mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-09-27 21:02:24 +08:00
267 lines
9.8 KiB
Python
267 lines
9.8 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import time
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import uuid
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import numpy as np
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from fastdeploy.input.preprocess import InputPreprocessor
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from fastdeploy.inter_communicator import IPCSignal, ZmqClient
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from fastdeploy.metrics.work_metrics import work_process_metrics
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from fastdeploy.utils import EngineError, api_server_logger
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class EngineClient:
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"""
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EngineClient is a class that handles the communication between the client and the server.
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"""
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def __init__(
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self,
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tokenizer,
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max_model_len,
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tensor_parallel_size,
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pid,
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limit_mm_per_prompt,
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mm_processor_kwargs,
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enable_mm=False,
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reasoning_parser=None,
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):
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input_processor = InputPreprocessor(
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tokenizer,
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reasoning_parser,
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limit_mm_per_prompt,
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mm_processor_kwargs,
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enable_mm,
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)
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self.enable_mm = enable_mm
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self.reasoning_parser = reasoning_parser
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self.data_processor = input_processor.create_processor()
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self.max_model_len = max_model_len
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self.worker_healthy_live_recorded_time_array = np.zeros(shape=[tensor_parallel_size], dtype=np.int32)
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self.worker_healthy_live_signal = IPCSignal(
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name="worker_healthy_live_signal",
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array=self.worker_healthy_live_recorded_time_array,
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dtype=np.int32,
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suffix=pid,
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create=False,
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)
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model_weights_status = np.zeros([1], dtype=np.int32)
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self.model_weights_status_signal = IPCSignal(
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name="model_weights_status",
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array=model_weights_status,
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dtype=np.int32,
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suffix=pid,
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create=False,
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)
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def create_zmq_client(self, model, mode):
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"""
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Create a ZMQ client.
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"""
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self.zmq_client = ZmqClient(model, mode)
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self.zmq_client.connect()
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def format_and_add_data(self, prompts: dict):
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"""
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Format the request data and send the request to the server.
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"""
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if "request_id" in prompts:
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prompts["request_id"] = prompts["request_id"]
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if "request_id" not in prompts:
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request_id = str(uuid.uuid4())
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prompts["request_id"] = request_id
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if "max_tokens" not in prompts:
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prompts["max_tokens"] = self.max_model_len - 1
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self.add_requests(prompts)
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return prompts["prompt_token_ids"]
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def add_requests(self, task):
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"""
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Add a new request to the queue.
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Args:
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task: Request A dictionary representing the request.
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sampling_params: A dictionary representing the sampling parameters.
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Returns:
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None
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"""
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task["preprocess_start_time"] = time.time()
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try:
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self.data_processor.process_request_dict(task, self.max_model_len)
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task["prompt_token_ids_len"] = len(task["prompt_token_ids"])
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input_ids_len = task["prompt_token_ids_len"]
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task["max_tokens"] = min(self.max_model_len - input_ids_len, task.get("max_tokens"))
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if task.get("reasoning_max_tokens", None) is None:
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task["reasoning_max_tokens"] = max(int(task["max_tokens"] * 0.8), 1)
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min_tokens = task.get("min_tokens", 1)
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if "messages" in task:
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del task["messages"]
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api_server_logger.info(f"task['max_tokens']:{task['max_tokens']}")
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work_process_metrics.request_params_max_tokens.observe(task["max_tokens"])
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work_process_metrics.prompt_tokens_total.inc(input_ids_len)
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work_process_metrics.request_prompt_tokens.observe(input_ids_len)
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except Exception as e:
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api_server_logger.error(e)
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raise EngineError(str(e), error_code=400)
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if input_ids_len + min_tokens >= self.max_model_len:
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error_msg = (
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f"Input text is too long, input_ids_len ({input_ids_len}) "
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f"+ min_tokens({min_tokens}) >= max_model_len({self.max_model_len})"
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)
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api_server_logger.error(error_msg)
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raise EngineError(error_msg, error_code=400)
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if input_ids_len > self.max_model_len:
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error_msg = (
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f"Length of input token({input_ids_len}) exceeds the limit max_model_len({self.max_model_len})."
