cleaning up tokenizer tests structure (at last) - last remaining ppb refs

README.md

@@ -345,8 +345,13 @@ tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')
 
 ### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
 
-The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer.
-The new optimizer `AdamW` matches PyTorch `Adam` optimizer API.
+The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences:
+
+- it only implements weights decay correction,
+- schedules are now externals (see below),
+- gradient clipping is now also external (see below).
+
+The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping.
 
 The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore.
 
@@ -355,6 +360,7 @@ Here is a conversion examples from `BertAdam` with a linear warmup and decay sch
 ```python
 # Parameters:
 lr = 1e-3
+max_grad_norm = 1.0
 num_total_steps = 1000
 num_warmup_steps = 100
 warmup_proportion = float(num_warmup_steps) / float(num_total_steps)  # 0.1
@@ -374,6 +380,7 @@ scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_tot
 for batch in train_data:
     loss = model(batch)
     loss.backward()
+    torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
     scheduler.step()
     optimizer.step()
 ```

docs/source/migration.md

@@ -68,8 +68,13 @@ tokenizer = BertTokenizer.from_pretrained('./my_saved_model_directory/')
 
 ### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
 
-The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer.
-The new optimizer `AdamW` matches PyTorch `Adam` optimizer API.
+The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer which has a few differences:
+
+- it only implements weights decay correction,
+- schedules are now externals (see below),
+- gradient clipping is now also external (see below).
+
+The new optimizer `AdamW` matches PyTorch `Adam` optimizer API and let you use standard PyTorch or apex methods for the schedule and clipping.
 
 The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore.
 
@@ -78,6 +83,7 @@ Here is a conversion examples from `BertAdam` with a linear warmup and decay sch
 ```python
 # Parameters:
 lr = 1e-3
+max_grad_norm = 1.0
 num_total_steps = 1000
 num_warmup_steps = 100
 warmup_proportion = float(num_warmup_steps) / float(num_total_steps)  # 0.1
@@ -97,6 +103,7 @@ scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_tot
 for batch in train_data:
     loss = model(batch)
     loss.backward()
+    torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
     scheduler.step()
     optimizer.step()
 ```

docs/source/serialization.rst

@@ -122,7 +122,7 @@ Here is the recommended way of saving the model, configuration and vocabulary to
 
 .. code-block:: python
 
-   from pytorch_pretrained_bert import WEIGHTS_NAME, CONFIG_NAME
+   from pytorch_transformers import WEIGHTS_NAME, CONFIG_NAME
 
    output_dir = "./models/"
 

docs/source/torchscript.rst

@@ -74,7 +74,7 @@ according to a ``BertConfig`` class and then saved to disk under the filename ``
 
 .. code-block:: python
 
-    from pytorch_pretrained_bert import BertModel, BertTokenizer, BertConfig
+    from pytorch_transformers import BertModel, BertTokenizer, BertConfig
     import torch
 
     enc = BertTokenizer.from_pretrained("bert-base-uncased")
@@ -105,6 +105,9 @@ according to a ``BertConfig`` class and then saved to disk under the filename ``
     # The model needs to be in evaluation mode
     model.eval()
 
+    # If you are instantiating the model with `from_pretrained` you can also easily set the TorchScript flag
+    model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
+
     # Creating the trace
     traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
     torch.jit.save(traced_model, "traced_bert.pt")

pytorch_transformers/__init__.py

@@ -39,4 +39,4 @@ from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
 from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
                            WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
 
