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08/27/2022 00:02:42 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1 distributed training: True, 16-bits training: True
08/27/2022 00:02:42 - INFO - __main__ - Training/evaluation parameters OurTrainingArguments(output_dir='out/mabel-joint-cl-al1-mlm-bs-32-lr-5e-5-msl-128-ep-2', overwrite_output_dir=True, do_train=True, do_eval=True, do_predict=False, evaluation_strategy=<EvaluationStrategy.NO: 'no'>, prediction_loss_only=False, per_device_train_batch_size=32, per_device_eval_batch_size=8, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=2.0, max_steps=-1, lr_scheduler_type=<SchedulerType.LINEAR: 'linear'>, warmup_steps=0, logging_dir='runs/Aug27_00-02-42_a11-03.hpc.usc.edu', logging_first_step=False, logging_steps=500, save_steps=125, save_total_limit=None, no_cuda=False, seed=42, fp16=True, fp16_opt_level='O1', fp16_backend='auto', local_rank=0, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name='out/mabel-joint-cl-al1-mlm-bs-32-lr-5e-5-msl-128-ep-2', disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=True, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, eval_transfer=False, report_to='wandb')
08/27/2022 00:02:42 - WARNING - __main__ - Process rank: 1, device: cuda:1, n_gpu: 1 distributed training: True, 16-bits training: True
08/27/2022 00:02:42 - WARNING - __main__ - Process rank: 2, device: cuda:2, n_gpu: 1 distributed training: True, 16-bits training: True
08/27/2022 00:02:42 - WARNING - __main__ - Process rank: 3, device: cuda:3, n_gpu: 1 distributed training: True, 16-bits training: True
08/27/2022 00:02:42 - WARNING - datasets.builder - Using custom data configuration default-2f6794b69ce47e79
08/27/2022 00:02:42 - WARNING - datasets.builder - Using custom data configuration default-2f6794b69ce47e79
08/27/2022 00:02:42 - WARNING - datasets.builder - Using custom data configuration default-2f6794b69ce47e79
08/27/2022 00:02:43 - WARNING - datasets.builder - Using custom data configuration default-2f6794b69ce47e79
08/27/2022 00:02:44 - WARNING - datasets.builder - Reusing dataset csv (.cache/csv/default-2f6794b69ce47e79/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a)
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08/27/2022 00:02:44 - WARNING - datasets.builder - Reusing dataset csv (.cache/csv/default-2f6794b69ce47e79/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a)
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[INFO|configuration_utils.py:445] 2022-08-27 00:02:44,795 >> loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at .cache/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e
[INFO|configuration_utils.py:481] 2022-08-27 00:02:44,796 >> Model config BertConfig {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"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": "4.2.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
[INFO|configuration_utils.py:445] 2022-08-27 00:02:45,107 >> loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at .cache/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e
[INFO|configuration_utils.py:481] 2022-08-27 00:02:45,108 >> Model config BertConfig {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"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": "4.2.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
[INFO|tokenization_utils_base.py:1766] 2022-08-27 00:02:45,697 >> loading file https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt from cache at .cache/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99
[INFO|tokenization_utils_base.py:1766] 2022-08-27 00:02:45,697 >> loading file https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json from cache at .cache/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4
[INFO|modeling_utils.py:1027] 2022-08-27 00:02:46,093 >> loading weights file https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin from cache at .cache/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMabel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'bert.pooler.dense.weight', 'bert.pooler.dense.bias']
- This IS expected if you are initializing BertForMabel 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 BertForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMabel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'bert.pooler.dense.weight', 'bert.pooler.dense.bias']
