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srun: job 2085180 has been allocated resources
srun: Job 2085180 scheduled successfully!
Current QUOTA_TYPE is [spot], which means the job has occupied quota in SPOT_TOTAL under your partition.
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2023-12-04 21:29:09,765 INFO launch.py:557 in try_bind_numa -- Try bind numa failed! Package import error, if numa is not installed, please implement: pip install --upgrade py-libnuma, Ref: https://pypi.org/project/py-libnuma/
2023-12-04 21:29:10,043 INFO parallel_context.py:555 in set_device -- process rank 6 is bound to host:HOST-10-140-60-82 device: 6
2023-12-04 21:29:10,047 INFO parallel_context.py:555 in set_device -- process rank 0 is bound to host:HOST-10-140-60-82 device: 0
2023-12-04 21:29:10,050 INFO parallel_context.py:555 in set_device -- process rank 4 is bound to host:HOST-10-140-60-82 device: 4
2023-12-04 21:29:10,080 INFO parallel_context.py:555 in set_device -- process rank 1 is bound to host:HOST-10-140-60-82 device: 1
2023-12-04 21:29:10,081 INFO parallel_context.py:555 in set_device -- process rank 7 is bound to host:HOST-10-140-60-82 device: 7
2023-12-04 21:29:10,111 INFO parallel_context.py:555 in set_device -- process rank 2 is bound to host:HOST-10-140-60-82 device: 2
2023-12-04 21:29:10,111 INFO parallel_context.py:555 in set_device -- process rank 5 is bound to host:HOST-10-140-60-82 device: 5
2023-12-04 21:29:10,141 INFO parallel_context.py:555 in set_device -- process rank 3 is bound to host:HOST-10-140-60-82 device: 3
2023-12-04 21:29:13,419 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 0, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO launch.py:411 in launch -- Distributed environment is initialized, data parallel size: 8, pipeline parallel size: 1, tensor parallel size: 1
2023-12-04 21:29:13,419 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 6, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO launch.py:114 in args_sanity_check -- gradient_accumulation size will be setted to 1.
2023-12-04 21:29:13,420 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 3, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO launch.py:142 in args_sanity_check -- +++++++++++++++ Data Info +++++++++++++++
2023-12-04 21:29:13,420 INFO launch.py:143 in args_sanity_check -- seq_len: 2048
2023-12-04 21:29:13,420 INFO launch.py:144 in args_sanity_check -- micro_num: 1
2023-12-04 21:29:13,420 INFO launch.py:145 in args_sanity_check -- micro_bsz: 4
2023-12-04 21:29:13,420 INFO launch.py:146 in args_sanity_check -- packed_length: 8192
2023-12-04 21:29:13,420 INFO launch.py:147 in args_sanity_check -- pack_sample_into_one: False
2023-12-04 21:29:13,420 INFO launch.py:148 in args_sanity_check -- min_length: 50
2023-12-04 21:29:13,420 INFO launch.py:149 in args_sanity_check -- valid_micro_num: 4
2023-12-04 21:29:13,420 INFO launch.py:150 in args_sanity_check -- valid_every: 500
2023-12-04 21:29:13,420 INFO launch.py:203 in args_sanity_check -- +++++++++++++++ Ckpt Info +++++++++++++++
2023-12-04 21:29:13,420 INFO launch.py:204 in args_sanity_check -- is enable save ckpt: False
2023-12-04 21:29:13,420 INFO launch.py:205 in args_sanity_check -- save_ckpt_folder: None
2023-12-04 21:29:13,420 INFO launch.py:206 in args_sanity_check -- checkpoint_every: inf
2023-12-04 21:29:13,420 INFO launch.py:221 in args_sanity_check -- tensorboard_folder: None
2023-12-04 21:29:13,420 INFO launch.py:222 in args_sanity_check -- resume_tb_folder: None
2023-12-04 21:29:13,420 INFO launch.py:230 in args_sanity_check -- +++++++++++++++ Other Info +++++++++++++++
2023-12-04 21:29:13,420 INFO launch.py:231 in args_sanity_check -- cudnn.benchmark: False
2023-12-04 21:29:13,420 INFO launch.py:232 in args_sanity_check -- cudnn.deterministic: False
2023-12-04 21:29:13,420 INFO launch.py:233 in args_sanity_check -- clip_grad_norm: 1.0
2023-12-04 21:29:13,420 INFO launch.py:270 in args_sanity_check -- +++++++++++++++ Model Info +++++++++++++++
2023-12-04 21:29:13,420 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 4, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO launch.py:271 in args_sanity_check -- Model: {'checkpoint': 0, 'num_attention_heads': 32, 'embed_split_hidden': True, 'vocab_size': 103168, 'embed_grad_scale': 1, 'parallel_output': True, 'hidden_size': 4096, 'num_layers': 32, 'mlp_ratio': 2.6666666666666665, 'apply_post_layer_norm': False, 'dtype': torch.float16, 'norm_type': 'rmsnorm', 'layer_norm_epsilon': 1e-06, 'use_flash_attn': True, 'num_chunks': 1}
2023-12-04 21:29:13,420 INFO launch.py:273 in args_sanity_check -- +++++++++++++++ grad_scaler Info +++++++++++++++
2023-12-04 21:29:13,420 INFO launch.py:274 in args_sanity_check -- grad_scaler: {'fp16': {'initial_scale': 65536, 'min_scale': 1, 'growth_interval': 1000}, 'growth_factor': 2, 'backoff_factor': 0.5, 'max_scale': 16777216, 'hysteresis': 2}
