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 →

• 🚀 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

• 🔄 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

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

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

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

import tritonparse.structured_logging
import tritonparse.parse.utils

# Initialize logging with full tracing options
tritonparse.structured_logging.init(
    "./logs/",
    enable_trace_launch=True,                 # Capture kernel launch events (enables torch.compile tracing automatically)
    enable_more_tensor_information=True,      # Optional: collect tensor statistics (min/max/mean/std)
)

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

# Parse and generate trace files
tritonparse.parse.utils.unified_parse("./logs/", out="./parsed_output")

💡 Note: enable_trace_launch=True automatically enables tracing for both native Triton kernels (@triton.jit) and torch.compile / TorchInductor kernels.

================================================================================
📁 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!

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

# Generate reproducer for the first launch event
# (--line is 0-based: line 0 is compilation event, line 1 is first launch event)
tritonparseoss reproduce ./parsed_output/trace.ndjson.gz --line 1 --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"
)

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!