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

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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>
This commit is contained in:
wangxiyuan
2025-07-24 10:23:34 +08:00
committed by GitHub
parent b5ad70e1a6
commit 846555cdb5
7 changed files with 41 additions and 93 deletions
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20
vllm_ascend/attention/attention_v1.py
View File

@@ -130,10 +130,8 @@ class AscendMetadata:
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
@@ -141,18 +139,9 @@ class AscendMetadata:
# 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
# TODO: Indicates whether there are only prefill requests.
# FlashAttention can be used when there are only prefill requests.
# FlashAttention has better performance than PageAtttention,
# but it does not support decode requests.
is_only_prefill: bool = False
# Current state of this attention run.
attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
attn_mask: Optional[torch.Tensor] = None
# For logging.
num_input_tokens: int = 0 # Number of tokens including padding.
with_prefill_across_dp: bool = False
@@ -169,7 +158,6 @@ class AscendAttentionMetadataBuilder:
num_reqs,
num_actual_tokens,
max_query_len,
common_prefix_len,
max_num_tokens_across_dp: int = 0,
with_prefill_across_dp: bool = False):
@@ -224,10 +212,10 @@ class AscendAttentionBackendImpl(AttentionImpl):
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
use_irope: bool = False,
logits_soft_cap: Optional[float],
attn_type: str,
kv_sharing_target_layer_name: Optional[str],
**kwargs,
) -> None:
self.num_heads = num_heads
self.head_size = head_size

23
vllm_ascend/attention/attention_v1_torchair.py
View File

@@ -37,7 +37,7 @@ class AscendAttentionTorchairBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "ASCEND"
return "ASCEND_TORCHAIR"
@staticmethod
def get_impl_cls() -> Type["AscendAttentionTorchairBackendImpl"]:
@@ -129,10 +129,8 @@ class AscendTorchairMetadata:
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
@@ -140,20 +138,10 @@ class AscendTorchairMetadata:
# 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
# TODO: Indicates whether there are only prefill requests.
# FlashAttention can be used when there are only prefill requests.
# FlashAttention has better performance than PageAtttention,
# but it does not support decode requests.
is_only_prefill: bool = False
# Current state of this attention run.
attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
attn_mask: Optional[torch.Tensor] = None
# For logging.
num_input_tokens: int = 0 # Number of tokens including padding.
with_prefill_across_dp: bool = False
decode: Optional[AscendDecodeMetadata] = None
@@ -236,7 +224,6 @@ class AscendAttentionTorchairMetadataBuilder:
num_reqs,
num_actual_tokens,
max_query_len,
common_prefix_len,
graph_pad_size: int = -1,
max_num_tokens_across_dp: int = 0,
with_prefill_across_dp: bool = False):
@@ -335,10 +322,10 @@ class AscendAttentionTorchairBackendImpl(AttentionImpl):
alibi_slopes: Optional[List[float]],
sliding_window: Optional[int],
kv_cache_dtype: str,
logits_soft_cap: Optional[float] = None,
attn_type: str = AttentionType.DECODER,
kv_sharing_target_layer_name: Optional[str] = None,
use_irope: bool = False,
logits_soft_cap: Optional[float],
attn_type: str,
kv_sharing_target_layer_name: Optional[str],
**kwargs,
) -> None:
self.num_heads = num_heads
self.head_size = head_size

23
vllm_ascend/attention/mla_v1.py
View File

@@ -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