TransformAndMapHuggingFaceDatasetJob and ExtractTextFromHuggingFaceDatasetJob

datasets/huggingface.py

@@ -2,12 +2,18 @@
 https://huggingface.co/docs/datasets/
 """
 
-from typing import Optional, Any, Union
-from sisyphus import Job, Task, gs
+from __future__ import annotations
+from typing import TYPE_CHECKING, Optional, Any, Union, Callable, Sequence, Dict
+from sisyphus import Job, Task, Path, gs
 from sisyphus.delayed_ops import DelayedBase
 
 from i6_core.util import instanciate_delayed
 
+if TYPE_CHECKING:
+    from datasets import Dataset, DatasetDict
+
+    TransformFuncT = Union[Callable[[DatasetDict], DatasetDict], Callable[[Dataset], Dataset]]
+
 
 class DownloadAndPrepareHuggingFaceDatasetJob(Job):
     """
@@ -112,3 +118,246 @@ def hash(cls, kwargs):
             "token": kwargs["token"],
         }
         return super().hash(d)
+
+
+class TransformAndMapHuggingFaceDatasetJob(Job):
+    """
+    Runs some functions (e.g. filtering, mapping, renaming columns, ...) on a HF dataset.
+
+    The map is handled with special logic, as this involves writing to disk.
+    We write to the work dir via cache_file_name(s).
+    Then we do a save_to_disk to the final output dir.
+    Then we clean up the work dir again.
+    """
+
+    def __init__(
+        self,
+        path: Union[str, Path],
+        name: Optional[str] = None,
+        *,
+        load_dataset_opts: Optional[Dict[str, Any]] = None,  # e.g. "split", "revision", ...
+        non_hashed_load_dataset_opts: Optional[Dict[str, Any]] = None,  # e.g. {"num_proc": 8}
+        transform: Union[None, TransformFuncT, Sequence[TransformFuncT]] = None,
+        map_func: Optional[Callable] = None,
+        map_opts: Union[
+            None, Dict[str, Any], Callable[[Dataset], Dict[str, Any]], Callable[[DatasetDict], Dict[str, Any]]
+        ] = None,
+        non_hashed_map_opts: Optional[Dict[str, Any]] = None,
+        num_shards: Union[None, int, Dict[str, int]] = None,
+        max_shard_size: Union[None, str, int] = None,
+    ):
+        """
+        :param path: for :func:`datasets.load_dataset`,
+            :func:`datasets.Dataset.load_from_disk` or :func:`datasets.DatasetDict.load_from_disk`.
+            We automatically detect which one to use.
+        :param name: for :func:`datasets.load_dataset`
+        :param load_dataset_opts: other options for :func:`datasets.load_dataset`
+            or :func:`datasets.Dataset.load_from_disk` or :func:`datasets.DatasetDict.load_from_disk`.
+            E.g. "split", "revision", ...
+        :param non_hashed_load_dataset_opts: like ``load_dataset_opts``, but not hashed.
+            E.g. ``{"num_proc": 8}``.
+        :param transform: function or list of functions to transform the dataset
+            ((Dataset) -> Dataset or (DatasetDict) -> DatasetDict).
+            E.g. filtering, renaming columns, ...
+        :param map_func: function to map the dataset examples, or batch of examples.
+            This is passed to :func:`datasets.Dataset.map` or :func:`datasets.DatasetDict.map`.
+            None (default) means identity.
+        :param map_opts: further options passed :func:`datasets.Dataset.map` or :func:`datasets.DatasetDict.map`,
+            or a function that returns such options (e.g. depending on the dataset size).
+            E.g. ``{"batched": True, "batch_size": 1000}``.
+        :param non_hashed_map_opts: like ``map_opts``, but not hashed.
+        :param num_shards: how many shards to write via :func:`datasets.Dataset.save_to_disk`
+            or :func:`datasets.DatasetDict.save_to_disk`.
+            If not given, will be auto-detected based on the dataset size and ``max_shard_size``.
+        :param max_shard_size: maximum size of each shard.
+            If not given, will use ``"500MB"``.
+        """
+        super().__init__()
+
+        if max_shard_size is not None and num_shards is not None:
+            raise ValueError(f"{self}: please specify either max_shard_size or num_shards, but not both.")
+
+        self.path = path
+        self.name = name
+        self.load_dataset_opts = load_dataset_opts
+        self.non_hashed_load_dataset_opts = non_hashed_load_dataset_opts
+        self.transform = transform
+        self.map_func = map_func
+        self.map_opts = map_opts
+        self.non_hashed_map_opts = non_hashed_map_opts
+        self.num_shards = num_shards
+        self.max_shard_size = max_shard_size
+
+        self.rqmt = {"cpu": 16, "mem": 16, "time": 12}
+
+        self.out_dir = self.output_path("dataset", directory=True)
+
+    @classmethod
+    def hash(cls, kwargs):
+        kwargs.pop("non_hashed_load_dataset_opts")
+        kwargs.pop("non_hashed_map_opts")
+        return super().hash(kwargs)
+
+    def tasks(self):
+        yield Task("run", resume="run", rqmt=self.rqmt)
+
+    def run(self):
+        import os
+        import shutil
+        from datasets import load_dataset, Dataset, DatasetDict
+        from datasets.utils.py_utils import convert_file_size_to_int
+        from datasets import config
+
+        dataset_path = instanciate_delayed(self.path)
+        split = None
+        load_dataset_opts = (instanciate_delayed(self.load_dataset_opts) or {}).copy()
+        load_dataset_opts.update(instanciate_delayed(self.non_hashed_load_dataset_opts) or {})
+        if self.name is not None:
+            load_dataset_opts["name"] = self.name
+        if "split" in load_dataset_opts:
+            split = load_dataset_opts["split"]
+        path_ext = f"{dataset_path}/{split}" if split is not None else dataset_path
+        ds = None
+        if os.path.exists(path_ext):
+            if os.path.isdir(path_ext):
+                if os.path.exists(f"{path_ext}/{config.DATASET_INFO_FILENAME}") and os.path.exists(
+                    f"{path_ext}/{config.DATASET_STATE_JSON_FILENAME}"
+                ):
+                    load_dataset_opts.pop("split", None)
+                    ds = Dataset.load_from_disk(path_ext, **load_dataset_opts)
+                elif os.path.exists(f"{path_ext}/{config.DATASETDICT_JSON_FILENAME}"):
+                    load_dataset_opts.pop("split", None)
+                    ds = DatasetDict.load_from_disk(path_ext, **load_dataset_opts)
+            elif path_ext.endswith(".arrow"):
+                load_dataset_opts.pop("split", None)
+                ds = Dataset.from_file(path_ext, **load_dataset_opts)
+
+        if ds is None:
+            # Use load_dataset.
+            # That can potentially download the dataset, so make sure that HF_HOME is set.
+            assert os.environ.get("HF_HOME"), (
+                "HF_HOME env var not set,"
+                " set this in your settings.py DEFAULT_ENVIRONMENT_SET"
+                " (if not CLEANUP_ENVIRONMENT, otherwise in your current env),"
+                " or via job.set_env"
+            )
+
+            ds = load_dataset(dataset_path, **load_dataset_opts)
+            assert isinstance(ds, (Dataset, DatasetDict))
+
+        if self.transform:
+            if callable(self.transform):
+                ds = self.transform(ds)
+                assert isinstance(ds, (Dataset, DatasetDict)), f"After {self.transform} got {type(ds)}"
+            else:
+                for func in self.transform:
+                    ds = func(ds)
+                    assert isinstance(ds, (Dataset, DatasetDict)), f"After {func} got {type(ds)}"
+
+        work_out_d = "tmp-map-output"
+        if os.path.exists(work_out_d):
+            shutil.rmtree(work_out_d)
+        os.makedirs(work_out_d)
+        map_opts = self.map_opts
+        if callable(map_opts):
+            map_opts = map_opts(ds)
+        map_extra_opts = {}
+        if self.non_hashed_map_opts and "num_proc" in self.non_hashed_map_opts:
+            num_proc = self.non_hashed_map_opts["num_proc"]
+        else:
+            num_proc = self.rqmt["cpu"] * 2
+            map_extra_opts["num_proc"] = num_proc
+        if self.map_func:
+            ds = ds.map(
+                self.map_func,
+                **(map_opts or {}),
+                **(self.non_hashed_map_opts or {}),
+                **({"cache_file_name": f"{work_out_d}/data.arrow"} if isinstance(ds, Dataset) else {}),
+                **(
+                    {"cache_file_names": {k: f"{work_out_d}/data-{k}.arrow" for k in ds.keys()}}
+                    if isinstance(ds, DatasetDict)
+                    else {}
+                ),
+                **map_extra_opts,
+            )
+
+        num_shards = self.num_shards
+        max_shard_size = self.max_shard_size or "500MB"
+        max_shard_size = convert_file_size_to_int(max_shard_size)
+        if num_shards is None:
+            # This code is adapted from Dataset.save_to_disk to determine the number of shards.
+            # We make this independent of num_proc (because num_proc is not hashed).
+            if isinstance(ds, DatasetDict):
+                # noinspection PyProtectedMember
+                num_shards = {k: int(ds_._estimate_nbytes() / max_shard_size) + 1 for k, ds_ in ds.items()}
+            elif isinstance(ds, Dataset):
+                # noinspection PyProtectedMember
+                num_shards = int(ds._estimate_nbytes() / max_shard_size) + 1
+            else:
+                raise TypeError(f"Unexpected type: {type(ds)}")
+
+        ds.save_to_disk(self.out_dir.get_path(), num_shards=num_shards, num_proc=num_proc)
+        del ds
+        shutil.rmtree(work_out_d)
+
+
+class ExtractTextFromHuggingFaceDatasetJob(Job):
+    """
+    Extract a text column from a HF dataset and write it to a gzipped text file.
+    """
+
+    def __init__(
+        self,
+        path: Union[str, Path],
+        name: Optional[str] = None,
+        *,
+        split: Optional[str] = "train",
+        column_name: str = "text",
+    ):
+        """
+        :param path: for :func:`datasets.load_dataset`
+        :param name: for :func:`datasets.load_dataset`
+        :param split: for :func:`datasets.load_dataset`
+        :param column_name: name of the text column to extract
+        """
+        super().__init__()
+        self.path = path
+        self.name = name
+        self.split = split
+        self.column_name = column_name
+
+        self.rqmt = {"cpu": 4, "mem": 8, "time": 10}
+
+        self.out_text = self.output_path("text.txt.gz")
+
+    def tasks(self):
+        yield Task("run", resume="run", rqmt=self.rqmt)
+
+    def run(self):
+        import sys
+        import gzip
+        import time
+        from datasets import load_dataset, Dataset
+
+        ds = load_dataset(self.path, self.name, split=self.split)
+        assert isinstance(ds, Dataset), f"Expected a Dataset, got {type(ds)} {ds}"
+        assert self.column_name in ds.column_names, f"Column name {self.column_name} not in columns {ds.column_names}"
+
+        def _hms(s):
+            m, s = divmod(s, 60)
+            h, m = divmod(m, 60)
+            return "%d:%02d:%02d" % (h, m, s)
+
+        size = ds.num_rows
+        start_time = time.monotonic()
+        with gzip.open(self.out_text.get_path(), "wt", encoding="utf-8") as f:
+            for i, item in enumerate(ds):
+                if (i + 1) % 10000 == 0 or i + 1 == size:
+                    elapsed = time.monotonic() - start_time
+                    speed = (i + 1) / elapsed if elapsed > 0 else 0
+                    eta = (size - (i + 1)) / speed if speed > 0 else float("inf")
+                    eta_str = _hms(eta) if eta != float("inf") else "inf"
+                    print(f"Line {i + 1}/{size}, {((i + 1) / size * 100):.1f}%, {speed:.1f} it/s, ETA {eta_str}")
+                    sys.stdout.flush()
+                f.write(item[self.column_name])
+                f.write("\n")