fix tests

README.md

@@ -60,7 +60,7 @@ This package comprises the following classes that can be imported in Python and
   - `BertTokenizer` - perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
 
 - One optimizer:
-  - `BERTAdam` - Bert version of Adam algorithm with weight decay fix, warmup and linear decay of the learning rate.
+  - `BertAdam` - Bert version of Adam algorithm with weight decay fix, warmup and linear decay of the learning rate.
 
 - A configuration class:
   - `BertConfig` - Configuration class to store the configuration of a `BertModel` with utilisities to read and write from JSON configuration files.
@@ -155,7 +155,7 @@ Here is a detailed documentation of the classes in the package and how to use th
 | [Loading Google AI's pre-trained weigths](#Loading-Google-AIs-pre-trained-weigths-and-PyTorch-dump) | How to load Google AI's pre-trained weight or a PyTorch saved instance |
 | [PyTorch models](#PyTorch-models) | API of the six PyTorch model classes: `BertModel`, `BertForMaskedLM`, `BertForNextSentencePrediction`, `BertForPreTraining`, `BertForSequenceClassification` or `BertForQuestionAnswering` |
 | [Tokenizer: `BertTokenizer`](#Tokenizer-BertTokenizer) | API of the `BertTokenizer` class|
-| [Optimizer: `BERTAdam`](#Optimizer-BERTAdam) |  API of the `BERTAdam` class |
+| [Optimizer: `BertAdam`](#Optimizer-BertAdam) |  API of the `BertAdam` class |
 
 ### Loading Google AI's pre-trained weigths and PyTorch dump
 
@@ -294,12 +294,12 @@ and three methods:
 
 Please refer to the doc strings and code in [`tokenization.py`](./pytorch_pretrained_bert/tokenization.py) for the details of the `BasicTokenizer` and `WordpieceTokenizer` classes. In general it is recommended to use `BertTokenizer` unless you know what you are doing.
 
-### Optimizer: `BERTAdam`
+### Optimizer: `BertAdam`
 
-`BERTAdam` is a `torch.optimizer` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch Adam optimizer are the following:
+`BertAdam` is a `torch.optimizer` adapted to be closer to the optimizer used in the TensorFlow implementation of Bert. The differences with PyTorch Adam optimizer are the following:
 
-- BERTAdam implements weight decay fix,
-- BERTAdam doesn't compensate for bias as in the regular Adam optimizer.
+- BertAdam implements weight decay fix,
+- BertAdam doesn't compensate for bias as in the regular Adam optimizer.
 
 The optimizer accepts the following arguments:
 

examples/extract_features.py

@@ -29,7 +29,7 @@ from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_pretrained_bert.tokenization import convert_to_unicode, BertTokenizer
-from pytorch_pretrained_bert.modeling import BertConfig, BertModel
+from pytorch_pretrained_bert.modeling import BertModel
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
                     datefmt = '%m/%d/%Y %H:%M:%S',

examples/run_classifier.py

@@ -31,8 +31,8 @@ from torch.utils.data import TensorDataset, DataLoader, RandomSampler, Sequentia
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_pretrained_bert.tokenization import printable_text, convert_to_unicode, BertTokenizer
-from pytorch_pretrained_bert.modeling import BertConfig, BertForSequenceClassification
-from pytorch_pretrained_bert.optimization import BERTAdam
+from pytorch_pretrained_bert.modeling import BertForSequenceClassification
+from pytorch_pretrained_bert.optimization import BertAdam
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -512,7 +512,7 @@ def main():
         {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01},
         {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0}
         ]
-    optimizer = BERTAdam(optimizer_grouped_parameters,
+    optimizer = BertAdam(optimizer_grouped_parameters,
                          lr=args.learning_rate,
                          warmup=args.warmup_proportion,
                          t_total=num_train_steps)

examples/run_squad.py

@@ -33,8 +33,8 @@ from torch.utils.data import TensorDataset, DataLoader, RandomSampler, Sequentia
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_pretrained_bert.tokenization import printable_text, whitespace_tokenize, BasicTokenizer, BertTokenizer
-from pytorch_pretrained_bert.modeling import BertConfig, BertForQuestionAnswering
-from pytorch_pretrained_bert.optimization import BERTAdam
+from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
+from pytorch_pretrained_bert.optimization import BertAdam
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s', 
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -847,7 +847,7 @@ def main():
         {'params': [p for n, p in param_optimizer if n not in no_decay], 'weight_decay_rate': 0.01},
         {'params': [p for n, p in param_optimizer if n in no_decay], 'weight_decay_rate': 0.0}
         ]
-    optimizer = BERTAdam(optimizer_grouped_parameters,
+    optimizer = BertAdam(optimizer_grouped_parameters,
                          lr=args.learning_rate,
                          warmup=args.warmup_proportion,
                          t_total=num_train_steps)

pytorch_pretrained_bert/__init__.py

@@ -2,4 +2,4 @@ from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer
 from .modeling import (BertConfig, BertModel, BertForPreTraining,
                        BertForMaskedLM, BertForNextSentencePrediction,
                        BertForSequenceClassification, BertForQuestionAnswering)
-from .optimization import BERTAdam
+from .optimization import BertAdam

pytorch_pretrained_bert/optimization.py

@@ -41,7 +41,7 @@ SCHEDULES = {
 }
 
 
-class BERTAdam(Optimizer):
+class BertAdam(Optimizer):
     """Implements BERT version of Adam algorithm with weight decay fix.
     Params:
         lr: learning rate
@@ -73,7 +73,7 @@ class BERTAdam(Optimizer):
         defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
                         b1=b1, b2=b2, e=e, weight_decay_rate=weight_decay_rate,
                         max_grad_norm=max_grad_norm)
-        super(BERTAdam, self).__init__(params, defaults)
+        super(BertAdam, self).__init__(params, defaults)
 
     def get_lr(self):
         lr = []

tests/modeling_test.py

@@ -22,7 +22,7 @@ import random
 
 import torch
 
-import modeling
+from pytorch_pretrained_bert import BertConfig, BertModel
 
 
 class BertModelTest(unittest.TestCase):
@@ -77,8 +77,8 @@ class BertModelTest(unittest.TestCase):
             if self.use_token_type_ids:
                 token_type_ids = BertModelTest.ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
 
-            config = modeling.BertConfig(
-                vocab_size=self.vocab_size,
+            config = BertConfig(
+                vocab_size_or_config_json_file=self.vocab_size,
                 hidden_size=self.hidden_size,
                 num_hidden_layers=self.num_hidden_layers,
                 num_attention_heads=self.num_attention_heads,
@@ -90,7 +90,7 @@ class BertModelTest(unittest.TestCase):
                 type_vocab_size=self.type_vocab_size,
                 initializer_range=self.initializer_range)
 
-            model = modeling.BertModel(config=config)
+            model = BertModel(config=config)
 
             all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
 
@@ -112,7 +112,7 @@ class BertModelTest(unittest.TestCase):
         self.run_tester(BertModelTest.BertModelTester(self))
 
     def test_config_to_json_string(self):
-        config = modeling.BertConfig(vocab_size=99, hidden_size=37)
+        config = BertConfig(vocab_size_or_config_json_file=99, hidden_size=37)
         obj = json.loads(config.to_json_string())
         self.assertEqual(obj["vocab_size"], 99)
         self.assertEqual(obj["hidden_size"], 37)

tests/optimization_test.py

@@ -20,7 +20,7 @@ import unittest
 
 import torch
 
-import optimization
+from pytorch_pretrained_bert import BertAdam
 
 class OptimizationTest(unittest.TestCase):
 
@@ -34,7 +34,7 @@ class OptimizationTest(unittest.TestCase):
         target = torch.tensor([0.4, 0.2, -0.5])
         criterion = torch.nn.MSELoss(reduction='elementwise_mean')
         # No warmup, constant schedule, no gradient clipping
-        optimizer = optimization.BERTAdam(params=[w], lr=2e-1,
+        optimizer = BertAdam(params=[w], lr=2e-1,
                                           weight_decay_rate=0.0,
                                           max_grad_norm=-1)
         for _ in range(100):

tests/tokenization_test.py

@@ -19,7 +19,8 @@ from __future__ import print_function
 import os
 import unittest
 
-import tokenization
+from pytorch_pretrained_bert.tokenization import (BertTokenizer, BasicTokenizer, WordpieceTokenizer,
+                                                  _is_whitespace, _is_control, _is_punctuation)
 
 
 class TokenizationTest(unittest.TestCase):
@@ -34,7 +35,7 @@ class TokenizationTest(unittest.TestCase):
 
             vocab_file = vocab_writer.name
 
-        tokenizer = tokenization.BertTokenizer(vocab_file)
+        tokenizer = BertTokenizer(vocab_file)
         os.remove(vocab_file)
 
         tokens = tokenizer.tokenize(u"UNwant\u00E9d,running")
@@ -44,14 +45,14 @@ class TokenizationTest(unittest.TestCase):
             tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9])
 
