llama.cpp/README-sycl.md

# llama.cpp for SYCL

- [Background](#background)
- [News](#news)
- [OS](#os)
- [Hardware](#hardware)
- [Docker](#docker)
- [Linux](#linux)
- [Windows](#windows)
- [Environment Variable](#environment-variable)
- [Known Issue](#known-issues)
- [Q&A](#qa)
- [TODO](#todo)

## Background

**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.

**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:

- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. oneMKL - Math Kernel Library)*.
- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.

### Llama.cpp + SYCL

The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it could support other vendor GPUs: Nvidia GPU (*AMD GPU coming*).

When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.

It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.

## News

- 2024.4
  - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.

- 2024.3
  - Release binary files of Windows.
  - A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
  - New base line is ready: [tag b2437](https://github.com/ggerganov/llama.cpp/tree/b2437).
  - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
  - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
  - Support detecting all GPUs with level-zero and same top **Max compute units**.
  - Support OPs
    - hardsigmoid
    - hardswish
    - pool2d

- 2024.1
  - Create SYCL backend for Intel GPU.
  - Support Windows build

## OS

| OS      | Status  | Verified                           |
|---------|---------|------------------------------------|
| Linux   | Support | Ubuntu 22.04, Fedora Silverblue 39 |
| Windows | Support | Windows 11                         |


## Hardware

### Intel GPU

**Verified devices**

| Intel GPU                     | Status  | Verified Model                        |
|-------------------------------|---------|---------------------------------------|
| Intel Data Center Max Series  | Support | Max 1550, 1100                        |
| Intel Data Center Flex Series | Support | Flex 170                              |
| Intel Arc Series              | Support | Arc 770, 730M                         |
| Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake       |
| Intel iGPU                    | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |

*Notes:*

- **Memory**
  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/main`.

  - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.

- **Execution Unit (EU)**
  - If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.

### Other Vendor GPU

**Verified devices**

| Nvidia GPU               | Status  | Verified Model |
|--------------------------|---------|----------------|
| Ampere Series            | Support | A100, A4000    |
| Ampere Series *(Mobile)* | Support | RTX 40 Series  |

## Docker
The docker build option is currently limited to *intel GPU* targets.

### Build image
```sh
# Using FP16
docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
```

*Notes*:

To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="LLAMA_SYCL_F16=ON"` argument from the previous command.

You can also use the `.devops/server-intel.Dockerfile`, which builds the *"server"* alternative.

### Run container

```sh
# First, find all the DRI cards
ls -la /dev/dri
# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
```

*Notes:*
- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.

## Linux

### I. Setup Environment

1. **Install GPU drivers**

  - **Intel GPU**

Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).

*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).

Once installed, add the user(s) to the `video` and `render` groups.

```sh
sudo usermod -aG render $USER
sudo usermod -aG video $USER
```

*Note*: logout/re-login for the changes to take effect.

Verify installation through `clinfo`:

```sh
sudo apt install clinfo
sudo clinfo -l
```

Sample output:

```sh
Platform #0: Intel(R) OpenCL Graphics
 `-- Device #0: Intel(R) Arc(TM) A770 Graphics

Platform #0: Intel(R) OpenCL HD Graphics
 `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
```

- **Nvidia GPU**

In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.

2. **Install Intel® oneAPI Base toolkit**

- **For Intel GPU**

The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.

Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.

Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.

Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI MKL for intel GPUs.

- **Adding support to Nvidia GPUs**

**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.


**oneMKL for cuBlas**: The current oneMKL releases *(shipped with the oneAPI base-toolkit)* do not contain the cuBLAS backend. A build from source of the upstream [oneMKL](https://github.com/oneapi-src/oneMKL) with the *cuBLAS* backend enabled is thus required to run it on Nvidia GPUs.

```sh
git clone https://github.com/oneapi-src/oneMKL
cd oneMKL
mkdir -p buildWithCublas && cd buildWithCublas
cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
make
```


3. **Verify installation and environment**

In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
```sh
source /opt/intel/oneapi/setvars.sh
sycl-ls
```

- **Intel GPU**

When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`ext_oneapi_level_zero:gpu:0`] in the sample output below:

```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [23.30.26918.50]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
```

- **Nvidia GPU**

Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`ext_oneapi_cuda:gpu`] as bellow:
```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.12.0.12_195853.xmain-hotfix]
[opencl:cpu:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.2]
```

### II. Build llama.cpp

#### Intel GPU
```sh
# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh

# Build LLAMA with MKL BLAS acceleration for intel GPU
mkdir -p build && cd build

# Option 1: Use FP16 for better performance in long-prompt inference
#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON

# Option 2: Use FP32 by default
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

#build all binary
cmake --build . --config Release -j -v
```

#### Nvidia GPU
```sh
# Export relevant ENV variables
export LD_LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=/path/to/oneMKL/buildWithCublas/lib:$LIBRARY_PATH
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_DIR
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR

# Build LLAMA with Nvidia BLAS acceleration through SYCL
mkdir -p build && cd build

# Option 1: Use FP16 for better performance in long-prompt  inference
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON

# Option 2: Use FP32 by default
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

#build all binary
cmake --build . --config Release -j -v

```

### III. Run the inference

1. Retrieve and prepare model

You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.

