Replaced torch.load for loading the pretrained vocab of TransformerXL...

Replaced torch.load for loading the pretrained vocab of TransformerXL tokenizer to pickle.load (#6935)

* Replaced torch.load for loading the pretrained vocab of TransformerXL to pickle.load

* Replaced torch.save with pickle.dump when saving the vocabulary

* updating transformer-xl

* uploaded on S3 - compatibility

* fix tests

* style

* Address review comments
Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

src/transformers/file_utils.py

@@ -203,6 +203,19 @@ def is_faiss_available():
     return _faiss_available
 
 
+def torch_only_method(fn):
+    def wrapper(*args, **kwargs):
+        if not _torch_available:
+            raise ImportError(
+                "You need to install pytorch to use this method or class, "
+                "or activate it with environment variables USE_TORCH=1 and USE_TF=0."
+            )
+        else:
+            return fn(*args, **kwargs)
+
+    return wrapper
+
+
 def is_sklearn_available():
     return _has_sklearn
 

src/transformers/tokenization_transfo_xl.py

@@ -36,7 +36,7 @@ from tokenizers.normalizers import Lowercase, Sequence, Strip, unicode_normalize
 from tokenizers.pre_tokenizers import CharDelimiterSplit, WhitespaceSplit
 from tokenizers.processors import BertProcessing
 
-from .file_utils import cached_path, is_torch_available
+from .file_utils import cached_path, is_torch_available, torch_only_method
 from .tokenization_utils import PreTrainedTokenizer
 from .tokenization_utils_fast import PreTrainedTokenizerFast
 from .utils import logging
@@ -48,12 +48,16 @@ if is_torch_available():
 
 logger = logging.get_logger(__name__)
 
-VOCAB_FILES_NAMES = {"pretrained_vocab_file": "vocab.bin", "vocab_file": "vocab.txt"}
+VOCAB_FILES_NAMES = {
+    "pretrained_vocab_file": "vocab.pkl",
+    "pretrained_vocab_file_torch": "vocab.bin",
+    "vocab_file": "vocab.txt",
+}
 VOCAB_FILES_NAMES_FAST = {"pretrained_vocab_file": "vocab.json", "vocab_file": "vocab.json"}
 
 PRETRAINED_VOCAB_FILES_MAP = {
     "pretrained_vocab_file": {
-        "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.bin",
+        "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.pkl",
     }
 }
 
@@ -139,8 +143,9 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
             File containing the vocabulary (from the original implementation).
         pretrained_vocab_file (:obj:`str`, `optional`):
             File containing the vocabulary as saved with the :obj:`save_pretrained()` method.
-        never_split (xxx, `optional`):
-            Fill me with intesting stuff.
+        never_split (:obj:`List[str]`, `optional`):
+            List of tokens that should never be split. If no list is specified, will simply use the existing
+            special tokens.
         unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
             The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
             token instead.
@@ -165,7 +170,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         lower_case=False,
         delimiter=None,
         vocab_file=None,
-        pretrained_vocab_file=None,
+        pretrained_vocab_file: str = None,
         never_split=None,
         unk_token="<unk>",
         eos_token="<eos>",
@@ -197,23 +202,40 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         self.moses_tokenizer = sm.MosesTokenizer(language)
         self.moses_detokenizer = sm.MosesDetokenizer(language)
 
+        # This try... catch... is not beautiful but honestly this tokenizer was not made to be used
+        # in a library like ours, at all.
         try:
+            vocab_dict = None
             if pretrained_vocab_file is not None:
-                # Hack because, honestly this tokenizer was not made to be used
-                # in a library like ours, at all.
+                # Priority on pickle files (support PyTorch and TF)
+                with open(pretrained_vocab_file, "rb") as f:
+                    vocab_dict = pickle.load(f)
+
+                # Loading a torch-saved transfo-xl vocab dict with pickle results in an integer
+                # Entering this if statement means that we tried to load a torch-saved file with pickle, and we failed.
+                # We therefore load it with torch, if it's available.
+                if type(vocab_dict) == int:
+                    if not is_torch_available():
+                        raise ImportError(
+                            "Not trying to load dict with PyTorch as you need to install pytorch to load "
+                            "from a PyTorch pretrained vocabulary, "
+                            "or activate it with environment variables USE_TORCH=1 and USE_TF=0."
+                        )
                     vocab_dict = torch.load(pretrained_vocab_file)
+
+            if vocab_dict is not None:
                 for key, value in vocab_dict.items():
                     if key not in self.__dict__:
                         self.__dict__[key] = value
-
-            if vocab_file is not None:
+            elif vocab_file is not None:
                 self.build_vocab()
-        except Exception:
+
+        except Exception as e:
             raise ValueError(
                 "Unable to parse file {}. Unknown format. "
                 "If you tried to load a model saved through TransfoXLTokenizerFast,"
                 "please note they are not compatible.".format(pretrained_vocab_file)
-            )
+            ) from e
 
         if vocab_file is not None:
             self.build_vocab()
@@ -286,7 +308,8 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
             vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["pretrained_vocab_file"])
         else:
             vocab_file = vocab_path
-        torch.save(self.__dict__, vocab_file)
+        with open(vocab_file, "wb") as f:
+            pickle.dump(self.__dict__, f)
         return (vocab_file,)
 
     def build_vocab(self):
@@ -309,6 +332,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
 
             logger.info("final vocab size {} from {} unique tokens".format(len(self), len(self.counter)))
 
+    @torch_only_method
     def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False):
         if verbose:
             logger.info("encoding file {} ...".format(path))
@@ -326,6 +350,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
 
         return encoded
 
+    @torch_only_method
     def encode_sents(self, sents, ordered=False, verbose=False):
         if verbose:
             logger.info("encoding {} sents ...".format(len(sents)))
@@ -436,6 +461,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         out_string = self.moses_detokenizer.detokenize(tokens)
         return detokenize_numbers(out_string).strip()
 
+    @torch_only_method
     def convert_to_tensor(self, symbols):
         return torch.LongTensor(self.convert_tokens_to_ids(symbols))
 
@@ -706,6 +732,7 @@ class LMShuffledIterator(object):
         for idx in epoch_indices:
             yield self.data[idx]
 
+    @torch_only_method
     def stream_iterator(self, sent_stream):
         # streams for each data in the batch
         streams = [None] * self.bsz
@@ -795,6 +822,7 @@ class LMMultiFileIterator(LMShuffledIterator):
 
 class TransfoXLCorpus(object):
     @classmethod
+    @torch_only_method
     def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
         """
         Instantiate a pre-processed corpus.
@@ -892,10 +920,14 @@ class TransfoXLCorpus(object):
                 data_iter = LMOrderedIterator(data, *args, **kwargs)
             elif self.dataset == "lm1b":
                 data_iter = LMShuffledIterator(data, *args, **kwargs)
+        else:
+            data_iter = None
+            raise ValueError(f"Split not recognized: {split}")
 
         return data_iter
 
 
+@torch_only_method
 def get_lm_corpus(datadir, dataset):
     fn = os.path.join(datadir, "cache.pt")
     fn_pickle = os.path.join(datadir, "cache.pkl")