Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time".

Overview

FastBERT

Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time".

Good News

2021/10/29 - Code: Code of FastPLM is released on both Pypi and Github.

2021/09/08 - Paper: Journal version of FastBERT (FastPLM) is accepted by IEEE TNNLS. "An Empirical Study on Adaptive Inference for Pretrained Language Model".

2020/07/05 - Update: Pypi version of FastBERT has been launched. Please see fastbert-pypi.

Install fastbert with pip

$ pip install fastbert

Requirements

python >= 3.4.0, Install all the requirements with pip.

$ pip install -r requirements.txt

Quick start on the Chinese Book review dataset

Download the pre-trained Chinese BERT parameters from here, and save it to the models directory with the name of "Chinese_base_model.bin".

Run the following command to validate our FastBERT with Speed=0.5 on the Book review datasets.

$ CUDA_VISIBLE_DEVICES="0" python3 -u run_fastbert.py \
        --pretrained_model_path ./models/Chinese_base_model.bin \
        --vocab_path ./models/google_zh_vocab.txt \
        --train_path ./datasets/douban_book_review/train.tsv \
        --dev_path ./datasets/douban_book_review/dev.tsv \
        --test_path ./datasets/douban_book_review/test.tsv \
        --epochs_num 3 --batch_size 32 --distill_epochs_num 5 \
        --encoder bert --fast_mode --speed 0.5 \
        --output_model_path  ./models/douban_fastbert.bin

Meaning of each option.

usage: --pretrained_model_path Path to initialize model parameters.
       --vocab_path Path to the vocabulary.
       --train_path Path to the training dataset.
       --dev_path Path to the validating dataset.
       --test_path Path to the testing dataset.
       --epochs_num The epoch numbers of fine-tuning.
       --batch_size Batch size.
       --distill_epochs_num The epoch numbers of the self-distillation.
       --encoder The type of encoder.
       --fast_mode Whether to enable the fast mode of FastBERT.
       --speed The Speed value in the paper.
       --output_model_path Path to the output model parameters.

Test results on the Book review dataset.

Test results at fine-tuning epoch 3 (Baseline): Acc.=0.8688;  FLOPs=21785247744;
Test results at self-distillation epoch 1     : Acc.=0.8698;  FLOPs=6300902177;
Test results at self-distillation epoch 2     : Acc.=0.8691;  FLOPs=5844839008;
Test results at self-distillation epoch 3     : Acc.=0.8664;  FLOPs=5170940850;
Test results at self-distillation epoch 4     : Acc.=0.8664;  FLOPs=5170940327;
Test results at self-distillation epoch 5     : Acc.=0.8664;  FLOPs=5170940327;

Quick start on the English Ag.news dataset

Download the pre-trained English BERT parameters from here, and save it to the models directory with the name of "English_uncased_base_model.bin".

Download the ag_news.zip from here, and then unzip it to the datasets directory.

Run the following command to validate our FastBERT with Speed=0.5 on the Ag.news datasets.

$ CUDA_VISIBLE_DEVICES="0" python3 -u run_fastbert.py \
        --pretrained_model_path ./models/English_uncased_base_model.bin \
        --vocab_path ./models/google_uncased_en_vocab.txt \
        --train_path ./datasets/ag_news/train.tsv \
        --dev_path ./datasets/ag_news/test.tsv \
        --test_path ./datasets/ag_news/test.tsv \
        --epochs_num 3 --batch_size 32 --distill_epochs_num 5 \
        --encoder bert --fast_mode --speed 0.5 \
        --output_model_path  ./models/ag_news_fastbert.bin

Test results on the Ag.news dataset.

Test results at fine-tuning epoch 3 (Baseline): Acc.=0.9447;  FLOPs=21785247744;
Test results at self-distillation epoch 1     : Acc.=0.9308;  FLOPs=2172009009;
Test results at self-distillation epoch 2     : Acc.=0.9311;  FLOPs=2163471246;
Test results at self-distillation epoch 3     : Acc.=0.9314;  FLOPs=2108341649;
Test results at self-distillation epoch 4     : Acc.=0.9314;  FLOPs=2108341649;
Test results at self-distillation epoch 5     : Acc.=0.9314;  FLOPs=2108341649;

Datasets

More datasets can be downloaded from here.

