Dataset: implement global dataset_distribution option

returnn/datasets/basic.py

@@ -20,7 +20,7 @@
 import numpy
 import functools
 import typing
-from typing import TYPE_CHECKING, Optional, Any, Union, Type, Dict, Sequence, List, Callable
+from typing import TYPE_CHECKING, Optional, Any, Tuple, Union, Type, Dict, Sequence, List, Callable
 
 from returnn.log import log
 from returnn.engine.batch import Batch, BatchSetGenerator
@@ -68,6 +68,12 @@ def set_or_remove(key, value):
         set_or_remove("min_chunk_size", config.opt_typed_value("min_chunk_size", 0) or None)
         set_or_remove("chunking_variance", config.float("chunking_variance", 0))
 
+        dd_cfg = config.typed_value("dataset_distribution", "random_seed_offset")
+        assert dd_cfg in ["random_seed_offset", "shard"]
+        shard_index, num_shards = Dataset._get_sharding_rank_and_size(config) if dd_cfg == "shard" else 0, 1
+        set_or_remove("num_shards", num_shards)
+        set_or_remove("shard_index", shard_index)
+
     @staticmethod
     def get_default_kwargs_eval(config: Config) -> Dict[str, Any]:
         """
@@ -112,8 +118,8 @@ def __init__(
         min_chunk_size=0,
         chunking_variance=0,
         estimated_num_seqs=None,
-        _num_shards=1,
-        _shard_index=0,
+        num_shards: int = 1,
+        shard_index: int = 0,
     ):
         """
         :param str name: e.g. "train" or "eval"
@@ -137,8 +143,8 @@ def __init__(
         :param str|None seq_order_seq_lens_file: for seq order, use the seq length given by this file
         :param int shuffle_frames_of_nseqs: shuffles the frames. not always supported
         :param None|int estimated_num_seqs: for progress reporting in case the real num_seqs is unknown
-        :param int _num_shards: number of shards the data is split into
-        :param int _shard_index: local shard index, when sharding is enabled
+        :param num_shards: number of shards the data is split into
+        :param shard_index: local shard index, when sharding is enabled
         """
         self.name = name or ("dataset_id%s" % id(self))
         self.lock = None  # type: Optional[RLock]  # Used when manipulating our data potentially from multiple threads.
@@ -172,9 +178,9 @@ def __init__(
         self._chunking = chunking
         self.chunk_size, self.chunk_step, self.custom_chunking_func = self._parse_chunking(chunking)
         self._context_window = context_window
-        assert 0 <= _shard_index < _num_shards
-        self._num_shards = _num_shards
-        self._shard_index = _shard_index
+        assert 0 <= shard_index < num_shards
+        self.num_shards = num_shards
+        self.shard_index = shard_index
         if isinstance(context_window, (tuple, list)):
             assert len(context_window) == 2
             for elem in context_window:
@@ -250,6 +256,37 @@ def __reduce__(self):
         state = {attr: getattr(self, attr) for attr in ["epoch", "zpad"]}
         return Dataset._create_from_reduce, (self.__class__, kwargs, state)
 
+    def set_shard_idx_and_num_shards(self, shard_index: int, num_shards: int):
+        """set sharding config for the dataset"""
+        assert 0 <= shard_index < num_shards
+        assert num_shards == 1 or self.supports_sharding()
+        self.num_shards = num_shards
+        self.shard_index = shard_index
+
+    @staticmethod
+    def _get_sharding_rank_and_size(config: Optional[Config] = None) -> Tuple[int, int]:
+        """
+        :return: tuple (rank, size): the global rank and size for distributed trainings
+        """
+        if config is None:
+            from returnn.config import get_global_config
+
+            config = get_global_config(return_empty_if_none=True)
+        if config.typed_value("torch_distributed") is not None:
+            import returnn.torch.distributed
+
+            ctx = returnn.torch.distributed.get_ctx(config=config)
+            return ctx.rank(), ctx.size()
+        elif config.is_true("use_horovod"):
+            assert config.bool("use_tensorflow", False) or config.value("backend", "").startswith("tensorflow")
+
+            import returnn.tf.horovod
+
+            ctx = returnn.tf.horovod.get_ctx(config=config)
+            return ctx.rank(), ctx.size()
+        else:
+            return 0, 1
+
     @property
     def random_seed_offset(self) -> int:
         """:return: random seed offset for shuffling"""
@@ -259,10 +296,10 @@ def random_seed_offset(self) -> int:
 
