add Qwen3VL model

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))
+}