Pre-release

This release is a pre-release and may not be stable for production use.

Liger Kernel: Efficient Triton Kernels for LLM Training

Stable	Nightly	Discord	Gurubase (experimental)

Installation | Getting Started | Examples | APIs | Cite our work

Supercharge Your Model with Liger Kernel

Banner

With one line of code, Liger Kernel can increase throughput by more than 20% and reduce memory usage by 60%, thereby enabling longer context lengths, larger batch sizes, and massive vocabularies.

Speed Up	Memory Reduction

Note:

Benchmark conditions: LLaMA 3-8B, Batch Size = 8, Data Type = bf16, Optimizer = AdamW, Gradient Checkpointing = True, Distributed Strategy = FSDP1 on 8 A100s.

Hugging Face models start to OOM at a 4K context length, whereas Hugging Face + Liger Kernel scales up to 16K.

Examples

Use Case	Description
Hugging Face Trainer	Train LLaMA 3-8B ~20% faster with over 40% memory reduction on Alpaca dataset using 4 A100s with FSDP
Lightning Trainer	Increase 15% throughput and reduce memory usage by 40% with LLaMA3-8B on MMLU dataset using 8 A100s with DeepSpeed ZeRO3
Medusa Multi-head LLM (Retraining Phase)	Reduce memory usage by 80% with 5 LM heads and improve throughput by 40% using 8 A100s with FSDP
Vision-Language Model SFT	Finetune Qwen2-VL on image-text data using 4 A100s with FSDP

Key Features

Ease of use: Simply patch your Hugging Face model with one line of code, or compose your own model using our Liger Kernel modules.
Time and memory efficient: In the same spirit as Flash-Attn, but for layers like RMSNorm, RoPE, SwiGLU, and CrossEntropy! Increases multi-GPU training throughput by 20% and reduces memory usage by 60% with kernel fusion, in-place replacement, and chunking techniques.
Exact: Computation is exact—no approximations! Both forward and backward passes are implemented with rigorous unit tests and undergo convergence testing against training runs without Liger Kernel to ensure accuracy.
Lightweight: Liger Kernel has minimal dependencies, requiring only Torch and Triton—no extra libraries needed! Say goodbye to dependency headaches!
Multi-GPU supported: Compatible with multi-GPU setups (PyTorch FSDP, DeepSpeed, DDP, etc.).
Trainer Framework Integration: Axolotl, LLaMa-Factory, SFTTrainer, Hugging Face Trainer, SWIFT

Installation

Dependencies

CUDA

torch >= 2.1.2
triton >= 2.3.0

ROCm

torch >= 2.5.0 Install according to the instruction in Pytorch official webpage.
triton >= 3.0.0 Install from pypi. (e.g. pip install triton==3.0.0)

Optional Dependencies

transformers >= 4.x: Required if you plan to use the transformers models patching APIs. The specific model you are working will dictate the minimum version of transformers.

Note: Our kernels inherit the full spectrum of hardware compatibility offered by Triton.

To install the stable version:

$ pip install liger-kernel

To install the nightly version:

$ pip install liger-kernel-nightly

To install from source:

git clone https://github.com/linkedin/Liger-Kernel.git
cd Liger-Kernel
pip install -e .
# or if using transformers
pip install -e .[transformers]

Getting Started

There are a couple of ways to apply Liger kernels, depending on the level of customization required.

1. Use AutoLigerKernelForCausalLM

Using the AutoLigerKernelForCausalLM is the simplest approach, as you don't have to import a model-specific patching API. If the model type is supported, the modeling code will be automatically patched using the default settings.

from liger_kernel.transformers import AutoLigerKernelForCausalLM

# This AutoModel wrapper class automatically monkey-patches the
# model with the optimized Liger kernels if the model is supported.
model = AutoLigerKernelForCausalLM.from_pretrained("path/to/some/model")

2. Apply Model-Specific Patching APIs

Using the patching APIs, you can swap Hugging Face models with optimized Liger Kernels.

import transformers
from liger_kernel.transformers import apply_liger_kernel_to_llama

# 1a. Adding this line automatically monkey-patches the model with the optimized Liger kernels
apply_liger_kernel_to_llama()

