migrate AG_NEWS to datapipes.

test/data/test_builtin_datasets.py

@@ -1,6 +1,5 @@
 #!/user/bin/env python3
 # Note that all the tests in this module require dataset (either network access or cached)
-import torch
 import torchtext
 import json
 import hashlib
@@ -24,20 +23,6 @@ class TestDataset(TorchtextTestCase):
     def setUpClass(cls):
         check_cache_status()
 
-    def _helper_test_func(self, length, target_length, results, target_results):
-        self.assertEqual(length, target_length)
-        if isinstance(target_results, list):
-            target_results = torch.tensor(target_results, dtype=torch.int64)
-        if isinstance(target_results, tuple):
-            target_results = tuple(torch.tensor(item, dtype=torch.int64) for item in target_results)
-        self.assertEqual(results, target_results)
-
-    def test_raw_ag_news(self):
-        train_iter, test_iter = torchtext.datasets.AG_NEWS()
-        self._helper_test_func(len(train_iter), 120000, next(train_iter)[1][:25], 'Wall St. Bears Claw Back ')
-        self._helper_test_func(len(test_iter), 7600, next(test_iter)[1][:25], 'Fears for T N pension aft')
-        del train_iter, test_iter
-
     @parameterized.expand(
         load_params('raw_datasets.jsonl'),
         name_func=_raw_text_custom_name_func)
@@ -74,16 +59,3 @@ def test_raw_datasets_split_argument(self, dataset_name):
             break
         # Exercise default constructor
         _ = dataset()
-
-    def test_next_method_dataset(self):
-        train_iter, test_iter = torchtext.datasets.AG_NEWS()
-        for_count = 0
-        next_count = 0
-        for line in train_iter:
-            for_count += 1
-            try:
-                next(train_iter)
-                next_count += 1
-            except:
-                break
-        self.assertEqual((for_count, next_count), (60000, 60000))

torchtext/datasets/ag_news.py

@@ -1,12 +1,13 @@
-from torchtext.utils import (
-    download_from_url,
-)
+from torchtext._internal.module_utils import is_module_available
+from typing import Union, Tuple
+
+if is_module_available("torchdata"):
+    from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper
+
 from torchtext.data.datasets_utils import (
-    _RawTextIterableDataset,
     _wrap_split_argument,
     _add_docstring_header,
     _create_dataset_directory,
-    _create_data_from_csv,
 )
 import os
 
@@ -30,11 +31,18 @@
 
 @_add_docstring_header(num_lines=NUM_LINES, num_classes=4)
 @_create_dataset_directory(dataset_name=DATASET_NAME)
-@_wrap_split_argument(('train', 'test'))
-def AG_NEWS(root, split):
-    path = download_from_url(URL[split], root=root,
-                             path=os.path.join(root, split + ".csv"),
-                             hash_value=MD5[split],
-                             hash_type='md5')
-    return _RawTextIterableDataset(DATASET_NAME, NUM_LINES[split],
-                                   _create_data_from_csv(path))
+@_wrap_split_argument(("train", "test"))
+def AG_NEWS(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[split]])
+    cache_dp = url_dp.on_disk_cache(
+        filepath_fn=lambda x: os.path.join(root, split + ".csv"),
+        hash_dict={os.path.join(root, split + ".csv"): MD5[split]},
+        hash_type="md5"
+    )
+    cache_dp = HttpReader(cache_dp)
+    cache_dp = cache_dp.end_caching(mode="w", same_filepath_fn=True)
+    cache_dp = FileOpener(cache_dp, mode="r")
+    return cache_dp.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))