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#!/usr/bin/env python3
import torch
import torch.nn as nn
import torch.nn.functional as F
class SuperConv(nn.Conv2d):
def __init__(self, *args, is_lora=False, **kwargs):
super().__init__(*args, **kwargs)
self.is_lora = is_lora
def forward(self, *args, **kwargs):
if self.is_lora:
return 3 + super().forward(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
# Define a simple Convolutional Neural Network
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = SuperConv(3, 6, 5) # Assuming input images are RGB, so 3 input channels
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = SuperConv(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# Create the network
net = SimpleCNN()
# Initialize weights with dummy values
for m in net.modules():
if isinstance(m, nn.Conv2d):
nn.init.constant_(m.weight, 0.1)
nn.init.constant_(m.bias, 0.1)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 0.1)
nn.init.constant_(m.bias, 0.1)
# Perform inference
input = torch.randn(1, 3, 32, 32).to("cuda")
net = net.to("cuda")
output = net(input)
print(output)
net = torch.compile(net, mode="reduce-overhead", fullgraph=True)
output = net(input)
print(output)
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