utils.py 12 KB

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  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. # type: ignore[reportUnusedImport]
  4. import subprocess
  5. import os
  6. import re
  7. import json
  8. import sys
  9. import threading
  10. import requests
  11. import time
  12. from concurrent.futures import ThreadPoolExecutor, as_completed
  13. from typing import (
  14. Any,
  15. Callable,
  16. ContextManager,
  17. Iterable,
  18. Iterator,
  19. List,
  20. Literal,
  21. Tuple,
  22. Set,
  23. )
  24. from re import RegexFlag
  25. class ServerResponse:
  26. headers: dict
  27. status_code: int
  28. body: dict | Any
  29. class ServerProcess:
  30. # default options
  31. debug: bool = False
  32. server_port: int = 8080
  33. server_host: str = "127.0.0.1"
  34. model_hf_repo: str = "ggml-org/models"
  35. model_hf_file: str = "tinyllamas/stories260K.gguf"
  36. model_alias: str = "tinyllama-2"
  37. temperature: float = 0.8
  38. seed: int = 42
  39. # custom options
  40. model_alias: str | None = None
  41. model_url: str | None = None
  42. model_file: str | None = None
  43. n_threads: int | None = None
  44. n_gpu_layer: int | None = None
  45. n_batch: int | None = None
  46. n_ubatch: int | None = None
  47. n_ctx: int | None = None
  48. n_ga: int | None = None
  49. n_ga_w: int | None = None
  50. n_predict: int | None = None
  51. n_prompts: int | None = 0
  52. slot_save_path: str | None = None
  53. id_slot: int | None = None
  54. cache_prompt: bool | None = None
  55. n_slots: int | None = None
  56. server_continuous_batching: bool | None = False
  57. server_embeddings: bool | None = False
  58. server_reranking: bool | None = False
  59. server_metrics: bool | None = False
  60. draft: int | None = None
  61. api_key: str | None = None
  62. response_format: str | None = None
  63. lora_files: List[str] | None = None
  64. disable_ctx_shift: int | None = False
  65. # session variables
  66. process: subprocess.Popen | None = None
  67. def __init__(self):
  68. if "N_GPU_LAYERS" in os.environ:
  69. self.n_gpu_layer = int(os.environ["N_GPU_LAYERS"])
  70. if "DEBUG" in os.environ:
  71. self.debug = True
  72. if "PORT" in os.environ:
  73. self.server_port = int(os.environ["PORT"])
  74. def start(self, timeout_seconds: int = 10) -> None:
  75. if "LLAMA_SERVER_BIN_PATH" in os.environ:
  76. server_path = os.environ["LLAMA_SERVER_BIN_PATH"]
  77. elif os.name == "nt":
  78. server_path = "../../../build/bin/Release/llama-server.exe"
  79. else:
  80. server_path = "../../../build/bin/llama-server"
  81. server_args = [
  82. "--slots", # requires to get slot status via /slots endpoint
  83. "--host",
  84. self.server_host,
  85. "--port",
  86. self.server_port,
  87. "--temp",
  88. self.temperature,
  89. "--seed",
  90. self.seed,
  91. ]
  92. if self.model_file:
  93. server_args.extend(["--model", self.model_file])
  94. if self.model_url:
  95. server_args.extend(["--model-url", self.model_url])
  96. if self.model_hf_repo:
  97. server_args.extend(["--hf-repo", self.model_hf_repo])
  98. if self.model_hf_file:
  99. server_args.extend(["--hf-file", self.model_hf_file])
  100. if self.n_batch:
  101. server_args.extend(["--batch-size", self.n_batch])
  102. if self.n_ubatch:
  103. server_args.extend(["--ubatch-size", self.n_ubatch])
  104. if self.n_threads:
  105. server_args.extend(["--threads", self.n_threads])
  106. if self.n_gpu_layer:
  107. server_args.extend(["--n-gpu-layers", self.n_gpu_layer])
  108. if self.draft is not None:
  109. server_args.extend(["--draft", self.draft])
  110. if self.server_continuous_batching:
  111. server_args.append("--cont-batching")
  112. if self.server_embeddings:
  113. server_args.append("--embedding")
  114. if self.server_reranking:
  115. server_args.append("--reranking")
  116. if self.server_metrics:
  117. server_args.append("--metrics")
  118. if self.model_alias:
  119. server_args.extend(["--alias", self.model_alias])
