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eriknw marked this pull request as ready for review August 4, 2026 16:07

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eriknw removed this pull request from stack #634 October 8, 2026 21:02

…ns (#591)

* Reject record UDT pairs whose leaf counts differ

`_check_udt_pair` rejects two record operands that disagree on shape, but it
compared only top-level field names. Two records can share those and still
nest differently: `[("a", f8), ("b", f8)]` against
`[("a", [("n1", f8), ("n2", f8)]), ("b", f8)]` both report fields
`["a", "b"]` while contributing two leaves and three. The codegen walks one
operand's leaf paths and applies them to both, so applying the scalar path
`["a"]` to the nested side asks Numba to add a float to a record.

Measured before this change, that pair gets through the checks and comes
back from Numba's typing pass as:

    UdfParseError: binary.plus does not work with (_NestFlat, _NestDeep):
    No implementation of function Function(<built-in function add>) found
    for signature: >>> add(float64, Record(nst_n1[type=float64;offset=0],
    nst_n2[type=float64;offset=8];16;False))

That reports a compile failure for what is the same shape disagreement the
three sibling checks in the same function already report as a KeyError.
Comparing leaf counts makes it the fourth of those checks.

The exception class changes, and that is visible in both directions:
UdfParseError derives from GraphblasException, not from KeyError, so code
catching KeyError to mean "no such op" now catches this pair, and code
catching UdfParseError no longer does. `OpBase.__contains__` catches both,
so `udt in binary.plus` is unaffected either way.

* Ignore the UDT repr size warning in the nesting mismatch test

* Compare record leaf paths and field shapes, not leaf counts

Equal leaf counts do not make two records pair up. The codegen reads
both operands at the left one's leaf paths, so records nested under
different fields, or with differently named sub-records, still reached
Numba's typing: a UdfParseError for plus, a raw TypingError for eq. The
check now requires the same leaf paths in the same order, as it already
does for top-level names. That also rejects reordered sub-record
fields. They work here, but they give wrong answers once #593 pairs leaf
dtypes by position and #595 branches on them.

Array fields whose shapes do not broadcast were worse: (3,) with (4,)
raised inside the cfunc, unseen, and left garbage in the result. Those
are now KeyErrors, as is a scalar field on the left of an array one,
which never compiled. The arithmetic ops also need the broadcast to fit
the left field. A (1,) field left of a (3,) one was caught only by the
UDF return check, worded as a UDF the user did not write. eq and ne
reduce each field to one bool and keep accepting that pair.

Fields that broadcast, such as (3,) with () or (1, 3), still combine;
across 64 field-shape pairs under plus, min and eq, everything that
worked before still does. The user guide now states the pairing rules.

* Type UDT ops like built-in ops, cast UDT stores, broadcast array UDTs

Lifted ops (plus, minus, times, truediv, floordiv, min, max, eq, ne,
ainv, abs) type each UDT element or record leaf as the built-in op
types that dtype, so results never narrow. A mixed pair gives an
operand's type if it holds the result, else the promoted layout.
Records pair on field names, order, nesting and field shapes; array
UDTs broadcast as numpy arrays do.

A Python number beside a UDT is weak per leaf (NEP 50), the same on
numpy 1 and 2. A literal that must become a UDT element (tuple, array,
user-op literal, ewise_union default) is converted only if no value
changes. apply and ewise ops with a Monoid on a mixed pair use its
BinaryOp.

Storing into an object of another UDT casts each leaf as GraphBLAS
casts built-ins; incompatible layouts and UDT/built-in pairs raise
DomainMismatch. Assigning or setting an element from another UDT used
to read raw bytes, past the end of a smaller element. ewise_add
converts operands to the result type, which GraphBLAS cannot cast.

Array codegen uses loops, in Numba and in C: typing an op on
FP64[64,64] took ~112 s and now takes ~0.02 s. Also fixes several
errors, eq on 2-D array fields, array-field truediv on small ints, a
UDF registered under a lifted op's name, and packed vs aligned
records. Docs and tests updated.

* Cast UDT stores without temporaries, type UDT literals, fold in #592-#595

Store casts: an existing object casts in one pass, and element-wise lifted
ops (apply, ewise_mult, ewise_union) write the target type directly. Any
other store into another UDT type raises DomainMismatch and suggests
obj << expr.new(). ewise_add on mixed-type Vectors/Matrices raises; infix
+ takes the union with each operand's zero (-0.0 in float fields).

Literals beside a UDT: Python numbers and tuples/lists/dicts of them are
weak per field (NEP 50), numpy values and Scalars are strong. A value that
must become a UDT element (user op literal, select/IndexUnaryOp thunk,
ewise_union default) must fit by type; one that fits only by value (0 for
a bool field, 2.0 for an int field) still converts, with a
DeprecationWarning. Lossy values raise.

From #592-#595: min/max (fmin semantics, fixed -0.0 tie), floordiv,
integer and complex division, and complex multiplication give the same
bits on the Numba and C JIT paths. Array fields in records compute
element by element.

Also: extracting an element into another UDT type casts it instead of
copying raw bytes (a segfault on main); Monoid literals are typed as their
BinaryOp's; outer works on mixed types; the user guide's UDT rules are two
tables.

* Type Python numbers beside built-in dtypes as numpy 2 does (NEP 50)

A Python number beside a Vector, Matrix or Scalar of a built-in dtype was
typed through numpy, with a 0-d array of value 0 standing in for it
(dtypes.unify). numpy 1 judged that array by its value, so the collection's
dtype won and a large literal wrapped (int8_vec * 1000 multiplied by -24,
int8_vec < 300 compared with 44); numpy 2 (NEP 50) made 0-d arrays strong,
so every int was INT64 and every float FP64 (int8_vec + 1 was INT64,
uint64_vec + 1 FP64, losing precision). The same script gave other dtypes,
and on numpy 1 wrong answers, depending on the installed numpy.

A Python bool, int, float or complex operand is now weak, by the table UDT
leaves already use: it takes the other operand's dtype when that dtype's
kind holds it, else INT64, FP64 or FC64, on numpy 1 and 2 alike. So
int8_vec + 1 is INT8 and wraps as numpy does, int8_vec * 0.5 is FP64,
fp32_vec * 0.5 is FP32, int8_vec + 300 raises OverflowError, and
fp32_vec + 1e300 (or + 2**200) is inf with a RuntimeWarning, as in numpy 2.
A strong operand keeps the old widening: int8_vec + np.int64(1). This covers
apply (infix ops, binary.plus(v, 1), Monoids), Scalar ewise ops,
ewise_union defaults, value thunks of select and IndexUnaryOps, and UDFs.
A comparison keeps no result type, so an int out of range compares exactly
(int8_vec < 300 is all True), and so does an op whose result is another
type anyway: int8_vec / 300 is FP64, as numpy divides integers in float64,
for UDT fields too. A to_dense fill value that does not fit widens, an int
or a float (fp32_vec.to_dense(fill_value=1e300) is float64), rather than
raising or becoming inf. numpy scalars, Scalars and typed ops stay strong,
and a thunk that is not compared with the values, such as a row index,
keeps its own type. unify no longer stands a scalar in as a value-0 array.

eq and ne beside a UDT now type a Python number or sequence the same way,
leaf by leaf, with the same exact fallback: fp32_udt == 0.1 compares in
float32, as fp32_vec == 0.1 and numpy do.

