namisan
commented
2 years ago Here is my script and log for electra. Hope it helps. _export CUDA_VISIBLE_DEVICES=0,1,2,3; bash experiments/glue/run_glue_finetuning.sh data/canonicaldata/ electra base mnli 128 4
Training Log
02/12/2022 06:15:34 Launching the MT-DNN training 02/12/2022 06:15:34 Loading data/canonical_data//bert-base-uncased/mnli_train.json as task 0 02/12/2022 06:15:47 #################### 02/12/2022 06:15:47 {'log_file': 'glue_app/mnli/bert-base-uncased/mt-dnn-train.log', 'tensorboard': False, 'tensorboard_logdir': 'tensorboard_logdir', 'init_checkpoint': 'google/electra-base-discriminator', 'data_dir': 'data/canonical_data//bert-base-uncased', 'data_sort_on': False, 'name': 'farmer', 'task_def': 'experiments/glue/glue_task_def.yml', 'train_datasets': ['mnli'], 'test_datasets': ['mnli_matched', 'mnli_mismatched'], 'glue_format_on': False, 'mkd_opt': 0, 'do_padding': False, 'update_bert_opt': 0, 'multi_gpu_on': False, 'mem_cum_type': 'simple', 'answer_num_turn': 5, 'answer_mem_drop_p': 0.1, 'answer_att_hidden_size': 128, 'answer_att_type': 'bilinear', 'answer_rnn_type': 'gru', 'answer_sum_att_type': 'bilinear', 'answer_merge_opt': 1, 'answer_mem_type': 1, 'max_answer_len': 20, 'answer_dropout_p': 0.1, 'answer_weight_norm_on': False, 'dump_state_on': False, 'answer_opt': 1, 'pooler_actf': 'tanh', 'mtl_opt': 0, 'ratio': 0, 'mix_opt': 0, 'max_seq_len': 512, 'init_ratio': 1, 'encoder_type': 7, 'num_hidden_layers': -1, 'bert_model_type': 'bert-base-uncased', 'do_lower_case': False, 'masked_lm_prob': 0.15, 'short_seq_prob': 0.2, 'max_predictions_per_seq': 128, 'bin_on': False, 'bin_size': 64, 'bin_grow_ratio': 0.5, 'local_rank': 0, 'world_size': 4, 'master_addr': 'localhost', 'master_port': '6600', 'backend': 'nccl', 'cuda': True, 'log_per_updates': 500, 'save_per_updates': 10000, 'save_per_updates_on': False, 'epochs': 3, 'batch_size': 128, 'batch_size_eval': 8, 'optimizer': 'adamax', 'grad_clipping': 0, 'global_grad_clipping': 1.0, 'weight_decay': 0, 'learning_rate': 5e-05, 'momentum': 0, 'warmup': 0.1, 'warmup_schedule': 'warmup_linear', 'adam_eps': 1e-06, 'vb_dropout': True, 'dropout_p': 0.1, 'dropout_w': 0.0, 'bert_dropout_p': 0.1, 'model_ckpt': 'checkpoints/model_0.pt', 'resume': False, 'have_lr_scheduler': True, 'multi_step_lr': '10,20,30', 'lr_gamma': 0.5, 'scheduler_type': 'ms', 'output_dir': 'glue_app/mnli/bert-base-uncased', 'seed': 2018, 'grad_accumulation_step': 1, 'fp16': False, 'fp16_opt_level': 'O1', 'adv_train': False, 'adv_opt': 0, 'adv_norm_level': 0, 'adv_p_norm': 'inf', 'adv_alpha': 1, 'adv_k': 1, 'adv_step_size': 1e-05, 'adv_noise_var': 1e-05, 'adv_epsilon': 1e-06, 'encode_mode': False, 'debug': False, 'transformer_cache': '.cache', 'rank': 0, 'task_def_list': [{'adv_loss': '<LossCriterion.SymKlCriterion: 7>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'loss': '<LossCriterion.CeCriterion: 0>', 'dropout_p': '0.1', 'enable_san': 'False', 'split_names': "['train', 'matched_dev', 'mismatched_dev', 'matched_test', 'mismatched_test']", 'metric_meta': '(<Metric.ACC: 0>,)', 'task_type': '<TaskType.Classification: 