Interactive mode: no SLURM_JOB_NODELIST
Head Node IP: 172.17.0.2; NNODES: 1
I0326 21:42:52.236000 10491 torch/distributed/run.py:657] Using nproc_per_node=8.
W0326 21:42:52.236000 10491 torch/distributed/run.py:774] 
W0326 21:42:52.236000 10491 torch/distributed/run.py:774] *****************************************
W0326 21:42:52.236000 10491 torch/distributed/run.py:774] 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. 
W0326 21:42:52.236000 10491 torch/distributed/run.py:774] *****************************************
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199] Starting elastic_operator with launch configs:
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   entrypoint         : pretrain.py
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   min_nodes          : 1
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   max_nodes          : 1
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   nproc_per_node     : 8
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   run_id             : 25240
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   rdzv_backend       : c10d
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   rdzv_endpoint      : 172.17.0.2:29523
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   rdzv_configs       : {'timeout': 900}
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   max_restarts       : 0
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   monitor_interval   : 0.1
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   log_dir            : /tmp/torchelastic_0w32qhsi
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   metrics_cfg        : {}
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199]   event_log_handler  : null
I0326 21:42:52.236000 10491 torch/distributed/launcher/api.py:199] 
I0326 21:42:52.297000 10491 torch/distributed/elastic/agent/server/api.py:869] [default] starting workers for entrypoint: python
I0326 21:42:52.297000 10491 torch/distributed/elastic/agent/server/api.py:677] [default] Rendezvous'ing worker group
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523] [default] Rendezvous complete for workers. Result:
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   restart_count=0
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   master_addr=5902e1728240
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   master_port=46677
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   group_rank=0
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   group_world_size=1
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   local_ranks=[0, 1, 2, 3, 4, 5, 6, 7]
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   role_ranks=[0, 1, 2, 3, 4, 5, 6, 7]
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   global_ranks=[0, 1, 2, 3, 4, 5, 6, 7]
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   role_world_sizes=[8, 8, 8, 8, 8, 8, 8, 8]
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   global_world_sizes=[8, 8, 8, 8, 8, 8, 8, 8]
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523]   event_log_handler=null
I0326 21:42:52.522000 10491 torch/distributed/elastic/agent/server/api.py:523] 
I0326 21:42:52.523000 10491 torch/distributed/elastic/agent/server/api.py:685] [default] Starting worker group
I0326 21:42:52.523000 10491 torch/distributed/elastic/agent/server/local_elastic_agent.py:296] use_agent_store: True
I0326 21:42:52.523000 10491 torch/distributed/elastic/agent/server/local_elastic_agent.py:192] Environment variable 'TORCHELASTIC_ENABLE_FILE_TIMER' not found. Do not start FileTimerServer.
I0326 21:42:52.523000 10491 torch/distributed/elastic/agent/server/local_elastic_agent.py:236] Environment variable 'TORCHELASTIC_HEALTH_CHECK_PORT' not found. Do not start health check.
Training new model from scratch - Total Size=371.83M parameters
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:root:No validation split found in HuggingFaceFW/fineweb-edu. Eval/predict will be disabled.
*** Datasets Loaded ***
WARNING: No validation split available. Setting do_eval=False and do_predict=False.
No checkpoint found, starting from scratch
*** Set ignore_data_skip=True for streaming mode to save time ***
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': None}.
WARNING:accelerate.accelerator:[RANK 0] Gradient accumulation steps mismatch: GradientAccumulationPlugin has 1, DeepSpeed config has 2. Using DeepSpeed's value.

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Bernoulli prior theta first: 0.72, last: 0.20, size: 4096
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{'loss': 187.9167, 'grad_norm': 142.12176513671875, 'learning_rate': 0.00039999996012889783, 'epoch': 0.01}

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{'loss': 115.2655, 'grad_norm': 14.66339111328125, 'learning_rate': 0.0003998963052169914, 'epoch': 0.02}

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{'loss': 101.3912, 'grad_norm': 40.80771255493164, 'learning_rate': 0.0003995934280199486, 'epoch': 0.03}

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{'loss': 91.8026, 'grad_norm': 9.904938697814941, 'learning_rate': 0.0003990916639525919, 'epoch': 0.04}

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{'loss': 84.7724, 'grad_norm': 10.307001113891602, 'learning_rate': 0.00039839156868272125, 'epoch': 0.05}

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{'loss': 80.5052, 'grad_norm': 11.138967514038086, 'learning_rate': 0.00039749391751575094, 'epoch': 0.06}

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{'loss': 77.0708, 'grad_norm': 12.443509101867676, 'learning_rate': 0.000396399704536115, 'epoch': 0.07}

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{'loss': 74.4126, 'grad_norm': 10.101991653442383, 'learning_rate': 0.00039511014150638945, 'epoch': 0.08}

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{'loss': 72.0413, 'grad_norm': 9.71811294555664, 'learning_rate': 0.0003936266565253521, 'epoch': 0.09}

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{'loss': 70.4722, 'grad_norm': 9.624479293823242, 'learning_rate': 0.00039195089244646516, 'epoch': 0.1}

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{'loss': 68.8479, 'grad_norm': 11.965486526489258, 'learning_rate': 0.00039008470505853306, 'epoch': 0.12}

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{'loss': 67.265, 'grad_norm': 12.420802116394043, 'learning_rate': 0.0003880301610305485, 'epoch': 0.13}

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{'loss': 66.278, 'grad_norm': 9.341446876525879, 'learning_rate': 0.0003857895356230049, 'epoch': 0.14}

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{'loss': 65.0851, 'grad_norm': 10.808452606201172, 'learning_rate': 0.00038336531016820805, 'epoch': 0.15}

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{'loss': 64.021, 'grad_norm': 8.981714248657227, 'learning_rate': 0.00038076016932237823, 'epoch': 0.16}

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{'loss': 62.9501, 'grad_norm': 9.801749229431152, 'learning_rate': 0.0003779769980925852, 'epoch': 0.17}

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{'loss': 62.3853, 'grad_norm': 9.63443660736084, 'learning_rate': 0.00037501887864180994, 'epoch': 0.18}

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{'loss': 61.3032, 'grad_norm': 10.845137596130371, 'learning_rate': 0.0003718890868756691, 'epoch': 0.19}

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{'loss': 60.4652, 'grad_norm': 10.274904251098633, 'learning_rate': 0.00036859108881458436, 'epoch': 0.2}

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{'loss': 59.7255, 'grad_norm': 9.491048812866211, 'learning_rate': 0.00036512853675541246, 'epoch': 0.21}

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{'loss': 59.087, 'grad_norm': 10.574953079223633, 'learning_rate': 0.0003615052652267882, 'epoch': 0.22}

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{'loss': 58.403, 'grad_norm': 9.089303970336914, 'learning_rate': 0.00035772528674265803, 'epoch': 0.23}

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{'loss': 57.705, 'grad_norm': 8.02743148803711, 'learning_rate': 0.0003537927873587081, 'epoch': 0.24}

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{'loss': 57.3199, 'grad_norm': 8.855215072631836, 'learning_rate': 0.0003497121220366071, 'epoch': 0.25}

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{'loss': 56.7108, 'grad_norm': 8.211740493774414, 'learning_rate': 0.00034548780982119806, 'epoch': 0.26}

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{'loss': 56.2852, 'grad_norm': 8.441597938537598, 'learning_rate': 0.0003411245288359792, 'epoch': 0.27}

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{'loss': 55.9327, 'grad_norm': 9.115478515625, 'learning_rate': 0.00033662711110241697, 'epoch': 0.28}

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{'loss': 55.5417, 'grad_norm': 7.044861316680908, 'learning_rate': 0.00033200053718882835, 'epoch': 0.29}

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{'loss': 55.111, 'grad_norm': 8.60493278503418, 'learning_rate': 0.00032724993069475764, 'epoch': 0.3}

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{'loss': 54.872, 'grad_norm': 7.99247407913208, 'learning_rate': 0.00032238055257695645, 'epoch': 0.31}

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{'loss': 54.5064, 'grad_norm': 8.24665355682373, 'learning_rate': 0.0003173977953232508, 'epoch': 0.33}

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{'loss': 54.2565, 'grad_norm': 7.757580280303955, 'learning_rate': 0.0003123071769807465, 'epoch': 0.34}

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{'loss': 53.8782, 'grad_norm': 6.928912162780762, 'learning_rate': 0.0003071143350449872, 'epoch': 0.35}

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{'loss': 53.6208, 'grad_norm': 8.050333976745605, 'learning_rate': 0.0003018250202168308, 'epoch': 0.36}

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{'loss': 53.3105, 'grad_norm': 6.607926368713379, 'learning_rate': 0.00029644509003396075, 'epoch': 0.37}

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{'loss': 53.7924, 'grad_norm': 6.494936466217041, 'learning_rate': 0.00029098050238408137, 'epoch': 0.38}

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{'loss': 53.4344, 'grad_norm': 6.646099090576172, 'learning_rate': 0.00028543730890698353, 'epoch': 0.39}

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{'loss': 53.2198, 'grad_norm': 7.492635250091553, 'learning_rate': 0.00027982164829278754, 'epoch': 0.4}

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{'loss': 53.0853, 'grad_norm': 6.944725036621094, 'learning_rate': 0.0002741397394837821, 'epoch': 0.41}

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{'loss': 52.9595, 'grad_norm': 6.289498329162598, 'learning_rate': 0.0002683978747873912, 'epoch': 0.42}

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{'loss': 52.5747, 'grad_norm': 6.555839538574219, 'learning_rate': 0.0002626024129078938, 'epoch': 0.43}

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{'loss': 52.4925, 'grad_norm': 5.928042411804199, 'learning_rate': 0.000256759771904614, 'epoch': 0.44}

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{'loss': 52.3647, 'grad_norm': 7.0436692237854, 'learning_rate': 0.00025087642208437936, 'epoch': 0.45}

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{'loss': 52.193, 'grad_norm': 7.129555702209473, 'learning_rate': 0.0002449588788361192, 'epoch': 0.46}

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{'loss': 52.1653, 'grad_norm': 6.446074485778809, 'learning_rate': 0.0002390136954155377, 'epoch': 0.47}

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{'loss': 51.9882, 'grad_norm': 6.451213359832764, 'learning_rate': 0.0002330474556878519, 'epoch': 0.48}

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{'loss': 51.9818, 'grad_norm': 5.596757411956787, 'learning_rate': 0.00022706676683663239, 'epoch': 0.49}

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{'loss': 51.8086, 'grad_norm': 7.200987339019775, 'learning_rate': 0.0002210782520468199, 'epoch': 0.5}

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{'loss': 51.6531, 'grad_norm': 5.844482898712158, 'learning_rate': 0.00021508854317002244, 'epoch': 0.51}

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{'loss': 51.5264, 'grad_norm': 6.054821014404297, 'learning_rate': 0.00020910427338021354, 'epoch': 0.52}

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{'loss': 51.4704, 'grad_norm': 5.831844329833984, 'learning_rate': 0.00020313206982796743, 'epoch': 0.53}

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{'loss': 51.0902, 'grad_norm': 5.882756233215332, 'learning_rate': 0.0001971785463013636, 'epoch': 0.55}

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{'loss': 51.159, 'grad_norm': 5.256133079528809, 'learning_rate': 0.0001912502959016897, 'epoch': 0.56}

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{'loss': 50.9444, 'grad_norm': 5.332986831665039, 'learning_rate': 0.0001853538837420539, 'epoch': 0.57}

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{'loss': 50.8519, 'grad_norm': 4.923770427703857, 'learning_rate': 0.00017949583967699168, 'epoch': 0.58}

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{'loss': 50.7116, 'grad_norm': 5.525134086608887, 'learning_rate': 0.00017368265107111944, 'epoch': 0.59}

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{'loss': 50.3727, 'grad_norm': 4.826198101043701, 'learning_rate': 0.00016792075561484216, 'epoch': 0.6}

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{'loss': 50.4392, 'grad_norm': 4.938926696777344, 'learning_rate': 0.00016221653419507207, 'epoch': 0.61}

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{'loss': 50.3025, 'grad_norm': 5.869336128234863, 'learning_rate': 0.0001565763038288528, 'epoch': 0.62}

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{'loss': 50.3506, 'grad_norm': 5.609067440032959, 'learning_rate': 0.00015100631066771562, 'epoch': 0.63}

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{'loss': 50.339, 'grad_norm': 5.190382957458496, 'learning_rate': 0.0001455127230805128, 'epoch': 0.64}

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{'loss': 50.1942, 'grad_norm': 5.355462074279785, 'learning_rate': 0.00014010162482239088, 'epoch': 0.65}

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{'loss': 50.1128, 'grad_norm': 5.277245044708252, 'learning_rate': 0.00013477900829746725, 'epoch': 0.66}

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{'loss': 49.9096, 'grad_norm': 4.316271781921387, 'learning_rate': 0.00012955076792267134, 'epoch': 0.67}

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{'loss': 49.7664, 'grad_norm': 4.940814018249512, 'learning_rate': 0.00012442269360009943, 'epoch': 0.68}

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{'loss': 49.8506, 'grad_norm': 5.167801380157471, 'learning_rate': 0.0001194004643051128, 'epoch': 0.69}

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{'loss': 49.6501, 'grad_norm': 5.062017917633057, 'learning_rate': 0.00011448964179727846, 'epoch': 0.7}

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{'loss': 49.7153, 'grad_norm': 4.997366905212402, 'learning_rate': 0.00010969566446111879, 'epoch': 0.71}

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{'loss': 49.6482, 'grad_norm': 4.51628303527832, 'learning_rate': 0.00010502384128348954, 'epoch': 0.72}

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{'loss': 49.3829, 'grad_norm': 4.526364803314209, 'learning_rate': 0.00010047934597425688, 'epoch': 0.73}

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{'loss': 49.3359, 'grad_norm': 4.674807548522949, 'learning_rate': 9.606721123678415e-05, 'epoch': 0.74}

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{'loss': 49.3209, 'grad_norm': 4.839710235595703, 'learning_rate': 9.179232319457263e-05, 'epoch': 0.76}

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{'loss': 49.2645, 'grad_norm': 4.820715427398682, 'learning_rate': 8.765941598022974e-05, 'epoch': 0.77}

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{'loss': 49.0976, 'grad_norm': 4.89091157913208, 'learning_rate': 8.36730664927557e-05, 'epoch': 0.78}

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{'loss': 49.1636, 'grad_norm': 4.598087310791016, 'learning_rate': 7.983768932895552e-05, 'epoch': 0.79}

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{'loss': 49.0768, 'grad_norm': 4.417520523071289, 'learning_rate': 7.615753189458901e-05, 'epoch': 0.8}

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{'loss': 49.0259, 'grad_norm': 4.7677717208862305, 'learning_rate': 7.263666970067252e-05, 'epoch': 0.81}

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{'loss': 48.9406, 'grad_norm': 4.528140544891357, 'learning_rate': 6.927900185014261e-05, 'epoch': 0.82}

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{'loss': 48.8781, 'grad_norm': 3.976332664489746, 'learning_rate': 6.608824671987803e-05, 'epoch': 0.83}

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{'loss': 48.807, 'grad_norm': 4.136193752288818, 'learning_rate': 6.306793784286387e-05, 'epoch': 0.84}

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{'loss': 48.8375, 'grad_norm': 4.744948387145996, 'learning_rate': 6.022141999505628e-05, 'epoch': 0.85}

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{'loss': 48.787, 'grad_norm': 4.454591751098633, 'learning_rate': 5.755184549128258e-05, 'epoch': 0.86}

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{'loss': 48.7356, 'grad_norm': 4.378818035125732, 'learning_rate': 5.506217069427819e-05, 'epoch': 0.87}

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{'loss': 48.6228, 'grad_norm': 4.370756149291992, 'learning_rate': 5.2755152740726445e-05, 'epoch': 0.88}

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{'loss': 48.5525, 'grad_norm': 4.3349409103393555, 'learning_rate': 5.0633346487926995e-05, 'epoch': 0.89}

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{'loss': 48.4855, 'grad_norm': 4.078170299530029, 'learning_rate': 4.8699101684474055e-05, 'epoch': 0.9}

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{'loss': 48.4749, 'grad_norm': 4.331348419189453, 'learning_rate': 4.69545603680782e-05, 'epoch': 0.91}

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{'loss': 48.4557, 'grad_norm': 5.18147087097168, 'learning_rate': 4.540165449341264e-05, 'epoch': 0.92}

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{'loss': 48.3142, 'grad_norm': 4.004964828491211, 'learning_rate': 4.404210379261189e-05, 'epoch': 0.93}

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{'loss': 48.3927, 'grad_norm': 3.968560218811035, 'learning_rate': 4.2877413870791265e-05, 'epoch': 0.94}

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{'loss': 48.485, 'grad_norm': 4.037226676940918, 'learning_rate': 4.1908874538697176e-05, 'epoch': 0.95}

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{'loss': 48.4193, 'grad_norm': 3.877131462097168, 'learning_rate': 4.113755838433387e-05, 'epoch': 0.96}

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{'loss': 48.2447, 'grad_norm': 4.14885139465332, 'learning_rate': 4.056431958514902e-05, 'epoch': 0.98}

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{'loss': 48.2203, 'grad_norm': 4.344592571258545, 'learning_rate': 4.018979296209327e-05, 'epoch': 0.99}

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{'loss': 48.3511, 'grad_norm': 4.123725414276123, 'learning_rate': 4.0014393276601606e-05, 'epoch': 1.0}

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{'train_runtime': 17591.4021, 'train_samples_per_second': 138.773, 'train_steps_per_second': 0.271, 'train_loss': 57.252746492424265, 'epoch': 1.0}

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Fatal Python error: PyGILState_Release: thread state 0x7a61bcc7dba0 must be current when releasing
Python runtime state: finalizing (tstate=0x0000000000a6acb8)

