Trainer() shows no log for validation loss when using PEFT

🤗Transformers
1

I modified the code from the notebook provided in this course.(Fine-tuning a model with the Trainer API).The following code produces validation loss while training and uses the compute metric when I am not using PEFT. But when I am using PEFT, it is showing “no log” as validation loss and skips the compute metric. what can be the problem?

from peft import LoraConfig , prepare_model_for_kbit_training , get_peft_model

lora_config = LoraConfig(
    r=8,
    target_modules=["query", "key","value"],
    bias="none",
    lora_alpha=16,
    lora_dropout=0.1
)

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)

model = prepare_model_for_kbit_training(model,use_gradient_checkpointing=False)
model = get_peft_model(model, lora_config)

def compute_metrics(eval_preds):
    metric = evaluate.load("glue", "mrpc")
    logits, labels = eval_preds
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

training_args = TrainingArguments("test-trainer",
                                  report_to = 'wandb',
                                  evaluation_strategy="epoch",
                                  disable_tqdm=False,
                                  per_device_train_batch_size=4,
                                  per_device_eval_batch_size=4,
                                  gradient_accumulation_steps=16,
                                  save_steps=10,
                                  logging_steps=10,
                                  max_grad_norm=0.3,
                                  num_train_epochs=3,
                                  warmup_ratio=0.03,
                                  )

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)