tutorial_pipe_multiprocess.py

import os

import torch
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

import fairscale
from fairscale.nn.model_parallel import initialize_model_parallel


def run(rank, world_size):
    os.environ["MASTER_ADDR"] = "localhost"
    os.environ["MASTER_PORT"] = "10638"
    torch.distributed.init_process_group(backend="nccl", rank=rank, world_size=world_size)
    os.environ["MASTER_PORT"] = "10639"
    torch.distributed.rpc.init_rpc(f"worker{rank}", rank=rank, world_size=world_size)
    initialize_model_parallel(1, world_size)

    model = nn.Sequential(torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 5))
    target = torch.randint(0, 2, size=(20, 1)).squeeze()
    data = torch.randn(20, 10)
    loss_fn = F.nll_loss

    device = torch.device("cuda", rank)

    model = fairscale.nn.Pipe(
        model,
        balance=[2, 1],
        style=fairscale.nn.Pipe.MultiProcess,
        worker_map={0: "worker0", 1: "worker1"},  # Needed to convert ranks to RPC worker names
        input_device=device,
    ).to(device)

    # define optimizer and loss function
    optimizer = optim.SGD(model.parameters(), lr=0.001)

    # zero the parameter gradients
    optimizer.zero_grad()

    # outputs and target need to be on the same device
    # forward step
    outputs = model(data.to(device))
    # compute loss
    if rank == 1:
        loss = loss_fn(outputs.to(device), target.to(device))

        # backward + optimize
        loss.backward()
        optimizer.step()
    else:
        model.back_helper(outputs)

    print(f"Finished Training Step on {rank}")

    del model


if __name__ == "__main__":
    world_size = 2
    mp.spawn(run, args=(world_size,), nprocs=world_size, join=True)