2023-04-10 14:38:24.700309: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-10 14:38:24.840794: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-04-10 14:38:25.603565: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64:
2023-04-10 14:38:25.603723: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64:
2023-04-10 14:38:25.603751: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
04/10/2023 14:38:27 - INFO - __main__ - Distributed environment: NO
Num processes: 1
Process index: 0
Local process index: 0
Device: cuda
Mixed precision type: no

04/10/2023 14:38:31 - WARNING - datasets.builder - Found cached dataset glue (/home/dingdaizong/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)

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loading configuration file config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json
Model config BertConfig {
  "_name_or_path": "bert-base-uncased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "finetuning_task": "sst2",
  "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": "4.26.1",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 30522
}

loading configuration file config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json
Model config BertConfig {
  "_name_or_path": "bert-base-uncased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "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": "4.26.1",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 30522
}

loading file vocab.txt from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/vocab.txt
loading file tokenizer.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/tokenizer.json
loading file added_tokens.json from cache at None
loading file special_tokens_map.json from cache at None
loading file tokenizer_config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/tokenizer_config.json
loading configuration file config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json
Model config BertConfig {
  "_name_or_path": "bert-base-uncased",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "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": "4.26.1",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 30522
}

loading weights file pytorch_model.bin from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/pytorch_model.bin
Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight']
- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.skim_predictors.5.predictor.2.bias', 'bert.encoder.skim_predictors.5.predictor.4.weight', 'bert.encoder.skim_predictors.4.predictor.0.weight', 'bert.encoder.skim_predictors.8.predictor.1.bias', 'bert.encoder.skim_predictors.6.predictor.0.weight', 'bert.encoder.skim_predictors.6.predictor.1.bias', 'bert.encoder.skim_predictors.11.predictor.2.weight', 'bert.encoder.skim_predictors.2.predictor.1.bias', 'bert.encoder.skim_predictors.11.predictor.1.bias', 'classifier.bias', 'bert.encoder.skim_predictors.9.predictor.1.bias', 'bert.encoder.skim_predictors.7.predictor.2.bias', 'bert.encoder.skim_predictors.7.predictor.0.weight', 'bert.encoder.skim_predictors.1.predictor.2.bias', 'bert.encoder.skim_predictors.5.predictor.4.bias', 'bert.encoder.skim_predictors.8.predictor.2.bias', 'classifier.weight', 'bert.encoder.skim_predictors.1.predictor.1.bias', 'bert.encoder.skim_predictors.2.predictor.0.weight', 'bert.encoder.skim_predictors.9.predictor.2.weight', 'bert.encoder.skim_predictors.9.predictor.4.bias', 'bert.encoder.skim_predictors.5.predictor.0.bias', 'bert.encoder.skim_predictors.3.predictor.2.weight', 'bert.encoder.skim_predictors.8.predictor.1.weight', 'bert.encoder.skim_predictors.6.predictor.2.weight', 'bert.encoder.skim_predictors.3.predictor.1.bias', 'bert.encoder.skim_predictors.0.predictor.2.bias', 'bert.encoder.skim_predictors.8.predictor.4.weight', 'bert.encoder.skim_predictors.11.predictor.0.bias', 'bert.encoder.skim_predictors.5.predictor.1.weight', 'bert.encoder.skim_predictors.6.predictor.0.bias', 'bert.encoder.skim_predictors.6.predictor.4.weight', 'bert.encoder.skim_predictors.1.predictor.0.bias', 'bert.encoder.skim_predictors.7.predictor.2.weight', 'bert.encoder.skim_predictors.0.predictor.0.bias', 'bert.encoder.skim_predictors.7.predictor.4.bias', 'bert.encoder.skim_predictors.1.predictor.4.bias', 'bert.encoder.skim_predictors.2.predictor.2.weight', 'bert.encoder.skim_predictors.0.predictor.2.weight', 'bert.encoder.skim_predictors.10.predictor.1.weight', 'bert.encoder.skim_predictors.0.predictor.1.weight', 'bert.encoder.skim_predictors.2.predictor.1.weight', 'bert.encoder.skim_predictors.7.predictor.4.weight', 'bert.encoder.skim_predictors.1.predictor.1.weight', 'bert.encoder.skim_predictors.3.predictor.4.bias', 'bert.encoder.skim_predictors.4.predictor.4.weight', 'bert.encoder.skim_predictors.5.predictor.0.weight', 'bert.encoder.skim_predictors.0.predictor.4.bias', 'bert.encoder.skim_predictors.11.predictor.4.bias', 'bert.encoder.skim_predictors.8.predictor.0.bias', 'bert.encoder.skim_predictors.10.predictor.2.bias', 'bert.encoder.skim_predictors.4.predictor.2.bias', 'bert.encoder.skim_predictors.7.predictor.0.bias', 'bert.encoder.skim_predictors.0.predictor.1.bias', 'bert.encoder.skim_predictors.10.predictor.1.bias', 'bert.encoder.skim_predictors.11.predictor.4.weight', 'bert.encoder.skim_predictors.3.predictor.0.weight', 'bert.encoder.skim_predictors.10.predictor.0.bias', 'bert.encoder.skim_predictors.2.predictor.4.bias', 'bert.encoder.skim_predictors.2.predictor.2.bias', 'bert.encoder.skim_predictors.5.predictor.2.weight', 'bert.encoder.skim_predictors.10.predictor.0.weight', 'bert.encoder.skim_predictors.1.predictor.4.weight', 'bert.encoder.skim_predictors.8.predictor.2.weight', 'bert.encoder.skim_predictors.6.predictor.4.bias', 'bert.encoder.skim_predictors.11.predictor.0.weight', 'bert.encoder.skim_predictors.3.predictor.2.bias', 'bert.encoder.skim_predictors.11.predictor.1.weight', 'bert.encoder.skim_predictors.5.predictor.1.bias', 'bert.encoder.skim_predictors.3.predictor.1.weight', 'bert.encoder.skim_predictors.6.predictor.1.weight', 'bert.encoder.skim_predictors.7.predictor.1.weight', 'bert.encoder.skim_predictors.1.predictor.2.weight', 'bert.encoder.skim_predictors.4.predictor.1.bias', 'bert.encoder.skim_predictors.8.predictor.0.weight', 'bert.encoder.skim_predictors.9.predictor.2.bias', 'bert.encoder.skim_predictors.9.predictor.1.weight', 'bert.encoder.skim_predictors.0.predictor.4.weight', 'bert.encoder.skim_predictors.9.predictor.0.weight', 'bert.encoder.skim_predictors.1.predictor.0.weight', 'bert.encoder.skim_predictors.3.predictor.4.weight', 'bert.encoder.skim_predictors.11.predictor.2.bias', 'bert.encoder.skim_predictors.4.predictor.2.weight', 'bert.encoder.skim_predictors.9.predictor.4.weight', 'bert.encoder.skim_predictors.7.predictor.1.bias', 'bert.encoder.skim_predictors.10.predictor.4.bias', 'bert.encoder.skim_predictors.0.predictor.0.weight', 'bert.encoder.skim_predictors.2.predictor.4.weight', 'bert.encoder.skim_predictors.10.predictor.4.weight', 'bert.encoder.skim_predictors.2.predictor.0.bias', 'bert.encoder.skim_predictors.3.predictor.0.bias', 'bert.encoder.skim_predictors.10.predictor.2.weight', 'bert.encoder.skim_predictors.8.predictor.4.bias', 'bert.encoder.skim_predictors.4.predictor.1.weight', 'bert.encoder.skim_predictors.4.predictor.0.bias', 'bert.encoder.skim_predictors.6.predictor.2.bias', 'bert.encoder.skim_predictors.9.predictor.0.bias', 'bert.encoder.skim_predictors.4.predictor.4.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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Running tokenizer on dataset:  21%|██        | 14000/67349 [00:00<00:02, 24192.62 examples/s]
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Running tokenizer on dataset: 100%|██████████| 67349/67349 [00:02<00:00, 34104.73 examples/s]
                                                                                             

