A Memory-saving Training Framework for Transformers

This is the official PyTorch implementation for Mesa: A Memory-saving Training Framework for Transformers.

By Zizheng Pan, Peng Chen, Haoyu He, Jing Liu, Jianfei Cai and Bohan Zhuang.

Installation

1. Create a virtual environment with anaconda.

   conda create -n mesa python=3.7 -y
   conda activate mesa
   
   # Install PyTorch, we use PyTorch 1.7.1 with CUDA 10.1 
   pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
   
   # Install ninja
   pip install ninja

2. Build and install Mesa.

   # clone this repo
   git clone https://github.com/zhuang-group/Mesa
   # build
   cd Mesa/
   # You need to have an NVIDIA GPU
   python setup.py develop

Usage

1. Prepare your policy and save as a text file, e.g. policy.txt.

   on gelu: # layer tag, choices: fc, conv, gelu, bn, relu, softmax, matmul, layernorm
       by_index: all # layer index
       enable: True # enable for compressing
       level: 256 # we adopt 8-bit quantization by default
       ema_decay: 0.9 # the decay rate for running estimates
       
       by_index: 1 2 # e.g. exluding GELU layers that indexed by 1 and 2.
       enable: False

2. Next, you can wrap your model with Mesa by:

   import mesa as ms
   ms.policy.convert_by_num_groups(model, 3)
   # or convert by group size with ms.policy.convert_by_group_size(model, 64)
   
   # setup compression policy
   ms.policy.deploy_on_init(model, '[path to policy.txt]', verbose=print, override_verbose=False)

   That's all you need to use Mesa for memory saving.

   Note that convert_by_num_groups and convert_by_group_size only recognize nn.XXX, if your code has functional operations, such as Q@K and F.Softmax, you may need to manually setup these layers. For example:

   import mesa as ms
   # matrix multipcation (before)
   out = Q@K.transpose(-2, -1)
   # with Mesa
   self.mm = ms.MatMul(quant_groups=3)
   out = self.mm(q, k.transpose(-2, -1))
   
   # sofmtax (before)
   attn = attn.softmax(dim=-1)
   # with Mesa
   self.softmax = ms.Softmax(dim=-1, quant_groups=3)
   attn = self.softmax(attn)

3. You can also target one layer by:

   import mesa as ms
   # previous 
   self.act = nn.GELU()
   # with Mesa
   self.act = ms.GELU(quant_groups=[num of quantization groups])

Demo projects for DeiT and Swin

We provide demo projects to replicate our results of training DeiT and Swin with Mesa, please refer to DeiT-Mesa and Swin-Mesa.

Results on ImageNet

Model	Param (M)	FLOPs (G)	Train Memory (MB)	Top-1 (%)
DeiT-Ti	5	1.3	4,171	71.9
DeiT-Ti w/ Mesa	5	1.3	1,858	72.1
DeiT-S	22	4.6	8,459	79.8
DeiT-S w/ Mesa	22	4.6	3,840	80.0
DeiT-B	86	17.5	17,691	81.8
DeiT-B w/ Mesa	86	17.5	8,616	81.8
Swin-Ti	29	4.5	11,812	81.3
Swin-Ti w/ Mesa	29	4.5	5,371	81.3
PVT-Ti	13	1.9	7,800	75.1
PVT-Ti w/ Mesa	13	1.9	3,782	74.9

Memory footprint at training time is measured with a batch size of 128 and an image resolution of 224x224 on a single GPU.

Citation

If you find our work interesting or helpful to your research, please consider citing Mesa.

@article{pan2021mesa,
      title={Mesa: A Memory-saving Training Framework for Transformers}, 
      author={Zizheng Pan and Peng Chen and Haoyu He and Jing Liu and Jianfei Cai and Bohan Zhuang},
      journal={arXiv preprint arXiv:2111.11124}
      year={2021}
}

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Acknowledgments

This repository has adopted part of the quantization codes from ActNN, we thank the authors for their open-sourced code.