feat: add payload limit

backends/trtllm/src/main.rs

@@ -62,6 +62,8 @@ struct Args {
     executor_worker: PathBuf,
     #[clap(default_value = "on", long, env)]
     usage_stats: usage_stats::UsageStatsLevel,
+    #[clap(default_value = "2000000", long, env)]
+    payload_limit: usize,
 }
 
 async fn get_tokenizer(
@@ -217,6 +219,7 @@ async fn main() -> Result<(), TensorRtLlmBackendError> {
         auth_token,
         executor_worker,
         usage_stats,
+        payload_limit,
     } = args;
 
     // Launch Tokio runtime
@@ -287,6 +290,7 @@ async fn main() -> Result<(), TensorRtLlmBackendError> {
         tokenizer_name,
         tokenizer_config_path,
         revision,
+        false,
         hostname,
         port,
         cors_allow_origin,
@@ -296,6 +300,7 @@ async fn main() -> Result<(), TensorRtLlmBackendError> {
         true,
         max_client_batch_size,
         usage_stats,
+        payload_limit,
     )
     .await?;
     Ok(())

backends/v2/src/main.rs

@@ -70,6 +70,8 @@ struct Args {
     max_client_batch_size: usize,
     #[clap(default_value = "on", long, env)]
     usage_stats: usage_stats::UsageStatsLevel,
+    #[clap(default_value = "2000000", long, env)]
+    payload_limit: usize,
 }
 
 #[derive(Debug, Subcommand)]
@@ -114,6 +116,7 @@ async fn main() -> Result<(), RouterError> {
         disable_grammar_support,
         max_client_batch_size,
         usage_stats,
+        payload_limit,
     } = args;
 
     if let Some(Commands::PrintSchema) = command {
@@ -194,6 +197,7 @@ async fn main() -> Result<(), RouterError> {
         disable_grammar_support,
         max_client_batch_size,
         usage_stats,
+        payload_limit,
     )
     .await?;
     Ok(())

backends/v3/src/main.rs

@@ -70,6 +70,8 @@ struct Args {
     max_client_batch_size: usize,
     #[clap(default_value = "on", long, env)]
     usage_stats: usage_stats::UsageStatsLevel,
+    #[clap(default_value = "2000000", long, env)]
+    payload_limit: usize,
 }
 
 #[derive(Debug, Subcommand)]
@@ -114,6 +116,7 @@ async fn main() -> Result<(), RouterError> {
         disable_grammar_support,
         max_client_batch_size,
         usage_stats,
+        payload_limit,
     } = args;
 
     if let Some(Commands::PrintSchema) = command {
@@ -210,6 +213,7 @@ async fn main() -> Result<(), RouterError> {
         disable_grammar_support,
         max_client_batch_size,
         usage_stats,
+        payload_limit,
     )
     .await?;
     Ok(())

launcher/src/main.rs

@@ -687,6 +687,12 @@ struct Args {
     /// Defaul is on.
     #[clap(default_value = "on", long, env)]
     usage_stats: UsageStatsLevel,
+
+    /// Payload size limit in bytes
+    ///
+    /// Default is 2MB
+    #[clap(default_value = "2000000", long, env)]
+    payload_limit: usize,
 }
 
 #[derive(Debug)]
@@ -1474,6 +1480,8 @@ fn spawn_webserver(
         format!("{}-0", args.shard_uds_path),
         "--tokenizer-name".to_string(),
         args.model_id,
+        "--payload-limit".to_string(),
+        args.payload_limit.to_string(),
     ];
     if let Some(max_input_tokens) = max_input_tokens {
         router_args.extend_from_slice(&[

router/src/server.rs

@@ -31,7 +31,7 @@ use crate::{
 use crate::{FunctionDefinition, HubPreprocessorConfig, ToolCall, ToolChoice, ToolType};
 use crate::{ModelInfo, ModelsInfo};
 use async_stream::__private::AsyncStream;
-use axum::extract::Extension;
+use axum::extract::{DefaultBodyLimit, Extension};
 use axum::http::{HeaderMap, HeaderValue, Method, StatusCode};
 use axum::response::sse::{Event, KeepAlive, Sse};
 use axum::response::{IntoResponse, Response};
@@ -1673,6 +1673,7 @@ pub async fn run(
     disable_grammar_support: bool,
     max_client_batch_size: usize,
     usage_stats_level: usage_stats::UsageStatsLevel,
+    payload_limit: usize,
 ) -> Result<(), WebServerError> {
     // CORS allowed origins
     // map to go inside the option and then map to parse from String to HeaderValue
@@ -1926,6 +1927,7 @@ pub async fn run(
         model_info,
         compat_return_full_text,
         allow_origin,
+        payload_limit,
     )
     .await;
 
@@ -1985,6 +1987,7 @@ async fn start(
     model_info: HubModelInfo,
     compat_return_full_text: bool,
     allow_origin: Option<AllowOrigin>,
+    payload_limit: usize,
 ) -> Result<(), WebServerError> {
     // Determine the server port based on the feature and environment variable.
     let port = if cfg!(feature = "google") {
@@ -2382,6 +2385,7 @@ async fn start(
         .layer(Extension(compute_type))
         .layer(Extension(prom_handle.clone()))
         .layer(OtelAxumLayer::default())
+        .layer(DefaultBodyLimit::max(payload_limit))
         .layer(cors_layer);
 
     tracing::info!("Connected");

server/text_generation_server/models/flash_causal_lm.py

@@ -962,9 +962,9 @@ class FlashCausalLMBatch(Batch):
         self.input_lengths_tensor = torch.tensor(
             self.input_lengths, dtype=torch.int32, device=device
         )
-        self.cu_seqlen_prefill = torch.nn.functional.pad(
-            torch.cumsum(self.input_lengths_tensor, dim=0), (1, 0)
-        ).to(torch.int32)
+        cu_seqlen_prefill = self.input_lengths_tensor.new_zeros(len(self) + 1)
+        torch.cumsum(self.input_lengths_tensor, out=cu_seqlen_prefill[1:], dim=0)
+        self.cu_seqlen_prefill = cu_seqlen_prefill.to(torch.int32)
         self.cache_lengths_tensor = torch.tensor(
             self.cache_lengths, dtype=torch.int32, device=device
         )
@@ -2020,9 +2020,8 @@ class FlashCausalLM(Model):
 
         # For each member of the batch
         # Cumulative length
-        cu_accepted_ids = torch.nn.functional.pad(
-            torch.cumsum(accepted_ids, dim=0), (1, 0)
-        )
+        cu_accepted_ids = accepted_ids.new_zeros(accepted_ids.shape[0] + 1)
+        torch.cumsum(accepted_ids, dim=0, out=cu_accepted_ids[1:])
         cumulative_length = 0
         for i, (
             request,

server/text_generation_server/models/metadata_kernels.py

@@ -66,8 +66,9 @@ def block_tables_to_ragged(
     )
 
     if has_triton():
-        cu_seqlen = torch.nn.functional.pad(
-            torch.cumsum(input_lengths_tensor + cache_lengths_tensor, dim=0), (1, 0)
+        cu_seqlen = input_lengths_tensor.new_zeros(input_lengths_tensor.shape[0] + 1)
+        torch.cumsum(
+            input_lengths_tensor + cache_lengths_tensor, out=cu_seqlen[1:], dim=0
         )
 
         def grid(meta):