server: init functional tests (#5566)

* server: tests: init scenarios
 - health and slots endpoints
 - completion endpoint
 - OAI compatible chat completion requests w/ and without streaming
 - completion multi users scenario
 - multi users scenario on OAI compatible endpoint with streaming
 - multi users with total number of tokens to predict exceeds the KV Cache size
 - server wrong usage scenario, like in Infinite loop of "context shift" #3969
 - slots shifting
 - continuous batching
 - embeddings endpoint
 - multi users embedding endpoint: Segmentation fault #5655
 - OpenAI-compatible embeddings API
 - tokenize endpoint
 - CORS and api key scenario

* server: CI GitHub workflow


---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

1 files changed, 709 insertions, 0 deletions

diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py
new file mode 100644
index 00000000..50f2b641
--- /dev/null
+++ b/examples/server/tests/features/steps/steps.py
@@ -0,0 +1,709 @@
+import asyncio
+import json
+import os
+import re
+import socket
+import subprocess
+import time
+from contextlib import closing
+from re import RegexFlag
+
+import aiohttp
+import openai
+from behave import step
+from behave.api.async_step import async_run_until_complete
+
+
+@step(u"a server listening on {server_fqdn}:{server_port}")
+def step_server_config(context, server_fqdn, server_port):
+    context.server_fqdn = server_fqdn
+    context.server_port = int(server_port)
+    if 'PORT' in os.environ:
+        context.server_port = int(os.environ['PORT'])
+        print(f"$PORT set, overriding server port with to {context.server_port}")
+
+    context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
+
+    context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
+    context.model_alias = None
+    context.n_ctx = None
+    context.n_predict = None
+    context.n_server_predict = None
+    context.n_slots = None
+    context.server_api_key = None
+    context.server_continuous_batching = False
+    context.server_embeddings = False
+    context.server_seed = None
+    context.user_api_key = None
+
+    context.tasks_result = []
+    context.concurrent_tasks = []
+    context.prompts = []
+
+
+@step(u'a model file {model_file}')
+def step_model_file(context, model_file):
+    context.model_file = model_file
+
+
+@step(u'a model alias {model_alias}')
+def step_model_alias(context, model_alias):
+    context.model_alias = model_alias
+
+
+@step(u'{seed} as server seed')
+def step_seed(context, seed):
+    context.server_seed = int(seed)
+
+
+@step(u'{n_ctx} KV cache size')
+def step_n_ctx(context, n_ctx):
+    context.n_ctx = int(n_ctx)
+
+
+@step(u'{n_slots} slots')
+def step_n_slots(context, n_slots):
+    context.n_slots = int(n_slots)
+
+
+@step(u'{n_predict} server max tokens to predict')
+def step_server_n_predict(context, n_predict):
+    context.n_server_predict = int(n_predict)
+
+
+@step(u'continuous batching')
+def step_server_continuous_batching(context):
+    context.server_continuous_batching = True
+
+
+@step(u'embeddings extraction')
+def step_server_embeddings(context):
+    context.server_embeddings = True
+
+
+@step(u"the server is starting")
+def step_start_server(context):
+    start_server_background(context)
+    attempts = 0
+    while True:
+        with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+            result = sock.connect_ex((context.server_fqdn, context.server_port))
+            if result == 0:
+                print("\x1b[33;46mserver started!\x1b[0m")
+                return
+            attempts += 1
+            if attempts > 20:
+                assert False, "server not started"
+            print(f"waiting for server to start, connect error code = {result}...")
