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.metadata = SequentialOperator()
self.standardization = SequentialOperator()
self.processing = SequentialOperator()
self.verblization = SequentialOperator()
self.finalize = SequentialOperator()
self.steps = [
self.loading,
self.metadata,
self.standardization,
self.processing,
self.verblization,
self.finalize,
]
self.inference_instance = SequentialOperator()
self.inference_instance.steps = [
self.metadata,
self.processing,
]
self.inference_demos = SourceSequentialOperator()
self.inference_demos.steps = [
self.loading,
self.metadata,
self.standardization,
self.processing,
]
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):
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": {
"card": self.card,
"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:
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