-from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path)
+from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE, cached_path)

pytorch_transformers/convert_pytorch_checkpoint_to_tf.py

@@ -20,7 +20,7 @@ import argparse
 import torch
 import numpy as np
 import tensorflow as tf
-from pytorch_pretrained_bert.modeling import BertModel
+from pytorch_transformers.modeling import BertModel
 
 
 def convert_pytorch_checkpoint_to_tf(model:BertModel, ckpt_dir:str, model_name:str):

pytorch_transformers/file_utils.py

@@ -38,10 +38,13 @@ except ImportError:
 try:
     from pathlib import Path
     PYTORCH_PRETRAINED_BERT_CACHE = Path(
-        os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path))
+        os.getenv('PYTORCH_TRANSFORMERS_CACHE', os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)))
 except (AttributeError, ImportError):
-    PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
-                                              default_cache_path)
+    PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_TRANSFORMERS_CACHE',
+                                              os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
+                                                        default_cache_path))
+
+PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
@@ -70,7 +73,7 @@ def filename_to_url(filename, cache_dir=None):
     Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
     """
     if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+        cache_dir = PYTORCH_TRANSFORMERS_CACHE
     if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
         cache_dir = str(cache_dir)
 
@@ -98,7 +101,7 @@ def cached_path(url_or_filename, cache_dir=None):
     make sure the file exists and then return the path.
     """
     if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+        cache_dir = PYTORCH_TRANSFORMERS_CACHE
     if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
         url_or_filename = str(url_or_filename)
     if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
@@ -187,7 +190,7 @@ def get_from_cache(url, cache_dir=None):
     If it's not there, download it. Then return the path to the cached file.
     """
     if cache_dir is None:
-        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+        cache_dir = PYTORCH_TRANSFORMERS_CACHE
     if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
         cache_dir = str(cache_dir)
     if sys.version_info[0] == 2 and not isinstance(cache_dir, str):

pytorch_transformers/tests/tokenization_bert_test.py

@@ -24,26 +24,33 @@ from pytorch_transformers.tokenization_bert import (BasicTokenizer,
                                                     _is_control, _is_punctuation,
                                                     _is_whitespace, VOCAB_FILES_NAMES)
 
-from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from .tokenization_tests_commons import CommonTestCases
 
-class TokenizationTest(unittest.TestCase):
+class BertTokenizationTest(CommonTestCases.CommonTokenizerTester):
+
+    tokenizer_class = BertTokenizer
+
+    def setUp(self):
+        super(BertTokenizationTest, self).setUp()
 
-    def test_full_tokenizer(self):
         vocab_tokens = [
             "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
             "##ing", ",", "low", "lowest",
         ]
-        with TemporaryDirectory() as tmpdirname:
-            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
-            with open(vocab_file, "w", encoding='utf-8') as vocab_writer:
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        with open(self.vocab_file, "w", encoding='utf-8') as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
+    def get_tokenizer(self):
+        return BertTokenizer.from_pretrained(self.tmpdirname)
+
+    def get_input_output_texts(self):
         input_text = u"UNwant\u00E9d,running"
         output_text = u"unwanted, running"
+        return input_text, output_text
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, BertTokenizer, tmpdirname)
-
-            tokenizer = BertTokenizer(vocab_file)
+    def test_full_tokenizer(self):
+        tokenizer = BertTokenizer(self.vocab_file)
 
         tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
         self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])

pytorch_transformers/tests/tokenization_gpt2_test.py

@@ -20,33 +20,40 @@ import json
 
 from pytorch_transformers.tokenization_gpt2 import GPT2Tokenizer, VOCAB_FILES_NAMES
 
-from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from .tokenization_tests_commons import CommonTestCases
 
-class GPT2TokenizationTest(unittest.TestCase):
+class GPT2TokenizationTest(CommonTestCases.CommonTokenizerTester):
 
-    def test_full_tokenizer(self):
-        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+    tokenizer_class = GPT2Tokenizer
+
+    def setUp(self):
+        super(GPT2TokenizationTest, self).setUp()
+
+        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
                  "lo", "low", "er",
                  "low", "lowest", "newer", "wider", "<unk>"]
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
         merges = ["#version: 0.2", "l o", "lo w", "e r", ""]
-        special_tokens_map = {"unk_token": "<unk>"}
+        self.special_tokens_map = {"unk_token": "<unk>"}
 
-        with TemporaryDirectory() as tmpdirname:
-            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
-            merges_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['merges_file'])
-            with open(vocab_file, "w") as fp:
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
+        with open(self.vocab_file, "w") as fp:
             fp.write(json.dumps(vocab_tokens))
-            with open(merges_file, "w") as fp:
+        with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
+    def get_tokenizer(self):
+        return GPT2Tokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
+
+    def get_input_output_texts(self):
         input_text = u"lower newer"
         output_text = u"lower<unk>newer"
+        return input_text, output_text
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, GPT2Tokenizer, tmpdirname, **special_tokens_map)
-
-            tokenizer = GPT2Tokenizer(vocab_file, merges_file, **special_tokens_map)
+    def test_full_tokenizer(self):
+        tokenizer = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
         text = "lower"
         bpe_tokens = ["low", "er"]
         tokens = tokenizer.tokenize(text)

pytorch_transformers/tests/tokenization_openai_test.py

@@ -20,13 +20,17 @@ import json
 
 from pytorch_transformers.tokenization_openai import OpenAIGPTTokenizer, VOCAB_FILES_NAMES
 
-from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from .tokenization_tests_commons import CommonTestCases
 
 
-class OpenAIGPTTokenizationTest(unittest.TestCase):
+class OpenAIGPTTokenizationTest(CommonTestCases.CommonTokenizerTester):
 
-    def test_full_tokenizer(self):
-        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+    tokenizer_class = OpenAIGPTTokenizer
+
+    def setUp(self):
+        super(OpenAIGPTTokenizationTest, self).setUp()
+
+        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
                  "w</w>", "r</w>", "t</w>",
                  "lo", "low", "er</w>",
@@ -34,20 +38,24 @@ class OpenAIGPTTokenizationTest(unittest.TestCase):
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
         merges = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
 
-        with TemporaryDirectory() as tmpdirname:
-            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
-            merges_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['merges_file'])
-            with open(vocab_file, "w") as fp:
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
+        with open(self.vocab_file, "w") as fp:
             fp.write(json.dumps(vocab_tokens))
-            with open(merges_file, "w") as fp:
+        with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
+    def get_tokenizer(self):
+        return OpenAIGPTTokenizer.from_pretrained(self.tmpdirname)
+
+    def get_input_output_texts(self):
         input_text = u"lower newer"
         output_text = u"lower newer"
+        return input_text, output_text
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, OpenAIGPTTokenizer, tmpdirname)
 
-            tokenizer = OpenAIGPTTokenizer(vocab_file, merges_file)
+    def test_full_tokenizer(self):
+        tokenizer = OpenAIGPTTokenizer(self.vocab_file, self.merges_file)
 
         text = "lower"
         bpe_tokens = ["low", "er</w>"]

pytorch_transformers/tests/tokenization_tests_commons.py

@@ -19,6 +19,7 @@ import sys
 from io import open
 import tempfile
 import shutil
+import unittest
 
 if sys.version_info[0] == 2:
     import cPickle as pickle
@@ -36,8 +37,26 @@ else:
     unicode = str
 
 
-def create_and_check_save_and_load_tokenizer(tester, tokenizer_class, *inputs, **kwargs):
-    tokenizer = tokenizer_class.from_pretrained(*inputs, **kwargs)
+class CommonTestCases:
+
+    class CommonTokenizerTester(unittest.TestCase):
+
+        tokenizer_class = None
+
+        def setUp(self):
+            self.tmpdirname = tempfile.mkdtemp()
+
+        def tearDown(self):
+            shutil.rmtree(self.tmpdirname)
+
+        def get_tokenizer(self):
+            raise NotImplementedError
+
+        def get_input_output_texts(self):
+            raise NotImplementedError
+
+        def test_save_and_load_tokenizer(self):
+            tokenizer = self.get_tokenizer()
 
             before_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
 
@@ -46,11 +65,11 @@ def create_and_check_save_and_load_tokenizer(tester, tokenizer_class, *inputs, *
                 tokenizer = tokenizer.from_pretrained(tmpdirname)
 
             after_tokens = tokenizer.encode(u"He is very happy, UNwant\u00E9d,running")
-    tester.assertListEqual(before_tokens, after_tokens)
+            self.assertListEqual(before_tokens, after_tokens)
 