+            time.sleep(0.1)
+
+
+@step(u"the server is {expecting_status}")
+@async_run_until_complete
+async def step_wait_for_the_server_to_be_started(context, expecting_status):
+    match expecting_status:
+        case 'healthy':
+            await wait_for_health_status(context, context.base_url, 200, 'ok')
+
+        case 'ready' | 'idle':
+            await wait_for_health_status(context, context.base_url, 200, 'ok',
+                                         params={'fail_on_no_slot': 0, 'include_slots': 0},
+                                         slots_idle=context.n_slots,
+                                         slots_processing=0,
+                                         expected_slots=[{'id': slot_id, 'state': 0}
+                                                         for slot_id in range(context.n_slots)])
+        case 'busy':
+            await wait_for_health_status(context, context.base_url, 503,
+                                         'no slot available',
+                                         params={'fail_on_no_slot': 0, 'include_slots': 0},
+                                         slots_idle=0,
+                                         slots_processing=context.n_slots,
+                                         expected_slots=[{'id': slot_id, 'state': 1}
+                                                         for slot_id in range(context.n_slots)])
+        case _:
+            assert False, "unknown status"
+
+
+@step(u'all slots are {expected_slot_status_string}')
+@async_run_until_complete
+async def step_all_slots_status(context, expected_slot_status_string):
+    match expected_slot_status_string:
+        case 'idle':
+            expected_slot_status = 0
+        case 'busy':
+            expected_slot_status = 1
+        case _:
+            assert False, "unknown status"
+
+    expected_slots = [{'id': slot_id, 'state': expected_slot_status}
+                      for slot_id in range(context.n_slots)]
+    await request_slots_status(context, expected_slots)
+
+
+@step(u'a completion request with {api_error} api error')
+@async_run_until_complete
+async def step_request_completion(context, api_error):
+    expect_api_error = api_error == 'raised'
+    completion = await request_completion(context.prompts.pop(),
+                                          context.base_url,
+                                          debug=context.debug,
+                                          n_predict=context.n_predict,
+                                          server_seed=context.server_seed,
+                                          expect_api_error=expect_api_error,
+                                          user_api_key=context.user_api_key)
+    context.tasks_result.append(completion)
+    if context.debug:
+        print(f"Completion response: {completion}")
+    if expect_api_error:
+        assert completion == 401, f"completion must be an 401 status code: {completion}"
+
+
+@step(u'{predicted_n} tokens are predicted matching {re_content}')
+def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
+    assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content)
+
+
+@step(u'{predicted_n} tokens are predicted')
+def step_n_tokens_predicted(context, predicted_n):
+    assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n))
+
+
+@step(u'a user prompt {user_prompt}')
+def step_user_prompt(context, user_prompt):
+    context.prompts.append(user_prompt)
+
+
+@step(u'a system prompt {system_prompt}')
+def step_system_prompt(context, system_prompt):
+    context.system_prompt = system_prompt
+
+
+@step(u'a model {model}')
+def step_model(context, model):
+    context.model = model
+
+
+@step(u'{max_tokens} max tokens to predict')
+def step_max_tokens(context, max_tokens):
+    context.n_predict = int(max_tokens)
+
+
+@step(u'streaming is {enable_streaming}')
+def step_streaming(context, enable_streaming):
+    context.enable_streaming = enable_streaming == 'enabled'
+
+
+@step(u'a user api key {user_api_key}')
+def step_user_api_key(context, user_api_key):
+    context.user_api_key = user_api_key
+
+
+@step(u'no user api key')
+def step_no_user_api_key(context):
+    context.user_api_key = None
+
+
+@step(u'a user api key ')
+def step_no_user_api_key_space(context):
+    context.user_api_key = None
+
+
+@step(u'a server api key {server_api_key}')
+def step_server_api_key(context, server_api_key):
+    context.server_api_key = server_api_key
+
+
+@step(u'an OAI compatible chat completions request with {api_error} api error')
+@async_run_until_complete
+async def step_oai_chat_completions(context, api_error):
+    if context.debug:
+        print(f"Submitting OAI compatible completions request...")
