| WEBVTT | |
| X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:144533 | |
| 1 | |
| 00:00:00.000 --> 00:00:01.533 | |
| Welcome to this short course | |
| 2 | |
| 00:00:01.533 --> 00:00:05.100 | |
| "Quantization in Depth," | |
| built in partnership with Hugging Face. | |
| 3 | |
| 00:00:05.800 --> 00:00:09.200 | |
| In this course | |
| you deep dive into the core technical | |
| 4 | |
| 00:00:09.200 --> 00:00:13.533 | |
| building blocks of quantization, | |
| which is a key part of the AI software | |
| 5 | |
| 00:00:13.533 --> 00:00:17.233 | |
| stack for compressing | |
| large language models and other models. | |
| 6 | |
| 00:00:17.500 --> 00:00:18.800 | |
| You implement from scratch | |
| 7 | |
| 00:00:18.800 --> 00:00:22.533 | |
| the most common variants of linear | |
| quantization, called | |
| 8 | |
| 00:00:22.600 --> 00:00:26.400 | |
| asymmetric and symmetric modes, | |
| which relate to whether | |
| 9 | |
| 00:00:26.733 --> 00:00:30.500 | |
| compression algorithm maps zero | |
| in the original representation, | |
| 10 | |
| 00:00:30.933 --> 00:00:33.933 | |
| to zero in decompress representation, | |
| 11 | |
| 00:00:33.933 --> 00:00:37.233 | |
| or if is allowed to shift | |
| the location of that zero. | |
| 12 | |
| 00:00:37.933 --> 00:00:42.200 | |
| You also implement different forms | |
| of quantization, such as a per tensor | |
| 13 | |
| 00:00:42.600 --> 00:00:46.500 | |
| per channel, and per group quantization | |
| using PyTorch, | |
| 14 | |
| 00:00:46.833 --> 00:00:48.100 | |
| in which you can decide | |
| 15 | |
| 00:00:48.100 --> 00:00:51.600 | |
| how big a chunk of your model | |
| you want to quantize at one time. | |
| 16 | |
| 00:00:52.500 --> 00:00:56.100 | |
| You end up building a quantizer | |
| to quantize any model | |
| 17 | |
| 00:00:56.100 --> 00:01:00.433 | |
| in eight-bit precision | |
| using per channel linear quantization. | |
| 18 | |
| 00:01:00.733 --> 00:01:05.200 | |
| If some of the terms I use don't make | |
| sense yet, don't worry about it. | |
| 19 | |
| 00:01:05.233 --> 00:01:06.100 | |
| These are all key | |
| 20 | |
| 00:01:06.100 --> 00:01:09.833 | |
| technical concepts in quantization | |
| that you learn about in this course. | |
| 21 | |
| 00:01:10.033 --> 00:01:14.300 | |
| And in addition to understanding | |
| all these quantization options, | |
| 22 | |
| 00:01:14.400 --> 00:01:19.400 | |
| you also hone your intuition | |
| about when to apply which technique. | |
| 23 | |
| 00:01:19.700 --> 00:01:23.000 | |
| I'm delighted to introduce our instructors | |
| for this course. | |
| 24 | |
| 00:01:23.433 --> 00:01:26.700 | |
| Younes Belkada, a machine | |
| learning engineer at Hugging Face | |
| 25 | |
| 00:01:27.100 --> 00:01:29.600 | |
| has been involved in the open source team, | |
| 26 | |
| 00:01:29.600 --> 00:01:32.933 | |
| where he works at the intersection | |
| of many open source tools | |
| 27 | |
| 00:01:33.100 --> 00:01:37.533 | |
| developed by Hugging Face | |
| such as transformers, PETF, and TRL. | |
| 28 | |
| 00:01:38.300 --> 00:01:42.300 | |
| And also Marc Sun, who's also a machine | |
| learning engineer at Hugging Face. | |
| 29 | |
| 00:01:42.733 --> 00:01:46.100 | |
| Marc is part of the Open source team, | |
| where he contributes to libraries | |
| 30 | |
| 00:01:46.100 --> 00:01:49.100 | |
| such as transformers or Accelerate. | |
| 31 | |
| 00:01:49.633 --> 00:01:52.733 | |
| Marc and Younes are also deeply involved | |
| in quantization | |
| 32 | |
| 00:01:52.733 --> 00:01:56.300 | |
| in order to make large models | |
| accessible to the community. | |
| 33 | |
| 00:01:57.700 --> 00:01:58.800 | |
| Thanks, Andrew. | |
| 34 | |
| 00:01:58.800 --> 00:02:01.200 | |
| We are excited to work with you | |
| and your team on this. | |
| 35 | |
| 00:02:01.200 --> 00:02:05.133 | |
| In this course, you will directly try | |
| your hand on implementing | |
| 36 | |
| 00:02:05.133 --> 00:02:08.300 | |
| from scratch | |
| different variants of linear quantization, | |
| 37 | |
| 00:02:08.400 --> 00:02:10.500 | |
| symmetric and asymmetric mode. | |
| 38 | |
| 00:02:10.500 --> 00:02:14.033 | |
| You will also implement | |
| different quantization granularities, such | |
| 39 | |
| 00:02:14.033 --> 00:02:19.033 | |
| as per tensor, per channel | |
| and per group quantization in pure PyTorch. | |
| 40 | |
| 00:02:19.033 --> 00:02:23.400 | |
| Each one of these algorithms having | |
| their own advantages and drawbacks. | |
| 41 | |
| 00:02:23.800 --> 00:02:26.600 | |
| After that, | |
| you'll build your own quantizer | |
| 42 | |
| 00:02:26.600 --> 00:02:29.600 | |
| in order to quantize any model | |
| in eight-bit precision. | |
| 43 | |
| 00:02:29.633 --> 00:02:33.200 | |
| Using the per channel quantization scheme | |
| that you have seen right before. | |
| 44 | |
| 00:02:33.600 --> 00:02:35.400 | |
| You will see that you'll be able | |
| to apply this | |
| 45 | |
| 00:02:35.400 --> 00:02:39.333 | |
| method to any model regardless | |
| of its modality, meaning you can apply | |
| 46 | |
| 00:02:39.333 --> 00:02:43.033 | |
| to a text, vision, audio, | |
| or even a multimodal model. | |
| 47 | |
| 00:02:43.200 --> 00:02:46.133 | |
| Once you are happy with the quantizer, | |
| it will try your hands on | |
| 48 | |
| 00:02:46.133 --> 00:02:49.133 | |
| addressing common challenges | |
| in quantization. | |
| 49 | |
| 00:02:49.233 --> 00:02:52.700 | |
| At the time, we speak the most common | |
| way of storing low-bit precision | |
| 50 | |
| 00:02:52.700 --> 00:02:56.533 | |
| weights, such as four-bit or two-bit, | |
| seemed to be weight spiking. | |
| 51 | |
| 00:02:57.033 --> 00:03:00.300 | |
| With weight spiking, | |
| you can pack altogether 2 or 4 bits | |
| 52 | |
| 00:03:00.300 --> 00:03:04.233 | |
| tensors in a larger eight-bit tensor | |
| without allocating any extra memory. | |
| 53 | |
| 00:03:04.733 --> 00:03:06.600 | |
| We will see together | |
| why this is important, | |
| 54 | |
| 00:03:06.600 --> 00:03:09.900 | |
| and you will implement from scratch | |
| packing and unpacking algorithms. | |
| 55 | |
| 00:03:10.100 --> 00:03:12.933 | |
| Finally, we will learn together | |
| about other challenges | |
| 56 | |
| 00:03:12.933 --> 00:03:16.000 | |
| when it comes to quantizing large models | |
| such as LLMS. | |
| 57 | |
| 00:03:16.400 --> 00:03:20.300 | |
| We will review together current state | |
| of the art approaches in order to perform | |
| 58 | |
| 00:03:20.300 --> 00:03:21.800 | |
| no performance degradation | |
| 59 | |
| 00:03:21.800 --> 00:03:26.100 | |
| quantization on LLMs and go through | |
| how to do that within the Hugging Face | |
| 60 | |
| 00:03:26.100 --> 00:03:27.133 | |
| ecosystem. | |
| 61 | |
| 00:03:27.133 --> 00:03:29.800 | |
| Quantization is a really important part | |
| 62 | |
| 00:03:29.800 --> 00:03:32.800 | |
| of practical use of large models today. | |
| 63 | |
| 00:03:32.833 --> 00:03:35.833 | |
| So having in-depth knowledge of it | |
| will help you to build, | |
| 64 | |
| 00:03:35.900 --> 00:03:38.900 | |
| deploy, and use models more effectively. | |
| 65 | |
| 00:03:39.300 --> 00:03:41.633 | |
| Many people have worked | |
| to create this course. | |
| 66 | |
| 00:03:41.633 --> 00:03:43.300 | |
| I like to thank on the Hugging Face | |
| 67 | |
| 00:03:43.300 --> 00:03:47.100 | |
| side, the entire Hugging Face team | |
| for the review of this course content, | |
| 68 | |
| 00:03:47.200 --> 00:03:50.233 | |
| as well as the Hugging Face community | |
| for their contributions | |
| 69 | |
| 00:03:50.233 --> 00:03:54.300 | |
| to open source models and quantization | |
| methods. From DeepLearning.AI, | |
| 70 | |
| 00:03:54.533 --> 00:03:57.800 | |
| Eddy Shyu, | |
| had also contributed to this course. | |
| 71 | |
| 00:03:58.000 --> 00:04:01.000 | |
| Quantization is a fairly technical topic. | |
| 72 | |
| 00:04:01.600 --> 00:04:04.633 | |
| After this course, | |
| I hope you deeply understand it | |
| 73 | |
| 00:04:04.633 --> 00:04:07.100 | |
| so you better say to others, "I now get it. | |
| 74 | |
| 00:04:07.100 --> 00:04:09.700 | |
| I'm not worried about model compression." | |
| 75 | |
| 00:04:09.700 --> 00:04:13.800 | |
| In other words, you can say: | |
| "I'm not sweating the small stuff." | |
| 76 | |
| 00:04:14.633 --> 00:04:16.733 | |
| Let's go on to the | |
| next video and get started. | |