Merge pull request #489 from huggingface/tokenization_serialization

Better serialization for Tokenizers and Configuration classes - Also fix #466

README.md

@@ -131,6 +131,7 @@ This package comprises the following classes that can be imported in Python and
 - Configuration classes for BERT, OpenAI GPT and Transformer-XL (in the respective [`modeling.py`](./pytorch_pretrained_bert/modeling.py), [`modeling_openai.py`](./pytorch_pretrained_bert/modeling_openai.py), [`modeling_transfo_xl.py`](./pytorch_pretrained_bert/modeling_transfo_xl.py) files):
   - `BertConfig` - Configuration class to store the configuration of a `BertModel` with utilities to read and write from JSON configuration files.
   - `OpenAIGPTConfig` - Configuration class to store the configuration of a `OpenAIGPTModel` with utilities to read and write from JSON configuration files.
+  - `GPT2Config` - Configuration class to store the configuration of a `GPT2Model` with utilities to read and write from JSON configuration files.
   - `TransfoXLConfig` - Configuration class to store the configuration of a `TransfoXLModel` with utilities to read and write from JSON configuration files.
 
 The repository further comprises:
@@ -461,10 +462,12 @@ Here is a detailed documentation of the classes in the package and how to use th
 
 | Sub-section | Description |
 |-|-|
-| [Loading Google AI's/OpenAI's pre-trained weights](#loading-google-ai-or-openai-pre-trained-weights-or-pytorch-dump) | How to load Google AI/OpenAI's pre-trained weight or a PyTorch saved instance |
-| [PyTorch models](#PyTorch-models) | API of the BERT, GPT, GPT-2 and Transformer-XL PyTorch model classes |
+| [Loading pre-trained weights](#loading-google-ai-or-openai-pre-trained-weights-or-pytorch-dump) | How to load Google AI/OpenAI's pre-trained weight or a PyTorch saved instance |
+| [Serialization best-practices](#serialization-best-practices) | How to save and reload a fine-tuned model |
+| [Configurations](#configurations) | API of the configuration classes for BERT, GPT, GPT-2 and Transformer-XL |
+| [Models](#models) | API of the PyTorch model classes for BERT, GPT, GPT-2 and Transformer-XL |
 | [Tokenizers](#tokenizers) | API of the tokenizers class for BERT, GPT, GPT-2 and Transformer-XL|
-| [Optimizers](#optimizerss) |  API of the optimizers |
+| [Optimizers](#optimizers) |  API of the optimizers |
 
