push to device

src/transformers/loss/loss_utils.py

@@ -34,6 +34,7 @@ def ForCausalLMLoss(
 ):
     # Upcast to float if we need to compute the loss to avoid potential precision issues
     logits = logits.float()
+    labels = labels.to(logits.device)
     # Shift so that tokens < n predict n
     shift_logits = logits[..., :-1, :].contiguous()
     shift_labels = labels[..., 1:].contiguous()
@@ -52,6 +53,7 @@ def ForMaskedLMLoss(
 ):
     # Upcast to float if we need to compute the loss to avoid potential precision issues
     logits = logits.float()
+    labels = labels.to(logits.device)
 
     # Flatten the tokens
     logits = logits.view(-1, vocab_size)
@@ -73,6 +75,7 @@ def ForSequenceClassificationLoss(labels, pooled_logits, config, **kwargs):
         else:
             config.problem_type = "multi_label_classification"
 
+    labels = labels.to(pooled_logits.device)
     if config.problem_type == "regression":
         loss_fct = MSELoss()
         if num_labels == 1:
@@ -109,7 +112,7 @@ def ForQuestionAnsweringLoss(start_logits, end_logits, start_positions, end_posi
 def ForTokenClassification(logits, labels, config, **kwargs):
     # Upcast to float if we need to compute the loss to avoid potential precision issues
     logits = logits.view(-1, config.num_labels)
-    labels = labels.view(-1)
+    labels = labels.view(-1).to(logits.device)
     logits = logits.float()
     # Flatten the tokens
     return fixed_cross_entropy(logits, labels, **kwargs)