update links to new weights

Typo with LM Fine tuning script

.circleci/config.yml

@@ -1,7 +1,7 @@
 version: 2
 jobs:
     build_py3_torch_and_tf:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         resource_class: xlarge
@@ -13,10 +13,10 @@ jobs:
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: codecov
     build_py3_torch:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         resource_class: xlarge
@@ -27,11 +27,11 @@ jobs:
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: python -m pytest -sv ./examples/
             - run: codecov
     build_py3_tf:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         resource_class: xlarge
@@ -42,10 +42,10 @@ jobs:
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
             - run: sudo pip install tensorboardX scikit-learn
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: codecov
     build_py2_torch:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         resource_class: large
         parallelism: 1
         docker:
@@ -55,10 +55,10 @@ jobs:
             - run: sudo pip install torch
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: codecov
     build_py2_tf:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         resource_class: large
         parallelism: 1
         docker:
@@ -68,10 +68,10 @@ jobs:
             - run: sudo pip install tensorflow==2.0.0-rc0
             - run: sudo pip install --progress-bar off .
             - run: sudo pip install pytest codecov pytest-cov
-            - run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
+            - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: codecov
     deploy_doc:
-        working_directory: ~/pytorch-transformers
+        working_directory: ~/transformers
         docker:
             - image: circleci/python:3.5
         steps:
@@ -81,7 +81,6 @@ jobs:
             - checkout
             - run: sudo pip install --progress-bar off -r docs/requirements.txt
             - run: sudo pip install --progress-bar off -r requirements.txt
-            - run: cd docs/source && ln -s ../../examples/README.md examples.md && cd -
             - run: cd docs && make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
 workflow_filters: &workflow_filters
     filters:

.coveragerc

@@ -1,5 +1,5 @@
 [run]
-source=pytorch_transformers
+source=transformers
 omit =
     # skip convertion scripts from testing for now
     */convert_*

.github/ISSUE_TEMPLATE/migration.md

@@ -1,6 +1,6 @@
 ---
 name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
-about: Report a problem when migrating from PyTorch-pretrained-Bert to PyTorch-Transformers
+about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
 ---
 
 ## 📚 Migration

README.md

@@ -1,47 +1,86 @@
-# 👾 PyTorch-Transformers
+<p align="center">
+    <br>
+    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
+    <br>
+<p>
+<p align="center">
+    <a href="https://circleci.com/gh/huggingface/transformers">
+        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
+    </a>
+    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
+        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
+    </a>
+    <a href="https://huggingface.co/transformers/index.html">
+        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
+    </a>
+    <a href="https://github.com/huggingface/transformers/releases">
+        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
+    </a>
+</p>
 
-[![CircleCI](https://circleci.com/gh/huggingface/pytorch-transformers.svg?style=svg)](https://circleci.com/gh/huggingface/pytorch-transformers)
+<h3 align="center">
+<p>State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
+</h3>
 
-PyTorch-Transformers (formerly known as `pytorch-pretrained-bert`) is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
+🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
 
-The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
+### Features
 
-1. **[BERT](https://github.com/google-research/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-2. **[GPT](https://github.com/openai/finetune-transformer-lm)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-3. **[GPT-2](https://blog.openai.com/better-language-models/)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-4. **[Transformer-XL](https://github.com/kimiyoung/transformer-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-5. **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-6. **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-8. **[DistilBERT](https://github.com/huggingface/pytorch-transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the blogpost [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5
-) by Victor Sanh, Lysandre Debut and Thomas Wolf.
+- As easy to use as pytorch-transformers
+- As powerful and concise as Keras
+- High performance on NLU and NLG tasks
+- Low barrier to entry for educators and practitioners
+
+State-of-the-art NLP for everyone
+- Deep learning researchers
+- Hands-on practitioners
+- AI/ML/NLP teachers and educators
+
+Lower compute costs, smaller carbon footprint
+- Researchers can share trained models instead of always retraining
+- Practitioners can reduce compute time and production costs
+- 8 architectures with over 30 pretrained models, some in more than 100 languages
+
+Choose the right framework for every part of a model's lifetime
+- Train state-of-the-art models in 3 lines of code
+- Deep interoperability between TensorFlow 2.0 and PyTorch models
+- Move a single model between TF2.0/PyTorch frameworks at will
+- Seamlessly pick the right framework for training, evaluation, production
 
-These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/pytorch-transformers/examples.html).
 
 | Section | Description |
 |-|-|
 | [Installation](#installation) | How to install the package |
+| [Model architectures](#model-architectures) | Architectures (with pretrained weights) |
 | [Online demo](#online-demo) | Experimenting with this repo’s text generation capabilities |
 | [Quick tour: Usage](#quick-tour) | Tokenizers & models usage: Bert and GPT-2 |
+| [Quick tour: TF 2.0 and PyTorch ](#Quick-tour-TF-20-training-and-PyTorch-interoperability) | Train a TF 2.0 model in 10 lines of code, load it in PyTorch |
 | [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
-| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) | Migrating your code from pytorch-pretrained-bert to pytorch-transformers |
-| [Documentation](https://huggingface.co/pytorch-transformers/) | Full API documentation and more |
+| [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
+| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
+| [Documentation](https://huggingface.co/transformers/) | Full API documentation and more |
 
 ## Installation
 
-This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.0.0+
+This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+), PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1
 
 ### With pip
 
-PyTorch-Transformers can be installed by pip as follows:
+First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
+Please refere to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
+
+When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
 
 ```bash
-pip install pytorch-transformers
+pip install transformers
 ```
 
 ### From source
 
-Clone the repository and run:
+Here also, you first need to install one of, or both, TensorFlow 2.0 and PyTorch.
+Please refere to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
+
+When TensorFlow 2.0 and/or PyTorch has been installed, you can install from source by cloning the repository and running:
 
 ```bash
 pip install [--editable] .
@@ -49,14 +88,16 @@ pip install [--editable] .
 
 ### Tests
 
-A series of tests is included for the library and the example scripts. Library tests can be found in the [tests folder](https://github.com/huggingface/pytorch-transformers/tree/master/pytorch_transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/pytorch-transformers/tree/master/examples).
+A series of tests are included for the library and the example scripts. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
 
 These tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
 
+Depending on which framework is installed (TensorFlow 2.0 and/or PyTorch), the irrelevant tests will be skipped. Ensure that both frameworks are installed if you want to execute all tests.
+
 You can run the tests from the root of the cloned repository with the commands:
 
 ```bash
-python -m pytest -sv ./pytorch_transformers/tests/
+python -m pytest -sv ./transformers/tests/
 python -m pytest -sv ./examples/
 ```
 
@@ -66,8 +107,22 @@ You should check out our [`swift-coreml-transformers`](https://github.com/huggin
 
 It contains an example of a conversion script from a Pytorch trained Transformer model (here, `GPT-2`) to a CoreML model that runs on iOS devices.
 
-At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML,
-or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!
+At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models to productizing them in CoreML, or prototype a model or an app in CoreML then research its hyperparameters or architecture from TensorFlow 2.0 and/or PyTorch. Super exciting!
+
+## Model architectures
+
+🤗 Transformers currently provides 8 NLU/NLG architectures:
+
+1. **[BERT](https://github.com/google-research/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
+2. **[GPT](https://github.com/openai/finetune-transformer-lm)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
+3. **[GPT-2](https://blog.openai.com/better-language-models/)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
+4. **[Transformer-XL](https://github.com/kimiyoung/transformer-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+5. **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+6. **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
+7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+8. **[DistilBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation).
+
+These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
 
 ## Online demo
 
@@ -80,22 +135,25 @@ You can use it to experiment with completions generated by `GPT2Model`, `Transfo
 
 ## Quick tour
 
-Let's do a very quick overview of PyTorch-Transformers. Detailed examples for each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [full documentation](https://huggingface.co/pytorch-transformers/).
+Let's do a very quick overview of the model architectures in 🤗 Transformers. Detailed examples for each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [full documentation](https://huggingface.co/transformers/).
 
 ```python
 import torch
-from pytorch_transformers import *
+from transformers import *
 
-# PyTorch-Transformers has a unified API
-# for 7 transformer architectures and 30 pretrained weights.
+# Transformers has a unified API
+# for 8 transformer architectures and 30 pretrained weights.
 #          Model          | Tokenizer          | Pretrained weights shortcut
-MODELS = [(BertModel,       BertTokenizer,      'bert-base-uncased'),
-          (OpenAIGPTModel,  OpenAIGPTTokenizer, 'openai-gpt'),
-          (GPT2Model,       GPT2Tokenizer,      'gpt2'),
-          (TransfoXLModel,  TransfoXLTokenizer, 'transfo-xl-wt103'),
-          (XLNetModel,      XLNetTokenizer,     'xlnet-base-cased'),
-          (XLMModel,        XLMTokenizer,       'xlm-mlm-enfr-1024'),
-          (RobertaModel,    RobertaTokenizer,   'roberta-base')]
+MODELS = [(BertModel,       BertTokenizer,       'bert-base-uncased'),
+          (OpenAIGPTModel,  OpenAIGPTTokenizer,  'openai-gpt'),
+          (GPT2Model,       GPT2Tokenizer,       'gpt2'),
+          (TransfoXLModel,  TransfoXLTokenizer,  'transfo-xl-wt103'),
+          (XLNetModel,      XLNetTokenizer,      'xlnet-base-cased'),
+          (XLMModel,        XLMTokenizer,        'xlm-mlm-enfr-1024'),
+          (DistilBertModel, DistilBertTokenizer, 'distilbert-base-uncased'),
+          (RobertaModel,    RobertaTokenizer,    'roberta-base')]
+
+# To use TensorFlow 2.0 versions of the models, simply prefix the class names with 'TF', e.g. `TFRobertaModel` is the TF 2.0 counterpart of the PyTorch model `RobertaModel`
 
 # Let's encode some text in a sequence of hidden-states using each model:
 for model_class, tokenizer_class, pretrained_weights in MODELS:
@@ -121,24 +179,71 @@ for model_class in BERT_MODEL_CLASSES:
     # Load pretrained model/tokenizer
     model = model_class.from_pretrained('bert-base-uncased')
 
-# Models can return full list of hidden-states & attentions weights at each layer
-model = model_class.from_pretrained(pretrained_weights,
-                                    output_hidden_states=True,
-                                    output_attentions=True)
-input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
-all_hidden_states, all_attentions = model(input_ids)[-2:]
+    # Models can return full list of hidden-states & attentions weights at each layer
+    model = model_class.from_pretrained(pretrained_weights,
+                                        output_hidden_states=True,
+                                        output_attentions=True)
+    input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
+    all_hidden_states, all_attentions = model(input_ids)[-2:]
 
-# Models are compatible with Torchscript
-model = model_class.from_pretrained(pretrained_weights, torchscript=True)
-traced_model = torch.jit.trace(model, (input_ids,))
+    # Models are compatible with Torchscript
+    model = model_class.from_pretrained(pretrained_weights, torchscript=True)
+    traced_model = torch.jit.trace(model, (input_ids,))
 
-# Simple serialization for models and tokenizers
-model.save_pretrained('./directory/to/save/')  # save
-model = model_class.from_pretrained('./directory/to/save/')  # re-load
-tokenizer.save_pretrained('./directory/to/save/')  # save
-tokenizer = tokenizer_class.from_pretrained('./directory/to/save/')  # re-load
+    # Simple serialization for models and tokenizers
+    model.save_pretrained('./directory/to/save/')  # save
+    model = model_class.from_pretrained('./directory/to/save/')  # re-load
+    tokenizer.save_pretrained('./directory/to/save/')  # save
+    tokenizer = BertTokenizer.from_pretrained('./directory/to/save/')  # re-load
 
-# SOTA examples for GLUE, SQUAD, text generation...
+    # SOTA examples for GLUE, SQUAD, text generation...
+```
+
+## Quick tour TF 2.0 training and PyTorch interoperability
+
+Let's do a quick example of how a TensorFlow 2.0 model can be trained in 12 lines of code with 🤗 Transformers and then loaded in PyTorch for fast inspection/tests.
+
+```python
+import tensorflow as tf
+import tensorflow_datasets
+from transformers import *
+
+# Load dataset, tokenizer, model from pretrained model/vocabulary
+tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
+data = tensorflow_datasets.load('glue/mrpc')
+
+# Prepare dataset for GLUE as a tf.data.Dataset instance
+train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task='mrpc')
+valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, max_length=128, task='mrpc')
+train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
+valid_dataset = valid_dataset.batch(64)
+
+# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule 
+optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
+loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
+model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
+
+# Train and evaluate using tf.keras.Model.fit()
+history = model.fit(train_dataset, epochs=2, steps_per_epoch=115,
+                    validation_data=valid_dataset, validation_steps=7)
+
+# Load the TensorFlow model in PyTorch for inspection
+model.save_pretrained('./save/')
+pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
+
+# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
+sentence_0 = "This research was consistent with his findings."
+sentence_1 = "His findings were compatible with this research."
+sentence_2 = "His findings were not compatible with this research."
+inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
+inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
+
+pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
+pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
+print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
+print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sentence_0")
 ```
 
 ## Quick tour of the fine-tuning/usage scripts
@@ -288,7 +393,7 @@ This is the model provided as `bert-large-uncased-whole-word-masking-finetuned-s
 ### `run_generation.py`: Text generation with GPT, GPT-2, Transformer-XL and XLNet
 
 A conditional generation script is also included to generate text from a prompt.
-The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed by Aman Rusia to get high quality generation with memory models like Transformer-XL and XLNet (include a predefined text to make short inputs longer).
+The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed by Aman Rusia to get high-quality generation with memory models like Transformer-XL and XLNet (include a predefined text to make short inputs longer).
 
 Here is how to run the script with the small version of OpenAI GPT-2 model:
 
@@ -299,19 +404,32 @@ python ./examples/run_generation.py \
     --model_name_or_path=gpt2 \
 ```
 
-## Migrating from pytorch-pretrained-bert to pytorch-transformers
+## Migrating from pytorch-transformers to transformers
 
-Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `pytorch-transformers`
+Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
+
+### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
+
+To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed.
+
+If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change.
+
+If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments.
+
+
+## Migrating from pytorch-pretrained-bert to transformers
+
+Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`.
 
 ### Models always output `tuples`
 
-The main breaking change when migrating from `pytorch-pretrained-bert` to `pytorch-transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
+The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
 
-The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/pytorch-transformers/).
+The exact content of the tuples for each model is detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
 
 In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
 
-Here is a `pytorch-pretrained-bert` to `pytorch-transformers` conversion example for a `BertForSequenceClassification` classification model:
+Here is a `pytorch-pretrained-bert` to `transformers` conversion example for a `BertForSequenceClassification` classification model:
 
 ```python
 # Let's load our model
@@ -320,11 +438,11 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 # If you used to have this line in pytorch-pretrained-bert:
 loss = model(input_ids, labels=labels)
 
-# Now just use this line in pytorch-transformers to extract the loss from the output tuple:
+# Now just use this line in transformers to extract the loss from the output tuple:
 outputs = model(input_ids, labels=labels)
 loss = outputs[0]
 
-# In pytorch-transformers you can also have access to the logits:
+# In transformers you can also have access to the logits:
 loss, logits = outputs[:2]
 
 # And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
@@ -333,13 +451,17 @@ outputs = model(input_ids, labels=labels)
 loss, logits, attentions = outputs
 ```
 
+### Using hidden states
+
+By enabling the configuration option `output_hidden_states`, it was possible to retrieve the last hidden states of the encoder. In `pytorch-transformers` as well as `transformers` the return value has changed slightly: `all_hidden_states` now also includes the hidden state of the embeddings in addition to those of the encoding layers. This allows users to easily access the embeddings final state.
+
 ### Serialization
 
-Breaking change in the `from_pretrained()`method:
+Breaking change in the `from_pretrained()` method:
 
 1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
 
-2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/pytorch-transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
+2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
 
 Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.
 
@@ -396,7 +518,7 @@ for batch in train_data:
     loss.backward()
     optimizer.step()
 
-### In PyTorch-Transformers, optimizer and schedules are splitted and instantiated like this:
+### In Transformers, optimizer and schedules are splitted and instantiated like this:
 optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
 scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps)  # PyTorch scheduler
 ### and used like this:
@@ -411,4 +533,4 @@ for batch in train_data:
 
 ## Citation
 
-At the moment, there is no paper associated to PyTorch-Transformers but we are working on preparing one. In the meantime, please include a mention of the library and a link to the present repository if you use this work in a published or open-source project.
+At the moment, there is no paper associated with Transformers but we are working on preparing one. In the meantime, please include a mention of the library and a link to the present repository if you use this work in a published or open-source project.

docker/Dockerfile

@@ -2,6 +2,6 @@ FROM pytorch/pytorch:latest
 
 RUN git clone https://github.com/NVIDIA/apex.git && cd apex && python setup.py install --cuda_ext --cpp_ext
 
-RUN pip install pytorch_transformers
+RUN pip install transformers
 
 WORKDIR /workspace

docs/README.md

@@ -34,11 +34,11 @@ pip install recommonmark
 
 ## Building the documentation
 
-Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the followig 
+Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following 
 command to generate it:
 
 ```bash
-ln -s ../../examples/README.md source/examples.md
+ln -s ../../examples/README.md examples.md
 ```
 
 Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:

docs/requirements.txt

@@ -26,4 +26,7 @@ sphinxcontrib-jsmath==1.0.1
 sphinxcontrib-qthelp==1.0.2
 sphinxcontrib-serializinghtml==1.1.3
 urllib3==1.25.3
-sphinx-markdown-tables==0.0.9
+sphinx-markdown-tables==0.0.9
+numpy==1.17.2
+tensorflow==2.0.0rc2
+torch==1.2.0

docs/source/_static/js/custom.js

@@ -1,5 +1,5 @@
 function addIcon() {
-    const huggingFaceLogo = "http://lysand.re/huggingface_logo.svg";
+    const huggingFaceLogo = "https://huggingface.co/assets/transformers-docs/huggingface_logo.svg";
     const image = document.createElement("img");
     image.setAttribute("src", huggingFaceLogo);
 
@@ -9,14 +9,14 @@ function addIcon() {
     div.style.paddingTop = '30px';
     div.style.backgroundColor = '#6670FF';
 
-    const scrollDiv = document.getElementsByClassName("wy-side-scroll")[0];
+    const scrollDiv = document.querySelector(".wy-side-scroll");
     scrollDiv.prepend(div);
 }
 
 function addCustomFooter() {
     const customFooter = document.createElement("div");
     const questionOrIssue = document.createElement("div");
-    questionOrIssue.innerHTML = "Stuck? Read our <a href='https://medium.com/huggingface'>Blog posts</a> or <a href='https://github.com/huggingface/pytorch_transformers'>Create an issue</a>";
+    questionOrIssue.innerHTML = "Stuck? Read our <a href='https://medium.com/huggingface'>Blog posts</a> or <a href='https://github.com/huggingface/transformers'>Create an issue</a>";
     customFooter.appendChild(questionOrIssue);
     customFooter.classList.add("footer");
 
@@ -24,10 +24,10 @@ function addCustomFooter() {
     social.classList.add("footer__Social");
 
     const imageDetails = [
-        { link: "https://huggingface.co", imageLink: "http://lysand.re/icons/website.svg" },
-        { link: "https://twitter.com/huggingface", imageLink: "http://lysand.re/icons/twitter.svg" },
-        { link: "https://github.com/huggingface", imageLink: "http://lysand.re/icons/github.svg" },
-        { link: "https://www.linkedin.com/company/huggingface/", imageLink: "http://lysand.re/icons/linkedin.svg" }
+        { link: "https://huggingface.co", imageLink: "https://huggingface.co/assets/transformers-docs/website.svg" },
+        { link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/twitter.svg" },
+        { link: "https://github.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/github.svg" },
+        { link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/assets/transformers-docs/linkedin.svg" }
     ];
 
     imageDetails.forEach(imageLinks => {
@@ -42,13 +42,38 @@ function addCustomFooter() {
     });
 
     customFooter.appendChild(social);
-    document.getElementsByTagName("footer")[0].appendChild(customFooter);
+    document.querySelector("footer").appendChild(customFooter);
 }
 
+function addGithubButton() {
+    const div = `
+        <div class="github-repo">
+            <a 
+                class="github-button"
+                href="https://github.com/huggingface/transformers" data-size="large" data-show-count="true" aria-label="Star huggingface/pytorch-transformers on GitHub">
+                Star
+            </a>
+        </div>
+    `;
+    document.querySelector(".wy-side-nav-search .icon-home").insertAdjacentHTML('afterend', div);
+}
+
+/*!
+ * github-buttons v2.2.10
+ * (c) 2019 なつき
+ * @license BSD-2-Clause
+ */
+/**
+ * modified to run programmatically
+ */
+function parseGithubButtons (){"use strict";var e=window.document,t=e.location,o=window.encodeURIComponent,r=window.decodeURIComponent,n=window.Math,a=window.HTMLElement,i=window.XMLHttpRequest,l="https://unpkg.com/github-buttons@2.2.10/dist/buttons.html",c=i&&i.prototype&&"withCredentials"in i.prototype,d=c&&a&&a.prototype.attachShadow&&!a.prototype.attachShadow.prototype,s=function(e,t,o){e.addEventListener?e.addEventListener(t,o):e.attachEvent("on"+t,o)},u=function(e,t,o){e.removeEventListener?e.removeEventListener(t,o):e.detachEvent("on"+t,o)},h=function(e,t,o){var r=function(n){return u(e,t,r),o(n)};s(e,t,r)},f=function(e,t,o){var r=function(n){if(t.test(e.readyState))return u(e,"readystatechange",r),o(n)};s(e,"readystatechange",r)},p=function(e){return function(t,o,r){var n=e.createElement(t);if(o)for(var a in o){var i=o[a];null!=i&&(null!=n[a]?n[a]=i:n.setAttribute(a,i))}if(r)for(var l=0,c=r.length;l<c;l++){var d=r[l];n.appendChild("string"==typeof d?e.createTextNode(d):d)}return n}},g=p(e),b=function(e){var t;return function(){t||(t=1,e.apply(this,arguments))}},m="body{margin:0}a{color:#24292e;text-decoration:none;outline:0}.octicon{display:inline-block;vertical-align:text-top;fill:currentColor}.widget{ display:inline-block;overflow:hidden;font-family:-apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif;font-size:0;white-space:nowrap;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.btn,.social-count{display:inline-block;height:14px;padding:2px 5px;font-size:11px;font-weight:600;line-height:14px;vertical-align:bottom;cursor:pointer;border:1px solid #c5c9cc;border-radius:0.25em}.btn{background-color:#eff3f6;background-image:-webkit-linear-gradient(top, #fafbfc, #eff3f6 90%);background-image:-moz-linear-gradient(top, #fafbfc, #eff3f6 90%);background-image:linear-gradient(180deg, #fafbfc, #eff3f6 90%);background-position:-1px -1px;background-repeat:repeat-x;background-size:110% 110%;border-color:rgba(27,31,35,0.2);-ms-filter:\"progid:DXImageTransform.Microsoft.Gradient(startColorstr='#FFFAFBFC', endColorstr='#FFEEF2F5')\";*filter:progid:DXImageTransform.Microsoft.Gradient(startColorstr='#FFFAFBFC', endColorstr='#FFEEF2F5')}.btn:active{background-color:#e9ecef;background-image:none;border-color:#a5a9ac;border-color:rgba(27,31,35,0.35);box-shadow:inset 0 0.15em 0.3em rgba(27,31,35,0.15)}.btn:focus,.btn:hover{background-color:#e6ebf1;background-image:-webkit-linear-gradient(top, #f0f3f6, #e6ebf1 90%);background-image:-moz-linear-gradient(top, #f0f3f6, #e6ebf1 90%);background-image:linear-gradient(180deg, #f0f3f6, #e6ebf1 90%);border-color:#a5a9ac;border-color:rgba(27,31,35,0.35);-ms-filter:\"progid:DXImageTransform.Microsoft.Gradient(startColorstr='#FFF0F3F6', endColorstr='#FFE5EAF0')\";*filter:progid:DXImageTransform.Microsoft.Gradient(startColorstr='#FFF0F3F6', endColorstr='#FFE5EAF0')}.social-count{position:relative;margin-left:5px;background-color:#fff}.social-count:focus,.social-count:hover{color:#0366d6}.social-count b,.social-count i{position:absolute;top:50%;left:0;display:block;width:0;height:0;margin:-4px 0 0 -4px;border:solid transparent;border-width:4px 4px 4px 0;_line-height:0;_border-top-color:red !important;_border-bottom-color:red !important;_border-left-color:red !important;_filter:chroma(color=red)}.social-count b{border-right-color:#c5c9cc}.social-count i{margin-left:-3px;border-right-color:#fff}.lg .btn,.lg .social-count{height:16px;padding:5px 10px;font-size:12px;line-height:16px}.lg .social-count{margin-left:6px}.lg .social-count b,.lg .social-count i{margin:-5px 0 0 -5px;border-width:5px 5px 5px 0}.lg .social-count i{margin-left:-4px}\n",v={"mark-github":{width:16,height:16,path:'<path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"/>'},eye:{width:16,height:16,path:'<path fill-rule="evenodd" d="M8.06 2C3 2 0 8 0 8s3 6 8.06 6C13 14 16 8 16 8s-3-6-7.94-6zM8 12c-2.2 0-4-1.78-4-4 0-2.2 1.8-4 4-4 2.22 0 4 1.8 4 4 0 2.22-1.78 4-4 4zm2-4c0 1.11-.89 2-2 2-1.11 0-2-.89-2-2 0-1.11.89-2 2-2 1.11 0 2 .89 2 2z"/>'},star:{width:14,height:16,path:'<path fill-rule="evenodd" d="M14 6l-4.9-.64L7 1 4.9 5.36 0 6l3.6 3.26L2.67 14 7 11.67 11.33 14l-.93-4.74L14 6z"/>'},"repo-forked":{width:10,height:16,path:'<path fill-rule="evenodd" d="M8 1a1.993 1.993 0 0 0-1 3.72V6L5 8 3 6V4.72A1.993 1.993 0 0 0 2 1a1.993 1.993 0 0 0-1 3.72V6.5l3 3v1.78A1.993 1.993 0 0 0 5 15a1.993 1.993 0 0 0 1-3.72V9.5l3-3V4.72A1.993 1.Line truncated
+
+
 function onLoad() {
     addIcon();
     addCustomFooter();
+    addGithubButton();
+    parseGithubButtons();
 }
 
 window.addEventListener("load", onLoad);
-

docs/source/bertology.rst

@@ -15,4 +15,4 @@ In order to help this new field develop, we have included a few additional featu
 * accessing all the attention weights for each head of BERT/GPT/GPT-2,
 * retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
 
-To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
+To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.

docs/source/conf.py

@@ -19,14 +19,14 @@ sys.path.insert(0, os.path.abspath('../..'))
 
