torch-circuit 0.9.1

A PyTorch extension for easy skip connections and repeatable blocks

torch-circuit

A PyTorch extension for building neural networks with skip connections and repeatable blocks.

Features

• Named Skip Connections: Easily implement ResNet-style skip connections with named references
• Repeatable Blocks: Define blocks once and repeat them multiple times without code duplication
• Visualization: Generate circuit diagrams of your network architecture
• PyTorch Compatible: Works seamlessly with existing PyTorch code and training loops

Installation

From Source

git clone https://github.com/your-username/torch-circuit.git
cd torch-circuit
pip install -e .

From PyPI (when published)

pip install torch-circuit

Quick Start

import torch
import torch.nn as nn
from torch_circuit import Circuit, SaveInput, GetInput, StartBlock, EndBlock

# Create a simple ResNet-style model with skip connections
model = Circuit(
    nn.Conv2d(3, 64, 3, padding=1),
    nn.BatchNorm2d(64),
    nn.ReLU(),
        
    # Repeatable ResNet block
    StartBlock("resnet", num_repeats=3),
    SaveInput("residual"),
    nn.Conv2d(64, 64, 3, padding=1),
    nn.BatchNorm2d(64),
    nn.ReLU(),
    nn.Conv2d(64, 64, 3, padding=1),
    nn.BatchNorm2d(64),
    GetInput("residual", op=torch.add),  # Add skip connection
    nn.ReLU(),
    EndBlock("resnet"),
    
    nn.AdaptiveAvgPool2d((1, 1)),
    nn.Flatten(),
    nn.Linear(64, 10)
)

# Use like any PyTorch model
x = torch.randn(1, 3, 32, 32)
output = model(x)

# Visualize the architecture
model.visualize(save_path="resnet_example.pdf")

Key Components

Circuit

The main container class that supports skip connections and repeatable blocks.

SaveInput / GetInput

• SaveInput(name): Save the input tensor with a given name
• GetInput(name, op=torch.add): Retrieve saved tensor and combine it with the current tensor using an operation (e.g. addition, concatenation)

StartBlock / EndBlock

• StartBlock(name, num_repeats=N): Mark the beginning of a repeatable block
• EndBlock(name): Mark the end of a repeatable block

Examples

See the examples/ directory for a complete example demonstrating equivalence to standard PyTorch implementations:

• examples/resnet_mnist.py: ResNet architecture on MNIST dataset

Architecture Visualization

torch-circuit can generate simple visual diagrams of your network architecture:

model.visualize(save_path="architecture.pdf")

Advanced Usage

Custom Operations

You can use custom operations for combining skip connections:

# Element-wise multiplication instead of addition
GetInput("residual", op=torch.mul)

# Custom lambda function
GetInput("residual", op=lambda x, y: x + 0.5 * y)

License

This project is licensed under the MIT License - see the LICENSE file for details.