  120. if self.n_ctx:
  121. server_args.extend(["--ctx-size", self.n_ctx])
  122. if self.n_slots:
  123. server_args.extend(["--parallel", self.n_slots])
  124. if self.n_predict:
  125. server_args.extend(["--n-predict", self.n_predict])
  126. if self.slot_save_path:
  127. server_args.extend(["--slot-save-path", self.slot_save_path])
  128. if self.n_ga:
  129. server_args.extend(["--grp-attn-n", self.n_ga])
  130. if self.n_ga_w:
  131. server_args.extend(["--grp-attn-w", self.n_ga_w])
  132. if self.debug:
  133. server_args.append("--verbose")
  134. if self.lora_files:
  135. for lora_file in self.lora_files:
  136. server_args.extend(["--lora", lora_file])
  137. if self.disable_ctx_shift:
  138. server_args.extend(["--no-context-shift"])
  139. if self.api_key:
  140. server_args.extend(["--api-key", self.api_key])
  141. args = [str(arg) for arg in [server_path, *server_args]]
  142. print(f"bench: starting server with: {' '.join(args)}")
  143. flags = 0
  144. if "nt" == os.name:
  145. flags |= subprocess.DETACHED_PROCESS
  146. flags |= subprocess.CREATE_NEW_PROCESS_GROUP
  147. flags |= subprocess.CREATE_NO_WINDOW
  148. self.process = subprocess.Popen(
  149. [str(arg) for arg in [server_path, *server_args]],
  150. creationflags=flags,
  151. stdout=subprocess.PIPE,
  152. stderr=subprocess.PIPE,
  153. env={**os.environ, "LLAMA_CACHE": "tmp"},
  154. )
  155. server_instances.add(self)
  156. def server_log(in_stream, out_stream):
  157. for line in iter(in_stream.readline, b""):
  158. print(line.decode("utf-8"), end="", file=out_stream)
  159. thread_stdout = threading.Thread(
  160. target=server_log, args=(self.process.stdout, sys.stdout), daemon=True
  161. )
  162. thread_stdout.start()
  163. thread_stderr = threading.Thread(
  164. target=server_log, args=(self.process.stderr, sys.stderr), daemon=True
  165. )
  166. thread_stderr.start()
  167. print(f"server pid={self.process.pid}, pytest pid={os.getpid()}")
  168. # wait for server to start
  169. start_time = time.time()
  170. while time.time() - start_time < timeout_seconds:
  171. try:
  172. response = self.make_request("GET", "/slots", headers={
  173. "Authorization": f"Bearer {self.api_key}" if self.api_key else None
  174. })
  175. if response.status_code == 200:
  176. self.ready = True
  177. return # server is ready
  178. except Exception as e:
  179. pass
  180. print(f"Waiting for server to start...")
  181. time.sleep(0.5)
  182. raise TimeoutError(f"Server did not start within {timeout_seconds} seconds")
  183. def stop(self) -> None:
  184. server_instances.remove(self)
  185. if self.process:
  186. print(f"Stopping server with pid={self.process.pid}")
  187. self.process.kill()
  188. self.process = None
  189. def make_request(
  190. self,
  191. method: str,
  192. path: str,
  193. data: dict | Any | None = None,
  194. headers: dict | None = None,
  195. ) -> ServerResponse:
  196. url = f"http://{self.server_host}:{self.server_port}{path}"
  197. parse_body = False
  198. if method == "GET":
  199. response = requests.get(url, headers=headers)
  200. parse_body = True
  201. elif method == "POST":
  202. response = requests.post(url, headers=headers, json=data)
  203. parse_body = True
  204. elif method == "OPTIONS":
  205. response = requests.options(url, headers=headers)
  206. else:
  207. raise ValueError(f"Unimplemented method: {method}")
  208. result = ServerResponse()
  209. result.headers = dict(response.headers)
  210. result.status_code = response.status_code
  211. result.body = response.json() if parse_body else None
  212. print("Response from server", result.body)
  213. return result
  214. def make_stream_request(
  215. self,
  216. method: str,
  217. path: str,
  218. data: dict | None = None,
  219. headers: dict | None = None,
  220. ) -> Iterator[dict]:
  221. url = f"http://{self.server_host}:{self.server_port}{path}"
  222. if method == "POST":
  223. response = requests.post(url, headers=headers, json=data, stream=True)
