Merge pull request #4738 from ppt0011/main

[legacy] remove deterministic data loader test

tests/test_data/test_deterministic_dataloader.py

@@ -1,73 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-import os
-from pathlib import Path
-
-import pytest
-import torch
-import torch.distributed as dist
-from torchvision import datasets, transforms
-
-import colossalai
-from colossalai.context import Config, ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.testing import rerun_if_address_is_in_use, spawn
-from colossalai.utils import get_dataloader
-
-CONFIG = Config(
-    dict(
-        train_data=dict(
-            dataset=dict(
-                type='CIFAR10',
-                root=Path(os.environ['DATA']),
-                train=True,
-                download=True,
-            ),
-            dataloader=dict(num_workers=2, batch_size=2, shuffle=True),
-        ),
-        parallel=dict(
-            pipeline=dict(size=1),
-            tensor=dict(size=1, mode=None),
-        ),
-        seed=1024,
-    ))
-
-
-def run_data_sampler(rank, world_size, port):
-    dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
-    colossalai.launch(**dist_args)
-
-    # build dataset
-    transform_pipeline = [transforms.ToTensor(), transforms.RandomCrop(size=32, padding=4)]
-    transform_pipeline = transforms.Compose(transform_pipeline)
-    dataset = datasets.CIFAR10(root=Path(os.environ['DATA']), train=True, download=True, transform=transform_pipeline)
-
-    # build dataloader
-    dataloader = get_dataloader(dataset, batch_size=8, add_sampler=False)
-
-    data_iter = iter(dataloader)
-    img, label = data_iter.next()
-    img = img[0]
-
-    if gpc.get_local_rank(ParallelMode.DATA) != 0:
-        img_to_compare = img.clone()
-    else:
-        img_to_compare = img
-    dist.broadcast(img_to_compare, src=0, group=gpc.get_group(ParallelMode.DATA))
-
-    if gpc.get_local_rank(ParallelMode.DATA) != 0:
-        # this is without sampler
-        # this should be false if data parallel sampler to given to the dataloader
-        assert torch.equal(img,
-                           img_to_compare), 'Same image was distributed across ranks and expected it to be the same'
-    torch.cuda.empty_cache()
-
-
-@rerun_if_address_is_in_use()
-def test_data_sampler():
-    spawn(run_data_sampler, 4)
-
-
-if __name__ == '__main__':
-    test_data_sampler()