[LLM] First commit the llm deployment code

fastdeploy/model_executor/models/model_base.py

@@ -0,0 +1,98 @@
+"""
+# Copyright (c) 2024 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 typing import Dict, Union
+
+import numpy as np
+import paddle
+from paddle import nn
+
+
+class ModelRegistry:
+    """
+    Used to register and retrieve model classes.
+    """
+    _registry = {}
+
+    @classmethod
+    def register(cls, model_class):
+        if issubclass(
+                model_class,
+                ModelForCasualLM) and model_class is not ModelForCasualLM:
+            cls._registry[model_class.name()] = model_class
+        return model_class
+
+    @classmethod
+    def get_class(cls, name):
+        if name not in cls._registry:
+            raise ValueError(f"Model '{name}' is not registered!")
+        return cls._registry[name]
+
+
+class ModelForCasualLM(nn.Layer, ABC):
+    """
+    Base class for LM
+    """
+
+    def __init__(self, configs):
+        """
+        Args:
+            configs (dict): Configurations including parameters such as max_dec_len, min_dec_len, decode_strategy,
+                ori_vocab_size, use_topp_sampling, etc.
+        """
+        super(ModelForCasualLM, self).__init__()
+
+    @abstractmethod
+    def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray,
+                                                         paddle.Tensor]]):
+        """
+        Load model parameters from a given state dictionary.
+
+        Args:
+            state_dict (dict[str, np.ndarray | paddle.Tensor]):
+                A dictionary containing model parameters, where keys are parameter names
+                and values are NumPy arrays or PaddlePaddle tensors.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def forward(
+        self,
+        input_ids=None,
+        pos_emb=None,
+        **model_kwargs,
+    ):
+        """
+        Defines the forward pass of the model for generating text.
+
+        Args:
+            input_ids (Tensor, optional): The input token ids to the model.
+            pos_emb (Tensor, optional): position Embeddings for model.
+            **model_kwargs: Additional keyword arguments for the model.
+
+        Returns:
+            Tensor or list of Tensors: Generated tokens or decoded outputs.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def compute_logits(self, hidden_state, **logits_prosessor_kwargs):
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def name(self):
+        raise NotImplementedError