README.md

Distributed SGD for Computer Vision

Implement local SGD and apply it to train imageneta and cifar.

 Introduction: First perform the initial step method for $initial_steps steps, and then perform the local SGD algorithm, i.e., each process steps on its own samples, and all processes average the models after every $local_steps steps. When $initial_steps = 0, (post)-local SGD algorithm reduces to local SGD. ["Lin, T., Stich, S. U., Patel, K. K., & Jaggi, M. (2018). Don't Use Large Mini-Batches, Use Local SGD. arXiv preprint arXiv:1808.07217."]

You will need at least two CUDA devices to make the programs perform as expected.

Sample commands to run cifar.py

path=~/datasets/cifar-10-batches-py
local_steps=4
initial_steps=100
initial_step_method=single_process
# initial_step_method=multiple_processes

python cifar.py $path \
    --dist-url 'tcp://127.0.0.1:23453' --dist-backend 'nccl' \
    -p 10 --epochs 5 --batch-size 256 \
    --local-steps $local_steps \
    --initial-steps $initial_steps \
    --initial-step-method $initial_step_method \
    | tee cifar.log

Sample commands to run image.py

path=~/datasets/imagenet
local_steps=4
initial_steps=100
initial_step_method=single_process
# initial_step_method=multiple_processesrint("")

python imtest.py $path -a resnet18 \
    --dist-url 'tcp://127.0.0.1:23451' --dist-backend 'nccl' \
    --multiprocessing-distributed \
    --world-size 1 --rank 0 --epochs 2 \
    --local-sgd --local-steps $local_steps \
    --initial_steps $initial_steps \
    --initial_step_method $initial_step_method \
    | tee imagenet.log

How to use distributed_optimization.py:

First, add arguments t= arg_dic. Second, use a local_optimizer (type torch.optim.optimizer) and pass it to distributed.optimization.get_distributed_optimizer() to get a distributed_optimizer. Third, use distributed_optimizer just like torch.optim.optimizer.

Sample code:

local_optimizer = torch.optim.SGD(
    model.parameters(), 
    args.lr,
    momentum=args.momentum, 
    weight_decay=args.weight_decay
)
arg_dict = {
    'local_steps': args.local_steps,
    'initial_steps': args.initial_steps,
    'initial_step_method': args.initial_step_method,
}
optimizer = get_distributed_optimizer(
    'local_sgd', 
    local_optimizer, 
    args.rank,
    args.world_size, 
    args.group, 
    arg_dict
)
    
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
for steps:
    output = model(images)
    loss = criterion(output, target)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Document:

def get_distributed_optimizer(alg, local_optimizer, rank, world_size, group, arg_dict):
    """

    Public interface to get the distributed optimizer specified by alg

    Arguments:
        alg (str): specifies the distributed optimization algorithm.
            Current version supports
                -(Post-)local SGD. When initial_steps = 0, Post-local SGD
                    reduces to local SGD
            Raise error if alg is not specified or supported

        local_optimizer (torch.optim.optimizer): The optimizer used for the
            local optimization

        rank (int): the rank of this process

        world_size (int): number of processes in the group this process
            belongs to

        group (list of int): the list of process ids that belong to this process
            group

        arg_dict (dictionary <str key type>): specifies the arguments for
            the distributed optimizer.

    Returns an instace of the distrbuted optimizer for the specified algorithm

    Note: If want to let some param_groups not processed by distributed
         optimizer, then add a pair ('allow_data_parallelism', False) in the
        corresponding param_group of the local_optimizer.

    Supported algorthms:
    1. (Post)-Local SGD.
        Introduction:
            First perform the initial step method for $initial_steps steps, and
            then perform the local SGD algorithm, i.e., each process steps on
            its own samples, and all processes average the models after every
            $local_steps steps. When $initial_steps = 0, (post)-local SGD
            algorithm reduces to local SGD.
            "Lin, T., Stich, S. U., Patel, K. K., & Jaggi, M. (2018).
            Don't Use Large Mini-Batches, Use Local SGD.
            arXiv preprint arXiv:1808.07217."
        Arguments:
            'initial_steps' (int): number of initial global steps (default 0)
            'local_steps' (int): number of local steps betwen two weights
                average (default 4)
            'init_step_method' (str): method for running initial steps (default
                'single_process')
        init_step_method:
            1. 'multiple_processes': Run initial steps on all processes and
                average models after each step.
            2. 'single_process': Run initial steps on one process and then
                copy the model to other processes.

    """