debert TypeError: _softmax_backward_data(): argument 'input_dtype' (position 4) must be torch.dtype, not Tensor
def get_training_corpus():
    dataset = list(data["msg"])
    for start_idx in range(0, len(dataset), 1000):
        samples = dataset[start_idx: start_idx + 1000]
        yield samples

DOWNLOADED_MODEL_PATH = 'model'
MODEL_NAME = 'microsoft/deberta-base'
if DOWNLOADED_MODEL_PATH=='model':
    os.makedirs('model', exist_ok=True)
    old_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    training_corpus = get_training_corpus()
    BertTokenizer_tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 10000)
    BertTokenizer_tokenizer.save_pretrained('model')
    BertTokenizer_tokenizer = AutoTokenizer.from_pretrained('model')
    config = DebertaConfig.from_pretrained(MODEL_NAME)
    config.save_pretrained('model')
    # config = BertConfig.from_pretrained('aaa_model')
else:
    BertTokenizer_tokenizer = AutoTokenizer.from_pretrained('model')
    config = DebertaConfig.from_pretrained('model')
dataset = LineByLineTextDataset(
    tokenizer=BertTokenizer_tokenizer,
    file_path="aa.txt",
    block_size=512,
)
data_collator = DataCollatorForLanguageModeling(tokenizer=BertTokenizer_tokenizer,mlm=True, mlm_probability=0.15)
persian_model = DebertaForMaskedLM(config=config)

batch_size = 15
training_args = TrainingArguments(
    output_dir='aaa_debert_model',
    overwrite_output_dir=True,
    num_train_epochs=3,
    learning_rate=5e-05,
    per_device_train_batch_size=batch_size,
    save_steps=len(dataset) / batch_size,
    save_total_limit=2,
    prediction_loss_only=True
)

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

)

trainer.train()
trainer.save_model('aaa_debert_model/pretrain')
BertTokenizer_tokenizer.save_pretrained('aaa_debert_model/aaa_model')

error message:
trainer.train()
  File "C:\Users\HP\.conda\envs\tensorflow26-py37\lib\site-packages\transformers\trainer.py", line 1332, in train
    tr_loss_step = self.training_step(model, inputs)
  File "C:\Users\HP\.conda\envs\tensorflow26-py37\lib\site-packages\transformers\trainer.py", line 1909, in training_step
    loss.backward()
  File "C:\Users\HP\.conda\envs\tensorflow26-py37\lib\site-packages\torch\_tensor.py", line 363, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "C:\Users\HP\.conda\envs\tensorflow26-py37\lib\site-packages\torch\autograd\__init__.py", line 175, in backward
    allow_unreachable=True, accumulate_grad=True)  # Calls into the C++ engine to run the backward pass
  File "C:\Users\HP\.conda\envs\tensorflow26-py37\lib\site-packages\torch\autograd\function.py", line 253, in apply
    return user_fn(self, *args)
  File "C:\Users\HP\.conda\envs\tensorflow26-py37\lib\site-packages\transformers\models\deberta\modeling_deberta.py", line 114, in backward
    inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)
TypeError: _softmax_backward_data(): argument 'input_dtype' (position 4) must be torch.dtype, not Tensor

bert roberta no problem。Am I doing something wrong? Python 3.9 transformers 4.15.0 win10 torch 1.11.0
Yes I have the same issue... I am using transformers 4.17.0<|||||>Same issue. Is it a version issue?<|||||>
If anyone solved this problem, hope to give some advice, thanks！<|||||>> Yes I have the same issue... I am using transformers 4.17.0

I also tried 4.17.0, the same problem<|||||>same issue<|||||>Should be fixed by #16043 in 4.18.0.<|||||>Downgrade your pytorch to 1.10 and you won't have this problem.  I'll make a pull request to get this fixed soon.<|||||>> Downgrade your pytorch to 1.10 and you won't have this problem. I'll make a pull request to get this fixed soon.

Got it, thanks.<|||||>Worked by downgrading pytorch to 1.10. Thanks!<|||||>Hi I am using transformers `4.14.1` and pytorch `1.12.0` and the issue persists<|||||>transformers should be > 4.19 