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Browse files- attnserver.run_attnserver.slurm.sh.343188.out.log +630 -0
- attnserver.run_attnserver.slurm.sh.343195.out.log +290 -0
- attnserver.run_attnserver.slurm.sh.343196.out.log +0 -0
- attnserver.run_attnserver.slurm.sh.343200.err.log +0 -0
- attnserver.run_attnserver.slurm.sh.343200.out.log +0 -0
- attnserver.run_attnserver.slurm.sh.343202.err.log +307 -0
- attnserver.run_attnserver.slurm.sh.343202.out.log +19 -0
- attnserver.run_attnserver.slurm.sh.343203.err.log +101 -0
- attnserver.run_attnserver.slurm.sh.343203.out.log +0 -0
- attnserver.run_attnserver.slurm.sh.343204.err.log +0 -0
- attnserver.run_attnserver.slurm.sh.343204.out.log +0 -0
- attnserver.run_attnserver.slurm.sh.343205.err.log +0 -0
- attnserver.run_attnserver.slurm.sh.343206.err.log +0 -0
- attnserver.run_attnserver.slurm.sh.343206.out.log +0 -0
- attnserver.run_attnserver.slurm.sh.343207.err.log +141 -0
- attnserver.run_attnserver.slurm.sh.343207.out.log +10 -0
- attnserver.run_attnserver.slurm.sh.343208.err.log +141 -0
- attnserver.run_attnserver.slurm.sh.343208.out.log +10 -0
- attnserver.run_attnserver.slurm.sh.343209.err.log +149 -0
- attnserver.run_attnserver.slurm.sh.343209.out.log +537 -0
- attnserver.run_attnserver.slurm.sh.343210.err.log +149 -0
- attnserver.run_attnserver.slurm.sh.343210.out.log +536 -0
attnserver.run_attnserver.slurm.sh.343188.out.log
CHANGED
@@ -124818,3 +124818,633 @@ batch tensor after cp: position_ids torch.Size([1, 16384])
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Start exporting trace 8
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Done exporting trace 8
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124820 |
[2025-06-21 21:18:21] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 128291.4 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 16777216.0 | number of skipped iterations: 1 | number of nan iterations: 0 |
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124818 |
Start exporting trace 8
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124819 |
Done exporting trace 8
|
124820 |
[2025-06-21 21:18:21] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 128291.4 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 16777216.0 | number of skipped iterations: 1 | number of nan iterations: 0 |
|
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+
batch tensor: tokens torch.Size([1, 131072])
|
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+
batch tensor: labels torch.Size([1, 131072])
|
124823 |
+
batch tensor: loss_mask torch.Size([1, 131072])
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+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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+
batch tensor: position_ids torch.Size([1, 131072])
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124826 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
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+
batch tensor after cp: labels torch.Size([1, 16384])
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+
batch tensor after cp: loss_mask torch.Size([1, 16384])
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+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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+
batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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+
batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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+
batch tensor after cp: position_ids torch.Size([1, 16384])
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+
batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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+
batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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+
batch tensor after cp: labels torch.Size([1, 16384])
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+
batch tensor after cp: loss_mask torch.Size([1, 16384])
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batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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batch tensor: position_ids torch.Size([1, 131072])
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batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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+
batch tensor after cp: position_ids torch.Size([1, 16384])
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+
batch tensor: tokens torch.Size([1, 131072])
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+
batch tensor: labels torch.Size([1, 131072])
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+
batch tensor: loss_mask torch.Size([1, 131072])
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+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124905 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124906 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
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+
batch tensor after cp: labels torch.Size([1, 16384])
|
124908 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124909 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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124910 |
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batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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+
batch tensor: loss_mask torch.Size([1, 131072])
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batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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batch tensor: position_ids torch.Size([1, 131072])
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+
batch tensor after cp: tokens torch.Size([1, 16384])
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124917 |
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batch tensor after cp: labels torch.Size([1, 16384])
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batch tensor after cp: loss_mask torch.Size([1, 16384])
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+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124920 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124921 |
+
batch tensor: tokens torch.Size([1, 131072])
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+
batch tensor: labels torch.Size([1, 131072])
|
124923 |
+
batch tensor: loss_mask torch.Size([1, 131072])
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124924 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124925 |
+
batch tensor: position_ids torch.Size([1, 131072])
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124926 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
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batch tensor after cp: labels torch.Size([1, 16384])
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+
batch tensor after cp: loss_mask torch.Size([1, 16384])
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+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
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+
batch tensor after cp: position_ids torch.Size([1, 16384])
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batch tensor: tokens torch.Size([1, 131072])
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batch tensor: labels torch.Size([1, 131072])
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124933 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124934 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124935 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124936 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124937 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124938 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124939 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124940 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124941 |
+
batch tensor: tokens torch.Size([1, 131072])
|
124942 |
+
batch tensor: labels torch.Size([1, 131072])
|
124943 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124944 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124945 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124946 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124947 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124948 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124949 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124950 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124951 |
+
batch tensor: tokens torch.Size([1, 131072])
|
124952 |
+
batch tensor: labels torch.Size([1, 131072])
|
124953 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124954 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124955 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124956 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124957 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124958 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124959 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124960 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124961 |
+
batch tensor: tokens torch.Size([1, 131072])
|
124962 |
+
batch tensor: labels torch.Size([1, 131072])
|
124963 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124964 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124965 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124966 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124967 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124968 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124969 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124970 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124971 |
+
batch tensor: tokens torch.Size([1, 131072])
|
124972 |
+
batch tensor: labels torch.Size([1, 131072])
|
124973 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124974 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124975 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124976 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124977 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124978 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124979 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124980 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124981 |
+
batch tensor: tokens torch.Size([1, 131072])
|
124982 |
+
batch tensor: labels torch.Size([1, 131072])
|
124983 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124984 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124985 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124986 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124987 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124988 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124989 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
124990 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
124991 |
+
batch tensor: tokens torch.Size([1, 131072])
|
124992 |
+
batch tensor: labels torch.Size([1, 131072])
|
124993 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
124994 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
124995 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
124996 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
124997 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
124998 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
124999 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125000 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125001 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125002 |
+
batch tensor: labels torch.Size([1, 131072])
|
125003 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125004 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125005 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125006 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125007 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125008 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125009 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125010 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125011 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125012 |
+
batch tensor: labels torch.Size([1, 131072])
|
125013 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125014 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125015 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125016 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125017 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125018 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125019 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125020 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125021 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125022 |
+
batch tensor: labels torch.Size([1, 131072])
|
125023 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125024 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125025 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125026 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125027 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125028 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125029 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125030 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125031 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125032 |
+
batch tensor: labels torch.Size([1, 131072])
|
125033 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125034 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125035 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125036 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125037 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125038 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125039 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125040 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125041 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125042 |
+
batch tensor: labels torch.Size([1, 131072])
|
125043 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125044 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125045 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125046 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125047 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125048 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125049 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125050 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125051 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125052 |
+
batch tensor: labels torch.Size([1, 131072])
|
125053 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125054 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125055 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125056 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125057 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125058 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125059 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125060 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125061 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125062 |
+
batch tensor: labels torch.Size([1, 131072])
|
125063 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125064 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125065 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125066 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125067 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125068 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125069 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125070 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125071 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125072 |
+
batch tensor: labels torch.Size([1, 131072])
|
125073 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125074 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125075 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125076 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125077 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125078 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125079 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125080 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125081 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125082 |
+
batch tensor: labels torch.Size([1, 131072])
|
125083 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125084 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125085 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125086 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125087 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125088 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125089 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125090 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125091 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125092 |
+
batch tensor: labels torch.Size([1, 131072])
|
125093 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125094 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125095 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125096 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125097 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125098 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125099 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125100 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125101 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125102 |
+
batch tensor: labels torch.Size([1, 131072])
|
125103 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125104 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125105 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125106 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125107 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125108 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125109 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125110 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125111 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125112 |
+
batch tensor: labels torch.Size([1, 131072])
|
125113 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125114 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125115 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125116 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125117 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125118 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125119 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125120 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125121 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125122 |
+
batch tensor: labels torch.Size([1, 131072])
|
125123 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125124 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125125 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125126 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125127 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125128 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125129 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125130 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125131 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125132 |
+
batch tensor: labels torch.Size([1, 131072])
|
125133 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125134 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125135 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125136 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125137 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125138 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125139 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125140 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125141 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125142 |
+
batch tensor: labels torch.Size([1, 131072])
|
125143 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125144 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125145 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125146 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125147 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125148 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125149 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125150 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125151 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125152 |
+
batch tensor: labels torch.Size([1, 131072])
|
125153 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125154 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125155 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125156 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125157 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125158 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125159 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125160 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125161 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125162 |
+
batch tensor: labels torch.Size([1, 131072])
|
125163 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125164 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125165 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125166 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125167 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125168 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125169 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125170 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125171 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125172 |
+
batch tensor: labels torch.Size([1, 131072])
|
125173 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125174 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125175 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125176 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125177 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125178 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125179 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125180 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125181 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125182 |
+
batch tensor: labels torch.Size([1, 131072])
|
125183 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125184 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125185 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125186 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125187 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125188 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125189 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125190 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125191 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125192 |
+
batch tensor: labels torch.Size([1, 131072])
|
125193 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125194 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125195 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125196 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125197 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125198 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125199 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125200 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125201 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125202 |
+
batch tensor: labels torch.Size([1, 131072])
|
125203 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125204 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125205 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125206 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125207 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125208 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125209 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125210 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125211 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125212 |
+
batch tensor: labels torch.Size([1, 131072])
|
125213 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125214 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125215 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125216 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125217 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125218 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125219 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125220 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125221 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125222 |
+
batch tensor: labels torch.Size([1, 131072])
|
125223 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125224 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125225 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125226 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125227 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125228 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125229 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125230 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125231 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125232 |
+
batch tensor: labels torch.Size([1, 131072])
|
125233 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125234 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125235 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125236 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125237 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125238 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125239 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125240 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125241 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125242 |
+
batch tensor: labels torch.Size([1, 131072])
|
125243 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125244 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125245 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125246 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125247 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125248 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125249 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125250 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125251 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125252 |
+
batch tensor: labels torch.Size([1, 131072])
|
125253 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125254 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125255 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125256 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125257 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125258 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125259 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125260 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125261 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125262 |
+
batch tensor: labels torch.Size([1, 131072])
|
125263 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125264 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125265 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125266 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125267 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125268 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125269 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125270 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125271 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125272 |
+
batch tensor: labels torch.Size([1, 131072])
|
125273 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125274 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125275 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125276 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125277 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125278 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125279 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125280 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125281 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125282 |
+
batch tensor: labels torch.Size([1, 131072])
|
125283 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125284 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125285 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125286 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125287 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125288 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125289 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125290 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125291 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125292 |
+
batch tensor: labels torch.Size([1, 131072])
|
125293 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125294 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125295 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125296 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125297 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125298 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125299 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125300 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125301 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125302 |
+
batch tensor: labels torch.Size([1, 131072])
|
125303 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125304 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125305 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125306 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125307 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125308 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125309 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125310 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125311 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125312 |
+
batch tensor: labels torch.Size([1, 131072])
|
125313 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125314 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125315 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125316 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125317 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125318 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125319 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125320 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125321 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125322 |
+
batch tensor: labels torch.Size([1, 131072])
|
125323 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125324 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125325 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125326 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125327 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125328 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125329 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125330 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125331 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125332 |
+
batch tensor: labels torch.Size([1, 131072])
|
125333 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125334 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125335 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125336 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125337 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125338 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125339 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125340 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125341 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125342 |
+
batch tensor: labels torch.Size([1, 131072])
|
125343 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125344 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125345 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125346 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125347 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125348 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125349 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125350 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125351 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125352 |
+
batch tensor: labels torch.Size([1, 131072])
|
125353 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125354 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125355 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125356 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125357 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125358 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125359 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125360 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125361 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125362 |
+
batch tensor: labels torch.Size([1, 131072])
|
125363 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125364 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125365 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125366 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125367 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125368 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125369 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125370 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125371 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125372 |
+
batch tensor: labels torch.Size([1, 131072])
|
125373 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125374 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125375 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125376 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125377 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125378 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125379 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125380 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125381 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125382 |
+
batch tensor: labels torch.Size([1, 131072])
|
125383 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125384 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125385 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125386 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125387 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125388 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125389 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125390 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125391 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125392 |
+
batch tensor: labels torch.Size([1, 131072])
|
125393 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125394 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125395 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125396 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125397 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125398 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125399 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125400 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125401 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125402 |
+
batch tensor: labels torch.Size([1, 131072])
|
125403 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125404 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125405 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125406 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125407 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125408 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125409 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125410 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125411 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125412 |
+
batch tensor: labels torch.Size([1, 131072])
|
125413 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125414 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125415 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125416 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125417 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125418 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125419 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125420 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125421 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125422 |
+
batch tensor: labels torch.Size([1, 131072])
|
125423 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125424 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125425 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125426 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125427 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125428 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125429 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125430 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125431 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125432 |
+
batch tensor: labels torch.Size([1, 131072])
|
125433 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125434 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125435 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125436 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125437 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125438 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125439 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125440 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
125441 |
+
batch tensor: tokens torch.Size([1, 131072])
|
125442 |
+
batch tensor: labels torch.Size([1, 131072])
|
125443 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
125444 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
125445 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
125446 |
+
batch tensor after cp: tokens torch.Size([1, 16384])
|
125447 |
+
batch tensor after cp: labels torch.Size([1, 16384])
|
125448 |
+
batch tensor after cp: loss_mask torch.Size([1, 16384])
|
125449 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
|
125450 |
+
batch tensor after cp: position_ids torch.Size([1, 16384])
|
attnserver.run_attnserver.slurm.sh.343195.out.log
CHANGED
@@ -67763,3 +67763,293 @@ batch tensor after cp: position_ids torch.Size([1, 32768])
|
|
67763 |
Start exporting trace 6
|
67764 |
Done exporting trace 6
|
67765 |
[2025-06-21 21:18:27] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 152588.3 | 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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|
67763 |
Start exporting trace 6
|
67764 |
Done exporting trace 6
|
67765 |
[2025-06-21 21:18:27] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 152588.3 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 67108864.0 | number of skipped iterations: 1 | number of nan iterations: 0 |
|
67766 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67767 |
+
batch tensor: labels torch.Size([1, 131072])
|
67768 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67769 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67770 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67771 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67772 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67773 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67774 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67775 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67776 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67777 |
+
batch tensor: labels torch.Size([1, 131072])
|
67778 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67779 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67780 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67781 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67782 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67783 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67784 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67785 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67786 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67787 |
+
batch tensor: labels torch.Size([1, 131072])
|
67788 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67789 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67790 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67791 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67792 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67793 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67794 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67795 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67796 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67797 |
+
batch tensor: labels torch.Size([1, 131072])
|
67798 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67799 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67800 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67801 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67802 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67803 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67804 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67805 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67806 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67807 |
+
batch tensor: labels torch.Size([1, 131072])
|
67808 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67809 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67810 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67811 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67812 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67813 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67814 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67815 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67816 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67817 |
+
batch tensor: labels torch.Size([1, 131072])
|
67818 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67819 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67820 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67821 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67822 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67823 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67824 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67825 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67826 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67827 |
+
batch tensor: labels torch.Size([1, 131072])
|
67828 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67829 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67830 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67831 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67832 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67833 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67834 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67835 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67836 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67837 |
+
batch tensor: labels torch.Size([1, 131072])
|
67838 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67839 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67840 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67841 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67842 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67843 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67844 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67845 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67846 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67847 |
+
batch tensor: labels torch.Size([1, 131072])
|
67848 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67849 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67850 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67851 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67852 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67853 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67854 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67855 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67856 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67857 |
+
batch tensor: labels torch.Size([1, 131072])
|
67858 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67859 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67860 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67861 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67862 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67863 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67864 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67865 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67866 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67867 |
+
batch tensor: labels torch.Size([1, 131072])
|
67868 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67869 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67870 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67871 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67872 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67873 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67874 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67875 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67876 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67877 |
+
batch tensor: labels torch.Size([1, 131072])
|
67878 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67879 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67880 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67881 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67882 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67883 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67884 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67885 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67886 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67887 |
+
batch tensor: labels torch.Size([1, 131072])
|
67888 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67889 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67890 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67891 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67892 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67893 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67894 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67895 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67896 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67897 |
+
batch tensor: labels torch.Size([1, 131072])
|
67898 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67899 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67900 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67901 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67902 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67903 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67904 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67905 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67906 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67907 |
+
batch tensor: labels torch.Size([1, 131072])
|
67908 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67909 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67910 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67911 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67912 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67913 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67914 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67915 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67916 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67917 |
+
batch tensor: labels torch.Size([1, 131072])
|
67918 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67919 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67920 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67921 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67922 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67923 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67924 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67925 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67926 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67927 |
+
batch tensor: labels torch.Size([1, 131072])
|
67928 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67929 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67930 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67931 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67932 |
+
batch tensor: labels torch.Size([1, 131072])
|
67933 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67934 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67935 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67936 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67937 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67938 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67939 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67940 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67941 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67942 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67943 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67944 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67945 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67946 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67947 |
+
batch tensor: labels torch.Size([1, 131072])
|
67948 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67949 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67950 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67951 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67952 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67953 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67954 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67955 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67956 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67957 |
+
batch tensor: labels torch.Size([1, 131072])
|
67958 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67959 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67960 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67961 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67962 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67963 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67964 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67965 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67966 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67967 |
+
batch tensor: labels torch.Size([1, 131072])
|
67968 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67969 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67970 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67971 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67972 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67973 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67974 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67975 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67976 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67977 |
+
batch tensor: labels torch.Size([1, 131072])
|
67978 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67979 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67980 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67981 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67982 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67983 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67984 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67985 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67986 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67987 |
+
batch tensor: labels torch.Size([1, 131072])
|
67988 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67989 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
67990 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
67991 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
67992 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
67993 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
67994 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
67995 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
67996 |
+
batch tensor: tokens torch.Size([1, 131072])
|
67997 |
+
batch tensor: labels torch.Size([1, 131072])
|
67998 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
67999 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
68000 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
68001 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
68002 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
68003 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
68004 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
68005 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
68006 |
+
batch tensor: tokens torch.Size([1, 131072])
|
68007 |
+
batch tensor: labels torch.Size([1, 131072])
|
68008 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
68009 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
68010 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
68011 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
68012 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
68013 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
68014 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
68015 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
68016 |
+
batch tensor: tokens torch.Size([1, 131072])
|
68017 |
+
batch tensor: labels torch.Size([1, 131072])
|
68018 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
68019 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
68020 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
68021 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
68022 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
68023 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
68024 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
68025 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
68026 |
+
batch tensor: tokens torch.Size([1, 131072])
|
68027 |
+
batch tensor: labels torch.Size([1, 131072])
|
68028 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
68029 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
68030 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
68031 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
68032 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
68033 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
68034 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
68035 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
68036 |
+
batch tensor: tokens torch.Size([1, 131072])
|
68037 |
+
batch tensor: labels torch.Size([1, 131072])
|
68038 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
68039 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
68040 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
68041 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
68042 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
68043 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
68044 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
68045 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
68046 |
+
batch tensor: tokens torch.Size([1, 131072])
|
68047 |
+
batch tensor: labels torch.Size([1, 131072])
|
68048 |
+
batch tensor: loss_mask torch.Size([1, 131072])
|
68049 |
+
batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
|
68050 |
+
batch tensor: position_ids torch.Size([1, 131072])
|
68051 |
+
batch tensor after cp: tokens torch.Size([1, 32768])
|
68052 |
+
batch tensor after cp: labels torch.Size([1, 32768])
|
68053 |
+
batch tensor after cp: loss_mask torch.Size([1, 32768])
|
68054 |
+
batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
|
68055 |
+
batch tensor after cp: position_ids torch.Size([1, 32768])
|
attnserver.run_attnserver.slurm.sh.343196.out.log
CHANGED
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attnserver.run_attnserver.slurm.sh.343200.err.log
CHANGED
The diff for this file is too large to render.
