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Merge branch 'master' into fix-ctrl-past

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Thomas Wolf
2019-11-27 17:25:46 +01:00
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5
.circleci/deploy.sh
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@@ -5,8 +5,12 @@ function deploy_doc(){
git checkout $1
if [ ! -z "$2" ]
then
if [ -d "$dir/$2" ]; then
echo "Directory" $2 "already exists"
else
echo "Pushing version" $2
make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
fi
else
echo "Pushing master"
make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
@@ -19,3 +23,4 @@ deploy_doc "fe02e45" v1.1.0
deploy_doc "89fd345" v1.2.0
deploy_doc "fc9faa8" v2.0.0
deploy_doc "3ddce1d" v2.1.1
deploy_doc "3616209" v2.2.0

1
.github/ISSUE_TEMPLATE/--new-model-addition.md vendored
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@@ -17,6 +17,7 @@ assignees: ''
* [ ] the model implementation is available: (give details)
* [ ] the model weights are available: (give details)
* [ ] who are the authors: (mention them)
## Additional context

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.gitignore vendored
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@@ -138,3 +138,4 @@ serialization_dir
# emacs
*.*~
debug.env

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CONTRIBUTING.md
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@@ -106,7 +106,7 @@ Follow these steps to start contributing:
```bash
$ git clone git@github.com:<your Github handle>/transformers.git
$ cd transformers
$ git remote add upstream git@github.com:huggingface/transformers.git
$ git remote add upstream https://github.com/huggingface/transformers.git
```
3. Create a new branch to hold your development changes:

33
README.md
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@@ -58,7 +58,7 @@ Choose the right framework for every part of a model's lifetime
| [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
| [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](https://huggingface.co/transformers/) [(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) | 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
@@ -86,6 +86,17 @@ When TensorFlow 2.0 and/or PyTorch has been installed, you can install from sour
pip install [--editable] .
```
### Run the examples
Examples are included in the repository but are not shipped with the library.
Therefore, in order to run the latest versions of the examples you also need to install from source. To do so, create a new virtual environment and follow these steps:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install [--editable] .
```
### Tests
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).
@@ -122,7 +133,9 @@ At some point in the future, you'll be able to seamlessly move from pre-training
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).
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. 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.
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/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).
@@ -171,8 +184,7 @@ for model_class, tokenizer_class, pretrained_weights in MODELS:
# Each architecture is provided with several class for fine-tuning on down-stream tasks, e.g.
BERT_MODEL_CLASSES = [BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
BertForQuestionAnswering]
BertForSequenceClassification, BertForTokenClassification, BertForQuestionAnswering]
# All the classes for an architecture can be initiated from pretrained weights for this architecture
# Note that additional weights added for fine-tuning are only initialized
@@ -253,6 +265,11 @@ print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sen
## Quick tour of the fine-tuning/usage scripts
**Important**
Before running the fine-tuning scripts, please read the
[instructions](#run-the-examples) on how to
setup your environment to run the examples.
The library comprises several example scripts with SOTA performances for NLU and NLG tasks:
- `run_glue.py`: an example fine-tuning Bert, XLNet and XLM on nine different GLUE tasks (*sequence-level classification*)
@@ -521,12 +538,12 @@ Here is a conversion examples from `BertAdam` with a linear warmup and decay sch
# Parameters:
lr = 1e-3
max_grad_norm = 1.0
num_total_steps = 1000
num_training_steps = 1000
num_warmup_steps = 100
warmup_proportion = float(num_warmup_steps) / float(num_total_steps) # 0.1
warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1
### Previously BertAdam optimizer was instantiated like this:
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, t_total=num_total_steps)
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, t_total=num_training_steps)
### and used like this:
for batch in train_data:
loss = model(batch)
@@ -535,7 +552,7 @@ for batch in train_data:
### In Transformers, optimizer and schedules are splitted and instantiated like this:
optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False) # To reproduce BertAdam specific behavior set correct_bias=False
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps) # PyTorch scheduler
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch scheduler
### and used like this:
for batch in train_data:
model.train()

22
deploy_multi_version_doc.sh Normal file
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@@ -0,0 +1,22 @@
cd docs
function deploy_doc(){
echo "Creating doc at commit $1 and pushing to folder $2"
git checkout $1
if [ ! -z "$2" ]
then
echo "Pushing version" $2
make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
else
echo "Pushing master"
make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
fi
}
deploy_doc "master"
deploy_doc "b33a385" v1.0.0
deploy_doc "fe02e45" v1.1.0
deploy_doc "89fd345" v1.2.0
deploy_doc "fc9faa8" v2.0.0
deploy_doc "3ddce1d" v2.1.1
deploy_doc "f2f3294" v2.2.0

10
docs/source/_static/js/custom.js
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@@ -1,5 +1,5 @@
function addIcon() {
const huggingFaceLogo = "https://huggingface.co/assets/transformers-docs/huggingface_logo.svg";
const huggingFaceLogo = "https://huggingface.co/landing/assets/transformers-docs/huggingface_logo.svg";
const image = document.createElement("img");
image.setAttribute("src", huggingFaceLogo);
@@ -24,10 +24,10 @@ function addCustomFooter() {
social.classList.add("footer__Social");
const imageDetails = [
{ link: "https://huggingface.co", imageLink: "https://huggingface.co/assets/transformers-docs/website.svg" },
{ link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/twitter.svg" },
{ link: "https://github.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/github.svg" },
{ link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/assets/transformers-docs/linkedin.svg" }
{ link: "https://huggingface.co", imageLink: "https://huggingface.co/landing/assets/transformers-docs/website.svg" },
{ link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/landing/assets/transformers-docs/twitter.svg" },
{ link: "https://github.com/huggingface", imageLink: "https://huggingface.co/landing/assets/transformers-docs/github.svg" },
{ link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/landing/assets/transformers-docs/linkedin.svg" }
];
imageDetails.forEach(imageLinks => {

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docs/source/conf.py
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@@ -26,7 +26,7 @@ author = u'huggingface'
# The short X.Y version
version = u''
# The full version, including alpha/beta/rc tags
release = u'2.1.1'
release = u'2.2.0'
# -- General configuration ---------------------------------------------------

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docs/source/index.rst
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@@ -47,6 +47,9 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
8. `DistilBERT <https://huggingface.co/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/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
@@ -89,3 +92,5 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
model_doc/roberta
model_doc/distilbert
model_doc/ctrl
model_doc/camembert
model_doc/albert

14
docs/source/main_classes/optimizer_schedules.rst
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@@ -18,19 +18,17 @@ Schedules
Learning Rate Schedules
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: transformers.ConstantLRSchedule
:members:
.. autofunction:: transformers.get_constant_schedule
.. autoclass:: transformers.WarmupConstantSchedule
:members:
.. autofunction:: transformers.get_constant_schedule_with_warmup
.. image:: /imgs/warmup_constant_schedule.png
:target: /imgs/warmup_constant_schedule.png
:alt:
.. autoclass:: transformers.WarmupCosineSchedule
.. autofunction:: transformers.get_cosine_schedule_with_warmup
:members:
.. image:: /imgs/warmup_cosine_schedule.png
@@ -38,8 +36,7 @@ Learning Rate Schedules
:alt:
.. autoclass:: transformers.WarmupCosineWithHardRestartsSchedule
:members:
.. autofunction:: transformers.get_cosine_with_hard_restarts_schedule_with_warmup
.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
:target: /imgs/warmup_cosine_hard_restarts_schedule.png
@@ -47,8 +44,7 @@ Learning Rate Schedules
.. autoclass:: transformers.WarmupLinearSchedule
:members:
.. autofunction:: transformers.get_linear_schedule_with_warmup
.. image:: /imgs/warmup_linear_schedule.png
:target: /imgs/warmup_linear_schedule.png

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docs/source/main_classes/processors.rst
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@@ -56,3 +56,26 @@ 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.
XNLI
~~~~~~~~~~~~~~~~~~~~~
`The Cross-Lingual NLI Corpus (XNLI) <https://www.nyu.edu/projects/bowman/xnli/>`__ is a benchmark that evaluates
the quality of cross-lingual text representations.
XNLI is crowd-sourced dataset based on `MultiNLI <http://www.nyu.edu/projects/bowman/multinli/>`: pairs of text are labeled with textual entailment
annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
It was released together with the paper
`XNLI: Evaluating Cross-lingual Sentence Representations <https://arxiv.org/abs/1809.05053>`__
This library hosts the processor to load the XNLI data:
- :class:`~transformers.data.processors.utils.XnliProcessor`
Please note that since the gold labels are available on the test set, evaluation is performed on the test set.
Example usage
^^^^^^^^^^^^^^^^^^^^^^^^^
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.

8
docs/source/migration.md
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@@ -84,12 +84,12 @@ Here is a conversion examples from `BertAdam` with a linear warmup and decay sch
# Parameters:
lr = 1e-3
max_grad_norm = 1.0
num_total_steps = 1000
num_training_steps = 1000
num_warmup_steps = 100
warmup_proportion = float(num_warmup_steps) / float(num_total_steps) # 0.1
warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1
### Previously BertAdam optimizer was instantiated like this:
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, t_total=num_total_steps)
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
### and used like this:
for batch in train_data:
loss = model(batch)
@@ -98,7 +98,7 @@ for batch in train_data:
### In Transformers, optimizer and schedules are splitted and instantiated like this:
optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False) # To reproduce BertAdam specific behavior set correct_bias=False
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps) # PyTorch scheduler
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch scheduler
### and used like this:
for batch in train_data:
loss = model(batch)

64
docs/source/model_doc/albert.rst Normal file
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@@ -0,0 +1,64 @@
ALBERT
----------------------------------------------------
``AlbrtConfig``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertConfig
:members:
``AlbertTokenizer``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertTokenizer
:members:
``AlbertModel``
~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertModel
:members:
``AlbertForMaskedLM``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForMaskedLM
:members:
``AlbertForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForSequenceClassification
:members:
``AlbertForQuestionAnswering``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.AlbertForQuestionAnswering
:members:
``TFAlbertModel``
~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertModel
:members:
``TFAlbertForMaskedLM``
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForMaskedLM
:members:
``TFAlbertForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFAlbertForSequenceClassification
:members:

50
docs/source/model_doc/camembert.rst Normal file
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@@ -0,0 +1,50 @@
CamemBERT
----------------------------------------------------
``CamembertConfig``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertConfig
:members:
``CamembertTokenizer``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertTokenizer
:members:
``CamembertModel``
~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertModel
:members:
``CamembertForMaskedLM``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForMaskedLM
:members:
``CamembertForSequenceClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForSequenceClassification
:members:
``CamembertForMultipleChoice``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForMultipleChoice
:members:
``CamembertForTokenClassification``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.CamembertForTokenClassification
:members:

48
docs/source/pretrained_models.rst
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@@ -73,6 +73,9 @@ Here is the full list of the currently provided pretrained models together with
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``gpt2-large`` | | 36-layer, 1280-hidden, 20-heads, 774M parameters. |
| | | | OpenAI's Large-sized GPT-2 English model |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``gpt2-xl`` | | 48-layer, 1600-hidden, 25-heads, 1558M parameters. |
| | | | OpenAI's XL-sized GPT-2 English model |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| Transformer-XL | ``transfo-xl-wt103`` | | 18-layer, 1024-hidden, 16-heads, 257M parameters. |
| | | | English model trained on wikitext-103 |
@@ -124,6 +127,14 @@ Here is the full list of the currently provided pretrained models together with
| | ``roberta-large-mnli`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
| | | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__. |
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``roberta-base-openai-detector`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
| | | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``roberta-large-openai-detector`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
| | | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| DistilBERT | ``distilbert-base-uncased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint |
@@ -144,5 +155,42 @@ Here is the full list of the currently provided pretrained models together with
| CTRL | ``ctrl`` | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters |
| | | | Salesforce's Large-sized CTRL English model |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| CamemBERT | ``camembert-base`` | | 12-layer, 768-hidden, 12-heads, 110M parameters |
| | | | CamemBERT using the BERT-base architecture |
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__) |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| 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/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/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/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/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/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/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/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/google-research/tree/master/albert>`__) |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
.. <https://huggingface.co/transformers/examples.html>`__

32
docs/source/quickstart.md
View File

@@ -188,3 +188,35 @@ assert predicted_text == 'Who was Jim Henson? Jim Henson was a man'
```
Examples for each model class of each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [documentation](#documentation).
#### Using the past
GPT-2 as well as some other models (GPT, XLNet, Transfo-XL, CTRL) make use of a `past` or `mems` attribute which can be used to prevent re-computing the key/value pairs when using sequential decoding. It is useful when generating sequences as a big part of the attention mechanism benefits from previous computations.
Here is a fully-working example using the `past` with `GPT2LMHeadModel` and argmax decoding (which should only be used as an example, as argmax decoding introduces a lot of repetition):
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained('gpt2')
generated = tokenizer.encode("The Manhattan bridge")
context = torch.tensor([generated])
past = None
for i in range(100):
print(i)
output, past = model(context, past=past)
token = torch.argmax(output[0, :])
generated += [token.tolist()]
context = token.unsqueeze(0)
sequence = tokenizer.decode(generated)
print(sequence)
```
The model only requires a single token as input as all the previous tokens' key/value pairs are contained in the `past`.

2
docs/source/serialization.rst
View File

@@ -106,7 +106,7 @@ This section explain how you can save and re-load a fine-tuned model (BERT, GPT,
There are three types of files you need to save to be able to reload a fine-tuned model:
* the model it-self which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`__\ ,
* the model itself which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`__\ ,
* the configuration file of the model which is saved as a JSON file, and
* the vocabulary (and the merges for the BPE-based models GPT and GPT-2).

60
examples/README.md
View File

@@ -3,6 +3,15 @@
In this section a few examples are put together. All of these examples work for several models, making use of the very
similar API between the different models.
**Important**
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] .
```
| Section | Description |
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks.
@@ -12,6 +21,7 @@ similar API between the different models.
| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
| [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
@@ -554,6 +564,16 @@ 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)
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):
| Model | F-Score Dev | F-Score Test
| --------------------------------- | ------- | --------
| `bert-large-cased` | 95.59 | 91.70
| `roberta-large` | 95.96 | 91.87
| `distilbert-base-uncased` | 94.34 | 90.32
## Abstractive summarization
Based on the script
@@ -581,3 +601,43 @@ python run_summarization_finetuning.py \
--do_train \
--data_path=$DATA_PATH \
```
## XNLI
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-ressource language such as English and low-ressource languages such as Swahili).
#### Fine-tuning on XNLI
This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a
`$XNLI_DIR` directory.
* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip)
* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip)
```bash
export XNLI_DIR=/path/to/XNLI
python run_xnli.py \
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--language de \
--train_language en \
--do_train \
--do_eval \
--data_dir $XNLI_DIR \
--per_gpu_train_batch_size 32 \
--learning_rate 5e-5 \
--num_train_epochs 2.0 \
--max_seq_length 128 \
--output_dir /tmp/debug_xnli/ \
--save_steps -1
```
Training with the previously defined hyper-parameters yields the following results on the **test** set:
```bash
acc = 0.7093812375249501
```

48
examples/contrib/run_camembert.py Normal file
View File

@@ -0,0 +1,48 @@
from pathlib import Path
import tarfile
import urllib.request
import torch
from transformers.tokenization_camembert import CamembertTokenizer
from transformers.modeling_camembert import CamembertForMaskedLM
def fill_mask(masked_input, model, tokenizer, topk=5):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('<mask>') == 1
input_ids = torch.tensor(tokenizer.encode(masked_input, add_special_tokens=True)).unsqueeze(0) # Batch size 1
logits = model(input_ids)[0] # The last hidden-state is the first element of the output tuple
masked_index = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
logits = logits[0, masked_index, :]
prob = logits.softmax(dim=0)
values, indices = prob.topk(k=topk, dim=0)
topk_predicted_token_bpe = ' '.join([tokenizer.convert_ids_to_tokens(indices[i].item())
for i in range(len(indices))])
masked_token = tokenizer.mask_token
topk_filled_outputs = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
predicted_token = predicted_token_bpe.replace('\u2581', ' ')
if " {0}".format(masked_token) in masked_input:
topk_filled_outputs.append((
masked_input.replace(
' {0}'.format(masked_token), predicted_token
),
values[index].item(),
predicted_token,
))
else:
topk_filled_outputs.append((
masked_input.replace(masked_token, predicted_token),
values[index].item(),
predicted_token,
))
return topk_filled_outputs
tokenizer = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()
masked_input = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))

6
examples/contrib/run_openai_gpt.py
View File

@@ -41,7 +41,7 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
from transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
WarmupLinearSchedule)
get_linear_schedule_with_warmup)
ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz"
@@ -211,7 +211,7 @@ def main():
{'params': [p for n, p in param_optimizer 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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
if args.do_train:
nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
@@ -237,7 +237,7 @@ def main():
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)

4
examples/contrib/run_swag.py
View File

@@ -42,7 +42,7 @@ from tqdm import tqdm, trange
from transformers import (WEIGHTS_NAME, BertConfig,
BertForMultipleChoice, BertTokenizer)
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
logger = logging.getLogger(__name__)
@@ -322,7 +322,7 @@ def train(args, train_dataset, model, tokenizer):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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

8
examples/distillation/distiller.py
View File

@@ -35,7 +35,7 @@ try:
except:
from tensorboardX import SummaryWriter
from transformers import WarmupLinearSchedule
from transformers import get_linear_schedule_with_warmup
from utils import logger
from lm_seqs_dataset import LmSeqsDataset
@@ -137,9 +137,9 @@ class Distiller:
betas=(0.9, 0.98))
warmup_steps = math.ceil(num_train_optimization_steps * params.warmup_prop)
self.scheduler = WarmupLinearSchedule(self.optimizer,
warmup_steps=warmup_steps,
t_total=num_train_optimization_steps)
self.scheduler = get_linear_schedule_with_warmup(self.optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=num_train_optimization_steps)
if self.fp16:
try:

4
examples/distillation/run_squad_w_distillation.py
View File

@@ -46,7 +46,7 @@ from transformers import (WEIGHTS_NAME, BertConfig,
XLNetTokenizer,
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
from ..utils_squad import (read_squad_examples, convert_examples_to_features,
RawResult, write_predictions,
@@ -101,7 +101,7 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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

7
examples/run_bertology.py
View File

@@ -39,8 +39,9 @@ from transformers import (WEIGHTS_NAME,
from run_glue import set_seed, load_and_cache_examples, ALL_MODELS, MODEL_CLASSES
from utils_glue import (compute_metrics, convert_examples_to_features,
output_modes, processors)
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
logger = logging.getLogger(__name__)
@@ -233,6 +234,8 @@ def main():
help="If > 0: limit the data to a subset of data_subset instances.")
parser.add_argument("--overwrite_output_dir", action='store_true',
help="Whether to overwrite data in output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--dont_normalize_importance_by_layer", action='store_true',
help="Don't normalize importance score by layers")

53
examples/run_glue.py
View File

@@ -47,9 +47,13 @@ from transformers import (WEIGHTS_NAME, BertConfig,
XLNetTokenizer,
DistilBertConfig,
DistilBertForSequenceClassification,
DistilBertTokenizer)
DistilBertTokenizer,
AlbertConfig,
AlbertForSequenceClassification,
AlbertTokenizer,
)
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_output_modes as output_modes
@@ -66,7 +70,8 @@ MODEL_CLASSES = {
'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
'albert': (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer)
}
@@ -99,8 +104,9 @@ def train(args, train_dataset, model, tokenizer):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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
@@ -158,7 +164,7 @@ def train(args, train_dataset, model, tokenizer):
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0 and not args.tpu:
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
@@ -189,11 +195,6 @@ def train(args, train_dataset, model, tokenizer):
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
if args.tpu:
args.xla_model.optimizer_step(optimizer, barrier=True)
model.zero_grad()
global_step += 1
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
@@ -224,6 +225,10 @@ def evaluate(args, model, tokenizer, prefix=""):
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
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
@@ -393,15 +398,6 @@ def main():
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--tpu', action='store_true',
help="Whether to run on the TPU defined in the environment variables")
parser.add_argument('--tpu_ip_address', type=str, default='',
help="TPU IP address if none are set in the environment variables")
parser.add_argument('--tpu_name', type=str, default='',
help="TPU name if none are set in the environment variables")
parser.add_argument('--xrt_tpu_config', type=str, default='',
help="XRT TPU config if none are set in the environment variables")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
@@ -435,23 +431,6 @@ def main():
args.n_gpu = 1
args.device = device
if args.tpu:
if args.tpu_ip_address:
os.environ["TPU_IP_ADDRESS"] = args.tpu_ip_address
if args.tpu_name:
os.environ["TPU_NAME"] = args.tpu_name
if args.xrt_tpu_config:
os.environ["XRT_TPU_CONFIG"] = args.xrt_tpu_config
assert "TPU_IP_ADDRESS" in os.environ
assert "TPU_NAME" in os.environ
assert "XRT_TPU_CONFIG" in os.environ
import torch_xla
import torch_xla.core.xla_model as xm
args.device = xm.xla_device()
args.xla_model = xm
# Setup logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
@@ -505,7 +484,7 @@ def main():
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0) and not args.tpu:
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)

17
examples/run_lm_finetuning.py
View File

@@ -42,7 +42,7 @@ except:
from tqdm import tqdm, trange
from transformers import (WEIGHTS_NAME, AdamW, WarmupLinearSchedule,
from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
BertConfig, BertForMaskedLM, BertTokenizer,
GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
@@ -63,12 +63,12 @@ MODEL_CLASSES = {
class TextDataset(Dataset):
def __init__(self, tokenizer, file_path='train', block_size=512):
def __init__(self, tokenizer, args, file_path='train', block_size=512):
assert os.path.isfile(file_path)
directory, filename = os.path.split(file_path)
cached_features_file = os.path.join(directory, 'cached_lm_' + str(block_size) + '_' + filename)
cached_features_file = os.path.join(directory, args.model_name_or_path + '_cached_lm_' + str(block_size) + '_' + filename)
if os.path.exists(cached_features_file):
if os.path.exists(cached_features_file) and not args.overwrite_cache:
logger.info("Loading features from cached file %s", cached_features_file)
with open(cached_features_file, 'rb') as handle:
self.examples = pickle.load(handle)
@@ -99,7 +99,7 @@ class TextDataset(Dataset):
def load_and_cache_examples(args, tokenizer, evaluate=False):
dataset = TextDataset(tokenizer, file_path=args.eval_data_file if evaluate else args.train_data_file, block_size=args.block_size)
dataset = TextDataset(tokenizer, args, file_path=args.eval_data_file if evaluate else args.train_data_file, block_size=args.block_size)
return dataset
@@ -185,7 +185,7 @@ def train(args, train_dataset, model, tokenizer):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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
@@ -215,6 +215,7 @@ def train(args, train_dataset, model, tokenizer):
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.resize_token_embeddings(len(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 reproducibility (even between python 2 and 3)
@@ -300,6 +301,10 @@ def evaluate(args, model, tokenizer, prefix=""):
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
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))

8
examples/run_multiple_choice.py
View File

@@ -43,7 +43,7 @@ from transformers import (WEIGHTS_NAME, BertConfig,
XLNetTokenizer, RobertaConfig,
RobertaForMultipleChoice, RobertaTokenizer)
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
from utils_multiple_choice import (convert_examples_to_features, processors)
@@ -101,7 +101,7 @@ def train(args, train_dataset, model, tokenizer):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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
@@ -229,6 +229,10 @@ def evaluate(args, model, tokenizer, prefix="", test=False):
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
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))

26
examples/run_ner.py
View File

@@ -33,19 +33,23 @@ from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import WEIGHTS_NAME, BertConfig, BertForTokenClassification, BertTokenizer
from transformers import RobertaConfig, RobertaForTokenClassification, RobertaTokenizer
from transformers import DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer
from transformers import CamembertConfig, CamembertForTokenClassification, CamembertTokenizer
logger = logging.getLogger(__name__)
ALL_MODELS = sum(
(tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig)),
(tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig, DistilBertConfig)),
())
MODEL_CLASSES = {
"bert": (BertConfig, BertForTokenClassification, BertTokenizer),
"roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer)
"roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
"distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
"camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
}
@@ -80,7 +84,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
{"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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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
@@ -121,9 +125,10 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
batch = tuple(t.to(args.device) for t in batch)
inputs = {"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2] if args.model_type in ["bert", "xlnet"] else None,
# XLM and RoBERTa don"t use segment_ids
"labels": batch[3]}
if args.model_type != "distilbert":
inputs["token_type_ids"] = batch[2] if args.model_type in ["bert", "xlnet"] else None # XLM and RoBERTa don"t use segment_ids
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
@@ -191,6 +196,10 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""
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
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation %s *****", prefix)
logger.info(" Num examples = %d", len(eval_dataset))
@@ -206,9 +215,9 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""
with torch.no_grad():
inputs = {"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2] if args.model_type in ["bert", "xlnet"] else None,
# XLM and RoBERTa don"t use segment_ids
"labels": batch[3]}
if args.model_type != "distilbert":
inputs["token_type_ids"] = batch[2] if args.model_type in ["bert", "xlnet"] else None # XLM and RoBERTa don"t use segment_ids
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
@@ -520,3 +529,4 @@ def main():
if __name__ == "__main__":
main()

22
examples/run_squad.py
View File

@@ -43,9 +43,10 @@ from transformers import (WEIGHTS_NAME, BertConfig,
XLMTokenizer, XLNetConfig,
XLNetForQuestionAnswering,
XLNetTokenizer,
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer,
AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
from utils_squad import (read_squad_examples, convert_examples_to_features,
RawResult, write_predictions,
@@ -65,7 +66,8 @@ MODEL_CLASSES = {
'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
}
def set_seed(args):
@@ -100,7 +102,7 @@ def train(args, train_dataset, model, tokenizer):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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
@@ -128,7 +130,7 @@ def train(args, train_dataset, model, tokenizer):
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
global_step = 1
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
@@ -217,6 +219,10 @@ def evaluate(args, model, tokenizer, prefix=""):
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
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
@@ -398,7 +404,7 @@ def main():
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("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
help="Weight decay 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,
@@ -533,7 +539,7 @@ def main():
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
model = model_class.from_pretrained(args.output_dir, force_download=True)
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
model.to(args.device)
@@ -551,7 +557,7 @@ def main():
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
model = model_class.from_pretrained(checkpoint, force_download=True)
model.to(args.device)
# Evaluate

4
examples/run_summarization_finetuning.py
View File

@@ -275,6 +275,10 @@ def evaluate(args, model, tokenizer, prefix=""):
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)

2
examples/run_tf_glue.py
View File

@@ -73,6 +73,8 @@ model.save_pretrained('./save/')
if TASK == "mrpc":
# Load the TensorFlow model in PyTorch for inspection
# This is to demo the interoperability between the two frameworks, you don't have to
# do this in real life (you can run the inference on the TF model).
pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task

515
examples/run_xnli.py Normal file
View File

@@ -0,0 +1,515 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" Finetuning multi-lingual models on XNLI (Bert, DistilBERT, XLM).
Adapted from `examples/run_glue.py`"""
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensorboard import SummaryWriter
except:
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
from transformers import (WEIGHTS_NAME,
BertConfig, BertForSequenceClassification, BertTokenizer,
XLMConfig, XLMForSequenceClassification, XLMTokenizer,
DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import xnli_compute_metrics as compute_metrics
from transformers import xnli_output_modes as output_modes
from transformers import xnli_processors as processors
from transformers import glue_convert_examples_to_features as convert_examples_to_features
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, DistilBertConfig, XLMConfig)), ())
MODEL_CLASSES = {
'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
}
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def train(args, train_dataset, model, tokenizer):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
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)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# 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)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
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],
'labels': batch[3]}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2] if args.model_type in ['bert'] else None # XLM and DistilBERT don't use segment_ids
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
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():
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
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)
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
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
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
def evaluate(args, model, tokenizer, prefix=""):
eval_task_names = (args.task_name,)
eval_outputs_dirs = (args.output_dir,)
results = {}
for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_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(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
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
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
preds = None
out_label_ids = None
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],
'labels': batch[3]}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = batch[2] if args.model_type in ['bert'] else None # XLM and DistilBERT don't use segment_ids
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = inputs['labels'].detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
if args.output_mode == "classification":
preds = np.argmax(preds, axis=1)
else:
raise ValueError('No other `output_mode` for XNLI.')
result = compute_metrics(eval_task, preds, out_label_ids)
results.update(result)
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
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 results
def load_and_cache_examples(args, task, tokenizer, evaluate=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
processor = processors[task](language=args.language, train_language=args.train_language)
output_mode = output_modes[task]
# Load data features from cache or dataset file
cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}_{}'.format(
'test' if evaluate else 'train',
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length),
str(task),
str(args.train_language if (not evaluate and args.train_language is not None) else args.language)))
if os.path.exists(cached_features_file) and not args.overwrite_cache:
logger.info("Loading features from cached file %s", cached_features_file)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", args.data_dir)
label_list = processor.get_labels()
examples = processor.get_test_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
features = convert_examples_to_features(examples,
tokenizer,
label_list=label_list,
max_length=args.max_seq_length,
output_mode=output_mode,
pad_on_left=False,
pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
pad_token_segment_id=0,
)
if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s", 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_attention_mask = 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)
if output_mode == "classification":
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
else:
raise ValueError('No other `output_mode` for XNLI.')
dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
return dataset
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir", default=None, type=str, required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
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,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--language", default=None, type=str, required=True,
help="Evaluation language. Also train language if `train_language` is set to None.")
parser.add_argument("--train_language", default=None, type=str,
help="Train language if is different of the evaluation language.")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
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,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--cache_dir", default="", type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length", default=128, type=int,
help="The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train", action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval", action='store_true',
help="Whether to run eval on the test set.")
parser.add_argument("--evaluate_during_training", action='store_true',
help="Rul evaluation during training at each logging step.")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for evaluation.")
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("--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 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,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=3.0, type=float,
help="Total number of training epochs to perform.")
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("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument('--logging_steps', type=int, default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--overwrite_output_dir', action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
args = parser.parse_args()
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))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
set_seed(args)
# Prepare XNLI task
args.task_name = 'xnli'
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name](language=args.language, train_language=args.train_language)
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
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,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None)
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)
model = model_class.from_pretrained(args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
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 logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
return results
if __name__ == "__main__":
main()

1
examples/utils_squad.py
View File

@@ -240,6 +240,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
assert max_tokens_for_doc > 0
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks

2
setup.py
View File

@@ -38,7 +38,7 @@ from setuptools import find_packages, setup
setup(
name="transformers",
version="2.1.1",
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",

4
templates/adding_a_new_example_script/run_xxx.py
View File

@@ -43,7 +43,7 @@ from transformers import (WEIGHTS_NAME, BertConfig,
XLNetTokenizer,
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
from transformers import AdamW, WarmupLinearSchedule
from transformers import AdamW, get_linear_schedule_with_warmup
from utils_squad import (read_squad_examples, convert_examples_to_features,
RawResult, write_predictions,
@@ -98,7 +98,7 @@ def train(args, train_dataset, model, tokenizer):
{'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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
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

14
templates/adding_a_new_model/README.md
View File

@@ -7,7 +7,7 @@ The library is designed to incorporate a variety of models and code bases. As su
One important point though is that the library has the following goals impacting the way models are incorporated:
- one specific feature of the API is the capability to run the model and tokenizer inline. The tokenization code thus often have to be slightly adapted to allow for running in the python interpreter.
- the package is also designed to be as self-consistent and with a small and reliable set of packages dependencies. In consequence, additional dependencies are usually not allowed when adding a model but can be allowed for the inclusion of a new tokenizer (recent examples of dependencies added for tokenizer specificites includes `sentencepiece` and `sacremoses`). Please make sure to check the existing dependencies when possible before adding a new one.
- the package is also designed to be as self-consistent and with a small and reliable set of packages dependencies. In consequence, additional dependencies are usually not allowed when adding a model but can be allowed for the inclusion of a new tokenizer (recent examples of dependencies added for tokenizer specificities include `sentencepiece` and `sacremoses`). Please make sure to check the existing dependencies when possible before adding a new one.
For a quick overview of the library organization, please check the [QuickStart section of the documentation](https://huggingface.co/transformers/quickstart.html).
@@ -20,7 +20,7 @@ Here an overview of the general workflow:
- [ ] add tests
- [ ] finalize
Let's details what should be done at each step
Let's detail what should be done at each step
## Adding model/configuration/tokenization classes
@@ -28,16 +28,16 @@ Here is the workflow for adding model/configuration/tokenization classes:
- [ ] copy the python files from the present folder to the main folder and rename them, replacing `xxx` with your model name,
- [ ] edit the files to replace `XXX` (with various casing) with your model name
- [ ] copy-past or create a simple configuration class for your model in the `configuration_...` file
- [ ] copy-past or create the code for your model in the `modeling_...` files (PyTorch and TF 2.0)
- [ ] copy-past or create a tokenizer class for your model in the `tokenization_...` file
- [ ] copy-paste or create a simple configuration class for your model in the `configuration_...` file
- [ ] copy-paste or create the code for your model in the `modeling_...` files (PyTorch and TF 2.0)
- [ ] copy-paste or create a tokenizer class for your model in the `tokenization_...` file
# Adding conversion scripts
Here is the workflow for the conversion scripts:
- [ ] copy the conversion script (`convert_...`) from the present folder to the main folder.
- [ ] edit this scipt to convert your original checkpoint weights to the current pytorch ones.
- [ ] edit this script to convert your original checkpoint weights to the current pytorch ones.
# Adding tests:
@@ -58,5 +58,5 @@ You can then finish the addition step by adding imports for your classes in the
- [ ] add your models and tokenizer to `pipeline.py`
- [ ] add a link to your conversion script in the main conversion utility (currently in `__main__` but will be moved to the `commands` subfolder in the near future)
- [ ] edit the PyTorch to TF 2.0 conversion script to add your model in the `convert_pytorch_checkpoint_to_tf2.py` file
- [ ] add a mention of your model in the doc: `README.md` and the documentation it-self at `docs/source/pretrained_models.rst`.
- [ ] add a mention of your model in the doc: `README.md` and the documentation itself at `docs/source/pretrained_models.rst`.
- [ ] upload the pretrained weigths, configurations and vocabulary files.

6
templates/adding_a_new_model/modeling_tf_xxx.py
View File

@@ -49,7 +49,7 @@ TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP = {
####################################################
# 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)
# - TFPreTrainedModel for the models (itself a sub-class of tf.keras.Model)
####################################################
####################################################
@@ -255,6 +255,10 @@ XXX_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare Xxx Model transformer outputing raw hidden-states without any specific head on top.",

50
templates/adding_a_new_model/modeling_xxx.py
View File

@@ -120,7 +120,7 @@ def load_tf_weights_in_xxx(model, config, tf_checkpoint_path):
####################################################
# 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)
# - PreTrainedModel for the models (itself a sub-class of torch.nn.Module)
####################################################
####################################################
@@ -238,6 +238,10 @@ XXX_INPUTS_DOCSTRING = r"""
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.
"""
@add_start_docstrings("The bare Xxx Model transformer outputting raw hidden-states without any specific head on top.",
@@ -280,7 +284,6 @@ class XxxModel(XxxPreTrainedModel):
self.init_weights()
@property
def get_input_embeddings(self):
return self.embeddings.word_embeddings
@@ -295,11 +298,22 @@ class XxxModel(XxxPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
@@ -333,7 +347,7 @@ class XxxModel(XxxPreTrainedModel):
##################################
# Replace this with your model code
embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask)
sequence_output = encoder_outputs[0]
outputs = (sequence_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here
@@ -384,14 +398,15 @@ class XxxForMaskedLM(XxxPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None):
outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
@@ -449,14 +464,15 @@ class XxxForSequenceClassification(XxxPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
pooled_output = outputs[1]
@@ -520,14 +536,15 @@ class XxxForTokenClassification(XxxPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
@@ -603,14 +620,15 @@ class XxxForQuestionAnswering(XxxPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
start_positions=None, end_positions=None):
outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]

2
templates/adding_a_new_model/tokenization_xxx.py
View File

@@ -172,7 +172,7 @@ class XxxTokenizer(PreTrainedTokenizer):
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:

29
transformers/__init__.py
View File

@@ -1,4 +1,4 @@
__version__ = "2.1.1"
__version__ = "2.2.0"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
@@ -25,10 +25,11 @@ from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH
from .data import (is_sklearn_available,
InputExample, InputFeatures, DataProcessor,
glue_output_modes, glue_convert_examples_to_features,
glue_processors, glue_tasks_num_labels)
glue_processors, glue_tasks_num_labels,
xnli_output_modes, xnli_processors, xnli_tasks_num_labels)
if is_sklearn_available():
from .data import glue_compute_metrics
from .data import glue_compute_metrics, xnli_compute_metrics
# Tokenizers
from .tokenization_utils import (PreTrainedTokenizer)
@@ -42,6 +43,8 @@ from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
from .tokenization_albert import AlbertTokenizer
from .tokenization_camembert import CamembertTokenizer
# Configurations
from .configuration_utils import PretrainedConfig
@@ -56,6 +59,8 @@ 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
# Modeling
if is_torch_available():
@@ -72,6 +77,7 @@ if is_torch_available():
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)
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
@@ -93,12 +99,21 @@ if is_torch_available():
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DistilBertForTokenClassification,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_camembert import (CamembertForMaskedLM, CamembertModel,
CamembertForSequenceClassification, CamembertForMultipleChoice,
CamembertForTokenClassification,
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
from .modeling_albert import (AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification,
AlbertForQuestionAnswering,
load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
# Optimization
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup)
# TensorFlow
@@ -154,6 +169,10 @@ if is_tf_available():
TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_albert import (TFAlbertPreTrainedModel, TFAlbertModel, TFAlbertForMaskedLM,
TFAlbertForSequenceClassification,
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
# 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,

100
transformers/configuration_albert.py Normal file
View File

@@ -0,0 +1,100 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" ALBERT model configuration """
from .configuration_utils import PretrainedConfig
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-config.json",
'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-config.json",
'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-config.json",
'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-config.json",
'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-config.json",
'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-config.json",
'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-config.json",
'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-config.json",
}
class AlbertConfig(PretrainedConfig):
"""Configuration for `AlbertModel`.
The default settings match the configuration of model `albert_xxlarge`.
