GeezerVison

Its docstring said None was "only" for an unsupported field type or a
field name that is not a C identifier. It is also None for a packed record
(numpy's default without align=True), a record with an array field, and a
UDT that is neither a record nor an array (bytes, a date).

"Select a UDT's elements equal or unequal to a value, as == compares
them" built the binary.eq (or ne) apply only when the select expression
ran, so its errors moved from the call to .new(), and a thunk of another
type lost the select's own message:

    v.select("==", Scalar of a record UDT with other field names)
    before: an expression; .new() raised eq's KeyError
    after:  KeyError at the call (record UDTs must share field names)
    v.select("==", (1, 2.0)) without Numba: the same, eq's KeyError at the call
    v.select("==", some_vector), v.select("==", object())
    before: TypeError: Vector can't be directly converted to a numpy array /
            int() argument must be ... not 'object'
    after:  TypeError: Bad type for keyword argument `thunk=` in Vector.select

Vector.select and Matrix.select now convert the thunk in the one block
every select uses, typed by eq for a UDT comparison, and build the apply
(typed, not computed) at the call; _select_by_comparison computes it when
the expression runs. From rB's patch.

"Compare an int beyond 64 bits in Scalar.isequal as == compares it" left
isclose with the same path:

    Scalar.from_value(1, INT8).isclose(2**63)
    before: OverflowError: int too big to convert
    after:  False
    Scalar.from_value(2.0**64).isclose(2**64): before OverflowError, after True

Such an int is compared in Python by isclose's formula; one too large for
a float, beside a float, is not close.

"Compare a str that bytes cannot hold as unequal to a bytes UDT" put its
branch under the check that the literal is no numpy value, so an np.str_
(a str subclass, and a numpy scalar) missed it:

    s4_vec == np.str_("\xe9")
    before: KeyError: binary.eq does not work with (S4, dtype('<U1')): a UDT
            that is neither a record nor an array pairs only with its own type.
    after:  all False, as numpy and s4_vec == "\xe9" give

The bytes check now runs first, for any str.

"Refuse an array literal that does not broadcast with an array UDT by
name" typed such an array in its own shape, which for an empty array is
a layout no UDT can have:

    i8x3_vec + np.array([])   (also ==, np.zeros((2, 0)), np.zeros((0, 3)))
    before: ValueError: Unknown dtype: ('<f8', (0,)) ...
    after:  ValueError: could not broadcast input array from shape (0,)
            into shape (3,)

as before that commit for (0,), and now for the empty arrays that
broadcast too.

"Refuse a tuple of types an op does not take with a TypeError" said
"binary.plus[...] takes one dtype or two, not 1" for binary.plus[("INT64",)],
which reads as a contradiction. It now says "takes a dtype or a pair of
dtypes, not a tuple of 1" (and of 3), and an IndexBinaryOp "takes a dtype,
or a tuple of two (x and y) or three (x, y and theta), not a tuple of 4".

"They need Numba" covered the whole list above it, but pair, count,
agg.ss.first and agg.ss.last work on a record UDT without Numba (first,
second, any, identity and the lifted ops raise KeyError).

import graphblas.core.ss.binary as the first graphblas import raised
ImportError ("cannot import name 'register_new' from partially initialized
module"), and so did graphblas.core.ss.unary, on main too. The module
imported the operator package at its top; that initializes the operators,
which imports graphblas.binary.ss, which takes register_new from the
module before it was defined.

Now register_new comes first and the operator imports and the
TypedJitBinaryOp (or TypedJitUnaryOp) class follow it, so register_new is
there when the operators look for it. Nothing else changes; the function
uses those names only when called.

…ses cmake

The module's setup called fix_jit_config(), which sets "on" whenever it
finds a compiler to repair from. Where SuiteSparse compiles through cmake
(always when it is built with MSVC), that would compile every kernel of
the module through cmake, about 10 s each on Windows CI, if a runner has
conda's clang.exe or the build machine's cl.exe. UDT work already leaves
the default "run" alone there (_enable_jit_for_udt); the setup now does
the same, so only an "on" set before the tests compiles there. The setup
moves into _configure_jit_for_tests, which a test drives with a stand-in
config on every host.

GraphBLAS stores an array UDT's element as one contiguous block, in row
or column order of the entries. In "fullc" and "bitmapc", export built
the values with psg's claim_buffer_2d in Fortran order, which made the
element's own axes the slowest, and import made the whole array Fortran
order before handing it over. Both scrambled the numbers, on every
SuiteSparse version, and an unpack then pack round trip worked only
because the two scrambles cancelled:

    exp = np.arange(24).reshape(2, 3, 4); A = Matrix.from_dense(exp)
    A.ss.export("fullc")["values"][0, 1]       before: [2, 4, 18, 20]
    Matrix.ss.import_fullc(values=exp)[0, 1]   before: [5, 17, 9, 21]
    after: both [4, 5, 6, 7], as A[0, 1]

Column-order export now reshapes the claimed buffer to
(ncols, nrows, *element) and swaps the first two axes; row order stays
with claim_buffer_2d, which gets it right and whose array owns the
buffer, so unpack then pack(take_ownership=True) stays zero-copy (a test
checks it). Import swaps the axes into a C-contiguous copy, so a
column-major array UDT given one element per entry is never taken
without a copy. Numbers given flat, in column order, import as before.
The values are converted once, to see their shape: straight to Fortran
order unless they are an array-UDT ndarray, and not copied again when
that made a new array.

export(raw=True) gives an array UDT's values as a list of elements, of
shape (n, *element), and in "fullc" and "bitmapc" that list is in
column order already. Import made it Fortran order, as for a 2-d array
of entries, so the numbers went in scrambled and the raw round trip
failed, on every SuiteSparse version:

    A = Matrix.from_dense(np.arange(24).reshape(2, 3, 4))
    B = Matrix.ss.import_any(**A.ss.export("fullc", raw=True))
    before: B[0, 1] is [5, 17, 9, 21], and B.isequal(A) is False
    after:  B[0, 1] is [4, 5, 6, 7], and B.isequal(A) is True

Such a list, numbers flat in column order, and one element (an iso
value; one of INT64[2, 2] was read transposed) are now kept in C order,
so take_ownership=True takes them without a copy. The docstrings of
import_fullc and import_bitmapc say so. Values whose trailing axes fit
none of these now raise ValueError, where they were scrambled the same
way:

    Matrix.ss.import_fullc(values=np.arange(24).reshape(2, 3, 4), dtype="INT64[2, 2]")
    before: [0, 1] is [[5, 17], [9, 21]] (import_fullr gives [[4, 5], [6, 7]])
    after:  ValueError: values of INT64[2, 2] in column order must have
            shape (nrows, ncols, 2, 2), an element per entry, ...

GraphBLAS may hand over a values buffer with more elements than
nrows * ncols: SuiteSparse 7's memory pool rounds every allocation up to
a power of two, and a full matrix resized smaller keeps its buffer. For
an array UDT, psg's claim_buffer_2d then reshaped the values to
(nrows, ncols), which raised ValueError. GraphBLAS had already given the
buffer away, so unpack left the Matrix empty:

    SuiteSparse 7.4: Matrix.from_dense(np.arange(24).reshape(2, 3, 4)).to_dense()
    SuiteSparse 10.5.2:
        A = Matrix.from_dense(np.arange(36).reshape(3, 3, 4)); A.resize(2, 3)
        A.ss.unpack("fullr")
    before: ValueError: cannot reshape array of size 24 into shape (2,3)
            (on main; the second also leaves A with nvals 0)
    after:  the values, of shape (2, 3, 4)

The spare elements are now cut off before the reshape, as psg does for
other types. On SuiteSparse 7 every dense export of an array UDT failed,
test_subarray_dtypes among them. Row order with an exactly sized buffer
still goes to claim_buffer_2d, whose array owns the buffer, so unpack
then pack(take_ownership=True) stays zero-copy.

Its loop over the to_* and from_* methods rebuilt B and asserted
nothing, and its from_dense(to_dense()) check compared Full2 with
itself. Both now compare with the matrix they came from. They pass
before and after; the test only checks more.

…nstructor

A Monoid over an is_udt=True BinaryOp compiles per dtype in
_compile_udt, which always called GrB_Monoid_new_UDT. For a built-in
type that the op returns, SuiteSparse refuses that constructor, since
the op's output is not a user-defined type:

    op = BinaryOp.register_anonymous(lambda x, y: x + y, is_udt=True)
    Monoid.register_anonymous(op, np.float64(0.0))[FP64]
    before: DomainMismatch with an empty message
    after:  the monoid, FP64 -> FP64, and v.reduce(it) sums

A built-in return type now goes through GrB_Monoid_new_<type>, as a
monoid over an ordinary BinaryOp does in _build.

to_dense, to_coo, to_csr and the like convert the exported values with
values.astype(dtype.np_type). The values already hold each element's
own axes last, and numpy's astype to an array dtype adds that dtype's
axes again, repeating each number into a whole element:

    v = Vector.from_coo([0, 1], np.arange(8).reshape(2, 4))   # INT64[4]
    v.to_dense(dtype="FP64[4]").shape     (an iso v too, and Matrix)
    before: (2, 4, 4)
    after:  (2, 4), the same numbers as FP64

Element shapes follow the store rule that to_dense(fill_value, dtype)
already uses: they must match apart from leading axes of length 1, so
FP32[1, 4] gives (2, 1, 4) and FP64[2] raises DomainMismatch, where it
gave (2, 4, 2).