+    expect_api_error = api_error == 'raised'
+    completion = await oai_chat_completions(context.prompts.pop(),
+                                            context.system_prompt,
+                                            context.base_url,
+                                            False,
+                                            model=context.model if hasattr(context, 'model') else None,
+
+                                            n_predict=context.n_predict
+                                            if hasattr(context, 'n_predict') else None,
+
+                                            enable_streaming=context.enable_streaming
+                                            if hasattr(context, 'enable_streaming') else None,
+
+                                            server_seed=context.server_seed
+                                            if hasattr(context, 'server_seed') else None,
+
+                                            user_api_key=context.user_api_key
+                                            if hasattr(context, 'user_api_key') else None,
+
+                                            expect_api_error=expect_api_error)
+    context.tasks_result.append(completion)
+    if context.debug:
+        print(f"Completion response: {completion}")
+    if expect_api_error:
+        assert completion == 401, f"completion must be an 401 status code: {completion}"
+
+    if context.debug:
+        print(f"Completion response: {completion}")
+
+
+@step(u'a prompt')
+def step_a_prompt(context):
+    context.prompts.append(context.text)
+
+
+@step(u'a prompt {prompt}')
+def step_a_prompt_prompt(context, prompt):
+    context.prompts.append(prompt)
+
+
+@step(u'concurrent completion requests')
+@async_run_until_complete()
+async def step_concurrent_completion_requests(context):
+    await concurrent_completion_requests(context,
+                                         request_completion,
+                                         # prompt is inserted automatically
+                                         context.base_url,
+                                         debug=context.debug,
+                                         n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
+                                         server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
+                                         user_api_key=context.user_api_key if hasattr(context,
+                                                                                      'user_api_key') else None)
+
+
+@step(u'concurrent OAI completions requests')
+@async_run_until_complete
+async def step_oai_chat_completions(context):
+    await concurrent_completion_requests(context, oai_chat_completions,
+                                         # user_prompt is inserted automatically
+                                         context.system_prompt,
+                                         context.base_url,
+                                         True,  # async_client
+                                         model=context.model
+                                         if hasattr(context, 'model') else None,
+                                         n_predict=context.n_predict
+                                         if hasattr(context, 'n_predict') else None,
+                                         enable_streaming=context.enable_streaming
+                                         if hasattr(context, 'enable_streaming') else None,
+                                         server_seed=context.server_seed
+                                         if hasattr(context, 'server_seed') else None,
+                                         user_api_key=context.user_api_key
+                                         if hasattr(context, 'user_api_key') else None)
+
+
+@step(u'all prompts are predicted')
+@async_run_until_complete
+async def step_all_prompts_are_predicted(context):
+    await all_prompts_are_predicted(context)
+
+
+@step(u'all prompts are predicted with {n_predict} tokens')
+@async_run_until_complete
+async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict):
+    expected_predicted_n = int(n_predict)
+    await all_prompts_are_predicted(context, expected_predicted_n)
+
+
+async def all_prompts_are_predicted(context, expected_predicted_n=None):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions > 0
+    for i in range(n_completions):
+        assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n)
+    assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
+
+
+@step(u'embeddings are computed for')
+@async_run_until_complete
+async def step_compute_embedding(context):
+    content = context.text
+    base_url = context.base_url
+    context.embeddings = await request_embedding(content, base_url)
+
+
+@step(u'embeddings are generated')
+def step_assert_embeddings(context):
+    assert_embeddings(context.embeddings)
+
+
+@step(u'an OAI compatible embeddings computation request for')
+def step_oai_compute_embedding(context):
+    openai.api_key = 'nope'  # openai client always expects an api_keu
+    if context.user_api_key is not None:
+        openai.api_key = context.user_api_key
+    openai.api_base = f'{context.base_url}/v1'
+    embeddings = openai.Embedding.create(
+        model=context.model,
+        input=context.text,
+    )
+    context.embeddings = embeddings
+
+
