mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-01 07:30:17 +01:00
804 lines
33 KiB
Python
804 lines
33 KiB
Python
import asyncio
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import collections
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import json
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import os
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import re
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import socket
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import subprocess
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import time
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from contextlib import closing
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from re import RegexFlag
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import aiohttp
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import openai
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from behave import step
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from behave.api.async_step import async_run_until_complete
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from prometheus_client import parser
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@step(u"a server listening on {server_fqdn}:{server_port}")
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def step_server_config(context, server_fqdn, server_port):
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context.server_fqdn = server_fqdn
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context.server_port = int(server_port)
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if 'PORT' in os.environ:
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context.server_port = int(os.environ['PORT'])
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print(f"$PORT set, overriding server port with to {context.server_port}")
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context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
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context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
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context.model_alias = None
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context.n_ctx = None
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context.n_predict = None
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context.n_server_predict = None
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context.n_slots = None
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context.server_api_key = None
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context.server_continuous_batching = False
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context.server_embeddings = False
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context.server_metrics = False
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context.server_process = None
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context.server_seed = None
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context.user_api_key = None
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context.tasks_result = []
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context.concurrent_tasks = []
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context.prompts = []
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@step(u'a model file {model_file}')
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def step_model_file(context, model_file):
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context.model_file = model_file
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@step(u'a model alias {model_alias}')
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def step_model_alias(context, model_alias):
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context.model_alias = model_alias
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@step(u'{seed} as server seed')
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def step_seed(context, seed):
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context.server_seed = int(seed)
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@step(u'{n_ctx} KV cache size')
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def step_n_ctx(context, n_ctx):
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context.n_ctx = int(n_ctx)
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@step(u'{n_slots} slots')
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def step_n_slots(context, n_slots):
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context.n_slots = int(n_slots)
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@step(u'{n_predict} server max tokens to predict')
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def step_server_n_predict(context, n_predict):
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context.n_server_predict = int(n_predict)
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@step(u'continuous batching')
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def step_server_continuous_batching(context):
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context.server_continuous_batching = True
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@step(u'embeddings extraction')
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def step_server_embeddings(context):
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context.server_embeddings = True
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@step(u'prometheus compatible metrics exposed')
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def step_server_metrics(context):
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context.server_metrics = True
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@step(u"the server is starting")
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def step_start_server(context):
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start_server_background(context)
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attempts = 0
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while True:
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with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
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result = sock.connect_ex((context.server_fqdn, context.server_port))
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if result == 0:
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print("\x1b[33;46mserver started!\x1b[0m")
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return
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attempts += 1
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if attempts > 20:
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assert False, "server not started"
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print(f"waiting for server to start, connect error code = {result}...")
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time.sleep(0.1)
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@step(u"the server is {expecting_status}")
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@async_run_until_complete
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async def step_wait_for_the_server_to_be_started(context, expecting_status):
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match expecting_status:
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case 'healthy':
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await wait_for_health_status(context, context.base_url, 200, 'ok')
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case 'ready' | 'idle':
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await wait_for_health_status(context, context.base_url, 200, 'ok',
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params={'fail_on_no_slot': 0, 'include_slots': 0},
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slots_idle=context.n_slots,
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slots_processing=0,
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expected_slots=[{'id': slot_id, 'state': 0}
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for slot_id in range(context.n_slots)])
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case 'busy':
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await wait_for_health_status(context, context.base_url, 503,
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'no slot available',
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params={'fail_on_no_slot': 0, 'include_slots': 0},
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slots_idle=0,
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slots_processing=context.n_slots,
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expected_slots=[{'id': slot_id, 'state': 1}
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for slot_id in range(context.n_slots)])
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case _:
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assert False, "unknown status"
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@step(u'all slots are {expected_slot_status_string}')
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@async_run_until_complete
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async def step_all_slots_status(context, expected_slot_status_string):
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match expected_slot_status_string:
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case 'idle':
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expected_slot_status = 0
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case 'busy':
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expected_slot_status = 1
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case _:
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assert False, "unknown status"
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expected_slots = [{'id': slot_id, 'state': expected_slot_status}
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for slot_id in range(context.n_slots)]
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await request_slots_status(context, expected_slots)
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@step(u'a completion request with {api_error} api error')
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@async_run_until_complete
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async def step_request_completion(context, api_error):
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expect_api_error = api_error == 'raised'
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completion = await request_completion(context.prompts.pop(),
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context.base_url,
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debug=context.debug,
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n_predict=context.n_predict,
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server_seed=context.server_seed,
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expect_api_error=expect_api_error,
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user_api_key=context.user_api_key)
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context.tasks_result.append(completion)
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if context.debug:
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print(f"Completion response: {completion}")
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if expect_api_error:
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assert completion == 401, f"completion must be an 401 status code: {completion}"
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@step(u'{predicted_n} tokens are predicted matching {re_content}')
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def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
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assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content)
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@step(u'{predicted_n} tokens are predicted')
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def step_n_tokens_predicted(context, predicted_n):
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assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n))
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@step(u'a user prompt {user_prompt}')
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def step_user_prompt(context, user_prompt):
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context.prompts.append(user_prompt)
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@step(u'a system prompt {system_prompt}')
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def step_system_prompt(context, system_prompt):
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context.system_prompt = system_prompt
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@step(u'a model {model}')
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def step_model(context, model):
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context.model = model
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@step(u'{max_tokens} max tokens to predict')
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def step_max_tokens(context, max_tokens):
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context.n_predict = int(max_tokens)
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@step(u'streaming is {enable_streaming}')
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def step_streaming(context, enable_streaming):
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context.enable_streaming = enable_streaming == 'enabled'
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@step(u'a user api key {user_api_key}')
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def step_user_api_key(context, user_api_key):
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context.user_api_key = user_api_key
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@step(u'no user api key')
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def step_no_user_api_key(context):
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context.user_api_key = None
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@step(u'a user api key ')
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def step_no_user_api_key_space(context):
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context.user_api_key = None
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@step(u'a server api key {server_api_key}')
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def step_server_api_key(context, server_api_key):
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context.server_api_key = server_api_key
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@step(u'an OAI compatible chat completions request with {api_error} api error')
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@async_run_until_complete
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async def step_oai_chat_completions(context, api_error):
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if context.debug:
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print(f"Submitting OAI compatible completions request...")
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expect_api_error = api_error == 'raised'
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completion = await oai_chat_completions(context.prompts.pop(),
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context.system_prompt,
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context.base_url,
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False,
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model=context.model if hasattr(context, 'model') else None,
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n_predict=context.n_predict
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if hasattr(context, 'n_predict') else None,
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enable_streaming=context.enable_streaming
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if hasattr(context, 'enable_streaming') else None,
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server_seed=context.server_seed
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if hasattr(context, 'server_seed') else None,
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user_api_key=context.user_api_key
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if hasattr(context, 'user_api_key') else None,
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expect_api_error=expect_api_error)
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context.tasks_result.append(completion)
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if context.debug:
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print(f"Completion response: {completion}")
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if expect_api_error:
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assert completion == 401, f"completion must be an 401 status code: {completion}"
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if context.debug:
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print(f"Completion response: {completion}")
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@step(u'a prompt')
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def step_a_prompt(context):
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context.prompts.append(context.text)
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@step(u'a prompt {prompt}')
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def step_a_prompt_prompt(context, prompt):
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context.prompts.append(prompt)
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@step(u'concurrent completion requests')
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@async_run_until_complete()
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async def step_concurrent_completion_requests(context):
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await concurrent_requests(context,
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request_completion,
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# prompt is inserted automatically
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context.base_url,
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debug=context.debug,
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n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
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server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
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user_api_key=context.user_api_key if hasattr(context,
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'user_api_key') else None)
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@step(u'concurrent OAI completions requests')
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@async_run_until_complete
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async def step_oai_chat_completions(context):
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await concurrent_requests(context, oai_chat_completions,
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# user_prompt is inserted automatically
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context.system_prompt,
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context.base_url,
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True, # async_client
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model=context.model
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if hasattr(context, 'model') else None,
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n_predict=context.n_predict
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if hasattr(context, 'n_predict') else None,
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enable_streaming=context.enable_streaming
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if hasattr(context, 'enable_streaming') else None,
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server_seed=context.server_seed
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if hasattr(context, 'server_seed') else None,
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user_api_key=context.user_api_key
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if hasattr(context, 'user_api_key') else None)
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@step(u'all prompts are predicted')
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@async_run_until_complete
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async def step_all_prompts_are_predicted(context):
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await all_prompts_are_predicted(context)
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@step(u'all prompts are predicted with {n_predict} tokens')
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@async_run_until_complete
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async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict):
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expected_predicted_n = int(n_predict)
