TritonParse

A comprehensive visualization and analysis tool for Triton IR files — helping developers analyze, debug, and understand Triton kernel compilation processes.

🌐 Try it online →

✨ Key Features

• 🚀 Launch Difference Analysis - Automatically detect and visualize variations in kernel launch parameters, helping you pinpoint performance bottlenecks and debug launch configurations.
• 🔍 Interactive Visualization - Explore Triton kernels with detailed metadata and stack traces
• 📊 Multi-format IR Support - View TTGIR, TTIR, LLIR, PTX, and AMDGCN in one place
• 🔄 Side-by-side Comparison - Compare IR stages with synchronized highlighting
• 📝 Structured Logging - Capture detailed compilation and launch events with source mapping
• 🌐 Ready-to-use Interface - No installation required, works in your browser
• 🔒 Privacy-first - All processing happens locally in your browser, no data uploaded

🚀 Quick Start

1. Generate Traces

import tritonparse.structured_logging

# Initialize logging with launch tracing enabled
tritonparse.structured_logging.init("./logs/", enable_trace_launch=True)

# Your Triton/PyTorch code here
# ... your kernels ...

# Parse and generate trace files
import tritonparse.utils
tritonparse.utils.unified_parse("./logs/")

The example terminal output is:

tritonparse log file list: /tmp/tmp1gan7zky/log_file_list.json
INFO:tritonparse:Copying parsed logs from /tmp/tmp1gan7zky to /scratch/findhao/tritonparse/tests/parsed_output

================================================================================
📁 TRITONPARSE PARSING RESULTS
================================================================================
📂 Parsed files directory: /scratch/findhao/tritonparse/tests/parsed_output
📊 Total files generated: 2

📄 Generated files:
--------------------------------------------------
   1. 📝 dedicated_log_triton_trace_findhao__mapped.ndjson.gz (7.2KB)
   2. 📝 log_file_list.json (181B)
================================================================================
✅ Parsing completed successfully!
================================================================================

2. Visualize Results

Visit https://meta-pytorch.org/tritonparse/ and open your local trace files (.ndjson.gz format).

🔒 Privacy Note: Your trace files are processed entirely in your browser - nothing is uploaded to any server!

🛠️ Installation

For basic usage (trace generation):

git clone https://github.com/meta-pytorch/tritonparse.git
cd tritonparse
pip install -e .

Prerequisites: Python ≥ 3.10, Triton ≥ 3.4.0, GPU required (NVIDIA/AMD)

TritonParse relies on new features in Triton. Please install the latest version of Triton:

pip install triton

📚 Complete Documentation

📖 Guide	Description
🏠 Wiki Home	Complete documentation and navigation
📦 Installation Guide	Detailed setup for all scenarios
📋 Usage Guide	Complete workflow and examples
🌐 Web Interface Guide	Master the visualization interface
🔧 Developer Guide	Contributing and development setup
❓ FAQ	Frequently asked questions

🛠️ Tech Stack

• Frontend: React 19, TypeScript, Vite, Tailwind CSS, Monaco Editor
• Backend: Python with Triton integration, structured logging
• Deployment: GitHub Pages, automatic deployment

📊 Understanding Triton Compilation

TritonParse visualizes the complete Triton compilation pipeline:

Python Source → TTIR → TTGIR → LLIR → PTX/AMDGCN

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

🤝 Contributing

We welcome contributions! Please see our Developer Guide for:

• Development setup
• Code formatting standards
• Pull request process
• Architecture overview

📞 Support & Community

• 🐛 Report Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📚 Documentation: TritonParse Wiki

📄 License

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

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✨ Ready to get started? Visit our Installation Guide or try the online tool directly!