138 lines
3.2 KiB
Markdown
138 lines
3.2 KiB
Markdown
# `llama`
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<<<<<<< Updated upstream
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This package integrates llama.cpp as a Go package that's easy to build with tags for different CPU and GPU processors.
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=======
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This package integrates the [llama.cpp](https://github.com/ggerganov/llama.cpp) library as a Go package and makes it easy to build it with tags for different CPU and GPU processors.
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>>>>>>> Stashed changes
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Supported:
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- [x] CPU
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- [x] avx, avx2
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- [x] macOS Metal
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- [x] Windows CUDA
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- [x] Windows ROCm
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- [x] Linux CUDA
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- [x] Linux ROCm
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- [x] Llava
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<<<<<<< Updated upstream
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=======
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- [ ] Parallel Requests
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Extra build steps are required for CUDA and ROCm on Windows since `nvcc` and `hipcc` both require using msvc as the host compiler. For these small dlls are created:
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- `ggml-cuda.dll`
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- `ggml-hipblas.dll`
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>>>>>>> Stashed changes
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> Note: it's important that memory is allocated and freed by the same compiler (e.g. entirely by code compiled with msvc or mingw). Issues from this should be rare, but there are some places where pointers are returned by the CUDA or HIP runtimes and freed elsewhere, causing a a crash. In a future change the same runtime should be used in both cases to avoid crashes.
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## Building
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```
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go build .
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```
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### AVX
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```shell
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go build -tags avx .
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```
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### AVX2
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```shell
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# go doesn't recognize `-mfma` as a valid compiler flag
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# see https://github.com/golang/go/issues/17895
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go env -w "CGO_CFLAGS_ALLOW=-mfma|-mf16c"
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go env -w "CGO_CXXFLAGS_ALLOW=-mfma|-mf16c"
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go build -tags=avx,avx2 .
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```
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## Linux
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### CUDA
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<<<<<<< Updated upstream
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Install the [CUDA toolkit v11.3.1](https://developer.nvidia.com/cuda-11-3-1-download-archive):
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=======
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Install the [CUDA toolkit v11.3.1](https://developer.nvidia.com/cuda-11-3-1-download-archive) then build `libggml-cuda.so`:
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```shell
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./build_cuda.sh
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```
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>>>>>>> Stashed changes
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Then build the package with the `cuda` tag:
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```shell
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go build -tags=cuda .
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```
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## Windows
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<<<<<<< Updated upstream
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Download [w64devkit](https://github.com/skeeto/w64devkit/releases/latest) for a simple MinGW development environment.
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=======
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>>>>>>> Stashed changes
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### CUDA
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Install the [CUDA toolkit v11.3.1](https://developer.nvidia.com/cuda-11-3-1-download-archive) then build the cuda code:
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Build `ggml-cuda.dll`:
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```shell
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<<<<<<< Updated upstream
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make ggml_cuda.dll
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=======
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./build_cuda.ps1
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>>>>>>> Stashed changes
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```
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Then build the package with the `cuda` tag:
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```shell
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go build -tags=cuda .
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```
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### ROCm
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<<<<<<< Updated upstream
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Install [ROCm 5.7.1](https://rocm.docs.amd.com/en/docs-5.7.1/).
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```shell
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make ggml_hipblas.dll
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=======
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Install [ROCm 5.7.1](https://rocm.docs.amd.com/en/docs-5.7.1/) and [Strawberry Perl](https://strawberryperl.com/).
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Then, build `ggml-hipblas.dll`:
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```shell
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./build_hipblas.sh
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>>>>>>> Stashed changes
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```
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Then build the package with the `rocm` tag:
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```shell
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go build -tags=rocm .
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```
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## Syncing with llama.cpp
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<<<<<<< Updated upstream
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To update this package to the latest llama.cpp code, use the `sync_llama.sh` script from the root of this repo:
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```
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./sync_llama.sh ../../llama.cpp
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=======
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To update this package to the latest llama.cpp code, use the `scripts/sync_llama.sh` script from the root of this repo, providing the location of a llama.cpp checkout:
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```
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cd ollama
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./scripts/sync_llama.sh ../llama.cpp
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>>>>>>> Stashed changes
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```
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