Upload folder using huggingface_hub

attnserver.run_attnserver.slurm.sh.343188.out.log

@@ -123499,3 +123499,666 @@ batch tensor after cp: labels torch.Size([1, 16384])
 batch tensor after cp: loss_mask torch.Size([1, 16384])
 batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
 batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+Start exporting trace 6
+Done exporting trace 6
+ [2025-06-21 21:14:01] iteration        7/      10 | consumed samples:            7 | elapsed time per iteration (ms): 121597.2 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 67108864.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])

attnserver.run_attnserver.slurm.sh.343195.out.log

@@ -67117,3 +67117,293 @@ batch tensor after cp: position_ids torch.Size([1, 32768])
 Start exporting trace 4
 Done exporting trace 4
  [2025-06-21 21:13:17] iteration        5/      10 | consumed samples:            5 | elapsed time per iteration (ms): 158672.1 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 268435456.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor:torch.Size([1, 131072]) loss_mask torch.Size([1, 131072])
+
+batch tensor: batch tensor:attention_mask  labels torch.Size([1, 1, 131072, 131072])torch.Size([1, 131072])
+
+batch tensor:batch tensor:  loss_maskposition_ids  torch.Size([1, 131072])torch.Size([1, 131072])
+
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labelsbatch tensor: torch.Size([1, 32768])
+batch tensor after cp: loss_mask  tokenstorch.Size([1, 32768]) 
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])

attnserver.run_attnserver.slurm.sh.343196.err.log

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7b57691e143fa8e49e77bdf2990f89b57fb8b5d0dae348b9778a1a91e63cb072
-size 30593387
+oid sha256:981e42ff9a48c2631e3df93380753e3ae9fc1cd27622449f48607ac2c0bc2ae5
+size 30620791

attnserver.run_attnserver.slurm.sh.343199.out.log

@@ -19772,3 +19772,294 @@ INFO:megatron.training.initialize:Setting logging level to 0
 INFO:megatron.training.initialize:Setting logging level to 0
 INFO:megatron.training.initialize:Setting logging level to 0
 INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 8
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+INFO:megatron.training.initialize:Setting logging level to 0
+> compiling dataset index builder ...
+INFO:megatron.training.initialize:Setting logging level to 0
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.081 seconds
+WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.838 seconds
+time to initialize megatron (seconds): 10.241
+[after megatron is initialized] datetime: 2025-06-21 21:13:48 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 607188480
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+ > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 607188480
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (607188480 elements, 607188480 padded size):
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.embedding.position_embeddings.weight
+	module.embedding.word_embeddings.weight
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.weight
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+>>> embedding
+>>> decoder
+>>> output_layer
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x1514d73bdca0>, config_logger_dir='')
+ > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 607188480
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 607188480
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 607188480
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 607188480
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (2.60, 3.98)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:13:56 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=131072, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x1514d6782690>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.007666 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 520
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001612 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 520
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001397 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 520
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:13:56 
+done with setup ...
+training ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (7280.81, 7310.26)
+    train/valid/test-data-iterators-setup ..........: (21.80, 150.57)
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:13:56 
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 3 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiBWARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 7 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 5 has a total capacity of 139.81 GiB of which 132.16 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 4 has a total capacity of 139.81 GiB of which 132.17 GiB is free. Including non-PyTorch memory, this process has 7.63 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 2 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+ is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 0 has a total capacity of 139.81 GiB of which 132.14 GiB is free. Including non-PyTorch memory, this process has 7.67 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']
+WARNING:megatron.core.utils:CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
+['Traceback (most recent call last):\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 446, in forward_step\n    (tokens, labels, loss_mask, attention_mask, position_ids), token_lens = get_batch(data_iterator)\n                                                                            ^^^^^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 284, in get_batch\n    batch = next(global_batches)\n            ^^^^^^^^^^^^^^^^^^^^\n', '  File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 226, in setup_batches\n    attention_mask = torch.ones(\n                     ^^^^^^^^^^^\n', 'torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 65536.00 GiB. GPU 1 has a total capacity of 139.81 GiB of which 132.15 GiB is free. Including non-PyTorch memory, this process has 7.65 GiB memory in use. Of the allocated memory 5.15 GiB is allocated by PyTorch, and 1007.51 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n']

attnserver.run_attnserver.slurm.sh.343202.err.log

@@ -6627,3 +6627,122 @@ W0621 21:10:00.022000 3918653 site-packages/torch/distributed/run.py:766] ******
 [rank10]:[W621 21:13:34.257100442 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
 [rank9]:[W621 21:13:35.336997844 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
 [rank8]:[W621 21:13:35.509281143 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
++ set +x
++ set +x
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=65536
++ PROF_CTX_LENGTH=65536
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L65536*tp8.cp2.bs2.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L65536*tp8.cp2.bs2.json' ']'
++ echo 'Running ctx_length=65536, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=2'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343202 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-728:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 65536 --max-position-embeddings 65536 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343202 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-728:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 65536 --max-position-embeddings 65536 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:13:47.330000 2522629 site-packages/torch/distributed/run.py:766] 
+W0621 21:13:47.330000 2522629 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:13:47.330000 2522629 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:13:47.330000 2522629 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:13:47.545000 3922086 site-packages/torch/distributed/run.py:766] 
+W0621 21:13:47.545000 3922086 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:13:47.545000 3922086 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:13:47.545000 3922086 site-packages/torch/distributed/run.py:766] *****************************************
+[rank0]:[W621 21:14:10.917895880 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:14:10.969841440 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank14]:[W621 21:14:10.504313725 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank8]:[W621 21:14:10.524287366 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:14:10.004414749 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank1]:[W621 21:14:10.004913310 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank12]:[W621 21:14:10.539119097 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:14:10.006866612 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank9]:[W621 21:14:10.540305615 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank3]:[W621 21:14:10.009074874 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank13]:[W621 21:14:10.542462813 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank2]:[W621 21:14:10.010101574 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank10]:[W621 21:14:10.543118008 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank7]:[W621 21:14:10.011325301 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank15]:[W621 21:14:10.544664712 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank11]:[W621 21:14:10.546167454 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/models/gpt/gpt_layer_specs.py:94: UserWarning: The fp8 argument in "get_gpt_layer_with_transformer_engine_spec" has been deprecated and will be removed soon. Please update your code accordingly.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/transformer_engine/pytorch/cpu_offload.py:595: DeprecationWarning: Offloading weights is deprecated. Using offload_weights=True does not have any effect.
+  warnings.warn(

