[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/attention/test_attention_v1.py

@@ -53,6 +53,7 @@ class TestAscendAttentionMetadataBuilder(TestBase):
         self.mock_vllm_config = MagicMock()
         self.mock_vllm_config.speculative_config = None
         self.mock_vllm_config.model_config.max_model_len = 640
+        self.mock_vllm_config.model_config.hf_text_config.sliding_window = None
         self.mock_vllm_config.cache_config.block_size = 64
         self.mock_vllm_config.compilation_config.cudagraph_mode = None
         self.mock_vllm_config.scheduler_config.max_num_seqs = 10
@@ -89,8 +90,6 @@ class TestAscendAttentionMetadataBuilder(TestBase):
             slot_mapping=torch.tensor(range(20)),
             actual_seq_lengths_q=torch.tensor([0, 1, 2]),
             positions=torch.tensor([10, 10]),
-            attn_mask=torch.ones((15, 15)),
-            spec_attn_mask=None,
             attn_state=AscendAttentionState.ChunkedPrefill,
             num_computed_tokens_cpu=None,
             seq_lens=None,