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docs: sycl build in docker

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ngxson 2024-01-31 00:10:44 +01:00
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README_sycl.md
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@ -44,7 +44,7 @@ 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|
|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|
## Linux
@ -59,7 +59,7 @@ 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
```
@ -68,7 +68,7 @@ Note: re-login to enable it.
c. Check
```
```sh
sudo apt install clinfo
sudo clinfo -l
```
@ -86,6 +86,7 @@ Platform #0: Intel(R) OpenCL HD Graphics
2. Install Intel® oneAPI Base toolkit.
Note: You can skip step this if you want to build inside docker container
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).
@ -95,7 +96,7 @@ 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
@ -114,35 +115,48 @@ 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.
Method using **docker**:
```sh
# For F16:
#docker build -t llama-cpp-sycl:latest --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
```
or, without docker:
```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
```
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**
@ -155,12 +169,14 @@ source /opt/intel/oneapi/setvars.sh
3. List device ID
(Skip this step if you're using docker)
Run without parameter:
```
```sh
./build/bin/ls-sycl-device
or
# or running the "main" executable and look at the output log:
./build/bin/main
```
@ -189,12 +205,24 @@ found 4 SYCL devices:
Set device ID = 0 by **GGML_SYCL_DEVICE=0**
Using docker image built from step 2:
```sh
# Firstly, find all the DRI cards:
ls -la /dev/dri
# Then, pick the card that you want to use. For example "/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
```
or, without docker:
```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:
```
```sh
./examples/sycl/run_llama2.sh
```