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await all_prompts_are_predicted(context, expected_predicted_n)
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async def all_prompts_are_predicted(context, expected_predicted_n=None):
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n_completions = await gather_tasks_results(context)
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assert n_completions > 0
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for i in range(n_completions):
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assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n)
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assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
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@step(u'embeddings are computed for')
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@async_run_until_complete
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async def step_compute_embedding(context):
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context.embeddings = await request_embedding(context.text, base_url=context.base_url)
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@step(u'embeddings are generated')
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def step_assert_embeddings(context):
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if len(context.prompts) == 0:
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assert_embeddings(context.embeddings)
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else:
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assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n"
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f"context.prompts={context.prompts}\n"
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f"context.embeddings={context.embeddings}")
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for embedding in context.embeddings:
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context.prompts.pop()
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assert_embeddings(embedding)
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@step(u'an OAI compatible embeddings computation request for')
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@async_run_until_complete
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async def step_oai_compute_embeddings(context):
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context.embeddings = await request_oai_embeddings(context.text,
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base_url=context.base_url,
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user_api_key=context.user_api_key,
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model=context.model)
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@step(u'an OAI compatible embeddings computation request for multiple inputs')
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@async_run_until_complete
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async def step_oai_compute_embeddings_multiple_inputs(context):
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context.embeddings = await request_oai_embeddings(context.prompts,
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base_url=context.base_url,
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user_api_key=context.user_api_key,
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model=context.model)
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@step(u'concurrent embedding requests')
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@async_run_until_complete()
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async def step_concurrent_embedding_requests(context):
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await concurrent_requests(context,
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request_embedding,
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# prompt is inserted automatically
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base_url=context.base_url)
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@step(u'concurrent OAI embedding requests')
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@async_run_until_complete()
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async def step_concurrent_oai_embedding_requests(context):
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await concurrent_requests(context,
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request_oai_embeddings,
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# prompt is inserted automatically
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base_url=context.base_url,
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async_client=True,
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model=context.model)
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@step(u'all embeddings are generated')
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@async_run_until_complete()
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async def all_embeddings_are_generated(context):
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n_embedding_requests = await gather_tasks_results(context)
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assert n_embedding_requests > 0
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for i in range(n_embedding_requests):
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assert_embeddings(context.tasks_result.pop())
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@step(u'tokenizing')
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@async_run_until_complete
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async def step_tokenize(context):
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context.tokenized_text = context.text
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async with aiohttp.ClientSession() as session:
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async with session.post(f'{context.base_url}/tokenize',
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json={
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"content": context.tokenized_text,
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}) as response:
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assert response.status == 200
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tokenize_json = await response.json()
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context.tokens = tokenize_json['tokens']
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@step(u'tokens can be detokenize')
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@async_run_until_complete
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async def step_detokenize(context):
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assert len(context.tokens) > 0
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async with aiohttp.ClientSession() as session:
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async with session.post(f'{context.base_url}/detokenize',
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json={
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"tokens": context.tokens,
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}) as response:
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assert response.status == 200
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detokenize_json = await response.json()
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# SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15
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assert context.tokenized_text == detokenize_json['content'].strip()
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@step(u'an OPTIONS request is sent from {origin}')
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@async_run_until_complete
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async def step_options_request(context, origin):
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async with aiohttp.ClientSession() as session:
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async with session.options(f'{context.base_url}/v1/chat/completions',
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headers={"Origin": origin}) as response:
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assert response.status == 200
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context.options_response = response
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@step(u'CORS header {cors_header} is set to {cors_header_value}')
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def step_check_options_header_value(context, cors_header, cors_header_value):
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assert context.options_response.headers[cors_header] == cors_header_value
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@step(u'prometheus metrics are exposed')
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@async_run_until_complete
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async def step_prometheus_metrics_exported(context):
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async with aiohttp.ClientSession() as session:
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async with await session.get(f'{context.base_url}/metrics') as metrics_response:
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assert metrics_response.status == 200
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assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
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metrics_raw = await metrics_response.text()
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metric_exported = False
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for metric in parser.text_string_to_metric_families(metrics_raw):
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match metric.name:
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case "llamacpp:kv_cache_usage_ratio":
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assert len(metric.samples) > 0
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metric_exported = True
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assert metric_exported, "No metrics exported"
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async def concurrent_requests(context, f_completion, *args, **kwargs):
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n_prompts = len(context.prompts)
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if context.debug:
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print(f"starting {n_prompts} concurrent completion requests...")
