feat: add SchedulerV3 (#1996)

- Refactor code to allow supporting multiple versions of the
generate.proto at the same time
- Add v3/generate.proto (ISO to generate.proto for now but allow for
future changes without impacting v2 backends)
- Add Schedule trait to abstract queuing and batching mechanisms that
will be different in the future
- Add SchedulerV2/V3 impl

router/src/infer/mod.rs

+mod health;
+pub(crate) mod v2;
+pub(crate) mod v3;
+
+pub(crate) use health::HealthCheck;
+
+use crate::validation::{ValidGenerateRequest, Validation, ValidationError};
+use crate::{
+    ChatTemplateInputs, ChatTemplateVersions, FinishReason, GenerateRequest, HubProcessorConfig,
+    HubTokenizerConfig, Message, MessageChunk, PrefillToken, Text, TextMessage, Token,
+};
+use crate::{FunctionRef, FunctionsMap, GrammarType, Properties, Tool, ToolType, Tools};
+use futures::future::try_join_all;
+use minijinja::{Environment, ErrorKind, Template};
+use serde_json::{json, Map, Value};
+use std::collections::HashMap;
+use std::sync::Arc;
+use thiserror::Error;
+use tokio::sync::{OwnedSemaphorePermit, Semaphore, TryAcquireError};
+use tokio::time::Instant;
+use tokio_stream::wrappers::UnboundedReceiverStream;
+use tokio_stream::StreamExt;
+use tracing::instrument;
+
+pub(crate) trait Scheduler {
+    fn schedule(
+        &self,
+        request: ValidGenerateRequest,
+        permit: OwnedSemaphorePermit,
+    ) -> Result<GenerateStreamResponse, InferError>;
+}
+
+/// Inference struct
+#[derive(Clone)]
+pub struct Infer {
+    /// Validation
+    validation: Validation,
+    /// Request scheduler
+    scheduler: Arc<dyn Scheduler + Send + Sync>,
+    /// Chat template
+    chat_template: Option<ChatTemplate>,
+    /// Inference limit
+    limit_concurrent_requests: Arc<Semaphore>,
+}
+
+impl Infer {
+    #[allow(clippy::too_many_arguments)]
+    pub(crate) fn new(
+        scheduler: Arc<dyn Scheduler + Send + Sync>,
+        validation: Validation,
+        max_concurrent_requests: usize,
+        tokenizer_config: HubTokenizerConfig,
+        processor_config: HubProcessorConfig,
+    ) -> Self {
+        let chat_template = tokenizer_config
+            .chat_template
+            .or(processor_config.chat_template)
+            .and_then(|t| match t {
+                ChatTemplateVersions::Single(template) => Some(template),
+                ChatTemplateVersions::Multiple(templates) => templates
+                    .into_iter()
+                    .find(|t| t.name == "default")
+                    .map(|t| t.template),
+            })
+            .map(|t| {
+                // .strip() is not supported in minijinja
+                // .capitalize() is not supported in minijinja but we can use | capitalize
+                let t = t
+                    .replace(".strip()", " | trim")
+                    .replace(".capitalize()", " | capitalize");
+                ChatTemplate::new(t, tokenizer_config.bos_token, tokenizer_config.eos_token)
+            });
+
+        // Inference limit with a semaphore
+        let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
+
+        Self {
+            validation,
+            scheduler,
+            chat_template,
+            limit_concurrent_requests: semaphore,
+        }
+    }
+
+    /// 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
+        })?;
+
+        self.scheduler.schedule(valid_request, permit)
+    }
+
+    /// Tokenizer the input
+    #[instrument(skip_all)]
+    pub(crate) async fn tokenize(
+        &self,
+        request: GenerateRequest,
+    ) -> Result<Option<tokenizers::Encoding>, InferError> {
+        // Tokenize request
+        let inputs = request.inputs;
+        let truncate = request.parameters.truncate;
+        let encoding = self
+            .validation
+            .tokenize(inputs, truncate)
+            .await
+            .map_err(|err| {
+                tracing::error!("Tokenization {err}");
+                err
+            })?;
+
+        // Return Encoding
+        Ok(encoding.map(|(encoding, _)| encoding))
+    }
+
+    /// Apply the chat template to the chat request
+    #[instrument(skip_all)]
+    pub(crate) fn apply_chat_template(
+        &self,
+        messages: Vec<Message>,
+        grammar_with_prompt: Option<(GrammarType, String)>,
+    ) -> Result<String, InferError> {
+        self.chat_template
+            .as_ref()
+            .ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
+            .apply(messages, grammar_with_prompt)
+            .map_err(|e| {
+                metrics::increment_counter!("tgi_request_failure", "err" => "template");
+                tracing::error!("{e}");
+                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(prefill_tokens) => {
+                    result_prefill = prefill_tokens;
+                }
+                // 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))
+    }
+}
+
+/// Raise a exception (custom function) used in the chat templates
+fn raise_exception(err_text: String) -> Result<String, minijinja::Error> {
+    Err(minijinja::Error::new(ErrorKind::SyntaxError, err_text))
+}
+
+#[derive(Clone)]
+struct ChatTemplate {
+    template: Template<'static, 'static>,
+    bos_token: Option<String>,
+    eos_token: Option<String>,
+    use_default_tool_template: bool,
+}
+
+impl ChatTemplate {
+    fn new(template: String, bos_token: Option<String>, eos_token: Option<String>) -> Self {
+        let mut env = Box::new(Environment::new());
+        let template_str = template.into_boxed_str();
+        env.add_function("raise_exception", raise_exception);
+
+        // check if contains the tools variable within the template
+        let use_default_tool_template =
+            !template_str.as_ref().replace(' ', "").contains("{{tools}}");
+        // leaking env and template_str as read-only, static resources for performance.
+        let template = Box::leak(env)
+            .template_from_str(Box::leak(template_str))
+            .unwrap();
+
+        Self {
+            template,
+            bos_token,
+            eos_token,
+            use_default_tool_template,
+        }
+    }
+
+    fn apply(
+        &self,
+        mut messages: Vec<Message>,
+        grammar_with_prompt: Option<(GrammarType, String)>,
+    ) -> Result<String, InferError> {
+        if self.use_default_tool_template {
+            if let Some(last_message) = messages.last_mut() {
+                if let Some((GrammarType::Json(tools), tool_prompt)) = grammar_with_prompt {
+                    last_message.content.push(MessageChunk::Text(Text {
+                        text: format!("\n---\n{}\n{}", tool_prompt, tools),
+                    }));
+                }
+            }
+        }
+
+        let messages: Vec<TextMessage> = messages.into_iter().map(|c| c.into()).collect();
+
+        self.template
+            .render(ChatTemplateInputs {
+                messages,
+                bos_token: self.bos_token.as_deref(),
+                eos_token: self.eos_token.as_deref(),
+                add_generation_prompt: true,
+                tools: None,
+                tools_prompt: None,
+            })
+            .map_err(InferError::TemplateError)
+    }
+}
+
+pub struct ToolGrammar {}
+
+impl ToolGrammar {
+    pub fn apply(
+        tools: Option<Vec<Tool>>,
+        tool_choice: Option<ToolType>,
+    ) -> Result<Option<Tools>, InferError> {
+        if let Some((req_tools, tool_choice)) = tools.zip(tool_choice) {
+            // let tool_prompt = tool_prompt.unwrap_or_default();
+            let tools_to_use = match tool_choice {
+                ToolType::FunctionName(name) => {
+                    vec![req_tools
+                        .iter()
+                        .find(|tool| tool.function.name == *name)
+                        .unwrap_or_else(|| panic!("Tool with name {} not found", name))
+                        .clone()]
+                }
+                ToolType::OneOf => req_tools.to_owned(),
+            };
+
+            // adds the error notification function for LLM feedback if required
+            let mut text_response_properties = Map::new();
+            text_response_properties.insert(
+                "error".to_string(),
+                serde_json::json!({
+                    "type": "string",
+                    "description": "The error or issue to notify"
+                }),
+            );
+            text_response_properties.insert(
+                "_name".to_string(),
+                serde_json::json!({
+                    "type": "string",
+                    "const": "notify_error"
+                }),
+            );
+
+            let functions: HashMap<String, serde_json::Value> = tools_to_use
+                .iter()
+                .map(|tool| {
+                    let func = tool.function.clone();
+
+                    // Clone the existing parameters, which are expected to be a JSON object
+                    let mut params = if let Value::Object(params) = &func.arguments {
+                        params.clone()
+                    } else {
+                        Map::new()
+                    };
+
+                    // Insert the function's description at the top level, outside of properties
+                    params.insert(
+                        "description".to_string(),
+                        Value::String(func.description.clone().unwrap_or_default()),
+                    );
+
+                    // Ensure 'properties' exists and is an object
+                    let properties = params
+                        .entry("properties".to_string())
+                        .or_insert_with(|| json!({}))
+                        .as_object_mut()
+                        .unwrap();
+
+                    // Insert the constant for the function name inside 'properties'
+                    properties.insert(
+                        "_name".to_string(),
+                        json!({
+                            "type": "string",
+                            "const": func.name.clone(),
+                            // "description": "The name of the function"
+                        }),
+                    );
+
+                    // Check if 'required' exists, and it is an array. If not, create an empty array.
+                    let required = params
+                        .entry("required".to_string())
+                        .or_insert_with(|| json!([]))
+                        .as_array_mut()
+                        .unwrap();
+
+                    // Add 'name' to the 'required' array if it is not already present
+                    if !required.iter().any(|r| r == "_name") {
+                        required.push(json!("_name"));
+                    }
+
+                    (func.name, Value::Object(params))
+                })
+                .chain([(
+                    "notify_error".to_string(),
+                    serde_json::json!({
+                        "properties": text_response_properties,
+                        "required": ["error", "_name"],
+                        "type": "object"
+                    }),
+                )])
+                .collect();
+
+            let tools = Tools {
+                functions_map: FunctionsMap { functions },
+                properties: Properties {
+                    function: tools_to_use
+                        .iter()
+                        .map(|tool| FunctionRef {
+                            ref_path: format!("#/$functions/{}", tool.function.name.clone()),
+                        })
+                        .chain(std::iter::once(FunctionRef {
+                            ref_path: "#/$functions/notify_error".to_string(),
+                        }))
+                        .collect(),
+                },
+            };
+
+            return Ok(Some(tools));
+        }
+        // Err(InferError::ToolError("No tools provided".to_string()))
+        Ok(None)
+    }
+}
+
+/// Type alias for generation responses
+pub(crate) type GenerateStreamResponse = (
+    OwnedSemaphorePermit,
+    u32, // input_length
+    UnboundedReceiverStream<Result<InferStreamResponse, InferError>>,
+);
+
+#[derive(Debug)]
+pub(crate) struct GeneratedText {
+    pub(crate) text: String,
+    pub(crate) generated_tokens: u32,
+    pub(crate) finish_reason: FinishReason,
+    pub(crate) seed: Option<u64>,
+}
+
+#[derive(Debug)]
+pub(crate) enum InferStreamResponse {
+    // Optional first message
+    Prefill(Vec<PrefillToken>),
+    // 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),
+    #[error("Tool error: {0}")]
+    ToolError(String),
+}
+
+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",
+            InferError::ToolError(_) => "tool_error",
+        }
+    }
+}

router/src/infer/v2/mod.rs

+mod queue;
+mod scheduler;
+
+pub(crate) use scheduler::SchedulerV2;

