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https://github.com/ggerganov/llama.cpp.git
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86 lines
3.0 KiB
Markdown
86 lines
3.0 KiB
Markdown
## Overview
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> [!IMPORTANT]
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> This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and
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> insecure. **Never run the RPC server on an open network or in a sensitive environment!**
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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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local["Backend (CUDA,Metal,etc.)"]<-->ggml[llama-cli]
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ggml[llama-cli]<-->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` for the local backend and add `-DGGML_RPC=ON` to the build options.
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Finally, when running `llama-cli`, 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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This way you can offload model layers to both local and remote devices.
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### Local cache
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The RPC server can use a local cache to store large tensors and avoid transferring them over the network.
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This can speed up model loading significantly, especially when using large models.
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To enable the cache, use the `-c` option:
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```bash
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$ bin/rpc-server -c
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```
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By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable.
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