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|
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attnserver.run_attnserver.slurm.sh.343200.out.log
CHANGED
The diff for this file is too large to render.
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|
|
attnserver.run_attnserver.slurm.sh.343202.err.log
CHANGED
@@ -6782,3 +6782,310 @@ W0621 21:13:47.545000 3922086 site-packages/torch/distributed/run.py:766] ******
|
|
6782 |
[rank0]: return io.open(self, mode, buffering, encoding, errors, newline)
|
6783 |
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
6784 |
[rank0]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010/.metadata.tmp'
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|
6782 |
[rank0]: return io.open(self, mode, buffering, encoding, errors, newline)
|
6783 |
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
6784 |
[rank0]: FileNotFoundError: [Errno 2] No such file or directory: 'gpt-checkpoint/iter_0000010/.metadata.tmp'
|
6785 |
+
[rank0]:[W621 21:19:04.979946813 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())
|
6786 |
+
W0621 21:19:13.223000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922159 closing signal SIGTERM
|
6787 |
+
W0621 21:19:13.227000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922160 closing signal SIGTERM
|
6788 |
+
W0621 21:19:13.230000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922161 closing signal SIGTERM
|
6789 |
+
W0621 21:19:13.231000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922162 closing signal SIGTERM
|
6790 |
+
W0621 21:19:13.236000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922163 closing signal SIGTERM
|
6791 |
+
W0621 21:19:13.240000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922164 closing signal SIGTERM
|
6792 |
+
W0621 21:19:13.260000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 3922165 closing signal SIGTERM
|
6793 |
+
E0621 21:19:15.487000 3922086 site-packages/torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 3922158) of binary: /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
6794 |
+
Traceback (most recent call last):
|
6795 |
+
File "<frozen runpy>", line 198, in _run_module_as_main
|
6796 |
+
File "<frozen runpy>", line 88, in _run_code
|
6797 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in <module>
|
6798 |
+
main()
|
6799 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper
|
6800 |
+
return arg(*args, **kwargs)
|
6801 |
+
^^^^^^^^^^^^^^^^^^^^
|
6802 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main
|
6803 |
+
launch(args)
|
6804 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch
|
6805 |
+
run(args)
|
6806 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run
|
6807 |
+
elastic_launch(
|
6808 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__
|
6809 |
+
return launch_agent(self._config, self._entrypoint, list(args))
|
6810 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
6811 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent
|
6812 |
+
raise ChildFailedError(
|
6813 |
+
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
|
6814 |
+
============================================================
|
6815 |
+
./pretrain_gpt_profile.py FAILED
|
6816 |
+
------------------------------------------------------------
|
6817 |
+
Failures:
|
6818 |
+
<NO_OTHER_FAILURES>
|
6819 |
+
------------------------------------------------------------
|
6820 |
+
Root Cause (first observed failure):
|
6821 |
+
[0]:
|
6822 |
+
time : 2025-06-21_21:19:13
|
6823 |
+
host : fs-mbz-gpu-728
|
6824 |
+
rank : 0 (local_rank: 0)
|
6825 |
+
exitcode : 1 (pid: 3922158)
|
6826 |
+
error_file: <N/A>
|
6827 |
+
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
|
6828 |
+
============================================================
|
6829 |
+
+ set +x
|
6830 |
+
[rank14]:[W621 21:19:16.597385554 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57156, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6831 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6832 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x15088b9785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6833 |
+
frame #1: <unknown function> + 0x5ba8afe (0x15087485aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6834 |
+
frame #2: <unknown function> + 0x5baae40 (0x15087485ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6835 |
+
frame #3: <unknown function> + 0x5bab74a (0x15087485d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6836 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x1508748571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6837 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x150831a509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6838 |
+
frame #6: <unknown function> + 0xd3b6d (0x15088b4f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6839 |
+
frame #7: <unknown function> + 0x94ac3 (0x15088ca36ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6840 |
+
frame #8: <unknown function> + 0x126850 (0x15088cac8850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6841 |
+
|
6842 |
+
[rank15]:[W621 21:19:16.597385455 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57224, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6843 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6844 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x145f943785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6845 |
+
frame #1: <unknown function> + 0x5ba8afe (0x145f7d25aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6846 |
+
frame #2: <unknown function> + 0x5baae40 (0x145f7d25ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6847 |
+
frame #3: <unknown function> + 0x5bab74a (0x145f7d25d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6848 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x145f7d2571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6849 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x145f3a4509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6850 |
+
frame #6: <unknown function> + 0xd3b6d (0x145f93ef1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6851 |
+
frame #7: <unknown function> + 0x94ac3 (0x145f953b8ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6852 |
+
frame #8: <unknown function> + 0x126850 (0x145f9544a850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6853 |
+
|
6854 |
+
[rank10]:[W621 21:19:16.597522826 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57206, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6855 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6856 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x14d75bd785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6857 |
+
frame #1: <unknown function> + 0x5ba8afe (0x14d744c5aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6858 |
+
frame #2: <unknown function> + 0x5baae40 (0x14d744c5ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6859 |
+
frame #3: <unknown function> + 0x5bab74a (0x14d744c5d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6860 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x14d744c571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6861 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x14d701e509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6862 |
+
frame #6: <unknown function> + 0xd3b6d (0x14d75b8f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6863 |
+
frame #7: <unknown function> + 0x94ac3 (0x14d75cdc5ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6864 |
+
frame #8: <unknown function> + 0x126850 (0x14d75ce57850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6865 |
+
|
6866 |
+
[rank14]:[W621 21:19:16.601790151 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 14] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6867 |
+
[rank15]:[W621 21:19:16.601801332 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 15] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6868 |
+
[rank10]:[W621 21:19:16.601904060 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 10] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6869 |
+
[rank8]:[W621 21:19:16.629327826 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=75, addr=[fs-mbz-gpu-865]:57148, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6870 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6871 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1465d09785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6872 |
+
frame #1: <unknown function> + 0x5ba8afe (0x1465b985aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6873 |
+
frame #2: <unknown function> + 0x5baae40 (0x1465b985ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6874 |
+
frame #3: <unknown function> + 0x5bab74a (0x1465b985d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6875 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x1465b98571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6876 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x146576a509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6877 |
+
frame #6: <unknown function> + 0xd3b6d (0x1465d04f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6878 |
+
frame #7: <unknown function> + 0x94ac3 (0x1465d1a14ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6879 |
+
frame #8: <unknown function> + 0x126850 (0x1465d1aa6850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6880 |
+
|
6881 |
+
[rank8]:[W621 21:19:16.633647516 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 8] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6882 |
+
[rank11]:[W621 21:19:16.688718409 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57220, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6883 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6884 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x146437b785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6885 |
+
frame #1: <unknown function> + 0x5ba8afe (0x146420e5aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6886 |
+
frame #2: <unknown function> + 0x5baae40 (0x146420e5ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6887 |
+
frame #3: <unknown function> + 0x5bab74a (0x146420e5d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6888 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x146420e571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6889 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x1463de0509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6890 |
+
frame #6: <unknown function> + 0xd3b6d (0x1463ce019b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6891 |
+
frame #7: <unknown function> + 0x94ac3 (0x146438f6dac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6892 |
+
frame #8: <unknown function> + 0x126850 (0x146438fff850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6893 |
+
|
6894 |
+
[rank11]:[W621 21:19:16.692730257 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 11] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6895 |
+
[rank12]:[W621 21:19:16.688662135 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57166, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6896 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6897 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x14776f3785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6898 |
+
frame #1: <unknown function> + 0x5ba8afe (0x14775865aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6899 |
+
frame #2: <unknown function> + 0x5baae40 (0x14775865ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6900 |
+
frame #3: <unknown function> + 0x5bab74a (0x14775865d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6901 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x1477586571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6902 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x1477158509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6903 |
+
frame #6: <unknown function> + 0xd3b6d (0x147705819b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6904 |
+
frame #7: <unknown function> + 0x94ac3 (0x147770710ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6905 |
+
frame #8: <unknown function> + 0x126850 (0x1477707a2850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6906 |
+
|
6907 |
+
[rank9]:[W621 21:19:16.688664327 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57180, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6908 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6909 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x14d335b785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6910 |
+
frame #1: <unknown function> + 0x5ba8afe (0x14d31ea5aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6911 |
+
frame #2: <unknown function> + 0x5baae40 (0x14d31ea5ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6912 |
+
frame #3: <unknown function> + 0x5bab74a (0x14d31ea5d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6913 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x14d31ea571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6914 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x14d2dbc509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6915 |
+
frame #6: <unknown function> + 0xd3b6d (0x14d3356f1b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6916 |
+
frame #7: <unknown function> + 0x94ac3 (0x14d336be8ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6917 |
+
frame #8: <unknown function> + 0x126850 (0x14d336c7a850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6918 |
+
|
6919 |
+
[rank13]:[W621 21:19:16.688672768 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=95, addr=[fs-mbz-gpu-865]:57196, remote=[fs-mbz-gpu-728]:37481): failed to recv, got 0 bytes
|
6920 |
+
Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:678 (most recent call first):
|
6921 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1512e83785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6922 |
+
frame #1: <unknown function> + 0x5ba8afe (0x1512d165aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6923 |
+
frame #2: <unknown function> + 0x5baae40 (0x1512d165ce40 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6924 |
+
frame #3: <unknown function> + 0x5bab74a (0x1512d165d74a in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6925 |
+
frame #4: c10d::TCPStore::check(std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > const&) + 0x2a9 (0x1512d16571a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6926 |
+
frame #5: c10d::ProcessGroupNCCL::heartbeatMonitor() + 0x379 (0x15128e8509a9 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cuda.so)
|
6927 |
+
frame #6: <unknown function> + 0xd3b6d (0x15127e819b6d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/../lib/libstdc++.so.6)
|
6928 |
+
frame #7: <unknown function> + 0x94ac3 (0x1512e96b9ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6929 |
+
frame #8: <unknown function> + 0x126850 (0x1512e974b850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6930 |
+
|
6931 |
+
[rank12]:[W621 21:19:16.692870336 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 12] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6932 |
+
[rank9]:[W621 21:19:16.692884070 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 9] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6933 |
+
[rank13]:[W621 21:19:16.692933680 ProcessGroupNCCL.cpp:1659] [PG ID 0 PG GUID 0(default_pg) Rank 13] Failed to check the "should dump" flag on TCPStore, (maybe TCPStore server has shut down too early), with error: failed to recv, got 0 bytes
|
6934 |
+
W0621 21:19:16.473000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522699 closing signal SIGTERM
|
6935 |
+
W0621 21:19:16.478000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522700 closing signal SIGTERM
|
6936 |
+
W0621 21:19:16.480000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522701 closing signal SIGTERM
|
6937 |
+
W0621 21:19:16.485000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522702 closing signal SIGTERM
|
6938 |
+
W0621 21:19:16.487000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522703 closing signal SIGTERM
|
6939 |
+
W0621 21:19:16.489000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522704 closing signal SIGTERM
|
6940 |
+
W0621 21:19:16.533000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522705 closing signal SIGTERM
|
6941 |
+
W0621 21:19:16.548000 2522629 site-packages/torch/distributed/elastic/multiprocessing/api.py:900] Sending process 2522707 closing signal SIGTERM
|
6942 |
+
[W621 21:19:18.748504106 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-865]:42814, remote=[fs-mbz-gpu-728]:29500): Broken pipe
|
6943 |
+
Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first):
|
6944 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1474ef7785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6945 |
+
frame #1: <unknown function> + 0x5ba8afe (0x1474d865aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6946 |
+
frame #2: <unknown function> + 0x5baa358 (0x1474d865c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6947 |
+
frame #3: <unknown function> + 0x5babb3e (0x1474d865db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6948 |
+
frame #4: c10d::TCPStore::doWait(c10::ArrayRef<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x1a6 (0x1474d8657ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6949 |
+
frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x33 (0x1474d8657ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6950 |
+
frame #6: c10d::TCPStore::get(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xab (0x1474d8658f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6951 |
+
frame #7: <unknown function> + 0xc0f526 (0x1474e798b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
6952 |
+
frame #8: <unknown function> + 0x37f17d (0x1474e70fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
6953 |
+
<omitting python frames>
|
6954 |
+
frame #17: <unknown function> + 0x94ac3 (0x1474f07f3ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6955 |
+
frame #18: <unknown function> + 0x126850 (0x1474f0885850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6956 |
+
|
6957 |
+
W0621 21:19:18.428000 2522629 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1341] The node 'fs-mbz-gpu-865_2522629_0' has failed to send a keep-alive heartbeat to the rendezvous '343202' due to an error of type RendezvousConnectionError.
|
6958 |
+
[W621 21:19:23.757992050 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-865]:42814, remote=[fs-mbz-gpu-728]:29500): Broken pipe
|
6959 |
+
Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first):
|
6960 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1474ef7785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6961 |
+
frame #1: <unknown function> + 0x5ba8afe (0x1474d865aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6962 |
+
frame #2: <unknown function> + 0x5baa358 (0x1474d865c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6963 |
+
frame #3: <unknown function> + 0x5babb3e (0x1474d865db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6964 |
+
frame #4: c10d::TCPStore::doWait(c10::ArrayRef<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x1a6 (0x1474d8657ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6965 |
+
frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x33 (0x1474d8657ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6966 |
+
frame #6: c10d::TCPStore::get(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xab (0x1474d8658f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6967 |
+
frame #7: <unknown function> + 0xc0f526 (0x1474e798b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
6968 |
+
frame #8: <unknown function> + 0x37f17d (0x1474e70fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
6969 |
+
<omitting python frames>
|
6970 |
+
frame #17: <unknown function> + 0x94ac3 (0x1474f07f3ac3 in /lib/x86_64-linux-gnu/libc.so.6)
|
6971 |
+
frame #18: <unknown function> + 0x126850 (0x1474f0885850 in /lib/x86_64-linux-gnu/libc.so.6)
|
6972 |
+
|
6973 |
+
W0621 21:19:23.435000 2522629 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1341] The node 'fs-mbz-gpu-865_2522629_0' has failed to send a keep-alive heartbeat to the rendezvous '343202' due to an error of type RendezvousConnectionError.
|
6974 |
+
[W621 21:19:23.821313515 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-865]:42814, remote=[fs-mbz-gpu-728]:29500): Broken pipe
|
6975 |
+
Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first):
|
6976 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1474ef7785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6977 |
+
frame #1: <unknown function> + 0x5ba8afe (0x1474d865aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6978 |
+
frame #2: <unknown function> + 0x5baa358 (0x1474d865c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6979 |
+
frame #3: <unknown function> + 0x5babb3e (0x1474d865db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6980 |
+
frame #4: c10d::TCPStore::doWait(c10::ArrayRef<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x1a6 (0x1474d8657ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6981 |
+
frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x33 (0x1474d8657ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6982 |
+
frame #6: c10d::TCPStore::get(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xab (0x1474d8658f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6983 |
+
frame #7: <unknown function> + 0xc0f526 (0x1474e798b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
6984 |
+
frame #8: <unknown function> + 0x37f17d (0x1474e70fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
6985 |
+
<omitting python frames>
|
6986 |
+
frame #26: <unknown function> + 0x29d90 (0x1474f0788d90 in /lib/x86_64-linux-gnu/libc.so.6)
|
6987 |
+
frame #27: __libc_start_main + 0x80 (0x1474f0788e40 in /lib/x86_64-linux-gnu/libc.so.6)
|
6988 |
+
|
6989 |
+
W0621 21:19:23.505000 2522629 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-865_2522629_0' has failed to shutdown the rendezvous '343202' due to an error of type RendezvousConnectionError.
