mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
1087 lines
43 KiB
Python
1087 lines
43 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 numpy as np
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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 huggingface_hub import hf_hub_download
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from prometheus_client import parser
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@step("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"$PORT set, overriding server port with to {context.server_port}")
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if 'FQDN' in os.environ:
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context.server_fqdn = os.environ['FQDN']
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print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
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context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
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context.model_alias = 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_gpu_layer = 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.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.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('a model file {hf_file} from HF repo {hf_repo}')
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def step_download_hf_model(context, hf_file, hf_repo):
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context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
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if context.debug:
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print(f"model file: {context.model_file}\n")
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@step('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('{seed:d} as server seed')
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def step_seed(context, seed):
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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):
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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_ctx:d} KV cache size')
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def step_n_ctx(context, n_ctx):
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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):
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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):
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context.n_server_predict = n_predict
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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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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 > 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):
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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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timeout=10,
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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):
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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):
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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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seed=await completions_seed(context),
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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}\n")
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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('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('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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context.seed = seed
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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)}```\n")
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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...\n")
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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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'/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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seed=await completions_seed(context),
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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('a prompt')
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def step_a_prompt(context):
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context.prompts.append(context_text(context))
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context.n_prompts = len(context.prompts)
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@step('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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context.n_prompts = len(context.prompts)
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@step('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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prompt_prefix=context.prompt_prefix,
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prompt_suffix=context.prompt_suffix,
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n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
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seed=await completions_seed(context),
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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('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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'/v1/chat/completions',
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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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seed=await completions_seed(context),
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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('concurrent OAI completions requests no v1')
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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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'/chat/completions',
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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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seed=context.seed
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if hasattr(context, 'seed') else
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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('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('all prompts are predicted with {n_expected_predicted:d} 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_expected_predicted):
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await all_prompts_are_predicted(context, n_expected_predicted)
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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('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.n_prompts = 1
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context.embeddings = await request_embedding(context_text(context), base_url=context.base_url)
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@step('all embeddings are the same')
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@async_run_until_complete
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async def step_all_embeddings_are_the_same(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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embeddings = []
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for i in range(n_embedding_requests):
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embedding = context.tasks_result.pop().pop()
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embeddings.append(embedding)
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assert_embeddings(embedding)
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n = len(embeddings)
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for i in range(n-1):
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for j in range(i+1, n):
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embedding1 = np.array(embeddings[i])
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embedding2 = np.array(embeddings[j])
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if context.debug:
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print(f"embedding1: {embedding1[-8:]}\n")
|
|
print(f"embedding2: {embedding2[-8:]}\n")
|
|
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}\n")
|
|
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),
|
|
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,
|
|
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('tokenizing')
|
|
@async_run_until_complete
|
|
async def step_tokenize(context):
|
|
context.tokenized_text = context_text(context)
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{context.base_url}/tokenize',
|
|
json={
|
|
"content": context.tokenized_text,
|
|
}) as response:
|
|
assert response.status == 200
|
|
tokenize_json = await response.json()
|
|
context.tokens = tokenize_json['tokens']
|
|
|
|
|
|
@step('tokens can be detokenize')
|
|
@async_run_until_complete
|
|
async def step_detokenize(context):
|
|
assert len(context.tokens) > 0
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{context.base_url}/detokenize',
|
|
json={
|
|
"tokens": context.tokens,
|
|
}) as response:
|
|
assert response.status == 200
|
|
detokenize_json = await response.json()
|
|
# SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15
|
|
assert context.tokenized_text == detokenize_json['content'].strip()
|
|
|
|
|
|
@step('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}\n")
|
|
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.api_base = f'{context.base_url}/v1'
|
|
context.models = openai.Model.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, param, preposition, param_value):
|
|
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
|
|
for prompt_no in range(context.n_prompts):
|
|
shifted_args = [context.prompts.pop(), *args]
|
|
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
|
|
await asyncio.sleep(0.1)
|
|
|
|
|
|
async def request_completion(prompt,
|
|
base_url,
|
|
debug=False,
|
|
prompt_prefix=None,
|
|
prompt_suffix=None,
|
|
n_predict=None,
|
|
seed=None,
|
|
expect_api_error=None,
|
|
user_api_key=None):
|
|
if debug:
|
|
print(f"Sending completion request: {prompt}")
|
|
origin = "my.super.domain"
|
|
headers = {
|
|
'Origin': origin
|
|
}
|
|
if user_api_key is not None:
|
|
if debug:
|
|
print(f"Set user_api_key: {user_api_key}")
|
|
headers['Authorization'] = f'Bearer {user_api_key}'
|
|
|
|
async with aiohttp.ClientSession() as session:
|
|
async with session.post(f'{base_url}/completion',
|
|
json={
|
|
"input_prefix": prompt_prefix,
|
|
"prompt": prompt,
|
|
"input_suffix": prompt_suffix,
|
|
"n_predict": n_predict if n_predict is not None else -1,
|
|
"seed": seed if seed is not None else 42
|
|
},
|
|
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,
|
|
system_prompt,
|
|
base_url,
|
|
base_path,
|
|
async_client,
|
|
debug=False,
|
|
model=None,
|
|
n_predict=None,
|
|
enable_streaming=None,
|
|
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 = 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,
|
|
"seed": seed
|
|
}
|
|
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('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'],
|
|
'prompt_n': chat_completion_raw['usage']['prompt_tokens']
|
|
}
|
|
}
|
|
else:
|
|
return response.status
|
|
else:
|
|
try:
|
|
openai.api_key = user_api_key
|
|
openai.api_base = f'{base_url}{base_path}'
|
|
chat_completion = openai.Completion.create(
|
|
messages=payload['messages'],
|
|
model=model,
|
|
max_tokens=n_predict,
|
|
stream=enable_streaming,
|
|
seed=seed
|
|
)
|
|
except openai.error.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:
|
|
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
|
|
completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
|
|
else:
|
|
assert len(chat_completion.choices) == 1
|
|
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, 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'
|
|
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, collections.abc.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.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 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}\n")
|
|
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}')
|
|
|
|
|
|
async def gather_tasks_results(context):
|
|
n_tasks = len(context.concurrent_tasks)
|
|
if context.debug:
|
|
print(f"Waiting for all {n_tasks} tasks results...\n")
|
|
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}\n")
|
|
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):
|
|
return context.seed if hasattr(context, 'seed') and context.seed is not None \
|
|
else context.server_seed if hasattr(context, 'server_seed') else 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/server.exe'
|
|
else:
|
|
context.server_path = '../../../build/bin/server'
|
|
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
|
|
context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
|
|
server_listen_addr = context.server_fqdn
|
|
if os.name == 'nt':
|
|
server_listen_addr = '0.0.0.0'
|
|
server_args = [
|
|
'--host', server_listen_addr,
|
|
'--port', context.server_port,
|
|
'--model', context.model_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_gpu_layer:
|
|
server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
|
|
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.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"])
|
|
print(f"starting server with: {context.server_path} {server_args}\n")
|
|
flags = 0
|
|
if 'nt' == os.name:
|
|
flags |= subprocess.DETACHED_PROCESS
|
|
flags |= subprocess.CREATE_NEW_PROCESS_GROUP
|
|
flags |= subprocess.CREATE_NO_WINDOW
|
|
|
|
pkwargs = {
|
|
'creationflags': flags,
|
|
}
|
|
context.server_process = subprocess.Popen(
|
|
[str(arg) for arg in [context.server_path, *server_args]],
|
|
**pkwargs)
|
|
print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
|