mirror of https://github.com/hpcaitech/ColossalAI
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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import os
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from functools import partial
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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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import colossalai
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from colossalai.context import MOE_CONTEXT
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from colossalai.nn.layer.moe import load_moe_model, save_moe_model
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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, get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext
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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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from tests.test_zero.common import CONFIG
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def exam_moe_checkpoint():
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with ColoInitContext(device=get_current_device()):
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model = MoeModel(checkpoint=True)
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save_moe_model(model, 'temp_path.pth')
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with ColoInitContext(device=get_current_device()):
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other_model = MoeModel(checkpoint=True)
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load_moe_model(other_model, 'temp_path.pth')
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state_0 = model.state_dict()
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state_1 = other_model.state_dict()
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for k, v in state_0.items():
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u = state_1.get(k)
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assert torch.equal(u.data, v.data)
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if dist.get_rank() == 0:
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os.remove('temp_path.pth')
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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_checkpoint()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [2, 4])
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@rerun_if_address_is_in_use()
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def test_moe_checkpoint(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_checkpoint(world_size=4)
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