     def _uses_custom_distributed_sharding(self) -> bool:
         """
-        :return: if dataset has its own data sharding logic independent of TF/PT.
+        :return: if the dataset has its own data sharding logic independent of TF/PT.
             Leads to a fixed random_seed_offset independent of the workers local rank.
         """
-        return False
+        return self.num_shards > 1
 
     def _get_default_random_seed_offset(self):
         """
@@ -643,9 +680,9 @@ def get_seq_order_for_epoch(
             seq_index = [
                 i for i in seq_index if (all_seq_tags[i] not in used_seq_tags, used_seq_tags.add(all_seq_tags[i]))[0]
             ]
-        if partition_epoch > 1 or self._num_shards > 1:
+        if partition_epoch > 1 or self.num_shards > 1:
             seq_index = self._apply_partition_epoch_and_sharding(
-                seq_index, partition_epoch, epoch, self._num_shards, self._shard_index
+                seq_index, partition_epoch, epoch, self.num_shards, self.shard_index
             )
         if repeat_epoch > 1:
             seq_index = list(seq_index) * repeat_epoch
@@ -737,8 +774,8 @@ def init_seq_order(self, epoch=None, seq_list=None, seq_order=None):
         self.epoch = epoch
         self.rnd_seq_drop = Random(self._get_random_seed_for_epoch(epoch=epoch))
         assert (
-            self._num_shards == 1 or self.supports_sharding()
-        ), f"{self}: does not support sharding, but got num_shards == {self._num_shards}"
+            self.num_shards == 1 or self.supports_sharding()
+        ), f"{self}: does not support sharding, but got num_shards == {self.num_shards}"
         return False
 
     def finish_epoch(self, *, free_resources: bool = False):
@@ -1588,9 +1625,9 @@ def _dataset_extend_default_kwargs_from_parent_dataset(
     default_kwargs = default_kwargs.copy() if default_kwargs else {}
     default_kwargs.setdefault("random_seed_offset", parent_dataset.random_seed_offset)
     # noinspection PyProtectedMember
-    default_kwargs.setdefault("_num_shards", parent_dataset._num_shards)
+    default_kwargs.setdefault("num_shards", parent_dataset.num_shards)
     # noinspection PyProtectedMember
-    default_kwargs.setdefault("_shard_index", parent_dataset._shard_index)
+    default_kwargs.setdefault("shard_index", parent_dataset.shard_index)
     return default_kwargs
 
 

returnn/datasets/distrib_files.py

@@ -137,9 +137,8 @@ def __init__(
         get_sub_epoch_dataset: Callable[[List[FileTree]], Dict[str, Any]],
         preload_next_n_sub_epochs: int = 1,
         buffer_size: int = 1,
-        distrib_shard_files: bool = False,
+        distrib_shard_files: Optional[bool] = None,
         _meta_info_cache: Optional[Dict[str, Any]] = None,
-        _distrib_info: Optional[Dict[str, int]] = None,
         **kwargs,
     ):
         """
@@ -148,10 +147,10 @@ def __init__(
         :param get_sub_epoch_dataset: callable which returns a dataset dict for a given subset of files
         :param preload_next_n_sub_epochs: how many sub epoch datasets to preload
         :param buffer_size: buffer size for each worker, amount of seqs to prefetch
-        :param distrib_shard_files: set to true to shard the data across worker processes in
-            distributed training scenaria
+        :param distrib_shard_files: deprecated. Replaced by global config option ``dataset_distribution="shard"``.
+
+            Set to true to shard the data across worker processes in distributed training scenaria.
         :param _meta_info_cache: for internal use
-        :param _distrib_info: for internal use
         """
         super().__init__(**kwargs)
         self.files = files
@@ -166,21 +165,13 @@ def __init__(
         self._workers: Dict[int, _WorkerProcParent] = {}  # epoch -> worker
         self._files_order_cache: Dict[int, List[List[FileTree]]] = {}  # full epoch (0-indexed) -> files order
 
-        self.distrib_shard_files = distrib_shard_files
-        if distrib_shard_files:
-            assert self._num_shards == 1 and self._shard_index == 0, (  # ensure defaults are set
-                f"{self}: Cannot use both dataset-sharding via properties _num_shards and _shard index "
-                f"and {self.__class__.__name__}'s own sharding implementation based on the trainings rank and size."
+        if distrib_shard_files is not None:
+            log.print_deprecation_warning(
+                f"{self.__class__.__name__}' `distrib_shard_files` config option is set. "
+                "Use global config option `dataset_distribution` instead "
+                "for the same behavior across more types of datasets."
             )
-            if _distrib_info:
-                # If we're in a child process `_get_rank_and_size()` no longer works,
-                # so we pass the info about the shards via a pickled property.
-                # See also Dataset.__reduce__.
-                self._shard_index = _distrib_info["_shard_index"]
-                self._num_shards = _distrib_info["_num_shards"]
-            else:
-                self._shard_index, self._num_shards = _get_rank_and_size()
-        assert 0 <= self._shard_index < self._num_shards
+            self._validate_global_shard_cfg(distrib_shard_files)
 
         if _meta_info_cache:
             # This allows to skip the lazy init in self.initialize().
@@ -204,10 +195,6 @@ def supports_sharding(self) -> bool:
         """:return: whether the dataset supports sharding based on the seq_order"""
         return True
 
-    @property
-    def _distrib_info(self):
-        return {"_num_shards": self._num_shards, "_shard_index": self._shard_index}
-
     @property
     def _meta_info_cache(self):
         if not self.num_outputs:
@@ -220,9 +207,6 @@ def _meta_info_cache(self):
             "file_sizes": self._file_sizes,
         }
 
-    def _uses_custom_distributed_sharding(self) -> bool:
-        return self._num_shards > 1
-
     def _lazy_init_num_outputs(self):
         if self.num_outputs:
             return
@@ -290,11 +274,11 @@ def init_seq_order(self, epoch: Optional[int] = None, seq_list=None, seq_order=N
             else:
                 raise ValueError(f"{self}: seq_ordering {self.seq_ordering!r} not supported")
             file_bins = self._distribute_evenly_by_size(
-                num_bins=self._num_shards * self.partition_epoch,
+                num_bins=self.num_shards * self.partition_epoch,
                 file_sizes=self._file_sizes,
                 files_order=files_order_flat,
             )
-            self_index_base = self.partition_epoch * self._shard_index
+            self_index_base = self.partition_epoch * self.shard_index
             self_index_end = self_index_base + self.partition_epoch
             self._files_order_cache[full_epoch_0idx_] = file_bins[self_index_base:self_index_end]
 
@@ -328,6 +312,10 @@ def _get_sub_dataset_dict(self, files: List[FileTree]) -> Dict[str, Any]:
 
         dataset_dict = self.get_sub_epoch_dataset(files)
         dataset_dict = extend_dataset_dict_from_parent_dataset(dataset_dict, parent_dataset=self)
+        # We shard by splitting the files list into shards, the sub datasets must not shard any further by themselves
+        if self.num_shards > 1:
+            dataset_dict["num_shards"] = 1
+            dataset_dict["shard_index"] = 0
 
         flat_sub_dset = tree.flatten_with_path(dataset_dict)
 
@@ -452,6 +440,21 @@ def get_data_keys(self) -> List[str]:
             self._lazy_init_num_outputs()
         return self._data_keys
 
+    @classmethod
+    def _validate_global_shard_cfg(cls, distrib_shard_files: bool):
+        from returnn.config import get_global_config
+
+        config = get_global_config(raise_exception=False)
+        if not config:
+            return
+
+        dd_cfg = config.typed_value("dataset_distribution", None)
+        if dd_cfg and (distrib_shard_files and dd_cfg != "shard") or (not distrib_shard_files and dd_cfg == "shard"):
+            raise ValueError(
+                f"{cls.__name__}: `distrib_shard_files` config ({distrib_shard_files}) mismatch "
+                f"with global config option `dataset_distribution` ({dd_cfg})."
+            )
+
 
 def _get_key_for_file_tree(t: FileTree) -> str:
     """generates a deterministic key given a file tree"""
@@ -460,32 +463,6 @@ def _get_key_for_file_tree(t: FileTree) -> str:
     return ":".join(tree.flatten(t))
 
 
-def _get_rank_and_size() -> Tuple[int, int]:
-    """
-    :return: tuple (rank, size): the global rank and size for distributed trainings
-    """
-
-    from returnn.config import get_global_config
-
-    config = get_global_config(raise_exception=False)
-    if not config:
-        return 0, 1
-    if config.typed_value("torch_distributed") is not None:
-        import returnn.torch.distributed
-
-        ctx = returnn.torch.distributed.get_ctx(config=config)
-        return ctx.rank(), ctx.size()
-    elif config.is_true("use_horovod"):
-        assert config.bool("use_tensorflow", False) or config.value("backend", "").startswith("tensorflow")
-
-        import returnn.tf.horovod
-
-        ctx = returnn.tf.horovod.get_ctx(config=config)
-        return ctx.rank(), ctx.size()
-    else:
-        return 0, 1
-
-
 class _WorkerProcParent:
     def __init__(
         self,

returnn/datasets/meta.py

@@ -1165,9 +1165,9 @@ def _get_random_dataset_seq_order(self, total_num_seqs):
 
         assert sum(counters) == total_num_seqs
 
-        if self.partition_epoch or self._num_shards > 1:
+        if self.partition_epoch or self.num_shards > 1:
             seq_order = self._apply_partition_epoch_and_sharding(
-                seq_order, self.partition_epoch, self.epoch, self._num_shards, self._shard_index
+                seq_order, self.partition_epoch, self.epoch, self.num_shards, self.shard_index
             )
         if self.repeat_epoch:
             seq_order = seq_order * self.repeat_epoch

returnn/datasets/multi_proc.py

@@ -94,6 +94,10 @@ def initialize(self):
             self._lazy_init()
         super().initialize()
 
+    def supports_sharding(self):
+        """this dataset supports sharding on the ``dedicated`` sharding method"""
+        return self._sharding_method == "dedicated"
+
     @property
     def _meta_info_cache(self):
         if not self.num_outputs:
@@ -144,7 +148,11 @@ def _lazy_init(self):
                     self._sharding_method,
                 )
             elif self._sharding_method == "dedicated":
-                sub_dataset = {**self.dataset, "_num_shards": self.num_workers, "_shard_index": i}
+                sub_dataset = {
+                    **self.dataset,
+                    "num_shards": self.num_workers * self.num_shards,
+                    "shard_index": (self.shard_index * self.num_workers) + i,
+                }
                 args = (
                     i,
                     sub_dataset,

returnn/torch/data/returnn_dataset_wrapper.py

@@ -41,8 +41,22 @@ class ReturnnDatasetResetMpSharedEpochCallback:
     def __init__(self, dataset: ReturnnDataset, epoch_mp_shared: torch.multiprocessing.Value):
         self.dataset = dataset
         self.epoch_mp_shared = epoch_mp_shared
+        self._sharding_config_set = False
 
     def __call__(self):
+        # Include local worker rank in the dataset sharding config
+        if not self._sharding_config_set:
+            worker_info = torch.utils.data.get_worker_info()
+            if worker_info is not None:
+                assert self.dataset.supports_sharding() or worker_info.num_workers == 1, (
+                    f"Using {worker_info.num_workers} torch data loading workers "
+                    "but dataset does not support sharding. This will result in repeated training data."
+                )
+                self.dataset.set_shard_idx_and_num_shards(
+                    self.dataset.shard_index + worker_info.id, self.dataset.num_shards * worker_info.num_workers
+                )
+            self._sharding_config_set = True
+
         # dataset is likely a copy of the original dataset, either in the main process or in a worker process
         # Use epoch_mp_shared to get the current epoch correctly in worked processes
         epoch = self.epoch_mp_shared.value or None