 ### Loading Google AI or OpenAI pre-trained weights or PyTorch dump
 
@@ -524,7 +527,101 @@ model = GPT2Model.from_pretrained('gpt2')
 
 ```
 
-### PyTorch models
+### Serialization best-practices
+
+This section explain how you can save and re-load a fine-tuned model (BERT, GPT, GPT-2 and Transformer-XL).
+There are three types of files you need to save to be able to reload a fine-tuned model:
+
+- the model it-self which should be saved following PyTorch serialization [best practices](https://pytorch.org/docs/stable/notes/serialization.html#best-practices),
+- the configuration file of the model which is saved as a JSON file, and
+- the vocabulary (and the merges for the BPE-based models GPT and GPT-2).
+
+Here is the recommended way of saving the model, configuration and vocabulary to an `output_dir` directory and reloading the model and tokenizer afterwards:
+
+```python
+from pytorch_pretrained_bert import WEIGHTS_NAME, CONFIG_NAME
+
+output_dir = "./models/"
+
+# Step 1: Save a model, configuration and vocabulary that you have fine-tuned
+
+# If we have a distributed model, save only the encapsulated model
+# (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
+model_to_save = model.module if hasattr(model, 'module') else model
+
+# If we save using the predefined names, we can load using `from_pretrained`
+output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
+output_config_file = os.path.join(output_dir, CONFIG_NAME)
+
+torch.save(model_to_save.state_dict(), output_model_file)
+model_to_save.config.to_json_file(output_config_file)
+tokenizer.save_vocabulary(output_dir)
+
+# Step 2: Re-load the saved model and vocabulary
+
+# Example for a Bert model
+model = BertForQuestionAnswering.from_pretrained(output_dir)
+tokenizer = BertTokenizer.from_pretrained(output_dir, do_lower_case=args.do_lower_case)  # Add specific options if needed
+# Example for a GPT model
+model = OpenAIGPTDoubleHeadsModel.from_pretrained(output_dir)
+tokenizer = OpenAIGPTTokenizer.from_pretrained(output_dir)
+```
+
+Here is another way you can save and reload the model if you want to use specific paths for each type of files:
+
+```python
+output_model_file = "./models/my_own_model_file.bin"
+output_config_file = "./models/my_own_config_file.bin"
+output_vocab_file = "./models/my_own_vocab_file.bin"
+
+# Step 1: Save a model, configuration and vocabulary that you have fine-tuned
+
+# If we have a distributed model, save only the encapsulated model
+# (it was wrapped in PyTorch DistributedDataParallel or DataParallel)
+model_to_save = model.module if hasattr(model, 'module') else model
+
+torch.save(model_to_save.state_dict(), output_model_file)
+model_to_save.config.to_json_file(output_config_file)
+tokenizer.save_vocabulary(output_vocab_file)
+
+# Step 2: Re-load the saved model and vocabulary
+
+# We didn't save using the predefined WEIGHTS_NAME, CONFIG_NAME names, we cannot load using `from_pretrained`.
+# Here is how to do it in this situation:
+
+# Example for a Bert model
+config = BertConfig.from_json_file(output_config_file)
+model = BertForQuestionAnswering(config)
+state_dict = torch.load(output_model_file)
+model.load_state_dict(state_dict)
+tokenizer = BertTokenizer(output_vocab_file, do_lower_case=args.do_lower_case)
+
+# Example for a GPT model
+config = OpenAIGPTConfig.from_json_file(output_config_file)
+model = OpenAIGPTDoubleHeadsModel(config)
+state_dict = torch.load(output_model_file)
+model.load_state_dict(state_dict)
+tokenizer = OpenAIGPTTokenizer(output_vocab_file)
+```
+
+### Configurations
+
+Models (BERT, GPT, GPT-2 and Transformer-XL) are defined and build from configuration classes which containes the parameters of the models (number of layers, dimensionalities...) and a few utilities to read and write from JSON configuration files. The respective configuration classes are:
+
+- `BertConfig` for `BertModel` and BERT classes instances.
+- `OpenAIGPTConfig` for `OpenAIGPTModel` and OpenAI GPT classes instances.
+- `GPT2Config` for `GPT2Model` and OpenAI GPT-2 classes instances.
+- `TransfoXLConfig` for `TransfoXLModel` and Transformer-XL classes instances.
+
+These configuration classes contains a few utilities to load and save configurations:
+
+- `from_dict(cls, json_object)`: A class method to construct a configuration from a Python dictionary of parameters. Returns an instance of the configuration class.
+- `from_json_file(cls, json_file)`: A class method to construct a configuration from a json file of parameters. Returns an instance of the configuration class.
+- `to_dict()`: Serializes an instance to a Python dictionary. Returns a dictionary.
+- `to_json_string()`: Serializes an instance to a JSON string. Returns a string.
+- `to_json_file(json_file_path)`: Save an instance to a json file.
+
+### Models
 
 #### 1. `BertModel`
 
@@ -796,8 +893,7 @@ This model *outputs*:
   - `multiple_choice_logits`: the multiple choice logits as a torch.FloatTensor of size [batch_size, num_choices]
   - `presents`: a list of pre-computed hidden-states (key and values in each attention blocks) as a torch.FloatTensors. They can be reused to speed up sequential decoding (see the `run_gpt2.py` example).
 
-
-### Tokenizers:
+### Tokenizers
 
 #### `BertTokenizer`
 
@@ -816,6 +912,7 @@ and three methods:
 - `tokenize(text)`: convert a `str` in a list of `str` tokens by (1) performing basic tokenization and (2) WordPiece tokenization.
 - `convert_tokens_to_ids(tokens)`: convert a list of `str` tokens in a list of `int` indices in the vocabulary.
 - `convert_ids_to_tokens(tokens)`: convert a list of `int` indices in a list of `str` tokens in the vocabulary.
+- `save_vocabulary(directory_path)`: save the vocabulary file to `directory_path`. Return the path to the saved vocabulary file: `vocab_file_path`. The vocabulary can be reloaded with `BertTokenizer.from_pretrained('vocab_file_path')` or `BertTokenizer.from_pretrained('directory_path')`.
 
 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.
 
@@ -837,6 +934,7 @@ and five methods:
 - `convert_ids_to_tokens(tokens)`: convert a list of `int` indices in a list of `str` tokens in the vocabulary.
 - `set_special_tokens(self, special_tokens)`: update the list of special tokens (see above arguments)
 - `decode(ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)`: decode a list of `int` indices in a string and do some post-processing if needed: (i) remove special tokens from the output and (ii) clean up tokenization spaces.
+- `save_vocabulary(directory_path)`: save the vocabulary, merge and special tokens files to `directory_path`. Return the path to the three files: `vocab_file_path`, `merge_file_path`, `special_tokens_file_path`. The vocabulary can be reloaded with `OpenAIGPTTokenizer.from_pretrained('directory_path')`.
 
 Please refer to the doc strings and code in [`tokenization_openai.py`](./pytorch_pretrained_bert/tokenization_openai.py) for the details of the `OpenAIGPTTokenizer`.
 
@@ -844,6 +942,8 @@ Please refer to the doc strings and code in [`tokenization_openai.py`](./pytorch
 
 `TransfoXLTokenizer` perform word tokenization. This tokenizer can be used for adaptive softmax and has utilities for counting tokens in a corpus to create a vocabulary ordered by toekn frequency (for adaptive softmax). See the adaptive softmax paper ([Efficient softmax approximation for GPUs](http://arxiv.org/abs/1609.04309)) for more details.
 
+The API is similar to the API of `BertTokenizer` (see above).
+
 Please refer to the doc strings and code in [`tokenization_transfo_xl.py`](./pytorch_pretrained_bert/tokenization_transfo_xl.py) for the details of these additional methods in `TransfoXLTokenizer`.
 
 #### `GPT2Tokenizer`
@@ -860,11 +960,11 @@ and two methods:
 
 - `encode(text)`: convert a `str` in a list of `int` tokens by performing byte-level BPE.
 - `decode(tokens)`: convert back a list of `int` tokens in a `str`.
+- `save_vocabulary(directory_path)`: save the vocabulary, merge and special tokens files to `directory_path`. Return the path to the three files: `vocab_file_path`, `merge_file_path`, `special_tokens_file_path`. The vocabulary can be reloaded with `OpenAIGPTTokenizer.from_pretrained('directory_path')`.
 
 Please refer to [`tokenization_gpt2.py`](./pytorch_pretrained_bert/tokenization_gpt2.py) for more details on the `GPT2Tokenizer`.
 
-
-### Optimizers:
+### Optimizers
 
 #### `BertAdam`
 
@@ -1174,18 +1274,20 @@ To get these results we used a combination of:
 
 Here is the full list of hyper-parameters for this run:
 ```bash
+export SQUAD_DIR=/path/to/SQUAD
+
 python ./run_squad.py \
   --bert_model bert-large-uncased \
   --do_train \
   --do_predict \
   --do_lower_case \
-  --train_file $SQUAD_TRAIN \
-  --predict_file $SQUAD_EVAL \
+  --train_file $SQUAD_DIR/train-v1.1.json \
+  --predict_file $SQUAD_DIR/dev-v1.1.json \
   --learning_rate 3e-5 \
   --num_train_epochs 2 \
   --max_seq_length 384 \
   --doc_stride 128 \
-  --output_dir $OUTPUT_DIR \
+  --output_dir /tmp/debug_squad/ \
   --train_batch_size 24 \
   --gradient_accumulation_steps 2
 ```
@@ -1194,18 +1296,20 @@ If you have a recent GPU (starting from NVIDIA Volta series), you should try **1
 
 Here is an example of hyper-parameters for a FP16 run we tried:
 ```bash
+export SQUAD_DIR=/path/to/SQUAD
+
 python ./run_squad.py \
   --bert_model bert-large-uncased \
   --do_train \
   --do_predict \
   --do_lower_case \
-  --train_file $SQUAD_TRAIN \
-  --predict_file $SQUAD_EVAL \
+  --train_file $SQUAD_DIR/train-v1.1.json \
+  --predict_file $SQUAD_DIR/dev-v1.1.json \
   --learning_rate 3e-5 \
   --num_train_epochs 2 \
   --max_seq_length 384 \
   --doc_stride 128 \
-  --output_dir $OUTPUT_DIR \
+  --output_dir /tmp/debug_squad/ \
   --train_batch_size 24 \
   --fp16 \
   --loss_scale 128

