ptlai
commented
2 weeks ago Hi @michaelanekson,
Here's a more native and fluent version of the message: Hi Michael, Thanks for bringing this error to our attention. It could be due to either an environment issue or a problem with the input PubTator file.
I've updated the requirements.txt to address potential environment-related issues.
As for the input file, would you mind sharing yours with me? Alternatively, you could try using the bc8_biored_task1_val.pubtator file from the BC8-BioRED dataset. You can find it in this ZIP archive: https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/BC8_BioRED_Subtask1_PubTator.zip. Give it a shot and see if the program runs without errors.
Let me know if you need any further assistance!
Best, Po-Ting
michaelanekson
Darrshan-Sankar
Hello, I am trying to run your code for "predicting new data" part. I follow all your command from creating new environment, install the requirement based on your txt file, until running the bash script. However, when I ran the bash script, I get this issue:
(biorex) [michaela95@BLOOM BioREx]$ bash scripts/run_biorex_new.sh Converting the dataset into BioREx input format 2024-07-04 14:00:33.031276: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-07-04 14:00:33.054684: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-07-04 14:00:33.054743: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-07-04 14:00:33.071248: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-07-04 14:00:34.221688: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT number_unique_YES_instances 0 Generating RE predictions 2024-07-04 14:00:38.500149: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-07-04 14:00:38.524087: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-07-04 14:00:38.524137: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-07-04 14:00:38.540547: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-07-04 14:00:39.539068: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT [INFO|training_args.py:804] 2024-07-04 14:00:42,112 >> using,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-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=biorex_model/runs/Jul04_14-00-42_BLOOM,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
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=10.0,
optim=OptimizerNames.ADAMW_HF,
output_dir=biorex_model,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=32,
per_device_train_batch_size=16,
poly_power=1.0,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=biorex_model,
save_on_each_node=False,
save_steps=10,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
tpu_metrics_debug=False,
tpu_name=None,
tpu_num_cores=None,
tpu_zone=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
xla=False,
xpu_backend=None,
)
[INFO|tokenization_utils_base.py:1776] 2024-07-04 14:00:42,124 >> loading file pretrained_model/vocab.txt
[INFO|tokenization_utils_base.py:1776] 2024-07-04 14:00:42,124 >> loading file pretrained_model/tokenizer.json
[INFO|tokenization_utils_base.py:1776] 2024-07-04 14:00:42,124 >> loading file pretrained_model/added_tokens.json
[INFO|tokenization_utils_base.py:1776] 2024-07-04 14:00:42,124 >> loading file pretrained_model/special_tokens_map.json
[INFO|tokenization_utils_base.py:1776] 2024-07-04 14:00:42,124 >> loading file pretrained_model/tokenizer_config.json
=======================>label2id {'None': 0, 'Association': 1, 'Bind': 2, 'Comparison': 3, 'Conversion': 4, 'Cotreatment': 5, 'Drug_Interaction': 6, 'Negative_Correlation': 7, 'Positive_Correlation': 8, 'None-CID': 9, 'CID': 10, 'None-PPIm': 11, 'PPIm': 12, 'None-AIMED': 13, 'None-DDI': 14, 'None-BC7': 15, 'None-phargkb': 16, 'None-GDA': 17, 'None-DISGENET': 18, 'None-EMU_BC': 19, 'None-EMU_PC': 20, 'None-HPRD50': 21, 'None-PHARMGKB': 22, 'ACTIVATOR': 23, 'AGONIST': 24, 'AGONIST-ACTIVATOR': 25, 'AGONIST-INHIBITOR': 26, 'ANTAGONIST': 27, 'DIRECT-REGULATOR': 28, 'INDIRECT-DOWNREGULATOR': 29, 'INDIRECT-UPREGULATOR': 30, 'INHIBITOR': 31, 'PART-OF': 32, 'PRODUCT-OF': 33, 'SUBSTRATE': 34, 'SUBSTRATE_PRODUCT-OF': 35, 'mechanism': 36, 'int': 37, 'effect': 38, 'advise': 39, 'AIMED-Association': 40, 'HPRD-Association': 41, 'EUADR-Association': 42, 'None-EUADR': 43, 'Indirect_conversion': 44, 'Non_conversion': 45}
=======================>positive_label
=======================>use_balanced_neg False
=======================>max_neg_scale 2
07/04/2024 14:00:42 - INFO - main - pos_label_ids
07/04/2024 14:00:42 - INFO - main - [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 16, 17, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45]
