PR vllm-project#26956: Squashed commit of the following:

commit ad717d4
Author: Richard Zou <zou3519@gmail.com>
Date:   Wed Oct 15 16:29:49 2025 -0700

    [BugFix] Work around graph partition x torch.compile cache issue

    In PyTorch 2.9, torch.compile has a bug where the graph
    partition is not taken into account during caching.
    Because vLLM's Mode.VLLM_COMPILE is the only mode that uses
    Inductor graph partition, and VLLM_COMPILE implies there
    is a PostGradPassManager, we put the list of operators to graph
    partition into the PostGradPassManager's uuid (which
    then gets incorporated into Inductor's FX graph cache key).
    Remove this hack whenever torch.compile fixes it.

    Signed-off-by: Richard Zou <zou3519@gmail.com>

Signed-off-by: ProExpertProg <lgovedic@redhat.com>

Author: ProExpertProg

tests/compile/piecewise/test_toy_llama.py

@@ -337,9 +337,8 @@ def run_model(llama_config, compile_config: CompilationConfig) -> torch.Tensor:
 def test_toy_llama(
     backend: str, use_inductor_graph_partition: bool, monkeypatch, tmp_path
 ):
-    # We disable the vLLM compile cache into a new tmp dir for 2 reasons:
+    # We disable the vLLM compile cache into a new tmp dir for 1 reason:
     # 1. To make sure we can properly track the number of Inductor compilations.
-    # 2. Inductor partitioning does not play nicely with Autograd cache (below)
     monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
 
     if use_inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
@@ -369,15 +368,6 @@ def test_toy_llama(
         cudagraph_capture_sizes=[1, 2],
     )
 
-    # FIXME(luka/boyuan): the graph from the previous test case
-    #  (no inductor partition) gets cached by AotAutograd so then the
-    #  compilation with inductor partitioning incorrectly loads an unpartitioned
-    #  graph and never partitions. I think this is a bug with custom inductor
-    #  partitioning but does not affect vLLM more generally as vLLM uses its own
-    #  cache (which takes inductor partitioning into account).
-    if use_inductor_graph_partition:
-        compile_config_no_split.inductor_compile_config["force_disable_caches"] = True
-
     compile_config_split = deepcopy(compile_config_no_split)
     compile_config_split.splitting_ops = ["silly::attention"]
 

vllm/compilation/pass_manager.py

@@ -110,6 +110,27 @@ def configure(self, config: VllmConfig):
         self.post_cleanup = PostCleanupPass(config)
         self.fix_functionalization = FixFunctionalizationPass(config)
 
+        # [HACK: Bug with Inductor graph partition and torch.compile cache]
+        # In PyTorch 2.9, torch.compile has a bug where the graph
+        # partition is not taken into account during caching.
+        # Because vLLM's Mode.VLLM_COMPILE is the only mode that uses
+        # Inductor graph partition, and VLLM_COMPILE implies there
+        # is a PostGradPassManager, we put the list of operators to graph
+        # partition into the PostGradPassManager's uuid (which
+        # then gets incorporated into Inductor's FX graph cache key).
+        # Remove this hack whenever torch.compile fixes it.
+
+        # This is the list of operators that vLLM asks Inductor to split.
+        self.inductor_splitting_ops = []
+        if (
+            config.compilation_config.use_inductor_graph_partition
+            and config.compilation_config.splitting_ops is not None
+        ):
+            # Sort them so we're not dependent on the ordering.
+            self.inductor_splitting_ops = sorted(
+                config.compilation_config.splitting_ops
+            )
+
     def add(self, pass_: InductorPass):
         assert isinstance(pass_, InductorPass)
         self.passes.append(pass_)
@@ -120,8 +141,16 @@ def uuid(self):
         affects compilation caching. Its uuid depends on the UUIDs of all
         dependent passes and the pass config. See InductorPass for more info.
         """
-        state = {"pass_config": self.pass_config.uuid(), "passes": []}
+        state = {
+            "pass_config": self.pass_config.uuid(),
+            "passes": [],
+            "inductor_splitting_ops": [],
+        }
         for pass_ in self.passes:
             state["passes"].append(pass_.uuid())
         state["passes"].append(self.fix_functionalization.uuid())
+
+        # See [HACK: Bug with Inductor graph partition and torch.compile cache]
+        state["inductor_splitting_ops"].extend(self.inductor_splitting_ops)
+
         return InductorPass.hash_dict(state)