QAT(quantize aware training) for classification with MQBench

Ling Zhang

Last update: Nov 18, 2022

Related tags

Deep Learning lsq resnet pact quantization qat dsq efficientnetb0 mqbench regnetx

Overview

MQBench Quantization Aware Training with PyTorch

I am using MQBench(Model Quantization Benchmark)(http://mqbench.tech/) to quantize the model for deployment.

MQBench is a benchmark and framework for evluating the quantization algorithms under real world hardware deployments.

Prerequisites

Python 3.7+
PyTorch 1.8.1+

Install MQBench Lib

Before run this repository, you should install MQBench:

git clone https://github.com/ModelTC/MQBench.git
cd MQBench
python setup.py build
python setup.py install

Training Fp32 Model

# Start training fp32 model with: 
# model_name can be ResNet18, MobileNet, ...
python main.py model_name

# You can manually config the training with: 
python main.py --resume --lr=0.01

Training Quantize Model

# Start training quantize model with: 
# model_name can be ResNet18, MobileNet, ...
python main.py model_name --quantize

# You can manually config the training with: 
python main.py --resume --parallel DP --BackendType Tensorrt --quantize
python -m torch.distributed.launch main.py --local_rank 0 --parallel DDP --resume  --BackendType Tensorrt --quantize

Fp32 Accuracy

Model	Acc.
VGG16	92.64%
ResNet18	93.02%
ResNet50	93.62%
ResNet101	93.75%
RegNetX_200MF	94.24%
RegNetY_400MF	94.29%
MobileNetV2	94.43%
ResNeXt29(32x4d)	94.73%
ResNeXt29(2x64d)	94.82%
SimpleDLA	94.89%
DenseNet121	95.04%
PreActResNet18	95.11%
DPN92	95.16%
DLA	95.47%

Comments

run error

stty: 标准输入: 对设备不适当的 ioctl 操作 Traceback (most recent call last): File "/home/chenxin/disk1/github/MQBench_Quantize/main.py", line 21, in from utils import progress_bar, choose_model, choose_backend File "/home/chenxin/disk1/github/MQBench_Quantize/utils.py", line 49, in _, term_width = os.popen('stty size', 'r').read().split() ValueError: not enough values to unpack (expected 2, got 0)

opened by mathpopo 2
quanitze model to 4 bits

Hello, Have you ever tried to quanitze model to 4 bits with MQbench in ImageNet. I found that it would have a gradient explosion in the second epoch. Do you know why?

opened by haoxuanwang37 0

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QAT(quantize aware training) for classification with MQBench

Related tags

Overview

MQBench Quantization Aware Training with PyTorch

Prerequisites

Install MQBench Lib

Training Fp32 Model

Training Quantize Model

Fp32 Accuracy

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Comments

run error

quanitze model to 4 bits

Owner

Ling Zhang

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