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+ unset _mlre _mlIFS _mlshdbg | |
+ '[' 0 = 1 ']' | |
+ '[' -n x ']' | |
+ _mlIFS=' | |
' | |
+ IFS=' ' | |
+ '[' -n '' ']' | |
++ /usr/bin/tclsh /usr/lib/x86_64-linux-gnu/modulecmd.tcl bash autoinit | |
+ eval 'module()' '{ | |
' unset _mlre _mlIFS '_mlshdbg; | |
' if '[' '"${MODULES_SILENT_SHELL_DEBUG:-0}"' = ''\''1'\''' '];' 'then | |
' case '"$-"' 'in | |
' '*v*x*)' set '+vx;' '_mlshdbg='\''vx'\''' ';; | |
' '*v*)' set '+v;' '_mlshdbg='\''v'\''' ';; | |
' '*x*)' set '+x;' '_mlshdbg='\''x'\''' ';; | |
' '*)' '_mlshdbg='\'''\''' ';; | |
' 'esac; | |
' 'fi; | |
' if '[' -n '"${IFS+x}"' '];' 'then | |
' '_mlIFS=$IFS; | |
' 'fi; | |
' 'IFS='\''' ''\''; | |
' for _mlv in '${MODULES_RUN_QUARANTINE:-};' 'do | |
' if '[' '"${_mlv}"' = '"${_mlv##*[!A-Za-z0-9_]}"' -a '"${_mlv}"' = '"${_mlv#[0-9]}"' '];' 'then | |
' if '[' -n '"`eval' ''\''echo' '${'\''$_mlv'\''+x}'\''`"' '];' 'then | |
' '_mlre="${_mlre:-}${_mlv}_modquar='\''`eval' ''\''echo' '${'\''$_mlv'\''}'\''`'\''' '"; | |
' 'fi; | |
' '_mlrv="MODULES_RUNENV_${_mlv}"; | |
' '_mlre="${_mlre:-}${_mlv}='\''`eval' ''\''echo' '${'\''$_mlrv'\'':-}'\''`'\''' '"; | |
' 'fi; | |
' 'done; | |
' if '[' -n '"${_mlre:-}"' '];' 'then | |
' '_mlre="eval' '${_mlre}"; | |
' 'fi; | |
' eval '`${_mlre:-}/usr/bin/tclsh' /usr/lib/x86_64-linux-gnu/modulecmd.tcl bash '$*`; | |
' '_mlstatus=$?; | |
' if '[' -n '"${_mlIFS+x}"' '];' 'then | |
' 'IFS=$_mlIFS; | |
' 'else | |
' unset 'IFS; | |
' 'fi; | |
' if '[' -n '"${_mlshdbg:-}"' '];' 'then | |
' set '-$_mlshdbg; | |
' 'fi; | |
' unset _mlre _mlv _mlrv _mlIFS '_mlshdbg; | |
' return '$_mlstatus; | |
}; | |
MODULES_CMD=/usr/lib/x86_64-linux-gnu/modulecmd.tcl;' export 'MODULES_CMD; | |
MODULESHOME=/usr/share/modules;' export 'MODULESHOME; | |
test' '0;' | |
++ MODULES_CMD=/usr/lib/x86_64-linux-gnu/modulecmd.tcl | |
++ export MODULES_CMD | |
++ MODULESHOME=/usr/share/modules | |
++ export MODULESHOME | |
++ test 0 | |
+ '[' -n x ']' | |
+ IFS=' | |
' | |
+ unset _mlIFS | |
+ unset _mlre _mlv _mlrv | |
+ '[' 0 = 1 ']' | |
+ '[' -t 1 ']' | |
+ export -f module | |
+ export -f switchml | |
+ ENV=/usr/share/modules/init/profile.sh | |
+ export ENV | |
+ BASH_ENV=/usr/share/modules/init/bash | |
+ export BASH_ENV | |
+ '[' 4 -ge 3 ']' | |
+ [[ hxB =~ i ]] | |
+ [[ ! :/home/ubuntu/.local/bin:/usr/local/cuda/bin:/usr/local/bin:/opt/aws/bin:/home/ubuntu/.dl_binaries/bin:/usr/local/mpi/bin:/opt/aws/neuron/bin:/opt/amazon/openmpi/bin/:/opt/amazon/efa/bin/:/home/ubuntu/.local/bin:/usr/local/cuda/bin:/usr/local/bin:/opt/aws/bin:/home/ubuntu/.dl_binaries/bin:/usr/local/mpi/bin:/opt/aws/neuron/bin:/opt/amazon/openmpi/bin/:/opt/amazon/efa/bin/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin: =~ :/usr/bin: ]] | |
++ manpath | |
+ manpath=/opt/aws/neuron/share/man:/opt/aws/neuron/share/man:/usr/local/man:/usr/local/share/man:/opt/aws/neuron/share/man:/usr/share/man | |
+ [[ ! :/opt/aws/neuron/share/man:/opt/aws/neuron/share/man:/usr/local/man:/usr/local/share/man:/opt/aws/neuron/share/man:/usr/share/man: =~ :/usr/share/man: ]] | |
+ '[' -n '' ']' | |
+ export SQUAD_DIR=/home/ubuntu/squad | |
+ SQUAD_DIR=/home/ubuntu/squad | |
+ python3 -m torch.distributed.launch --nproc_per_node=4 ./examples/question-answering/run_squad.py --model_type albert --model_name_or_path albert-base-v2 --do_train --do_eval --version_2_with_negative --train_file /home/ubuntu/squad/train-v2.0.json --predict_file /home/ubuntu/squad/dev-v2.0.json --learning_rate 3e-5 --weight_decay 0.01 --max_grad_norm 1.0 --num_train_epochs 3 --warmup_ratio 0.1 --max_seq_length 512 --doc_stride 128 --output_dir ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/ --per_gpu_eval_batch_size=24 --fp16 --per_gpu_train_batch_size=12 --gradient_accumulation_steps=1 --overwrite_cache --threads 8 --overwrite_output_dir | |
11/25/2020 17:09:34 - WARNING - __main__ - Process rank: 3, device: cuda:3, n_gpu: 1, distributed training: True, 16-bits training: True | |
11/25/2020 17:09:34 - WARNING - __main__ - Process rank: 1, device: cuda:1, n_gpu: 1, distributed training: True, 16-bits training: True | |
11/25/2020 17:09:34 - WARNING - __main__ - Process rank: 2, device: cuda:2, n_gpu: 1, distributed training: True, 16-bits training: True | |
11/25/2020 17:09:34 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: True, 16-bits training: True | |
[INFO|configuration_utils.py:413] 2020-11-25 17:09:39,724 >> loading configuration file https://huggingface.co/albert-base-v2/resolve/main/config.json from cache at /home/ubuntu/.cache/torch/transformers/e48be00f755a5f765e36a32885e8d6a573081df3321c9e19428d12abadf7dba2.b8f28145885741cf994c0e8a97b724f6c974460c297002145e48e511d2496e88 | |
[INFO|configuration_utils.py:449] 2020-11-25 17:09:39,725 >> Model config AlbertConfig { | |
"architectures": [ | |
"AlbertForMaskedLM" | |
], | |
"attention_probs_dropout_prob": 0, | |
"bos_token_id": 2, | |
"classifier_dropout_prob": 0.1, | |
"down_scale_factor": 1, | |
"embedding_size": 128, | |
"eos_token_id": 3, | |
"gap_size": 0, | |
"hidden_act": "gelu_new", | |
"hidden_dropout_prob": 0, | |
"hidden_size": 768, | |
"initializer_range": 0.02, | |
"inner_group_num": 1, | |
"intermediate_size": 3072, | |
"layer_norm_eps": 1e-12, | |
"max_position_embeddings": 512, | |
"model_type": "albert", | |
"net_structure_type": 0, | |
"num_attention_heads": 12, | |
"num_hidden_groups": 1, | |
"num_hidden_layers": 12, | |
"num_memory_blocks": 0, | |
"pad_token_id": 0, | |
"type_vocab_size": 2, | |
"vocab_size": 30000 | |
} | |
[INFO|configuration_utils.py:413] 2020-11-25 17:09:39,837 >> loading configuration file https://huggingface.co/albert-base-v2/resolve/main/config.json from cache at /home/ubuntu/.cache/torch/transformers/e48be00f755a5f765e36a32885e8d6a573081df3321c9e19428d12abadf7dba2.b8f28145885741cf994c0e8a97b724f6c974460c297002145e48e511d2496e88 | |
[INFO|configuration_utils.py:449] 2020-11-25 17:09:39,837 >> Model config AlbertConfig { | |
"architectures": [ | |
"AlbertForMaskedLM" | |
], | |
"attention_probs_dropout_prob": 0, | |
"bos_token_id": 2, | |
"classifier_dropout_prob": 0.1, | |
"down_scale_factor": 1, | |
"embedding_size": 128, | |
"eos_token_id": 3, | |
"gap_size": 0, | |
"hidden_act": "gelu_new", | |
"hidden_dropout_prob": 0, | |
"hidden_size": 768, | |
"initializer_range": 0.02, | |
"inner_group_num": 1, | |
"intermediate_size": 3072, | |
"layer_norm_eps": 1e-12, | |
"max_position_embeddings": 512, | |
"model_type": "albert", | |
"net_structure_type": 0, | |
"num_attention_heads": 12, | |
"num_hidden_groups": 1, | |
"num_hidden_layers": 12, | |
"num_memory_blocks": 0, | |
"pad_token_id": 0, | |
"type_vocab_size": 2, | |
"vocab_size": 30000 | |
} | |
[INFO|tokenization_utils_base.py:1650] 2020-11-25 17:09:39,925 >> loading file https://huggingface.co/albert-base-v2/resolve/main/spiece.model from cache at /home/ubuntu/.cache/torch/transformers/10be6ce6d3508f1fdce98a57a574283b47c055228c1235f8686f039287ff8174.d6110e25022b713452eb83d5bfa8ae64530995a93d8e694fe52e05aa85dd3a7d | |
[INFO|modeling_utils.py:940] 2020-11-25 17:09:40,051 >> loading weights file https://huggingface.co/albert-base-v2/resolve/main/pytorch_model.bin from cache at /home/ubuntu/.cache/torch/transformers/bf1986d976e9a8320cbd3a0597e610bf299d639ce31b7ca581cbf54be3aaa6d3.d6d54047dfe6ae844e3bf6e7a7d0aff71cb598d3df019361e076ba7639b1da9b | |
[WARNING|modeling_utils.py:1048] 2020-11-25 17:09:40,539 >> Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForQuestionAnswering: ['predictions.bias', 'predictions.LayerNorm.weight', 'predictions.LayerNorm.bias', 'predictions.dense.weight', 'predictions.dense.bias', 'predictions.decoder.weight', 'predictions.decoder.bias'] | |
- This IS expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). | |
- This IS NOT expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). | |
[WARNING|modeling_utils.py:1059] 2020-11-25 17:09:40,539 >> Some weights of AlbertForQuestionAnswering were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias'] | |
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. | |
11/25/2020 17:09:43 - INFO - __main__ - Training/evaluation parameters Namespace(adam_betas='(0.9, 0.999)', adam_epsilon=1e-08, cache_dir='', config_name='', data_dir=None, device=device(type='cuda', index=0), do_eval=True, do_lower_case=False, do_train=True, doc_stride=128, eval_all_checkpoints=False, evaluate_during_training=False, fp16=True, fp16_opt_level='O1', gradient_accumulation_steps=1, lang_id=0, learning_rate=3e-05, local_rank=0, logging_steps=500, max_answer_length=30, max_grad_norm=1.0, max_query_length=64, max_seq_length=512, max_steps=-1, model_name_or_path='albert-base-v2', model_type='albert', n_best_size=20, n_gpu=1, no_cuda=False, null_score_diff_threshold=0.0, num_train_epochs=3.0, output_dir='./examples/models/albert-base-v2_finetuned_squad2.0-fp16/', overwrite_cache=True, overwrite_output_dir=True, per_gpu_eval_batch_size=24, per_gpu_train_batch_size=12, predict_file='/home/ubuntu/squad/dev-v2.0.json', save_steps=500, seed=42, server_ip='', server_port='', threads=8, tokenizer_name='', train_file='/home/ubuntu/squad/train-v2.0.json', verbose_logging=False, version_2_with_negative=True, warmup_ratio=0.1, warmup_steps=None, weight_decay=0.01) | |
11/25/2020 17:09:43 - INFO - __main__ - Creating features from dataset file at . | |
Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForQuestionAnswering: ['predictions.bias', 'predictions.LayerNorm.weight', 'predictions.LayerNorm.bias', 'predictions.dense.weight', 'predictions.dense.bias', 'predictions.decoder.weight', 'predictions.decoder.bias'] | |
