[squad] make examples and dataset accessible from SquadDataset object (…

…huggingface#6710)

* [squad] make examples and dataset accessible from SquadDataset object

* [squad] add support for legacy cache files

src/transformers/data/datasets/squad.py

@@ -103,6 +103,7 @@ def __init__(
         mode: Union[str, Split] = Split.train,
         is_language_sensitive: Optional[bool] = False,
         cache_dir: Optional[str] = None,
+        dataset_format: Optional[str] = "pt",
     ):
         self.args = args
         self.is_language_sensitive = is_language_sensitive
@@ -128,28 +129,43 @@ def __init__(
         with FileLock(lock_path):
             if os.path.exists(cached_features_file) and not args.overwrite_cache:
                 start = time.time()
-                self.features = torch.load(cached_features_file)
+                self.old_features = torch.load(cached_features_file)
+
+                # Legacy cache files have only features, while new cache files
+                # will have dataset and examples also.
+                self.features = self.old_features["features"]
+                self.dataset = self.old_features.get("dataset", None)
+                self.examples = self.old_features.get("examples", None)
                 logger.info(
                     f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
                 )
+
+                if self.dataset is None or self.examples is None:
+                    logger.warn(
+                        f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in future run"
+                    )
             else:
                 if mode == Split.dev:
-                    examples = self.processor.get_dev_examples(args.data_dir)
+                    self.examples = self.processor.get_dev_examples(args.data_dir)
                 else:
-                    examples = self.processor.get_train_examples(args.data_dir)
+                    self.examples = self.processor.get_train_examples(args.data_dir)
 
-                self.features = squad_convert_examples_to_features(
-                    examples=examples,
+                self.features, self.dataset = squad_convert_examples_to_features(
+                    examples=self.examples,
                     tokenizer=tokenizer,
                     max_seq_length=args.max_seq_length,
                     doc_stride=args.doc_stride,
                     max_query_length=args.max_query_length,
                     is_training=mode == Split.train,
                     threads=args.threads,
+                    return_dataset=dataset_format,
                 )
 
                 start = time.time()
-                torch.save(self.features, cached_features_file)
+                torch.save(
+                    {"features": self.features, "dataset": self.dataset, "examples": self.examples},
+                    cached_features_file,
+                )
                 # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                 logger.info(
                     "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start