Numba sparse: Allow no-op tocsc and tocsr

pytensor/link/numba/dispatch/sparse/variable.py

@@ -289,8 +289,15 @@ def overload_sparse_astype(matrix, dtype):
     return astype
 
 
-@overload_method(CSCMatrixType, "tocsr")
+@overload_method(CSMatrixType, "tocsr")
 def overload_tocsr(matrix):
+    if isinstance(matrix, CSRMatrixType):
+
+        def to_csr(matrix):
+            return matrix
+
+    else:  # CSCMatrix
+
         def to_csr(matrix):
             n_row, n_col = matrix.shape
             csc_ptr = matrix.indptr.view(np.uint32)
@@ -337,8 +344,15 @@ def overload_tocsr(matrix):
     return to_csr
 
 
-@overload_method(CSRMatrixType, "tocsc")
+@overload_method(CSMatrixType, "tocsc")
 def overload_tocsc(matrix):
+    if isinstance(matrix, CSCMatrixType):
+
+        def to_csc(matrix):
+            return matrix
+
+    else:  # CSRMatrix
+
         def to_csc(matrix):
             n_row, n_col = matrix.shape
             csr_ptr = matrix.indptr.view(np.uint32)

tests/link/numba/sparse/test_basic.py

@@ -276,3 +276,26 @@ def test_sparse_dense_from_sparse(format):
     x_test = sp.sparse.random(5, 3, density=0.5, format=format)
     y = ps.dense_from_sparse(x)
     compare_numba_and_py_sparse([x], y, [x_test])
+
+
+def test_sparse_conversion():
+    @numba.njit
+    def to_csr(matrix):
+        return matrix.tocsr()
+
+    @numba.njit
+    def to_csc(matrix):
+        return matrix.tocsc()
+
+    x_csr = scipy.sparse.random(5, 5, density=0.5, format="csr")
+    x_csc = x_csr.tocsc()
+    x_dense = x_csr.todense()
+
+    for x_inp in (x_csr, x_csc):
+        for output_format in ("csr", "csc"):
+            if output_format == "csr":
+                res = to_csr(x_inp)
+            else:
+                res = to_csc(x_inp)
+            assert res.format == output_format
+            np.testing.assert_array_equal(res.todense(), x_dense)