🌍 中文文档

Sylvan is an educational, modern C++ deep learning framework built from scratch. It supports CUDA for high-performance GPU computing and Bazel for a robust build system. The main goal of this project is to help developers get a deep, practical understanding of how AI frameworks operate under the hood.

The core components of this framework are implemented in under 5000 lines of C++/CUDA code, making it a concise and approachable codebase for learning.

This project is developed with a "composition over inheritance" philosophy, favoring a functional-style API, which promotes modularity, reusability, and testability by avoiding complex class hierarchies and enabling easier combination of operations. The codebase is very easy to understand and maintain.

• Modern C++: Utilizes modern C++ features for clean, safe, and expressive code.
• CUDA-First: All core computations are designed to run on the GPU. No CPU fallback is planned to maintain focus.
• Function-Style API: Operations are free functions (ops::add(a, b)) rather than member functions (a.add(b)), promoting composition and testability.
• No Inheritance for Layers/Ops: Avoids complex class hierarchies.
• Bazel with Bzlmod: A modern, reproducible, and scalable build system.

• sylvan_tensor library for core tensor operations (creation, element-wise ops, matmul, sum, reshape, transpose, slice, fill, uniform initialization, ReLU, Softmax, LayerNorm, Embedding lookup)
• Advanced GPU Memory Management (Allocator/Pool) (Basic RAII via std::shared_ptr for Tensor data is implemented)
• sylvan_core library with:
  • Dynamic Computation Graph
  • Autograd Engine (backward pass for all implemented ops)
  • Basic Layers (Linear, ReLU, LayerNorm, Embedding)
  • Attention Mechanisms (Multi-Head Attention, Scaled Dot-Product Attention)
  • Transformer Architecture (Encoder, Decoder, Full Transformer)
  • Optimizers (SGD, Adam)
• Convolutional Layers (Conv2D, MaxPooling) using cuDNN
• sylvan_infer library for optimized inference
• Model serialization (saving/loading weights)
• Dataloader (multiple formats, parallelly)

Sylvan's design prioritizes clarity and a hands-on understanding of deep learning internals. The codebase is extensively commented, especially in the core sylvan/core and sylvan/tensor directories. Each Variable operation, neural network layer, and GPU kernel includes detailed explanations of its purpose, parameters, and mathematical derivation. This focus on documentation aims to provide a clear roadmap for anyone looking to delve into the foundational concepts of modern AI frameworks.

• CUDA Toolkit 11.0 or later (configured via $CUDA_PATH)
• Bazel 8.2.1 or later

This project uses Bazel. Ensure you have a recent version of Bazel and the NVIDIA CUDA Toolkit installed.

1. Clone the repository:

   git clone https://github.com/pluveto/sylvan.git
   cd sylvan

2. Sync dependencies (first time only):

   bazel mod tidy

3. Build all targets: All CUDA-related build flags are managed via the .bazelrc file.

   bazel build --config=cuda //...

4. Run all tests:

   bazel test --config=cuda //...

5. Run an example:

   # Run a linear regression example
   bazel run --config=cuda //examples:linear_regression
   # Run a transformer example
   bazel run --config=cuda //examples:number_translator

• sylvan/: Main source code.
  • tensor/: The core tensor library. Doesn't know about autograd.
  • core/: The deep learning framework (autograd, layers, optimizers).
  • infer/: (Future) The inference-only library.
• tests/: Unit tests for all libraries (using GTest).
• examples/: Standalone examples showing how to use the framework.

Run CC=clang bazel run @hedron_compile_commands//:refresh_all to generate a compilation database for your editor.

Install Nsight Visual Studio Code Edition for better debugging experience.

This project is licensed under the MIT License. See the LICENSE file for details.