Attorch

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attorch is an AI Frameworks & Infra tool that provides a subset of PyTorch's neural network modules, rewritten in Python using OpenAI's Triton. It aims to be an easily hackable, self-contained, and readable collection of modules.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is attorch?

attorch offers a collection of PyTorch's neural network modules, re-implemented in Python using OpenAI's Triton. The project's core goal is to provide an easily hackable, self-contained, and readable set of deep learning operations, maintaining or improving efficiency compared to standard PyTorch implementations. It serves as an accessible starting point for developers looking to create custom deep learning operations without the speed limitations of pure PyTorch or the complexity of writing CUDA kernels. Unlike many Triton-powered frameworks focused on Transformers, attorch includes layers for diverse applications like computer vision. It supports both forward and backward passes, making it suitable for training and inference, and offers an interface with PyTorch fallback for seamless integration.

Best used for

Ideal for developers who need to implement custom deep learning operations, accelerate neural network modules, and integrate Triton-based layers with existing PyTorch workflows. Especially valuable for those seeking an easily hackable and self-contained library for both training and inference, extending beyond NLP applications.

Common actions

develop custom neural networks

accelerate deep learning operations

integrate Triton with PyTorch

implement computer vision models

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Capabilities

Key features

Triton-powered neural network modules
Automatic mixed precision support
PyTorch fallback integration
Forward and backward pass support
Diverse layer implementations

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the primary dependencies for attorch?

attorch primarily depends on specific versions of PyTorch and Triton. Users need to install `torch==2.4.0` and `triton==3.0.0` to get started. This ensures compatibility and optimal performance with the custom neural network modules.

Does attorch support both training and inference?

Yes, attorch fully supports both forward and backward passes, meaning it can be used effectively during both training and inference phases of deep learning workflows. While it supports both, its performance for inference might not always match dedicated inference engines.

How does attorch handle layers not implemented in its library?

attorch provides an interface, `attorch.nn`, which offers a seamless fallback to PyTorch's modules if a desired layer is not available within attorch. This allows for easier integration and ensures functionality even when specific custom layers are not yet implemented.

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