Revert "[Seq2SeqTrainer] Move import to init to make file self-contai…

…ned (huggingface#8194)"

This reverts commit 4aec1b6.

examples/seq2seq/seq2seq_trainer.py

@@ -20,6 +20,12 @@
 from transformers.trainer_pt_utils import get_tpu_sampler
 
 
+try:
+    from .utils import label_smoothed_nll_loss
+except ImportError:
+    from utils import label_smoothed_nll_loss
+
+
 logger = logging.get_logger(__name__)
 
 arg_to_scheduler = {
@@ -58,17 +64,6 @@ def __init__(self, config=None, data_args=None, *args, **kwargs):
                 f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for padding.."
             )
 
-        if self.args.label_smoothing == 0:
-            self.loss_fn = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
-        else:
-            # dynamically import label_smoothed_nll_loss
-            try:
-                from .utils import label_smoothed_nll_loss
-            except ImportError:
-                from utils import label_smoothed_nll_loss
-
-            self.loss_fn = label_smoothed_nll_loss
-
     def create_optimizer_and_scheduler(self, num_training_steps: int):
         """
         Setup the optimizer and the learning rate scheduler.
@@ -140,15 +135,19 @@ def _compute_loss(self, model, inputs, labels):
             if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                 # force training to ignore pad token
                 logits = model(**inputs, use_cache=False)[0]
-                loss = self.loss_fn(logits.view(-1, logits.shape[-1]), labels.view(-1))
+
+                loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
+                loss = loss_fct(logits.view(-1, logits.shape[-1]), labels.view(-1))
             else:
                 # compute usual loss via models
                 loss, logits = model(**inputs, labels=labels, use_cache=False)[:2]
         else:
             # compute label smoothed loss
             logits = model(**inputs, use_cache=False)[0]
             lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
-            loss, _ = self.loss_fn(lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id)
+            loss, _ = label_smoothed_nll_loss(
+                lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id
+            )
         return loss, logits
 
     def compute_loss(self, model, inputs):