-def create_and_check_pickle_tokenizer(tester, tokenizer_class, *inputs, **kwargs):
-    tokenizer = tokenizer_class.from_pretrained(*inputs, **kwargs)
-    tester.assertIsNotNone(tokenizer)
+        def test_pickle_tokenizer(self):
+            tokenizer = self.get_tokenizer()
+            self.assertIsNotNone(tokenizer)
 
             text = u"Munich and Berlin are nice cities"
             subwords = tokenizer.tokenize(text)
@@ -64,32 +83,32 @@ def create_and_check_pickle_tokenizer(tester, tokenizer_class, *inputs, **kwargs
 
             subwords_loaded = tokenizer_new.tokenize(text)
 
-    tester.assertListEqual(subwords, subwords_loaded)
+            self.assertListEqual(subwords, subwords_loaded)
 
 
-def create_and_check_add_tokens_tokenizer(tester, tokenizer_class, *inputs, **kwargs):
-    tokenizer = tokenizer_class.from_pretrained(*inputs, **kwargs)
+        def test_add_tokens_tokenizer(self):
+            tokenizer = self.get_tokenizer()
 
             vocab_size = tokenizer.vocab_size
             all_size = len(tokenizer)
 
-    tester.assertNotEqual(vocab_size, 0)
-    tester.assertEqual(vocab_size, all_size)
+            self.assertNotEqual(vocab_size, 0)
+            self.assertEqual(vocab_size, all_size)
 
             new_toks = ["aaaaabbbbbb", "cccccccccdddddddd"]
             added_toks = tokenizer.add_tokens(new_toks)
             vocab_size_2 = tokenizer.vocab_size
             all_size_2 = len(tokenizer)
 
-    tester.assertNotEqual(vocab_size_2, 0)
-    tester.assertEqual(vocab_size, vocab_size_2)
-    tester.assertEqual(added_toks, len(new_toks))
-    tester.assertEqual(all_size_2, all_size + len(new_toks))
+            self.assertNotEqual(vocab_size_2, 0)
+            self.assertEqual(vocab_size, vocab_size_2)
+            self.assertEqual(added_toks, len(new_toks))
+            self.assertEqual(all_size_2, all_size + len(new_toks))
 
             tokens = tokenizer.encode("aaaaabbbbbb low cccccccccdddddddd l")
-    tester.assertGreaterEqual(len(tokens), 4)
-    tester.assertGreater(tokens[0], tokenizer.vocab_size - 1)
-    tester.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
+            self.assertGreaterEqual(len(tokens), 4)
+            self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
+            self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
 
             new_toks_2 = {'eos_token': ">>>>|||<||<<|<<",
                         'pad_token': "<<<<<|||>|>>>>|>"}
@@ -97,52 +116,45 @@ def create_and_check_add_tokens_tokenizer(tester, tokenizer_class, *inputs, **kw
             vocab_size_3 = tokenizer.vocab_size
             all_size_3 = len(tokenizer)
 
-    tester.assertNotEqual(vocab_size_3, 0)
-    tester.assertEqual(vocab_size, vocab_size_3)
-    tester.assertEqual(added_toks_2, len(new_toks_2))
-    tester.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
+            self.assertNotEqual(vocab_size_3, 0)
+            self.assertEqual(vocab_size, vocab_size_3)
+            self.assertEqual(added_toks_2, len(new_toks_2))
+            self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
 
             tokens = tokenizer.encode(">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l")
 
-    tester.assertGreaterEqual(len(tokens), 6)
-    tester.assertGreater(tokens[0], tokenizer.vocab_size - 1)
-    tester.assertGreater(tokens[0], tokens[1])
-    tester.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
-    tester.assertGreater(tokens[-2], tokens[-3])
-    tester.assertEqual(tokens[0], tokenizer.convert_tokens_to_ids(tokenizer.eos_token))
-    tester.assertEqual(tokens[-2], tokenizer.convert_tokens_to_ids(tokenizer.pad_token))
+            self.assertGreaterEqual(len(tokens), 6)
+            self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
+            self.assertGreater(tokens[0], tokens[1])
+            self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
+            self.assertGreater(tokens[-2], tokens[-3])
+            self.assertEqual(tokens[0], tokenizer.convert_tokens_to_ids(tokenizer.eos_token))
+            self.assertEqual(tokens[-2], tokenizer.convert_tokens_to_ids(tokenizer.pad_token))
 
