jhqxxx
56 lines
2.0 KiB
Rust
56 lines
2.0 KiB
Rust
use std::time::Instant;
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use aha::{models::{qwen2_5vl::generate::Qwen2_5VLGenerateModel, GenerateModel}, ModelType};
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use anyhow::{Result};
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use candle_core::{DType, Device};
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use openai_dive::v1::resources::chat::ChatCompletionParameters;
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#[test]
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fn qwen2_5vl_generate() -> Result<()> {
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// test with cpu :(太慢了, : RUST_BACKTRACE=1 cargo test qwen2_5vl_generate -- --nocapture
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// test with cuda: RUST_BACKTRACE=1 cargo test -F cuda qwen2_5vl_generate -- --nocapture
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// test with cuda+flash-attn: RUST_BACKTRACE=1 cargo test -F cuda,flash-attn qwen2_5vl_generate -- --nocapture
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let device = Device::cuda_if_available(0)?;
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let dtype = DType::BF16;
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let model_path = "/home/jhq/huggingface_model/Qwen/Qwen2.5-VL-3B-Instruct/";
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let message = r#"
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{
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"model": "qwen2.5vl",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image_url":
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{
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"url": "file://./assets/img/ocr_test.png"
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}
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},
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{
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"type": "text",
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"text": "请分析图片并提取所有可见文本内容，按从左到右、从上到下的布局，返回纯文本"
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}
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]
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}
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]
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}
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"#;
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let mes:ChatCompletionParameters = serde_json::from_str(message)?;
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let i_start = Instant::now();
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// let mut model = Qwen2_5VLGenerateModel::init(model_path, &device, dtype)?;
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let mut model = ModelType::init(ModelType::Qwen2_5VL, model_path, None, None)?;
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let i_duration = i_start.elapsed();
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println!("Time elapsed in load model is: {:?}", i_duration);
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let i_start = Instant::now();
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let result = model.generate(mes)?;
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println!("generate: \n{}", result);
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let i_duration = i_start.elapsed();
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println!("Time elapsed in generate is: {:?}", i_duration);
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Ok(())
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} |