From d9f94e0d7d96e52a636899a1b104cf610dd1a905 Mon Sep 17 00:00:00 2001
From: Animesh Jain <anijain@umich.edu>
Date: Fri, 17 Oct 2025 16:38:12 -0700
Subject: [PATCH] [dynamo] Support fx.traceback.annotate as decorator (#165805)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165805
Approved by: https://github.com/Lucaskabela, https://github.com/SherlockNoMad, https://github.com/yushangdi
---
 test/dynamo/test_fx_annotate.py  | 50 ++++++++++++++++++++++++++++++++
 torch/_dynamo/variables/torch.py |  6 +++-
 2 files changed, 55 insertions(+), 1 deletion(-)

diff --git a/test/dynamo/test_fx_annotate.py b/test/dynamo/test_fx_annotate.py
index ede0b51ef123..337ce0f5764c 100644
--- a/test/dynamo/test_fx_annotate.py
+++ b/test/dynamo/test_fx_annotate.py
@@ -238,6 +238,56 @@ class AnnotateTests(torch._dynamo.test_case.TestCase):
 ('call_function', 'getitem_5', {'compile_inductor': 0})""",  # noqa: B950
         )
 
+    def test_as_decorator(self):
+        class Mod(torch.nn.Module):
+            @fx_traceback.annotate({"fdsp_bucket": 0})
+            def sin(self, x):
+                return torch.sin(x)
+
+            def forward(self, x):
+                with fx_traceback.annotate({"pp_stage": 0}):
+                    sin = self.sin(x)
+                    sub = sin - 2
+                    mul = sub * 2
+                div = mul / 3
+                return div
+
+        m = Mod()
+        backend = AotEagerAndRecordGraphs()
+        opt_m = torch.compile(m, backend=backend, fullgraph=True)
+        x = torch.randn(10, requires_grad=True)
+        m(x)
+        opt_m(x).sum().backward()
+
+        self.assertEqual(len(backend.fw_graphs), 1)
+        self.assertEqual(len(backend.bw_graphs), 1)
+
+        dynamo_metadata = fx_traceback._get_custom_metadata(backend.graphs[0])
+        fw_metadata = fx_traceback._get_custom_metadata(backend.fw_graphs[0])
+        bw_metadata = fx_traceback._get_custom_metadata(backend.bw_graphs[0])
+        self.assertExpectedInline(
+            str(dynamo_metadata),
+            """\
+('placeholder', 'l_x_', {'pp_stage': 0, 'fdsp_bucket': 0})
+('call_function', 'sin', {'pp_stage': 0, 'fdsp_bucket': 0})
+('call_function', 'sub', {'pp_stage': 0})
+('call_function', 'mul', {'pp_stage': 0})""",  # noqa: B950
+        )
+        self.assertExpectedInline(
+            str(fw_metadata),
+            """\
+('call_function', 'sin', {'pp_stage': 0, 'fdsp_bucket': 0})
+('call_function', 'sub', {'pp_stage': 0})
+('call_function', 'mul', {'pp_stage': 0})""",  # noqa: B950
+        )
+        self.assertExpectedInline(
+            str(bw_metadata),
+            """\
+('call_function', 'mul_1', {'pp_stage': 0})
+('call_function', 'cos', {'pp_stage': 0, 'fdsp_bucket': 0})
+('call_function', 'mul_2', {'pp_stage': 0, 'fdsp_bucket': 0})""",  # noqa: B950
+        )
+
 
 if __name__ == "__main__":
     run_tests()
diff --git a/torch/_dynamo/variables/torch.py b/torch/_dynamo/variables/torch.py
index d659f3a24d86..1e39187274cc 100644
--- a/torch/_dynamo/variables/torch.py
+++ b/torch/_dynamo/variables/torch.py
@@ -126,6 +126,7 @@ supported_ctx_manager_classes = dict.fromkeys(
         torch.cpu.amp.autocast_mode.autocast,
         torch.cuda.amp.autocast_mode.autocast,
         torch.fx.traceback.annotate,
+        torch.fx.traceback.annotate.__wrapped__,  # type: ignore[attr-defined]
         # We'll let Dynamo inline into the contextlib part of these context
         # manager instances, all the way till it invokes the wrapped function
         # itself (at which point we wrap it back to special context manager
@@ -364,7 +365,10 @@ class TorchCtxManagerClassVariable(BaseTorchVariable):
             assert len(args) <= 1 and len(kwargs) == 0
             inf_mode = args[0].as_python_constant() if len(args) == 1 else True
             return InferenceModeVariable.create(tx, inf_mode)
-        elif self.value is torch.fx.traceback.annotate:
+        elif self.value in (
+            torch.fx.traceback.annotate,
+            torch.fx.traceback.annotate.__wrapped__,  # type: ignore[attr-defined]
+        ):
             assert len(args) <= 1 and len(kwargs) == 0
             return FxTracebackAnnotateVariable(
                 args[0].as_python_constant(), source=self.source