Fix (GPFQ): use max/mean to avoid running out of memory

src/brevitas/graph/gpfq.py

@@ -221,12 +221,18 @@ def update_batch(self, module, input, current_layer):
             if self.float_input is None:
                 self.float_input = inp_processed
             else:
-                self.float_input = torch.cat([self.float_input, inp_processed], dim=1)
+                self.float_input = torch.max(self.float_input, inp_processed)
+                # self.float_input = torch.stack([self.float_input, inp_processed])
+                # self.float_input = self.float_input.mean(dim=0)
+                # self.float_input = torch.cat([self.float_input, inp_processed], dim=1)
         else:
             if self.quantized_input is None:
                 self.quantized_input = inp_processed
             else:
-                self.quantized_input = torch.cat([self.quantized_input, inp_processed], dim=1)
+                self.quantized_input = torch.max(self.quantized_input, inp_processed)
+                # self.quantized_input = torch.stack([self.quantized_input, inp_processed])
+                # self.quantized_input = self.quantized_input.mean(dim=0)
+                # self.quantized_input = torch.cat([self.quantized_input, inp_processed], dim=1)
         # If we are executing GPFQ with group of parallel layers, we keep track of how many forward
         # we executed. Once we executed as many as the number of parallel_layers, we raise
         # StopFwdException