jhqxxx
99 lines
3.5 KiB
Rust
99 lines
3.5 KiB
Rust
use crate::models::common::{
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MultiModalData,
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generate::{GenerationDataProvider, PrepareData},
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};
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use anyhow::Result;
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use candle_core::{DType, Device};
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use candle_nn::VarBuilder;
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use crate::{
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models::deepseek_ocr::{
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config::DeepseekOCRConfig, model::DeepseekOCRModel, processor::DeepseekOCRProcessor,
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},
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tokenizer::TokenizerModel,
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utils::{extract_metadata_value, find_type_files, get_device, get_dtype},
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};
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pub struct DeepseekOCRGenerateModel {
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tokenizer: TokenizerModel,
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processor: DeepseekOCRProcessor,
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model: DeepseekOCRModel,
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device: Device,
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size: Vec<u32>,
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model_name: String,
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version: usize,
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}
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impl DeepseekOCRGenerateModel {
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pub fn init(path: &str, device: Option<&Device>, dtype: Option<DType>) -> Result<Self> {
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let tokenizer = TokenizerModel::init(path)?;
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let config_path = path.to_string() + "/config.json";
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let cfg: DeepseekOCRConfig = serde_json::from_slice(&std::fs::read(config_path)?)?;
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let cfg_dtype = cfg.language_config.torch_dtype.clone();
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let device = &get_device(device);
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let dtype = get_dtype(dtype, &cfg_dtype);
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let model_name = std::path::Path::new(path)
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.file_name()
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.and_then(|s| s.to_str())
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.unwrap_or("deepseek-ai/DeepSeek-OCR")
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.to_string();
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let version = if model_name.contains("2") || cfg.vision_config.width.qwen2_0_5b.is_some() {
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2usize
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} else {
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1usize
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};
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let processor = DeepseekOCRProcessor::new(device, dtype, version)?;
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let model_list = find_type_files(path, "safetensors")?;
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
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let model = DeepseekOCRModel::new(vb, cfg, version)?;
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let size = vec![512u32, 640, 1024, 1280];
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Ok(Self {
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tokenizer,
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processor,
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model,
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device: device.clone(),
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size,
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model_name: model_name.to_string(),
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version,
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})
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}
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}
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impl GenerationDataProvider for DeepseekOCRGenerateModel {
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fn get_data(&self, mes: &crate::params::chat::ChatCompletionParameters) -> Result<PrepareData> {
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let base_size = extract_metadata_value::<u32>(&mes.metadata, "base_size").unwrap_or(640);
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let base_size = if self.size.contains(&base_size) {
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base_size
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} else {
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640
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};
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let image_size = extract_metadata_value::<u32>(&mes.metadata, "image_size").unwrap_or(640);
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let image_size = if self.size.contains(&image_size) {
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image_size
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} else {
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640
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};
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let base_size = if self.version == 2 { 1024 } else { base_size };
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let image_size = if self.version == 2 { 768 } else { image_size };
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let crop_mode = extract_metadata_value::<bool>(&mes.metadata, "crop_mode").unwrap_or(false);
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let (input_ids, images_ori, image_crop, images_seq_mask, images_spatial_crop_t) = self
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.processor
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.process_info(mes, &self.tokenizer, base_size, image_size, crop_mode)?;
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let data_vec = vec![
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Some(images_ori),
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Some(image_crop),
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Some(images_seq_mask),
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Some(images_spatial_crop_t),
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];
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let multi_model_data = MultiModalData::new(data_vec);
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Ok(PrepareData {
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in_reasoning: false,
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input_ids,
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multi_model_data,
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})
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}
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}
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crate::impl_generate_model!(DeepseekOCRGenerateModel);
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