openai_dive::v1::resources::chat::ChatCompletionParameters;
+use rocket::futures::StreamExt;
+
+#[test]
+fn qwen3vl_generate() -> Result<()> {
+    // test with cuda: RUST_BACKTRACE=1 cargo test -F cuda qwen3vl_generate -- --nocapture
+
+    let model_path = "/home/jhq/huggingface_model/Qwen/Qwen3-VL-2B-Instruct/";
+
+    let message = r#"
+    {
+        "model": "qwen3vl",
+        "messages": [
+            {
+                "role": "user",
+                "content": [ 
+                    {
+                        "type": "video",
+                        "video_url": 
+                        {
+                            "url": "./assets/video/video_test.mp4"
+                        }
+                    },               
+                    {
+                        "type": "text", 
+                        "text": "视频中发生了什么."
+                    }
+                ]
+            }
+        ]
+    }
+    "#;
+    let mes: ChatCompletionParameters = serde_json::from_str(message)?;
+    let i_start = Instant::now();
+    let mut qwen3vl = Qwen3VLGenerateModel::init(model_path, None, None)?;
+    let i_duration = i_start.elapsed();
+    println!("Time elapsed in load model is: {:?}", i_duration);
+
+    let i_start = Instant::now();
+    let res = qwen3vl.generate(mes)?;
+    let i_duration = i_start.elapsed();
+    println!("generate: \n {:?}", res);
+    println!("Time elapsed in generate is: {:?}", i_duration);
+    Ok(())
+}
+
+#[tokio::test]
+async fn qwen3vl_stream() -> Result<()> {
+    // test with cuda: RUST_BACKTRACE=1 cargo test -F cuda qwen3vl_stream -- --nocapture
+
+    let model_path = "/home/jhq/huggingface_model/Qwen/Qwen3-VL-2B-Instruct/";
+
+    let message = r#"
+    {
+        "model": "qwen3vl",
+        "messages": [
+            {
+                "role": "user",
+                "content": [ 
+                    {
+                        "type": "image",
+                        "image_url": 
+                        {
+                            "url": "file://./assets/img/voxcpm.png"
+                        }
+                    },              
+                    {
+                        "type": "text", 
+                        "text": "描述这张图片"
+                    }
+                ]
+            }
+        ]
+    }
+    "#;
+    let mes: ChatCompletionParameters = serde_json::from_str(message)?;
+    let i_start = Instant::now();
+    let mut qwen3vl = Qwen3VLGenerateModel::init(model_path, None, None)?;
+    let i_duration = i_start.elapsed();
+    println!("Time elapsed in load model is: {:?}", i_duration);
+
+    let i_start = Instant::now();
+    let mut stream = pin!(qwen3vl.generate_stream(mes)?);
+    while let Some(item) = stream.next().await {
+        println!("generate: \n {:?}", item);
+    }
+    let i_duration = i_start.elapsed();
+    println!("Time elapsed in generate is: {:?}", i_duration);
+    Ok(())
+}

tests/weight_test.rs

@@ -45,3 +45,19 @@ fn voxcpm_weight() -> Result<()> {
     );
     Ok(())
 }
+
+#[test]
+fn qwen3vl_weight() -> Result<()> {
+    let model_path = "/home/jhq/huggingface_model/Qwen/Qwen3-VL-4B-Instruct/";
+    let model_list = find_type_files(model_path, "safetensors")?;
+    
+    let device = Device::Cpu;
+    for m in &model_list {
+        let weights = safetensors::load(m, &device)?;
+        for (key, tensor) in weights.iter() {
+            println!("=== {} === {:?}", key, tensor.shape());
+        }
+    }
+    println!("model_list: {:?}", model_list);
+    Ok(())
+}