# Post-Training Quantization Best Practices¶

## Introduction¶

The Default Quantization of the Post-training Optimization Tool (POT) is the fastest and easiest way to get a quantized model because it requires only some unannotated representative dataset to be provided in most cases. Thus, it is recommended to use it as a starting point when it comes to model optimization. However, it can lead to significant accuracy deviation in some cases. This document is aimed at providing tips to address this issue.

Note

POT uses inference on the CPU during model optimization. It means the ability to infer the original floating-point model is a prerequisite for model optimization. It is also worth mentioning that in the case of 8-bit quantization it is recommended to run POT on the same CPU architecture when optimizing for CPU or VNNI-based CPU when quantizing for a non-CPU device, such as GPU, VPU, or GNA. It should help to avoid the impact of the saturation issue that occurs on AVX and SSE based CPU devices.

## Improving accuracy after the Default Quantization¶

Parameters of the Default Quantization algorithm with basic settings are shown below:

{
"name": "DefaultQuantization", # Optimization algorithm name
"params": {
"preset": "performance", # Preset [performance, mixed] which controls
# the quantization scheme. For the CPU:
# performance - symmetric quantization  of weights and activations
# mixed - symmetric weights and asymmetric activations
"stat_subset_size": 300  # Size of subset to calculate activations statistics that can be used
# for quantization parameters calculation
}
}

In the case of substantial accuracy degradation after applying this method there are two alternatives:

1. Hyperparameters tuning

2. AccuracyAwareQuantization algorithm

### Tuning Hyperparameters of the Default Quantization¶

The Default Quantization algorithm provides multiple hyperparameters which can be used in order to improve accuracy results for the fully-quantized model. Below is a list of best practices that can be applied to improve accuracy without a substantial performance reduction with respect to default settings:

1. The first recommended option is to change the preset from performance to mixed. This enables asymmetric quantization of activations and can be helpful for models with non-ReLU activation functions, for example, YOLO, EfficientNet, etc.

2. The next option is use_fast_bias. Setting this option to false enables a different bias correction method which is more accurate, in general, and applied after model quantization as a part of the Default Quantization algorithm.

Note

Changing this option can substantially increase quantization time in the POT tool.

1. Another important option is a range_estimator. It defines how to calculate the minimum and maximum of quantization range for weights and activations. For example, the following range_estimator for activations can improve the accuracy for Faster R-CNN based networks:

{
"name": "DefaultQuantization",
"params": {
"preset": "performance",
"stat_subset_size": 300

"activations": {                 # defines activation
"range_estimator": {         # defines how to estimate statistics
"max": {                 # right border of the quantizating floating-point range
"aggregator": "max", # use max(x) to aggregate statistics over calibration dataset
"type": "abs_max"    # use abs(max(x)) to get per-sample statistics
}
}
}
}
}

Find the possible options and their description in the configs/default_quantization_spec.json file in the POT directory.

1. The next option is stat_subset_size. It controls the size of the calibration dataset used by POT to collect statistics for quantization parameters initialization. It is assumed that this dataset should contain a sufficient number of representative samples. Thus, varying this parameter may affect accuracy (higher is better). However, we empirically found that 300 samples are sufficient to get representative statistics in most cases.

2. The last option is ignored_scope. It allows excluding some layers from the quantization process, i.e. their inputs will not be quantized. It may be helpful for some patterns for which it is known in advance that they drop accuracy when executing in low-precision. For example, DetectionOutput layer of SSD model expressed as a subgraph should not be quantized to preserve the accuracy of Object Detection models. One of the sources for the ignored scope can be the Accuracy-aware algorithm which can revert layers back to the original precision (see details below).

## Accuracy-aware Quantization¶

In case when the steps above do not lead to the accurate quantized model you may use the so-called Accuracy-aware Quantization algorithm which leads to mixed-precision models. A fragment of Accuracy-aware Quantization configuration with default settings is shown below below:

{
"name": "AccuracyAwareQuantization",
"params": {
"preset": "performance",
"stat_subset_size": 300,

"maximal_drop": 0.01 # Maximum accuracy drop which has to be achieved after the quantization
}
}

Since the Accuracy-aware Quantization calls the Default Quantization at the first step it means that all the parameters of the latter one are also valid and can be applied to the accuracy-aware scenario.

Note

In general case, possible speedup after applying the Accuracy-aware Quantization algorithm is less than after the Default Quantization when the model gets fully quantized.

### Reducing the performance gap of Accuracy-aware Quantization¶

To improve model performance after Accuracy-aware Quantization, you can try the "tune_hyperparams" setting and set it to True. It will enable searching for optimal quantization parameters before reverting layers to the “backup” precision. Note, that this can increase the overall quantization time.

If you do not achieve the desired accuracy and performance after applying the Accuracy-aware Quantization algorithm or you need an accurate fully-quantized model, we recommend either using Quantization-Aware Training from NNCF.