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04/10/2023 14:38:37 - INFO - __main__ - Sample 14592 of the training set: {'input_ids': [101, 1037, 2307, 3185, 102], 'token_type_ids': [0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1], 'labels': 1}.
04/10/2023 14:38:37 - INFO - __main__ - Sample 3278 of the training set: {'input_ids': [101, 14036, 1010, 2065, 5399, 16367, 1010, 2895, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}.
04/10/2023 14:38:37 - INFO - __main__ - Sample 36048 of the training set: {'input_ids': [101, 2130, 2043, 2045, 2024, 11320, 12718, 1010, 1996, 6699, 4025, 14469, 1010, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}.
/home/dingdaizong/mlsnrs/pxd/Finetune_GPT-J_6B_8-bit/chatgpt/lib/python3.9/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(
/home/dingdaizong/mlsnrs/zsy/Transkimmer/src/run_glue_no_trainer.py:472: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co./docs/evaluate
  metric = load_metric("glue", args.task_name)
04/10/2023 14:38:41 - INFO - __main__ - ***** Running training *****
04/10/2023 14:38:41 - INFO - __main__ -   Num examples = 67349
04/10/2023 14:38:41 - INFO - __main__ -   Num Epochs = 3
04/10/2023 14:38:41 - INFO - __main__ -   Instantaneous batch size per device = 32
04/10/2023 14:38:41 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 64
04/10/2023 14:38:41 - INFO - __main__ -   Gradient Accumulation steps = 2
04/10/2023 14:38:41 - INFO - __main__ -   Total optimization steps = 3159

  0%|          | 0/3159 [00:00<?, ?it/s]You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
/home/dingdaizong/mlsnrs/pxd/Finetune_GPT-J_6B_8-bit/chatgpt/lib/python3.9/site-packages/transformers/modeling_utils.py:810: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
  warnings.warn(

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100%|██████████| 3159/3159 [08:54<00:00,  5.68it/s]04/10/2023 14:47:36 - INFO - __main__ - epoch 2: {'accuracy': 0.9105504587155964}, skim_loss: 0.26928749680519104, tokens_remained: 0.5101129412651062, layers: 0 0.9598081707954407 1 0.9589188694953918 2 0.6685288548469543 3 0.6680372953414917 4 0.6655147075653076 5 0.5209729671478271 6 0.40471377968788147 7 0.40040579438209534 8 0.4000111520290375 9 0.38064661622047424 10 0.05123211815953255 11 0.042564891278743744
04/10/2023 14:47:37 - INFO - __main__ - eval: {'accuracy': 0.9036697247706422}, skim_loss: 0.2691411077976227, tokens_remained: 0.5098466873168945, layers: 0 0.9600897431373596 1 0.9590127468109131 2 0.6699763536453247 3 0.6696270704269409 4 0.6664778590202332 5 0.5199223160743713 6 0.4035145342350006 7 0.39916759729385376 8 0.398638516664505 9 0.3795333802700043 10 0.0497579500079155 11 0.04244190827012062
Configuration saved in model/glue/sst2/time_04-10-14-38/config.json
Model weights saved in model/glue/sst2/time_04-10-14-38/pytorch_model.bin

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