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SYCL: Update to oneAPI 2025.2 (#16371)

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* update oneapi to 2025.2, use deep-learning-essentials to replace base-tool

* update to 2025.2 use deeplearn essi to replace base toolkit

* add missed dll

* add deep learning essentials

* add sycl-ls

---------

Co-authored-by: Zhang Jianyu <zhang.jianyu@outlook.com>
This commit is contained in:
Neo Zhang Jianyu
2025-10-02 15:16:25 +08:00
committed by GitHub
parent 95ce098544
commit 2be72c2b12
4 changed files with 49 additions and 15 deletions
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48
docs/backend/SYCL.md
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@@ -145,12 +145,13 @@ The docker build option is currently limited to *Intel GPU* targets.
```sh
# Using FP16
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
# Using FP32
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --target light -f .devops/intel.Dockerfile .
```
*Notes*:
To build in default FP32 *(Slower than FP16 alternative)*, set `--build-arg="GGML_SYCL_F16=OFF"` in the previous command.
You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
Check the [documentation for Docker](../docker.md) to see the available images.
@@ -160,7 +161,7 @@ Check the [documentation for Docker](../docker.md) to see the available images.
# 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
docker run -it --rm -v "/path/to/models:/models" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 llama-cpp-sycl -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -c 4096 -s 0
```
*Notes:*
@@ -215,9 +216,19 @@ To target AMD GPUs with SYCL, the ROCm stack must be installed first.
2. **Install Intel® oneAPI Base toolkit**
SYCL backend depends on:
- Intel® oneAPI DPC++/C++ compiler/running-time.
- Intel® oneAPI DPC++/C++ library (oneDPL).
- Intel® oneAPI Deep Neural Network Library (oneDNN).
- Intel® oneAPI Math Kernel Library (oneMKL).
- **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.
All above are included in both **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** packages.
It's recommended to install **Intel® Deep Learning Essentials** which only provides the necessary libraries with less size.
The **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** 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)*.
@@ -225,6 +236,12 @@ Following guidelines/code snippets assume the default installation values. Other
Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.
|Verified release|
|-|
|2025.2.1|
|2025.1|
|2024.1|
- **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.
@@ -255,10 +272,11 @@ sycl-ls
When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via `sycl-ls`, for instance `[level_zero:gpu]` in the sample output below:
```
[opencl:acc][opencl: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][opencl: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][opencl:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50]
[level_zero:gpu][level_zero:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
[level_zero:gpu][level_zero:0] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) Arc(TM) A770 Graphics 12.55.8 [1.3.29735+27]
[level_zero:gpu][level_zero:1] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) UHD Graphics 730 12.2.0 [1.3.29735+27]
[opencl:cpu][opencl:0] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i5-13400 OpenCL 3.0 (Build 0) [2025.20.8.0.06_160000]
[opencl:gpu][opencl:1] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [24.39.31294]
[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO [24.39.31294]
```
- **Nvidia GPU**
@@ -353,7 +371,7 @@ cmake --build build --config Release -j -v
#### Retrieve and prepare model
You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf?download=true) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
##### Check device
@@ -466,7 +484,17 @@ If you already have a recent version of Microsoft Visual Studio, you can skip th
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.
SYCL backend depends on:
- Intel® oneAPI DPC++/C++ compiler/running-time.
- Intel® oneAPI DPC++/C++ library (oneDPL).
- Intel® oneAPI Deep Neural Network Library (oneDNN).
- Intel® oneAPI Math Kernel Library (oneMKL).
All above are included in both **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** packages.
It's recommended to install **Intel® Deep Learning Essentials** which only provides the necessary libraries with less size.
The **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** 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)*.