[inductor] fix index.Tensor fallback (#144736)

The original issue is we see accuracy problem in a meta internal model [meta internal link](https://fb.workplace.com/groups/1075192433118967/posts/1567334737238065/).  The debugging is hard but the root cause is relatively simple. The root cause is that the model has mix-device inputs for index.Tensor which causes Inductor to fallback. And the meta kernel for index.Tensor returns a tensor with inconsistent strides to the eager kernel.

The following code snippet
```
import torch
from torch._subclasses import FakeTensorMode

device = "cuda"

x = torch.randn((24, 16, 32, 32), device=device).to(memory_format=torch.channels_last)
x = x.view(2, 12, 16, 32, 32)

i1 = torch.arange(2).unsqueeze(-1)
i2 = torch.argsort(torch.rand(2, 12), dim=-1)[:, :3]

print(f"Eager stride: {x[i1, i2].stride()}")

mode = FakeTensorMode()
with mode:
    f_x = mode.from_tensor(x)
    f_i1 = mode.from_tensor(i1)
    f_i2 = mode.from_tensor(i2)
    f_out = f_x[f_i1, f_i2]
    print(f"Meta stride: {f_out.stride()}")
```

would output:
```
Eager stride: (49152, 16384, 1, 512, 16)
Meta stride: (49152, 16384, 1024, 32, 1)
```

In this PR, I fix the problem to run eager kernel to get the index.Tensor fallback's output layout. A better solution would be to change meta/eager kernel implementation so that their output layout matches. But I'm not sure how to properly do that.
In the index.Tensor meta kernel, we always produce dense output:  6d56277682/torch/_meta_registrations.py (L3184) . While the eager kernel seems to leverage TensorIteratorBase to decide some dimension permutation: 6d56277682/aten/src/ATen/TensorIterator.cpp (L232-L308) .  We can duplicate this logic to the meta kernel implementation if we really want meta matches eager. I can follow up on this if people have strong opinion to do this.

And here is an issue https://github.com/pytorch/pytorch/issues/144717 for asserting size/strides for fallback kernels. With that, the issue debugged here would be much easier to root cause.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144736
Approved by: https://github.com/jansel

test/test_meta.py

@@ -1805,6 +1805,24 @@ class TestMeta(TestCase):
         self.assertEqual(nz.stride(), torch.Size([1, 24]))
 
 
+    def test_stride_for_index_Tensor(self):
+        from torch._subclasses import FakeTensorMode
+        x = torch.randn((24, 16, 32, 32)).to(memory_format=torch.channels_last)
+        x = x.view(2, 12, 16, 32, 32)
+
+        i1 = torch.arange(2).unsqueeze(-1)
+        i2 = torch.argsort(torch.rand(2, 12), dim=-1)[:, :3]
+
+        out = x[i1, i2]
+
+        mode = FakeTensorMode()
+        with mode:
+            f_x = mode.from_tensor(x)
+            f_i1 = mode.from_tensor(i1)
+            f_i2 = mode.from_tensor(i2)
+            f_out = f_x[f_i1, f_i2]
+
+        self.assertEqual(out.stride(), f_out.stride())
 
 instantiate_device_type_tests(TestMeta, globals())