docstring updates, attempt to make shape/len more precise

src/tiledbsoma_ml/pytorch.py

@@ -84,14 +84,14 @@ def open_experiment(self) -> Generator[soma.Experiment, None, None]:
 
 
 class ExperimentAxisQueryIterable(Iterable[XObsDatum]):
-    """An :class:`Iterator` which reads ``X`` and ``obs`` data from a :class:`tiledbsoma.Experiment`, as
+    """An :class:`Iterable` which reads ``X`` and ``obs`` data from a :class:`tiledbsoma.Experiment`, as
     selected by a user-specified :class:`tiledbsoma.ExperimentAxisQuery`. Each step of the iterator
-    produces equal sized ``X`` and ``obs`` data, in the form of a :class:`numpy.ndarray` and
+    produces a batch containing equal-sized ``X`` and ``obs`` data, in the form of a :class:`numpy.ndarray` and
     :class:`pandas.DataFrame`, respectively.
 
     Private base class for subclasses of :class:`torch.utils.data.IterableDataset` and
     :class:`torchdata.datapipes.iter.IterDataPipe`. Refer to :class:`ExperimentAxisQueryIterableDataset`
-    and `ExperimentAxisQueryIterDataPipe` for more details on usage.
+    and :class:`ExperimentAxisQueryIterDataPipe` for more details on usage.
 
     Lifecycle:
         experimental
@@ -136,14 +136,10 @@ def __init__(
                 If ``True``, will return the ``X`` data as a :class:`scipy.sparse.csr_matrix`. If ``False`` (the
                 default), will return ``X`` data as a :class:`numpy.ndarray`.
             use_eager_fetch:
-                Fetch the next SOMA chunk of ``obs`` and ``X`` data immediately after a previously fetched SOMA chunk is made
-                available for processing via the iterator. This allows network (or filesystem) requests to be made in
-                parallel with client-side processing of the SOMA data, potentially improving overall performance at the
-                cost of doubling memory utilization. Defaults to ``True``.
-
-        Returns:
-            An ``iterable``, which can be iterated over using the Python ``iter()`` statement, or passed directly to
-            a :class:`torch.utils.data.DataLoader` instance.
+                Fetch the next SOMA chunk of ``obs`` and ``X`` data immediately after a previously fetched SOMA chunk is
+                made available for processing via the iterator. This allows network (or filesystem) requests to be made
+                in parallel with client-side processing of the SOMA data, potentially improving overall performance at
+                the cost of doubling memory utilization. Defaults to ``True``.
 
         Raises:
             ``ValueError`` on various unsupported or malformed parameter values.
@@ -300,16 +296,16 @@ def __iter__(self) -> Iterator[XObsDatum]:
             yield from _mini_batch_iter
 
     def __len__(self) -> int:
-        """Return the approximate number of batches this iterable will produce. If run in the context of :class:`torch.distributed` or
-        as a multi-process loader (i.e., :class:`torch.utils.data.DataLoader` instantiated with num_workers > 0), the obs (cell)
-        count will reflect the size of the data partition assigned to the active process.
+        """Return the number of batches this iterable will produce. If run in the context of :class:`torch.distributed`
+        or as a multi-process loader (i.e., :class:`torch.utils.data.DataLoader` instantiated with num_workers > 0), the
+        batch count will reflect the size of the data partition assigned to the active process.
 
         See important caveats in the PyTorch
         [:class:`torch.utils.data.DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)
         documentation regarding ``len(dataloader)``, which also apply to this class.
 
         Returns:
-            An ``int``.
+            ``int`` (Number of batches).
 
         Lifecycle:
             experimental
@@ -318,25 +314,31 @@ def __len__(self) -> int:
 
     @property
     def shape(self) -> Tuple[int, int]:
-        """Get the approximate shape of the data that will be returned by this :class:`tiledbsoma_ml.ExperimentAxisQueryIterable`.
-        This is the number of obs (cell) and var (feature) counts in the returned data. If used in multiprocessing mode
-        (i.e. :class:`torch.utils.data.DataLoader` instantiated with num_workers > 0), the obs (cell) count will reflect
-        the size of the data partition assigned to the active process.
+        """Return the number of batches and features that will be yielded from this :class:`tiledbsoma_ml.ExperimentAxisQueryIterable`.
+
+        If used in multiprocessing mode (i.e. :class:`torch.utils.data.DataLoader` instantiated with num_workers > 0),
+        the number of batches will reflect the size of the data partition assigned to the active process.
 
         Returns:
-            A tuple of two ``int`` values: number of obs, number of vars.
+            A tuple of two ``int`` values: number of batches, number of vars.
 
         Lifecycle:
             experimental
         """
         self._init_once()
         assert self._obs_joinids is not None
         assert self._var_joinids is not None
-        world_size, _ = _get_distributed_world_rank()
-        n_workers, _ = _get_worker_world_rank()
-        partition_len = len(self._obs_joinids) // world_size // n_workers
-        div, rem = divmod(partition_len, self.batch_size)
-        return div + bool(rem), len(self._var_joinids)
+        world_size, rank = _get_distributed_world_rank()
+        n_workers, worker_id = _get_worker_world_rank()
+        obs_per_proc, obs_rem = divmod(len(self._obs_joinids), world_size)
+        # obs rows assigned to this "distributed" process
+        n_proc_obs = obs_per_proc + bool(rank < obs_rem)
+        obs_per_worker, obs_rem = divmod(n_proc_obs, n_workers)
+        # obs rows assigned to this worker process
+        n_worker_obs = obs_per_worker + bool(worker_id < obs_rem)
+        n_batches, rem = divmod(n_worker_obs, self.batch_size)
+        # (num batches this worker will produce, num features)
+        return n_batches + bool(rem), len(self._var_joinids)
 
     def __getitem__(self, index: int) -> XObsDatum:
         raise NotImplementedError(
@@ -349,11 +351,9 @@ def _io_batch_iter(
         X: soma.SparseNDArray,
         obs_joinid_iter: Iterator[npt.NDArray[np.int64]],
     ) -> Iterator[Tuple[sparse.csr_matrix, pd.DataFrame]]:
-        """Iterate over IO batches, i.e., SOMA query/read, producing a tuple of
-        (X: csr_array, obs: DataFrame).
+        """Iterate over IO batches, i.e., SOMA query reads, producing tuples of ``(X: csr_array, obs: DataFrame)``.
 
-        obs joinids read are controlled by the obs_joinid_iter. Iterator results will
-        be reindexed.
+        ``obs`` joinids read are controlled by the ``obs_joinid_iter``. Iterator results will be reindexed.
 
         Private method.
         """
@@ -475,7 +475,7 @@ class ExperimentAxisQueryIterDataPipe(
         torch.utils.data.dataset.Dataset[XObsDatum]
     ],
 ):
-    """A :class:`torch.utils.data.IterableDataset` implementation that loads from a :class:`tiledbsoma.SOMAExperiment`.
+    """A :class:`torchdata.datapipes.iter.IterDataPipe` implementation that loads from a :class:`tiledbsoma.SOMAExperiment`.
 
     This class is based upon the now-deprecated :class:`torchdata.datapipes` API, and should only be used for
     legacy code. See [GitHub issue #1196](https://github.com/pytorch/data/issues/1196) and the
@@ -534,7 +534,7 @@ def __len__(self) -> int:
         Lifecycle:
             deprecated
         """
-        return self._exp_iter.__len__()
+        return len(self._exp_iter)
 
     @property
     def shape(self) -> Tuple[int, int]:
@@ -640,10 +640,6 @@ def __init__(
                 parallel with client-side processing of the SOMA data, potentially improving overall performance at the
                 cost of doubling memory utilization. Defaults to ``True``.
 
-        Returns:
-            An ``iterable``, which can be iterated over using the Python ``iter()`` statement, or passed directly to
-            a :class:`torch.data.utils.DataLoader` instance.
-
         Raises:
             ``ValueError`` on various unsupported or malformed parameter values.
 
@@ -663,7 +659,8 @@ def __init__(
         )
 
     def __iter__(self) -> Iterator[XObsDatum]:
-        """Create Iterator yielding tuples of :class:`numpy.ndarray` and :class:`pandas.DataFrame`.
+        """Create ``Iterator`` yielding "mini-batch" tuples of :class:`numpy.ndarray` (or :class:`scipy.csr_matrix`) and
+        :class:`pandas.DataFrame`.
 
         Returns:
             ``iterator``
@@ -678,30 +675,29 @@ def __iter__(self) -> Iterator[XObsDatum]:
             yield X, obs
 
     def __len__(self) -> int:
-        """Return approximate number of batches this iterable will produce.
+        """Return number of batches this iterable will produce.
 
         See important caveats in the PyTorch
         [:class:`torch.utils.data.DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)
         documentation regarding ``len(dataloader)``, which also apply to this class.
 
         Returns:
-            An ``int``.
+            ``int`` (number of batches).
 
         Lifecycle:
             experimental
         """
-        return self._exp_iter.__len__()
+        return len(self._exp_iter)
 
     @property
     def shape(self) -> Tuple[int, int]:
-        """Get the shape of the data that will be returned by this :class:`tiledbsoma_ml.ExperimentAxisQueryIterableDataset`.
+        """Return the number of batches and features that will be yielded from this :class:`tiledbsoma_ml.ExperimentAxisQueryIterable`.
 
-        This is the number of obs (cell) and var (feature) counts in the returned data. If used in multiprocessing mode
-        (i.e. :class:`torch.utils.data.DataLoader` instantiated with num_workers > 0), the obs (cell) count will reflect
-        the size of the partition of the data assigned to the active process.
+        If used in multiprocessing mode (i.e. :class:`torch.utils.data.DataLoader` instantiated with num_workers > 0),
+        the number of batches will reflect the size of the data partition assigned to the active process.
 
         Returns:
-            A tuple of ``int``s, for obs and var counts, respectively.
+            A tuple of two ``int`` values: number of batches, number of vars.
 
         Lifecycle:
             experimental