- This IS expected if you are initializing BertForMabel 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 BertForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForMabel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['lm_head.bias', 'lm_head.transform.dense.weight', 'lm_head.transform.dense.bias', 'lm_head.transform.LayerNorm.weight', 'lm_head.transform.LayerNorm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.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 BertForMabel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['lm_head.bias', 'lm_head.transform.dense.weight', 'lm_head.transform.dense.bias', 'lm_head.transform.LayerNorm.weight', 'lm_head.transform.LayerNorm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:1135] 2022-08-27 00:02:55,669 >> Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMabel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'bert.pooler.dense.weight', 'bert.pooler.dense.bias']
- This IS expected if you are initializing BertForMabel 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 BertForMabel 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:1146] 2022-08-27 00:02:55,669 >> Some weights of BertForMabel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['lm_head.bias', 'lm_head.transform.dense.weight', 'lm_head.transform.dense.bias', 'lm_head.transform.LayerNorm.weight', 'lm_head.transform.LayerNorm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.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 bert-base-uncased were not used when initializing BertForMabel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'bert.pooler.dense.weight', 'bert.pooler.dense.bias']
- This IS expected if you are initializing BertForMabel 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 BertForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForMabel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['lm_head.bias', 'lm_head.transform.dense.weight', 'lm_head.transform.dense.bias', 'lm_head.transform.LayerNorm.weight', 'lm_head.transform.LayerNorm.bias', 'lm_head.decoder.weight', 'lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|configuration_utils.py:445] 2022-08-27 00:02:55,967 >> loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at .cache/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e
[INFO|configuration_utils.py:481] 2022-08-27 00:02:55,968 >> Model config BertConfig {
"architectures": [
"BertForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"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": "4.2.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
[INFO|modeling_utils.py:1027] 2022-08-27 00:02:56,268 >> loading weights file https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin from cache at .cache/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f
Some weights of BertForPreTraining were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['cls.predictions.decoder.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|modeling_utils.py:1143] 2022-08-27 00:03:05,402 >> All model checkpoint weights were used when initializing BertForPreTraining.
[WARNING|modeling_utils.py:1146] 2022-08-27 00:03:05,415 >> Some weights of BertForPreTraining were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['cls.predictions.decoder.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 BertForPreTraining were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['cls.predictions.decoder.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 BertForPreTraining were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['cls.predictions.decoder.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
08/27/2022 00:03:05 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at .cache/csv/default-2f6794b69ce47e79/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-51922e954a887dd0.arrow
08/27/2022 00:03:05 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at .cache/csv/default-2f6794b69ce47e79/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-51922e954a887dd0.arrow
08/27/2022 00:03:05 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at .cache/csv/default-2f6794b69ce47e79/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-51922e954a887dd0.arrow
08/27/2022 00:03:05 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at .cache/csv/default-2f6794b69ce47e79/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a/cache-51922e954a887dd0.arrow
Dataset({
features: ['bin_mask', 'input_ids', 'token_type_ids', 'attention_mask'],
num_rows: 142158
})Dataset({
features: ['bin_mask', 'input_ids', 'token_type_ids', 'attention_mask'],
num_rows: 142158
})Dataset({
features: ['bin_mask', 'input_ids', 'token_type_ids', 'attention_mask'],
num_rows: 142158
})Dataset({
features: ['bin_mask', 'input_ids', 'token_type_ids', 'attention_mask'],
num_rows: 142158
})
[INFO|trainer.py:442] 2022-08-27 00:03:19,523 >> The following columns in the training set don't have a corresponding argument in `BertForMabel.forward` and have been ignored: .
