Generate Function call throughs error when "inputs_embeds" argument passed
When using `inputs_embeds` as the argument instead of 'input_ids' while trying to generate text with GPT2 model, an error pops up about `input_ids`. 

```
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
import transformers

import torch
import torch.nn as nn

model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

some_random_texts = "This is a nice place to eat"
tokenized_text = tokenizer.encode(some_random_texts, return_tensors='pt')
tokenized_text_embeds = model.transformer.wte(tokenized_text)

output = model.generate(inputs_embeds=tokenized_text_embeds, max_length=50)
```


The error generated is:
```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-2-643fe4803ba4> in <module>
     12 tokenized_text_embeds = model.transformer.wte(tokenized_text)
     13 
---> 14 output = model.generate(inputs_embeds=tokenized_text_embeds, max_length=50)

C:\ProgramData\Anaconda3\envs\hugging_face\lib\site-packages\torch\autograd\grad_mode.py in decorate_context(*args, **kwargs)
     24         def decorate_context(*args, **kwargs):
     25             with self.__class__():
---> 26                 return func(*args, **kwargs)
     27         return cast(F, decorate_context)
     28 

C:\ProgramData\Anaconda3\envs\hugging_face\lib\site-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, max_time, 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, forced_bos_token_id, forced_eos_token_id, remove_invalid_values, synced_gpus, **model_kwargs)
    891             # init `attention_mask` depending on `pad_token_id`
    892             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
--> 893                 input_ids, pad_token_id, eos_token_id
    894             )
    895 

C:\ProgramData\Anaconda3\envs\hugging_face\lib\site-packages\transformers\generation_utils.py in _prepare_attention_mask_for_generation(self, input_ids, pad_token_id, eos_token_id)
    401         if is_pad_token_in_inputs_ids and is_pad_token_not_equal_to_eos_token_id:
    402             return input_ids.ne(pad_token_id).long()
--> 403         return input_ids.new_ones(input_ids.shape, dtype=torch.long)
    404 
    405     def _prepare_encoder_decoder_kwargs_for_generation(

AttributeError: 'NoneType' object has no attribute 'new_ones'
```


While solving this issue, I find that 'attention_mask' argument needs to be included too, to avoid this `self._prepare_attention_mask_for_generation` function being called altogether, another error pops ups.

The changes made are as follows:
```
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
import transformers

import torch
import torch.nn as nn

model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

some_random_texts = "This is a nice place to eat"
tokenized_text = tokenizer.encode(some_random_texts, return_tensors='pt')
tokenized_text_embeds = model.transformer.wte(tokenized_text)

att_mask = torch.ones(tokenized_text.shape[1])

output = model.generate(inputs_embeds=tokenized_text_embeds, attention_mask=att_mask, max_length=50)
```

and the error that pops up now is:
```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-17cdf18aa52b> in <module>
     14 att_mask = torch.ones(tokenized_text.shape[1])
     15 
---> 16 output = model.generate(inputs_embeds=tokenized_text_embeds, attention_mask=att_mask, max_length=50)

C:\ProgramData\Anaconda3\envs\hugging_face\lib\site-packages\torch\autograd\grad_mode.py in decorate_context(*args, **kwargs)
     24         def decorate_context(*args, **kwargs):
     25             with self.__class__():
---> 26                 return func(*args, **kwargs)
     27         return cast(F, decorate_context)
     28 

C:\ProgramData\Anaconda3\envs\hugging_face\lib\site-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, max_time, 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, forced_bos_token_id, forced_eos_token_id, remove_invalid_values, synced_gpus, **model_kwargs)
    917                 raise ValueError("Make sure that `model_kwargs` include `encoder_outputs` of type `ModelOutput`.")
    918 
--> 919         if input_ids.shape[-1] >= max_length:
    920             input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
    921             logger.warning(

AttributeError: 'NoneType' object has no attribute 'shape'
```

Am I doing something wrong here or is there is a bug in the code of `generation_utils.GenerationMixin.generate()`?

Version control:
transformers: 4.6.1
torch: 1.7.1
python: 3.7.4

@patrickvonplaten 
Hey @abhikasd6523,

I don't think that `generate()` currently supports `inputs_embeds` correctly. It would require quite some changes in `generate()` to make it work I'm afraid. Can you give me some more background on your use-case for passing `inputs_embeds` instead of `input_ids`. If it's a general enough use-case, I think we could try to make the required changes to `generate()`<|||||>Thanks a lot for replying.
I am trying to connect a custom encoder to the GPT2 model and would want to pass the vectored last layer values of the Encoder as input embeds. The goal is to generate a random sentence with this type of connection and architecture.<|||||>I see - did you try to directly use the `sample(...)` method? Think that this could work<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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