 
-def create_and_check_required_methods_tokenizer(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs):
-    tokenizer = tokenizer_class.from_pretrained(*inputs, **kwargs)
+        def test_required_methods_tokenizer(self):
+            tokenizer = self.get_tokenizer()
+            input_text, output_text = self.get_input_output_texts()
 
             tokens = tokenizer.tokenize(input_text)
             ids = tokenizer.convert_tokens_to_ids(tokens)
             ids_2 = tokenizer.encode(input_text)
-    tester.assertListEqual(ids, ids_2)
+            self.assertListEqual(ids, ids_2)
 
             tokens_2 = tokenizer.convert_ids_to_tokens(ids)
             text_2 = tokenizer.decode(ids)
 
-    tester.assertEqual(text_2, output_text)
+            self.assertEqual(text_2, output_text)
 
-    tester.assertNotEqual(len(tokens_2), 0)
-    tester.assertIsInstance(text_2, (str, unicode))
+            self.assertNotEqual(len(tokens_2), 0)
+            self.assertIsInstance(text_2, (str, unicode))
 
 
-def create_and_check_pretrained_model_lists(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs):
-    weights_list = list(tokenizer_class.max_model_input_sizes.keys())
+        def test_pretrained_model_lists(self):
+            weights_list = list(self.tokenizer_class.max_model_input_sizes.keys())
             weights_lists_2 = []
-    for file_id, map_list in tokenizer_class.pretrained_vocab_files_map.items():
+            for file_id, map_list in self.tokenizer_class.pretrained_vocab_files_map.items():
                 weights_lists_2.append(list(map_list.keys()))
 
             for weights_list_2 in weights_lists_2:
-        tester.assertListEqual(weights_list, weights_list_2)
-
-
-def create_and_check_tokenizer_commons(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs):
-    create_and_check_pretrained_model_lists(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs)
-    create_and_check_required_methods_tokenizer(tester, input_text, output_text, tokenizer_class, *inputs, **kwargs)
-    create_and_check_add_tokens_tokenizer(tester, tokenizer_class, *inputs, **kwargs)
-    create_and_check_save_and_load_tokenizer(tester, tokenizer_class, *inputs, **kwargs)
-    create_and_check_pickle_tokenizer(tester, tokenizer_class, *inputs, **kwargs)
+                self.assertListEqual(weights_list, weights_list_2)

pytorch_transformers/tests/tokenization_transfo_xl_test.py

@@ -20,26 +20,33 @@ from io import open
 
 from pytorch_transformers.tokenization_transfo_xl import TransfoXLTokenizer, VOCAB_FILES_NAMES
 
-from.tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from.tokenization_tests_commons import CommonTestCases
 
-class TransfoXLTokenizationTest(unittest.TestCase):
+class TransfoXLTokenizationTest(CommonTestCases.CommonTokenizerTester):
+
+    tokenizer_class = TransfoXLTokenizer
+
+    def setUp(self):
+        super(TransfoXLTokenizationTest, self).setUp()
 
-    def test_full_tokenizer(self):
         vocab_tokens = [
             "<unk>", "[CLS]", "[SEP]", "want", "unwanted", "wa", "un",
             "running", ",", "low", "l",
         ]
-        with TemporaryDirectory() as tmpdirname:
-            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
-            with open(vocab_file, "w", encoding='utf-8') as vocab_writer:
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        with open(self.vocab_file, "w", encoding='utf-8') as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
 
