chapter, we introduce Quantization, a model compression technique that addresses both these issues. We’ll start with a gentle introduction to the idea of compression. Details of quantization and its applications after. The quantization section delves into the implementation details using code samples. We finish with a hands-on project that will walk you through the process of applying quantization in practical the next section we introduce Quantization, a popular compression technique which is also used in various fields of computer science in addition to deep learning. Quantization Before we jump to working

compression techniques. By ‘advanced’ we mean that these techniques are slightly more involved than quantization (as discussed in the second chapter). But that doesn’t mean they are harder to learn or implement particular clustering is a generalization of quantization. If you noticed, quantization ensures that any two weights that lie within the same quantization bin, are mapped to the same quantized weight value value. That is an implicit form for weight sharing. However, quantization falls behind in case the data that we are quantizing is not uniformly distributed, i.e. the data is more likely to take values

results. For example, between quantization and clustering, which one is preferable? What is the performance impact when both are used together? We have four options: none, quantization, clustering, and both. earlier example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering. A $$True$$ value

these approaches are generic enough to be used across architectures. A classical example is Quantization (see Figure 1-8), which tries to compress the weight matrix of a layer, by reducing its precision precision (eg., from 32-bit floating point values to 8-bit unsigned / signed integers). Quantization can generally be applied to any network which has a weight matrix. It can often help reduce the model size size 2 - 8x, while also speeding up the inference latency. Figure 1-8: An illustration of the quantization process: mapping of continuous high-precision values to discrete fixed-point integer values. Another

quality is within the acceptable parameters. For on-device models, TFLite offers post-training quantization as described in chapter 2. We could also incorporate compression techniques such as sparsity, a range of mobile and edge devices. Do you recall a technique that can reduce it further? Yes, Quantization! We will leave it for you as an exercise. Tell us how well it works! Summary This chapter was architectures for your deep learning projects. They can often be combined with other approaches like quantization, distillation, data augmentation, that we already learned. In the next chapter we will explore

JupyterLab 2.3.2 including Jupyter-TensorBoard ‣ TransformerEngine 0.10.0+96ed6fc ‣ PyTorch quantization wheel 2.1.2 PyTorch Release 23.07 PyTorch RN-08516-001_v23.07 | 6 Driver Requirements 2.6.2 ‣ JupyterLab 2.3.2 including Jupyter-TensorBoard ‣ TransformerEngine 0.9.0 ‣ PyTorch quantization wheel 2.1.2 PyTorch Release 23.06 PyTorch RN-08516-001_v23.07 | 14 Driver Requirements MAGMA 2.6.2 ‣ JupyterLab 2.3.2 including Jupyter-TensorBoard ‣ TransformerEngine 0.8 ‣ PyTorch quantization wheel 2.1.2 PyTorch Release 23.05 PyTorch RN-08516-001_v23.07 | 22 Driver Requirements

Keutzer, and A. Gholami. Kvquant: Towards 10 million context length LLM inference with KV cache quantization. CoRR, abs/2401.18079, 2024. URL https://doi.org/10.48550/arXiv.2401.18079. S. Hu, Y. Tu, X. Zhu, Z. Ye, L. Chen, S. Zheng, L. Ceze, A. Krishnamurthy, T. Chen, and B. Kasikci. Atom: Low-bit quantization for efficient and accurate LLM serving. CoRR, abs/2310.19102, 2023. URL https://doi.org/10.48550/arXiv

selecting DCT sizes and quantization steps. 5. Hare – Enables Gaborish Filtering, Chroma from Luma and estimates quantization steps. 6. Wombat – Enables error diffusion quantization and DCT heuristics. context clustering. 8. Kitten – Optimizes the adaptive quantization for a psychovisual metric. 9. Tortoise – Enables a more thorough adaptive quantization search. You can force-enable several of the options applied on this mathematical function is also finetuned by the encoder, this is called Adaptive Quantization. Because the encoder is able to pick the best solution for the compression (Depending on what

Contrast" operations, and it is possible to create others as well. 1This is sometimes referred to as Quantization, which is described in the Glossary. GNU Image Manipulation Program 249 / 653 13.2.5 Histogram estimation filter), as edge enhancement is the direct opposite of smoothing. For reducing color quantization noise in images (ie. turning .gif files back into 24 bit files) you could try a pass of the optimal of information makes it very difficult to maintain up-to-date support for PSD files. Q Quantization Quantization is the process of reducing the color of a pixel into one of a number of fixed values by

different application. Use quality settings from original image If a particular quality setting (or “quantization table”) was attached to the image when it was loaded, then this option allows you to use them same quality and file size as the original image. This will minimize the losses caused by the quantization step, compared to what would happen if you used different quality setting. If the quality setting it will approximate them by using the nearest color available. This is sometimes referred to as Quantization. If the colormap is too limited or poorly chosen, this can easily produce very poor image quality