server.rs

/// HTTP Server logic
use crate::infer::{InferError, InferResponse, InferStreamResponse};
use crate::validation::ValidationError;
use crate::{
    BestOfSequence, CompatGenerateRequest, Details, ErrorResponse, FinishReason,
    GenerateParameters, GenerateRequest, GenerateResponse, Infer, Info, ModelInfo, PrefillToken,
    StreamDetails, StreamResponse, Token, Validation,
};
use axum::extract::Extension;
use axum::http::{HeaderMap, Method, StatusCode};
use axum::response::sse::{Event, KeepAlive, Sse};
use axum::response::{IntoResponse, Response};
use axum::routing::{get, post};
use axum::{http, Json, Router};
use axum_tracing_opentelemetry::opentelemetry_tracing_layer;
use futures::stream::StreamExt;
use futures::Stream;
use metrics_exporter_prometheus::{Matcher, PrometheusBuilder, PrometheusHandle};
use std::convert::Infallible;
use std::net::SocketAddr;
use text_generation_client::ShardedClient;
use tokenizers::Tokenizer;
use tokio::signal;
use tokio::time::Instant;
use tower_http::cors::{AllowOrigin, CorsLayer};
use tracing::{info_span, instrument, Instrument};
use utoipa::OpenApi;
use utoipa_swagger_ui::SwaggerUi;

/// Generate tokens if `stream == false` or a stream of token if `stream == true`
#[utoipa::path(
    post,
    tag = "Text Generation Inference",
    path = "/",
    request_body = CompatGenerateRequest,
    responses(
        (status = 200, description = "See /generate or /generate_stream"),
        (status = 424, description = "Generation Error", body = ErrorResponse,
            example = json ! ({"error": "Request failed during generation"})),
        (status = 429, description = "Model is overloaded", body = ErrorResponse,
            example = json ! ({"error": "Model is overloaded"})),
        (status = 422, description = "Input validation error", body = ErrorResponse,
            example = json ! ({"error": "Input validation error"})),
        (status = 500, description = "Incomplete generation", body = ErrorResponse,
            example = json ! ({"error": "Incomplete generation"})),
    )
)]
#[instrument(skip(infer))]
async fn compat_generate(
    default_return_full_text: Extension<bool>,
    infer: Extension<Infer>,
    req: Json<CompatGenerateRequest>,
) -> Result<Response, (StatusCode, Json<ErrorResponse>)> {
    let mut req = req.0;

    // default return_full_text given the pipeline_tag
    if req.parameters.return_full_text.is_none() {
        req.parameters.return_full_text = Some(default_return_full_text.0)
    }

    // switch on stream
    if req.stream {
        Ok(generate_stream(infer, Json(req.into()))
            .await
            .into_response())
    } else {
        let (headers, generation) = generate(infer, Json(req.into())).await?;
        // wrap generation inside a Vec to match api-inference
        Ok((headers, Json(vec![generation.0])).into_response())
    }
}

/// Text Generation Inference endpoint info
#[utoipa::path(
    get,
    tag = "Text Generation Inference",
    path = "/info",
    responses((status = 200, description = "Served model info", body = Info))
)]
#[instrument]
async fn get_model_info(model_info: Extension<ModelInfo>) -> Json<Info> {
    let model_info = model_info.0;
    let info = Info {
        version: env!("CARGO_PKG_VERSION"),
        sha: option_env!("VERGEN_GIT_SHA"),
        model_id: model_info.model_id,
        model_sha: model_info.sha,
        model_pipeline_tag: model_info.pipeline_tag,
    };
    Json(info)
}

/// Health check method
#[instrument(skip(infer))]
async fn health(infer: Extension<Infer>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
    // TODO: while this is the best health check we can do, it is a bit on the heavy side and might
    //       be a bit too slow for a health check.
    //       What we should do instead is check if the gRPC channels are still healthy.

    // Send a small inference request
    infer
        .generate(GenerateRequest {
            inputs: "liveness".to_string(),
            parameters: GenerateParameters {
                best_of: None,
                temperature: None,
                repetition_penalty: None,
                top_k: None,
                top_p: None,
                typical_p: None,
                do_sample: false,
                max_new_tokens: 1,
                return_full_text: None,
                stop: Vec::new(),
                truncate: None,
                watermark: false,
                details: false,
                seed: None,
            },
        })
        .await?;
    Ok(())
}

/// Generate tokens
#[utoipa::path(
    post,
    tag = "Text Generation Inference",
    path = "/generate",
    request_body = GenerateRequest,
    responses(
        (status = 200, description = "Generated Text", body = GenerateResponse),
        (status = 424, description = "Generation Error", body = ErrorResponse,
            example = json ! ({"error": "Request failed during generation"})),
        (status = 429, description = "Model is overloaded", body = ErrorResponse,
            example = json ! ({"error": "Model is overloaded"})),
        (status = 422, description = "Input validation error", body = ErrorResponse,
            example = json ! ({"error": "Input validation error"})),
        (status = 500, description = "Incomplete generation", body = ErrorResponse,
            example = json ! ({"error": "Incomplete generation"})),
    )
)]
#[instrument(
    skip(infer),
    fields(
        total_time,
        validation_time,
        queue_time,
        inference_time,
        time_per_token,
        seed,
    )
)]
async fn generate(
    infer: Extension<Infer>,
    req: Json<GenerateRequest>,
) -> Result<(HeaderMap, Json<GenerateResponse>), (StatusCode, Json<ErrorResponse>)> {
    let span = tracing::Span::current();
    let start_time = Instant::now();
    metrics::increment_counter!("tgi_request_count");

    let compute_characters = req.0.inputs.chars().count();
    let mut add_prompt = None;
    if req.0.parameters.return_full_text.unwrap_or(false) {
        add_prompt = Some(req.0.inputs.clone());
    }

    let details = req.0.parameters.details;

    // Inference
    let (response, best_of_responses) = match req.0.parameters.best_of {
        Some(best_of) if best_of > 1 => {
            let (response, best_of_responses) = infer.generate_best_of(req.0, best_of).await?;
            (response, Some(best_of_responses))
        }
        _ => (infer.generate(req.0).await?, None),
    };

    // Token details
    let details = match details {
        true => {
            // convert best_of_responses
            let best_of_sequences = best_of_responses.map(|responses: Vec<InferResponse>| {
                responses
                    .into_iter()
                    .map(|response: InferResponse| {
                        // Add prompt if return_full_text
                        let mut output_text = response.generated_text.text;
                        if let Some(prompt) = &add_prompt {
                            output_text = prompt.clone() + &output_text;
                        }

                        BestOfSequence {
                            generated_text: output_text,
                            finish_reason: FinishReason::from(
                                response.generated_text.finish_reason,
                            ),
                            generated_tokens: response.generated_text.generated_tokens,
                            prefill: response.prefill,
                            tokens: response.tokens,
                            seed: response.generated_text.seed,
                        }
                    })
                    .collect()
            });

            Some(Details {
                finish_reason: FinishReason::from(response.generated_text.finish_reason),
                generated_tokens: response.generated_text.generated_tokens,
                prefill: response.prefill,
                tokens: response.tokens,
                seed: response.generated_text.seed,
                best_of_sequences,
            })
        }
        false => None,
    };

    // Timings
    let total_time = start_time.elapsed();
    let validation_time = response.queued - start_time;
    let queue_time = response.start - response.queued;
    let inference_time = Instant::now() - response.start;
    let time_per_token = inference_time / response.generated_text.generated_tokens;

