can't run (TF)BartForConditionalGeneration.generation on GPU, it's speed very very very slow
### System Info

```shell
transformers==4.19
tensorflow-gpu==2.3
torch==1.11
```


### Who can help?

@patil-suraj@patrickvonplaten, @Narsil, @gante@Rocketknight1

### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

from transformers import BertTokenizer, TFBartForConditionalGeneration
tokenizer = BertTokenizer.from_pretrained("fnlp/bart-base-chinese")
model = TFBartForConditionalGeneration.from_pretrained("fnlp/bart-base-chinese", from_pt=True)
batch_data = ['北京是[MASK]的首都']*64
for i in range(20):
    batch_dict = tokenizer.batch_encode_plus(batch_data, return_token_type_ids=False, return_tensors='tf')
    result = model.generate(**batch_dict, max_length=20)
    result = tokenizer.batch_decode(result, skip_special_tokens=True)
print(result)

### Expected behavior

```shell
1 . when I run CUDA_VISIBLE_DEVICES=1 python test.py, GPU's memory is used,GPU utilization is almost zero，and generate speed is very very very slow, cpu utilization is 100%.
2. when i replace TFBartForConditionalGeneration with BartForConditionalGeneration, GPU's memory is used,
GPU utilization is almost zero，cpu utilization greater than 100%，speed is normal,but it mean that, generate is on cpu not GPU.
```

Hey @TheHonestBob 👋  We are aware of the generate speed problems with TensorFlow, and will be releasing an update very soon. It is not a bug, but rather how Eager Execution works, sadly. Stay tuned 🤞 <|||||>> Hey @TheHonestBob 👋 We are aware of the generate speed problems with TensorFlow, and will be releasing an update very soon. It is not a bug, but rather how Eager Execution works, sadly. Stay tuned 🤞

thanks for your reply，what can I do before update to solve it.<|||||>My advice would be to go with the PyTorch version, if performance is a bottleneck to you and you need something working in the next ~2 weeks. If you can afford to wait ~2 weeks, then you can have a look at the guides we are writing up at the moment :) <|||||>> My advice would be to go with the PyTorch version, if performance is a bottleneck to you and you need something working in the next ~2 weeks. If you can afford to wait ~2 weeks, then you can have a look at the guides we are writing up at the moment :)

OK, I will continue to pay attention no it<|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.<|||||>@TheHonestBob -- some of the functionality to speed up has been merged recently. If you try running a modified version of your script and you have a GPU, you will see it is much much faster.

```python
import tensorflow as tf
from transformers import BertTokenizer, TFBartForConditionalGeneration
tokenizer = BertTokenizer.from_pretrained("fnlp/bart-base-chinese")
model = TFBartForConditionalGeneration.from_pretrained("fnlp/bart-base-chinese", from_pt=True)
batch_data = ['北京是[MASK]的首都']*64
xla_generate = tf.function(model.generate, jit_compile=True)
for i in range(20):
    batch_dict = tokenizer.batch_encode_plus(batch_data, return_token_type_ids=False, return_tensors='tf')
    result = xla_generate(**batch_dict, max_length=20, no_repeat_ngram_size=0, num_beams=1)
    result = tokenizer.batch_decode(result, skip_special_tokens=True)
    print(result)
```

To enable bigger values of `num_beams`, which should increase the quality of the generation, [this PR](https://github.com/huggingface/transformers/pull/17857) has to be merged first :)<|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.<|||||>@TheHonestBob The newest release (v4.21) fixes this issue. Check our recent blog post -- https://huggingface.co/blog/tf-xla-generate<|||||>> @TheHonestBob The newest release (v4.21) fixes this issue. Check our recent blog post -- https://huggingface.co/blog/tf-xla-generate

thanks a lot, I'll try it