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130 lines
3.1 KiB
Python
130 lines
3.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from abc import ABC, abstractmethod
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from enum import Enum
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from typing import Any, Dict, Generic, Iterator, Literal, Protocol, TypeVar, Union
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from llama_models.schema_utils import json_schema_type, webmethod
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from pydantic import BaseModel, Field
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from typing_extensions import Annotated
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# A sample (row) from dataset
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TDatasetSample = TypeVar("TDatasetSample")
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@json_schema_type
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class DatasetSample(BaseModel): ...
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@json_schema_type
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class DictSample(DatasetSample):
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data: Dict[str, Any]
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# A sample (row) from evals intermediate dataset
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TProcessedSample = TypeVar("TProcessedSample")
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@json_schema_type
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class PredictionSample(BaseModel):
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completion_message: str
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@json_schema_type
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class ProcessedDictSample(DictSample):
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preprocessed: Optional[Dict[str, Any]] = None
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prediction: Optional[PredictionSample] = None
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postprocessed: Optional[Dict[str, Any]] = None
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@json_schema_type
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class DatasetType(Enum):
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custom = "custom"
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huggingface = "huggingface"
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@json_schema_type
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class HuggingfaceDatasetDef(BaseModel):
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type: Literal[DatasetType.huggingface.value] = DatasetType.huggingface.value
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identifier: str = Field(
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description="A unique name for the dataset",
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)
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dataset_name: str = Field(
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description="The name of the dataset into HF (e.g. hellawag)",
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)
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kwargs: Dict[str, Any] = Field(
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description="Any additional arguments to get Huggingface (e.g. split, trust_remote_code)",
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default_factory=dict,
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)
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@json_schema_type
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class CustomDatasetDef(BaseModel):
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type: Literal[DatasetType.custom.value] = DatasetType.custom.value
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identifier: str = Field(
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description="A unique name for the dataset",
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)
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url: str = Field(
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description="The URL to the dataset",
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)
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DatasetDef = Annotated[
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Union[
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HuggingfaceDatasetDef,
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CustomDatasetDef,
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],
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Field(discriminator="type"),
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]
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class BaseDataset(ABC, Generic[TDatasetSample]):
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def __init__(self) -> None:
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self.type: str = self.__class__.__name__
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@property
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@abstractmethod
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def dataset_id(self) -> str:
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raise NotImplementedError()
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@abstractmethod
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def __iter__(self) -> Iterator[TDatasetSample]:
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raise NotImplementedError()
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@abstractmethod
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def __str__(self) -> str:
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raise NotImplementedError()
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@abstractmethod
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def __len__(self) -> int:
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raise NotImplementedError()
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@abstractmethod
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def load(self) -> None:
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raise NotImplementedError()
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class Datasets(Protocol):
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@webmethod(route="/datasets/create")
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def create_dataset(
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self,
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dataset: DatasetDef,
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) -> None: ...
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@webmethod(route="/datasets/get")
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def get_dataset(
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self,
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dataset_identifier: str,
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) -> DatasetDef: ...
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@webmethod(route="/datasets/delete")
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def delete_dataset(
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self,
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dataset_uuid: str,
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) -> None: ...
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