[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/compilation/acl_graph.py

@@ -340,7 +340,7 @@ def update_mla_attn_params(update_stream, forward_context, runtime_shape,
                 graph_params.events[runtime_shape],
         ):
             (q_nope, k_nope, q_pe, k_pe, num_heads, num_kv_heads, input_layout,
-             spec_attn_mask, sparse_mode, scale, block_table, block_size,
+             attn_mask, sparse_mode, scale, block_table, block_size,
              seq_lens_list, actual_seq_lengths, attn_output,
              softmax_lse) = param
             seq_lens_list = forward_context.attn_metadata[
@@ -380,7 +380,7 @@ def update_mla_attn_params(update_stream, forward_context, runtime_shape,
                 num_heads=num_heads,
                 num_key_value_heads=num_kv_heads,
                 input_layout=input_layout,
-                atten_mask=spec_attn_mask,
+                atten_mask=attn_mask,
                 sparse_mode=sparse_mode,
                 scale=scale,
                 antiquant_mode=0,
@@ -480,7 +480,7 @@ def update_mla_attn_dcp_pcp_params(update_stream, forward_context,
             seq_len = decode_meta.cp_seq_len
 
             # For pcp + spec decode, we flatten seq_lens
-            # to avoid irregular spec_attn_mask shape,
+            # to avoid irregular attn_mask shape,
             # so there's no need to divide runtime_shape by spec_multiple
             pad_length = runtime_shape - len(seq_len)
             pad_tensor = torch.zeros(pad_length,