This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling.
The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation engine and graph-based operation recording, which enables kernel fusion compilation to merge multiple operations into optimized execution units. These capabilities are complemented by stream-based execution control, which manages hardware-level concurrency to maximize throughput during intensive tensor processing.
Beyond its core execution model, the framework supports a broad range of capabilities including distributed sharding infrastructure for scaling workloads across multiple devices, and extensive utilities for model weight management and serialization. It provides a deep library of mathematical and statistical operations, alongside specialized functions for quantized matrix multiplication and autoregressive text generation.
The project is implemented in C++ and includes build-time configuration options to tailor hardware backends and compilation settings for specific deployment environments.
