Merge branch 'main' into add-convert-int64-ops-to-int32-pass

Author: YufengShi-dudu

exir/tests/test_tracer.py

@@ -307,76 +307,6 @@ def f(x: torch.Tensor, y: List[torch.Tensor]) -> Dict[str, torch.Tensor]:
 
         self.assertEqual(prog(*inp), f(*inp))
 
-    def test_aot_buffer_mutation(self) -> None:
-        class Module(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.register_buffer(
-                    "_bin_num_examples",
-                    torch.empty([42]).fill_(
-                        0.0,
-                    ),
-                )
-
-            def forward(self, x, y, z):
-                self._bin_num_examples.index_copy_(
-                    dim=0,
-                    index=y,
-                    source=z,
-                )
-                self._bin_num_examples.index_add_(
-                    dim=0, index=torch.arange(4), source=x
-                )
-                return self._bin_num_examples - 1, x * z
-
-        model = Module()
-        example_inputs = (
-            torch.randn(4, requires_grad=True),
-            torch.tensor(0),
-            torch.tensor(3.14),
-        )
-
-        with self.assertRaisesRegex(
-            RuntimeError,
-            "Found a graph input that requires gradients, and received a mutation.",
-        ):
-            _ = exir.capture(
-                model,
-                example_inputs,
-                exir.CaptureConfig(
-                    enable_aot=True,
-                ),
-            )
-
-        # Note that model._bin_num_examples is mutated during exir.capture
-        # We need to create a new_model
-        new_model = Module()
-        example_inputs = (
-            torch.randn(4),
-            torch.tensor(0),
-            torch.tensor(3.14),
-        )
-
-        ep = exir.capture(
-            new_model,
-            example_inputs,
-            exir.CaptureConfig(
-                enable_aot=True,
-            ),
-        )
-
-        test_inputs = (
-            torch.randn(4),
-            torch.tensor(0),
-            torch.tensor(2.1),
-        )
-        graph_outputs = ep(*test_inputs)
-        eager_outputs = Module()(*test_inputs)
-        self.assertEqual(len(graph_outputs), 2)
-        self.assertEqual(len(eager_outputs), 2)
-        self.assertTrue(torch.allclose(graph_outputs[0], eager_outputs[0]))
-        self.assertTrue(torch.allclose(graph_outputs[1], eager_outputs[1]))
-
     def test_assume_constant_by_default_prop(self) -> None:
         def foo(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
             if x.shape[0] > 3:

pyproject.toml

@@ -63,7 +63,7 @@ dependencies=[
   "parameterized",
   "pytest",
   "pytest-xdist",
-  "pytest-rerunfailures",
+  "pytest-rerunfailures==15.1",
   "pyyaml",
   "ruamel.yaml",
   "sympy",