Other implementations

There are some other excellent implementations of FastBERT.

Acknowledgement

This work is funded by 2019 Tencent Rhino-Bird Elite Training Program. Work done while this author was an intern at Tencent.

If you use this code, please cite this paper:

@inproceedings{weijie2020fastbert,
  title={{FastBERT}: a Self-distilling BERT with Adaptive Inference Time},
  author={Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Haotang Deng, Qi Ju},
  booktitle={Proceedings of ACL 2020},
  year={2020}
}
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

In-Place Activated BatchNorm In-Place Activated BatchNorm for Memory-Optimized Training of DNNs In-Place Activated BatchNorm (InPlace-ABN) is a novel

1.3k Dec 29, 2022
Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"

KSTER Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples" [paper]. Usage Download the processed datas

jiangqn 23 Nov 24, 2022
ruptures: change point detection in Python

Welcome to ruptures ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation

Charles T. 1.1k Jan 03, 2023
Pixel-level Crack Detection From Images Of Levee Systems : A Comparative Study

PIXEL-LEVEL CRACK DETECTION FROM IMAGES OF LEVEE SYSTEMS : A COMPARATIVE STUDY G

Manisha Panta 2 Jul 23, 2022
PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset.

FACT This repo provides a demo for the CVPR 2021 paper "A Fourier-based Framework for Domain Generalization" on the PACS dataset. To cite, please use:

105 Dec 17, 2022
Pytorch code for "State-only Imitation with Transition Dynamics Mismatch" (ICLR 2020)

This repo contains code for our paper State-only Imitation with Transition Dynamics Mismatch published at ICLR 2020. The code heavily uses the RL mach

20 Sep 08, 2022
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Li Yang 1.1k Dec 19, 2022
Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021) Overview of paths used in DIG and IG. w is the word being attributed. The

INK Lab @ USC 17 Oct 27, 2022
This program can detect your face and add an Christams hat on the top of your head

Auto_Christmas This program can detect your face and add a Christmas hat to the top of your head. just run the Auto_Christmas.py, then you can see the

3 Dec 22, 2021
The Codebase for Causal Distillation for Language Models.

Causal Distillation for Language Models Zhengxuan Wu*,Atticus Geiger*, Josh Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, Noah D.

Zen 20 Dec 31, 2022
A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

Tensorpack is a neural network training interface based on TensorFlow. Features: It's Yet Another TF high-level API, with speed, and flexibility built

Tensorpack 6.2k Jan 01, 2023
This implementation contains the application of GPlearn's symbolic transformer on a commodity futures sector of the financial market.

GPlearn_finiance_stock_futures_extension This implementation contains the application of GPlearn's symbolic transformer on a commodity futures sector

Chengwei <a href=[email protected]"> 189 Dec 25, 2022
Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"

This is the codebase for the paper: Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs Directory Structur

Peter Hase 19 Aug 21, 2022
[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. This repo contains the PyTorch code and implementation for the paper E

Akuchi 18 Dec 22, 2022
Advancing mathematics by guiding human intuition with AI

Advancing mathematics by guiding human intuition with AI This repo contains two colab notebooks which accompany the paper, available online at https:/

DeepMind 315 Dec 26, 2022
Spatial-Temporal Transformer for Dynamic Scene Graph Generation, ICCV2021

Spatial-Temporal Transformer for Dynamic Scene Graph Generation Pytorch Implementation of our paper Spatial-Temporal Transformer for Dynamic Scene Gra

Yuren Cong 119 Jan 01, 2023
Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" (RSS 2022)

Intro Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" Robotics:Science and

Yunho Kim 21 Dec 07, 2022
CVPR 2021 - Official code repository for the paper: On Self-Contact and Human Pose.

SMPLify-XMC This repo is part of our project: On Self-Contact and Human Pose. [Project Page] [Paper] [MPI Project Page] License Software Copyright Lic

Lea Müller 83 Dec 14, 2022
Dynamica causal Bayesian optimisation

Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th

nd308 18 Nov 22, 2022