2023-12-04 21:29:13,420 INFO launch.py:276 in args_sanity_check -- +++++++++++++++ hybrid_zero_optimizer Info +++++++++++++++
2023-12-04 21:29:13,420 INFO launch.py:277 in args_sanity_check -- hybrid_zero_optimizer: {'overlap_sync_grad': True, 'overlap_sync_param': False, 'reduce_bucket_size': 536870912, 'clip_grad_norm': 1.0}
2023-12-04 21:29:13,420 INFO launch.py:279 in args_sanity_check -- +++++++++++++++ adam Info +++++++++++++++
2023-12-04 21:29:13,420 INFO launch.py:280 in args_sanity_check -- adam: {'lr': 2e-06, 'adam_beta1': 0.9, 'adam_beta2': 0.999, 'adam_beta2_c': 0, 'adam_eps': 1e-08, 'weight_decay': 0.01}
2023-12-04 21:29:13,420 INFO launch.py:282 in args_sanity_check -- +++++++++++++++ beta2_scheduler Info +++++++++++++++
2023-12-04 21:29:13,421 INFO launch.py:283 in args_sanity_check -- beta2_scheduler: {'init_beta2': 0.95, 'c': 0, 'cur_iter': -1}
2023-12-04 21:29:13,421 INFO launch.py:340 in args_sanity_check -- overlap_sync_grad:True, overlap_sync_param:False
2023-12-04 21:29:13,420 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 2, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 5, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 7, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,420 INFO parallel_context.py:593 in set_seed -- initialized seed on rank 1, numpy: 1024, python random: 1024, ParallelMode.DATA: 1024, ParallelMode.DUMMY: 1024, ParallelMode.TENSOR: 1024,the default parallel seed is ParallelMode.DATA.
2023-12-04 21:29:13,471 INFO modeling_internlm.py:459 in _build_generic_model_1d -- The layer sharding is [[(0, 32)]].
2023-12-04 21:29:19,251 INFO packed_dataset.py:378 in get_packed_dataset_without_short_length -- Reading /mnt/petrelfs/share_data/lijiaxing/wikitext-2-tokenize/train...
0it [00:00, ?it/s]
2023-12-04 21:29:19,256 INFO packed_dataset.py:378 in get_packed_dataset_without_short_length -- Reading /mnt/petrelfs/share_data/lijiaxing/wikitext-2-tokenize/train/en...
0%| | 0/2 [00:00<?, ?it/s]
2023-12-04 21:29:19,279 INFO packed_dataset.py:415 in get_packed_dataset_without_short_length -- Find `1` datasets, 295 samples, delete `9473` because of short length
2023-12-04 21:29:19,282 INFO training_internlm.py:334 in get_validation_data_loader -- load validation dataset en with valid batch size 16 and samples 11.
2023-12-04 21:29:19,340 INFO hybrid_zero_optim.py:268 in _partition_param_list -- Number of elements on ranks: [907415552, 907411456, 910163968, 910163968, 921698304, 921698304, 921698304, 921698304], rank:0
2023-12-04 21:29:35,932 INFO hybrid_zero_optim.py:268 in _partition_param_list -- Number of elements on ranks: [0, 0, 0, 0, 0, 0, 0, 0], rank:0
2023-12-04 21:29:35,937 WARNING model_checkpoint.py:786 in __init__ -- no set stop_file_path, quit_signal_handler is disable
2023-12-04 21:29:35,937 INFO model_checkpoint.py:208 in try_load_internlm_ckpt -- Try load_ckpt_folder: /mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt
2023-12-04 21:29:35,937 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:35,941 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt/model_tp0_pp0.pt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:53,062 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt/context.pt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:53,063 INFO model_checkpoint.py:664 in load_context -- reload train_state:{
"batch_count": 1,
"inf_nan_skip_batches": 0,
"num_consumed_samples_in_epoch": 0,
"num_consumed_tokens": 0,
"step_count": 0
}
2023-12-04 21:29:53,063 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:53,071 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt/optimizer_tp0_pp0_zo0.pt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:56,663 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt/gpus-8_pp-0_tp-0_zo-0.pt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:56,699 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt/schedulder.pt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:56,704 INFO model_checkpoint.py:696 in load_scheduler -- reload load_scheduler:{
"_get_lr_called_within_step": false,
"_init_steps": 0,
"_last_lr": [
6.666666666666667e-08,
6.666666666666667e-08
],
"_step_count": 1,
"_warmup_steps": 30,
"after_scheduler_dict": {
"T_max": 970,
"_get_lr_called_within_step": false,
"_last_lr": [
2e-06,
2e-06
],
"_step_count": 1,
"base_lrs": [
2e-06,
2e-06
],
"eta_min": 1e-05,
"last_epoch": 0,
"verbose": false
},
"after_scheduler_type": "CosineAnnealingLR",
"base_lrs": [
2e-06,
2e-06
],
"finished": false,
"last_epoch": 1,
"verbose": false,
"warmup_epochs": 30
}
2023-12-04 21:29:56,704 WARNING storage_manager.py:285 in try_get_storage_backend -- path: '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt/sampler.pt' not start with backend prefix, guess it is the backend of local.
2023-12-04 21:29:56,725 INFO model_checkpoint.py:656 in load_sampler -- reload sampler_states:{'batch_size': 1, 'raw_rampup_batch_size': '1 1 1', 'epoch': 0, 'seed': 1024, 'data_world_size': 8, 'num_consumed_samples_in_epoch': 0, 'batch_count': 0}
2023-12-04 21:29:56,725 WARNING model_checkpoint.py:256 in try_load_internlm_ckpt -- CheckpointManager has no 'data_state_dict', skip reload data_state_dict checkpoint!
2023-12-04 21:29:56,725 INFO model_checkpoint.py:1012 in try_resume_training -- load_ckpt_info : {'path': '/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt', 'content': {'optimizer', 'scheduler', 'sampler', 'model'}., 'ckpt_type': <CheckpointLoadType.INTERNLM: 'internlm'>}
2023-12-04 21:29:56,725 INFO model_checkpoint.py:1013 in try_resume_training -- ===========Resume training from `/mnt/petrelfs/share/lijiaxing/Huawei_init_ckpt` Dec04_21-29-13 on host:HOST-10-140-60-82===========
2023-12-04 21:29:56,725 INFO model_checkpoint.py:1018 in try_resume_training -- ===========Load contents are: model, optimizer, scheduler, sampler,
2023-12-04T21:29:56.726+08:00 INFO [writer.py, line 57, in init_tb_writer] - pid=13773 : Login tensorboard logs to: hw_7B_wiki/Dec04_21-29-13/tensorboards
fwd hidden_states embedding output: tensor([[[ 0.0032, -0.0058, 0.0047, ..., 0.0020, -0.0094, -0.0037],
[ 0.0022, 0.0049, -0.0017, ..., 0.0052, 0.0074, 0.0013],
[ 0.0035, 0.0006, -0.0002, ..., 0.0065, -0.0012, 0.0007],
...,
[-0.0060, 0.0068, 0.0008, ..., -0.0005, 0.0059, -0.0005],
[-0.0060, 0.0068, 0.0008, ..., -0.0005, 0.0059, -0.0005],
[-0.0060, 0.0068, 0.0008, ..., -0.0005, 0.0059, -0.0005]]],
device='cuda:0', dtype=torch.float16), shape:torch.Size([1, 8192, 4096])
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
fwd block 0 dropout1+norm1: tensor([[ 0.5889, -1.0645, 0.8765, ..., 0.3772, -1.7451, -0.6895],
[ 0.4214, 0.9590, -0.3362, ..., 1.0176, 1.4424, 0.2605],
[ 0.6533, 0.1079, -0.0458, ..., 1.2100, -0.2299, 0.1229],
...,
[-1.1475, 1.2939, 0.1488, ..., -0.0983, 1.1221, -0.0894],
[-1.1475, 1.2939, 0.1488, ..., -0.0983, 1.1221, -0.0894],
[-1.1475, 1.2939, 0.1488, ..., -0.0983, 1.1221, -0.0894]],
device='cuda:0', dtype=torch.float16), shape:torch.Size([8192, 4096])
fwd block 0 qkv: tensor([[ 0.0233, 0.1367, 0.4971, ..., 0.9688, -0.5342, 0.0586],
[-0.6387, -0.0337, -0.0786, ..., -0.6523, -0.4207, -0.0371],
[-0.2412, -0.4653, -0.1932, ..., -0.1326, -0.3643, 0.1268],
...,
[ 0.4270, 0.5815, -0.5161, ..., -0.0453, 0.0557, 0.1555],
[ 0.4270, 0.5815, -0.5161, ..., -0.0453, 0.0557, 0.1555],
[ 0.4270, 0.5815, -0.5161, ..., -0.0453, 0.0557, 0.1555]],
device='cuda:0', dtype=torch.float16), shape: torch.Size([8192, 12288])
fwd block 0 rotary_emb: tensor([[[[ 2.3315e-02, 1.3672e-01, 4.9707e-01, ..., -7.3340e-01,
-1.3013e-01, -6.0596e-01],
[ 3.7744e-01, -2.0618e-01, 3.7964e-02, ..., -1.9128e-01,
-1.0815e-01, -2.7740e-02],
[-3.4668e-01, -5.4248e-01, 9.3994e-02, ..., 3.0591e-01,
2.3315e-01, 5.7764e-01],
...,
[ 7.6318e-01, 3.5132e-01, -2.1387e-01, ..., 6.4502e-01,
4.2847e-01, 3.4448e-01],
[-3.4888e-01, 6.0352e-01, 6.1310e-02, ..., -2.1228e-01,
3.9478e-01, -2.7393e-01],
[-1.3904e-01, -1.0815e-01, -3.6206e-01, ..., -6.6040e-02,
3.6694e-01, 5.1178e-02]],
[[ 4.3365e-02, 4.4629e-01, -7.2559e-01, ..., 1.4905e-01,
4.1211e-01, 2.5098e-01],
[ 9.1400e-03, 1.3086e-01, 4.7998e-01, ..., -4.4702e-01,
-5.9375e-01, 1.8774e-01],
[ 1.2732e-01, 4.9976e-01, 1.2158e-01, ..., 8.2031e-02,
3.7292e-02, -5.3271e-01],
...,
[-3.2886e-01, 2.0435e-01, -1.6479e-01, ..., -1.2231e-01,
-3.4473e-01, -3.7427e-01],
[-1.8689e-01, -3.5645e-01, -2.5513e-01, ..., -3.3594e-01,
-5.3418e-01, 1.0291e-01],
[ 1.1407e-01, 2.1436e-01, -2.9922e-02, ..., 1.4563e-01,
-1.1530e-01, -1.8875e-02]],
[[-2.4438e-01, -1.0455e-01, 6.7432e-01, ..., -1.6272e-01,
1.6626e-01, -4.7729e-01],
[ 9.6680e-02, -1.6663e-01, -1.8176e-01, ..., 5.1660e-01,
-1.5051e-01, -3.8483e-02],
[-8.3545e-01, -4.9805e-02, 4.9048e-01, ..., 2.1558e-01,
-4.5435e-01, 2.7246e-01],
...,
[ 6.4636e-02, 5.4932e-01, -1.3489e-01, ..., -3.2666e-01,
8.6914e-01, -4.6851e-01],
[ 5.0146e-01, -1.7175e-01, 1.2947e-02, ..., 2.4194e-01,
1.0381e+00, -9.9268e-01],
[-2.0740e-01, -1.8738e-01, -4.6143e-02, ..., 9.6875e-01,
-5.3418e-01, 5.8563e-02]]],
[[[-9.1260e-01, -4.0430e-01, -6.7932e-02, ..., 4.1284e-01,
-2.3608e-01, 1.0321e-01],
[ 3.5376e-01, -5.5664e-02, -1.7358e-01, ..., 3.3252e-01,
-7.1239e-04, -4.6338e-01],
[-5.9229e-01, -6.0156e-01, 3.7964e-01, ..., -1.9788e-01,
2.1863e-01, 4.4922e-01],
...,
[ 9.6436e-02, 3.3643e-01, 2.5244e-01, ..., 7.6074e-01,
4.8242e-01, -2.0435e-01],
[ 2.6172e-01, 2.7808e-01, -6.1768e-01, ..., 2.4500e-01,
2.8052e-01, 1.1230e+00],
[-7.6562e-01, -5.5762e-01, -1.5454e-01, ..., -1.6394e-01,
-2.9810e-01, 5.3174e-01]],
[[ 2.2888e-02, 5.1855e-01, -4.6216e-01, ..., 2.9297e-01,
2.6367e-01, 1.6821e-01],
[-1.7944e-01, -1.8835e-01, 1.0059e-01, ..., -2.5024e-01,
-1.4551e-01, -4.7583e-01],
[-5.1208e-02, -7.2705e-01, 5.0293e-01, ..., 8.8135e-02,
-4.2505e-01, 1.8628e-01],
...,
[-4.6948e-01, -2.5317e-01, -4.1577e-01, ..., -5.4688e-01,
3.1348e-01, -2.8418e-01],
[-4.9934e-03, 4.9463e-01, -2.9980e-01, ..., -3.9111e-01,
2.1988e-02, 1.1804e-01],
[ 2.1960e-01, -3.7622e-01, 2.9272e-01, ..., 1.0098e+00,
1.6089e-01, 4.6338e-01]],
[[ 3.5858e-02, 6.5771e-01, 3.9966e-01, ..., -8.8574e-01,
3.0930e-02, -9.5398e-02],
[ 5.8545e-01, 4.2236e-01, -2.6221e-01, ..., -2.7856e-01,
-1.6418e-01, -3.5571e-01],
[ 1.5295e-01, 1.5601e-01, 1.3867e-01, ..., -6.7285e-01,
-2.8394e-01, -1.1748e+00],
...,
[ 1.5820e-01, 1.0748e-01, 5.3174e-01, ..., -1.1115e-01,
-4.9268e-01, -6.9153e-02],
[ 7.7246e-01, 1.6174e-01, -2.7759e-01, ..., 5.1660e-01,
8.2275e-02, -8.0273e-01],
[-2.0276e-01, 6.0400e-01, -3.4760e-02, ..., -6.5234e-01,
-4.2065e-01, -3.7079e-02]]],
[[[ 1.8219e-02, 8.2336e-02, -5.6543e-01, ..., 3.6133e-01,
-5.1660e-01, -4.4952e-02],
[ 6.0889e-01, 4.1534e-02, 3.8086e-01, ..., -1.1401e-01,
8.3057e-01, -2.1716e-01],
[-1.2891e-01, 2.0300e-01, -5.6396e-02, ..., 2.1265e-01,
-1.8823e-01, -5.8252e-01],
...,
[-6.6455e-01, 3.0591e-01, 1.7224e-01, ..., -3.7939e-01,
6.4111e-01, -3.6523e-01],
[ 4.3579e-01, -7.4121e-01, 6.0150e-02, ..., 2.2205e-01,
8.7967e-03, 2.7817e-02],
[-2.2095e-02, 3.7524e-01, -6.2891e-01, ..., 1.4214e-02,
4.2676e-01, 8.1482e-03]],
[[ 1.0944e-01, 3.4277e-01, -4.1382e-01, ..., -3.4448e-01,
-2.5195e-01, -7.0264e-01],
[-1.8478e-02, -5.6122e-02, 3.1152e-01, ..., -2.6660e-01,
-5.4736e-01, -5.4102e-01],
[-8.6792e-02, -2.8784e-01, 2.7759e-01, ..., 3.2739e-01,
-1.1694e-01, 3.1152e-01],
...,
[-4.0405e-01, -5.0293e-01, -6.2939e-01, ..., 1.3147e-01,
-1.6833e-01, -3.3472e-01],
[-5.4102e-01, 3.8062e-01, -2.5952e-01, ..., 4.1553e-01,
-7.0374e-02, -8.4717e-02],
[ 5.2795e-02, -9.5276e-02, 5.0537e-02, ..., -3.4741e-01,
-2.9443e-01, 4.2017e-01]],
[[-3.2202e-01, 2.3694e-01, -1.3379e-01, ..., -2.9907e-01,
-5.1904e-01, -6.5674e-01],
[-1.0273e+00, -3.4521e-01, 2.4268e-01, ..., 3.4961e-01,
-3.6865e-01, 1.6492e-01],
[-3.7402e-01, 1.7322e-01, 3.9014e-01, ..., 1.9128e-01,
-7.3242e-01, -3.4814e-01],
...,
[-3.6182e-01, 1.7563e-02, -5.7666e-01, ..., -1.5759e-01,
4.2261e-01, 2.8595e-02],
[-1.3329e-02, -2.3425e-01, 3.9844e-01, ..., 1.9519e-01,
-1.2903e-01, 6.5674e-02],
[ 2.6465e-01, -1.8774e-01, -2.8046e-02, ..., -1.3257e-01,
-3.6426e-01, 1.2683e-01]]],
...,
[[[ 1.5857e-01, 6.2305e-01, 4.9268e-01, ..., -1.2830e-01,
-3.4637e-02, -1.6296e-01],
[ 1.6281e-02, 2.2083e-01, 3.3081e-01, ..., -5.8990e-02,
-6.9824e-02, 6.0547e-01],
[ 1.3770e-01, 6.6309e-01, -4.0234e-01, ..., 2.1378e-02,
1.6931e-01, 1.4771e-01],
...,
[ 4.8999e-01, -8.6914e-02, -2.4255e-01, ..., 1.0919e-01,
1.9958e-01, 2.6538e-01],
[-1.1060e-01, -5.9668e-01, 1.7126e-01, ..., -3.0444e-01,
-2.2046e-01, -6.6406e-02],
[ 3.5498e-01, -4.1089e-01, -3.9502e-01, ..., 2.1582e-01,
-3.3521e-01, -2.7664e-02]],
[[-2.2632e-01, 4.2798e-01, -1.2231e-01, ..., 1.3989e-01,
2.1191e-01, 2.5464e-01],
[ 2.5732e-01, -1.1481e-01, -1.4685e-01, ..., -7.9150e-01,
5.3894e-02, -4.5410e-01],
[ 1.9751e-01, 1.1151e-01, 1.3878e-02, ..., 5.6396e-01,
-5.0537e-01, 7.4890e-02],
...,
[-6.9458e-02, 9.2468e-02, 6.1816e-01, ..., 7.2632e-02,
6.3281e-01, -8.3557e-02],
[ 8.4521e-01, 7.1533e-02, 1.4087e-01, ..., -1.5686e-01,
-3.5693e-01, 2.9956e-01],
[ 5.8545e-01, 2.6758e-01, 1.4717e-02, ..., 6.6452e-03,
-3.9307e-02, 3.0151e-01]],
[[-1.6223e-01, 7.2693e-02, -5.1514e-01, ..., 2.1289e-01,
-1.4880e-01, 2.5220e-01],
[ 3.7036e-01, -1.2732e-01, 2.5391e-01, ..., 3.2324e-01,
-5.5939e-02, 7.5293e-01],
[ 3.8330e-01, 9.2676e-01, -2.7173e-01, ..., -1.5823e-02,
1.0553e-01, -5.1221e-01],
...,
[-3.7549e-01, -4.5337e-01, -2.2729e-01, ..., -4.9219e-01,
-4.7974e-01, -3.4204e-01],
[ 4.5581e-01, -3.8501e-01, -9.6497e-02, ..., 2.0401e-02,
-8.5266e-02, 3.2739e-01],
[-2.0386e-01, 2.3010e-01, -5.5713e-01, ..., -4.5288e-02,
5.5725e-02, 1.5552e-01]]],
[[[ 4.4873e-01, 1.8652e-01, 2.2693e-01, ..., -1.2842e-01,
-3.4637e-02, -1.6333e-01],
[-2.2864e-01, 5.4785e-01, 1.3708e-01, ..., -5.8960e-02,
-6.9824e-02, 6.0547e-01],
[ 1.4038e-01, 4.8389e-01, -2.7002e-01, ..., 2.1408e-02,
1.6931e-01, 1.4758e-01],
...,
[-1.7529e-01, 3.9575e-01, -1.2128e-01, ..., 1.0913e-01,
1.9958e-01, 2.6562e-01],
[-2.5589e-02, -1.7371e-01, -1.2610e-01, ..., -3.0469e-01,
-2.2046e-01, -6.6895e-02],
[ 8.1726e-02, -5.2637e-01, -5.1172e-01, ..., 2.1594e-01,
-3.3521e-01, -2.7802e-02]],
[[-2.7878e-02, -3.1006e-01, 4.6600e-02, ..., 1.3989e-01,
2.1191e-01, 2.5488e-01],
[ 2.5513e-01, 2.8711e-01, 7.0740e-02, ..., -7.9150e-01,
5.3894e-02, -4.5459e-01],
[ 4.8035e-02, 3.6328e-01, -1.0815e-01, ..., 5.6348e-01,
-5.0537e-01, 7.5317e-02],
...,
[-2.9907e-01, -5.0732e-01, 6.7480e-01, ..., 7.2693e-02,
6.3281e-01, -8.3618e-02],
[ 8.1934e-01, -2.1838e-01, 5.5078e-01, ..., -1.5686e-01,
-3.5693e-01, 2.9907e-01],
[ 2.3840e-01, 5.1074e-01, -4.0796e-01, ..., 6.6452e-03,
-3.9307e-02, 3.0200e-01]],
[[-1.6223e-01, 7.2693e-02, -5.1514e-01, ..., 2.1289e-01,
-1.4880e-01, 2.5220e-01],
[ 3.7036e-01, -1.2732e-01, 2.5391e-01, ..., 3.2324e-01,
-5.5939e-02, 7.5293e-01],
[ 3.8330e-01, 9.2676e-01, -2.7173e-01, ..., -1.5823e-02,
1.0553e-01, -5.1221e-01],
...,
[-3.7549e-01, -4.5337e-01, -2.2729e-01, ..., -4.9219e-01,
-4.7974e-01, -3.4204e-01],
[ 4.5581e-01, -3.8501e-01, -9.6497e-02, ..., 2.0401e-02,
-8.5266e-02, 3.2739e-01],
[-2.0386e-01, 2.3010e-01, -5.5713e-01, ..., -4.5288e-02,
5.5725e-02, 1.5552e-01]]],
[[[ 3.2617e-01, -3.8110e-01, -1.6077e-01, ..., -1.2854e-01,
-3.4637e-02, -1.6333e-01],
[-2.6343e-01, 4.8877e-01, -1.3025e-01, ..., -5.8899e-02,
-6.9824e-02, 6.0547e-01],
[ 1.3931e-02, -3.6224e-02, 7.0648e-03, ..., 2.1423e-02,
1.6931e-01, 1.4758e-01],
...,
[-6.7969e-01, 5.9961e-01, 6.5063e-02, ..., 1.0907e-01,
1.9958e-01, 2.6562e-01],
[ 8.3008e-02, 3.7158e-01, -3.5596e-01, ..., -3.0469e-01,
-2.2046e-01, -6.6895e-02],
[-2.6685e-01, -2.7124e-01, -3.5400e-01, ..., 2.1594e-01,
-3.3521e-01, -2.7802e-02]],
[[ 1.9629e-01, -8.2959e-01, 1.9055e-01, ..., 1.3989e-01,
2.1191e-01, 2.5488e-01],
[ 1.8204e-02, 4.8657e-01, 2.5049e-01, ..., -7.9150e-01,
5.3894e-02, -4.5459e-01],
[-1.4563e-01, 3.5938e-01, -1.7224e-01, ..., 5.6348e-01,
-5.0537e-01, 7.5317e-02],
...,
[-2.5391e-01, -7.5000e-01, 3.6963e-01, ..., 7.2815e-02,
6.3281e-01, -8.3618e-02],
[ 4.0161e-02, -3.5449e-01, 6.6504e-01, ..., -1.5686e-01,
-3.5693e-01, 2.9907e-01],
[-3.2788e-01, 3.9404e-01, -6.1182e-01, ..., 6.6452e-03,
-3.9307e-02, 3.0200e-01]],
[[-1.6223e-01, 7.2693e-02, -5.1514e-01, ..., 2.1289e-01,
-1.4880e-01, 2.5220e-01],
[ 3.7036e-01, -1.2732e-01, 2.5391e-01, ..., 3.2324e-01,
-5.5939e-02, 7.5293e-01],
[ 3.8330e-01, 9.2676e-01, -2.7173e-01, ..., -1.5823e-02,
1.0553e-01, -5.1221e-01],
...,
[-3.7549e-01, -4.5337e-01, -2.2729e-01, ..., -4.9219e-01,
-4.7974e-01, -3.4204e-01],
[ 4.5581e-01, -3.8501e-01, -9.6497e-02, ..., 2.0401e-02,
-8.5266e-02, 3.2739e-01],
[-2.0386e-01, 2.3010e-01, -5.5713e-01, ..., -4.5288e-02,
5.5725e-02, 1.5552e-01]]]], device='cuda:0', dtype=torch.float16), shape: torch.Size([8192, 3, 32, 128])
fwd block 0 inner_attn: tensor([[[-0.2444, -0.1046, 0.6743, ..., -0.1627, 0.1663, -0.4773],
[ 0.0967, -0.1666, -0.1818, ..., 0.5166, -0.1505, -0.0385],
[-0.8354, -0.0498, 0.4905, ..., 0.2156, -0.4543, 0.2725],
...,
[ 0.0646, 0.5493, -0.1349, ..., -0.3267, 0.8691, -0.4685],
[ 0.5015, -0.1718, 0.0129, ..., 0.2419, 1.0381, -0.9927],
[-0.2074, -0.1874, -0.0461, ..., 0.9688, -0.5342, 0.0586]],
[[-0.0975, 0.2952, 0.5303, ..., -0.5420, 0.0953, -0.2771],
[ 0.3682, 0.1605, -0.2264, ..., 0.0749, -0.1581, -0.2147],
[-0.3630, 0.0486, 0.3223, ..., -0.2091, -0.3728, -0.4192],
...,
[ 0.1113, 0.3291, 0.1971, ..., -0.2192, 0.1907, -0.2695],
[ 0.6147, -0.0324, -0.1085, ..., 0.3567, 0.6387, -0.9136],
[-0.2048, 0.2603, -0.0397, ..., 0.0516, -0.4700, 0.0045]],
[[-0.1688, 0.2776, 0.3176, ..., -0.4653, -0.1013, -0.3977],
[-0.1248, -0.0288, -0.0629, ..., 0.1901, -0.2302, -0.0758],
[-0.3474, 0.0880, 0.3350, ..., -0.1119, -0.4719, -0.4265],
...,
[-0.0091, 0.2426, 0.0078, ..., -0.2003, 0.2305, -0.1870],
[ 0.4124, -0.0836, 0.0508, ..., 0.3162, 0.3188, -0.5635],
[-0.0726, 0.1346, -0.0364, ..., -0.0011, -0.4402, 0.0388]],
...,
[[-0.1622, 0.0727, -0.5151, ..., 0.2129, -0.1488, 0.2522],
[ 0.3704, -0.1273, 0.2539, ..., 0.3232, -0.0559, 0.7529],
[ 0.3833, 0.9268, -0.2717, ..., -0.0158, 0.1055, -0.5122],
...,
[-0.3755, -0.4534, -0.2273, ..., -0.4922, -0.4797, -0.3420],
[ 0.4558, -0.3850, -0.0965, ..., 0.0204, -0.0853, 0.3274],
[-0.2039, 0.2301, -0.5571, ..., -0.0453, 0.0557, 0.1555]],
[[-0.1622, 0.0727, -0.5151, ..., 0.2129, -0.1488, 0.2522],
[ 0.3704, -0.1273, 0.2539, ..., 0.3232, -0.0559, 0.7529],
[ 0.3833, 0.9268, -0.2717, ..., -0.0158, 0.1055, -0.5122],
...,
[-0.3755, -0.4534, -0.2273, ..., -0.4922, -0.4797, -0.3420],
[ 0.4558, -0.3850, -0.0965, ..., 0.0204, -0.0853, 0.3274],
[-0.2039, 0.2301, -0.5571, ..., -0.0453, 0.0557, 0.1555]],
[[-0.1622, 0.0727, -0.5151, ..., 0.2129, -0.1488, 0.2522],
[ 0.3704, -0.1273, 0.2539, ..., 0.3232, -0.0559, 0.7529],
