Maybe it is a limitation on M1 devices, I am able to see avx and avx2 on my Intel after specifying cpu as host, avx, avx2.

It is definitely not available for M1 devices as qemu-system-aarch64 -cpu help | grep avx returns no output.

I also saw avx enabled with kvm64, avx, avx2, however anything other than qemu64 cpu type is very slow on M1 devices.

The best performance would've been qemu64, avx, avx2 but it is not supported for avx from my test. You can try with kvm64 cpu type but I doubt the speed would be bearable.

Thank you for the incredibly quick response!

I'll try kvm64 and see if something is worse than nothing ;-)

Edit: kvm64 didn't work, reasoning below.

$ colima start --arch x86_64 --cpu 2 --memory 4 --cpu-type kvm64, avx, avx2 --disk 60
…                                    
$ colima ssh
colima:~$ grep avx /proc/cpuinfo 
colima:~$

I'm learning up on the scene a bit and finding some references to HVF. Apparently, there are some builds of qemu that might enable qemu to use macOS Hypervisor.framework and expose AVX through that if and only if it's actually been implemented.

• https://wiki.qemu.org/Features/HVF
• https://github.com/simnalamburt/homebrew-x#qemu-hvf
• Test on ARM macOS lima-vm/lima#42

HVF may already be there and only relevant to aarch64:

$ qemu-system-aarch64 -accel help
Accelerators supported in QEMU binary:
hvf
tcg
$ qemu-system-x86_64 -accel help
Accelerators supported in QEMU binary:
tcg

Yeah, confirmed. That qemu-hvf fork was upstreamed as of qemu v6.2.0.

qemu upstream issue: qemu x86 TCG doesn't support AVX insns

Ticket Close gap for x86_64-v3 ABI in TCG - CPU support for fma, f16c, avx, avx2 features required pointed me to this mailing list patchset that would implement AVX, it seems. It's not merged yet. The author is hosting their work at https://github.com/pbrook/qemu/tree/avx.

TL;DR As of this comment's timestamp, qemu doesn’t support AVX for aarch64 hosts and x86_64 guests yet, but there’s a patchset that may enable it in development.

If you're finding this issue looking for running Tensorflow inside of a colima/lima/qemu container on an M1 Mac, the short is "you can't" and I'm working on a workaround of some kind.

Some other additional context, mostly geared toward my particular predicament with trying to have Tensorflow start inside of a colima x86_64 container:

• Tensorflow shipping AVX-enabled by default Prebuilt binaries do not work with CPUs that do not have AVX instruction sets. tensorflow/tensorflow#19584
• Tensorflow may be silence-able with the methods available in silence-tensorflow but in practice this seems not to work anymore…?
• This SO Q/A gets into details about AVX and recommends the same approach from the previous bullet

qemu 7.2.0 came out this week and I've started playing with it, starting with getting qemu 7.2.0 into Homebrew. My preliminary results aren't looking great.

I've started qemu via colima with

colima start --arch x86_64 --cpu 2 --cpu-type "qemu64, sse4.2, sse4.1, sse, sse2, avx, avx2" --memory 4

and it starts correctly. However, when I try to run my containers, I'm seeing shell processes exit with 132 or 139 exit codes… indicating an illegal instruction (132 - SIGILL) or segmentation fault (139 - SIGSEGV) when running bash or sh respectively. I haven't yet tried destroying the VM entirely.

@colindean I tried deleting the VM by running colima delete and starting qemu using the same command as you did:

colima start --arch x86_64 --cpu 2 --cpu-type "qemu64, sse4.2, sse4.1, sse, sse2, avx, avx2" --memory 4

And I am getting similar error codes. I have qemu 7.2.0 here as well, with HEAD colima compiled from source using homebrew.

I also tried running with Rosetta 2 and some different parameters:

colima start --cpu 4 --memory 6 --disk 100 --arch amd64 --cpu-type "qemu64, sse4.2, sse4.1, sse, sse2, avx, avx2" --vm-
type=vz --vz-rosetta

But got similar results.

Did you manage to resolve this?

I have not resolved this yet 😞

Install the latest qemu (8.0.0) https://www.qemu.org/, it should work now. Thanks for the great work!

Yes! Going to give 8.0.0 a shot next week and see if I can get Tensorflow working!

Tried with the following command with Qemu 8.0.0:

colima start --arch x86_64 --cpu 4 --cpu-type "max" --memory 8
docker run --rm -it tensorflow/tensorflow:1.7.1 bash

The container started up correctly but, when I run import tensorflow on python, it goes onto a long wait and timeout

Tried with the following command with Qemu 8.0.0:

colima start --arch x86_64 --cpu 4 --cpu-type "max" --memory 8
docker run --rm -it tensorflow/tensorflow:1.7.1 bash

The container started up correctly but, when I run import tensorflow on python, it goes onto a long wait and timeout

Can confirm
Tried different versions of tensorflow and it seems to always hang somewhere here:

python -v
>>> import tensorflow
`import 'numpy.ma' # <_frozen_importlib_external.SourceFileLoader object at 0x7ff84f2f2b20>` 

Basically making colima unresponsive
Myself ended up using tensorflow-macos, had high hopes for colima and qemu though.

colima start --arch x86_64 --cpu 8 --cpu-type "max" --memory 16 and tensorflow 2.13.0 works at first glance.
lscpu also shows AVX instructions and tensorflow prints that it will use those in performance criticial operation
However running anything proper is terribly slow. Also qemu process shows only 100% usage , hence apparently using only a single core

Also tried colima start --cpu 8 --memory 16 --vm-type vz --vz-rosetta --mount-type virtiofs and starting docker containers with export DOCKER_DEFAULT_PLATFORM="linux/amd64" .
docker jumps into emulation then, but lscpu reports that the cpu acthitecture is only 32 bit .
Its the same behaviour when enable rosetta on official docker desktop for MacOs

As of now, there isn't really a way to run performant x86_64 docker container on Apple Silicon.

I spend my whole day rebuilding my script, running from one machine to another, and i seriously was just supposed to stop freaking out and read. But thanks for the solution, its plain simple and easy "import os nl os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'