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[0.9.1][Perf] Optimize the number of rope-related index selections in deepseek. #1614

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Jul 8, 2025
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[0.9.1][Perf] Optimize the number of rope-related index selections in deepseek. #1614

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76 changes: 54 additions & 22 deletions vllm_ascend/attention/mla_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,6 +93,8 @@ class ChunkedContextMetadata:
max_query_len: int
max_seq_lens: int
chunked_context: Optional[ChunkedContextMetadata] = None
sin: torch.Tensor = None
cos: torch.Tensor = None


@dataclass
Expand All @@ -106,6 +108,8 @@ class AscendMLADecodeMetadata:
seq_lens_list: list[int]
actual_seq_q_lens: Optional[list[int]] = None
attn_mask: Optional[torch.Tensor] = None
sin: torch.Tensor = None
cos: torch.Tensor = None


@dataclass
Expand Down Expand Up @@ -217,6 +221,9 @@ def __init__(self,
)
ascend_config = get_ascend_config()
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
self.rope_dim = self.runner.model_config.hf_text_config.qk_rope_head_dim
self.cos_cache = None
self.sin_cache = None

def reorder_batch(self, input_batch: "InputBatch",
scheduler_output: "SchedulerOutput") -> bool:
Expand Down Expand Up @@ -348,6 +355,18 @@ def build_torchair_graph_dummy(
else:
attn_state = AscendAttentionState.DecodeOnly
num_decode_tokens = 1
sin = torch.ones(num_reqs,
1,
1,
self.rope_dim,
dtype=self.runner.dtype,
device=device)
cos = torch.ones(num_reqs,
1,
1,
self.rope_dim,
dtype=self.runner.dtype,
device=device)
decode_metadata = AscendMLADecodeMetadata(
input_positions=input_positions,
block_table=block_table,
Expand All @@ -356,7 +375,8 @@ def build_torchair_graph_dummy(
max_seq_lens=1,
attn_mask=self.runner.spec_attn_mask,
actual_seq_q_lens=self.runner.actual_seq_q_lens[:num_reqs],
)
sin=sin,
cos=cos)
return self.metadata_cls( # type: ignore
num_input_tokens=num_actual_tokens,
num_actual_tokens=num_actual_tokens,
Expand Down Expand Up @@ -408,6 +428,16 @@ def build(
max_query_len = query_lens.max().item()
max_seq_lens = seq_lens.max().item()
query_start_loc = common_attn_metadata.query_start_loc
if self.cos_cache is None:
self.cos_cache = self.runner.get_model(
).model.layers[0].self_attn.rotary_emb.cos_cached
self.sin_cache = self.runner.get_model(
).model.layers[0].self_attn.rotary_emb.sin_cached
if self.cos_cache.dtype != self.runner.dtype: # type: ignore
self.cos_cache = self.cos_cache.to( # type: ignore
self.runner.dtype) # type: ignore
self.sin_cache = self.sin_cache.to( # type: ignore
self.runner.dtype) # type: ignore

prefill_metadata = None
chunked_context_metadata = None
Expand Down Expand Up @@ -454,18 +484,26 @@ def build(
chunk_seq_lens=chunk_seq_lens,
workspace=self.chunked_prefill_workspace,
)

prefill_input_positions = input_positions[tokens_start:]
cos = self.cos_cache[
prefill_input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
sin = self.sin_cache[
prefill_input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
prefill_metadata = AscendMLAPrefillMetadata(
attn_mask=self.runner.attn_mask,
query_lens=query_lens[tokens_start:],
seq_lens=seq_lens,
context_lens=seq_lens[tokens_start:],
input_positions=input_positions[tokens_start:],
input_positions=prefill_input_positions,
block_table=block_table[reqs_start:, ...],
max_query_len=max_query_len,
max_seq_lens=max_seq_lens,
query_start_loc=prefill_query_start_loc,
chunked_context=chunked_context_metadata,
sin=sin,
cos=cos,
)

decode_metadata = None
Expand Down Expand Up @@ -510,8 +548,15 @@ def build(
actual_seq_q_lens = query_start_loc[1:].tolist(
) + self.runner.actual_seq_q_lens[num_reqs:num_reqs +
num_reqs_pad_size]
cos = self.cos_cache[
input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
sin = self.sin_cache[
input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
else:
seq_lens_list = seq_lens.tolist()
cos, sin = None, None

decode_metadata = AscendMLADecodeMetadata(
input_positions=input_positions,
Expand All @@ -521,7 +566,8 @@ def build(
max_seq_lens=max_seq_lens,
attn_mask=self.runner.spec_attn_mask,
actual_seq_q_lens=actual_seq_q_lens,
)
sin=sin,
cos=cos)

return self.metadata_cls( # type: ignore
num_actual_tokens=num_actual_tokens,
Expand Down Expand Up @@ -1113,15 +1159,8 @@ def forward(
decode_k_nope = None
assert attn_metadata.decode is not None
if self.running_in_graph:
seq_len = self.rotary_emb.max_position_embeddings * self.rotary_emb.scaling_factor
cos = self.rotary_emb.cos_cached[:seq_len].to(
dtype=decode_hs_or_q_c.dtype)
sin = self.rotary_emb.sin_cached[:seq_len].to(
dtype=decode_hs_or_q_c.dtype)
cos = cos[attn_metadata.decode.input_positions]
sin = sin[attn_metadata.decode.input_positions]
cos = cos[:, None, None, :]
sin = sin[:, None, None, :]
cos = attn_metadata.decode.cos
sin = attn_metadata.decode.sin
# Without explicitly controlling the order, IndexByTensor operations
# would be placed after `matmul W_KV_T` hindering the overlapping of
# KvRmsNormRopeCache and SingleRope.
Expand Down Expand Up @@ -1156,15 +1195,8 @@ def forward(
prefill_q_nope = prefill_q[..., :self.qk_nope_head_dim]
if self.torchair_graph_enabled:
num_tokens = prefill_hs_or_q_c.shape[0]
seq_len = self.rotary_emb.max_position_embeddings * self.rotary_emb.scaling_factor
cos = self.rotary_emb.cos_cached[:seq_len].to(
dtype=prefill_q_pe.dtype)
sin = self.rotary_emb.sin_cached[:seq_len].to(
dtype=prefill_q_pe.dtype)
cos = cos[attn_metadata.prefill.input_positions]
sin = sin[attn_metadata.prefill.input_positions]
cos = cos[:, None, None, :]
sin = sin[:, None, None, :]
cos = attn_metadata.prefill.cos
sin = attn_metadata.prefill.sin

prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
prefill_k_pe, prefill_k_nope = self.exec_kv_prefill(
Expand Down
2 changes: 2 additions & 0 deletions vllm_ascend/worker/model_runner_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1647,6 +1647,8 @@ def _dummy_run(
attn_metadata.decode.block_table)
torch._dynamo.mark_static(
attn_metadata.decode.input_positions)
torch._dynamo.mark_static(attn_metadata.decode.sin)
torch._dynamo.mark_static(attn_metadata.decode.cos)
torch._dynamo.mark_static(attn_metadata.slot_mapping)
for kv in self.kv_caches:
assert isinstance(
Expand Down