* Compare select value thunks exactly; isequal compares arrays as np.array_equal

SuiteSparse:GraphBLAS's select with a value op (valueeq, valuelt, ...)
typed for another type than the input's reads the thunk's bytes as the
input's type (8.0.2 through 10.6.0; apply with the same op is right; fix
proposed upstream in DrTimothyAldenDavis/GraphBLAS#463). On an INT8 vector,
select(valueeq, 1.0) kept nothing and select(valuelt, 300) compared with
44, on main too. select now compares in the input's own type with a thunk
that keeps the same entries (x < 2.5 is x < 3 for integers).

isequal on array UDTs compares as np.array_equal: elements must have the
same shape, apart from leading axes of length 1, so an FP64[3] of [1, 1, 1]
is no longer isequal to an FP64[1] of [1]. == still broadcasts, and a
Scalar's ==, which returns a bool, now computes binary.eq, as == on a Vector
does (it called isequal). A literal beside a UDT Scalar is compared as ==
types it, so [0, 0, 0] == 0.5 is False (0.5 was truncated to 0), and
Scalar(FP32 0.1) == 0.1 compares in FP32, as numpy does.

A float too large for FP32 in a comparison (fp32_vec == 1e300) compares
with infinity, as numpy 2 does, and now warns at the caller's line, as
arithmetic does. A Python int from 2**63 to 2**64 - 1 now goes into a
UINT64 Scalar, Vector or Matrix element (Scalar.from_value(2**63, "UINT64")
and uint64_vec[0] = 2**63 raised OverflowError, on main too).

* Compare a Python int beyond a 64-bit element's range exactly

Commit 2 typed an int beyond an integer element's range by a type that
holds it, so a comparison no longer raised, but beside INT64 and UINT64
that comparison runs in FP64, which rounds both: int64_vec < 2**63 was
False for 2**63 - 1, uint64_vec == 2**64 was True for 2**64 - 1, and
Scalar(2**63 - 1) == 2**63 was True, as for UDT int64 and uint64 leaves
(main raised OverflowError). Such an int now compares as infinity of its
sign, which every integer element compares with as with the int, so infix
comparisons, apply, ewise_union defaults and Scalar == are exact, as in
numpy 2, and beside a UDT a literal with one equals no element. An int too
large for a float compares too (int64_vec < 10**400 and
select(valueeq, 10**400) raised OverflowError).

A float too large for a float32 UDT leaf (fp32_udt + 1e300, or a store)
now warns at the caller's line, as a built-in FP32 does, instead of from
inside python-graphblas, where the default filter showed it once per
process. Drops a test helper nothing used.

* Give each UDT layout its own JIT C name; Scalar == without Numba; CI fixes

SuiteSparse's JIT knows a type by its C name alone, and a UDT's C name
was the name it was registered under, so two layouts registered under
one name shared JIT kernels: making an iso Vector non-iso ran a kernel
compiled for the other layout, which corrupted the heap or gave wrong
values (on main too; the new broadcasting test registers four layouts
as _BcastX, and crashed 9 Linux CI jobs). A name already given to
another layout now gets a fresh _gbudt_NNN, and gb.ss.register_new
refuses a name another type has.

Scalar == on a UDT runs binary.eq, which needs Numba; without Numba it
raised KeyError, where it compared in Python before. It now compares the
values in numpy, leaf by leaf in the type binary.eq compares in, and
raises binary.eq's KeyError for UDTs that do not pair.

Tests: guard the cases that need Numba, skip the 32 KB UDT on Windows
(MSVC builds of SuiteSparse refuse a UDT over 1024 bytes), give the JIT
name tests array lengths no other test registers, and expect numpy's own
int dtype (int32 on Windows with numpy 1).

* Spell out complex * and / for UDTs; Numba 0.68 follows Python 3.14

From Python 3.14, CPython recovers infinities that the textbook complex
formulas leave as nan (C99 Annex G), and Numba 0.68 follows it there:
1j * (inf+infj) is -inf+infj and (inf+nanj) / (1+1j) is inf-infj, where
numpy and the C JIT kernel give nan+nanj. The UDT cfunc used Numba's
operators, so it disagreed with the C JIT kernel on CI's macOS job with
Python 3.14 and Numba 0.68. The cfunc now spells out the formulas the C
kernel uses (the textbook product, and Smith's division as CPython
divided before 3.14), so the two agree on any Python and Numba.

The user guide no longer calls a complex quotient numpy's: numpy
multiplies by the divisor's reciprocal, so the last bit can differ, and
numpy's overflows where the quotient is finite.

* Move UDT and UDF logic out of the general modules

Code moves, plus import changes and a few small helpers, so scalar,
vector, matrix and base read mostly as the built-in path.

- core/udt.py (new): rules for UDT elements: layout, op typing,
  store casts, the ewise_add rule, equality without Numba, and
  element values as bytes. The type rules moved here from udt_utils,
  which now only generates code and is renamed udt_codegen.
- core/literals.py (new): NEP 50 typing of literal operands, for
  built-in and UDT types (moved from scalar.py and udt_utils).
- operator/udf.py (new): the Numba-only UDF-on-UDT block from
  operator/base.py, plus _bool_to_int8.
- scalar._union_default replaces the ewise_union default block, which
  was copied six times across Scalar, Vector and Matrix.
- New helpers pulled out of scalar.py's inline code:
  _scalar_ewise_add_converted, _udt_eq_without_numba, _element_bytes
  and _element_value.

core/udt.py imports operator, base and scalar names inside functions,
after the built-in early returns, so built-in calls pay nothing extra.

* Pass CODECOV_TOKEN to the coverage upload

Codecov takes tokenless uploads only from forks. Every upload from this
PR's CI runs was refused with "Token required because branch is
protected", so the PR had no coverage report, and the PRs stacked on it
had no base to compare against. The repository now has the secret.

* Cover how core/literals.py types literal operands

Tests for the literal-typing paths CI never ran:

- Beside built-in dtypes: a comparison op given as a string with an
  int out of range (ewise_union with "<"), an op string that names no
  BinaryOp (the op reports it), an int beyond INT64 beside a bool
  (OverflowError, as numpy's bools & 2**70), a user's BinaryOp with an
  int out of range, and a complex literal on a backend without complex
  types (vanilla only).
- Beside UDTs: an infinite or NaN Decimal, literals not laid out like
  an element (ragged, too long or too short for a field, not
  broadcastable) or not numbers, which numpy then rejects, a number
  beside a bytes field, a nested record that is not the last field,
  and the conversions that lose a number (an imaginary part, a float32
  overflow, a fraction in a record's array field) or keep a bytes field
  as it is.
- Scalar.isequal with a ragged literal on an array UDT (ValueError).

* Cover select's rewrite of value thunks of another type

Tests for the branches of operator/select.py's
_value_select_in_own_type that CI never ran: a complex thunk beside
integers and floats (only valueeq and valuene, which have an answer),
a NaN thunk of another float type, an infinite thunk beside integers,
and an empty thunk Scalar, which GraphBLAS rejects.

* Cover UDT store casts, ewise_add casts and fused stores

Tests for the paths in core/udt.py, Matrix and udt_codegen that CI
never ran:

- store casts between records whose field shapes differ or whose
  bytes fields differ in width (DomainMismatch saying why), a Scalar
  expression assigned to many elements of another UDT type, one
  element extracted from a Matrix into a Scalar of another type, and
  Matrix.power with a semiring whose result is another type;
- without Numba no UDT cast compiles, so every such store raises
  (monkeypatched, so it runs everywhere);
- Matrix.ewise_add with operands of another type than the result's
  (DomainMismatch, and DimensionMismatch first for other shapes), and
  Scalar.ewise_add with a Monoid and with a non-lifted op;
- a fused store with a built-in Scalar operand, and fused stores that
  cannot be made (first, a user's op);
- (x | y) + z of non-BOOL vectors, which reports the infix error.

* Cover UDF return-type errors and the Numba error summary

Tests for operator/udf.py: a UDF on a UDT that returns None, or an
array with no array UDT input, or a tuple beside a UDT that is neither
a record nor an array, raises UdfParseError naming the type; built-in
ops on such a UDT raise their KeyError. The one-line summary of a
Numba error falls back to the first non-empty line, then to a fixed
sentence.

* Cover the JIT layout checks for records a C struct cannot match

A record with explicit offsets can have a C struct's size with a field
elsewhere, as can such a record nested in one whose own offsets match,
and a nested record with an array field has no C typedef. Each skips
the JIT and still computes with the cfunc.

