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| Author | SHA1 | Date | |
|---|---|---|---|
 thomwolf
|
dfb61caf77 |
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+2
-7
@@ -5,12 +5,8 @@ function deploy_doc(){
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git checkout $1
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if [ ! -z "$2" ]
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then
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if [ -d "$dir/$2" ]; then
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echo "Directory" $2 "already exists"
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else
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echo "Pushing version" $2
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make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
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fi
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echo "Pushing version" $2
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make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
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else
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echo "Pushing master"
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make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
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@@ -23,4 +19,3 @@ deploy_doc "fe02e45" v1.1.0
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deploy_doc "89fd345" v1.2.0
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deploy_doc "fc9faa8" v2.0.0
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deploy_doc "3ddce1d" v2.1.1
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deploy_doc "3616209" v2.2.0
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@@ -17,7 +17,6 @@ assignees: ''
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* [ ] the model implementation is available: (give details)
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* [ ] the model weights are available: (give details)
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* [ ] who are the authors: (mention them)
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## Additional context
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+1
-2
@@ -137,5 +137,4 @@ examples/runs
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serialization_dir
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# emacs
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*.*~
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debug.env
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*.*~
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+1
-1
@@ -106,7 +106,7 @@ Follow these steps to start contributing:
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```bash
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$ git clone git@github.com:<your Github handle>/transformers.git
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$ cd transformers
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$ git remote add upstream https://github.com/huggingface/transformers.git
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$ git remote add upstream git@github.com:huggingface/transformers.git
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```
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3. Create a new branch to hold your development changes:
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@@ -58,7 +58,7 @@ Choose the right framework for every part of a model's lifetime
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| [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
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| [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers |
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| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
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| [Documentation][(v2.2.0/v2.2.1)](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 |
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| [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 |
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## Installation
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@@ -86,17 +86,6 @@ When TensorFlow 2.0 and/or PyTorch has been installed, you can install from sour
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pip install [--editable] .
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```
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### Run the examples
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Examples are included in the repository but are not shipped with the library.
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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:
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```bash
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git clone https://github.com/huggingface/transformers
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cd transformers
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pip install [--editable] .
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```
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### Tests
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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).
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@@ -133,9 +122,7 @@ At some point in the future, you'll be able to seamlessly move from pre-training
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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.
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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).
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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.
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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.
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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.
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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.
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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.
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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).
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@@ -184,7 +171,8 @@ for model_class, tokenizer_class, pretrained_weights in MODELS:
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# Each architecture is provided with several class for fine-tuning on down-stream tasks, e.g.
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BERT_MODEL_CLASSES = [BertModel, BertForPreTraining, BertForMaskedLM, BertForNextSentencePrediction,
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BertForSequenceClassification, BertForTokenClassification, BertForQuestionAnswering]
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BertForSequenceClassification, BertForMultipleChoice, BertForTokenClassification,
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BertForQuestionAnswering]
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# All the classes for an architecture can be initiated from pretrained weights for this architecture
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# Note that additional weights added for fine-tuning are only initialized
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@@ -265,11 +253,6 @@ print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sen
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## Quick tour of the fine-tuning/usage scripts
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**Important**
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Before running the fine-tuning scripts, please read the
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[instructions](#run-the-examples) on how to
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setup your environment to run the examples.
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The library comprises several example scripts with SOTA performances for NLU and NLG tasks:
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- `run_glue.py`: an example fine-tuning Bert, XLNet and XLM on nine different GLUE tasks (*sequence-level classification*)
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@@ -538,12 +521,12 @@ Here is a conversion examples from `BertAdam` with a linear warmup and decay sch
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# Parameters:
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lr = 1e-3
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max_grad_norm = 1.0
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num_training_steps = 1000
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num_total_steps = 1000
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num_warmup_steps = 100
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warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1
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warmup_proportion = float(num_warmup_steps) / float(num_total_steps) # 0.1
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### Previously BertAdam optimizer was instantiated like this:
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optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, t_total=num_training_steps)
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optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, t_total=num_total_steps)
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### and used like this:
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for batch in train_data:
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loss = model(batch)
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@@ -552,7 +535,7 @@ for batch in train_data:
|
||||
|
||||
### In Transformers, optimizer and schedules are splitted and instantiated like this:
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||||
optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False) # To reproduce BertAdam specific behavior set correct_bias=False
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch scheduler
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scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps) # PyTorch scheduler
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||||
### and used like this:
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for batch in train_data:
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model.train()
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@@ -1,22 +0,0 @@
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cd docs
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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
|
||||
}
|
||||
|
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deploy_doc "master"
|
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deploy_doc "b33a385" v1.0.0
|
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deploy_doc "fe02e45" v1.1.0
|
||||
deploy_doc "89fd345" v1.2.0
|
||||
deploy_doc "fc9faa8" v2.0.0
|
||||
deploy_doc "3ddce1d" v2.1.1
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deploy_doc "f2f3294" v2.2.0
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@@ -1,5 +1,5 @@
|
||||
function addIcon() {
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||||
const huggingFaceLogo = "https://huggingface.co/landing/assets/transformers-docs/huggingface_logo.svg";
|
||||
const huggingFaceLogo = "https://huggingface.co/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/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" }
|
||||
{ link: "https://huggingface.co", imageLink: "https://huggingface.co/assets/transformers-docs/website.svg" },
|
||||
{ link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/twitter.svg" },
|
||||
{ link: "https://github.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/github.svg" },
|
||||
{ link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/assets/transformers-docs/linkedin.svg" }
|
||||
];
|
||||
|
||||
imageDetails.forEach(imageLinks => {
|
||||
|
||||
+1
-1
@@ -26,7 +26,7 @@ author = u'huggingface'
|
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# The short X.Y version
|
||||
version = u''
|
||||
# The full version, including alpha/beta/rc tags
|
||||
release = u'2.2.1'
|
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release = u'2.1.1'
|
||||
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||||
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# -- General configuration ---------------------------------------------------
|
||||
|
||||
@@ -47,9 +47,6 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
|
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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.
|
||||
|
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.. toctree::
|
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:maxdepth: 2
|
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@@ -92,5 +89,3 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
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model_doc/roberta
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model_doc/distilbert
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model_doc/ctrl
|
||||
model_doc/camembert
|
||||
model_doc/albert
|
||||
@@ -18,17 +18,19 @@ Schedules
|
||||
Learning Rate Schedules
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
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.. autofunction:: transformers.get_constant_schedule
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.. autoclass:: transformers.ConstantLRSchedule
|
||||
:members:
|
||||
|
||||
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||||
.. autofunction:: transformers.get_constant_schedule_with_warmup
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||||
.. autoclass:: transformers.WarmupConstantSchedule
|
||||
:members:
|
||||
|
||||
.. image:: /imgs/warmup_constant_schedule.png
|
||||
:target: /imgs/warmup_constant_schedule.png
|
||||
:alt:
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_cosine_schedule_with_warmup
|
||||
.. autoclass:: transformers.WarmupCosineSchedule
|
||||
:members:
|
||||
|
||||
.. image:: /imgs/warmup_cosine_schedule.png
|
||||
@@ -36,7 +38,8 @@ Learning Rate Schedules
|
||||
:alt:
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_cosine_with_hard_restarts_schedule_with_warmup
|
||||
.. autoclass:: transformers.WarmupCosineWithHardRestartsSchedule
|
||||
:members:
|
||||
|
||||
.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
|
||||
:target: /imgs/warmup_cosine_hard_restarts_schedule.png
|
||||
@@ -44,7 +47,8 @@ Learning Rate Schedules
|
||||
|
||||
|
||||
|
||||
.. autofunction:: transformers.get_linear_schedule_with_warmup
|
||||
.. autoclass:: transformers.WarmupLinearSchedule
|
||||
:members:
|
||||
|
||||
.. image:: /imgs/warmup_linear_schedule.png
|
||||
:target: /imgs/warmup_linear_schedule.png
|
||||
|
||||
@@ -55,27 +55,4 @@ 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.
|
||||
`run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py>`__ script.
|
||||
@@ -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_training_steps = 1000
|
||||
num_total_steps = 1000
|
||||
num_warmup_steps = 100
|
||||
warmup_proportion = float(num_warmup_steps) / float(num_training_steps) # 0.1
|
||||
warmup_proportion = float(num_warmup_steps) / float(num_total_steps) # 0.1
|
||||
|
||||
### Previously BertAdam optimizer was instantiated like this:
|
||||
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, num_training_steps=num_training_steps)
|
||||
optimizer = BertAdam(model.parameters(), lr=lr, schedule='warmup_linear', warmup=warmup_proportion, t_total=num_total_steps)
|
||||
### and used like this:
|
||||
for batch in train_data:
|
||||
loss = model(batch)
|
||||
@@ -98,12 +98,12 @@ 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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) # PyTorch scheduler
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps) # PyTorch scheduler
|
||||
### and used like this:
|
||||
for batch in train_data:
|
||||
loss = model(batch)
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm) # Gradient clipping is not in AdamW anymore (so you can use amp without issue)
|
||||
optimizer.step()
|
||||
scheduler.step()
|
||||
optimizer.step()
|
||||
```
|
||||
@@ -1,64 +0,0 @@
|
||||
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:
|
||||
@@ -1,50 +0,0 @@
|
||||
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:
|
||||
@@ -73,9 +73,6 @@ 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 |
|
||||
@@ -127,14 +124,6 @@ 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 |
|
||||
@@ -151,50 +140,9 @@ Here is the full list of the currently provided pretrained models together with
|
||||
| | ``distilroberta-base`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
|
||||
| | | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint. |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-german-cased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint. |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| 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>`__
|
||||
.. <https://huggingface.co/transformers/examples.html>`__
|
||||
@@ -188,35 +188,3 @@ 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`.
|
||||
@@ -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 itself which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`__\ ,
|
||||
* the model it-self which should be saved following PyTorch serialization `best practices <https://pytorch.org/docs/stable/notes/serialization.html#best-practices>`__\ ,
|
||||
* the configuration file of the model which is saved as a JSON file, and
|
||||
* the vocabulary (and the merges for the BPE-based models GPT and GPT-2).
|
||||
|
||||
|
||||
@@ -3,17 +3,6 @@
|
||||
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 and install some specific requirements for the examples.
|
||||
Execute the following steps in a new virtual environment:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/huggingface/transformers
|
||||
cd transformers
|
||||
pip install [--editable] .
|
||||
pip install -r ./examples/requirements.txt
|
||||
```
|
||||
|
||||
| Section | Description |
|
||||
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks.
|
||||
@@ -23,7 +12,6 @@ pip install -r ./examples/requirements.txt
|
||||
| [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
|
||||
@@ -427,76 +415,6 @@ exact_match = 86.91
|
||||
This fine-tuned model is available as a checkpoint under the reference
|
||||
`bert-large-uncased-whole-word-masking-finetuned-squad`.
|
||||
|
||||
### `run_semeval.py`: relationship classification using R-Bert
|
||||
|
||||
R-BERT is a relationship classification head for BERT and RoBERTa, described [here](https://arxiv.org/pdf/1905.08284.pdf").
|
||||
|
||||
This example code fine-tunes R-BERT on the semeval 2010 Task 8 dataset:
|
||||
|
||||
```bash
|
||||
python ./examples/run_semeval.py \
|
||||
--data_dir $SEMEVAL_DIR \
|
||||
--output_dir $RESULTS_DIR \
|
||||
--model_name_or_path bert-base-uncased \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--overwrite_output_dir \
|
||||
--num_train_epochs 8.0 \
|
||||
--per_gpu_train_batch_size 16 \
|
||||
--per_gpu_eval_batch_size 16 \
|
||||
--learning_rate 2e-5 \
|
||||
--max_seq_length 128 \
|
||||
--task_name semeval2010_task8 \
|
||||
--train_on_other_labels \
|
||||
--eval_on_other_labels \
|
||||
--include_directionality
|
||||
|
||||
```
|
||||
The ```$SEMEVAL_DIR``` should point to the extracted archive.
|
||||
|
||||
The ```--include_directionality``` flag trains a classifier using all 18 semeval classes. The
|
||||
```--train_on_other_labels``` and ```--eval_on_other_labels``` flags also include instances labeled as 'Other' in the
|
||||
training and evaluation respectively. Include all of these to be able to use the official evaluation script.
|
||||
|
||||
Note, although an F1 score is calculated in the python code, two additional files are also written out at the checkpoint
|
||||
intervals ```{global_step}_semeval_results.tsv``` that may be used with the official Semeval evaluation script
|
||||
(supplied in the semeval data archive). The dataset is available under Creative Commons Atrribution 3.0
|
||||
Unported Licence (http://creativecommons.org/licenses/by/3.0/) and is available
|
||||
[here](http://docs.google.com/leaf?id=0B_jQiLugGTAkMDQ5ZjZiMTUtMzQ1Yy00YWNmLWJlZDYtOWY1ZDMwY2U4YjFk&sort=name&layout=list&num=50).
|
||||
|
||||
for example:
|
||||
|
||||
```bash
|
||||
./semeval2010_task8_scorer-v1.2.pl $SEMEVAL_DIR/{global_step}_semeval_results.tsv $SEMEVAL_DIR/TEST_FILE_SEMEVAL_SCRIPT_FORMAT.tsv
|
||||
```
|
||||
|
||||
|
||||
Using the ```bert-base-uncased``` should give around 87.6 undirected F1 (excluding other), with the official script.
|
||||
However, the RoBERTa model can also be used with this head:
|
||||
|
||||
```bash
|
||||
python ./examples/run_semeval.py \
|
||||
--data_dir $SEMEVAL_DIR \
|
||||
--output_dir $RESULTS_DIR \
|
||||
--model_name_or_path roberta-large \
|
||||
--model_type roberta \
|
||||
--do_train \
|
||||
--do_eval \
|
||||
--overwrite_output_dir \
|
||||
--num_train_epochs 8.0 \
|
||||
--per_gpu_train_batch_size 16 \
|
||||
--per_gpu_eval_batch_size 16 \
|
||||
--learning_rate 2e-5 \
|
||||
--max_seq_length 128 \
|
||||
--task_name semeval2010_task8 \
|
||||
--train_on_other_labels \
|
||||
--eval_on_other_labels \
|
||||
--include_directionality
|
||||
|
||||
```
|
||||
|
||||
This should give an undirected F1 of aroubd 90.8 (excluding other) with the official script.
|
||||
|
||||
#### Fine-tuning XLNet on SQuAD
|
||||
|
||||
This example code fine-tunes XLNet on the SQuAD dataset. See above to download the data for SQuAD .
|
||||
@@ -636,16 +554,6 @@ On the test dataset the following results could be achieved:
|
||||
10/04/2019 00:42:42 - INFO - __main__ - recall = 0.8624150210424085
|
||||
```
|
||||
|
||||
### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
|
||||
|
||||
Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run):
|
||||
|
||||
| Model | F-Score Dev | F-Score Test
|
||||
| --------------------------------- | ------- | --------
|
||||
| `bert-large-cased` | 95.59 | 91.70
|
||||
| `roberta-large` | 95.96 | 91.87
|
||||
| `distilbert-base-uncased` | 94.34 | 90.32
|
||||
|
||||
## Abstractive summarization
|
||||
|
||||
Based on the script
|
||||
@@ -673,43 +581,3 @@ python run_summarization_finetuning.py \
|
||||
--do_train \
|
||||
--data_path=$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
|
||||
```
|
||||
@@ -1,48 +0,0 @@
|
||||
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))
|
||||
@@ -41,7 +41,7 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
|
||||
|
||||
from transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
|
||||
AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
|
||||
get_linear_schedule_with_warmup)
|
||||
WarmupLinearSchedule)
|
||||
|
||||
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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=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 itself
|
||||
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
|
||||
|
||||
# If we save using the predefined names, we can load using `from_pretrained`
|
||||
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
|
||||
|
||||
@@ -42,7 +42,7 @@ from tqdm import tqdm, trange
|
||||
from transformers import (WEIGHTS_NAME, BertConfig,
|
||||
BertForMultipleChoice, BertTokenizer)
|
||||
|
||||
from transformers import AdamW, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
|
||||
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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
|
||||
@@ -2,8 +2,6 @@
|
||||
|
||||
This folder contains the original code used to train Distil* as well as examples showcasing how to use DistilBERT, DistilRoBERTa and DistilGPT2.
|
||||
|
||||
**November 19th, 2019 - Update** We release German **DistilBERT**: 98.8% of `bert-base-german-dbmdz-cased` on NER tasks.
|
||||
|
||||
**October 23rd, 2019 - Update** We release **DistilRoBERTa**: 95% of `RoBERTa-base`'s performance on GLUE, twice as fast as RoBERTa while being 35% smaller.
|
||||
|
||||
**October 3rd, 2019 - Update** We release our [NeurIPS workshop paper](https://arxiv.org/abs/1910.01108) explaining our approach on **DistilBERT**. It includes updated results and further experiments. We applied the same method to GPT2 and release the weights of **DistilGPT2**. DistilGPT2 is two times faster and 33% smaller than GPT2. **The paper superseeds our [previous blogpost](https://medium.com/huggingface/distilbert-8cf3380435b5) with a different distillation loss and better performances. Please use the paper as a reference when comparing/reporting results on DistilBERT.**
|
||||
@@ -47,11 +45,10 @@ This part of the library has only be tested with Python3.6+. There are few speci
|
||||
|
||||
## How to use DistilBERT
|
||||
|
||||
Transformers includes five pre-trained Distil* models, currently only provided for English and German (we are investigating the possibility to train and release a multilingual version of DistilBERT):
|
||||
Transformers includes two pre-trained Distil* models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):
|
||||
|
||||
- `distilbert-base-uncased`: DistilBERT English language model pretrained on the same data used to pretrain Bert (concatenation of the Toronto Book Corpus and full English Wikipedia) using distillation with the supervision of the `bert-base-uncased` version of Bert. The model has 6 layers, 768 dimension and 12 heads, totalizing 66M parameters.
|
||||
- `distilbert-base-uncased-distilled-squad`: A finetuned version of `distilbert-base-uncased` finetuned using (a second step of) knwoledge distillation on SQuAD 1.0. This model reaches a F1 score of 86.9 on the dev set (for comparison, Bert `bert-base-uncased` version reaches a 88.5 F1 score).
|
||||
- `distilbert-base-german-cased`: DistilBERT German language model pretrained on 1/2 of the data used to pretrain Bert using distillation with the supervision of the `bert-base-german-dbmdz-cased` version of German DBMDZ Bert. For NER tasks the model reaches a F1 score of 83.49 on the CoNLL-2003 test set (for comparison, `bert-base-german-dbmdz-cased` reaches a 84.52 F1 score), and a F1 score of 85.23 on the GermEval 2014 test set (`bert-base-german-dbmdz-cased` reaches a 86.89 F1 score).
|
||||
- `distilgpt2`: DistilGPT2 English language model pretrained with the supervision of `gpt2` (the smallest version of GPT2) on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 124M parameters for GPT2). On average, DistilGPT2 is two times faster than GPT2.
|
||||
- `distilroberta-base`: DistilRoBERTa English language model pretrained with the supervision of `roberta-base` solely on [OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/), a reproduction of OpenAI's WebText dataset (it is ~4 times less training data than the teacher RoBERTa). The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.
|
||||
- and more to come! 🤗🤗🤗
|
||||
|
||||
@@ -35,7 +35,7 @@ try:
|
||||
except:
|
||||
from tensorboardX import SummaryWriter
|
||||
|
||||
from transformers import get_linear_schedule_with_warmup
|
||||
from transformers import WarmupLinearSchedule
|
||||
|
||||
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 = get_linear_schedule_with_warmup(self.optimizer,
|
||||
num_warmup_steps=warmup_steps,
|
||||
num_training_steps=num_train_optimization_steps)
|
||||
self.scheduler = WarmupLinearSchedule(self.optimizer,
|
||||
warmup_steps=warmup_steps,
|
||||
t_total=num_train_optimization_steps)
|
||||
|
||||
if self.fp16:
|
||||
try:
|
||||
|
||||
@@ -46,7 +46,7 @@ from transformers import (WEIGHTS_NAME, BertConfig,
|
||||
XLNetTokenizer,
|
||||
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
|
||||
|
||||
from transformers import AdamW, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
|
||||
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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
|
||||
@@ -1,61 +0,0 @@
|
||||
# PPLM
|
||||
|
||||
This folder contains the original code used to run the Plug and Play Language Model (PPLM).
|
||||
|
||||
|
||||
## Plug and Play Language Models: a Simple Approach to Steerable Text Generation
|
||||
Authors: [Sumanth Dathathri](https://dathath.github.io/), Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, [Piero Molino](https://w4nderlu.st/), [Jason Yosinski](http://yosinski.com/), and [Rosanne Liu](http://www.rosanneliu.com/)
|
||||
|
||||
PPLM allows a user to flexibly plug in one or more tiny attribute models representing the desired steering objective into a large, unconditional LM. The method has the key property that it uses the LM _as is_---no training or fine-tuning is required---which enables researchers to leverage best-in-class LMs even if they do not have the extensive hardware required to train them.
|
||||
|
||||
Paper link:
|
||||
|
||||
Blog link: https://eng.uber.com/pplm
|
||||
|
||||
|
||||
## Setup
|
||||
|
||||
```bash
|
||||
git clone https://github.com/huggingface/transformers && cd transformers
|
||||
pip install [--editable] .
|
||||
pip install nltk torchtext # additional requirements.
|
||||
cd examples/pplm
|
||||
```
|
||||
|
||||
## PPLM-BoW
|
||||
|
||||
### Example command for bag-of-words control
|
||||
|
||||
```bash
|
||||
python run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 1 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
|
||||
```
|
||||
|
||||
### Tuning hyperparameters for bag-of-words control
|
||||
|
||||
1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model.
|
||||
|
||||
2. If the language being generated is repetitive (For e.g. "science science experiment experiment"), there are several options to consider: </br>
|
||||
a) Reduce the `--stepsize` </br>
|
||||
b) Increase `--kl_scale` (the KL-loss coefficient) or decrease `--gm_scale` (the gm-scaling term) </br>
|
||||
c) Add `--grad-length xx` where xx is an (integer <= length, e.g. `--grad-length 30`).</br>
|
||||
|
||||
|
||||
## PPLM-Discrim
|
||||
|
||||
### Example command for discriminator based sentiment control
|
||||
|
||||
```bash
|
||||
python run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 10 --num_samples 1 --stepsize 0.03 --kl_scale 0.01 --gm_scale 0.95
|
||||
```
|
||||
|
||||
### Tuning hyperparameters for discriminator control
|
||||
|
||||
1. Increase `--stepsize` to intensify topic control, and decrease its value to soften the control. `--stepsize 0` recovers the original uncontrolled GPT-2 model.
|
||||
|
||||
2. Use `--class_label 3` for negative, and `--class_label 2` for positive
|
||||
|
||||
### Example command for detoxificiation:
|
||||
|
||||
```bash
|
||||
python run_pplm.py -D toxicity --length 100 --num_iterations 10 --cond-text 'TH PEOPLEMan goddreams Blacks' --gamma 1.0 --num_samples 10 --stepsize 0.02
|
||||
```
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 653 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 664 KiB |
@@ -1,18 +0,0 @@
|
||||
import torch
|
||||
|
||||
class ClassificationHead(torch.nn.Module):
|
||||
"""Classification Head for transformer encoders"""
|
||||
|
||||
def __init__(self, class_size, embed_size):
|
||||
super(ClassificationHead, self).__init__()
|
||||
self.class_size = class_size
|
||||
self.embed_size = embed_size
|
||||
# self.mlp1 = torch.nn.Linear(embed_size, embed_size)
|
||||
# self.mlp2 = (torch.nn.Linear(embed_size, class_size))
|
||||
self.mlp = torch.nn.Linear(embed_size, class_size)
|
||||
|
||||
def forward(self, hidden_state):
|
||||
# hidden_state = F.relu(self.mlp1(hidden_state))
|
||||
# hidden_state = self.mlp2(hidden_state)
|
||||
logits = self.mlp(hidden_state)
|
||||
return logits
|
||||
@@ -1,889 +0,0 @@
|
||||
#! /usr/bin/env python3
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Uber AI 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.
|
||||
|
||||
"""
|
||||
Example command with bag of words:
|
||||
python examples/run_pplm.py -B space --cond_text "The president" --length 100 --gamma 1.5 --num_iterations 3 --num_samples 10 --stepsize 0.01 --window_length 5 --kl_scale 0.01 --gm_scale 0.95
|
||||
|
||||
Example command with discriminator:
|
||||
python examples/run_pplm.py -D sentiment --class_label 3 --cond_text "The lake" --length 10 --gamma 1.0 --num_iterations 30 --num_samples 10 --stepsize 0.01 --kl_scale 0.01 --gm_scale 0.95
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
from operator import add
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch.autograd import Variable
|
||||
from tqdm import trange
|
||||
|
||||
from transformers import GPT2Tokenizer
|
||||
from transformers.file_utils import cached_path
|
||||
from transformers.modeling_gpt2 import GPT2LMHeadModel
|
||||
from pplm_classification_head import ClassificationHead
|
||||
|
||||
PPLM_BOW = 1
|
||||
PPLM_DISCRIM = 2
|
||||
PPLM_BOW_DISCRIM = 3
|
||||
SMALL_CONST = 1e-15
|
||||
BIG_CONST = 1e10
|
||||
|
||||
BAG_OF_WORDS_ARCHIVE_MAP = {
|
||||
'kitchen': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/kitchen.txt",
|
||||
'legal': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/legal.txt",
|
||||
'military': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/military.txt",
|
||||
'monsters': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/monsters.txt",
|
||||
'politics': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/politics.txt",
|
||||
'positive_words': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/positive_words.txt",
|
||||
'religion': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/religion.txt",
|
||||
'science': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/science.txt",
|
||||
'space': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/space.txt",
|
||||
'technology': "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/bow/technology.txt",
|
||||
}
|
||||
|
||||
DISCRIMINATOR_MODELS_PARAMS = {
|
||||
"clickbait": {
|
||||
"url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/clickbait_classifier_head.pt",
|
||||
"class_size": 2,
|
||||
"embed_size": 1024,
|
||||
"class_vocab": {"non_clickbait": 0, "clickbait": 1},
|
||||
"default_class": 1,
|
||||
"pretrained_model": "gpt2-medium",
|
||||
},
|
||||
"sentiment": {
|
||||
"url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/SST_classifier_head.pt",
|
||||
"class_size": 5,
|
||||
"embed_size": 1024,
|
||||
"class_vocab": {"very_positive": 2, "very_negative": 3},
|
||||
"default_class": 3,
|
||||
"pretrained_model": "gpt2-medium",
|
||||
},
|
||||
"toxicity": {
|
||||
"url": "https://s3.amazonaws.com/models.huggingface.co/bert/pplm/discriminators/toxic_classifier_head.pt",
|
||||
"class_size": 2,
|
||||
"embed_size": 1024,
|
||||
"class_vocab": {"non_toxic": 0, "toxic": 1},
|
||||
"default_class": 0,
|
||||
"pretrained_model": "gpt2-medium",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def to_var(x, requires_grad=False, volatile=False, device='cuda'):
|
||||
if torch.cuda.is_available() and device == 'cuda':
|
||||
x = x.cuda()
|
||||
elif device != 'cuda':
|
||||
x = x.to(device)
|
||||
return Variable(x, requires_grad=requires_grad, volatile=volatile)
|
||||
|
||||
|
||||
def top_k_filter(logits, k, probs=False):
|
||||
"""
|
||||
Masks everything but the k top entries as -infinity (1e10).
|
||||
Used to mask logits such that e^-infinity -> 0 won't contribute to the
|
||||
sum of the denominator.
|
||||
"""
|
||||
if k == 0:
|
||||
return logits
|
||||
else:
|
||||
values = torch.topk(logits, k)[0]
|
||||
batch_mins = values[:, -1].view(-1, 1).expand_as(logits)
|
||||
if probs:
|
||||
return torch.where(logits < batch_mins,
|
||||
torch.ones_like(logits) * 0.0, logits)
|
||||
return torch.where(logits < batch_mins,
|
||||
torch.ones_like(logits) * -BIG_CONST,
|
||||
logits)
|
||||
|
||||
|
||||
def perturb_past(
|
||||
past,
|
||||
model,
|
||||
last,
|
||||
unpert_past=None,
|
||||
unpert_logits=None,
|
||||
accumulated_hidden=None,
|
||||
grad_norms=None,
|
||||
stepsize=0.01,
|
||||
one_hot_bows_vectors=None,
|
||||
classifier=None,
|
||||
class_label=None,
|
||||
loss_type=0,
|
||||
num_iterations=3,
|
||||
horizon_length=1,
|
||||
window_length=0,
|
||||
decay=False,
|
||||
gamma=1.5,
|
||||
kl_scale=0.01,
|
||||
device='cuda',
|
||||
):
|
||||
# Generate inital perturbed past
|
||||
grad_accumulator = [
|
||||
(np.zeros(p.shape).astype("float32"))
|
||||
for p in past
|
||||
]
|
||||
|
||||
if accumulated_hidden is None:
|
||||
accumulated_hidden = 0
|
||||
|
||||
if decay:
|
||||
decay_mask = torch.arange(
|
||||
0.,
|
||||
1.0 + SMALL_CONST,
|
||||
1.0 / (window_length)
|
||||
)[1:]
|
||||
else:
|
||||
decay_mask = 1.0
|
||||
|
||||
# TODO fix this comment (SUMANTH)
|
||||
# Generate a mask is gradient perturbated is based on a past window
|
||||
_, _, _, curr_length, _ = past[0].shape
|
||||
|
||||
if curr_length > window_length and window_length > 0:
|
||||
ones_key_val_shape = (
|
||||
tuple(past[0].shape[:-2])
|
||||
+ tuple([window_length])
|
||||
+ tuple(past[0].shape[-1:])
|
||||
)
|
||||
|
||||
zeros_key_val_shape = (
|
||||
tuple(past[0].shape[:-2])
|
||||
+ tuple([curr_length - window_length])
|
||||
+ tuple(past[0].shape[-1:])
|
||||
)
|
||||
|
||||
ones_mask = torch.ones(ones_key_val_shape)
|
||||
ones_mask = decay_mask * ones_mask.permute(0, 1, 2, 4, 3)
|
||||
ones_mask = ones_mask.permute(0, 1, 2, 4, 3)
|
||||
|
||||
window_mask = torch.cat(
|
||||
(ones_mask, torch.zeros(zeros_key_val_shape)),
|
||||
dim=-2
|
||||
).to(device)
|
||||
else:
|
||||
window_mask = torch.ones_like(past[0]).to(device)
|
||||
|
||||
# accumulate perturbations for num_iterations
|
||||
loss_per_iter = []
|
||||
new_accumulated_hidden = None
|
||||
for i in range(num_iterations):
|
||||
print("Iteration ", i + 1)
|
||||
curr_perturbation = [
|
||||
to_var(torch.from_numpy(p_), requires_grad=True, device=device)
|
||||
for p_ in grad_accumulator
|
||||
]
|
||||
|
||||
# Compute hidden using perturbed past
|
||||
perturbed_past = list(map(add, past, curr_perturbation))
|
||||
_, _, _, curr_length, _ = curr_perturbation[0].shape
|
||||
all_logits, _, all_hidden = model(last, past=perturbed_past)
|
||||
hidden = all_hidden[-1]
|
||||
new_accumulated_hidden = accumulated_hidden + torch.sum(
|
||||
hidden,
|
||||
dim=1
|
||||
).detach()
|
||||
# TODO: Check the layer-norm consistency of this with trained discriminator (Sumanth)
|
||||
logits = all_logits[:, -1, :]
|
||||
probs = F.softmax(logits, dim=-1)
|
||||
|
||||
loss = 0.0
|
||||
loss_list = []
|
||||
if loss_type == PPLM_BOW or loss_type == PPLM_BOW_DISCRIM:
|
||||
for one_hot_bow in one_hot_bows_vectors:
|
||||
bow_logits = torch.mm(probs, torch.t(one_hot_bow))
|
||||
bow_loss = -torch.log(torch.sum(bow_logits))
|
||||
loss += bow_loss
|
||||
loss_list.append(bow_loss)
|
||||
print(" pplm_bow_loss:", loss.data.cpu().numpy())
|
||||
|
||||
if loss_type == 2 or loss_type == 3:
|
||||
ce_loss = torch.nn.CrossEntropyLoss()
|
||||
# TODO why we need to do this assignment and not just using unpert_past? (Sumanth)
|
||||
curr_unpert_past = unpert_past
|
||||
curr_probs = torch.unsqueeze(probs, dim=1)
|
||||
wte = model.resize_token_embeddings()
|
||||
for _ in range(horizon_length):
|
||||
inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
|
||||
_, curr_unpert_past, curr_all_hidden = model(
|
||||
past=curr_unpert_past,
|
||||
inputs_embeds=inputs_embeds
|
||||
)
|
||||
curr_hidden = curr_all_hidden[-1]
|
||||
new_accumulated_hidden = new_accumulated_hidden + torch.sum(
|
||||
curr_hidden, dim=1)
|
||||
|
||||
prediction = classifier(new_accumulated_hidden /
|
||||
(curr_length + 1 + horizon_length))
|
||||
|
||||
label = torch.tensor(prediction.shape[0] * [class_label],
|
||||
device=device,
|
||||
dtype=torch.long)
|
||||
discrim_loss = ce_loss(prediction, label)
|
||||
print(" pplm_discrim_loss:", discrim_loss.data.cpu().numpy())
|
||||
loss += discrim_loss
|
||||
loss_list.append(discrim_loss)
|
||||
|
||||
kl_loss = 0.0
|
||||
if kl_scale > 0.0:
|
||||
unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
|
||||
unpert_probs = (
|
||||
unpert_probs + SMALL_CONST *
|
||||
(unpert_probs <= SMALL_CONST).float().to(device).detach()
|
||||
)
|
||||
correction = SMALL_CONST * (probs <= SMALL_CONST).float().to(
|
||||
device).detach()
|
||||
corrected_probs = probs + correction.detach()
|
||||
kl_loss = kl_scale * (
|
||||
(corrected_probs * (corrected_probs / unpert_probs).log()).sum()
|
||||
)
|
||||
print(' kl_loss', kl_loss.data.cpu().numpy())
|
||||
loss += kl_loss
|
||||
|
||||
loss_per_iter.append(loss.data.cpu().numpy())
|
||||
print(' pplm_loss', (loss - kl_loss).data.cpu().numpy())
|
||||
|
||||
# compute gradients
|
||||
loss.backward()
|
||||
|
||||
# calculate gradient norms
|
||||
if grad_norms is not None and loss_type == PPLM_BOW:
|
||||
grad_norms = [
|
||||
torch.max(grad_norms[index], torch.norm(p_.grad * window_mask))
|
||||
for index, p_ in enumerate(curr_perturbation)
|
||||
]
|
||||
else:
|
||||
grad_norms = [
|
||||
(torch.norm(p_.grad * window_mask) + SMALL_CONST)
|
||||
for index, p_ in enumerate(curr_perturbation)
|
||||
]
|
||||
|
||||
# normalize gradients
|
||||
grad = [
|
||||
-stepsize *
|
||||
(p_.grad * window_mask / grad_norms[
|
||||
index] ** gamma).data.cpu().numpy()
|
||||
for index, p_ in enumerate(curr_perturbation)
|
||||
]
|
||||
|
||||
# accumulate gradient
|
||||
grad_accumulator = list(map(add, grad, grad_accumulator))
|
||||
|
||||
# reset gradients, just to make sure
|
||||
for p_ in curr_perturbation:
|
||||
p_.grad.data.zero_()
|
||||
|
||||
# removing past from the graph
|
||||
new_past = []
|
||||
for p_ in past:
|
||||
new_past.append(p_.detach())
|
||||
past = new_past
|
||||
|
||||
# apply the accumulated perturbations to the past
|
||||
grad_accumulator = [
|
||||
to_var(torch.from_numpy(p_), requires_grad=True, device=device)
|
||||
for p_ in grad_accumulator
|
||||
]
|
||||
pert_past = list(map(add, past, grad_accumulator))
|
||||
|
||||
return pert_past, new_accumulated_hidden, grad_norms, loss_per_iter
|
||||
|
||||
|
||||
def get_classifier(
|
||||
name: Optional[str], class_label: Union[str, int],
|
||||
device: str
|
||||
) -> Tuple[Optional[ClassificationHead], Optional[int]]:
|
||||
if name is None:
|
||||
return None, None
|
||||
|
||||
params = DISCRIMINATOR_MODELS_PARAMS[name]
|
||||
classifier = ClassificationHead(
|
||||
class_size=params['class_size'],
|
||||
embed_size=params['embed_size']
|
||||
).to(device)
|
||||
if "url" in params:
|
||||
resolved_archive_file = cached_path(params["url"])
|
||||
elif "path" in params:
|
||||
resolved_archive_file = params["path"]
|
||||
else:
|
||||
raise ValueError("Either url or path have to be specified "
|
||||
"in the discriminator model parameters")
|
||||
classifier.load_state_dict(
|
||||
torch.load(resolved_archive_file, map_location=device))
|
||||
classifier.eval()
|
||||
|
||||
if isinstance(class_label, str):
|
||||
if class_label in params["class_vocab"]:
|
||||
label_id = params["class_vocab"][class_label]
|
||||
else:
|
||||
label_id = params["default_class"]
|
||||
print("class_label {} not in class_vocab".format(class_label))
|
||||
print("available values are: {}".format(params["class_vocab"]))
|
||||
print("using default class {}".format(label_id))
|
||||
|
||||
elif isinstance(class_label, int):
|
||||
if class_label in set(params["class_vocab"].values()):
|
||||
label_id = class_label
|
||||
else:
|
||||
label_id = params["default_class"]
|
||||
print("class_label {} not in class_vocab".format(class_label))
|
||||
print("available values are: {}".format(params["class_vocab"]))
|
||||
print("using default class {}".format(label_id))
|
||||
|
||||
else:
|
||||
label_id = params["default_class"]
|
||||
|
||||
return classifier, label_id
|
||||
|
||||
|
||||
def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \
|
||||
List[List[List[int]]]:
|
||||
bow_indices = []
|
||||
for id_or_path in bag_of_words_ids_or_paths:
|
||||
if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP:
|
||||
filepath = cached_path(BAG_OF_WORDS_ARCHIVE_MAP[id_or_path])
|
||||
else:
|
||||
filepath = id_or_path
|
||||
with open(filepath, "r") as f:
|
||||
words = f.read().strip().split("\n")
|
||||
bow_indices.append(
|
||||
[tokenizer.encode(word.strip(), add_prefix_space=True) for word in
|
||||
words])
|
||||
return bow_indices
|
||||
|
||||
|
||||
def build_bows_one_hot_vectors(bow_indices, tokenizer, device='cuda'):
|
||||
if bow_indices is None:
|
||||
return None
|
||||
|
||||
one_hot_bows_vectors = []
|
||||
for single_bow in bow_indices:
|
||||
single_bow = list(filter(lambda x: len(x) <= 1, single_bow))
|
||||
single_bow = torch.tensor(single_bow).to(device)
|
||||
num_words = single_bow.shape[0]
|
||||
one_hot_bow = torch.zeros(num_words, tokenizer.vocab_size).to(device)
|
||||
one_hot_bow.scatter_(1, single_bow, 1)
|
||||
one_hot_bows_vectors.append(one_hot_bow)
|
||||
return one_hot_bows_vectors
|
||||
|
||||
|
||||
def full_text_generation(
|
||||
model,
|
||||
tokenizer,
|
||||
context=None,
|
||||
num_samples=1,
|
||||
device="cuda",
|
||||
bag_of_words=None,
|
||||
discrim=None,
|
||||
class_label=None,
|
||||
length=100,
|
||||
stepsize=0.02,
|
||||
temperature=1.0,
|
||||
top_k=10,
|
||||
sample=False,
|
||||
num_iterations=3,
|
||||
grad_length=10000,
|
||||
horizon_length=1,
|
||||
window_length=0,
|
||||
decay=False,
|
||||
gamma=1.5,
|
||||
gm_scale=0.9,
|
||||
kl_scale=0.01,
|
||||
**kwargs
|
||||
):
|
||||
classifier, class_id = get_classifier(
|
||||
discrim,
|
||||
class_label,
|
||||
device
|
||||
)
|
||||
|
||||
bow_indices = []
|
||||
if bag_of_words:
|
||||
bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
|
||||
tokenizer)
|
||||
|
||||
if bag_of_words and classifier:
|
||||
print("Both PPLM-BoW and PPLM-Discrim are on. This is not optimized.")
|
||||
loss_type = PPLM_BOW_DISCRIM
|
||||
|
||||
elif bag_of_words:
|
||||
loss_type = PPLM_BOW
|
||||
print("Using PPLM-BoW")
|
||||
|
||||
elif classifier is not None:
|
||||
loss_type = PPLM_DISCRIM
|
||||
print("Using PPLM-Discrim")
|
||||
|
||||
else:
|
||||
raise Exception("Specify either a bag of words or a discriminator")
|
||||
|
||||
unpert_gen_tok_text, _, _ = generate_text_pplm(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
context=context,
|
||||
device=device,
|
||||
length=length,
|
||||
sample=sample,
|
||||
perturb=False
|
||||
)
|
||||
if device == 'cuda':
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
pert_gen_tok_texts = []
|
||||
discrim_losses = []
|
||||
losses_in_time = []
|
||||
|
||||
for i in range(num_samples):
|
||||
pert_gen_tok_text, discrim_loss, loss_in_time = generate_text_pplm(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
context=context,
|
||||
device=device,
|
||||
perturb=True,
|
||||
bow_indices=bow_indices,
|
||||
classifier=classifier,
|
||||
class_label=class_id,
|
||||
loss_type=loss_type,
|
||||
length=length,
|
||||
stepsize=stepsize,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
sample=sample,
|
||||
num_iterations=num_iterations,
|
||||
grad_length=grad_length,
|
||||
horizon_length=horizon_length,
|
||||
window_length=window_length,
|
||||
decay=decay,
|
||||
gamma=gamma,
|
||||
gm_scale=gm_scale,
|
||||
kl_scale=kl_scale,
|
||||
)
|
||||
pert_gen_tok_texts.append(pert_gen_tok_text)
|
||||
if classifier is not None:
|
||||
discrim_losses.append(discrim_loss.data.cpu().numpy())
|
||||
losses_in_time.append(loss_in_time)
|
||||
|
||||
if device == 'cuda':
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
|
||||
|
||||
|
||||
def generate_text_pplm(
|
||||
model,
|
||||
tokenizer,
|
||||
context=None,
|
||||
past=None,
|
||||
device="cuda",
|
||||
perturb=True,
|
||||
bow_indices=None,
|
||||
classifier=None,
|
||||
class_label=None,
|
||||
loss_type=0,
|
||||
length=100,
|
||||
stepsize=0.02,
|
||||
temperature=1.0,
|
||||
top_k=10,
|
||||
sample=False,
|
||||
num_iterations=3,
|
||||
grad_length=10000,
|
||||
horizon_length=1,
|
||||
window_length=0,
|
||||
decay=False,
|
||||
gamma=1.5,
|
||||
gm_scale=0.9,
|
||||
kl_scale=0.01,
|
||||
):
|
||||
output_so_far = None
|
||||
if context:
|
||||
context_t = torch.tensor(context, device=device, dtype=torch.long)
|
||||
while len(context_t.shape) < 2:
|
||||
context_t = context_t.unsqueeze(0)
|
||||
output_so_far = context_t
|
||||
|
||||
# collect one hot vectors for bags of words
|
||||
one_hot_bows_vectors = build_bows_one_hot_vectors(bow_indices, tokenizer,
|
||||
device)
|
||||
|
||||
grad_norms = None
|
||||
last = None
|
||||
unpert_discrim_loss = 0
|
||||
loss_in_time = []
|
||||
for i in trange(length, ascii=True):
|
||||
|
||||
# Get past/probs for current output, except for last word
|
||||
# Note that GPT takes 2 inputs: past + current_token
|
||||
|
||||
# run model forward to obtain unperturbed
|
||||
if past is None and output_so_far is not None:
|
||||
last = output_so_far[:, -1:]
|
||||
if output_so_far.shape[1] > 1:
|
||||
_, past, _ = model(output_so_far[:, :-1])
|
||||
|
||||
unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far)
|
||||
unpert_last_hidden = unpert_all_hidden[-1]
|
||||
|
||||
# check if we are abowe grad max length
|
||||
if i >= grad_length:
|
||||
current_stepsize = stepsize * 0
|
||||
else:
|
||||
current_stepsize = stepsize
|
||||
|
||||
# modify the past if necessary
|
||||
if not perturb or num_iterations == 0:
|
||||
pert_past = past
|
||||
|
||||
else:
|
||||
accumulated_hidden = unpert_last_hidden[:, :-1, :]
|
||||
accumulated_hidden = torch.sum(accumulated_hidden, dim=1)
|
||||
|
||||
if past is not None:
|
||||
pert_past, _, grad_norms, loss_this_iter = perturb_past(
|
||||
past,
|
||||
model,
|
||||
last,
|
||||
unpert_past=unpert_past,
|
||||
unpert_logits=unpert_logits,
|
||||
accumulated_hidden=accumulated_hidden,
|
||||
grad_norms=grad_norms,
|
||||
stepsize=current_stepsize,
|
||||
one_hot_bows_vectors=one_hot_bows_vectors,
|
||||
classifier=classifier,
|
||||
class_label=class_label,
|
||||
loss_type=loss_type,
|
||||
num_iterations=num_iterations,
|
||||
horizon_length=horizon_length,
|
||||
window_length=window_length,
|
||||
decay=decay,
|
||||
gamma=gamma,
|
||||
kl_scale=kl_scale,
|
||||
device=device,
|
||||
)
|
||||
loss_in_time.append(loss_this_iter)
|
||||
else:
|
||||
pert_past = past
|
||||
|
||||
pert_logits, past, pert_all_hidden = model(last, past=pert_past)
|
||||
pert_logits = pert_logits[:, -1, :] / temperature # + SMALL_CONST
|
||||
pert_probs = F.softmax(pert_logits, dim=-1)
|
||||
|
||||
if classifier is not None:
|
||||
ce_loss = torch.nn.CrossEntropyLoss()
|
||||
prediction = classifier(torch.mean(unpert_last_hidden, dim=1))
|
||||
label = torch.tensor([class_label], device=device,
|
||||
dtype=torch.long)
|
||||
unpert_discrim_loss = ce_loss(prediction, label)
|
||||
print(
|
||||
"unperturbed discrim loss",
|
||||
unpert_discrim_loss.data.cpu().numpy()
|
||||
)
|
||||
else:
|
||||
unpert_discrim_loss = 0
|
||||
|
||||
# Fuse the modified model and original model
|
||||
if perturb:
|
||||
|
||||
unpert_probs = F.softmax(unpert_logits[:, -1, :], dim=-1)
|
||||
|
||||
pert_probs = ((pert_probs ** gm_scale) * (
|
||||
unpert_probs ** (1 - gm_scale))) # + SMALL_CONST
|
||||
pert_probs = top_k_filter(pert_probs, k=top_k,
|
||||
probs=True) # + SMALL_CONST
|
||||
|
||||
# rescale
|
||||
if torch.sum(pert_probs) <= 1:
|
||||
pert_probs = pert_probs / torch.sum(pert_probs)
|
||||
|
||||
else:
|
||||
pert_logits = top_k_filter(pert_logits, k=top_k) # + SMALL_CONST
|
||||
pert_probs = F.softmax(pert_logits, dim=-1)
|
||||
|
||||
# sample or greedy
|
||||
if sample:
|
||||
last = torch.multinomial(pert_probs, num_samples=1)
|
||||
|
||||
else:
|
||||
_, last = torch.topk(pert_probs, k=1, dim=-1)
|
||||
|
||||
# update context/output_so_far appending the new token
|
||||
output_so_far = (
|
||||
last if output_so_far is None
|
||||
else torch.cat((output_so_far, last), dim=1)
|
||||
)
|
||||
|
||||
print(tokenizer.decode(output_so_far.tolist()[0]))
|
||||
|
||||
return output_so_far, unpert_discrim_loss, loss_in_time
|
||||
|
||||
|
||||
def set_generic_model_params(discrim_weights, discrim_meta):
|
||||
if discrim_weights is None:
|
||||
raise ValueError('When using a generic discriminator, '
|
||||
'discrim_weights need to be specified')
|
||||
if discrim_meta is None:
|
||||
raise ValueError('When using a generic discriminator, '
|
||||
'discrim_meta need to be specified')
|
||||
|
||||
with open(discrim_meta, 'r') as discrim_meta_file:
|
||||
meta = json.load(discrim_meta_file)
|
||||
meta['path'] = discrim_weights
|
||||
DISCRIMINATOR_MODELS_PARAMS['generic'] = meta
|
||||
|
||||
|
||||
def run_pplm_example(
|
||||
pretrained_model="gpt2-medium",
|
||||
cond_text="",
|
||||
uncond=False,
|
||||
num_samples=1,
|
||||
bag_of_words=None,
|
||||
discrim=None,
|
||||
discrim_weights=None,
|
||||
discrim_meta=None,
|
||||
class_label=-1,
|
||||
length=100,
|
||||
stepsize=0.02,
|
||||
temperature=1.0,
|
||||
top_k=10,
|
||||
sample=False,
|
||||
num_iterations=3,
|
||||
grad_length=10000,
|
||||
horizon_length=1,
|
||||
window_length=0,
|
||||
decay=False,
|
||||
gamma=1.5,
|
||||
gm_scale=0.9,
|
||||
kl_scale=0.01,
|
||||
seed=0,
|
||||
no_cuda=False,
|
||||
colorama=False
|
||||
):
|
||||
# set Random seed
|
||||
torch.manual_seed(seed)
|
||||
np.random.seed(seed)
|
||||
|
||||
# set the device
|
||||
device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
|
||||
|
||||
if discrim == 'generic':
|
||||
set_generic_model_params(discrim_weights, discrim_meta)
|
||||
|
||||
if discrim is not None:
|
||||
pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][
|
||||
"pretrained_model"
|
||||
]
|
||||
print("discrim = {}, pretrained_model set "
|
||||
"to discriminator's = {}".format(discrim, pretrained_model))
|
||||
|
||||
# load pretrained model
|
||||
model = GPT2LMHeadModel.from_pretrained(
|
||||
pretrained_model,
|
||||
output_hidden_states=True
|
||||
)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
# load tokenizer
|
||||
tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
|
||||
|
||||
# Freeze GPT-2 weights
|
||||
for param in model.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
# figure out conditioning text
|
||||
if uncond:
|
||||
tokenized_cond_text = tokenizer.encode(
|
||||
[tokenizer.bos_token]
|
||||
)
|
||||
else:
|
||||
raw_text = cond_text
|
||||
while not raw_text:
|
||||
print("Did you forget to add `--cond_text`? ")
|
||||
raw_text = input("Model prompt >>> ")
|
||||
tokenized_cond_text = tokenizer.encode(tokenizer.bos_token + raw_text)
|
||||
|
||||
print("= Prefix of sentence =")
|
||||
print(tokenizer.decode(tokenized_cond_text))
|
||||
print()
|
||||
|
||||
# generate unperturbed and perturbed texts
|
||||
|
||||
# full_text_generation returns:
|
||||
# unpert_gen_tok_text, pert_gen_tok_texts, discrim_losses, losses_in_time
|
||||
unpert_gen_tok_text, pert_gen_tok_texts, _, _ = full_text_generation(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
context=tokenized_cond_text,
|
||||
device=device,
|
||||
num_samples=num_samples,
|
||||
bag_of_words=bag_of_words,
|
||||
discrim=discrim,
|
||||
class_label=class_label,
|
||||
length=length,
|
||||
stepsize=stepsize,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
sample=sample,
|
||||
num_iterations=num_iterations,
|
||||
grad_length=grad_length,
|
||||
horizon_length=horizon_length,
|
||||
window_length=window_length,
|
||||
decay=decay,
|
||||
gamma=gamma,
|
||||
gm_scale=gm_scale,
|
||||
kl_scale=kl_scale,
|
||||
)
|
||||
|
||||
# untokenize unperturbed text
|
||||
unpert_gen_text = tokenizer.decode(unpert_gen_tok_text.tolist()[0])
|
||||
|
||||
print("=" * 80)
|
||||
print("= Unperturbed generated text =")
|
||||
print(unpert_gen_text)
|
||||
print()
|
||||
|
||||
generated_texts = []
|
||||
|
||||
bow_word_ids = set()
|
||||
if bag_of_words and colorama:
|
||||
bow_indices = get_bag_of_words_indices(bag_of_words.split(";"),
|
||||
tokenizer)
|
||||
for single_bow_list in bow_indices:
|
||||
# filtering all words in the list composed of more than 1 token
|
||||
filtered = list(filter(lambda x: len(x) <= 1, single_bow_list))
|
||||
# w[0] because we are sure w has only 1 item because previous fitler
|
||||
bow_word_ids.update(w[0] for w in filtered)
|
||||
|
||||
# iterate through the perturbed texts
|
||||
for i, pert_gen_tok_text in enumerate(pert_gen_tok_texts):
|
||||
try:
|
||||
# untokenize unperturbed text
|
||||
if colorama:
|
||||
import colorama
|
||||
|
||||
pert_gen_text = ''
|
||||
for word_id in pert_gen_tok_text.tolist()[0]:
|
||||
if word_id in bow_word_ids:
|
||||
pert_gen_text += '{}{}{}'.format(
|
||||
colorama.Fore.RED,
|
||||
tokenizer.decode([word_id]),
|
||||
colorama.Style.RESET_ALL
|
||||
)
|
||||
else:
|
||||
pert_gen_text += tokenizer.decode([word_id])
|
||||
else:
|
||||
pert_gen_text = tokenizer.decode(pert_gen_tok_text.tolist()[0])
|
||||
|
||||
print("= Perturbed generated text {} =".format(i + 1))
|
||||
print(pert_gen_text)
|
||||
print()
|
||||
except:
|
||||
pass
|
||||
|
||||
# keep the prefix, perturbed seq, original seq for each index
|
||||
generated_texts.append(
|
||||
(tokenized_cond_text, pert_gen_tok_text, unpert_gen_tok_text)
|
||||
)
|
||||
|
||||
return
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--pretrained_model",
|
||||
"-M",
|
||||
type=str,
|
||||
default="gpt2-medium",
|
||||
help="pretrained model name or path to local checkpoint",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--cond_text", type=str, default="The lake",
|
||||
help="Prefix texts to condition on"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--uncond", action="store_true",
|
||||
help="Generate from end-of-text as prefix"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num_samples",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Number of samples to generate from the modified latents",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--bag_of_words",
|
||||
"-B",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Bags of words used for PPLM-BoW. "
|
||||
"Either a BOW id (see list in code) or a filepath. "
|
||||
"Multiple BoWs separated by ;",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--discrim",
|
||||
"-D",
|
||||
type=str,
|
||||
default=None,
|
||||
choices=("clickbait", "sentiment", "toxicity", "generic"),
|
||||
help="Discriminator to use",
|
||||
)
|
||||
parser.add_argument('--discrim_weights', type=str, default=None,
|
||||
help='Weights for the generic discriminator')
|
||||
parser.add_argument('--discrim_meta', type=str, default=None,
|
||||
help='Meta information for the generic discriminator')
|
||||
parser.add_argument(
|
||||
"--class_label",
|
||||
type=int,
|
||||
default=-1,
|
||||
help="Class label used for the discriminator",
|
||||
)
|
||||
parser.add_argument("--length", type=int, default=100)
|
||||
parser.add_argument("--stepsize", type=float, default=0.02)
|
||||
parser.add_argument("--temperature", type=float, default=1.0)
|
||||
parser.add_argument("--top_k", type=int, default=10)
|
||||
parser.add_argument(
|
||||
"--sample", action="store_true",
|
||||
help="Generate from end-of-text as prefix"
|
||||
)
|
||||
parser.add_argument("--num_iterations", type=int, default=3)
|
||||
parser.add_argument("--grad_length", type=int, default=10000)
|
||||
parser.add_argument(
|
||||
"--window_length",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Length of past which is being optimized; "
|
||||
"0 corresponds to infinite window length",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--horizon_length",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Length of future to optimize over",
|
||||
)
|
||||
parser.add_argument("--decay", action="store_true",
|
||||
help="whether to decay or not")
|
||||
parser.add_argument("--gamma", type=float, default=1.5)
|
||||
parser.add_argument("--gm_scale", type=float, default=0.9)
|
||||
parser.add_argument("--kl_scale", type=float, default=0.01)
|
||||
parser.add_argument("--seed", type=int, default=0)
|
||||
parser.add_argument("--no_cuda", action="store_true", help="no cuda")
|
||||
parser.add_argument("--colorama", action="store_true",
|
||||
help="colors keywords")
|
||||
|
||||
args = parser.parse_args()
|
||||
run_pplm_example(**vars(args))
|
||||
@@ -1,576 +0,0 @@
|
||||
#! /usr/bin/env python3
|
||||
# coding=utf-8
|
||||
|
||||
# This code is licensed under a non-commercial license.
|
||||
|
||||
import argparse
|
||||
import csv
|
||||
import json
|
||||
import math
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.optim
|
||||
import torch.optim as optim
|
||||
import torch.utils.data as data
|
||||
from nltk.tokenize.treebank import TreebankWordDetokenizer
|
||||
from torchtext import data as torchtext_data
|
||||
from torchtext import datasets
|
||||
from tqdm import tqdm, trange
|
||||
|
||||
from transformers import GPT2Tokenizer, GPT2LMHeadModel
|
||||
from pplm_classification_head import ClassificationHead
|
||||
|
||||
torch.manual_seed(0)
|
||||
np.random.seed(0)
|
||||
EPSILON = 1e-10
|
||||
example_sentence = "This is incredible! I love it, this is the best chicken I have ever had."
|
||||
max_length_seq = 100
|
||||
|
||||
|
||||
|
||||
|
||||
class Discriminator(torch.nn.Module):
|
||||
"""Transformer encoder followed by a Classification Head"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
class_size,
|
||||
pretrained_model="gpt2-medium",
|
||||
cached_mode=False,
|
||||
device='cpu'
|
||||
):
|
||||
super(Discriminator, self).__init__()
|
||||
self.tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model)
|
||||
self.encoder = GPT2LMHeadModel.from_pretrained(pretrained_model)
|
||||
self.embed_size = self.encoder.transformer.config.hidden_size
|
||||
self.classifier_head = ClassificationHead(
|
||||
class_size=class_size,
|
||||
embed_size=self.embed_size
|
||||
)
|
||||
self.cached_mode = cached_mode
|
||||
self.device = device
|
||||
|
||||
def get_classifier(self):
|
||||
return self.classifier_head
|
||||
|
||||
def train_custom(self):
|
||||
for param in self.encoder.parameters():
|
||||
param.requires_grad = False
|
||||
self.classifier_head.train()
|
||||
|
||||
def avg_representation(self, x):
|
||||
mask = x.ne(0).unsqueeze(2).repeat(
|
||||
1, 1, self.embed_size
|
||||
).float().to(self.device).detach()
|
||||
hidden, _ = self.encoder.transformer(x)
|
||||
masked_hidden = hidden * mask
|
||||
avg_hidden = torch.sum(masked_hidden, dim=1) / (
|
||||
torch.sum(mask, dim=1).detach() + EPSILON
|
||||
)
|
||||
return avg_hidden
|
||||
|
||||
def forward(self, x):
|
||||
if self.cached_mode:
|
||||
avg_hidden = x.to(self.device)
|
||||
else:
|
||||
avg_hidden = self.avg_representation(x.to(self.device))
|
||||
|
||||
logits = self.classifier_head(avg_hidden)
|
||||
probs = F.log_softmax(logits, dim=-1)
|
||||
|
||||
return probs
|
||||
|
||||
|
||||
class Dataset(data.Dataset):
|
||||
def __init__(self, X, y):
|
||||
"""Reads source and target sequences from txt files."""
|
||||
self.X = X
|
||||
self.y = y
|
||||
|
||||
def __len__(self):
|
||||
return len(self.X)
|
||||
|
||||
def __getitem__(self, index):
|
||||
"""Returns one data pair (source and target)."""
|
||||
data = {}
|
||||
data["X"] = self.X[index]
|
||||
data["y"] = self.y[index]
|
||||
return data
|
||||
|
||||
|
||||
def collate_fn(data):
|
||||
def pad_sequences(sequences):
|
||||
lengths = [len(seq) for seq in sequences]
|
||||
|
||||
padded_sequences = torch.zeros(
|
||||
len(sequences),
|
||||
max(lengths)
|
||||
).long() # padding value = 0
|
||||
|
||||
for i, seq in enumerate(sequences):
|
||||
end = lengths[i]
|
||||
padded_sequences[i, :end] = seq[:end]
|
||||
|
||||
return padded_sequences, lengths
|
||||
|
||||
item_info = {}
|
||||
for key in data[0].keys():
|
||||
item_info[key] = [d[key] for d in data]
|
||||
|
||||
x_batch, _ = pad_sequences(item_info["X"])
|
||||
y_batch = torch.tensor(item_info["y"], dtype=torch.long)
|
||||
|
||||
return x_batch, y_batch
|
||||
|
||||
|
||||
def cached_collate_fn(data):
|
||||
item_info = {}
|
||||
for key in data[0].keys():
|
||||
item_info[key] = [d[key] for d in data]
|
||||
|
||||
x_batch = torch.cat(item_info["X"], 0)
|
||||
y_batch = torch.tensor(item_info["y"], dtype=torch.long)
|
||||
|
||||
return x_batch, y_batch
|
||||
|
||||
|
||||
def train_epoch(data_loader, discriminator, optimizer,
|
||||
epoch=0, log_interval=10, device='cpu'):
|
||||
samples_so_far = 0
|
||||
discriminator.train_custom()
|
||||
for batch_idx, (input_t, target_t) in enumerate(data_loader):
|
||||
input_t, target_t = input_t.to(device), target_t.to(device)
|
||||
|
||||
optimizer.zero_grad()
|
||||
|
||||
output_t = discriminator(input_t)
|
||||
loss = F.nll_loss(output_t, target_t)
|
||||
loss.backward(retain_graph=True)
|
||||
optimizer.step()
|
||||
|
||||
samples_so_far += len(input_t)
|
||||
|
||||
if batch_idx % log_interval == 0:
|
||||
print(
|
||||
"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
|
||||
epoch + 1,
|
||||
samples_so_far, len(data_loader.dataset),
|
||||
100 * samples_so_far / len(data_loader.dataset), loss.item()
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def evaluate_performance(data_loader, discriminator, device='cpu'):
|
||||
discriminator.eval()
|
||||
test_loss = 0
|
||||
correct = 0
|
||||
with torch.no_grad():
|
||||
for input_t, target_t in data_loader:
|
||||
input_t, target_t = input_t.to(device), target_t.to(device)
|
||||
output_t = discriminator(input_t)
|
||||
# sum up batch loss
|
||||
test_loss += F.nll_loss(output_t, target_t, reduction="sum").item()
|
||||
# get the index of the max log-probability
|
||||
pred_t = output_t.argmax(dim=1, keepdim=True)
|
||||
correct += pred_t.eq(target_t.view_as(pred_t)).sum().item()
|
||||
|
||||
test_loss /= len(data_loader.dataset)
|
||||
|
||||
print(
|
||||
"Performance on test set: "
|
||||
"Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
|
||||
test_loss, correct, len(data_loader.dataset),
|
||||
100. * correct / len(data_loader.dataset)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def predict(input_sentence, model, classes, cached=False, device='cpu'):
|
||||
input_t = model.tokenizer.encode(input_sentence)
|
||||
input_t = torch.tensor([input_t], dtype=torch.long, device=device)
|
||||
if cached:
|
||||
input_t = model.avg_representation(input_t)
|
||||
|
||||
log_probs = model(input_t).data.cpu().numpy().flatten().tolist()
|
||||
print("Input sentence:", input_sentence)
|
||||
print("Predictions:", ", ".join(
|
||||
"{}: {:.4f}".format(c, math.exp(log_prob)) for c, log_prob in
|
||||
zip(classes, log_probs)
|
||||
))
|
||||
|
||||
|
||||
def get_cached_data_loader(dataset, batch_size, discriminator,
|
||||
shuffle=False, device='cpu'):
|
||||
data_loader = torch.utils.data.DataLoader(dataset=dataset,
|
||||
batch_size=batch_size,
|
||||
collate_fn=collate_fn)
|
||||
|
||||
xs = []
|
||||
ys = []
|
||||
for batch_idx, (x, y) in enumerate(tqdm(data_loader, ascii=True)):
|
||||
with torch.no_grad():
|
||||
x = x.to(device)
|
||||
avg_rep = discriminator.avg_representation(x).cpu().detach()
|
||||
avg_rep_list = torch.unbind(avg_rep.unsqueeze(1))
|
||||
xs += avg_rep_list
|
||||
ys += y.cpu().numpy().tolist()
|
||||
|
||||
data_loader = torch.utils.data.DataLoader(
|
||||
dataset=Dataset(xs, ys),
|
||||
batch_size=batch_size,
|
||||
shuffle=shuffle,
|
||||
collate_fn=cached_collate_fn)
|
||||
|
||||
return data_loader
|
||||
|
||||
|
||||
def train_discriminator(
|
||||
dataset, dataset_fp=None, pretrained_model="gpt2-medium",
|
||||
epochs=10, batch_size=64, log_interval=10,
|
||||
save_model=False, cached=False, no_cuda=False):
|
||||
device = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
|
||||
|
||||
print("Preprocessing {} dataset...".format(dataset))
|
||||
start = time.time()
|
||||
|
||||
if dataset == "SST":
|
||||
idx2class = ["positive", "negative", "very positive", "very negative",
|
||||
"neutral"]
|
||||
class2idx = {c: i for i, c in enumerate(idx2class)}
|
||||
|
||||
discriminator = Discriminator(
|
||||
class_size=len(idx2class),
|
||||
pretrained_model=pretrained_model,
|
||||
cached_mode=cached,
|
||||
device=device
|
||||
).to(device)
|
||||
|
||||
text = torchtext_data.Field()
|
||||
label = torchtext_data.Field(sequential=False)
|
||||
train_data, val_data, test_data = datasets.SST.splits(
|
||||
text,
|
||||
label,
|
||||
fine_grained=True,
|
||||
train_subtrees=True,
|
||||
)
|
||||
|
||||
x = []
|
||||
y = []
|
||||
for i in trange(len(train_data), ascii=True):
|
||||
seq = TreebankWordDetokenizer().detokenize(
|
||||
vars(train_data[i])["text"]
|
||||
)
|
||||
seq = discriminator.tokenizer.encode(seq)
|
||||
seq = torch.tensor([50256] + seq, device=device, dtype=torch.long)
|
||||
x.append(seq)
|
||||
y.append(class2idx[vars(train_data[i])["label"]])
|
||||
train_dataset = Dataset(x, y)
|
||||
|
||||
test_x = []
|
||||
test_y = []
|
||||
for i in trange(len(test_data), ascii=True):
|
||||
seq = TreebankWordDetokenizer().detokenize(
|
||||
vars(test_data[i])["text"]
|
||||
)
|
||||
seq = discriminator.tokenizer.encode(seq)
|
||||
seq = torch.tensor([50256] + seq, device=device, dtype=torch.long)
|
||||
test_x.append(seq)
|
||||
test_y.append(class2idx[vars(test_data[i])["label"]])
|
||||
test_dataset = Dataset(test_x, test_y)
|
||||
|
||||
discriminator_meta = {
|
||||
"class_size": len(idx2class),
|
||||
"embed_size": discriminator.embed_size,
|
||||
"pretrained_model": pretrained_model,
|
||||
"class_vocab": class2idx,
|
||||
"default_class": 2,
|
||||
}
|
||||
|
||||
elif dataset == "clickbait":
|
||||
idx2class = ["non_clickbait", "clickbait"]
|
||||
class2idx = {c: i for i, c in enumerate(idx2class)}
|
||||
|
||||
discriminator = Discriminator(
|
||||
class_size=len(idx2class),
|
||||
pretrained_model=pretrained_model,
|
||||
cached_mode=cached,
|
||||
device=device
|
||||
).to(device)
|
||||
|
||||
with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
|
||||
data = []
|
||||
for i, line in enumerate(f):
|
||||
try:
|
||||
data.append(eval(line))
|
||||
except:
|
||||
print("Error evaluating line {}: {}".format(
|
||||
i, line
|
||||
))
|
||||
continue
|
||||
x = []
|
||||
y = []
|
||||
with open("datasets/clickbait/clickbait_train_prefix.txt") as f:
|
||||
for i, line in enumerate(tqdm(f, ascii=True)):
|
||||
try:
|
||||
d = eval(line)
|
||||
seq = discriminator.tokenizer.encode(d["text"])
|
||||
|
||||
if len(seq) < max_length_seq:
|
||||
seq = torch.tensor(
|
||||
[50256] + seq, device=device, dtype=torch.long
|
||||
)
|
||||
else:
|
||||
print("Line {} is longer than maximum length {}".format(
|
||||
i, max_length_seq
|
||||
))
|
||||
continue
|
||||
x.append(seq)
|
||||
y.append(d["label"])
|
||||
except:
|
||||
print("Error evaluating / tokenizing"
|
||||
" line {}, skipping it".format(i))
|
||||
pass
|
||||
|
||||
full_dataset = Dataset(x, y)
|
||||
train_size = int(0.9 * len(full_dataset))
|
||||
test_size = len(full_dataset) - train_size
|
||||
train_dataset, test_dataset = torch.utils.data.random_split(
|
||||
full_dataset, [train_size, test_size]
|
||||
)
|
||||
|
||||
discriminator_meta = {
|
||||
"class_size": len(idx2class),
|
||||
"embed_size": discriminator.embed_size,
|
||||
"pretrained_model": pretrained_model,
|
||||
"class_vocab": class2idx,
|
||||
"default_class": 1,
|
||||
}
|
||||
|
||||
elif dataset == "toxic":
|
||||
idx2class = ["non_toxic", "toxic"]
|
||||
class2idx = {c: i for i, c in enumerate(idx2class)}
|
||||
|
||||
discriminator = Discriminator(
|
||||
class_size=len(idx2class),
|
||||
pretrained_model=pretrained_model,
|
||||
cached_mode=cached,
|
||||
device=device
|
||||
).to(device)
|
||||
|
||||
x = []
|
||||
y = []
|
||||
with open("datasets/toxic/toxic_train.txt") as f:
|
||||
for i, line in enumerate(tqdm(f, ascii=True)):
|
||||
try:
|
||||
d = eval(line)
|
||||
seq = discriminator.tokenizer.encode(d["text"])
|
||||
|
||||
if len(seq) < max_length_seq:
|
||||
seq = torch.tensor(
|
||||
[50256] + seq, device=device, dtype=torch.long
|
||||
)
|
||||
else:
|
||||
print("Line {} is longer than maximum length {}".format(
|
||||
i, max_length_seq
|
||||
))
|
||||
continue
|
||||
x.append(seq)
|
||||
y.append(int(np.sum(d["label"]) > 0))
|
||||
except:
|
||||
print("Error evaluating / tokenizing"
|
||||
" line {}, skipping it".format(i))
|
||||
pass
|
||||
|
||||
full_dataset = Dataset(x, y)
|
||||
train_size = int(0.9 * len(full_dataset))
|
||||
test_size = len(full_dataset) - train_size
|
||||
train_dataset, test_dataset = torch.utils.data.random_split(
|
||||
full_dataset, [train_size, test_size]
|
||||
)
|
||||
|
||||
discriminator_meta = {
|
||||
"class_size": len(idx2class),
|
||||
"embed_size": discriminator.embed_size,
|
||||
"pretrained_model": pretrained_model,
|
||||
"class_vocab": class2idx,
|
||||
"default_class": 0,
|
||||
}
|
||||
|
||||
else: # if dataset == "generic":
|
||||
# This assumes the input dataset is a TSV with the following structure:
|
||||
# class \t text
|
||||
|
||||
if dataset_fp is None:
|
||||
raise ValueError("When generic dataset is selected, "
|
||||
"dataset_fp needs to be specified aswell.")
|
||||
|
||||
classes = set()
|
||||
with open(dataset_fp) as f:
|
||||
csv_reader = csv.reader(f, delimiter="\t")
|
||||
for row in tqdm(csv_reader, ascii=True):
|
||||
if row:
|
||||
classes.add(row[0])
|
||||
|
||||
idx2class = sorted(classes)
|
||||
class2idx = {c: i for i, c in enumerate(idx2class)}
|
||||
|
||||
discriminator = Discriminator(
|
||||
class_size=len(idx2class),
|
||||
pretrained_model=pretrained_model,
|
||||
cached_mode=cached,
|
||||
device=device
|
||||
).to(device)
|
||||
|
||||
x = []
|
||||
y = []
|
||||
with open(dataset_fp) as f:
|
||||
csv_reader = csv.reader(f, delimiter="\t")
|
||||
for i, row in enumerate(tqdm(csv_reader, ascii=True)):
|
||||
if row:
|
||||
label = row[0]
|
||||
text = row[1]
|
||||
|
||||
try:
|
||||
seq = discriminator.tokenizer.encode(text)
|
||||
if (len(seq) < max_length_seq):
|
||||
seq = torch.tensor(
|
||||
[50256] + seq,
|
||||
device=device,
|
||||
dtype=torch.long
|
||||
)
|
||||
|
||||
else:
|
||||
print(
|
||||
"Line {} is longer than maximum length {}".format(
|
||||
i, max_length_seq
|
||||
))
|
||||
continue
|
||||
|
||||
x.append(seq)
|
||||
y.append(class2idx[label])
|
||||
|
||||
except:
|
||||
print("Error tokenizing line {}, skipping it".format(i))
|
||||
pass
|
||||
|
||||
full_dataset = Dataset(x, y)
|
||||
train_size = int(0.9 * len(full_dataset))
|
||||
test_size = len(full_dataset) - train_size
|
||||
train_dataset, test_dataset = torch.utils.data.random_split(
|
||||
full_dataset,
|
||||
[train_size, test_size]
|
||||
)
|
||||
|
||||
discriminator_meta = {
|
||||
"class_size": len(idx2class),
|
||||
"embed_size": discriminator.embed_size,
|
||||
"pretrained_model": pretrained_model,
|
||||
"class_vocab": class2idx,
|
||||
"default_class": 0,
|
||||
}
|
||||
|
||||
end = time.time()
|
||||
print("Preprocessed {} data points".format(
|
||||
len(train_dataset) + len(test_dataset))
|
||||
)
|
||||
print("Data preprocessing took: {:.3f}s".format(end - start))
|
||||
|
||||
if cached:
|
||||
print("Building representation cache...")
|
||||
|
||||
start = time.time()
|
||||
|
||||
train_loader = get_cached_data_loader(
|
||||
train_dataset, batch_size, discriminator,
|
||||
shuffle=True, device=device
|
||||
)
|
||||
|
||||
test_loader = get_cached_data_loader(
|
||||
test_dataset, batch_size, discriminator, device=device
|
||||
)
|
||||
|
||||
end = time.time()
|
||||
print("Building representation cache took: {:.3f}s".format(end - start))
|
||||
|
||||
else:
|
||||
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
|
||||
batch_size=batch_size,
|
||||
shuffle=True,
|
||||
collate_fn=collate_fn)
|
||||
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
|
||||
batch_size=batch_size,
|
||||
collate_fn=collate_fn)
|
||||
|
||||
if save_model:
|
||||
with open("{}_classifier_head_meta.json".format(dataset),
|
||||
"w") as meta_file:
|
||||
json.dump(discriminator_meta, meta_file)
|
||||
|
||||
optimizer = optim.Adam(discriminator.parameters(), lr=0.0001)
|
||||
|
||||
for epoch in range(epochs):
|
||||
start = time.time()
|
||||
print("\nEpoch", epoch + 1)
|
||||
|
||||
train_epoch(
|
||||
discriminator=discriminator,
|
||||
data_loader=train_loader,
|
||||
optimizer=optimizer,
|
||||
epoch=epoch,
|
||||
log_interval=log_interval,
|
||||
device=device
|
||||
)
|
||||
evaluate_performance(
|
||||
data_loader=test_loader,
|
||||
discriminator=discriminator,
|
||||
device=device
|
||||
)
|
||||
|
||||
end = time.time()
|
||||
print("Epoch took: {:.3f}s".format(end - start))
|
||||
|
||||
print("\nExample prediction")
|
||||
predict(example_sentence, discriminator, idx2class,
|
||||
cached=cached, device=device)
|
||||
|
||||
if save_model:
|
||||
# torch.save(discriminator.state_dict(),
|
||||
# "{}_discriminator_{}.pt".format(
|
||||
# args.dataset, epoch + 1
|
||||
# ))
|
||||
torch.save(discriminator.get_classifier().state_dict(),
|
||||
"{}_classifier_head_epoch_{}.pt".format(dataset,
|
||||
epoch + 1))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Train a discriminator on top of GPT-2 representations")
|
||||
parser.add_argument("--dataset", type=str, default="SST",
|
||||
choices=("SST", "clickbait", "toxic", "generic"),
|
||||
help="dataset to train the discriminator on."
|
||||
"In case of generic, the dataset is expected"
|
||||
"to be a TSBV file with structure: class \\t text")
|
||||
parser.add_argument("--dataset_fp", type=str, default="",
|
||||
help="File path of the dataset to use. "
|
||||
"Needed only in case of generic datadset")
|
||||
parser.add_argument("--pretrained_model", type=str, default="gpt2-medium",
|
||||
help="Pretrained model to use as encoder")
|
||||
parser.add_argument("--epochs", type=int, default=10, metavar="N",
|
||||
help="Number of training epochs")
|
||||
parser.add_argument("--batch_size", type=int, default=64, metavar="N",
|
||||
help="input batch size for training (default: 64)")
|
||||
parser.add_argument("--log_interval", type=int, default=10, metavar="N",
|
||||
help="how many batches to wait before logging training status")
|
||||
parser.add_argument("--save_model", action="store_true",
|
||||
help="whether to save the model")
|
||||
parser.add_argument("--cached", action="store_true",
|
||||
help="whether to cache the input representations")
|
||||
parser.add_argument("--no_cuda", action="store_true",
|
||||
help="use to turn off cuda")
|
||||
args = parser.parse_args()
|
||||
|
||||
train_discriminator(**(vars(args)))
|
||||
@@ -39,9 +39,8 @@ from transformers import (WEIGHTS_NAME,
|
||||
|
||||
from run_glue import set_seed, load_and_cache_examples, ALL_MODELS, MODEL_CLASSES
|
||||
|
||||
from transformers import glue_compute_metrics as compute_metrics
|
||||
from transformers import glue_output_modes as output_modes
|
||||
from transformers import glue_processors as processors
|
||||
from utils_glue import (compute_metrics, convert_examples_to_features,
|
||||
output_modes, processors)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -234,8 +233,6 @@ 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")
|
||||
|
||||
+44
-32
@@ -22,7 +22,6 @@ import glob
|
||||
import logging
|
||||
import os
|
||||
import random
|
||||
import json
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
@@ -48,13 +47,9 @@ from transformers import (WEIGHTS_NAME, BertConfig,
|
||||
XLNetTokenizer,
|
||||
DistilBertConfig,
|
||||
DistilBertForSequenceClassification,
|
||||
DistilBertTokenizer,
|
||||
AlbertConfig,
|
||||
AlbertForSequenceClassification,
|
||||
AlbertTokenizer,
|
||||
)
|
||||
DistilBertTokenizer)
|
||||
|
||||
from transformers import AdamW, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
|
||||
from transformers import glue_compute_metrics as compute_metrics
|
||||
from transformers import glue_output_modes as output_modes
|
||||
@@ -71,8 +66,7 @@ MODEL_CLASSES = {
|
||||
'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
|
||||
'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
|
||||
'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
|
||||
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
|
||||
'albert': (AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer)
|
||||
'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer)
|
||||
}
|
||||
|
||||
|
||||
@@ -105,9 +99,8 @@ 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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
@@ -165,7 +158,7 @@ def train(args, train_dataset, model, tokenizer):
|
||||
loss.backward()
|
||||
|
||||
tr_loss += loss.item()
|
||||
if (step + 1) % args.gradient_accumulation_steps == 0:
|
||||
if (step + 1) % args.gradient_accumulation_steps == 0 and not args.tpu:
|
||||
if args.fp16:
|
||||
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
|
||||
else:
|
||||
@@ -177,23 +170,15 @@ def train(args, train_dataset, model, tokenizer):
|
||||
global_step += 1
|
||||
|
||||
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
|
||||
logs = {}
|
||||
# Log metrics
|
||||
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
|
||||
results = evaluate(args, model, tokenizer)
|
||||
for key, value in results.items():
|
||||
eval_key = 'eval_{}'.format(key)
|
||||
logs[eval_key] = value
|
||||
|
||||
loss_scalar = (tr_loss - logging_loss) / args.logging_steps
|
||||
learning_rate_scalar = scheduler.get_lr()[0]
|
||||
logs['learning_rate'] = learning_rate_scalar
|
||||
logs['loss'] = loss_scalar
|
||||
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
|
||||
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
|
||||
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
|
||||
logging_loss = tr_loss
|
||||
|
||||
for key, value in logs.items():
|
||||
tb_writer.add_scalar(key, value, global_step)
|
||||
print(json.dumps({**logs, **{'step': global_step}}))
|
||||
|
||||
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
|
||||
# Save model checkpoint
|
||||
output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
|
||||
@@ -204,6 +189,11 @@ 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
|
||||
@@ -231,13 +221,9 @@ def evaluate(args, model, tokenizer, prefix=""):
|
||||
|
||||
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
|
||||
# Note that DistributedSampler samples randomly
|
||||
eval_sampler = SequentialSampler(eval_dataset)
|
||||
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
|
||||
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||||
|
||||
# multi-gpu eval
|
||||
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))
|
||||
@@ -332,7 +318,7 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
|
||||
all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
|
||||
elif output_mode == "regression":
|
||||
all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
|
||||
|
||||
|
||||
dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
|
||||
return dataset
|
||||
|
||||
@@ -376,7 +362,7 @@ def main():
|
||||
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.")
|
||||
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,
|
||||
@@ -407,6 +393,15 @@ 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',
|
||||
@@ -440,6 +435,23 @@ 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',
|
||||
@@ -493,7 +505,7 @@ 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):
|
||||
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0) and not args.tpu:
|
||||
# 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)
|
||||
|
||||
@@ -42,13 +42,12 @@ except:
|
||||
|
||||
from tqdm import tqdm, trange
|
||||
|
||||
from transformers import (WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup,
|
||||
from transformers import (WEIGHTS_NAME, AdamW, WarmupLinearSchedule,
|
||||
BertConfig, BertForMaskedLM, BertTokenizer,
|
||||
GPT2Config, GPT2LMHeadModel, GPT2Tokenizer,
|
||||
OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer,
|
||||
RobertaConfig, RobertaForMaskedLM, RobertaTokenizer,
|
||||
DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer,
|
||||
CamembertConfig, CamembertForMaskedLM, CamembertTokenizer)
|
||||
DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -59,18 +58,17 @@ MODEL_CLASSES = {
|
||||
'openai-gpt': (OpenAIGPTConfig, OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
|
||||
'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
|
||||
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
|
||||
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
|
||||
'camembert': (CamembertConfig, CamembertForMaskedLM, CamembertTokenizer)
|
||||
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer)
|
||||
}
|
||||
|
||||
|
||||
class TextDataset(Dataset):
|
||||
def __init__(self, tokenizer, args, file_path='train', block_size=512):
|
||||
def __init__(self, tokenizer, file_path='train', block_size=512):
|
||||
assert os.path.isfile(file_path)
|
||||
directory, filename = os.path.split(file_path)
|
||||
cached_features_file = os.path.join(directory, args.model_name_or_path + '_cached_lm_' + str(block_size) + '_' + filename)
|
||||
cached_features_file = os.path.join(directory, 'cached_lm_' + str(block_size) + '_' + filename)
|
||||
|
||||
if os.path.exists(cached_features_file) and not args.overwrite_cache:
|
||||
if os.path.exists(cached_features_file):
|
||||
logger.info("Loading features from cached file %s", cached_features_file)
|
||||
with open(cached_features_file, 'rb') as handle:
|
||||
self.examples = pickle.load(handle)
|
||||
@@ -101,7 +99,7 @@ class TextDataset(Dataset):
|
||||
|
||||
|
||||
def load_and_cache_examples(args, tokenizer, evaluate=False):
|
||||
dataset = TextDataset(tokenizer, args, file_path=args.eval_data_file if evaluate else args.train_data_file, block_size=args.block_size)
|
||||
dataset = TextDataset(tokenizer, file_path=args.eval_data_file if evaluate else args.train_data_file, block_size=args.block_size)
|
||||
return dataset
|
||||
|
||||
|
||||
@@ -187,7 +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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
@@ -217,10 +215,6 @@ def train(args, train_dataset, model, tokenizer):
|
||||
|
||||
global_step = 0
|
||||
tr_loss, logging_loss = 0.0, 0.0
|
||||
|
||||
model_to_resize = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
|
||||
model_to_resize.resize_token_embeddings(len(tokenizer))
|
||||
|
||||
model.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)
|
||||
@@ -303,13 +297,9 @@ def evaluate(args, model, tokenizer, prefix=""):
|
||||
|
||||
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
|
||||
# Note that DistributedSampler samples randomly
|
||||
eval_sampler = SequentialSampler(eval_dataset)
|
||||
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
|
||||
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||||
|
||||
# multi-gpu evaluate
|
||||
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))
|
||||
@@ -437,7 +427,7 @@ def main():
|
||||
parser.add_argument('--server_port', type=str, default='', help="For distant debugging.")
