Raven Ridge/APUs

[lsr0]

I understand APUs are officially not supported, but as of rocm 1.9/kernel 4.19 the runtime, thunk, opencl and amdkfd components are now in place; the biggest missing piece is hcc (and therefore HIP).

Asking for clarity on whether there are any plans to make hcc functional on APUs, and if so, some kind of order of magnitude on time.

Rationale-wise, we are early stage evaluating these chips for our devices, and have existing code that would hugely benefit from HIP support (not to mention hc seems an enjoyable language to code in), not to mention the benefit of access to the NN frameworks supported via hcc.

[Johnreidsilver]

Waiting for hcc/hip to use ROCm tensorflow too.
"Simple" OpenCL+SVM seems to be working on APU's:
https://community.amd.com/thread/232266
I'm delighted to find with kernel 4.19-rc3 which includes the kfd with support for Raven Ridge it seems to 'just work', including OpenCL 1.2 with SVM!

Understandably dev team has higher priorities like "selling" ROCm to datacenters:
we have many users who want Device Enqueue

[Johnreidsilver]

These are also related to enabling APU's:

At the moment we don't support either page migration or Carrizo so do you agree that we don't need to address +xnack in this PR?

#840

This adds back all targets supported by ROCm`

#692

[ghostplant]

I think ROCm for APU has actually been supported using Linux kernel 4.20 + a special docker image with APU patches, whose gcnArch is recognized as gfx902.

[rohitsharma123123]

I’m having the same trouble on my gfx902 @ghostplant can you share more details on the docker image with APU patches .

[ghostplant]

@rohitsharma123123 Firstly, you should have Ubuntu 18.04, then upgrade your linux kernel to 5.0 for your system.

Then install rocm-2.0-with-gfx902-patched from here: https://github.com/ghostplant/public/releases/download/ubuntu/ubuntu_bionic-rocm2_gfx902.tar.gz

After that, you can compile any HIP sources in this environment which is executable on gfx902.
Official pre-built rocm applications (e.g. tensorflow-rocm) still don't work on gfx902 because they are not compiled to contain gfx902 device code. You have to rebuild them in your environment from source.

If you don't want to recompile them by yourself. here is a pre-built tensorflow-rocm with gfx902 device code added: https://github.com/ghostplant/public/releases/download/ubuntu/tensorflow-1.12.0_gfx902-cp36-cp36m-linux_x86_64.whl

[rohitsharma123123]

Thank you so much @ghostplant

[rohitsharma123123]

@ghostplant my current installation is 16.04, I’m guessing that shouldn’t be a problem when installing/compiling rocm as its supported ? My work is also focused mainly on Pytorch , is there a Pytorch-rocm installation available or do i need to rebuild them ?

[ghostplant]

@rohitsharma123123 If you use 16.04, upgrade kernel to 5.0 is still needed, and you can download a 18.04 docker image and install packages inside. I don't have pre-built pytorch-rocm for gfx902.

[ghostplant]

@rohitsharma123123 Actually, gfx902 performs only about 20x faster than single CPU thread, so you should not expect it can do much useful work.

[rohitsharma123123]

Ok @ghostplant will try it out or else will install 18.04
Thanks for the details.

[ourhut]

@ghostplant have you documented your steps for patching to create these gfx902 builds? I'd like to rebuild the latest tensorflow with this support. Thanks.

[ghostplant]

You can use https://github.com/ghostplant/public/releases/download/ubuntu/ubuntu_bionic-rocm2_gfx902.tar.gz to create a ubuntu:18.04 docker image, then inside the image, you can follow standard way to compile latest https://github.com/ROCmSoftwarePlatform/tensorflow-upstream. Might have some slight failures during the compilation since the gfx902 build only works and matches ROCm 2.0 API.