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70 lines
2.0 KiB
70 lines
2.0 KiB
import torch
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import torch.nn as nn
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import torch.optim as optim
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from coati.models import convert_to_lora_module
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from torch.utils.data import DataLoader, TensorDataset
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class SimpleNN(nn.Module):
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def __init__(self, input_size, hidden_size, num_classes):
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super(SimpleNN, self).__init__()
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self.fc1 = nn.Linear(input_size, hidden_size)
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self.relu = nn.ReLU()
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self.fc2 = nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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out = self.fc1(x)
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out = self.relu(out)
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out = self.fc2(out)
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return out
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def test_overfit():
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input_size = 1000
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hidden_size = 200
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num_classes = 5
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batch_size = 64
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learning_rate = 0.01
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num_epochs = 200
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# Synthesized dataset
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X = torch.randn(batch_size, input_size)
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Y = torch.randint(0, num_classes, (batch_size,))
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# Convert to DataLoader
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dataset = TensorDataset(X, Y)
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loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
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# Build and convert model
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model = SimpleNN(input_size, hidden_size, num_classes)
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weight_to_compare = model.fc1.weight.detach().clone()
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model = convert_to_lora_module(model, lora_rank=30)
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# Loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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# Train the model
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for _ in range(num_epochs):
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for i, (inputs, labels) in enumerate(loader):
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# Forward pass
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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print(loss)
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# Backward and optimize
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# Check if model has overfitted
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outputs = model(X)
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_, predicted = torch.max(outputs.data, 1)
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total = labels.size(0)
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correct = (predicted == Y).sum().item()
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assert (correct / total > 0.95, "The model has not overfitted to the synthesized dataset")
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assert (weight_to_compare - model.fc1.weight).sum() < 0.01
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if __name__ == "__main__":
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test_overfit()
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