Remove graph_pool as member of VllmBackend and argument to CUDAGraphWrapper (#23385)

Signed-off-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: ProExpertProg <11367180+ProExpertProg@users.noreply.github.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>

vllm/compilation/backends.py

@@ -294,13 +294,12 @@ class PiecewiseCompileInterpreter(torch.fx.Interpreter):
 
     def __init__(self, module: torch.fx.GraphModule,
                  compile_submod_names: list[str], vllm_config: VllmConfig,
-                 graph_pool, vllm_backend: "VllmBackend"):
+                 vllm_backend: "VllmBackend"):
         super().__init__(module)
         from torch._guards import detect_fake_mode
         self.fake_mode = detect_fake_mode()
         self.compile_submod_names = compile_submod_names
         self.compilation_config = vllm_config.compilation_config
-        self.graph_pool = graph_pool
         self.vllm_config = vllm_config
         self.vllm_backend = vllm_backend
         # When True, it annoyingly dumps the torch.fx.Graph on errors.
@@ -359,7 +358,6 @@ class PiecewiseCompileInterpreter(torch.fx.Interpreter):
                     runnable=piecewise_backend,
                     vllm_config=self.vllm_config,
                     runtime_mode=CUDAGraphMode.PIECEWISE,
-                    graph_pool=self.graph_pool,
                     cudagraph_options=CUDAGraphOptions(
                         debug_log_enable=piecewise_backend.is_first_graph,
                         gc_disable=not piecewise_backend.is_first_graph,
@@ -405,7 +403,6 @@ class VllmBackend:
 
     vllm_config: VllmConfig
     compilation_config: CompilationConfig
-    graph_pool: Any
     _called: bool = False
     # the graph we compiled
     graph: fx.GraphModule
@@ -433,13 +430,6 @@ class VllmBackend:
         # them, e.g. backbone (default), eagle_head, etc.
         self.prefix = prefix or model_tag
 
-        global_graph_pool = current_platform.get_global_graph_pool()
-
-        # TODO: in the future, if we want to use multiple
-        # streams, it might not be safe to share a global pool.
-        # only investigate this when we use multiple streams
-        self.graph_pool = global_graph_pool
-
         # Passes to run on the graph post-grad.
         self.post_grad_pass_manager = PostGradPassManager()
 
@@ -586,7 +576,7 @@ class VllmBackend:
         # propagate the split graph to the piecewise backend,
         # compile submodules with symbolic shapes
         PiecewiseCompileInterpreter(self.split_gm, submod_names_to_compile,
-                                    self.vllm_config, self.graph_pool,
+                                    self.vllm_config,
                                     self).run(*example_inputs)
 
         graph_path = os.path.join(local_cache_dir, "computation_graph.py")

vllm/compilation/base_static_graph.py

@@ -13,7 +13,7 @@ class AbstractStaticGraphWrapper(Protocol):
     """
 
     def __init__(self, runnable: Callable, vllm_config: VllmConfig,
-                 runtime_mode: CUDAGraphMode, graph_pool: Any, **kwargs):
+                 runtime_mode: CUDAGraphMode, **kwargs):
         """
         Initializes the StaticGraphWrapper class with graph capturing and
         execution-related configurations.
@@ -25,9 +25,6 @@ class AbstractStaticGraphWrapper(Protocol):
                 graph runtime. See CUDAGraphMode in vllm/config.py.
                 Note that only the subset enum `NONE`, `PIECEWISE` and `FULL`
                 are used as concrete runtime mode for cudagraph dispatching.
-            graph_pool (Any):
-                Graph memory pool handle, e.g.,
-                    `torch.cuda.graph_pool_handle()`.
         Keyword Args:
             kwargs: Additional keyword arguments for platform-specific
                 configurations.

vllm/compilation/cuda_graph.py

@@ -67,11 +67,9 @@ class CUDAGraphWrapper:
                  runnable: Callable,
                  vllm_config: VllmConfig,
                  runtime_mode: CUDAGraphMode,
-                 graph_pool: Any = None,
                  cudagraph_options: Optional[CUDAGraphOptions] = None):
         self.runnable = runnable
         self.vllm_config = vllm_config
-        self.graph_pool = graph_pool
         self.runtime_mode = runtime_mode
         self.compilation_config = vllm_config.compilation_config
 
@@ -81,8 +79,10 @@ class CUDAGraphWrapper:
         # assert runtime_mode is not NONE(no cudagraph), otherwise, we don't
         # need to initialize a CUDAGraphWrapper.
         assert self.runtime_mode != CUDAGraphMode.NONE
-        if self.graph_pool is None:
-            self.graph_pool = current_platform.get_global_graph_pool()
+        # TODO: in the future, if we want to use multiple
+        # streams, it might not be safe to share a global pool.
+        # only investigate this when we use multiple streams
+        self.graph_pool = current_platform.get_global_graph_pool()
 
         if cudagraph_options is None:
             cudagraph_options = CUDAGraphOptions()