#
GPTQ

Try GPTQ quantization with PEFT in this notebook and learn more about it’s details in this blog post!

Both GPTQModel and AutoGPTQ libraries implement the GPTQ algorithm, a post-training quantization technique where each row of the weight matrix is quantized independently to find a version of the weights that minimizes error. These weights are quantized to int4, stored as int32 (int4 x 8) and dequantized (restored) to fp16 on the fly during inference. This can save memory by almost 4x because the int4 weights are often dequantized in a fused kernel. You can also expect a substantial speedup in inference due to lower bandwidth requirements for lower bitwidth.

GPTQModel started as a maintained fork of AutoGPTQ but has since differentiated itself with the following major differences.

- Model support: GPTQModel continues to support all of the latest LLM models.
- Multimodal support: GPTQModel supports accurate quantization of Qwen 2-VL and Ovis 1.6-VL image-to-text models.
- Platform support: Linux, macOS (Apple Silicon), and Windows 11.
- Hardware support: NVIDIA CUDA, AMD ROCm, Apple Silicon M1/MPS /CPU, Intel/AMD CPU, and Intel Datacenter Max/Arc GPUs.
- Asymmetric support: Asymmetric quantization can potentially introduce lower quantization errors compared to symmetric quantization. However, it is not backward compatible with AutoGPTQ, and not all kernels, such as Marlin, support asymmetric quantization.
- IPEX kernel for Intel/AMD accelerated CPU and Intel GPU (Datacenter Max/Arc GPUs) support.
- Updated Marlin kernel from Neural Magic optimized for A100 (Ampere).
- Updated kernels with auto-padding for legacy model support and models with non-uniform in/out-features.
- Faster quantization, lower memory usage, and more accurate default quantization via GPTQModel quantization APIs.
- User and developer friendly APIs.

AutoGPTQ will likely be deprecated in the future due the lack of continued support for new models and features.

Before you begin, make sure the following libraries are installed and updated to the latest release:

    pip install --upgrade accelerate optimum transformers

Then install either GPTQModel or AutoGPTQ.

    pip install gptqmodel --no-build-isolation

or

    pip install auto-gptq --no-build-isolation

To quantize a model (currently only supported for text models), you need to create a GPTQConfig class and set the number of bits to quantize to, a dataset to calibrate the weights for quantization, and a tokenizer to prepare the dataset.

    from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig

    model_id = "facebook/opt-125m"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    gptq_config = GPTQConfig(bits=4, dataset="c4", tokenizer=tokenizer)

You could also pass your own dataset as a list of strings, but it is highly recommended to use the same dataset from the GPTQ paper.

    dataset = ["auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."]
    gptq_config = GPTQConfig(bits=4, dataset=dataset, tokenizer=tokenizer)

Load a model to quantize and pass the `gptq_config` to the from_pretrained() method. Set `device_map="auto"` to automatically offload the model to a CPU to help fit the model in memory, and allow the model modules to be moved between the CPU and GPU for quantization.

    quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config)

If you’re running out of memory because a dataset is too large, disk offloading is not supported. If this is the case, try passing the `max_memory` parameter to allocate the amount of memory to use on your device (GPU and CPU):

    quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", max_memory={0: "30GiB", 1: "46GiB", "cpu": "30GiB"}, quantization_config=gptq_config)

Depending on your hardware, it can take some time to quantize a model from scratch. It can take ~5 minutes to quantize the facebook/opt-350m model on a free-tier Google Colab GPU, but it’ll take ~4 hours to quantize a 175B parameter model on a NVIDIA A100. Before you quantize a model, it is a good idea to check the Hub if a GPTQ-quantized version of the model already exists.

Once your model is quantized, you can push the model and tokenizer to the Hub where it can be easily shared and accessed. Use the push_to_hub() method to save the GPTQConfig:

    quantized_model.push_to_hub("opt-125m-gptq")
    tokenizer.push_to_hub("opt-125m-gptq")

You could also save your quantized model locally with the save_pretrained() method. If the model was quantized with the `device_map` parameter, make sure to move the entire model to a GPU or CPU before saving it. For example, to save the model on a CPU:

    quantized_model.save_pretrained("opt-125m-gptq")
    tokenizer.save_pretrained("opt-125m-gptq")

    # if quantized with device_map set
    quantized_model.to("cpu")
    quantized_model.save_pretrained("opt-125m-gptq")

Reload a quantized model with the from_pretrained() method, and set `device_map="auto"` to automatically distribute the model on all available GPUs to load the model faster without using more memory than needed.

    from transformers import AutoModelForCausalLM

    model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto")

##
Marlin

Marlin is a 4-bit only CUDA GPTQ kernel, highly optimized for the NVIDIA A100 GPU (Ampere) architecture. Loading, dequantization, and execution of post-dequantized weights are highly parallelized, offering a substantial inference improvement versus the original CUDA GPTQ kernel. Marlin is only available for quantized inference and does not support model quantization.

Marlin inference can be activated with the `backend` parameter in GPTQConfig.

    from transformers import AutoModelForCausalLM, GPTQConfig

    model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto", quantization_config=GPTQConfig(bits=4, backend="marlin"))

##
ExLlama

ExLlama is a CUDA implementation of the Llama model that is designed for faster inference with 4-bit GPTQ weights (check out these benchmarks). The ExLlama kernel is activated by default when you create a GPTQConfig object. To boost inference speed even further, use the ExLlamaV2 kernels by configuring the `exllama_config` parameter:

    import torch
    from transformers import AutoModelForCausalLM, GPTQConfig

    gptq_config = GPTQConfig(bits=4, exllama_config={"version":2})
    model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto", quantization_config=gptq_config)

Only 4-bit models are supported, and we recommend deactivating the ExLlama kernels if you’re finetuning a quantized model with PEFT.

The ExLlama kernels are only supported when the entire model is on the GPU. If you’re doing inference on a CPU with AutoGPTQ or GPTQModel, then you’ll need to disable the ExLlama kernel. This overwrites the attributes related to the ExLlama kernels in the quantization config of the config.json file.

    import torch
    from transformers import AutoModelForCausalLM, GPTQConfig
    gptq_config = GPTQConfig(bits=4, use_exllama=False)
    model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="cpu", quantization_config=gptq_config)

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