#
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`.

    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.

    >>> 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.

    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`:

    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:

    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 `save_pretrained` method in a `Repository`:

    from huggingface_hub import Repository

    repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
    classifier.save_pretrained("test-dynamic-pipeline")
    repo.push_to_hub()

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.