attnserver.run_attnserver.slurm.sh.343202.out.log

@@ -27186,3 +27186,860 @@ WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMo
 ----------------------------------------------------------------------------------------------------------
  validation loss at iteration 10 on test set | lm loss value: 1.165397E+01 | lm loss PPL: 1.151480E+05 | 
 ----------------------------------------------------------------------------------------------------------
+Running ctx_length=65536, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=2
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 65536
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 65536
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 16, data-parallel size: 1, context-parallel size: 2, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 8, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 2
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 65536
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 8
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 16
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 65536
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+INFO:megatron.training.initialize:Setting logging level to 0
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 65536
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+INFO:megatron.training.initialize:Setting logging level to 0
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+INFO:megatron.training.initialize:Setting logging level to 0
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 8
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+INFO:megatron.training.initialize:Setting logging level to 0
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+INFO:megatron.training.initialize:Setting logging level to 0
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+INFO:megatron.training.initialize:Setting logging level to 0
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+INFO:megatron.training.initialize:Setting logging level to 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+INFO:megatron.training.initialize:Setting logging level to 0
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 16
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+ > padded vocab (size: 50257) with 943 dummy tokens (new size: 51200)
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 8
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.047 seconds
+WARNING: constraints for invoking optimized fused softmax kernel are not met. We default back to unfused kernel invocations.
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.465 seconds
+time to initialize megatron (seconds): 7.480
+[after megatron is initialized] datetime: 2025-06-21 21:14:16 
+building GPT model ...
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 338753024
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 338753024
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 338753024
+INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False)
+INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1
+Params for bucket 1 (338753024 elements, 338753024 padded size):
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight
+	module.embedding.word_embeddings.weight
+	module.decoder.final_layernorm.weight
+	module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.1.self_attention.linear_qkv.bias
+	module.decoder.layers.1.mlp.linear_fc1.weight
+	module.decoder.layers.0.mlp.linear_fc1.bias
+	module.decoder.layers.1.mlp.linear_fc2.bias
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias
+	module.decoder.layers.1.mlp.linear_fc1.bias
+	module.decoder.layers.0.mlp.linear_fc2.weight
+	module.decoder.layers.0.self_attention.linear_proj.weight
+	module.decoder.layers.1.self_attention.linear_qkv.weight
+	module.decoder.layers.1.self_attention.linear_proj.weight
+	module.decoder.layers.0.mlp.linear_fc2.bias
+	module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight
+	module.decoder.layers.0.self_attention.linear_qkv.bias
+	module.decoder.layers.0.self_attention.linear_proj.bias
+	module.embedding.position_embeddings.weight
+	module.decoder.final_layernorm.bias
+	module.decoder.layers.1.mlp.linear_fc2.weight
+	module.decoder.layers.1.self_attention.linear_proj.bias
+	module.decoder.layers.0.mlp.linear_fc1.weight
+INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=<megatron.core.timers.Timers object at 0x14935fdf1e50>, config_logger_dir='')
+INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine
+>>> embedding
+>>> decoder
+>>> output_layer
+ > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 338753024
+WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt
+    will not load any checkpoints and will start from random
+(min, max) time across ranks (ms):
+    load-checkpoint ................................: (2.91, 3.26)
+[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 21:14:19 
+> building train, validation, and test datasets ...
+ > datasets target sizes (minimum size):
+    train:      10
+    validation: 1
+    test:       1
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True
+INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)]
+> building train, validation, and test datasets for GPT ...
+INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=65536, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=<megatron.training.tokenizer.tokenizer._GPT2BPETokenizer object at 0x149363295520>, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None)
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.004369 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1040
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001585 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1040
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices
+DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False
+WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None
+DEBUG:megatron.core.datasets.gpt_dataset:	> time elapsed: 0.001322 seconds
+INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 1041
+INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1
+> finished creating GPT datasets ...
+[after dataloaders are built] datetime: 2025-06-21 21:14:20 
+done with setup ...
+(min, max) time across ranks (ms):
+    model-and-optimizer-setup ......................: (3069.19, 3071.22)
+    train/valid/test-data-iterators-setup ..........: (14.70, 112.75)
+training ...
+Setting rerun_state_machine.current_iteration to 0...
+[before the start of training step] datetime: 2025-06-21 21:14:20 
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])
+batch tensor: tokens torch.Size([2, 131072])
+batch tensor: labels torch.Size([2, 131072])
+batch tensor: loss_mask torch.Size([2, 131072])
+batch tensor: attention_mask torch.Size([2, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([2, 131072])
+batch tensor after cp: tokens torch.Size([2, 65536])
+batch tensor after cp: labels torch.Size([2, 65536])
+batch tensor after cp: loss_mask torch.Size([2, 65536])
+batch tensor after cp: attention_mask torch.Size([2, 1, 65536, 131072])
+batch tensor after cp: position_ids torch.Size([2, 65536])

