Hey,
I am trying to figure out how to freeze layers of a model and read that I had to use
for param in model.base_model.parameters():
    param.requires_grad = False

if I wanted to freeze the encoder of a pretrained MLM for example. But how do I use this with the Trainer?
I tried the following:
from transformers import BertTokenizer, BertForMaskedLM. LineByLineTextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

for param in model.base_model.parameters():
    param.requires_grad = False

dataset = LineByLineTextDataset(
    tokenizer=tokenizer,
    file_path=in_path,
    block_size=512,
)

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)

training_args = TrainingArguments(
    output_dir=out_path,
    overwrite_output_dir=True,
    num_train_epochs=25,
    per_device_train_batch_size=48,
    save_steps=500,
    save_total_limit=2,
    seed=1
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset
)

trainer.train()

If the encoder was frozen I would expect it to produce the same outputs as a fresh instance of the pretrained encoder, but it doesn’t:
model_fresh = BertForMaskedLM.from_pretrained('bert-base-uncased')
inputs = tokenizer("This is a boring test sentence", return_tensors="pt")
torch.all(model.bert(**inputs)[0].eq(model_fresh.bert(**inputs)[0]))
--> tensor(false)

So I must be doing somethin wrong here, I guess the Trainer is reseting the requires_grad attribute and I have to overwrite it somehow after I instanciated the trainer?
Thanks in advance!
Johannes