mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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30 lines
1.0 KiB
30 lines
1.0 KiB
import pytest |
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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, spawn |
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def check_alpha_beta(rank, world_size, port, physical_devices): |
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disable_existing_loggers() |
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launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl") |
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profiler = AlphaBetaProfiler(physical_devices) |
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best_logical_mesh = profiler.search_best_logical_mesh() |
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if physical_devices == [0, 1, 2, 3]: |
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assert best_logical_mesh == [[0, 1], [2, 3]] |
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elif physical_devices == [0, 3]: |
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assert best_logical_mesh == [[0, 3]] |
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@pytest.mark.skip(reason="Skip because assertion may fail 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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spawn(check_alpha_beta, 4, physical_devices=physical_devices) |
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if __name__ == "__main__": |
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test_profile_alpha_beta()
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