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InternVL

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[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] pld_enabled .................. False
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] pld_params ................... False
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] prescale_gradients ........... False
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] scheduler_name ............... None
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] scheduler_params ............. None
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] sparse_attention ............. None
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] sparse_gradients_enabled ..... False
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] steps_per_print .............. inf
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] train_batch_size ............. 8
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] train_micro_batch_size_per_gpu 2
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] use_node_local_storage ....... False
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] wall_clock_breakdown ......... True
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] world_size ................... 4
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] zero_allow_untested_optimizer False
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] zero_config .................. stage=1 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=1000000000 allgather_partitions=True allgather_bucket_size=1000000000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=None offload_optimizer=None sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=50,000,000 param_persistence_threshold=100,000 model_persistence_threshold=sys.maxsize max_live_parameters=1,000,000,000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] zero_enabled ................. True
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] zero_force_ds_cpu_optimizer .. True
[2024-10-22 17:17:09,677] [INFO] [config.py:964:print] zero_optimization_stage ...... 1
[2024-10-22 17:17:09,678] [INFO] [config.py:950:print_user_config] json = {
"zero_optimization": {
"stage": 1,
"allgather_partitions": true,
"allgather_bucket_size": 1.000000e+09,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1.000000e+09,
"contiguous_gradients": true
},
"fp16": {
"enabled": false,
"auto_cast": true,
"loss_scale": 0,
"initial_scale_power": 32,
"loss_scale_window": 1000,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": true
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": 4e-05,
"betas": [0.9, 0.999],
"eps": 1e-08,
"weight_decay": 0.01
}
},
"gradient_accumulation_steps": 1,
"gradient_clipping": 1.0,
"steps_per_print": inf,
"train_batch_size": 8,
"train_micro_batch_size_per_gpu": 2,
"wall_clock_breakdown": true
}
[INFO|trainer.py:1721] 2024-10-22 17:17:09,678 >> ***** Running training *****
[INFO|trainer.py:1722] 2024-10-22 17:17:09,678 >> Num examples = 9,681
[INFO|trainer.py:1723] 2024-10-22 17:17:09,678 >> Num Epochs = 4
[INFO|trainer.py:1724] 2024-10-22 17:17:09,678 >> Instantaneous batch size per device = 2
[INFO|trainer.py:1727] 2024-10-22 17:17:09,678 >> Total train batch size (w. parallel, distributed & accumulation) = 8
[INFO|trainer.py:1728] 2024-10-22 17:17:09,678 >> Gradient Accumulation steps = 1
[INFO|trainer.py:1729] 2024-10-22 17:17:09,678 >> Total optimization steps = 4,844
[INFO|trainer.py:1730] 2024-10-22 17:17:09,681 >> Number of trainable parameters = 15,728,640
0%| | 0/4844 [00:00<?, ?it/s][2024-10-22 17:17:11,360] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:11,369] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:11,399] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:11,407] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:14,386] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:14,459] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:14,472] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:14,511] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:17,276] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:17,450] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:17,499] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:17,518] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:20,146] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:20,313] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:20,434] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-10-22 17:17:20,520] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there apparent damage to the bus in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the pair of shoes on the left of the single shoe?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many animal species are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)