[Refactor] Replace npu_ring_mla with FIA in MLA prefill (#5704)

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

**Refactor: Replace npu_ring_mla with FIA in MLA prefill**

This PR refactors the MLA (Multi-Layer Attention) prefill implementation
by replacing `npu_ring_mla` with `npu_fused_infer_attention_score` (FIA)
operator, unifying the attention backend with the standard attention
implementation.

**Key changes:**

1. **Core prefill refactoring (`mla_v1.py`)**
- Replace `npu_ring_mla` with `npu_fused_infer_attention_score` in
`_forward_prefill` and `_compute_prefill_context`
   - Use TND layout with `softmax_lse_flag=True` for prefill attention
- Use `npu_attention_update` to merge multiple chunk outputs with LSE
(Log-Sum-Exp)
- Change `attn_mask` from `get_final_mla_mask()` to
`get_splitfuse_attn_mask()` for FIA compatibility

2. **Data type handling**
- Add automatic float16 → bfloat16 conversion (FIA with TND layout only
supports bfloat16)
   - Convert output back to original dtype after FIA computation

3. **Metadata optimization**
   - Pre-calculate `actual_seq_lengths_q` in `AscendMLAPrefillMetadata`
- Pre-calculate `chunk_actual_seq_lengths_kv_list` in
`ChunkedContextMetadata`
- Move `torch.cumsum` operations from forward pass to metadata building
phase

4. **CP compatibility (`mla_cp.py`)**
- Add `_ring_mla_mask_builder` to get `npu_ring_mla`-compatible masks
for Context Parallel scenarios
- Add `chunk_actual_seq_lengths_kv_list` field to
`CPChunkedContextMetadata`

**Why we need it:**
- **Backend unification**: Aligns MLA prefill with standard attention
implementation (`attention_v1.py`)
- **Better chunked context support**: FIA + `npu_attention_update`
provides native LSE-based output merging
- **Future compatibility**: Prepares for eventual `npu_ring_mla` removal
across the codebase

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

**No.** This is a pure refactoring with no functional changes - same
behavior, unified backend.

---
- Related issue: #5463 (item 7)
- vLLM version: v0.14.1

Signed-off-by: lico67373 <918688502@qq.com>

tests/ut/attention/test_mla_v1.py

@@ -102,7 +102,8 @@ class TestAscendMLAPrefillMetadata(TestBase):
             max_seq_lens=max_seq_lens,
             workspace=workspace,
             chunk_seq_lens=chunk_seq_lens,
-            chunk_seq_lens_npu=chunk_seq_lens)
+            chunk_seq_lens_npu=chunk_seq_lens,
+            chunk_actual_seq_lengths_kv_list=[[2, 4]])
 
         metadata = AscendMLAPrefillMetadata(
             attn_mask=torch.tensor([[1, 0], [1, 1]], dtype=torch.bool),
@@ -886,8 +887,9 @@ class TestAscendMLAImpl(TestBase):
         self.assertTrue(torch.equal(prefix_lse, lse))
 
     @patch("torch_npu.atb.npu_paged_cache_load")
-    @patch("torch_npu.atb.npu_ring_mla")
-    def test_compute_prefill_context(self, mock_ring, mock_load):
+    @patch("torch_npu.npu_attention_update")
+    @patch("torch_npu.npu_fused_infer_attention_score")
+    def test_compute_prefill_context(self, mock_fia, mock_update, mock_load):
         S, N, D, VD = 2, self.impl.num_heads, self.impl.qk_head_dim, self.impl.v_head_dim
         _, AND = self.impl.qk_rope_head_dim, self.impl.qk_nope_head_dim
         latent_kv_dim = self.impl.kv_lora_rank
@@ -898,11 +900,16 @@ class TestAscendMLAImpl(TestBase):
         kv_cache_0 = torch.randn(num_blocks, block_size, N, latent_kv_dim)
         kv_cache_1 = torch.randn(num_blocks, block_size, N, D)
         kv_cache = [kv_cache_0, kv_cache_1]
-        prefix_out = torch.randn(S, N, 128)
-        prefix_lse = torch.randn(S, N)
+        prefix_out = torch.randn(S, N, VD)
+        prefix_lse = torch.randn(N, S)
 
         self.impl.kv_b_proj.return_value = (torch.randn(8, N, VD + AND), )
 
+        # Mock FIA to return output and lse
+        mock_fia.return_value = (torch.randn(S, N, VD), torch.randn(N, S))
+        # Mock attention_update to return merged output
+        mock_update.return_value = (torch.randn(S * N, VD), None)
+
         chunk_ctx = MagicMock()
         chunk_ctx.seq_tot = [8]
         chunk_ctx.chunk_seq_lens = [torch.tensor([8])]
@@ -911,7 +918,7 @@ class TestAscendMLAImpl(TestBase):
 
         prefill_meta = MagicMock()
         prefill_meta.chunked_context = chunk_ctx
-        prefill_meta.query_lens = [8]
+        prefill_meta.query_lens = torch.tensor([S])
         prefill_meta.block_table = torch.randint(0, 100, (S, 4))
 
         meta = MagicMock()
@@ -924,10 +931,10 @@ class TestAscendMLAImpl(TestBase):
                                                       prefix_lse)
 
         mock_load.assert_called_once()
-        mock_ring.assert_called_once()
+        mock_fia.assert_called_once()
+        mock_update.assert_called_once()
 
         self.assertEqual(out.shape, prefix_out.shape)
-        self.assertEqual(lse.shape, prefix_lse.shape)
 
     @patch('vllm_ascend.ascend_forward_context.get_forward_context')
     @patch("vllm_ascend.attention.mla_v1.AscendMLAImpl._v_up_proj")