SPHINX: Transferring Optimization Predictors Across MLIR Dialects Using Program Graphs

SPHINX is a research project focused on transferring machine learning-based optimization predictors across MLIR dialects by representing programs as ProGraML graphs. This repository contains both the core C++ graph generation infrastructure and the Python-based machine learning experiments.

Repository Structure

This monorepo is divided into two main components:

• mlir-to-programl/ (C++): The core tool that translates MLIR code into ProGraML graph representations.
• ml-experiments/ (Python): Machine learning models, training scripts, and reproduction experiments.

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Getting Started

1. The Graph Generator (C++)

Located in mlir-to-programl/. This tool reads MLIR files and outputs the corresponding graphs.

Prerequisites:

• CMake >= 3.20
• C++20 Compiler (GCC 10+ or Clang 10+)
• LLVM/MLIR (Installed and configured)
• Google Protobuf
• Google Abseil (Abseil-cpp)

Build Instructions:

1. Configure LLVM Path: The build system expects to find your LLVM installation. By default, it looks in $HOME/llvm_install. If your LLVM is installed elsewhere, export the path before building:

   export CMAKE_PREFIX_PATH=/path/to/your/llvm/lib/cmake:$CMAKE_PREFIX_PATH

2. Build:

   cd mlir-to-programl
   mkdir build && cd build
   cmake ..
   make -j8

Usage:

1. Single File Mode: Converts a single MLIR file. If the output path is omitted, it defaults to replacing the extension with .ProgramGraph.pb.

   ./mlir-to-programl <input.mlir> [output.ProgramGraph.pb]

2. Dataset Mode: Processes an entire directory. It detects if the input is a folder and automatically converts all contained MLIR files.

   ./mlir-to-programl <dataset_folder>

2. The Experiments (Python)

Located in ml-experiments/. Contains GNN models and training/evaluation scripts.

Prerequisites:

• Python 3.8+
• CUDA (optional but recommended for training)

Setup:

We provide a unified environment for all experiments:

cd ml-experiments

# 1. Create a virtual environment
python3 -m venv venv
source venv/bin/activate

# 2. Install dependencies (PyTorch, PyG, etc.)
pip install -r requirements.txt

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