2025-12-10 00:05:46 +08:00
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use std::time::Instant;
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use aha::models::{GenerateModel, qwen2_5vl::generate::Qwen2_5VLGenerateModel};
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use aha_openai_dive::v1::resources::chat::ChatCompletionParameters;
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use anyhow::Result;
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#[test]
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fn robo_brain_generate() -> Result<()> {
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// test with cuda: RUST_BACKTRACE=1 cargo test -F cuda robo_brain_generate -r -- --nocapture
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let model_path = "/home/jhq/huggingface_model/BAAI/RoboBrain2.0-3B/";
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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": "text",
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"text": "hello RoboBrain"
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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, 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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let i_duration = i_start.elapsed();
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2025-12-10 16:44:20 +08:00
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println!("generate: \n {:?}", result);
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if result.usage.is_some() {
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let num_token = result.usage.as_ref().unwrap().total_tokens;
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let duration_secs = i_duration.as_secs_f64();
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let tps = num_token as f64 / duration_secs;
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println!("Tokens per second (TPS): {:.2}", tps);
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}
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2025-12-10 00:05:46 +08:00
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println!("Time elapsed in generate is: {:?}", i_duration);
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Ok(())
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}
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