1>', 'data_type': '<DataFormat.PremiseAndOneHypothesis: 2>', 'n_class': '3', 'label_vocab': '<data_utils.vocab.Vocabulary object at 0x7f3753a44668>', 'self': '{}', 'class': "<class 'experiments.exp_def.TaskDef'>"}]} 02/12/2022 06:15:47 #################### 02/12/2022 06:15:47 ############# Gradient Accumulation Info ############# 02/12/2022 06:15:47 number of step: 9204 02/12/2022 06:15:47 number of grad grad_accumulation step: 1 02/12/2022 06:15:47 adjusted number of step: 9204 02/12/2022 06:15:47 ############# Gradient Accumulation Info ############# 02/12/2022 06:15:57 02/12/2022 06:15:57 Total number of params: 109484547 02/12/2022 06:15:57 At epoch 0 02/12/2022 06:16:03 Task [ 0] updates[ 1] train loss[1.10139] remaining[4:41:51] 02/12/2022 06:18:17 Task [ 0] updates[ 500] train loss[0.81959] remaining[0:11:59] 02/12/2022 06:20:30 Task [ 0] updates[ 1000] train loss[0.65015] remaining[0:09:24] 02/12/2022 06:22:48 Task [ 0] updates[ 1500] train loss[0.57299] remaining[0:07:09] 02/12/2022 06:25:08 Task [ 0] updates[ 2000] train loss[0.53009] remaining[0:04:53] 02/12/2022 06:27:22 Task [ 0] updates[ 2500] train loss[0.49991] remaining[0:02:35] 02/12/2022 06:29:36 Task [ 0] updates[ 3000] train loss[0.47922] remaining[0:00:18] 02/12/2022 06:29:54 Evaluation 02/12/2022 06:30:13 Task mnli_matched -- epoch 0 -- Dev ACC: 87.937 02/12/2022 06:30:32 Task mnli_mismatched -- epoch 0 -- Dev ACC: 87.917 02/12/2022 06:30:32 Evaluation 02/12/2022 06:31:10 [new test scores at 0 saved.] 02/12/2022 06:31:13 At epoch 1 02/12/2022 06:33:20 Task [ 0] updates[ 3500] train loss[0.46232] remaining[0:12:59] 02/12/2022 06:35:34 Task [ 0] updates[ 4000] train loss[0.44551] remaining[0:09:59] 02/12/2022 06:37:47 Task [ 0] updates[ 4500] train loss[0.42968] remaining[0:07:30] 02/12/2022 06:40:03 Task [ 0] updates[ 5000] train loss[0.41715] remaining[0:05:12] 02/12/2022 06:42:22 Task [ 0] updates[ 5500] train loss[0.40635] remaining[0:02:55] 02/12/2022 06:44:36 Task [ 0] updates[ 6000] train loss[0.39754] remaining[0:00:37] 02/12/2022 06:45:13 Evaluation 02/12/2022 06:45:32 Task mnli_matched -- epoch 1 -- Dev ACC: 88.630 02/12/2022 06:45:52 Task mnli_mismatched -- epoch 1 -- Dev ACC: 88.548 02/12/2022 06:45:52 Evaluation 02/12/2022 06:46:30 [new test scores at 1 saved.] 02/12/2022 06:46:33 At epoch 2 02/12/2022 06:48:13 Task [ 0] updates[ 6500] train loss[0.38976] remaining[0:12:29] 02/12/2022 06:50:31 Task [ 0] updates[ 7000] train loss[0.38173] remaining[0:10:08] 02/12/2022 06:52:44 Task [ 0] updates[ 7500] train loss[0.37323] remaining[0:07:44] 02/12/2022 06:55:04 Task [ 0] updates[ 8000] train loss[0.36603] remaining[0:05:30] 02/12/2022 06:57:23 Task [ 0] updates[ 8500] train loss[0.35957] remaining[0:03:13] 02/12/2022 06:59:37 Task [ 0] updates[ 9000] train loss[0.35410] remaining[0:00:55] 02/12/2022 07:00:33 Evaluation 02/12/2022 07:00:51 Task mnli_matched -- epoch 2 -- Dev ACC: 88.762 02/12/2022 07:01:11 Task mnli_mismatched -- epoch 2 -- Dev ACC: 88.670
AJSVB
Running on an internal data set. Only 1 multi-classification task.
