| | import copy |
| | from abc import abstractmethod |
| | from typing import Generator, List, Optional |
| |
|
| | from .dataclass import NonPositionalField |
| | from .operator import SourceOperator |
| | from .random_utils import new_random_generator |
| | from .stream import MultiStream, Stream |
| |
|
| |
|
| | class BaseFusion(SourceOperator): |
| | """BaseFusion operator that combines multiple streams into one. |
| | |
| | Args: |
| | include_splits: List of splits to include. If None, all splits are included. |
| | """ |
| |
|
| | origins: List[SourceOperator] |
| | include_splits: Optional[List[str]] = NonPositionalField(default=None) |
| |
|
| | @abstractmethod |
| | def fusion_generator(self, split) -> Generator: |
| | pass |
| |
|
| | def splits(self) -> Generator: |
| | splits = [] |
| | for origin in self.origins: |
| | for s in origin().keys(): |
| | if s not in splits: |
| | if self.include_splits is None or s in self.include_splits: |
| | splits.append(s) |
| | return splits |
| |
|
| | def process( |
| | self, |
| | ) -> MultiStream: |
| | result = {} |
| | for split in self.splits(): |
| | result[split] = Stream(self.fusion_generator, gen_kwargs={"split": split}) |
| | return MultiStream(result) |
| |
|
| |
|
| | class FixedFusion(BaseFusion): |
| | """FixedFusion operator that combines multiple streams into one based on a fixed number of examples per task. |
| | |
| | Args: |
| | origins: List of SourceOperator objects. |
| | examples_per_task: Number of examples per task. If None, all examples are returned. |
| | splits: List of splits to include. If None, all splits are included. |
| | """ |
| |
|
| | max_instances_per_origin: Optional[int] = None |
| |
|
| | def fusion_generator(self, split) -> Generator: |
| | for origin in self.origins: |
| | iterator = iter(origin()[split]) |
| | if self.max_instances_per_origin is not None: |
| | for _ in range(self.max_instances_per_origin): |
| | try: |
| | yield next(iterator) |
| | except StopIteration: |
| | break |
| | else: |
| | yield from iterator |
| |
|
| |
|
| | class WeightedFusion(BaseFusion): |
| | """Fusion operator that combines multiple streams based. |
| | |
| | Args: |
| | origins: List of SourceOperator objects. |
| | weights: List of weights for each origin. |
| | max_total_examples: Total number of examples to return. If None, all examples are returned. |
| | """ |
| |
|
| | origins: List[SourceOperator] = None |
| | weights: List[float] = None |
| | max_total_examples: int = None |
| |
|
| | def verify(self): |
| | super().verify() |
| | assert self.origins is not None, "origins must be specified" |
| | assert self.weights is not None, "weights must be specified" |
| | assert len(self.origins) == len( |
| | self.weights |
| | ), "origins and weights must have the same length" |
| |
|
| | def fusion_generator(self, split) -> Generator: |
| | weights = copy.deepcopy(self.weights) |
| | iterators = [iter(origin()[split]) for origin in self.origins] |
| | total_examples = 0 |
| | random_generator = new_random_generator(sub_seed="weighted_fusion_" + split) |
| | while ( |
| | self.max_total_examples is None or total_examples <= self.max_total_examples |
| | ) and len(iterators) > 0: |
| | iterator = random_generator.choices(population=iterators, weights=weights)[ |
| | 0 |
| | ] |
| | try: |
| | yield next(iterator) |
| | total_examples += 1 |
| | except StopIteration: |
| | index = iterators.index(iterator) |
| | iterators.pop(index) |
| | weights.pop(index) |
| |
|