mirror of
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Merge branch 'upstream' into concedo_experimental
# Conflicts: # .devops/full-cuda.Dockerfile # .devops/full-rocm.Dockerfile # .devops/full.Dockerfile # .devops/llama-cpp-clblast.srpm.spec # .devops/llama-cpp-cuda.srpm.spec # .devops/llama-cpp.srpm.spec # .devops/nix/package.nix # .devops/server-cuda.Dockerfile # .devops/server-intel.Dockerfile # .devops/server-rocm.Dockerfile # .devops/server-vulkan.Dockerfile # .devops/server.Dockerfile # .github/workflows/build.yml # .github/workflows/code-coverage.yml # .github/workflows/docker.yml # .github/workflows/editorconfig.yml # .github/workflows/gguf-publish.yml # .github/workflows/nix-ci-aarch64.yml # .github/workflows/nix-ci.yml # .github/workflows/python-check-requirements.yml # .github/workflows/python-lint.yml # .github/workflows/server.yml # .github/workflows/zig-build.yml # CMakeLists.txt # Makefile # README-sycl.md # README.md # ci/run.sh # examples/gguf-split/gguf-split.cpp # flake.lock # flake.nix # llama.cpp # scripts/compare-llama-bench.py # scripts/sync-ggml-am.sh # scripts/sync-ggml.last # scripts/sync-ggml.sh # tests/CMakeLists.txt # tests/test-backend-ops.cpp # tests/test-chat-template.cpp
This commit is contained in:
Concedo
+76
-78
@@ -16,52 +16,50 @@ The project is under active development, and we are [looking for feedback and co
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**Command line options:**
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- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching, this parameter is used only if one token is to be processed on CPU backend.
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- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend.
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- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU.
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- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`)
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- `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`
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- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
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- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (default: unused).
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- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository (default: unused).
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- `-hff FILE, --hf-file FILE`: Hugging Face model file (default: unused).
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- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused
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- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository. Default: unused
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- `-hff FILE, --hf-file FILE`: Hugging Face model file. Default: unused
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- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
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- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
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- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is `512`, but LLaMA models were built with a context of `2048`, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of `4096`.
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- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
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- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
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- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
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- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`.
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- `-ub N`, `--ubatch-size N`: physical maximum batch size. Default: `512`.
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- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
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- `-mg i, --main-gpu i`: When using multiple GPUs, this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default, GPU `0` is used.
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- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs, this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance.
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- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
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- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
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- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
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- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
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- `--numa STRATEGY`: Attempt one of the below optimization strategies that help on some NUMA systems
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- `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems
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- `--numa distribute`: Spread execution evenly over all nodes
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- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
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- `--numa numactl`: Use the CPU map provided by numactl
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if run without this previously, it is recommended to drop the system page cache before using this
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see https://github.com/ggerganov/llama.cpp/issues/1437
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- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system
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page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437
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- `--numa`: Attempt optimizations that help on some NUMA systems.
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- `--numa`: Attempt optimizations that may help on some NUMA systems.
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- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
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- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
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- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
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- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
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- `--port`: Set the port to listen. Default: `8080`.
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- `--path`: path from which to serve static files (default: disabled)
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- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
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- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s.
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- `--embedding`: Enable embedding extraction, Default: disabled.
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- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)
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- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
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- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
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- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`
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- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`
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- `--port`: Set the port to listen. Default: `8080`
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- `--path`: Path from which to serve static files. Default: disabled
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- `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
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- `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s.
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- `--embedding`: Enable embedding extraction. Default: disabled
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- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`
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- `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching). Default: disabled
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- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime)
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- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
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- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`
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- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`
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- `-n N, --n-predict N`: Set the maximum tokens to predict (default: -1)
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- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend. Used together with group attention width `--grp-attn-w`. Default: `1`, which is disabled.
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- `--grp-attn-w`: Set the group attention width to extend context size through self-extend. Used together with group attention factor `--grp-attn-n`. Default: `512`
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- `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
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- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
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- `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled)
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- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
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- `--log-disable`: Output logs to stdout only, not to `llama.log`. default: enabled.
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- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
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- `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
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- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name. Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
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- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
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- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
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**If compiled with `LLAMA_SERVER_SSL=ON`**
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- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
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@@ -69,7 +67,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
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## Build
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server is build alongside everything else from the root of the project
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`server` is built alongside everything else from the root of the project
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- Using `make`:
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@@ -85,7 +83,7 @@ server is build alongside everything else from the root of the project
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## Build with SSL
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server can also be built with SSL support using OpenSSL 3
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`server` can also be built with SSL support using OpenSSL 3
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- Using `make`:
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@@ -135,7 +133,7 @@ docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/
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## Testing with CURL
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Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
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Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS.
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```sh
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curl --request POST \
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@@ -159,7 +157,7 @@ mkdir llama-client
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cd llama-client
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```
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Create a index.js file and put inside this:
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Create a index.js file and put this inside:
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```javascript
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const prompt = `Building a website can be done in 10 simple steps:`;
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@@ -190,8 +188,8 @@ node index.js
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- 503 -> `{"status": "loading model"}` if the model is still being loaded.
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- 500 -> `{"status": "error"}` if the model failed to load.
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- 200 -> `{"status": "ok", "slots_idle": 1, "slots_processing": 2 }` if the model is successfully loaded and the server is ready for further requests mentioned below.
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- 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slot are currently available.
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- 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slot are currently available.
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- 200 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if no slots are currently available.
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- 503 -> `{"status": "no slot available", "slots_idle": 0, "slots_processing": 32}` if the query parameter `fail_on_no_slot` is provided and no slots are currently available.
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If the query parameter `include_slots` is passed, `slots` field will contain internal slots data except if `--slots-endpoint-disable` is set.
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@@ -205,75 +203,75 @@ node index.js
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- The model's `tokenizer.ggml.add_bos_token` metadata is `true`
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- The system prompt is empty
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`temperature`: Adjust the randomness of the generated text (default: 0.8).
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`temperature`: Adjust the randomness of the generated text. Default: `0.8`
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`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` (default: 0.0, 0.0 = disabled).
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`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
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`dynatemp_exponent`: Dynamic temperature exponent (default: 1.0).
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`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`
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`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
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`top_k`: Limit the next token selection to the K most probable tokens. Default: `40`
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`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
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`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`
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`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token (default: 0.05).
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`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`
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`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
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`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
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`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
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By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt.
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By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
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`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
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`stop`: Specify a JSON array of stopping strings.
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These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
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These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
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`tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
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`tfs_z`: Enable tail free sampling with parameter z. Default: `1.0`, which is disabled.
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`typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
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`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
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`repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).
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`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
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`repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
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`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
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`penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).
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`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
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`presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).
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`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
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`frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
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`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
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`penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens (default: `null` = use the original `prompt`).
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`penalty_prompt`: This will replace the `prompt` for the purpose of the penalty evaluation. Can be either `null`, a string or an array of numbers representing tokens. Default: `null`, which is to use the original `prompt`.
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`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
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`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
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`mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
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`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
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`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
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`mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1`
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`grammar`: Set grammar for grammar-based sampling (default: no grammar)
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`grammar`: Set grammar for grammar-based sampling. Default: no grammar
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`seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
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`seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed.
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`ignore_eos`: Ignore end of stream token and continue generating (default: false).
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`ignore_eos`: Ignore end of stream token and continue generating. Default: `false`
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`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. (default: []).
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`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
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`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
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`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token. Default: `0`
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`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum (default: 0)
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`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
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`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
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`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
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`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1`
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`cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. (default: false)
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`cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. Default: `false`
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`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
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|
||||
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. (default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values)
|
||||
`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "tfs_z", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values.
|
||||
|
||||
### Result JSON
|
||||
|
||||
- Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
|
||||
- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
|
||||
|
||||
- `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
|
||||
|
||||
@@ -287,7 +285,7 @@ node index.js
|
||||
},
|
||||
{
|
||||
"prob": float,
|
||||
"tok_str": "<second most likely tonen>"
|
||||
"tok_str": "<second most likely token>"
|
||||
},
|
||||
...
|
||||
]
|
||||
@@ -357,14 +355,14 @@ Notice that each `probs` is an array of length `n_probs`.
|
||||
|
||||
- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
|
||||
- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
|
||||
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
|
||||
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
|
||||
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
|
||||
|
||||
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used.
|
||||
|
||||
*Options:*
|
||||
|
||||
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
|
||||
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
|
||||
|
||||
*Examples:*
|
||||
|
||||
@@ -514,16 +512,16 @@ Available metrics:
|
||||
- `llamacpp:tokens_predicted_total`: Number of generation tokens processed.
|
||||
- `llamacpp:prompt_tokens_seconds`: Average prompt throughput in tokens/s.
|
||||
- `llamacpp:predicted_tokens_seconds`: Average generation throughput in tokens/s.
|
||||
- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. 1 means 100 percent usage.
|
||||
- `llamacpp:kv_cache_usage_ratio`: KV-cache usage. `1` means 100 percent usage.
|
||||
- `llamacpp:kv_cache_tokens`: KV-cache tokens.
|
||||
- `llamacpp:requests_processing`: Number of request processing.
|
||||
- `llamacpp:requests_deferred`: Number of request deferred.
