[Feature] Initial support for multi-LoRA serving (#1307)

python/sglang/srt/server_args.py

@@ -101,6 +101,10 @@ class ServerArgs:
     enable_mla: bool = False
     triton_attention_reduce_in_fp32: bool = False
 
+    # LoRA
+    lora_paths: Optional[List[str]] = None
+    max_loras_per_batch: int = 8
+
     def __post_init__(self):
         # Set missing default values
         if self.tokenizer_path is None:
@@ -522,6 +526,21 @@ class ServerArgs:
             help="Turn on memory efficient weight loading with quantization (quantize per layer during loading).",
         )
 
+        # LoRA options
+        parser.add_argument(
+            "--lora-paths",
+            type=str,
+            nargs="*",
+            default=None,
+            help="The list of LoRA adapters.",
+        )
+        parser.add_argument(
+            "--max-loras-per-batch",
+            type=int,
+            default=8,
+            help="Maximum number of adapters for a running batch, include base-only request",
+        )
+
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace):
         args.tp_size = args.tensor_parallel_size
@@ -539,6 +558,12 @@ class ServerArgs:
         assert not (
             self.dp_size > 1 and self.node_rank is not None
         ), "multi-node data parallel is not supported"
+        assert (
+            self.max_loras_per_batch > 0
+            # FIXME
+            and (self.lora_paths is None or self.disable_cuda_graph)
+            and (self.lora_paths is None or self.disable_radix_cache)
+        ), "compatibility of lora and cuda graph and radix attention is in progress"
 
 
 def prepare_server_args(argv: List[str]) -> ServerArgs: