Backport PR #1744: (fix): cache arrays in BaseCompressedSparseDataset

docs/release-notes/1744.bugfix.md

@@ -0,0 +1 @@
+Cache accesses to the `data` and `indices` arrays in {class}`~anndata.abc.CSRDataset` and {class}`~anndata.abc.CSCDataset` {user}`ilan-gold`

src/anndata/_core/sparse_dataset.py

@@ -38,6 +38,7 @@
     from scipy.sparse._compressed import _cs_matrix
 
     from .._types import GroupStorageType
+    from ..compat import H5Array
     from .index import Index
 else:
     from scipy.sparse import spmatrix as _cs_matrix
@@ -380,7 +381,7 @@ def backend(self) -> Literal["zarr", "hdf5"]:
     @property
     def dtype(self) -> np.dtype:
         """The :class:`numpy.dtype` of the `data` attribute of the sparse matrix."""
-        return self.group["data"].dtype
+        return self._data.dtype
 
     @classmethod
     def _check_group_format(cls, group):
@@ -545,16 +546,18 @@ def append(self, sparse_matrix: ss.csr_matrix | ss.csc_matrix | SpArray) -> None
         indptr[orig_data_size:] = (
             sparse_matrix.indptr[1:].astype(np.int64) + indptr_offset
         )
-        # Clear cached property
-        if hasattr(self, "indptr"):
-            del self._indptr
 
         # indices
         indices = self.group["indices"]
         orig_data_size = indices.shape[0]
         indices.resize((orig_data_size + sparse_matrix.indices.shape[0],))
         indices[orig_data_size:] = sparse_matrix.indices
 
+        # Clear cached property
+        for attr in ["_indptr", "_indices", "_data"]:
+            if hasattr(self, attr):
+                delattr(self, attr)
+
     @cached_property
     def _indptr(self) -> np.ndarray:
         """\
@@ -565,11 +568,25 @@ def _indptr(self) -> np.ndarray:
         arr = self.group["indptr"][...]
         return arr
 
+    @cached_property
+    def _indices(self) -> H5Array | ZarrArray:
+        """\
+        Cache access to the indices to prevent unnecessary reads of the zarray
+        """
+        return self.group["indices"]
+
+    @cached_property
+    def _data(self) -> H5Array | ZarrArray:
+        """\
+        Cache access to the data to prevent unnecessary reads of the zarray
+        """
+        return self.group["data"]
+
     def _to_backed(self) -> BackedSparseMatrix:
         format_class = get_backed_class(self.format)
         mtx = format_class(self.shape, dtype=self.dtype)
-        mtx.data = self.group["data"]
-        mtx.indices = self.group["indices"]
+        mtx.data = self._data
+        mtx.indices = self._indices
         mtx.indptr = self._indptr
         return mtx
 
@@ -578,8 +595,8 @@ def to_memory(self) -> ss.csr_matrix | ss.csc_matrix | SpArray:
             self.format, use_sparray_in_io=settings.use_sparse_array_on_read
         )
         mtx = format_class(self.shape, dtype=self.dtype)
-        mtx.data = self.group["data"][...]
-        mtx.indices = self.group["indices"][...]
+        mtx.data = self._data[...]
+        mtx.indices = self._indices[...]
         mtx.indptr = self._indptr
         return mtx
 

src/anndata/_io/specs/lazy_methods.py

@@ -3,25 +3,24 @@
 from contextlib import contextmanager
 from functools import partial
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, overload
 
 import h5py
 import numpy as np
 from scipy import sparse
 
 import anndata as ad
+from anndata.abc import CSCDataset, CSRDataset
 
 from ..._core.file_backing import filename, get_elem_name
 from ...compat import H5Array, H5Group, ZarrArray, ZarrGroup
 from .registry import _LAZY_REGISTRY, IOSpec
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Generator, Mapping, Sequence
+    from collections.abc import Generator, Mapping, Sequence
     from typing import Literal, ParamSpec, TypeVar
 
-    from ..._core.sparse_dataset import _CSCDataset, _CSRDataset
-    from ..._types import ArrayStorageType, StorageType
-    from ...compat import DaskArray
+    from ...compat import DaskArray, H5File, SpArray
     from .registry import DaskReader
 
     BlockInfo = Mapping[
@@ -31,16 +30,25 @@
 
     P = ParamSpec("P")
     R = TypeVar("R")
+    D = TypeVar("D")
 
 
+@overload
 @contextmanager
 def maybe_open_h5(
-    path_or_group: Path | ZarrGroup, elem_name: str
-) -> Generator[StorageType, None, None]:
-    if not isinstance(path_or_group, Path):
-        yield path_or_group
+    path_or_other: Path, elem_name: str
+) -> Generator[H5File, None, None]: ...
+@overload
+@contextmanager
+def maybe_open_h5(path_or_other: D, elem_name: str) -> Generator[D, None, None]: ...
+@contextmanager
+def maybe_open_h5(
+    path_or_other: H5File | D, elem_name: str
+) -> Generator[H5File | D, None, None]:
+    if not isinstance(path_or_other, Path):
+        yield path_or_other
         return
-    file = h5py.File(path_or_group, "r")
+    file = h5py.File(path_or_other, "r")
     try:
         yield file[elem_name]
     finally:
@@ -61,20 +69,17 @@ def compute_chunk_layout_for_axis_shape(
 
 
 def make_dask_chunk(
-    path_or_group: Path | ZarrGroup,
+    path_or_sparse_dataset: Path | D,
     elem_name: str,
     block_info: BlockInfo | None = None,
-    *,
-    wrap: Callable[[ArrayStorageType], ArrayStorageType]
-    | Callable[[H5Group | ZarrGroup], _CSRDataset | _CSCDataset] = lambda g: g,
-):
+) -> sparse.csr_matrix | sparse.csc_matrix | SpArray:
     if block_info is None:
         msg = "Block info is required"
         raise ValueError(msg)
     # We need to open the file in each task since `dask` cannot share h5py objects when using `dask.distributed`
     # https://github.com/scverse/anndata/issues/1105
-    with maybe_open_h5(path_or_group, elem_name) as f:
-        mtx = wrap(f)
+    with maybe_open_h5(path_or_sparse_dataset, elem_name) as f:
+        mtx = ad.io.sparse_dataset(f) if isinstance(f, H5Group) else f
         idx = tuple(
             slice(start, stop) for start, stop in block_info[None]["array-location"]
         )
@@ -94,10 +99,17 @@ def read_sparse_as_dask(
 ) -> DaskArray:
     import dask.array as da
 
-    path_or_group = Path(filename(elem)) if isinstance(elem, H5Group) else elem
+    path_or_sparse_dataset = (
+        Path(filename(elem))
+        if isinstance(elem, H5Group)
+        else ad.io.sparse_dataset(elem)
+    )
     elem_name = get_elem_name(elem)
     shape: tuple[int, int] = tuple(elem.attrs["shape"])
-    dtype = elem["data"].dtype
+    if isinstance(path_or_sparse_dataset, CSRDataset | CSCDataset):
+        dtype = path_or_sparse_dataset.dtype
+    else:
+        dtype = elem["data"].dtype
     is_csc: bool = elem.attrs["encoding-type"] == "csc_matrix"
 
     stride: int = _DEFAULT_STRIDE
@@ -123,9 +135,7 @@ def read_sparse_as_dask(
         (chunks_minor, chunks_major) if is_csc else (chunks_major, chunks_minor)
     )
     memory_format = sparse.csc_matrix if is_csc else sparse.csr_matrix
-    make_chunk = partial(
-        make_dask_chunk, path_or_group, elem_name, wrap=ad.io.sparse_dataset
-    )
+    make_chunk = partial(make_dask_chunk, path_or_sparse_dataset, elem_name)
     da_mtx = da.map_blocks(
         make_chunk,
         dtype=dtype,

tests/test_backed_sparse.py

@@ -13,7 +13,8 @@
 import anndata as ad
 from anndata._core.anndata import AnnData
 from anndata._core.sparse_dataset import sparse_dataset
-from anndata.compat import CAN_USE_SPARSE_ARRAY, SpArray
+from anndata._io.specs.registry import read_elem_as_dask
+from anndata.compat import CAN_USE_SPARSE_ARRAY, DaskArray, SpArray
 from anndata.experimental import read_dispatched
 from anndata.tests.helpers import AccessTrackingStore, assert_equal, subset_func
 
@@ -26,6 +27,9 @@
     from numpy.typing import ArrayLike, NDArray
     from pytest_mock import MockerFixture
 
+    from anndata.abc import CSCDataset, CSRDataset
+    from anndata.compat import ZarrGroup
+
     Idx = slice | int | NDArray[np.integer] | NDArray[np.bool_]
 
 
@@ -281,6 +285,25 @@ def test_dataset_append_memory(
     assert_equal(fromdisk, frommem)
 
 
+def test_append_array_cache_bust(tmp_path: Path, diskfmt: Literal["h5ad", "zarr"]):
+    path = tmp_path / f"test.{diskfmt.replace('ad', '')}"
+    a = sparse.random(100, 100, format="csr")
+    if diskfmt == "zarr":
+        f = zarr.open_group(path, "a")
+    else:
+        f = h5py.File(path, "a")
+    ad.io.write_elem(f, "mtx", a)
+    ad.io.write_elem(f, "mtx_2", a)
+    diskmtx = sparse_dataset(f["mtx"])
+    old_array_shapes = {}
+    array_names = ["indptr", "indices", "data"]
+    for name in array_names:
+        old_array_shapes[name] = getattr(diskmtx, f"_{name}").shape
+    diskmtx.append(sparse_dataset(f["mtx_2"]))
+    for name in array_names:
+        assert old_array_shapes[name] != getattr(diskmtx, f"_{name}").shape
+
+
 @pytest.mark.parametrize("sparse_format", [sparse.csr_matrix, sparse.csc_matrix])
 @pytest.mark.parametrize(
     ("subset_func", "subset_func2"),
@@ -354,16 +377,18 @@ def test_dataset_append_disk(
 
 
 @pytest.mark.parametrize("sparse_format", [sparse.csr_matrix, sparse.csc_matrix])
-def test_indptr_cache(
+def test_lazy_array_cache(
     tmp_path: Path,
     sparse_format: Callable[[ArrayLike], sparse.spmatrix],
 ):
+    elems = {"indptr", "indices", "data"}
     path = tmp_path / "test.zarr"
     a = sparse_format(sparse.random(10, 10))
     f = zarr.open_group(path, "a")
     ad.io.write_elem(f, "X", a)
     store = AccessTrackingStore(path)
-    store.initialize_key_trackers(["X/indptr"])
+    for elem in elems:
+        store.initialize_key_trackers([f"X/{elem}"])
     f = zarr.open_group(store, "a")
     a_disk = sparse_dataset(f["X"])
     a_disk[:1]
@@ -372,6 +397,14 @@ def test_indptr_cache(
     a_disk[8:9]
     # one each for .zarray and actual access
     assert store.get_access_count("X/indptr") == 2
+    for elem_not_indptr in elems - {"indptr"}:
+        assert (
+            sum(
+                ".zarray" in key_accessed
+                for key_accessed in store.get_accessed_keys(f"X/{elem_not_indptr}")
+            )
+            == 1
+        )
 
 
 Kind = Literal["slice", "int", "array", "mask"]
@@ -421,27 +454,38 @@ def width_idx_kinds(
         (
             [0],
             slice(None, None),
-            ["X/data/.zarray", "X/data/.zarray", "X/data/0"],
+            ["X/data/.zarray", "X/data/0"],
         ),
         (
             [0],
             slice(None, 3),
-            ["X/data/.zarray", "X/data/.zarray", "X/data/0"],
+            ["X/data/.zarray", "X/data/0"],
         ),
         (
             [3, 4, 5],
             slice(None, None),
-            ["X/data/.zarray", "X/data/.zarray", "X/data/3", "X/data/4", "X/data/5"],
+            ["X/data/.zarray", "X/data/3", "X/data/4", "X/data/5"],
         ),
         l=10,
     ),
 )
+@pytest.mark.parametrize(
+    "open_func",
+    [
+        sparse_dataset,
+        lambda x: read_elem_as_dask(
+            x, chunks=(1, -1) if x.attrs["encoding-type"] == "csr_matrix" else (-1, 1)
+        ),
+    ],
+    ids=["sparse_dataset", "read_elem_as_dask"],
+)
 def test_data_access(
     tmp_path: Path,
     sparse_format: Callable[[ArrayLike], sparse.spmatrix],
     idx_maj: Idx,
     idx_min: Idx,
     exp: Sequence[str],
+    open_func: Callable[[ZarrGroup], CSRDataset | CSCDataset | DaskArray],
 ):
     path = tmp_path / "test.zarr"
     a = sparse_format(np.eye(10, 10))
@@ -454,19 +498,19 @@ def test_data_access(
     store = AccessTrackingStore(path)
     store.initialize_key_trackers(["X/data"])
     f = zarr.open_group(store)
-    a_disk = sparse_dataset(f["X"])
-
-    # Do the slicing with idx
-    store.reset_key_trackers()
-    if a_disk.format == "csr":
-        a_disk[idx_maj, idx_min]
+    a_disk = AnnData(X=open_func(f["X"]))
+    if a.format == "csr":
+        subset = a_disk[idx_maj, idx_min]
     else:
-        a_disk[idx_min, idx_maj]
+        subset = a_disk[idx_min, idx_maj]
+    if isinstance(subset.X, DaskArray):
+        subset.X.compute(scheduler="single-threaded")
 
     assert store.get_access_count("X/data") == len(exp), store.get_accessed_keys(
         "X/data"
     )
-    assert store.get_accessed_keys("X/data") == exp
+    # dask access order is not guaranteed so need to sort
+    assert sorted(store.get_accessed_keys("X/data")) == sorted(exp)
 
 
 @pytest.mark.parametrize(

tests/test_concatenate.py

@@ -1044,7 +1044,9 @@ def gen_list(n):
 
 
 def gen_sparse(n):
-    return sparse.random(np.random.randint(1, 100), np.random.randint(1, 100))
+    return sparse.random(
+        np.random.randint(1, 100), np.random.randint(1, 100), format="csr"
+    )
 
 
 def gen_something(n):