Models in TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Quantization Overview • Represent FP32 numbers with a lower-precision INT8 numbers • Integer number stands All rights reserved. Quantization in TVM • Quantization within TVM - Automatic Quantization • TVM stack ingests a FP32 graph and a small dataset • Finds suitable quantization scale • Produces a quantized its Affiliates. All rights reserved. Quantization Appraoches in TVM Framework FP32 Graph MXNet Parser TF parser …. Relay FP32 Graph Relay Automatic Quantization Relay Int8 Graph Framework Pre-quantized

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

以了解它们。 1.4.3 生成你的 GGUF 文件 We introduce the method of creating and quantizing GGUF files in quantization/llama.cpp. You can refer to that document for more information. 1.4.4 PPL 评测 llama.cpp 为我们提供了评估 AutoAWQForCausalLM from transformers import AutoTokenizer # Specify paths and hyperparameters for quantization model_path = "your_model_path" quant_path = "your_quantized_model_path" quant_config = { "zero_point": BaseQuantizeConfig from transformers import AutoTokenizer # Specify paths and hyperparameters for quantization (续下页) 16 Chapter 1. 文档 Qwen (接上页) model_path = "your_model_path" quant_path = "your_quantized_model_path"

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

/c Weight Adjustment 和 90% 而 Baseline 国 INT8 quantization w/o WA 忻 INT8 quantization w/ WA 80% 70% 60% 50%6 MobileNet v1 MobileNet v1 0.5

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