attnserver.run_attnserver.slurm.sh.343203.err.log

@@ -0,0 +1,172 @@
++ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
+++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
+++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export _CE_M=
++++ _CE_M=
++++ export _CE_CONDA=
++++ _CE_CONDA=
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ '[' -z x ']'
+++ conda activate
+++ local cmd=activate
+++ case "$cmd" in
+++ __conda_activate activate
+++ '[' -n '' ']'
+++ local ask_conda
++++ PS1=
++++ __conda_exe shell.posix activate
++++ '[' -n '' ']'
++++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
+++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++++ unset _CE_M
++++ unset _CE_CONDA
++++ PS1='(base) '
++++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ export CONDA_SHLVL=1
++++ CONDA_SHLVL=1
++++ export 'CONDA_PROMPT_MODIFIER=(base) '
++++ CONDA_PROMPT_MODIFIER='(base) '
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ __conda_hashr
+++ '[' -n '' ']'
+++ '[' -n '' ']'
+++ hash -r
++ conda activate junda-attnserver
++ local cmd=activate
++ case "$cmd" in
++ __conda_activate activate junda-attnserver
++ '[' -n '' ']'
++ local ask_conda
+++ PS1='(base) '
+++ __conda_exe shell.posix activate junda-attnserver
+++ '[' -n '' ']'
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ unset _CE_M
+++ unset _CE_CONDA
+++ PS1='(junda-attnserver) '
+++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ export CONDA_SHLVL=2
+++ CONDA_SHLVL=2
+++ export CONDA_DEFAULT_ENV=junda-attnserver
+++ CONDA_DEFAULT_ENV=junda-attnserver
+++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
+++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
+++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++ __conda_hashr
++ '[' -n '' ']'
++ '[' -n '' ']'
++ hash -r
++ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ export PROF_TP_SIZE=8
++ PROF_TP_SIZE=8
++ export PROF_CP_SIZE=2
++ PROF_CP_SIZE=2
++ export PROF_BS=4
++ PROF_BS=4
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=1024
++ PROF_CTX_LENGTH=1024
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs4.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs4.json' ']'
++ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=4'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343203 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-274:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343203 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-274:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:25.580000 745731 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:25.580000 745731 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:25.580000 745731 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:25.580000 745731 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:25.581000 1023500 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:25.581000 1023500 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:25.581000 1023500 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:25.581000 1023500 site-packages/torch/distributed/run.py:766] *****************************************
+[rank0]:[W621 21:14:50.312096532 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank7]:[W621 21:14:50.525353333 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank8]:[W621 21:14:50.183904831 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank15]:[W621 21:14:50.186063934 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank1]:[W621 21:14:50.547624534 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:14:50.548731566 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank9]:[W621 21:14:50.211465708 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank3]:[W621 21:14:50.550947312 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank13]:[W621 21:14:50.211955200 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank11]:[W621 21:14:50.214092084 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:14:50.553329856 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank2]:[W621 21:14:50.554580721 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:14:50.555759595 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank10]:[W621 21:14:50.217879933 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank14]:[W621 21:14:50.219224621 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank12]:[W621 21:14:50.219311960 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.

attnserver.run_attnserver.slurm.sh.343203.out.log

@@ -0,0 +1,553 @@
+Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=4
+Cleaning up checkpoint directory: gpt-checkpoint
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+using world size: 16, data-parallel size: 1, context-parallel size: 2, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 8, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 2
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 1024
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 8
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 16
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 1024
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 1024
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 8
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 16
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+ > padded vocab (size: 50257) with 943 dummy tokens (new size: 51200)
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+> initialized tensor model parallel with size 8
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.042 seconds
+> compiling and loading fused kernels ...
+INFO:megatron.training.initialize:Setting logging level to 0

attnserver.run_attnserver.slurm.sh.343204.err.log

@@ -0,0 +1,172 @@
++ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
+++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
+++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export _CE_M=
++++ _CE_M=
++++ export _CE_CONDA=
++++ _CE_CONDA=
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ '[' -z x ']'
+++ conda activate
+++ local cmd=activate
+++ case "$cmd" in
+++ __conda_activate activate
+++ '[' -n '' ']'
+++ local ask_conda
++++ PS1=
++++ __conda_exe shell.posix activate
++++ '[' -n '' ']'
++++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
+++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++++ unset _CE_M
++++ unset _CE_CONDA
++++ PS1='(base) '
++++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ export CONDA_SHLVL=1
++++ CONDA_SHLVL=1
++++ export 'CONDA_PROMPT_MODIFIER=(base) '
++++ CONDA_PROMPT_MODIFIER='(base) '
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ __conda_hashr
+++ '[' -n '' ']'
+++ '[' -n '' ']'
+++ hash -r
++ conda activate junda-attnserver
++ local cmd=activate
++ case "$cmd" in
++ __conda_activate activate junda-attnserver
++ '[' -n '' ']'
++ local ask_conda
+++ PS1='(base) '
+++ __conda_exe shell.posix activate junda-attnserver
+++ '[' -n '' ']'
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ unset _CE_M
+++ unset _CE_CONDA
+++ PS1='(junda-attnserver) '
+++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ export CONDA_SHLVL=2
+++ CONDA_SHLVL=2
+++ export CONDA_DEFAULT_ENV=junda-attnserver
+++ CONDA_DEFAULT_ENV=junda-attnserver
+++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
+++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
+++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++ __conda_hashr
++ '[' -n '' ']'
++ '[' -n '' ']'
++ hash -r
++ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ export PROF_TP_SIZE=8
++ PROF_TP_SIZE=8
++ export PROF_CP_SIZE=2
++ PROF_CP_SIZE=2
++ export PROF_BS=8
++ PROF_BS=8
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=1024
++ PROF_CTX_LENGTH=1024
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs8.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs8.json' ']'
++ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=8'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343204 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-600:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343204 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-600:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:24.484000 714964 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:24.484000 714964 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:24.484000 714964 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:24.484000 714964 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:24.530000 1696942 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:24.530000 1696942 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:24.530000 1696942 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:24.530000 1696942 site-packages/torch/distributed/run.py:766] *****************************************
+[rank11]:[W621 21:14:47.056171966 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 11]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank3]:[W621 21:14:47.466190298 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 3]  using GPU 3 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank12]:[W621 21:14:47.078944088 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 12]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank8]:[W621 21:14:47.079965203 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 8]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank4]:[W621 21:14:47.502964203 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 4]  using GPU 4 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank14]:[W621 21:14:47.237324628 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 14]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank6]:[W621 21:14:47.651750207 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 6]  using GPU 6 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank0]:[W621 21:14:47.678463549 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 0]  using GPU 0 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank7]:[W621 21:14:47.698089708 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 7]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank2]:[W621 21:14:47.698584426 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 2]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank15]:[W621 21:14:47.290367699 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 15]  using GPU 7 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank5]:[W621 21:14:47.700404013 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 5]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank10]:[W621 21:14:47.291263320 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 10]  using GPU 2 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank1]:[W621 21:14:47.701985784 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 1]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank9]:[W621 21:14:47.294021018 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 9]  using GPU 1 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.
+[rank13]:[W621 21:14:47.294471701 ProcessGroupNCCL.cpp:4715] [PG ID 0 PG GUID 0 Rank 13]  using GPU 5 as device used by this process is currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. You can pecify device_id in init_process_group() to force use of a particular device.

