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[Perf] vectorize PCP/DCP loops in attention_cp.py (#4944)

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### What this PR does / why we need it?
- Add explicit .contiguous() after permute/view to ensure mem-friendly
layout
- Replace nested PCP/DCP Python loops with fully vectorized tensor
operations

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: F.Liu <liufeng248@huawei.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>
This commit is contained in:
Feng Liu
2025-12-22 11:06:19 +08:00
committed by GitHub
parent 904c18f929
commit 49838d4bec
2 changed files with 119 additions and 109 deletions
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154
vllm_ascend/attention/attention_cp.py
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@@ -428,26 +428,36 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
attn_lse.shape[0] * attn_lse.shape[1] * attn_lse.shape[2])
return attn_out, attn_lse
def _npu_attention_update(
self, attn_out_lse_list: List[torch.Tensor]) -> torch.Tensor:
def _npu_attention_update(self,
attn_out_lse: torch.Tensor) -> torch.Tensor:
B_total, H_total, D_plus_1 = attn_out_lse.shape
S = B_total // self.pcp_size
H = H_total // self.dcp_size
D = self.head_size
update_type = 0
batch = attn_out_lse_list[0].shape[0]
num_heads = attn_out_lse_list[0].shape[1]
head_dim = attn_out_lse_list[0].shape[2] - 1
attn_out_split_cp = []
attn_lse_split_cp = []
for i in attn_out_lse_list:
attn_out_allgather, attn_lse_allgather = self._out_lse_reshape(
*torch.split(i, [self.head_size, 1], dim=-1))
attn_out_split_cp.append(attn_out_allgather)
attn_lse_split_cp.append(attn_lse_allgather)
assert D_plus_1 == D + 1
# [PCP, S, DCP, H, D+1]
x = attn_out_lse.view(self.pcp_size, S, self.dcp_size, H, D_plus_1)
# [PCP, DCP, S, H, D+1]
x = x.permute(0, 2, 1, 3, 4).contiguous()
# Flatten [N, S, H, D+1], N = pcp_size * dcp_size
x = x.view(-1, S, H, D_plus_1)
# Split out lse
# [N, S, H, D], [N, S, H, 1]
out_flat, lse_flat = torch.split(x, [D, 1], dim=-1)
# out: [N, S, H, D] -> [N, S*H, D]
# lse: [N, S, H, 1] -> [N, S*H]
out_flat = out_flat.flatten(1, 2)
lse_flat = lse_flat.squeeze(-1).flatten(1)
# unbind to list
# [S*H, D]
out_list = out_flat.unbind(0)
# [S*H]
lse_list = lse_flat.unbind(0)
attn_out, attn_lse = torch_npu.npu_attention_update(
attn_lse_split_cp, attn_out_split_cp, update_type)
attn_out = attn_out.view(batch, num_heads, head_dim)
lse_list, out_list, update_type)
attn_out = attn_out.view(S, H, D)
return attn_out
@@ -539,17 +549,10 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
attn_out, attn_lse = torch_npu.npu_fused_infer_attention_score(
query, k_nope, value, **common_kwargs)
out_mask = attn_metadata.decode_meta.batch_seq_mask[:, None,
None].expand_as(
attn_out)
attn_out = torch.where(out_mask, 0, attn_out)
lse_mask = attn_metadata.decode_meta.batch_seq_mask[:, None,
None].expand_as(
attn_lse)
attn_lse = torch.where(lse_mask, -torch.inf, attn_lse)
attn_out_lse_list = []
# Concat out&lse: [bs,num_heads,v_head_dim] + [bs,num_heads,1] -> [bs,num_heads,v_head_dim+1]
attn_out_lse = torch.cat([attn_out, attn_lse], dim=-1)
if self.dcp_size > 1:
@@ -559,30 +562,14 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
dist.all_to_all_single(attn_out_lse_all2all,
attn_out_lse,
group=self.dcp_group)
# permute: [num_heads, v_head_dim+1, bs] -> [bs, num_heads, v_head_dim+1]
attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1])
if self.pcp_size > 1:
attn_out_lse = attn_out_lse_all2all.contiguous()
attn_out_lse_list = list(
torch.chunk(attn_out_lse_all2all, self.dcp_size, dim=1))
attn_out_lse = attn_out_lse_all2all.permute([2, 0, 1])
if self.pcp_size > 1:
# AllGather out&lse within CP group
attn_out_lse_list = [
torch.empty_like(attn_out_lse) for _ in range(self.pcp_size)
]
dist.all_gather(attn_out_lse_list,
attn_out_lse,
group=self.pcp_group)
if self.dcp_size > 1 and self.pcp_size > 1:
attn_out_lse_list_pcp_dcp = []
