For Datasets, refactor local functions to be global so that they can …

…be pickled (#1726)

For Datasets, refactor local functions to be global so that they can be pickled

torchtext/datasets/ag_news.py

@@ -1,4 +1,5 @@
 import os
+from functools import partial
 from typing import Union, Tuple
 
 from torchtext._internal.module_utils import is_module_available
@@ -29,6 +30,14 @@
 DATASET_NAME = "AG_NEWS"
 
 
+def _filepath_fn(root, split, _=None):
+    return os.path.join(root, split + ".csv")
+
+
+def _modify_res(t):
+    return int(t[0]), " ".join(t[1:])
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(("train", "test"))
 def AG_NEWS(root: str, split: Union[Tuple[str], str]):
@@ -52,16 +61,10 @@ def AG_NEWS(root: str, split: Union[Tuple[str], str]):
             "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
         )
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, split + ".csv")
-
-    def _modify_res(t):
-        return int(t[0]), " ".join(t[1:])
-
     url_dp = IterableWrapper([URL[split]])
     cache_dp = url_dp.on_disk_cache(
-        filepath_fn=_filepath_fn,
-        hash_dict={_filepath_fn(): MD5[split]},
+        filepath_fn=partial(_filepath_fn, root, split),
+        hash_dict={_filepath_fn(root, split): MD5[split]},
         hash_type="md5",
     )
     cache_dp = HttpReader(cache_dp)

torchtext/datasets/amazonreviewfull.py

@@ -1,4 +1,5 @@
 import os
+from functools import partial
 from typing import Union, Tuple
 
 from torchtext._internal.module_utils import is_module_available
@@ -35,6 +36,22 @@
 DATASET_NAME = "AmazonReviewFull"
 
 
+def _filepath_fn(root, _=None):
+    return os.path.join(root, _PATH)
+
+
+def _extracted_filepath_fn(root, split, _=None):
+    return os.path.join(root, _EXTRACTED_FILES[split])
+
+
+def _filter_fn(split, x):
+    return _EXTRACTED_FILES[split] in x[0]
+
+
+def _modify_res(t):
+    return int(t[0]), " ".join(t[1:])
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(("train", "test"))
 def AmazonReviewFull(root: str, split: Union[Tuple[str], str]):
@@ -58,28 +75,18 @@ def AmazonReviewFull(root: str, split: Union[Tuple[str], str]):
             "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
         )
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, _PATH)
-
-    def _extracted_filepath_fn(_=None):
-        return os.path.join(root, _EXTRACTED_FILES[split])
-
-    def _filter_fn(x):
-        return _EXTRACTED_FILES[split] in x[0]
-
-    def _modify_res(t):
-        return int(t[0]), " ".join(t[1:])
-
     url_dp = IterableWrapper([URL])
     cache_compressed_dp = url_dp.on_disk_cache(
-        filepath_fn=_filepath_fn,
-        hash_dict={_filepath_fn(): MD5},
+        filepath_fn=partial(_filepath_fn, root),
+        hash_dict={_filepath_fn(root): MD5},
         hash_type="md5",
     )
     cache_compressed_dp = GDriveReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
 
-    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=_extracted_filepath_fn)
-    cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(_filter_fn)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
+    cache_decompressed_dp = (
+        FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(partial(_filter_fn, split))
+    )
     cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
 
     data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")

torchtext/datasets/amazonreviewpolarity.py

@@ -1,4 +1,5 @@
 import os
+from functools import partial
 from typing import Union, Tuple
 
 from torchtext._internal.module_utils import is_module_available
@@ -31,6 +32,22 @@
 DATASET_NAME = "AmazonReviewPolarity"
 
