transformers/docs/source/en/model_doc/bigbird_pegasus.md

BigBirdPegasus

Overview

The BigBird model was proposed in Big Bird: Transformers for Longer Sequences 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.

The original code can be found here.

Usage tips

• For an in-detail explanation on how BigBird's attention works, see this blog post.
• 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.
• BigBirdPegasus uses the PegasusTokenizer.
• BigBird is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than the left.

Resources

• Text classification task guide
• Question answering task guide
• Causal language modeling task guide
• Translation task guide
• Summarization task guide

BigBirdPegasusConfig

autodoc BigBirdPegasusConfig - all

BigBirdPegasusModel

autodoc BigBirdPegasusModel - forward

BigBirdPegasusForConditionalGeneration

autodoc BigBirdPegasusForConditionalGeneration - forward

BigBirdPegasusForSequenceClassification

autodoc BigBirdPegasusForSequenceClassification - forward

BigBirdPegasusForQuestionAnswering

autodoc BigBirdPegasusForQuestionAnswering - forward

BigBirdPegasusForCausalLM

autodoc BigBirdPegasusForCausalLM - forward