soft launch distilbert multilingual

.circleci/config.yml

@@ -70,27 +70,6 @@ jobs:
             - run: sudo pip install pytest codecov pytest-cov
             - run: python -m pytest -sv ./transformers/tests/ --cov
             - run: codecov
-    build_py3_custom_tokenizers:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        steps:
-            - checkout
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
-            - run: sudo pip install mecab-python3
-            - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
-    build_py2_custom_tokenizers:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:2.7
-        steps:
-            - checkout
-            - run: sudo pip install --progress-bar off .
-            - run: sudo pip install pytest
-            - run: sudo apt-get -y install libmecab-dev mecab mecab-ipadic-utf8 swig
-            - run: sudo pip install mecab-python
-            - run: RUN_CUSTOM_TOKENIZERS=1 python -m pytest -sv ./transformers/tests/tokenization_bert_japanese_test.py
     deploy_doc:
         working_directory: ~/transformers
         docker:
@@ -103,16 +82,6 @@ jobs:
             - run: sudo pip install --progress-bar off -r docs/requirements.txt
             - run: sudo pip install --progress-bar off -r requirements.txt
             - run: ./.circleci/deploy.sh
-    repository_consistency:
-        working_directory: ~/transformers
-        docker:
-            - image: circleci/python:3.5
-        resource_class: small
-        parallelism: 1
-        steps:
-            - checkout
-            - run: sudo pip install requests
-            - run: python ./utils/link_tester.py
 workflow_filters: &workflow_filters
     filters:
         branches:
@@ -122,9 +91,6 @@ workflows:
     version: 2
     build_and_test:
         jobs:
-            - repository_consistency
-            - build_py3_custom_tokenizers
-            - build_py2_custom_tokenizers
             - build_py3_torch_and_tf
             - build_py3_torch
             - build_py3_tf

README.md

@@ -56,10 +56,9 @@ Choose the right framework for every part of a model's lifetime
 | [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 |
-| [Quick tour: Share your models ](#Quick-tour-of-model-sharing) | Upload and share your fine-tuned models with the community |
 | [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers 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][(v2.2.0/v2.2.1/v2.2.2)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more |
+| [Documentation][(v2.2.0)](https://huggingface.co/transformers/v2.2.0) [(v2.1.1)](https://huggingface.co/transformers/v2.1.1) [(v2.0.0)](https://huggingface.co/transformers/v2.0.0) [(v1.2.0)](https://huggingface.co/transformers/v1.2.0) [(v1.1.0)](https://huggingface.co/transformers/v1.1.0) [(v1.0.0)](https://huggingface.co/transformers/v1.0.0) [(master)](https://huggingface.co/transformers) | Full API documentation and more |
 
 ## Installation
 
@@ -102,26 +101,17 @@ pip install [--editable] .
 
 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 `unittest` or `pytest` (install pytest if needed with `pip install pytest`).
+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 unittest discover -s transformers/tests -p "*test.py" -t .
-python -m unittest discover -s examples -p "*test.py" -t examples
-```
-
-or
-
 ```bash
 python -m pytest -sv ./transformers/tests/
 python -m pytest -sv ./examples/
 ```
 
-By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them.
-
 ### Do you want to run a Transformer model on a mobile device?
 
 You should check out our [`swift-coreml-transformers`](https://github.com/huggingface/swift-coreml-transformers) repo.
@@ -141,12 +131,11 @@ At some point in the future, you'll be able to seamlessly move from pre-training
 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), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
+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).
 9. **[CTRL](https://github.com/salesforce/ctrl/)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
 10. **[CamemBERT](https://camembert-model.fr)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-11. **[ALBERT](https://github.com/google-research/ALBERT)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-12. **[T5](https://github.com/google-research/text-to-text-transfer-transformer)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-13. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
+11. **[ALBERT](https://github.com/google-research/google-research/tree/master/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+11. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
 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).
 
@@ -447,46 +436,6 @@ python ./examples/run_generation.py \
     --repetition_penalty=1.2 \
 ```
 
-## Quick tour of model sharing
-
-New in `v2.2.2`: you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
-
-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
-
-```shell
-transformers-cli login
-# log in using the same credentials as on huggingface.co
-```
-Upload your model:
-```shell
-transformers-cli upload ./path/to/pretrained_model/
-
-# ^^ Upload folder containing weights/tokenizer/config
-# saved via `.save_pretrained()`
-
-transformers-cli upload ./config.json [--filename foobar.json]
-
-# ^^ Upload a single file
-# (you can optionally override its filename)
-```
-
-Your model will then be accessible through its identifier:
-```python
-"username/model_name"
-```
-
-Anyone can load it from code:
-```python
-tokenizer = AutoTokenizer.from_pretrained("username/model_name")
-model = AutoModel.from_pretrained("username/model_name")
-```
-
-Finally, list all your files on S3:
-```shell
-transformers-cli ls
-# List all your S3 objects.
-```
-
 ## Migrating from pytorch-transformers to transformers
 
 Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.

docs/source/conf.py

@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'2.2.2'
+release = u'2.2.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/index.rst

@@ -49,7 +49,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
 8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (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>`_.
 9. `CTRL <https://github.com/pytorch/fairseq/tree/master/examples/ctrl>`_ (from Salesforce), released together with the paper `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://www.github.com/salesforce/ctrl>`_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
 10. `CamemBERT <https://huggingface.co/transformers/model_doc/camembert.html>`_ (from FAIR, Inria, Sorbonne Université) released together with the paper `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`_ by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot.
-11. `ALBERT <https://github.com/google-research/ALBERT>`_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+11. `ALBERT <https://github.com/pytorch/fairseq/tree/master/examples/albert>`_ (from Google Research), released together with the paper a `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_ by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 
 .. toctree::
     :maxdepth: 2
@@ -58,7 +58,6 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     installation
     quickstart
     pretrained_models
-    model_sharing
     examples
     notebooks
     serialization

docs/source/installation.md

@@ -24,24 +24,15 @@ pip install [--editable] .
 
 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 `unittest` or `pytest` (install pytest if needed with `pip install pytest`).
+Tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
 
 Run all the tests from the root of the cloned repository with the commands:
 
-```bash
-python -m unittest discover -s transformers/tests -p "*test.py" -t .
-python -m unittest discover -s examples -p "*test.py" -t examples
-```
-
-or
-
 ``` bash
 python -m pytest -sv ./transformers/tests/
 python -m pytest -sv ./examples/
 ```
 
-By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to `yes` to run them.
-
 ## OpenAI GPT original tokenization workflow
 
 If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (use version 4.4.3 if you are using Python 2) and `SpaCy`:

docs/source/main_classes/optimizer_schedules.rst

@@ -5,7 +5,6 @@ The ``.optimization`` module provides:
 
 - an optimizer with weight decay fixed that can be used to fine-tuned models, and
 - several schedules in the form of schedule objects that inherit from ``_LRSchedule``:
-- a gradient accumulation class to accumulate the gradients of multiple batches
 
 ``AdamW``
 ~~~~~~~~~~~~~~~~
@@ -13,15 +12,6 @@ The ``.optimization`` module provides:
 .. autoclass:: transformers.AdamW
     :members:
 
-``AdamWeightDecay``
-~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AdamWeightDecay
-    :members:
-
-.. autofunction:: transformers.create_optimizer
-    :members:
-
 Schedules
 ----------------------------------------------------
 
@@ -59,17 +49,3 @@ Learning Rate Schedules
 .. image:: /imgs/warmup_linear_schedule.png
     :target: /imgs/warmup_linear_schedule.png
     :alt:
-
-``Warmup``
-~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.Warmup
-    :members:
-
-Gradient Strategies
-----------------------------------------------------
-
-``GradientAccumulator``
-~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.GradientAccumulator

docs/source/main_classes/processors.rst

@@ -54,7 +54,8 @@ Additionally, the following method  can be used to load values from a data file
 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.
+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.
 
 
 XNLI
@@ -73,81 +74,8 @@ This library hosts the processor to load the XNLI data:
 
 Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
 
-An example using these processors is given in the
-`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_xnli.py>`__ script.
-
-
-SQuAD
-~~~~~~~~~~~~~~~~~~~~~
-
-`The Stanford Question Answering Dataset (SQuAD) <https://rajpurkar.github.io/SQuAD-explorer//>`__ is a benchmark that evaluates
-the performance of models on question answering. Two versions are available, v1.1 and v2.0. The first version (v1.1) was released together with the paper
-`SQuAD: 100,000+ Questions for Machine Comprehension of Text <https://arxiv.org/abs/1606.05250>`__. The second version (v2.0) was released alongside 
-the paper `Know What You Don't Know: Unanswerable Questions for SQuAD <https://arxiv.org/abs/1806.03822>`__.
-
-This library hosts a processor for each of the two versions:
-
-Processors
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Those processors are:
-    - :class:`~transformers.data.processors.utils.SquadV1Processor`
-    - :class:`~transformers.data.processors.utils.SquadV2Processor`
-
-They both inherit from the abstract class :class:`~transformers.data.processors.utils.SquadProcessor`
-
-.. autoclass:: transformers.data.processors.squad.SquadProcessor
-    :members:
-
-Additionally, the following method can be used to convert SQuAD examples into :class:`~transformers.data.processors.utils.SquadFeatures`
-that can be used as model inputs.
-
-.. automethod:: transformers.data.processors.squad.squad_convert_examples_to_features
-
-These processors as well as the aforementionned method can be used with files containing the data as well as with the `tensorflow_datasets` package.
-Examples are given below.
-
-
 Example usage
 ^^^^^^^^^^^^^^^^^^^^^^^^^
-Here is an example using the processors as well as the conversion method using data files:
 
-Example::
-
-    # Loading a V2 processor
-    processor = SquadV2Processor()
-    examples = processor.get_dev_examples(squad_v2_data_dir)
-
-    # Loading a V1 processor
-    processor = SquadV1Processor()
-    examples = processor.get_dev_examples(squad_v1_data_dir)
-
-    features = squad_convert_examples_to_features( 
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=args.doc_stride,
-        max_query_length=max_query_length,
-        is_training=not evaluate,
-    )
-
-Using `tensorflow_datasets` is as easy as using a data file:
-
-Example::
-
-    # tensorflow_datasets only handle Squad V1.
-    tfds_examples = tfds.load("squad")
-    examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
-
-    features = squad_convert_examples_to_features( 
-        examples=examples,
-        tokenizer=tokenizer,
-        max_seq_length=max_seq_length,
-        doc_stride=args.doc_stride,
-        max_query_length=max_query_length,
-        is_training=not evaluate,
-    )
-
-
-Another example using these processors is given in the
-`run_squad.py <https://github.com/huggingface/transformers/blob/master/examples/run_squad.py>`__ script.
+An example using these processors is given in the
+`run_xnli.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_xnli.py>`__ script.

docs/source/migration.md

@@ -104,6 +104,6 @@ for batch in train_data:
     loss = model(batch)
     loss.backward()
     torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)  # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
-    optimizer.step()
     scheduler.step()
+    optimizer.step()
 ```

docs/source/model_sharing.md

@@ -1,40 +0,0 @@
-# Model upload and sharing
-
-Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.
-
-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:
-
-```shell
-transformers-cli login
-# log in using the same credentials as on huggingface.co
-```
-Upload your model:
-```shell
-transformers-cli upload ./path/to/pretrained_model/
-
-# ^^ Upload folder containing weights/tokenizer/config
-# saved via `.save_pretrained()`
-
-transformers-cli upload ./config.json [--filename foobar.json]
-
-# ^^ Upload a single file
-# (you can optionally override its filename)
-```
-
-Your model will then be accessible through its identifier:
-```python
-"username/model_name"
-```
-
-Anyone can load it from code:
-```python
-tokenizer = AutoTokenizer.from_pretrained("username/model_name")
-model = AutoModel.from_pretrained("username/model_name")
-```
-
-Finally, list all your files on S3:
-```shell
-transformers-cli ls
-# List all your S3 objects.
-```
-

docs/source/pretrained_models.rst

@@ -61,24 +61,6 @@ Here is the full list of the currently provided pretrained models together with
 |                   | ``bert-base-german-dbmdz-uncased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese``                                     | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
-|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-whole-word-masking``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
-|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-char``                                | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``bert-base-japanese-char-whole-word-masking``             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
-|                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | GPT               | ``openai-gpt``                                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | OpenAI GPT English model                                                                                                            |
@@ -169,14 +151,6 @@ Here is the full list of the currently provided pretrained models together with
 |                   | ``distilroberta-base``                                     | | 6-layer, 768-hidden, 12-heads, 82M parameters                                                                                       |
 |                   |                                                            | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint.                                                 |
 |                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-german-cased``                           | | 6-layer, 768-hidden, 12-heads, 66M parameters                                                                                       |
-|                   |                                                            | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint.                   |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``distilbert-base-multilingual-cased``                     | | 6-layer, 768-hidden, 12-heads, 134M parameters                                                                                      |
-|                   |                                                            | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint.             |
-|                   |                                                            | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__)                                     |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | CTRL              | ``ctrl``                                                   | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters                                                                                    |
 |                   |                                                            | | Salesforce's Large-sized CTRL English model                                                                                         |
@@ -187,50 +161,35 @@ Here is the full list of the currently provided pretrained models together with
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | ALBERT            | ``albert-base-v1``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
 |                   |                                                            | | ALBERT base model                                                                                                                   |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-large-v1``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
 |                   |                                                            | | ALBERT large model                                                                                                                  |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xlarge-v1``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
 |                   |                                                            | | ALBERT xlarge model                                                                                                                 |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xxlarge-v1``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
 |                   |                                                            | | ALBERT xxlarge model                                                                                                                |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-base-v2``                                         | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters                                                            |
 |                   |                                                            | | ALBERT base model with no dropout, additional training data and longer training                                                     |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-large-v2``                                        | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters                                                           |
 |                   |                                                            | | ALBERT large model with no dropout, additional training data and longer training                                                    |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xlarge-v2``                                       | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters                                                           |
 |                   |                                                            | | ALBERT xlarge model with no dropout, additional training data and longer training                                                   |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``albert-xxlarge-v2``                                      | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters                                                           |
 |                   |                                                            | | ALBERT xxlarge model with no dropout, additional training data and longer training                                                  |
-|                   |                                                            | (see `details <https://github.com/google-research/ALBERT>`__)                                                                         |
-+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| T5                | ``t5-small``                                               | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads,                                           |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-base``                                                | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads,                                        |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-large``                                               | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-3B``                                                  | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads,                                      |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``t5-11B``                                                 | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads,                                      |
-|                   |                                                            | | Trained on English text: the Colossal Clean Crawled Corpus (C4)                                                                     |
+|                   |                                                            | (see `details <https://github.com/google-research/google-research/tree/master/albert>`__)                                             |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 
 

examples/README.md

@@ -4,14 +4,12 @@ In this section a few examples are put together. All of these examples work for
 similar API between the different models.
 
 **Important**  
-To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
-Execute the following steps in a new virtual environment:
+To run the latest versions of the examples, you have to install from source. Execute the following steps in a new virtual environment:
 
 ```bash
 git clone https://github.com/huggingface/transformers
 cd transformers
 pip install [--editable] .
-pip install -r ./examples/requirements.txt
 ```
 
 | Section                    | Description                                                                                                                                                |
@@ -24,6 +22,7 @@ pip install -r ./examples/requirements.txt
 | [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. 
 | [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training.                                                                                  |
 | [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
+| [Abstractive summarization](#abstractive-summarization) | Fine-tuning the library models for abstractive summarization tasks on the CNN/Daily Mail dataset. |
 
 ## TensorFlow 2.0 Bert models on GLUE
 
@@ -466,8 +465,7 @@ Training with the previously defined hyper-parameters yields the following resul
 
 ## Named Entity Recognition
 
-Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py) for Pytorch and
-[`run_tf_ner.py`(https://github.com/huggingface/transformers/blob/master/examples/run_tf_ner.py)] for Tensorflow 2.
+Based on the script [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py).
 This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
 Details and results for the fine-tuning provided by @stefan-it.
 
@@ -512,7 +510,7 @@ The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so
 cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
 ```
 
-### Prepare the run
+### Training
 
 Additional environment variables must be set:
 
@@ -524,8 +522,6 @@ export SAVE_STEPS=750
 export SEED=1
 ```
 
-### Run the Pytorch version
-
 To start training, just run:
 
 ```bash
@@ -546,7 +542,7 @@ python3 run_ner.py --data_dir ./ \
 
 If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
 
-#### Evaluation
+### Evaluation
 
 Evaluation on development dataset outputs the following for our example:
 
@@ -568,7 +564,7 @@ On the test dataset the following results could be achieved:
 10/04/2019 00:42:42 - INFO - __main__ -     recall = 0.8624150210424085
 ```
 
-#### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
+### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
 
 Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run):
 
@@ -578,70 +574,32 @@ Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) a
 | `roberta-large`                  | 95.96 | 91.87
 | `distilbert-base-uncased` | 94.34 | 90.32
 
-### Run the Tensorflow 2 version
+## Abstractive summarization
 
-To start training, just run:
+Based on the script
+[`run_summarization_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_summarization_finetuning.py).
+
+Before running this script you should download **both** CNN and Daily Mail
+datasets from [Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/)  (the
+links next to "Stories") in the same folder. Then uncompress the archives by running:
 
 ```bash
-python3 run_tf_ner.py --data_dir ./ \
---model_type bert \
---labels ./labels.txt \
---model_name_or_path $BERT_MODEL \
---output_dir $OUTPUT_DIR \
---max_seq_length  $MAX_LENGTH \
---num_train_epochs $NUM_EPOCHS \
---per_device_train_batch_size $BATCH_SIZE \
---save_steps $SAVE_STEPS \
---seed $SEED \
---do_train \
---do_eval \
---do_predict
+tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
 ```
 
-Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
+note that the finetuning script **will not work** if you do not download both
+datasets. We will refer as `$DATA_PATH` the path to where you uncompressed both
+archive.
 
-#### Evaluation
-
-Evaluation on development dataset outputs the following for our example:
 ```bash
-           precision    recall  f1-score   support
+export DATA_PATH=/path/to/dataset/
 
