llama.cpp/examples/server/tests/unit/test_chat_completion.py

import pytest
from openai import OpenAI
from utils import *

server = ServerPreset.tinyllama2()


@pytest.fixture(scope="module", autouse=True)
def create_server():
    global server
    server = ServerPreset.tinyllama2()


@pytest.mark.parametrize(
    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
    [
        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
    ]
)
def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
    global server
    server.start()
    res = server.make_request("POST", "/chat/completions", data={
        "model": model,
        "max_tokens": max_tokens,
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ],
    })
    assert res.status_code == 200
    assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
    assert res.body["model"] == model if model is not None else server.model_alias
    assert res.body["usage"]["prompt_tokens"] == n_prompt
    assert res.body["usage"]["completion_tokens"] == n_predicted
    choice = res.body["choices"][0]
    assert "assistant" == choice["message"]["role"]
    assert match_regex(re_content, choice["message"]["content"])
    assert choice["finish_reason"] == finish_reason


@pytest.mark.parametrize(
    "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
    [
        ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
        ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
    ]
)
def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
    global server
    server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL
    server.start()
    res = server.make_stream_request("POST", "/chat/completions", data={
        "max_tokens": max_tokens,
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ],
        "stream": True,
    })
    content = ""
    last_cmpl_id = None
    for data in res:
        choice = data["choices"][0]
        assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
        if last_cmpl_id is None:
            last_cmpl_id = data["id"]
        assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
        if choice["finish_reason"] in ["stop", "length"]:
            assert data["usage"]["prompt_tokens"] == n_prompt
            assert data["usage"]["completion_tokens"] == n_predicted
            assert "content" not in choice["delta"]
            assert match_regex(re_content, content)
            assert choice["finish_reason"] == finish_reason
        else:
            assert choice["finish_reason"] is None
            content += choice["delta"]["content"]


def test_chat_completion_with_openai_library():
    global server
    server.start()
    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
    res = client.chat.completions.create(
        model="gpt-3.5-turbo-instruct",
        messages=[
            {"role": "system", "content": "Book"},
            {"role": "user", "content": "What is the best book"},
        ],
        max_tokens=8,
        seed=42,
        temperature=0.8,
    )
    assert res.choices[0].finish_reason == "length"
    assert res.choices[0].message.content is not None
    assert match_regex("(Suddenly)+", res.choices[0].message.content)


@pytest.mark.parametrize("response_format,n_predicted,re_content", [
    ({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""),
    ({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"),
    ({"type": "json_object"}, 10, "(\\{|John)+"),
    ({"type": "sound"}, 0, None),
    # invalid response format (expected to fail)
    ({"type": "json_object", "schema": 123}, 0, None),
    ({"type": "json_object", "schema": {"type": 123}}, 0, None),
    ({"type": "json_object", "schema": {"type": "hiccup"}}, 0, None),
])
def test_completion_with_response_format(response_format: dict, n_predicted: int, re_content: str | None):
    global server
    server.start()
    res = server.make_request("POST", "/chat/completions", data={
        "max_tokens": n_predicted,
        "messages": [
            {"role": "system", "content": "You are a coding assistant."},
            {"role": "user", "content": "Write an example"},
        ],
        "response_format": response_format,
    })
    if re_content is not None:
        assert res.status_code == 200
        choice = res.body["choices"][0]
        assert match_regex(re_content, choice["message"]["content"])
    else:
        assert res.status_code != 200
        assert "error" in res.body


@pytest.mark.parametrize("messages", [
    None,
    "string",
    [123],
    [{}],
    [{"role": 123}],
    [{"role": "system", "content": 123}],
    # [{"content": "hello"}], # TODO: should not be a valid case
    [{"role": "system", "content": "test"}, {}],
])
def test_invalid_chat_completion_req(messages):
    global server
    server.start()
    res = server.make_request("POST", "/chat/completions", data={
        "messages": messages,
    })
    assert res.status_code == 400 or res.status_code == 500
    assert "error" in res.body


def test_chat_completion_with_timings_per_token():
    global server
    server.start()
    res = server.make_stream_request("POST", "/chat/completions", data={
        "max_tokens": 10,
        "messages": [{"role": "user", "content": "test"}],
        "stream": True,
        "timings_per_token": True,
    })
    for data in res:
        assert "timings" in data
        assert "prompt_per_second" in data["timings"]
        assert "predicted_per_second" in data["timings"]
        assert "predicted_n" in data["timings"]
        assert data["timings"]["predicted_n"] <= 10


def test_logprobs():
    global server
    server.start()
    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
    res = client.chat.completions.create(
        model="gpt-3.5-turbo-instruct",
        temperature=0.0,
        messages=[
            {"role": "system", "content": "Book"},
            {"role": "user", "content": "What is the best book"},
        ],
        max_tokens=5,
        logprobs=True,
        top_logprobs=10,
    )
    output_text = res.choices[0].message.content
    aggregated_text = ''
    assert res.choices[0].logprobs is not None
    assert res.choices[0].logprobs.content is not None
    for token in res.choices[0].logprobs.content:
        aggregated_text += token.token
        assert token.logprob <= 0.0
        assert token.bytes is not None
        assert len(token.top_logprobs) > 0
    assert aggregated_text == output_text


def test_logprobs_stream():
    global server
    server.start()
    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
    res = client.chat.completions.create(
        model="gpt-3.5-turbo-instruct",
        temperature=0.0,
        messages=[
            {"role": "system", "content": "Book"},
            {"role": "user", "content": "What is the best book"},
        ],
        max_tokens=5,
        logprobs=True,
        top_logprobs=10,
        stream=True,
    )
    output_text = ''
    aggregated_text = ''
    for data in res:
        choice = data.choices[0]
        if choice.finish_reason is None:
            if choice.delta.content:
                output_text += choice.delta.content
            assert choice.logprobs is not None
            assert choice.logprobs.content is not None
            for token in choice.logprobs.content:
                aggregated_text += token.token
                assert token.logprob <= 0.0
                assert token.bytes is not None
                assert token.top_logprobs is not None
                assert len(token.top_logprobs) > 0
    assert aggregated_text == output_text