feat(backend): wrap Arc tokenizer to avoid duplicating

backends/llamacpp/src/backend.rs

@@ -53,10 +53,10 @@ pub(crate) struct GenerationContext {
     pub(crate) sampling_params: SamplingParams,
 }
 
-pub(crate) struct InferContext {
+pub(crate) struct InferContext<'a> {
     pub(crate) start: Instant,
     pub(crate) stream: UnboundedSender<InferResult>,
-    pub(crate) tokenizer: Tokenizer,
+    pub(crate) tokenizer: &'a Tokenizer,
     pub(crate) generation: GenerationContext,
 }
 
@@ -69,11 +69,6 @@ pub enum LlamaCppBackendError {
     ModelInitializationFailed(PathBuf, String),
 }
 
-// pub struct LlamaCppBackend {
-//     backlog: Sender<(GenerationContext, UnboundedSender<InferResult>)>,
-//     _scheduler_handle: JoinHandle<()>,
-// }
-
 struct LlamaCppWorker {
     sender: Sender<(GenerationContext, UnboundedSender<InferResult>)>,
     handle: JoinHandle<()>,
@@ -95,7 +90,7 @@ impl LlamaCppBackend {
 
     pub fn new<P: AsRef<Path>>(
         model_path: P,
-        tokenizer: Tokenizer,
+        tokenizer: Arc<Tokenizer>,
         num_cores_per_instance: u16,
     ) -> Result<Self, LlamaCppBackendError> {
         let shared_path = Arc::new(model_path);
@@ -110,7 +105,7 @@ impl LlamaCppBackend {
             0 => {
                 let worker = Self::allocate_worker(path)?;
                 let (sender, receiver) = channel();
-                let handle = spawn(|| scheduler_loop(worker, tokenizer, receiver));
+                let handle = spawn(move || scheduler_loop(worker, tokenizer, receiver));
                 LlamaCppBackend::Single(LlamaCppWorker { sender, handle })
             }
             _ => panic!("No supported yet"),
@@ -186,7 +181,7 @@ fn llama_generate_callback(
 
 fn scheduler_loop(
     mut backend: UniquePtr<LlamaCppWorkerFrontend>,
-    tokenizer: Tokenizer,
+    tokenizer: Arc<Tokenizer>,
     backlog: Receiver<(GenerationContext, UnboundedSender<InferResult>)>,
 ) {
     // This loop will mostly decode single token at every step, so no need to rely on parallelism
@@ -195,37 +190,34 @@ fn scheduler_loop(
     loop {
         if let Ok((generation, stream)) = backlog.recv() {
             let start = Instant::now();
-            let tokenizer = tokenizer.clone();
             let generation_params = generation.generation_params; // copy
             let sampling_params = generation.sampling_params; // copy
             let input_tokens = Arc::clone(&generation.input_tokens);
 
             // Creating the whole InferContext and pushing it to the heap
-            {
-                let ctx = Box::new(InferContext {
-                    start,
-                    stream,
-                    tokenizer,
-                    generation,
-                });
+            let ctx = Box::new(InferContext {
+                start,
+                stream,
+                tokenizer: &tokenizer,
+                generation,
+            });
 
-                // We leak the box to avoid it being freed after the first callback call
-                // when going out of scope
-                unsafe {
-                    let boxed_ctx = Box::into_raw(ctx);
-                    if let Err(e) = backend.pin_mut().stream(
-                        &input_tokens,
-                        generation_params,
-                        &sampling_params,
-                        boxed_ctx,
-                        llama_generate_callback,
-                    ) {
-                        error!("Error while decoding tokens... {}", e.what());
-                    }
-
-                    // Make sure we re-keep track of the OpaqueStream box
-                    let _ = Box::from_raw(boxed_ctx);
+            // We leak the box to avoid it being freed after the first callback call
+            // when going out of scope
+            unsafe {
+                let boxed_ctx = Box::into_raw(ctx);
+                if let Err(e) = backend.pin_mut().stream(
+                    &input_tokens,
+                    generation_params,
+                    &sampling_params,
+                    boxed_ctx,
+                    llama_generate_callback,
+                ) {
+                    error!("Error while decoding tokens... {}", e.what());
                 }
+
+                // Make sure we re-keep track of the OpaqueStream box
+                let _ = Box::from_raw(boxed_ctx);
             }
         } else {
             info!("IPC channel is closed, exiting the scheduler loop");

backends/llamacpp/src/lib.rs

@@ -33,7 +33,7 @@ mod ffi {
     }
 
     extern "Rust" {
-        type InferContext;
+        type InferContext<'a>;
     }
 
     unsafe extern "C++" {

backends/llamacpp/src/main.rs

@@ -1,5 +1,6 @@
 use clap::{Parser, Subcommand};
 use std::path::PathBuf;
+use std::sync::Arc;
 use text_generation_backend_llamacpp::backend::{LlamaCppBackend, LlamaCppBackendError};
 use text_generation_router::server::ApiDoc;
 use text_generation_router::{server, usage_stats};
@@ -162,8 +163,10 @@ async fn main() -> Result<(), RouterError> {
         user_agent: Default::default(),
         auth_token,
     };
-    let tokenizer = tokenizers::Tokenizer::from_pretrained(tokenizer_name.clone(), Some(options))
-        .expect("Failed to retrieve tokenizer");
+    let tokenizer = Arc::new(
+        tokenizers::Tokenizer::from_pretrained(tokenizer_name.clone(), Some(options))
+            .expect("Failed to retrieve tokenizer"),
+    );
     let backend = LlamaCppBackend::new(gguf_path, tokenizer, num_cores_per_instance.unwrap_or(0))?;
 
     // Run server