BigBird (#10183)

* init bigbird

* model.__init__ working, conversion script ready, config updated

* add conversion script

* BigBirdEmbeddings working :)

* slightly update conversion script

* BigBirdAttention working :) ; some bug in layer.output.dense

* add debugger-notebook

* forward() working for BigBirdModel :) ; replaced gelu with gelu_fast

* tf code adapted to torch till rand_attn in bigbird_block_sparse_attention ; till now everything working :)

* BigBirdModel working in block-sparse attention mode :)

* add BigBirdForPreTraining

* small fix

* add tokenizer for BigBirdModel

* fix config & hence modeling

* fix base prefix

* init testing

* init tokenizer test

* pos_embed must be absolute, attn_type=original_full when add_cross_attn=True , nsp loss is optional in BigBirdForPreTraining, add assert statements

* remove position_embedding_type arg

* complete normal tests

* add comments to block sparse attention

* add attn_probs for sliding & global tokens

* create fn for block sparse attn mask creation

* add special tests

* restore pos embed arg

* minor fix

* attn probs update

* make big bird fully gpu friendly

* fix tests

* remove pruning

* correct tokenzier & minor fixes

* update conversion script , remove norm_type

* tokenizer-inference test add

* remove extra comments

* add docs

* save intermediate

* finish trivia_qa conversion

* small update to forward

* correct qa and layer

* better error message

* BigBird QA ready

* fix rebased

* add triva-qa debugger notebook

* qa setup

* fixed till embeddings

* some issue in q/k/v_layer

* fix bug in conversion-script

* fixed till self-attn

* qa fixed except layer norm

* add qa end2end test

* fix gradient ckpting ; other qa test

* speed-up big bird a bit

* hub_id=google

* clean up

* make quality

* speed up einsum with bmm

* finish perf improvements for big bird

* remove wav2vec2 tok

* fix tokenizer

* include docs

* correct docs

* add helper to auto pad block size

* make style

* remove fast tokenizer for now

* fix some

* add pad test

* finish

* fix some bugs

* fix another bug

* fix buffer tokens

* fix comment and merge from master

* add comments

* make style

* commit some suggestions
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Fix typos

* fix some more suggestions

* add another patch
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix copies

* another path
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* update

* update nit suggestions

* make style
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

README.md

@@ -194,6 +194,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[BigBird-RoBERTa](https://huggingface.co/transformers/model_doc/bigbird.html)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
 1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
 1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.