examples/run_classifier.py

@@ -35,14 +35,11 @@ from torch.nn import CrossEntropyLoss, MSELoss
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import matthews_corrcoef, f1_score
 
-from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
-from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig, WEIGHTS_NAME, CONFIG_NAME
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
+from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig
 from pytorch_pretrained_bert.tokenization import BertTokenizer
 from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -697,6 +694,11 @@ def main():
         n_gpu = 1
         # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.distributed.init_process_group(backend='nccl')
+
+    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                        datefmt = '%m/%d/%Y %H:%M:%S',
+                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
+
     logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
         device, n_gpu, bool(args.local_rank != -1), args.fp16))
 
@@ -857,18 +859,21 @@ def main():
                     optimizer.zero_grad()
                     global_step += 1
 
-        # Save a trained model and the associated configuration
+    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Save a trained model, configuration and tokenizer
         model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+
+        # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
-        torch.save(model_to_save.state_dict(), output_model_file)
         output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
-        with open(output_config_file, 'w') as f:
-            f.write(model_to_save.config.to_json_string())
 
-        # Load a trained model and config that you have fine-tuned
-        config = BertConfig(output_config_file)
-        model = BertForSequenceClassification(config, num_labels=num_labels)
-        model.load_state_dict(torch.load(output_model_file))
+        torch.save(model_to_save.state_dict(), output_model_file)
+        model_to_save.config.to_json_file(output_config_file)
+        tokenizer.save_vocabulary(args.output_dir)
+
+        # Load a trained model and vocabulary that you have fine-tuned
+        model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
+        tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
     else:
         model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
     model.to(device)

examples/run_openai_gpt.py

@@ -39,7 +39,8 @@ import torch
 from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                               TensorDataset)
 
-from pytorch_pretrained_bert import OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer, OpenAIAdam, cached_path
+from pytorch_pretrained_bert import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
+                                     OpenAIAdam, cached_path, WEIGHTS_NAME, CONFIG_NAME)
 
 ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz"
 
@@ -218,15 +219,20 @@ def main():
 
     # Save a trained model
     if args.do_train:
+        # Save a trained model, configuration and tokenizer
         model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
-        output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
-        config = model.config
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
+        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
+
         torch.save(model_to_save.state_dict(), output_model_file)
+        model_to_save.config.to_json_file(output_config_file)
+        tokenizer.save_vocabulary(args.output_dir)
 
