mirror of https://github.com/hpcaitech/ColossalAI
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import os
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from functools import partial
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from pathlib import Path
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import pytest
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from torchvision import transforms
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from torch.utils.data import DataLoader
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import colossalai
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from colossalai.builder import build_dataset, build_transform
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from colossalai.context import ParallelMode, Config
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from colossalai.core import global_context as gpc
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from colossalai.utils import free_port
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from colossalai.testing import rerun_on_exception
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CONFIG = Config(
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dict(
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train_data=dict(dataset=dict(
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type='CIFAR10',
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root=Path(os.environ['DATA']),
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train=True,
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download=True,
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),
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dataloader=dict(num_workers=2, batch_size=2, shuffle=True),
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transform_pipeline=[
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dict(type='ToTensor'),
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dict(type='RandomCrop', size=32),
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dict(type='Normalize', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
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]),
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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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seed=1024,
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))
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def run_data_sampler(rank, world_size, port):
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dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
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colossalai.launch(**dist_args)
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dataset_cfg = gpc.config.train_data.dataset
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dataloader_cfg = gpc.config.train_data.dataloader
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transform_cfg = gpc.config.train_data.transform_pipeline
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# build transform
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transform_pipeline = [build_transform(cfg) for cfg in transform_cfg]
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transform_pipeline = transforms.Compose(transform_pipeline)
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dataset_cfg['transform'] = transform_pipeline
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# build dataset
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dataset = build_dataset(dataset_cfg)
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# build dataloader
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dataloader = DataLoader(dataset=dataset, **dataloader_cfg)
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data_iter = iter(dataloader)
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img, label = data_iter.next()
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img = img[0]
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if gpc.get_local_rank(ParallelMode.DATA) != 0:
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img_to_compare = img.clone()
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else:
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img_to_compare = img
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dist.broadcast(img_to_compare, src=0, group=gpc.get_group(ParallelMode.DATA))
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if gpc.get_local_rank(ParallelMode.DATA) != 0:
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# this is without sampler
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# this should be false if data parallel sampler to given to the dataloader
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assert torch.equal(img,
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img_to_compare), 'Same image was distributed across ranks and expected it to be the same'
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torch.cuda.empty_cache()
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@pytest.mark.cpu
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@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
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def test_data_sampler():
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world_size = 4
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test_func = partial(run_data_sampler, world_size=world_size, port=free_port())
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mp.spawn(test_func, nprocs=world_size)
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if __name__ == '__main__':
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test_data_sampler()
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