docs/source/index.rst

@@ -97,130 +97,133 @@ and conversion utilities for the following models:
 5. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
    Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
    Narayan, Aliaksei Severyn.
-6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+6. :doc:`BigBird-RoBERTa <model_doc/bigbird>` (from Google Research) released with the paper `Big Bird: Transformers
+   for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua
+   Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+7. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
    open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
    Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-7. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building an
+8. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building an
    open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
    Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-8. :doc:`BORT <model_doc/bort>` (from Alexa) released with the paper `Optimal Subarchitecture Extraction For BERT
+9. :doc:`BORT <model_doc/bort>` (from Alexa) released with the paper `Optimal Subarchitecture Extraction For BERT
    <https://arxiv.org/abs/2010.10499>`__ by Adrian de Wynter and Daniel J. Perry.
-9. :doc:`CamemBERT <model_doc/camembert>` (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.
-10. :doc:`ConvBERT <model_doc/convbert>` (from YituTech) released with the paper `ConvBERT: Improving BERT with
+10. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+    French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
+    Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+11. :doc:`ConvBERT <model_doc/convbert>` (from YituTech) released with the paper `ConvBERT: Improving BERT with
     Span-based Dynamic Convolution <https://arxiv.org/abs/2008.02496>`__ by Zihang Jiang, Weihao Yu, Daquan Zhou,
     Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-11. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+12. :doc:`CTRL <model_doc/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.
-12. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT with
+13. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT with
     Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu
     Chen.
-13. :doc:`DeBERTa-v2 <model_doc/deberta_v2>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT
+14. :doc:`DeBERTa-v2 <model_doc/deberta_v2>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT
     with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
     Weizhu Chen.
-14. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+15. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
     Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
     Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-15. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+16. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
     distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
     Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
     <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
     `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
     version of DistilBERT.
-16. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+17. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
     Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
     Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-17. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+18. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
     Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
     Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-18. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+19. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
     Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
     Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-19. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+20. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
     Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
     Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-20. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+21. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
     Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
     and Ilya Sutskever.
-21. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+22. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
     Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
     Luan, Dario Amodei** and Ilya Sutskever**.
-22. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
+23. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
     <https://arxiv.org/abs/2101.01321>`__ by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer
-23. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+24. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
     of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
     Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-24. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
+25. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
     <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-25. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+26. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
     Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-26. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+27. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
     Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
     by Hao Tan and Mohit Bansal.
-27. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
+28. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
     Machine Translation <https://arxiv.org/abs/2010.11125>`__ by by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi
     Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman
     Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-28. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+29. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
     Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
     Translator Team.
-29. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+30. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
     Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
     Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-30. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
+31. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
     Multilingual Pretraining and Finetuning <https://arxiv.org/abs/2008.00401>`__ by Yuqing Tang, Chau Tran, Xian Li,
     Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-31. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
+32. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
     Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
     Jianfeng Lu, Tie-Yan Liu.
-32. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+33. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
     text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
     Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-33. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+34. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
     Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
     Mohammad Saleh and Peter J. Liu.
-34. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+35. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
     Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
     Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-35. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+36. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
     Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-36. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+37. :doc:`RoBERTa <model_doc/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.
-37. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
+38. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
     `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
     Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-38. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+39. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
     about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
     Krishna, and Kurt W. Keutzer.
-39. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+40. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
     Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-40. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+41. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
     Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
     Francesco Piccinno and Julian Martin Eisenschlos.
-41. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+42. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
     Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
     Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-42. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
+43. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
     Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
     Zhou, Abdelrahman Mohamed, Michael Auli.
-43. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+44. :doc:`XLM <model_doc/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.
-44. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+45. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-45. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+46. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-46. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+47. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
     Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
     Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-47. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
+48. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
     Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
     Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 
@@ -247,6 +250,8 @@ TensorFlow and/or Flax.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|           BigBird           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |       BlenderbotSmall       |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
@@ -407,6 +412,7 @@ TensorFlow and/or Flax.
     model_doc/bert
     model_doc/bertweet
     model_doc/bertgeneration
+    model_doc/bigbird
     model_doc/blenderbot
     model_doc/blenderbot_small
     model_doc/bort

docs/source/model_doc/bigbird.rst

+.. 
+    Copyright 2021 The HuggingFace Team. 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.
+
+BigBird
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The BigBird model was proposed in `Big Bird: Transformers for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by
+Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
+Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention
+based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse
+attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it
+has been shown that applying sparse, global, and random attention approximates full attention, while being
+computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context,
+BigBird has shown improved performance on various long document NLP tasks, such as question answering and
+summarization, compared to BERT or RoBERTa.
+
+The abstract from the paper is the following:
+
+*Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP.
+Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence
+length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that
+reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and
+is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our
+theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire
+sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to
+8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context,
+BigBird drastically improves performance on various NLP tasks such as question answering and summarization. We also
+propose novel applications to genomics data.*
+
+Tips:
+
+- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
+  **original_full** is advised as there is no benefit in using **block_sparse** attention.
+- The code currently uses window size of 3 blocks and 2 global blocks.
+- Sequence length must be divisible by block size.
+- Current implementation supports only **ITC**.
+- Current implementation doesn't support **num_random_blocks = 0**
+
+The original code can be found `here <https://github.com/google-research/bigbird>`__.
+
+BigBirdConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdConfig
+    :members:
+
+
+BigBirdTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+BigBird specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput
+    :members:
+
+
+BigBirdModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdModel
+    :members: forward
+
+
+BigBirdForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForPreTraining
+    :members: forward
+
+
+BigBirdForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForCausalLM
+    :members: forward
+
+
+BigBirdForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForMaskedLM
+    :members: forward
+
+
+BigBirdForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForSequenceClassification
+    :members: forward
+
+
+BigBirdForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForMultipleChoice
+    :members: forward
+
+
+BigBirdForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForTokenClassification
+    :members: forward
+
+
+BigBirdForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BigBirdForQuestionAnswering
+    :members: forward

