AutoGPTQ

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.

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News or Update

2023-05-12 - (In Progress) - peft + auto-gptq + multi-modal data = easily fine tune LLMs to gain multi-modal instruction following ability with low resources, stay tune!
2023-05-04 - (Update) - Support using faster cuda kernel when not desc_act or group_size == -1.
2023-04-29 - (Update) - Support loading quantized model from arbitrary quantize_config and model_basename.
2023-04-28 - (Update) - Support CPU offload and quantize/inference on multiple devices, support gpt2 type models.

For more histories please turn to here

Installation

Quick Installation

You can install the latest stable release of AutoGPTQ from pip:

pip install auto-gptq

disable cuda extensions

By default, cuda extensions will be installed when torch and cuda is already installed in your machine, if you don't want to use them, using:

BUILD_CUDA_EXT=0 pip install auto-gptq

And to make sure autogptq_cuda is not ever in your virtual environment, run:

pip uninstall autogptq_cuda -y

to support LLaMa model

For some people want to try LLaMa and whose transformers version not meet the newest one that supports it, using:

pip install auto-gptq[llama]

to support triton speedup

To integrate with triton, using:

warning: currently triton only supports linux; 3-bit quantization is not supported when using triton

pip install auto-gptq[triton]

Install from source

click to see details

Clone the source code:

git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ

Then, install from source:

pip install .

Like quick installation, you can also set BUILD_CUDA_EXT=0 to disable pytorch extension building.

Use .[llama] if you want to try LLaMa model.

Use .[triton] if you want to integrate with triton and it's available on your operating system.

Quick Tour

Quantization and Inference

warning: this is just a showcase of the usage of basic apis in AutoGPTQ, which uses only one sample to quantize a much small model, quality of quantized model using such little samples may not good.

Below is an example for the simplest use of auto_gptq to quantize a model and inference after quantization:

from transformers import AutoTokenizer, TextGenerationPipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig


pretrained_model_dir = "facebook/opt-125m"
quantized_model_dir = "opt-125m-4bit"


tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
examples = [
    tokenizer(
        "auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."
    )
]

quantize_config = BaseQuantizeConfig(
    bits=4,  # quantize model to 4-bit
    group_size=128,  # it is recommended to set the value to 128
    desc_act=False,  # set to False can significantly speed up inference but the perplexity may slightly bad 
)

# load un-quantized model, by default, the model will always be loaded into CPU memory
model = AutoGPTQForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)

# quantize model, the examples should be list of dict whose keys can only be "input_ids" and "attention_mask"
model.quantize(examples)

# save quantized model
model.save_quantized(quantized_model_dir)

# save quantized model using safetensors
model.save_quantized(quantized_model_dir, use_safetensors=True)

# load quantized model to the first GPU
model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir)

# inference with model.generate
print(tokenizer.decode(model.generate(**tokenizer("auto_gptq is", return_tensors="pt").to(model.device))[0]))

# or you can also use pipeline
pipeline = TextGenerationPipeline(model=model, tokenizer=tokenizer)
print(pipeline("auto-gptq is")[0]["generated_text"])

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

Customize Model

Below is an example to extend `auto_gptq` to support `OPT` model, as you will see, it's very easy:

from auto_gptq.modeling import BaseGPTQForCausalLM


class OPTGPTQForCausalLM(BaseGPTQForCausalLM):
    # chained attribute name of transformer layer block
    layers_block_name = "model.decoder.layers"
    # chained attribute names of other nn modules that in the same level as the transformer layer block
    outside_layer_modules = [
        "model.decoder.embed_tokens", "model.decoder.embed_positions", "model.decoder.project_out",
        "model.decoder.project_in", "model.decoder.final_layer_norm"
    ]
    # chained attribute names of linear layers in transformer layer module
    # normally, there are four sub lists, for each one the modules in it can be seen as one operation, 
    # and the order should be the order when they are truly executed, in this case (and usually in most cases), 
    # they are: attention q_k_v projection, attention output projection, MLP project input, MLP project output
    inside_layer_modules = [
        ["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"],
        ["self_attn.out_proj"],
        ["fc1"],
        ["fc2"]
    ]

After this, you can use OPTGPTQForCausalLM.from_pretrained and other methods as shown in Basic.

Evaluation on Downstream Tasks

You can use tasks defined in auto_gptq.eval_tasks to evaluate model's performance on specific down-stream task before and after quantization.

The predefined tasks support all causal-language-models implemented in 🤗 transformers and in this project.

Below is an example to evaluate `EleutherAI/gpt-j-6b` on sequence-classification task using `cardiffnlp/tweet_sentiment_multilingual` dataset:

from functools import partial

import datasets
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from auto_gptq.eval_tasks import SequenceClassificationTask


MODEL = "EleutherAI/gpt-j-6b"
DATASET = "cardiffnlp/tweet_sentiment_multilingual"
TEMPLATE = "Question:What's the sentiment of the given text? Choices are {labels}.\nText: {text}\nAnswer:"
ID2LABEL = {
    0: "negative",
    1: "neutral",
    2: "positive"
}
LABELS = list(ID2LABEL.values())


def ds_refactor_fn(samples):
    text_data = samples["text"]
    label_data = samples["label"]

    new_samples = {"prompt": [], "label": []}
    for text, label in zip(text_data, label_data):
        prompt = TEMPLATE.format(labels=LABELS, text=text)
        new_samples["prompt"].append(prompt)
        new_samples["label"].append(ID2LABEL[label])

    return new_samples


#  model = AutoModelForCausalLM.from_pretrained(MODEL).eval().half().to("cuda:0")
model = AutoGPTQForCausalLM.from_pretrained(MODEL, BaseQuantizeConfig())
tokenizer = AutoTokenizer.from_pretrained(MODEL)

task = SequenceClassificationTask(
        model=model,
        tokenizer=tokenizer,
        classes=LABELS,
        data_name_or_path=DATASET,
        prompt_col_name="prompt",
        label_col_name="label",
        **{
            "num_samples": 1000,  # how many samples will be sampled to evaluation
            "sample_max_len": 1024,  # max tokens for each sample
            "block_max_len": 2048,  # max tokens for each data block
            # function to load dataset, one must only accept data_name_or_path as input 
            # and return datasets.Dataset
            "load_fn": partial(datasets.load_dataset, name="english"),  
            # function to preprocess dataset, which is used for datasets.Dataset.map, 
            # must return Dict[str, list] with only two keys: [prompt_col_name, label_col_name]
            "preprocess_fn": ds_refactor_fn,  
            # truncate label when sample's length exceed sample_max_len
            "truncate_prompt": False  
        }
    )

# note that max_new_tokens will be automatically specified internally based on given classes
print(task.run())

# self-consistency
print(
    task.run(
        generation_config=GenerationConfig(
            num_beams=3,
            num_return_sequences=3,
            do_sample=True
        )
    )
)

Learn More

tutorials provide step-by-step guidance to integrate auto_gptq with your own project and some best practice principles.

examples provide plenty of example scripts to use auto_gptq in different ways.

Supported Models

Currently, auto_gptq supports: bloom, gpt2, gpt_neox, gptj, llama, moss and opt; more Transformer models will come soon!

Supported Evaluation Tasks

Currently, auto_gptq supports: LanguageModelingTask, SequenceClassificationTask and TextSummarizationTask; more Tasks will come soon!

Acknowledgement

Specially thanks Elias Frantar, Saleh Ashkboos, Torsten Hoefler and Dan Alistarh for proposing GPTQ algorithm and open source the code.
Specially thanks qwopqwop200, for code in this project that relevant to quantization are mainly referenced from GPTQ-for-LLaMa.

Dessix/AutoGPTQ