Paddle/paddle/fluid/inference/tests/api/int8_mkldnn_quantization.md

INT8 MKL-DNN quantization

This document describes how to use Paddle inference Engine to convert the FP32 model to INT8 model on ResNet-50 and MobileNet-V1. We provide the instructions on enabling INT8 MKL-DNN quantization in Paddle inference and show the ResNet-50 and MobileNet-V1 results in accuracy and performance.

0. Install PaddlePaddle

Follow PaddlePaddle installation instruction to install PaddlePaddle. If you build PaddlePaddle yourself, please use the following cmake arguments.

cmake ..  -DWITH_TESTING=ON -WITH_FLUID_ONLY=ON -DWITH_GPU=OFF -DWITH_MKL=ON  -WITH_SWIG_PY=OFF -DWITH_INFERENCE_API_TEST=ON -DON_INFER=ON

Note: MKL-DNN and MKL are required.

1. Enable INT8 MKL-DNN quantization

For reference, please examine the code of unit test enclosed in analyzer_int8_image_classification_tester.cc.

• Create Analysis config

INT8 quantization is one of the optimizations in analysis config. More information about analysis config can be found here

• Create quantize config by analysis config

We enable the MKL-DNN quantization procedure by calling an appropriate method from analysis config. Afterwards, all the required quantization parameters (quantization op names, quantization strategies etc.) can be set through quantizer config which is present in the analysis config. It is also necessary to specify a pre-processed warmup dataset and desired batch size.

//Enable MKL-DNN quantization
cfg.EnableMkldnnQuantizer();

//use analysis config to call the MKL-DNN quantization config
cfg.mkldnn_quantizer_config()->SetWarmupData(warmup_data); 
cfg.mkldnn_quantizer_config()->SetWarmupBatchSize(100);

2. Accuracy and Performance benchmark

We provide the results of accuracy and performance measured on Intel(R) Xeon(R) Gold 6271 on single core.

I. Top-1 Accuracy on Intel(R) Xeon(R) Gold 6271

Model	Dataset	FP32 Accuracy	INT8 Accuracy	Accuracy Diff
ResNet-50	Full ImageNet Val	76.63%	76.48%	0.15%
MobileNet-V1	Full ImageNet Val	70.78%	70.36%	0.42%

II. Throughput on Intel(R) Xeon(R) Gold 6271 (batch size 1 on single core)

Model	Dataset	FP32 Throughput	INT8 Throughput	Ratio(INT8/FP32)
ResNet-50	Full ImageNet Val	13.17 images/s	49.84 images/s	3.78
MobileNet-V1	Full ImageNet Val	75.49 images/s	232.38 images/s	3.07

Notes:

• Measurement of accuracy requires a model which accepts two inputs: data and labels.
• Different sampling batch size data may cause slight difference on INT8 top accuracy.
• CAPI performance data is better than python API performance data because of the python overhead. Especially for the small computational model, python overhead will be more obvious.

3. Commands to reproduce the above accuracy and performance benchmark

• Full dataset (Single core)

  • Download full ImageNet Validation Dataset

cd /PATH/TO/PADDLE/build
python ../paddle/fluid/inference/tests/api/full_ILSVRC2012_val_preprocess.py

The converted data binary file is saved by default in ~/.cache/paddle/dataset/int8/download/int8_full_val.bin

• ResNet50 Full dataset benchmark

./paddle/fluid/inference/tests/api/test_analyzer_int8_resnet50 --infer_model=third_party/inference_demo/int8v2/resnet50/model --infer_data=/path/to/converted/int8_full_val.bin --batch_size=1 --paddle_num_threads=1

• Mobilenet-v1 Full dataset benchmark

./paddle/fluid/inference/tests/api/test_analyzer_int8_mobilenet --infer_model=third_party/inference_demo/int8v2/mobilenet/model --infer_data=/path/to/converted/int8_full_val.bin --batch_size=1 --paddle_num_threads=1