src/transformers/__init__.py

@@ -150,6 +150,7 @@ _import_structure = {
     "models.bert_generation": ["BertGenerationConfig"],
     "models.bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"],
     "models.bertweet": ["BertweetTokenizer"],
+    "models.big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdTokenizer"],
     "models.blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig", "BlenderbotTokenizer"],
     "models.blenderbot_small": [
         "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
@@ -484,6 +485,22 @@ if is_torch_available():
             "load_tf_weights_in_bert_generation",
         ]
     )
+    _import_structure["models.big_bird"].extend(
+        [
+            "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "BigBirdForCausalLM",
+            "BigBirdForMaskedLM",
+            "BigBirdForMultipleChoice",
+            "BigBirdForPreTraining",
+            "BigBirdForQuestionAnswering",
+            "BigBirdForSequenceClassification",
+            "BigBirdForTokenClassification",
+            "BigBirdLayer",
+            "BigBirdModel",
+            "BigBirdPreTrainedModel",
+            "load_tf_weights_in_big_bird",
+        ]
+    )
     _import_structure["models.blenderbot"].extend(
         [
             "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -1376,6 +1393,7 @@ if TYPE_CHECKING:
     from .models.bert_generation import BertGenerationConfig
     from .models.bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
     from .models.bertweet import BertweetTokenizer
+    from .models.big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdTokenizer
     from .models.blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotTokenizer
     from .models.blenderbot_small import (
         BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -1678,6 +1696,20 @@ if TYPE_CHECKING:
             BertGenerationEncoder,
             load_tf_weights_in_bert_generation,
         )
+        from .models.big_bird import (
+            BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
+            BigBirdForCausalLM,
+            BigBirdForMaskedLM,
+            BigBirdForMultipleChoice,
+            BigBirdForPreTraining,
+            BigBirdForQuestionAnswering,
+            BigBirdForSequenceClassification,
+            BigBirdForTokenClassification,
+            BigBirdLayer,
+            BigBirdModel,
+            BigBirdPreTrainedModel,
+            load_tf_weights_in_big_bird,
+        )
         from .models.blenderbot import (
             BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
             BlenderbotForCausalLM,

src/transformers/models/__init__.py

@@ -25,6 +25,7 @@ from . import (
     bert_generation,
     bert_japanese,
     bertweet,
+    big_bird,
     blenderbot,
     blenderbot_small,
     camembert,

src/transformers/models/auto/configuration_auto.py

@@ -22,6 +22,7 @@ from ..albert.configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
 from ..bart.configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig
 from ..bert.configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
 from ..bert_generation.configuration_bert_generation import BertGenerationConfig
+from ..big_bird.configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig
 from ..blenderbot.configuration_blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig
 from ..blenderbot_small.configuration_blenderbot_small import (
     BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -80,6 +81,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
     (key, value)
     for pretrained_map in [
         # Add archive maps here
+        BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -127,6 +129,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
 CONFIG_MAPPING = OrderedDict(
     [
         # Add configs here
+        ("big_bird", BigBirdConfig),
         ("speech_to_text", Speech2TextConfig),
         ("wav2vec2", Wav2Vec2Config),
         ("m2m_100", M2M100Config),
@@ -180,6 +183,7 @@ CONFIG_MAPPING = OrderedDict(
 MODEL_NAMES_MAPPING = OrderedDict(
     [
         # Add full (and cased) model names here
+        ("big_bird", "BigBird"),
         ("speech_to_text", "Speech2Text"),
         ("wav2vec2", "Wav2Vec2"),
         ("m2m_100", "M2M100"),

