test_trainer_with_pipe_schedule.py

import colossalai
import os
import pytest
import torch
import torch.nn as nn
import torch.multiprocessing as mp

from pathlib import Path
from torchvision import transforms
from torch.optim import Adam
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.trainer import Trainer
from colossalai.utils import get_dataloader
from colossalai.engine.schedule import PipelineSchedule
from torchvision.models import resnet18
from torchvision.datasets import CIFAR10
from functools import partial


BATCH_SIZE = 16
IMG_SIZE = 32
NUM_EPOCHS = 200

CONFIG = dict(
    parallel=dict(
        pipeline=2,
    ),
)


def run_trainer_with_pipeline(rank, world_size):
    colossalai.launch(
        config=CONFIG,
        rank=rank,
        world_size=world_size,
        host='localhost',
        port=29931,
        backend='nccl'
    )

    # build model
    model = resnet18(num_classes=10)

    if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
        model = nn.Sequential(
            model.conv1,
            model.bn1,
            model.relu,
            model.maxpool,
            model.layer1,
            model.layer2
        )
    elif gpc.get_local_rank(ParallelMode.PIPELINE) == 1:
        from functools import partial

        class Flatten(nn.Module):

            def forward(self, x):
                return torch.flatten(x, 1)

        model = nn.Sequential(
            model.layer3,
            model.layer4,
            model.avgpool,
            Flatten(),
            model.fc
        )

    # build dataloaders
    train_dataset = CIFAR10(
        root=Path(os.environ['DATA']),
        download=True,
        transform=transforms.Compose(
            [
                transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
                transforms.ToTensor(),
                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
            ]
        )
    )

    test_dataset = CIFAR10(
        root=Path(os.environ['DATA']),
        train=False,
        download=True,
        transform=transforms.Compose(
            [
                transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
                transforms.ToTensor(),
                transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
            ]
        )
    )

    train_dataloader = get_dataloader(dataset=train_dataset,
                                      shuffle=True,
                                      batch_size=BATCH_SIZE,
                                      pin_memory=True,
                                      drop_last=True)

    test_dataloader = get_dataloader(dataset=test_dataset,
                                     batch_size=BATCH_SIZE,
                                     pin_memory=True,
                                     drop_last=True)

    # build optimizer
    optimizer = Adam(model.parameters(), lr=0.001)
    criterion = nn.CrossEntropyLoss()

    engine, train_dataloader, *args = colossalai.initialize(
        model=model,
        optimizer=optimizer,
        criterion=criterion,
        train_dataloader=train_dataloader
    )

    logger = get_dist_logger()
    logger.info("engine is built", ranks=[0])
    pipe_schedule = PipelineSchedule(num_microbatches=4)
    trainer = Trainer(engine=engine,
                      schedule=pipe_schedule,
                      logger=logger)
    logger.info("trainer is built", ranks=[0])

    logger.info("start training", ranks=[0])

    trainer.fit(
        train_dataloader=train_dataloader,
        test_dataloader=test_dataloader,
        epochs=NUM_EPOCHS,
        max_steps=100,
        display_progress=True,
        test_interval=5
    )
    gpc.destroy()
    torch.cuda.empty_cache()


@pytest.mark.dist
def test_trainer_with_pipeline():
    world_size = 4
    run_func = partial(run_trainer_with_pipeline, world_size=world_size)
    mp.spawn(run_func, nprocs=world_size)


if __name__ == '__main__':
    test_trainer_with_pipeline()