[INFO|configuration_utils.py:652] 2024-07-04 14:00:42,153 >> loading configuration file pretrained_model/config.json
[INFO|configuration_utils.py:690] 2024-07-04 14:00:42,154 >> Model config BertConfig {
"_name_or_path": "pretrained_model",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"finetuning_task": "text-classification",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "None",
"1": "Association",
"2": "Bind",
"3": "Comparison",
"4": "Conversion",
"5": "Cotreatment",
"6": "Drug_Interaction",
"7": "Negative_Correlation",
"8": "Positive_Correlation",
"9": "None-CID",
"10": "CID",
"11": "None-PPIm",
"12": "PPIm",
"13": "None-AIMED",
"14": "None-DDI",
"15": "None-BC7",
"16": "None-phargkb",
"17": "None-GDA",
"18": "None-DISGENET",
"19": "None-EMU_BC",
"20": "None-EMU_PC",
"21": "None-HPRD50",
"22": "None-PHARMGKB",
"23": "ACTIVATOR",
"24": "AGONIST",
"25": "AGONIST-ACTIVATOR",
"26": "AGONIST-INHIBITOR",
"27": "ANTAGONIST",
"28": "DIRECT-REGULATOR",
"29": "INDIRECT-DOWNREGULATOR",
"30": "INDIRECT-UPREGULATOR",
"31": "INHIBITOR",
"32": "PART-OF",
"33": "PRODUCT-OF",
"34": "SUBSTRATE",
"35": "SUBSTRATE_PRODUCT-OF",
"36": "mechanism",
"37": "int",
"38": "effect",
"39": "advise",
"40": "AIMED-Association",
"41": "HPRD-Association",
"42": "EUADR-Association",
"43": "None-EUADR",
"44": "Indirect_conversion",
"45": "Non_conversion"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"ACTIVATOR": 23,
"AGONIST": 24,
"AGONIST-ACTIVATOR": 25,
"AGONIST-INHIBITOR": 26,
"AIMED-Association": 40,
"ANTAGONIST": 27,
"Association": 1,
"Bind": 2,
"CID": 10,
"Comparison": 3,
"Conversion": 4,
"Cotreatment": 5,
"DIRECT-REGULATOR": 28,
"Drug_Interaction": 6,
"EUADR-Association": 42,
"HPRD-Association": 41,
"INDIRECT-DOWNREGULATOR": 29,
"INDIRECT-UPREGULATOR": 30,
"INHIBITOR": 31,
"Indirect_conversion": 44,
"Negative_Correlation": 7,
"Non_conversion": 45,
"None": 0,
"None-AIMED": 13,
"None-BC7": 15,
"None-CID": 9,
"None-DDI": 14,
"None-DISGENET": 18,
"None-EMU_BC": 19,
"None-EMU_PC": 20,
"None-EUADR": 43,
"None-GDA": 17,
"None-HPRD50": 21,
"None-PHARMGKB": 22,
"None-PPIm": 11,
"None-phargkb": 16,
"PART-OF": 32,
"PPIm": 12,
"PRODUCT-OF": 33,
"Positive_Correlation": 8,
"SUBSTRATE": 34,
"SUBSTRATE_PRODUCT-OF": 35,
"advise": 39,
"effect": 38,
"int": 37,
"mechanism": 36
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.18.0",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 28933
}
logging_stepsto initializeeval_stepsto 10 [INFO|training_args.py:1023] 2024-07-04 14:00:42,112 >> PyTorch: setting up devices [INFO|training_args.py:885] 2024-07-04 14:00:42,113 >> The default value for the training argument--report_towill change in v5 (from all installed integrations to none). In v5, you will need to use--report_to allto get the same behavior as now. You should start updating your code and make this info disappear :-). [INFO|training_args_tf.py:189] 2024-07-04 14:00:42,114 >> Tensorflow: setting up strategy 07/04/2024 14:00:42 - INFO - main - n_replicas: 1, distributed training: False, 16-bits training: False 07/04/2024 14:00:42 - INFO - main - Training/evaluation parameters TFTrainingArguments( _n_gpu=0, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, bf16=False, bf16_full_eval=False, data_seed=None, 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=True, do_train=False, eval_accumulation_steps=None, eval_delay=0, eval_steps=10, evaluation_strategy=IntervalStrategy.STEPS, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, gcp_project=None, 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=[INFO|modeling_tf_utils.py:1776] 2024-07-04 14:00:42,177 >> loading weights file pretrained_model/tf_model.h5 /media/data/biorex/lib/python3.10/site-packages/keras/src/layers/layer.py:1331: UserWarning: Layer 'tf_bert_for_sequence_classification' looks like it has unbuilt state, but Keras is not able to trace the layer
call()in order to build it automatically. Possible causes:call()method of your layer may be crashing. Try to__call__()the layer eagerly on some test input first to see if it works. E.g.x = np.random.random((3, 4)); y = layer(x)call()method is correct, then you may need to implement thedef build(self, input_shape)method on your layer. It should create all variables used by the layer (e.g. by callinglayer.build()on all its children layers). Exception encountered: ''Exception encountered when calling TFBertMainLayer.call().'NoneType' object has no attribute 'shape'
Arguments received by TFBertMainLayer.call(): • input_ids=tf.Tensor(shape=(3, 5), dtype=int32) • attention_mask=None • token_type_ids=None • position_ids=None • head_mask=None • inputs_embeds=None • encoder_hidden_states=None • encoder_attention_mask=None • past_key_values=None • use_cache=None • output_attentions=False • output_hidden_states=False • return_dict=True • training=False'' warnings.warn( /media/data/biorex/lib/python3.10/site-packages/keras/src/layers/layer.py:372: UserWarning:
main()
File "/home/michaela95/NLP_immune_checkpoint_gene/BERT_testing/BioREx/src/run_ncbi_rel_exp.py", line 687, in main
model = TFAutoModelForSequenceClassification.from_pretrained(
File "/media/data/biorex/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 446, in from_pretrained
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, kwargs)
File "/media/data/biorex/lib/python3.10/site-packages/transformers/modeling_tf_utils.py", line 1803, in from_pretrained
model(model.dummy_inputs) # build the network with dummy inputs
File "/media/data/biorex/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/media/data/biorex/lib/python3.10/site-packages/transformers/modeling_tf_utils.py", line 383, in run_call_with_unpacked_inputs
return func(self, unpacked_inputs)
File "/media/data/biorex/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py", line 1633, in call
outputs = self.bert(
File "/media/data/biorex/lib/python3.10/site-packages/transformers/modeling_tf_utils.py", line 383, in run_call_with_unpacked_inputs
return func(self, *unpacked_inputs)
File "/media/data/biorex/lib/python3.10/site-packages/transformers/models/bert/modeling_tf_bert.py", line 850, in call
encoder_outputs = self.encoder(
File "/media/data/biorex/lib/python3.10/site-packages/optree/ops.py", line 594, in tree_map
return treespec.unflatten(map(func, flat_args))
AttributeError: Exception encountered when calling TFBertMainLayer.call().
build()was called on layer 'tf_bert_for_sequence_classification', however the layer does not have abuild()method implemented and it looks like it has unbuilt state. This will cause the layer to be marked as built, despite not being actually built, which may cause failures down the line. Make sure to implement a properbuild()method. warnings.warn( Traceback (most recent call last): File "/home/michaela95/NLP_immune_checkpoint_gene/BERT_testing/BioREx/src/run_ncbi_rel_exp.py", line 884, in'NoneType' object has no attribute 'shape'
Arguments received by TFBertMainLayer.call(): • input_ids=tf.Tensor(shape=(3, 5), dtype=int32) • attention_mask=None • token_type_ids=None • position_ids=None • head_mask=None • inputs_embeds=None • encoder_hidden_states=None • encoder_attention_mask=None • past_key_values=None • use_cache=None • output_attentions=False • output_hidden_states=False • return_dict=True • training=False cp: cannot stat 'biorex_model/test_results.tsv': No such file or directory 2024-07-04 14:00:46.035576: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-07-04 14:00:46.059361: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-07-04 14:00:46.059424: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered 2024-07-04 14:00:46.075918: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-07-04 14:00:47.150909: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT Traceback (most recent call last): File "/home/michaela95/NLP_immune_checkpoint_gene/BERT_testing/BioREx/src/utils/run_pubtator_eval.py", line 1557, in
dump_pred_2_pubtator_file(in_pubtator_file = in_test_pubtator_file,
File "/home/michaela95/NLP_immune_checkpoint_gene/BERT_testing/BioREx/src/utils/run_pubtator_eval.py", line 206, in dump_pred_2_pubtator_file
add_relation_pairs_dict(
File "/home/michaela95/NLP_immune_checkpoint_gene/BERT_testing/BioREx/src/utils/run_pubtator_eval.py", line 83, in add_relation_pairs_dict
testdf = pd.read_csv(in_gold_tsv_file, sep="\t", index_col=0)
File "/media/data/biorex/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
return _read(filepath_or_buffer, kwds)
File "/media/data/biorex/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 620, in _read
parser = TextFileReader(filepath_or_buffer, kwds)
File "/media/data/biorex/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 1620, in init
self._engine = self._make_engine(f, self.engine)
File "/media/data/biorex/lib/python3.10/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
return mapping[engine](f, self.options)
File "/media/data/biorex/lib/python3.10/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in init
self._reader = parsers.TextReader(src, **kwds)
File "parsers.pyx", line 581, in pandas._libs.parsers.TextReader.cinit
pandas.errors.EmptyDataError: No columns to parse from file
Would you tell what should I do in dealing with this situation?