    // Tracing metadata
    span.record("total_time", format!("{total_time:?}"));
    span.record("validation_time", format!("{validation_time:?}"));
    span.record("queue_time", format!("{queue_time:?}"));
    span.record("inference_time", format!("{inference_time:?}"));
    span.record("time_per_token", format!("{time_per_token:?}"));
    span.record("seed", format!("{:?}", response.generated_text.seed));

    // Headers
    let mut headers = HeaderMap::new();
    headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
    headers.insert(
        "x-compute-time",
        total_time.as_millis().to_string().parse().unwrap(),
    );
    headers.insert(
        "x-compute-characters",
        compute_characters.to_string().parse().unwrap(),
    );
    headers.insert(
        "x-total-time",
        total_time.as_millis().to_string().parse().unwrap(),
    );
    headers.insert(
        "x-validation-time",
        validation_time.as_millis().to_string().parse().unwrap(),
    );
    headers.insert(
        "x-queue-time",
        queue_time.as_millis().to_string().parse().unwrap(),
    );
    headers.insert(
        "x-inference-time",
        inference_time.as_millis().to_string().parse().unwrap(),
    );
    headers.insert(
        "x-time-per-token",
        time_per_token.as_millis().to_string().parse().unwrap(),
    );

    // Metrics
    metrics::increment_counter!("tgi_request_success");
    metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
    metrics::histogram!(
        "tgi_request_validation_duration",
        validation_time.as_secs_f64()
    );
    metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
    metrics::histogram!(
        "tgi_request_inference_duration",
        inference_time.as_secs_f64()
    );
    metrics::histogram!(
        "tgi_request_mean_time_per_token_duration",
        time_per_token.as_secs_f64()
    );
    metrics::histogram!(
        "tgi_request_generated_tokens",
        response.generated_text.generated_tokens as f64
    );

    // Send response
    let mut output_text = response.generated_text.text;
    if let Some(prompt) = add_prompt {
        output_text = prompt + &output_text;
    }

    tracing::info!("Output: {}", output_text);

    let response = GenerateResponse {
        generated_text: output_text,
        details,
    };
    Ok((headers, Json(response)))
}

/// Generate a stream of token using Server-Sent Events
#[utoipa::path(
    post,
    tag = "Text Generation Inference",
    path = "/generate_stream",
    request_body = GenerateRequest,
    responses(
        (status = 200, description = "Generated Text", body = StreamResponse,
            content_type = "text/event-stream"),
        (status = 424, description = "Generation Error", body = ErrorResponse,
            example = json ! ({"error": "Request failed during generation"}),
            content_type = "text/event-stream"),
        (status = 429, description = "Model is overloaded", body = ErrorResponse,
            example = json ! ({"error": "Model is overloaded"}),
            content_type = "text/event-stream"),
        (status = 422, description = "Input validation error", body = ErrorResponse,
            example = json ! ({"error": "Input validation error"}),
            content_type = "text/event-stream"),
        (status = 500, description = "Incomplete generation", body = ErrorResponse,
            example = json ! ({"error": "Incomplete generation"}),
            content_type = "text/event-stream"),
    )
)]
#[instrument(
    skip(infer),
    fields(
        total_time,
        validation_time,
        queue_time,
        inference_time,
        time_per_token,
        seed,
    )
)]
async fn generate_stream(
    infer: Extension<Infer>,
    req: Json<GenerateRequest>,
) -> (
    HeaderMap,
    Sse<impl Stream<Item = Result<Event, Infallible>>>,
) {
    let span = tracing::Span::current();
    let start_time = Instant::now();
    metrics::increment_counter!("tgi_request_count");

    let compute_characters = req.0.inputs.chars().count();

    let mut headers = HeaderMap::new();
    headers.insert("x-compute-type", "gpu+optimized".parse().unwrap());
    headers.insert(
        "x-compute-characters",
        compute_characters.to_string().parse().unwrap(),
    );

    let stream = async_stream::stream! {
        // Inference
        let mut end_reached = false;
        let mut error = false;

        let mut add_prompt = None;
        if req.0.parameters.return_full_text.unwrap_or(false) {
            add_prompt = Some(req.0.inputs.clone());
        }
        let details = req.0.parameters.details;

        let best_of = req.0.parameters.best_of.unwrap_or(1);
        if best_of == 1 {
            match infer.generate_stream(req.0).instrument(info_span!(parent: &span, "async_stream")).await {
                Ok(mut response_stream) => {
                    // Server-Sent Event stream
                    while let Some(response) = response_stream.next().await {
                        match response {
                            Ok(response) => {
                                match response {
                                    // Prefill is ignored
                                    InferStreamResponse::Prefill(_) => {}
                                    // Yield event for every new token
                                    InferStreamResponse::Token(token) => {
                                        // StreamResponse
                                        let stream_token = StreamResponse {
                                            token,
                                            generated_text: None,
                                            details: None,
                                        };

                                        yield Ok(Event::default().json_data(stream_token).unwrap())
                                    }
                                    // Yield event for last token and compute timings
                                    InferStreamResponse::End {
                                        token,
                                        generated_text,
                                        start,
                                        queued,
                                    } => {
                                        // Token details
                                        let details = match details {
                                            true => Some(StreamDetails {
                                                finish_reason: FinishReason::from(generated_text.finish_reason),
                                                generated_tokens: generated_text.generated_tokens,
                                                seed: generated_text.seed,
                                            }),
                                            false => None,
                                        };

                                        // Timings
                                        let total_time = start_time.elapsed();
                                        let validation_time = queued - start_time;
                                        let queue_time = start - queued;
                                        let inference_time = Instant::now() - start;
                                        let time_per_token = inference_time / generated_text.generated_tokens;

                                        // Tracing metadata
                                        span.record("total_time", format!("{total_time:?}"));
                                        span.record("validation_time", format!("{validation_time:?}"));
                                        span.record("queue_time", format!("{queue_time:?}"));
                                        span.record("inference_time", format!("{inference_time:?}"));
                                        span.record("time_per_token", format!("{time_per_token:?}"));
                                        span.record("seed", format!("{:?}", generated_text.seed));

                                        // Metrics
                                        metrics::increment_counter!("tgi_request_success");
                                        metrics::histogram!("tgi_request_duration", total_time.as_secs_f64());
                                        metrics::histogram!("tgi_request_validation_duration", validation_time.as_secs_f64());
                                        metrics::histogram!("tgi_request_queue_duration", queue_time.as_secs_f64());
                                        metrics::histogram!("tgi_request_inference_duration", inference_time.as_secs_f64());
                                        metrics::histogram!("tgi_request_mean_time_per_token_duration", time_per_token.as_secs_f64());
                                        metrics::histogram!("tgi_request_generated_tokens", generated_text.generated_tokens as f64);

                                        // StreamResponse
                                        end_reached = true;

                                        let mut output_text = generated_text.text;
                                        if let Some(prompt) = add_prompt {
                                            output_text = prompt + &output_text;
                                        }

                                        tracing::info!(parent: &span, "Output: {}", output_text);

                                        let stream_token = StreamResponse {
                                            token,
                                            generated_text: Some(output_text),
                                            details
                                        };

                                        yield Ok(Event::default().json_data(stream_token).unwrap());
                                        break;
                                    }
                                }
                            }
                            // yield error
                            Err(err) => {
                                error = true;
                                yield Ok(Event::from(err));
                                break;
                            }
                        }
                    }
                },
                // yield error
                Err(err) => {
                    error = true;
                    yield Ok(Event::from(err));
                }
            }
            // Check if generation reached the end
            // Skip if we already sent an error
            if !end_reached && !error {
                let err = InferError::IncompleteGeneration;
                metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
                tracing::error!("{err}");
                yield Ok(Event::from(err));
            }
        } else {
            let err = InferError::from(ValidationError::BestOfStream);
            metrics::increment_counter!("tgi_request_failure", "err" => "validation");
            tracing::error!("{err}");
            yield Ok(Event::from(err));
        }
    };

