mirror of https://github.com/hpcaitech/ColossalAI
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
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from functools import partial
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import colossalai
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import pytest
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import torch
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import torch.multiprocessing as mp
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import torch.distributed as dist
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from colossalai.testing import parameterize
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from colossalai.utils import free_port
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from colossalai.context import MOE_CONTEXT
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from colossalai.tensor import ColoParameter
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import get_current_device
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from tests.test_zero.common import CONFIG
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from tests.test_moe.test_moe_zero_init import MoeModel
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from tests.test_tensor.common_utils import debug_print
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@parameterize("init_device_type", ['cpu', 'cuda'])
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def exam_moe_colo_init(init_device_type):
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world_size = dist.get_world_size()
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if init_device_type == 'cuda':
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init_device = get_current_device()
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elif init_device_type == 'cpu':
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init_device = torch.device("cpu")
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else:
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raise NotImplementedError("Unknown device found.")
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with ColoInitContext(device=init_device):
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model = MoeModel(checkpoint=True)
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for name, param in model.named_parameters():
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assert isinstance(param, ColoParameter), "parameter `{}` has an init problem".format(name)
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if hasattr(param, "moe_info"):
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param.set_process_group(param.moe_info.pg)
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if hasattr(param, "moe_info"):
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assert param.process_group.dp_world_size() == param.moe_info.dp_size
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else:
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assert param.process_group.dp_world_size() == world_size
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def _run_dist(rank, world_size, port):
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colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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MOE_CONTEXT.setup(seed=42)
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exam_moe_colo_init()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [4])
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@rerun_if_address_is_in_use()
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def test_moe_colo_init(world_size):
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run_func = partial(_run_dist, 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_moe_colo_init(world_size=4)
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