Add support for SciPy CSC and CSR sparse types to Numba

aesara/link/numba/dispatch/__init__.py

@@ -13,5 +13,6 @@
 import aesara.link.numba.dispatch.random
 import aesara.link.numba.dispatch.elemwise
 import aesara.link.numba.dispatch.scan
+import aesara.link.numba.dispatch.sparse
 
 # isort: on

aesara/link/numba/dispatch/sparse.py

@@ -0,0 +1,142 @@
+import scipy as sp
+import scipy.sparse
+from numba.core import cgutils, types
+from numba.extending import (
+    NativeValue,
+    box,
+    make_attribute_wrapper,
+    models,
+    register_model,
+    typeof_impl,
+    unbox,
+)
+
+
+class CSMatrixType(types.Type):
+    """A Numba `Type` modeled after the base class `scipy.sparse.compressed._cs_matrix`."""
+
+    name: str
+    instance_class: type
+
+    def __init__(self, dtype):
+        self.dtype = dtype
+        self.data = types.Array(dtype, 1, "A")
+        self.indices = types.Array(types.int32, 1, "A")
+        self.indptr = types.Array(types.int32, 1, "A")
+        self.shape = types.UniTuple(types.int64, 2)
+        super().__init__(self.name)
+
+
+make_attribute_wrapper(CSMatrixType, "data", "data")
+make_attribute_wrapper(CSMatrixType, "indices", "indices")
+make_attribute_wrapper(CSMatrixType, "indptr", "indptr")
+make_attribute_wrapper(CSMatrixType, "shape", "shape")
+
+
+class CSRMatrixType(CSMatrixType):
+    name = "csr_matrix"
+
+    @staticmethod
+    def instance_class(data, indices, indptr, shape):
+        return sp.sparse.csr_matrix((data, indices, indptr), shape, copy=False)
+
+
+class CSCMatrixType(CSMatrixType):
+    name = "csc_matrix"
+
+    @staticmethod
+    def instance_class(data, indices, indptr, shape):
+        return sp.sparse.csc_matrix((data, indices, indptr), shape, copy=False)
+
+
+@typeof_impl.register(sp.sparse.csc_matrix)
+def typeof_csc_matrix(val, c):
+    data = typeof_impl(val.data, c)
+    return CSCMatrixType(data.dtype)
+
+
+@typeof_impl.register(sp.sparse.csr_matrix)
+def typeof_csr_matrix(val, c):
+    data = typeof_impl(val.data, c)
+    return CSRMatrixType(data.dtype)
+
+
+@register_model(CSRMatrixType)
+class CSRMatrixModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("data", fe_type.data),
+            ("indices", fe_type.indices),
+            ("indptr", fe_type.indptr),
+            ("shape", fe_type.shape),
+        ]
+        super().__init__(dmm, fe_type, members)
+
+
+@register_model(CSCMatrixType)
+class CSCMatrixModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("data", fe_type.data),
+            ("indices", fe_type.indices),
+            ("indptr", fe_type.indptr),
+            ("shape", fe_type.shape),
+        ]
+        super().__init__(dmm, fe_type, members)
+
+
+@unbox(CSCMatrixType)
+@unbox(CSRMatrixType)
+def unbox_matrix(typ, obj, c):
+
+    struct_ptr = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+
+    data = c.pyapi.object_getattr_string(obj, "data")
+    indices = c.pyapi.object_getattr_string(obj, "indices")
+    indptr = c.pyapi.object_getattr_string(obj, "indptr")
+    shape = c.pyapi.object_getattr_string(obj, "shape")
+
+    struct_ptr.data = c.unbox(typ.data, data).value
+    struct_ptr.indices = c.unbox(typ.indices, indices).value
+    struct_ptr.indptr = c.unbox(typ.indptr, indptr).value
+    struct_ptr.shape = c.unbox(typ.shape, shape).value
+
+    c.pyapi.decref(data)
+    c.pyapi.decref(indices)
+    c.pyapi.decref(indptr)
+    c.pyapi.decref(shape)
+
+    is_error_ptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+    is_error = c.builder.load(is_error_ptr)
+
+    res = NativeValue(struct_ptr._getvalue(), is_error=is_error)
+
+    return res
+
+
+@box(CSCMatrixType)
+@box(CSRMatrixType)
+def box_matrix(typ, val, c):
+    struct_ptr = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+
+    data_obj = c.box(typ.data, struct_ptr.data)
+    indices_obj = c.box(typ.indices, struct_ptr.indices)
+    indptr_obj = c.box(typ.indptr, struct_ptr.indptr)
+    shape_obj = c.box(typ.shape, struct_ptr.shape)
+
+    c.pyapi.incref(data_obj)
+    c.pyapi.incref(indices_obj)
+    c.pyapi.incref(indptr_obj)
+    c.pyapi.incref(shape_obj)
+
+    cls_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.instance_class))
+    obj = c.pyapi.call_function_objargs(
+        cls_obj, (data_obj, indices_obj, indptr_obj, shape_obj)
+    )
+
+    c.pyapi.decref(data_obj)
+    c.pyapi.decref(indices_obj)
+    c.pyapi.decref(indptr_obj)
+    c.pyapi.decref(shape_obj)
+
+    return obj

tests/link/numba/test_sparse.py

@@ -0,0 +1,40 @@
+import numba
+import numpy as np
+import scipy as sp
+
+# Load Numba customizations
+import aesara.link.numba.dispatch.sparse  # noqa: F401
+
+
+def test_sparse_unboxing():
+    @numba.njit
+    def test_unboxing(x, y):
+        return x.shape, y.shape
+
+    x_val = sp.sparse.csr_matrix(np.eye(100))
+    y_val = sp.sparse.csc_matrix(np.eye(101))
+
+    res = test_unboxing(x_val, y_val)
+
+    assert res == (x_val.shape, y_val.shape)
+
+
+def test_sparse_boxing():
+    @numba.njit
+    def test_boxing(x, y):
+        return x, y
+
+    x_val = sp.sparse.csr_matrix(np.eye(100))
+    y_val = sp.sparse.csc_matrix(np.eye(101))
+
+    res_x_val, res_y_val = test_boxing(x_val, y_val)
+
+    assert np.array_equal(res_x_val.data, x_val.data)
+    assert np.array_equal(res_x_val.indices, x_val.indices)
+    assert np.array_equal(res_x_val.indptr, x_val.indptr)
+    assert res_x_val.shape == x_val.shape
+
+    assert np.array_equal(res_y_val.data, y_val.data)
+    assert np.array_equal(res_y_val.indices, y_val.indices)
+    assert np.array_equal(res_y_val.indptr, y_val.indptr)
+    assert res_y_val.shape == y_val.shape