[fix] model attn_mask

model/model_minimind.py

@@ -193,16 +193,8 @@ class Attention(nn.Module):
             repeat_kv(xv, self.n_rep).transpose(1, 2)
         )
 
-        if self.flash and seq_len > 1:
+        if self.flash and seq_len > 1 and (attention_mask is None or torch.all(attention_mask == 1)):
-            if attention_mask is None or torch.all(attention_mask == 1):
+            output = F.scaled_dot_product_attention(xq, xk, xv, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
-                attn_mask, is_causal = None, True
-            else:
-                causal_mask = torch.triu(torch.full((seq_len, seq_len), float("-inf"), device=xq.device), diagonal=1)
-                extended_mask = (1.0 - attention_mask.unsqueeze(1).unsqueeze(2)) * float("-inf")
-                attn_mask, is_causal = causal_mask.unsqueeze(0) + extended_mask, False
-            
-            dropout_p = self.dropout if self.training else 0.0
-            output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=is_causal)
         else:
             scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
             scores = scores + torch.triu(