fix

src/brevitas/core/quant/float.py

@@ -52,9 +52,6 @@ def __init__(
         if scaling_impl is None:
             scaling_impl = ConstScaling(1., device=device, dtype=dtype)
 
-        if float_scaling_impl is None:
-            float_scaling_impl = ConstScaling(1., device=device, dtype=dtype)
-
         # Zero-point is currently hardcoded to 0
         self.zero_point_impl = StatelessBuffer(torch.tensor(0., device=device, dtype=dtype))
         self.float_scaling_impl = float_scaling_impl
@@ -68,10 +65,13 @@ def __init__(
 
     @brevitas.jit.script_method
     def quantize(self, x: torch.Tensor):
-        scaling_impl_value = self.scaling_impl(x)
-        float_scaling_impl_value = self.float_scaling_impl(
-            self.exponent_bit_width(), self.mantissa_bit_width(), self.exponent_bias())
-        scale = scaling_impl_value / float_scaling_impl_value
+        scale = self.scaling_impl(x)
+
+        if self.float_scaling_impl is not None:
+            float_scaling_impl_value = self.float_scaling_impl(
+                self.exponent_bit_width(), self.mantissa_bit_width(), self.exponent_bias())
+            scale = scale / float_scaling_impl_value
+
         scaled_x = x / scale
         internal_scale = float_internal_scale(
             scaled_x, self.mantissa_bit_width(), self.fp_internal_scale_min(), self.eps)