diff --git a/vllm_ascend/attention/mla_v1.py b/vllm_ascend/attention/mla_v1.py
index da17cf21da..9db13534a9 100644
--- a/vllm_ascend/attention/mla_v1.py
+++ b/vllm_ascend/attention/mla_v1.py
@@ -378,12 +378,6 @@ def __init__(
         self.qk_rope_head_dim = qk_rope_head_dim
         self.qk_head_dim = qk_head_dim
         self.v_head_dim = v_head_dim
-        # TODO: below padding should be removed after kernel is ready
-        # we found npu_flash_attention can only works on 128 divisible head_dim, we pad it to target size here
-        # and slice the final result to guarantee its functionality.
-        self.padding_head_dim = (
-            (self.qk_nope_head_dim + self.qk_rope_head_dim - 1) // 128 +
-            1) * 128
 
         # Hack for V1 for now to avoid torch library overhead (since we are
         # already inside an attention custom op), pull out the forward
@@ -523,7 +517,7 @@ def _forward_prefill(
         elif attn_metadata.attn_state == AscendAttentionState.PrefillNoCache:
             attn_output = torch.empty(num_tokens,
                                       self.num_heads,
-                                      self.padding_head_dim,
+                                      self.v_head_dim,
                                       dtype=query.dtype,
                                       device=query.device)
             k_nope, value = self.kv_b_proj(kv_c_normed)[0].view(
@@ -532,31 +526,17 @@ def _forward_prefill(
                     [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
             key = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
                             dim=-1)
-            pad_query = torch.nn.functional.pad(query, [
-                0, self.padding_head_dim - self.qk_rope_head_dim -
-                self.qk_nope_head_dim
-            ],
-                                                value=0)
-            pad_key = torch.nn.functional.pad(key, [
-                0, self.padding_head_dim - self.qk_rope_head_dim -
-                self.qk_nope_head_dim
-            ],
-                                              value=0)
-            pad_value = torch.nn.functional.pad(
-                value, [0, self.padding_head_dim - self.v_head_dim], value=0)
             torch_npu._npu_flash_attention(
-                query=pad_query,
-                key=pad_key,
-                value=pad_value,
+                query=query,
+                key=key,
+                value=value,
                 mask=attn_metadata.attn_mask,
                 seq_len=attn_metadata.prefill.context_lens,
                 scale_value=self.scale,
                 num_heads=self.num_heads,
                 num_kv_heads=self.num_heads,
                 out=attn_output)
-            attn_output = attn_output.view(
-                -1, self.num_heads,
-                self.padding_head_dim)[:, :, :self.v_head_dim]
+            attn_output = attn_output.view(-1, self.num_heads, self.v_head_dim)
         else:
             raise RuntimeError(
                 "Unexpected path reached, AscendMLAImpl should only have PrefillNoCache and ChunkedPrefill scenario in forward prefill, please file a bug to vllm-ascend !"