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Nashmi

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llama
chemistry
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text-generation-inference
Inference Endpoints
Model card Files Community

Inference Speed

The result is generated using this script, batch size of input is 1, decode strategy is beam search and enforce the model to generate 512 tokens, speed metric is tokens/s (the larger, the better).

The quantized model is loaded using the setup that can gain the fastest inference speed.

model GPU num_beams fp16 gptq-int4
llama-7b 1xA100-40G 1 18.87 25.53
llama-7b 1xA100-40G 4 68.79 91.30
moss-moon 16b 1xA100-40G 1 12.48 15.25
moss-moon 16b 1xA100-40G 4 OOM 42.67
moss-moon 16b 2xA100-40G 1 06.83 06.78
moss-moon 16b 2xA100-40G 4 13.10 10.80
gpt-j 6b 1xRTX3060-12G 1 OOM 29.55
gpt-j 6b 1xRTX3060-12G 4 OOM 47.36

Perplexity

For perplexity comparison, you can turn to here and here

Installation

Quick Installation

You can install the latest stable release of AutoGPTQ from pip with pre-built wheels compatible with PyTorch 2.0.1:

  • For CUDA 11.7: pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/
  • For CUDA 11.8: pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
  • For RoCm 5.4.2: pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/rocm542/

Warning: These wheels are not expected to work on PyTorch nightly. Please install AutoGPTQ from source when using PyTorch nightly.

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

To install from source for AMD GPUs supporting RoCm, please specify the ROCM_VERSION environment variable. The compilation can be speeded up by specifying the PYTORCH_ROCM_ARCH variable (reference), for example gfx90a for MI200 series devices. Example:

ROCM_VERSION=5.6 pip install .

For RoCm systems, the packages rocsparse-dev, hipsparse-dev, rocthrust-dev, rocblas-dev and hipblas-dev are required to build.

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
import logging

logging.basicConfig(
    format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
)

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)

# push quantized model to Hugging Face Hub.
# to use use_auth_token=True, Login first via huggingface-cli login.
# or pass explcit token with: use_auth_token="hf_xxxxxxx"
# (uncomment the following three lines to enable this feature)
# repo_id = f"YourUserName/{quantized_model_dir}"
# commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
# model.push_to_hub(repo_id, commit_message=commit_message, use_auth_token=True)

# alternatively you can save and push at the same time
# (uncomment the following three lines to enable this feature)
# repo_id = f"YourUserName/{quantized_model_dir}"
# commit_message = f"AutoGPTQ model for {pretrained_model_dir}: {quantize_config.bits}bits, gr{quantize_config.group_size}, desc_act={quantize_config.desc_act}"
# model.push_to_hub(repo_id, save_dir=quantized_model_dir, use_safetensors=True, commit_message=commit_message, use_auth_token=True)

# load quantized model to the first GPU
model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir, device="cuda:0")

# download quantized model from Hugging Face Hub and load to the first GPU
# model = AutoGPTQForCausalLM.from_quantized(repo_id, device="cuda:0", use_safetensors=True, use_triton=False)

# 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

you can use model.config.model_type to compare with the table below to check whether the model you use is supported by auto_gptq.

for example, model_type of WizardLM, vicuna and gpt4all are all llama, hence they are all supported by auto_gptq.

model type quantization inference peft-lora peft-ada-lora peft-adaption_prompt
bloom ✅ ✅ ✅ ✅
gpt2 ✅ ✅ ✅ ✅
gpt_neox ✅ ✅ ✅ ✅ ✅requires this peft branch
gptj ✅ ✅ ✅ ✅ ✅requires this peft branch
llama ✅ ✅ ✅ ✅ ✅
moss ✅ ✅ ✅ ✅ ✅requires this peft branch
opt ✅ ✅ ✅ ✅
gpt_bigcode ✅ ✅ ✅ ✅
codegen ✅ ✅ ✅ ✅
falcon(RefinedWebModel/RefinedWeb) ✅ ✅ ✅ ✅

Supported Evaluation Tasks

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

Running tests

Tests can be run with:

pytest tests/ -s

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.

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Dataset used to train PetraAI/Nashmi