router/src/infer/v2/queue.rs

+use crate::infer::{InferError, InferStreamResponse};
+use crate::validation::{
+    ValidGenerateRequest, ValidGrammar, ValidParameters, ValidStoppingParameters,
+};
+use nohash_hasher::{BuildNoHashHasher, IntMap};
+use std::cmp::min;
+use std::collections::VecDeque;
+use text_generation_client::v2::{
+    Batch, GrammarType, NextTokenChooserParameters, Request, StoppingCriteriaParameters,
+};
+use text_generation_client::ChunksToString;
+use tokio::sync::{mpsc, oneshot};
+use tokio::time::Instant;
+use tracing::{info_span, instrument, Span};
+
+/// Queue entry
+#[derive(Debug)]
+pub(crate) struct Entry {
+    /// Request
+    pub request: ValidGenerateRequest,
+    /// Response sender to communicate between the Infer struct and the batching_task
+    pub response_tx: mpsc::UnboundedSender<Result<InferStreamResponse, InferError>>,
+    /// Span that will live as long as entry
+    pub span: Span,
+    /// Temporary span used as a guard when logging inference, wait times...
+    pub temp_span: Option<Span>,
+    /// Instant when this entry was queued
+    pub queue_time: Instant,
+    /// Instant when this entry was added to a batch
+    pub batch_time: Option<Instant>,
+}
+
+/// Request Queue
+#[derive(Debug, Clone)]
+pub(crate) struct Queue {
+    /// Channel to communicate with the background queue task
+    queue_sender: mpsc::UnboundedSender<QueueCommand>,
+}
+
+impl Queue {
+    pub(crate) fn new(
+        requires_padding: bool,
+        block_size: u32,
+        window_size: Option<u32>,
+        speculate: u32,
+    ) -> Self {
+        // Create channel
+        let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
+
+        // Launch background queue task
+        tokio::spawn(queue_task(
+            requires_padding,
+            block_size,
+            window_size,
+            speculate,
+            queue_receiver,
+        ));
+
+        Self { queue_sender }
+    }
+
+    #[instrument(skip_all)]
+    pub(crate) fn append(&self, entry: Entry) {
+        // Send append command to the background task managing the state
+        // Unwrap is safe here
+        self.queue_sender
+            .send(QueueCommand::Append(Box::new(entry), Span::current()))
+            .unwrap();
+    }
+
+    // Get the next batch
+    #[instrument(skip(self))]
+    pub(crate) async fn next_batch(
+        &self,
+        min_size: Option<usize>,
+        max_size: Option<usize>,
+        prefill_token_budget: u32,
+        token_budget: u32,
+    ) -> Option<NextBatch> {
+        // Create response channel
+        let (response_sender, response_receiver) = oneshot::channel();
+        // Send next batch command to the background task managing the state
+        // Unwrap is safe here
+        self.queue_sender
+            .send(QueueCommand::NextBatch {
+                min_size,
+                max_size,
+                prefill_token_budget,
+                token_budget,
+                response_sender,
+                span: Span::current(),
+            })
+            .unwrap();
+        // Await on response channel
+        // Unwrap is safe here
+        response_receiver.await.unwrap()
+    }
+}
+
+// Background task responsible of the queue state
+async fn queue_task(
+    requires_padding: bool,
+    block_size: u32,
+    window_size: Option<u32>,
+    speculate: u32,
+    mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
+) {
+    let mut state = State::new(requires_padding, block_size, window_size, speculate);
+
+    while let Some(cmd) = receiver.recv().await {
+        match cmd {
+            QueueCommand::Append(entry, span) => {
+                span.in_scope(|| state.append(*entry));
+                metrics::increment_gauge!("tgi_queue_size", 1.0);
+            }
+            QueueCommand::NextBatch {
+                min_size,
+                max_size,
+                prefill_token_budget,
+                token_budget,
+                response_sender,
+                span,
+            } => span.in_scope(|| {
+                let next_batch =
+                    state.next_batch(min_size, max_size, prefill_token_budget, token_budget);
+                response_sender.send(next_batch).unwrap();
+                metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
+            }),
+        }
+    }
+}
+
+/// Queue State
+#[derive(Debug)]
+struct State {
+    /// Queue entries organized in a Vec
+    entries: VecDeque<(u64, Entry)>,
+
+    /// Id of the next entry
+    next_id: u64,
+
+    /// Id of the next batch
+    next_batch_id: u64,
+
+    /// Whether the model is using padding
+    requires_padding: bool,
+
+    /// Paged Attention block size
+    block_size: u32,
+
+    /// Sliding window
+    window_size: Option<u32>,
+
+    /// Speculation amount
+    speculate: u32,
+}
+
+impl State {
+    fn new(
+        requires_padding: bool,
+        block_size: u32,
+        window_size: Option<u32>,
+        speculate: u32,
+    ) -> Self {
+        Self {
+            entries: VecDeque::with_capacity(128),
+            next_id: 0,
+            next_batch_id: 0,
+            requires_padding,
+            block_size,
+            window_size,
+            speculate,
+        }
+    }
+
+    /// Append an entry to the queue
+    fn append(&mut self, mut entry: Entry) {
+        // Create a span that will live as long as the entry is in the queue waiting to be batched
+        let queue_span = info_span!(parent: &entry.span, "queued");
+        entry.temp_span = Some(queue_span);
+
+        // Push entry in the queue
+        self.entries.push_back((self.next_id, entry));
+        self.next_id += 1;
+    }
+
+    // Get the next batch
+    fn next_batch(
+        &mut self,
+        min_size: Option<usize>,
+        max_size: Option<usize>,
+        prefill_token_budget: u32,
+        token_budget: u32,
+    ) -> Option<NextBatch> {
+        if self.entries.is_empty() {
+            tracing::debug!("No queue");
+            return None;
+        }
+
+        // Check if we have enough entries
+        if let Some(min_size) = min_size {
+            if self.entries.len() < min_size {
+                tracing::debug!("Not enough entries");
+                return None;
+            }
+        }
+
+        // Pad prefill_token_budget to be a multiple of block size
+        let prefill_token_budget =
+            ((prefill_token_budget + self.block_size - 1) / self.block_size) * self.block_size;
+
+        // Create span for this batch to add context to inference calls
+        let next_batch_span = info_span!(parent: None, "batch", batch_size = tracing::field::Empty);
+        next_batch_span.follows_from(&Span::current());
+
+        let mut batch_requests = Vec::with_capacity(self.entries.len());
+        let mut batch_entries =
+            IntMap::with_capacity_and_hasher(self.entries.len(), BuildNoHashHasher::default());
+
+        let mut max_input_length = 0;
+        let mut prefill_tokens: u32 = 0;
+        let mut decode_tokens: u32 = 0;
+
+        // Pop entries starting from the front of the queue
+        while let Some((id, mut entry)) = self.entries.pop_front() {
+            // Filter entries where the response receiver was dropped (== entries where the request
+            // was dropped by the client)
+            if entry.response_tx.is_closed() {
+                metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
+                tracing::debug!("Dropping entry");
+                continue;
+            }
+
+            if self.requires_padding {
+                // We pad to max input length in the Python shards
+                // We need to take these padding tokens into the equation
+                max_input_length = max_input_length.max(entry.request.input_length);
+                prefill_tokens = (batch_requests.len() + 1) as u32 * max_input_length
+            } else {
+                // pad to block size
+                prefill_tokens += ((entry.request.input_length + self.block_size - 1)
+                    / self.block_size)
+                    * self.block_size;
+            }
+
+            if self.requires_padding {
+                decode_tokens += entry.request.stopping_parameters.max_new_tokens;
+            } else {
+                let max_new_tokens = match self.window_size {
+                    None => entry.request.stopping_parameters.max_new_tokens,
+                    Some(window_size) => min(
+                        window_size.saturating_sub(entry.request.input_length),
+                        entry.request.stopping_parameters.max_new_tokens,
+                    ),
+                };
+
+                // pad to block size
+                decode_tokens +=
+                    ((max_new_tokens + self.block_size - 1) / self.block_size) * self.block_size;
+            }
+
+            if prefill_tokens > prefill_token_budget
+                || (prefill_tokens + decode_tokens + self.speculate) > token_budget
+            {
+                // Entry is over budget
+                // Add it back to the front
+                tracing::debug!("Over budget: prefill_tokens={prefill_tokens} > {prefill_token_budget} || {prefill_tokens} + {decode_tokens} + {} > {token_budget}", self.speculate);
+                self.entries.push_front((id, entry));
+                break;
+            }
+
+            tracing::debug!("Accepting 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);
+
+            batch_requests.push(Request {
+                id,
+                prefill_logprobs: entry.request.decoder_input_details,
+                inputs: entry.request.inputs.chunks_to_string(),
+                truncate: entry.request.truncate,
+                parameters: Some(NextTokenChooserParameters::from(
+                    entry.request.parameters.clone(),
+                )),
+                stopping_parameters: Some(StoppingCriteriaParameters::from(
+                    entry.request.stopping_parameters.clone(),
+                )),
+                top_n_tokens: entry.request.top_n_tokens,
+            });
+            // Set batch_time
+            entry.batch_time = Some(Instant::now());
+            // Insert in batch_entries IntMap
+            batch_entries.insert(id, entry);
+
+            // Check if max_size
+            if Some(batch_requests.len()) == max_size {
+                break;
+            }
+        }
+
+        // Empty batch
+        if batch_requests.is_empty() {
+            tracing::debug!("Filtered out all entries");
+            return None;
+        }
+
+        // Check if our batch is big enough
+        if let Some(min_size) = min_size {
+            // Batch is too small
+            if batch_requests.len() < min_size {
+                // Add back entries to the queue in the correct order
+                for r in batch_requests.into_iter().rev() {
+                    let id = r.id;
+                    let entry = batch_entries.remove(&id).unwrap();
+                    self.entries.push_front((id, entry));
+                }
+
+                return None;
+            }
+        }
+
+        // Final batch size
+        let size = batch_requests.len() as u32;
+        next_batch_span.record("batch_size", size);
+
+        let batch = Batch {
+            id: self.next_batch_id,
+            requests: batch_requests,
+            size,
+            max_tokens: (prefill_tokens + decode_tokens),
+        };
+        // Increment batch id
+        self.next_batch_id += 1;
+
+        metrics::histogram!("tgi_batch_next_size", batch.size as f64);
+
+        Some((batch_entries, batch, next_batch_span))
+    }
+}
+
+type NextBatch = (IntMap<u64, Entry>, Batch, Span);
+
+#[derive(Debug)]
+enum QueueCommand {
+    Append(Box<Entry>, Span),
+    NextBatch {
+        min_size: Option<usize>,
+        max_size: Option<usize>,
+        prefill_token_budget: u32,
+        token_budget: u32,
+        response_sender: oneshot::Sender<Option<NextBatch>>,
+        span: Span,
+    },
+}
+
+impl From<ValidParameters> for NextTokenChooserParameters {
+    fn from(value: ValidParameters) -> Self {
+        let (grammar, grammar_type) = match value.grammar {
+            None => (String::new(), GrammarType::None),
+
+            Some(grammar) => match grammar {
+                ValidGrammar::Json(grammar_string) => (grammar_string, GrammarType::Json),
+                ValidGrammar::Regex(grammar_string) => (grammar_string, GrammarType::Regex),
+            },
+        };
+
+        Self {
+            temperature: value.temperature,
+            top_k: value.top_k,
+            top_p: value.top_p,
+            typical_p: value.typical_p,
+            do_sample: value.do_sample,
+            seed: value.seed,
+            repetition_penalty: value.repetition_penalty,
+            frequency_penalty: value.frequency_penalty,
+            watermark: value.watermark,
+            grammar,
+            grammar_type: grammar_type.into(),
+        }
+    }
+}
+
+impl From<ValidStoppingParameters> for StoppingCriteriaParameters {
+    fn from(value: ValidStoppingParameters) -> Self {
+        Self {
+            max_new_tokens: value.max_new_tokens,
+            stop_sequences: value.stop_sequences,
+            ignore_eos_token: value.ignore_eos_token,
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use tracing::info_span;
+
+    fn default_entry() -> (
+        Entry,
+        mpsc::UnboundedReceiver<Result<InferStreamResponse, InferError>>,
+    ) {
+        let (response_tx, receiver_tx) = mpsc::unbounded_channel();
+
+        let entry = Entry {
+            request: ValidGenerateRequest {
+                inputs: vec![],
+                input_length: 0,
+                truncate: 0,
+                decoder_input_details: false,
+                parameters: ValidParameters {
+                    temperature: 0.0,
+                    top_k: 0,
+                    top_p: 0.0,
+                    typical_p: 0.0,
+                    do_sample: false,
+                    seed: 0,
+                    repetition_penalty: 0.0,
+                    frequency_penalty: 0.0,
+                    watermark: false,
+                    grammar: None,
+                },
+                stopping_parameters: ValidStoppingParameters {
+                    ignore_eos_token: false,
+                    max_new_tokens: 1,
+                    stop_sequences: vec![],
+                },
+                top_n_tokens: 0,
+            },
+            response_tx,
+            span: info_span!("entry"),
+            temp_span: None,
+            queue_time: Instant::now(),
+            batch_time: None,
+        };
+        (entry, receiver_tx)
+    }
+
+    #[test]
+    fn test_append() {
+        let mut state = State::new(false, 1, None, 0);
+        let (entry, _guard) = default_entry();
+
+        assert_eq!(state.next_id, 0);
+        assert_eq!(state.entries.len(), 0);
+
+        state.append(entry);
+
+        assert_eq!(state.next_id, 1);
+        assert_eq!(state.entries.len(), 1);
+        let (id, _) = state.entries.remove(0).unwrap();
+        assert_eq!(id, 0);
+    }
+
+    #[test]
+    fn test_next_batch_empty() {
+        let mut state = State::new(false, 1, None, 0);
+
+        assert!(state.next_batch(None, None, 1, 1).is_none());
+        assert!(state.next_batch(Some(1), None, 1, 1).is_none());
+    }
+
+    #[test]
+    fn test_next_batch_min_size() {
+        let mut state = State::new(false, 1, None, 0);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        state.append(entry1);
+        state.append(entry2);
+
+        let (entries, batch, _) = state.next_batch(None, None, 2, 2).unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&0));
+        assert!(entries.contains_key(&1));
+        assert!(entries.get(&0).unwrap().batch_time.is_some());
+        assert!(entries.get(&1).unwrap().batch_time.is_some());
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 2);
+
+        assert_eq!(state.next_id, 2);
+        assert_eq!(state.entries.len(), 0);
+        assert_eq!(state.next_batch_id, 1);
+
+        let (entry3, _guard3) = default_entry();
+        state.append(entry3);
+
+        assert!(state.next_batch(Some(2), None, 2, 2).is_none());
+
+        assert_eq!(state.next_id, 3);
+        assert_eq!(state.entries.len(), 1);
+        let (id, _) = state.entries.remove(0).unwrap();
+        assert_eq!(id, 2);
+    }
+
+    #[test]
+    fn test_next_batch_max_size() {
+        let mut state = State::new(false, 1, None, 0);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        state.append(entry1);
+        state.append(entry2);
+
+        let (entries, batch, _) = state.next_batch(None, Some(1), 2, 2).unwrap();
+        assert_eq!(entries.len(), 1);
+        assert!(entries.contains_key(&0));
+        assert!(entries.get(&0).unwrap().batch_time.is_some());
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 1);
+
+        assert_eq!(state.next_id, 2);
+        assert_eq!(state.entries.len(), 1);
+        assert_eq!(state.next_batch_id, 1);
+    }
+
+    #[test]
+    fn test_next_batch_token_budget() {
+        let mut state = State::new(false, 1, None, 0);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        state.append(entry1);
+        state.append(entry2);
+
+        let (entries, batch, _) = state.next_batch(None, None, 1, 1).unwrap();
+        assert_eq!(entries.len(), 1);
+        assert!(entries.contains_key(&0));
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 1);
+
+        assert_eq!(state.next_id, 2);
+        assert_eq!(state.entries.len(), 1);
+        assert_eq!(state.next_batch_id, 1);
+
+        let (entry3, _guard3) = default_entry();
+        state.append(entry3);
+
+        let (entries, batch, _) = state.next_batch(None, None, 3, 3).unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&1));
+        assert!(entries.contains_key(&2));
+        assert_eq!(batch.id, 1);
+        assert_eq!(batch.size, 2);
+
+        assert_eq!(state.next_id, 3);
+        assert_eq!(state.entries.len(), 0);
+        assert_eq!(state.next_batch_id, 2);
+    }
+
+    #[tokio::test]
+    async fn test_queue_append() {
+        let queue = Queue::new(false, 1, None, 0);
+        let (entry, _guard) = default_entry();
+        queue.append(entry);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_empty() {
+        let queue = Queue::new(false, 1, None, 0);
+
+        assert!(queue.next_batch(None, None, 1, 1).await.is_none());
+        assert!(queue.next_batch(Some(1), None, 1, 1).await.is_none());
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_min_size() {
+        let queue = Queue::new(false, 1, None, 0);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        let (entries, batch, _) = queue.next_batch(None, None, 2, 2).await.unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&0));
+        assert!(entries.contains_key(&1));
+        assert!(entries.get(&0).unwrap().batch_time.is_some());
+        assert!(entries.get(&1).unwrap().batch_time.is_some());
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 2);
+
+        let (entry3, _guard3) = default_entry();
+        queue.append(entry3);
+
+        // Not enough requests pending
+        assert!(queue.next_batch(Some(2), None, 2, 2).await.is_none());
+        // Not enough token budget
+        assert!(queue.next_batch(Some(1), None, 0, 0).await.is_none());
+        // Ok
+        let (entries2, batch2, _) = queue.next_batch(Some(1), None, 2, 2).await.unwrap();
+        assert_eq!(entries2.len(), 1);
+        assert!(entries2.contains_key(&2));
+        assert!(entries2.get(&2).unwrap().batch_time.is_some());
+        assert_eq!(batch2.id, 1);
+        assert_eq!(batch2.size, 1);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_max_size() {
+        let queue = Queue::new(false, 1, None, 0);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        let (entries, batch, _) = queue.next_batch(None, Some(1), 2, 2).await.unwrap();
+        assert_eq!(entries.len(), 1);
+        assert!(entries.contains_key(&0));
+        assert!(entries.get(&0).unwrap().batch_time.is_some());
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 1);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_token_budget() {
+        let queue = Queue::new(false, 1, None, 0);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        let (entries, batch, _) = queue.next_batch(None, None, 1, 1).await.unwrap();
+        assert_eq!(entries.len(), 1);
+        assert!(entries.contains_key(&0));
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 1);
+
+        let (entry3, _guard3) = default_entry();
+        queue.append(entry3);
+
+        let (entries, batch, _) = queue.next_batch(None, None, 3, 3).await.unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&1));
+        assert!(entries.contains_key(&2));
+        assert_eq!(batch.id, 1);
+        assert_eq!(batch.size, 2);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_token_speculate() {
+        let queue = Queue::new(false, 1, None, 2);
+        let (entry1, _guard1) = default_entry();
+        let (entry2, _guard2) = default_entry();
+        queue.append(entry1);
+        queue.append(entry2);
+
+        // Budget of 1 is not enough
+        assert!(queue.next_batch(None, None, 1, 1).await.is_none());
+
+        let (entries, batch, _) = queue.next_batch(None, None, 6, 6).await.unwrap();
+        assert_eq!(entries.len(), 2);
+        assert!(entries.contains_key(&0));
+        assert!(entries.contains_key(&1));
+        assert_eq!(batch.id, 0);
+        assert_eq!(batch.size, 2);
+    }
+
+    #[tokio::test]
+    async fn test_queue_next_batch_dropped_receiver() {
+        let queue = Queue::new(false, 1, None, 0);
+        let (entry, _) = default_entry();
+        queue.append(entry);
+
+        assert!(queue.next_batch(None, None, 1, 1).await.is_none());
+    }
+}

