Model Training Failed while fine-tuning bloomberg/KBIR model
Here is the output:
(venv) debanjan@deb-research:~/code/research/dlkp/examples/ke$ CUDA_VISIBLE_DEVICES=1 python ke_tagger_transformers.py
03/15/2022 21:56:26 - WARNING - dlkp.extraction.train_eval_kp_tagger - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False
03/15/2022 21:56:26 - INFO - dlkp.extraction.train_eval_kp_tagger - Training/evaluation parameters KETrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
bf16=False,
bf16_full_eval=False,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_steps=100,
evaluation_strategy=IntervalStrategy.STEPS,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=3e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=-1,
log_level=-1,
log_level_replica=-1,
log_on_each_node=True,
logging_dir=../../outputs/runs/Mar15_21-56-26_deb-research,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=100,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=2,
optim=OptimizerNames.ADAMW_HF,
output_dir=../../outputs,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=4,
per_device_train_batch_size=4,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=../../outputs,
save_on_each_node=False,
save_steps=1000,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xpu_backend=None,
)
[INFO|configuration_utils.py:648] 2022-03-15 21:56:27,153 >> loading configuration file https://huggingface.co/bloomberg/KBIR/resolve/main/config.json from cache at /home/debanjan/.cache/huggingface/transformers/e3e4e9cc0f5071082c8ddd8a31edf789b32dc26ba7dec62e3fbe88190bb22206.a2909b3b000ff4512970d210c555be4a09eeb3e22fbde068618b6919d78ddc34
[INFO|configuration_utils.py:684] 2022-03-15 21:56:27,154 >> Model config RobertaConfig {
"_name_or_path": "bloomberg/KBIR",
"architectures": [
"KLMForReplacementAndMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.17.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
[INFO|tokenization_utils_base.py:1786] 2022-03-15 21:56:28,110 >> loading file https://huggingface.co/bloomberg/KBIR/resolve/main/vocab.json from cache at /home/debanjan/.cache/huggingface/transformers/f9fcf68a490a2d176769963ac10ab2c59182602353f3484d14da025d9d7585ab.647b4548b6d9ea817e82e7a9231a320231a1c9ea24053cc9e758f3fe68216f05
[INFO|tokenization_utils_base.py:1786] 2022-03-15 21:56:28,110 >> loading file https://huggingface.co/bloomberg/KBIR/resolve/main/merges.txt from cache at /home/debanjan/.cache/huggingface/transformers/ef76f4aba99dea7f976cccdc74652fdc84f73480f539d10abe709d5182fe94ed.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b
[INFO|tokenization_utils_base.py:1786] 2022-03-15 21:56:28,110 >> loading file https://huggingface.co/bloomberg/KBIR/resolve/main/tokenizer.json from cache at /home/debanjan/.cache/huggingface/transformers/5af92da1cf1b5bbe300ffd058e94205c37374f4f5de77f2be7d41d559f9aa59b.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730
[INFO|tokenization_utils_base.py:1786] 2022-03-15 21:56:28,110 >> loading file https://huggingface.co/bloomberg/KBIR/resolve/main/added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:1786] 2022-03-15 21:56:28,110 >> loading file https://huggingface.co/bloomberg/KBIR/resolve/main/special_tokens_map.json from cache at /home/debanjan/.cache/huggingface/transformers/214f7e5010364e3b2a1b4768a7c94d3925495de7d1df2b3a4bf3bc9e60fec3a9.cb2244924ab24d706b02fd7fcedaea4531566537687a539ebb94db511fd122a0
[INFO|tokenization_utils_base.py:1786] 2022-03-15 21:56:28,110 >> loading file https://huggingface.co/bloomberg/KBIR/resolve/main/tokenizer_config.json from cache at /home/debanjan/.cache/huggingface/transformers/499673c10e6c643617124841edb2de871366d6c41fc6216f2cb4fea18705f2df.ad1eb0115e1dcd62936cffaee6ce0d19c9b8fea6a4ad255cc807ce29db906ec2
[INFO|configuration_utils.py:648] 2022-03-15 21:56:28,234 >> loading configuration file https://huggingface.co/bloomberg/KBIR/resolve/main/config.json from cache at /home/debanjan/.cache/huggingface/transformers/e3e4e9cc0f5071082c8ddd8a31edf789b32dc26ba7dec62e3fbe88190bb22206.a2909b3b000ff4512970d210c555be4a09eeb3e22fbde068618b6919d78ddc34
[INFO|configuration_utils.py:684] 2022-03-15 21:56:28,235 >> Model config RobertaConfig {
"_name_or_path": "bloomberg/KBIR",
"architectures": [
"KLMForReplacementAndMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.17.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
03/15/2022 21:56:29 - WARNING - datasets.builder - Reusing dataset inspec (/home/debanjan/.cache/huggingface/datasets/midas___inspec/extraction/0.0.1/debd18641afb7048a36cee2b7bb8dfbf2cd1a68899118653a42fd760cf84284e)
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[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:29,649 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding. | 0/1 [00:00<?, ?ba/s]
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:29,649 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:29,676 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:29,676 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. [00:00<?, ?ba/s]
[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:29,692 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:29,692 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:29,737 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:29,737 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:29,798 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
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#0: 0%| | 0/1 [00:00<?, ?ba/s][WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:30,301 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
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[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:30,991 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:30,991 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:30,996 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. [00:00<?, ?ba/s]
[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:31,031 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:31,031 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:31,066 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:31,066 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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[WARNING|tokenization_utils_base.py:2334] 2022-03-15 21:56:31,094 >> Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no padding.
[WARNING|tokenization_utils_base.py:2347] 2022-03-15 21:56:31,095 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
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[INFO|configuration_utils.py:648] 2022-03-15 21:56:31,430 >> loading configuration file https://huggingface.co/bloomberg/KBIR/resolve/main/config.json from cache at /home/debanjan/.cache/huggingface/transformers/e3e4e9cc0f5071082c8ddd8a31edf789b32dc26ba7dec62e3fbe88190bb22206.a2909b3b000ff4512970d210c555be4a09eeb3e22fbde068618b6919d78ddc34
[INFO|configuration_utils.py:684] 2022-03-15 21:56:31,430 >> Model config RobertaConfig { | 0/1 [00:00<?, ?ba/s]
"_name_or_path": "bloomberg/KBIR",
"architectures": [
"KLMForReplacementAndMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.17.0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
[INFO|modeling_utils.py:1431] 2022-03-15 21:56:31,803 >> loading weights file https://huggingface.co/bloomberg/KBIR/resolve/main/pytorch_model.bin from cache at /home/debanjan/.cache/huggingface/transformers/dcbcac674440cfdfe901cc2259b02eb80c5069676d931c4416a862656ebf0f49.5aec73b8a150e8021bbc6d6d1de4d9548566e457a9a5044790d0f685dcb48c6b
[WARNING|modeling_utils.py:1693] 2022-03-15 21:56:34,052 >> Some weights of the model checkpoint at bloomberg/KBIR were not used when initializing RobertaForTokenClassification: ['infilling_head.mlp_layer_norm.layer_norm1.bias', 'infilling_head.position_embeddings.weight', 'infilling_head.mlp_layer_norm.layer_norm2.bias', 'lm_head.dense.weight', 'replacement_classification_head.classifier.weight', 'lm_head.dense.bias', 'infilling_head.mlp_layer_norm.linear1.weight', 'infilling_head.decoder.weight', 'infilling_head.mlp_layer_norm.layer_norm1.weight', 'infilling_head.num_tok_classifier.bias', 'infilling_head.mlp_layer_norm.linear2.bias', 'replacement_classification_head.classifier.bias', 'infilling_head.mlp_layer_norm.layer_norm2.weight', 'lm_head.bias', 'lm_head.layer_norm.weight', 'infilling_head.mlp_layer_norm.linear2.weight', 'infilling_head.num_tok_classifier.weight', 'infilling_head.mlp_layer_norm.linear1.bias', 'replacement_classification_head.bias', 'infilling_head.bias', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias']
- This IS expected if you are initializing RobertaForTokenClassification 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 RobertaForTokenClassification 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:1704] 2022-03-15 21:56:34,052 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at bloomberg/KBIR and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|trainer.py:570] 2022-03-15 21:56:37,210 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, doc_bio_tags, special_tokens_mask, document. If id, doc_bio_tags, special_tokens_mask, document are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
warnings.warn(
[INFO|trainer.py:1279] 2022-03-15 21:56:37,258 >> ***** Running training *****
[INFO|trainer.py:1280] 2022-03-15 21:56:37,258 >> Num examples = 1000
[INFO|trainer.py:1281] 2022-03-15 21:56:37,258 >> Num Epochs = 2
[INFO|trainer.py:1282] 2022-03-15 21:56:37,258 >> Instantaneous batch size per device = 4
[INFO|trainer.py:1283] 2022-03-15 21:56:37,258 >> Total train batch size (w. parallel, distributed & accumulation) = 4
[INFO|trainer.py:1284] 2022-03-15 21:56:37,259 >> Gradient Accumulation steps = 1
[INFO|trainer.py:1285] 2022-03-15 21:56:37,259 >> Total optimization steps = 500
3%|█████▋ | 15/500 [00:02<01:17, 6.25it/s]Traceback (most recent call last):
File "ke_tagger_transformers.py", line 40, in <module>
KeyphraseTagger.train_and_eval(
File "/home/debanjan/code/research/dlkp/src/dlkp/extraction/tagger.py", line 95, in train_and_eval
return train_eval_extraction_model(
File "/home/debanjan/code/research/dlkp/src/dlkp/extraction/train_eval_kp_tagger.py", line 165, in train_eval_extraction_model
train_result = trainer.train(resume_from_checkpoint=checkpoint)
File "/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1400, in train
tr_loss_step = self.training_step(model, inputs)
File "/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/transformers/trainer.py", line 1984, in training_step
loss = self.compute_loss(model, inputs)
File "/home/debanjan/code/research/dlkp/src/dlkp/extraction/trainer.py", line 219, in compute_loss
outputs = model(**inputs)
File "/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py", line 1397, in forward
outputs = self.roberta(
File "/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/home/debanjan/code/research/dlkp/venv/lib/python3.8/site-packages/transformers/models/roberta/modeling_roberta.py", line 816, in forward
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
RuntimeError: The expanded size of the tensor (527) must match the existing size (514) at non-singleton dimension 1. Target sizes: [4, 527]. Tensor sizes: [1, 514]
3%|█████▋
debanjanbhucs
commented
2 years ago
Model Training Failed while fine-tuning
bloomberg/KBIRmodelHere is the output:
Code Used:
`