diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 5fa6a1714a1..6cc658729b4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -118,6 +118,14 @@ jobs: uv run --all-packages pytest --benchmark-disable -n auto test/ working-directory: vortex-python/ + # The datasets integration relies on private `datasets` APIs, so exercise the oldest + # supported release in addition to the locked one. + - name: Pytest - Vortex (datasets 4.x floor) + run: | + uv run --all-packages --with 'datasets==4.0.0' --with 'huggingface-hub<1' \ + pytest --benchmark-disable test/test_hf_datasets.py + working-directory: vortex-python/ + - name: Setup benchmark environment run: sudo bash scripts/setup-benchmark.sh diff --git a/docs/api/python/datasets.rst b/docs/api/python/datasets.rst new file mode 100644 index 00000000000..6d7597a7b13 --- /dev/null +++ b/docs/api/python/datasets.rst @@ -0,0 +1,52 @@ +Hugging Face Datasets +===================== + +Vortex files can be loaded directly as Hugging Face ``datasets`` objects. Install the optional +dependencies with: + +.. code-block:: bash + + pip install vortex-data[hf] + +``vortex.datasets.load_dataset`` accepts a local path or glob, a URL readable by Vortex +(``https://``, ``s3://``, ``gs://``, ``az://``), a Hugging Face Hub dataset repository id, or an +``hf://`` URI of the form ``hf://datasets//[@][/]`` (revisions +containing ``/`` must be percent-encoded, e.g. ``@refs%2Fconvert%2Fparquet``). +Unlike ``datasets.load_dataset``, it defaults to ``streaming=True`` and returns a streaming +dataset that keeps Vortex in charge of reading. Column selection, Vortex filter expressions, and +row limits are pushed down into each Vortex scan before examples are yielded to Hugging Face +transforms. + +Hub repositories are streamed in place: files are read with HTTP range requests, so only the +projected columns and matching rows are ever transferred. Private and gated repositories +authenticate with the ``token`` argument or the locally saved login. Files are downloaded (with +the usual Hub caching) only when ``streaming=False`` or ``local_files_only=True``. + +.. code-block:: python + + import vortex as vx + + # Stream a local directory of Vortex files as a Hugging Face IterableDataset. + ds = vx.datasets.load_dataset("path/to/dir", split="train") + + # Stream a Hub dataset directly, without downloading it. + ds = vx.datasets.load_dataset("username/dataset", split="train") + + # hf:// URIs select a revision and a path or glob within the repository. + ds = vx.datasets.load_dataset("hf://datasets/username/dataset@main/data/*.vortex", split="train") + + # Column projection and Vortex filter expressions are pushed into the scan. + ds = ds.select_columns(["text", "label"]).filter(vx.expr.column("label") == 1) + + for example in ds.take(100): + ... + + # Materialize an in-memory dataset instead, optionally selecting splits via data_files. + splits = vx.datasets.load_dataset( + "username/dataset", + data_files={"train": "train-*.vortex", "validation": "validation.vortex"}, + streaming=False, + ) + +When loading multiple splits, pass a ``data_files`` mapping from split name to file patterns; a +list of splits without such a mapping cannot be resolved and raises an error. diff --git a/docs/api/python/index.rst b/docs/api/python/index.rst index 1ef85a4acb6..04bcd1c08d2 100644 --- a/docs/api/python/index.rst +++ b/docs/api/python/index.rst @@ -16,7 +16,7 @@ Optional integrations can be installed as extras: .. code-block:: bash - pip install vortex-data[polars,pandas,numpy,duckdb,ray] + pip install vortex-data[polars,pandas,numpy,duckdb,ray,hf] Compatibility @@ -66,5 +66,6 @@ API Reference io store dataset + datasets runtime type_aliases diff --git a/uv.lock b/uv.lock index 72b8d1998b7..2155dfa28a0 100644 --- a/uv.lock +++ b/uv.lock @@ -26,6 +26,146 @@ wheels = [ { url = 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"2026-07-20T02:07:43.461Z" }, +] diff --git a/vortex-python/pyproject.toml b/vortex-python/pyproject.toml index 8ee24e94fa5..2e40ad9bc0d 100644 --- a/vortex-python/pyproject.toml +++ b/vortex-python/pyproject.toml @@ -43,6 +43,8 @@ pandas = ["pandas>=2.2.0"] numpy = ["numpy>=1.26.0"] duckdb = ["duckdb>=1.1.2"] ray = ["ray>=2.48"] +# The datasets integration relies on private `datasets` APIs, so cap the next major version. +hf = ["datasets>=4.0.0,<6", "huggingface-hub>=0.34.0"] [project.urls] Documentation = "https://docs.vortex.dev" @@ -72,7 +74,9 @@ include = [ [dependency-groups] dev = [ + "datasets>=4.0.0,<6", "duckdb>=1.1.2", + "huggingface-hub>=0.34.0", "maturin>=1.10.2", "numpy>=2.2.2", "pandas-stubs>=2.2.3.241126", diff --git a/vortex-python/python/vortex/__init__.py b/vortex-python/python/vortex/__init__.py index ce4e57064ff..acb90629832 100644 --- a/vortex-python/python/vortex/__init__.py +++ b/vortex-python/python/vortex/__init__.py @@ -1,6 +1,7 @@ # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright the Vortex contributors +import importlib import importlib.metadata import importlib.util @@ -118,6 +119,17 @@ def cuda_extension_installed() -> bool: return importlib.util.find_spec("vortex_cuda") is not None +def __getattr__(name: str): + # `datasets` is exposed lazily and deliberately kept out of __all__: importing it pulls in the + # optional `vortex-data[hf]` dependencies, so it must not be imported by `from vortex import *` + # or by merely importing `vortex`. + if name == "datasets": + module = importlib.import_module(".datasets", __name__) + globals()[name] = module + return module + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + __all__ = [ # --- Modules --- "arrays", diff --git a/vortex-python/python/vortex/datasets.py b/vortex-python/python/vortex/datasets.py new file mode 100644 index 00000000000..faab4d28d43 --- /dev/null +++ b/vortex-python/python/vortex/datasets.py @@ -0,0 +1,1024 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright the Vortex contributors +# pyright: reportAny=false +# pyright: reportMissingTypeStubs=false +# pyright: reportMissingTypeArgument=false +# pyright: reportPrivateUsage=false +# pyright: reportUnannotatedClassAttribute=false +# pyright: reportUnknownArgumentType=false +# pyright: reportUnknownMemberType=false +# pyright: reportUnknownParameterType=false +# pyright: reportUnknownVariableType=false + +from __future__ import annotations + +import copy +import functools +import glob +import inspect +import re +from collections.abc import Callable, Iterable, Mapping, Sequence +from pathlib import Path +from typing import TypeAlias, cast, final +from urllib.parse import quote, unquote + +import pyarrow as pa +from typing_extensions import override + +import vortex as vx +from vortex.expr import Expr, and_ +from vortex.store import ( + AzureStore, + ClientConfig, + GCSStore, + HTTPStore, + LocalStore, + MemoryStore, + S3Store, +) + +# The stores `vx.open` accepts. This is narrower than `vortex.store.ObjectStore`, which also +# covers the OpenDAL-backed `CosStore` that `vx.open` cannot read from. +ObjectStore: TypeAlias = AzureStore | GCSStore | HTTPStore | LocalStore | MemoryStore | S3Store + +try: + import datasets as hf_datasets + from datasets.iterable_dataset import ( + FormattingConfig, + _BaseExamplesIterable, + get_format_type_from_alias, + ) + from datasets.table import InMemoryTable + from huggingface_hub import HfApi, get_token, hf_hub_url, snapshot_download + from huggingface_hub import constants as hf_hub_constants +except ImportError as e: # pragma: no cover - exercised only without optional deps. + raise ImportError("Install vortex-data[hf] to use vortex.datasets.") from e + +try: + from datasets.iterable_dataset import DataSourcesShufflingDisallowed +except ImportError: # datasets 4.x signals disallowed source shuffling by returning self instead. + DataSourcesShufflingDisallowed = None + + +_DEFAULT_SPLIT = "train" +_DEFAULT_DATA_FILES = "**/*.vortex" +_ITERABLE_DATASET_HAS_SHUFFLING = "shuffling" in inspect.signature(hf_datasets.IterableDataset).parameters + + +def load_dataset( + path: str | Path, + *, + data_files: str | Sequence[str] | Mapping[str, str | Sequence[str]] | None = None, + split: str | Sequence[str] | None = None, + streaming: bool = True, + columns: Sequence[str] | None = None, + filter: Expr | None = None, + batch_size: int | None = None, + limit: int | None = None, + cache_dir: str | Path | None = None, + keep_in_memory: bool = False, + num_proc: int | None = None, + revision: str | None = None, + token: bool | str | None = None, + local_files_only: bool = False, +) -> ( + VortexIterableDataset + | hf_datasets.Dataset + | hf_datasets.IterableDatasetDict + | hf_datasets.DatasetDict + | list[VortexIterableDataset | hf_datasets.Dataset] +): + """Load Vortex files as Hugging Face Datasets objects. + + ``path`` may be a local path or glob, a URL readable by Vortex (``https://``, ``s3://``, + ``gs://``, ``az://``), a Hugging Face Hub dataset repository id, or an ``hf://`` URI of the + form ``hf://datasets//[@][/]`` (percent-encode revisions + containing ``/``, e.g. ``@refs%2Fconvert%2Fparquet``). + + Unlike ``datasets.load_dataset``, this defaults to ``streaming=True``. The streaming path + keeps Vortex in charge of reading and pushes column selection, Vortex expressions, and row + limits into each Vortex scan before examples are yielded to Hugging Face Datasets transforms. + Hub repositories are streamed directly with HTTP range requests instead of being downloaded; + private and gated repositories authenticate with ``token`` or the locally saved login. Pass + ``streaming=False`` to eagerly materialize an in-memory ``datasets.Dataset``, which downloads + Hub files first (as does ``local_files_only=True``). + """ + + split_to_files, store = _resolve_data_files( + path, + data_files=data_files, + split=split, + revision=revision, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + streaming=streaming, + ) + + def build_one(split_name: str): + files = split_to_files[split_name] + if streaming: + return VortexIterableDataset( + files, + columns=columns, + filter=filter, + limit=limit, + batch_size=batch_size, + store=store, + split=split_name, + ) + return _materialize_dataset( + files, + columns=columns, + filter=filter, + limit=limit, + batch_size=batch_size, + split=split_name, + cache_dir=None if cache_dir is None else str(cache_dir), + keep_in_memory=keep_in_memory, + num_proc=num_proc, + ) + + if split is None: + if streaming: + return hf_datasets.IterableDatasetDict( + (name, cast(hf_datasets.IterableDataset, build_one(name))) for name in split_to_files + ) + return hf_datasets.DatasetDict((name, cast(hf_datasets.Dataset, build_one(name))) for name in split_to_files) + + if isinstance(split, str): + if split not in split_to_files: + raise ValueError(f"Unknown split {split!r}. Available splits: {list(split_to_files)}") + return build_one(split) + + unknown = [split_name for split_name in split if split_name not in split_to_files] + if unknown: + raise ValueError(f"Unknown splits {unknown}. Available splits: {list(split_to_files)}") + return [build_one(split_name) for split_name in split] + + +@final +class VortexIterableDataset(hf_datasets.IterableDataset): + """A Hugging Face IterableDataset backed by Vortex scans.""" + + def __init__( + self, + files: Sequence[str | Path], + *, + columns: Sequence[str] | None = None, + filter: Expr | None = None, + limit: int | None = None, + batch_size: int | None = None, + store: ObjectStore | None = None, + split: str = _DEFAULT_SPLIT, + formatting: FormattingConfig | None = None, + shuffling: object | None = None, + distributed: object | None = None, + token_per_repo_id: dict[str, bool | str | None] | None = None, + features: hf_datasets.Features | None = None, + ): + file_names = tuple(str(file) for file in files) + if not file_names: + raise ValueError("VortexIterableDataset requires at least one Vortex file") + + self._vortex_files = file_names + self._vortex_columns = _normalize_columns(columns) + self._vortex_filter = filter + self._vortex_limit = limit + self._vortex_batch_size = batch_size + self._vortex_store = store + + if features is None: + features = _features_for_files(file_names, self._vortex_columns, store=store) + info = hf_datasets.DatasetInfo(features=features) + ex_iterable = _VortexExamplesIterable( + file_names, + columns=self._vortex_columns, + filter=filter, + limit=limit, + batch_size=batch_size, + store=store, + features=features, + ) + if _ITERABLE_DATASET_HAS_SHUFFLING: + super().__init__( + ex_iterable=ex_iterable, + info=info, + split=hf_datasets.Split(split), + formatting=formatting, + shuffling=shuffling, # pyright: ignore[reportCallIssue] + distributed=distributed, + token_per_repo_id=token_per_repo_id, + ) + else: + super().__init__( + ex_iterable=ex_iterable, + info=info, + split=hf_datasets.Split(split), + formatting=formatting, + distributed=distributed, # pyright: ignore[reportArgumentType] + token_per_repo_id=token_per_repo_id, + ) + + @override + def select_columns(self, column_names: str | list[str]) -> VortexIterableDataset: + if isinstance(column_names, str): + column_names = [column_names] + current_columns = self._current_column_names() + available = set(current_columns) + missing = set(column_names) - available + if missing: + raise ValueError( + f"Column name {list(missing)} not in the dataset. Columns in the dataset: {current_columns}." + ) + return self._with_pushdown(columns=column_names) + + @override + def remove_columns(self, column_names: str | list[str]) -> VortexIterableDataset: + if isinstance(column_names, str): + column_names = [column_names] + current_columns = self._current_column_names() + remove = set(column_names) + missing = remove - set(current_columns) + if missing: + raise ValueError( + f"Column name {list(missing)} not in the dataset. Columns in the dataset: {current_columns}." + ) + return self._with_pushdown(columns=[column for column in current_columns if column not in remove]) + + @override + def take(self, n: int) -> VortexIterableDataset | hf_datasets.IterableDataset: + if getattr(self, "_shuffling", None) is not None or self._distributed is not None: + return super().take(n) + limit = n if self._vortex_limit is None else min(self._vortex_limit, n) + return self._with_pushdown(limit=limit) + + @override + def filter( + self, + function: Callable[..., object] | Expr | None = None, + with_indices: bool = False, + input_columns: str | list[str] | None = None, + batched: bool = False, + batch_size: int | None = 1000, + fn_kwargs: dict[str, object] | None = None, + ) -> VortexIterableDataset | hf_datasets.IterableDataset: + if isinstance(function, Expr): + # An Expr cannot be delegated to the base filter(), which expects a callable, so any + # argument that prevents pushdown must be rejected here rather than failing obscurely + # at iteration time. + if with_indices or input_columns is not None or batched or fn_kwargs is not None: + raise ValueError( + "with_indices, input_columns, batched, and fn_kwargs are not supported with a Vortex expression." + ) + # Vortex applies the predicate before the scan limit, so folding a filter into a + # dataset that already carries a pushed-down limit (e.g. from take()) would filter the + # whole file and then limit, rather than limiting first and then filtering. That + # silently reorders the operations, so refuse it. + if self._vortex_limit is not None: + raise ValueError( + "Cannot push a filter expression down after a row limit (e.g. take()); filter before limiting." + ) + row_filter = function if self._vortex_filter is None else and_(self._vortex_filter, function) + return self._with_pushdown(filter=row_filter) + return super().filter( + function=function, + with_indices=with_indices, + input_columns=input_columns, + batched=batched, + batch_size=batch_size, + fn_kwargs=fn_kwargs, + ) + + @override + def with_format(self, type: str | None = None) -> VortexIterableDataset: + return self._with_pushdown(formatting=FormattingConfig(format_type=get_format_type_from_alias(type))) + + def _with_pushdown( + self, + *, + columns: Sequence[str] | None | object = ..., + filter: Expr | None | object = ..., + limit: int | None | object = ..., + formatting: FormattingConfig | None | object = ..., + ) -> VortexIterableDataset: + new_columns = self._vortex_columns if columns is ... else _normalize_columns(columns) # pyright: ignore[reportArgumentType] + # Derive the new features from the current ones instead of re-reading the file schema: + # select_columns/remove_columns only ever narrow the current selection. + if new_columns == self._vortex_columns: + features = self.features + elif new_columns is not None and self.features is not None: + features = hf_datasets.Features({name: self.features[name] for name in new_columns}) + else: + features = None + return VortexIterableDataset( + self._vortex_files, + columns=new_columns, + filter=self._vortex_filter if filter is ... else filter, # pyright: ignore[reportArgumentType] + limit=self._vortex_limit if limit is ... else limit, # pyright: ignore[reportArgumentType] + batch_size=self._vortex_batch_size, + store=self._vortex_store, + split=str(self._split), + formatting=self._formatting if formatting is ... else formatting, # pyright: ignore[reportArgumentType] + shuffling=copy.deepcopy(getattr(self, "_shuffling", None)), + distributed=copy.deepcopy(self._distributed), + token_per_repo_id=self._token_per_repo_id, + features=features, + ) + + def _current_column_names(self) -> list[str]: + column_names = self.column_names + if column_names is not None: + return list(column_names) + features = self.features + if features is None: + return [] + return list(features) + + +class _VortexExamplesIterable(_BaseExamplesIterable): + def __init__( + self, + files: Sequence[str], + *, + columns: Sequence[str] | None, + filter: Expr | None, + limit: int | None, + batch_size: int | None, + store: ObjectStore | None, + features: hf_datasets.Features, + ): + super().__init__() + # datasets>=5 reads this flag on every leaf iterable when tearing down prefetch threads + # (shuffle(), interleave_datasets()); the base-class fallback assumes a wrapper with an + # `ex_iterable(s)` attribute and would raise AttributeError on this leaf. + self._sleep_on_threads_shutdown = False + self.files: tuple[str, ...] = tuple(files) + self.columns: tuple[str, ...] | None = _normalize_columns(columns) + self.filter: Expr | None = filter + self.limit: int | None = limit + self.batch_size: int | None = batch_size + self.store: ObjectStore | None = store + self._features: hf_datasets.Features = features + + @property + @override + def iter_arrow(self): + return self._iter_arrow + + @property + @override + def is_typed(self) -> bool: + return True + + @property + @override + def features(self) -> hf_datasets.Features: + return self._features + + @property + @override + def num_shards(self) -> int: + return max(1, len(self.files)) + + @override + def _init_state_dict(self) -> dict[str, int | str]: + self._state_dict = { + "file_idx": 0, + "file_row_idx": 0, + "num_yielded": 0, + "type": self.__class__.__name__, + } + return self._state_dict + + @override + def __iter__(self): + # The resume state must advance per row here: a state_dict() checkpoint taken while the + # consumer is mid-batch would otherwise record the whole batch as consumed and resume + # would silently skip its remaining rows. + for file_idx, row_start, yielded_before, table in self._resumed_batches(): + state = self._state() + for offset, row in enumerate(table.to_pylist()): + if state is not None: + state["file_row_idx"] = row_start + offset + 1 + state["num_yielded"] = yielded_before + offset + 1 + yield f"{file_idx}:{row_start + offset}", row + + def _iter_arrow(self): + # Batches are the unit of consumption on this path, so the resume state advances per + # batch; mid-batch checkpoints are handled by RebatchedArrowExamplesIterable, which + # replays from the last batch boundary and crops. + for file_idx, row_start, yielded_before, table in self._resumed_batches(): + state = self._state() + if state is not None: + state["file_row_idx"] = row_start + len(table) + state["num_yielded"] = yielded_before + len(table) + yield f"{file_idx}:{row_start}", table + + def _resumed_batches(self): + """Scan the files from the resume state, yielding ``(file_idx, row_start, yielded_before, table)``. + + Rows already consumed per the resume state are skipped and the row limit is enforced. + This generator only writes the per-file portion of the resume state (advancing to the + next file); the consumer must write ``file_row_idx``/``num_yielded`` at its own + granularity before each item it yields, since the state is only observed at yield points. + """ + state = self._state() + start_file_idx = state["file_idx"] if state is not None else 0 + start_file_row_idx = state["file_row_idx"] if state is not None else 0 + yielded = state["num_yielded"] if state is not None else 0 + + for file_idx, file_name in enumerate(self.files[start_file_idx:], start=start_file_idx): + file_row_idx = 0 + # On resume we re-scan the resume file from row 0 and skip the rows already yielded + # (below), so the scan must read those skipped rows in addition to the rows still owed + # against the limit; otherwise the limit cuts the scan short and we under-read. + skip = start_file_row_idx if file_idx == start_file_idx else 0 + for table in _scan_file_as_tables( + file_name, + columns=self.columns, + filter=self.filter, + limit=None if self.limit is None else self.limit - yielded + skip, + batch_size=self.batch_size, + store=self.store, + ): + if self.limit is not None and yielded >= self.limit: + return + + if file_idx == start_file_idx and file_row_idx + len(table) <= start_file_row_idx: + file_row_idx += len(table) + continue + + if file_idx == start_file_idx and file_row_idx < start_file_row_idx: + offset = start_file_row_idx - file_row_idx + table = table.slice(offset) + file_row_idx = start_file_row_idx + + if self.limit is not None and yielded + len(table) > self.limit: + table = table.slice(0, self.limit - yielded) + + if len(table) == 0: + continue + + yield file_idx, file_row_idx, yielded, table + yielded += len(table) + file_row_idx += len(table) + + state = self._state() + if state is not None: + state["file_idx"] = file_idx + 1 + state["file_row_idx"] = 0 + + @override + def shuffle_data_sources(self, generator) -> _VortexExamplesIterable: # pyright: ignore[reportMissingParameterType] + if self.limit is not None: + # A pushed-down limit (take()) must keep selecting the same rows, so the file order + # cannot be permuted — mirroring TakeExamplesIterable. datasets>=5 signals this with + # an exception (shuffle() then falls back to a buffer-only shuffle); datasets 4.x + # expects `self` back. + if DataSourcesShufflingDisallowed is not None: + raise DataSourcesShufflingDisallowed() + return self + indices = generator.permutation(len(self.files)) + return self._with_files(tuple(self.files[int(idx)] for idx in indices), limit=self.limit) + + @override + def shard_data_sources(self, num_shards: int, index: int, contiguous: bool = True) -> _VortexExamplesIterable: + shard_indices = self.split_shard_indices_by_worker(num_shards, index, contiguous=contiguous) + # A pushed-down limit is a global row count, so divide it across the shards (mirroring + # TakeExamplesIterable.split_number); keeping it whole would yield up to + # `limit * num_shards` rows under DataLoader worker or distributed sharding. + limit = self.limit + if limit is not None: + limit = limit // num_shards + (1 if index < limit % num_shards else 0) + return self._with_files(tuple(self.files[i] for i in shard_indices), limit=limit) + + @override + def reshard_data_sources(self) -> _VortexExamplesIterable: + # Shards are whole Vortex files and cannot be split further; the base class documents + # returning self in that case (the default implementation raises instead). + return self + + def _with_files(self, files: Sequence[str], *, limit: int | None) -> _VortexExamplesIterable: + return _VortexExamplesIterable( + files, + columns=self.columns, + filter=self.filter, + limit=limit, + batch_size=self.batch_size, + store=self.store, + features=self._features, + ) + + def _state(self) -> dict[str, int] | None: + if isinstance(self._state_dict, dict): + return cast(dict[str, int], self._state_dict) + return None + + +def _materialize_dataset( + files: Sequence[str], + *, + columns: Sequence[str] | None, + filter: Expr | None, + limit: int | None, + batch_size: int | None, + split: str, + cache_dir: str | None, + keep_in_memory: bool, + num_proc: int | None, +) -> hf_datasets.Dataset: + features = _features_for_files(files, _normalize_columns(columns)) + if filter is not None: + # vortex.Expr cannot be pickled, so it cannot pass through Dataset.from_generator's + # gen_kwargs (which Hugging Face hashes for the cache fingerprint). Read the filtered rows + # in-process and build an in-memory dataset from the resulting Arrow tables instead; + # cache_dir, keep_in_memory, and num_proc do not apply to this path. + return _materialize_filtered( + files, + columns=columns, + filter=filter, + limit=limit, + batch_size=batch_size, + split=split, + features=features, + ) + gen_kwargs = { + "files": list(files), + # Keep `columns` a tuple (never a list): Hugging Face shards `num_proc` work across the + # list-valued gen_kwargs entries, so `files` must be the only list. See + # datasets.utils.sharding._number_of_shards_in_gen_kwargs. + "columns": _normalize_columns(columns), + "filter": filter, + "limit": limit, + "batch_size": batch_size, + } + # `cache_dir=None` is the Hugging Face default, so a single call covers both cases; the stub + # mistypes the parameter as `str`, hence the ignore. + return hf_datasets.Dataset.from_generator( + _generate_rows, + features=features, + cache_dir=cache_dir, # pyright: ignore[reportArgumentType] + keep_in_memory=keep_in_memory, + gen_kwargs=gen_kwargs, + # A global row limit cannot be divided across processes without overshooting, so force + # single-process generation whenever a limit is set. + num_proc=None if limit is not None else num_proc, + split=hf_datasets.Split(split), + ) + + +def _materialize_filtered( + files: Sequence[str], + *, + columns: Sequence[str] | None, + filter: Expr | None, + limit: int | None, + batch_size: int | None, + split: str, + features: hf_datasets.Features, +) -> hf_datasets.Dataset: + tables: list[pa.Table] = [] + yielded = 0 + for file_name in files: + if limit is not None and yielded >= limit: + break + for table in _scan_file_as_tables( + file_name, + columns=_normalize_columns(columns), + filter=filter, + limit=None if limit is None else limit - yielded, + batch_size=batch_size, + ): + if limit is not None and yielded + len(table) > limit: + table = table.slice(0, limit - yielded) + if len(table) == 0: + continue + tables.append(table) + yielded += len(table) + if limit is not None and yielded >= limit: + break + combined = pa.concat_tables(tables) if tables else features.arrow_schema.empty_table() + return hf_datasets.Dataset( + InMemoryTable(combined), + info=hf_datasets.DatasetInfo(features=features), + split=hf_datasets.Split(split), + ) + + +def _generate_rows( + files: Sequence[str], + columns: Sequence[str] | None, + filter: Expr | None, + limit: int | None, + batch_size: int | None, +): + yielded = 0 + for file_name in files: + remaining = None if limit is None else limit - yielded + if remaining is not None and remaining <= 0: + return + for table in _scan_file_as_tables( + file_name, + columns=columns, + filter=filter, + limit=remaining, + batch_size=batch_size, + ): + for row in table.to_pylist(): + yielded += 1 + yield row + if limit is not None and yielded >= limit: + return + + +def _scan_file_as_tables( + file_name: str, + *, + columns: Sequence[str] | None, + filter: Expr | None, + limit: int | None, + batch_size: int | None, + store: ObjectStore | None = None, +) -> Iterable[pa.Table]: + projection = None if columns is None else list(columns) + # Vortex cannot push a filter and a limit into the same scan. When both are set we scan with + # only the filter and let the caller enforce the limit while consuming the lazy reader, which + # still stops early once enough rows have been yielded. + scan_limit = None if filter is not None else limit + reader = vx.open(file_name, store=store).to_arrow( + projection=projection, expr=filter, limit=scan_limit, batch_size=batch_size + ) + for batch in reader: + yield _to_hf_compatible_table(pa.Table.from_batches([batch], schema=reader.schema)) + + +def _features_for_files( + files: Sequence[str], columns: Sequence[str] | None, store: ObjectStore | None = None +) -> hf_datasets.Features: + # Assumes every file shares the schema of the first; mixed-schema datasets would mistype + # because the features are derived from files[0] alone. + schema = vx.open(files[0], store=store).dtype.to_arrow_schema() + if columns is not None: + schema = pa.schema([schema.field(column) for column in columns]) + schema = _hf_compatible_schema(schema) + return hf_datasets.Features.from_arrow_schema(schema) + + +def _to_hf_compatible_table(table: pa.Table) -> pa.Table: + schema = _hf_compatible_schema(table.schema) + if table.schema.equals(schema, check_metadata=False): + return table + return table.cast(schema) + + +def _hf_compatible_schema(schema: pa.Schema) -> pa.Schema: + metadata = cast(dict[bytes | str, bytes | str] | None, schema.metadata) + return pa.schema([_hf_compatible_field(field) for field in schema], metadata=metadata) + + +def _hf_compatible_field(field: pa.Field) -> pa.Field: + return pa.field( + field.name, + _hf_compatible_type(field.type), + nullable=field.nullable, + metadata=field.metadata, + ) + + +def _hf_compatible_type(dtype: pa.DataType) -> pa.DataType: + if pa.types.is_string_view(dtype): + return pa.string() + if pa.types.is_binary_view(dtype): + return pa.binary() + if pa.types.is_list(dtype) or pa.types.is_large_list(dtype) or pa.types.is_fixed_size_list(dtype): + value_field = _hf_compatible_field(dtype.value_field) + if pa.types.is_large_list(dtype): + return pa.large_list(value_field) + if pa.types.is_fixed_size_list(dtype): + return pa.list_(value_field, dtype.list_size) + return pa.list_(value_field) + if pa.types.is_struct(dtype): + return pa.struct([_hf_compatible_field(field) for field in dtype]) + return dtype + + +def _normalize_columns(columns: Sequence[str] | None) -> tuple[str, ...] | None: + if columns is None: + return None + return tuple(columns) + + +def _resolve_data_files( + path: str | Path, + *, + data_files: str | Sequence[str] | Mapping[str, str | Sequence[str]] | None, + split: str | Sequence[str] | None, + revision: str | None, + token: bool | str | None, + cache_dir: str | Path | None, + local_files_only: bool, + streaming: bool, +) -> tuple[dict[str, list[str]], ObjectStore | None]: + """Resolve ``path``/``data_files`` to per-split file lists plus the store to read them with. + + The store is ``None`` except for streamed Hub repositories that need one (credentialed reads + or revisions containing ``/``), where the files are paths relative to the returned store. + """ + normalized = _normalize_data_files(data_files, split=split) + path_str = str(path) + + if path_str.startswith("hf://"): + repo_id, uri_revision, uri_path = _parse_hf_uri(path_str) + if uri_revision is not None: + if revision is not None and revision != uri_revision: + raise ValueError( + f"Conflicting revisions: {revision!r} from `revision` and {uri_revision!r} from {path_str!r}" + ) + revision = uri_revision + if uri_path is not None: + if data_files is not None: + raise ValueError("data_files cannot be combined with an hf:// URL that already names a path.") + normalized = _normalize_data_files(uri_path, split=split) + return _resolve_repo_files( + repo_id, + normalized, + revision=revision, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + streaming=streaming, + ) + + if _is_url(path_str): + if data_files is not None: + raise ValueError("data_files cannot be combined with a URL path; pass URLs in one or the other.") + split_name = split if isinstance(split, str) else _DEFAULT_SPLIT + return {split_name: [path_str]}, None + + path_obj = Path(path).expanduser() + if path_obj.exists() or _looks_like_local_path(path_str): + if not path_obj.exists() and data_files is None: + split_name = split if isinstance(split, str) else _DEFAULT_SPLIT + return _resolve_local_files(Path("."), {split_name: [str(path_obj)]}), None + return _resolve_local_files(path_obj, normalized), None + + return _resolve_repo_files( + path_str, + normalized, + revision=revision, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + streaming=streaming, + ) + + +def _parse_hf_uri(uri: str) -> tuple[str, str | None, str | None]: + """Parse ``hf://datasets//[@][/]``. + + Returns ``(repo_id, revision, path)`` with ``revision``/``path`` as ``None`` when absent. + A revision containing ``/`` (e.g. ``refs/convert/parquet``) must be percent-encoded, as in + ``HfFileSystem`` paths. + """ + rest = uri.removeprefix("hf://") + if not rest.startswith("datasets/"): + raise ValueError(f"Unsupported hf:// URL {uri!r}: only dataset repositories (hf://datasets/...) are supported") + parts = rest.removeprefix("datasets/").split("/", 2) + if len(parts) < 2 or not parts[0] or not parts[1]: + raise ValueError(f"Invalid hf:// URL {uri!r}: expected hf://datasets//[@revision][/path]") + org, name = parts[0], parts[1] + path = parts[2] if len(parts) == 3 and parts[2] else None + revision = None + if "@" in name: + name, _, revision = name.partition("@") + if not name or not revision: + raise ValueError(f"Invalid hf:// URL {uri!r}: expected hf://datasets//[@revision][/path]") + revision = unquote(revision) + return f"{org}/{name}", revision, path + + +def _resolve_repo_files( + repo_id: str, + split_to_patterns: Mapping[str, Sequence[str]], + *, + revision: str | None, + token: bool | str | None, + cache_dir: str | Path | None, + local_files_only: bool, + streaming: bool, +) -> tuple[dict[str, list[str]], ObjectStore | None]: + if streaming and not local_files_only: + return _resolve_hub_files(repo_id, split_to_patterns, revision=revision, token=token) + + # snapshot_download filters with fnmatch, so expand `**/` into its zero-directory variants + # (fnmatch alone would skip repository-root files for the default `**/*.vortex` pattern). + # Over-inclusion is fine: _resolve_local_files applies the exact glob afterwards. + allow_patterns = sorted( + { + variant + for patterns in split_to_patterns.values() + for pattern in patterns + for candidate in _pattern_with_directory_fallback(pattern) + for variant in _fnmatch_variants(candidate) + } + ) + snapshot_dir = Path( + snapshot_download( + repo_id, + repo_type="dataset", + revision=revision, + token=token, + cache_dir=cache_dir, + local_files_only=local_files_only, + allow_patterns=allow_patterns, + ) + ) + return _resolve_local_files(snapshot_dir, split_to_patterns), None + + +def _resolve_hub_files( + repo_id: str, + split_to_patterns: Mapping[str, Sequence[str]], + *, + revision: str | None, + token: bool | str | None, +) -> tuple[dict[str, list[str]], ObjectStore | None]: + """Resolve Hub repository files to locations Vortex can stream without downloading them. + + Anonymous reads use plain ``resolve`` URLs. When credentials are available (an explicit + ``token`` or the saved login) the reads must carry an authorization header, so the files are + returned as endpoint-relative paths together with an authenticated HTTP store. Revisions + containing ``/`` also require a store, rooted at the percent-encoded ``resolve`` prefix, + because the Hub only routes the encoded form and percent escapes cannot round-trip through + inferred object store paths. + """ + repo_files = HfApi(token=token).list_repo_files(repo_id, repo_type="dataset", revision=revision) + + split_to_matches: dict[str, list[str]] = {} + for split_name, patterns in split_to_patterns.items(): + candidates = [candidate for pattern in patterns for candidate in _pattern_with_directory_fallback(pattern)] + matches = sorted({file for file in repo_files if any(_glob_match(file, candidate) for candidate in candidates)}) + if not matches: + raise FileNotFoundError( + f"No Vortex files matched split {split_name!r} patterns {list(patterns)!r} in repository {repo_id!r}" + ) + split_to_matches[split_name] = matches + + headers = _hub_auth_headers(token) + client_options: ClientConfig | None = {"default_headers": headers} if headers else None + + if revision is not None and "/" in revision: + base = f"{hf_hub_constants.ENDPOINT}/datasets/{repo_id}/resolve/{quote(revision, safe='')}" + return split_to_matches, HTTPStore(base, client_options=client_options) + + split_to_urls = { + name: [hf_hub_url(repo_id, file, repo_type="dataset", revision=revision) for file in files] + for name, files in split_to_matches.items() + } + if client_options is None: + return split_to_urls, None + + endpoint = hf_hub_constants.ENDPOINT + return { + name: [url.removeprefix(endpoint).lstrip("/") for url in urls] for name, urls in split_to_urls.items() + }, HTTPStore(endpoint, client_options=client_options) + + +def _hub_auth_headers(token: bool | str | None) -> dict[str, str]: + """Authorization headers for Hub reads: the explicit token, or the saved login unless + ``token=False`` opts out. Empty when no credentials are available (anonymous access).""" + if token is False: + return {} + resolved = token if isinstance(token, str) else get_token() + if resolved is None: + return {} + return {"authorization": f"Bearer {resolved}"} + + +def _is_url(path: str) -> bool: + return "://" in path + + +def _has_glob_chars(pattern: str) -> bool: + return any(char in pattern for char in "*?[") + + +def _pattern_with_directory_fallback(pattern: str) -> list[str]: + """A glob-free pattern may name a directory; also match the default files beneath it.""" + if _has_glob_chars(pattern): + return [pattern] + return [pattern, f"{pattern.rstrip('/')}/{_DEFAULT_DATA_FILES}"] + + +def _glob_match(file_name: str, pattern: str) -> bool: + return _glob_to_regex(pattern).match(file_name) is not None + + +@functools.lru_cache(maxsize=128) +def _glob_to_regex(pattern: str) -> re.Pattern[str]: + """Translate a glob pattern to a regex where ``*``/``?`` stop at ``/`` and ``**`` does not. + + ``**/`` also matches zero directory levels, mirroring ``glob.glob(recursive=True)``. + """ + parts: list[str] = [] + i = 0 + while i < len(pattern): + char = pattern[i] + if char == "*": + if pattern.startswith("**/", i): + parts.append("(?:.*/)?") + i += 3 + elif pattern.startswith("**", i): + parts.append(".*") + i += 2 + else: + parts.append("[^/]*") + i += 1 + elif char == "?": + parts.append("[^/]") + i += 1 + elif char == "[": + end = i + 1 + if end < len(pattern) and pattern[end] == "!": + end += 1 + if end < len(pattern) and pattern[end] == "]": + end += 1 + while end < len(pattern) and pattern[end] != "]": + end += 1 + if end >= len(pattern): + parts.append(re.escape(char)) + i += 1 + else: + inner = pattern[i + 1 : end] + if inner.startswith("!"): + inner = "^" + inner[1:] + parts.append(f"[{inner}]") + i = end + 1 + else: + parts.append(re.escape(char)) + i += 1 + return re.compile("".join(parts) + r"\Z") + + +def _fnmatch_variants(pattern: str) -> list[str]: + """Expand each ``**/`` into present/absent variants for fnmatch-based matchers.""" + head, sep, tail = pattern.partition("**/") + if not sep: + return [pattern] + return [ + head + variant for tail_variant in _fnmatch_variants(tail) for variant in (tail_variant, "**/" + tail_variant) + ] + + +def _normalize_data_files( + data_files: str | Sequence[str] | Mapping[str, str | Sequence[str]] | None, + *, + split: str | Sequence[str] | None, +) -> dict[str, list[str]]: + if isinstance(data_files, Mapping): + return {str(name): _as_list(patterns) for name, patterns in data_files.items()} + if split is not None and not isinstance(split, str): + raise ValueError(f"Requesting multiple splits {list(split)!r} requires a `data_files` mapping; pass one.") + split_name = split if isinstance(split, str) else _DEFAULT_SPLIT + return {split_name: _as_list(data_files or _DEFAULT_DATA_FILES)} + + +def _resolve_local_files(base: Path, split_to_patterns: Mapping[str, Sequence[str]]) -> dict[str, list[str]]: + resolved: dict[str, list[str]] = {} + for split_name, patterns in split_to_patterns.items(): + files: list[str] = [] + for pattern in patterns: + if pattern.startswith("hf://"): + raise ValueError("hf:// URLs are not supported in data_files; pass the hf:// URL as `path` instead.") + if _is_url(pattern): + files.append(pattern) + continue + for candidate in _pattern_with_directory_fallback(pattern): + candidate_path = Path(candidate) + if candidate_path.is_absolute(): + matches = sorted(glob.glob(str(candidate_path), recursive=True)) + elif base.is_file() and candidate == _DEFAULT_DATA_FILES: + matches = [str(base)] + else: + matches = sorted(glob.glob(str(base / candidate), recursive=True)) + files.extend(match for match in matches if Path(match).is_file()) + if not files: + raise FileNotFoundError(f"No Vortex files matched split {split_name!r} patterns {list(patterns)!r}") + resolved[split_name] = files + return resolved + + +def _as_list(value: str | Sequence[str] | None) -> list[str]: + if value is None: + return [_DEFAULT_DATA_FILES] + if isinstance(value, str): + return [value] + return [str(item) for item in value] + + +def _looks_like_local_path(path: str) -> bool: + return path.startswith((".", "/", "~")) or any(char in path for char in "*?[]") + + +__all__ = ["VortexIterableDataset", "load_dataset"] diff --git a/vortex-python/src/object_store/registry.rs b/vortex-python/src/object_store/registry.rs index 144a0d0e536..ab0bfce52e1 100644 --- a/vortex-python/src/object_store/registry.rs +++ b/vortex-python/src/object_store/registry.rs @@ -15,7 +15,6 @@ use std::sync::Arc; use object_store::ObjectStore; use object_store::parse_url_opts; use object_store::path::Path; -use object_store::path::PathPart; use object_store::registry::ObjectStoreRegistry; use parking_lot::RwLock; use url::Url; @@ -93,9 +92,12 @@ impl ObjectStoreRegistry for Registry { // 2. Build the store and the path *within* that store. `build_store` reports the path // the way `parse_url_opts` does — i.e. the path is what the store will see as the // key — which is what keeps every scheme on the one caching rule in - // `cache_and_resolve`. The depth is then just the difference in segment counts. + // `cache_and_resolve`. The depth is then the difference in segment counts. let (store, path) = build_store(to_resolve)?; - let depth = num_segments(to_resolve.path()) - num_segments(path.as_ref()); + // The raw URL path counts a percent-encoded segment (e.g. `refs%2Fconvert%2Fparquet`) + // as one segment while `path` is percent-decoded and may count more, so saturate to + // mount the store at the URL root rather than underflowing. + let depth = num_segments(to_resolve.path()).saturating_sub(num_segments(path.as_ref())); // 3. Cache the store and return it alongside the path that lives inside the store. self.cache_and_resolve(to_resolve, store, depth) @@ -169,15 +171,16 @@ fn num_segments(s: &str) -> usize { /// Returns the path of `url` skipping the first `depth` segments fn path_suffix(url: &Url, depth: usize) -> Result { - let segments = path_segments(url.path()).skip(depth); - let path = segments - .map(PathPart::parse) - .collect::>() - .map_err(|e| object_store::Error::Generic { - store: "ObjectStoreRegistry", - source: Box::new(e), - })?; - Ok(path) + // The segments come from a URL, so percent-decode them (one raw segment may decode to + // several path parts, e.g. `refs%2Fconvert%2Fparquet`). + let suffix = path_segments(url.path()) + .skip(depth) + .collect::>() + .join("/"); + Path::from_url_path(suffix).map_err(|e| object_store::Error::Generic { + store: "ObjectStoreRegistry", + source: Box::new(e), + }) } /// Walks to the [`PathEntry`] for `key` sitting `depth` segments into `path`, creating the @@ -201,6 +204,7 @@ mod tests { use std::sync::Arc; use object_store::ObjectStore; + use object_store::path::Path; use object_store::registry::ObjectStoreRegistry; use url::Url; @@ -228,6 +232,23 @@ mod tests { } } + #[test] + fn test_resolve_percent_encoded_path() { + let registry = Registry::default(); + let url = + Url::parse("https://example.com/datasets/org/name/resolve/refs%2Fconvert%2Fparquet/dir/file.vortex") + .unwrap(); + let expected = Path::from("datasets/org/name/resolve/refs/convert/parquet/dir/file.vortex"); + + // First resolution parses and registers the store; it must decode the path, not panic. + let (_store, path) = registry.resolve(&url).unwrap(); + assert_eq!(path, expected); + + // Second resolution takes the cached-store branch and must agree. + let (_store, path) = registry.resolve(&url).unwrap(); + assert_eq!(path, expected); + } + #[test] #[expect(clippy::use_debug)] fn test_resolve_url() { diff --git a/vortex-python/test/test_hf_datasets.py b/vortex-python/test/test_hf_datasets.py new file mode 100644 index 00000000000..6642ab5a14c --- /dev/null +++ b/vortex-python/test/test_hf_datasets.py @@ -0,0 +1,577 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright the Vortex contributors +# pyright: reportMissingTypeStubs=false +# pyright: reportUnknownMemberType=false +# pyright: reportUnknownArgumentType=false +# pyright: reportUnknownVariableType=false + +import re +import threading +from collections.abc import Iterator +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path +from typing import ClassVar, cast + +import datasets as hf_datasets +import pyarrow as pa +import pytest +import vortex.datasets as vx_datasets +from huggingface_hub import hf_hub_url +from typing_extensions import override +from vortex.store import HTTPStore + +import vortex as vx +import vortex.expr as ve + + +def test_datasets_module_is_lazy_exported(): + assert vx.datasets is vx_datasets + + +def write_vortex(path: Path, rows: list[dict[str, object]]) -> None: + vx.io.write(pa.Table.from_pylist(rows), str(path)) + + +class _RangeRequestHandler(BaseHTTPRequestHandler): + """Serves files from `directory` with just enough HTTP range support for Vortex scans.""" + + directory: ClassVar[Path] + + def do_HEAD(self) -> None: + data = self._read_file() + if data is None: + return + self.send_response(200) + self.send_header("Content-Length", str(len(data))) + self.send_header("Accept-Ranges", "bytes") + self.end_headers() + + def do_GET(self) -> None: + data = self._read_file() + if data is None: + return + range_match = re.match(r"bytes=(\d*)-(\d*)", self.headers.get("Range") or "") + if range_match is None: + self.send_response(200) + self.send_header("Content-Length", str(len(data))) + self.end_headers() + _ = self.wfile.write(data) + return + start_group, end_group = range_match.groups() + if start_group: + start = int(start_group) + end = int(end_group) if end_group else len(data) - 1 + else: + start, end = max(0, len(data) - int(end_group)), len(data) - 1 + end = min(end, len(data) - 1) + chunk = data[start : end + 1] + self.send_response(206) + self.send_header("Content-Range", f"bytes {start}-{end}/{len(data)}") + self.send_header("Content-Length", str(len(chunk))) + self.end_headers() + _ = self.wfile.write(chunk) + + def _read_file(self) -> bytes | None: + file = self.directory / self.path.lstrip("/") + if not file.is_file(): + self.send_error(404) + return None + return file.read_bytes() + + @override + def log_message(self, format: str, *args: object) -> None: + pass + + +@pytest.fixture +def http_file_server(tmp_path: Path) -> Iterator[str]: + handler = type("Handler", (_RangeRequestHandler,), {"directory": tmp_path}) + server = ThreadingHTTPServer(("127.0.0.1", 0), handler) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + yield f"http://127.0.0.1:{server.server_address[1]}" + server.shutdown() + + +def test_load_dataset_streaming_local_splits(tmp_path: Path): + write_vortex( + tmp_path / "train-0000.vortex", + [ + {"text": "zero", "label": 0, "tokens": 10}, + {"text": "one", "label": 1, "tokens": 11}, + ], + ) + write_vortex( + tmp_path / "train-0001.vortex", + [ + {"text": "two", "label": 0, "tokens": 12}, + {"text": "three", "label": 1, "tokens": 13}, + ], + ) + write_vortex(tmp_path / "validation.vortex", [{"text": "valid", "label": 1, "tokens": 20}]) + + dataset = vx_datasets.load_dataset( + tmp_path, + data_files={"train": "train-*.vortex", "validation": "validation.vortex"}, + ) + + assert isinstance(dataset, hf_datasets.IterableDatasetDict) + assert list(dataset) == ["train", "validation"] + assert list(dataset["train"].take(3)) == [ + {"label": 0, "text": "zero", "tokens": 10}, + {"label": 1, "text": "one", "tokens": 11}, + {"label": 0, "text": "two", "tokens": 12}, + ] + assert list(dataset["validation"]) == [{"label": 1, "text": "valid", "tokens": 20}] + + +def test_streaming_select_columns_pushes_projection(tmp_path: Path): + write_vortex(tmp_path / "train.vortex", [{"text": "zero", "label": 0}, {"text": "one", "label": 1}]) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train"), + ) + selected = dataset.select_columns(["text"]) + + assert isinstance(selected, vx_datasets.VortexIterableDataset) + assert selected._vortex_columns == ("text",) # pyright: ignore[reportPrivateUsage] + assert list(selected) == [{"text": "zero"}, {"text": "one"}] + + +def test_streaming_filter_accepts_vortex_expression(tmp_path: Path): + write_vortex( + tmp_path / "train.vortex", + [ + {"text": "zero", "label": 0}, + {"text": "one", "label": 1}, + {"text": "two", "label": 0}, + ], + ) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train"), + ) + filtered = dataset.filter(ve.column("label") == 1) + + assert isinstance(filtered, vx_datasets.VortexIterableDataset) + assert list(filtered) == [{"label": 1, "text": "one"}] + + +def test_load_dataset_materializes_to_hf_dataset(tmp_path: Path): + write_vortex(tmp_path / "train.vortex", [{"text": "zero", "label": 0}, {"text": "one", "label": 1}]) + + dataset = vx_datasets.load_dataset( + tmp_path / "train.vortex", + split="train", + streaming=False, + columns=["text", "label"], + keep_in_memory=True, + ) + + assert isinstance(dataset, hf_datasets.Dataset) + assert dataset.to_list() == [{"text": "zero", "label": 0}, {"text": "one", "label": 1}] + + +def test_streaming_resume_with_limit_reads_full_limit(tmp_path: Path): + rows: list[dict[str, object]] = [{"idx": i} for i in range(10)] + write_vortex(tmp_path / "train.vortex", rows) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train", limit=8, batch_size=2), + ) + examples = cast( + vx_datasets._VortexExamplesIterable, # pyright: ignore[reportPrivateUsage] + dataset._ex_iterable, # pyright: ignore[reportPrivateUsage] + ) + # Simulate resuming after the first two rows of the file were already yielded. + examples._state_dict = { # pyright: ignore[reportPrivateUsage] + "file_idx": 0, + "file_row_idx": 2, + "num_yielded": 2, + "type": type(examples).__name__, + } + + produced = [row for _key, table in examples._iter_arrow() for row in table.to_pylist()] # pyright: ignore[reportPrivateUsage] + + # The limit of 8 must still be honored: six rows remain after the two already yielded. + assert produced == rows[2:8] + + +def test_streaming_state_dict_resume_mid_batch_is_exact(tmp_path: Path): + rows: list[dict[str, object]] = [{"idx": i} for i in range(10)] + write_vortex(tmp_path / "train.vortex", rows) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train", batch_size=4), + ) + iterator = iter(dataset) + # Stop mid-batch: five rows consumed, one row into the second batch of four. + consumed = [next(iterator) for _ in range(5)] + state = dataset.state_dict() + + resumed = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train", batch_size=4), + ) + resumed.load_state_dict(state) + + assert consumed + list(resumed) == rows + + +def write_sharded_dataset(tmp_path: Path, num_files: int = 4, rows_per_file: int = 3) -> list[int]: + for file_idx in range(num_files): + rows: list[dict[str, object]] = [{"idx": file_idx * rows_per_file + i} for i in range(rows_per_file)] + write_vortex(tmp_path / f"train-{file_idx:04d}.vortex", rows) + return list(range(num_files * rows_per_file)) + + +def test_streaming_shuffle_multiple_files(tmp_path: Path): + # Regression test: shuffle() interleaves shards through iterables that read + # sleep_on_threads_shutdown on every leaf; this used to raise AttributeError. + indices = write_sharded_dataset(tmp_path) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path, split="train"), + ) + shuffled = dataset.shuffle(seed=42, buffer_size=4) + + assert sorted(row["idx"] for row in shuffled) == indices + + +def test_interleave_vortex_datasets(tmp_path: Path): + write_vortex(tmp_path / "a.vortex", [{"idx": 0}, {"idx": 1}]) + write_vortex(tmp_path / "b.vortex", [{"idx": 10}, {"idx": 11}]) + + left = cast(vx_datasets.VortexIterableDataset, vx_datasets.load_dataset(tmp_path / "a.vortex", split="train")) + right = cast(vx_datasets.VortexIterableDataset, vx_datasets.load_dataset(tmp_path / "b.vortex", split="train")) + interleaved = hf_datasets.interleave_datasets([left, right], stopping_strategy="all_exhausted") + + assert sorted(row["idx"] for row in interleaved) == [0, 1, 10, 11] + + +def test_streaming_take_splits_limit_across_shards(tmp_path: Path): + _ = write_sharded_dataset(tmp_path) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path, split="train"), + ) + limited = dataset.take(4) + assert isinstance(limited, vx_datasets.VortexIterableDataset) + examples = limited._ex_iterable # pyright: ignore[reportPrivateUsage] + + # DataLoader worker / distributed sharding must split the pushed-down limit so the shards + # together yield exactly take(n) rows, mirroring TakeExamplesIterable.split_number. + shards = [examples.shard_data_sources(2, index) for index in range(2)] + rows = [row["idx"] for shard in shards for _key, row in shard] + + assert rows == [0, 1, 6, 7] + + +def test_streaming_take_then_shuffle_keeps_row_set(tmp_path: Path): + _ = write_sharded_dataset(tmp_path) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path, split="train"), + ) + limited = dataset.take(4) + + # Shuffling after take() may only reorder the taken rows, never select different ones. + taken = sorted(row["idx"] for row in limited) + shuffled = sorted(row["idx"] for row in limited.shuffle(seed=7, buffer_size=16)) + + assert shuffled == taken + + +@pytest.mark.skipif( + not hasattr(hf_datasets.IterableDataset, "reshard"), reason="IterableDataset.reshard requires datasets>=5" +) +def test_streaming_reshard_keeps_all_rows(tmp_path: Path): + rows: list[dict[str, object]] = [{"idx": i} for i in range(4)] + write_vortex(tmp_path / "train.vortex", rows) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train"), + ) + + assert list(dataset.reshard()) == rows + + +def test_streaming_filter_expr_with_kwargs_is_rejected(tmp_path: Path): + write_vortex(tmp_path / "train.vortex", [{"text": "zero", "label": 0}]) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train"), + ) + with pytest.raises(ValueError, match="not supported with a Vortex expression"): + _ = dataset.filter(ve.column("label") == 1, with_indices=True) + + +def test_streaming_filter_after_take_is_rejected(tmp_path: Path): + write_vortex(tmp_path / "train.vortex", [{"text": "zero", "label": 0}, {"text": "one", "label": 1}]) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train"), + ) + limited = dataset.take(1) + assert isinstance(limited, vx_datasets.VortexIterableDataset) + with pytest.raises(ValueError, match="after a row limit"): + _ = limited.filter(ve.column("label") == 1) + + +def test_streaming_filter_then_take_pushes_down(tmp_path: Path): + write_vortex( + tmp_path / "train.vortex", + [{"text": "a", "label": 0}, {"text": "b", "label": 1}, {"text": "c", "label": 1}], + ) + + dataset = cast( + vx_datasets.VortexIterableDataset, + vx_datasets.load_dataset(tmp_path / "train.vortex", split="train"), + ) + result = dataset.filter(ve.column("label") == 1).take(1) + + assert isinstance(result, vx_datasets.VortexIterableDataset) + assert list(result) == [{"text": "b", "label": 1}] + + +def test_streaming_filter_and_limit_combined(tmp_path: Path): + write_vortex( + tmp_path / "train.vortex", + [{"text": "a", "label": 0}, {"text": "b", "label": 1}, {"text": "c", "label": 1}], + ) + + # Vortex cannot scan with a filter and a limit at once; load_dataset must still honor both. + dataset = vx_datasets.load_dataset( + tmp_path / "train.vortex", split="train", filter=ve.column("label") == 1, limit=1 + ) + + assert isinstance(dataset, vx_datasets.VortexIterableDataset) + assert list(dataset) == [{"text": "b", "label": 1}] + + +def test_materialize_filter_and_limit_combined(tmp_path: Path): + write_vortex( + tmp_path / "train.vortex", + [{"text": "a", "label": 0}, {"text": "b", "label": 1}, {"text": "c", "label": 1}], + ) + + dataset = vx_datasets.load_dataset( + tmp_path / "train.vortex", + split="train", + streaming=False, + filter=ve.column("label") == 1, + limit=1, + keep_in_memory=True, + ) + + assert isinstance(dataset, hf_datasets.Dataset) + assert dataset.to_list() == [{"text": "b", "label": 1}] + + +def test_load_dataset_multi_split_without_mapping_raises(tmp_path: Path): + write_vortex(tmp_path / "train.vortex", [{"text": "zero"}]) + + with pytest.raises(ValueError, match="data_files"): + _ = vx_datasets.load_dataset(tmp_path, split=["train", "validation"]) + + +def test_load_dataset_streams_from_http_url(tmp_path: Path, http_file_server: str, monkeypatch: pytest.MonkeyPatch): + # The test server is plain http; object_store only allows https unless ALLOW_HTTP is set. + monkeypatch.setenv("ALLOW_HTTP", "true") + rows: list[dict[str, object]] = [{"idx": i, "text": f"row {i}"} for i in range(20)] + write_vortex(tmp_path / "train.vortex", rows) + + dataset = vx_datasets.load_dataset(f"{http_file_server}/train.vortex", split="train", batch_size=8) + + assert isinstance(dataset, vx_datasets.VortexIterableDataset) + assert list(dataset.select_columns(["idx"]).take(3)) == [{"idx": 0}, {"idx": 1}, {"idx": 2}] + assert list(dataset) == rows + + +def test_load_dataset_url_in_data_files(tmp_path: Path, http_file_server: str, monkeypatch: pytest.MonkeyPatch): + monkeypatch.setenv("ALLOW_HTTP", "true") + write_vortex(tmp_path / "remote.vortex", [{"idx": 0}]) + + dataset = vx_datasets.load_dataset( + tmp_path, data_files={"train": f"{http_file_server}/remote.vortex"}, split="train" + ) + + assert list(dataset) == [{"idx": 0}] + + +class _FakeHfApi: + repo_files: ClassVar[list[str]] = ["README.md", "train.vortex", "data/validation.vortex", "notes/readme.txt"] + + def __init__(self, token: bool | str | None = None): + self.token: bool | str | None = token + + def list_repo_files(self, repo_id: str, *, repo_type: str | None = None, revision: str | None = None) -> list[str]: + assert repo_id == "org/name" + assert repo_type == "dataset" + assert revision in (None, "refs/convert/parquet") + return list(self.repo_files) + + +def test_hub_streaming_resolves_to_urls_without_download(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(vx_datasets, "HfApi", _FakeHfApi) + + files, store = vx_datasets._resolve_data_files( # pyright: ignore[reportPrivateUsage] + "org/name", + data_files=None, + split="train", + revision=None, + token=False, + cache_dir=None, + local_files_only=False, + streaming=True, + ) + + assert store is None + expected = [ + hf_hub_url("org/name", file, repo_type="dataset") for file in ["data/validation.vortex", "train.vortex"] + ] + assert files == {"train": expected} + + +def test_hub_streaming_with_token_uses_authenticated_store(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(vx_datasets, "HfApi", _FakeHfApi) + + files, store = vx_datasets._resolve_data_files( # pyright: ignore[reportPrivateUsage] + "org/name", + data_files=None, + split="train", + revision=None, + token="hf_fake_token", + cache_dir=None, + local_files_only=False, + streaming=True, + ) + + assert isinstance(store, HTTPStore) + assert files == { + "train": [ + "datasets/org/name/resolve/main/data/validation.vortex", + "datasets/org/name/resolve/main/train.vortex", + ] + } + + +@pytest.mark.parametrize( + ("uri", "expected"), + [ + ("hf://datasets/org/name", ("org/name", None, None)), + ("hf://datasets/org/name@main", ("org/name", "main", None)), + ("hf://datasets/org/name/train.vortex", ("org/name", None, "train.vortex")), + ("hf://datasets/org/name/data/nested/*.vortex", ("org/name", None, "data/nested/*.vortex")), + ( + "hf://datasets/org/name@refs%2Fconvert%2Fparquet/data/*.vortex", + ("org/name", "refs/convert/parquet", "data/*.vortex"), + ), + ], +) +def test_parse_hf_uri(uri: str, expected: tuple[str, str | None, str | None]): + assert vx_datasets._parse_hf_uri(uri) == expected # pyright: ignore[reportPrivateUsage] + + +@pytest.mark.parametrize("uri", ["hf://org/name/file.vortex", "hf://datasets/name-only", "hf://datasets/org/@main"]) +def test_parse_hf_uri_invalid(uri: str): + with pytest.raises(ValueError, match="hf://"): + _ = vx_datasets._parse_hf_uri(uri) # pyright: ignore[reportPrivateUsage] + + +def test_hf_uri_streaming_resolves_file_and_directory(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(vx_datasets, "HfApi", _FakeHfApi) + + def resolve(path: str): + return vx_datasets._resolve_data_files( # pyright: ignore[reportPrivateUsage] + path, + data_files=None, + split="train", + revision=None, + token=False, + cache_dir=None, + local_files_only=False, + streaming=True, + ) + + files, store = resolve("hf://datasets/org/name/train.vortex") + assert store is None + assert files == {"train": [hf_hub_url("org/name", "train.vortex", repo_type="dataset")]} + + # A glob-free path naming a directory selects the default Vortex files beneath it. + files, _store = resolve("hf://datasets/org/name/data") + assert files == {"train": [hf_hub_url("org/name", "data/validation.vortex", repo_type="dataset")]} + + +def test_hf_uri_slash_revision_uses_prefix_rooted_store(monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(vx_datasets, "HfApi", _FakeHfApi) + + files, store = vx_datasets._resolve_data_files( # pyright: ignore[reportPrivateUsage] + "hf://datasets/org/name@refs%2Fconvert%2Fparquet", + data_files=None, + split="train", + revision=None, + token=False, + cache_dir=None, + local_files_only=False, + streaming=True, + ) + + # The Hub only routes percent-encoded revisions, so the store is rooted at the encoded + # resolve prefix and the files are in-repo paths. + assert isinstance(store, HTTPStore) + assert store.url.endswith("/datasets/org/name/resolve/refs%2Fconvert%2Fparquet") + assert files == {"train": ["data/validation.vortex", "train.vortex"]} + + +def test_hf_uri_revision_conflict_raises(): + with pytest.raises(ValueError, match="Conflicting revisions"): + _ = vx_datasets.load_dataset("hf://datasets/org/name@main", revision="other") + + +def test_hf_uri_with_path_and_data_files_raises(): + with pytest.raises(ValueError, match="data_files"): + _ = vx_datasets.load_dataset("hf://datasets/org/name/train.vortex", data_files="x.vortex") + + +def test_hf_url_in_data_files_rejected(tmp_path: Path): + with pytest.raises(ValueError, match="data_files"): + _ = vx_datasets.load_dataset(tmp_path, data_files={"train": "hf://datasets/org/name/train.vortex"}) + + +def test_local_directory_in_data_files(tmp_path: Path): + (tmp_path / "sub").mkdir() + write_vortex(tmp_path / "sub" / "train.vortex", [{"idx": 0}]) + + dataset = vx_datasets.load_dataset(tmp_path, data_files={"train": "sub"}, split="train") + + assert list(dataset) == [{"idx": 0}] + + +@pytest.mark.parametrize( + ("pattern", "path", "expected"), + [ + ("**/*.vortex", "train.vortex", True), + ("**/*.vortex", "data/nested/train.vortex", True), + ("**/*.vortex", "train.parquet", False), + ("*.vortex", "train.vortex", True), + ("*.vortex", "data/train.vortex", False), + ("data/**/*.vortex", "data/train.vortex", True), + ("data/**/*.vortex", "data/a/b/train.vortex", True), + ("data/**/*.vortex", "other/train.vortex", False), + ("train-?.vortex", "train-1.vortex", True), + ("train-[01].vortex", "train-2.vortex", False), + ], +) +def test_glob_match(pattern: str, path: str, expected: bool): + assert vx_datasets._glob_match(path, pattern) is expected # pyright: ignore[reportPrivateUsage]