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
|
||||
if args.model_type in ["bert", "roberta", "distilbert"] and not args.mlm:
|
||||
raise ValueError("BERT and RoBERTa do not have LM heads but masked LM heads. They must be run using the --mlm "
|
||||
"flag (masked language modeling).")
|
||||
if args.eval_data_file is None and args.do_eval:
|
||||
|
||||
@@ -43,7 +43,7 @@ from transformers import (WEIGHTS_NAME, BertConfig,
|
||||
XLNetTokenizer, RobertaConfig,
|
||||
RobertaForMultipleChoice, RobertaTokenizer)
|
||||
|
||||
from transformers import AdamW, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
|
||||
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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
@@ -226,13 +226,9 @@ def evaluate(args, model, tokenizer, prefix="", test=False):
|
||||
|
||||
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
|
||||
# Note that DistributedSampler samples randomly
|
||||
eval_sampler = SequentialSampler(eval_dataset)
|
||||
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
|
||||
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||||
|
||||
# multi-gpu evaluate
|
||||
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))
|
||||
|
||||
+9
-19
@@ -33,23 +33,19 @@ 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, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
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, DistilBertConfig)),
|
||||
(tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, RobertaConfig)),
|
||||
())
|
||||
|
||||
MODEL_CLASSES = {
|
||||
"bert": (BertConfig, BertForTokenClassification, BertTokenizer),
|
||||
"roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer),
|
||||
"distilbert": (DistilBertConfig, DistilBertForTokenClassification, DistilBertTokenizer),
|
||||
"camembert": (CamembertConfig, CamembertForTokenClassification, CamembertTokenizer),
|
||||
"roberta": (RobertaConfig, RobertaForTokenClassification, RobertaTokenizer)
|
||||
}
|
||||
|
||||
|
||||
@@ -84,7 +80,7 @@ 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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
@@ -125,10 +121,9 @@ 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)
|
||||
|
||||
@@ -158,7 +153,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
|
||||
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, labels, pad_token_label_id, mode="dev")
|
||||
results, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id)
|
||||
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)
|
||||
@@ -196,10 +191,6 @@ 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))
|
||||
@@ -215,9 +206,9 @@ 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]
|
||||
|
||||
@@ -529,4 +520,3 @@ def main():
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@@ -1,483 +0,0 @@
|
||||
# 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 the library models for sequence classification on Semeval (Bert)."""
|
||||
|
||||
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 tensorboardX import SummaryWriter
|
||||
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler)
|
||||
from torch.utils.data.distributed import DistributedSampler
|
||||
from tqdm import tqdm, trange
|
||||
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
from transformers import (WEIGHTS_NAME, BertConfig, RobertaConfig,
|
||||
BertForRelationshipClassification, RobertaForRelationshipClassification,
|
||||
BertTokenizer, RobertaTokenizer)
|
||||
from examples.utils_semeval import (processors, output_modes, convert_examples_to_features,
|
||||
convert_features_to_dataset, compute_metrics, RBertUtils,
|
||||
get_entity_seperator_token_ids)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MODEL_CLASSES = {
|
||||
'bert': (BertConfig, BertForRelationshipClassification, BertTokenizer),
|
||||
'roberta': (RobertaConfig, RobertaForRelationshipClassification, RobertaTokenizer)
|
||||
}
|
||||
|
||||
RBERT_UTILS = RBertUtils()
|
||||
|
||||
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 = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=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],
|
||||
'token_type_ids': batch[2],
|
||||
'labels': batch[3]}
|
||||
outputs = model(**inputs)
|
||||
loss = outputs[0] # model outputs are always tuple in pytorch-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()
|
||||
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
|
||||
else:
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
|
||||
|
||||
tr_loss += loss.item()
|
||||
if (step + 1) % args.gradient_accumulation_steps == 0:
|
||||
scheduler.step() # Update learning rate schedule
|
||||
optimizer.step()
|
||||
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, eval_dataset, prefix=""):
|
||||
eval_task = args.task_name
|
||||
eval_output_dir = args.output_dir
|
||||
|
||||
results = {}
|
||||
|
||||
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)
|
||||
|
||||
# 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
|
||||
out_instance_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],
|
||||
'token_type_ids': batch[2],
|
||||
'labels': batch[3]}
|
||||
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()
|
||||
out_instance_ids = batch[4].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)
|
||||
out_instance_ids = np.append(out_instance_ids, batch[4].detach().cpu().numpy(), axis=0)
|
||||
|
||||
eval_loss = eval_loss / nb_eval_steps
|
||||
preds = np.argmax(preds, axis=1)
|
||||
result = compute_metrics(eval_task, preds, out_label_ids)
|
||||
results.update(result)
|
||||
|
||||
output_eval_file = os.path.join(eval_output_dir, "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, np.stack([out_instance_ids,preds],axis=1)
|
||||
|
||||
|
||||
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](include_directionality=args.include_directionality)
|
||||
output_mode = output_modes[task]
|
||||
# Load data features from cache or dataset file
|
||||
cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format(
|
||||
'dev' if evaluate else 'train',
|
||||
list(filter(None, args.model_name_or_path.split('/'))).pop(),
|
||||
str(args.max_seq_length),
|
||||
str(task)))
|
||||
if os.path.exists(cached_features_file):
|
||||
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()
|
||||
if evaluate:
|
||||
examples = list(processor.get_dev_examples(args.data_dir,include_other=args.eval_on_other_labels))
|
||||
# so that the official scoring script can be used
|
||||
processor.write_dev_examples_to_official_format(args.output_dir,examples=examples)
|
||||
else:
|
||||
examples = list(processor.get_train_examples(args.data_dir,include_other=args.train_on_other_labels))
|
||||
features = convert_examples_to_features(examples, label_list,args.max_seq_length, tokenizer,RBERT_UTILS)
|
||||
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
|
||||
dataset = convert_features_to_dataset(features, output_mode=output_mode)
|
||||
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_name_or_path", default='bert-base-uncased', type=str)
|
||||
parser.add_argument("--model_type", default='bert', type=str)
|
||||
parser.add_argument("--output_dir", default=None, type=str, required=True,
|
||||
help="The output directory where the model predictions and checkpoints will be written.")
|
||||
parser.add_argument("--task_name", default='semeval2010_task8', type=str)
|
||||
## 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 dev 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.")
|
||||
parser.add_argument("--include_directionality", action='store_true',help='train on all 19 classes for semeval 2010 task 8. Exclude this flag to train on the 9 relation labels only')
|
||||
parser.add_argument("--train_on_other_labels", action='store_true',help='For semeval 2010 task 8, do not include instances with the label "Other" in the training data')
|
||||
parser.add_argument("--eval_on_other_labels", action='store_true',help='For semeval 2010 task 8, Do not include instances with the label "Other" in the evaluation data')
|
||||
|
||||
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 semeval task
|
||||
args.task_name = args.task_name.lower()
|
||||
if args.task_name not in processors:
|
||||
raise ValueError("Task not found: %s" % (args.task_name))
|
||||
processor = processors[args.task_name](include_directionality=args.include_directionality)
|
||||
args.output_mode = output_modes[args.task_name]
|
||||
label_list = processor.get_labels()
|
||||
logger.info(f"labels are: {','.join(label_list)}")
|
||||
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]
|
||||
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)
|
||||
|
||||
entity_1_token_id,entity_2_token_id = get_entity_seperator_token_ids(RBERT_UTILS, tokenizer)
|
||||
|
||||
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,
|
||||
entity_1_token_id=entity_1_token_id,
|
||||
entity_2_token_id=entity_2_token_id,
|
||||
)
|
||||
|
||||
model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
|
||||
|
||||
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.model_name_or_path, do_lower_case=args.do_lower_case)
|
||||
model.to(args.device)
|
||||
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if args.do_eval and args.local_rank in [-1, 0]:
|
||||
checkpoints = [args.output_dir]
|
||||
if args.eval_all_checkpoints:
|
||||
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
|
||||
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
|
||||
logger.info("Evaluate the following checkpoints: %s", checkpoints)
|
||||
for checkpoint in checkpoints:
|
||||
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else "best"
|
||||
model = model_class.from_pretrained(checkpoint)
|
||||
model.to(args.device)
|
||||
eval_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=True)
|
||||
result , instance_result_array = evaluate(args, model, eval_dataset, prefix=global_step)
|
||||
result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
|
||||
results.update(result)
|
||||
# output file in the format for the official semeval script
|
||||
output_semeval_file = os.path.join(args.output_dir , f"{global_step}_semeval_results.tsv")
|
||||
with open(output_semeval_file, "w") as writer:
|
||||
label_map = {i: label for i, label in enumerate(label_list)}
|
||||
for x in instance_result_array:
|
||||
writer.write(str(x[0])+'\t'+label_map[x[1]]+'\n')
|
||||
return results
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+9
-20
@@ -22,7 +22,6 @@ import logging
|
||||
import os
|
||||
import random
|
||||
import glob
|
||||
import timeit
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
@@ -43,10 +42,9 @@ from transformers import (WEIGHTS_NAME, BertConfig,
|
||||
XLMTokenizer, XLNetConfig,
|
||||
XLNetForQuestionAnswering,
|
||||
XLNetTokenizer,
|
||||
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer,
|
||||
AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
|
||||
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
|
||||
|
||||
from transformers import AdamW, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
|
||||
from utils_squad import (read_squad_examples, convert_examples_to_features,
|
||||
RawResult, write_predictions,
|
||||
@@ -66,8 +64,7 @@ MODEL_CLASSES = {
|
||||
'bert': (BertConfig, BertForQuestionAnswering, BertTokenizer),
|
||||
'xlnet': (XLNetConfig, XLNetForQuestionAnswering, XLNetTokenizer),
|
||||
'xlm': (XLMConfig, XLMForQuestionAnswering, XLMTokenizer),
|
||||
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer),
|
||||
'albert': (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
|
||||
'distilbert': (DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
|
||||
}
|
||||
|
||||
def set_seed(args):
|
||||
@@ -102,7 +99,7 @@ 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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
@@ -130,7 +127,7 @@ 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 = 1
|
||||
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])
|
||||
@@ -216,19 +213,14 @@ def evaluate(args, model, tokenizer, prefix=""):
|
||||
|
||||
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
|
||||
# Note that DistributedSampler samples randomly
|
||||
eval_sampler = SequentialSampler(dataset)
|
||||
eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
|
||||
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||||
|
||||
# multi-gpu evaluate
|
||||
if args.n_gpu > 1:
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
# Eval!
|
||||
logger.info("***** Running evaluation {} *****".format(prefix))
|
||||
logger.info(" Num examples = %d", len(dataset))
|
||||
logger.info(" Batch size = %d", args.eval_batch_size)
|
||||
all_results = []
|
||||
start_time = timeit.default_timer()
|
||||
for batch in tqdm(eval_dataloader, desc="Evaluating"):
|
||||
model.eval()
|
||||
batch = tuple(t.to(args.device) for t in batch)
|
||||
@@ -261,9 +253,6 @@ def evaluate(args, model, tokenizer, prefix=""):
|
||||
end_logits = to_list(outputs[1][i]))
|
||||
all_results.append(result)
|
||||
|
||||
evalTime = timeit.default_timer() - start_time
|
||||
logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
|
||||
|
||||
# Compute predictions
|
||||
output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
|
||||
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
|
||||
@@ -404,7 +393,7 @@ 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 decay if we apply some.")
|
||||
help="Weight deay if we apply some.")
|
||||
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
|
||||
help="Epsilon for Adam optimizer.")
|
||||
parser.add_argument("--max_grad_norm", default=1.0, type=float,
|
||||
@@ -539,7 +528,7 @@ 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, force_download=True)
|
||||
model = model_class.from_pretrained(args.output_dir)
|
||||
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
||||
model.to(args.device)
|
||||
|
||||
@@ -557,7 +546,7 @@ 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, force_download=True)
|
||||
model = model_class.from_pretrained(checkpoint)
|
||||
model.to(args.device)
|
||||
|
||||
# Evaluate
|
||||
|
||||
@@ -275,10 +275,6 @@ 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)
|
||||
|
||||
@@ -73,8 +73,6 @@ 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
|
||||
|
||||
@@ -1,515 +0,0 @@
|
||||
# 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)
|
||||
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()
|
||||
@@ -1,309 +0,0 @@
|
||||
# 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 the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
|
||||
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import torch
|
||||
from torch.utils.data import (TensorDataset)
|
||||
from sklearn.metrics import f1_score
|
||||
|
||||
from transformers import RobertaTokenizer
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RBertUtils:
|
||||
"""
|
||||
Utility class to prepare text for RBERT
|
||||
|
||||
|
||||
Args:
|
||||
same as BertTokenizer, apart from
|
||||
replace_conflicting_entity_offset_char: since the forward method of RBert classification models detects
|
||||
the special characters that bound each entity, any pre-existing characters
|
||||
of this type will cause a conflict. To prevent this, we replace any affected
|
||||
characters with this, prior to encoding. Defaults to '|'
|
||||
ent1_sep_token: the char to delimit entity 1. Default is '$'
|
||||
ent2_sep_token: the char to delimit entity 2. Default is '#'
|
||||
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, replace_conflicting_entity_offset_char='|',
|
||||
ent1_sep_token="$", ent2_sep_token="#", ):
|
||||
self.ent1_sep_token = ent1_sep_token
|
||||
self.ent2_sep_token = ent2_sep_token
|
||||
self.replace_conflicting_entity_offset_char = replace_conflicting_entity_offset_char
|
||||
|
||||
def _clean_special_chars(self, text: str):
|
||||
return text.replace(self.ent1_sep_token,
|
||||
self.replace_conflicting_entity_offset_char).replace(self.ent2_sep_token,
|
||||
self.replace_conflicting_entity_offset_char)
|
||||
|
||||
def _insert_relationship_special_chars(self, text, e1_offset_tup, e2_offset_tup):
|
||||
reverse_ordered_tups = sorted([e1_offset_tup, e2_offset_tup], reverse=True, key=lambda x: x[1])
|
||||
last_token_to_process = True
|
||||
for start, end in reverse_ordered_tups:
|
||||
before = text[:start]
|
||||
during = text[start:end]
|
||||
after = text[end:]
|
||||
if last_token_to_process:
|
||||
text = before + " " + self.ent2_sep_token + " " + during + " " + self.ent2_sep_token + " " + after
|
||||
else:
|
||||
text = before + " " + self.ent1_sep_token + " " + during + " " + self.ent1_sep_token + " " + after
|
||||
last_token_to_process = False
|
||||
return text
|
||||
|
||||
def insert_special_chars_into_text(self, text, e1_offset_tup, e2_offset_tup):
|
||||
text = self._clean_special_chars(text)
|
||||
text = self._insert_relationship_special_chars(text, e1_offset_tup, e2_offset_tup)
|
||||
return text
|
||||
|
||||
class InputExample(object):
|
||||
"""A single SemEval 2010 Task 8 example"""
|
||||
|
||||
def __init__(self, id: int, text: str, label: str, comment: str):
|
||||
self.comment = comment
|
||||
self.label = label
|
||||
self.text = text
|
||||
self.id = id
|
||||
|
||||
|
||||
class InputFeatures(object):
|
||||
"""A single set of features of data."""
|
||||
|
||||
def __init__(self, input_ids, input_mask, segment_ids, label_id, instance_id):
|
||||
self.instance_id = instance_id
|
||||
self.input_ids = input_ids
|
||||
self.input_mask = input_mask
|
||||
self.segment_ids = segment_ids
|
||||
self.label_id = label_id
|
||||
|
||||
|
||||
|
||||
class SemEval2010Task8DataProcessor():
|
||||
"""Processor for the SemEval 2010 Task 8 Dataset. Note, it's not clear from the RBert paper how many classes the
|
||||
model is trained on. Clearly this has a big impact on the result.
|
||||
|
||||
|
||||
"""
|
||||
def __init__(self,include_directionality=True):
|
||||
self.include_directionality = include_directionality
|
||||
self.e1_e2_labels = [
|
||||
'Product-Producer(e1,e2)',
|
||||
'Entity-Origin(e1,e2)',
|
||||
'Entity-Destination(e1,e2)',
|
||||
'Message-Topic(e1,e2)',
|
||||
'Component-Whole(e1,e2)',
|
||||
'Content-Container(e1,e2)',
|
||||
'Instrument-Agency(e1,e2)',
|
||||
'Cause-Effect(e1,e2)',
|
||||
'Member-Collection(e1,e2)'
|
||||
]
|
||||
self.e2_e1_labels = [
|
||||
'Product-Producer(e2,e1)',
|
||||
'Entity-Origin(e2,e1)',
|
||||
'Entity-Destination(e2,e1)',
|
||||
'Message-Topic(e2,e1)',
|
||||
'Component-Whole(e2,e1)',
|
||||
'Content-Container(e2,e1)',
|
||||
'Instrument-Agency(e2,e1)',
|
||||
'Cause-Effect(e2,e1)',
|
||||
'Member-Collection(e2,e1)'
|
||||
]
|
||||
self.undirected_labels_mapping = dict(zip(self.e2_e1_labels,self.e1_e2_labels))
|
||||
self.other_label = ['Other']
|
||||
self.undirected_labels_mapping[self.other_label[0]] = self.other_label[0]
|
||||
|
||||
if self.include_directionality:
|
||||
self.all_labels = self.e1_e2_labels + self.e2_e1_labels + self.other_label
|
||||
else:
|
||||
self.all_labels = self.e1_e2_labels + self.other_label
|
||||
|
||||
def _instance_generator(self, data,include_other):
|
||||
for i in range(0, len(data), 4):
|
||||
id, text = data[i].split('\t')
|
||||
text = text.strip()
|
||||
label = str(data[i + 1]).strip()
|
||||
# map all labels to single direction if not using directionality
|
||||
if not self.include_directionality and label in self.undirected_labels_mapping:
|
||||
label = self.undirected_labels_mapping[label]
|
||||
# Don't include Other label in training set
|
||||
if label == 'Other' and not include_other:
|
||||
logger.info(f'Skipping Other labeled instance at {id}')
|
||||
else:
|
||||
comment = data[i + 2].strip()
|
||||
yield InputExample(id=id, text=text, label=label, comment=comment)
|
||||
|
||||
|
||||
def _strip_direction(self,label):
|
||||
return re.sub(r'\(.*', "", label)
|
||||
|
||||
def _create_examples(self, path,include_other):
|
||||
with open(path, 'r') as f:
|
||||
data = f.readlines()
|
||||
return self._instance_generator(data,include_other)
|
||||
|
||||
def get_train_examples(self, data_dir,include_other):
|
||||
return self._create_examples(os.path.join(data_dir, "SemEval2010_task8_training","TRAIN_FILE.TXT"),
|
||||
include_other)
|
||||
|
||||
def get_dev_examples(self, data_dir,include_other):
|
||||
return self._create_examples(os.path.join(data_dir, "SemEval2010_task8_testing_keys","TEST_FILE_FULL.TXT"),
|
||||
include_other)
|
||||
|
||||
def write_dev_examples_to_official_format(self,data_dir,examples):
|
||||
with open(os.path.join(data_dir,"TEST_FILE_SEMEVAL_SCRIPT_FORMAT.tsv"),'w') as f:
|
||||
for example in examples:
|
||||
f.write(str(example.id)+'\t'+example.label+'\n')
|
||||
|
||||
|
||||
def get_labels(self):
|
||||
return self.all_labels
|
||||
|
||||
def mod_text(text, hit):
|
||||
before = text[:hit.start()]
|
||||
during = hit.group(2)
|
||||
after = text[hit.end():]
|
||||
|
||||
start = len(before)
|
||||
end = len(before) + len(during)
|
||||
return (before + during + after, [start, end],)
|
||||
|
||||
|
||||
def find_ents_and_modify_string(text):
|
||||
e1_hits = re.search("(<e1>)(.*)(</e1>)", text)
|
||||
new_text, e1_offsets = mod_text(text, e1_hits)
|
||||
e2_hits = re.search("(<e2>)(.*)(</e2>)", new_text)
|
||||
new_text, e2_offsets = mod_text(new_text, e2_hits)
|
||||
|
||||
return new_text, e1_offsets, e2_offsets
|
||||
|
||||
def get_entity_seperator_token_ids(rbert_utils,tokenizer):
|
||||
if isinstance(tokenizer, RobertaTokenizer):
|
||||
entity_1_token_id = \
|
||||
tokenizer.encode(rbert_utils.ent1_sep_token, text_pair=None, add_special_tokens=True, add_prefix_space=True)[1]
|
||||
entity_2_token_id = \
|
||||
tokenizer.encode(rbert_utils.ent2_sep_token, text_pair=None, add_special_tokens=True, add_prefix_space=True)[1]
|
||||
else:
|
||||
entity_1_token_id = tokenizer.encode(rbert_utils.ent1_sep_token)[0]
|
||||
entity_2_token_id = tokenizer.encode(rbert_utils.ent2_sep_token)[0]
|
||||
|
||||
return entity_1_token_id, entity_2_token_id
|
||||
|
||||
def get_input_ids(text, tokenizer):
|
||||
if isinstance(tokenizer, RobertaTokenizer):
|
||||
input_ids = tokenizer.encode(text, text_pair=None, add_special_tokens=True, add_prefix_space=True)
|
||||
else:
|
||||
input_ids = tokenizer.encode(text, text_pair=None, add_special_tokens=True)
|
||||
return input_ids
|
||||
|
||||
def convert_examples_to_features(examples, label_list, max_seq_length,
|
||||
tokenizer, rbert_utils,pad_token=0,
|
||||
mask_padding_with_zero=True):
|
||||
label_map = {label: i for i, label in enumerate(label_list)}
|
||||
features = []
|
||||
for (ex_index, example) in enumerate(examples):
|
||||
if ex_index % 5000 == 0:
|
||||
logger.info("Writing example %d" % (ex_index))
|
||||
|
||||
|
||||
text_with_tags_removed, e1_offsets, e2_offsets = find_ents_and_modify_string(example.text)
|
||||
new_text = rbert_utils.insert_special_chars_into_text(text_with_tags_removed,e1_offsets,e2_offsets)
|
||||
|
||||
entity_1_token_id, entity_2_token_id = get_entity_seperator_token_ids(rbert_utils,tokenizer)
|
||||
input_ids = get_input_ids(new_text,tokenizer)
|
||||
|
||||
|
||||
|
||||
|
||||
#check that the special tokens have been encoded in the right order
|
||||
special_tokens_ordered = list(filter(lambda n: n in [entity_1_token_id, entity_2_token_id], input_ids))
|
||||
assert special_tokens_ordered[0] == entity_1_token_id
|
||||
assert special_tokens_ordered[1] == entity_1_token_id
|
||||
assert special_tokens_ordered[2] == entity_2_token_id
|
||||
assert special_tokens_ordered[3] == entity_2_token_id
|
||||
|
||||
# Account for [CLS] and [SEP] with "- 2"
|
||||
if len(input_ids) > max_seq_length - 2:
|
||||
input_ids = input_ids[:(max_seq_length - 2)]
|
||||
|
||||
# The mask has 1 for real tokens and 0 for padding tokens. Only real
|
||||
# tokens are attended to.
|
||||
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
|
||||
|
||||
# Zero-pad up to the sequence length.
|
||||
padding_length = max_seq_length - len(input_ids)
|
||||
|
||||
input_ids = input_ids + ([pad_token] * padding_length)
|
||||
input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
|
||||
|
||||
assert len(input_ids) == max_seq_length
|
||||
assert len(input_mask) == max_seq_length
|
||||
|
||||
label_id = label_map[example.label]
|
||||
features.append(
|
||||
InputFeatures(input_ids=input_ids,
|
||||
input_mask=input_mask,
|
||||
segment_ids=[0] * len(input_ids),
|
||||
label_id=label_id,
|
||||
instance_id=int(example.id)
|
||||
))
|
||||
return features
|
||||
|
||||
|
||||
def convert_features_to_dataset(features, output_mode='classification'):
|
||||
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
|
||||
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
|
||||
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
|
||||
if output_mode == "classification":
|
||||
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
|
||||
else:
|
||||
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)
|
||||
instance_ids = torch.tensor([f.instance_id for f in features], dtype=torch.int)
|
||||
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids,instance_ids)
|
||||
return dataset
|
||||
|
||||
def simple_accuracy(preds, labels):
|
||||
return (preds == labels).mean()
|
||||
|
||||
def acc_and_f1(preds, labels):
|
||||
acc = simple_accuracy(preds, labels)
|
||||
f1 = f1_score(y_true=labels, y_pred=preds,average='macro')
|
||||
return {
|
||||
"acc": acc,
|
||||
"f1": f1,
|
||||
"acc_and_f1": (acc + f1) / 2,
|
||||
}
|
||||
def compute_metrics(task_name, preds, labels):
|
||||
assert len(preds) == len(labels)
|
||||
if task_name == "semeval2010_task8":
|
||||
return acc_and_f1(labels, preds)
|
||||
else:
|
||||
raise KeyError(task_name)
|
||||
|
||||
|
||||
processors = {
|
||||
"semeval2010_task8": SemEval2010Task8DataProcessor
|
||||
}
|
||||
|
||||
output_modes = {
|
||||
"semeval2010_task8": "classification"
|
||||
}
|
||||
@@ -23,7 +23,6 @@ import logging
|
||||
import math
|
||||
import collections
|
||||
from io import open
|
||||
from tqdm import tqdm
|
||||
|
||||
from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
|
||||
|
||||
@@ -203,7 +202,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
|
||||
# f = np.zeros((max_N, max_M), dtype=np.float32)
|
||||
|
||||
features = []
|
||||
for (example_index, example) in enumerate(tqdm(examples)):
|
||||
for (example_index, example) in enumerate(examples):
|
||||
|
||||
# if example_index % 100 == 0:
|
||||
# logger.info('Converting %s/%s pos %s neg %s', example_index, len(examples), cnt_pos, cnt_neg)
|
||||
@@ -240,7 +239,6 @@ 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
|
||||
|
||||
@@ -36,15 +36,9 @@ To create the package for pypi.
|
||||
from io import open
|
||||
from setuptools import find_packages, setup
|
||||
|
||||
|
||||
extras = {
|
||||
'serving': ['uvicorn', 'fastapi']
|
||||
}
|
||||
extras['all'] = [package for package in extras.values()]
|
||||
|
||||
setup(
|
||||
name="transformers",
|
||||
version="2.2.1",
|
||||
version="2.1.1",
|
||||
author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
|
||||
author_email="thomas@huggingface.co",
|
||||
description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",
|
||||
@@ -67,10 +61,6 @@ setup(
|
||||
"transformers=transformers.__main__:main",
|
||||
]
|
||||
},
|
||||
extras_require=extras,
|
||||
scripts=[
|
||||
'transformers-cli'
|
||||
],
|
||||
# python_requires='>=3.5.0',
|
||||
tests_require=['pytest'],
|
||||
classifiers=[
|
||||
|
||||
@@ -43,7 +43,7 @@ from transformers import (WEIGHTS_NAME, BertConfig,
|
||||
XLNetTokenizer,
|
||||
DistilBertConfig, DistilBertForQuestionAnswering, DistilBertTokenizer)
|
||||
|
||||
from transformers import AdamW, get_linear_schedule_with_warmup
|
||||
from transformers import AdamW, WarmupLinearSchedule
|
||||
|
||||
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 = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
|
||||
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
|
||||
if args.fp16:
|
||||
try:
|
||||
from apex import amp
|
||||
|
||||
@@ -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 specificities include `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 specificites includes `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 detail what should be done at each step
|
||||
Let's details 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-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
|
||||
- [ ] 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
|
||||
|
||||
# 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 script to convert your original checkpoint weights to the current pytorch ones.
|
||||
- [ ] edit this scipt 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 itself at `docs/source/pretrained_models.rst`.
|
||||
- [ ] add a mention of your model in the doc: `README.md` and the documentation it-self at `docs/source/pretrained_models.rst`.
|
||||
- [ ] upload the pretrained weigths, configurations and vocabulary files.
|
||||
@@ -32,7 +32,7 @@ import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
from .configuration_xxx import XxxConfig
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer
|
||||
from .file_utils import add_start_docstrings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -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 (itself a sub-class of tf.keras.Model)
|
||||
# - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
|
||||
####################################################
|
||||
|
||||
####################################################
|
||||
@@ -121,9 +121,9 @@ class TFXxxMainLayer(tf.keras.layers.Layer):
|
||||
input_ids = inputs
|
||||
|
||||
if attention_mask is None:
|
||||
attention_mask = tf.fill(shape_list(input_ids), 1)
|
||||
attention_mask = tf.fill(tf.shape(input_ids), 1)
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(shape_list(input_ids), 0)
|
||||
token_type_ids = tf.fill(tf.shape(input_ids), 0)
|
||||
|
||||
# We create a 3D attention mask from a 2D tensor mask.
|
||||
# Sizes are [batch_size, 1, 1, to_seq_length]
|
||||
@@ -255,10 +255,6 @@ 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.",
|
||||
|
||||
@@ -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 (itself a sub-class of torch.nn.Module)
|
||||
# - PreTrainedModel for the models (it-self a sub-class of torch.nn.Module)
|
||||
####################################################
|
||||
|
||||
####################################################
|
||||
@@ -238,10 +238,6 @@ 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.",
|
||||
@@ -284,6 +280,7 @@ class XxxModel(XxxPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
@property
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
@@ -298,22 +295,11 @@ class XxxModel(XxxPreTrainedModel):
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.encoder.layer[layer].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
|
||||
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(input_shape, device=device)
|
||||
attention_mask = torch.ones_like(input_ids)
|
||||
if token_type_ids is None:
|
||||
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
||||
token_type_ids = torch.zeros_like(input_ids)
|
||||
|
||||
# We create a 3D attention mask from a 2D tensor mask.
|
||||
# Sizes are [batch_size, 1, 1, to_seq_length]
|
||||
@@ -347,7 +333,7 @@ class XxxModel(XxxPreTrainedModel):
|
||||
|
||||
##################################
|
||||
# Replace this with your model code
|
||||
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
|
||||
embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
|
||||
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
|
||||
@@ -398,15 +384,14 @@ class XxxForMaskedLM(XxxPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
prediction_scores = self.cls(sequence_output)
|
||||
@@ -464,15 +449,14 @@ class XxxForSequenceClassification(XxxPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
pooled_output = outputs[1]
|
||||
|
||||
@@ -536,15 +520,14 @@ class XxxForTokenClassification(XxxPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
@@ -620,15 +603,14 @@ class XxxForQuestionAnswering(XxxPreTrainedModel):
|
||||
|
||||
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,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
|
||||
@@ -172,7 +172,7 @@ class XxxTokenizer(PreTrainedTokenizer):
|
||||
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.
|
||||
A list of integers in the range [0, 1]: 0 for a special token, 1 for a sequence token.
