remove SharedDDP as it is deprecated (#25702)

* remove SharedDDP as it was drepracated

* apply review suggestion

* make style

* Oops,forgot to remove the compute_loss context manager in Seq2SeqTrainer.

* remove the unnecessary conditional statement

* keep the logic of IPEX

* clean code

* mix precision setup & make fixup

---------

Co-authored-by: statelesshz <jihuazhong1@huawei.com>

examples/legacy/seq2seq/seq2seq_trainer.py

@@ -19,7 +19,6 @@ from torch import nn
 from torch.utils.data import DistributedSampler, RandomSampler
 
 from transformers import PreTrainedModel, Trainer, logging
-from transformers.integrations import is_fairscale_available
 from transformers.models.fsmt.configuration_fsmt import FSMTConfig
 from transformers.optimization import (
     Adafactor,
@@ -36,10 +35,6 @@ from transformers.training_args import ParallelMode
 from transformers.utils import is_torch_tpu_available
 
 
-if is_fairscale_available():
-    from fairscale.optim import OSS
-
-
 logger = logging.get_logger(__name__)
 
 arg_to_scheduler = {
@@ -118,14 +113,7 @@ class Seq2SeqTrainer(Trainer):
                     "eps": self.args.adam_epsilon,
                 }
             optimizer_kwargs["lr"] = self.args.learning_rate
-            if self.sharded_ddp:
-                self.optimizer = OSS(
-                    params=optimizer_grouped_parameters,
-                    optim=optimizer_cls,
-                    **optimizer_kwargs,
-                )
-            else:
-                self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
+            self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
 
         if self.lr_scheduler is None:
             self.lr_scheduler = self._get_lr_scheduler(num_training_steps)