router/src/infer.rs → router/src/infer/v2/scheduler.rs

 /// Batching and inference logic
-use crate::validation::{Validation, ValidationError};
-use crate::{
-    ChatTemplateInputs, ChatTemplateVersions, Entry, GenerateRequest, GenerateStreamResponse,
-    HubProcessorConfig, HubTokenizerConfig, Message, MessageChunk, PrefillToken, Queue, Text,
-    TextMessage, Token,
+use crate::infer::v2::queue::{Entry, Queue};
+use crate::infer::{
+    GenerateStreamResponse, GeneratedText, InferError, InferStreamResponse, Scheduler,
 };
-use crate::{FunctionRef, FunctionsMap, GrammarType, Properties, Tool, ToolType, Tools};
-use futures::future::try_join_all;
-use minijinja::{Environment, ErrorKind, Template};
+use crate::validation::ValidGenerateRequest;
+use crate::{FinishReason, PrefillToken, Token};
 use nohash_hasher::IntMap;
-use serde_json::{json, Map, Value};
-use std::collections::HashMap;
 use std::sync::{
     atomic::{AtomicBool, Ordering},
     Arc,
 };
-use text_generation_client::{
-    Batch, CachedBatch, ClientError, GeneratedText, Generation, ShardedClient, Tokens,
-};
-use thiserror::Error;
+use text_generation_client::v2::{Batch, CachedBatch, Generation, ShardedClient};
+use text_generation_client::ClientError;
 use tokio::sync::mpsc::error::SendError;
-use tokio::sync::{mpsc, Notify, Semaphore, TryAcquireError};
+use tokio::sync::{mpsc, Notify, OwnedSemaphorePermit};
 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,
+pub(crate) struct SchedulerV2 {
     /// Request queue
     queue: Queue,
-    /// Shared state
-    shared: Arc<Shared>,
-    /// Chat template
-    chat_template: Option<ChatTemplate>,
-    /// Inference limit
-    limit_concurrent_requests: Arc<Semaphore>,
-}
-
-/// Infer shared state
-struct Shared {
-    /// Batching background Tokio task notifier
-    batching_task: Notify,
+    /// Notify batcher on queue appends
+    batching_task_notifier: Arc<Notify>,
 }
 
-/// Raise a exception (custom function) used in the chat templates
-fn raise_exception(err_text: String) -> Result<String, minijinja::Error> {
-    Err(minijinja::Error::new(ErrorKind::SyntaxError, err_text))
-}
-
-impl Infer {
+impl SchedulerV2 {
     #[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_batch_size: Option<usize>,
-        max_concurrent_requests: usize,
         requires_padding: bool,
         window_size: Option<u32>,
         speculate: u32,
         generation_health: Arc<AtomicBool>,
-        tokenizer_config: HubTokenizerConfig,
-        processor_config: HubProcessorConfig,
     ) -> Self {
         let queue = Queue::new(requires_padding, 16, window_size, speculate);
-        let shared = Arc::new(Shared {
-            batching_task: Notify::new(),
-        });
+        let batching_task_notifier = Arc::new(Notify::new());
 
         // Spawn batching background task that contains all the inference logic
         tokio::spawn(batching_task(
@@ -84,72 +51,31 @@ impl Infer {
             max_waiting_tokens,
             max_batch_size,
             queue.clone(),
-            shared.clone(),
+            batching_task_notifier.clone(),
             generation_health,
         ));
 
-        let chat_template = tokenizer_config
-            .chat_template
-            .or(processor_config.chat_template)
-            .and_then(|t| match t {
-                ChatTemplateVersions::Single(template) => Some(template),
-                ChatTemplateVersions::Multiple(templates) => templates
-                    .into_iter()
-                    .find(|t| t.name == "default")
-                    .map(|t| t.template),
-            })
-            .map(|t| {
-                // .strip() is not supported in minijinja
-                // .capitalize() is not supported in minijinja but we can use | capitalize
-                let t = t
-                    .replace(".strip()", " | trim")
-                    .replace(".capitalize()", " | capitalize");
-                ChatTemplate::new(t, tokenizer_config.bos_token, tokenizer_config.eos_token)
-            });
-
-        // Inference limit with a semaphore
-        let semaphore = Arc::new(Semaphore::new(max_concurrent_requests));
-
         Self {
-            validation,
             queue,
-            shared,
-            chat_template,
-            limit_concurrent_requests: semaphore,
+            batching_task_notifier,
         }
     }
+}
 
-    /// Add a new request to the queue and return a stream of InferStreamResponse
+impl Scheduler for SchedulerV2 {
     #[instrument(skip_all)]
-    pub(crate) async fn generate_stream(
+    fn schedule(
         &self,
-        request: GenerateRequest,
+        request: ValidGenerateRequest,
+        permit: OwnedSemaphorePermit,
     ) -> 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;
+        let input_length = request.input_length;
 
         // Append the request to the queue
         self.queue.append(Entry {
-            request: valid_request,
+            request,
             response_tx,
             span: Span::current(),
             temp_span: None,
@@ -159,7 +85,7 @@ impl Infer {
 
         // Notify the background task that we have a new entry in the queue that needs
         // to be batched
-        self.shared.batching_task.notify_one();
+        self.batching_task_notifier.notify_one();
 
         // Return stream
         Ok((
@@ -168,343 +94,6 @@ impl Infer {
             UnboundedReceiverStream::new(response_rx),
         ))
     }
-
-    /// Tokenizer the input
-    #[instrument(skip_all)]
-    pub(crate) async fn tokenize(
-        &self,
-        request: GenerateRequest,
-    ) -> Result<Option<tokenizers::Encoding>, InferError> {
-        // Tokenize request
-        let inputs = request.inputs;
-        let truncate = request.parameters.truncate;
-        let encoding = self
-            .validation
-            .tokenize(inputs, truncate)
-            .await
-            .map_err(|err| {
-                tracing::error!("Tokenization {err}");
-                err
-            })?;
-
-        // Return Encoding
-        Ok(encoding.map(|(encoding, _)| encoding))
-    }
-
-    /// Apply the chat template to the chat request
-    #[instrument(skip_all)]
-    pub(crate) fn apply_chat_template(
-        &self,
-        messages: Vec<Message>,
-        grammar_with_prompt: Option<(GrammarType, String)>,
-    ) -> Result<String, InferError> {
-        self.chat_template
-            .as_ref()
-            .ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
-            .apply(messages, grammar_with_prompt)
-            .map_err(|e| {
-                metrics::increment_counter!("tgi_request_failure", "err" => "template");
-                tracing::error!("{e}");
-                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))
-    }
-}
-
-#[derive(Clone)]
-struct ChatTemplate {
-    template: Template<'static, 'static>,
-    bos_token: Option<String>,
-    eos_token: Option<String>,
-    use_default_tool_template: bool,
-}
-
-impl ChatTemplate {
-    fn new(template: String, bos_token: Option<String>, eos_token: Option<String>) -> Self {
-        let mut env = Box::new(Environment::new());
-        let template_str = template.into_boxed_str();
-        env.add_function("raise_exception", raise_exception);
-
-        // check if contains the tools variable within the template
-        let use_default_tool_template =
-            !template_str.as_ref().replace(' ', "").contains("{{tools}}");
-        // leaking env and template_str as read-only, static resources for performance.
-        let template = Box::leak(env)
-            .template_from_str(Box::leak(template_str))
-            .unwrap();
-
-        Self {
-            template,
-            bos_token,
-            eos_token,
-            use_default_tool_template,
-        }
-    }
-
-    fn apply(
-        &self,
-        mut messages: Vec<Message>,
-        grammar_with_prompt: Option<(GrammarType, String)>,
-    ) -> Result<String, InferError> {
-        if self.use_default_tool_template {
-            if let Some(last_message) = messages.last_mut() {
-                if let Some((GrammarType::Json(tools), tool_prompt)) = grammar_with_prompt {
-                    last_message.content.push(MessageChunk::Text(Text {
-                        text: format!("\n---\n{}\n{}", tool_prompt, tools),
-                    }));
-                }
-            }
-        }
-
-        let messages: Vec<TextMessage> = messages.into_iter().map(|c| c.into()).collect();
-
-        self.template
-            .render(ChatTemplateInputs {
-                messages,
-                bos_token: self.bos_token.as_deref(),
-                eos_token: self.eos_token.as_deref(),
-                add_generation_prompt: true,
-                tools: None,
-                tools_prompt: None,
-            })
-            .map_err(InferError::TemplateError)
-    }
-}
-
-pub struct ToolGrammar {}
-
-impl ToolGrammar {
-    pub fn apply(
-        tools: Option<Vec<Tool>>,
-        tool_choice: Option<ToolType>,
-    ) -> Result<Option<Tools>, InferError> {
-        if let Some((req_tools, tool_choice)) = tools.zip(tool_choice) {
-            // let tool_prompt = tool_prompt.unwrap_or_default();
-            let tools_to_use = match tool_choice {
-                ToolType::FunctionName(name) => {
-                    vec![req_tools
-                        .iter()
-                        .find(|tool| tool.function.name == *name)
-                        .unwrap_or_else(|| panic!("Tool with name {} not found", name))
-                        .clone()]
-                }
-                ToolType::OneOf => req_tools.to_owned(),
-            };
-
-            // adds the error notification function for LLM feedback if required
-            let mut text_response_properties = Map::new();
-            text_response_properties.insert(
-                "error".to_string(),
-                serde_json::json!({
-                    "type": "string",
-                    "description": "The error or issue to notify"
-                }),
-            );
-            text_response_properties.insert(
-                "_name".to_string(),
-                serde_json::json!({
-                    "type": "string",
-                    "const": "notify_error"
-                }),
-            );
-
-            let functions: HashMap<String, serde_json::Value> = tools_to_use
-                .iter()
-                .map(|tool| {
-                    let func = tool.function.clone();
-
-                    // Clone the existing parameters, which are expected to be a JSON object
-                    let mut params = if let Value::Object(params) = &func.arguments {
-                        params.clone()
-                    } else {
-                        Map::new()
-                    };
-
-                    // Insert the function's description at the top level, outside of properties
-                    params.insert(
-                        "description".to_string(),
-                        Value::String(func.description.clone().unwrap_or_default()),
-                    );
-
-                    // Ensure 'properties' exists and is an object
-                    let properties = params
-                        .entry("properties".to_string())
-                        .or_insert_with(|| json!({}))
-                        .as_object_mut()
-                        .unwrap();
-
-                    // Insert the constant for the function name inside 'properties'
-                    properties.insert(
-                        "_name".to_string(),
-                        json!({
-                            "type": "string",
-                            "const": func.name.clone(),
-                            // "description": "The name of the function"
-                        }),
-                    );
-
-                    // Check if 'required' exists, and it is an array. If not, create an empty array.
-                    let required = params
-                        .entry("required".to_string())
-                        .or_insert_with(|| json!([]))
-                        .as_array_mut()
-                        .unwrap();
-
-                    // Add 'name' to the 'required' array if it is not already present
-                    if !required.iter().any(|r| r == "_name") {
-                        required.push(json!("_name"));
-                    }
-
-                    (func.name, Value::Object(params))
-                })
-                .chain([(
-                    "notify_error".to_string(),
-                    serde_json::json!({
-                        "properties": text_response_properties,
-                        "required": ["error", "_name"],
-                        "type": "object"
-                    }),
-                )])
-                .collect();
-
-            let tools = Tools {
-                functions_map: FunctionsMap { functions },
-                properties: Properties {
-                    function: tools_to_use
-                        .iter()
-                        .map(|tool| FunctionRef {
-                            ref_path: format!("#/$functions/{}", tool.function.name.clone()),
-                        })
-                        .chain(std::iter::once(FunctionRef {
-                            ref_path: "#/$functions/notify_error".to_string(),
-                        }))
-                        .collect(),
-                },
-            };
-
-            return Ok(Some(tools));
-        }
-        // Err(InferError::ToolError("No tools provided".to_string()))
-        Ok(None)
-    }
 }
 
