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

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zzzzwwjj merged 1 commit into from
Feb 11, 2026
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[main][Quant] Remove unused rotation functions and parameters from W4A4 LAOS quantization #6648

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38 changes: 0 additions & 38 deletions vllm_ascend/quantization/methods/w4a4_laos_dynamic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)}
Expand All @@ -58,32 +44,8 @@ def get_perchannel_param(self, output_size: int, params_dtype: torch.dtype) -> d
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,
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