[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>

tests/ut/worker/test_pcp_manager.py

@@ -70,7 +70,7 @@ def test_generate_pcp_metadata_basic(pcp_size, dcp_size, num_reqs, query_lens,
     input_batch.num_tokens = torch.tensor(num_tokens)
 
     query_lens = torch.tensor(query_lens)
-    result = pcp_manager.generate_pcp_metadata(total_tokens, query_lens, None,
+    result = pcp_manager.generate_pcp_metadata(total_tokens, query_lens,
                                                input_batch)
 
     if not expect_not_none:
@@ -97,21 +97,6 @@ def test_generate_pcp_metadata_basic(pcp_size, dcp_size, num_reqs, query_lens,
                 assert hasattr(result, 'head_attn_nomask_seqlens')
                 assert hasattr(result, 'tail_attn_nomask_seqlens')
 
-                if hasattr(result, 'pcp_prefill_mask'
-                           ) and result.pcp_prefill_mask is not None:
-                    if use_mla:
-                        assert result.pcp_prefill_mask.shape == (512, 512)
-                    else:
-                        assert result.pcp_prefill_mask.shape == (2048, 2048)
-            else:
-                if hasattr(result, 'pcp_prefill_mask'):
-                    if result.pcp_prefill_mask is not None:
-                        if use_mla:
-                            assert result.pcp_prefill_mask.shape == (512, 512)
-                        else:
-                            assert result.pcp_prefill_mask.shape == (2048,
-                                                                     2048)
-
 
 @pytest.mark.parametrize(
     "tokens, num_reqs, num_computed_tokens, num_prompt_tokens, pcp_size, pcp_rank, expected_pcp_tokens",