 # -- Project information -----------------------------------------------------
 
-project = u'pytorch-transformers'
+project = u'transformers'
 copyright = u'2019, huggingface'
 author = u'huggingface'
 
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'1.2.0'
+release = u'2.0.0'
 
 
 # -- General configuration ---------------------------------------------------
@@ -109,7 +109,7 @@ html_static_path = ['_static']
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'pytorch-transformersdoc'
+htmlhelp_basename = 'transformersdoc'
 
 
 # -- Options for LaTeX output ------------------------------------------------
@@ -136,7 +136,7 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'pytorch-transformers.tex', u'pytorch-transformers Documentation',
+    (master_doc, 'transformers.tex', u'transformers Documentation',
      u'huggingface', 'manual'),
 ]
 
@@ -146,7 +146,7 @@ latex_documents = [
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
+    (master_doc, 'transformers', u'transformers Documentation',
      [author], 1)
 ]
 
@@ -157,8 +157,8 @@ man_pages = [
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
-     author, 'pytorch-transformers', 'One line description of project.',
+    (master_doc, 'transformers', u'transformers Documentation',
+     author, 'transformers', 'One line description of project.',
      'Miscellaneous'),
 ]
 

docs/source/converting_tensorflow_models.rst

@@ -6,7 +6,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
 BERT
 ^^^^
 
-You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
+You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
 
 This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
 
@@ -20,7 +20,7 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
 
    export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
 
-   pytorch_transformers bert \
+   transformers bert \
      $BERT_BASE_DIR/bert_model.ckpt \
      $BERT_BASE_DIR/bert_config.json \
      $BERT_BASE_DIR/pytorch_model.bin
@@ -36,7 +36,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT model,
 
    export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
 
-   pytorch_transformers gpt \
+   transformers gpt \
      $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
      $PYTORCH_DUMP_OUTPUT \
      [OPENAI_GPT_CONFIG]
@@ -50,7 +50,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
 
    export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
 
-   pytorch_transformers gpt2 \
+   transformers gpt2 \
      $OPENAI_GPT2_CHECKPOINT_PATH \
      $PYTORCH_DUMP_OUTPUT \
      [OPENAI_GPT2_CONFIG]
@@ -64,7 +64,7 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo
 
    export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
 
-   pytorch_transformers transfo_xl \
+   transformers transfo_xl \
      $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
      $PYTORCH_DUMP_OUTPUT \
      [TRANSFO_XL_CONFIG]
@@ -80,7 +80,7 @@ Here is an example of the conversion process for a pre-trained XLNet model, fine
    export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
    export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
 
-   pytorch_transformers xlnet \
+   transformers xlnet \
      $TRANSFO_XL_CHECKPOINT_PATH \
      $TRANSFO_XL_CONFIG_PATH \
      $PYTORCH_DUMP_OUTPUT \
@@ -96,6 +96,6 @@ Here is an example of the conversion process for a pre-trained XLM model:
 
    export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
 
-   pytorch_transformers xlm \
+   transformers xlm \
      $XLM_CHECKPOINT_PATH \
      $PYTORCH_DUMP_OUTPUT \

docs/source/examples.md

@@ -0,0 +1 @@
+../../examples/README.md

docs/source/index.rst

@@ -1,9 +1,43 @@
-Pytorch-Transformers
+Transformers
 ================================================================================================================================================
 
-PyTorch-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
+🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose architectures
+(BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation
+(NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
 
-The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
+This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__.
+
+Features
+---------------------------------------------------
+
+- As easy to use as pytorch-transformers
+- As powerful and concise as Keras
+- High performance on NLU and NLG tasks
+- Low barrier to entry for educators and practitioners
+
+State-of-the-art NLP for everyone:
+
+- Deep learning researchers
+- Hands-on practitioners
+- AI/ML/NLP teachers and educators
+
+Lower compute costs, smaller carbon footprint:
+
+- Researchers can share trained models instead of always retraining
+- Practitioners can reduce compute time and production costs
+- 8 architectures with over 30 pretrained models, some in more than 100 languages
+
+Choose the right framework for every part of a model's lifetime:
+
+- Train state-of-the-art models in 3 lines of code
+- Deep interoperability between TensorFlow 2.0 and PyTorch models
+- Move a single model between TF2.0/PyTorch frameworks at will
+- Seamlessly pick the right framework for training, evaluation, production
+
+Contents
+---------------------------------
+
+The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
 
 1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
@@ -12,7 +46,8 @@ The library currently contains PyTorch implementations, pre-trained model weight
 5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
 7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-8. `DistilBERT <https://huggingface.co/pytorch-transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the blog post `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf.
+8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the blog post `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf.
+
 
 .. toctree::
     :maxdepth: 2
@@ -37,6 +72,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
     main_classes/model
     main_classes/tokenizer
     main_classes/optimizer_schedules
+    main_classes/processors
 
 .. toctree::
     :maxdepth: 2

docs/source/installation.rst

@@ -1,7 +1,7 @@
 Installation
 ================================================
 
-PyTorch-Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
+Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
 
 With pip
 ^^^^^^^^
@@ -10,7 +10,7 @@ PyTorch Transformers can be installed using pip as follows:
 
 .. code-block:: bash
 
-   pip install pytorch-transformers
+   pip install transformers
 
 From source
 ^^^^^^^^^^^
@@ -19,15 +19,15 @@ To install from source, clone the repository and install with:
 
 .. code-block:: bash
 
-    git clone https://github.com/huggingface/pytorch-transformers.git
-    cd pytorch-transformers
+    git clone https://github.com/huggingface/transformers.git
+    cd transformers
     pip install [--editable] .
 
 
 Tests
 ^^^^^
 
-An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the `tests folder <https://github.com/huggingface/pytorch-transformers/tree/master/pytorch_transformers/tests>`_ and examples tests in the `examples folder <https://github.com/huggingface/pytorch-transformers/tree/master/examples>`_.
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the `tests folder <https://github.com/huggingface/transformers/tree/master/transformers/tests>`_ and examples tests in the `examples folder <https://github.com/huggingface/transformers/tree/master/examples>`_.
 
 Tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
 
@@ -35,7 +35,7 @@ Run all the tests from the root of the cloned repository with the commands:
 
 .. code-block:: bash
 
-    python -m pytest -sv ./pytorch_transformers/tests/
+    python -m pytest -sv ./transformers/tests/
     python -m pytest -sv ./examples/
 
 

docs/source/main_classes/configuration.rst

@@ -6,5 +6,5 @@ The base class ``PretrainedConfig`` implements the common methods for loading/sa
 ``PretrainedConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.PretrainedConfig
+.. autoclass:: transformers.PretrainedConfig
     :members:

docs/source/main_classes/model.rst

@@ -11,5 +11,11 @@ The base class ``PreTrainedModel`` implements the common methods for loading/sav
 ``PreTrainedModel``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.PreTrainedModel
+.. autoclass:: transformers.PreTrainedModel
+    :members:
+
+``TFPreTrainedModel``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFPreTrainedModel
     :members:

docs/source/main_classes/optimizer_schedules.rst

@@ -9,7 +9,7 @@ The ``.optimization`` module provides:
 ``AdamW``
 ~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.AdamW
+.. autoclass:: transformers.AdamW
     :members:
 
 Schedules
@@ -18,11 +18,11 @@ Schedules
 Learning Rate Schedules
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. autoclass:: pytorch_transformers.ConstantLRSchedule
+.. autoclass:: transformers.ConstantLRSchedule
     :members:
 
 
-.. autoclass:: pytorch_transformers.WarmupConstantSchedule
+.. autoclass:: transformers.WarmupConstantSchedule
     :members:
 
 .. image:: /imgs/warmup_constant_schedule.png
@@ -30,7 +30,7 @@ Learning Rate Schedules
     :alt:
 
 
-.. autoclass:: pytorch_transformers.WarmupCosineSchedule
+.. autoclass:: transformers.WarmupCosineSchedule
     :members:
 
 .. image:: /imgs/warmup_cosine_schedule.png
@@ -38,7 +38,7 @@ Learning Rate Schedules
     :alt:
 
 
-.. autoclass:: pytorch_transformers.WarmupCosineWithHardRestartsSchedule
+.. autoclass:: transformers.WarmupCosineWithHardRestartsSchedule
     :members:
 
 .. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
@@ -47,7 +47,7 @@ Learning Rate Schedules
 
 
 
-.. autoclass:: pytorch_transformers.WarmupLinearSchedule
+.. autoclass:: transformers.WarmupLinearSchedule
     :members:
 
 .. image:: /imgs/warmup_linear_schedule.png

docs/source/main_classes/processors.rst

@@ -0,0 +1,58 @@
+Processors
+----------------------------------------------------
+
+This library includes processors for several traditional tasks. These processors can be used to process a dataset into
+examples that can be fed to a model.
+
+Processors
+~~~~~~~~~~~~~~~~~~~~~
+
+All processors follow the same architecture which is that of the
+:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
+of :class:`~transformers.data.processors.utils.InputExample`. These
+:class:`~transformers.data.processors.utils.InputExample` can be converted to
+:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
+
+.. autoclass:: transformers.data.processors.utils.DataProcessor
+    :members:
+
+
+.. autoclass:: transformers.data.processors.utils.InputExample
+    :members:
+
+
+.. autoclass:: transformers.data.processors.utils.InputFeatures
+    :members:
+
+
+GLUE
+~~~~~~~~~~~~~~~~~~~~~
+
+`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
+the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
+`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__
+
+This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
+CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.
+
+Those processors are:
+    - :class:`~transformers.data.processors.utils.MrpcProcessor`
+    - :class:`~transformers.data.processors.utils.MnliProcessor`
+    - :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
+    - :class:`~transformers.data.processors.utils.Sst2Processor`
+    - :class:`~transformers.data.processors.utils.StsbProcessor`
+    - :class:`~transformers.data.processors.utils.QqpProcessor`
+    - :class:`~transformers.data.processors.utils.QnliProcessor`
+    - :class:`~transformers.data.processors.utils.RteProcessor`
+    - :class:`~transformers.data.processors.utils.WnliProcessor`
+
+Additionally, the following method  can be used to load values from a data file and convert them to a list of
+:class:`~transformers.data.processors.utils.InputExample`.
+
+.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
+
+Example usage
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+An example using these processors is given in the
+`run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py>`__ script.

docs/source/main_classes/tokenizer.rst

@@ -12,5 +12,5 @@ The base class ``PreTrainedTokenizer`` implements the common methods for loading
 ``PreTrainedTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.PreTrainedTokenizer
+.. autoclass:: transformers.PreTrainedTokenizer
     :members:

docs/source/migration.md

@@ -1,17 +1,17 @@
 # Migrating from pytorch-pretrained-bert
 
 
-Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `pytorch-transformers`
+Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
 
 ### Models always output `tuples`
 
-The main breaking change when migrating from `pytorch-pretrained-bert` to `pytorch-transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
+The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
 
-The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/pytorch-transformers/).
+The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
 
 In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
 
-Here is a `pytorch-pretrained-bert` to `pytorch-transformers` conversion example for a `BertForSequenceClassification` classification model:
+Here is a `pytorch-pretrained-bert` to `transformers` conversion example for a `BertForSequenceClassification` classification model:
 
 ```python
 # Let's load our model
@@ -20,11 +20,11 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
 # If you used to have this line in pytorch-pretrained-bert:
 loss = model(input_ids, labels=labels)
 
-# Now just use this line in pytorch-transformers to extract the loss from the output tuple:
+# Now just use this line in transformers to extract the loss from the output tuple:
 outputs = model(input_ids, labels=labels)
 loss = outputs[0]
 
-# In pytorch-transformers you can also have access to the logits:
+# In transformers you can also have access to the logits:
 loss, logits = outputs[:2]
 
 # And even the attention weigths if you configure the model to output them (and other outputs too, see the docstrings and documentation)
@@ -96,7 +96,7 @@ for batch in train_data:
     loss.backward()
     optimizer.step()
 
-### In PyTorch-Transformers, optimizer and schedules are splitted and instantiated like this:
+### In Transformers, optimizer and schedules are splitted and instantiated like this:
 optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
 scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps)  # PyTorch scheduler
 ### and used like this:

docs/source/model_doc/auto.rst

@@ -11,19 +11,19 @@ Instantiating one of ``AutoModel``, ``AutoConfig`` and ``AutoTokenizer`` will di
 ``AutoConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.AutoConfig
+.. autoclass:: transformers.AutoConfig
     :members:
 
 
 ``AutoModel``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.AutoModel
+.. autoclass:: transformers.AutoModel
     :members:
 
 
 ``AutoTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.AutoTokenizer
+.. autoclass:: transformers.AutoTokenizer
     :members:

docs/source/model_doc/bert.rst

@@ -4,69 +4,125 @@ BERT
 ``BertConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertConfig
+.. autoclass:: transformers.BertConfig
     :members:
 
 
 ``BertTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertTokenizer
+.. autoclass:: transformers.BertTokenizer
     :members:
 
 
 ``BertModel``
 ~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertModel
+.. autoclass:: transformers.BertModel
     :members:
 
 
 ``BertForPreTraining``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForPreTraining
+.. autoclass:: transformers.BertForPreTraining
     :members:
 
 
 ``BertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForMaskedLM
+.. autoclass:: transformers.BertForMaskedLM
     :members:
 
 
 ``BertForNextSentencePrediction``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForNextSentencePrediction
+.. autoclass:: transformers.BertForNextSentencePrediction
     :members:
 
 
 ``BertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForSequenceClassification
+.. autoclass:: transformers.BertForSequenceClassification
     :members:
 
 
 ``BertForMultipleChoice``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForMultipleChoice
+.. autoclass:: transformers.BertForMultipleChoice
     :members:
 
 
 ``BertForTokenClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForTokenClassification
+.. autoclass:: transformers.BertForTokenClassification
     :members:
 
 
 ``BertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.BertForQuestionAnswering
+.. autoclass:: transformers.BertForQuestionAnswering
+    :members:
+
+
+``TFBertModel``
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertModel
+    :members:
+
+
+``TFBertForPreTraining``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForPreTraining
+    :members:
+
+
+``TFBertForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForMaskedLM
+    :members:
+
+
+``TFBertForNextSentencePrediction``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForNextSentencePrediction
+    :members:
+
+
+``TFBertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForSequenceClassification
+    :members:
+
+
+``TFBertForMultipleChoice``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForMultipleChoice
+    :members:
+
+
+``TFBertForTokenClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForTokenClassification
+    :members:
+
+
+``TFBertForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFBertForQuestionAnswering
     :members:
 

docs/source/model_doc/distilbert.rst

@@ -4,40 +4,67 @@ DistilBERT
 ``DistilBertConfig``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.DistilBertConfig
+.. autoclass:: transformers.DistilBertConfig
     :members:
 
 
 ``DistilBertTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.DistilBertTokenizer
+.. autoclass:: transformers.DistilBertTokenizer
     :members:
 
 
 ``DistilBertModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.DistilBertModel
+.. autoclass:: transformers.DistilBertModel
     :members:
 
 
 ``DistilBertForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.DistilBertForMaskedLM
+.. autoclass:: transformers.DistilBertForMaskedLM
     :members:
 
 
 ``DistilBertForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.DistilBertForSequenceClassification
+.. autoclass:: transformers.DistilBertForSequenceClassification
     :members:
 
 
 ``DistilBertForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.DistilBertForQuestionAnswering
+.. autoclass:: transformers.DistilBertForQuestionAnswering
+    :members:
+
+``TFDistilBertModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDistilBertModel
+    :members:
+
+
+``TFDistilBertForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDistilBertForMaskedLM
+    :members:
+
+
+``TFDistilBertForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDistilBertForSequenceClassification
+    :members:
+
+
+``TFDistilBertForQuestionAnswering``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFDistilBertForQuestionAnswering
     :members:

docs/source/model_doc/gpt.rst

@@ -4,33 +4,54 @@ OpenAI GPT
 ``OpenAIGPTConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.OpenAIGPTConfig
+.. autoclass:: transformers.OpenAIGPTConfig
     :members:
 
 
 ``OpenAIGPTTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.OpenAIGPTTokenizer
+.. autoclass:: transformers.OpenAIGPTTokenizer
     :members:
 
 
 ``OpenAIGPTModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.OpenAIGPTModel
+.. autoclass:: transformers.OpenAIGPTModel
     :members:
 
 
 ``OpenAIGPTLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.OpenAIGPTLMHeadModel
+.. autoclass:: transformers.OpenAIGPTLMHeadModel
     :members:
 
 
 ``OpenAIGPTDoubleHeadsModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.OpenAIGPTDoubleHeadsModel
+.. autoclass:: transformers.OpenAIGPTDoubleHeadsModel
+    :members:
+
+
+``TFOpenAIGPTModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFOpenAIGPTModel
+    :members:
+
+
+``TFOpenAIGPTLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFOpenAIGPTLMHeadModel
+    :members:
+
+
+``TFOpenAIGPTDoubleHeadsModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFOpenAIGPTDoubleHeadsModel
     :members:

docs/source/model_doc/gpt2.rst

@@ -4,33 +4,54 @@ OpenAI GPT2
 ``GPT2Config``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.GPT2Config
+.. autoclass:: transformers.GPT2Config
     :members:
 
 
 ``GPT2Tokenizer``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.GPT2Tokenizer
+.. autoclass:: transformers.GPT2Tokenizer
     :members:
 
 
 ``GPT2Model``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.GPT2Model
+.. autoclass:: transformers.GPT2Model
     :members:
 
 
 ``GPT2LMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.GPT2LMHeadModel
+.. autoclass:: transformers.GPT2LMHeadModel
     :members:
 
 
 ``GPT2DoubleHeadsModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.GPT2DoubleHeadsModel
+.. autoclass:: transformers.GPT2DoubleHeadsModel
+    :members:
+
+
+``TFGPT2Model``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFGPT2Model
+    :members:
+
+
+``TFGPT2LMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFGPT2LMHeadModel
+    :members:
+
+
+``TFGPT2DoubleHeadsModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFGPT2DoubleHeadsModel
     :members:

docs/source/model_doc/roberta.rst

@@ -4,33 +4,54 @@ RoBERTa
 ``RobertaConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.RobertaConfig
+.. autoclass:: transformers.RobertaConfig
     :members:
 
 
 ``RobertaTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.RobertaTokenizer
+.. autoclass:: transformers.RobertaTokenizer
     :members:
 
 
 ``RobertaModel``
 ~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.RobertaModel
+.. autoclass:: transformers.RobertaModel
     :members:
 
 
 ``RobertaForMaskedLM``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.RobertaForMaskedLM
+.. autoclass:: transformers.RobertaForMaskedLM
     :members:
 
 
 ``RobertaForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.RobertaForSequenceClassification
+.. autoclass:: transformers.RobertaForSequenceClassification
+    :members:
+
+
+``TFRobertaModel``
+~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRobertaModel
+    :members:
+
+
+``TFRobertaForMaskedLM``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRobertaForMaskedLM
+    :members:
+
+
+``TFRobertaForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRobertaForSequenceClassification
     :members:

docs/source/model_doc/transformerxl.rst

@@ -5,26 +5,40 @@ Transformer XL
 ``TransfoXLConfig``
 ~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.TransfoXLConfig
+.. autoclass:: transformers.TransfoXLConfig
     :members:
 
 
 ``TransfoXLTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.TransfoXLTokenizer
+.. autoclass:: transformers.TransfoXLTokenizer
     :members:
 
 
 ``TransfoXLModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.TransfoXLModel
+.. autoclass:: transformers.TransfoXLModel
     :members:
 
 
 ``TransfoXLLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.TransfoXLLMHeadModel
+.. autoclass:: transformers.TransfoXLLMHeadModel
+    :members:
+
+
+``TFTransfoXLModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTransfoXLModel
+    :members:
+
+
+``TFTransfoXLLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFTransfoXLLMHeadModel
     :members:

docs/source/model_doc/xlm.rst

@@ -4,38 +4,66 @@ XLM
 ``XLMConfig``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLMConfig
+.. autoclass:: transformers.XLMConfig
     :members:
 
 ``XLMTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLMTokenizer
+.. autoclass:: transformers.XLMTokenizer
     :members:
 
 ``XLMModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLMModel
+.. autoclass:: transformers.XLMModel
     :members:
 
 
 ``XLMWithLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLMWithLMHeadModel
+.. autoclass:: transformers.XLMWithLMHeadModel
     :members:
 
 
 ``XLMForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLMForSequenceClassification
+.. autoclass:: transformers.XLMForSequenceClassification
     :members:
 
 
 ``XLMForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLMForQuestionAnswering
+.. autoclass:: transformers.XLMForQuestionAnswering
+    :members:
+
+
+``TFXLMModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMModel
+    :members:
+
+
+``TFXLMWithLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMWithLMHeadModel
+    :members:
+
+
+``TFXLMForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMForSequenceClassification
+    :members:
+
+
+``TFXLMForQuestionAnsweringSimple``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLMForQuestionAnsweringSimple
     :members:

docs/source/model_doc/xlnet.rst

@@ -4,40 +4,68 @@ XLNet
 ``XLNetConfig``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLNetConfig
+.. autoclass:: transformers.XLNetConfig
     :members:
 
 
 ``XLNetTokenizer``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLNetTokenizer
+.. autoclass:: transformers.XLNetTokenizer
     :members:
 
 
 ``XLNetModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLNetModel
+.. autoclass:: transformers.XLNetModel
     :members:
 
 
 ``XLNetLMHeadModel``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLNetLMHeadModel
+.. autoclass:: transformers.XLNetLMHeadModel
     :members:
 
 
 ``XLNetForSequenceClassification``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLNetForSequenceClassification
+.. autoclass:: transformers.XLNetForSequenceClassification
     :members:
 
 
 ``XLNetForQuestionAnswering``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pytorch_transformers.XLNetForQuestionAnswering
+.. autoclass:: transformers.XLNetForQuestionAnswering
+    :members:
+
+
+``TFXLNetModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLNetModel
+    :members:
+
+
+``TFXLNetLMHeadModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLNetLMHeadModel
+    :members:
+
+
+``TFXLNetForSequenceClassification``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLNetForSequenceClassification
+    :members:
+
+
+``TFXLNetForQuestionAnsweringSimple``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFXLNetForQuestionAnsweringSimple
     :members:

docs/source/notebooks.rst

@@ -1,16 +1,16 @@
 Notebooks
 ================================================
 
-We include `three Jupyter Notebooks <https://github.com/huggingface/pytorch-transformers/tree/master/notebooks>`_ that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
+We include `three Jupyter Notebooks <https://github.com/huggingface/transformers/tree/master/notebooks>`_ that can be used to check that the predictions of the PyTorch model are identical to the predictions of the original TensorFlow model.
 
 
 *
-  The first NoteBook (\ `Comparing-TF-and-PT-models.ipynb <https://github.com/huggingface/pytorch-transformers/blob/master/notebooks/Comparing-TF-and-PT-models.ipynb>`_\ ) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
+  The first NoteBook (\ `Comparing-TF-and-PT-models.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models.ipynb>`_\ ) extracts the hidden states of a full sequence on each layers of the TensorFlow and the PyTorch models and computes the standard deviation between them. In the given example, we get a standard deviation of 1.5e-7 to 9e-7 on the various hidden state of the models.
 
 *
-  The second NoteBook (\ `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/pytorch-transformers/blob/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_\ ) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the ``BertForQuestionAnswering`` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
+  The second NoteBook (\ `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`_\ ) compares the loss computed by the TensorFlow and the PyTorch models for identical initialization of the fine-tuning layer of the ``BertForQuestionAnswering`` and computes the standard deviation between them. In the given example, we get a standard deviation of 2.5e-7 between the models.
 
 *
-  The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/pytorch-transformers/blob/master/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
+  The third NoteBook (\ `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/transformers/blob/master/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`_\ ) compares the predictions computed by the TensorFlow and the PyTorch models for masked token language modeling using the pre-trained masked language modeling model.
 
 Please follow the instructions given in the notebooks to run and modify them.

docs/source/pretrained_models.rst

@@ -44,15 +44,15 @@ Here is the full list of the currently provided pretrained models together with
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-uncased-whole-word-masking-finetuned-squad``  | | 24-layer, 1024-hidden, 16-heads, 340M parameters.                                                                                   |
 |                   |                                                            | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD                                                             |
-|                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/pytorch-transformers/tree/master/examples>`__).   |
+|                   |                                                            | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__).           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-large-cased-whole-word-masking-finetuned-squad``    | | 24-layer, 1024-hidden, 16-heads, 340M parameters                                                                                    |
 |                   |                                                            | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD                                                               |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/pytorch-transformers/examples.html>`__)                   |
+|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``bert-base-cased-finetuned-mrpc``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | The ``bert-base-cased`` model fine-tuned on MRPC                                                                                    |
-|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/pytorch-transformers/examples.html>`__)                   |
+|                   |                                                            | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__)                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | OpenAI GPT English model                                                                                                            |
@@ -120,4 +120,4 @@ Here is the full list of the currently provided pretrained models together with
 |                   |                                                            | (see `details <https://medium.com/huggingface/distilbert-8cf3380435b5>`__)                                                            |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 
-.. <https://huggingface.co/pytorch-transformers/examples.html>`__
+.. <https://huggingface.co/transformers/examples.html>`__

docs/source/quickstart.md

@@ -2,7 +2,7 @@
 
 ## Philosophy
 
-PyTorch-Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.
+Transformers is an opinionated library built for NLP researchers seeking to use/study/extend large-scale transformers models.
 
 The library was designed with two strong goals in mind:
 
@@ -19,12 +19,12 @@ The library was designed with two strong goals in mind:
 
 A few other goals:
 
-- expose the models internals as consistently as possible:
+- expose the models' internals as consistently as possible:
 
   - we give access, using a single API to the full hidden-states and attention weights,
   - tokenizer and base model's API are standardized to easily switch between models.
 
-- incorporate a subjective selection of promising tools for fine-tuning/investiguating these models:
+- incorporate a subjective selection of promising tools for fine-tuning/investigating these models:
 
   - a simple/consistent way to add new tokens to the vocabulary and embeddings for fine-tuning,
   - simple ways to mask and prune transformer heads.
@@ -33,13 +33,13 @@ A few other goals:
 
 The library is build around three type of classes for each models:
 
-- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 6 models architectures currently provided in the library, e.g. `BertModel`
+- **model classes** which are PyTorch models (`torch.nn.Modules`) of the 8 models architectures currently provided in the library, e.g. `BertModel`
 - **configuration classes** which store all the parameters required to build a model, e.g. `BertConfig`. You don't always need to instantiate these your-self, in particular if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model)
 - **tokenizer classes** which store the vocabulary for each model and provide methods for encoding/decoding strings in list of token embeddings indices to be fed to a model, e.g. `BertTokenizer`
 
 All these classes can be instantiated from pretrained instances and saved locally using two methods:
 
-- `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/pytorch-transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
+- `from_pretrained()` let you instantiate a model/configuration/tokenizer from a pretrained version either provided by the library itself (currently 27 models are provided as listed [here](https://huggingface.co/transformers/pretrained_models.html)) or stored locally (or on a server) by the user,
 - `save_pretrained()` let you save a model/configuration/tokenizer locally so that it can be reloaded using `from_pretrained()`.
 
 We'll finish this quickstart tour by going through a few simple quick-start examples to see how we can instantiate and use these classes. The rest of the documentation is organized in two parts:
@@ -51,7 +51,7 @@ We'll finish this quickstart tour by going through a few simple quick-start exam
 
 Here are two examples showcasing a few `Bert` and `GPT2` classes and pre-trained models.
 
-See full API reference for examples for each model classe.
+See full API reference for examples for each model class.
 
 ### BERT example
 
@@ -59,7 +59,7 @@ Let's start by preparing a tokenized input (a list of token embeddings indices t
 
