mirror of https://github.com/hpcaitech/ColossalAI
119 lines
3.2 KiB
Python
119 lines
3.2 KiB
Python
import colossalai
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import os
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import pytest
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import torch
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import torch.multiprocessing as mp
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import torch.nn as nn
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from functools import partial
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from pathlib import Path
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from torchvision import transforms
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from torch.optim import Adam
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from colossalai.core import global_context as gpc
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from colossalai.logging import get_dist_logger
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from colossalai.utils import report_memory_usage, get_dataloader
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from colossalai.initialize import get_default_parser
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from torchvision.models import resnet18
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from torchvision.datasets import CIFAR10
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# Config
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BATCH_SIZE = 16
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IMG_SIZE = 224
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NUM_CLASSES = 10
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CONFIG = dict(
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parallel=dict(
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pipeline=dict(size=1),
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tensor=dict(size=1, mode=None)
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),
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clip_grad_norm=1.0,
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gradient_accumulation=4
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)
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def run_no_pipeline(rank, world_size):
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# init dist env
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colossalai.launch(
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config=CONFIG,
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rank=rank,
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world_size=world_size,
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host='localhost',
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port=29500,
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backend='nccl'
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)
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# build model
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model = resnet18(num_classes=10)
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# build dataloaders
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train_dataset = CIFAR10(
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root=Path(os.environ['DATA']),
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download=True,
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transform=transforms.Compose(
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[
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transforms.Resize(size=(IMG_SIZE, IMG_SIZE)),
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transforms.ToTensor(),
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transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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]
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)
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)
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train_dataloader = get_dataloader(dataset=train_dataset,
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shuffle=True,
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batch_size=BATCH_SIZE,
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pin_memory=True,
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drop_last=True)
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# build optimizer
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optimizer = Adam(model.parameters(), lr=0.001)
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criterion = nn.CrossEntropyLoss()
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engine, train_dataloader, *args = colossalai.initialize(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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train_dataloader=train_dataloader
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)
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logger = get_dist_logger()
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rank = torch.distributed.get_rank()
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param_track = []
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grad_track = []
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next(model.parameters()).retain_grad()
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engine.train()
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step = 0
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for img, label in train_dataloader:
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engine.zero_grad()
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img = img.cuda()
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label = label.cuda()
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output = engine(img)
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loss = engine.criterion(output, label)
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engine.backward(loss)
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engine.step()
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# check
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param_track.append(next(model.parameters())[0].clone())
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grad_track.append(next(model.parameters()).grad[0].clone())
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step += 1
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if step == CONFIG['gradient_accumulation']:
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break
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assert not torch.all(grad_track[0] == grad_track[-1]), 'grad should be different in different iterations'
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assert torch.all(param_track[0] == param_track[1]) and not torch.all(param_track[0] == param_track[-1]), \
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'param should be the same in the first few iterations and only changed in the last iteration'
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gpc.destroy()
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torch.cuda.empty_cache()
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@pytest.mark.dist
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def test_engine():
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world_size = 4
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func = partial(run_no_pipeline, world_size=world_size)
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mp.spawn(func, nprocs=world_size)
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if __name__ == '__main__':
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test_engine()
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