Sync v2.0 version of code to github repo

fastdeploy/worker/model_runner_base.py

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+"""
+# 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.
+"""
+from abc import ABC, abstractmethod
+
+from paddle import nn
+
+from fastdeploy.config import FDConfig
+from fastdeploy.utils import get_logger
+from fastdeploy.worker.output import ModelRunnerOutput
+
+logger = get_logger("model_runner_base", "model_runner_base.log")
+
+
+class ModelRunnerBase(ABC):
+    """
+        Engine -> (WIP)Executor -> Worker -> ModelRunner -> Model
+        ModelRunner interface abstracts the model execution logic that
+        contain input preparation, token generation, and tokenprocessing.
+    """
+
+    def __init__(self, fd_config: FDConfig, device: str) -> None:
+        """
+            Initialize FDConfig
+        """
+        self.fd_config = fd_config
+        self.model_config = fd_config.model_config
+        self.load_config = fd_config.load_config
+        self.device_config = fd_config.device_config
+        self.speculative_config = fd_config.speculative_config
+        self.parallel_config = fd_config.parallel_config
+        self.graph_opt_config = fd_config.graph_opt_config
+        self.quant_config = fd_config.quant_config
+        # ... config
+
+        self.device = device
+
+    @abstractmethod
+    def load_model(self) -> None:
+        """
+            Load model from local path or remote(will download) path
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_model(self) -> nn.Layer:
+        """
+            Get current model
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def execute_model(self, ) -> ModelRunnerOutput:
+        """
+            Execute model with and get output
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def profile_run(self) -> None:
+        """
+            Execute a forward pass with dummy inputs to profile the memory usage of the model."
+        """
+        raise NotImplementedError