[Large PR] Entire rework of pipelines. (#13308)

* Enabling dataset iteration on pipelines.

Enabling dataset iteration on pipelines.

Unifying parameters under `set_parameters` function.

Small fix.

Last fixes after rebase

Remove print.

Fixing text2text `generate_kwargs`

No more `self.max_length`.

Fixing tf only conversational.

Consistency in start/stop index over TF/PT.

Speeding up drastically on TF (nasty bug where max_length would increase
a ton.)

Adding test for support for non fast tokenizers.

Fixign GPU usage on zero-shot.

Fix working on Tf.

Update src/transformers/pipelines/base.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/pipelines/base.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Small cleanup.

Remove all asserts + simple format.

* Fixing audio-classification for large PR.

* Overly explicity null checking.

* Encapsulating GPU/CPU pytorch manipulation directly within `base.py`.

* Removed internal state for parameters of the  pipeline.

Instead of overriding implicitly internal state, we moved
to real named arguments on every `preprocess`, `_forward`,
`postprocess` function.

Instead `_sanitize_parameters` will be used to split all kwargs
of both __init__ and __call__ into the 3 kinds of named parameters.

* Move import warnings.

* Small fixes.

* Quality.

* Another small fix, using the CI to debug faster.

* Last fixes.

* Last fix.

* Small cleanup of tensor moving.

* is not None.

* Adding a bunch of docs + a iteration test.

* Fixing doc style.

* KeyDataset = None guard.

* RRemoving the Cuda test for pipelines (was testing).

* Even more simple iteration test.

* Correct import .

* Long day.

* Fixes in docs.

* [WIP] migrating object detection.

* Fixed the target_size bug.

* Fixup.

* Bad variable name.

* Fixing `ensure_on_device` respects original ModelOutput.

docs/source/main_classes/pipelines.rst

@@ -46,12 +46,53 @@ The pipeline abstraction
 The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
 pipeline but requires an additional argument which is the `task`.
 
+Simple call on one item:
+
+.. code-block::
+
+    >>> pipe = pipeline("text-classification")
+    >>> pipe("This restaurant is awesome")
+    [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
+
+To call a pipeline on many items, you can either call with a `list`.
+
+.. code-block::
+
+    >>> pipe = pipeline("text-classification")
+    >>> pipe(["This restaurant is awesome", "This restaurant is aweful"])
+    [{'label': 'POSITIVE', 'score': 0.9998743534088135},
+     {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
+
+
+To iterate of full datasets it is recommended to use a :obj:`dataset` directly. This means you don't need to allocate
+the whole dataset at once, nor do you need to do batching yourself. This should work just as fast as custom loops on
+GPU. If it doesn't don't hesitate to create an issue.
+
+.. code-block::
+
+    pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
+    dataset = datasets.load_dataset("superb", name="asr", split="test")
+
+    # KeyDataset (only `pt`) will simply return the item in the dict returned by the dataset item
+    # as we're not interested in the `target` part of the dataset.
+    for out in tqdm.tqdm(pipe(KeyDataset(dataset, "file"))):
+        print(out)
+        # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
+        # {"text": ....}
+        # ....
+
+
 .. autofunction:: transformers.pipeline
 
+Implementing a pipeline
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:doc:`Implementing a new pipeline <../add_new_pipeline>`
 
 The task specific pipelines
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+
 AudioClassificationPipeline
 =======================================================================================================================