mirror of https://github.com/hpcaitech/ColossalAI
60 lines
2.2 KiB
Python
60 lines
2.2 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.multiprocessing as mp
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import colossalai
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from colossalai.nn.parallel import GeminiDDP
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from colossalai.nn.parallel.utils import get_static_torch_model
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from colossalai.tensor import ColoParameter
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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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from colossalai.utils.cuda import get_current_device
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from tests.components_to_test.registry import non_distributed_component_funcs
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@parameterize('model_name', ['hanging_param_model', 'resnet18', 'gpt2'])
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def run_convert_torch_module(model_name: str):
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, _, _, _, _ = get_components_func()
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with ColoInitContext(device=torch.device("cpu")):
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model = model_builder(checkpoint=False)
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model = GeminiDDP(model, device=get_current_device(), placement_policy='auto', pin_memory=True)
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pytorch_model = get_static_torch_model(model, only_rank_0=False)
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for n, p in pytorch_model.named_parameters():
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assert type(p) == torch.nn.Parameter, f"type error: {n} is a {type(p)}"
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# get the static model should not change the original model
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for n, p in model.named_parameters():
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assert isinstance(p, ColoParameter)
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for (pn, pm), (cn, cm) in zip(pytorch_model.named_modules(), model.named_modules()):
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assert pn == cn
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assert id(pm) != id(cm)
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for pp, cp in zip(pm.parameters(recurse=False), cm.parameters(recurse=False)):
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assert id(pp) != id(cp)
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assert pp.shape == cp.shape
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def run_dist(rank, world_size, port):
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config = {}
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_convert_torch_module()
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@rerun_if_address_is_in_use()
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def test_convert_torch_module(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_convert_torch_module(2)
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