[INFO|trainer.py:358] 2022-08-27 00:03:19,528 >> Using amp fp16 backend
08/27/2022 00:03:20 - INFO - trainer - ***** Running training *****
08/27/2022 00:03:20 - INFO - trainer - Num examples = 142158
08/27/2022 00:03:20 - INFO - trainer - Num Epochs = 2
08/27/2022 00:03:20 - INFO - trainer - Instantaneous batch size per device = 32
08/27/2022 00:03:20 - INFO - trainer - Total train batch size (w. parallel, distributed & accumulation) = 128
08/27/2022 00:03:20 - INFO - trainer - Gradient Accumulation steps = 1
08/27/2022 00:03:20 - INFO - trainer - Total optimization steps = 2222
0%| | 0/2222 [00:00<?, ?it/s][W reducer.cpp:1303] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1303] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1303] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1303] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. 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)
/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. 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)
/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. 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)
/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. 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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{'loss': 3.6766, 'learning_rate': 3.874887488748875e-05, 'epoch': 0.45}
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{'loss': 2.2606, 'learning_rate': 2.7497749774977498e-05, 'epoch': 0.9}
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{'loss': 2.0216, 'learning_rate': 1.6246624662466247e-05, 'epoch': 1.35}
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{'loss': 1.9092, 'learning_rate': 4.995499549954996e-06, 'epoch': 1.8}
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100%|██████████| 2222/2222 [39:59<00:00, 1.01s/it]08/27/2022 00:43:20 - INFO - trainer -
Training completed. Do not forget to share your model on huggingface.co/models =)
Traceback (most recent call last):
File "train.py", line 629, in <module>
Traceback (most recent call last):
File "train.py", line 629, in <module>
Traceback (most recent call last):
File "train.py", line 629, in <module>
{'train_runtime': 2399.7982, 'train_samples_per_second': 0.926, 'epoch': 2.0}
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main()
File "train.py", line 611, in main
[INFO|trainer.py:1344] 2022-08-27 00:43:20,039 >> Saving model checkpoint to out/mabel-joint-cl-al1-mlm-bs-32-lr-5e-5-msl-128-ep-2
main()
File "train.py", line 611, in main
[INFO|configuration_utils.py:300] 2022-08-27 00:43:20,042 >> Configuration saved in out/mabel-joint-cl-al1-mlm-bs-32-lr-5e-5-msl-128-ep-2/config.json
results = trainer.evaluate(eval_senteval_transfer=True)
TypeError: evaluate() got an unexpected keyword argument 'eval_senteval_transfer'
main()
File "train.py", line 611, in main
results = trainer.evaluate(eval_senteval_transfer=True)
TypeError: evaluate() got an unexpected keyword argument 'eval_senteval_transfer'
results = trainer.evaluate(eval_senteval_transfer=True)
TypeError: evaluate() got an unexpected keyword argument 'eval_senteval_transfer'
[INFO|modeling_utils.py:817] 2022-08-27 00:43:21,328 >> Model weights saved in out/mabel-joint-cl-al1-mlm-bs-32-lr-5e-5-msl-128-ep-2/pytorch_model.bin
08/27/2022 00:43:21 - INFO - __main__ - ***** Train results *****
08/27/2022 00:43:21 - INFO - __main__ - epoch = 2.0
08/27/2022 00:43:21 - INFO - __main__ - train_runtime = 2399.7982
08/27/2022 00:43:21 - INFO - __main__ - train_samples_per_second = 0.926
08/27/2022 00:43:21 - INFO - __main__ - *** Evaluate ***
Traceback (most recent call last):
File "train.py", line 629, in <module>
main()
File "train.py", line 611, in main
results = trainer.evaluate(eval_senteval_transfer=True)
TypeError: evaluate() got an unexpected keyword argument 'eval_senteval_transfer'
/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/launch.py:186: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
FutureWarning,
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 24668 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 1 (pid: 24669) of binary: /project/jonmay_231/jacqueline/miniconda3/envs/env/bin/python
Traceback (most recent call last):
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
main()
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
launch(args)
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
run(args)
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/run.py", line 713, in run
)(*cmd_args)
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/project/jonmay_231/jacqueline/miniconda3/envs/env/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 261, in launch_agent
failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
train.py FAILED
------------------------------------------------------------
Failures:
[1]:
time : 2022-08-27_00:43:23
host : a11-03.hpc.usc.edu
rank : 2 (local_rank: 2)
exitcode : 1 (pid: 24670)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
time : 2022-08-27_00:43:23
host : a11-03.hpc.usc.edu
rank : 3 (local_rank: 3)
exitcode : 1 (pid: 24671)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2022-08-27_00:43:23
host : a11-03.hpc.usc.edu
rank : 1 (local_rank: 1)
exitcode : 1 (pid: 24669)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
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