mirror of https://github.com/hpcaitech/ColossalAI
150 lines
3.9 KiB
Python
150 lines
3.9 KiB
Python
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()
|