graphcodebert-robust/training.log

2026-04-15 17:47:48,928 - INFO - train_pipeline - Logging to ./output_checkpoints/graphcodebert-robust/training.log
2026-04-15 17:47:48,933 - INFO - train_pipeline - Training config: TrainConfig(model_name='microsoft/graphcodebert-base', output_dir='./output_checkpoints/graphcodebert-robust', num_epochs=10, batch_size=32, learning_rate=2e-05, max_length=512, num_labels=2, use_wandb=True, freeze_base=True, loss_type='r-drop', focal_alpha=1.0, focal_gamma=2.0, r_drop_alpha=4.0, infonce_temperature=0.07, infonce_weight=0.5, seed=42, resume_from_checkpoint=None, label_smoothing=0.1, adversarial_epsilon=0.5, use_swa=False, swa_start_epoch=2, swa_lr=1e-05, data_augmentation=True, aug_rename_prob=0.3, aug_format_prob=0.3, device=device(type='cuda'))
2026-04-15 17:47:48,936 - INFO - train_pipeline - Loading model & tokenizer for 'microsoft/graphcodebert-base'
2026-04-15 17:47:51,171 - INFO - train_pipeline - Model placed on cuda
2026-04-15 17:47:51,174 - INFO - train_pipeline - Base model weights frozen – only classifier head will be trained.
2026-04-15 17:47:51,177 - INFO - train_pipeline - ===== Model Architecture =====
2026-04-15 17:47:51,180 - INFO - train_pipeline - 
RobertaForSequenceClassification(
  (roberta): RobertaModel(
    (embeddings): RobertaEmbeddings(
      (word_embeddings): Embedding(50265, 768, padding_idx=1)
      (position_embeddings): Embedding(514, 768, padding_idx=1)
      (token_type_embeddings): Embedding(1, 768)
      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
      (dropout): Dropout(p=0.2, inplace=False)
    )
    (encoder): RobertaEncoder(
      (layer): ModuleList(
        (0-11): 12 x RobertaLayer(
          (attention): RobertaAttention(
            (self): RobertaSdpaSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.2, inplace=False)
            )
            (output): RobertaSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
              (dropout): Dropout(p=0.2, inplace=False)
            )
          )
          (intermediate): RobertaIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): RobertaOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
            (dropout): Dropout(p=0.2, inplace=False)
          )
        )
      )
    )
  )
  (classifier): RobertaClassificationHead(
    (dense): Linear(in_features=768, out_features=768, bias=True)
    (dropout): Dropout(p=0.2, inplace=False)
    (out_proj): Linear(in_features=768, out_features=2, bias=True)
  )
)
2026-04-15 17:47:51,186 - INFO - train_pipeline - ===== Parameter Summary =====
2026-04-15 17:47:51,189 - INFO - train_pipeline - Total Parameters:         124,647,170
2026-04-15 17:47:51,191 - INFO - train_pipeline - Trainable Parameters:     592,130
2026-04-15 17:47:51,193 - INFO - train_pipeline - Non-trainable Parameters: 124,055,040
2026-04-15 17:47:51,195 - INFO - train_pipeline - ===== Tokenizer Summary =====
2026-04-15 17:47:51,224 - INFO - train_pipeline - Vocab size: 50265 | Special tokens: ['<s>', '</s>', '<unk>', '<pad>', '<mask>']
2026-04-15 17:47:51,227 - INFO - train_pipeline - ===== End of Architecture Log =====
2026-04-15 17:47:54,338 - INFO - train_pipeline - Data augmentation enabled (rename=0.3, format=0.3)
2026-04-15 17:47:54,374 - INFO - train_pipeline - === Starting training with robust regularisation ===