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Quantization in Pytorch

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Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


Quantization in PyTorch means simplifying the numbers used in a deep learning model. It's like rounding off numbers with lots of decimal places to make them shorter and easier to handle. This helps the model work faster and use less computer memory, while trying to keep its accuracy as close as possible to the original. It's like giving a powerful computer a little break, so it can work faster and be more friendly to devices like phones.

Quantization In Pytorch

Let’s discuss about Quantization in Pytorch

Why Does Quantization Matter?

Imagine you have a powerful computer, but it uses a lot of energy and takes up a lot of space. Now, what if you could make that computer smaller and use less energy while still getting it to do the same tasks?

That's where quantization comes into play. It helps make our deep learning models smaller, faster, and more efficient.

Due to its usefulness and influence on deep learning models, quantization in PyTorch is quite important. Here is why it's important:

1. Increase in Efficiency 

Quantization simplifies the data that deep learning models employ. Less digits are used to represent data, which lowers the amount of memory and processing power needed for calculations. This increase in efficiency translates into quicker operation and greater hardware usage.

2. Quicker Inference

Quantization results in numbers that are simplified, which speeds up computations. For real-time applications, like autonomous driving or immediate language translation, where quick decision-making is necessary, this speed advantage is extremely important.

3. Device Compatibility

A lot of electronics, including smartphones, wearables, and Internet of Things devices, have processing and memory capacities. Models may be modified for these resource-constrained situations via quantization, which enables AI applications to run on a wider variety of hardware.

4. Energy Savings

Quantization reduces computing complexity, which results in less energy being used. In addition to being ecologically benign, energy-efficient versions significantly increase the battery life of mobile and edge devices.

5. Cost-Efficient Scaling

Using unquantized models at scale can be costly in terms of infrastructure and processing resources. Large-scale deployment becomes more affordable as a result of quantization's reduction of these overheads.

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Why Do Deep Learning Models Need Quantization?

Quantization plays a crucial role in PyTorch when it comes to deep learning models.

Let's talk about deep learning models. 

Imagine these models as super-smart friends who can solve puzzles really quickly. But here's the thing – they use some really fancy tools to do their puzzle-solving. These tools work with numbers, kind of like maths problems.

Now, imagine you're helping your friend solve a giant puzzle with lots of pieces. Some of these pieces are itty-bitty and have super tiny details. It's like doing a puzzle with really small letters – it can be a bit slow and tiring, right?

Well, deep learning models work with these tiny numbers to solve their puzzles. 

But guess what? 

They don't always need all those tiny details. Sometimes, it's like solving a puzzle with bigger, easier-to-see pieces. It's faster and still gets the job done.

That's where quantization comes into play. It's like using a magic tool that takes those super tiny numbers and makes them a bit bigger and simpler. These "bigger" numbers might not have all the fancy details, but they still give pretty good answers to the puzzles.

Now, why is this important? 

These slightly bigger numbers are like using a bigger font to write – you can read it faster. When deep learning models use these simpler numbers, they can solve puzzles quicker. Plus, these simpler numbers take up less space in the model's "brain," so it doesn't get too full and slow.

And guess what?

 When models use less space and solve puzzles faster, they can work on small devices like phones without making them tired. So, in a nutshell, deep learning models need quantization in PyTorch to work faster, save space, and be friendly to small gadgets. It's like giving them a turbo boost for puzzle-solving.

Quantization in Pytorch 

Quantization steps in to simplify these precise measurements. It's like rounding off those measurements to the nearest whole number or a smaller unit. This makes the numbers simpler, but still close enough to the original values.

In PyTorch, when we apply quantization, we're taking these complex numbers that the models use and making them less detailed. 

This matters because working with simpler numbers is faster and more efficient.

Imagine doing maths with whole numbers instead of dealing with lots of decimal places – it's quicker and less complex.

Now, when deep learning models use these quantized numbers, their calculations become speedier. This speed boost matters, especially in applications where quick decision-making is crucial, like self-driving cars or real-time language translation.

Furthermore, quantization also reduces the memory these numbers occupy. Think of it as compressing a file to take up less space on your computer. This memory efficiency is particularly useful for deploying models on devices with limited resources, like smartphones or edge devices.

So, the relationship between quantization and PyTorch is like optimising the toolbox.

PyTorch provides the tools for creating sophisticated models, and quantization fine-tunes these tools to work faster and more efficiently by using simpler numbers. It's like giving those tools a special edge to handle tasks with greater speed and agility.


PyTorch is like a super-smart friend who can do tricky maths really fast. But sometimes, this friend needs a little help to be even faster and more efficient. That's where quantization comes in.

Imagine you have a big box of colorful markers. Each marker has a different shade, just like how numbers in PyTorch have lots of details. But here's the thing: your friend doesn't need all those shades all the time. Sometimes, a few main shades are enough.

Quantization is like picking out those main shades and using only those markers. It's like simplifying the maths stuff, so your super-smart friend can work quicker. And when things are quicker, they're also less tiring.

So, PyTorch and quantization work together like a dynamic duo. PyTorch does the smart stuff, and quantization gives it a boost to do things faster and smoother. It's like making a team of superheroes even stronger by giving them the perfect sidekick.

Various Pytorch Quantization Methods

There are various methods of Quantization in Pytorch.So, let's define PyTorch quantization methods.

Post-training Quantization

Post-training quantization is a method in which an already trained deep learning model undergoes an optimization process to reduce computational complexity and memory usage. This process involves converting the model's original high-precision numerical parameters, including weights and activations, into lower-precision fixed-point representations.

During post-training quantization, the precision of these numerical values is reduced by truncating or rounding them to fewer bits.

This results in a compact representation that occupies less memory and requires fewer computational resources during inference. While this reduction in precision can lead to a marginal loss in model accuracy, post-training quantization aims to strike a balance between efficiency gains and acceptable performance degradation.


Imagine you have a beautiful painting that you're happy with, but you want to make it a bit smaller so it fits in a smaller frame.

In the same way, post-training quantization works with an already trained deep learning model. This model is like your painting. The model uses special numbers for its calculations, and these numbers have lots of details, like tiny dots in a picture.

Quantization steps in and simplifies these numbers a little, just like making your painting a bit smaller. This simplification makes the model use less memory and run faster, without changing its overall ability.

So, post-training quantization is like refining a masterpiece to make it more efficient and quicker, while keeping its original brilliance intact.

Implementing Post-training Quantization

If you haven't already, you can install PyTorch using the following steps:
It seems like the error is occurring because the 'torch' module is not installed in your environment. To resolve this error and successfully run the code, you need to make sure you have PyTorch installed. 

If you haven't already, you can install PyTorch using the following steps:

Step 1

Open a command prompt or terminal.

Step 2

Run the appropriate command based on your system and hardware:
For CPU-only installation:

pip install torch torchvision torchaudio

For GPU installation (if you have CUDA-compatible GPU):

pip install torch torchvision torchaudio -f

Step 3

Wait for the installation to complete.

Once PyTorch is installed, you can try running the code again. Make sure you're running the code in an environment where PyTorch is available.

If you continue to face issues, ensure that your Python environment is set up correctly and that there are no conflicts with other installed packages.

Follow the Implementation Steps:


  • Python


import torch
import torchvision.models as models
import torch.quantization

# Step 1: Load a pre-trained model
model = models.resnet18(pretrained=True)

# Step 2: Set the model to evaluation mode

# Step 3: Create a dummy input
dummy_input = torch.randn(1, 3, 224, 224) # Example input shape

# Step 4: Apply quantization
quantized_model = torch.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8

# Step 5: Compare performance before and after quantization
with torch.no_grad():
original_output = model(dummy_input)
quantized_output = quantized_model(dummy_input)

print("Original Model Output:", original_output)
print("Quantized Model Output:", quantized_output)



Here's a simplified breakdown of what each step does:

1. We import the necessary libraries, including pre-trained models from torchvision and the quantization module from PyTorch.

2. The model is set to evaluation mode using model.eval(). This is important as it affects certain behaviours, like dropout.

3. We create a dummy input that matches the expected input shape of the model. This is necessary for quantization.

4. We use the torch.quantization.quantize_dynamic() function to quantize the model. This function takes the model, a set of module types to quantize (in this case, linear layers), and the desired data type for quantization (int8 in this example).

5. We compare the outputs of the original and quantized models using the dummy input.

Quantization-Aware Training

Quantization-aware training is like teaching a clever parrot to talk in simpler words without losing its smartness.

Imagine you're training a parrot to repeat sentences. At first, you teach it exactly as people talk, with all the words and details. But then, you want the parrot to talk faster and use fewer words, like a summary.

In the same way, quantization-aware training works with a smart model. When you train the model, you make it understand both the complex numbers and how to simplify them. It's like teaching the parrot to talk normally and then showing it how to speak in shorter sentences.

This training helps the model to be smart not only with the original numbers but also when they're simplified. It's like teaching the parrot to be good at both long conversations and quick chats.

In short, quantization-aware training is like training a parrot to speak well in both detailed and simplified ways. It's about making sure the model understands complex maths and how to talk about it in simpler terms.


Illustrating Quantization-Aware Training with Simple Terms

Alright, let's break down quantization-aware training in a simple way.

Imagine you're teaching a smart robot to understand colors, but you want it to use just a few main colors instead of all the shades. Quantization-aware training is like training that robot to work with those main colors, even before it starts its mission.

Here's how it works step by step:

Getting Ready

Imagine you're preparing the robot's backpack before its journey. In quantization-aware training, you're getting the robot ready to use fewer colors (or numbers) from the beginning.

Teaching Time

Now, you're teaching the robot about these main colors. You train it to do tasks using those simplified colors, which makes its learning process faster.

Smart Conversations

The robot is now like a smart friend who speaks using only those main colors. When it talks, it uses the simpler colors you taught it. This makes its conversations quicker and easier.

Going on Missions

Finally, the robot is ready for its adventures! It uses what it learned about those main colors to perform tasks efficiently. It's like having a superhero with a small but powerful set of tools.

So, quantization-aware training is like giving the robot a head start, teaching it to understand and work with a smaller set of colors (or numbers) for faster and smarter actions. Just like a superhero using a special toolkit to save the day.

Dynamic Quantization 

Dynamic quantization is a clever way to make complicated maths faster and simpler for computers.

Imagine you're doing a puzzle with numbers, but instead of using all the numbers exactly, you round them off to the nearest whole number. Dynamic quantization does something similar for computers.

Here's how it works:

Numbers on Demand

Just like you take out puzzle pieces when you need them, dynamic quantization simplifies numbers when the computer needs to use them. It's like having a smart helper that only brings out the necessary puzzle pieces.

Quick Maths

When you round off numbers in the puzzle, it becomes easier to solve. Similarly, the computer works faster with simpler numbers. This speed-up is handy when you want quick answers.

Saving Memory

Imagine you have a box to keep your puzzle pieces. When you simplify numbers, they take up less space in the computer's memory. This is like fitting more pieces in a smaller box.

Efficient Thinking

Dynamic quantization helps the computer think quickly by using less detailed numbers. It's like making decisions faster without getting caught up in tiny details.

Real-World Example

Let's consider a real-world scenario involving a language translation app. Imagine you're using an app that instantly translates sentences from one language to another. Dynamic quantization comes into play here to make this process faster and more efficient.

Here's how it works:

Scenario: Language Translation App


Regular Calculation

Without dynamic quantization, the app would use very precise numbers to figure out the translations. Imagine these precise numbers are like measuring distances down to the millimetre.

Dynamic Quantization in Action

Now, with dynamic quantization, the app is smarter. It realises that translating with super precise numbers isn't necessary all the time. Just like you might say "around 5 kilometres" instead of "4.8742 kilometres," the app simplifies its calculations by rounding off numbers.

Speed and Memory Benefits

By using less detailed numbers, the app can calculate translations faster. This is crucial for real-time use – you want your translation quickly, like in a conversation. Additionally, the simplified numbers take up less space in the app's "memory," which means the app can handle more translations at once.

Balancing Accuracy and Speed

It's important to mention that while dynamic quantization speeds up the app, it doesn't compromise accuracy significantly. Just like "around 5 kilometres" is accurate enough for most purposes, the app's translations remain reliable.

Static Quantization

Static quantization is like putting different items into labelled boxes to keep things organised. In the world of computers, it's a smart way to make complex maths calculations simpler and faster.


Imagine Organising a Collection of Toys

Grouping Similar Toys

Think of your toys. You have cars, action figures, and blocks. In static quantization, numbers are like these toys. They're grouped into categories based on their values.

Fixed Categories

Once you decide which toys go in each category, it doesn't change. Just like action figures always go in the "action figure" box, numbers in static quantization have their fixed groups.

Labels for Categories

You put labels on each box so you know exactly what's inside. Similarly, numbers in static quantization have labels that help the computer quickly figure out which group they belong to.

Quick and Easy Access

When you want to play with action figures, you go straight to the labelled box. Computers do the same with categorised numbers – they access them directly without needing complex calculations.

Efficient Storage 

Your organised boxes take up less space. Static quantization also saves space in computers' "memory" because it arranges numbers neatly.

Selection of Appropriate Quantization Method 

Picking the right quantization method is a bit like choosing the best tool for a job. You want to use the one that fits the task perfectly and makes everything work smoothly.

Different Tools for Different Steps

When you bake cookies, you use various tools – mixing bowls, measuring cups, and the oven. Each tool has its job. Similarly, in quantization, there are different methods, and each is good for specific situations.

Matching the Task

Just as you wouldn't use a tiny spoon to mix a big batch of dough, you don't use the same quantization method for every situation. You pick the method that suits the complexity of your data and the task you're doing.

Balance of Speed and Accuracy

Sometimes, you want cookies fast and don't mind if they're not picture-perfect. Other times, you're making special cookies and need them to look and taste amazing. Quantization methods also balance speed and accuracy in different ways.

Efficiency and Simplicity

If you're baking a lot of cookies, you might choose methods that are quick and straightforward. Similarly, for simple tasks, you might go for quantization methods that are efficient and easy to apply.

Precision Matters

Just as you carefully measure ingredients for delicate cookies, in certain cases, you need precision in quantization to maintain the quality of your results.


In short, choosing the right quantization method is like picking the right tool for baking. You match the method to the task, balancing speed, accuracy, efficiency, and precision. This way, you get the best results without unnecessary complications.


Advantages of Using Quantization

Using quantization is like having a magic spell to make maths faster and computers more efficient. Let's explore why it's so handy.

Imagine a Super Speed Potion.

1. Faster Calculations

Quantization is like a potion that makes maths super fast. It simplifies numbers, making them easier for computers to handle. Just like a calculator becomes quicker when it uses simpler numbers.

2. Saving Space

Think of it as a bag that shrinks things down. Quantization makes numbers take up less room in a computer's "memory." This means you can do more with the same space.

3. Efficient Conversations

Like speaking in short sentences, quantization helps computers talk faster. They communicate using simpler numbers, making tasks speedier and smoother.

4. Less Energy, More Power

Just as a car uses less fuel when it's lighter, computers save energy with quantization. They do calculations with less effort, which is especially important for devices like phones.

5. Fitting Big Data

Quantization is like arranging puzzle pieces to fit perfectly. It's great for handling large amounts of data, helping computers process big tasks more easily.


In a nutshell, quantization is like giving computers a secret potion to become faster, smarter, and more efficient. It simplifies maths, saves space, and lets them do their tasks with less energy, making them powerful problem solvers.

Frequently Asked Questions

How does quantization impact the accuracy of PyTorch models? 

Quantization can have a minor impact on model accuracy. Reducing precision may lead to a small loss of accuracy due to rounding errors. However, PyTorch provides techniques like quantization-aware training, where models are trained to be more robust to quantization effects, helping maintain accuracy while reaping efficiency benefits.

What is the impact of quantization on memory usage in PyTorch? 

Quantization reduces memory usage in PyTorch models. By representing numbers with fewer bits, the memory footprint decreases. This is particularly advantageous when deploying models on devices with limited memory, such as edge devices.

How can I assess the trade-off between accuracy and performance when using quantization in PyTorch?

Assessing the trade-off involves evaluating the model's accuracy before and after quantization, along with measuring its performance in terms of inference speed and memory usage. You can use tools provided by PyTorch to monitor these metrics and determine the optimal balance between accuracy and efficiency.


Quantization in PyTorch offers a powerful way to boost the speed and efficiency of deep learning models. By simplifying numbers and optimising memory usage, quantization enhances performance while maintaining a balance between accuracy and speed. PyTorch's versatile quantization methods provide developers with tools to optimise models for various hardware platforms, making it a valuable technique for real-world applications.

You can read these articles to learn more.


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Topics covered
Why Does Quantization Matter?
Why Do Deep Learning Models Need Quantization?
Quantization in Pytorch 
Various Pytorch Quantization Methods
Post-training Quantization
Implementing Post-training Quantization
Quantization-Aware Training
Dynamic Quantization 
Real-World Example
Static Quantization
Selection of Appropriate Quantization Method 
Different Tools for Different Steps
Advantages of Using Quantization
1. Faster Calculations
2. Saving Space
3. Efficient Conversations
4. Less Energy, More Power
5. Fitting Big Data
Frequently Asked Questions
How does quantization impact the accuracy of PyTorch models? 
What is the impact of quantization on memory usage in PyTorch? 
How can I assess the trade-off between accuracy and performance when using quantization in PyTorch?