[Static Runtime] Manage temporary Tensors for aten::layer_norm (#64078)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/64078
This change converts `aten::layer_norm -> output Tensor` to `static_runtime::layer_norm -> (output Tensor, temp1 Tensor, tmp2 Tensor)` to manage `tmp1` and `tmp2` Tensors by the static runtime.
Currently the out-variant of `aten::layer_norm` creates two temporary Tensors inside it:
```
at::Tensor mean = create_empty_from({M}, *X);
at::Tensor rstd = create_empty_from({M}, *X);
```
that the static runtime misses an opportunity to manage.
This change puts them into (unused) output Tensors of a new placeholder op `static_runtime::layer_norm` so that the static runtime can mange them since the static runtime as of now chooses to manage only output tensors.
Test Plan:
- Enhanced `StaticRuntime.LayerNorm` to ensure that `static_runtime::layer_norm` gets activated.
- Confirmed that the new op gets activated during testing:
```
V0825 12:51:50.017890 2265227 impl.cpp:1396] Switch to out variant for node: %8 : Tensor, %9 : Tensor, %10 : Tensor = static_runtime::layer_norm(%input.1, %normalized_shape.1, %4, %4, %5, %3)
```
Reviewed By: hlu1
Differential Revision: D30486475
fbshipit-source-id: 5121c44ab58c2d8a954aa0bbd9dfeb7468347a2d
