intel/auto-round

SOTA Weight-only Quantization Algorithm for LLMs. This is official implementation of "Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs"

AutoRound

Advanced Weight-Only Quantization Algorithm for LLMs

AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It's tailored for a wide range of models and consistently delivers noticeable improvements, often significantly outperforming SignRound with the cost of more tuning time for quantization.

Our method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, which competes impressively against recent methods without introducing any additional inference overhead. The below image presents an overview of AutoRound.

Prerequisites

• Python 3.9 or higher

Installation

Build from Source

pip install -r requirements.txt
python setup.py install

Install from pypi

pip install auto-round

Model quantization

Gaudi2/ CPU/ GPU

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meta-llama/Llama-2-7b-hf"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

from auto_round import AutoRound

bits, group_size, sym = 4, 128, False
##device:Optional["auto", None, "hpu", "cpu", "cuda"]
autoround = AutoRound(model, tokenizer, bits=bits, group_size=group_size, sym=sym, device=None)
autoround.quantize()
output_dir = "./tmp_autoround"
autoround.save_quantized(output_dir)

Detailed Hyperparameters

• model: The PyTorch model to be quantized.

• tokenizer: An optional tokenizer for processing input data. If none is provided, a dataloader must be supplied.

• bits (int): Number of bits for quantization (default is 4).

• group_size (int): Size of the quantization group (default is 128).

• sym (bool): Whether to use symmetric quantization.

• enable_quanted_input (bool): Whether to use the output of the previous quantized block as the input for the current block (default is True).

• enable_minmax_tuning (bool): Whether to enable weight min-max tuning (default is True).

• iters (int): Number of tuning iterations (default is 200).

• lr (float): The learning rate for rounding value (default is None, it will be set to 1.0/iters automatically).

• minmax_lr (float): The learning rate for min-max tuning (default is None, it will be set to lr automatically).

• n_samples (int): Number of samples for tuning (default is 512).

• seqlen (int): Data length of the sequence for tuning (default is 2048).

• batch_size (int): Batch size for training (default is 8).

• scale_dtype (str): The data type of quantization scale to be used (default is "float32"), different kernels have different choices.

• amp (bool): Whether to use automatic mixed precision (default is True).

• n_blocks (int): Packing several blocks as one for tuning together (default is 1).

• gradient_accumulate_steps (int): Number of gradient accumulation steps (default is 1).

• low_gpu_mem_usage (bool): Whether to save GPU memory at the cost of a little tuning time (default is True).

• dataset (str): The default dataset name for tuning (default is "NeelNanda/pile-10k").

• dataset_split (str): The split of the dataset to be used for tuning (default is "train").

• dataloader: The dataloader for tuning data.

• weight_config (dict): Configuration for weight quantization (default is an empty dictionary), mainly for mixed bits or mixed precision.

• device: The device to be used for tuning. The default is set to 'auto', allowing for automatic detection.

Model inference

Please run the quantization code first.

CPU

##Install the latest https://github.com/intel/intel-extension-for-transformers from source first.
from intel_extension_for_transformers.transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

quantized_model_path = "./tmp_autoround"
model = AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(quantized_model_path, use_fast=True)
text = "There is a girl who likes adventure,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))

GPU

from transformers import AutoModelForCausalLM, AutoTokenizer

quantized_model_path = "./tmp_autoround"
model = AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(quantized_model_path, use_fast=True)
text = "There is a girl who likes adventure,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))

