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Make SDNQDequantizer a dataclass

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Disty0
2025-12-08 22:29:45 +03:00
parent 3f161b5532
commit 1c2a81ee2d
1 changed files with 21 additions and 6 deletions
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modules/sdnq/dequantizer.py
+21 -6
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@@ -1,6 +1,7 @@
# pylint: disable=redefined-builtin,no-member,protected-access
from typing import List, Tuple, Optional
from dataclasses import dataclass
import torch
@@ -206,7 +207,9 @@ def dequantize_sdnq_model(model: torch.nn.Module):
return model
class SDNQDequantizer():
# SDNQDequantizer has to be a dataclass for torch.compile
@dataclass
class SDNQDequantizer:
def __init__(
self,
result_dtype: torch.dtype,
@@ -257,19 +260,31 @@ class SDNQDequantizer():
return re_quantize_matmul_symmetric_compiled(weight, scale, self.quantized_matmul_dtype, svd_up=svd_up, svd_down=svd_down, result_shape=self.result_shape)
@devices.inference_context()
def __call__(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul: bool = False, dtype: torch.dtype = None): # pylint: disable=unused-argument
def __call__(self, weight, scale, zero_point, svd_up, svd_down, skip_quantized_matmul: bool = False, skip_compile: bool = False, dtype: torch.dtype = None): # pylint: disable=unused-argument
if dtype is None:
dtype = self.result_dtype
if self.is_packed:
if self.is_unsigned:
return dequantize_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
if skip_compile: # compiled training needs to be traced with the original function
return dequantize_packed_int_asymmetric(weight, scale, zero_point, self.quantized_weight_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
else:
return dequantize_packed_int_asymmetric_compiled(weight, scale, zero_point, self.quantized_weight_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
else:
return dequantize_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=self.re_quantize_for_matmul)
if skip_compile:
return dequantize_packed_int_symmetric(weight, scale, self.quantized_weight_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=self.re_quantize_for_matmul)
else:
return dequantize_packed_int_symmetric_compiled(weight, scale, self.quantized_weight_shape, self.weights_dtype, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=self.re_quantize_for_matmul)
else:
if self.is_unsigned:
return dequantize_asymmetric_compiled(weight, scale, zero_point, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
if skip_compile:
return dequantize_asymmetric(weight, scale, zero_point, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
else:
return dequantize_asymmetric_compiled(weight, scale, zero_point, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul)
else:
return dequantize_symmetric_compiled(weight, scale, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=self.re_quantize_for_matmul)
if skip_compile:
return dequantize_symmetric(weight, scale, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=self.re_quantize_for_matmul)
else:
return dequantize_symmetric_compiled(weight, scale, svd_up=svd_up, svd_down=svd_down, dtype=dtype, result_shape=self.result_shape, skip_quantized_matmul=skip_quantized_matmul, re_quantize_for_matmul=self.re_quantize_for_matmul)
dequantize_asymmetric_compiled = compile_func(dequantize_asymmetric)