src/transformers/models/auto/modeling_auto.py

@@ -51,6 +51,16 @@ from ..bert.modeling_bert import (
     BertModel,
 )
 from ..bert_generation.modeling_bert_generation import BertGenerationDecoder, BertGenerationEncoder
+from ..big_bird.modeling_big_bird import (
+    BigBirdForCausalLM,
+    BigBirdForMaskedLM,
+    BigBirdForMultipleChoice,
+    BigBirdForPreTraining,
+    BigBirdForQuestionAnswering,
+    BigBirdForSequenceClassification,
+    BigBirdForTokenClassification,
+    BigBirdModel,
+)
 from ..blenderbot.modeling_blenderbot import BlenderbotForCausalLM, BlenderbotForConditionalGeneration, BlenderbotModel
 from ..blenderbot_small.modeling_blenderbot_small import (
     BlenderbotSmallForCausalLM,
@@ -263,6 +273,7 @@ from .configuration_auto import (
     BartConfig,
     BertConfig,
     BertGenerationConfig,
+    BigBirdConfig,
     BlenderbotConfig,
     BlenderbotSmallConfig,
     CamembertConfig,
@@ -315,6 +326,7 @@ logger = logging.get_logger(__name__)
 MODEL_MAPPING = OrderedDict(
     [
         # Base model mapping
+        (BigBirdConfig, BigBirdModel),
         (Speech2TextConfig, Speech2TextModel),
         (Wav2Vec2Config, Wav2Vec2Model),
         (M2M100Config, M2M100Model),
@@ -380,6 +392,7 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
         (RobertaConfig, RobertaForMaskedLM),
         (SqueezeBertConfig, SqueezeBertForMaskedLM),
         (BertConfig, BertForPreTraining),
+        (BigBirdConfig, BigBirdForPreTraining),
         (OpenAIGPTConfig, OpenAIGPTLMHeadModel),
         (GPT2Config, GPT2LMHeadModel),
         (MobileBertConfig, MobileBertForPreTraining),
@@ -402,6 +415,7 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
 MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
     [
         # Model with LM heads mapping
+        (BigBirdConfig, BigBirdForMaskedLM),
         (Speech2TextConfig, Speech2TextForConditionalGeneration),
         (Wav2Vec2Config, Wav2Vec2ForMaskedLM),
         (M2M100Config, M2M100ForConditionalGeneration),
@@ -444,6 +458,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
 MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
     [
         # Model for Causal LM mapping
+        (BigBirdConfig, BigBirdForCausalLM),
         (CamembertConfig, CamembertForCausalLM),
         (XLMRobertaConfig, XLMRobertaForCausalLM),
         (RobertaConfig, RobertaForCausalLM),
@@ -473,6 +488,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
 MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
     [
         # Model for Masked LM mapping
+        (BigBirdConfig, BigBirdForMaskedLM),
         (Wav2Vec2Config, Wav2Vec2ForMaskedLM),
         (ConvBertConfig, ConvBertForMaskedLM),
         (LayoutLMConfig, LayoutLMForMaskedLM),
@@ -523,6 +539,7 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Sequence Classification mapping
+        (BigBirdConfig, BigBirdForSequenceClassification),
         (ConvBertConfig, ConvBertForSequenceClassification),
         (LEDConfig, LEDForSequenceClassification),
         (DistilBertConfig, DistilBertForSequenceClassification),
@@ -558,6 +575,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
     [
         # Model for Question Answering mapping
+        (BigBirdConfig, BigBirdForQuestionAnswering),
         (ConvBertConfig, ConvBertForQuestionAnswering),
         (LEDConfig, LEDForQuestionAnswering),
         (DistilBertConfig, DistilBertForQuestionAnswering),
@@ -595,6 +613,7 @@ MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = OrderedDict(
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Token Classification mapping
+        (BigBirdConfig, BigBirdForTokenClassification),
         (ConvBertConfig, ConvBertForTokenClassification),
         (LayoutLMConfig, LayoutLMForTokenClassification),
         (DistilBertConfig, DistilBertForTokenClassification),
@@ -622,6 +641,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
     [
         # Model for Multiple Choice mapping
+        (BigBirdConfig, BigBirdForMultipleChoice),
         (ConvBertConfig, ConvBertForMultipleChoice),
         (CamembertConfig, CamembertForMultipleChoice),
         (ElectraConfig, ElectraForMultipleChoice),

src/transformers/models/auto/tokenization_auto.py

@@ -60,6 +60,7 @@ from .configuration_auto import (
     BartConfig,
     BertConfig,
     BertGenerationConfig,
+    BigBirdConfig,
     BlenderbotConfig,
     BlenderbotSmallConfig,
     CamembertConfig,
@@ -111,6 +112,7 @@ if is_sentencepiece_available():
     from ..albert.tokenization_albert import AlbertTokenizer
     from ..barthez.tokenization_barthez import BarthezTokenizer
     from ..bert_generation.tokenization_bert_generation import BertGenerationTokenizer
+    from ..big_bird.tokenization_big_bird import BigBirdTokenizer
     from ..camembert.tokenization_camembert import CamembertTokenizer
     from ..deberta_v2.tokenization_deberta_v2 import DebertaV2Tokenizer
     from ..m2m_100 import M2M100Tokenizer
@@ -129,6 +131,7 @@ else:
     AlbertTokenizer = None
     BarthezTokenizer = None
     BertGenerationTokenizer = None
+    BigBirdTokenizer = None
     CamembertTokenizer = None
     DebertaV2Tokenizer = None
     MarianTokenizer = None
@@ -258,6 +261,7 @@ TOKENIZER_MAPPING = OrderedDict(
         (TapasConfig, (TapasTokenizer, None)),
         (LEDConfig, (LEDTokenizer, LEDTokenizerFast)),
         (ConvBertConfig, (ConvBertTokenizer, ConvBertTokenizerFast)),
+        (BigBirdConfig, (BigBirdTokenizer, None)),
         (IBertConfig, (RobertaTokenizer, RobertaTokenizerFast)),
         (Wav2Vec2Config, (Wav2Vec2CTCTokenizer, None)),
     ]

src/transformers/models/big_bird/__init__.py

+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2021 The HuggingFace Team. 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.
+from typing import TYPE_CHECKING
+
+from ...file_utils import _BaseLazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig"],
+    "tokenization_big_bird": ["BigBirdTokenizer"],
+}
+
+if is_torch_available():
+    _import_structure["modeling_big_bird"] = [
+        "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BigBirdForCausalLM",
+        "BigBirdForMaskedLM",
+        "BigBirdForMultipleChoice",
+        "BigBirdForPreTraining",
+        "BigBirdForQuestionAnswering",
+        "BigBirdForSequenceClassification",
+        "BigBirdForTokenClassification",
+        "BigBirdLayer",
+        "BigBirdModel",
+        "BigBirdPreTrainedModel",
+        "load_tf_weights_in_big_bird",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig
+    from .tokenization_big_bird import BigBirdTokenizer
+
+    if is_torch_available():
+        from .modeling_big_bird import (
+            BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
+            BigBirdForCausalLM,
+            BigBirdForMaskedLM,
+            BigBirdForMultipleChoice,
+            BigBirdForPreTraining,
+            BigBirdForQuestionAnswering,
+            BigBirdForSequenceClassification,
+            BigBirdForTokenClassification,
+            BigBirdLayer,
+            BigBirdModel,
+            BigBirdPreTrainedModel,
+            load_tf_weights_in_big_bird,
+        )
+
+
+else:
+    import importlib
+    import os
+    import sys
+
+    class _LazyModule(_BaseLazyModule):
+        """
+        Module class that surfaces all objects but only performs associated imports when the objects are requested.
+        """
+
+        __file__ = globals()["__file__"]
+        __path__ = [os.path.dirname(__file__)]
+
+        def _get_module(self, module_name: str):
+            return importlib.import_module("." + module_name, self.__name__)
+
+    sys.modules[__name__] = _LazyModule(__name__, _import_structure)

src/transformers/models/big_bird/configuration_big_bird.py

+# coding=utf-8
+# Copyright 2021 Google Research and The HuggingFace Inc. team. 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.
+""" BigBird model configuration """
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
+    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
+    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
+    # See all BigBird models at https://huggingface.co/models?filter=big_bird
+}
+
+
+class BigBirdConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.BigBirdModel`. It is used to
+    instantiate an BigBird model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the BigBird
+    `google/bigbird-roberta-base <https://huggingface.co/google/bigbird-roberta-base>`__ architecture.
+
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
+    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
+
+
+    Args:
+        vocab_size (:obj:`int`, `optional`, defaults to 50358):
+            Vocabulary size of the BigBird model. Defines the number of different tokens that can be represented by the
+            :obj:`inputs_ids` passed when calling :class:`~transformers.BigBirdModel`.
+        hidden_size (:obj:`int`, `optional`, defaults to 768):
+            Dimension of the encoder layers and the pooler layer.
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
+            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu_fast"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            :obj:`"gelu"`, :obj:`"gelu_fast"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 4096):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 1024 or 2048 or 4096).
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.BigBirdModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if ``config.is_decoder=True``.
+        attention_type (:obj:`str`, `optional`, defaults to :obj:`"block_sparse"`)
+            Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
+            layer (with n^2 complexity). Possible values are :obj:`"original_full"` and :obj:`"block_sparse"`.
+        use_bias (:obj:`bool`, `optional`, defaults to :obj:`True`)
+            Whether to use bias in query, key, value.
+        rescale_embeddings (:obj:`bool`, `optional`, defaults to :obj:`False`)
+            Whether to rescale embeddings with (hidden_size ** 0.5).
+        block_size (:obj:`int`, `optional`, defaults to 64)
+            Size of each block. Useful only when :obj:`attention_type == "block_sparse"`.
+        num_random_blocks (:obj:`int`, `optional`, defaults to 3)
+            Each query is going to attend these many number of random blocks. Useful only when :obj:`attention_type ==
+            "block_sparse"`.
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
+
+        Example::
+
+        >>> from transformers import BigBirdModel, BigBirdConfig
+
+        >>> # Initializing a BigBird google/bigbird-roberta-base style configuration
+        >>> configuration = BigBirdConfig()
+
+        >>> # Initializing a model from the google/bigbird-roberta-base style configuration
+        >>> model = BigBirdModel(configuration)
+
+        >>> # Accessing the model configuration
+        >>> configuration = model.config
+    """
+    model_type = "big_bird"
+
+    def __init__(
+        self,
+        vocab_size=50358,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu_fast",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=4096,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        use_cache=True,
+        is_encoder_decoder=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        sep_token_id=66,
+        attention_type="block_sparse",
+        use_bias=True,
+        rescale_embeddings=False,
+        block_size=64,
+        num_random_blocks=3,
+        gradient_checkpointing=False,
+        **kwargs
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            sep_token_id=sep_token_id,
+            **kwargs,
+        )
+
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.type_vocab_size = type_vocab_size
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.is_encoder_decoder = is_encoder_decoder
+        self.gradient_checkpointing = gradient_checkpointing
+
+        self.rescale_embeddings = rescale_embeddings
+        self.attention_type = attention_type
+        self.use_bias = use_bias
+        self.block_size = block_size
+        self.num_random_blocks = num_random_blocks

src/transformers/models/big_bird/convert_bigbird_original_tf_checkpoint_to_pytorch.py

+# coding=utf-8
+# Copyright 2021 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 BigBird checkpoint."""
+
+
+import argparse
+
+from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+
+
+def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, big_bird_config_file, pytorch_dump_path, is_trivia_qa):
+    # Initialise PyTorch model
+    config = BigBirdConfig.from_json_file(big_bird_config_file)
+    print("Building PyTorch model from configuration: {}".format(str(config)))
+
+    if is_trivia_qa:
+        model = BigBirdForQuestionAnswering(config)
+    else:
+        model = BigBirdForPreTraining(config)
+
+    # Load weights from tf checkpoint
+    load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=is_trivia_qa)
+
+    # Save pytorch-model
+    print(f"Save PyTorch model to {pytorch_dump_path}")
+    model.save_pretrained(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(
+        "--big_bird_config_file",
+        default=None,
+        type=str,
+        required=True,
+        help="The config json file corresponding to the pre-trained BERT 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."
+    )
+    parser.add_argument(
+        "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
+    )
+    args = parser.parse_args()
+    convert_tf_checkpoint_to_pytorch(
+        args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
+    )