- LOCderiv     0.7619    0.6154    0.6809        52
-  PERpart     0.8724    0.8997    0.8858      4057
-  OTHpart     0.9360    0.9466    0.9413       711
-  ORGpart     0.7015    0.6989    0.7002       269
-  LOCpart     0.7668    0.8488    0.8057       496
-      LOC     0.8745    0.9191    0.8963       235
- ORGderiv     0.7723    0.8571    0.8125        91
- OTHderiv     0.4800    0.6667    0.5581        18
-      OTH     0.5789    0.6875    0.6286        16
- PERderiv     0.5385    0.3889    0.4516        18
-      PER     0.5000    0.5000    0.5000         2
-      ORG     0.0000    0.0000    0.0000         3
-
-micro avg     0.8574    0.8862    0.8715      5968
-macro avg     0.8575    0.8862    0.8713      5968
-```
-
-On the test dataset the following results could be achieved:
-```bash
-           precision    recall  f1-score   support
-
-  PERpart     0.8847    0.8944    0.8896      9397
-  OTHpart     0.9376    0.9353    0.9365      1639
-  ORGpart     0.7307    0.7044    0.7173       697
-      LOC     0.9133    0.9394    0.9262       561
-  LOCpart     0.8058    0.8157    0.8107      1150
-      ORG     0.0000    0.0000    0.0000         8
- OTHderiv     0.5882    0.4762    0.5263        42
- PERderiv     0.6571    0.5227    0.5823        44
-      OTH     0.4906    0.6667    0.5652        39
- ORGderiv     0.7016    0.7791    0.7383       172
- LOCderiv     0.8256    0.6514    0.7282       109
-      PER     0.0000    0.0000    0.0000        11
-
-micro avg     0.8722    0.8774    0.8748     13869
-macro avg     0.8712    0.8774    0.8740     13869
+python run_summarization_finetuning.py \
+    --output_dir=output \
+    --model_type=bert2bert \
+    --model_name_or_path=bert2bert \
+    --do_train \
+    --data_path=$DATA_PATH \
 ```
 
 ## XNLI

examples/distillation/README.md

@@ -2,10 +2,6 @@
 
 This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.
 
-**December 6th, 2019 - Update** We release **DistilmBERT**: 92% of `bert-base-multilingual-cased` on XNLI. The model supports 104 different languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
-
-**November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
-
 **October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.
 
 **October 3rd, 2019 - 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. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
@@ -19,9 +15,8 @@ Distil* is a class of compressed models that started with DistilBERT. DistilBERT
 
 We have applied the same method to other Transformer architectures and released the weights:
 - GPT2: on the [WikiText-103](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/) benchmark, GPT2 reaches a perplexity on the test set of 15.0 compared to 18.5 for **DistilGPT2** (after fine-tuning on the train set).
-- RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base`'s performance on GLUE while being twice faster and 35% smaller.
-- German BERT: **German DistilBERT** reaches 99% of `bert-base-german-dbmdz-cased`'s performance on German NER (CoNLL-2003).
-- Multilingual BERT: **DistilmBERT** reaches 92% of Multilingual BERT's performance on XNLI while being twice faster and 25% smaller. The model supports 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages).
+- RoBERTa: **DistilRoBERTa** reaches 95% of `RoBERTa-base` performance on GLUE while being twice faster and 35% smaller.
+- and more to come! 🤗🤗🤗
 
 For more information on DistilBERT, please refer to our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108).
 
@@ -32,7 +27,7 @@ Here are the results on the dev sets of GLUE:
 | 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              |
 | ---                       |    ---                         |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  --- |  ---              |
-| RoBERTa-base (reported)   |  **83.2**/**86.4**<sup>2</sup> | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.7<sup>3</sup>  |
+| RoBERTa-base (reported)   |  **83.2**/**86.4**<sup>2</sup> | 63.6 | 87.6 | 90.2 | 92.8 | 91.9 | 78.7 | 94.8 | 91.2 | 57.7<sup>3</sup> |
 | DistilRoBERTa<sup>1</sup> |  **79.0**/**82.3**<sup>2</sup> | 59.4 | 83.9 | 86.6 | 90.8 | 89.4 | 67.9 | 92.5 | 88.3 | 52.1              |
 
 <sup>1</sup> We did not use the MNLI checkpoint for fine-tuning but directy perform transfer learning on the pre-trained DistilRoBERTa.
@@ -41,14 +36,6 @@ Here are the results on the dev sets of GLUE:
 
 <sup>3</sup> We compute this score ourselves for completeness.
 
-Here are the results on the *test* sets for 6 of the languages available in XNLI. The results are computed in the zero shot setting (trained on the English portion and evaluated on the target language portion):
-
-| Model                        | English | Spanish | Chinese | German | Arabic  | Urdu |
-| :---:                        | :---:   | :---:   | :---:   | :---:  | :---:   | :---:|
-| mBERT base cased (computed)  | 82.1    | 74.6    | 69.1    | 72.3   | 66.4    | 58.5 |
-| mBERT base uncased (reported)| 81.4    | 74.3    | 63.8    | 70.5   | 62.1    | 58.3 |
-| DistilmBERT                  | 78.2    | 69.1    | 64.0    | 66.3   | 59.1    | 54.7 |
-
 ## 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`. 
@@ -58,14 +45,13 @@ This part of the library has only be tested with Python3.6+. There are few speci
 
 ## How to use DistilBERT
 
-Transformers includes five pre-trained Distil* models, currently only provided for English and German (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 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
-- `distilbert-base-german-cased`: DistilBERT German language model pretrained on 1/2 of the data used to pretrain Bert using distillation with the supervision of the `bert-base-german-dbmdz-cased` version of German DBMDZ Bert. For NER tasks the model reaches a F1 score of 83.49 on the CoNLL-2003 test set (for comparison, `bert-base-german-dbmdz-cased` reaches a 84.52 F1 score), and a F1 score of 85.23 on the GermEval 2014 test set (`bert-base-german-dbmdz-cased` reaches a 86.89 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. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2.
 - `distilroberta-base`: DistilRoBERTa English language model pretrained with the supervision of `roberta-base` solely on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.
-- `distilbert-base-multilingual-cased`: DistilmBERT multilingual model pretrained with the supervision of `bert-base-multilingual-cased` on the concatenation of Wikipedia in 104 different languages. The model supports the 104 languages listed [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages). The model has 6 layers, 768 dimension and 12 heads, totalizing 134M parameters (compared to 177M parameters for mBERT-base). On average DistilmBERT is twice as fast as mBERT-base.
+- 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.
 
@@ -81,7 +67,6 @@ last_hidden_states = outputs[0]  # The last hidden-state is the first element of
 Similarly, using the other Distil* models simply consists in calling the base classes with a different pretrained checkpoint:
 - DistilGPT2: `model = GPT2Model.from_pretrained('distilgpt2')`
 - DistilRoBERTa: `model = RobertaModel.from_pretrained('distilroberta-base')`
-- DistilmBERT: `model = DistilBertModel.from_pretrained('distilbert-base-multilingual-cased')`
 
 
 ## How to train Distil*

examples/distillation/distiller.py

@@ -21,6 +21,7 @@ import psutil
 import time
 from tqdm import trange, tqdm
 import numpy as np
+import psutil
 
 import torch
 import torch.nn as nn

examples/distillation/requirements.txt

@@ -3,4 +3,4 @@ tensorboard>=1.14.0
 tensorboardX==1.8
 psutil==5.6.3
 scipy==1.3.1
-transformers
+transformers==2.0.0

examples/pplm/README.md

@@ -1,54 +0,0 @@
-# Plug and Play Language Models: a Simple Approach to Controlled Text Generation
-
-Authors: [Sumanth Dathathri](https://dathath.github.io/), [Andrea Madotto](https://andreamad8.github.io/), Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/)
-
-This folder contains the original code used to run the Plug and Play Language Model (PPLM).
-
-Paper link: https://arxiv.org/abs/1912.02164
-
-Blog link: https://eng.uber.com/pplm
-
-Please check out the repo under uber-research for more information: https://github.com/uber-research/PPLM
-
-
-## Setup
-
-```bash
-git clone https://github.com/huggingface/transformers && cd transformers
-pip install [--editable] .
-pip install nltk torchtext # additional requirements.
-cd examples/pplm
-```
-
-## PPLM-BoW 
-
-### Example command for bag-of-words control
-
-```bash
-python run_pplm.py -B military --cond_text "The potato" --length 50 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.03 --window_length 5 --kl_scale 0.01 --gm_scale 0.99 --colorama --sample
-```
-
-### Tuning hyperparameters for bag-of-words control
-
-1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. 
-
-2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider: </br>
-	a) Reduce the `--stepsize` </br>
-	b) Increase `--kl_scale` (the KL-loss coefficient) or decrease `--gm_scale` (the gm-scaling term) </br>
-	c) Add `--grad-length xx` where xx is an (integer <= length, e.g. `--grad-length 30`).</br>
-
-
-## PPLM-Discrim
-
-### Example command for discriminator based sentiment control
-
-```bash
-python run_pplm.py -D sentiment --class_label 2 --cond_text "My dog died" --length 50 --gamma 1.0 --num_iterations 10 --num_samples 10 --stepsize 0.04 --kl_scale 0.01 --gm_scale 0.95 --sample
-```
-
-### Tuning hyperparameters for discriminator control
-
-1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model. 
-
-2. Use `--class_label 3` for negative, and `--class_label 2` for positive
-

examples/pplm/pplm_classification_head.py

@@ -1,18 +0,0 @@
-import torch
-
-class ClassificationHead(torch.nn.Module):
-    """Classification Head for  transformer encoders"""
-
-    def __init__(self, class_size, embed_size):
-        super(ClassificationHead, self).__init__()
-        self.class_size = class_size
-        self.embed_size = embed_size
-        # self.mlp1 = torch.nn.Linear(embed_size, embed_size)
-        # self.mlp2 = (torch.nn.Linear(embed_size, class_size))
-        self.mlp = torch.nn.Linear(embed_size, class_size)
-
-    def forward(self, hidden_state):
-        # hidden_state = F.relu(self.mlp1(hidden_state))
-        # hidden_state = self.mlp2(hidden_state)
-        logits = self.mlp(hidden_state)
-        return logits

examples/pplm/run_pplm.py

@@ -1,879 +0,0 @@
-#! /usr/bin/env python3
-# coding=utf-8
-
-#Copyright (c) 2019 Uber Technologies, 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.
-
-"""
-Example command with bag of words:
-python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
-
-Example command with discriminator:
-python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
-"""
-
-import argparse
-import json
-from operator import add
-from typing import List, Optional, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch.autograd import Variable
-from tqdm import trange
-
-from transformers import GPT2Tokenizer
-from transformers.file_utils import cached_path
-from transformers.modeling_gpt2 import GPT2LMHeadModel
-from pplm_classification_head import ClassificationHead
-
-PPLM_BOW = 1
-PPLM_DISCRIM = 2
-PPLM_BOW_DISCRIM = 3
-SMALL_CONST = 1e-15
-BIG_CONST = 1e10
-
-BAG_OF_WORDS_ARCHIVE_MAP = {
-    'legal': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/legal.txt",
-    'military': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/military.txt",
-    'politics': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/politics.txt",
-    'religion': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/religion.txt",
-    'science': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/science.txt",
-    'space': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/space.txt",
-    'technology': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/technology.txt",
-}
-
-DISCRIMINATOR_MODELS_PARAMS = {
-    "clickbait": {
-        "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifier_head.pt",
-        "class_size": 2,
-        "embed_size": 1024,
-        "class_vocab": {"non_clickbait": 0, "clickbait": 1},
-        "default_class": 1,
-        "pretrained_model": "gpt2-medium",
-    },
-    "sentiment": {
-        "url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/SST_classifier_head.pt",
-        "class_size": 5,
-        "embed_size": 1024,
-        "class_vocab": {"very_positive": 2, "very_negative": 3},
-        "default_class": 3,
-        "pretrained_model": "gpt2-medium",
-    },
-}
-
-
-def to_var(x, requires_grad=False, volatile=False, device='cuda'):
-    if torch.cuda.is_available() and device == 'cuda':
-        x = x.cuda()
-    elif device != 'cuda':
-        x = x.to(device)
-    return Variable(x, requires_grad=requires_grad, volatile=volatile)
-
-
-def top_k_filter(logits, k, probs=False):
-    """
-    Masks everything but the k top entries as -infinity (1e10).
-    Used to mask logits such that e^-infinity -> 0 won't contribute to the
-    sum of the denominator.
-    """
-    if k == 0:
-        return logits
-    else:
-        values = torch.topk(logits, k)[0]
-        batch_mins = values[:, -1].view(-1, 1).expand_as(logits)
-        if probs:
-            return torch.where(logits < batch_mins,
-                               torch.ones_like(logits) * 0.0, logits)
-        return torch.where(logits < batch_mins,
-                           torch.ones_like(logits) * -BIG_CONST,
-                           logits)
-
-
-def perturb_past(
-        past,
-        model,
-        last,
-        unpert_past=None,
-        unpert_logits=None,
-        accumulated_hidden=None,
-        grad_norms=None,
-        stepsize=0.01,
-        one_hot_bows_vectors=None,
-        classifier=None,
-        class_label=None,
-        loss_type=0,
-        num_iterations=3,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        kl_scale=0.01,
-        device='cuda',
-):
-    # Generate inital perturbed past
-    grad_accumulator = [
-        (np.zeros(p.shape).astype("float32"))
-        for p in past
-    ]
-
-    if accumulated_hidden is None:
-        accumulated_hidden = 0
-
-    if decay:
-        decay_mask = torch.arange(
-            0.,
-            1.0 + SMALL_CONST,
-            1.0 / (window_length)
-        )[1:]
-    else:
-        decay_mask = 1.0
-
-    # TODO fix this comment (SUMANTH)
-    # Generate a mask is gradient perturbated is based on a past window
-    _, _, _, curr_length, _ = past[0].shape
-
-    if curr_length > window_length and window_length > 0:
-        ones_key_val_shape = (
-                tuple(past[0].shape[:-2])
-                + tuple([window_length])
-                + tuple(past[0].shape[-1:])
-        )
-
-        zeros_key_val_shape = (
-                tuple(past[0].shape[:-2])
-                + tuple([curr_length - window_length])
-                + tuple(past[0].shape[-1:])
-        )
-
-        ones_mask = torch.ones(ones_key_val_shape)
-        ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3)
-        ones_mask = ones_mask.permute(0, 1, 2, 4, 3)
-
-        window_mask = torch.cat(
-            (ones_mask, torch.zeros(zeros_key_val_shape)),
-            dim=-2
-        ).to(device)
-    else:
-        window_mask = torch.ones_like(past[0]).to(device)
-
-    # accumulate perturbations for num_iterations
-    loss_per_iter = []
-    new_accumulated_hidden = None
-    for i in range(num_iterations):
-        print("Iteration ", i + 1)
-        curr_perturbation = [
-            to_var(torch.from_numpy(p_), requires_grad=True, device=device)
-            for p_ in grad_accumulator
-        ]
-
-        # Compute hidden using perturbed past
-        perturbed_past = list(map(add, past, curr_perturbation))
-        _, _, _, curr_length, _ = curr_perturbation[0].shape
-        all_logits, _, all_hidden = model(last, past=perturbed_past)
-        hidden = all_hidden[-1]
-        new_accumulated_hidden = accumulated_hidden + torch.sum(
-            hidden,
-            dim=1
-        ).detach()
-        # TODO: Check the layer-norm consistency of this with trained discriminator (Sumanth)
-        logits = all_logits[:, -1, :]
-        probs = F.softmax(logits, dim=-1)
-
-        loss = 0.0
-        loss_list = []
-        if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM:
-            for one_hot_bow in one_hot_bows_vectors:
-                bow_logits = torch.mm(probs, torch.t(one_hot_bow))
-                bow_loss = -torch.log(torch.sum(bow_logits))
-                loss += bow_loss
-                loss_list.append(bow_loss)
-            print(" pplm_bow_loss:", loss.data.cpu().numpy())
-
-        if loss_type == 2 or loss_type == 3:
-            ce_loss = torch.nn.CrossEntropyLoss()
-            # TODO why we need to do this assignment and not just using unpert_past? (Sumanth)
-            curr_unpert_past = unpert_past
-            curr_probs = torch.unsqueeze(probs, dim=1)
-            wte = model.resize_token_embeddings()
-            for _ in range(horizon_length):
-                inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
-                _, curr_unpert_past, curr_all_hidden = model(
-                    past=curr_unpert_past,
-                    inputs_embeds=inputs_embeds
-                )
-                curr_hidden = curr_all_hidden[-1]
-                new_accumulated_hidden = new_accumulated_hidden + torch.sum(
-                    curr_hidden, dim=1)
-
-            prediction = classifier(new_accumulated_hidden /
-                                    (curr_length + 1 + horizon_length))
-
-            label = torch.tensor(prediction.shape[0] * [class_label],
-                                 device=device,
-                                 dtype=torch.long)
-            discrim_loss = ce_loss(prediction, label)
-            print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy())
-            loss += discrim_loss
-            loss_list.append(discrim_loss)
-
-        kl_loss = 0.0
-        if kl_scale > 0.0:
-            unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
-            unpert_probs = (
-                    unpert_probs + SMALL_CONST *
-                    (unpert_probs <= SMALL_CONST).float().to(device).detach()
-            )
-            correction = SMALL_CONST * (probs <= SMALL_CONST).float().to(
-                device).detach()
-            corrected_probs = probs + correction.detach()
-            kl_loss = kl_scale * (
-                (corrected_probs * (corrected_probs / unpert_probs).log()).sum()
-            )
-            print(' kl_loss', kl_loss.data.cpu().numpy())
-            loss += kl_loss
-
-        loss_per_iter.append(loss.data.cpu().numpy())
-        print(' pplm_loss', (loss - kl_loss).data.cpu().numpy())
-
-        # compute gradients
-        loss.backward()
-
-        # calculate gradient norms
-        if grad_norms is not None and loss_type == PPLM_BOW:
-            grad_norms = [
-                torch.max(grad_norms[index], torch.norm(p_.grad * window_mask))
-                for index, p_ in enumerate(curr_perturbation)
-            ]
-        else:
-            grad_norms = [
-                (torch.norm(p_.grad * window_mask) + SMALL_CONST)
-                for index, p_ in enumerate(curr_perturbation)
-            ]
-
-        # normalize gradients
-        grad = [
-            -stepsize *
-            (p_.grad * window_mask / grad_norms[
-                index] ** gamma).data.cpu().numpy()
-            for index, p_ in enumerate(curr_perturbation)
-        ]
-
-        # accumulate gradient
-        grad_accumulator = list(map(add, grad, grad_accumulator))
-
-        # reset gradients, just to make sure
-        for p_ in curr_perturbation:
-            p_.grad.data.zero_()
-
-        # removing past from the graph
-        new_past = []
-        for p_ in past:
-            new_past.append(p_.detach())
-        past = new_past
-
-    # apply the accumulated perturbations to the past
-    grad_accumulator = [
-        to_var(torch.from_numpy(p_), requires_grad=True, device=device)
-        for p_ in grad_accumulator
-    ]
-    pert_past = list(map(add, past, grad_accumulator))
-
-    return pert_past, new_accumulated_hidden, grad_norms, loss_per_iter
-
-
-def get_classifier(
-        name: Optional[str], class_label: Union[str, int],
-        device: str
-) -> Tuple[Optional[ClassificationHead], Optional[int]]:
-    if name is None:
-        return None, None
-
-    params = DISCRIMINATOR_MODELS_PARAMS[name]
-    classifier = ClassificationHead(
-        class_size=params['class_size'],
-        embed_size=params['embed_size']
-    ).to(device)
-    if "url" in params:
-        resolved_archive_file = cached_path(params["url"])
-    elif "path" in params:
-        resolved_archive_file = params["path"]
-    else:
-        raise ValueError("Either url or path have to be specified "
-                         "in the discriminator model parameters")
-    classifier.load_state_dict(
-        torch.load(resolved_archive_file, map_location=device))
-    classifier.eval()
-
-    if isinstance(class_label, str):
-        if class_label in params["class_vocab"]:
-            label_id = params["class_vocab"][class_label]
-        else:
-            label_id = params["default_class"]
-            print("class_label {} not in class_vocab".format(class_label))
-            print("available values are: {}".format(params["class_vocab"]))
-            print("using default class {}".format(label_id))
-
-    elif isinstance(class_label, int):
-        if class_label in set(params["class_vocab"].values()):
-            label_id = class_label
-        else:
-            label_id = params["default_class"]
-            print("class_label {} not in class_vocab".format(class_label))
-            print("available values are: {}".format(params["class_vocab"]))
-            print("using default class {}".format(label_id))
-
-    else:
-        label_id = params["default_class"]
-
-    return classifier, label_id
-
-
-def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \
-        List[List[List[int]]]:
-    bow_indices = []
-    for id_or_path in bag_of_words_ids_or_paths:
-        if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP:
-            filepath = cached_path(BAG_OF_WORDS_ARCHIVE_MAP[id_or_path])
-        else:
-            filepath = id_or_path
-        with open(filepath, "r") as f:
-            words = f.read().strip().split("\n")
-        bow_indices.append(
-            [tokenizer.encode(word.strip(), add_prefix_space=True) for word in
-             words])
-    return bow_indices
-
-
-def build_bows_one_hot_vectors(bow_indices, tokenizer, device='cuda'):
-    if bow_indices is None:
-        return None
-
-    one_hot_bows_vectors = []
-    for single_bow in bow_indices:
-        single_bow = list(filter(lambda x: len(x) <= 1, single_bow))
-        single_bow = torch.tensor(single_bow).to(device)
-        num_words = single_bow.shape[0]
-        one_hot_bow = torch.zeros(num_words, tokenizer.vocab_size).to(device)
-        one_hot_bow.scatter_(1, single_bow, 1)
-        one_hot_bows_vectors.append(one_hot_bow)
-    return one_hot_bows_vectors
-
-
-def full_text_generation(
-        model,
-        tokenizer,
-        context=None,
-        num_samples=1,
-        device="cuda",
-        bag_of_words=None,
-        discrim=None,
-        class_label=None,
-        length=100,
-        stepsize=0.02,
-        temperature=1.0,
-        top_k=10,
-        sample=False,
-        num_iterations=3,
-        grad_length=10000,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        gm_scale=0.9,
-        kl_scale=0.01,
-        **kwargs
-):
-    classifier, class_id = get_classifier(
-        discrim,
-        class_label,
-        device
-    )
-
-    bow_indices = []
-    if bag_of_words:
-        bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
-                                               tokenizer)
-
-    if bag_of_words and classifier:
-        print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.")
-        loss_type = PPLM_BOW_DISCRIM
-
-    elif bag_of_words:
-        loss_type = PPLM_BOW
-        print("Using PPLM-BoW")
-
-    elif classifier is not None:
-        loss_type = PPLM_DISCRIM
-        print("Using PPLM-Discrim")
-
-    else:
-        raise Exception("Specify either a bag of words or a discriminator")
-
-    unpert_gen_tok_text, _, _ = generate_text_pplm(
-        model=model,
-        tokenizer=tokenizer,
-        context=context,
-        device=device,
-        length=length,
-        sample=sample,
-        perturb=False
-    )
-    if device == 'cuda':
-        torch.cuda.empty_cache()
-
-    pert_gen_tok_texts = []
-    discrim_losses = []
-    losses_in_time = []
-
-    for i in range(num_samples):
-        pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm(
-            model=model,
-            tokenizer=tokenizer,
-            context=context,
-            device=device,
-            perturb=True,
-            bow_indices=bow_indices,
-            classifier=classifier,
-            class_label=class_id,
-            loss_type=loss_type,
-            length=length,
-            stepsize=stepsize,
-            temperature=temperature,
-            top_k=top_k,
-            sample=sample,
-            num_iterations=num_iterations,
-            grad_length=grad_length,
-            horizon_length=horizon_length,
-            window_length=window_length,
-            decay=decay,
-            gamma=gamma,
-            gm_scale=gm_scale,
-            kl_scale=kl_scale,
-        )
-        pert_gen_tok_texts.append(pert_gen_tok_text)
-        if classifier is not None:
-            discrim_losses.append(discrim_loss.data.cpu().numpy())
-        losses_in_time.append(loss_in_time)
-
-    if device == 'cuda':
-        torch.cuda.empty_cache()
-
-    return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
-
-
-def generate_text_pplm(
-        model,
-        tokenizer,
-        context=None,
-        past=None,
-        device="cuda",
-        perturb=True,
-        bow_indices=None,
-        classifier=None,
-        class_label=None,
-        loss_type=0,
-        length=100,
-        stepsize=0.02,
-        temperature=1.0,
-        top_k=10,
-        sample=False,
-        num_iterations=3,
-        grad_length=10000,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        gm_scale=0.9,
-        kl_scale=0.01,
-):
-    output_so_far = None
-    if context:
-        context_t = torch.tensor(context, device=device, dtype=torch.long)
-        while len(context_t.shape) < 2:
-            context_t = context_t.unsqueeze(0)
-        output_so_far = context_t
-
-    # collect one hot vectors for bags of words
-    one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer,
-                                                      device)
-
-    grad_norms = None
-    last = None
-    unpert_discrim_loss = 0
-    loss_in_time = []
-    for i in trange(length, ascii=True):
-
-        # Get past/probs for current output, except for last word
-        # Note that GPT takes 2 inputs: past + current_token
-
-        # run model forward to obtain unperturbed
-        if past is None and output_so_far is not None:
-            last = output_so_far[:, -1:]
-            if output_so_far.shape[1] > 1:
-                _, past, _ = model(output_so_far[:, :-1])
-
-        unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far)
-        unpert_last_hidden = unpert_all_hidden[-1]
-
-        # check if we are abowe grad max length
-        if i >= grad_length:
-            current_stepsize = stepsize * 0
-        else:
-            current_stepsize = stepsize
-
-        # modify the past if necessary
-        if not perturb or num_iterations == 0:
-            pert_past = past
-
-        else:
-            accumulated_hidden = unpert_last_hidden[:, :-1, :]
-            accumulated_hidden = torch.sum(accumulated_hidden, dim=1)
-
-            if past is not None:
-                pert_past, _, grad_norms, loss_this_iter = perturb_past(
-                    past,
-                    model,
-                    last,
-                    unpert_past=unpert_past,
-                    unpert_logits=unpert_logits,
-                    accumulated_hidden=accumulated_hidden,
-                    grad_norms=grad_norms,
-                    stepsize=current_stepsize,
-                    one_hot_bows_vectors=one_hot_bows_vectors,
-                    classifier=classifier,
-                    class_label=class_label,
-                    loss_type=loss_type,
-                    num_iterations=num_iterations,
-                    horizon_length=horizon_length,
-                    window_length=window_length,
-                    decay=decay,
-                    gamma=gamma,
-                    kl_scale=kl_scale,
-                    device=device,
-                )
-                loss_in_time.append(loss_this_iter)
-            else:
-                pert_past = past
-
-        pert_logits, past, pert_all_hidden = model(last, past=pert_past)
-        pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST
-        pert_probs = F.softmax(pert_logits, dim=-1)
-
-        if classifier is not None:
-            ce_loss = torch.nn.CrossEntropyLoss()
-            prediction = classifier(torch.mean(unpert_last_hidden, dim=1))
-            label = torch.tensor([class_label], device=device,
-                                 dtype=torch.long)
-            unpert_discrim_loss = ce_loss(prediction, label)
-            print(
-                "unperturbed discrim loss",
-                unpert_discrim_loss.data.cpu().numpy()
-            )
-        else:
-            unpert_discrim_loss = 0
-
-        # Fuse the modified model and original model
-        if perturb:
-
-            unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
-
-            pert_probs = ((pert_probs ** gm_scale) * (
-                    unpert_probs ** (1 - gm_scale)))  # + SMALL_CONST
-            pert_probs = top_k_filter(pert_probs, k=top_k,
-                                      probs=True)  # + SMALL_CONST
-
-            # rescale
-            if torch.sum(pert_probs) <= 1:
-                pert_probs = pert_probs / torch.sum(pert_probs)
-
-        else:
-            pert_logits = top_k_filter(pert_logits, k=top_k)  # + SMALL_CONST
-            pert_probs = F.softmax(pert_logits, dim=-1)
-
-        # sample or greedy
-        if sample:
-            last = torch.multinomial(pert_probs, num_samples=1)
-
-        else:
-            _, last = torch.topk(pert_probs, k=1, dim=-1)
-
-        # update context/output_so_far appending the new token
-        output_so_far = (
-            last if output_so_far is None
-            else torch.cat((output_so_far, last), dim=1)
-        )
-
-        print(tokenizer.decode(output_so_far.tolist()[0]))
-
-    return output_so_far, unpert_discrim_loss, loss_in_time
-
-
-def set_generic_model_params(discrim_weights, discrim_meta):
-    if discrim_weights is None:
-        raise ValueError('When using a generic discriminator, '
-                         'discrim_weights need to be specified')
-    if discrim_meta is None:
-        raise ValueError('When using a generic discriminator, '
-                         'discrim_meta need to be specified')
-
-    with open(discrim_meta, 'r') as discrim_meta_file:
-        meta = json.load(discrim_meta_file)
-    meta['path'] = discrim_weights
-    DISCRIMINATOR_MODELS_PARAMS['generic'] = meta
-
-
-def run_pplm_example(
-        pretrained_model="gpt2-medium",
-        cond_text="",
-        uncond=False,
-        num_samples=1,
-        bag_of_words=None,
-        discrim=None,
-        discrim_weights=None,
-        discrim_meta=None,
-        class_label=-1,
-        length=100,
-        stepsize=0.02,
-        temperature=1.0,
-        top_k=10,
-        sample=False,
-        num_iterations=3,
-        grad_length=10000,
-        horizon_length=1,
-        window_length=0,
-        decay=False,
-        gamma=1.5,
-        gm_scale=0.9,
-        kl_scale=0.01,
-        seed=0,
-        no_cuda=False,
-        colorama=False
-):
-    # set Random seed
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-
-    # set the device
-    device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
-
-    if discrim == 'generic':
-        set_generic_model_params(discrim_weights, discrim_meta)
-
-    if discrim is not None:
-        pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][
-            "pretrained_model"
-        ]
-        print("discrim = {}, pretrained_model set "
-              "to discriminator's = {}".format(discrim, pretrained_model))
-
-    # load pretrained model
-    model = GPT2LMHeadModel.from_pretrained(
-        pretrained_model,
-        output_hidden_states=True
-    )
-    model.to(device)
-    model.eval()
-
-    # load tokenizer
-    tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
-
-    # Freeze GPT-2 weights
-    for param in model.parameters():
-        param.requires_grad = False
-
-    # figure out conditioning text
-    if uncond:
-        tokenized_cond_text = tokenizer.encode(
-            [tokenizer.bos_token]
-        )
-    else:
-        raw_text = cond_text
-        while not raw_text:
-            print("Did you forget to add `--cond_text`? ")
-            raw_text = input("Model prompt >>> ")
-        tokenized_cond_text = tokenizer.encode(tokenizer.bos_token + raw_text)
-
-    print("= Prefix of sentence =")
-    print(tokenizer.decode(tokenized_cond_text))
-    print()
-
-    # generate unperturbed and perturbed texts
-
-    # full_text_generation returns:
-    # unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
-    unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation(
-        model=model,
-        tokenizer=tokenizer,
-        context=tokenized_cond_text,
-        device=device,
-        num_samples=num_samples,
-        bag_of_words=bag_of_words,
-        discrim=discrim,
-        class_label=class_label,
-        length=length,
-        stepsize=stepsize,
-        temperature=temperature,
-        top_k=top_k,
-        sample=sample,
-        num_iterations=num_iterations,
-        grad_length=grad_length,
-        horizon_length=horizon_length,
-        window_length=window_length,
-        decay=decay,
-        gamma=gamma,
-        gm_scale=gm_scale,
-        kl_scale=kl_scale,
-    )
-
-    # untokenize unperturbed text
-    unpert_gen_text = tokenizer.decode(unpert_gen_tok_text.tolist()[0])
-
-    print("=" * 80)
-    print("= Unperturbed generated text =")
-    print(unpert_gen_text)
-    print()
-
-    generated_texts = []
-
-    bow_word_ids = set()
-    if bag_of_words and colorama:
-        bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
-                                               tokenizer)
-        for single_bow_list in bow_indices:
-            # filtering all words in the list composed of more than 1 token
-            filtered = list(filter(lambda x: len(x) <= 1, single_bow_list))
-            # w[0] because we are sure w has only 1 item because previous fitler
-            bow_word_ids.update(w[0] for w in filtered)
-
-    # iterate through the perturbed texts
-    for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts):
-        try:
-            # untokenize unperturbed text
-            if colorama:
-                import colorama
-
-                pert_gen_text = ''
-                for word_id in pert_gen_tok_text.tolist()[0]:
-                    if word_id in bow_word_ids:
-                        pert_gen_text += '{}{}{}'.format(
-                            colorama.Fore.RED,
-                            tokenizer.decode([word_id]),
-                            colorama.Style.RESET_ALL
-                        )
-                    else:
-                        pert_gen_text += tokenizer.decode([word_id])
-            else:
-                pert_gen_text = tokenizer.decode(pert_gen_tok_text.tolist()[0])
-
-            print("= Perturbed generated text {} =".format(i + 1))
-            print(pert_gen_text)
-            print()
-        except:
-            pass
-
-        # keep the prefix, perturbed seq, original seq for each index
-        generated_texts.append(
-            (tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text)
-        )
-
-    return
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--pretrained_model",
-        "-M",
-        type=str,
-        default="gpt2-medium",
-        help="pretrained model name or path to local checkpoint",
-    )
-    parser.add_argument(
-        "--cond_text", type=str, default="The lake",
-        help="Prefix texts to condition on"
-    )
-    parser.add_argument(
-        "--uncond", action="store_true",
-        help="Generate from end-of-text as prefix"
-    )
-    parser.add_argument(
-        "--num_samples",
-        type=int,
-        default=1,
-        help="Number of samples to generate from the modified latents",
-    )
-    parser.add_argument(
-        "--bag_of_words",
-        "-B",
-        type=str,
-        default=None,
-        help="Bags of words used for PPLM-BoW. "
-             "Either a BOW id (see list in code) or a filepath. "
-             "Multiple BoWs separated by ;",
-    )
-    parser.add_argument(
-        "--discrim",
-        "-D",
-        type=str,
-        default=None,
-        choices=("clickbait", "sentiment", "toxicity", "generic"),
-        help="Discriminator to use",
-    )
-    parser.add_argument('--discrim_weights', type=str, default=None,
-                        help='Weights for the generic discriminator')
-    parser.add_argument('--discrim_meta', type=str, default=None,
-                        help='Meta information for the generic discriminator')
-    parser.add_argument(
-        "--class_label",
-        type=int,
-        default=-1,
-        help="Class label used for the discriminator",
-    )
-    parser.add_argument("--length", type=int, default=100)
-    parser.add_argument("--stepsize", type=float, default=0.02)
-    parser.add_argument("--temperature", type=float, default=1.0)
-    parser.add_argument("--top_k", type=int, default=10)
-    parser.add_argument(
-        "--sample", action="store_true",
-        help="Generate from end-of-text as prefix"
-    )
-    parser.add_argument("--num_iterations", type=int, default=3)
-    parser.add_argument("--grad_length", type=int, default=10000)
-    parser.add_argument(
-        "--window_length",
-        type=int,
-        default=0,
-        help="Length of past which is being optimized; "
-             "0 corresponds to infinite window length",
-    )
-    parser.add_argument(
-        "--horizon_length",
-        type=int,
-        default=1,
-        help="Length of future to optimize over",
-    )
-    parser.add_argument("--decay", action="store_true",
-                        help="whether to decay or not")
-    parser.add_argument("--gamma", type=float, default=1.5)
-    parser.add_argument("--gm_scale", type=float, default=0.9)
-    parser.add_argument("--kl_scale", type=float, default=0.01)
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--no_cuda", action="store_true", help="no cuda")
-    parser.add_argument("--colorama", action="store_true",
-                        help="colors keywords")
-
-    args = parser.parse_args()
-    run_pplm_example(**vars(args))

examples/pplm/run_pplm_discrim_train.py

@@ -1,588 +0,0 @@
-#! /usr/bin/env python3
-# coding=utf-8
-
-#Copyright (c) 2019 Uber Technologies, 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.
-
-import argparse
-import csv
-import json
-import math
-import time
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.optim
-import torch.optim as optim
-import torch.utils.data as data
-from nltk.tokenize.treebank import TreebankWordDetokenizer
-from torchtext import data as torchtext_data
-from torchtext import datasets
-from tqdm import tqdm, trange
-
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-from pplm_classification_head import ClassificationHead
-
-torch.manual_seed(0)
-np.random.seed(0)
-EPSILON = 1e-10
-example_sentence = "This is incredible! I love it, this is the best chicken I have ever had."
-max_length_seq = 100
-
-
-
-
-class Discriminator(torch.nn.Module):
-    """Transformer encoder followed by a Classification Head"""
-
-    def __init__(
-            self,
-            class_size,
-            pretrained_model="gpt2-medium",
-            cached_mode=False,
-            device='cpu'
-    ):
-        super(Discriminator, self).__init__()
-        self.tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
-        self.encoder = GPT2LMHeadModel.from_pretrained(pretrained_model)
-        self.embed_size = self.encoder.transformer.config.hidden_size
-        self.classifier_head = ClassificationHead(
-            class_size=class_size,
-            embed_size=self.embed_size
-        )
-        self.cached_mode = cached_mode
-        self.device = device
-
-    def get_classifier(self):
-        return self.classifier_head
-
-    def train_custom(self):
-        for param in self.encoder.parameters():
-            param.requires_grad = False
-        self.classifier_head.train()
-
-    def avg_representation(self, x):
-        mask = x.ne(0).unsqueeze(2).repeat(
-            1, 1, self.embed_size
-        ).float().to(self.device).detach()
-        hidden, _ = self.encoder.transformer(x)
-        masked_hidden = hidden * mask
-        avg_hidden = torch.sum(masked_hidden, dim=1) / (
-                torch.sum(mask, dim=1).detach() + EPSILON
-        )
-        return avg_hidden
-
-    def forward(self, x):
-        if self.cached_mode:
-            avg_hidden = x.to(self.device)
-        else:
-            avg_hidden = self.avg_representation(x.to(self.device))
-
-        logits = self.classifier_head(avg_hidden)
-        probs = F.log_softmax(logits, dim=-1)
-
-        return probs
-
-
-class Dataset(data.Dataset):
-    def __init__(self, X, y):
-        """Reads source and target sequences from txt files."""
-        self.X = X
-        self.y = y
-
-    def __len__(self):
-        return len(self.X)
-
-    def __getitem__(self, index):
-        """Returns one data pair (source and target)."""
-        data = {}
-        data["X"] = self.X[index]
-        data["y"] = self.y[index]
-        return data
-
-
-def collate_fn(data):
-    def pad_sequences(sequences):
-        lengths = [len(seq) for seq in sequences]
-
-        padded_sequences = torch.zeros(
-            len(sequences),
-            max(lengths)
-        ).long()  # padding value = 0
-
-        for i, seq in enumerate(sequences):
-            end = lengths[i]
-            padded_sequences[i, :end] = seq[:end]
-
-        return padded_sequences, lengths
-
-    item_info = {}
-    for key in data[0].keys():
-        item_info[key] = [d[key] for d in data]
-
-    x_batch, _ = pad_sequences(item_info["X"])
-    y_batch = torch.tensor(item_info["y"], dtype=torch.long)
-
-    return x_batch, y_batch
-
-
-def cached_collate_fn(data):
-    item_info = {}
-    for key in data[0].keys():
-        item_info[key] = [d[key] for d in data]
-
-    x_batch = torch.cat(item_info["X"], 0)
-    y_batch = torch.tensor(item_info["y"], dtype=torch.long)
-
-    return x_batch, y_batch
-
-
-def train_epoch(data_loader, discriminator, optimizer,
-                epoch=0, log_interval=10, device='cpu'):
-    samples_so_far = 0
-    discriminator.train_custom()
-    for batch_idx, (input_t, target_t) in enumerate(data_loader):
-        input_t, target_t = input_t.to(device), target_t.to(device)
-
-        optimizer.zero_grad()
-
-        output_t = discriminator(input_t)
-        loss = F.nll_loss(output_t, target_t)
-        loss.backward(retain_graph=True)
-        optimizer.step()
-
-        samples_so_far += len(input_t)
-
-        if batch_idx % log_interval == 0:
-            print(
-                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
-                    epoch + 1,
-                    samples_so_far, len(data_loader.dataset),
-                    100 * samples_so_far / len(data_loader.dataset), loss.item()
-                )
-            )
-
-
-def evaluate_performance(data_loader, discriminator, device='cpu'):
-    discriminator.eval()
-    test_loss = 0
-    correct = 0
-    with torch.no_grad():
-        for input_t, target_t in data_loader:
-            input_t, target_t = input_t.to(device), target_t.to(device)
-            output_t = discriminator(input_t)
-            # sum up batch loss
-            test_loss += F.nll_loss(output_t, target_t, reduction="sum").item()
-            # get the index of the max log-probability
-            pred_t = output_t.argmax(dim=1, keepdim=True)
-            correct += pred_t.eq(target_t.view_as(pred_t)).sum().item()
-
-    test_loss /= len(data_loader.dataset)
-
-    print(
-        "Performance on test set: "
-        "Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
-            test_loss, correct, len(data_loader.dataset),
-            100. * correct / len(data_loader.dataset)
-        )
-    )
-
-
-def predict(input_sentence, model, classes, cached=False, device='cpu'):
-    input_t = model.tokenizer.encode(input_sentence)
-    input_t = torch.tensor([input_t], dtype=torch.long, device=device)
-    if cached:
-        input_t = model.avg_representation(input_t)
-
-    log_probs = model(input_t).data.cpu().numpy().flatten().tolist()
-    print("Input sentence:", input_sentence)
-    print("Predictions:", ", ".join(
-        "{}: {:.4f}".format(c, math.exp(log_prob)) for c, log_prob in
-        zip(classes, log_probs)
-    ))
-
-
-def get_cached_data_loader(dataset, batch_size, discriminator,
-                           shuffle=False, device='cpu'):
-    data_loader = torch.utils.data.DataLoader(dataset=dataset,
-                                              batch_size=batch_size,
-                                              collate_fn=collate_fn)
-
-    xs = []
-    ys = []
-    for batch_idx, (x, y) in enumerate(tqdm(data_loader, ascii=True)):
-        with torch.no_grad():
-            x = x.to(device)
-            avg_rep = discriminator.avg_representation(x).cpu().detach()
-            avg_rep_list = torch.unbind(avg_rep.unsqueeze(1))
-            xs += avg_rep_list
-            ys += y.cpu().numpy().tolist()
-
-    data_loader = torch.utils.data.DataLoader(
-        dataset=Dataset(xs, ys),
-        batch_size=batch_size,
-        shuffle=shuffle,
-        collate_fn=cached_collate_fn)
-
-    return data_loader
-
-
-def train_discriminator(
-        dataset, dataset_fp=None, pretrained_model="gpt2-medium",
-        epochs=10, batch_size=64, log_interval=10,
-        save_model=False, cached=False, no_cuda=False):
-    device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
-
-    print("Preprocessing {} dataset...".format(dataset))
-    start = time.time()
-
-    if dataset == "SST":
-        idx2class = ["positive", "negative", "very positive", "very negative",
-                     "neutral"]
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        text = torchtext_data.Field()
-        label = torchtext_data.Field(sequential=False)
-        train_data, val_data, test_data = datasets.SST.splits(
-            text,
-            label,
-            fine_grained=True,
-            train_subtrees=True,
-        )
-
-        x = []
-        y = []
-        for i in trange(len(train_data), ascii=True):
-            seq = TreebankWordDetokenizer().detokenize(
-                vars(train_data[i])["text"]
-            )
-            seq = discriminator.tokenizer.encode(seq)
-            seq = torch.tensor([50256] + seq, device=device, dtype=torch.long)
-            x.append(seq)
-            y.append(class2idx[vars(train_data[i])["label"]])
-        train_dataset = Dataset(x, y)
-
-        test_x = []
-        test_y = []
-        for i in trange(len(test_data), ascii=True):
-            seq = TreebankWordDetokenizer().detokenize(
-                vars(test_data[i])["text"]
-            )
-            seq = discriminator.tokenizer.encode(seq)
-            seq = torch.tensor([50256] + seq, device=device, dtype=torch.long)
-            test_x.append(seq)
-            test_y.append(class2idx[vars(test_data[i])["label"]])
-        test_dataset = Dataset(test_x, test_y)
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 2,
-        }
-
-    elif dataset == "clickbait":
-        idx2class = ["non_clickbait", "clickbait"]
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
-            data = []
-            for i, line in enumerate(f):
-                try:
-                    data.append(eval(line))
-                except:
-                    print("Error evaluating line {}: {}".format(
-                        i, line
-                    ))
-                    continue
-        x = []
-        y = []
-        with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
-            for i, line in enumerate(tqdm(f, ascii=True)):
-                try:
-                    d = eval(line)
-                    seq = discriminator.tokenizer.encode(d["text"])
-
-                    if len(seq) < max_length_seq:
-                        seq = torch.tensor(
-                            [50256] + seq, device=device, dtype=torch.long
-                        )
-                    else:
-                        print("Line {} is longer than maximum length {}".format(
-                            i, max_length_seq
-                        ))
-                        continue
-                    x.append(seq)
-                    y.append(d["label"])
-                except:
-                    print("Error evaluating / tokenizing"
-                          " line {}, skipping it".format(i))
-                    pass
-
-        full_dataset = Dataset(x, y)
-        train_size = int(0.9 * len(full_dataset))
-        test_size = len(full_dataset) - train_size
-        train_dataset, test_dataset = torch.utils.data.random_split(
-            full_dataset, [train_size, test_size]
-        )
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 1,
-        }
-
-    elif dataset == "toxic":
-        idx2class = ["non_toxic", "toxic"]
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        x = []
-        y = []
-        with open("datasets/toxic/toxic_train.txt") as f:
-            for i, line in enumerate(tqdm(f, ascii=True)):
-                try:
-                    d = eval(line)
-                    seq = discriminator.tokenizer.encode(d["text"])
-
-                    if len(seq) < max_length_seq:
-                        seq = torch.tensor(
-                            [50256] + seq, device=device, dtype=torch.long
-                        )
-                    else:
-                        print("Line {} is longer than maximum length {}".format(
-                            i, max_length_seq
-                        ))
-                        continue
-                    x.append(seq)
-                    y.append(int(np.sum(d["label"]) > 0))
-                except:
-                    print("Error evaluating / tokenizing"
-                          " line {}, skipping it".format(i))
-                    pass
-
-        full_dataset = Dataset(x, y)
-        train_size = int(0.9 * len(full_dataset))
-        test_size = len(full_dataset) - train_size
-        train_dataset, test_dataset = torch.utils.data.random_split(
-            full_dataset, [train_size, test_size]
-        )
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 0,
-        }
-
-    else:  # if dataset == "generic":
-        # This assumes the input dataset is a TSV with the following structure:
-        # class \t text
-
-        if dataset_fp is None:
-            raise ValueError("When generic dataset is selected, "
-                             "dataset_fp needs to be specified aswell.")
-
-        classes = set()
-        with open(dataset_fp) as f:
-            csv_reader = csv.reader(f, delimiter="\t")
-            for row in tqdm(csv_reader, ascii=True):
-                if row:
-                    classes.add(row[0])
-
-        idx2class = sorted(classes)
-        class2idx = {c: i for i, c in enumerate(idx2class)}
-
-        discriminator = Discriminator(
-            class_size=len(idx2class),
-            pretrained_model=pretrained_model,
-            cached_mode=cached,
-            device=device
-        ).to(device)
-
-        x = []
-        y = []
-        with open(dataset_fp) as f:
-            csv_reader = csv.reader(f, delimiter="\t")
-            for i, row in enumerate(tqdm(csv_reader, ascii=True)):
-                if row:
-                    label = row[0]
-                    text = row[1]
-
-                    try:
-                        seq = discriminator.tokenizer.encode(text)
-                        if (len(seq) < max_length_seq):
-                            seq = torch.tensor(
-                                [50256] + seq,
-                                device=device,
-                                dtype=torch.long
-                            )
-
-                        else:
-                            print(
-                                "Line {} is longer than maximum length {}".format(
-                                    i, max_length_seq
-                                ))
-                            continue
-
-                        x.append(seq)
-                        y.append(class2idx[label])
-
-                    except:
-                        print("Error tokenizing line {}, skipping it".format(i))
-                        pass
-
-        full_dataset = Dataset(x, y)
-        train_size = int(0.9 * len(full_dataset))
-        test_size = len(full_dataset) - train_size
-        train_dataset, test_dataset = torch.utils.data.random_split(
-            full_dataset,
-            [train_size, test_size]
-        )
-
-        discriminator_meta = {
-            "class_size": len(idx2class),
-            "embed_size": discriminator.embed_size,
-            "pretrained_model": pretrained_model,
-            "class_vocab": class2idx,
-            "default_class": 0,
-        }
-
-    end = time.time()
-    print("Preprocessed {} data points".format(
-        len(train_dataset) + len(test_dataset))
-    )
-    print("Data preprocessing took: {:.3f}s".format(end - start))
-
-    if cached:
-        print("Building representation cache...")
-
-        start = time.time()
-
-        train_loader = get_cached_data_loader(
-            train_dataset, batch_size, discriminator,
-            shuffle=True, device=device
-        )
-
-        test_loader = get_cached_data_loader(
-            test_dataset, batch_size, discriminator, device=device
-        )
-
-        end = time.time()
-        print("Building representation cache took: {:.3f}s".format(end - start))
-
-    else:
-        train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
-                                                   batch_size=batch_size,
-                                                   shuffle=True,
-                                                   collate_fn=collate_fn)
-        test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
-                                                  batch_size=batch_size,
-                                                  collate_fn=collate_fn)
-
-    if save_model:
-        with open("{}_classifier_head_meta.json".format(dataset),
-                  "w") as meta_file:
-            json.dump(discriminator_meta, meta_file)
-
-    optimizer = optim.Adam(discriminator.parameters(), lr=0.0001)
-
-    for epoch in range(epochs):
-        start = time.time()
-        print("\nEpoch", epoch + 1)
-
-        train_epoch(
-            discriminator=discriminator,
-            data_loader=train_loader,
-            optimizer=optimizer,
-            epoch=epoch,
-            log_interval=log_interval,
-            device=device
-        )
-        evaluate_performance(
-            data_loader=test_loader,
-            discriminator=discriminator,
-            device=device
-        )
-
-        end = time.time()
-        print("Epoch took: {:.3f}s".format(end - start))
-
-        print("\nExample prediction")
-        predict(example_sentence, discriminator, idx2class,
-                cached=cached, device=device)
-
-        if save_model:
-            # torch.save(discriminator.state_dict(),
-            #           "{}_discriminator_{}.pt".format(
-            #               args.dataset, epoch + 1
-            #               ))
-            torch.save(discriminator.get_classifier().state_dict(),
-                       "{}_classifier_head_epoch_{}.pt".format(dataset,
-                                                               epoch + 1))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Train a discriminator on top of GPT-2 representations")
-    parser.add_argument("--dataset", type=str, default="SST",
-                        choices=("SST", "clickbait", "toxic", "generic"),
-                        help="dataset to train the discriminator on."
-                             "In case of generic, the dataset is expected"
-                             "to be a TSBV file with structure: class \\t text")
-    parser.add_argument("--dataset_fp", type=str, default="",
-                        help="File path of the dataset to use. "
-                             "Needed only in case of generic datadset")
-    parser.add_argument("--pretrained_model", type=str, default="gpt2-medium",
-                        help="Pretrained model to use as encoder")
-    parser.add_argument("--epochs", type=int, default=10, metavar="N",
-                        help="Number of training epochs")
-    parser.add_argument("--batch_size", type=int, default=64, metavar="N",
-                        help="input batch size for training (default: 64)")
-    parser.add_argument("--log_interval", type=int, default=10, metavar="N",
-                        help="how many batches to wait before logging training status")
-    parser.add_argument("--save_model", action="store_true",
-                        help="whether to save the model")
-    parser.add_argument("--cached", action="store_true",
-                        help="whether to cache the input representations")
-    parser.add_argument("--no_cuda", action="store_true",
-                        help="use to turn off cuda")
-    args = parser.parse_args()
-
-    train_discriminator(**(vars(args)))

examples/run_glue.py

@@ -22,7 +22,6 @@ import glob
 import logging
 import os
 import random
-import json
 
 import numpy as np
 import torch
@@ -177,23 +176,15 @@ def train(args, train_dataset, model, tokenizer):
                 global_step += 1
 
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    logs = {}
+                    # Log metrics
                     if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
                         results = evaluate(args, model, tokenizer)
                         for key, value in results.items():
-                            eval_key = 'eval_{}'.format(key)
-                            logs[eval_key] = value
-
-                    loss_scalar = (tr_loss - logging_loss) / args.logging_steps
-                    learning_rate_scalar = scheduler.get_lr()[0]
-                    logs['learning_rate'] = learning_rate_scalar
-                    logs['loss'] = loss_scalar
+                            tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
+                    tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
+                    tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                     logging_loss = tr_loss
 
-                    for key, value in logs.items():
-                        tb_writer.add_scalar(key, value, global_step)
-                    print(json.dumps({**logs, **{'step': global_step}}))
-
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     # Save model checkpoint
                     output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
@@ -231,7 +222,7 @@ def evaluate(args, model, tokenizer, prefix=""):
 
         args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
         # Note that DistributedSampler samples randomly
-        eval_sampler = SequentialSampler(eval_dataset)
+        eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
         eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
         # multi-gpu eval
@@ -380,7 +371,7 @@ def main():
     parser.add_argument("--learning_rate", default=5e-5, type=float,
                         help="The initial learning rate for Adam.")
     parser.add_argument("--weight_decay", default=0.0, type=float,
-                        help="Weight decay if we apply some.")
+                        help="Weight deay if we apply some.")
     parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                         help="Epsilon for Adam optimizer.")
     parser.add_argument("--max_grad_norm", default=1.0, type=float,

examples/run_lm_finetuning.py

@@ -47,8 +47,7 @@ from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
                                   GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
                                   OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
                                   RobertaConfig, RobertaForMaskedLM, RobertaTokenizer,
-                                  DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer,
-                                  CamembertConfig, CamembertForMaskedLM, CamembertTokenizer)
+                                  DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
 
 
 logger = logging.getLogger(__name__)
@@ -59,8 +58,7 @@ MODEL_CLASSES = {
     'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
     'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
     'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
-    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
-    'camembert': (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer)
+    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
 }
 
 
@@ -188,13 +186,6 @@ def train(args, train_dataset, model, tokenizer):
         ]
     optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
     scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
-
-    # Check if saved optimizer or scheduler states exist
-    if os.path.isfile(os.path.join(args.model_name_or_path, 'optimizer.pt')) and os.path.isfile(os.path.join(args.model_name_or_path, 'scheduler.pt')):
-        # Load in optimizer and scheduler states
-        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'optimizer.pt')))
-        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, 'scheduler.pt')))
-
     if args.fp16:
         try:
             from apex import amp
@@ -223,37 +214,14 @@ def train(args, train_dataset, model, tokenizer):
     logger.info("  Total optimization steps = %d", t_total)
 
     global_step = 0
-    epochs_trained = 0
-    steps_trained_in_current_epoch = 0
-    # Check if continuing training from a checkpoint
-    if os.path.exists(args.model_name_or_path):
-        # set global_step to gobal_step of last saved checkpoint from model path
-        global_step = int(args.model_name_or_path.split('-')[-1].split('/')[0])
-        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
-        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
-
-        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-        logger.info("  Continuing training from epoch %d", epochs_trained)
-        logger.info("  Continuing training from global step %d", global_step)
-        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
-
     tr_loss, logging_loss = 0.0, 0.0
-
-    model_to_resize = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
-    model_to_resize.resize_token_embeddings(len(tokenizer))
-
+    model.resize_token_embeddings(len(tokenizer))
     model.zero_grad()
-    train_iterator = trange(epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
+    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
     set_seed(args)  # Added here for reproducibility (even between python 2 and 3)
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-            
-            # Skip past any already trained steps if resuming training
-            if steps_trained_in_current_epoch > 0:
-                steps_trained_in_current_epoch -= 1
-                continue
-
             inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
             inputs = inputs.to(args.device)
             labels = labels.to(args.device)
@@ -301,17 +269,11 @@ def train(args, train_dataset, model, tokenizer):
                         os.makedirs(output_dir)
                     model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
                     model_to_save.save_pretrained(output_dir)
-                    tokenizer.save_pretrained(output_dir)
-
                     torch.save(args, os.path.join(output_dir, 'training_args.bin'))
                     logger.info("Saving model checkpoint to %s", output_dir)
 
                     _rotate_checkpoints(args, checkpoint_prefix)
 
-                    torch.save(optimizer.state_dict(), os.path.join(output_dir, 'optimizer.pt'))
-                    torch.save(scheduler.state_dict(), os.path.join(output_dir, 'scheduler.pt'))
-                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
-
             if args.max_steps > 0 and global_step > args.max_steps:
                 epoch_iterator.close()
                 break
@@ -336,7 +298,7 @@ def evaluate(args, model, tokenizer, prefix=""):
 
     args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
     # Note that DistributedSampler samples randomly
-    eval_sampler = SequentialSampler(eval_dataset)
+    eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
     eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
     # multi-gpu evaluate
@@ -470,7 +432,7 @@ def main():
     parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
     args = parser.parse_args()
 
-    if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
+    if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm:
         raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm "
                          "flag (masked language modeling).")
     if args.eval_data_file is None and args.do_eval:

examples/run_multiple_choice.py

@@ -226,7 +226,7 @@ def evaluate(args, model, tokenizer, prefix="", test=False):
 
         args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
         # Note that DistributedSampler samples randomly
-        eval_sampler = SequentialSampler(eval_dataset)
+        eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
         eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
         # multi-gpu evaluate

examples/run_squad.py

@@ -16,8 +16,6 @@
 """ Finetuning the library models for question-answering on SQuAD (DistilBERT, Bert, XLM, XLNet)."""
 
 from __future__ import absolute_import, division, print_function
