[Misc][Refactor] Generalize linear_method to be quant_method (#4373)

vllm/model_executor/layers/quantization/base_config.py

@@ -2,8 +2,33 @@ from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
 import torch
+from torch import nn
 
-from vllm.model_executor.layers.linear import LinearMethodBase
+
+class QuantizeMethodBase(ABC):
+    """Base class for different quantized methods."""
+
+    @abstractmethod
+    def create_weights(self, layer: torch.nn.Module, *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: torch.nn.Module, *args, **kwargs) -> torch.Tensor:
+        """Apply the weights in layer to the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
+        raise NotImplementedError
+
+    def process_weights_after_loading(self, layer: nn.Module) -> None:
+        """Process the weight after loading.
+
+        This can be used for example, to transpose weights for computation.
+        """
+        return
 
 
 class QuantizationConfig(ABC):
@@ -51,8 +76,8 @@ class QuantizationConfig(ABC):
                          "quantization config.")
 
     @abstractmethod
-    def get_linear_method(self) -> LinearMethodBase:
-        """Get the linear method to use for the quantized linear layer."""
+    def get_quant_method(self, layer: torch.nn.Module) -> QuantizeMethodBase:
+        """Get the quantize method to use for the quantized layer."""
         raise NotImplementedError
 
     @abstractmethod