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Liger Kernel: Efficient Triton Kernels for LLM Training

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Installation | Getting Started | Examples | High-level APIs | Low-level APIs | Cite our work

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Liger Kernel is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. We have implemented Hugging Face Compatible RMSNorm, RoPE, SwiGLU, CrossEntropy, FusedLinearCrossEntropy, and more to come. The kernel works out of the box with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed. We welcome contributions from the community to gather the best kernels for LLM training.

We've also added optimized Post-Training kernels that deliver up to 80% memory savings for alignment and distillation tasks. We support losses like DPO, CPO, ORPO, SimPO, JSD, and many more. Check out how we optimize the memory.

Supercharge Your Model with Liger Kernel

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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
Speed up Memory

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.

Optimize Post Training with Liger Kernel

Post Training

We provide optimized post training kernels like DPO, ORPO, SimPO, and more which can reduce memory usage by up to 80%. You can easily use them as python modules.

from liger_kernel.chunked_loss import LigerFusedLinearDPOLoss
orpo_loss = LigerFusedLinearORPOLoss()
y = orpo_loss(lm_head.weight, x, target)

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
Liger ORPO Trainer Align Llama 3.2 using Liger ORPO Trainer with FSDP with 50% memory reduction

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

# Install Default Dependencies
# Setup.py will detect whether you are using AMD or NVIDIA
pip install -e .

# Setup Development Dependencies
pip install -e ".[dev]"

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()

High-level 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

Low-level APIs

  • Fused Linear kernels combine linear layers with losses, reducing memory usage by up to 80% - ideal for HBM-constrained workloads.
  • Other kernels use fusion and in-place techniques for memory and performance optimization.

Model 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
Fused Linear CrossEntropy liger_kernel.transformers.LigerFusedLinearCrossEntropyLoss

Alignment Kernels

Kernel API
Fused Linear CPO Loss liger_kernel.chunked_loss.LigerFusedLinearCPOLoss
Fused Linear DPO Loss liger_kernel.chunked_loss.LigerFusedLinearDPOLoss
Fused Linear ORPO Loss liger_kernel.chunked_loss.LigerFusedLinearORPOLoss
Fused Linear SimPO Loss liger_kernel.chunked_loss.LigerFusedLinearSimPOLoss

Distillation Kernels

Kernel API
KLDivergence liger_kernel.transformers.LigerKLDIVLoss
JSD liger_kernel.transformers.LigerJSD
Fused Linear JSD liger_kernel.transformers.LigerFusedLinearJSD

Experimental Kernels

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

Contributing, Acknowledgements, and License

Sponsorship and Collaboration

  • AMD: Providing AMD GPUs for our AMD CI.
  • Intel: Providing Intel GPUs for our Intel CI.
  • Modal: Free 3000 credits from GPU MODE IRL for our NVIDIA CI.
  • EmbeddedLLM: Making Liger Kernel run fast and stable on AMD.
  • HuggingFace: Integrating Liger Kernel into Hugging Face Transformers and TRL.
  • Lightning AI: Integrating Liger Kernel into Lightning Thunder.
  • Axolotl: Integrating Liger Kernel into Axolotl.
  • Llama-Factory: Integrating Liger Kernel into Llama-Factory.

Contact

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},
}

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