 ```python
 import torch
-from pytorch_transformers import BertTokenizer, BertModel, BertForMaskedLM
+from transformers import BertTokenizer, BertModel, BertForMaskedLM
 
 # OPTIONAL: if you want to have more information on what's happening under the hood, activate the logger as follows
 import logging
@@ -93,8 +93,8 @@ Let's see how we can use `BertModel` to encode our inputs in hidden-states:
 # Load pre-trained model (weights)
 model = BertModel.from_pretrained('bert-base-uncased')
 
-# Set the model in evaluation mode to desactivate the DropOut modules
-# This is IMPORTANT to have reproductible results during evaluation!
+# Set the model in evaluation mode to deactivate the DropOut modules
+# This is IMPORTANT to have reproducible results during evaluation!
 model.eval()
 
 # If you have a GPU, put everything on cuda
@@ -106,7 +106,7 @@ model.to('cuda')
 with torch.no_grad():
     # See the models docstrings for the detail of the inputs
     outputs = model(tokens_tensor, token_type_ids=segments_tensors)
-    # PyTorch-Transformers models always output tuples.
+    # Transformers models always output tuples.
     # See the models docstrings for the detail of all the outputs
     # In our case, the first element is the hidden state of the last layer of the Bert model
     encoded_layers = outputs[0]
@@ -145,7 +145,7 @@ First let's prepare a tokenized input from our text string using `GPT2Tokenizer`
 
 ```python
 import torch
-from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
 # OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
 import logging
@@ -168,8 +168,8 @@ Let's see how to use `GPT2LMHeadModel` to generate the next token following our
 # Load pre-trained model (weights)
 model = GPT2LMHeadModel.from_pretrained('gpt2')
 
-# Set the model in evaluation mode to desactivate the DropOut modules
-# This is IMPORTANT to have reproductible results during evaluation!
+# Set the model in evaluation mode to deactivate the DropOut modules
+# This is IMPORTANT to have reproducible results during evaluation!
 model.eval()
 
 # If you have a GPU, put everything on cuda

docs/source/serialization.rst

@@ -45,7 +45,7 @@ where
     * ``bert_config.json`` or ``openai_gpt_config.json`` a configuration file for the model, and
     * ``pytorch_model.bin`` a PyTorch dump of a pre-trained instance of ``BertForPreTraining``\ , ``OpenAIGPTModel``\ , ``TransfoXLModel``\ , ``GPT2LMHeadModel`` (saved with the usual ``torch.save()``\ )
 
-  If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here <https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/modeling_bert.py>`__\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ).
+  If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here <https://github.com/huggingface/transformers/blob/master/transformers/modeling_bert.py>`__\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ).
 
 *
   ``cache_dir`` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example ``cache_dir='./pretrained_model_{}'.format(args.local_rank)`` (see the section on distributed training for more information).
@@ -122,7 +122,7 @@ Here is the recommended way of saving the model, configuration and vocabulary to
 
 .. code-block:: python
 
-   from pytorch_transformers import WEIGHTS_NAME, CONFIG_NAME
+   from transformers import WEIGHTS_NAME, CONFIG_NAME
 
    output_dir = "./models/"
 

docs/source/torchscript.rst

@@ -12,7 +12,7 @@ According to Pytorch's documentation: "TorchScript is a way to create serializab
 Pytorch's two modules `JIT and TRACE <https://pytorch.org/docs/stable/jit.html>`_ allow the developer to export
 their model to be re-used in other programs, such as efficiency-oriented C++ programs.
 
-We have provided an interface that allows the export of `pytorch-transformers` models to TorchScript so that they can
+We have provided an interface that allows the export of `transformers` models to TorchScript so that they can
 be reused in a different environment than a Pytorch-based python program. Here we explain how to use our models so that
 they can be exported, and what to be mindful of when using these models with TorchScript.
 
@@ -74,7 +74,7 @@ according to a ``BertConfig`` class and then saved to disk under the filename ``
 
 .. code-block:: python
 
-    from pytorch_transformers import BertModel, BertTokenizer, BertConfig
+    from transformers import BertModel, BertTokenizer, BertConfig
     import torch
 
     enc = BertTokenizer.from_pretrained("bert-base-uncased")

examples/README.md

@@ -13,7 +13,7 @@ similar API between the different models.
 
 ## Language model fine-tuning
 
-Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_lm_finetuning.py).
+Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py).
 
 Fine-tuning the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT 
 to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa 
@@ -75,7 +75,7 @@ python run_lm_finetuning.py \
 
 ## Language generation
 
-Based on the script [`run_generation.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_generation.py).
+Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py).
 
 Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet.
 A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
@@ -91,26 +91,26 @@ python run_generation.py \
 
 ## GLUE
 
-Based on the script [`run_glue.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py).
+Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py).
 
 Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding 
 Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa. 
 
 GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
-uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8  V100 GPUs with a total train
+uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train
 batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results
 between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.
 
 | Task  | Metric                       | Result      |
 |-------|------------------------------|-------------|
-| CoLA  | Matthew's corr               | 55.75       |
-| SST-2 | Accuracy                     | 92.09       |
-| MRPC  | F1/Accuracy                  | 90.48/86.27 |
-| STS-B | Person/Spearman corr.        | 89.03/88.64 |
-| QQP   | Accuracy/F1                  | 90.92/87.72 |
-| MNLI  | Matched acc./Mismatched acc. | 83.74/84.06 |
-| QNLI  | Accuracy                     | 91.07       |
-| RTE   | Accuracy                     | 68.59       |
+| CoLA  | Matthew's corr               | 48.87       |
+| SST-2 | Accuracy                     | 91.74       |
+| MRPC  | F1/Accuracy                  | 90.70/86.27 |
+| STS-B | Person/Spearman corr.        | 91.39/91.04 |
+| QQP   | Accuracy/F1                  | 90.79/87.66 |
+| MNLI  | Matched acc./Mismatched acc. | 83.70/84.83 |
+| QNLI  | Accuracy                     | 89.31       |
+| RTE   | Accuracy                     | 71.43       |
 | WNLI  | Accuracy                     | 43.66       |
 
 Some of these results are significantly different from the ones reported on the test set
@@ -319,7 +319,7 @@ eval_loss = 0.44457291918821606
 
 ## SQuAD
 
-Based on the script [`run_squad.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_squad.py).
+Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/run_squad.py).
 
 #### Fine-tuning on SQuAD
 

examples/contrib/run_openai_gpt.py

@@ -39,7 +39,7 @@ import torch
 from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                               TensorDataset)
 
-from pytorch_transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
+from transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
                                      AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
                                      WarmupLinearSchedule)
 

examples/contrib/run_swag.py

@@ -35,10 +35,10 @@ from tqdm import tqdm, trange
 
 from tensorboardX import SummaryWriter
 
-from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
+from transformers import (WEIGHTS_NAME, BertConfig,
                                   BertForMultipleChoice, BertTokenizer)
 
-from pytorch_transformers import AdamW, WarmupLinearSchedule
+from transformers import AdamW, WarmupLinearSchedule
 
 logger = logging.getLogger(__name__)
 
@@ -365,7 +365,7 @@ def train(args, train_dataset, model, tokenizer):
             #     inputs.update({'cls_index': batch[5],
             #                    'p_mask':       batch[6]})
             outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
+            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
                 loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
@@ -647,7 +647,7 @@ def main():
 
         if args.eval_all_checkpoints:
             checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
+            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
 
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
 

examples/contrib/run_transfo_xl.py

@@ -28,7 +28,7 @@ import math
 
 import torch
 
-from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
+from transformers import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                     datefmt = '%m/%d/%Y %H:%M:%S',

examples/distillation/README.md

@@ -1,26 +1,40 @@
-# DistilBERT
+# Distil*
 
-This folder contains the original code used to train DistilBERT as well as examples showcasing how to use DistilBERT.
+This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT and DistilGPT2.
 
-## What is DistilBERT
+**2019, October 3rd - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2.
 
-DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
+**2019, September 19th - Update:** We fixed bugs in the code and released an upadted version of the weights trained with a modification of the distillation loss. DistilBERT now reaches 97% of `BERT-base`'s performance on GLUE, and 86.9 F1 score on SQuAD v1.1 dev set (compared to 88.5 for `BERT-base`). We will publish a formal write-up of our approach in the near future!
 
-For more information on DistilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
-).
+## What is Distil*
+
+Distil* is a class of compressed models that started with DistilBERT. DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and light Transformer model based on Bert architecture. It has 40% less parameters than `bert-base-uncased`, runs 60% faster while preserving 97% of BERT's performances as measured on the GLUE language understanding benchmark. DistilBERT is trained using knowledge distillation, a technique to compress a large model called the teacher into a smaller model called the student. By distillating Bert, we obtain a smaller Transformer model that bears a lot of similarities with the original BERT model while being lighter, smaller and faster to run. DistilBERT is thus an interesting option to put large-scaled trained Transformer model into production.
+
+We have applied the same method to GPT2 and release the weights of the compressed model. On the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test of 15.8 compared to 19.3 for DistilGPT2 (after fine-tuning on the train set).
+
+For more information on DistilBERT, please refer to our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108). The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances.
+
+Here are the results on the dev sets of GLUE:
+
+| Model      | Macro-score | CoLA | MNLI | MRPC | QNLI | QQP  | RTE  | SST-2| STS-B| WNLI |
+| :---:      |    :---:    | :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:| :---:|
+| BERT-base  |  **77.6**   | 48.9 | 84.3 | 88.6 | 89.3 | 89.5 | 71.3 | 91.7 | 91.2 | 43.7 |
+| DistilBERT |  **76.8**   | 49.1 | 81.8 | 90.2 | 90.2 | 89.2 | 62.9 | 92.7 | 90.7 | 44.4 |
 
 ## Setup
 
 This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. 
 
-**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0). It is important to note that there is a small internal bug in the current version of PyTorch available on pip that causes a memory leak in our training/distillation. It has been recently fixed and will likely be integrated into the next release. For the moment, we recommend to [compile PyTorch from source](https://github.com/pytorch/pytorch#from-source). Please refer to [issue 1179](https://github.com/huggingface/pytorch-transformers/issues/1179) for more details.
+**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0). It is important to note that there is a small internal bug in the current version of PyTorch available on pip that causes a memory leak in our training/distillation. It has been recently fixed and will likely be integrated into the next release. For the moment, we recommend to [compile PyTorch from source](https://github.com/pytorch/pytorch#from-source). Please refer to [issue 1179](https://github.com/huggingface/transformers/issues/1179) for more details.
 
 ## How to use DistilBERT
 
-PyTorch-Transformers includes two pre-trained DistilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):
+Transformers includes two pre-trained Distil* models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):
 
 - `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
-- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.2 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
+- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
+- `distilgpt2`: DistilGPT2 English language model pretrained with the supervision of `gpt2` (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset and . The model has 6 layers, 768 dimension and 12 heads, totalizing 82M (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2.
+- and more to come! 🤗🤗🤗
 
 Using DistilBERT is very similar to using BERT. DistilBERT share the same tokenizer as BERT's `bert-base-uncased` even though we provide a link to this tokenizer under the `DistilBertTokenizer` name to have a consistent naming between the library models.
 
@@ -33,9 +47,11 @@ outputs = model(input_ids)
 last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
 ```
 
-## How to train DistilBERT
+Similarly, using DistilGPT2 simply consists in calling the GPT2 classes from a different pretrained checkpoint: `model = GPT2Model.from_pretrained('distilgpt2')`.
 
-In the following, we will explain how you can train your own compressed model.
+## How to train Distil*
+
+In the following, we will explain how you can train DistilBERT.
 
 ### A. Preparing the data
 
@@ -48,7 +64,8 @@ First, we will binarize the data, i.e. tokenize the data and convert each token
 ```bash
 python scripts/binarized_data.py \
     --file_path data/dump.txt \
-    --bert_tokenizer bert-base-uncased \
+    --tokenizer_type bert \
+    --tokenizer_name bert-base-uncased \
     --dump_file data/binarized_text
 ```
 
@@ -57,7 +74,8 @@ Our implementation of masked language modeling loss follows [XLM](https://github
 ```bash
 python scripts/token_counts.py \
     --data_file data/binarized_text.bert-base-uncased.pickle \
-    --token_counts_dump data/token_counts.bert-base-uncased.pickle
+    --token_counts_dump data/token_counts.bert-base-uncased.pickle \
+    --vocab_size 30522
 ```
 
 ### B. Training
@@ -66,6 +84,12 @@ Training with distillation is really simple once you have pre-processed the data
 
 ```bash
 python train.py \
+    --student_type distilbert \
+    --student_config training_configs/distilbert-base-uncased.json \
+    --teacher_type bert \
+    --teacher_name bert-base-uncased \
+    --alpha_ce 5.0 --alpha_mlm 2.0 --alpha_cos 1.0 --mlm \
+    --freeze_pos_embs \
     --dump_path serialization_dir/my_first_training \
     --data_file data/binarized_text.bert-base-uncased.pickle \
     --token_counts data/token_counts.bert-base-uncased.pickle \
@@ -74,7 +98,7 @@ python train.py \
 
 By default, this will launch a training on a single GPU (even if more are available on the cluster). Other parameters are available in the command line, please look in `train.py` or run `python train.py --help` to list them.
 
-We highly encourage you to use distributed training for training DistilBert as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
+We highly encourage you to use distributed training for training DistilBERT as the training corpus is quite large. Here's an example that runs a distributed training on a single node having 4 GPUs:
 
 ```bash
 export NODE_RANK=0
@@ -96,11 +120,17 @@ python -m torch.distributed.launch \
     train.py \
         --force \
         --n_gpu $WORLD_SIZE \
+        --student_type distilbert \
+        --student_config training_configs/distilbert-base-uncased.json \
+        --teacher_type bert \
+        --teacher_name bert-base-uncased \
+        --alpha_ce 0.33 --alpha_mlm 0.33 --alpha_cos 0.33 --mlm \
+        --freeze_pos_embs \
+        --dump_path serialization_dir/my_first_training \
         --data_file data/binarized_text.bert-base-uncased.pickle \
-        --token_counts data/token_counts.bert-base-uncased.pickle \
-        --dump_path serialization_dir/my_first_distillation
+        --token_counts data/token_counts.bert-base-uncased.pickle
 ```
 
-**Tips:** Starting distillated training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract_for_distil.py` to create a valid initialization checkpoint and use `--from_pretrained_weights` and `--from_pretrained_config` arguments to use this initialization for the distilled training!
+**Tips:** Starting distillated training with good initialization of the model weights is crucial to reach decent performance. In our experiments, we initialized our model from a few layers of the teacher (Bert) itself! Please refer to `scripts/extract.py` and `scripts/extract_distilbert.py` to create a valid initialization checkpoint and use `--student_pretrained_weights` argument to use this initialization for the distilled training!
 
 Happy distillation!

examples/distillation/distiller.py

@@ -12,29 +12,35 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" The distiller to distil DistilBERT
-    adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
+""" The distiller to distil the student.
+    Adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
 """
 import os
 import math
 import psutil
+import time
 from tensorboardX import SummaryWriter
 from tqdm import trange, tqdm
 import numpy as np
+import psutil
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.optim import AdamW
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.data import RandomSampler, BatchSampler, DataLoader
 
-from pytorch_transformers import AdamW, WarmupLinearSchedule
+from transformers import WarmupLinearSchedule
 
 from utils import logger
-from dataset import Dataset
+from lm_seqs_dataset import LmSeqsDataset
+from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
 
 class Distiller:
     def __init__(self,
                  params: dict,
-                 dataloader: Dataset,
+                 dataset: LmSeqsDataset,
                  token_probs: torch.tensor,
                  student: nn.Module,
                  teacher: nn.Module):
@@ -47,31 +53,47 @@ class Distiller:
         self.student = student
         self.teacher = teacher
 
-        self.dataloader = dataloader
-        if self.params.n_gpu > 1:
-            self.dataloader.split()
-        self.get_iterator(seed=params.seed)
+        self.student_config = student.config
+        self.vocab_size = student.config.vocab_size
+
+        if params.n_gpu <= 1:
+            sampler = RandomSampler(dataset)
+        else:
+            sampler = DistributedSampler(dataset)
+
+        if params.group_by_size:
+            groups = create_lengths_groups(lengths=dataset.lengths, k=params.max_model_input_size)
+            sampler = GroupedBatchSampler(sampler=sampler, group_ids=groups, batch_size=params.batch_size)
+        else:
+            sampler = BatchSampler(sampler=sampler, batch_size=params.batch_size, drop_last=False)
+
+        self.dataloader = DataLoader(dataset=dataset,
+                                     batch_sampler=sampler,
+                                     collate_fn=dataset.batch_sequences)
 
         self.temperature = params.temperature
         assert self.temperature > 0.
 
         self.alpha_ce = params.alpha_ce
         self.alpha_mlm = params.alpha_mlm
+        self.alpha_clm = params.alpha_clm
         self.alpha_mse = params.alpha_mse
-        assert self.alpha_ce >= 0.
-        assert self.alpha_mlm >= 0.
-        assert self.alpha_mse >= 0.
-        assert self.alpha_ce + self.alpha_mlm + self.alpha_mse > 0.
+        self.alpha_cos = params.alpha_cos
 
-        self.mlm_mask_prop = params.mlm_mask_prop
-        assert 0.0 <= self.mlm_mask_prop <= 1.0
-        assert params.word_mask + params.word_keep + params.word_rand == 1.0
-        self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
-        self.pred_probs = self.pred_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else self.pred_probs
-        self.token_probs = token_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else token_probs
-        if self.fp16:
-            self.pred_probs = self.pred_probs.half()
-            self.token_probs = self.token_probs.half()
+        self.mlm = params.mlm
+        if self.mlm:
+            logger.info(f'Using MLM loss for LM step.')
+            self.mlm_mask_prop = params.mlm_mask_prop
+            assert 0.0 <= self.mlm_mask_prop <= 1.0
+            assert params.word_mask + params.word_keep + params.word_rand == 1.0
+            self.pred_probs = torch.FloatTensor([params.word_mask, params.word_keep, params.word_rand])
+            self.pred_probs = self.pred_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else self.pred_probs
+            self.token_probs = token_probs.to(f'cuda:{params.local_rank}') if params.n_gpu > 0 else token_probs
+            if self.fp16:
+                self.pred_probs = self.pred_probs.half()
+                self.token_probs = self.token_probs.half()
+        else:
+            logger.info(f'Using CLM loss for LM step.')
 
         self.epoch = 0
         self.n_iter = 0
@@ -81,17 +103,22 @@ class Distiller:
         self.last_loss = 0
         self.last_loss_ce = 0
         self.last_loss_mlm = 0
-        self.last_loss_mse = 0
+        self.last_loss_clm = 0
+        if self.alpha_mse > 0.: self.last_loss_mse = 0
+        if self.alpha_cos > 0.: self.last_loss_cos = 0
+        self.last_log = 0
 
         self.ce_loss_fct = nn.KLDivLoss(reduction='batchmean')
-        self.mlm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
-        self.mse_loss_fct = nn.MSELoss(reduction='sum')
+        self.lm_loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
+        if self.alpha_mse > 0.:
+            self.mse_loss_fct = nn.MSELoss(reduction='sum')
+        if self.alpha_cos > 0.:
+            self.cosine_loss_fct = nn.CosineEmbeddingLoss(reduction='mean')
 
         logger.info('--- Initializing model optimizer')
         assert params.gradient_accumulation_steps >= 1
-        self.num_steps_epoch = int(len(self.dataloader) / params.batch_size) + 1
+        self.num_steps_epoch = len(self.dataloader)
         num_train_optimization_steps = int(self.num_steps_epoch / params.gradient_accumulation_steps * params.n_epoch) + 1
-        warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
 
         no_decay = ['bias', 'LayerNorm.weight']
         optimizer_grouped_parameters = [
@@ -104,9 +131,11 @@ class Distiller:
                                lr=params.learning_rate,
                                eps=params.adam_epsilon,
                                betas=(0.9, 0.98))
+
+        warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
         self.scheduler = WarmupLinearSchedule(self.optimizer,
-                                              warmup_steps=warmup_steps,
-                                              t_total=num_train_optimization_steps)
+                                                warmup_steps=warmup_steps,
+                                                t_total=num_train_optimization_steps)
 
         if self.fp16:
             try:
@@ -129,43 +158,18 @@ class Distiller:
                 logger.info("Using nn.parallel.DistributedDataParallel for distributed training.")
                 self.student = DistributedDataParallel(self.student,
                                                        device_ids=[params.local_rank],
-                                                       output_device=params.local_rank)
+                                                       output_device=params.local_rank,
+                                                       find_unused_parameters=True)
 
         self.is_master = params.is_master
         if self.is_master:
             logger.info('--- Initializing Tensorboard')
             self.tensorboard = SummaryWriter(log_dir=os.path.join(self.dump_path, 'log', 'train'))
-            self.tensorboard.add_text(tag='config', text_string=str(self.params), global_step=0)
+            self.tensorboard.add_text(tag='config/training', text_string=str(self.params), global_step=0)
+            self.tensorboard.add_text(tag='config/student', text_string=str(self.student_config), global_step=0)
 
-    def get_iterator(self,
-                     seed: int = None):
-        """
-        Initialize the data iterator.
-        Each process has its own data iterator (iterating on his own random portion of the dataset).
-
-        Input:
-        ------
-            seed: `int` - The random seed.
-        """
-        logger.info('--- Initializing Data Iterator')
-        self.data_iterator = self.dataloader.get_iterator(seed=seed)
-
-    def get_batch(self):
-        """
-        Call the data iterator to output a new batch.
-        If the data iterator went through the whole dataset, create a new iterator.
-        """
-        assert hasattr(self, 'data_iterator')
-        try:
-            x = next(self.data_iterator)
-        except StopIteration:
-            logger.warning('--- Went through the whole dataset. Creating new data iterator.')
-            self.data_iterator = self.dataloader.get_iterator()
-            x = next(self.data_iterator)
-        return x
-
-    def prepare_batch(self,
-                      batch):
+    def prepare_batch_mlm(self,
+                          batch):
         """
         Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the masked label for MLM.
 
@@ -211,7 +215,7 @@ class Distiller:
                 assert pred_mask.sum().item() % 8 == 0, pred_mask.sum().item()
 
         _token_ids_real = token_ids[pred_mask]
-        _token_ids_rand = _token_ids_real.clone().random_(self.params.vocab_size)
+        _token_ids_rand = _token_ids_real.clone().random_(self.vocab_size)
         _token_ids_mask = _token_ids_real.clone().fill_(self.params.special_tok_ids['mask_token'])
         probs = torch.multinomial(self.pred_probs, len(_token_ids_real), replacement=True)
         _token_ids = _token_ids_mask * (probs == 0).long() + _token_ids_real * (probs == 1).long() + _token_ids_rand * (probs == 2).long()
@@ -219,8 +223,41 @@ class Distiller:
 
         mlm_labels[~pred_mask] = -1 # previously `mlm_labels[1-pred_mask] = -1`, cf pytorch 1.2.0 compatibility
 
+        # sanity checks
+        assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
+
         return token_ids, attn_mask, mlm_labels
 
+    def prepare_batch_clm(self,
+                          batch):
+        """
+        Prepare the batch: from the token_ids and the lenghts, compute the attention mask and the labels for CLM.
+
+        Input:
+        ------
+            batch: `Tuple`
+                token_ids: `torch.tensor(bs, seq_length)` - The token ids for each of the sequence. It is padded.
+                lengths: `torch.tensor(bs)` - The lengths of each of the sequences in the batch.
+
+        Output:
+        -------
+            token_ids: `torch.tensor(bs, seq_length)` - The token ids after the modifications for MLM.
+            attn_mask: `torch.tensor(bs, seq_length)` - The attention mask for the self-attention.
+            clm_labels: `torch.tensor(bs, seq_length)` - The causal languge modeling labels. There is a -1 where there is nothing to predict.
+        """
+        token_ids, lengths = batch
+        token_ids, lengths = self.round_batch(x=token_ids, lengths=lengths)
+        assert token_ids.size(0) == lengths.size(0)
+
+        attn_mask = (torch.arange(token_ids.size(1), dtype=torch.long, device=lengths.device) < lengths[:, None])
+        clm_labels = token_ids.new(token_ids.size()).copy_(token_ids)
+        clm_labels[~attn_mask] = -1 # previously `clm_labels[1-attn_mask] = -1`, cf pytorch 1.2.0 compatibility
+
+        # sanity checks
+        assert 0 <= token_ids.min() <= token_ids.max() < self.vocab_size
+
+        return token_ids, attn_mask, clm_labels
+
     def round_batch(self,
                     x: torch.tensor,
                     lengths: torch.tensor):
@@ -258,7 +295,10 @@ class Distiller:
         if ml1 % 8 != 0:
             pad = 8 - (ml1 % 8)
             ml2 = ml1 + pad
-            pad_id = self.params.special_tok_ids['pad_token']
+            if self.mlm:
+                pad_id = self.params.special_tok_ids['pad_token']
+            else:
+                pad_id = self.params.special_tok_ids['unk_token']
             padding_tensor = torch.zeros(bs2, pad, dtype=torch.long, device=x.device).fill_(pad_id)
             x = torch.cat([x, padding_tensor], 1)
             assert x.size() == (bs2, ml2)
@@ -272,20 +312,25 @@ class Distiller:
         The real training loop.
         """
         if self.is_master: logger.info('Starting training')
+        self.last_log = time.time()
         self.student.train()
         self.teacher.eval()
 
         for _ in range(self.params.n_epoch):
             if self.is_master: logger.info(f'--- Starting epoch {self.epoch}/{self.params.n_epoch-1}')
+            if self.multi_gpu:
+                torch.distributed.barrier()
 
-            iter_bar = trange(self.num_steps_epoch, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
-            for __ in range(self.num_steps_epoch):
-                batch = self.get_batch()
+            iter_bar = tqdm(self.dataloader, desc="-Iter", disable=self.params.local_rank not in [-1, 0])
+            for batch in iter_bar:
                 if self.params.n_gpu > 0:
                     batch = tuple(t.to(f'cuda:{self.params.local_rank}') for t in batch)
-                token_ids, attn_mask, mlm_labels = self.prepare_batch(batch=batch)
 
-                self.step(input_ids=token_ids, attention_mask=attn_mask, mlm_labels=mlm_labels)
+                if self.mlm:
+                    token_ids, attn_mask, lm_labels = self.prepare_batch_mlm(batch=batch)
+                else:
+                    token_ids, attn_mask, lm_labels = self.prepare_batch_clm(batch=batch)
+                self.step(input_ids=token_ids, attention_mask=attn_mask, lm_labels=lm_labels)
 
                 iter_bar.update()
                 iter_bar.set_postfix({'Last_loss': f'{self.last_loss:.2f}',
@@ -303,7 +348,7 @@ class Distiller:
     def step(self,
              input_ids: torch.tensor,
              attention_mask: torch.tensor,
-             mlm_labels: torch.tensor):
+             lm_labels: torch.tensor):
         """
         One optimization step: forward of student AND teacher, backward on the loss (for gradient accumulation),
         and possibly a parameter update (depending on the gradient accumulation).
@@ -312,17 +357,22 @@ class Distiller:
         ------
         input_ids: `torch.tensor(bs, seq_length)` - The token ids.
         attention_mask: `torch.tensor(bs, seq_length)` - The attention mask for self attention.
-        mlm_labels: `torch.tensor(bs, seq_length)` - The masked language modeling labels.
+        lm_labels: `torch.tensor(bs, seq_length)` - The language modeling labels (mlm labels for MLM and clm labels for CLM).
         """
-        s_logits = self.student(input_ids=input_ids, attention_mask=attention_mask)[0]     # (bs, seq_length, voc_size)
-        with torch.no_grad():
-            t_logits = self.teacher(input_ids=input_ids, attention_mask=attention_mask)[0] # (bs, seq_length, voc_size)
+        if self.mlm:
+            s_logits, s_hidden_states = self.student(input_ids=input_ids, attention_mask=attention_mask)     # (bs, seq_length, voc_size)
+            with torch.no_grad():
+                t_logits, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=attention_mask) # (bs, seq_length, voc_size)
+        else:
+            s_logits, _, s_hidden_states = self.student(input_ids=input_ids, attention_mask=None)            # (bs, seq_length, voc_size)
+            with torch.no_grad():
+                t_logits, _, t_hidden_states = self.teacher(input_ids=input_ids, attention_mask=None)           # (bs, seq_length, voc_size)
         assert s_logits.size() == t_logits.size()
 
         #https://github.com/peterliht/knowledge-distillation-pytorch/blob/master/model/net.py#L100
         #https://github.com/peterliht/knowledge-distillation-pytorch/issues/2
         if self.params.restrict_ce_to_mask:
-            mask = (mlm_labels>-1).unsqueeze(-1).expand_as(s_logits)   # (bs, seq_lenth, voc_size)
+            mask = (lm_labels>-1).unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
         else:
             mask = attention_mask.unsqueeze(-1).expand_as(s_logits)    # (bs, seq_lenth, voc_size)
         s_logits_slct = torch.masked_select(s_logits, mask)            # (bs * seq_length * voc_size) modulo the 1s in mask
@@ -334,20 +384,47 @@ class Distiller:
         loss_ce = self.ce_loss_fct(F.log_softmax(s_logits_slct/self.temperature, dim=-1),
                                    F.softmax(t_logits_slct/self.temperature, dim=-1)) * (self.temperature)**2
         loss = self.alpha_ce*loss_ce
+
         if self.alpha_mlm > 0.:
-            loss_mlm = self.mlm_loss_fct(s_logits.view(-1, s_logits.size(-1)), mlm_labels.view(-1))
+            loss_mlm = self.lm_loss_fct(s_logits.view(-1, s_logits.size(-1)), lm_labels.view(-1))
             loss += self.alpha_mlm * loss_mlm
+        if self.alpha_clm > 0.:
+            shift_logits = s_logits[..., :-1, :].contiguous()
+            shift_labels = lm_labels[..., 1:].contiguous()
+            loss_clm = self.lm_loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
+                                        shift_labels.view(-1))
+            loss += self.alpha_clm * loss_clm
+
         if self.alpha_mse > 0.:
             loss_mse = self.mse_loss_fct(s_logits_slct, t_logits_slct)/s_logits_slct.size(0) # Reproducing batchmean reduction
             loss += self.alpha_mse * loss_mse
+        if self.alpha_cos > 0.:
+            s_hidden_states = s_hidden_states[-1]                              # (bs, seq_length, dim)
+            t_hidden_states = t_hidden_states[-1]                              # (bs, seq_length, dim)
+            mask = attention_mask.unsqueeze(-1).expand_as(s_hidden_states)     # (bs, seq_length, dim)
+            assert s_hidden_states.size() == t_hidden_states.size()
+            dim = s_hidden_states.size(-1)
+            
+            s_hidden_states_slct = torch.masked_select(s_hidden_states, mask)        # (bs * seq_length * dim)
+            s_hidden_states_slct = s_hidden_states_slct.view(-1, dim)                # (bs * seq_length, dim)
+            t_hidden_states_slct = torch.masked_select(t_hidden_states, mask)        # (bs * seq_length * dim)
+            t_hidden_states_slct = t_hidden_states_slct.view(-1, dim)                # (bs * seq_length, dim)
+        
+            target = s_hidden_states_slct.new(s_hidden_states_slct.size(0)).fill_(1) # (bs * seq_length,)
+            loss_cos = self.cosine_loss_fct(s_hidden_states_slct, t_hidden_states_slct, target)
+            loss += self.alpha_cos * loss_cos
 
         self.total_loss_epoch += loss.item()
         self.last_loss = loss.item()
         self.last_loss_ce = loss_ce.item()
         if self.alpha_mlm > 0.:
             self.last_loss_mlm = loss_mlm.item()
+        if self.alpha_clm > 0.:
+            self.last_loss_clm = loss_clm.item()
         if self.alpha_mse > 0.:
             self.last_loss_mse = loss_mse.item()
+        if self.alpha_cos > 0.:
+            self.last_loss_cos = loss_cos.item()
 
         self.optimize(loss)
 
@@ -396,6 +473,7 @@ class Distiller:
 
         if self.n_total_iter % self.params.log_interval == 0:
             self.log_tensorboard()
+            self.last_log = time.time()
         if self.n_total_iter % self.params.checkpoint_interval == 0:
             self.save_checkpoint()
 
@@ -419,11 +497,16 @@ class Distiller:
         self.tensorboard.add_scalar(tag="losses/loss_ce", scalar_value=self.last_loss_ce, global_step=self.n_total_iter)
         if self.alpha_mlm > 0.:
             self.tensorboard.add_scalar(tag="losses/loss_mlm", scalar_value=self.last_loss_mlm, global_step=self.n_total_iter)
+        if self.alpha_clm > 0.:
+            self.tensorboard.add_scalar(tag="losses/loss_clm", scalar_value=self.last_loss_clm, global_step=self.n_total_iter)
         if self.alpha_mse > 0.:
             self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter)
+        if self.alpha_cos > 0.:
+            self.tensorboard.add_scalar(tag="losses/loss_cos", scalar_value=self.last_loss_cos, global_step=self.n_total_iter)
         self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter)
         
         self.tensorboard.add_scalar(tag="global/memory_usage", scalar_value=psutil.virtual_memory()._asdict()['used']/1_000_000, global_step=self.n_total_iter)
+        self.tensorboard.add_scalar(tag="global/speed", scalar_value=time.time()-self.last_log, global_step=self.n_total_iter)
 
     def end_epoch(self):
         """

examples/distillation/grouped_batch_sampler.py

@@ -0,0 +1,105 @@
+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team and Facebook, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Adapted from PyTorch Vision (https://github.com/pytorch/vision/blob/master/references/detection/group_by_aspect_ratio.py)
+"""
+import bisect
+import copy
+from collections import defaultdict
+import numpy as np
+
+from torch.utils.data.sampler import BatchSampler, Sampler
+
+from utils import logger
+
+def _quantize(x, bins):
+    bins = copy.deepcopy(bins)
+    bins = sorted(bins)
+    quantized = list(map(lambda y: bisect.bisect_right(bins, y), x))
+    return quantized
+
+def create_lengths_groups(lengths, k=0):
+    bins = np.arange(start=3, stop=k, step=4).tolist() if k > 0 else [10]
+    groups = _quantize(lengths, bins)
+    # count number of elements per group
+    counts = np.unique(groups, return_counts=True)[1]
+    fbins = [0] + bins + [np.inf]
+    logger.info("Using {} as bins for aspect lengths quantization".format(fbins))
+    logger.info("Count of instances per bin: {}".format(counts))
+    return groups
+
+class GroupedBatchSampler(BatchSampler):
+    """
+    Wraps another sampler to yield a mini-batch of indices.
+    It enforces that the batch only contain elements from the same group.
+    It also tries to provide mini-batches which follows an ordering which is
+    as close as possible to the ordering from the original sampler.
+    Arguments:
+        sampler (Sampler): Base sampler.
+        group_ids (list[int]): If the sampler produces indices in range [0, N),
+            `group_ids` must be a list of `N` ints which contains the group id of each sample.
+            The group ids must be a continuous set of integers starting from
+            0, i.e. they must be in the range [0, num_groups).
+        batch_size (int): Size of mini-batch.
+    """
+    def __init__(self, sampler, group_ids, batch_size):
+        if not isinstance(sampler, Sampler):
+            raise ValueError(
+                "sampler should be an instance of "
+                "torch.utils.data.Sampler, but got sampler={}".format(sampler)
+            )
+        self.sampler = sampler
+        self.group_ids = group_ids
+        self.batch_size = batch_size
+
+    def __iter__(self):
+        buffer_per_group = defaultdict(list)
+        samples_per_group = defaultdict(list)
+
+        num_batches = 0
+        for idx in self.sampler:
+            group_id = self.group_ids[idx]
+            buffer_per_group[group_id].append(idx)
+            samples_per_group[group_id].append(idx)
+            if len(buffer_per_group[group_id]) == self.batch_size:
+                yield buffer_per_group[group_id] #TODO
+                num_batches += 1
+                del buffer_per_group[group_id]
+            assert len(buffer_per_group[group_id]) < self.batch_size
+
+        # now we have run out of elements that satisfy
+        # the group criteria, let's return the remaining
+        # elements so that the size of the sampler is
+        # deterministic
+        expected_num_batches = len(self)
+        num_remaining = expected_num_batches - num_batches
+        if num_remaining > 0:
+            # for the remaining batches, group the batches by similar lengths
+            batch_idx = []
+            for group_id, idxs in sorted(buffer_per_group.items(), key=lambda x: x[0]):
+                batch_idx.extend(idxs)
+                if len(batch_idx) >= self.batch_size:
+                    yield batch_idx[:self.batch_size]
+                    batch_idx = batch_idx[self.batch_size:]
+                    num_remaining -= 1
+            if len(batch_idx) > 0:
+                yield batch_idx
+                num_remaining -= 1
+        assert num_remaining == 0
+
+    def __len__(self):
+        """
+        Return the number of mini-batches rather than the number of samples.
+        """
+        return (len(self.sampler) + self.batch_size - 1) // self.batch_size

examples/distillation/lm_seqs_dataset.py

@@ -12,30 +12,33 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Dataloaders to train DistilBERT
+""" Dataset to distilled models
     adapted in part from Facebook, Inc XLM model (https://github.com/facebookresearch/XLM)
 """
-from typing import List
-import math
-from itertools import chain
-from collections import Counter
-import numpy as np
 import torch
+from torch.utils.data import Dataset
 
+import numpy as np
 from utils import logger
 
-class Dataset:
+class LmSeqsDataset(Dataset):
+    """Custom Dataset wrapping language modeling sequences.
+
+    Each sample will be retrieved by indexing the list of token_ids and their corresponding lengths.
+
+    Input:
+    ------
+        params: `NameSpace` parameters
+        data: `List[np.array[int]]
+    """
+
     def __init__(self,
                  params,
                  data):
         self.params = params
-        self.tokens_per_batch = params.tokens_per_batch
-        self.batch_size = params.batch_size
-        self.shuffle = params.shuffle
-        self.group_by_size = params.group_by_size
 
         self.token_ids = np.array(data)
-        self.lengths = np.uint16([len(t) for t in data])
+        self.lengths = np.array([len(t) for t in data])
 
         self.check()
         self.remove_long_sequences()
@@ -43,6 +46,9 @@ class Dataset:
         self.check()
         self.print_statistics()
 
+    def __getitem__(self, index):
+        return (self.token_ids[index], self.lengths[index])
+
     def __len__(self):
         return len(self.lengths)
 
@@ -51,12 +57,14 @@ class Dataset:
         Some sanity checks
         """
         assert len(self.token_ids) == len(self.lengths)
+        assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths))) 
 
     def remove_long_sequences(self):
         """
-        Sequences that are too long are splitted by chunk of max_position_embeddings.
+        Sequences that are too long are splitted by chunk of max_model_input_size.
         """
-        indices = self.lengths >= self.params.max_position_embeddings
+        max_len = self.params.max_model_input_size
+        indices = self.lengths > max_len
         logger.info(f'Splitting {sum(indices)} too long sequences.')
 
         def divide_chunks(l, n):
@@ -64,10 +72,13 @@ class Dataset:
 
         new_tok_ids = []
         new_lengths = []
-        cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
-        max_len = self.params.max_position_embeddings
+        if self.params.mlm:
+            cls_id, sep_id = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
+        else:
+            cls_id, sep_id = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
 
         for seq_, len_ in zip(self.token_ids, self.lengths):
+            assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
             if len_ <= max_len:
                 new_tok_ids.append(seq_)
                 new_lengths.append(len_)
@@ -79,6 +90,7 @@ class Dataset:
                     if sub_s[-1] != sep_id:
                         sub_s = np.insert(sub_s, len(sub_s), sep_id)
                     assert len(sub_s) <= max_len
+                    assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                     sub_seqs.append(sub_s)
 
                 new_tok_ids.extend(sub_seqs)
@@ -92,11 +104,11 @@ class Dataset:
         Too short sequences are simply removed. This could be tunedd.
         """
         init_size = len(self)
-        indices = self.lengths > 5
+        indices = self.lengths > 11
         self.token_ids = self.token_ids[indices]
         self.lengths = self.lengths[indices]
         new_size = len(self)
-        logger.info(f'Remove {init_size - new_size} too short (<=5 tokens) sequences.')
+        logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.')
 
     def print_statistics(self):
         """
@@ -113,89 +125,27 @@ class Dataset:
         # nb_unkown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unkown} unknown tokens (covering {100*nb_unkown/data_len:.2f}% of the data)')
 
-    def select_data(self, a: int, b: int):
-        """
-        Select a subportion of the data.
-        """
-        n_sequences = len(self)
-        assert 0 <= a < b <= n_sequences, ValueError(f'`0 <= a < b <= n_sequences` is not met with a={a} and b={b}')
-
-        logger.info(f'Selecting sequences from {a} to {b} (excluded).')
-        self.token_ids = self.token_ids[a:b]
-        self.lengths = self.lengths[a:b]
-
-        self.check()
-
-    def split(self):
-        """
-        Distributed training: split the data accross the processes.
-        """
-        assert self.params.n_gpu > 1
-        logger.info('Splitting the data accross the processuses.')
-        n_seq = len(self)
-        n_seq_per_procesus = n_seq // self.params.world_size
-        a = n_seq_per_procesus * self.params.global_rank
-        b = a + n_seq_per_procesus
-        self.select_data(a=a, b=b)
-
     def batch_sequences(self,
-                        token_ids: List[List[int]],
-                        lengths: List[int]):
+                        batch):
         """
         Do the padding and transform into torch.tensor.
         """
+        token_ids = [t[0] for t in batch]
+        lengths = [t[1] for t in batch]
         assert len(token_ids) == len(lengths)
 
         # Max for paddings
         max_seq_len_ = max(lengths)
 
         # Pad token ids
-        pad_idx = self.params.special_tok_ids['pad_token']
+        if self.params.mlm:
+            pad_idx = self.params.special_tok_ids['pad_token']
+        else:
+            pad_idx = self.params.special_tok_ids['unk_token']
         tk_ = [list(t.astype(int)) + [pad_idx]*(max_seq_len_-len(t)) for t in token_ids]
         assert len(tk_) == len(token_ids)
         assert all(len(t) == max_seq_len_ for t in tk_)
 
-        tk_t = torch.tensor(tk_)                  # (bs, max_seq_len_)
-        lg_t = torch.tensor(lengths.astype(int))  # (bs)
+        tk_t = torch.tensor(tk_)      # (bs, max_seq_len_)
+        lg_t = torch.tensor(lengths)  # (bs)
         return tk_t, lg_t
-
-    def get_batches_iterator(self,
-                             batches):
-        """
-        Return an iterator over batches.
-        """
-        for sequences_ids in batches:
-            token_ids, lengths = self.batch_sequences(self.token_ids[sequences_ids],
-                                                    self.lengths[sequences_ids])
-            yield (token_ids, lengths)
-
-    def get_iterator(self,
-                     seed: int = None):
-        """
-        Return a data iterator.
-        """
-        rng = np.random.RandomState(seed)
-
-        n_sequences = len(self)
-        indices = np.arange(n_sequences)
-
-        if self.group_by_size:
-            indices = indices[np.argsort(self.lengths[indices], kind='mergesort')]
-
-        if self.tokens_per_batch == -1:
-            batches = np.array_split(indices, math.ceil(len(indices) * 1. / self.batch_size))
-        else:
-            assert self.tokens_per_batch > 0
-            batch_ids = np.cumsum(self.lengths[indices]) // self.tokens_per_batch
-            _, bounds = np.unique(batch_ids, return_index=True)
-            batches = [indices[bounds[i]:bounds[i + 1]] for i in range(len(bounds) - 1)]
-            if bounds[-1] < len(indices):
-                batches.append(indices[bounds[-1]:])
-
-        if self.shuffle:
-            rng.shuffle(batches)
-
-        assert n_sequences == sum([len(x) for x in batches])
-        assert self.lengths[indices].sum() == sum([self.lengths[x].sum() for x in batches])
-
-        return self.get_batches_iterator(batches=batches)

examples/distillation/requirements.txt

@@ -2,3 +2,5 @@ gitpython==3.0.2
 tensorboard>=1.14.0
 tensorboardX==1.8
 psutil==5.6.3
+scipy==1.3.1
+transformers==2.0.0

examples/distillation/scripts/binarized_data.py

@@ -13,14 +13,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Preprocessing script before training DistilBERT.
+Preprocessing script before distillation.
 """
 import argparse
 import pickle
 import random
 import time
 import numpy as np
-from pytorch_transformers import BertTokenizer
+from transformers import BertTokenizer, RobertaTokenizer, GPT2Tokenizer
 import logging
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
@@ -32,16 +32,27 @@ def main():
     parser = argparse.ArgumentParser(description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).")
     parser.add_argument('--file_path', type=str, default='data/dump.txt',
                         help='The path to the data.')
-    parser.add_argument('--bert_tokenizer', type=str, default='bert-base-uncased',
+    parser.add_argument('--tokenizer_type', type=str, default='bert', choices=['bert', 'roberta', 'gpt2'])
+    parser.add_argument('--tokenizer_name', type=str, default='bert-base-uncased',
                         help="The tokenizer to use.")
     parser.add_argument('--dump_file', type=str, default='data/dump',
                         help='The dump file prefix.')
     args = parser.parse_args()
 
 
-    logger.info(f'Loading Tokenizer ({args.bert_tokenizer})')
-    bert_tokenizer = BertTokenizer.from_pretrained(args.bert_tokenizer)
-
+    logger.info(f'Loading Tokenizer ({args.tokenizer_name})')
+    if args.tokenizer_type == 'bert':
+        tokenizer = BertTokenizer.from_pretrained(args.tokenizer_name)
+        bos = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
+        sep = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
+    elif args.tokenizer_type == 'roberta':
+        tokenizer = RobertaTokenizer.from_pretrained(args.tokenizer_name)
+        bos = tokenizer.special_tokens_map['cls_token'] # `<s>`
+        sep = tokenizer.special_tokens_map['sep_token'] # `</s>`
+    elif args.tokenizer_type == 'gpt2':
+        tokenizer = GPT2Tokenizer.from_pretrained(args.tokenizer_name)
+        bos = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
+        sep = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`    
 
     logger.info(f'Loading text from {args.file_path}')
     with open(args.file_path, 'r', encoding='utf8') as fp:
@@ -56,8 +67,8 @@ def main():
     interval = 10000
     start = time.time()
     for text in data:
-        text = f'[CLS] {text.strip()} [SEP]'
-        token_ids = bert_tokenizer.encode(text)
+        text = f'{bos} {text.strip()} {sep}'
+        token_ids = tokenizer.encode(text)
         rslt.append(token_ids)
 
         iter += 1
@@ -69,7 +80,7 @@ def main():
     logger.info(f'{len(data)} examples processed.')
 
 
-    dp_file = f'{args.dump_file}.{args.bert_tokenizer}.pickle'
+    dp_file = f'{args.dump_file}.{args.tokenizer_name}.pickle'
     rslt_ = [np.uint16(d) for d in rslt]
     random.shuffle(rslt_)
     logger.info(f'Dump to {dp_file}')

examples/distillation/scripts/extract.py

@@ -0,0 +1,89 @@
+# coding=utf-8
+# Copyright 2019-present, the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Preprocessing script before training the distilled model.
+Specific to RoBERTa -> DistilRoBERTa and GPT2 -> DistilGPT2.
+"""
+from transformers import BertForMaskedLM, RobertaForMaskedLM, GPT2LMHeadModel
+import torch
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned Distillation")
+    parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
+    parser.add_argument("--model_name", default='roberta-large', type=str)
+    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_roberta_048131723.pth', type=str)
+    parser.add_argument("--vocab_transform", action='store_true')
+    args = parser.parse_args()
+
+
+    if args.model_type == 'roberta':
+        model = RobertaForMaskedLM.from_pretrained(args.model_name)
+        prefix = 'roberta'
+    elif args.model_type == 'gpt2':
+        model = GPT2LMHeadModel.from_pretrained(args.model_name)
+        prefix = 'transformer'
+
+    state_dict = model.state_dict()
+    compressed_sd = {}
+
+    ### Embeddings ###
+    if args.model_type == 'gpt2':
+        for param_name in ['wte.weight', 'wpe.weight']:
+            compressed_sd[f'{prefix}.{param_name}'] = state_dict[f'{prefix}.{param_name}']
+    else:
+        for w in ['word_embeddings', 'position_embeddings', 'token_type_embeddings']:
+            param_name = f'{prefix}.embeddings.{w}.weight'
+            compressed_sd[param_name] = state_dict[param_name]
+        for w in ['weight', 'bias']:
+            param_name = f'{prefix}.embeddings.LayerNorm.{w}'
+            compressed_sd[param_name] = state_dict[param_name]
+
+    ### Transformer Blocks ###
+    std_idx = 0
+    for teacher_idx in [0, 2, 4, 7, 9, 11]:
+        if args.model_type == 'gpt2':
+            for layer in ['ln_1', 'attn.c_attn', 'attn.c_proj', 'ln_2', 'mlp.c_fc', 'mlp.c_proj']:
+                for w in ['weight', 'bias']:
+                    compressed_sd[f'{prefix}.h.{std_idx}.{layer}.{w}'] = \
+                        state_dict[f'{prefix}.h.{teacher_idx}.{layer}.{w}']
+            compressed_sd[f'{prefix}.h.{std_idx}.attn.bias'] = state_dict[f'{prefix}.h.{teacher_idx}.attn.bias']
+        else:
+            for layer in ['attention.self.query', 'attention.self.key', 'attention.self.value',
+                        'attention.output.dense', 'attention.output.LayerNorm',
+                        'intermediate.dense', 'output.dense', 'output.LayerNorm']:
+                for w in ['weight', 'bias']:
+                    compressed_sd[f'{prefix}.encoder.layer.{std_idx}.{layer}.{w}'] = \
+                        state_dict[f'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}']
+        std_idx += 1
+
+    ### Language Modeling Head ###s
+    if args.model_type == 'roberta':
+        for layer in ['lm_head.decoder.weight', 'lm_head.bias']:
+            compressed_sd[f'{layer}'] = state_dict[f'{layer}']
+        if args.vocab_transform:
+            for w in ['weight', 'bias']:
+                compressed_sd[f'lm_head.dense.{w}'] = state_dict[f'lm_head.dense.{w}']
+                compressed_sd[f'lm_head.layer_norm.{w}'] = state_dict[f'lm_head.layer_norm.{w}']
+    elif args.model_type == 'gpt2':
+        for w in ['weight', 'bias']:
+            compressed_sd[f'{prefix}.ln_f.{w}'] = state_dict[f'{prefix}.ln_f.{w}']
+        compressed_sd[f'lm_head.weight'] = state_dict[f'lm_head.weight']
+
+    print(f'N layers selected for distillation: {std_idx}')
+    print(f'Number of params transfered for distillation: {len(compressed_sd.keys())}')
+
+    print(f'Save transfered checkpoint to {args.dump_checkpoint}.')
+    torch.save(compressed_sd, args.dump_checkpoint)

examples/distillation/scripts/extract_distilbert.py

@@ -14,52 +14,58 @@
 # limitations under the License.
 """
 Preprocessing script before training DistilBERT.
+Specific to BERT -> DistilBERT.
 """
-from pytorch_transformers import BertForPreTraining
+from transformers import BertForMaskedLM, RobertaForMaskedLM
 import torch
 import argparse
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForPreTraining for Transfer Learned Distillation")
-    parser.add_argument("--bert_model", default='bert-base-uncased', type=str)
-    parser.add_argument("--dump_checkpoint", default='serialization_dir/transfer_learning_checkpoint_0247911.pth', type=str)
+    parser = argparse.ArgumentParser(description="Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned Distillation")
+    parser.add_argument("--model_type", default="bert", choices=["bert"])
+    parser.add_argument("--model_name", default='bert-base-uncased', type=str)
+    parser.add_argument("--dump_checkpoint", default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
     parser.add_argument("--vocab_transform", action='store_true')
     args = parser.parse_args()
 
 
-    model = BertForPreTraining.from_pretrained(args.bert_model)
+    if args.model_type == 'bert':
+        model = BertForMaskedLM.from_pretrained(args.model_name)
+        prefix = 'bert'
+    else:
+        raise ValueError(f'args.model_type should be "bert".')
 
     state_dict = model.state_dict()
     compressed_sd = {}
 
     for w in ['word_embeddings', 'position_embeddings']:
         compressed_sd[f'distilbert.embeddings.{w}.weight'] = \
-            state_dict[f'bert.embeddings.{w}.weight']
+            state_dict[f'{prefix}.embeddings.{w}.weight']
     for w in ['weight', 'bias']:
         compressed_sd[f'distilbert.embeddings.LayerNorm.{w}'] = \
-            state_dict[f'bert.embeddings.LayerNorm.{w}']
+            state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
 
     std_idx = 0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
         for w in ['weight', 'bias']:
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.query.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}']
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.key.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}']
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.self.value.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}']
 
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}']
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}']
 
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}']
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.output.dense.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}']
             compressed_sd[f'distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}'] = \
-                state_dict[f'bert.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
+                state_dict[f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}']
         std_idx += 1
 
     compressed_sd[f'vocab_projector.weight'] = state_dict[f'cls.predictions.decoder.weight']

examples/distillation/scripts/token_counts.py

@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Preprocessing script before training DistilBERT.
+Preprocessing script before training the distilled model.
 """
 from collections import Counter
 import argparse

examples/distillation/train.py

@@ -13,7 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Training DistilBERT.
+Training the distilled model.
+Supported architectures include: BERT -> DistilBERT, RoBERTa -> DistilRoBERTa, GPT2 -> DistilGPT2.
 """
 import os
 import argparse
@@ -23,64 +24,96 @@ import shutil
 import numpy as np
 import torch
 
-from pytorch_transformers import BertTokenizer, BertForMaskedLM
-from pytorch_transformers import DistilBertForMaskedLM, DistilBertConfig
+from transformers import BertConfig, BertForMaskedLM, BertTokenizer
+from transformers import RobertaConfig, RobertaForMaskedLM, RobertaTokenizer
+from transformers import DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer
+from transformers import GPT2Config, GPT2LMHeadModel, GPT2Tokenizer
 
 from distiller import Distiller
 from utils import git_log, logger, init_gpu_params, set_seed
-from dataset import Dataset
+from lm_seqs_dataset import LmSeqsDataset
 
 
+MODEL_CLASSES = {
+    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
+    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
+    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
+    'gpt2': (GPT2Config, GPT2LMHeadModel, GPT2Tokenizer)
+}
+
+def sanity_checks(args):
+    """
+    A bunch of args sanity checks to perform even starting...
+    """
+    assert (args.mlm and args.alpha_mlm > 0.) or (not args.mlm and args.alpha_mlm == 0.)
+    assert (args.alpha_mlm > 0. and args.alpha_clm == 0.) or (args.alpha_mlm == 0. and args.alpha_clm > 0.)
+    if args.mlm:
+        assert os.path.isfile(args.token_counts)
+        assert (args.student_type in ['roberta', 'distilbert']) and (args.teacher_type in ['roberta', 'bert'])
+    else:
+        assert (args.student_type in ['gpt2']) and (args.teacher_type in ['gpt2'])
+
+    assert args.teacher_type == args.student_type or (args.student_type=='distilbert' and args.teacher_type=='bert')
+    assert os.path.isfile(args.student_config)
+    if args.student_pretrained_weights is not None:
+        assert os.path.isfile(args.student_pretrained_weights)
+
+    if args.freeze_token_type_embds: assert args.student_type in ['roberta']
+
+    assert args.alpha_ce >= 0.
+    assert args.alpha_mlm >= 0.
+    assert args.alpha_clm >= 0.
+    assert args.alpha_mse >= 0.
+    assert args.alpha_cos >= 0.
+    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.
+
+def freeze_pos_embeddings(student, args):
+    if args.student_type == 'roberta':
+        student.roberta.embeddings.position_embeddings.weight.requires_grad = False
+    elif args.student_type == 'gpt2':
+        student.transformer.wpe.weight.requires_grad = False
+
+def freeze_token_type_embeddings(student, args):
+    if args.student_type == 'roberta':
+        student.roberta.embeddings.token_type_embeddings.weight.requires_grad = False
+
 def main():
     parser = argparse.ArgumentParser(description="Training")
+    parser.add_argument("--force", action='store_true',
+                        help="Overwrite dump_path if it already exists.")
 
     parser.add_argument("--dump_path", type=str, required=True,
                         help="The output directory (log, checkpoints, parameters, etc.)")
     parser.add_argument("--data_file", type=str, required=True,
                         help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.")
-    parser.add_argument("--token_counts", type=str, required=True,
-                        help="The token counts in the data_file for MLM.")
-    parser.add_argument("--force", action='store_true',
-                        help="Overwrite dump_path if it already exists.")
 
-    parser.add_argument("--vocab_size", default=30522, type=int,
-                        help="The vocabulary size.")
-    parser.add_argument("--max_position_embeddings", default=512, type=int,
-                        help="Maximum sequence length we can model (including [CLS] and [SEP]).")
-    parser.add_argument("--sinusoidal_pos_embds", action='store_false',
-                        help="If true, the position embeddings are simply fixed with sinusoidal embeddings.")
-    parser.add_argument("--n_layers", default=6, type=int,
-                        help="Number of Transformer blocks.")
-    parser.add_argument("--n_heads", default=12, type=int,
-                        help="Number of heads in the self-attention module.")
-    parser.add_argument("--dim", default=768, type=int,
-                        help="Dimension through the network. Must be divisible by n_heads")
-    parser.add_argument("--hidden_dim", default=3072, type=int,
-                        help="Intermediate dimension in the FFN.")
-    parser.add_argument("--dropout", default=0.1, type=float,
-                        help="Dropout.")
-    parser.add_argument("--attention_dropout", default=0.1, type=float,
-                        help="Dropout in self-attention.")
-    parser.add_argument("--activation", default='gelu', type=str,
-                        help="Activation to use in self-attention")
-    parser.add_argument("--tie_weights_", action='store_false',
-                        help="If true, we tie the embeddings matrix with the projection over the vocabulary matrix. Default is true.")
-
-    parser.add_argument("--from_pretrained_weights", default=None, type=str,
+    parser.add_argument("--student_type", type=str, choices=["distilbert", "roberta", "gpt2"], required=True,
+                        help="The student type (DistilBERT, RoBERTa).")
+    parser.add_argument("--student_config", type=str, required=True,
+                        help="Path to the student configuration.")
+    parser.add_argument("--student_pretrained_weights", default=None, type=str,
                         help="Load student initialization checkpoint.")
-    parser.add_argument("--from_pretrained_config", default=None, type=str,
-                        help="Load student initialization architecture config.")
-    parser.add_argument("--bert_model", default='bert-base-uncased', type=str,
-                        help="The teacher BERT model.")
+
+    parser.add_argument("--teacher_type", choices=["bert", "roberta", "gpt2"], required=True,
+                        help="Teacher type (BERT, RoBERTa).")
+    parser.add_argument("--teacher_name", type=str, required=True,
+                        help="The teacher model.")
 
     parser.add_argument("--temperature", default=2., type=float,
                         help="Temperature for the softmax temperature.")
     parser.add_argument("--alpha_ce", default=0.5, type=float,
                         help="Linear weight for the distillation loss. Must be >=0.")
-    parser.add_argument("--alpha_mlm", default=0.5, type=float,
-                        help="Linear weight for the MLM loss. Must be >=0.")
+    parser.add_argument("--alpha_mlm", default=0.0, type=float,
+                        help="Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.")
+    parser.add_argument("--alpha_clm", default=0.5, type=float,
+                        help="Linear weight for the CLM loss. Must be >=0.")
     parser.add_argument("--alpha_mse", default=0.0, type=float,
                         help="Linear weight of the MSE loss. Must be >=0.")
+    parser.add_argument("--alpha_cos", default=0.0, type=float,
+                        help="Linear weight of the cosine embedding loss. Must be >=0.")
+
+    parser.add_argument("--mlm", action="store_true",
+                        help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.")
     parser.add_argument("--mlm_mask_prop", default=0.15, type=float,
                         help="Proportion of tokens for which we need to make a prediction.")
     parser.add_argument("--word_mask", default=0.8, type=float,
@@ -91,17 +124,20 @@ def main():
                         help="Proportion of tokens to randomly replace.")
     parser.add_argument("--mlm_smoothing", default=0.7, type=float,
                         help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).")
+    parser.add_argument("--token_counts", type=str,
+                        help="The token counts in the data_file for MLM.")
+
     parser.add_argument("--restrict_ce_to_mask", action='store_true',
                         help="If true, compute the distilation loss only the [MLM] prediction distribution.")
+    parser.add_argument("--freeze_pos_embs", action="store_true",
+                        help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.")
+    parser.add_argument("--freeze_token_type_embds", action="store_true",
+                        help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.")
 
     parser.add_argument("--n_epoch", type=int, default=3,
                         help="Number of pass on the whole dataset.")
     parser.add_argument("--batch_size", type=int, default=5,
                         help="Batch size (for each process).")
-    parser.add_argument("--tokens_per_batch", type=int, default=-1,
-                        help="If specified, modify the batches so that they have approximately this number of tokens.")
-    parser.add_argument("--shuffle", action='store_false',
-                        help="If true, shuffle the sequence order. Default is true.")
     parser.add_argument("--group_by_size", action='store_false',
                         help="If true, group sequences that have similar length into the same batch. Default is true.")
 
@@ -137,6 +173,7 @@ def main():
     parser.add_argument("--checkpoint_interval", type=int, default=4000,
                         help="Checkpoint interval.")
     args = parser.parse_args()
+    sanity_checks(args)
 
 
     ## ARGS ##
@@ -160,18 +197,19 @@ def main():
         with open(os.path.join(args.dump_path, 'parameters.json'), 'w') as f:
             json.dump(vars(args), f, indent=4)
         git_log(args.dump_path)
-    assert (args.from_pretrained_weights is None and args.from_pretrained_config is None) or \
-           (args.from_pretrained_weights is not None and args.from_pretrained_config is not None)
 
+    student_config_class, student_model_class, _ = MODEL_CLASSES[args.student_type]
+    teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[args.teacher_type]
 
     ### TOKENIZER ###
-    bert_tokenizer = BertTokenizer.from_pretrained(args.bert_model)
+    tokenizer = teacher_tokenizer_class.from_pretrained(args.teacher_name)
     special_tok_ids = {}
-    for tok_name, tok_symbol in bert_tokenizer.special_tokens_map.items():
-        idx = bert_tokenizer.all_special_tokens.index(tok_symbol)
-        special_tok_ids[tok_name] = bert_tokenizer.all_special_ids[idx]
+    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
+        idx = tokenizer.all_special_tokens.index(tok_symbol)
+        special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
     logger.info(f'Special tokens {special_tok_ids}')
     args.special_tok_ids = special_tok_ids
+    args.max_model_input_size = tokenizer.max_model_input_sizes[args.teacher_name]
 
 
     ## DATA LOADER ##
@@ -180,34 +218,34 @@ def main():
         data = pickle.load(fp)
 
 
-    assert os.path.isfile(args.token_counts)
-    logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
-    with open(args.token_counts, 'rb') as fp:
-        counts = pickle.load(fp)
-        assert len(counts) == args.vocab_size
-    token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
-    for idx in special_tok_ids.values():
-        token_probs[idx] = 0.  # do not predict special tokens
-    token_probs = torch.from_numpy(token_probs)
+    if args.mlm:
+        logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)')
+        with open(args.token_counts, 'rb') as fp:
+            counts = pickle.load(fp)
+        
+        token_probs = np.maximum(counts, 1) ** -args.mlm_smoothing
+        for idx in special_tok_ids.values():
+            token_probs[idx] = 0.  # do not predict special tokens
+        token_probs = torch.from_numpy(token_probs)
+    else:
+        token_probs = None
 
 
-    train_dataloader = Dataset(params=args, data=data)
+    train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
     logger.info(f'Data loader created.')
 
 
     ## STUDENT ##
-    if args.from_pretrained_weights is not None:
-        assert os.path.isfile(os.path.join(args.from_pretrained_weights))
-        assert os.path.isfile(os.path.join(args.from_pretrained_config))
-        logger.info(f'Loading pretrained weights from {args.from_pretrained_weights}')
-        logger.info(f'Loading pretrained config from {args.from_pretrained_config}')
-        stu_architecture_config = DistilBertConfig.from_json_file(args.from_pretrained_config)
-        student = DistilBertForMaskedLM.from_pretrained(args.from_pretrained_weights,
-                                                     config=stu_architecture_config)
+    logger.info(f'Loading student config from {args.student_config}')
+    stu_architecture_config = student_config_class.from_pretrained(args.student_config)
+    stu_architecture_config.output_hidden_states = True
+
+    if args.student_pretrained_weights is not None:
+        logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}')
+        student = student_model_class.from_pretrained(args.student_pretrained_weights,
+                                                      config=stu_architecture_config)
     else:
-        args.vocab_size_or_config_json_file = args.vocab_size
-        stu_architecture_config = DistilBertConfig(**vars(args))
-        student = DistilBertForMaskedLM(stu_architecture_config)
+        student = student_model_class(stu_architecture_config)
 
 
     if args.n_gpu > 0:
@@ -216,15 +254,31 @@ def main():
 
 
     ## TEACHER ##
-    teacher = BertForMaskedLM.from_pretrained(args.bert_model)
+    teacher = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=True)
     if args.n_gpu > 0:
         teacher.to(f'cuda:{args.local_rank}')
-    logger.info(f'Teacher loaded from {args.bert_model}.')
+    logger.info(f'Teacher loaded from {args.teacher_name}.')
+
+
+    ## FREEZING ##
+    if args.freeze_pos_embs:
+        freeze_pos_embeddings(student, args)
+    if args.freeze_token_type_embds:
+        freeze_token_type_embeddings(student, args)
+
+
+    ## SANITY CHECKS ##
+    assert student.config.vocab_size == teacher.config.vocab_size
+    assert student.config.hidden_size == teacher.config.hidden_size
+    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
+    if args.mlm:
+        assert token_probs.size(0) == stu_architecture_config.vocab_size
+
 
     ## DISTILLER ##
     torch.cuda.empty_cache()
     distiller = Distiller(params=args,
-                          dataloader=train_dataloader,
+                          dataset=train_lm_seq_dataset,
                           token_probs=token_probs,
                           student=student,
                           teacher=teacher)

examples/distillation/training_configs/distilbert-base-uncased.json

@@ -0,0 +1,15 @@
+{
+	"activation": "gelu",
+	"attention_dropout": 0.1,
+	"dim": 768,
+	"dropout": 0.1,
+	"hidden_dim": 3072,
+	"initializer_range": 0.02,
+	"max_position_embeddings": 512,
+	"n_heads": 12,
+	"n_layers": 6,
+	"sinusoidal_pos_embds": true,
+	"tie_weights_": true,
+	"vocab_size": 30522
+  }
+  

examples/distillation/training_configs/distilgpt2.json

@@ -0,0 +1,10 @@
+{
+	"initializer_range": 0.02,
+	"layer_norm_epsilon": 0.00001,
+	"n_ctx": 1024,
+	"n_embd": 768,
+	"n_head": 12,
+	"n_layer": 6,
+	"n_positions": 1024,
+	"vocab_size": 50257
+}

examples/run_bertology.py

@@ -32,7 +32,7 @@ from torch.utils.data import DataLoader, SequentialSampler, TensorDataset, Subse
 from torch.utils.data.distributed import DistributedSampler
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from pytorch_transformers import (WEIGHTS_NAME,
+from transformers import (WEIGHTS_NAME,
                                   BertConfig, BertForSequenceClassification, BertTokenizer,
                                   XLMConfig, XLMForSequenceClassification, XLMTokenizer,
                                   XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer)

examples/run_generation.py

@@ -26,12 +26,12 @@ import torch
 import torch.nn.functional as F
 import numpy as np
 
-from pytorch_transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
+from transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
 
-from pytorch_transformers import GPT2LMHeadModel, GPT2Tokenizer
-from pytorch_transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
-from pytorch_transformers import XLNetLMHeadModel, XLNetTokenizer
-from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
+from transformers import XLNetLMHeadModel, XLNetTokenizer
+from transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
 
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',

examples/run_glue.py

@@ -31,7 +31,7 @@ from torch.utils.data.distributed import DistributedSampler
 from tensorboardX import SummaryWriter
 from tqdm import tqdm, trange
 
-from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
+from transformers import (WEIGHTS_NAME, BertConfig,
                                   BertForSequenceClassification, BertTokenizer,
                                   RobertaConfig,
                                   RobertaForSequenceClassification,
@@ -44,12 +44,12 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
                                   DistilBertForSequenceClassification,
                                   DistilBertTokenizer)
 
-from pytorch_transformers import AdamW, WarmupLinearSchedule
+from transformers import AdamW, WarmupLinearSchedule
 
-from pytorch_transformers import glue_compute_metrics as compute_metrics
-from pytorch_transformers import glue_output_modes as output_modes
-from pytorch_transformers import glue_processors as processors
-from pytorch_transformers import glue_convert_examples_to_features as convert_examples_to_features
+from transformers import glue_compute_metrics as compute_metrics
+from transformers import glue_output_modes as output_modes
+from transformers import glue_processors as processors
+from transformers import glue_convert_examples_to_features as convert_examples_to_features
 
 logger = logging.getLogger(__name__)
 
@@ -134,10 +134,11 @@ def train(args, train_dataset, model, tokenizer):
             batch = tuple(t.to(args.device) for t in batch)
             inputs = {'input_ids':      batch[0],
                       'attention_mask': batch[1],
-                      'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM, DistilBERT and RoBERTa don't use segment_ids
                       'labels':         batch[3]}
+            if args.model_type != 'distilbert':
+                inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None  # XLM, DistilBERT and RoBERTa don't use segment_ids
             outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
+            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
                 loss = loss.mean() # mean() to average on multi-gpu parallel training
@@ -154,8 +155,8 @@ def train(args, train_dataset, model, tokenizer):
 
             tr_loss += loss.item()
             if (step + 1) % args.gradient_accumulation_steps == 0:
-                scheduler.step()  # Update learning rate schedule
                 optimizer.step()
+                scheduler.step()  # Update learning rate schedule
                 model.zero_grad()
                 global_step += 1
 
@@ -224,8 +225,9 @@ def evaluate(args, model, tokenizer, prefix=""):
             with torch.no_grad():
                 inputs = {'input_ids':      batch[0],
                           'attention_mask': batch[1],
-                          'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM, DistilBERT and RoBERTa don't use segment_ids
                           'labels':         batch[3]}
+                if args.model_type != 'distilbert':
+                    inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None  # XLM, DistilBERT and RoBERTa don't use segment_ids
                 outputs = model(**inputs)
                 tmp_eval_loss, logits = outputs[:2]
 
@@ -483,7 +485,7 @@ def main():
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
         for checkpoint in checkpoints:
             global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""

examples/run_lm_finetuning.py

@@ -35,7 +35,7 @@ from torch.utils.data.distributed import DistributedSampler
 from tensorboardX import SummaryWriter
 from tqdm import tqdm, trange
 
-from pytorch_transformers import (WEIGHTS_NAME, AdamW, WarmupLinearSchedule,
+from transformers import (WEIGHTS_NAME, AdamW, WarmupLinearSchedule,
                                   BertConfig, BertForMaskedLM, BertTokenizer,
                                   GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
                                   OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
@@ -59,7 +59,7 @@ class TextDataset(Dataset):
     def __init__(self, tokenizer, file_path='train', block_size=512):
         assert os.path.isfile(file_path)
         directory, filename = os.path.split(file_path)
-        cached_features_file = os.path.join(directory, f'cached_lm_{block_size}_{filename}')
+        cached_features_file = os.path.join(directory, 'cached_lm_{}_{}'.format(block_size, filename))
 
         if os.path.exists(cached_features_file):
             logger.info("Loading features from cached file %s", cached_features_file)
@@ -74,9 +74,8 @@ class TextDataset(Dataset):
 
             tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
 
-            while len(tokenized_text) >= block_size:  # Truncate in block of block_size
-                self.examples.append(tokenizer.add_special_tokens_single_sequence(tokenized_text[:block_size]))
-                tokenized_text = tokenized_text[block_size:]
+            for i in range(0, len(tokenized_text)-block_size+1, block_size): # Truncate in block of block_size
+                self.examples.append(tokenizer.add_special_tokens_single_sequence(tokenized_text[i:i+block_size]))
             # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
             # If your dataset is small, first you should loook for a bigger one :-) and second you
             # can change this behavior by adding (model specific) padding.
@@ -188,7 +187,7 @@ def train(args, train_dataset, model, tokenizer):
             labels = labels.to(args.device)
             model.train()
             outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
-            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
+            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
                 loss = loss.mean()  # mean() to average on multi-gpu parallel training
@@ -481,7 +480,7 @@ def main():
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
         for checkpoint in checkpoints:
             global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""

examples/run_multiple_choice.py

@@ -32,13 +32,13 @@ from torch.utils.data.distributed import DistributedSampler
 from tensorboardX import SummaryWriter
 from tqdm import tqdm, trange
 
-from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
+from transformers import (WEIGHTS_NAME, BertConfig,
                                   BertForMultipleChoice, BertTokenizer,
                                   XLNetConfig, XLNetForMultipleChoice,
                                   XLNetTokenizer, RobertaConfig,
                                   RobertaForMultipleChoice, RobertaTokenizer)
 
-from pytorch_transformers import AdamW, WarmupLinearSchedule
+from transformers import AdamW, WarmupLinearSchedule
 
 from utils_multiple_choice import (convert_examples_to_features, processors)
 
@@ -141,7 +141,7 @@ def train(args, train_dataset, model, tokenizer):
                       'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,  # XLM don't use segment_ids
                       'labels':         batch[3]}
             outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
+            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
                 loss = loss.mean() # mean() to average on multi-gpu parallel training
@@ -508,7 +508,7 @@ def main():
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
         for checkpoint in checkpoints:
             global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
@@ -524,7 +524,7 @@ def main():
         checkpoints = [args.output_dir]
         # if args.eval_all_checkpoints: # can not use this to do test!!
         #     checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-        #     logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
+        #     logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
         for checkpoint in checkpoints:
             global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""

examples/run_squad.py

@@ -32,7 +32,7 @@ from tqdm import tqdm, trange
 
 from tensorboardX import SummaryWriter
 
-from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
+from transformers import (WEIGHTS_NAME, BertConfig,
                                   BertForQuestionAnswering, BertTokenizer,
                                   XLMConfig, XLMForQuestionAnswering,
                                   XLMTokenizer, XLNetConfig,
@@ -40,7 +40,7 @@ from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
                                   XLNetTokenizer,
                                   DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
 
-from pytorch_transformers import AdamW, WarmupLinearSchedule
+from transformers import AdamW, WarmupLinearSchedule
 
 from utils_squad import (read_squad_examples, convert_examples_to_features,
                          RawResult, write_predictions,
@@ -142,7 +142,7 @@ def train(args, train_dataset, model, tokenizer):
                 inputs.update({'cls_index': batch[5],
                                'p_mask':       batch[6]})
             outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in pytorch-transformers (see doc)
+            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
             if args.n_gpu > 1:
                 loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
@@ -510,7 +510,7 @@ def main():
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
-            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
+            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
 
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
 

examples/run_tf_glue.py

@@ -0,0 +1,40 @@
+import tensorflow as tf
+import tensorflow_datasets
+from transformers import BertTokenizer, TFBertForSequenceClassification, glue_convert_examples_to_features, BertForSequenceClassification
+
+# Load dataset, tokenizer, model from pretrained model/vocabulary
+tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
+data = tensorflow_datasets.load('glue/mrpc')
+
+# Prepare dataset for GLUE as a tf.data.Dataset instance
+train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, 128, 'mrpc')
+valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, 128, 'mrpc')
+train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
+valid_dataset = valid_dataset.batch(64)
+
+# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule 
+optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
+loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
+model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
+
+# Train and evaluate using tf.keras.Model.fit()
+history = model.fit(train_dataset, epochs=2, steps_per_epoch=115,
+                    validation_data=valid_dataset, validation_steps=7)
+
+# Load the TensorFlow model in PyTorch for inspection
+model.save_pretrained('./save/')
+pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
+
+# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
+sentence_0 = "This research was consistent with his findings."
+sentence_1 = "His findings were compatible with this research."
+sentence_2 = "His findings were not compatible with this research."
+inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
+inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
+
+pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
+pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
+print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
+print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sentence_0")

examples/utils_squad.py

@@ -24,7 +24,7 @@ import math
 import collections
 from io import open
 
-from pytorch_transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
+from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
 
 # Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
 from utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get_raw_scores

hubconf.py

@@ -1,7 +1,7 @@
-from pytorch_transformers import (
+from transformers import (
     AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering
 )
-from pytorch_transformers.file_utils import add_start_docstrings
+from transformers.file_utils import add_start_docstrings
 
 dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses']
 
@@ -11,12 +11,12 @@ def config(*args, **kwargs):
                 # Using torch.hub !
                 import torch
 
-                config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased')  # Download configuration from S3 and cache.
-                config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
-                config = torch.hub.load('huggingface/pytorch-transformers', 'config', './test/bert_saved_model/my_configuration.json')
-                config = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
+                config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased')  # Download configuration from S3 and cache.
+                config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
+                config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json')
+                config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
                 assert config.output_attention == True
-                config, unused_kwargs = torch.hub.load('huggingface/pytorch-transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
+                config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
                 assert config.output_attention == True
                 assert unused_kwargs == {'foo': False}
 
@@ -31,8 +31,8 @@ def tokenizer(*args, **kwargs):
         # Using torch.hub !
         import torch
 
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from S3 and cache.
-        tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', './test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
+        tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased')    # Download vocabulary from S3 and cache.
+        tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', './test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
 
     """
 
@@ -45,13 +45,13 @@ def model(*args, **kwargs):
             # Using torch.hub !
             import torch
 
-            model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
-            model = torch.hub.load('huggingface/pytorch-transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
             assert model.config.output_attention == True
             # Loading from a TF checkpoint file instead of a PyTorch model (slower)
             config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
-            model = torch.hub.load('huggingface/pytorch-transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+            model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
 
@@ -63,13 +63,13 @@ def modelWithLMHead(*args, **kwargs):
         # Using torch.hub !
         import torch
 
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
         assert model.config.output_attention == True
         # Loading from a TF checkpoint file instead of a PyTorch model (slower)
         config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
     """
     return AutoModelWithLMHead.from_pretrained(*args, **kwargs)
@@ -81,13 +81,13 @@ def modelForSequenceClassification(*args, **kwargs):
             # Using torch.hub !
             import torch
 
-            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
-            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
             assert model.config.output_attention == True
             # Loading from a TF checkpoint file instead of a PyTorch model (slower)
             config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
-            model = torch.hub.load('huggingface/pytorch-transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+            model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
 
@@ -100,13 +100,13 @@ def modelForQuestionAnswering(*args, **kwargs):
         # Using torch.hub !
         import torch
 
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
+        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
+        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
         assert model.config.output_attention == True
         # Loading from a TF checkpoint file instead of a PyTorch model (slower)
         config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
-        model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
     """
     return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)

setup.py

@@ -13,11 +13,11 @@ To create the package for pypi.
 4. Build both the sources and the wheel. Do not change anything in setup.py between
    creating the wheel and the source distribution (obviously).
 
-   For the wheel, run: "python setup.py bdist_wheel" in the top level allennlp directory.
+   For the wheel, run: "python setup.py bdist_wheel" in the top level directory.
    (this will build a wheel for the python version you use to build it - make sure you use python 3.x).
 
    For the sources, run: "python setup.py sdist"
-   You should now have a /dist directory with both .whl and .tar.gz source versions of allennlp.
+   You should now have a /dist directory with both .whl and .tar.gz source versions.
 
 5. Check that everything looks correct by uploading the package to the pypi test server:
 
@@ -25,7 +25,7 @@ To create the package for pypi.
    (pypi suggest using twine as other methods upload files via plaintext.)
 
    Check that you can install it in a virtualenv by running:
-   pip install -i https://testpypi.python.org/pypi pytorch-transformers
+   pip install -i https://testpypi.python.org/pypi transformers
 
 6. Upload the final version to actual pypi:
    twine upload dist/* -r pypi
@@ -37,16 +37,16 @@ from io import open
 from setuptools import find_packages, setup
 
 setup(
-    name="pytorch_transformers",
-    version="1.2.0",
-    author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors",
+    name="transformers",
+    version="2.0.0",
+    author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
     author_email="thomas@huggingface.co",
-    description="Repository of pre-trained NLP Transformer models: BERT & RoBERTa, GPT & GPT-2, Transformer-XL, XLNet and XLM",
+    description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
     long_description=open("README.md", "r", encoding='utf-8').read(),
     long_description_content_type="text/markdown",
-    keywords='NLP deep learning transformer pytorch BERT GPT GPT-2 google openai CMU',
+    keywords='NLP deep learning transformer pytorch tensorflow BERT GPT GPT-2 google openai CMU',
     license='Apache',
-    url="https://github.com/huggingface/pytorch-transformers",
+    url="https://github.com/huggingface/transformers",
     packages=find_packages(exclude=["*.tests", "*.tests.*",
                                     "tests.*", "tests"]),
     install_requires=['numpy',
@@ -58,7 +58,7 @@ setup(
                       'sacremoses'],
     entry_points={
       'console_scripts': [
-        "pytorch_transformers=pytorch_transformers.__main__:main",
+        "transformers=transformers.__main__:main",
       ]
     },
     # python_requires='>=3.5.0',

transformers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.2.0"
+__version__ = "2.0.0"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -16,7 +16,21 @@ import logging
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
-# Tokenizer
+# Files and general utilities
+from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
+                         cached_path, add_start_docstrings, add_end_docstrings,
+                         WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
+                         is_tf_available, is_torch_available)
+
+from .data import (is_sklearn_available,
+                   InputExample, InputFeatures, DataProcessor,
+                   glue_output_modes, glue_convert_examples_to_features,
+                   glue_processors, glue_tasks_num_labels)
+
+if is_sklearn_available():
+    from .data import glue_compute_metrics
+
+# Tokenizers
 from .tokenization_utils import (PreTrainedTokenizer)
 from .tokenization_auto import AutoTokenizer
 from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
@@ -41,15 +55,7 @@ from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCH
 from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 
 # Modeling
-try:
-    import torch
-    _torch_available = True  # pylint: disable=invalid-name
-except ImportError:
-    _torch_available = False  # pylint: disable=invalid-name
-
-if _torch_available:
-    logger.info("PyTorch version {} available.".format(torch.__version__))
-
+if is_torch_available():
     from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
     from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
                                 AutoModelWithLMHead)
@@ -68,14 +74,15 @@ if _torch_available:
                                 GPT2LMHeadModel, GPT2DoubleHeadsModel,
                                 load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
-                                XLNetForSequenceClassification, XLNetForQuestionAnsweringSimple,
-                                XLNetForQuestionAnswering,
+                                XLNetForSequenceClassification, XLNetForMultipleChoice,
+                                XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
                                 load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
                             XLMWithLMHeadModel, XLMForSequenceClassification,
                             XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
                             XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
-    from .modeling_roberta import (RobertaForMaskedLM, RobertaModel, RobertaForSequenceClassification,
+    from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
+                                RobertaForSequenceClassification, RobertaForMultipleChoice,
                                 ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
                                 DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
@@ -87,16 +94,7 @@ if _torch_available:
 
 
 # TensorFlow
-try:
-    import tensorflow as tf
-    assert int(tf.__version__[0]) >= 2
-    _tf_available = True  # pylint: disable=invalid-name
-except (ImportError, AssertionError):
-    _tf_available = False  # pylint: disable=invalid-name
-
-if _tf_available:
-    logger.info("TensorFlow version {} available.".format(tf.__version__))
-
+if is_tf_available():
     from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
     from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
                                    TFAutoModelWithLMHead)
@@ -151,7 +149,8 @@ if _tf_available:
                                          load_distilbert_pt_weights_in_tf2,
                                          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
-if _tf_available and _torch_available:
+# TF 2.0 <=> PyTorch conversion utilities
+if is_tf_available() and is_torch_available():
     from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
                                             load_pytorch_checkpoint_in_tf2_model,
                                             load_pytorch_weights_in_tf2_model,
@@ -160,16 +159,7 @@ if _tf_available and _torch_available:
                                             load_tf2_weights_in_pytorch_model,
                                             load_tf2_model_in_pytorch_model)
 
-# Files and general utilities
-from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
-                         cached_path, add_start_docstrings, add_end_docstrings,
-                         WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
-                         is_tf_available, is_torch_available)
-
-from .data import (is_sklearn_available,
-                   InputExample, InputFeatures, DataProcessor,
-                   glue_output_modes, glue_convert_examples_to_features,
-                   glue_processors, glue_tasks_num_labels)
-
-if is_sklearn_available():
-    from .data import glue_compute_metrics
+if not is_tf_available() and not is_torch_available():
+    logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
+                   "Models won't be available and only tokenizers, configuration"
+                   "and file/data utilities can be used.")

transformers/__main__.py

@@ -5,25 +5,25 @@ def main():
         print(
         "This command line utility let you convert original (author released) model checkpoint to pytorch.\n"
         "It 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] or \n"
-        ">> pytorch_transformers xlm XLM_CHECKPOINT_PATH PYTORCH_DUMP_OUTPUT")
+        ">> transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT, \n"
+        ">> transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG], \n"
+        ">> transformers transfo_xl TF_CHECKPOINT_OR_DATASET PYTORCH_DUMP_OUTPUT [TF_CONFIG] or \n"
+        ">> transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [GPT2_CONFIG] or \n"
+        ">> transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME] or \n"
+        ">> transformers xlm XLM_CHECKPOINT_PATH PYTORCH_DUMP_OUTPUT")
     else:
         if sys.argv[1] == "bert":
             try:
                 from .convert_bert_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
             except ImportError:
-                print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
+                print("transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
                     "In that case, it requires TensorFlow to be installed. Please see "
                     "https://www.tensorflow.org/install/ for installation instructions.")
                 raise
 
             if len(sys.argv) != 5:
                 # pylint: disable=line-too-long
-                print("Should be used as `pytorch_transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`")
+                print("Should be used as `transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`")
             else:
                 PYTORCH_DUMP_OUTPUT = sys.argv.pop()
                 TF_CONFIG = sys.argv.pop()
@@ -33,7 +33,7 @@ def main():
             from .convert_openai_original_tf_checkpoint_to_pytorch import convert_openai_checkpoint_to_pytorch
             if len(sys.argv) < 4 or len(sys.argv) > 5:
                 # pylint: disable=line-too-long
-                print("Should be used as `pytorch_transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`")
+                print("Should be used as `transformers gpt OPENAI_GPT_CHECKPOINT_FOLDER_PATH PYTORCH_DUMP_OUTPUT [OPENAI_GPT_CONFIG]`")
             else:
                 OPENAI_GPT_CHECKPOINT_FOLDER_PATH = sys.argv[2]
                 PYTORCH_DUMP_OUTPUT = sys.argv[3]
@@ -48,13 +48,13 @@ def main():
             try:
                 from .convert_transfo_xl_original_tf_checkpoint_to_pytorch import convert_transfo_xl_checkpoint_to_pytorch
             except ImportError:
-                print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
+                print("transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
                     "In that case, it requires TensorFlow to be installed. Please see "
                     "https://www.tensorflow.org/install/ for installation instructions.")
                 raise
             if len(sys.argv) < 4 or len(sys.argv) > 5:
                 # pylint: disable=line-too-long
-                print("Should be used as `pytorch_transformers transfo_xl TF_CHECKPOINT/TF_DATASET_FILE PYTORCH_DUMP_OUTPUT [TF_CONFIG]`")
+                print("Should be used as `transformers transfo_xl TF_CHECKPOINT/TF_DATASET_FILE PYTORCH_DUMP_OUTPUT [TF_CONFIG]`")
             else:
                 if 'ckpt' in sys.argv[2].lower():
                     TF_CHECKPOINT = sys.argv[2]
@@ -72,14 +72,14 @@ def main():
             try:
                 from .convert_gpt2_original_tf_checkpoint_to_pytorch import convert_gpt2_checkpoint_to_pytorch
             except ImportError:
-                print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
+                print("transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
                     "In that case, it requires TensorFlow to be installed. Please see "
                     "https://www.tensorflow.org/install/ for installation instructions.")
                 raise
 
             if len(sys.argv) < 4 or len(sys.argv) > 5:
                 # pylint: disable=line-too-long
-                print("Should be used as `pytorch_transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [TF_CONFIG]`")
+                print("Should be used as `transformers gpt2 TF_CHECKPOINT PYTORCH_DUMP_OUTPUT [TF_CONFIG]`")
             else:
                 TF_CHECKPOINT = sys.argv[2]
                 PYTORCH_DUMP_OUTPUT = sys.argv[3]
@@ -92,14 +92,14 @@ def main():
             try:
                 from .convert_xlnet_original_tf_checkpoint_to_pytorch import convert_xlnet_checkpoint_to_pytorch
             except ImportError:
-                print("pytorch_transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
+                print("transformers can only be used from the commandline to convert TensorFlow models in PyTorch, "
                     "In that case, it requires TensorFlow to be installed. Please see "
                     "https://www.tensorflow.org/install/ for installation instructions.")
                 raise
 
             if len(sys.argv) < 5 or len(sys.argv) > 6:
                 # pylint: disable=line-too-long
-                print("Should be used as `pytorch_transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`")
+                print("Should be used as `transformers xlnet TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT [FINETUNING_TASK_NAME]`")
             else:
                 TF_CHECKPOINT = sys.argv[2]
                 TF_CONFIG = sys.argv[3]
@@ -118,7 +118,7 @@ def main():
 
             if len(sys.argv) != 4:
                 # pylint: disable=line-too-long
-                print("Should be used as `pytorch_transformers xlm XLM_CHECKPOINT_PATH PYTORCH_DUMP_OUTPUT`")
+                print("Should be used as `transformers xlm XLM_CHECKPOINT_PATH PYTORCH_DUMP_OUTPUT`")
             else:
                 XLM_CHECKPOINT_PATH = sys.argv[2]
                 PYTORCH_DUMP_OUTPUT = sys.argv[3]

transformers/configuration_auto.py

@@ -31,7 +31,7 @@ logger = logging.getLogger(__name__)
 
 
 class AutoConfig(object):
-    r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
+    r""":class:`~transformers.AutoConfig` is a generic configuration class
         that will be instantiated as one of the configuration classes of the library
         when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -76,7 +76,7 @@ class AutoConfig(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
                 - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
 
             cache_dir: (`optional`) string:

transformers/configuration_bert.py

@@ -45,7 +45,7 @@ BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class BertConfig(PretrainedConfig):
     r"""
-        :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
+        :class:`~transformers.BertConfig` is the configuration class to store the configuration of a
         `BertModel`.
 
 

transformers/configuration_gpt2.py

@@ -28,7 +28,8 @@ logger = logging.getLogger(__name__)
 
 GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
                                       "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
-                                      "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json"}
+                                      "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json",
+                                      "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-config.json",}
 
 class GPT2Config(PretrainedConfig):
     """Configuration class to store the configuration of a `GPT2Model`.

transformers/configuration_openai.py

@@ -36,7 +36,6 @@ class OpenAIGPTConfig(PretrainedConfig):
 
     Args:
         vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `OpenAIGPTModel` or a configuration json file.
-        n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...)
         n_positions: Number of positional embeddings.
         n_ctx: Size of the causal mask (usually same as n_positions).
         n_embd: Dimensionality of the embeddings and hidden states.

transformers/configuration_utils.py

@@ -59,7 +59,7 @@ class PretrainedConfig(object):
 
     def save_pretrained(self, save_directory):
         """ Save a configuration object to the directory `save_directory`, so that it
-            can be re-loaded using the :func:`~pytorch_transformers.PretrainedConfig.from_pretrained` class method.
+            can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
         """
         assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
 
@@ -67,16 +67,17 @@ class PretrainedConfig(object):
         output_config_file = os.path.join(save_directory, CONFIG_NAME)
 
         self.to_json_file(output_config_file)
+        logger.info("Configuration saved in {}".format(output_config_file))
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        r""" Instantiate a :class:`~pytorch_transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
+        r""" Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
 
         Parameters:
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing a configuration file saved using the :func:`~pytorch_transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
                 - a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
 
             cache_dir: (`optional`) string:

transformers/convert_bert_original_tf_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 import argparse
 import torch
 
-from pytorch_transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
+from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
 
 import logging
 logging.basicConfig(level=logging.INFO)

transformers/convert_bert_pytorch_checkpoint_to_original_tf.py

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

transformers/convert_gpt2_original_tf_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from io import open
 
 import torch
 
-from pytorch_transformers import (CONFIG_NAME, WEIGHTS_NAME,
+from transformers import (CONFIG_NAME, WEIGHTS_NAME,
                                                      GPT2Config,
                                                      GPT2Model,
                                                      load_tf_weights_in_gpt2)

