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# What does this PR do? - move folder ## Test Plan **Unit Test** ``` pytest -v -s -m "huggingface" datasetio/test_datasetio.py ``` **E2E** ``` llama stack run ``` ``` llama-stack-client eval run_benchmark meta-reference-mmlu --num-examples 5 --output-dir ./ --eval-task-config ~/eval_task_config.json --visualize ``` <img width="657" alt="image" src="https://github.com/user-attachments/assets/63d53f9d-6c7e-4667-af8c-9d16c91ae6e3"> ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
96 lines
3 KiB
Python
96 lines
3 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 Optional
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from llama_stack.apis.datasetio import * # noqa: F403
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import datasets as hf_datasets
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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 llama_stack.providers.utils.kvstore import kvstore_impl
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from .config import HuggingfaceDatasetIOConfig
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DATASETS_PREFIX = "datasets:"
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def load_hf_dataset(dataset_def: Dataset):
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if dataset_def.metadata.get("path", None):
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return hf_datasets.load_dataset(**dataset_def.metadata)
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df = get_dataframe_from_url(dataset_def.url)
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if df is None:
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raise ValueError(f"Failed to load dataset from {dataset_def.url}")
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dataset = hf_datasets.Dataset.from_pandas(df)
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return dataset
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class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
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def __init__(self, config: HuggingfaceDatasetIOConfig) -> 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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self.kvstore = None
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async def initialize(self) -> None:
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self.kvstore = await kvstore_impl(self.config.kvstore)
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# Load existing datasets from kvstore
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start_key = DATASETS_PREFIX
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end_key = f"{DATASETS_PREFIX}\xff"
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stored_datasets = await self.kvstore.range(start_key, end_key)
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for dataset in stored_datasets:
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dataset = Dataset.model_validate_json(dataset)
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self.dataset_infos[dataset.identifier] = dataset
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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: Dataset,
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) -> None:
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# Store in kvstore
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key = f"{DATASETS_PREFIX}{dataset_def.identifier}"
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await self.kvstore.set(
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key=key,
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value=dataset_def.json(),
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)
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self.dataset_infos[dataset_def.identifier] = dataset_def
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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_def = self.dataset_infos[dataset_id]
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loaded_dataset = load_hf_dataset(dataset_def)
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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(loaded_dataset)
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else:
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end = min(start + rows_in_page, len(loaded_dataset))
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rows = [loaded_dataset[i] for i in range(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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