diff --git "a/attnserver.run_attnserver.slurm.sh.343195.out.log" "b/attnserver.run_attnserver.slurm.sh.343195.out.log"
--- "a/attnserver.run_attnserver.slurm.sh.343195.out.log"
+++ "b/attnserver.run_attnserver.slurm.sh.343195.out.log"
@@ -52277,3 +52277,2264 @@ batch tensor after cp: position_ids torch.Size([1, 16384])
 Start exporting trace 1
 Done exporting trace 1
  [2025-06-21 20:28:07] iteration        2/      10 | consumed samples:            2 | elapsed time per iteration (ms): 21463.0 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 2147483648.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
+batch tensor: labels torch.Size([1, 65536])
+batch tensor: loss_mask torch.Size([1, 65536])
+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 65536])
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+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+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, 65536])
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+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
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+batch tensor after cp: loss_mask torch.Size([1, 16384])
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+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
+batch tensor: position_ids torch.Size([1, 65536])
+batch tensor after cp: tokens torch.Size([1, 16384])
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+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 65536])
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+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
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+batch tensor: position_ids torch.Size([1, 65536])
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+batch tensor: position_ids torch.Size([1, 65536])
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+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 65536])
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+batch tensor: position_ids torch.Size([1, 65536])
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+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 65536])
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+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 65536])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+Start exporting trace 2
+Done exporting trace 2
+ [2025-06-21 20:28:17] iteration        3/      10 | consumed samples:            3 | elapsed time per iteration (ms): 10417.3 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 1073741824.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 3
+Done exporting trace 3
+ [2025-06-21 20:28:28] iteration        4/      10 | consumed samples:            4 | elapsed time per iteration (ms): 10068.6 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 536870912.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 4
+Done exporting trace 4
+ [2025-06-21 20:28:38] iteration        5/      10 | consumed samples:            5 | elapsed time per iteration (ms): 10320.6 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 268435456.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+batch tensor after cp: position_ids torch.Size([1, 16384])
+Start exporting trace 5
+Done exporting trace 5
+ [2025-06-21 20:28:48] iteration        6/      10 | consumed samples:            6 | elapsed time per iteration (ms): 10121.0 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 134217728.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 6
+Done exporting trace 6
+ [2025-06-21 20:28:58] iteration        7/      10 | consumed samples:            7 | elapsed time per iteration (ms): 10122.5 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 67108864.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
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+batch tensor: attention_mask torch.Size([1, 1, 65536, 65536])
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+Start exporting trace 7
+Done exporting trace 7
+ [2025-06-21 20:29:08] iteration        8/      10 | consumed samples:            8 | elapsed time per iteration (ms): 10103.1 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 33554432.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 8
+Done exporting trace 8
+ [2025-06-21 20:29:18] iteration        9/      10 | consumed samples:            9 | elapsed time per iteration (ms): 10063.8 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 16777216.0 | number of skipped iterations:   1 | number of nan iterations:   0 |