-from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor, SquadResult
-from transformers.data.metrics.squad_metrics import compute_predictions_logits, compute_predictions_log_probs, squad_evaluate
 
 import argparse
 import logging
@@ -25,9 +23,11 @@ import os
 import random
 import glob
 import timeit
+
 import numpy as np
 import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
+from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
+                              TensorDataset)
 from torch.utils.data.distributed import DistributedSampler
 
 try:
@@ -44,11 +44,18 @@ from transformers import (WEIGHTS_NAME, BertConfig,
                                   XLNetForQuestionAnswering,
                                   XLNetTokenizer,
                                   DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer,
-                                  AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer,
-                                  XLMConfig, XLMForQuestionAnswering, XLMTokenizer,
-                                  )
+                                  AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
 
-from transformers import AdamW, get_linear_schedule_with_warmup, squad_convert_examples_to_features
+from transformers import AdamW, get_linear_schedule_with_warmup
+
+from utils_squad import (read_squad_examples, convert_examples_to_features,
+                         RawResult, write_predictions,
+                         RawResultExtended, write_predictions_extended)
+
+# The follwing import is the official SQuAD evaluation script (2.0).
+# You can remove it from the dependencies if you are using this script outside of the library
+# We've added it here for automated tests (see examples/test_examples.py file)
+from utils_squad_evaluate import EVAL_OPTS, main as evaluate_on_squad
 
 logger = logging.getLogger(__name__)
 
@@ -60,8 +67,7 @@ MODEL_CLASSES = {
     'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
     'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
     'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
-    'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer),
-    'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer)
+    'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
 }
 
 def set_seed(args):
@@ -94,16 +100,14 @@ def train(args, train_dataset, model, tokenizer):
     optimizer_grouped_parameters = [
         {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
         {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
-    ]
+        ]
     optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
     scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
-
     if args.fp16:
         try:
             from apex import amp
         except ImportError:
             raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        
         model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
 
     # multi-gpu training (should be after apex fp16 initialization)
@@ -131,26 +135,20 @@ def train(args, train_dataset, model, tokenizer):
     model.zero_grad()
     train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
     set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
-    
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
             model.train()
             batch = tuple(t.to(args.device) for t in batch)
-
-            inputs = {
-                'input_ids':       batch[0],
-                'attention_mask':  batch[1],
-                'start_positions': batch[3],
-                'end_positions':   batch[4]
-            }
-
+            inputs = {'input_ids':       batch[0],
+                      'attention_mask':  batch[1],
+                      'start_positions': batch[3],
+                      'end_positions':   batch[4]}
             if args.model_type != 'distilbert':
                 inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2]
-
             if args.model_type in ['xlnet', 'xlm']:
-                inputs.update({'cls_index': batch[5], 'p_mask': batch[6]})
-
+                inputs.update({'cls_index': batch[5],
+                               'p_mask':       batch[6]})
             outputs = model(**inputs)
             loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
@@ -177,8 +175,8 @@ def train(args, train_dataset, model, tokenizer):
                 model.zero_grad()
                 global_step += 1
 
-                # Log metrics
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
+                    # Log metrics
                     if args.local_rank == -1 and args.evaluate_during_training:  # Only evaluate when single GPU otherwise metrics may not average well
                         results = evaluate(args, model, tokenizer)
                         for key, value in results.items():
@@ -187,8 +185,8 @@ def train(args, train_dataset, model, tokenizer):
                     tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                     logging_loss = tr_loss
 
-                # Save model checkpoint
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
+                    # Save model checkpoint
                     output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
                     if not os.path.exists(output_dir):
                         os.makedirs(output_dir)
@@ -217,9 +215,8 @@ def evaluate(args, model, tokenizer, prefix=""):
         os.makedirs(args.output_dir)
 
     args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-
     # Note that DistributedSampler samples randomly
-    eval_sampler = SequentialSampler(dataset)
+    eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
     eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
     # multi-gpu evaluate
@@ -230,59 +227,38 @@ def evaluate(args, model, tokenizer, prefix=""):
     logger.info("***** Running evaluation {} *****".format(prefix))
     logger.info("  Num examples = %d", len(dataset))
     logger.info("  Batch size = %d", args.eval_batch_size)
-
     all_results = []
     start_time = timeit.default_timer()
-
     for batch in tqdm(eval_dataloader, desc="Evaluating"):
         model.eval()
         batch = tuple(t.to(args.device) for t in batch)
-
         with torch.no_grad():
-            inputs = {
-                'input_ids':      batch[0],
-                'attention_mask': batch[1]
-            }
-            
+            inputs = {'input_ids':      batch[0],
+                      'attention_mask': batch[1]
+                      }
             if args.model_type != 'distilbert':
                 inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2]  # XLM don't use segment_ids
-
             example_indices = batch[3]
-            
-            # XLNet and XLM use more arguments for their predictions
             if args.model_type in ['xlnet', 'xlm']:
-                inputs.update({'cls_index': batch[4], 'p_mask': batch[5]})
-
+                inputs.update({'cls_index': batch[4],
+                               'p_mask':    batch[5]})
             outputs = model(**inputs)
 
         for i, example_index in enumerate(example_indices):
             eval_feature = features[example_index.item()]
             unique_id = int(eval_feature.unique_id)
-
-            output = [to_list(output[i]) for output in outputs]
-
-            # Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
-            # models only use two.
-            if len(output) >= 5:
-                start_logits = output[0]
-                start_top_index = output[1]
-                end_logits = output[2]
-                end_top_index = output[3]
-                cls_logits = output[4]
-
-                result = SquadResult(
-                    unique_id, start_logits, end_logits, 
-                    start_top_index=start_top_index, 
-                    end_top_index=end_top_index, 
-                    cls_logits=cls_logits
-                )
-
+            if args.model_type in ['xlnet', 'xlm']:
+                # XLNet uses a more complex post-processing procedure
+                result = RawResultExtended(unique_id            = unique_id,
+                                           start_top_log_probs  = to_list(outputs[0][i]),
+                                           start_top_index      = to_list(outputs[1][i]),
+                                           end_top_log_probs    = to_list(outputs[2][i]),
+                                           end_top_index        = to_list(outputs[3][i]),
+                                           cls_logits           = to_list(outputs[4][i]))
             else:
-                start_logits, end_logits = output
-                result = SquadResult(
-                    unique_id, start_logits, end_logits
-                )
-
+                result = RawResult(unique_id    = unique_id,
+                                   start_logits = to_list(outputs[0][i]),
+                                   end_logits   = to_list(outputs[1][i]))
             all_results.append(result)
 
     evalTime = timeit.default_timer() - start_time
@@ -291,88 +267,84 @@ def evaluate(args, model, tokenizer, prefix=""):
     # Compute predictions
     output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
     output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
-
     if args.version_2_with_negative:
         output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
     else:
         output_null_log_odds_file = None
 
-    # XLNet and XLM use a more complex post-processing procedure
     if args.model_type in ['xlnet', 'xlm']:
-        start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top
-        end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top
-
-        predictions = compute_predictions_log_probs(examples, features, all_results, args.n_best_size,
+        # XLNet uses a more complex post-processing procedure
+        write_predictions_extended(examples, features, all_results, args.n_best_size,
                         args.max_answer_length, output_prediction_file,
-                        output_nbest_file, output_null_log_odds_file,
-                        start_n_top, end_n_top,
+                        output_nbest_file, output_null_log_odds_file, args.predict_file,
+                        model.config.start_n_top, model.config.end_n_top,
                         args.version_2_with_negative, tokenizer, args.verbose_logging)
     else:
-        predictions = compute_predictions_logits(examples, features, all_results, args.n_best_size,
+        write_predictions(examples, features, all_results, args.n_best_size,
                         args.max_answer_length, args.do_lower_case, output_prediction_file,
                         output_nbest_file, output_null_log_odds_file, args.verbose_logging,
                         args.version_2_with_negative, args.null_score_diff_threshold)
 
-    # Compute the F1 and exact scores.
-    results = squad_evaluate(examples, predictions)
+    # Evaluate with the official SQuAD script
+    evaluate_options = EVAL_OPTS(data_file=args.predict_file,
+                                 pred_file=output_prediction_file,
+                                 na_prob_file=output_null_log_odds_file)
+    results = evaluate_on_squad(evaluate_options)
     return results
 
+
 def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
     if args.local_rank not in [-1, 0] and not evaluate:
         torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
 
     # Load data features from cache or dataset file
-    input_dir = args.data_dir if args.data_dir else "."
-    cached_features_file = os.path.join(input_dir, 'cached_{}_{}_{}'.format(
+    input_file = args.predict_file if evaluate else args.train_file
+    cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
         'dev' if evaluate else 'train',
         list(filter(None, args.model_name_or_path.split('/'))).pop(),
-        str(args.max_seq_length))
-    )
-
-    # Init features and dataset from cache if it exists
+        str(args.max_seq_length)))
     if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
         logger.info("Loading features from cached file %s", cached_features_file)
-        features_and_dataset = torch.load(cached_features_file)
-        features, dataset = features_and_dataset["features"], features_and_dataset["dataset"]
+        features = torch.load(cached_features_file)
     else:
-        logger.info("Creating features from dataset file at %s", input_dir)
-
-        if not args.data_dir and ((evaluate and not args.predict_file) or (not evaluate and not args.train_file)):
-            try:
-                import tensorflow_datasets as tfds
-            except ImportError:
-                raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
-
-            if args.version_2_with_negative:
-                logger.warn("tensorflow_datasets does not handle version 2 of SQuAD.")
-
-            tfds_examples = tfds.load("squad")
-            examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
-        else:
-            processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
-
-            if evaluate:
-                examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
-            else:
-                examples = processor.get_train_examples(args.data_dir, filename=args.train_file)
-
-        features, dataset = squad_convert_examples_to_features( 
-            examples=examples,
-            tokenizer=tokenizer,
-            max_seq_length=args.max_seq_length,
-            doc_stride=args.doc_stride,
-            max_query_length=args.max_query_length,
-            is_training=not evaluate,
-            return_dataset='pt'
-        )
-
+        logger.info("Creating features from dataset file at %s", input_file)
+        examples = read_squad_examples(input_file=input_file,
+                                                is_training=not evaluate,
+                                                version_2_with_negative=args.version_2_with_negative)
+        features = convert_examples_to_features(examples=examples,
+                                                tokenizer=tokenizer,
+                                                max_seq_length=args.max_seq_length,
+                                                doc_stride=args.doc_stride,
+                                                max_query_length=args.max_query_length,
+                                                is_training=not evaluate,
+                                                cls_token_segment_id=2 if args.model_type in ['xlnet'] else 0,
+                                                pad_token_segment_id=3 if args.model_type in ['xlnet'] else 0,
+                                                cls_token_at_end=True if args.model_type in ['xlnet'] else False,
+                                                sequence_a_is_doc=True if args.model_type in ['xlnet'] else False)
         if args.local_rank in [-1, 0]:
             logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save({"features": features, "dataset": dataset}, cached_features_file)
+            torch.save(features, cached_features_file)
 
     if args.local_rank == 0 and not evaluate:
         torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
 
+    # Convert to Tensors and build dataset
+    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
+    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
+    all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
+    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
+    if evaluate:
+        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
+        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                all_example_index, all_cls_index, all_p_mask)
+    else:
+        all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
+        all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
+        dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
+                                all_start_positions, all_end_positions,
+                                all_cls_index, all_p_mask)
+
     if output_examples:
         return dataset, examples, features
     return dataset
@@ -382,6 +354,10 @@ def main():
     parser = argparse.ArgumentParser()
 
     ## Required parameters
+    parser.add_argument("--train_file", default=None, type=str, required=True,
+                        help="SQuAD json for training. E.g., train-v1.1.json")
+    parser.add_argument("--predict_file", default=None, type=str, required=True,
+                        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
     parser.add_argument("--model_type", default=None, type=str, required=True,
                         help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
     parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
@@ -390,15 +366,6 @@ def main():
                         help="The output directory where the model checkpoints and predictions will be written.")
 
     ## Other parameters
-    parser.add_argument("--data_dir", default=None, type=str,
-                        help="The input data dir. Should contain the .json files for the task." +
-                             "If no data dir or train/predict files are specified, will run with tensorflow_datasets.")
-    parser.add_argument("--train_file", default=None, type=str,
-                        help="The input training file. If a data dir is specified, will look for the file there" +
-                             "If no data dir or train/predict files are specified, will run with tensorflow_datasets.")
-    parser.add_argument("--predict_file", default=None, type=str,
-                        help="The input evaluation file. If a data dir is specified, will look for the file there" +
-                             "If no data dir or train/predict files are specified, will run with tensorflow_datasets.")
     parser.add_argument("--config_name", default="", type=str,
                         help="Pretrained config name or path if not the same as model_name")
     parser.add_argument("--tokenizer_name", default="", type=str,
@@ -580,16 +547,10 @@ def main():
     # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
-
-        if args.do_train:
-            logger.info("Loading checkpoints saved during training for evaluation")
-            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("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
-        else:
-            logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path)
-            checkpoints = [args.model_name_or_path]
+        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("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
 
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
 

examples/run_summarization_finetuning.py

@@ -0,0 +1,492 @@
+# coding=utf-8
+# Copyright 2019 The HuggingFace Inc. team.
+# Copyright (c) 2019 The HuggingFace Inc.  All rights reserved.
+#
+# 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.
+""" Finetuning seq2seq models for sequence generation."""
+
+import argparse
+import functools
+import logging
+import os
+import random
+import sys
+
+import numpy as np
+from tqdm import tqdm, trange
+import torch
+from torch.optim import Adam
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+
+from transformers import (
+    AutoTokenizer,
+    BertForMaskedLM,
+    BertConfig,
+    PreTrainedEncoderDecoder,
+    Model2Model,
+)
+
+from utils_summarization import (
+    CNNDailyMailDataset,
+    encode_for_summarization,
+    fit_to_block_size,
+    build_lm_labels,
+    build_mask,
+    compute_token_type_ids,
+)
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+
+
+def set_seed(args):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+
+
+# ------------
+# Load dataset
+# ------------
+
+
+def load_and_cache_examples(args, tokenizer):
+    dataset = CNNDailyMailDataset(tokenizer, data_dir=args.data_dir)
+    return dataset
+
+
+def collate(data, tokenizer, block_size):
+    """ List of tuple as an input. """
+    # remove the files with empty an story/summary, encode and fit to block
+    data = filter(lambda x: not (len(x[0]) == 0 or len(x[1]) == 0), data)
+    data = [
+        encode_for_summarization(story, summary, tokenizer) for story, summary in data
+    ]
+    data = [
+        (
+            fit_to_block_size(story, block_size, tokenizer.pad_token_id),
+            fit_to_block_size(summary, block_size, tokenizer.pad_token_id),
+        )
+        for story, summary in data
+    ]
+
+    stories = torch.tensor([story for story, summary in data])
+    summaries = torch.tensor([summary for story, summary in data])
+    encoder_token_type_ids = compute_token_type_ids(stories, tokenizer.cls_token_id)
+    encoder_mask = build_mask(stories, tokenizer.pad_token_id)
+    decoder_mask = build_mask(summaries, tokenizer.pad_token_id)
+    lm_labels = build_lm_labels(summaries, tokenizer.pad_token_id)
+
+    return (
+        stories,
+        summaries,
+        encoder_token_type_ids,
+        encoder_mask,
+        decoder_mask,
+        lm_labels,
+    )
+
+
+# ----------
+# Optimizers
+# ----------
+
+
+class BertSumOptimizer(object):
+    """ Specific optimizer for BertSum.
+
+    As described in [1], the authors fine-tune BertSum for abstractive
+    summarization using two Adam Optimizers with different warm-up steps and
+    learning rate. They also use a custom learning rate scheduler.
+
+    [1] Liu, Yang, and Mirella Lapata. "Text summarization with pretrained encoders."
+        arXiv preprint arXiv:1908.08345 (2019).
+    """
+
+    def __init__(self, model, lr, warmup_steps, beta_1=0.99, beta_2=0.999, eps=1e-8):
+        self.encoder = model.encoder
+        self.decoder = model.decoder
+        self.lr = lr
+        self.warmup_steps = warmup_steps
+
+        self.optimizers = {
+            "encoder": Adam(
+                model.encoder.parameters(),
+                lr=lr["encoder"],
+                betas=(beta_1, beta_2),
+                eps=eps,
+            ),
+            "decoder": Adam(
+                model.decoder.parameters(),
+                lr=lr["decoder"],
+                betas=(beta_1, beta_2),
+                eps=eps,
+            ),
+        }
+
+        self._step = 0
+
+    def _update_rate(self, stack):
+        return self.lr[stack] * min(
+            self._step ** (-0.5), self._step * self.warmup_steps[stack] ** (-0.5)
+        )
+
+    def zero_grad(self):
+        self.optimizer_decoder.zero_grad()
+        self.optimizer_encoder.zero_grad()
+
+    def step(self):
+        self._step += 1
+        for stack, optimizer in self.optimizers.items():
+            new_rate = self._update_rate(stack)
+            for param_group in optimizer.param_groups:
+                param_group["lr"] = new_rate
+            optimizer.step()
+
+
+# ------------
+# Train
+# ------------
+
+
+def train(args, model, tokenizer):
+    """ Fine-tune the pretrained model on the corpus. """
+    set_seed(args)
+
+    # Load the data
+    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
+    train_dataset = load_and_cache_examples(args, tokenizer)
+    train_sampler = RandomSampler(train_dataset)
+    model_collate_fn = functools.partial(collate, tokenizer=tokenizer, block_size=512)
+    train_dataloader = DataLoader(
+        train_dataset,
+        sampler=train_sampler,
+        batch_size=args.train_batch_size,
+        collate_fn=model_collate_fn,
+    )
+
+    # Training schedule
+    if args.max_steps > 0:
+        t_total = args.max_steps
+        args.num_train_epochs = t_total // (
+            len(train_dataloader) // args.gradient_accumulation_steps + 1
+        )
+    else:
+        t_total = (
+            len(train_dataloader)
+            // args.gradient_accumulation_steps
+            * args.num_train_epochs
+        )
+
+    # Prepare the optimizer
+    lr = {"encoder": 0.002, "decoder": 0.2}
+    warmup_steps = {"encoder": 20000, "decoder": 10000}
+    optimizer = BertSumOptimizer(model, lr, warmup_steps)
+
+    # Train
+    logger.info("***** Running training *****")
+    logger.info("  Num examples = %d", len(train_dataset))
+    logger.info("  Num Epochs = %d", args.num_train_epochs)
+    logger.info(
+        "  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size
+    )
+    logger.info(
+        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
+        args.train_batch_size * args.gradient_accumulation_steps
+        # * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
+    )
+    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
+    logger.info("  Total optimization steps = %d", t_total)
+
+    model.zero_grad()
+    train_iterator = trange(args.num_train_epochs, desc="Epoch", disable=True)
+
+    global_step = 0
+    tr_loss = 0.0
+    for _ in train_iterator:
+        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=True)
+        for step, batch in enumerate(epoch_iterator):
+            source, target, encoder_token_type_ids, encoder_mask, decoder_mask, lm_labels = batch
+
+            source = source.to(args.device)
+            target = target.to(args.device)
+            encoder_token_type_ids = encoder_token_type_ids.to(args.device)
+            encoder_mask = encoder_mask.to(args.device)
+            decoder_mask = decoder_mask.to(args.device)
+            lm_labels = lm_labels.to(args.device)
+
+            model.train()
+            outputs = model(
+                source,
+                target,
+                encoder_token_type_ids=encoder_token_type_ids,
+                encoder_attention_mask=encoder_mask,
+                decoder_attention_mask=decoder_mask,
+                decoder_lm_labels=lm_labels,
+            )
+
+            loss = outputs[0]
+            print(loss)
+            if args.gradient_accumulation_steps > 1:
+                loss /= args.gradient_accumulation_steps
+
+            loss.backward()
+
+            tr_loss += loss.item()
+            if (step + 1) % args.gradient_accumulation_steps == 0:
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+                optimizer.step()
+                model.zero_grad()
+                global_step += 1
+
+            if args.max_steps > 0 and global_step > args.max_steps:
+                epoch_iterator.close()
+                break
+
+        if args.max_steps > 0 and global_step > args.max_steps:
+            train_iterator.close()
+            break
+
+    return global_step, tr_loss / global_step
+
+
+# ------------
+# Train
+# ------------
+
+
+def evaluate(args, model, tokenizer, prefix=""):
+    set_seed(args)
+
+    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
+    eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)
+    eval_sampler = SequentialSampler(eval_dataset)
+    eval_dataloader = DataLoader(
+        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
+    )
+
+    # multi-gpu evaluate
+    if args.n_gpu > 1:
+        model = torch.nn.DataParallel(model)
+        
+    logger.info("***** Running evaluation {} *****".format(prefix))
+    logger.info("  Num examples = %d", len(eval_dataset))
+    logger.info("  Batch size = %d", args.eval_batch_size)
+    eval_loss = 0.0
+    nb_eval_steps = 0
+    model.eval()
+
+    for batch in tqdm(eval_dataloader, desc="Evaluating"):
+        source, target, encoder_token_type_ids, encoder_mask, decoder_mask, lm_labels = batch
+
+        source = source.to(args.device)
+        target = target.to(args.device)
+        encoder_token_type_ids = encoder_token_type_ids.to(args.device)
+        encoder_mask = encoder_mask.to(args.device)
+        decoder_mask = decoder_mask.to(args.device)
+        lm_labels = lm_labels.to(args.device)
+
+        with torch.no_grad():
+            outputs = model(
+                source,
+                target,
+                encoder_token_type_ids=encoder_token_type_ids,
+                encoder_attention_mask=encoder_mask,
+                decoder_attention_mask=decoder_mask,
+                decoder_lm_labels=lm_labels,
+            )
+            lm_loss = outputs[0]
+            eval_loss += lm_loss.mean().item()
+        nb_eval_steps += 1
+
+    eval_loss = eval_loss / nb_eval_steps
+    perplexity = torch.exp(torch.tensor(eval_loss))
+
+    result = {"perplexity": perplexity}
+
+    # Save the evaluation's results
+    output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
+
+    with open(output_eval_file, "w") as writer:
+        logger.info("***** Eval results {} *****".format(prefix))
+        for key in sorted(result.keys()):
+            logger.info("  %s = %s", key, str(result[key]))
+            writer.write("%s = %s\n" % (key, str(result[key])))
+
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    # Required parameters
+    parser.add_argument(
+        "--data_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The input training data file (a text file).",
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+
+    # Optional parameters
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--do_evaluate",
+        type=bool,
+        default=False,
+        help="Run model evaluation on out-of-sample data.",
+    )
+    parser.add_argument("--do_train", type=bool, default=False, help="Run training.")
+    parser.add_argument(
+        "--do_overwrite_output_dir",
+        type=bool,
+        default=False,
+        help="Whether to overwrite the output dir.",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        default="bert-base-cased",
+        type=str,
+        help="The model checkpoint to initialize the encoder and decoder's weights with.",
+    )
+    parser.add_argument(
+        "--model_type",
+        default="bert",
+        type=str,
+        help="The decoder architecture to be fine-tuned.",
+    )
+    parser.add_argument(
+        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
+    )
+    parser.add_argument(
+        "--max_steps",
+        default=-1,
+        type=int,
+        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
+    )
+    parser.add_argument(
+        "--to_cpu", default=False, type=bool, help="Whether to force training on CPU."
+    )
+    parser.add_argument(
+        "--num_train_epochs",
+        default=10,
+        type=int,
+        help="Total number of training epochs to perform.",
+    )
+    parser.add_argument(
+        "--per_gpu_train_batch_size",
+        default=4,
+        type=int,
+        help="Batch size per GPU/CPU for training.",
+    )
+    parser.add_argument("--seed", default=42, type=int)
+    args = parser.parse_args()
+
+    if (
+        os.path.exists(args.output_dir)
+        and os.listdir(args.output_dir)
+        and args.do_train
+        and not args.do_overwrite_output_dir
+    ):
+        raise ValueError(
+            "Output directory ({}) already exists and is not empty. Use --do_overwrite_output_dir to overwrite.".format(
+                args.output_dir
+            )
+        )
+
+    # Set up training device
+    if args.to_cpu or not torch.cuda.is_available():
+        args.device = torch.device("cpu")
+        args.n_gpu = 0
+    else:
+        args.device = torch.device("cuda")
+        args.n_gpu = torch.cuda.device_count()
+
+    # Load pretrained model and tokenizer. The decoder's weights are randomly initialized.
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
+    config = BertConfig.from_pretrained(args.model_name_or_path)
+    decoder_model = BertForMaskedLM(config)
+    model = Model2Model.from_pretrained(
+        args.model_name_or_path, decoder_model=decoder_model
+    )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.warning(
+        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
+        0,
+        args.device,
+        args.n_gpu,
+        False,
+        False,
+    )
+
+    logger.info("Training/evaluation parameters %s", args)
+
+    # Train the model
+    model.to(args.device)
+    if args.do_train:
+        global_step, tr_loss = train(args, model, tokenizer)
+        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
+
+        if not os.path.exists(args.output_dir):
+            os.makedirs(args.output_dir)
+
+        logger.info("Saving model checkpoint to %s", args.output_dir)
+
+        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
+        # They can then be reloaded using `from_pretrained()`
+        model_to_save = (
+            model.module if hasattr(model, "module") else model
+        )  # Take care of distributed/parallel training
+        model_to_save.save_pretrained(args.output_dir)
+        tokenizer.save_pretrained(args.output_dir)
+        torch.save(args, os.path.join(args.output_dir, "training_arguments.bin"))
+
+    # Evaluate the model
+    results = {}
+    if args.do_evaluate:
+        checkpoints = []
+        logger.info("Evaluate the following checkpoints: %s", checkpoints)
+        for checkpoint in checkpoints:
+            encoder_checkpoint = os.path.join(checkpoint, "encoder")
+            decoder_checkpoint = os.path.join(checkpoint, "decoder")
+            model = PreTrainedEncoderDecoder.from_pretrained(
+                encoder_checkpoint, decoder_checkpoint
+            )
+            model.to(args.device)
+            results = "placeholder"
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

examples/run_tf_ner.py

@@ -1,615 +0,0 @@
-# coding=utf-8
-import datetime
-import os
-import math
-import glob
-import re
-import tensorflow as tf
-import collections
-import numpy as np
-from seqeval import metrics
-import _pickle as pickle
-from absl import logging
-from transformers import TF2_WEIGHTS_NAME, BertConfig, BertTokenizer, TFBertForTokenClassification
-from transformers import RobertaConfig, RobertaTokenizer, TFRobertaForTokenClassification
-from transformers import DistilBertConfig, DistilBertTokenizer, TFDistilBertForTokenClassification
-from transformers import create_optimizer, GradientAccumulator
-from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
-from fastprogress import master_bar, progress_bar
-from absl import flags
-from absl import app
-
-
-ALL_MODELS = sum(
-    (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, DistilBertConfig)),
-    ())
-
-MODEL_CLASSES = {
-    "bert": (BertConfig, TFBertForTokenClassification, BertTokenizer),
-    "roberta": (RobertaConfig, TFRobertaForTokenClassification, RobertaTokenizer),
-    "distilbert": (DistilBertConfig, TFDistilBertForTokenClassification, DistilBertTokenizer)
-}
-
-
-flags.DEFINE_string(
-    "data_dir", None,
-    "The input data dir. Should contain the .conll files (or other data files) "
-    "for the task.")
-
-flags.DEFINE_string(
-    "model_type", None,
-    "Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
-
-flags.DEFINE_string(
-    "model_name_or_path", None,
-    "Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
-
-flags.DEFINE_string(
-    "output_dir", None,
-    "The output directory where the model checkpoints will be written.")
-
-flags.DEFINE_string(
-    "labels", "",
-    "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.")
-
-flags.DEFINE_string(
-    "config_name", "",
-    "Pretrained config name or path if not the same as model_name")
-
-flags.DEFINE_string(
-    "tokenizer_name", "",
-    "Pretrained tokenizer name or path if not the same as model_name")
-
-flags.DEFINE_string(
-    "cache_dir", "",
-    "Where do you want to store the pre-trained models downloaded from s3")
-
-flags.DEFINE_integer(
-    "max_seq_length", 128,
-    "The maximum total input sentence length after tokenization. "
-    "Sequences longer than this will be truncated, sequences shorter "
-    "will be padded.")
-
-flags.DEFINE_string(
-    "tpu", None,
-    "The Cloud TPU to use for training. This should be either the name "
-    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
-    "url.")
-
-flags.DEFINE_integer(
-    "num_tpu_cores", 8,
-    "Total number of TPU cores to use.")
-
-flags.DEFINE_boolean(
-    "do_train", False,
-    "Whether to run training.")
-
-flags.DEFINE_boolean(
-    "do_eval", False,
-    "Whether to run eval on the dev set.")
-
-flags.DEFINE_boolean(
-    "do_predict", False,
-    "Whether to run predictions on the test set.")
-
-flags.DEFINE_boolean(
-    "evaluate_during_training", False,
-    "Whether to run evaluation during training at each logging step.")
-
-flags.DEFINE_boolean(
-    "do_lower_case", False,
-    "Set this flag if you are using an uncased model.")
-
-flags.DEFINE_integer(
-    "per_device_train_batch_size", 8,
-    "Batch size per GPU/CPU/TPU for training.")
-
-flags.DEFINE_integer(
-    "per_device_eval_batch_size", 8,
-    "Batch size per GPU/CPU/TPU for evaluation.")
-
-flags.DEFINE_integer(
-    "gradient_accumulation_steps", 1,
-    "Number of updates steps to accumulate before performing a backward/update pass.")
-
-flags.DEFINE_float(
-    "learning_rate", 5e-5,
-    "The initial learning rate for Adam.")
-
-flags.DEFINE_float(
-    "weight_decay", 0.0,
-    "Weight decay if we apply some.")
-
-flags.DEFINE_float(
-    "adam_epsilon", 1e-8,
-    "Epsilon for Adam optimizer.")
-
-flags.DEFINE_float(
-    "max_grad_norm", 1.0,
-    "Max gradient norm.")
-
-flags.DEFINE_integer(
-    "num_train_epochs", 3,
-    "Total number of training epochs to perform.")
-
-flags.DEFINE_integer(
-    "max_steps", -1,
-    "If > 0: set total number of training steps to perform. Override num_train_epochs.")
-
-flags.DEFINE_integer(
-    "warmup_steps", 0,
-    "Linear warmup over warmup_steps.")
-
-flags.DEFINE_integer(
-    "logging_steps", 50,
-    "Log every X updates steps.")
-
-flags.DEFINE_integer(
-    "save_steps", 50,
-    "Save checkpoint every X updates steps.")
-
-flags.DEFINE_boolean(
-    "eval_all_checkpoints", False,
-    "Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
-
-flags.DEFINE_boolean(
-    "no_cuda", False,
-    "Avoid using CUDA when available")
-
-flags.DEFINE_boolean(
-    "overwrite_output_dir", False,
-    "Overwrite the content of the output directory")
-
-flags.DEFINE_boolean(
-    "overwrite_cache", False,
-    "Overwrite the cached training and evaluation sets")
-
-flags.DEFINE_integer(
-    "seed", 42,
-    "random seed for initialization")
-
-flags.DEFINE_boolean(
-    "fp16", False,
-    "Whether to use 16-bit (mixed) precision instead of 32-bit")
-
-flags.DEFINE_string(
-    "gpus", "0",
-    "Comma separated list of gpus devices. If only one, switch to single "
-    "gpu strategy, if None takes all the gpus available.")
-
-
-def train(args, strategy, train_dataset, tokenizer, model, num_train_examples, labels, train_batch_size, pad_token_label_id):
-    if args['max_steps'] > 0:
-        num_train_steps = args['max_steps'] * args['gradient_accumulation_steps']
-        args['num_train_epochs'] = 1
-    else:
-        num_train_steps = math.ceil(num_train_examples / train_batch_size) // args['gradient_accumulation_steps'] * args['num_train_epochs']
-
-    writer = tf.summary.create_file_writer("/tmp/mylogs")
-
-    with strategy.scope():
-        loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)
-        optimizer = create_optimizer(args['learning_rate'], num_train_steps, args['warmup_steps'])
-
-        if args['fp16']:
-            optimizer = tf.keras.mixed_precision.experimental.LossScaleOptimizer(optimizer, 'dynamic')
-
-        loss_metric = tf.keras.metrics.Mean(name='loss', dtype=tf.float32)
-        gradient_accumulator = GradientAccumulator()
-        
-    logging.info("***** Running training *****")
-    logging.info("  Num examples = %d", num_train_examples)
-    logging.info("  Num Epochs = %d", args['num_train_epochs'])
-    logging.info("  Instantaneous batch size per device = %d", args['per_device_train_batch_size'])
-    logging.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
-                train_batch_size * args['gradient_accumulation_steps'])
-    logging.info("  Gradient Accumulation steps = %d", args['gradient_accumulation_steps'])
-    logging.info("  Total training steps = %d", num_train_steps)
-
-    model.summary()
-
-    @tf.function
-    def apply_gradients():
-        grads_and_vars = []
-
-        for gradient, variable in zip(gradient_accumulator.gradients, model.trainable_variables):
-            if gradient is not None:
-                scaled_gradient = gradient / (args['n_device'] * args['gradient_accumulation_steps'])
-                grads_and_vars.append((scaled_gradient, variable))
-            else:
-                grads_and_vars.append((gradient, variable))
-
-        optimizer.apply_gradients(grads_and_vars, args['max_grad_norm'])
-        gradient_accumulator.reset()
-
-    @tf.function
-    def train_step(train_features, train_labels):
-        def step_fn(train_features, train_labels):
-            inputs = {'attention_mask': train_features['input_mask'], 'training': True}
-
-            if args['model_type'] != "distilbert":
-                inputs["token_type_ids"] = train_features['segment_ids'] if args['model_type'] in ["bert", "xlnet"] else None
-
-            with tf.GradientTape() as tape:
-                logits = model(train_features['input_ids'], **inputs)[0]
-                logits = tf.reshape(logits, (-1, len(labels) + 1))
-                active_loss = tf.reshape(train_features['input_mask'], (-1,))
-                active_logits = tf.boolean_mask(logits, active_loss)
-                train_labels = tf.reshape(train_labels, (-1,))
-                active_labels = tf.boolean_mask(train_labels, active_loss)
-                cross_entropy = loss_fct(active_labels, active_logits)
-                loss = tf.reduce_sum(cross_entropy) * (1.0 / train_batch_size)
-                grads = tape.gradient(loss, model.trainable_variables)
-
-                gradient_accumulator(grads)
-
-            return cross_entropy
-
-        per_example_losses = strategy.experimental_run_v2(step_fn, args=(train_features, train_labels))
-        mean_loss = strategy.reduce(tf.distribute.ReduceOp.MEAN, per_example_losses, axis=0)
-
-        return mean_loss
-
-    current_time = datetime.datetime.now()
-    train_iterator = master_bar(range(args['num_train_epochs']))
-    global_step = 0
-    logging_loss = 0.0
-
-    for epoch in train_iterator:
-        epoch_iterator = progress_bar(train_dataset, total=num_train_steps, parent=train_iterator, display=args['n_device'] > 1)
-        step = 1
-
-        with strategy.scope():
-            for train_features, train_labels in epoch_iterator:
-                loss = train_step(train_features, train_labels)
-
-                if step % args['gradient_accumulation_steps'] == 0:
-                    strategy.experimental_run_v2(apply_gradients)
-
-                    loss_metric(loss)
-
-                    global_step += 1
-
-                    if args['logging_steps'] > 0 and global_step % args['logging_steps'] == 0:
-                        # Log metrics
-                        if args['n_device'] == 1 and args['evaluate_during_training']:  # Only evaluate when single GPU otherwise metrics may not average well
-                            y_true, y_pred, eval_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev")
-                            report = metrics.classification_report(y_true, y_pred, digits=4)
-                            
-                            logging.info("Eval at step " + str(global_step) + "\n" + report)
-                            logging.info("eval_loss: " + str(eval_loss))
-                            
-                            precision = metrics.precision_score(y_true, y_pred)
-                            recall = metrics.recall_score(y_true, y_pred)
-                            f1 = metrics.f1_score(y_true, y_pred)
-
-                            with writer.as_default():
-                                tf.summary.scalar("eval_loss", eval_loss, global_step)
-                                tf.summary.scalar("precision", precision, global_step)
-                                tf.summary.scalar("recall", recall, global_step)
-                                tf.summary.scalar("f1", f1, global_step)
-                        
-                        lr = optimizer.learning_rate
-                        learning_rate = lr(step)
-
-                        with writer.as_default():
-                            tf.summary.scalar("lr", learning_rate, global_step)
-                            tf.summary.scalar("loss", (loss_metric.result() - logging_loss) / args['logging_steps'], global_step)
-                        
-                        logging_loss = loss_metric.result()
-
-                    with writer.as_default():
-                        tf.summary.scalar("loss", loss_metric.result(), step=step)
-
-                    if args['save_steps'] > 0 and global_step % args['save_steps'] == 0:
-                        # Save model checkpoint
-                        output_dir = os.path.join(args['output_dir'], "checkpoint-{}".format(global_step))
-
-                        if not os.path.exists(output_dir):
-                            os.makedirs(output_dir)
-                        
-                        model.save_pretrained(output_dir)
-                        logging.info("Saving model checkpoint to %s", output_dir)
-                
-                train_iterator.child.comment = f'loss : {loss_metric.result()}'
-                step += 1
-
-        train_iterator.write(f'loss epoch {epoch + 1}: {loss_metric.result()}')
-
-        loss_metric.reset_states()
-
-    logging.info("  Training took time = {}".format(datetime.datetime.now() - current_time))
-
-
-def evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode):
-    eval_batch_size = args['per_device_eval_batch_size'] * args['n_device']
-    eval_dataset, size = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode=mode)
-    eval_dataset = strategy.experimental_distribute_dataset(eval_dataset)
-    preds = None
-    num_eval_steps = math.ceil(size / eval_batch_size)
-    master = master_bar(range(1))
-    eval_iterator = progress_bar(eval_dataset, total=num_eval_steps, parent=master, display=args['n_device'] > 1)
-    loss_fct = tf.keras.losses.SparseCategoricalCrossentropy(reduction=tf.keras.losses.Reduction.NONE)
-    loss = 0.0
-
-    logging.info("***** Running evaluation *****")
-    logging.info("  Num examples = %d", size)
-    logging.info("  Batch size = %d", eval_batch_size)
-
-    for eval_features, eval_labels in eval_iterator:
-        inputs = {'attention_mask': eval_features['input_mask'], 'training': False}
-
-        if args['model_type'] != "distilbert":
-            inputs["token_type_ids"] = eval_features['segment_ids'] if args['model_type'] in ["bert", "xlnet"] else None
-
-        with strategy.scope():
-            logits = model(eval_features['input_ids'], **inputs)[0]
-            tmp_logits = tf.reshape(logits, (-1, len(labels) + 1))
-            active_loss = tf.reshape(eval_features['input_mask'], (-1,))
-            active_logits = tf.boolean_mask(tmp_logits, active_loss)
-            tmp_eval_labels = tf.reshape(eval_labels, (-1,))
-            active_labels = tf.boolean_mask(tmp_eval_labels, active_loss)
-            cross_entropy = loss_fct(active_labels, active_logits)
-            loss += tf.reduce_sum(cross_entropy) * (1.0 / eval_batch_size)
-
-        if preds is None:
-            preds = logits.numpy()
-            label_ids = eval_labels.numpy()
-        else:
-            preds = np.append(preds, logits.numpy(), axis=0)
-            label_ids = np.append(label_ids, eval_labels.numpy(), axis=0)
-
-    preds = np.argmax(preds, axis=2)
-    y_pred = [[] for _ in range(label_ids.shape[0])]
-    y_true = [[] for _ in range(label_ids.shape[0])]
-    loss = loss / num_eval_steps
-
-    for i in range(label_ids.shape[0]):
-        for j in range(label_ids.shape[1]):
-            if label_ids[i, j] != pad_token_label_id:
-                y_pred[i].append(labels[preds[i, j] - 1])
-                y_true[i].append(labels[label_ids[i, j] - 1])
-
-    return y_true, y_pred, loss.numpy()
-
-
-def load_cache(cached_file, max_seq_length):
-    name_to_features = {
-        "input_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64),
-        "input_mask": tf.io.FixedLenFeature([max_seq_length], tf.int64),
-        "segment_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64),
-        "label_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64),
-    }
-
-    def _decode_record(record):
-        example = tf.io.parse_single_example(record, name_to_features)
-        features = {}
-        features['input_ids'] = example['input_ids']
-        features['input_mask'] = example['input_mask']
-        features['segment_ids'] = example['segment_ids']
-
-        return features, example['label_ids']
-
-    d = tf.data.TFRecordDataset(cached_file)
-    d = d.map(_decode_record, num_parallel_calls=4)
-    count = d.reduce(0, lambda x, _: x + 1)
-
-    return d, count.numpy()
-
-
-def save_cache(features, cached_features_file):
-    writer = tf.io.TFRecordWriter(cached_features_file)
-
-    for (ex_index, feature) in enumerate(features):
-        if ex_index % 5000 == 0:
-            logging.info("Writing example %d of %d" % (ex_index, len(features)))
-
-        def create_int_feature(values):
-            f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
-            return f
-
-        record_feature = collections.OrderedDict()
-        record_feature["input_ids"] = create_int_feature(feature.input_ids)
-        record_feature["input_mask"] = create_int_feature(feature.input_mask)
-        record_feature["segment_ids"] = create_int_feature(feature.segment_ids)
-        record_feature["label_ids"] = create_int_feature(feature.label_ids)
-
-        tf_example = tf.train.Example(features=tf.train.Features(feature=record_feature))
-
-        writer.write(tf_example.SerializeToString())
-
-    writer.close()
-
-
-def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, batch_size, mode):
-    drop_remainder = True if args['tpu'] or mode == 'train' else False
-
-    # Load data features from cache or dataset file
-    cached_features_file = os.path.join(args['data_dir'], "cached_{}_{}_{}.tf_record".format(mode,
-        list(filter(None, args['model_name_or_path'].split("/"))).pop(),
-        str(args['max_seq_length'])))
-    if os.path.exists(cached_features_file) and not args['overwrite_cache']:
-        logging.info("Loading features from cached file %s", cached_features_file)
-        dataset, size = load_cache(cached_features_file, args['max_seq_length'])
-    else:
-        logging.info("Creating features from dataset file at %s", args['data_dir'])
-        examples = read_examples_from_file(args['data_dir'], mode)
-        features = convert_examples_to_features(examples, labels, args['max_seq_length'], tokenizer,
-                                                cls_token_at_end=bool(args['model_type'] in ["xlnet"]),
-                                                # xlnet has a cls token at the end
-                                                cls_token=tokenizer.cls_token,
-                                                cls_token_segment_id=2 if args['model_type'] in ["xlnet"] else 0,
-                                                sep_token=tokenizer.sep_token,
-                                                sep_token_extra=bool(args['model_type'] in ["roberta"]),
-                                                # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
-                                                pad_on_left=bool(args['model_type'] in ["xlnet"]),
-                                                # pad on the left for xlnet
-                                                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
-                                                pad_token_segment_id=4 if args['model_type'] in ["xlnet"] else 0,
-                                                pad_token_label_id=pad_token_label_id
-                                                )
-        logging.info("Saving features into cached file %s", cached_features_file)
-        save_cache(features, cached_features_file)
-        dataset, size = load_cache(cached_features_file, args['max_seq_length'])
-
-    if mode == 'train':
-        dataset = dataset.repeat()
-        dataset = dataset.shuffle(buffer_size=8192, seed=args['seed'])
-
-    dataset = dataset.batch(batch_size, drop_remainder)
-    dataset = dataset.prefetch(buffer_size=batch_size)
-
-    return dataset, size
-
-
-def main(_):
-    logging.set_verbosity(logging.INFO)
-    args = flags.FLAGS.flag_values_dict()
-
-    if os.path.exists(args['output_dir']) and os.listdir(
-            args['output_dir']) and args['do_train'] and not args['overwrite_output_dir']:
-        raise ValueError(
-            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                args['output_dir']))
-
-    if args['fp16']:
-        tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
-
-    if args['tpu']:
-        resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=args['tpu'])
-        tf.config.experimental_connect_to_cluster(resolver)
-        tf.tpu.experimental.initialize_tpu_system(resolver)
-        strategy = tf.distribute.experimental.TPUStrategy(resolver)
-        args['n_device'] = args['num_tpu_cores']
-    elif len(args['gpus'].split(',')) > 1:
-        args['n_device'] = len([f"/gpu:{gpu}" for gpu in args['gpus'].split(',')])
-        strategy = tf.distribute.MirroredStrategy(devices=[f"/gpu:{gpu}" for gpu in args['gpus'].split(',')])
-    elif args['no_cuda']:
-        args['n_device'] = 1
-        strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
-    else:
-        args['n_device'] = len(args['gpus'].split(','))
-        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:" + args['gpus'].split(',')[0])
-
-    logging.warning("n_device: %s, distributed training: %s, 16-bits training: %s",
-                   args['n_device'], bool(args['n_device'] > 1), args['fp16'])
-
-    labels = get_labels(args['labels'])
-    num_labels = len(labels) + 1
-    pad_token_label_id = 0
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args['model_type']]
-    config = config_class.from_pretrained(args['config_name'] if args['config_name'] else args['model_name_or_path'],
-                                          num_labels=num_labels,
-                                          cache_dir=args['cache_dir'] if args['cache_dir'] else None)
-
-    logging.info("Training/evaluation parameters %s", args)
-
-    # Training
-    if args['do_train']:
-        tokenizer = tokenizer_class.from_pretrained(args['tokenizer_name'] if args['tokenizer_name'] else args['model_name_or_path'],
-                                                    do_lower_case=args['do_lower_case'],
-                                                    cache_dir=args['cache_dir'] if args['cache_dir'] else None)
-
-        with strategy.scope():
-            model = model_class.from_pretrained(args['model_name_or_path'],
-                                                from_pt=bool(".bin" in args['model_name_or_path']),
-                                                config=config,
-                                                cache_dir=args['cache_dir'] if args['cache_dir'] else None)
-            model.layers[-1].activation = tf.keras.activations.softmax
-
-        train_batch_size = args['per_device_train_batch_size'] * args['n_device']
-        train_dataset, num_train_examples = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, train_batch_size, mode="train")
-        train_dataset = strategy.experimental_distribute_dataset(train_dataset)
-        train(args, strategy, train_dataset, tokenizer, model, num_train_examples, labels, train_batch_size, pad_token_label_id)
-
-        if not os.path.exists(args['output_dir']):
-            os.makedirs(args['output_dir'])
-
-        logging.info("Saving model to %s", args['output_dir'])
-
-        model.save_pretrained(args['output_dir'])
-        tokenizer.save_pretrained(args['output_dir'])
-
-    # Evaluation
-    if args['do_eval']:
-        tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case'])
-        checkpoints = []
-        results = []
-
-        if args['eval_all_checkpoints']:
-            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args['output_dir'] + "/**/" + TF2_WEIGHTS_NAME, recursive=True), key=lambda f: int(''.join(filter(str.isdigit, f)) or -1)))
-        
-        logging.info("Evaluate the following checkpoints: %s", checkpoints)
-
-        if len(checkpoints) == 0:
-            checkpoints.append(args['output_dir'])
-        
-        for checkpoint in checkpoints:
-            global_step = checkpoint.split("-")[-1] if re.match(".*checkpoint-[0-9]", checkpoint) else "final"
-
-            with strategy.scope():
-                model = model_class.from_pretrained(checkpoint)
-
-            y_true, y_pred, eval_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="dev")
-            report = metrics.classification_report(y_true, y_pred, digits=4)
-
-            if global_step:
-                results.append({global_step + "_report": report, global_step + "_loss": eval_loss})
-
-        output_eval_file = os.path.join(args['output_dir'], "eval_results.txt")
-        
-        with tf.io.gfile.GFile(output_eval_file, "w") as writer:
-            for res in results:
-                for key, val in res.items():
-                    if "loss" in key:
-                        logging.info(key + " = " + str(val))
-                        writer.write(key + " = " + str(val))
-                        writer.write("\n")
-                    else:
-                        logging.info(key)
-                        logging.info("\n" + report)
-                        writer.write(key + "\n")
-                        writer.write(report)
-                        writer.write("\n")
-
-    if args['do_predict']:
-        tokenizer = tokenizer_class.from_pretrained(args['output_dir'], do_lower_case=args['do_lower_case'])
-        model = model_class.from_pretrained(args['output_dir'])
-        eval_batch_size = args['per_device_eval_batch_size'] * args['n_device']
-        predict_dataset, _ = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, eval_batch_size, mode="test")
-        y_true, y_pred, pred_loss = evaluate(args, strategy, model, tokenizer, labels, pad_token_label_id, mode="test")
-        output_test_results_file = os.path.join(args['output_dir'], "test_results.txt")
-        output_test_predictions_file = os.path.join(args['output_dir'], "test_predictions.txt")
-        report = metrics.classification_report(y_true, y_pred, digits=4)
-
-        with tf.io.gfile.GFile(output_test_results_file, "w") as writer:
-            report = metrics.classification_report(y_true, y_pred, digits=4)
-            
-            logging.info("\n" + report)
-            
-            writer.write(report)
-            writer.write("\n\nloss = " + str(pred_loss))
-
-        with tf.io.gfile.GFile(output_test_predictions_file, "w") as writer:
-            with tf.io.gfile.GFile(os.path.join(args['data_dir'], "test.txt"), "r") as f:
-                example_id = 0
-
-                for line in f:
-                    if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                        writer.write(line)
-
-                        if not y_pred[example_id]:
-                            example_id += 1
-                    elif y_pred[example_id]:
-                        output_line = line.split()[0] + " " + y_pred[example_id].pop(0) + "\n"
-                        writer.write(output_line)
-                    else:
-                        logging.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
-
-
-if __name__ == "__main__":
-    flags.mark_flag_as_required("data_dir")
-    flags.mark_flag_as_required("output_dir")
-    flags.mark_flag_as_required("model_name_or_path")
-    flags.mark_flag_as_required("model_type")
-    app.run(main)

examples/run_xnli.py

@@ -206,7 +206,7 @@ def evaluate(args, model, tokenizer, prefix=""):
 
         args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
         # Note that DistributedSampler samples randomly
-        eval_sampler = SequentialSampler(eval_dataset)
+        eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
         eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
         # multi-gpu eval

examples/summarization/README.md

@@ -1,61 +0,0 @@
-# Text Summarization with Pretrained Encoders
-
-This folder contains part of the code necessary to reproduce the results on abstractive summarization from the article [Text Summarization with Pretrained Encoders](https://arxiv.org/pdf/1908.08345.pdf) by [Yang Liu](https://nlp-yang.github.io/) and [Mirella Lapata](https://homepages.inf.ed.ac.uk/mlap/). It can also be used to summarize any document.
-
-The original code can be found on the Yang Liu's [github repository](https://github.com/nlpyang/PreSumm).
-
-The model is loaded with the pre-trained weights for the abstractive summarization model trained on the CNN/Daily Mail dataset with an extractive and then abstractive tasks.
-
-## Setup
-
-```
-git clone https://github.com/huggingface/transformers && cd transformers
-pip install [--editable] .
-pip install nltk py-rouge
-cd examples/summarization
-```
-
-## Reproduce the authors' results on ROUGE
-
-To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
-
-```bash
-tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
-```
-
-And move all the stories to the same folder. We will refer as `$DATA_PATH` the path to where you uncompressed both archive. Then run the following in the same folder as `run_summarization.py`:
-
-```bash
-python run_summarization.py \
-    --documents_dir $DATA_PATH \
-    --summaries_output_dir $SUMMARIES_PATH \ # optional
-    --no_cuda false \
-    --batch_size 4 \
-    --min_length 50 \
-    --max_length 200 \
-    --beam_size 5 \
-    --alpha 0.95 \
-    --block_trigram true \
-    --compute_rouge true
-```
-
-The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not suported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize).
-
-## Summarize any text
-
-Put the documents that you would like to summarize in a folder (the path to which is referred to as `$DATA_PATH` below) and run the following in the same folder as `run_summarization.py`:
-
-```bash
-python run_summarization.py \
-    --documents_dir $DATA_PATH \
-    --summaries_output_dir $SUMMARIES_PATH \ # optional
-    --no_cuda false \
-    --batch_size 4 \
-    --min_length 50 \
-    --max_length 200 \
-    --beam_size 5 \
-    --alpha 0.95 \
-    --block_trigram true \
-```
-
-You may want to play around with `min_length`, `max_length` and `alpha` to suit your use case. If you want to compute ROUGE on another dataset you will need to tweak the stories/summaries import in `utils_summarization.py` and tell it where to fetch the reference summaries.

examples/summarization/configuration_bertabs.py

@@ -1,119 +0,0 @@
-# coding=utf-8
-# Copyright 2019 The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# 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.
-""" BertAbs configuration """
-import json
-import logging
-import sys
-
-from transformers import PretrainedConfig
-
-
-logger = logging.getLogger(__name__)
-
-
-BERTABS_FINETUNED_CONFIG_MAP = {
-    "bertabs-finetuned-cnndm": "https://s3.amazonaws.com/models.huggingface.co/bert/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization-config.json",
-}
-
-
-class BertAbsConfig(PretrainedConfig):
-    r""" Class to store the configuration of the BertAbs model.
-
-    Arguments:
-        max_pos: int
-            The maximum sequence length that this model will be used with.
-        enc_layer: int
-            The numner of hidden layers in the Transformer encoder.
-        enc_hidden_size: int
-            The size of the encoder's layers.
-        enc_heads: int
-            The number of attention heads for each attention layer in the encoder.
-        enc_ff_size: int
-            The size of the encoder's feed-forward layers.
-        enc_dropout: int
-            The dropout probabilitiy for all fully connected layers in the
-            embeddings, layers, pooler and also the attention probabilities in
-            the encoder.
-        dec_layer: int
-            The numner of hidden layers in the decoder.
-        dec_hidden_size: int
-            The size of the decoder's layers.
-        dec_heads: int
-            The number of attention heads for each attention layer in the decoder.
-        dec_ff_size: int
-            The size of the decoder's feed-forward layers.
-        dec_dropout: int
-            The dropout probabilitiy for all fully connected layers in the
-            embeddings, layers, pooler and also the attention probabilities in
-            the decoder.
-    """
-
-    pretrained_config_archive_map = BERTABS_FINETUNED_CONFIG_MAP
-
-    def __init__(
-        self,
-        vocab_size_or_config_json_file=30522,
-        max_pos=512,
-        enc_layers=6,
-        enc_hidden_size=512,
-        enc_heads=8,
-        enc_ff_size=512,
-        enc_dropout=0.2,
-        dec_layers=6,
-        dec_hidden_size=768,
-        dec_heads=8,
-        dec_ff_size=2048,
-        dec_dropout=0.2,
-        **kwargs,
-    ):
-        super(BertAbsConfig, self).__init__(**kwargs)
-
-        if self._input_is_path_to_json(vocab_size_or_config_json_file):
-            path_to_json = vocab_size_or_config_json_file
-            with open(path_to_json, "r", encoding="utf-8") as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif isinstance(vocab_size_or_config_json_file, int):
-            self.vocab_size = vocab_size_or_config_json_file
-            self.max_pos = max_pos
-
-            self.enc_layers = enc_layers
-            self.enc_hidden_size = enc_hidden_size
-            self.enc_heads = enc_heads
-            self.enc_ff_size = enc_ff_size
-            self.enc_dropout = enc_dropout
-
-            self.dec_layers = dec_layers
-            self.dec_hidden_size = dec_hidden_size
-            self.dec_heads = dec_heads
-            self.dec_ff_size = dec_ff_size
-            self.dec_dropout = dec_dropout
-        else:
-            raise ValueError(
-                "First argument must be either a vocabulary size (int)"
-                "or the path to a pretrained model config file (str)"
-            )
-
-    def _input_is_path_to_json(self, first_argument):
-        """ Checks whether the first argument passed to config
-        is the path to a JSON file that contains the config.
-        """
-        is_python_2 = sys.version_info[0] == 2
-        if is_python_2:
-            return isinstance(first_argument, unicode)
-        else:
-            return isinstance(first_argument, str)

examples/summarization/convert_bertabs_original_pytorch_checkpoint.py

@@ -1,163 +0,0 @@
-# coding=utf-8
-# Copyright 2018 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.
-""" Convert BertExtAbs's checkpoints.
-