     def test_chinese(self):
-        tokenizer = tokenization.BasicTokenizer()
+        tokenizer = BasicTokenizer()
     
         self.assertListEqual(
             tokenizer.tokenize(u"ah\u535A\u63A8zz"),
             [u"ah", u"\u535A", u"\u63A8", u"zz"])  
 
     def test_basic_tokenizer_lower(self):
-        tokenizer = tokenization.BasicTokenizer(do_lower_case=True)
+        tokenizer = BasicTokenizer(do_lower_case=True)
 
         self.assertListEqual(
             tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
@@ -59,7 +60,7 @@ class TokenizationTest(unittest.TestCase):
         self.assertListEqual(tokenizer.tokenize(u"H\u00E9llo"), ["hello"])
 
     def test_basic_tokenizer_no_lower(self):
-        tokenizer = tokenization.BasicTokenizer(do_lower_case=False)
+        tokenizer = BasicTokenizer(do_lower_case=False)
 
         self.assertListEqual(
             tokenizer.tokenize(u" \tHeLLo!how  \n Are yoU?  "),
@@ -74,7 +75,7 @@ class TokenizationTest(unittest.TestCase):
         vocab = {}
         for (i, token) in enumerate(vocab_tokens):
             vocab[token] = i
-        tokenizer = tokenization.WordpieceTokenizer(vocab=vocab)
+        tokenizer = WordpieceTokenizer(vocab=vocab)
 
         self.assertListEqual(tokenizer.tokenize(""), [])
 
@@ -85,46 +86,32 @@ class TokenizationTest(unittest.TestCase):
         self.assertListEqual(
             tokenizer.tokenize("unwantedX running"), ["[UNK]", "runn", "##ing"])
 
-    def test_convert_tokens_to_ids(self):
-        vocab_tokens = [
-            "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
-            "##ing"
-        ]
-
-        vocab = {}
-        for (i, token) in enumerate(vocab_tokens):
-            vocab[token] = i
-
-        self.assertListEqual(
-            tokenization.convert_tokens_to_ids(
-                vocab, ["un", "##want", "##ed", "runn", "##ing"]), [7, 4, 5, 8, 9])
-
     def test_is_whitespace(self):
-        self.assertTrue(tokenization._is_whitespace(u" "))
-        self.assertTrue(tokenization._is_whitespace(u"\t"))
-        self.assertTrue(tokenization._is_whitespace(u"\r"))
-        self.assertTrue(tokenization._is_whitespace(u"\n"))
-        self.assertTrue(tokenization._is_whitespace(u"\u00A0"))
+        self.assertTrue(_is_whitespace(u" "))
+        self.assertTrue(_is_whitespace(u"\t"))
+        self.assertTrue(_is_whitespace(u"\r"))
+        self.assertTrue(_is_whitespace(u"\n"))
+        self.assertTrue(_is_whitespace(u"\u00A0"))
 
-        self.assertFalse(tokenization._is_whitespace(u"A"))
-        self.assertFalse(tokenization._is_whitespace(u"-"))
+        self.assertFalse(_is_whitespace(u"A"))
+        self.assertFalse(_is_whitespace(u"-"))
 
     def test_is_control(self):
-        self.assertTrue(tokenization._is_control(u"\u0005"))
+        self.assertTrue(_is_control(u"\u0005"))
 
-        self.assertFalse(tokenization._is_control(u"A"))
-        self.assertFalse(tokenization._is_control(u" "))
-        self.assertFalse(tokenization._is_control(u"\t"))
-        self.assertFalse(tokenization._is_control(u"\r"))
+        self.assertFalse(_is_control(u"A"))
+        self.assertFalse(_is_control(u" "))
+        self.assertFalse(_is_control(u"\t"))
+        self.assertFalse(_is_control(u"\r"))
 
     def test_is_punctuation(self):
-        self.assertTrue(tokenization._is_punctuation(u"-"))
-        self.assertTrue(tokenization._is_punctuation(u"$"))
-        self.assertTrue(tokenization._is_punctuation(u"`"))
-        self.assertTrue(tokenization._is_punctuation(u"."))
+        self.assertTrue(_is_punctuation(u"-"))
+        self.assertTrue(_is_punctuation(u"$"))
+        self.assertTrue(_is_punctuation(u"`"))
+        self.assertTrue(_is_punctuation(u"."))
 
-        self.assertFalse(tokenization._is_punctuation(u"A"))
-        self.assertFalse(tokenization._is_punctuation(u" "))
+        self.assertFalse(_is_punctuation(u"A"))
+        self.assertFalse(_is_punctuation(u" "))
 
 
 if __name__ == '__main__':