mirror of https://github.com/hpcaitech/ColossalAI
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
import os
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from functools import partial
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from pathlib import Path
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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import torch.nn as nn
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.engine.schedule import PipelineSchedule
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from colossalai.logging import get_dist_logger
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from colossalai.trainer import Trainer
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from colossalai.utils import MultiTimer, free_port, get_dataloader
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from torch.optim import Adam
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from torchvision import transforms
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from torchvision.datasets import CIFAR10
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from torchvision.models import resnet18
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from colossalai.testing import rerun_if_address_is_in_use
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BATCH_SIZE = 4
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IMG_SIZE = 32
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NUM_EPOCHS = 200
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CONFIG = dict(
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NUM_MICRO_BATCHES=2,
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parallel=dict(pipeline=2),
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)
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def run_trainer_with_pipeline(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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# build model
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model = resnet18(num_classes=10)
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if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
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model = nn.Sequential(model.conv1, model.bn1, model.relu, model.maxpool, model.layer1, model.layer2)
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elif gpc.get_local_rank(ParallelMode.PIPELINE) == 1:
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class Flatten(nn.Module):
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def forward(self, x):
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return torch.flatten(x, 1)
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model = nn.Sequential(model.layer3, model.layer4, model.avgpool, Flatten(), model.fc)
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# build dataloaders
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train_dataset = CIFAR10(root=Path(os.environ['DATA']),
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download=True,
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transform=transforms.Compose([
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transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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]))
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train_dataloader = get_dataloader(dataset=train_dataset,
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shuffle=True,
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batch_size=BATCH_SIZE,
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pin_memory=True,
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drop_last=True)
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# build optimizer
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optimizer = Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss()
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engine, train_dataloader, *args = colossalai.initialize(model=model,
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optimizer=optimizer,
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criterion=criterion,
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train_dataloader=train_dataloader)
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logger = get_dist_logger()
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logger.info("engine is built", ranks=[0])
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timer = MultiTimer()
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trainer = Trainer(engine=engine, logger=logger, timer=timer)
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logger.info("trainer is built", ranks=[0])
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logger.info("start training", ranks=[0])
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trainer.fit(train_dataloader=train_dataloader,
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epochs=NUM_EPOCHS,
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max_steps=3,
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display_progress=True,
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test_interval=5)
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gpc.destroy()
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torch.cuda.empty_cache()
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_trainer_with_pipeline():
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world_size = 4
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run_func = partial(run_trainer_with_pipeline, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_trainer_with_pipeline()
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