attnserver.run_attnserver.slurm.sh.343204.out.log

@@ -0,0 +1,554 @@
+Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=8
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+using world size: 16, data-parallel size: 1, context-parallel size: 2, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 8, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0
+Number of virtual stages per pipeline stage: None
+WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
+using torch.float16 for parameters ...
+------------------------ arguments ------------------------
+  account_for_embedding_in_pipeline_split ......... False
+  account_for_loss_in_pipeline_split .............. False
+  accumulate_allreduce_grads_in_fp32 .............. False
+  adam_beta1 ...................................... 0.9
+  adam_beta2 ...................................... 0.999
+  adam_eps ........................................ 1e-08
+  add_bias_linear ................................. True
+  add_position_embedding .......................... True
+  add_qkv_bias .................................... True
+  adlr_autoresume ................................. False
+  adlr_autoresume_interval ........................ 1000
+  align_grad_reduce ............................... True
+  align_param_gather .............................. False
+  app_tag_run_name ................................ None
+  app_tag_run_version ............................. 0.0.0
+  apply_layernorm_1p .............................. False
+  apply_query_key_layer_scaling ................... False
+  apply_residual_connection_post_layernorm ........ False
+  apply_rope_fusion ............................... False
+  async_save ...................................... None
+  async_tensor_model_parallel_allreduce ........... True
+  attention_backend ............................... AttnBackend.auto
+  attention_dropout ............................... 0.1
+  attention_softmax_in_fp32 ....................... False
+  auto_detect_ckpt_format ......................... False
+  barrier_with_L1_time ............................ True
+  bert_binary_head ................................ True
+  bert_embedder_type .............................. megatron
+  bert_load ....................................... None
+  bf16 ............................................ False
+  bias_dropout_fusion ............................. True
+  bias_gelu_fusion ................................ True
+  bias_swiglu_fusion .............................. True
+  biencoder_projection_dim ........................ 0
+  biencoder_shared_query_context_model ............ False
+  block_data_path ................................. None
+  calc_ft_timeouts ................................ False
+  calculate_per_token_loss ........................ False
+  check_for_large_grads ........................... False
+  check_for_nan_in_loss_and_grad .................. False
+  check_for_spiky_loss ............................ False
+  check_weight_hash_across_dp_replicas_interval ... None
+  ckpt_assume_constant_structure .................. False
+  ckpt_convert_format ............................. None
+  ckpt_convert_save ............................... None
+  ckpt_convert_update_legacy_dist_opt_format ...... False
+  ckpt_format ..................................... torch_dist
+  ckpt_fully_parallel_load ........................ False
+  ckpt_fully_parallel_save ........................ True
+  ckpt_fully_parallel_save_deprecated ............. False
+  ckpt_step ....................................... None
+  classes_fraction ................................ 1.0
+  clip_grad ....................................... 1.0
+  clone_scatter_output_in_embedding ............... True
+  config_logger_dir ............................... 
+  consumed_train_samples .......................... 0
+  consumed_valid_samples .......................... 0
+  context_parallel_size ........................... 2
+  cp_comm_type .................................... ['p2p']
+  create_attention_mask_in_dataloader ............. True
+  cross_entropy_fusion_impl ....................... native
+  cross_entropy_loss_fusion ....................... False
+  cuda_graph_scope ................................ full
+  cuda_graph_warmup_steps ......................... 3
+  data_args_path .................................. None
+  data_cache_path ................................. None
+  data_parallel_random_init ....................... False
+  data_parallel_sharding_strategy ................. no_shard
+  data_parallel_size .............................. 1
+  data_path ....................................... None
+  data_per_class_fraction ......................... 1.0
+  data_sharding ................................... True
+  dataloader_type ................................. single
+  ddp_average_in_collective ....................... False
+  ddp_bucket_size ................................. None
+  ddp_num_buckets ................................. None
+  ddp_pad_buckets_for_high_nccl_busbw ............. False
+  decoder_first_pipeline_num_layers ............... None
+  decoder_last_pipeline_num_layers ................ None
+  decoder_num_layers .............................. None
+  decoder_seq_length .............................. None
+  decoupled_lr .................................... None
+  decoupled_min_lr ................................ None
+  decrease_batch_size_if_needed ................... False
+  defer_embedding_wgrad_compute ................... False
+  deprecated_use_mcore_models ..................... False
+  deterministic_mode .............................. False
+  dino_bottleneck_size ............................ 256
+  dino_freeze_last_layer .......................... 1
+  dino_head_hidden_size ........................... 2048
+  dino_local_crops_number ......................... 10
+  dino_local_img_size ............................. 96
+  dino_norm_last_layer ............................ False
+  dino_teacher_temp ............................... 0.07
+  dino_warmup_teacher_temp ........................ 0.04
+  dino_warmup_teacher_temp_epochs ................. 30
+  disable_bf16_reduced_precision_matmul ........... False
+  disable_mamba_mem_eff_path ...................... False
+  disable_straggler_on_startup .................... False
+  dist_ckpt_format_deprecated ..................... None
+  dist_ckpt_strictness ............................ assume_ok_unexpected
+  distribute_saved_activations .................... False
+  distributed_backend ............................. nccl
+  distributed_timeout_minutes ..................... 10
+  embedding_path .................................. None
+  empty_unused_memory_level ....................... 0
+  enable_cuda_graph ............................... False
+  enable_ft_package ............................... False
+  enable_gloo_process_groups ...................... True
+  enable_msc ...................................... True
+  enable_one_logger ............................... True
+  encoder_num_layers .............................. 2
+  encoder_pipeline_model_parallel_size ............ 0
+  encoder_seq_length .............................. 1024
+  encoder_tensor_model_parallel_size .............. 0
+  end_weight_decay ................................ 0.1
+  eod_mask_loss ................................... False
+  error_injection_rate ............................ 0
+  error_injection_type ............................ transient_error
+  eval_interval ................................... 16