|
6990 |
+
[W621 21:19:23.835472402 TCPStore.cpp:106] [c10d] sendBytes failed on SocketImpl(fd=4, addr=[fs-mbz-gpu-865]:42814, remote=[fs-mbz-gpu-728]:29500): Broken pipe
|
6991 |
+
Exception raised from sendBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:653 (most recent call first):
|
6992 |
+
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x98 (0x1474ef7785e8 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libc10.so)
|
6993 |
+
frame #1: <unknown function> + 0x5ba8afe (0x1474d865aafe in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6994 |
+
frame #2: <unknown function> + 0x5baa358 (0x1474d865c358 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6995 |
+
frame #3: <unknown function> + 0x5babb3e (0x1474d865db3e in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6996 |
+
frame #4: c10d::TCPStore::doWait(c10::ArrayRef<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x1a6 (0x1474d8657ac6 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6997 |
+
frame #5: c10d::TCPStore::doGet(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x33 (0x1474d8657ea3 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6998 |
+
frame #6: c10d::TCPStore::get(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xab (0x1474d8658f8b in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so)
|
6999 |
+
frame #7: <unknown function> + 0xc0f526 (0x1474e798b526 in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
7000 |
+
frame #8: <unknown function> + 0x37f17d (0x1474e70fb17d in /mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/lib/libtorch_python.so)
|
7001 |
+
<omitting python frames>
|
7002 |
+
frame #26: <unknown function> + 0x29d90 (0x1474f0788d90 in /lib/x86_64-linux-gnu/libc.so.6)
|
7003 |
+
frame #27: __libc_start_main + 0x80 (0x1474f0788e40 in /lib/x86_64-linux-gnu/libc.so.6)
|
7004 |
+
|
7005 |
+
W0621 21:19:23.516000 2522629 site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py:1292] The node 'fs-mbz-gpu-865_2522629_0' has failed to shutdown the rendezvous '343202' due to an error of type RendezvousConnectionError.
|
7006 |
+
Traceback (most recent call last):
|
7007 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 117, in _call_store
|
7008 |
+
return getattr(self._store, store_op)(*args, **kwargs)
|
7009 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
7010 |
+
torch.distributed.DistNetworkError: failed to recv, got 0 bytes
|
7011 |
+
|
7012 |
+
The above exception was the direct cause of the following exception:
|
7013 |
+
|
7014 |
+
Traceback (most recent call last):
|
7015 |
+
File "<frozen runpy>", line 198, in _run_module_as_main
|
7016 |
+
File "<frozen runpy>", line 88, in _run_code
|
7017 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 207, in <module>
|
7018 |
+
main()
|
7019 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/typing_extensions.py", line 3253, in wrapper
|
7020 |
+
return arg(*args, **kwargs)
|
7021 |
+
^^^^^^^^^^^^^^^^^^^^
|
7022 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 203, in main
|
7023 |
+
launch(args)
|
7024 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py", line 188, in launch
|
7025 |
+
run(args)
|
7026 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/run.py", line 883, in run
|
7027 |
+
elastic_launch(
|
7028 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 139, in __call__
|
7029 |
+
return launch_agent(self._config, self._entrypoint, list(args))
|
7030 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
7031 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launcher/api.py", line 261, in launch_agent
|
7032 |
+
result = agent.run()
|
7033 |
+
^^^^^^^^^^^
|
7034 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/metrics/api.py", line 138, in wrapper
|
7035 |
+
result = f(*args, **kwargs)
|
7036 |
+
^^^^^^^^^^^^^^^^^^
|
7037 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/agent/server/api.py", line 711, in run
|
7038 |
+
result = self._invoke_run(role)
|
7039 |
+
^^^^^^^^^^^^^^^^^^^^^^
|
7040 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/agent/server/api.py", line 906, in _invoke_run
|
7041 |
+
num_nodes_waiting = rdzv_handler.num_nodes_waiting()
|
7042 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
7043 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 1263, in num_nodes_waiting
|
7044 |
+
self._state_holder.sync()
|
7045 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py", line 437, in sync
|
7046 |
+
get_response = self._backend.get_state()
|
7047 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^
|
7048 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 75, in get_state
|
7049 |
+
base64_state: bytes = self._call_store("get", self._key)
|
7050 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
7051 |
+
File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py", line 119, in _call_store
|
7052 |
+
raise RendezvousConnectionError(
|
7053 |
+
torch.distributed.elastic.rendezvous.api.RendezvousConnectionError: The connection to the C10d store has failed. See inner exception for details.
|
7054 |
+
+ set +x
|
7055 |
+
+ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
|
7056 |
+
+ export PROF_CTX_LENGTH=81920
|
7057 |
+
+ PROF_CTX_LENGTH=81920
|
7058 |
+
+ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L81920*tp8.cp2.bs2.json'
|
7059 |
+
+ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L81920*tp8.cp2.bs2.json' ']'
|
7060 |
+
+ echo 'Running ctx_length=81920, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=2'
|
7061 |
+
+ srun bash ./attnserver.sh
|
7062 |
+
+ which python3
|
7063 |
+
+ 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 81920 --max-position-embeddings 81920 --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/
|
7064 |
+
+ which python3
|
7065 |
+
+ 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 81920 --max-position-embeddings 81920 --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/
|
7066 |
+
/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
|
7067 |
+
and will be removed in future. Use torchrun.
|
7068 |
+
Note that --use-env is set by default in torchrun.
|
7069 |
+
If your script expects `--local-rank` argument to be set, please
|
7070 |
+
change it to read from `os.environ['LOCAL_RANK']` instead. See
|
7071 |
+
https://pytorch.org/docs/stable/distributed.html#launch-utility for
|
7072 |
+
further instructions
|
7073 |
+
|
7074 |
+
main()
|
7075 |
+
W0621 21:19:26.938000 2525849 site-packages/torch/distributed/run.py:766]
|
7076 |
+
W0621 21:19:26.938000 2525849 site-packages/torch/distributed/run.py:766] *****************************************
|
7077 |
+
W0621 21:19:26.938000 2525849 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.
|
7078 |
+
W0621 21:19:26.938000 2525849 site-packages/torch/distributed/run.py:766] *****************************************
|
7079 |
+
/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
|
7080 |
+
and will be removed in future. Use torchrun.
|
7081 |
+
Note that --use-env is set by default in torchrun.
|
7082 |
+
If your script expects `--local-rank` argument to be set, please
|
7083 |
+
change it to read from `os.environ['LOCAL_RANK']` instead. See
|
7084 |
+
https://pytorch.org/docs/stable/distributed.html#launch-utility for
|
7085 |
+
further instructions
|
7086 |
+
|
7087 |
+
main()
|
7088 |
+
W0621 21:19:27.143000 3925433 site-packages/torch/distributed/run.py:766]
|
7089 |
+
W0621 21:19:27.143000 3925433 site-packages/torch/distributed/run.py:766] *****************************************
|
7090 |
+
W0621 21:19:27.143000 3925433 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.
|
7091 |
+
W0621 21:19:27.143000 3925433 site-packages/torch/distributed/run.py:766] *****************************************
|
attnserver.run_attnserver.slurm.sh.343202.out.log
CHANGED
@@ -29921,3 +29921,22 @@ DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540712.95
|
|
29921 |
DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540712.9507585, 15, gather: 1.8799049854278564
|
29922 |
DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540712.9546258, 0, gather: 0.007202863693237305
|
29923 |
DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0099s
|
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|
29921 |
DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540712.9507585, 15, gather: 1.8799049854278564
|
29922 |
DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750540712.9546258, 0, gather: 0.007202863693237305
|
29923 |
DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0099s
|
29924 |
+
Running ctx_length=81920, TP_SIZE=8, CP_SIZE=2, BATCH_SIZE=2
|
29925 |
+
Cleaning up checkpoint directory: gpt-checkpoint
|
29926 |
+
Cleaning up checkpoint directory: gpt-checkpoint
|
29927 |
+
--------------------------------
|
29928 |
+
CTX_LENGTH: 81920
|
29929 |
+
TP_SIZE: 8
|
29930 |
+
CP_SIZE: 2
|
29931 |
+
CHECKPOINT_PATH: gpt-checkpoint
|
29932 |
+
PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
|
29933 |
+
--------------------------------
|
29934 |
+
/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
29935 |
+
--------------------------------
|
29936 |
+
CTX_LENGTH: 81920
|
29937 |
+
TP_SIZE: 8
|
29938 |
+
CP_SIZE: 2
|
29939 |
+
CHECKPOINT_PATH: gpt-checkpoint
|
29940 |
+
PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
|
29941 |
+
--------------------------------
|
29942 |
+
/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
attnserver.run_attnserver.slurm.sh.343203.err.log
CHANGED
@@ -695,3 +695,104 @@ W0621 21:18:19.257000 758676 site-packages/torch/distributed/run.py:766]
|
|
695 |
W0621 21:18:19.257000 758676 site-packages/torch/distributed/run.py:766] *****************************************
|
696 |
W0621 21:18:19.257000 758676 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.
|
697 |
W0621 21:18:19.257000 758676 site-packages/torch/distributed/run.py:766] *****************************************
|
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|
695 |
W0621 21:18:19.257000 758676 site-packages/torch/distributed/run.py:766] *****************************************
|
696 |
W0621 21:18:19.257000 758676 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.
|
697 |
W0621 21:18:19.257000 758676 site-packages/torch/distributed/run.py:766] *****************************************
|
698 |
+
[rank5]:[W621 21:18:42.008152355 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.
|
699 |
+
[rank13]:[W621 21:18:42.669951592 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.
|
700 |
+
[rank1]:[W621 21:18:42.009315982 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.
|
701 |
+
[rank9]:[W621 21:18:42.671191991 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.
|
702 |
+
[rank15]:[W621 21:18:42.671262833 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.
|
703 |
+
[rank12]:[W621 21:18:42.671775299 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.
|
704 |
+
[rank7]:[W621 21:18:42.012603791 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.
|
705 |
+
[rank4]:[W621 21:18:42.014142683 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.
|
706 |
+
[rank8]:[W621 21:18:43.829344186 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.
|
707 |
+
[rank0]:[W621 21:18:44.272155162 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.
|
708 |
+
[rank14]:[W621 21:18:44.950915815 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.
|
709 |
+
[rank11]:[W621 21:18:44.951789519 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.
|
710 |
+
[rank10]:[W621 21:18:44.952640951 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.
|
711 |
+
[rank3]:[W621 21:18:44.294979689 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.
|
712 |
+
[rank2]:[W621 21:18:44.295832026 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.
|
713 |
+
[rank6]:[W621 21:18:44.297861170 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.
|
714 |
+
/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.
|
715 |
+
warnings.warn(
|
716 |
+
/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.
|
717 |
+
warnings.warn(
|
718 |
+
/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.
|
719 |
+
warnings.warn(
|
720 |
+
/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.
|
721 |
+
warnings.warn(
|
722 |
+
/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.
|
723 |
+
warnings.warn(
|
724 |
+
/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.
|
725 |
+
warnings.warn(
|
726 |
+
/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.
|
727 |
+
warnings.warn(
|
728 |
+
/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.
|
729 |
+
warnings.warn(
|
730 |
+
/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.
|
731 |
+
warnings.warn(
|
732 |
+
/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.
|
733 |
+
warnings.warn(
|
734 |
+
/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.
|
735 |
+
warnings.warn(
|
736 |
+
/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.
|
737 |
+
warnings.warn(
|
738 |
+
/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.
|
739 |
+
warnings.warn(
|
740 |
+
/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.
|
741 |
+
warnings.warn(
|
742 |
+
/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.
|
743 |
+
warnings.warn(
|
744 |
+
/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.
|
745 |
+
warnings.warn(
|
746 |
+
/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.
|
747 |
+
warnings.warn(
|
748 |
+
/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.
|
749 |
+
warnings.warn(
|
750 |
+
/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.
|
751 |
+
warnings.warn(
|
752 |
+
/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.
|
753 |
+
warnings.warn(
|
754 |
+
/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.
|
755 |
+
warnings.warn(
|
756 |
+
/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.
|
757 |
+
warnings.warn(
|
758 |
+
/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.
|
759 |
+
warnings.warn(
|
760 |
+
/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.
|
761 |
+
warnings.warn(
|
762 |
+
/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.
|
763 |
+
warnings.warn(
|
764 |
+
/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.
|
765 |
+
warnings.warn(
|
766 |
+
/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.
|
767 |
+
warnings.warn(
|
768 |
+
/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.
|
769 |
+
warnings.warn(
|
770 |
+
/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.
|
771 |
+
warnings.warn(
|
772 |
+
/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.
|
773 |
+
warnings.warn(
|
774 |
+
/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.
|
775 |
+
warnings.warn(
|
776 |
+
/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.
|
777 |
+
warnings.warn(
|
778 |
+
[rank0]: Traceback (most recent call last):
|
779 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/./pretrain_gpt_profile.py", line 554, in <module>
|
780 |
+
[rank0]: pretrain(
|
781 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/training.py", line 879, in pretrain
|
782 |
+
[rank0]: save_checkpoint(
|
783 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/training/checkpointing.py", line 469, in save_checkpoint
|
784 |
+
[rank0]: async_save_request = dist_checkpointing.save(state_dict, checkpoint_name, save_strategy,
|
785 |
+
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
786 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/dist_checkpointing/serialization.py", line 386, in save
|
787 |
+
[rank0]: common_strategy.save_common(state_dict, checkpoint_dir)
|
788 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/dist_checkpointing/strategies/common.py", line 48, in save_common
|
789 |
+
[rank0]: torch.save(common_state_dict, path)
|
790 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/serialization.py", line 964, in save
|
791 |
+
[rank0]: with _open_zipfile_writer(f) as opened_zipfile:
|
792 |
+
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^
|
793 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/serialization.py", line 828, in _open_zipfile_writer
|
794 |
+
[rank0]: return container(name_or_buffer)
|
795 |
+
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^
|
796 |
+
[rank0]: File "/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/serialization.py", line 792, in __init__
|
797 |
+
[rank0]: torch._C.PyTorchFileWriter(
|
798 |
+
[rank0]: RuntimeError: Parent directory gpt-checkpoint/iter_0000010 does not exist.
|
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|
attnserver.run_attnserver.slurm.sh.343204.err.log
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attnserver.run_attnserver.slurm.sh.343204.out.log
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|
|
attnserver.run_attnserver.slurm.sh.343205.err.log
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|
|
attnserver.run_attnserver.slurm.sh.343206.err.log
CHANGED
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|
|
attnserver.run_attnserver.slurm.sh.343206.out.log
CHANGED
The diff for this file is too large to render.
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|
|
attnserver.run_attnserver.slurm.sh.343207.err.log
ADDED
@@ -0,0 +1,141 @@
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|
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|
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|
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|
|
|
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|
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|
|
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|
|
|
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|
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|
|
|
|
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|
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|
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|
|
|
|
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|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
+ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
|
2 |
+
++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
|
3 |
+
++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
|
4 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
5 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
6 |
+
+++ export _CE_M=
|
7 |
+
+++ _CE_M=
|
8 |
+
+++ export _CE_CONDA=
|
9 |
+
+++ _CE_CONDA=
|
10 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
11 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
12 |
+
+++ '[' -z x ']'
|
13 |
+
++ conda activate
|
14 |
+
++ local cmd=activate
|
15 |
+
++ case "$cmd" in
|
16 |
+
++ __conda_activate activate
|
17 |
+
++ '[' -n '' ']'
|
18 |
+
++ local ask_conda
|
19 |
+
+++ PS1=
|
20 |
+
+++ __conda_exe shell.posix activate
|
21 |
+
+++ '[' -n '' ']'
|
22 |
+
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
|
23 |
+
++ ask_conda='unset _CE_M
|
24 |
+
unset _CE_CONDA
|
25 |
+
PS1='\''(base) '\''
|
26 |
+
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'\''
|
27 |
+
export CONDA_SHLVL='\''1'\''
|
28 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
29 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
30 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
31 |
+
++ eval 'unset _CE_M
|
32 |
+
unset _CE_CONDA
|
33 |
+
PS1='\''(base) '\''
|
34 |
+
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'\''
|
35 |
+
export CONDA_SHLVL='\''1'\''
|
36 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
37 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
38 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
39 |
+
+++ unset _CE_M
|
40 |
+
+++ unset _CE_CONDA
|
41 |
+
+++ PS1='(base) '
|
42 |
+
+++ 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
|
43 |
+
+++ 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
|
44 |
+
+++ export CONDA_SHLVL=1
|
45 |
+
+++ CONDA_SHLVL=1
|
46 |
+
+++ export 'CONDA_PROMPT_MODIFIER=(base) '
|
47 |
+
+++ CONDA_PROMPT_MODIFIER='(base) '
|
48 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
49 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
50 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
51 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
52 |
+
++ __conda_hashr
|
53 |
+
++ '[' -n '' ']'
|
54 |
+
++ '[' -n '' ']'
|
55 |
+
++ hash -r
|
56 |
+
+ conda activate junda-attnserver
|
57 |
+
+ local cmd=activate
|
58 |
+
+ case "$cmd" in
|
59 |
+
+ __conda_activate activate junda-attnserver
|
60 |
+
+ '[' -n '' ']'
|
61 |
+
+ local ask_conda
|
62 |
+
++ PS1='(base) '
|
63 |
+
++ __conda_exe shell.posix activate junda-attnserver
|
64 |
+
++ '[' -n '' ']'
|
65 |
+
++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
|
66 |
+
+ ask_conda='unset _CE_M
|
67 |
+
unset _CE_CONDA
|
68 |
+
PS1='\''(junda-attnserver) '\''
|
69 |
+
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'\''
|
70 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
71 |
+
export CONDA_SHLVL='\''2'\''
|
72 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
73 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
74 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
75 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
76 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
77 |
+
+ eval 'unset _CE_M
|
78 |
+
unset _CE_CONDA
|
79 |
+
PS1='\''(junda-attnserver) '\''
|
80 |
+
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'\''
|
81 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
82 |
+
export CONDA_SHLVL='\''2'\''
|
83 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
84 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
85 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
86 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
87 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
88 |
+
++ unset _CE_M
|
89 |
+
++ unset _CE_CONDA
|
90 |
+
++ PS1='(junda-attnserver) '
|
91 |
+
++ 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
|
92 |
+
++ 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
|
93 |
+
++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
94 |
+
++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
95 |
+
++ export CONDA_SHLVL=2
|
96 |
+
++ CONDA_SHLVL=2
|
97 |
+
++ export CONDA_DEFAULT_ENV=junda-attnserver
|
98 |
+
++ CONDA_DEFAULT_ENV=junda-attnserver
|
99 |
+
++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
|
100 |
+
++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
|
101 |
+
++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
102 |
+
++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
103 |
+
++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
104 |
+
++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
105 |
+
++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
106 |
+
++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
107 |
+
+ __conda_hashr
|
108 |
+
+ '[' -n '' ']'
|
109 |
+
+ '[' -n '' ']'
|
110 |
+
+ hash -r
|
111 |
+
+ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
112 |
+
+ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
113 |
+
+ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
114 |
+
+ export PROF_TP_SIZE=8
|
115 |
+
+ PROF_TP_SIZE=8
|
116 |
+
+ export PROF_CP_SIZE=1
|
117 |
+
+ PROF_CP_SIZE=1
|
118 |
+
+ export PROF_BS=1
|
119 |
+
+ PROF_BS=1
|
120 |
+
+ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
|
121 |
+
+ export PROF_CTX_LENGTH=1024
|
122 |
+
+ PROF_CTX_LENGTH=1024
|
123 |
+
+ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs1.json'
|
124 |
+
+ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs1.json' ']'
|
125 |
+
+ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=1'
|
126 |
+
+ srun bash ./attnserver.sh
|
127 |
+
+ which python3
|
128 |
+
+ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 1 --node_rank 0 --rdzv_id 343207 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-661:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 1 --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/
|
129 |
+
/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
|
130 |
+
and will be removed in future. Use torchrun.
|
131 |
+
Note that --use-env is set by default in torchrun.
|
132 |
+
If your script expects `--local-rank` argument to be set, please
|
133 |
+
change it to read from `os.environ['LOCAL_RANK']` instead. See
|
134 |
+
https://pytorch.org/docs/stable/distributed.html#launch-utility for
|
135 |
+
further instructions
|
136 |
+
|
137 |
+
main()
|
138 |
+
W0621 21:19:26.076000 1511074 site-packages/torch/distributed/run.py:766]
|
139 |
+
W0621 21:19:26.076000 1511074 site-packages/torch/distributed/run.py:766] *****************************************
|
140 |
+
W0621 21:19:26.076000 1511074 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.
|
141 |
+
W0621 21:19:26.076000 1511074 site-packages/torch/distributed/run.py:766] *****************************************
|
attnserver.run_attnserver.slurm.sh.343207.out.log
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=1
|
2 |
+
Cleaning up checkpoint directory: gpt-checkpoint
|
3 |
+
--------------------------------
|
4 |
+
CTX_LENGTH: 1024
|
5 |
+
TP_SIZE: 8
|
6 |
+
CP_SIZE: 1
|
7 |
+
CHECKPOINT_PATH: gpt-checkpoint
|
8 |
+
PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
|
9 |
+
--------------------------------
|
10 |
+
/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
attnserver.run_attnserver.slurm.sh.343208.err.log
ADDED
@@ -0,0 +1,141 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
+ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
|
2 |
+
++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
|
3 |
+
++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
|
4 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
5 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
6 |
+
+++ export _CE_M=
|
7 |
+
+++ _CE_M=
|
8 |
+
+++ export _CE_CONDA=
|
9 |
+
+++ _CE_CONDA=
|
10 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
11 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
12 |
+
+++ '[' -z x ']'
|
13 |
+
++ conda activate
|
14 |
+
++ local cmd=activate
|
15 |
+
++ case "$cmd" in
|
16 |
+
++ __conda_activate activate
|
17 |
+
++ '[' -n '' ']'
|
18 |
+
++ local ask_conda
|
19 |
+
+++ PS1=
|
20 |
+
+++ __conda_exe shell.posix activate
|
21 |
+
+++ '[' -n '' ']'
|
22 |
+
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
|
23 |
+
++ ask_conda='unset _CE_M
|
24 |
+
unset _CE_CONDA
|
25 |
+
PS1='\''(base) '\''
|
26 |
+
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'\''
|
27 |
+
export CONDA_SHLVL='\''1'\''
|
28 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
29 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
30 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
31 |
+
++ eval 'unset _CE_M
|
32 |
+
unset _CE_CONDA
|
33 |
+
PS1='\''(base) '\''
|
34 |
+
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'\''
|
35 |
+
export CONDA_SHLVL='\''1'\''
|
36 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
37 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
38 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
39 |
+
+++ unset _CE_M
|
40 |
+
+++ unset _CE_CONDA
|
41 |
+
+++ PS1='(base) '
|
42 |
+
+++ 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
|
43 |
+
+++ 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
|
44 |
+
+++ export CONDA_SHLVL=1
|
45 |
+
+++ CONDA_SHLVL=1
|
46 |
+
+++ export 'CONDA_PROMPT_MODIFIER=(base) '
|
47 |
+
+++ CONDA_PROMPT_MODIFIER='(base) '
|
48 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
49 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
50 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
51 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
52 |
+
++ __conda_hashr
|
53 |
+
++ '[' -n '' ']'
|
54 |
+
++ '[' -n '' ']'
|
55 |
+
++ hash -r
|
56 |
+
+ conda activate junda-attnserver
|
57 |
+
+ local cmd=activate
|
58 |
+
+ case "$cmd" in
|
59 |
+
+ __conda_activate activate junda-attnserver
|
60 |
+
+ '[' -n '' ']'
|
61 |
+
+ local ask_conda
|
62 |
+
++ PS1='(base) '
|
63 |
+
++ __conda_exe shell.posix activate junda-attnserver
|
64 |
+
++ '[' -n '' ']'
|
65 |
+
++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
|
66 |
+
+ ask_conda='unset _CE_M
|
67 |
+
unset _CE_CONDA
|
68 |
+
PS1='\''(junda-attnserver) '\''
|
69 |
+
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'\''
|
70 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
71 |
+
export CONDA_SHLVL='\''2'\''
|
72 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
73 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
74 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
75 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
76 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
77 |
+
+ eval 'unset _CE_M
|
78 |
+
unset _CE_CONDA
|
79 |
+
PS1='\''(junda-attnserver) '\''
|
80 |
+
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'\''
|
81 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
82 |
+
export CONDA_SHLVL='\''2'\''
|
83 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
84 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
85 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
86 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
87 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
88 |
+
++ unset _CE_M
|
89 |
+
++ unset _CE_CONDA
|
90 |
+
++ PS1='(junda-attnserver) '
|
91 |
+
++ 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
|
92 |
+
++ 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
|
93 |
+
++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
94 |
+
++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
95 |
+
++ export CONDA_SHLVL=2
|
96 |
+
++ CONDA_SHLVL=2
|
97 |
+
++ export CONDA_DEFAULT_ENV=junda-attnserver
|
98 |
+
++ CONDA_DEFAULT_ENV=junda-attnserver
|
99 |
+
++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
|
100 |
+
++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
|
101 |
+
++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
102 |
+
++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
103 |
+
++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
104 |
+
++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
105 |
+
++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
106 |
+
++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
107 |
+
+ __conda_hashr
|
108 |
+
+ '[' -n '' ']'
|
109 |
+
+ '[' -n '' ']'
|
110 |
+
+ hash -r
|
111 |
+
+ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
112 |
+
+ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
113 |
+
+ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
114 |
+
+ export PROF_TP_SIZE=8
|
115 |
+
+ PROF_TP_SIZE=8
|
116 |
+
+ export PROF_CP_SIZE=1
|
117 |
+
+ PROF_CP_SIZE=1
|
118 |
+
+ export PROF_BS=2
|
119 |
+
+ PROF_BS=2
|
120 |
+
+ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
|
121 |
+
+ export PROF_CTX_LENGTH=1024
|
122 |
+
+ PROF_CTX_LENGTH=1024
|
123 |
+
+ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs2.json'
|
124 |
+
+ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs2.json' ']'
|
125 |
+
+ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=2'
|
126 |
+
+ srun bash ./attnserver.sh
|
127 |
+
+ which python3
|
128 |
+
+ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 1 --node_rank 0 --rdzv_id 343208 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-886:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 1 --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/
|
129 |
+
/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
|
130 |
+
and will be removed in future. Use torchrun.
|
131 |
+
Note that --use-env is set by default in torchrun.
|
132 |
+
If your script expects `--local-rank` argument to be set, please
|
133 |
+
change it to read from `os.environ['LOCAL_RANK']` instead. See
|
134 |
+
https://pytorch.org/docs/stable/distributed.html#launch-utility for
|
135 |
+
further instructions
|
136 |
+
|
137 |
+
main()
|
138 |
+
W0621 21:19:26.532000 1692678 site-packages/torch/distributed/run.py:766]
|
139 |
+
W0621 21:19:26.532000 1692678 site-packages/torch/distributed/run.py:766] *****************************************
|
140 |
+
W0621 21:19:26.532000 1692678 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.
|
141 |
+
W0621 21:19:26.532000 1692678 site-packages/torch/distributed/run.py:766] *****************************************
|
attnserver.run_attnserver.slurm.sh.343208.out.log
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=2
|
2 |
+
Cleaning up checkpoint directory: gpt-checkpoint
|
3 |
+
--------------------------------
|
4 |
+
CTX_LENGTH: 1024
|
5 |
+
TP_SIZE: 8
|
6 |
+
CP_SIZE: 1
|
7 |
+
CHECKPOINT_PATH: gpt-checkpoint
|
8 |
+
PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
|
9 |
+
--------------------------------
|
10 |
+
/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
attnserver.run_attnserver.slurm.sh.343209.err.log
ADDED
@@ -0,0 +1,149 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
+ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
|
2 |
+
++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
|
3 |
+
++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
|
4 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
5 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
6 |
+
+++ export _CE_M=
|
7 |
+
+++ _CE_M=
|
8 |
+
+++ export _CE_CONDA=
|
9 |
+
+++ _CE_CONDA=
|
10 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
11 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
12 |
+
+++ '[' -z x ']'
|
13 |
+
++ conda activate
|
14 |
+
++ local cmd=activate
|
15 |
+
++ case "$cmd" in
|
16 |
+
++ __conda_activate activate
|
17 |
+
++ '[' -n '' ']'
|
18 |
+
++ local ask_conda
|
19 |
+
+++ PS1=
|
20 |
+
+++ __conda_exe shell.posix activate
|
21 |
+
+++ '[' -n '' ']'
|
22 |
+
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
|
23 |
+
++ ask_conda='unset _CE_M
|
24 |
+
unset _CE_CONDA
|
25 |
+
PS1='\''(base) '\''
|
26 |
+
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'\''
|
27 |
+
export CONDA_SHLVL='\''1'\''
|
28 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
29 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
30 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
31 |
+
++ eval 'unset _CE_M
|
32 |
+
unset _CE_CONDA
|
33 |
+
PS1='\''(base) '\''
|
34 |
+
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'\''
|
35 |
+
export CONDA_SHLVL='\''1'\''
|
36 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
37 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
38 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
39 |
+
+++ unset _CE_M
|
40 |
+
+++ unset _CE_CONDA
|
41 |
+
+++ PS1='(base) '
|
42 |
+
+++ 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
|
43 |
+
+++ 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
|
44 |
+
+++ export CONDA_SHLVL=1
|
45 |
+
+++ CONDA_SHLVL=1
|
46 |
+
+++ export 'CONDA_PROMPT_MODIFIER=(base) '
|
47 |
+
+++ CONDA_PROMPT_MODIFIER='(base) '
|
48 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
49 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
50 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
51 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
52 |
+
++ __conda_hashr
|
53 |
+
++ '[' -n '' ']'
|
54 |
+
++ '[' -n '' ']'
|
55 |
+
++ hash -r
|
56 |
+
+ conda activate junda-attnserver
|
57 |
+
+ local cmd=activate
|
58 |
+
+ case "$cmd" in
|
59 |
+
+ __conda_activate activate junda-attnserver
|
60 |
+
+ '[' -n '' ']'
|
61 |
+
+ local ask_conda
|
62 |
+
++ PS1='(base) '
|
63 |
+
++ __conda_exe shell.posix activate junda-attnserver
|
64 |
+
++ '[' -n '' ']'
|
65 |
+
++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
|
66 |
+
+ ask_conda='unset _CE_M
|
67 |
+
unset _CE_CONDA
|
68 |
+
PS1='\''(junda-attnserver) '\''
|
69 |
+
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'\''
|
70 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
71 |
+
export CONDA_SHLVL='\''2'\''
|
72 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
73 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
74 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
75 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
76 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
77 |
+
+ eval 'unset _CE_M
|
78 |
+
unset _CE_CONDA
|
79 |
+
PS1='\''(junda-attnserver) '\''
|
80 |
+
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'\''
|
81 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
82 |
+
export CONDA_SHLVL='\''2'\''
|
83 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
84 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
85 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
86 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
87 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
88 |
+
++ unset _CE_M
|
89 |
+
++ unset _CE_CONDA
|
90 |
+
++ PS1='(junda-attnserver) '
|
91 |
+
++ 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
|
92 |
+
++ 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
|
93 |
+
++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
94 |
+
++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
95 |
+
++ export CONDA_SHLVL=2
|
96 |
+
++ CONDA_SHLVL=2
|
97 |
+
++ export CONDA_DEFAULT_ENV=junda-attnserver
|
98 |
+
++ CONDA_DEFAULT_ENV=junda-attnserver
|
99 |
+
++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
|
100 |
+
++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
|
101 |
+
++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
102 |
+
++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
103 |
+
++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
104 |
+
++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
105 |
+
++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
106 |
+
++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
107 |
+
+ __conda_hashr
|
108 |
+
+ '[' -n '' ']'
|
109 |
+
+ '[' -n '' ']'
|
110 |
+
+ hash -r
|
111 |
+
+ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
112 |
+
+ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
113 |
+
+ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
114 |
+
+ export PROF_TP_SIZE=8
|
115 |
+
+ PROF_TP_SIZE=8
|
116 |
+
+ export PROF_CP_SIZE=1
|
117 |
+
+ PROF_CP_SIZE=1
|
118 |
+
+ export PROF_BS=4
|
119 |
+
+ PROF_BS=4
|
120 |
+
+ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
|
121 |
+
+ export PROF_CTX_LENGTH=1024
|
122 |
+
+ PROF_CTX_LENGTH=1024
|
123 |
+
+ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs4.json'
|
124 |
+
+ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs4.json' ']'
|
125 |
+
+ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=4'
|
126 |
+
+ srun bash ./attnserver.sh
|
127 |
+
+ which python3
|
128 |
+
+ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 1 --node_rank 0 --rdzv_id 343209 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-702:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 1 --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/
|
129 |
+
/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
|
130 |
+
and will be removed in future. Use torchrun.
|
131 |
+
Note that --use-env is set by default in torchrun.
|
132 |
+
If your script expects `--local-rank` argument to be set, please
|
133 |
+
change it to read from `os.environ['LOCAL_RANK']` instead. See
|
134 |
+
https://pytorch.org/docs/stable/distributed.html#launch-utility for
|
135 |
+
further instructions
|
136 |
+
|
137 |
+
main()
|
138 |
+
W0621 21:19:25.037000 1978718 site-packages/torch/distributed/run.py:766]
|
139 |
+
W0621 21:19:25.037000 1978718 site-packages/torch/distributed/run.py:766] *****************************************
|
140 |
+
W0621 21:19:25.037000 1978718 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.
|
141 |
+
W0621 21:19:25.037000 1978718 site-packages/torch/distributed/run.py:766] *****************************************
|
142 |
+
[rank1]:[W621 21:19:45.103630717 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.
|
143 |
+
[rank3]:[W621 21:19:46.254546836 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.
|
144 |
+
[rank0]:[W621 21:19:46.278781300 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.
|
145 |
+
[rank4]:[W621 21:19:46.282298864 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.
|
146 |
+
[rank7]:[W621 21:19:46.283689693 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.
|
147 |
+
[rank5]:[W621 21:19:46.293795584 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.
|
148 |
+
[rank6]:[W621 21:19:46.294037250 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.
|
149 |
+
[rank2]:[W621 21:19:46.295639773 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.
|
attnserver.run_attnserver.slurm.sh.343209.out.log
ADDED
@@ -0,0 +1,537 @@
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|
1 |
+
Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=4
|
2 |
+
Cleaning up checkpoint directory: gpt-checkpoint
|
3 |
+
--------------------------------
|
4 |
+
CTX_LENGTH: 1024
|
5 |
+
TP_SIZE: 8
|
6 |
+
CP_SIZE: 1
|
7 |
+
CHECKPOINT_PATH: gpt-checkpoint
|
8 |
+
PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
|
9 |
+
--------------------------------
|
10 |
+
/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
11 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
12 |
+
using world size: 8, data-parallel size: 1, context-parallel size: 1, 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
|
13 |
+
Number of virtual stages per pipeline stage: None
|
14 |
+
WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
|
15 |
+
using torch.float16 for parameters ...
|
16 |
+
------------------------ arguments ------------------------
|
17 |
+
account_for_embedding_in_pipeline_split ......... False
|
18 |
+
account_for_loss_in_pipeline_split .............. False
|
19 |
+
accumulate_allreduce_grads_in_fp32 .............. False
|
20 |
+
adam_beta1 ...................................... 0.9
|
21 |
+
adam_beta2 ...................................... 0.999
|
22 |
+
adam_eps ........................................ 1e-08
|
23 |
+
add_bias_linear ................................. True
|
24 |
+
add_position_embedding .......................... True
|
25 |
+
add_qkv_bias .................................... True
|
26 |
+
adlr_autoresume ................................. False
|
27 |
+
adlr_autoresume_interval ........................ 1000
|
28 |
+
align_grad_reduce ............................... True
|
29 |
+
align_param_gather .............................. False
|
30 |
+
app_tag_run_name ................................ None
|
31 |
+
app_tag_run_version ............................. 0.0.0
|
32 |
+
apply_layernorm_1p .............................. False
|
33 |
+
apply_query_key_layer_scaling ................... False
|
34 |
+
apply_residual_connection_post_layernorm ........ False
|
35 |
+
apply_rope_fusion ............................... False
|
36 |
+
async_save ...................................... None
|
37 |
+
async_tensor_model_parallel_allreduce ........... True
|
38 |
+
attention_backend ............................... AttnBackend.auto
|
39 |
+
attention_dropout ............................... 0.1
|
40 |
+
attention_softmax_in_fp32 ....................... False
|
41 |
+
auto_detect_ckpt_format ......................... False
|
42 |
+
barrier_with_L1_time ............................ True
|
43 |
+
bert_binary_head ................................ True
|
44 |
+
bert_embedder_type .............................. megatron
|
45 |
+
bert_load ....................................... None
|
46 |
+
bf16 ............................................ False
|
47 |
+
bias_dropout_fusion ............................. True
|
48 |
+
bias_gelu_fusion ................................ True
|
49 |
+
bias_swiglu_fusion .............................. True
|
50 |
+
biencoder_projection_dim ........................ 0
|
51 |
+
biencoder_shared_query_context_model ............ False
|
52 |
+
block_data_path ................................. None
|
53 |
+
calc_ft_timeouts ................................ False
|
54 |
+
calculate_per_token_loss ........................ False
|
55 |
+
check_for_large_grads ........................... False
|
56 |
+
check_for_nan_in_loss_and_grad .................. False
|
57 |
+
check_for_spiky_loss ............................ False
|
58 |
+
check_weight_hash_across_dp_replicas_interval ... None
|
59 |
+
ckpt_assume_constant_structure .................. False
|
60 |
+
ckpt_convert_format ............................. None
|
61 |
+
ckpt_convert_save ............................... None
|
62 |
+
ckpt_convert_update_legacy_dist_opt_format ...... False
|
63 |
+
ckpt_format ..................................... torch_dist
|
64 |
+
ckpt_fully_parallel_load ........................ False
|
65 |
+
ckpt_fully_parallel_save ........................ True
|
66 |
+
ckpt_fully_parallel_save_deprecated ............. False
|
67 |
+
ckpt_step ....................................... None
|
68 |
+
classes_fraction ................................ 1.0
|
69 |
+
clip_grad ....................................... 1.0
|
70 |
+
clone_scatter_output_in_embedding ............... True
|
71 |
+
config_logger_dir ...............................
|
72 |
+
consumed_train_samples .......................... 0
|
73 |
+
consumed_valid_samples .......................... 0
|
74 |
+
context_parallel_size ........................... 1
|
75 |
+
cp_comm_type .................................... ['p2p']
|
76 |
+
create_attention_mask_in_dataloader ............. True
|
77 |
+
cross_entropy_fusion_impl ....................... native
|
78 |
+
cross_entropy_loss_fusion ....................... False
|
79 |
+
cuda_graph_scope ................................ full
|
80 |
+
cuda_graph_warmup_steps ......................... 3
|
81 |
+
data_args_path .................................. None
|
82 |
+
data_cache_path ................................. None
|
83 |
+
data_parallel_random_init ....................... False
|
84 |
+
data_parallel_sharding_strategy ................. no_shard
|
85 |
+
data_parallel_size .............................. 1
|
86 |
+
data_path ....................................... None
|
87 |
+
data_per_class_fraction ......................... 1.0
|
88 |
+
data_sharding ................................... True
|
89 |
+
dataloader_type ................................. single
|
90 |
+
ddp_average_in_collective ....................... False
|
91 |
+
ddp_bucket_size ................................. None
|
92 |
+
ddp_num_buckets ................................. None
|
93 |
+
ddp_pad_buckets_for_high_nccl_busbw ............. False
|
94 |
+
decoder_first_pipeline_num_layers ............... None
|
95 |
+
decoder_last_pipeline_num_layers ................ None
|
96 |
+
decoder_num_layers .............................. None
|
97 |
+
decoder_seq_length .............................. None
|
98 |
+
decoupled_lr .................................... None
|
99 |
+
decoupled_min_lr ................................ None
|
100 |
+
decrease_batch_size_if_needed ................... False
|
101 |
+
defer_embedding_wgrad_compute ................... False
|
102 |
+
deprecated_use_mcore_models ..................... False
|
103 |
+
deterministic_mode .............................. False
|
104 |
+
dino_bottleneck_size ............................ 256
|
105 |
+
dino_freeze_last_layer .......................... 1
|
106 |
+
dino_head_hidden_size ........................... 2048
|
107 |
+
dino_local_crops_number ......................... 10
|
108 |
+
dino_local_img_size ............................. 96
|
109 |
+
dino_norm_last_layer ............................ False
|
110 |
+
dino_teacher_temp ............................... 0.07
|
111 |
+
dino_warmup_teacher_temp ........................ 0.04
|
112 |
+
dino_warmup_teacher_temp_epochs ................. 30
|
113 |
+
disable_bf16_reduced_precision_matmul ........... False
|
114 |
+
disable_mamba_mem_eff_path ...................... False
|
115 |
+
disable_straggler_on_startup .................... False
|
116 |
+
dist_ckpt_format_deprecated ..................... None
|
117 |
+
dist_ckpt_strictness ............................ assume_ok_unexpected
|
118 |
+
distribute_saved_activations .................... False
|
119 |
+
distributed_backend ............................. nccl
|
120 |
+
distributed_timeout_minutes ..................... 10
|
121 |
+
embedding_path .................................. None
|
122 |
+
empty_unused_memory_level ....................... 0
|
123 |
+
enable_cuda_graph ............................... False
|
124 |
+
enable_ft_package ............................... False
|
125 |
+
enable_gloo_process_groups ...................... True
|
126 |
+
enable_msc ...................................... True
|
127 |
+
enable_one_logger ............................... True
|
128 |
+
encoder_num_layers .............................. 2
|
129 |
+
encoder_pipeline_model_parallel_size ............ 0
|
130 |
+
encoder_seq_length .............................. 1024
|
131 |
+
encoder_tensor_model_parallel_size .............. 0
|
132 |
+
end_weight_decay ................................ 0.1
|
133 |
+
eod_mask_loss ................................... False
|
134 |
+
error_injection_rate ............................ 0
|
135 |
+
error_injection_type ............................ transient_error
|
136 |
+
eval_interval ................................... 16
|
137 |
+
eval_iters ...................................... 1
|
138 |
+
evidence_data_path .............................. None
|
139 |
+
exit_duration_in_mins ........................... None
|
140 |
+
exit_interval ................................... None
|
141 |
+
exit_on_missing_checkpoint ...................... False
|
142 |
+
exit_signal_handler ............................. False
|
143 |
+
exp_avg_dtype ................................... torch.float32
|
144 |
+
exp_avg_sq_dtype ................................ torch.float32
|
145 |
+
expert_model_parallel_size ...................... 1
|
146 |
+
expert_tensor_parallel_size ..................... 8
|
147 |
+
external_cuda_graph ............................. False
|
148 |
+
ffn_hidden_size ................................. 16384
|
149 |
+
finetune ........................................ False
|
150 |
+
first_last_layers_bf16 .......................... False
|
151 |
+
flash_decode .................................... False
|
152 |
+
fp16 ............................................ True
|
153 |
+
fp16_lm_cross_entropy ........................... False
|
154 |
+
fp32_residual_connection ........................ False
|
155 |
+
fp8 ............................................. None
|
156 |
+
fp8_amax_compute_algo ........................... most_recent
|
157 |
+
fp8_amax_history_len ............................ 1
|
158 |
+
fp8_interval .................................... 1
|
159 |
+
fp8_margin ...................................... 0
|
160 |
+
fp8_param_gather ................................ False
|
161 |
+
fp8_recipe ...................................... delayed
|
162 |
+
fp8_wgrad ....................................... True
|
163 |
+
fsdp_double_buffer .............................. False
|
164 |
+
global_batch_size ............................... 1
|
165 |
+
grad_reduce_in_bf16 ............................. False
|
166 |
+
gradient_accumulation_fusion .................... True
|
167 |
+
gradient_reduce_div_fusion ...................... True
|
168 |
+
group_query_attention ........................... True
|
169 |
+
head_lr_mult .................................... 1.0
|
170 |
+
heterogeneous_layers_config_encoded_json ........ None
|
171 |
+
heterogeneous_layers_config_path ................ None
|
172 |
+
hidden_dropout .................................. 0.1
|
173 |
+
hidden_size ..................................... 4096
|
174 |
+
hierarchical_context_parallel_sizes ............. None
|
175 |
+
high_priority_stream_groups ..................... []
|
176 |
+
hybrid_attention_ratio .......................... 0.0
|
177 |
+
hybrid_mlp_ratio ................................ 0.0
|
178 |
+
hybrid_override_pattern ......................... None
|
179 |
+
hysteresis ...................................... 2
|
180 |
+
ict_head_size ................................... None
|
181 |
+
ict_load ........................................ None
|
182 |
+
img_h ........................................... 224
|
183 |
+
img_w ........................................... 224
|
184 |
+
indexer_batch_size .............................. 128
|
185 |
+
indexer_log_interval ............................ 1000
|
186 |
+
inference_batch_times_seqlen_threshold .......... -1
|
187 |
+
inference_dynamic_batching ...................... False
|
188 |
+
inference_dynamic_batching_buffer_guaranteed_fraction 0.2
|
189 |
+
inference_dynamic_batching_buffer_overflow_factor None
|
190 |
+
inference_dynamic_batching_buffer_size_gb ....... 40.0
|
191 |
+
inference_dynamic_batching_chunk_size ........... 256
|
192 |
+
inference_dynamic_batching_max_requests_override None
|
193 |
+
inference_dynamic_batching_max_tokens_override .. None
|
194 |
+
inference_max_batch_size ........................ 8
|
195 |
+
inference_max_seq_length ........................ 2560
|
196 |
+
inference_rng_tracker ........................... False
|
197 |
+
init_method_std ................................. 0.02
|
198 |
+
init_method_xavier_uniform ...................... False
|
199 |
+
init_model_with_meta_device ..................... False
|
200 |
+
initial_loss_scale .............................. 4294967296
|
201 |
+
inprocess_active_world_size ..................... 8
|
202 |
+
inprocess_barrier_timeout ....................... 120
|
203 |
+
inprocess_completion_timeout .................... 120
|
204 |
+
inprocess_empty_cuda_cache ...................... False
|
205 |
+
inprocess_granularity ........................... node
|
206 |
+
inprocess_hard_timeout .......................... 90
|
207 |
+
inprocess_heartbeat_interval .................... 30
|
208 |
+
inprocess_heartbeat_timeout ..................... 60
|
209 |
+
inprocess_last_call_wait ........................ 1
|
210 |
+
inprocess_max_iterations ........................ None
|
211 |
+
inprocess_monitor_process_interval .............. 1.0
|
212 |
+
inprocess_monitor_thread_interval ............... 1.0
|
213 |
+
inprocess_progress_watchdog_interval ............ 1.0
|
214 |
+
inprocess_restart ............................... False
|
215 |
+
inprocess_soft_timeout .......................... 60
|
216 |
+
inprocess_termination_grace_time ................ 1
|
217 |
+
is_hybrid_model ................................. False
|
218 |
+
iter_per_epoch .................................. 1250
|
219 |
+
iterations_to_skip .............................. []
|
220 |
+
keep_fp8_transpose_cache_when_using_custom_fsdp . False
|
221 |
+
kv_channels ..................................... 64
|
222 |
+
kv_lora_rank .................................... 32
|
223 |
+
lazy_mpu_init ................................... None
|
224 |
+
load ............................................ gpt-checkpoint
|
225 |
+
load_model_opt_format ........................... False
|
226 |
+
local_rank ...................................... 0
|
227 |
+
log_interval .................................... 1
|
228 |
+
log_loss_scale_to_tensorboard ................... True
|
229 |
+
log_memory_to_tensorboard ....................... False
|
230 |
+
log_num_zeros_in_grad ........................... False
|
231 |
+
log_params_norm ................................. False
|
232 |
+
log_progress .................................... False
|
233 |
+
log_straggler ................................... False
|
234 |
+
log_throughput .................................. False
|
235 |
+
log_timers_to_tensorboard ....................... False
|
236 |
+
log_validation_ppl_to_tensorboard ............... False
|
237 |
+
log_world_size_to_tensorboard ................... False
|
238 |
+
logging_level ................................... 0
|
239 |
+
loss_scale ...................................... None
|
240 |
+
loss_scale_window ............................... 1000
|
241 |
+
lr .............................................. 0.0005
|
242 |
+
lr_decay_iters .................................. 150000
|
243 |
+
lr_decay_samples ................................ None
|
244 |
+
lr_decay_style .................................. cosine
|
245 |
+
lr_warmup_fraction .............................. None
|
246 |
+
lr_warmup_init .................................. 0.0
|
247 |
+
lr_warmup_iters ................................. 2
|
248 |
+
lr_warmup_samples ............................... 0
|
249 |
+
lr_wsd_decay_iters .............................. None
|
250 |
+
lr_wsd_decay_samples ............................ None
|
251 |
+
lr_wsd_decay_style .............................. exponential
|
252 |
+
main_grads_dtype ................................ torch.float32
|
253 |
+
main_params_dtype ............................... torch.float32
|
254 |
+
make_vocab_size_divisible_by .................... 128
|
255 |
+
mamba_head_dim .................................. 64
|
256 |
+
mamba_num_groups ................................ 8
|
257 |
+
mamba_num_heads ................................. None
|
258 |
+
mamba_state_dim ................................. 128
|
259 |
+
manual_gc ....................................... False
|
260 |
+
manual_gc_eval .................................. True
|
261 |
+
manual_gc_interval .............................. 0
|
262 |
+
mask_factor ..................................... 1.0
|
263 |
+
mask_prob ....................................... 0.15
|
264 |
+
mask_type ....................................... random
|
265 |
+
masked_softmax_fusion ........................... True
|
266 |
+
max_position_embeddings ......................... 1024
|
267 |
+
max_tokens_to_oom ............................... 12000
|
268 |
+
memory_snapshot_path ............................ snapshot.pickle
|
269 |
+
merge_file ...................................... merges.txt
|
270 |
+
micro_batch_size ................................ 1
|
271 |
+
microbatch_group_size_per_vp_stage .............. None
|
272 |
+
mid_level_dataset_surplus ....................... 0.005
|
273 |
+
min_loss_scale .................................. 1.0
|
274 |
+
min_lr .......................................... 0.0
|
275 |
+
mlp_chunks_for_prefill .......................... 1
|
276 |
+
mmap_bin_files .................................. True
|
277 |
+
mock_data ....................................... True
|
278 |
+
moe_apply_probs_on_input ........................ False
|
279 |
+
moe_aux_loss_coeff .............................. 0.0
|
280 |
+
moe_enable_deepep ............................... False
|
281 |
+
moe_expert_capacity_factor ...................... None
|
282 |
+
moe_extended_tp ................................. False
|
283 |
+
moe_ffn_hidden_size ............................. None
|
284 |
+
moe_grouped_gemm ................................ False
|
285 |
+
moe_input_jitter_eps ............................ None
|
286 |
+
moe_layer_freq .................................. 1
|
287 |
+
moe_layer_recompute ............................. False
|
288 |
+
moe_pad_expert_input_to_capacity ................ False
|
289 |
+
moe_per_layer_logging ........................... False
|
290 |
+
moe_permute_fusion .............................. False
|
291 |
+
moe_router_bias_update_rate ..................... 0.001
|
292 |
+
moe_router_dtype ................................ None
|
293 |
+
moe_router_enable_expert_bias ................... False
|
294 |
+
moe_router_force_load_balancing ................. False
|
295 |
+
moe_router_group_topk ........................... None
|
296 |
+
moe_router_load_balancing_type .................. aux_loss
|
297 |
+
moe_router_num_groups ........................... None
|
298 |
+
moe_router_padding_for_fp8 ...................... False
|
299 |
+
moe_router_pre_softmax .......................... False
|
300 |
+
moe_router_score_function ....................... softmax
|
301 |
+
moe_router_topk ................................. 2
|
302 |
+
moe_router_topk_scaling_factor .................. None
|
303 |
+
moe_shared_expert_intermediate_size ............. None
|
304 |
+
moe_shared_expert_overlap ....................... False
|
305 |
+
moe_token_dispatcher_type ....................... allgather
|
306 |
+
moe_token_drop_policy ........................... probs
|
307 |
+
moe_use_legacy_grouped_gemm ..................... False
|
308 |
+
moe_use_upcycling ............................... False
|
309 |
+
moe_z_loss_coeff ................................ None
|
310 |
+
mrope_section ................................... None
|
311 |
+
mscale .......................................... 1.0
|
312 |
+
mscale_all_dim .................................. 1.0
|
313 |
+
mtp_loss_scaling_factor ......................... 0.1
|
314 |
+
mtp_num_layers .................................. None
|
315 |
+
multi_latent_attention .......................... False
|
316 |
+
nccl_all_reduce_for_prefill ..................... False
|
317 |
+
nccl_communicator_config_path ................... None
|
318 |
+
nccl_ub ......................................... False
|
319 |
+
no_load_optim ................................... None
|
320 |
+
no_load_rng ..................................... None
|
321 |
+
no_persist_layer_norm ........................... False
|
322 |
+
no_rope_freq .................................... None
|
323 |
+
no_save_optim ................................... None
|
324 |
+
no_save_rng ..................................... None
|
325 |
+
non_persistent_ckpt_type ........................ None
|
326 |
+
non_persistent_global_ckpt_dir .................. None
|
327 |
+
non_persistent_local_ckpt_algo .................. fully_parallel
|
328 |
+
non_persistent_local_ckpt_dir ................... None
|
329 |
+
non_persistent_save_interval .................... None
|
330 |
+
norm_epsilon .................................... 1e-05
|
331 |
+
normalization ................................... LayerNorm
|
332 |
+
num_attention_heads ............................. 64
|
333 |
+
num_channels .................................... 3
|
334 |
+
num_classes ..................................... 1000
|
335 |
+
num_dataset_builder_threads ..................... 1
|
336 |
+
num_distributed_optimizer_instances ............. 1
|
337 |
+
num_experts ..................................... None
|
338 |
+
num_layers ...................................... 2
|
339 |
+
num_layers_at_end_in_bf16 ....................... 1
|
340 |
+
num_layers_at_start_in_bf16 ..................... 1
|
341 |
+
num_layers_per_virtual_pipeline_stage ........... None
|
342 |
+
num_query_groups ................................ 16
|
343 |
+
num_virtual_stages_per_pipeline_rank ............ None
|
344 |
+
num_workers ..................................... 2
|
345 |
+
object_storage_cache_path ....................... None
|
346 |
+
one_logger_async ................................ False
|
347 |
+
one_logger_project .............................. megatron-lm
|
348 |
+
one_logger_run_name ............................. None
|
349 |
+
onnx_safe ....................................... None
|
350 |
+
openai_gelu ..................................... False
|
351 |
+
optimizer ....................................... adam
|
352 |
+
optimizer_cpu_offload ........................... False
|
353 |
+
optimizer_offload_fraction ...................... 1.0
|
354 |
+
output_bert_embeddings .......................... False
|
355 |
+
overlap_cpu_optimizer_d2h_h2d ................... False
|
356 |
+
overlap_grad_reduce ............................. False
|
357 |
+
overlap_p2p_comm ................................ False
|
358 |
+
overlap_p2p_comm_warmup_flush ................... False
|
359 |
+
overlap_param_gather ............................ False
|
360 |
+
overlap_param_gather_with_optimizer_step ........ False
|
361 |
+
override_opt_param_scheduler .................... False
|
362 |
+
params_dtype .................................... torch.float16
|
363 |
+
patch_dim ....................................... 16
|
364 |
+
per_split_data_args_path ........................ None
|
365 |
+
perform_initialization .......................... True
|
366 |
+
pin_cpu_grads ................................... True
|
367 |
+
pin_cpu_params .................................. True
|
368 |
+
pipeline_model_parallel_comm_backend ............ None
|
369 |
+
pipeline_model_parallel_size .................... 1
|
370 |
+
pipeline_model_parallel_split_rank .............. None
|
371 |
+
position_embedding_type ......................... learned_absolute
|
372 |
+
pretrained_checkpoint ........................... None
|
373 |
+
profile ......................................... False
|
374 |
+
profile_ranks ................................... [0]
|
375 |
+
profile_step_end ................................ 12
|
376 |
+
profile_step_start .............................. 10
|
377 |
+
q_lora_rank ..................................... None
|
378 |
+
qk_head_dim ..................................... 128
|
379 |
+
qk_l2_norm ...................................... False
|
380 |
+
qk_layernorm .................................... False
|
381 |
+
qk_pos_emb_head_dim ............................. 64
|
382 |
+
query_in_block_prob ............................. 0.1
|
383 |
+
rampup_batch_size ............................... None
|
384 |
+
rank ............................................ 0
|
385 |
+
recompute_granularity ........................... None
|
386 |
+
recompute_method ................................ None
|
387 |
+
recompute_modules ............................... None
|
388 |
+
recompute_num_layers ............................ None
|
389 |
+
record_memory_history ........................... False
|
390 |
+
relative_attention_max_distance ................. 128
|
391 |
+
relative_attention_num_buckets .................. 32
|
392 |
+
replication ..................................... False
|
393 |
+
replication_factor .............................. 2
|
394 |
+
replication_jump ................................ None
|
395 |
+
rerun_mode ...................................... disabled
|
396 |
+
reset_attention_mask ............................ False
|
397 |
+
reset_position_ids .............................. False
|
398 |
+
result_rejected_tracker_filename ................ None
|
399 |
+
retriever_report_topk_accuracies ................ []
|
400 |
+
retriever_score_scaling ......................... False
|
401 |
+
retriever_seq_length ............................ 256
|
402 |
+
retro_add_retriever ............................. False
|
403 |
+
retro_attention_gate ............................ 1
|
404 |
+
retro_cyclic_train_iters ........................ None
|
405 |
+
retro_encoder_attention_dropout ................. 0.1
|
406 |
+
retro_encoder_hidden_dropout .................... 0.1
|
407 |
+
retro_encoder_layers ............................ 2
|
408 |
+
retro_num_neighbors ............................. 2
|
409 |
+
retro_num_retrieved_chunks ...................... 2
|
410 |
+
retro_project_dir ............................... None
|
411 |
+
retro_verify_neighbor_count ..................... True
|
412 |
+
rope_scaling_factor ............................. 8.0
|
413 |
+
rotary_base ..................................... 10000
|
414 |
+
rotary_interleaved .............................. False
|
415 |
+
rotary_percent .................................. 1.0
|
416 |
+
rotary_scaling_factor ........................... 1.0
|
417 |
+
rotary_seq_len_interpolation_factor ............. None
|
418 |
+
run_workload_inspector_server ................... False
|
419 |
+
sample_rate ..................................... 1.0
|
420 |
+
save ............................................ gpt-checkpoint
|
421 |
+
save_interval ................................... 16
|
422 |
+
scatter_gather_tensors_in_pipeline .............. True
|
423 |
+
seed ............................................ 1234
|
424 |
+
seq_length ...................................... 1024
|
425 |
+
sequence_parallel ............................... False
|
426 |
+
sgd_momentum .................................... 0.9
|
427 |
+
short_seq_prob .................................. 0.1
|
428 |
+
skip_train ...................................... False
|
429 |
+
skipped_train_samples ........................... 0
|
430 |
+
spec ............................................ None
|
431 |
+
split ........................................... None
|
432 |
+
squared_relu .................................... False
|
433 |
+
start_weight_decay .............................. 0.1
|
434 |
+
straggler_ctrlr_port ............................ 65535
|
435 |
+
straggler_minmax_count .......................... 1
|
436 |
+
suggested_communication_unit_size ............... None
|
437 |
+
swiglu .......................................... False
|
438 |
+
swin_backbone_type .............................. tiny
|
439 |
+
symmetric_ar_type ............................... None
|
440 |
+
te_rng_tracker .................................. False
|
441 |
+
tensor_model_parallel_size ...................... 8
|
442 |
+
tensorboard_dir ................................. tensorboard-logs/
|
443 |
+
tensorboard_log_interval ........................ 1
|
444 |
+
tensorboard_queue_size .......................... 1000
|
445 |
+
test_data_path .................................. None
|
446 |
+
test_mode ....................................... False
|
447 |
+
tiktoken_num_special_tokens ..................... 1000
|
448 |
+
tiktoken_pattern ................................ None
|
449 |
+
tiktoken_special_tokens ......................... None
|
450 |
+
timing_log_level ................................ 0
|
451 |
+
timing_log_option ............................... minmax
|
452 |
+
titles_data_path ................................ None
|
453 |
+
tokenizer_model ................................. None
|
454 |
+
tokenizer_type .................................. GPT2BPETokenizer
|
455 |
+
torch_fsdp2_reshard_after_forward ............... True
|
456 |
+
tp_comm_bootstrap_backend ....................... nccl
|
457 |
+
tp_comm_bulk_dgrad .............................. True
|
458 |
+
tp_comm_bulk_wgrad .............................. True
|
459 |
+
tp_comm_overlap ................................. False
|
460 |
+
tp_comm_overlap_ag .............................. True
|
461 |
+
tp_comm_overlap_cfg ............................. None
|
462 |
+
tp_comm_overlap_rs .............................. True
|
463 |
+
tp_comm_overlap_rs_dgrad ........................ False
|
464 |
+
tp_comm_split_ag ................................ True
|
465 |
+
tp_comm_split_rs ................................ True
|
466 |
+
train_data_path ................................. None
|
467 |
+
train_iters ..................................... 10
|
468 |
+
train_samples ................................... None
|
469 |
+
train_sync_interval ............................. None
|
470 |
+
transformer_impl ................................ transformer_engine
|
471 |
+
transformer_pipeline_model_parallel_size ........ 1
|
472 |
+
untie_embeddings_and_output_weights ............. False
|
473 |
+
use_checkpoint_args ............................. False
|
474 |
+
use_checkpoint_opt_param_scheduler .............. False
|
475 |
+
use_cpu_initialization .......................... None
|
476 |
+
use_custom_fsdp ................................. False
|
477 |
+
use_dist_ckpt ................................... True
|
478 |
+
use_dist_ckpt_deprecated ........................ False
|
479 |
+
use_distributed_optimizer ....................... False
|
480 |
+
use_flash_attn .................................. False
|
481 |
+
use_legacy_models ............................... False
|
482 |
+
use_mp_args_from_checkpoint_args ................ False
|
483 |
+
use_one_sent_docs ............................... False
|
484 |
+
use_persistent_ckpt_worker ...................... False
|
485 |
+
use_precision_aware_optimizer ................... False
|
486 |
+
use_pytorch_profiler ............................ False
|
487 |
+
use_ring_exchange_p2p ........................... False
|
488 |
+
use_rope_scaling ................................ False
|
489 |
+
use_rotary_position_embeddings .................. False
|
490 |
+
use_sharp ....................................... False
|
491 |
+
use_tokenizer_model_from_checkpoint_args ........ True
|
492 |
+
use_torch_fsdp2 ................................. False
|
493 |
+
use_torch_optimizer_for_cpu_offload ............. False
|
494 |
+
use_tp_pp_dp_mapping ............................ False
|
495 |
+
v_head_dim ...................................... 128
|
496 |
+
valid_data_path ................................. None
|
497 |
+
variable_seq_lengths ............................ False
|
498 |
+
virtual_pipeline_model_parallel_size ............ None
|
499 |
+
vision_backbone_type ............................ vit
|
500 |
+
vision_pretraining .............................. False
|
501 |
+
vision_pretraining_type ......................... classify
|
502 |
+
vocab_extra_ids ................................. 0
|
503 |
+
vocab_file ...................................... vocab.json
|
504 |
+
vocab_size ...................................... None
|
505 |
+
wandb_exp_name ..................................
|
506 |
+
wandb_project ...................................
|
507 |
+
wandb_save_dir ..................................
|
508 |
+
weight_decay .................................... 0.1
|
509 |
+
weight_decay_incr_style ......................... constant
|
510 |
+
wgrad_deferral_limit ............................ 0
|
511 |
+
world_size ...................................... 8
|
512 |
+
yaml_cfg ........................................ None
|
513 |
+
-------------------- end of arguments ---------------------
|
514 |
+
INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
|
515 |
+
> building GPT2BPETokenizer tokenizer ...
|
516 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
517 |
+
> padded vocab (size: 50257) with 943 dummy tokens (new size: 51200)
|
518 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
519 |
+
WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
|
520 |
+
> initializing torch distributed ...
|
521 |
+
> initialized tensor model parallel with size 8
|
522 |
+
> initialized pipeline model parallel with size 1
|
523 |
+
> setting random seeds to 1234 ...
|
524 |
+
> compiling dataset index builder ...
|
525 |
+
make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
|
526 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
527 |
+
WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
|
528 |
+
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
|
529 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
530 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
531 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
532 |
+
make: Nothing to be done for 'default'.
|
533 |
+
make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
|
534 |
+
>>> done with dataset index builder. Compilation time: 0.044 seconds
|
535 |
+
> compiling and loading fused kernels ...
|
536 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
537 |
+
>>> done with compiling and loading fused kernels. Compilation time: 2.457 seconds
|
attnserver.run_attnserver.slurm.sh.343210.err.log
ADDED
@@ -0,0 +1,149 @@
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
+ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
|
2 |
+
++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
|
3 |
+
++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
|
4 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
5 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
6 |
+
+++ export _CE_M=
|
7 |
+
+++ _CE_M=
|
8 |
+
+++ export _CE_CONDA=
|
9 |
+
+++ _CE_CONDA=
|
10 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
11 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
12 |
+
+++ '[' -z x ']'
|
13 |
+
++ conda activate
|
14 |
+
++ local cmd=activate
|
15 |
+
++ case "$cmd" in
|
16 |
+
++ __conda_activate activate
|
17 |
+
++ '[' -n '' ']'
|
18 |
+
++ local ask_conda
|
19 |
+
+++ PS1=
|
20 |
+
+++ __conda_exe shell.posix activate
|
21 |
+
+++ '[' -n '' ']'
|
22 |
+
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
|
23 |
+
++ ask_conda='unset _CE_M
|
24 |
+
unset _CE_CONDA
|
25 |
+
PS1='\''(base) '\''
|
26 |
+
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'\''
|
27 |
+
export CONDA_SHLVL='\''1'\''
|
28 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
29 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
30 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
31 |
+
++ eval 'unset _CE_M
|
32 |
+
unset _CE_CONDA
|
33 |
+
PS1='\''(base) '\''
|
34 |
+
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'\''
|
35 |
+
export CONDA_SHLVL='\''1'\''
|
36 |
+
export CONDA_PROMPT_MODIFIER='\''(base) '\''
|
37 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
38 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
39 |
+
+++ unset _CE_M
|
40 |
+
+++ unset _CE_CONDA
|
41 |
+
+++ PS1='(base) '
|
42 |
+
+++ 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
|
43 |
+
+++ 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
|
44 |
+
+++ export CONDA_SHLVL=1
|
45 |
+
+++ CONDA_SHLVL=1
|
46 |
+
+++ export 'CONDA_PROMPT_MODIFIER=(base) '
|
47 |
+
+++ CONDA_PROMPT_MODIFIER='(base) '
|
48 |
+
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
49 |
+
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
50 |
+
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
51 |
+
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
52 |
+
++ __conda_hashr
|
53 |
+
++ '[' -n '' ']'
|
54 |
+
++ '[' -n '' ']'
|
55 |
+
++ hash -r
|
56 |
+
+ conda activate junda-attnserver
|
57 |
+
+ local cmd=activate
|
58 |
+
+ case "$cmd" in
|
59 |
+
+ __conda_activate activate junda-attnserver
|
60 |
+
+ '[' -n '' ']'
|
61 |
+
+ local ask_conda
|
62 |
+
++ PS1='(base) '
|
63 |
+
++ __conda_exe shell.posix activate junda-attnserver
|
64 |
+
++ '[' -n '' ']'
|
65 |
+
++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
|
66 |
+
+ ask_conda='unset _CE_M
|
67 |
+
unset _CE_CONDA
|
68 |
+
PS1='\''(junda-attnserver) '\''
|
69 |
+
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'\''
|
70 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
71 |
+
export CONDA_SHLVL='\''2'\''
|
72 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
73 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
74 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
75 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
76 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
77 |
+
+ eval 'unset _CE_M
|
78 |
+
unset _CE_CONDA
|
79 |
+
PS1='\''(junda-attnserver) '\''
|
80 |
+
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'\''
|
81 |
+
export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
|
82 |
+
export CONDA_SHLVL='\''2'\''
|
83 |
+
export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
|
84 |
+
export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
|
85 |
+
export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
|
86 |
+
export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
|
87 |
+
export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
|
88 |
+
++ unset _CE_M
|
89 |
+
++ unset _CE_CONDA
|
90 |
+
++ PS1='(junda-attnserver) '
|
91 |
+
++ 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
|
92 |
+
++ 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
|
93 |
+
++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
94 |
+
++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
|
95 |
+
++ export CONDA_SHLVL=2
|
96 |
+
++ CONDA_SHLVL=2
|
97 |
+
++ export CONDA_DEFAULT_ENV=junda-attnserver
|
98 |
+
++ CONDA_DEFAULT_ENV=junda-attnserver
|
99 |
+
++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
|
100 |
+
++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
|
101 |
+
++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
102 |
+
++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
|
103 |
+
++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
104 |
+
++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
|
105 |
+
++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
106 |
+
++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
|
107 |
+
+ __conda_hashr
|
108 |
+
+ '[' -n '' ']'
|
109 |
+
+ '[' -n '' ']'
|
110 |
+
+ hash -r
|
111 |
+
+ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
112 |
+
+ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
113 |
+
+ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
|
114 |
+
+ export PROF_TP_SIZE=8
|
115 |
+
+ PROF_TP_SIZE=8
|
116 |
+
+ export PROF_CP_SIZE=1
|
117 |
+
+ PROF_CP_SIZE=1
|
118 |
+
+ export PROF_BS=8
|
119 |
+
+ PROF_BS=8
|
120 |
+
+ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
|
121 |
+
+ export PROF_CTX_LENGTH=1024
|
122 |
+
+ PROF_CTX_LENGTH=1024
|
123 |
+
+ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs8.json'
|
124 |
+
+ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp1.bs8.json' ']'
|
125 |
+
+ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=8'
|
126 |
+
+ srun bash ./attnserver.sh
|
127 |
+
+ which python3
|
128 |
+
+ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 1 --node_rank 0 --rdzv_id 343210 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-768:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 1 --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/
|
129 |
+
/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
|
130 |
+
and will be removed in future. Use torchrun.