for s in attn_out_lse_list:
attn_out_lse_list_split = list(
torch.chunk(s, self.dcp_size, dim=1))
attn_out_lse_list_pcp_dcp += attn_out_lse_list_split
attn_out_lse_list = attn_out_lse_list_pcp_dcp
# Update out&lse
attn_out = self._npu_attention_update(attn_out_lse_list)
attn_out_lse = get_pcp_group().all_gather(
attn_out_lse.contiguous(), dim=0)
attn_out = self._npu_attention_update(attn_out_lse)
return attn_out
def _update_out_and_lse(self, out_list: torch.Tensor,
@@ -739,35 +726,28 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
dist.all_to_all_single(attn_out_lse_all2all,
chunk_attn_out_lse,
group=self.dcp_group)
attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1])
if self.pcp_size > 1:
chunk_attn_out_lse = attn_out_lse_all2all.contiguous()
attn_out_lse_list = list(
torch.chunk(attn_out_lse_all2all, self.dcp_size, dim=1))
chunk_attn_out_lse = attn_out_lse_all2all.permute([2, 0, 1])
if self.pcp_size > 1:
attn_out_lse_list = [
torch.empty_like(chunk_attn_out_lse)
for _ in range(self.pcp_size)
]
dist.all_gather(attn_out_lse_list,
chunk_attn_out_lse,
group=self.pcp_group)
# AllGather out&lse within CP group
chunk_attn_out_lse = get_pcp_group().all_gather(
chunk_attn_out_lse.contiguous(), dim=0)
if self.dcp_size > 1 and self.pcp_size > 1:
attn_out_lse_list_pcp_dcp = []
for s in attn_out_lse_list:
attn_out_lse_list_split = list(
torch.chunk(s, self.dcp_size, dim=1))
attn_out_lse_list_pcp_dcp += attn_out_lse_list_split
attn_out_lse_list = attn_out_lse_list_pcp_dcp
attn_out_lse_allgather = torch.stack(
attn_out_lse_list,
dim=0) # [pcp, batch_size, num_heads, head_size+1]
attn_out_allgather, attn_lse_allgather = torch.split(
attn_out_lse_allgather, [self.head_size, 1], dim=-1)
B_total, H_total, D_plus_1 = chunk_attn_out_lse.shape
S = B_total // self.pcp_size
H = H_total // self.dcp_size
D = self.head_size
assert D_plus_1 == D + 1
# [PCP, S, DCP, H, D+1]
x = chunk_attn_out_lse.view(self.pcp_size, S, self.dcp_size, H,
D_plus_1)
# [PCP, DCP, S, H, D+1]
x = x.permute(0, 2, 1, 3, 4).contiguous()
# Flatten [N, S, H, D+1], N = pcp_size * dcp_size
x = x.view(-1, S, H, D_plus_1)
# Split out lse.
# [N, S, H, D], [N, S, H, 1]
attn_out_allgather, attn_lse_allgather = torch.split(x, [D, 1], dim=-1)
prefix_output, prefix_lse = self._update_out_and_lse(
attn_out_allgather, attn_lse_allgather)
@@ -842,19 +822,21 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
pcp_allgather_restore_idx)
key, value = all_kv.split([self.head_size, self.head_size],
dim=-1)
prefill_key = key[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded]
prefill_value = value[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded]
slot_mapping = attn_metadata.slot_mapping[
self.pcp_size * num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded]
torch_npu._npu_reshape_and_cache(key=prefill_key,
value=prefill_value,
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=slot_mapping)
torch_npu._npu_reshape_and_cache(
key=key[self.pcp_size * num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
value=value[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded],
key_cache=self.key_cache,
value_cache=self.value_cache,
slot_indices=attn_metadata.
slot_mapping[self.pcp_size *
num_decode_tokens:attn_metadata.
num_actual_tokens_pcp_padded])
return key, value
def forward_impl(
@@ -879,9 +861,9 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
assert attn_metadata.prefill is not None
num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
prefill_query = query[
num_decode_tokens:num_actual_tokens_pcp_padded]
key = key[self.pcp_size * num_decode_tokens:]
value = value[self.pcp_size * num_decode_tokens:]
num_decode_tokens:num_actual_tokens_pcp_padded].contiguous()
key = key[self.pcp_size * num_decode_tokens:].contiguous()
value = value[self.pcp_size * num_decode_tokens:].contiguous()
if self.pcp_size > 1:
# Scenario of Enabling PCP or PCP&DCP
attn_output_prefill, attn_lse_prefill = self._forward_prefill_cp(