Official Pytorch Implementation of Length-Adaptive Transformer (ACL 2021)

Overview

Length-Adaptive Transformer

This is the official Pytorch implementation of Length-Adaptive Transformer. For detailed information about the method, please refer to our paper.

Our code is based on HuggingFace's ( 🤗 ) Transformers library. Currently, it only supports limited transformers (BERT and DistilBERT) and downstream tasks (SQuAD 1.1 and GLUE benchmark). We will extend it one-by-one to support other transformers and tasks. You can easily apply our method to any other use cases beforehand.

Getting Started

Requirements

  • Python 3
  • PyTorch
  • 🤗 Transformers
  • torchprofile (to measure FLOPs)

Dataset Preparation

(Standard) Finetuning pretrained transformer

For SQuAD 1.1, use run_squad.py slightly modified from 🤗 Transformers' question-answering example.

python run_squad.py \
  --model_type bert \
  --model_name_or_path bert-base-uncased \
  --do_train \
  --do_eval \
  --evaluate_during_training \
  --save_only_best \
  --do_lower_case \
  --data_dir $SQUAD_DIR \
  --train_file train-v1.1.json \
  --predict_file dev-v1.1.json \
  --per_gpu_train_batch_size 32 \
  --per_gpu_eval_batch_size 32 \
  --learning_rate 5e-5 \
  --num_train_epochs 3.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir $SQUAD_OUTPUT_DIR/standard

For GLUE, use run_glue.py slightly modified from 🤗 Transformers' text-classification example.

python run_glue.py \
  --model_name_or_path bert-base-cased \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --data_dir $GLUE_DIR/$TASK_NAME \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --per_device_eval_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3.0 \
  --output_dir $GLUE_OUTPUT_DIR/$TASK_NAME/standard

Training with LengthDrop

Starting from a checkpoint finetuned without Drop-and-Restore, continue finetuning for additional steps with Drop-and-Restore and LengthDrop.

python run_squad.py \
  --model_type bert \
  --model_name_or_path $SQUAD_OUTPUT_DIR/standard/checkpoint-best \
  --do_train \
  --do_eval \
  --evaluate_during_training \
  --save_only_best \
  --do_lower_case \
  --data_dir $SQUAD_DIR \
  --train_file train-v1.1.json \
  --predict_file dev-v1.1.json \
  --per_gpu_train_batch_size 32 \
  --per_gpu_eval_batch_size 32 \
  --learning_rate 5e-5 \
  --num_train_epochs 5.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir $SQUAD_OUTPUT_DIR/length_adaptive \
  --length_adaptive \
  --num_sandwich 2 \
  --length_drop_ratio_bound 0.2 \
  --layer_dropout_prob 0.2 \
python run_glue.py \
  --model_name_or_path $GLUE_OUTPUT_DIR/$TASK_NAME/standard/checkpoint-best \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --data_dir $GLUE_DIR/$TASK_NAME \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --per_device_eval_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 5.0 \
  --output_dir $GLUE_OUTPUT_DIR/$TASK_NAME/length_adaptive
  --length_adaptive \
  --num_sandwich 2 \
  --length_drop_ratio_bound 0.2 \
  --layer_dropout_prob 0.2 \

Evolutionary Search of Length Configurations

After training with LengthDrop, perform an evolutionary search to find length configurations for anytime prediction.

python run_squad.py \
  --model_type bert \
  --model_name_or_path $SQUAD_OUTPUT_DIR/length_adaptive/checkpoint-best \
  --do_search \
  --do_lower_case \
  --data_dir $SQUAD_DIR \
  --train_file train-v1.1.json \
  --predict_file dev-v1.1.json \
  --per_gpu_eval_batch_size 32 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir $SQUAD_OUTPUT_DIR/evolutionary_search \
  --evo_iter 30 \
  --mutation_size 30 \
  --crossover_size 30 \
python run_glue.py \
  --model_name_or_path $GLUE_OUTPUT_DIR/$TASK_NAME/length_adaptive/checkpoint-best \
  --task_name $TASK_NAME \
  --do_search \
  --data_dir $GLUE_DIR/$TASK_NAME \
  --max_seq_length 128 \
  --per_device_eval_batch_size 32 \
  --output_dir $GLUE_OUTPUT_DIR/$TASK_NAME/evolutionary_search
  --evo_iter 30 \
  --mutation_size 30 \
  --crossover_size 30 \

License

Copyright 2020-present NAVER Corp.