-        # Load a trained model that you have fine-tuned
-        model_state_dict = torch.load(output_model_file)
-        model = OpenAIGPTDoubleHeadsModel(config)
-        model.load_state_dict(model_state_dict)
+        # Load a trained model and vocabulary that you have fine-tuned
+        model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
+        tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
         model.to(device)
 
     if args.do_eval:

examples/run_squad.py

@@ -34,8 +34,8 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
-from pytorch_pretrained_bert.modeling import BertForQuestionAnswering, BertConfig, WEIGHTS_NAME, CONFIG_NAME
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
+from pytorch_pretrained_bert.modeling import BertForQuestionAnswering, BertConfig
 from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
 from pytorch_pretrained_bert.tokenization import (BasicTokenizer,
                                                   BertTokenizer,
@@ -46,9 +46,6 @@ if sys.version_info[0] == 2:
 else:
     import pickle
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
 logger = logging.getLogger(__name__)
 
 
@@ -837,7 +834,17 @@ def main():
     parser.add_argument('--null_score_diff_threshold',
                         type=float, default=0.0,
                         help="If null_score - best_non_null is greater than the threshold predict null.")
+    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
+    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
     args = parser.parse_args()
+    print(args)
+
+    if args.server_ip and args.server_port:
+        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
+        import ptvsd
+        print("Waiting for debugger attach")
+        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
+        ptvsd.wait_for_attach()
 
     if args.local_rank == -1 or args.no_cuda:
         device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
@@ -848,6 +855,11 @@ def main():
         n_gpu = 1
         # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
         torch.distributed.init_process_group(backend='nccl')
+
+    logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
+                        datefmt = '%m/%d/%Y %H:%M:%S',
+                        level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
+
     logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
         device, n_gpu, bool(args.local_rank != -1), args.fp16))
 
@@ -983,7 +995,7 @@ def main():
 
         model.train()
         for _ in trange(int(args.num_train_epochs), desc="Epoch"):
-            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
+            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
                 if n_gpu == 1:
                     batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self
                 input_ids, input_mask, segment_ids, start_positions, end_positions = batch
@@ -1008,19 +1020,21 @@ def main():
                     optimizer.zero_grad()
                     global_step += 1
 
-    if args.do_train:
-        # Save a trained model and the associated configuration
+    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
+        # Save a trained model, configuration and tokenizer
         model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+
+        # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
-        torch.save(model_to_save.state_dict(), output_model_file)
         output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
-        with open(output_config_file, 'w') as f:
-            f.write(model_to_save.config.to_json_string())
 
-        # Load a trained model and config that you have fine-tuned
-        config = BertConfig(output_config_file)
-        model = BertForQuestionAnswering(config)
-        model.load_state_dict(torch.load(output_model_file))
+        torch.save(model_to_save.state_dict(), output_model_file)
+        model_to_save.config.to_json_file(output_config_file)
+        tokenizer.save_vocabulary(args.output_dir)
+
+        # Load a trained model and vocabulary that you have fine-tuned
+        model = BertForQuestionAnswering.from_pretrained(args.output_dir)
+        tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
     else:
         model = BertForQuestionAnswering.from_pretrained(args.bert_model)
 
@@ -1054,7 +1068,7 @@ def main():
         model.eval()
         all_results = []
         logger.info("Start evaluating")
-        for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating"):
+        for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating", disable=args.local_rank not in [-1, 0]):
             if len(all_results) % 1000 == 0:
                 logger.info("Processing example: %d" % (len(all_results)))
             input_ids = input_ids.to(device)

examples/run_swag.py

@@ -32,8 +32,8 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
-from pytorch_pretrained_bert.modeling import (BertForMultipleChoice, BertConfig, WEIGHTS_NAME, CONFIG_NAME)
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
+from pytorch_pretrained_bert.modeling import BertForMultipleChoice, BertConfig
 from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
 from pytorch_pretrained_bert.tokenization import BertTokenizer
 
@@ -473,18 +473,20 @@ def main():
 
 
     if args.do_train:
-        # Save a trained model and the associated configuration
+        # Save a trained model, configuration and tokenizer
         model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
+
+        # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
-        torch.save(model_to_save.state_dict(), output_model_file)
         output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
-        with open(output_config_file, 'w') as f:
-            f.write(model_to_save.config.to_json_string())
 
-        # Load a trained model and config that you have fine-tuned
-        config = BertConfig(output_config_file)
-        model = BertForMultipleChoice(config, num_choices=4)
-        model.load_state_dict(torch.load(output_model_file))
+        torch.save(model_to_save.state_dict(), output_model_file)
+        model_to_save.config.to_json_file(output_config_file)
+        tokenizer.save_vocabulary(args.output_dir)
+
+        # Load a trained model and vocabulary that you have fine-tuned
+        model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4)
+        tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
     else:
         model = BertForMultipleChoice.from_pretrained(args.bert_model, num_choices=4)
     model.to(device)

examples/run_transfo_xl.py

@@ -28,7 +28,7 @@ import math
 
 import torch
 
-from pytorch_pretrained_bert import TransfoXLLMHeadModel, TransfoXLCorpus
+from pytorch_pretrained_bert import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -80,6 +80,7 @@ def main():
     # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
     # and tokenizing the dataset
     # The pre-processed corpus is a convertion (using the conversion script )
+    tokenizer = TransfoXLTokenizer.from_pretrained(args.model_name)
     corpus = TransfoXLCorpus.from_pretrained(args.model_name)
     ntokens = len(corpus.vocab)
 

pytorch_pretrained_bert/__init__.py

@@ -21,4 +21,4 @@ from .modeling_gpt2 import (GPT2Config, GPT2Model,
 from .optimization import BertAdam
 from .optimization_openai import OpenAIAdam
 