"""
pretrained_config_archive_map = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file=30000,
embedding_size=128,
hidden_size=4096,
num_hidden_layers=12,
num_hidden_groups=1,
num_attention_heads=64,
intermediate_size=16384,
inner_group_num=1,
hidden_act="gelu_new",
hidden_dropout_prob=0,
attention_probs_dropout_prob=0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12, **kwargs):
"""Constructs AlbertConfig.
Args:
vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
embedding_size: size of voc embeddings.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_hidden_groups: Number of group for the hidden layers, parameters in
the same group are shared.
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.
inner_group_num: int, number of inner repetition of attention and ffn.
down_scale_factor: float, the scale to apply
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler.
hidden_dropout_prob: The dropout probability 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
`AlbertModel`.
initializer_range: The stdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
super(AlbertConfig, self).__init__(**kwargs)
self.vocab_size = vocab_size_or_config_json_file
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_hidden_groups = num_hidden_groups
self.num_attention_heads = num_attention_heads
self.inner_group_num = inner_group_num
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps

10
transformers/configuration_auto.py
View File

@@ -27,6 +27,7 @@ from .configuration_xlm import XLMConfig
from .configuration_roberta import RobertaConfig
from .configuration_distilbert import DistilBertConfig
from .configuration_ctrl import CTRLConfig
from .configuration_camembert import CamembertConfig
logger = logging.getLogger(__name__)
@@ -50,6 +51,7 @@ class AutoConfig(object):
- 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).
"""
@@ -72,6 +74,7 @@ class AutoConfig(object):
- 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:
@@ -92,6 +95,9 @@ 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.
@@ -116,6 +122,8 @@ class AutoConfig(object):
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertConfig.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:
return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
@@ -134,4 +142,4 @@ 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', 'ctrl'".format(pretrained_model_name_or_path))
"'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path))

33
transformers/configuration_camembert.py Normal file
View File

@@ -0,0 +1,33 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" CamemBERT configuration """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
from .configuration_roberta import RobertaConfig
logger = logging.getLogger(__name__)
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'camembert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-config.json",
}
class CamembertConfig(RobertaConfig):
pretrained_config_archive_map = CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP

3
transformers/configuration_distilbert.py
View File

@@ -27,7 +27,8 @@ 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-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json",
'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-config.json",
}

1
transformers/configuration_gpt2.py
View File

@@ -29,6 +29,7 @@ logger = logging.getLogger(__name__)
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-config.json",
"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-config.json",
"gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-config.json",
"gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-config.json",
"distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-config.json",}
class GPT2Config(PretrainedConfig):

2
transformers/configuration_roberta.py
View File

@@ -29,6 +29,8 @@ ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-config.json",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-config.json",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-config.json",
}

7
transformers/configuration_utils.py
View File

@@ -94,6 +94,9 @@ 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.
@@ -120,6 +123,7 @@ 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)
@@ -131,7 +135,8 @@ class PretrainedConfig(object):
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)
resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download,
proxies=proxies, resume_download=resume_download)
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(

67
transformers/convert_albert_original_tf_checkpoint_to_pytorch.py Normal file
View File

@@ -0,0 +1,67 @@
# 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 ALBERT checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import torch
from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert
import logging
logging.basicConfig(level=logging.INFO)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = AlbertConfig.from_json_file(albert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = AlbertForMaskedLM(config)
# Load weights from tf checkpoint
load_tf_weights_in_albert(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("--albert_config_file",
default = None,
type = str,
required = True,
help = "The config json file corresponding to the pre-trained ALBERT 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.albert_config_file,
args.pytorch_dump_path)

13
transformers/convert_pytorch_checkpoint_to_tf2.py
View File

@@ -33,7 +33,8 @@ from transformers import (load_pytorch_checkpoint_in_tf2_model,
OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaConfig, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP)
CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
if is_torch_available():
import torch
@@ -46,7 +47,8 @@ if is_torch_available():
OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
CTRLLMHeadModel, CTRL_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,
@@ -56,7 +58,8 @@ else:
OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
RobertaForMaskedLM, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
DistilBertForMaskedLM, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) = (
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) = (
None, None, None, None,
None, None,
None, None,
@@ -65,6 +68,7 @@ else:
None, None,
None, None, None,
None, None, None,
None, None,
None, None)
@@ -85,7 +89,8 @@ MODEL_CLASSES = {
'roberta-large-mnli': (RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP),
'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)
'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)
}
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):

3
transformers/data/__init__.py
View File

@@ -1,6 +1,7 @@
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 xnli_output_modes, xnli_processors, xnli_tasks_num_labels
from .metrics import is_sklearn_available
if is_sklearn_available():
from .metrics import glue_compute_metrics
from .metrics import glue_compute_metrics, xnli_compute_metrics

8
transformers/data/metrics/__init__.py
View File

@@ -81,3 +81,11 @@ if _has_sklearn:
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)
def xnli_compute_metrics(task_name, preds, labels):
assert len(preds) == len(labels)
if task_name == "xnli":
return {"acc": simple_accuracy(preds, labels)}
else:
raise KeyError(task_name)

2
transformers/data/processors/__init__.py
View File

@@ -1,3 +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 .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels

85
transformers/data/processors/xnli.py Normal file
View File

@@ -0,0 +1,85 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team 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.
""" XNLI utils (dataset loading and evaluation) """
from __future__ import absolute_import, division, print_function
import logging
import os
from .utils import DataProcessor, InputExample
logger = logging.getLogger(__name__)
class XnliProcessor(DataProcessor):
"""Processor for the XNLI dataset.
Adapted from https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/run_classifier.py#L207"""
def __init__(self, language, train_language = None):
self.language = language
self.train_language = train_language
def get_train_examples(self, data_dir):
"""See base class."""
lg = self.language if self.train_language is None else self.train_language
lines = self._read_tsv(os.path.join(data_dir, "XNLI-MT-1.0/multinli/multinli.train.{}.tsv".format(lg)))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % ('train', i)
text_a = line[0]
text_b = line[1]
label = "contradiction" if line[2] == "contradictory" else line[2]
assert isinstance(text_a, str) and isinstance(text_b, str) and isinstance(label, str)
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_test_examples(self, data_dir):
"""See base class."""
lines = self._read_tsv(os.path.join(data_dir, "XNLI-1.0/xnli.test.tsv"))
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
language = line[0]
if language != self.language:
continue
guid = "%s-%s" % ('test', i)
text_a = line[6]
text_b = line[7]
label = line[1]
assert isinstance(text_a, str) and isinstance(text_b, str) and isinstance(label, str)
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
xnli_processors = {
"xnli": XnliProcessor,
}
xnli_output_modes = {
"xnli": "classification",
}
xnli_tasks_num_labels = {
"xnli": 3,
}

47
transformers/file_utils.py
View File

@@ -22,6 +22,7 @@ from botocore.config import Config
from botocore.exceptions import ClientError
import requests
from tqdm import tqdm
from contextlib import contextmanager
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
@@ -152,7 +153,7 @@ 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):
def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None, resume_download=False):
"""
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
@@ -161,6 +162,7 @@ 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
@@ -173,7 +175,9 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N
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)
return get_from_cache(url_or_filename, cache_dir=cache_dir,
force_download=force_download, proxies=proxies,
resume_download=resume_download)
elif os.path.exists(url_or_filename):
# File, and it exists.
return url_or_filename
@@ -234,19 +238,22 @@ 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):
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):
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", total=total, initial=resume_size)
for chunk in response.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):
def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10, resume_download=False):
"""
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.
@@ -289,17 +296,35 @@ 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 tempfile.NamedTemporaryFile() as temp_file:
with temp_file_manager() 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)
http_get(url, temp_file, proxies=proxies, resume_size=resume_size)
# we are copying the file before closing it, so flush to avoid truncation
temp_file.flush()

764
transformers/modeling_albert.py Normal file
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@@ -0,0 +1,764 @@
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and 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.
"""PyTorch ALBERT model. """
import os
import math
import logging
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.modeling_utils import PreTrainedModel
from transformers.configuration_albert import AlbertConfig
from transformers.modeling_bert import BertEmbeddings, BertSelfAttention, prune_linear_layer, ACT2FN
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin",
'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin",
'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin",
'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin",
'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-pytorch_model.bin",
'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-pytorch_model.bin",
'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-pytorch_model.bin",
'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-pytorch_model.bin",
}
def load_tf_weights_in_albert(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 = []
arrays = []
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)
arrays.append(array)
for name, array in zip(names, arrays):
print(name)
for name, array in zip(names, arrays):
original_name = name
name = name.replace("ffn_1", "ffn")
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("inner_group_", "albert_layers/")
name = name.replace("group_", "albert_layer_groups/")
name = name.split('/')
pointer = model
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] == 'kernel' or l[0] == 'gamma':
pointer = getattr(pointer, 'weight')
elif l[0] == 'output_bias' or l[0] == 'beta':
pointer = getattr(pointer, 'bias')
elif l[0] == 'output_weights':
pointer = getattr(pointer, 'weight')
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 m_name[-11:] == '_embeddings':
pointer = getattr(pointer, 'weight')
elif m_name == 'kernel':
array = np.transpose(array)
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
print("Initialize PyTorch weight {} from {}".format(name, original_name))
pointer.data = torch.from_numpy(array)
return model
class AlbertEmbeddings(BertEmbeddings):
"""
Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config):
super(AlbertEmbeddings, self).__init__(config)
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
class AlbertAttention(BertSelfAttention):
def __init__(self, config):
super(AlbertAttention, self).__init__(config)
self.output_attentions = config.output_attentions
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
self.attention_head_size = config.hidden_size // config.num_attention_heads
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
mask = torch.ones(self.num_attention_heads, self.attention_head_size)
heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads
for head in heads:
# Compute how many pruned heads are before the head and move the index accordingly
head = 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.query = prune_linear_layer(self.query, index)
self.key = prune_linear_layer(self.key, index)
self.value = prune_linear_layer(self.value, index)
self.dense = prune_linear_layer(self.dense, index, dim=1)
# Update hyper params and store pruned heads
self.num_attention_heads = self.num_attention_heads - len(heads)
self.all_head_size = self.attention_head_size * self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(self, input_ids, attention_mask=None, head_mask=None):
mixed_query_layer = self.query(input_ids)
mixed_key_layer = self.key(input_ids)
mixed_value_layer = self.value(input_ids)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
reshaped_context_layer = context_layer.view(*new_context_layer_shape)
# Should find a better way to do this
w = self.dense.weight.t().view(self.num_attention_heads, self.attention_head_size, self.hidden_size).to(context_layer.dtype)
b = self.dense.bias.to(context_layer.dtype)
projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b
projected_context_layer_dropout = self.dropout(projected_context_layer)
layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout)
return (layernormed_context_layer, attention_probs) if self.output_attentions else (layernormed_context_layer,)
class AlbertLayer(nn.Module):
def __init__(self, config):
super(AlbertLayer, self).__init__()
self.config = config
self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attention = AlbertAttention(config)
self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
self.activation = ACT2FN[config.hidden_act]
def forward(self, hidden_states, attention_mask=None, head_mask=None):
attention_output = self.attention(hidden_states, attention_mask, head_mask)
ffn_output = self.ffn(attention_output[0])
ffn_output = self.activation(ffn_output)
ffn_output = self.ffn_output(ffn_output)
hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
return (hidden_states,) + attention_output[1:] # add attentions if we output them
class AlbertLayerGroup(nn.Module):
def __init__(self, config):
super(AlbertLayerGroup, self).__init__()
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
def forward(self, hidden_states, attention_mask=None, head_mask=None):
layer_hidden_states = ()
layer_attentions = ()
for layer_index, albert_layer in enumerate(self.albert_layers):
layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index])
hidden_states = layer_output[0]
if self.output_attentions:
layer_attentions = layer_attentions + (layer_output[1],)
if self.output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (layer_hidden_states,)
if self.output_attentions:
outputs = outputs + (layer_attentions,)
return outputs # last-layer hidden state, (layer hidden states), (layer attentions)
class AlbertTransformer(nn.Module):
def __init__(self, config):
super(AlbertTransformer, self).__init__()
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])
def forward(self, hidden_states, attention_mask=None, head_mask=None):
hidden_states = self.embedding_hidden_mapping_in(hidden_states)
all_attentions = ()
if self.output_hidden_states:
all_hidden_states = (hidden_states,)
for i in range(self.config.num_hidden_layers):
# Number of layers in a hidden group
layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
# Index of the hidden group
group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
# Index of the layer inside the group
layer_idx = int(i - group_idx * layers_per_group)
layer_group_output = self.albert_layer_groups[group_idx](hidden_states, attention_mask, head_mask[group_idx*layers_per_group:(group_idx+1)*layers_per_group])
hidden_states = layer_group_output[0]
if self.output_attentions:
all_attentions = all_attentions + layer_group_output[-1]
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)
class AlbertPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = AlbertConfig
pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "albert"
def _init_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if isinstance(module, (nn.Linear)) and module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in
`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`_
by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
two parameter-reduction techniques to lower memory consumption and increase the trainig speed of BERT.
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.
.. _`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`:
https://arxiv.org/abs/1909.11942
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.AlbertConfig`): 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.
"""
ALBERT_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, BERT 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]``
``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``
Albert is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.AlbertTokenizer`.
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.
**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``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**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 ALBERT Model transformer outputting raw hidden-states without any specific head on top.",
ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertModel(AlbertPreTrainedModel):
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.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**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.
"""
config_class = AlbertConfig
pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_albert
base_model_prefix = "albert"
def __init__(self, config):
super(AlbertModel, self).__init__(config)
self.config = config
self.embeddings = AlbertEmbeddings(config)
self.encoder = AlbertTransformer(config)
self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
self.pooler_activation = nn.Tanh()
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _resize_token_embeddings(self, new_num_tokens):
old_embeddings = self.embeddings.word_embeddings
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.embeddings.word_embeddings = new_embeddings
return self.embeddings.word_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}
ALBERT has a different architecture in that its layers are shared across groups, which then has inner groups.
If an ALBERT model has 12 hidden layers and 2 hidden groups, with two inner groups, there
is a total of 4 different layers.
These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer,
while [2,3] correspond to the two inner groups of the second hidden layer.
Any layer with in index other than [0,1,2,3] will result in an error.
See base class PreTrainedModel for more information about head pruning
"""
for layer, heads in heads_to_prune.items():
group_idx = int(layer / self.config.inner_group_num)
inner_group_idx = int(layer - group_idx * self.config.inner_group_num)
self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
inputs_embeds=None):
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 = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
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)
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
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_hidden_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_hidden_layers
embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds)
encoder_outputs = self.encoder(embedding_output,
extended_attention_mask,
head_mask=head_mask)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0]))
outputs = (sequence_output, pooled_output) + encoder_outputs[1:] # add hidden_states and attentions if they are here
return outputs
class AlbertMLMHead(nn.Module):
def __init__(self, config):
super(AlbertMLMHead, self).__init__()
self.LayerNorm = nn.LayerNorm(config.embedding_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
self.activation = ACT2FN[config.hidden_act]
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
hidden_states = self.decoder(hidden_states)
prediction_scores = hidden_states + self.bias
return prediction_scores
@add_start_docstrings("Bert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertForMaskedLM(AlbertPreTrainedModel):
r"""
**masked_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 ``masked_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.
"""
def __init__(self, config):
super(AlbertForMaskedLM, self).__init__(config)
self.albert = AlbertModel(config)
self.predictions = AlbertMLMHead(config)
self.init_weights()
self.tie_weights()
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self._tie_or_clone_weights(self.predictions.decoder,
self.albert.embeddings.word_embeddings)
def get_output_embeddings(self):
return self.predictions.decoder
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None):
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds
)
sequence_outputs = outputs[0]
prediction_scores = self.predictions(sequence_outputs)
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
if masked_lm_labels is not None:
loss_fct = CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1))
outputs = (masked_lm_loss,) + outputs
return outputs
@add_start_docstrings("""Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertForSequenceClassification(AlbertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
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 (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (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 = AlbertTokenizer.from_pretrained('albert-base-v2')
model = AlbertForSequenceClassification.from_pretrained('albert-base-v2')
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]
"""
def __init__(self, config):
super(AlbertForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), logits, (hidden_states), (attentions)
@add_start_docstrings("""Albert 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`). """,
ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class AlbertForQuestionAnswering(AlbertPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
**end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
**start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (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 = AlbertTokenizer.from_pretrained('albert-base-v2')
model = AlbertForQuestionAnswering.from_pretrained('albert-base-v2')
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))]
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)
print(' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]))
# a nice puppet
"""
def __init__(self, config):
super(AlbertForQuestionAnswering, self).__init__(config)
self.num_labels = config.num_labels
self.albert = AlbertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
inputs_embeds=None, start_positions=None, end_positions=None):
outputs = self.albert(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,) + outputs[2:]
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
outputs = (total_loss,) + outputs
return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions)

21
transformers/modeling_auto.py
View File

@@ -27,6 +27,7 @@ from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassi
from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
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_utils import PreTrainedModel, SequenceSummary
@@ -48,6 +49,7 @@ 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 `distilbert`: DistilBertModel (DistilBERT model)
- contains `camembert`: CamembertModel (CamemBERT model)
- contains `roberta`: RobertaModel (RoBERTa model)
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
@@ -71,6 +73,7 @@ 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 `distilbert`: DistilBertModel (DistilBERT model)
- contains `camembert`: CamembertModel (CamemBERT model)
- contains `roberta`: RobertaModel (RoBERTa model)
- contains `bert`: BertModel (Bert model)
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
@@ -112,6 +115,9 @@ 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.
@@ -138,6 +144,8 @@ class AutoModel(object):
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertModel.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:
return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
@@ -172,6 +180,7 @@ 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 `distilbert`: DistilBertForMaskedLM (DistilBERT model)
- contains `camembert`: CamembertForMaskedLM (CamemBERT model)
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
- contains `bert`: BertForMaskedLM (Bert model)
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
@@ -198,6 +207,7 @@ 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 `distilbert`: DistilBertForMaskedLM (DistilBERT model)
- contains `camembert`: CamembertForMaskedLM (CamemBERT model)
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
- contains `bert`: BertForMaskedLM (Bert model)
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
@@ -237,6 +247,8 @@ 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'}.
@@ -264,6 +276,8 @@ class AutoModelWithLMHead(object):
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertForMaskedLM.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:
return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
@@ -298,6 +312,7 @@ 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 `camembert`: CamembertForSequenceClassification (CamemBERT model)
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
- contains `bert`: BertForSequenceClassification (Bert model)
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
@@ -320,6 +335,7 @@ 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 `camembert`: CamembertForSequenceClassification (CamemBERT model)
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
- contains `bert`: BertForSequenceClassification (Bert model)
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
@@ -357,6 +373,9 @@ 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.
@@ -383,6 +402,8 @@ class AutoModelForSequenceClassification(object):
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertForSequenceClassification.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:
return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
elif 'bert' in pretrained_model_name_or_path:

97
transformers/modeling_bert.py
View File

@@ -158,19 +158,26 @@ class BertEmbeddings(nn.Module):
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids, token_type_ids=None, position_ids=None):
seq_length = input_ids.size(1)
if position_ids is None:
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
words_embeddings = self.word_embeddings(input_ids)
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = words_embeddings + position_embeddings + token_type_embeddings
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
@@ -271,7 +278,7 @@ class BertAttention(nn.Module):
if len(heads) == 0:
return
mask = torch.ones(self.self.num_attention_heads, self.self.attention_head_size)
heads = set(heads) - self.pruned_heads # Convert to set and emove already pruned heads
heads = set(heads) - self.pruned_heads # Convert to set and remove already pruned heads
for head in heads:
# Compute how many pruned heads are before the head and move the index accordingly
head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
@@ -550,6 +557,10 @@ BERT_INPUTS_DOCSTRING = r"""
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.
