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29 changes: 29 additions & 0 deletions CONTRIBUTING.md
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# How to Contribute

We'd love to accept your patches and contributions to this project. There are
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## Community Guidelines

This project follows
[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
202 changes: 202 additions & 0 deletions LICENSE
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164 changes: 154 additions & 10 deletions README.md
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# Big Bird: Transformers for Longer Sequences

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. 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.

Code release in progress.
Not an official Google product.

# What is BigBird?
BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle.

As a consequence of the capability to handle longer context,
BigBird drastically improves performance on various NLP tasks such as question answering and summarization.

More details and comparisons can be found in our [presentation](https://docs.google.com/presentation/d/1FdMNqG2b8XYc89_v7-_2sba7Iz6YAlXXWuMxUbrKFK0/preview).


# Citation
If you find this useful, please cite our [NeurIPS 2020 paper](https://papers.nips.cc/paper/2020/hash/c8512d142a2d849725f31a9a7a361ab9-Abstract.html):
```
@article{zaheer2020bigbird,
title={Big bird: Transformers for longer sequences},
author={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},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
```


# Code

The most important directory is `core`.
There are three main files in `core`.

* [attention.py](bigbird/core/attention.py):
Contains BigBird linear attention mechanism
* [encoder.py](bigbird/core/encoder.py):
Contains the main long sequence encoder stack
* [modeling.py](bigbird/core/modeling):
Contains packaged BERT and seq2seq transformer models with BigBird attention


### Colab/IPython Notebook

A quick fine-tuning demonstration for text classification is provided in
[imdb.ipynb](bigbird/classifier/imdb.ipynb)


### Create GCP Instance
Please create a project first and create an instance in a zone which has quota as follows

```bash
gcloud compute instances create \
bigbird \
--zone=europe-west4-a \
--machine-type=n1-standard-16 \
--boot-disk-size=50GB \
--image-project=ml-images \
--image-family=tf-2-3-1 \
--maintenance-policy TERMINATE \
--restart-on-failure \
--scopes=cloud-platform

gcloud compute tpus create \
bigbird \
--zone=europe-west4-a \
--accelerator-type=v3-32 \
--version=2.3.1

gcloud compute ssh --zone "europe-west4-a" "bigbird"

```

For illustration we used instance name `bigbird` and zone `europe-west4-a`, but feel free to change them.
More details about creating Google Cloud TPU can be found in [online documentations](https://cloud.google.com/tpu/docs/creating-deleting-tpus#setup_TPU_only).


### Install and download dependencies
```bash
git clone https://github.com/google-research/bigbird.git
cd bigbird
pip3 install -e .
```
You can find pretrained and fine-tuned checkpoints in our [Google Cloud Storage Bucket](https://console.cloud.google.com/storage/browser/bigbird-transformer).

Optionally, you can download them using `gsutil` as
```bash
mkdir -p bigbird/ckpt
gsutil cp -r gs://bigbird-transformer/ bigbird/ckpt/
```

The storage bucket contains:
* pretrained BERT model for base and large size
* pretrained Pegasus Encoder-Decoder Transformer in large size
* fine-tuned `tf.SavedModel` for long document summarization


### Running Classification

For quickly starting with BigBird, one can start by running the classification experiment code in `classifier` directory.
To run the code simply execute

```shell
export GCP_PROJECT_NAME=bigbird-project # Replace by your project name
export GCP_EXP_BUCKET=gs://bigbird-transformer-training/ # Replace
sh -x bigbird/classifier/base_size.sh
```


## Using BigBird Encoder instead BERT/RoBERTa

To directly use the encoder instead of say BERT model, we can use the following
code.

```python
from bigbird.core import modeling

bigb_encoder = modeling.BertModel(...)
```

It can easily replace [BERT's](https://arxiv.org/abs/1810.04805) encoder.


Alternatively, one can also try playing with layers of BigBird encoder

```python
from bigbird.core import encoder

only_layers = encoder.EncoderStack(...)
```


## Understanding Flags & Config

All the flags and config are explained in
`core/flags.py`. Here we explain
some of the important config paramaters.

`attention_type` is used to select the type of attention we would use. Setting
it to `block_sparse` runs the BigBird attention module.

```python
flags.DEFINE_enum(
"attention_type", "block_sparse",
["original_full", "simulated_sparse", "block_sparse"],
"Selecting attention implementation. "
"'original_full': full attention from original bert. "
"'simulated_sparse': simulated sparse attention. "
"'block_sparse': blocked implementation of sparse attention.")
```

`block_size` is used to define the size of blocks, whereas `num_rand_blocks` is
used to set the number of random blocks. The code currently uses window size of
3 blocks and 2 global blocks. The current code only supports static tensors.

Important points to note:
* Hidden dimension should be divisible by the number of heads.
* For sequene length less than 1024, using `original_full` is advised as there
is no benefit in using sparse BigBird attention.
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