-from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE, cached_path
+from .file_utils import PYTORCH_PRETRAINED_BERT_CACHE, cached_path, WEIGHTS_NAME, CONFIG_NAME

pytorch_pretrained_bert/file_utils.py

@@ -33,6 +33,9 @@ except (AttributeError, ImportError):
     PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
                                               os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_bert'))
 
+CONFIG_NAME = "config.json"
+WEIGHTS_NAME = "pytorch_model.bin"
+
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
 

pytorch_pretrained_bert/modeling.py

@@ -32,7 +32,7 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss
 
-from .file_utils import cached_path
+from .file_utils import cached_path, WEIGHTS_NAME, CONFIG_NAME
 
 logger = logging.getLogger(__name__)
 
@@ -45,8 +45,7 @@ PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
     'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
 }
-CONFIG_NAME = 'bert_config.json'
-WEIGHTS_NAME = 'pytorch_model.bin'
+BERT_CONFIG_NAME = 'bert_config.json'
 TF_WEIGHTS_NAME = 'model.ckpt'
 
 def load_tf_weights_in_bert(model, tf_checkpoint_path):
@@ -220,6 +219,11 @@ class BertConfig(object):
         """Serializes this instance to a JSON string."""
         return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
+
 try:
     from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
 except ImportError:
@@ -581,13 +585,16 @@ class BertPreTrainedModel(nn.Module):
             serialization_dir = tempdir
         # Load config
         config_file = os.path.join(serialization_dir, CONFIG_NAME)
+        if not os.path.exists(config_file):
+            # Backward compatibility with old naming format
+            config_file = os.path.join(serialization_dir, BERT_CONFIG_NAME)
         config = BertConfig.from_json_file(config_file)
         logger.info("Model config {}".format(config))
         # Instantiate model.
         model = cls(config, *inputs, **kwargs)
         if state_dict is None and not from_tf:
             weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
-            state_dict = torch.load(weights_path, map_location='cpu' if not torch.cuda.is_available() else None)
+            state_dict = torch.load(weights_path, map_location='cpu')
         if tempdir:
             # Clean up temp dir
             shutil.rmtree(tempdir)

pytorch_pretrained_bert/modeling_gpt2.py

@@ -34,7 +34,7 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .file_utils import cached_path
+from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
 from .modeling import BertLayerNorm as LayerNorm
 
 logger = logging.getLogger(__name__)
@@ -42,9 +42,6 @@ logger = logging.getLogger(__name__)
 PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin"}
 PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json"}
 
-CONFIG_NAME = "config.json"
-WEIGHTS_NAME = "pytorch_model.bin"
-
 def load_tf_weights_in_gpt2(model, gpt2_checkpoint_path):
     """ Load tf checkpoints in a pytorch model
     """
@@ -180,6 +177,11 @@ class GPT2Config(object):
         """Serializes this instance to a JSON string."""
         return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
+
 
 class Conv1D(nn.Module):
     def __init__(self, nf, nx):
@@ -416,7 +418,7 @@ class GPT2PreTrainedModel(nn.Module):
         # Instantiate model.
         model = cls(config, *inputs, **kwargs)
         if state_dict is None and not from_tf:
-            state_dict = torch.load(resolved_archive_file, map_location='cpu' if not torch.cuda.is_available() else None)
+            state_dict = torch.load(resolved_archive_file, map_location='cpu')
         if from_tf:
             # Directly load from a TensorFlow checkpoint (stored as NumPy array)
             return load_tf_weights_in_gpt2(model, resolved_archive_file)

pytorch_pretrained_bert/modeling_openai.py

@@ -34,7 +34,7 @@ import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .file_utils import cached_path
+from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
 from .modeling import BertLayerNorm as LayerNorm
 
 logger = logging.getLogger(__name__)
@@ -42,8 +42,6 @@ logger = logging.getLogger(__name__)
 PRETRAINED_MODEL_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-pytorch_model.bin"}
 PRETRAINED_CONFIG_ARCHIVE_MAP = {"openai-gpt": "https://s3.amazonaws.com/models.huggingface.co/bert/openai-gpt-config.json"}
 
-CONFIG_NAME = "config.json"
-WEIGHTS_NAME = "pytorch_model.bin"
 
 def load_tf_weights_in_openai_gpt(model, openai_checkpoint_folder_path):
     """ Load tf pre-trained weights in a pytorch model (from NumPy arrays here)
@@ -225,6 +223,11 @@ class OpenAIGPTConfig(object):
         """Serializes this instance to a JSON string."""
         return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
+
 
 class Conv1D(nn.Module):
     def __init__(self, nf, rf, nx):
@@ -473,7 +476,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
         # Instantiate model.
         model = cls(config, *inputs, **kwargs)
         if state_dict is None and not from_tf:
-            state_dict = torch.load(resolved_archive_file, map_location='cpu' if not torch.cuda.is_available() else None)
+            state_dict = torch.load(resolved_archive_file, map_location='cpu')
         if from_tf:
             # Directly load from a TensorFlow checkpoint (stored as NumPy array)
             return load_tf_weights_in_openai_gpt(model, resolved_archive_file)

pytorch_pretrained_bert/modeling_transfo_xl.py

@@ -40,7 +40,7 @@ from torch.nn.parameter import Parameter
 
 from .modeling import BertLayerNorm as LayerNorm
 from .modeling_transfo_xl_utilities import ProjectedAdaptiveLogSoftmax, sample_logits
-from .file_utils import cached_path
+from .file_utils import cached_path, CONFIG_NAME, WEIGHTS_NAME
 
 logger = logging.getLogger(__name__)
 
