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

@@ -122183,3 +122183,33 @@ batch tensor after cp: labels torch.Size([1, 16384])
 batch tensor after cp: loss_mask torch.Size([1, 16384])
 batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
 batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 16384])
+batch tensor after cp: labels torch.Size([1, 16384])
+batch tensor after cp: loss_mask torch.Size([1, 16384])
+batch tensor after cp: attention_mask torch.Size([1, 1, 16384, 131072])
+batch tensor after cp: position_ids torch.Size([1, 16384])

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

@@ -66428,3 +66428,326 @@ batch tensor after cp: labels torch.Size([1, 32768])
 batch tensor after cp: loss_mask torch.Size([1, 32768])
 batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
 batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+Start exporting trace 2
+Done exporting trace 2
+ [2025-06-21 21:07:56] iteration        3/      10 | consumed samples:            3 | elapsed time per iteration (ms): 174501.8 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 1073741824.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor:batch tensor: attention_mask  tokens torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokensbatch tensor after cp:  tokens torch.Size([1, 32768])torch.Size([1, 32768])
+
+batch tensor after cp:batch tensor after cp:  labelslabels  torch.Size([1, 32768])torch.Size([1, 32768])
+
+batch tensor after cp:batch tensor after cp:  loss_maskloss_mask  torch.Size([1, 32768])torch.Size([1, 32768])
+
+batch tensor after cp:batch tensor after cp:  attention_maskattention_mask  torch.Size([1, 1, 32768, 131072])
+torch.Size([1, 1, 32768, 131072])batch tensor after cp:
+ position_idsbatch tensor after cp:  position_idstorch.Size([1, 32768]) 
+torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
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+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
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+batch tensor after cp: tokens torch.Size([1, 32768])
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+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])
+batch tensor: tokens torch.Size([1, 131072])
+batch tensor: labels torch.Size([1, 131072])
+batch tensor: loss_mask torch.Size([1, 131072])
+batch tensor: attention_mask torch.Size([1, 1, 131072, 131072])
+batch tensor: position_ids torch.Size([1, 131072])
+batch tensor after cp: tokens torch.Size([1, 32768])
+batch tensor after cp: labels torch.Size([1, 32768])
+batch tensor after cp: loss_mask torch.Size([1, 32768])
+batch tensor after cp: attention_mask torch.Size([1, 1, 32768, 131072])
+batch tensor after cp: position_ids torch.Size([1, 32768])

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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2218be73f47fe164c296b4e0d629a3d3b6542a149ea0e99d6fdd7751b7de4983
-size 30416020
+oid sha256:c78b4349417cacd0f3c66f837e9bed0c1241276553a9acecd79541d563f8e013
+size 30470641

