Make GPT2 traceable in meta state (#28054)

* Put device in tensor constructor instead of to()

* Fix copy

src/transformers/models/decision_transformer/modeling_decision_transformer.py

@@ -185,7 +185,7 @@ class DecisionTransformerGPT2Attention(nn.Module):
             mask_value = torch.finfo(attn_weights.dtype).min
             # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
             # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
-            mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+            mask_value = torch.full([], mask_value, dtype=attn_weights.dtype, device=attn_weights.device)
             attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value)
 
         if attention_mask is not None:

src/transformers/models/gpt2/modeling_gpt2.py

@@ -198,7 +198,7 @@ class GPT2Attention(nn.Module):
             mask_value = torch.finfo(attn_weights.dtype).min
             # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.
             # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`
-            mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+            mask_value = torch.full([], mask_value, dtype=attn_weights.dtype, device=attn_weights.device)
             attn_weights = torch.where(causal_mask, attn_weights.to(attn_weights.dtype), mask_value)
 
         if attention_mask is not None: