nohup: ignoring input
master_addr is only used for static rdzv_backend and when rdzv_endpoint is not specified.
WARNING:torch.distributed.run:
*****************************************
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. 
*****************************************
[2024-01-02 17:39:28,646][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 2
[2024-01-02 17:39:28,651][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 1
[2024-01-02 17:39:28,680][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 6
[2024-01-02 17:39:28,712][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 0
[2024-01-02 17:39:28,713][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 7
[2024-01-02 17:39:28,720][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 3
[2024-01-02 17:39:28,722][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 4
[2024-01-02 17:39:28,728][distributed_c10d.py][INFO] Added key: store_based_barrier_key:1 to store for rank: 5
[2024-01-02 17:39:28,728][distributed_c10d.py][INFO] Rank 5: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,728][distributed_c10d.py][INFO] Rank 2: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,731][distributed_c10d.py][INFO] Rank 3: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,731][distributed_c10d.py][INFO] Rank 6: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,733][distributed_c10d.py][INFO] Rank 4: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,733][distributed_c10d.py][INFO] Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,734][distributed_c10d.py][INFO] Rank 1: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
[2024-01-02 17:39:28,734][distributed_c10d.py][INFO] Rank 7: Completed store-based barrier for key:store_based_barrier_key:1 with 8 nodes.
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
tokens per iteration will be: 32,768
breaks down as: 1 grad accum steps * 8 processes * 8 batch size * 512 max seq len
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
memmap:True train data.shape:(293670029, 512)
downloading finished.....
Initializing a new model from scratch
总参数量为: 1005591552
总参数量为: 1005591552
总参数量为: 1005591552
总参数量为: 1005591552
总参数量为: 1005591552
总参数量为: 1005591552
总参数量为: 1005591552
总参数量为: 1005591552
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
num decayed parameter tensors: 225, with 1,005,491,712 parameters
num non-decayed parameter tensors: 65, with 99,840 parameters
using fused AdamW: True
[2024-01-02 17:40:18,090][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.339 lr:0.0000000 epoch_Time:2333086.0min:
[2024-01-02 17:40:18,093][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.359 lr:0.0000000 epoch_Time:2330913.0min:
[2024-01-02 17:40:18,093][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.385 lr:0.0000000 epoch_Time:2332028.0min:
[2024-01-02 17:40:18,096][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.342 lr:0.0000000 epoch_Time:2332113.0min:
[2024-01-02 17:40:18,097][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.393 lr:0.0000000 epoch_Time:2334802.0min:
[2024-01-02 17:40:18,098][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.395 lr:0.0000000 epoch_Time:2332419.0min:
[2024-01-02 17:40:18,100][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:18,101][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:18,101][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:23,492][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.396 lr:0.0000000 epoch_Time:2742879.0min:
[2024-01-02 17:40:23,494][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:23,496][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:24,796][model8_pretrain.py][INFO] Epoch:[0/2](0/4588595) loss:11.414 lr:0.0000000 epoch_Time:2844006.0min:
[2024-01-02 17:40:24,799][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:24,799][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:40:24,799][distributed.py][INFO] Reducer buckets have been rebuilt in this iteration.
[2024-01-02 17:41:01,758][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.210 lr:0.0000300 epoch_Time:56164.0min:
[2024-01-02 17:41:01,758][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.346 lr:0.0000300 epoch_Time:56141.0min:
[2024-01-02 17:41:01,758][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.100 lr:0.0000300 epoch_Time:56140.0min:
[2024-01-02 17:41:01,758][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.115 lr:0.0000300 epoch_Time:56152.0min:
[2024-01-02 17:41:01,758][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.118 lr:0.0000300 epoch_Time:56152.0min:
[2024-01-02 17:41:01,759][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.180 lr:0.0000300 epoch_Time:56152.0min:
[2024-01-02 17:41:01,759][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.233 lr:0.0000300 epoch_Time:56179.0min:
[2024-01-02 17:41:01,759][model8_pretrain.py][INFO] Epoch:[0/2](100/4588595) loss:8.211 lr:0.0000300 epoch_Time:56150.0min:
[2024-01-02 17:41:38,670][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.767 lr:0.0000600 epoch_Time:42253.0min:
[2024-01-02 17:41:38,670][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.478 lr:0.0000600 epoch_Time:42265.0min:
[2024-01-02 17:41:38,670][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.602 lr:0.0000600 epoch_Time:42259.0min:
[2024-01-02 17:41:38,671][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.547 lr:0.0000600 epoch_Time:42259.0min:
[2024-01-02 17:41:38,671][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.386 lr:0.0000600 epoch_Time:42272.0min:
[2024-01-02 17:41:38,671][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.533 lr:0.0000600 epoch_Time:42254.0min:
[2024-01-02 17:41:38,671][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.719 lr:0.0000600 epoch_Time:42258.0min:
[2024-01-02 17:41:38,671][model8_pretrain.py][INFO] Epoch:[0/2](200/4588595) loss:7.560 lr:0.0000600 epoch_Time:42259.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:7.410 lr:0.0000900 epoch_Time:37597.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:7.087 lr:0.0000900 epoch_Time:37593.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:6.866 lr:0.0000900 epoch_Time:37593.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:7.182 lr:0.0000900 epoch_Time:37601.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:6.785 lr:0.0000900 epoch_Time:37596.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:7.290 lr:0.0000900 epoch_Time:37605.0min:
[2024-01-02 17:42:15,584][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:6.996 lr:0.0000900 epoch_Time:37597.0min:
[2024-01-02 17:42:15,585][model8_pretrain.py][INFO] Epoch:[0/2](300/4588595) loss:6.967 lr:0.0000900 epoch_Time:37597.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.753 lr:0.0001200 epoch_Time:36746.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.811 lr:0.0001200 epoch_Time:36749.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.687 lr:0.0001200 epoch_Time:36752.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.655 lr:0.0001200 epoch_Time:36746.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.974 lr:0.0001200 epoch_Time:36748.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.632 lr:0.0001200 epoch_Time:36749.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.642 lr:0.0001200 epoch_Time:36755.0min:
[2024-01-02 17:43:00,307][model8_pretrain.py][INFO] Epoch:[0/2](400/4588595) loss:6.716 lr:0.0001200 epoch_Time:36749.0min:
[2024-01-02 17:43:37,235][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.493 lr:0.0001500 epoch_Time:35050.0min:
[2024-01-02 17:43:37,235][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.015 lr:0.0001500 epoch_Time:35048.0min:
[2024-01-02 17:43:37,235][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.041 lr:0.0001500 epoch_Time:35053.0min:
[2024-01-02 17:43:37,235][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.653 lr:0.0001500 epoch_Time:35048.0min:
[2024-01-02 17:43:37,235][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.189 lr:0.0001500 epoch_Time:35050.0min:
[2024-01-02 17:43:37,236][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.283 lr:0.0001500 epoch_Time:35050.0min:
[2024-01-02 17:43:37,236][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.149 lr:0.0001500 epoch_Time:35055.0min:
[2024-01-02 17:43:37,236][model8_pretrain.py][INFO] Epoch:[0/2](500/4588595) loss:6.319 lr:0.0001500 epoch_Time:35050.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:5.909 lr:0.0001800 epoch_Time:33911.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:6.013 lr:0.0001800 epoch_Time:33915.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:5.877 lr:0.0001800 epoch_Time:33917.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:6.153 lr:0.0001800 epoch_Time:33911.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:6.032 lr:0.0001800 epoch_Time:33913.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:5.923 lr:0.0001800 epoch_Time:33913.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:5.827 lr:0.0001800 epoch_Time:33913.0min:
[2024-01-02 17:44:14,144][model8_pretrain.py][INFO] Epoch:[0/2](600/4588595) loss:5.634 lr:0.0001800 epoch_Time:33913.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:6.019 lr:0.0002100 epoch_Time:33098.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:5.657 lr:0.0002100 epoch_Time:33100.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:5.606 lr:0.0002100 epoch_Time:33100.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:5.410 lr:0.0002100 epoch_Time:33098.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:5.606 lr:0.0002100 epoch_Time:33104.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:5.873 lr:0.0002100 epoch_Time:33102.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:5.719 lr:0.0002100 epoch_Time:33100.0min:
[2024-01-02 17:44:51,052][model8_pretrain.py][INFO] Epoch:[0/2](700/4588595) loss:6.092 lr:0.0002100 epoch_Time:33100.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.251 lr:0.0002400 epoch_Time:32492.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.469 lr:0.0002400 epoch_Time:32494.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.254 lr:0.0002400 epoch_Time:32493.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.536 lr:0.0002400 epoch_Time:32494.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.269 lr:0.0002400 epoch_Time:32492.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.615 lr:0.0002400 epoch_Time:32495.0min:
[2024-01-02 17:45:27,988][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.228 lr:0.0002400 epoch_Time:32494.0min:
[2024-01-02 17:45:27,992][model8_pretrain.py][INFO] Epoch:[0/2](800/4588595) loss:5.535 lr:0.0002400 epoch_Time:32497.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.372 lr:0.0002700 epoch_Time:32022.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.445 lr:0.0002700 epoch_Time:32023.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.731 lr:0.0002700 epoch_Time:32023.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.999 lr:0.0002700 epoch_Time:32026.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.171 lr:0.0002700 epoch_Time:32023.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.595 lr:0.0002700 epoch_Time:32024.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.122 lr:0.0002700 epoch_Time:32023.0min:
[2024-01-02 17:46:04,951][model8_pretrain.py][INFO] Epoch:[0/2](900/4588595) loss:5.550 lr:0.0002700 epoch_Time:32022.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.277 lr:0.0003000 epoch_Time:31644.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.118 lr:0.0003000 epoch_Time:31645.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.034 lr:0.0003000 epoch_Time:31644.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.279 lr:0.0003000 epoch_Time:31645.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.184 lr:0.0003000 epoch_Time:31645.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.347 lr:0.0003000 epoch_Time:31647.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:5.359 lr:0.0003000 epoch_Time:31648.0min:
[2024-01-02 17:46:41,889][model8_pretrain.py][INFO] Epoch:[0/2](1000/4588595) loss:4.929 lr:0.0003000 epoch_Time:31645.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:5.343 lr:0.0003000 epoch_Time:31333.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:5.150 lr:0.0003000 epoch_Time:31334.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:5.248 lr:0.0003000 epoch_Time:31334.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:4.960 lr:0.0003000 epoch_Time:31337.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:5.045 lr:0.0003000 epoch_Time:31336.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:5.098 lr:0.0003000 epoch_Time:31335.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:4.868 lr:0.0003000 epoch_Time:31335.0min:
[2024-01-02 17:47:18,817][model8_pretrain.py][INFO] Epoch:[0/2](1100/4588595) loss:5.231 lr:0.0003000 epoch_Time:31335.0min:
[2024-01-02 17:48:01,751][model8_pretrain.py][INFO] Epoch:[0/2](1200/4588595) loss:5.233 lr:0.0003000 epoch_Time:31457.0min:
[2024-01-02 17:48:01,751][model8_pretrain.py][INFO] Epoch:[0/2](1200/4588595) loss:4.877 lr:0.0003000 epoch_Time:31457.0min:
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