    (headers, Sse::new(stream).keep_alive(KeepAlive::default()))
}

/// Prometheus metrics scrape endpoint
#[utoipa::path(
    get,
    tag = "Text Generation Inference",
    path = "/metrics",
    responses((status = 200, description = "Prometheus Metrics", body = String))
)]
async fn metrics(prom_handle: Extension<PrometheusHandle>) -> String {
    prom_handle.render()
}

/// Serving method
#[allow(clippy::too_many_arguments)]
pub async fn run(
    model_info: ModelInfo,
    compat_return_full_text: bool,
    max_concurrent_requests: usize,
    max_best_of: usize,
    max_stop_sequences: usize,
    max_input_length: usize,
    max_total_tokens: usize,
    max_batch_size: usize,
    max_waiting_tokens: usize,
    client: ShardedClient,
    tokenizer: Option<Tokenizer>,
    validation_workers: usize,
    addr: SocketAddr,
    allow_origin: Option<AllowOrigin>,
) {
    // OpenAPI documentation
    #[derive(OpenApi)]
    #[openapi(
        paths(
            get_model_info,
            compat_generate,
            generate,
            generate_stream,
            metrics,
        ),
        components(
            schemas(
                Info,
                CompatGenerateRequest,
                GenerateRequest,
                GenerateParameters,
                PrefillToken,
                Token,
                GenerateResponse,
                BestOfSequence,
                Details,
                FinishReason,
                StreamResponse,
                StreamDetails,
                ErrorResponse,
            )
        ),
        tags(
            (name = "Text Generation Inference", description = "Hugging Face Text Generation Inference API")
        ),
        info(
            title = "Text Generation Inference",
            license(
                name = "Apache 2.0",
                url = "https://www.apache.org/licenses/LICENSE-2.0"
            )
        )
    )]
    struct ApiDoc;

    // Create state
    let validation = Validation::new(
        validation_workers,
        tokenizer,
        max_best_of,
        max_stop_sequences,
        max_input_length,
        max_total_tokens,
    );
    let infer = Infer::new(
        client,
        validation,
        max_batch_size,
        max_waiting_tokens,
        max_concurrent_requests,
    );

    // Duration buckets
    let duration_matcher = Matcher::Suffix(String::from("duration"));
    let n_duration_buckets = 35;
    let mut duration_buckets = Vec::with_capacity(n_duration_buckets);
    // Minimum duration in seconds
    let mut value = 0.0001;
    for _ in 0..n_duration_buckets {
        // geometric sequence
        value *= 1.5;
        duration_buckets.push(value);
    }
    // Input Length buckets
    let input_length_matcher = Matcher::Full(String::from("tgi_request_input_length"));
    let input_length_buckets: Vec<f64> = (0..100)
        .map(|x| (max_input_length as f64 / 100.0) * (x + 1) as f64)
        .collect();
    // Generated tokens buckets
    let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
    let generated_tokens_buckets: Vec<f64> = (0..100)
        .map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
        .collect();
    // Input Length buckets
    let max_new_tokens_matcher = Matcher::Full(String::from("tgi_request_max_new_tokens"));
    let max_new_tokens_buckets: Vec<f64> = (0..100)
        .map(|x| (max_total_tokens as f64 / 100.0) * (x + 1) as f64)
        .collect();
    // Batch size buckets
    let batch_size_matcher = Matcher::Full(String::from("tgi_batch_next_size"));
    let batch_size_buckets: Vec<f64> = (0..max_batch_size).map(|x| (x + 1) as f64).collect();