* Remove branches no caller can reach in the UDT code

Behavior-neutral; each guard tests a state its callers never produce:

- literals._check_literal_fits: every caller passes a UDT, never None.
- select._value_select_in_own_type: an int too large for any float
  never reaches the float branch (beside a float type it is typed as
  that type, or float() raises first, as in numpy).
- udt_codegen._c_aligned_version is only called with records.
- udt_codegen._c_expr_unary: both callers pass the field's dtype.
- udt_codegen._make_jit_c_comparison_definition: _udt_c_typedef has
  already returned None for a UDT that is neither array nor record.
- udt_codegen.set_jit_c_on_op and set_jit_c_comparison_on_op: callers
  check _has_jit_set first, since the setter they pass does not exist
  without it.

* Exclude Numba-compiled bodies and unreachable guards from coverage

- numba: _complex_times and _complex_truediv run only inside compiled
  code, as do two UDFs in test_op.py.
- safety: guards no public call reaches: a record leaf lookup_dtype
  refuses (a void field fails to register first), a Numba error in the
  UDF probe (the wrapper has the same operand types), a tuple whose
  length differs from the record _resolve_udt_return_type matched, a
  C expression for an op or dtype no lifted op gives, and the frame
  walk in _warn_from_caller running off the stack.
- only on failure: the udt_op_path fixture's demotion check, and the
  re-raise for a large UDT off Windows.

* Drop test conditions that cannot change and a needless Numba skip

- test_dtype: the JIT name test is skipped unless _has_jit_set, which
  needs the suitesparse backend, so it always has gb.ss.
- test_ssjit: the two complex JIT tests are skipped without Numba, so
  their own "numba is None" skips never ran.
- test_op: test_compile_codegen_helper compiles plain Python, so its
  Numba skip was needless; it now runs without Numba too, which covers
  _compile_codegen's no-Numba namespace in CI's no-Numba jobs.

* Fix select on complex values with a thunk of another type

Behavior fix: wrong results before, on main too (not a #591
regression). SuiteSparse:GraphBLAS reads the thunk of a value select as
the input's type when the op is typed for another type, and the
workaround from 69173110 converted the thunk only for bool, integer and
float inputs. On an FC32 vector of [1, 2+1j, 3, nan],
select(valueeq, np.complex128(1)) kept nothing (now [0]), valueeq with
np.float64(3) or np.int64(3) kept nothing (now [2]), and valuene with
np.complex128(2+1j) kept everything (now [0, 2, 3]); apply with the same
ops was right. (Main also got a Python 3 wrong there; #591's typing of
a weak int as FC32 already fixed that.)

A complex input now gets the thunk in its own type when that converts
exactly; otherwise (or with a NaN part) the thunk equals no element, so
valueeq keeps nothing and valuene keeps everything. "Exactly" is as for
float inputs: the thunk in the input's type must equal the thunk, part
by part, in Python's int and float comparison, so np.int64(2**53 + 1)
equals no complex64 element, as it equals no float32 one, where apply
compares in complex128 and matches 2**53. The ordered value ops still raise
KeyError on complex values, which have no order. A real input with a
complex thunk keeps its rule: an imaginary part of 0 converts, any
other equals nothing.

* Compare UDTs that are neither records nor arrays in eq and ne

Behavior fix. A UDT of bytes, dates or the like holds one value per
element. binary.eq and binary.ne on it raised TypeError ('NoneType'
object is not subscriptable), and so did Vector and Matrix isequal and
==: on main too. A Scalar's == raised it as well, where main compared
the values (a #591 regression, from 69173110's Scalar == through
binary.eq), as did == with a literal (b"ab"), which the literal checks
read as an array UDT. On the vanilla backend a complex literal is such
a UDT, so int8_vec == 1j raised the same TypeError.

eq and ne now compare the two values as numpy does, as a record field
of that type compares (bytes by value, NaT equal to nothing), and only
two elements of the one UDT compare: any other pair raises KeyError,
from _check_udt_pair, with or without Numba. So int8_vec == 1j on
vanilla is a KeyError, and so is a Scalar's == with another type
(another bytes UDT, or a number), where main compared the values (True
or False). A literal beside such a UDT is checked as one value.

A Scalar's == on such a UDT compares in numpy, as without Numba, so it
needs no Numba type for the element: a float16 UDT, which Numba cannot
compile, compares as on main. Vector and Matrix == on float16 still
need Numba and raise its NotImplementedError, as main raised there too.

* Type each product of Matrix.power for its UDT operands

Behavior fix of a #591 regression: main, which typed UDT ops
otherwise, computed int8 results here. power types the semiring for A,
and GraphBLAS casts a built-in operand of another type to it, but not a
UDT. With a semiring whose result is another type, such as plus_truediv
on an int8 array UDT (float64 results), power(2) worked, while from
n = 3 on the products of float64 squares with A or with each other
raised a bare GrB_DOMAIN_MISMATCH from GrB_mxm.

Each product of UDT operands that are not of the types the semiring
was typed for now takes the semiring typed for its own operands, as any
mxm of them does. power(n) into a float64 array object now equals the
power of a float64 copy of A, as for built-in INT8, for every n.

Unchanged: A.power(n, plus_truediv).new() makes an object of A's int8
type, and an mxm cannot cast a UDT as it stores, so it raises
DomainMismatch by #591's store rule, where main computed int8 results
(built-in INT8 truncates as it stores).

* Select with a float64 thunk too large for FP32 without a warning

Behavior fix, small: f32_vec.select(valueeq, np.float64(1e300)) gave the
right result but numpy warned "overflow encountered in cast" from
inside python-graphblas (an error under -W error), as the select
workaround from 69173110 converted the thunk to float32. A #591
regression: main had no such conversion (it let SuiteSparse read the
thunk's bytes as float32, which was wrong in another way). numpy's own
f32_array == np.float64(1e300) does not warn.

The conversion now ignores the overflow: the thunk is infinity in
float32, which compares with every element as 1e300 does, so the
selected elements are unchanged.

* Refuse values that a bytes, date or duration leaf of a UDT would not keep

Behavior fix. A literal that must become an element of a UDT and does
not fit it by type converts, with a DeprecationWarning, only when its
values convert exactly (#591's MAINT rule). The check skipped every
leaf that holds no number, so whatever was given for a bytes, str, date
or duration leaf counted as exact. Under a user's op, (1.5, 1) for a
record of a bytes field and an int8 field became (b"1.5", 1), and 1/3
became b"0.33", with a warning that said "Its values convert exactly";
np.timedelta64(5, "ms") for a timedelta of seconds became 0 s, and a
structured literal's six bytes became four, under the same warning.

- main: converted silently (1.5 became b"1.5").
- 0e1ad03c: a record field converted with that warning; a bare UDT of
  bytes or durations raised "does not fit" (the check failed on its
  layout).
- the commit "Compare UDTs that are neither records nor arrays in eq
  and ne" let a bare UDT convert with the warning too.
- now: such a leaf keeps a value only if it converts back to it, so a
  Python number, a numpy bool, a string cut short or a duration in a
  coarser unit raises ValueError "does not fit". bytes or str that fit
  still convert, as does np.timedelta64(5000, "ms") as 5 s (with the
  warning, since ms to s is not a safe cast).

Only a literal whose type does not fit reaches this check. A numpy
number that numpy casts safely to the leaf's type fits by type: np.uint8(5)
beside a 3-byte bytes UDT is b"5" without a warning, by numpy's rule.
bytes or str alone beside a bare bytes UDT has no type, so numpy still
cuts b"abcdef" to b"abc" there. A record literal holding a numpy
number, text or a date gets no type at all and so no check; the next
commit types it.