|
||||
- `llamacpp:requests_processing`: Number of requests processing.
|
||||
- `llamacpp:requests_deferred`: Number of requests deferred.
|
||||
|
||||
## More examples
|
||||
|
||||
### Change system prompt on runtime
|
||||
|
||||
To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it.
|
||||
To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt`. This only needs to be used once.
|
||||
|
||||
`prompt`: Specify a context that you want all connecting clients to respect.
|
||||
|
||||
@@ -562,11 +560,11 @@ bash chat.sh
|
||||
|
||||
### OAI-like API
|
||||
|
||||
The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi
|
||||
The HTTP `server` supports an OAI-like API: https://github.com/openai/openai-openapi
|
||||
|
||||
### API errors
|
||||
|
||||
Server returns error in the same format as OAI: https://github.com/openai/openai-openapi
|
||||
`server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
|
||||
|
||||
Example of an error:
|
||||
|
||||
|
||||
@@ -2,13 +2,15 @@
|
||||
|
||||
Benchmark is using [k6](https://k6.io/).
|
||||
|
||||
##### Install k6
|
||||
##### Install k6 and sse extension
|
||||
|
||||
Follow instruction from: https://k6.io/docs/get-started/installation/
|
||||
SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
|
||||
|
||||
Example for ubuntu:
|
||||
Example:
|
||||
```shell
|
||||
snap install k6
|
||||
go install go.k6.io/xk6/cmd/xk6@latest
|
||||
xk6 build master \
|
||||
--with github.com/phymbert/xk6-sse
|
||||
```
|
||||
|
||||
#### Download a dataset
|
||||
@@ -46,7 +48,7 @@ server --host localhost --port 8080 \
|
||||
|
||||
For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
|
||||
```shell
|
||||
k6 run script.js --duration 10m --iterations 500 --vus 8
|
||||
./k6 run script.js --duration 10m --iterations 500 --vus 8
|
||||
```
|
||||
|
||||
The benchmark values can be overridden with:
|
||||
@@ -86,3 +88,33 @@ K6 metrics might be compared against [server metrics](../README.md), with:
|
||||
```shell
|
||||
curl http://localhost:8080/metrics
|
||||
```
|
||||
|
||||
### Using the CI python script
|
||||
The `bench.py` script does several steps:
|
||||
- start the server
|
||||
- define good variable for k6
|
||||
- run k6 script
|
||||
- extract metrics from prometheus
|
||||
|
||||
It aims to be used in the CI, but you can run it manually:
|
||||
|
||||
```shell
|
||||
LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/server python bench.py \
|
||||
--runner-label local \
|
||||
--name local \
|
||||
--branch `git rev-parse --abbrev-ref HEAD` \
|
||||
--commit `git rev-parse HEAD` \
|
||||
--scenario script.js \
|
||||
--duration 5m \
|
||||
--hf-repo ggml-org/models \
|
||||
--hf-file phi-2/ggml-model-q4_0.gguf \
|
||||
--model-path-prefix models \
|
||||
--parallel 4 \
|
||||
-ngl 33 \
|
||||
--batch-size 2048 \
|
||||
--ubatch-size 256 \
|
||||
--ctx-size 4096 \
|
||||
--n-prompts 200 \
|
||||
--max-prompt-tokens 256 \
|
||||
--max-tokens 256
|
||||
```
|
||||
|
||||
@@ -0,0 +1,308 @@
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import signal
|
||||
import socket
|
||||
import subprocess
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import traceback
|
||||
from contextlib import closing
|
||||
from datetime import datetime
|
||||
|
||||
import matplotlib
|
||||
import matplotlib.dates
|
||||
import matplotlib.pyplot as plt
|
||||
import requests
|
||||
from statistics import mean
|
||||
|
||||
|
||||
def main(args_in: list[str] | None = None) -> None:
|
||||
parser = argparse.ArgumentParser(description="Start server benchmark scenario")
|
||||
parser.add_argument("--name", type=str, help="Bench name", required=True)
|
||||
parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
|
||||
parser.add_argument("--branch", type=str, help="Branch name", default="detached")
|
||||
parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
|
||||
parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
|
||||
parser.add_argument("--port", type=int, help="Server listen host", default="8080")
|
||||
parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
|
||||
parser.add_argument("--n-prompts", type=int,
|
||||
help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
|
||||
parser.add_argument("--max-prompt-tokens", type=int,
|
||||
help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
|
||||
required=True)
|
||||
parser.add_argument("--max-tokens", type=int,
|
||||
help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
|
||||
required=True)
|
||||
parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
|
||||
parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
|
||||
parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
|
||||
parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
|
||||
parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
|
||||
parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
|
||||
parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
|
||||
parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
|
||||
parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
|
||||
|
||||
args = parser.parse_args(args_in)
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# Start the server and performance scenario
|
||||
try:
|
||||
server_process = start_server(args)
|
||||
except Exception:
|
||||
print("bench: server start error :")
|
||||
traceback.print_exc(file=sys.stdout)
|
||||
sys.exit(1)
|
||||
|
||||
# start the benchmark
|
||||
try:
|
||||
start_benchmark(args)
|
||||
|
||||
iterations = 0
|
||||
with open("results.github.env", 'w') as github_env:
|
||||
# parse output
|
||||
with open('k6-results.json', 'r') as bench_results:
|
||||
# Load JSON data from file
|
||||
data = json.load(bench_results)
|
||||
for metric_name in data['metrics']:
|
||||
for metric_metric in data['metrics'][metric_name]:
|
||||
value = data['metrics'][metric_name][metric_metric]
|
||||
if isinstance(value, float) or isinstance(value, int):
|
||||
value = round(value, 2)
|
||||
data['metrics'][metric_name][metric_metric]=value
|
||||
github_env.write(
|
||||
f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
|
||||
iterations = data['root_group']['checks']['success completion']['passes']
|
||||
|
||||
except Exception:
|
||||
print("bench: error :")
|
||||
traceback.print_exc(file=sys.stdout)
|
||||
|
||||
# Stop the server
|
||||
if server_process:
|
||||
try:
|
||||
print(f"bench: shutting down server pid={server_process.pid} ...")
|
||||
if os.name == 'nt':
|
||||
interrupt = signal.CTRL_C_EVENT
|
||||
else:
|
||||
interrupt = signal.SIGINT
|
||||
server_process.send_signal(interrupt)
|
||||
server_process.wait(0.5)
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
|
||||
server_process.kill() # SIGKILL
|
||||
server_process.wait()
|
||||
|
||||
while is_server_listening(args.host, args.port):
|
||||
time.sleep(0.1)
|
||||
|
||||
title = (f"llama.cpp {args.name} on {args.runner_label}\n "
|
||||
f"duration={args.duration} {iterations} iterations")
|
||||
xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
|
||||
f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
|
||||
f"branch={args.branch} commit={args.commit}")
|
||||
|
||||
# Prometheus
|
||||
end_time = time.time()
|
||||
prometheus_metrics = {}
|
||||
if is_server_listening("0.0.0.0", 9090):
|
||||
metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
|
||||
'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
|
||||
|
||||
for metric in metrics:
|
||||
resp = requests.get(f"http://localhost:9090/api/v1/query_range",
|
||||
params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
|
||||
|
||||
with open(f"{metric}.json", 'w') as metric_json:
|
||||
metric_json.write(resp.text)
|
||||
|
||||
if resp.status_code != 200:
|
||||
print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
|
||||
else:
|
||||
metric_data = resp.json()
|
||||
values = metric_data['data']['result'][0]['values']
|
||||
timestamps, metric_values = zip(*values)
|
||||
metric_values = [float(value) for value in metric_values]
|
||||
prometheus_metrics[metric] = metric_values
|
||||
timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
|
||||
plt.figure(figsize=(16, 10), dpi=80)
|
||||
plt.plot(timestamps_dt, metric_values, label=metric)
|
||||
plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
|
||||
plt.yticks(fontsize=12, alpha=.7)
|
||||
|
||||
ylabel = f"llamacpp:{metric}"
|
||||
plt.title(title,
|
||||
fontsize=14, wrap=True)
|
||||
plt.grid(axis='both', alpha=.3)
|
||||
plt.ylabel(ylabel, fontsize=22)
|
||||
plt.xlabel(xlabel, fontsize=14, wrap=True)
|
||||
plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
|
||||
plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
|
||||
plt.gcf().autofmt_xdate()
|
||||
|
||||
# Remove borders
|
||||
plt.gca().spines["top"].set_alpha(0.0)
|
||||
plt.gca().spines["bottom"].set_alpha(0.3)
|
||||
plt.gca().spines["right"].set_alpha(0.0)
|
||||
plt.gca().spines["left"].set_alpha(0.3)
|
||||
|
||||
# Save the plot as a jpg image
|
||||
plt.savefig(f'{metric}.jpg', dpi=60)
|
||||
plt.close()
|
||||
|
||||
# Mermaid format in case images upload failed
|
||||
with (open(f"{metric}.mermaid", 'w') as mermaid_f):
|
||||
mermaid = (
|
||||
f"""---
|
||||
config:
|
||||
xyChart:
|
||||
titleFontSize: 12
|
||||
width: 900
|
||||
height: 600
|
||||
themeVariables:
|
||||
xyChart:
|
||||
titleColor: "#000000"
|
||||
---
|
||||
xychart-beta
|
||||
title "{title}"
|
||||
y-axis "llamacpp:{metric}"
|
||||
x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
|
||||
line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