transformers/convert_openai_original_tf_checkpoint_to_pytorch.py

@@ -21,7 +21,7 @@ from io import open
 
 import torch
 
-from pytorch_transformers import (CONFIG_NAME, WEIGHTS_NAME,
+from transformers import (CONFIG_NAME, WEIGHTS_NAME,
                                                      OpenAIGPTConfig,
                                                      OpenAIGPTModel,
                                                      load_tf_weights_in_openai_gpt)

transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -22,9 +22,9 @@ import os
 import argparse
 import tensorflow as tf
 
-from pytorch_transformers import is_torch_available, cached_path
+from transformers import is_torch_available, cached_path
 
-from pytorch_transformers import (BertConfig, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, load_bert_pt_weights_in_tf2, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+from transformers import (BertConfig, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, load_bert_pt_weights_in_tf2, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
                                   GPT2Config, TFGPT2LMHeadModel, load_gpt2_pt_weights_in_tf2, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
                                   XLNetConfig, TFXLNetLMHeadModel, load_xlnet_pt_weights_in_tf2, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
                                   XLMConfig, TFXLMWithLMHeadModel, load_xlm_pt_weights_in_tf2, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -36,7 +36,7 @@ from pytorch_transformers import (BertConfig, TFBertForPreTraining, TFBertForQue
 if is_torch_available():
     import torch
     import numpy as np
-    from pytorch_transformers import (BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    from transformers import (BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
                                       GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
                                       XLNetLMHeadModel, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
                                       XLMWithLMHeadModel, XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
@@ -228,6 +228,7 @@ if __name__ == "__main__":
     convert_all_pt_checkpoints_to_tf(args.model_type.lower() if args.model_type is not None else None,
                                         args.tf_dump_path,
                                         model_shortcut_names_or_path=[args.pytorch_checkpoint_path] if args.pytorch_checkpoint_path is not None else None,
+                                        config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
                                         compare_with_pt_model=args.compare_with_pt_model,
                                         use_cached_models=args.use_cached_models,
                                         only_convert_finetuned_models=args.only_convert_finetuned_models)

transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -23,12 +23,12 @@ import torch
 
 from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
-from pytorch_transformers import (BertConfig, BertEncoder,
+from transformers import (BertConfig, BertEncoder,
                                                 BertIntermediate, BertLayer,
                                                 BertModel, BertOutput,
                                                 BertSelfAttention,
                                                 BertSelfOutput)
-from pytorch_transformers import (RobertaEmbeddings,
+from transformers import (RobertaEmbeddings,
                                                    RobertaForMaskedLM,
                                                    RobertaForSequenceClassification,
                                                    RobertaModel)

transformers/convert_transfo_xl_original_tf_checkpoint_to_pytorch.py

@@ -23,12 +23,12 @@ from io import open
 
 import torch
 
-import pytorch_transformers.tokenization_transfo_xl as data_utils
+import transformers.tokenization_transfo_xl as data_utils
 
-from pytorch_transformers import CONFIG_NAME, WEIGHTS_NAME
-from pytorch_transformers import (TransfoXLConfig, TransfoXLLMHeadModel,
+from transformers import CONFIG_NAME, WEIGHTS_NAME
+from transformers import (TransfoXLConfig, TransfoXLLMHeadModel,
                                                       load_tf_weights_in_transfo_xl)
-from pytorch_transformers.tokenization_transfo_xl import (CORPUS_NAME, VOCAB_FILES_NAMES)
+from transformers.tokenization_transfo_xl import (CORPUS_NAME, VOCAB_FILES_NAMES)
 
 if sys.version_info[0] == 2:
     import cPickle as pickle

transformers/convert_xlm_original_pytorch_checkpoint_to_pytorch.py

@@ -23,8 +23,8 @@ from io import open
 import torch
 import numpy
 
-from pytorch_transformers import CONFIG_NAME, WEIGHTS_NAME
-from pytorch_transformers.tokenization_xlm import VOCAB_FILES_NAMES
+from transformers import CONFIG_NAME, WEIGHTS_NAME
+from transformers.tokenization_xlm import VOCAB_FILES_NAMES
 
 import logging
 logging.basicConfig(level=logging.INFO)

transformers/convert_xlnet_original_tf_checkpoint_to_pytorch.py

@@ -22,7 +22,7 @@ import os
 import argparse
 import torch
 
-from pytorch_transformers import (CONFIG_NAME, WEIGHTS_NAME,
+from transformers import (CONFIG_NAME, WEIGHTS_NAME,
                                                     XLNetConfig,
                                                     XLNetLMHeadModel, XLNetForQuestionAnswering,
                                                     XLNetForSequenceClassification,

transformers/data/processors/glue.py

@@ -26,6 +26,7 @@ if is_tf_available():
 
 logger = logging.getLogger(__name__)
 
+
 def glue_convert_examples_to_features(examples, tokenizer,
                                       max_length=512,
                                       task=None,
@@ -36,7 +37,27 @@ def glue_convert_examples_to_features(examples, tokenizer,
                                       pad_token_segment_id=0,
                                       mask_padding_with_zero=True):
     """
-    Loads a data file into a list of `InputBatch`s
+    Loads a data file into a list of ``InputFeatures``
+
+    Args:
+        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
+        tokenizer: Instance of a tokenizer that will tokenize the examples
+        max_length: Maximum example length
+        task: GLUE task
+        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
+        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
+        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
+        pad_token: Padding token
+        pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
+        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
+            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
+            actual values)
+
+    Returns:
+        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
+        containing the task-specific features. If the input is a list of ``InputExamples``, will return
+        a list of task-specific ``InputFeatures`` which can be fed to the model.
+
     """
     is_tf_dataset = False
     if is_tf_available() and isinstance(examples, tf.data.Dataset):
@@ -58,10 +79,7 @@ def glue_convert_examples_to_features(examples, tokenizer,
         if ex_index % 10000 == 0:
             logger.info("Writing example %d" % (ex_index))
         if is_tf_dataset:
-            example = InputExample(example['idx'].numpy(),
-                                   example['sentence1'].numpy().decode('utf-8'),
-                                   example['sentence2'].numpy().decode('utf-8'),
-                                   str(example['label'].numpy()))
+            example = processor.get_example_from_tensor_dict(example)
 
         inputs = tokenizer.encode_plus(
             example.text_a,
@@ -136,6 +154,13 @@ def glue_convert_examples_to_features(examples, tokenizer,
 class MrpcProcessor(DataProcessor):
     """Processor for the MRPC data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['sentence1'].numpy().decode('utf-8'),
+                            tensor_dict['sentence2'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
@@ -169,6 +194,13 @@ class MrpcProcessor(DataProcessor):
 class MnliProcessor(DataProcessor):
     """Processor for the MultiNLI data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['premise'].numpy().decode('utf-8'),
+                            tensor_dict['hypothesis'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -212,6 +244,13 @@ class MnliMismatchedProcessor(MnliProcessor):
 class ColaProcessor(DataProcessor):
     """Processor for the CoLA data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['sentence'].numpy().decode('utf-8'),
+                            None,
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -241,6 +280,13 @@ class ColaProcessor(DataProcessor):
 class Sst2Processor(DataProcessor):
     """Processor for the SST-2 data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['sentence'].numpy().decode('utf-8'),
+                            None,
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -272,6 +318,13 @@ class Sst2Processor(DataProcessor):
 class StsbProcessor(DataProcessor):
     """Processor for the STS-B data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['sentence1'].numpy().decode('utf-8'),
+                            tensor_dict['sentence2'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -304,6 +357,13 @@ class StsbProcessor(DataProcessor):
 class QqpProcessor(DataProcessor):
     """Processor for the QQP data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['question1'].numpy().decode('utf-8'),
+                            tensor_dict['question2'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -339,6 +399,13 @@ class QqpProcessor(DataProcessor):
 class QnliProcessor(DataProcessor):
     """Processor for the QNLI data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['question'].numpy().decode('utf-8'),
+                            tensor_dict['sentence'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -372,6 +439,13 @@ class QnliProcessor(DataProcessor):
 class RteProcessor(DataProcessor):
     """Processor for the RTE data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['sentence1'].numpy().decode('utf-8'),
+                            tensor_dict['sentence2'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(
@@ -404,6 +478,13 @@ class RteProcessor(DataProcessor):
 class WnliProcessor(DataProcessor):
     """Processor for the WNLI data set (GLUE version)."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(tensor_dict['idx'].numpy(),
+                            tensor_dict['sentence1'].numpy().decode('utf-8'),
+                            tensor_dict['sentence2'].numpy().decode('utf-8'),
+                            str(tensor_dict['label'].numpy()))
+
     def get_train_examples(self, data_dir):
         """See base class."""
         return self._create_examples(

transformers/data/processors/utils.py

@@ -17,21 +17,22 @@
 import csv
 import sys
 import copy
+import json
 
 class InputExample(object):
-    """A single training/test example for simple sequence classification."""
-    def __init__(self, guid, text_a, text_b=None, label=None):
-        """Constructs a InputExample.
+    """
+    A single training/test example for simple sequence classification.
 
-        Args:
-            guid: Unique id for the example.
-            text_a: string. The untokenized text of the first sequence. For single
-            sequence tasks, only this sequence must be specified.
-            text_b: (Optional) string. The untokenized text of the second sequence.
-            Only must be specified for sequence pair tasks.
-            label: (Optional) string. The label of the example. This should be
-            specified for train and dev examples, but not for test examples.
-        """
+    Args:
+        guid: Unique id for the example.
+        text_a: string. The untokenized text of the first sequence. For single
+        sequence tasks, only this sequence must be specified.
+        text_b: (Optional) string. The untokenized text of the second sequence.
+        Only must be specified for sequence pair tasks.
+        label: (Optional) string. The label of the example. This should be
+        specified for train and dev examples, but not for test examples.
+    """
+    def __init__(self, guid, text_a, text_b=None, label=None):
         self.guid = guid
         self.text_a = text_a
         self.text_b = text_b
@@ -51,7 +52,17 @@ class InputExample(object):
 
 
 class InputFeatures(object):
-    """A single set of features of data."""
+    """
+    A single set of features of data.
+
+    Args:
+        input_ids: Indices of input sequence tokens in the vocabulary.
+        attention_mask: Mask to avoid performing attention on padding token indices.
+            Mask values selected in ``[0, 1]``:
+            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
+        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
+        label: Label corresponding to the input
+    """
 
     def __init__(self, input_ids, attention_mask, token_type_ids, label):
         self.input_ids = input_ids
@@ -75,6 +86,15 @@ class InputFeatures(object):
 class DataProcessor(object):
     """Base class for data converters for sequence classification data sets."""
 
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """Gets an example from a dict with tensorflow tensors
+
+        Args:
+            tensor_dict: Keys and values should match the corresponding Glue
+                tensorflow_dataset examples.
+        """
+        raise NotImplementedError()
+
     def get_train_examples(self, data_dir):
         """Gets a collection of `InputExample`s for the train set."""
         raise NotImplementedError()

transformers/file_utils.py

@@ -23,16 +23,20 @@ from botocore.exceptions import ClientError
 import requests
 from tqdm import tqdm
 
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
 try:
     import tensorflow as tf
     assert int(tf.__version__[0]) >= 2
     _tf_available = True  # pylint: disable=invalid-name
+    logger.info("TensorFlow version {} available.".format(tf.__version__))
 except (ImportError, AssertionError):
     _tf_available = False  # pylint: disable=invalid-name
 
 try:
     import torch
     _torch_available = True  # pylint: disable=invalid-name
+    logger.info("PyTorch version {} available.".format(torch.__version__))
 except ImportError:
     _torch_available = False  # pylint: disable=invalid-name
 
@@ -44,7 +48,7 @@ except ImportError:
     torch_cache_home = os.path.expanduser(
         os.getenv('TORCH_HOME', os.path.join(
             os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch')))
-default_cache_path = os.path.join(torch_cache_home, 'pytorch_transformers')
+default_cache_path = os.path.join(torch_cache_home, 'transformers')
 
 try:
     from urllib.parse import urlparse
@@ -61,14 +65,13 @@ except (AttributeError, ImportError):
                                                         default_cache_path))
 
 PYTORCH_TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
+TRANSFORMERS_CACHE = PYTORCH_PRETRAINED_BERT_CACHE  # Kept for backward compatibility
 
 WEIGHTS_NAME = "pytorch_model.bin"
 TF2_WEIGHTS_NAME = 'tf_model.h5'
 TF_WEIGHTS_NAME = 'model.ckpt'
 CONFIG_NAME = "config.json"
 
-logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
-
 def is_torch_available():
     return _torch_available
 
@@ -129,7 +132,7 @@ 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_TRANSFORMERS_CACHE
+        cache_dir = TRANSFORMERS_CACHE
     if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
         cache_dir = str(cache_dir)
 
@@ -160,7 +163,7 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N
         force_download: if True, re-dowload the file even if it's already cached in the cache dir.
     """
     if cache_dir is None:
-        cache_dir = PYTORCH_TRANSFORMERS_CACHE
+        cache_dir = 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):
@@ -249,7 +252,7 @@ def get_from_cache(url, cache_dir=None, force_download=False, proxies=None):
     If it's not there, download it. Then return the path to the cached file.
     """
     if cache_dir is None:
-        cache_dir = PYTORCH_TRANSFORMERS_CACHE
+        cache_dir = 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):

transformers/modeling_auto.py

@@ -36,7 +36,7 @@ logger = logging.getLogger(__name__)
 
 class AutoModel(object):
     r"""
-        :class:`~pytorch_transformers.AutoModel` is a generic model class
+        :class:`~transformers.AutoModel` is a generic model class
         that will be instantiated as one of the base model classes of the library
         when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -84,23 +84,23 @@ class AutoModel(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
 
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -120,7 +120,7 @@ class AutoModel(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 
@@ -157,7 +157,7 @@ class AutoModel(object):
 
 class AutoModelWithLMHead(object):
     r"""
-        :class:`~pytorch_transformers.AutoModelWithLMHead` is a generic model class
+        :class:`~transformers.AutoModelWithLMHead` is a generic model class
         that will be instantiated as one of the language modeling model classes of the library
         when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -208,23 +208,23 @@ class AutoModelWithLMHead(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
 
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -244,7 +244,7 @@ class AutoModelWithLMHead(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 
@@ -281,7 +281,7 @@ class AutoModelWithLMHead(object):
 
 class AutoModelForSequenceClassification(object):
     r"""
-        :class:`~pytorch_transformers.AutoModelForSequenceClassification` is a generic model class
+        :class:`~transformers.AutoModelForSequenceClassification` is a generic model class
         that will be instantiated as one of the sequence classification model classes of the library
         when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -326,23 +326,23 @@ class AutoModelForSequenceClassification(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
 
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -362,7 +362,7 @@ class AutoModelForSequenceClassification(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 
@@ -392,7 +392,7 @@ class AutoModelForSequenceClassification(object):
 
 class AutoModelForQuestionAnswering(object):
     r"""
-        :class:`~pytorch_transformers.AutoModelForQuestionAnswering` is a generic model class
+        :class:`~transformers.AutoModelForQuestionAnswering` is a generic model class
         that will be instantiated as one of the question answering model classes of the library
         when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -435,23 +435,23 @@ class AutoModelForQuestionAnswering(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
 
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -471,7 +471,7 @@ class AutoModelForQuestionAnswering(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 

transformers/modeling_bert.py

@@ -138,11 +138,7 @@ def swish(x):
 ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new}
 
 
-try:
-    from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
-except (ImportError, AttributeError) as e:
-    logger.info("Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .")
-    BertLayerNorm = torch.nn.LayerNorm
+BertLayerNorm = torch.nn.LayerNorm
 
 class BertEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings.
@@ -486,9 +482,9 @@ BERT_START_DOCSTRING = r"""    The BERT model was proposed in
         https://pytorch.org/docs/stable/nn.html#module
 
     Parameters:
-        config (:class:`~pytorch_transformers.BertConfig`): Model configuration class with all the parameters of the model. 
+        config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model. 
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 BERT_INPUTS_DOCSTRING = r"""
@@ -512,9 +508,9 @@ BERT_INPUTS_DOCSTRING = r"""
             Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
 
-            Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.BertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:

transformers/modeling_distilbert.py

@@ -372,9 +372,9 @@ DISTILBERT_START_DOCSTRING = r"""
         https://medium.com/huggingface/distilbert-8cf3380435b5
 
     Parameters:
-        config (:class:`~pytorch_transformers.DistilBertConfig`): Model configuration class with all the parameters of the model. 
+        config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model. 
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 DISTILBERT_INPUTS_DOCSTRING = r"""

transformers/modeling_gpt2.py

@@ -38,7 +38,8 @@ logger = logging.getLogger(__name__)
 
 GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin",
-                                     "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-pytorch_model.bin"}
+                                     "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-pytorch_model.bin",
+                                     "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-pytorch_model.bin",}
 
 def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
     """ Load tf checkpoints in a pytorch model
@@ -280,9 +281,9 @@ GPT2_START_DOCSTRING = r"""    OpenAI GPT-2 model was proposed in
         https://pytorch.org/docs/stable/nn.html#module
 
     Parameters:
-        config (:class:`~pytorch_transformers.GPT2Config`): Model configuration class with all the parameters of the model.
+        config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 GPT2_INPUTS_DOCSTRING = r"""    Inputs:
@@ -290,9 +291,9 @@ GPT2_INPUTS_DOCSTRING = r"""    Inputs:
             Indices of input sequence tokens in the vocabulary.
             GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
-            Indices can be obtained using :class:`pytorch_transformers.GPT2Tokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.GPT2Tokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **past**:
             list of ``torch.FloatTensor`` (one for each layer):
             that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
@@ -493,7 +494,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
     Examples::
 
         import torch
-        from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel
+        from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         model = GPT2LMHeadModel.from_pretrained('gpt2')
@@ -589,7 +590,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     Examples::
 
         import torch
-        from pytorch_transformers import GPT2Tokenizer, GPT2DoubleHeadsModel
+        from transformers import GPT2Tokenizer, GPT2DoubleHeadsModel
         
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         model = GPT2DoubleHeadsModel.from_pretrained('gpt2')

transformers/modeling_openai.py

@@ -294,9 +294,9 @@ OPENAI_GPT_START_DOCSTRING = r"""    OpenAI GPT model was proposed in
         https://pytorch.org/docs/stable/nn.html#module
 
     Parameters:
-        config (:class:`~pytorch_transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
+        config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
@@ -304,9 +304,9 @@ OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
             Indices of input sequence tokens in the vocabulary.
             GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
-            Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:

transformers/modeling_roberta.py

@@ -43,6 +43,9 @@ class RobertaEmbeddings(BertEmbeddings):
     def __init__(self, config):
         super(RobertaEmbeddings, self).__init__(config)
         self.padding_idx = 1
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size,
+                                                padding_idx=self.padding_idx)
 
     def forward(self, input_ids, token_type_ids=None, position_ids=None):
         seq_length = input_ids.size(1)
@@ -77,9 +80,9 @@ ROBERTA_START_DOCSTRING = r"""    The RoBERTa model was proposed in
         https://pytorch.org/docs/stable/nn.html#module
 
     Parameters:
-        config (:class:`~pytorch_transformers.RobertaConfig`): Model configuration class with all the parameters of the 
+        config (:class:`~transformers.RobertaConfig`): Model configuration class with all the parameters of the 
             model. Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 ROBERTA_INPUTS_DOCSTRING = r"""
@@ -102,8 +105,8 @@ ROBERTA_INPUTS_DOCSTRING = r"""
             RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
 
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
@@ -361,9 +364,9 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
 
                 ``token_type_ids:   0   0   0   0  0     0   0``
 
-            Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.BertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
             Segment token indices to indicate first and second portions of the inputs.
             The second dimension of the input (`num_choices`) indicates the number of choices to score.

transformers/modeling_tf_auto.py

@@ -34,7 +34,7 @@ logger = logging.getLogger(__name__)
 
 class TFAutoModel(object):
     r"""
-        :class:`~pytorch_transformers.TFAutoModel` is a generic model class
+        :class:`~transformers.TFAutoModel` is a generic model class
         that will be instantiated as one of the base model classes of the library
         when created with the `TFAutoModel.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -79,7 +79,7 @@ class TFAutoModel(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
 
             from_pt: (`Optional`) Boolean
@@ -88,17 +88,17 @@ class TFAutoModel(object):
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -118,7 +118,7 @@ class TFAutoModel(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 
@@ -155,7 +155,7 @@ class TFAutoModel(object):
 
 class TFAutoModelWithLMHead(object):
     r"""
-        :class:`~pytorch_transformers.TFAutoModelWithLMHead` is a generic model class
+        :class:`~transformers.TFAutoModelWithLMHead` is a generic model class
         that will be instantiated as one of the language modeling model classes of the library
         when created with the `TFAutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -203,7 +203,7 @@ class TFAutoModelWithLMHead(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
 
             from_pt: (`Optional`) Boolean
@@ -212,17 +212,17 @@ class TFAutoModelWithLMHead(object):
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -242,7 +242,7 @@ class TFAutoModelWithLMHead(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 
@@ -279,7 +279,7 @@ class TFAutoModelWithLMHead(object):
 
 class TFAutoModelForSequenceClassification(object):
     r"""
-        :class:`~pytorch_transformers.TFAutoModelForSequenceClassification` is a generic model class
+        :class:`~transformers.TFAutoModelForSequenceClassification` is a generic model class
         that will be instantiated as one of the sequence classification model classes of the library
         when created with the `TFAutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -324,7 +324,7 @@ class TFAutoModelForSequenceClassification(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
 
             from_pt: (`Optional`) Boolean
@@ -333,17 +333,17 @@ class TFAutoModelForSequenceClassification(object):
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -363,7 +363,7 @@ class TFAutoModelForSequenceClassification(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 
@@ -393,7 +393,7 @@ class TFAutoModelForSequenceClassification(object):
 
 class TFAutoModelForQuestionAnswering(object):
     r"""
-        :class:`~pytorch_transformers.TFAutoModelForQuestionAnswering` is a generic model class
+        :class:`~transformers.TFAutoModelForQuestionAnswering` is a generic model class
         that will be instantiated as one of the question answering model classes of the library
         when created with the `TFAutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
         class method.
@@ -436,7 +436,7 @@ class TFAutoModelForQuestionAnswering(object):
             pretrained_model_name_or_path: either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a path to a `directory` containing model weights saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
+                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                 - a path or url to a `PyTorch, TF 1.X or TF 2.0 checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In the case of a PyTorch checkpoint, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument.
 
             from_pt: (`Optional`) Boolean
@@ -445,17 +445,17 @@ class TFAutoModelForQuestionAnswering(object):
             model_args: (`optional`) Sequence of positional arguments:
                 All remaning positional arguments will be passed to the underlying model's ``__init__`` method
 
-            config: (`optional`) instance of a class derived from :class:`~pytorch_transformers.PretrainedConfig`:
+            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
 
                 - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
-                - the model was saved using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
+                - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                 - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
 
             state_dict: (`optional`) dict:
                 an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                 This option can be used if you want to create a model from a pretrained configuration but load your own weights.
-                In this case though, you should check if using :func:`~pytorch_transformers.PreTrainedModel.save_pretrained` and :func:`~pytorch_transformers.PreTrainedModel.from_pretrained` is not a simpler option.
+                In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
 
             cache_dir: (`optional`) string:
                 Path to a directory in which a downloaded pre-trained model
@@ -475,7 +475,7 @@ class TFAutoModelForQuestionAnswering(object):
                 Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
 
                 - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
-                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~pytorch_transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
+                - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
 
         Examples::
 

transformers/modeling_tf_bert.py

@@ -28,7 +28,7 @@ import numpy as np
 import tensorflow as tf
 
 from .configuration_bert import BertConfig
-from .modeling_tf_utils import TFPreTrainedModel
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer
 from .file_utils import add_start_docstrings
 from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
 
@@ -100,9 +100,16 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
         super(TFBertEmbeddings, self).__init__(**kwargs)
         self.vocab_size = config.vocab_size
         self.hidden_size = config.hidden_size
+        self.initializer_range = config.initializer_range
 
-        self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings, config.hidden_size, name='position_embeddings')
-        self.token_type_embeddings = tf.keras.layers.Embedding(config.type_vocab_size, config.hidden_size, name='token_type_embeddings')
