[main][Quant] Remove unused rotation functions and parameters from W4A4 LAOS quantization (#6648)

## Summary
- Remove unused `set_rotation_config` and `apply_rotation` methods from
`AscendW4A4LaosDynamicLinearMethod`
- Remove unused `rotation_type` field and associated conditional
quantization parameters (`heads_rotation`, `kronecker_rotation_n`,
`kronecker_rotation_m`)

These rotation-related functions and parameters are never called in the
current W4A4 LAOS dynamic quantization workflow.

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>

vllm_ascend/quantization/methods/w4a4_laos_dynamic.py

@@ -35,20 +35,6 @@ class AscendW4A4LaosDynamicLinearMethod(AscendLinearScheme):
 
     def __init__(self):
         self.transpose_weight = True
-        self.rotation_type = None
-
-    def set_rotation_config(self, prefix: str, metadata: dict) -> str | None:
-        """Set rotation config based on prefix and metadata."""
-        layer_idx = prefix.split(".")[2]
-        if prefix.endswith("o_proj"):
-            layers = metadata["quarot"]["heads_rotation"]["layers"]
-            if layer_idx in layers:
-                return "heads_rotation"
-        if prefix.endswith("down_proj"):
-            layers = metadata["quarot"]["kronecker_rotation"]["layers"]
-            if layer_idx in layers:
-                return "kronecker_rotation"
-        return None
 
     def get_weight(self, input_size: int, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
         params_dict = {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}
@@ -58,32 +44,8 @@ class AscendW4A4LaosDynamicLinearMethod(AscendLinearScheme):
         params_dict = {}
         params_dict["weight_scale"] = torch.empty(output_size, 1, dtype=torch.float32)
         params_dict["weight_offset"] = torch.empty(output_size, 1, dtype=torch.float32)
-        if self.rotation_type == "heads_rotation":
-            params_dict["heads_rotation"] = torch.zeros((64, 64), dtype=torch.float32)
-        if self.rotation_type == "kronecker_rotation":
-            params_dict["kronecker_rotation_n"] = torch.zeros((160, 160), dtype=torch.float32)
-            params_dict["kronecker_rotation_m"] = torch.zeros((160, 160), dtype=torch.float32)
         return params_dict
 
-    def apply_rotation(self, layer: torch.nn.Module, x: torch.Tensor) -> torch.Tensor:
-        """Apply rotation transformation to input tensor."""
-        init_shape = x.shape
-        dtype = x.dtype
-        if self.rotation_type == "heads_rotation":
-            Q1 = layer.heads_rotation
-            scaled_x = x.reshape(-1, Q1.shape[1], 128)
-            scaled_x = torch.matmul(Q1.T, scaled_x).reshape(init_shape)
-            return scaled_x.to(dtype)
-        if self.rotation_type == "kronecker_rotation":
-            Q1 = layer.kronecker_rotation_m
-            Q2 = layer.kronecker_rotation_n
-            scaled_x = x.reshape(-1, Q1.shape[0], Q2.shape[0])
-            scaled_x = torch.matmul(scaled_x, Q2)
-            scaled_x = torch.matmul(Q1.T, scaled_x)
-            scaled_x = scaled_x.reshape(init_shape)
-            return scaled_x.to(dtype)
-        return x
-
     def apply(
         self,
         layer: torch.nn.Module,