SpinQuant
Code repo for the paper "SpinQuant LLM quantization with learned rotations"
Stars: 76
SpinQuant is a tool designed for LLM quantization with learned rotations. It focuses on optimizing rotation matrices to enhance the performance of quantized models, narrowing the accuracy gap to full precision models. The tool implements rotation optimization and PTQ evaluation with optimized rotation, providing arguments for model name, batch sizes, quantization bits, and rotation options. SpinQuant is based on the findings that rotation helps in removing outliers and improving quantization, with specific enhancements achieved through learning rotation with Cayley optimization.
README:
This repository contains the code of SpinQuant introduced in our work: "SpinQuant: LLM Quantization with Learned Rotations"
In this work, we found that
- Rotation is a principle way to remove outliers in the LLMs and assist quantization;
- Not all rotation helps equally and random rotations produce a large variance in quantized models;
- Learning rotation with Cayley optimization greatly enhance the final performance.
As a result, SpinQuant narrows the accuracy gap of W4A4KV4 quantization with full precision to merely 2.9 points for the LLaMA-2 7B model on zero-shot reasoning tasks, surpassing LLM-QAT by 19.1 points and SmoothQuant by 25.0 points.
If you find our code useful for your research, please consider citing:
@article{liu2024spinquant,
title={SpinQuant--LLM quantization with learned rotations},
author={Liu, Zechun and Zhao, Changsheng and Fedorov, Igor and Soran, Bilge and Choudhary, Dhruv and Krishnamoorthi, Raghuraman and Chandra, Vikas and Tian, Yuandong and Blankevoort, Tijmen},
journal={arXiv preprint arXiv:2405.16406},
year={2024}
}
- python 3.9, pytorch >= 2.0
- pip install -r requirement.txt
- git clone https://github.com/Dao-AILab/fast-hadamard-transform.git
cd fast-hadamard-transform
pip install .
Step 1: Optimize Rotation Matrix
- For LLaMA-2 7B/13B and LLaMA-3 8B models:
bash10_optimize_rotation.sh $model_name $w_bit $a_bit $kv_bit
e.g.,bash 10_optimize_rotation.sh meta-llama/Llama-2-7b 4 4 4for 4-bit weight 4-bit activation and 4-bit kv-cache on Llama-2-7b model. - For LLaMA-2 70B and LLaMA-3 70B models:
bash11_optimize_rotation_fsdp.sh $model_name $w_bit $a_bit $kv_bit
e.g.,bash 11_optimize_rotation_fsdp.sh meta-llama/Llama-2-70b 4 4 4for 4-bit weight 4-bit activation and 4-bit kv-cache on Llama-2-70b model.
Step 2: Run PTQ evaluation with optimized rotation
- bash
2_eval_ptq.sh $model_name $w_bit $a_bit $kv_bit
Others
- If using GPTQ quantization method in Step 2 for quantizing both weight and activations, we optimize the rotation matrices with respect to a network where only activations are quantized.
e.g.bash 10_optimize_rotation.sh meta-llama/Llama-2-7b 16 4 4followed bybash 2_eval_ptq.sh meta-llama/Llama-2-7b 4 4 4with the--optimized_rotation_pathpointing to the rotation optimized for W16A4KV4.
-
--input_model: The model name (or path to the weights) -
--output_rotation_path: The local path we want to store the oprimized rotation matrix -
--per_device_train_batch_size: The batch size for rotation optimization -
--per_device_eval_batch_size: The batch size for PPL evaluation -
--a_bits: The number of bits for activation quantization -
--w_bits: The number of bits for weight quantization -
--v_bits: The number of bits for value quantization -
--k_bits: The number of bits for key quantization -
--w_clip: Whether using the grid search to find best weight clipping range -
--w_rtn: Whether we want to use round-to-nearest quantization. If not having--w_rtn, we are using GPTQ quantization. -
--rotate: Whether we want to rotate the model -
--optimized_rotation_path: The checkpoint path of optimized rotation; Use random rotation if path is not given
| Model | LLaMA-3 8B | LLaMA-3 70B | LLaMA-2 7B | LLaMA-2 13B | LLaMA-2 70B | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Method | Zero-shot | Wiki2 | Zero-shot | Wiki2 | Zero-shot | Wiki2 | Zero-shot | Wiki2 | Zero-shot | Wiki2 |
| FloatingPoint | 69.6 | 6.1 | 74.5 | 2.8 | 66.9 | 5.5 | 68.3 | 5.0 | 72.9 | 3.3 |
| W4A16KV16 | ||||||||||
| RTN | 65.4 | 7.8 | 35.5 | 1e5 | 63.6 | 7.2 | 57.9 | 6.4 | 69.2 | 4.6 |
| SmoothQuant | 61.0 | 10.7 | 66.9 | 12.0 | 59.1 | 7.5 | 63.3 | 6.1 | 70.2 | 4.1 |
| LLM-QAT | 67.7 | 7.1 | -- | -- | 64.9 | 5.9 | -- | -- | -- | -- |
| GPTQ | 66.5 | 7.2 | 35.7 | 1e5 | 64.5 | 11.3 | 64.7 | 5.6 | 71.9 | 3.9 |
| QuaRot | 68.4 | 6.4 | 70.3 | 7.9 | 65.8 | 5.6 | 68.3 | 5.0 | 72.2 | 3.5 |
| SpinQuant | 68.5 | 6.4 | 71.6 | 4.8 | 65.9 | 5.6 | 68.5 | 5.0 | 72.6 | 3.5 |
| W4A4KV16 | ||||||||||
| RTN | 38.5 | 9e2 | 35.6 | 1e5 | 35.6 | 2e3 | 35.3 | 7e3 | 35.1 | 2e5 |
| SmoothQuant | 40.3 | 8e2 | 55.3 | 18.0 | 41.8 | 2e2 | 44.9 | 34.5 | 64.6 | 57.1 |
| LLM-QAT | 44.9 | 42.9 | -- | -- | 47.8 | 12.9 | -- | -- | -- | -- |
| GPTQ | 37.0 | 9e2 | 35.3 | 1e5 | 36.8 | 8e3 | 35.3 | 5e3 | 35.5 | 2e6 |
| QuaRot | 63.8 | 7.9 | 65.4 | 20.4 | 63.5 | 6.1 | 66.7 | 5.4 | 70.4 | 3.9 |
| SpinQuant | 65.8 | 7.1 | 69.5 | 5.5 | 64.1 | 5.9 | 67.2 | 5.2 | 71.0 | 3.8 |
| W4A4KV4 | ||||||||||
| RTN | 38.2 | 1e3 | 35.2 | 1e5 | 37.1 | 2e3 | 35.4 | 7e3 | 35.0 | 2e5 |
| SmoothQuant | 38.7 | 1e3 | 52.4 | 22.1 | 39.0 | 6e2 | 40.5 | 56.6 | 55.9 | 10.5 |
| LLM-QAT | 43.2 | 52.5 | -- | -- | 44.9 | 14.9 | -- | -- | -- | -- |
| GPTQ | 37.1 | 1e3 | 35.1 | 1e5 | 36.8 | 9e3 | 35.2 | 5e3 | 35.6 | 1e6 |
| QuaRot | 63.3 | 8.0 | 65.1 | 20.2 | 62.5 | 6.4 | 66.2 | 5.4 | 70.3 | 3.9 |
| SpinQuant | 65.2 | 7.3 | 69.3 | 5.5 | 64.0 | 5.9 | 66.9 | 5.3 | 71.2 | 3.8 |
You can download the optimized rotation matrices here.
The results reported in the paper is run with the internal LLaMA codebase in Meta. We reproduced our experiments with HuggingFace codebase and released code here, which partially based on HuggingFace transformers, QuaRot, QuIP# and Optimization-on-Stiefel-Manifold-via-Cayley-Transform.
Zechun Liu, Reality Labs, Meta Inc (zechunliu at meta dot com)
Changsheng Zhao, Reality Labs, Meta Inc (cszhao at meta dot com)
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases [Paper] [Code]
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models [Paper] [Code]
BiT is CC-BY-NC 4.0 licensed as of now.
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