[Inductor] avoid CUDA__equal when constant tensors are from different device (#163529)

Summary:
otherwise, may hit
```
Exception: Expected all tensors to be on the same device, but got other is on cuda:0, different from other tensors on cpu (when checking argument in method wrapper_CUDA__equal)
```

Test Plan: UTs

Reviewed By: yushangdi

Differential Revision: D82974062

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163529
Approved by: https://github.com/yushangdi, https://github.com/Skylion007

test/inductor/test_aot_inductor.py

@@ -1829,7 +1829,7 @@ class AOTInductorTestsTemplate:
                 self.user_float_feature_idx = user_float_feature_idx
                 self.register_buffer(
                     "_tensor_constant0",
-                    torch.ones(1, device=device, dtype=torch.float32),
+                    torch.ones(5, device=device, dtype=torch.float32),
                     persistent=True,
                 )
                 self.register_buffer(
@@ -1840,6 +1840,7 @@ class AOTInductorTestsTemplate:
                 self.sub_mod = SubModule(device)
 
             def forward(self, x):
+                self._tensor_constant0[1:2] = 1
                 return (
                     torch.index_select(
                         x, 1, torch.tensor(self.user_float_feature_idx, device=x.device)
@@ -7291,8 +7292,6 @@ MPS_TEST_FAILURES = {
     "test_index_put_with_none_index": fail_mps(),
     # Error device may not be nil
     "test_zero_size_weight": fail_mps(is_skip=True),
-    # RuntimeError: Cannot compare two tensors on different devices. Got: cpu and mps:0
-    "test_aoti_constant_tensor_name_collision": fail_mps(is_skip=True),
     # MPSGraph does not support tensor dims > INT_MAX
     "test_upper_bound_i64": fail_mps(is_skip=True),
     # MPS doesn't support triton

torch/_inductor/compile_fx.py

@@ -370,9 +370,12 @@ def _resolve_name_collision(mod: GraphModule, gm: GraphModule) -> None:
                 ):
                     continue
             elif (
-                torch.equal(gm_target, model_target)
+                gm_target.device == model_target.device
                 and gm_target.dtype == model_target.dtype
+                and torch.equal(gm_target, model_target)
             ):
+                # If tensors with same name from gm and model are indeed the same, we don't need to rename
+                # Check device first, to avoid torch.equal(wrapper_CUDA__equal) raise when different device
                 continue
 
             prefix = (