mirror of https://github.com/hpcaitech/ColossalAI
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from colossalai.legacy.nn import CheckpointModule
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from .registry import non_distributed_component_funcs
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from .utils import DummyDataGenerator
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class SubNet(nn.Module):
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def __init__(self, out_features) -> None:
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super().__init__()
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self.bias = nn.Parameter(torch.zeros(out_features))
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def forward(self, x, weight):
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return F.linear(x, weight, self.bias)
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class NestedNet(CheckpointModule):
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def __init__(self, checkpoint=False) -> None:
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super().__init__(checkpoint)
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self.fc1 = nn.Linear(5, 5)
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self.sub_fc = SubNet(5)
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self.fc2 = nn.Linear(5, 2)
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def forward(self, x):
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x = self.fc1(x)
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x = self.sub_fc(x, self.fc1.weight)
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x = self.fc1(x)
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x = self.fc2(x)
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return x
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class DummyDataLoader(DummyDataGenerator):
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def generate(self):
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data = torch.rand(16, 5)
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label = torch.randint(low=0, high=2, size=(16,))
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return data, label
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@non_distributed_component_funcs.register(name="nested_model")
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def get_training_components():
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def model_builder(checkpoint=False):
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return NestedNet(checkpoint)
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trainloader = DummyDataLoader()
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testloader = DummyDataLoader()
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criterion = torch.nn.CrossEntropyLoss()
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return model_builder, trainloader, testloader, torch.optim.Adam, criterion
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