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Reapply "[Refactor] Unify full-graph parameter update logic (#6041)" (#6227) (#6231)

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This reverts commit 95649344aa.

The CI failure doesn't related to this change. Let's reapply it.

- vLLM version: v0.14.0
- vLLM main:
d68209402d
This commit is contained in:
wangxiyuan
2026-01-26 09:04:54 +08:00
committed by GitHub
parent c38c838d03
commit 4e3919e965
10 changed files with 420 additions and 415 deletions
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82
vllm_ascend/attention/mla_v1.py
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@@ -720,6 +720,88 @@ class AscendMLAImpl(MLAAttentionImpl):
)
register_all_layers_to_shard_weight_series(self.layer_sharding_kwargs)
@staticmethod
def update_graph_params(
update_stream,
forward_context,
num_tokens,
vllm_config=None,
speculative_config=None,
num_dcp_pcp_tokens=None,
):
if forward_context.is_draft_model:
graph_params = get_draft_graph_params()
else:
graph_params = get_graph_params()
# FIXME: Behold! We are using a temporary hack here to update the args
# for each layer's attention op in the graph.
with torch.npu.stream(update_stream):
for key, param, handle, event in zip(
forward_context.attn_metadata,
graph_params.attn_params[num_tokens],
graph_params.handles[num_tokens],
graph_params.events[num_tokens],
):
(
q_nope,
k_nope,
q_pe,
k_pe,
num_heads,
num_kv_heads,
input_layout,
attn_mask,
sparse_mode,
scale,
block_table,
block_size,
seq_lens_list,
actual_seq_lengths,
attn_output,
softmax_lse,
) = param
seq_lens_list = forward_context.attn_metadata[key].decode.seq_lens_list
if speculative_config and speculative_config.method == "mtp" and not forward_context.is_draft_model:
actual_seq_lengths = forward_context.attn_metadata[key].decode.actual_seq_lengths_q
spec_multiple = speculative_config.num_speculative_tokens + 1
seq_lens_list = seq_lens_list + [0] * (num_tokens // spec_multiple - len(seq_lens_list))
actual_seq_lengths = [spec_multiple * (i + 1) for i in range(num_tokens // spec_multiple)]
elif forward_context.is_draft_model:
actual_seq_lengths = forward_context.attn_metadata[key].decode.actual_seq_lengths_q
block_table = forward_context.attn_metadata[key].decode.block_table
# TODO: This is a hack and should be fixed in the future.
if speculative_config.disable_padded_drafter_batch:
block_table = block_table[: len(actual_seq_lengths)]
seq_lens_list = seq_lens_list + [0] * (len(actual_seq_lengths) - len(seq_lens_list))
else:
seq_lens_list = seq_lens_list + [0] * (num_tokens - len(seq_lens_list))
torch.npu.graph_task_update_begin(update_stream, handle)
torch_npu.npu_fused_infer_attention_score.out(
q_nope,
k_nope,
k_nope,
query_rope=q_pe,
key_rope=k_pe,
num_heads=num_heads,
num_key_value_heads=num_kv_heads,
input_layout=input_layout,
atten_mask=attn_mask,
sparse_mode=sparse_mode,
scale=scale,
antiquant_mode=0,
antiquant_scale=None,
block_table=block_table,
block_size=block_size,
actual_seq_lengths_kv=seq_lens_list,
actual_seq_lengths=actual_seq_lengths,
workspace=graph_params.workspaces.get(num_tokens),
out=[attn_output, softmax_lse],
)
torch.npu.graph_task_update_end(update_stream)
event.record(update_stream)
def _v_up_proj(self, x):
# Convert from (N, B, L)/(N, B, 1, L) to (N, B, L)
x = x.view(self.num_heads, -1, self.kv_lora_rank)