[Misc] Clean up uesless code in attention (#1933)

Before do attention module refactor, we can do some code cleanup to make
the next step easier.

What this PR does:

1. remove uesless `common_prefix_len` for attention builder
2. remove uesless `is_only_prefill` and `num_input_tokens` in attention
metadata.
3. remove `CommonAttentionMetadata` and ues `query_start_loc` instead,
`CommonAttentionMetadata` is over designed and uesless
4. update the attention backend input parameters to keep the same as
vLLM.
5. Rename attention name to the same style with `ASCEND` prefix

- vLLM version: v0.9.2
- vLLM main:
107111a859

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/attention/mla_v1.py

@@ -31,27 +31,13 @@ if TYPE_CHECKING:
 _ALLOWED_NUM_QUERIES_PER_KV = [32, 64, 128]
 
 
-@dataclass
-class CommonAttentionMetadata:
-    """
-    Attention metadata attributes that can be shared by layers in different KV
-    cache groups and thus having different block table.
-    """
-
-    query_start_loc: torch.Tensor
-    """(batch_size + 1,), the start location of each request in query Tensor"""
-    seq_lens: torch.Tensor
-    """(batch_size,), the length of each request including both computed tokens
-    and newly scheduled tokens"""
-
-
 class AscendMLABackend(AttentionBackend):
 
     accept_output_buffer: bool = True
 
     @staticmethod
     def get_name() -> str:
-        return "VLLM_ASCEND_MLA"
+        return "ASCEND_MLA"
 
     @staticmethod
     def get_metadata_cls() -> type["AttentionMetadata"]:
@@ -368,11 +354,10 @@ class AscendMLAMetadataBuilder:
         num_reqs: int,
         num_actual_tokens: int,
         max_query_len: int,
-        common_attn_metadata: CommonAttentionMetadata,
-        common_prefix_len: Optional[int] = None,
         graph_pad_size: int = -1,
         max_num_tokens_across_dp: int = 0,
         with_prefill_across_dp: bool = False,
+        query_start_loc: torch.Tensor = None,
     ) -> AscendMLAMetadata:
         assert self._num_decodes + self._num_prefills == num_reqs
 
@@ -394,7 +379,6 @@ class AscendMLAMetadataBuilder:
         seq_lens = seq_lens_cpu
         max_query_len = query_lens.max().item()
         max_seq_lens = seq_lens.max().item()
-        query_start_loc = common_attn_metadata.query_start_loc
 
         prefill_metadata = None
         chunked_context_metadata = None
@@ -403,7 +387,6 @@ class AscendMLAMetadataBuilder:
             tokens_start = self._num_decode_tokens
             max_query_len = query_lens[tokens_start:].max().item()
             max_seq_lens = seq_lens[tokens_start:].max().item()
-            query_start_loc = common_attn_metadata.query_start_loc
             prefill_query_start_loc = query_start_loc[
                 reqs_start:] - query_start_loc[reqs_start]
 
@@ -539,7 +522,7 @@ class AscendMLAImpl(MLAAttentionImpl):
         kv_cache_dtype: str,
         logits_soft_cap: Optional[float],
         attn_type: str,
-        kv_sharing_target_layer_name: Optional[str] = None,
+        kv_sharing_target_layer_name: Optional[str],
         **kwargs,
     ) -> None:
         self.num_heads = num_heads