Add cache_dir to save features TextDataset (#6879)

* Add cache_dir to save features TextDataset

This is in case the dataset is in a RO filesystem, for which is the case
in tests (GKE TPU tests).

* style

examples/language-modeling/run_language_modeling.py

@@ -125,13 +125,22 @@ class DataTrainingArguments:
     )
 
 
-def get_dataset(args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate=False):
+def get_dataset(
+    args: DataTrainingArguments,
+    tokenizer: PreTrainedTokenizer,
+    evaluate: bool = False,
+    cache_dir: Optional[str] = None,
+):
     file_path = args.eval_data_file if evaluate else args.train_data_file
     if args.line_by_line:
         return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
     else:
         return TextDataset(
-            tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache
+            tokenizer=tokenizer,
+            file_path=file_path,
+            block_size=args.block_size,
+            overwrite_cache=args.overwrite_cache,
+            cache_dir=cache_dir,
         )
 
 
@@ -229,8 +238,14 @@ def main():
 
     # Get datasets
 
-    train_dataset = get_dataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
+    train_dataset = (
-    eval_dataset = get_dataset(data_args, tokenizer=tokenizer, evaluate=True) if training_args.do_eval else None
+        get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
+    )
+    eval_dataset = (
+        get_dataset(data_args, tokenizer=tokenizer, evaluate=True, cache_dir=model_args.cache_dir)
+        if training_args.do_eval
+        else None
+    )
     if config.model_type == "xlnet":
         data_collator = DataCollatorForPermutationLanguageModeling(
             tokenizer=tokenizer,

src/transformers/data/datasets/language_modeling.py

@@ -1,6 +1,7 @@
 import os
 import pickle
 import time
+from typing import Optional
 
 import torch
 from torch.utils.data.dataset import Dataset
@@ -26,6 +27,7 @@ class TextDataset(Dataset):
         file_path: str,
         block_size: int,
         overwrite_cache=False,
+        cache_dir: Optional[str] = None,
     ):
         assert os.path.isfile(file_path), f"Input file path {file_path} not found"
 
@@ -33,7 +35,7 @@ class TextDataset(Dataset):
 
         directory, filename = os.path.split(file_path)
         cached_features_file = os.path.join(
-            directory,
+            cache_dir if cache_dir is not None else directory,
             "cached_lm_{}_{}_{}".format(
                 tokenizer.__class__.__name__,
                 str(block_size),