* server : refactoring (wip) * server : remove llava/clip objects from build * server : fix empty prompt handling + all slots idle logic * server : normalize id vars * server : code style * server : simplify model chat template validation * server : code style * server : minor * llama : llama_chat_apply_template support null buf * server : do not process embedding requests when disabled * server : reorganize structs and enums + naming fixes * server : merge oai.hpp in utils.hpp * server : refactor system prompt update at start * server : disable cached prompts with self-extend * server : do not process more than n_batch tokens per iter * server: tests: embeddings use a real embeddings model (#5908) * server, tests : bump batch to fit 1 embedding prompt * server: tests: embeddings fix build type Debug is randomly failing (#5911) * server: tests: embeddings, use different KV Cache size * server: tests: embeddings, fixed prompt do not exceed n_batch, increase embedding timeout, reduce number of concurrent embeddings * server: tests: embeddings, no need to wait for server idle as it can timout * server: refactor: clean up http code (#5912) * server : avoid n_available var ggml-ci * server: refactor: better http codes * server : simplify json parsing + add comment about t_last * server : rename server structs * server : allow to override FQDN in tests ggml-ci * server : add comments --------- Co-authored-by: Pierrick Hymbert <pierrick.hymbert@gmail.com> |
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.. | ||
features | ||
README.md | ||
requirements.txt | ||
tests.sh |
Server tests
Python based server tests scenario using BDD and behave:
- issues.feature Pending issues scenario
- parallel.feature Scenario involving multi slots and concurrent requests
- security.feature Security, CORS and API Key
- server.feature Server base scenario: completion, embedding, tokenization, etc...
Tests target GitHub workflows job runners with 4 vCPU.
Requests are using aiohttp, asyncio based http client.
Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail.
To mitigate it, you can increase values in n_predict
, kv_size
.
Install dependencies
pip install -r requirements.txt
Run tests
- Build the server
cd ../../..
mkdir build
cd build
cmake ../
cmake --build . --target server
- Start the test:
./tests.sh
It's possible to override some scenario steps values with environment variables:
variable | description |
---|---|
PORT |
context.server_port to set the listening port of the server during scenario, default: 8080 |
LLAMA_SERVER_BIN_PATH |
to change the server binary path, default: ../../../build/bin/server |
DEBUG |
"ON" to enable steps and server verbose mode --verbose |
SERVER_LOG_FORMAT_JSON |
if set switch server logs to json format |
N_GPU_LAYERS |
number of model layers to offload to VRAM -ngl --n-gpu-layers |
Run @bug, @wip or @wrong_usage annotated scenario
Feature or Scenario must be annotated with @llama.cpp
to be included in the default scope.
@bug
annotation aims to link a scenario with a GitHub issue.@wrong_usage
are meant to show user issue that are actually an expected behavior@wip
to focus on a scenario working in progress@slow
heavy test, disabled by default
To run a scenario annotated with @bug
, start:
DEBUG=ON ./tests.sh --no-skipped --tags bug
After changing logic in steps.py
, ensure that @bug
and @wrong_usage
scenario are updated.
./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile"