[Refactor] Add comments for Metadata classes in attention module (#5789)

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

Add docstrings for Metadata and MetadataBuilder classes in the attention
module to improve code readability.

Related to #5463 (Item 11: Add some comments for CommonMetadata and
others)

**Modified files:**
- `vllm_ascend/attention/context_parallel/common_cp.py`: Added comments
for `AscendPCPMetadata`, `CPChunkedContextMetadata`,
`AscendMetadataForPrefill`, `AscendMetadataForDecode`
- `vllm_ascend/attention/utils.py`: Added comments for
`AscendPrefillContextParallelMetadata`
- `vllm_ascend/attention/mla_v1.py`: Added comments for
`ChunkedContextMetadata`, `AscendMLADecodeMetadata`
- `vllm_ascend/attention/attention_v1.py`: Added comments for
`AscendMetadata`, `AscendAttentionMetadataBuilder`
- `vllm_ascend/attention/context_parallel/attention_cp.py`: Added
comments for `AscendAttentionCPMetadataBuilder`

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

No.

### How was this patch tested?

Documentation only, no functional changes.

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

vllm_ascend/attention/attention_v1.py

@@ -132,6 +132,12 @@ class AscendAttentionState(Enum):
 
 @dataclass
 class AscendMetadata:
+    """
+    Per-layer attention metadata for Ascend FlashAttention backend.
+
+    Contains attention masks, token counts, sequence lengths and KV cache
+    related properties for attention computation.
+    """
     # **************************** Basic Properties ************************** #
     attn_mask: Optional[torch.Tensor] = None
     # Current state of this attention run.
@@ -186,7 +192,12 @@ class AscendMetadata:
 
 
 class AscendAttentionMetadataBuilder(AttentionMetadataBuilder[AscendMetadata]):
-    # AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+    """
+    Builder for constructing AscendMetadata from CommonAttentionMetadata.
+
+    Handles attention mask generation and metadata preparation for
+    Ascend FlashAttention backend.
+    """
     # Does this backend/builder reorder the batch?
     # If not, set this to None. Otherwise set it to the query
     # length that will be pulled into the front of the batch.

vllm_ascend/attention/context_parallel/attention_cp.py

@@ -45,7 +45,11 @@ from vllm_ascend.utils import cp_chunkedprefill_comm_stream, weak_ref_tensors
 
 
 class AscendAttentionCPMetadataBuilder(AscendAttentionMetadataBuilder):
-    # AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+    """
+    Builder for constructing AscendMetadata with Context Parallelism support.
+
+    Extends AscendAttentionMetadataBuilder with PCP/DCP metadata handling.
+    """
     # Does this backend/builder reorder the batch?
     # If not, set this to None. Otherwise set it to the query
     # length that will be pulled into the front of the batch.

vllm_ascend/attention/context_parallel/common_cp.py

@@ -11,6 +11,12 @@ from vllm.distributed import (get_dcp_group,
 
 @dataclass
 class AscendPCPMetadata:
+    """
+    Metadata for Prefill Context Parallelism (PCP) on Ascend devices.
+
+    Stores index tensors and sequence lengths for routing attention
+    computations across PCP ranks during long sequence processing.
+    """
     q_head_idx: torch.Tensor = None
     q_tail_idx: torch.Tensor = None
     kv_with_q_head_nomask_idx: torch.Tensor = None
@@ -26,7 +32,11 @@ class AscendPCPMetadata:
 
 @dataclass
 class CPChunkedContextMetadata:
-    # New for MLA (compared to FlashAttention)
+    """
+    Metadata for chunked context handling in Context Parallelism (CP).
+
+    Extends chunked prefill with per-rank chunk information for PCP/DCP.
+    """
     # For handling chunked prefill
     cu_seq_lens: torch.Tensor
     starts: torch.Tensor
@@ -46,9 +56,11 @@ class CPChunkedContextMetadata:
 
 @dataclass
 class AscendMetadataForPrefill:
+    """ Prefill-specific metadata for Ascend attention with Context Parallelism."""
 
