--- /dev/null
+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}")
overview / pkg / ggml / sources / llama.cpp / commitdiff