mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
3fd62a6b1c
* py : type-check all Python scripts with Pyright * server-tests : use trailing slash in openai base_url * server-tests : add more type annotations * server-tests : strip "chat" from base_url in oai_chat_completions * server-tests : model metadata is a dict * ci : disable pip cache in type-check workflow The cache is not shared between branches, and it's 250MB in size, so it would become quite a big part of the 10GB cache limit of the repo. * py : fix new type errors from master branch * tests : fix test-tokenizer-random.py Apparently, gcc applies optimisations even when pre-processing, which confuses pycparser. * ci : only show warnings and errors in python type-check The "information" level otherwise has entries from 'examples/pydantic_models_to_grammar.py', which could be confusing for someone trying to figure out what failed, considering that these messages can safely be ignored even though they look like errors.
1361 lines
54 KiB
Python
1361 lines
54 KiB
Python
import asyncio
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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 sys
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import threading
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import time
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from collections.abc import Sequence
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from contextlib import closing
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from re import RegexFlag
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from typing import Any, Literal, cast
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import aiohttp
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import numpy as np
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import openai
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from openai.types.chat import ChatCompletionChunk
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from behave import step # pyright: ignore[reportAttributeAccessIssue]
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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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# pyright: reportRedeclaration=false
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@step("a server listening on {server_fqdn}:{server_port}")
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def step_server_config(context, server_fqdn: str, server_port: str):
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context.server_fqdn = server_fqdn
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context.server_port = int(server_port)
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context.n_threads = None
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context.n_gpu_layer = None
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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"$PORT set, overriding server port with to {context.server_port}")
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if 'FQDN' in os.environ:
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context.server_fqdn = os.environ['FQDN']
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print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
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if 'N_GPU_LAYERS' in os.environ:
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context.n_gpu_layer = int(os.environ['N_GPU_LAYERS'])
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print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}")
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context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
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context.model_alias = None
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context.model_file = None
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context.model_hf_repo = None
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context.model_hf_file = None
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context.model_url = None
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context.n_batch = None
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context.n_ubatch = None
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context.n_ctx = None
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context.n_ga = None
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context.n_ga_w = None
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context.n_predict = None
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context.n_prompts = 0
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context.n_server_predict = None
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context.slot_save_path = None
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context.id_slot = None
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context.cache_prompt = None
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context.n_slots = None
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context.prompt_prefix = None
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context.prompt_suffix = 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.seed = None
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context.draft = None
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context.server_seed = None
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context.user_api_key = None
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context.response_format = None
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context.temperature = 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('a model file {hf_file} from HF repo {hf_repo}')
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def step_download_hf_model(context, hf_file: str, hf_repo: str):
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context.model_hf_repo = hf_repo
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context.model_hf_file = hf_file
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context.model_file = os.path.basename(hf_file)
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@step('a model file {model_file}')
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def step_model_file(context, model_file: str):
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context.model_file = model_file
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@step('a model url {model_url}')
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def step_model_url(context, model_url: str):
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context.model_url = model_url
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@step('a model alias {model_alias}')
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def step_model_alias(context, model_alias: str):
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context.model_alias = model_alias
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@step('{seed:d} as server seed')