 /// Batching logic
@@ -512,7 +101,7 @@ impl ToolGrammar {
 ///
 /// Batches requests and sends them to the inference server
 #[allow(clippy::too_many_arguments)]
-async fn batching_task(
+pub(crate) async fn batching_task(
     mut client: ShardedClient,
     waiting_served_ratio: f32,
     max_batch_prefill_tokens: u32,
@@ -520,13 +109,13 @@ async fn batching_task(
     max_waiting_tokens: usize,
     max_batch_size: Option<usize>,
     queue: Queue,
-    shared: Arc<Shared>,
+    notifier: Arc<Notify>,
     generation_health: Arc<AtomicBool>,
 ) {
     // Infinite loop
     loop {
         // Wait for a notification from the Infer struct
-        shared.batching_task.notified().await;
+        notifier.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
@@ -792,6 +381,16 @@ fn send_responses(
     let mut stopped = false;
 
     if let Some(prefill_tokens) = generation.prefill_tokens {
+        // Create Token objects
+        // We do that here instead of in the Python code as Rust for loops are faster
+        let prefill_tokens = prefill_tokens
+            .ids
+            .into_iter()
+            .zip(prefill_tokens.logprobs)
+            .zip(prefill_tokens.texts)
+            .map(|((id, logprob), text)| PrefillToken { id, text, logprob })
+            .collect();
+
         // Send message
         entry
             .response_tx
@@ -842,7 +441,7 @@ fn send_responses(
                 entry.response_tx.send(Ok(InferStreamResponse::End {
                     token,
                     top_tokens,
-                    generated_text: generated_text.clone(),
+                    generated_text: GeneratedText::from(generated_text.clone()),
                     queued: entry.queue_time,
                     start: entry.batch_time.unwrap(),
                 }))?;
@@ -877,64 +476,21 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
     });
 }
 
-#[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),
-    #[error("Tool error: {0}")]
-    ToolError(String),
-}
+impl From<text_generation_client::v2::GeneratedText> for GeneratedText {
+    fn from(value: text_generation_client::v2::GeneratedText) -> Self {
+        let v2_finish_reason =
+            text_generation_client::v2::FinishReason::try_from(value.finish_reason).unwrap();
+        let finish_reason = match v2_finish_reason {
+            text_generation_client::v2::FinishReason::Length => FinishReason::Length,
+            text_generation_client::v2::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
+            text_generation_client::v2::FinishReason::StopSequence => FinishReason::StopSequence,
+        };
 
-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",
-            InferError::ToolError(_) => "tool_error",
+        Self {
+            text: value.text,
+            generated_tokens: value.generated_tokens,
+            finish_reason,
+            seed: value.seed,
         }
     }
 }
@@ -1355,11 +911,11 @@ mod tests {
                 chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
                 input: ChatTemplateInputs {
                     messages: vec![
-                        TextMessage{
+                        TextMessage {
                             role: "system".to_string(),
                             content: "You are a friendly chatbot who always responds in the style of a pirate".to_string(),
                         },
-                        TextMessage{
+                        TextMessage {
                             role: "user".to_string(),
                             content: "How many helicopters can a human eat in one sitting?".to_string(),
                         },

router/src/infer/v3/mod.rs

+mod queue;
+mod scheduler;
+
+pub(crate) use scheduler::SchedulerV3;

router/src/queue.rs → router/src/infer/v3/queue.rs

-use crate::infer::InferError;
-use crate::infer::InferStreamResponse;
-use crate::validation::ValidGenerateRequest;
+use crate::infer::{InferError, InferStreamResponse};
+use crate::validation::{
+    ValidGenerateRequest, ValidGrammar, ValidParameters, ValidStoppingParameters,
+};
 use nohash_hasher::{BuildNoHashHasher, IntMap};
 use std::cmp::min;
 use std::collections::VecDeque;
-use text_generation_client::ChunksToString;
-use text_generation_client::Input;
-use text_generation_client::{Batch, Request};
+use text_generation_client::v3::{
+    Batch, GrammarType, NextTokenChooserParameters, Request, StoppingCriteriaParameters,
+};
+use text_generation_client::{ChunksToString, Input};
 use tokio::sync::{mpsc, oneshot};
 use tokio::time::Instant;
 use tracing::{info_span, instrument, Span};
@@ -57,7 +59,6 @@ impl Queue {
         Self { queue_sender }
     }
 
-    /// Append an entry to the queue
     #[instrument(skip_all)]
     pub(crate) fn append(&self, entry: Entry) {
         // Send append command to the background task managing the state
@@ -280,13 +281,17 @@ impl State {
             batch_requests.push(Request {
                 id,
                 prefill_logprobs: entry.request.decoder_input_details,
+                inputs: entry.request.inputs.chunks_to_string(),
                 input_chunks: Some(Input {
                     chunks: entry.request.inputs.clone(),
                 }),
-                inputs: entry.request.inputs.chunks_to_string(),
                 truncate: entry.request.truncate,
-                parameters: Some(entry.request.parameters.clone()),
-                stopping_parameters: Some(entry.request.stopping_parameters.clone()),
+                parameters: Some(NextTokenChooserParameters::from(
+                    entry.request.parameters.clone(),
+                )),
+                stopping_parameters: Some(StoppingCriteriaParameters::from(
+                    entry.request.stopping_parameters.clone(),
+                )),
                 top_n_tokens: entry.request.top_n_tokens,
             });
             // Set batch_time
@@ -355,12 +360,46 @@ enum QueueCommand {
     },
 }
 
+impl From<ValidParameters> for NextTokenChooserParameters {
+    fn from(value: ValidParameters) -> Self {
+        let (grammar, grammar_type) = match value.grammar {
+            None => (String::new(), GrammarType::None),
+
+            Some(grammar) => match grammar {
+                ValidGrammar::Json(grammar_string) => (grammar_string, GrammarType::Json),
+                ValidGrammar::Regex(grammar_string) => (grammar_string, GrammarType::Regex),
+            },
+        };
+
+        Self {
+            temperature: value.temperature,
+            top_k: value.top_k,
+            top_p: value.top_p,
+            typical_p: value.typical_p,
+            do_sample: value.do_sample,
+            seed: value.seed,
+            repetition_penalty: value.repetition_penalty,
+            frequency_penalty: value.frequency_penalty,
+            watermark: value.watermark,
+            grammar,
+            grammar_type: grammar_type.into(),
+        }
+    }
+}
+
+impl From<ValidStoppingParameters> for StoppingCriteriaParameters {
+    fn from(value: ValidStoppingParameters) -> Self {
+        Self {
+            max_new_tokens: value.max_new_tokens,
+            stop_sequences: value.stop_sequences,
+            ignore_eos_token: value.ignore_eos_token,
+        }
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;
-    use text_generation_client::{
-        GrammarType as ProtoGrammarType, NextTokenChooserParameters, StoppingCriteriaParameters,
-    };
     use tracing::info_span;
 
     fn default_entry() -> (
@@ -375,7 +414,7 @@ mod tests {
                 input_length: 0,
                 truncate: 0,
                 decoder_input_details: false,
-                parameters: NextTokenChooserParameters {
+                parameters: ValidParameters {
                     temperature: 0.0,
                     top_k: 0,
                     top_p: 0.0,
@@ -385,10 +424,9 @@ mod tests {
                     repetition_penalty: 0.0,
                     frequency_penalty: 0.0,
                     watermark: false,
-                    grammar: String::new(),
-                    grammar_type: ProtoGrammarType::None as i32,
+                    grammar: None,
                 },
-                stopping_parameters: StoppingCriteriaParameters {
+                stopping_parameters: ValidStoppingParameters {
                     ignore_eos_token: false,
                     max_new_tokens: 1,
                     stop_sequences: vec![],

router/src/infer/v3/scheduler.rs

+/// Batching and inference logic
+use crate::infer::v3::queue::{Entry, Queue};
+use crate::infer::{
+    GenerateStreamResponse, GeneratedText, InferError, InferStreamResponse, Scheduler,
+};
+use crate::validation::ValidGenerateRequest;
+use crate::{FinishReason, PrefillToken, Token};
+use nohash_hasher::IntMap;
+use std::sync::{
+    atomic::{AtomicBool, Ordering},
+    Arc,
+};
+use text_generation_client::v3::{Batch, CachedBatch, Generation, ShardedClient};
+use text_generation_client::ClientError;
+use tokio::sync::mpsc::error::SendError;
+use tokio::sync::{mpsc, Notify, OwnedSemaphorePermit};
+use tokio::time::Instant;
+use tokio_stream::wrappers::UnboundedReceiverStream;
+use tracing::{info_span, instrument, Instrument, Span};
+
+pub(crate) struct SchedulerV3 {
+    /// Request queue
+    queue: Queue,
+    /// Notify batcher on queue appends
+    batching_task_notifier: Arc<Notify>,
+}
+
+impl SchedulerV3 {
+    #[allow(clippy::too_many_arguments)]
+    pub(crate) fn new(
+        client: ShardedClient,
+        waiting_served_ratio: f32,
+        max_batch_prefill_tokens: u32,
+        max_batch_total_tokens: u32,
+        max_waiting_tokens: usize,
+        max_batch_size: Option<usize>,
+        requires_padding: bool,
+        window_size: Option<u32>,
+        speculate: u32,
+        generation_health: Arc<AtomicBool>,
+    ) -> Self {
+        let queue = Queue::new(requires_padding, 16, window_size, speculate);
+        let batching_task_notifier = Arc::new(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,
+            max_batch_size,
+            queue.clone(),
+            batching_task_notifier.clone(),
+            generation_health,
+        ));
+
+        Self {
+            queue,
+            batching_task_notifier,
+        }
+    }
+}
+
+impl Scheduler for SchedulerV3 {
+    #[instrument(skip_all)]
+    fn schedule(
+        &self,
+        request: ValidGenerateRequest,
+        permit: OwnedSemaphorePermit,
+    ) -> Result<GenerateStreamResponse, InferError> {
+        // MPSC channel to communicate with the background batching task
+        let (response_tx, response_rx) = mpsc::unbounded_channel();
+        let input_length = request.input_length;
+
+        // Append the request to the queue
+        self.queue.append(Entry {
+            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.batching_task_notifier.notify_one();
+
+        // Return stream
+        Ok((
+            permit,
+            input_length,
+            UnboundedReceiverStream::new(response_rx),
+        ))
+    }
+}
+
+/// Batching logic
+/// Will be launched in a background Tokio task
+///
+/// Batches requests and sends them to the inference server
+#[allow(clippy::too_many_arguments)]
+pub(crate) 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,
+    max_batch_size: Option<usize>,
+    queue: Queue,
+    notifier: Arc<Notify>,
+    generation_health: Arc<AtomicBool>,
+) {
+    // Infinite loop
+    loop {
+        // Wait for a notification from the Infer struct
+        notifier.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_size,
+                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);
+                let max_size = max_batch_size.map(|max_size| max_size - batch_size as usize);
+
+                // Try to get a new batch
+                if let Some((mut new_entries, new_batch, span)) = queue
+                    .next_batch(min_size, max_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 {
+        // Create Token objects
+        // We do that here instead of in the Python code as Rust for loops are faster
+        let prefill_tokens = prefill_tokens
+            .ids
+            .into_iter()
+            .zip(prefill_tokens.logprobs)
+            .zip(prefill_tokens.texts)
+            .map(|((id, logprob), text)| PrefillToken { id, text, logprob })
+            .collect();
+
+        // 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: GeneratedText::from(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(());
+    });
+}
+
+impl From<text_generation_client::v3::GeneratedText> for GeneratedText {
+    fn from(value: text_generation_client::v3::GeneratedText) -> Self {
+        let v3_finish_reason =
+            text_generation_client::v3::FinishReason::try_from(value.finish_reason).unwrap();
+        let finish_reason = match v3_finish_reason {
+            text_generation_client::v3::FinishReason::Length => FinishReason::Length,
+            text_generation_client::v3::FinishReason::EosToken => FinishReason::EndOfSequenceToken,
+            text_generation_client::v3::FinishReason::StopSequence => FinishReason::StopSequence,
+        };
+
+        Self {
+            text: value.text,
+            generated_tokens: value.generated_tokens,
+            finish_reason,
+            seed: value.seed,
+        }
+    }
+}
+
+// tests
+#[cfg(test)]
+mod tests {
+    use crate::infer::raise_exception;
+    use crate::{ChatTemplateInputs, TextMessage};
+    use minijinja::Environment;
+
+    #[test]
+    fn test_chat_template() {
+        let env = Environment::new();
+
+        let source = r#"
+        {% for message in messages %}
+            {% if message['role'] == 'system' %}
+                {% if message['content']%}
+                    {{'### System:\n' + message['content']+'\n\n'}}
+                {% endif %}
+            {% elif message['role'] == 'user' %}
+                {{'### User:\n' + message['content']+'\n\n'}}
+            {% elif message['role'] == 'assistant' %}
+                {{'### Assistant:\n'  + message['content']}}
+            {% endif %}
+            {% if loop.last and add_generation_prompt %}
+                {{ '### Assistant:\n' }}
+            {% endif %}
+        {% endfor %}"#;
+
+        // trim all the whitespace
+        let source = source
+            .lines()
+            .map(|line| line.trim())
+            .collect::<Vec<&str>>()
+            .join("");
+
+        let tmpl = env.template_from_str(&source);
+
+        let chat_template_inputs = ChatTemplateInputs {
+            messages: vec![
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "Hi!".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "Hello how can I help?".to_string(),
+                },
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "What is Deep Learning?".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "magic!".to_string(),
+                },
+            ],
+            bos_token: Some("[BOS]"),
+            eos_token: Some("[EOS]"),
+            add_generation_prompt: true,
+            ..Default::default()
+        };
+
+        let result = tmpl.unwrap().render(chat_template_inputs).unwrap();
+
+        assert_eq!(
+            result,
+            "### User:\nHi!\n\n### Assistant:\nHello how can I help?### User:\nWhat is Deep Learning?\n\n### Assistant:\nmagic!### Assistant:\n"
+        );
+    }
+
+    #[test]
+    fn test_chat_template_invalid_with_raise() {
+        let mut env = Environment::new();
+        env.add_function("raise_exception", raise_exception);
+
+        let source = r#"
+        {{ bos_token }}
+        {% for message in messages %}
+        {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
+        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
+        {% endif %}
+        {% if message['role'] == 'user' %}
+        {{ '[INST] ' + message['content'] + ' [/INST]' }}
+        {% elif message['role'] == 'assistant' %}
+        {{ message['content'] + eos_token}}
+        {% else %}
+        {{ raise_exception('Only user and assistant roles are supported!') }}
+        {% endif %}
+        {% endfor %}"#;
+
+        // trim all the whitespace
+        let source = source
+            .lines()
+            .map(|line| line.trim())
+            .collect::<Vec<&str>>()
+            .join("");
+
+        let tmpl = env.template_from_str(&source);
+
+        let chat_template_inputs = ChatTemplateInputs {
+            messages: vec![
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "Hi!".to_string(),
+                },
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "Hi again!".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "Hello how can I help?".to_string(),
+                },
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "What is Deep Learning?".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "magic!".to_string(),
+                },
+            ],
+            bos_token: Some("[BOS]"),
+            eos_token: Some("[EOS]"),
+            add_generation_prompt: true,
+            ..Default::default()
+        };
+
+        let result = tmpl.unwrap().render(chat_template_inputs); //.err().unwrap();
+
+        match result {
+            Ok(_) => panic!("Should have failed"),
+            Err(e) => {
+                assert_eq!(
+                    e.detail().unwrap(),
+                    "Conversation roles must alternate user/assistant/user/assistant/..."
+                );
+            }
+        }
+    }
+
+    #[test]
+    fn test_chat_template_valid_with_raise() {
+        let mut env = Environment::new();
+        env.add_function("raise_exception", raise_exception);
+
+        let source = r#"
+        {{ bos_token }}
+        {% for message in messages %}
+        {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
+        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
+        {% endif %}
+        {% if message['role'] == 'user' %}
+        {{ '[INST] ' + message['content'] + ' [/INST]' }}
+        {% elif message['role'] == 'assistant' %}
+        {{ message['content'] + eos_token}}
+        {% else %}
+        {{ raise_exception('Only user and assistant roles are supported!') }}
+        {% endif %}
+        {% endfor %}"#;
+
+        // trim all the whitespace
+        let source = source
+            .lines()
+            .map(|line| line.trim())
+            .collect::<Vec<&str>>()
+            .join("");
+
+        let tmpl = env.template_from_str(&source);
+
+        let chat_template_inputs = ChatTemplateInputs {
+            messages: vec![
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "Hi!".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "Hello how can I help?".to_string(),
+                },
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "What is Deep Learning?".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "magic!".to_string(),
+                },
+            ],
+            bos_token: Some("[BOS]"),
+            eos_token: Some("[EOS]"),
+            add_generation_prompt: true,
+            ..Default::default()
+        };
+
+        let result = tmpl.unwrap().render(chat_template_inputs).unwrap();
+        assert_eq!(result, "[BOS][INST] Hi! [/INST]Hello how can I help?[EOS][INST] What is Deep Learning? [/INST]magic![EOS]");
+    }
+
+    #[test]
+    fn test_chat_template_valid_with_add_generation_prompt() {
+        let mut env = Environment::new();
+        env.add_function("raise_exception", raise_exception);
+
+        let source = r#"
+        {% for message in messages %}
+        {{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}
+        {% endfor %}
+        {% if add_generation_prompt %}
+            {{ '<|im_start|>assistant\n' }}
+        {% endif %}"#;
+
+        // trim all the whitespace
+        let source = source
+            .lines()
+            .map(|line| line.trim())
+            .collect::<Vec<&str>>()
+            .join("");
+
+        let tmpl = env.template_from_str(&source);
+
+        let chat_template_inputs = ChatTemplateInputs {
+            messages: vec![
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "Hi!".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "Hello how can I help?".to_string(),
+                },
+                TextMessage {
+                    role: "user".to_string(),
+                    content: "What is Deep Learning?".to_string(),
+                },
+                TextMessage {
+                    role: "assistant".to_string(),
+                    content: "magic!".to_string(),
+                },
+            ],
+            bos_token: Some("[BOS]"),
+            eos_token: Some("[EOS]"),
+            add_generation_prompt: true,
+            ..Default::default()
+        };
+
+        let result = tmpl.unwrap().render(chat_template_inputs).unwrap();
+        assert_eq!(result, "<|im_start|>user\nHi!<|im_end|>\n<|im_start|>assistant\nHello how can I help?<|im_end|>\n<|im_start|>user\nWhat is Deep Learning?<|im_end|>\n<|im_start|>assistant\nmagic!<|im_end|>\n<|im_start|>assistant\n");
+    }
+
+    struct ChatTemplateTestItem {
+        name: &'static str,
+        chat_template: &'static str,
+        input: ChatTemplateInputs<'static>,
+        target: &'static str,
+    }
+
+    #[test]
+    fn test_many_chat_templates() {
+        let example_chat = vec![
+            TextMessage {
+                role: "user".to_string(),
+                content: "Hello, how are you?".to_string(),
+            },
+            TextMessage {
+                role: "assistant".to_string(),
+                content: "I'm doing great. How can I help you today?".to_string(),
+            },
+            TextMessage {
+                role: "user".to_string(),
+                content: "I'd like to show off how chat templating works!".to_string(),
+            },
+        ];
+
+        let example_chat_with_system = [TextMessage {
+            role: "system".to_string(),
+            content: "You are a friendly chatbot who always responds in the style of a pirate"
+                .to_string(),
+        }]
+        .iter()
+        .chain(&example_chat)
+        .cloned()
+        .collect::<Vec<_>>();
+
+        let test_default_templates = vec![
+            ChatTemplateTestItem {
+                name: "_base",
+                chat_template: "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some(""),
+                    ..Default::default()
+                },
+                target: "<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n",
+            },
+            ChatTemplateTestItem {
+                name: "blenderbot",
+                chat_template: "{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: " Hello, how are you?  I'm doing great. How can I help you today?   I'd like to show off how chat templating works!</s>",
+            },
+            ChatTemplateTestItem {
+                name: "blenderbot_small",
+                chat_template: "{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: " Hello, how are you?  I'm doing great. How can I help you today?   I'd like to show off how chat templating works!</s>",
+            },
+            ChatTemplateTestItem {
+                name: "bloom",
+                chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "Hello, how are you?</s>I'm doing great. How can I help you today?</s>I'd like to show off how chat templating works!</s>",
+            },
+            ChatTemplateTestItem {
+                name: "gpt_neox",
+                chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some("<|endoftext|>"),
+                    ..Default::default()
+                },
+                target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>",
+            },
+            ChatTemplateTestItem {
+                name: "gpt2",
+                chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some("<|endoftext|>"),
+                    ..Default::default()
+                },
+                target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>",
+            },
+            ChatTemplateTestItem {
+                name: "llama",
+                // NOTE: the `.strip()` has been replaced with `| trim` in the following template
+                chat_template: "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token +'[INST] ' + content | trim + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>\\n' + content | trim + '\\n<</SYS>>\\n\\n' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content | trim + ' ' + eos_token }}{% endif %}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat_with_system.clone(),
+                    add_generation_prompt: true,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<s>[INST] <<SYS>>\nYou are a friendly chatbot who always responds in the style of a pirate\n<</SYS>>\n\nHello, how are you? [/INST] I'm doing great. How can I help you today? </s><s>[INST] I'd like to show off how chat templating works! [/INST]",
+            },
+            ChatTemplateTestItem {
+                name: "whisper",
+                chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: true,
+                    bos_token: Some(""),
+                    eos_token: Some("<|endoftext|>"),
+                    ..Default::default()
+                },
+                target: "Hello, how are you?<|endoftext|>I'm doing great. How can I help you today?<|endoftext|>I'd like to show off how chat templating works!<|endoftext|>",
+            },
+        ];
+
+        #[allow(unused_variables)] // name is unused
+        for ChatTemplateTestItem {
+            name,
+            chat_template,
+            input,
+            target,
+        } in test_default_templates
+        {
+            let mut env = Environment::new();
+            env.add_function("raise_exception", raise_exception);
+            let tmpl = env.template_from_str(chat_template);
+            let result = tmpl.unwrap().render(input).unwrap();
+            assert_eq!(result, target);
+        }
+
+        let test_custom_templates = vec![
+            ChatTemplateTestItem {
+                name: "HuggingFaceH4/zephyr-7b-beta (add_generation_prompt=false)",
+                chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat_with_system.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some(""),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate</s><|user|>\nHello, how are you?</s><|assistant|>\nI'm doing great. How can I help you today?</s><|user|>\nI'd like to show off how chat templating works!</s>",
+            },
+            ChatTemplateTestItem {
+                name: "HuggingFaceH4/zephyr-7b-beta (add_generation_prompt=true)",
+                chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: vec![
+                        TextMessage {
+                            role: "system".to_string(),
+                            content: "You are a friendly chatbot who always responds in the style of a pirate".to_string(),
+                        },
+                        TextMessage {
+                            role: "user".to_string(),
+                            content: "How many helicopters can a human eat in one sitting?".to_string(),
+                        },
+                    ],
+                    add_generation_prompt: true,
+                    bos_token: Some(""),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<|system|>\nYou are a friendly chatbot who always responds in the style of a pirate</s><|user|>\nHow many helicopters can a human eat in one sitting?</s><|assistant|>",
+            },
+            ChatTemplateTestItem {
+                name: "HuggingFaceH4/zephyr-7b-gemma-v0.1",
+                chat_template: "{% if messages[0]['role'] == 'user' or messages[0]['role'] == 'system' %}{{ bos_token }}{% endif %}{% for message in messages %}{{ '<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% elif messages[-1]['role'] == 'assistant' %}{{ eos_token }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<bos>"),
+                    eos_token: Some("<eos>"),
+                    ..Default::default()
+                },
+                target: "<bos><|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n",
+            },
+            ChatTemplateTestItem {
+                name: "mistralai/Mistral-7B-Instruct-v0.1",
+                chat_template: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]",
+            },
+            ChatTemplateTestItem {
+                name: "mistralai/Mixtral-8x7B-Instruct-v0.1",
+                chat_template: "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s>[INST] I'd like to show off how chat templating works! [/INST]",
+            },
+            ChatTemplateTestItem {
+                name: "cognitivecomputations/dolphin-2.5-mixtral-8x7b",
+                chat_template: "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n",
+            },
+            ChatTemplateTestItem {
+                name: "openchat/openchat-3.5-0106",
+                // `.title()` has been replaced with `| upper` in the following template
+                chat_template: "{{ bos_token }}{% for message in messages %}{{ 'GPT4 Correct ' + (message['role'] | title) + ': ' + message['content'] + '<|end_of_turn|>'}}{% endfor %}{% if add_generation_prompt %}{{ 'GPT4 Correct Assistant:' }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<s>GPT4 Correct User: Hello, how are you?<|end_of_turn|>GPT4 Correct Assistant: I'm doing great. How can I help you today?<|end_of_turn|>GPT4 Correct User: I'd like to show off how chat templating works!<|end_of_turn|>",
+            },
+            ChatTemplateTestItem {
+                name: "upstage/SOLAR-10.7B-Instruct-v1.0",
+                chat_template: "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "Hello, how are you?</s>I'm doing great. How can I help you today?</s>I'd like to show off how chat templating works!</s>",
+            },
+            ChatTemplateTestItem {
+                name: "codellama/CodeLlama-70b-Instruct-hf",
+                // NOTE: `.strip()` has been replaced with `| trim` in the following template
+                chat_template: "{% if messages[0]['role'] == 'system' %}{% set user_index = 1 %}{% else %}{% set user_index = 0 %}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != ((loop.index0 + user_index) % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 %}{{ '<s>' }}{% endif %}{% set content = 'Source: ' + message['role'] + '\\n\\n ' + message['content'] | trim %}{{ content + ' <step> ' }}{% endfor %}{{'Source: assistant\\nDestination: user\\n\\n '}}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<s>Source: user\n\n Hello, how are you? <step> Source: assistant\n\n I'm doing great. How can I help you today? <step> Source: user\n\n I'd like to show off how chat templating works! <step> Source: assistant\nDestination: user\n\n ",
+            },
+            ChatTemplateTestItem {
+                name: "Deci/DeciLM-7B-instruct",
+                chat_template: "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '### User:\\n' + message['content'] }}\n{% elif message['role'] == 'system' %}\n{{ '### System:\\n' + message['content'] }}\n{% elif message['role'] == 'assistant' %}\n{{ '### Assistant:\\n'  + message['content'] }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '### Assistant:' }}\n{% endif %}\n{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "### User:\nHello, how are you?### Assistant:\nI'm doing great. How can I help you today?### User:\nI'd like to show off how chat templating works!",
+            },
+            ChatTemplateTestItem {
+                name: "Qwen/Qwen1.5-72B-Chat",
+                chat_template: "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!",
+            },
+            ChatTemplateTestItem {
+                name: "deepseek-ai/deepseek-llm-7b-chat",
+                chat_template: "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\\n\\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\\n\\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<｜begin▁of▁sentence｜>"),
+                    eos_token: Some("<｜end▁of▁sentence｜>"),
+                    ..Default::default()
+                },
+                target: "<｜begin▁of▁sentence｜>User: Hello, how are you?\n\nAssistant: I'm doing great. How can I help you today?<｜end▁of▁sentence｜>User: I'd like to show off how chat templating works!\n\n",
+            },
+            ChatTemplateTestItem {
+                name: "h2oai/h2o-danube-1.8b-chat",
+                chat_template: "{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|prompt|>' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ '<|system|>' + message['content'] + eos_token }}{% elif message['role'] == 'assistant' %}{{ '<|answer|>'  + message['content'] + eos_token }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|answer|>' }}{% endif %}{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<|prompt|>Hello, how are you?</s><|answer|>I'm doing great. How can I help you today?</s><|prompt|>I'd like to show off how chat templating works!</s>",
+            },
+            ChatTemplateTestItem {
+                name: "internlm/internlm2-chat-7b",
+                chat_template: "{% if messages[0]['role'] == 'user' or messages[0]['role'] == 'system' %}{{ bos_token }}{% endif %}{% for message in messages %}{{ '<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% elif messages[-1]['role'] == 'assistant' %}{{ eos_token }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "<s><|im_start|>user\nHello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing great. How can I help you today?<|im_end|>\n<|im_start|>user\nI'd like to show off how chat templating works!<|im_end|>\n",
+            },
+            ChatTemplateTestItem {
+                name: "TheBloke/deepseek-coder-33B-instruct-AWQ",
+                chat_template: "{%- set found_item = false -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set found_item = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{%- if not found_item -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{{'### Response:\\n'}}\n",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<｜begin▁of▁sentence｜>"),
+                    eos_token: Some("<|EOT|>"),
+                    ..Default::default()
+                },
+                target: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\n### Instruction:\nHello, how are you?\n### Response:\nI'm doing great. How can I help you today?\n<|EOT|>\n### Instruction:\nI'd like to show off how chat templating works!\n### Response:\n",
+            },
+            ChatTemplateTestItem {
+                name: "ericzzz/falcon-rw-1b-chat",
+                // `.strip()` has been replaced with `| trim` in the following template
+                chat_template: "{% for message in messages %}{% if loop.index > 1 and loop.previtem['role'] != 'assistant' %}{{ ' ' }}{% endif %}{% if message['role'] == 'system' %}{{ '[SYS] ' + message['content'] | trim }}{% elif message['role'] == 'user' %}{{ '[INST] ' + message['content'] | trim }}{% elif message['role'] == 'assistant' %}{{ '[RESP] '  + message['content'] + eos_token }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ ' [RESP] ' }}{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<|endoftext|>"),
+                    eos_token: Some("<|endoftext|>"),
+                    ..Default::default()
+                },
+                target: "[INST] Hello, how are you? [RESP] I'm doing great. How can I help you today?<|endoftext|>[INST] I'd like to show off how chat templating works!",
+            },
+            ChatTemplateTestItem {
+                name: "abacusai/Smaug-34B-v0.1",
+                chat_template: "{%- for idx in range(0, messages|length) -%}\n{%- if messages[idx]['role'] == 'user' -%}\n{%- if idx > 1 -%}\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\n{%- else -%}\n{{- messages[idx]['content'] + ' [/INST]' -}}\n{%- endif -%}\n{% elif messages[idx]['role'] == 'system' %}\n{{- '[INST] <<SYS>>\\n' + messages[idx]['content'] + '\\n<</SYS>>\\n\\n' -}}\n{%- elif messages[idx]['role'] == 'assistant' -%}\n{{- ' '  + messages[idx]['content'] + ' ' + eos_token -}}\n{% endif %}\n{% endfor %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "Hello, how are you? [/INST] I'm doing great. How can I help you today? </s><s>[INST] I'd like to show off how chat templating works! [/INST]",
+            },
+            ChatTemplateTestItem {
+                name: "maywell/Synatra-Mixtral-8x7B",
+                chat_template: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{% for message in messages %}{% if message['role'] == 'user' %}### Instruction:\n{{ message['content']|trim -}}{% if not loop.last %}{% endif %}\n{% elif message['role'] == 'assistant' %}### Response:\n{{ message['content']|trim -}}{% if not loop.last %}{% endif %}\n{% elif message['role'] == 'system' %}{{ message['content']|trim -}}{% if not loop.last %}{% endif %}\n{% endif %}\n{% endfor %}\n{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}\n### Response:\n{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "Below is an instruction that describes a task. Write a response that appropriately completes the request.### Instruction:Hello, how are you?### Response:I'm doing great. How can I help you today?### Instruction:I'd like to show off how chat templating works!",
+            },
+            ChatTemplateTestItem {
+                name: "deepseek-ai/deepseek-coder-33b-instruct",
+                chat_template: "{% if not add_generation_prompt is defined %}\n{% set add_generation_prompt = false %}\n{% endif %}\n{%- set ns = namespace(found=false) -%}\n{%- for message in messages -%}\n    {%- if message['role'] == 'system' -%}\n        {%- set ns.found = true -%}\n    {%- endif -%}\n{%- endfor -%}\n{{bos_token}}{%- if not ns.found -%}\n{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\\n'}}\n{%- endif %}\n{%- for message in messages %}\n    {%- if message['role'] == 'system' %}\n{{ message['content'] }}\n    {%- else %}\n        {%- if message['role'] == 'user' %}\n{{'### Instruction:\\n' + message['content'] + '\\n'}}\n        {%- else %}\n{{'### Response:\\n' + message['content'] + '\\n<|EOT|>\\n'}}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{% if add_generation_prompt %}\n{{'### Response:'}}\n{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<｜begin▁of▁sentence｜>"),
+                    eos_token: Some("</EOT>"),
+                    ..Default::default()
+                },
+                target: "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n### Instruction:\nHello, how are you?\n### Response:\nI'm doing great. How can I help you today?\n<|EOT|>\n### Instruction:\nI'd like to show off how chat templating works!\n",
+            },
+            // NOT INCLUDED
+            // - meetkai/functionary-medium-v3.2
+            // - fireworks-ai/firefunction-v1
+            // https://github
+            ChatTemplateTestItem {
+                name: "maywell/PiVoT-MoE",
+                chat_template: "{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}{% for message in messages %}{% if message['role'] == 'system' %}{{ message['content']|trim }}{% elif message['role'] == 'user' %}### Instruction: {{ message['content']|trim }}{% elif message['role'] == 'assistant' %}### Response: {{ message['content']|trim }}{% elif message['role'] == 'user_context' %}### Input: {{ message['content']|trim }}{% endif %}{% if not loop.last %}\n{% endif %}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}### Response:{% endif %}",
+                input: ChatTemplateInputs {
+                    messages: example_chat_with_system.clone(),
+                    add_generation_prompt: false,
+                    bos_token: Some("<s>"),
+                    eos_token: Some("</s>"),
+                    ..Default::default()
+                },
+                target: "You are a friendly chatbot who always responds in the style of a pirateYou are a friendly chatbot who always responds in the style of a pirate### Instruction: Hello, how are you?### Response: I'm doing great. How can I help you today?### Instruction: I'd like to show off how chat templating works!",
+            },
+        ];
+
+        #[allow(unused_variables)] // name is unused
+        for ChatTemplateTestItem {
+            name,
+            chat_template,
+            input,
+            target,
+        } in test_custom_templates
+        {
+            let mut env = Environment::new();
+            env.add_function("raise_exception", raise_exception);
+            // trim all the whitespace
+            let chat_template = chat_template
+                .lines()
+                .map(|line| line.trim())
+                .collect::<Vec<&str>>()
+                .join("");
+
+            let tmpl = env.template_from_str(&chat_template);
+            let result = tmpl.unwrap().render(input).unwrap();
+            assert_eq!(result, target);
+        }
+    }
+}

router/src/lib.rs

-pub mod config;
-mod health;
 /// Text Generation Inference Webserver
+pub mod config;
 mod infer;
-mod queue;
 pub mod server;
 mod validation;
 
-use infer::{Infer, InferError, InferStreamResponse};
-use queue::{Entry, Queue};
 use serde::{Deserialize, Serialize};
-use tokio::sync::OwnedSemaphorePermit;
-use tokio_stream::wrappers::UnboundedReceiverStream;
 use tracing::warn;
 use utoipa::ToSchema;
 use validation::Validation;
 
-/// Type alias for generation responses
-pub(crate) type GenerateStreamResponse = (
-    OwnedSemaphorePermit,
-    u32, // input_length
-    UnboundedReceiverStream<Result<InferStreamResponse, InferError>>,
-);
-
 #[derive(Clone, Deserialize, ToSchema)]
 pub(crate) struct VertexInstance {
     #[schema(example = "What is Deep Learning?")]
@@ -158,7 +145,7 @@ pub struct Info {
     #[schema(example = "4")]
     pub max_stop_sequences: usize,
     #[schema(example = "1024")]
-    pub max_input_length: usize,
+    pub max_input_tokens: usize,
     #[schema(example = "2048")]
     pub max_total_tokens: usize,
     #[schema(example = "1.2")]
@@ -1087,7 +1074,7 @@ pub struct SimpleToken {
     stop: usize,
 }
 
