Generally Intelligent

Tri Dao, Stanford: FlashAttention and sparsity, quantization, and efficient inference

20 snips
Aug 9, 2023
Tri Dao explores why machine learning breakthroughs depend on getting algorithms to work with real hardware, from FlashAttention and GPU-friendly sparsity to low-bit quantization. The conversation ranges across data augmentation, recurrent models, and alternatives to attention for handling longer contexts. They also discuss the growing importance of efficient inference, personalization, and hands-on engineering.
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1
Introduction
00:00 • 5min
2
The Importance of Hardware in the Operations Field
05:16 • 2min
3
The Theory of Modern Data Augmentation
07:07 • 2min
4
The Kernel Theory of Data Augmentation
09:19 • 3min
5
The Importance of Data Augmentation
12:17 • 2min
6
The Butterfly Factorization, Nicely Modeled 2019
13:50 • 4min
7
The Evolution of Butterfly Matrixes in Machine Learning
17:46 • 3min
8
The Importance of Sparsity in GPUs
20:31 • 2min
9
The Impact of Sparse Training on Language Models
22:41 • 3min
10
The Inductive Bias in Language Model Training
25:50 • 2min
11
The Future of Intuitive Learning
27:37 • 4min
12
How to Preserve Quality if We Are Zero Up With the Percent of the Entries
31:49 • 2min
13
The Future of Recurrent Models
33:25 • 2min
14
The Evolution of Attention in Language Models
35:52 • 3min
15
The Opposition to Attention
38:31 • 2min
16
The Importance of Larger Models in Research
40:34 • 3min
17
The Future of Recurrent Networks
44:01 • 2min
18
How to Scale to Longer Context Longevity
46:01 • 4min
19
ML Perp: A Low-Level Implementation of Bert From NVIDIA
49:42 • 4min
20
The Importance of Static Sparsity in Truning
53:12 • 3min
21
The Performance of Sparse Attention for Language Models
55:46 • 2min
22
The Right Sparsity Schedule for Language Models
57:24 • 2min
23
The Importance of Adaptive Sparsity
59:14 • 2min
24
The Future of Flash Tension
01:01:21 • 4min
25
The Future of Language Models
01:05:38 • 2min
26
The Future of Inference
01:08:03 • 2min
27
The Importance of Feedback in Inference
01:09:41 • 3min
28
The Future of Machine Learning
01:12:37 • 3min
29
How to Get Better at Engineering
01:15:09 • 3min
30
The Importance of Optimizing Hardware for Faster Attention
01:18:16 • 2min