**encoder_hidden_states**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``:
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model
is configured as a decoder.
@@ -615,8 +626,8 @@ class BertModel(BertPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None,
head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None,
head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None):
""" Forward pass on the Model.
The model can behave as an encoder (with only self-attention) as well
@@ -632,12 +643,23 @@ class BertModel(BertPreTrainedModel):
https://arxiv.org/abs/1706.03762
"""
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 = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
attention_mask = torch.ones(input_shape, device=device)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones_like(input_ids)
encoder_attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=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.
@@ -649,8 +671,8 @@ class BertModel(BertPreTrainedModel):
# - 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_ids.size()
seq_ids = torch.arange(seq_length, device=input_ids.device)
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]
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
else:
@@ -689,7 +711,7 @@ class BertModel(BertPreTrainedModel):
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
encoder_outputs = self.encoder(embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
@@ -754,14 +776,15 @@ class BertForPreTraining(BertPreTrainedModel):
def get_output_embeddings(self):
return self.cls.predictions.decoder
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None, next_sentence_label=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
@@ -829,7 +852,7 @@ class BertForMaskedLM(BertPreTrainedModel):
def get_output_embeddings(self):
return self.cls.predictions.decoder
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None, encoder_hidden_states=None, encoder_attention_mask=None, lm_labels=None, ):
outputs = self.bert(input_ids,
@@ -837,6 +860,7 @@ class BertForMaskedLM(BertPreTrainedModel):
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask)
@@ -908,14 +932,15 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
next_sentence_label=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
pooled_output = outputs[1]
@@ -975,14 +1000,15 @@ class BertForSequenceClassification(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
pooled_output = outputs[1]
@@ -1049,8 +1075,8 @@ class BertForMultipleChoice(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
num_choices = input_ids.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1))
@@ -1062,7 +1088,8 @@ class BertForMultipleChoice(BertPreTrainedModel):
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
pooled_output = outputs[1]
@@ -1123,14 +1150,15 @@ class BertForTokenClassification(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
@@ -1207,14 +1235,15 @@ class BertForQuestionAnswering(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
start_positions=None, end_positions=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]

293
transformers/modeling_camembert.py Normal file
View File

@@ -0,0 +1,293 @@
# coding=utf-8
# Copyright 2019 Inria, Facebook AI Research 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.
"""PyTorch CamemBERT model. """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification, RobertaForMultipleChoice, RobertaForTokenClassification
from .configuration_camembert import CamembertConfig
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
'camembert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-pytorch_model.bin",
}
CAMEMBERT_START_DOCSTRING = r""" The CamemBERT model was proposed in
`CamemBERT: a Tasty French Language Model`_
by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019.
It is a model trained on 138GB of French text.
This implementation is the same as RoBERTa.
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.
.. _`CamemBERT: a Tasty French Language Model`:
https://arxiv.org/abs/1911.03894
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~transformers.CamembertConfig`): 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.
"""
CAMEMBERT_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, CamemBERT input sequence should be formatted with <s> and </s> tokens as follows:
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the CamembertTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
CamemBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`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.
**token_type_ids**: (`optional` need to be trained) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs.
This embedding matrice is not trained (not pretrained during CamemBERT pretraining), you will have to train it
during finetuning.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``.
**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.
"""
@add_start_docstrings("The bare CamemBERT Model transformer outputting raw hidden-states without any specific head on top.",
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertModel(RobertaModel):
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.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
eo match pre-training, CamemBERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] [SEP] no it is not . [SEP]``
``token_type_ids: 0 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``
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**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 = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertModel.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !")).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
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT Model with a `language modeling` head on top. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForMaskedLM(RobertaForMaskedLM):
r"""
**masked_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 ``masked_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 = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForMaskedLM.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForSequenceClassification(RobertaForSequenceClassification):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels]``.
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
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 (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (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 = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForSequenceClassification.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForMultipleChoice(RobertaForMultipleChoice):
r"""
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.
**classification_scores**: ``torch.FloatTensor`` of shape ``(batch_size, num_choices)`` where `num_choices` is the size of the second dimension
of the input tensors. (see `input_ids` above).
Classification scores (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 = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForMultipleChoice.from_pretrained('camembert-base')
choices = ["J'aime le camembert !", "Je deteste le camembert !"]
input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) 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]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP
@add_start_docstrings("""CamemBERT 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. """,
CAMEMBERT_START_DOCSTRING, CAMEMBERT_INPUTS_DOCSTRING)
class CamembertForTokenClassification(RobertaForTokenClassification):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the token classification loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
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).
**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 = CamembertTokenizer.from_pretrained('camembert-base')
model = CamembertForTokenClassification.from_pretrained('camembert-base')
input_ids = torch.tensor(tokenizer.encode("J'aime le camembert !", add_special_tokens=True)).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]
"""
config_class = CamembertConfig
pretrained_model_archive_map = CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP

36
transformers/modeling_ctrl.py
View File

@@ -221,7 +221,8 @@ CTRL_INPUTS_DOCSTRING = r""" Inputs:
**past**:
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past` output below). Can be used to speed up sequential decoding.
(see `past` output below). Can be used 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.
**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]``:
@@ -237,6 +238,10 @@ CTRL_INPUTS_DOCSTRING = r""" Inputs:
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.
"""
@add_start_docstrings("The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
@@ -249,7 +254,8 @@ class CTRLModel(CTRLPreTrainedModel):
**past**:
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.
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.
**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)``:
@@ -303,17 +309,26 @@ class CTRLModel(CTRLPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
@@ -355,9 +370,10 @@ class CTRLModel(CTRLPreTrainedModel):
token_type_embeds = 0
position_ids = position_ids.view(-1, input_shape[-1])
if inputs_embeds is None:
inputs_embeds = self.w(input_ids)
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
seq_len = input_ids.shape[-1]
seq_len = input_shape.shape[-1]
mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device)
inputs_embeds *= np.sqrt(self.d_model_size)
@@ -424,7 +440,8 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
**past**:
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.
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.
**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)``:
@@ -456,14 +473,15 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]

115
transformers/modeling_distilbert.py
View File

@@ -30,6 +30,7 @@ import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from .modeling_utils import PreTrainedModel, prune_linear_layer
from .configuration_distilbert import DistilBertConfig
@@ -41,7 +42,8 @@ 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-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin",
'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-pytorch_model.bin",
}
@@ -387,6 +389,10 @@ DISTILBERT_INPUTS_DOCSTRING = r"""
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.
"""
@add_start_docstrings("The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",
@@ -436,9 +442,20 @@ class DistilBertModel(DistilBertPreTrainedModel):
self.transformer.layer[layer].attention.prune_heads(heads)
def forward(self,
input_ids, attention_mask=None, head_mask=None):
input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None):
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 = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
attention_mask = torch.ones_like(input_ids) # (bs, seq_length)
attention_mask = torch.ones(input_shape, device=device) # (bs, seq_length)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
@@ -455,8 +472,9 @@ class DistilBertModel(DistilBertPreTrainedModel):
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(input_ids) # (bs, seq_length, dim)
tfmr_output = self.transformer(x=embedding_output,
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids) # (bs, seq_length, dim)
tfmr_output = self.transformer(x=inputs_embeds,
attn_mask=attention_mask,
head_mask=head_mask)
hidden_state = tfmr_output[0]
@@ -514,10 +532,11 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
def get_output_embeddings(self):
return self.vocab_projector
def forward(self, input_ids, attention_mask=None, head_mask=None, masked_lm_labels=None):
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, masked_lm_labels=None):
dlbrt_output = self.distilbert(input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = dlbrt_output[0] # (bs, seq_length, dim)
prediction_logits = self.vocab_transform(hidden_states) # (bs, seq_length, dim)
prediction_logits = gelu(prediction_logits) # (bs, seq_length, dim)
@@ -578,10 +597,11 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None):
distilbert_output = self.distilbert(input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_state = distilbert_output[0] # (bs, seq_len, dim)
pooled_output = hidden_state[:, 0] # (bs, dim)
pooled_output = self.pre_classifier(pooled_output) # (bs, dim)
@@ -652,10 +672,11 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, head_mask=None, start_positions=None, end_positions=None):
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None):
distilbert_output = self.distilbert(input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = distilbert_output[0] # (bs, max_query_len, dim)
hidden_states = self.dropout(hidden_states) # (bs, max_query_len, dim)
@@ -683,3 +704,75 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
outputs = (total_loss,) + outputs
return outputs # (loss), start_logits, end_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 DistilBertForTokenClassification(DistilBertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the token classification loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
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).
**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 = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForTokenClassification.from_pretrained('distilbert-base-uncased')
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]
"""
def __init__(self, config):
super(DistilBertForTokenClassification, self).__init__(config)
self.num_labels = config.num_labels
self.distilbert = DistilBertModel(config)
self.dropout = nn.Dropout(config.dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None, head_mask=None,
inputs_embeds=None, labels=None):
outputs = self.distilbert(input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
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 # (loss), scores, (hidden_states), (attentions)

43
transformers/modeling_gpt2.py
View File

@@ -39,6 +39,7 @@ logger = logging.getLogger(__name__)
GPT2_PRETRAINED_MODEL_ARCHIVE_MAP = {"gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-pytorch_model.bin",
"gpt2-medium": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-pytorch_model.bin",
"gpt2-large": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-pytorch_model.bin",
"gpt2-xl": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-pytorch_model.bin",
"distilgpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-pytorch_model.bin",}
def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
@@ -297,7 +298,8 @@ GPT2_INPUTS_DOCSTRING = r""" Inputs:
**past**:
list of ``torch.FloatTensor`` (one for each layer):
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past` output below). Can be used to speed up sequential decoding.
(see `past` output below). Can be used 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.
**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]``:
@@ -313,6 +315,10 @@ GPT2_INPUTS_DOCSTRING = r""" Inputs:
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.
"""
@add_start_docstrings("The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.",
@@ -325,7 +331,8 @@ class GPT2Model(GPT2PreTrainedModel):
**past**:
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.
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.
**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)``:
@@ -370,9 +377,17 @@ class GPT2Model(GPT2PreTrainedModel):
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
@@ -384,8 +399,9 @@ class GPT2Model(GPT2PreTrainedModel):
else:
past_length = past[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
@@ -419,6 +435,7 @@ class GPT2Model(GPT2PreTrainedModel):
else:
head_mask = [None] * self.config.n_layer
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
@@ -488,7 +505,8 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
**past**:
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.
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.
**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)``:
@@ -520,14 +538,15 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
@@ -579,7 +598,8 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
**past**:
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.
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.
**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)``:
@@ -623,14 +643,15 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
mc_token_ids=None, lm_labels=None, mc_labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]

41
transformers/modeling_openai.py
View File

@@ -322,6 +322,10 @@ OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
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.
"""
@add_start_docstrings("The bare OpenAI GPT transformer model outputting raw hidden-states without any specific head on top.",
@@ -373,14 +377,22 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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 = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if position_ids is None:
# This was used when we had a single embedding matrice from position and token embeddings
# start = self.config.vocab_size + self.config.n_special
# end = start + input_ids.size(-1)
# position_ids = torch.arange(start, end, dtype=torch.long, device=input_ids.device)
position_ids = torch.arange(input_ids.size(-1), dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
# Code is different from when we had a single embedding matrice from position and token embeddings
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
@@ -413,10 +425,7 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
else:
head_mask = [None] * self.config.n_layer
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_ids.size(-1))
position_ids = position_ids.view(-1, position_ids.size(-1))
if inputs_embeds is None:
inputs_embeds = self.tokens_embed(input_ids)
position_embeds = self.positions_embed(position_ids)
if token_type_ids is not None:
@@ -495,13 +504,14 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
@@ -587,13 +597,14 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
mc_token_ids=None, lm_labels=None, mc_labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)

49
transformers/modeling_roberta.py
View File

@@ -35,6 +35,8 @@ ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-pytorch_model.bin",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-openai-detector-pytorch_model.bin",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-openai-detector-pytorch_model.bin",
}
class RobertaEmbeddings(BertEmbeddings):
@@ -48,16 +50,24 @@ class RobertaEmbeddings(BertEmbeddings):
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size,
padding_idx=self.padding_idx)
def forward(self, input_ids, token_type_ids=None, position_ids=None):
seq_length = input_ids.size(1)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
if position_ids is None:
# Position numbers begin at padding_idx+1. Padding symbols are ignored.
# cf. fairseq's `utils.make_positions`
position_ids = torch.arange(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
position_ids = torch.arange(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
return super(RobertaEmbeddings, self).forward(input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids)
position_ids=position_ids,
inputs_embeds=inputs_embeds)
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
@@ -126,6 +136,10 @@ ROBERTA_INPUTS_DOCSTRING = r"""
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.
"""
@add_start_docstrings("The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.",
@@ -222,13 +236,14 @@ class RobertaForMaskedLM(BertPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head.decoder
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None):
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
@@ -309,13 +324,14 @@ class RobertaForSequenceClassification(BertPreTrainedModel):
self.roberta = RobertaModel(config)
self.classifier = RobertaClassificationHead(config)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
@@ -372,6 +388,10 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
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
@@ -415,8 +435,8 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
position_ids=None, head_mask=None):
def forward(self, input_ids=None, token_type_ids=None, attention_mask=None, labels=None,
position_ids=None, head_mask=None, inputs_embeds=None):
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
@@ -487,14 +507,15 @@ class RobertaForTokenClassification(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
outputs = self.roberta(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]

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transformers/modeling_tf_albert.py Normal file
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# coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 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
from .modeling_tf_bert import ACT2FN, TFBertSelfAttention
from .file_utils import add_start_docstrings
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-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",
'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-tf_model.h5",
}
class TFAlbertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config, **kwargs):
super(TFAlbertEmbeddings, self).__init__(**kwargs)
self.config = config
self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings,
config.embedding_size,
embeddings_initializer=get_initializer(
self.config.initializer_range),
name='position_embeddings')
self.token_type_embeddings = tf.keras.layers.Embedding(config.type_vocab_size,
config.embedding_size,
embeddings_initializer=get_initializer(
self.config.initializer_range),
name='token_type_embeddings')
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.config.vocab_size, self.config.embedding_size],
initializer=get_initializer(self.config.initializer_range))
super(TFAlbertEmbeddings, self).build(input_shape)
def call(self, inputs, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
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
"""
if mode == "embedding":
return self._embedding(inputs, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
if input_ids is not None:
input_shape = tf.shape(input_ids)
else:
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, :]
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, embedding_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
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])
class TFAlbertSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFAlbertSelfAttention, self).__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads))
self.output_attentions = config.output_attentions
self.num_attention_heads = config.num_attention_heads
assert config.hidden_size % config.num_attention_heads == 0
self.attention_head_size = int(
config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = tf.keras.layers.Dense(self.all_head_size,
kernel_initializer=get_initializer(
config.initializer_range),
name='query')
self.key = tf.keras.layers.Dense(self.all_head_size,
kernel_initializer=get_initializer(
config.initializer_range),
name='key')
self.value = tf.keras.layers.Dense(self.all_head_size,
kernel_initializer=get_initializer(
config.initializer_range),
name='value')
self.dropout = tf.keras.layers.Dropout(
config.attention_probs_dropout_prob)
def transpose_for_scores(self, x, batch_size):
x = tf.reshape(
x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
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)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (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(tf.shape(key_layer)[-1], tf.float32)
attention_scores = attention_scores / tf.math.sqrt(dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFAlbertModel call() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
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,
(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,)
return outputs
class TFAlbertSelfOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFAlbertSelfOutput, self).__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(
config.initializer_range),
name='dense')
self.LayerNorm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, inputs, training=False):
hidden_states, input_tensor = inputs
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class TFAlbertAttention(TFBertSelfAttention):
def __init__(self, config, **kwargs):
super(TFAlbertAttention, self).__init__(config, **kwargs)
self.hidden_size = config.hidden_size
self.dense = tf.keras.layers.Dense(config.hidden_size,
kernel_initializer=get_initializer(
config.initializer_range),
name='dense')
self.LayerNorm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name='LayerNorm')
self.pruned_heads = set()
def prune_heads(self, heads):
raise NotImplementedError
def call(self, inputs, training=False):
input_tensor, attention_mask, head_mask = inputs
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)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (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(tf.shape(key_layer)[-1], tf.float32)
attention_scores = attention_scores / tf.math.sqrt(dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
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,
(batch_size, -1, self.all_head_size)) # (batch_size, seq_len_q, all_head_size)
self_outputs = (context_layer, attention_probs) if self.output_attentions else (
context_layer,)
hidden_states = self_outputs[0]
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
attention_output = self.LayerNorm(hidden_states + input_tensor)
# add attentions if we output them
outputs = (attention_output,) + self_outputs[1:]
return outputs
class TFAlbertLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFAlbertLayer, self).__init__(**kwargs)
self.attention = TFAlbertAttention(config, name='attention')
self.ffn = tf.keras.layers.Dense(config.intermediate_size, kernel_initializer=get_initializer(
config.initializer_range), name='ffn')
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
self.activation = ACT2FN[config.hidden_act]
else:
self.activation = config.hidden_act
self.ffn_output = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer(
config.initializer_range), name='ffn_output')
self.full_layer_layer_norm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name='full_layer_layer_norm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
attention_outputs = self.attention(
[hidden_states, attention_mask, head_mask], training=training)
ffn_output = self.ffn(attention_outputs[0])
ffn_output = self.activation(ffn_output)
ffn_output = self.ffn_output(ffn_output)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.full_layer_layer_norm(
ffn_output + attention_outputs[0])
# add attentions if we output them
outputs = (hidden_states,) + attention_outputs[1:]
return outputs
class TFAlbertLayerGroup(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFAlbertLayerGroup, self).__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.albert_layers = [TFAlbertLayer(config, name="albert_layers_._{}".format(
i)) for i in range(config.inner_group_num)]
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
layer_hidden_states = ()
layer_attentions = ()
for layer_index, albert_layer in enumerate(self.albert_layers):
layer_output = albert_layer(
[hidden_states, attention_mask, head_mask[layer_index]], training=training)
hidden_states = layer_output[0]
if self.output_attentions:
layer_attentions = layer_attentions + (layer_output[1],)
if self.output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (layer_hidden_states,)
if self.output_attentions:
outputs = outputs + (layer_attentions,)
# last-layer hidden state, (layer hidden states), (layer attentions)
return outputs
class TFAlbertTransformer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFAlbertTransformer, self).__init__(**kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.embedding_hidden_mapping_in = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer(
config.initializer_range), name='embedding_hidden_mapping_in')
self.albert_layer_groups = [TFAlbertLayerGroup(
config, name="albert_layer_groups_._{}".format(i)) for i in range(config.num_hidden_groups)]
def call(self, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
hidden_states = self.embedding_hidden_mapping_in(hidden_states)
all_attentions = ()
if self.output_hidden_states:
all_hidden_states = (hidden_states,)
for i in range(self.config.num_hidden_layers):
# Number of layers in a hidden group
layers_per_group = int(
self.config.num_hidden_layers / self.config.num_hidden_groups)
# Index of the hidden group
group_idx = int(
i / (self.config.num_hidden_layers / self.config.num_hidden_groups))
layer_group_output = self.albert_layer_groups[group_idx](
[hidden_states, attention_mask, head_mask[group_idx*layers_per_group:(group_idx+1)*layers_per_group]], training=training)
hidden_states = layer_group_output[0]
if self.output_attentions:
all_attentions = all_attentions + layer_group_output[-1]
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,)
# last-layer hidden state, (all hidden states), (all attentions)
return outputs
class TFAlbertPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = AlbertConfig
pretrained_model_archive_map = TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "albert"
class TFAlbertMLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super(TFAlbertMLMHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.dense = tf.keras.layers.Dense(config.embedding_size,
kernel_initializer=get_initializer(
config.initializer_range),
name='dense')
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
self.activation = ACT2FN[config.hidden_act]
else:
self.activation = config.hidden_act
self.LayerNorm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name='LayerNorm')
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
self.decoder_bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='decoder/bias')
super(TFAlbertMLMHead, self).build(input_shape)
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
hidden_states = self.decoder(hidden_states, mode="linear") + self.decoder_bias
hidden_states = hidden_states + self.bias
return hidden_states
ALBERT_START_DOCSTRING = r""" The ALBERT model was proposed in
`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`_
by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents
two parameter-reduction techniques to lower memory consumption and increase the trainig speed of BERT.
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.
.. _`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations`:
https://arxiv.org/abs/1909.11942
.. _`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.AlbertConfig`): 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.
"""
ALBERT_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, ALBERT 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]``
``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``
Albert is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
Indices can be obtained using :class:`transformers.AlbertTokenizer`.
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.
**token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` 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``
corresponds to a `sentence B` token
(see `ALBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**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 Albert Model transformer outputing raw hidden-states without any specific head on top.",
ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class TFAlbertModel(TFAlbertPreTrainedModel):
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.
**pooler_output**: ``tf.Tensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Albert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**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 AlbertTokenizer, TFAlbertModel
tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
model = TFAlbertModel.from_pretrained('bert-base-uncased')
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, **kwargs):
super(TFAlbertModel, self).__init__(config, **kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
self.encoder = TFAlbertTransformer(config, name="encoder")
self.pooler = tf.keras.layers.Dense(config.hidden_size, kernel_initializer=get_initializer(
config.initializer_range), activation='tanh', name='pooler')
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
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
"""
raise NotImplementedError
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
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 = tf.shape(input_ids)
elif inputs_embeds is not None:
input_shape = inputs_embeds.shape[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# 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.
extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
# 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)
embedding_output = self.embeddings(
[input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
encoder_outputs = self.encoder(
[embedding_output, extended_attention_mask, head_mask], training=training)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output[:, 0])
# add hidden_states and attentions if they are here
outputs = (sequence_output, pooled_output,) + encoder_outputs[1:]
# sequence_output, pooled_output, (hidden_states), (attentions)
return outputs
@add_start_docstrings("""Albert Model with a `language modeling` head on top. """,
ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class TFAlbertForMaskedLM(TFAlbertPreTrainedModel):
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 AlbertTokenizer, TFAlbertForMaskedLM
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
model = TFAlbertForMaskedLM.from_pretrained('albert-base-v2')
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(TFAlbertForMaskedLM, self).__init__(config, *inputs, **kwargs)
self.albert = TFAlbertModel(config, name='albert')
self.predictions = TFAlbertMLMHead(
config, self.albert.embeddings, name='predictions')
def get_output_embeddings(self):
return self.albert.embeddings
def call(self, inputs, **kwargs):
outputs = self.albert(inputs, **kwargs)
sequence_output = outputs[0]
prediction_scores = self.predictions(
sequence_output, training=kwargs.get('training', False))
# Add hidden states and attention if they are here
outputs = (prediction_scores,) + outputs[2:]
return outputs # prediction_scores, (hidden_states), (attentions)
@add_start_docstrings("""Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
ALBERT_START_DOCSTRING, ALBERT_INPUTS_DOCSTRING)
class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) 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 AlbertTokenizer, TFAlbertForSequenceClassification
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
model = TFAlbertForSequenceClassification.from_pretrained('albert-base-v2')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFAlbertForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.albert = TFAlbertModel(config, name='albert')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
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.albert(inputs, **kwargs)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=kwargs.get('training', False))
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # logits, (hidden_states), (attentions)

12
transformers/modeling_tf_auto.py
View File

@@ -109,6 +109,9 @@ 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.
@@ -237,6 +240,9 @@ 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.
@@ -360,6 +366,9 @@ 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.
@@ -472,6 +481,9 @@ 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.

68
transformers/modeling_tf_bert.py
View File

@@ -142,19 +142,25 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids = inputs
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
seq_length = tf.shape(input_ids)[1]
if input_ids is not None:
input_shape = tf.shape(input_ids)
else:
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, :]
if token_type_ids is None:
token_type_ids = tf.fill(tf.shape(input_ids), 0)
token_type_ids = tf.fill(input_shape, 0)
words_embeddings = tf.gather(self.word_embeddings, input_ids)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = words_embeddings + position_embeddings + token_type_embeddings
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
@@ -460,6 +466,9 @@ class TFBertMainLayer(tf.keras.layers.Layer):
self.encoder = TFBertEncoder(config, name='encoder')
self.pooler = TFBertPooler(config, name='pooler')
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
@@ -470,28 +479,39 @@ class TFBertMainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
assert len(inputs) <= 5, "Too many inputs."
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 5, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
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 = input_ids.shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.shape[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(tf.shape(input_ids), 1)
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
token_type_ids = tf.fill(tf.shape(input_ids), 0)
token_type_ids = tf.fill(input_shape, 0)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
@@ -520,7 +540,7 @@ class TFBertMainLayer(tf.keras.layers.Layer):
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids], training=training)
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
sequence_output = encoder_outputs[0]
@@ -616,6 +636,10 @@ BERT_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
@@ -698,6 +722,9 @@ class TFBertForPreTraining(TFBertPreTrainedModel):
self.nsp = TFBertNSPHead(config, name='nsp___cls')
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name='mlm___cls')
def get_output_embeddings(self):
return self.bert.embeddings
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
@@ -743,6 +770,9 @@ class TFBertForMaskedLM(TFBertPreTrainedModel):
self.bert = TFBertMainLayer(config, name='bert')
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name='mlm___cls')
def get_output_embeddings(self):
return self.bert.embeddings
def call(self, inputs, **kwargs):
outputs = self.bert(inputs, **kwargs)
@@ -888,33 +918,39 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel):
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
assert len(inputs) <= 5, "Too many inputs."
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 5, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
num_choices = tf.shape(input_ids)[1]
seq_length = tf.shape(input_ids)[2]
else:
num_choices = tf.shape(inputs_embeds)[1]
seq_length = tf.shape(inputs_embeds)[2]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length))
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
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_inputs = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask]
flat_inputs = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds]
outputs = self.bert(flat_inputs, training=training)

31
transformers/modeling_tf_ctrl.py
View File

@@ -192,6 +192,9 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
name='h_._{}'.format(i)) for i in range(config.n_layer)]
self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="layernorm")
def get_input_embeddings(self):
return self.w
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
@@ -201,7 +204,7 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
past = inputs[1] if len(inputs) > 1 else past
@@ -209,7 +212,8 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
head_mask = inputs[5] if len(inputs) > 5 else head_mask
assert len(inputs) <= 6, "Too many inputs."
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
assert len(inputs) <= 7, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
past = inputs.get('past', past)
@@ -217,12 +221,20 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 6, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 7, "Too many inputs."
else:
input_ids = inputs
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_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past is None:
past_length = 0
@@ -230,8 +242,8 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
else:
past_length = shape_list(past[0][0])[-2]
if position_ids is None:
position_ids = tf.range(past_length, shape_list(input_ids)[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
position_ids = tf.tile(position_ids, [shape_list(input_ids)[0], 1])
position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
position_ids = tf.tile(position_ids, [input_shape[0], 1])
# Attention mask.
if attention_mask is not None:
@@ -270,8 +282,8 @@ class TFCTRLMainLayer(tf.keras.layers.Layer):
token_type_embeds = 0
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
inputs_embeds = self.w(input_ids, mode='embedding')
# x = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
seq_len = input_shape[-1]
mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
@@ -374,6 +386,10 @@ CTRL_INPUTS_DOCSTRING = r""" Inputs:
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
@@ -476,6 +492,9 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel):
self.lm_head = TFCTRLLMHead(config, self.transformer.w, name="lm_head")
def get_output_embeddings(self):
return self.lm_head.input_embeddings
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_states = transformer_outputs[0]

46
transformers/modeling_tf_distilbert.py
View File

@@ -96,7 +96,7 @@ class TFEmbeddings(tf.keras.layers.Layer):
initializer=get_initializer(self.initializer_range))
super(TFEmbeddings, self).build(input_shape)
def call(self, inputs, mode="embedding", training=False):
def call(self, inputs, inputs_embeds=None, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
@@ -112,13 +112,13 @@ class TFEmbeddings(tf.keras.layers.Layer):
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs, training=training)
return self._embedding(inputs, inputs_embeds=inputs_embeds, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, training=False):
def _embedding(self, inputs, inputs_embeds=None, training=False):
"""
Parameters
----------
@@ -136,14 +136,19 @@ class TFEmbeddings(tf.keras.layers.Layer):
else:
input_ids, position_ids = inputs
if input_ids is not None:
seq_length = tf.shape(input_ids)[1]
else:
seq_length = tf.shape(inputs_embeds)[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
word_embeddings = tf.gather(self.word_embeddings, input_ids)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
embeddings = word_embeddings + position_embeddings # (bs, max_seq_length, dim)
embeddings = inputs_embeds + position_embeddings # (bs, max_seq_length, dim)
embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim)
embeddings = self.dropout(embeddings, training=training) # (bs, max_seq_length, dim)
return embeddings
@@ -398,28 +403,42 @@ class TFDistilBertMainLayer(tf.keras.layers.Layer):
self.embeddings = TFEmbeddings(config, name="embeddings") # Embeddings
self.transformer = TFTransformer(config, name="transformer") # Encoder
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def call(self, inputs, attention_mask=None, head_mask=None, training=False):
def call(self, inputs, attention_mask=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
head_mask = inputs[2] if len(inputs) > 2 else head_mask
assert len(inputs) <= 3, "Too many inputs."
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
assert len(inputs) <= 4, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 3, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 4, "Too many inputs."
else:
input_ids = inputs
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)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.ones(shape_list(input_ids)) # (bs, seq_length)
attention_mask = tf.ones(input_shape) # (bs, seq_length)
attention_mask = tf.cast(attention_mask, dtype=tf.float32)
# Prepare head mask if needed
@@ -432,7 +451,7 @@ class TFDistilBertMainLayer(tf.keras.layers.Layer):
else:
head_mask = [None] * self.num_hidden_layers
embedding_output = self.embeddings(input_ids) # (bs, seq_length, dim)
embedding_output = self.embeddings(input_ids, inputs_embeds=inputs_embeds) # (bs, seq_length, dim)
tfmr_output = self.transformer([embedding_output, attention_mask, head_mask], training=training)
return tfmr_output # last-layer hidden-state, (all hidden_states), (all attentions)
@@ -508,6 +527,10 @@ DISTILBERT_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
@@ -609,6 +632,9 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")
def get_output_embeddings(self):
return self.vocab_projector.input_embeddings
def call(self, inputs, **kwargs):
distilbert_output = self.distilbert(inputs, **kwargs)

53
transformers/modeling_tf_gpt2.py
View File

@@ -219,6 +219,9 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
name='h_._{}'.format(i)) for i in range(config.n_layer)]
self.ln_f = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name='ln_f')
def get_input_embeddings(self):
return self.wte
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
@@ -228,7 +231,7 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
past = inputs[1] if len(inputs) > 1 else past
@@ -236,7 +239,8 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
head_mask = inputs[5] if len(inputs) > 5 else head_mask
assert len(inputs) <= 6, "Too many inputs."
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
assert len(inputs) <= 7, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
past = inputs.get('past', past)
@@ -244,17 +248,28 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 6, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 7, "Too many inputs."
else:
input_ids = inputs
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_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = shape_list(past[0][0])[-2]
if position_ids is None:
position_ids = tf.range(past_length, shape_list(input_ids)[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
@@ -286,10 +301,9 @@ class TFGPT2MainLayer(tf.keras.layers.Layer):
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids, mode='embedding')
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
@@ -408,6 +422,10 @@ GPT2_INPUTS_DOCSTRING = r""" Inputs:
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare GPT2 Model transformer outputing raw hidden-states without any specific head on top.",
@@ -486,6 +504,9 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel):
super(TFGPT2LMHeadModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name='transformer')
def get_output_embeddings(self):
return self.transformer.wte
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_states = transformer_outputs[0]
@@ -556,7 +577,10 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
self.transformer = TFGPT2MainLayer(config, name='transformer')
self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
def get_output_embeddings(self):
return self.transformer.wte
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, mc_token_ids=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
past = inputs[1] if len(inputs) > 1 else past
@@ -564,8 +588,9 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
head_mask = inputs[5] if len(inputs) > 5 else head_mask
mc_token_ids = inputs[6] if len(inputs) > 6 else mc_token_ids
assert len(inputs) <= 7, "Too many inputs."
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
mc_token_ids = inputs[7] if len(inputs) > 7 else mc_token_ids
assert len(inputs) <= 8, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
past = inputs.get('past', past)
@@ -573,21 +598,25 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
mc_token_ids = inputs.get('mc_token_ids', mc_token_ids)
assert len(inputs) <= 7, "Too many inputs."
assert len(inputs) <= 8, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
input_shapes = shape_list(input_ids)
else:
input_shapes = shape_list(inputs_embeds)[:-1]
seq_length = input_shapes[-1]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length))
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
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_inputs = [flat_input_ids, past, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask]
flat_inputs = [flat_input_ids, past, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds]
transformer_outputs = self.transformer(flat_inputs, training=training)
hidden_states = transformer_outputs[0]

53
transformers/modeling_tf_openai.py
View File

@@ -217,6 +217,9 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
scale=True,
name='h_._{}'.format(i)) for i in range(config.n_layer)]
def get_input_embeddings(self):
return self.tokens_embed
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
@@ -226,26 +229,38 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
assert len(inputs) <= 5, "Too many inputs."
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
assert len(inputs) <= 6, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 5, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 6, "Too many inputs."
else:
input_ids = inputs
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_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if position_ids is None:
position_ids = tf.range(shape_list(input_ids)[-1], dtype=tf.int32)[tf.newaxis, :]
position_ids = tf.range(input_shape[-1], dtype=tf.int32)[tf.newaxis, :]
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
@@ -277,10 +292,9 @@ class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
inputs_embeds = self.tokens_embed(input_ids, mode='embedding')
position_embeds = self.positions_embed(position_ids)
if token_type_ids is not None:
@@ -389,6 +403,10 @@ OPENAI_GPT_INPUTS_DOCSTRING = r""" Inputs:
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare OpenAI GPT transformer model outputing raw hidden-states without any specific head on top.",
@@ -458,6 +476,9 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel):
super(TFOpenAIGPTLMHeadModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFOpenAIGPTMainLayer(config, name='transformer')
def get_output_embeddings(self):
return self.transformer.tokens_embed
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_states = transformer_outputs[0]
@@ -520,36 +541,44 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
self.transformer = TFOpenAIGPTMainLayer(config, name='transformer')
self.multiple_choice_head = TFSequenceSummary(config, initializer_range=config.initializer_range, name='multiple_choice_head')
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
def get_output_embeddings(self):
return self.transformer.tokens_embed
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, mc_token_ids=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
mc_token_ids = inputs[5] if len(inputs) > 5 else mc_token_ids
assert len(inputs) <= 6, "Too many inputs."
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
mc_token_ids = inputs[6] if len(inputs) > 6 else mc_token_ids
assert len(inputs) <= 7, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
mc_token_ids = inputs.get('mc_token_ids', mc_token_ids)
assert len(inputs) <= 6, "Too many inputs."
assert len(inputs) <= 7, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
input_shapes = shape_list(input_ids)
else:
input_shapes = shape_list(inputs_embeds)[:-1]
seq_length = input_shapes[-1]
flat_input_ids = tf.reshape(input_ids, (-1, seq_length))
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
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_inputs = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask]
flat_inputs = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask, inputs_embeds]
transformer_outputs = self.transformer(flat_inputs, training=training)
hidden_states = transformer_outputs[0]

18
transformers/modeling_tf_roberta.py
View File

@@ -48,13 +48,17 @@ class TFRobertaEmbeddings(TFBertEmbeddings):
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids = inputs
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
if input_ids is not None:
seq_length = tf.shape(input_ids)[1]
else:
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, :]
return super(TFRobertaEmbeddings, self)._embedding([input_ids, position_ids, token_type_ids], training=training)
return super(TFRobertaEmbeddings, self)._embedding([input_ids, position_ids, token_type_ids, inputs_embeds], training=training)
class TFRobertaMainLayer(TFBertMainLayer):
@@ -65,6 +69,9 @@ class TFRobertaMainLayer(TFBertMainLayer):
super(TFRobertaMainLayer, self).__init__(config, **kwargs)
self.embeddings = TFRobertaEmbeddings(config, name='embeddings')
def get_input_embeddings(self):
return self.embeddings
class TFRobertaPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
@@ -157,6 +164,10 @@ ROBERTA_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
@@ -276,6 +287,9 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
def get_output_embeddings(self):
return self.lm_head.decoder
def call(self, inputs, **kwargs):
outputs = self.roberta(inputs, **kwargs)

54
transformers/modeling_tf_transfo_xl.py
View File

@@ -413,6 +413,9 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
name='r_r_bias')
super(TFTransfoXLMainLayer, self).build(input_shape)
def get_input_embeddings(self):
return self.word_emb
def _resize_token_embeddings(self, new_num_tokens):
return self.word_emb
@@ -427,11 +430,11 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
def _prune_heads(self, heads):
raise NotImplementedError
def init_mems(self, data):
def init_mems(self, bsz):
if self.mem_len > 0:
mems = []
for i in range(self.n_layer):
empty = tf.zeros([self.mem_len, shape_list(data)[1], self.d_model])
empty = tf.zeros([self.mem_len, bsz, self.d_model])
mems.append(empty)
return mems
@@ -461,28 +464,37 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
return new_mems
def call(self, inputs, mems=None, head_mask=None, training=False):
def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
mems = inputs[1] if len(inputs) > 1 else mems
head_mask = inputs[2] if len(inputs) > 2 else head_mask
assert len(inputs) <= 3, "Too many inputs."