-#[derive(Serialize, ToSchema)]
+#[derive(Debug, Serialize, ToSchema)]
 #[serde(rename_all(serialize = "snake_case"))]
 #[schema(example = "Length")]
 pub(crate) enum FinishReason {

router/src/main.rs

@@ -12,7 +12,6 @@ use std::fs::File;
 use std::io::BufReader;
 use std::net::{IpAddr, Ipv4Addr, SocketAddr};
 use std::path::{Path, PathBuf};
-use text_generation_client::{ClientError, ShardedClient};
 use text_generation_router::config::Config;
 use text_generation_router::{server, HubModelInfo, HubProcessorConfig, HubTokenizerConfig};
 use thiserror::Error;
@@ -315,59 +314,6 @@ async fn main() -> Result<(), RouterError> {
         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
-        .map_err(RouterError::Connection)?;
-    // Clear the cache; useful if the webserver rebooted
-    sharded_client
-        .clear_cache(None)
-        .await
-        .map_err(RouterError::Cache)?;
-    // Get info from the shard
-    let shard_info = sharded_client.info().await.map_err(RouterError::Info)?;
-
-    // Warmup model
-    tracing::info!("Warming up model");
-    let max_supported_batch_total_tokens = match sharded_client
-        .warmup(
-            max_input_tokens as u32,
-            max_batch_prefill_tokens,
-            max_total_tokens as u32,
-            max_batch_size,
-        )
-        .await
-        .map_err(RouterError::Warmup)?
-    {
-        // Older models do not support automatic max-batch-total-tokens
-        None => {
-            let max_batch_total_tokens = max_batch_total_tokens
-                .unwrap_or(16000.max((max_total_tokens as u32).max(max_batch_prefill_tokens)));
-            tracing::warn!("Model does not support automatic max batch total tokens");
-            max_batch_total_tokens
-        }
-        // Flash attention models return their max supported total tokens
-        Some(max_supported_batch_total_tokens) => {
-            // Warn if user added his own max-batch-total-tokens as we will ignore it
-            if max_batch_total_tokens.is_some() {
-                tracing::warn!(
-                    "`--max-batch-total-tokens` is deprecated for Flash \
-                        Attention models."
-                );
-                tracing::warn!(
-                    "Inferred max batch total tokens: {max_supported_batch_total_tokens}"
-                );
-            }
-            if max_total_tokens as u32 > max_supported_batch_total_tokens {
-                return Err(RouterError::ArgumentValidation(format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_supported_batch_total_tokens}")));
-            }
-
-            max_supported_batch_total_tokens
-        }
-    };
-    tracing::info!("Setting max batch total tokens to {max_supported_batch_total_tokens}");
-    tracing::info!("Connected");
-
     // Determine the server port based on the feature and environment variable.
     let port = if cfg!(feature = "google") {
         std::env::var("AIP_HTTP_PORT")
@@ -387,8 +333,8 @@ async fn main() -> Result<(), RouterError> {
 
     // Run server
     server::run(
+        master_shard_uds_path,
         model_info,
-        shard_info,
         compat_return_full_text,
         max_concurrent_requests,
         max_best_of,
@@ -398,10 +344,9 @@ async fn main() -> Result<(), RouterError> {
         max_total_tokens,
         waiting_served_ratio,
         max_batch_prefill_tokens,
-        max_supported_batch_total_tokens,
+        max_batch_total_tokens,
         max_waiting_tokens,
         max_batch_size,
-        sharded_client,
         tokenizer,
         config,
         validation_workers,
@@ -557,16 +502,8 @@ pub async fn get_tokenizer_config(api_repo: &ApiRepo) -> Option<HubTokenizerConf
 enum RouterError {
     #[error("Argument validation error: {0}")]
     ArgumentValidation(String),
-    #[error("Unable to connect to the Python model shards: {0}")]
-    Connection(ClientError),
-    #[error("Unable to clear the Python model shards cache: {0}")]
-    Cache(ClientError),
-    #[error("Unable to get the Python model shards info: {0}")]
-    Info(ClientError),
-    #[error("Unable to warmup the Python model shards: {0}")]
-    Warmup(ClientError),
+    #[error("WebServer error: {0}")]
+    WebServer(#[from] server::WebServerError),
     #[error("Tokio runtime failed to start: {0}")]
     Tokio(#[from] std::io::Error),
-    #[error("Axum webserver failed: {0}")]
-    Axum(#[from] axum::BoxError),
 }

router/src/server.rs

-use crate::config::Config;
 /// HTTP Server logic
-use crate::health::Health;
-use crate::infer::{InferError, InferResponse, InferStreamResponse, ToolGrammar};
+use crate::config::Config;
+use crate::infer::v2::SchedulerV2;
+use crate::infer::v3::SchedulerV3;
+use crate::infer::{HealthCheck, Scheduler};
+use crate::infer::{Infer, InferError, InferResponse, InferStreamResponse, ToolGrammar};
 use crate::validation::ValidationError;
 use crate::{
     BestOfSequence, Details, ErrorResponse, FinishReason, GenerateParameters, GenerateRequest,
-    GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Infer,
-    Info, Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token,
-    TokenizeResponse, Usage, Validation,
+    GenerateResponse, GrammarType, HubModelInfo, HubProcessorConfig, HubTokenizerConfig, Info,
+    Message, PrefillToken, SimpleToken, StreamDetails, StreamResponse, Token, TokenizeResponse,
+    Usage, Validation,
 };
 use crate::{
     ChatCompletion, ChatCompletionChoice, ChatCompletionChunk, ChatCompletionComplete,
@@ -34,7 +36,8 @@ use std::convert::Infallible;
 use std::net::SocketAddr;
 use std::sync::atomic::AtomicBool;
 use std::sync::Arc;
-use text_generation_client::{ShardInfo, ShardedClient};
+use text_generation_client::{v2, v3, ClientError, ShardInfo};
+use thiserror::Error;
 use tokenizers::Tokenizer;
 use tokio::select;
 use tokio::signal;
@@ -115,7 +118,9 @@ example = json ! ({"error": "unhealthy", "error_type": "healthcheck"})),
 )]
 #[instrument(skip(health))]
 /// Health check method
-async fn health(mut health: Extension<Health>) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
+async fn health(
+    mut health: Extension<HealthCheck>,
+) -> Result<(), (StatusCode, Json<ErrorResponse>)> {
     match health.check().await {
         true => Ok(()),
         false => Err((
@@ -213,9 +218,7 @@ async fn generate_internal(
 
                         BestOfSequence {
                             generated_text: output_text,
-                            finish_reason: FinishReason::from(
-                                response.generated_text.finish_reason,
-                            ),
+                            finish_reason: response.generated_text.finish_reason,
                             generated_tokens: response.generated_text.generated_tokens,
                             prefill: response.prefill,
                             tokens: response.tokens,
@@ -227,7 +230,7 @@ async fn generate_internal(
             });
 
             Some(Details {
-                finish_reason: FinishReason::from(response.generated_text.finish_reason),
+                finish_reason: response.generated_text.finish_reason,
                 generated_tokens: response.generated_text.generated_tokens,
                 prefill: response.prefill,
                 tokens: response.tokens,
@@ -468,7 +471,7 @@ async fn generate_stream_internal(
                                         // Token details
                                         let details = match details {
                                             true => Some(StreamDetails {
-                                                finish_reason: FinishReason::from(generated_text.finish_reason),
+                                                finish_reason: generated_text.finish_reason,
                                                 generated_tokens: generated_text.generated_tokens,
                                                 seed: generated_text.seed,
                                             }),
@@ -556,38 +559,38 @@ async fn generate_stream_internal(
 
 /// Generate tokens
 #[utoipa::path(
-    post,
-    tag = "Text Generation Inference",
-    path = "/v1/completions",
-    request_body = CompletionRequest,
-    responses(
-    (status = 200, description = "Generated Chat Completion",
-    content(
-    ("application/json" = Completion),
-    ("text/event-stream" = CompletionCompleteChunk),
-    )),
-    (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"})),
-    )
-    )]
+post,
+tag = "Text Generation Inference",
+path = "/v1/completions",
+request_body = CompletionRequest,
+responses(
+(status = 200, description = "Generated Chat Completion",
+content(
+("application/json" = Completion),
+("text/event-stream" = CompletionCompleteChunk),
+)),
+(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_all,
-    fields(
-    // parameters = ? req.parameters,
-    total_time,
-    validation_time,
-    queue_time,
-    inference_time,
-    time_per_token,
-    seed,
-    )
-    )]
+skip_all,
+fields(
+// parameters = ? req.parameters,
+total_time,
+validation_time,
+queue_time,
+inference_time,
+time_per_token,
+seed,
+)
+)]
 async fn completions(
     Extension(infer): Extension<Infer>,
     Extension(compute_type): Extension<ComputeType>,
@@ -961,38 +964,38 @@ async fn completions(
 
 /// Generate tokens
 #[utoipa::path(
-    post,
-    tag = "Text Generation Inference",
-    path = "/v1/chat/completions",
-    request_body = ChatRequest,
-    responses(
-    (status = 200, description = "Generated Chat Completion",
-    content(
-    ("application/json" = ChatCompletion),
-    ("text/event-stream" = ChatCompletionChunk),
-    )),
-    (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"})),
-    )
-    )]
+post,
+tag = "Text Generation Inference",
+path = "/v1/chat/completions",
+request_body = ChatRequest,
+responses(
+(status = 200, description = "Generated Chat Completion",
+content(
+("application/json" = ChatCompletion),
+("text/event-stream" = ChatCompletionChunk),
+)),
+(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_all,
-    fields(
-    // parameters = ? req.parameters,
-    total_time,
-    validation_time,
-    queue_time,
-    inference_time,
-    time_per_token,
-    seed,
-    )
-    )]
+skip_all,
+fields(
+// parameters = ? req.parameters,
+total_time,
+validation_time,
+queue_time,
+inference_time,
+time_per_token,
+seed,
+)
+)]
 async fn chat_completions(
     Extension(infer): Extension<Infer>,
     Extension(compute_type): Extension<ComputeType>,
@@ -1217,22 +1220,22 @@ async fn chat_completions(
 
 /// Generate tokens from Vertex request
 #[utoipa::path(
-    post,
-    tag = "Text Generation Inference",
-    path = "/vertex",
-    request_body = VertexRequest,
-    responses(
-    (status = 200, description = "Generated Text", body = VertexResponse),
-    (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"})),
-    )
-    )]
+post,
+tag = "Text Generation Inference",
+path = "/vertex",
+request_body = VertexRequest,
+responses(
+(status = 200, description = "Generated Text", body = VertexResponse),
+(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_all,
     fields(
@@ -1310,16 +1313,16 @@ async fn vertex_compatibility(
 
 /// Tokenize inputs
 #[utoipa::path(
-    post,
-    tag = "Text Generation Inference",
-    path = "/tokenize",
-    request_body = GenerateRequest,
-    responses(
-    (status = 200, description = "Tokenized ids", body = TokenizeResponse),
-    (status = 404, description = "No tokenizer found", body = ErrorResponse,
-    example = json ! ({"error": "No fast tokenizer available"})),
-    )
-    )]
+post,
+tag = "Text Generation Inference",
+path = "/tokenize",
+request_body = GenerateRequest,
+responses(
+(status = 200, description = "Tokenized ids", body = TokenizeResponse),
+(status = 404, description = "No tokenizer found", body = ErrorResponse,
+example = json ! ({"error": "No fast tokenizer available"})),
+)
+)]
 #[instrument(skip_all)]
 async fn tokenize(
     Extension(infer): Extension<Infer>,
@@ -1372,21 +1375,20 @@ pub(crate) struct ComputeType(String);
 /// Serving method
 #[allow(clippy::too_many_arguments)]
 pub async fn run(
+    master_shard_uds_path: String,
     model_info: HubModelInfo,
-    shard_info: ShardInfo,
     compat_return_full_text: bool,
     max_concurrent_requests: usize,
     max_best_of: usize,
     max_stop_sequences: usize,
     max_top_n_tokens: u32,
-    max_input_length: usize,
+    max_input_tokens: usize,
     max_total_tokens: usize,
     waiting_served_ratio: f32,
     max_batch_prefill_tokens: u32,
-    max_batch_total_tokens: u32,
+    max_batch_total_tokens: Option<u32>,
     max_waiting_tokens: usize,
     max_batch_size: Option<usize>,
-    client: ShardedClient,
     tokenizer: Option<Tokenizer>,
     config: Option<Config>,
     validation_workers: usize,
@@ -1400,7 +1402,7 @@ pub async fn run(
     messages_api_enabled: bool,
     grammar_support: bool,
     max_client_batch_size: usize,
-) -> Result<(), axum::BoxError> {
+) -> Result<(), WebServerError> {
     // OpenAPI documentation
     #[derive(OpenApi)]
     #[openapi(
@@ -1470,6 +1472,141 @@ pub async fn run(
     struct ApiDoc;
 
