tinygrad: A Simple Neural Network Framework Based on Three Core Operation Types
By
albelfio
Toasted golden, schmeared with insight. Top of the rack.
Summary
The article introduces tinygrad, a neural network framework that simplifies complex networks into three fundamental operation types: ElementwiseOps (UnaryOps, BinaryOps, TernaryOps like SQRT, ADD, MUL), ReduceOps (operations that reduce tensor size like SUM, MAX), and MovementOps (virtual operations that rearrange data copy-free like RESHAPE, PERMUTE). The framework is described as extremely simple and the fastest growing in its category, breaking down complex neural network operations into these core components.
Key quotes
· 5 pulledWe write and maintain tinygrad, the fastest growing neural network framework
It's extremely simple, and breaks down the most complex networks into 3 OpTypes
ElementwiseOps are UnaryOps, BinaryOps, and TernaryOps. They operate on 1-3 tensors and run elementwise
ReduceOps operate on one tensor and return a smaller tensor
MovementOps are virtual ops that operate on one tensor and move the data around copy-free
You might also wanna read
Timber: AOT Compiler Converts Classical ML Models to Native C99 Code for High-Performance Inference
Timber is an open-source tool that compiles classical machine learning models (XGBoost, LightGBM, scikit-learn, CatBoost, ONNX) into native
github.com·3mo ago
Implementing HNSW Algorithm for Vector Search in PHP: A Practical Guide
This article explains the Hierarchical Navigable Small World (HNSW) algorithm for efficient vector similarity search, contrasting it with br
ONNX Runtime May Silently Convert Models to FP16 on Apple MPS Backend: Causes and Solutions
The article details a technical issue discovered in ONNX Runtime where models may be silently converted to FP16 (half-precision) when runnin
Debugging a PyTorch Bug: How a Training Loss Plateau Revealed Deep Framework Insights
A developer shares their experience debugging a training loss plateau in PyTorch that they initially assumed was their own mistake with hype
elanapearl.github.io·7mo ago
PyTorch Monarch: A New Framework for Complex, Dynamic Machine Learning Workflows
PyTorch Monarch is a new framework designed to address the challenges of modern ML workflows that are heterogeneous, asynchronous, and dynam
pytorch.org·7mo ago
Luminal: High-Performance Deep Learning Library Using Search-Based Compilation
Luminal is a deep learning library that uses search-based compilation to achieve high performance. It's a Rust-based framework that allows u
github.com·9mo ago