Post-train quant bugfix: squeeze scale factor shape when quantizing bias

distiller/quantization/range_linear.py

@@ -359,7 +359,7 @@ class RangeLinearQuantParamLayerWrapper(RangeLinearQuantWrapper):
         self.has_bias = hasattr(wrapped_module, 'bias') and wrapped_module.bias is not None
         if self.has_bias:
             if self.preset_act_stats:
-                linear_quantize_clamp(wrapped_module.bias.data, self.accum_scale, 0,
+                linear_quantize_clamp(wrapped_module.bias.data, self.accum_scale.squeeze(), 0,
                                       self.accum_min_q_val, self.accum_max_q_val, inplace=True)
             else:
                 b_scale, b_zero_point = _get_quant_params_from_tensor(wrapped_module.bias, num_bits_params, self.mode)
@@ -387,7 +387,7 @@ class RangeLinearQuantParamLayerWrapper(RangeLinearQuantWrapper):
             if self.has_bias:
                 # Re-quantize bias to match x * w scale: b_q' = (in_scale * w_scale / b_scale) * (b_q + b_zero_point)
                 self.wrapped_module.bias.data = linear_quantize_clamp(self.base_b_q + self.b_zero_point,
-                                                                      self.accum_scale / self.b_scale, 0,
+                                                                      self.accum_scale.squeeze() / self.b_scale, 0,
                                                                       self.accum_min_q_val, self.accum_max_q_val)
 
         # Note the main terms within the summation is: