documentation/_add_new_pipeline.txt

How to create a custom pipeline?
In this guide, we will see how to create a custom pipeline and share it on the Hub or add it to the
🤗 Transformers library.
First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible
as it makes compatibility easier (even through other languages via JSON). Those will be the inputs of the
pipeline (preprocess).
Then define the outputs. Same policy as the inputs. The simpler, the better. Those will be the outputs of
postprocess method.
Start by inheriting the base class Pipeline with the 4 methods needed to implement preprocess,
_forward, postprocess, and _sanitize_parameters.
thon
from transformers import Pipeline
class MyPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if "maybe_arg" in kwargs:
            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
        return preprocess_kwargs, {}, {}
def preprocess(self, inputs, maybe_arg=2):
    model_input = Tensor(inputs["input_ids"])
    return {"model_input": model_input}

def _forward(self, model_inputs):
    # model_inputs == {"model_input": model_input}
    outputs = self.model(**model_inputs)
    # Maybe {"logits": Tensor()}
    return outputs

def postprocess(self, model_outputs):
    best_class = model_outputs["logits"].softmax(-1)
    return best_class

The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
pre/postprocessing on the CPU on different threads
preprocess will take the originally defined inputs, and turn them into something feedable to the model. It might
contain more information and is usually a Dict.
_forward is the implementation detail and is not meant to be called directly. forward is the preferred
called method as it contains safeguards to make sure everything is working on the expected device. If anything is
linked to a real model it belongs in the _forward method, anything else is in the preprocess/postprocess.
postprocess methods will take the output of _forward and turn it into the final output that was decided
earlier.
_sanitize_parameters exists to allow users to pass any parameters whenever they wish, be it at initialization
time pipeline(., maybe_arg=4) or at call time pipe = pipeline(); output = pipe(., maybe_arg=4).
The returns of _sanitize_parameters are the 3 dicts of kwargs that will be passed directly to preprocess,
_forward, and postprocess. Don't fill anything if the caller didn't call with any extra parameter. That
allows to keep the default arguments in the function definition which is always more "natural".
A classic example would be a top_k argument in the post processing in classification tasks.
thon

pipe = pipeline("my-new-task")
pipe("This is a test")
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
pipe("This is a test", top_k=2)
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]

In order to achieve that, we'll update our postprocess method with a default parameter to 5. and edit
_sanitize_parameters to allow this new parameter.
thon
def postprocess(self, model_outputs, top_k=5):
    best_class = model_outputs["logits"].softmax(-1)
    # Add logic to handle top_k
    return best_class
def _sanitize_parameters(self, **kwargs):
    preprocess_kwargs = {}
    if "maybe_arg" in kwargs:
        preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
postprocess_kwargs = {}
if "top_k" in kwargs:
    postprocess_kwargs["top_k"] = kwargs["top_k"]
return preprocess_kwargs, {}, postprocess_kwargs

Try to keep the inputs/outputs very simple and ideally JSON-serializable as it makes the pipeline usage very easy
without requiring users to understand new kinds of objects. It's also relatively common to support many different types
of arguments for ease of use (audio files, which can be filenames, URLs or pure bytes)
Adding it to the list of supported tasks
To register your new-task to the list of supported tasks, you have to add it to the PIPELINE_REGISTRY:
thon
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline(
    "new-task",
    pipeline_class=MyPipeline,
    pt_model=AutoModelForSequenceClassification,
)

You can specify a default model if you want, in which case it should come with a specific revision (which can be the name of a branch or a commit hash, here we took "abcdef") as well as the type:
python
PIPELINE_REGISTRY.register_pipeline(
    "new-task",
    pipeline_class=MyPipeline,
    pt_model=AutoModelForSequenceClassification,
    default={"pt": ("user/awesome_model", "abcdef")},
    type="text",  # current support type: text, audio, image, multimodal
)
Share your pipeline on the Hub
To share your custom pipeline on the Hub, you just have to save the custom code of your Pipeline subclass in a
python file. For instance, let's say we want to use a custom pipeline for sentence pair classification like this:

import numpy as np
from transformers import Pipeline
def softmax(outputs):
    maxes = np.max(outputs, axis=-1, keepdims=True)
    shifted_exp = np.exp(outputs - maxes)
    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if "second_text" in kwargs:
            preprocess_kwargs["second_text"] = kwargs["second_text"]
        return preprocess_kwargs, {}, {}
def preprocess(self, text, second_text=None):
    return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)

def _forward(self, model_inputs):
    return self.model(**model_inputs)

def postprocess(self, model_outputs):
    logits = model_outputs.logits[0].numpy()
    probabilities = softmax(logits)

    best_class = np.argmax(probabilities)
    label = self.model.config.id2label[best_class]
    score = probabilities[best_class].item()
    logits = logits.tolist()
    return {"label": label, "score": score, "logits": logits}

The implementation is framework agnostic, and will work for PyTorch and TensorFlow models. If we have saved this in
a file named pair_classification.py, we can then import it and register it like this:

from pair_classification import PairClassificationPipeline
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
PIPELINE_REGISTRY.register_pipeline(
    "pair-classification",
    pipeline_class=PairClassificationPipeline,
    pt_model=AutoModelForSequenceClassification,
    tf_model=TFAutoModelForSequenceClassification,
)

Once this is done, we can use it with a pretrained model. For instance sgugger/finetuned-bert-mrpc has been
fine-tuned on the MRPC dataset, which classifies pairs of sentences as paraphrases or not.

from transformers import pipeline
classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")

Then we can share it on the Hub by using the push_to_hub method:
py
classifier.push_to_hub("test-dynamic-pipeline")
This will copy the file where you defined PairClassificationPipeline inside the folder "test-dynamic-pipeline",
along with saving the model and tokenizer of the pipeline, before pushing everything into the repository
{your_username}/test-dynamic-pipeline. After that, anyone can use it as long as they provide the option
trust_remote_code=True:

from transformers import pipeline
classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True)

Add the pipeline to 🤗 Transformers
If you want to contribute your pipeline to 🤗 Transformers, you will need to add a new module in the pipelines submodule
with the code of your pipeline, then add it to the list of tasks defined in pipelines/__init__.py.
Then you will need to add tests. Create a new file tests/test_pipelines_MY_PIPELINE.py with examples of the other tests.
The run_pipeline_test function will be very generic and run on small random models on every possible
architecture as defined by model_mapping and tf_model_mapping.
This is very important to test future compatibility, meaning if someone adds a new model for
XXXForQuestionAnswering then the pipeline test will attempt to run on it. Because the models are random it's
impossible to check for actual values, that's why there is a helper ANY that will simply attempt to match the
output of the pipeline TYPE.
You also need to implement 2 (ideally 4) tests.

test_small_model_pt : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
  and test the pipeline outputs. The results should be the same as test_small_model_tf.
test_small_model_tf : Define 1 small model for this pipeline (doesn't matter if the results don't make sense)
  and test the pipeline outputs. The results should be the same as test_small_model_pt.
test_large_model_pt (optional): Tests the pipeline on a real pipeline where the results are supposed to
  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
  sure there is no drift in future releases.
test_large_model_tf (optional): Tests the pipeline on a real pipeline where the results are supposed to
  make sense. These tests are slow and should be marked as such. Here the goal is to showcase the pipeline and to make
  sure there is no drift in future releases.