82 lines
1.8 KiB
Markdown
82 lines
1.8 KiB
Markdown
# `llama`
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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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- [x] CPU
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- [x] avx, avx2
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- [ ] avx512
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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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- [ ] Linux CUDA
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- [ ] Linux ROCm
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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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> 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"
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go env -w "CGO_CXXFLAGS_ALLOW=-mfma"
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go build -tags=avx2 .
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```
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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 ggml-cuda:
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Build `ggml-cuda.dll`:
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```shell
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./build_cuda.sh
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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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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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```
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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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To update this package to the latest llama.cpp code, use the `sync.sh` script.
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```
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./sync.sh ../../llama.cpp
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```
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