A serialized object keeps its type's C name. For a type registered with
dtypes.ss.register_new, that is the name it is registered under in
dtypes.ss, where lookup_dtype does not look. The fallback, the numpy
repr serialize writes as GrB_NAME, is empty for such a type, and
literal_eval("") raised:

    T = dtypes.ss.register_new("pair", "typedef struct { int64_t a ; double b ; } pair ;")
    Matrix.ss.deserialize(Matrix(T, 2, 2).ss.serialize())
    before: SyntaxError: invalid syntax
    after:  a Matrix of T (SuiteSparse checks the blob against it, as
            for dtype=T)

dtypes.ss is tried last, after GrB_NAME, so a numpy UDT's own repr
still wins. When nothing resolves, deserialize now raises a ValueError
that says to pass the type as dtype=: a blob of such a type that this
process has not registered, or of an anonymous UDT whose numpy repr was
too long to keep (two tests expected that bare SyntaxError).

On Numba 0.58 and 0.59, a UDT UDF reading a field its record lacks
raised NumbaPendingDeprecationWarning under -W error, as in our tests:
the warning filter in _numba_compile never matched, because the
warning's text need not start with the words it looked for. Without
-W error, users got the first line of Numba's old_style TypingError:

    b = BinaryOp.register_anonymous(lambda x, y: x.zzz, is_udt=True)
    b[R]   before: UdfParseError: binary.b does not work with (R, R):
                   Failed in nopython mode pipeline (step: nopython frontend)
           after:  UdfParseError: binary.b does not work with (R, R):
                   Field 'zzz' was not found in the record

as Numba 0.60 and later already gave. x["zzz"] likewise now gives
Numba's "Field 'zzz' was not found in record with fields (...)" line,
which old_style quotes, instead of that first line.

…the type

Before SuiteSparse 9 a blob has no GrB_NAME to fall back to. There,
deserializing a type from C source that this process has not
registered, or a UDT whose C name does not resolve, said only:

    Matrix deserialize cannot resolve dtype 'pair' and
    GxB_Serialized_get_String is unavailable in this SuiteSparse build

which test_jit_udt_deserializes_without_dtype did not expect, so it
failed on SuiteSparse 8. Every build now gives the message that later
ones give, with the remedy:

    Matrix deserialize cannot tell the dtype from the type name 'pair';
    pass it as dtype=

Numba 0.60 raises a bare KeyError for x["zzz"] on a record without that
field, as for x.zzz, but its argument is Numba's sentence rather than
the name, and the KeyError branch wrapped it once more:

    before: Field "Field 'zzz' was not found in record with fields
            ('a', 'b')" was not found in the record
    after:  Field 'zzz' was not found in record with fields ('a', 'b')

as other Numba versions say. Only an argument that is an identifier, as
x.name needs, is taken for the field's name.

Scalar.isclose compares an int that INT64 cannot hold in Python, with the
formula written out, which lacked isclose's infinity check:

    Scalar.from_value(inf).isclose(2**70)
    before: True
    after:  False, as math.isclose says

It now calls the function that binary.isclose compiles, as the other
paths do since "Say an infinity is close only to itself in isclose, as
math.isclose does".

"Select a UDT's elements equal or unequal to a value, as == compares them"
sends UDT eq and ne through select's rewrite of A.select(A == x). Before
"Select by a transposed comparison as by its mask, not by the untransposed
one", that rewrite also matched A.select(A.T == x) and dropped the
transpose. test_udt_select_by_equality now uses a 2 x 3 matrix with
entries off the diagonal, so that a lost transpose would show, checks
A.T.select(A.T == x) and the DimensionMismatch of A.select(A.T == x), and
assigns a select through a mask with replace.

An iso Matrix exports as "coo" with its indices and one value. The
indices came from GrB_Matrix_extractTuples_<type name>, which for a UDT
does not exist, and the value from a reduce with monoid.any, which a UDT
can do only with Numba:

    A = Matrix.ss.import_fullr(values=[[1, 2, 3, 4]], nrows=2, ncols=3, dtype="INT64[4]", is_iso=True)
    A.ss.export("coo")
    before: AttributeError: ... no function ... named 'GrB_Matrix_extractTuples_<name>'
    after:  values [[1, 2, 3, 4]], of shape (1, 4), and import_any gives A back

A UDT now uses GrB_Matrix_extractTuples_UDT, as to_coo does, and the
value is read with iso_value's iterator into an array of one element,
so an array UDT's value is (1, *element), as other iso exports give it
(np.array([value], dtype) repeated each number into an element). An iso
Matrix with no entries still exports 0, as the reduce gave.