+@step(u'concurrent embedding requests')
+@async_run_until_complete()
+async def step_concurrent_embedding_requests(context):
+    await concurrent_completion_requests(context,
+                                         request_embedding,
+                                         # prompt is inserted automatically
+                                         context.base_url)
+
+
+@step(u'all embeddings are generated')
+@async_run_until_complete()
+async def all_embeddings_are_generated(context):
+    n_embedding_requests = await gather_tasks_results(context)
+    assert n_embedding_requests > 0
+    for i in range(n_embedding_requests):
+        assert_embeddings(context.tasks_result.pop())
+
+
+@step(u'tokenizing')
+@async_run_until_complete
+async def step_tokenize(context):
+    context.tokenized_text = context.text
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/tokenize',
+                                json={
+                                    "content": context.tokenized_text,
+                                }) as response:
+            assert response.status == 200
+            tokenize_json = await response.json()
+            context.tokens = tokenize_json['tokens']
+
+
+@step(u'tokens can be detokenize')
+@async_run_until_complete
+async def step_detokenize(context):
+    assert len(context.tokens) > 0
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/detokenize',
+                                json={
+                                    "tokens": context.tokens,
+                                }) as response:
+            assert response.status == 200
+            detokenize_json = await response.json()
+            # SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15
+            assert context.tokenized_text == detokenize_json['content'].strip()
+
+
+@step(u'an OPTIONS request is sent from {origin}')
+@async_run_until_complete
+async def step_options_request(context, origin):
+    async with aiohttp.ClientSession() as session:
+        async with session.options(f'{context.base_url}/v1/chat/completions',
+                                   headers={"Origin": origin}) as response:
+            assert response.status == 200
+            context.options_response = response
+
+
+@step(u'CORS header {cors_header} is set to {cors_header_value}')
+def step_check_options_header_value(context, cors_header, cors_header_value):
+    assert context.options_response.headers[cors_header] == cors_header_value
+
+
+async def concurrent_completion_requests(context, f_completion, *args, **kwargs):
+    n_prompts = len(context.prompts)
+    if context.debug:
+        print(f"starting {n_prompts} concurrent completion requests...")
+    assert n_prompts > 0
+    for prompt_no in range(n_prompts):
+        shifted_args = [context.prompts.pop(), *args]
+        context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
+    await asyncio.sleep(0.1)
+
+
+async def request_completion(prompt,
+                             base_url,
+                             debug=False,
+                             n_predict=None,
+                             server_seed=None,
+                             expect_api_error=None,
+                             user_api_key=None):
+    if debug:
+        print(f"Sending completion request: {prompt}")
+    origin = "my.super.domain"
+    headers = {
+        'Origin': origin
+    }
+    if user_api_key is not None:
+        if debug:
+            print(f"Set user_api_key: {user_api_key}")
+        headers['Authorization'] = f'Bearer {user_api_key}'
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{base_url}/completion',
+                                json={
+                                    "prompt": prompt,
+                                    "n_predict": int(n_predict) if n_predict is not None else -1,
+                                    "seed": server_seed if server_seed is not None else 42
+                                },
+                                headers=headers) as response:
+            if expect_api_error is None or not expect_api_error:
+                assert response.status == 200
+                assert response.headers['Access-Control-Allow-Origin'] == origin
+                return await response.json()
+            else:
+                return response.status
+
+
+async def oai_chat_completions(user_prompt,
+                               system_prompt,
+                               base_url,
+                               async_client,
+                               debug=False,
+                               model=None,
+                               n_predict=None,
+                               enable_streaming=None,
+                               server_seed=None,
+                               user_api_key=None,
+                               expect_api_error=None):
+    if debug:
+        print(f"Sending OAI Chat completions request: {user_prompt}")
+    # openai client always expects an api key
+    user_api_key = user_api_key if user_api_key is not None else 'nope'
+    seed = server_seed if server_seed is not None else 42
+    enable_streaming = enable_streaming if enable_streaming is not None else False
+    payload = {
+        "messages": [
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+            {
+                "role": "user",
+                "content": user_prompt,
+            }
+        ],
+        "model": model,