|
||||
"""
|
||||
|
||||
if already_has_special_tokens:
|
||||
|
||||
@@ -1,23 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
from argparse import ArgumentParser
|
||||
|
||||
from transformers.commands.user import UserCommands
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = ArgumentParser(description='Transformers CLI tool', usage='transformers-cli <command> [<args>]')
|
||||
commands_parser = parser.add_subparsers(help='transformers-cli command helpers')
|
||||
|
||||
# Register commands
|
||||
UserCommands.register_subcommand(commands_parser)
|
||||
|
||||
# Let's go
|
||||
args = parser.parse_args()
|
||||
|
||||
if not hasattr(args, 'func'):
|
||||
parser.print_help()
|
||||
exit(1)
|
||||
|
||||
# Run
|
||||
service = args.func(args)
|
||||
service.run()
|
||||
+11
-33
@@ -1,4 +1,4 @@
|
||||
__version__ = "2.2.1"
|
||||
__version__ = "2.1.1"
|
||||
|
||||
# Work around to update TensorFlow's absl.logging threshold which alters the
|
||||
# default Python logging output behavior when present.
|
||||
@@ -25,11 +25,10 @@ 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,
|
||||
xnli_output_modes, xnli_processors, xnli_tasks_num_labels)
|
||||
glue_processors, glue_tasks_num_labels)
|
||||
|
||||
if is_sklearn_available():
|
||||
from .data import glue_compute_metrics, xnli_compute_metrics
|
||||
from .data import glue_compute_metrics
|
||||
|
||||
# Tokenizers
|
||||
from .tokenization_utils import (PreTrainedTokenizer)
|
||||
@@ -43,8 +42,6 @@ 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
|
||||
@@ -59,8 +56,6 @@ 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():
|
||||
@@ -71,13 +66,12 @@ if is_torch_available():
|
||||
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
|
||||
BertForMaskedLM, BertForNextSentencePrediction,
|
||||
BertForSequenceClassification, BertForMultipleChoice,
|
||||
BertForTokenClassification, BertForQuestionAnswering, BertForRelationshipClassification,
|
||||
BertForTokenClassification, BertForQuestionAnswering,
|
||||
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
|
||||
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
|
||||
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
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,
|
||||
@@ -86,41 +80,30 @@ if is_torch_available():
|
||||
CTRLLMHeadModel,
|
||||
CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
|
||||
XLNetForSequenceClassification, XLNetForTokenClassification,
|
||||
XLNetForMultipleChoice, XLNetForQuestionAnsweringSimple,
|
||||
XLNetForQuestionAnswering, load_tf_weights_in_xlnet,
|
||||
XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
XLNetForSequenceClassification, XLNetForMultipleChoice,
|
||||
XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
|
||||
load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
|
||||
XLMWithLMHeadModel, XLMForSequenceClassification,
|
||||
XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
|
||||
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
|
||||
RobertaForSequenceClassification, RobertaForMultipleChoice,
|
||||
RobertaForRelationshipClassification,
|
||||
RobertaForTokenClassification,
|
||||
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_distilbert import (DistilBertPreTrainedModel, DistilBertForMaskedLM, DistilBertModel,
|
||||
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
|
||||
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
|
||||
DistilBertForTokenClassification,
|
||||
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
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 (AlbertPreTrainedModel, AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification,
|
||||
AlbertForQuestionAnswering,
|
||||
load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
|
||||
# Optimization
|
||||
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)
|
||||
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
|
||||
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
|
||||
|
||||
|
||||
# TensorFlow
|
||||
if is_tf_available():
|
||||
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary, shape_list
|
||||
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, TFSequenceSummary
|
||||
from .modeling_tf_auto import (TFAutoModel, TFAutoModelForSequenceClassification, TFAutoModelForQuestionAnswering,
|
||||
TFAutoModelWithLMHead)
|
||||
|
||||
@@ -146,7 +129,6 @@ if is_tf_available():
|
||||
from .modeling_tf_xlnet import (TFXLNetPreTrainedModel, TFXLNetMainLayer,
|
||||
TFXLNetModel, TFXLNetLMHeadModel,
|
||||
TFXLNetForSequenceClassification,
|
||||
TFXLNetForTokenClassification,
|
||||
TFXLNetForQuestionAnsweringSimple,
|
||||
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
|
||||
@@ -172,10 +154,6 @@ 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,
|
||||
|
||||
@@ -1,12 +0,0 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from argparse import ArgumentParser
|
||||
|
||||
class BaseTransformersCLICommand(ABC):
|
||||
@staticmethod
|
||||
@abstractmethod
|
||||
def register_subcommand(parser: ArgumentParser):
|
||||
raise NotImplementedError()
|
||||
|
||||
@abstractmethod
|
||||
def run(self):
|
||||
raise NotImplementedError()
|
||||
@@ -1,165 +0,0 @@
|
||||
from argparse import ArgumentParser
|
||||
from getpass import getpass
|
||||
import os
|
||||
|
||||
from transformers.commands import BaseTransformersCLICommand
|
||||
from transformers.hf_api import HfApi, HfFolder, HTTPError
|
||||
|
||||
|
||||
class UserCommands(BaseTransformersCLICommand):
|
||||
@staticmethod
|
||||
def register_subcommand(parser: ArgumentParser):
|
||||
login_parser = parser.add_parser('login')
|
||||
login_parser.set_defaults(func=lambda args: LoginCommand(args))
|
||||
whoami_parser = parser.add_parser('whoami')
|
||||
whoami_parser.set_defaults(func=lambda args: WhoamiCommand(args))
|
||||
logout_parser = parser.add_parser('logout')
|
||||
logout_parser.set_defaults(func=lambda args: LogoutCommand(args))
|
||||
list_parser = parser.add_parser('ls')
|
||||
list_parser.set_defaults(func=lambda args: ListObjsCommand(args))
|
||||
# upload
|
||||
upload_parser = parser.add_parser('upload')
|
||||
upload_parser.add_argument('file', type=str, help='Local filepath of the file to upload.')
|
||||
upload_parser.add_argument('--filename', type=str, default=None, help='Optional: override object filename on S3.')
|
||||
upload_parser.set_defaults(func=lambda args: UploadCommand(args))
|
||||
|
||||
|
||||
|
||||
class ANSI:
|
||||
"""
|
||||
Helper for en.wikipedia.org/wiki/ANSI_escape_code
|
||||
"""
|
||||
_bold = u"\u001b[1m"
|
||||
_reset = u"\u001b[0m"
|
||||
@classmethod
|
||||
def bold(cls, s):
|
||||
return "{}{}{}".format(cls._bold, s, cls._reset)
|
||||
|
||||
|
||||
class BaseUserCommand:
|
||||
def __init__(self, args):
|
||||
self.args = args
|
||||
self._api = HfApi()
|
||||
|
||||
|
||||
class LoginCommand(BaseUserCommand):
|
||||
def run(self):
|
||||
print("""
|
||||
_| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|
|
||||
_| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|
|
||||
_|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|
|
||||
_| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|
|
||||
_| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|
|
||||
|
||||
""")
|
||||
username = input("Username: ")
|
||||
password = getpass()
|
||||
try:
|
||||
token = self._api.login(username, password)
|
||||
except HTTPError as e:
|
||||
# probably invalid credentials, display error message.
|
||||
print(e)
|
||||
exit(1)
|
||||
HfFolder.save_token(token)
|
||||
print("Login successful")
|
||||
print("Your token:", token, "\n")
|
||||
print("Your token has been saved to", HfFolder.path_token)
|
||||
|
||||
|
||||
class WhoamiCommand(BaseUserCommand):
|
||||
def run(self):
|
||||
token = HfFolder.get_token()
|
||||
if token is None:
|
||||
print("Not logged in")
|
||||
exit()
|
||||
try:
|
||||
user = self._api.whoami(token)
|
||||
print(user)
|
||||
except HTTPError as e:
|
||||
print(e)
|
||||
|
||||
|
||||
class LogoutCommand(BaseUserCommand):
|
||||
def run(self):
|
||||
token = HfFolder.get_token()
|
||||
if token is None:
|
||||
print("Not logged in")
|
||||
exit()
|
||||
HfFolder.delete_token()
|
||||
self._api.logout(token)
|
||||
print("Successfully logged out.")
|
||||
|
||||
|
||||
class ListObjsCommand(BaseUserCommand):
|
||||
def tabulate(self, rows, headers):
|
||||
# type: (List[List[Union[str, int]]], List[str]) -> str
|
||||
"""
|
||||
Inspired by:
|
||||
stackoverflow.com/a/8356620/593036
|
||||
stackoverflow.com/questions/9535954/printing-lists-as-tabular-data
|
||||
"""
|
||||
col_widths = [max(len(str(x)) for x in col) for col in zip(*rows, headers)]
|
||||
row_format = ("{{:{}}} " * len(headers)).format(*col_widths)
|
||||
lines = []
|
||||
lines.append(
|
||||
row_format.format(*headers)
|
||||
)
|
||||
lines.append(
|
||||
row_format.format(*["-" * w for w in col_widths])
|
||||
)
|
||||
for row in rows:
|
||||
lines.append(
|
||||
row_format.format(*row)
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
def run(self):
|
||||
token = HfFolder.get_token()
|
||||
if token is None:
|
||||
print("Not logged in")
|
||||
exit(1)
|
||||
try:
|
||||
objs = self._api.list_objs(token)
|
||||
except HTTPError as e:
|
||||
print(e)
|
||||
exit(1)
|
||||
if len(objs) == 0:
|
||||
print("No shared file yet")
|
||||
exit()
|
||||
rows = [ [
|
||||
obj.filename,
|
||||
obj.LastModified,
|
||||
obj.ETag,
|
||||
obj.Size
|
||||
] for obj in objs ]
|
||||
print(
|
||||
self.tabulate(rows, headers=["Filename", "LastModified", "ETag", "Size"])
|
||||
)
|
||||
|
||||
|
||||
class UploadCommand(BaseUserCommand):
|
||||
def run(self):
|
||||
token = HfFolder.get_token()
|
||||
if token is None:
|
||||
print("Not logged in")
|
||||
exit(1)
|
||||
filepath = os.path.join(os.getcwd(), self.args.file)
|
||||
filename = self.args.filename if self.args.filename is not None else os.path.basename(filepath)
|
||||
print(
|
||||
"About to upload file {} to S3 under filename {}".format(
|
||||
ANSI.bold(filepath), ANSI.bold(filename)
|
||||
)
|
||||
)
|
||||
|
||||
choice = input("Proceed? [Y/n] ").lower()
|
||||
if not(choice == "" or choice == "y" or choice == "yes"):
|
||||
print("Abort")
|
||||
exit()
|
||||
print(
|
||||
ANSI.bold("Uploading... This might take a while if file is large")
|
||||
)
|
||||
access_url = self._api.presign_and_upload(
|
||||
token=token, filename=filename, filepath=filepath
|
||||
)
|
||||
print("Your file now lives at:")
|
||||
print(access_url)
|
||||
@@ -1,100 +0,0 @@
|
||||
# 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
|
||||
@@ -27,8 +27,6 @@ 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
|
||||
from .configuration_albert import AlbertConfig
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -45,15 +43,13 @@ class AutoConfig(object):
|
||||
The base model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `distilbert`: DistilBertConfig (DistilBERT model)
|
||||
- contains `albert`: AlbertConfig (ALBERT model)
|
||||
- contains `camembert`: CamembertConfig (CamemBERT model)
|
||||
- contains `roberta`: RobertaConfig (RoBERTa model)
|
||||
- contains `bert`: BertConfig (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
|
||||
- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetConfig (XLNet model)
|
||||
- contains `xlm`: XLMConfig (XLM model)
|
||||
- contains `roberta`: RobertaConfig (RoBERTa model)
|
||||
- contains `ctrl` : CTRLConfig (CTRL model)
|
||||
This class cannot be instantiated using `__init__()` (throw an error).
|
||||
"""
|
||||
@@ -69,15 +65,13 @@ class AutoConfig(object):
|
||||
The configuration class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `distilbert`: DistilBertConfig (DistilBERT model)
|
||||
- contains `albert`: AlbertConfig (ALBERT model)
|
||||
- contains `camembert`: CamembertConfig (CamemBERT model)
|
||||
- contains `roberta`: RobertaConfig (RoBERTa model)
|
||||
- contains `bert`: BertConfig (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
|
||||
- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetConfig (XLNet model)
|
||||
- contains `xlm`: XLMConfig (XLM model)
|
||||
- contains `roberta`: RobertaConfig (RoBERTa model)
|
||||
- contains `ctrl` : CTRLConfig (CTRL model)
|
||||
Params:
|
||||
pretrained_model_name_or_path: either:
|
||||
@@ -98,9 +92,6 @@ class AutoConfig(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -125,10 +116,6 @@ class AutoConfig(object):
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
elif 'albert' in pretrained_model_name_or_path:
|
||||
return AlbertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
elif 'camembert' in pretrained_model_name_or_path:
|
||||
return CamembertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
elif 'roberta' in pretrained_model_name_or_path:
|
||||
return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
@@ -147,4 +134,4 @@ class AutoConfig(object):
|
||||
return CTRLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert'".format(pretrained_model_name_or_path))
|
||||
"'xlm', 'roberta', 'ctrl'".format(pretrained_model_name_or_path))
|
||||
@@ -89,30 +89,27 @@ class BertConfig(PretrainedConfig):
|
||||
type_vocab_size=2,
|
||||
initializer_range=0.02,
|
||||
layer_norm_eps=1e-12,
|
||||
entity_1_token_id=1001,
|
||||
entity_2_token_id=1002,
|
||||
**kwargs):
|
||||
super(BertConfig, self).__init__(**kwargs)
|
||||
self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
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
|
||||
self.entity_2_token_id = entity_2_token_id
|
||||
self.entity_1_token_id = entity_1_token_id
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
|
||||
json_config = json.loads(reader.read())
|
||||
for key, value in json_config.items():
|
||||
self.__dict__[key] = value
|
||||
elif not isinstance(vocab_size_or_config_json_file, int):
|
||||
elif isinstance(vocab_size_or_config_json_file, int):
|
||||
self.vocab_size = vocab_size_or_config_json_file
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
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
|
||||
else:
|
||||
raise ValueError("First argument must be either a vocabulary size (int)"
|
||||
" or the path to a pretrained model config file (str)")
|
||||
@@ -1,33 +0,0 @@
|
||||
# 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
|
||||
@@ -27,9 +27,7 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json",
|
||||
'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json",
|
||||
'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-config.json",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
|
||||
}
|
||||
|
||||
|
||||
@@ -53,27 +51,29 @@ class DistilBertConfig(PretrainedConfig):
|
||||
seq_classif_dropout=0.2,
|
||||
**kwargs):
|
||||
super(DistilBertConfig, self).__init__(**kwargs)
|
||||
self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.sinusoidal_pos_embds = sinusoidal_pos_embds
|
||||
self.n_layers = n_layers
|
||||
self.n_heads = n_heads
|
||||
self.dim = dim
|
||||
self.hidden_dim = hidden_dim
|
||||
self.dropout = dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.activation = activation
|
||||
self.initializer_range = initializer_range
|
||||
self.tie_weights_ = tie_weights_
|
||||
self.qa_dropout = qa_dropout
|
||||
self.seq_classif_dropout = seq_classif_dropout
|
||||
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
|
||||
json_config = json.loads(reader.read())
|
||||
for key, value in json_config.items():
|
||||
self.__dict__[key] = value
|
||||
elif not isinstance(vocab_size_or_config_json_file, int):
|
||||
elif isinstance(vocab_size_or_config_json_file, int):
|
||||
self.vocab_size = vocab_size_or_config_json_file
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.sinusoidal_pos_embds = sinusoidal_pos_embds
|
||||
self.n_layers = n_layers
|
||||
self.n_heads = n_heads
|
||||
self.dim = dim
|
||||
self.hidden_dim = hidden_dim
|
||||
self.dropout = dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.activation = activation
|
||||
self.initializer_range = initializer_range
|
||||
self.tie_weights_ = tie_weights_
|
||||
self.qa_dropout = qa_dropout
|
||||
self.seq_classif_dropout = seq_classif_dropout
|
||||
else:
|
||||
raise ValueError("First argument must be either a vocabulary size (int)"
|
||||
" or the path to a pretrained model config file (str)")
|
||||
@property
|
||||
|
||||
@@ -29,7 +29,6 @@ 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):
|
||||
@@ -96,31 +95,33 @@ class GPT2Config(PretrainedConfig):
|
||||
initializing all weight matrices.
|
||||
"""
|
||||
super(GPT2Config, self).__init__(**kwargs)
|
||||
self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
|
||||
self.n_ctx = n_ctx
|
||||
self.n_positions = n_positions
|
||||
self.n_embd = n_embd
|
||||
self.n_layer = n_layer
|
||||
self.n_head = n_head
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attn_pdrop = attn_pdrop
|
||||
self.layer_norm_epsilon = layer_norm_epsilon
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_first_dropout = summary_first_dropout
|
||||
self.summary_proj_to_labels = summary_proj_to_labels
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
|
||||
json_config = json.loads(reader.read())
|
||||
for key, value in json_config.items():
|
||||
self.__dict__[key] = value
|
||||
elif not isinstance(vocab_size_or_config_json_file, int):
|
||||
elif isinstance(vocab_size_or_config_json_file, int):
|
||||
self.vocab_size = vocab_size_or_config_json_file
|
||||
self.n_ctx = n_ctx
|
||||
self.n_positions = n_positions
|
||||
self.n_embd = n_embd
|
||||
self.n_layer = n_layer
|
||||
self.n_head = n_head
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attn_pdrop = attn_pdrop
|
||||
self.layer_norm_epsilon = layer_norm_epsilon
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_first_dropout = summary_first_dropout
|
||||
self.summary_proj_to_labels = summary_proj_to_labels
|
||||
else:
|
||||
raise ValueError(
|
||||
"First argument must be either a vocabulary size (int)"
|
||||
"or the path to a pretrained model config file (str)"
|
||||
|
||||
@@ -83,33 +83,35 @@ class OpenAIGPTConfig(PretrainedConfig):
|
||||
"""Constructs OpenAIGPTConfig.
|
||||
"""
|
||||
super(OpenAIGPTConfig, self).__init__(**kwargs)
|
||||
self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
|
||||
self.n_ctx = n_ctx
|
||||
self.n_positions = n_positions
|
||||
self.n_embd = n_embd
|
||||
self.n_layer = n_layer
|
||||
self.n_head = n_head
|
||||
self.afn = afn
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attn_pdrop = attn_pdrop
|
||||
self.layer_norm_epsilon = layer_norm_epsilon
|
||||
self.initializer_range = initializer_range
|
||||
self.predict_special_tokens = predict_special_tokens
|
||||
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_first_dropout = summary_first_dropout
|
||||
self.summary_proj_to_labels = summary_proj_to_labels
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
|
||||
json_config = json.loads(reader.read())
|
||||
for key, value in json_config.items():
|
||||
self.__dict__[key] = value
|
||||
elif not isinstance(vocab_size_or_config_json_file, int):
|
||||
elif isinstance(vocab_size_or_config_json_file, int):
|
||||
self.vocab_size = vocab_size_or_config_json_file
|
||||
self.n_ctx = n_ctx
|
||||
self.n_positions = n_positions
|
||||
self.n_embd = n_embd
|
||||
self.n_layer = n_layer
|
||||
self.n_head = n_head
|
||||
self.afn = afn
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attn_pdrop = attn_pdrop
|
||||
self.layer_norm_epsilon = layer_norm_epsilon
|
||||
self.initializer_range = initializer_range
|
||||
self.predict_special_tokens = predict_special_tokens
|
||||
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_first_dropout = summary_first_dropout
|
||||
self.summary_proj_to_labels = summary_proj_to_labels
|
||||
else:
|
||||
raise ValueError(
|
||||
"First argument must be either a vocabulary size (int)"
|
||||
"or the path to a pretrained model config file (str)"
|
||||
|
||||
@@ -29,8 +29,6 @@ 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",
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -94,9 +94,6 @@ class PretrainedConfig(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -123,7 +120,6 @@ class PretrainedConfig(object):
|
||||
"""
|
||||
cache_dir = kwargs.pop('cache_dir', None)
|
||||
force_download = kwargs.pop('force_download', False)
|
||||
resume_download = kwargs.pop('resume_download', False)
|
||||
proxies = kwargs.pop('proxies', None)
|
||||
return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)
|
||||
|
||||
@@ -135,8 +131,7 @@ 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, resume_download=resume_download)
|
||||
resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
|
||||
except EnvironmentError:
|
||||
if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
|
||||
msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
|
||||
|
||||
@@ -117,44 +117,46 @@ class XLMConfig(PretrainedConfig):
|
||||
"""Constructs XLMConfig.
|
||||
"""
|
||||
super(XLMConfig, self).__init__(**kwargs)
|
||||
self.n_words = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
|
||||
self.emb_dim = emb_dim
|
||||
self.n_layers = n_layers
|
||||
self.n_heads = n_heads
|
||||
self.dropout = dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.gelu_activation = gelu_activation
|
||||
self.sinusoidal_embeddings = sinusoidal_embeddings
|
||||
self.causal = causal
|
||||
self.asm = asm
|
||||
self.n_langs = n_langs
|
||||
self.use_lang_emb = use_lang_emb
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.bos_index = bos_index
|
||||
self.eos_index = eos_index
|
||||
self.pad_index = pad_index
|
||||
self.unk_index = unk_index
|
||||
self.mask_index = mask_index
|
||||
self.is_encoder = is_encoder
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.embed_init_std = embed_init_std
|
||||
self.init_std = init_std
|
||||
self.finetuning_task = finetuning_task
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_proj_to_labels = summary_proj_to_labels
|
||||
self.summary_first_dropout = summary_first_dropout
|
||||
self.start_n_top = start_n_top
|
||||
self.end_n_top = end_n_top
|
||||
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
|
||||
json_config = json.loads(reader.read())
|
||||
for key, value in json_config.items():
|
||||
self.__dict__[key] = value
|
||||
elif not isinstance(vocab_size_or_config_json_file, int):
|
||||
elif isinstance(vocab_size_or_config_json_file, int):
|
||||
self.n_words = vocab_size_or_config_json_file
|
||||
self.emb_dim = emb_dim
|
||||
self.n_layers = n_layers
|
||||
self.n_heads = n_heads
|
||||
self.dropout = dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.gelu_activation = gelu_activation
|
||||
self.sinusoidal_embeddings = sinusoidal_embeddings
|
||||
self.causal = causal
|
||||
self.asm = asm
|
||||
self.n_langs = n_langs
|
||||
self.use_lang_emb = use_lang_emb
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.bos_index = bos_index
|
||||
self.eos_index = eos_index
|
||||
self.pad_index = pad_index
|
||||
self.unk_index = unk_index
|
||||
self.mask_index = mask_index
|
||||
self.is_encoder = is_encoder
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.embed_init_std = embed_init_std
|
||||
self.init_std = init_std
|
||||
self.finetuning_task = finetuning_task
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_proj_to_labels = summary_proj_to_labels
|
||||
self.summary_first_dropout = summary_first_dropout
|
||||
self.start_n_top = start_n_top
|
||||
self.end_n_top = end_n_top
|
||||
else:
|
||||
raise ValueError("First argument must be either a vocabulary size (int)"
|
||||
" or the path to a pretrained model config file (str)")
|
||||
|
||||
|
||||
@@ -77,6 +77,7 @@ class XLNetConfig(PretrainedConfig):
|
||||
n_layer=24,
|
||||
n_head=16,
|
||||
d_inner=4096,
|
||||
max_position_embeddings=512,
|
||||
ff_activation="gelu",
|
||||
untie_r=True,
|
||||
attn_type="bi",
|
||||
@@ -103,42 +104,44 @@ class XLNetConfig(PretrainedConfig):
|
||||
"""Constructs XLNetConfig.
|
||||
"""
|
||||
super(XLNetConfig, self).__init__(**kwargs)
|
||||
self.n_token = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
|
||||
self.d_model = d_model
|
||||
self.n_layer = n_layer
|
||||
self.n_head = n_head
|
||||
assert d_model % n_head == 0
|
||||
self.d_head = d_model // n_head
|
||||
self.ff_activation = ff_activation
|
||||
self.d_inner = d_inner
|
||||
self.untie_r = untie_r
|
||||
self.attn_type = attn_type
|
||||
|
||||
self.initializer_range = initializer_range
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
|
||||
self.dropout = dropout
|
||||
self.mem_len = mem_len
|
||||
self.reuse_len = reuse_len
|
||||
self.bi_data = bi_data
|
||||
self.clamp_len = clamp_len
|
||||
self.same_length = same_length
|
||||
|
||||
self.finetuning_task = finetuning_task
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_last_dropout = summary_last_dropout
|
||||
self.start_n_top = start_n_top
|
||||
self.end_n_top = end_n_top
|
||||
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
|
||||
and isinstance(vocab_size_or_config_json_file, unicode)):
|
||||
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
|
||||
json_config = json.loads(reader.read())
|
||||
for key, value in json_config.items():
|
||||
self.__dict__[key] = value
|
||||
elif not isinstance(vocab_size_or_config_json_file, int):
|
||||
setattr(config, key, value)
|
||||
elif isinstance(vocab_size_or_config_json_file, int):
|
||||
self.n_token = vocab_size_or_config_json_file
|
||||
self.d_model = d_model
|
||||
self.n_layer = n_layer
|
||||
self.n_head = n_head
|
||||
assert d_model % n_head == 0
|
||||
self.d_head = d_model // n_head
|
||||
self.ff_activation = ff_activation
|
||||
self.d_inner = d_inner
|
||||
self.untie_r = untie_r
|
||||
self.attn_type = attn_type
|
||||
|
||||
self.initializer_range = initializer_range
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
|
||||
self.dropout = dropout
|
||||
self.mem_len = mem_len
|
||||
self.reuse_len = reuse_len
|
||||
self.bi_data = bi_data
|
||||
self.clamp_len = clamp_len
|
||||
self.same_length = same_length
|
||||
|
||||
self.finetuning_task = finetuning_task
|
||||
self.num_labels = num_labels
|
||||
self.summary_type = summary_type
|
||||
self.summary_use_proj = summary_use_proj
|
||||
self.summary_activation = summary_activation
|
||||
self.summary_last_dropout = summary_last_dropout
|
||||
self.start_n_top = start_n_top
|
||||
self.end_n_top = end_n_top
|
||||
else:
|
||||
raise ValueError("First argument must be either a vocabulary size (int)"
|
||||
" or the path to a pretrained model config file (str)")
|
||||
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Convert 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)
|
||||
|
||||
@@ -33,8 +33,7 @@ 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,
|
||||
AlbertConfig, TFAlbertForMaskedLM, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
|
||||
CTRLConfig, TFCTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP)
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
@@ -47,8 +46,7 @@ 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,
|
||||
AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
else:
|
||||
(BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
|
||||
GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
|
||||
@@ -58,8 +56,7 @@ 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,
|
||||
AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP) = (
|
||||
CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP) = (
|
||||
None, None, None, None,
|
||||
None, None,
|
||||
None, None,
|
||||
@@ -68,7 +65,6 @@ else:
|
||||
None, None,
|
||||
None, None, None,
|
||||
None, None, None,
|
||||
None, None,
|
||||
None, None)
|
||||
|
||||
|
||||
@@ -89,8 +85,7 @@ 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),
|
||||
'albert': (AlbertConfig, TFAlbertForMaskedLM, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP)
|
||||
'ctrl': (CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRL_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):
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
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, xnli_compute_metrics
|
||||
from .metrics import glue_compute_metrics
|
||||
@@ -81,11 +81,3 @@ 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)
|
||||
@@ -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
|
||||
|
||||
@@ -1,85 +0,0 @@
|
||||
# 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,
|
||||
}
|
||||
+11
-36
@@ -22,7 +22,6 @@ 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
|
||||
|
||||
@@ -153,7 +152,7 @@ def filename_to_url(filename, cache_dir=None):
|
||||
return url, etag
|
||||
|
||||
|
||||
def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None, resume_download=False):
|
||||
def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=None):
|
||||
"""
|
||||
Given something that might be a URL (or might be a local path),
|
||||
determine which. If it's a URL, download the file and cache it, and
|
||||
@@ -162,7 +161,6 @@ def cached_path(url_or_filename, cache_dir=None, force_download=False, proxies=N
|
||||
Args:
|
||||
cache_dir: specify a cache directory to save the file to (overwrite the default cache dir).
|
||||
force_download: if True, re-dowload the file even if it's already cached in the cache dir.
|
||||
resume_download: if True, resume the download if incompletly recieved file is found.
|
||||
"""
|
||||
if cache_dir is None:
|
||||
cache_dir = TRANSFORMERS_CACHE
|
||||
@@ -175,9 +173,7 @@ 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,
|
||||
resume_download=resume_download)
|
||||
return get_from_cache(url_or_filename, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
|
||||
elif os.path.exists(url_or_filename):
|
||||
# File, and it exists.
|
||||
return url_or_filename
|
||||
@@ -238,22 +234,19 @@ def s3_get(url, temp_file, proxies=None):
|
||||
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
|
||||
|
||||
|
||||
def http_get(url, temp_file, proxies=None, resume_size=0):
|
||||
headers={'Range':'bytes=%d-'%(resume_size,)} if resume_size > 0 else None
|
||||
response = requests.get(url, stream=True, proxies=proxies, headers=headers)
|
||||
if response.status_code == 416: # Range not satisfiable
|
||||
return
|
||||
content_length = response.headers.get('Content-Length')
|
||||
total = resume_size + int(content_length) if content_length is not None else None
|
||||
progress = tqdm(unit="B", total=total, initial=resume_size)
|
||||
for chunk in response.iter_content(chunk_size=1024):
|
||||
def http_get(url, temp_file, proxies=None):
|
||||
req = requests.get(url, stream=True, proxies=proxies)
|
||||
content_length = req.headers.get('Content-Length')
|
||||
total = int(content_length) if content_length is not None else None
|
||||
progress = tqdm(unit="B", total=total)
|
||||
for chunk in req.iter_content(chunk_size=1024):
|
||||
if chunk: # filter out keep-alive new chunks
|
||||
progress.update(len(chunk))
|
||||
temp_file.write(chunk)
|
||||
progress.close()
|
||||
|
||||
|
||||
def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10, resume_download=False):
|
||||
def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag_timeout=10):
|
||||
"""
|
||||
Given a URL, look for the corresponding dataset in the local cache.
|
||||
If it's not there, download it. Then return the path to the cached file.
|
||||
@@ -296,35 +289,17 @@ def get_from_cache(url, cache_dir=None, force_download=False, proxies=None, etag
|
||||
if matching_files:
|
||||
cache_path = os.path.join(cache_dir, matching_files[-1])
|
||||
|
||||
if resume_download:
|
||||
incomplete_path = cache_path + '.incomplete'
|
||||
@contextmanager
|
||||
def _resumable_file_manager():
|
||||
with open(incomplete_path,'a+b') as f:
|
||||
yield f
|
||||
os.remove(incomplete_path)
|
||||
temp_file_manager = _resumable_file_manager
|
||||
if os.path.exists(incomplete_path):
|
||||
resume_size = os.stat(incomplete_path).st_size
|
||||
else:
|
||||
resume_size = 0
|
||||
else:
|
||||
temp_file_manager = tempfile.NamedTemporaryFile
|
||||
resume_size = 0
|
||||
|
||||
if not os.path.exists(cache_path) or force_download:
|
||||
# Download to temporary file, then copy to cache dir once finished.
|
||||
# Otherwise you get corrupt cache entries if the download gets interrupted.
|
||||
with temp_file_manager() as temp_file:
|
||||
with tempfile.NamedTemporaryFile() as temp_file:
|
||||
logger.info("%s not found in cache or force_download set to True, downloading to %s", url, temp_file.name)
|
||||
|
||||
# GET file object
|
||||
if url.startswith("s3://"):
|
||||
if resume_download:
|
||||
logger.warn('Warning: resumable downloads are not implemented for "s3://" urls')
|
||||
s3_get(url, temp_file, proxies=proxies)
|
||||
else:
|
||||
http_get(url, temp_file, proxies=proxies, resume_size=resume_size)
|
||||
http_get(url, temp_file, proxies=proxies)
|
||||
|
||||
# we are copying the file before closing it, so flush to avoid truncation
|
||||
temp_file.flush()
|
||||
|
||||
@@ -1,196 +0,0 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2019-present, the HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import os
|
||||
from os.path import expanduser
|
||||
import six
|
||||
|
||||
import requests
|
||||
from requests.exceptions import HTTPError
|
||||
|
||||
ENDPOINT = "https://huggingface.co"
|
||||
|
||||
class S3Obj:
|
||||
def __init__(
|
||||
self,
|
||||
filename, # type: str
|
||||
LastModified, # type: str
|
||||
ETag, # type: str
|
||||
Size, # type: int
|
||||
**kwargs
|
||||
):
|
||||
self.filename = filename
|
||||
self.LastModified = LastModified
|
||||
self.ETag = ETag
|
||||
self.Size = Size
|
||||
|
||||
|
||||
class PresignedUrl:
|
||||
def __init__(
|
||||
self,
|
||||
write, # type: str
|
||||
access, # type: str
|
||||
type, # type: str
|
||||
**kwargs
|
||||
):
|
||||
self.write = write
|
||||
self.access = access
|
||||
self.type = type # mime-type to send to S3.
|
||||
|
||||
|
||||
class HfApi:
|
||||
def __init__(self, endpoint=None):
|
||||
self.endpoint = endpoint if endpoint is not None else ENDPOINT
|
||||
|
||||
def login(
|
||||
self,
|
||||
username, # type: str
|
||||
password, # type: str
|
||||
):
|
||||
# type: (...) -> str
|
||||
"""
|
||||
Call HF API to sign in a user and get a token if credentials are valid.
|
||||
|
||||
Outputs:
|
||||
token if credentials are valid
|
||||
|
||||
Throws:
|
||||
requests.exceptions.HTTPError if credentials are invalid
|
||||
"""
|
||||
path = "{}/api/login".format(self.endpoint)
|
||||
r = requests.post(path, json={"username": username, "password": password})
|
||||
r.raise_for_status()
|
||||
d = r.json()
|
||||
return d["token"]
|
||||
|
||||
def whoami(
|
||||
self,
|
||||
token, # type: str
|
||||
):
|
||||
# type: (...) -> str
|
||||
"""
|
||||
Call HF API to know "whoami"
|
||||
"""
|
||||
path = "{}/api/whoami".format(self.endpoint)
|
||||
r = requests.get(path, headers={"authorization": "Bearer {}".format(token)})
|
||||
r.raise_for_status()
|
||||
d = r.json()
|
||||
return d["user"]
|
||||
|
||||
def logout(self, token):
|
||||
# type: (...) -> void
|
||||
"""
|
||||
Call HF API to log out.
|
||||
"""
|
||||
path = "{}/api/logout".format(self.endpoint)
|
||||
r = requests.post(path, headers={"authorization": "Bearer {}".format(token)})
|
||||
r.raise_for_status()
|
||||
|
||||
def presign(self, token, filename):
|
||||
# type: (...) -> PresignedUrl
|
||||
"""
|
||||
Call HF API to get a presigned url to upload `filename` to S3.
|
||||
"""
|
||||
path = "{}/api/presign".format(self.endpoint)
|
||||
r = requests.post(
|
||||
path,
|
||||
headers={"authorization": "Bearer {}".format(token)},
|
||||
json={"filename": filename},
|
||||
)
|
||||
r.raise_for_status()
|
||||
d = r.json()
|
||||
return PresignedUrl(**d)
|
||||
|
||||
def presign_and_upload(self, token, filename, filepath):
|
||||
# type: (...) -> str
|
||||
"""
|
||||
Get a presigned url, then upload file to S3.
|
||||
|
||||
Outputs:
|
||||
url: Read-only url for the stored file on S3.
|
||||
"""
|
||||
urls = self.presign(token, filename=filename)
|
||||
# streaming upload:
|
||||
# https://2.python-requests.org/en/master/user/advanced/#streaming-uploads
|
||||
#
|
||||
# Even though we presign with the correct content-type,
|
||||
# the client still has to specify it when uploading the file.
|
||||
with open(filepath, "rb") as f:
|
||||
r = requests.put(urls.write, data=f, headers={
|
||||
"content-type": urls.type,
|
||||
})
|
||||
r.raise_for_status()
|
||||
return urls.access
|
||||
|
||||
def list_objs(self, token):
|
||||
# type: (...) -> List[S3Obj]
|
||||
"""
|
||||
Call HF API to list all stored files for user.
|
||||
"""
|
||||
path = "{}/api/listObjs".format(self.endpoint)
|
||||
r = requests.get(path, headers={"authorization": "Bearer {}".format(token)})
|
||||
r.raise_for_status()
|
||||
d = r.json()
|
||||
return [S3Obj(**x) for x in d]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class HfFolder:
|
||||
path_token = expanduser("~/.huggingface/token")
|
||||
|
||||
@classmethod
|
||||
def save_token(cls, token):
|
||||
"""
|
||||
Save token, creating folder as needed.
|
||||
"""
|
||||
if six.PY3:
|
||||
os.makedirs(os.path.dirname(cls.path_token), exist_ok=True)
|
||||
else:
|
||||
# Python 2
|
||||
try:
|
||||
os.makedirs(os.path.dirname(cls.path_token))
|
||||
except OSError as e:
|
||||
if e.errno != os.errno.EEXIST:
|
||||
raise e
|
||||
pass
|
||||
with open(cls.path_token, 'w+') as f:
|
||||
f.write(token)
|
||||
|
||||
@classmethod
|
||||
def get_token(cls):
|
||||
"""
|
||||
Get token or None if not existent.
|
||||
"""
|
||||
try:
|
||||
with open(cls.path_token, 'r') as f:
|
||||
return f.read()
|
||||
except:
|
||||
# this is too wide. When Py2 is dead use:
|
||||
# `except FileNotFoundError:` instead
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def delete_token(cls):
|
||||
"""
|
||||
Delete token.
|
||||
Do not fail if token does not exist.
|
||||
"""
|
||||
try:
|
||||
os.remove(cls.path_token)
|
||||
except:
|
||||
return
|
||||
@@ -1,801 +0,0 @@
|
||||
|
||||
# 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
|
||||
|
||||
# If saved from the TF HUB module
|
||||
name = name.replace("module/", "")
|
||||
|
||||
# Renaming and simplifying
|
||||
name = name.replace("ffn_1", "ffn")
|
||||
name = name.replace("bert/", "albert/")
|
||||
name = name.replace("attention_1", "attention")
|
||||
name = name.replace("transform/", "")
|
||||
name = name.replace("LayerNorm_1", "full_layer_layer_norm")
|
||||
name = name.replace("LayerNorm", "attention/LayerNorm")
|
||||
name = name.replace("transformer/", "")
|
||||
|
||||
# The feed forward layer had an 'intermediate' step which has been abstracted away
|
||||
name = name.replace("intermediate/dense/", "")
|
||||
name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")
|
||||
|
||||
# ALBERT attention was split between self and output which have been abstracted away
|
||||
name = name.replace("/output/", "/")
|
||||
name = name.replace("/self/", "/")
|
||||
|
||||
# The pooler is a linear layer
|
||||
name = name.replace("pooler/dense", "pooler")
|
||||
|
||||
# The classifier was simplified to predictions from cls/predictions
|
||||
name = name.replace("cls/predictions", "predictions")
|
||||
name = name.replace("predictions/attention", "predictions")
|
||||
|
||||
# Naming was changed to be more explicit
|
||||
name = name.replace("embeddings/attention", "embeddings")
|
||||
name = name.replace("inner_group_", "albert_layers/")
|
||||
name = name.replace("group_", "albert_layer_groups/")
|
||||
|
||||
# Classifier
|
||||
if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name):
|
||||
name = "classifier/" + name
|
||||
|
||||
# No ALBERT model currently handles the next sentence prediction task
|
||||
if "seq_relationship" in name:
|
||||
continue
|
||||
|
||||
name = name.split('/')
|
||||
|
||||
# Ignore the gradients applied by the LAMB/ADAM optimizers.
|
||||
if "adam_m" in name or "adam_v" in name or "global_step" in name:
|
||||
logger.info("Skipping {}".format("/".join(name)))
|
||||
continue
|
||||
|
||||
pointer = model
|
||||
for m_name in name:
|
||||
if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
|
||||
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)
|
||||
@@ -27,9 +27,6 @@ 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_camembert import CamembertModel, CamembertForMaskedLM, CamembertForSequenceClassification, CamembertForMultipleChoice
|
||||
from .modeling_albert import AlbertModel, AlbertForMaskedLM, AlbertForSequenceClassification, AlbertForQuestionAnswering
|
||||
|
||||
from .modeling_utils import PreTrainedModel, SequenceSummary
|
||||
|
||||
@@ -51,16 +48,14 @@ 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 `albert`: AlbertModel (ALBERT model)
|
||||
- contains `camembert`: CamembertModel (CamemBERT model)
|
||||
- contains `roberta`: RobertaModel (RoBERTa model)
|
||||
- contains `bert`: BertModel (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
|
||||
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
|
||||
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetModel (XLNet model)
|
||||
- contains `xlm`: XLMModel (XLM model)
|
||||
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
|
||||
|
||||
This class cannot be instantiated using `__init__()` (throws an error).
|
||||
"""
|
||||
@@ -76,16 +71,14 @@ 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 `albert`: AlbertModel (ALBERT model)
|
||||
- contains `camembert`: CamembertModel (CamemBERT model)
|
||||
- contains `roberta`: RobertaModel (RoBERTa model)
|
||||
- contains `bert`: BertModel (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
|
||||
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
|
||||
- contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetModel (XLNet model)
|
||||
- contains `xlm`: XLMModel (XLM model)
|
||||
- contains `ctrl`: CTRLModel (Salesforce CTRL model)
|
||||
|
||||
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
|
||||
To train the model, you should first set it back in training mode with `model.train()`
|
||||
@@ -119,9 +112,6 @@ class AutoModel(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -148,10 +138,6 @@ class AutoModel(object):
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'albert' in pretrained_model_name_or_path:
|
||||
return AlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'camembert' in pretrained_model_name_or_path:
|
||||
return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'roberta' in pretrained_model_name_or_path:
|
||||
return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
@@ -170,7 +156,7 @@ class AutoModel(object):
|
||||
return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm', 'roberta, 'ctrl', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path))
|
||||
"'xlm', 'roberta, 'ctrl'".format(pretrained_model_name_or_path))
|
||||
|
||||
|
||||
class AutoModelWithLMHead(object):
|
||||
@@ -186,16 +172,14 @@ 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 `albert`: AlbertForMaskedLM (ALBERT model)
|
||||
- contains `camembert`: CamembertForMaskedLM (CamemBERT model)
|
||||
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
|
||||
- contains `bert`: BertForMaskedLM (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
|
||||
- contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
|
||||
- contains `ctrl`: CTRLLMModel (Salesforce CTRL model)
|
||||
- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetLMHeadModel (XLNet model)
|
||||
- contains `xlm`: XLMWithLMHeadModel (XLM model)
|
||||
- contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
|
||||
|
||||
This class cannot be instantiated using `__init__()` (throws an error).
|
||||
"""
|
||||
@@ -214,8 +198,6 @@ 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 `albert`: AlbertForMaskedLM (ALBERT model)
|
||||
- contains `camembert`: CamembertForMaskedLM (CamemBERT model)
|
||||
- contains `roberta`: RobertaForMaskedLM (RoBERTa model)
|
||||
- contains `bert`: BertForMaskedLM (Bert model)
|
||||
- contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
|
||||
@@ -223,7 +205,6 @@ class AutoModelWithLMHead(object):
|
||||
- contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
|
||||
- contains `xlnet`: XLNetLMHeadModel (XLNet model)
|
||||
- contains `xlm`: XLMWithLMHeadModel (XLM model)
|
||||
- contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
|
||||
|
||||
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
|
||||
To train the model, you should first set it back in training mode with `model.train()`
|
||||
@@ -256,8 +237,6 @@ class AutoModelWithLMHead(object):
|
||||
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
@@ -285,10 +264,6 @@ class AutoModelWithLMHead(object):
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'albert' in pretrained_model_name_or_path:
|
||||
return AlbertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'camembert' in pretrained_model_name_or_path:
|
||||
return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'roberta' in pretrained_model_name_or_path:
|
||||
return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
@@ -307,7 +282,7 @@ class AutoModelWithLMHead(object):
|
||||
return CTRLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
|
||||
"'xlm', 'roberta','ctrl', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path))
|
||||
"'xlm', 'roberta','ctrl'".format(pretrained_model_name_or_path))
|
||||
|
||||
|
||||
class AutoModelForSequenceClassification(object):
|
||||
@@ -323,8 +298,6 @@ class AutoModelForSequenceClassification(object):
|
||||
The model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
|
||||
- contains `albert`: AlbertForSequenceClassification (ALBERT model)
|
||||
- contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
|
||||
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
|
||||
- contains `bert`: BertForSequenceClassification (Bert model)
|
||||
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
|
||||
@@ -347,8 +320,6 @@ class AutoModelForSequenceClassification(object):
|
||||
The model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `distilbert`: DistilBertForSequenceClassification (DistilBERT model)
|
||||
- contains `albert`: AlbertForSequenceClassification (ALBERT model)
|
||||
- contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
|
||||
- contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
|
||||
- contains `bert`: BertForSequenceClassification (Bert model)
|
||||
- contains `xlnet`: XLNetForSequenceClassification (XLNet model)
|
||||
@@ -386,9 +357,6 @@ class AutoModelForSequenceClassification(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -415,10 +383,6 @@ class AutoModelForSequenceClassification(object):
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
return DistilBertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'albert' in pretrained_model_name_or_path:
|
||||
return AlbertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'camembert' in pretrained_model_name_or_path:
|
||||
return CamembertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'roberta' in pretrained_model_name_or_path:
|
||||
return RobertaForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
@@ -429,7 +393,7 @@ class AutoModelForSequenceClassification(object):
|
||||
return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'xlnet', 'xlm', 'roberta', 'distilbert', 'camembert', 'albert'".format(pretrained_model_name_or_path))
|
||||
"'bert', 'xlnet', 'xlm', 'roberta'".format(pretrained_model_name_or_path))
|
||||
|
||||
|
||||
class AutoModelForQuestionAnswering(object):
|
||||
@@ -445,7 +409,6 @@ class AutoModelForQuestionAnswering(object):
|
||||
The model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
|
||||
- contains `albert`: AlbertForQuestionAnswering (ALBERT model)
|
||||
- contains `bert`: BertForQuestionAnswering (Bert model)
|
||||
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
||||
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
||||
@@ -467,7 +430,6 @@ class AutoModelForQuestionAnswering(object):
|
||||
The model class to instantiate is selected as the first pattern matching
|
||||
in the `pretrained_model_name_or_path` string (in the following order):
|
||||
- contains `distilbert`: DistilBertForQuestionAnswering (DistilBERT model)
|
||||
- contains `albert`: AlbertForQuestionAnswering (ALBERT model)
|
||||
- contains `bert`: BertForQuestionAnswering (Bert model)
|
||||
- contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
|
||||
- contains `xlm`: XLMForQuestionAnswering (XLM model)
|
||||
@@ -530,8 +492,6 @@ class AutoModelForQuestionAnswering(object):
|
||||
"""
|
||||
if 'distilbert' in pretrained_model_name_or_path:
|
||||
return DistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'albert' in pretrained_model_name_or_path:
|
||||
return AlbertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'bert' in pretrained_model_name_or_path:
|
||||
return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
elif 'xlnet' in pretrained_model_name_or_path:
|
||||
@@ -540,4 +500,4 @@ class AutoModelForQuestionAnswering(object):
|
||||
return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
|
||||
|
||||
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
|
||||
"'bert', 'xlnet', 'xlm', 'distilbert', 'albert'".format(pretrained_model_name_or_path))
|
||||
"'bert', 'xlnet', 'xlm'".format(pretrained_model_name_or_path))
|
||||
+47
-210
@@ -138,11 +138,7 @@ def swish(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
def mish(x):
|
||||
return x * torch.tanh(nn.functional.softplus(x))
|
||||
|
||||
|
||||
ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
|
||||
ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new}
|
||||
|
||||
|
||||
BertLayerNorm = torch.nn.LayerNorm
|
||||
@@ -162,26 +158,19 @@ 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=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
|
||||
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=device)
|
||||
position_ids = position_ids.unsqueeze(0).expand(input_shape)
|
||||
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(input_shape, dtype=torch.long, device=device)
|
||||
token_type_ids = torch.zeros_like(input_ids)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.word_embeddings(input_ids)
|
||||
words_embeddings = 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 = words_embeddings + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings)
|
||||
return embeddings
|
||||
@@ -282,7 +271,7 @@ 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 remove already pruned heads
|
||||
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)
|
||||
@@ -478,58 +467,6 @@ class BertPreTrainingHeads(nn.Module):
|
||||
return prediction_scores, seq_relationship_score
|
||||
|
||||
|
||||
class RBertClassificationHead(nn.Module):
|
||||
def __init__(self, config):
|
||||
"""
|
||||
:param config: a BertConfig or aRobertaConfig.
|
||||
"""
|
||||
super(RBertClassificationHead, self).__init__()
|
||||
self.ent_2_index_id = config.entity_2_token_id
|
||||
self.ent_1_index_id = config.entity_1_token_id
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.sentence_layer = torch.nn.Sequential(
|
||||
self.dropout,
|
||||
torch.nn.Linear(config.hidden_size, config.hidden_size),
|
||||
torch.nn.modules.activation.Tanh(),
|
||||
)
|
||||
self.entity_layer = torch.nn.Sequential(
|
||||
self.dropout,
|
||||
torch.nn.Linear(config.hidden_size, config.hidden_size),
|
||||
torch.nn.modules.activation.Tanh()
|
||||
)
|
||||
|
||||
self.classifier = nn.Linear(config.hidden_size * 3, config.num_labels)
|
||||
|
||||
def get_indices_tensor(self, instance_input_ids, entity_char_id):
|
||||
index_tensors = (instance_input_ids == entity_char_id).nonzero()
|
||||
start_index = index_tensors[0].item() + 1 # do not include the symbol itself in the calculation
|
||||
end_index = index_tensors[1].item()
|
||||
return torch.arange(start_index, end_index, device=instance_input_ids.device.type)
|
||||
|
||||
def average_entity_vectors(self, last_hidden_states, input_ids, entity_char_id):
|
||||
batch_return_list = []
|
||||
for instance_hidden_state, instance_input_ids in zip(last_hidden_states, input_ids):
|
||||
lookup_tensor = self.get_indices_tensor(instance_input_ids, entity_char_id)
|
||||
average_of_entity_vectors = torch.index_select(instance_hidden_state, 0, lookup_tensor).unsqueeze(0).mean(1)
|
||||
batch_return_list.append(average_of_entity_vectors)
|
||||
return_tensor = torch.cat(batch_return_list)
|
||||
return_tensor[return_tensor.ne(return_tensor)] = 0 #set any nan's created by mean operation to 0
|
||||
return return_tensor
|
||||
|
||||
def forward(self, input_ids, last_hidden_states):
|
||||
cls_tensor = self.sentence_layer(
|
||||
last_hidden_states[:, 0]) # get the sentence embedding and pass through FC layer
|
||||
ent1_tensor = self.entity_layer(self.average_entity_vectors(last_hidden_states, input_ids,
|
||||
self.ent_1_index_id)) # average wordpieces of ent1 and pass through FC entity layer
|
||||
ent2_tensor = self.entity_layer(self.average_entity_vectors(last_hidden_states, input_ids,
|
||||
self.ent_2_index_id)) # ditto - ent2 shared parameters with ent1
|
||||
|
||||
entities_and_cls_tensor = torch.cat((cls_tensor, ent1_tensor, ent2_tensor),
|
||||
dim=1) # concat all and pass through classifier layer
|
||||
logits = self.classifier(entities_and_cls_tensor)
|
||||
return logits
|
||||
|
||||
|
||||
class BertPreTrainedModel(PreTrainedModel):
|
||||
""" An abstract class to handle weights initialization and
|
||||
a simple interface for dowloading and loading pretrained models.
|
||||
@@ -613,10 +550,6 @@ 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.
|
||||
@@ -653,7 +586,7 @@ class BertModel(BertPreTrainedModel):
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertModel.from_pretrained('bert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids)
|
||||
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
|
||||
|
||||
@@ -682,8 +615,8 @@ class BertModel(BertPreTrainedModel):
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.encoder.layer[layer].attention.prune_heads(heads)
|
||||
|
||||
def forward(self, input_ids=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):
|
||||
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):
|
||||
""" Forward pass on the Model.
|
||||
|
||||
The model can behave as an encoder (with only self-attention) as well
|
||||
@@ -699,21 +632,12 @@ 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(input_shape, device=device)
|
||||
attention_mask = torch.ones_like(input_ids)
|
||||
if encoder_attention_mask is None:
|
||||
encoder_attention_mask = torch.ones_like(input_ids)
|
||||
if token_type_ids is None:
|
||||
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
|
||||
token_type_ids = torch.zeros_like(input_ids)
|
||||
|
||||
# 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.
|
||||
@@ -725,8 +649,8 @@ 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_shape
|
||||
seq_ids = torch.arange(seq_length, device=device)
|
||||
batch_size, seq_length = input_ids.size()
|
||||
seq_ids = torch.arange(seq_length, device=input_ids.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:
|
||||
@@ -742,19 +666,13 @@ class BertModel(BertPreTrainedModel):
|
||||
|
||||
# If a 2D ou 3D attention mask is provided for the cross-attention
|
||||
# we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
|
||||
if self.config.is_decoder:
|
||||
if encoder_attention_mask is None:
|
||||
encoder_attention_mask = torch.ones(input_shape, device=device)
|
||||
if encoder_attention_mask.dim() == 3:
|
||||
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
|
||||
if encoder_attention_mask.dim() == 2:
|
||||
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
|
||||
|
||||
if encoder_attention_mask.dim() == 3:
|
||||
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
|
||||
if encoder_attention_mask.dim() == 2:
|
||||
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
|
||||
|
||||
encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
|
||||
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
|
||||
else:
|
||||
encoder_extended_attention_mask = None
|
||||
encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility
|
||||
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
@@ -771,7 +689,7 @@ class BertModel(BertPreTrainedModel):
|
||||
else:
|
||||
head_mask = [None] * self.config.num_hidden_layers
|
||||
|
||||
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
|
||||
embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
|
||||
encoder_outputs = self.encoder(embedding_output,
|
||||
attention_mask=extended_attention_mask,
|
||||
head_mask=head_mask,
|
||||
@@ -820,7 +738,7 @@ class BertForPreTraining(BertPreTrainedModel):
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForPreTraining.from_pretrained('bert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids)
|
||||
prediction_scores, seq_relationship_scores = outputs[:2]
|
||||
|
||||
@@ -836,15 +754,14 @@ class BertForPreTraining(BertPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.cls.predictions.decoder
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output, pooled_output = outputs[:2]
|
||||
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
|
||||
@@ -896,7 +813,7 @@ class BertForMaskedLM(BertPreTrainedModel):
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, masked_lm_labels=input_ids)
|
||||
loss, prediction_scores = outputs[:2]
|
||||
|
||||
@@ -912,7 +829,7 @@ class BertForMaskedLM(BertPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.cls.predictions.decoder
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
|
||||
masked_lm_labels=None, encoder_hidden_states=None, encoder_attention_mask=None, lm_labels=None, ):
|
||||
|
||||
outputs = self.bert(input_ids,
|
||||
@@ -920,7 +837,6 @@ 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)
|
||||
|
||||
@@ -979,7 +895,7 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids)
|
||||
seq_relationship_scores = outputs[0]
|
||||
|
||||
@@ -992,15 +908,14 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
|
||||
|
||||
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,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
pooled_output = outputs[1]
|
||||
|
||||
@@ -1015,80 +930,6 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
|
||||
return outputs # (next_sentence_loss), seq_relationship_score, (hidden_states), (attentions)
|
||||
|
||||
|
||||
@add_start_docstrings(r"""Bert Model transformer with an R-BERT head on top, making use of
|
||||
entity offsets for relationship classification. See the details in the R-Bert paper https://arxiv.org/pdf/1905.08284.pdf""",
|
||||
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
|
||||
class BertForRelationshipClassification(BertPreTrainedModel):
|
||||
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::
|
||||
|
||||
test_string = "The $ cat $ sat on the # mat # ."
|
||||
ent1_sep_token = '$'
|
||||
ent2_sep_token = '#'
|
||||
|
||||
model_name = 'bert-base-uncased'
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained(model_name)
|
||||
ent1_sep_token_id = tokenizer.encode(ent1_sep_token)[0]
|
||||
ent2_sep_token_id = tokenizer.encode(ent2_sep_token)[0]
|
||||
config = BertConfig.from_pretrained(model_name, entity_1_token_id=ent1_sep_token_id, entity_2_token_id=ent2_sep_token_id)
|
||||
model = BertForRelationshipClassification.from_pretrained(model_name, config=config)
|
||||
input_ids = torch.tensor(tokenizer.encode(test_string,add_special_tokens=True)).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(BertForRelationshipClassification, self).__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.bert = BertModel(config)
|
||||
self.rbert = RBertClassificationHead(config)
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=None, labels=None):
|
||||
|
||||
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
|
||||
attention_mask=attention_mask, head_mask=head_mask)
|
||||
last_hidden_states = outputs[0]
|
||||
logits = self.rbert(input_ids, last_hidden_states)
|
||||
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("""Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of
|
||||
the pooled output) e.g. for GLUE tasks. """,
|
||||
BERT_START_DOCSTRING,
|
||||
@@ -1118,7 +959,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss, logits = outputs[:2]
|
||||
@@ -1134,15 +975,14 @@ class BertForSequenceClassification(BertPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
pooled_output = outputs[1]
|
||||
|
||||
@@ -1194,7 +1034,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForMultipleChoice.from_pretrained('bert-base-uncased')
|
||||
choices = ["Hello, my dog is cute", "Hello, my cat is amazing"]
|
||||
input_ids = torch.tensor([tokenizer.encode(s, add_special_tokens=True) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
|
||||
input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
|
||||
labels = torch.tensor(1).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss, classification_scores = outputs[:2]
|
||||
@@ -1209,8 +1049,8 @@ class BertForMultipleChoice(BertPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=None, labels=None):
|
||||
num_choices = input_ids.shape[1]
|
||||
|
||||
input_ids = input_ids.view(-1, input_ids.size(-1))
|
||||
@@ -1222,8 +1062,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
position_ids=position_ids,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
pooled_output = outputs[1]
|
||||
|
||||
@@ -1268,7 +1107,7 @@ class BertForTokenClassification(BertPreTrainedModel):
|
||||
|
||||
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
||||
model = BertForTokenClassification.from_pretrained('bert-base-uncased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
loss, scores = outputs[:2]
|
||||
@@ -1284,15 +1123,14 @@ class BertForTokenClassification(BertPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
@@ -1369,15 +1207,14 @@ class BertForQuestionAnswering(BertPreTrainedModel):
|
||||
|
||||
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,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
|
||||
@@ -1,293 +0,0 @@
|
||||
# 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
|
||||
@@ -63,8 +63,7 @@ def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=N
|
||||
scaled_attention_logits = matmul_qk / np.sqrt(dk)
|
||||
|
||||
if mask is not None:
|
||||
nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1)
|
||||
scaled_attention_logits += (mask[ns-nd:ns, :ns] * -1e4)
|
||||
scaled_attention_logits += (mask * -1e4)
|
||||
|
||||
if attention_mask is not None:
|
||||
# Apply the attention mask
|
||||
@@ -221,8 +220,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
(see `past` output below). Can be used to speed up sequential decoding.
|
||||
**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]``:
|
||||
@@ -238,10 +236,6 @@ 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.",
|
||||
@@ -254,8 +248,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
Can be used (see `past` input) to speed up sequential decoding.
|
||||
**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)``:
|
||||
@@ -309,26 +302,17 @@ class CTRLModel(CTRLPreTrainedModel):
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.h[layer].attn.prune_heads(heads)
|
||||
|
||||
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")
|
||||
|
||||
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
|
||||
input_shape = input_ids.size()
|
||||
input_ids = input_ids.view(-1, input_shape[-1])
|
||||
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:
|
||||
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])
|
||||
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)
|
||||
|
||||
# Attention mask.
|
||||
if attention_mask is not None:
|
||||
@@ -370,11 +354,10 @@ 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 = self.w(input_ids)
|
||||
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
|
||||
seq_len = input_shape[-1]
|
||||
mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device)
|
||||
seq_len = input_ids.shape[-1]
|
||||
mask = torch.triu(torch.ones(seq_len, seq_len), 1).to(inputs_embeds.device)
|
||||
|
||||
inputs_embeds *= np.sqrt(self.d_model_size)
|
||||
|
||||
@@ -440,8 +423,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
Can be used (see `past` input) to speed up sequential decoding.
|
||||
**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)``:
|
||||
@@ -473,15 +455,14 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
|
||||
@@ -30,7 +30,6 @@ 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
|
||||
@@ -42,9 +41,7 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
|
||||
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-pytorch_model.bin",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin",
|
||||
'distilbert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-pytorch_model.bin",
|
||||
'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-pytorch_model.bin",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-pytorch_model.bin"
|
||||
}
|
||||
|
||||
|
||||
@@ -390,10 +387,6 @@ 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.",
|
||||
@@ -443,20 +436,9 @@ class DistilBertModel(DistilBertPreTrainedModel):
|
||||
self.transformer.layer[layer].attention.prune_heads(heads)
|
||||
|
||||
def forward(self,
|
||||
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
|
||||
|
||||
input_ids, attention_mask=None, head_mask=None):
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(input_shape, device=device) # (bs, seq_length)
|
||||
attention_mask = torch.ones_like(input_ids) # (bs, seq_length)
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
@@ -473,9 +455,8 @@ class DistilBertModel(DistilBertPreTrainedModel):
|
||||
else:
|
||||
head_mask = [None] * self.config.num_hidden_layers
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embeddings(input_ids) # (bs, seq_length, dim)
|
||||
tfmr_output = self.transformer(x=inputs_embeds,
|
||||
embedding_output = self.embeddings(input_ids) # (bs, seq_length, dim)
|
||||
tfmr_output = self.transformer(x=embedding_output,
|
||||
attn_mask=attention_mask,
|
||||
head_mask=head_mask)
|
||||
hidden_state = tfmr_output[0]
|
||||
@@ -533,11 +514,10 @@ class DistilBertForMaskedLM(DistilBertPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.vocab_projector
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, masked_lm_labels=None):
|
||||
def forward(self, input_ids, attention_mask=None, head_mask=None, masked_lm_labels=None):
|
||||
dlbrt_output = self.distilbert(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
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)
|
||||
@@ -598,11 +578,10 @@ class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None):
|
||||
def forward(self, input_ids, attention_mask=None, head_mask=None, labels=None):
|
||||
distilbert_output = self.distilbert(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
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)
|
||||
@@ -673,11 +652,10 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None):
|
||||
def forward(self, input_ids, attention_mask=None, head_mask=None, start_positions=None, end_positions=None):
|
||||
distilbert_output = self.distilbert(input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
hidden_states = distilbert_output[0] # (bs, max_query_len, dim)
|
||||
|
||||
hidden_states = self.dropout(hidden_states) # (bs, max_query_len, dim)
|
||||
@@ -705,75 +683,3 @@ 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)
|
||||
@@ -39,7 +39,6 @@ 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):
|
||||
@@ -298,8 +297,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
(see `past` output below). Can be used to speed up sequential decoding.
|
||||
**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]``:
|
||||
@@ -315,10 +313,6 @@ 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.",
|
||||
@@ -331,8 +325,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
Can be used (see `past` input) to speed up sequential decoding.
|
||||
**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)``:
|
||||
@@ -377,17 +370,9 @@ class GPT2Model(GPT2PreTrainedModel):
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.h[layer].attn.prune_heads(heads)
|
||||
|
||||
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")
|
||||
|
||||
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
|
||||
input_shape = input_ids.size()
|
||||
input_ids = input_ids.view(-1, input_shape[-1])
|
||||
if token_type_ids is not None:
|
||||
token_type_ids = token_type_ids.view(-1, input_shape[-1])
|
||||
if position_ids is not None:
|
||||
@@ -399,9 +384,8 @@ class GPT2Model(GPT2PreTrainedModel):
|
||||
else:
|
||||
past_length = past[0][0].size(-2)
|
||||
if position_ids is None:
|
||||
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])
|
||||
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)
|
||||
|
||||
# Attention mask.
|
||||
if attention_mask is not None:
|
||||
@@ -435,8 +419,7 @@ class GPT2Model(GPT2PreTrainedModel):
|
||||
else:
|
||||
head_mask = [None] * self.config.n_layer
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.wte(input_ids)
|
||||
inputs_embeds = self.wte(input_ids)
|
||||
position_embeds = self.wpe(position_ids)
|
||||
if token_type_ids is not None:
|
||||
token_type_embeds = self.wte(token_type_ids)
|
||||
@@ -505,8 +488,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
Can be used (see `past` input) to speed up sequential decoding.
|
||||
**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)``:
|
||||
@@ -538,15 +520,14 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
lm_logits = self.lm_head(hidden_states)
|
||||
@@ -598,8 +579,7 @@ 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. The token ids which have their past given to this model
|
||||
should not be passed as input ids as they have already been computed.
|
||||
Can be used (see `past` input) to speed up sequential decoding.
|
||||
**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)``:
|
||||
@@ -643,15 +623,14 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
|
||||
@@ -322,10 +322,6 @@ 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.",
|
||||
@@ -377,22 +373,14 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.h[layer].attn.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()
|
||||
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")
|
||||
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
|
||||
if position_ids is None:
|
||||
# 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])
|
||||
# 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)
|
||||
|
||||
# Attention mask.
|
||||
if attention_mask is not None:
|
||||
@@ -425,8 +413,11 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
|
||||
else:
|
||||
head_mask = [None] * self.config.n_layer
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.tokens_embed(input_ids)
|
||||
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))
|
||||
|
||||
inputs_embeds = self.tokens_embed(input_ids)
|
||||
position_embeds = self.positions_embed(position_ids)
|
||||
if token_type_ids is not None:
|
||||
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
|
||||
@@ -504,14 +495,13 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
hidden_states = transformer_outputs[0]
|
||||
lm_logits = self.lm_head(hidden_states)
|
||||
|
||||
@@ -597,14 +587,13 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
lm_logits = self.lm_head(hidden_states)
|
||||
|
||||
@@ -24,7 +24,7 @@ import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import CrossEntropyLoss, MSELoss
|
||||
|
||||
from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu, RBertClassificationHead
|
||||
from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
|
||||
from .configuration_roberta import RobertaConfig
|
||||
from .file_utils import add_start_docstrings
|
||||
|
||||
@@ -35,8 +35,6 @@ 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):
|
||||
@@ -50,24 +48,16 @@ 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=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
|
||||
|
||||
def forward(self, input_ids, token_type_ids=None, position_ids=None):
|
||||
seq_length = input_ids.size(1)
|
||||
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=device)
|
||||
position_ids = position_ids.unsqueeze(0).expand(input_shape)
|
||||
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)
|
||||
return super(RobertaEmbeddings, self).forward(input_ids,
|
||||
token_type_ids=token_type_ids,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=inputs_embeds)
|
||||
position_ids=position_ids)
|
||||
|
||||
|
||||
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
|
||||
@@ -136,10 +126,6 @@ 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.",
|
||||
@@ -236,14 +222,13 @@ class RobertaForMaskedLM(BertPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head.decoder
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
sequence_output = outputs[0]
|
||||
prediction_scores = self.lm_head(sequence_output)
|
||||
|
||||
@@ -324,14 +309,13 @@ class RobertaForSequenceClassification(BertPreTrainedModel):
|
||||
self.roberta = RobertaModel(config)
|
||||
self.classifier = RobertaClassificationHead(config)
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
sequence_output = outputs[0]
|
||||
logits = self.classifier(sequence_output)
|
||||
|
||||
@@ -388,10 +372,6 @@ 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
|
||||
@@ -435,8 +415,8 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
|
||||
position_ids=None, head_mask=None):
|
||||
num_choices = input_ids.shape[1]
|
||||
|
||||
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
||||
@@ -460,86 +440,6 @@ class RobertaForMultipleChoice(BertPreTrainedModel):
|
||||
|
||||
return outputs # (loss), reshaped_logits, (hidden_states), (attentions)
|
||||
|
||||
@add_start_docstrings("""Roberta Model transformer with an R-BERT head on top, making use of
|
||||
entity offsets for relationship classification. See the details in the R-Bert paper https://arxiv.org/pdf/1905.08284.pdf""",
|
||||
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
|
||||
class RobertaForRelationshipClassification(BertPreTrainedModel):
|
||||
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::
|
||||
|
||||
test_string = "The $ cat $ sat on the # mat # ."
|
||||
ent1_sep_token = '$'
|
||||
ent2_sep_token = '#'
|
||||
|
||||
model_name = 'roberta-large'
|
||||
|
||||
tokenizer = RobertaTokenizer.from_pretrained(model_name)
|
||||
ent1_sep_token_id = tokenizer.encode(ent1_sep_token, text_pair=None, add_special_tokens=True, add_prefix_space=True)[1]
|
||||
ent2_sep_token_id = tokenizer.encode(ent2_sep_token, text_pair=None, add_special_tokens=True, add_prefix_space=True)[1]
|
||||
config = RobertaConfig.from_pretrained(model_name,entity_1_token_id=ent1_sep_token_id, entity_2_token_id=ent2_sep_token_id)
|
||||
model = RobertaForRelationshipClassification.from_pretrained(model_name, config=config)
|
||||
input_ids = torch.tensor(tokenizer.encode(test_string,add_special_tokens=True)).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 = RobertaConfig
|
||||
pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
|
||||
base_model_prefix = "roberta"
|
||||
|
||||
def __init__(self, config):
|
||||
super(RobertaForRelationshipClassification, self).__init__(config)
|
||||
|
||||
self.num_labels = config.num_labels
|
||||
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
||||
self.roberta = RobertaModel(config)
|
||||
self.rbert = RBertClassificationHead(config)
|
||||
|
||||
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.roberta(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
position_ids=position_ids,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
last_hidden_states = outputs[0]
|
||||
logits = self.rbert(input_ids, last_hidden_states)
|
||||
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("""Roberta 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. """,
|
||||
@@ -587,15 +487,14 @@ class RobertaForTokenClassification(BertPreTrainedModel):
|
||||
|
||||
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):
|
||||
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
|
||||
position_ids=None, head_mask=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,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
|
||||
@@ -1,794 +0,0 @@
|
||||
# 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 logging
|
||||
import sys
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
from .configuration_albert import AlbertConfig
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
|
||||
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-v1-tf_model.h5",
|
||||
'albert-large-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v1-tf_model.h5",
|
||||
'albert-xlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v1-tf_model.h5",
|
||||
'albert-xxlarge-v1': "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v1-tf_model.h5",
|
||||
'albert-base-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 = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(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 = shape_list(inputs)[0]
|
||||
length = shape_list(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 = shape_list(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(shape_list(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 = shape_list(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(shape_list(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 = 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.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)
|
||||
@@ -109,9 +109,6 @@ class TFAutoModel(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -240,9 +237,6 @@ class TFAutoModelWithLMHead(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -366,9 +360,6 @@ class TFAutoModelForSequenceClassification(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -481,9 +472,6 @@ class TFAutoModelForQuestionAnswering(object):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
|
||||
@@ -28,7 +28,7 @@ import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
from .configuration_bert import BertConfig
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer
|
||||
from .file_utils import add_start_docstrings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@@ -142,25 +142,19 @@ 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_embeds = inputs
|
||||
input_ids, position_ids, token_type_ids = inputs
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
|
||||
seq_length = input_shape[1]
|
||||
seq_length = tf.shape(input_ids)[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)
|
||||
token_type_ids = tf.fill(tf.shape(input_ids), 0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
words_embeddings = 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 = words_embeddings + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
return embeddings
|
||||
@@ -172,8 +166,8 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
batch_size = tf.shape(inputs)[0]
|
||||
length = tf.shape(inputs)[1]
|
||||
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
@@ -214,7 +208,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
|
||||
def call(self, inputs, training=False):
|
||||
hidden_states, attention_mask, head_mask = inputs
|
||||
|
||||
batch_size = shape_list(hidden_states)[0]
|
||||
batch_size = tf.shape(hidden_states)[0]
|
||||
mixed_query_layer = self.query(hidden_states)
|
||||
mixed_key_layer = self.key(hidden_states)
|
||||
mixed_value_layer = self.value(hidden_states)
|
||||
@@ -225,7 +219,7 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
|
||||
|
||||
# Take the dot product between "query" and "key" to get the raw attention scores.
|
||||
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) # (batch size, num_heads, seq_len_q, seq_len_k)
|
||||
dk = tf.cast(shape_list(key_layer)[-1], tf.float32) # scale attention_scores
|
||||
dk = tf.cast(tf.shape(key_layer)[-1], tf.float32) # scale attention_scores
|
||||
attention_scores = attention_scores / tf.math.sqrt(dk)
|
||||
|
||||
if attention_mask is not None:
|
||||
@@ -466,9 +460,6 @@ 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
|
||||
|
||||
@@ -479,39 +470,28 @@ 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, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
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."
|
||||
assert len(inputs) <= 5, "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."
|
||||
assert len(inputs) <= 5, "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.fill(input_shape, 1)
|
||||
attention_mask = tf.fill(tf.shape(input_ids), 1)
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
token_type_ids = tf.fill(tf.shape(input_ids), 0)
|
||||
|
||||
# We create a 3D attention mask from a 2D tensor mask.
|
||||
# Sizes are [batch_size, 1, 1, to_seq_length]
|
||||
@@ -540,7 +520,7 @@ 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, inputs_embeds], training=training)
|
||||
embedding_output = self.embeddings([input_ids, position_ids, token_type_ids], training=training)
|
||||
encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
|
||||
|
||||
sequence_output = encoder_outputs[0]
|
||||
@@ -636,10 +616,6 @@ 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.",
|
||||
@@ -722,9 +698,6 @@ 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)
|
||||
|
||||
@@ -770,9 +743,6 @@ 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)
|
||||
|
||||
@@ -918,39 +888,33 @@ 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, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
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."
|
||||
assert len(inputs) <= 5, "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."
|
||||
assert len(inputs) <= 5, "Too many inputs."
|
||||
else:
|
||||
input_ids = inputs
|
||||
|
||||
if input_ids is not None:
|
||||
num_choices = shape_list(input_ids)[1]
|
||||
seq_length = shape_list(input_ids)[2]
|
||||
else:
|
||||
num_choices = shape_list(inputs_embeds)[1]
|
||||
seq_length = shape_list(inputs_embeds)[2]
|
||||
num_choices = tf.shape(input_ids)[1]
|
||||
seq_length = tf.shape(input_ids)[2]
|
||||
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length))
|
||||
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, inputs_embeds]
|
||||
flat_inputs = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask]
|
||||
|
||||
outputs = self.bert(flat_inputs, training=training)
|
||||
|
||||
|
||||
@@ -95,7 +95,7 @@ class TFMultiHeadAttention(tf.keras.layers.Layer):
|
||||
|
||||
def call(self, inputs, training=False):
|
||||
v, k, q, mask, layer_past, attention_mask, head_mask = inputs
|
||||
batch_size = shape_list(q)[0]
|
||||
batch_size = q.shape[0]
|
||||
|
||||
q = self.Wq(q)
|
||||
k = self.Wk(k)
|
||||
@@ -192,9 +192,6 @@ 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
|
||||
|
||||
@@ -204,7 +201,7 @@ 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, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
past = inputs[1] if len(inputs) > 1 else past
|
||||
@@ -212,8 +209,7 @@ 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
|
||||
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
|
||||
assert len(inputs) <= 7, "Too many inputs."
|
||||
assert len(inputs) <= 6, "Too many inputs."
|
||||
elif isinstance(inputs, dict):
|
||||
input_ids = inputs.get('input_ids')
|
||||
past = inputs.get('past', past)
|
||||
@@ -221,20 +217,12 @@ 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)
|
||||
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
|
||||
assert len(inputs) <= 7, "Too many inputs."
|
||||
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")
|
||||
input_shape = shape_list(input_ids)
|
||||
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
|
||||
|
||||
if past is None:
|
||||
past_length = 0
|
||||
@@ -242,8 +230,8 @@ 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, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
position_ids = tf.tile(position_ids, [input_shape[0], 1])
|
||||
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])
|
||||
|
||||
# Attention mask.
|
||||
if attention_mask is not None:
|
||||
@@ -282,8 +270,8 @@ 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')
|
||||
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)
|
||||
|
||||
@@ -386,10 +374,6 @@ 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.",
|
||||
@@ -492,9 +476,6 @@ 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]
|
||||
|
||||
@@ -37,8 +37,7 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
|
||||
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-tf_model.h5",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5",
|
||||
'distilbert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-tf_model.h5",
|
||||
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5"
|
||||
}
|
||||
|
||||
|
||||
@@ -97,7 +96,7 @@ class TFEmbeddings(tf.keras.layers.Layer):
|
||||
initializer=get_initializer(self.initializer_range))
|
||||
super(TFEmbeddings, self).build(input_shape)
|
||||
|
||||
def call(self, inputs, inputs_embeds=None, mode="embedding", training=False):
|
||||
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)
|
||||
@@ -113,13 +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, inputs_embeds=inputs_embeds, training=training)
|
||||
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, inputs_embeds=None, training=False):
|
||||
def _embedding(self, inputs, training=False):
|
||||
"""
|
||||
Parameters
|
||||
----------
|
||||
@@ -137,19 +136,14 @@ class TFEmbeddings(tf.keras.layers.Layer):
|
||||
else:
|
||||
input_ids, position_ids = inputs
|
||||
|
||||
if input_ids is not None:
|
||||
seq_length = shape_list(input_ids)[1]
|
||||
else:
|
||||
seq_length = shape_list(inputs_embeds)[1]
|
||||
|
||||
seq_length = tf.shape(input_ids)[1]
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
word_embeddings = tf.gather(self.word_embeddings, input_ids)
|
||||
position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
|
||||
|
||||
embeddings = inputs_embeds + position_embeddings # (bs, max_seq_length, dim)
|
||||
embeddings = word_embeddings + 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
|
||||
@@ -161,8 +155,8 @@ class TFEmbeddings(tf.keras.layers.Layer):
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
batch_size = tf.shape(inputs)[0]
|
||||
length = tf.shape(inputs)[1]
|
||||
|
||||
x = tf.reshape(inputs, [-1, self.dim])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
@@ -404,42 +398,28 @@ 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, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, attention_mask=None, head_mask=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
|
||||
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
|
||||
assert len(inputs) <= 4, "Too many inputs."
|
||||
assert len(inputs) <= 3, "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)
|
||||
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
|
||||
assert len(inputs) <= 4, "Too many inputs."
|
||||
assert len(inputs) <= 3, "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(input_shape) # (bs, seq_length)
|
||||
attention_mask = tf.ones(shape_list(input_ids)) # (bs, seq_length)
|
||||
attention_mask = tf.cast(attention_mask, dtype=tf.float32)
|
||||
|
||||
# Prepare head mask if needed
|
||||
@@ -452,7 +432,7 @@ class TFDistilBertMainLayer(tf.keras.layers.Layer):
|
||||
else:
|
||||
head_mask = [None] * self.num_hidden_layers
|
||||
|
||||
embedding_output = self.embeddings(input_ids, inputs_embeds=inputs_embeds) # (bs, seq_length, dim)
|
||||
embedding_output = self.embeddings(input_ids) # (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)
|
||||
@@ -528,10 +508,6 @@ 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.",
|
||||
@@ -633,9 +609,6 @@ 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)
|
||||
|
||||
|
||||
@@ -92,7 +92,7 @@ class TFAttention(tf.keras.layers.Layer):
|
||||
# q, k, v have shape [batch, heads, sequence, features]
|
||||
w = tf.matmul(q, k, transpose_b=True)
|
||||
if self.scale:
|
||||
dk = tf.cast(shape_list(k)[-1], tf.float32) # scale attention_scores
|
||||
dk = tf.cast(tf.shape(k)[-1], tf.float32) # scale attention_scores
|
||||
w = w / tf.math.sqrt(dk)
|
||||
|
||||
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
|
||||
@@ -219,9 +219,6 @@ 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
|
||||
|
||||
@@ -231,7 +228,7 @@ 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, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
past = inputs[1] if len(inputs) > 1 else past
|
||||
@@ -239,8 +236,7 @@ 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
|
||||
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
|
||||
assert len(inputs) <= 7, "Too many inputs."
|
||||
assert len(inputs) <= 6, "Too many inputs."
|
||||
elif isinstance(inputs, dict):
|
||||
input_ids = inputs.get('input_ids')
|
||||
past = inputs.get('past', past)
|
||||
@@ -248,28 +244,17 @@ 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)
|
||||
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
|
||||
assert len(inputs) <= 7, "Too many inputs."
|
||||
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 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, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
position_ids = tf.range(past_length, shape_list(input_ids)[-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.
|
||||
@@ -301,10 +286,11 @@ 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')
|
||||
inputs_embeds = self.wte(input_ids, mode='embedding')
|
||||
position_embeds = self.wpe(position_ids)
|
||||
if token_type_ids is not None:
|
||||
token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
|
||||
@@ -422,10 +408,6 @@ 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.",
|
||||
@@ -504,9 +486,6 @@ 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]
|
||||
@@ -577,10 +556,7 @@ 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 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):
|
||||
def call(self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
past = inputs[1] if len(inputs) > 1 else past
|
||||
@@ -588,9 +564,8 @@ 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
|
||||
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."
|
||||
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')
|
||||
past = inputs.get('past', past)
|
||||
@@ -598,25 +573,21 @@ 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) <= 8, "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]
|
||||
input_shapes = shape_list(input_ids)
|
||||
|
||||
seq_length = input_shapes[-1]
|
||||
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length))
|
||||
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, inputs_embeds]
|
||||
flat_inputs = [flat_input_ids, past, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask]
|
||||
|
||||
transformer_outputs = self.transformer(flat_inputs, training=training)
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
@@ -98,7 +98,7 @@ class TFAttention(tf.keras.layers.Layer):
|
||||
# q, k, v have shape [batch, heads, sequence, features]
|
||||
w = tf.matmul(q, k, transpose_b=True)
|
||||
if self.scale:
|
||||
dk = tf.cast(shape_list(k)[-1], tf.float32) # scale attention_scores
|
||||
dk = tf.cast(tf.shape(k)[-1], tf.float32) # scale attention_scores
|
||||
w = w / tf.math.sqrt(dk)
|
||||
|
||||
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
|
||||
@@ -217,9 +217,6 @@ 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
|
||||
|
||||
@@ -229,38 +226,26 @@ 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, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
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."
|
||||
assert len(inputs) <= 5, "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."
|
||||
assert len(inputs) <= 5, "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(input_shape[-1], dtype=tf.int32)[tf.newaxis, :]
|
||||
position_ids = tf.range(shape_list(input_ids)[-1], dtype=tf.int32)[tf.newaxis, :]
|
||||
|
||||
if attention_mask is not None:
|
||||
# We create a 3D attention mask from a 2D tensor mask.
|
||||
@@ -292,10 +277,11 @@ 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')
|
||||
inputs_embeds = self.tokens_embed(input_ids, mode='embedding')
|
||||
position_embeds = self.positions_embed(position_ids)
|
||||
if token_type_ids is not None:
|
||||
token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
|
||||
@@ -403,10 +389,6 @@ 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.",
|
||||
@@ -476,9 +458,6 @@ 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]
|
||||
@@ -541,44 +520,36 @@ 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 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):
|
||||
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, mc_token_ids=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
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
|
||||
mc_token_ids = inputs[6] if len(inputs) > 6 else mc_token_ids
|
||||
assert len(inputs) <= 7, "Too many inputs."
|
||||
mc_token_ids = inputs[5] if len(inputs) > 5 else mc_token_ids
|
||||
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)
|
||||
mc_token_ids = inputs.get('mc_token_ids', mc_token_ids)
|
||||
assert len(inputs) <= 7, "Too many inputs."
|
||||
assert len(inputs) <= 6, "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]
|
||||
input_shapes = shape_list(input_ids)
|
||||
|
||||
seq_length = input_shapes[-1]
|
||||
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
|
||||
flat_input_ids = tf.reshape(input_ids, (-1, seq_length))
|
||||
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, inputs_embeds]
|
||||
flat_inputs = [flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, head_mask]
|
||||
|
||||
transformer_outputs = self.transformer(flat_inputs, training=training)
|
||||
hidden_states = transformer_outputs[0]
|
||||
|
||||
@@ -118,9 +118,6 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
|
||||
new_key = key.replace('gamma', 'weight')
|
||||
if 'beta' in key:
|
||||
new_key = key.replace('beta', 'bias')
|
||||
# DialoGPT format
|
||||
if key == 'lm_head.decoder.weight':
|
||||
new_key = 'lm_head.weight'
|
||||
if new_key:
|
||||
old_keys.append(key)
|
||||
new_keys.append(new_key)
|
||||
|
||||
@@ -24,7 +24,7 @@ import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
from .configuration_roberta import RobertaConfig
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
|
||||
from .modeling_tf_utils import TFPreTrainedModel, get_initializer
|
||||
from .file_utils import add_start_docstrings
|
||||
|
||||
from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu, gelu_new
|
||||
@@ -48,17 +48,13 @@ class TFRobertaEmbeddings(TFBertEmbeddings):
|
||||
|
||||
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:
|
||||
seq_length = shape_list(input_ids)[1]
|
||||
else:
|
||||
seq_length = shape_list(inputs_embeds)[1]
|
||||
input_ids, position_ids, token_type_ids = inputs
|
||||
|
||||
seq_length = tf.shape(input_ids)[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, inputs_embeds], training=training)
|
||||
return super(TFRobertaEmbeddings, self)._embedding([input_ids, position_ids, token_type_ids], training=training)
|
||||
|
||||
|
||||
class TFRobertaMainLayer(TFBertMainLayer):
|
||||
@@ -69,9 +65,6 @@ 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
|
||||
@@ -164,10 +157,6 @@ 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.",
|
||||
@@ -287,9 +276,6 @@ 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)
|
||||
|
||||
|
||||
@@ -337,7 +337,7 @@ class TFAdaptiveEmbedding(tf.keras.layers.Layer):
|
||||
emb_i = tf.einsum('id,de->ie', emb_i, self.emb_projs[i])
|
||||
|
||||
mask_idx = tf.cast(tf.where(mask_i), dtype=tf.int64)
|
||||
emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(shape_list(emb_flat), dtype=tf.int64))
|
||||
emb_flat += tf.scatter_nd(mask_idx, emb_i, tf.cast(tf.shape(emb_flat), dtype=tf.int64))
|
||||
|
||||
embed_shape = shape_list(inp) + [self.d_proj]
|
||||
embed = tf.reshape(emb_flat, embed_shape)
|
||||
@@ -413,9 +413,6 @@ 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
|
||||
|
||||
@@ -430,11 +427,11 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
|
||||
def _prune_heads(self, heads):
|
||||
raise NotImplementedError
|
||||
|
||||
def init_mems(self, bsz):
|
||||
def init_mems(self, data):
|
||||
if self.mem_len > 0:
|
||||
mems = []
|
||||
for i in range(self.n_layer):
|
||||
empty = tf.zeros([self.mem_len, bsz, self.d_model])
|
||||
empty = tf.zeros([self.mem_len, shape_list(data)[1], self.d_model])
|
||||
mems.append(empty)
|
||||
|
||||
return mems
|
||||
@@ -464,37 +461,28 @@ class TFTransfoXLMainLayer(tf.keras.layers.Layer):
|
||||
|
||||
return new_mems
|
||||
|
||||
def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, training=False):
|
||||
def call(self, inputs, mems=None, head_mask=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
|
||||
inputs_embeds = inputs[3] if len(inputs) > 3 else inputs_embeds
|
||||
assert len(inputs) <= 4, "Too many inputs."
|
||||
assert len(inputs) <= 3, "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)
|
||||
assert len(inputs) <= 4, "Too many inputs."
|
||||
assert len(inputs) <= 3, "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")
|
||||
input_ids = tf.transpose(input_ids, perm=(1, 0))
|
||||
|
||||
if mems is None:
|
||||
mems = self.init_mems(bsz)
|
||||
mems = self.init_mems(input_ids)
|
||||
|
||||
qlen, bsz = shape_list(input_ids)
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
@@ -506,10 +494,7 @@ 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)
|
||||
word_emb = self.word_emb(input_ids)
|
||||
|
||||
mlen = shape_list(mems[0])[0] if mems is not None else 0
|
||||
klen = mlen + qlen
|
||||
@@ -641,10 +626,6 @@ 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.",
|
||||
@@ -735,33 +716,28 @@ 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, bsz):
|
||||
return self.transformer.init_mems(bsz)
|
||||
def init_mems(self, data):
|
||||
return self.transformer.init_mems(data)
|
||||
|
||||
def call(self, inputs, mems=None, head_mask=None, inputs_embeds=None, labels=None, training=False):
|
||||
def call(self, inputs, mems=None, head_mask=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
|
||||
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."
|
||||
labels = inputs[3] if len(inputs) > 3 else labels
|
||||
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)
|
||||
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
|
||||
labels = inputs.get('labels', labels)
|
||||
assert len(inputs) <= 5, "Too many inputs."
|
||||
assert len(inputs) <= 4, "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]
|
||||
bsz, tgt_len = shape_list(input_ids)[:2]
|
||||
|
||||
transformer_outputs = self.transformer([input_ids, mems, head_mask, inputs_embeds], training=training)
|
||||
transformer_outputs = self.transformer([input_ids, mems, head_mask], training=training)
|
||||
|
||||
last_hidden = transformer_outputs[0]
|
||||
pred_hid = last_hidden[:, -tgt_len:]
|
||||
|
||||
@@ -105,7 +105,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
|
||||
|
||||
@staticmethod
|
||||
def _gather_logprob(logprob, target):
|
||||
lp_size = shape_list(logprob)
|
||||
lp_size = tf.shape(logprob)
|
||||
r = tf.range(lp_size[0])
|
||||
idx = tf.stack([r, target], 1)
|
||||
return tf.gather_nd(logprob, idx)
|
||||
@@ -159,7 +159,7 @@ class TFAdaptiveSoftmaxMask(tf.keras.layers.Layer):
|
||||
cur_logprob = self._gather_logprob(cur_tail_logprob, cur_target)
|
||||
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
|
||||
if target is not None:
|
||||
loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(shape_list(loss), dtype=tf.int64))
|
||||
loss += tf.scatter_nd(mask_idx, -cur_logprob, tf.cast(tf.shape(loss), dtype=tf.int64))
|
||||
out = tf.concat(out, axis=-1)
|
||||
|
||||
if target is not None:
|
||||
|
||||
@@ -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 common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
|
||||
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.
|
||||
|
||||
Class attributes (overridden by derived classes):
|
||||
- ``config_class``: a class derived from :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
|
||||
@@ -51,15 +51,7 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
config_class = None
|
||||
pretrained_model_archive_map = {}
|
||||
base_model_prefix = ""
|
||||
|
||||
@property
|
||||
def dummy_inputs(self):
|
||||
""" Dummy inputs to build the network.