-The script looks like it is doing something trivial but it is not. The "weights"
-proposed by the authors are actually the entire model pickled. We need to load
-the model within the original codebase to be able to only save its `state_dict`.
-"""
-
-import argparse
-from collections import namedtuple
-import logging
-import torch
-
-from models.model_builder import AbsSummarizer  # The authors' implementation
-from model_bertabs import BertAbsSummarizer
-
-from transformers import BertTokenizer
-
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-SAMPLE_TEXT = 'Hello world! cécé herlolip'
-
-
-BertAbsConfig = namedtuple(
-    "BertAbsConfig",
-    ["temp_dir", "large", "use_bert_emb", "finetune_bert", "encoder", "share_emb", "max_pos", "enc_layers", "enc_hidden_size", "enc_heads", "enc_ff_size", "enc_dropout", "dec_layers", "dec_hidden_size", "dec_heads", "dec_ff_size", "dec_dropout"],
-)
-
-
-def convert_bertabs_checkpoints(path_to_checkpoints, dump_path):
-    """ Copy/paste and tweak the pre-trained weights provided by the creators
-    of BertAbs for the internal architecture.
-    """
-
-    # Instantiate the authors' model with the pre-trained weights
-    config = BertAbsConfig(
-        temp_dir=".",
-        finetune_bert=False,
-        large=False,
-        share_emb=True,
-        use_bert_emb=False,
-        encoder="bert",
-        max_pos=512,
-        enc_layers=6,
-        enc_hidden_size=512,
-        enc_heads=8,
-        enc_ff_size=512,
-        enc_dropout=0.2,
-        dec_layers=6,
-        dec_hidden_size=768,
-        dec_heads=8,
-        dec_ff_size=2048,
-        dec_dropout=0.2,
-    )
-    checkpoints = torch.load(path_to_checkpoints, lambda storage, loc: storage)
-    original = AbsSummarizer(config, torch.device("cpu"), checkpoints)
-    original.eval()
-
-    new_model = BertAbsSummarizer(config, torch.device("cpu"))
-    new_model.eval()
-
-    # -------------------
-    # Convert the weights
-    # -------------------
-
-    logging.info("convert the model")
-    new_model.bert.load_state_dict(original.bert.state_dict())
-    new_model.decoder.load_state_dict(original.decoder.state_dict())
-    new_model.generator.load_state_dict(original.generator.state_dict())
-
-    # ----------------------------------
-    # Make sure the outpus are identical
-    # ----------------------------------
-
-    logging.info("Make sure that the models' outputs are identical")
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-
-    # prepare the model inputs
-    encoder_input_ids = tokenizer.encode("This is sample éàalj'-.")
-    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(encoder_input_ids)))
-    encoder_input_ids = torch.tensor(encoder_input_ids).unsqueeze(0)
-    decoder_input_ids = tokenizer.encode("This is sample 3 éàalj'-.")
-    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(decoder_input_ids)))
-    decoder_input_ids = torch.tensor(decoder_input_ids).unsqueeze(0)
-
-    # failsafe to make sure the weights reset does not affect the
-    # loaded weights.
-    assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0
-
-    # forward pass
-    src = encoder_input_ids
-    tgt = decoder_input_ids
-    segs = token_type_ids = None
-    clss = None
-    mask_src = encoder_attention_mask = None
-    mask_tgt = decoder_attention_mask = None
-    mask_cls = None
-
-    # The original model does not apply the geneator layer immediatly but rather in
-    # the beam search (where it combines softmax + linear layer). Since we already
-    # apply the softmax in our generation process we only apply the linear layer here.
-    # We make sure that the outputs of the full stack are identical
-    output_original_model = original(src, tgt, segs, clss, mask_src, mask_tgt, mask_cls)[0]
-    output_original_generator = original.generator(output_original_model)
-
-    output_converted_model = new_model(encoder_input_ids, decoder_input_ids, token_type_ids, encoder_attention_mask, decoder_attention_mask)[0]
-    output_converted_generator = new_model.generator(output_converted_model)
-
-    maximum_absolute_difference = torch.max(torch.abs(output_converted_model - output_original_model)).item()
-    print("Maximum absolute difference beween weights: {:.2f}".format(maximum_absolute_difference))
-    maximum_absolute_difference = torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
-    print("Maximum absolute difference beween weights: {:.2f}".format(maximum_absolute_difference))
-
-    are_identical = torch.allclose(output_converted_model, output_original_model, atol=1e-3)
-    if are_identical:
-        logging.info("all weights are equal up to 1e-3")
-    else:
-        raise ValueError("the weights are different. The new model is likely different from the original one.")
-
-    # The model has been saved with torch.save(model) and this is bound to the exact
-    # directory structure. We save the state_dict instead.
-    logging.info("saving the model's state dictionary")
-    torch.save(new_model.state_dict(), "bertabs-finetuned-cnndm-extractive-abstractive-summarization-pytorch_model.bin")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--bertabs_checkpoint_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path the official PyTorch dump.",
-    )
-    parser.add_argument(
-        "--pytorch_dump_folder_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to the output PyTorch model.",
-    )
-    args = parser.parse_args()
-
-    convert_bertabs_checkpoints(
-        args.bertabs_checkpoint_path,
-        args.pytorch_dump_folder_path,
-    )

examples/summarization/requirements.txt

@@ -1,9 +0,0 @@
-# progress bars in model download and training scripts
-tqdm
-# Accessing files from S3 directly.
-boto3
-# Used for downloading models over HTTP
-requests
-# For ROUGE
-nltk
-py-rouge

examples/summarization/run_summarization.py

@@ -1,344 +0,0 @@
-#! /usr/bin/python3
-import argparse
-from collections import namedtuple
-import logging
-import os
-import sys
-
-import torch
-from torch.utils.data import DataLoader, SequentialSampler
-from tqdm import tqdm
-
-from transformers import BertTokenizer
-
-from modeling_bertabs import BertAbs, build_predictor
-
-from utils_summarization import (
-    SummarizationDataset,
-    encode_for_summarization,
-    build_mask,
-    fit_to_block_size,
-    compute_token_type_ids,
-)
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(stream=sys.stdout, level=logging.INFO)
-
-
-Batch = namedtuple(
-    "Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"]
-)
-
-
-def evaluate(args):
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", do_lower_case=True)
-    model = BertAbs.from_pretrained("bertabs-finetuned-cnndm")
-    model.to(args.device)
-    model.eval()
-
-    symbols = {
-        "BOS": tokenizer.vocab["[unused0]"],
-        "EOS": tokenizer.vocab["[unused1]"],
-        "PAD": tokenizer.vocab["[PAD]"],
-    }
-
-    if args.compute_rouge:
-        reference_summaries = []
-        generated_summaries = []
-
-        import rouge
-        import nltk
-        nltk.download('punkt')
-        rouge_evaluator = rouge.Rouge(
-            metrics=['rouge-n', 'rouge-l'],
-            max_n=2,
-            limit_length=True,
-            length_limit=args.beam_size,
-            length_limit_type='words',
-            apply_avg=True,
-            apply_best=False,
-            alpha=0.5,  # Default F1_score
-            weight_factor=1.2,
-            stemming=True,
-        )
-
-    # these (unused) arguments are defined to keep the compatibility
-    # with the legacy code and will be deleted in a next iteration.
-    args.result_path = ""
-    args.temp_dir = ""
-
-    data_iterator = build_data_iterator(args, tokenizer)
-    predictor = build_predictor(args, tokenizer, symbols, model)
-
-    logger.info("***** Running evaluation *****")
-    logger.info("  Number examples = %d", len(data_iterator.dataset))
-    logger.info("  Batch size = %d", args.batch_size)
-    logger.info("")
-    logger.info("***** Beam Search parameters *****")
-    logger.info("  Beam size = %d", args.beam_size)
-    logger.info("  Minimum length = %d", args.min_length)
-    logger.info("  Maximum length = %d", args.max_length)
-    logger.info("  Alpha (length penalty) = %.2f", args.alpha)
-    logger.info("  Trigrams %s be blocked", ("will" if args.block_trigram else "will NOT"))
-
-    for batch in tqdm(data_iterator):
-        batch_data = predictor.translate_batch(batch)
-        translations = predictor.from_batch(batch_data)
-        summaries = [format_summary(t) for t in translations]
-        save_summaries(summaries, args.summaries_output_dir, batch.document_names)
-
-        if args.compute_rouge:
-            reference_summaries += batch.tgt_str
-            generated_summaries += summaries
-
-    if args.compute_rouge:
-        scores = rouge_evaluator.get_scores(generated_summaries, reference_summaries)
-        str_scores = format_rouge_scores(scores)
-        save_rouge_scores(str_scores)
-        print(str_scores)
-
-
-def save_summaries(summaries, path, original_document_name):
-    """ Write the summaries in fies that are prefixed by the original
-    files' name with the `_summary` appended.
-
-    Attributes:
-        original_document_names: List[string]
-            Name of the document that was summarized.
-        path: string
-            Path were the summaries will be written
-        summaries: List[string]
-            The summaries that we produced.
-    """
-    for summary, document_name in zip(summaries, original_document_name):
-        # Prepare the summary file's name
-        if "." in document_name:
-            bare_document_name = ".".join(document_name.split(".")[:-1])
-            extension = document_name.split(".")[-1]
-            name = bare_document_name + "_summary." + extension
-        else:
-            name = document_name + "_summary"
-
-        file_path = os.path.join(path, name)
-        with open(file_path, "w") as output:
-            output.write(summary)
-
-
-def format_summary(translation):
-    """ Transforms the output of the `from_batch` function
-    into nicely formatted summaries.
-    """
-    raw_summary, _, _ = translation
-    summary = (
-        raw_summary.replace("[unused0]", "")
-        .replace("[unused3]", "")
-        .replace("[PAD]", "")
-        .replace("[unused1]", "")
-        .replace(r" +", " ")
-        .replace(" [unused2] ", ". ")
-        .replace("[unused2]", "")
-        .strip()
-    )
-
-    return summary
-
-
-def format_rouge_scores(scores):
-    return """\n
-****** ROUGE SCORES ******
-
-** ROUGE 1
-F1        >> {:.3f}
-Precision >> {:.3f}
-Recall    >> {:.3f}
-
-** ROUGE 2
-F1        >> {:.3f}
-Precision >> {:.3f}
-Recall    >> {:.3f}
-
-** ROUGE L
-F1        >> {:.3f}
-Precision >> {:.3f}
-Recall    >> {:.3f}""".format(
-        scores['rouge-1']['f'],
-        scores['rouge-1']['p'],
-        scores['rouge-1']['r'],
-        scores['rouge-2']['f'],
-        scores['rouge-2']['p'],
-        scores['rouge-2']['r'],
-        scores['rouge-l']['f'],
-        scores['rouge-l']['p'],
-        scores['rouge-l']['r'],
-    )
-
-
-def save_rouge_scores(str_scores):
-    with open("rouge_scores.txt", "w") as output:
-        output.write(str_scores)
-
-
-#
-# LOAD the dataset
-#
-
-
-def build_data_iterator(args, tokenizer):
-    dataset = load_and_cache_examples(args, tokenizer)
-    sampler = SequentialSampler(dataset)
-    collate_fn = lambda data: collate(data, tokenizer, block_size=512, device=args.device)
-    iterator = DataLoader(
-        dataset, sampler=sampler, batch_size=args.batch_size, collate_fn=collate_fn,
-    )
-
-    return iterator
-
-
-def load_and_cache_examples(args, tokenizer):
-    dataset = SummarizationDataset(args.documents_dir)
-    return dataset
-
-
-def collate(data, tokenizer, block_size, device):
-    """ Collate formats the data passed to the data loader.
-
-    In particular we tokenize the data batch after batch to avoid keeping them
-    all in memory. We output the data as a namedtuple to fit the original BertAbs's
-    API.
-    """
-    data = [x for x in data if not len(x[1]) == 0]  # remove empty_files
-    names = [name for name, _, _ in data]
-    summaries = [" ".join(summary_list) for _, _, summary_list in data]
-
-    encoded_text = [
-        encode_for_summarization(story, summary, tokenizer) for _, story, summary in data
-    ]
-    encoded_stories = torch.tensor(
-        [
-            fit_to_block_size(story, block_size, tokenizer.pad_token_id)
-            for story, _ in encoded_text
-        ]
-    )
-    encoder_token_type_ids = compute_token_type_ids(encoded_stories, tokenizer.cls_token_id)
-    encoder_mask = build_mask(encoded_stories, tokenizer.pad_token_id)
-
-    batch = Batch(
-        document_names=names,
-        batch_size=len(encoded_stories),
-        src=encoded_stories.to(device),
-        segs=encoder_token_type_ids.to(device),
-        mask_src=encoder_mask.to(device),
-        tgt_str=summaries,
-    )
-
-    return batch
-
-
-def decode_summary(summary_tokens, tokenizer):
-    """ Decode the summary and return it in a format
-    suitable for evaluation.
-    """
-    summary_tokens = summary_tokens.to("cpu").numpy()
-    summary = tokenizer.decode(summary_tokens)
-    sentences = summary.split(".")
-    sentences = [s + "." for s in sentences]
-    return sentences
-
-
-def main():
-    """ The main function defines the interface with the users.
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--documents_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The folder where the documents to summarize are located.",
-    )
-    parser.add_argument(
-        "--summaries_output_dir",
-        default=None,
-        type=str,
-        required=False,
-        help="The folder in wich the summaries should be written. Defaults to the folder where the documents are",
-    )
-    parser.add_argument(
-        "--compute_rouge",
-        default=False,
-        type=bool,
-        required=False,
-        help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.",
-    )
-    # EVALUATION options
-    parser.add_argument(
-        "--no_cuda",
-        default=False,
-        type=bool,
-        help="Whether to force the execution on CPU.",
-    )
-    parser.add_argument(
-        "--batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.",
-    )
-    # BEAM SEARCH arguments
-    parser.add_argument(
-        "--min_length",
-        default=50,
-        type=int,
-        help="Minimum number of tokens for the summaries.",
-    )
-    parser.add_argument(
-        "--max_length",
-        default=200,
-        type=int,
-        help="Maixmum number of tokens for the summaries.",
-    )
-    parser.add_argument(
-        "--beam_size",
-        default=5,
-        type=int,
-        help="The number of beams to start with for each example.",
-    )
-    parser.add_argument(
-        "--alpha",
-        default=0.95,
-        type=float,
-        help="The value of alpha for the length penalty in the beam search.",
-    )
-    parser.add_argument(
-        "--block_trigram",
-        default=True,
-        type=bool,
-        help="Whether to block the existence of repeating trigrams in the text generated by beam search.",
-    )
-    args = parser.parse_args()
-
-    # Select device (distibuted not available)
-    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-
-    # Check the existence of directories
-    if not args.summaries_output_dir:
-        args.summaries_output_dir = args.documents_dir
-
-    if not documents_dir_is_valid(args.documents_dir):
-        raise FileNotFoundError(
-            "We could not find the directory you specified for the documents to summarize, or it was empty. Please specify a valid path."
-        )
-    os.makedirs(args.summaries_output_dir, exist_ok=True)
-
-    evaluate(args)
-
-
-def documents_dir_is_valid(path):
-    if not os.path.exists(path):
-        return False
-
-    file_list = os.listdir(path)
-    if len(file_list) == 0:
-        return False
-
-    return True
-
-
-if __name__ == "__main__":
-    main()

examples/test_examples.py

@@ -72,7 +72,8 @@ class ExamplesTests(unittest.TestCase):
         logger.addHandler(stream_handler)
 
         testargs = ["run_squad.py",
-                    "--data_dir=./examples/tests_samples/SQUAD",
+                    "--train_file=./examples/tests_samples/SQUAD/dev-v2.0-small.json",
+                    "--predict_file=./examples/tests_samples/SQUAD/dev-v2.0-small.json",
                     "--model_name=bert-base-uncased",
                     "--output_dir=./examples/tests_samples/temp_dir",
                     "--max_steps=10",

examples/tests_samples/SQUAD/train-v2.0.json

@@ -1,140 +0,0 @@
-{
-    "version": "v2.0",
-    "data": [{
-        "title": "Normans",
-        "paragraphs": [{
-            "qas": [{
-                "question": "In what country is Normandy located?",
-                "id": "56ddde6b9a695914005b9628",
-                "answers": [{
-                    "text": "France",
-                    "answer_start": 159
-                }],
-                "is_impossible": false
-            }, {
-                "question": "When were the Normans in Normandy?",
-                "id": "56ddde6b9a695914005b9629",
-                "answers": [{
-                    "text": "10th and 11th centuries",
-                    "answer_start": 94
-                }],
-                "is_impossible": false
-            }, {
-                "question": "From which countries did the Norse originate?",
-                "id": "56ddde6b9a695914005b962a",
-                "answers": [{
-                    "text": "Denmark, Iceland and Norway",
-                    "answer_start": 256
-                }],
-                "is_impossible": false
-            }, {
-                "plausible_answers": [{
-                    "text": "Rollo",
-                    "answer_start": 308
-                }],
-                "question": "Who did King Charles III swear fealty to?",
-                "id": "5ad39d53604f3c001a3fe8d3",
-                "answers": [],
-                "is_impossible": true
-            }, {
-                "plausible_answers": [{
-                    "text": "10th century",
-                    "answer_start": 671
-                }],
-                "question": "When did the Frankish identity emerge?",
-                "id": "5ad39d53604f3c001a3fe8d4",
-                "answers": [],
-                "is_impossible": true
-            }],
-            "context": "The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave their name to Normandy, a region in France. They were descended from Norse (\"Norman\" comes from \"Norseman\") raiders and pirates from Denmark, Iceland and Norway who, under their leader Rollo, agreed to swear fealty to King Charles III of West Francia. Through generations of assimilation and mixing with the native Frankish and Roman-Gaulish populations, their descendants would gradually merge with the Carolingian-based cultures of West Francia. The distinct cultural and ethnic identity of the Normans emerged initially in the first half of the 10th century, and it continued to evolve over the succeeding centuries."
-        }, {
-            "qas": [{
-                "question": "Who was the duke in the battle of Hastings?",
-                "id": "56dddf4066d3e219004dad5f",
-                "answers": [{
-                    "text": "William the Conqueror",
-                    "answer_start": 1022
-                }],
-                "is_impossible": false
-            }, {
-                "plausible_answers": [{
-                    "text": "Antioch",
-                    "answer_start": 1295
-                }],
-                "question": "What principality did William the conquerer found?",
-                "id": "5ad3a266604f3c001a3fea2b",
-                "answers": [],
-                "is_impossible": true
-            }],
-            "context": "The Norman dynasty had a major political, cultural and military impact on medieval Europe and even the Near East. The Normans were famed for their martial spirit and eventually for their Christian piety, becoming exponents of the Catholic orthodoxy into which they assimilated. They adopted the Gallo-Romance language of the Frankish land they settled, their dialect becoming known as Norman, Normaund or Norman French, an important literary language. The Duchy of Normandy, which they formed by treaty with the French crown, was a great fief of medieval France, and under Richard I of Normandy was forged into a cohesive and formidable principality in feudal tenure. The Normans are noted both for their culture, such as their unique Romanesque architecture and musical traditions, and for their significant military accomplishments and innovations. Norman adventurers founded the Kingdom of Sicily under Roger II after conquering southern Italy on the Saracens and Byzantines, and an expedition on behalf of their duke, William the Conqueror, led to the Norman conquest of England at the Battle of Hastings in 1066. Norman cultural and military influence spread from these new European centres to the Crusader states of the Near East, where their prince Bohemond I founded the Principality of Antioch in the Levant, to Scotland and Wales in Great Britain, to Ireland, and to the coasts of north Africa and the Canary Islands."
-        }]
-    }, {
-        "title": "Computational_complexity_theory",
-        "paragraphs": [{
-            "qas": [{
-                "question": "What branch of theoretical computer science deals with broadly classifying computational problems by difficulty and class of relationship?",
-                "id": "56e16182e3433e1400422e28",
-                "answers": [{
-                    "text": "Computational complexity theory",
-                    "answer_start": 0
-                }],
-                "is_impossible": false
-            }, {
-                "plausible_answers": [{
-                    "text": "algorithm",
-                    "answer_start": 472
-                }],
-                "question": "What is a manual application of mathematical steps?",
-                "id": "5ad5316b5b96ef001a10ab76",
-                "answers": [],
-                "is_impossible": true
-            }],
-            "context": "Computational complexity theory is a branch of the theory of computation in theoretical computer science that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm."
-        }, {
-            "qas": [{
-                "question": "What measure of a computational problem broadly defines the inherent difficulty of the solution?",
-                "id": "56e16839cd28a01900c67887",
-                "answers": [{
-                    "text": "if its solution requires significant resources",
-                    "answer_start": 46
-                }],
-                "is_impossible": false
-            }, {
-                "question": "What method is used to intuitively assess or quantify the amount of resources required to solve a computational problem?",
-                "id": "56e16839cd28a01900c67888",
-                "answers": [{
-                    "text": "mathematical models of computation",
-                    "answer_start": 176
-                }],
-                "is_impossible": false
-            }, {
-                "question": "What are two basic primary resources used to guage complexity?",
-                "id": "56e16839cd28a01900c67889",
-                "answers": [{
-                    "text": "time and storage",
-                    "answer_start": 305
-                }],
-                "is_impossible": false
-            }, {
-                "plausible_answers": [{
-                    "text": "the number of gates in a circuit",
-                    "answer_start": 436
-                }],
-                "question": "What unit is measured to determine circuit simplicity?",
-                "id": "5ad532575b96ef001a10ab7f",
-                "answers": [],
-                "is_impossible": true
-            }, {
-                "plausible_answers": [{
-                    "text": "the number of processors",
-                    "answer_start": 502
-                }],
-                "question": "What number is used in perpendicular computing?",
-                "id": "5ad532575b96ef001a10ab80",
-                "answers": [],
-                "is_impossible": true
-            }],
-            "context": "A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do."
-        }]
-    }]
-}

examples/utils_squad_evaluate.py

@@ -0,0 +1,330 @@
+""" Official evaluation script for SQuAD version 2.0.
+    Modified by XLNet authors to update `find_best_threshold` scripts for SQuAD V2.0
+
+In addition to basic functionality, we also compute additional statistics and
+plot precision-recall curves if an additional na_prob.json file is provided.
+This file is expected to map question ID's to the model's predicted probability
+that a question is unanswerable.
+"""
+import argparse
+import collections
+import json
+import numpy as np
+import os
+import re
+import string
+import sys
+
+class EVAL_OPTS():
+  def __init__(self, data_file, pred_file, out_file="",
+               na_prob_file="na_prob.json", na_prob_thresh=1.0,
+               out_image_dir=None, verbose=False):
+    self.data_file = data_file
+    self.pred_file = pred_file
+    self.out_file = out_file
+    self.na_prob_file = na_prob_file
+    self.na_prob_thresh = na_prob_thresh
+    self.out_image_dir = out_image_dir
+    self.verbose = verbose
+
+OPTS = None
+
+def parse_args():
+  parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
+  parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
+  parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
+  parser.add_argument('--out-file', '-o', metavar='eval.json',
+                      help='Write accuracy metrics to file (default is stdout).')
+  parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
+                      help='Model estimates of probability of no answer.')
+  parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
+                      help='Predict "" if no-answer probability exceeds this (default = 1.0).')
+  parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
+                      help='Save precision-recall curves to directory.')
+  parser.add_argument('--verbose', '-v', action='store_true')
+  if len(sys.argv) == 1:
+    parser.print_help()
+    sys.exit(1)
+  return parser.parse_args()
+
+def make_qid_to_has_ans(dataset):
+  qid_to_has_ans = {}
+  for article in dataset:
+    for p in article['paragraphs']:
+      for qa in p['qas']:
+        qid_to_has_ans[qa['id']] = bool(qa['answers'])
+  return qid_to_has_ans
+
+def normalize_answer(s):
+  """Lower text and remove punctuation, articles and extra whitespace."""
+  def remove_articles(text):
+    regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
+    return re.sub(regex, ' ', text)
+  def white_space_fix(text):
+    return ' '.join(text.split())
+  def remove_punc(text):
+    exclude = set(string.punctuation)
+    return ''.join(ch for ch in text if ch not in exclude)
+  def lower(text):
+    return text.lower()
+  return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+def get_tokens(s):
+  if not s: return []
+  return normalize_answer(s).split()
+
+def compute_exact(a_gold, a_pred):
+  return int(normalize_answer(a_gold) == normalize_answer(a_pred))
+
+def compute_f1(a_gold, a_pred):
+  gold_toks = get_tokens(a_gold)
+  pred_toks = get_tokens(a_pred)
+  common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
+  num_same = sum(common.values())
+  if len(gold_toks) == 0 or len(pred_toks) == 0:
+    # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
+    return int(gold_toks == pred_toks)
+  if num_same == 0:
+    return 0
+  precision = 1.0 * num_same / len(pred_toks)
+  recall = 1.0 * num_same / len(gold_toks)
+  f1 = (2 * precision * recall) / (precision + recall)
+  return f1
+
+def get_raw_scores(dataset, preds):
+  exact_scores = {}
+  f1_scores = {}
+  for article in dataset:
+    for p in article['paragraphs']:
+      for qa in p['qas']:
+        qid = qa['id']
+        gold_answers = [a['text'] for a in qa['answers']
+                        if normalize_answer(a['text'])]
+        if not gold_answers:
+          # For unanswerable questions, only correct answer is empty string
+          gold_answers = ['']
+        if qid not in preds:
+          print('Missing prediction for %s' % qid)
+          continue
+        a_pred = preds[qid]
+        # Take max over all gold answers
+        exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
+        f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
+  return exact_scores, f1_scores
+
+def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
+  new_scores = {}
+  for qid, s in scores.items():
+    pred_na = na_probs[qid] > na_prob_thresh
+    if pred_na:
+      new_scores[qid] = float(not qid_to_has_ans[qid])
+    else:
+      new_scores[qid] = s
+  return new_scores
+
+def make_eval_dict(exact_scores, f1_scores, qid_list=None):
+  if not qid_list:
+    total = len(exact_scores)
+    return collections.OrderedDict([
+        ('exact', 100.0 * sum(exact_scores.values()) / total),
+        ('f1', 100.0 * sum(f1_scores.values()) / total),
+        ('total', total),
+    ])
+  else:
+    total = len(qid_list)
+    return collections.OrderedDict([
+        ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
+        ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
+        ('total', total),
+    ])
+
+def merge_eval(main_eval, new_eval, prefix):
+  for k in new_eval:
+    main_eval['%s_%s' % (prefix, k)] = new_eval[k]
+
+def plot_pr_curve(precisions, recalls, out_image, title):
+  plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
+  plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
+  plt.xlabel('Recall')
+  plt.ylabel('Precision')
+  plt.xlim([0.0, 1.05])
+  plt.ylim([0.0, 1.05])
+  plt.title(title)
+  plt.savefig(out_image)
+  plt.clf()
+
+def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
+                               out_image=None, title=None):
+  qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+  true_pos = 0.0
+  cur_p = 1.0
+  cur_r = 0.0
+  precisions = [1.0]
+  recalls = [0.0]
+  avg_prec = 0.0
+  for i, qid in enumerate(qid_list):
+    if qid_to_has_ans[qid]:
+      true_pos += scores[qid]
+    cur_p = true_pos / float(i+1)
+    cur_r = true_pos / float(num_true_pos)
+    if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
+      # i.e., if we can put a threshold after this point
+      avg_prec += cur_p * (cur_r - recalls[-1])
+      precisions.append(cur_p)
+      recalls.append(cur_r)
+  if out_image:
+    plot_pr_curve(precisions, recalls, out_image, title)
+  return {'ap': 100.0 * avg_prec}
+
+def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs, 
+                                  qid_to_has_ans, out_image_dir):
+  if out_image_dir and not os.path.exists(out_image_dir):
+    os.makedirs(out_image_dir)
+  num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
+  if num_true_pos == 0:
+    return
+  pr_exact = make_precision_recall_eval(
+      exact_raw, na_probs, num_true_pos, qid_to_has_ans,
+      out_image=os.path.join(out_image_dir, 'pr_exact.png'),
+      title='Precision-Recall curve for Exact Match score')
+  pr_f1 = make_precision_recall_eval(
+      f1_raw, na_probs, num_true_pos, qid_to_has_ans,
+      out_image=os.path.join(out_image_dir, 'pr_f1.png'),
+      title='Precision-Recall curve for F1 score')
+  oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
+  pr_oracle = make_precision_recall_eval(
+      oracle_scores, na_probs, num_true_pos, qid_to_has_ans,
+      out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
+      title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
+  merge_eval(main_eval, pr_exact, 'pr_exact')
+  merge_eval(main_eval, pr_f1, 'pr_f1')
+  merge_eval(main_eval, pr_oracle, 'pr_oracle')
+
+def histogram_na_prob(na_probs, qid_list, image_dir, name):
+  if not qid_list:
+    return
+  x = [na_probs[k] for k in qid_list]
+  weights = np.ones_like(x) / float(len(x))
+  plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
+  plt.xlabel('Model probability of no-answer')
+  plt.ylabel('Proportion of dataset')
+  plt.title('Histogram of no-answer probability: %s' % name)
+  plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
+  plt.clf()
+
+def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
+  num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
+  cur_score = num_no_ans
+  best_score = cur_score
+  best_thresh = 0.0
+  qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+  for i, qid in enumerate(qid_list):
+    if qid not in scores: continue
+    if qid_to_has_ans[qid]:
+      diff = scores[qid]
+    else:
+      if preds[qid]:
+        diff = -1
+      else:
+        diff = 0
+    cur_score += diff
+    if cur_score > best_score:
+      best_score = cur_score
+      best_thresh = na_probs[qid]
+  return 100.0 * best_score / len(scores), best_thresh
+
+def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
+  num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
+  cur_score = num_no_ans
+  best_score = cur_score
+  best_thresh = 0.0
+  qid_list = sorted(na_probs, key=lambda k: na_probs[k])
+  for i, qid in enumerate(qid_list):
+    if qid not in scores: continue
+    if qid_to_has_ans[qid]:
+      diff = scores[qid]
+    else:
+      if preds[qid]:
+        diff = -1
+      else:
+        diff = 0
+    cur_score += diff
+    if cur_score > best_score:
+      best_score = cur_score
+      best_thresh = na_probs[qid]
+
+  has_ans_score, has_ans_cnt = 0, 0
+  for qid in qid_list:
+    if not qid_to_has_ans[qid]: continue
+    has_ans_cnt += 1
+
+    if qid not in scores: continue
+    has_ans_score += scores[qid]
+
+  return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
+
+def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
+  best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
+  best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
+  main_eval['best_exact'] = best_exact
+  main_eval['best_exact_thresh'] = exact_thresh
+  main_eval['best_f1'] = best_f1
+  main_eval['best_f1_thresh'] = f1_thresh
+
+def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
+  best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
+  best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
+  main_eval['best_exact'] = best_exact
+  main_eval['best_exact_thresh'] = exact_thresh
+  main_eval['best_f1'] = best_f1
+  main_eval['best_f1_thresh'] = f1_thresh
+  main_eval['has_ans_exact'] = has_ans_exact
+  main_eval['has_ans_f1'] = has_ans_f1
+
+def main(OPTS):
+  with open(OPTS.data_file) as f:
+    dataset_json = json.load(f)
+    dataset = dataset_json['data']
+  with open(OPTS.pred_file) as f:
+    preds = json.load(f)
+  if OPTS.na_prob_file:
+    with open(OPTS.na_prob_file) as f:
+      na_probs = json.load(f)
+  else:
+    na_probs = {k: 0.0 for k in preds}
+  qid_to_has_ans = make_qid_to_has_ans(dataset)  # maps qid to True/False
+  has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
+  no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
+  exact_raw, f1_raw = get_raw_scores(dataset, preds)
+  exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
+                                        OPTS.na_prob_thresh)
+  f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
+                                     OPTS.na_prob_thresh)
+  out_eval = make_eval_dict(exact_thresh, f1_thresh)
+  if has_ans_qids:
+    has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
+    merge_eval(out_eval, has_ans_eval, 'HasAns')
+  if no_ans_qids:
+    no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
+    merge_eval(out_eval, no_ans_eval, 'NoAns')
+  if OPTS.na_prob_file:
+    find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
+  if OPTS.na_prob_file and OPTS.out_image_dir:
+    run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs, 
+                                  qid_to_has_ans, OPTS.out_image_dir)
+    histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
+    histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
+  if OPTS.out_file:
+    with open(OPTS.out_file, 'w') as f:
+      json.dump(out_eval, f)
+  else:
+    print(json.dumps(out_eval, indent=2))
+  return out_eval
+
+if __name__ == '__main__':
+  OPTS = parse_args()
+  if OPTS.out_image_dir:
+    import matplotlib
+    matplotlib.use('Agg')
+    import matplotlib.pyplot as plt 
+  main(OPTS)

examples/utils_summarization.py

@@ -10,14 +10,9 @@ from torch.utils.data import Dataset
 # ------------
 
 
-class SummarizationDataset(Dataset):
+class CNNDailyMailDataset(Dataset):
     """ Abstracts the dataset used to train seq2seq models.
 
-    The class will process the documents that are located in the specified
-    folder. The preprocessing will work on any document that is reasonably
-    formatted. On the CNN/DailyMail dataset it will extract both the story
-    and the summary.
-
     CNN/Daily News:
 
     The CNN/Daily News raw datasets are downloaded from [1]. The stories are
@@ -30,33 +25,33 @@ class SummarizationDataset(Dataset):
     [2] https://github.com/abisee/cnn-dailymail/
     """
 
-    def __init__(self, path="", prefix="train"):
-        """ We initialize the class by listing all the documents to summarize.
-        Files are not read in memory due to the size of some datasets (like CNN/DailyMail).
-        """
-        assert os.path.isdir(path)
+    def __init__(self, tokenizer, prefix="train", data_dir=""):
+        assert os.path.isdir(data_dir)
+        self.tokenizer = tokenizer
 
-        self.documents = []
-        story_filenames_list = os.listdir(path)
-        for story_filename in story_filenames_list:
-            if "summary" in story_filename:
-                continue
-            path_to_story = os.path.join(path, story_filename)
-            if not os.path.isfile(path_to_story):
-                continue
-            self.documents.append(path_to_story)
+        # We initialize the class by listing all the files that contain
+        # stories and summaries. Files are not read in memory given
+        # the size of the corpus.
+        self.stories_path = []
+        datasets = ("cnn", "dailymail")
+        for dataset in datasets:
+            path_to_stories = os.path.join(data_dir, dataset, "stories")
+            story_filenames_list = os.listdir(path_to_stories)
+            for story_filename in story_filenames_list:
+                path_to_story = os.path.join(path_to_stories, story_filename)
+                if not os.path.isfile(path_to_story):
+                    continue
+                self.stories_path.append(path_to_story)
 
     def __len__(self):
-        """ Returns the number of documents. """
-        return len(self.documents)
+        return len(self.stories_path)
 
     def __getitem__(self, idx):
-        document_path = self.documents[idx]
-        document_name = document_path.split("/")[-1]
-        with open(document_path, encoding="utf-8") as source:
+        story_path = self.stories_path[idx]
+        with open(story_path, encoding="utf-8") as source:
             raw_story = source.read()
             story_lines, summary_lines = process_story(raw_story)
-        return document_name, story_lines, summary_lines
+        return story_lines, summary_lines
 
 
 def process_story(raw_story):
@@ -86,7 +81,7 @@ def process_story(raw_story):
             story_lines.append(element)
         except IndexError:
             # if "@highlight" is absent from the file we pop
-            # all elements until there is None, raising an exception.
+            # all elements until there is None.
             return story_lines, []
 
     # gather summary lines
@@ -109,22 +104,31 @@ def _add_missing_period(line):
 # --------------------------
 
 
-def fit_to_block_size(sequence, block_size, pad_token_id):
+def fit_to_block_size(sequence, block_size, pad_token):
     """ Adapt the source and target sequences' lengths to the block size.
-    If the sequence is shorter we append padding token to the right of the sequence.
+    If the sequence is shorter than the block size we pad it with -1 ids
+    which correspond to padding tokens.
     """
     if len(sequence) > block_size:
         return sequence[:block_size]
     else:
-        sequence.extend([pad_token_id] * (block_size - len(sequence)))
+        sequence.extend([pad_token] * (block_size - len(sequence)))
         return sequence
 
 
-def build_mask(sequence, pad_token_id):
+def build_lm_labels(sequence, pad_token):
+    """ Padding token, encoded as 0, are represented by the value -1 so they
+    are not taken into account in the loss computation. """
+    padded = sequence.clone()
+    padded[padded == pad_token] = -1
+    return padded
+
+
+def build_mask(sequence, pad_token):
     """ Builds the mask. The attention mechanism will only attend to positions
     with value 1. """
     mask = torch.ones_like(sequence)
-    idx_pad_tokens = sequence == pad_token_id
+    idx_pad_tokens = sequence == pad_token
     mask[idx_pad_tokens] = 0
     return mask
 
@@ -134,11 +138,18 @@ def encode_for_summarization(story_lines, summary_lines, tokenizer):
     as specified in [1] by using `[SEP] [CLS]` tokens to separate
     sentences.
     """
-    story_lines_token_ids = [tokenizer.encode(line) for line in story_lines]
+    story_lines_token_ids = [
+        tokenizer.add_special_tokens_single_sequence(tokenizer.encode(line))
+        for line in story_lines
+    ]
+    summary_lines_token_ids = [
+        tokenizer.add_special_tokens_single_sequence(tokenizer.encode(line))
+        for line in summary_lines
+    ]
+
     story_token_ids = [
         token for sentence in story_lines_token_ids for token in sentence
     ]
-    summary_lines_token_ids = [tokenizer.encode(line) for line in summary_lines]
     summary_token_ids = [
         token for sentence in summary_lines_token_ids for token in sentence
     ]
@@ -163,7 +174,7 @@ def compute_token_type_ids(batch, separator_token_id):
     """
     batch_embeddings = []
     for sequence in batch:
-        sentence_num = -1
+        sentence_num = 0
         embeddings = []
         for s in sequence:
             if s == separator_token_id:

examples/utils_summarization_test.py

@@ -21,6 +21,7 @@ from utils_summarization import (
     compute_token_type_ids,
     fit_to_block_size,
     build_mask,
+    build_lm_labels,
     process_story,
 )
 
@@ -87,6 +88,20 @@ class SummarizationDataProcessingTest(unittest.TestCase):
         expected_summary_lines = ["It was the best of times."]
         self.assertEqual(expected_summary_lines, summary_lines)
 
+    def test_build_lm_labels_no_padding(self):
+        sequence = torch.tensor([1, 2, 3, 4])
+        expected = sequence
+        np.testing.assert_array_equal(
+            build_lm_labels(sequence, 0).numpy(), expected.numpy()
+        )
+
+    def test_build_lm_labels(self):
+        sequence = torch.tensor([1, 2, 3, 4, 0, 0, 0])
+        expected = torch.tensor([1, 2, 3, 4, -1, -1, -1])
+        np.testing.assert_array_equal(
+            build_lm_labels(sequence, 0).numpy(), expected.numpy()
+        )
+
     def test_build_mask_no_padding(self):
         sequence = torch.tensor([1, 2, 3, 4])
         expected = torch.tensor([1, 1, 1, 1])
@@ -110,7 +125,7 @@ class SummarizationDataProcessingTest(unittest.TestCase):
             [[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]
         )
         expected = torch.tensor(
-            [[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]
+            [[0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1], [0, 1, 1, 1, 0, 0]]
         )
 
         result = compute_token_type_ids(batch, separator)

setup.py

@@ -36,15 +36,9 @@ To create the package for pypi.
 from io import open
 from setuptools import find_packages, setup
 
-
-extras = {
-    'serving': ['uvicorn', 'fastapi']
-}
-extras['all'] = [package for package in extras.values()]
-
 setup(
     name="transformers",
-    version="2.2.2",
+    version="2.2.0",
     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="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
@@ -67,11 +61,8 @@ setup(
         "transformers=transformers.__main__:main",
       ]
     },
-    extras_require=extras,
-    scripts=[
-        'transformers-cli'
-    ],
     # python_requires='>=3.5.0',
+    tests_require=['pytest'],
     classifiers=[
           'Intended Audience :: Science/Research',
           'License :: OSI Approved :: Apache Software License',

templates/adding_a_new_model/configuration_xxx.py

@@ -84,7 +84,7 @@ class XxxConfig(PretrainedConfig):
                  summary_first_dropout=0.1,
                  **kwargs):
         super(XxxConfig, self).__init__(**kwargs)
-        self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
+        self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, six.string_types) else -1
         self.n_ctx = n_ctx
         self.n_positions = n_positions
         self.n_embd = n_embd

templates/adding_a_new_model/convert_xxx_original_tf_checkpoint_to_pytorch.py

@@ -26,9 +26,9 @@ from transformers import XxxConfig, XxxForPreTraining, load_tf_weights_in_xxx
 import logging
 logging.basicConfig(level=logging.INFO)
 
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
+def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, xxx_config_file, pytorch_dump_path):
     # Initialise PyTorch model
-    config = XxxConfig.from_json_file(config_file)
+    config = XxxConfig.from_json_file(xxx_config_file)
     print("Building PyTorch model from configuration: {}".format(str(config)))
     model = XxxForPreTraining(config)
 
@@ -48,11 +48,11 @@ if __name__ == "__main__":
                         type = str,
                         required = True,
                         help = "Path to the TensorFlow checkpoint path.")
-    parser.add_argument("--config_file",
+    parser.add_argument("--xxx_config_file",
                         default = None,
                         type = str,
                         required = True,
-                        help = "The config json file corresponding to the pre-trained model. \n"
+                        help = "The config json file corresponding to the pre-trained XXX model. \n"
                             "This specifies the model architecture.")
     parser.add_argument("--pytorch_dump_path",
                         default = None,
@@ -61,5 +61,5 @@ if __name__ == "__main__":
                         help = "Path to the output PyTorch model.")
     args = parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path,
-                                     args.config_file,
+                                     args.xxx_config_file,
                                      args.pytorch_dump_path)

templates/adding_a_new_model/modeling_tf_xxx.py

@@ -26,15 +26,13 @@ import logging
 import math
 import os
 import sys
-import copy
-import itertools
 from io import open
 
 import numpy as np
 import tensorflow as tf
 
 from .configuration_xxx import XxxConfig
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer
 from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
@@ -123,9 +121,9 @@ class TFXxxMainLayer(tf.keras.layers.Layer):
             input_ids = inputs
 
         if attention_mask is None:
-            attention_mask = tf.fill(shape_list(input_ids), 1)
+            attention_mask = tf.fill(tf.shape(input_ids), 1)
         if token_type_ids is None:
-            token_type_ids = tf.fill(shape_list(input_ids), 0)
+            token_type_ids = tf.fill(tf.shape(input_ids), 0)
 
         # We create a 3D attention mask from a 2D tensor mask.
         # Sizes are [batch_size, 1, 1, to_seq_length]

templates/adding_a_new_model/modeling_xxx.py

@@ -25,8 +25,6 @@ import logging
 import math
 import os
 import sys
-import copy
-import itertools
 from io import open
 
 import torch

templates/adding_a_new_model/tests/modeling_tf_xxx_test.py

@@ -18,11 +18,11 @@ from __future__ import print_function
 
 import unittest
 import shutil
+import pytest
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
 
 from transformers import XxxConfig, is_tf_available
 
@@ -33,9 +33,10 @@ if is_tf_available():
                                                TFXxxForTokenClassification,
                                                TFXxxForQuestionAnswering,
                                                TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP)
+else:
+    pytestmark = pytest.mark.skip("Require TensorFlow")
 
 
-@require_tf
 class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester):
 
     all_model_classes = (TFXxxModel, TFXxxForMaskedLM, TFXxxForQuestionAnswering,
@@ -243,7 +244,7 @@ class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs)
 
-    @slow
+    @pytest.mark.slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in ['xxx-base-uncased']:

templates/adding_a_new_model/tests/modeling_xxx_test.py

@@ -18,12 +18,12 @@ from __future__ import print_function
 
 import unittest
 import shutil
+import pytest
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (XxxConfig, XxxModel, XxxForMaskedLM,
@@ -31,9 +31,10 @@ if is_torch_available():
                                         XxxForQuestionAnswering, XxxForSequenceClassification,
                                         XxxForTokenClassification, XxxForMultipleChoice)
     from transformers.modeling_xxx import XXX_PRETRAINED_MODEL_ARCHIVE_MAP
+else:
+    pytestmark = pytest.mark.skip("Require Torch")
 
 
-@require_torch
 class XxxModelTest(CommonTestCases.CommonModelTester):
 
     all_model_classes = (XxxModel, XxxForMaskedLM, XxxForQuestionAnswering,
@@ -130,7 +131,6 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xxx_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = XxxModel(config=config)
-            model.to(torch_device)
             model.eval()
             sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
             sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
@@ -148,7 +148,6 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xxx_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = XxxForMaskedLM(config=config)
-            model.to(torch_device)
             model.eval()
             loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
             result = {
@@ -163,7 +162,6 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_xxx_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = XxxForQuestionAnswering(config=config)
-            model.to(torch_device)
             model.eval()
             loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
                                                    start_positions=sequence_labels, end_positions=sequence_labels)
@@ -184,7 +182,6 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xxx_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             config.num_labels = self.num_labels
             model = XxxForSequenceClassification(config)
-            model.to(torch_device)
             model.eval()
             loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
             result = {
@@ -200,7 +197,6 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_xxx_for_token_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             config.num_labels = self.num_labels
             model = XxxForTokenClassification(config=config)
-            model.to(torch_device)
             model.eval()
             loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
             result = {
@@ -247,7 +243,7 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xxx_for_token_classification(*config_and_inputs)
 
-    @slow
+    @pytest.mark.slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers-cli

@@ -1,23 +0,0 @@
-#!/usr/bin/env python
-from argparse import ArgumentParser
-
-from transformers.commands.user import UserCommands
-
-
-if __name__ == '__main__':
-    parser = ArgumentParser(description='Transformers CLI tool', usage='transformers-cli <command> [<args>]')
-    commands_parser = parser.add_subparsers(help='transformers-cli command helpers')
-
-    # Register commands
-    UserCommands.register_subcommand(commands_parser)
-
-    # Let's go
-    args = parser.parse_args()
-
-    if not hasattr(args, 'func'):
-        parser.print_help()
-        exit(1)
-
-    # Run
-    service = args.func(args)
-    service.run()

transformers/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "2.2.2"
+__version__ = "2.2.0"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.
@@ -26,9 +26,7 @@ from .data import (is_sklearn_available,
                    InputExample, InputFeatures, DataProcessor,
                    glue_output_modes, glue_convert_examples_to_features,
                    glue_processors, glue_tasks_num_labels,
-                   xnli_output_modes, xnli_processors, xnli_tasks_num_labels,
-                   squad_convert_examples_to_features, SquadFeatures, 
-                   SquadExample, SquadV1Processor, SquadV2Processor)
+                   xnli_output_modes, xnli_processors, xnli_tasks_num_labels)
 
 if is_sklearn_available():
     from .data import glue_compute_metrics, xnli_compute_metrics
@@ -37,7 +35,6 @@ if is_sklearn_available():
 from .tokenization_utils import (PreTrainedTokenizer)
 from .tokenization_auto import AutoTokenizer
 from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
-from .tokenization_bert_japanese import BertJapaneseTokenizer, MecabTokenizer, CharacterTokenizer
 from .tokenization_openai import OpenAIGPTTokenizer
 from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
 from .tokenization_gpt2 import GPT2Tokenizer
@@ -48,7 +45,6 @@ from .tokenization_roberta import RobertaTokenizer
 from .tokenization_distilbert import DistilBertTokenizer
 from .tokenization_albert import AlbertTokenizer
 from .tokenization_camembert import CamembertTokenizer
-from .tokenization_t5 import T5Tokenizer
 
 # Configurations
 from .configuration_utils import PretrainedConfig
@@ -59,12 +55,12 @@ from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CON
 from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
+from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_albert import AlbertConfig, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
 from .configuration_camembert import CamembertConfig, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
-from .configuration_t5 import T5Config, T5_PRETRAINED_CONFIG_ARCHIVE_MAP
 
 # Modeling
 if is_torch_available():
@@ -78,8 +74,8 @@ if is_torch_available():
                                 BertForTokenClassification, BertForQuestionAnswering,
                                 load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
-                                  OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
-                                  load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
+                                OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
+                                load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
                                     AdaptiveEmbedding,
                                     load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
@@ -90,10 +86,9 @@ if is_torch_available():
                                 CTRLLMHeadModel,
                                 CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
-                                XLNetForSequenceClassification, XLNetForTokenClassification,
-                                XLNetForMultipleChoice, XLNetForQuestionAnsweringSimple,
-                                XLNetForQuestionAnswering, load_tf_weights_in_xlnet,
-                                XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
+                                XLNetForSequenceClassification, XLNetForMultipleChoice,
+                                XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
+                                load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
                             XLMWithLMHeadModel, XLMForSequenceClassification,
                             XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
@@ -102,7 +97,7 @@ if is_torch_available():
                                 RobertaForSequenceClassification, RobertaForMultipleChoice,
                                 RobertaForTokenClassification,
                                 ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
-    from .modeling_distilbert import (DistilBertPreTrainedModel, DistilBertForMaskedLM, DistilBertModel,
+    from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
                                 DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
                                 DistilBertForTokenClassification,
                                 DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
@@ -111,11 +106,8 @@ if is_torch_available():
                                 CamembertForTokenClassification,
                                 CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
     from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
-    from .modeling_t5 import (T5PreTrainedModel, T5Model, T5WithLMHeadModel,
-                              load_tf_weights_in_t5,
-                              T5_PRETRAINED_MODEL_ARCHIVE_MAP)
 
-    from .modeling_albert import (AlbertPreTrainedModel, AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification,
+    from .modeling_albert import (AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification,
                                 AlbertForQuestionAnswering,
                                 load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
@@ -126,7 +118,7 @@ if is_torch_available():
 
 # TensorFlow
 if is_tf_available():
-    from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list
+    from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
     from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
                                    TFAutoModelWithLMHead)
 
@@ -152,7 +144,6 @@ if is_tf_available():
     from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
                                     TFXLNetModel, TFXLNetLMHeadModel,
                                     TFXLNetForSequenceClassification,
-                                    TFXLNetForTokenClassification,
                                     TFXLNetForQuestionAnsweringSimple,
                                     TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
 
@@ -171,7 +162,6 @@ if is_tf_available():
     from .modeling_tf_distilbert import (TFDistilBertPreTrainedModel, TFDistilBertMainLayer,
                                          TFDistilBertModel, TFDistilBertForMaskedLM,
                                          TFDistilBertForSequenceClassification,
-                                         TFDistilBertForTokenClassification,
                                          TFDistilBertForQuestionAnswering,
                                          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
@@ -183,12 +173,6 @@ if is_tf_available():
                                      TFAlbertForSequenceClassification,
                                     TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 
-    from .modeling_tf_t5 import (TFT5PreTrainedModel, TFT5Model, TFT5WithLMHeadModel,
-                                 TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
-
-    # Optimization
-    from .optimization_tf import (WarmUp, create_optimizer, AdamWeightDecay, GradientAccumulator)
-
 # TF 2.0 <=> PyTorch conversion utilities
 from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
                                         load_pytorch_checkpoint_in_tf2_model,

transformers/__main__.py

@@ -6,7 +6,6 @@ def main():
         "This command line utility let you convert original (author released) model checkpoint to pytorch.\n"
         "It should be used as one of: \n"
         ">> transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT, \n"
-        ">> transformers t5 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"
@@ -22,23 +21,6 @@ def main():
                     "https://www.tensorflow.org/install/ for installation instructions.")
                 raise
 
-            if len(sys.argv) != 5:
-                # pylint: disable=line-too-long
-                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()
-                TF_CHECKPOINT = sys.argv.pop()
-                convert_tf_checkpoint_to_pytorch(TF_CHECKPOINT, TF_CONFIG, PYTORCH_DUMP_OUTPUT)
-        elif sys.argv[1] == "t5":
-            try:
-                from .convert_t5_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
-            except ImportError:
-                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 `transformers bert TF_CHECKPOINT TF_CONFIG PYTORCH_DUMP_OUTPUT`")

transformers/commands/__init__.py

@@ -1,12 +0,0 @@
-from abc import ABC, abstractmethod
-from argparse import ArgumentParser
-
-class BaseTransformersCLICommand(ABC):
-    @staticmethod
-    @abstractmethod
-    def register_subcommand(parser: ArgumentParser):
-        raise NotImplementedError()
-
-    @abstractmethod
-    def run(self):
-        raise NotImplementedError()

transformers/commands/user.py

@@ -1,194 +0,0 @@
-from argparse import ArgumentParser
-from getpass import getpass
-import os
-
-from transformers.commands import BaseTransformersCLICommand
-from transformers.hf_api import HfApi, HfFolder, HTTPError
-
-
-class UserCommands(BaseTransformersCLICommand):
-    @staticmethod
-    def register_subcommand(parser: ArgumentParser):
-        login_parser = parser.add_parser('login')
-        login_parser.set_defaults(func=lambda args: LoginCommand(args))
-        whoami_parser = parser.add_parser('whoami')
-        whoami_parser.set_defaults(func=lambda args: WhoamiCommand(args))
-        logout_parser = parser.add_parser('logout')
-        logout_parser.set_defaults(func=lambda args: LogoutCommand(args))
-        list_parser = parser.add_parser('ls')
-        list_parser.set_defaults(func=lambda args: ListObjsCommand(args))
-        # upload
-        upload_parser = parser.add_parser('upload')
-        upload_parser.add_argument('path', type=str, help='Local path of the folder or individual file to upload.')
-        upload_parser.add_argument('--filename', type=str, default=None, help='Optional: override individual object filename on S3.')
-        upload_parser.set_defaults(func=lambda args: UploadCommand(args))
-
-
-
-class ANSI:
-    """
-    Helper for en.wikipedia.org/wiki/ANSI_escape_code
-    """
-    _bold = u"\u001b[1m"
-    _reset = u"\u001b[0m"
-    @classmethod
-    def bold(cls, s):
-        return "{}{}{}".format(cls._bold, s, cls._reset)
-
-
-class BaseUserCommand:
-    def __init__(self, args):
-        self.args = args
-        self._api = HfApi()
-
-
-class LoginCommand(BaseUserCommand):
-    def run(self):
-        print("""
-        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|  
-        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|        
-        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|    
-        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|        
-        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|  
-
-        """)
-        username = input("Username: ")
-        password = getpass()
-        try:
-            token = self._api.login(username, password)
-        except HTTPError as e:
-            # probably invalid credentials, display error message.
-            print(e)
-            exit(1)
-        HfFolder.save_token(token)
-        print("Login successful")
-        print("Your token:", token, "\n")
-        print("Your token has been saved to", HfFolder.path_token)
-
-
-class WhoamiCommand(BaseUserCommand):
-    def run(self):
-        token = HfFolder.get_token()
-        if token is None:
-            print("Not logged in")
-            exit()
-        try:
-            user = self._api.whoami(token)
-            print(user)
-        except HTTPError as e:
-            print(e)
-
-
-class LogoutCommand(BaseUserCommand):
-    def run(self):
-        token = HfFolder.get_token()
-        if token is None:
-            print("Not logged in")
-            exit()
-        HfFolder.delete_token()
-        self._api.logout(token)
-        print("Successfully logged out.")
-
-
-class ListObjsCommand(BaseUserCommand):
-    def tabulate(self, rows, headers):
-        # type: (List[List[Union[str, int]]], List[str]) -> str
-        """
-        Inspired by:
-        stackoverflow.com/a/8356620/593036
-        stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
-        """
-        col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
-        row_format = ("{{:{}}} " * len(headers)).format(*col_widths)
-        lines = []
-        lines.append(
-            row_format.format(*headers)
-        )
-        lines.append(
-            row_format.format(*["-" * w for w in col_widths])
-        )
-        for row in rows:
-            lines.append(
-                row_format.format(*row)
-            )
-        return "\n".join(lines)
-
-    def run(self):
-        token = HfFolder.get_token()
-        if token is None:
-            print("Not logged in")
-            exit(1)
-        try:
-            objs = self._api.list_objs(token)
-        except HTTPError as e:
-            print(e)
-            exit(1)
-        if len(objs) == 0:
-            print("No shared file yet")
-            exit()
-        rows = [ [
-            obj.filename,
-            obj.LastModified,
-            obj.ETag,
-            obj.Size
-        ] for obj in objs ]
-        print(
-            self.tabulate(rows, headers=["Filename", "LastModified", "ETag", "Size"])
-        )
-
-
-class UploadCommand(BaseUserCommand):
-    def walk_dir(self, rel_path):
-        """
-        Recursively list all files in a folder.
-        """
-        entries: List[os.DirEntry] = list(os.scandir(rel_path))
-        files = [
-            (
-                os.path.join(os.getcwd(), f.path),  # filepath
-                f.path  # filename
-            )
-            for f in entries if f.is_file()
-        ]
-        for f in entries:
-            if f.is_dir():
-                files += self.walk_dir(f.path)
-        return files
-
-    def run(self):
-        token = HfFolder.get_token()
-        if token is None:
-            print("Not logged in")
-            exit(1)
-        local_path = os.path.abspath(self.args.path)
-        if os.path.isdir(local_path):
-            if self.args.filename is not None:
-                raise ValueError("Cannot specify a filename override when uploading a folder.")
-            rel_path = os.path.basename(local_path)
-            files = self.walk_dir(rel_path)
-        elif os.path.isfile(local_path):
-            filename = self.args.filename if self.args.filename is not None else os.path.basename(local_path)
-            files = [(local_path, filename)]
-        else:
-            raise ValueError("Not a valid file or directory: {}".format(local_path))
-
-        for filepath, filename in files:
-            print(
-                "About to upload file {} to S3 under filename {}".format(
-                    ANSI.bold(filepath), ANSI.bold(filename)
-                )
-            )
-
-        choice = input("Proceed? [Y/n] ").lower()
-        if not(choice == "" or choice == "y" or choice == "yes"):
-            print("Abort")
-            exit()
-        print(
-            ANSI.bold("Uploading... This might take a while if files are large")
-        )
-        for filepath, filename in files:
-            access_url = self._api.presign_and_upload(
-                token=token, filename=filename, filepath=filepath
-            )
-            print("Your file now lives at:")
-            print(access_url)

transformers/configuration_auto.py

@@ -28,8 +28,6 @@ from .configuration_roberta import RobertaConfig
 from .configuration_distilbert import DistilBertConfig
 from .configuration_ctrl import CTRLConfig
 from .configuration_camembert import CamembertConfig
-from .configuration_albert import AlbertConfig
-from .configuration_t5 import T5Config
 
 logger = logging.getLogger(__name__)
 
@@ -46,15 +44,14 @@ class AutoConfig(object):
         The base model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
             - contains `distilbert`: DistilBertConfig (DistilBERT model)
-            - contains `albert`: AlbertConfig (ALBERT model)
-            - contains `camembert`: CamembertConfig (CamemBERT model)
-            - contains `roberta`: RobertaConfig (RoBERTa model)
             - contains `bert`: BertConfig (Bert model)
             - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
             - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
             - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
             - contains `xlnet`: XLNetConfig (XLNet model)
             - contains `xlm`: XLMConfig (XLM model)
+            - contains `roberta`: RobertaConfig (RoBERTa model)
+            - contains `camembert`: CamembertConfig (CamemBERT model)
             - contains `ctrl` : CTRLConfig (CTRL model)
         This class cannot be instantiated using `__init__()` (throw an error).
     """
@@ -69,23 +66,20 @@ class AutoConfig(object):
 
         The configuration class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5Config (T5 model)
             - contains `distilbert`: DistilBertConfig (DistilBERT model)
-            - contains `albert`: AlbertConfig (ALBERT model)
-            - contains `camembert`: CamembertConfig (CamemBERT model)
-            - contains `roberta`: RobertaConfig (RoBERTa model)
             - contains `bert`: BertConfig (Bert model)
             - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
             - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
             - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
             - contains `xlnet`: XLNetConfig (XLNet model)
             - contains `xlm`: XLMConfig (XLM model)
+            - contains `roberta`: RobertaConfig (RoBERTa model)
+            - contains `camembert`: CamembertConfig (CamemBERT model)
             - contains `ctrl` : CTRLConfig (CTRL model)
         Params:
             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 string with the `identifier name` of a pre-trained model configuration that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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``.
 
@@ -101,9 +95,6 @@ class AutoConfig(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -126,12 +117,8 @@ class AutoConfig(object):
             assert unused_kwargs == {'foo': False}
 
         """
-        if 't5' in pretrained_model_name_or_path:
-            return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'distilbert' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
             return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        elif 'albert' in pretrained_model_name_or_path:
-            return AlbertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif 'camembert' in pretrained_model_name_or_path:
             return CamembertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
@@ -152,4 +139,4 @@ class AutoConfig(object):
             return CTRLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
         raise ValueError("Unrecognized model identifier in {}. Should contains one of "
                          "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
-                         "'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert'".format(pretrained_model_name_or_path))
+                         "'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path))

transformers/configuration_bert.py

@@ -42,10 +42,6 @@ BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
     'bert-base-german-dbmdz-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json",
     'bert-base-german-dbmdz-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json",
-    'bert-base-japanese': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-config.json",
-    'bert-base-japanese-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-config.json",
-    'bert-base-japanese-char': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-config.json",
-    'bert-base-japanese-char-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-config.json"
 }
 
 

transformers/configuration_distilbert.py

@@ -28,7 +28,6 @@ logger = logging.getLogger(__name__)
 DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
     'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
     'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json",
-    'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json",
     'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-config.json",
 }
 

transformers/configuration_t5.py

@@ -1,119 +0,0 @@
-# coding=utf-8
-# Copyright 2010, The T5 Authors and HuggingFace 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.
-""" T5 model configuration """
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import json
-import logging
-import sys
-import six
-from io import open
-
-from .configuration_utils import PretrainedConfig
-
-logger = logging.getLogger(__name__)
-
-T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json",
-    't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-config.json",
-    't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-config.json",
-    't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-config.json",
-    't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-config.json",
-}
-
-
-class T5Config(PretrainedConfig):
-    r"""
-        :class:`~transformers.T5Config` is the configuration class to store the configuration of a
-        `T5Model`.
-
-
-        Arguments:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `T5Model`.
-            hidden_size: Size of the encoder layers and the pooler layer.
-            num_hidden_layers: Number of hidden layers in the Transformer encoder.
-            num_attention_heads: Number of attention heads for each attention layer in
-                the Transformer encoder.
-            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
-                layer in the Transformer encoder.
-            hidden_act: The non-linear activation function (function or string) in the
-                encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
-            hidden_dropout_prob: The dropout probabilitiy for all fully connected
-                layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob: The dropout ratio for the attention
-                probabilities.
-            max_position_embeddings: The maximum sequence length that this model might
-                ever be used with. Typically set this to something large just in case
-                (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
-                `T5Model`.
-            initializer_factor: A factor for initializing all weight matrices (should be kept to 1.0, used for initialization testing).
-            layer_norm_eps: The epsilon used by LayerNorm.
-    """
-    pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP
-
-    def __init__(self,
-                 vocab_size_or_config_json_file=32128,
-                 n_positions=512,
-                 d_model=512,
-                 d_kv=64,
-                 d_ff=2048,
-                 num_layers=6,
-                 num_heads=8,
-                 relative_attention_num_buckets=32,
-                 dropout_rate=0.1,
-                 layer_norm_epsilon=1e-6,
-                 initializer_factor=1.0,
-                 **kwargs):
-        super(T5Config, self).__init__(**kwargs)
-        self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
-        self.n_positions = n_positions
-        self.d_model = d_model
-        self.d_kv = d_kv
-        self.d_ff = d_ff
-        self.num_layers = num_layers
-        self.num_heads = num_heads
-        self.relative_attention_num_buckets = relative_attention_num_buckets
-        self.dropout_rate = dropout_rate
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_factor = initializer_factor
-
-        if isinstance(vocab_size_or_config_json_file, six.string_types):
-            with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
-                json_config = json.loads(reader.read())
-            for key, value in json_config.items():
-                self.__dict__[key] = value
-        elif not isinstance(vocab_size_or_config_json_file, int):
-            raise ValueError(
-                "First argument must be either a vocabulary size (int)"
-                "or the path to a pretrained model config file (str)"
-            )
-
-    @property
-    def max_position_embeddings(self):
-        return self.n_positions
-
-    @property
-    def hidden_size(self):
-        return self.d_model
-
-    @property
-    def num_attention_heads(self):
-        return self.num_heads
-
-    @property
-    def num_hidden_layers(self):
-        return self.num_layers

transformers/configuration_utils.py

@@ -24,7 +24,7 @@ import logging
 import os
 from io import open
 
-from .file_utils import CONFIG_NAME, cached_path, is_remote_url, hf_bucket_url
+from .file_utils import cached_path, CONFIG_NAME
 
 logger = logging.getLogger(__name__)
 
@@ -79,7 +79,6 @@ class PretrainedConfig(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 string with the `identifier name` of a pre-trained model configuration that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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``.
 
@@ -95,9 +94,6 @@ class PretrainedConfig(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -124,7 +120,6 @@ class PretrainedConfig(object):
         """
         cache_dir = kwargs.pop('cache_dir', None)
         force_download = kwargs.pop('force_download', False)
-        resume_download = kwargs.pop('resume_download', False)
         proxies = kwargs.pop('proxies', None)
         return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
 
@@ -132,14 +127,11 @@ class PretrainedConfig(object):
             config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
         elif os.path.isdir(pretrained_model_name_or_path):
             config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
-        elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
-            config_file = pretrained_model_name_or_path
         else:
-            config_file = hf_bucket_url(pretrained_model_name_or_path, postfix=CONFIG_NAME)
+            config_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
-            resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download,
-                                               proxies=proxies, resume_download=resume_download)
+            resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
         except EnvironmentError:
             if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
                 msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
@@ -190,7 +182,7 @@ class PretrainedConfig(object):
 
     @classmethod
     def from_json_file(cls, json_file):
-        """Constructs a `Config` from a json file of parameters."""
+        """Constructs a `BertConfig` from a json file of parameters."""
         with open(json_file, "r", encoding='utf-8') as reader:
             text = reader.read()
         return cls.from_dict(json.loads(text))

transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -34,8 +34,7 @@ from transformers import (load_pytorch_checkpoint_in_tf2_model,
                                   RobertaConfig, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
                                   DistilBertConfig, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
                                   CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                                  AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-                                  T5Config, TFT5WithLMHeadModel, T5_PRETRAINED_CONFIG_ARCHIVE_MAP)
+                                  AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
 
 if is_torch_available():
     import torch
@@ -49,8 +48,7 @@ if is_torch_available():
                                       RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
                                       DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
                                       CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
-                                      AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-                                      T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP)
+                                      AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
 else:
     (BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
@@ -61,8 +59,7 @@ else:
     RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
     DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
-    AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-    T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP) = (
+    AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) = (
         None, None, None, None,
         None, None,
         None, None,
@@ -72,7 +69,6 @@ else:
         None, None, None,
         None, None, None,
         None, None,
-        None, None,
         None, None)
 
 
@@ -94,8 +90,7 @@ MODEL_CLASSES = {
     'distilbert': (DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
     'distilbert-base-uncased-distilled-squad': (DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
     'ctrl': (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP),
-    'albert': (AlbertConfig, TFAlbertForMaskedLM, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP),
-    't5': (T5Config, TFT5WithLMHeadModel, T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5_PRETRAINED_CONFIG_ARCHIVE_MAP),
+    'albert': (AlbertConfig, TFAlbertForMaskedLM, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
 }
 
 def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True):
@@ -120,21 +115,23 @@ def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file
     tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)
 
     if compare_with_pt_model:
-        tfo = tf_model(tf_model.dummy_inputs, training=False)  # build the network
+        inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
+        tf_inputs = tf.constant(inputs_list)
+        tfo = tf_model(tf_inputs, training=False)  # build the network
 
-        state_dict = torch.load(pytorch_checkpoint_path, map_location='cpu')
-        pt_model = pt_model_class.from_pretrained(pretrained_model_name_or_path=None,
+        pt_model = pt_model_class.from_pretrained(None,
                                                   config=config,
-                                                  state_dict=state_dict)
-
+                                                  state_dict=torch.load(pytorch_checkpoint_path,
+                                                                        map_location='cpu'))
+        pt_inputs = torch.tensor(inputs_list)
         with torch.no_grad():
-            pto = pt_model(**pt_model.dummy_inputs)
+            pto = pt_model(pt_inputs)
 
-        np_pt = pto[0].numpy()
+        np_pt = pto[0].detach().numpy()
         np_tf = tfo[0].numpy()
         diff = np.amax(np.abs(np_pt - np_tf))
         print("Max absolute difference between models outputs {}".format(diff))
-        assert diff <= 2e-2, "Error, model absolute difference is >2e-2: {}".format(diff)
+        assert diff <= 2e-2, "Error, model absolute difference is >2e-2"
 
     # Save pytorch-model
     print("Save TensorFlow model to {}".format(tf_dump_path))
@@ -142,7 +139,7 @@ def convert_pt_checkpoint_to_tf(model_type, pytorch_checkpoint_path, config_file
 
 
 def convert_all_pt_checkpoints_to_tf(args_model_type, tf_dump_path, model_shortcut_names_or_path=None, config_shortcut_names_or_path=None,
-                                     compare_with_pt_model=False, use_cached_models=False, remove_cached_files=False, only_convert_finetuned_models=False):
+                                     compare_with_pt_model=False, use_cached_models=False, only_convert_finetuned_models=False):
     assert os.path.isdir(args.tf_dump_path), "--tf_dump_path should be a directory"
 
     if args_model_type is None:
@@ -190,15 +187,13 @@ def convert_all_pt_checkpoints_to_tf(args_model_type, tf_dump_path, model_shortc
 
             if os.path.isfile(model_shortcut_name):
                 model_shortcut_name = 'converted_model'
-
             convert_pt_checkpoint_to_tf(model_type=model_type,
                                         pytorch_checkpoint_path=model_file,
                                         config_file=config_file,
                                         tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + '-tf_model.h5'),
                                         compare_with_pt_model=compare_with_pt_model)
-            if remove_cached_files:
-                os.remove(config_file)
-                os.remove(model_file)
+            os.remove(config_file)
+            os.remove(model_file)
 
 
 if __name__ == "__main__":
@@ -231,9 +226,6 @@ if __name__ == "__main__":
     parser.add_argument("--use_cached_models",
                         action='store_true',
                         help = "Use cached models if possible instead of updating to latest checkpoint versions.")
-    parser.add_argument("--remove_cached_files",
-                        action='store_true',
-                        help = "Remove pytorch models after conversion (save memory when converting in batches).")
     parser.add_argument("--only_convert_finetuned_models",
                         action='store_true',
                         help = "Only convert finetuned models.")
@@ -253,5 +245,4 @@ if __name__ == "__main__":
                                         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,
-                                        remove_cached_files=args.remove_cached_files,
                                         only_convert_finetuned_models=args.only_convert_finetuned_models)

transformers/convert_t5_original_tf_checkpoint_to_pytorch.py

@@ -1,65 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The T5 authors and 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.
-"""Convert T5 checkpoint."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import torch
-
-from transformers import T5Config, T5Model, load_tf_weights_in_t5
-
-import logging
-logging.basicConfig(level=logging.INFO)
-
-def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
-    # Initialise PyTorch model
-    config = T5Config.from_json_file(config_file)
-    print("Building PyTorch model from configuration: {}".format(str(config)))
-    model = T5Model(config)
-
-    # Load weights from tf checkpoint
-    load_tf_weights_in_t5(model, config, tf_checkpoint_path)
-
-    # Save pytorch-model
-    print("Save PyTorch model to {}".format(pytorch_dump_path))
-    torch.save(model.state_dict(), pytorch_dump_path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    ## Required parameters
-    parser.add_argument("--tf_checkpoint_path",
-                        default = None,
-                        type = str,
-                        required = True,
-                        help = "Path to the TensorFlow checkpoint path.")
-    parser.add_argument("--config_file",
-                        default = None,
-                        type = str,
-                        required = True,
-                        help = "The config json file corresponding to the pre-trained T5 model. \n"
-                            "This specifies the model architecture.")
-    parser.add_argument("--pytorch_dump_path",
-                        default = None,
-                        type = str,
-                        required = True,
-                        help = "Path to the output PyTorch model.")
-    args = parser.parse_args()
-    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path,
-                                     args.config_file,
-                                     args.pytorch_dump_path)

transformers/data/__init__.py

@@ -1,6 +1,5 @@
-from .processors import InputExample, InputFeatures, DataProcessor, SquadFeatures
+from .processors import InputExample, InputFeatures, DataProcessor
 from .processors import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features
-from .processors import squad_convert_examples_to_features, SquadExample, SquadV1Processor, SquadV2Processor
 from .processors import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
 
 from .metrics import is_sklearn_available

transformers/data/metrics/squad_metrics.py

@@ -1,763 +0,0 @@
-""" Very heavily inspired by the official evaluation script for SQuAD version 2.0 which was
-modified by XLNet authors to update `find_best_threshold` scripts for SQuAD V2.0
-
-In addition to basic functionality, we also compute additional statistics and
-plot precision-recall curves if an additional na_prob.json file is provided.
-This file is expected to map question ID's to the model's predicted probability
-that a question is unanswerable.
-"""
-
-
-import json
-import logging
-import math
-import collections
-from io import open
-from tqdm import tqdm
-import string
-import re
-
-from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
-
-logger = logging.getLogger(__name__)
-
-
-def normalize_answer(s):
-    """Lower text and remove punctuation, articles and extra whitespace."""
-    def remove_articles(text):
-        regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
-        return re.sub(regex, ' ', text)
-
-    def white_space_fix(text):
-        return ' '.join(text.split())
-
-    def remove_punc(text):
-        exclude = set(string.punctuation)
-        return ''.join(ch for ch in text if ch not in exclude)
-
-    def lower(text):
-        return text.lower()
-    return white_space_fix(remove_articles(remove_punc(lower(s))))
-
-
-def get_tokens(s):
-    if not s:
-        return []
-    return normalize_answer(s).split()
-
-
-def compute_exact(a_gold, a_pred):
-    return int(normalize_answer(a_gold) == normalize_answer(a_pred))
-
-
-def compute_f1(a_gold, a_pred):
-    gold_toks = get_tokens(a_gold)
-    pred_toks = get_tokens(a_pred)
-    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
-    num_same = sum(common.values())
-    if len(gold_toks) == 0 or len(pred_toks) == 0:
-        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
-        return int(gold_toks == pred_toks)
-    if num_same == 0:
-        return 0
-    precision = 1.0 * num_same / len(pred_toks)
-    recall = 1.0 * num_same / len(gold_toks)
-    f1 = (2 * precision * recall) / (precision + recall)
-    return f1
-
-
-def get_raw_scores(examples, preds):
-    """
-    Computes the exact and f1 scores from the examples and the model predictions
-    """
-    exact_scores = {}
-    f1_scores = {}
-
-    for example in examples:
-        qas_id = example.qas_id
-        gold_answers = [answer['text'] for answer in example.answers if normalize_answer(answer['text'])]
-
-        if not gold_answers:
-            # For unanswerable questions, only correct answer is empty string
-            gold_answers = ['']
-
-        if qas_id not in preds:
-            print('Missing prediction for %s' % qas_id)
-            continue
-
-        prediction = preds[qas_id]
-        exact_scores[qas_id] = max(compute_exact(a, prediction) for a in gold_answers)
-        f1_scores[qas_id] = max(compute_f1(a, prediction) for a in gold_answers)
-
-    return exact_scores, f1_scores
-
-
-def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
-    new_scores = {}
-    for qid, s in scores.items():
-        pred_na = na_probs[qid] > na_prob_thresh
-        if pred_na:
-            new_scores[qid] = float(not qid_to_has_ans[qid])
-        else:
-            new_scores[qid] = s
-    return new_scores
-
-
-def make_eval_dict(exact_scores, f1_scores, qid_list=None):
-    if not qid_list:
-        total = len(exact_scores)
-        return collections.OrderedDict([
-            ('exact', 100.0 * sum(exact_scores.values()) / total),
-            ('f1', 100.0 * sum(f1_scores.values()) / total),
-            ('total', total),
-        ])
-    else:
-        total = len(qid_list)
-        return collections.OrderedDict([
-            ('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
-            ('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
-            ('total', total),
-        ])
-
-
-def merge_eval(main_eval, new_eval, prefix):
-    for k in new_eval:
-        main_eval['%s_%s' % (prefix, k)] = new_eval[k]
-
-
-def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
-    num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
-    cur_score = num_no_ans
-    best_score = cur_score
-    best_thresh = 0.0
-    qid_list = sorted(na_probs, key=lambda k: na_probs[k])
-    for i, qid in enumerate(qid_list):
-        if qid not in scores:
-            continue
-        if qid_to_has_ans[qid]:
-            diff = scores[qid]
-        else:
-            if preds[qid]:
-                diff = -1
-            else:
-                diff = 0
-        cur_score += diff
-        if cur_score > best_score:
-            best_score = cur_score
-            best_thresh = na_probs[qid]
-
-    has_ans_score, has_ans_cnt = 0, 0
-    for qid in qid_list:
-        if not qid_to_has_ans[qid]:
-            continue
-        has_ans_cnt += 1
-
-        if qid not in scores:
-            continue
-        has_ans_score += scores[qid]
-
-    return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
-
-
-def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
-    best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(
-        preds, exact_raw, na_probs, qid_to_has_ans)
-    best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(
-        preds, f1_raw, na_probs, qid_to_has_ans)
-    main_eval['best_exact'] = best_exact
-    main_eval['best_exact_thresh'] = exact_thresh
-    main_eval['best_f1'] = best_f1
-    main_eval['best_f1_thresh'] = f1_thresh
-    main_eval['has_ans_exact'] = has_ans_exact
-    main_eval['has_ans_f1'] = has_ans_f1
-
-
-def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
-    num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
-    cur_score = num_no_ans
-    best_score = cur_score
-    best_thresh = 0.0
-    qid_list = sorted(na_probs, key=lambda k: na_probs[k])
-    for _, qid in enumerate(qid_list):
-        if qid not in scores:
-            continue
-        if qid_to_has_ans[qid]:
-            diff = scores[qid]
-        else:
-            if preds[qid]:
-                diff = -1
-            else:
-                diff = 0
-        cur_score += diff
-        if cur_score > best_score:
-            best_score = cur_score
-            best_thresh = na_probs[qid]
-    return 100.0 * best_score / len(scores), best_thresh
-
-
-def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
-    best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
-    best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
-
-    main_eval['best_exact'] = best_exact
-    main_eval['best_exact_thresh'] = exact_thresh
-    main_eval['best_f1'] = best_f1
-    main_eval['best_f1_thresh'] = f1_thresh
-
-
-def squad_evaluate(examples, preds, no_answer_probs=None, no_answer_probability_threshold=1.0):
-    qas_id_to_has_answer = {example.qas_id: bool(example.answers) for example in examples}
-    has_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if has_answer]
-    no_answer_qids = [qas_id for qas_id, has_answer in qas_id_to_has_answer.items() if not has_answer]
-
-    if no_answer_probs is None:
-        no_answer_probs = {k: 0.0 for k in preds}
-
-    exact, f1 = get_raw_scores(examples, preds)
-
-    exact_threshold = apply_no_ans_threshold(exact, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold)
-    f1_threshold = apply_no_ans_threshold(f1, no_answer_probs, qas_id_to_has_answer, no_answer_probability_threshold)
-
-    evaluation = make_eval_dict(exact_threshold, f1_threshold)
-
-    if has_answer_qids:
-        has_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=has_answer_qids)
-        merge_eval(evaluation, has_ans_eval, 'HasAns')
-
-    if no_answer_qids:
-        no_ans_eval = make_eval_dict(exact_threshold, f1_threshold, qid_list=no_answer_qids)
-        merge_eval(evaluation, no_ans_eval, 'NoAns')
-
-    if no_answer_probs:
-        find_all_best_thresh(evaluation, preds, exact, f1, no_answer_probs, qas_id_to_has_answer)
-
-    return evaluation
-
-
-def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
-    """Project the tokenized prediction back to the original text."""
-
-    # When we created the data, we kept track of the alignment between original
-    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
-    # now `orig_text` contains the span of our original text corresponding to the
-    # span that we predicted.
-    #
-    # However, `orig_text` may contain extra characters that we don't want in
-    # our prediction.
-    #
-    # For example, let's say:
-    #   pred_text = steve smith
-    #   orig_text = Steve Smith's
-    #
-    # We don't want to return `orig_text` because it contains the extra "'s".
-    #
-    # We don't want to return `pred_text` because it's already been normalized
-    # (the SQuAD eval script also does punctuation stripping/lower casing but
-    # our tokenizer does additional normalization like stripping accent
-    # characters).
-    #
-    # What we really want to return is "Steve Smith".
-    #
-    # Therefore, we have to apply a semi-complicated alignment heuristic between
-    # `pred_text` and `orig_text` to get a character-to-character alignment. This
-    # can fail in certain cases in which case we just return `orig_text`.
-
-    def _strip_spaces(text):
-        ns_chars = []
-        ns_to_s_map = collections.OrderedDict()
-        for (i, c) in enumerate(text):
-            if c == " ":
-                continue
-            ns_to_s_map[len(ns_chars)] = i
-            ns_chars.append(c)
-        ns_text = "".join(ns_chars)
-        return (ns_text, ns_to_s_map)
-
-    # We first tokenize `orig_text`, strip whitespace from the result
-    # and `pred_text`, and check if they are the same length. If they are
-    # NOT the same length, the heuristic has failed. If they are the same
-    # length, we assume the characters are one-to-one aligned.
-    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
-
-    tok_text = " ".join(tokenizer.tokenize(orig_text))
-
-    start_position = tok_text.find(pred_text)
-    if start_position == -1:
-        if verbose_logging:
-            logger.info(
-                "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
-        return orig_text
-    end_position = start_position + len(pred_text) - 1
-
-    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
-    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
-
-    if len(orig_ns_text) != len(tok_ns_text):
-        if verbose_logging:
-            logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
-                        orig_ns_text, tok_ns_text)
-        return orig_text
-
-    # We then project the characters in `pred_text` back to `orig_text` using
-    # the character-to-character alignment.
-    tok_s_to_ns_map = {}
-    for (i, tok_index) in tok_ns_to_s_map.items():
-        tok_s_to_ns_map[tok_index] = i
-
-    orig_start_position = None
-    if start_position in tok_s_to_ns_map:
-        ns_start_position = tok_s_to_ns_map[start_position]
-        if ns_start_position in orig_ns_to_s_map:
-            orig_start_position = orig_ns_to_s_map[ns_start_position]
-
-    if orig_start_position is None:
-        if verbose_logging:
-            logger.info("Couldn't map start position")
-        return orig_text
-
-    orig_end_position = None
-    if end_position in tok_s_to_ns_map:
-        ns_end_position = tok_s_to_ns_map[end_position]
-        if ns_end_position in orig_ns_to_s_map:
-            orig_end_position = orig_ns_to_s_map[ns_end_position]
-
-    if orig_end_position is None:
-        if verbose_logging:
-            logger.info("Couldn't map end position")
-        return orig_text
-
-    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
-    return output_text
-
-
-def _get_best_indexes(logits, n_best_size):
-    """Get the n-best logits from a list."""
-    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
-
-    best_indexes = []
-    for i in range(len(index_and_score)):
-        if i >= n_best_size:
-            break
-        best_indexes.append(index_and_score[i][0])
-    return best_indexes
-
-
-def _compute_softmax(scores):
-    """Compute softmax probability over raw logits."""
-    if not scores:
-        return []
-
-    max_score = None
-    for score in scores:
-        if max_score is None or score > max_score:
-            max_score = score
-
-    exp_scores = []
-    total_sum = 0.0
-    for score in scores:
-        x = math.exp(score - max_score)
-        exp_scores.append(x)
-        total_sum += x
-
-    probs = []
-    for score in exp_scores:
-        probs.append(score / total_sum)
-    return probs
-
-
-def compute_predictions_logits(
-    all_examples,
-    all_features,
-    all_results,
-    n_best_size,
-    max_answer_length,
-    do_lower_case,
-    output_prediction_file,
-    output_nbest_file,
-    output_null_log_odds_file,
-    verbose_logging,
-    version_2_with_negative,
-    null_score_diff_threshold
-):
-    """Write final predictions to the json file and log-odds of null if needed."""
-    logger.info("Writing predictions to: %s" % (output_prediction_file))
-    logger.info("Writing nbest to: %s" % (output_nbest_file))
-
-    example_index_to_features = collections.defaultdict(list)
-    for feature in all_features:
-        example_index_to_features[feature.example_index].append(feature)
-
-    unique_id_to_result = {}
-    for result in all_results:
-        unique_id_to_result[result.unique_id] = result
-
-    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "PrelimPrediction",
-        ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
-
-    all_predictions = collections.OrderedDict()
-    all_nbest_json = collections.OrderedDict()
-    scores_diff_json = collections.OrderedDict()
-
-    for (example_index, example) in enumerate(all_examples):
-        features = example_index_to_features[example_index]
-
-        prelim_predictions = []
-        # keep track of the minimum score of null start+end of position 0
-        score_null = 1000000  # large and positive
-        min_null_feature_index = 0  # the paragraph slice with min null score
-        null_start_logit = 0  # the start logit at the slice with min null score
-        null_end_logit = 0  # the end logit at the slice with min null score
-        for (feature_index, feature) in enumerate(features):
-            result = unique_id_to_result[feature.unique_id]
-            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
-            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
-            # if we could have irrelevant answers, get the min score of irrelevant
-            if version_2_with_negative:
-                feature_null_score = result.start_logits[0] + result.end_logits[0]
-                if feature_null_score < score_null:
-                    score_null = feature_null_score
-                    min_null_feature_index = feature_index
-                    null_start_logit = result.start_logits[0]
-                    null_end_logit = result.end_logits[0]
-            for start_index in start_indexes:
-                for end_index in end_indexes:
-                    # We could hypothetically create invalid predictions, e.g., predict
-                    # that the start of the span is in the question. We throw out all
-                    # invalid predictions.
-                    if start_index >= len(feature.tokens):
-                        continue
-                    if end_index >= len(feature.tokens):
-                        continue
-                    if start_index not in feature.token_to_orig_map:
-                        continue
-                    if end_index not in feature.token_to_orig_map:
-                        continue
-                    if not feature.token_is_max_context.get(start_index, False):
-                        continue
-                    if end_index < start_index:
-                        continue
-                    length = end_index - start_index + 1
-                    if length > max_answer_length:
-                        continue
-                    prelim_predictions.append(
-                        _PrelimPrediction(
-                            feature_index=feature_index,
-                            start_index=start_index,
-                            end_index=end_index,
-                            start_logit=result.start_logits[start_index],
-                            end_logit=result.end_logits[end_index]))
-        if version_2_with_negative:
-            prelim_predictions.append(
-                _PrelimPrediction(
-                    feature_index=min_null_feature_index,
-                    start_index=0,
-                    end_index=0,
-                    start_logit=null_start_logit,
-                    end_logit=null_end_logit))
-        prelim_predictions = sorted(
-            prelim_predictions,
-            key=lambda x: (x.start_logit + x.end_logit),
-            reverse=True)
-
-        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-            "NbestPrediction", ["text", "start_logit", "end_logit"])
-
-        seen_predictions = {}
-        nbest = []
-        for pred in prelim_predictions:
-            if len(nbest) >= n_best_size:
-                break
-            feature = features[pred.feature_index]
-            if pred.start_index > 0:  # this is a non-null prediction
-                tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
-                orig_doc_start = feature.token_to_orig_map[pred.start_index]
-                orig_doc_end = feature.token_to_orig_map[pred.end_index]
-                orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
-                tok_text = " ".join(tok_tokens)
-
-                # De-tokenize WordPieces that have been split off.
-                tok_text = tok_text.replace(" ##", "")
-                tok_text = tok_text.replace("##", "")
-
-                # Clean whitespace
-                tok_text = tok_text.strip()
-                tok_text = " ".join(tok_text.split())
-                orig_text = " ".join(orig_tokens)
-
-                final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
-                if final_text in seen_predictions:
-                    continue
-
-                seen_predictions[final_text] = True
-            else:
-                final_text = ""
-                seen_predictions[final_text] = True
-
-            nbest.append(
-                _NbestPrediction(
-                    text=final_text,
-                    start_logit=pred.start_logit,
-                    end_logit=pred.end_logit))
-        # if we didn't include the empty option in the n-best, include it
-        if version_2_with_negative:
-            if "" not in seen_predictions:
-                nbest.append(
-                    _NbestPrediction(
-                        text="",
-                        start_logit=null_start_logit,
-                        end_logit=null_end_logit))
-
-            # In very rare edge cases we could only have single null prediction.
-            # So we just create a nonce prediction in this case to avoid failure.
-            if len(nbest) == 1:
-                nbest.insert(0,
-                             _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
-
-        # In very rare edge cases we could have no valid predictions. So we
-        # just create a nonce prediction in this case to avoid failure.
-        if not nbest:
-            nbest.append(
-                _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
-
-        assert len(nbest) >= 1
-
-        total_scores = []
-        best_non_null_entry = None
-        for entry in nbest:
-            total_scores.append(entry.start_logit + entry.end_logit)
-            if not best_non_null_entry:
-                if entry.text:
-                    best_non_null_entry = entry
-
-        probs = _compute_softmax(total_scores)
-
-        nbest_json = []
-        for (i, entry) in enumerate(nbest):
-            output = collections.OrderedDict()
-            output["text"] = entry.text
-            output["probability"] = probs[i]
-            output["start_logit"] = entry.start_logit
-            output["end_logit"] = entry.end_logit
-            nbest_json.append(output)
-
-        assert len(nbest_json) >= 1
-
-        if not version_2_with_negative:
-            all_predictions[example.qas_id] = nbest_json[0]["text"]
-        else:
-            # predict "" iff the null score - the score of best non-null > threshold
-            score_diff = score_null - best_non_null_entry.start_logit - (
-                best_non_null_entry.end_logit)
-            scores_diff_json[example.qas_id] = score_diff
-            if score_diff > null_score_diff_threshold:
-                all_predictions[example.qas_id] = ""
-            else:
-                all_predictions[example.qas_id] = best_non_null_entry.text
-        all_nbest_json[example.qas_id] = nbest_json
-
-    with open(output_prediction_file, "w") as writer:
-        writer.write(json.dumps(all_predictions, indent=4) + "\n")
-
-    with open(output_nbest_file, "w") as writer:
-        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
-
-    if version_2_with_negative:
-        with open(output_null_log_odds_file, "w") as writer:
-            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
-
-    return all_predictions
-
-
-def compute_predictions_log_probs(
-    all_examples,
-    all_features,
-    all_results,
-    n_best_size,
-    max_answer_length,
-    output_prediction_file,
-    output_nbest_file,
-    output_null_log_odds_file,
-    start_n_top,
-    end_n_top,
-    version_2_with_negative,
-    tokenizer,
-    verbose_logging
-):
-    """ XLNet write prediction logic (more complex than Bert's).
-        Write final predictions to the json file and log-odds of null if needed.
-
-        Requires utils_squad_evaluate.py
-    """
-    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "PrelimPrediction",
-        ["feature_index", "start_index", "end_index",
-         "start_log_prob", "end_log_prob"])
-
-    _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "NbestPrediction", ["text", "start_log_prob", "end_log_prob"])
-
-    logger.info("Writing predictions to: %s", output_prediction_file)
-    # logger.info("Writing nbest to: %s" % (output_nbest_file))
-
-    example_index_to_features = collections.defaultdict(list)
-    for feature in all_features:
-        example_index_to_features[feature.example_index].append(feature)
-
-    unique_id_to_result = {}
-    for result in all_results:
-        unique_id_to_result[result.unique_id] = result
-
-    all_predictions = collections.OrderedDict()
-    all_nbest_json = collections.OrderedDict()
-    scores_diff_json = collections.OrderedDict()
-
-    for (example_index, example) in enumerate(all_examples):
-        features = example_index_to_features[example_index]
-
-        prelim_predictions = []
-        # keep track of the minimum score of null start+end of position 0
-        score_null = 1000000  # large and positive
-
-        for (feature_index, feature) in enumerate(features):
-            result = unique_id_to_result[feature.unique_id]
-
-            cur_null_score = result.cls_logits
-
-            # if we could have irrelevant answers, get the min score of irrelevant
-            score_null = min(score_null, cur_null_score)
-
-            for i in range(start_n_top):
-                for j in range(end_n_top):
-                    start_log_prob = result.start_logits[i]
-                    start_index = result.start_top_index[i]
-
-                    j_index = i * end_n_top + j
-
-                    end_log_prob = result.end_logits[j_index]
-                    end_index = result.end_top_index[j_index]
-
-                    # We could hypothetically create invalid predictions, e.g., predict
-                    # that the start of the span is in the question. We throw out all
-                    # invalid predictions.
-                    if start_index >= feature.paragraph_len - 1:
-                        continue
-                    if end_index >= feature.paragraph_len - 1:
-                        continue
-
-                    if not feature.token_is_max_context.get(start_index, False):
-                        continue
-                    if end_index < start_index:
-                        continue
-                    length = end_index - start_index + 1
-                    if length > max_answer_length:
-                        continue
-
-                    prelim_predictions.append(
-                        _PrelimPrediction(
-                            feature_index=feature_index,
-                            start_index=start_index,
-                            end_index=end_index,
-                            start_log_prob=start_log_prob,
-                            end_log_prob=end_log_prob))
-
-        prelim_predictions = sorted(
-            prelim_predictions,
-            key=lambda x: (x.start_log_prob + x.end_log_prob),
-            reverse=True)
-
-        seen_predictions = {}
-        nbest = []
-        for pred in prelim_predictions:
-            if len(nbest) >= n_best_size:
-                break
-            feature = features[pred.feature_index]
-
-            # XLNet un-tokenizer
-            # Let's keep it simple for now and see if we need all this later.
-            #
-            # tok_start_to_orig_index = feature.tok_start_to_orig_index
-            # tok_end_to_orig_index = feature.tok_end_to_orig_index
-            # start_orig_pos = tok_start_to_orig_index[pred.start_index]
-            # end_orig_pos = tok_end_to_orig_index[pred.end_index]
-            # paragraph_text = example.paragraph_text
-            # final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
-
-            # Previously used Bert untokenizer
-            tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
-            orig_doc_start = feature.token_to_orig_map[pred.start_index]
-            orig_doc_end = feature.token_to_orig_map[pred.end_index]
-            orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
-            tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
-
-            # Clean whitespace
-            tok_text = tok_text.strip()
-            tok_text = " ".join(tok_text.split())
-            orig_text = " ".join(orig_tokens)
-
-            if hasattr(tokenizer, "do_lower_case"):
-                do_lower_case = tokenizer.do_lower_case
-            else:
-                do_lower_case = tokenizer.do_lowercase_and_remove_accent
-
-            final_text = get_final_text(tok_text, orig_text, do_lower_case,
-                                        verbose_logging)
-
-            if final_text in seen_predictions:
-                continue
-
-            seen_predictions[final_text] = True
-
-            nbest.append(
-                _NbestPrediction(
-                    text=final_text,
-                    start_log_prob=pred.start_log_prob,
-                    end_log_prob=pred.end_log_prob))
-
-        # In very rare edge cases we could have no valid predictions. So we
-        # just create a nonce prediction in this case to avoid failure.
-        if not nbest:
-            nbest.append(
-                _NbestPrediction(text="", start_log_prob=-1e6,
-                end_log_prob=-1e6))
-
-        total_scores = []
-        best_non_null_entry = None
-        for entry in nbest:
-            total_scores.append(entry.start_log_prob + entry.end_log_prob)
-            if not best_non_null_entry:
-                best_non_null_entry = entry
-
-        probs = _compute_softmax(total_scores)
-
-        nbest_json = []
-        for (i, entry) in enumerate(nbest):
-            output = collections.OrderedDict()
-            output["text"] = entry.text
-            output["probability"] = probs[i]
-            output["start_log_prob"] = entry.start_log_prob
-            output["end_log_prob"] = entry.end_log_prob
-            nbest_json.append(output)
-
-        assert len(nbest_json) >= 1
-        assert best_non_null_entry is not None
-
-        score_diff = score_null
-        scores_diff_json[example.qas_id] = score_diff
-        # note(zhiliny): always predict best_non_null_entry
-        # and the evaluation script will search for the best threshold
-        all_predictions[example.qas_id] = best_non_null_entry.text
-
-        all_nbest_json[example.qas_id] = nbest_json
-
-    with open(output_prediction_file, "w") as writer:
-        writer.write(json.dumps(all_predictions, indent=4) + "\n")
-
-    with open(output_nbest_file, "w") as writer:
-        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
-
-    if version_2_with_negative:
-        with open(output_null_log_odds_file, "w") as writer:
-            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
-
-    return all_predictions

transformers/data/processors/__init__.py

@@ -1,4 +1,3 @@
 from .utils import InputExample, InputFeatures, DataProcessor
 from .glue import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features
-from .squad import squad_convert_examples_to_features, SquadFeatures, SquadExample, SquadV1Processor, SquadV2Processor
-from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
+from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels

transformers/data/processors/glue.py

@@ -133,7 +133,7 @@ def glue_convert_examples_to_features(examples, tokenizer,
     if is_tf_available() and is_tf_dataset:
         def gen():
             for ex in features:
-                yield ({'input_ids': ex.input_ids,
+                yield  ({'input_ids': ex.input_ids,
                          'attention_mask': ex.attention_mask,
                          'token_type_ids': ex.token_type_ids},
                         ex.label)

transformers/data/processors/squad.py

@@ -1,653 +0,0 @@
-from tqdm import tqdm
-import collections
-import logging
-import os
-import json
-import numpy as np
-
-from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
-from .utils import DataProcessor, InputExample, InputFeatures
-from ...file_utils import is_tf_available, is_torch_available
-
-if is_torch_available():
-    import torch
-    from torch.utils.data import TensorDataset
-
-if is_tf_available():
-    import tensorflow as tf
-
-logger = logging.getLogger(__name__)
-
-
-def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
-    """Returns tokenized answer spans that better match the annotated answer."""
-    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
-
-    for new_start in range(input_start, input_end + 1):
-        for new_end in range(input_end, new_start - 1, -1):
-            text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
-            if text_span == tok_answer_text:
-                return (new_start, new_end)
-
-    return (input_start, input_end)
-
-
-def _check_is_max_context(doc_spans, cur_span_index, position):
-    """Check if this is the 'max context' doc span for the token."""
-    best_score = None
-    best_span_index = None
-    for (span_index, doc_span) in enumerate(doc_spans):
-        end = doc_span.start + doc_span.length - 1
-        if position < doc_span.start:
-            continue
-        if position > end:
-            continue
-        num_left_context = position - doc_span.start
-        num_right_context = end - position
-        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
-        if best_score is None or score > best_score:
-            best_score = score
-            best_span_index = span_index
-
-    return cur_span_index == best_span_index
-
-
-def _new_check_is_max_context(doc_spans, cur_span_index, position):
-    """Check if this is the 'max context' doc span for the token."""
-    # if len(doc_spans) == 1:
-    # return True
-    best_score = None
-    best_span_index = None
-    for (span_index, doc_span) in enumerate(doc_spans):
-        end = doc_span["start"] + doc_span["length"] - 1
-        if position < doc_span["start"]:
-            continue
-        if position > end:
-            continue
-        num_left_context = position - doc_span["start"]
-        num_right_context = end - position
-        score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
-        if best_score is None or score > best_score:
-            best_score = score
-            best_span_index = span_index
-
-    return cur_span_index == best_span_index
-
-
-def _is_whitespace(c):
-    if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
-        return True
-    return False
-
-
-def squad_convert_examples_to_features(
-    examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, return_dataset=False
-):
-    """
-    Converts a list of examples into a list of features that can be directly given as input to a model.
-    It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
-
-    Args:
-        examples: list of :class:`~transformers.data.processors.squad.SquadExample`
-        tokenizer: an instance of a child of :class:`~transformers.PreTrainedTokenizer`
-        max_seq_length: The maximum sequence length of the inputs.
-        doc_stride: The stride used when the context is too large and is split across several features.
-        max_query_length: The maximum length of the query.
-        is_training: whether to create features for model evaluation or model training.
-        return_dataset: Default False. Either 'pt' or 'tf'.
-            if 'pt': returns a torch.data.TensorDataset,
-            if 'tf': returns a tf.data.Dataset
-
-    Returns:
-        list of :class:`~transformers.data.processors.squad.SquadFeatures`
-
-    Example::
-
-        processor = SquadV2Processor()
-        examples = processor.get_dev_examples(data_dir)
-
-        features = squad_convert_examples_to_features( 
-            examples=examples,
-            tokenizer=tokenizer,
-            max_seq_length=args.max_seq_length,
-            doc_stride=args.doc_stride,
-            max_query_length=args.max_query_length,
-            is_training=not evaluate,
-        )
-    """
-
-    # Defining helper methods
-    unique_id = 1000000000
-
-    features = []
-    for (example_index, example) in enumerate(tqdm(examples, desc="Converting examples to features")):
-        if is_training and not example.is_impossible:
-            # Get start and end position
-            start_position = example.start_position
-            end_position = example.end_position
-
-            # If the answer cannot be found in the text, then skip this example.
-            actual_text = " ".join(example.doc_tokens[start_position : (end_position + 1)])
-            cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
-            if actual_text.find(cleaned_answer_text) == -1:
-                logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
-                continue
-
-        tok_to_orig_index = []
-        orig_to_tok_index = []
-        all_doc_tokens = []
-        for (i, token) in enumerate(example.doc_tokens):
-            orig_to_tok_index.append(len(all_doc_tokens))
-            sub_tokens = tokenizer.tokenize(token)
-            for sub_token in sub_tokens:
-                tok_to_orig_index.append(i)
-                all_doc_tokens.append(sub_token)
-
-        if is_training and not example.is_impossible:
-            tok_start_position = orig_to_tok_index[example.start_position]
-            if example.end_position < len(example.doc_tokens) - 1:
-                tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
-            else:
-                tok_end_position = len(all_doc_tokens) - 1
-
-            (tok_start_position, tok_end_position) = _improve_answer_span(
-                all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
-            )
-
-        spans = []
-
-        truncated_query = tokenizer.encode(
-            example.question_text, add_special_tokens=False, max_length=max_query_length
-        )
-        sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence
-        sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
-
-        span_doc_tokens = all_doc_tokens
-        while len(spans) * doc_stride < len(all_doc_tokens):
-
-            encoded_dict = tokenizer.encode_plus(
-                truncated_query if tokenizer.padding_side == "right" else span_doc_tokens,
-                span_doc_tokens if tokenizer.padding_side == "right" else truncated_query,
-                max_length=max_seq_length,
-                return_overflowing_tokens=True,
-                pad_to_max_length=True,
-                stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
-                truncation_strategy="only_second" if tokenizer.padding_side == "right" else "only_first",
-            )
-
-            paragraph_len = min(
-                len(all_doc_tokens) - len(spans) * doc_stride,
-                max_seq_length - len(truncated_query) - sequence_pair_added_tokens,
-            )
-
-            if tokenizer.pad_token_id in encoded_dict["input_ids"]:
-                non_padded_ids = encoded_dict["input_ids"][: encoded_dict["input_ids"].index(tokenizer.pad_token_id)]
-            else:
-                non_padded_ids = encoded_dict["input_ids"]
-
-            tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
-
-            token_to_orig_map = {}
-            for i in range(paragraph_len):
-                index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
-                token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
-
-            encoded_dict["paragraph_len"] = paragraph_len
-            encoded_dict["tokens"] = tokens
-            encoded_dict["token_to_orig_map"] = token_to_orig_map
-            encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
-            encoded_dict["token_is_max_context"] = {}
-            encoded_dict["start"] = len(spans) * doc_stride
-            encoded_dict["length"] = paragraph_len
-
-            spans.append(encoded_dict)
-
-            if "overflowing_tokens" not in encoded_dict:
-                break
-            span_doc_tokens = encoded_dict["overflowing_tokens"]
-
-        for doc_span_index in range(len(spans)):
-            for j in range(spans[doc_span_index]["paragraph_len"]):
-                is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
-                index = (
-                    j
-                    if tokenizer.padding_side == "left"
-                    else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
-                )
-                spans[doc_span_index]["token_is_max_context"][index] = is_max_context
-
-        for span in spans:
-            # Identify the position of the CLS token
-            cls_index = span["input_ids"].index(tokenizer.cls_token_id)
-
-            # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
-            # Original TF implem also keep the classification token (set to 0) (not sure why...)
-            p_mask = np.array(span["token_type_ids"])
-
-            p_mask = np.minimum(p_mask, 1)
-
-            if tokenizer.padding_side == "right":
-                # Limit positive values to one
-                p_mask = 1 - p_mask
-
-            p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 1
-
-            # Set the CLS index to '0'
-            p_mask[cls_index] = 0
-
-            span_is_impossible = example.is_impossible
-            start_position = 0
-            end_position = 0
-            if is_training and not span_is_impossible:
-                # For training, if our document chunk does not contain an annotation
-                # we throw it out, since there is nothing to predict.
-                doc_start = span["start"]
-                doc_end = span["start"] + span["length"] - 1
-                out_of_span = False
-
-                if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
-                    out_of_span = True
-
-                if out_of_span:
-                    start_position = cls_index
-                    end_position = cls_index
-                    span_is_impossible = True
-                else:
-                    if tokenizer.padding_side == "left":
-                        doc_offset = 0
-                    else:
-                        doc_offset = len(truncated_query) + sequence_added_tokens
-
-                    start_position = tok_start_position - doc_start + doc_offset
-                    end_position = tok_end_position - doc_start + doc_offset
-
-            features.append(
-                SquadFeatures(
-                    span["input_ids"],
-                    span["attention_mask"],
-                    span["token_type_ids"],
-                    cls_index,
-                    p_mask.tolist(),
-                    example_index=example_index,
-                    unique_id=unique_id,
-                    paragraph_len=span["paragraph_len"],
-                    token_is_max_context=span["token_is_max_context"],
-                    tokens=span["tokens"],
-                    token_to_orig_map=span["token_to_orig_map"],
-                    start_position=start_position,
-                    end_position=end_position,
-                )
-            )
-
-            unique_id += 1
-
-    if return_dataset == "pt":
-        if not is_torch_available():
-            raise ImportError("Pytorch must be installed to return a pytorch dataset.")
-
-        # Convert to Tensors and build dataset
-        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-        all_attention_masks = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
-        all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
-        all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
-        all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
-
-        if not is_training:
-            all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
-            dataset = TensorDataset(
-                all_input_ids, all_attention_masks, all_token_type_ids, all_example_index, all_cls_index, all_p_mask
-            )
-        else:
-            all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
-            all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
-            dataset = TensorDataset(
-                all_input_ids,
-                all_attention_masks,
-                all_token_type_ids,
-                all_start_positions,
-                all_end_positions,
-                all_cls_index,
-                all_p_mask,
-            )
-
-        return features, dataset
-    elif return_dataset == "tf":
-        if not is_tf_available():
-            raise ImportError("TensorFlow must be installed to return a TensorFlow dataset.")
-
-        def gen():
-            for ex in features:
-                yield (
-                    {
-                        "input_ids": ex.input_ids,
-                        "attention_mask": ex.attention_mask,
-                        "token_type_ids": ex.token_type_ids,
-                    }, {
-                        "start_position": ex.start_position,
-                        "end_position": ex.end_position,
-                        "cls_index": ex.cls_index,
-                        "p_mask": ex.p_mask,
-                    }
-                )
-
-        return tf.data.Dataset.from_generator(
-            gen,
-            (
-                {"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32},
-                {"start_position": tf.int64, "end_position": tf.int64, "cls_index": tf.int64, "p_mask": tf.int32},
-            ),
-            (
-                {
-                    "input_ids": tf.TensorShape([None]),
-                    "attention_mask": tf.TensorShape([None]),
-                    "token_type_ids": tf.TensorShape([None]),
-                },
-                {
-                    "start_position": tf.TensorShape([]),
-                    "end_position": tf.TensorShape([]),
-                    "cls_index": tf.TensorShape([]),
-                    "p_mask": tf.TensorShape([None]),
-                },
-            ),
-        )
-
-    return features
-
-
-class SquadProcessor(DataProcessor):
-    """
-    Processor for the SQuAD data set.
-    Overriden by SquadV1Processor and SquadV2Processor, used by the version 1.1 and version 2.0 of SQuAD, respectively.
-    """
-
-    train_file = None
-    dev_file = None
-
-    def _get_example_from_tensor_dict(self, tensor_dict, evaluate=False):
-        if not evaluate:
-            answer = tensor_dict["answers"]["text"][0].numpy().decode("utf-8")
-            answer_start = tensor_dict["answers"]["answer_start"][0].numpy()
-            answers = []
-        else:
-            answers = [
-                {"answer_start": start.numpy(), "text": text.numpy().decode("utf-8")}
-                for start, text in zip(tensor_dict["answers"]["answer_start"], tensor_dict["answers"]["text"])
-            ]
-
-            answer = None
-            answer_start = None
-
-        return SquadExample(
-            qas_id=tensor_dict["id"].numpy().decode("utf-8"),
-            question_text=tensor_dict["question"].numpy().decode("utf-8"),
-            context_text=tensor_dict["context"].numpy().decode("utf-8"),
-            answer_text=answer,
-            start_position_character=answer_start,
-            title=tensor_dict["title"].numpy().decode("utf-8"),
-            answers=answers,
-        )
-
-    def get_examples_from_dataset(self, dataset, evaluate=False):
-        """
-        Creates a list of :class:`~transformers.data.processors.squad.SquadExample` using a TFDS dataset.
-
-        Args:
-            dataset: The tfds dataset loaded from `tensorflow_datasets.load("squad")`
-            evaluate: boolean specifying if in evaluation mode or in training mode
-
-        Returns:
-            List of SquadExample
-
-        Examples::
-
-            import tensorflow_datasets as tfds
-            dataset = tfds.load("squad")
-
-            training_examples = get_examples_from_dataset(dataset, evaluate=False)
-            evaluation_examples = get_examples_from_dataset(dataset, evaluate=True)
-        """
-
-        if evaluate:
-            dataset = dataset["validation"]
-        else:
-            dataset = dataset["train"]
-
-        examples = []
-        for tensor_dict in tqdm(dataset):
-            examples.append(self._get_example_from_tensor_dict(tensor_dict, evaluate=evaluate))
-
-        return examples
-
-    def get_train_examples(self, data_dir, filename=None):
-        """
-        Returns the training examples from the data directory.
-
-        Args:
-            data_dir: Directory containing the data files used for training and evaluating.
-            filename: None by default, specify this if the training file has a different name than the original one
-                which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
-
-        """
-        if data_dir is None:
-            data_dir = ""
-
-        if self.train_file is None:
-            raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
-
-        with open(
-            os.path.join(data_dir, self.train_file if filename is None else filename), "r", encoding="utf-8"
-        ) as reader:
-            input_data = json.load(reader)["data"]
-        return self._create_examples(input_data, "train")
-
-    def get_dev_examples(self, data_dir, filename=None):
-        """
-        Returns the evaluation example from the data directory.
-
-        Args:
-            data_dir: Directory containing the data files used for training and evaluating.
-            filename: None by default, specify this if the evaluation file has a different name than the original one
-                which is `train-v1.1.json` and `train-v2.0.json` for squad versions 1.1 and 2.0 respectively.
-        """
-        if data_dir is None:
-            data_dir = ""
-
-        if self.dev_file is None:
-            raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
-
-        with open(
-            os.path.join(data_dir, self.dev_file if filename is None else filename), "r", encoding="utf-8"
-        ) as reader:
-            input_data = json.load(reader)["data"]
-        return self._create_examples(input_data, "dev")
-
-    def _create_examples(self, input_data, set_type):
-        is_training = set_type == "train"
-        examples = []
-        for entry in tqdm(input_data):
-            title = entry["title"]
-            for paragraph in entry["paragraphs"]:
-                context_text = paragraph["context"]
-                for qa in paragraph["qas"]:
-                    qas_id = qa["id"]
-                    question_text = qa["question"]
-                    start_position_character = None
-                    answer_text = None
-                    answers = []
-
-                    if "is_impossible" in qa:
-                        is_impossible = qa["is_impossible"]
-                    else:
-                        is_impossible = False
-
-                    if not is_impossible:
-                        if is_training:
-                            answer = qa["answers"][0]
-                            answer_text = answer["text"]
-                            start_position_character = answer["answer_start"]
-                        else:
-                            answers = qa["answers"]
-
-                    example = SquadExample(
-                        qas_id=qas_id,
-                        question_text=question_text,
-                        context_text=context_text,
-                        answer_text=answer_text,
-                        start_position_character=start_position_character,
-                        title=title,
-                        is_impossible=is_impossible,
-                        answers=answers,
-                    )
-
-                    examples.append(example)
-        return examples
-
-
-class SquadV1Processor(SquadProcessor):
-    train_file = "train-v1.1.json"
-    dev_file = "dev-v1.1.json"
-
-
-class SquadV2Processor(SquadProcessor):
-    train_file = "train-v2.0.json"
-    dev_file = "dev-v2.0.json"
-
-
-class SquadExample(object):
-    """
-    A single training/test example for the Squad dataset, as loaded from disk.
-
-    Args:
-        qas_id: The example's unique identifier
-        question_text: The question string
-        context_text: The context string
-        answer_text: The answer string
-        start_position_character: The character position of the start of the answer
-        title: The title of the example
-        answers: None by default, this is used during evaluation. Holds answers as well as their start positions.
-        is_impossible: False by default, set to True if the example has no possible answer.
-    """
-
-    def __init__(
-        self,
-        qas_id,
-        question_text,
-        context_text,
-        answer_text,
-        start_position_character,
-        title,
-        answers=[],
-        is_impossible=False,
-    ):
-        self.qas_id = qas_id
-        self.question_text = question_text
-        self.context_text = context_text
-        self.answer_text = answer_text
-        self.title = title
-        self.is_impossible = is_impossible
-        self.answers = answers
-
-        self.start_position, self.end_position = 0, 0
-
-        doc_tokens = []
-        char_to_word_offset = []
-        prev_is_whitespace = True
-
-        # Split on whitespace so that different tokens may be attributed to their original position.
-        for c in self.context_text:
-            if _is_whitespace(c):
-                prev_is_whitespace = True
-            else:
-                if prev_is_whitespace:
-                    doc_tokens.append(c)
-                else:
-                    doc_tokens[-1] += c
-                prev_is_whitespace = False
-            char_to_word_offset.append(len(doc_tokens) - 1)
-
-        self.doc_tokens = doc_tokens
-        self.char_to_word_offset = char_to_word_offset
-
-        # Start end end positions only has a value during evaluation.
-        if start_position_character is not None and not is_impossible:
-            self.start_position = char_to_word_offset[start_position_character]
-            self.end_position = char_to_word_offset[start_position_character + len(answer_text) - 1]
-
-
-class SquadFeatures(object):
-    """
-    Single squad example features to be fed to a model.
-    Those features are model-specific and can be crafted from :class:`~transformers.data.processors.squad.SquadExample`
-    using the :method:`~transformers.data.processors.squad.squad_convert_examples_to_features` method.
-
-    Args:
-        input_ids: Indices of input sequence tokens in the vocabulary.
-        attention_mask: Mask to avoid performing attention on padding token indices.
-        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
-        cls_index: the index of the CLS token.
-        p_mask: Mask identifying tokens that can be answers vs. tokens that cannot.
-            Mask with 1 for tokens than cannot be in the answer and 0 for token that can be in an answer
-        example_index: the index of the example
-        unique_id: The unique Feature identifier
-        paragraph_len: The length of the context
-        token_is_max_context: List of booleans identifying which tokens have their maximum context in this feature object.
-            If a token does not have their maximum context in this feature object, it means that another feature object
-            has more information related to that token and should be prioritized over this feature for that token.
-        tokens: list of tokens corresponding to the input ids
-        token_to_orig_map: mapping between the tokens and the original text, needed in order to identify the answer.
-        start_position: start of the answer token index 
-        end_position: end of the answer token index 
-    """
-
-    def __init__(
-        self,
-        input_ids,
-        attention_mask,
-        token_type_ids,
-        cls_index,
-        p_mask,
-        example_index,
-        unique_id,
-        paragraph_len,
-        token_is_max_context,
-        tokens,
-        token_to_orig_map,
-        start_position,
-        end_position,
-    ):
-        self.input_ids = input_ids
-        self.attention_mask = attention_mask
-        self.token_type_ids = token_type_ids
-        self.cls_index = cls_index
-        self.p_mask = p_mask
-
-        self.example_index = example_index
-        self.unique_id = unique_id
-        self.paragraph_len = paragraph_len
-        self.token_is_max_context = token_is_max_context
-        self.tokens = tokens
-        self.token_to_orig_map = token_to_orig_map
-
-        self.start_position = start_position
-        self.end_position = end_position
-
-
-class SquadResult(object):
-    """
-    Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
-
-    Args:
-        unique_id: The unique identifier corresponding to that example.
-        start_logits: The logits corresponding to the start of the answer
-        end_logits: The logits corresponding to the end of the answer
-    """
-
-    def __init__(self, unique_id, start_logits, end_logits, start_top_index=None, end_top_index=None, cls_logits=None):
-        self.start_logits = start_logits
-        self.end_logits = end_logits
-        self.unique_id = unique_id
-
-        if start_top_index:
-            self.start_top_index = start_top_index
-            self.end_top_index = end_top_index
-            self.cls_logits = cls_logits

transformers/file_utils.py

@@ -21,8 +21,7 @@ import boto3
 from botocore.config import Config
 from botocore.exceptions import ClientError
 import requests
-from tqdm.auto import tqdm
-from contextlib import contextmanager
+from tqdm import tqdm
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
@@ -73,13 +72,6 @@ TF2_WEIGHTS_NAME = 'tf_model.h5'
 TF_WEIGHTS_NAME = 'model.ckpt'
 CONFIG_NAME = "config.json"
 
-
-DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
-DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
-
-S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
-
-
 def is_torch_available():
     return _torch_available
 
@@ -110,18 +102,6 @@ else:
             return fn
         return docstring_decorator
 
-
-def is_remote_url(url_or_filename):
-    parsed = urlparse(url_or_filename)
-    return parsed.scheme in ('http', 'https', 's3')
-
-def hf_bucket_url(identifier, postfix=None):
-    if postfix is None:
-        return "/".join((S3_BUCKET_PREFIX, identifier))
-    else:
-        return "/".join((S3_BUCKET_PREFIX, identifier, postfix))
-
-
 def url_to_filename(url, etag=None):
     """
     Convert `url` into a hashed filename in a repeatable way.
@@ -172,7 +152,7 @@ def filename_to_url(filename, cache_dir=None):
     return url, etag
 
 
-def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None, resume_download=False):
+def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None):
     """
     Given something that might be a URL (or might be a local path),
     determine which. If it's a URL, download the file and cache it, and
@@ -181,7 +161,6 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N
     Args:
         cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
         force_download: if True, re-dowload the file even if it's already cached in the cache dir.
-        resume_download: if True, resume the download if incompletly recieved file is found.
     """
     if cache_dir is None:
         cache_dir = TRANSFORMERS_CACHE
@@ -190,15 +169,15 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N
     if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
         cache_dir = str(cache_dir)
 
-    if is_remote_url(url_or_filename):
+    parsed = urlparse(url_or_filename)
+
+    if parsed.scheme in ('http', 'https', 's3'):
         # URL, so get it from the cache (downloading if necessary)
-        return get_from_cache(url_or_filename, cache_dir=cache_dir,
-            force_download=force_download, proxies=proxies,
-            resume_download=resume_download)
+        return get_from_cache(url_or_filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
     elif os.path.exists(url_or_filename):
         # File, and it exists.
         return url_or_filename
-    elif urlparse(url_or_filename).scheme == '':
+    elif parsed.scheme == '':
         # File, but it doesn't exist.
         raise EnvironmentError("file {} not found".format(url_or_filename))
     else:
@@ -255,22 +234,19 @@ def s3_get(url, temp_file, proxies=None):
     s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
 
 
-def http_get(url, temp_file, proxies=None, resume_size=0):
-    headers={'Range':'bytes=%d-'%(resume_size,)} if resume_size > 0 else None
-    response = requests.get(url, stream=True, proxies=proxies, headers=headers)
-    if response.status_code == 416:  # Range not satisfiable
-        return
-    content_length = response.headers.get('Content-Length')
-    total = resume_size + int(content_length) if content_length is not None else None
-    progress = tqdm(unit="B", unit_scale=True, total=total, initial=resume_size, desc="Downloading")
-    for chunk in response.iter_content(chunk_size=1024):
+def http_get(url, temp_file, proxies=None):
+    req = requests.get(url, stream=True, proxies=proxies)
+    content_length = req.headers.get('Content-Length')
+    total = int(content_length) if content_length is not None else None
+    progress = tqdm(unit="B", total=total)
+    for chunk in req.iter_content(chunk_size=1024):
         if chunk: # filter out keep-alive new chunks
             progress.update(len(chunk))
             temp_file.write(chunk)
     progress.close()
 
 
-def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10, resume_download=False):
+def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10):
     """
     Given a URL, look for the corresponding dataset in the local cache.
     If it's not there, download it. Then return the path to the cached file.
@@ -313,35 +289,17 @@ def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag
         if matching_files:
             cache_path = os.path.join(cache_dir, matching_files[-1])
 
-    if resume_download:
-        incomplete_path = cache_path + '.incomplete'
-        @contextmanager
-        def _resumable_file_manager():
-            with open(incomplete_path,'a+b') as f:
-                yield f
-            os.remove(incomplete_path)
-        temp_file_manager = _resumable_file_manager
-        if os.path.exists(incomplete_path):
-            resume_size = os.stat(incomplete_path).st_size
-        else:
-            resume_size = 0
-    else:
-        temp_file_manager = tempfile.NamedTemporaryFile
-        resume_size = 0
-
     if not os.path.exists(cache_path) or force_download:
         # Download to temporary file, then copy to cache dir once finished.
         # Otherwise you get corrupt cache entries if the download gets interrupted.
-        with temp_file_manager() as temp_file:
+        with tempfile.NamedTemporaryFile() as temp_file:
             logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
 
             # GET file object
             if url.startswith("s3://"):
-                if resume_download:
-                    logger.warn('Warning: resumable downloads are not implemented for "s3://" urls')
                 s3_get(url, temp_file, proxies=proxies)
             else:
-                http_get(url, temp_file, proxies=proxies, resume_size=resume_size)
+                http_get(url, temp_file, proxies=proxies)
 
             # we are copying the file before closing it, so flush to avoid truncation
             temp_file.flush()

transformers/hf_api.py

@@ -1,229 +0,0 @@
-# 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.
-from __future__ import absolute_import, division, print_function
-
-import os
-from os.path import expanduser
-
-import requests
-import six
-from requests.exceptions import HTTPError
-from tqdm import tqdm
-
-ENDPOINT = "https://huggingface.co"
-
-class S3Obj:
-    def __init__(
-        self,
-        filename,     # type: str
-        LastModified, # type: str
-        ETag,         # type: str
-        Size,         # type: int
-        **kwargs
-    ):
-        self.filename = filename
-        self.LastModified = LastModified
-        self.ETag = ETag
-        self.Size = Size
-
-
-class PresignedUrl:
-    def __init__(
-        self,
-        write,  # type: str
-        access, # type: str
-        type,   # type: str
-        **kwargs
-    ):
-        self.write = write
-        self.access = access
-        self.type = type # mime-type to send to S3.
-
-
-class HfApi:
-    def __init__(self, endpoint=None):
-        self.endpoint = endpoint if endpoint is not None else ENDPOINT
-
-    def login(
-        self,
-        username, # type: str
-        password, # type: str
-    ):
-        # type: (...) -> str
-        """
-        Call HF API to sign in a user and get a token if credentials are valid.
-
-        Outputs:
-            token if credentials are valid
-
-        Throws:
-            requests.exceptions.HTTPError if credentials are invalid
-        """
-        path = "{}/api/login".format(self.endpoint)
-        r = requests.post(path, json={"username": username, "password": password})
-        r.raise_for_status()
-        d = r.json()
-        return d["token"]
-
-    def whoami(
-        self,
-        token, # type: str
-    ):
-        # type: (...) -> str
-        """
-        Call HF API to know "whoami"
-        """
-        path = "{}/api/whoami".format(self.endpoint)
-        r = requests.get(path, headers={"authorization": "Bearer {}".format(token)})
-        r.raise_for_status()
-        d = r.json()
-        return d["user"]
-
-    def logout(self, token):
-        # type: (...) -> void
-        """
-        Call HF API to log out.
-        """
-        path = "{}/api/logout".format(self.endpoint)
-        r = requests.post(path, headers={"authorization": "Bearer {}".format(token)})
-        r.raise_for_status()
-
-    def presign(self, token, filename):
-        # type: (...) -> PresignedUrl
-        """
-        Call HF API to get a presigned url to upload `filename` to S3.
-        """
-        path = "{}/api/presign".format(self.endpoint)
-        r = requests.post(
-            path,
-            headers={"authorization": "Bearer {}".format(token)},
-            json={"filename": filename},
-        )
-        r.raise_for_status()
-        d = r.json()
-        return PresignedUrl(**d)
-
-    def presign_and_upload(self, token, filename, filepath):
-        # type: (...) -> str
-        """
-        Get a presigned url, then upload file to S3.
-
-        Outputs:
-            url: Read-only url for the stored file on S3.
-        """
-        urls = self.presign(token, filename=filename)
-        # streaming upload:
-        # https://2.python-requests.org/en/master/user/advanced/#streaming-uploads
-        # 
-        # Even though we presign with the correct content-type,
-        # the client still has to specify it when uploading the file.
-        with open(filepath, "rb") as f:
-            pf = TqdmProgressFileReader(f)
-            data = f if pf.total_size > 0 else ""
-
-            r = requests.put(urls.write, data=data, headers={
-                "content-type": urls.type,
-            })
-            r.raise_for_status()
-            pf.close()
-        return urls.access
-
-    def list_objs(self, token):
-        # type: (...) -> List[S3Obj]
-        """
-        Call HF API to list all stored files for user.
-        """
-        path = "{}/api/listObjs".format(self.endpoint)
-        r = requests.get(path, headers={"authorization": "Bearer {}".format(token)})
-        r.raise_for_status()
-        d = r.json()
-        return [S3Obj(**x) for x in d]
-
-
-
-class TqdmProgressFileReader:
-    """
-    Wrap an io.BufferedReader `f` (such as the output of `open(…, "rb")`)
-    and override `f.read()` so as to display a tqdm progress bar.
-
-    see github.com/huggingface/transformers/pull/2078#discussion_r354739608
-    for implementation details.
-    """
-    def __init__(
-        self,
-        f   # type: io.BufferedReader
-    ):
-        self.f = f
-        self.total_size = os.fstat(f.fileno()).st_size # type: int
-        self.pbar = tqdm(total=self.total_size, leave=False)
-        if six.PY3:
-            # does not work unless PY3
-            # no big deal as the CLI does not currently support PY2 anyways.
-            self.read = f.read
-            f.read = self._read
-
-    def _read(self, n=-1):
-        self.pbar.update(n)
-        return self.read(n)
-
-    def close(self):
-        self.pbar.close()
-
-
-
-class HfFolder:
-    path_token = expanduser("~/.huggingface/token")
-
-    @classmethod
-    def save_token(cls, token):
-        """
-        Save token, creating folder as needed.
-        """
-        if six.PY3:
-            os.makedirs(os.path.dirname(cls.path_token), exist_ok=True)
-        else:
-            # Python 2
-            try:
-                os.makedirs(os.path.dirname(cls.path_token))
-            except OSError as e:
-                if e.errno != os.errno.EEXIST:
-                    raise e
-                pass
-        with open(cls.path_token, 'w+') as f:
-            f.write(token)
-
-    @classmethod
-    def get_token(cls):
-        """
-        Get token or None if not existent.
-        """
-        try:
-            with open(cls.path_token, 'r') as f:
-                return f.read()
-        except:
-            # this is too wide. When Py2 is dead use:
-            # `except FileNotFoundError:` instead
-            return None
-
-    @classmethod
-    def delete_token(cls):
-        """
-        Delete token.
-        Do not fail if token does not exist.
-        """
-        try:
-            os.remove(cls.path_token)
-        except:
-            return

transformers/modeling_albert.py

@@ -68,54 +68,17 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
     
     for name, array in zip(names, arrays):
         original_name = name
-
-        # If saved from the TF HUB module
-        name = name.replace("module/", "")
-
-        # Renaming and simplifying
         name = name.replace("ffn_1", "ffn")
-        name = name.replace("bert/", "albert/")
+        name = name.replace("/bert/", "/albert/")
+        name = name.replace("ffn/intermediate/output", "ffn_output")
         name = name.replace("attention_1", "attention")   
+        name = name.replace("cls/predictions", "predictions")
         name = name.replace("transform/", "")
         name = name.replace("LayerNorm_1", "full_layer_layer_norm")    
-        name = name.replace("LayerNorm", "attention/LayerNorm")   
-        name = name.replace("transformer/", "")
-
-        # The feed forward layer had an 'intermediate' step which has been abstracted away
-        name = name.replace("intermediate/dense/", "")
-        name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")
-
-        # ALBERT attention was split between self and output which have been abstracted away
-        name = name.replace("/output/", "/")
-        name = name.replace("/self/", "/")
-
-        # The pooler is a linear layer
-        name = name.replace("pooler/dense", "pooler")
-
-        # The classifier was simplified to predictions from cls/predictions
-        name = name.replace("cls/predictions", "predictions")
-        name = name.replace("predictions/attention", "predictions")
-
-        # Naming was changed to be more explicit
-        name = name.replace("embeddings/attention", "embeddings")    
+        name = name.replace("LayerNorm", "attention/LayerNorm")    
         name = name.replace("inner_group_", "albert_layers/") 
-        name = name.replace("group_", "albert_layer_groups/")   
-
-        # Classifier
-        if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name):
-            name = "classifier/" + name
-
-        # No ALBERT model currently handles the next sentence prediction task 
-        if "seq_relationship" in name:
-            continue
-
+        name = name.replace("group_", "albert_layer_groups/")    
         name = name.split('/')
-
-        # Ignore the gradients applied by the LAMB/ADAM optimizers.
-        if "adam_m" in name or "adam_v" in name or "global_step" in name:
-            logger.info("Skipping {}".format("/".join(name)))
-            continue
-
         pointer = model
         for m_name in name:
             if re.fullmatch(r'[A-Za-z]+_\d+', m_name):

transformers/modeling_auto.py

@@ -28,8 +28,6 @@ from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassifica
 from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
 from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification
 from .modeling_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice
-from .modeling_albert import AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering
-from .modeling_t5 import T5Model, T5WithLMHeadModel
 
 from .modeling_utils import PreTrainedModel, SequenceSummary
 
@@ -50,18 +48,16 @@ class AutoModel(object):
 
         The base model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5Model (T5 model)
             - contains `distilbert`: DistilBertModel (DistilBERT model)
-            - contains `albert`: AlbertModel (ALBERT model)
             - contains `camembert`: CamembertModel (CamemBERT model)
             - contains `roberta`: RobertaModel (RoBERTa model)
             - contains `bert`: BertModel (Bert model)
             - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
             - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
+            - contains `ctrl`: CTRLModel (Salesforce CTRL  model)
             - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
             - contains `xlnet`: XLNetModel (XLNet model)
             - contains `xlm`: XLMModel (XLM model)
-            - contains `ctrl`: CTRLModel (Salesforce CTRL  model)
 
         This class cannot be instantiated using `__init__()` (throws an error).
     """
@@ -76,18 +72,16 @@ class AutoModel(object):
 
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5Model (T5 model)
             - contains `distilbert`: DistilBertModel (DistilBERT model)
-            - contains `albert`: AlbertModel (ALBERT model)
             - contains `camembert`: CamembertModel (CamemBERT model)
             - contains `roberta`: RobertaModel (RoBERTa model)
             - contains `bert`: BertModel (Bert model)
             - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
             - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
+            - contains `ctrl`: CTRLModel (Salesforce CTRL  model)
             - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
             - contains `xlnet`: XLNetModel (XLNet model)
             - contains `xlm`: XLMModel (XLM model)
-            - contains `ctrl`: CTRLModel (Salesforce CTRL model)
 
             The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
             To train the model, you should first set it back in training mode with `model.train()`
@@ -96,7 +90,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -122,9 +115,6 @@ class AutoModel(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -149,12 +139,8 @@ class AutoModel(object):
             model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
-        if 't5' in pretrained_model_name_or_path:
-            return T5Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'distilbert' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
             return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'albert' in pretrained_model_name_or_path:
-            return AlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'camembert' in pretrained_model_name_or_path:
             return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
@@ -175,7 +161,7 @@ class AutoModel(object):
             return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         raise ValueError("Unrecognized model identifier in {}. Should contains one of "
                          "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
-                         "'xlm', 'roberta, 'ctrl', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path))
+                         "'xlm', 'roberta, 'ctrl'".format(pretrained_model_name_or_path))
 
 
 class AutoModelWithLMHead(object):
@@ -190,18 +176,16 @@ class AutoModelWithLMHead(object):
 
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5ModelWithLMHead (T5 model)
             - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
-            - contains `albert`: AlbertForMaskedLM (ALBERT model)
             - contains `camembert`: CamembertForMaskedLM (CamemBERT model)
             - contains `roberta`: RobertaForMaskedLM (RoBERTa model)
             - contains `bert`: BertForMaskedLM (Bert model)
             - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
             - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
+            - contains `ctrl`: CTRLLMModel (Salesforce CTRL model)
             - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
             - contains `xlnet`: XLNetLMHeadModel (XLNet model)
             - contains `xlm`: XLMWithLMHeadModel (XLM model)
-            - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
 
         This class cannot be instantiated using `__init__()` (throws an error).
     """
@@ -219,9 +203,7 @@ class AutoModelWithLMHead(object):
 
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: T5ModelWithLMHead (T5 model)
             - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
-            - contains `albert`: AlbertForMaskedLM (ALBERT model)
             - contains `camembert`: CamembertForMaskedLM (CamemBERT model)
             - contains `roberta`: RobertaForMaskedLM (RoBERTa model)
             - contains `bert`: BertForMaskedLM (Bert model)
@@ -230,7 +212,6 @@ class AutoModelWithLMHead(object):
             - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
             - contains `xlnet`: XLNetLMHeadModel (XLNet model)
             - contains `xlm`: XLMWithLMHeadModel (XLM model)
-            - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
 
         The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
         To train the model, you should first set it back in training mode with `model.train()`
@@ -239,7 +220,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -264,8 +244,6 @@ class AutoModelWithLMHead(object):
 
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
 
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
@@ -291,12 +269,8 @@ class AutoModelWithLMHead(object):
             model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
-        if 't5' in pretrained_model_name_or_path:
-            return T5WithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'distilbert' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
             return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'albert' in pretrained_model_name_or_path:
-            return AlbertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'camembert' in pretrained_model_name_or_path:
             return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
@@ -317,7 +291,7 @@ class AutoModelWithLMHead(object):
             return CTRLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         raise ValueError("Unrecognized model identifier in {}. Should contains one of "
                          "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
-                         "'xlm', 'roberta','ctrl', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path))
+                         "'xlm', 'roberta','ctrl'".format(pretrained_model_name_or_path))
 
 
 class AutoModelForSequenceClassification(object):
@@ -333,7 +307,6 @@ class AutoModelForSequenceClassification(object):
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
             - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
-            - contains `albert`: AlbertForSequenceClassification (ALBERT model)
             - contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
             - contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
             - contains `bert`: BertForSequenceClassification (Bert model)
@@ -357,7 +330,6 @@ class AutoModelForSequenceClassification(object):
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
             - contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
-            - contains `albert`: AlbertForSequenceClassification (ALBERT model)
             - contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
             - contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
             - contains `bert`: BertForSequenceClassification (Bert model)
@@ -371,7 +343,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -397,9 +368,6 @@ class AutoModelForSequenceClassification(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -426,8 +394,6 @@ class AutoModelForSequenceClassification(object):
         """
         if 'distilbert' in pretrained_model_name_or_path:
             return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'albert' in pretrained_model_name_or_path:
-            return AlbertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'camembert' in pretrained_model_name_or_path:
             return CamembertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
@@ -440,7 +406,7 @@ class AutoModelForSequenceClassification(object):
             return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
 
         raise ValueError("Unrecognized model identifier in {}. Should contains one of "
-                         "'bert', 'xlnet', 'xlm', 'roberta', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path))
+                         "'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path))
 
 
 class AutoModelForQuestionAnswering(object):
@@ -456,7 +422,6 @@ class AutoModelForQuestionAnswering(object):
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
             - contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
-            - contains `albert`: AlbertForQuestionAnswering (ALBERT model)
             - contains `bert`: BertForQuestionAnswering (Bert model)
             - contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
             - contains `xlm`: XLMForQuestionAnswering (XLM model)
@@ -478,7 +443,6 @@ class AutoModelForQuestionAnswering(object):
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
             - contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
-            - contains `albert`: AlbertForQuestionAnswering (ALBERT model)
             - contains `bert`: BertForQuestionAnswering (Bert model)
             - contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
             - contains `xlm`: XLMForQuestionAnswering (XLM model)
@@ -490,7 +454,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -542,8 +505,6 @@ class AutoModelForQuestionAnswering(object):
         """
         if 'distilbert' in pretrained_model_name_or_path:
             return DistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'albert' in pretrained_model_name_or_path:
-            return AlbertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'bert' in pretrained_model_name_or_path:
             return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'xlnet' in pretrained_model_name_or_path:
@@ -552,4 +513,4 @@ class AutoModelForQuestionAnswering(object):
             return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
 
         raise ValueError("Unrecognized model identifier in {}. Should contains one of "
-                         "'bert', 'xlnet', 'xlm', 'distilbert', 'albert'".format(pretrained_model_name_or_path))
+                         "'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))

transformers/modeling_beam_search.py

@@ -0,0 +1,271 @@
+# coding=utf-8
+# Copyright (c) 2019 Yang Liu
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+A general wrapper around models with LM heads to generate sequences
+using beam search.
+"""
+import torch
+from torch import nn
+
+
+class TransformerBeamSearch(nn.Module):
+    def __init__(
+        self,
+        model,
+        tokenizer,
+        batch_size,
+        beam_size,
+        min_length,
+        max_length,
+        alpha=0,
+        block_repeating_trigram=True,
+    ):
+        """
+        Attributes:
+            mask_word_id: token id that corresponds to the mask
+        """
+        super(TransformerBeamSearch, self).__init__()
+        self.model = model
+        self.tokenizer = tokenizer
+
+        self.start_token_id = tokenizer.start_token_id
+        self.end_token_id = tokenizer.end_token_id
+        self.pad_token_id = tokenizer.pad_token_id
+
+        self.beam_size = beam_size
+        self.min_length = min_length
+        self.max_length = max_length
+
+        self.block_repeating_trigram = block_repeating_trigram
+        self.apply_length_penalty = False if alpha == 0 else True
+        self.alpha = alpha
+
+        # State of the beam
+        self.hypotheses = [[] for _ in range(batch_size)]
+        self.batch_offset = torch.arange(batch_size, dtype=torch.long)
+        self.beam_offset = torch.arange(
+            0, batch_size * self.beam_size, step=self.beam_size, dtype=torch.long
+        )
+        self.growing_beam = torch.full(
+            (batch_size * self.beam_size, 1), self.start_token_id, dtype=torch.long
+        )
+        self.topk_log_probabilities = torch.tensor(
+            [0.0] + [float("-inf")] * (self.beam_size - 1), dtype=torch.float
+        ).repeat(batch_size)
+        self.results = {
+            "prediction": [[] for _ in batch_size],
+            "scores": [[] for _ in batch_size],
+        }
+        self._step = 0
+        self.is_done = False
+
+    def step(self, log_probabilities):
+        """ Grows the beam by one step. """
+        self._step += 1
+
+        # The batch size changes as some beams finish so we define _B
+        vocab_size = log_probabilities.size(-1)
+        _B = log_probabilities.size(0) // self.beam_size
+
+        # Multiply each beam probability with the probability of the
+        # next token (conditioned on the words in the beam).
+        log_probabilities += self.topk_log_probabilities.view(-1, 1)
+
+        self.enforce_min_length(log_probabilities)
+        if self.block_repeating_trigram:
+            self.remove_repeating_trigrams(log_probabilities, _B)
+
+        # Find the `beam_size` (previous_beam + token) combinations with
+        # the highest score
+        topk_log_probabilities, topk_ids = log_probabilities.topk(
+            log_probabilities.view(_B, self.beam_size * vocab_size),
+            self.beam_size,
+            dim=1,
+        )
+
+        # Apply the length penalty. The +1 accounts for the [EOS] token
+        # that will be added if the beam ends.
+        topk_scores = topk_log_probabilities / self.length_penalty()
+
+        # Retrieve the corresponding respective beam and token id
+        # topk_token_ids[i] will be added to topk_beam_ids[i]
+        topk_beam_ids = topk_ids.div(vocab_size)
+        topk_token_ids = topk_ids.fmod(vocab_size)
+
+        # Retrieve the row index of the surviving beams in the original
+        # view of the log_probabilities tensor
+        surviving_beams_rows = (topk_beam_ids + self.beam_offset[:_B].view(-1, 1)).view(
+            -1
+        )
+
+        # Append the last predictions
+        self.growing_beam = torch.cat(
+            [
+                self.growing_beam.index_select(0, surviving_beams_rows),
+                topk_token_ids.view(-1, 1),
+            ],
+            1,
+        )
+
+        # Check if any of the beam searches has ended during this
+        # growth step. Also if top beam (most probable) has ended
+        # for one element of the batch.
+        is_finished = topk_token_ids.eq(self.end_token_id)
+        self.enforce_max_length()
+        is_top_beam_finished = is_finished[:, 0].eq(1)
+
+        # Save the finished searches
+        if is_finished.any():
+            predictions = self.growing_beam.view(
+                -1, self.beam_size, self.growing_beam.size(1)
+            )
+            for i in range(is_finished.size(0)):
+                if is_top_beam_finished[i]:
+                    is_finished[i].fill_(1)
+                finished_hyp = is_finished[i].nonzero().view(-1)
+
+                # Store finished hypotheses for this batch.
+                b = self.batch_offset[i]
+                for j in finished_hyp:
+                    self.hypotheses[b].append((topk_scores[i, j], predictions[i, j, :]))
+
+                # If the batch reached the end, save the best hypotheses
+                # in terms of length-penalized score.
+                if is_top_beam_finished[i]:
+                    best_hyp = sorted(
+                        self.hypotheses[b], key=lambda x: x[0], reverse=True
+                    )
+                    best_score, best_prediction = best_hyp[0]
+                    self.results["scores"][b].append(best_score)
+                    self.results["predictions"][b].append(best_prediction)
+
+            non_finished = is_top_beam_finished.eq(0).nonzero().view(-1)
+            if len(non_finished) == 0:
+                self.is_done = True
+
+            # Remove finished batches for the next step.
+            topk_log_probabilities = topk_log_probabilities.index_select(
+                0, non_finished
+            )
+            self.batch_offset = self.batch_offset.index_select(0, non_finished)
+            self.growing_beam = predictions.index_select(0, non_finished).view(
+                -1, self.growing_beam.size(-1)
+            )
+
+            surviving_beams_rows = surviving_beams_rows.index_select(0, non_finished)
+
+        return surviving_beams_rows
+
+    def forward(self, encoder_input_ids, **kwargs):
+        # keyword arguments come in 3 flavors: encoder-specific (prefixed by
+        # `encoder_`), decoder-specific (prefixed by `decoder_`) and those
+        # that apply to the model as whole.
+        # We let the specific kwargs override the common ones in case of conflict.
+        kwargs_encoder = {
+            argument[len("encoder_"):]: value
+            for argument, value in kwargs.items()
+            if argument.startswith("encoder_")
+        }
+        kwargs_decoder = {
+            argument[len("decoder_"):]: value
+            for argument, value in kwargs.items()
+            if argument.startswith("decoder_")
+        }
+        kwargs_common = {
+            argument: value
+            for argument, value in kwargs.items()
+            if not (argument.startswith("encoder_") or argument.startswith("decoder_"))
+        }
+        kwargs_decoder = dict(kwargs_common, **kwargs_decoder)
+        kwargs_encoder = dict(kwargs_common, **kwargs_encoder)
+
+        # forward pass on the encoder
+        encoder_outputs = self.model.encoder.forward(encoder_input_ids, kwargs_encoder)
+        kwargs_decoder["encoder_hidden_states"] = tile(
+            encoder_outputs, self.beam_size, dim=0
+        )
+
+        # grow the beam by generating sequences in an autoregressive way
+        self.growing_beam = torch.full(
+            (self.batch_size * self.beam_size, 1), self.start_token_id, dtype=torch.long
+        )
+        for step in range(self.max_length):
+            decoder_input = self.growing_beam[:, -1]
+            outputs = self.model.decoder(decoder_input, kwargs_decoder)
+            log_probabilities = torch.nn.functional.log_softmax(outputs[1])
+            surviving_beams_rows = self.step(log_probabilities)
+            if self.is_done:
+                break
+
+            kwargs_decoder["encoder_hidden_states"] = kwargs_decoder[
+                "encoder_hidden_states"
+            ].index_select(0, surviving_beams_rows)
+
+        return self.results
+
+    def remove_repeating_trigrams(self, log_probabilities, _B):
+        if(self._step + 1 > 3):
+            for i in range(_B * self.beam_size):
+                tokens = [t for t in self.growing_beam[i]]
+                trigrams = [(tokens[i-1], tokens[i], tokens[i+1]) for i in range(1, len(words) - 1)]
+                last_trigram = tuple(trigrams[-1])
+                if last_trigram in trigrams[:-1]:
+                    log_probabilities[i] = -1e20
+
+    def enforce_min_length(self):
+        if self._step < self.min_length:
+            self.log_probabilities[self.end_token_id] = -1e20
+
+    def enforce_max_length(self):
+        if self._step + 1 == self.max_length:
+            self.is_finished.fill_(1)
+
+    def length_penalty(self):
+        return ((5.0 + (self._step + 1)) / 6.0) ** self.alpha
+
+
+def tile(x, count, dim=0):
+    """
+    Tiles `x` along dimension `dim` `count` times.
+
+    Example:
+        >> ex = torch.tensor([1,2],[3,4])
+        >> tile(ex, 2, 0)
+        torch.Tensor([[1,2],[1,2],[3,4],[3,4]])
+    """
+    perm = list(range(len(x.size())))
+    if dim != 0:
+        perm[0], perm[dim] = perm[dim], perm[0]
+        x = x.permute(perm).contiguous()
+    out_size = list(x.size())
+    out_size[0] *= count
+    batch = x.size(0)
+    x = (
+        x.view(batch, -1)
+        .transpose(0, 1)
+        .repeat(count, 1)
+        .transpose(0, 1)
+        .contiguous()
+        .view(*out_size)
+    )
+    if dim != 0:
+        x = x.permute(perm).contiguous()
+    return x

transformers/modeling_bert.py

@@ -48,10 +48,6 @@ BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-pytorch_model.bin",
     'bert-base-german-dbmdz-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin",
     'bert-base-german-dbmdz-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin",
-    'bert-base-japanese': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-pytorch_model.bin",
-    'bert-base-japanese-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-pytorch_model.bin",
-    'bert-base-japanese-char': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-pytorch_model.bin",
-    'bert-base-japanese-char-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-pytorch_model.bin"
 }
 
 
@@ -142,11 +138,7 @@ def swish(x):
     return x * torch.sigmoid(x)
 
 
-def mish(x):
-    return x * torch.tanh(nn.functional.softplus(x))
-
-
-ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
+ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new}
 
 
 BertLayerNorm = torch.nn.LayerNorm
@@ -605,7 +597,7 @@ class BertModel(BertPreTrainedModel):
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertModel.from_pretrained('bert-base-uncased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids)
         last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
 
@@ -664,6 +656,8 @@ class BertModel(BertPreTrainedModel):
 
         if attention_mask is None:
             attention_mask = torch.ones(input_shape, device=device)
+        if encoder_attention_mask is None:
+            encoder_attention_mask = torch.ones(input_shape, device=device)
         if token_type_ids is None:
             token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
 
@@ -671,20 +665,18 @@ class BertModel(BertPreTrainedModel):
         # ourselves in which case we just need to make it broadcastable to all heads.
         if attention_mask.dim() == 3:
             extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-            # Provided a padding mask of dimensions [batch_size, seq_length]
-            # - if the model is a decoder, apply a causal mask in addition to the padding mask
-            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+
+        # Provided a padding mask of dimensions [batch_size, seq_length]
+        # - if the model is a decoder, apply a causal mask in addition to the padding mask
+        # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if attention_mask.dim() == 2:
             if self.config.is_decoder:
                 batch_size, seq_length = input_shape
                 seq_ids = torch.arange(seq_length, device=device)
                 causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                causal_mask = causal_mask.to(torch.long)  # not converting to long will cause errors with pytorch version < 1.3
                 extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
             else:
                 extended_attention_mask = attention_mask[:, None, None, :]
-        else:
-            raise ValueError("Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(input_shape, attention_mask.shape))
 
         # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
         # masked positions, this operation will create a tensor which is 0.0 for
@@ -696,24 +688,13 @@ class BertModel(BertPreTrainedModel):
 
         # If a 2D ou 3D attention mask is provided for the cross-attention
         # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
-        if self.config.is_decoder and encoder_hidden_states is not None:
-            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
-            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
-            if encoder_attention_mask is None:
-                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+        if encoder_attention_mask.dim() == 3:
+            encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
+        if encoder_attention_mask.dim() == 2:
+            encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
 
-            if encoder_attention_mask.dim() == 3:
-                encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
-            elif encoder_attention_mask.dim() == 2:
-                encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
-            else:
-                raise ValueError("Wrong shape for encoder_hidden_shape (shape {}) or encoder_attention_mask (shape {})".format(encoder_hidden_shape,
-                                                                                                                               encoder_attention_mask.shape))
-
-            encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
-            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
-        else:
-            encoder_extended_attention_mask = None
+        encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
 
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
@@ -779,7 +760,7 @@ class BertForPreTraining(BertPreTrainedModel):
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForPreTraining.from_pretrained('bert-base-uncased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids)
         prediction_scores, seq_relationship_scores = outputs[:2]
 
@@ -855,7 +836,7 @@ class BertForMaskedLM(BertPreTrainedModel):
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForMaskedLM.from_pretrained('bert-base-uncased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids, masked_lm_labels=input_ids)
         loss, prediction_scores = outputs[:2]
 
@@ -938,7 +919,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids)
         seq_relationship_scores = outputs[0]
 
@@ -1003,7 +984,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids, labels=labels)
         loss, logits = outputs[:2]
@@ -1079,7 +1060,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForMultipleChoice.from_pretrained('bert-base-uncased')
         choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
-        input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
+        input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0)  # Batch size 1, 2 choices
         labels = torch.tensor(1).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids, labels=labels)
         loss, classification_scores = outputs[:2]
@@ -1153,7 +1134,7 @@ class BertForTokenClassification(BertPreTrainedModel):
 
         tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
         model = BertForTokenClassification.from_pretrained('bert-base-uncased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
         labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
         outputs = model(input_ids, labels=labels)
         loss, scores = outputs[:2]
@@ -1237,9 +1218,9 @@ class BertForQuestionAnswering(BertPreTrainedModel):
         question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
         input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]"
         input_ids = tokenizer.encode(input_text)
-        token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]
+        token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))] 
         start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids]))
-        all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
+        all_tokens = tokenizer.convert_ids_to_tokens(input_ids)  
         print(' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]))
         # a nice puppet
 

transformers/modeling_ctrl.py

@@ -63,8 +63,7 @@ def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=N
     scaled_attention_logits = matmul_qk / np.sqrt(dk)
 
     if mask is not None:
-        nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1)
-        scaled_attention_logits += (mask[ns-nd:ns, :ns] * -1e4)
+        scaled_attention_logits += (mask * -1e4)
 
     if attention_mask is not None:
         # Apply the attention mask
@@ -252,7 +251,7 @@ class CTRLModel(CTRLPreTrainedModel):
         **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the last layer of the model.
         **past**:
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model 
             should not be passed as input ids as they have already been computed.
@@ -374,7 +373,7 @@ class CTRLModel(CTRLPreTrainedModel):
             inputs_embeds = self.w(input_ids)
         # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
         seq_len = input_shape[-1]
-        mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device)
+        mask = torch.triu(torch.ones(seq_len, seq_len), 1).to(inputs_embeds.device)
 
         inputs_embeds *= np.sqrt(self.d_model_size)
 
@@ -438,7 +437,7 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
         **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         **past**:
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model 
             should not be passed as input ids as they have already been computed.

transformers/modeling_distilbert.py

@@ -43,7 +43,6 @@ logger = logging.getLogger(__name__)
 DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin",
     'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin",
-    'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-pytorch_model.bin",
     'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-pytorch_model.bin",
 }
 

transformers/modeling_encoder_decoder.py

@@ -59,14 +59,12 @@ class PreTrainedEncoderDecoder(nn.Module):
             encoder_pretrained_model_name_or_path: information necessary to initiate the encoder. Either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/encoder``.
                 - 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.
 
             decoder_pretrained_model_name_or_path: information necessary to initiate the decoder. Either:
 
                 - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
-                - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/decoder``.
                 - 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.
 
@@ -219,7 +217,9 @@ class PreTrainedEncoderDecoder(nn.Module):
         encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
         if encoder_hidden_states is None:
             encoder_outputs = self.encoder(encoder_input_ids, **kwargs_encoder)
-            encoder_hidden_states = encoder_outputs[0]
+            encoder_hidden_states = encoder_outputs[
+                0
+            ]  # output the last layer hidden state
         else:
             encoder_outputs = ()
 

transformers/modeling_gpt2.py

@@ -329,7 +329,7 @@ class GPT2Model(GPT2PreTrainedModel):
         **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the last layer of the model.
         **past**:
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model 
             should not be passed as input ids as they have already been computed.
@@ -503,7 +503,7 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         **past**:
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model 
             should not be passed as input ids as they have already been computed.
@@ -596,7 +596,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         **mc_prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)``
             Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
         **past**:
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding. The token ids which have their past given to this model 
             should not be passed as input ids as they have already been computed.

transformers/modeling_openai.py

@@ -50,10 +50,8 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
 
     logger.info("Loading weights from {}".format(openai_checkpoint_folder_path))
 
-    with open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8') as names_handle:
-        names = json.load(names_handle)
-    with open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8') as shapes_handle:
-        shapes = json.load(shapes_handle)
+    names = json.load(open(openai_checkpoint_folder_path + '/parameters_names.json', "r", encoding='utf-8'))
+    shapes = json.load(open(openai_checkpoint_folder_path + '/params_shapes.json', "r", encoding='utf-8'))
     offsets = np.cumsum([np.prod(shape) for shape in shapes])
     init_params = [np.load(openai_checkpoint_folder_path + '/params_{}.npy'.format(n)) for n in range(10)]
     init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]

transformers/modeling_t5.py

@@ -1,886 +0,0 @@
-# coding=utf-8
-# Copyright 2018 Mesh TensorFlow authors, T5 Authors and 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.
-""" PyTorch T5 model. """
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import json
-import logging
-import math
-import os
-import sys
-import copy
-import itertools
-from io import open
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn import CrossEntropyLoss, MSELoss
-
-from .modeling_utils import PreTrainedModel, prune_linear_layer
-from .configuration_t5 import T5Config
-from .file_utils import add_start_docstrings, DUMMY_INPUTS, DUMMY_MASK
-
-logger = logging.getLogger(__name__)
-
-####################################################
-# This dict contrains shortcut names and associated url
-# for the pretrained weights provided with the models
-####################################################
-T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin",
-    't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-pytorch_model.bin",
-    't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-pytorch_model.bin",
-    't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-pytorch_model.bin",
-    't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-pytorch_model.bin",
-}
-
-####################################################
-# This is a conversion method from TF 1.0 to PyTorch
-# More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
-####################################################
-def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
-    """ Load tf checkpoints in a pytorch model.
-    """
-    try:
-        import re
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions.")
-        raise
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    tf_weights = {}
-    for name, shape in init_vars:
-        logger.info("Loading TF weight {} with shape {}".format(name, shape))
-        array = tf.train.load_variable(tf_path, name)
-        names.append(name)
-        tf_weights[name] = array
-
-    for txt_name in names:
-        name = txt_name.split('/')
-        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
-        # which are not required for using pretrained model
-        if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
-            logger.info("Skipping {}".format("/".join(name)))
-            tf_weights.pop(txt_name, None)
-            continue
-        if '_slot_' in name[-1]:
-            logger.info("Skipping {}".format("/".join(name)))
-            tf_weights.pop(txt_name, None)
-            continue
-        pointer = model
-        array = tf_weights[txt_name]
-        for m_name in name:
-            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
-                l = re.split(r'_(\d+)', m_name)
-            else:
-                l = [m_name]
-            if l[0] in ['kernel', 'scale', 'embedding']:
-                pointer = getattr(pointer, 'weight')
-            # elif l[0] == 'scale':
-            #     pointer = getattr(pointer, 'weight')
-            # elif l[0] == 'output_bias' or l[0] == 'beta':
-            #     pointer = getattr(pointer, 'bias')
-            # elif l[0] == 'squad':
-            #     pointer = getattr(pointer, 'classifier')
-            else:
-                try:
-                    pointer = getattr(pointer, l[0])
-                except AttributeError:
-                    logger.info("Skipping {}".format("/".join(name)))
-                    continue
-            if len(l) >= 2:
-                num = int(l[1])
-                pointer = pointer[num]
-        if l[0] not in ['kernel', 'scale', 'embedding']:
-            pointer = getattr(pointer, 'weight')
-        if l[0] != 'embedding':
-            logger.info("Transposing numpy weight of shape {} for {}".format(array.shape, name))
-            array = np.transpose(array)
-        try:
-            assert pointer.shape == array.shape
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        logger.info("Initialize PyTorch weight {}".format(name))
-        pointer.data = torch.from_numpy(array.astype(np.float32))
-        tf_weights.pop(txt_name, None)
-
-    logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
-    # logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
-    return model
-
-
-####################################################
-# PyTorch Models are constructed by sub-classing
-# - torch.nn.Module for the layers and
-# - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module)
-####################################################
-
-class T5LayerNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """ Construct a layernorm module in the T5 style
-            No bias and no substraction of mean.
-        """
-        super(T5LayerNorm, self).__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, x):
-        variance = x.pow(2).mean(-1, keepdim=True)
-        x = x / torch.sqrt(variance + self.variance_epsilon)
-        return self.weight * x
-
-
-class T5DenseReluDense(nn.Module):
-    def __init__(self, config):
-        super(T5DenseReluDense, self).__init__()
-        self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
-        self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, hidden_states):
-        h = self.wi(hidden_states)
-        h = F.relu(h)
-        h = self.dropout(h)
-        h = self.wo(h)
-        return h
-
-
-class T5LayerFF(nn.Module):
-    def __init__(self, config):
-        super(T5LayerFF, self).__init__()
-        self.DenseReluDense = T5DenseReluDense(config)
-        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, hidden_states):
-        norm_x = self.layer_norm(hidden_states)
-        y = self.DenseReluDense(norm_x)
-        layer_output = hidden_states + self.dropout(y)
-        return layer_output
-
-
-class T5Attention(nn.Module):
-    NEW_ID = itertools.count()
-
-    def __init__(self, config, has_relative_attention_bias=False):
-        super(T5Attention, self).__init__()
-        self.layer_id = next(T5Attention.NEW_ID)
-        self.is_decoder = config.is_decoder
-        self.has_relative_attention_bias = has_relative_attention_bias
-
-        self.output_attentions = config.output_attentions
-        self.relative_attention_num_buckets = config.relative_attention_num_buckets
-        self.d_model = config.d_model
-        self.d_kv = config.d_kv
-        self.n_heads = config.num_heads
-        self.dropout = config.dropout_rate
-        self.inner_dim = self.n_heads * self.d_kv
-
-        # Mesh TensorFlow initialization to avoid scaling before softmax
-        self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
-        self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
-        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
-        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
-
-        if self.has_relative_attention_bias:
-            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        mask = torch.ones(self.n_heads, self.d_kv)
-        heads = set(heads) - self.pruned_heads
-        for head in heads:
-            head -= sum(1 if h < head else 0 for h in self.pruned_heads)
-            mask[head] = 0
-        mask = mask.view(-1).contiguous().eq(1)
-        index = torch.arange(len(mask))[mask].long()
-        # Prune linear layers
-        self.q = prune_linear_layer(self.q, index)
-        self.k = prune_linear_layer(self.k, index)
-        self.v = prune_linear_layer(self.v, index)
-        self.o = prune_linear_layer(self.o, index, dim=1)
-        # Update hyper params
-        self.n_heads = self.n_heads - len(heads)
-        self.inner_dim = self.d_kv * self.n_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    @staticmethod
-    def _relative_position_bucket(relative_position,
-                                  bidirectional=True,
-                                  num_buckets=32,
-                                  max_distance=128):
-        """
-        Adapted from Mesh Tensorflow:
-        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-
-        Translate relative position to a bucket number for relative attention.
-        The relative position is defined as memory_position - query_position, i.e.
-        the distance in tokens from the attending position to the attended-to
-        position.  If bidirectional=False, then positive relative positions are
-        invalid.
-        We use smaller buckets for small absolute relative_position and larger buckets
-        for larger absolute relative_positions.  All relative positions >=max_distance
-        map to the same bucket.  All relative positions <=-max_distance map to the
-        same bucket.  This should allow for more graceful generalization to longer
-        sequences than the model has been trained on.
-        Args:
-            relative_position: an int32 Tensor
-            bidirectional: a boolean - whether the attention is bidirectional
-            num_buckets: an integer
-            max_distance: an integer
-        Returns:
-            a Tensor with the same shape as relative_position, containing int32
-            values in the range [0, num_buckets)
-        """
-        ret = 0
-        n = -relative_position
-        if bidirectional:
-            num_buckets //= 2
-            ret += (n < 0).to(torch.long) * num_buckets  # mtf.to_int32(mtf.less(n, 0)) * num_buckets
-            n = torch.abs(n)
-        else:
-            n = torch.max(n, torch.zeros_like(n))
-        # now n is in the range [0, inf)
-
-        # half of the buckets are for exact increments in positions
-        max_exact = num_buckets // 2
-        is_small = (n < max_exact)
-
-        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
-        val_if_large = max_exact + (
-            torch.log(n.float() / max_exact)
-            / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long)
-        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
-
-        ret += torch.where(is_small, n, val_if_large)
-        return ret
-
-    def compute_bias(self, qlen, klen):
-        """ Compute binned relative position bias """
-        context_position = torch.arange(qlen, dtype=torch.long)[:, None]
-        memory_position = torch.arange(klen, dtype=torch.long)[None, :]
-        relative_position = memory_position - context_position  # shape (qlen, klen)
-        rp_bucket = self._relative_position_bucket(relative_position,  # shape (qlen, klen)
-                                                   bidirectional=not self.is_decoder,
-                                                   num_buckets=self.relative_attention_num_buckets)
-        values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
-        values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, qlen, klen)
-        return values
-
-    def forward(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None):
-        """
-        Self-attention (if kv is None) or attention over source sentence (provided by kv).
-        """
-        # Input is (bs, qlen, dim)
-        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
-        bs, qlen, dim = input.size()
-        if kv is None:
-            klen = qlen if cache is None else cache['slen'] + qlen
-        else:
-            klen = kv.size(1)
-
-        def shape(x):
-            """  projection """
-            return x.view(bs, -1, self.n_heads, self.d_kv).transpose(1, 2)
-
-        def unshape(x):
-            """  compute context """
-            return x.transpose(1, 2).contiguous().view(bs, -1, self.inner_dim)
-
-        q = shape(self.q(input))                                          # (bs, n_heads, qlen, dim_per_head)
-        if kv is None:
-            k = shape(self.k(input))                                      # (bs, n_heads, qlen, dim_per_head)
-            v = shape(self.v(input))                                      # (bs, n_heads, qlen, dim_per_head)
-        elif cache is None or self.layer_id not in cache:
-            k = v = kv
-            k = shape(self.k(k))                                          # (bs, n_heads, qlen, dim_per_head)
-            v = shape(self.v(v))                                          # (bs, n_heads, qlen, dim_per_head)
-
-        if cache is not None:
-            if self.layer_id in cache:
-                if kv is None:
-                    k_, v_ = cache[self.layer_id]
-                    k = torch.cat([k_, k], dim=2)                             # (bs, n_heads, klen, dim_per_head)
-                    v = torch.cat([v_, v], dim=2)                             # (bs, n_heads, klen, dim_per_head)
-                else:
-                    k, v = cache[self.layer_id]
-            cache[self.layer_id] = (k, v)
-
-        # q = q / math.sqrt(dim_per_head)                                     # No scaling in T5
-        scores = torch.einsum('bnqd,bnkd->bnqk', q, k)                        # (bs, n_heads, qlen, klen)
-
-        if position_bias is None:
-            if not self.has_relative_attention_bias:
-                raise ValueError("No position_bias provided and no weights to compute position_bias")
-            position_bias = self.compute_bias(qlen, klen)
-            if mask is not None:
-                position_bias = position_bias + mask                          # (bs, n_heads, qlen, klen)
-
-        scores += position_bias
-        weights = F.softmax(scores.float(), dim=-1).type_as(scores)           # (bs, n_heads, qlen, klen)
-        weights = F.dropout(weights, p=self.dropout, training=self.training)  # (bs, n_heads, qlen, klen)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            weights = weights * head_mask
-
-        context = torch.matmul(weights, v)                                    # (bs, n_heads, qlen, dim_per_head)
-        context = unshape(context)                                            # (bs, qlen, dim)
-
-        context = self.o(context)
-
-        outputs = (context,)
-        if self.output_attentions:
-            outputs = outputs + (weights,)
-        if self.has_relative_attention_bias:
-            outputs = outputs + (position_bias,)
-        return outputs
-
-
-class T5LayerSelfAttention(nn.Module):
-    def __init__(self, config, has_relative_attention_bias=False):
-        super(T5LayerSelfAttention, self).__init__()
-        self.SelfAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
-        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, hidden_states, attention_mask=None, position_bias=None, head_mask=None):
-        norm_x = self.layer_norm(hidden_states)
-        attention_output = self.SelfAttention(norm_x,
-                                              mask=attention_mask,
-                                              position_bias=position_bias,
-                                              head_mask=head_mask)
-        y = attention_output[0]
-        layer_output = hidden_states + self.dropout(y)
-        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
-        return outputs
-
-
-class T5LayerCrossAttention(nn.Module):
-    def __init__(self, config, has_relative_attention_bias=False):
-        super(T5LayerCrossAttention, self).__init__()
-        self.EncDecAttention = T5Attention(config, has_relative_attention_bias=has_relative_attention_bias)
-        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-    def forward(self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None):
-        norm_x = self.layer_norm(hidden_states)
-        attention_output = self.EncDecAttention(norm_x,
-                                                mask=attention_mask,
-                                                kv=kv,
-                                                position_bias=position_bias,
-                                                head_mask=head_mask)
-        y = attention_output[0]
-        layer_output = hidden_states + self.dropout(y)
-        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
-        return outputs
-
-
-class T5Block(nn.Module):
-    def __init__(self, config, has_relative_attention_bias=False):
-        super(T5Block, self).__init__()
-        self.is_decoder = config.is_decoder
-        self.layer = nn.ModuleList()
-        self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
-        if self.is_decoder:
-            self.layer.append(T5LayerCrossAttention(config, has_relative_attention_bias=has_relative_attention_bias))
-            self.layer.append(T5LayerFF(config))
-        else:
-            self.layer.append(T5LayerFF(config))
-
-    def forward(self, hidden_states, attention_mask=None, position_bias=None,
-                encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
-                head_mask=None):
-        self_attention_outputs = self.layer[0](hidden_states,
-                                                attention_mask=attention_mask,
-                                                position_bias=position_bias,
-                                                head_mask=head_mask)
-        hidden_states = self_attention_outputs[0]
-        outputs = self_attention_outputs[1:]  # Keep self-attention outputs and relative position weights
-
-        if not self.is_decoder:
-            hidden_states = self.layer[1](hidden_states)
-        else:
-            cross_attention_outputs = self.layer[1](hidden_states,
-                                                    kv=encoder_hidden_states,
-                                                    attention_mask=encoder_attention_mask,
-                                                    position_bias=encoder_decoder_position_bias,
-                                                    head_mask=head_mask)
-            hidden_states = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:]  # Keep cross-attention outputs and relative position weights
-            hidden_states = self.layer[2](hidden_states)
-
-        outputs = (hidden_states,) + outputs  # add attentions if we output them
-        return outputs  # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
-
-
-class T5PreTrainedModel(PreTrainedModel):
-    """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
-    """
-    config_class = T5Config
-    pretrained_model_archive_map = T5_PRETRAINED_MODEL_ARCHIVE_MAP
-    load_tf_weights = load_tf_weights_in_t5
-    base_model_prefix = "transformer"
-
-    @property
-    def dummy_inputs(self):
-        input_ids = torch.tensor(DUMMY_INPUTS)
-        input_mask = torch.tensor(DUMMY_MASK)
-        dummy_inputs = {'decoder_input_ids': input_ids,
-                        'encoder_input_ids': input_ids,
-                        'decoder_attention_mask': input_mask}
-        return dummy_inputs
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        factor = self.config.initializer_factor  # Used for testing weights initialization
-        if isinstance(module, T5LayerNorm):
-            module.weight.data.fill_(factor*1.0)
-        elif isinstance(module, (T5Model, T5WithLMHeadModel)):
-            # Mesh TensorFlow embeddings initialization
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
-            module.shared.weight.data.normal_(mean=0.0, std=factor*1.0)
-        elif isinstance(module, T5DenseReluDense):
-            # Mesh TensorFlow FF initialization
-            # See https://github.com/tensorflow/mesh/blob/master/mesh_tensorflow/transformer/transformer_layers.py#L56
-            # and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
-            module.wi.weight.data.normal_(mean=0.0, std=factor*((self.config.d_model) ** -0.5))
-            if hasattr(module.wi, 'bias') and module.wi.bias is not None:
-                module.wi.bias.data.zero_()
-            module.wo.weight.data.normal_(mean=0.0, std=factor*((self.config.d_ff) ** -0.5))
-            if hasattr(module.wo, 'bias') and module.wo.bias is not None:
-                module.wo.bias.data.zero_()
-        elif isinstance(module, T5Attention):
-            # Mesh TensorFlow attention initialization to avoid scaling before softmax
-            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/attention.py#L136
-            d_model = self.config.d_model
-            d_kv = self.config.d_kv
-            n_heads = self.config.num_heads
-            module.q.weight.data.normal_(mean=0.0, std=factor*((d_model * d_kv) ** -0.5))
-            module.k.weight.data.normal_(mean=0.0, std=factor*(d_model ** -0.5))
-            module.v.weight.data.normal_(mean=0.0, std=factor*(d_model ** -0.5))
-            module.o.weight.data.normal_(mean=0.0, std=factor*((n_heads * d_kv) ** -0.5))
-            if module.has_relative_attention_bias:
-                module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor*((d_model) ** -0.5))
-
-
-class T5Stack(T5PreTrainedModel):
-    def __init__(self, config):
-        super(T5Stack, self).__init__(config)
-        self.output_attentions = config.output_attentions
-        self.output_hidden_states = config.output_hidden_states
-        self.is_decoder = config.is_decoder
-
-        self.block = nn.ModuleList([T5Block(config, has_relative_attention_bias=bool(i == 0))
-                                    for i in range(config.num_layers)])
-        self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
-        self.dropout = nn.Dropout(config.dropout_rate)
-
-        self.init_weights()
-
-    def forward(self,
-                hidden_states,
-                attention_mask=None,
-                encoder_hidden_states=None,
-                encoder_attention_mask=None,
-                head_mask=None):
-
-        batch_size, seq_length = hidden_states.shape[0], hidden_states.shape[1]
-        if attention_mask is None:
-            attention_mask = torch.ones(batch_size, seq_length).to(hidden_states.device)
-        if self.is_decoder and encoder_attention_mask is None:
-            encoder_seq_length = encoder_hidden_states.shape[1]
-            encoder_attention_mask = torch.ones(batch_size, encoder_seq_length).to(hidden_states.device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        if attention_mask.dim() == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif attention_mask.dim() == 2:
-        # Provided a padding mask of dimensions [batch_size, seq_length]
-        # - if the model is a decoder, apply a causal mask in addition to the padding mask
-        # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if self.config.is_decoder:
-                seq_ids = torch.arange(seq_length, device=hidden_states.device)
-                causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
-                causal_mask = causal_mask.to(attention_mask)
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -1e9 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-
-        # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
-        # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
-        # extended_attention_mask = (extended_attention_mask == extended_attention_mask.transpose(-1, -2))
-
-        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
-        extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
-
-        if self.is_decoder:
-            # If a 2D ou 3D attention mask is provided for the cross-attention
-            # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
-            if encoder_attention_mask.dim() == 3:
-                encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
-            if encoder_attention_mask.dim() == 2:
-                encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
-
-            # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
-            # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
-            # encoder_extended_attention_mask = (encoder_extended_attention_mask == encoder_extended_attention_mask.transpose(-1, -2))
-
-            encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
-            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        if head_mask is not None:
-            if head_mask.dim() == 1:
-                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-                head_mask = head_mask.expand(self.config.num_layers, -1, -1, -1, -1)
-            elif head_mask.dim() == 2:
-                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)  # We can specify head_mask for each layer
-            head_mask = head_mask.to(dtype=next(self.parameters()).dtype) # switch to fload if need + fp16 compatibility
-        else:
-            head_mask = [None] * self.config.num_layers
-
-        all_hidden_states = ()
-        all_attentions = ()
-        position_bias = None
-        encoder_decoder_position_bias = None
-
-        hidden_states = self.dropout(hidden_states)
-        for i, layer_module in enumerate(self.block):
-            if self.output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_outputs = layer_module(hidden_states,
-                                         attention_mask=extended_attention_mask,
-                                         position_bias=position_bias,
-                                         encoder_hidden_states=encoder_hidden_states,
-                                         encoder_attention_mask=encoder_extended_attention_mask,
-                                         encoder_decoder_position_bias=encoder_decoder_position_bias,
-                                         head_mask=head_mask[i])
-            # layer_outputs is a tuple with:
-            # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
-            hidden_states = layer_outputs[0]
-            if i == 0:
-                # We share the position biases between the layers - the first layer store them
-                # layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
-                position_bias = layer_outputs[2 if self.output_attentions else 1]
-                if self.is_decoder:
-                    encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2]
-
-            if self.output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)  # We keep only self-attention weights for now
-
-        hidden_states = self.final_layer_norm(hidden_states)
-        layer_output = self.dropout(hidden_states)
-
-        # Add last layer
-        if self.output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        outputs = (hidden_states,)
-        if self.output_hidden_states:
-            outputs = outputs + (all_hidden_states,)
-        if self.output_attentions:
-            outputs = outputs + (all_attentions,)
-        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
-
-
-T5_START_DOCSTRING = r"""    The T5 model was proposed in
-    `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
-    by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
-    It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
-
-    This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
-    refer to the PyTorch documentation for all matter related to general usage and behavior.
-
-    .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`:
-        https://arxiv.org/abs/1910.10683
-
-    .. _`torch.nn.Module`:
-        https://pytorch.org/docs/stable/nn.html#module
-
-    Parameters:
-        config (:class:`~transformers.T5Config`): 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:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
-"""
-
-T5_INPUTS_DOCSTRING = r"""
-    Inputs:
-        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
-            Indices of input sequence tokens in the vocabulary.
-            To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:
-
-            (a) For sequence pairs:
-
-                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
-
-            (b) For single sequences:
-
-                ``tokens:         [CLS] the dog is hairy . [SEP]``
-
-            T5 is a model with relative position embeddings so you should be able to pad the inputs on
-            the right or the left.
-
-            Indices can be obtained using :class:`transformers.T5Tokenizer`.
-            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]``:
-            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
-        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
-            Mask to nullify selected heads of the self-attention modules.
-            Mask values selected in ``[0, 1]``:
-            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
-"""
-
-@add_start_docstrings("The bare T5 Model transformer outputting raw hidden-states"
-                      "without any specific head on top.",
-                      T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
-class T5Model(T5PreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
-            Sequence of hidden-states at the output of the last layer of the model.
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        tokenizer = T5Tokenizer.from_pretrained('t5-small')
-        model = T5Model.from_pretrained('t5-small')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids)
-        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
-
-    """
-    def __init__(self, config):
-        super(T5Model, self).__init__(config)
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
-        encoder_config = copy.deepcopy(config)
-        self.encoder = T5Stack(encoder_config)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        self.decoder = T5Stack(decoder_config)
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def set_input_embeddings(self, new_embeddings):
-        self.shared = new_embeddings
-
-    def _prune_heads(self, heads_to_prune):
-        """ Prunes heads of the model.
-            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
-            See base class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def forward(self, **kwargs):
-        # keyword arguments come in 3 flavors: encoder-specific (prefixed by
-        # `encoder_`), decoder-specific (prefixed by `decoder_`) and those
-        # that apply to the model as whole.
-        # We let the specific kwargs override the common ones in case of conflict.
-        kwargs_common = dict((k, v) for k, v in kwargs.items()
-                             if not k.startswith("encoder_") and not k.startswith("decoder_"))
-        kwargs_encoder = kwargs_common.copy()
-        kwargs_decoder = kwargs_common.copy()
-        kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_")))
-        kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_")))
-
-        # Encode if needed (training, first prediction pass)
-        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
-        encoder_attention_mask = kwargs_encoder.get("attention_mask", None)
-        if encoder_hidden_states is None:
-            # Convert encoder inputs in embeddings if needed
-            hidden_states = kwargs_encoder.pop("inputs_embeds", None)
-            if hidden_states is None:
-                encoder_inputs_ids = kwargs_encoder.pop("input_ids")
-                hidden_states = self.shared(encoder_inputs_ids)  # Convert inputs in embeddings
-
-            if encoder_attention_mask is not None:
-                # Apply masking
-                encoder_attention_mask = (encoder_attention_mask != 0).to(hidden_states)
-                hidden_states = hidden_states * encoder_attention_mask.unsqueeze(-1)
-
-            encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
-            encoder_hidden_states = encoder_outputs[0]
-        else:
-            encoder_outputs = ()
-
-        # Decode
-        # Convert decoder inputs in embeddings if needed
-        hidden_states = kwargs_decoder.pop("inputs_embeds", None)
-        if hidden_states is None:
-            decoder_inputs_ids = kwargs_decoder.pop("input_ids")
-            hidden_states = self.shared(decoder_inputs_ids)
-
-        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
-        kwargs_decoder["encoder_attention_mask"] = encoder_attention_mask
-        decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
-
-        return decoder_outputs + encoder_outputs
-
-
-@add_start_docstrings("""T5 Model with a `language modeling` head on top. """,
-    T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
-class T5WithLMHeadModel(T5PreTrainedModel):
-    r"""
-        **lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
-            Labels for computing the masked language modeling loss.
-            Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
-            Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
-            in ``[0, ..., config.vocab_size]``
-
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **loss**: (`optional`, returned when ``lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Masked language modeling loss.
-        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        tokenizer = T5Tokenizer.from_pretrained('t5-small')
-        model = T5WithLMHeadModel.from_pretrained('t5-small')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, lm_labels=input_ids)
-        loss, prediction_scores = outputs[:2]
-
-    """
-    def __init__(self, config):
-        super(T5WithLMHeadModel, self).__init__(config)
-        self.model_dim = config.d_model
-
-        self.shared = nn.Embedding(config.vocab_size, config.d_model)
-
-        encoder_config = copy.deepcopy(config)
-        self.encoder = T5Stack(encoder_config)
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        self.decoder = T5Stack(decoder_config)
-
-        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def set_input_embeddings(self, new_embeddings):
-        self.shared = new_embeddings
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def forward(self, **kwargs):
-        # keyword arguments come in 3 flavors: encoder-specific (prefixed by
-        # `encoder_`), decoder-specific (prefixed by `decoder_`) and those
-        # that apply to the model as whole.
-        # We let the specific kwargs override the common ones in case of conflict.
-
-        lm_labels = kwargs.pop('decoder_lm_labels', None)
-
-        kwargs_common = dict((k, v) for k, v in kwargs.items()
-                             if not k.startswith("encoder_") and not k.startswith("decoder_"))
-        kwargs_encoder = kwargs_common.copy()
-        kwargs_decoder = kwargs_common.copy()
-        kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_")))
-        kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_")))
-
-        # Encode if needed (training, first prediction pass)
-        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
-        if encoder_hidden_states is None:
-            # Convert encoder inputs in embeddings if needed
-            hidden_states = kwargs_encoder.pop("inputs_embeds", None)
-            if hidden_states is None:
-                encoder_inputs_ids = kwargs_encoder.pop("input_ids")
-                hidden_states = self.shared(encoder_inputs_ids)  # Convert inputs in embeddings
-
-            encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
-            encoder_hidden_states = encoder_outputs[0]
-        else:
-            encoder_outputs = ()
-
-        # Decode
-        # Convert decoder inputs in embeddings if needed
-        hidden_states = kwargs_decoder.pop("inputs_embeds", None)
-        if hidden_states is None:
-            decoder_inputs_ids = kwargs_decoder.pop("input_ids")
-            hidden_states = self.shared(decoder_inputs_ids)
-
-        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
-        kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
-        decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
-
-        sequence_output = decoder_outputs[0]
-        # Rescale output before projecting on vocab
-        # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
-        sequence_output = sequence_output * (self.model_dim ** -0.5)
-        lm_logits = self.lm_head(sequence_output)
-
-        decoder_outputs = (lm_logits,) + decoder_outputs[1:]  # Add hidden states and attention if they are here
-        if lm_labels is not None:
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = lm_labels[..., 1:].contiguous()
-            loss_fct = CrossEntropyLoss(ignore_index=-1)
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
-                            shift_labels.view(-1))
-            decoder_outputs = (loss,) + decoder_outputs  # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
-
-        return decoder_outputs + encoder_outputs

transformers/modeling_tf_albert.py

@@ -16,13 +16,18 @@
 """ TF 2.0 ALBERT model. """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
+import json
 import logging
+import math
+import os
 import sys
+from io import open
 
+import numpy as np
 import tensorflow as tf
 
 from .configuration_albert import AlbertConfig
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer
 from .modeling_tf_bert import ACT2FN, TFBertSelfAttention
 from .file_utils import add_start_docstrings
 
@@ -31,10 +36,10 @@ import logging
 logger = logging.getLogger(__name__)
 
 TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v1-tf_model.h5",
-    'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v1-tf_model.h5",
-    'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v1-tf_model.h5",
-    'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v1-tf_model.h5",
+    'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-tf_model.h5",
+    'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-tf_model.h5",
+    'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-tf_model.h5",
+    'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-tf_model.h5",
     'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-tf_model.h5",
     'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-tf_model.h5",
     'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-tf_model.h5",
@@ -105,9 +110,9 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer):
         input_ids, position_ids, token_type_ids, inputs_embeds = inputs
 
         if input_ids is not None:
-            input_shape = shape_list(input_ids)
+            input_shape = tf.shape(input_ids)
         else:
-            input_shape = shape_list(inputs_embeds)[:-1]
+            input_shape = tf.shape(inputs_embeds)[:-1]
 
         seq_length = input_shape[1]
         if position_ids is None:
@@ -132,8 +137,8 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer):
             Returns:
                 float32 tensor with shape [batch_size, length, vocab_size].
         """
-        batch_size = shape_list(inputs)[0]
-        length = shape_list(inputs)[1]
+        batch_size = tf.shape(inputs)[0]
+        length = tf.shape(inputs)[1]
         x = tf.reshape(inputs, [-1, self.config.embedding_size])
         logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
         return tf.reshape(logits, [batch_size, length, self.config.vocab_size])
@@ -178,7 +183,7 @@ class TFAlbertSelfAttention(tf.keras.layers.Layer):
     def call(self, inputs, training=False):
         hidden_states, attention_mask, head_mask = inputs
 
-        batch_size = shape_list(hidden_states)[0]
+        batch_size = tf.shape(hidden_states)[0]
         mixed_query_layer = self.query(hidden_states)
         mixed_key_layer = self.key(hidden_states)
         mixed_value_layer = self.value(hidden_states)
@@ -191,7 +196,7 @@ class TFAlbertSelfAttention(tf.keras.layers.Layer):
         # (batch size, num_heads, seq_len_q, seq_len_k)
         attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
         # scale attention_scores
-        dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
+        dk = tf.cast(tf.shape(key_layer)[-1], tf.float32)
         attention_scores = attention_scores / tf.math.sqrt(dk)
 
         if attention_mask is not None:
@@ -259,7 +264,7 @@ class TFAlbertAttention(TFBertSelfAttention):
     def call(self, inputs, training=False):
         input_tensor, attention_mask, head_mask = inputs
 
-        batch_size = shape_list(input_tensor)[0]
+        batch_size = tf.shape(input_tensor)[0]
         mixed_query_layer = self.query(input_tensor)
         mixed_key_layer = self.key(input_tensor)
         mixed_value_layer = self.value(input_tensor)
@@ -272,7 +277,7 @@ class TFAlbertAttention(TFBertSelfAttention):
         # (batch size, num_heads, seq_len_q, seq_len_k)
         attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
         # scale attention_scores
-        dk = tf.cast(shape_list(key_layer)[-1], tf.float32)
+        dk = tf.cast(tf.shape(key_layer)[-1], tf.float32)
         attention_scores = attention_scores / tf.math.sqrt(dk)
 
         if attention_mask is not None:
@@ -640,9 +645,9 @@ class TFAlbertModel(TFAlbertPreTrainedModel):
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
-            input_shape = shape_list(input_ids)
+            input_shape = tf.shape(input_ids)
         elif inputs_embeds is not None:
-            input_shape = shape_list(inputs_embeds)[:-1]
+            input_shape = inputs_embeds.shape[:-1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 

transformers/modeling_tf_auto.py

@@ -27,7 +27,6 @@ from .modeling_tf_xlm import TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceC
 from .modeling_tf_roberta import TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification
 from .modeling_tf_distilbert import TFDistilBertModel, TFDistilBertForQuestionAnswering, TFDistilBertForMaskedLM, TFDistilBertForSequenceClassification
 from .modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel
-from .modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel
 
 from .file_utils import add_start_docstrings
 
@@ -46,7 +45,6 @@ class TFAutoModel(object):
 
         The base model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: TFT5Model (T5 model)
             - contains `distilbert`: TFDistilBertModel (DistilBERT model)
             - contains `roberta`: TFRobertaModel (RoBERTa model)
             - contains `bert`: TFBertModel (Bert model)
@@ -70,7 +68,6 @@ class TFAutoModel(object):
 
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: TFT5Model (T5 model)
             - contains `distilbert`: TFDistilBertModel (DistilBERT model)
             - contains `roberta`: TFRobertaModel (RoBERTa model)
             - contains `bert`: TFTFBertModel (Bert model)
@@ -84,7 +81,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -113,9 +109,6 @@ class TFAutoModel(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -140,9 +133,7 @@ class TFAutoModel(object):
             model = TFAutoModel.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
 
         """
-        if 't5' in pretrained_model_name_or_path:
-            return TFT5Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'distilbert' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
             return TFDistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
             return TFRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
@@ -178,7 +169,6 @@ class TFAutoModelWithLMHead(object):
 
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: TFT5WithLMHeadModel (T5 model)
             - contains `distilbert`: TFDistilBertForMaskedLM (DistilBERT model)
             - contains `roberta`: TFRobertaForMaskedLM (RoBERTa model)
             - contains `bert`: TFBertForMaskedLM (Bert model)
@@ -205,7 +195,6 @@ class TFAutoModelWithLMHead(object):
 
         The model class to instantiate is selected as the first pattern matching
         in the `pretrained_model_name_or_path` string (in the following order):
-            - contains `t5`: TFT5WithLMHeadModel (T5 model)
             - contains `distilbert`: TFDistilBertForMaskedLM (DistilBERT model)
             - contains `roberta`: TFRobertaForMaskedLM (RoBERTa model)
             - contains `bert`: TFBertForMaskedLM (Bert model)
@@ -220,7 +209,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -249,9 +237,6 @@ class TFAutoModelWithLMHead(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -276,9 +261,7 @@ class TFAutoModelWithLMHead(object):
             model = TFAutoModelWithLMHead.from_pretrained('./pt_model/bert_pytorch_model.bin', from_pt=True, config=config)
 
         """
-        if 't5' in pretrained_model_name_or_path:
-            return TFT5WithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        elif 'distilbert' in pretrained_model_name_or_path:
+        if 'distilbert' in pretrained_model_name_or_path:
             return TFDistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
         elif 'roberta' in pretrained_model_name_or_path:
             return TFRobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
@@ -349,7 +332,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -378,9 +360,6 @@ class TFAutoModelForSequenceClassification(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -465,7 +444,6 @@ 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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
 
@@ -494,9 +472,6 @@ class TFAutoModelForQuestionAnswering(object):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.

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, get_initializer, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer
 from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
@@ -48,10 +48,6 @@ TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-tf_model.h5",
     'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-tf_model.h5",
     'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-tf_model.h5",
-    'bert-base-japanese': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-tf_model.h5",
-    'bert-base-japanese-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-whole-word-masking-tf_model.h5",
-    'bert-base-japanese-char': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-tf_model.h5",
-    'bert-base-japanese-char-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/cl-tohoku/bert-base-japanese-char-whole-word-masking-tf_model.h5"
 }
 
 
@@ -133,7 +129,7 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
                 linear tensor, float32 with shape [batch_size, length, vocab_size].
         Raises:
             ValueError: if mode is not valid.
-
+        
         Shared weights logic adapted from
             https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
         """
@@ -149,10 +145,10 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
         input_ids, position_ids, token_type_ids, inputs_embeds = inputs
 
         if input_ids is not None:
-            input_shape = shape_list(input_ids)
+            input_shape = tf.shape(input_ids)
         else:
-            input_shape = shape_list(inputs_embeds)[:-1]
-
+            input_shape = tf.shape(inputs_embeds)[:-1]
+        
         seq_length = input_shape[1]
         if position_ids is None:
             position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
@@ -176,8 +172,8 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
             Returns:
                 float32 tensor with shape [batch_size, length, vocab_size].
         """
-        batch_size = shape_list(inputs)[0]
-        length = shape_list(inputs)[1]
+        batch_size = tf.shape(inputs)[0]
+        length = tf.shape(inputs)[1]
 
         x = tf.reshape(inputs, [-1, self.hidden_size])
         logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
@@ -218,7 +214,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
     def call(self, inputs, training=False):
         hidden_states, attention_mask, head_mask = inputs
 
-        batch_size = shape_list(hidden_states)[0]
+        batch_size = tf.shape(hidden_states)[0]
         mixed_query_layer = self.query(hidden_states)
         mixed_key_layer = self.key(hidden_states)
         mixed_value_layer = self.value(hidden_states)
@@ -229,7 +225,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
 
         # Take the dot product between "query" and "key" to get the raw attention scores.
         attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)  # (batch size, num_heads, seq_len_q, seq_len_k)
-        dk = tf.cast(shape_list(key_layer)[-1], tf.float32) # scale attention_scores
+        dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) # scale attention_scores
         attention_scores = attention_scores / tf.math.sqrt(dk)
 
         if attention_mask is not None:
@@ -250,7 +246,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
         context_layer = tf.matmul(attention_probs, value_layer)
 
         context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
-        context_layer = tf.reshape(context_layer,
+        context_layer = tf.reshape(context_layer, 
                                   (batch_size, -1, self.all_head_size))  # (batch_size, seq_len_q, all_head_size)
 
         outputs = (context_layer, attention_probs) if self.output_attentions else (context_layer,)
@@ -506,9 +502,9 @@ class TFBertMainLayer(tf.keras.layers.Layer):
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
-            input_shape = shape_list(input_ids)
+            input_shape = input_ids.shape
         elif inputs_embeds is not None:
-            input_shape = shape_list(inputs_embeds)[:-1]
+            input_shape = inputs_embeds.shape[:-1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
@@ -595,7 +591,7 @@ BERT_START_DOCSTRING = r"""    The BERT model was proposed in
             `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
 
     Parameters:
-        config (:class:`~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:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
 """
@@ -609,13 +605,13 @@ BERT_INPUTS_DOCSTRING = r"""
             (a) For sequence pairs:
 
                 ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
-
+                
                 ``token_type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1``
 
             (b) For single sequences:
 
                 ``tokens:         [CLS] the dog is hairy . [SEP]``
-
+                
                 ``token_type_ids:   0   0   0   0  0     0   0``
 
             Bert is a model with absolute position embeddings so it's usually advised to pad the inputs on
@@ -943,11 +939,11 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
             input_ids = inputs
 
         if input_ids is not None:
-            num_choices = shape_list(input_ids)[1]
-            seq_length = shape_list(input_ids)[2]
+            num_choices = tf.shape(input_ids)[1]
+            seq_length = tf.shape(input_ids)[2]
         else:
-            num_choices = shape_list(inputs_embeds)[1]
-            seq_length = shape_list(inputs_embeds)[2]
+            num_choices = tf.shape(inputs_embeds)[1]
+            seq_length = tf.shape(inputs_embeds)[2]
 
         flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
         flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None

transformers/modeling_tf_ctrl.py

@@ -95,7 +95,7 @@ class TFMultiHeadAttention(tf.keras.layers.Layer):
 
     def call(self, inputs, training=False):
         v, k, q, mask, layer_past, attention_mask, head_mask = inputs
-        batch_size = shape_list(q)[0]
+        batch_size = q.shape[0]
 
         q = self.Wq(q)
         k = self.Wk(k)
@@ -400,7 +400,7 @@ class TFCTRLModel(TFCTRLPreTrainedModel):
         **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the last layer of the model.
         **past**:
-            list of ``tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding.
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
@@ -462,7 +462,7 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
         **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         **past**:
-            list of ``tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding.
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

transformers/modeling_tf_distilbert.py

@@ -37,8 +37,7 @@ logger = logging.getLogger(__name__)
 
 TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-tf_model.h5",
-    'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5",
-    'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-tf_model.h5",
+    'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5"
 }
 
 
@@ -138,9 +137,9 @@ class TFEmbeddings(tf.keras.layers.Layer):
             input_ids, position_ids = inputs
 
         if input_ids is not None:
-            seq_length = shape_list(input_ids)[1]
+            seq_length = tf.shape(input_ids)[1]
         else:
-            seq_length = shape_list(inputs_embeds)[1]
+            seq_length = tf.shape(inputs_embeds)[1]
 
         if position_ids is None:
             position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
@@ -161,8 +160,8 @@ class TFEmbeddings(tf.keras.layers.Layer):
             Returns:
                 float32 tensor with shape [batch_size, length, vocab_size].
         """
-        batch_size = shape_list(inputs)[0]
-        length = shape_list(inputs)[1]
+        batch_size = tf.shape(inputs)[0]
+        length = tf.shape(inputs)[1]
 
         x = tf.reshape(inputs, [-1, self.dim])
         logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
@@ -704,53 +703,6 @@ class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
         return outputs  # logits, (hidden_states), (attentions)
 
 
-@add_start_docstrings("""DistilBert Model with a token classification head on top (a linear layer on top of
-    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
-    DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
-class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
-            Classification scores (before SoftMax).
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-    Examples::
-        import tensorflow as tf
-        from transformers import DistilBertTokenizer, TFDistilBertForTokenClassification
-        tokenizer = DistilBertTokenizer.from_pretrained('bert-base-uncased')
-        model = TFDistilBertForTokenClassification.from_pretrained('bert-base-uncased')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        scores = outputs[0]
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFDistilBertForTokenClassification, self).__init__(config, *inputs, **kwargs)
-        self.num_labels = config.num_labels
-
-        self.distilbert = TFDistilBertMainLayer(config, name='distilbert')
-        self.dropout = tf.keras.layers.Dropout(config.dropout)
-        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.distilbert(inputs, **kwargs)
-
-        sequence_output = outputs[0]
-
-        sequence_output = self.dropout(sequence_output, training=kwargs.get('training', False))
-        logits = self.classifier(sequence_output)
-
-        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here
-
-        return outputs  # scores, (hidden_states), (attentions)
-
-
 @add_start_docstrings("""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
                          the hidden-states output to compute `span start logits` and `span end logits`). """,
                       DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)

transformers/modeling_tf_gpt2.py

@@ -92,7 +92,7 @@ class TFAttention(tf.keras.layers.Layer):
         # q, k, v have shape [batch, heads, sequence, features]
         w = tf.matmul(q, k, transpose_b=True)
         if self.scale:
-            dk = tf.cast(shape_list(k)[-1], tf.float32) # scale attention_scores
+            dk = tf.cast(tf.shape(k)[-1], tf.float32) # scale attention_scores
             w = w / tf.math.sqrt(dk)
 
         # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
@@ -436,7 +436,7 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
         **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
             Sequence of hidden-states at the last layer of the model.
         **past**:
-            list of ``tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding.
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
@@ -476,7 +476,7 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
         **prediction_scores**: `tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
             Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
         **past**:
-            list of `tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of `tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding.
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
@@ -535,7 +535,7 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
         **mc_prediction_scores**: `tf.Tensor`` of shape ``(batch_size, num_choices)``
             Prediction scores of the multiplechoice classification head (scores for each choice before SoftMax).
         **past**:
-            list of `tf.Tensor`` (one for each layer) of shape ``(2, batch_size, num_heads, sequence_length, embed_size_per_head)``:
+            list of `tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             that contains pre-computed hidden-states (key and values in the attention blocks).
             Can be used (see `past` input) to speed up sequential decoding.
         **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)

transformers/modeling_tf_openai.py

@@ -98,7 +98,7 @@ class TFAttention(tf.keras.layers.Layer):
         # q, k, v have shape [batch, heads, sequence, features]
         w = tf.matmul(q, k, transpose_b=True)
         if self.scale:
-            dk = tf.cast(shape_list(k)[-1], tf.float32) # scale attention_scores
+            dk = tf.cast(tf.shape(k)[-1], tf.float32) # scale attention_scores
             w = w / tf.math.sqrt(dk)
 
         # w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.

transformers/modeling_tf_pytorch_utils.py

@@ -78,7 +78,6 @@ def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_i
     logger.info("Loading PyTorch weights from {}".format(pt_path))
 
     pt_state_dict = torch.load(pt_path, map_location='cpu')
-    logger.info("PyTorch checkpoint contains {:,} parameters".format(sum(t.numel() for t in pt_state_dict.values())))
 
     return load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys)
 
@@ -119,9 +118,6 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             new_key = key.replace('gamma', 'weight')
         if 'beta' in key:
             new_key = key.replace('beta', 'bias')
-        # DialoGPT format
-        if key == 'lm_head.decoder.weight':
-            new_key = 'lm_head.weight'
         if new_key:
             old_keys.append(key)
             new_keys.append(new_key)
@@ -135,7 +131,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
         start_prefix_to_remove = tf_model.base_model_prefix + '.'
 
     symbolic_weights = tf_model.trainable_weights + tf_model.non_trainable_weights
-    tf_loaded_numel = 0
+
     weight_value_tuples = []
     all_pytorch_weights = set(list(pt_state_dict.keys()))
     for symbolic_weight in symbolic_weights:
@@ -143,11 +139,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
         name, transpose = convert_tf_weight_name_to_pt_weight_name(sw_name, start_prefix_to_remove=start_prefix_to_remove)
 
         # Find associated numpy array in pytorch model state dict
-        if name not in pt_state_dict:
-            if allow_missing_keys:
-                continue
-            raise AttributeError("{} not found in PyTorch model".format(name))
-
+        assert name in pt_state_dict, "{} not found in PyTorch model".format(name)
         array = pt_state_dict[name].numpy()
 
         if transpose:
@@ -164,8 +156,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             e.args += (symbolic_weight.shape, array.shape)
             raise e
 
-        tf_loaded_numel += array.size
-        # logger.warning("Initialize TF weight {}".format(symbolic_weight.name))
+        logger.info("Initialize TF weight {}".format(symbolic_weight.name))
 
         weight_value_tuples.append((symbolic_weight, array))
         all_pytorch_weights.discard(name)
@@ -175,8 +166,6 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
     if tf_inputs is not None:
         tfo = tf_model(tf_inputs, training=False)  # Make sure restore ops are run
 
-    logger.info("Loaded {:,} parameters in the TF 2.0 model.".format(tf_loaded_numel))
-
     logger.info("Weights or buffers not loaded from PyTorch model: {}".format(all_pytorch_weights))
 
     return tf_model
@@ -254,7 +243,6 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
     all_tf_weights = set(list(tf_weights_map.keys()))
     loaded_pt_weights_data_ptr = {}
-    missing_keys_pt = []
     for pt_weight_name, pt_weight in current_pt_params_dict.items():
         # Handle PyTorch shared weight ()not duplicated in TF 2.0
         if pt_weight.data_ptr() in loaded_pt_weights_data_ptr:
@@ -263,10 +251,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
         # Find associated numpy array in pytorch model state dict
         if pt_weight_name not in tf_weights_map:
-            if allow_missing_keys:
-                missing_keys_pt.append(pt_weight_name)
-                continue
-            raise AttributeError("{} not found in TF 2.0 model".format(pt_weight_name))
+            raise ValueError("{} not found in TF 2.0 model".format(pt_weight_name))
 
         array, transpose = tf_weights_map[pt_weight_name]
 
@@ -284,14 +269,13 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
             e.args += (pt_weight.shape, array.shape)
             raise e
 
-        # logger.warning("Initialize PyTorch weight {}".format(pt_weight_name))
+        logger.info("Initialize PyTorch weight {}".format(pt_weight_name))
 
         new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)
         loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)
         all_tf_weights.discard(pt_weight_name)
 
     missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False)
-    missing_keys += missing_keys_pt
 
     if len(missing_keys) > 0:
         logger.info("Weights of {} not initialized from TF 2.0 model: {}".format(

transformers/modeling_tf_roberta.py

@@ -24,7 +24,7 @@ import numpy as np
 import tensorflow as tf
 
 from .configuration_roberta import RobertaConfig
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer
 from .file_utils import add_start_docstrings
 
 from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu, gelu_new
@@ -51,9 +51,9 @@ class TFRobertaEmbeddings(TFBertEmbeddings):
         input_ids, position_ids, token_type_ids, inputs_embeds = inputs
 
         if input_ids is not None:
-            seq_length = shape_list(input_ids)[1]
+            seq_length = tf.shape(input_ids)[1]
         else:
-            seq_length = shape_list(inputs_embeds)[1]
+            seq_length = tf.shape(inputs_embeds)[1]
 
         if position_ids is None:
             position_ids = tf.range(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=tf.int32)[tf.newaxis, :]

transformers/modeling_tf_t5.py

@@ -1,775 +0,0 @@
-# coding=utf-8
-# Copyright 2018 T5 Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# 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.
-""" TF 2.0 T5 model. """
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import logging
-import math
-import copy
-import itertools
-
-import tensorflow as tf
-
-from .configuration_t5 import T5Config
-from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
-from .file_utils import add_start_docstrings, DUMMY_INPUTS, DUMMY_MASK
-
-logger = logging.getLogger(__name__)
-
-TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-tf_model.h5",
-    't5-base': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-tf_model.h5",
-    't5-large': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-tf_model.h5",
-    't5-3b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-tf_model.h5",
-    't5-11b': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-tf_model.h5",
-}
-
-####################################################
-# TF 2.0 Models are constructed using Keras imperative API by sub-classing
-# - tf.keras.layers.Layer for the layers and
-# - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
-####################################################
-
-class TFT5LayerNorm(tf.keras.layers.Layer):
-    def __init__(self, epsilon=1e-6, **kwargs):
-        """ Construct a layernorm module in the T5 style
-            No bias and no substraction of mean.
-        """
-        super(TFT5LayerNorm, self).__init__(**kwargs)
-        self.variance_epsilon = epsilon
-
-    def build(self, input_shape):
-        """Build shared word embedding layer """
-        self.weight = self.add_weight(
-            "weight",
-            shape=(input_shape[-1],),
-            initializer='ones')
-        super(TFT5LayerNorm, self).build(input_shape)
-
-    def call(self, x):
-        variance = tf.math.reduce_mean(tf.math.square(x), axis=-1, keepdims=True)
-        x = x * tf.math.rsqrt(variance + self.variance_epsilon)
-        return self.weight * x
-
-
-class TFT5DenseReluDense(tf.keras.layers.Layer):
-    def __init__(self, config, **kwargs):
-        super(TFT5DenseReluDense, self).__init__(**kwargs)
-        self.wi = tf.keras.layers.Dense(config.d_ff, use_bias=False, name='wi')
-        self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name='wo')
-        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
-        self.act = tf.keras.activations.relu
-
-    def call(self, hidden_states, training=False):
-        h = self.wi(hidden_states)
-        h = self.act(h)
-        h = self.dropout(h, training=training)
-        h = self.wo(h)
-        return h
-
-
-class TFT5LayerFF(tf.keras.layers.Layer):
-    def __init__(self, config, **kwargs):
-        super(TFT5LayerFF, self).__init__(**kwargs)
-        self.DenseReluDense = TFT5DenseReluDense(config, name='DenseReluDense')
-        self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon,
-                                        name='layer_norm')
-        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
-
-    def call(self, hidden_states, training=False):
-        norm_x = self.layer_norm(hidden_states)
-        y = self.DenseReluDense(norm_x, training=training)
-        layer_output = hidden_states + self.dropout(y, training=training)
-        return layer_output
-
-
-class TFT5Attention(tf.keras.layers.Layer):
-    NEW_ID = itertools.count()
-
-    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
-        super(TFT5Attention, self).__init__(**kwargs)
-        self.layer_id = next(TFT5Attention.NEW_ID)
-        self.is_decoder = config.is_decoder
-        self.has_relative_attention_bias = has_relative_attention_bias
-
-        self.output_attentions = config.output_attentions
-        self.relative_attention_num_buckets = config.relative_attention_num_buckets
-        self.d_model = config.d_model
-        self.d_kv = config.d_kv
-        self.n_heads = config.num_heads
-        self.inner_dim = self.n_heads * self.d_kv
-
-        # Mesh TensorFlow initialization to avoid scaling before softmax
-        self.q = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name='q')
-        self.k = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name='k')
-        self.v = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name='v')
-        self.o = tf.keras.layers.Dense(self.d_model, use_bias=False, name='o')
-        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
-
-        if self.has_relative_attention_bias:
-            self.relative_attention_bias = tf.keras.layers.Embedding(self.relative_attention_num_buckets,
-                                                                     self.n_heads,
-                                                                     name='relative_attention_bias')
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        raise NotImplementedError
-
-    @staticmethod
-    def _relative_position_bucket(relative_position,
-                                  bidirectional=True,
-                                  num_buckets=32,
-                                  max_distance=128):
-        """
-        Adapted from Mesh Tensorflow:
-        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
-
-        Translate relative position to a bucket number for relative attention.
-        The relative position is defined as memory_position - query_position, i.e.
-        the distance in tokens from the attending position to the attended-to
-        position.  If bidirectional=False, then positive relative positions are
-        invalid.
-        We use smaller buckets for small absolute relative_position and larger buckets
-        for larger absolute relative_positions.  All relative positions >=max_distance
-        map to the same bucket.  All relative positions <=-max_distance map to the
-        same bucket.  This should allow for more graceful generalization to longer
-        sequences than the model has been trained on.
-        Args:
-            relative_position: an int32 Tensor
-            bidirectional: a boolean - whether the attention is bidirectional
-            num_buckets: an integer
-            max_distance: an integer
-        Returns:
-            a Tensor with the same shape as relative_position, containing int32
-            values in the range [0, num_buckets)
-        """
-        ret = 0
-        n = -relative_position
-        if bidirectional:
-            num_buckets //= 2
-            ret += tf.dtypes.cast(tf.math.less(n, 0), tf.int32) * num_buckets
-            n = tf.math.abs(n)
-        else:
-            n = tf.math.maximum(n, 0)
-        # now n is in the range [0, inf)
-        max_exact = num_buckets // 2
-        is_small = tf.math.less(n, max_exact)
-        val_if_large = max_exact + tf.dtypes.cast(
-            tf.math.log(tf.dtypes.cast(n, tf.float32) / max_exact)
-            / math.log(max_distance / max_exact) * (num_buckets - max_exact), tf.int32)
-        val_if_large = tf.math.minimum(val_if_large, num_buckets - 1)
-        ret += tf.where(is_small, n, val_if_large)
-        return ret
-
-    def compute_bias(self, qlen, klen):
-        """ Compute binned relative position bias """
-        context_position = tf.range(qlen)[:, None]
-        memory_position = tf.range(klen)[None, :]
-        relative_position = memory_position - context_position  # shape (qlen, klen)
-        rp_bucket = self._relative_position_bucket(relative_position,
-                                                   bidirectional=not self.is_decoder,
-                                                   num_buckets=self.relative_attention_num_buckets)
-        values = self.relative_attention_bias(rp_bucket)  # shape (qlen, klen, num_heads)
-        values = tf.expand_dims(tf.transpose(values, [2, 0, 1]), axis=0) # shape (1, num_heads, qlen, klen)
-        return values
-
-    def call(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None, training=False):
-        """
-        Self-attention (if kv is None) or attention over source sentence (provided by kv).
-        """
-        # Input is (bs, qlen, dim)
-        # Mask is (bs, klen) (non-causal) or (bs, klen, klen)
-        bs, qlen, dim = shape_list(input)
-        if kv is None:
-            klen = qlen if cache is None else cache['slen'] + qlen
-        else:
-            klen = shape_list(kv)[1]
-
-        def shape(x):
-            """  projection """
-            return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, self.d_kv)), perm=(0, 2, 1, 3))
-
-        def unshape(x):
-            """  compute context """
-            return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.inner_dim))
-
-        q = shape(self.q(input))                                          # (bs, n_heads, qlen, dim_per_head)
-        if kv is None:
-            k = shape(self.k(input))                                      # (bs, n_heads, qlen, dim_per_head)
-            v = shape(self.v(input))                                      # (bs, n_heads, qlen, dim_per_head)
-        elif cache is None or self.layer_id not in cache:
-            k = v = kv
-            k = shape(self.k(k))                                          # (bs, n_heads, qlen, dim_per_head)
-            v = shape(self.v(v))                                          # (bs, n_heads, qlen, dim_per_head)
-
-        if cache is not None:
-            if self.layer_id in cache:
-                if kv is None:
-                    k_, v_ = cache[self.layer_id]
-                    k = tf.concat([k_, k], axis=2)                             # (bs, n_heads, klen, dim_per_head)
-                    v = tf.concat([v_, v], axis=2)                             # (bs, n_heads, klen, dim_per_head)
-                else:
-                    k, v = cache[self.layer_id]
-            cache[self.layer_id] = (k, v)
-
-        # q = q / math.sqrt(dim_per_head)                                     # No scaling in T5
-        # scores = tf.matmul(q, k, transpose_b=True)                            # (bs, n_heads, qlen, klen)
-        scores = tf.einsum('bnqd,bnkd->bnqk', q, k)                        # (bs, n_heads, qlen, klen)
-
-        if position_bias is None:
-            if not self.has_relative_attention_bias:
-                raise ValueError("No position_bias provided and no weights to compute position_bias")
-            position_bias = self.compute_bias(qlen, klen)
-            if mask is not None:
-                position_bias = position_bias + mask
-                # mask = (mask == 0).expand_as(scores)                              # (bs, n_heads, qlen, klen)
-                # scores.masked_fill_(mask, -float('inf'))                          # (bs, n_heads, qlen, klen)
-
-        scores += position_bias
-        weights = tf.nn.softmax(scores, axis=-1)                              # (bs, n_heads, qlen, klen)
-        weights = self.dropout(weights, training=training)                    # (bs, n_heads, qlen, klen)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            weights = weights * head_mask
-
-        context = tf.matmul(weights, v)                                    # (bs, n_heads, qlen, dim_per_head)
-        context = unshape(context)                                            # (bs, qlen, dim)
-
-        context = self.o(context)
-
-        outputs = (context,)
-        if self.output_attentions:
-            outputs = outputs + (weights,)
-        if self.has_relative_attention_bias:
-            outputs = outputs + (position_bias,)
-        return outputs
-
-
-class TFT5LayerSelfAttention(tf.keras.layers.Layer):
-    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
-        super(TFT5LayerSelfAttention, self).__init__(**kwargs)
-        self.SelfAttention = TFT5Attention(config,
-                                           has_relative_attention_bias=has_relative_attention_bias,
-                                           name='SelfAttention')
-        self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon,
-                                        name='layer_norm')
-        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
-
-    def call(self, hidden_states, attention_mask=None, position_bias=None,
-             head_mask=None, training=False):
-        norm_x = self.layer_norm(hidden_states)
-        attention_output = self.SelfAttention(norm_x,
-                                              mask=attention_mask,
-                                              position_bias=position_bias,
-                                              head_mask=head_mask,
-                                              training=training)
