Fix PyTensor-to-Numba type resolution for sparse variables

pytensor/link/numba/dispatch/basic.py

@@ -25,6 +25,7 @@ from pytensor.graph.basic import Apply, NoParams
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.type import Type
 from pytensor.ifelse import IfElse
+from pytensor.link.numba.dispatch.sparse import CSCMatrixType, CSRMatrixType
 from pytensor.link.utils import (
     compile_function_src,
     fgraph_to_python,
@@ -32,6 +33,7 @@ from pytensor.link.utils import (
 )
 from pytensor.scalar.basic import ScalarType
 from pytensor.scalar.math import Softplus
+from pytensor.sparse import SparseTensorType
 from pytensor.tensor.blas import BatchedDot
 from pytensor.tensor.math import Dot
 from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
@@ -105,6 +107,15 @@ def get_numba_type(
         dtype = np.dtype(pytensor_type.dtype)
         numba_dtype = numba.from_dtype(dtype)
         return numba_dtype
+    elif isinstance(pytensor_type, SparseTensorType):
+        dtype = pytensor_type.numpy_dtype
+        numba_dtype = numba.from_dtype(dtype)
+        if pytensor_type.format == "csr":
+            return CSRMatrixType(numba_dtype)
+        if pytensor_type.format == "csc":
+            return CSCMatrixType(numba_dtype)
+
+        raise NotImplementedError()
     else:
         raise NotImplementedError(f"Numba type not implemented for {pytensor_type}")
 

pytensor/link/numba/dispatch/sparse.py

@@ -19,7 +19,10 @@ class CSMatrixType(types.Type):
     """A Numba `Type` modeled after the base class `scipy.sparse.compressed._cs_matrix`."""
 
     name: str
-    instance_class: type
+
+    @staticmethod
+    def instance_class(data, indices, indptr, shape):
+        raise NotImplementedError()
 
     def __init__(self, dtype):
         self.dtype = dtype
@@ -29,6 +32,10 @@ class CSMatrixType(types.Type):
         self.shape = types.UniTuple(types.int64, 2)
         super().__init__(self.name)
 
+    @property
+    def key(self):
+        return (self.name, self.dtype)
+
 
 make_attribute_wrapper(CSMatrixType, "data", "data")
 make_attribute_wrapper(CSMatrixType, "indices", "indices")
@@ -152,7 +159,6 @@ def overload_sparse_shape(x):
 
 @overload_attribute(CSMatrixType, "ndim")
 def overload_sparse_ndim(inst):
-
     if not isinstance(inst, CSMatrixType):
         return
 

tests/link/numba/test_sparse.py

 import numba
 import numpy as np
+import pytest
 import scipy as sp
 
-# Load Numba customizations
+# Make sure the Numba customizations are loaded
 import pytensor.link.numba.dispatch.sparse  # noqa: F401
+from pytensor import config
+from pytensor.sparse import Dot, SparseTensorType
+from tests.link.numba.test_basic import compare_numba_and_py
+
+
+pytestmark = pytest.mark.filterwarnings("error")
 
 
 def test_sparse_unboxing():
@@ -62,3 +69,19 @@ def test_sparse_ndim():
     res = test_fn(x_val)
 
     assert res == 2
+
+
+def test_sparse_objmode():
+    x = SparseTensorType("csc", dtype=config.floatX)()
+    y = SparseTensorType("csc", dtype=config.floatX)()
+
+    out = Dot()(x, y)
+
+    x_val = sp.sparse.random(2, 2, density=0.25, dtype=config.floatX)
+    y_val = sp.sparse.random(2, 2, density=0.25, dtype=config.floatX)
+
+    with pytest.warns(
+        UserWarning,
+        match="Numba will use object mode to run SparseDot's perform method",
+    ):
+        compare_numba_and_py(((x, y), (out,)), [x_val, y_val])