Type-fixes / errata

pyproject.toml

@@ -56,9 +56,6 @@ version = {attr = "tiledbsoma_ml.__version__"}
 [tool.setuptools.package-data]
 "tiledbsoma_ml" = ["py.typed"]
 
-[tool.setuptools_scm]
-root = "../../.."
-
 [tool.mypy]
 show_error_codes = true
 ignore_missing_imports = true

src/tiledbsoma_ml/pytorch.py

@@ -328,15 +328,13 @@ def __iter__(self) -> Iterator[XObsDatum]:
             experimental
         """
 
-        if (
-            self.return_sparse_X
-            and torch.utils.data.get_worker_info()
-            and torch.utils.data.get_worker_info().num_workers > 0
-        ):
-            raise NotImplementedError(
-                "torch does not work with sparse tensors in multi-processing mode "
-                "(see https://github.com/pytorch/pytorch/issues/20248)"
-            )
+        if self.return_sparse_X:
+            worker_info = torch.utils.data.get_worker_info()
+            if worker_info and worker_info.num_workers > 0:
+                raise NotImplementedError(
+                    "torch does not work with sparse tensors in multi-processing mode "
+                    "(see https://github.com/pytorch/pytorch/issues/20248)"
+                )
 
         world_size, rank = _get_distributed_world_rank()
         n_workers, worker_id = _get_worker_world_rank()
@@ -426,7 +424,7 @@ def set_epoch(self, epoch: int) -> None:
 
     def __getitem__(self, index: int) -> XObsDatum:
         raise NotImplementedError(
-            "``ExperimentAxisQueryIterable can only be iterated - does not support mapping"
+            "`ExperimentAxisQueryIterable` can only be iterated - does not support mapping"
         )
 
     def _io_batch_iter(

tests/test_pytorch.py

@@ -8,7 +8,7 @@
 import sys
 from functools import partial
 from pathlib import Path
-from typing import Any, Callable, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Optional, Sequence, Tuple, Type, Union
 from unittest.mock import patch
 
 import numpy as np
@@ -35,15 +35,21 @@
 
 # These control which classes are tested (for most, but not all tests).
 # Centralized to allow easy add/delete of specific test parameters.
-PipeClassType = Union[
-    ExperimentAxisQueryIterable,
+IterableWrapperType = Union[
+    Type[ExperimentAxisQueryIterDataPipe],
+    Type[ExperimentAxisQueryIterableDataset],
+]
+IterableWrappers = (
     ExperimentAxisQueryIterDataPipe,
     ExperimentAxisQueryIterableDataset,
+)
+PipeClassType = Union[
+    Type[ExperimentAxisQueryIterable],
+    IterableWrapperType,
 ]
 PipeClasses = (
     ExperimentAxisQueryIterable,
-    ExperimentAxisQueryIterDataPipe,
-    ExperimentAxisQueryIterableDataset,
+    *IterableWrappers,
 )
 XValueGen = Callable[[range, range], spmatrix]
 
@@ -450,24 +456,22 @@ def test_batching__partial_soma_batches_are_concatenated(
 @pytest.mark.parametrize(
     "obs_range,var_range,X_value_gen", [(6, 3, pytorch_x_value_gen)]
 )
-@pytest.mark.parametrize(
-    "PipeClass",
-    (ExperimentAxisQueryIterDataPipe, ExperimentAxisQueryIterableDataset),
-)
+@pytest.mark.parametrize("PipeClass", IterableWrappers)
 def test_multiprocessing__returns_full_result(
-    PipeClass: ExperimentAxisQueryIterDataPipe | ExperimentAxisQueryIterableDataset,
+    PipeClass: IterableWrapperType,
     soma_experiment: Experiment,
 ) -> None:
-    """Tests the ExperimentAxisQueryIterDataPipe provides all data, as collected from multiple processes that are managed by a
-    PyTorch DataLoader with multiple workers configured."""
+    """Tests that ``ExperimentAxisQueryIterDataPipe`` / ``ExperimentAxisQueryIterableDataset``
+    provide all data, as collected from multiple processes that are managed by a PyTorch DataLoader
+    with multiple workers configured."""
     with soma_experiment.axis_query(measurement_name="RNA") as query:
         dp = PipeClass(
             query,
             X_name="raw",
             obs_column_names=["soma_joinid", "label"],
             io_batch_size=3,  # two chunks, one per worker
         )
-        # Note we're testing the ExperimentAxisQueryIterDataPipe via a DataLoader, since this is what sets up the multiprocessing
+        # Wrap with a DataLoader, which sets up the multiprocessing
         dl = experiment_dataloader(dp, num_workers=2)
 
         full_result = list(iter(dl))
@@ -593,12 +597,9 @@ def test_distributed_and_multiprocessing__returns_data_partition_for_rank(
     "obs_range,var_range,X_value_gen,use_eager_fetch",
     [(3, 3, pytorch_x_value_gen, use_eager_fetch) for use_eager_fetch in (True, False)],
 )
-@pytest.mark.parametrize(
-    "PipeClass",
-    (ExperimentAxisQueryIterDataPipe, ExperimentAxisQueryIterableDataset),
-)
+@pytest.mark.parametrize("PipeClass", IterableWrappers)
 def test_experiment_dataloader__non_batched(
-    PipeClass: ExperimentAxisQueryIterDataPipe | ExperimentAxisQueryIterableDataset,
+    PipeClass: IterableWrapperType,
     soma_experiment: Experiment,
     use_eager_fetch: bool,
 ) -> None:
@@ -624,12 +625,9 @@ def test_experiment_dataloader__non_batched(
     "obs_range,var_range,X_value_gen,use_eager_fetch",
     [(6, 3, pytorch_x_value_gen, use_eager_fetch) for use_eager_fetch in (True, False)],
 )
-@pytest.mark.parametrize(
-    "PipeClass",
-    (ExperimentAxisQueryIterDataPipe, ExperimentAxisQueryIterableDataset),
-)
+@pytest.mark.parametrize("PipeClass", IterableWrappers)
 def test_experiment_dataloader__batched(
-    PipeClass: ExperimentAxisQueryIterDataPipe | ExperimentAxisQueryIterableDataset,
+    PipeClass: IterableWrapperType,
     soma_experiment: Experiment,
     use_eager_fetch: bool,
 ) -> None:
@@ -656,12 +654,9 @@ def test_experiment_dataloader__batched(
         for use_eager_fetch in (True, False)
     ],
 )
-@pytest.mark.parametrize(
-    "PipeClass",
-    (ExperimentAxisQueryIterDataPipe, ExperimentAxisQueryIterableDataset),
-)
+@pytest.mark.parametrize("PipeClass", IterableWrappers)
 def test_experiment_dataloader__batched_length(
-    PipeClass: ExperimentAxisQueryIterDataPipe | ExperimentAxisQueryIterableDataset,
+    PipeClass: IterableWrapperType,
     soma_experiment: Experiment,
     use_eager_fetch: bool,
 ) -> None:
@@ -682,12 +677,9 @@ def test_experiment_dataloader__batched_length(
     "obs_range,var_range,X_value_gen,batch_size",
     [(10, 3, pytorch_x_value_gen, batch_size) for batch_size in (1, 3, 10)],
 )
-@pytest.mark.parametrize(
-    "PipeClass",
-    (ExperimentAxisQueryIterDataPipe, ExperimentAxisQueryIterableDataset),
-)
+@pytest.mark.parametrize("PipeClass", IterableWrappers)
 def test_experiment_dataloader__collate_fn(
-    PipeClass: ExperimentAxisQueryIterDataPipe | ExperimentAxisQueryIterableDataset,
+    PipeClass: IterableWrapperType,
     soma_experiment: Experiment,
     batch_size: int,
 ) -> None: