llama.cpp/examples/server/tests
compilade f98eb31c51
convert-hf : save memory with lazy evaluation (#7075)
* convert-hf : begin refactoring write_tensor

* convert : upgrade to sentencepiece v0.2.0

* convert-hf : remove unused n_dims in extra_*_tensors

* convert-hf : simplify MoE weights stacking

* convert-hf : flake8 linter doesn't like semicolons

* convert-hf : allow unusual model part names

For example, loading `model-00001-of-00001.safetensors` now works.

* convert-hf : fix stacking MoE expert tensors

`torch.stack` and `torch.cat` don't do the same thing.

* convert-hf : fix Mamba conversion

Tested to work even with a SentencePiece-based tokenizer.

* convert : use a string for the SentencePiece tokenizer path

* convert-hf : display tensor shape

* convert-hf : convert norms to f32 by default

* convert-hf : sort model part names

`os.listdir` is said to list files in arbitrary order.
Sorting the file names should let "model-00009-of-00042.safetensors"
be loaded before "model-00010-of-00042.safetensors".

* convert-hf : use an ABC for Model again

It seems Protocol can't be used as a statically type-checked ABC,
because its subclasses also can't be instantiated. (why did it seem to work?)

At least there's still a way to throw an error when forgetting to define
the `model_arch` property of any registered Model subclasses.

* convert-hf : use a plain class for Model, and forbid direct instantiation

There are no abstract methods used anyway,
so using ABC isn't really necessary.

* convert-hf : more consistent formatting of cmdline args

* convert-hf : align the message logged for converted tensors

* convert-hf : fix Refact conversion

* convert-hf : save memory with lazy evaluation

* convert-hf : flake8 doesn't like lowercase L as a variable name

* convert-hf : remove einops requirement for InternLM2

* convert-hf : faster model parts loading

Instead of pre-loading them all into a dict, iterate on the tensors
in the model parts progressively as needed in Model.write_tensors

Conversion for some architectures relies on checking for the presence
of specific tensor names, so for multi-part models, the weight map is read
from the relevant json file to quickly get these names up-front.

* convert-hf : minor changes for consistency

* gguf-py : add tqdm as a dependency

It's small, and used for a progress bar
in GGUFWriter.write_tensors_to_file
2024-05-08 18:16:38 -04:00
..
features convert-hf : save memory with lazy evaluation (#7075) 2024-05-08 18:16:38 -04:00
README.md doc : server tests require llama to be built with curl enabled (#6788) 2024-04-20 18:29:50 +02:00
requirements.txt server tests : more pythonic process management; fix bare except: (#6146) 2024-03-20 06:33:49 +01:00
tests.sh tests : minor bash stuff (#6902) 2024-04-25 14:27:20 +03:00

Server tests

Python based server tests scenario using BDD and behave:

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

  1. Build the server
cd ../../..
mkdir build
cd build
cmake -DLLAMA_CURL=ON ../
cmake --build . --target server
  1. 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 --stop

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"