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Architecture

This document outlines the architecture and design principles of TorchEBM, providing insights into how the library is structured and how its components interact.

Core Components

TorchEBM is designed around several key components that work together to provide a flexible and powerful framework for energy-based modeling:

Energy Functions

The EnergyFunction base class defines the interface for all energy functions. It provides methods for:

  • Computing energy values
  • Computing gradients
  • Handling batches of inputs
  • CUDA acceleration

Implemented energy functions include Gaussian, Double Well, Rastrigin, Rosenbrock, and more.

Samplers

The BaseSampler class defines the interface for sampling algorithms. Key features include:

  • Generating samples from energy functions
  • Running sampling chains
  • Collecting diagnostics
  • Parallelized sampling

Implemented samplers include Langevin Dynamics and Hamiltonian Monte Carlo.

Loss Functions

Loss functions are used to train energy-based models. They include:

  • Contrastive Divergence (CD)
  • Persistent Contrastive Divergence (PCD)
  • Parallel Tempering Contrastive Divergence
  • Score Matching (planned)

Models

Neural network models that can be used as energy functions:

  • Base model interfaces
  • Integration with PyTorch modules
  • Support for custom architectures
  • GPU acceleration

Architecture Diagram

graph TD
    A[Energy Functions] --> C[Samplers]
    B[Models] --> A
    C --> D[Loss Functions]
    D --> B
    E[CUDA Accelerators] --> A
    E --> C
    F[Utils] --> A
    F --> B
    F --> C
    F --> D

Design Principles

Key Design Principles

TorchEBM follows these core design principles:

  1. Modularity: Components can be used independently and combined flexibly
  2. Extensibility: Easy to add new energy functions, samplers, and loss functions
  3. Performance: Optimized for both CPU and GPU execution
  4. Compatibility: Seamless integration with PyTorch ecosystem
  5. Usability: Clear, consistent API with comprehensive documentation

Component Interactions

Energy Function and Sampler Interaction

The energy function provides the landscape that the sampler traverses:

# Energy function computes energy and gradients
energy = energy_fn(x)  # Forward pass
gradient = energy_fn.gradient(x)  # Gradient computation

# Sampler uses gradients for updates
x_new = x - step_size * gradient + noise

Sampler and Loss Function Interaction

Samplers are used by loss functions to generate negative samples during training:

# Loss function uses sampler to generate negative samples
negative_samples = sampler.sample_chain(x_init, n_steps=10)

# Loss computation uses both data samples and negative samples
loss = loss_fn(data_samples, negative_samples)

Module Organization

TorchEBM's codebase is organized into the following modules:

Module Description Key Classes
torchebm.core Core functionality and base classes EnergyFunction, BaseSampler
torchebm.samplers Sampling algorithms LangevinDynamics, HamiltonianMonteCarlo
torchebm.losses Loss functions ContrastiveDivergence, PersistentContrastiveDivergence
torchebm.models Neural network models BaseModel
torchebm.cuda CUDA-accelerated implementations Various CUDA kernels
torchebm.utils Utility functions and helpers Visualization tools, diagnostics

Performance Considerations

TorchEBM is designed with performance in mind:

  • Vectorization: Operations are vectorized for efficient batch processing
  • GPU Acceleration: Most operations can run on CUDA devices
  • Memory Management: Careful memory management to avoid unnecessary allocations
  • Parallel Sampling: Samples can be generated in parallel for better utilization of hardware

Extension Points

TorchEBM is designed to be extended in several ways:

  1. Custom Energy Functions: Create your own energy functions by subclassing EnergyFunction
  2. Custom Samplers: Implement new sampling algorithms by subclassing BaseSampler
  3. Custom Loss Functions: Create new training objectives for energy-based models
  4. Neural Network Energy Functions: Use neural networks as energy functions

For more details on implementing extensions, see our API Design documentation.