Using Horovod with Tune#

import numpy as np
import time
import torch

import ray
from ray import tune
from ray.train.horovod import HorovodTrainer
from ray.train import ScalingConfig
from ray.tune.tune_config import TuneConfig
from ray.tune.tuner import Tuner


def sq(x):
    m2 = 1.0
    m1 = -20.0
    m0 = 50.0
    return m2 * x * x + m1 * x + m0


def qu(x):
    m3 = 10.0
    m2 = 5.0
    m1 = -20.0
    m0 = -5.0
    return m3 * x * x * x + m2 * x * x + m1 * x + m0


class Net(torch.nn.Module):
    def __init__(self, mode="sq"):
        super(Net, self).__init__()

        if mode == "square":
            self.mode = 0
            self.param = torch.nn.Parameter(torch.FloatTensor([1.0, -1.0]))
        else:
            self.mode = 1
            self.param = torch.nn.Parameter(torch.FloatTensor([1.0, -1.0, 1.0]))

    def forward(self, x):
        if ~self.mode:
            return x * x + self.param[0] * x + self.param[1]
        else:
            return_val = 10 * x * x * x
            return_val += self.param[0] * x * x
            return_val += self.param[1] * x + self.param[2]
            return return_val


def train_loop_per_worker(config):
    import torch
    import horovod.torch as hvd

    hvd.init()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    mode = config["mode"]
    net = Net(mode).to(device)
    optimizer = torch.optim.SGD(
        net.parameters(),
        lr=config["lr"],
    )
    optimizer = hvd.DistributedOptimizer(optimizer)

    num_steps = 5
    print(hvd.size())
    np.random.seed(1 + hvd.rank())
    torch.manual_seed(1234)
    # To ensure consistent initialization across workers,
    hvd.broadcast_parameters(net.state_dict(), root_rank=0)
    hvd.broadcast_optimizer_state(optimizer, root_rank=0)

    start = time.time()
    x_max = config["x_max"]
    for step in range(1, num_steps + 1):
        features = torch.Tensor(np.random.rand(1) * 2 * x_max - x_max).to(device)
        if mode == "square":
            labels = sq(features)
        else:
            labels = qu(features)
        optimizer.zero_grad()
        outputs = net(features)
        loss = torch.nn.MSELoss()(outputs, labels)
        loss.backward()

        optimizer.step()
        time.sleep(0.1)
        tune.report(dict(loss=loss.item()))
    total = time.time() - start
    print(f"Took {total:0.3f} s. Avg: {total / num_steps:0.3f} s.")


def tune_horovod(num_workers, num_samples, use_gpu, mode="square", x_max=1.0):
    horovod_trainer = HorovodTrainer(
        train_loop_per_worker=train_loop_per_worker,
        scaling_config=ScalingConfig(
            trainer_resources={"CPU": 0}, num_workers=num_workers, use_gpu=use_gpu
        ),
        train_loop_config={"mode": mode, "x_max": x_max},
    )

    tuner = Tuner(
        horovod_trainer,
        param_space={"train_loop_config": {"lr": tune.uniform(0.1, 1)}},
        tune_config=TuneConfig(mode="min", metric="loss", num_samples=num_samples),
    )

    result_grid = tuner.fit()

    print("Best hyperparameters found were: ", result_grid.get_best_result().config)


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--mode", type=str, default="square", choices=["square", "cubic"]
    )
    parser.add_argument(
        "--learning_rate", type=float, default=0.1, dest="learning_rate"
    )
    parser.add_argument("--x_max", type=float, default=1.0, dest="x_max")
    parser.add_argument("--gpu", action="store_true")
    parser.add_argument(
        "--smoke-test", action="store_true", help=("Finish quickly for testing.")
    )
    parser.add_argument("--num-workers", type=int, default=2)
    args, _ = parser.parse_known_args()

    if args.smoke_test:
        # Smoke test with 2 samples x 2 workers x 1 CPU/worker
        # (and allocating 0 CPUs for the trainers)
        ray.init(num_cpus=4)

    tune_horovod(
        num_workers=args.num_workers,
        num_samples=2 if args.smoke_test else 10,
        use_gpu=args.gpu,
        mode=args.mode,
        x_max=args.x_max,
    )