Project Basic Structure

I. Overall Design Overview

The project adopts a four-layer architecture design, abstracting layer by layer from bottom to top, extending from hardware and computation scheduling to upper-level public interfaces and application layers. Each layer has clear responsibilities and well-defined boundaries.

II. Four-Layer Division

Layer 1: Low-Level Computation and Hardware Abstraction Layer

Core Responsibilities:
Direct interaction with low-level hardware and computational resources, providing unified computation and device abstraction.

Key Components:

  • Compute Dispatcher
  • Device abstract class
  • Low-level hardware interface interaction layer

Characteristics:

  • Shields differences between different hardware implementations
  • Provides unified, extensible computation scheduling capabilities for upper layers

Layer 2: Data Abstraction and Data Operation Layer

Core Responsibilities:
Abstracts computational data and provides intermediate-level data operation capabilities.

Key Components:

  • Tensor
  • Tensor Operator
  • Linear Algebra Operator
  • Various intermediate-level data operation classes

Characteristics:

  • Unified data representation
  • Handles operations and transformations of tensors and structured data
  • Acts as a bridge between computation and algorithm layers

Layer 3: Machine Learning and Computation Graph Core Layer

Core Responsibilities:
Builds high-level computation and learning capabilities, serving as the project's core logic layer.

Key Components:

  • Machine Learning module
  • Deep Learning module
  • Dataset support system
  • Computation Graph
  • Core computation structures such as DA graphs

Characteristics:

  • Responsible for model construction, training, and inference logic
  • Unifies core mechanisms such as computation graphs and automatic differentiation

Layer 4: Public Interface and Upper Application Layer

Core Responsibilities:
Provides unified, easy-to-use methods and interfaces externally, targeting public internet or upper-level applications.

Key Components:

  • Method-level operation interfaces
  • Public internet-facing access and invocation layer

Characteristics:

  • Provides stable APIs
  • Shields internal complex implementations
  • Supports integration with upper-level applications and external systems

III. Summary

The project achieves the following through the four-layer structure:

  • Bottom layer decouples hardware
  • Middle layer unifies data
  • Core layer focuses on machine learning and computation graphs
  • Top layer provides public interfaces and application capabilities

The overall design has good extensibility, maintainability, and engineering clarity.

The project is built using Meson/CMake.