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def step_seed(context, seed: int):
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context.server_seed = seed
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@step('{ngl:d} GPU offloaded layers')
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def step_n_gpu_layer(context, ngl: int):
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if 'N_GPU_LAYERS' in os.environ:
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new_ngl = int(os.environ['N_GPU_LAYERS'])
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if context.debug:
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print(f"-ngl upgraded from {ngl} to {new_ngl}")
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ngl = new_ngl
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context.n_gpu_layer = ngl
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@step('{n_threads:d} threads')
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def step_n_threads(context, n_threads: int):
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context.n_thread = n_threads
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@step('{draft:d} as draft')
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def step_draft(context, draft: int):
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context.draft = draft
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@step('{n_ctx:d} KV cache size')
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def step_n_ctx(context, n_ctx: int):
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context.n_ctx = n_ctx
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@step('{n_slots:d} slots')
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def step_n_slots(context, n_slots: int):
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context.n_slots = n_slots
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@step('{n_predict:d} server max tokens to predict')
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def step_server_n_predict(context, n_predict: int):
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context.n_server_predict = n_predict
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@step('{slot_save_path} as slot save path')
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def step_slot_save_path(context, slot_save_path: str):
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context.slot_save_path = slot_save_path
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@step('using slot id {id_slot:d}')
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def step_id_slot(context, id_slot: int):
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context.id_slot = id_slot
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@step('prompt caching is enabled')
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def step_enable_prompt_cache(context):
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context.cache_prompt = True
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@step('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('embeddings extraction')
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def step_server_embeddings(context):
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context.server_embeddings = True
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@step('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("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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max_attempts = 20
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if 'GITHUB_ACTIONS' in os.environ:
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max_attempts *= 2
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addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM)
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family, typ, proto, _, sockaddr = addrs[0]
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while True:
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with closing(socket.socket(family, typ, proto)) as sock:
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result = sock.connect_ex(sockaddr)
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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 > max_attempts:
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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("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: Literal['healthy', 'ready', 'idle', 'busy'] | str):
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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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timeout=30)
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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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timeout=30,
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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
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range(context.n_slots if context.n_slots else 1)])
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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
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range(context.n_slots if context.n_slots else 1)])
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case _:
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assert False, "unknown status"
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@step('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: Literal['idle', 'busy'] | str):
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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('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: Literal['raised'] | str):
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expect_api_error = api_error == 'raised'
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seeds = await completions_seed(context, num_seeds=1)
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completion = await request_completion(context.prompts.pop(),
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seeds[0] if seeds is not None else seeds,
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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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cache_prompt=context.cache_prompt,
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id_slot=context.id_slot,
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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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temperature=context.temperature)
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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('{predicted_n:d} 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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context.completion = context.tasks_result.pop()
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assert_n_tokens_predicted(context.completion, predicted_n, re_content)
