…on using Vec256" (#31127)

Summary:
Pull Request resolved: #31127

Original commit changeset: d22448b90843

On Skylake T6:

Single Core:
(Note that our benchmark generates batch_size=47 for first case and batch_size=56 for the second case. In spite of that, the vectorized version is still faster than the original reference C version without vectorization.)
- Before the PR:
```
native_layer_norm        0.81%            5.884ms          0.81%            5.884ms          122.580us        NaN              0.000us          0.000us          48               [[47, 1, 1024], [1024], [1024]]
```

- After the PR:
```
native_layer_norm        0.68%            5.053ms          0.68%            5.053ms          105.272us        NaN              0.000us          0.000us          48               [[56, 1, 1024], [1024], [1024]]
```

20 Cores:
- Before the PR:
```
native_layer_norm        1.65%            41.682ms         1.65%            41.682ms         868.365us        NaN              0.000us          0.000us          48               [[61, 64, 1024], [1024], [1024]]
```

- After the PR:
```
native_layer_norm        1.34%            33.829ms         1.34%            33.829ms         704.771us        NaN              0.000us          0.000us          48               [[61, 64, 1024], [1024], [1024]]
```
ghstack-source-id: 95420889

Test Plan:
buck test mode/dev-nosan //caffe2/test:nn -- "LayerNorm"

buck test mode/dev-nosan //caffe2/test:nn -- "test_LayerNorm_1d_no_elementwise_affine_eval"

 python run_test.py -i nn -- TestNN.test_LayerNorm_1d_no_elementwise_affine_eval

Differential Revision: D18936428

fbshipit-source-id: 8cae33d35fb338b5ac49b1597c2709152612d6e5