+    def get_tokenizer(self):
+        return TransfoXLTokenizer.from_pretrained(self.tmpdirname, lower_case=True)
+
+    def get_input_output_texts(self):
         input_text = u"<unk> UNwanted , running"
         output_text = u"<unk> unwanted, running"
+        return input_text, output_text
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, TransfoXLTokenizer, tmpdirname, lower_case=True)
-
-            tokenizer = TransfoXLTokenizer(vocab_file=vocab_file, lower_case=True)
+    def test_full_tokenizer(self):
+        tokenizer = TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=True)
 
         tokens = tokenizer.tokenize(u"<unk> UNwanted , running")
         self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])

pytorch_transformers/tests/tokenization_xlm_test.py

@@ -20,12 +20,16 @@ import json
 
 from pytorch_transformers.tokenization_xlm import XLMTokenizer, VOCAB_FILES_NAMES
 
-from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from .tokenization_tests_commons import CommonTestCases
 
-class XLMTokenizationTest(unittest.TestCase):
+class XLMTokenizationTest(CommonTestCases.CommonTokenizerTester):
 
-    def test_full_tokenizer(self):
-        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+    tokenizer_class = XLMTokenizer
+
+    def setUp(self):
+        super(XLMTokenizationTest, self).setUp()
+
+        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
                  "w</w>", "r</w>", "t</w>",
                  "lo", "low", "er</w>",
@@ -33,20 +37,24 @@ class XLMTokenizationTest(unittest.TestCase):
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
         merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
 
-        with TemporaryDirectory() as tmpdirname:
-            vocab_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
-            merges_file = os.path.join(tmpdirname, VOCAB_FILES_NAMES['merges_file'])
-            with open(vocab_file, "w") as fp:
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
+        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
+        with open(self.vocab_file, "w") as fp:
             fp.write(json.dumps(vocab_tokens))
-            with open(merges_file, "w") as fp:
+        with open(self.merges_file, "w") as fp:
             fp.write("\n".join(merges))
 
+    def get_tokenizer(self):
+        return XLMTokenizer.from_pretrained(self.tmpdirname)
+
+    def get_input_output_texts(self):
         input_text = u"lower newer"
         output_text = u"lower newer"
+        return input_text, output_text
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, XLMTokenizer, tmpdirname)
-
-            tokenizer = XLMTokenizer(vocab_file, merges_file)
+    def test_full_tokenizer(self):
+        """ Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt """
+        tokenizer = XLMTokenizer(self.vocab_file, self.merges_file)
 
         text = "lower"
         bpe_tokens = ["low", "er</w>"]

pytorch_transformers/tests/tokenization_xlnet_test.py

@@ -19,23 +19,33 @@ import unittest
 
 from pytorch_transformers.tokenization_xlnet import (XLNetTokenizer, SPIECE_UNDERLINE)
 
-from .tokenization_tests_commons import create_and_check_tokenizer_commons, TemporaryDirectory
+from .tokenization_tests_commons import CommonTestCases
 
 SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                     'fixtures/test_sentencepiece.model')
 
-class XLNetTokenizationTest(unittest.TestCase):
+class XLNetTokenizationTest(CommonTestCases.CommonTokenizerTester):
 
-    def test_full_tokenizer(self):
+    tokenizer_class = XLNetTokenizer
+
+    def setUp(self):
+        super(XLNetTokenizationTest, self).setUp()
+
+        # We have a SentencePiece fixture for testing
         tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
+        tokenizer.save_pretrained(self.tmpdirname)
 
-        with TemporaryDirectory() as tmpdirname:
-            tokenizer.save_pretrained(tmpdirname)
+    def get_tokenizer(self):
+        return XLNetTokenizer.from_pretrained(self.tmpdirname)
 
+    def get_input_output_texts(self):
         input_text = u"This is a test"
         output_text = u"This is a test"
+        return input_text, output_text
 
