mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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27 lines
833 B
27 lines
833 B
1 year ago
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import torch
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import torch.nn as nn
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from colossalai.shardformer.shard.utils import set_tensors_to_none
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class Net(nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.layers = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3))
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self.out = nn.Linear(3, 1)
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def test_release_layer():
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orig_cuda_allocated = torch.cuda.memory_allocated()
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model = Net().cuda()
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set_tensors_to_none(model, exclude={model.layers[0]})
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assert model.layers[1].weight is None
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assert model.layers[1].bias is None
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assert model.out.weight is None
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assert model.out.bias is None
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set_tensors_to_none(model)
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assert model.layers[0].weight is None
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assert model.layers[0].bias is None
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assert len(list(model.parameters())) == 0
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assert torch.cuda.memory_allocated() == orig_cuda_allocated
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