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)
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api_server_logger.error(error_msg)
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raise EngineError(error_msg, error_code=400)
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task["preprocess_end_time"] = time.time()
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preprocess_cost_time = task["preprocess_end_time"] - task["preprocess_start_time"]
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api_server_logger.info(
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f"Cache request with request_id ({task.get('request_id')}), " f"cost {time.time() - preprocess_cost_time}"
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)
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self.vaild_parameters(task)
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api_server_logger.debug(f"Recieve task: {task}")
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try:
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if not self.enable_mm:
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self.zmq_client.send_json(task)
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else:
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self.zmq_client.send_pyobj(task)
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except Exception as e:
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api_server_logger.error(e)
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raise EngineError(str(e), error_code=400)
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def vaild_parameters(self, data):
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"""
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Validate stream options
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"""
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if data.get("n"):
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if data["n"] != 1:
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raise ValueError("n only support 1.")
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if data.get("max_tokens"):
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if data["max_tokens"] < 1 or data["max_tokens"] >= self.max_model_len:
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raise ValueError(f"max_tokens can be defined [1, {self.max_model_len}).")
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if data.get("reasoning_max_tokens"):
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if data["reasoning_max_tokens"] > data["max_tokens"] or data["reasoning_max_tokens"] < 1:
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raise ValueError("reasoning_max_tokens must be between max_tokens and 1")
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if data.get("top_p"):
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if data["top_p"] > 1 or data["top_p"] < 0:
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raise ValueError("top_p value can only be defined [0, 1].")
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if data.get("frequency_penalty"):
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if not -2.0 <= data["frequency_penalty"] <= 2.0:
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raise ValueError("frequency_penalty must be in [-2, 2]")
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if data.get("temperature"):
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if data["temperature"] < 0:
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raise ValueError("temperature must be non-negative")
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if data.get("presence_penalty"):
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if not -2.0 <= data["presence_penalty"] <= 2.0:
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raise ValueError("presence_penalty must be in [-2, 2]")
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if data.get("seed"):
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if not 0 <= data["seed"] <= 922337203685477580:
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raise ValueError("seed must be in [0, 922337203685477580]")
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if data.get("stream_options") and not data.get("stream"):
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raise ValueError("Stream options can only be defined when `stream=True`.")
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def check_health(self, time_interval_threashold=30):
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"""
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Check the health of the model server by checking whether all workers are alive.
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"""
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if self.worker_healthy_live_signal.value[0]:
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elapsed_time = time.time() - self.worker_healthy_live_signal.value[0]
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if elapsed_time > time_interval_threashold:
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return False, "Worker Service Not Healthy"
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return True, ""
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def is_workers_alive(self):
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"""
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Check the health of the model server by checking whether all workers are alive.
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"""
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if self.model_weights_status_signal.value[0] == 0:
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return True, ""
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else:
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return False, "No model weight enabled"
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def update_model_weight(self, timeout=300):
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"""
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Update the model weight by sending a signal to the server.
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1 : worker receive the signal and start to update model weight
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2 : worker update finish and notify client
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"""
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if self.model_weights_status_signal.value[0] == 0:
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return True, ""
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if self.model_weights_status_signal.value[0] == 1:
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return False, "updating model weight already"
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self.model_weights_status_signal.value[0] = 1
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api_server_logger.info(f"start update model weight {self.model_weights_status_signal.value}")
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while self.model_weights_status_signal.value[0] != 0 and timeout != 0:
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time.sleep(1)
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timeout -= 1
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continue
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if self.model_weights_status_signal.value[0] != 0:
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return False, "Update model weight timeout"
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time.sleep(1)
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return True, ""
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def clear_load_weight(self, timeout=300):
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"""
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Clear the load weight status.
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-1 : worker receive the signal and start to clear model weight
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-2 : worker clear finish and notify client
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"""
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if self.model_weights_status_signal.value[0] == -2:
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return True, ""
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if self.model_weights_status_signal.value[0] == -1:
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return False, "clearing model weight already"
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self.model_weights_status_signal.value[0] = -1
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api_server_logger.info(f"start clear model weight {self.model_weights_status_signal.value}")
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while self.model_weights_status_signal.value[0] != -2 and timeout != 0:
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time.sleep(1)
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timeout -= 1
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continue
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if self.model_weights_status_signal.value[0] != -2:
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return False, "clear model weight timeout"
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time.sleep(1)
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return True, ""
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