-        y = attention_output[0]
-        layer_output = hidden_states + self.dropout(y, training=training)
-        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
-        return outputs
-
-
-class TFT5LayerCrossAttention(tf.keras.layers.Layer):
-    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
-        super(TFT5LayerCrossAttention, self).__init__(**kwargs)
-        self.EncDecAttention = TFT5Attention(config,
-                                           has_relative_attention_bias=has_relative_attention_bias,
-                                           name='EncDecAttention')
-        self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon,
-                                        name='layer_norm')
-        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
-
-    def call(self, hidden_states, kv, attention_mask=None, position_bias=None,
-             head_mask=None, training=False):
-        norm_x = self.layer_norm(hidden_states)
-        attention_output = self.EncDecAttention(norm_x,
-                                                mask=attention_mask,
-                                                kv=kv,
-                                                position_bias=position_bias,
-                                                head_mask=head_mask,
-                                                training=training)
-        y = attention_output[0]
-        layer_output = hidden_states + self.dropout(y, training=training)
-        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
-        return outputs
-
-
-class TFT5Block(tf.keras.layers.Layer):
-    def __init__(self, config, has_relative_attention_bias=False, **kwargs):
-        super(TFT5Block, self).__init__(**kwargs)
-        self.is_decoder = config.is_decoder
-        self.layer = []
-        self.layer.append(TFT5LayerSelfAttention(config,
-                                                 has_relative_attention_bias=has_relative_attention_bias,
-                                                 name='layer_._0'))
-        if self.is_decoder:
-            self.layer.append(TFT5LayerCrossAttention(config,
-                                                      has_relative_attention_bias=has_relative_attention_bias,
-                                                      name='layer_._1'))
-            self.layer.append(TFT5LayerFF(config, name='layer_._2'))
-        else:
-            self.layer.append(TFT5LayerFF(config, name='layer_._1'))
-
-    def call(self, hidden_states, attention_mask=None, position_bias=None,
-             encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
-             head_mask=None, training=False):
-        self_attention_outputs = self.layer[0](hidden_states,
-                                                attention_mask=attention_mask,
-                                                position_bias=position_bias,
-                                                head_mask=head_mask,
-                                                training=training)
-        hidden_states = self_attention_outputs[0]
-        outputs = self_attention_outputs[1:]
-
-        if not self.is_decoder:
-            hidden_states = self.layer[1](hidden_states, training=training)
-        else:
-            cross_attention_outputs = self.layer[1](hidden_states,
-                                                    kv=encoder_hidden_states,
-                                                    attention_mask=encoder_attention_mask,
-                                                    position_bias=encoder_decoder_position_bias,
-                                                    head_mask=head_mask,
-                                                    training=training)
-            hidden_states = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:]
-            hidden_states = self.layer[2](hidden_states, training=training)
-
-        outputs = (hidden_states,) + outputs  # add attentions if we output them
-        return outputs # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
-
-
-####################################################
-# The full model without a specific pretrained or finetuning head is
-# provided as a tf.keras.layers.Layer usually called "TFT5MainLayer"
-####################################################
-class TFT5MainLayer(tf.keras.layers.Layer):
-    def __init__(self, config, **kwargs):
-        super(TFT5MainLayer, self).__init__(**kwargs)
-        self.output_attentions = config.output_attentions
-        self.output_hidden_states = config.output_hidden_states
-        self.is_decoder = config.is_decoder
-        self.config = config
-        self.num_hidden_layers = config.num_layers
-
-        self.block = [TFT5Block(config,
-                                has_relative_attention_bias=bool(i == 0),
-                                name='block_._{}'.format(i))
-                        for i in range(config.num_layers)]
-        self.final_layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon,
-                                              name='final_layer_norm')
-        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
-
-    def _resize_token_embeddings(self, new_num_tokens):
-        raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
-
-    def _prune_heads(self, heads_to_prune):
-        raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
-
-    def call(self, hidden_states, attention_mask=None, encoder_hidden_states=None,
-             encoder_attention_mask=None, head_mask=None, training=False):
-
-        batch_size, seq_length = shape_list(hidden_states)[:2]
-        if attention_mask is None:
-            attention_mask = tf.fill((batch_size, seq_length), 1)
-        if self.is_decoder and encoder_attention_mask is None:
-            encoder_seq_length = encoder_hidden_states.shape[1]
-            encoder_attention_mask = tf.fill((batch_size, encoder_seq_length), 1)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        attention_mask = tf.cast(attention_mask, dtype=tf.float32)
-        num_dims_attention_mask = len(shape_list(attention_mask))
-        if num_dims_attention_mask == 3:
-            extended_attention_mask = attention_mask[:, None, :, :]
-        elif num_dims_attention_mask == 2:
-        # Provided a padding mask of dimensions [batch_size, seq_length]
-        # - if the model is a decoder, apply a causal mask in addition to the padding mask
-        # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
-            if self.config.is_decoder:
-                seq_ids = tf.range(seq_length)
-                causal_mask = tf.less_equal(tf.tile(seq_ids[None, None, :], (batch_size, seq_length, 1)),
-                                            seq_ids[None, :, None])
-                causal_mask = tf.cast(causal_mask, dtype=tf.float32)
-                extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
-            else:
-                extended_attention_mask = attention_mask[:, None, None, :]
-
-        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
-        # masked positions, this operation will create a tensor which is 0.0 for
-        # positions we want to attend and -10000.0 for masked positions.
-        # Since we are adding it to the raw scores before the softmax, this is
-        # effectively the same as removing these entirely.
-
-        # T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion
-        # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
-        # extended_attention_mask = tf.math.equal(extended_attention_mask,
-        #                                         tf.transpose(extended_attention_mask, perm=(-1, -2)))
-
-        extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
-
-        if self.is_decoder:
-            # If a 2D ou 3D attention mask is provided for the cross-attention
-            # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
-            encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=tf.float32)
-            num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
-            if num_dims_encoder_attention_mask == 3:
-                encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
-            if num_dims_encoder_attention_mask == 2:
-                encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
-
-            # T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion
-            # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
-            # encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
-            #                                         tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
-
-            encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
-        else:
-            encoder_extended_attention_mask = None
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        if not head_mask is None:
-            raise NotImplementedError
-        else:
-            head_mask = [None] * self.num_hidden_layers
-            # head_mask = tf.constant([0] * self.num_hidden_layers)
-
-        all_hidden_states = ()
-        all_attentions = ()
-        position_bias = None
-        encoder_decoder_position_bias = None
-        for i, layer_module in enumerate(self.block):
-            if self.output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_outputs = layer_module(hidden_states,
-                                         attention_mask=extended_attention_mask,
-                                         position_bias=position_bias,
-                                         encoder_hidden_states=encoder_hidden_states,
-                                         encoder_attention_mask=encoder_extended_attention_mask,
-                                         encoder_decoder_position_bias=encoder_decoder_position_bias,
-                                         head_mask=head_mask[i],
-                                         training=training)
-            hidden_states = layer_outputs[0]
-            if i == 0:
-                # We share the position biases between the layers - the first layer store them
-                # layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
-                position_bias = layer_outputs[2 if self.output_attentions else 1]
-                if self.is_decoder:
-                    encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2]
-
-            if self.output_attentions:
-                all_attentions = all_attentions + (layer_outputs[1],)
-
-        hidden_states = self.final_layer_norm(hidden_states)
-        layer_output = self.dropout(hidden_states, training=training)
-
-        # Add last layer
-        if self.output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        outputs = (hidden_states,)
-        if self.output_hidden_states:
-            outputs = outputs + (all_hidden_states,)
-        if self.output_attentions:
-            outputs = outputs + (all_attentions,)
-        return outputs  # last-layer hidden state, (all hidden states), (all attentions)
-
-
-####################################################
-# TFT5PreTrainedModel is a sub-class of tf.keras.Model
-# which take care of loading and saving pretrained weights
-# and various common utilities.
-# Here you just need to specify a few (self-explanatory)
-# pointers for your model.
-####################################################
-class TFT5PreTrainedModel(TFPreTrainedModel):
-    """ An abstract class to handle weights initialization and
-        a simple interface for dowloading and loading pretrained models.
-    """
-    config_class = T5Config
-    pretrained_model_archive_map = TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
-    base_model_prefix = "transformer"
-
-    @property
-    def dummy_inputs(self):
-        input_ids = tf.constant(DUMMY_INPUTS)
-        input_mask = tf.constant(DUMMY_MASK)
-        dummy_inputs = {'decoder_input_ids': input_ids,
-                        'encoder_input_ids': input_ids,
-                        'decoder_attention_mask': input_mask}
-        return dummy_inputs
-
-
-T5_START_DOCSTRING = r"""    The T5 model was proposed in
-    `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
-    by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
-    It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
-
-    This model is a tf.keras.Model `tf.keras.Model`_ sub-class. Use it as a regular TF 2.0 Keras Model and
-    refer to the TF 2.0 documentation for all matter related to general usage and behavior.
-
-    .. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`:
-        https://arxiv.org/abs/1910.10683
-
-    .. _`tf.keras.Model`:
-        https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
-
-    Note on the model inputs:
-        TF 2.0 models accepts two formats as inputs:
-
-            - having all inputs as keyword arguments (like PyTorch models), or
-            - having all inputs as a list, tuple or dict in the first positional arguments.
-
-        This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
-
-        If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
-
-        - a single Tensor with input_ids only and nothing else: `model(inputs_ids)
-        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
-            `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-        - a dictionary with one or several input Tensors associaed to the input names given in the docstring:
-            `model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
-
-    Parameters:
-        config (:class:`~transformers.T5Config`): 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:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
-"""
-
-T5_INPUTS_DOCSTRING = r"""
-    Inputs:
-        **input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
-            Indices of input sequence tokens in the vocabulary.
-            To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:
-
-            (a) For sequence pairs:
-
-                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
-
-            (b) For single sequences:
-
-                ``tokens:         [CLS] the dog is hairy . [SEP]``
-
-
-            T5 is a model with relative position embeddings so you should be able to pad the inputs on
-            the right or the left.
-
-            Indices can be obtained using :class:`transformers.T5Tokenizer`.
-            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]``:
-            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
-        **head_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
-            Mask to nullify selected heads of the self-attention modules.
-            Mask values selected in ``[0, 1]``:
-            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
-"""
-
-@add_start_docstrings("The bare T5 Model transformer outputting raw hidden-states"
-                      "without any specific head on top.",
-                      T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
-class TFT5Model(TFT5PreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **last_hidden_state**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, hidden_size)``
-            Sequence of hidden-states at the output of the last layer of the model.
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        import tensorflow as tf
-        from transformers import T5Tokenizer, TFT5Model
-
-        tokenizer = T5Tokenizer.from_pretrained('t5-small')
-        model = TFT5Model.from_pretrained('t5-small')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
-
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFT5Model, self).__init__(config, *inputs, **kwargs)
-        self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model,
-                                         name='shared')
-
-        encoder_config = copy.deepcopy(config)
-        self.encoder = TFT5MainLayer(encoder_config, name='encoder')
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        self.decoder = TFT5MainLayer(decoder_config, name='decoder')
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def get_output_embeddings(self):
-        return self.shared
-
-    def call(self, decoder_input_ids, **kwargs):
-        # We allow two types of multi-inputs:
-        # - traditional keyword arguments in the call method
-        # - all the arguments provided as a dict in the first positional argument of call
-        # The last option is useful to use the tf.keras fit() method.
-
-        if isinstance(decoder_input_ids, dict):
-            kwargs.update(decoder_input_ids)
-        else:
-            kwargs['decoder_input_ids'] = decoder_input_ids
-
-        kwargs_common = dict((k, v) for k, v in kwargs.items()
-                             if not k.startswith("encoder_") and not k.startswith("decoder_"))
-        kwargs_encoder = kwargs_common.copy()
-        kwargs_decoder = kwargs_common.copy()
-        kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_")))
-        kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_")))
-
-        # Encode if needed (training, first prediction pass)
-        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
-        if encoder_hidden_states is None:
-            # Convert encoder inputs in embeddings if needed
-            hidden_states = kwargs_encoder.pop("inputs_embeds", None)
-            if hidden_states is None:
-                encoder_inputs_ids = kwargs_encoder.pop("input_ids")
-                hidden_states = self.shared(encoder_inputs_ids)  # Convert inputs in embeddings
-
-            encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
-            encoder_hidden_states = encoder_outputs[0]
-        else:
-            encoder_outputs = ()
-
-        # Decode
-        # Convert decoder inputs in embeddings if needed
-        hidden_states = kwargs_decoder.pop("inputs_embeds", None)
-        if hidden_states is None:
-            decoder_inputs_ids = kwargs_decoder.pop("input_ids")
-            hidden_states = self.shared(decoder_inputs_ids)
-
-        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
-        kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
-        decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
-
-        return decoder_outputs + encoder_outputs
-
-
-@add_start_docstrings("""T5 Model with a `language modeling` head on top. """,
-    T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
-class TFT5WithLMHeadModel(TFT5PreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **prediction_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``Numpy array`` or ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        import tensorflow as tf
-        from transformers import T5Tokenizer, TFT5WithLMHeadModel
-
-        tokenizer = T5Tokenizer.from_pretrained('t5-small')
-        model = TFT5WithLMHeadModel.from_pretrained('t5-small')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        prediction_scores = outputs[0]
-
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFT5WithLMHeadModel, self).__init__(config, *inputs, **kwargs)
-        self.model_dim = config.d_model
-
-        self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model,
-                                         name='shared')
-
-        encoder_config = copy.deepcopy(config)
-        self.encoder = TFT5MainLayer(encoder_config, name='encoder')
-
-        decoder_config = copy.deepcopy(config)
-        decoder_config.is_decoder = True
-        self.decoder = TFT5MainLayer(decoder_config, name='decoder')
-
-    def get_input_embeddings(self):
-        return self.shared
-
-    def get_output_embeddings(self):
-        return self.shared
-
-    def call(self, decoder_input_ids, **kwargs):
-        # We allow two types of multi-inputs:
-        # - traditional keyword arguments in the call method
-        # - all the arguments provided as a dict in the first positional argument of call
-        # The last option is useful to use the tf.keras fit() method.
-
-        if isinstance(decoder_input_ids, dict):
-            kwargs.update(decoder_input_ids)
-        else:
-            kwargs['decoder_input_ids'] = decoder_input_ids
-
-        kwargs_common = dict((k, v) for k, v in kwargs.items()
-                             if not k.startswith("encoder_") and not k.startswith("decoder_"))
-        kwargs_encoder = kwargs_common.copy()
-        kwargs_decoder = kwargs_common.copy()
-        kwargs_encoder.update(dict((k[len("encoder_"):], v) for k, v in kwargs.items() if k.startswith("encoder_")))
-        kwargs_decoder.update(dict((k[len("decoder_"):], v) for k, v in kwargs.items() if k.startswith("decoder_")))
-
-        # Encode if needed (training, first prediction pass)
-        encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
-        if encoder_hidden_states is None:
-            # Convert encoder inputs in embeddings if needed
-            hidden_states = kwargs_encoder.pop("inputs_embeds", None)
-            if hidden_states is None:
-                encoder_inputs_ids = kwargs_encoder.pop("input_ids")
-                hidden_states = self.shared(encoder_inputs_ids)  # Convert inputs in embeddings
-
-            encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
-            encoder_hidden_states = encoder_outputs[0]
-        else:
-            encoder_outputs = ()
-
-        # Decode
-        # Convert decoder inputs in embeddings if needed
-        hidden_states = kwargs_decoder.pop("inputs_embeds", None)
-        if hidden_states is None:
-            decoder_inputs_ids = kwargs_decoder.pop("input_ids")
-            hidden_states = self.shared(decoder_inputs_ids)
-
-        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
-        kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
-        decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
-
-        sequence_output = decoder_outputs[0] * (self.model_dim ** -0.5)
-        lm_logits = self.shared(sequence_output, mode="linear")
-        decoder_outputs = (lm_logits,) + decoder_outputs[1:]
-
-        return decoder_outputs + encoder_outputs

transformers/modeling_tf_transfo_xl.py

@@ -337,7 +337,7 @@ class TFAdaptiveEmbedding(tf.keras.layers.Layer):
                 emb_i = tf.einsum('id,de->ie', emb_i, self.emb_projs[i])
 
                 mask_idx = tf.cast(tf.where(mask_i), dtype=tf.int64)
-                emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(shape_list(emb_flat), dtype=tf.int64))
+                emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(tf.shape(emb_flat), dtype=tf.int64))
 
             embed_shape = shape_list(inp) + [self.d_proj]
             embed = tf.reshape(emb_flat, embed_shape)

transformers/modeling_tf_transfo_xl_utilities.py

@@ -105,7 +105,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
 
     @staticmethod
     def _gather_logprob(logprob, target):
-        lp_size = shape_list(logprob)
+        lp_size = tf.shape(logprob)
         r = tf.range(lp_size[0])
         idx = tf.stack([r, target], 1)
         return tf.gather_nd(logprob, idx)
@@ -159,7 +159,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
                         cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target)
                         cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                 if target is not None:
-                    loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(shape_list(loss), dtype=tf.int64))
+                    loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(tf.shape(loss), dtype=tf.int64))
             out = tf.concat(out, axis=-1)
 
         if target is not None:

transformers/modeling_tf_utils.py

@@ -24,12 +24,13 @@ import os
 import tensorflow as tf
 
 from .configuration_utils import PretrainedConfig
-from .file_utils import (TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, WEIGHTS_NAME, DUMMY_INPUTS,
-                         cached_path, hf_bucket_url, is_remote_url)
+from .file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME, TF2_WEIGHTS_NAME
 from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
 
 logger = logging.getLogger(__name__)
 
+DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
+
 class TFPreTrainedModel(tf.keras.Model):
     r""" Base class for all TF models.
 
@@ -50,15 +51,7 @@ class TFPreTrainedModel(tf.keras.Model):
     config_class = None
     pretrained_model_archive_map = {}
     base_model_prefix = ""
-
-    @property
-    def dummy_inputs(self):
-        """ Dummy inputs to build the network.
-
-        Returns:
-            tf.Tensor with dummy inputs
-        """
-        return {'input_ids': tf.constant(DUMMY_INPUTS)}
+    dummy_inputs = tf.constant(DUMMY_INPUTS)  # dummy inputs to build the network
 
     def __init__(self, config, *inputs, **kwargs):
         super(TFPreTrainedModel, self).__init__(*inputs, **kwargs)
@@ -175,7 +168,6 @@ class TFPreTrainedModel(tf.keras.Model):
             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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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 state_dict save file` (e.g. `./pt_model/pytorch_model.bin`). In this case, ``from_pt`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the PyTorch checkpoint in a TensorFlow model using the provided conversion scripts and loading the TensorFlow model afterwards.
 
@@ -199,9 +191,6 @@ class TFPreTrainedModel(tf.keras.Model):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -227,7 +216,6 @@ class TFPreTrainedModel(tf.keras.Model):
         cache_dir = kwargs.pop('cache_dir', None)
         from_pt = kwargs.pop('from_pt', False)
         force_download = kwargs.pop('force_download', False)
-        resume_download = kwargs.pop('resume_download', False)
         proxies = kwargs.pop('proxies', None)
 
         # Load config
@@ -236,7 +224,6 @@ class TFPreTrainedModel(tf.keras.Model):
                 pretrained_model_name_or_path, *model_args,
                 cache_dir=cache_dir, return_unused_kwargs=True,
                 force_download=force_download,
-                resume_download=resume_download,
                 **kwargs
             )
         else:
@@ -257,19 +244,14 @@ class TFPreTrainedModel(tf.keras.Model):
                     raise EnvironmentError("Error no file named {} found in directory {} or `from_pt` set to False".format(
                         [WEIGHTS_NAME, TF2_WEIGHTS_NAME],
                         pretrained_model_name_or_path))
-            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+            elif os.path.isfile(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path
-            elif os.path.isfile(pretrained_model_name_or_path + ".index"):
-                archive_file = pretrained_model_name_or_path + ".index"
             else:
-                archive_file = hf_bucket_url(pretrained_model_name_or_path, postfix=TF2_WEIGHTS_NAME)
-                if from_pt:
-                    raise EnvironmentError("Loading a TF model from a PyTorch checkpoint is not supported when using a model identifier name.")
+                raise EnvironmentError("Error file {} not found".format(pretrained_model_name_or_path))
 
             # redirect to the cache, if necessary
             try:
-                resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download,
-                                                    resume_download=resume_download, proxies=proxies)
+                resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
             except EnvironmentError as e:
                 if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                     logger.error(
@@ -297,7 +279,7 @@ class TFPreTrainedModel(tf.keras.Model):
 
         if from_pt:
             # Load from a PyTorch checkpoint
-            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
+            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file)
 
         ret = model(model.dummy_inputs, training=False)  # build the network with dummy inputs
 
@@ -472,7 +454,7 @@ class TFSequenceSummary(tf.keras.layers.Layer):
         elif self.summary_type == 'first':
             output = hidden_states[:, 0]
         elif self.summary_type == 'mean':
-            output = tf.reduce_mean(hidden_states, axis=1)
+            output = tf.mean(hidden_states, axis=1)
         elif self.summary_type == 'cls_index':
             hidden_shape = shape_list(hidden_states)  # e.g. [batch, num choices, seq length, hidden dims]
             if cls_index is None:

transformers/modeling_tf_xlm.py

@@ -460,7 +460,7 @@ class TFXLMPreTrainedModel(TFPreTrainedModel):
             langs_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
         else:
             langs_list = None
-        return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list}
+        return [inputs_list, attns_list, langs_list]
 
 
 XLM_START_DOCSTRING = r"""    The XLM model was proposed in

transformers/modeling_tf_xlnet.py

@@ -112,7 +112,8 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
     def prune_heads(self, heads):
         raise NotImplementedError
 
-    def rel_shift(self, x, klen=-1):
+    @staticmethod
+    def rel_shift(x, klen=-1):
         """perform relative shift to form the relative attention score."""
         x_size = shape_list(x)
 
@@ -134,7 +135,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
 
         # position based attention score
         bd = tf.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r)
-        bd = self.rel_shift(bd, klen=shape_list(ac)[1])
+        bd = self.rel_shift(bd, klen=ac.shape[1])
 
         # segment based attention score
         if seg_mat is None:
@@ -191,7 +192,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
         if g is not None:
             ###### Two-stream attention with relative positional encoding.
             # content based attention score
-            if mems is not None and len(shape_list(mems)) > 1:
+            if mems is not None and mems.shape.ndims > 1:
                 cat = tf.concat([mems, h], axis=0)
             else:
                 cat = h
@@ -251,7 +252,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
 
         else:
             ###### Multi-head attention with relative positional encoding
-            if mems is not None and len(shape_list(mems)) > 1:
+            if mems is not None and mems.shape.ndims > 1:
                 cat = tf.concat([mems, h], axis=0)
             else:
                 cat = h
@@ -564,7 +565,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
 
         if data_mask is not None:
             # all mems can be attended to
-            mems_mask = tf.zeros([shape_list(data_mask)[0], mlen, bsz],
+            mems_mask = tf.zeros([tf.shape(data_mask)[0], mlen, bsz],
                                 dtype=dtype_float)
             data_mask = tf.concat([mems_mask, data_mask], axis=1)
             if attn_mask is None:
@@ -589,7 +590,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
             word_emb_k = self.word_embedding(input_ids)
         output_h = self.dropout(word_emb_k, training=training)
         if target_mapping is not None:
-            word_emb_q = tf.tile(self.mask_emb, [shape_list(target_mapping)[0], bsz, 1])
+            word_emb_q = tf.tile(self.mask_emb, [tf.shape(target_mapping)[0], bsz, 1])
         # else:  # We removed the inp_q input which was same as target mapping
         #     inp_q_ext = inp_q[:, :, None]
         #     word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
@@ -938,59 +939,6 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel):
         return outputs  # return logits, (mems), (hidden states), (attentions)
 
 
-@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of
-    the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
-    XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
-class TFXLNetForTokenClassification(TFXLNetPreTrainedModel):
-    r"""
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
-            Classification scores (before SoftMax).
-        **mems**: (`optional`, returned when ``config.mem_len > 0``)
-            list of ``tf.Tensor`` (one for each layer):
-            that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
-            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
-            See details in the docstring of the `mems` input above.
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        import tensorflow as tf
-        from transformers import XLNetTokenizer, TFXLNetForTokenClassification
-
-        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
-        model = TFXLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
-        input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
-        outputs = model(input_ids)
-        scores = outputs[0]
-
-    """
-    def __init__(self, config, *inputs, **kwargs):
-        super(TFXLNetForTokenClassification, self).__init__(config, *inputs, **kwargs)
-        self.num_labels = config.num_labels
-
-        self.transformer = TFXLNetMainLayer(config, name='transformer')
-        self.classifier = tf.keras.layers.Dense(config.num_labels,
-                                                kernel_initializer=get_initializer(config.initializer_range),
-                                                name='classifier')
-
-    def call(self, inputs, **kwargs):
-        transformer_outputs = self.transformer(inputs, **kwargs)
-        output = transformer_outputs[0]
-
-        logits = self.classifier(output)
-
-        outputs = (logits,) + transformer_outputs[1:]  # Keep mems, hidden states, attentions if there are in it
-
-        return outputs  # return logits, (mems), (hidden states), (attentions)
-
-
 # @add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
 #     the hidden-states output to compute `span start logits` and `span end logits`). """,
 #     XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)

transformers/modeling_utils.py

@@ -31,11 +31,11 @@ from torch.nn import CrossEntropyLoss
 from torch.nn import functional as F
 
 from .configuration_utils import PretrainedConfig
-from .file_utils import (TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, WEIGHTS_NAME, DUMMY_INPUTS,
-                         cached_path, hf_bucket_url, is_remote_url)
+from .file_utils import cached_path, WEIGHTS_NAME, TF_WEIGHTS_NAME, TF2_WEIGHTS_NAME
 
 logger = logging.getLogger(__name__)
 
+
 try:
     from torch.nn import Identity
 except ImportError:
@@ -71,15 +71,6 @@ class PreTrainedModel(nn.Module):
     load_tf_weights = lambda model, config, path: None
     base_model_prefix = ""
 
-    @property
-    def dummy_inputs(self):
-        """ Dummy inputs to do a forward pass in the network.
-
-        Returns:
-            torch.Tensor with dummy inputs
-        """
-        return {'input_ids': torch.tensor(DUMMY_INPUTS)}
-
     def __init__(self, config, *inputs, **kwargs):
         super(PreTrainedModel, self).__init__()
         if not isinstance(config, PretrainedConfig):
@@ -169,7 +160,8 @@ class PreTrainedModel(nn.Module):
         base_model.vocab_size = new_num_tokens
 
         # Tie weights again if needed
-        self.tie_weights()
+        if hasattr(self, 'tie_weights'):
+            self.tie_weights()
 
         return model_embeds
 
@@ -273,7 +265,6 @@ class PreTrainedModel(nn.Module):
             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 string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                 - 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.
                 - None if you are both providing the configuration and state dictionary (resp. with keyword arguments ``config`` and ``state_dict``)
@@ -300,9 +291,6 @@ class PreTrainedModel(nn.Module):
             force_download: (`optional`) boolean, default False:
                 Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
 
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
-
             proxies: (`optional`) dict, default None:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
                 The proxies are used on each request.
@@ -327,8 +315,7 @@ class PreTrainedModel(nn.Module):
             model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
-        if pretrained_model_name_or_path is not None and (
-                "albert" in pretrained_model_name_or_path and "v2" in pretrained_model_name_or_path):
+        if "albert" in pretrained_model_name_or_path and "v2" in pretrained_model_name_or_path:
             logger.warning("There is currently an upstream reproducibility issue with ALBERT v2 models. Please see " +
                            "https://github.com/google-research/google-research/issues/119 for more information.")
 
@@ -337,7 +324,6 @@ class PreTrainedModel(nn.Module):
         cache_dir = kwargs.pop('cache_dir', None)
         from_tf = kwargs.pop('from_tf', False)
         force_download = kwargs.pop('force_download', False)
-        resume_download = kwargs.pop('resume_download', False)
         proxies = kwargs.pop('proxies', None)
         output_loading_info = kwargs.pop('output_loading_info', False)
 
@@ -347,7 +333,6 @@ class PreTrainedModel(nn.Module):
                 pretrained_model_name_or_path, *model_args,
                 cache_dir=cache_dir, return_unused_kwargs=True,
                 force_download=force_download,
-                resume_download=resume_download,
                 proxies=proxies,
                 **kwargs
             )
@@ -372,21 +357,15 @@ class PreTrainedModel(nn.Module):
                     raise EnvironmentError("Error no file named {} found in directory {} or `from_tf` set to False".format(
                         [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
                         pretrained_model_name_or_path))
-            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+            elif os.path.isfile(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path
-            elif os.path.isfile(pretrained_model_name_or_path + ".index"):
-                assert from_tf, "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
-                    pretrained_model_name_or_path + ".index")
-                archive_file = pretrained_model_name_or_path + ".index"
             else:
-                archive_file = hf_bucket_url(pretrained_model_name_or_path, postfix=WEIGHTS_NAME)
-                if from_tf:
-                    raise EnvironmentError("Loading a PyTorch model from a TF checkpoint is not supported when using a model identifier name.")
+                assert from_tf, "Error finding file {}, no file or TF 1.X checkpoint found".format(pretrained_model_name_or_path)
+                archive_file = pretrained_model_name_or_path + ".index"
 
             # redirect to the cache, if necessary
             try:
-                resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download,
-                                                    proxies=proxies, resume_download=resume_download)
+                resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
             except EnvironmentError:
                 if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
                     msg = "Couldn't reach server at '{}' to download pretrained weights.".format(
@@ -442,8 +421,6 @@ class PreTrainedModel(nn.Module):
                     new_key = key.replace('gamma', 'weight')
                 if 'beta' in key:
                     new_key = key.replace('beta', 'bias')
-                if key == 'lm_head.decoder.weight':
-                    new_key = 'lm_head.weight'
                 if new_key:
                     old_keys.append(key)
                     new_keys.append(new_key)
@@ -485,7 +462,8 @@ class PreTrainedModel(nn.Module):
                 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                                 model.__class__.__name__, "\n\t".join(error_msgs)))
 
-        model.tie_weights()  # make sure word embedding weights are still tied if needed
+        if hasattr(model, 'tie_weights'):
+            model.tie_weights()  # make sure word embedding weights are still tied
 
         # Set model in evaluation mode to desactivate DropOut modules by default
         model.eval()
@@ -754,7 +732,7 @@ class SequenceSummary(nn.Module):
     def __init__(self, config):
         super(SequenceSummary, self).__init__()
 
-        self.summary_type = config.summary_type if hasattr(config, 'summary_type') else 'last'
+        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
         if self.summary_type == 'attn':
             # We should use a standard multi-head attention module with absolute positional embedding for that.
             # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276

transformers/modeling_xlm.py

@@ -227,16 +227,6 @@ class XLMPreTrainedModel(PreTrainedModel):
     def __init__(self, *inputs, **kwargs):
         super(XLMPreTrainedModel, self).__init__(*inputs, **kwargs)
 
-    @property
-    def dummy_inputs(self):
-        inputs_list = torch.tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
-        attns_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
-        if self.config.use_lang_emb and self.config.n_langs > 1:
-            langs_list = torch.tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
-        else:
-            langs_list = None
-        return {'input_ids': inputs_list, 'attention_mask': attns_list, 'langs': langs_list}
-
     def _init_weights(self, module):
         """ Initialize the weights. """
         if isinstance(module, nn.Embedding):
@@ -656,7 +646,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
                                                langs=langs,
                                                token_type_ids=token_type_ids,
                                                position_ids=position_ids,
-                                               lengths=lengths,
+                                               lengths=lengths, 
                                                cache=cache,
                                                head_mask=head_mask,
                                                inputs_embeds=inputs_embeds)

transformers/modeling_xlnet.py

@@ -583,7 +583,6 @@ class XLNetModel(XLNetPreTrainedModel):
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -879,11 +878,7 @@ class XLNetModel(XLNetPreTrainedModel):
                 hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)
             outputs = outputs + (hidden_states,)
         if self.output_attentions:
-            if target_mapping is not None:
-                # when target_mapping is provided, there are 2-tuple of attentions
-                attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions)
-            else:
-                attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
+            attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
             outputs = outputs + (attentions,)
 
         return outputs  # outputs, (new_mems), (hidden_states), (attentions)
@@ -918,7 +913,6 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1001,7 +995,6 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1053,106 +1046,6 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
 
         return outputs  # return (loss), logits, (mems), (hidden states), (attentions)
 
-@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of
-                      the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
-                      XLNET_START_DOCSTRING,
-                      XLNET_INPUTS_DOCSTRING)
-class XLNetForTokenClassification(XLNetPreTrainedModel):
-    r"""
-    Inputs:
-        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
-            Indices of input sequence tokens in the vocabulary.
-            The second dimension of the input (`num_choices`) indicates the number of choices to scores.
-        **token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
-            Segment token indices to indicate first and second portions of the inputs.
-            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
-        **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]``:
-            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
-        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
-            Mask to nullify selected heads of the self-attention modules.
-            Mask values selected in ``[0, 1]``:
-            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
-        **inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
-            Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
-            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
-            than the model's internal embedding lookup matrix.
-        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
-            Labels for computing the multiple choice classification loss.
-            Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
-            of the input tensors. (see `input_ids` above)
-
-    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
-        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
-            Classification loss.
-        **scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
-            Classification scores (before SoftMax).
-        **mems**: (`optional`, returned when ``config.mem_len > 0``)
-            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
-            if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
-            See details in the docstring of the `mems` input above.
-        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
-            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
-            of shape ``(batch_size, sequence_length, hidden_size)``:
-            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
-        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
-            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-
-    Examples::
-
-        tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
-        model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
-        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
-        labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0)  # Batch size 1
-        outputs = model(input_ids, labels=labels)
-        scores = outputs[0]
-
-    """
-    def __init__(self, config):
-        super(XLNetForTokenClassification, self).__init__(config)
-        self.num_labels = config.num_labels
-
-        self.transformer = XLNetModel(config)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        self.init_weights()
-
-    def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
-                token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):
-
-        outputs = self.transformer(input_ids,
-                            attention_mask=attention_mask,
-                            mems=mems,
-                            perm_mask=perm_mask,
-                            target_mapping=target_mapping,
-                            token_type_ids=token_type_ids,
-                            input_mask=input_mask,
-                            head_mask=head_mask,
-                            inputs_embeds=inputs_embeds)
-
-        sequence_output = outputs[0]
-
-        logits = self.classifier(sequence_output)
-
-        outputs = (logits,) + outputs[1:]  # Keep mems, hidden states, attentions if there are in it
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            # Only keep active parts of the loss
-            if attention_mask is not None:
-                active_loss = attention_mask.view(-1) == 1
-                active_logits = logits.view(-1, self.num_labels)[active_loss]
-                active_labels = labels.view(-1)[active_loss]
-                loss = loss_fct(active_logits, active_labels)
-            else:
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            outputs = (loss,) + outputs
-
-        return outputs  # return (loss), logits, (mems), (hidden states), (attentions)
-
-
 @add_start_docstrings("""XLNet Model with a multiple choice classification head on top (a linear layer on top of
     the pooled output and a softmax) e.g. for RACE/SWAG tasks. """,
     XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
@@ -1202,7 +1095,6 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1288,7 +1180,6 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 
@@ -1403,7 +1294,6 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
         **attentions**: (`optional`, returned when ``config.output_attentions=True``)
             list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
             Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
-            When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
 
     Examples::
 

transformers/optimization_tf.py

@@ -1,254 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# 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.
-# ==============================================================================
-"""Functions and classes related to optimization (weight updates)."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import re
-
-import tensorflow as tf
-
-
-class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
-  """Applys a warmup schedule on a given learning rate decay schedule."""
-
-  def __init__(
-      self,
-      initial_learning_rate,
-      decay_schedule_fn,
-      warmup_steps,
-      power=1.0,
-      name=None):
-    super(WarmUp, self).__init__()
-    self.initial_learning_rate = initial_learning_rate
-    self.warmup_steps = warmup_steps
-    self.power = power
-    self.decay_schedule_fn = decay_schedule_fn
-    self.name = name
-
-  def __call__(self, step):
-    with tf.name_scope(self.name or 'WarmUp') as name:
-      # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
-      # learning rate will be `global_step/num_warmup_steps * init_lr`.
-      global_step_float = tf.cast(step, tf.float32)
-      warmup_steps_float = tf.cast(self.warmup_steps, tf.float32)
-      warmup_percent_done = global_step_float / warmup_steps_float
-      warmup_learning_rate = (
-          self.initial_learning_rate *
-          tf.math.pow(warmup_percent_done, self.power))
-      return tf.cond(global_step_float < warmup_steps_float,
-                     lambda: warmup_learning_rate,
-                     lambda: self.decay_schedule_fn(step),
-                     name=name)
-
-  def get_config(self):
-    return {
-        'initial_learning_rate': self.initial_learning_rate,
-        'decay_schedule_fn': self.decay_schedule_fn,
-        'warmup_steps': self.warmup_steps,
-        'power': self.power,
-        'name': self.name
-    }
-
-
-def create_optimizer(init_lr, num_train_steps, num_warmup_steps):
-  """Creates an optimizer with learning rate schedule."""
-  # Implements linear decay of the learning rate.
-  learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
-      initial_learning_rate=init_lr,
-      decay_steps=num_train_steps,
-      end_learning_rate=0.0)
-  if num_warmup_steps:
-    learning_rate_fn = WarmUp(initial_learning_rate=init_lr,
-                              decay_schedule_fn=learning_rate_fn,
-                              warmup_steps=num_warmup_steps)
-  optimizer = AdamWeightDecay(
-      learning_rate=learning_rate_fn,
-      weight_decay_rate=0.01,
-      beta_1=0.9,
-      beta_2=0.999,
-      epsilon=1e-6,
-      exclude_from_weight_decay=['layer_norm', 'bias'])
-  return optimizer
-
-
-class AdamWeightDecay(tf.keras.optimizers.Adam):
-  """Adam enables L2 weight decay and clip_by_global_norm on gradients.
-
-  Just adding the square of the weights to the loss function is *not* the
-  correct way of using L2 regularization/weight decay with Adam, since that will
-  interact with the m and v parameters in strange ways.
-
-  Instead we want ot decay the weights in a manner that doesn't interact with
-  the m/v parameters. This is equivalent to adding the square of the weights to
-  the loss with plain (non-momentum) SGD.
-  """
-
-  def __init__(self,
-               learning_rate=0.001,
-               beta_1=0.9,
-               beta_2=0.999,
-               epsilon=1e-7,
-               amsgrad=False,
-               weight_decay_rate=0.0,
-               include_in_weight_decay=None,
-               exclude_from_weight_decay=None,
-               name='AdamWeightDecay',
-               **kwargs):
-    super(AdamWeightDecay, self).__init__(
-        learning_rate, beta_1, beta_2, epsilon, amsgrad, name, **kwargs)
-    self.weight_decay_rate = weight_decay_rate
-    self._include_in_weight_decay = include_in_weight_decay
-    self._exclude_from_weight_decay = exclude_from_weight_decay
-
-  @classmethod
-  def from_config(cls, config):
-    """Creates an optimizer from its config with WarmUp custom object."""
-    custom_objects = {'WarmUp': WarmUp}
-    return super(AdamWeightDecay, cls).from_config(
-        config, custom_objects=custom_objects)
-
-  def _prepare_local(self, var_device, var_dtype, apply_state):
-    super(AdamWeightDecay, self)._prepare_local(var_device, var_dtype,
-                                                apply_state)
-    apply_state['weight_decay_rate'] = tf.constant(
-        self.weight_decay_rate, name='adam_weight_decay_rate')
-
-  def _decay_weights_op(self, var, learning_rate, apply_state):
-    do_decay = self._do_use_weight_decay(var.name)
-    if do_decay:
-      return var.assign_sub(
-          learning_rate * var *
-          apply_state['weight_decay_rate'],
-          use_locking=self._use_locking)
-    return tf.no_op()
-
-  def apply_gradients(self, grads_and_vars, clip_norm, name=None):
-    grads, tvars = list(zip(*grads_and_vars))
-    (grads, _) = tf.clip_by_global_norm(grads, clip_norm=clip_norm)
-    return super(AdamWeightDecay, self).apply_gradients(zip(grads, tvars))
-
-  def _get_lr(self, var_device, var_dtype, apply_state):
-    """Retrieves the learning rate with the given state."""
-    if apply_state is None:
-      return self._decayed_lr_t[var_dtype], {}
-
-    apply_state = apply_state or {}
-    coefficients = apply_state.get((var_device, var_dtype))
-    if coefficients is None:
-      coefficients = self._fallback_apply_state(var_device, var_dtype)
-      apply_state[(var_device, var_dtype)] = coefficients
-
-    return coefficients['lr_t'], dict(apply_state=apply_state)
-
-  def _resource_apply_dense(self, grad, var, apply_state=None):
-    lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
-    decay = self._decay_weights_op(var, lr_t, apply_state)
-    with tf.control_dependencies([decay]):
-      return super(AdamWeightDecay, self)._resource_apply_dense(
-          grad, var, **kwargs)
-
-  def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
-    lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype, apply_state)
-    decay = self._decay_weights_op(var, lr_t, apply_state)
-    with tf.control_dependencies([decay]):
-      return super(AdamWeightDecay, self)._resource_apply_sparse(
-          grad, var, indices, **kwargs)
-
-  def get_config(self):
-    config = super(AdamWeightDecay, self).get_config()
-    config.update({
-        'weight_decay_rate': self.weight_decay_rate,
-    })
-    return config
-
-  def _do_use_weight_decay(self, param_name):
-    """Whether to use L2 weight decay for `param_name`."""
-    if self.weight_decay_rate == 0:
-      return False
-
-    if self._include_in_weight_decay:
-      for r in self._include_in_weight_decay:
-        if re.search(r, param_name) is not None:
-          return True
-
-    if self._exclude_from_weight_decay:
-      for r in self._exclude_from_weight_decay:
-        if re.search(r, param_name) is not None:
-          return False
-    return True
-
-
-## Inspired from https://github.com/OpenNMT/OpenNMT-tf/blob/master/opennmt/optimizers/utils.py
-class GradientAccumulator(object):
-    """Distribution strategies-aware gradient accumulation utility."""
-
-    def __init__(self):
-        """Initializes the accumulator."""
-        self._gradients = []
-        self._accum_steps = tf.Variable(
-            initial_value=0,
-            dtype=tf.int64,
-            trainable=False,
-            aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA)
-
-    @property
-    def step(self):
-        """Number of accumulated steps."""
-        return self._accum_steps.value()
-
-    @property
-    def gradients(self):
-        """The accumulated gradients."""
-        return list(gradient.value() if gradient is not None else gradient for gradient in self._get_replica_gradients())
-
-    def __call__(self, gradients):
-        """Accumulates :obj:`gradients`."""
-        if not self._gradients:
-            self._gradients.extend([tf.Variable(tf.zeros_like(gradient), trainable=False) if gradient is not None else gradient for gradient in gradients])
-
-        if len(gradients) != len(self._gradients):
-            raise ValueError("Expected %s gradients, but got %d" % (len(self._gradients), len(gradients)))
-
-        for accum_gradient, gradient in zip(self._get_replica_gradients(), gradients):
-            if accum_gradient is not None:
-                accum_gradient.assign_add(gradient)
-
-        self._accum_steps.assign_add(1)
-
-    def reset(self):
-        """Resets the accumulated gradients."""
-        if self._gradients:
-            self._accum_steps.assign(0)
-
-        for gradient in self._get_replica_gradients():
-            if gradient is not None:
-                gradient.assign(tf.zeros_like(gradient))
-
-    def _get_replica_gradients(self):
-        if tf.distribute.has_strategy():
-            # In a replica context, we want to accumulate gradients on each replica
-            # without synchronization, so we directly assign the value of the
-            # current replica.
-            replica_context = tf.distribute.get_replica_context()
-
-            if replica_context is None or tf.distribute.get_strategy().num_replicas_in_sync == 1:
-                return self._gradients
-
-            return (gradient.device_map.select_for_current_replica(gradient.values, replica_context) for gradient in self._gradients)
-        else:
-            return self._gradients

transformers/tests/conftest.py

@@ -0,0 +1,31 @@
+# content of conftest.py
+
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--runslow", action="store_true", default=False, help="run slow tests"
+    )
+    parser.addoption(
+        "--use_cuda", action="store_true", default=False, help="run tests on gpu"
+    )
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "slow: mark test as slow to run")
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption("--runslow"):
+        # --runslow given in cli: do not skip slow tests
+        return
+    skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+    for item in items:
+        if "slow" in item.keywords:
+            item.add_marker(skip_slow)
+
+@pytest.fixture
+def use_cuda(request):
+    """ Run test on gpu """
+    return request.config.getoption("--use_cuda")

transformers/tests/hf_api_test.py

@@ -1,120 +0,0 @@
-# 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.
-from __future__ import absolute_import, division, print_function
-
-import os
-import time
-import unittest
-
-import requests
-import six
-
-from transformers.hf_api import HfApi, HfFolder, HTTPError, PresignedUrl, S3Obj
-
-USER = "__DUMMY_TRANSFORMERS_USER__"
-PASS = "__DUMMY_TRANSFORMERS_PASS__"
-FILES = [
-    (
-        "Test-{}.txt".format(int(time.time())),
-        os.path.join(
-            os.path.dirname(os.path.abspath(__file__)), "fixtures/input.txt"
-        )
-    ),
-    (
-        "yoyo {}.txt".format(int(time.time())), # space is intentional
-        os.path.join(
-            os.path.dirname(os.path.abspath(__file__)), "fixtures/empty.txt"
-        )
-    ),
-]
-
-
-
-class HfApiCommonTest(unittest.TestCase):
-    _api = HfApi(endpoint="https://moon-staging.huggingface.co")
-
-
-class HfApiLoginTest(HfApiCommonTest):
-    def test_login_invalid(self):
-        with self.assertRaises(HTTPError):
-            self._api.login(username=USER, password="fake")
-
-    def test_login_valid(self):
-        token = self._api.login(username=USER, password=PASS)
-        self.assertIsInstance(token, six.string_types)
-
-
-class HfApiEndpointsTest(HfApiCommonTest):
-    @classmethod
-    def setUpClass(cls):
-        """
-        Share this valid token in all tests below.
-        """
-        cls._token = cls._api.login(username=USER, password=PASS)
-
-    def test_whoami(self):
-        user = self._api.whoami(token=self._token)
-        self.assertEqual(user, USER)
-
-    def test_presign(self):
-        for FILE_KEY, FILE_PATH in FILES:
-            urls = self._api.presign(token=self._token, filename=FILE_KEY)
-            self.assertIsInstance(urls, PresignedUrl)
-            self.assertEqual(urls.type, "text/plain")
-
-    def test_presign_and_upload(self):
-        for FILE_KEY, FILE_PATH in FILES:
-            access_url = self._api.presign_and_upload(
-                token=self._token, filename=FILE_KEY, filepath=FILE_PATH
-            )
-            self.assertIsInstance(access_url, six.string_types)
-            with open(FILE_PATH, 'r') as f:
-                body = f.read()
-            r = requests.get(access_url)
-            self.assertEqual(r.text, body)
-
-    def test_list_objs(self):
-        objs = self._api.list_objs(token=self._token)
-        self.assertIsInstance(objs, list)
-        if len(objs) > 0:
-            o = objs[-1]
-            self.assertIsInstance(o, S3Obj)
-
-
-
-class HfFolderTest(unittest.TestCase):
-    def test_token_workflow(self):
-        """
-        Test the whole token save/get/delete workflow,
-        with the desired behavior with respect to non-existent tokens.
-        """
-        token = "token-{}".format(int(time.time()))
-        HfFolder.save_token(token)
-        self.assertEqual(
-            HfFolder.get_token(),
-            token
-        )
-        HfFolder.delete_token()
-        HfFolder.delete_token()
-        # ^^ not an error, we test that the
-        # second call does not fail.
-        self.assertEqual(
-            HfFolder.get_token(),
-            None
-        )
-
-
-if __name__ == "__main__":
-    unittest.main()

transformers/tests/modeling_albert_test.py

@@ -18,21 +18,22 @@ from __future__ import print_function
 
 import unittest
 import shutil
+import pytest
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM,
                               AlbertForSequenceClassification, AlbertForQuestionAnswering,
                               )
     from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
+else:
+    pytestmark = pytest.mark.skip("Require Torch")
 
 
-@require_torch
 class AlbertModelTest(CommonTestCases.CommonModelTester):
 
     all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else ()
@@ -132,7 +133,6 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = AlbertModel(config=config)
-            model.to(torch_device)
             model.eval()
             sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
             sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
@@ -150,7 +150,6 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = AlbertForMaskedLM(config=config)
-            model.to(torch_device)
             model.eval()
             loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
             result = {
@@ -164,7 +163,6 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
 
         def create_and_check_albert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             model = AlbertForQuestionAnswering(config=config)
-            model.to(torch_device)
             model.eval()
             loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
                                                    start_positions=sequence_labels, end_positions=sequence_labels)
@@ -185,7 +183,6 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
         def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
             config.num_labels = self.num_labels
             model = AlbertForSequenceClassification(config)
-            model.to(torch_device)
             model.eval()
             loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
             result = {
@@ -228,7 +225,7 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_sequence_classification(*config_and_inputs)
 
-    @slow
+    @pytest.mark.slow
     def test_model_from_pretrained(self):
         cache_dir = "/tmp/transformers_test/"
         for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:

transformers/tests/modeling_auto_test.py

@@ -18,12 +18,11 @@ from __future__ import print_function
 
 import unittest
 import shutil
+import pytest
 import logging
 
 from transformers import is_torch_available
 
-from .utils import require_torch, slow, SMALL_MODEL_IDENTIFIER
-
 if is_torch_available():
     from transformers import (AutoConfig, BertConfig,
                                     AutoModel, BertModel,
@@ -34,11 +33,12 @@ if is_torch_available():
 
     from .modeling_common_test import (CommonTestCases, ids_tensor)
     from .configuration_common_test import ConfigTester
+else:
+    pytestmark = pytest.mark.skip("Require Torch")
 
 
-@require_torch
 class AutoModelTest(unittest.TestCase):
-    @slow
+    @pytest.mark.slow
     def test_model_from_pretrained(self):
         logging.basicConfig(level=logging.INFO)
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
@@ -53,7 +53,7 @@ class AutoModelTest(unittest.TestCase):
             for value in loading_info.values():
                 self.assertEqual(len(value), 0)
 
-    @slow
+    @pytest.mark.slow
     def test_lmhead_model_from_pretrained(self):
         logging.basicConfig(level=logging.INFO)
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
@@ -66,7 +66,7 @@ class AutoModelTest(unittest.TestCase):
             self.assertIsNotNone(model)
             self.assertIsInstance(model, BertForMaskedLM)
 
-    @slow
+    @pytest.mark.slow
     def test_sequence_classification_model_from_pretrained(self):
         logging.basicConfig(level=logging.INFO)
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
@@ -79,7 +79,7 @@ class AutoModelTest(unittest.TestCase):
             self.assertIsNotNone(model)
             self.assertIsInstance(model, BertForSequenceClassification)
 
-    @slow
+    @pytest.mark.slow
     def test_question_answering_model_from_pretrained(self):
         logging.basicConfig(level=logging.INFO)
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
@@ -92,11 +92,6 @@ class AutoModelTest(unittest.TestCase):
             self.assertIsNotNone(model)
             self.assertIsInstance(model, BertForQuestionAnswering)
 
-    def test_from_pretrained_identifier(self):
-        logging.basicConfig(level=logging.INFO)
-        model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
-        self.assertIsInstance(model, BertForMaskedLM)
-
 
 if __name__ == "__main__":
     unittest.main()