GPTQModel

Production ready LLM model compression/quantization toolkit with accelerated inference support for both cpu/gpu via HF, vLLM, and SGLang.

News

• 11/29/2024 1.3.1 Olmo2 model support. Intel XPU acceleration via IPEX. Model sharding Transformer compat fix due to api deprecation in HF. Removed triton dependency. Triton kernel now optionally dependent on triton pkg.
• 11/26/2024 1.3.0 Zero-Day Hymba model support. Removed tqdm and rogue dependency.
• 11/24/2024 1.2.3 HF GLM model support. ClearML logging integration. Use device-smi and replace gputil + psutil depends. Fixed model unit tests.
• 11/11/2024 🚀 1.2.1 Meta MobileLLM model support added. lm-eval[gptqmodel] integration merged upstream. Intel/IPEX cpu inference merged replacing QBits (deprecated). Auto-fix/patch ChatGLM-3/GLM-4 compat with latest transformers. New .load() and .save() api.
• 10/29/2024 🚀 1.1.0 IBM Granite model support. Full auto-buildless wheel install from pypi. Reduce max cpu memory usage by >20% during quantization. 100% CI model/feature coverage.
• 10/12/2024 ✨ 1.0.9 Move AutoRound to optional and fix pip install regression in v1.0.8.
• 10/11/2024 ✨ 1.0.8 Add wheel for python 3.12 and cuda 11.8.
• 10/08/2024 ✨ 1.0.7 Fixed marlin (faster) kernel was not auto-selected for some models.
• 09/26/2024 ✨ 1.0.6 Fixed quantized Llama 3.2 vision quantized loader.
• 09/26/2024 ✨ 1.0.5 Partial Llama 3.2 Vision model support (mllama): only text-layer quantization layers are supported for now.

Archived News:

* 09/26/2024 ✨ [1.0.4](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.0.4) Integrated Liger Kernel support for ~1/2 memory reduction on some models during quantization. Added control toggle disable parallel packing. * 09/18/2024 ✨ [1.0.3](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.0.3) Added Microsoft GRIN-MoE and MiniCPM3 support. * 08/16/2024 ✨ [1.0.2](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.0.2) Support Intel/AutoRound v0.3, pre-built whl packages, and PyPI release. * 08/14/2024 ✨ [1.0.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v1.0.0) 40% faster `packing`, Fixed Python 3.9 compat, added `lm_eval` api. * 08/10/2024 🚀 [0.9.11](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.11) Added LG EXAONE 3.0 model support. New `dynamic` per layer/module flexible quantization where each layer/module may have different bits/params. Added proper sharding support to `backend.BITBLAS`. Auto-heal quantization errors due to small damp values. * 07/31/2024 🚀 [0.9.10](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.10) Ported vllm/nm `gptq_marlin` inference kernel with expanded bits (8bits), group_size (64,32), and desc_act support for all GPTQ models with `FORMAT.GPTQ`. Auto calculate auto-round nsamples/seglen parameters based on calibration dataset. Fixed save_quantized() called on pre-quantized models with non-supported backends. HF transformers depend updated to ensure Llama 3.1 fixes are correctly applied to both quant and inference. * 07/25/2024 🚀 [0.9.9](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.9): Added Llama-3.1 support, Gemma2 27B quant inference support via vLLM, auto pad_token normalization, fixed auto-round quant compat for vLLM/SGLang, and more. * 07/13/2024 🚀 [0.9.8](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.8): Run quantized models directly using GPTQModel using fast `vLLM` or `SGLang` backend! Both vLLM and SGLang are optimized for dyanamic batching inference for maximum `TPS` (check usage under examples). Marlin backend also got full end-to-end in/out features padding to enhance current/future model compatibility. * 07/08/2024 🚀 [0.9.7](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.7): InternLM 2.5 model support added. * 07/08/2024 🚀 [0.9.6](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.6): [Intel/AutoRound](https://github.com/intel/auto-round) QUANT_METHOD support added for a potentially higher quality quantization with `lm_head` module quantization support for even more vram reduction: format export to `FORMAT.GPTQ` for max inference compatibility. * 07/05/2024 🚀 [0.9.5](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.5): Cuda kernels have been fully deprecated in favor of Exllama(v1/v2)/Marlin/Triton. * 07/03/2024 🚀 [0.9.4](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.4): HF Transformers integration added and bug fixed Gemma 2 support. * 07/02/2024 🚀 [0.9.3](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.3): Added Gemma 2 support, faster PPL calculations on gpu, and more code/arg refractor. * 06/30/2024 🚀 [0.9.2](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.2): Added auto-padding of model in/out-features for exllama and exllama v2. Fixed quantization of OPT and DeepSeek V2-Lite models. Fixed inference for DeepSeek V2-Lite. * 06/29/2024 🚀 [0.9.1](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.1): With 3 new models (DeepSeek-V2, DeepSeek-V2-Lite, DBRX Converted), BITBLAS new format/kernel, proper batching of calibration dataset resulting > 50% quantization speedup, security hash check of loaded model weights, tons of refractor/usability improvements, bugs fixes and much more. * 06/20/2924 ✨ [0.9.0](https://github.com/ModelCloud/GPTQModel/releases/tag/v0.9.0): Thanks for all the work from ModelCloud team and the opensource ML community for their contributions!