|
||||
""")
|
||||
mermaid_f.write(mermaid)
|
||||
|
||||
# 140 chars max for commit status description
|
||||
bench_results = {
|
||||
"i": iterations,
|
||||
"req": {
|
||||
"p95": round(data['metrics']["http_req_duration"]["p(95)"], 2),
|
||||
"avg": round(data['metrics']["http_req_duration"]["avg"], 2),
|
||||
},
|
||||
"pp": {
|
||||
"p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2),
|
||||
"avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2),
|
||||
"0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2),
|
||||
},
|
||||
"tg": {
|
||||
"p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2),
|
||||
"avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2),
|
||||
"0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2),
|
||||
},
|
||||
}
|
||||
with open("results.github.env", 'a') as github_env:
|
||||
github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
|
||||
github_env.write(f"BENCH_ITERATIONS={iterations}\n")
|
||||
|
||||
title = title.replace('\n', ' ')
|
||||
xlabel = xlabel.replace('\n', ' ')
|
||||
github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
|
||||
github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
|
||||
|
||||
|
||||
def start_benchmark(args):
|
||||
k6_path = './k6'
|
||||
if 'BENCH_K6_BIN_PATH' in os.environ:
|
||||
k6_path = os.environ['BENCH_K6_BIN_PATH']
|
||||
k6_args = [
|
||||
'run', args.scenario,
|
||||
'--no-color',
|
||||
]
|
||||
k6_args.extend(['--duration', args.duration])
|
||||
k6_args.extend(['--iterations', args.n_prompts])
|
||||
k6_args.extend(['--vus', args.parallel])
|
||||
k6_args.extend(['--summary-export', 'k6-results.json'])
|
||||
args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
|
||||
args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
|
||||
print(f"bench: starting k6 with: {args}")
|
||||
k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
|
||||
if k6_completed.returncode != 0:
|
||||
raise Exception("bench: unable to run k6")
|
||||
|
||||
|
||||
def start_server(args):
|
||||
server_process = start_server_background(args)
|
||||
|
||||
attempts = 0
|
||||
max_attempts = 20
|
||||
if 'GITHUB_ACTIONS' in os.environ:
|
||||
max_attempts *= 2
|
||||
|
||||
while not is_server_listening(args.host, args.port):
|
||||
attempts += 1
|
||||
if attempts > max_attempts:
|
||||
assert False, "server not started"
|
||||
print(f"bench: waiting for server to start ...")
|
||||
time.sleep(0.5)
|
||||
|
||||
print("bench: server started.")
|
||||
return server_process
|
||||
|
||||
|
||||
def start_server_background(args):
|
||||
# Start the server
|
||||
server_path = '../../../build/bin/server'
|
||||
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
|
||||
server_path = os.environ['LLAMA_SERVER_BIN_PATH']
|
||||
server_args = [
|
||||
'--host', args.host,
|
||||
'--port', args.port,
|
||||
]
|
||||
model_file = args.model_path_prefix + os.path.sep + args.hf_file
|
||||
model_dir = os.path.dirname(model_file)
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
server_args.extend(['--model', model_file])
|
||||
server_args.extend(['--hf-repo', args.hf_repo])
|
||||
server_args.extend(['--hf-file', args.hf_file])
|
||||
server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
|
||||
server_args.extend(['--ctx-size', args.ctx_size])
|
||||
server_args.extend(['--parallel', args.parallel])
|
||||
server_args.extend(['--batch-size', args.batch_size])
|
||||
server_args.extend(['--ubatch-size', args.ubatch_size])
|
||||
server_args.extend(['--n-predict', args.max_tokens * 2])
|
||||
server_args.extend(['--defrag-thold', "0.1"])
|
||||
server_args.append('--cont-batching')
|
||||
server_args.append('--metrics')
|
||||
server_args.extend(['--log-format', "text"])
|
||||
args = [str(arg) for arg in [server_path, *server_args]]
|
||||
print(f"bench: starting server with: {' '.join(args)}")
|
||||
pkwargs = {
|
||||
'stdout': subprocess.PIPE,
|
||||
'stderr': subprocess.PIPE
|
||||
}
|
||||
server_process = subprocess.Popen(
|
||||
args,
|
||||
**pkwargs)
|
||||
|
||||
def server_log(in_stream, out_stream):
|
||||
for line in iter(in_stream.readline, b''):
|
||||
print(line.decode('utf-8'), end='', file=out_stream)
|
||||
|
||||
thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
|
||||
thread_stdout.start()
|
||||
thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
|
||||
thread_stderr.start()
|
||||
|
||||
return server_process
|
||||
|
||||
|
||||
def is_server_listening(server_fqdn, server_port):
|
||||
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
|
||||
result = sock.connect_ex((server_fqdn, server_port))
|
||||
_is_server_listening = result == 0
|
||||
if _is_server_listening:
|
||||
print(f"server is listening on {server_fqdn}:{server_port}...")
|
||||
return _is_server_listening
|
||||
|
||||
|
||||
def escape_metric_name(metric_name):
|
||||
return re.sub('[^A-Z0-9]', '_', metric_name.upper())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,9 @@
|
||||
global:
|
||||
scrape_interval: 10s
|
||||
external_labels:
|
||||
llamacpp: 'server'
|
||||
|
||||
scrape_configs:
|
||||
- job_name: 'llama.cpp server'
|
||||
static_configs:
|
||||
- targets: ['localhost:8080']
|
||||
@@ -0,0 +1,2 @@
|
||||
matplotlib
|
||||
requests
|
||||
@@ -1,4 +1,4 @@
|
||||
import http from 'k6/http'
|
||||
import sse from 'k6/x/sse'
|
||||
import {check, sleep} from 'k6'
|
||||
import {SharedArray} from 'k6/data'
|
||||
import {Counter, Rate, Trend} from 'k6/metrics'
|
||||
@@ -53,7 +53,9 @@ const data = new SharedArray('conversations', function () {
|
||||
|
||||
const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
|
||||
const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
|
||||
|
||||
const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
|
||||
const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second')
|
||||
|
||||
const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
|
||||
const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
|
||||
@@ -86,35 +88,62 @@ export default function () {
|
||||
}
|
||||
],
|
||||
"model": model,
|
||||
"stream": false,
|
||||
"stream": true,
|
||||
"seed": 42,
|
||||
"max_tokens": max_tokens
|
||||
}
|
||||
|
||||
const body = JSON.stringify(payload)
|
||||
const params = {method: 'POST', body: JSON.stringify(payload)};
|
||||
|
||||
let res = http.post(`${server_url}/chat/completions`, body, {
|
||||
headers: {'Content-Type': 'application/json'},
|
||||
timeout: '300s'
|
||||
const startTime = new Date()
|
||||
let promptEvalEndTime = null
|
||||
let prompt_tokens = 0
|
||||
let completions_tokens = 0
|
||||
let finish_reason = null
|
||||
const res = sse.open(`${server_url}/chat/completions`, params, function (client) {
|
||||
client.on('event', function (event) {
|
||||
if (promptEvalEndTime == null) {
|
||||
promptEvalEndTime = new Date()
|
||||
}
|
||||
|
||||
let chunk = JSON.parse(event.data)
|
||||
let choice = chunk.choices[0]
|
||||
if (choice.finish_reason) {
|
||||
finish_reason = choice.finish_reason
|
||||
}
|
||||
|
||||
if (chunk.usage) {
|
||||
prompt_tokens = chunk.usage.prompt_tokens
|
||||
llamacpp_prompt_tokens.add(prompt_tokens)
|
||||
llamacpp_prompt_tokens_total_counter.add(prompt_tokens)
|
||||
|
||||
completions_tokens = chunk.usage.completion_tokens
|
||||
llamacpp_completion_tokens.add(completions_tokens)
|
||||
llamacpp_completion_tokens_total_counter.add(completions_tokens)
|
||||
}
|
||||
})
|
||||
|
||||
client.on('error', function (e) {
|
||||
console.log('An unexpected error occurred: ', e.error());
|
||||
throw e;
|
||||
})
|
||||
})
|
||||
|
||||
check(res, {'success completion': (r) => r.status === 200})
|
||||
|
||||
if (res.status === 200) {
|
||||
const completions = res.json()
|
||||
const endTime = new Date()
|
||||
|
||||
llamacpp_prompt_tokens.add(completions.usage.prompt_tokens)
|
||||
llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens)
|
||||
|
||||
llamacpp_completion_tokens.add(completions.usage.completion_tokens)
|
||||
llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens)
|
||||
|
||||
llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length')
|
||||
llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop')
|
||||
|
||||
llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3)
|
||||
} else {
|
||||
console.error(`response: ${res.body} request=${payload}`)
|
||||
const promptEvalTime = promptEvalEndTime - startTime
|
||||
if (promptEvalTime > 0) {
|
||||
llamacpp_prompt_processing_second.add(prompt_tokens / (promptEvalEndTime - startTime) * 1.e3)
|
||||
}
|
||||
|
||||
const completion_time = endTime - promptEvalEndTime
|
||||
if (completions_tokens > 0 && completion_time > 0) {
|
||||
llamacpp_tokens_second.add(completions_tokens / completion_time * 1.e3)
|
||||
}
|
||||
llamacpp_completions_truncated_rate.add(finish_reason === 'length')
|
||||
llamacpp_completions_stop_rate.add(finish_reason === 'stop')
|
||||
|
||||
sleep(0.3)
|
||||
}
|
||||
|
||||
+438
-428
@@ -43,444 +43,454 @@ unsigned char completion_js[] = {
|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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||||
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|
||||
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|
||||
};
|
||||
size_t completion_js_len = 5796;
|
||||
unsigned int completion_js_len = 5909;
|
||||
|
||||
+2462
-2412
File diff suppressed because it is too large
Load Diff
@@ -1928,4 +1928,4 @@ unsigned char index_js[] = {
|
||||
0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73,
|
||||
0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
|
||||
};
|
||||
size_t index_js_len = 23136;
|
||||
unsigned int index_js_len = 23136;
|
||||
|
||||
@@ -21,6 +21,7 @@ let generation_settings = null;
|
||||
//
|
||||
export async function* llama(prompt, params = {}, config = {}) {
|
||||
let controller = config.controller;
|
||||
const api_url = config.api_url || "";
|
||||
|
||||
if (!controller) {
|
||||
controller = new AbortController();
|
||||
@@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {
|
||||
|
||||
const completionParams = { ...paramDefaults, ...params, prompt };
|
||||
|
||||
const response = await fetch("/completion", {
|
||||
const response = await fetch(`${api_url}/completion`, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify(completionParams),
|
||||
headers: {
|
||||
@@ -193,9 +194,10 @@ export const llamaComplete = async (params, controller, callback) => {
|
||||
}
|
||||
|
||||
// Get the model info from the server. This is useful for getting the context window and so on.
|
||||
export const llamaModelInfo = async () => {
|
||||
export const llamaModelInfo = async (config = {}) => {
|
||||
if (!generation_settings) {
|
||||
const props = await fetch("/props").then(r => r.json());
|
||||
const api_url = config.api_url || "";
|
||||
const props = await fetch(`${api_url}/props`).then(r => r.json());
|
||||
generation_settings = props.default_generation_settings;
|
||||
}
|
||||
return generation_settings;
|
||||
|
||||
@@ -199,10 +199,10 @@
|
||||
<script type="module">
|
||||
import {
|
||||
html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
|
||||
} from '/index.js';
|
||||
} from './index.js';
|
||||
|
||||
import { llama } from '/completion.js';
|
||||
import { SchemaConverter } from '/json-schema-to-grammar.mjs';
|
||||
import { llama } from './completion.js';
|
||||
import { SchemaConverter } from './json-schema-to-grammar.mjs';
|
||||
let selected_image = false;
|
||||
var slot_id = -1;
|
||||
|
||||
@@ -222,6 +222,7 @@
|
||||
temperature: 0.7,
|
||||
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
|
||||
repeat_penalty: 1.18, // 1.0 = disabled
|
||||
penalize_nl: false,
|
||||
top_k: 40, // <= 0 to use vocab size
|
||||
top_p: 0.95, // 1.0 = disabled
|
||||
min_p: 0.05, // 0 = disabled
|
||||
@@ -405,7 +406,7 @@
|
||||
throw new Error("already running");
|
||||
}
|
||||
controller.value = new AbortController();
|
||||
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
|
||||
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
|
||||
const data = chunk.data;
|
||||
|
||||
if (data.stop) {
|
||||
@@ -627,6 +628,7 @@
|
||||
const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
|
||||
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
|
||||
const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
|
||||
const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked }
|
||||
|
||||
const grammarJsonSchemaPropOrder = signal('')
|
||||
const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value
|
||||
@@ -670,6 +672,15 @@
|
||||
`
|
||||
};
|
||||
|
||||
const BoolField = ({ label, name, value }) => {
|
||||
return html`
|
||||
<div>
|
||||
<label for="${name}">${label}</label>
|
||||
<input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} />
|
||||
</div>
|
||||
`
|
||||
};
|
||||
|
||||
const userTemplateReset = (e) => {
|
||||
e.preventDefault();
|
||||
userTemplateResetToDefaultAndApply()
|
||||
@@ -769,6 +780,7 @@
|
||||
${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
|
||||
${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
|
||||
${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
|
||||
${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
|
||||
${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
|
||||
${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
|
||||
${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}
|
||||
|
||||
@@ -2190,8 +2190,6 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
|
||||
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
|
||||
printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
|
||||
printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch);
|
||||
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
|
||||
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
|
||||
if (llama_supports_mlock()) {
|
||||
printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n");
|
||||
}
|
||||
@@ -2214,6 +2212,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
|
||||
printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
|
||||
printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n");
|
||||
printf(" or for intermediate results and KV (with split-mode = row)\n");
|
||||
printf(" -nkvo, --no-kv-offload\n");
|
||||
printf(" disable KV offload\n");
|
||||
}
|
||||
printf(" -m FNAME, --model FNAME\n");
|
||||
printf(" model path (default: %s)\n", params.model.c_str());
|
||||
@@ -2499,6 +2499,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
|
||||
"See main README.md for information on enabling GPU BLAS support",
|
||||
{{"n_gpu_layers", params.n_gpu_layers}});
|
||||
}
|
||||
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
|
||||
params.no_kv_offload = true;
|
||||
} else if (arg == "--split-mode" || arg == "-sm") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
@@ -3567,6 +3569,7 @@ int main(int argc, char ** argv) {
|
||||
sigemptyset (&sigint_action.sa_mask);
|
||||
sigint_action.sa_flags = 0;
|
||||
sigaction(SIGINT, &sigint_action, NULL);
|
||||
sigaction(SIGTERM, &sigint_action, NULL);
|
||||
#elif defined (_WIN32)
|
||||
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
|
||||
return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
|
||||
|
||||
@@ -1114,7 +1114,10 @@ def start_server_background(context):
|
||||
server_args.append('--verbose')
|
||||
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
|
||||
server_args.extend(['--log-format', "text"])
|
||||
print(f"starting server with: {context.server_path} {server_args}")
|
||||
|
||||
args = [str(arg) for arg in [context.server_path, *server_args]]
|
||||
print(f"bench: starting server with: {' '.join(args)}")
|
||||
|
||||
flags = 0
|
||||
if 'nt' == os.name:
|
||||
flags |= subprocess.DETACHED_PROCESS
|
||||
@@ -1130,16 +1133,14 @@ def start_server_background(context):
|
||||
[str(arg) for arg in [context.server_path, *server_args]],
|
||||
**pkwargs)
|
||||
|
||||
def log_stdout(process):
|
||||
for line in iter(process.stdout.readline, b''):
|
||||
print(line.decode('utf-8'), end='')
|
||||
thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,))
|
||||
def server_log(in_stream, out_stream):
|
||||
for line in iter(in_stream.readline, b''):
|
||||
print(line.decode('utf-8'), end='', file=out_stream)
|
||||
|
||||
thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
|
||||
thread_stdout.start()
|
||||
|
||||
def log_stderr(process):
|
||||
for line in iter(process.stderr.readline, b''):
|
||||
print(line.decode('utf-8'), end='', file=sys.stderr)
|
||||
thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,))
|
||||
thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
|
||||
thread_stderr.start()
|
||||
|
||||
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
|
||||
|
||||
@@ -49,12 +49,23 @@ extern bool server_log_json;
|
||||
#define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
|
||||
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
|
||||
|
||||
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra);
|
||||
|
||||
template <typename T>
|
||||
static T json_value(const json &body, const std::string &key, const T &default_value) {
|
||||
// Fallback null to default value
|
||||
return body.contains(key) && !body.at(key).is_null()
|
||||
? body.value(key, default_value)
|
||||
: default_value;
|
||||
if (body.contains(key) && !body.at(key).is_null()){
|
||||
try {
|
||||
return body.value(key, default_value);
|
||||
}
|
||||
catch (nlohmann::json_abi_v3_11_3::detail::type_error const&){
|
||||
std::string message = "Wrong type supplied for parameter '" + key + "'. Expected '" + typeid(default_value).name() + "', using default value.";
|
||||
server_log("WARN", __func__, __LINE__, message.c_str(), body);
|
||||
return default_value;
|
||||
}
|
||||
} else {
|
||||
return default_value;
|
||||
}
|
||||
}
|
||||
|
||||
static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
|
||||
@@ -556,6 +567,15 @@ static std::vector<json> format_partial_response_oaicompat(json result, const st
|
||||
{"model", modelname},
|
||||
{"object", "chat.completion.chunk"}
|
||||
};
|
||||
if (!finish_reason.empty()) {
|
||||
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
|
||||
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
|
||||
ret.push_back({"usage", json {
|
||||
{"completion_tokens", num_tokens_predicted},
|
||||
{"prompt_tokens", num_prompt_tokens},
|
||||
{"total_tokens", num_tokens_predicted + num_prompt_tokens}
|
||||
}});
|
||||
}
|
||||
|
||||
return std::vector<json>({ret});
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user