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
assert len(inputs) <= 4, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
mems = inputs.get('mems', mems)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 3, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 4, "Too many inputs."
else:
input_ids = inputs
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz]
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_ids = tf.transpose(input_ids, perm=(1, 0))
qlen, bsz = shape_list(input_ids)
elif inputs_embeds is not None:
inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
qlen, bsz = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if mems is None:
mems = self.init_mems(input_ids)
qlen, bsz = shape_list(input_ids)
mems = self.init_mems(bsz)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
@@ -494,6 +506,9 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
else:
head_mask = [None] * self.n_layer
if inputs_embeds is not None:
word_emb = inputs_embeds
else:
word_emb = self.word_emb(input_ids)
mlen = shape_list(mems[0])[0] if mems is not None else 0
@@ -626,6 +641,10 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare Bert Model transformer outputing raw hidden-states without any specific head on top.",
@@ -716,28 +735,33 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
def reset_length(self, tgt_len, ext_len, mem_len):
self.transformer.reset_length(tgt_len, ext_len, mem_len)
def init_mems(self, data):
return self.transformer.init_mems(data)
def init_mems(self, bsz):
return self.transformer.init_mems(bsz)
def call(self, inputs, mems=None, head_mask=None, labels=None, training=False):
def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, labels=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
mems = inputs[1] if len(inputs) > 1 else mems
head_mask = inputs[2] if len(inputs) > 2 else head_mask
labels = inputs[3] if len(inputs) > 3 else labels
assert len(inputs) <= 4, "Too many inputs."
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
labels = inputs[4] if len(inputs) > 4 else labels
assert len(inputs) <= 5, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
mems = inputs.get('mems', mems)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
labels = inputs.get('labels', labels)
assert len(inputs) <= 4, "Too many inputs."
assert len(inputs) <= 5, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
bsz, tgt_len = shape_list(input_ids)[:2]
else:
bsz, tgt_len = shape_list(inputs_embeds)[:2]
transformer_outputs = self.transformer([input_ids, mems, head_mask], training=training)
transformer_outputs = self.transformer([input_ids, mems, head_mask, inputs_embeds], training=training)
last_hidden = transformer_outputs[0]
pred_hid = last_hidden[:, -tgt_len:]

25
transformers/modeling_tf_utils.py
View File

@@ -35,7 +35,7 @@ class TFPreTrainedModel(tf.keras.Model):
r""" Base class for all TF models.
:class:`~transformers.TFPreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
@@ -65,6 +65,21 @@ class TFPreTrainedModel(tf.keras.Model):
# Save config in model
self.config = config
def get_input_embeddings(self):
""" Get model's input embeddings
"""
base_model = getattr(self, self.base_model_prefix, self)
if base_model is not self:
return base_model.get_input_embeddings()
else:
raise NotImplementedError
def get_output_embeddings(self):
""" Get model's output embeddings
Return None if the model doesn't have output embeddings
"""
return None # Overwrite for models with output embeddings
def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
""" Build a resized Embedding Variable from a provided token Embedding Module.
Increasing the size will add newly initialized vectors at the end
@@ -176,6 +191,9 @@ 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.
@@ -201,6 +219,7 @@ 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
@@ -209,6 +228,7 @@ 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:
@@ -236,7 +256,8 @@ class TFPreTrainedModel(tf.keras.Model):
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download,
resume_download=resume_download, proxies=proxies)
except EnvironmentError as e:
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
logger.error(

38
transformers/modeling_tf_xlm.py
View File

@@ -277,6 +277,9 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
self.prune_heads({int(layer): list(map(int, heads))})
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
@@ -288,7 +291,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
raise NotImplementedError
def call(self, inputs, attention_mask=None, langs=None, token_type_ids=None,
position_ids=None, lengths=None, cache=None, head_mask=None,
position_ids=None, lengths=None, cache=None, head_mask=None, inputs_embeds=None,
training=False): # removed: src_enc=None, src_len=None
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
@@ -299,7 +302,8 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
lengths = inputs[5] if len(inputs) > 5 else lengths
cache = inputs[6] if len(inputs) > 6 else cache
head_mask = inputs[7] if len(inputs) > 7 else head_mask
assert len(inputs) <= 8, "Too many inputs."
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
assert len(inputs) <= 9, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
@@ -309,16 +313,28 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
lengths = inputs.get('lengths', lengths)
cache = inputs.get('cache', cache)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 8, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 9, "Too many inputs."
else:
input_ids = inputs
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:
bs, slen = shape_list(input_ids)
elif inputs_embeds is not None:
bs, slen = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if lengths is None:
if input_ids is not None:
lengths = tf.reduce_sum(tf.cast(tf.not_equal(input_ids, self.pad_index), dtype=tf.int32), axis=1)
else:
lengths = tf.convert_to_tensor([slen]*bs, tf.int32)
# mask = input_ids != self.pad_index
# check inputs
bs, slen = shape_list(input_ids)
# assert shape_list(lengths)[0] == bs
tf.debugging.assert_equal(shape_list(lengths)[0], bs)
# assert lengths.max().item() <= slen
@@ -358,7 +374,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
head_mask = [None] * self.n_layers
# do not recompute cached elements
if cache is not None:
if cache is not None and input_ids is not None:
_slen = slen - cache['slen']
input_ids = input_ids[:, -_slen:]
position_ids = position_ids[:, -_slen:]
@@ -368,8 +384,10 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
attn_mask = attn_mask[:, -_slen:]
# embeddings
tensor = self.embeddings(input_ids)
tensor = tensor + self.position_embeddings(position_ids)
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
tensor = inputs_embeds + self.position_embeddings(position_ids)
if langs is not None and self.use_lang_emb:
tensor = tensor + self.lang_embeddings(langs)
if token_type_ids is not None:
@@ -530,6 +548,10 @@ XLM_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare XLM Model transformer outputing raw hidden-states without any specific head on top.",
@@ -637,6 +659,8 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
self.transformer = TFXLMMainLayer(config, name='transformer')
self.pred_layer = TFXLMPredLayer(config, self.transformer.embeddings, name='pred_layer_._proj')
def get_output_embeddings(self):
return self.pred_layer.input_embeddings
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)

32
transformers/modeling_tf_xlnet.py
View File

@@ -371,6 +371,9 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
self.layer = [TFXLNetLayer(config, name='layer_._{}'.format(i)) for i in range(config.n_layer)]
self.dropout = tf.keras.layers.Dropout(config.dropout)
def get_input_embeddings(self):
return self.word_embedding
def build(self, input_shape):
initializer = get_initializer(self.initializer_range)
self.mask_emb = self.add_weight(shape=(1, 1, self.d_model),
@@ -484,7 +487,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
return pos_emb
def call(self, inputs, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, training=False):
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
@@ -494,7 +497,8 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
token_type_ids = inputs[5] if len(inputs) > 5 else token_type_ids
input_mask = inputs[6] if len(inputs) > 6 else input_mask
head_mask = inputs[7] if len(inputs) > 7 else head_mask
assert len(inputs) <= 8, "Too many inputs."
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
assert len(inputs) <= 9, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
@@ -504,7 +508,8 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
token_type_ids = inputs.get('token_type_ids', token_type_ids)
input_mask = inputs.get('input_mask', input_mask)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 8, "Too many inputs."
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 9, "Too many inputs."
else:
input_ids = inputs
@@ -512,14 +517,23 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
# but we want a unified interface in the library with the batch size on the first dimension
# so we move here the first dimension (batch) to the end
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_ids = tf.transpose(input_ids, perm=(1, 0))
qlen, bsz = shape_list(input_ids)[:2]
elif inputs_embeds is not None:
inputs_embeds = tf.transpose(inputs_embeds, perm=(1, 0, 2))
qlen, bsz = shape_list(inputs_embeds)[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
token_type_ids = tf.transpose(token_type_ids, perm=(1, 0)) if token_type_ids is not None else None
input_mask = tf.transpose(input_mask, perm=(1, 0)) if input_mask is not None else None
attention_mask = tf.transpose(attention_mask, perm=(1, 0)) if attention_mask is not None else None
perm_mask = tf.transpose(perm_mask, perm=(1, 2, 0)) if perm_mask is not None else None
target_mapping = tf.transpose(target_mapping, perm=(1, 2, 0)) if target_mapping is not None else None
qlen, bsz = shape_list(input_ids)[:2]
mlen = shape_list(mems[0])[0] if mems is not None and mems[0] is not None else 0
klen = mlen + qlen
@@ -570,6 +584,9 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
non_tgt_mask = None
##### Word embeddings and prepare h & g hidden states
if inputs_embeds is not None:
word_emb_k = inputs_embeds
else:
word_emb_k = self.word_embedding(input_ids)
output_h = self.dropout(word_emb_k, training=training)
if target_mapping is not None:
@@ -762,6 +779,10 @@ XLNET_INPUTS_DOCSTRING = r"""
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`) ``Numpy array`` or ``tf.Tensor`` 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.
"""
@add_start_docstrings("The bare XLNet Model transformer outputing raw hidden-states without any specific head on top.",
@@ -850,6 +871,9 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel):
self.transformer = TFXLNetMainLayer(config, name='transformer')
self.lm_loss = TFXLNetLMHead(config, self.transformer.word_embedding, name='lm_loss')
def get_output_embeddings(self):
return self.lm_loss.input_embeddings
def call(self, inputs, **kwargs):
transformer_outputs = self.transformer(inputs, **kwargs)
hidden_state = transformer_outputs[0]

42
transformers/modeling_transfo_xl.py
View File

@@ -553,6 +553,10 @@ TRANSFO_XL_INPUTS_DOCSTRING = r"""
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.
"""
@add_start_docstrings("The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
@@ -657,12 +661,12 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
logger.info("Head pruning is not implemented for Transformer-XL model")
pass
def init_mems(self, data):
def init_mems(self, bsz):
if self.mem_len > 0:
mems = []
param = next(self.parameters())
for i in range(self.n_layer):
empty = torch.zeros(self.mem_len, data.size(1), self.config.d_model,
empty = torch.zeros(self.mem_len, bsz, self.config.d_model,
dtype=param.dtype, device=param.device)
mems.append(empty)
@@ -693,15 +697,22 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
return new_mems
def forward(self, input_ids, mems=None, head_mask=None):
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None):
# the original code for Transformer-XL used shapes [len, bsz] but we want a unified interface in the library
# so we transpose here from shape [bsz, len] to shape [len, bsz]
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_ids = input_ids.transpose(0, 1).contiguous()
qlen, bsz = input_ids.size()
elif inputs_embeds is not None:
inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if mems is None:
mems = self.init_mems(input_ids)
qlen, bsz = input_ids.size()
mems = self.init_mems(bsz)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
@@ -718,6 +729,9 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
else:
head_mask = [None] * self.n_layer
if inputs_embeds is not None:
word_emb = inputs_embeds
else:
word_emb = self.word_emb(input_ids)
mlen = mems[0].size(0) if mems is not None else 0
@@ -860,14 +874,18 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
def reset_length(self, tgt_len, ext_len, mem_len):
self.transformer.reset_length(tgt_len, ext_len, mem_len)
def init_mems(self, data):
return self.transformer.init_mems(data)
def init_mems(self, bsz):
return self.transformer.init_mems(bsz)
def forward(self, input_ids, mems=None, head_mask=None, labels=None):
bsz = input_ids.size(0)
tgt_len = input_ids.size(1)
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None, labels=None):
if input_ids is not None:
bsz, tgt_len = input_ids.size(0), input_ids.size(1)
elif inputs_embeds is not None:
bsz, tgt_len = inputs_embeds.size(0), inputs_embeds.size(1)
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
transformer_outputs = self.transformer(input_ids, mems=mems, head_mask=head_mask)
transformer_outputs = self.transformer(input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds)
last_hidden = transformer_outputs[0]
pred_hid = last_hidden[:, -tgt_len:]

16
transformers/modeling_utils.py
View File

@@ -53,7 +53,7 @@ class PreTrainedModel(nn.Module):
r""" Base class for all models.
:class:`~transformers.PreTrainedModel` takes care of storing the configuration of the models and handles methods for loading/downloading/saving models
as well as a few methods commons to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
as well as a few methods common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
Class attributes (overridden by derived classes):
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
@@ -238,7 +238,7 @@ class PreTrainedModel(nn.Module):
"""
assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
# Only save the model it-self if we are using distributed training
# Only save the model itself if we are using distributed training
model_to_save = self.module if hasattr(self, 'module') else self
# Save configuration file
@@ -291,6 +291,9 @@ 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.
@@ -315,11 +318,16 @@ class PreTrainedModel(nn.Module):
model = BertModel.from_pretrained('./tf_model/my_tf_checkpoint.ckpt.index', from_tf=True, config=config)
"""
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.")
config = kwargs.pop('config', None)
state_dict = kwargs.pop('state_dict', None)
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)
@@ -329,6 +337,7 @@ 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
)
@@ -361,7 +370,8 @@ class PreTrainedModel(nn.Module):
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download,
proxies=proxies, resume_download=resume_download)
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(

59
transformers/modeling_xlm.py
View File

@@ -311,6 +311,10 @@ XLM_INPUTS_DOCSTRING = r"""
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.
"""
@add_start_docstrings("The bare XLM Model transformer outputting raw hidden-states without any specific head on top.",
@@ -421,14 +425,21 @@ class XLMModel(XLMPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.attentions[layer].prune_heads(heads)
def forward(self, input_ids, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None): # removed: src_enc=None, src_len=None
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None): # removed: src_enc=None, src_len=None
if input_ids is not None:
bs, slen = input_ids.size()
else:
bs, slen = inputs_embeds.size()[:-1]
if lengths is None:
if input_ids is not None:
lengths = (input_ids != self.pad_index).sum(dim=1).long()
else:
lengths = torch.LongTensor([slen]*bs)
# mask = input_ids != self.pad_index
# check inputs
bs, slen = input_ids.size()
assert lengths.size(0) == bs
assert lengths.max().item() <= slen
# input_ids = input_ids.transpose(0, 1) # batch size as dimension 0
@@ -442,10 +453,12 @@ class XLMModel(XLMPreTrainedModel):
# if self.is_decoder and src_enc is not None:
# src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None]
device = input_ids.device if input_ids is not None else inputs_embeds.device
# position_ids
if position_ids is None:
position_ids = input_ids.new((slen,)).long()
position_ids = torch.arange(slen, out=position_ids).unsqueeze(0)
position_ids = torch.arange(slen, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand((bs, slen))
else:
assert position_ids.size() == (bs, slen) # (slen, bs)
# position_ids = position_ids.transpose(0, 1)
@@ -471,7 +484,7 @@ class XLMModel(XLMPreTrainedModel):
head_mask = [None] * self.n_layers
# do not recompute cached elements
if cache is not None:
if cache is not None and input_ids is not None:
_slen = slen - cache['slen']
input_ids = input_ids[:, -_slen:]
position_ids = position_ids[:, -_slen:]
@@ -481,8 +494,10 @@ class XLMModel(XLMPreTrainedModel):
attn_mask = attn_mask[:, -_slen:]
# embeddings
tensor = self.embeddings(input_ids)
tensor = tensor + self.position_embeddings(position_ids).expand_as(tensor)
if inputs_embeds is None:
inputs_embeds = self.embeddings(input_ids)
tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
if langs is not None and self.use_lang_emb:
tensor = tensor + self.lang_embeddings(langs)
if token_type_ids is not None:
@@ -624,8 +639,8 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
def get_output_embeddings(self):
return self.pred_layer.proj
def forward(self, input_ids, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
@@ -633,7 +648,8 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
outputs = self.pred_layer(output, labels)
@@ -685,8 +701,8 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, labels=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
@@ -694,7 +710,8 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
logits = self.sequence_summary(output)
@@ -768,8 +785,8 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, start_positions=None, end_positions=None):
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
langs=langs,
@@ -777,7 +794,8 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = transformer_outputs[0]
@@ -863,8 +881,8 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, start_positions=None, end_positions=None,
def forward(self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None,
lengths=None, cache=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None,
is_impossible=None, cls_index=None, p_mask=None):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
@@ -873,7 +891,8 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]

61
transformers/modeling_xlnet.py
View File

@@ -558,6 +558,10 @@ XLNET_INPUTS_DOCSTRING = r"""
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.
"""
@add_start_docstrings("The bare XLNet Model transformer outputting raw hidden-states without any specific head on top.",
@@ -712,19 +716,29 @@ class XLNetModel(XLNetPreTrainedModel):
pos_emb = pos_emb.to(next(self.parameters()))
return pos_emb
def forward(self, input_ids, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None):
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):
# the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension
# so we move here the first dimension (batch) to the end
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_ids = input_ids.transpose(0, 1).contiguous()
qlen, bsz = input_ids.shape[0], input_ids.shape[1]
elif inputs_embeds is not None:
inputs_embeds.transpose(0, 1).contiguous()
qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None
qlen, bsz = input_ids.shape[0], input_ids.shape[1]
mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0
klen = mlen + qlen
@@ -777,6 +791,9 @@ class XLNetModel(XLNetPreTrainedModel):
non_tgt_mask = None
##### Word embeddings and prepare h & g hidden states
if inputs_embeds is not None:
word_emb_k = inputs_embeds
else:
word_emb_k = self.word_embedding(input_ids)
output_h = self.dropout(word_emb_k)
if target_mapping is not None:
@@ -924,8 +941,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
def get_output_embeddings(self):
return self.lm_loss
def forward(self, input_ids, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, labels=None):
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):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
mems=mems,
@@ -933,7 +950,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
logits = self.lm_loss(transformer_outputs[0])
@@ -998,8 +1016,8 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None, labels=None):
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):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
mems=mems,
@@ -1007,7 +1025,8 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
output = self.sequence_summary(output)
@@ -1049,6 +1068,10 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
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
@@ -1093,9 +1116,9 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
def forward(self, input_ids=None, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None,
labels=None, head_mask=None):
labels=None, head_mask=None, inputs_embeds=None):
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
@@ -1106,7 +1129,7 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
transformer_outputs = self.transformer(flat_input_ids, token_type_ids=flat_token_type_ids,
input_mask=flat_input_mask, attention_mask=flat_attention_mask,
mems=mems, perm_mask=perm_mask, target_mapping=target_mapping,
head_mask=head_mask)
head_mask=head_mask, inputs_embeds=inputs_embeds)
output = transformer_outputs[0]
@@ -1178,8 +1201,8 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None,
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,
start_positions=None, end_positions=None):
outputs = self.transformer(input_ids,
@@ -1189,7 +1212,8 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
sequence_output = outputs[0]
@@ -1294,8 +1318,8 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
self.init_weights()
def forward(self, input_ids, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
token_type_ids=None, input_mask=None, head_mask=None,
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,
start_positions=None, end_positions=None, is_impossible=None, cls_index=None, p_mask=None,):
transformer_outputs = self.transformer(input_ids,
attention_mask=attention_mask,
@@ -1304,7 +1328,8 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
target_mapping=target_mapping,
token_type_ids=token_type_ids,
input_mask=input_mask,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)
hidden_states = transformer_outputs[0]
start_logits = self.start_logits(hidden_states, p_mask=p_mask)

104
transformers/optimization.py
View File

@@ -23,85 +23,65 @@ from torch.optim.lr_scheduler import LambdaLR
logger = logging.getLogger(__name__)
class ConstantLRSchedule(LambdaLR):
""" Constant learning rate schedule.
def get_constant_schedule(optimizer, last_epoch=-1):
""" Create a schedule with a constant learning rate.
"""
def __init__(self, optimizer, last_epoch=-1):
super(ConstantLRSchedule, self).__init__(optimizer, lambda _: 1.0, last_epoch=last_epoch)
return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
class WarmupConstantSchedule(LambdaLR):
""" Linear warmup and then constant.
Linearly increases learning rate schedule from 0 to 1 over `warmup_steps` training steps.
Keeps learning rate schedule equal to 1. after warmup_steps.
def get_constant_schedule_with_warmup(optimizer, num_warmup_steps, last_epoch=-1):
""" Create a schedule with a constant learning rate preceded by a warmup
period during which the learning rate increases linearly between 0 and 1.
"""
def __init__(self, optimizer, warmup_steps, last_epoch=-1):
self.warmup_steps = warmup_steps
super(WarmupConstantSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)
def lr_lambda(self, step):
if step < self.warmup_steps:
return float(step) / float(max(1.0, self.warmup_steps))
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1.0, num_warmup_steps))
return 1.
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
class WarmupLinearSchedule(LambdaLR):
""" Linear warmup and then linear decay.
Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps.
Linearly decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps.
def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1):
""" Create a schedule with a learning rate that decreases linearly after
linearly increasing during a warmup period.
"""
def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1):
self.warmup_steps = warmup_steps
self.t_total = t_total
super(WarmupLinearSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps)))
def lr_lambda(self, step):
if step < self.warmup_steps:
return float(step) / float(max(1, self.warmup_steps))
return max(0.0, float(self.t_total - step) / float(max(1.0, self.t_total - self.warmup_steps)))
return LambdaLR(optimizer, lr_lambda, last_epoch)
class WarmupCosineSchedule(LambdaLR):
""" Linear warmup and then cosine decay.
Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps.
Decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps following a cosine curve.
If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=.5, last_epoch=-1):
""" Create a schedule with a learning rate that decreases following the
values of the cosine function between 0 and `pi * cycles` after a warmup
period during which it increases linearly between 0 and 1.
"""
def __init__(self, optimizer, warmup_steps, t_total, cycles=.5, last_epoch=-1):
self.warmup_steps = warmup_steps
self.t_total = t_total
self.cycles = cycles
super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return max(0., 0.5 * (1. + math.cos(math.pi * float(num_cycles) * 2. * progress)))
def lr_lambda(self, step):
if step < self.warmup_steps:
return float(step) / float(max(1.0, self.warmup_steps))
# progress after warmup
progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))
return max(0.0, 0.5 * (1. + math.cos(math.pi * float(self.cycles) * 2.0 * progress)))
return LambdaLR(optimizer, lr_lambda, last_epoch)
class WarmupCosineWithHardRestartsSchedule(LambdaLR):
""" Linear warmup and then cosine cycles with hard restarts.
Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps.
If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
learning rate (with hard restarts).
def get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=1., last_epoch=-1):
""" Create a schedule with a learning rate that decreases following the
values of the cosine function with several hard restarts, after a warmup
period during which it increases linearly between 0 and 1.
"""
def __init__(self, optimizer, warmup_steps, t_total, cycles=1., last_epoch=-1):
self.warmup_steps = warmup_steps
self.t_total = t_total
self.cycles = cycles
super(WarmupCosineWithHardRestartsSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch)
def lr_lambda(self, step):
if step < self.warmup_steps:
return float(step) / float(max(1, self.warmup_steps))
# progress after warmup
progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps))
if progress >= 1.0:
return 0.0
return max(0.0, 0.5 * (1. + math.cos(math.pi * ((float(self.cycles) * progress) % 1.0))))
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
if progress >= 1.:
return 0.
return max(0., 0.5 * (1. + math.cos(math.pi * ((float(num_cycles) * progress) % 1.))))
return LambdaLR(optimizer, lr_lambda, last_epoch)
class AdamW(Optimizer):

12
transformers/tests/conftest.py
View File

@@ -7,6 +7,13 @@ 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):
@@ -17,3 +24,8 @@ def pytest_collection_modifyitems(config, items):
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")

BIN
transformers/tests/fixtures/spiece.model vendored Normal file
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237
transformers/tests/modeling_albert_test.py Normal file
View File

@@ -0,0 +1,237 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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
from __future__ import division
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
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")
class AlbertModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else ()
class AlbertModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
embedding_size=16,
hidden_size=36,
num_hidden_layers=6,
num_hidden_groups=6,
num_attention_heads=6,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.num_hidden_groups = num_hidden_groups
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = AlbertConfig(
vocab_size_or_config_json_file=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
num_hidden_groups=self.num_hidden_groups)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_loss_output(self, result):
self.parent.assertListEqual(
list(result["loss"].size()),
[])
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.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)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output,
"pooled_output": pooled_output,
}
self.parent.assertListEqual(
list(result["sequence_output"].size()),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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.eval()
loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
result = {
"loss": loss,
"prediction_scores": prediction_scores,
}
self.parent.assertListEqual(
list(result["prediction_scores"].size()),
[self.batch_size, self.seq_length, self.vocab_size])
self.check_loss_output(result)
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.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)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(
list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(
list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
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.eval()
loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.num_labels])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = AlbertModelTest.AlbertModelTester(self)
self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_albert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_for_masked_lm(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_for_sequence_classification(*config_and_inputs)
@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]:
model = AlbertModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()

21
transformers/tests/modeling_bert_test.py
View File

@@ -35,6 +35,7 @@ else:
pytestmark = pytest.mark.skip("Require Torch")
@pytest.mark.usefixtures("use_cuda")
class BertModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
@@ -66,6 +67,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
num_labels=3,
num_choices=4,
scope=None,
device='cpu',
):
self.parent = parent
self.batch_size = batch_size
@@ -89,25 +91,26 @@ class BertModelTest(CommonTestCases.CommonModelTester):
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.device = device
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).to(self.device)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2).to(self.device)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size).to(self.device)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size).to(self.device)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels).to(self.device)
choice_labels = ids_tensor([self.batch_size], self.num_choices).to(self.device)
config = BertConfig(
vocab_size_or_config_json_file=self.vocab_size,
@@ -141,6 +144,7 @@ class BertModelTest(CommonTestCases.CommonModelTester):
def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertModel(config=config)
model.to(input_ids.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)
@@ -309,7 +313,10 @@ class BertModelTest(CommonTestCases.CommonModelTester):
def test_config(self):
self.config_tester.run_common_tests()
def test_bert_model(self):
def test_bert_model(self, use_cuda=False):
# ^^ This could be a real fixture
if use_cuda:
self.model_tester.device = "cuda"
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_model(*config_and_inputs)

25
transformers/tests/modeling_common_test.py
View File

@@ -35,7 +35,7 @@ if is_torch_available():
import torch
import numpy as np
from transformers import (PretrainedConfig, PreTrainedModel,
from transformers import (AdaptiveEmbedding, PretrainedConfig, PreTrainedModel,
BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
@@ -468,9 +468,15 @@ class CommonTestCases:
for model_class in self.all_model_classes:
model = model_class(config)
model.get_input_embeddings()
self.assertIsInstance(
model.get_input_embeddings(),
(torch.nn.Embedding, AdaptiveEmbedding)
)
model.set_input_embeddings(torch.nn.Embedding(10, 10))
model.get_output_embeddings()
x = model.get_output_embeddings()
self.assertTrue(
x is None or isinstance(x, torch.nn.Linear)
)
def test_tie_model_weights(self):
if not self.test_torchscript:
@@ -525,6 +531,19 @@ class CommonTestCases:
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
wte = model.get_input_embeddings()
inputs_dict["inputs_embeds"] = wte(input_ids)
outputs = model(**inputs_dict)
class GPTModelTester(CommonModelTester):

20
transformers/tests/modeling_distilbert_test.py
View File

@@ -23,6 +23,7 @@ from transformers import is_torch_available
if is_torch_available():
from transformers import (DistilBertConfig, DistilBertModel, DistilBertForMaskedLM,
DistilBertForTokenClassification,
DistilBertForQuestionAnswering, DistilBertForSequenceClassification)
else:
pytestmark = pytest.mark.skip("Require Torch")
@@ -180,6 +181,21 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester):
[self.batch_size, self.num_labels])
self.check_loss_output(result)
def create_and_check_distilbert_for_token_classification(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = DistilBertForTokenClassification(config=config)
model.eval()
loss, logits = model(input_ids, attention_mask=input_mask, labels=token_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(
list(result["logits"].size()),
[self.batch_size, self.seq_length, self.num_labels])
self.check_loss_output(result)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
@@ -209,6 +225,10 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*config_and_inputs)
# @pytest.mark.slow
# def test_model_from_pretrained(self):
# cache_dir = "/tmp/transformers_test/"

231
transformers/tests/modeling_tf_albert_test.py Normal file
View File

@@ -0,0 +1,231 @@
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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
from __future__ import division
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 transformers import AlbertConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_albert import (TFAlbertModel, TFAlbertForMaskedLM,
TFAlbertForSequenceClassification,
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (
TFAlbertModel,
TFAlbertForMaskedLM,
TFAlbertForSequenceClassification
) if is_tf_available() else ()
class TFAlbertModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
embedding_size=16,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor(
[self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor(
[self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor(
[self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor(
[self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor(
[self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = AlbertConfig(
vocab_size_or_config_json_file=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFAlbertModel(config=config)
# inputs = {'input_ids': input_ids,
# 'attention_mask': input_mask,
# 'token_type_ids': token_type_ids}
# sequence_output, pooled_output = model(**inputs)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output, pooled_output = model(inputs)
inputs = [input_ids, input_mask]
sequence_output, pooled_output = model(inputs)
sequence_output, pooled_output = model(input_ids)
result = {
"sequence_output": sequence_output.numpy(),
"pooled_output": pooled_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].shape), [
self.batch_size, self.hidden_size])
def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFAlbertForMaskedLM(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores, = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
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 = TFAlbertForSequenceClassification(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
logits, = model(inputs)
result = {
"logits": logits.numpy(),
}
self.parent.assertListEqual(
list(result["logits"].shape),
[self.batch_size, self.num_labels])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids,
'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = TFAlbertModelTest.TFAlbertModelTester(self)
self.config_tester = ConfigTester(
self, config_class=AlbertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_albert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_for_masked_lm(
*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_albert_for_sequence_classification(
*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
# for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ['albert-base-uncased']:
model = TFAlbertModel.from_pretrained(
model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()

4
transformers/tests/modeling_tf_bert_test.py
View File

@@ -131,10 +131,6 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
def create_and_check_bert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = TFBertModel(config=config)
# inputs = {'input_ids': input_ids,
# 'attention_mask': input_mask,
# 'token_type_ids': token_type_ids}
# sequence_output, pooled_output = model(**inputs)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}

39
transformers/tests/modeling_tf_common_test.py
View File

@@ -360,6 +360,16 @@ class TFCommonTestCases:
# self.assertTrue(models_equal)
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
x = model.get_output_embeddings()
assert x is None or isinstance(x, tf.keras.layers.Layer)
def test_tie_model_weights(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -401,6 +411,35 @@ class TFCommonTestCases:
first, second = model(inputs_dict, training=False)[0], model(inputs_dict, training=False)[0]
self.assertTrue(tf.math.equal(first, second).numpy().all())
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
del inputs_dict["input_ids"]
for model_class in self.all_model_classes:
model = model_class(config)
wte = model.get_input_embeddings()
try:
x = wte(input_ids, mode="embedding")
except:
try:
x = wte([input_ids], mode="embedding")
except:
try:
x = wte([input_ids, None, None, None], mode="embedding")
except:
if hasattr(self.model_tester, "embedding_size"):
x = tf.ones(input_ids.shape + [self.model_tester.embedding_size], dtype=tf.dtypes.float32)
else:
x = tf.ones(input_ids.shape + [self.model_tester.hidden_size], dtype=tf.dtypes.float32)
# ^^ In our TF models, the input_embeddings can take slightly different forms,
# so we try a few of them.
# We used to fall back to just synthetically creating a dummy tensor of ones:
#
inputs_dict["inputs_embeds"] = x
outputs = model(inputs_dict)
def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
"""Creates a random int32 tensor of the shape within the vocab size."""

29
transformers/tests/optimization_test.py
View File

@@ -25,8 +25,12 @@ from transformers import is_torch_available
if is_torch_available():
import torch
from transformers import (AdamW, ConstantLRSchedule, WarmupConstantSchedule,
WarmupCosineSchedule, WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
from transformers import (AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup)
else:
pytestmark = pytest.mark.skip("Require Torch")
@@ -87,59 +91,60 @@ class ScheduleInitTest(unittest.TestCase):
self.assertAlmostEqual(a, b, delta=tol)
def test_constant_scheduler(self):
scheduler = ConstantLRSchedule(self.optimizer)
scheduler = get_constant_schedule(self.optimizer)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [10.] * self.num_steps
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = ConstantLRSchedule(self.optimizer)
scheduler = get_constant_schedule(self.optimizer)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_constant_scheduler(self):
scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = WarmupConstantSchedule(self.optimizer, warmup_steps=4)
scheduler = get_constant_schedule_with_warmup(self.optimizer, num_warmup_steps=4)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_linear_scheduler(self):
scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListEqual([l[0] for l in lrs], expected_learning_rates)
scheduler = WarmupLinearSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_linear_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_scheduler(self):
scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
scheduler = WarmupCosineSchedule(self.optimizer, warmup_steps=2, t_total=10)
scheduler = get_cosine_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
def test_warmup_cosine_hard_restart_scheduler(self):
scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10)
lrs = unwrap_schedule(scheduler, self.num_steps)
expected_learning_rates = [5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46, 0.0]
self.assertEqual(len(lrs[0]), 1)
self.assertListAlmostEqual([l[0] for l in lrs], expected_learning_rates, tol=1e-2)
scheduler = WarmupCosineWithHardRestartsSchedule(self.optimizer, warmup_steps=2, cycles=2, t_total=10)
scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(self.optimizer, num_warmup_steps=2, num_cycles=2, num_training_steps=10)
lrs_2 = unwrap_and_save_reload_schedule(scheduler, self.num_steps)
self.assertListEqual([l[0] for l in lrs], [l[0] for l in lrs_2])
if __name__ == "__main__":
unittest.main()

78
transformers/tests/tokenization_albert_test.py Normal file
View File

@@ -0,0 +1,78 @@
# coding=utf-8
# Copyright 2019 Hugging Face 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.
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import unittest
from transformers.tokenization_albert import (AlbertTokenizer, SPIECE_UNDERLINE)
from .tokenization_tests_commons import CommonTestCases
SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'fixtures/spiece.model')
class AlbertTokenizationTest(CommonTestCases.CommonTokenizerTester):
tokenizer_class = AlbertTokenizer
def setUp(self):
super(AlbertTokenizationTest, self).setUp()
# We have a SentencePiece fixture for testing
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AlbertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self):
input_text = u"this is a test"
output_text = u"this is a test"
return input_text, output_text
def test_full_tokenizer(self):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize(u'This is a test')
self.assertListEqual(tokens, [u'▁this', u'▁is', u'▁a', u'▁test'])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens), [48, 25, 21, 1289])
tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
self.assertListEqual(tokens, [u'▁i', u'▁was', u'▁born', u'▁in', u'▁9', u'2000', u',', u'▁and', u'▁this', u'▁is', u'▁fal', u's', u'é', u'.'])
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(ids, [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(back_tokens, ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'])
def test_sequence_builders(self):
tokenizer = AlbertTokenizer(SAMPLE_VOCAB)
text = tokenizer.encode("sequence builders")
text_2 = tokenizer.encode("multi-sequence build")
encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [tokenizer.sep_token_id]
if __name__ == '__main__':
unittest.main()

71
transformers/tests/tokenization_tests_commons.py
View File

@@ -110,6 +110,36 @@ class CommonTestCases:
self.assertListEqual(subwords, subwords_loaded)
def test_added_tokens_do_lower_case(self):
tokenizer = self.get_tokenizer(do_lower_case=True)
text = "aaaaa bbbbbb low cccccccccdddddddd l"
text2 = "AAAAA BBBBBB low CCCCCCCCCDDDDDDDD l"
toks0 = tokenizer.tokenize(text) # toks before adding new_toks
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd", 'AAAAA BBBBBB', 'CCCCCCCCCDDDDDDDD']
added = tokenizer.add_tokens(new_toks)
self.assertEqual(added, 2)
toks = tokenizer.tokenize(text)
toks2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks), len(toks2))
self.assertNotEqual(len(toks), len(toks0)) # toks0 should be longer
self.assertListEqual(toks, toks2)
tokenizer = self.get_tokenizer(do_lower_case=False)
added = tokenizer.add_tokens(new_toks)
self.assertEqual(added, 4)
toks = tokenizer.tokenize(text)
toks2 = tokenizer.tokenize(text2)
self.assertEqual(len(toks), len(toks2)) # Length should still be the same
self.assertNotEqual(len(toks), len(toks0))
self.assertNotEqual(toks[0], toks2[0]) # But at least the first tokens should differ
def test_add_tokens_tokenizer(self):
tokenizer = self.get_tokenizer()
@@ -160,6 +190,26 @@ class CommonTestCases:
self.assertEqual(tokens[0], tokenizer.eos_token_id)
self.assertEqual(tokens[-2], tokenizer.pad_token_id)
def test_add_special_tokens(self):
tokenizer = self.get_tokenizer()
input_text, output_text = self.get_input_output_texts()
special_token = "[SPECIAL TOKEN]"
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
assert len(encoded_special_token) == 1
text = " ".join([input_text, special_token, output_text])
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
output_encoded = tokenizer.encode(output_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
assert encoded == input_encoded + special_token_id + output_encoded
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
assert special_token not in decoded
def test_required_methods_tokenizer(self):
tokenizer = self.get_tokenizer()
@@ -223,7 +273,11 @@ class CommonTestCases:
sequence = tokenizer.encode(seq_0, add_special_tokens=False)
num_added_tokens = tokenizer.num_added_tokens()
total_length = len(sequence) + num_added_tokens
information = tokenizer.encode_plus(seq_0, max_length=total_length - 2, add_special_tokens=True, stride=stride)
information = tokenizer.encode_plus(seq_0,
max_length=total_length - 2,
add_special_tokens=True,
stride=stride,
return_overflowing_tokens=True)
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
@@ -250,10 +304,12 @@ class CommonTestCases:
)
information = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2, add_special_tokens=True,
stride=stride, truncation_strategy='only_second')
stride=stride, truncation_strategy='only_second',
return_overflowing_tokens=True)
information_first_truncated = tokenizer.encode_plus(seq_0, seq_1, max_length=len(sequence) - 2,
add_special_tokens=True, stride=stride,
truncation_strategy='only_first')
truncation_strategy='only_first',
return_overflowing_tokens=True)
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
@@ -285,7 +341,7 @@ class CommonTestCases:
# Testing single inputs
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True, return_special_tokens_mask=True)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
@@ -297,7 +353,8 @@ class CommonTestCases:
# Testing inputs pairs
encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) + tokenizer.encode(sequence_1,
add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, sequence_1, add_special_tokens=True,
return_special_tokens_mask=True)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
@@ -309,7 +366,9 @@ class CommonTestCases:
# Testing with already existing special tokens
if tokenizer.cls_token_id == tokenizer.unk_token_id and tokenizer.cls_token_id == tokenizer.unk_token_id:
tokenizer.add_special_tokens({'cls_token': '</s>', 'sep_token': '<s>'})
encoded_sequence_dict = tokenizer.encode_plus(sequence_0, add_special_tokens=True)
encoded_sequence_dict = tokenizer.encode_plus(sequence_0,
add_special_tokens=True,
return_special_tokens_mask=True)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask_orig = encoded_sequence_dict["special_tokens_mask"]
special_tokens_mask = tokenizer.get_special_tokens_mask(encoded_sequence_w_special, already_has_special_tokens=True)

252
transformers/tokenization_albert.py Normal file
View File

@@ -0,0 +1,252 @@
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and 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.
