ROCm VS CTranslate2

Yep, did exactly that. IMO he threw a fit, even though AMD was working with him squashing bugs. https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...

Ok, I wonder what's wrong. maybe it's this? https://stackoverflow.com/questions/4959621/error-1001-in-cl...

Nope. Anything about this on the arch wiki? Nope

This bug report[2] from 2021? Maybe I need to update my groups.

[2]: https://github.com/RadeonOpenCompute/ROCm/issues/1411

    $ ls -la /dev/kfd

HMM is, I believe, a Linux feature.

AMD added HMM support in ROCm 5.0 according to this: https://github.com/RadeonOpenCompute/ROCm/blob/develop/CHANG...

Woot AMD now supports APU? I sold my notebook as i hit a wall when trying rocm [1] Is there a list oft Wirkung apu's ?

[1] https://github.com/RadeonOpenCompute/ROCm/issues/1587

Last I heard he's abandoned working with AMD products.

https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...

They're talking about the meltdown he had on stream [1] (in front of the mentioned pirate flag), that ended with him saying he'd stop using AMD hardware [2]. He recanted this two weeks after talking with AMD [3].

Maybe he'll succeed, but this definitely doesn't scream stability to me. I'd be wary of investing money into his ventures (but then I'm not a VC, so what do I know).

[1] https://www.youtube.com/watch?v=Mr0rWJhv9jU

[2] https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...

[3] https://twitter.com/realGeorgeHotz/status/166980346408248934...

As for rocm support on consumer devices, AMD wont even clarify what devices are supported. https://github.com/RadeonOpenCompute/ROCm/pull/1738

He flamed out, then is back after Lisa Su called him (lmao)

https://geohot.github.io/blog/jekyll/update/2023/05/24/the-t...

https://www.youtube.com/watch?v=Mr0rWJhv9jU

https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...

https://geohot.github.io/blog/jekyll/update/2023/06/07/a-div...

On a personal level that youtube doesn't make him come off looking that good... like people are trying to get patches to him and generally soothe him/damage control and he's just being a bit of a manchild. And it sounds like that's the general course of events around a lot of his "efforts".

On the other hand he's not wrong either, having this private build inside AMD and not even validating official, supported configurations for the officially supported non-private builds they show to the world isn't a good look, and that's just the very start of the problems around ROCm. AMD's OpenCL runtime was never stable or good either and every experience I've heard with it was "we spent so much time fighting AMD-specific runtime bugs and specs jank that what we ended up with was essentially vendor-proprietary anyway".

On the other other hand, it sounds like AMD know this is a mess and has some big stability/maturity improvements in the pipeline. It seems clear from some of the smoke coming out of the building that they're cooking on more general ROCm support for RDNA cards, and generally working to patch the maturity and stability issues he's talking about. I hate the "wait for drivers/new software release bro it's gonna fix everything" that surrounds AMD products but in this case I'm at least hopeful they seem to understand the problem, even if it's completely absurdly late.

Some of what he was viewing as "the process happening in secret" was likely people doing rush patches on the latest build to accommodate him, and he comes off as berating them over it. Again, like, that stream just comes off as "mercurial manchild" not coding genius. And everyone knew the driver situation is bad, that's why there's notionally alpha for him to realize here in the first place. He's bumping into moneymakers, and getting mad about it.

I tried to train another model using InceptionResNetV2 and the same issues happens. Also, this happens even using the model.predict() method if using the GPU. Probably this is an issue related to the AMD Radeon RX 6700 XT or some mine misconfiguration. System Inormation: ArchLinux 6.1.32-1-lts - AMD Radeon RX 6700 XT - gfx1031 Opened issues: - https://github.com/RadeonOpenCompute/ROCm/issues/2250 - https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/issues/2125

Just a point of clarification - faster-whisper references it but ctranslate2[0] is what's really doing the magic here.

Ctranslate2 is a sleeper powerhouse project that enables a lot. They should be up front and center and get the credit they deserve.