|
||||
|
||||
Returns:
|
||||
tf.Tensor with dummy inputs
|
||||
"""
|
||||
return tf.constant(DUMMY_INPUTS)
|
||||
dummy_inputs = tf.constant(DUMMY_INPUTS) # dummy inputs to build the network
|
||||
|
||||
def __init__(self, config, *inputs, **kwargs):
|
||||
super(TFPreTrainedModel, self).__init__(*inputs, **kwargs)
|
||||
@@ -73,21 +65,6 @@ 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
|
||||
@@ -199,9 +176,6 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -227,7 +201,6 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
cache_dir = kwargs.pop('cache_dir', None)
|
||||
from_pt = kwargs.pop('from_pt', False)
|
||||
force_download = kwargs.pop('force_download', False)
|
||||
resume_download = kwargs.pop('resume_download', False)
|
||||
proxies = kwargs.pop('proxies', None)
|
||||
|
||||
# Load config
|
||||
@@ -236,7 +209,6 @@ class TFPreTrainedModel(tf.keras.Model):
|
||||
pretrained_model_name_or_path, *model_args,
|
||||
cache_dir=cache_dir, return_unused_kwargs=True,
|
||||
force_download=force_download,
|
||||
resume_download=resume_download,
|
||||
**kwargs
|
||||
)
|
||||
else:
|
||||
@@ -264,8 +236,7 @@ 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,
|
||||
resume_download=resume_download, proxies=proxies)
|
||||
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
|
||||
except EnvironmentError as e:
|
||||
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
|
||||
logger.error(
|
||||
@@ -468,7 +439,7 @@ class TFSequenceSummary(tf.keras.layers.Layer):
|
||||
elif self.summary_type == 'first':
|
||||
output = hidden_states[:, 0]
|
||||
elif self.summary_type == 'mean':
|
||||
output = tf.reduce_mean(hidden_states, axis=1)
|
||||
output = tf.mean(hidden_states, axis=1)
|
||||
elif self.summary_type == 'cls_index':
|
||||
hidden_shape = shape_list(hidden_states) # e.g. [batch, num choices, seq length, hidden dims]
|
||||
if cls_index is None:
|
||||
@@ -506,10 +477,10 @@ def shape_list(x):
|
||||
return [dynamic[i] if s is None else s for i, s in enumerate(static)]
|
||||
|
||||
def get_initializer(initializer_range=0.02):
|
||||
"""Creates a `tf.initializers.truncated_normal` with the given range.
|
||||
Args:
|
||||
initializer_range: float, initializer range for stddev.
|
||||
Returns:
|
||||
TruncatedNormal initializer with stddev = `initializer_range`.
|
||||
"""
|
||||
return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)
|
||||
"""Creates a `tf.initializers.truncated_normal` with the given range.
|
||||
Args:
|
||||
initializer_range: float, initializer range for stddev.
|
||||
Returns:
|
||||
TruncatedNormal initializer with stddev = `initializer_range`.
|
||||
"""
|
||||
return tf.keras.initializers.TruncatedNormal(stddev=initializer_range)
|
||||
@@ -277,9 +277,6 @@ 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
|
||||
|
||||
@@ -291,7 +288,7 @@ 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, inputs_embeds=None,
|
||||
position_ids=None, lengths=None, cache=None, head_mask=None,
|
||||
training=False): # removed: src_enc=None, src_len=None
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
@@ -302,8 +299,7 @@ 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
|
||||
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
|
||||
assert len(inputs) <= 9, "Too many inputs."
|
||||
assert len(inputs) <= 8, "Too many inputs."
|
||||
elif isinstance(inputs, dict):
|
||||
input_ids = inputs.get('input_ids')
|
||||
attention_mask = inputs.get('attention_mask', attention_mask)
|
||||
@@ -313,28 +309,16 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
|
||||
lengths = inputs.get('lengths', lengths)
|
||||
cache = inputs.get('cache', cache)
|
||||
head_mask = inputs.get('head_mask', head_mask)
|
||||
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
|
||||
assert len(inputs) <= 9, "Too many inputs."
|
||||
assert len(inputs) <= 8, "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)
|
||||
lengths = tf.reduce_sum(tf.cast(tf.not_equal(input_ids, self.pad_index), dtype=tf.int32), axis=1)
|
||||
# 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
|
||||
@@ -374,7 +358,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
|
||||
head_mask = [None] * self.n_layers
|
||||
|
||||
# do not recompute cached elements
|
||||
if cache is not None and input_ids is not None:
|
||||
if cache is not None:
|
||||
_slen = slen - cache['slen']
|
||||
input_ids = input_ids[:, -_slen:]
|
||||
position_ids = position_ids[:, -_slen:]
|
||||
@@ -384,10 +368,8 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
|
||||
attn_mask = attn_mask[:, -_slen:]
|
||||
|
||||
# embeddings
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embeddings(input_ids)
|
||||
|
||||
tensor = inputs_embeds + self.position_embeddings(position_ids)
|
||||
tensor = self.embeddings(input_ids)
|
||||
tensor = tensor + 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:
|
||||
@@ -548,10 +530,6 @@ 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.",
|
||||
@@ -659,8 +637,6 @@ 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)
|
||||
|
||||
@@ -112,7 +112,8 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
|
||||
def prune_heads(self, heads):
|
||||
raise NotImplementedError
|
||||
|
||||
def rel_shift(self, x, klen=-1):
|
||||
@staticmethod
|
||||
def rel_shift(x, klen=-1):
|
||||
"""perform relative shift to form the relative attention score."""
|
||||
x_size = shape_list(x)
|
||||
|
||||
@@ -134,7 +135,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
|
||||
|
||||
# position based attention score
|
||||
bd = tf.einsum('ibnd,jbnd->ijbn', q_head + self.r_r_bias, k_head_r)
|
||||
bd = self.rel_shift(bd, klen=shape_list(ac)[1])
|
||||
bd = self.rel_shift(bd, klen=ac.shape[1])
|
||||
|
||||
# segment based attention score
|
||||
if seg_mat is None:
|
||||
@@ -191,7 +192,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
|
||||
if g is not None:
|
||||
###### Two-stream attention with relative positional encoding.
|
||||
# content based attention score
|
||||
if mems is not None and len(shape_list(mems)) > 1:
|
||||
if mems is not None and mems.shape.ndims > 1:
|
||||
cat = tf.concat([mems, h], axis=0)
|
||||
else:
|
||||
cat = h
|
||||
@@ -251,7 +252,7 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
|
||||
|
||||
else:
|
||||
###### Multi-head attention with relative positional encoding
|
||||
if mems is not None and len(shape_list(mems)) > 1:
|
||||
if mems is not None and mems.shape.ndims > 1:
|
||||
cat = tf.concat([mems, h], axis=0)
|
||||
else:
|
||||
cat = h
|
||||
@@ -370,9 +371,6 @@ 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),
|
||||
@@ -486,7 +484,7 @@ 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, inputs_embeds=None, training=False):
|
||||
token_type_ids=None, input_mask=None, head_mask=None, training=False):
|
||||
if isinstance(inputs, (tuple, list)):
|
||||
input_ids = inputs[0]
|
||||
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
|
||||
@@ -496,8 +494,7 @@ 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
|
||||
inputs_embeds = inputs[8] if len(inputs) > 8 else inputs_embeds
|
||||
assert len(inputs) <= 9, "Too many inputs."
|
||||
assert len(inputs) <= 8, "Too many inputs."
|
||||
elif isinstance(inputs, dict):
|
||||
input_ids = inputs.get('input_ids')
|
||||
attention_mask = inputs.get('attention_mask', attention_mask)
|
||||
@@ -507,8 +504,7 @@ 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)
|
||||
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
|
||||
assert len(inputs) <= 9, "Too many inputs."
|
||||
assert len(inputs) <= 8, "Too many inputs."
|
||||
else:
|
||||
input_ids = inputs
|
||||
|
||||
@@ -516,23 +512,14 @@ 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")
|
||||
|
||||
input_ids = tf.transpose(input_ids, perm=(1, 0))
|
||||
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
|
||||
|
||||
@@ -552,7 +539,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
|
||||
assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) " \
|
||||
"or attention_mask (uses 0 for padding, added for compatbility with BERT). Please choose one."
|
||||
if input_mask is None and attention_mask is not None:
|
||||
input_mask = 1.0 - attention_mask
|
||||
input_mask = 1.0 - tf.cast(attention_mask, dtype=dtype_float)
|
||||
if input_mask is not None and perm_mask is not None:
|
||||
data_mask = input_mask[None] + perm_mask
|
||||
elif input_mask is not None and perm_mask is None:
|
||||
@@ -564,7 +551,7 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
|
||||
|
||||
if data_mask is not None:
|
||||
# all mems can be attended to
|
||||
mems_mask = tf.zeros([shape_list(data_mask)[0], mlen, bsz],
|
||||
mems_mask = tf.zeros([tf.shape(data_mask)[0], mlen, bsz],
|
||||
dtype=dtype_float)
|
||||
data_mask = tf.concat([mems_mask, data_mask], axis=1)
|
||||
if attn_mask is None:
|
||||
@@ -583,13 +570,10 @@ 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)
|
||||
word_emb_k = self.word_embedding(input_ids)
|
||||
output_h = self.dropout(word_emb_k, training=training)
|
||||
if target_mapping is not None:
|
||||
word_emb_q = tf.tile(self.mask_emb, [shape_list(target_mapping)[0], bsz, 1])
|
||||
word_emb_q = tf.tile(self.mask_emb, [tf.shape(target_mapping)[0], bsz, 1])
|
||||
# else: # We removed the inp_q input which was same as target mapping
|
||||
# inp_q_ext = inp_q[:, :, None]
|
||||
# word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
|
||||
@@ -778,10 +762,6 @@ 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.",
|
||||
@@ -870,9 +850,6 @@ 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]
|
||||
@@ -938,59 +915,6 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel):
|
||||
return outputs # return logits, (mems), (hidden states), (attentions)
|
||||
|
||||
|
||||
@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of
|
||||
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
|
||||
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
|
||||
class TFXLNetForTokenClassification(TFXLNetPreTrainedModel):
|
||||
r"""
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**scores**: ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
|
||||
Classification scores (before SoftMax).
|
||||
**mems**: (`optional`, returned when ``config.mem_len > 0``)
|
||||
list of ``tf.Tensor`` (one for each layer):
|
||||
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
||||
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
|
||||
See details in the docstring of the `mems` input above.
|
||||
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
||||
list of ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
|
||||
of shape ``(batch_size, sequence_length, hidden_size)``:
|
||||
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``tf.Tensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
|
||||
Examples::
|
||||
|
||||
import tensorflow as tf
|
||||
from transformers import XLNetTokenizer, TFXLNetForTokenClassification
|
||||
|
||||
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
|
||||
model = TFXLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
|
||||
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
|
||||
outputs = model(input_ids)
|
||||
scores = outputs[0]
|
||||
|
||||
"""
|
||||
def __init__(self, config, *inputs, **kwargs):
|
||||
super(TFXLNetForTokenClassification, self).__init__(config, *inputs, **kwargs)
|
||||
self.num_labels = config.num_labels
|
||||
|
||||
self.transformer = TFXLNetMainLayer(config, name='transformer')
|
||||
self.classifier = tf.keras.layers.Dense(config.num_labels,
|
||||
kernel_initializer=get_initializer(config.initializer_range),
|
||||
name='classifier')
|
||||
|
||||
def call(self, inputs, **kwargs):
|
||||
transformer_outputs = self.transformer(inputs, **kwargs)
|
||||
output = transformer_outputs[0]
|
||||
|
||||
logits = self.classifier(output)
|
||||
|
||||
outputs = (logits,) + transformer_outputs[1:] # Keep mems, hidden states, attentions if there are in it
|
||||
|
||||
return outputs # return logits, (mems), (hidden states), (attentions)
|
||||
|
||||
|
||||
# @add_start_docstrings("""XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
|
||||
# the hidden-states output to compute `span start logits` and `span end logits`). """,
|
||||
# XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
|
||||
|
||||
@@ -553,10 +553,6 @@ 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.",
|
||||
@@ -661,12 +657,12 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
|
||||
logger.info("Head pruning is not implemented for Transformer-XL model")
|
||||
pass
|
||||
|
||||
def init_mems(self, bsz):
|
||||
def init_mems(self, data):
|
||||
if self.mem_len > 0:
|
||||
mems = []
|
||||
param = next(self.parameters())
|
||||
for i in range(self.n_layer):
|
||||
empty = torch.zeros(self.mem_len, bsz, self.config.d_model,
|
||||
empty = torch.zeros(self.mem_len, data.size(1), self.config.d_model,
|
||||
dtype=param.dtype, device=param.device)
|
||||
mems.append(empty)
|
||||
|
||||
@@ -697,22 +693,15 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
|
||||
|
||||
return new_mems
|
||||
|
||||
def forward(self, input_ids=None, mems=None, head_mask=None, inputs_embeds=None):
|
||||
def forward(self, input_ids, mems=None, head_mask=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")
|
||||
input_ids = input_ids.transpose(0, 1).contiguous()
|
||||
|
||||
if mems is None:
|
||||
mems = self.init_mems(bsz)
|
||||
mems = self.init_mems(input_ids)
|
||||
|
||||
qlen, bsz = input_ids.size()
|
||||
|
||||
# Prepare head mask if needed
|
||||
# 1.0 in head_mask indicate we keep the head
|
||||
@@ -729,10 +718,7 @@ 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)
|
||||
word_emb = self.word_emb(input_ids)
|
||||
|
||||
mlen = mems[0].size(0) if mems is not None else 0
|
||||
klen = mlen + qlen
|
||||
@@ -874,18 +860,14 @@ 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, bsz):
|
||||
return self.transformer.init_mems(bsz)
|
||||
def init_mems(self, data):
|
||||
return self.transformer.init_mems(data)
|
||||
|
||||
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")
|
||||
def forward(self, input_ids, mems=None, head_mask=None, labels=None):
|
||||
bsz = input_ids.size(0)
|
||||
tgt_len = input_ids.size(1)
|
||||
|
||||
transformer_outputs = self.transformer(input_ids, mems=mems, head_mask=head_mask, inputs_embeds=inputs_embeds)
|
||||
transformer_outputs = self.transformer(input_ids, mems=mems, head_mask=head_mask)
|
||||
|
||||
last_hidden = transformer_outputs[0]
|
||||
pred_hid = last_hidden[:, -tgt_len:]
|
||||
|
||||
@@ -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 common to all models to (i) resize the input embeddings and (ii) prune heads in the self-attention heads.
|
||||
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.
|
||||
|
||||
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 itself if we are using distributed training
|
||||
# Only save the model it-self if we are using distributed training
|
||||
model_to_save = self.module if hasattr(self, 'module') else self
|
||||
|
||||
# Save configuration file
|
||||
@@ -291,9 +291,6 @@ class PreTrainedModel(nn.Module):
|
||||
force_download: (`optional`) boolean, default False:
|
||||
Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
|
||||
|
||||
resume_download: (`optional`) boolean, default False:
|
||||
Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
|
||||
|
||||
proxies: (`optional`) dict, default None:
|
||||
A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
|
||||
The proxies are used on each request.
|
||||
@@ -318,16 +315,11 @@ 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)
|
||||
|
||||
@@ -337,7 +329,6 @@ class PreTrainedModel(nn.Module):
|
||||
pretrained_model_name_or_path, *model_args,
|
||||
cache_dir=cache_dir, return_unused_kwargs=True,
|
||||
force_download=force_download,
|
||||
resume_download=resume_download,
|
||||
proxies=proxies,
|
||||
**kwargs
|
||||
)
|
||||
@@ -370,8 +361,7 @@ 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, resume_download=resume_download)
|
||||
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
|
||||
except EnvironmentError:
|
||||
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
|
||||
msg = "Couldn't reach server at '{}' to download pretrained weights.".format(
|
||||
@@ -427,8 +417,6 @@ class PreTrainedModel(nn.Module):
|
||||
new_key = key.replace('gamma', 'weight')
|
||||
if 'beta' in key:
|
||||
new_key = key.replace('beta', 'bias')
|
||||
if key == 'lm_head.decoder.weight':
|
||||
new_key = 'lm_head.weight'
|
||||
if new_key:
|
||||
old_keys.append(key)
|
||||
new_keys.append(new_key)
|
||||
@@ -740,7 +728,7 @@ class SequenceSummary(nn.Module):
|
||||
def __init__(self, config):
|
||||
super(SequenceSummary, self).__init__()
|
||||
|
||||
self.summary_type = config.summary_type if hasattr(config, 'summary_type') else 'last'
|
||||
self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
|
||||
if self.summary_type == 'attn':
|
||||
# We should use a standard multi-head attention module with absolute positional embedding for that.
|
||||
# Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
|
||||
|
||||
@@ -311,10 +311,6 @@ 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.",
|
||||
@@ -425,21 +421,14 @@ class XLMModel(XLMPreTrainedModel):
|
||||
for layer, heads in heads_to_prune.items():
|
||||
self.attentions[layer].prune_heads(heads)
|
||||
|
||||
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]
|
||||
|
||||
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
|
||||
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)
|
||||
lengths = (input_ids != self.pad_index).sum(dim=1).long()
|
||||
# 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
|
||||
@@ -453,12 +442,10 @@ 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 = torch.arange(slen, dtype=torch.long, device=device)
|
||||
position_ids = position_ids.unsqueeze(0).expand((bs, slen))
|
||||
position_ids = input_ids.new((slen,)).long()
|
||||
position_ids = torch.arange(slen, out=position_ids).unsqueeze(0)
|
||||
else:
|
||||
assert position_ids.size() == (bs, slen) # (slen, bs)
|
||||
# position_ids = position_ids.transpose(0, 1)
|
||||
@@ -484,7 +471,7 @@ class XLMModel(XLMPreTrainedModel):
|
||||
head_mask = [None] * self.n_layers
|
||||
|
||||
# do not recompute cached elements
|
||||
if cache is not None and input_ids is not None:
|
||||
if cache is not None:
|
||||
_slen = slen - cache['slen']
|
||||
input_ids = input_ids[:, -_slen:]
|
||||
position_ids = position_ids[:, -_slen:]
|
||||
@@ -494,10 +481,8 @@ class XLMModel(XLMPreTrainedModel):
|
||||
attn_mask = attn_mask[:, -_slen:]
|
||||
|
||||
# embeddings
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embeddings(input_ids)
|
||||
|
||||
tensor = inputs_embeds + self.position_embeddings(position_ids).expand_as(inputs_embeds)
|
||||
tensor = self.embeddings(input_ids)
|
||||
tensor = tensor + self.position_embeddings(position_ids).expand_as(tensor)
|
||||
if langs is not None and self.use_lang_emb:
|
||||
tensor = tensor + self.lang_embeddings(langs)
|
||||
if token_type_ids is not None:
|
||||
@@ -639,8 +624,8 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.pred_layer.proj
|
||||
|
||||
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):
|
||||
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):
|
||||
transformer_outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
langs=langs,
|
||||
@@ -648,8 +633,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
|
||||
position_ids=position_ids,
|
||||
lengths=lengths,
|
||||
cache=cache,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
output = transformer_outputs[0]
|
||||
outputs = self.pred_layer(output, labels)
|
||||
@@ -701,8 +685,8 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
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):
|
||||
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):
|
||||
transformer_outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
langs=langs,
|
||||
@@ -710,8 +694,7 @@ class XLMForSequenceClassification(XLMPreTrainedModel):
|
||||
position_ids=position_ids,
|
||||
lengths=lengths,
|
||||
cache=cache,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
output = transformer_outputs[0]
|
||||
logits = self.sequence_summary(output)
|
||||
@@ -785,8 +768,8 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
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):
|
||||
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):
|
||||
transformer_outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
langs=langs,
|
||||
@@ -794,8 +777,7 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
|
||||
position_ids=position_ids,
|
||||
lengths=lengths,
|
||||
cache=cache,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = transformer_outputs[0]
|
||||
|
||||
@@ -881,8 +863,8 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
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,
|
||||
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,
|
||||
is_impossible=None, cls_index=None, p_mask=None):
|
||||
transformer_outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
@@ -891,8 +873,7 @@ class XLMForQuestionAnswering(XLMPreTrainedModel):
|
||||
position_ids=position_ids,
|
||||
lengths=lengths,
|
||||
cache=cache,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
output = transformer_outputs[0]
|
||||
|
||||
|
||||
+21
-156
@@ -558,10 +558,6 @@ 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.",
|
||||
@@ -583,7 +579,6 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
|
||||
|
||||
Examples::
|
||||
|
||||
@@ -717,29 +712,19 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
pos_emb = pos_emb.to(next(self.parameters()))
|
||||
return pos_emb
|
||||
|
||||
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):
|
||||
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):
|
||||
# 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")
|
||||
|
||||
input_ids = input_ids.transpose(0, 1).contiguous()
|
||||
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
|
||||
|
||||
@@ -792,10 +777,7 @@ 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)
|
||||
word_emb_k = self.word_embedding(input_ids)
|
||||
output_h = self.dropout(word_emb_k)
|
||||
if target_mapping is not None:
|
||||
word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
|
||||
@@ -879,11 +861,7 @@ class XLNetModel(XLNetPreTrainedModel):
|
||||
hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)
|
||||
outputs = outputs + (hidden_states,)
|
||||
if self.output_attentions:
|
||||
if target_mapping is not None:
|
||||
# when target_mapping is provided, there are 2-tuple of attentions
|
||||
attentions = tuple(tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions)
|
||||
else:
|
||||
attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
|
||||
attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)
|
||||
outputs = outputs + (attentions,)
|
||||
|
||||
return outputs # outputs, (new_mems), (hidden_states), (attentions)
|
||||
@@ -918,7 +896,6 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
|
||||
|
||||
Examples::
|
||||
|
||||
@@ -947,8 +924,8 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_loss
|
||||
|
||||
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):
|
||||
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):
|
||||
transformer_outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
mems=mems,
|
||||
@@ -956,8 +933,7 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
|
||||
target_mapping=target_mapping,
|
||||
token_type_ids=token_type_ids,
|
||||
input_mask=input_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
logits = self.lm_loss(transformer_outputs[0])
|
||||
|
||||
@@ -1001,7 +977,6 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
|
||||
|
||||
Examples::
|
||||
|
||||
@@ -1023,8 +998,8 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
|
||||
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):
|
||||
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):
|
||||
transformer_outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
mems=mems,
|
||||
@@ -1032,8 +1007,7 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
target_mapping=target_mapping,
|
||||
token_type_ids=token_type_ids,
|
||||
input_mask=input_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
output = transformer_outputs[0]
|
||||
|
||||
output = self.sequence_summary(output)
|
||||
@@ -1053,106 +1027,6 @@ class XLNetForSequenceClassification(XLNetPreTrainedModel):
|
||||
|
||||
return outputs # return (loss), logits, (mems), (hidden states), (attentions)
|
||||
|
||||
@add_start_docstrings("""XLNet Model with a token classification head on top (a linear layer on top of
|
||||
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
|
||||
XLNET_START_DOCSTRING,
|
||||
XLNET_INPUTS_DOCSTRING)
|
||||
class XLNetForTokenClassification(XLNetPreTrainedModel):
|
||||
r"""
|
||||
Inputs:
|
||||
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
|
||||
Indices of input sequence tokens in the vocabulary.
|
||||
The second dimension of the input (`num_choices`) indicates the number of choices to scores.
|
||||
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
|
||||
Segment token indices to indicate first and second portions of the inputs.
|
||||
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
|
||||
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
|
||||
Mask to avoid performing attention on padding token indices.
|
||||
Mask values selected in ``[0, 1]``:
|
||||
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
|
||||
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
|
||||
Mask to nullify selected heads of the self-attention modules.
|
||||
Mask values selected in ``[0, 1]``:
|
||||
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
|
||||
**inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
|
||||
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
|
||||
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
||||
than the model's internal embedding lookup matrix.
|
||||
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
|
||||
Labels for computing the multiple choice classification loss.
|
||||
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
|
||||
of the input tensors. (see `input_ids` above)
|
||||
|
||||
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
|
||||
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
|
||||
Classification loss.
|
||||
**scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
|
||||
Classification scores (before SoftMax).
|
||||
**mems**: (`optional`, returned when ``config.mem_len > 0``)
|
||||
list of ``torch.FloatTensor`` (one for each layer):
|
||||
that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
|
||||
if config.mem_len > 0 else tuple of None. Can be used to speed up sequential decoding and attend to longer context.
|
||||
See details in the docstring of the `mems` input above.
|
||||
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
|
||||
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
|
||||
of shape ``(batch_size, sequence_length, hidden_size)``:
|
||||
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
|
||||
Examples::
|
||||
|
||||
tokenizer = XLNetTokenizer.from_pretrained('xlnet-large-cased')
|
||||
model = XLNetForSequenceClassification.from_pretrained('xlnet-large-cased')
|
||||
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
|
||||
labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
|
||||
outputs = model(input_ids, labels=labels)
|
||||
scores = outputs[0]
|
||||
|
||||
"""
|
||||
def __init__(self, config):
|
||||
super(XLNetForTokenClassification, self).__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
|
||||
self.transformer = XLNetModel(config)
|
||||
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
|
||||
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None, labels=None):
|
||||
|
||||
outputs = self.transformer(input_ids,
|
||||
attention_mask=attention_mask,
|
||||
mems=mems,
|
||||
perm_mask=perm_mask,
|
||||
target_mapping=target_mapping,
|
||||
token_type_ids=token_type_ids,
|
||||
input_mask=input_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
logits = self.classifier(sequence_output)
|
||||
|
||||
outputs = (logits,) + outputs[1:] # Keep mems, hidden states, attentions if there are in it
|
||||
if labels is not None:
|
||||
loss_fct = CrossEntropyLoss()
|
||||
# Only keep active parts of the loss
|
||||
if attention_mask is not None:
|
||||
active_loss = attention_mask.view(-1) == 1
|
||||
active_logits = logits.view(-1, self.num_labels)[active_loss]
|
||||
active_labels = labels.view(-1)[active_loss]
|
||||
loss = loss_fct(active_logits, active_labels)
|
||||
else:
|
||||
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
||||
outputs = (loss,) + outputs
|
||||
|
||||
return outputs # return (loss), logits, (mems), (hidden states), (attentions)
|
||||
|
||||
|
||||
@add_start_docstrings("""XLNet Model with a multiple choice classification head on top (a linear layer on top of
|
||||
the pooled output and a softmax) e.g. for RACE/SWAG tasks. """,
|
||||
XLNET_START_DOCSTRING, XLNET_INPUTS_DOCSTRING)
|
||||
@@ -1175,10 +1049,6 @@ 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
|
||||
@@ -1202,7 +1072,6 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
|
||||
|
||||
Examples::
|
||||
|
||||
@@ -1224,9 +1093,9 @@ class XLNetForMultipleChoice(XLNetPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, token_type_ids=None, input_mask=None, attention_mask=None,
|
||||
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
|
||||
mems=None, perm_mask=None, target_mapping=None,
|
||||
labels=None, head_mask=None, inputs_embeds=None):
|
||||
labels=None, head_mask=None):
|
||||
num_choices = input_ids.shape[1]
|
||||
|
||||
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
|
||||
@@ -1237,7 +1106,7 @@ 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, inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
|
||||
output = transformer_outputs[0]
|
||||
@@ -1288,7 +1157,6 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
|
||||
|
||||
Examples::
|
||||
|
||||
@@ -1310,8 +1178,8 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
|
||||
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None,
|
||||
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,
|
||||
start_positions=None, end_positions=None):
|
||||
|
||||
outputs = self.transformer(input_ids,
|
||||
@@ -1321,8 +1189,7 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
|
||||
target_mapping=target_mapping,
|
||||
token_type_ids=token_type_ids,
|
||||
input_mask=input_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
|
||||
sequence_output = outputs[0]
|
||||
|
||||
@@ -1403,7 +1270,6 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
|
||||
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
|
||||
list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
|
||||
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
|
||||
When ``target_mapping is not None``, the attentions outputs are a list of 2-tuple of ``torch.FloatTensor``.
|
||||
|
||||
Examples::
|
||||
|
||||
@@ -1428,8 +1294,8 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
|
||||
|
||||
self.init_weights()
|
||||
|
||||
def forward(self, input_ids=None, attention_mask=None, mems=None, perm_mask=None, target_mapping=None,
|
||||
token_type_ids=None, input_mask=None, head_mask=None, inputs_embeds=None,
|
||||
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,
|
||||
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,
|
||||
@@ -1438,8 +1304,7 @@ class XLNetForQuestionAnswering(XLNetPreTrainedModel):
|
||||
target_mapping=target_mapping,
|
||||
token_type_ids=token_type_ids,
|
||||
input_mask=input_mask,
|
||||
head_mask=head_mask,
|
||||
inputs_embeds=inputs_embeds)
|
||||
head_mask=head_mask)
|
||||
hidden_states = transformer_outputs[0]
|
||||
start_logits = self.start_logits(hidden_states, p_mask=p_mask)
|
||||
|
||||
|
||||
@@ -23,65 +23,85 @@ from torch.optim.lr_scheduler import LambdaLR
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def get_constant_schedule(optimizer, last_epoch=-1):
|
||||
""" Create a schedule with a constant learning rate.
|
||||
class ConstantLRSchedule(LambdaLR):
|
||||
""" Constant learning rate schedule.
|
||||
"""
|
||||
return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
|
||||
def __init__(self, optimizer, last_epoch=-1):
|
||||
super(ConstantLRSchedule, self).__init__(optimizer, lambda _: 1.0, last_epoch=last_epoch)
|
||||
|
||||
|
||||
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.
|
||||
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 lr_lambda(current_step):
|
||||
if current_step < num_warmup_steps:
|
||||
return float(current_step) / float(max(1.0, num_warmup_steps))
|
||||
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))
|
||||
return 1.
|
||||
|
||||
return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
|
||||
|
||||
|
||||
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.
|
||||
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 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 __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)
|
||||
|
||||
return LambdaLR(optimizer, lr_lambda, last_epoch)
|
||||
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)))
|
||||
|
||||
|
||||
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.
|
||||
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 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 __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)
|
||||
|
||||
return LambdaLR(optimizer, lr_lambda, last_epoch)
|
||||
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)))
|
||||
|
||||
|
||||
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.
|
||||
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 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.))))
|
||||
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))))
|
||||
|
||||
return LambdaLR(optimizer, lr_lambda, last_epoch)
|
||||
|
||||
|
||||
class AdamW(Optimizer):
|
||||
|
||||
@@ -7,13 +7,6 @@ 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):
|
||||
@@ -24,8 +17,3 @@ 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")
|
||||
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Reference in new issue
Block a user