[Misc] Refactor AscendMetaData Comments to Make It Clearer (#1967)

### What this PR does / why we need it?
Refactor the comments of `AscendMetaData` to make it clearer.

- vLLM version: v0.9.2
- vLLM main:
f3137cdd81

---------

Signed-off-by: shen-shanshan <467638484@qq.com>

vllm_ascend/attention/attention_v1.py

@@ -121,28 +121,40 @@ class AscendAttentionState(Enum):
 
 @dataclass
 class AscendMetadata:
-    num_actual_tokens: int  # Number of tokens excluding padding.
-    # (batch_size, max_blocks_per_seq).
-    # Block addresses per sequence. (Seq id -> list of physical block)
-    block_tables: torch.Tensor
-    # (batch_size,). The sequence length per sequence. Sequence length means
-    # the computed tokens + new tokens None if it is a decoding.
-    query_start_loc: torch.Tensor
-    query_lens: torch.Tensor
-    seq_lens: torch.Tensor
-    # max value of number of tokens across dp group
-    max_num_tokens_across_dp: int = 0
-    # Maximum query length in the batch. None for decoding.
-    max_query_len: Optional[int] = None
-    # (num_tokens,). The indices of the token slots that input tokens will be
-    # stored into. E.g., if `slot_mapping` is [35, 2, 17] and the block size
-    # is 16, the three tokens are stored in the 3rd slot in block 2, 2nd slot
-    # in block 0, and 1st slot in block 1, respectively.
-    slot_mapping: torch.Tensor = None
+    # **************************** Basic Properties ****************************
+    attn_mask: Optional[torch.Tensor] = None
     # Current state of this attention run.
     attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
-    attn_mask: Optional[torch.Tensor] = None
+
+    # Number of tokens excluding padding.
+    num_actual_tokens: int = 0
+
+    # The sequence length per sequence. Sequence length means the computed
+    # tokens + new tokens (is None if it is a decoding).
+    # (batch_size,)
+    seq_lens: torch.Tensor = None
+
+    query_start_loc: torch.Tensor = None
+    query_lens: torch.Tensor = None
+    # Maximum query length in the batch (None for decoding).
+    max_query_len: Optional[int] = None
+
+    # ********************** KV Cache Related Properties ***********************
+    # Block addresses per sequence (Seq id -> list of physical block).
+    # (batch_size, max_blocks_per_seq)
+    block_tables: torch.Tensor = None
+
+    # The indices of the token slots that input tokens will be stored into.
+    # E.g., if `slot_mapping` is [35, 2, 17] and the block size is 16, the
+    # three tokens are stored in the 3rd slot in block 2, 2nd slot in block 0,
+    # and 1st slot in block 1, respectively.
+    # (num_tokens,)
+    slot_mapping: torch.Tensor = None
+
+    # ************************* DP Related Properties **************************
     with_prefill_across_dp: bool = False
+    # Maximum number of tokens across dp group
+    max_num_tokens_across_dp: int = 0
 
 
 class AscendAttentionMetadataBuilder: