Support src_mask and src_key_padding_mask for Better Transformer (#88488)
Fixes T135842750 (follow-up for #87377)
## Description
At present, having both `src_key_padding_mask` and `src_mask` at the same time is not supported on the fastpath in Transformer and Multi-Head Attention.
This PR enables using both masks on the fastpath on CPU and GPU: if both masks are passed, we merge them into a 4D mask in Python and change mask type to 2 before passing downstream.
Downstream processing in native code is not changed, as it already supports 4D mask. Indeed, it is done depending on the device:
- on CUDA, by `SoftMax.cu::masked_softmax_cuda`. When mask type is 2, it calls either `dispatch_softmax_forward` -> `softmax_warp_forward` or `at::softmax` (depending on the input size). In both cases 4D mask is supported.
- on CPU, by `SoftMax.cpp::masked_softmax_cpp`. It calls `hosted_softmax` which supports 4D mask.
## Tests
- Extended `test_mask_check_fastpath` to check that fast path is indeed taken in Transformer when two masks are passed
- Added `test_multihead_self_attn_two_masks_fast_path_mock` to check that fast path is taken in MHA when two masks are passed
- Added `test_multihead_self_attn_two_masks_fast_path` to check that fast and slow paths give the same result when two masks are passed in MHA
- `test_masked_softmax_mask_types` now covers mask type 2
- `test_transformerencoderlayer_fast_path` (CPU smoke test) is expanded to the case of both masks provided simultaneously
- `test_masked_softmax_devices_parity` checks that mask type 2 is accepted by CPU and CUDA paths
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88488
Approved by: https://github.com/mikekgfb
