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Frequently Asked Questions (FAQ)

General Info & Concepts

GGUF format usage with IPEX-LLM?

IPEX-LLM supports running GGUF/AWQ/GPTQ models on both CPU and GPU.

Please also refer to here for our latest support.

How to Resolve Errors

Fail to install ipex-llm via pip on Intel GPU

If you encounter errors when installing ipex-llm on Intel GPU using either

pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/

or

pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/

You can try install ipex-llm dependencies from source archives:

  • For Windows system, refer to here for the steps.
  • For Linux system, refer to here for the steps.

PyTorch is not linked with support for xpu devices

  1. Before running on Intel GPUs, please make sure you've prepared environment follwing installation instruction.
  2. If you are using an older version of ipex-llm (specifically, older than 2.5.0b20240104), you need to manually add import intel_extension_for_pytorch as ipex at the beginning of your code.
  3. After optimizing the model with IPEX-LLM, you need to move model to GPU through model = model.to('xpu').
  4. If you have mutil GPUs, you could refer to here for details about GPU selection.
  5. If you do inference using the optimized model on Intel GPUs, you also need to set to('xpu') for input tensors.

Import intel_extension_for_pytorch error on Windows GPU

Please refer to here for detailed guide. We list the possible missing requirements in environment which could lead to this error.

XPU device count is zero

It's recommended to reinstall driver:

  • For Windows system, refer to here for the steps.
  • For Linux system, refer to here for the steps.

Error such as The size of tensor a (33) must match the size of tensor b (17) at non-singleton dimension 2 duing attention forward function

If you are using IPEX-LLM PyTorch API, please try to set optimize_llm=False manually when call optimize_model function to work around. As for IPEX-LLM transformers-style API, you could try to set optimize_model=False manually when call from_pretrained function to work around.

ValueError: Unrecognized configuration class

This error is not quite relevant to IPEX-LLM. It could be that you're using the incorrect AutoClass, or the transformers version is not updated, or transformers does not support using AutoClasses to load this model. You need to refer to the model card in huggingface to confirm these information. Besides, if you load the model from local path, please also make sure you download the complete model files.

mixed dtype (CPU): expect input to have scalar type of BFloat16 during inference

You could solve this error by converting the optimized model to bf16 through model.to(torch.bfloat16) before inference.

Native API failed. Native API returns: -5 (PI_ERROR_OUT_OF_RESOURCES) -5 (PI_ERROR_OUT_OF_RESOURCES)

This error is caused by out of GPU memory. Some possible solutions to decrease GPU memory uage:

  1. If you run several models continuously, please make sure you have released GPU memory of previous model through del model timely.
  2. You could try model = model.float16() or model = model.bfloat16() before moving model to GPU to use less GPU memory.
  3. You could try set cpu_embedding=True when call from_pretrained of AutoClass or optimize_model function.

Failed to enable AMX

You could use export BIGDL_LLM_AMX_DISABLED=1 to disable AMX manually and solve this error.

oneCCL: comm_selector.cpp:57 create_comm_impl: EXCEPTION: ze_data was not initialized

You may encounter this error during finetuning on multi GPUs. Please try sudo apt install level-zero-dev to fix it.

Random and unreadable output of Gemma-7b-it on Arc770 ubuntu 22.04 due to driver and OneAPI missmatching.

If driver and OneAPI missmatching, it will lead to some error when IPEX-LLM uses XMX(short prompts) for speeding up. The output of What's AI? may like below:

wiedzy Artificial Intelligence meliti: Artificial Intelligence undenti beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng beng

If you meet this error. Please check your driver version and OneAPI version. Commnad is sudo apt list --installed | egrep "intel-basekit|intel-level-zero-gpu". Make sure intel-basekit>=2024.0.1-43 and intel-level-zero-gpu>=1.3.27191.42-775~22.04.

Too many open files

You may encounter this error during finetuning, expecially when run 70B model. Please raise the system open file limit using ulimit -n 1048576.

RuntimeError: could not create a primitive on Windows

This error may happen when multiple GPUs exists for Windows Users. To solve this error, you can open Device Manager (search "Device Manager" in your start menu). Then click the "Display adapter" option, and disable all the GPU device you do not want to use. Restart your computer and try again. IPEX-LLM should work fine this time.

The first time to run model on Meteor Lake's iGPU(Intel Core™ Ultra integrated GPU) will takes 5-10 minutes.

This is caused by intel-extension-for-pytorch's compilation. For windows user, you can install this intel-extension-for-pytorch with Meteor Lake's ahead-of-time-compilation. Please notice the packages only support python 3.10, you can use below commands after you have installed ipex-llm[xpu]:

pip install https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.1.20%2Bmtl%2Boneapi/intel_extension_for_pytorch-2.1.20+git4849f3b-cp310-cp310-win_amd64.whl
pip install https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.1.20%2Bmtl%2Boneapi/torch-2.1.0a0+git7bcf7da-cp310-cp310-win_amd64.whl
pip install https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.1.20%2Bmtl%2Boneapi/torchvision-0.16.0+fbb4cc5-cp310-cp310-win_amd64.whl