update conversion scripts and __main__

pytorch_transformers/__main__.py

@@ -1,14 +1,15 @@
 # coding: utf8
 def main():
     import sys
-    if (len(sys.argv) < 4 or len(sys.argv) > 6) or sys.argv[1] not in ["bert", "gpt", "transfo_xl", "gpt2", "xlnet"]:
+    if (len(sys.argv) < 4 or len(sys.argv) > 6) or sys.argv[1] not in ["bert", "gpt", "transfo_xl", "gpt2", "xlnet", "xlm"]:
         print(
         "Should be used as one of: \n"
-        ">> `pytorch_transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`, \n"
-        ">> `pytorch_transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`, \n"
-        ">> `pytorch_transformers transfo_xl TF_CHECKPOINT_OR_DATASET PYTORCH_DUMP_OUTPUT [TF_CONFIG]` or \n"
-        ">> `pytorch_transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [GPT2_CONFIG]` or \n"
-        ">> `pytorch_transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`")
+        ">> pytorch_transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT, \n"
+        ">> pytorch_transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG], \n"
+        ">> pytorch_transformers transfo_xl TF_CHECKPOINT_OR_DATASET PYTORCH_DUMP_OUTPUT [TF_CONFIG] or \n"
+        ">> pytorch_transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [GPT2_CONFIG] or \n"
+        ">> pytorch_transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME] or \n"
+        ">> pytorch_transformers xlm XLM_CHECKPOINT_PATH PYTORCH_DUMP_OUTPUT")
     else:
         if sys.argv[1] == "bert":
             try:
@@ -86,7 +87,7 @@ def main():
                 else:
                     TF_CONFIG = ""
                 convert_gpt2_checkpoint_to_pytorch(TF_CHECKPOINT, TF_CONFIG, PYTORCH_DUMP_OUTPUT)
-        else:
+        elif sys.argv[1] == "xlnet":
             try:
                 from .convert_xlnet_checkpoint_to_pytorch import convert_xlnet_checkpoint_to_pytorch
             except ImportError:
@@ -104,11 +105,24 @@ def main():
                 PYTORCH_DUMP_OUTPUT = sys.argv[4]
                 if len(sys.argv) == 6:
                     FINETUNING_TASK = sys.argv[5]
+                else:
+                    FINETUNING_TASK = None
 
                 convert_xlnet_checkpoint_to_pytorch(TF_CHECKPOINT,
                                                     TF_CONFIG,
                                                     PYTORCH_DUMP_OUTPUT,
                                                     FINETUNING_TASK)
+        elif sys.argv[1] == "xlm":
+            from .convert_xlm_checkpoint_to_pytorch import convert_xlm_checkpoint_to_pytorch
+
+            if len(sys.argv) != 4:
+                # pylint: disable=line-too-long
+                print("Should be used as `pytorch_transformers xlm XLM_CHECKPOINT_PATH PYTORCH_DUMP_OUTPUT`")
+            else:
+                XLM_CHECKPOINT_PATH = sys.argv[2]
+                PYTORCH_DUMP_OUTPUT = sys.argv[3]
+
+                convert_xlm_checkpoint_to_pytorch(XLM_CHECKPOINT_PATH, PYTORCH_DUMP_OUTPUT)
 
 if __name__ == '__main__':
     main()

pytorch_transformers/convert_gpt2_checkpoint_to_pytorch.py

@@ -26,6 +26,9 @@
                                                      GPT2Model,
                                                      load_tf_weights_in_gpt2)
 
+import logging
+logging.basicConfig(level=logging.INFO)
+
 
 def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
     # Construct model
@@ -36,7 +39,7 @@ def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, p
     model = GPT2Model(config)
 
     # Load weights from numpy
-    load_tf_weights_in_gpt2(model, gpt2_checkpoint_path)
+    load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
 
     # Save pytorch-model
     pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME

pytorch_transformers/convert_openai_checkpoint_to_pytorch.py

@@ -26,6 +26,9 @@
                                                      OpenAIGPTModel,
                                                      load_tf_weights_in_openai_gpt)
 
+import logging
+logging.basicConfig(level=logging.INFO)
+
 
 def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
     # Construct model
@@ -36,7 +39,7 @@ def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_c
     model = OpenAIGPTModel(config)
 
     # Load weights from numpy
-    load_tf_weights_in_openai_gpt(model, openai_checkpoint_folder_path)
+    load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path)
 
     # Save pytorch-model
     pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME

pytorch_transformers/convert_tf_checkpoint_to_pytorch.py

@@ -18,23 +18,22 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
-import re
 import argparse
-import tensorflow as tf
 import torch
-import numpy as np
 
 from pytorch_transformers.modeling_bert import BertConfig, BertForPreTraining, load_tf_weights_in_bert
 
