History

Pierrick Hymbert 9731134296 server: tests: passkey challenge / self-extend with context shift demo (#5832 ) * server: tests: add models endpoint scenario * server: /v1/models add some metadata * server: tests: add debug field in context before scenario * server: tests: download model from HF, add batch size * server: tests: add passkey test * server: tests: add group attention params * server: do not truncate prompt tokens if self-extend through group attention is enabled * server: logs: do not truncate log values * server: tests - passkey - first good working value of nga * server: tests: fix server timeout * server: tests: fix passkey, add doc, fix regex content matching, fix timeout * server: tests: fix regex content matching * server: tests: schedule slow tests on master * server: metrics: fix when no prompt processed * server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1 * server: tests: increase timeout for completion * server: tests: keep only the PHI-2 test * server: tests: passkey add a negative test		2024-03-02 22:00:14 +01:00
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features	server: tests: passkey challenge / self-extend with context shift demo (#5832 )	2024-03-02 22:00:14 +01:00
README.md	server: tests: passkey challenge / self-extend with context shift demo (#5832 )	2024-03-02 22:00:14 +01:00
requirements.txt	server: tests: passkey challenge / self-extend with context shift demo (#5832 )	2024-03-02 22:00:14 +01:00
tests.sh	server: tests: passkey challenge / self-extend with context shift demo (#5832 )	2024-03-02 22:00:14 +01:00

README.md

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"