 
+def _filepath_fn(root, _=None):
+    return os.path.join(root, _PATH)
+
+
+def _extracted_filepath_fn(root, split, _=None):
+    return os.path.join(root, _EXTRACTED_FILES[split])
+
+
+def _filter_fn(split, x):
+    return _EXTRACTED_FILES[split] in x[0]
+
+
+def _modify_res(t):
+    return int(t[0]), " ".join(t[1:])
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(("train", "test"))
 def AmazonReviewPolarity(root: str, split: Union[Tuple[str], str]):
@@ -55,28 +72,18 @@ def AmazonReviewPolarity(root: str, split: Union[Tuple[str], str]):
             "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
         )
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, _PATH)
-
-    def _extracted_filepath_fn(_=None):
-        return os.path.join(root, _EXTRACTED_FILES[split])
-
-    def _filter_fn(x):
-        return _EXTRACTED_FILES[split] in x[0]
-
-    def _modify_res(t):
-        return int(t[0]), " ".join(t[1:])
-
     url_dp = IterableWrapper([URL])
     cache_compressed_dp = url_dp.on_disk_cache(
-        filepath_fn=_filepath_fn,
-        hash_dict={_filepath_fn(): MD5},
+        filepath_fn=partial(_filepath_fn, root),
+        hash_dict={_filepath_fn(root): MD5},
         hash_type="md5",
     )
     cache_compressed_dp = GDriveReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
 
-    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=_extracted_filepath_fn)
-    cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(_filter_fn)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
+    cache_decompressed_dp = (
+        FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(partial(_filter_fn, split))
+    )
     cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
 
     data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")

torchtext/datasets/cc100.py

@@ -1,4 +1,5 @@
 import os.path
+from functools import partial
 
 from torchtext._internal.module_utils import is_module_available
 from torchtext.data.datasets_utils import (
@@ -135,6 +136,18 @@
 DATASET_NAME = "CC100"
 
 
+def _filepath_fn(root, url, _=None):
+    return os.path.join(root, os.path.basename(url))
+
+
+def _decompressed_filepath_fn(root, x):
+    return os.path.join(root, os.path.basename(x).rstrip(".xz"))
+
+
+def _modify_res(language_code, x):
+    return language_code, x
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 def CC100(root: str, language_code: str = "en"):
     """CC100 Dataset
@@ -151,25 +164,16 @@ def CC100(root: str, language_code: str = "en"):
     if language_code not in VALID_CODES:
         raise ValueError(f"Invalid language code {language_code}")
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, os.path.basename(url))
-
-    def _decompressed_filepath_fn(x):
-        return os.path.join(root, os.path.basename(x).rstrip(".xz"))
-
-    def _modify_res(x):
-        return language_code, x
-
     url = URL % language_code
     url_dp = IterableWrapper([url])
-    cache_compressed_dp = url_dp.on_disk_cache(filepath_fn=_filepath_fn)
+    cache_compressed_dp = url_dp.on_disk_cache(filepath_fn=partial(_filepath_fn, root, url))
 
     cache_compressed_dp = HttpReader(cache_compressed_dp)
     cache_compressed_dp = cache_compressed_dp.end_caching(mode="wb", same_filepath_fn=True)
 
-    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=_decompressed_filepath_fn)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_decompressed_filepath_fn, root))
     cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_xz()
     cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb")
 
     data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8").readlines(return_path=False)
-    return data_dp.map(_modify_res)
+    return data_dp.map(partial(_modify_res, language_code))

torchtext/datasets/conll2000chunking.py

@@ -1,4 +1,5 @@
 import os
+from functools import partial
 from typing import Union, Tuple
 
 from torchtext._internal.module_utils import is_module_available
@@ -31,6 +32,14 @@
 DATASET_NAME = "CoNLL2000Chunking"
 
 
+def _filepath_fn(root, split, _=None):
+    return os.path.join(root, os.path.basename(URL[split]))
+
+
+def _extracted_filepath_fn(root, split, _=None):
+    return os.path.join(root, _EXTRACTED_FILES[split])
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(("train", "test"))
 def CoNLL2000Chunking(root: str, split: Union[Tuple[str], str]):
@@ -55,24 +64,18 @@ def CoNLL2000Chunking(root: str, split: Union[Tuple[str], str]):
             "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
         )
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, os.path.basename(URL[split]))
-
-    def _extracted_filepath_fn(_=None):
-        return os.path.join(root, _EXTRACTED_FILES[split])
-
     url_dp = IterableWrapper([URL[split]])
 