+  eval_iters ...................................... 1
+  evidence_data_path .............................. None
+  exit_duration_in_mins ........................... None
+  exit_interval ................................... None
+  exit_on_missing_checkpoint ...................... False
+  exit_signal_handler ............................. False
+  exp_avg_dtype ................................... torch.float32
+  exp_avg_sq_dtype ................................ torch.float32
+  expert_model_parallel_size ...................... 1
+  expert_tensor_parallel_size ..................... 8
+  external_cuda_graph ............................. False
+  ffn_hidden_size ................................. 16384
+  finetune ........................................ False
+  first_last_layers_bf16 .......................... False
+  flash_decode .................................... False
+  fp16 ............................................ True
+  fp16_lm_cross_entropy ........................... False
+  fp32_residual_connection ........................ False
+  fp8 ............................................. None
+  fp8_amax_compute_algo ........................... most_recent
+  fp8_amax_history_len ............................ 1
+  fp8_interval .................................... 1
+  fp8_margin ...................................... 0
+  fp8_param_gather ................................ False
+  fp8_recipe ...................................... delayed
+  fp8_wgrad ....................................... True
+  fsdp_double_buffer .............................. False
+  global_batch_size ............................... 1
+  grad_reduce_in_bf16 ............................. False
+  gradient_accumulation_fusion .................... True
+  gradient_reduce_div_fusion ...................... True
+  group_query_attention ........................... True
+  head_lr_mult .................................... 1.0
+  heterogeneous_layers_config_encoded_json ........ None
+  heterogeneous_layers_config_path ................ None
+  hidden_dropout .................................. 0.1
+  hidden_size ..................................... 4096
+  hierarchical_context_parallel_sizes ............. None
+  high_priority_stream_groups ..................... []
+  hybrid_attention_ratio .......................... 0.0
+  hybrid_mlp_ratio ................................ 0.0
+  hybrid_override_pattern ......................... None
+  hysteresis ...................................... 2
+  ict_head_size ................................... None
+  ict_load ........................................ None
+  img_h ........................................... 224
+  img_w ........................................... 224
+  indexer_batch_size .............................. 128
+  indexer_log_interval ............................ 1000
+  inference_batch_times_seqlen_threshold .......... -1
+  inference_dynamic_batching ...................... False
+  inference_dynamic_batching_buffer_guaranteed_fraction  0.2
+  inference_dynamic_batching_buffer_overflow_factor  None
+  inference_dynamic_batching_buffer_size_gb ....... 40.0
+  inference_dynamic_batching_chunk_size ........... 256
+  inference_dynamic_batching_max_requests_override  None
+  inference_dynamic_batching_max_tokens_override .. None
+  inference_max_batch_size ........................ 8
+  inference_max_seq_length ........................ 2560
+  inference_rng_tracker ........................... False
+  init_method_std ................................. 0.02
+  init_method_xavier_uniform ...................... False
+  init_model_with_meta_device ..................... False
+  initial_loss_scale .............................. 4294967296
+  inprocess_active_world_size ..................... 16
+  inprocess_barrier_timeout ....................... 120
+  inprocess_completion_timeout .................... 120
+  inprocess_empty_cuda_cache ...................... False
+  inprocess_granularity ........................... node
+  inprocess_hard_timeout .......................... 90
+  inprocess_heartbeat_interval .................... 30
+  inprocess_heartbeat_timeout ..................... 60
+  inprocess_last_call_wait ........................ 1
+  inprocess_max_iterations ........................ None
+  inprocess_monitor_process_interval .............. 1.0
+  inprocess_monitor_thread_interval ............... 1.0
+  inprocess_progress_watchdog_interval ............ 1.0
+  inprocess_restart ............................... False
+  inprocess_soft_timeout .......................... 60
+  inprocess_termination_grace_time ................ 1
+  is_hybrid_model ................................. False
+  iter_per_epoch .................................. 1250
+  iterations_to_skip .............................. []
+  keep_fp8_transpose_cache_when_using_custom_fsdp . False
+  kv_channels ..................................... 64
+  kv_lora_rank .................................... 32
+  lazy_mpu_init ................................... None
+  load ............................................ gpt-checkpoint
+  load_model_opt_format ........................... False
+  local_rank ...................................... 0
+  log_interval .................................... 1
+  log_loss_scale_to_tensorboard ................... True
+  log_memory_to_tensorboard ....................... False
+  log_num_zeros_in_grad ........................... False
+  log_params_norm ................................. False
+  log_progress .................................... False
+  log_straggler ................................... False
+  log_throughput .................................. False
+  log_timers_to_tensorboard ....................... False
+  log_validation_ppl_to_tensorboard ............... False
+  log_world_size_to_tensorboard ................... False
+  logging_level ................................... 0
+  loss_scale ...................................... None
+  loss_scale_window ............................... 1000
+  lr .............................................. 0.0005
+  lr_decay_iters .................................. 150000
+  lr_decay_samples ................................ None
+  lr_decay_style .................................. cosine
+  lr_warmup_fraction .............................. None
+  lr_warmup_init .................................. 0.0
+  lr_warmup_iters ................................. 2
+  lr_warmup_samples ............................... 0
+  lr_wsd_decay_iters .............................. None
+  lr_wsd_decay_samples ............................ None
+  lr_wsd_decay_style .............................. exponential
+  main_grads_dtype ................................ torch.float32
+  main_params_dtype ............................... torch.float32
+  make_vocab_size_divisible_by .................... 128
+  mamba_head_dim .................................. 64
+  mamba_num_groups ................................ 8
+  mamba_num_heads ................................. None
+  mamba_state_dim ................................. 128
+  manual_gc ....................................... False
+  manual_gc_eval .................................. True
+  manual_gc_interval .............................. 0
+  mask_factor ..................................... 1.0
+  mask_prob ....................................... 0.15
+  mask_type ....................................... random
+  masked_softmax_fusion ........................... True
+  max_position_embeddings ......................... 1024
+  max_tokens_to_oom ............................... 12000