* Type every leaf of a record literal, numpy numbers, text and dates too

Behavior fix, in #591's literal typing (older than its last commits;
main had it too). A tuple or dict literal for a record UDT got no type
at all when a leaf held anything but Python numbers: a numpy number,
text or a date. Then nothing checked it, and numpy converted it into
the record, losing value where a Python number alone raises. In an int8
record, under a user's op, (np.float64(1.5), 1) became (1, 1) while
(1.5, 1) raised "does not fit"; under a lifted op,
int8_rec + (np.float64(1.5), 1) computed with (1, 1), and
int8_rec == (np.float64(1.5), 2) was True. A record of a bytes and an
int8 field holding (b"cd", 1) had (b"cd", 1.5) == it True, and a user's
op stored (b"cd", 1.5) as (b"cd", 1); a record with a date field the
same.

- main and 0e1ad03c: all of these as above, silently.
- now every leaf is typed. A numpy number or bool is strong, as alone
  or beside a built-in type: the leaf takes its dtype, promoted with any
  Python numbers in it, and a numpy number may stand for a whole nested
  record. Text or a date in a text or date field takes the field's own
  type when it converts there and back unchanged, so ops still pair the
  field with itself (rec + (b"ab", 1) is (b"abab", 2), and
  (np.timedelta64(5, "m"), 1) adds 300 s to a field of seconds, as on
  main); otherwise it keeps its own type, which does not fit: text at
  its own length, a numpy date or duration in its unit. Other dates
  (datetime, str) take the field's type. So the user's op raises "does
  not fit" for np.float64(1.5) or 1.5 in an int8 field next to text or
  a date, for text longer than its field (it was cut short) and for a
  date or duration in a finer unit that loses value (5 ms in seconds);
  np.int64(2) converts with the DeprecationWarning (exact, but not a
  safe cast) and np.int16(2) fits an int16 field; the lifted op gives a
  float64 field (2.5, 3), and + with 5 ms in a field of seconds raises
  KeyError (main added 0 s); and the comparisons are False.

In a comparison, an int beyond its integer leaf's range now becomes
infinity in that leaf only, not in the whole literal, so a text leaf
beside it still pairs: rec == (b"ab", 300) is False (it raised). A text
date is compared as a date in the exactness check.

A sequence for an array UDT keeps its rule, the array numpy makes of
it.

* Refuse UDT ops on float16 elements by name; compare them in numpy for Scalar ==

Behavior fix. Numba has a float16 type but no code for it, so every op
compiled on a UDT with a float16 leaf (a field, an array's element, or
a bare float16 UDT) failed with a bare NotImplementedError: float16.

- A record with a float16 field, + (1.0,) or == (1.5,): NotImplementedError
  on main, at 0e1ad03c and before this commit; now KeyError.
- Its Scalar == (1.5,): True on main, NotImplementedError at 0e1ad03c
  and before; now True again.
- A bare float16 UDT, Vector == or isequal: TypeError on main and at
  0e1ad03c, NotImplementedError before; now KeyError.
- An int8 record + (np.float16(1.5), 1) or == (np.float16(1.5), 2): main
  and 0e1ad03c cut 1.5 to 1 and answered (2, 3) and True, both wrong;
  before, KeyError ("no common type") and NotImplementedError; now
  KeyError.

Every UDT op's _compile_udt (unary, binary, indexunary, select,
indexbinary) now refuses a float16 leaf up front with the KeyError an
op gives for a type it does not take: "binary.eq does not work with
(...): Numba cannot compute on float16, which ... holds." A Scalar's ==
on a UDT with a float16 leaf compares in numpy, as for a UDT that is
neither a record nor an array. A store that would cast to or from a UDT
with a float16 leaf (another record or array UDT, or dup(dtype=...)),
which raised the same NotImplementedError on main, at 0e1ad03c and
before, now raises the DomainMismatch of a store that cannot cast,
saying why: "... Numba cannot compute on float16, which ... holds."
Widening float16 to float32 was not done: the result type would change,
and results stored back as float16 would round.

* Type a record's array field as an array UDT types its literal

Behavior fix. A sequence for an array UDT is typed by the rule #591
gives it: Python numbers alone are weak, each taking the element's
type when its kind holds them, and a numpy array, or a sequence holding
a numpy value, is the array numpy makes of it. The same sequence for an
array field of a record was typed element by element, numpy values
strong and Python numbers weak beside the field, and a numpy array was
read as Python numbers. So, with an int8[3] array UDT and a record with
an int8[3] field:

- (np.int8(1), 300, 3): the array UDT gives int64[3] and computes
  [2, 302, 6]; the record raised OverflowError (main and 0e1ad03c too).
- np.array([1, 300, 3]): the array UDT gives int64[3]; the record
  raised OverflowError (0e1ad03c), and main wrapped 300 to 44 silently.
- (np.float64(1.5), 2, 3): main and 0e1ad03c cut 1.5 to 1; the commit
  "Type every leaf of a record literal, numpy numbers, text and dates
  too" already gave a float64 field.

A record's array field now takes the array UDT's rule: an array, or a
sequence holding a numpy number or bool, is the array numpy makes of
it, broadcast to the field's shape. So the record gives an int64[3]
field and [2, 302, 6], as the array UDT does. Python numbers alone, and
a numpy number for the whole field, are typed as before.

A comparison keeps a Python int beyond the element's range exact, as it
does everywhere (int8_vec == 300 is False), also inside such an array:
the int is infinity in its place before numpy makes the array. So
rec == ((np.int8(1), 2**70, 3), 4) and int8_array_udt ==
(np.int8(1), 2**70, 3) are False and != True. The array UDT raised
numpy's OverflowError there (older than this commit), and the record
field, False before, would have raised it under the array rule alone.
A user's op with such a literal still raises OverflowError.

* Refuse == between text, dates and numbers with eq's KeyError

Behavior fix. binary.eq and ne compared each pair of record leaves with
Numba, which cannot compare bytes with a number, so such a pair raised
UdfParseError carrying Numba's typing error:

- a record with a bytes and a float64 field, eq with a record of two
  float64 fields: GrB_DOMAIN_MISMATCH on main, UdfParseError at
  0e1ad03c and before this commit;
- the same record == 1 or != (1.0, 1.0), and its Scalar == (1.0, 1.0):
  main converted the number into the bytes field (1 became b"1") and
  compared the converted value; UdfParseError at 0e1ad03c and before;
- a nested record == (b"x", np.float64(1)), whose number stands for a
  nested record holding a bytes field: False on main and at 0e1ad03c,
  UdfParseError before this commit.

Text, a date, a duration and a number now compare only with their own
kind; any other pair of leaves raises the KeyError binary.eq gives for
a pair it does not take: "binary.eq does not work with (...): elements
of |S4 and float64 do not compare." Scalar == raises it with or without
Numba. This follows the rules #591 already has: plus finds no common
type for such leaves (a KeyError), and a UDT that is neither a record
nor an array pairs only with its own type. Answering False instead
(a number for a text leaf can never be equal) would turn the record
pair, an error since main, into an answer, and would differ from main
anyway where main compared b"1" with 1 as equal.

* Test the text-date and duration-for-a-date branches of record literal typing

Tests only. Two branches of the record literal typing had no row:

- a date given as text shorter than numpy's own date string
  ("1970-01-01" for a field of seconds), which _converts_back compares
  as a date: with a number that converts exactly, the literal converts
  with the DeprecationWarning, and without the branch it raised "does
  not fit";
- a numpy duration given for a date field, which keeps its own type:
  numpy converts np.timedelta64(5, "s") to a date by value, so without
  the guard it took the date field's type and failed in numpy's own
  conversion; with it, it raises "does not fit".

* Let a UDF return an operand of a UDT that is neither a record nor an array

Behavior fix. A user's op on a UDT of bytes, str or the like that
returns an operand as it is (lambda x, y: x) failed to compile: Numba
types the element as a char sequence, which no DataType is registered
for, so the return type was not found.

- main: UdfParseError "UDT UDF returned an unsupported type [char x 8]".
- 0e1ad03c: TypeError 'NoneType' object is not subscriptable (the
  literal beside it was read as an array UDT).
- before this commit: the UdfParseError again.
- now: the return takes the operand's UDT when its Numba type is that
  UDT's, so the op computes; [b"abc"] for x, the literal for y.

