add qwen2.5vl model

src/models/qwen2_5vl/generate.rs

@@ -0,0 +1,137 @@
+use crate::models::qwen2_5vl::config::Config;
+use crate::utils::utils::{find_safetensors_files, get_device, get_dtype};
+use crate::{
+    chat_template::chat_template::ChatTemplate,
+    models::{
+        GenerateModel,
+        qwen2_5vl::{model::Qwen2_5VLModel, processor::Qwen2_5VLProcessor},
+    },
+    tokenizer::tokenizer::TokenizerModel,
+};
+use anyhow::{Result, anyhow};
+use candle_core::{D, DType, Device, IndexOp, Tensor};
+use candle_nn::VarBuilder;
+use candle_transformers::generation::LogitsProcessor;
+use openai_dive::v1::resources::chat::ChatCompletionParameters;
+
+pub struct Qwen2_5VLGenerateModel<'a> {
+    chat_template: ChatTemplate<'a>,
+    tokenizer: TokenizerModel,
+    pre_processor: Qwen2_5VLProcessor,
+    qwen2_5_vl: Qwen2_5VLModel,
+    device: Device,
+    dtype: DType,
+}
+
+impl<'a> GenerateModel for Qwen2_5VLGenerateModel<'a> {
+    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: Config = serde_json::from_slice(&std::fs::read(config_path)?)?;
+        let device = &get_device(device);
+        let cfg_dtype = cfg.torch_dtype.as_str();
+        let dtype = get_dtype(dtype, cfg_dtype);
+        let pre_processor = Qwen2_5VLProcessor::new(device, dtype)?;       
+        
+        let model_list = find_safetensors_files(&path)?;
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
+        let qwen2_5_vl = Qwen2_5VLModel::new(cfg, vb)?;
+        Ok(Qwen2_5VLGenerateModel {
+            chat_template,
+            tokenizer,
+            pre_processor,
+            qwen2_5_vl,
+            device: device.clone(),
+            dtype: dtype,
+        })
+    }
+    fn generate(&mut self, mes: ChatCompletionParameters) -> Result<String> {
+        let temperature = match mes.temperature {
+            Some(temp) => Some(temp as f64),
+            None => None,
+        };
+        let top_p = match mes.top_p {
+            Some(tp) => Some(tp as f64),
+            None => None,
+        };
+        let mut logit_processor = LogitsProcessor::new(34562, temperature, top_p);
+        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 end_of_text_id = self.qwen2_5_vl.cfg.bos_token_id as u32;
+        let im_end_id = self.qwen2_5_vl.cfg.eos_token_id as u32;
+        let mut pixel_values = if input.pixel_values.is_some() {
+            Some(&input.pixel_values.unwrap().clone())
+        } else {
+            None
+        };
+        let image_grid_thw = if input.image_grid_thw.is_some() {
+            Some(&input.image_grid_thw.unwrap().clone())
+        } else {
+            None
+        };
+        let mut pixel_values_video = if input.pixel_values_video.is_some() {
+            Some(&input.pixel_values_video.unwrap().clone())
+        } else {
+            None
+        };
+        let video_grid_thw = if input.video_grid_thw.is_some() {
+            Some(&input.video_grid_thw.unwrap().clone())
+        } else {
+            None
+        };
+        let second_per_grid_ts = if input.second_per_grid_ts.is_some() {
+            Some(input.second_per_grid_ts.unwrap().clone())
+        } else {
+            None
+        };
+
+        let mut mask = Tensor::ones_like(&input_ids)?;
+        let mut cache_position = Tensor::ones_like(&input_ids.i(0)?)?
+            .to_dtype(candle_core::DType::F64)?
+            .cumsum(D::Minus1)?
+            .to_dtype(candle_core::DType::U32)?
+            .broadcast_sub(&Tensor::new(vec![1_u32], input_ids.device())?)?;
+
+        let mut generate = Vec::new();
+        let sample_len = match mes.max_tokens {
+            Some(max) => max,
+            None => 512,
+        };
+        for _ in 0..sample_len {
+            let logits = self.qwen2_5_vl.forward(
+                &input_ids,
+                pixel_values,
+                image_grid_thw,
+                pixel_values_video,
+                video_grid_thw,
+                &mask,
+                Some(&cache_position),
+                seqlen_offset,
+                second_per_grid_ts.clone(),
+            )?;
+            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 == end_of_text_id || next_token == im_end_id {
+                break;
+            }
+            seqlen_offset += seq_len;
+            seq_len = 1;
+            input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
+            let appendd_mask = Tensor::ones((1, 1), mask.dtype(), &self.device)?;
+            mask = Tensor::cat(&[mask, appendd_mask], 1)?;
+            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.qwen2_5_vl.clear_kv_cache();
+        Ok(res)
+    }
+}