-            create_and_check_tokenizer_commons(self, input_text, output_text, XLNetTokenizer, tmpdirname)
+
+    def test_full_tokenizer(self):
+        tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)
 
         tokens = tokenizer.tokenize(u'This is a test')
         self.assertListEqual(tokens, [u'▁This', u'▁is', u'▁a', u'▁t', u'est'])

pytorch_transformers/tokenization_bert.py

@@ -86,7 +86,7 @@ def whitespace_tokenize(text):
 class BertTokenizer(PreTrainedTokenizer):
     r"""
     Constructs a BertTokenizer.
-    :class:`~pytorch_pretrained_bert.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiece
+    :class:`~pytorch_transformers.BertTokenizer` runs end-to-end tokenization: punctuation splitting + wordpiece
 
     Args:
         vocab_file: Path to a one-wordpiece-per-line vocabulary file

pytorch_transformers/tokenization_utils.py

@@ -125,42 +125,34 @@ class PreTrainedTokenizer(object):
 
     @bos_token.setter
     def bos_token(self, value):
-        self.add_tokens([value]) 
         self._bos_token = value
 
     @eos_token.setter
     def eos_token(self, value):
-        self.add_tokens([value]) 
         self._eos_token = value
 
     @unk_token.setter
     def unk_token(self, value):
-        self.add_tokens([value]) 
         self._unk_token = value
 
     @sep_token.setter
     def sep_token(self, value):
-        self.add_tokens([value]) 
         self._sep_token = value
 
     @pad_token.setter
     def pad_token(self, value):
-        self.add_tokens([value]) 
         self._pad_token = value
 
     @cls_token.setter
     def cls_token(self, value):
-        self.add_tokens([value]) 
         self._cls_token = value
 
     @mask_token.setter
     def mask_token(self, value):
-        self.add_tokens([value]) 
         self._mask_token = value
 
     @additional_special_tokens.setter
     def additional_special_tokens(self, value):
-        self.add_tokens(value) 
         self._additional_special_tokens = value
 
     def __init__(self, max_len=None, **kwargs):
@@ -179,6 +171,10 @@ class PreTrainedTokenizer(object):
 
         for key, value in kwargs.items():
             if key in self.SPECIAL_TOKENS_ATTRIBUTES:
+                if key == 'additional_special_tokens':
+                    assert isinstance(value, (list, tuple)) and all(isinstance(t, str) or (six.PY2 and isinstance(t, unicode)) for t in value)
+                else:
+                    assert isinstance(value, str) or (six.PY2 and isinstance(value, unicode))
                 setattr(self, key, value)
 
 
@@ -415,15 +411,39 @@ class PreTrainedTokenizer(object):
                 
                     Tokens are only added if they are not already in the vocabulary (tested by checking if the tokenizer assign the index of the ``unk_token`` to them).
 
+            Returns:
+                Number of tokens added to the vocabulary.
+
+        Examples::
+
+            # Let's see how to add a new classification token to GPT-2
+            tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+            model = GPT2Model.from_pretrained('gpt2')
+
+            special_tokens_dict = {'cls_token': '<CLS>'}
+
+            num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
+            print('We have added', num_added_toks, 'tokens')
+            model.resize_token_embeddings(len(tokenizer))  # Notice: resize_token_embeddings expect to receive the full size of the new vocabulary, i.e. the length of the tokenizer.
+
+            assert tokenizer.cls_token == '<CLS>'
         """
         if not special_tokens_dict:
             return 0
 
+        added_tokens = 0
         for key, value in special_tokens_dict.items():
             assert key in self.SPECIAL_TOKENS_ATTRIBUTES
+            if key == 'additional_special_tokens':
+                assert isinstance(value, (list, tuple)) and all(isinstance(t, str) or (six.PY2 and isinstance(t, unicode)) for t in value)
+                added_tokens += self.add_tokens(value)
+            else:
+                assert isinstance(value, str) or (six.PY2 and isinstance(value, unicode))
+                added_tokens += self.add_tokens([value])
             logger.info("Assigning %s to the %s key of the tokenizer", value, key)
             setattr(self, key, value)
 
+        return added_tokens
 
     def tokenize(self, text, **kwargs):
         """ Converts a string in a sequence of tokens (string), using the tokenizer.