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SDNQ fix conv matmul

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Disty0
2025-09-16 17:12:00 +03:00
parent 4c03b22887
commit 39027ccfea
1 changed files with 6 additions and 4 deletions
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modules/sdnq/__init__.py
+6 -4
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@@ -75,6 +75,10 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
use_quantized_matmul = group_channel_size >= 32 and output_channel_size >= 32
if use_quantized_matmul and not dtype_dict[weights_dtype]["is_integer"]:
use_quantized_matmul = output_channel_size % 16 == 0 and group_channel_size % 16 == 0
if use_quantized_matmul and dtype_dict[weights_dtype]["num_bits"] == 8:
result_shape = layer.weight.shape
layer.weight.data = layer.weight.flatten(1,-1)
reduction_axes = -1
elif layer_class_name in conv_transpose_types:
if not quant_conv:
return layer
@@ -128,7 +132,8 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
num_of_groups = int(num_of_groups)
if num_of_groups > 1:
result_shape = layer.weight.shape
if result_shape is None:
result_shape = layer.weight.shape
new_shape = list(result_shape)
if is_conv_type:
# output_channel_size, channel_size, X, X
@@ -165,9 +170,6 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
re_quantize_for_matmul = (num_of_groups > 1 or zero_point is not None)
if use_quantized_matmul and not re_quantize_for_matmul:
if is_conv_type:
result_shape = layer.weight.shape
layer.weight.data = layer.weight.reshape(output_channel_size, -1)
scale.transpose_(0,1)
layer.weight.transpose_(0,1)
if not dtype_dict[weights_dtype]["is_integer"]: