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Class RayExecutor

lmdeploy/pytorch/engine/executor/ray_executor.py:233–718  ·  view source on GitHub ↗

Ray executor.

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231
232
233class RayExecutor(ExecutorBase):
234 """Ray executor."""
235
236 def __init__(
237 self,
238 model_path: str,
239 model_config: ModelConfig,
240 cache_config: CacheConfig,
241 backend_config: BackendConfig,
242 dist_config: DistConfig,
243 misc_config: MiscConfig,
244 adapters: dict[str, str] = None,
245 device_type: str = 'cuda',
246 dtype: str = 'auto',
247 specdecode_config: SpecDecodeConfig = None,
248 trust_remote_code: bool = False,
249 ):
250 """Initialize Executor."""
251 super().__init__(
252 model_path=model_path,
253 model_config=model_config,
254 cache_config=cache_config,
255 backend_config=backend_config,
256 dist_config=dist_config,
257 misc_config=misc_config,
258 adapters=adapters,
259 device_type=device_type,
260 specdecode_config=specdecode_config,
261 trust_remote_code=trust_remote_code,
262 )
263
264 device_ctx = DeviceContext(device_type)
265 with get_device_manager().context(device_ctx):
266 logger.info('Init ray cluster.')
267 attn_tp = dist_config.attn_tp
268 self.ray_ctx = RayContext(attn_tp, dp=dist_config.dp, device_type=device_type)
269 placement_group = self.ray_ctx.get_placement_group()
270 self.placement_group = placement_group
271
272 if self.dp == 1:
273 self.master_addr = _get_master_addr()
274 self.master_port = _get_master_port()
275 else:
276 self.master_addr = _envs.dp_master_addr
277 self.master_port = _envs.dp_master_port
278 if self.master_addr is None or self.master_port is None:
279 raise RuntimeError('DP > 1 requires "LMDEPLOY_DP_MASTER_ADDR" and "LMDEPLOY_DP_MASTER_PORT".')
280
281 # create workerwrapper actors
282 worker_kwargs = dict(
283 model_path=model_path,
284 cache_config=cache_config,
285 model_config=model_config,
286 backend_config=backend_config,
287 dist_config=dist_config,
288 misc_config=misc_config,
289 adapters=adapters,
290 device_type=device_type,

Callers 1

build_executorFunction · 0.85

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