[upgrade]Upgrade transformers (#6320)

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colossalai/shardformer/modeling/sam.py

@@ -1,6 +1,8 @@
 import torch
+from torch import nn
 
 
+# Same as the SamVisionAttention forward method in the v4.51.3 transformers
 def forward_fn():
     def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
         batch_size, height, width, _ = hidden_states.shape
@@ -16,16 +18,15 @@ def forward_fn():
         attn_weights = (query * self.scale) @ key.transpose(-2, -1)
 
         if self.use_rel_pos:
-            attn_weights = self.add_decomposed_rel_pos(
-                attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
+            decomposed_rel_pos = self.get_decomposed_rel_pos(
+                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
             )
+            decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights)
+            attn_weights = attn_weights + decomposed_rel_pos
 
         attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)
 
-        # replace dropout process with added DropoutForParallelInput layer
-        # origin code:
-        # attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-        attn_probs = self.dropout_layer(attn_weights)
+        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
 
         attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
         attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)