    // Prometheus handler
    let builder = PrometheusBuilder::new()
        .set_buckets_for_metric(duration_matcher, &duration_buckets)
        .unwrap()
        .set_buckets_for_metric(input_length_matcher, &input_length_buckets)
        .unwrap()
        .set_buckets_for_metric(generated_tokens_matcher, &generated_tokens_buckets)
        .unwrap()
        .set_buckets_for_metric(max_new_tokens_matcher, &max_new_tokens_buckets)
        .unwrap()
        .set_buckets_for_metric(batch_size_matcher, &batch_size_buckets)
        .unwrap();
    let prom_handle = builder
        .install_recorder()
        .expect("failed to install metrics recorder");

    // CORS layer
    let allow_origin = allow_origin.unwrap_or(AllowOrigin::any());
    let cors_layer = CorsLayer::new()
        .allow_methods([Method::GET, Method::POST])
        .allow_headers([http::header::CONTENT_TYPE])
        .allow_origin(allow_origin);

    // Create router
    let app = Router::new()
        .merge(SwaggerUi::new("/docs").url("/api-doc/openapi.json", ApiDoc::openapi()))
        // Base routes
        .route("/", post(compat_generate))
        .route("/info", get(get_model_info))
        .route("/generate", post(generate))
        .route("/generate_stream", post(generate_stream))
        // AWS Sagemaker route
        .route("/invocations", post(compat_generate))
        // Base Health route
        .route("/health", get(health))
        // Inference API health route
        .route("/", get(health))
        // AWS Sagemaker health route
        .route("/ping", get(health))
        // Prometheus metrics route
        .route("/metrics", get(metrics))
        .layer(Extension(model_info))
        .layer(Extension(compat_return_full_text))
        .layer(Extension(infer))
        .layer(Extension(prom_handle))
        .layer(opentelemetry_tracing_layer())
        .layer(cors_layer);

    // Run server
    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        // Wait until all requests are finished to shut down
        .with_graceful_shutdown(shutdown_signal())
        .await
        .unwrap();
}

/// Shutdown signal handler
async fn shutdown_signal() {
    let ctrl_c = async {
        signal::ctrl_c()
            .await
            .expect("failed to install Ctrl+C handler");
    };

    #[cfg(unix)]
    let terminate = async {
        signal::unix::signal(signal::unix::SignalKind::terminate())
            .expect("failed to install signal handler")
            .recv()
            .await;
    };

    #[cfg(not(unix))]
    let terminate = std::future::pending::<()>();

    tokio::select! {
        _ = ctrl_c => {},
        _ = terminate => {},
    }

    tracing::info!("signal received, starting graceful shutdown");
    opentelemetry::global::shutdown_tracer_provider();
}

impl From<i32> for FinishReason {
    fn from(finish_reason: i32) -> Self {
        let finish_reason = text_generation_client::FinishReason::from_i32(finish_reason).unwrap();
        match finish_reason {
            text_generation_client::FinishReason::Length => FinishReason::Length,
            text_generation_client::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
            text_generation_client::FinishReason::StopSequence => FinishReason::StopSequence,
        }
    }
}

/// Convert to Axum supported formats
impl From<InferError> for (StatusCode, Json<ErrorResponse>) {
    fn from(err: InferError) -> Self {
        let status_code = match err {
            InferError::GenerationError(_) => StatusCode::FAILED_DEPENDENCY,
            InferError::Overloaded(_) => StatusCode::TOO_MANY_REQUESTS,
            InferError::ValidationError(_) => StatusCode::UNPROCESSABLE_ENTITY,
            InferError::IncompleteGeneration => StatusCode::INTERNAL_SERVER_ERROR,
        };

        (
            status_code,
            Json(ErrorResponse {
                error: err.to_string(),
                error_type: err.error_type().to_string(),
            }),
        )
    }
}

impl From<InferError> for Event {
    fn from(err: InferError) -> Self {
        Event::default()
            .json_data(ErrorResponse {
                error: err.to_string(),
                error_type: err.error_type().to_string(),
            })
            .unwrap()
    }
}