@@ -50,8 +50,7 @@ PRETRAINED_MODEL_ARCHIVE_MAP = {
 PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'transfo-xl-wt103': "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-config.json",
 }
-CONFIG_NAME = 'config.json'
-WEIGHTS_NAME = 'pytorch_model.bin'
+
 TF_WEIGHTS_NAME = 'model.ckpt'
 
 def build_tf_to_pytorch_map(model, config):
@@ -316,6 +315,11 @@ class TransfoXLConfig(object):
         """Serializes this instance to a JSON string."""
         return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
+
 
 class PositionalEmbedding(nn.Module):
     def __init__(self, demb):
@@ -940,7 +944,7 @@ class TransfoXLPreTrainedModel(nn.Module):
         # Instantiate model.
         model = cls(config, *inputs, **kwargs)
         if state_dict is None and not from_tf:
-            state_dict = torch.load(resolved_archive_file, map_location='cpu' if not torch.cuda.is_available() else None)
+            state_dict = torch.load(resolved_archive_file, map_location='cpu')
         if from_tf:
             # Directly load from a TensorFlow checkpoint
             return load_tf_weights_in_transfo_xl(model, config, pretrained_model_name_or_path)

pytorch_pretrained_bert/tokenization.py

@@ -134,6 +134,21 @@ class BertTokenizer(object):
             tokens.append(self.ids_to_tokens[i])
         return tokens
 
+    def save_vocabulary(self, vocab_path):
+        """Save the tokenizer vocabulary to a directory or file."""
+        index = 0
+        if os.path.isdir(vocab_path):
+            vocab_file = os.path.join(vocab_path, VOCAB_NAME)
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive."
+                                   " Please check that the vocabulary is not corrupted!".format(vocab_file))
+                    index = token_index
+                writer.write(token + u'\n')
+                index += 1
+        return vocab_file
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
         """

pytorch_pretrained_bert/tokenization_gpt2.py

@@ -45,6 +45,7 @@ PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
 }
 VOCAB_NAME = 'vocab.json'
 MERGES_NAME = 'merges.txt'
+SPECIAL_TOKENS_NAME = 'special_tokens.txt'
 
 @lru_cache()
 def bytes_to_unicode():
@@ -97,6 +98,11 @@ class GPT2Tokenizer(object):
         else:
             vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
             merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
+            special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
+            if not os.path.exists(special_tokens_file):
+                special_tokens_file = None
+            else:
+                logger.info("loading special tokens file {}".format(special_tokens_file))
         # redirect to the cache, if necessary
         try:
             resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
@@ -125,7 +131,11 @@ class GPT2Tokenizer(object):
             max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
             kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
         # Instantiate tokenizer.
-        tokenizer = cls(resolved_vocab_file, resolved_merges_file, *inputs, **kwargs)
+        if special_tokens_file and 'special_tokens' not in kwargs:
+            special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
+        else:
+            special_tokens = kwargs.pop('special_tokens', [])
+        tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs)
         return tokenizer
 
     def __init__(self, vocab_file, merges_file, errors='replace', max_len=None):
@@ -187,6 +197,35 @@ class GPT2Tokenizer(object):
         self.cache[token] = word
         return word
 
+    def save_vocabulary(self, vocab_path):
+        """Save the tokenizer vocabulary and merge files to a directory."""
+        if not os.path.isdir(vocab_path):
+            logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
+            return
+        vocab_file = os.path.join(vocab_path, VOCAB_NAME)
+        merge_file = os.path.join(vocab_path, MERGES_NAME)
+        special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
+
+        with open(vocab_file, 'w', encoding='utf-8') as f:
+            f.write(json.dumps(self.encoder, ensure_ascii=False))
+
+        index = 0
+        with open(merge_file, "w", encoding="utf-8") as writer:
+            writer.write(u'#version: 0.2\n')
+            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
+                                   " Please check that the tokenizer is not corrupted!".format(merge_file))
+                    index = token_index
+                writer.write(' '.join(bpe_tokens) + u'\n')
+                index += 1
+
+        with open(special_tokens_file, 'w', encoding='utf-8') as writer:
+            for token in sorted(self.special_tokens.keys(), key=lambda kv: kv[1]):
+                writer.write(token + u'\n')
+
+        return vocab_file, merge_file, special_tokens_file
+
     def encode(self, text):
         bpe_tokens = []
         for token in re.findall(self.pat, text):

pytorch_pretrained_bert/tokenization_openai.py

@@ -41,6 +41,7 @@ PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = {
 }
 VOCAB_NAME = 'vocab.json'
 MERGES_NAME = 'merges.txt'
+SPECIAL_TOKENS_NAME = 'special_tokens.txt'
 
