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WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:144533 1 00:00:00.000 --> 00:00:03.100 Quantization methods are used to make models smaller, 2 00:00:03.100 --> 00:00:06.200 which makes them more accessible to the AI community. 3 00:00:06.200 --> 00:00:09.800 In this lesson, you'll get an overview of what Quantization is, 4 00:00:09.800 --> 00:00:12.500 and how it works. Let's get started. 5 00:00:12.500 --> 00:00:15.500 We have seen previously that quantization is an exciting topic 6 00:00:15.500 --> 00:00:19.733 as it enables us to shrink models for better accessibility to the community. 7 00:00:19.900 --> 00:00:22.000 In this lesson, we will learn how to implement 8 00:00:22.000 --> 00:00:25.400 some quantization primitives from scratch, and we will also implement 9 00:00:25.400 --> 00:00:29.933 our own model quantizer and cover some challenges that anyone can face 10 00:00:29.933 --> 00:00:33.700 when it comes to lower bit quantization, such as weights packing. 11 00:00:34.300 --> 00:00:35.433 Let's get started. 12 00:00:35.433 --> 00:00:38.300 So let's first have a quick glance on what we have learned 13 00:00:38.300 --> 00:00:40.200 so far from the first course. 14 00:00:40.200 --> 00:00:44.100 So, in the introduction of the first course, we listed all available techniques 15 00:00:44.100 --> 00:00:47.433 that one could use in order to compress a model in general. 16 00:00:48.000 --> 00:00:50.733 So first of all, quantization aims at representing 17 00:00:50.733 --> 00:00:53.733 parameters of the model in a lower precision. 18 00:00:53.900 --> 00:00:57.700 With knowledge distillation, you can train a student model 19 00:00:57.700 --> 00:01:00.700 using the bigger teacher model outputs. 20 00:01:00.833 --> 00:01:04.300 And finally, with pruning, you can simply remove some connections 21 00:01:04.300 --> 00:01:09.300 inside the model, meaning removing weights to make the model more sparse. 22 00:01:10.100 --> 00:01:12.533 We also covered common data types in machine 23 00:01:12.533 --> 00:01:16.700 learning, such as INT8 or float. We also performed 24 00:01:16.800 --> 00:01:21.100 linear quantization using Hugging Face's quantum library with few lines of code. 25 00:01:21.400 --> 00:01:24.200 And finally, we wrapped up the course with an overview 26 00:01:24.200 --> 00:01:27.200 how quantization can be leveraged in different use cases, 27 00:01:27.233 --> 00:01:29.900 such as large language models, finetuning. 28 00:01:29.900 --> 00:01:32.900 So let's see together what we are going to cover exactly in this course. 29 00:01:33.333 --> 00:01:37.433 So, first of all, we are going to deep dive together into the internals 30 00:01:37.433 --> 00:01:41.733 of linear quantization and implement some of their variants from scratch, 31 00:01:42.000 --> 00:01:46.000 such as per channel, per tensor or per group quantization. 32 00:01:46.700 --> 00:01:50.200 We will study what are the advantages and drawbacks for each of these methods, 33 00:01:50.200 --> 00:01:53.100 and we will see their impact on some random tensors. 34 00:01:53.100 --> 00:01:56.700 And next, we will try to build our own quantizer 35 00:01:56.900 --> 00:01:58.800 to quantize any model in eight-bit 36 00:01:58.800 --> 00:02:02.500 precision using one of the quantization schemes presented before. 37 00:02:02.833 --> 00:02:06.200 Note the quantization scheme is agnostic to modalities, 38 00:02:06.433 --> 00:02:11.000 meaning you can apply to any model as long as your model contains linear layers. 39 00:02:11.400 --> 00:02:14.400 Technically, you will be able to use your quantizer to quantize 40 00:02:14.400 --> 00:02:17.900 a vision, text, audio, or even a multimodal model. 41 00:02:18.200 --> 00:02:21.433 And finally, we will wrap up the course by learning more 42 00:02:21.500 --> 00:02:26.033 about some challenges that you can face when it comes to extreme quantization, 43 00:02:26.033 --> 00:02:29.533 such as weight packing, which is a common challenge these days. 44 00:02:30.000 --> 00:02:32.933 As of the time we speak, PyTorch does not have 45 00:02:32.933 --> 00:02:36.100 a native support for two-bit or four-bit precision weights. 46 00:02:36.400 --> 00:02:40.000 One way to address this issue is to pack these low precision 47 00:02:40.000 --> 00:02:43.433 weights into a higher precision tensor, for example INT8. 48 00:02:43.833 --> 00:02:45.600 And we will deep dive into that 49 00:02:45.600 --> 00:02:49.300 and implement packing and unpacking algorithms together. 50 00:02:49.400 --> 00:02:53.300 And we will end the course by covering what are the other common challenges 51 00:02:53.300 --> 00:02:56.333 when it comes to quantizing large models such as LLMs. 52 00:02:56.500 --> 00:03:00.600 And review together some state of the art quantization methods together. 53 00:03:00.833 --> 00:03:03.600 Yeah, so let's try to get started and shrink some models. |