Fix Ascend NPU RMSNorm and fused-attention mask shapes

[mengchengTang]

What does this PR do?

Fixes two Ascend NPU incompatibilities hit when running LTX-2 with
attn_backend=_native_npu:

1. RMSNorm with elementwise_affine=False leaves weight=None.
   torch_npu.npu_rms_norm requires a gamma tensor, so we only use the
   fused kernel when weight is not None, and otherwise fall back to the
   existing PyTorch path (same as CUDA).

2. Fused attention mask: Ascend FA does not broadcast a singleton
   query-length dim. Expand masks shaped [B, N, 1, Skv] (e.g. LTX
   cross-attn) to [B, N, Sq, Skv], generalizing the existing
   [B, 1, 1, Skv] handling.

Screenshots

Error 1 — RMSNorm / gamma is None

Error 2 — FA mask shape [1, 32, 1, 1024]

Fixes # (issue)

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[github-actions]

Hi @mengchengTang, thanks for the PR! It does not appear to link an issue it fixes. If this PR addresses an existing issue, please add a closing keyword (e.g. Fixes #1234) to the PR description so the issue is linked. See the contribution guide for more details. If this PR intentionally does not fix a tracked issue, a maintainer can add the no-issue-needed label to silence this reminder.