+  memory_snapshot_path ............................ snapshot.pickle
+  merge_file ...................................... merges.txt
+  micro_batch_size ................................ 1
+  microbatch_group_size_per_vp_stage .............. None
+  mid_level_dataset_surplus ....................... 0.005
+  min_loss_scale .................................. 1.0
+  min_lr .......................................... 0.0
+  mlp_chunks_for_prefill .......................... 1
+  mmap_bin_files .................................. True
+  mock_data ....................................... True
+  moe_apply_probs_on_input ........................ False
+  moe_aux_loss_coeff .............................. 0.0
+  moe_enable_deepep ............................... False
+  moe_expert_capacity_factor ...................... None
+  moe_extended_tp ................................. False
+  moe_ffn_hidden_size ............................. None
+  moe_grouped_gemm ................................ False
+  moe_input_jitter_eps ............................ None
+  moe_layer_freq .................................. 1
+  moe_layer_recompute ............................. False
+  moe_pad_expert_input_to_capacity ................ False
+  moe_per_layer_logging ........................... False
+  moe_permute_fusion .............................. False
+  moe_router_bias_update_rate ..................... 0.001
+  moe_router_dtype ................................ None
+  moe_router_enable_expert_bias ................... False
+  moe_router_force_load_balancing ................. False
+  moe_router_group_topk ........................... None
+  moe_router_load_balancing_type .................. aux_loss
+  moe_router_num_groups ........................... None
+  moe_router_padding_for_fp8 ...................... False
+  moe_router_pre_softmax .......................... False
+  moe_router_score_function ....................... softmax
+  moe_router_topk ................................. 2
+  moe_router_topk_scaling_factor .................. None
+  moe_shared_expert_intermediate_size ............. None
+  moe_shared_expert_overlap ....................... False
+  moe_token_dispatcher_type ....................... allgather
+  moe_token_drop_policy ........................... probs
+  moe_use_legacy_grouped_gemm ..................... False
+  moe_use_upcycling ............................... False
+  moe_z_loss_coeff ................................ None
+  mrope_section ................................... None
+  mscale .......................................... 1.0
+  mscale_all_dim .................................. 1.0
+  mtp_loss_scaling_factor ......................... 0.1
+  mtp_num_layers .................................. None
+  multi_latent_attention .......................... False
+  nccl_all_reduce_for_prefill ..................... False
+  nccl_communicator_config_path ................... None
+  nccl_ub ......................................... False
+  no_load_optim ................................... None
+  no_load_rng ..................................... None
+  no_persist_layer_norm ........................... False
+  no_rope_freq .................................... None
+  no_save_optim ................................... None
+  no_save_rng ..................................... None
+  non_persistent_ckpt_type ........................ None
+  non_persistent_global_ckpt_dir .................. None
+  non_persistent_local_ckpt_algo .................. fully_parallel
+  non_persistent_local_ckpt_dir ................... None
+  non_persistent_save_interval .................... None
+  norm_epsilon .................................... 1e-05
+  normalization ................................... LayerNorm
+  num_attention_heads ............................. 64
+  num_channels .................................... 3
+  num_classes ..................................... 1000
+  num_dataset_builder_threads ..................... 1
+  num_distributed_optimizer_instances ............. 1
+  num_experts ..................................... None
+  num_layers ...................................... 2
+  num_layers_at_end_in_bf16 ....................... 1
+  num_layers_at_start_in_bf16 ..................... 1
+  num_layers_per_virtual_pipeline_stage ........... None
+  num_query_groups ................................ 16
+  num_virtual_stages_per_pipeline_rank ............ None
+  num_workers ..................................... 2
+  object_storage_cache_path ....................... None
+  one_logger_async ................................ False
+  one_logger_project .............................. megatron-lm
+  one_logger_run_name ............................. None
+  onnx_safe ....................................... None
+  openai_gelu ..................................... False
+  optimizer ....................................... adam
+  optimizer_cpu_offload ........................... False
+  optimizer_offload_fraction ...................... 1.0
+  output_bert_embeddings .......................... False
+  overlap_cpu_optimizer_d2h_h2d ................... False
+  overlap_grad_reduce ............................. False
+  overlap_p2p_comm ................................ False
+  overlap_p2p_comm_warmup_flush ................... False
+  overlap_param_gather ............................ False
+  overlap_param_gather_with_optimizer_step ........ False
+  override_opt_param_scheduler .................... False
+  params_dtype .................................... torch.float16
+  patch_dim ....................................... 16
+  per_split_data_args_path ........................ None
+  perform_initialization .......................... True
+  pin_cpu_grads ................................... True
+  pin_cpu_params .................................. True
+  pipeline_model_parallel_comm_backend ............ None
+  pipeline_model_parallel_size .................... 1
+  pipeline_model_parallel_split_rank .............. None
+  position_embedding_type ......................... learned_absolute
+  pretrained_checkpoint ........................... None
+  profile ......................................... False
+  profile_ranks ................................... [0]
+  profile_step_end ................................ 12
+  profile_step_start .............................. 10
+  q_lora_rank ..................................... None
+  qk_head_dim ..................................... 128
+  qk_l2_norm ...................................... False
+  qk_layernorm .................................... False
+  qk_pos_emb_head_dim ............................. 64
+  query_in_block_prob ............................. 0.1
+  rampup_batch_size ............................... None
+  rank ............................................ 0
+  recompute_granularity ........................... None
+  recompute_method ................................ None
+  recompute_modules ............................... None
+  recompute_num_layers ............................ None
+  record_memory_history ........................... False
+  relative_attention_max_distance ................. 128
+  relative_attention_num_buckets .................. 32
+  replication ..................................... False
+  replication_factor .............................. 2
+  replication_jump ................................ None
+  rerun_mode ...................................... disabled
+  reset_attention_mask ............................ False
+  reset_position_ids .............................. False
