Revert "[dynamo] Error when user nests FX with dynamo (#87797)"

This reverts commit 1da5aeb97b73664ff0fe2f4bb48379655cede969.

Reverted https://github.com/pytorch/pytorch/pull/87797 on behalf of https://github.com/ezyang due to breaks nvfuser stack, needs more investigation

test/dynamo/test_misc.py

@@ -2732,20 +2732,6 @@ class MiscTests(torch._dynamo.test_case.TestCase):
             dynamo_result = graph(x)
             self.assertTrue(same(real, dynamo_result))
 
-    def test_error_on_nested_fx_trace(self):
-        input = torch.rand(2, 3)
-
-        def f(x):
-            x + x
-
-        real = f(input)
-
-        optimized = torch._dynamo.optimize("eager")(f)
-        self.assertTrue(same(optimized(input), real))
-
-        with self.assertRaisesRegex(RuntimeError, "Detected that you are using FX"):
-            gm = torch.fx.symbolic_trace(optimized)
-
 
 class CustomFunc(torch.autograd.Function):
     @staticmethod

test/test_prims.py

@@ -8,14 +8,7 @@ import unittest
 
 import torch
 from torch.testing import make_tensor
-from torch.testing._internal.common_utils import (
-    parametrize,
-    run_tests,
-    TestCase,
-    TEST_SCIPY,
-    skipCUDAMemoryLeakCheckIf,
-    skipIfTorchDynamo,
-)
+from torch.testing._internal.common_utils import parametrize, run_tests, TestCase, TEST_SCIPY, skipCUDAMemoryLeakCheckIf
 from torch.testing._internal.common_device_type import (
     instantiate_device_type_tests,
     onlyCUDA,
@@ -394,7 +387,6 @@ class TestPrims(TestCase):
         actual = execute(gm, a.mT, executor="nvfuser")
         self.assertEqual(expected, actual)
 
-    @skipIfTorchDynamo
     def test_nvfuser_capability_context(self, device):
         # This test is to ensure that the torch calls are replaced with refs
         # based on the nvfuser+prims capability

torch/_dynamo/config.py

@@ -153,10 +153,6 @@ dynamo_import = __name__.replace(".config", "")
 # How to import torchinductor, either torchinductor or torch.inductor
 inductor_import = dynamo_import.replace("dynamo", "inductor")
 
-# If true, error with a better message if we symbolically trace over a
-# dynamo-optimized function. If false, silently suppress dynamo.
-error_on_nested_fx_trace = True
-
 # root folder of the project
 if "torch." in dynamo_import:
     base_dir = dirname(dirname(dirname(abspath(__file__))))

torch/_dynamo/eval_frame.py

@@ -14,7 +14,6 @@ from unittest.mock import patch
 
 import torch
 import torch.utils._pytree as pytree
-from torch.fx._symbolic_trace import is_fx_tracing
 from torch.fx.experimental.proxy_tensor import make_fx
 from torch.nn.parallel.distributed import DistributedDataParallel
 
@@ -150,14 +149,6 @@ class _TorchDynamoContext:
 
         @functools.wraps(fn)
         def _fn(*args, **kwargs):
-            if is_fx_tracing():
-                if config.error_on_nested_fx_trace:
-                    raise RuntimeError(
-                        "Detected that you are using FX to symbolically trace "
-                        "a dynamo-optimized function. This is not supported at the moment."
-                    )
-                return fn
-
             on_enter()
             prior = set_eval_frame(callback)
             backend_ctx = backend_ctx_ctor()