text-generation-inference/router/src/infer.rs

/// Batching and inference logic
use crate::validation::{Validation, ValidationError};
use crate::HubTokenizerConfig;
use crate::{ChatRequest, GenerateRequest, GenerateStreamResponse, PrefillToken};
use crate::{Entry, Queue, Token};
use futures::future::try_join_all;
use minijinja::{Environment, ErrorKind, Template};
use nohash_hasher::IntMap;
use std::sync::{
    atomic::{AtomicBool, Ordering},
    Arc,
};
use text_generation_client::{
    Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
};
use thiserror::Error;
use tokio::sync::mpsc::error::SendError;
use tokio::sync::{mpsc, Notify, Semaphore, TryAcquireError};
use tokio::time::Instant;
use tokio_stream::wrappers::UnboundedReceiverStream;
use tokio_stream::StreamExt;
use tracing::{info_span, instrument, Instrument, Span};

/// Inference struct
#[derive(Clone)]
pub struct Infer {
    /// Validation
    validation: Validation,
    /// Request queue
    queue: Queue,
    /// Shared state
    shared: Arc<Shared>,
    /// Inference limit
    limit_concurrent_requests: Arc<Semaphore>,
    /// Chat template
    template: Option<Template<'static, 'static>>,
}

/// Infer shared state
struct Shared {
    /// Batching background Tokio task notifier
    batching_task: Notify,
}

impl Infer {
    #[allow(clippy::too_many_arguments)]
    pub(crate) fn new(
        client: ShardedClient,
        validation: Validation,
        waiting_served_ratio: f32,
        max_batch_prefill_tokens: u32,
        max_batch_total_tokens: u32,
        max_waiting_tokens: usize,
        max_concurrent_requests: usize,
        requires_padding: bool,
        window_size: Option<u32>,
        speculate: u32,
        generation_health: Arc<AtomicBool>,
        tokenizer_config: HubTokenizerConfig,
    ) -> Self {
        // Infer shared state
        let queue = Queue::new(requires_padding, 16, window_size, speculate);
        let shared = Arc::new(Shared {
            batching_task: Notify::new(),
        });

        // Spawn batching background task that contains all the inference logic
        tokio::spawn(batching_task(
            client,
            waiting_served_ratio,
            max_batch_prefill_tokens,
            max_batch_total_tokens,
            max_waiting_tokens,
            queue.clone(),
            shared.clone(),
            generation_health,
        ));

        // Inference limit with a semaphore
        let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));

        let template = tokenizer_config.chat_template.map(|t| {
            let env = Box::new(Environment::new());
            let template_str = t.into_boxed_str();
            // leaking env and template_str as read-only, static resources for performance.
            Box::leak(env)
                .template_from_str(Box::leak(template_str))
                .unwrap()
        });

        Self {
            validation,
            queue,
            shared,
            limit_concurrent_requests: semaphore,
            template,
        }
    }

    /// Add a new request to the queue and return a stream of InferStreamResponse
    #[instrument(skip_all)]
    pub(crate) async fn generate_stream(
        &self,
        request: GenerateRequest,
    ) -> Result<GenerateStreamResponse, InferError> {
        // Limit concurrent requests by acquiring a permit from the semaphore
        let permit = self
            .clone()
            .limit_concurrent_requests
            .try_acquire_owned()
            .map_err(|err| {
                metrics::increment_counter!("tgi_request_failure", "err" => "overloaded");
                tracing::error!("{err}");
                err
            })?;

        // Validate request
        let valid_request = self.validation.validate(request).await.map_err(|err| {
            metrics::increment_counter!("tgi_request_failure", "err" => "validation");
            tracing::error!("{err}");
            err
        })?;

        // MPSC channel to communicate with the background batching task
        let (response_tx, response_rx) = mpsc::unbounded_channel();
        let input_length = valid_request.input_length;

        // Append the request to the queue
        self.queue.append(Entry {
            request: valid_request,
            response_tx,
            span: Span::current(),
            temp_span: None,
            queue_time: Instant::now(),
            batch_time: None,
        });

        // Notify the background task that we have a new entry in the queue that needs
        // to be batched
        self.shared.batching_task.notify_one();

        // Return stream
        Ok((
            permit,
            input_length,
            UnboundedReceiverStream::new(response_rx),
        ))
    }

    /// Apply the chat template to the chat request
    #[instrument(skip_all)]
    pub(crate) fn apply_chat_template(&self, chat: ChatRequest) -> Result<String, InferError> {
        self.template
            .as_ref()
            .ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
            .render(chat)
            .map_err(|e| {
                metrics::increment_counter!("tgi_request_failure", "err" => "template");
                tracing::error!("{e}");
                InferError::TemplateError(e)
            })
    }

    /// Add a new request to the queue and return a InferResponse
    #[instrument(skip_all)]
    pub(crate) async fn generate(
        &self,
        request: GenerateRequest,
    ) -> Result<InferResponse, InferError> {
        let use_top_tokens = request.parameters.top_n_tokens.is_some_and(|x| x > 0);

        // Create stream and keep semaphore permit as long as generate lives
        let (_permit, _input_length, mut stream) = self.generate_stream(request).await?;

        // Return values
        let mut result_prefill = Vec::new();
        let mut result_tokens = Vec::new();
        let mut result_top_tokens = Vec::new();
        let mut result_generated_text = None;
        let mut result_start = None;
        let mut result_queued = None;

        // Iterate on stream
        while let Some(response) = stream.next().await {
            match response? {
                // Add prefill tokens
                InferStreamResponse::Prefill(tokens) => {
                    // Create Token objects
                    // We do that here instead of in the Python code as Rust for loops are faster
                    result_prefill = tokens
                        .ids
                        .into_iter()
                        .zip(tokens.logprobs.into_iter())
                        .zip(tokens.texts.into_iter())
                        .map(|((id, logprob), text)| PrefillToken { id, text, logprob })
                        .collect();
                }
                // Push last token
                InferStreamResponse::Intermediate { token, top_tokens } => {
                    result_tokens.push(token);
                    result_top_tokens.push(top_tokens);
                }
                // Final message
                // Set return values
                InferStreamResponse::End {
                    token,
                    generated_text,
                    start,
                    queued,
                    top_tokens,
                } => {
                    result_tokens.push(token);
                    result_top_tokens.push(top_tokens);
                    result_generated_text = Some(generated_text);
                    result_start = Some(start);
                    result_queued = Some(queued)
                }
            }
        }

        // Check that we received a `InferStreamResponse::End` message
        if let (Some(generated_text), Some(queued), Some(start)) =
            (result_generated_text, result_queued, result_start)
        {
            Ok(InferResponse {
                prefill: result_prefill,
                _input_length,
                tokens: result_tokens,
                generated_text,
                queued,
                start,
                top_tokens: if use_top_tokens {
                    result_top_tokens
                } else {
                    Vec::new()
                },
            })
        } else {
            let err = InferError::IncompleteGeneration;
            metrics::increment_counter!("tgi_request_failure", "err" => "incomplete");
            tracing::error!("{err}");
            Err(err)
        }
    }
    /// Add best_of new requests to the queue and return a InferResponse of the sequence with
    /// the highest log probability per token
    #[instrument(skip(self, request))]
    pub(crate) async fn generate_best_of(
        &self,
        request: GenerateRequest,
        best_of: usize,
    ) -> Result<(InferResponse, Vec<InferResponse>), InferError> {
        // validate  best_of parameter separately
        let best_of = self.validation.validate_best_of(best_of)?;

        // create multiple generate requests
        let mut infer_responses: Vec<InferResponse> =
            try_join_all((0..best_of).map(|_| self.generate(request.clone()))).await?;

        // get the sequence with the highest log probability per token
        let mut max_index = 0;
        let mut max_logprob: f32 = f32::MIN;

        for (i, response) in infer_responses.iter().enumerate() {
            // mean logprobs of the generated tokens
            let sequence_logprob = response
                .tokens
                .iter()
                .map(|token| token.logprob)
                .sum::<f32>()
                / response.tokens.len() as f32;

            // set best sequence
            if sequence_logprob > max_logprob {
                max_index = i;
                max_logprob = sequence_logprob;
            }
        }
        let best_response = infer_responses.remove(max_index);
        Ok((best_response, infer_responses))
    }
}

/// Batching logic
/// Will be launched in a background Tokio task
///
/// Batches requests and sends them to the inference server
#[allow(clippy::too_many_arguments)]
async fn batching_task(
    mut client: ShardedClient,
    waiting_served_ratio: f32,
    max_batch_prefill_tokens: u32,
    max_batch_total_tokens: u32,
    max_waiting_tokens: usize,
    queue: Queue,
    shared: Arc<Shared>,
    generation_health: Arc<AtomicBool>,
) {
    // Infinite loop
    loop {
        // Wait for a notification from the Infer struct
        shared.batching_task.notified().await;

