mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 06:39:25 +01:00
Neo Zhang
GitHub
parent
33309f661a
commit
a21c6fd450
@ -80,7 +80,14 @@ The following release is verified with good quality:
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### Intel GPU
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**Verified devices**
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SYCL backend supports Intel GPU Family:
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- Intel Data Center Max Series
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- Intel Flex Series, Arc Series
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- Intel Built-in Arc GPU
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- Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)).
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#### Verified devices
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| Intel GPU | Status | Verified Model |
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|-------------------------------|---------|---------------------------------------|
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@ -88,7 +95,7 @@ The following release is verified with good quality:
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| Intel Data Center Flex Series | Support | Flex 170 |
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| Intel Arc Series | Support | Arc 770, 730M, Arc A750 |
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| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake |
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| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
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| Intel iGPU | Support | iGPU in 13700k, i5-1250P, i7-1260P, i7-1165G7 |
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*Notes:*
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@ -237,6 +244,13 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic
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### II. Build llama.cpp
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#### Intel GPU
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```
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./examples/sycl/build.sh
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```
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or
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```sh
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# Export relevant ENV variables
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source /opt/intel/oneapi/setvars.sh
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@ -276,23 +290,26 @@ cmake --build build --config Release -j -v
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### III. Run the inference
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1. Retrieve and prepare model
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#### Retrieve and prepare model
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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.
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2. Enable oneAPI running environment
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##### Check device
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1. Enable oneAPI running environment
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```sh
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source /opt/intel/oneapi/setvars.sh
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```
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3. List devices information
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2. List devices information
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Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
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```sh
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./build/bin/llama-ls-sycl-device
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```
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This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
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```
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found 2 SYCL devices:
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@ -304,12 +321,37 @@ found 2 SYCL devices:
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| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
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```
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#### Choose level-zero devices
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4. Launch inference
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|Chosen Device ID|Setting|
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|-|-|
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|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
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|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
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|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
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#### Execute
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Choose one of following methods to run.
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1. Script
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- Use device 0:
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```sh
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./examples/sycl/run_llama2.sh 0
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```
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- Use multiple devices:
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```sh
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./examples/sycl/run_llama2.sh
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```
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2. Command line
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Launch inference
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There are two device selection modes:
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- Single device: Use one device target specified by the user.
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- Single device: Use one device assigned by user. Default device id is 0.
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- Multiple devices: Automatically choose the devices with the same backend.
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In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
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@ -326,11 +368,6 @@ Examples:
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```sh
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ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -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
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```
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or run by script:
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```sh
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./examples/sycl/run_llama2.sh 0
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```
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- Use multiple devices:
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@ -338,12 +375,6 @@ or run by script:
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ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -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
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```
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Otherwise, you can run the script:
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```sh
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./examples/sycl/run_llama2.sh
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```
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*Notes:*
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- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
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@ -390,7 +421,7 @@ c. Verify installation
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In the oneAPI command line, run the following to print the available SYCL devices:
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```
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sycl-ls
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sycl-ls.exe
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```
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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:
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@ -411,6 +442,18 @@ b. The new Visual Studio will install Ninja as default. (If not, please install
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### II. Build llama.cpp
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You could download the release package for Windows directly, which including binary files and depended oneAPI dll files.
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Choose one of following methods to build from source code.
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1. Script
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```sh
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.\examples\sycl\win-build-sycl.bat
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```
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2. CMake
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On the oneAPI command line window, step into the llama.cpp main directory and run the following:
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```
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@ -425,12 +468,8 @@ cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPI
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cmake --build build --config Release -j
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```
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Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
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```sh
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.\examples\sycl\win-build-sycl.bat
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```
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Or, use CMake presets to build:
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```sh
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cmake --preset x64-windows-sycl-release
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cmake --build build-x64-windows-sycl-release -j --target llama-cli
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@ -442,7 +481,9 @@ cmake --preset x64-windows-sycl-debug
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cmake --build build-x64-windows-sycl-debug -j --target llama-cli
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```
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Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project.
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3. Visual Studio
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You can use Visual Studio to open llama.cpp folder as a CMake project. Choose the sycl CMake presets (`x64-windows-sycl-release` or `x64-windows-sycl-debug`) before you compile the project.
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*Notes:*
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@ -450,23 +491,25 @@ Or, you can use Visual Studio to open llama.cpp folder as a CMake project. Choos
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### III. Run the inference
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1. Retrieve and prepare model
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#### Retrieve and prepare model
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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.
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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.
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2. Enable oneAPI running environment
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##### Check device
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1. Enable oneAPI running environment
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On the oneAPI command line window, run the following and step into the llama.cpp directory:
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```
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"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
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```
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3. List devices information
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2. List devices information
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Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
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```
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build\bin\ls-sycl-device.exe
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build\bin\llama-ls-sycl-device.exe
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```
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This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
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@ -479,9 +522,27 @@ found 2 SYCL devices:
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| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
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```
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#### Choose level-zero devices
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|Chosen Device ID|Setting|
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|-|-|
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|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
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|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
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|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
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4. Launch inference
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#### Execute
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Choose one of following methods to run.
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1. Script
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```
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examples\sycl\win-run-llama2.bat
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```
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2. Command line
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Launch inference
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There are two device selection modes:
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@ -508,11 +569,7 @@ build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website ca
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```
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build\bin\llama-cli.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
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```
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Otherwise, run the following wrapper script:
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```
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.\examples\sycl\win-run-llama2.bat
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```
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Note:
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@ -526,17 +583,18 @@ Or
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use 1 SYCL GPUs: [0] with Max compute units:512
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```
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## Environment Variable
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#### Build
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| Name | Value | Function |
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|--------------------|-----------------------------------|---------------------------------------------|
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| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
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| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
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| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. |
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| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. |
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| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. |
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| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
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| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. |
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| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
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#### Runtime
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@ -572,9 +630,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512
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```
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Otherwise, please double-check the GPU driver installation steps.
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- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend?
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No. We can't support Ollama issue directly, because we aren't familiar with Ollama.
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Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it.
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It's same for other projects including llama.cpp SYCL backend.
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### **GitHub contribution**:
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Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
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## TODO
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- Support row layer split for multiple card runs.
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- NA
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Loading…
Reference in New Issue
Block a user