T5 GPU Runtime Degradation
## Environment info
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     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.2.1 VS 3.4.0
- Platform: Colab (K80 GPU)
- Python version: 3.6.9
- PyTorch version (GPU?): 1.7.0+cu101
- Tensorflow version (GPU?): N.A.
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @jplu

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @n1t0, @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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@patrickvonplaten, @patil-suraj

## Information

Model I am using (Bert, XLNet ...): T5

The problem arises when using:
* [x] the official example scripts: (give details below)
* [] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

Hello,

I’ve noticed that the running time of T5 on a GPU has increased between v3.4.0 and the current version (v4.2.1). When running inference on a single example on a K80 GPU (Google Colab), the average runtime of a generate() call for a single example (the one in the transformers documentation) with t5-base in v3.4.0 is 539 ± 13 ms, while the runtime for v4.2.1 is 627 ± 13 ms.
On t5-large, the difference is 1004 ± 22 ms, compared to 1242 ± 15 ms.

I made two colab notebooks that compare the two versions:
https://colab.research.google.com/drive/1Rm9RFdfLUFFHOvjAOg816-6oXw8zm_tE?usp=sharing#scrollTo=eeJ0sS_g7-X2
https://colab.research.google.com/drive/1U2QPA4MR48xPCpn4XiG5KBk3qZGYeoIJ?usp=sharing

I’m aware of a at least one bug fix that was made to the attention mechanism of T5 in v4.0.0 (#8158), but I don’t think this change should have caused such a degradation.
Any idea why such a degradation occurred?

Thanks!

## To reproduce

See Colab notebooks attached. See the following code snippet as well:
```
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
print(f"Using device: {device}")

t5_tokenizer = T5TokenizerFast.from_pretrained('t5-base')
t5_model = T5ForConditionalGeneration.from_pretrained('t5-base')
t5_model = t5_model.to(device)

t5_input_ids = t5_tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="pt").input_ids  # Batch size 1
t5_input_ids = t5_input_ids.to(device)

import time
import numpy as np
N = 100
times = []
for _ in range(N):
  start = time.time()
  t5_outputs = t5_model.generate(t5_input_ids)
  end = time.time()
  times.append(end-start)
print(f"transformers version: {transformers_version}")
print(f"torch version: {torch_version}")
print(f"{1000*np.mean(times):.0f} ms \u00B1 {1000*np.std(times):.2f} ms per loop (mean \u00B1 std of {N} runs)")
```

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Thanks a lot for this issue @dsgissin! Will take a look this week!<|||||>Hey! 
Did you get a chance to look into the runtime degradation?

Thanks<|||||>Looking now! Sorry for the delay<|||||>Okey, I can reproduce the degradation! Will try to fix it today<|||||>I think this PR should fix it: https://github.com/huggingface/transformers/pull/10496

Let me know if you still encounter a degradation!

Thanks a mille for spotting this degradation - you probably now made T5 faster for the whole community :-)<|||||>Great, thanks a lot for the quick fix!