---
license: mit
---
# triton-kernels

triton-kernels is a set of kernels that enable fast moe on different architectures. These kernels are compatible with different precision (e.g bf16, mxfp4)

Original code here https://github.com/triton-lang/triton/tree/main/python/triton_kernels

The current version is the following commit 7d0efaa7231661299284a603512fce4fa255e62c


## Quickstart

```bash
uv run https://huggingface.co/kernels-community/triton_kernels/raw/main/readme_example.py
```

```python
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "torch",
#     "triton",
#     "numpy",
#     "kernels",
# ]
# ///

import torch
import sys
from kernels import get_kernel

torch.manual_seed(42)
torch.cuda.manual_seed(42)

# Load triton_kernels module via kernels library
triton_kernels = get_kernel("kernels-community/triton_kernels")

# Access modules directly from the loaded kernel
swiglu = triton_kernels.swiglu
routing = triton_kernels.routing

# Setup
device = "cuda" if torch.cuda.is_available() else "cpu"

# SwiGLU example
x = torch.randn(512, 1024, device=device, dtype=torch.bfloat16)
y = swiglu.swiglu_torch(x, 0.5, swiglu.PrecisionConfig(limit=1.0))
print(f"SwiGLU: {x.shape} -> {y.shape}")

# Routing example
logits = torch.randn(128, 8, device=device, dtype=torch.float16)
routing_data, gather_idx, scatter_idx = routing.routing_torch(logits, n_expts_act=2)
print(f"Routing: {routing_data.expt_hist.sum()} tokens routed")

# MoE integrated
n_tokens = routing_data.expt_hist.sum().item()
x_moe = torch.randn(n_tokens, 512, device=device, dtype=torch.bfloat16)
y_moe = swiglu.swiglu_torch(x_moe, 0.5, swiglu.PrecisionConfig(limit=1.0))
print(f"MoE SwiGLU: {x_moe.shape} -> {y_moe.shape}")
```