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@step('{predicted_n:d} tokens are predicted')
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def step_n_tokens_predicted(context, predicted_n):
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context.completion = context.tasks_result.pop()
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assert_n_tokens_predicted(context.completion, predicted_n)
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@step('all predictions are equal')
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@async_run_until_complete
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async def step_predictions_equal(context):
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n_completions = await gather_tasks_results(context)
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assert n_completions >= 2, "need at least 2 completions"
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assert_all_predictions_equal(context.tasks_result)
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context.tasks_result = []
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@step('all predictions are different')
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@async_run_until_complete
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async def step_predictions_different(context):
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n_completions = await gather_tasks_results(context)
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assert n_completions >= 2, "need at least 2 completions"
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assert_all_predictions_different(context.tasks_result)
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context.tasks_result = []
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@step('all token probabilities are equal')
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@async_run_until_complete
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async def step_token_probabilities_equal(context):
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n_completions = await gather_tasks_results(context)
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assert n_completions >= 2, "need at least 2 completions"
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assert_all_token_probabilities_equal(context.tasks_result)
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context.tasks_result = []
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@step('the completion is truncated')
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def step_assert_completion_truncated(context):
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step_assert_completion_truncated(context, '')
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@step('the completion is {truncated} truncated')
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def step_assert_completion_truncated(context, truncated):
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truncated = truncated != "not"
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assert context.completion['truncated'] == truncated, f'{context.completion}'
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@step('{n_prompt:d} prompt tokens are processed')
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def step_impl(context, n_prompt):
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assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}"
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@step('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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context.n_prompts = len(context.prompts)
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@step('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('a model {model}')
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def step_model(context, model):
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context.model = model
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@step('{max_tokens:d} max tokens to predict')
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def step_max_tokens(context, max_tokens):
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context.n_predict = max_tokens
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@step('a response format {response_format}')
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def step_response_format(context, response_format):
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context.response_format = json.loads(response_format)
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@step('{temperature:f} temperature')
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def step_temperature(context, temperature):
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context.temperature = temperature
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@step('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('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('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('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('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('{n_junk:d} as number of junk')
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def step_n_junk(context, n_junk):
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context.n_junk = n_junk
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@step('{n_batch:d} as batch size')
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def step_n_batch(context, n_batch):
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context.n_batch = n_batch
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@step('{n_ubatch:d} as ubatch size')
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def step_n_ubatch(context, n_ubatch):
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context.n_ubatch = n_ubatch
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@step('{seed:d} as seed')
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def step_seed(context, seed):
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if context.seed is None:
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context.seed = [seed]
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else:
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context.seed.append(seed)
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@step('BOS token is {bos:d}')
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def step_bos_token(context, bos):
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context.bos = bos
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@step('a prefix prompt')
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def step_prompt_prefix(context):
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context.prompt_prefix = context_text(context)
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@step('a junk suffix prompt')
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def step_prompt_junk_suffix(context):
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context.prompt_junk_suffix = context_text(context)
