forked from phoenix-oss/llama-stack-mirror
2b7d70ba86
* wip * scoring fn api * eval api * eval task * evaluate api update * pre commit * unwrap context -> config * config field doc * typo * naming fix * separate benchmark / app eval * api name * rename * wip tests * wip * datasetio test * delete unused * fixture * scoring resolve * fix scoring register * scoring test pass * score batch * scoring fix * fix eval * test eval works * huggingface provider * datasetdef files * mmlu scoring fn * test wip * remove type ignore * api refactor * add default task_eval_id for routing * add eval_id for jobs * remove type ignore * huggingface provider * wip huggingface register * only keep 1 run_eval * fix optional * register task required * register task required * delete old tests * fix * mmlu loose * refactor * msg * fix tests * move benchmark task def to file * msg * gen openapi * openapi gen * move dataset to hf llamastack repo * remove todo * refactor * add register model to unit test * rename * register to client * delete preregistered dataset/eval task * comments * huggingface -> remote adapter * openapi gen
133 lines
4.1 KiB
Python
133 lines
4.1 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 typing import List, Optional
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import pandas
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.datasetio import * # noqa: F403
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from llama_stack.providers.datatypes import DatasetsProtocolPrivate
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from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
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from .config import MetaReferenceDatasetIOConfig
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class BaseDataset(ABC):
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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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 __getitem__(self, idx):
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raise NotImplementedError()
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@abstractmethod
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def load(self):
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raise NotImplementedError()
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@dataclass
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class DatasetInfo:
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dataset_def: DatasetDef
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dataset_impl: BaseDataset
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class PandasDataframeDataset(BaseDataset):
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def __init__(self, dataset_def: DatasetDef, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self.dataset_def = dataset_def
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self.df = None
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def __len__(self) -> int:
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assert self.df is not None, "Dataset not loaded. Please call .load() first"
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return len(self.df)
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def __getitem__(self, idx):
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assert self.df is not None, "Dataset not loaded. Please call .load() first"
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if isinstance(idx, slice):
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return self.df.iloc[idx].to_dict(orient="records")
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else:
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return self.df.iloc[idx].to_dict()
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def _validate_dataset_schema(self, df) -> pandas.DataFrame:
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# note that we will drop any columns in dataset that are not in the schema
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df = df[self.dataset_def.dataset_schema.keys()]
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# check all columns in dataset schema are present
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assert len(df.columns) == len(self.dataset_def.dataset_schema)
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# TODO: type checking against column types in dataset schema
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return df
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def load(self) -> None:
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if self.df is not None:
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return
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df = get_dataframe_from_url(self.dataset_def.url)
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if df is None:
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raise ValueError(f"Failed to load dataset from {self.dataset_def.url}")
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self.df = self._validate_dataset_schema(df)
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class MetaReferenceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
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def __init__(self, config: MetaReferenceDatasetIOConfig) -> None:
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self.config = config
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# local registry for keeping track of datasets within the provider
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self.dataset_infos = {}
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async def initialize(self) -> None: ...
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async def shutdown(self) -> None: ...
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async def register_dataset(
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self,
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dataset_def: DatasetDef,
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) -> None:
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dataset_impl = PandasDataframeDataset(dataset_def)
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self.dataset_infos[dataset_def.identifier] = DatasetInfo(
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dataset_def=dataset_def,
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dataset_impl=dataset_impl,
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)
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async def list_datasets(self) -> List[DatasetDef]:
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return [i.dataset_def for i in self.dataset_infos.values()]
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async def get_rows_paginated(
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self,
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dataset_id: str,
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rows_in_page: int,
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page_token: Optional[str] = None,
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filter_condition: Optional[str] = None,
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) -> PaginatedRowsResult:
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dataset_info = self.dataset_infos.get(dataset_id)
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dataset_info.dataset_impl.load()
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if page_token and not page_token.isnumeric():
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raise ValueError("Invalid page_token")
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if page_token is None or len(page_token) == 0:
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next_page_token = 0
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else:
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next_page_token = int(page_token)
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start = next_page_token
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if rows_in_page == -1:
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end = len(dataset_info.dataset_impl)
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else:
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end = min(start + rows_in_page, len(dataset_info.dataset_impl))
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rows = dataset_info.dataset_impl[start:end]
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return PaginatedRowsResult(
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rows=rows,
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total_count=len(rows),
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next_page_token=str(end),
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)
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