- This IS expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). | |
- This IS NOT expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). | |
Some weights of AlbertForQuestionAnswering were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias'] | |
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. | |
Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForQuestionAnswering: ['predictions.bias', 'predictions.LayerNorm.weight', 'predictions.LayerNorm.bias', 'predictions.dense.weight', 'predictions.dense.bias', 'predictions.decoder.weight', 'predictions.decoder.bias'] | |
- This IS expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). | |
- This IS NOT expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). | |
Some weights of AlbertForQuestionAnswering were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias'] | |
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. | |
Some weights of the model checkpoint at albert-base-v2 were not used when initializing AlbertForQuestionAnswering: ['predictions.bias', 'predictions.LayerNorm.weight', 'predictions.LayerNorm.bias', 'predictions.dense.weight', 'predictions.dense.bias', 'predictions.decoder.weight', 'predictions.decoder.bias'] | |
- This IS expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). | |
- This IS NOT expected if you are initializing AlbertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). | |
Some weights of AlbertForQuestionAnswering were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias'] | |
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. | |
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11/25/2020 17:12:08 - INFO - __main__ - Saving features into cached file ./cached_train_albert-base-v2_512 | |
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4%|▍ | 18/442 [00:02<00:46, 9.22it/s]Selected optimization level O1: Insert automatic casts around Pytorch functions and Tensor methods. | |
Defaults for this optimization level are: | |
enabled : True | |
opt_level : O1 | |
cast_model_type : None | |
patch_torch_functions : True | |
keep_batchnorm_fp32 : None | |
master_weights : None | |
loss_scale : dynamic | |
Processing user overrides (additional kwargs that are not None)... | |
After processing overrides, optimization options are: | |
enabled : True | |
opt_level : O1 | |
cast_model_type : None | |
patch_torch_functions : True | |
keep_batchnorm_fp32 : None | |
master_weights : None | |
loss_scale : dynamic | |
5%|▍ | 22/442 [00:02<00:35, 11.68it/s]Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ModuleNotFoundError("No module named 'amp_C'",) | |
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11/25/2020 17:17:44 - INFO - __main__ - ***** Running training ***** | |
11/25/2020 17:17:44 - INFO - __main__ - Num examples = 132998 | |
11/25/2020 17:17:44 - INFO - __main__ - Num Epochs = 3 | |
11/25/2020 17:17:44 - INFO - __main__ - Instantaneous batch size per GPU = 12 | |
11/25/2020 17:17:44 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 48 | |
11/25/2020 17:17:44 - INFO - __main__ - Gradient Accumulation steps = 1 | |
11/25/2020 17:17:44 - INFO - __main__ - Total optimization steps = 8313 | |
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32768.0 | |
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32768.0 | |
/home/ubuntu/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:114: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate | |
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) | |
/home/ubuntu/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:114: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate | |
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) | |
/home/ubuntu/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:114: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate | |
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) | |
/home/ubuntu/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:114: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate | |
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning) | |
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0 | |
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0 | |
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2048.0 | |
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warnings.warn("To get the last learning rate computed by the scheduler, " | |
[INFO|configuration_utils.py:282] 2020-11-25 17:23:01,076 >> Configuration saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-500/config.json | |
[INFO|modeling_utils.py:740] 2020-11-25 17:23:01,160 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-500/pytorch_model.bin | |