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@step('a suffix prompt')
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def step_prompt_suffix(context):
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context.prompt_suffix = context_text(context)
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@step('{n_ga:d} group attention factor'
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' to extend context size through self-extend')
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def step_impl(context, n_ga):
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context.n_ga = n_ga
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@step('{n_ga_w:d} group attention width to extend context size through self-extend')
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def step_impl(context, n_ga_w):
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context.n_ga_w = n_ga_w
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@step('a passkey prompt template')
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def step_prompt_passkey(context):
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context.prompt_passkey = context_text(context)
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@step('{n_prompts:d} fixed prompts')
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def step_fixed_prompts(context, n_prompts):
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context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)])
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context.n_prompts = n_prompts
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@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
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def step_prompt_passkey(context, passkey, i_pos):
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prompt = ""
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for i in range(context.n_junk):
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if i % context.n_junk == i_pos:
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prompt += context.prompt_passkey # the passkey is already substituted
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prompt += context.prompt_junk_suffix
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if context.debug:
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passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
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print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```")
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context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
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context.n_prompts = len(context.prompts)
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@step('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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seeds = await completions_seed(context, num_seeds=1),
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completion = await oai_chat_completions(context.prompts.pop(),
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seeds[0] if seeds is not None else seeds,
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context.system_prompt,
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context.base_url,
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'/v1/chat',
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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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response_format=context.response_format
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if hasattr(context, 'response_format') 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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|
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if context.debug:
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print(f"Completion response: {completion}")
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|
|
|
|
@step('a prompt')
|
|
def step_a_prompt(context):
|
|
context.prompts.append(context_text(context))
|
|
context.n_prompts = len(context.prompts)
|
|
|
|
|
|
@step('a prompt {prompt}')
|
|
def step_a_prompt_prompt(context, prompt):
|
|
context.prompts.append(prompt)
|
|
context.n_prompts = len(context.prompts)
|
|
|
|
|
|
@step('{num_prompts:d} prompts {prompt} with seed {seed:d}')
|
|
def step_many_prompts(context, num_prompts, prompt, seed):
|
|
if context.seed is None:
|
|
context.seed = []
|
|
for _ in range(num_prompts):
|
|
context.seed.append(seed)
|
|
context.prompts.append(prompt)
|
|
context.n_prompts = len(context.prompts)
|
|
|
|
|
|
@step('concurrent completion requests')
|
|
@async_run_until_complete()
|
|
async def step_concurrent_completion_requests(context):
|
|
await concurrent_requests(
|
|
context,
|
|
request_completion,
|
|
# prompt is inserted automatically
|
|
context.base_url,
|
|
debug=context.debug,
|
|
prompt_prefix=context.prompt_prefix,
|
|
prompt_suffix=context.prompt_suffix,
|
|
n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
|
|
user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None,
|
|
temperature=context.temperature,
|
|
)
|
|
|
|
|
|
@step('concurrent OAI completions requests')
|
|
@async_run_until_complete
|
|
async def step_oai_chat_completions(context):
|
|
await concurrent_requests(context, oai_chat_completions,
|
|
# user_prompt is inserted automatically
|
|
context.system_prompt,
|
|
context.base_url,
|
|
'/v1/chat/completions',
|
|
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,
|
|
response_format=context.response_format
|
|
if hasattr(context, 'response_format') else None,
|
|
user_api_key=context.user_api_key
|
|
if hasattr(context, 'user_api_key') else None)
|
|
|
|
|
|
@step('concurrent OAI completions requests no v1')
|
|
@async_run_until_complete
|
|
async def step_oai_chat_completions(context):
|
|
await concurrent_requests(context, oai_chat_completions,
|
|
# user_prompt is inserted automatically
|
|
context.system_prompt,
|
|
context.base_url,
|
|
'/chat/completions',
|
|
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,
|
|
response_format=context.response_format
|
|
if hasattr(context, 'response_format') else None,
|
|
user_api_key=context.user_api_key
|
|
if hasattr(context, 'user_api_key') else None)
|
|
|
|
|
|
@step('all prompts are predicted')
|
|
@async_run_until_complete
|
|
async def step_all_prompts_are_predicted(context):
|
|
await all_prompts_are_predicted(context)
|
|
|
|
|
|
@step('all prompts are predicted with {n_expected_predicted:d} tokens')
|
|
@async_run_until_complete
|
|
async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
|
|
await all_prompts_are_predicted(context, n_expected_predicted)
|
|
|
|
|
|
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('embeddings are computed for')
|
|
@async_run_until_complete
|
|
async def step_compute_embedding(context):
|
|
context.n_prompts = 1
|
|
context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url)
|
|
|
|
|
|
