This folder contains examples of quantizing a FP32 model with oneAPI Deep Neural Network Library (oneDNN) to (U)INT8 model.
oneDNN supports quantization with subgraph features on Intel® CPU Platform and can bring performance improvements on the Intel® Xeon® Scalable Platform.
usage: python imagenet_gen_qsym_onednn.py [-h] [--model MODEL] [--epoch EPOCH]
[--no-pretrained] [--batch-size BATCH_SIZE]
[--calib-dataset CALIB_DATASET]
[--image-shape IMAGE_SHAPE]
[--data-nthreads DATA_NTHREADS]
[--num-calib-batches NUM_CALIB_BATCHES]
[--exclude-first-conv] [--shuffle-dataset]
[--calib-mode CALIB_MODE]
[--quantized-dtype {auto,int8,uint8}]
[--quiet]
Generate a calibrated quantized model from a FP32 model with oneDNN support
optional arguments:
-h, --help show this help message and exit
--model MODEL model to be quantized. If no-pretrained is set then
model must be provided to `model` directory in the same path
as this python script, default is `resnet50_v1`
--epoch EPOCH number of epochs, default is `0`
--no-pretrained If enabled, will not download pretrained model from
MXNet or Gluon-CV modelzoo, default is `False`
--batch-size BATCH_SIZE
batch size to be used when calibrating model, default is `32`
--calib-dataset CALIB_DATASET
path of the calibration dataset, default is `data/val_256_q90.rec`
--image-shape IMAGE_SHAPE
number of channels, height and width of input image separated by comma,
default is `3,224,224`
--data-nthreads DATA_NTHREADS
number of threads for data loading, default is `0`
--num-calib-batches NUM_CALIB_BATCHES
number of batches for calibration, default is `10`
--exclude-first-conv excluding quantizing the first conv layer since the
input data may have negative value which doesn't
support at moment
--shuffle-dataset shuffle the calibration dataset
--calib-mode CALIB_MODE
calibration mode used for generating calibration table
for the quantized symbol; supports 1. none: no
calibration will be used. The thresholds for
quantization will be calculated on the fly. This will
result in inference speed slowdown and loss of
accuracy in general. 2. naive: simply take min and max
values of layer outputs as thresholds for
quantization. In general, the inference accuracy
worsens with more examples used in calibration. It is
recommended to use `entropy` mode as it produces more
accurate inference results. 3. entropy: calculate KL
divergence of the FP32 output and quantized output for
optimal thresholds. This mode is expected to produce
the best inference accuracy of all three kinds of
quantized models if the calibration dataset is
representative enough of the inference dataset.
default is `entropy`
--quantized-dtype {auto,int8,uint8}
quantization destination data type for input data,
default is `auto`
--quiet suppress most of log
A new benchmark script launch_inference_onednn.sh has been designed to launch performance benchmark for FP32 or INT8 image-classification models with oneDNN.
usage: bash ./launch_inference_onednn.sh -s symbol_file [-b batch_size] [-iter iteraton] [-ins instance] [-c cores/instance] [-h]
arguments:
-h, --help show this help message and exit
-s, --symbol_file symbol file for benchmark, required
-b, --batch_size inference batch size
default: 64
-iter, --iteration inference iteration
default: 500
-ins, --instance launch multi-instance inference
default: one instance per socket
-c, --core number of cores per instance
default: divide full physical cores
example: resnet INT8 performance benchmark on c5.24xlarge(duo sockets, 24 physical cores per socket).
bash ./launch_inference_onednn.sh -s ./model/resnet50_v1-quantized-5batches-naive-symbol.json
will launch two instances for throughput benchmark and each instance will use 24 physical cores.
The following models have been tested on Linux systems. Accuracy is collected on Intel XEON Cascade Lake CPU. For CPU with Skylake Lake or eariler architecture, the accuracy may not be the same.
| Model | Source | Dataset | FP32 Accuracy (top-1/top-5) | INT8 Accuracy (top-1/top-5) |
|---|---|---|---|---|
| ResNet18-V1 | MXNet ModelZoo | Validation Dataset | 70.45%/89.55% | 70.22%/89.38% |
| ResNet50-V1 | MXNet ModelZoo | Validation Dataset | 76.36%/93.49% | 76.04%/93.30% |
| ResNet101-V1 | MXNet ModelZoo | Validation Dataset | 78.23%/93.99% | 77.85%/93.69% |
| MobileNet v2 1.0 | MXNet ModelZoo | Validation Dataset | 71.72%/90.28% | 71.22%/89.92% |
| VGG16 | MXNet ModelZoo | Validation Dataset | 72.83%/91.11% | 72.81%/91.10% |
| VGG19 | MXNet ModelZoo | Validation Dataset | 73.67%/91.63% | 73.67%/91.67% |
| Measured on validation ImageNet (ILSVRC2012) with batch-size=64, num-calib-batches=10 and calib-mode=entropy |
The following command is to download the pre-trained model from MXNet ModelZoo and transfer it into the symbolic model which would be finally quantized. The validation dataset is available for testing the pre-trained models:
python imagenet_gen_qsym_onednn.py --model=resnet50_v1 --num-calib-batches=5 --calib-mode=naive
The model would be automatically replaced in fusion and quantization format. It is then saved as the quantized symbol and parameter files in the ./model directory. Set --model to one of above listed verified models to quantize them. The following command is to launch inference.
# Launch FP32 Inference
python imagenet_inference.py --symbol-file=./model/resnet50_v1-symbol.json --param-file=./model/resnet50_v1-0000.params --rgb-mean=0.485,0.456,0.406 --rgb-std=0.229,0.224,0.225 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec
# Launch INT8 Inference
python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-5batches-naive-symbol.json --param-file=./model/resnet50_v1-quantized-0000.params --rgb-mean=0.485,0.456,0.406 --rgb-std=0.229,0.224,0.225 --num-skipped-batches=50 --batch-size=64 --num-inference-batches=500 --dataset=./data/val_256_q90.rec
# Launch dummy data Inference
bash ./launch_inference_onednn.sh -s ./model/resnet50_v1-symbol.json
bash ./launch_inference_onednn.sh -s ./model/resnet50_v1-quantized-5batches-naive-symbol.json
This script also supports custom symbolic models. Quantization layer configs can easily be added in imagenet_gen_qsym_onednn.py like below:
if logger:
frameinfo = getframeinfo(currentframe())
logger.info(F'Please set proper RGB configs inside this script below {frameinfo.filename}:{frameinfo.lineno} for model {args.model}!')
# add rgb mean/std of your model.
rgb_mean = '0,0,0'
rgb_std = '0,0,0'
# add layer names that shouldn't be quantized.
if logger:
frameinfo = getframeinfo(currentframe())
logger.info(F'Please set proper excluded_sym_names inside this script below {frameinfo.filename}:{frameinfo.lineno} for model {args.model} if required!')
excluded_sym_names += []
if exclude_first_conv:
excluded_sym_names += []
Some tips on quantization configs:
- First, data, symbol file (custom-symbol.json) and parameter file (custom-0000.params) of FP32 symbolic model should be prepared.
- Then, following command should be run to verify that FP32 symbolic model runs inference as expected.
# Launch FP32 Inference
python imagenet_inference.py --symbol-file=./model/custom-symbol.json --param-file=./model/custom-0000.params --rgb-mean=* --rgb-std=* --num-skipped-batches=* --batch-size=* --num-inference-batches=*--dataset=./data/*
-
Proper
rgb_mean,rgb_stdandexcluded_sym_namesshould be added inimagenet_gen_qsym_onednn.pyscript. -
Run following command for quantization:
python imagenet_gen_qsym_onednn.py --model=custom --num-calib-batches=5 --calib-mode=naive
-
After quantization, the quantized symbol and parameter files will be saved in the
model/directory. -
Finally, INT8 inference can be run:
# Launch INT8 Inference
python imagenet_inference.py --symbol-file=./model/resnet50_v1-quantized-10batches-entropy-symbol.json --param-file=./model/resnet50_v1-quantized-10batches-entropy-0000.params --benchmark
# Launch dummy data Inference
bash ./launch_inference_onednn.sh -s ./model/*.json