An implementation of Fastformer: Additive Attention Can Be All You Need in TensorFlow

Related tags

Deep Learning machine-learning deep-learning tensorflow keras transformers artificial-intelligence attention-mechanism
Overview

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This repo implements Fastformer: Additive Attention Can Be All You Need by Wu et al. in TensorFlow. Fast Transformer is a Transformer variant based on additive attention that can handle long sequences efficiently with linear complexity. Fastformer is much more efficient than many existing Transformer models and can meanwhile achieve comparable or even better long text modeling performance.

Installation

Run the following to install:

pip install fast-transformer

Developing fast-transformer

To install fast-transformer, along with tools you need to develop and test, run the following in your virtualenv:

git clone https://github.com/Rishit-dagli/Fast-Transformer.git
# or clone your own fork

cd fast-transformer
pip install -e .[dev]

Usage

import tensorflow as tf
from fast_transformer import FastTransformer

mask = tf.ones([1, 4096], dtype=tf.bool)
model = FastTransformer(
    num_tokens = 20000,
    dim = 512,
    depth = 2,
    max_seq_len = 4096,
    absolute_pos_emb = True, # Absolute positional embeddings
    mask = mask
)
x = tf.experimental.numpy.random.randint(0, 20000, (1, 4096))

logits = model(x) # (1, 4096, 20000)

Want to Contribute šŸ™‹ā€ā™‚ļø ?

Awesome! If you want to contribute to this project, you're always welcome! See Contributing Guidelines. You can also take a look at open issues for getting more information about current or upcoming tasks.

Want to discuss? šŸ’¬

Have any questions, doubts or want to present your opinions, views? You're always welcome. You can start discussions.

Citation

@misc{wu2021fastformer,
    title   = {Fastformer: Additive Attention is All You Need}, 
    author  = {Chuhan Wu and Fangzhao Wu and Tao Qi and Yongfeng Huang},
    year    = {2021},
    eprint  = {2108.09084},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

Yannic Kilcher's video was super helpful while building this.

License

Copyright 2020 Rishit Dagli

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.
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Comments
  • Implement Additive Attention

    Implement Additive Attention

    Implement Additive Attention as a TensorFlow layer:

    • [x] Figure out using rotary embeddings
    • [x] Add masking functionality
    • [x] Relative Position embeddings
    • [x] Calculate query attention logits
    • [x] Calculate Global Query tokens
    • [x] Calculate key attention logits
    • [x] Calculate Global Key tokens
    • [x] Add queries as residuals
    opened by Rishit-dagli 0
Releases(v0.2.0)

  • v0.2.0(Jan 16, 2022)

    āœ… Bug Fixes / Improvements

    • Unit Tests for output rank and shape
    • Looser dependency requirements (now supports all TensorFlow versions >= 2.5.0)
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Sep 3, 2021)

    This is the initial release of Fast Transformer and implements Fast Transformer as a subclassed TensorFlow model.

    Classes

    • FastAttention: Implements additive attention as a TensorFlow Keras layer, and supports using relative positional encodings.
    • PreNorm: Normalize the activations of the previous layer for each given example in a batch independently and apply some function to it, implemented as a TensorFlow Keras Layer.
    • FeedForward: Create a FeedForward neural net with two Dense layers and GELU activation, implemented as a TensorFlow Keras Layer.
    • FastTransformer: Implements the FastTransformer model using all the other classes, allows using rotary embeddings, weight tie projections, and converts to logits. Implemented as a TensorFlow Keras Model.
    Source code(tar.gz)
    Source code(zip)
Owner
Rishit Dagli
High School,TEDx,2xTED-Ed speaker | International Speaker | Microsoft Student Ambassador | Mentor, @TFUGMumbai | Organize @KotlinMumbai
Rishit Dagli
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