onnxruntime/docs/performance/quantization.md

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Quantize ONNX Models	Performance	3	/docs/how-to/quantization

Quantize ONNX Models

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Contents

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Quantization Overview

Quantization in ONNX Runtime refers to 8 bit linear quantization of an ONNX model.

During quantization, the floating point values are mapped to an 8 bit quantization space of the form: val_fp32 = scale * (val_quantized - zero_point)

scale is a positive real number used to map the floating point numbers to a quantization space. It is calculated as follows:

For asymmetric quantization:

scale = (data_range_max - data_range_min) / (quantization_range_max - quantization_range_min)

For symmetric quantization:

scale = max(abs(data_range_max), abs(data_range_min)) * 2 / (quantization_range_max - quantization_range_min)

zero_point represents zero in the quantization space. It is important that the floating point zero value be exactly representable in quantization space. This is because zero padding is used in many CNNs. If it is not possible to represent 0 uniquely after quantization, it will result in accuracy errors.

ONNX quantization representation format

There are two ways to represent quantized ONNX models:

• Operator-oriented (QOperator). All the quantized operators have their own ONNX definitions, like QLinearConv, MatMulInteger and etc.
• Tensor-oriented (QDQ; Quantize and DeQuantize). This format inserts DeQuantizeLinear(QuantizeLinear(tensor)) between the original operators to simulate the quantization and dequantization process. The QuantizeLinear and DeQuantizeLinear operators also carry the quantization parameters.Models generated in the following ways are in the QDQ format:
  • Models quantized by quantize_static API below with quant_format=QuantFormat.QDQ.
  • Quantization-Aware training (QAT) models converted from Tensorflow or exported from PyTorch.
  • Quantized models converted from TFLite and other frameworks.

For the latter two cases, you don't need to quantize the model with the quantization tool. ONNX Runtime can run them directly as a quantized model.

The picture below shows the equivalent representation with the QOperator and QDQ formats for quantized Conv. This end-to-end example demonstrates the two formats.

Quantizing an ONNX model

There are two ways of quantizing a model: dynamic and static.

• Dynamic quantization: This method calculates the quantization parameters (scale and zero point) for activations dynamically.

• Static quantization: leverages calibration data to calculate the quantization parameters of activations. Our quantization tool supports three calibration methods: MinMax, Entropy and Percentile. Please refer to calibrate.py for details.

Quantization API

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Quantization has two main APIs, corresponding to the two quantization methods: quantize_dynamic() and quantize_static().

Please refer to quantize.py for the quantization options for each method.

Example

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• Dynamic quantization:

import onnx
from onnxruntime.quantization import quantize_dynamic, QuantType

model_fp32 = 'path/to/the/model.onnx'
model_quant = 'path/to/the/model.quant.onnx'
quantized_model = quantize_dynamic(model_fp32, model_quant)

• Static quantization: please refer to the end-to-end examples.

Method selection

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The main difference between dynamic and static quantization is how the scale and zero point of activations are calculated. For static quantization, they are calculated in advance (offline) using a calibration data set. The activations thus have the same scale and zero point during each forward pass. For dynamic quantization, they are calculated on-the-fly (online) and are specific for each forward pass. They are thus more accurate but introduce an extra computational overhead.

In general, it is recommended to use dynamic quantization for RNNs and transformer-based models, and static quantization for CNN models.

If neither post-training quantization method can meet your accuracy goal, you can try using quantization-aware training (QAT) to retrain the model. ONNX Runtime does not provide retraining at this time, but you can retrain your models with the original framework and convert them back to ONNX.

Data type selection

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The quantized values are 8 bits wide and can be either signed (int8) or unsigned (uint8). We can choose the signedness of the activations and the weights separately, so the data format can be (activations: uint8, weights: uint8), (activations: uint8, weights: int8), etc. Let's use U8U8 as a shorthand for (activations: uint8, weights: uint8), U8S8 for (activations: uint8, weights: int8), and similarly S8U8 and S8S8 for the remaining two formats.

ONNX Runtime quantization on CPU can run U8U8, U8S8 and S8S8. S8S8 with QDQ is the default setting and balances performance and accuracy. It should be the first choice. Only in cases that the accuracy drops a lot, you can try U8U8. Note that S8S8 with QOperator will be slow on x86-64 CPUs and should be avoided in general. ONNX Runtime quantization on GPU only supports S8S8.

When and why do I need to try U8U8?

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On x86-64 machines with AVX2 and AVX512 extensions, ONNX Runtime uses the VPMADDUBSW instruction for U8S8 for performance. This instruction might suffer from saturation issues: it can happen that the output does not fit into a 16-bit integer and has to be clamped (saturated) to fit. Generally, this is not a big issue for the final result. However, if you do encounter a large accuracy drop, it may be caused by saturation. In this case, you can either try reduce_range or the U8U8 format which doesn't have saturation issues.

There is no such issue on other CPU architectures (x64 with VNNI and ARM).

List of Supported Quantized Ops

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Please refer to the registry for the list of supported Ops.

Quantization and model opset versions

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Models must be opset10 or higher to be quantized. Models with opset < 10 must be reconverted to ONNX from their original framework using a later opset.

Transformer-based models

There are specific optimizations for transformer-based models, such as QAttention for quantization of attention layers. In order to leverage these optimizations, you need to optimize your models using the Transformer Model Optimization Tool before quantizing the model.

This notebook demonstrates the process.

Quantization on GPU

Hardware support is required to achieve better performance with quantization on GPUs. You need a device that supports Tensor Core int8 computation, like T4 or A100. Older hardware will not benefit from quantization.

ONNX Runtime leverages the TensorRT Execution Provider for quantization on GPU now. Unlike the CPU Execution Provider, TensorRT takes in a full precision model and a calibration result for inputs. It decides how to quantize with their own logic. The overall procedure to leverage TensorRT EP quantization is:

• Implement a CalibrationDataReader.
• Compute quantization parameters using a calibration data set. Note: In order to include all tensors from the model for better calibration, please run symbolic_shape_infer.py first. Please refer to here for details.
• Save quantization parameters into a flatbuffer file
• Load model and quantization parameter file and run with the TensorRT EP.

We provide two end-to end examples: Yolo V3 and resnet50.

FAQ

Why am I not seeing performance improvements?

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The performance improvement depends on your model and hardware. The performance gain from quantization has two aspects: compute and memory. Old hardware has none or few of the instructions needed to perform efficient inference in int8. And quantization has overhead (from quantizing and dequantizing), so it is not rare to get worse performance on old devices.

x86-64 with VNNI, GPU with Tensor Core int8 support and ARM with dot-product instructions can get better performance in general.

Which quantization method should I choose, dynamic or static?

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Please refer to the Method selection section.

When to use reduce-range and per-channel quantization?

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Reduce-range will quantize the weights to 7 bits. It is designed for the U8S8 format on AVX2 and AVX512 (non-VNNI) machines to mitigate saturation issues. This is not needed on machines supporting VNNI.

Per-channel quantization can improve the accuracy for models whose weight ranges are large. Try it if the accuracy loss is large. On AVX2 and AVX512 machines, you will generally need to enable reduce-range as well if per-channel is enabled.

Why are operators like MaxPool not quantized?

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8-bit type support for certain operators such as MaxPool was added in ONNX opset 12. Please check your model version and upgrade it to opset 12 and above.