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Nexesenex authored Aug 11, 2024
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145 changes: 106 additions & 39 deletions docs/backend/SYCL.md
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Expand Up @@ -80,15 +80,22 @@ The following release is verified with good quality:

### Intel GPU

**Verified devices**
SYCL backend supports Intel GPU Family:

- Intel Data Center Max Series
- Intel Flex Series, Arc Series
- Intel Built-in Arc GPU
- 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)).

#### Verified devices

| Intel GPU | Status | Verified Model |
|-------------------------------|---------|---------------------------------------|
| Intel Data Center Max Series | Support | Max 1550, 1100 |
| Intel Data Center Flex Series | Support | Flex 170 |
| Intel Arc Series | Support | Arc 770, 730M, Arc A750 |
| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake |
| Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 |
| Intel iGPU | Support | iGPU in 13700k, i5-1250P, i7-1260P, i7-1165G7 |

*Notes:*

Expand Down Expand Up @@ -237,6 +244,13 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic
### II. Build llama.cpp

#### Intel GPU

```
./examples/sycl/build.sh
```

or

```sh
# Export relevant ENV variables
source /opt/intel/oneapi/setvars.sh
Expand Down Expand Up @@ -276,23 +290,26 @@ cmake --build build --config Release -j -v

### III. Run the inference

1. Retrieve and prepare model
#### Retrieve and prepare model

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.

2. Enable oneAPI running environment
##### Check device

1. Enable oneAPI running environment

```sh
source /opt/intel/oneapi/setvars.sh
```

3. List devices information
2. List devices information

Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:

```sh
./build/bin/llama-ls-sycl-device
```

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:
```
found 2 SYCL devices:
Expand All @@ -304,12 +321,37 @@ found 2 SYCL devices:
| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
```

#### Choose level-zero devices

|Chosen Device ID|Setting|
|-|-|
|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|

#### Execute

Choose one of following methods to run.

1. Script

- Use device 0:

```sh
./examples/sycl/run_llama2.sh 0
```
- Use multiple devices:

```sh
./examples/sycl/run_llama2.sh
```

4. Launch inference
2. Command line
Launch inference

There are two device selection modes:

- Single device: Use one device target specified by the user.
- Single device: Use one device assigned by user. Default device id is 0.
- Multiple devices: Automatically choose the devices with the same backend.

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.
Expand All @@ -326,24 +368,13 @@ Examples:
```sh
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
```
or run by script:

```sh
./examples/sycl/run_llama2.sh 0
```

- Use multiple devices:

```sh
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
```

Otherwise, you can run the script:

```sh
./examples/sycl/run_llama2.sh
```

*Notes:*

- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
Expand Down Expand Up @@ -390,7 +421,7 @@ c. Verify installation
In the oneAPI command line, run the following to print the available SYCL devices:

```
sycl-ls
sycl-ls.exe
```

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:
Expand All @@ -411,6 +442,18 @@ b. The new Visual Studio will install Ninja as default. (If not, please install

### II. Build llama.cpp

You could download the release package for Windows directly, which including binary files and depended oneAPI dll files.

Choose one of following methods to build from source code.

1. Script

```sh
.\examples\sycl\win-build-sycl.bat
```

2. CMake

On the oneAPI command line window, step into the llama.cpp main directory and run the following:

```
Expand All @@ -425,12 +468,8 @@ cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPI
cmake --build build --config Release -j
```

Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
```sh
.\examples\sycl\win-build-sycl.bat
```

Or, use CMake presets to build:

```sh
cmake --preset x64-windows-sycl-release
cmake --build build-x64-windows-sycl-release -j --target llama-cli
Expand All @@ -442,31 +481,35 @@ cmake --preset x64-windows-sycl-debug
cmake --build build-x64-windows-sycl-debug -j --target llama-cli
```

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.
3. Visual Studio

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.

*Notes:*

- In case of a minimal experimental setup, the user can build the inference executable only through `cmake --build build --config Release -j --target llama-cli`.

### III. Run the inference

1. Retrieve and prepare model
#### Retrieve and prepare model

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.
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.

2. Enable oneAPI running environment
##### Check device

1. Enable oneAPI running environment

On the oneAPI command line window, run the following and step into the llama.cpp directory:
```
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
```

3. List devices information
2. List devices information

Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:

```
build\bin\ls-sycl-device.exe
build\bin\llama-ls-sycl-device.exe
```

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:
Expand All @@ -478,10 +521,28 @@ found 2 SYCL devices:
| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
```
#### Choose level-zero devices

|Chosen Device ID|Setting|
|-|-|
|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|

#### Execute

Choose one of following methods to run.

1. Script

```
examples\sycl\win-run-llama2.bat
```

2. Command line

4. Launch inference
Launch inference

There are two device selection modes:

Expand All @@ -508,11 +569,7 @@ build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website ca
```
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
```
Otherwise, run the following wrapper script:

```
.\examples\sycl\win-run-llama2.bat
```

Note:

Expand All @@ -526,17 +583,18 @@ Or
use 1 SYCL GPUs: [0] with Max compute units:512
```


## Environment Variable

#### Build

| Name | Value | Function |
|--------------------|-----------------------------------|---------------------------------------------|
| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA | Set the SYCL target device type. |
| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. |
| CMAKE_C_COMPILER | icx | Set *icx* compiler for SYCL code path. |
| CMAKE_CXX_COMPILER | icpx *(Linux)*, icx *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
| 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. |

#### Runtime

Expand Down Expand Up @@ -572,9 +630,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512
```
Otherwise, please double-check the GPU driver installation steps.

- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend?

No. We can't support Ollama issue directly, because we aren't familiar with Ollama.

Sugguest reproducing on llama.cpp and report similar issue to llama.cpp. We will surpport it.

It's same for other projects including llama.cpp SYCL backend.


### **GitHub contribution**:
Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.

## TODO

- Support row layer split for multiple card runs.
- NA
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