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run_squad_albert-base-v2_fp16.log
+ 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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convert squad examples to features: 0%| | 0/130319 [00:00<?, ?it/s]/home/ubuntu/transformers/src/transformers/tokenization_utils_base.py:1423: FutureWarning: The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead.
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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
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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
Epoch: 0%| | 0/3 [00:00<?, ?it/s]
Iteration: 0%| | 0/2771 [00:00<?, ?it/s]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
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)
Iteration: 0%| | 1/2771 [00:00<32:16, 1.43it/s]Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 16384.0
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8192.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8192.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 8192.0
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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 4096.0
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
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 2048.0
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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Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1024.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1024.0
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 1024.0
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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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