人工智能（AI）模型在近年来取得了惊人的进步，但随之而来的是它们体量的不断膨胀。一个庞大的AI模型，就像一头力大无穷的巨兽，虽然能力超群，但也意味着它需要消耗大量的计算资源和内存。这对于数据中心里强大的服务器来说或许不是问题，但当我们想把AI带到手机、智能音箱、摄像头这些“小个子”设备上时，这些巨兽就显得太“重”了，难以施展拳脚。
为了让AI模型“瘦身”并跑得更快，同时又不损失太多智能，科学家们想出了各种“减肥”方法，其中之一就是“量化”（Quantization）。
一、什么是量化？——给数字“瘦身”
想象一下，你有一张非常精美的彩色照片，每一颗像素的颜色都用数百万种不同的色调来精确表示（比如32位浮点数）。这张照片占用的存储空间很大，如果要在老旧的手机上快速打开或处理，可能会很慢。
“量化”就像是给这张照片“压缩颜色”：我们决定不再使用数百万种颜色，而是只用256种（比如8位整数）。虽然颜色种类变少了，但如果我们选择得当，照片看起来可能依然很棒，甚至普通人看不出太大区别，但文件大小和处理速度却能大大优化。
在AI领域，模型内部进行了大量的数学运算，这些运算的数据（比如模型的权重和激活值）通常以高精度的浮点数（32位浮点数，就像那数百万种颜色）表示。量化的目标就是将这些高精度的浮点数，转换成低精度的整数（比如8位或4位整数，就像256种颜色）。
这样做的好处显而易见：
- 节省内存： 低精度数据占用更少的存储空间，模型更小。
- 加速计算： 处理器处理整数运算比浮点运算更快、能耗更低。
- 方便部署： 使得AI模型更容易部署到资源有限的边缘设备（如手机、物联网设备）上。
二、动态量化：智能的“实时调色师”
量化技术又分为几种，其中一种被称为“动态量化”（Dynamic Quantization）。要理解它，我们可以先简单了解一下它的“兄弟”——静态量化。
1. 静态量化（Static Quantization）
静态量化就像是一位“预先设定好的调色师”。在模型开始工作之前，它会先看几张示例照片（称为“校准数据”），然后根据这些照片统计出各种颜色的分布范围，提前定好一套统一的256种颜色调色板。之后，所有要处理的照片都使用这套固定的调色板。
这种方法效率很高，因为调色板是固定的，模型可以直接使用。但缺点是，如果新来的照片和之前用于校准的示例照片风格差异很大，那么这套预设的调色板可能就不太适用，照片的“失真”会比较严重。尤其是在处理序列模型（如处理语言的循环神经网络）时，其输出的数值范围变化很大，静态量化可能难以表现良好。
2. 动态量化（Dynamic Quantization）——按需分配，灵活应变
动态量化则更像一个“实时的智能调色师”。它不像静态量化那样需要提前准备校准数据。当模型处理每一张照片（或者说每一个输入数据）时，它会即时地分析当前这张照片的颜色分布，然后根据这个分布，动态地计算并生成256种最适合当前照片的调色板。
具体来说：
- 权重（模型固有的“画笔和颜料”）：模型的参数（权重）是模型训练好后就固定不变的，它们通常会在部署前被离线量化成低精度的整数。
- 激活值（模型处理数据时产生的“中间画作”）：模型在处理输入数据过程中会产生大量的中间结果，叫做激活值。这些激活值的数值范围是不断变化的。动态量化会在程序运行的“当下”，根据每一个激活值的实际数值范围（最小值和最大值），实时地确定如何将其映射到低精度的整数范围。
打个比方：
如果说静态量化是画一幅画前，先根据看过的几幅画，定好你将要用的所有颜色，然后从头到尾都用这一套颜色来画。那么动态量化就是，当你画到天空时，实时分析天空的颜色，选择一个局部最优的256种蓝色调；当你画到大地时，又实时分析大地的颜色，选择一个局部最优的256种棕色调。这样，虽然总量都是256种颜色，但对于每一部分的刻画，都会更精准。
或者，我们可以把AI中的浮点数想象成测量物体长度时用的精密尺子，可以精确到毫米甚至微米。而量化就是换成一把只有厘米刻度的尺子。动态量化则是在每次测量时，会先看看物体的实际大小范围，然后“智能”地调整厘米尺子的起点和终点，让它能尽可能准确地覆盖当前的测量范围，以减少误差。
三、动态量化的优势与局限
优势：
- 无需校准数据： 动态量化最大的特点就是不需要额外的校准数据集来预设激活值的范围。这使得它部署起来非常方便，特别是对于那些没有足够代表性校准数据的场景。
- 节省内存和加速推理： 与静态量化一样，它也能有效减小模型体积，并加速模型推理速度，特别是在CPU上运行时效果显著。
- 对特定模型类型友好： 对于一些激活值分布难以预测或动态范围变化较大的模型，如循环神经网络（RNN）或Transformer模型，动态量化往往能获得比静态量化更好的效果和更小的精度损失。
局限性：
- 性能略低于完美静态量化： 由于需要在推理过程中实时计算激活值的量化参数，这会引入一些额外的计算开销。因此，如果静态量化经过精心调优，且校准数据非常具有代表性，那么静态量化的推理速度可能会略快于动态量化。
- 仍存在精度损失： 尽管动态量化试图最小化精度损失，但将高精度浮点数转换为低精度整数本身就是一个信息压缩过程，不可避免地会带来一定程度的精度损失。 不过，这种损失通常在可接受范围内。
四、最新进展与应用
随着大模型时代的到来，模型量化技术（包括动态量化）的重要性日益凸显。许多主流AI框架，如PyTorch和TensorFlow，都提供了对动态量化的支持，使得开发者能够方便地将他们的模型进行量化优化。
目前，AI模型量化技术正朝着更低比特（如INT4甚至更低）发展，同时也在探索自动化量化工具链、专用硬件协同优化、以及与混合精度等其他优化技术的融合，以在精度和效率之间找到最佳平衡。 动态量化作为一种简单而有效的模型优化手段，在推动AI模型在边缘设备上普及和应用方面，发挥着不可或缺的作用。 想象一下，未来的智能眼镜、自动驾驶汽车、智能工厂等，都将因为这些“瘦身”后的AI模型而变得更加智能、高效。
