diff --git a/fastdeploy/model_executor/layers/attention/mla_attention_backend.py b/fastdeploy/model_executor/layers/attention/mla_attention_backend.py
index d7d18526f9..8293b6936f 100644
--- a/fastdeploy/model_executor/layers/attention/mla_attention_backend.py
+++ b/fastdeploy/model_executor/layers/attention/mla_attention_backend.py
@@ -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
@@ -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
diff --git a/fastdeploy/model_executor/models/deepseek_v3.py b/fastdeploy/model_executor/models/deepseek_v3.py
index faa76be8d2..5c9070a701 100644
--- a/fastdeploy/model_executor/models/deepseek_v3.py
+++ b/fastdeploy/model_executor/models/deepseek_v3.py
@@ -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,
@@ -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)