+        "max_tokens": n_predict,
+        "stream": enable_streaming,
+        "seed": seed
+    }
+    completion_response = {
+        'content': '',
+        'timings': {
+            'predicted_n': 0
+        }
+    }
+    if async_client:
+        origin = 'llama.cpp'
+        headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
+        async with aiohttp.ClientSession() as session:
+            async with session.post(f'{base_url}/v1/chat/completions',
+                                    json=payload,
+                                    headers=headers) as response:
+                if enable_streaming:
+                    assert response.status == 200
+                    assert response.headers['Access-Control-Allow-Origin'] == origin
+                    assert response.headers['Content-Type'] == "text/event-stream"
+                    event_received = True
+                    while event_received:
+                        event_received = False
+                        async for line_in_bytes in response.content:
+                            line = line_in_bytes.decode('utf8')
+                            line = line.rstrip('\n').rstrip('\r')
+                            if line == '':
+                                continue
+                            event_data = line.split(': ', 1)
+                            assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```'
+                            chunk_raw = event_data[1]
+
+                            chunk = json.loads(chunk_raw)
+                            assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```"
+                            delta = chunk['choices'][0]['delta']
+                            if 'content' in delta:
+                                completion_response['content'] += delta['content']
+                                completion_response['timings']['predicted_n'] += 1
+                else:
+                    if expect_api_error is None or not expect_api_error:
+                        assert response.status == 200
+                        assert response.headers['Access-Control-Allow-Origin'] == origin
+                        assert response.headers['Content-Type'] == "application/json; charset=utf-8"
+                        chat_completion_raw = await response.json()
+                        completion_response = {
+                            'content': chat_completion_raw['choices'][0]['message'],
+                            'timings': {
+                                'predicted_n': chat_completion_raw['usage']['completion_tokens']
+                            }
+                        }
+                    else:
+                        return response.status
+    else:
+        try:
+            openai.api_key = user_api_key
+            openai.api_base = f'{base_url}/v1/chat'
+            chat_completion = openai.Completion.create(
+                messages=payload['messages'],
+                model=model,
+                max_tokens=n_predict,
+                stream=enable_streaming,
+                seed=seed
+            )
+        except openai.error.APIError as e:
+            if expect_api_error is not None and expect_api_error:
+                return 401
+            else:
+                assert False, f'error raised: {e}'
+
+        if enable_streaming:
+            for chunk in chat_completion:
+                assert len(chunk.choices) == 1
+                delta = chunk.choices[0].delta
+                if 'content' in delta:
+                    completion_response['content'] += delta['content']
+                    completion_response['timings']['predicted_n'] += 1
+        else:
+            assert len(chat_completion.choices) == 1
+            completion_response = {
+                'content': chat_completion.choices[0].message.content,
+                'timings': {
+                    'predicted_n': chat_completion.usage.completion_tokens
+                }
+            }
+    if debug:
+        print("OAI response formatted to llama.cpp:", completion_response)
+    return completion_response
+
+
+async def request_embedding(content, base_url):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{base_url}/embedding',
+                                json={
+                                    "content": content,
+                                }) as response:
+            assert response.status == 200
+            response_json = await response.json()
+            return response_json['embedding']
+
+
+def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
+    content = completion_response['content']
+    n_predicted = completion_response['timings']['predicted_n']
+    assert len(content) > 0, "no token predicted"
+    if expected_predicted_n is not None:
+        assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
+                                                     f' {n_predicted} <> {expected_predicted_n}')
+    if re_content is not None:
+        re_content = '^.*' + re_content.replace('<or>', '|') + '.*$'
+        assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), (
+            f'invalid tokens predicted:'
+            f' ```\n{content}\n``` do not match /{re_content}/')
+
+
+async def gather_tasks_results(context):
+    n_tasks = len(context.concurrent_tasks)
+    if context.debug:
+        print(f"Waiting for all {n_tasks} tasks results...")
