change constant torch.tensor to torch.full (#20061)

src/transformers/models/decision_transformer/modeling_decision_transformer.py

@@ -170,8 +170,8 @@ class DecisionTransformerGPT2Attention(nn.Module):
         attn_weights = torch.matmul(query, key.transpose(-1, -2))
 
         if self.scale_attn_weights:
-            attn_weights = attn_weights / torch.tensor(
-                value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
+            attn_weights = attn_weights / torch.full(
+                [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
             )
 
         # Layer-wise attention scaling
@@ -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.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+            mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
             attn_weights = torch.where(causal_mask, attn_weights, mask_value)
 
         if attention_mask is not None:

src/transformers/models/gpt2/modeling_gpt2.py

@@ -182,8 +182,8 @@ class GPT2Attention(nn.Module):
         attn_weights = torch.matmul(query, key.transpose(-1, -2))
 
         if self.scale_attn_weights:
-            attn_weights = attn_weights / torch.tensor(
-                value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
+            attn_weights = attn_weights / torch.full(
+                [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device
             )
 
         # Layer-wise attention scaling
@@ -197,7 +197,7 @@ 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.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
+            mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(attn_weights.device)
             attn_weights = torch.where(causal_mask, attn_weights, mask_value)
 
         if attention_mask is not None: