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docker : add build for SYCL, Vulkan + update readme (#5228)

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* add vulkan dockerfile

* intel dockerfile: compile sycl by default

* fix vulkan dockerfile

* add docs for vulkan

* docs: sycl build in docker

* docs: remove trailing spaces

* docs: sycl: add docker section

* docs: clarify install vulkan SDK outside docker

* sycl: use intel/oneapi-basekit docker image

* docs: correct TOC

* docs: correct docker image for Intel oneMKL
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102
README-sycl.md
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@ -1,22 +1,15 @@
# llama.cpp for SYCL
[Background](#background)
[OS](#os)
[Intel GPU](#intel-gpu)
[Linux](#linux)
[Windows](#windows)
[Environment Variable](#environment-variable)
[Known Issue](#known-issue)
[Q&A](#q&a)
[Todo](#todo)
- [Background](#background)
- [OS](#os)
- [Intel GPU](#intel-gpu)
- [Docker](#docker)
- [Linux](#linux)
- [Windows](#windows)
- [Environment Variable](#environment-variable)
- [Known Issue](#known-issue)
- [Q&A](#q&a)
- [Todo](#todo)
## Background
@ -36,7 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
|OS|Status|Verified|
|-|-|-|
|Linux|Support|Ubuntu 22.04|
|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
|Windows|Support|Windows 11|
@ -50,7 +43,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
|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, i7-1165G7|
|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use.
@ -64,6 +57,38 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla
For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
## Docker
Note:
- Only docker on Linux is tested. Docker on WSL may not work.
- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that)
### Build the image
You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
```sh
# For F16:
#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
# Or, for F32:
docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
```
### Run
```sh
# Firstly, find all the DRI cards:
ls -la /dev/dri
# Then, pick the card that you want to use.
# For example with "/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
```
## Linux
### Setup Environment
@ -76,7 +101,7 @@ Note: for iGPU, please install the client GPU driver.
b. Add user to group: video, render.
```
```sh
sudo usermod -aG render username
sudo usermod -aG video username
```
@ -85,7 +110,7 @@ Note: re-login to enable it.
c. Check
```
```sh
sudo apt install clinfo
sudo clinfo -l
```
@ -103,7 +128,6 @@ Platform #0: Intel(R) OpenCL HD Graphics
2. Install Intel® oneAPI Base toolkit.
a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
Recommend to install to default folder: **/opt/intel/oneapi**.
@ -112,7 +136,7 @@ Following guide use the default folder as example. If you use other folder, plea
b. Check
```
```sh
source /opt/intel/oneapi/setvars.sh
sycl-ls
@ -131,21 +155,25 @@ Output (example):
2. Build locally:
```
Note:
- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
```sh
mkdir -p build
cd build
source /opt/intel/oneapi/setvars.sh
#for FP16
#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference
# For FP16:
#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
#for FP32
# Or, for FP32:
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build example/main only
# Build example/main only
#cmake --build . --config Release --target main
#build all binary
# Or, build all binary
cmake --build . --config Release -v
cd ..
@ -153,14 +181,10 @@ cd ..
or
```
```sh
./examples/sycl/build.sh
```
Note:
- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
### Run
1. Put model file to folder **models**
@ -177,10 +201,10 @@ source /opt/intel/oneapi/setvars.sh
Run without parameter:
```
```sh
./build/bin/ls-sycl-device
or
# or running the "main" executable and look at the output log:
./build/bin/main
```
@ -209,13 +233,13 @@ found 4 SYCL devices:
Set device ID = 0 by **GGML_SYCL_DEVICE=0**
```
```sh
GGML_SYCL_DEVICE=0 ./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
```
or run by script:
```
./examples/sycl/run-llama2.sh
```sh
./examples/sycl/run_llama2.sh
```
Note: