Remove dependency on examples/seq2seq from rag (#7395)

Co-authored-by: Your Name <you@example.com>

examples/rag/finetune.py

@@ -34,22 +34,23 @@ from transformers import logging as transformers_logging
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
 
 from examples.lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa: E402 # isort:skip
-from examples.rag.callbacks import get_checkpoint_callback  # noqa: E402 # isort:skip
+from examples.rag.callbacks import (  # noqa: E402 # isort:skip
+    get_checkpoint_callback,
+    get_early_stopping_callback,
+    Seq2SeqLoggingCallback,
+)
 from examples.rag.distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
 from examples.rag.utils import (  # noqa: E402 # isort:skip
-    Seq2SeqDataset,
     calculate_exact_match,
-    is_rag_model,
-    set_extra_model_params,
-)
-from examples.seq2seq.callbacks import Seq2SeqLoggingCallback, get_early_stopping_callback  # noqa: E402 # isort:skip
-from examples.seq2seq.utils import (  # noqa: E402 # isort:skip
     flatten_list,
     get_git_info,
+    is_rag_model,
     lmap,
     pickle_save,
     save_git_info,
     save_json,
+    set_extra_model_params,
+    Seq2SeqDataset,
 )
 
 logging.basicConfig(level=logging.INFO)
@@ -303,11 +304,6 @@ class GenerativeQAModule(BaseTransformer):
 
     def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
         dataset = self.get_dataset(type_path)
-        sampler = None
-        if self.hparams.sortish_sampler and type_path == "train":
-            assert self.hparams.gpus <= 1  # TODO: assert earlier
-            sampler = dataset.make_sortish_sampler(batch_size)
-            shuffle = False
 
         dataloader = DataLoader(
             dataset,
@@ -315,7 +311,6 @@ class GenerativeQAModule(BaseTransformer):
             collate_fn=dataset.collate_fn,
             shuffle=shuffle,
             num_workers=self.num_workers,
-            sampler=sampler,
         )
         return dataloader
 
@@ -379,7 +374,6 @@ class GenerativeQAModule(BaseTransformer):
             help="The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded.",
         )
-        parser.add_argument("--sortish_sampler", action="store_true", default=False)
         parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
         parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
         parser.add_argument("--n_val", type=int, default=-1, required=False, help="# examples. -1 means use all.")