@step('all embeddings are the same')
|
|
@async_run_until_complete
|
|
async def step_all_embeddings_are_the_same(context):
|
|
n_embedding_requests = await gather_tasks_results(context)
|
|
assert n_embedding_requests > 0
|
|
embeddings = []
|
|
for i in range(n_embedding_requests):
|
|
embedding = context.tasks_result.pop().pop()
|
|
embeddings.append(embedding)
|
|
assert_embeddings(embedding)
|
|
n = len(embeddings)
|
|
for i in range(n-1):
|
|
for j in range(i+1, n):
|
|
embedding1 = np.array(embeddings[i])
|
|
embedding2 = np.array(embeddings[j])
|
|
if context.debug:
|
|
print(f"embedding1: {embedding1[-8:]}")
|
|
print(f"embedding2: {embedding2[-8:]}")
|
|
similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
|
|
msg = f"Similarity between {i} and {j}: {similarity:.10f}"
|
|
if context.debug:
|
|
print(f"{msg}")
|
|
assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
|
|
|
|
|
|
@step('embeddings are generated')
|
|
def step_assert_embeddings(context):
|
|
assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
|
|
f"context.n_prompts={context.n_prompts}\n"
|
|
f"context.embeddings={context.embeddings}")
|
|
for embedding in context.embeddings:
|
|
assert_embeddings(embedding)
|
|
|
|
|
|
@step('an OAI compatible embeddings computation request for')
|
|
@async_run_until_complete
|
|
async def step_oai_compute_embeddings(context):
|
|
context.n_prompts = 1
|
|
context.embeddings = await request_oai_embeddings(context_text(context), None,
|
|
base_url=context.base_url,
|
|
user_api_key=context.user_api_key,
|
|
model=context.model)
|
|
|
|
|
|
@step('an OAI compatible embeddings computation request for multiple inputs')
|
|
@async_run_until_complete
|
|
async def step_oai_compute_embeddings_multiple_inputs(context):
|
|
context.embeddings = await request_oai_embeddings(context.prompts, None,
|
|
base_url=context.base_url,
|
|
user_api_key=context.user_api_key,
|
|
model=context.model)
|
|
context.prompts.clear()
|
|
|
|
|
|
@step('concurrent embedding requests')
|
|
@async_run_until_complete()
|
|
async def step_concurrent_embedding_requests(context):
|
|
await concurrent_requests(context,
|
|
request_embedding,
|
|
# prompt is inserted automatically
|
|
base_url=context.base_url)
|
|
|
|
|
|
@step('concurrent OAI embedding requests')
|
|
@async_run_until_complete()
|
|
async def step_concurrent_oai_embedding_requests(context):
|
|
await concurrent_requests(context,
|
|
request_oai_embeddings,
|
|
# prompt is inserted automatically
|
|
base_url=context.base_url,
|
|
async_client=True,
|
|
model=context.model)
|
|
|
|
|
|
@step('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 == context.n_prompts
|
|
for i in range(n_embedding_requests):
|
|
assert_embeddings(context.tasks_result.pop().pop())
|
|
|
|
|
|
@step('adding special tokens')
|
|
def step_tokenize_set_add_special(context):
|
|
context.tokenize_add_special = True
|
|
|
|
|
|
@step('tokenizing')
|
|
@async_run_until_complete
|
|
async def step_tokenize(context):
|
|
context.tokenized_text = context_text(context)
|
|
async with aiohttp.ClientSession() as session:
|
|
tokenize_args = {
|
|
"content": context.tokenized_text,
|
|
}
|
|
if getattr(context, 'tokenize_add_special', None) is not None:
|
|
tokenize_args['add_special'] = context.tokenize_add_special
|
|
async with session.post(f'{context.base_url}/tokenize',
|
|
json=tokenize_args) as response:
|
|
assert response.status == 200
|
|
tokenize_json = await response.json()
|
|
context.tokens = tokenize_json['tokens']
|
|
|
|
|
|
@step('tokens can be detokenized')
|
|
@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('tokens begin with BOS')
|
|
def step_strings_for_tokenization(context):
|
|
assert context.tokens[0] == context.bos
|
|
|
|
|
|
@step('tokens do not begin with BOS')
|
|
def step_strings_for_tokenization(context):
|
|
assert context.tokens[0] != context.bos
|
|
|
|
|
|
@step('first token is removed')
|
|
def step_strings_for_tokenization(context):
|
|
context.tokens = context.tokens[1:]
|
|
|
|
|
|
@step('an OPTIONS request is sent from {origin}')
|
|
@async_run_until_complete
|
|
async def step_options_request(context, origin):
|
|
async with aiohttp.ClientSession() as session:
|
|
headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
|
|
async with session.options(f'{context.base_url}/v1/chat/completions',
|
|
headers=headers) as response:
|
|
assert response.status == 200
|
|
context.options_response = response
|
|
|
|
|
|
@step('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
|
|
|
|
|
|
@step('prometheus metrics are exposed')
|
|
@async_run_until_complete
|
|
async def step_prometheus_metrics_exported(context):
|
|
async with aiohttp.ClientSession() as session:
|
|
async with await session.get(f'{context.base_url}/metrics') as metrics_response:
|
|
assert metrics_response.status == 200
|
|
assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
|
|
metrics_raw = await metrics_response.text()
|
|
metric_exported = False
|
|
if context.debug:
|
|
print(f"/metrics answer:\n{metrics_raw}")
|
|
context.metrics = {}
|
|
for metric in parser.text_string_to_metric_families(metrics_raw):
|
|
match metric.name:
|
|
case "llamacpp:kv_cache_usage_ratio":
|
|
assert len(metric.samples) > 0
|
|
metric_exported = True
|
|
context.metrics[metric.name] = metric
|
|
assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time"
|
|
assert metric_exported, "No metrics exported"
|
|
|
|
|
|
@step('metric {metric_name} is {metric_value:d}')
|
|
def step_assert_metric_value(context, metric_name, metric_value):
|
|
if metric_name not in context.metrics:
|
|
assert False, f"no metric {metric_name} in {context.metrics.keys()}"
|
|
assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
|
|
|
|
|
|
@step('available models')
|
|
def step_available_models(context):
|
|
# openai client always expects an api_key
|
|
openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
|
|
openai.base_url = f'{context.base_url}/v1/'
|
|
context.models = openai.models.list().data
|
|
|
|
|
|
@step('{n_model:d} models are supported')
|
|
def step_supported_models(context, n_model):
|
|
if context.debug:
|
|
print("server models available:", context.models)
|
|
assert len(context.models) == n_model
|
|
|
|
|
|
@step('model {i_model:d} is {param} {preposition} {param_value}')
|
|
def step_supported_models(context, i_model: int, param: Literal['identified', 'trained'] | str, preposition: str, param_value: str):
|
|
assert i_model < len(context.models)
|
|
model = context.models[i_model]
|
|
|
|
param_value = param_value.split(' ', 1)[0]
|
|
match param:
|
|
case 'identified':
|
|
value = model.id
|
|
case 'trained':
|
|
value = str(model.meta["n_ctx_train"])
|
|
case _:
|
|
assert False, "param {param} not supported"
|
|
assert param_value == value, f"model param {param} {value} != {param_value}"
|
|
|
|
|
|
async def concurrent_requests(context, f_completion, *args, **kwargs):
|
|
context.n_prompts = len(context.prompts)
|
|
if context.debug:
|
|
print(f"starting {context.n_prompts} concurrent completion requests...")