2. Enable oneAPI running environment

```sh
source /opt/intel/oneapi/setvars.sh
```

3. List devices information

Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:

```sh
./build/bin/ls-sycl-device
```
A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
```
found 6 SYCL devices:
|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
```

| Attribute              | Note                                                        |
|------------------------|-------------------------------------------------------------|
| compute capability 1.3 | Level-zero driver/runtime, recommended                      |
| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |

4. Launch inference

There are two device selection modes:

- Single device: Use one device target specified by the user.
- Multiple devices: Automatically select the devices with the same largest Max compute-units.

| Device selection | Parameter                              |
|------------------|----------------------------------------|
| Single device    | --split-mode none --main-gpu DEVICE_ID |
| Multiple devices | --split-mode layer (default)           |

Examples:

- Use device 0:

```sh
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
```
or run by script:

```sh
./examples/sycl/run_llama2.sh 0
```

- Use multiple devices:

```sh
ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
```

Otherwise, you can run the script:

```sh
./examples/sycl/run_llama2.sh
```

*Notes:*

- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:

```sh
detect 1 SYCL GPUs: [0] with top Max compute units:512
```
Or
```sh
use 1 SYCL GPUs: [0] with Max compute units:512
```

## Windows

### I. Setup Environment

1. Install GPU driver

Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).

2. Install Visual Studio

If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).

3. Install Intel® oneAPI Base toolkit

The base toolkit can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.

Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.

Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.

b. Enable oneAPI running environment:

- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.

- On the command prompt, enable the runtime environment with the following:
```
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
```

c. Verify installation

In the oneAPI command line, run the following to print the available SYCL devices:

```
sycl-ls
```

There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:

Output (example):
```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO  [31.0.101.5186]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
```

4. Install build tools

a. Download & install cmake for Windows: https://cmake.org/download/

b. Download & install mingw-w64 make for Windows provided by w64devkit

- Download the 1.19.0 version of [w64devkit](https://github.com/skeeto/w64devkit/releases/download/v1.19.0/w64devkit-1.19.0.zip).

- Extract `w64devkit` on your pc.

- Add the **bin** folder path in the Windows system PATH environment (for e.g. `C:\xxx\w64devkit\bin\`).

### II. Build llama.cpp

On the oneAPI command line window, step into the llama.cpp main directory and run the following:

```
mkdir -p build
cd build
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force

cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON

make -j
```

Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
```sh
.\examples\sycl\win-build-sycl.bat
```

*Notes:*

- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make main`.

### III. Run the inference

1. Retrieve and prepare model

You can refer to the general [*Prepare and Quantize*](README#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.

2. Enable oneAPI running environment

On the oneAPI command line window, run the following and step into the llama.cpp directory:
```
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
```

3. List devices information

Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:

```
build\bin\ls-sycl-device.exe
```

The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
```
found 6 SYCL devices:
|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
| 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
| 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|

```

| Attribute              | Note                                                      |
|------------------------|-----------------------------------------------------------|
| compute capability 1.3 | Level-zero running time, recommended                      |
| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |


4. Launch inference

There are two device selection modes:

- Single device: Use one device assigned by user.
- Multiple devices: Automatically choose the devices with the same biggest Max compute units.

| Device selection | Parameter                              |
|------------------|----------------------------------------|
| Single device    | --split-mode none --main-gpu DEVICE_ID |
| Multiple devices | --split-mode layer (default)           |

Examples:

- Use device 0:

```
build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
```

- Use multiple devices:

```
build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
```
Otherwise, run the following wrapper script:

```
.\examples\sycl\win-run-llama2.bat
```

Note:

- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:

```sh
detect 1 SYCL GPUs: [0] with top Max compute units:512
```
Or
```sh
use 1 SYCL GPUs: [0] with Max compute units:512
```

## Environment Variable

#### Build

| Name               | Value                             | Function                                    |
|--------------------|-----------------------------------|---------------------------------------------|
| LLAMA_SYCL         | ON (mandatory)                    | Enable build with SYCL code path.           |
| LLAMA_SYCL_TARGET  | INTEL *(default)* \| NVIDIA       | Set the SYCL target device type.            |
| LLAMA_SYCL_F16     | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path.      |
| CMAKE_C_COMPILER   | icx                               | Set *icx* compiler for SYCL code path.      |
| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |

#### Runtime

| Name              | Value            | Function                                                                                                                  |
|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
| GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |

## Known Issues

- `Split-mode:[row]` is not supported.

## Q&A

- Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.

  - Potential cause: Unavailable oneAPI installation or not set ENV variables.
  - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.

- General compiler error:

  - Remove **build** folder or try a clean-build.

- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.

  Please double-check with `sudo sycl-ls`.

  If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:

  ```
  sudo usermod -aG render $USER
  sudo usermod -aG video $USER
  ```
  Otherwise, please double-check the GPU driver installation steps.

### **GitHub contribution**:
Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.

## TODO

- Support row layer split for multiple card runs.