     # Cache and check HTTP response
     cache_compressed_dp = url_dp.on_disk_cache(
-        filepath_fn=_filepath_fn,
-        hash_dict={_filepath_fn(): MD5[split]},
+        filepath_fn=partial(_filepath_fn, root, split),
+        hash_dict={_filepath_fn(root, split): MD5[split]},
         hash_type="md5",
     )
     cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
 
     # Cache and check the gzip extraction for relevant split
-    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=_extracted_filepath_fn)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
     cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").extract(file_type="gzip")
     cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
 

torchtext/datasets/dbpedia.py

@@ -1,4 +1,5 @@
 import os
+from functools import partial
 from typing import Union, Tuple
 
 from torchtext._internal.module_utils import is_module_available
@@ -30,6 +31,22 @@
 DATASET_NAME = "DBpedia"
 
 
+def _filepath_fn(root, _=None):
+    return os.path.join(root, _PATH)
+
+
+def _extracted_filepath_fn(root, split, _=None):
+    return os.path.join(root, _EXTRACTED_FILES[split])
+
+
+def _filter_fn(split, x):
+    return _EXTRACTED_FILES[split] in x[0]
+
+
+def _modify_res(t):
+    return int(t[0]), " ".join(t[1:])
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 @_wrap_split_argument(("train", "test"))
 def DBpedia(root: str, split: Union[Tuple[str], str]):
@@ -54,28 +71,18 @@ def DBpedia(root: str, split: Union[Tuple[str], str]):
             "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
         )
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, _PATH)
-
-    def _extracted_filepath_fn(_=None):
-        return os.path.join(root, _EXTRACTED_FILES[split])
-
-    def _filter_fn(x):
-        return _EXTRACTED_FILES[split] in x[0]
-
-    def _modify_res(t):
-        return int(t[0]), " ".join(t[1:])
-
     url_dp = IterableWrapper([URL])
     cache_compressed_dp = url_dp.on_disk_cache(
-        filepath_fn=_filepath_fn,
-        hash_dict={_filepath_fn(): MD5},
+        filepath_fn=partial(_filepath_fn, root),
+        hash_dict={_filepath_fn(root): MD5},
         hash_type="md5",
     )
     cache_compressed_dp = GDriveReader(cache_compressed_dp).end_caching(mode="wb", same_filepath_fn=True)
 
-    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=_extracted_filepath_fn)
-    cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(_filter_fn)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
+    cache_decompressed_dp = (
+        FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(partial(_filter_fn, split))
+    )
     cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
 
     data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")

torchtext/datasets/enwik9.py

@@ -1,4 +1,5 @@
 import os
+from functools import partial
 
 from torchtext._internal.module_utils import is_module_available
 from torchtext.data.datasets_utils import _create_dataset_directory
@@ -18,6 +19,14 @@
 DATASET_NAME = "EnWik9"
 
 
+def _filepath_fn(root, _=None):
+    return os.path.join(root, _PATH)
+
+
+def _extracted_filepath_fn(root, _=None):
+    return os.path.join(root, os.path.splitext(_PATH)[0])
+
+
 @_create_dataset_directory(dataset_name=DATASET_NAME)
 def EnWik9(root: str):
     """EnWik9 dataset
@@ -37,21 +46,15 @@ def EnWik9(root: str):
             "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
         )
 
-    def _filepath_fn(_=None):
-        return os.path.join(root, _PATH)
-
-    def _extracted_filepath_fn(_=None):
-        return os.path.join(root, os.path.splitext(_PATH)[0])
-
     url_dp = IterableWrapper([URL])
     cache_compressed_dp = url_dp.on_disk_cache(
-        filepath_fn=_filepath_fn,
-        hash_dict={_filepath_fn(): MD5},
+        filepath_fn=partial(_filepath_fn, root),
+        hash_dict={_filepath_fn(root): 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=_extracted_filepath_fn)
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root))
     cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").load_from_zip()
     cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)