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[CP&SP] Integrate FIA operator in mla_cp._forward_decode (#5641)

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### What this PR does / why we need it?
Replace the npu_multi_head_latent_attention with FIA operator in
mla_cp.py _forward_decode.
Adjust mla_attn_dpc_pcp in acl_graph.py

### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: Bai Yongbin <845473182@qq.com>
Signed-off-by: tongyuzhou <t00886357@china.huawei.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: tongyuzhou <t00886357@china.huawei.com>
This commit is contained in:
Bai Yongbin
2026-01-22 20:02:30 +08:00
committed by GitHub
parent 88632cf976
commit 7f91ac2649
4 changed files with 123 additions and 81 deletions
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127
vllm_ascend/attention/context_parallel/mla_cp.py
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@@ -14,6 +14,8 @@ from vllm.forward_context import ForwardContext, get_forward_context
from vllm.utils.math_utils import cdiv
from vllm.v1.kv_cache_interface import AttentionSpec, MLAAttentionSpec
from vllm_ascend.attention.attention_v1 import AscendAttentionState
# isort: off
from vllm_ascend.attention.mla_v1 import (
AscendMLADecodeMetadata,
@@ -244,8 +246,12 @@ class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder):
self.batch_seq_mask_buf[: batch_seq_mask.shape[0]].copy_(batch_seq_mask, non_blocking=True)
batch_seq_mask = self.batch_seq_mask_buf[: batch_seq_mask.shape[0]]
cp_seq_len = torch.where(cp_seq_len == 0, 1, cp_seq_len)
decode_metadata.cp_seq_len = cp_seq_len
decode_metadata.cp_seq_len = cp_seq_len.tolist()
decode_metadata.batch_seq_mask = batch_seq_mask
actual_seq_lengths_q = torch.arange(self.num_decodes_flatten) + 1
decode_metadata.actual_seq_lengths_q = actual_seq_lengths_q
return decode_metadata
@@ -535,18 +541,53 @@ class AscendMlaCPImpl(AscendMLAImpl):
num_heads = self.num_heads * self.dcp_size
else:
num_heads = self.num_heads
k_nope = k_nope.view(-1, block_size, self.num_kv_heads, self.kv_lora_rank)
k_pe = k_pe.view(-1, block_size, self.num_kv_heads, self.qk_rope_head_dim)
q_nope = q_nope.view(num_tokens, num_heads, -1)
q_pe = q_pe.view(num_tokens, num_heads, -1)
# use pcp & dcp split computed token nums from scheduler to compute actual seq_len and seq_mask
seq_len = decode_meta.cp_seq_len
k_nope = k_nope.view(-1, self.num_kv_heads, block_size, self.kv_lora_rank)
k_pe = k_pe.view(-1, self.num_kv_heads, block_size, self.qk_rope_head_dim)
actual_seq_lengths = None
input_layout = "BNSD"
if (
attn_metadata.attn_state
in [
AscendAttentionState.SpecDecoding,
AscendAttentionState.ChunkedPrefill,
AscendAttentionState.DecodeOnly,
]
and self.speculative_config is not None
):
input_layout = "TND"
# TODO: If the driver is upgraded later, the contiguous function can be deleted.
q_nope = q_nope.view(num_tokens, num_heads, -1).contiguous()
q_pe = q_pe.view(num_tokens, num_heads, -1)
sparse_mode = 3
spec_attn_mask = attn_metadata.decode.attn_mask # type:ignore
actual_seq_lengths = decode_meta.actual_seq_lengths_q
else:
q_nope = q_nope.view(num_tokens, num_heads, 1, -1).contiguous()
q_pe = q_pe.view(num_tokens, num_heads, 1, -1)
sparse_mode = 0
spec_attn_mask = None
common_kwargs = {
"return_lse": True,
"calc_type": "calc_type_ring",
"query_rope": q_pe,
"key_rope": k_pe,
"num_heads": num_heads,
"num_key_value_heads": self.num_kv_heads,
"input_layout": input_layout,
"atten_mask": spec_attn_mask,
"sparse_mode": sparse_mode,
"scale": self.scale,
"antiquant_mode": 0,
"antiquant_scale": None,
"block_table": decode_meta.block_table,
"block_size": block_size,
"actual_seq_lengths": actual_seq_lengths,
"actual_seq_lengths_kv": decode_meta.cp_seq_len,
"softmax_lse_flag": True,
}
forward_context: ForwardContext = get_forward_context()
if forward_context.is_draft_model:
graph_params = get_draft_graph_params()
@@ -560,72 +601,58 @@ class AscendMlaCPImpl(AscendMLAImpl):
graph_params.events[num_tokens].append(event)
workspace = graph_params.workspaces.get(num_tokens)
if workspace is None:
workspace = torch_npu.atb._npu_multi_head_latent_attention_get_workspace(
workspace = torch_npu._npu_fused_infer_attention_score_get_max_workspace(
q_nope,
q_pe,
k_nope,
k_pe,
decode_meta.block_table,
seq_len,
num_heads,
self.scale,
self.num_kv_heads,
k_nope,
**common_kwargs,
)
update_graph_params_workspaces(num_tokens, workspace)
attn_output = torch.empty_like(q_nope)
softmax_lse = torch.empty((num_tokens, num_heads, 1), dtype=q_nope.dtype, device=q_nope.device)
if input_layout == "BNSD":
softmax_lse = torch.empty((num_tokens, num_heads, 1, 1), dtype=torch.float, device=q_nope.device)
else:
softmax_lse = torch.empty((num_tokens, num_heads, 1), dtype=torch.float, device=q_nope.device)
graph_params.attn_params[num_tokens].append(
(
weak_ref_tensors(q_nope),
weak_ref_tensors(q_pe),
weak_ref_tensors(k_nope),
weak_ref_tensors(q_pe),
weak_ref_tensors(k_pe),
decode_meta.block_table,
seq_len,
num_heads,
self.scale,
self.num_kv_heads,
input_layout,
weak_ref_tensors(spec_attn_mask) if spec_attn_mask is not None else None,
sparse_mode,
self.scale,
weak_ref_tensors(decode_meta.block_table),
block_size,
actual_seq_lengths,
decode_meta.cp_seq_len,
weak_ref_tensors(attn_output),
weak_ref_tensors(softmax_lse),
)
)
torch.npu.graph_task_group_begin(stream)
torch_npu.atb.npu_multi_head_latent_attention(
q_nope,
q_pe,
k_nope,
k_pe,
decode_meta.block_table,
seq_len,
num_heads,
self.scale,
self.num_kv_heads,
**common_kwargs,
workspace=workspace,
output=attn_output,
lse=softmax_lse,
torch_npu.npu_fused_infer_attention_score.out(
q_nope, k_nope, k_nope, **common_kwargs, workspace=workspace, out=[attn_output, softmax_lse]
)
handle = torch.npu.graph_task_group_end(stream)
graph_params.handles[num_tokens].append(handle)
else:
attn_output = torch.empty_like(q_nope)
softmax_lse = torch.empty((num_tokens, num_heads, 1), dtype=q_nope.dtype, device=q_nope.device)
torch_npu.atb.npu_multi_head_latent_attention(
attn_output, softmax_lse = torch_npu.npu_fused_infer_attention_score(
q_nope,
q_pe,
k_nope,
k_pe,
decode_meta.block_table,
seq_len,
num_heads,
self.scale,
self.num_kv_heads,
return_lse=True,
calc_type="calc_type_ring",
output=attn_output,
lse=softmax_lse,
k_nope,
**common_kwargs,
)
if input_layout == "BNSD":
B_attn, N_attn, S, D = attn_output.shape
B_lse, N_lse, Q_S, _ = softmax_lse.shape
attn_output = attn_output.permute(0, 2, 1, 3).reshape(B_attn * S, N_attn, D)
softmax_lse = softmax_lse.permute(0, 2, 1, 3).reshape(B_lse * Q_S, N_lse, 1)
# Update out&lse
attn_out_lse = _process_attn_out_lse(attn_output, softmax_lse, decode_meta.batch_seq_mask)