Chris Lattner: evaluation

Transcript context

…Let's start diving deeper. So as you mentioned, you said between the framework level and the hardware level. So when it first got announced, I went on the website and I was like, wow, I wonder how many petaflops they get on an A100. And then I open and it's all CPUs. So my question is, everybody's trying to make GPUs go brr. Why are you making CPUs go brr first? So this is the problem with doing first principles work. Is that you have to do all of the work from the beginning. And if you do it right, you shouldn't skip over important steps. What is an AI system today? Lots of people say, oh, it's a GPU. People are fighting over GPUs. They're always talking about, it's all about GPUs, right? AI, in my opinion, is actually a large-scale, heterogeneous, parallel compute problem. And so AI traditionally starts with data loading. GPUs don't load data, right? And so you have to do data loading, preprocessing, networking, a whole bunch of stuff. And then you do a lot of matrix multiplications. You do all the things that people usually talk about. But then you do post-processing and you send stuff out over a network or under disk, right? And so CPUs, it turns out, are necessary to drive the GPUs, right? And a lot of the systems, again, when you say, let's bring up software for the accelerator, what you end up doing is you say, okay, well, what can the accelerator do? It turns out it's a subset of the problem because they decided that the matrix multiplications or whatever they thought was important is the important part of the problem. So you then go build a system that does exactly what the chip will do. And you never have time to go solve the big problem. And so it's really funny when you look at something like a TensorFlow or like a PyTorch, so much of that host side compute problem, the CPU work, ends up being in Python, ends up being in these things like tf.data and stuff like this. Not programmable, not extensible, really slow in many cases, very difficult to distribute. And so there's a huge mess here. Also, if you look at CPUs, it turns out they are accelerators. So CPUs these days have tensor cores. They just get funny names like AMX instructions and things like this, right? And the reason for that is that it used to be that CPUs and GPUs were completely different things. What's happened over time is GPUs get more programmable and more like CPUs, and CPUs get more parallel. And so what's happening is we're getting a spectrum of this technology. And so when we started modular, we said, okay, well, let's look at this from a technology perspective. Hey, it makes sense to build a general thing because once you have a general thing, you can specialize. As I've seen with XLA and some of these other stacks, like it's very hard to start with the specialized thing and then generalize it. Also, it turns out that, you know, where's the spend in AI? Well, I mean, different people are spending different amounts of money, different things, but training scales the size of your research team, inference scales the size of your product and user base and everything else. And so a lot of inference these days is still done on CPU. So what we decided to do is we said, okay, well, let's start with CPU. Let's get improve the architecture. and user base and everything else. And so a lot of inference these days is still done on CPU. So what we decided to do is we said, okay, well, let's start with CPU. Let's get improve the architecture. CPUs are also easier to work with and they don't stock out and they, you know, they're easier for a variety of other reasons. And let's prove that we can build a very general architecture that can scale across different families. And so what we showed is we showed, okay, we can do Intel, AMD, we can do this arm Graviton thing and showed a lot of support for, you know, all the different weird permutations of things within even an Intel CPU. There's all these different vector lengths and all this stuff going on and showing that we could beat the vendor software with much more general and flexible programming approaches. And then from there, yes, we're doing GPU. We'll have GPUs coming out soon. And then when you build into that, right, what you get is you get the benefit of a well considered, well layered stack that has got all the right DNA in it. And so then you can scale into these different kinds of accelerators over time.…

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