[Refactor] Fix AttentionMaskBuilder singleton and remove redundant pcp_prefill_mask (#4870)

## What this PR does / why we need it?

This PR fixes the `AttentionMaskBuilder` singleton initialization issue
introduced in PR #4779 and removes the unused `pcp_prefill_mask` field.

### Background

After PR #4779 made `AttentionMaskBuilder` a singleton with `@singleton`
decorator, the class constructor now requires a `device` parameter.
However, two initialization sites were still using the old parameterless
constructor, causing failures.

### Changes

1. **Fix singleton initialization**
- Fixed `AttentionMaskBuilder()` → `AttentionMaskBuilder(self.device)`
in `AscendMLAMetadataBuilder.__init__()`
- Fixed `AttentionMaskBuilder()` → `AttentionMaskBuilder(self.device)`
in `AscendAttentionMetadataBuilder.__init__()`

2. **Remove unused field**
- Removed `pcp_prefill_mask` field from
`AscendPrefillContextParallelMetadata` (never used in codebase)
   - Updated related test assertions

### Related

- Issue #5463
- PR #4779 (Unify all mask generation methods)
- PR #5389 (Make AttentionMaskBuilder singleton)

## Does this PR introduce _any_ user-facing change?

No. This is an internal refactoring.

## How was this patch tested?

- ✅ Local testing: No linter errors
- ✅ Unit tests for attention modules verified
- ⏳ CI pipeline

Signed-off-by: lico67373 <918688502@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>

vllm_ascend/attention/context_parallel/mla_cp.py

@@ -118,7 +118,6 @@ class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder):
             tail_attn_nomask_seqlens=common_long_seq_metadata.
             tail_attn_nomask_seqlens,
             q_full_idx=common_long_seq_metadata.q_full_idx,
-            pcp_prefill_mask=common_long_seq_metadata.pcp_prefill_mask,
             pcp_allgather_restore_idx=common_long_seq_metadata.
             pcp_allgather_restore_idx)
 
@@ -195,7 +194,7 @@ class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder):
         ).item()
         if build_metadata_step == BUILD_METADATA_STEP_PREFILL:
             # For pcp + spec decode, we flatten seq_lens and block_table
-            # to avoid irregular spec_attn_mask shape
+            # to avoid irregular attn_mask shape
             return self.num_decodes_flatten + self.num_prefills
         else:
             return self.num_decodes_flatten
@@ -420,7 +419,6 @@ class AscendMlaCPImpl(AscendMLAImpl):
         attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens
         head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens
         tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens
-        mask = attn_metadata.prefill.pcp_metadata.pcp_prefill_mask
         output_head, lse_head = self._attention_with_mask_and_nomask(
             q_nope=torch.index_select(q_nope, 0, q_head_idx),
             q_pe=torch.index_select(q_pe, 0, q_head_idx),
@@ -431,7 +429,7 @@ class AscendMlaCPImpl(AscendMLAImpl):
             kv_nomask_idx=kv_with_q_head_nomask_idx,
             attn_mask_seqlens=attn_mask_seqlens,
             attn_nomask_seqlens=head_attn_nomask_seqlens,
-            mask=mask)
+            mask=attn_metadata.attn_mask)
 
         output_tail, lse_tail = self._attention_with_mask_and_nomask(
             q_nope=torch.index_select(q_nope, 0, q_tail_idx),
@@ -443,7 +441,7 @@ class AscendMlaCPImpl(AscendMLAImpl):
             kv_nomask_idx=kv_with_q_tail_nomask_idx,
             attn_mask_seqlens=attn_mask_seqlens,
             attn_nomask_seqlens=tail_attn_nomask_seqlens,
-            mask=mask)
+            mask=attn_metadata.attn_mask)
 
         q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx
         attn_output = torch.index_select(