        // Get the next batch from the queue
        // This batch might be smaller than the maximum batch size if there are not enough requests
        // waiting in the queue
        while let Some((mut entries, batch, span)) = queue
            .next_batch(None, max_batch_prefill_tokens, max_batch_total_tokens)
            .await
        {
            let mut cached_batch = prefill(&mut client, batch, &mut entries, &generation_health)
                .instrument(span)
                .await;
            let mut waiting_tokens = 1;

            // We loop until we do not receive any cached batch from the inference server (== until
            // all requests have met their stopping criteria)
            while let Some(batch) = cached_batch {
                // Get current batch info
                let batch_size = batch.size;
                let batch_max_tokens = batch.max_tokens;
                let mut batches = vec![batch];
                metrics::gauge!("tgi_batch_current_size", batch_size as f64);
                metrics::gauge!("tgi_batch_current_max_tokens", batch_max_tokens as f64);

                let min_size = if waiting_tokens >= max_waiting_tokens {
                    // If we didn't onboard any new requests since >= max_waiting_tokens, we try
                    // to add a new batch even though its size might be small
                    None
                } else {
                    // Minimum batch size
                    Some((batch_size as f32 * waiting_served_ratio).floor() as usize)
                };

                let token_budget = max_batch_total_tokens.saturating_sub(batch_max_tokens);

                // Try to get a new batch
                if let Some((mut new_entries, new_batch, span)) = queue
                    .next_batch(min_size, max_batch_prefill_tokens, token_budget)
                    .await
                {
                    // Tracking metrics
                    if min_size.is_some() {
                        metrics::increment_counter!("tgi_batch_concat", "reason" => "backpressure");
                    } else {
                        metrics::increment_counter!("tgi_batch_concat", "reason" => "wait_exceeded");
                    }

                    entries.iter_mut().for_each(|(_, entry)| {
                        // Create a new span to add the info that this entry is waiting
                        // because a new batch is being computed
                        let entry_waiting_span = info_span!(parent: &entry.span, "waiting");
                        // Add relationships
                        span.follows_from(&entry_waiting_span);
                        entry_waiting_span.follows_from(&span);
                        // Update entry
                        entry.temp_span = Some(entry_waiting_span);
                    });

                    // Generate one token for this new batch to have the attention past in cache
                    let new_cached_batch =
                        prefill(&mut client, new_batch, &mut new_entries, &generation_health)
                            .instrument(span)
                            .await;
                    // Reset waiting counter
                    waiting_tokens = 1;
                    // Extend current batch with the new batch
                    if let Some(new_cached_batch) = new_cached_batch {
                        entries.extend(new_entries);
                        batches.push(new_cached_batch);
                    }
                }

                // Create span for this batch to add context to inference calls
                let next_batch_size = entries.len();
                let next_batch_span =
                    info_span!(parent: None, "batch", batch_size = next_batch_size);
                entries.iter_mut().for_each(|(_, entry)| {
                    // Create a new span to link the batch back to this entry
                    let entry_batch_span = info_span!(parent: &entry.span, "infer");
                    // Add relationships
                    next_batch_span.follows_from(&entry_batch_span);
                    entry_batch_span.follows_from(&next_batch_span);
                    // Update entry
                    entry.temp_span = Some(entry_batch_span);
                });

                cached_batch = decode(&mut client, batches, &mut entries, &generation_health)
                    .instrument(next_batch_span)
                    .await;
                waiting_tokens += 1;
            }
            metrics::gauge!("tgi_batch_current_size", 0.0);
            metrics::gauge!("tgi_batch_current_max_tokens", 0.0);
        }
    }
}

#[instrument(skip_all)]
async fn prefill(
    client: &mut ShardedClient,
    batch: Batch,
    entries: &mut IntMap<u64, Entry>,
    generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
    let start_time = Instant::now();
    let batch_id = batch.id;
    metrics::increment_counter!("tgi_batch_inference_count", "method" => "prefill");

    match client.prefill(batch).await {
        Ok((generations, next_batch, timings)) => {
            // Update health
            generation_health.store(true, Ordering::SeqCst);

            let start_filtering_time = Instant::now();
            // Send generated tokens and filter stopped entries
            filter_send_generations(generations, entries);

            // Filter next batch and remove requests that were stopped
            let next_batch = filter_batch(client, next_batch, entries).await;

            metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "prefill");
            metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "prefill");
            metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "prefill");
            metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "prefill");
            metrics::increment_counter!("tgi_batch_inference_success", "method" => "prefill");
            next_batch
        }
        // If we have an error, we discard the whole batch
        Err(err) => {
            // Update health
            generation_health.store(false, Ordering::SeqCst);
            let _ = client.clear_cache(Some(batch_id)).await;
            send_errors(err, entries);
            metrics::increment_counter!("tgi_batch_inference_failure", "method" => "prefill");
            None
        }
    }
}

#[instrument(skip_all)]
async fn decode(
    client: &mut ShardedClient,
    batches: Vec<CachedBatch>,
    entries: &mut IntMap<u64, Entry>,
    generation_health: &Arc<AtomicBool>,
) -> Option<CachedBatch> {
    let start_time = Instant::now();
    let batch_ids: Vec<u64> = batches.iter().map(|b| b.id).collect();
    metrics::increment_counter!("tgi_batch_inference_count", "method" => "decode");

    match client.decode(batches).await {
        Ok((generations, next_batch, timings)) => {
            // Update health
            generation_health.store(true, Ordering::SeqCst);

            let start_filtering_time = Instant::now();
            // Send generated tokens and filter stopped entries
            filter_send_generations(generations, entries);

            // Filter next batch and remove requests that were stopped
            let next_batch = filter_batch(client, next_batch, entries).await;

            if let Some(concat_duration) = timings.concat {
                metrics::histogram!("tgi_batch_concat_duration", concat_duration.as_secs_f64(), "method" => "decode");
            }
            metrics::histogram!("tgi_batch_forward_duration", timings.forward.as_secs_f64(), "method" => "decode");
            metrics::histogram!("tgi_batch_decode_duration", timings.decode.as_secs_f64(), "method" => "decode");
            metrics::histogram!("tgi_batch_filter_duration", start_filtering_time.elapsed().as_secs_f64(), "method" => "decode");
            metrics::histogram!("tgi_batch_inference_duration", start_time.elapsed().as_secs_f64(), "method" => "decode");
            metrics::increment_counter!("tgi_batch_inference_success", "method" => "decode");
            next_batch
        }
        // If we have an error, we discard the whole batch
        Err(err) => {
            generation_health.store(false, Ordering::SeqCst);
            for id in batch_ids {
                let _ = client.clear_cache(Some(id)).await;
            }
            send_errors(err, entries);
            metrics::increment_counter!("tgi_batch_inference_failure", "method" => "decode");
            None
        }
    }
}

/// Filter a `batch` and remove all requests not present in `entries`
#[instrument(skip_all)]
async fn filter_batch(
    client: &mut ShardedClient,
    next_batch: Option<CachedBatch>,
    entries: &IntMap<u64, Entry>,
) -> Option<CachedBatch> {
    let mut batch = next_batch?;

    // No need to filter
    if batch.size as usize == entries.len() {
        return Some(batch);
    }

    let id = batch.id;

    // Retain only requests that are still in entries
    batch.request_ids.retain(|id| entries.contains_key(id));

    if batch.request_ids.is_empty() {
        // All requests have been filtered out
        // Next batch is now empty
        // Clear it from the Python shards cache
        // We unwrap here as we need to panic since we cannot recover if this method fails
        client.clear_cache(Some(id)).await.unwrap();
        None
    } else {
        // Filter Python shard cache
        // We unwrap here as we need to panic since we cannot recover if this method fails
        client.filter_batch(id, batch.request_ids).await.unwrap()
    }
}

/// Send one or multiple `InferStreamResponse` to Infer for all `entries`
/// and filter entries
#[instrument(skip_all)]
fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u64, Entry>) {
    generations.into_iter().for_each(|generation| {
        let id = generation.request_id;
        // Get entry
        // We can `expect` here as the request id should always be in the entries
        let entry = entries
            .get(&id)
            .expect("ID not found in entries. This is a bug.");

        // Create and enter a span to link this function back to the entry
        let _span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_generation", generation = ?generation).entered();
        // Send generation responses back to the infer task
        // If the receive an error from the Flume channel, it means that the client dropped the
        // request and we need to stop generating hence why we unwrap_or(true)
        let stopped = send_responses(generation, entry).map_err(|err| {
            tracing::error!("Entry response channel error.");
            metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
            err
        }).unwrap_or(true);
        if stopped {
            entries.remove(&id).expect("ID not found in entries. This is a bug.");
        }
    });
}