|
|
assert context.n_prompts > 0
|
|
seeds = await completions_seed(context)
|
|
assert seeds is not None
|
|
for prompt_no in range(context.n_prompts):
|
|
shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
|
|
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
|
|
await asyncio.sleep(0.1)
|
|
|
|
|
|
@step('the slot {slot_id:d} is saved with filename "{filename}"')
|
|
@async_run_until_complete
|
|
async def step_save_slot(context, slot_id, filename):
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{context.base_url}/slots/{slot_id}?action=save',
|
|
json={"filename": filename},
|
|
headers={"Content-Type": "application/json"}) as response:
|
|
context.response = response
|
|
|
|
|
|
@step('the slot {slot_id:d} is restored with filename "{filename}"')
|
|
@async_run_until_complete
|
|
async def step_restore_slot(context, slot_id, filename):
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore',
|
|
json={"filename": filename},
|
|
headers={"Content-Type": "application/json"}) as response:
|
|
context.response = response
|
|
|
|
|
|
@step('the slot {slot_id:d} is erased')
|
|
@async_run_until_complete
|
|
async def step_erase_slot(context, slot_id):
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase',
|
|
headers={"Content-Type": "application/json"}) as response:
|
|
context.response = response
|
|
|
|
|
|
@step('the server responds with status code {status_code:d}')
|
|
def step_server_responds_with_status_code(context, status_code):
|
|
assert context.response.status == status_code
|
|
|
|
|
|
async def request_completion(prompt,
|
|
seed,
|
|
base_url,
|
|
debug=False,
|
|
prompt_prefix=None,
|
|
prompt_suffix=None,
|
|
n_predict=None,
|
|
cache_prompt=False,
|
|
id_slot=None,
|
|
expect_api_error=None,
|
|
user_api_key=None,
|
|
temperature=None) -> int | dict[str, Any]:
|
|
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={
|
|
"input_prefix": prompt_prefix,
|
|
"prompt": prompt,
|
|
"input_suffix": prompt_suffix,
|
|
"n_predict": n_predict if n_predict is not None else -1,
|
|
"cache_prompt": cache_prompt,
|
|
"id_slot": id_slot,
|
|
"seed": seed if seed is not None else 42,
|
|
"temperature": temperature if temperature is not None else 0.8,
|
|
"n_probs": 2,
|
|
},
|
|
headers=headers,
|
|
timeout=3600) 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,
|
|
seed,
|
|
system_prompt,
|
|
base_url: str,
|
|
base_path: str,
|
|
async_client,
|
|
debug=False,
|
|
temperature=None,
|
|
model=None,
|
|
n_predict=None,
|
|
enable_streaming=None,
|
|
response_format=None,
|
|
user_api_key=None,
|
|
expect_api_error=None) -> int | dict[str, Any]:
|
|
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 = seed if 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,
|
|
"temperature": temperature if temperature is not None else 0.0,
|
|
"seed": seed,
|
|
}
|
|
if response_format is not None:
|
|
payload['response_format'] = response_format
|
|
completion_response = {
|
|
'content': '',
|
|
'timings': {
|
|
'predicted_n': 0,
|
|
'prompt_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}{base_path}',
|
|
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('utf-8')
|
|
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'],
|
|
'prompt_n': chat_completion_raw['usage']['prompt_tokens']
|
|
}
|
|
}
|
|
else:
|
|
return response.status
|
|
else:
|
|
try:
|
|
openai.api_key = user_api_key
|
|
openai.base_url = f'{base_url}{base_path.removesuffix("chat")}'
|
|
assert model is not None
|
|
chat_completion = openai.chat.completions.create(
|
|
messages=payload['messages'],
|
|
model=model,
|
|
max_tokens=n_predict,
|
|
stream=enable_streaming,
|
|
response_format=payload.get('response_format') or openai.NOT_GIVEN,
|
|
seed=seed,
|
|
temperature=payload['temperature']
|
|
)
|
|
except openai.AuthenticationError 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:
|
|
chat_completion = cast(openai.Stream[ChatCompletionChunk], chat_completion)
|
|
for chunk in chat_completion:
|
|
assert len(chunk.choices) == 1
|
|
delta = chunk.choices[0].delta
|
|
if delta.content is not None:
|
|
completion_response['content'] += delta.content
|
|
completion_response['timings']['predicted_n'] += 1
|
|
completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
|
|
else:
|