attnserver.run_attnserver.slurm.sh.343200.err.log

@@ -0,0 +1,186 @@
++ source /mnt/weka/home/hao.zhang/conda/miniconda/bin/activate
+++ _CONDA_ROOT=/mnt/weka/home/hao.zhang/conda/miniconda
+++ . /mnt/weka/home/hao.zhang/conda/miniconda/etc/profile.d/conda.sh
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export _CE_M=
++++ _CE_M=
++++ export _CE_CONDA=
++++ _CE_CONDA=
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ '[' -z x ']'
+++ conda activate
+++ local cmd=activate
+++ case "$cmd" in
+++ __conda_activate activate
+++ '[' -n '' ']'
+++ local ask_conda
++++ PS1=
++++ __conda_exe shell.posix activate
++++ '[' -n '' ']'
++++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate
+++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(base) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_SHLVL='\''1'\''
+export CONDA_PROMPT_MODIFIER='\''(base) '\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++++ unset _CE_M
++++ unset _CE_CONDA
++++ PS1='(base) '
++++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
++++ export CONDA_SHLVL=1
++++ CONDA_SHLVL=1
++++ export 'CONDA_PROMPT_MODIFIER=(base) '
++++ CONDA_PROMPT_MODIFIER='(base) '
++++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
++++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ __conda_hashr
+++ '[' -n '' ']'
+++ '[' -n '' ']'
+++ hash -r
++ conda activate junda-attnserver
++ local cmd=activate
++ case "$cmd" in
++ __conda_activate activate junda-attnserver
++ '[' -n '' ']'
++ local ask_conda
+++ PS1='(base) '
+++ __conda_exe shell.posix activate junda-attnserver
+++ '[' -n '' ']'
+++ /mnt/weka/home/hao.zhang/conda/miniconda/bin/conda shell.posix activate junda-attnserver
++ ask_conda='unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
++ eval 'unset _CE_M
+unset _CE_CONDA
+PS1='\''(junda-attnserver) '\''
+export PATH='\''/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin'\''
+export CONDA_PREFIX='\''/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver'\''
+export CONDA_SHLVL='\''2'\''
+export CONDA_DEFAULT_ENV='\''junda-attnserver'\''
+export CONDA_PROMPT_MODIFIER='\''(junda-attnserver) '\''
+export CONDA_PREFIX_1='\''/mnt/weka/home/hao.zhang/conda/miniconda'\''
+export CONDA_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda'\''
+export CONDA_PYTHON_EXE='\''/mnt/weka/home/hao.zhang/conda/miniconda/bin/python'\'''
+++ unset _CE_M
+++ unset _CE_CONDA
+++ PS1='(junda-attnserver) '
+++ export PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ PATH=/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/.local/bin:/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin:/mnt/weka/home/hao.zhang/conda/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
+++ export CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ CONDA_PREFIX=/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver
+++ export CONDA_SHLVL=2
+++ CONDA_SHLVL=2
+++ export CONDA_DEFAULT_ENV=junda-attnserver
+++ CONDA_DEFAULT_ENV=junda-attnserver
+++ export 'CONDA_PROMPT_MODIFIER=(junda-attnserver) '
+++ CONDA_PROMPT_MODIFIER='(junda-attnserver) '
+++ export CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ CONDA_PREFIX_1=/mnt/weka/home/hao.zhang/conda/miniconda
+++ export CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ CONDA_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/conda
+++ export CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
+++ CONDA_PYTHON_EXE=/mnt/weka/home/hao.zhang/conda/miniconda/bin/python
++ __conda_hashr
++ '[' -n '' ']'
++ '[' -n '' ']'
++ hash -r
++ export CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ CHROME_TRACE_PREFIX=/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ mkdir -p /mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5
++ export PROF_TP_SIZE=8
++ PROF_TP_SIZE=8
++ export PROF_CP_SIZE=4
++ PROF_CP_SIZE=4
++ export PROF_BS=32
++ PROF_BS=32
++ for ctx_length in 1024 2048 4096 8192 12288 16384 24576 32768 40960 49152 65536 81920 98304 131072
++ export PROF_CTX_LENGTH=1024
++ PROF_CTX_LENGTH=1024
++ name='/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp4.bs32.json'
++ '[' -f '/mnt/sharefs/users/hao.zhang/junda/megatron-prof-data--unstable-v5/mytrace.L1024*tp8.cp4.bs32.json' ']'
++ echo 'Running ctx_length=1024, TP_SIZE=8, CP_SIZE=4, BATCH_SIZE=32'
++ srun bash ./attnserver.sh
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 4 --node_rank 3 --rdzv_id 343200 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-852:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 4 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 4 --node_rank 2 --rdzv_id 343200 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-852:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 4 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 4 --node_rank 0 --rdzv_id 343200 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-852:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 4 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
++ which python3
++ python3 -m torch.distributed.launch --nproc_per_node 8 --nnodes 4 --node_rank 1 --rdzv_id 343200 --rdzv_backend c10d --rdzv_endpoint fs-mbz-gpu-852:29500 ./pretrain_gpt_profile.py --tensor-model-parallel-size 8 --context-parallel-size 4 --num-layers 2 --hidden-size 4096 --num-attention-heads 64 --group-query-attention --num-query-groups 16 --seq-length 1024 --max-position-embeddings 1024 --micro-batch-size 1 --global-batch-size 1 --lr 0.0005 --train-iters 10 --lr-decay-iters 150000 --lr-decay-style cosine --lr-warmup-iters 2 --weight-decay .1 --adam-beta2 .999 --fp16 --log-interval 1 --save-interval 16 --eval-interval 16 --eval-iters 1 --vocab-file vocab.json --merge-file merges.txt --save gpt-checkpoint --load gpt-checkpoint --logging-level 0 --mock-data --tensorboard-dir tensorboard-logs/
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:08:49.911000 121562 site-packages/torch/distributed/run.py:766] 
+W0621 21:08:49.911000 121562 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:08:49.911000 121562 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:08:49.911000 121562 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:08:49.997000 2041342 site-packages/torch/distributed/run.py:766] 
+W0621 21:08:49.997000 2041342 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:08:49.997000 2041342 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:08:49.997000 2041342 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:08:49.997000 3410371 site-packages/torch/distributed/run.py:766] 
+W0621 21:08:49.997000 3410371 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:08:49.997000 3410371 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:08:49.997000 3410371 site-packages/torch/distributed/run.py:766] *****************************************
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/lib/python3.12/site-packages/torch/distributed/launch.py:207: FutureWarning: The module torch.distributed.launch is deprecated
+and will be removed in future. Use torchrun.
+Note that --use-env is set by default in torchrun.
+If your script expects `--local-rank` argument to be set, please
+change it to read from `os.environ['LOCAL_RANK']` instead. See 
+https://pytorch.org/docs/stable/distributed.html#launch-utility for 
+further instructions
+
+  main()
+W0621 21:08:50.019000 3341756 site-packages/torch/distributed/run.py:766] 
+W0621 21:08:50.019000 3341756 site-packages/torch/distributed/run.py:766] *****************************************
+W0621 21:08:50.019000 3341756 site-packages/torch/distributed/run.py:766] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0621 21:08:50.019000 3341756 site-packages/torch/distributed/run.py:766] *****************************************

attnserver.run_attnserver.slurm.sh.343200.out.log

@@ -0,0 +1,37 @@
+Running ctx_length=1024, TP_SIZE=8, CP_SIZE=4, BATCH_SIZE=32
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3
+Cleaning up checkpoint directory: gpt-checkpoint
+--------------------------------
+CTX_LENGTH: 1024
+TP_SIZE: 8
+CP_SIZE: 4
+CHECKPOINT_PATH: gpt-checkpoint
+PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron
+--------------------------------
+/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3

