chore: import upstream snapshot with attribution

vllm/model_executor/layers/quantization/inc/inc_linear.py

@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import TYPE_CHECKING
+
+import torch
+
+from vllm.model_executor.layers.linear import LinearMethodBase
+
+if TYPE_CHECKING:
+    from .schemes.inc_scheme import INCLinearScheme
+
+
+class INCLinearMethod(LinearMethodBase):
+    def __init__(self, scheme: "INCLinearScheme") -> None:
+        self.scheme = scheme
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        return self.scheme.create_weights(
+            layer=layer,
+            input_size_per_partition=input_size_per_partition,
+            output_partition_sizes=output_partition_sizes,
+            input_size=input_size,
+            output_size=output_size,
+            params_dtype=params_dtype,
+            **extra_weight_attrs,
+        )
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        return self.scheme.process_weights_after_loading(layer)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        return self.scheme.apply_weights(layer, x, bias)