diff --git a/colossalai/shardformer/modeling/gpt2.py b/colossalai/shardformer/modeling/gpt2.py
index a9a38bce235f..fbb1691ec595 100644
--- a/colossalai/shardformer/modeling/gpt2.py
+++ b/colossalai/shardformer/modeling/gpt2.py
@@ -4,14 +4,11 @@
 
 __all__ = ['get_gpt2_forward']
 
+
 def get_gpt2_forward():
 
-    try:
-        from xformers.ops import memory_efficient_attention as me_attention
-        from xformers.ops.fmha.attn_bias import LowerTriangularMask
-    except:
-        raise ImportError("Error: xformers module is not installed. Please install it to use flash attention.")
-        
+    from colossalai.kernel.cuda_native.flash_attention import AttnMaskType, ColoAttention
+
     def gpt2_flash_attention_forward(
         self,
         hidden_states: Optional[Tuple[torch.FloatTensor]],
@@ -30,8 +27,7 @@ def gpt2_flash_attention_forward(
             if not hasattr(self, "q_attn"):
                 raise ValueError(
                     "If class is used as cross attention, the weights `q_attn` have to be defined. "
-                    "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`."
-                )
+                    "Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`.")
 
             query = self.q_attn(hidden_states)
             key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
@@ -52,31 +48,38 @@ def gpt2_flash_attention_forward(
             present = (key, value)
         else:
             present = None
-        
-        attn_bias = None
+
         if not self.is_cross_attention:
-            attn_bias = LowerTriangularMask()
+            attn_mask_type = AttnMaskType.causal
+            flash_attention_mask = None
         if attention_mask != None:
-            if attn_bias:
-                attn_bias.add_bias(attention_mask)
+            if attn_mask_type == AttnMaskType.causal:
+                attn_mask_type == AttnMaskType.paddedcausal
             else:
-                batch_size, _, tgt_len, src_len = attention_mask.size()
-                attn_bias = attention_mask.expand(batch_size, self.num_heads, tgt_len, src_len).contiguous()
-        
-        scale = value.size(-1) ** -0.5
+                attn_mask_type = AttnMaskType.padding
+            flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous()
+
+        scale = value.size(-1)**-0.5
         if self.scale_attn_by_inverse_layer_idx:
             scale = scale * (1 / float(self.layer_idx + 1))
-        attn_output = me_attention(query=query, key=key, value=value, attn_bias=attn_bias, p=self.attn_dropout.p, scale=scale)
-        
-        attn_output = merge_heads(attn_output, self.num_heads, self.head_dim)
+
+        # use coloattention
+        attention = ColoAttention(embed_dim=self.embed_dim,
+                                  num_heads=self.num_heads,
+                                  dropout=self.attn_dropout.p,
+                                  scale=scale)
+
+        attn_output = attention(query, key, value, attn_mask=flash_attention_mask, attn_mask_type=attn_mask_type)
+
         attn_output = self.c_proj(attn_output)
         attn_output = self.resid_dropout(attn_output)
         outputs = (attn_output, present, None)
 
         return outputs
-    
+
     return gpt2_flash_attention_forward
 
+
 def split_heads(tensor, num_heads, attn_head_size):
     """
     Splits hidden_size dim into attn_head_size and num_heads
@@ -84,10 +87,3 @@ def split_heads(tensor, num_heads, attn_head_size):
     new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
     tensor = tensor.view(new_shape)
     return tensor
-
-def merge_heads(tensor, num_heads, attn_head_size):
-    """
-    Merges attn_head_size dim and num_attn_heads dim into hidden_size
-    """
-    new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
-    return tensor.view(new_shape)
\ No newline at end of file