From 10cf1e482c4d4b0304b099f8a219ddfcecc4449b Mon Sep 17 00:00:00 2001
From: yewentao256 <zhyanwentao@126.com>
Date: Mon, 22 Dec 2025 21:12:46 +0000
Subject: [PATCH] reset

---
 vllm/compilation/cuda_graph.py | 86 +++++-----------------------------
 1 file changed, 13 insertions(+), 73 deletions(-)

diff --git a/vllm/compilation/cuda_graph.py b/vllm/compilation/cuda_graph.py
index d82d8f2820c..4c1cfbfa848 100644
--- a/vllm/compilation/cuda_graph.py
+++ b/vllm/compilation/cuda_graph.py
@@ -126,11 +126,6 @@ class CUDAGraphEntry:
     # during capture, and check if they are the same during replay
     input_addresses: list[int] | None = None
 
-    # CUDA graphs will replay using the capture-time input buffers;
-    # keeping references here ensures they stay alive and enables
-    # optionally copying runtime inputs into these buffers before replay.
-    input_tensors: list[torch.Tensor] | None = None
-
 
 @dataclasses.dataclass
 class CUDAGraphOptions:
@@ -207,43 +202,6 @@ class CUDAGraphWrapper:
         # in case we need to access the original runnable.
         return self.runnable
 
-    @staticmethod
-    def _tensor_inputs(args) -> list[torch.Tensor]:
-        # preserve the same ordering as the original address debug check.
-        return [x for x in args if isinstance(x, torch.Tensor)]
-
-    @staticmethod
-    def _copy_runtime_inputs_into_captured_inputs(
-        entry: CUDAGraphEntry, runtime_inputs: list[torch.Tensor]
-    ) -> None:
-        """Copy runtime inputs into capture-time inputs so replay sees new values"""
-        captured_inputs = entry.input_tensors
-        if captured_inputs is None:
-            return
-        if len(runtime_inputs) != len(captured_inputs):
-            raise RuntimeError(
-                "CUDAGraph replay input tensor count mismatch. "
-                f"Expected {len(captured_inputs)}, got {len(runtime_inputs)}."
-            )
-
-        for i, (captured, runtime) in enumerate(zip(captured_inputs, runtime_inputs)):
-            if runtime.data_ptr() == captured.data_ptr():
-                continue
-            if (
-                runtime.device != captured.device
-                or runtime.dtype != captured.dtype
-                or runtime.shape != captured.shape
-            ):
-                raise RuntimeError(
-                    "CUDAGraph replay input tensor mismatch at index "
-                    f"{i}: captured(device={captured.device}, dtype={captured.dtype}, "
-                    f"shape={tuple(captured.shape)}), runtime(device={runtime.device}, "
-                    f"dtype={runtime.dtype}, shape={tuple(runtime.shape)}). "
-                    "Disable CUDA graphs (e.g. --enforce-eager / cudagraph_mode=NONE) "
-                    "or ensure inputs are passed from stable, preallocated buffers."
-                )
-            captured.copy_(runtime, non_blocking=True)
-
     def __call__(self, *args, **kwargs):
         forward_context = get_forward_context()
         batch_descriptor = forward_context.batch_descriptor
@@ -283,9 +241,9 @@ class CUDAGraphWrapper:
             # validate that cudagraph capturing is legal at this point.
             validate_cudagraph_capturing_enabled()
 
-            tensor_inputs = self._tensor_inputs(args)
-            entry.input_tensors = tensor_inputs
-            entry.input_addresses = [x.data_ptr() for x in tensor_inputs]
+            input_addresses = [
+                x.data_ptr() for x in args if isinstance(x, torch.Tensor)
+            ]
             cudagraph = torch.cuda.CUDAGraph()
 
             with ExitStack() as stack:
@@ -327,34 +285,16 @@ class CUDAGraphWrapper:
             # the weak ref of the output, so that pytorch can correctly
             # manage the memory during cuda graph capture
             return output
-
-        # if runtime input tensor addresses differ from the capture-time addresses,
-        # CUDA graph replay would read stale values.
-        # we need tocopy runtime inputs into the capture-time buffers before replaying.
-        copy_inputs = self.compilation_config.cudagraph_copy_inputs
-        if self.is_debugging_mode or copy_inputs:
-            runtime_inputs = self._tensor_inputs(args)
-            new_input_addresses = [x.data_ptr() for x in runtime_inputs]
-            if new_input_addresses != entry.input_addresses:
-                if copy_inputs:
-                    self._copy_runtime_inputs_into_captured_inputs(
-                        entry, runtime_inputs
-                    )
-                    if self.is_debugging_mode:
-                        logger.warning_once(
-                            "CUDAGraph replay saw different input addresses, s"
-                            "copied runtime inputs into captured input buffers.",
-                            scope="local",
-                        )
-                else:
-                    assert new_input_addresses == entry.input_addresses, (
-                        f"Input addresses for cudagraphs are different "
-                        f"during replay. Expected {entry.input_addresses}, "
-                        f"got {new_input_addresses}. "
-                        "Set compilation_config.cudagraph_copy_inputs=True to copy "
-                        "runtime inputs into captured buffers before replay, or "
-                        "disable CUDA graphs."
-                    )
+        if self.is_debugging_mode:
+            # check if the input addresses are the same
+            new_input_addresses = [
+                x.data_ptr() for x in args if isinstance(x, torch.Tensor)
+            ]
+            assert new_input_addresses == entry.input_addresses, (
+                f"Input addresses for cudagraphs are different "
+                f"during replay. Expected {entry.input_addresses}, "
+                f"got {new_input_addresses}"
+            )
 
         entry.cudagraph.replay()
         return entry.output