+    for task_no in range(n_tasks):
+        context.tasks_result.append(await context.concurrent_tasks.pop())
+    n_completions = len(context.tasks_result)
+    return n_completions
+
+
+async def wait_for_health_status(context,
+                                 base_url,
+                                 expected_http_status_code,
+                                 expected_health_status,
+                                 params=None,
+                                 slots_idle=None,
+                                 slots_processing=None,
+                                 expected_slots=None):
+    if context.debug:
+        print(f"Starting checking for health for expected_health_status={expected_health_status}")
+    timeout = 3  # seconds
+    interval = 0.5
+    counter = 0
+    async with aiohttp.ClientSession() as session:
+        while True:
+            async with await session.get(f'{base_url}/health', params=params) as health_response:
+                status_code = health_response.status
+                health = await health_response.json()
+                if context.debug:
+                    print(f"HEALTH - response for expected health status='{expected_health_status}' on "
+                          f"'{base_url}/health'?{params} is {health}")
+                if (status_code == expected_http_status_code
+                        and health['status'] == expected_health_status
+                        and (slots_idle is None or health['slots_idle'] == slots_idle)
+                        and (slots_processing is None or health['slots_processing'] == slots_processing)):
+                    if expected_slots is not None:
+                        assert_slots_status(health['slots'], expected_slots)
+                    return
+                if (status_code == expected_http_status_code
+                        and health['status'] == expected_health_status
+                        and (slots_idle is None or health['slots_idle'] == slots_idle)
+                        and (slots_processing is None or health['slots_processing'] == slots_processing)):
+                    if expected_slots is not None:
+                        assert_slots_status(health['slots'], expected_slots)
+                    return
+            await asyncio.sleep(interval)
+
+            counter += interval
+            if counter >= timeout:
+                # Sometimes health requests are triggered after completions are predicted
+                if expected_http_status_code == 503:
+                    if len(context.tasks_result) == 0:
+                        print("\x1b[5;37;43mWARNING: forcing concurrent tasks,"
+                              " busy health check missed, probably too fast inference\x1b[0m")
+                        n_completions = await gather_tasks_results(context)
+                        if n_completions > 0:
+                            return
+
+                assert False, 'timeout exceeded'
+
+
+def assert_embeddings(embeddings):
+    assert len(embeddings) > 0
+    embeddings_computed = False
+    for emb in embeddings:
+        if emb != 0:
+            embeddings_computed = True
+    assert embeddings_computed, f"Embeddings: {embeddings}"
+
+
+async def request_slots_status(context, expected_slots):
+    async with aiohttp.ClientSession() as session:
+        async with await session.get(f'{context.base_url}/slots') as slots_response:
+            assert slots_response.status == 200
+            slots = await slots_response.json()
+            assert_slots_status(slots, expected_slots)
+
+
+def assert_slots_status(slots, expected_slots):
+    assert len(slots) == len(expected_slots)
+    for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)):
+        for key in expected:
+            assert expected[key] == slot[key], (f"invalid slot {slot_id}"
+                                                f" expected[{key}] != slot[{key}]"
+                                                f" = {expected[key]} != {slot[key]}")
+
+
+def start_server_background(context):
+    context.server_path = '../../../build/bin/server'
+    if 'LLAMA_SERVER_BIN_PATH' in os.environ:
+        context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
+    server_args = [
+        '--host', context.server_fqdn,
+        '--port', context.server_port,
+        '--model', context.model_file
+    ]
+    if context.server_continuous_batching:
+        server_args.append('--cont-batching')
+    if context.server_embeddings:
+        server_args.append('--embedding')
+    if context.model_alias is not None:
+        server_args.extend(['--alias', context.model_alias])
+    if context.n_ctx is not None:
+        server_args.extend(['--ctx-size', context.n_ctx])
+    if context.n_slots is not None:
+        server_args.extend(['--parallel', context.n_slots])
+    if context.n_server_predict is not None:
+        server_args.extend(['--n-predict', context.n_server_predict])
+    if context.server_api_key is not None:
+        server_args.extend(['--api-key', context.server_api_key])
+    if context.debug:
+        server_args.append('--verbose')
+    print(f"starting server with: {context.server_path}", *server_args)
+    context.server_process = subprocess.Popen(
+        [str(arg) for arg in [context.server_path, *server_args]],
+        close_fds=True)
+    print(f"server pid={context.server_process.pid}")