mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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33 lines
1.2 KiB
33 lines
1.2 KiB
from functools import partial |
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import pytest |
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import torch.multiprocessing as mp |
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from colossalai.device import AlphaBetaProfiler |
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from colossalai.initialize import launch |
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from colossalai.logging import disable_existing_loggers |
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from colossalai.testing import parameterize, rerun_if_address_is_in_use |
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from colossalai.utils import free_port |
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def check_alpha_beta(rank, physical_devices, world_size, port): |
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disable_existing_loggers() |
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launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') |
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profiler = AlphaBetaProfiler(physical_devices) |
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ab_dict = profiler.profile_ab() |
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for _, (alpha, beta) in ab_dict.items(): |
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assert alpha > 0 and alpha < 1e-4 and beta > 0 and beta < 1e-10 |
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@pytest.mark.skip(reason="Skip because assertion fails for CI devices") |
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@pytest.mark.dist |
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@parameterize('physical_devices', [[0, 1, 2, 3], [0, 3]]) |
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@rerun_if_address_is_in_use() |
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def test_profile_alpha_beta(physical_devices): |
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world_size = 4 |
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run_func = partial(check_alpha_beta, physical_devices=physical_devices, 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_profile_alpha_beta()
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