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https://github.com/likejazz/ggml-simple
A very simple performing matrix multiplication example for CPU / CUDA / METAL using GGML / llama.cpp
https://github.com/likejazz/ggml-simple
Last synced: 7 days ago
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A very simple performing matrix multiplication example for CPU / CUDA / METAL using GGML / llama.cpp
- Host: GitHub
- URL: https://github.com/likejazz/ggml-simple
- Owner: likejazz
- Created: 2024-07-06T15:59:45.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2024-07-07T05:16:05.000Z (4 months ago)
- Last Synced: 2024-07-07T17:47:00.793Z (4 months ago)
- Homepage:
- Size: 9.77 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ggml-simple
This example simply performs a matrix multiplication, as shown below:
$$
ggml\\_mul\\_mat(A, B^T) = C
$$
$$
ggml\\_mul\\_mat(
\begin{bmatrix}
2 & 8 \\
5 & 1 \\
4 & 2 \\
8 & 6 \\
\end{bmatrix}
,
\begin{bmatrix}
10 & 5 \\
9 & 9 \\
5 & 4 \\
\end{bmatrix}
)
\=
\begin{bmatrix}
60 & 90 & 42 \\
55 & 54 & 29 \\
50 & 54 & 28 \\
110 & 126 & 64 \\
\end{bmatrix}
$$
1. Simply put `add_subdirectory(ggml-simple)` to end of `examples/CMakeLists.txt` in llama.cpp project.
```
...
add_subdirectory(tokenize)
add_subdirectory(train-text-from-scratch)
add_subdirectory(ggml-simple) <-- HERE!
endif()
```
2. Build llama.cpp project using `CMake`:
```
$ cmake -B bld
```
if you have CUDA enabled device, you can build CUDA version:
```
$ cmake -B bld -DGGML_CUDA=on
```
3. `$ make ggml-simple` in `bld` directory.
4. Launch `./bin/ggml-simple`!
```shell
$ ./bin/ggml-simple
main: using Metal backend
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2
ggml_metal_init: picking default device: Apple M2
ggml_metal_init: using embedded metal library
ggml_metal_init: GPU name: Apple M2
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3 (5001)
ggml_metal_init: simdgroup reduction support = true
ggml_metal_init: simdgroup matrix mul. support = true
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 17179.89 MB
main: compute buffer size: 0.0625 KB
mul mat (4 x 3):
[ 60.00 90.00 42.00
55.00 54.00 29.00
50.00 54.00 28.00
110.00 126.00 64.00 ]
ggml_metal_free: deallocating
```
## References
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