diff --git a/egs/libriheavy/LM/zipformer1/subformer.py b/egs/libriheavy/LM/zipformer1/subformer.py
index 37873f245..071cf8ac3 100644
--- a/egs/libriheavy/LM/zipformer1/subformer.py
+++ b/egs/libriheavy/LM/zipformer1/subformer.py
@@ -862,9 +862,6 @@ class LearnedDownsamplingModule(nn.Module):
             d = self.downsampling_factor
             seq_len_reduced = (seq_len + d - 1) // d
 
-            if random.random() < 0.01 or __name__ == '__main__':
-                logging.info(f"mean weight={weights.mean()}, mean-abs-scores={scores.abs().mean()} positive-scores={(scores>0).to(torch.float32).mean()}, seq_len={seq_len}, seq_len_reduced={seq_len_reduced}")
-
 
             weights_discarded = weights[:, seq_len_reduced:2*seq_len_reduced]
             missing = weights_discarded.shape[1] - seq_len_reduced
@@ -875,6 +872,10 @@ class LearnedDownsamplingModule(nn.Module):
                                                            dtype=weights.dtype)),
                                               dim=1)
 
+            if random.random() < 0.01 or __name__ == '__main__':
+                logging.info(f"mean weight={weights.mean()}, mean-abs-scores={scores.abs().mean()} positive-scores={(scores>0).to(torch.float32).mean()}, discarded-weights={weights_discarded.mean()}, seq_len={seq_len}, seq_len_reduced={seq_len_reduced}")
+
+
 
             # randomly rotate `weights_discarded` on the sequence axis; this is
             # intended to ensure that it doesn't assign the highest scores to