|
131 |
+
Note that --use-env is set by default in torchrun.
|
132 |
+
If your script expects `--local-rank` argument to be set, please
|
133 |
+
change it to read from `os.environ['LOCAL_RANK']` instead. See
|
134 |
+
https://pytorch.org/docs/stable/distributed.html#launch-utility for
|
135 |
+
further instructions
|
136 |
+
|
137 |
+
main()
|
138 |
+
W0621 21:19:24.688000 2174748 site-packages/torch/distributed/run.py:766]
|
139 |
+
W0621 21:19:24.688000 2174748 site-packages/torch/distributed/run.py:766] *****************************************
|
140 |
+
W0621 21:19:24.688000 2174748 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.
|
141 |
+
W0621 21:19:24.688000 2174748 site-packages/torch/distributed/run.py:766] *****************************************
|
142 |
+
[rank1]:[W621 21:19:46.843638124 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.
|
143 |
+
[rank3]:[W621 21:19:46.273254932 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.
|
144 |
+
[rank7]:[W621 21:19:46.287551098 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.
|
145 |
+
[rank0]:[W621 21:19:46.300492516 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.
|
146 |
+
[rank5]:[W621 21:19:46.306446014 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.
|
147 |
+
[rank4]:[W621 21:19:46.306935911 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.
|
148 |
+
[rank6]:[W621 21:19:46.310121545 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.
|
149 |
+
[rank2]:[W621 21:19:46.317946415 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.
|
attnserver.run_attnserver.slurm.sh.343210.out.log
ADDED
@@ -0,0 +1,536 @@
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|
1 |
+
Running ctx_length=1024, TP_SIZE=8, CP_SIZE=1, BATCH_SIZE=8
|
2 |
+
Cleaning up checkpoint directory: gpt-checkpoint
|
3 |
+
--------------------------------
|
4 |
+
CTX_LENGTH: 1024
|
5 |
+
TP_SIZE: 8
|
6 |
+
CP_SIZE: 1
|
7 |
+
CHECKPOINT_PATH: gpt-checkpoint
|
8 |
+
PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
|
9 |
+
--------------------------------
|
10 |
+
/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
|
11 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
12 |
+
using world size: 8, data-parallel size: 1, context-parallel size: 1, 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
|
13 |
+
Number of virtual stages per pipeline stage: None
|
14 |
+
WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used
|
15 |
+
using torch.float16 for parameters ...
|
16 |
+
------------------------ arguments ------------------------
|
17 |
+
account_for_embedding_in_pipeline_split ......... False
|
18 |
+
account_for_loss_in_pipeline_split .............. False
|
19 |
+
accumulate_allreduce_grads_in_fp32 .............. False
|
20 |
+
adam_beta1 ...................................... 0.9
|
21 |
+
adam_beta2 ...................................... 0.999
|
22 |
+
adam_eps ........................................ 1e-08
|
23 |
+
add_bias_linear ................................. True
|
24 |
+
add_position_embedding .......................... True
|
25 |
+
add_qkv_bias .................................... True
|
26 |
+
adlr_autoresume ................................. False
|
27 |
+
adlr_autoresume_interval ........................ 1000
|
28 |
+
align_grad_reduce ............................... True
|
29 |
+
align_param_gather .............................. False
|
30 |
+
app_tag_run_name ................................ None
|
31 |
+
app_tag_run_version ............................. 0.0.0
|
32 |
+
apply_layernorm_1p .............................. False
|
33 |
+
apply_query_key_layer_scaling ................... False
|
34 |
+
apply_residual_connection_post_layernorm ........ False
|
35 |
+
apply_rope_fusion ............................... False
|
36 |
+
async_save ...................................... None
|
37 |
+
async_tensor_model_parallel_allreduce ........... True
|
38 |
+
attention_backend ............................... AttnBackend.auto
|
39 |
+
attention_dropout ............................... 0.1
|
40 |
+
attention_softmax_in_fp32 ....................... False
|
41 |
+
auto_detect_ckpt_format ......................... False
|
42 |
+
barrier_with_L1_time ............................ True
|
43 |
+
bert_binary_head ................................ True
|
44 |
+
bert_embedder_type .............................. megatron
|
45 |
+
bert_load ....................................... None
|
46 |
+
bf16 ............................................ False
|
47 |
+
bias_dropout_fusion ............................. True
|
48 |
+
bias_gelu_fusion ................................ True
|
49 |
+
bias_swiglu_fusion .............................. True
|
50 |
+
biencoder_projection_dim ........................ 0
|
51 |
+
biencoder_shared_query_context_model ............ False
|
52 |
+
block_data_path ................................. None
|
53 |
+
calc_ft_timeouts ................................ False
|
54 |
+
calculate_per_token_loss ........................ False
|
55 |
+
check_for_large_grads ........................... False
|
56 |
+
check_for_nan_in_loss_and_grad .................. False
|
57 |
+
check_for_spiky_loss ............................ False
|
58 |
+
check_weight_hash_across_dp_replicas_interval ... None
|
59 |
+
ckpt_assume_constant_structure .................. False
|
60 |
+
ckpt_convert_format ............................. None
|
61 |
+
ckpt_convert_save ............................... None
|
62 |
+
ckpt_convert_update_legacy_dist_opt_format ...... False
|
63 |
+
ckpt_format ..................................... torch_dist
|
64 |
+
ckpt_fully_parallel_load ........................ False
|
65 |
+
ckpt_fully_parallel_save ........................ True
|
66 |
+
ckpt_fully_parallel_save_deprecated ............. False
|
67 |
+
ckpt_step ....................................... None
|
68 |
+
classes_fraction ................................ 1.0
|
69 |
+
clip_grad ....................................... 1.0
|
70 |
+
clone_scatter_output_in_embedding ............... True
|
71 |
+
config_logger_dir ...............................
|
72 |
+
consumed_train_samples .......................... 0
|
73 |
+
consumed_valid_samples .......................... 0
|
74 |
+
context_parallel_size ........................... 1
|
75 |
+
cp_comm_type .................................... ['p2p']
|
76 |
+
create_attention_mask_in_dataloader ............. True
|
77 |
+
cross_entropy_fusion_impl ....................... native
|
78 |
+
cross_entropy_loss_fusion ....................... False
|
79 |
+
cuda_graph_scope ................................ full
|
80 |
+
cuda_graph_warmup_steps ......................... 3
|
81 |
+
data_args_path .................................. None
|
82 |
+
data_cache_path ................................. None
|
83 |
+
data_parallel_random_init ....................... False
|
84 |
+
data_parallel_sharding_strategy ................. no_shard
|
85 |
+
data_parallel_size .............................. 1
|
86 |
+
data_path ....................................... None
|
87 |
+
data_per_class_fraction ......................... 1.0
|
88 |
+
data_sharding ................................... True
|
89 |
+
dataloader_type ................................. single
|
90 |
+
ddp_average_in_collective ....................... False
|
91 |
+
ddp_bucket_size ................................. None
|
92 |
+
ddp_num_buckets ................................. None
|
93 |
+
ddp_pad_buckets_for_high_nccl_busbw ............. False
|
94 |
+
decoder_first_pipeline_num_layers ............... None
|
95 |
+
decoder_last_pipeline_num_layers ................ None
|
96 |
+
decoder_num_layers .............................. None
|
97 |
+
decoder_seq_length .............................. None
|
98 |
+
decoupled_lr .................................... None
|
99 |
+
decoupled_min_lr ................................ None
|
100 |
+
decrease_batch_size_if_needed ................... False
|
101 |
+
defer_embedding_wgrad_compute ................... False
|
102 |
+
deprecated_use_mcore_models ..................... False
|
103 |
+
deterministic_mode .............................. False
|
104 |
+
dino_bottleneck_size ............................ 256
|
105 |
+
dino_freeze_last_layer .......................... 1
|
106 |
+
dino_head_hidden_size ........................... 2048
|
107 |
+
dino_local_crops_number ......................... 10
|
108 |
+
dino_local_img_size ............................. 96
|
109 |
+
dino_norm_last_layer ............................ False
|
110 |
+
dino_teacher_temp ............................... 0.07
|
111 |
+
dino_warmup_teacher_temp ........................ 0.04
|
112 |
+
dino_warmup_teacher_temp_epochs ................. 30
|
113 |
+
disable_bf16_reduced_precision_matmul ........... False
|
114 |
+
disable_mamba_mem_eff_path ...................... False
|
115 |
+
disable_straggler_on_startup .................... False
|
116 |
+
dist_ckpt_format_deprecated ..................... None
|
117 |
+
dist_ckpt_strictness ............................ assume_ok_unexpected
|
118 |
+
distribute_saved_activations .................... False
|
119 |
+
distributed_backend ............................. nccl
|
120 |
+
distributed_timeout_minutes ..................... 10
|
121 |
+
embedding_path .................................. None
|
122 |
+
empty_unused_memory_level ....................... 0
|
123 |
+
enable_cuda_graph ............................... False
|
124 |
+
enable_ft_package ............................... False
|
125 |
+
enable_gloo_process_groups ...................... True
|
126 |
+
enable_msc ...................................... True
|
127 |
+
enable_one_logger ............................... True
|
128 |
+
encoder_num_layers .............................. 2
|
129 |
+
encoder_pipeline_model_parallel_size ............ 0
|
130 |
+
encoder_seq_length .............................. 1024
|
131 |
+
encoder_tensor_model_parallel_size .............. 0
|
132 |
+
end_weight_decay ................................ 0.1
|
133 |
+
eod_mask_loss ................................... False
|
134 |
+
error_injection_rate ............................ 0
|
135 |
+
error_injection_type ............................ transient_error
|
136 |
+
eval_interval ................................... 16
|
137 |
+
eval_iters ...................................... 1
|
138 |
+
evidence_data_path .............................. None
|
139 |
+
exit_duration_in_mins ........................... None
|
140 |
+
exit_interval ................................... None
|
141 |
+
exit_on_missing_checkpoint ...................... False
|
142 |
+
exit_signal_handler ............................. False
|
143 |
+
exp_avg_dtype ................................... torch.float32
|
144 |
+
exp_avg_sq_dtype ................................ torch.float32
|
145 |
+
expert_model_parallel_size ...................... 1
|
146 |
+
expert_tensor_parallel_size ..................... 8
|
147 |
+
external_cuda_graph ............................. False
|
148 |
+
ffn_hidden_size ................................. 16384
|
149 |
+
finetune ........................................ False
|
150 |
+
first_last_layers_bf16 .......................... False
|
151 |
+
flash_decode .................................... False
|
152 |
+
fp16 ............................................ True
|
153 |
+
fp16_lm_cross_entropy ........................... False
|
154 |
+
fp32_residual_connection ........................ False
|
155 |
+
fp8 ............................................. None
|
156 |
+
fp8_amax_compute_algo ........................... most_recent
|
157 |
+
fp8_amax_history_len ............................ 1
|
158 |
+
fp8_interval .................................... 1
|
159 |
+
fp8_margin ...................................... 0
|
160 |
+
fp8_param_gather ................................ False
|
161 |
+
fp8_recipe ...................................... delayed
|
162 |
+
fp8_wgrad ....................................... True
|
163 |
+
fsdp_double_buffer .............................. False
|
164 |
+
global_batch_size ............................... 1
|
165 |
+
grad_reduce_in_bf16 ............................. False
|
166 |
+
gradient_accumulation_fusion .................... True
|
167 |
+
gradient_reduce_div_fusion ...................... True
|
168 |
+
group_query_attention ........................... True
|
169 |
+
head_lr_mult .................................... 1.0
|
170 |
+
heterogeneous_layers_config_encoded_json ........ None
|
171 |
+
heterogeneous_layers_config_path ................ None
|
172 |
+
hidden_dropout .................................. 0.1
|
173 |
+
hidden_size ..................................... 4096
|
174 |
+
hierarchical_context_parallel_sizes ............. None
|
175 |
+
high_priority_stream_groups ..................... []
|
176 |
+
hybrid_attention_ratio .......................... 0.0
|
177 |
+
hybrid_mlp_ratio ................................ 0.0
|
178 |
+
hybrid_override_pattern ......................... None
|
179 |
+
hysteresis ...................................... 2
|
180 |
+
ict_head_size ................................... None
|
181 |
+
ict_load ........................................ None
|
182 |
+
img_h ........................................... 224
|
183 |
+
img_w ........................................... 224
|
184 |
+
indexer_batch_size .............................. 128
|
185 |
+
indexer_log_interval ............................ 1000
|
186 |
+
inference_batch_times_seqlen_threshold .......... -1
|
187 |
+
inference_dynamic_batching ...................... False
|
188 |
+
inference_dynamic_batching_buffer_guaranteed_fraction 0.2
|
189 |
+
inference_dynamic_batching_buffer_overflow_factor None
|
190 |
+
inference_dynamic_batching_buffer_size_gb ....... 40.0
|
191 |
+
inference_dynamic_batching_chunk_size ........... 256
|
192 |
+
inference_dynamic_batching_max_requests_override None
|
193 |
+
inference_dynamic_batching_max_tokens_override .. None
|
194 |
+
inference_max_batch_size ........................ 8
|
195 |
+
inference_max_seq_length ........................ 2560
|
196 |
+
inference_rng_tracker ........................... False
|
197 |
+
init_method_std ................................. 0.02
|
198 |
+
init_method_xavier_uniform ...................... False
|
199 |
+
init_model_with_meta_device ..................... False
|
200 |
+
initial_loss_scale .............................. 4294967296
|
201 |
+
inprocess_active_world_size ..................... 8
|
202 |
+
inprocess_barrier_timeout ....................... 120
|
203 |
+
inprocess_completion_timeout .................... 120
|
204 |
+
inprocess_empty_cuda_cache ...................... False
|
205 |
+
inprocess_granularity ........................... node
|
206 |
+
inprocess_hard_timeout .......................... 90
|
207 |
+
inprocess_heartbeat_interval .................... 30
|
208 |
+
inprocess_heartbeat_timeout ..................... 60
|
209 |
+
inprocess_last_call_wait ........................ 1
|
210 |
+
inprocess_max_iterations ........................ None
|
211 |
+
inprocess_monitor_process_interval .............. 1.0
|
212 |
+
inprocess_monitor_thread_interval ............... 1.0
|
213 |
+
inprocess_progress_watchdog_interval ............ 1.0
|
214 |
+
inprocess_restart ............................... False
|
215 |
+
inprocess_soft_timeout .......................... 60
|
216 |
+
inprocess_termination_grace_time ................ 1
|
217 |
+
is_hybrid_model ................................. False
|
218 |
+
iter_per_epoch .................................. 1250
|
219 |
+
iterations_to_skip .............................. []
|
220 |
+
keep_fp8_transpose_cache_when_using_custom_fsdp . False
|
221 |
+
kv_channels ..................................... 64
|
222 |
+
kv_lora_rank .................................... 32
|
223 |
+
lazy_mpu_init ................................... None
|
224 |
+
load ............................................ gpt-checkpoint
|
225 |
+
load_model_opt_format ........................... False
|
226 |
+
local_rank ...................................... 0
|
227 |
+
log_interval .................................... 1
|
228 |
+
log_loss_scale_to_tensorboard ................... True
|
229 |
+
log_memory_to_tensorboard ....................... False
|
230 |
+
log_num_zeros_in_grad ........................... False
|
231 |
+
log_params_norm ................................. False
|
232 |
+
log_progress .................................... False
|
233 |
+
log_straggler ................................... False
|
234 |
+
log_throughput .................................. False
|
235 |
+
log_timers_to_tensorboard ....................... False
|
236 |
+
log_validation_ppl_to_tensorboard ............... False
|
237 |
+
log_world_size_to_tensorboard ................... False
|
238 |
+
logging_level ................................... 0
|
239 |
+
loss_scale ...................................... None
|
240 |
+
loss_scale_window ............................... 1000
|
241 |
+
lr .............................................. 0.0005
|
242 |
+
lr_decay_iters .................................. 150000
|
243 |
+
lr_decay_samples ................................ None
|
244 |
+
lr_decay_style .................................. cosine
|
245 |
+
lr_warmup_fraction .............................. None
|
246 |
+
lr_warmup_init .................................. 0.0
|
247 |
+
lr_warmup_iters ................................. 2
|
248 |
+
lr_warmup_samples ............................... 0
|
249 |
+
lr_wsd_decay_iters .............................. None
|
250 |
+
lr_wsd_decay_samples ............................ None
|
251 |
+
lr_wsd_decay_style .............................. exponential
|
252 |
+
main_grads_dtype ................................ torch.float32
|
253 |
+
main_params_dtype ............................... torch.float32
|
254 |
+
make_vocab_size_divisible_by .................... 128
|
255 |
+
mamba_head_dim .................................. 64
|
256 |
+
mamba_num_groups ................................ 8
|
257 |
+
mamba_num_heads ................................. None
|
258 |
+
mamba_state_dim ................................. 128
|
259 |
+
manual_gc ....................................... False
|
260 |
+
manual_gc_eval .................................. True
|
261 |
+
manual_gc_interval .............................. 0
|
262 |
+
mask_factor ..................................... 1.0
|
263 |
+
mask_prob ....................................... 0.15
|
264 |
+
mask_type ....................................... random
|
265 |
+
masked_softmax_fusion ........................... True
|
266 |
+
max_position_embeddings ......................... 1024
|
267 |
+
max_tokens_to_oom ............................... 12000
|
268 |
+
memory_snapshot_path ............................ snapshot.pickle
|
269 |
+
merge_file ...................................... merges.txt
|
270 |
+
micro_batch_size ................................ 1
|
271 |
+
microbatch_group_size_per_vp_stage .............. None
|
272 |
+
mid_level_dataset_surplus ....................... 0.005
|
273 |
+
min_loss_scale .................................. 1.0
|
274 |
+
min_lr .......................................... 0.0
|
275 |
+
mlp_chunks_for_prefill .......................... 1
|
276 |
+
mmap_bin_files .................................. True
|
277 |
+
mock_data ....................................... True
|
278 |
+
moe_apply_probs_on_input ........................ False
|
279 |
+
moe_aux_loss_coeff .............................. 0.0
|
280 |
+
moe_enable_deepep ............................... False
|
281 |
+
moe_expert_capacity_factor ...................... None
|
282 |
+
moe_extended_tp ................................. False
|
283 |
+
moe_ffn_hidden_size ............................. None
|
284 |
+
moe_grouped_gemm ................................ False
|
285 |
+
moe_input_jitter_eps ............................ None
|
286 |
+
moe_layer_freq .................................. 1
|
287 |
+
moe_layer_recompute ............................. False
|
288 |
+
moe_pad_expert_input_to_capacity ................ False
|
289 |
+
moe_per_layer_logging ........................... False
|
290 |
+
moe_permute_fusion .............................. False
|
291 |
+
moe_router_bias_update_rate ..................... 0.001
|
292 |
+
moe_router_dtype ................................ None
|
293 |
+
moe_router_enable_expert_bias ................... False
|
294 |
+
moe_router_force_load_balancing ................. False
|
295 |
+
moe_router_group_topk ........................... None
|
296 |
+
moe_router_load_balancing_type .................. aux_loss
|
297 |
+
moe_router_num_groups ........................... None
|
298 |
+
moe_router_padding_for_fp8 ...................... False
|
299 |
+
moe_router_pre_softmax .......................... False
|
300 |
+
moe_router_score_function ....................... softmax
|
301 |
+
moe_router_topk ................................. 2
|
302 |
+
moe_router_topk_scaling_factor .................. None
|
303 |
+
moe_shared_expert_intermediate_size ............. None
|
304 |
+
moe_shared_expert_overlap ....................... False
|
305 |
+
moe_token_dispatcher_type ....................... allgather
|
306 |
+
moe_token_drop_policy ........................... probs
|
307 |
+
moe_use_legacy_grouped_gemm ..................... False
|
308 |
+
moe_use_upcycling ............................... False
|
309 |
+
moe_z_loss_coeff ................................ None
|
310 |
+
mrope_section ................................... None
|
311 |
+
mscale .......................................... 1.0
|
312 |
+
mscale_all_dim .................................. 1.0
|
313 |
+
mtp_loss_scaling_factor ......................... 0.1
|
314 |
+
mtp_num_layers .................................. None
|
315 |
+
multi_latent_attention .......................... False
|
316 |
+
nccl_all_reduce_for_prefill ..................... False
|
317 |
+
nccl_communicator_config_path ................... None
|
318 |
+
nccl_ub ......................................... False
|
319 |
+
no_load_optim ................................... None
|
320 |
+
no_load_rng ..................................... None
|
321 |
+
no_persist_layer_norm ........................... False
|
322 |
+
no_rope_freq .................................... None
|
323 |
+
no_save_optim ................................... None
|
324 |
+
no_save_rng ..................................... None
|
325 |
+
non_persistent_ckpt_type ........................ None
|
326 |
+
non_persistent_global_ckpt_dir .................. None
|
327 |
+
non_persistent_local_ckpt_algo .................. fully_parallel
|
328 |
+
non_persistent_local_ckpt_dir ................... None
|
329 |
+
non_persistent_save_interval .................... None
|
330 |
+
norm_epsilon .................................... 1e-05
|
331 |
+
normalization ................................... LayerNorm
|
332 |
+
num_attention_heads ............................. 64
|
333 |
+
num_channels .................................... 3
|
334 |
+
num_classes ..................................... 1000
|
335 |
+
num_dataset_builder_threads ..................... 1
|
336 |
+
num_distributed_optimizer_instances ............. 1
|
337 |
+
num_experts ..................................... None
|
338 |
+
num_layers ...................................... 2
|
339 |
+
num_layers_at_end_in_bf16 ....................... 1
|
340 |
+
num_layers_at_start_in_bf16 ..................... 1
|
341 |
+
num_layers_per_virtual_pipeline_stage ........... None
|
342 |
+
num_query_groups ................................ 16
|
343 |
+
num_virtual_stages_per_pipeline_rank ............ None
|
344 |
+
num_workers ..................................... 2
|
345 |
+
object_storage_cache_path ....................... None
|
346 |
+
one_logger_async ................................ False
|
347 |
+
one_logger_project .............................. megatron-lm
|
348 |
+
one_logger_run_name ............................. None
|
349 |
+
onnx_safe ....................................... None
|
350 |
+
openai_gelu ..................................... False
|
351 |
+
optimizer ....................................... adam
|
352 |
+
optimizer_cpu_offload ........................... False
|
353 |
+
optimizer_offload_fraction ...................... 1.0
|
354 |
+
output_bert_embeddings .......................... False
|
355 |
+
overlap_cpu_optimizer_d2h_h2d ................... False
|
356 |
+
overlap_grad_reduce ............................. False
|
357 |
+
overlap_p2p_comm ................................ False
|
358 |
+
overlap_p2p_comm_warmup_flush ................... False
|
359 |
+
overlap_param_gather ............................ False
|
360 |
+
overlap_param_gather_with_optimizer_step ........ False
|
361 |
+
override_opt_param_scheduler .................... False
|
362 |
+
params_dtype .................................... torch.float16
|
363 |
+
patch_dim ....................................... 16
|
364 |
+
per_split_data_args_path ........................ None
|
365 |
+
perform_initialization .......................... True
|
366 |
+
pin_cpu_grads ................................... True
|
367 |
+
pin_cpu_params .................................. True
|
368 |
+
pipeline_model_parallel_comm_backend ............ None
|
369 |
+
pipeline_model_parallel_size .................... 1
|
370 |
+
pipeline_model_parallel_split_rank .............. None
|
371 |
+
position_embedding_type ......................... learned_absolute
|
372 |
+
pretrained_checkpoint ........................... None
|
373 |
+
profile ......................................... False
|
374 |
+
profile_ranks ................................... [0]
|
375 |
+
profile_step_end ................................ 12
|
376 |
+
profile_step_start .............................. 10
|
377 |
+
q_lora_rank ..................................... None
|
378 |
+
qk_head_dim ..................................... 128
|
379 |
+
qk_l2_norm ...................................... False
|
380 |
+
qk_layernorm .................................... False
|
381 |
+
qk_pos_emb_head_dim ............................. 64
|
382 |
+
query_in_block_prob ............................. 0.1
|
383 |
+
rampup_batch_size ............................... None
|
384 |
+
rank ............................................ 0
|
385 |
+
recompute_granularity ........................... None
|
386 |
+
recompute_method ................................ None
|
387 |
+
recompute_modules ............................... None
|
388 |
+
recompute_num_layers ............................ None
|
389 |
+
record_memory_history ........................... False
|
390 |
+
relative_attention_max_distance ................. 128
|
391 |
+
relative_attention_num_buckets .................. 32
|
392 |
+
replication ..................................... False
|
393 |
+
replication_factor .............................. 2
|
394 |
+
replication_jump ................................ None
|
395 |
+
rerun_mode ...................................... disabled
|
396 |
+
reset_attention_mask ............................ False
|
397 |
+
reset_position_ids .............................. False
|
398 |
+
result_rejected_tracker_filename ................ None
|
399 |
+
retriever_report_topk_accuracies ................ []
|
400 |
+
retriever_score_scaling ......................... False
|
401 |
+
retriever_seq_length ............................ 256
|
402 |
+
retro_add_retriever ............................. False
|
403 |
+
retro_attention_gate ............................ 1
|
404 |
+
retro_cyclic_train_iters ........................ None
|
405 |
+
retro_encoder_attention_dropout ................. 0.1
|
406 |
+
retro_encoder_hidden_dropout .................... 0.1
|
407 |
+
retro_encoder_layers ............................ 2
|
408 |
+
retro_num_neighbors ............................. 2
|
409 |
+
retro_num_retrieved_chunks ...................... 2
|
410 |
+
retro_project_dir ............................... None
|
411 |
+
retro_verify_neighbor_count ..................... True
|
412 |
+
rope_scaling_factor ............................. 8.0
|
413 |
+
rotary_base ..................................... 10000
|
414 |
+
rotary_interleaved .............................. False
|
415 |
+
rotary_percent .................................. 1.0
|
416 |
+
rotary_scaling_factor ........................... 1.0
|
417 |
+
rotary_seq_len_interpolation_factor ............. None
|
418 |
+
run_workload_inspector_server ................... False
|
419 |
+
sample_rate ..................................... 1.0
|
420 |
+
save ............................................ gpt-checkpoint
|
421 |
+
save_interval ................................... 16
|
422 |
+
scatter_gather_tensors_in_pipeline .............. True
|
423 |
+
seed ............................................ 1234
|
424 |
+
seq_length ...................................... 1024
|
425 |
+
sequence_parallel ............................... False
|
426 |
+
sgd_momentum .................................... 0.9
|
427 |
+
short_seq_prob .................................. 0.1
|
428 |
+
skip_train ...................................... False
|
429 |
+
skipped_train_samples ........................... 0
|
430 |
+
spec ............................................ None
|
431 |
+
split ........................................... None
|
432 |
+
squared_relu .................................... False
|
433 |
+
start_weight_decay .............................. 0.1
|
434 |
+
straggler_ctrlr_port ............................ 65535
|
435 |
+
straggler_minmax_count .......................... 1
|
436 |
+
suggested_communication_unit_size ............... None
|
437 |
+
swiglu .......................................... False
|
438 |
+
swin_backbone_type .............................. tiny
|
439 |
+
symmetric_ar_type ............................... None
|
440 |
+
te_rng_tracker .................................. False
|
441 |
+
tensor_model_parallel_size ...................... 8
|
442 |
+
tensorboard_dir ................................. tensorboard-logs/
|
443 |
+
tensorboard_log_interval ........................ 1
|
444 |
+
tensorboard_queue_size .......................... 1000
|
445 |
+
test_data_path .................................. None
|
446 |
+
test_mode ....................................... False
|
447 |
+
tiktoken_num_special_tokens ..................... 1000
|
448 |
+
tiktoken_pattern ................................ None
|
449 |
+
tiktoken_special_tokens ......................... None
|
450 |
+
timing_log_level ................................ 0
|
451 |
+
timing_log_option ............................... minmax
|
452 |
+
titles_data_path ................................ None
|
453 |
+
tokenizer_model ................................. None
|
454 |
+
tokenizer_type .................................. GPT2BPETokenizer
|
455 |
+
torch_fsdp2_reshard_after_forward ............... True
|
456 |
+
tp_comm_bootstrap_backend ....................... nccl
|
457 |
+
tp_comm_bulk_dgrad .............................. True
|
458 |
+
tp_comm_bulk_wgrad .............................. True
|
459 |
+
tp_comm_overlap ................................. False
|
460 |
+
tp_comm_overlap_ag .............................. True
|
461 |
+
tp_comm_overlap_cfg ............................. None
|
462 |
+
tp_comm_overlap_rs .............................. True
|
463 |
+
tp_comm_overlap_rs_dgrad ........................ False
|
464 |
+
tp_comm_split_ag ................................ True
|
465 |
+
tp_comm_split_rs ................................ True
|
466 |
+
train_data_path ................................. None
|
467 |
+
train_iters ..................................... 10
|
468 |
+
train_samples ................................... None
|
469 |
+
train_sync_interval ............................. None
|
470 |
+
transformer_impl ................................ transformer_engine
|
471 |
+
transformer_pipeline_model_parallel_size ........ 1
|
472 |
+
untie_embeddings_and_output_weights ............. False
|
473 |
+
use_checkpoint_args ............................. False
|
474 |
+
use_checkpoint_opt_param_scheduler .............. False
|
475 |
+
use_cpu_initialization .......................... None
|
476 |
+
use_custom_fsdp ................................. False
|
477 |
+
use_dist_ckpt ................................... True
|
478 |
+
use_dist_ckpt_deprecated ........................ False
|
479 |
+
use_distributed_optimizer ....................... False
|
480 |
+
use_flash_attn .................................. False
|
481 |
+
use_legacy_models ............................... False
|
482 |
+
use_mp_args_from_checkpoint_args ................ False
|
483 |
+
use_one_sent_docs ............................... False
|
484 |
+
use_persistent_ckpt_worker ...................... False
|
485 |
+
use_precision_aware_optimizer ................... False
|
486 |
+
use_pytorch_profiler ............................ False
|
487 |
+
use_ring_exchange_p2p ........................... False
|
488 |
+
use_rope_scaling ................................ False
|
489 |
+
use_rotary_position_embeddings .................. False
|
490 |
+
use_sharp ....................................... False
|
491 |
+
use_tokenizer_model_from_checkpoint_args ........ True
|
492 |
+
use_torch_fsdp2 ................................. False
|
493 |
+
use_torch_optimizer_for_cpu_offload ............. False
|
494 |
+
use_tp_pp_dp_mapping ............................ False
|
495 |
+
v_head_dim ...................................... 128
|
496 |
+
valid_data_path ................................. None
|
497 |
+
variable_seq_lengths ............................ False
|
498 |
+
virtual_pipeline_model_parallel_size ............ None
|
499 |
+
vision_backbone_type ............................ vit
|
500 |
+
vision_pretraining .............................. False
|
501 |
+
vision_pretraining_type ......................... classify
|
502 |
+
vocab_extra_ids ................................. 0
|
503 |
+
vocab_file ...................................... vocab.json
|
504 |
+
vocab_size ...................................... None
|
505 |
+
wandb_exp_name ..................................
|
506 |
+
wandb_project ...................................
|
507 |
+
wandb_save_dir ..................................
|
508 |
+
weight_decay .................................... 0.1
|
509 |
+
weight_decay_incr_style ......................... constant
|
510 |
+
wgrad_deferral_limit ............................ 0
|
511 |
+
world_size ...................................... 8
|
512 |
+
yaml_cfg ........................................ None
|
513 |
+
-------------------- end of arguments ---------------------
|
514 |
+
INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
|
515 |
+
> building GPT2BPETokenizer tokenizer ...
|
516 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
517 |
+
WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written.
|
518 |
+
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
|
519 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
520 |
+
> padded vocab (size: 50257) with 943 dummy tokens (new size: 51200)
|
521 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
522 |
+
WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED
|
523 |
+
> initializing torch distributed ...
|
524 |
+
> initialized tensor model parallel with size 8
|
525 |
+
> initialized pipeline model parallel with size 1
|
526 |
+
> setting random seeds to 1234 ...
|
527 |
+
> compiling dataset index builder ...
|
528 |
+
make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
|
529 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
530 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
531 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
532 |
+
INFO:megatron.training.initialize:Setting logging level to 0
|
533 |
+
make: Nothing to be done for 'default'.
|
534 |
+
make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets'
|
535 |
+
>>> done with dataset index builder. Compilation time: 0.047 seconds
|
536 |
+
> compiling and loading fused kernels ...
|