BERT F1 score: ~85% ELECTRA / ROBERTA F1 score: ~75%
model.network of ELECTRA (last ~10 lines)
02/09/2022 01:12:40 {'log_file': 'log.log', 'tensorboard': False, 'tensorboard_logdir': 'tensorboard_logdir', 'init_checkpoint': 'electra.pt', 'data_dir': 'data', 'data_sort_on': False, 'name': 'farmer', 'task_def': 'config.yml', 'train_datasets': ['agg_data'], 'test_datasets': ['agg_data'], 'glue_format_on': False, 'mkd_opt': 0, 'do_padding': False, 'update_bert_opt': 0, 'multi_gpu_on': False, 'mem_cum_type': 'simple', 'answer_num_turn': 5, 'answer_mem_drop_p': 0.1, 'answer_att_hidden_size': 128, 'answer_att_type': 'bilinear', 'answer_rnn_type': 'gru', 'answer_sum_att_type': 'bilinear', 'answer_merge_opt': 1, 'answer_mem_type': 1, 'max_answer_len': 10, 'answer_dropout_p': 0.1, 'answer_weight_norm_on': False, 'dump_state_on': False, 'answer_opt': 1, 'pooler_actf': 'tanh', 'mtl_opt': 0, 'ratio': 0, 'mix_opt': 0, 'max_seq_len': 512, 'init_ratio': 1, 'encoder_type': 7, 'num_hidden_layers': -1, 'bert_model_type': 'bert-base-uncased', 'do_lower_case': False, 'masked_lm_prob': 0.15, 'short_seq_prob': 0.2, 'max_predictions_per_seq': 128, 'bin_on': False, 'bin_size': 64, 'bin_grow_ratio': 0.5, 'local_rank': -1, 'world_size': 1, 'master_addr': 'localhost', 'master_port': '6600', 'backend': 'nccl', 'cuda': True, 'log_per_updates': 500, 'save_per_updates': 10000, 'save_per_updates_on': False, 'epochs': 5, 'batch_size': 8, 'batch_size_eval': 8, 'optimizer': 'adamax', 'grad_clipping': 0, 'global_grad_clipping': 1.0, 'weight_decay': 0, 'learning_rate': 5e-05, 'momentum': 0, 'warmup': 0.1, 'warmup_schedule': 'warmup_linear', 'adam_eps': 1e-06, 'vb_dropout': True, 'dropout_p': 0.1, 'dropout_w': 0.0, 'bert_dropout_p': 0.1, 'model_ckpt': 'checkpoints/model_0.pt', 'resume': False, 'have_lr_scheduler': True, 'multi_step_lr': '10,20,30', 'lr_gamma': 0.5, 'scheduler_type': 'ms', 'output_dir': 'result/exp2', 'seed': 2018, 'grad_accumulation_step': 4, 'fp16': False, 'fp16_opt_level': 'O1', 'adv_train': False, 'adv_opt': 0, 'adv_norm_level': 0, 'adv_p_norm': 'inf', 'adv_alpha': 1, 'adv_k': 1, 'adv_step_size': 1e-05, 'adv_noise_var': 1e-05, 'adv_epsilon': 1e-06, 'encode_mode': False, 'debug': False, 'transformer_cache': '.cache', 'task_def_list': [{'adv_loss': '<LossCriterion.SymKlCriterion: 7>', 'kd_loss': '<LossCriterion.MseCriterion: 1>', 'loss': '<LossCriterion.CeCriterion: 0>', 'dropout_p': 'None', 'enable_san': 'False', 'split_names': "['train', 'dev', 'test']", 'metric_meta': '(<Metric.F1MAC: 9>, <Metric.F1MIC: 10>)', 'task_type': '<TaskType.Classification: 1>', 'data_type': '<DataFormat.PremiseOnly: 1>', 'n_class': '147', 'label_vocab': '<data_utils.vocab.Vocabulary object at 0x7fe531fc7f98>', 'self': '{}', '__class__': "<class 'experiments.exp_def.TaskDef'>"}]}Will try to reproduce on opensource data once I have time.