[Misc] Remove CP Redundant Variables after FIA operator enables for CANN 8.5 (#6013)

### What this PR does / why we need it?
PCP/DCP splits the kv-cache onto different cards. After introducing the
parameter cp-kv-cache-interleave-size, the first size tokens will be
cached at Card 0, and so on.
However, if there are too few tokens, some cards will not store the
key-value pairs, resulting in values ​​of 0, corrupted values, and
precision issues. Currently, additional operations are introduced to
avoid this precision problem.

After we integrate FIA operator in mla_cp._forward_decode and CANN
updates to 8.5.0, we now can remove these additional operations.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
passed all CI by CANN 8.5.0
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

Signed-off-by: dsxsteven <dsxsteven@sina.com>
Signed-off-by: dsxsteven <36877507+dsxsteven@users.noreply.github.com>

tests/ut/attention/test_mla_cp.py

@@ -439,11 +439,7 @@ class TestAscendMLAImpl(TestBase):
         decode_metadata = MagicMock()
         decode_metadata.actual_seq_lengths_q = MagicMock()
         decode_metadata.seq_lens_list = MagicMock()
-        decode_metadata.batch_seq_mask = torch.tensor([True, False],
-                                                      dtype=torch.bool)
-
-        result = _process_attn_out_lse(attn_output, softmax_lse,
-                                       decode_metadata.batch_seq_mask)
+        result = _process_attn_out_lse(attn_output, softmax_lse)
 
         self.assertEqual(result.shape[0], B * self.impl.pcp_size)
         self.assertEqual(result.shape[1], N)
@@ -478,8 +474,6 @@ class TestAscendMLAImpl(TestBase):
         attn_metadata.decode = MagicMock()
         attn_metadata.decode.actual_seq_lengths_q = MagicMock()
         attn_metadata.decode.seq_lens_list = MagicMock()
-        attn_metadata.decode.batch_seq_mask = torch.tensor([False, False],
-                                                           dtype=torch.bool)
 
         self.impl.enable_kv_nz = True
 
@@ -886,12 +880,9 @@ class TestAscendMLAImpl(TestBase):
             # Inputs
             attn_output = torch.randn(B, H, D)
             softmax_lse = torch.randn(B, H, 1)
-            batch_seq_mask = torch.tensor([False, True, False, False])  # [B]
             decode_meta = MagicMock()
-            decode_meta.batch_seq_mask = batch_seq_mask
 
-            result = _process_attn_out_lse(attn_output, softmax_lse,
-                                           batch_seq_mask)
+            result = _process_attn_out_lse(attn_output, softmax_lse)
             # [PCP * S, DCP * H, D + 1]
             self.assertIsInstance(result, torch.Tensor)
             assert result.shape == (B * self.impl.pcp_size, H, D + 1)