+  result_rejected_tracker_filename ................ None
+  retriever_report_topk_accuracies ................ []
+  retriever_score_scaling ......................... False
+  retriever_seq_length ............................ 256
+  retro_add_retriever ............................. False
+  retro_attention_gate ............................ 1
+  retro_cyclic_train_iters ........................ None
+  retro_encoder_attention_dropout ................. 0.1
+  retro_encoder_hidden_dropout .................... 0.1
+  retro_encoder_layers ............................ 2
+  retro_num_neighbors ............................. 2
+  retro_num_retrieved_chunks ...................... 2
+  retro_project_dir ............................... None
+  retro_verify_neighbor_count ..................... True
+  rope_scaling_factor ............................. 8.0
+  rotary_base ..................................... 10000
+  rotary_interleaved .............................. False
+  rotary_percent .................................. 1.0
+  rotary_scaling_factor ........................... 1.0
+  rotary_seq_len_interpolation_factor ............. None
+  run_workload_inspector_server ................... False
+  sample_rate ..................................... 1.0
+  save ............................................ gpt-checkpoint
+  save_interval ................................... 16
+  scatter_gather_tensors_in_pipeline .............. True
+  seed ............................................ 1234
+  seq_length ...................................... 1024
+  sequence_parallel ............................... False
+  sgd_momentum .................................... 0.9
+  short_seq_prob .................................. 0.1
+  skip_train ...................................... False
+  skipped_train_samples ........................... 0
+  spec ............................................ None
+  split ........................................... None
+  squared_relu .................................... False
+  start_weight_decay .............................. 0.1
+  straggler_ctrlr_port ............................ 65535
+  straggler_minmax_count .......................... 1
+  suggested_communication_unit_size ............... None
+  swiglu .......................................... False
+  swin_backbone_type .............................. tiny
+  symmetric_ar_type ............................... None
+  te_rng_tracker .................................. False
+  tensor_model_parallel_size ...................... 8
+  tensorboard_dir ................................. tensorboard-logs/
+  tensorboard_log_interval ........................ 1
+  tensorboard_queue_size .......................... 1000
+  test_data_path .................................. None
+  test_mode ....................................... False
+  tiktoken_num_special_tokens ..................... 1000
+  tiktoken_pattern ................................ None
+  tiktoken_special_tokens ......................... None
+  timing_log_level ................................ 0
+  timing_log_option ............................... minmax
+  titles_data_path ................................ None
+  tokenizer_model ................................. None
+  tokenizer_type .................................. GPT2BPETokenizer
+  torch_fsdp2_reshard_after_forward ............... True
+  tp_comm_bootstrap_backend ....................... nccl
+  tp_comm_bulk_dgrad .............................. True
+  tp_comm_bulk_wgrad .............................. True
+  tp_comm_overlap ................................. False
+  tp_comm_overlap_ag .............................. True
+  tp_comm_overlap_cfg ............................. None
+  tp_comm_overlap_rs .............................. True
+  tp_comm_overlap_rs_dgrad ........................ False
+  tp_comm_split_ag ................................ True
+  tp_comm_split_rs ................................ True
+  train_data_path ................................. None
+  train_iters ..................................... 10
+  train_samples ................................... None
+  train_sync_interval ............................. None
+  transformer_impl ................................ transformer_engine
+  transformer_pipeline_model_parallel_size ........ 1
+  untie_embeddings_and_output_weights ............. False
+  use_checkpoint_args ............................. False
+  use_checkpoint_opt_param_scheduler .............. False
+  use_cpu_initialization .......................... None
+  use_custom_fsdp ................................. False
+  use_dist_ckpt ................................... True
+  use_dist_ckpt_deprecated ........................ False
+  use_distributed_optimizer ....................... False
+  use_flash_attn .................................. False
+  use_legacy_models ............................... False
+  use_mp_args_from_checkpoint_args ................ False
+  use_one_sent_docs ............................... False
+  use_persistent_ckpt_worker ...................... False
+  use_precision_aware_optimizer ................... False
+  use_pytorch_profiler ............................ False
+  use_ring_exchange_p2p ........................... False
+  use_rope_scaling ................................ False
+  use_rotary_position_embeddings .................. False
+  use_sharp ....................................... False
+  use_tokenizer_model_from_checkpoint_args ........ True
+  use_torch_fsdp2 ................................. False
+  use_torch_optimizer_for_cpu_offload ............. False
+  use_tp_pp_dp_mapping ............................ False
+  v_head_dim ...................................... 128
+  valid_data_path ................................. None
+  variable_seq_lengths ............................ False
+  virtual_pipeline_model_parallel_size ............ None
+  vision_backbone_type ............................ vit
+  vision_pretraining .............................. False
+  vision_pretraining_type ......................... classify
+  vocab_extra_ids ................................. 0
+  vocab_file ...................................... vocab.json
+  vocab_size ...................................... None
+  wandb_exp_name .................................. 
+  wandb_project ................................... 
+  wandb_save_dir .................................. 
+  weight_decay .................................... 0.1
+  weight_decay_incr_style ......................... constant
+  wgrad_deferral_limit ............................ 0
+  world_size ...................................... 16
+  yaml_cfg ........................................ None
+-------------------- end of arguments ---------------------
+INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
+> building GPT2BPETokenizer tokenizer ...
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+ > padded vocab (size: 50257) with 943 dummy tokens (new size: 51200)
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
+> initializing torch distributed ...
+> initialized tensor model parallel with size 8
+> initialized pipeline model parallel with size 1
+> setting random seeds to 1234 ...
+> compiling dataset index builder ...
+make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+make: Nothing to be done for 'default'.
+make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
+>>> done with dataset index builder. Compilation time: 0.043 seconds
+> compiling and loading fused kernels ...
+>>> done with compiling and loading fused kernels. Compilation time: 2.710 seconds

