Feat (GPFA2Q): unify quant_input initialization

src/brevitas/graph/gpfq.py

@@ -297,6 +297,10 @@ def __init__(
         self.accumulator_bit_width = accumulator_bit_width
 
     def single_layer_update(self):
+        # raise error in case no quant-input is here
+        if self.quant_input is None:
+            raise ValueError(
+                'Expected quant input to calculate Upper Bound on L1 norm, but received None')
         weight = self.layer.weight.data
         dev = weight.device
         dtype = weight.dtype
@@ -311,8 +315,8 @@ def single_layer_update(self):
         self.quantized_input = self.quantized_input.to(dev)
 
         # get upper bound
-        input_bit_width = self.layer.quant_input_bit_width()
-        input_is_signed = self.layer.is_quant_input_signed
+        input_bit_width = self.quant_input.bit_width
+        input_is_signed = self.quant_input.signed
         T = get_upper_bound_on_l1_norm(self.accumulator_bit_width, input_bit_width, input_is_signed)
         s = self.layer.quant_weight_scale()
         s = s.view(self.groups, -1)  # [Groups, OC/Groups]

src/brevitas/graph/gpxq.py

@@ -182,6 +182,14 @@ def process_input(self, inp):
                     signed=inp.signed,
                     training=inp.training)
             inp = inp.value
+        elif self.layer.is_input_quant_enabled:
+            self.quant_input = QuantTensor(
+                value=None,
+                scale=self.layer.quant_input_scale(),
+                zero_point=self.layer.quant_input_zero_point(),
+                bit_width=self.layer.quant_input_bit_width(),
+                signed=self.layer.is_quant_input_signed,
+                training=self.layer.training)
 
         # If input is unbatched, add batch_size = 1
         if len(inp.shape) == 1: