Quantization

AutoGPTQ Integration

🤗 Optimum collaborated with AutoGPTQ library to provide a simple API that apply GPTQ quantization on language models. With GPTQ quantization, you can quantize your favorite language model to 8, 4, 3 or even 2 bits. This comes without a big drop of performance and with faster inference speed. This is supported by most GPU hardwares.

If you want to quantize 🤗 Transformers models with GPTQ, follow this documentation.

To learn more about the quantization technique used in GPTQ, please refer to:

• the GPTQ paper
• the AutoGPTQ library used as the backend

Note that the AutoGPTQ library provides more advanced usage (triton backend, fused attention, fused MLP) that are not integrated with Optimum. For now, we leverage only the CUDA kernel for GPTQ.

Requirements

You need to have the following requirements installed to run the code below:

• AutoGPTQ library: pip install auto-gptq

• Optimum library: pip install --upgrade optimum

• Install latest transformers library from source: pip install --upgrade git+https://github.com/huggingface/transformers.git

• Install latest accelerate library: pip install --upgrade accelerate

Load and quantize a model

The GPTQQuantizer class is used to quantize your model. In order to quantize your model, you need to provide a few arguemnts:

• the number of bits: bits
• the dataset used to calibrate the quantization: dataset
• the model sequence length used to process the dataset: model_seqlen
• the block name to quantize: block_name_to_quantize

With 🤗 Transformers integration, you don’t need to pass the block_name_to_quantize and model_seqlen as we can retrieve them. However, for custom model, you need to specify them. Also, make sure that your model is converted to torch.float16 before quantization.

from transformers import AutoModelForCausalLM, AutoTokenizer
from optimum.gptq import GPTQQuantizer, load_quantized_model
import torch
model_name = "facebook/opt-125m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

quantizer = GPTQQuantizer(bits=4, dataset="c4", block_name_to_quantize = "model.decoder.layers", model_seqlen = 2048)
quantized_model = quantizer.quantize_model(model, tokenizer)

Save the model

To save your model, use the save method from GPTQQuantizer class. It will create a folder with your model state dict along with the quantization config.

save_folder = "/path/to/save_folder/"
quantizer.save(model,save_folder)

Load quantized weights

You can load your quantized weights by using the load_quantized_model() function. Through the Accelerate library, it is possible to load a model faster with a lower memory usage. The model needs to be initialized using empty weights, with weights loaded as a next step.

from accelerate import init_empty_weights
with init_empty_weights():
    empty_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
empty_model.tie_weights()
quantized_model = load_quantized_model(empty_model, save_folder=save_folder, device_map="auto")

Exllama kernels for faster inference

For 4-bit model, you can use the exllama kernels in order to a faster inference speed. It is activated by default. If you want to change its value, you just need to pass disable_exllama in load_quantized_model(). In order to use these kernels, you need to have the entire model on gpus.

from optimum.gptq import GPTQQuantizer, load_quantized_model
import torch

from accelerate import init_empty_weights
with init_empty_weights():
    empty_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
empty_model.tie_weights()
quantized_model = load_quantized_model(empty_model, save_folder=save_folder, device_map="auto", disable_exllama=False)

Note that only 4-bit models are supported with exllama kernels for now. Furthermore, it is recommended to disable the exllama kernel when you are finetuning your model with peft.

Fine-tune a quantized model

With the official support of adapters in the Hugging Face ecosystem, you can fine-tune models that have been quantized with GPTQ. Please have a look at peft library for more details.