attnserver.run_attnserver.slurm.sh.343205.err.log

@@ -0,0 +1,156 @@
++ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
+++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
+++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export _CE_M=
++++ _CE_M=
++++ export _CE_CONDA=
++++ _CE_CONDA=
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ '[' -z x ']'
+++ conda activate
+++ local cmd=activate
+++ case "$cmd" in
+++ __conda_activate activate
+++ '[' -n '' ']'
+++ local ask_conda
++++ PS1=
++++ __conda_exe shell.posix activate
++++ '[' -n '' ']'
++++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
+++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++++ unset _CE_M
++++ unset _CE_CONDA
++++ PS1='(base) '
++++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ export CONDA_SHLVL=1
++++ CONDA_SHLVL=1
++++ export 'CONDA_PROMPT_MODIFIER=(base) '
++++ CONDA_PROMPT_MODIFIER='(base) '
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ __conda_hashr
+++ '[' -n '' ']'
+++ '[' -n '' ']'
+++ hash -r
++ conda activate junda-attnserver
++ local cmd=activate
++ case "$cmd" in
++ __conda_activate activate junda-attnserver
++ '[' -n '' ']'
++ local ask_conda
+++ PS1='(base) '
+++ __conda_exe shell.posix activate junda-attnserver
+++ '[' -n '' ']'
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ unset _CE_M
+++ unset _CE_CONDA
+++ PS1='(junda-attnserver) '
+++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ export CONDA_SHLVL=2
+++ CONDA_SHLVL=2
+++ export CONDA_DEFAULT_ENV=junda-attnserver
+++ CONDA_DEFAULT_ENV=junda-attnserver
+++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
+++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
+++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++ __conda_hashr
++ '[' -n '' ']'
++ '[' -n '' ']'
++ hash -r
++ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ export PROF_TP_SIZE=8
++ PROF_TP_SIZE=8
++ export PROF_CP_SIZE=2
++ PROF_CP_SIZE=2
++ export PROF_BS=16
++ PROF_BS=16
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=1024
++ PROF_CTX_LENGTH=1024
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs16.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs16.json' ']'
++ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=16'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343205 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-188:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343205 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-188:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:30.977000 2067583 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:30.977000 2067583 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:30.977000 2067583 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:30.977000 2067583 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:31.120000 722898 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:31.120000 722898 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:31.120000 722898 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:31.120000 722898 site-packages/torch/distributed/run.py:766] *****************************************

attnserver.run_attnserver.slurm.sh.343205.out.log

@@ -0,0 +1,19 @@
+Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=16
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+Cleaning up checkpoint directory: gpt-checkpoint
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3

attnserver.run_attnserver.slurm.sh.343206.err.log

@@ -0,0 +1,156 @@
++ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
+++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
+++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export _CE_M=
++++ _CE_M=
++++ export _CE_CONDA=
++++ _CE_CONDA=
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ '[' -z x ']'
+++ conda activate
+++ local cmd=activate
+++ case "$cmd" in
+++ __conda_activate activate
+++ '[' -n '' ']'
+++ local ask_conda
++++ PS1=
++++ __conda_exe shell.posix activate
++++ '[' -n '' ']'
++++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
+++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++++ unset _CE_M
++++ unset _CE_CONDA
++++ PS1='(base) '
++++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ export CONDA_SHLVL=1
++++ CONDA_SHLVL=1
++++ export 'CONDA_PROMPT_MODIFIER=(base) '
++++ CONDA_PROMPT_MODIFIER='(base) '
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ __conda_hashr
+++ '[' -n '' ']'
+++ '[' -n '' ']'
+++ hash -r
++ conda activate junda-attnserver
++ local cmd=activate
++ case "$cmd" in
++ __conda_activate activate junda-attnserver
++ '[' -n '' ']'
++ local ask_conda
+++ PS1='(base) '
+++ __conda_exe shell.posix activate junda-attnserver
+++ '[' -n '' ']'
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ unset _CE_M
+++ unset _CE_CONDA
+++ PS1='(junda-attnserver) '
+++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ export CONDA_SHLVL=2
+++ CONDA_SHLVL=2
+++ export CONDA_DEFAULT_ENV=junda-attnserver
+++ CONDA_DEFAULT_ENV=junda-attnserver
+++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
+++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
+++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++ __conda_hashr
++ '[' -n '' ']'
++ '[' -n '' ']'
++ hash -r
++ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ export PROF_TP_SIZE=8
++ PROF_TP_SIZE=8
++ export PROF_CP_SIZE=2
++ PROF_CP_SIZE=2
++ export PROF_BS=32
++ PROF_BS=32
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=1024
++ PROF_CTX_LENGTH=1024
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs32.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp2.bs32.json' ']'
++ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=32'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 0 --rdzv_id 343206 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 2 --node_rank 1 --rdzv_id 343206 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-239:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 2 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:31.024000 966285 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:31.024000 966285 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:31.024000 966285 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:31.024000 966285 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:14:31.036000 1892432 site-packages/torch/distributed/run.py:766] 
+W0621 21:14:31.036000 1892432 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:14:31.036000 1892432 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:14:31.036000 1892432 site-packages/torch/distributed/run.py:766] *****************************************

attnserver.run_attnserver.slurm.sh.343206.out.log

@@ -0,0 +1,29 @@
+Running ctx_length=1024, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=32
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 2
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
+WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0
+INFO:megatron.training.initialize:Setting logging level to 0