Artificial Intelligence (AI) models have made astonishing progress in recent years, but this has been accompanied by their continuously expanding size. A massive AI model is like a behemoth with immense strength; while it possesses superior capabilities, it also means it requires consuming vast amounts of computational resources and memory. This might not be a problem for powerful servers in data centers, but when we want to bring AI to “small-stature” devices like mobile phones, smart speakers, and cameras, these behemoths appear too “heavy” and struggle to perform.
To make AI models “slim down” and run faster without losing too much intelligence, scientists have come up with various “weight loss” methods, one of which is “Quantization”.
I. What is Quantization? — “Slimming Down” Numbers
Imagine you have a very exquisite color photo where the color of every pixel is precisely represented using millions of different shades (e.g., 32-bit floating-point numbers). This photo takes up a lot of storage space, and if you want to open or process it quickly on an old mobile phone, it might be very slow.
“Quantization” is like “compressing the colors” of this photo: we decide not to use millions of colors anymore, but only use 256 (e.g., 8-bit integers). Although the variety of colors is reduced, if we choose wisely, the photo can still look great—even ordinary people might not notice much difference—but the file size and processing speed can be greatly optimized.
In the field of AI, models perform massive amounts of mathematical operations. The data for these operations (such as the model’s weights and activation values) is usually represented as high-precision floating-point numbers (32-bit floating-point numbers, just like those millions of colors). The goal of quantization is to convert these high-precision floating-point numbers into low-precision integers (such as 8-bit or 4-bit integers, just like 256 colors).
The benefits of doing this are obvious:
- Save Memory: Low-precision data takes up less storage space, making the model smaller.
- Accelerate Computing: Processors handle integer operations faster and with lower energy consumption than floating-point operations.
- Facilitate Deployment: Makes it easier to deploy AI models on resource-constrained edge devices (such as mobile phones and IoT devices).
II. Dynamic Quantization: The Intelligent “Real-time Colorist”
Quantization technology is divided into several types, one of which is called “Dynamic Quantization”. To understand it, we can first briefly understand its “sibling”—Static Quantization.