[ 0.3833, 0.9268, -0.2717, ..., -0.0158, 0.1055, -0.5122],
...,
[-0.3755, -0.4534, -0.2273, ..., -0.4922, -0.4797, -0.3420],
[ 0.4558, -0.3850, -0.0965, ..., 0.0204, -0.0853, 0.3274],
[-0.2039, 0.2301, -0.5571, ..., -0.0453, 0.0557, 0.1555]]],
device='cuda:0', dtype=torch.float16), shape: torch.Size([8192, 32, 128])
fwd block 0 out_proj: tensor([[-0.0449, 0.2668, 0.0318, ..., -0.1669, 0.0184, 0.0685],
[-0.0646, 0.1694, 0.0032, ..., -0.0605, 0.0173, 0.0800],
[-0.0704, 0.1581, 0.0136, ..., -0.0740, 0.0375, 0.0423],
...,
[-0.0076, 0.1115, -0.1059, ..., 0.0121, -0.0580, 0.1011],
[-0.0076, 0.1115, -0.1059, ..., 0.0121, -0.0580, 0.1011],
[-0.0076, 0.1115, -0.1059, ..., 0.0121, -0.0580, 0.1011]],
device='cuda:0', dtype=torch.float16), shape: torch.Size([8192, 4096])
fwd block 0 dropout2+norm2: tensor([[-0.4109, 2.5684, 0.3599, ..., -1.6221, 0.0884, 0.6382],
[-0.8530, 2.3789, 0.0201, ..., -0.7539, 0.3372, 1.1104],
[-1.1201, 2.6582, 0.2231, ..., -1.1309, 0.6074, 0.7192],
...,
[-0.1346, 1.1719, -1.0400, ..., 0.1146, -0.5156, 0.9966],
[-0.1346, 1.1719, -1.0400, ..., 0.1146, -0.5156, 0.9966],
[-0.1346, 1.1719, -1.0400, ..., 0.1146, -0.5156, 0.9966]],
device='cuda:0', dtype=torch.float16), shape:torch.Size([8192, 4096])
fwd block 0 mlp: tensor([[-0.0121, -0.0072, -0.0016, ..., -0.0024, 0.0053, 0.0030],
[-0.0165, -0.0130, 0.0150, ..., -0.0087, -0.0036, -0.0043],
[-0.0029, -0.0028, 0.0016, ..., -0.0005, -0.0079, -0.0018],
...,
[-0.0006, -0.0102, -0.0053, ..., -0.0108, -0.0120, 0.0058],
[-0.0006, -0.0102, -0.0053, ..., -0.0108, -0.0120, 0.0058],
[-0.0006, -0.0102, -0.0053, ..., -0.0108, -0.0120, 0.0058]],
device='cuda:0', dtype=torch.float16), shape:torch.Size([8192, 4096])
fwd norm: tensor([[-1.7852, 0.8657, 0.0756, ..., -1.1943, -0.4158, 0.7007],
[-2.0840, 0.4084, 0.6230, ..., -0.8672, 0.0288, 0.6421],
[-1.8848, 0.4941, 1.0088, ..., -0.6123, 0.3828, 0.2512],
...,
[ 0.3508, 0.8120, 0.2200, ..., 1.3623, 1.0596, -0.2998],
[ 0.3508, 0.8120, 0.2200, ..., 1.3623, 1.0596, -0.2998],
[ 0.3508, 0.8120, 0.2200, ..., 1.3623, 1.0596, -0.2998]],
device='cuda:0', dtype=torch.float16), shapetorch.Size([8192, 4096])
fwd head: tensor([[ 0.1843, 0.4094, -0.2091, ..., 0.0714, 0.5176, 0.2795],
[ 0.1895, 0.5454, -0.5142, ..., 0.1703, 0.4844, 0.3513],
[ 0.2000, 0.5674, -0.6069, ..., 0.2366, 0.4680, 0.2693],
...,
[-0.0163, -0.4883, 0.5146, ..., -0.0645, -0.2766, 0.1595],
[-0.0163, -0.4883, 0.5146, ..., -0.0645, -0.2766, 0.1595],
[-0.0163, -0.4883, 0.5146, ..., -0.0645, -0.2766, 0.1595]],
device='cuda:0', dtype=torch.float16), shapetorch.Size([8192, 103168])
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
/mnt/petrelfs/share_data/llm_env/miniconda3-py39_4/envs/llm-flash2.0/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
|