 def get_pairs(word):
     """
@@ -86,9 +87,15 @@ class OpenAIGPTTokenizer(object):
         if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
             vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
             merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path]
+            special_tokens_file = None
         else:
             vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
             merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME)
+            special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME)
+            if not os.path.exists(special_tokens_file):
+                special_tokens_file = None
+            else:
+                logger.info("loading special tokens file {}".format(special_tokens_file))
         # redirect to the cache, if necessary
         try:
             resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
@@ -117,7 +124,11 @@ class OpenAIGPTTokenizer(object):
             max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path]
             kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
         # Instantiate tokenizer.
-        tokenizer = cls(resolved_vocab_file, resolved_merges_file, *inputs, **kwargs)
+        if special_tokens_file and 'special_tokens' not in kwargs:
+            special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1]
+        else:
+            special_tokens = kwargs.pop('special_tokens', [])
+        tokenizer = cls(resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs)
         return tokenizer
 
     def __init__(self, vocab_file, merges_file, special_tokens=None, max_len=None):
@@ -261,3 +272,32 @@ class OpenAIGPTTokenizer(object):
                     ).replace(" 's", "'s").replace(" t ", "'t ").replace(" s ", "'s ").replace(" m ", "'m "
                     ).replace(" 've", "'ve")
         return out_string
+
+    def save_vocabulary(self, vocab_path):
+        """Save the tokenizer vocabulary and merge files to a directory."""
+        if not os.path.isdir(vocab_path):
+            logger.error("Vocabulary path ({}) should be a directory".format(vocab_path))
+            return
+        vocab_file = os.path.join(vocab_path, VOCAB_NAME)
+        merge_file = os.path.join(vocab_path, MERGES_NAME)
+        special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME)
+
+        with open(vocab_file, 'w', encoding='utf-8') as f:
+            f.write(json.dumps(self.encoder, ensure_ascii=False))
+
+        index = 0
+        with open(merge_file, "w", encoding="utf-8") as writer:
+            writer.write(u'#version: 0.2\n')
+            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive."
+                                   " Please check that the tokenizer is not corrupted!".format(merge_file))
+                    index = token_index
+                writer.write(' '.join(bpe_tokens) + u'\n')
+                index += 1
+
+        with open(special_tokens_file, 'w', encoding='utf-8') as writer:
+            for token in sorted(self.special_tokens.keys(), key=lambda kv: kv[1]):
+                writer.write(token + u'\n')
+
+        return vocab_file, merge_file, special_tokens_file

pytorch_pretrained_bert/tokenization_transfo_xl.py

@@ -63,7 +63,10 @@ class TransfoXLTokenizer(object):
         if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
             vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
         else:
-            vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
+            if os.path.isdir(pretrained_model_name_or_path):
+                vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME)
+            else:
+                vocab_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
             resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
@@ -141,6 +144,14 @@ class TransfoXLTokenizer(object):
         else:
             raise ValueError('No <unkown> token in vocabulary')
 
+    def save_vocabulary(self, vocab_path):
+        """Save the tokenizer vocabulary to a directory or file."""
+        index = 0
+        if os.path.isdir(vocab_path):
+            vocab_file = os.path.join(vocab_path, VOCAB_NAME)
+        torch.save(self.__dict__, vocab_file)
+        return vocab_file
+
     def build_vocab(self):
         if self.vocab_file:
             print('building vocab from {}'.format(self.vocab_file))
@@ -245,82 +256,24 @@ class TransfoXLTokenizer(object):
     def __len__(self):
         return len(self.idx2sym)
 
-    def _run_split_on_punc(self, text):
-        """Splits punctuation on a piece of text."""
-        if text in self.never_split:
-            return [text]
-        chars = list(text)
-        i = 0
-        start_new_word = True
-        output = []
-        while i < len(chars):
-            char = chars[i]
-            if _is_punctuation(char):
-                output.append([char])
-                start_new_word = True
-            else:
-                if start_new_word:
-                    output.append([])
-                start_new_word = False
-                output[-1].append(char)
-            i += 1
-
-        return ["".join(x) for x in output]
-
-    def _run_strip_accents(self, text):
-        """Strips accents from a piece of text."""
-        text = unicodedata.normalize("NFD", text)
-        output = []
-        for char in text:
-            cat = unicodedata.category(char)
-            if cat == "Mn":
-                continue
-            output.append(char)
-        return "".join(output)
-
-    def _clean_text(self, text):
-        """Performs invalid character removal and whitespace cleanup on text."""
-        output = []
-        for char in text:
-            cp = ord(char)
-            if cp == 0 or cp == 0xfffd or _is_control(char):
-                continue
-            if _is_whitespace(char):
-                output.append(" ")
-            else:
-                output.append(char)
-        return "".join(output)
-
-    def whitespace_tokenize(self, text):
-        """Runs basic whitespace cleaning and splitting on a piece of text."""
-        text = text.strip()
-        if not text:
-            return []
-        if self.delimiter == '':
-            tokens = text
-        else:
-            tokens = text.split(self.delimiter)
-        return tokens
-
     def tokenize(self, line, add_eos=False, add_double_eos=False):
-        line = self._clean_text(line)
         line = line.strip()
+        # convert to lower case
+        if self.lower_case:
+            line = line.lower()
 
-        symbols = self.whitespace_tokenize(line)
-
-        split_symbols = []
-        for symbol in symbols:
-            if self.lower_case and symbol not in self.never_split:
-                symbol = symbol.lower()
-                symbol = self._run_strip_accents(symbol)
-            split_symbols.extend(self._run_split_on_punc(symbol))
+        # empty delimiter '' will evaluate False
+        if self.delimiter == '':
+            symbols = line
+        else:
+            symbols = line.split(self.delimiter)
 
         if add_double_eos: # lm1b
-            return ['<S>'] + split_symbols + ['<S>']
+            return ['<S>'] + symbols + ['<S>']
         elif add_eos:
-            return split_symbols + ['<eos>']
+            return symbols + ['<eos>']
         else:
-            return split_symbols
+            return symbols
 
 
 class LMOrderedIterator(object):
@@ -631,42 +584,3 @@ def get_lm_corpus(datadir, dataset):
         torch.save(corpus, fn)
 
     return corpus
-
-def _is_whitespace(char):
-    """Checks whether `chars` is a whitespace character."""
-    # \t, \n, and \r are technically contorl characters but we treat them
-    # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
-        return True
-    cat = unicodedata.category(char)
-    if cat == "Zs":
-        return True
-    return False
-
-
-def _is_control(char):
-    """Checks whether `chars` is a control character."""
-    # These are technically control characters but we count them as whitespace
-    # characters.
-    if char == "\t" or char == "\n" or char == "\r":
-        return False
-    cat = unicodedata.category(char)
-    if cat.startswith("C"):
-        return True
-    return False
-
-
-def _is_punctuation(char):
-    """Checks whether `chars` is a punctuation character."""
-    cp = ord(char)
-    # We treat all non-letter/number ASCII as punctuation.
-    # Characters such as "^", "$", and "`" are not in the Unicode
-    # Punctuation class but we treat them as punctuation anyways, for
-    # consistency.
-    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
-        return True
-    cat = unicodedata.category(char)
-    if cat.startswith("P"):
-        return True
-    return False

tests/modeling_gpt2_test.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import unittest
 import json
 import random
@@ -176,6 +177,14 @@ class GPT2ModelTest(unittest.TestCase):
         self.assertEqual(obj["vocab_size"], 99)
         self.assertEqual(obj["n_embd"], 37)
 
+    def test_config_to_json_file(self):
+        config_first = GPT2Config(vocab_size_or_config_json_file=99, n_embd=37)
+        json_file_path = "/tmp/config.json"
+        config_first.to_json_file(json_file_path)
+        config_second = GPT2Config.from_json_file(json_file_path)
+        os.remove(json_file_path)
+        self.assertEqual(config_second.to_dict(), config_first.to_dict())
+
     def run_tester(self, tester):
         config_and_inputs = tester.prepare_config_and_inputs()
         output_result = tester.create_gpt2_model(*config_and_inputs)

tests/modeling_openai_test.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import unittest
 import json
 import random
@@ -188,6 +189,14 @@ class OpenAIGPTModelTest(unittest.TestCase):
         self.assertEqual(obj["vocab_size"], 99)
         self.assertEqual(obj["n_embd"], 37)
 
+    def test_config_to_json_file(self):
+        config_first = OpenAIGPTConfig(vocab_size_or_config_json_file=99, n_embd=37)
+        json_file_path = "/tmp/config.json"
+        config_first.to_json_file(json_file_path)
+        config_second = OpenAIGPTConfig.from_json_file(json_file_path)
+        os.remove(json_file_path)
+        self.assertEqual(config_second.to_dict(), config_first.to_dict())
+
     def run_tester(self, tester):
         config_and_inputs = tester.prepare_config_and_inputs()
         output_result = tester.create_openai_model(*config_and_inputs)

tests/modeling_test.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import unittest
 import json
 import random
@@ -251,6 +252,14 @@ class BertModelTest(unittest.TestCase):
         self.assertEqual(obj["vocab_size"], 99)
         self.assertEqual(obj["hidden_size"], 37)
 
+    def test_config_to_json_file(self):
+        config_first = BertConfig(vocab_size_or_config_json_file=99, hidden_size=37)
+        json_file_path = "/tmp/config.json"
+        config_first.to_json_file(json_file_path)
+        config_second = BertConfig.from_json_file(json_file_path)
+        os.remove(json_file_path)
+        self.assertEqual(config_second.to_dict(), config_first.to_dict())
+
     def run_tester(self, tester):
         config_and_inputs = tester.prepare_config_and_inputs()
         output_result = tester.create_bert_model(*config_and_inputs)

tests/modeling_transfo_xl_test.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import os
 import unittest
 import json
 import random
@@ -186,6 +187,14 @@ class TransfoXLModelTest(unittest.TestCase):
         self.assertEqual(obj["n_token"], 96)
         self.assertEqual(obj["d_embed"], 37)
 
+    def test_config_to_json_file(self):
+        config_first = TransfoXLConfig(vocab_size_or_config_json_file=96, d_embed=37)
+        json_file_path = "/tmp/config.json"
+        config_first.to_json_file(json_file_path)
+        config_second = TransfoXLConfig.from_json_file(json_file_path)
+        os.remove(json_file_path)
+        self.assertEqual(config_second.to_dict(), config_first.to_dict())
+
     def run_tester(self, tester):
         config_and_inputs = tester.prepare_config_and_inputs()