Why should you use GPTQModel?

GPTQModel started out as a major refractor (fork) of AutoGTQP but has now morphed into a full-stand-in replacement with cleaner api, up-to-date model support, faster inference, faster quantization, higher quality quants and a pledge that ModelCloud, together with the open-source ML community, will take every effort to bring the library up-to-date with latest advancements and model support.

Why GPTQ and not other low-bit quantizers?

Public tests/papers and ModelCloud's internal tests have shown that GPTQ is on-par and/or exceeds other 4bit quantization methods in terms of both quality recovery and production level inference speed in both token latency and rps. GPTQ has the optimal blend of quality and inference speed you would want to use in a real-world production system.

Features

• 🚀 Extensive model support for: Olmo2, Hymba, GLM, IBM Granite, Llama 3.2 Vision, MiniCPM3, GRIN-Moe, Phi 3.5, EXAONE 3.0, InternLM 2.5, Gemma 2, DeepSeek-V2, DeepSeek-V2-Lite, ChatGLM, MiniCPM, Phi-3, Qwen2MoE, DBRX.
• ✨ 100% CI coverage for all supported models including quality/ppl regression.
• 🚀 vLLM and SGLang inference integration for quantized model where format = FORMAT.GPTQ
• 🚀 Intel/IPEX 4bit quantization/inference support on CPU (avx512_vnni) and Intel/XPU.
• 🚀 Intel/AutoRound QUANT_METHOD support added for a potentially higher quality quantization with lm_head module quantization support for even more vram reduction: format export to FORMAT.GPTQ for max inference compatibility.
• 🚀 BITBLAS format/inference support from Microsoft
• 🚀Sym=False Support. AutoGPTQ has unusable sym=false. (Re-quant required)
• 🚀lm_head module quant inference support for further VRAM reduction.
• 🚀 Faster quantization: More than 50% faster for TinyLlama + 4090 with batching and large calibration dataset.
• 🚀 Better quality quants as measured by PPL. (Test config: defaults + sym=True + FORMAT.GPTQ, TinyLlama)
• 🚀 Model weights sharding support
• 🚀 Security: hash check of model weights on load
• 🚀 Over 50% faster PPL calculations (OPT)
• 🚀 Over 40% faster packing stage in quantization (Llama 3.1 8B)

Quality: GPTQModel 4bit can match BF16:

🤗 ModelCloud quantized ultra-high recovery vortex-series models on HF

Model Support: 🚀 (Added by GPTQModel)

Model
Baichuan	✅	Falcon	✅	Llama 1/2/3	✅	OLMo2	🚀
Bloom	✅	Gemma 2	🚀	Llama 3.2 Vision	🚀	Phi/Phi-3	🚀
ChatGLM	🚀	GPTBigCod	✅	LongLLaMA	✅	Qwen	✅
CodeGen	✅	GPTNeoX	✅	MiniCPM3	✅	Qwen2MoE	🚀
Cohere	✅	GPT-2	✅	Mistral	✅	RefinedWeb	✅
DBRX Converted	🚀	GPT-J	✅	Mixtral	✅	StableLM	✅
Deci	✅	Granite	🚀	MobileLLM	🚀	StarCoder2	✅
DeepSeek-V2	🚀	GRIN-MoE	🚀	MOSS	✅	XVERSE	✅
DeepSeek-V2-Lite	🚀	Hymba	🚀	MPT	✅	Yi	✅
EXAONE 3.0	🚀	InternLM 1/2.5	🚀	OPT	✅

HW Accelerator Requirements

GPTQModel is validated for Linux x86_64 with the following devices:

Device
Nvidia GPU	✅	Ampere or Higher
Intel/AMD CPU	✅	avx512_vnni or amx
Intel XPU	✅	Intel Datacenter Max

Install

PIP/UV

# You can install optional modules like autoround, ipex, vllm, sglang, bitblas, and ipex.
# Example: pip install -v --no-build-isolation gptqmodel[vllm,sglang,bitblas,ipex,auto_round]
pip install -v gptqmodel --no-build-isolation
uv pip install -v gptqmodel --no-build-isolation

Install from source

# clone repo
git clone https://github.com/ModelCloud/GPTQModel.git && cd GPTQModel

# pip: compile and install
# You can install optional modules like autoround, ipex, vllm, sglang, bitblas, and ipex.
# Example: pip install -v --no-build-isolation .[vllm,sglang,bitblas,ipex,auto_round]
pip install -v . --no-build-isolation

Quantization and Inference

Below is a basic sample using GPTQModel to quantize a llm model and perform post-quantization inference:

from datasets import load_dataset
from transformers import AutoTokenizer
from gptqmodel import GPTQModel, QuantizeConfig, get_best_device

model_id = "meta-llama/Llama-3.2-1B-Instruct"
quant_path = "Llama-3.2-1B-Instruct-gptqmodel-4bit"

tokenizer = AutoTokenizer.from_pretrained(model_id)

calibration_dataset = [
  tokenizer(example["text"])
  for example in load_dataset(
    "allenai/c4",
    data_files="en/c4-train.00001-of-01024.json.gz",
    split="train"
  ).select(range(1024))
]

quant_config = QuantizeConfig(bits=4, group_size=128)

model = GPTQModel.load(model_id, quant_config)

model.quantize(calibration_dataset)

model.save(quant_path)

model = GPTQModel.load(quant_path, device=get_best_device())

result = model.generate(
  **tokenizer(
      "Uncovering deep insights begins with", return_tensors="pt"
  ).to(model.device)
)[0]

For more advanced features of model quantization, please reference to this script

How to Add Support for a New Model

Read the gptqmodel/models/llama.py code which explains in detail via comments how the model support is defined. Use it as guide to PR for to new models. Most models follow the same pattern.

Evaluation and Quality Benchmarks

GPTQModel inference is integrated into lm-evaluation-hardness and we highly recommend avoid using PPL and use lm-eval to validate post-quantization model quality.

# gptqmodel is integrated into lm-eval >= v0.4.6
pip install lm-eval[gptqmodel]

Which kernel is used by default?

• GPU: Marlin, Exllama v2, Triton kernels in that order for maximum inference performance. Optional Microsoft/BITBLAS kernel can be toggled.
• CPU: Intel/IPEX kernel
• XPU: Intel/IPEX kernel

Citation

@misc{gptqmodel,
    author = {ModelCloud.ai},
    title = {GPTQModel},
    year = {2024},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/modelcloud/gptqmodel}},
}

@article{frantar-gptq,
  title={{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers}, 
  author={Elias Frantar and Saleh Ashkboos and Torsten Hoefler and Dan Alistarh},
  year={2022},
  journal={arXiv preprint arXiv:2210.17323}
}

@article{frantar2024marlin,
  title={MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models},
  author={Frantar, Elias and Castro, Roberto L and Chen, Jiale and Hoefler, Torsten and Alistarh, Dan},
  journal={arXiv preprint arXiv:2408.11743},
  year={2024}
}