eindist

Differentiable einops-style wrapper over torch.distributed.

Install

pip install git+https://github.com/DanielHesslow/eindist

Example Usage

    TP = Distributed(None) # None is default process group. Distribute across everyone

    x = torch.randn(4,4, requires_grad = True)

    y = distribute("x y -> x (TP y)", x,  TP = TP) # split x
    z = distribute("x (TP y) -> x y", y,  TP = TP) # gather

    # or..
    y = distribute("x y -> x (TP y)", x,  TP = TP) # split x
    z = distribute("TP x y -> x y",   y,  TP = TP) # reduce sum

    # or even..
    y = distribute("(SP x) y -> x (TP y)", x,  TP = TP, SP=TP) # all-to-all
    z = distribute("x (TP y) -> (SP x) y", x,  TP = TP, SP=TP) # all-to-all

Why to use:

Simple, intuitive way to distribute workloads. Many different type of tensor parallelism is easy to implement.

And its all auto differentiable.

Why not to use

Currently only single distributed index can be specified at each side of the expression.

DistributedDataParallel is very good, when data parallel is enough, use it.