# 1b. You could alternatively specify exactly which kernels are applied
apply_liger_kernel_to_llama(
  rope=True,
  swiglu=True,
  cross_entropy=True,
  fused_linear_cross_entropy=False,
  rms_norm=False
)

# 2. Instantiate patched model
model = transformers.AutoModelForCausalLM("path/to/llama/model")

3. Compose Your Own Model

You can take individual kernels to compose your models.

from liger_kernel.transformers import LigerFusedLinearCrossEntropyLoss
import torch.nn as nn
import torch

model = nn.Linear(128, 256).cuda()

# fuses linear + cross entropy layers together and performs chunk-by-chunk computation to reduce memory
loss_fn = LigerFusedLinearCrossEntropyLoss()

input = torch.randn(4, 128, requires_grad=True, device="cuda")
target = torch.randint(256, (4, ), device="cuda")

loss = loss_fn(model.weight, input, target)
loss.backward()

APIs

AutoModel

AutoModel Variant	API
AutoModelForCausalLM	`liger_kernel.transformers.AutoLigerKernelForCausalLM`

Patching

Model	API	Supported Operations
LLaMA 2 & 3	`liger_kernel.transformers.apply_liger_kernel_to_llama`	RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy
LLaMA 3.2-Vision	`liger_kernel.transformers.apply_liger_kernel_to_mllama`	RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Mistral	`liger_kernel.transformers.apply_liger_kernel_to_mistral`	RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Mixtral	`liger_kernel.transformers.apply_liger_kernel_to_mixtral`	RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Gemma1	`liger_kernel.transformers.apply_liger_kernel_to_gemma`	RoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Gemma2	`liger_kernel.transformers.apply_liger_kernel_to_gemma2`	RoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Qwen2, Qwen2.5, & QwQ	`liger_kernel.transformers.apply_liger_kernel_to_qwen2`	RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Qwen2-VL	`liger_kernel.transformers.apply_liger_kernel_to_qwen2_vl`	RMSNorm, LayerNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy
Phi3 & Phi3.5	`liger_kernel.transformers.apply_liger_kernel_to_phi3`	RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy

Kernels

Kernel	API
RMSNorm	`liger_kernel.transformers.LigerRMSNorm`
LayerNorm	`liger_kernel.transformers.LigerLayerNorm`
RoPE	`liger_kernel.transformers.liger_rotary_pos_emb`
SwiGLU	`liger_kernel.transformers.LigerSwiGLUMLP`
GeGLU	`liger_kernel.transformers.LigerGEGLUMLP`
CrossEntropy	`liger_kernel.transformers.LigerCrossEntropyLoss`
FusedLinearCrossEntropy	`liger_kernel.transformers.LigerFusedLinearCrossEntropyLoss`
KLDivergence	`liger_kernel.transformers.LigerKLDIVLoss`
JSD	`liger_kernel.transformers.LigerJSD`
FusedLinearJSD	`liger_kernel.transformers.LigerFusedLinearJSD`

RMSNorm: RMSNorm, which normalizes activations using their root mean square, is implemented by fusing the normalization and scaling steps into a single Triton kernel, and achieves ~3X speedup with ~3X peak memory reduction.
LayerNorm: LayerNorm, which centers and normalizes activations across the feature dimension, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and achieves ~2X speedup.
GroupNorm: GroupNorm, which normalizes activations across the group dimension for a given sample. Channels are grouped in K groups over which the normalization is performed, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and can achieve up to ~2X speedup as the number of channels/groups increases.
RoPE: Rotary Positional Embedding is implemented by fusing the query and key embedding rotary into a single kernel with inplace replacement, and achieves ~3X speedup with ~3X peak memory reduction.
SwiGLU: Swish Gated Linear Units, given by $$\text{SwiGLU}(x)=\text{Swish}_{\beta}(xW+b)\otimes(xV+c)$$ , is implemented by fusing the elementwise multiplication (denoted by $\otimes$) into a single kernel with inplace replacement, and achieves parity speed with ~1.5X peak memory reduction.
GeGLU: GELU Gated Linear Units, given by $$\text{GeGLU}(x)=\text{GELU}(xW+b)\otimes(xV+c)$$ , is implemented by fusing the elementwise multiplication into a single kernel with inplace replacement, and achieves parity speed with ~1.5X peak memory reduction. Note that the tanh approximation form of GELU is used.
CrossEntropy: Cross entropy loss is implemented by computing both the loss and gradient in the forward pass with inplace replacement of input to reduce the peak memory by avoiding simultaneous materialization of both input logits and gradient. It achieves >2X speedup and >4X memory reduction for common vocab sizes (e.g., 32K, 128K, etc.).

FusedLinearCrossEntropy: Peak memory usage of cross entropy loss is further improved by fusing the model head with the CE loss and chunking the input for block-wise loss and gradient calculation, a technique inspired by Efficient Cross Entropy. It achieves >4X memory reduction for 128k vocab size. This is highly effective for large batch size, large sequence length, and large vocabulary sizes. Please refer to the Medusa example for individual kernel usage.
KLDivergence: KL Divergence is implemented by fusing the forward into a single triton kernel, with reduction done outside the kernel. It achieves ~1.5X speed and ~15% memory reduction for 128K vocab size.
JSD: Generalized JSD (Jensen-Shannon divergence), is implemented by computing both the loss and gradient in the forward pass. It achieves ~1.5X speed and ~54% memory reduction for 128k vocab size. NOTE: It implements forward/reverse KL when beta equals 0 and 1 respectively.
FusedLinearJSD: Peak memory usage of JSD loss is further improved by fusing the model head with the JSD and chunking the input for block-wise loss and gradient calculation. It achieves ~85% memory reduction for 128k vocab size where batch size $\times$ sequence length is 8192. NOTE: It implements forward/reverse KL when beta equals 0 and 1 respectively.

Experimental Kernels

Kernel	API
Embedding	`liger_kernel.transformers.experimental.LigerEmbedding`
Matmul int2xint8	`liger_kernel.transformers.experimental.matmul`

Embedding: Embedding is implemented by fusing embedding lookup and output operations. It achieves a peak speedup of ~1.5x in the forward pass and an overall speedup of ~1.1x.
Matmul int2xint8: is implemented by using the cache tiled matrix multiplication and by fusing the matmul with the unpacking process which achieves a considerable speed up and performs on par with @torch.compile

Contributing, Acknowledgements, and License

Contact

For issues, create a Github ticket in this repository
For open discussion, join our discord channel
For formal collaboration, send an email to byhsu@linkedin.com

Cite this work

Biblatex entry:

@article{hsu2024ligerkernelefficienttriton,
      title={Liger Kernel: Efficient Triton Kernels for LLM Training},
      author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
      year={2024},
      eprint={2410.10989},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.10989},
      journal={arXiv preprint arXiv:2410.10989},
}

Star History

Contributors

↑ Back to Top ↑

Release files for liger-kernel-nightly 0.4.2.dev20241204180758

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for liger-kernel-nightly 0.4.2.dev20241204180758
File	Size	Uploaded
liger_kernel_nightly-0.4.2.dev20241204180758.tar.gz	65.9 kB	Dec 4, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for liger-kernel-nightly 0.4.2.dev20241204180758
File	Interpreter	ABI	Platform	Reset
liger_kernel_nightly-0.4.2.dev20241204180758-py3-none-any.whl 98.7 kB Dec 4, 2024	Python 3	none	any	Details

Total release size: 164.7 kB

Release files / liger_kernel_nightly-0.4.2.dev20241204180758.tar.gz

Download URL	liger_kernel_nightly-0.4.2.dev20241204180758.tar.gz
Size	65.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`4f0e589c0af0858a21f0861547b84e67a511ea6636b660728e2bdc1a73ee4355`
BLAKE2b-256 checksum How to use checksums	`8b4d2eba657326d62132809001dd1ada8b4bf06f19c9a63a8beb017ecf38588d`
Upload date	Dec 4, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.8.18`

Release files / liger_kernel_nightly-0.4.2.dev20241204180758-py3-none-any.whl

Download URL	liger_kernel_nightly-0.4.2.dev20241204180758-py3-none-any.whl
Size	98.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`e1f9220b86b3e5817d95a67b175181f431180f944e0537962742f7fdc73dc707`
BLAKE2b-256 checksum How to use checksums	`5935277ba20472ffc6d9b174da9a9ac932b518799d4b6a9df83d15887aa0c1d8`
Upload date	Dec 4, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.0.1 CPython/3.8.18`