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Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. (#19246)

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User can't build up the software for Nvidia & AMD GPU.
rm the oneMath since it is only used in NV and AMD code path.
This commit is contained in:
Neo Zhang
2026-02-02 21:06:21 +08:00
committed by GitHub
parent 4d5e972673
commit bf38346d13
7 changed files with 60 additions and 274 deletions
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126
docs/backend/SYCL.md
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@@ -22,12 +22,11 @@
- **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. Intel oneMKL, oneMath and oneDNN)*.
- **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 primarily designed for **Intel GPUs**.
SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.
SYCL cross-platform capabilities enable support for other vendor GPUs as well.
## Recommended Release
@@ -42,6 +41,9 @@ The following releases are verified and recommended:
## News
- 2026.02
- Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. User can't build up the software for Nvidia & AMD GPU.
- 2025.11
- Support malloc memory on device more than 4GB.
@@ -111,8 +113,8 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the
|-------------------------------|---------|---------------------------------------|
| Intel Data Center Max Series | Support | Max 1550, 1100 |
| Intel Data Center Flex Series | Support | Flex 170 |
| Intel Arc A-Series | Support | Arc A770, Arc A730M, Arc A750 |
| Intel Arc B-Series | Support | Arc B580 |
| Intel Arc A-Series | Support | Arc A770, Arc A730M, Arc A750 |
| Intel Arc B-Series | Support | Arc B580 |
| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake |
| Intel iGPU | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7 |
@@ -127,20 +129,7 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the
### Other Vendor GPU
**Verified devices**
| Nvidia GPU | Status | Verified Model |
|--------------------------|-----------|----------------|
| Ampere Series | Supported | A100, A4000 |
| Ampere Series *(Mobile)* | Supported | RTX 40 Series |
| AMD GPU | Status | Verified Model |
|--------------------------|--------------|----------------|
| Radeon Pro | Experimental | W6800 |
| Radeon RX | Experimental | 6700 XT |
Note: AMD GPU support is highly experimental and is incompatible with F16.
Additionally, it only supports GPUs with a sub_group_size (warp size) of 32.
NA
## Docker
@@ -149,11 +138,11 @@ The docker build option is currently limited to *Intel GPU* targets.
### Build image
```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 .
# Using FP16
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
```
*Notes*:
@@ -212,14 +201,6 @@ 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.
- **AMD GPU**
To target AMD GPUs with SYCL, the ROCm stack must be installed first.
2. **Install Intel® oneAPI Base toolkit**
SYCL backend depends on:
@@ -248,23 +229,6 @@ Upon a successful installation, SYCL is enabled for the available intel devices,
|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.
**oneDNN**: The current oneDNN releases *(shipped with the oneAPI base-toolkit)* do not include the NVIDIA backend. Therefore, oneDNN must be compiled from source to enable the NVIDIA target:
```sh
git clone https://github.com/oneapi-src/oneDNN.git
cd oneDNN
cmake -GNinja -Bbuild-nvidia -DDNNL_CPU_RUNTIME=DPCPP -DDNNL_GPU_RUNTIME=DPCPP -DDNNL_GPU_VENDOR=NVIDIA -DONEDNN_BUILD_GRAPH=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
cmake --build build-nvidia --config Release
```
- **Adding support to AMD GPUs**
**oneAPI Plugin**: In order to enable SYCL support on AMD GPUs, please install the [Codeplay oneAPI Plugin for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/download). As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit.
3. **Verify installation and environment**
In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
@@ -285,25 +249,6 @@ When targeting an intel GPU, the user should expect one or more devices among th
[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO [24.39.31294]
```
- **Nvidia GPU**
Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`cuda:gpu`] as below:
```
[opencl:acc][opencl: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][opencl: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]
[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5]
```
- **AMD GPU**
For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:
```
[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000]
[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9]
```
### II. Build llama.cpp
#### Intel GPU
@@ -332,47 +277,6 @@ It is possible to come across some precision issues when running tests that stem
instructions, which can be circumvented by setting the environment variable `SYCL_PROGRAM_COMPILE_OPTIONS`
as `-cl-fp32-correctly-rounded-divide-sqrt`
#### Nvidia GPU
The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices.
By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.
```sh
# Build LLAMA with Nvidia BLAS acceleration through SYCL
# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance
GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture
# Option 1: Use FP32 (recommended for better performance in most cases)
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl
# Option 2: Use FP16
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl
# build all binary
cmake --build build --config Release -j -v
```
It is possible to come across some precision issues when running tests that stem from using faster
instructions, which can be circumvented by passing the `-fno-fast-math` flag to the compiler.
#### AMD GPU
The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices.
By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.
```sh
# Build LLAMA with rocBLAS acceleration through SYCL
## AMD
# Use FP32, FP16 is not supported
# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:'
GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# build all binary
cmake --build build --config Release -j -v
```
### III. Run the inference
#### Retrieve and prepare model
@@ -766,15 +670,15 @@ use 1 SYCL GPUs: [0] with Max compute units:512
| Name | Value | Function |
|--------------------|---------------------------------------|---------------------------------------------|
| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. |
| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
| GGML_SYCL_TARGET | INTEL *(default)* | Set the SYCL target device type. |
| GGML_SYCL_DEVICE_ARCH | Optional | Set the SYCL device architecture. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. (1.) |
| GGML_SYCL_GRAPH | ON *(default)* \|OFF *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
| GGML_SYCL_GRAPH | OFF *(default)* \|ON *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
| GGML_SYCL_DNN | ON *(default)* \|OFF *(Optional)* | Enable build with oneDNN. |
| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. |
| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds.
1. FP32 or FP16 have different performance impact to LLM. Recommended to test them for better prompt processing performance on your models. You need to rebuild the code after change `GGML_SYCL_F16=OFF/ON`.
#### Runtime
@@ -782,7 +686,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG |
| GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) |
| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
| GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
| 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 |
| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|