/// Send responses through the `entry` response channel
fn send_responses(
    generation: Generation,
    entry: &Entry,
) -> Result<bool, Box<SendError<Result<InferStreamResponse, InferError>>>> {
    // Return directly if the channel is disconnected
    if entry.response_tx.is_closed() {
        metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
        return Ok(true);
    }

    let mut stopped = false;

    if let Some(prefill_tokens) = generation.prefill_tokens {
        // Send message
        entry
            .response_tx
            .send(Ok(InferStreamResponse::Prefill(prefill_tokens)))?;
    }

    // Create last Token
    let tokens_ = generation.tokens.expect("Non empty tokens in generation");
    let n = tokens_.ids.len();
    metrics::histogram!("tgi_request_skipped_tokens", (n - 1) as f64);
    let mut iterator = tokens_
        .ids
        .into_iter()
        .zip(tokens_.logprobs)
        .zip(tokens_.texts)
        .zip(tokens_.is_special)
        .enumerate()
        .peekable();
    while let Some((i, (((id, logprob), text), special))) = iterator.next() {
        let token = Token {
            id,
            text,
            logprob,
            special,
        };
        let top_tokens = if let Some(top_tokens_) = generation.top_tokens.get(i) {
            top_tokens_
                .ids
                .iter()
                .zip(top_tokens_.logprobs.iter())
                .zip(top_tokens_.texts.iter())
                .zip(top_tokens_.is_special.iter())
                .map(|(((&id, &logprob), text), &special)| Token {
                    id,
                    text: text.to_string(),
                    logprob,
                    special,
                })
                .collect()
        } else {
            vec![]
        };
        match (&generation.generated_text, iterator.peek()) {
            (Some(generated_text), None) => {
                // Generation has ended
                stopped = true;
                // Send message
                entry.response_tx.send(Ok(InferStreamResponse::End {
                    token,
                    top_tokens,
                    generated_text: generated_text.clone(),
                    queued: entry.queue_time,
                    start: entry.batch_time.unwrap(),
                }))?;
            }
            _ => {
                // Send message
                entry
                    .response_tx
                    .send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
            }
        }
    }

    Ok(stopped)
}

/// Send errors to Infer for all `entries`
#[instrument(skip_all)]
fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
    entries.drain().for_each(|(_, entry)| {
        // Create and enter a span to link this function back to the entry
        let _send_error_span = info_span!(parent: entry.temp_span.as_ref().expect("batch_span is None. This is a bug."), "send_error").entered();
        let err = InferError::GenerationError(error.to_string());
        metrics::increment_counter!("tgi_request_failure", "err" => "generation");
        tracing::error!("{err}");

        // unwrap_or is valid here as we don't care if the receiver is gone.
        entry
            .response_tx
            .send(Err(err))
            .unwrap_or(());
    });
}

#[derive(Debug)]
pub(crate) enum InferStreamResponse {
    // Optional first message
    Prefill(Tokens),
    // Intermediate messages
    Intermediate {
        token: Token,
        top_tokens: Vec<Token>,
    },
    // Last message
    End {
        token: Token,
        top_tokens: Vec<Token>,
        generated_text: GeneratedText,
        start: Instant,
        queued: Instant,
    },
}

#[derive(Debug)]
pub(crate) struct InferResponse {
    /// input_length is the input as perceived by the rust tokenizer in the
    /// validation pathway. It is redundant with prefill.len() but prefill
    /// has data only if the user asked for it. This will always be filled.
    pub(crate) _input_length: u32,
    pub(crate) prefill: Vec<PrefillToken>,
    pub(crate) tokens: Vec<Token>,
    pub(crate) generated_text: GeneratedText,
    pub(crate) queued: Instant,
    pub(crate) start: Instant,
    pub(crate) top_tokens: Vec<Vec<Token>>,
}

#[derive(Debug, Error)]
pub enum InferError {
    #[error("Request failed during generation: {0}")]
    GenerationError(String),
    #[error("Model is overloaded")]
    Overloaded(#[from] TryAcquireError),
    #[error("Input validation error: {0}")]
    ValidationError(#[from] ValidationError),
    #[error("Incomplete generation")]
    IncompleteGeneration,
    #[error("Template error: {0}")]
    TemplateError(#[from] minijinja::Error),
}

impl InferError {
    pub(crate) fn error_type(&self) -> &str {
        match self {
            InferError::GenerationError(_) => "generation",
            InferError::Overloaded(_) => "overloaded",
            InferError::ValidationError(_) => "validation",
            InferError::IncompleteGeneration => "incomplete_generation",
            InferError::TemplateError(_) => "template_error",
        }
    }
}