Remove subclass for sortish sampler (#9907)

* Remove subclass for sortish sampler

* Use old Seq2SeqTrainer in script

* Styling

examples/seq2seq/finetune_trainer.py

@@ -20,6 +20,8 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import transformers
+from seq2seq_trainer import Seq2SeqTrainer
+from seq2seq_training_args import Seq2SeqTrainingArguments
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,
@@ -27,8 +29,6 @@ from transformers import (
     HfArgumentParser,
     MBartTokenizer,
     MBartTokenizerFast,
-    Seq2SeqTrainer,
-    Seq2SeqTrainingArguments,
     set_seed,
 )
 from transformers.trainer_utils import EvaluationStrategy, is_main_process
@@ -286,6 +286,7 @@ def main():
     trainer = Seq2SeqTrainer(
         model=model,
         args=training_args,
+        data_args=data_args,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         data_collator=Seq2SeqDataCollator(
@@ -323,9 +324,7 @@ def main():
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
-        metrics = trainer.evaluate(
-            metric_key_prefix="val", max_length=data_args.val_max_target_length, num_beams=data_args.eval_beams
-        )
+        metrics = trainer.evaluate(metric_key_prefix="val")
         metrics["val_n_objs"] = data_args.n_val
         metrics["val_loss"] = round(metrics["val_loss"], 4)
 
@@ -337,12 +336,7 @@ def main():
     if training_args.do_predict:
         logger.info("*** Predict ***")
 
-        test_output = trainer.predict(
-            test_dataset=test_dataset,
-            metric_key_prefix="test",
-            max_length=data_args.val_max_target_length,
-            num_beams=data_args.eval_beams,
-        )
+        test_output = trainer.predict(test_dataset=test_dataset, metric_key_prefix="test")
         metrics = test_output.metrics
         metrics["test_n_objs"] = data_args.n_test
 

src/transformers/trainer_seq2seq.py

@@ -17,14 +17,10 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import torch
 from packaging import version
 from torch import nn
-from torch.utils.data import DistributedSampler, RandomSampler
 from torch.utils.data.dataset import Dataset
 
-from .file_utils import is_torch_tpu_available
 from .trainer import Trainer
-from .trainer_pt_utils import get_tpu_sampler
 from .trainer_utils import PredictionOutput
-from .training_args import ParallelMode
 from .utils import logging
 
 
@@ -36,24 +32,6 @@ logger = logging.get_logger(__name__)
 
 
 class Seq2SeqTrainer(Trainer):
-    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
-        if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
-            return None
-        elif is_torch_tpu_available():
-            return get_tpu_sampler(self.train_dataset)
-        else:
-            if self.args.sortish_sampler:
-                self.train_dataset.make_sortish_sampler(
-                    self.args.per_device_train_batch_size,
-                    distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
-                )
-
-            return (
-                RandomSampler(self.train_dataset)
-                if self.args.local_rank == -1
-                else DistributedSampler(self.train_dataset)
-            )
-
     def evaluate(
         self,
         eval_dataset: Optional[Dataset] = None,