A comprehensive visualization and analysis tool for Triton kernel compilation and launch — helping developers analyze, debug, and understand Triton kernel compilation processes.
- 🚀 Launch Difference Analysis - Detect and visualize kernel launch parameter variations
- 📊 IR Code View - Side-by-side IR viewing with synchronized highlighting and line mapping
- 🔄 File Diff View - Compare kernels across different trace files side-by-side
- 📝 Multi-format IR Support - View TTGIR, TTIR, LLIR, PTX, and AMDGCN
- 🎯 Interactive Code Views - Click-to-highlight corresponding lines across IR stages
- 📝 Compilation & Launch Tracing - Capture detailed events with source mapping
- 🔍 Stack Trace Integration - Full Python stack traces for debugging
- 📈 Metadata Extraction - Comprehensive kernel statistics
- 🔧 Reproducer Generation - Generate standalone Python scripts to reproduce kernels
- 🌐 Browser-based Interface - No installation required, works in your browser
- 🔒 Privacy-first - All processing happens locally, no data uploaded
import tritonparse.structured_logging
import tritonparse.utils
# Initialize logging
tritonparse.structured_logging.init("./logs/", enable_trace_launch=True)
# Your Triton/PyTorch code here
# ... your kernels ...
# Parse and generate trace files
tritonparse.utils.unified_parse("./logs/", out="./parsed_output")📝 Example output (click to expand)
================================================================================
📁 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!
================================================================================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!
For basic usage (trace generation): Four options:
# install nightly version
pip install -U --pre tritonparse
# install stable version
pip install tritonparse
# install from source
git clone https://github.com/meta-pytorch/tritonparse.git
cd tritonparse
pip install -e .
# pip install the latest version from github
pip install git+https://github.com/meta-pytorch/tritonparse.gitPrerequisites: 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| 📖 Guide | Description |
|---|---|
| 🏠 Wiki Home | Complete documentation and quick navigation |
| 📦 Installation | Setup guide for all scenarios |
| 📋 Usage Guide | Complete workflow, examples, and reproducer |
| 🌐 Web Interface | Master the visualization interface |
| 🔧 Developer Guide | Contributing and architecture overview |
| 📝 Code Formatting | Formatting standards and tools |
| ❓ FAQ | Quick answers and troubleshooting |
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.
We welcome contributions! Please see our Developer Guide for:
- Development setup and prerequisites
- Code formatting standards (Formatting Guide)
- Pull request and code review process
- Testing guidelines
- Architecture overview
- 🐛 Report Issues: GitHub Issues
- 💬 Discussions: GitHub Discussions
- 📚 Documentation: TritonParse Wiki
This project is licensed under the BSD-3 License - see the LICENSE file for details.
✨ Ready to get started? Visit our Installation Guide or try the online tool directly!