main.rs

/// Text Generation Inference webserver entrypoint
use axum::http::HeaderValue;
use clap::Parser;
use opentelemetry::sdk::propagation::TraceContextPropagator;
use opentelemetry::sdk::trace;
use opentelemetry::sdk::trace::Sampler;
use opentelemetry::sdk::Resource;
use opentelemetry::{global, KeyValue};
use opentelemetry_otlp::WithExportConfig;
use std::net::{IpAddr, Ipv4Addr, SocketAddr};
use std::path::Path;
use std::time::Duration;
use text_generation_client::ShardedClient;
use text_generation_router::{server, HubModelInfo};
use tokenizers::{FromPretrainedParameters, Tokenizer};
use tower_http::cors::AllowOrigin;
use tracing_subscriber::layer::SubscriberExt;
use tracing_subscriber::util::SubscriberInitExt;
use tracing_subscriber::{EnvFilter, Layer};

/// App Configuration
#[derive(Parser, Debug)]
#[clap(author, version, about, long_about = None)]
struct Args {
    #[clap(default_value = "128", long, env)]
    max_concurrent_requests: usize,
    #[clap(default_value = "2", long, env)]
    max_best_of: usize,
    #[clap(default_value = "4", long, env)]
    max_stop_sequences: usize,
    #[clap(default_value = "1024", long, env)]
    max_input_length: usize,
    #[clap(default_value = "2048", long, env)]
    max_total_tokens: usize,
    #[clap(default_value = "1.2", long, env)]
    waiting_served_ratio: f32,
    #[clap(default_value = "4096", long, env)]
    max_batch_prefill_tokens: u32,
    #[clap(default_value = "16000", long, env)]
    max_batch_total_tokens: u32,
    #[clap(default_value = "20", long, env)]
    max_waiting_tokens: usize,
    #[clap(default_value = "3000", long, short, env)]
    port: u16,
    #[clap(default_value = "/tmp/text-generation-server-0", long, env)]
    master_shard_uds_path: String,
    #[clap(default_value = "bigscience/bloom", long, env)]
    tokenizer_name: String,
    #[clap(default_value = "main", long, env)]
    revision: String,
    #[clap(default_value = "2", long, env)]
    validation_workers: usize,
    #[clap(long, env)]
    json_output: bool,
    #[clap(long, env)]
    otlp_endpoint: Option<String>,
    #[clap(long, env)]
    cors_allow_origin: Option<Vec<String>>,
    #[clap(long, env)]
    ngrok: bool,
    #[clap(long, env)]
    ngrok_authtoken: Option<String>,
    #[clap(long, env)]
    ngrok_domain: Option<String>,
    #[clap(long, env)]
    ngrok_username: Option<String>,
    #[clap(long, env)]
    ngrok_password: Option<String>,
}

fn main() -> Result<(), std::io::Error> {
    // Get args
    let args = Args::parse();
    // Pattern match configuration
    let Args {
        max_concurrent_requests,
        max_best_of,
        max_stop_sequences,
        max_input_length,
        max_total_tokens,
        waiting_served_ratio,
        max_batch_prefill_tokens,
        max_batch_total_tokens,
        max_waiting_tokens,
        port,
        master_shard_uds_path,
        tokenizer_name,
        revision,
        validation_workers,
        json_output,
        otlp_endpoint,
        cors_allow_origin,
        ngrok,
        ngrok_authtoken,
        ngrok_domain,
        ngrok_username,
        ngrok_password,
    } = args;

    // Validate args
    if max_input_length as u32 > max_batch_prefill_tokens {
        panic!("{}", format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}"));
    }
    if max_batch_prefill_tokens > max_batch_total_tokens {
        panic!("{}", format!("`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {max_batch_prefill_tokens} and {max_batch_total_tokens}"));
    }
    if max_total_tokens as u32 > max_batch_total_tokens {
        panic!("{}", format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_batch_total_tokens}"));
    }
    if validation_workers == 0 {
        panic!("`validation_workers` must be > 0");
    }

    // CORS allowed origins
    // map to go inside the option and then map to parse from String to HeaderValue
    // Finally, convert to AllowOrigin
    let cors_allow_origin: Option<AllowOrigin> = cors_allow_origin.map(|cors_allow_origin| {
        AllowOrigin::list(
            cors_allow_origin
                .iter()
                .map(|origin| origin.parse::<HeaderValue>().unwrap()),
        )
    });

    // Parse Huggingface hub token
    let authorization_token = std::env::var("HUGGING_FACE_HUB_TOKEN").ok();

    // Tokenizer instance
    // This will only be used to validate payloads
    let local_path = Path::new(&tokenizer_name);
    let local_model = local_path.exists() && local_path.is_dir();
    let tokenizer = if local_model {
        // Load local tokenizer
        Tokenizer::from_file(local_path.join("tokenizer.json")).ok()
    } else {
        // Download and instantiate tokenizer
        // We need to download it outside of the Tokio runtime
        let params = FromPretrainedParameters {
            revision: revision.clone(),
            auth_token: authorization_token.clone(),
            ..Default::default()
        };
        Tokenizer::from_pretrained(tokenizer_name.clone(), Some(params)).ok()
    };

