kabicm / dt-fm

Decentralized Training of Foundation Models

We explore how to deploy the training of foundation models over a heterogeneous decentralized environment. This is a research project developed by DS3Lab@ETH Zurich, HazyResearch@Stanford and CRFM@Stanford.

Overview

• Udp_hole_punching_test directory includes our sample script for doing udp hole punching test, detail instruction about running hole punching test is provided.

• The other modules are self-document to support the distributed training within the scope of pipeline parallelism and data parallelism.

Cite Our Paper

@article{yuan2022decentralized,
  title={Decentralized Training of Foundation Models in Heterogeneous Environments}, 
  author={Binhang Yuan, Yongjun He, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Re, and Ce Zhang},
  year={2022},
  eprint={2206.01288},
  archivePrefix={arXiv},
  primaryClass={cs.DC}
}

AWS AMI

You can directly use our AWS AMI for easy configuration:

AMI Name	AMI ID	Region	Recommended Instances
DT-FM	ami-0652205be6faa6e2d	us-west-2	p3.2xlarge, p3.8xlarge, p3.16xlarge

Run our system:

Setup:

• If you use our AMI, you can ignore this section.

• Use AWS Deep Learning Base AMI

• Install PyTorch env:

  pip3 install torch==1.9.0+cu111 torchtext -f https://download.pytorch.org/whl/torch_stable.html
  pip3 install cupy-cuda110==8.6.0
  

• Download glue-qqp dataset for throughput benchmark.

• Setup network configuration:

  export GLOO_SOCKET_IFNAME=ens3
  export NCCL_SOCKET_IFNAME=ens3
  

Manually run cmd on each instance (not recommended)

• Use TC scripts to control network delay and bandwidth.

• From each terminal, run cmd:

  python dist_runner.py --dist-url tcp://XXX.XXX.XXX.XXX:9000 --world-size N --rank i (i=0,...,N-1)
  

Run with Advanced Scripts (recommended)

• Go to the scripts directory

• First update the public IPs and private IP of the rank-0 node in ip_list.sh.

• Allow SSH connects, on your local machine run:

  bash accept_ssh_keys.sh
  

• Enable environment: (This is optional but load conda env seems to be slow for the first time)

  bash aws_foo_load_lib.sh
  

• Setup heterogeneous network:

  • Update the private IPs in generate_heterogeneous_tc.py;

  • Sync the code to AWS, make sure this python script is updated with the IPs in all nodes.

  • Run:

      bash aws_generate_heter_tc.sh #HETER_CASE (3/4/5)
    

• Run Schedulers experiments (optional):

  • Go to scheduler/heuristic_evolutionary_solver directory

  • To get assignments and estimated cost, run Schedulers

     python scheduler.py
    

• Run Tasks (e.g.,):

  bash aws_run_batch_gpt3_optimal.sh 3/4/5
  bash aws_run_batch_gpt3_random.sh 3/4/5