TritonParse

A comprehensive visualization and analysis tool for Triton IR files, designed to help developers analyze, debug, and understand Triton kernel compilation processes.

🚀 Features

Visualization & Analysis

• Interactive Kernel Explorer: Display detailed kernel information and stack traces
• Multi-format IR Support: View and explore multiple Triton IR formats:
  • TTGIR (Triton GPU IR)
  • TTIR (Triton IR)
  • LLIR (LLVM IR)
  • PTX (Parallel Thread Execution)
  • AMDGCN (AMD GPU IR)
• Side-by-side Comparison: Compare TTGIR and PTX code with synchronized highlighting
• Interactive Code Views: Click-to-highlight corresponding lines across different formats
• Source Mapping: Trace relationships between different compilation stages

Structured Logging

• Compilation Tracing: Capture detailed Triton compilation events
• Stack Trace Integration: Full Python stack traces for compilation events
• Metadata Extraction: Comprehensive kernel metadata and compilation statistics
• NDJSON Output: Structured logging format for easy processing

Website Deployment Options

• GitHub Pages: Automatic deployment with GitHub Actions
• Standalone Build: Single HTML file with all assets inlined
• Local Development: Full development environment setup

🛠️ Tech Stack

Frontend:

• React 19 with TypeScript
• Vite for build tooling
• Tailwind CSS for styling
• Monaco Editor for code display
• React Syntax Highlighter for syntax highlighting
• React Resizable Panels for layout

Backend/Processing:

• Python with Triton integration
• Structured logging and event tracing
• Source mapping extraction utilities

📦 Installation

For Users (Trace Generation Only)

Prerequisites:

• Python >= 3.8
• Triton >= 3.3.1

Quick Start:

# Clone the repository
git clone https://github.com/pytorch-labs/tritonparse.git
cd tritonparse

# Install Python dependencies
pip install -e .

For Website Developers (Optional)

Additional Prerequisites:

• Node.js >= 18.0.0
• npm

Website Setup:

# Install website dependencies
cd website
npm install

🎯 Usage

1. Generate Triton Trace Files

First, integrate TritonParse with your Triton/PyTorch code to generate trace files:

import torch
import tritonparse.structured_logging

# Initialize structured logging to capture Triton compilation events
tritonparse.structured_logging.init("./logs/")

# Example: Using with torch.compile
def your_kernel():
    # Your PyTorch/Triton kernel code
    pass

compiled_kernel = torch.compile(your_kernel)
result = compiled_kernel()  # This will generate trace logs in ./logs/

# The trace files can then be analyzed using the web interface

2. Analyze with Web Interface

Option A: Online Interface (Recommended)

Visit https://pytorch-labs.github.io/tritonparse/ to use the tool directly in your browser:

1. Open your local trace file directly in the browser
2. Explore the visualization using the Overview and Code Comparison tabs

Option B: Local Development (For Contributors)

For contributors working on the website:

cd website
npm run dev

Access the application at http://localhost:5173

Available Scripts:

• npm run build - Standard build
• npm run build:single - Standalone HTML file
• npm run preview - Preview production build

📁 Project Structure

tritonparse/
├── tritonparse/              # Python package
│   ├── structured_logging.py # Main logging infrastructure
│   ├── extract_source_mappings.py # Source mapping utilities
│   ├── source_type.py        # Source type definitions
│   ├── utils.py              # Helper utilities
│   ├── common.py             # Common functions
│   └── tp_logger.py          # Logger configuration
├── website/                  # React web application
│   ├── src/                  # React source code
│   ├── public/               # Static assets and example files
│   ├── scripts/              # Build utilities (inline-html.js)
│   ├── node_modules/         # Dependencies
│   ├── package.json          # Node.js dependencies
│   ├── vite.config.ts        # Vite configuration
│   └── dist/                 # Built application (after build)
├── tests/                    # Test files and example traces
│   ├── test_add.py           # Example Triton kernel test
│   ├── unit_tests.py         # Unit tests
│   └── *.ndjson              # Example trace files
├── run.py                    # Main runner script
├── pyproject.toml            # Python package configuration
├── LICENSE                   # BSD-3 license
├── CONTRIBUTING.md           # Contribution guidelines
└── CODE_OF_CONDUCT.md        # Code of conduct

🔧 Development

Python Development

Install in development mode:

pip install -e .

Example test:

cd tests
python test_add.py

Website Development (For Contributors)

Start development server:

cd website
npm run dev

Available Scripts:

• npm run dev - Start development server
• npm run build - Production build
• npm run build:single - Standalone HTML build
• npm run lint - Run ESLint
• npm run preview - Preview production build

🚀 Deployment

Live Website

The TritonParse visualization tool is automatically deployed and available at: https://pytorch-labs.github.io/tritonparse/

For Contributors: Local Deployment

Build standalone version:

cd website
npm run build:single

The dist/standalone.html file contains the entire application and can be deployed anywhere.

📊 Understanding Triton Compilation

TritonParse helps visualize the Triton compilation pipeline:

1. Python Source → Triton kernel functions
2. TTIR → Triton's high-level IR
3. TTGIR → GPU-specific Triton IR
4. LLIR → LLVM IR representation
5. PTX → NVIDIA PTX assembly
6. AMDGCN → AMD GPU IR

Each stage can be inspected and compared to understand optimization transformations.

🔍 Advanced Features

Environment Variables

• TRITONPARSE_DEBUG=1 - Enable debug logging
• TRITONPARSE_NDJSON=1 - Output in NDJSON format (default)

Custom Log Directory

tritonparse.structured_logging.init("/custom/log/path/")

Source Mapping Extraction

python tritonparse/extract_source_mappings.py input.ndjson output_mapped.ndjson

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Run tests: npm test (website) and python -m pytest (Python)
5. Submit a pull request

📝 License

This project is licensed under the BSD-3 License - see the LICENSE file for details.

🔗 Related Projects

• OpenAI Triton - The Triton compiler and language
• PyTorch - Deep learning framework with Triton integration

📞 Support

• Issues: GitHub Issues
• Live Tool: https://pytorch-labs.github.io/tritonparse/
• Examples: Check the tests/ directory for usage examples

---

Note: This tool is designed for developers working with Triton kernels and GPU computing. Basic familiarity with CUDA, GPU programming concepts, and the Triton language is recommended for effective use.