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* scripts : update sync [no ci] * files : relocate [no ci] * ci : disable kompute build [no ci] * cmake : fixes [no ci] * server : fix mingw build ggml-ci * cmake : minor [no ci] * cmake : link math library [no ci] * cmake : build normal ggml library (not object library) [no ci] * cmake : fix kompute build ggml-ci * make,cmake : fix LLAMA_CUDA + replace GGML_CDEF_PRIVATE ggml-ci * move public backend headers to the public include directory (#8122) * move public backend headers to the public include directory * nix test * spm : fix metal header --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * scripts : fix sync paths [no ci] * scripts : sync ggml-blas.h [no ci] --------- Co-authored-by: slaren <slarengh@gmail.com>
75 lines
2.3 KiB
Markdown
75 lines
2.3 KiB
Markdown
## Overview
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The `rpc-server` allows running `ggml` backend on a remote host.
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The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them.
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This can be used for distributed LLM inference with `llama.cpp` in the following way:
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```mermaid
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flowchart TD
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rpcb---|TCP|srva
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rpcb---|TCP|srvb
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rpcb-.-|TCP|srvn
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subgraph hostn[Host N]
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srvn[rpc-server]-.-backend3["Backend (CUDA,Metal,etc.)"]
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end
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subgraph hostb[Host B]
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srvb[rpc-server]---backend2["Backend (CUDA,Metal,etc.)"]
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end
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subgraph hosta[Host A]
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srva[rpc-server]---backend["Backend (CUDA,Metal,etc.)"]
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end
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subgraph host[Main Host]
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ggml[llama.cpp]---rpcb[RPC backend]
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end
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style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5
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```
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Each host can run a different backend, e.g. one with CUDA and another with Metal.
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You can also run multiple `rpc-server` instances on the same host, each with a different backend.
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## Usage
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On each host, build the corresponding backend with `cmake` and add `-DGGML_RPC=ON` to the build options.
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For example, to build the CUDA backend with RPC support:
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```bash
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mkdir build-rpc-cuda
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cd build-rpc-cuda
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cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
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cmake --build . --config Release
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```
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Then, start the `rpc-server` with the backend:
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```bash
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$ bin/rpc-server -p 50052
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create_backend: using CUDA backend
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ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
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ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
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ggml_cuda_init: found 1 CUDA devices:
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Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
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Starting RPC server on 0.0.0.0:50052
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```
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When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.:
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```bash
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$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
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```
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This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
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On the main host build `llama.cpp` only with `-DGGML_RPC=ON`:
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```bash
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mkdir build-rpc
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cd build-rpc
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cmake .. -DGGML_RPC=ON
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cmake --build . --config Release
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```
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Finally, use the `--rpc` option to specify the host and port of each `rpc-server`:
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```bash
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$ bin/llama-cli -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99
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```
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