Migrate WikiText103 to datapipes

torchtext/datasets/wikitext103.py

@@ -1,16 +1,15 @@
-import logging
-from torchtext.utils import (
-    download_from_url,
-    extract_archive,
-)
+from torchtext._internal.module_utils import is_module_available
+
+if is_module_available("torchdata"):
+    from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper
+
+import os
 from torchtext.data.datasets_utils import (
-    _RawTextIterableDataset,
     _wrap_split_argument,
     _add_docstring_header,
-    _find_match,
     _create_dataset_directory,
-    _read_text_iterator,
 )
+from typing import Union, Tuple
 
 URL = 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip'
 
@@ -24,15 +23,30 @@
 
 DATASET_NAME = "WikiText103"
 
+_EXTRACTED_FILES = {
+    'train': os.path.join('wikitext-103', 'wiki.train.tokens'),
+    'test': os.path.join('wikitext-103', 'wiki.test.tokens'),
+    'valid': os.path.join('wikitext-103', 'wiki.valid.tokens'),
+}
+
 
 @_add_docstring_header(num_lines=NUM_LINES)
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(('train', 'valid', 'test'))
-def WikiText103(root, split):
-    dataset_tar = download_from_url(URL, root=root, hash_value=MD5, hash_type='md5')
-    extracted_files = extract_archive(dataset_tar)
-
-    path = _find_match(split, extracted_files)
-    logging.info('Creating {} data'.format(split))
-    return _RawTextIterableDataset(DATASET_NAME,
-                                   NUM_LINES[split], _read_text_iterator(path))
+def WikiText103(root: str, split: Union[Tuple[str], str]):
+    if not is_module_available("torchdata"):
+        raise ModuleNotFoundError("Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`")
+    url_dp = IterableWrapper([URL])
+    # cache data on-disk
+    cache_compressed_dp = url_dp.on_disk_cache(
+        filepath_fn=lambda x: os.path.join(root, os.path.basename(x)),
+        hash_dict={os.path.join(root, os.path.basename(URL)): MD5},
+        hash_type="md5",
+    )
+    cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=lambda x: os.path.join(root, _EXTRACTED_FILES[split]))
+    # Extract zip and filter the appropriate split file
+    cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").read_from_zip().filter(lambda x: _EXTRACTED_FILES[split] in x[0])
+    cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
+    data_dp = FileOpener(cache_decompressed_dp, mode='b')
+    return data_dp.readlines(strip_newline=False, decode=True, return_path=False)