[LLM] First commit the llm deployment code

fastdeploy/entrypoints/engine_client.py

@@ -0,0 +1,249 @@
+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+import zmq
+import time
+from random import randint
+import uuid
+import numpy as np
+
+from fastdeploy.input.preprocess import InputPreprocessor
+from fastdeploy.engine.request import Request
+from fastdeploy.inter_communicator import ZmqClient, IPCSignal
+from fastdeploy.utils import api_server_logger, EngineError
+
+
+class EngineClient:
+    """
+    EngineClient is a class that handles the communication between the client and the server.
+    """
+    def __init__(self, tokenizer, max_model_len, tensor_parallel_size, pid, enable_mm=False):
+        input_processor =  InputPreprocessor(tokenizer, enable_mm)
+        self.enable_mm = enable_mm
+        self.data_processor = input_processor.create_processor()
+        self.max_model_len = max_model_len
+        self.worker_healthy_live_recorded_time_array = np.zeros(shape=[tensor_parallel_size], dtype=np.int32)
+        self.worker_healthy_live_signal = IPCSignal(name="worker_healthy_live_signal",
+                    array=self.worker_healthy_live_recorded_time_array,
+                    dtype=np.int32,
+                    suffix=pid,
+                    create=False)
+
+        model_weights_status = np.zeros([1], dtype=np.int32)
+        self.model_weights_status_signal = IPCSignal(
+            name="model_weights_status",
+            array=model_weights_status,
+            dtype=np.int32,
+            suffix=pid,
+            create=False)
+
+    def create_zmq_client(self, model, mode):
+        """
+        Create a ZMQ client.
+        """
+        self.zmq_client = ZmqClient(model, mode)
+        self.zmq_client.connect()
+
+    def format_and_add_data(self, prompts: dict):
+        """
+        Format the request data and send the request to the server.
+        """
+        if "request_id" in prompts:
+            prompts["request_id"] = prompts["request_id"]
+
+        if "request_id" not in prompts:
+            request_id = str(uuid.uuid4())
+            prompts["request_id"] = request_id
+        query_list = []
+
+        if "max_tokens" not in prompts:
+            prompts["max_tokens"] = self.max_model_len - 1
+
+        self.add_requests(prompts)
+
+    def add_requests(self, task):
+        """
+        Add a new request to the queue.
+
+        Args:
+            task: Request A dictionary representing the request.
+            sampling_params: A dictionary representing the sampling parameters.
+
+        Returns:
+            None
+        """
+        self.vaild_parameters(task)
+
+        task["preprocess_start_time"] = time.time()
+        try:
+            self.data_processor.process_request_dict(task, self.max_model_len)
+
+            task["prompt_token_ids_len"] = len(task["prompt_token_ids"])
+            input_ids_len = task["prompt_token_ids_len"]
+            task["max_tokens"] = min(self.max_model_len - input_ids_len , task.get("max_tokens"))
+            min_tokens = task.get("min_tokens", 1)
+        except Exception as e:
+            api_server_logger.error(e)
+            raise EngineError(str(e), error_code=400)
+
+        if input_ids_len + min_tokens >= self.max_model_len:
+            error_msg = (
+                f"Input text is too long, input_ids_len ({input_ids_len}) "
+                f"+ min_dec_len ({min_tokens}) >= max_model_len "
+            )
+            api_server_logger.error(error_msg)
+            raise EngineError(error_msg, error_code=400)
+
+        if input_ids_len > self.max_model_len:
+            error_msg = (
+                f"Length of input token({input_ids_len}) exceeds the limit max_model_len({self.max_model_len})."
+            )
+            api_server_logger.error(error_msg)
+            raise EngineError(error_msg, error_code=400)
+
+        task["preprocess_end_time"] = time.time()
+        preprocess_cost_time = task["preprocess_end_time"] - task["preprocess_start_time"]
+        api_server_logger.info(
+            f"Cache request with request_id ({task.get('request_id')}), "
+            f"cost {time.time() - preprocess_cost_time}"
+        )
+        api_server_logger.debug(f"Recieve task: {task}")
+        try:
+            if not self.enable_mm:
+                self.zmq_client.send_json(task)
+            else:
+                self.zmq_client.send_pyobj(task)
+        except Exception as e:
+            api_server_logger.error(e)
+            raise EngineError(str(e), error_code=400)
+
+    def vaild_parameters(self, data):
+        """
+        Validate stream options
+        """
+
+
+        if data.get("n"):
+            if data["n"] != 1:
+                raise ValueError("n only support 1.")
