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15771072c7
* RPC support multiple devices * rpc : update documentation (#16441) Update the README file to match the newly added functionality of exposing multiple devices from a single server. Co-authored-by: Diego Devesa <slarengh@gmail.com> # Conflicts: # examples/rpc/README.md * Remove memory settings * rpc : cache and reuse compute graphs (#15405) Store the last computed graph and reuse it when possible. Also do not return response from GRAPH_COMPUTE and assume it always completes successfully. If this this is not the case, the server closes the connection. This saves us a network round trip to the server. * Add -cpu to include cpu backend --------- Co-authored-by: firecoperana <firecoperana> Co-authored-by: Radoslav Gerganov <rgerganov@gmail.com>
99 lines
3.5 KiB
Markdown
99 lines
3.5 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 exposing `ggml` devices 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]<-.->dev4["CUDA0"]
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srvn[rpc-server]<-.->dev5["CPU"]
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end
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subgraph hostb[Host B]
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srvb[rpc-server]<-->dev3["Metal"]
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end
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subgraph hosta[Host A]
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srva[rpc-server]<-->dev["CUDA0"]
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srva[rpc-server]<-->dev2["CUDA1"]
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end
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subgraph host[Main Host]
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local["Local devices"]<-->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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classDef devcls fill:#5B9BD5
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class local,dev,dev2,dev3,dev4,dev5 devcls
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```
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By default, `rpc-server` exposes all available accelerator devices on the host.
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If there are no accelerators, it exposes a single `CPU` device.
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## Usage
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### Remote hosts
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On each remote host, build the backends for each accelerator by adding `-DGGML_RPC=ON` to the build options.
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For example, to build the `rpc-server` with support for CUDA accelerators:
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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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When started, the `rpc-server` will detect and expose all available `CUDA` devices:
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```bash
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$ bin/rpc-server
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ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
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ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
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ggml_cuda_init: found 1 CUDA devices:
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Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes
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Starting RPC server v3.0.0
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endpoint : 127.0.0.1:50052
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local cache : n/a
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Devices:
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CUDA0: NVIDIA GeForce RTX 5090 (32109 MiB, 31588 MiB free)
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```
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You can control the set of exposed CUDA devices with the `CUDA_VISIBLE_DEVICES` environment variable or the `--device` command line option. The following two commands have the same effect:
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```bash
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$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
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$ bin/rpc-server --device CUDA0 -p 50052
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```
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### Main host
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On the main host build `llama.cpp` with the backends for the local devices and add `-DGGML_RPC=ON` to the build options.
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Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `rpc-server`:
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```bash
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$ llama-cli -hf ggml-org/gemma-3-1b-it-GGUF -ngl 99 --rpc 192.168.88.10:50052,192.168.88.11:50052
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```
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By default, llama.cpp distributes model weights and the KV cache across all available devices -- both local and remote -- in proportion to each device's available memory.
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You can override this behavior with the `--tensor-split` option and set custom proportions when splitting tensor data across devices.
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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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By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable.
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### Troubleshooting
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Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `rpc-server`:
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```bash
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$ GGML_RPC_DEBUG=1 bin/rpc-server
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```
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