Make as_symbolic work with sparse matrices

aesara/sparse/basic.py

@@ -14,6 +14,7 @@ import scipy.sparse
 from numpy.lib.stride_tricks import as_strided
 
 import aesara
+from aesara import _as_symbolic, as_symbolic
 from aesara import scalar as aes
 from aesara.configdefaults import config
 from aesara.gradient import DisconnectedType, grad_not_implemented, grad_undefined
@@ -128,6 +129,11 @@ def _is_dense(x):
     return isinstance(x, np.ndarray)
 
 
+@_as_symbolic.register(scipy.sparse.base.spmatrix)
+def as_symbolic_sparse(x, **kwargs):
+    return as_sparse_variable(x, **kwargs)
+
+
 def as_sparse_variable(x, name=None, ndim=None, **kwargs):
     """
     Wrapper around SparseVariable constructor to construct
@@ -174,26 +180,7 @@ def as_sparse_variable(x, name=None, ndim=None, **kwargs):
 as_sparse = as_sparse_variable
 
 
-def as_sparse_or_tensor_variable(x, name=None):
-    """
-    Same as `as_sparse_variable` but if we can't make a
-    sparse variable, we try to make a tensor variable.
-
-    Parameters
-    ----------
-    x
-        A sparse matrix.
-
-    Returns
-    -------
-    SparseVariable or TensorVariable version of `x`
-
-    """
-
-    try:
-        return as_sparse_variable(x, name)
-    except (ValueError, TypeError):
-        return at.as_tensor_variable(x, name)
+as_sparse_or_tensor_variable = as_symbolic
 
 
 def constant(x, name=None):