candle-examples 0.10.2

#[cfg(feature = "mkl")]
extern crate intel_mkl_src;

#[cfg(feature = "accelerate")]
extern crate accelerate_src;

use clap::{Parser, ValueEnum};
use std::io::Write;
use tokenizers::Tokenizer;

use candle::quantized::gguf_file;
use candle::Tensor;
use candle_transformers::generation::{LogitsProcessor, Sampling};

use candle_examples::token_output_stream::TokenOutputStream;
use candle_transformers::models::quantized_gemma3::ModelWeights;

const DEFAULT_PROMPT: &str = "Write a function to calculate fibonacci num";

#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
enum Which {
    #[value(name = "gemma3-4b-it")]
    Gemma3_4bIt,
}

#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// GGUF file to load, typically a .gguf file generated by quantization
    #[arg(long)]
    model: Option<String>,

    /// The initial prompt, use 'interactive' for entering multiple prompts in an interactive way
    /// and 'chat' for an interactive model where history of previous prompts and generated tokens
    /// is preserved.
    #[arg(long)]
    prompt: Option<String>,

    /// The length of the sample to generate (in tokens).
    #[arg(short = 'n', long, default_value_t = 1000)]
    sample_len: usize,

    /// The tokenizer config in json format.
    #[arg(long)]
    tokenizer: Option<String>,

    /// The temperature used to generate samples, use 0 for greedy sampling.
    #[arg(long, default_value_t = 0.8)]
    temperature: f64,

    /// Nucleus sampling probability cutoff.
    #[arg(long)]
    top_p: Option<f64>,

    /// Only sample among the top K samples.
    #[arg(long)]
    top_k: Option<usize>,

    /// The seed to use when generating random samples.
    #[arg(long, default_value_t = 299792458)]
    seed: u64,

    /// Enable tracing (generates a trace-timestamp.json file).
    #[arg(long)]
    tracing: bool,

    /// Process prompt elements separately.
    #[arg(long)]
    split_prompt: bool,

    /// Run on CPU rather than GPU even if a GPU is available.
    #[arg(long)]
    cpu: bool,

    /// Penalty to be applied for repeating tokens, 1. means no penalty.
    #[arg(long, default_value_t = 1.1)]
    repeat_penalty: f32,

    /// The context size to consider for the repeat penalty.
    #[arg(long, default_value_t = 64)]
    repeat_last_n: usize,

    /// The model size to use.
    #[arg(long, default_value = "gemma3-4b-it")]
    which: Which,
}

impl Args {
    fn tokenizer(&self) -> anyhow::Result<Tokenizer> {
        let tokenizer_path = match &self.tokenizer {
            Some(config) => std::path::PathBuf::from(config),
            None => {
                let api = hf_hub::api::sync::Api::new()?;
                let repo = "google/gemma-3-4b-it";
                println!("DEBUG: Downloading tokenizer from {repo}");
                let api = api.model(repo.to_string());
                api.get("tokenizer.json")?
            }
        };
        println!("DEBUG: Loading tokenizer from {tokenizer_path:?}");
        let tokenizer = Tokenizer::from_file(tokenizer_path).map_err(anyhow::Error::msg)?;

        Ok(tokenizer)
    }

    fn model(&self) -> anyhow::Result<std::path::PathBuf> {
        let model_path = match &self.model {
            Some(config) => std::path::PathBuf::from(config),
            None => {
                let (repo, filename) = match self.which {
                    Which::Gemma3_4bIt => (
                        "google/gemma-3-4b-it-qat-q4_0-gguf",
                        "gemma-3-4b-it-q4_0.gguf",
                    ),
                };
                let api = hf_hub::api::sync::Api::new()?;
                api.repo(hf_hub::Repo::with_revision(
                    repo.to_string(),
                    hf_hub::RepoType::Model,
                    "main".to_string(),
                ))
                .get(filename)?
            }
        };
        Ok(model_path)
    }
}

fn format_size(size_in_bytes: usize) -> String {
    if size_in_bytes < 1_000 {
        format!("{size_in_bytes}B")
    } else if size_in_bytes < 1_000_000 {
        format!("{:.2}KB", size_in_bytes as f64 / 1e3)
    } else if size_in_bytes < 1_000_000_000 {
        format!("{:.2}MB", size_in_bytes as f64 / 1e6)
    } else {
        format!("{:.2}GB", size_in_bytes as f64 / 1e9)
    }
}

#[derive(Debug)]
enum Prompt {
    Interactive,
    Chat,
    One(String),
}

fn main() -> anyhow::Result<()> {
    use tracing_chrome::ChromeLayerBuilder;
    use tracing_subscriber::prelude::*;

    let args = Args::parse();
    let _guard = if args.tracing {
        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
        tracing_subscriber::registry().with(chrome_layer).init();
        Some(guard)
    } else {
        None
    };

    println!(
        "avx: {}, neon: {}, simd128: {}, f16c: {}",
        candle::utils::with_avx(),
        candle::utils::with_neon(),
        candle::utils::with_simd128(),
        candle::utils::with_f16c()
    );
    println!(
        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
        args.temperature, args.repeat_penalty, args.repeat_last_n
    );

    let model_path = args.model()?;
    let mut file = std::fs::File::open(&model_path)?;
    let start = std::time::Instant::now();
    let device = candle_examples::device(args.cpu)?;

