use eval vs training mode to determine whether to call backprop on trainer forward

dalle2_pytorch/train.py

@@ -279,7 +279,9 @@ class DiffusionPriorTrainer(nn.Module):
                 loss = loss * chunk_size_frac
 
             total_loss += loss.item()
-            self.scaler.scale(loss).backward()
+
+            if self.training:
+                self.scaler.scale(loss).backward()
 
         return total_loss
 
@@ -406,6 +408,8 @@ class DecoderTrainer(nn.Module):
                 loss = loss * chunk_size_frac
 
             total_loss += loss.item()
-            self.scale(loss, unet_number = unet_number).backward()
+
+            if self.training:
+                self.scale(loss, unet_number = unet_number).backward()
 
         return total_loss

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.2.31',
+  version = '0.2.32',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',