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* server : fix logprobs, make it openai-compatible * update docs * add std::log * return pre-sampling p * sort before apply softmax * add comment * fix test * set p for sampled token * update docs * add --multi-token-probs * update docs * add `post_sampling_probs` option * update docs [no ci] * remove --multi-token-probs * "top_probs" with "post_sampling_probs" * resolve review comments * rename struct token_prob to prob_info * correct comment placement * fix setting prob for sampled token |
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| unit | ||
| .gitignore | ||
| conftest.py | ||
| README.md | ||
| requirements.txt | ||
| tests.sh | ||
| utils.py | ||
Server tests
Python based server tests scenario using pytest.
Tests target GitHub workflows job runners with 4 vCPU.
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 ../../..
cmake -B build -DLLAMA_CURL=ON
cmake --build build --target llama-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/llama-server |
DEBUG |
to enable steps and server verbose mode --verbose |
N_GPU_LAYERS |
number of model layers to offload to VRAM -ngl --n-gpu-layers |
To run slow tests:
SLOW_TESTS=1 ./tests.sh
To run with stdout/stderr display in real time (verbose output, but useful for debugging):
DEBUG=1 ./tests.sh -s -v -x
Hint: You can compile and run test in single command, useful for local developement:
cmake --build build -j --target llama-server && ./examples/server/tests/tests.sh
To see all available arguments, please refer to pytest documentation