switch to inference_mode from no_gard
# What does this PR do?

Use `torch.inference_mode()` for pipelines. 
https://pytorch.org/docs/stable/generated/torch.inference_mode.html

`with torch.no_grad()`
![Screenshot 2021-09-21 at 11 46 12 AM](https://user-images.githubusercontent.com/17096858/134121830-e16ddaae-77ea-4dc7-8464-6ce80b44ebf9.png)

`with torch.inference_mode()`
![Screenshot 2021-09-21 at 11 48 14 AM](https://user-images.githubusercontent.com/17096858/134121909-4e79ed6c-944e-4c23-9013-53e3d38bd450.png)



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@sgugger @Narsil 
Hi @kamalkraj,

Thanks for this ! Do you mind sharing on which hardware this was done ?
We ran some tests too and found very little difference between the 2 modes (<1%).

We decided against putting that line because `transformers` needs to support older versions of `pytorch` which do not have `inference_mode` (so we would need an extra switch and maintain remove it when necessary). Because the performance was so little we decided it was not worth the investment.

If we can show performance differences, we're more than happy to add such switches.

Here is a small benchmark I ran on a GTX970:

```python
from transformers import pipeline
from transformers.pipelines.base import KeyDataset
import datasets
import tqdm

pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
dataset = datasets.load_dataset("superb", name="asr", split="test")

print("New style of pipeline")
for i, out in tqdm.tqdm(enumerate(pipe(KeyDataset(dataset, "file"))), total=100):
    # print(out)
    if i >= 100:
        break
```

And the results are

`no_grad`:
```
Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
The model 'Wav2Vec2ForCTC' is not supported for automatic-speech-recognition. Supported models are [(<class 'transformers.models.speech_encoder_decoder.configuration_speech_encoder_decoder.SpeechEncoderDecoderConfig'>, <class 'transformers.models.speech_encoder_decoder.modeling_speech_encoder_decoder.SpeechEncoderDecoderModel'>), (<class 'transformers.models.speech_to_text.configuration_speech_to_text.Speech2TextConfig'>, <class 'transformers.models.speech_to_text.modeling_speech_to_text.Speech2TextForConditionalGeneration'>), (<class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>, <class 'transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC'>), (<class 'transformers.models.hubert.configuration_hubert.HubertConfig'>, <class 'transformers.models.hubert.modeling_hubert.HubertForCTC'>)].
Reusing dataset superb (/home/nicolas/.cache/huggingface/datasets/superb/asr/1.9.0/b185de2966d0d6025cd53df6b41f89e2d100ee17139797b793f62b2c7c7612bd)
New style of pipeline
100%|███████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:08<00:00, 12.22it/s]

real    0m21.202s
user    0m23.972s
sys     0m17.884s
```

`inference_mode`:
```
Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.masked_spec_embed']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
The model 'Wav2Vec2ForCTC' is not supported for automatic-speech-recognition. Supported models are [(<class 'transformers.models.speech_encoder_decoder.configuration_speech_encoder_decoder.SpeechEncoderDecoderConfig'>, <class 'transformers.models.speech_encoder_decoder.modeling_speech_encoder_decoder.SpeechEncoderDecoderModel'>), (<class 'transformers.models.speech_to_text.configuration_speech_to_text.Speech2TextConfig'>, <class 'transformers.models.speech_to_text.modeling_speech_to_text.Speech2TextForConditionalGeneration'>), (<class 'transformers.models.wav2vec2.configuration_wav2vec2.Wav2Vec2Config'>, <class 'transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC'>), (<class 'transformers.models.hubert.configuration_hubert.HubertConfig'>, <class 'transformers.models.hubert.modeling_hubert.HubertForCTC'>)].
Reusing dataset superb (/home/nicolas/.cache/huggingface/datasets/superb/asr/1.9.0/b185de2966d0d6025cd53df6b41f89e2d100ee17139797b793f62b2c7c7612bd)
New style of pipeline
100%|███████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:08<00:00, 12.30it/s]

real    0m21.570s
user    0m23.568s
sys     0m17.807s
```

I ran the benchmark multiple times and basically the numbers are exactly the same for each mode.<|||||>Hi @Narsil,

The above screenshot I shared is using TITAN RTX. I have also tested the same on A100.  Similar performance as TITAN RTX

But when I use the below benchmark, I don't really see any performance improvement. (Only tested on A100)
getting the same performance as you shared.
> Here is a small benchmark I ran on a GTX970:
> 
> ```python
> from transformers import pipeline
> from transformers.pipelines.base import KeyDataset
> import datasets
> import tqdm
> 
> pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
> dataset = datasets.load_dataset("superb", name="asr", split="test")
> 
> print("New style of pipeline")
> for i, out in tqdm.tqdm(enumerate(pipe(KeyDataset(dataset, "file"))), total=100):
>     # print(out)
>     if i >= 100:
>         break
> ```

- `transformers` version: 4.11.0.dev0
- Platform: Linux-5.4.0-84-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.9.0+cu111 (True)
<|||||>https://twitter.com/PyTorch/status/1437838236529868803/photo/
![E_Q4D8cXIAki7Yx](https://user-images.githubusercontent.com/17096858/134164937-a12a228d-c546-470c-9d5f-b71dddff45a2.jpeg)
2<|||||>Thanks you @kamalkraj.

I checked the numbers and had similar results for `distilbert-base-uncased-finetuned-sst-2-english` as you.
the odd part is that I also had similar total runtime numbers on a much smaller GPU.

I think this model is super small and so the pipeline does showcase a bit more the difference in `inference_mode`.
But only because we're not feeding it enough, so it's starving (and probably inference_mode sync is faster ?)

As the tweet mentions:

> Note that the highest speedups are for lightweight operations that are bottlenecked by the tracking overhead.

However I am currently thinking that maybe we're just under utilizing the GPU for this and re enabling batching could speedup this use case which would result in similar times for both `no_grad` and `inference_mode` again (both much faster than current implementation but with batching which has it's own set of caveats at inference time).

So my opinion, is that we should:

1- Enable the switch as it's is indeed faster on some cases by more than what was anticipated (but current PR needs to be modified to support older `torch` versions)
2- Add again `batch_size` support for pipeline but with a sane default of `1`, clear explanations of the caveats (alignment issues can really blow up the results and worsen inference times) in the docs and enable users to speed up on small-models with pipelines where data is sufficiently aligned. This was already planned, but having a clear use case will definitely help !

<|||||>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/master/CONTRIBUTING.md) are likely to be ignored.<|||||>unstale<|||||>Thank you for your work @kamalkraj!