* Keep text that a bare text UDT would cut short out of it, and compare text widths

Behavior fix. Text given beside a UDT of bytes or str (not a record)
had no type, so numpy cut it to the UDT's width: with an S4 element
b"abcd", b"abcdef" became b"abcd".

- v == b"abcdef": TypeError on main and at 0e1ad03c; True before this
  commit; now False (!= is True).
- Scalar == and Scalar.isequal with b"abcdef": True on main (isequal
  True at 0e1ad03c too), now False.
- a user's op with b"abcdef": main and the commits before this one cut
  it silently (0e1ad03c raised TypeError); now ValueError "does not fit".

Such text is now typed as a record's text field is: the UDT itself
when it converts into it and back unchanged (b"ab", "ab" into S4, so
bare_s4 == b"ab" works as before), else its own type at its own length,
which does not fit. For eq and ne, that literal is text of the same
kind at another width, so this commit also loosens, there and only
there, the pairing rule of "Compare UDTs that are neither records nor
arrays in eq and ne": two UDTs of text of the same kind (bytes with
bytes, str with str) at different widths now pair under eq and ne and
compare as text, as numpy compares them and as a record's S4 and S2
fields already do. An S4 and an S6 Vector compare element by element
(they raised KeyError before; TypeError on main and at 0e1ad03c).
Bytes against str, a number, float16 or any other kind still raise the
pairing KeyError, as does every other op on such a pair.

Stores are unchanged: v[0] = b"abcdef" still stores b"abcd", as every
literal store converts by numpy's cast (int8_vec[0] = 1.5 stores 1).

* Keep a NaT duration NaT in another unit on numpy before 2.3

Behavior fix. numpy before 2.3 converts a NaT timedelta64 scalar into
another unit as the integer NaT is stored as: NaT ms assigned into
timedelta64[s] is 9223372036854774 s, and NaT minutes is 0 s. An array
of durations (astype) and a date convert NaT correctly, and numpy 2.3
and later convert it correctly every way.

On numpy 2.2 (numpy 1.26 converts NaT the same way), with a UDT of
timedelta64[s] or a record with a timedelta64[s] field:

- Scalar.from_value, v[0] = ..., Scalar.value = ..., Vector.from_coo,
  and the Scalar a literal becomes, given NaT ms or NaT minutes: stored
  9223372036854774 s or 0 s, as on main; now NaT.
- A NaT ms literal beside the bare UDT: ValueError "does not fit", since
  it converted to a number and so not exactly; now it converts with the
  DeprecationWarning, as on numpy 2.4.
- (NaT ms, 1) beside the record: typed as timedelta64[ms], so
  binary.plus raised KeyError "no common type"; now it takes the field's
  type, as on numpy 2.4.

The conversion into an element, and the checks of whether a literal
converts exactly, now hand numpy a NaT of no unit in place of each NaT
duration (alone, or in a tuple or list), which every numpy converts to
NaT in any unit. numpy 2.4 gives the same results before and after.

* Keep the JIT layout test's record short enough to name on SuiteSparse 8

SuiteSparse before 9 has no GrB_NAME setter, so registration names a
UDT by its numpy repr, which must fit in 128 bytes, and warns when it
does not; pytest makes the warning an error.
test_record_layouts_c_cannot_declare_skip_jit registered its record
with an array field with align=True, which gives the dict form of the
repr: 143 bytes. Its float64 fields have the same offsets packed or
aligned, so it is now registered packed (a 71-byte repr); a C struct
still cannot declare it, and the test runs the same lines.

Every other UDT the tests register with a repr over 128 bytes is in a
test that ignores that warning.

* Convert a NaT duration in its leaf's unit, not as a NaT of no unit

"Keep a NaT duration NaT in another unit on numpy before 2.3" handed
numpy a NaT of no unit, np.timedelta64("NaT"), in place of each NaT
duration. numpy 2.5 deprecates that generic unit ("The 'generic' unit
for NumPy timedelta is deprecated"), so on numpy 2.5 every store or
literal of a NaT duration into a duration UDT or field warned with a
DeprecationWarning, which the test suite's filterwarnings makes an
error: test_udt_nat_duration_in_another_unit_stays_nat,
test_udt_sequence_and_array_literals_type_like_numbers and
test_udt_literal_converts_only_when_exact failed.

Each NaT duration is now converted into the unit of the leaf it fills
by converting it as an array (astype), which keeps NaT on every numpy,
before numpy assigns it. The value is walked beside the dtype: a tuple
of a record's length as its fields, a list (or a tuple for any other
dtype) as a sequence of elements, and an array UDT by its element type.
Nothing makes a generic unit.

numpy 2.5.4: DeprecationWarning before, NaT with no warning now. numpy
1.26, 2.2 and 2.4: NaT in the target unit before and after. New rows:
a nested record's duration field, and an array UDT of durations given
a list, where Numba is absent (Numba cannot type an array of durations,
so registering one fails with it).

* Skip the JIT part of the one-name UDT layouts test without a C compiler

test_udt_layouts_under_one_name_get_their_own_jit_c_names set
jit_c_control to 'on' and compiled kernels for both layouts, so on a host
whose JIT cannot compile (no C compiler, or a broken one) it failed with
JitError instead of skipping. CI always installs a compiler, so CI never
saw it.

It now asks _enable_jit_for_udt, which probes with a real compile once
per process, and skips the kernel part when the answer is no. The C name
checks and the register_new check need no compiler, so they come first
and run everywhere.

* Expand an iso array UDT's value without an extra axis in exports

Behavior fix. SuiteSparse exports the iso value of an array UDT with
shape (1, *subarray shape), and normalize_values broadcast it to
shape + values.shape, keeping that axis of length 1. With the
suitesparse backend, for an iso Vector or Matrix of an array UDT such
as INT64[4] (as v[:] = [0, 1, 2, 3] makes):

- Vector.to_dense: TypeError "unsupported operand type(s) for +: 'int'
  and 'tuple'" on main and before this commit (it passed its size as
  an int); now an array of shape (size, 4).
- Matrix.to_dense: shape (nrows, ncols, 1, 4); now (nrows, ncols, 4).
- Matrix.to_csr, to_csc, to_dcsr and to_dcsc: values of shape
  (nvals, 1, 4); now (nvals, 4).
- Matrix.to_dicts, which uses to_dcsr or to_dcsc: each value as
  [[0, 1, 2, 3]]; now [0, 1, 2, 3].

Vector.to_coo, Matrix.to_coo, the vanilla backend and records were
already right. normalize_values now broadcasts to
shape + values.shape[1:], and Vector.to_dense passes (size,).

CI found it through #652's test_udt_mixed_jit_c_name_is_its_own (17
jobs, e.g. job 122633175811): SuiteSparse before 10.5 makes a vector
built from equal values iso, so that test's constant inputs gave an
iso result, and to_dense raised. With 10.5 the vectors are not iso.

* Refuse a zero-size UDT, so a str literal raises TypeError on SuiteSparse 7

Behavior fix. register_anonymous passed a dtype's itemsize to
GrB_Type_new without a check. SuiteSparse 8 and later refuse a size of
0, but SuiteSparse 7 takes it as 1. lookup_dtype auto-registers what it
does not know, so on SuiteSparse 7 a str became the UDT "<U0", which
holds nothing.

- register_anonymous of str, bytes, np.dtype("U"), an empty record or
  a subarray of length 0: InvalidValue with no message on SuiteSparse
  8 and later, a UDT on 7; now ValueError "dtype must not have a size
  of 0" on every version.
- lookup_dtype(str): ValueError "Unknown dtype" on 8 and later, the
  "<U0" UDT on 7; now ValueError "Unknown dtype" on every version.
- Scalar.from_value("x"), Scalar + "x" and v.apply(binary.plus,
  right="x"): on SuiteSparse 7, ValueError "got more than 0 values to
  unpack" from cffi; now the TypeError they raise on 8 and later.

CI found it in wave 2 through #654's test_bad_ewise case (s1, "x"),
which expects the TypeError, in its one job with
python-suitesparse-graphblas 7.4.2 (122632718294).

* Note that SuiteSparse:GraphBLAS 10.5.2 fixed select with a value op

SuiteSparse:GraphBLAS 10.5.2 fixed the bug _value_select_in_own_type
works around (DrTimothyAldenDavis/GraphBLAS#463): select with a VALUE*
op of another type than the input's now casts both to the op's type.
Its MAINT note said to delete the function once every supported version
has the fix, but that would change two results that
test_select_value_thunk_of_another_type pins: with the function
bypassed on 10.5.2, an INT64 thunk of 2**53 + 1 beside FP32 or FC32
values of 2**53 rounds to 2**53 in the op's type and keeps that element.
The other 61 cases of the test match. The note now says so, and gives
the versions with the bug as 8.0.2 through 10.5.1. Comments only.

* Say which array Numba cannot type when registering a UDT

Behavior change. Registering a UDT that holds an array of bool,
datetime64, timedelta64, str or bytes (as the UDT, or as a field of a
record) raised Numba's own error, which does not say what to change:

    dtypes.register_anonymous(np.dtype((np.bool_, (2,))))
    # was AttributeError: 'Boolean' object has no attribute 'bitwidth'
    # now ValueError: Numba, which python-graphblas uses for UDTs, cannot
    #     type an array of bool, as in ('?', (2,)); use an array of uint8
    #     instead

The ValueError, the type of the other errors register_anonymous raises
for a dtype it refuses, is chained from Numba's AttributeError. The
np_type of dtypes.ss.register_new gets the same error. Without Numba
these dtypes still register, as before.

Numba now types the dtype before GrB_Type_new, so a dtype it cannot type
no longer leaves an unused GrB_Type behind.

lookup_dtype, which registers a numpy dtype it has not seen, still says
"Unknown dtype" for these, as for any dtype it cannot register.

* Type an IndexBinaryOp bound to theta when it runs, with theta of its own type

Binding theta to an IndexBinaryOp of no type, op(theta), took the op's type
from theta alone, and the inputs were cast to it. Now op(theta) returns a
BinaryOp whose type is chosen each time it runs, as any other BinaryOp's is.
Theta is typed by its value when it is given, as apply types an IndexUnaryOp's
thunk: a Python int is INT64, a float FP64 and a bool BOOL, and a numpy scalar
or a Scalar keeps its type. A numpy array is typed as its array layout, as
other array literals are. The default theta, False, changes no type.

An op registered without is_udt runs at the type that holds both inputs and
theta. One with is_udt=True is compiled for the types of x, y and theta as
they are, and SuiteSparse gets theta's type as the op's theta_type, which
GxB_IndexBinaryOp_new takes apart from x's and y's. So theta is never
converted. The compiled ops are cached per (x, y, theta) types, and
op[X, Y, T] names all three; op[X, Y] and op[T] give theta y's type, as
before. A Semiring built from op(theta) is polymorphic in the same way.
op[T](theta) and op(theta, dtype=T) still bind at T. IndexBinaryOp has not
been released, so nothing is deprecated.

Behavior changes (before -> after):

- A.ewise_mult(B, op()) on INT64: a BOOL op, so every value became True ->
  INT64, the values op computes.
- u.ewise_mult(w, op(2)) on FP64: computed in INT64 (1.5 + 2 * 2.25 gave 5)
  -> FP64 (6.0). op(np.int8(2)): computed in INT8, where 300.0 became 127
  and wrapped -> FP64.
- A Semiring from op(0.5) with INT64, FP32 or INT8 matrices: KeyError ->
  computed in FP64, with or without is_udt.
- The SuiteSparse user guide's argmax (an is_udt multiplier that makes the
  pair (k, A(i, k)), theta unused) on a matrix of pairs: iop(), iop(False),
  iop(0) and iop(0.0) were ops on BOOL, INT64 or FP64 values, which failed
  to compile for the pairs -> all four run, compiled for (pair, BOOL, BOOL),
  (pair, BOOL, INT64) and (pair, BOOL, FP64).
- An is_udt op on INT64 data: op(0.5) or an FP32 Scalar theta made the op
  FP64 or FP32 and cast the inputs to it -> the inputs stay INT64, theta keeps
  its type, and Numba types the result (FP64 for x + y + theta).
- An is_udt op on int32[3] data: op(np.array([10, 20, 30], np.int32)) raised
  "Cannot infer a dtype for theta" -> theta is an int32[3] value; an int64
  array is an int64[3] one. op(5) gave GrB_DOMAIN_MISMATCH -> theta is an
  INT64. op((10, 20, 30)) still raises TypeError, now saying to give theta
  as a Scalar of its UDT; an op without is_udt raises KeyError for an array
  theta, as for a UDT Scalar.
- A JIT op (indexbinary.ss.register_new) is registered for its x, y and theta
  types. op(theta) took the one registered at theta's type T for x, y and
  theta, and cast the inputs to T -> it takes the one for the inputs' types
  and theta's, without casting, as a JIT IndexUnaryOp takes its thunk: with
  an op registered at FP64, op(10.0) on FP64 data runs as before, op(10.0)
  on FP32 data ran in FP64 -> KeyError naming (FP32, FP32, FP64), and op(10)
  raised KeyError when bound -> when it runs. op.types is keyed
  (x, y, theta), so registering the same x and theta types for another y no
  longer raises "already defined", and op["FP64", "FP32"] is x and y, no
  longer x and theta: op["FP64", "FP64", "FP32"] is the op with an FP32
  theta.
- A bound op pickles with all its types: op["INT64", "FP64"](1) came back as
  op["INT64"](1), whose y is INT64.
- op(theta).type: the bound type -> AttributeError, as for any untyped op;
  op(theta)[T] is op[T](theta). repr(op(2)): binary.f[INT64] ->
  indexbinary.f(2). op(theta)(A & B) works, as for other BinaryOps.
- A Scalar theta is copied when given, so a later change to it does not
  reach the op, as when the op bound it at once.

find_opclass now reads an opclass attribute on an OpBase subclass, so
op(theta) is still a BinaryOp there. The user guide's examples drop the
op[float](0.5) that hid the old typing. The comments on IndexUnaryOp's and
SelectOp's UDT caches now say they are keyed by (dtype, thunk_type).

* Refuse a record literal whose array field part does not broadcast to the field

Behavior fix. Where a literal for a record UDT is typed (a lifted op such as
+, ==, a user's op, an IndexUnaryOp thunk, an ewise_union default, and ==
on a Scalar), a part for an array field must now broadcast to the field's
shape, or ValueError names both shapes. An array UDT's element takes the
shape it broadcasts to with a literal, as numpy does, so INT8[3] plus
np.array([[1, 300, 3]]) is INT64[1, 3]; that is unchanged. A record's field
keeps its shape, so for the record the typing gave up, and the literal was
converted into the field as numpy assigns it: the leading axis was dropped
and 300 wrapped to 44, silently, even beside a user's op, which refuses a
literal that does not fit. Stores (r[0] = ...) still convert as numpy
assigns, as they do for an array UDT.

With r a Vector of a record with an int8[3] field a and an int64 field b,
holding ([1, 2, 3], 0) (before -> after):

- r + (np.array([[1, 300, 3]]), 0): ([2, 46, 6], 0) -> ValueError
  "array([[  1, 300,   3]]) has shape (1, 3), which does not fit field
  'a' of R, of shape (3,): ...".
- r.apply(user_op, right=(np.array([[1, 300, 3]]), 0)), with user_op
  returning y: ([1, 44, 3], 0) -> the same ValueError.
- r == (np.array([[1, 300, 3]]), 0): False -> the same ValueError; with
  np.array([[1, 2, 3]], np.int8) it was True.
- r + ([[1, 300, 3]], 0), and the other paths with it: numpy's "setting an
  array element with a sequence" ValueError -> this ValueError.
- r + ([1, 2], 0): numpy's "could not broadcast input array from shape (2,)
  into shape (3,)" -> this ValueError.
- r[0] = (np.array([[1, 300, 3]]), 0): ([1, 44, 3], 0), unchanged.

