[LLM] First commit the llm deployment code

fastdeploy/model_executor/layers/quantization/quant_base.py

@@ -0,0 +1,85 @@
+"""
+# 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 typing import Any, Optional
+
+
+class QuantMethodBase(ABC):
+    """Base class for different quantized methods."""
+
+    @abstractmethod
+    def create_weights(self, layer, *weight_args, **extra_weight_attrs):
+        """Create weights for a layer.
+
+        The weights will be set as attributes of the layer."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply(self, layer, *args, **kwargs):
+        """Apply the weights in layer to the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
+        raise NotImplementedError
+
+    def process_loaded_weights(self, layer, weights):
+        """Process the weight after loading.
+
+        This can be used for example, to transpose weights for computation.
+        """
+        return
+
+
+class QuantConfigBase(ABC):
+    """Base class for quantization configs."""
+
+    def __init__(self):
+        super().__init__()
+        self.quant_round_type = None
+        self.quant_max_bound = None
+        self.quant_min_bound = None
+
+    @abstractmethod
+    def get_name(self) -> str:
+        """Name of the quantization method."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def from_config(cls, config: dict) -> "QuantConfigBase":
+        """Create a config class from the model's quantization config."""
+        raise NotImplementedError
+
+    @staticmethod
+    def get_from_keys(config: dict[str, Any], keys: list[str]) -> Any:
+        """Get a value from the model's quantization config."""
+        for key in keys:
+            if key in config:
+                return config[key]
+        raise ValueError(f"Cannot find any of {keys} in the model's "
+                         "quantization config.")
+
+    @abstractmethod
+    def get_quant_method(self, layer, prefix) -> Optional[QuantMethodBase]:
+        """Get the quantize method to use for the quantized layer.
+
+        Args:
+            layer: The layer for the quant method.
+            prefix: The full name of the layer in the state dict
+        Returns:
+            The quantize method. None if the given layer doesn't support quant
+            method.
+        """
+        raise NotImplementedError