llama.cpp/examples/rpc
Olivier Chafik 1c641e6aac
build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809)
* `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew

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gitignore llama-server

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fix examples/main ref

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* main: target name -> llama-cli

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* Updating docs for eval-callback binary to use new `llama-` prefix.

* Updating a few lingering doc references for rename of main to llama-cli

* Updating `run-with-preset.py` to use new binary names.
Updating docs around `perplexity` binary rename.

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* Revert "update llama-rpc-server bin name + doc"

This reverts commit e474ef1df4.

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---------

Co-authored-by: HanClinto <hanclinto@gmail.com>
2024-06-13 00:41:52 +01:00
..
CMakeLists.txt ggml : add RPC backend (#6829) 2024-05-14 14:27:19 +03:00
README.md build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809) 2024-06-13 00:41:52 +01:00
rpc-server.cpp Revert "[SYCL] Update rpc-server.cpp to include SYCL backend (#7682)" (#7808) 2024-06-09 01:43:39 +02:00

Overview

The rpc-server allows running ggml backend on a remote host. The RPC backend communicates with one or several instances of rpc-server and offloads computations to them. This can be used for distributed LLM inference with llama.cpp in the following way:

flowchart TD
    rpcb---|TCP|srva
    rpcb---|TCP|srvb
    rpcb-.-|TCP|srvn
    subgraph hostn[Host N]
    srvn[rpc-server]-.-backend3["Backend (CUDA,Metal,etc.)"]
    end
    subgraph hostb[Host B]
    srvb[rpc-server]---backend2["Backend (CUDA,Metal,etc.)"]
    end
    subgraph hosta[Host A]
    srva[rpc-server]---backend["Backend (CUDA,Metal,etc.)"]
    end
    subgraph host[Main Host]
    ggml[llama.cpp]---rpcb[RPC backend]
    end
    style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5

Each host can run a different backend, e.g. one with CUDA and another with Metal. You can also run multiple rpc-server instances on the same host, each with a different backend.

Usage

On each host, build the corresponding backend with cmake and add -DLLAMA_RPC=ON to the build options. For example, to build the CUDA backend with RPC support:

mkdir build-rpc-cuda
cd build-rpc-cuda
cmake .. -DLLAMA_CUDA=ON -DLLAMA_RPC=ON
cmake --build . --config Release

Then, start the rpc-server with the backend:

$ bin/rpc-server -p 50052
create_backend: using CUDA backend
ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
  Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
Starting RPC server on 0.0.0.0:50052

When using the CUDA backend, you can specify the device with the CUDA_VISIBLE_DEVICES environment variable, e.g.:

$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052

This way you can run multiple rpc-server instances on the same host, each with a different CUDA device.

On the main host build llama.cpp only with -DLLAMA_RPC=ON:

mkdir build-rpc
cd build-rpc
cmake .. -DLLAMA_RPC=ON
cmake --build . --config Release

Finally, use the --rpc option to specify the host and port of each rpc-server:

$ 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