TypeError: forward() got an unexpected keyword argument 'return_dict'

I am trying to perform inference with a finetuned GPT2HeadWithValueModel. I’m using the model.generate() method from generation_utils.py inside this function.

def top_p_sampling(text, model, tokenizer):
  encoding = tokenizer(text, return_tensors="pt")['input_ids']
  output_tensor = model.generate(
    top_p= .92,      
    temperature= .9,
  return tokenizer.decode(output_tensor[0], skip_special_tokens=True).strip()

But when i try:

text = "this is an example of input text"
comp = top_p_sampling(text, model_name, tokenizer_name)

I get the following error:

TypeError: forward() got an unexpected keyword argument 'return_dict'

Full traceback:

TypeError                                 Traceback (most recent call last)
<ipython-input-24-cc7c3f8aa367> in <module>()
      1 text = "this is an example of input text"
----> 2 comp = top_p_sampling(text, model_name, tokenizer_name)

4 frames
<ipython-input-23-a5241487f309> in top_p_sampling(text, model, tokenizer)
      9     temperature=temp,
--->10   early_stopping=False,
     12   return tokenizer.decode(output_tensor[0], skip_special_tokens=True).strip()

/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     26         def decorate_context(*args, **kwargs):
     27             with self.__class__():
---> 28                 return func(*args, **kwargs)
     29         return cast(F, decorate_context)

/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in generate(self, input_ids, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, **model_kwargs)
    938                 output_scores=output_scores,
    939                 return_dict_in_generate=return_dict_in_generate,
--> 940                 **model_kwargs,
    941             )

/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in sample(self, input_ids, logits_processor, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, **model_kwargs)
   1383                 return_dict=True,
   1384                 output_attentions=output_attentions,
-> 1385                 output_hidden_states=output_hidden_states,
   1386             )
   1387             next_token_logits = outputs.logits[:, -1, :]

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
   1100         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1101                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102             return forward_call(*input, **kwargs)
   1103         # Do not call functions when jit is used
   1104         full_backward_hooks, non_full_backward_hooks = [], []

TypeError: forward() got an unexpected keyword argument 'return_dict'

I’m a bit of a rookie, so I hope someone can point out what I’m doing wrong. Thanks a lot