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[BugFix] Fix a bug of running chunked-prefill with torchair. (#1378) #1844

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[BugFix] Fix a bug of running chunked-prefill with torchair. (#1378) #1844

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28 changes: 14 additions & 14 deletions vllm_ascend/attention/mla_v1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1051,11 +1051,10 @@ def forward(
]
num_actual_toks = attn_metadata.num_actual_tokens
if k_pe is None and not self.running_in_graph:
if not self.torchair_graph_enabled:
kv_c, k_pe = self.kv_a_proj_with_mqa(
hidden_states_or_kv_c_normed)[0].split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
kv_c, k_pe = self.kv_a_proj_with_mqa(
hidden_states_or_kv_c_normed)[0].split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
else:
kv_c_normed = hidden_states_or_kv_c_normed
assert attn_metadata.num_decodes is not None and \
Expand All @@ -1074,12 +1073,13 @@ def forward(
if not self.running_in_graph:
hidden_states_or_q_c = hidden_states_or_q_c[:num_actual_toks, ...]
prefill_hs_or_q_c = hidden_states_or_q_c[num_decode_tokens:]
if not self.torchair_graph_enabled:
decode_hs_or_q_c = hidden_states_or_q_c[:num_decode_tokens]
k_pe = k_pe[:num_actual_toks, ...]
k_pe = k_pe.unsqueeze(1)
decode_k_pe = k_pe[:num_decode_tokens]
prefill_k_pe = k_pe[num_decode_tokens:]
decode_hs_or_q_c = hidden_states_or_q_c[:num_decode_tokens]
prefill_hs = hidden_states_or_kv_c_normed[num_decode_tokens:]
# if not self.torchair_graph_enabled:
k_pe = k_pe[:num_actual_toks, ...]
k_pe = k_pe.unsqueeze(1)
decode_k_pe = k_pe[:num_decode_tokens]
prefill_k_pe = k_pe[num_decode_tokens:]
else:
decode_hs_or_q_c = hidden_states_or_q_c
if has_decode:
Expand Down Expand Up @@ -1153,11 +1153,11 @@ def forward(

prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
prefill_k_pe, prefill_k_nope = self.exec_kv_prefill(
hidden_states_or_kv_c_normed, cos, sin, kv_cache,
attn_metadata.slot_mapping)
prefill_hs, cos, sin, kv_cache,
attn_metadata.slot_mapping[num_decode_tokens:])

kv_c_normed = prefill_k_nope[:num_actual_toks, ...]
prefill_k_c_normed = prefill_k_nope[num_decode_tokens:]
prefill_k_c_normed = prefill_k_nope
prefill_k_pe = prefill_k_pe.view(num_tokens, self.num_kv_heads,
-1)
prefill_q = torch.cat([prefill_q_nope, prefill_q_pe], dim=-1)
Expand Down
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