Fix: audio encoder uses full attention instead of windowed on non-FA2 backends

[Dvad]

Bug: Qwen3ASRAudioEncoder.forward() passes cu_seqlens to attention layers but never builds a 4D attention mask. Only Flash Attention 2 (CUDA) interprets cu_seqlens for windowed attention boundaries.

On SDPA/eager backends (MPS, CPU), cu_seqlens is ignored and the encoder performs full global self-attention over all tokens instead of the trained windowed attention pattern.

This causes significant quality degradation on non-CUDA hardware (~340 words transcribed vs ~555 expected on a 5-minute test clip).

Fix: Call the existing _prepare_attention_mask() method (which already returns None for FA2) and pass the resulting block-diagonal mask to each encoder layer.

           cu_seqlens = torch.tensor(cu_chunk_lens, device=aftercnn_lens.device).cumsum(-1, dtype=torch.int32)
  +        attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens)
           for encoder_layer in self.layers:
               layer_outputs = encoder_layer(
                   hidden_states,
                   cu_seqlens,
  +                attention_mask=attention_mask,
               )

Verified on: MPS (Apple Silicon), CPU.

FA2 path is unchanged (_prepare_attention_mask returns None for flash_attention_2).