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assert n_prompts > 0
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for prompt_no in range(n_prompts):
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shifted_args = [context.prompts.pop(), *args]
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context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
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await asyncio.sleep(0.1)
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async def request_completion(prompt,
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base_url,
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debug=False,
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n_predict=None,
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server_seed=None,
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expect_api_error=None,
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user_api_key=None):
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if debug:
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print(f"Sending completion request: {prompt}")
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origin = "my.super.domain"
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headers = {
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'Origin': origin
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}
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if user_api_key is not None:
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if debug:
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print(f"Set user_api_key: {user_api_key}")
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headers['Authorization'] = f'Bearer {user_api_key}'
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async with aiohttp.ClientSession() as session:
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async with session.post(f'{base_url}/completion',
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json={
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"prompt": prompt,
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"n_predict": int(n_predict) if n_predict is not None else -1,
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"seed": server_seed if server_seed is not None else 42
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},
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headers=headers) as response:
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if expect_api_error is None or not expect_api_error:
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assert response.status == 200
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assert response.headers['Access-Control-Allow-Origin'] == origin
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return await response.json()
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else:
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return response.status
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|
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=None):
|
|
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']
|
|
|
|
|
|
async def request_oai_embeddings(input,
|
|
base_url=None, user_api_key=None,
|
|
model=None, async_client=False):
|
|
# openai client always expects an api_key
|
|
user_api_key = user_api_key if user_api_key is not None else 'nope'
|
|
if async_client:
|
|
origin = 'llama.cpp'
|
|
if user_api_key is not None:
|
|
headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{base_url}/v1/embeddings',
|
|
json={
|
|
"input": input,
|
|
"model": model,
|
|
},
|
|
headers=headers) as response:
|
|
assert response.status == 200, f"received status code not expected: {response.status}"
|
|
assert response.headers['Access-Control-Allow-Origin'] == origin
|
|
assert response.headers['Content-Type'] == "application/json; charset=utf-8"
|
|
response_json = await response.json()
|
|
assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
|
|
assert response_json['object'] == 'list'
|
|
return response_json['data']
|
|
else:
|
|
openai.api_key = user_api_key
|
|
openai.api_base = f'{base_url}/v1'
|
|
oai_embeddings = openai.Embedding.create(
|
|
model=model,
|
|
input=input,
|
|
)
|
|
|
|
if isinstance(input, collections.abc.Sequence):
|
|
embeddings = []
|
|
for an_oai_embeddings in oai_embeddings.data:
|
|
embeddings.append(an_oai_embeddings.embedding)
|
|
else:
|
|
embeddings = oai_embeddings.data.embedding
|
|
return embeddings
|
|
|
|
|
|
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
|
|
if expected_health_status == 'ok':
|
|
timeout = 10 # CI slow inference
|
|
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, f'{expected_health_status} timeout exceeded {counter}s>={timeout}'
|
|
|
|
|
|
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.server_metrics:
|
|
server_args.append('--metrics')
|
|
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')
|
|
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
|
|
server_args.extend(['--log-format', "text"])
|
|
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}")
|