You'd either need to dedicate an overwhelming team to shock and awe this (IE support and performance for AMD chips is best in class for all frameworks, period). This is 50 people, at least, who all work really well together, if you want to deliver it in the next year.
Or, you can bide your time, dedicate those resources to better and better hardware, and when the frameworks field shakes out a bit, have a better shot.
None of these people care about running on nvidia gpus (and in fact, the vendors pushing them don't want to be locked in either), so your main concern there is hand tuning and cuda kernel integration they do. The switching cost is something but not huge, and isn't increasing that much over time (unlike the x86 switching cost, for example). So waiting doesn't lose you a lot.
So in the meantime, you make two bets: 1. You try to take over the intermediate IR of frameworks, and make it good enough that people stop writing hand tuned nvidia kernels. This is unlikely to work out, but worth a shot.
2. You slowly decide what customers you want, look at what they are writing hand-tuned kernels for, and try to tackle making the frameworks they use good enough to not need it.
(in case #1 doesn't work out).
the real preoccupation is that these building blocks need to be as fast or faster than Nvidia's
If what they have works for them, precisely none of these people will bother.
AMD's management just doesn't seem to be that interested in ML.
You’re right that doing 10 things at once is a recipe for failure, but the reality is, majority of frameworks don’t really matter all that much, and if a couple of solid integrations existed, they could just reuse that work on their own.
AMD has a number of initiatives that haven't panned out as well as NVidia's AI investment. AMD's "HSA" technology is actually quite interesting, although unpopular. AMD's push for HSA has made it faster at Video Rendering tasks (see Blender for instance) and random tasks like GPGPU-accelerated WinRAR decompression or LibreOffice Spreadsheets.
IIRC, AMD Vega beats NVidia Titan XP in Creo and Solidworks benchmarks (CAD programs). So its not like AMD is sleeping on its laurels here, they're just focusing on other, still profitable, corners of the market.
Of course, the current is behind AI, Tensors, and NVidia at the moment. AMD can't afford to fall further behind. The current trend is AI and Machine Learning, and it seems reasonable for AMD to at least get PyTorch running on AMD cards (if not "beating" NVidia, but at least they can play along).
Like literally, NVidia will just pay 3 people to do nothing but argue with those 3 people on mailing lists and keep them from landing patches at a reasonable rate.
It would cost nothing compared to the benefit of slowing your only competitor down in a space worth this much.
(and i've literally seen not-great companies do this to people's open source projects, so ...)