Support List

Model	Supported
Intel/neural-chat-7b-v3-3	HF-int4-model, accuracy, recipe, example
Intel/neural-chat-7b-v3-1	HF-int4-model, accuracy, recipe, example
mistralai/Mistral-7B-v0.1	HF-int4-model, accuracy, recipe, example
microsoft/phi-2	HF-int4-model, accuracy, recipe, example
tiiuae/falcon-7b	HF-int4-model, accuracy, recipe, example
google/gemma-2b	HF-int4-model, accuracy, recipe, example
mistralai/Mistral-7B-Instruct-v0.2	HF-int4-model (under review), accuracy, recipe, example
google/gemma-7b	HF-int4-model (under review), accuracy, recipe, example
google/gemma-7b-it	HF-int4-model (under review), accuracy, recipe, example
mistralai/Mixtral-8x7B-Instruct-v0.1	HF-int4-model (under review), accuracy, recipe, example
mistralai/Mixtral-8x7B-v0.1	HF-int4-model (under review), accuracy, recipe, example
meta-llama/Meta-Llama-3-8B-Instruct	accuracy, recipe, example
meta-llama/Llama-2-7b-chat-hf	accuracy, recipe, example
Qwen/Qwen1.5-7B-Chat	accuracy, sym recipe, asym recipe , example
baichuan-inc/Baichuan2-7B-Chat	accuracy, recipe, example
01-ai/Yi-6B-Chat	accuracy, recipe, example
facebook/opt-2.7b	accuracy, recipe, example
bigscience/bloom-3b	accuracy, recipe, example
EleutherAI/gpt-j-6b	accuracy, recipe, example
Salesforce/codegen25-7b-multi	example
huggyllama/llama-7b	example
mosaicml/mpt-7b	example
THUDM/chatglm3-6b	example
MBZUAI/LaMini-GPT-124M	example
EleutherAI/gpt-neo-125m	example
databricks/dolly-v2-3b	example
stabilityai/stablelm-base-alpha-3b	example

Comparison with other methods

We provide a comprehensive analysis with other methods in our accuracy data section. In summary, our approach achieved superior performance compared to GPTQ, scoring 30/32, AWQ with 27/32, HQQ with 15/16, and OmniQuant with a perfect score of 16/16 across llamv1/llamav2/mistral-7b on W4G-1, W4G128, W3G128, and W2G128, based on the average accuracies of 11 zero-shot tasks.

Tips

1 Consider increasing tuning steps to achieve better results, albeit with increased tuning time.

2 Setting 'enable_quanted_input' to False has been observed to occasionally yield improved results.

3 Setting 'minmax_lr' to 2.0/iters has been observed to occasionally yield improved results.

Reference

If you find SignRound useful for your research, please cite our paper:

@article{cheng2023optimize,
  title={Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs},
  author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao},
  journal={arXiv preprint arXiv:2309.05516},
  year={2023}
}

auto-round's Issues

GPU memery use

How much GPU memory and CPU size are required when I quantize the Chatglm3-6B model，I used A100-40G，but get an error ”killed“。

support F.linear and matmul in some moe models

https://huggingface.co/databricks/dbrx-instruct/blob/main/modeling_dbrx.py
A simple but engineering ugly solution is to follow https://huggingface.co/databricks/dbrx-instruct/discussions/10 to change the matmul to linear, let's follow this way to add a patch for this model

8-bit quantization support

I was wondering if it supports 8-bit quantization. The example code is as below.

python main.py \ --model_name "" \ --bits 8 \ --group_size 128 \ --train_bs 8 \ --gradient_accumulate_steps 8 \ --deployment_device 'gpu' \ --output_dir "./save_ckpt"

Quantization/layer speed is very slow

Current testing PR #87 and running into very slow quants for a Tinyllama 1.1B test model.

I am geting ~96s per layer in quantization on 4090 gpu with n_blocks = 1 and ~75s per layer with n_blocks = 2.

Is this the norm? I am new to autoround so don't have a baseline metric to go by.

Is gpu quant faster or slower than cpu quant? Are there optimizations to make it faster other than using a smaller model for testing? Thanks!

Env:
Ubuntu 22.04
Torch 2.2.2
Cpu: AMD ZEN3

env CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=5 python3 main.py \
--model_name /local/TinyLlama-1.1B-intermediate-step-1341k-3T \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--use_quant_input \
--quant_lm_head \
--n_blocks 4 \
--deployment_device 'gpu' \
--disable_low_gpu_mem_usage \
--output_dir "./tmp_autoround"

ceval, cmmlu acc is not matched with lm-eval results

fix it or just call lm-eval