MQBench Quantization Aware Training with PyTorch

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Deep Learning lsq pact quantization qat dsq mqbench ptq dorefa-net
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
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Comments
  • run error

    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

    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
Owner
Ling Zhang
Ling Zhang
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