Radeon Limitations and recommended settings

7.0.2 release known issues#

Note
ROCm 7.0.2 is a preview release, meaning that stability and performance are not yet optimized. Furthermore, only Pytorch is currently available on Windows - the rest of the ROCm stack is only supported on Linux.
AMD is aware and actively working on resolving these issues for future releases.

Linux#

Known issues#

  • Intermittent script failure may be observed while running text-to-image inference workloads with PyTorch.

  • Intermittent system crash may be observed while running Llama 3 inference workloads with PyTorch and vLLM on multiple (4x) AMD Radeon™ AI PRO R9700 graphics products.

  • Intermittent script failure may be observed while running Mixtral-8x7b-instruct-v0.1 inference workloads with PyTorch and vLLM on Radeon™ PRO W7900 series graphics products.

  • Lower than expected performance may be observed while running inference workloads with vLLM on AMD Radeon™ graphics products.

  • Intermittent script failure (out of memory) may be observed while running FP8 LLM inference workloads on Radeon™ RX 9060 series graphics products.

Multi-GPU configuration#

AMD has identified common errors when running ROCm™ on Radeon™ multi-GPU configuration at this time, along with the applicable recommendations.

See mGPU known issues and limitations for a complete list of mGPU known issues and limitations.

Windows#

Note
The following Windows known issues and limitations are applicable to the 6.4.4 preview release.

Note
If you encounter errors related to missing .dll libraries, install Visual C++ 2015-2022 Redistributables.

Known issues#

  • If you encounter an error relating to Application Control Policy blocking DLL loading, check that Smart App Control is OFF. Note that to re-enable Smart App Control, you will need to reinstall windows. A future release will fix this requirement.

  • Intermittent application crash or driver timeout may be observed while running inference workloads with PyTorch on Windows while also running other applications (such as games or web browsers).

  • Failure to launch may be observed after installation while running ComfyUI with Smart App Control enabled.

Limitations#

  • No backward pass support (essential for ML training).

  • Only Python 3.12 is supported.

  • On Windows, only Pytorch is supported, not the entire ROCm stack.

  • On Windows, the latest version of transformers should be installed, via pip install. Some older versions of transformers (<4.55.5) might not be supported.

  • On Windows, only LLM batch sizes of 1 are officially supported.

  • On Windows, the torch.distributed module is currently not supported (may impact A1111 as an example. Some functions from diffusers and accelerate module may get affected).

WSL#

Known issues#

  • Intermittent script failure may be observed while running Llama 3 inference workloads with vLLM in WSL2. End users experiencing this issue are recommended to follow vLLM setup instructions here.

  • Intermittent script failure or driver timeout may be observed while running Stable Diffusion 3 inference workloads with JAX.

  • Lower than expected performance may be observed while running inference workloads with JAX in WSL2.

  • Intermittent script failure may be observed while running Resnet50, BERT, or InceptionV3 training workloads with ONNX runtime.

  • Output error message (resource leak) may be observed while running Llama 3.2 workloads with vLLM.

  • Output error message (VaMgr) may be observed while running PyTorch workloads in WSL2.

  • Intermittent script failure or driver timeout may be observed while running Stable Diffusion inference workloads with TensorFlow.

  • Intermittent application crash may be observed while running Stable Diffusion workloads with ComfyUI and MIGraphX on Radeon™ RX 9060 series graphics products.

  • Intermittent script failure may occur while running Stable Diffusion 2 workloads with PyTorch and MIGraphX

  • Intermittent script failure may occur while running LLM workloads with PyTorch on Radeon™ PRO W7700 graphics products.

  • Lower than expected performance (compared to native Linux) may be observed while running inference workloads (eg. Llama2, BERT) in WSL2.

Important!
Radeon™ PRO Series graphics cards are not designed nor recommended for datacenter usage. Use in a datacenter setting may adversely affect manageability, efficiency, reliability, and/or performance. GD-239.

Important!
ROCm is not officially supported on any mobile SKUs.

ROCm support in WSL environments#

Due to WSL architectural limitations for native Linux User Kernel Interface (UKI), rocm-smi is not supported.

Issue

Limitations

UKI does not currently support rocm-smi

No current support for:
Active compute processes
GPU utilization
Modifiable state features

Not currently supported.

Not currently supported.

Running PyTorch in virtual environments

Running PyTorch in virtual environments requires a manual libhsa-runtime64.so update.

When using the WSL usecase and hsa-runtime-rocr4wsl-amdgpu package (installed with PyTorch wheels), users are required to update to a WSL compatible runtime lib.

Solution:

Enter the following commands:

location=`pip show torch | grep Location | awk -F ": " '{print $2}'`
cd ${location}/torch/lib/
rm libhsa-runtime64.so*
cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so
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