mirror of https://github.com/hpcaitech/ColossalAI
208 lines
7.5 KiB
Python
208 lines
7.5 KiB
Python
import argparse
|
|
import os
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.nn as nn
|
|
import torchvision
|
|
import torchvision.transforms as transforms
|
|
from torch.optim import Optimizer
|
|
from torch.optim.lr_scheduler import MultiStepLR
|
|
from torch.utils.data import DataLoader
|
|
from tqdm import tqdm
|
|
|
|
import colossalai
|
|
from colossalai.booster import Booster
|
|
from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
|
|
from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
|
|
from colossalai.cluster import DistCoordinator
|
|
from colossalai.nn.optimizer import HybridAdam
|
|
from colossalai.utils import get_current_device
|
|
|
|
# ==============================
|
|
# Prepare Hyperparameters
|
|
# ==============================
|
|
NUM_EPOCHS = 80
|
|
LEARNING_RATE = 1e-3
|
|
|
|
|
|
def build_dataloader(batch_size: int, coordinator: DistCoordinator, plugin: DPPluginBase):
|
|
# transform
|
|
transform_train = transforms.Compose(
|
|
[transforms.Pad(4), transforms.RandomHorizontalFlip(), transforms.RandomCrop(32), transforms.ToTensor()]
|
|
)
|
|
transform_test = transforms.ToTensor()
|
|
|
|
# CIFAR-10 dataset
|
|
data_path = os.environ.get("DATA", "./data")
|
|
with coordinator.priority_execution():
|
|
train_dataset = torchvision.datasets.CIFAR10(
|
|
root=data_path, train=True, transform=transform_train, download=True
|
|
)
|
|
test_dataset = torchvision.datasets.CIFAR10(
|
|
root=data_path, train=False, transform=transform_test, download=True
|
|
)
|
|
|
|
# Data loader
|
|
train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=batch_size, shuffle=True, drop_last=True)
|
|
test_dataloader = plugin.prepare_dataloader(test_dataset, batch_size=batch_size, shuffle=False, drop_last=False)
|
|
return train_dataloader, test_dataloader
|
|
|
|
|
|
@torch.no_grad()
|
|
def evaluate(model: nn.Module, test_dataloader: DataLoader, coordinator: DistCoordinator) -> float:
|
|
model.eval()
|
|
correct = torch.zeros(1, dtype=torch.int64, device=get_current_device())
|
|
total = torch.zeros(1, dtype=torch.int64, device=get_current_device())
|
|
for images, labels in test_dataloader:
|
|
images = images.cuda()
|
|
labels = labels.cuda()
|
|
outputs = model(images)
|
|
_, predicted = torch.max(outputs.data, 1)
|
|
total += labels.size(0)
|
|
correct += (predicted == labels).sum().item()
|
|
dist.all_reduce(correct)
|
|
dist.all_reduce(total)
|
|
accuracy = correct.item() / total.item()
|
|
if coordinator.is_master():
|
|
print(f"Accuracy of the model on the test images: {accuracy * 100:.2f} %")
|
|
return accuracy
|
|
|
|
|
|
def train_epoch(
|
|
epoch: int,
|
|
model: nn.Module,
|
|
optimizer: Optimizer,
|
|
criterion: nn.Module,
|
|
train_dataloader: DataLoader,
|
|
booster: Booster,
|
|
coordinator: DistCoordinator,
|
|
):
|
|
model.train()
|
|
with tqdm(train_dataloader, desc=f"Epoch [{epoch + 1}/{NUM_EPOCHS}]", disable=not coordinator.is_master()) as pbar:
|
|
for images, labels in pbar:
|
|
images = images.cuda()
|
|
labels = labels.cuda()
|
|
# Forward pass
|
|
outputs = model(images)
|
|
loss = criterion(outputs, labels)
|
|
|
|
# Backward and optimize
|
|
booster.backward(loss, optimizer)
|
|
optimizer.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Print log info
|
|
pbar.set_postfix({"loss": loss.item()})
|
|
|
|
|
|
def main():
|
|
# ==============================
|
|
# Parse Arguments
|
|
# ==============================
|
|
parser = argparse.ArgumentParser()
|
|
# FIXME(ver217): gemini is not supported resnet now
|
|
parser.add_argument(
|
|
"-p",
|
|
"--plugin",
|
|
type=str,
|
|
default="torch_ddp",
|
|
choices=["torch_ddp", "torch_ddp_fp16", "low_level_zero"],
|
|
help="plugin to use",
|
|
)
|
|
parser.add_argument("-r", "--resume", type=int, default=-1, help="resume from the epoch's checkpoint")
|
|
parser.add_argument("-c", "--checkpoint", type=str, default="./checkpoint", help="checkpoint directory")
|
|
parser.add_argument("-i", "--interval", type=int, default=5, help="interval of saving checkpoint")
|
|
parser.add_argument(
|
|
"--target_acc", type=float, default=None, help="target accuracy. Raise exception if not reached"
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
# ==============================
|
|
# Prepare Checkpoint Directory
|
|
# ==============================
|
|
if args.interval > 0:
|
|
Path(args.checkpoint).mkdir(parents=True, exist_ok=True)
|
|
|
|
# ==============================
|
|
# Launch Distributed Environment
|
|
# ==============================
|
|
colossalai.launch_from_torch(config={})
|
|
coordinator = DistCoordinator()
|
|
|
|
# update the learning rate with linear scaling
|
|
# old_gpu_num / old_lr = new_gpu_num / new_lr
|
|
global LEARNING_RATE
|
|
LEARNING_RATE *= coordinator.world_size
|
|
|
|
# ==============================
|
|
# Instantiate Plugin and Booster
|
|
# ==============================
|
|
booster_kwargs = {}
|
|
if args.plugin == "torch_ddp_fp16":
|
|
booster_kwargs["mixed_precision"] = "fp16"
|
|
if args.plugin.startswith("torch_ddp"):
|
|
plugin = TorchDDPPlugin()
|
|
elif args.plugin == "gemini":
|
|
plugin = GeminiPlugin(placement_policy="static", strict_ddp_mode=True, initial_scale=2**5)
|
|
elif args.plugin == "low_level_zero":
|
|
plugin = LowLevelZeroPlugin(initial_scale=2**5)
|
|
|
|
booster = Booster(plugin=plugin, **booster_kwargs)
|
|
|
|
# ==============================
|
|
# Prepare Dataloader
|
|
# ==============================
|
|
train_dataloader, test_dataloader = build_dataloader(100, coordinator, plugin)
|
|
|
|
# ====================================
|
|
# Prepare model, optimizer, criterion
|
|
# ====================================
|
|
# resent50
|
|
model = torchvision.models.resnet18(num_classes=10)
|
|
|
|
# Loss and optimizer
|
|
criterion = nn.CrossEntropyLoss()
|
|
optimizer = HybridAdam(model.parameters(), lr=LEARNING_RATE)
|
|
|
|
# lr scheduler
|
|
lr_scheduler = MultiStepLR(optimizer, milestones=[20, 40, 60, 80], gamma=1 / 3)
|
|
|
|
# ==============================
|
|
# Boost with ColossalAI
|
|
# ==============================
|
|
model, optimizer, criterion, _, lr_scheduler = booster.boost(
|
|
model, optimizer, criterion=criterion, lr_scheduler=lr_scheduler
|
|
)
|
|
|
|
# ==============================
|
|
# Resume from checkpoint
|
|
# ==============================
|
|
if args.resume >= 0:
|
|
booster.load_model(model, f"{args.checkpoint}/model_{args.resume}.pth")
|
|
booster.load_optimizer(optimizer, f"{args.checkpoint}/optimizer_{args.resume}.pth")
|
|
booster.load_lr_scheduler(lr_scheduler, f"{args.checkpoint}/lr_scheduler_{args.resume}.pth")
|
|
|
|
# ==============================
|
|
# Train model
|
|
# ==============================
|
|
start_epoch = args.resume if args.resume >= 0 else 0
|
|
for epoch in range(start_epoch, NUM_EPOCHS):
|
|
train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator)
|
|
lr_scheduler.step()
|
|
|
|
# save checkpoint
|
|
if args.interval > 0 and (epoch + 1) % args.interval == 0:
|
|
booster.save_model(model, f"{args.checkpoint}/model_{epoch + 1}.pth")
|
|
booster.save_optimizer(optimizer, f"{args.checkpoint}/optimizer_{epoch + 1}.pth")
|
|
booster.save_lr_scheduler(lr_scheduler, f"{args.checkpoint}/lr_scheduler_{epoch + 1}.pth")
|
|
|
|
accuracy = evaluate(model, test_dataloader, coordinator)
|
|
if args.target_acc is not None:
|
|
assert accuracy >= args.target_acc, f"Accuracy {accuracy} is lower than target accuracy {args.target_acc}"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|