mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
96 lines
3.1 KiB
96 lines
3.1 KiB
import os
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.optim import Adam
|
|
from torchvision import transforms
|
|
from torchvision.datasets import CIFAR10
|
|
from torchvision.models import resnet18
|
|
|
|
import colossalai
|
|
from colossalai.core import global_context as gpc
|
|
from colossalai.logging import get_dist_logger
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
from colossalai.utils import get_dataloader
|
|
|
|
# Config
|
|
BATCH_SIZE = 2
|
|
NUM_CLASSES = 10
|
|
|
|
CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)),
|
|
clip_grad_norm=1.0,
|
|
gradient_accumulation=4)
|
|
|
|
|
|
def run_no_pipeline(rank, world_size, port):
|
|
|
|
# init dist env
|
|
colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
|
|
|
|
# build model
|
|
model = resnet18(num_classes=10)
|
|
|
|
# build dataloaders
|
|
train_dataset = CIFAR10(root=Path(os.environ['DATA']),
|
|
download=True,
|
|
transform=transforms.Compose([
|
|
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)
|
|
|
|
# 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()
|
|
rank = torch.distributed.get_rank()
|
|
param_track = []
|
|
grad_track = []
|
|
next(model.parameters()).retain_grad()
|
|
|
|
engine.train()
|
|
step = 0
|
|
for img, label in train_dataloader:
|
|
engine.zero_grad()
|
|
img = img.cuda()
|
|
label = label.cuda()
|
|
output = engine(img)
|
|
loss = engine.criterion(output, label)
|
|
engine.backward(loss)
|
|
engine.step()
|
|
|
|
# check
|
|
param_track.append(next(model.parameters())[0].clone())
|
|
grad_track.append(next(model.parameters()).grad[0].clone())
|
|
step += 1
|
|
if step == CONFIG['gradient_accumulation']:
|
|
break
|
|
|
|
assert not torch.all(grad_track[0] == grad_track[-1]), 'grad should be different in different iterations'
|
|
assert torch.all(param_track[0] == param_track[1]) and not torch.all(param_track[0] == param_track[-1]), \
|
|
'param should be the same in the first few iterations and only changed in the last iteration'
|
|
|
|
gpc.destroy()
|
|
torch.cuda.empty_cache()
|
|
|
|
|
|
@pytest.mark.dist
|
|
@rerun_if_address_is_in_use()
|
|
def test_engine():
|
|
spawn(run_no_pipeline, 4)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_engine()
|