1. Static Quantization
Static Quantization is like a “pre-configured colorist”. Before the model starts working, it first looks at a few sample photos (called “calibration data”), and then statistically analyzes the distribution range of various colors based on these photos to determine a unified palette of 256 colors in advance. Afterwards, all photos to be processed use this fixed palette.
This method is very efficient because the palette is fixed and the model can use it directly. However, the downside is that if the style of a new photo differs significantly from the sample photos used for calibration, this preset palette might not be suitable, and the “distortion” of the photo can be quite serious. Especially when dealing with sequence models (such as Recurrent Neural Networks processing language), where the range of output values varies greatly, static quantization may struggle to perform well.
2. Dynamic Quantization — Allocation on Demand, Flexible Adaptation
Dynamic Quantization is more like a “real-time intelligent colorist”. Unlike static quantization, it does not need to prepare calibration data in advance. When the model processes each photo (or rather, each input data), it analyzes the color distribution of the current photo on the fly, and then, based on this distribution, dynamically computes and generates the 256 colors best suited for the current photo.
Specifically:
- Weights (The model’s inherent “brushes and paints”): The parameters (weights) of the model are fixed after training. They are usually quantized offline into low-precision integers before deployment.
- Activations (The “intermediate paintings” produced during data processing): The model generates large amounts of intermediate results, called activation values, during the process of handling input data. The numerical range of these activation values is constantly changing. Dynamic quantization determines, at “runtime”, how to map these values to a low-precision integer range based on the actual numerical range (min and max values) of each activation.
Analogy:
If static quantization is like deciding on all the colors you will use based on a few paintings you’ve seen before starting to paint, and then using this set of colors primarily from start to finish. Then dynamic quantization is like when you paint the sky, you analyze the color of the sky in real-time and select a locally optimal set of 256 blue shades; when you paint the ground, you analyze the color of the earth in real-time and select a locally optimal set of 256 brown shades. In this way, although the total is 256 colors, the portrayal of each part will be more precise.
Or, we can imagine floating-point numbers in AI as precision rulers used to measure object lengths, accurate to millimeters or even micrometers. Quantization is switching to a ruler with only centimeter markings. Dynamic quantization, then, is looking at the actual size range of the object before each measurement, and “intelligently” adjusting the start and end points of the centimeter ruler so that it covers the current measurement range as accurately as possible to reduce error.
III. Advantages and Limitations of Dynamic Quantization
Advantages:
- No Calibration Data Needed: The biggest feature of dynamic quantization is that it does not require an additional calibration dataset to preset the range of activation values. This makes deployment very convenient, especially for scenarios without sufficiently representative calibration data.
- Save Memory and Accelerate Inference: Like static quantization, it can effectively reduce model size and accelerate model inference speed, with significant effects especially when running on CPUs.
- Friendly to Specific Model Types: For models where activation value distributions are hard to predict or have large dynamic range variations, such as Recurrent Neural Networks (RNNs) or Transformer models, dynamic quantization often achieves better results and less accuracy loss than static quantization.
Limitations:
- Performance Slightly Lower than Perfect Static Quantization: Because quantization parameters for activation values need to be calculated in real-time during inference, this introduces some extra computational overhead. Therefore, if static quantization is carefully tuned and the calibration data is very representative, the inference speed of static quantization might be slightly faster than dynamic quantization.
- Accuracy Loss Still Exists: Although dynamic quantization attempts to minimize accuracy loss, converting high-precision floating-point numbers to low-precision integers is inherently an information compression process, which inevitably brings a certain degree of accuracy loss. However, this loss is usually within an acceptable range.
IV. Recent Progress and Applications
With the arrival of the era of large models, the importance of model quantization technology (including dynamic quantization) has become increasingly prominent. Many mainstream AI frameworks, such as PyTorch and TensorFlow, provide support for dynamic quantization, allowing developers to conveniently optimize their models with quantization.
Currently, AI model quantization technology is moving towards lower bits (such as INT4 or even lower), while also exploring automated quantization toolchains, collaborative optimization with specialized hardware, and integration with other optimization techniques like mixed precision, to find the best balance between accuracy and efficiency. Dynamic quantization, as a simple and effective model optimization method, plays an indispensable role in promoting the popularity and application of AI models on edge devices. Imagine that future smart glasses, autonomous vehicles, smart factories, etc., will all become more intelligent and efficient because of these “slimmed down” AI models.