Fix dynamo tracing into AOTAutogradCache results

ghstack-source-id: c8acaf0
Pull Request resolved: #155251

Author: jamesjwu

test/dynamo/test_aot_autograd_cache.py

@@ -1,5 +1,6 @@
 # Owner(s): ["module: dynamo"]
 
+import copy
 import os
 import shutil
 import unittest
@@ -822,6 +823,44 @@ def fn(a, b):
         self.assertEqual(a.grad, a2.grad)
         self.assertEqual(b.grad, b2.grad)
 
+    @inductor_config.patch("fx_graph_remote_cache", False)
+    @inductor_config.patch({"fx_graph_cache": True})
+    @functorch_config.patch({"enable_autograd_cache": True})
+    @functorch_config.patch({"strict_autograd_cache": True})
+    def test_autograd_no_dynamo_trace_backward(self):
+        """
+        Test that dynamo does not trace into the backward compiled function,
+        even on cache hit.
+        """
+        torch._dynamo.eval_frame.clear_dynamo_tls()
+
+        @torch.compile
+        def fn(x):
+            # Calls x.sum().backward() during forward execution of fn
+            (x_grad,) = torch.autograd.grad(x.sum(), x)
+            return x_grad
+
+        a = torch.randn(10, 10, requires_grad=True, device="cpu")
+        result = fn(a)
+        self.assertEqual(counters["aot_autograd"]["autograd_cache_miss"], 1)
+        self.assertEqual(counters["aot_autograd"]["autograd_cache_hit"], 0)
+        # Backward of `sum` will run during execution of graph break
+        self.assertEqual(counters["aot_autograd"]["autograd_cache_saved"], 1)
+        traced_frame_infos = copy.deepcopy(
+            torch._dynamo.eval_frame.dynamo_tls.traced_frame_infos
+        )
+
+        torch._dynamo.reset()
+        torch._dynamo.eval_frame.clear_dynamo_tls()
+        result2 = fn(a)
+        self.assertEqual(counters["aot_autograd"]["autograd_cache_miss"], 1)
+        self.assertEqual(counters["aot_autograd"]["autograd_cache_hit"], 1)
+        self.assertEqual(counters["aot_autograd"]["autograd_cache_saved"], 1)
+        new_traced_frame_infos = torch._dynamo.eval_frame.dynamo_tls.traced_frame_infos
+        self.assertEqual(result, result2)
+        # Dynamo should trace exactly the same frames on cache hit
+        self.assertEqual(traced_frame_infos, new_traced_frame_infos)
+
     @inductor_config.patch("fx_graph_remote_cache", False)
     @inductor_config.patch("fx_graph_cache", True)
     @functorch_config.patch({"enable_autograd_cache": True})

torch/_functorch/_aot_autograd/autograd_cache.py

@@ -589,6 +589,14 @@ class CompiledBackward(GenericCompiledBackward[CompiledFxGraph], FxGraphCacheLoa
     def _is_backward(self) -> bool:
         return True
 
+    def post_compile(
+        self, result: CompiledFxGraph, fx_config: _CompileFxKwargs
+    ) -> CompiledFxGraph:
+        compiled_bw = super().post_compile(result, fx_config)
+        # This is done by _wrapped_bw_compiler in torch/_dynamo/backends/common.py
+        # But since on cache hit we do not call the bw_compiler, we need to reapply the disable
+        return torch._dynamo.disable(compiled_bw, reason="do not trace generated backwards pass")  # type: ignore[return-value]
+
 
 # Forward types don't have any extra parameters, so this is just a TypeAlias, in essence
 class BundledCompiledForward(CompiledFxGraphLoadable):
@@ -599,7 +607,13 @@ class BundledCompiledForward(CompiledFxGraphLoadable):
 class BundledCompiledBackward(
     GenericCompiledBackward[CompiledFxGraph], CompiledFxGraphLoadable
 ):
-    pass
+    def post_compile(
+        self, result: CompiledFxGraph, fx_config: _CompileFxKwargs
+    ) -> CompiledFxGraph:
+        compiled_bw = super().post_compile(result, fx_config)
+        # This is done by _wrapped_bw_compiler in torch/_dynamo/backends/common.py
+        # But since on cache hit we do not call the bw_compiler, we need to reapply the disable
+        return torch._dynamo.disable(compiled_bw, reason="do not trace generated backwards pass")  # type: ignore[return-value]
 
 
 TForward = TypeVar("TForward", bound=InductorOutput)