JunXue-tech / efficient-attention

[EVA ICLR'23; LARA ICML'22] Efficient attention mechanisms via control variates, random features, and importance sampling

Efficient Attentions

This repository contains the official implementation of experiments conducted in

• EVA: Efficient attention via control variates (ICLR 2023)
• LARA: Linear complexity randomized self-attention mechanism (ICML 2022)

🌲 Repo structure:

• efficient-attention: a small self-contained codebase that implements various efficient attention mechanisms. Please see the usage for more details.
• vit: codebase for image classification experiments, which is adapted from
  • DeiT for the file structure, and
  • PvT for PvTv2 model classes.
• fairseq: a modified fork of fairseq for language tasks, including machine translation and autoregressive language modeling.
• main.sh: a bash script for launching all experiments.
  • See the script for the list of arguments.
  • Note that arguments after -e True are directly passed to the training command. You can pass custom arguments to the training command by appending them after -e True.

Dependencies

To setup the environment, run the following commands to install the required dependencies (recommended in a virtual environment):

# install packages
pip install -r requirements.txt
# install efficient-attention library
pip install -e efficient-attention

# OPTIONAL: install fairseq library for running language tasks
cd fairseq
python3 setup.py build develop
cd ..

Basic Usage of the Efficient Attention Library

efficient-attention is a small self-contained codebase that collects several efficient attention mechanisms.

Passing Attention-specific Arguments to Argparse

• For arguments specific to each attention mechanism, please check the add_attn_specific_args() class method in the corresponding python file.
• To pass these arguments to the argparse parser, follow the following code snippet:

import argparse
from efficient_attention import AttentionFactory
# ...
parser = argparse.ArgumentParser()
parser.add_argument('--attn-name', default='softmax', type=str, metavar='ATTN',
                        help='Name of attention model to use')
# ...
temp_args, _ = parser.parse_known_args()
# add attention-specific arguments to the parser
# struct_name: name of the inner namespace to store all attention-specific arguments
# prefix: prefix to prepend to all argument names
#         for example, if prefix = encoder-attn, then for the argument --window-size 
#         we need to pass --encoder-attn-window-size
#         this is useful to avoid argument name conflicts.
efficient_attention.AttentionFactory.add_attn_specific_args(parser, temp_args.attn_name, struct_name="attn_args", prefix="")
# parse arguments to a namespace that supports nested attributes
args = parser.parse_args(namespace=efficient_attention.NestedNamespace())
# now we can access the attention-specific arguments via args.attn_args
print(args.attn_args.window_size)

Create an Efficient Attention Module

In a torch.nn.Module class, you can create an efficient attention module as follows:

# we might want to pass attention-specific arguments to the attention module
# along with other related arguments
attn_args = {
    **vars(args.attn_args),
    **{
    'dim': args.embed_dim, 
    'num_heads': args.num_heads, 
    'qkv_bias': args.qkv_bias, 
    'attn_drop': args.attn_drop_rate, 
    'proj_drop': args.drop_rate,
    }
}
self.attn = AttentionFactory.build_attention(attn_name = attn_name, attn_args = attn_args)

# the module can then be used as a normal function as
x = self.attn(x)

Image Classification on ImageNet1k

Data Preparation

We follow the setup similar to DeiT to pre-process the ImageNet dataset. Download ImageNet train and val images and place them in the following directory structure so that it can be compatible with the torchvision datasets.ImageFolder

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class2/
      img4.jpeg

Training & Evaluation

The following commands are used for training and evaluating various vision transformers with LARA/EVA. The training is assumed to be conducted with 8 GPUs.

ImageNet Classification on DeiT (sequence length 784(suffix:*_p8)/196(suffix:*_p16))

To use LARA/EVA in different DeiT architectures:

# LARA: DeiT-tiny-p8
bash main.sh -m evit_tiny_p8 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name lara --mis-type mis-opt --proposal-gen pool-mixed --alpha-coeff 2.0 --num-landmarks 49

# LARA: DeiT-tiny-p16
bash main.sh -m evit_tiny_p16 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name lara --mis-type mis-opt --proposal-gen pool-mixed --alpha-coeff 2.0 --num-landmarks 49

# LARA: DeiT-small-p16
bash main.sh -m evit_small_p16 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name lara --mis-type mis-opt --proposal-gen pool-mixed --alpha-coeff 2.0 --num-landmarks 49

# EVA: DeiT-tiny-p8
bash main.sh -m evit_tiny_p8 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name eva --num-landmarks 49 --adaptive-proj default --window-size 7 --attn-2d --use-rpe

# EVA: DeiT-tiny-p16
bash main.sh -m evit_tiny_p16 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name eva --num-landmarks 49 --adaptive-proj default --window-size 7 --attn-2d --use-rpe

# EVA: DeiT-small-p16
bash main.sh -m evit_small_p16 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name eva --num-landmarks 49 --adaptive-proj default --window-size 7 --attn-2d --use-rpe

ImageNet Classification on PVTv2-B3 (sequence length: 3136 -> 784 -> 196 -> 49)

To adapt LARA/EVA in PvTv2 architectures:

# LARA Attention
bash main.sh -m pvt_medium2 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 1.0 --drop-path-rate 0.3 --warmup-epochs 10 --seed 1 --attn-name lara --pool-module-type dense --mis-type mis-opt --proposal-gen pool-mixed --num-landmarks 49 --alpha-coeff 2.0 --repeated-aug

# EVA Attention
bash main.sh -m pvt_medium2 -p <dir-of-imagenet-data> -g 8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --drop-path-rate 0.3 --warmup-epochs 10 --seed 1 --attn-name eva --num-landmarks 49 --adaptive-proj default --window-size 7 --attn-2d --use-rpe --repeated-aug

The usage of other attention mechanisms:

Alternatively, you may want to try out other attention mechanisms:

# Softmax Attention
bash main.sh -m evit_tiny_p8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name softmax
# RFA/Performer
bash main.sh -m evit_tiny_p8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name performer --proj-method favorp --approx-attn-dim 64
# Local Attention
bash main.sh -m evit_tiny_p8 -d imagenet -e TRUE --dist-eval --num-workers 16 --clip-grad 5.0 --warmup-epochs 10 --seed 1 --attn-name local --window-size 7 --attn-2d --use-rpe

Language Tasks

Data Preparation

We use the standard pre-processing fairseq to prepare the data for language tasks.

• For machine translation, please follow here to prepare for the binarized WMT'14 EN-DE data;
• For autoregressive language modeling, follow here to process the Wikitext-103 dataset.

Training

• -r <resume-ckpt-DIR> specifies the DIRECTORY that stores your checkpoints during training and can be used to resume training.
• Note that all attention-specific arguments need to be associated with prefix --encoder-attn- (for encoder-side) / --decoder-attn- (for decoder-side). See the examples below.

Machine Translation

## LARA
CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -d wmt -s lara_8 -g 4 -e TRUE --attn-name-encoder lara --encoder-attn-num-landmarks 8 --encoder-attn-proposal-gen adaptive-1d --encoder-attn-mis-type mis-opt

CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -d wmt -s lara_16 -g 4 -e TRUE --attn-name-encoder lara --encoder-attn-num-landmarks 16 --encoder-attn-proposal-gen adaptive-1d --encoder-attn-mis-type mis-opt

CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -d wmt -s lara_32 -g 4 -e TRUE --attn-name-encoder lara --encoder-attn-num-landmarks 32 --encoder-attn-proposal-gen adaptive-1d --encoder-attn-mis-type mis-opt

## EVA
CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -d wmt -s eva_8_8 -g 4 -e TRUE --attn-name-encoder eva --encoder-attn-window-size 8 --encoder-attn-num-landmarks 8 --encoder-attn-adaptive-proj no-ln --encoder-attn-use-t5-rpe --encoder-attn-overlap-window

CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -d wmt -s eva_16_8 -g 4 -e TRUE --attn-name-encoder eva --encoder-attn-window-size 16 --encoder-attn-num-landmarks 8 --encoder-attn-adaptive-proj no-ln --encoder-attn-use-t5-rpe --encoder-attn-overlap-window

CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -d wmt -s eva_32_8 -g 4 -e TRUE --attn-name-encoder eva --encoder-attn-window-size 32 --encoder-attn-num-landmarks 8 --encoder-attn-adaptive-proj no-ln --encoder-attn-use-t5-rpe --encoder-attn-overlap-window

Autoregressive Language Modeling

# Currently, LARA does not support causal masking yet.

# EVA on a 16-layer Transformer LM
CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -m 16layers -d wikitext103 -s eva_128_8_16layers -g 4 -e TRUE --attn-name-decoder causal_eva --decoder-attn-window-size 128 --decoder-attn-causal --decoder-attn-adaptive-proj qk --decoder-attn-chunk-size 8 --decoder-attn-use-t5-rpe

# EVA on a 32-layer Transformer LM
CUDA_VISIBLE_DEVICES=0,1,2,3 bash main.sh -p <dir-of-your-bin-data> -m 32layers -d wikitext103 -s eva_128_8_32layers -g 4 -e TRUE --attn-name-decoder causal_eva --decoder-attn-window-size 128 --decoder-attn-causal --decoder-attn-adaptive-proj qk --decoder-attn-chunk-size 8 --decoder-attn-use-t5-rpe

Generation & Evaluation

For generation & evaluation, simply pass argument -i true when calling main.sh to perform the inference procedure only. The checkpoint path can be specified as -c <your-ckpt-path>. For example,

# Machine Translation
CUDA_VISIBLE_DEVICES=0 bash main.sh -i true -c <your-possibly-avg-checkpoint.pt> -p <dir-of-your-bin-data> -d wmt -g 1

# Autoregressive Language Modeling
CUDA_VISIBLE_DEVICES=0 bash main.sh -i true -c <your-checkpoint_last.pt> -p <dir-of-your-bin-data> -d wikitext103 -g 1

Pre-trained Models

We also provide trained EVA model checkpoints in OneDrive for machine translation and language modeling tasks:

• wikitext103-eva-16layers-lm
• wikitext103-eva-32layers-lm
• wmt14ende-eva-e32_c8-mt
• wmt14ende-eva-e8_c8-mt

Citation

@inproceedings{zheng2023efficient,
  title={Efficient Attention via Control Variates},
  author={Lin Zheng and Jianbo Yuan and Chong Wang and Lingpeng Kong},
  booktitle={International Conference on Learning Representations},
  year={2023},
  url={https://openreview.net/forum?id=G-uNfHKrj46}
}

@inproceedings{zheng2022linear,
  title={Linear complexity randomized self-attention mechanism},
  author={Lin Zheng and Chong Wang and Lingpeng Kong},
  booktitle={International Conference on Machine Learning},
  pages={27011--27041},
  year={2022},
  organization={PMLR}
}