     @dataclass
     class ChunkedContextMetadata:
+        """Metadata for chunked context processing within prefill phase."""
         actual_chunk_seq_lengths: torch.Tensor
         actual_seq_lengths_kv: torch.Tensor
         starts: torch.Tensor
@@ -69,7 +81,7 @@ class AscendMetadataForPrefill:
 
 @dataclass
 class AscendMetadataForDecode:
-    """ Decode Specific Metadata for Ascend"""
+    """ Decode-specific metadata for Ascend attention with Context Parallelism."""
     num_computed_tokens_of_pcp_dcp: Optional[list[list[list[int]]]] = None
     batch_seq_mask: torch.Tensor = None
     block_tables: torch.Tensor = None

vllm_ascend/attention/mla_v1.py

@@ -84,8 +84,11 @@ class AscendMLABackend(AttentionBackend):
 
 @dataclass
 class ChunkedContextMetadata:
-    # New for MLA (compared to FlashAttention)
-    # For handling chunked prefill
+    """
+    Metadata for chunked context handling in MLA attention.
+
+    Manages sequence boundaries and workspace for chunked prefill processing.
+    """
     cu_seq_lens: torch.Tensor
     starts: torch.Tensor
     seq_tot: list[int]
@@ -116,7 +119,8 @@ class AscendMLAPrefillMetadata:
 
 @dataclass
 class AscendMLADecodeMetadata:
-    # Input positions for rotrary embeddings since for MLA the rotary
+    """ Decode-specific metadata for Ascend MLA attention."""
+    # Input positions for rotary embeddings since for MLA the rotary
     # position embeddings are applied inside the attention backend
     input_positions: torch.Tensor
     block_table: torch.Tensor

vllm_ascend/attention/utils.py

@@ -36,8 +36,12 @@ def enable_cp():
 
 
 @dataclass
-# class AscendCommonLongSequenceMetadata:
 class AscendPrefillContextParallelMetadata:
+    """
+    Metadata for Prefill Context Parallelism (PCP) in CommonAttentionMetadata.
+
+    Contains index tensors and sequence lengths for PCP operations.
+    """
     pcp_allgather_restore_idx: torch.Tensor = None
 
     cp_kv_recover_idx_for_chunk: torch.Tensor = None
@@ -81,24 +85,36 @@ class AscendCommonAttentionMetadata(CommonAttentionMetadata):
 
     For many of the tensors we keep both NPU and CPU versions.
     """
+    # CPU tensor of sequence lengths for host-side operations.
+    # E.g., tensor([128, 256, 64]) for 3 requests with different seq lengths.
     seq_lens_cpu: torch.Tensor = None
+
+    # CPU tensor of already computed tokens count per request.
+    # E.g., tensor([100, 200, 50]) means req0 has 100 tokens already computed.
     num_computed_tokens_cpu: torch.Tensor = None
 
+    # Number of decode tokens per request, used for speculative decoding.
+    # E.g., 1 for normal decoding, >1 for speculative decoding.
     decode_token_per_req: int = 1
-    """decode token number per request"""
 
+    # Actual query sequence lengths for each token in the batch (CPU list).
+    # E.g., [1, 1, 1, 128] for 3 decode tokens and 1 prefill with 128 tokens.
     actual_seq_lengths_q: list[int] = field(default_factory=list)
 
+    # NPU tensor of position indices for rotary embeddings computation.
+    # E.g., tensor([0, 1, 2, ...]) indicating token positions in sequence.
     positions: torch.Tensor = None
 
+    # Current attention state (e.g., ChunkedPrefill, DecodeOnly).
     attn_state: Any = None
 
+    # Padding size for graph capture, -1 means not in graph mode.
     graph_pad_size: int = -1
 
-    # num_input_tokens refers to total number of tokens including
-    # padding tokens. It is used to handle some padding operations.
+    # Total number of tokens including padding, used for padding operations.
     num_input_tokens: int = 0
 
+    # Metadata for Prefill Context Parallelism (PCP) operations.
     prefill_context_parallel_metadata: Optional[
         AscendPrefillContextParallelMetadata] = None