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distributed_training_with_flexible_runner.py
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distributed_training_with_flexible_runner.py
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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from mmengine.evaluator import BaseMetric
from mmengine.model import BaseModel
from mmengine.runner._flexible_runner import FlexibleRunner
class MMResNet50(BaseModel):
def __init__(self):
super().__init__()
self.resnet = torchvision.models.resnet50()
def forward(self, imgs, labels, mode):
x = self.resnet(imgs)
if mode == 'loss':
return {'loss': F.cross_entropy(x, labels)}
elif mode == 'predict':
return x, labels
class Accuracy(BaseMetric):
def process(self, data_batch, data_samples):
score, gt = data_samples
self.results.append({
'batch_size': len(gt),
'correct': (score.argmax(dim=1) == gt).sum().cpu(),
})
def compute_metrics(self, results):
total_correct = sum(item['correct'] for item in results)
total_size = sum(item['batch_size'] for item in results)
return dict(accuracy=100 * total_correct / total_size)
def parse_args():
parser = argparse.ArgumentParser(description='Distributed Training')
parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
parser.add_argument('--use-fsdp', action='store_true')
parser.add_argument('--use-deepspeed', action='store_true')
parser.add_argument('--use-colossalai', action='store_true')
args = parser.parse_args()
return args
def main():
args = parse_args()
norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
train_set = torchvision.datasets.CIFAR10(
'data/cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
]))
valid_set = torchvision.datasets.CIFAR10(
'data/cifar10',
train=False,
download=True,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(**norm_cfg)]))
train_dataloader = dict(
batch_size=128,
dataset=train_set,
sampler=dict(type='DefaultSampler', shuffle=True),
collate_fn=dict(type='default_collate'))
val_dataloader = dict(
batch_size=128,
dataset=valid_set,
sampler=dict(type='DefaultSampler', shuffle=False),
collate_fn=dict(type='default_collate'))
if args.use_deepspeed:
strategy = dict(
type='DeepSpeedStrategy',
fp16=dict(
enabled=True,
fp16_master_weights_and_grads=False,
loss_scale=0,
loss_scale_window=500,
hysteresis=2,
min_loss_scale=1,
initial_scale_power=15,
),
inputs_to_half=[0],
# bf16=dict(
# enabled=True,
# ),
zero_optimization=dict(
stage=3,
allgather_partitions=True,
reduce_scatter=True,
allgather_bucket_size=50000000,
reduce_bucket_size=50000000,
overlap_comm=True,
contiguous_gradients=True,
cpu_offload=False),
)
optim_wrapper = dict(
type='DeepSpeedOptimWrapper',
optimizer=dict(type='AdamW', lr=1e-3))
elif args.use_fsdp:
from functools import partial
from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy
size_based_auto_wrap_policy = partial(
size_based_auto_wrap_policy, min_num_params=1e7)
strategy = dict(
type='FSDPStrategy',
model_wrapper=dict(auto_wrap_policy=size_based_auto_wrap_policy))
optim_wrapper = dict(
type='AmpOptimWrapper', optimizer=dict(type='AdamW', lr=1e-3))
elif args.use_colossalai:
from colossalai.tensor.op_wrapper import colo_op_impl
# ColossalAI overwrite some torch ops with their custom op to
# make it compatible with `ColoTensor`. However, a backward error
# is more likely to happen if there are inplace operation in the
# model.
# For example, layers like `conv` + `bn` + `relu` is OK when `relu` is
# inplace since PyTorch builtin ops `batch_norm` could handle it.
# However, if `relu` is an `inplaced` op while `batch_norm` is an
# custom op, an error will be raised since PyTorch thinks the custom op
# could not handle the backward graph modification caused by inplace
# op.
# In this example, the inplace op `add_` in resnet could raise an error
# since PyTorch consider the custom op before it could not handle the
# backward graph modification
colo_op_impl(torch.Tensor.add_)(torch.add)
strategy = dict(type='ColossalAIStrategy')
optim_wrapper = dict(optimizer=dict(type='HybridAdam', lr=1e-3))
else:
strategy = None
optim_wrapper = dict(
type='AmpOptimWrapper', optimizer=dict(type='AdamW', lr=1e-3))
runner = FlexibleRunner(
model=MMResNet50(),
work_dir='./work_dirs',
strategy=strategy,
train_dataloader=train_dataloader,
optim_wrapper=optim_wrapper,
param_scheduler=dict(type='LinearLR'),
train_cfg=dict(by_epoch=True, max_epochs=10, val_interval=1),
val_dataloader=val_dataloader,
val_cfg=dict(),
val_evaluator=dict(type=Accuracy))
runner.train()
if __name__ == '__main__':
# torchrun --nproc-per-node 2 distributed_training_with_flexible_runner.py --use-fsdp # noqa: 501
# torchrun --nproc-per-node 2 distributed_training_with_flexible_runner.py --use-deepspeed # noqa: 501
# torchrun --nproc-per-node 2 distributed_training_with_flexible_runner.py
# python distributed_training_with_flexible_runner.py
main()