Expose GxB_pack_HyperHash and GxB_unpack_HyperHash (python-graphb…

…las#305)

* Expose `GxB_pack_HyperHash` and `GxB_unpack_HyperHash`

* add test

* oops

graphblas/core/matrix.py

@@ -6,7 +6,7 @@
 from .. import backend, binary, monoid, select, semiring
 from ..dtypes import _INDEX, FP64, lookup_dtype, unify
 from ..exceptions import DimensionMismatch, NoValue, check_status
-from . import NULL, automethods, ffi, lib, utils
+from . import automethods, ffi, lib, utils
 from .base import BaseExpression, BaseType, _check_mask, call
 from .expr import AmbiguousAssignOrExtract, IndexerResolver, Updater
 from .mask import Mask, StructuralMask, ValueMask
@@ -2248,7 +2248,7 @@ def to_pygraphblas(self):  # pragma: no cover
         import pygraphblas as pg
 
         matrix = pg.Matrix(self.gb_obj, pg.types._gb_type_to_type(self.dtype.gb_obj))
-        self.gb_obj = NULL
+        self.gb_obj = ffi_new("GrB_Matrix*")
         return matrix
 
     @classmethod
@@ -2274,7 +2274,7 @@ def from_pygraphblas(cls, matrix):  # pragma: no cover
         rv = cls(matrix._matrix, dtype)
         rv._nrows = matrix.nrows
         rv._ncols = matrix.ncols
-        matrix._matrix = NULL
+        matrix._matrix = ffi_new("GrB_Matrix*")
         return rv
 
 

graphblas/core/ss/matrix.py

@@ -3545,6 +3545,39 @@ def _import_any(
         else:
             raise ValueError(f"Invalid format: {format}")
 
+    def unpack_hyperhash(self, *, compute=False, name=None):
+        """Unpacks the hyper_hash of a hypersparse matrix if possible.
+
+        Will return None if the matrix is not hypersparse or if the hash is not computed.
+        Use ``compute=True`` to compute the hyper_hash if the input is hypersparse.
+
+        Use ``pack_hyperhash`` to move a hyper_hash matrix that was previously unpacked
+        back into a matrix.
+
+        This may be used before unpacking a HyperCSR or HyperCSC matrix to preserve the
+        full underlying data structure that can be packed back into an empty matrix.
+        """
+        from ..matrix import Matrix
+
+        if compute and self.format.startswith("hypercs"):
+            self._parent.wait()
+        rv = Matrix._from_obj(ffi_new("GrB_Matrix*"), INT64, 0, 0, name=name)
+        call("GxB_unpack_HyperHash", [self._parent, _Pointer(rv), None])
+        if rv.gb_obj[0] == NULL:
+            return
+        rv._nrows = rv.nrows
+        rv._ncols = rv.ncols
+        return rv
+
+    def pack_hyperhash(self, Y):
+        """Pack a hyper_hash matrix Y into the current hypersparse matrix.
+
+        The hyper_hash matrix Y should be from ``unpack_hyperhash`` and unmodified.
+
+        This uses move semantics. Y will become an invalid matrix.
+        """
+        call("GxB_pack_HyperHash", [self._parent, _Pointer(Y), None])
+
     @wrapdoc(head)
     def head(self, n=10, dtype=None, *, sort=False):
         return head(self._parent, n, dtype, sort=sort)

graphblas/core/vector.py

@@ -6,7 +6,7 @@
 from .. import backend, binary, monoid, select, semiring
 from ..dtypes import _INDEX, FP64, INT64, lookup_dtype, unify
 from ..exceptions import DimensionMismatch, NoValue, check_status
-from . import NULL, automethods, ffi, lib, utils
+from . import automethods, ffi, lib, utils
 from .base import BaseExpression, BaseType, _check_mask, call
 from .expr import AmbiguousAssignOrExtract, IndexerResolver, Updater
 from .mask import Mask, StructuralMask, ValueMask
@@ -1582,7 +1582,7 @@ def to_pygraphblas(self):  # pragma: no cover
         import pygraphblas as pg
 
         vector = pg.Vector(self.gb_obj, pg.types._gb_type_to_type(self.dtype.gb_obj))
-        self.gb_obj = NULL
+        self.gb_obj = ffi_new("GrB_Vector*")
         return vector
 
     @classmethod
@@ -1607,7 +1607,7 @@ def from_pygraphblas(cls, vector):  # pragma: no cover
         dtype = lookup_dtype(vector.gb_type)
         rv = cls(vector._vector, dtype)
         rv._size = vector.size
-        vector._vector = NULL
+        vector._vector = ffi_new("GrB_Vector*")
         return rv
 
 

graphblas/tests/test_matrix.py

@@ -3746,3 +3746,20 @@ def test_to_csr_from_csc(A):
     assert expected.isequal(B, check_dtype=True)
     assert Matrix.from_csr(*B.to_csr(), ncols=2).isequal(B)
     assert Matrix.from_csr(*B.to_csr()).isequal(B[:, 0:0].new())
+
+
+def test_ss_pack_hyperhash(A):
+    A.ss.config["sparsity_control"] = "sparse"
+    assert A.ss.unpack_hyperhash() is None
+
+    C = Matrix(int, 20000, 200000)
+    C.ss.config["sparsity_control"] = "hypersparse"
+    C[100, 2000] = 2
+    C[10, 20] = 1
+    Y = C.ss.unpack_hyperhash()
+    Y = C.ss.unpack_hyperhash(compute=True)
+    assert C.ss.unpack_hyperhash() is None
+    assert Y.nrows == C.nrows
+    C.ss.pack_hyperhash(Y)
+    assert Y.gb_obj[0] == graphblas.core.NULL
+    assert C.ss.unpack_hyperhash() is not None

setup.py

@@ -25,7 +25,7 @@
     url="https://github.com/python-graphblas/python-graphblas",
     packages=find_packages(),
     python_requires=">=3.8",
-    install_requires=["suitesparse-graphblas >=7.2.0.0, <7.4", "numba", "donfig", "pyyaml"],
+    install_requires=["suitesparse-graphblas >=7.3.0.0, <7.4", "numba", "donfig", "pyyaml"],
     extras_require=extras_require,
     include_package_data=True,
     license="Apache License 2.0",