perf: add ability to write downcasted indices

docs/release-notes/2159.perf.md

@@ -0,0 +1 @@
+Add a `write_csr_csc_indices_with_min_possible_dtype` option to {attr}`anndata.settings` to enable downcasting of the `indices` of csr and csc matrices to a smaller dtype when writing. For example, if your csr matrix only has 30000 columns, then you can write out the `indices` of that matrix as `uint16` instead of `int64`. {user}`ilan-gold`

src/anndata/_io/specs/methods.py

@@ -727,6 +727,24 @@ def write_sparse_compressed(
     for attr_name in ["data", "indices", "indptr"]:
         attr = getattr(value, attr_name)
         dtype = indptr_dtype if attr_name == "indptr" else attr.dtype
+        if (
+            attr_name == "indices"
+            and settings.write_csr_csc_indices_with_min_possible_dtype
+        ):
+            # np.min_scalar_type can return things like np.ulonglong which zarr doesn't understand
+            # and I find this clearer as to what the result type is i.e., unsigned or signed.
+            # For example `np.iinfo(np.uint16).max + 1` could be either `uint32` or `int32`,
+            # and there's nothing in numpy's docs disallowing this output to change.
+            if (minor_axis_size := value.shape[value.format == "csr"]) <= np.iinfo(
+                np.uint8
+            ).max:
+                dtype = np.dtype("uint8")
+            elif minor_axis_size <= np.iinfo(np.uint16).max:
+                dtype = np.dtype("uint16")
+            elif minor_axis_size <= np.iinfo(np.uint32).max:
+                dtype = np.dtype("uint32")
+            elif minor_axis_size <= np.iinfo(np.uint64).max:
+                dtype = np.dtype("uint64")
         if isinstance(f, H5Group) or is_zarr_v2():
             g.create_dataset(
                 attr_name, data=attr, shape=attr.shape, dtype=dtype, **dataset_kwargs

src/anndata/_settings.py

@@ -500,6 +500,14 @@ def validate_sparse_settings(val: Any, settings: SettingsManager) -> None:
     get_from_env=check_and_get_bool,
 )
 
+settings.register(
+    "write_csr_csc_indices_with_min_possible_dtype",
+    default_value=False,
+    description="Write a csr or csc matrix with the minimum possible data type for `indices`, always unsigned integer.",
+    validate=validate_bool,
+    get_from_env=check_and_get_bool,
+)
+
 settings.register(
     "auto_shard_zarr_v3",
     default_value=False,

src/anndata/_settings.pyi

@@ -44,6 +44,7 @@ class _AnnDataSettingsManager(SettingsManager):
     use_sparse_array_on_read: bool = False
     min_rows_for_chunked_h5_copy: int = 1000
     disallow_forward_slash_in_h5ad: bool = False
+    write_csr_csc_indices_with_min_possible_dtype: bool = False
     auto_shard_zarr_v3: bool = False
 
 settings: _AnnDataSettingsManager

tests/test_io_elementwise.py

@@ -430,6 +430,113 @@ def test_write_indptr_dtype_override(store, sparse_format):
     np.testing.assert_array_equal(store["X/indptr"][...], X.indptr)
 
 
+@pytest.mark.parametrize(
+    ("num_minor_axis", "expected_dtype"),
+    [
+        pytest.param(1, np.dtype("uint8"), id="one_col-expected_uint8_on_disk"),
+        pytest.param(
+            np.iinfo(np.uint8).max,
+            np.dtype("uint8"),
+            id="max_np.uint8-matching_dtype_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.int8).max,
+            np.dtype("uint8"),
+            id="max_np.int8-uint8_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.uint16).max,
+            np.dtype("uint16"),
+            id="max_np.uint16-matching_dtype_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.int16).max,
+            np.dtype("uint16"),
+            id="max_np.int16-uint16_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.uint32).max,
+            np.dtype("uint32"),
+            id="max_np.uint32-matching_dtype_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.int32).max,
+            np.dtype("uint32"),
+            id="max_np.int32-uint32_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.uint8).max + 1,
+            np.dtype("uint16"),
+            id="max_np.uint8_plus_one_cols-expected_uint16_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.uint16).max + 1,
+            np.dtype("uint32"),
+            id="max_np.uint16_plus_one_cols-expected_uint32_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.uint32).max + 1,
+            np.dtype("uint64"),
+            id="max_np.uint32_plus_one_cols-expected_uint64_on_disk",
+        ),
+        pytest.param(
+            np.iinfo(np.int64).max + 1,
+            np.dtype("uint64"),
+            id="max_np.int64_plus_one_cols-expected_uint64_on_disk",
+            marks=pytest.mark.xfail(
+                reason="scipy sparse does not support bigger than max(int64) values in indices and there is no uint128."
+            ),
+        ),
+        pytest.param(
+            np.iinfo(np.uint64).max + 1,
+            np.dtype("uint64"),
+            id="max_np.uint64_plus_one_cols-expected_uint64_on_disk",
+            marks=pytest.mark.xfail(
+                reason="scipy sparse does not support bigger than max(int64) values in indices and there is no uint128."
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize("format", ["csr", "csc"])
+def test_write_indices_min(
+    store: H5Group | ZarrGroup,
+    num_minor_axis: int,
+    expected_dtype: np.dtype,
+    format: Literal["csr", "csc"],
+):
+    minor_axis_index = np.array([num_minor_axis - 1])
+    major_axis_index = np.array([10])
+    row_cols = (
+        (minor_axis_index, major_axis_index)
+        if format == "csc"
+        else (major_axis_index, minor_axis_index)
+    )
+    shape = (num_minor_axis, 20) if format == "csc" else (20, num_minor_axis)
+    X = getattr(sparse, f"{format}_array")(
+        (np.array([10]), row_cols),
+        shape=shape,
+    )
+    assert X.nnz == 1
+    with ad.settings.override(write_csr_csc_indices_with_min_possible_dtype=True):
+        write_elem(store, "X", X)
+
+    assert store["X/indices"].dtype == expected_dtype
+    with ad.settings.override(use_sparse_array_on_read=True):
+        result = read_elem(store["X"])
+    assert_equal(result.data, X.data)
+    assert_equal(result.indices, X.indices)
+    assert_equal(result.indptr, X.indptr)
+    assert X.format == result.format
+    assert result.shape == X.shape
+    # != comparison converts to csr, which allocates a lot of memory or errors out with:
+    # ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
+    # Because the old, very large, minor axis is now the major axis and so either it fails to create or the indptr is very big.
+    # The above tests should be enough to capture the desired equality checks so this is mostly for being extra sure.
+    # See https://github.com/scipy/scipy/issues/23826
+    if not (format == "csc" and num_minor_axis > np.iinfo(np.uint16).max + 1):
+        assert (result != X).nnz == 0
+
+
 def test_io_spec_raw(store):
     adata = gen_adata((3, 2), **GEN_ADATA_NO_XARRAY_ARGS)
     adata.raw = adata.copy()