  224. else:
  225. raise ValueError(f"Unimplemented method: {method}")
  226. for line_bytes in response.iter_lines():
  227. line = line_bytes.decode("utf-8")
  228. if '[DONE]' in line:
  229. break
  230. elif line.startswith('data: '):
  231. data = json.loads(line[6:])
  232. print("Partial response from server", data)
  233. yield data
  234. server_instances: Set[ServerProcess] = set()
  235. class ServerPreset:
  236. @staticmethod
  237. def tinyllama2() -> ServerProcess:
  238. server = ServerProcess()
  239. server.model_hf_repo = "ggml-org/models"
  240. server.model_hf_file = "tinyllamas/stories260K.gguf"
  241. server.model_alias = "tinyllama-2"
  242. server.n_ctx = 256
  243. server.n_batch = 32
  244. server.n_slots = 2
  245. server.n_predict = 64
  246. server.seed = 42
  247. return server
  248. @staticmethod
  249. def bert_bge_small() -> ServerProcess:
  250. server = ServerProcess()
  251. server.model_hf_repo = "ggml-org/models"
  252. server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf"
  253. server.model_alias = "bert-bge-small"
  254. server.n_ctx = 512
  255. server.n_batch = 128
  256. server.n_ubatch = 128
  257. server.n_slots = 2
  258. server.seed = 42
  259. server.server_embeddings = True
  260. return server
  261. @staticmethod
  262. def tinyllama_infill() -> ServerProcess:
  263. server = ServerProcess()
  264. server.model_hf_repo = "ggml-org/models"
  265. server.model_hf_file = "tinyllamas/stories260K-infill.gguf"
  266. server.model_alias = "tinyllama-infill"
  267. server.n_ctx = 2048
  268. server.n_batch = 1024
  269. server.n_slots = 1
  270. server.n_predict = 64
  271. server.temperature = 0.0
  272. server.seed = 42
  273. return server
  274. @staticmethod
  275. def stories15m_moe() -> ServerProcess:
  276. server = ServerProcess()
  277. server.model_hf_repo = "ggml-org/stories15M_MOE"
  278. server.model_hf_file = "stories15M_MOE-F16.gguf"
  279. server.model_alias = "stories15m-moe"
  280. server.n_ctx = 2048
  281. server.n_batch = 1024
  282. server.n_slots = 1
  283. server.n_predict = 64
  284. server.temperature = 0.0
  285. server.seed = 42
  286. return server
  287. @staticmethod
  288. def jina_reranker_tiny() -> ServerProcess:
  289. server = ServerProcess()
  290. server.model_hf_repo = "ggml-org/models"
  291. server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf"
  292. server.model_alias = "jina-reranker"
  293. server.model_file = "./tmp/jina-reranker-v1-tiny-en.gguf"
  294. server.n_ctx = 512
  295. server.n_batch = 512
  296. server.n_slots = 1
  297. server.seed = 42
  298. server.server_reranking = True
  299. return server
  300. def parallel_function_calls(function_list: List[Tuple[Callable[..., Any], Tuple[Any, ...]]]) -> List[Any]:
  301. """
  302. Run multiple functions in parallel and return results in the same order as calls. Equivalent to Promise.all in JS.
  303. Example usage:
  304. results = parallel_function_calls([
  305. (func1, (arg1, arg2)),
  306. (func2, (arg3, arg4)),
  307. ])
  308. """
  309. results = [None] * len(function_list)
  310. exceptions = []
  311. def worker(index, func, args):
  312. try:
  313. result = func(*args)
  314. results[index] = result
  315. except Exception as e:
  316. exceptions.append((index, str(e)))
  317. with ThreadPoolExecutor() as executor:
  318. futures = []
  319. for i, (func, args) in enumerate(function_list):
  320. future = executor.submit(worker, i, func, args)
  321. futures.append(future)
  322. # Wait for all futures to complete
  323. for future in as_completed(futures):
  324. pass
  325. # Check if there were any exceptions
  326. if exceptions:
  327. print("Exceptions occurred:")
  328. for index, error in exceptions:
  329. print(f"Function at index {index}: {error}")
  330. return results
  331. def match_regex(regex: str, text: str) -> bool:
  332. return (
  333. re.compile(
  334. regex, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL
  335. ).search(text)
  336. is not None
  337. )