import logging
from typing import List
from .card import TaskCard
from .dataclass import InternalField, OptionalField
from .formats import ICLFormat
from .instructions import Instruction
from .operator import SourceSequentialOperator, StreamingOperator
from .operators import Augmentor, NullAugmentor, StreamRefiner
from .recipe import Recipe
from .renderers import StandardRenderer
from .schema import ToUnitxtGroup
from .splitters import Sampler, SeparateSplit, SpreadSplit
from .templates import Template
# 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
instruction: Instruction = None
format: ICLFormat = ICLFormat()
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_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 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 postive integers."
)
if self.demos_pool_size < self.num_demos:
raise ValueError(
f"demos_pool_size must be bigger than num_demos ({self.num_demos}), Got demos_pool_size={self.demos_pool_size}"
)
if self.loader_limit and self.demos_pool_size > self.loader_limit:
raise ValueError(
f"demos_pool_size must be bigger than 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 must be bigger than 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 must be bigger than 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 must be bigger than loader_limit ({self.loader_limit}), Got max_train_instances={self.max_train_instances}"
)
def prepare(self):
self.steps = [
self.card.loader,
]
if self.loader_limit:
self.card.loader.loader_limit = self.loader_limit
logging.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)],
)
)
if self.num_demos > 0:
sampler = self.card.sampler
if self.sampler is not None:
sampler = self.sampler
sampler.set_size(self.num_demos)
self.steps.append(
AddDemosField(
source_stream=self.demos_pool_name,
target_field=self.demos_field,
sampler=sampler,
)
)
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)
render = StandardRenderer(
instruction=self.instruction,
template=self.template,
format=self.format,
demos_field=self.demos_field,
)
self.steps.append(render)
if self.augmentor.augment_model_input:
self.steps.append(self.augmentor)
postprocessors = render.get_postprocessors()
self.steps.append(
ToUnitxtGroup(
group="unitxt",
metrics=self.card.task.metrics,
postprocessors=postprocessors,
)
)
class StandardRecipeWithIndexes(BaseRecipe):
instruction_card_index: int = None
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 = 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 in card. Available options: {options}"
) from e
assert (
self.instruction_card_index is None or self.instruction is None
), "Specify either instruction or instruction_card_index"
if self.instruction_card_index is not None:
self.instruction = self.card.instructions[int(self.instruction_card_index)]
super().prepare()
class StandardRecipe(StandardRecipeWithIndexes):
"""This class represents a standard recipe for data processing and preperation.
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.
instruction (Instruction, optional): Instruction 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 (ICLFormat, optional): ICLFormat object to be used for the recipe.
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".
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, or instruction and instruction_card_index
are specified at the same time.
"""
pass