TritonParse

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

🌐 Try it online →

✨ Key Features

🔍 Visualization & Analysis

🚀 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

🔧 Reproducer & Debugging Tools

🔄 Standalone Script Generation - Extract any kernel into a self-contained Python script
💾 Tensor Data Reconstruction - Preserve actual tensor data or use statistical approximation
🎯 Custom Templates - Flexible reproducer templates for different workflows
🐛 Bug Isolation - Share reproducible test cases for debugging and collaboration

📊 Structured Logging & Analysis

📝 Compilation & Launch Tracing - Capture detailed events with source mapping
🔍 Stack Trace Integration - Full Python stack traces for debugging
📈 Metadata Extraction - Comprehensive kernel statistics

🛠️ Developer Tools

🌐 Browser-based Interface - No installation required, works in your browser
🔒 Privacy-first - All processing happens locally, no data uploaded

🚀 Quick Start

1. Installation

Four options to install:

# 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.git

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

TritonParse relies on new features in Triton. If you're using nightly PyTorch, Triton is already included. Otherwise, install the latest Triton:

pip install triton

2. Generate Traces

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!
================================================================================

3. 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!

4. Generate Reproducers (Optional)

Extract any kernel into a standalone, executable Python script for debugging or testing:

# Generate reproducer from first launch event
tritonparseoss reproduce ./parsed_output/trace.ndjson.gz --line 2 --out-dir repro_output

# Run the generated reproducer
cd repro_output/<kernel_name>/
python repro_*.py

Python API:

from tritonparse.reproducer.orchestrator import reproduce

result = reproduce(
    input_path="./parsed_output/trace.ndjson.gz",
    line_index=0,           # 0-based index (first event is 0)
    out_dir="repro_output"
)

🎯 Common Reproducer Use Cases (click to expand)

🐛 Bug Isolation: Extract a failing kernel into a minimal standalone script
⚡ Performance Testing: Benchmark specific kernels without running the full application
🤝 Team Collaboration: Share reproducible test cases with colleagues or in bug reports
📊 Regression Testing: Compare kernel behavior and performance across different versions
🔍 Deep Debugging: Modify and experiment with kernel parameters in isolation

📚 Complete Documentation

📖 Guide	Description
🏠 Wiki Home	Complete documentation and quick navigation
📦 Installation	Setup guide for all scenarios
📋 Usage Guide	Complete workflow, reproducer generation, and examples
🌐 Web Interface	Master the visualization interface
🔧 Developer Guide	Contributing and architecture overview
📝 Code Formatting	Formatting standards and tools
❓ FAQ	Quick answers and troubleshooting

📊 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 and prerequisites
Code formatting standards (Formatting Guide)
Pull request and code review process
Testing guidelines
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.

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

njriasan/tritonparse