""" Tokenization classes for ALBERT model."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from .tokenization_utils import PreTrainedTokenizer
import logging
import unicodedata
import six
import os
from shutil import copyfile
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'spiece.model'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'albert-base-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-spiece.model",
'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-spiece.model",
'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-spiece.model",
'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-spiece.model",
'albert-base-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-spiece.model",
'albert-large-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-spiece.model",
'albert-xlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-spiece.model",
'albert-xxlarge-v2': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-spiece.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'albert-base-v1': 512,
'albert-large-v1': 512,
'albert-xlarge-v1': 512,
'albert-xxlarge-v1': 512,
'albert-base-v2': 512,
'albert-large-v2': 512,
'albert-xlarge-v2': 512,
'albert-xxlarge-v2': 512,
}
SPIECE_UNDERLINE = u'▁'
class AlbertTokenizer(PreTrainedTokenizer):
"""
SentencePiece based tokenizer. Peculiarities:
- requires `SentencePiece <https://github.com/google/sentencepiece>`_
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file,
do_lower_case=True, remove_space=True, keep_accents=False,
bos_token="[CLS]", eos_token="[SEP]", unk_token="<unk>", sep_token="[SEP]",
pad_token="<pad>", cls_token="[CLS]", mask_token="[MASK]>", **kwargs):
super(AlbertTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token,
unk_token=unk_token, sep_token=sep_token,
pad_token=pad_token, cls_token=cls_token,
mask_token=mask_token, **kwargs)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 3 # take into account special tokens
try:
import sentencepiece as spm
except ImportError:
logger.warning("You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece")
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(vocab_file)
@property
def vocab_size(self):
return len(self.sp_model)
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
try:
import sentencepiece as spm
except ImportError:
logger.warning("You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"
"pip install sentencepiece")
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file)
def preprocess_text(self, inputs):
if self.remove_space:
outputs = ' '.join(inputs.strip().split())
else:
outputs = inputs
outputs = outputs.replace("``", '"').replace("''", '"')
if six.PY2 and isinstance(outputs, str):
outputs = outputs.decode('utf-8')
if not self.keep_accents:
outputs = unicodedata.normalize('NFKD', outputs)
outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text, return_unicode=True, sample=False):
""" Tokenize a string.
return_unicode is used only for py2
"""
text = self.preprocess_text(text)
# note(zhiliny): in some systems, sentencepiece only accepts str for py2
if six.PY2 and isinstance(text, unicode):
text = text.encode('utf-8')
if not sample:
pieces = self.sp_model.EncodeAsPieces(text)
else:
pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit():
cur_pieces = self.sp_model.EncodeAsPieces(
piece[:-1].replace(SPIECE_UNDERLINE, ''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
# note(zhiliny): convert back to unicode for py2
if six.PY2 and return_unicode:
ret_pieces = []
for piece in new_pieces:
if isinstance(piece, str):
piece = piece.decode('utf-8')
ret_pieces.append(piece)
new_pieces = ret_pieces
return new_pieces
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index, return_unicode=True):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
token = self.sp_model.IdToPiece(index)
if six.PY2 and return_unicode and isinstance(token, str):
token = token.decode('utf-8')
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = ''.join(tokens).replace(SPIECE_UNDERLINE, ' ').strip()
return out_string
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
An ALBERT sequence has the following format:
single sequence: [CLS] X [SEP]
pair of sequences: [CLS] A [SEP] B [SEP]
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError("You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model.")
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
An ALBERT sequence pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
| first sequence | second sequence
if token_ids_1 is None, only returns the first portion of the mask (0's).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_vocabulary(self, save_directory):
""" Save the sentencepiece vocabulary (copy original file) and special tokens file
to a directory.
"""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)

12
transformers/tokenization_auto.py
View File

@@ -27,6 +27,7 @@ from .tokenization_xlnet import XLNetTokenizer
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
from .tokenization_camembert import CamembertTokenizer
logger = logging.getLogger(__name__)
@@ -41,6 +42,7 @@ class AutoTokenizer(object):
The tokenizer class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `camembert`: CamembertTokenizer (CamemBERT model)
- contains `distilbert`: DistilBertTokenizer (DistilBert model)
- contains `roberta`: RobertaTokenizer (RoBERTa model)
- contains `bert`: BertTokenizer (Bert model)
@@ -64,8 +66,9 @@ class AutoTokenizer(object):
The tokenizer class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `camembert`: CamembertTokenizer (CamemBERT model)
- contains `distilbert`: DistilBertTokenizer (DistilBert model)
- contains `roberta`: RobertaTokenizer (XLM model)
- contains `roberta`: RobertaTokenizer (RoBERTa model)
- contains `bert`: BertTokenizer (Bert model)
- contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
- contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
@@ -87,6 +90,9 @@ class AutoTokenizer(object):
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary 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.
@@ -103,6 +109,8 @@ class AutoTokenizer(object):
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'camembert' in pretrained_model_name_or_path:
return CamembertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
@@ -121,4 +129,4 @@ class AutoTokenizer(object):
return CTRLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta', 'ctrl'".format(pretrained_model_name_or_path))
"'xlm', 'roberta', 'camembert', 'ctrl'".format(pretrained_model_name_or_path))

2
transformers/tokenization_bert.py
View File

@@ -220,7 +220,7 @@ class BertTokenizer(PreTrainedTokenizer):
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:

157
transformers/tokenization_camembert.py Normal file
View File

@@ -0,0 +1,157 @@
# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and 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
""" Tokenization classes for Camembert model."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import os
from shutil import copyfile
import sentencepiece as spm
from transformers.tokenization_utils import PreTrainedTokenizer
logger = logging.getLogger(__name__)
VOCAB_FILES_NAMES = {'vocab_file': 'sentencepiece.bpe.model'}
PRETRAINED_VOCAB_FILES_MAP = {
'vocab_file':
{
'camembert-base': "https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-sentencepiece.bpe.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'camembert-base': None,
}
class CamembertTokenizer(PreTrainedTokenizer):
"""
Adapted from RobertaTokenizer and XLNetTokenizer
SentencePiece based tokenizer. Peculiarities:
- requires `SentencePiece <https://github.com/google/sentencepiece>`_
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, bos_token="<s>", eos_token="</s>", sep_token="</s>",
cls_token="<s>", unk_token="<unk>", pad_token='<pad>', mask_token='<mask>',
additional_special_tokens=['<s>NOTUSED', '<s>NOTUSED'], **kwargs):
super(CamembertTokenizer, self).__init__(max_len=512, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token,
sep_token=sep_token, cls_token=cls_token, pad_token=pad_token,
mask_token=mask_token, additional_special_tokens=additional_special_tokens,
**kwargs)
self.max_len_single_sentence = self.max_len - 2 # take into account special tokens
self.max_len_sentences_pair = self.max_len - 4 # take into account special tokens
self.sp_model = spm.SentencePieceProcessor()
self.sp_model.Load(str(vocab_file))
self.vocab_file = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
self.fairseq_tokens_to_ids = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
self.fairseq_offset = len(self.fairseq_tokens_to_ids)
self.fairseq_tokens_to_ids['<mask>'] = len(self.sp_model) + len(self.fairseq_tokens_to_ids)
self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks
by concatenating and adding special tokens.
A RoBERTa sequence has the following format:
single sequence: <s> X </s>
pair of sequences: <s> A </s></s> B </s>
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls + token_ids_0 + sep + sep + token_ids_1 + sep
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
Args:
token_ids_0: list of ids (must not contain special tokens)
token_ids_1: Optional list of ids (must not contain special tokens), necessary when fetching sequence ids
for sequence pairs
already_has_special_tokens: (default False) Set to True if the token list is already formated with
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError("You should not supply a second sequence if the provided sequence of "
"ids is already formated with special tokens for the model.")
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is None:
return [1] + ([0] * len(token_ids_0)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task.
A RoBERTa sequence pair mask has the following format:
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
| first sequence | second sequence
if token_ids_1 is None, only returns the first portion of the mask (0's).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep) * [0] + len(token_ids_1 + sep) * [1]
@property
def vocab_size(self):
return self.fairseq_offset + len(self.sp_model)
def _tokenize(self, text):
return self.sp_model.EncodeAsPieces(text)
def _convert_token_to_id(self, token):
""" Converts a token (str/unicode) in an id using the vocab. """
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
return self.fairseq_offset + self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (string/unicode) using the vocab."""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def save_vocabulary(self, save_directory):
""" Save the sentencepiece vocabulary (copy original file) and special tokens file
to a directory.
"""
if not os.path.isdir(save_directory):
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
return
out_vocab_file = os.path.join(save_directory, VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
copyfile(self.vocab_file, out_vocab_file)
return (out_vocab_file,)

2
transformers/tokenization_distilbert.py
View File

@@ -33,12 +33,14 @@ PRETRAINED_VOCAB_FILES_MAP = {
{
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt",
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt",
'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'distilbert-base-uncased': 512,
'distilbert-base-uncased-distilled-squad': 512,
'distilbert-base-multilingual-cased': 512,
}

11
transformers/tokenization_gpt2.py
View File

@@ -46,6 +46,7 @@ PRETRAINED_VOCAB_FILES_MAP = {
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json",
'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-vocab.json",
'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-vocab.json",
'gpt2-xl': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-vocab.json",
'distilgpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-vocab.json",
},
'merges_file':
@@ -53,6 +54,7 @@ PRETRAINED_VOCAB_FILES_MAP = {
'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt",
'gpt2-medium': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-medium-merges.txt",
'gpt2-large': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-large-merges.txt",
'gpt2-xl': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-xl-merges.txt",
'distilgpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/distilgpt2-merges.txt",
},
}
@@ -61,6 +63,7 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'gpt2': 1024,
'gpt2-medium': 1024,
'gpt2-large': 1024,
'gpt2-xl': 1024,
'distilgpt2': 1024,
}
@@ -104,10 +107,10 @@ class GPT2Tokenizer(PreTrainedTokenizer):
"""
GPT-2 BPE tokenizer. Peculiarities:
- Byte-level Byte-Pair-Encoding
- Requires a space to start the input string => the encoding methods should be called with the
- Requires a space to start the input string => the encoding and tokenize methods should be called with the
``add_prefix_space`` flag set to ``True``.
Otherwise, this tokenizer ``encode`` and ``decode`` method will not conserve
the absence of a space at the beginning of a string: `tokenizer.decode(tokenizer.encode("Hello")) = " Hello"`
Otherwise, this tokenizer's ``encode``, ``decode``, and ``tokenize`` methods will not conserve
the spaces at the beginning of a string: `tokenizer.decode(tokenizer.encode(" Hello")) = "Hello"`
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
@@ -181,7 +184,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
""" Tokenize a string.
Args:
- add_prefix_space (boolean, default False):
Begin the sentence with at least one space toto get invariance to word order in GPT-2 (and RoBERTa) tokenizers.
Begin the sentence with at least one space to get invariance to word order in GPT-2 (and RoBERTa) tokenizers.
"""
if add_prefix_space:
text = ' ' + text

8
transformers/tokenization_roberta.py
View File

@@ -47,6 +47,8 @@ PRETRAINED_VOCAB_FILES_MAP = {
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-vocab.json",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-vocab.json",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-vocab.json",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-vocab.json",
},
'merges_file':
{
@@ -54,6 +56,8 @@ PRETRAINED_VOCAB_FILES_MAP = {
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-merges.txt",
'distilroberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/distilroberta-base-merges.txt",
'roberta-base-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-merges.txt",
'roberta-large-openai-detector': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-merges.txt",
},
}
@@ -62,6 +66,8 @@ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
'roberta-large': 512,
'roberta-large-mnli': 512,
'distilroberta-base': 512,
'roberta-base-openai-detector': 512,
'roberta-large-openai-detector': 512,
}
@@ -114,7 +120,7 @@ class RobertaTokenizer(GPT2Tokenizer):
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:

93
transformers/tokenization_utils.py
View File

@@ -21,6 +21,7 @@ import os
import json
import six
import copy
import itertools
from io import open
from .file_utils import cached_path, is_tf_available, is_torch_available
@@ -251,6 +252,9 @@ class PreTrainedTokenizer(object):
force_download: (`optional`) boolean, default False:
Force to (re-)download the vocabulary 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.
@@ -286,6 +290,7 @@ class PreTrainedTokenizer(object):
def _from_pretrained(cls, pretrained_model_name_or_path, *init_inputs, **kwargs):
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)
s3_models = list(cls.max_model_input_sizes.keys())
@@ -352,7 +357,7 @@ class PreTrainedTokenizer(object):
if file_path is None:
resolved_vocab_files[file_id] = None
else:
resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies, resume_download=resume_download)
except EnvironmentError:
if pretrained_model_name_or_path in s3_models:
msg = "Couldn't reach server at '{}' to download vocabulary files."
@@ -512,6 +517,8 @@ class PreTrainedTokenizer(object):
to_add_tokens = []
for token in new_tokens:
assert isinstance(token, str) or (six.PY2 and isinstance(token, unicode))
if self.init_kwargs.get('do_lower_case', False):
token = token.lower()
if token != self.unk_token and \
self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and \
token not in to_add_tokens:
@@ -605,6 +612,9 @@ class PreTrainedTokenizer(object):
Take care of added tokens.
"""
if self.init_kwargs.get('do_lower_case', False):
text = text.lower()
def split_on_token(tok, text):
result = []
split_text = text.split(tok)
@@ -641,9 +651,9 @@ class PreTrainedTokenizer(object):
tokenized_text += [sub_text]
text_list = tokenized_text
return sum((self._tokenize(token, **kwargs) if token not \
return list(itertools.chain.from_iterable((self._tokenize(token, **kwargs) if token not \
in self.added_tokens_encoder and token not in self.all_special_tokens \
else [token] for token in tokenized_text), [])
else [token] for token in tokenized_text)))
added_tokens = list(self.added_tokens_encoder.keys()) + self.all_special_tokens
tokenized_text = split_on_tokens(added_tokens, text)
@@ -671,10 +681,6 @@ class PreTrainedTokenizer(object):
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
if len(ids) > self.max_len:
logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
"indexing errors".format(len(ids), self.max_len))
return ids
def _convert_token_to_id_with_added_voc(self, token):
@@ -744,6 +750,9 @@ class PreTrainedTokenizer(object):
stride=0,
truncation_strategy='longest_first',
return_tensors=None,
return_token_type_ids=True,
return_overflowing_tokens=False,
return_special_tokens_mask=False,
**kwargs):
"""
Returns a dictionary containing the encoded sequence or sequence pair and additional informations:
@@ -770,7 +779,30 @@ class PreTrainedTokenizer(object):
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True).
return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
**kwargs: passed to the `self.tokenize()` method
Return:
A Dictionary of shape::
{
input_ids: list[int],
token_type_ids: list[int] if return_token_type_ids is True (default)
overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
}
With the fields:
``input_ids``: list of token ids to be fed to a model
``token_type_ids``: list of token type ids to be fed to a model
``overflowing_tokens``: list of overflowing tokens if a max length is specified.
``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
def get_input_ids(text):
@@ -792,10 +824,17 @@ class PreTrainedTokenizer(object):
add_special_tokens=add_special_tokens,
stride=stride,
truncation_strategy=truncation_strategy,
return_tensors=return_tensors)
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask)
def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=True, stride=0,
truncation_strategy='longest_first', return_tensors=None):
truncation_strategy='longest_first',
return_tensors=None,
return_token_type_ids=True,
return_overflowing_tokens=False,
return_special_tokens_mask=False):
"""
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
It adds special tokens, truncates
@@ -820,21 +859,27 @@ class PreTrainedTokenizer(object):
- 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
or PyTorch torch.Tensor instead of a list of python integers.
return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True).
return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
Return:
A Dictionary of shape::
{
input_ids: list[int],
overflowing_tokens: list[int] if a ``max_length`` is specified, else None
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True``
token_type_ids: list[int] if return_token_type_ids is True (default)
overflowing_tokens: list[int] if a ``max_length`` is specified and return_overflowing_tokens is True
num_truncated_tokens: int if a ``max_length`` is specified and return_overflowing_tokens is True
special_tokens_mask: list[int] if ``add_special_tokens`` if set to ``True`` and return_special_tokens_mask is True
}
With the fields:
``input_ids``: list of tokens to be fed to a model
``input_ids``: list of token ids to be fed to a model
``token_type_ids``: list of token type ids to be fed to a model
``overflowing_tokens``: list of overflowing tokens if a max length is specified.
``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
tokens and 1 specifying sequence tokens.
"""
@@ -843,23 +888,31 @@ class PreTrainedTokenizer(object):
len_pair_ids = len(pair_ids) if pair else 0
encoded_inputs = {}
# Handle max sequence length
total_len = len_ids + len_pair_ids + (self.num_added_tokens(pair=pair) if add_special_tokens else 0)
if max_length and total_len > max_length:
ids, pair_ids, overflowing_tokens = self.truncate_sequences(ids, pair_ids=pair_ids,
num_tokens_to_remove=total_len-max_length,
truncation_strategy=truncation_strategy,
stride=stride)
if return_overflowing_tokens:
encoded_inputs["overflowing_tokens"] = overflowing_tokens
encoded_inputs["num_truncated_tokens"] = total_len - max_length
# Handle special_tokens
if add_special_tokens:
sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
special_tokens_mask = self.get_special_tokens_mask(ids, pair_ids)
else:
sequence = ids + pair_ids if pair else ids
token_type_ids = [0] * len(ids) + ([1] * len(pair_ids) if pair else [])
special_tokens_mask = [0] * (len(ids) + (len(pair_ids) if pair else 0))
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
# Prepare inputs as tensors if asked
if return_tensors == 'tf' and is_tf_available():
sequence = tf.constant([sequence])
token_type_ids = tf.constant([token_type_ids])
@@ -870,13 +923,21 @@ class PreTrainedTokenizer(object):
logger.warning("Unable to convert output to tensors format {}, PyTorch or TensorFlow is not available.".format(return_tensors))
encoded_inputs["input_ids"] = sequence
if return_token_type_ids:
encoded_inputs["token_type_ids"] = token_type_ids
if max_length and len(encoded_inputs["input_ids"]) > max_length:
encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length]
if return_token_type_ids:
encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
if return_special_tokens_mask:
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"][:max_length]
if max_length is None and len(encoded_inputs["input_ids"]) > self.max_len:
logger.warning("Token indices sequence length is longer than the specified maximum sequence length "
"for this model ({} > {}). Running this sequence through the model will result in "
"indexing errors".format(len(ids), self.max_len))
return encoded_inputs
def truncate_sequences(self, ids, pair_ids=None, num_tokens_to_remove=0, truncation_strategy='longest_first', stride=0):
@@ -951,7 +1012,7 @@ class PreTrainedTokenizer(object):
special tokens for the model
Returns:
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
return [0] * ((len(token_ids_1) if token_ids_1 else 0) + len(token_ids_0))
@@ -1055,7 +1116,7 @@ class PreTrainedTokenizer(object):
class attributes (cls_token, unk_token...).
"""
all_toks = self.all_special_tokens
all_ids = list(self._convert_token_to_id(t) for t in all_toks)
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids
@staticmethod

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