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SDNQ use channelwise only quant

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Disty0
2025-06-03 12:30:19 +03:00
parent 79ec23dd9d
commit ce3a6e27fa
1 changed files with 8 additions and 15 deletions
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modules/model_quant_sdnq.py
+8 -15
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@@ -57,26 +57,21 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
if layer_class_name in conv_types:
if not quant_conv:
return layer
reduction_axes = list(range(layer.weight.ndim))[1:]
use_quantized_matmul = False
is_conv_type = True
reduction_axes = 1
output_channel_size, channel_size = layer.weight.shape[:2]
use_quantized_matmul = False
if dtype_dict[weights_dtype]["num_bits"] < 4:
weights_dtype = "uint4"
elif dtype_dict[weights_dtype]["num_bits"] < 6:
weights_dtype = "int6"
elif layer_class_name in conv_transpose_types:
if not quant_conv:
return layer
reduction_axes = list(range(layer.weight.ndim))
reduction_axes.pop(1)
use_quantized_matmul = False
is_conv_transpose_type = True
reduction_axes = 0
channel_size, output_channel_size = layer.weight.shape[:2]
use_quantized_matmul = False
if dtype_dict[weights_dtype]["num_bits"] < 4:
weights_dtype = "uint4"
elif dtype_dict[weights_dtype]["num_bits"] < 6:
weights_dtype = "int6"
else:
is_linear_type = True
reduction_axes = -1
@@ -92,7 +87,8 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
if dtype_dict[weights_dtype]["num_bits"] < 6:
group_size = 2 ** (2 + dtype_dict[weights_dtype]["num_bits"])
else:
group_size = 2 ** dtype_dict[weights_dtype]["num_bits"]
if dtype_dict[weights_dtype]["num_bits"] < 8:
group_size = 2 ** (1 + dtype_dict[weights_dtype]["num_bits"])
if not use_quantized_matmul and group_size > 0:
if group_size >= channel_size:
@@ -118,16 +114,13 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
# output_channel_size, num_of_groups, group_size, X, X
new_shape[1] = group_size
new_shape.insert(1, num_of_groups)
reduction_axes.pop(0)
reduction_axes.append(layer.weight.ndim)
reduction_axes = 2
elif is_conv_transpose_type:
#channel_size, output_channel_size, X, X
#num_of_groups, group_size, output_channel_size, X, X
new_shape[0] = group_size
new_shape.insert(0, num_of_groups)
reduction_axes = list(range(layer.weight.ndim + 1))
reduction_axes.pop(2)
reduction_axes.pop(0)
reduction_axes = 1
elif is_linear_type:
# output_channel_size, channel_size
# output_channel_size, num_of_groups, group_size