Neighbors & umap fix (#82)

* Fixes for cupy anndata

* added release note

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/release-notes/0.9.2.md

@@ -0,0 +1,6 @@
+### 0.9.0 {small}`the future``
+
+```{rubric} Bug fixes
+```
+
+* Fixes implicit conversion issue for `Umap` and `neighbors` {pr}`82` {smaller}`S Dicks`

docs/release-notes/index.md

@@ -3,6 +3,9 @@
 # Release notes
 
 ## Version 0.9.0
+```{include} /release-notes/0.9.2.md
+``````
+
 ```{include} /release-notes/0.9.1.md
 ``````
 

src/rapids_singlecell/preprocessing/_neighbors.py

@@ -6,6 +6,7 @@
 from anndata import AnnData
 from cuml.manifold.simpl_set import fuzzy_simplicial_set
 from cuml.neighbors import NearestNeighbors
+from cupyx.scipy import sparse
 
 from rapids_singlecell.tools._utils import _choose_representation
 
@@ -124,7 +125,12 @@ def neighbors(
     nn = NearestNeighbors(
         n_neighbors=n_neighbors, algorithm=algorithm, metric=metric, output_type="cupy"
     )
-    X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
+    if isinstance(X, cp.ndarray):
+        X_contiguous = cp.ascontiguousarray(X, dtype=np.float32)
+    elif isinstance(X, sparse.spmatrix):
+        X_contiguous = X
+    else:
+        X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
     nn.fit(X_contiguous)
     distances = nn.kneighbors_graph(X_contiguous, mode="distance")
     n_obs = adata.shape[0]

src/rapids_singlecell/tools/_umap.py

@@ -1,9 +1,11 @@
 from typing import Literal, Optional
 
+import cupy as cp
 import numpy as np
 from anndata import AnnData
 from cuml import UMAP
 from cuml.manifold.umap_utils import find_ab_params
+from cupyx.scipy import sparse
 from scanpy._utils import NeighborsView
 from sklearn.utils import check_random_state
 
@@ -128,7 +130,14 @@ def umap(
         500 if maxiter is None else maxiter
     )  # 0 is not a valid value for rapids, unlike original umap
     metric = neigh_params.get("metric", "euclidean")
-    X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
+
+    if isinstance(X, cp.ndarray):
+        X_contiguous = cp.ascontiguousarray(X, dtype=np.float32)
+    elif isinstance(X, sparse.spmatrix):
+        X_contiguous = X
+    else:
+        X_contiguous = np.ascontiguousarray(X, dtype=np.float32)
+
     n_obs = adata.shape[0]
     if neigh_params.get("method") == "rapids":
         knn_dist = neighbors["distances"].data.reshape(n_obs, n_neighbors)