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			50 lines
		
	
	
		
			1.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			50 lines
		
	
	
		
			1.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| #!/usr/bin/env python
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| 
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| import torch
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| import torch.nn as nn
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| 
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| from colossalai.nn import CheckpointModule
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| 
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| from .registry import non_distributed_component_funcs
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| from .utils.dummy_data_generator import DummyDataGenerator
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| 
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| 
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| class NetWithRepeatedlyComputedLayers(CheckpointModule):
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|     """
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|     This model is to test with layers which go through forward pass multiple times.
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|     In this model, the fc1 and fc2 call forward twice
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|     """
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| 
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|     def __init__(self, checkpoint=False) -> None:
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|         super().__init__(checkpoint=checkpoint)
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|         self.fc1 = nn.Linear(5, 5)
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|         self.fc2 = nn.Linear(5, 5)
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|         self.fc3 = nn.Linear(5, 2)
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|         self.layers = [self.fc1, self.fc2, self.fc1, self.fc2, self.fc3]
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| 
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|     def forward(self, x):
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|         for layer in self.layers:
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|             x = layer(x)
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|         return x
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| 
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| 
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| class DummyDataLoader(DummyDataGenerator):
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| 
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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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| 
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| 
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| @non_distributed_component_funcs.register(name='repeated_computed_layers')
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| def get_training_components():
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| 
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|     def model_builder(checkpoint=False):
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|         return NetWithRepeatedlyComputedLayers(checkpoint)
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| 
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|     trainloader = DummyDataLoader()
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|     testloader = DummyDataLoader()
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| 
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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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