[mps] Fix NaN in Attention.get_attention_scores when attention_mask is None

[Shreyas-jk]

What does this PR do?

Fixes #11229

Attention.get_attention_scores allocates baddbmm_input with torch.empty() and uses beta=0, relying on baddbmm to ignore the uninitialized input. The MPS baddbmm kernel does not short-circuit on beta=0, so any NaN/Inf in the uninitialized memory propagates through 0 * NaN = NaN and poisons the attention output. CUDA happens to mask this because its allocator typically returns zero-initialized memory.

This change uses torch.zeros instead of torch.empty only on MPS, leaving the CUDA / CPU / XPU paths unchanged so they don't pay the extra fill cost.

In real workloads this surfaces as black/NaN images from StableDiffusionXLPipeline with enable_attention_slicing() on Apple Silicon + fp16, which is the standard memory-saving path on Macs with limited unified memory.

Reproduction

Minimal repro on M-series MPS (without the fix): 30/30 trials produce NaN. With the fix: 0/30. CPU baseline: 0/5. Verified on M4 MacBook Pro, torch 2.11.0, fp16 and fp32.

The added test GetAttentionScoresMPSTests.test_no_nan_when_attention_mask_is_none_on_mps reproduces the bug deterministically (fails 20/20 without the fix, passes 20/20 with it) by polluting the MPS allocator pool with NaN-filled tensors before each call.

Before submitting

• Did you read the contributor guideline?
• Did you write any new necessary tests?
• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Was this discussed/approved via a GitHub issue or the forum? --> Fixes existing issue enable_attention_slicing give NaN results for SDXL on MPS #11229.

Who can review?

@pcuenca (MPS / Apple Silicon maintainer per the PR template)