    // Launch Tokio runtime
    tokio::runtime::Builder::new_multi_thread()
        .enable_all()
        .build()
        .unwrap()
        .block_on(async {
            init_logging(otlp_endpoint, json_output);

            if tokenizer.is_none() {
                tracing::warn!(
                    "Could not find a fast tokenizer implementation for {tokenizer_name}"
                );
                tracing::warn!("Rust input length validation and truncation is disabled");
            }

            // Get Model info
            let model_info = match local_model {
                true => HubModelInfo {
                    model_id: tokenizer_name.clone(),
                    sha: None,
                    pipeline_tag: None,
                },
                false => get_model_info(&tokenizer_name, &revision, authorization_token)
                    .await
                    .unwrap_or_else(|| {
                        tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
                        HubModelInfo {
                            model_id: tokenizer_name.to_string(),
                            sha: None,
                            pipeline_tag: None,
                        }
                    }),
            };

            // if pipeline-tag == text-generation we default to return_full_text = true
            let compat_return_full_text = match &model_info.pipeline_tag {
                None => {
                    tracing::warn!("no pipeline tag found for model {tokenizer_name}");
                    false
                }
                Some(pipeline_tag) => pipeline_tag.as_str() == "text-generation",
            };

            // Instantiate sharded client from the master unix socket
            let mut sharded_client = ShardedClient::connect_uds(master_shard_uds_path)
                .await
                .expect("Could not connect to server");
            // Clear the cache; useful if the webserver rebooted
            sharded_client
                .clear_cache(None)
                .await
                .expect("Unable to clear cache");
            // Get info from the shard
            let shard_info = sharded_client
                .info()
                .await
                .expect("Unable to get shard info");

            // Warmup model
            tracing::info!("Warming up model");
            sharded_client
                .warmup(
                    max_input_length as u32,
                    max_batch_prefill_tokens,
                    max_batch_total_tokens,
                )
                .await
                .expect("Unable to warmup model");
            tracing::info!("Connected");

            // Binds on localhost
            let addr = SocketAddr::new(IpAddr::V4(Ipv4Addr::new(0, 0, 0, 0)), port);

            // Run server
            server::run(
                model_info,
                shard_info,
                compat_return_full_text,
                max_concurrent_requests,
                max_best_of,
                max_stop_sequences,
                max_input_length,
                max_total_tokens,
                waiting_served_ratio,
                max_batch_prefill_tokens,
                max_batch_total_tokens,
                max_waiting_tokens,
                sharded_client,
                tokenizer,
                validation_workers,
                addr,
                cors_allow_origin,
                ngrok,
                ngrok_authtoken,
                ngrok_domain,
                ngrok_username,
                ngrok_password,
            )
            .await;
            Ok(())
        })
}

/// Init logging using env variables LOG_LEVEL and LOG_FORMAT:
///     - otlp_endpoint is an optional URL to an Open Telemetry collector
///     - LOG_LEVEL may be TRACE, DEBUG, INFO, WARN or ERROR (default to INFO)
///     - LOG_FORMAT may be TEXT or JSON (default to TEXT)
fn init_logging(otlp_endpoint: Option<String>, json_output: bool) {
    let mut layers = Vec::new();

    // STDOUT/STDERR layer
    let fmt_layer = tracing_subscriber::fmt::layer()
        .with_file(true)
        .with_line_number(true);

    let fmt_layer = match json_output {
        true => fmt_layer.json().flatten_event(true).boxed(),
        false => fmt_layer.boxed(),
    };
    layers.push(fmt_layer);

    // OpenTelemetry tracing layer
    if let Some(otlp_endpoint) = otlp_endpoint {
        global::set_text_map_propagator(TraceContextPropagator::new());

        let tracer = opentelemetry_otlp::new_pipeline()
            .tracing()
            .with_exporter(
                opentelemetry_otlp::new_exporter()
                    .tonic()
                    .with_endpoint(otlp_endpoint),
            )
            .with_trace_config(
                trace::config()
                    .with_resource(Resource::new(vec![KeyValue::new(
                        "service.name",
                        "text-generation-inference.router",
                    )]))
                    .with_sampler(Sampler::AlwaysOn),
            )
            .install_batch(opentelemetry::runtime::Tokio);

        if let Ok(tracer) = tracer {
            layers.push(tracing_opentelemetry::layer().with_tracer(tracer).boxed());
            axum_tracing_opentelemetry::init_propagator().unwrap();
        };
    }

    // Filter events with LOG_LEVEL
    let env_filter =
        EnvFilter::try_from_env("LOG_LEVEL").unwrap_or_else(|_| EnvFilter::new("info"));

    tracing_subscriber::registry()
        .with(env_filter)
        .with(layers)
        .init();
}

/// get model info from the Huggingface Hub
pub async fn get_model_info(
    model_id: &str,
    revision: &str,
    token: Option<String>,
) -> Option<HubModelInfo> {
    let client = reqwest::Client::new();
    // Poor man's urlencode
    let revision = revision.replace('/', "%2F");
    let url = format!("https://huggingface.co/api/models/{model_id}/revision/{revision}");
    let mut builder = client.get(url).timeout(Duration::from_secs(5));
    if let Some(token) = token {
        builder = builder.bearer_auth(token);
    }

    let response = builder.send().await.ok()?;

    if response.status().is_success() {
        return serde_json::from_str(&response.text().await.ok()?).ok();
    }
    None
}