     // Create state
+
+    // Open connection, get model info and warmup
+    let (scheduler, health_ext, shard_info, max_batch_total_tokens): (
+        Arc<dyn Scheduler + Send + Sync>,
+        HealthCheck,
+        ShardInfo,
+        u32,
+    ) = {
+        // Helper function to check both v2 and v3
+        let check_max_batch_total_tokens = |max_supported_batch_total_tokens: Option<u32>| {
+            match max_supported_batch_total_tokens {
+                // Older models do not support automatic max-batch-total-tokens
+                None => {
+                    let max_batch_total_tokens = max_batch_total_tokens.unwrap_or(
+                        16000.max((max_total_tokens as u32).max(max_batch_prefill_tokens)),
+                    );
+                    tracing::warn!("Model does not support automatic max batch total tokens");
+                    Ok(max_batch_total_tokens)
+                }
+                // Flash attention models return their max supported total tokens
+                Some(max_supported_batch_total_tokens) => {
+                    // Warn if user added his own max-batch-total-tokens as we will ignore it
+                    if max_batch_total_tokens.is_some() {
+                        tracing::warn!(
+                            "`--max-batch-total-tokens` is deprecated for Flash \
+                        Attention models."
+                        );
+                        tracing::warn!(
+                            "Inferred max batch total tokens: {max_supported_batch_total_tokens}"
+                        );
+                    }
+                    if max_total_tokens as u32 > max_supported_batch_total_tokens {
+                        return Err(WebServerError::NotEnoughMemory(max_total_tokens));
+                    }
+
+                    Ok(max_supported_batch_total_tokens)
+                }
+            }
+        };
+
+        let generation_health = Arc::new(AtomicBool::new(false));
+
+        match v3::ShardedClient::connect_uds(master_shard_uds_path.clone()).await {
+            Ok(mut sharded_client) => {
+                // server is running on v3
+                // Clear the cache; useful if the webserver rebooted
+                sharded_client
+                    .clear_cache(None)
+                    .await
+                    .map_err(WebServerError::Cache)?;
+                // Get info from the shard
+                let shard_info = sharded_client.info().await.map_err(WebServerError::Info)?;
+
+                // Warmup model
+                tracing::info!("Warming up model");
+                let max_batch_total_tokens = check_max_batch_total_tokens(
+                    sharded_client
+                        .warmup(
+                            max_input_tokens as u32,
+                            max_batch_prefill_tokens,
+                            max_total_tokens as u32,
+                            max_batch_size,
+                        )
+                        .await
+                        .map_err(WebServerError::Warmup)?,
+                )?;
+
+                let health_ext =
+                    HealthCheck::new(Arc::new(sharded_client.clone()), generation_health.clone());
+                let scheduler = Arc::new(SchedulerV3::new(
+                    sharded_client,
+                    waiting_served_ratio,
+                    max_batch_prefill_tokens,
+                    max_batch_total_tokens,
+                    max_waiting_tokens,
+                    max_batch_size,
+                    shard_info.requires_padding,
+                    shard_info.window_size,
+                    shard_info.speculate,
+                    generation_health,
+                ));
+                tracing::info!("Using scheduler V3");
+
+                (scheduler, health_ext, shard_info, max_batch_total_tokens)
+            }
+            Err(_) => {
+                let mut sharded_client = v2::ShardedClient::connect_uds(master_shard_uds_path)
+                    .await
+                    .map_err(WebServerError::Connection)?;
+
+                // server is running on v2
+                // Clear the cache; useful if the webserver rebooted
+                sharded_client
+                    .clear_cache(None)
+                    .await
+                    .map_err(WebServerError::Cache)?;
+                // Get info from the shard
+                let shard_info = sharded_client.info().await.map_err(WebServerError::Info)?;
+
+                // Warmup model
+                tracing::info!("Warming up model");
+                let max_batch_total_tokens = check_max_batch_total_tokens(
+                    sharded_client
+                        .warmup(
+                            max_input_tokens as u32,
+                            max_batch_prefill_tokens,
+                            max_total_tokens as u32,
+                            max_batch_size,
+                        )
+                        .await
+                        .map_err(WebServerError::Warmup)?,
+                )?;
+
+                let health_ext =
+                    HealthCheck::new(Arc::new(sharded_client.clone()), generation_health.clone());
+                let scheduler = Arc::new(SchedulerV2::new(
+                    sharded_client,
+                    waiting_served_ratio,
+                    max_batch_prefill_tokens,
+                    max_batch_total_tokens,
+                    max_waiting_tokens,
+                    max_batch_size,
+                    shard_info.requires_padding,
+                    shard_info.window_size,
+                    shard_info.speculate,
+                    generation_health,
+                ));
+                tracing::info!("Using scheduler V2");
+
+                (scheduler, health_ext, shard_info, max_batch_total_tokens)
+            }
+        }
+    };
+    tracing::info!("Setting max batch total tokens to {max_batch_total_tokens}");
+
     let validation = Validation::new(
         validation_workers,
         tokenizer,
@@ -1477,25 +1614,15 @@ pub async fn run(
         max_best_of,
         max_stop_sequences,
         max_top_n_tokens,
-        max_input_length,
+        max_input_tokens,
         max_total_tokens,
         grammar_support,
     );
-    let generation_health = Arc::new(AtomicBool::new(false));
-    let health_ext = Health::new(client.clone(), generation_health.clone());
+
     let infer = Infer::new(
-        client,
+        scheduler,
         validation,
-        waiting_served_ratio,
-        max_batch_prefill_tokens,
-        max_batch_total_tokens,
-        max_waiting_tokens,
-        max_batch_size,
         max_concurrent_requests,
-        shard_info.requires_padding,
-        shard_info.window_size,
-        shard_info.speculate,
-        generation_health,
         tokenizer_config,
         processor_config,
     );
@@ -1514,7 +1641,7 @@ pub async fn run(
     // 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)
+        .map(|x| (max_input_tokens as f64 / 100.0) * (x + 1) as f64)
         .collect();
     // Generated tokens buckets
     let generated_tokens_matcher = Matcher::Full(String::from("tgi_request_generated_tokens"));
@@ -1568,7 +1695,7 @@ pub async fn run(
         max_concurrent_requests,
         max_best_of,
         max_stop_sequences,
-        max_input_length,
+        max_input_tokens,
         max_total_tokens,
         waiting_served_ratio,
         max_batch_total_tokens,
@@ -1664,6 +1791,8 @@ pub async fn run(
         .layer(OtelAxumLayer::default())
         .layer(cors_layer);
 
+    tracing::info!("Connected");
+
     if ngrok {
         #[cfg(feature = "ngrok")]
         {
@@ -1686,7 +1815,8 @@ pub async fn run(
         let listener = tokio::net::TcpListener::bind(&addr).await.unwrap();
         axum::serve(listener, app)
             .with_graceful_shutdown(shutdown_signal())
-            .await?;
+            .await
+            .map_err(|err| WebServerError::Axum(Box::new(err)))?;
     }
     Ok(())
 }
@@ -1719,17 +1849,6 @@ async fn shutdown_signal() {
     opentelemetry::global::shutdown_tracer_provider();
 }
 
-impl From<i32> for FinishReason {
-    fn from(finish_reason: i32) -> Self {
-        let finish_reason = text_generation_client::FinishReason::try_from(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 {
@@ -1762,3 +1881,19 @@ impl From<InferError> for Event {
             .unwrap()
     }
 }
+
+#[derive(Debug, Error)]
+pub enum WebServerError {
+    #[error("Unable to connect to the Python model shards: {0}")]
+    Connection(ClientError),
+    #[error("Unable to clear the Python model shards cache: {0}")]
+    Cache(ClientError),
+    #[error("Unable to get the Python model shards info: {0}")]
+    Info(ClientError),
+    #[error("Unable to warmup the Python model shards: {0}")]
+    Warmup(ClientError),
+    #[error("Not enough memory to handle `max_total_tokens={0}`")]
+    NotEnoughMemory(usize),
+    #[error("Axum error: {0}")]
+    Axum(#[from] axum::BoxError),
+}

router/src/validation.rs

-use crate::config::Config;
 /// Payload validation logic
+use crate::config::Config;
 use crate::validation::ValidationError::{BestOfSampling, BestOfSeed, EmptyInput};
 use crate::{GenerateParameters, GenerateRequest, GrammarType};
+use base64::{engine::general_purpose::STANDARD, Engine};
+use image::{io::Reader as ImageReader, ImageFormat};
 use jsonschema::{Draft, JSONSchema};
 use rand::{thread_rng, Rng};
 use serde_json::Value;
 use std::io::Cursor;
-use text_generation_client::{
-    Chunk, GrammarType as ProtoGrammarType, Image, InputChunk, NextTokenChooserParameters,
-    StoppingCriteriaParameters,
-};
+use text_generation_client::{Chunk, Image, InputChunk};
 use thiserror::Error;
 use tokenizers::tokenizer::Tokenizer;
-// use tokenizers::TruncationDirection;
-use base64::{engine::general_purpose::STANDARD, Engine};
-use image::{io::Reader as ImageReader, ImageFormat};
 use tokio::sync::mpsc;
 use tokio::sync::oneshot;
 use tracing::{instrument, Span};
@@ -173,10 +169,6 @@ impl Validation {
             // Validate MaxNewTokens
             if (input_length as u32 + max_new_tokens) > self.max_total_tokens as u32 {
                 input_length = input_length.saturating_sub(max_new_tokens as usize);
-                // return Err(ValidationError::MaxNewTokens(
-                //     self.max_total_tokens - self.max_input_length,
-                //     max_new_tokens,
-                // ));
             }
 
             Ok((
@@ -327,13 +319,13 @@ impl Validation {
         // compiler and use that to build the FSM here.
 
         // Validate grammar and unpack the grammar and type for the proto message
-        let (grammar, grammar_type) = match grammar {
+        let grammar = match grammar {
             Some(grammar) => {
                 // Ensure that grammar is not set if it's not supported
                 if self.disable_grammar_support {
                     return Err(ValidationError::Grammar);
                 }
-                match grammar {
+                let valid_grammar = match grammar {
                     GrammarType::Json(json) => {
                         let json = match json {
                             // if value is a string, we need to parse it again to make sure its
@@ -350,20 +342,20 @@ impl Validation {
                             .compile(&json)
                             .map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?;
 
-                        (
-                            // Serialize json to string
+                        // Serialize json to string
+                        ValidGrammar::Json(
                             serde_json::to_string(&json)
                                 .map_err(|e| ValidationError::InvalidGrammar(e.to_string()))?,
-                            ProtoGrammarType::Json.into(),
                         )
                     }
-                    GrammarType::Regex(regex) => (regex, ProtoGrammarType::Regex.into()),
-                }
+                    GrammarType::Regex(regex) => ValidGrammar::Regex(regex),
+                };
+                Some(valid_grammar)
             }
-            None => (String::new(), ProtoGrammarType::None.into()),
+            None => None,
         };
 
-        let parameters = NextTokenChooserParameters {
+        let parameters = ValidParameters {
             temperature,
             repetition_penalty,
             frequency_penalty,
@@ -374,9 +366,8 @@ impl Validation {
             seed,
             watermark,
             grammar,
-            grammar_type,
         };
-        let stopping_parameters = StoppingCriteriaParameters {
+        let stopping_parameters = ValidStoppingParameters {
             max_new_tokens,
             stop_sequences,
             ignore_eos_token: false,
@@ -458,6 +449,7 @@ fn format_from_mimetype(mimetype: &str) -> Option<ImageFormat> {
         _ => None,
     }
 }
+
 fn format_to_mimetype(format: ImageFormat) -> String {
     match format {
         ImageFormat::Png => "image/png",
@@ -636,14 +628,55 @@ type TokenizerRequest = (
     Span,
 );
 
+#[derive(Debug, Clone)]
+pub(crate) enum ValidGrammar {
+    Json(String),
+    Regex(String),
+}
+
+#[derive(Debug, Clone)]
+pub(crate) struct ValidParameters {
+    /// / exponential scaling output probability distribution
+    pub temperature: f32,
+    /// / restricting to the k highest probability elements
+    pub top_k: u32,
+    /// / restricting to top tokens summing to prob_cut_off <= prob_cut_off
+    pub top_p: f32,
+    /// / restricting to top tokens summing to prob_cut_off <= prob_cut_off
+    pub typical_p: f32,
+    /// / apply sampling on the logits
+    pub do_sample: bool,
+    /// / random seed for sampling
+    pub seed: u64,
+    /// / repetition penalty
+    pub repetition_penalty: f32,
+    /// / frequency penalty
+    pub frequency_penalty: f32,
+    /// / token watermarking using "A Watermark for Large Language Models"
+    pub watermark: bool,
+    /// / grammar (applied if not empty)
+    pub grammar: Option<ValidGrammar>,
+}
+
+#[derive(Debug, Clone)]
+pub(crate) struct ValidStoppingParameters {
+    /// / Maximum number of generated tokens
+    pub max_new_tokens: u32,
+    /// / Optional stopping sequences
+    pub stop_sequences: Vec<String>,
+    /// / Ignore end of sequence token
+    /// / used for benchmarking
+    pub ignore_eos_token: bool,
+}
+
 #[derive(Debug, Clone)]
 pub(crate) struct ValidGenerateRequest {
     pub inputs: Vec<InputChunk>,
     pub input_length: u32,
     pub truncate: u32,
     pub decoder_input_details: bool,
-    pub parameters: NextTokenChooserParameters,
-    pub stopping_parameters: StoppingCriteriaParameters,
+    pub parameters: ValidParameters,
+    pub stopping_parameters: ValidStoppingParameters,
     pub top_n_tokens: u32,
 }
 

server/Makefile

@@ -12,8 +12,8 @@ gen-server:
 	# Compile protos
 	pip install grpcio-tools==1.51.1 mypy-protobuf==3.4.0 'types-protobuf>=3.20.4' --no-cache-dir
 	mkdir text_generation_server/pb || true
-	python -m grpc_tools.protoc -I../proto --python_out=text_generation_server/pb \
-		--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/generate.proto
+	python -m grpc_tools.protoc -I../proto/v3 --python_out=text_generation_server/pb \
+		--grpc_python_out=text_generation_server/pb --mypy_out=text_generation_server/pb ../proto/v3/generate.proto
 	find text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
 	touch text_generation_server/pb/__init__.py