add mean to g3 nightly

examples/cadence/operators/facto_util.py

@@ -22,7 +22,16 @@ def apply_tensor_contraints(op_name: str, tensor_constraints: list[object]) -> N
             tensor_constraints.extend(
                 [
                     cp.Dtype.In(lambda deps: [torch.float]),
-                    cp.Rank.Le(lambda deps: 2**3),
+                    cp.Rank.Le(lambda deps: 2**2),
+                    cp.Value.Ge(lambda deps, dtype, struct: -2),
+                    cp.Value.Le(lambda deps, dtype, struct: 2),
+                ]
+            )
+        case "mean.dim":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float]),
+                    cp.Rank.Le(lambda deps: 2**2),
                 ]
             )
         case "exp.default":
@@ -86,8 +95,27 @@ def facto_testcase_gen(op_name: str) -> List[Tuple[List[str], OrderedDict[str, s
                         cp.Value.Le(lambda deps, dtype: 2),
                     ]
                 )
+        elif in_spec.type.is_scalar_type():
+            spec.inspec[index].constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: apply_scalar_contraints(op_name)),
+                ]
+            )
         elif in_spec.type.is_tensor():
             spec.inspec[index].constraints.extend(tensor_constraints)
+        elif in_spec.type.is_dim_list():
+            spec.inspec[index].constraints.extend(
+                [
+                    cp.Length.Ge(lambda deps: 1),
+                    cp.Optional.Eq(lambda deps: False),
+                ]
+            )
+        elif in_spec.type.is_bool():
+            spec.inspec[index].constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.bool]),
+                ]
+            )
 
     return [
         (posargs, inkwargs)

examples/cadence/operators/test_g3_ops.py

@@ -259,6 +259,35 @@ def test_g3__softmax_out(
 
         self.run_and_verify(model, (inputs,))
 
+    # pyre-ignore[16]: Module `parameterized.parameterized` has no attribute `expand`.
+    @parameterized.expand([*facto_util.facto_testcase_gen("mean.dim")])
+    def test_g3_mean_dim_out(
+        self,
+        posargs: List[int],
+        inkwargs: OrderedDict[str, str],
+    ) -> None:
+        class Meandim(nn.Module):
+            def forward(
+                self,
+                x: torch.Tensor,
+                dim_list: Tuple[int],
+                keepdim: bool,
+                dtype: torch.dtype = torch.float32,
+            ) -> torch.Tensor:
+                return torch.ops.aten.mean.dim(
+                    x,
+                    dim_list,
+                    keepdim,
+                    dtype=dtype,
+                )
+
+        model = Meandim()
+
+        self.run_and_verify(
+            model,
+            inputs=tuple(posargs),
+        )
+
 
 if __name__ == "__main__":
     unittest.main()