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65 lines
1.9 KiB
65 lines
1.9 KiB
import copy
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import pytest
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import torch
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import torch.nn as nn
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from lazy_init_utils import SUPPORT_LAZY
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from torch.nn import Parameter
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from colossalai.lazy import LazyInitContext
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@pytest.mark.skipif(not SUPPORT_LAZY, reason="requires torch >= 1.12.0")
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def test_lazy_ops():
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with LazyInitContext():
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x = torch.rand(2, 3)
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assert tuple(x.shape) == (2, 3)
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assert x.device.type == "cpu"
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x.requires_grad is False
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y = x.cuda()
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assert tuple(y.shape) == (2, 3)
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assert y.device.type == "cuda"
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assert y.requires_grad is False
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assert x.cpu() is x
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p = Parameter(torch.empty(2, 3))
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assert tuple(p.shape) == (2, 3)
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assert p.device.type == "cpu"
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assert p.requires_grad is True
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assert isinstance(p, Parameter)
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x.materialize()
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assert tuple(x.shape) == (2, 3)
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assert x.device.type == "cpu"
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assert x.requires_grad is False
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y.materialize()
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assert tuple(y.shape) == (2, 3)
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assert y.device.type == "cuda"
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assert y.requires_grad is False
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p.materialize()
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assert tuple(p.shape) == (2, 3)
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assert p.device.type == "cpu"
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assert p.requires_grad is True
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assert isinstance(p, Parameter)
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with LazyInitContext():
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x = torch.empty(2, 3)
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x.uniform_()
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x.materialize()
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assert tuple(x.shape) == (2, 3)
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with LazyInitContext():
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model = nn.Linear(3, 4)
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model = model.cuda()
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model_copied = copy.deepcopy(model)
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LazyInitContext.materialize(model)
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assert model.weight.device.type == "cuda"
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assert model.bias.device.type == "cuda"
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LazyInitContext.materialize(model_copied)
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assert model_copied.weight.device.type == "cuda"
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assert model_copied.bias.device.type == "cuda"
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assert torch.equal(model.weight, model_copied.weight)
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assert torch.equal(model.bias, model_copied.bias)
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if __name__ == "__main__":
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test_lazy_ops()
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