[0] - https://github.com/OpenNMT/CTranslate2

We'd love to move beyond Nvidia.

The issue (among others) is we achieve the speech recognition performance we do largely thanks to ctranslate2[0]. They've gone on the record saying that they essentially have no interest in ROCm[1].

Of course with open source anything is possible but we see this as being one of several fundamental issues in supporting AMD GPGPU hardware.

[0] - https://github.com/OpenNMT/CTranslate2

[1] - https://github.com/OpenNMT/CTranslate2/issues/1072

> While I agree that it's much more effort to get things working on AMD cards than it is with Nvidia, I was a bit surprised to see this comment mention Whisper being an example of "5-10x as performant".

It easily is. See the benchmarks[0] from faster-whisper which uses Ctranslate2. That's 5x faster than OpenAI reference code on a Tesla V100. Needless to say something like a 4080 easily multiplies that.

> https://www.tomshardware.com/news/whisper-audio-transcriptio... is a good example of Nvidia having no excuses being double the price when it comes to Whisper inference, with 7900XTX being directly comparable with 4080, albeit with higher power draw. To be fair it's not using ROCm but Direct3D 11, but for performance/price arguments sake that detail is not relevant.

With all due respect to the author of the article this is "my first entry into ML" territory. They talk about a 5-10 second delay, my project can do sub 1 second times[1] even with ancient GPUs thanks to Ctranslate2. I don't have an RTX 4080 but if you look at the performance stats for the closest thing (RTX 4090) the performance numbers are positively bonkers - completely untouchable for anything ROCm based. Same goes for the other projects I linked, lmdeploy does over 100 tokens/s in a single session with LLama2 13b on my RTX 4090 and almost 600 tokens/s across eight simultaneous sessions.

> EDIT: Also using CTranslate2 as an example is not great as it's actually a good showcase why ROCm is so far behind CUDA: It's all about adapting the tech and getting the popular libraries to support it. Things usually get implemented in CUDA first and then would need additional effort to add ROCm support that projects with low amount of (possibly hobbyist) maintainers might not have available. There's even an issue in CTranslate2 where they clearly state no-one is working to get ROCm supported in the library. ( https://github.com/OpenNMT/CTranslate2/issues/1072#issuecomm... )

I don't understand what you're saying here. It (along with the other projects I linked) are fantastic examples of just how far behind the ROCm ecosystem is. ROCm isn't even on the radar for most of them as your linked issue highlights.

Things always get implemented in CUDA first (ten years in this space and I've never seen ROCm first) and ROCm users either wait months (minimum) for sub-par performance or never get it at all.

[0] - https://github.com/guillaumekln/faster-whisper#benchmark

[1] - https://heywillow.io/components/willow-inference-server/#ben...

Etc.

Now, what this allows you to do is reuse the attention computed from the previous turns (since the prefix is the same).

In practice, people often have a system prompt before the conversation history, which (as far a I can tell) makes this technique not applicable (the input prefix will change as soon as the conversation history is long enough that we need to start dropping the oldest turns).

In such case, what you could do is to cache at least the system prompt. This is also possible with https://github.com/OpenNMT/CTranslate2/blob/2203ad5c8baf878a...

The original Whisper implementation from OpenAI uses the PyTorch deep learning framework. On the other hand, faster-whisper is implemented using CTranslate2 [1] which is a custom inference engine for Transformer models. So basically it is running the same model but using another backend, which is specifically optimized for inference workloads.

[1] https://github.com/OpenNMT/CTranslate2

FLAN-T5 models generally perform well for their size, but they are encode-decoder models, and they aren't as widely supported for efficient inference. I wanted students to be able to run everything locally on CPU, so I was ideally hoping for something that supported quantization for CPU inference. I explored llama.cpp and GGML, but ultimately landed on ctranslate2 for inference.

You can also check out the CTranslate2 library which supports efficient inference of T5 models, including 8-bit quantization on CPU and GPU. There is a usage example in the documentation.