---
id: ref-liger-kernel
repo: linkedin/Liger-Kernel
title: Liger Kernel Triton Kernels for LLM Training
url: https://github.com/linkedin/Liger-Kernel
source_type: source-reference
source_category: open-triton-kernel-library
architectures:
- amd
- nvidia
- rocm
- dcu
tags:
- triton
- liger-kernel
- llm-training
- rmsnorm
- rope
- swiglu
- cross-entropy
- fused-linear-cross-entropy
- loss
- amd
techniques:
- llm-training-kernel
- autograd-wrapper
- fused-epilogue
- memory-reduction
- benchmark
hardware_features:
- wavefront
- vectorization
- cache
kernel_types:
- normalization
- rotary
- activation
- loss
- fused-linear
languages:
- python
- triton
captured_at: '2026-05-26'
license: not-captured
source_paths:
- src/liger_kernel
- test
- benchmark
- examples
- docs
- README.md
---
# Liger Kernel Triton Kernels For LLM Training

- Repository: `linkedin/Liger-Kernel`
- Source: [linkedin/Liger-Kernel](https://github.com/linkedin/Liger-Kernel)

## Route Fit

Use Liger Kernel when the Triton task is training-side LLM work: RMSNorm, RoPE,
SwiGLU, cross entropy, fused linear + loss, or memory-saving fused epilogues.
It is most useful for direct-file Triton structure, PyTorch autograd wrappers,
and correctness/benchmark patterns around training kernels.

## What To Inspect

- `src/liger_kernel` for wrappers and Triton kernels.
- `test` for numerical tolerance and training-shape coverage.
- `benchmark` and `examples` for measuring memory and runtime tradeoffs.

## DCU Use Notes

Use Liger as an algorithmic and harness reference. When porting to DCU, verify
dtype support, generated ISA, and resource pressure on the target card before
claiming a performance win.

## Query Hooks

```bash
python3 scripts/query.py "liger triton rmsnorm swiglu" --type source-reference --compact
python3 scripts/query.py "liger fused linear cross entropy" --type source-reference --compact
python3 scripts/get_page.py ref-liger-kernel
```