mirror of https://github.com/hpcaitech/ColossalAI
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
import torch
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from colossalai.utils.model.lazy_init_context import LazyInitContext
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from torchvision.models import resnet34
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import random
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import numpy as np
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MANUAL_SEED = 0
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random.seed(MANUAL_SEED)
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np.random.seed(MANUAL_SEED)
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torch.manual_seed(MANUAL_SEED)
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def test_lazy_init_with_meta():
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ctx = LazyInitContext(to_meta=True)
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with ctx:
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model = resnet34(num_classes=10)
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for param in model.parameters():
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assert param.is_meta
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for buffer in model.buffers():
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assert buffer.is_meta
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ctx.lazy_init_parameters(model)
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for name, param in model.named_parameters():
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assert not param.is_meta, name
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for buffer in model.buffers():
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assert not buffer.is_meta
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def test_lazy_init_without_meta():
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ctx = LazyInitContext(to_meta=False)
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with ctx:
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model = resnet34(num_classes=10)
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for param in model.parameters():
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assert not param.is_meta
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for buffer in model.buffers():
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assert not buffer.is_meta
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conv1_weight_before_init = model.conv1.weight.clone()
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ctx.lazy_init_parameters(model)
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conv1_weight_after_init = model.conv1.weight.clone()
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assert not torch.allclose(conv1_weight_after_init, conv1_weight_before_init)
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if __name__ == '__main__':
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test_lazy_init_with_meta()
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test_lazy_init_without_meta()
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