BartForConditionalGeneration: `decoder_input_ids` should not be computed if `decoder_inputs_embeds` is set
## Environment info
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     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.8.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.10
- PyTorch version (GPU?): 1.9.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

### Who can help
@patrickvonplaten, @patil-suraj

## Information

- Model I am using: Bart. The specific class is BartForConditionalGeneration.
- Summary: Cannot provide `decoder_inputs_embeds` to `BartForConditionalGeneration.forward` if `labels` are provided.

In `BartForConditionalGeneration.forward`, there is the following code on line 1285 of modelling_bart.py in the current version (4.8.2):

    if labels is not None:
        if decoder_input_ids is None:
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

So, if `labels` are provided, `decoder_input_ids` are set to the `labels` shifted to the right. This is problematic: if `decoder_inputs_embeds` is also set, the call to `self.model`, which eventually gets to `BartDecoder.forward`, will raise an error. In particular, the snipped that does this in `BartDecoder.forward` is (line 967 of modelling_bart.py in version 4.8.2):

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")

The fix is quite simple, similar to what is there already in `BartModel.forward` (line 1152 in version 4.8.2). Mainly, we should not compute `decoder_input_ids` if `decoder_inputs_embeds` is provided. That is, the first snippet above in In `BartForConditionalGeneration.forward` should be:

    if labels is not None:
        if decoder_input_ids is None and decoder_inputs_embeds is None: # <- this line changed
            decoder_input_ids = shift_tokens_right(
                labels, self.config.pad_token_id, self.config.decoder_start_token_id
            )

## To reproduce

Steps to reproduce the behavior:

    model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
    input_ids = torch.tensor([[1, 2, 3]])
    labels = torch.tensor([[1, 2, 3]])
    decoder_input_ids = torch.tensor([[1, 2, 3]])
    decoder_inputs_embeds = model.get_input_embeddings()(decoder_input_ids)
    model(input_ids, decoder_inputs_embeds=decoder_inputs_embeds, labels=labels)    

## Expected behavior
`decoder_input_ids` should not be computed if `decoder_inputs_embeds` and `labels` are provided.

same problem here, my solution is removing argument  `labels`, but I have to calculate loss outside the forward function.<|||||>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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