allow for division of loss prior to scaling, for gradient accumulation purposes

dalle2_pytorch/train.py

@@ -169,7 +169,14 @@ class DecoderTrainer(nn.Module):
             ema_unet = self.ema_unets[index]
             ema_unet.update()
 
-    def forward(self, x, *, unet_number, **kwargs):
+    def forward(
+        self,
+        x,
+        *,
+        unet_number,
+        divisor = 1,
+        **kwargs
+    ):
         with autocast(enabled = self.amp):
             loss = self.decoder(x, unet_number = unet_number, **kwargs)
-        return self.scale(loss, unet_number = unet_number)
+        return self.scale(loss / divisor, unet_number = unet_number)