threshold fix

src/brevitas/core/scaling/pre_scaling.py

@@ -97,7 +97,7 @@ def forward(self, weights: Tensor) -> Tensor:
         weights = self.stats_input_view_shape_impl(weights)
         d_w = self.stats(weights)  # denominator for weight normalization
         g = abs_binary_sign_grad(self.restrict_clamp_scaling(self.value))  # g
-        s = self.scaling_impl(weights)  # s
+        s = self.scaling_impl(weights, torch.tensor(1.).type_as(weights))  # s
         value = (s * d_w) / g
         return value
 
@@ -184,7 +184,7 @@ def calc_max_l1_norm(self, input_bit_width: Tensor, input_is_signed: bool) -> Te
     def inner_forward(self, weights: Tensor, input_bit_width: Tensor, input_is_signed: bool):
         weights = self.stats_input_view_shape_impl(weights)
         d_w = self.stats(weights)  # denominator for weight normalization
-        s = self.scaling_impl(weights)  # s
+        s = self.scaling_impl(weights, torch.tensor(1.).type_as(weights))  # s
         g = abs_binary_sign_grad(self.restrict_clamp_scaling(self.value))  # g
         T = self.calc_max_l1_norm(input_bit_width, input_is_signed)  # T / s
         g = torch.clamp_max(g / s, T)