src/transformers/models/big_bird/tokenization_big_bird.py

+# coding=utf-8
+# Copyright 2021 Google Research and The HuggingFace Inc. team. 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.
+"""Tokenization classes for BigBird."""
+
+
+import os
+from shutil import copyfile
+from typing import List, Optional, Tuple
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
+        "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model",
+        "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "google/bigbird-roberta-base": 4096,
+    "google/bigbird-roberta-large": 4096,
+    "google/bigbird-base-trivia-itc": 4096,
+}
+
+
+class BigBirdTokenizer(PreTrainedTokenizer):
+    """
+    Construct a BigBird tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.
+
+    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
+    Users should refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (:obj:`str`):
+            `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
+            The end of sequence token.
+        bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
+            The begin of sequence token.
+        unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+    prefix_tokens: List[int] = []
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="<pad>",
+        sep_token="[SEP]",
+        mask_token="[MASK]",
+        cls_token="[CLS]",
+        **kwargs
+    ):
+        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
+        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
+
+        # Mask token behave like a normal word, i.e. include the space before it
+        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            sep_token=sep_token,
+            mask_token=mask_token,
+            cls_token=cls_token,
+            **kwargs,
+        )
+
+        self.vocab_file = vocab_file
+
+        self.sp_model = spm.SentencePieceProcessor()
+        self.sp_model.Load(vocab_file)
+
+    @property
+    def vocab_size(self):
+        return self.sp_model.get_piece_size()
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        self.sp_model = spm.SentencePieceProcessor()
+        self.sp_model.Load(self.vocab_file)
+
+    def _tokenize(self, text, sample=False):
+        """Take as input a string and return a list of strings (tokens) for words/sub-words"""
+        if not sample:
+            pieces = self.sp_model.EncodeAsPieces(text)
+        else:
+            pieces = self.sp_model.SampleEncodeAsPieces(text, 64, 0.1)
+        return pieces
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def convert_tokens_to_string(self, tokens):
+        """ Converts a sequence of tokens (string) in a single string. """
+        out_string = self.sp_model.decode_pieces(tokens)
+        return out_string
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A Big Bird sequence has the following format:
+
+        - single sequence: ``[CLS] X [SEP]``
+        - pair of sequences: ``[CLS] A [SEP] B [SEP]``
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer ``prepare_for_model`` method.
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            if token_ids_1 is not None:
+                raise ValueError(
+                    "You should not supply a second sequence if the provided sequence of "
+                    "ids is already formatted with special tokens for the model."
+                )
+            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

src/transformers/utils/dummy_pt_objects.py

@@ -613,6 +613,91 @@ def load_tf_weights_in_bert_generation(*args, **kwargs):
     requires_pytorch(load_tf_weights_in_bert_generation)
 
 
+BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class BigBirdForCausalLM:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdForPreTraining:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdLayer:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdModel:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+class BigBirdPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_pytorch(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_pytorch(self)
+
+
+def load_tf_weights_in_big_bird(*args, **kwargs):
+    requires_pytorch(load_tf_weights_in_big_bird)
+
+
 BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/modeling_auto_mapping.py

@@ -6,6 +6,7 @@ from collections import OrderedDict
 
 MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
     [
+        ("BigBirdConfig", "BigBirdForQuestionAnswering"),
         ("ConvBertConfig", "ConvBertForQuestionAnswering"),
         ("LEDConfig", "LEDForQuestionAnswering"),
         ("DistilBertConfig", "DistilBertForQuestionAnswering"),

tests/test_tokenization_big_bird.py

+# coding=utf-8
+# Copyright 2020 The HuggingFace Team. 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.
+
+
+import os
+import unittest
+
+from transformers import BigBirdTokenizer
+from transformers.file_utils import cached_property
+from transformers.testing_utils import require_sentencepiece, require_torch, slow
+
+from .test_tokenization_common import TokenizerTesterMixin
+
+
+SPIECE_UNDERLINE = "▁"
+
+SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
+
+
+@require_sentencepiece
+class BigBirdTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = BigBirdTokenizer
+
+    def setUp(self):
+        super().setUp()
+
+        tokenizer = BigBirdTokenizer(SAMPLE_VOCAB, keep_accents=True)
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def test_full_tokenizer(self):
+        tokenizer = BigBirdTokenizer(SAMPLE_VOCAB, keep_accents=True)
+
+        tokens = tokenizer.tokenize("This is a test")
+        self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
+
+        self.assertListEqual(
+            tokenizer.convert_tokens_to_ids(tokens),
+            [285, 46, 10, 170, 382],
+        )
+
+        tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
+        self.assertListEqual(
+            tokens,
+            [
+                SPIECE_UNDERLINE + "I",
+                SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b",
+                "or",
+                "n",
+                SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "",
+                "9",
+                "2",
+                "0",
+                "0",
+                "0",
+                ",",
+                SPIECE_UNDERLINE + "and",
+                SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is",
+                SPIECE_UNDERLINE + "f",
+                "al",
+                "s",
+                "é",
+                ".",
+            ],
+        )
+        ids = tokenizer.convert_tokens_to_ids(tokens)
+        self.assertListEqual(
+            ids,
+            [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
+        )
+
+        back_tokens = tokenizer.convert_ids_to_tokens(ids)
+        self.assertListEqual(
+            back_tokens,
+            [
+                SPIECE_UNDERLINE + "I",
+                SPIECE_UNDERLINE + "was",
+                SPIECE_UNDERLINE + "b",
+                "or",
+                "n",
+                SPIECE_UNDERLINE + "in",
+                SPIECE_UNDERLINE + "",
+                "<unk>",
+                "2",
+                "0",
+                "0",
+                "0",
+                ",",
+                SPIECE_UNDERLINE + "and",
+                SPIECE_UNDERLINE + "this",
+                SPIECE_UNDERLINE + "is",
+                SPIECE_UNDERLINE + "f",
+                "al",
+                "s",
+                "<unk>",
+                ".",
+            ],
+        )
+
+    @cached_property
+    def big_tokenizer(self):
+        return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
+
+    @slow
+    def test_tokenization_base_easy_symbols(self):
+        symbols = "Hello World!"
+        original_tokenizer_encodings = [65, 18536, 2260, 101, 66]
+
+        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
+
+    @slow
+    def test_tokenization_base_hard_symbols(self):
+        symbols = 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
+        # fmt: off
+        original_tokenizer_encodings = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
+        # fmt: on
+        self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
+
+    @require_torch
+    @slow
+    def test_torch_encode_plus_sent_to_model(self):
+        import torch
+
+        from transformers import BigBirdConfig, BigBirdModel
+
+        # Build sequence
+        first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
+        sequence = " ".join(first_ten_tokens)
+        encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False)
+        batch_encoded_sequence = self.big_tokenizer.batch_encode_plus(
+            [sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False
+        )
+
+        config = BigBirdConfig(attention_type="original_full")
+        model = BigBirdModel(config)
+
+        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
+
+        with torch.no_grad():
+            model(**encoded_sequence)
+            model(**batch_encoded_sequence)
+
+    @slow
+    def test_special_tokens(self):
+        """
+        To reproduce:
+
+        $ wget https://github.com/google-research/bigbird/blob/master/bigbird/vocab/gpt2.model?raw=true
+        $ mv gpt2.model?raw=true gpt2.model
+
+        ```
+        import tensorflow_text as tft
+        import tensorflow as tf
+
+        vocab_model_file = "./gpt2.model"
+        tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(vocab_model_file, "rb").read()))
+        ids = tokenizer.tokenize("Paris is the [MASK].")
+        ids = tf.concat([tf.constant([65]), ids, tf.constant([66])], axis=0)
+        detokenized = tokenizer.detokenize(ids)  # should give [CLS] Paris is the [MASK].[SEP]
+        """
+        tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
+        decoded_text = tokenizer.decode(tokenizer("Paris is the [MASK].").input_ids)
+
+        self.assertTrue(decoded_text == "[CLS] Paris is the [MASK].[SEP]")