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

@@ -22196,3 +22196,1165 @@ batch tensor after cp: labels torch.Size([2, 40960])
 batch tensor after cp: loss_mask torch.Size([2, 40960])
 batch tensor after cp: attention_mask torch.Size([2, 1, 40960, 81920])
 batch tensor after cp: position_ids torch.Size([2, 40960])
+Start exporting trace 0
+Done exporting trace 0
+Number of parameters in transformer block in billions:  0.35
+Number of parameters in embedding layers in billions: 0.21
+Total number of parameters in billions: 0.56
+Number of parameters in most loaded shard in billions: 0.0703
+Theoretical memory footprints: weight and optimizer=1206.09 MB
+ [2025-06-21 21:07:57] iteration        1/      10 | consumed samples:            1 | elapsed time per iteration (ms): 29934.7 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 4294967296.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
+[Rank 5] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+[Rank 1] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+[Rank 7] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+[Rank 14] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 12] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 11] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 10] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 3] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+[Rank 15] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 8] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 35508.0 | max reserved: 35508.0
+[Rank 2] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+[Rank 9] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 0] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 35588.0 | max reserved: 35588.0
+[Rank 13] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36148.0 | max reserved: 36148.0
+[Rank 6] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+[Rank 4] (after 1 iterations) memory (MB) | allocated: 22070.03955078125 | max allocated: 33779.52001953125 | reserved: 36108.0 | max reserved: 36108.0
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 40960])
+batch tensor after cp: labels torch.Size([2, 40960])
+batch tensor after cp: loss_mask torch.Size([2, 40960])
+batch tensor after cp: attention_mask torch.Size([2, 1, 40960, 81920])
+batch tensor after cp: position_ids torch.Size([2, 40960])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 40960])
+batch tensor after cp: labels torch.Size([2, 40960])
+batch tensor after cp: loss_mask torch.Size([2, 40960])
+batch tensor after cp: attention_mask torch.Size([2, 1, 40960, 81920])
+batch tensor after cp: position_ids torch.Size([2, 40960])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 40960])
+batch tensor after cp: labels torch.Size([2, 40960])
+batch tensor after cp: loss_mask torch.Size([2, 40960])
+batch tensor after cp: attention_mask torch.Size([2, 1, 40960, 81920])
+batch tensor after cp: position_ids torch.Size([2, 40960])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
+batch tensor: attention_mask torch.Size([2, 1, 81920, 81920])
+batch tensor: position_ids torch.Size([2, 81920])
+batch tensor after cp: tokens torch.Size([2, 40960])
+batch tensor after cp: labels torch.Size([2, 40960])
+batch tensor after cp: loss_mask torch.Size([2, 40960])
+batch tensor after cp: attention_mask torch.Size([2, 1, 40960, 81920])
+batch tensor after cp: position_ids torch.Size([2, 40960])
+batch tensor: tokens torch.Size([2, 81920])
+batch tensor: labels torch.Size([2, 81920])
+batch tensor: loss_mask torch.Size([2, 81920])
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+Start exporting trace 1
+Done exporting trace 1
+ [2025-06-21 21:08:05] iteration        2/      10 | consumed samples:            2 | elapsed time per iteration (ms): 8086.1 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 2147483648.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 2
+Done exporting trace 2
+ [2025-06-21 21:08:13] iteration        3/      10 | consumed samples:            3 | elapsed time per iteration (ms): 7945.0 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 1073741824.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 3
+Done exporting trace 3
+ [2025-06-21 21:08:21] iteration        4/      10 | consumed samples:            4 | elapsed time per iteration (ms): 7924.7 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 536870912.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 4
+Done exporting trace 4
+ [2025-06-21 21:08:28] iteration        5/      10 | consumed samples:            5 | elapsed time per iteration (ms): 7646.0 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 268435456.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 5
+Done exporting trace 5
+ [2025-06-21 21:08:36] iteration        6/      10 | consumed samples:            6 | elapsed time per iteration (ms): 7989.7 | learning rate: 0.000000E+00 | global batch size:     1 | loss scale: 134217728.0 | number of skipped iterations:   1 | number of nan iterations:   0 |
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+Start exporting trace 6
+Done exporting trace 6
+ [2025-06-21 21:08:44] iteration        7/      10 | consumed samples:            7 | elapsed time per iteration (ms): 7801.4 | 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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