init

vllm/model_executor/layers/quantization/base_config.py

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+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
+import torch
+from torch import nn
+
+
+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):
+    """Base class for quantization configs."""
+
+    @abstractmethod
+    def get_name(self) -> str:
+        """Name of the quantization method."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        """List of supported activation dtypes."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_min_capability(self) -> int:
+        """Minimum GPU capability to support the quantization method.
+
+        E.g., 70 for Volta, 75 for Turing, 80 for Ampere.
+        This requirement is due to the custom CUDA kernels used by the
+        quantization method.
+        """
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def get_config_filenames() -> List[str]:
+        """List of filenames to search for in the model directory."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def from_config(cls, config: Dict[str, Any]) -> "QuantizationConfig":
+        """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: torch.nn.Module) -> Optional[QuantizeMethodBase]:
+        """Get the quantize method to use for the quantized layer.
+        
+        Args:
+            layer: The layer for the quant method.
+        Returns:
+            The quantize method. None if the given layer doesn't support quant
+            method.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_scaled_act_names(self) -> List[str]:
+        """Returns the activation function names that should be post-scaled.
+
+        For now, this is only used by AWQ.
+        """
+        raise NotImplementedError