|
assert len(chat_completion.choices) == 1
|
|
assert chat_completion.usage is not None
|
|
completion_response = {
|
|
'content': chat_completion.choices[0].message.content,
|
|
'timings': {
|
|
'predicted_n': chat_completion.usage.completion_tokens,
|
|
'prompt_n': chat_completion.usage.prompt_tokens
|
|
},
|
|
'truncated': chat_completion.choices[0].finish_reason != 'stop'
|
|
}
|
|
if debug:
|
|
print("OAI response formatted to llama.cpp:", completion_response)
|
|
return completion_response
|
|
|
|
|
|
async def request_embedding(content, seed, base_url=None) -> list[list[float]]:
|
|
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, seed,
|
|
base_url=None, user_api_key=None,
|
|
model=None, async_client=False) -> list[list[float]]:
|
|
# 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'
|
|
headers=[]
|
|
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,
|
|
timeout=3600) 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'
|
|
if isinstance(input, Sequence):
|
|
embeddings = []
|
|
for an_oai_embeddings in response_json['data']:
|
|
embeddings.append(an_oai_embeddings['embedding'])
|
|
else:
|
|
embeddings = [response_json['data']['embedding']]
|
|
return embeddings
|
|
else:
|
|
openai.api_key = user_api_key
|
|
openai.base_url = f'{base_url}/v1/'
|
|
assert model is not None
|
|
oai_embeddings = openai.embeddings.create(
|
|
model=model,
|
|
input=input,
|
|
)
|
|
|
|
return [e.embedding for e in oai_embeddings.data]
|
|
|
|
|
|
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 re_content is not None:
|
|
p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL)
|
|
matches = p.finditer(content)
|
|
last_match = 0
|
|
highlighted = ''
|
|
for match in matches:
|
|
start, end = match.span()
|
|
highlighted += content[last_match: start]
|
|
highlighted += '\x1b[33m'
|
|
highlighted += content[start: end]
|
|
highlighted += '\x1b[0m'
|
|
last_match = end
|
|
highlighted += content[last_match:]
|
|
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
|
|
print(f"Checking completion response: {highlighted}")
|
|
assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
|
|
if expected_predicted_n and expected_predicted_n > 0:
|
|
assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
|
|
f' {n_predicted} <> {expected_predicted_n}')
|
|
|
|
def assert_all_predictions_equal(completion_responses):
|
|
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
|
|
for i, response_i in enumerate(completion_responses):
|
|
content_i = response_i['content']
|
|
print(f"content {i}: {content_i}")
|
|
for i, response_i in enumerate(completion_responses):
|
|
content_i = response_i['content']
|
|
for j, response_j in enumerate(completion_responses):
|
|
if i == j:
|
|
continue
|
|
content_j = response_j['content']
|
|
assert content_i == content_j, "contents not equal"
|
|
|
|
|
|
def assert_all_predictions_different(completion_responses):
|
|
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
|
|
for i, response_i in enumerate(completion_responses):
|
|
content_i = response_i['content']
|
|
print(f"content {i}: {content_i}")
|
|
for i, response_i in enumerate(completion_responses):
|
|
content_i = response_i['content']
|
|
for j, response_j in enumerate(completion_responses):
|
|
if i == j:
|
|
continue
|
|
content_j = response_j['content']
|
|
assert content_i != content_j, "contents not different"
|
|
|
|
|
|
def assert_all_token_probabilities_equal(completion_responses):
|
|
n_predict = len(completion_responses[0]['completion_probabilities'])
|
|
if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
|
|
for pos in range(n_predict):
|
|
for i, response_i in enumerate(completion_responses):
|
|
probs_i = response_i['completion_probabilities'][pos]['probs']
|
|
print(f"pos {pos}, probs {i}: {probs_i}")
|
|
for pos in range(n_predict):
|
|
for i, response_i in enumerate(completion_responses):
|
|
probs_i = response_i['completion_probabilities'][pos]['probs']
|
|
for j, response_j in enumerate(completion_responses):
|
|
if i == j:
|
|
continue
|
|
probs_j = response_j['completion_probabilities'][pos]['probs']
|
|
assert probs_i == probs_j, "contents not equal"
|
|
|
|
|
|
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,
|
|
timeout=3,
|
|
