| 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 SourceSequentialOperator, StreamingOperator |
| from .operators import AddFields, Augmentor, NullAugmentor, StreamRefiner |
| from .recipe import Recipe |
| from .schema import ToUnitxtGroup |
| from .splitters import Sampler, SeparateSplit, SpreadSplit |
| from .system_prompts import EmptySystemPrompt, SystemPrompt |
| from .templates import Template |
|
|
| logger = get_logger() |
|
|
|
|
| |
| 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: |
| 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.steps.append(self.train_refiner) |
|
|
| self.validation_refiner.max_instances = self.max_validation_instances |
| self.validation_refiner.apply_to_streams = ["validation"] |
| self.steps.append(self.validation_refiner) |
|
|
| self.test_refiner.max_instances = self.max_test_instances |
| self.test_refiner.apply_to_streams = ["test"] |
| self.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 prepare(self): |
| self.steps = [ |
| self.card.loader, |
| AddFields( |
| fields={ |
| "recipe_metadata": { |
| "card": self.card, |
| "template": self.template, |
| "system_prompt": self.system_prompt, |
| "format": self.format, |
| } |
| } |
| ), |
| ] |
|
|
| if self.loader_limit: |
| self.card.loader.loader_limit = self.loader_limit |
| logger.info(f"Loader line limit was set to {self.loader_limit}") |
| self.steps.append(StreamRefiner(max_instances=self.loader_limit)) |
|
|
| if self.card.preprocess_steps is not None: |
| self.steps.extend(self.card.preprocess_steps) |
|
|
| self.steps.append(self.card.task) |
|
|
| if self.augmentor.augment_task_input: |
| self.augmentor.set_task_input_fields(self.card.task.augmentable_inputs) |
| self.steps.append(self.augmentor) |
|
|
| if self.demos_pool_size is not None: |
| self.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.steps.append(self.template) |
| if self.num_demos > 0: |
| self.steps.append( |
| AddDemosField( |
| source_stream=self.demos_pool_name, |
| target_field=self.demos_field, |
| sampler=self.sampler, |
| ) |
| ) |
| self.steps.append(self.system_prompt) |
| self.steps.append(self.format) |
| if self.augmentor.augment_model_input: |
| self.steps.append(self.augmentor) |
|
|
| metrics, postprocessors = self.prepare_metrics_and_postprocessors() |
|
|
| self.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 |
|
|
