Can’t Import Sage: Fix No Module Named ‘Triton’ Error Quickly

Users working with SageAttention or related machine learning libraries sometimes encounter the error message: ModuleNotFoundError: No module named ‘triton’.

This issue typically arises during the import or runtime phase when the Python environment cannot locate the required triton module.

This article explores the reasons behind this error, the role of triton in SageAttention, and step-by-step solutions to resolve the problem. Detailed explanations and practical advice will help you get back on track quickly.

Understanding the Error

The message:

ModuleNotFoundError: No module named 'triton'

means that the Python interpreter tried to import a module named triton but failed to find it in the current environment. This is a common issue when dependencies are missing or improperly installed.

SageAttention is a library designed for efficient attention mechanisms in deep learning models. To accelerate computations, it leverages triton, a specialized compiler and runtime for writing high-performance GPU kernels.

Without triton, SageAttention cannot function properly.

What is Triton?

triton is an open-source language and compiler developed to simplify writing custom GPU kernels. It offers an alternative to CUDA, allowing researchers and developers to write fast GPU code in Python.

Many modern ML libraries use it to optimize performance-critical components.

Its presence is often mandatory for advanced attention layers and custom GPU operations. Failure to have triton installed or properly configured results in import errors.

Common Causes of the Error

Cause	Description	Impact
Missing Installation	triton package not installed in the Python environment.	ModuleNotFoundError during import.
Incorrect Python Environment	Using a different environment where triton is absent.	Same error despite previous installation.
Version Incompatibility	Installed triton version incompatible with SageAttention or Python version.	Import fails or runtime errors.
Missing GPU or CUDA Setup	No compatible GPU or CUDA drivers, leading to installation or runtime issues.	Failure during installation or import.
Installation Errors	Network issues, permissions, or pip errors during installation.	Partial or failed installation of triton.

How to Check if Triton is Installed

Before troubleshooting, check if triton is available in your environment. Open a terminal or command prompt and run:

python -c "import triton; print(triton.__version__)"

If the module is installed, you should see the version printed. If you get a ModuleNotFoundError, the package is missing or inaccessible.

Installing Triton

The recommended way to install triton is via pip. However, it requires a compatible GPU and CUDA environment.

Here’s the general installation command:

pip install triton

To ensure compatibility, verify your Python version (usually 3.7 or higher) and CUDA version (commonly 11.x or above). triton releases correspond to specific CUDA versions.

If you face issues installing the latest version, try installing a specific version compatible with your setup:

pip install triton==2.0.0

Replace 2.0.0 with the version that matches your environment.

Note on CUDA and GPU Drivers

Troubleshooting Installation Issues

Verify Python Environment

Ensure you are installing triton in the same environment where SageAttention runs. Use which python (Linux/macOS) or where python (Windows) to check the interpreter path.

Virtual environments or Conda environments may isolate packages. Activate your environment before installing:

source myenv/bin/activate  # Linux/macOS
conda activate myenv         # Conda environments
myenv\Scripts\activate       # Windows

Update pip and setuptools

Older versions of pip or setuptools may cause installation failures. Upgrade them before installing triton:

pip install --upgrade pip setuptools

Install with Verbose Output

For detailed error messages, run:

pip install triton --verbose

This provides clues on missing dependencies or permission problems.

Check GPU and CUDA Setup

Make sure CUDA is correctly installed and accessible. You can verify CUDA availability with:

nvcc --version

Also, confirm NVIDIA driver version with:

nvidia-smi

Both commands should return valid outputs without errors.

Alternative Installation Methods

If pip install triton fails, consider the following:

• Conda Forge: Some users manage installations through Conda channels:

conda install -c conda-forge triton

• Build from Source: For advanced users, cloning the Triton GitHub repository and building manually can solve version or compatibility issues.

Integrating Triton with SageAttention

Once triton is installed, SageAttention should automatically detect it. However, some additional steps may be necessary depending on your setup.

Step	Action	Description
1	Install Triton	Ensure triton is installed in the same environment.
2	Install SageAttention	Use pip install sageattention or source build.
3	Validate Imports	Run import triton and import sageattention in Python.
4	Test Functionality	Run example scripts or tests to verify GPU acceleration.

If import errors persist, double-check Python paths and environment variables.

Common Pitfalls and How to Avoid Them

• Multiple Python Installations: Having multiple Python versions can cause confusion. Always install and run packages in the same interpreter.
• Insufficient Permissions: Use --user flag or virtual environments if permission errors occur during installation.
• Outdated CUDA Drivers: Triton requires up-to-date drivers; outdated ones will cause failures.
• Incompatible Python Version: Triton supports Python 3.7 or newer. Older versions will not work.
• Network Issues: Firewalls or proxy servers may block pip installations. Use offline wheels if necessary.

Example: Fixing the Import Error Step-by-Step

User Report: “I installed SageAttention but get ‘No module named triton’ when running my script.”

1. Check triton installation: Run python -c "import triton". If error occurs, triton is not installed.
2. Install triton: Run pip install triton. If permission errors occur, try pip install --user triton.
3. Verify GPU and CUDA: Run nvidia-smi and nvcc --version to confirm setup.
4. Re-run your script: Import error should be resolved.

Additional Resources

Resource	Description	Link
Triton GitHub Repository	Official source code and documentation.	github.com/openai/triton
SageAttention Documentation	Installation and usage guides.	sageattention.readthedocs.io
CUDA Toolkit	Download and documentation for CUDA.	developer.nvidia.com/cuda-toolkit
NVIDIA Drivers	Latest GPU driver downloads.	nvidia.com/Download

Summary

The ModuleNotFoundError: No module named ‘triton’ error when importing SageAttention is caused by the absence or misconfiguration of the triton package.

Since triton plays a critical role in enabling GPU-accelerated custom kernels, it must be properly installed and compatible with your system environment.

Resolving this error involves verifying your Python environment, GPU and CUDA setup, and installing triton correctly with tools like pip or conda. Additionally, keeping dependencies up to date and ensuring version compatibility will help avoid similar issues.

Following the instructions in this article will allow you to overcome the import error and leverage the full power of SageAttention in your projects.