Adding support for `truncation` parameter on `feature-extraction` pipeline.
Fixes #14183


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Can you also include `padding` as well? Since we're extracting features, I'd like to be able to specify both padding and truncation strategies. Thanks! @Narsil <|||||>Padding for pipelines is something, I would like to keep orthogonal to business logic (See https://github.com/huggingface/transformers/pull/13724).

It's more batching than padding, but I imagine you pad mostly for the batch.
Currently, pipelines do not batch ever, meaning the padding is not used. Would adding padding change the results of `feature-extraction`?<|||||>Only slightly, I think. Right now you get embeddings of varying size depending on the size of your input sequence. If you want to use somehow these embeddings in a downstream task, it's weird to have varying size. In my case, I think I'm going to use only the embedding corresponding to the CLS, so I'm good. <|||||>@Narsil Note that padding is supported in other pipelines. I think as a user, it's maddening to have varied behavior depending on which pipeline you use. I personally think that lack of consistency across pipelines is problematic. Take a look at this pipeline code, note there is logic around padding:

https://github.com/huggingface/transformers/blob/026866df92afe40cdf928839864111015a62d3b5/src/transformers/pipelines/text2text_generation.py#L92
<|||||>>  it's maddening to have varied behavior depending on which pipeline you use. 

You're 100% correct, that's part of the reason of the large rewrite which is happening. 

For instance, the rewrite enables you to write either `pipeline(..., truncation=True)` or `pipe = pipeline(..); pipe(..., truncation=True)`. 
And that for all pipelines, and all parameters. This was far from the case before.

If anything dropping padding from the code you're quoting would be the way to go. (At least, deprecating it first, we have to maintain compatibility as much as possible). This code is currently legacy, and should be rewritten sometime in the future. The thing is there are a couple of directions to be considered for `text2text-generation` and we're also trying to align pipelines with other libraries. (https://github.com/huggingface/huggingface_hub/tree/main/api-inference-community)

Padding, is like batching, it was very spurious support across pipelines, we're closing the gap, but it takes time, and backward compatibility is important. The core idea is to get orthogonal behavior whereever possible. So as much as possible, individual pipelines should NOT handle them, all this logic should be enabled in the parent class. Not all models are even capable of padding (`gpt2` for instance).

Truncation for instance, is not orthogonal, since `question-answering` and `zero-shot-classification` will handle long prompts by chunking the input. Some pipelines input cannot really be chunked: `summarization` for instance uses and encoder-decoder, if the prompt does not fit the size of the model, then the summary cannot realistically chunk (or it will come with its own set of drawbacks let's say). 

That's also the reason why adding new parameters is something we try to think about before jumping to it.

`truncation` in `feature-extraction` is important because afaik, sentence embeddings do use the feature-extraction capability, and missing the last part of a sentence is indeed OK in a lot of cases (you want to use only the first token embedding, and missing part of the sentence is OK since it's only about matching later). It still needs to be opt-in as you need to explicitly know you can miss part of the sentence. Ideally, we would also prompt a warning since we're ignoring part of the sentence. And since a user sending text has no idea how long it is token-wise, it would be better to tell which part of the sentence is being chunked.

Hope this clears a bit what's going on.
Happy to receive feedback here too.