Making large AI models cheaper, faster and more accessible
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import pytest
import torch
from common_utils import tensor_equal
import colossalai
from colossalai.tensor import ColoParameter, ColoTensor, ColoTensorSpec, ProcessGroup
from colossalai.testing import free_port
@pytest.mark.skip
def test_multiinheritance():
colossalai.launch(config={}, rank=0, world_size=1, host='localhost', port=free_port(), backend='nccl')
colo_param = ColoParameter(None, requires_grad=True)
assert colo_param.dist_spec.placement.value == 'r'
assert isinstance(colo_param, ColoTensor)
assert isinstance(colo_param, torch.nn.Parameter)
# __deepcopy__ overload
import copy
colo_param2 = copy.deepcopy(colo_param)
assert isinstance(colo_param2, ColoParameter)
assert tensor_equal(colo_param.data, colo_param2.data)
assert colo_param.requires_grad == colo_param2.requires_grad
# __repr__ overload
assert 'ColoParameter' in str(colo_param)
# __torch_function__
clone_param = torch.clone(colo_param)
assert isinstance(clone_param, ColoTensor)
if __name__ == '__main__':
test_multiinheritance()