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.
Owner
Clova AI Research
Open source repository of Clova AI Research, NAVER & LINE
Clova AI Research
A collection of models for image<->text generation in ACM MM 2021.

Bi-directional Image and Text Generation UMT-BITG (image & text generator) Unifying Multimodal Transformer for Bi-directional Image and Text Generatio

Multimedia Research 63 Oct 30, 2022
This is a simple framework to make object detection dataset very quickly

FastAnnotation Table of contents General info Requirements Setup General info This is a simple framework to make object detection dataset very quickly

Serena Tetart 1 Jan 24, 2022
Prevent `CUDA error: out of memory` in just 1 line of code.

🐨 Koila Koila solves CUDA error: out of memory error painlessly. Fix it with just one line of code, and forget it. 🚀 Features 🙅 Prevents CUDA error

RenChu Wang 1.7k Jan 02, 2023
Hierarchical Metadata-Aware Document Categorization under Weak Supervision (WSDM'21)

Hierarchical Metadata-Aware Document Categorization under Weak Supervision This project provides a weakly supervised framework for hierarchical metada

Yu Zhang 53 Sep 17, 2022
For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training.

LongScientificFormer For encoding a text longer than 512 tokens, for example 800. Set max_pos to 800 during both preprocessing and training. Some code

Athar Sefid 6 Nov 02, 2022
A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.

3d-pose-baseline This is the code for the paper Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little. A simple yet effective baseline for 3

Julieta Martinez 1.3k Jan 03, 2023
Keras Image Embeddings using Contrastive Loss

Image to Embedding projection in vector space. Implementation in keras and tensorflow of batch all triplet loss for one-shot/few-shot learning.

Shravan Anand K 5 Mar 21, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
SymPy-powered, Wolfram|Alpha-like answer engine totally in your browser, without backend computation

SymPy Beta SymPy Beta is a fork of SymPy Gamma. The purpose of this project is to run a SymPy-powered, Wolfram|Alpha-like answer engine totally in you

Liumeo 25 Dec 21, 2022
This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs)

Description This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs) in [Gardy et

Ludovic Gardy 0 Feb 09, 2022
deep learning model that learns to code with drawing in the Processing language

sketchnet sketchnet - processing code generator can we teach a computer to draw pictures with code. We use Processing and java/jruby code paired with

41 Dec 12, 2022
PyTorch implementation of PP-LCNet: A Lightweight CPU Convolutional Neural Network

PyTorch implementation of PP-LCNet Reproduction of PP-LCNet architecture as described in PP-LCNet: A Lightweight CPU Convolutional Neural Network by C

Quan Nguyen (Fly) 47 Nov 02, 2022
salabim - discrete event simulation in Python

Object oriented discrete event simulation and animation in Python. Includes process control features, resources, queues, monitors. statistical distrib

181 Dec 21, 2022
Cross View SLAM

Cross View SLAM This is the associated code and dataset repository for our paper I. D. Miller et al., "Any Way You Look at It: Semantic Crossview Loca

Ian D. Miller 99 Dec 09, 2022
MusicYOLO framework uses the object detection model, YOLOx, to locate notes in the spectrogram.

MusicYOLO MusicYOLO framework uses the object detection model, YOLOX, to locate notes in the spectrogram. Its performance on the ISMIR2014 dataset, MI

Xianke Wang 2 Aug 02, 2022
A generalist algorithm for cell and nucleus segmentation.

Cellpose | A generalist algorithm for cell and nucleus segmentation. Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Cel

MouseLand 733 Dec 29, 2022
Bio-OFC gym implementation and Gym-Fly environment

Bio-OFC gym implementation and Gym-Fly environment This repository includes the gym compatible implementation of the Bio-OFC algorithm from the paper

Siavash Golkar 1 Nov 16, 2021
Official PyTorch implementation of Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, Yu

UT-Austin Robot Perception and Learning Lab 63 Jan 03, 2023
The reference baseline of final exam for XMU machine learning course

Mini-NICO Baseline The baseline is a reference method for the final exam of machine learning course. Requirements Installation we use /python3.7 /torc

JoaquinChou 3 Dec 29, 2021
This project aims to segment 4 common retinal lesions from Fundus Images.

This project aims to segment 4 common retinal lesions from Fundus Images.

Husam Nujaim 1 Oct 10, 2021