[Evals API][11/n] huggingface dataset provider + mmlu scoring fn (#392)

* 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
2024-11-11 14:49:50 -05:00 · 2024-11-11 14:49:50 -05:00 · 2b7d70ba86
commit 2b7d70ba86
parent b78ee3a0a5
20 changed files with 1607 additions and 718 deletions
--- a/llama_stack/providers/adapters/datasetio/huggingface/huggingface.py
+++ b/llama_stack/providers/adapters/datasetio/huggingface/huggingface.py
@ -0,0 +1,81 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import List, Optional
+
+from llama_stack.apis.datasetio import *  # noqa: F403
+
+
+import datasets as hf_datasets
+from llama_stack.providers.datatypes import DatasetsProtocolPrivate
+from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
+
+from .config import HuggingfaceDatasetIOConfig
+
+
+def load_hf_dataset(dataset_def: DatasetDef):
+    if dataset_def.metadata.get("path", None):
+        return hf_datasets.load_dataset(**dataset_def.metadata)
+
+    df = get_dataframe_from_url(dataset_def.url)
+
+    if df is None:
+        raise ValueError(f"Failed to load dataset from {dataset_def.url}")
+
+    dataset = hf_datasets.Dataset.from_pandas(df)
+    return dataset
+
+
+class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
+    def __init__(self, config: HuggingfaceDatasetIOConfig) -> None:
+        self.config = config
+        # local registry for keeping track of datasets within the provider
+        self.dataset_infos = {}
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None: ...
+
+    async def register_dataset(
+        self,
+        dataset_def: DatasetDef,
+    ) -> None:
+        self.dataset_infos[dataset_def.identifier] = dataset_def
+
+    async def list_datasets(self) -> List[DatasetDef]:
+        return list(self.dataset_infos.values())
+
+    async def get_rows_paginated(
+        self,
+        dataset_id: str,
+        rows_in_page: int,
+        page_token: Optional[str] = None,
+        filter_condition: Optional[str] = None,
+    ) -> PaginatedRowsResult:
+        dataset_def = self.dataset_infos[dataset_id]
+        loaded_dataset = load_hf_dataset(dataset_def)
+
+        if page_token and not page_token.isnumeric():
+            raise ValueError("Invalid page_token")
+
+        if page_token is None or len(page_token) == 0:
+            next_page_token = 0
+        else:
+            next_page_token = int(page_token)
+
+        start = next_page_token
+        if rows_in_page == -1:
+            end = len(loaded_dataset)
+        else:
+            end = min(start + rows_in_page, len(loaded_dataset))
+
+        rows = [loaded_dataset[i] for i in range(start, end)]
+
+        return PaginatedRowsResult(
+            rows=rows,
+            total_count=len(rows),
+            next_page_token=str(end),
+        )