Thread 0x00007a6656ed6000 (most recent call first):
  <no Python frame>

Extension modules: _brotli, charset_normalizer.md, yaml._yaml, regex._regex, numpy._core._multiarray_umath, numpy.linalg._umath_linalg, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, markupsafe._speedups, PIL._imaging, _cffi_backend, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, _cyutility, scipy._cyutility, scipy._lib._ccallback_c, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg.cython_lapack, scipy.linalg._cythonized_array_utils, scipy.linalg._solve_toeplitz, scipy.linalg._batched_linalg, scipy.linalg._decomp_lu_cython, scipy.linalg._matfuncs_schur_sqrtm, scipy.linalg._matfuncs_expm, scipy.linalg._linalg_pythran, scipy.linalg.cython_blas, scipy.linalg._decomp_update, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpacklib, scipy.sparse.linalg._propack, scipy.optimize._group_columns, scipy._lib.messagestream, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._slsqplib, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy._lib._uarray._uarray, scipy.special._ufuncs_cxx, scipy.special._ellip_harm_2, scipy.special._special_ufuncs, scipy.special._gufuncs, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.linalg._decomp_interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.spatial._ckdtree, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._hausdorff, scipy.spatial._distance_wrap, scipy.spatial.transform._rotation_cy, scipy.spatial.transform._rigid_transform_cy, scipy.optimize._direct, sklearn.__check_build._check_build, psutil._psutil_linux, psutil._psutil_posix, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.interpolate._fitpack, scipy.interpolate._dfitpack, scipy.interpolate._dierckx, scipy.interpolate._ppoly, scipy.interpolate._interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.special.cython_special, scipy.stats._stats, scipy.stats._biasedurn, scipy.stats._stats_pythran, scipy.stats._levy_stable.levyst, scipy.stats._ansari_swilk_statistics, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._rcont.rcont, scipy.stats._qmvnt_cy, scipy.ndimage._nd_image, scipy.ndimage._rank_filter_1d, _ni_label, scipy.ndimage._ni_label, pyarrow.lib, pandas._libs._cyutility, pandas._libs.tslibs.ccalendar, pandas._libs.tslibs.np_datetime, pandas._libs.tslibs.dtypes, pandas._libs.tslibs.base, pandas._libs.tslibs.nattype, pandas._libs.tslibs.timezones, pandas._libs.properties, pandas._libs.tslibs.fields, pandas._libs.tslibs.timedeltas, pandas._libs.tslibs.tzconversion, pandas._libs.tslibs.timestamps, pandas._libs.tslibs.offsets, pandas._libs.tslibs.strptime, pandas._libs.tslibs.parsing, pandas._libs.tslibs.conversion, pandas._libs.tslibs.period, pandas._libs.tslibs.vectorized, pandas._libs.ops_dispatch, pandas._libs.missing, pandas._libs.hashtable, pandas._libs.algos, pandas._libs.interval, pandas._libs.lib, pyarrow._compute, pandas._libs.ops, pandas._libs.hashing, pandas._libs.arrays, pandas._libs.tslib, pandas._libs.sparse, pandas._libs.internals, pandas._libs.indexing, pandas._libs.index, pandas._libs.writers, pandas._libs.join, pandas._libs.window.aggregations, pandas._libs.window.indexers, pandas._libs.reshape, pandas._libs.groupby, pandas._libs.json, pandas._libs.parsers, pandas._libs.testing, sklearn._cyutility, sklearn.utils._isfinite, sklearn.utils.sparsefuncs_fast, sklearn.utils.murmurhash, sklearn.utils._openmp_helpers, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.metrics._dist_metrics, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.utils._cython_blas, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._argkmin_classmode, sklearn.utils._vector_sentinel, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_distances_reduction._radius_neighbors_classmode, sklearn.metrics._pairwise_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.preprocessing._target_encoder_fast, zstandard.backend_c, pyarrow._acero, pyarrow._fs, pyarrow._csv, pyarrow._json, pyarrow._substrait, pyarrow._dataset, pyarrow._dataset_orc, pyarrow._parquet, pyarrow._azurefs, pyarrow._hdfs, pyarrow._gcsfs, pyarrow._s3fs, pyarrow._parquet_encryption, pyarrow._dataset_parquet_encryption, pyarrow._dataset_parquet, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, xxhash._xxhash, cuda_utils, msgpack._cmsgpack, sentencepiece._sentencepiece, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, __triton_launcher (total: 210)
E0327 02:36:34.905000 10491 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: -6) local_rank: 0 (pid: 10558) of binary: /usr/bin/python
I0327 02:36:34.909000 10491 torch/distributed/elastic/multiprocessing/errors/__init__.py:370] ('local_rank %s FAILED with no error file. Decorate your entrypoint fn with @record for traceback info. See: https://pytorch.org/docs/stable/elastic/errors.html', 0)
Traceback (most recent call last):
  File "/usr/local/bin/torchrun", line 7, in <module>
    sys.exit(main())
             ^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 357, in wrapper
    return f(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 901, in main
    run(args)
  File "/usr/local/lib/python3.11/dist-packages/torch/distributed/run.py", line 892, in run
    elastic_launch(
  File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 143, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/torch/distributed/launcher/api.py", line 277, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
======================================================
pretrain.py FAILED
------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2026-03-27_02:36:34
  host      : 5902e1728240
  rank      : 0 (local_rank: 0)
  exitcode  : -6 (pid: 10558)
  error_file: <N/A>
  traceback : Signal 6 (SIGABRT) received by PID 10558
======================================================