mirror of https://github.com/hpcaitech/ColossalAI
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
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from functools import partial
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from typing import List
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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 colossalai.communication.p2p_v2 import _send_object, _recv_object, init_process_group
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.initialize import launch
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from colossalai.utils import free_port, get_current_device
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.logging import disable_existing_loggers
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disable_existing_loggers()
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world_size = 4
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CONFIG = dict(parallel=dict(pipeline=world_size))
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torch.manual_seed(123)
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def check_layer(rank, world_size, port):
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disable_existing_loggers()
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launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl', verbose=False)
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rank = gpc.get_local_rank(ParallelMode.PIPELINE)
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if rank == 0:
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obj = [torch.randn(3,)]
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_send_object(obj, 1)
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if rank == 1:
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_recv_object(0)
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if rank == 2:
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_recv_object(3)
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if rank == 3:
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obj = [torch.randn(3,)]
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_send_object(obj, 2)
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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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@rerun_if_address_is_in_use()
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def test_object_list_p2p():
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disable_existing_loggers()
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run_func = partial(check_layer, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_object_list_p2p()
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