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}")
|
|
interval = 0.5
|
|
counter = 0
|
|
if 'GITHUB_ACTIONS' in os.environ:
|
|
timeout *= 2
|
|
|
|
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}\n")
|
|
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")
|
|
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 not isinstance(emb, float):
|
|
assert False, f"Bad embeddings: {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]}")
|
|
|
|
|
|
async def completions_seed(context, num_seeds=None):
|
|
if hasattr(context, "seed") and context.seed is not None:
|
|
assert len(context.seed) == context.n_prompts
|
|
if num_seeds is None:
|
|
num_seeds = context.n_prompts
|
|
assert num_seeds <= context.n_prompts
|
|
seeds = context.seed[:num_seeds]
|
|
context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None
|
|
return seeds
|
|
|
|
if hasattr(context, "server_seed") and context.server_seed is not None:
|
|
if num_seeds is None:
|
|
return [context.server_seed] * context.n_prompts
|
|
else:
|
|
return [context.server_seed] * num_seeds
|
|
return None
|
|
|
|
|
|
def context_text(context):
|
|
return context.text.replace('\r', '')
|
|
|
|
|
|
def start_server_background(context):
|
|
if os.name == 'nt':
|
|
context.server_path = '../../../build/bin/Release/llama-server.exe'
|
|
else:
|
|
context.server_path = '../../../build/bin/llama-server'
|
|
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
|
|
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
|
|
server_listen_addr = context.server_fqdn
|
|
server_args = [
|
|
'--host', server_listen_addr,
|
|
'--port', context.server_port,
|
|
]
|
|
if context.model_file:
|
|
server_args.extend(['--model', context.model_file])
|
|
if context.model_url:
|
|
server_args.extend(['--model-url', context.model_url])
|
|
if context.model_hf_repo:
|
|
server_args.extend(['--hf-repo', context.model_hf_repo])
|
|
if context.model_hf_file:
|
|
server_args.extend(['--hf-file', context.model_hf_file])
|
|
if context.n_batch:
|
|
server_args.extend(['--batch-size', context.n_batch])
|
|
if context.n_ubatch:
|
|
server_args.extend(['--ubatch-size', context.n_ubatch])
|
|
if context.n_threads:
|
|
server_args.extend(['--threads', context.threads])
|
|
if context.n_gpu_layer:
|
|
server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
|
|
if context.draft is not None:
|
|
server_args.extend(['--draft', context.draft])
|
|
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:
|
|
server_args.extend(['--alias', context.model_alias])
|
|
if context.n_ctx:
|
|
server_args.extend(['--ctx-size', context.n_ctx])
|
|
if context.n_slots:
|
|
server_args.extend(['--parallel', context.n_slots])
|
|
if context.n_server_predict:
|
|
server_args.extend(['--n-predict', context.n_server_predict])
|
|
if context.slot_save_path:
|
|
server_args.extend(['--slot-save-path', context.slot_save_path])
|
|
if context.server_api_key:
|
|
server_args.extend(['--api-key', context.server_api_key])
|
|
if context.n_ga:
|
|
server_args.extend(['--grp-attn-n', context.n_ga])
|
|
if context.n_ga_w:
|
|
server_args.extend(['--grp-attn-w', context.n_ga_w])
|
|
if context.debug:
|
|
server_args.append('--verbose')
|
|
if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
|
|
server_args.extend(['--log-format', "text"])
|
|
|
|
args = [str(arg) for arg in [context.server_path, *server_args]]
|
|
print(f"bench: starting server with: {' '.join(args)}")
|
|
|
|
flags = 0
|
|
if 'nt' == os.name:
|
|
flags |= subprocess.DETACHED_PROCESS
|
|
flags |= subprocess.CREATE_NEW_PROCESS_GROUP
|
|
flags |= subprocess.CREATE_NO_WINDOW
|
|
|
|
pkwargs = {
|
|
'creationflags': flags,
|
|
'stdout': subprocess.PIPE,
|
|
'stderr': subprocess.PIPE
|
|
}
|
|
context.server_process = subprocess.Popen(
|
|
[str(arg) for arg in [context.server_path, *server_args]],
|
|
**pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue]
|
|
|
|
def server_log(in_stream, out_stream):
|
|
for line in iter(in_stream.readline, b''):
|
|
print(line.decode('utf-8'), end='', file=out_stream)
|
|
|
|
thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
|
|
thread_stdout.start()
|
|
|
|
thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
|
|
thread_stderr.start()
|
|
|
|
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
|