+
+        if data.get("max_tokens"):
+            if data["max_tokens"] < 1 or data["max_tokens"] >= self.max_model_len:
+                raise ValueError(f"max_tokens can be defined [1, {self.max_model_len}).")
+
+        if data.get("top_p"):
+            if data["top_p"] > 1 or data["top_p"] < 0:
+                raise ValueError(
+                    "top_p value can only be defined [0, 1].")
+
+
+        if data.get("frequency_penalty"):
+            if  not -2.0 <= data["frequency_penalty"] <= 2.0:
+                raise ValueError("frequency_penalty must be in [-2, 2]")
+
+        if data.get("temperature"):
+            if data["temperature"] < 0:
+                raise ValueError(f"temperature must be non-negative")
+
+
+        if data.get("presence_penalty"):
+            if  not -2.0 <= data["presence_penalty"] <= 2.0:
+                raise ValueError("presence_penalty must be in [-2, 2]")
+
+
+
+        if data.get("seed"):
+            if not 0 <= data["seed"] <= 922337203685477580:
+                raise ValueError("seed must be in [0, 922337203685477580]")
+
+        if data.get("stream_options") and not data.get("stream"):
+            raise ValueError(
+                "Stream options can only be defined when `stream=True`.")
+
+
+
+    def check_health(self, time_interval_threashold=30):
+        """
+        Check the health of the model server by checking whether all workers are alive.
+
+        """
+        if self.worker_healthy_live_signal.value[0]:
+            elapsed_time = time.time() - self.worker_healthy_live_signal.value[0]
+            if elapsed_time > time_interval_threashold:
+                return False, "Worker Service Not Healthy"
+
+        return True, ""
+
+
+    def is_workers_alive(self):
+        """
+        Check the health of the model server by checking whether all workers are alive.
+
+        """
+        if self.model_weights_status_signal.value[0] == 0:
+            return True, ""
+        else:
+            return False, "No model weight enabled"
+
+
+
+    def update_model_weight(self, timeout = 300):
+        """
+        Update the model weight by sending a signal to the server.
+        1 : worker receive the signal and start to update model weight
+        2 : worker update finish and notify client
+        """
+        if self.model_weights_status_signal.value[0] == 0:
+            return True, ""
+        if self.model_weights_status_signal.value[0] == 1:
+            return False, "updating model weight already"
+
+        self.model_weights_status_signal.value[0] = 1
+        api_server_logger.info(f"start update model weight {self.model_weights_status_signal.value}")
+        while self.model_weights_status_signal.value[0] != 0  and timeout != 0:
+            time.sleep(1)
+            timeout -= 1
+            continue
+        if self.model_weights_status_signal.value[0] != 0:
+            return False, "Update model weight timeout"
+        time.sleep(1)
+        return True, ""
+
+
+
+    def clear_load_weight(self, timeout = 300):
+        """
+        Clear the load weight status.
+        -1 : worker receive the signal and start to clear model weight
+        -2 : worker clear finish and notify client
+        """
+        if self.model_weights_status_signal.value[0] == -2:
+            return True, ""
+        if self.model_weights_status_signal.value[0] == -1:
+            return False, "clearing model weight already"
+
+        self.model_weights_status_signal.value[0] = -1
+
+        api_server_logger.info(f"start clear model weight {self.model_weights_status_signal.value}")
+        while self.model_weights_status_signal.value[0] != -2  and timeout != 0:
+            time.sleep(1)
+            timeout -= 1
+            continue
+        if self.model_weights_status_signal.value[0] != -2:
+            return False, "clear model weight timeout"
+        time.sleep(1)
+        return True, ""
+