[s2s] Adafactor support for builtin trainer (#7522)

examples/seq2seq/seq2seq_trainer.py

@@ -7,6 +7,7 @@ from torch.utils.data import DistributedSampler, RandomSampler
 
 from transformers import Trainer
 from transformers.file_utils import is_torch_tpu_available
+from transformers.optimization import Adafactor, AdamW, get_linear_schedule_with_warmup
 from transformers.trainer import get_tpu_sampler
 
 
@@ -28,6 +29,43 @@ class Seq2SeqTrainer(Trainer):
         self.pad_token_id = self.config.pad_token_id
         self.vocab_size = self.config.vocab_size
 
+    def create_optimizer_and_scheduler(self, num_training_steps: int):
+        """
+        Setup the optimizer and the learning rate scheduler.
+
+        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
+        Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
+        """
+        if self.optimizer is None:
+            no_decay = ["bias", "LayerNorm.weight"]
+            optimizer_grouped_parameters = [
+                {
+                    "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
+                    "weight_decay": self.args.weight_decay,
+                },
+                {
+                    "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
+                    "weight_decay": 0.0,
+                },
+            ]
+            if self.args.adafactor:
+                self.optimizer = Adafactor(
+                    optimizer_grouped_parameters,
+                    lr=self.args.learning_rate,
+                    scale_parameter=False,
+                    relative_step=False,
+                )
+
+            else:
+                self.optimizer = AdamW(
+                    optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon
+                )
+
+        if self.lr_scheduler is None:
+            self.lr_scheduler = get_linear_schedule_with_warmup(
+                self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
+            )
+
     def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
         if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
             return None