mirror of https://github.com/hpcaitech/ColossalAI
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
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.legacy.core import global_context as gpc
|
|
from colossalai.legacy.utils import get_dataloader
|
|
from colossalai.logging import get_dist_logger
|
|
from colossalai.testing import rerun_if_address_is_in_use, spawn
|
|
|
|
# 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.legacy.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.legacy.initialize(
|
|
model=model, optimizer=optimizer, criterion=criterion, train_dataloader=train_dataloader
|
|
)
|
|
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()
|