standard.py

from typing import List

from .card import TaskCard
from .dataclass import Field, InternalField, NonPositionalField, OptionalField
from .formats import Format, SystemFormat
from .logging_utils import get_logger
from .operator import SequentialOperator, SourceSequentialOperator, StreamingOperator
from .operators import AddFields, Augmentor, NullAugmentor, StreamRefiner
from .recipe import Recipe
from .schema import ToUnitxtGroup
from .splitters import Sampler, SeparateSplit, SpreadSplit
from .stream import MultiStream
from .system_prompts import EmptySystemPrompt, SystemPrompt
from .templates import Template

logger = get_logger()


# Used to give meaningful name to recipe steps
class CreateDemosPool(SeparateSplit):
    pass


class AddDemosField(SpreadSplit):
    pass


class BaseRecipe(Recipe, SourceSequentialOperator):
    card: TaskCard
    template: Template = None
    system_prompt: SystemPrompt = Field(default_factory=EmptySystemPrompt)
    format: Format = Field(default_factory=SystemFormat)
    metrics: List[str] = NonPositionalField(default=None)
    postprocessors: List[str] = NonPositionalField(default=None)

    loader_limit: int = None

    max_train_instances: int = None
    max_validation_instances: int = None
    max_test_instances: int = None

    train_refiner: StreamRefiner = OptionalField(default_factory=StreamRefiner)
    validation_refiner: StreamRefiner = OptionalField(default_factory=StreamRefiner)
    test_refiner: StreamRefiner = OptionalField(default_factory=StreamRefiner)

    demos_pool_size: int = None
    num_demos: int = 0
    demos_removed_from_data: bool = True

    demos_pool_name: str = "demos_pool"
    demos_taken_from: str = "train"
    demos_field: str = "demos"
    sampler: Sampler = None

    augmentor: Augmentor = OptionalField(default_factory=NullAugmentor)

    steps: List[StreamingOperator] = InternalField(default_factory=list)

    def before_process_multi_stream(self):
        super().before_process_multi_stream()
        if self.sampler:  # e.g. when num_demos is 0, the sampler may not be initialized
            self.sampler.init_new_random_generator()

    def verify(self):
        super().verify()
        if self.num_demos > 0:
            if self.demos_pool_size is None or self.demos_pool_size < 1:
                raise ValueError(
                    "When using demonstrations both num_demos and demos_pool_size should be assigned with positive integers."
                )
            if self.demos_pool_size < self.num_demos:
                raise ValueError(
                    f"num_demos (got: {self.num_demos}) should not exceed demos_pool_size (got: {self.demos_pool_size})"
                )
            if self.loader_limit and self.demos_pool_size > self.loader_limit:
                raise ValueError(
                    f"demos_pool_size should not exceed loader_limit ({self.loader_limit}), Got demos_pool_size={self.demos_pool_size}"
                )

        if self.loader_limit:
            if self.max_test_instances and self.max_test_instances > self.loader_limit:
                raise ValueError(
                    f"max_test_instances should not exceed loader_limit ({self.loader_limit}), Got max_test_instances={self.max_test_instances}"
                )
            if (
                self.max_validation_instances
                and self.max_validation_instances > self.loader_limit
            ):
                raise ValueError(
                    f"max_validation_instances should not exceed loader_limit ({self.loader_limit}), Got max_validation_instances={self.max_validation_instances}"
                )
            if (
                self.max_train_instances
                and self.max_train_instances > self.loader_limit
            ):
                raise ValueError(
                    f"max_train_instances should not exceed loader_limit ({self.loader_limit}), Got max_train_instances={self.max_train_instances}"
                )

    def prepare_refiners(self):
        self.train_refiner.max_instances = self.max_train_instances
        self.train_refiner.apply_to_streams = ["train"]
        self.processing.steps.append(self.train_refiner)

        self.validation_refiner.max_instances = self.max_validation_instances
        self.validation_refiner.apply_to_streams = ["validation"]
        self.processing.steps.append(self.validation_refiner)

        self.test_refiner.max_instances = self.max_test_instances
        self.test_refiner.apply_to_streams = ["test"]
        self.processing.steps.append(self.test_refiner)

    def prepare_metrics_and_postprocessors(self):
        if self.postprocessors is None:
            postprocessors = self.template.get_postprocessors()
        else:
            postprocessors = self.postprocessors

        if self.metrics is None:
            metrics = self.card.task.metrics
        else:
            metrics = self.metrics
        return metrics, postprocessors

    def set_pipelines(self):
        self.loading = SequentialOperator()
        self.loading.__description__ = "Loading the data from the data source."
        self.metadata = SequentialOperator()
        self.metadata.__description__ = (
            "Adding metadata (e.g. format, system prompt, template)  "
        )
        self.standardization = SequentialOperator()
        self.standardization.__description__ = (
            "Standardizing the raw dataset fields to task field definition."
        )
        self.processing = SequentialOperator()
        self.processing.__description__ = (
            "Setting task fields (and selecting demos per sample if needed)."
        )
        self.verblization = SequentialOperator()
        self.verblization.__description__ = "Verbalizing the input to the model and gold references to the 'source', 'target' and 'references' fields."
        self.finalize = SequentialOperator()
        self.finalize.__description__ = "Adding post processors. Removing intermediate fields. Creating the final output dataset."

        self.steps = [
            self.loading,
            self.metadata,
            self.standardization,
            self.processing,
            self.metadata,
            self.verblization,
            self.finalize,
        ]

        self.inference_instance = SequentialOperator()

        self.inference_instance.steps = [
            self.metadata,
            self.processing,
            self.metadata,
        ]

        self.inference_demos = SourceSequentialOperator()

        self.inference_demos.steps = [
            self.loading,
            self.metadata,
            self.standardization,
            self.processing,
            self.metadata,
        ]

        self.inference = SequentialOperator()

        self.inference.steps = [self.verblization, self.finalize]

        self._demos_pool_cache = None

    def production_preprocess(self, task_instances):
        ms = MultiStream.from_iterables({"__inference__": task_instances})
        return list(self.inference_instance(ms)["__inference__"])

    def production_demos_pool(self):
        if self.num_demos > 0:
            if self._demos_pool_cache is None:
                self._demos_pool_cache = list(
                    self.inference_demos()[self.demos_pool_name]
                )
            return self._demos_pool_cache
        return []

    def produce(self, task_instances):
        """Use the recipe in production to produce model ready query from standard task instance."""
        self.before_process_multi_stream()
        multi_stream = MultiStream.from_iterables(
            {
                "__inference__": self.production_preprocess(task_instances),
                self.demos_pool_name: self.production_demos_pool(),
            }
        )
        multi_stream = self.inference(multi_stream)
        return list(multi_stream["__inference__"])

    def prepare(self):
        # To avoid the Python's mutable default list trap, we set the default value to None
        # and then set it to an empty list if it is None.
        if self.card.preprocess_steps is None:
            self.card.preprocess_steps = []

        self.set_pipelines()

        loader = self.card.loader
        if self.loader_limit:
            loader.loader_limit = self.loader_limit
            logger.info(f"Loader line limit was set to  {self.loader_limit}")
        self.loading.steps.append(loader)

        # This is required in case loader_limit is not enforced by the loader
        if self.loader_limit:
            self.loading.steps.append(StreamRefiner(max_instances=self.loader_limit))

        self.metadata.steps.append(
            AddFields(
                fields={
                    "recipe_metadata": {
                        "template": self.template,
                        "system_prompt": self.system_prompt,
                        "format": self.format,
                    }
                }
            )
        )

        self.standardization.steps.extend(self.card.preprocess_steps)

        self.processing.steps.append(self.card.task)

        if self.augmentor.augment_task_input:
            self.augmentor.set_task_input_fields(self.card.task.augmentable_inputs)
            self.processing.steps.append(self.augmentor)

        if self.demos_pool_size is not None and self.demos_pool_size > 0:
            self.processing.steps.append(
                CreateDemosPool(
                    from_split=self.demos_taken_from,
                    to_split_names=[self.demos_pool_name, self.demos_taken_from],
                    to_split_sizes=[int(self.demos_pool_size)],
                    remove_targets_from_source_split=self.demos_removed_from_data,
                )
            )

        if self.num_demos > 0:
            if self.sampler is None:
                if self.card.sampler is None:
                    raise ValueError(
                        "Unexpected None value for card.sampler. "
                        "To use num_demos > 0, please set a sampler on the TaskCard."
                    )
                self.sampler = self.card.sampler

            self.sampler.set_size(self.num_demos)

        self.prepare_refiners()

        self.verblization.steps.append(self.template)
        if self.num_demos > 0:
            self.verblization.steps.append(
                AddDemosField(
                    source_stream=self.demos_pool_name,
                    target_field=self.demos_field,
                    sampler=self.sampler,
                )
            )
        self.verblization.steps.append(self.system_prompt)
        self.verblization.steps.append(self.format)
        if self.augmentor.augment_model_input:
            self.verblization.steps.append(self.augmentor)

        metrics, postprocessors = self.prepare_metrics_and_postprocessors()

        self.finalize.steps.append(
            ToUnitxtGroup(
                group="unitxt",
                metrics=metrics,
                postprocessors=postprocessors,
            )
        )


class StandardRecipeWithIndexes(BaseRecipe):
    template_card_index: int = None

    def prepare(self):
        assert (
            self.template_card_index is None or self.template is None
        ), f"Specify either template ({self.template}) or template_card_index ({self.template_card_index}) but not both"
        assert not (
            self.template_card_index is None and self.template is None
        ), "Specify either template or template_card_index in card"
        if self.template_card_index is not None:
            try:
                self.template = self.card.templates[self.template_card_index]
            except Exception as e:
                if isinstance(self.card.templates, dict):
                    options = list(self.card.templates.keys())
                else:
                    options = list(range(0, len(self.card.templates)))
                raise ValueError(
                    f"card_template_index '{self.template_card_index}' is not defined in card. Possible card_template_index options: {options}"
                ) from e

        super().prepare()


class StandardRecipe(StandardRecipeWithIndexes):
    """This class represents a standard recipe for data processing and preparation.

    This class can be used to prepare a recipe.
    with all necessary steps, refiners and renderers included. It allows to set various
    parameters and steps in a sequential manner for preparing the recipe.

    Attributes:
        card (TaskCard): TaskCard object associated with the recipe.
        template (Template, optional): Template object to be used for the recipe.
        system_prompt (SystemPrompt, optional): SystemPrompt object to be used for the recipe.
        loader_limit (int, optional): Specifies the maximum number of instances per stream to be returned from the loader (used to reduce loading time in large datasets)
        format (SystemFormat, optional): SystemFormat object to be used for the recipe.
        metrics (List[str]): list of catalog metrics to use with this recipe.
        postprocessors (List[str]): list of catalog processors to apply at post processing. (Not recommended to use from here)
        train_refiner (StreamRefiner, optional): Train refiner to be used in the recipe.
        max_train_instances (int, optional): Maximum training instances for the refiner.
        validation_refiner (StreamRefiner, optional): Validation refiner to be used in the recipe.
        max_validation_instances (int, optional): Maximum validation instances for the refiner.
        test_refiner (StreamRefiner, optional): Test refiner to be used in the recipe.
        max_test_instances (int, optional): Maximum test instances for the refiner.
        demos_pool_size (int, optional): Size of the demos pool.
        num_demos (int, optional): Number of demos to be used.
        demos_pool_name (str, optional): Name of the demos pool. Default is "demos_pool".
        demos_taken_from (str, optional): Specifies from where the demos are taken. Default is "train".
        demos_field (str, optional): Field name for demos. Default is "demos".
        demos_removed_from_data (bool, optional): whether to remove the demos from the source data, Default is True
        sampler (Sampler, optional): Sampler object to be used in the recipe.
        steps (List[StreamingOperator], optional): List of StreamingOperator objects to be used in the recipe.
        augmentor (Augmentor) : Augmentor to be used to pseudo randomly augment the source text
        instruction_card_index (int, optional): Index of instruction card to be used
            for preparing the recipe.
        template_card_index (int, optional): Index of template card to be used for
            preparing the recipe.

    Methods:
        prepare(): This overridden method is used for preparing the recipe
            by arranging all the steps, refiners, and renderers in a sequential manner.

    Raises:
        AssertionError: If both template and template_card_index are specified at the same time.
    """

    pass