11/25/2020 17:23:01 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-500 | |
/home/ubuntu/.local/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:200: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler. | |
warnings.warn(SAVE_STATE_WARNING, UserWarning) | |
11/25/2020 17:23:01 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:28:18,496 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-1000/pytorch_model.bin | |
11/25/2020 17:28:18 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-1000 | |
11/25/2020 17:28:18 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-1000 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:33:35,415 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-1500/pytorch_model.bin | |
11/25/2020 17:33:35 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-1500 | |
11/25/2020 17:33:35 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-1500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:38:50,563 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-2000/pytorch_model.bin | |
11/25/2020 17:38:50 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-2000 | |
11/25/2020 17:38:50 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-2000 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:44:05,187 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-2500/pytorch_model.bin | |
11/25/2020 17:44:05 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-2500 | |
11/25/2020 17:44:05 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-2500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:49:19,647 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-3000/pytorch_model.bin | |
11/25/2020 17:49:19 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-3000 | |
11/25/2020 17:49:19 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-3000 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:54:34,225 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-3500/pytorch_model.bin | |
11/25/2020 17:54:34 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-3500 | |
11/25/2020 17:54:34 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-3500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 17:59:48,854 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-4000/pytorch_model.bin | |
11/25/2020 17:59:48 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-4000 | |
11/25/2020 17:59:48 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-4000 | |
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[INFO|modeling_utils.py:740] 2020-11-25 18:05:03,448 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-4500/pytorch_model.bin | |
11/25/2020 18:05:03 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-4500 | |
11/25/2020 18:05:03 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-4500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 18:10:18,026 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-5000/pytorch_model.bin | |
11/25/2020 18:10:18 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-5000 | |
11/25/2020 18:10:18 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-5000 | |
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[INFO|modeling_utils.py:740] 2020-11-25 18:15:32,918 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-5500/pytorch_model.bin | |
11/25/2020 18:15:32 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-5500 | |
11/25/2020 18:15:33 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-5500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 18:20:47,397 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-6000/pytorch_model.bin | |
11/25/2020 18:20:47 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-6000 | |
11/25/2020 18:20:47 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-6000 | |
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[INFO|modeling_utils.py:740] 2020-11-25 18:26:01,979 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-6500/pytorch_model.bin | |
11/25/2020 18:26:01 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-6500 | |
11/25/2020 18:26:02 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-6500 | |
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[INFO|modeling_utils.py:740] 2020-11-25 18:31:16,603 >> Model weights saved in ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-7000/pytorch_model.bin | |
11/25/2020 18:31:16 - INFO - __main__ - Saving model checkpoint to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-7000 | |
11/25/2020 18:31:16 - INFO - __main__ - Saving optimizer and scheduler states to ./examples/models/albert-base-v2_finetuned_squad2.0-fp16/checkpoint-7000 | |
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0 | |
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0 | |
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