fix: check max_num_batched_tokenz if max_model_len not set

src/engine_args.py

@@ -186,6 +186,24 @@ def get_speculative_config():
 
     return None
 
+def _resolve_max_model_len(model, trust_remote_code=False, revision=None):
+    """Resolve max_model_len from the model's HuggingFace config."""
+    try:
+        from transformers import AutoConfig
+        config = AutoConfig.from_pretrained(
+            model,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+        )
+        for attr in ('max_position_embeddings', 'n_positions', 'max_seq_len', 'seq_length'):
+            val = getattr(config, attr, None)
+            if val is not None:
+                logging.info(f"Resolved max_model_len={val} from model config ({attr})")
+                return val
+    except Exception as e:
+        logging.warning(f"Could not resolve max_model_len from model config: {e}")
+    return None
+
 limit_mm_env = os.getenv('LIMIT_MM_PER_PROMPT')
 if limit_mm_env is not None:
     DEFAULT_ARGS["limit_mm_per_prompt"] = convert_limit_mm_per_prompt(limit_mm_env)
@@ -268,11 +286,19 @@ def get_engine_args():
     #     os.environ["VLLM_ATTENTION_BACKEND"] = "FLASHINFER"
     #     logging.info("Using FLASHINFER for gemma-2 model.")
     
-    # When max_num_batched_tokens is None (env var was 0), set to max_model_len
-    # to preserve "unlimited" behavior. vLLM defaults None to 2048.
-    if args.get("max_num_batched_tokens") is None and args.get("max_model_len") is not None:
-        args["max_num_batched_tokens"] = args["max_model_len"]
-        logging.info(f"Setting max_num_batched_tokens to max_model_len ({args['max_model_len']}) for unlimited batching.")
+    # Set max_num_batched_tokens to max_model_len for unlimited batching.
+    # vLLM defaults max_num_batched_tokens to 2048 when None, which is too low.
+    if args.get("max_num_batched_tokens") is None:
+        max_model_len = args.get("max_model_len")
+        if max_model_len is None:
+            max_model_len = _resolve_max_model_len(
+                args.get("model"),
+                trust_remote_code=args.get("trust_remote_code", False),
+                revision=args.get("revision"),
+            )
+        if max_model_len is not None:
+            args["max_num_batched_tokens"] = max_model_len
+            logging.info(f"Setting max_num_batched_tokens to {max_model_len}")
     
     # VLLM_ATTENTION_BACKEND is deprecated, migrate to attention_backend
     if os.getenv('VLLM_ATTENTION_BACKEND'):