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