+        self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings,
+                                                             config.hidden_size,
+                                                             embeddings_initializer=get_initializer(self.initializer_range),
+                                                             name='position_embeddings')
+        self.token_type_embeddings = tf.keras.layers.Embedding(config.type_vocab_size,
+                                                               config.hidden_size,
+                                                               embeddings_initializer=get_initializer(self.initializer_range),
+                                                               name='token_type_embeddings')
 
         # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
         # any TensorFlow checkpoint file
@@ -117,8 +124,7 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
             self.word_embeddings = self.add_weight(
                 "weight",
                 shape=[self.vocab_size, self.hidden_size],
-                initializer=tf.random_normal_initializer(
-                    mean=0., stddev=self.hidden_size**-0.5))
+                initializer=get_initializer(self.initializer_range))
         super(TFBertEmbeddings, self).build(input_shape)
 
     def call(self, inputs, mode="embedding", training=False):
@@ -192,9 +198,15 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
         self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
         self.all_head_size = self.num_attention_heads * self.attention_head_size
 
-        self.query = tf.keras.layers.Dense(self.all_head_size, name='query')
-        self.key = tf.keras.layers.Dense(self.all_head_size, name='key')
-        self.value = tf.keras.layers.Dense(self.all_head_size, name='value')
+        self.query = tf.keras.layers.Dense(self.all_head_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name='query')
+        self.key = tf.keras.layers.Dense(self.all_head_size,
+                                         kernel_initializer=get_initializer(config.initializer_range),
+                                         name='key')
+        self.value = tf.keras.layers.Dense(self.all_head_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name='value')
 
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
 
@@ -247,7 +259,9 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
 class TFBertSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFBertSelfOutput, self).__init__(**kwargs)
-        self.dense = tf.keras.layers.Dense(config.hidden_size, name='dense')
+        self.dense = tf.keras.layers.Dense(config.hidden_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name='dense')
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
 
@@ -281,7 +295,9 @@ class TFBertAttention(tf.keras.layers.Layer):
 class TFBertIntermediate(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFBertIntermediate, self).__init__(**kwargs)
-        self.dense = tf.keras.layers.Dense(config.intermediate_size, name='dense')
+        self.dense = tf.keras.layers.Dense(config.intermediate_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name='dense')
         if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
             self.intermediate_act_fn = ACT2FN[config.hidden_act]
         else:
@@ -296,7 +312,9 @@ class TFBertIntermediate(tf.keras.layers.Layer):
 class TFBertOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFBertOutput, self).__init__(**kwargs)
-        self.dense = tf.keras.layers.Dense(config.hidden_size, name='dense')
+        self.dense = tf.keras.layers.Dense(config.hidden_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name='dense')
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
 
@@ -364,7 +382,10 @@ class TFBertEncoder(tf.keras.layers.Layer):
 class TFBertPooler(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFBertPooler, self).__init__(**kwargs)
-        self.dense = tf.keras.layers.Dense(config.hidden_size, activation='tanh', name='dense')
+        self.dense = tf.keras.layers.Dense(config.hidden_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           activation='tanh',
+                                           name='dense')
 
     def call(self, hidden_states):
         # We "pool" the model by simply taking the hidden state corresponding
@@ -377,7 +398,9 @@ class TFBertPooler(tf.keras.layers.Layer):
 class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFBertPredictionHeadTransform, self).__init__(**kwargs)
-        self.dense = tf.keras.layers.Dense(config.hidden_size, name='dense')
+        self.dense = tf.keras.layers.Dense(config.hidden_size,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name='dense')
         if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
             self.transform_act_fn = ACT2FN[config.hidden_act]
         else:
@@ -428,7 +451,9 @@ class TFBertMLMHead(tf.keras.layers.Layer):
 class TFBertNSPHead(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFBertNSPHead, self).__init__(**kwargs)
-        self.seq_relationship = tf.keras.layers.Dense(2, name='seq_relationship')
+        self.seq_relationship = tf.keras.layers.Dense(2,
+                                                      kernel_initializer=get_initializer(config.initializer_range),
+                                                      name='seq_relationship')
 
     def call(self, pooled_output):
         seq_relationship_score = self.seq_relationship(pooled_output)
@@ -454,8 +479,6 @@ class TFBertMainLayer(tf.keras.layers.Layer):
         """
         raise NotImplementedError
 
-    # def call(self, input_ids, attention_mask=None, token_type_ids=None,
-    #          position_ids=None, head_mask=None, training=False):
     def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
         if isinstance(inputs, (tuple, list)):
             input_ids = inputs[0]
@@ -558,9 +581,9 @@ BERT_START_DOCSTRING = r"""    The BERT model was proposed in
             `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
-        config (:class:`~pytorch_transformers.BertConfig`): Model configuration class with all the parameters of the model. 
+        config (:class:`~transformers.BertConfig`): Model configuration class with all the parameters of the model. 
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 BERT_INPUTS_DOCSTRING = r"""
@@ -584,9 +607,9 @@ BERT_INPUTS_DOCSTRING = r"""
             Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
 
-            Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.BertTokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
@@ -630,7 +653,7 @@ class TFBertModel(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertModel
+        from transformers import BertTokenizer, TFBertModel
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertModel.from_pretrained('bert-base-uncased')
@@ -669,7 +692,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForPreTraining
+        from transformers import BertTokenizer, TFBertForPreTraining
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForPreTraining.from_pretrained('bert-base-uncased')
@@ -715,7 +738,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForMaskedLM
+        from transformers import BertTokenizer, TFBertForMaskedLM
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForMaskedLM.from_pretrained('bert-base-uncased')
@@ -759,7 +782,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForNextSentencePrediction
+        from transformers import BertTokenizer, TFBertForNextSentencePrediction
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForNextSentencePrediction.from_pretrained('bert-base-uncased')
@@ -804,7 +827,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForSequenceClassification
+        from transformers import BertTokenizer, TFBertForSequenceClassification
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
@@ -819,7 +842,9 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel):
 
         self.bert = TFBertMainLayer(config, name='bert')
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(config.num_labels, name='classifier')
+        self.classifier = tf.keras.layers.Dense(config.num_labels,
+                                                kernel_initializer=get_initializer(config.initializer_range),
+                                                name='classifier')
 
     def call(self, inputs, **kwargs):
         outputs = self.bert(inputs, **kwargs)
@@ -854,7 +879,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForMultipleChoice
+        from transformers import BertTokenizer, TFBertForMultipleChoice
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForMultipleChoice.from_pretrained('bert-base-uncased')
@@ -869,7 +894,9 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
 
         self.bert = TFBertMainLayer(config, name='bert')
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(1, name='classifier')
+        self.classifier = tf.keras.layers.Dense(1,
+                                                kernel_initializer=get_initializer(config.initializer_range),
+                                                name='classifier')
 
     def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
         if isinstance(inputs, (tuple, list)):
@@ -931,7 +958,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForTokenClassification
+        from transformers import BertTokenizer, TFBertForTokenClassification
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForTokenClassification.from_pretrained('bert-base-uncased')
@@ -946,7 +973,9 @@ class TFBertForTokenClassification(TFBertPreTrainedModel):
 
         self.bert = TFBertMainLayer(config, name='bert')
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
-        self.classifier = tf.keras.layers.Dense(config.num_labels, name='classifier')
+        self.classifier = tf.keras.layers.Dense(config.num_labels,
+                                                kernel_initializer=get_initializer(config.initializer_range),
+                                                name='classifier')
 
     def call(self, inputs, **kwargs):
         outputs = self.bert(inputs, **kwargs)
@@ -982,7 +1011,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFBertForQuestionAnswering
+        from transformers import BertTokenizer, TFBertForQuestionAnswering
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = TFBertForQuestionAnswering.from_pretrained('bert-base-uncased')
@@ -996,7 +1025,9 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel):
         self.num_labels = config.num_labels
 
         self.bert = TFBertMainLayer(config, name='bert')
-        self.qa_outputs = tf.keras.layers.Dense(config.num_labels, name='qa_outputs')
+        self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
+                                                kernel_initializer=get_initializer(config.initializer_range),
+                                                name='qa_outputs')
 
     def call(self, inputs, **kwargs):
         outputs = self.bert(inputs, **kwargs)

transformers/modeling_tf_distilbert.py

@@ -29,7 +29,7 @@ import numpy as np
 import tensorflow as tf
 
 from .configuration_distilbert import DistilBertConfig
-from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list, get_initializer
 from .file_utils import add_start_docstrings
 from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
 
@@ -79,8 +79,15 @@ class TFEmbeddings(tf.keras.layers.Layer):
         super(TFEmbeddings, self).__init__(**kwargs)
         self.vocab_size = config.vocab_size
         self.dim = config.dim
-        self.word_embeddings = TFSharedEmbeddings(config.vocab_size, config.dim, name='word_embeddings')  # padding_idx=0)
-        self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings, config.dim, name='position_embeddings')
+        self.initializer_range = config.initializer_range
+        self.word_embeddings = TFSharedEmbeddings(config.vocab_size,
+                                                  config.dim,
+                                                  initializer_range=config.initializer_range,
+                                                  name='word_embeddings')  # padding_idx=0)
+        self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings,
+                                                             config.dim,
+                                                             embeddings_initializer=get_initializer(config.initializer_range),
+                                                             name='position_embeddings')
         if config.sinusoidal_pos_embds:
             raise NotImplementedError
 
@@ -95,8 +102,7 @@ class TFEmbeddings(tf.keras.layers.Layer):
             self.word_embeddings = self.add_weight(
                 "weight",
                 shape=[self.vocab_size, self.dim],
-                initializer=tf.random_normal_initializer(
-                    mean=0., stddev=self.dim**-0.5))
+                initializer=get_initializer(self.initializer_range))
         super(TFEmbeddings, self).build(input_shape)
 
     def call(self, inputs, mode="embedding", training=False):
@@ -178,10 +184,18 @@ class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
 
         assert self.dim % self.n_heads == 0
 
-        self.q_lin = tf.keras.layers.Dense(config.dim, name="q_lin")
-        self.k_lin = tf.keras.layers.Dense(config.dim, name="k_lin")
-        self.v_lin = tf.keras.layers.Dense(config.dim, name="v_lin")
-        self.out_lin = tf.keras.layers.Dense(config.dim, name="out_lin")
+        self.q_lin = tf.keras.layers.Dense(config.dim,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name="q_lin")
+        self.k_lin = tf.keras.layers.Dense(config.dim,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name="k_lin")
+        self.v_lin = tf.keras.layers.Dense(config.dim,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name="v_lin")
+        self.out_lin = tf.keras.layers.Dense(config.dim,
+                                           kernel_initializer=get_initializer(config.initializer_range),
+                                           name="out_lin")
 
         self.pruned_heads = set()
 
@@ -254,8 +268,12 @@ class TFFFN(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super(TFFFN, self).__init__(**kwargs)
         self.dropout = tf.keras.layers.Dropout(config.dropout)
-        self.lin1 = tf.keras.layers.Dense(config.hidden_dim, name="lin1")
-        self.lin2 = tf.keras.layers.Dense(config.dim, name="lin2")
+        self.lin1 = tf.keras.layers.Dense(config.hidden_dim,
+                                          kernel_initializer=get_initializer(config.initializer_range),
+                                          name="lin1")
+        self.lin2 = tf.keras.layers.Dense(config.dim,
+                                          kernel_initializer=get_initializer(config.initializer_range),
+                                          name="lin2")
         assert config.activation in ['relu', 'gelu'], "activation ({}) must be in ['relu', 'gelu']".format(config.activation)
         self.activation = tf.keras.layers.Activation(gelu) if config.activation=='gelu' else tf.keras.activations.relu
 
@@ -482,9 +500,9 @@ DISTILBERT_START_DOCSTRING = r"""
             `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
-        config (:class:`~pytorch_transformers.DistilBertConfig`): Model configuration class with all the parameters of the model. 
+        config (:class:`~transformers.DistilBertConfig`): Model configuration class with all the parameters of the model. 
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 DISTILBERT_INPUTS_DOCSTRING = r"""
@@ -522,7 +540,7 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import DistilBertTokenizer, TFDistilBertModel
+        from transformers import DistilBertTokenizer, TFDistilBertModel
 
         tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
         model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
@@ -580,7 +598,7 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import DistilBertTokenizer, TFDistilBertForMaskedLM
+        from transformers import DistilBertTokenizer, TFDistilBertForMaskedLM
 
         tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
         model = TFDistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
@@ -596,7 +614,9 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
         self.vocab_size = config.vocab_size
 
         self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
-        self.vocab_transform = tf.keras.layers.Dense(config.dim, name="vocab_transform")
+        self.vocab_transform = tf.keras.layers.Dense(config.dim,
+                                                     kernel_initializer=get_initializer(config.initializer_range),
+                                                     name="vocab_transform")
         self.act = tf.keras.layers.Activation(gelu)
         self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
         self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")
@@ -633,7 +653,7 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFDistilBertForSequenceClassification
+        from transformers import BertTokenizer, TFDistilBertForSequenceClassification
 
         tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
         model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
@@ -647,8 +667,13 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
         self.num_labels = config.num_labels
 
         self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
-        self.pre_classifier = tf.keras.layers.Dense(config.dim, activation='relu', name="pre_classifier")
-        self.classifier = tf.keras.layers.Dense(config.num_labels, name="classifier")
+        self.pre_classifier = tf.keras.layers.Dense(config.dim,
+                                                    kernel_initializer=get_initializer(config.initializer_range),
+                                                    activation='relu',
+                                                    name="pre_classifier")
+        self.classifier = tf.keras.layers.Dense(config.num_labels,
+                                                kernel_initializer=get_initializer(config.initializer_range),
+                                                name="classifier")
         self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
 
     def call(self, inputs, **kwargs):
@@ -685,7 +710,7 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import BertTokenizer, TFDistilBertForQuestionAnswering
+        from transformers import BertTokenizer, TFDistilBertForQuestionAnswering
 
         tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
         model = TFDistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
@@ -700,7 +725,9 @@ class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
         super(TFDistilBertForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
 
         self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
-        self.qa_outputs = tf.keras.layers.Dense(config.num_labels, name='qa_outputs')
+        self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
+                                                kernel_initializer=get_initializer(config.initializer_range),
+                                                name='qa_outputs')
         assert config.num_labels == 2
         self.dropout = tf.keras.layers.Dropout(config.qa_dropout)
 

transformers/modeling_tf_gpt2.py

@@ -29,7 +29,7 @@ import numpy as np
 import tensorflow as tf
 
 from .modeling_tf_utils import (TFPreTrainedModel, TFConv1D, TFSharedEmbeddings,
-                                TFSequenceSummary, shape_list)
+                                TFSequenceSummary, shape_list, get_initializer)
 from .configuration_gpt2 import GPT2Config
 from .file_utils import add_start_docstrings
 from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
@@ -38,7 +38,8 @@ logger = logging.getLogger(__name__)
 
 TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-tf_model.h5",
                                      "gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-tf_model.h5",
-                                     "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-tf_model.h5"}
+                                     "gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-tf_model.h5",
+                                     "distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-tf_model.h5",}
 
 
 def load_gpt2_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
@@ -76,8 +77,8 @@ class TFAttention(tf.keras.layers.Layer):
         self.split_size = n_state
         self.scale = scale
 
-        self.c_attn = TFConv1D(n_state * 3, nx, name='c_attn')
-        self.c_proj = TFConv1D(n_state, nx, name='c_proj')
+        self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name='c_attn')
+        self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name='c_proj')
         self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop)
         self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop)
         self.pruned_heads = set()
@@ -166,8 +167,8 @@ class TFMLP(tf.keras.layers.Layer):
     def __init__(self, n_state, config, **kwargs):
         super(TFMLP, self).__init__(**kwargs)
         nx = config.n_embd
-        self.c_fc = TFConv1D(n_state, nx, name='c_fc')
-        self.c_proj = TFConv1D(nx, n_state, name='c_proj')
+        self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name='c_fc')
+        self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name='c_proj')
         self.act = gelu
         self.dropout = tf.keras.layers.Dropout(config.resid_pdrop)
 
@@ -212,8 +213,14 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
         self.vocab_size = config.vocab_size
         self.n_embd = config.n_embd
 
-        self.wte = TFSharedEmbeddings(config.vocab_size, config.hidden_size, name='wte')
-        self.wpe = tf.keras.layers.Embedding(config.n_positions, config.n_embd, name='wpe')
+        self.wte = TFSharedEmbeddings(config.vocab_size,
+                                      config.hidden_size,
+                                      initializer_range=config.initializer_range,
+                                      name='wte')
+        self.wpe = tf.keras.layers.Embedding(config.n_positions,
+                                             config.n_embd,
+                                             embeddings_initializer=get_initializer(config.initializer_range),
+                                             name='wpe')
         self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
         self.h = [TFBlock(config.n_ctx,
                           config,
@@ -379,9 +386,9 @@ GPT2_START_DOCSTRING = r"""    OpenAI GPT-2 model was proposed in
             `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
-        config (:class:`~pytorch_transformers.GPT2Config`): Model configuration class with all the parameters of the model.
+        config (:class:`~transformers.GPT2Config`): Model configuration class with all the parameters of the model.
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 GPT2_INPUTS_DOCSTRING = r"""    Inputs:
@@ -389,9 +396,9 @@ GPT2_INPUTS_DOCSTRING = r"""    Inputs:
             Indices of input sequence tokens in the vocabulary.
             GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
-            Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **past**:
             list of ``Numpy array`` or ``tf.Tensor`` (one for each layer):
             that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
@@ -435,7 +442,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import GPT2Tokenizer, TFGPT2Model
+        from transformers import GPT2Tokenizer, TFGPT2Model
 
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         model = TFGPT2Model.from_pretrained('gpt2')
@@ -475,7 +482,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import GPT2Tokenizer, TFGPT2LMHeadModel
+        from transformers import GPT2Tokenizer, TFGPT2LMHeadModel
 
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         model = TFGPT2LMHeadModel.from_pretrained('gpt2')
@@ -531,7 +538,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel
+        from transformers import GPT2Tokenizer, TFGPT2DoubleHeadsModel
 
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
         model = TFGPT2DoubleHeadsModel.from_pretrained('gpt2')
@@ -557,7 +564,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
     def __init__(self, config, *inputs, **kwargs):
         super(TFGPT2DoubleHeadsModel, self).__init__(config, *inputs, **kwargs)
         self.transformer = TFGPT2MainLayer(config, name='transformer')
-        self.multiple_choice_head = TFSequenceSummary(config, name='multiple_choice_head')
+        self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
 
     def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
         if isinstance(inputs, (tuple, list)):

transformers/modeling_tf_openai.py

@@ -29,7 +29,7 @@ import numpy as np
 import tensorflow as tf
 
 from .modeling_tf_utils import (TFPreTrainedModel, TFConv1D, TFSharedEmbeddings,
-                                TFSequenceSummary, shape_list)
+                                TFSequenceSummary, shape_list, get_initializer)
 from .configuration_openai import OpenAIGPTConfig
 from .file_utils import add_start_docstrings
 from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
@@ -83,8 +83,8 @@ class TFAttention(tf.keras.layers.Layer):
         self.split_size = n_state
         self.scale = scale
 
-        self.c_attn = TFConv1D(n_state * 3, nx, name='c_attn')
-        self.c_proj = TFConv1D(n_state, nx, name='c_proj')
+        self.c_attn = TFConv1D(n_state * 3, nx, initializer_range=config.initializer_range, name='c_attn')
+        self.c_proj = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name='c_proj')
         self.attn_dropout = tf.keras.layers.Dropout(config.attn_pdrop)
         self.resid_dropout = tf.keras.layers.Dropout(config.resid_pdrop)
         self.pruned_heads = set()
@@ -168,8 +168,8 @@ class TFMLP(tf.keras.layers.Layer):
     def __init__(self, n_state, config, **kwargs):
         super(TFMLP, self).__init__(**kwargs)
         nx = config.n_embd
-        self.c_fc = TFConv1D(n_state, nx, name='c_fc')
-        self.c_proj = TFConv1D(nx, n_state, name='c_proj')
+        self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name='c_fc')
+        self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initializer_range, name='c_proj')
         self.act = gelu
         self.dropout = tf.keras.layers.Dropout(config.resid_pdrop)
 
@@ -212,8 +212,14 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
         self.vocab_size = config.vocab_size
         self.n_embd = config.n_embd
 
-        self.tokens_embed = TFSharedEmbeddings(config.vocab_size, config.n_embd, name='tokens_embed')
-        self.positions_embed = tf.keras.layers.Embedding(config.n_positions, config.n_embd, name='positions_embed')
+        self.tokens_embed = TFSharedEmbeddings(config.vocab_size,
+                                               config.n_embd,
+                                               initializer_range=config.initializer_range,
+                                               name='tokens_embed')
+        self.positions_embed = tf.keras.layers.Embedding(config.n_positions,
+                                                         config.n_embd,
+                                                         embeddings_initializer=get_initializer(config.initializer_range),
+                                                         name='positions_embed')
         self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
         self.h = [TFBlock(config.n_ctx,
                           config,
@@ -365,9 +371,9 @@ OPENAI_GPT_START_DOCSTRING = r"""    OpenAI GPT model was proposed in
             `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
-        config (:class:`~pytorch_transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
+        config (:class:`~transformers.OpenAIGPTConfig`): Model configuration class with all the parameters of the model.
             Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the :meth:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
+            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
 
 OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
@@ -375,9 +381,9 @@ OPENAI_GPT_INPUTS_DOCSTRING = r"""    Inputs:
             Indices of input sequence tokens in the vocabulary.
             GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on
             the right rather than the left.
-            Indices can be obtained using :class:`pytorch_transformers.BPT2Tokenizer`.
-            See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
-            :func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            Indices can be obtained using :class:`transformers.BPT2Tokenizer`.
+            See :func:`transformers.PreTrainedTokenizer.encode` and
+            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
         **attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
             Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
@@ -413,7 +419,7 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
+        from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
 
         tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
         model = TFOpenAIGPTModel.from_pretrained('openai-gpt')
@@ -449,7 +455,7 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import OpenAIGPTTokenizer, TFOpenAIGPTLMHeadModel
+        from transformers import OpenAIGPTTokenizer, TFOpenAIGPTLMHeadModel
 
         tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
         model = TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
@@ -500,7 +506,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
     Examples::
 
         import tensorflow as tf
-        from pytorch_transformers import OpenAIGPTTokenizer, TFOpenAIGPTDoubleHeadsModel
+        from transformers import OpenAIGPTTokenizer, TFOpenAIGPTDoubleHeadsModel
 
         tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
         model = TFOpenAIGPTDoubleHeadsModel.from_pretrained('openai-gpt')
@@ -522,7 +528,7 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
     def __init__(self, config, *inputs, **kwargs):
         super(TFOpenAIGPTDoubleHeadsModel, self).__init__(config, *inputs, **kwargs)
         self.transformer = TFOpenAIGPTMainLayer(config, name='transformer')
-        self.multiple_choice_head = TFSequenceSummary(config, name='multiple_choice_head')
+        self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
 
     def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
         if isinstance(inputs, (tuple, list)):

transformers/modeling_tf_pytorch_utils.py

@@ -189,14 +189,14 @@ def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs
             "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions.")
         raise e
 
-    import pytorch_transformers
+    import transformers
 
     tf_path = os.path.abspath(tf_checkpoint_path)
     logger.info("Loading TensorFlow weights from {}".format(tf_checkpoint_path))
 
     # Instantiate and load the associated TF 2.0 model
     tf_model_class_name = "TF" + pt_model.__class__.__name__  # Add "TF" at the beggining
-    tf_model_class = getattr(pytorch_transformers, tf_model_class_name)
+    tf_model_class = getattr(transformers, tf_model_class_name)
     tf_model = tf_model_class(pt_model.config)
 
     if tf_inputs is None: