perf: fast path for unchunked minor axis dask sparse reindexing when concating

.github/workflows/benchmark.yml

@@ -64,4 +64,4 @@ jobs:
         working-directory: ${{ env.ASV_DIR }}
         run: |
           asv machine --yes
-          asv run --quick --show-stderr --verbose
+          asv run --dry-run --quick --show-stderr --verbose HEAD^!

benchmarks/benchmarks/sparse_dataset.py

@@ -1,8 +1,10 @@
 from __future__ import annotations
 
 from types import MappingProxyType
+from typing import TYPE_CHECKING
 
 import numpy as np
+import pandas as pd
 import zarr
 from dask.array.core import Array as DaskArray
 from scipy import sparse
@@ -12,6 +14,9 @@
 from anndata._io.specs import write_elem
 from anndata.experimental import read_elem_lazy
 
+if TYPE_CHECKING:
+    from typing import Literal
+
 
 def make_alternating_mask(n):
     mask_alternating = np.ones(10_000, dtype=bool)
@@ -79,9 +84,12 @@ def peakmem_getitem_adata(self, *_):
             res.compute()
 
 
-class SparseCSRDask:
+class SparseCSRDaskConcat:
     filepath = "data.zarr"
 
+    params = (["inner", "outer"], [0, -1])
+    param_names = ("join", "fill_value")
+
     def setup_cache(self):
         X = sparse.random(
             10_000,
@@ -93,18 +101,59 @@ def setup_cache(self):
         g = zarr.group(self.filepath)
         write_elem(g, "X", X)
 
-    def setup(self):
+    def setup(self, *_):
         self.group = zarr.group(self.filepath)
-        self.adata = AnnData(X=read_elem_lazy(self.group["X"]))
+        self.adatas = [
+            AnnData(
+                var=pd.DataFrame(
+                    index=[
+                        f"gene_{j}{f'_{i}' if (j % 100 == 0) else ''}"
+                        for j in range(10_000)
+                    ]
+                ),
+                X=read_elem_lazy(self.group["X"]),
+            )
+            for i in range(10)
+        ]
+
+    def time_concat(self, join: Literal["inner", "outer"], fill_value: Literal[0, -1]):
+        concat(self.adatas, join=join, fill_value=fill_value)
+
+    def peakmem_concat(
+        self, join: Literal["inner", "outer"], fill_value: Literal[0, -1]
+    ):
+        concat(self.adatas, join=join, fill_value=fill_value)
+
+    def time_concat_with_mem(
+        self, join: Literal["inner", "outer"], fill_value: Literal[0, -1]
+    ):
+        concat(self.adatas, join=join, fill_value=fill_value).to_memory()
+
+    def peakmem_concat_with_mem(
+        self, join: Literal["inner", "outer"], fill_value: Literal[0, -1]
+    ):
+        concat(self.adatas, join=join, fill_value=fill_value).to_memory()
 
-    def time_concat(self):
-        concat([self.adata for i in range(100)])
 
-    def peakmem_concat(self):
-        concat([self.adata for i in range(100)])
+class SparseCSRDask:
+    filepath = "data.zarr"
+
+    def setup_cache(self):
+        X = sparse.random(
+            10_000,
+            10_000,
+            density=0.01,
+            format="csr",
+            random_state=np.random.default_rng(42),
+        )
+        g = zarr.group(self.filepath)
+        write_elem(g, "X", X)
+
+    def setup(self, *_):
+        self.group = zarr.group(self.filepath)
 
-    def time_read(self):
+    def time_read(self, *_):
         AnnData(X=read_elem_lazy(self.group["X"]))
 
-    def peakmem_read(self):
+    def peakmem_read(self, *_):
         AnnData(X=read_elem_lazy(self.group["X"]))

docs/release-notes/2395.perf.md

@@ -0,0 +1 @@
+Accelerate outer joins on dask-sparse matrices with unchunked minor axes in {func}`anndata.concat` {user}`ilan-gold`

src/anndata/_core/merge.py

@@ -583,6 +583,29 @@ def _apply_to_df_like(self, el: pd.DataFrame | Dataset2D, *, axis, fill_value=No
     def _apply_to_dask_array(self, el: DaskArray, *, axis, fill_value=None):
         import dask.array as da
 
+        indexer = self.idx
+        is_outer = any(indexer == -1)
+        # Fast path for the majority of sparse matrices whose minor-axis is unchunked and is being reindexed.
+        # This prevents 0's from being stored explicitly in the sparse matrices when outer joining, for example (see below).
+        if (
+            is_sparse_sub := isinstance(el._meta, CSArray | CSMatrix)
+            and el.chunksize[minor_axis := int(el._meta.format == "csr")]
+            == el.shape[minor_axis]
+            and axis == minor_axis
+            and is_outer
+        ):
+            return el.map_blocks(
+                partial(
+                    self._apply_to_sparse,
+                    axis=axis,
+                    fill_value=fill_value,
+                    keep_format=True,
+                ),
+                chunks=(el.chunks[0], len(self.new_idx))
+                if minor_axis == 1
+                else (len(self.new_idx), el.chunks[1]),
+                meta=el._meta,
+            )
         if fill_value is None:
             fill_value = default_fill_value([el])
         shape = list(el.shape)
@@ -591,12 +614,11 @@ def _apply_to_dask_array(self, el: DaskArray, *, axis, fill_value=None):
             shape[axis] = len(self.new_idx)
             return da.broadcast_to(fill_value, tuple(shape))
 
-        indexer = self.idx
         sub_el = _subset(el, make_slice(indexer, axis, len(shape)))
 
-        if any(indexer == -1):
+        if is_outer:
             # TODO: Remove this condition once https://github.com/dask/dask/pull/12078 is released
-            if isinstance(sub_el._meta, CSArray | CSMatrix) and np.isscalar(fill_value):
+            if is_sparse_sub and np.isscalar(fill_value):
                 fill_value = np.array([[fill_value]])
             sub_el[make_slice(indexer == -1, axis, len(shape))] = fill_value
 
@@ -658,7 +680,7 @@ def _apply_to_array_api(
         return xp.where(mask, fv, taken)
 
     def _apply_to_sparse(  # noqa: PLR0912
-        self, el: CSMatrix | CSArray, *, axis, fill_value=None
+        self, el: CSMatrix | CSArray, *, axis, fill_value=None, keep_format: bool = True
     ) -> CSMatrix:
         if isinstance(el, CupySparseMatrix):
             from cupyx.scipy import sparse
@@ -730,7 +752,8 @@ def _apply_to_sparse(  # noqa: PLR0912
 
         if fill_idxer is not None:
             out[fill_idxer] = fill_value
-
+        if keep_format:
+            out = out.tocsr() if el.format == "csr" else out.tocsc()
         return out
 
     def _apply_to_awkward(self, el: AwkArray, *, axis, fill_value=None):

tests/test_concatenate.py

@@ -1814,22 +1814,60 @@ def test_concat_on_var_outer_join(array_type):
     _ = concat([a, b], join="outer", axis=1)
 
 
-def test_concat_dask_sparse_matches_memory(join_type, merge_strategy):
+@pytest.mark.parametrize("format", ["csr", "csc"])
+@pytest.mark.parametrize(
+    "unchunked_minor_axis", [True, False], ids=["unchunked_minor", "chunked_minor"]
+)
+@pytest.mark.parametrize("fill_value", [0, -1])
+def test_concat_dask_sparse_matches_memory(
+    join_type,
+    merge_strategy,
+    format: Literal["csr", "csc"],
+    axis_name: Literal["obs", "var"],
+    fill_value: Literal[-1, 0],
+    *,
+    unchunked_minor_axis: bool,
+):
     import dask.array as da
 
-    X = sparse.random(50, 20, density=0.5, format="csr")
-    X_dask = da.from_array(X, chunks=(5, 20))
-    var_names_1 = [f"gene_{i}" for i in range(20)]
-    var_names_2 = [f"gene_{i}{'_foo' if (i % 2) else ''}" for i in range(20)]
+    X = sparse.random(50, 20, density=0.5, format=format)
+    X_dask = da.from_array(
+        X,
+        chunks=(
+            X.shape[0] if format == "csc" else 10,
+            X.shape[1] if format == "csr" else 5,
+        )
+        if unchunked_minor_axis
+        else (5, 10),
+    )
+    off_axis_idx = int(axis_name == "obs")
+    concat_axis_idx = int(axis_name == "var")
+    off_axis = "var" if axis_name == "obs" else "obs"
+    axis_names_1 = [f"off_axis_{i}" for i in range(X.shape[off_axis_idx])]
+    axis_names_2 = [
+        f"off_axis_{i}{'_foo' if (i % 2) else ''}" for i in range(X.shape[off_axis_idx])
+    ]
 
-    ad1 = AnnData(X=X, var=pd.DataFrame(index=var_names_1))
-    ad2 = AnnData(X=X, var=pd.DataFrame(index=var_names_2))
+    ad1 = AnnData(X=X, **{off_axis: pd.DataFrame(index=axis_names_1)})
+    ad2 = AnnData(X=X, **{off_axis: pd.DataFrame(index=axis_names_2)})
 
-    ad1_dask = AnnData(X=X_dask, var=pd.DataFrame(index=var_names_1))
-    ad2_dask = AnnData(X=X_dask, var=pd.DataFrame(index=var_names_2))
+    ad1_dask = AnnData(X=X_dask, **{off_axis: pd.DataFrame(index=axis_names_1)})
+    ad2_dask = AnnData(X=X_dask, **{off_axis: pd.DataFrame(index=axis_names_2)})
 
-    res_in_memory = concat([ad1, ad2], join=join_type, merge=merge_strategy)
-    res_dask = concat([ad1_dask, ad2_dask], join=join_type, merge=merge_strategy)
+    res_in_memory = concat(
+        [ad1, ad2],
+        join=join_type,
+        merge=merge_strategy,
+        axis=concat_axis_idx,
+        fill_value=fill_value,
+    )
+    res_dask = concat(
+        [ad1_dask, ad2_dask],
+        join=join_type,
+        merge=merge_strategy,
+        axis=concat_axis_idx,
+        fill_value=fill_value,
+    )
     assert_equal(res_in_memory, res_dask)