[Minor] Add more type annotations (#1237)

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -17,6 +17,7 @@ limitations under the License.
 
 import bisect
 from contextlib import contextmanager
+from typing import Callable, List
 
 import torch
 from flashinfer import BatchDecodeWithPagedKVCacheWrapper
@@ -53,12 +54,12 @@ def _to_torch(model: torch.nn.Module, reverse: bool = False):
 
 @contextmanager
 def patch_model(
-    model: torch.nn.Module, use_compile: bool, tp_group: "GroupCoordinator"
+    model: torch.nn.Module, enable_compile: bool, tp_group: "GroupCoordinator"
 ):
     backup_ca_comm = None
 
     try:
-        if use_compile:
+        if enable_compile:
             _to_torch(model)
             monkey_patch_vllm_all_gather()
             backup_ca_comm = tp_group.ca_comm
@@ -67,7 +68,7 @@ def patch_model(
         else:
             yield model.forward
     finally:
-        if use_compile:
+        if enable_compile:
             _to_torch(model, reverse=True)
             monkey_patch_vllm_all_gather(reverse=True)
             tp_group.ca_comm = backup_ca_comm
@@ -88,7 +89,7 @@ def set_torch_compile_config():
 class CudaGraphRunner:
     def __init__(
         self,
-        model_runner,
+        model_runner: "ModelRunner",
         max_batch_size_to_capture: int,
         use_torch_compile: bool,
         disable_padding: bool,
@@ -154,13 +155,13 @@ class CudaGraphRunner:
         if use_torch_compile:
             set_torch_compile_config()
 
-    def can_run(self, batch_size):
+    def can_run(self, batch_size: int):
         if self.disable_padding:
             return batch_size in self.graphs
         else:
             return batch_size <= self.max_bs
 
-    def capture(self, batch_size_list):
+    def capture(self, batch_size_list: List[int]):
         self.batch_size_list = batch_size_list
         with graph_capture() as graph_capture_context:
             self.stream = graph_capture_context.stream
@@ -181,7 +182,7 @@ class CudaGraphRunner:
                     self.output_buffers[bs] = output_buffers
                     self.flashinfer_handlers[bs] = flashinfer_handler
 
-    def capture_one_batch_size(self, bs, forward):
+    def capture_one_batch_size(self, bs: int, forward: Callable):
         graph = torch.cuda.CUDAGraph()
         stream = self.stream