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[Optimization] Delete the padding strategy of the MLA-prefill value #4472

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[Optimization] Delete the padding strategy of the MLA-prefill value #4472

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5 changes: 5 additions & 0 deletions fastdeploy/model_executor/layers/attention/mla_attention_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,7 @@ def __init__(
self.kv_lora_rank: int = fd_config.model_config.kv_lora_rank
self.qk_rope_head_dim: int = fd_config.model_config.qk_rope_head_dim
self.qk_head_dim: int = fd_config.model_config.qk_nope_head_dim + fd_config.model_config.qk_rope_head_dim
self.v_head_dim: int = fd_config.model_config.v_head_dim
self.attn_softmax_scale: float = self.qk_head_dim**-0.5
if fd_config.model_config.rope_scaling:
mscale_all_dim = fd_config.model_config.rope_scaling.get("mscale_all_dim", False) # 1.0
Expand Down Expand Up @@ -443,6 +444,10 @@ def forward_mixed(
**self.flash_attn_kwargs,
)[0]

# NOTE: (changwenbin)If you use Flash-attn2, you need to cut off the padding part.
if self.flash_attn_func is flash_attn_unpadded:
fmha_out = fmha_out[:, :, : self.v_head_dim]

return fmha_out

# Decode
Expand Down
5 changes: 2 additions & 3 deletions fastdeploy/model_executor/models/deepseek_v3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -369,8 +369,9 @@ def forward(
key = paddle.empty_like(query)
key[..., : self.qk_nope_head_dim] = key_nope
key[..., self.qk_nope_head_dim :] = key_pe
value = paddle.nn.functional.pad(value, [0, self.qk_head_dim - self.v_head_dim], value=0)

# NOTE: (changwenbin) Flash-attn3 does not need to padding the head-dim of Value,
# Flash-attn2 has already processed it when returning.
fmha_out_prefill = self.mla_attn(
q=query,
k=key,
Expand All @@ -381,8 +382,6 @@ def forward(
forward_meta=forward_meta,
)

fmha_out_prefill = fmha_out_prefill.reshape([-1, self.num_attention_heads_tp, self.qk_head_dim])
fmha_out_prefill = fmha_out_prefill[:, :, : self.v_head_dim]
fmha_out_prefill = fmha_out_prefill.reshape([-1, self.num_attention_heads_tp * self.v_head_dim])
fmha_out_prefill = fmha_out_prefill * mask_encoder_batch.cast(fmha_out_prefill.dtype)

Expand Down
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