    let mut model = {
        let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(&model_path))?;
        let mut total_size_in_bytes = 0;
        for (_, tensor) in model.tensor_infos.iter() {
            let elem_count = tensor.shape.elem_count();
            total_size_in_bytes +=
                elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
        }
        println!(
            "loaded {:?} tensors ({}) in {:.2}s",
            model.tensor_infos.len(),
            &format_size(total_size_in_bytes),
            start.elapsed().as_secs_f32(),
        );
        ModelWeights::from_gguf(model, &mut file, &device)?
    };
    println!("model built");

    let tokenizer = args.tokenizer()?;

    let mut tos = TokenOutputStream::new(tokenizer);
    println!(
        "DEBUG: Tokenizer vocabulary size: {}",
        tos.tokenizer().get_vocab(true).len()
    );

    let prompt = match args.prompt.as_deref() {
        Some("chat") => Prompt::Chat,
        Some("interactive") => Prompt::Interactive,
        Some(s) => Prompt::One(s.to_string()),
        None => Prompt::One(DEFAULT_PROMPT.to_string()),
    };

    let mut pre_prompt_tokens = vec![];
    for _ in 0.. {
        let prompt_str = match &prompt {
            Prompt::One(prompt) => prompt.clone(),
            Prompt::Interactive | Prompt::Chat => {
                print!("> ");
                std::io::stdout().flush()?;
                let mut prompt = String::new();
                std::io::stdin().read_line(&mut prompt)?;
                if prompt.ends_with('\n') {
                    prompt.pop();
                    if prompt.ends_with('\r') {
                        prompt.pop();
                    }
                }
                // Format for Gemma 3 chat/instruction format
                format!("<start_of_turn> user\n{prompt}<end_of_turn>\n<start_of_turn> model\n")
            }
        };
        print!("{}", &prompt_str);

        let tokens = tos
            .tokenizer()
            .encode(prompt_str, true)
            .map_err(anyhow::Error::msg)?;
        let prompt_tokens = [&pre_prompt_tokens, tokens.get_ids()].concat();

        let to_sample = args.sample_len.saturating_sub(1);
        let max_seq_len = 8192; // Gemma 3 context length
        let prompt_tokens = if prompt_tokens.len() + to_sample > max_seq_len - 10 {
            let to_remove = prompt_tokens.len() + to_sample + 10 - max_seq_len;
            prompt_tokens[prompt_tokens.len().saturating_sub(to_remove)..].to_vec()
        } else {
            prompt_tokens
        };
        let mut all_tokens = vec![];
        let mut logits_processor = {
            let temperature = args.temperature;
            let sampling = if temperature <= 0. {
                Sampling::ArgMax
            } else {
                match (args.top_k, args.top_p) {
                    (None, None) => Sampling::All { temperature },
                    (Some(k), None) => Sampling::TopK { k, temperature },
                    (None, Some(p)) => Sampling::TopP { p, temperature },
                    (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
                }
            };
            LogitsProcessor::from_sampling(args.seed, sampling)
        };

        let start_prompt_processing = std::time::Instant::now();
        let mut next_token = if !args.split_prompt {
            let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
            let logits = model.forward(&input, 0)?;
            let logits = logits.squeeze(0)?;
            logits_processor.sample(&logits)?
        } else {
            let mut next_token = 0;
            for (pos, token) in prompt_tokens.iter().enumerate() {
                let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
                let logits = model.forward(&input, pos)?;
                let logits = logits.squeeze(0)?;
                next_token = logits_processor.sample(&logits)?
            }
            next_token
        };
        let prompt_dt = start_prompt_processing.elapsed();
        all_tokens.push(next_token);
        if let Some(t) = tos.next_token(next_token)? {
            print!("{t}");
            std::io::stdout().flush()?;
        }

        // For Gemma 3, use the correct end of sequence token
        let eos_token = *tos
            .tokenizer()
            .get_vocab(true)
            .get("<end_of_turn>")
            .unwrap();

        let start_post_prompt = std::time::Instant::now();
        let mut sampled = 0;
        for index in 0..to_sample {
            let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
            let logits = model.forward(&input, prompt_tokens.len() + index)?;
            let logits = logits.squeeze(0)?;
            let logits = if args.repeat_penalty == 1. {
                logits
            } else {
                let start_at = all_tokens.len().saturating_sub(args.repeat_last_n);
                candle_transformers::utils::apply_repeat_penalty(
                    &logits,
                    args.repeat_penalty,
                    &all_tokens[start_at..],
                )?
            };
            next_token = logits_processor.sample(&logits)?;
            all_tokens.push(next_token);
            if let Some(t) = tos.next_token(next_token)? {
                print!("{t}");
                std::io::stdout().flush()?;
            }
            sampled += 1;
            if next_token == eos_token {
                break;
            };
        }
        if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
            print!("{rest}");
        }
        std::io::stdout().flush()?;
        let dt = start_post_prompt.elapsed();
        println!(
            "\n\n{:4} prompt tokens processed: {:.2} token/s",
            prompt_tokens.len(),
            prompt_tokens.len() as f64 / prompt_dt.as_secs_f64(),
        );
        println!(
            "{sampled:4} tokens generated: {:.2} token/s",
            sampled as f64 / dt.as_secs_f64(),
        );

        match prompt {
            Prompt::One(_) => break,
            Prompt::Interactive => {}
            Prompt::Chat => {
                pre_prompt_tokens = [prompt_tokens.as_slice(), all_tokens.as_slice()].concat()
            }
        }
    }

    Ok(())
}