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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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34 lines
1.1 KiB
34 lines
1.1 KiB
import pytest |
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import torch |
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from common_utils import tensor_equal |
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import colossalai |
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from colossalai.tensor import ColoParameter, ColoTensor, ColoTensorSpec, ProcessGroup |
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from colossalai.testing import free_port |
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@pytest.mark.skip |
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def test_multiinheritance(): |
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colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl') |
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colo_param = ColoParameter(None, requires_grad=True) |
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assert colo_param.dist_spec.placement.value == 'r' |
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assert isinstance(colo_param, ColoTensor) |
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assert isinstance(colo_param, torch.nn.Parameter) |
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# __deepcopy__ overload |
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import copy |
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colo_param2 = copy.deepcopy(colo_param) |
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assert isinstance(colo_param2, ColoParameter) |
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assert tensor_equal(colo_param.data, colo_param2.data) |
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assert colo_param.requires_grad == colo_param2.requires_grad |
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# __repr__ overload |
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assert 'ColoParameter' in str(colo_param) |
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# __torch_function__ |
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clone_param = torch.clone(colo_param) |
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assert isinstance(clone_param, ColoTensor) |
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if __name__ == '__main__': |
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test_multiinheritance()
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