+import logging
+logging.basicConfig(level=logging.INFO)
+
 def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
     # Initialise PyTorch model
     config = BertConfig.from_json_file(bert_config_file)
     print("Building PyTorch model from configuration: {}".format(str(config)))
     model = BertForPreTraining(config)
 
     # Load weights from tf checkpoint
-    load_tf_weights_in_bert(model, tf_checkpoint_path)
+    load_tf_weights_in_bert(model, config, tf_checkpoint_path)
 
     # Save pytorch-model
     print("Save PyTorch model to {}".format(pytorch_dump_path))

pytorch_transformers/convert_transfo_xl_checkpoint_to_pytorch.py

@@ -36,6 +36,9 @@
 else:
     import pickle
 
+import logging
+logging.basicConfig(level=logging.INFO)
+
 # We do this to be able to load python 2 datasets pickles
 # See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
 data_utils.Vocab = data_utils.TransfoXLTokenizer

pytorch_transformers/convert_xlm_checkpoint_to_pytorch.py

@@ -24,9 +24,10 @@
 import numpy
 
 from pytorch_transformers.modeling_utils import CONFIG_NAME, WEIGHTS_NAME
-from pytorch_transformers.modeling_xlm import (XLMConfig, XLMModel)
 from pytorch_transformers.tokenization_xlm import VOCAB_FILES_NAMES
 
+import logging
+logging.basicConfig(level=logging.INFO)
 
 def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
     # Load checkpoint

pytorch_transformers/convert_xlnet_checkpoint_to_pytorch.py

@@ -40,6 +40,8 @@
     "wnli": 2,
 }
 
+import logging
+logging.basicConfig(level=logging.INFO)
 
 def convert_xlnet_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_folder_path, finetuning_task=None):
     # Initialise PyTorch model
@@ -48,14 +50,17 @@ def convert_xlnet_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, py
     finetuning_task = finetuning_task.lower() if finetuning_task is not None else ""
     if finetuning_task in GLUE_TASKS_NUM_LABELS:
         print("Building PyTorch XLNetForSequenceClassification model from configuration: {}".format(str(config)))
-        model = XLNetForSequenceClassification(config, num_labels=GLUE_TASKS_NUM_LABELS[finetuning_task])
+        config.finetuning_task = finetuning_task
+        config.num_labels = GLUE_TASKS_NUM_LABELS[finetuning_task]
+        model = XLNetForSequenceClassification(config)
     elif 'squad' in finetuning_task:
+        config.finetuning_task = finetuning_task
         model = XLNetForQuestionAnswering(config)
     else:
         model = XLNetLMHeadModel(config)
 
     # Load weights from tf checkpoint
-    load_tf_weights_in_xlnet(model, config, tf_checkpoint_path, finetuning_task)
+    load_tf_weights_in_xlnet(model, config, tf_checkpoint_path)
 
     # Save pytorch-model
     pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)

pytorch_transformers/modeling_xlnet.py

@@ -37,9 +37,11 @@
 logger = logging.getLogger(__name__)
 
 XLNET_PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-pytorch_model.bin",
     'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-pytorch_model.bin",
 }
 XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-config.json",
     'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-config.json",
 }
 

pytorch_transformers/tokenization_transfo_xl.py

@@ -50,7 +50,7 @@
 }
 
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    'transfo-xl-wt103': 512,
+    'transfo-xl-wt103': None,
 }
 
 PRETRAINED_CORPUS_ARCHIVE_MAP = {

pytorch_transformers/tokenization_utils.py

@@ -208,7 +208,8 @@ def _from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs
             # if we're using a pretrained model, ensure the tokenizer
             # wont index sequences longer than the number of positional embeddings
             max_len = cls.max_model_input_sizes[pretrained_model_name_or_path]
-            kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
+            if max_len is not None and isinstance(max_len, (int, float)):
+                kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
 
         # Merge resolved_vocab_files arguments in kwargs.
         added_tokens_file = resolved_vocab_files.pop('added_tokens_file', None)

pytorch_transformers/tokenization_xlnet.py

@@ -32,12 +32,14 @@
 PRETRAINED_VOCAB_FILES_MAP = {
     'vocab_file':
     {
+    'xlnet-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-base-cased-spiece.model",
     'xlnet-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/xlnet-large-cased-spiece.model",
     }
 }
 
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    'xlnet-large-cased': 512,
+    'xlnet-base-cased': None,
+    'xlnet-large-cased': None,
 }
 
 SPIECE_UNDERLINE = u'▁'