vicuna-chat.rs

use clap::Parser;
use rustyline::error::ReadlineError;
use std::{convert::Infallible, io::Write, path::PathBuf};

#[derive(Parser)]
struct Args {
    architecture: String,
    path: PathBuf,
    #[arg(long, short = 'v')]
    pub vocabulary_path: Option<PathBuf>,
    #[arg(long, short = 'r')]
    pub vocabulary_repository: Option<String>,
}
impl Args {
    pub fn to_vocabulary_source(&self) -> llm::VocabularySource {
        match (&self.vocabulary_path, &self.vocabulary_repository) {
            (Some(_), Some(_)) => {
                panic!("Cannot specify both --vocabulary-path and --vocabulary-repository");
            }
            (Some(path), None) => llm::VocabularySource::HuggingFaceTokenizerFile(path.to_owned()),
            (None, Some(repo)) => llm::VocabularySource::HuggingFaceRemote(repo.to_owned()),
            (None, None) => llm::VocabularySource::Model,
        }
    }
}

fn main() {
    let args = Args::parse();

    let vocabulary_source = args.to_vocabulary_source();
    let architecture = args.architecture.parse().unwrap();
    let path = args.path;
    let model = llm::load_dynamic(
        architecture,
        &path,
        vocabulary_source,
        Default::default(),
        llm::load_progress_callback_stdout,
    )
    .unwrap_or_else(|err| panic!("Failed to load {architecture} model from {path:?}: {err}"));

    let mut session = model.start_session(Default::default());

    let character_name = "### Assistant";
    let user_name = "### Human";
    let persona = "A chat between a human and an assistant.";
    let history = format!(
        "{character_name}: Hello - How may I help you today?\n\
         {user_name}: What is the capital of France?\n\
         {character_name}:  Paris is the capital of France."
    );

    let inference_parameters = llm::InferenceParameters::default();

    session
        .feed_prompt(
            model.as_ref(),
            &inference_parameters,
            format!("{persona}\n{history}").as_str(),
            &mut Default::default(),
            llm::feed_prompt_callback(|resp| match resp {
                llm::InferenceResponse::PromptToken(t)
                | llm::InferenceResponse::InferredToken(t) => print_token(t),
                _ => Ok(llm::InferenceFeedback::Continue),
            }),
        )
        .expect("Failed to ingest initial prompt.");

    let mut rl = rustyline::DefaultEditor::new().expect("Failed to create input reader");

    let mut rng = rand::thread_rng();
    let mut res = llm::InferenceStats::default();
    let mut buf = String::new();

    loop {
        println!();
        let readline = rl.readline(format!("{user_name}: ").as_str());
        print!("{character_name}:");
        match readline {
            Ok(line) => {
                let stats = session
                    .infer(
                        model.as_ref(),
                        &mut rng,
                        &llm::InferenceRequest {
                            prompt: format!("{user_name}: {line}\n{character_name}:")
                                .as_str()
                                .into(),
                            parameters: &inference_parameters,
                            play_back_previous_tokens: false,
                            maximum_token_count: None,
                        },
                        &mut Default::default(),
                        inference_callback(String::from(user_name), &mut buf),
                    )
                    .unwrap_or_else(|e| panic!("{e}"));

                res.feed_prompt_duration = res
                    .feed_prompt_duration
                    .saturating_add(stats.feed_prompt_duration);
                res.prompt_tokens += stats.prompt_tokens;
                res.predict_duration = res.predict_duration.saturating_add(stats.predict_duration);
                res.predict_tokens += stats.predict_tokens;
            }
            Err(ReadlineError::Eof) | Err(ReadlineError::Interrupted) => {
                break;
            }
            Err(err) => {
                println!("{err}");
            }
        }
    }

    println!("\n\nInference stats:\n{res}");
}

fn inference_callback(
    stop_sequence: String,
    buf: &mut String,
) -> impl FnMut(llm::InferenceResponse) -> Result<llm::InferenceFeedback, Infallible> + '_ {
    move |resp| match resp {
        llm::InferenceResponse::InferredToken(t) => {
            let mut reverse_buf = buf.clone();
            reverse_buf.push_str(t.as_str());
            if stop_sequence.as_str().eq(reverse_buf.as_str()) {
                buf.clear();
                return Ok(llm::InferenceFeedback::Halt);
            } else if stop_sequence.as_str().starts_with(reverse_buf.as_str()) {
                buf.push_str(t.as_str());
                return Ok(llm::InferenceFeedback::Continue);
            }

            if buf.is_empty() {
                print_token(t)
            } else {
                print_token(reverse_buf)
            }
        }
        llm::InferenceResponse::EotToken => Ok(llm::InferenceFeedback::Halt),
        _ => Ok(llm::InferenceFeedback::Continue),
    }
}

fn print_token(t: String) -> Result<llm::InferenceFeedback, Infallible> {
    print!("{t}");
    std::io::stdout().flush().unwrap();

    Ok(llm::InferenceFeedback::Continue)
}