Quantization Recipe

This recipe demonstrates how to quantize a PyTorch model so it can run with reduced size and faster inference speed with about the same accuracy as the original model. Quantization can be applied to both server and mobile model deployment, but it can be especially important or even critical on mobile, because a non-quantized model’s size may exceed the limit that an iOS or Android app allows for, cause the deployment or OTA update to take too much time, and make the inference too slow for a good user experience.


Quantization is a technique that converts 32-bit floating numbers in the model parameters to 8-bit integers. With quantization, the model size and memory footprint can be reduced to 1/4 of its original size, and the inference can be made about 2-4 times faster, while the accuracy stays about the same.

There are overall three approaches or workflows to quantize a model: post training dynamic quantization, post training static quantization, and quantization aware training. But if the model you want to use already has a quantized version, you can use it directly without going through any of the three workflows above. For example, the torchvision library already includes quantized versions for models MobileNet v2, ResNet 18, ResNet 50, Inception v3, GoogleNet, among others. So we will make the last approach another workflow, albeit a simple one.


The quantization support is available for a limited set of operators. See this for more information.


PyTorch 1.6.0 or 1.7.0

torchvision 0.6.0 or 0.7.0


Use one of the four workflows below to quantize a model.

1. Use Pretrained Quantized MobileNet v2

To get the MobileNet v2 quantized model, simply do:

import torchvision
model_quantized = torchvision.models.quantization.mobilenet_v2(pretrained=True, quantize=True)

To compare the size difference of a non-quantized MobileNet v2 model with its quantized version:

model = torchvision.models.mobilenet_v2(pretrained=True)

import os
import torch

def print_model_size(mdl):, "")
    print("%.2f MB" %(os.path.getsize("")/1e6))


The outputs will be:

14.27 MB
3.63 MB

2. Post Training Dynamic Quantization

To apply Dynamic Quantization, which converts all the weights in a model from 32-bit floating numbers to 8-bit integers but doesn’t convert the activations to int8 till just before performing the computation on the activations, simply call torch.quantization.quantize_dynamic:

model_dynamic_quantized = torch.quantization.quantize_dynamic(
    model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8

where qconfig_spec specifies the list of submodule names in model to apply quantization to.


An important limitation of Dynamic Quantization, while it is the easiest workflow if you do not have a pre-trained quantized model ready for use, is that it currently only supports nn.Linear and nn.LSTM in qconfig_spec, meaning that you will have to use Static Quantization or Quantization Aware Training, to be discussed later, to quantize other modules such as nn.Conv2d.

The full documentation of the quantize_dynamic API call is here. Three other examples of using the post training dynamic quantization are the Bert example, an LSTM model example, and another demo LSTM example.

3. Post Training Static Quantization

This method converts both the weights and the activations to 8-bit integers beforehand so there won’t be on-the-fly conversion on the activations during the inference, as the dynamic quantization does, hence improving the performance significantly.

To apply static quantization on a model, run the following code:

backend = "qnnpack"
model.qconfig = torch.quantization.get_default_qconfig(backend)
torch.backends.quantized.engine = backend
model_static_quantized = torch.quantization.prepare(model, inplace=False)
model_static_quantized = torch.quantization.convert(model_static_quantized, inplace=False)

After this, running print_model_size(model_static_quantized) shows the static quantized model is 3.98MB.

A complete model definition and static quantization example is here. A dedicated static quantization tutorial is here.


To make the model run on mobile devices which normally have arm architecture, you need to use qnnpack for backend; to run the model on computer with x86 architecture, use fbgemm.

4. Quantization Aware Training

Quantization aware training inserts fake quantization to all the weights and activations during the model training process and results in higher inference accuracy than the post-training quantization methods. It is typically used in CNN models.

To enable a model for quantization aware traing, define in the __init__ method of the model definition a QuantStub and a DeQuantStub to convert tensors from floating point to quantized type and vice versa:

self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub()

Then in the beginning and the end of the forward method of the model definition, call x = self.quant(x) and x = self.dequant(x).

To do a quantization aware training, use the following code snippet:

model.qconfig = torch.quantization.get_default_qat_qconfig(backend)
model_qat = torch.quantization.prepare_qat(model, inplace=False)
# quantization aware training goes here
model_qat = torch.quantization.convert(model_qat.eval(), inplace=False)

For more detailed examples of the quantization aware training, see here and here.

A pre-trained quantized model can also be used for quantized aware transfer learning, using the same quant and dequant calls shown above. See here for a complete example.

After a quantized model is generated using one of the steps above, before the model can be used to run on mobile devices, it needs to be further converted to the TorchScript format and then optimized for mobile apps. See the Script and Optimize for Mobile recipe for details.

Learn More

For more info on the different workflows of quantization, see here and here.

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