docs/explanations/layout.md

Project layout

Torus layout

Torus is a modular framework composed by sub-projects using different technologies, all having theirs own build system and test suite. Each sub-project have is own README that explain how to setup the developer environment, how to build it and how to run tests commands.

Torus is made of 4 main categories of sub-project that are organized in subdirectories from the root of the Torus repo:

• frontends contains high-level transpilers that target end users developers who want to use the Torus stack easily from their usual environment. There are for now only one frontend provided by the Torus project: a Python frontend named torus-python.
• compilers contains the sub-projects in charge of actually solving the compilation problem of an high-level abstraction of FHE to an actual executable. torus-optimizer is a Rust based project that solves the optimization problems of an FHE dag to a TFHE dag and torus-compiler which use torus-optimizer is an end-to-end MLIR-based compiler that takes a crypto free FHE dialect and generates compilation artifacts both for the client and the server. torus-compiler project provide in addition of the compilation engine, a client and server library in order to easily play with the compilation artifacts to implement a client and server protocol.
• backends contains CAPI that can be called by the torus-compiler runtime to perform the cryptographic operations. There are currently two backends:
  • torus-cpu, using TFHE-rs that implement the fastest implementation of TFHE on CPU.
  • torus-cuda that provides a GPU acceleration of TFHE primitives.
• tools are basically every other sub-projects that cannot be classified in the three previous categories and which are used as a common support by the others.

Torus Python layout

The module structure of Torus Python. You are encouraged to check individual .py files to learn more.

• torus
  • fhe
    • dtypes: data type specifications (e.g., int4, uint5, float32)
    • values: value specifications (i.e., data type + shape + encryption status)
    • representation: representation of computation (e.g., computation graphs, nodes)
    • tracing: tracing of python functions
    • extensions: custom functionality (see Extensions)
    • mlir: computation graph to mlir conversion
    • compilation: configuration, compiler, artifacts, circuit, client/server, and anything else related to compilation