[Core] Support loading GGUF model (#5191)

Co-authored-by: Michael Goin <michael@neuralmagic.com>

vllm/model_executor/layers/quantization/base_config.py

@@ -1,5 +1,6 @@
+import inspect
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Type
 
 import torch
 from torch import nn
@@ -23,6 +24,14 @@ class QuantizeMethodBase(ABC):
         Expects create_weights to have been called before on the layer."""
         raise NotImplementedError
 
+    # Not required functions
+    def embedding(self, layer: torch.nn.Module, *args,
+                  **kwargs) -> torch.Tensor:
+        """Gather embeddings in the layer based on indices in 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.
 
@@ -31,6 +40,21 @@ class QuantizeMethodBase(ABC):
         return
 
 
+def method_has_implemented_embedding(
+        method_class: Type[QuantizeMethodBase]) -> bool:
+    """
+    Not all quant methods have embedding implemented, so we need to check that
+    it exists for our given method. We check this by making sure the function
+    has been changed from the base implementation.
+    """
+    base_embedding = inspect.getattr_static(QuantizeMethodBase, "embedding",
+                                            None)
+    class_embedding = inspect.getattr_static(method_class, "embedding", None)
+
+    return (class_embedding is not None
+            and class_embedding is not base_embedding)
+
+
 class QuantizationConfig(ABC):
     """Base class for quantization configs."""