* Cover more literals a UDT refuses and the Numba error of a lifted op

Literal typing had arms no test reached: a comparison with a record literal
of the wrong length (alone and nested beside an int beyond its leaf), a part
holding numpy values that numpy cannot make an array of or that is not
numbers, and text a bytes field cannot encode. An array of records now pins
the UdfParseError a lifted op gives when Numba cannot compile it, and the
NoJITWarning is checked to stay silent for a pair it warned about.

Two arms of the monoid identity are excluded from coverage: the binary op
compiles first, and it refuses what would reach them.

* Give each type signature of a JIT IndexBinaryOp its own C name

indexbinary.ss.register_new compiled every type signature of one op under
the op's own name. SuiteSparse finds a compiled kernel in memory by its op's
name and the types in the kernel's encoding, and an ewise or mxm kernel's
encoding has x's, y's and z's types but not theta's. So two signatures that
differ only in theta's type ran whichever kernel was compiled first. Now each
signature's C function is compiled as name_N_x_M_y_theta, with N and M the
lengths of the x and y parts (built-in types by name, UDTs by their C name):
the C definition is the user's with the function renamed. A name SuiteSparse
would cut at 127 characters is the op name's first 100 and a digest of the
whole. The op's jit_c_name and jit_c_source show the C name and the source
SuiteSparse compiles; jit_c_definition is still the definition given.
JIT IndexUnaryOps and SelectOps are not affected: the thunk's type is in
their kernels' encodings.

Behavior change (before -> after): with add_theta registered at FP64 for x,
y and theta, and then at an FP32 theta with
(*z) = (*x) + (*y) + 2 * (double)(*theta), the FP32 signature on FP64 data
with theta 10 gave [2, 2, 4, 0] (the FP64 kernel, reading the float as a
double) -> [22, 22, 24, 20]. op.jit_c_name: None ->
add_theta_4_FP64_4_FP64_FP32.

* Name each op registered from C by its full name and definition

The ss.register_new functions of unary, binary, indexunary (and select) gave
SuiteSparse the op's last name as its C name, and indexbinary's the last name
and types. SuiteSparse finds a compiled kernel in memory by its op's name,
and checks an op's definition only when it loads a kernel from disk. So, in
one process, an op registered again under its name with another definition
(after delattr, as when a notebook cell is run again), or an op of the same
last name in another namespace (ss.f and ss.nested.f), ran the kernel
compiled first.

Now every op registered from C source is compiled under a C name made of its
last name, the types for an IndexBinaryOp (whose theta type is not in its
kernels' encoding), and a 12-hex digest of these with the full dotted name
and the definition: f_3a1c90e2d4b7, or add_theta_4_FP64_4_FP64_4_FP32_...
for an IndexBinaryOp. A name SuiteSparse would cut at 127 characters is the
last name's first 100 characters and a 16-hex digest. The function is renamed
in the definition wherever it is named, except as a struct member (after "."
or "->"). The same name and definition give the same C name, so a kernel is
compiled once for them. jit_c_name and jit_c_source, None for these ops
before (except IndexBinaryOp, from the previous commit), show the C name and
the renamed source; jit_c_definition is still the definition given. The
helper is ss._utils._jit_c_source.

Behavior changes (before -> after), in one process:

- binary.ss.register_new("f", x + y) and ("nested.f", x - y), both FP64:
  nested.f computed x + y -> x - y.
- unary.ss.register_new("u", x * x), delattr(unary.ss, "u"), then
  register_new("u", x + 10): u computed x * x -> x + 10.

* Refuse an empty Scalar theta for an IndexBinaryOp

SuiteSparse binds theta without checking that it holds a value: it reads
the Scalar's value array. Binding an empty Scalar crashed the interpreter,
and op(s) with no type, which binds a copy of s, ran with theta 0. Both now
raise EmptyObject, as apply does for an empty IndexUnaryOp thunk, in every
bind: typed or not, is_udt or not, a JIT op's too.

Behavior change (before -> after), with s = Scalar(int):

- op[int](s) and op(s, dtype=int): segmentation fault -> EmptyObject.
- op(s): an op that ran with theta 0 -> EmptyObject, at the bind. With s a
  C scalar (is_cscalar=True): segmentation fault -> EmptyObject.

* Leave a dtypes.ss.register_new name free when the registration fails

register_new recorded the name as a JIT C type name as soon as
SuiteSparse made the type, before the numpy and Numba types were made.
When one of those raised (an np_type with an array of bool, which Numba
cannot type), the name stayed reserved, and a correct second call
raised "name for dtype is unavailable". Main allowed the second call; the
check against C type names came with "Give each UDT layout its own JIT C
name". The name is now recorded after the type is registered. The type
made by the failed call is never used, so no JIT kernel knows its name.

Behavior change (before -> after), with d the typedef of pt_t:

- register_new("pt_t", d, np_type=np.dtype((np.bool_, (2,)))) raises
  ValueError (unchanged), then register_new("pt_t", d,
  np_type=np.dtype([("x", "f8"), ("y", "f8")])): ValueError "'pt_t' name
  for dtype is unavailable" -> registers dtypes.ss.pt_t.

* Type a Python date or duration in a record literal as numpy does

A record literal's date or duration field takes the field's type only
when its value converts into the field and back unchanged; otherwise it
keeps its own type, so == compares it whole. A numpy date did this
already, but a Python datetime.date, datetime.datetime or
datetime.timedelta, or a date given as text, always took the field's type.
So it was cut to the field's unit before == compared it: noon compared
equal to its day in a field of days, and 5 s and 1 us equal to 5 s in a
field of seconds. Main gave the same wrong answers (it gave them for numpy
dates too). Now such a value is first made the numpy value it stands for
(a datetime in microseconds, a timedelta in microseconds, a date as text
in the unit it is written to), and is typed as that numpy value is.

Behavior change (before -> after), with v a Vector of a record of an
M8[D] and an m8[s] field holding (2020-01-01, 5 s), and s = v[0].new():

- v == (datetime(2020, 1, 1, 12), np.timedelta64(5, "s")), and with
  "2020-01-01T12:00" for the date: True -> False; the same for s ==.
- v == (np.datetime64("2020-01-01"), timedelta(seconds=5, microseconds=1)):
  True -> False.
- v.apply(user_op, right=(datetime(2020, 1, 1, 12), np.timedelta64(5,
  "s"))): stored the day, silently -> ValueError "does not fit", as for
  np.datetime64("2020-01-01T12:00").
- v + (datetime(2020, 1, 1, 12), ...): UdfParseError from Numba ->
  KeyError naming datetime64[D] and datetime64[us].
- A datetime at midnight, a date, and text such as "2020-01-01" still
  compare equal and fit the field.

* Compare a UDT of dates or durations with one in another unit as numpy does

A UDT that is a bare datetime64 or timedelta64 (not a record or an array)
converted a date or duration literal into its own unit before == compared
it, so noon compared equal to its day in a UDT of days. Main raised
TypeError here for a Vector, and gave True for a Scalar; "Compare UDTs that
are neither records nor arrays in eq and ne" made the Vector give True too.
A numpy duration in another unit was a KeyError instead, even when it was
the same duration.

Now such a literal is typed as in a record's date field: the UDT itself
when it converts into the UDT and back unchanged, else numpy's type for it
(a datetime is in microseconds, a timedelta too, a date as text in the unit
it is written to). Under eq and ne, a UDT of dates or durations pairs with
one in another unit, as text of one kind pairs at another width, and they
compare as numpy compares them.

Behavior change (before -> after), with day a Vector of an M8[D] UDT
holding 2020-01-01 and five one of an m8[s] UDT holding 5 s:

- day == np.datetime64("2020-01-01T12:00"), datetime(2020, 1, 1, 12) or
  "2020-01-01T12:00": True -> False; != the opposite; the same for
  day[0].new() ==.
- five == np.timedelta64(5000, "ms"): KeyError -> True; 5001 ms: KeyError
  -> False. five == timedelta(seconds=5, microseconds=1): True -> False.
- binary.eq(five & a Vector of m8[ms] holding 5000 ms), and five.isequal
  of it: KeyError -> True.
- day.apply(user_op, right=datetime(2020, 1, 1, 12)): stored the day,
  silently -> ValueError "does not fit".
- five.apply(user_op, right=np.timedelta64(5000, "ms")): converted with a
  DeprecationWarning -> converts with none, as in a record's field.
- day == timedelta(5): numpy's ValueError -> binary.eq's KeyError.

* Type a literal beside a typed op in the op's own input type

A literal operand beside a UDT became an element of that UDT even when
the op was typed and takes that operand in another type: op[A, INT64]
takes its thunk, or its second input, as an INT64. Apply and select pass
a UDT thunk or operand to SuiteSparse as a pointer to its bytes, so the op
read the bytes of the A element as an int64: silently wrong answers.
Main did the same. A Scalar of the op's type gave the right answer.

Now a typed op's own input type on the literal's side decides: its first
input type for a left operand, else its second input or thunk type.
GraphBLAS converts a value to a built-in input type, so the literal is
typed as beside a built-in typed op (its own type: 2.5 for an INT64 thunk
is converted to 2, as for an op typed on built-in types); a UDT input type
takes only a literal that fits it. _literal_operand takes left=True from
apply's left operand and from ewise_union's left default.

Behavior change (before -> after), with v a Vector of a record A of two
float64 fields holding (1.0, 2.0) and (3.0, 4.0), and user ops typed
[A, INT64] (or [INT64, A]) that compare or add field a with the other:

- v.apply(iu[A, INT64], 2): [False, False] -> [False, True], as with
  Scalar.from_value(2); the same for thunks 2.5 and np.int8(2).
- v.select(se[A, INT64], 2), on a Vector or Matrix: empty -> index 1.
- v.apply(f[A, INT64], right=2) and v.apply(g[INT64, A], left=2): about
  4.6e18 for each element -> [3.0, 5.0].
- With B a record of two int64 fields: v.apply(h[A, B], right=(2, 3)):
  about 4.6e18 -> [3.0, 5.0]; v.apply(iu_rec[A, B], (2, 0)): [False,
  False] -> [False, True], and with (2.5, 0): [False, False] -> ValueError
  "does not fit".
- Scalar(A).ewise_mult(2, f[A, INT64]): GraphBLAS's DomainMismatch -> 3.0.

* Accumulate into a UDT Scalar as into a built-in Scalar

The accumulator is typed on the object's dtype, and for a built-in Scalar
GraphBLAS converts the value to that type before it accumulates: an INT8
Scalar of -1 with s += 0.5 (Scalar.__iadd__ is s(binary.plus) << 0.5)
adds 0 and stays -1, as v(accum=binary.plus) << 0.5 does for a Vector of
either kind. A UDT Scalar used the untyped op instead (_scalar_accum_op),
so it computed -1 + 0.5 in FP64 and truncated the sum to 0. That was meant
to match a Vector's v += 0.5, but that is v << v + 0.5, not an accumulate.
Main gave the built-in answer.

Now a UDT Scalar accumulates with the typed op, and the value is converted
to the Scalar's type first, as storing it would convert it: a literal by
Scalar.from_value, a Scalar or an element of another UDT by the store cast.
A built-in Scalar into a UDT Scalar still raises DomainMismatch, as a store
of it does, into a Vector too.

Behavior change (before -> after), with s an INT8[3] UDT Scalar of
[-1, -1, -1]:

- s += 0.5 and s(accum=binary.plus) << 0.5: [0, 0, 0] -> [-1, -1, -1]
  (an INT8 Scalar gives -1; a Vector of the UDT gives [-1, -1, -1]).
- s(accum=binary.plus) << (0.5, 1.5, 2.5): [0, 0, 1] -> [-1, 0, 1], as for
  a Vector of the UDT.
- s(accum=binary.plus) << v[1], with v a Vector of an FP64[3] UDT holding
  [0.5, 1.5, 2.5]: [0, 0, 1] -> [-1, 0, 1], as with the same value in a
  Scalar of that UDT, which was cast first already.

* Pair an array literal with an array UDT when their shapes broadcast

udt.rst says a numpy array beside a UDT pairs as an array UDT of its own
shape, and array UDTs pair when their shapes broadcast. A numpy array
paired only when it broadcast to the UDT's shape, or had that shape apart
from leading 1s. Any other array, even one whose shape broadcasts with
the UDT's, was converted into the UDT, where numpy raised "could not
broadcast", while the same values as a UDT operand worked. Main raised
too (a KeyError for the UDT pair). Now such an array is typed in its own
shape and pairs as an array UDT operand does; the result has the shape
numpy's would. An array that broadcasts to the UDT's shape is typed in the
UDT's shape, as before, which a literal that must become an element needs.

Behavior change (before -> after), with v a Vector of an INT8[3] UDT
holding [1, 2, 3] and u one of an INT8[1] UDT holding [1]:

- v + np.array([[1, 2, 3], [4, 5, 6]]), and apply(binary.plus) with it on
  either side: ValueError "could not broadcast input array from shape
  (2,3) into shape (3,)" -> INT64[2, 3] [[2, 4, 6], [5, 7, 9]], as with
  the same values in an INT64[2, 3] UDT.
- v == np.array([[1, 2, 3], [1, 2, 3]]), on a Vector or a Scalar: the same
  ValueError -> True.
- u + np.array([1, 2, 3]), and u + (1, 2, 3): ValueError -> INT64[3]
  [2, 3, 4].
- v + [[1, 2, 3], [4, 5, 6]]: numpy's "setting an array element with a
  sequence" ValueError -> INT64[2, 3], as for the array.
- v.ewise_union(v, binary.plus, a 2-by-3 array, 0): numpy's ValueError ->
  ValueError "does not fit".

* Give IndexBinaryOp is_positional, so dtype in op and op[dtype] work

OpBase.__getitem__ reads is_positional for a type the op does not have,
and IndexBinaryOp had none, so asking an IndexBinaryOp (registered without
is_udt) for a type it does not take raised AttributeError. Main did the
same; IndexBinaryOp has not been released. It is now False, as on a bound
IndexBinaryOp and every other op class.

Behavior change (before -> after), with op an IndexBinaryOp of x + y +
theta and R a record UDT:

- R in op: AttributeError -> False, as for an IndexUnaryOp.
- op[R] and op(Scalar.from_value((1.0, 2), R), dtype=R): AttributeError ->
  KeyError "... does not work with R".

* Refuse a bound IndexBinaryOp in reduce with the usual TypeError

reduce turns a BinaryOp that is part of a Monoid into that Monoid, and
refuses any other BinaryOp with a TypeError naming it. It reads the op's
monoid for that, and the error message its parent's; neither a bound
IndexBinaryOp nor IndexBinaryOp had the attribute, so reduce raised
AttributeError, for ibo[T](theta) on main and for the new untyped
ibo(theta) too. Both now have monoid = None, as a BinaryOp of no Monoid
has. IndexBinaryOp has not been released.

Behavior change (before -> after), with v an INT64 Vector and ibo an
IndexBinaryOp of x + y + theta:

- v.reduce(ibo(1)) and v.reduce(ibo[int](1)): AttributeError
  "'_BoundIndexBinaryOp' object has no attribute 'monoid'" -> TypeError
  "Bad type for argument `op` in Vector.reduce(...) ... is not known to be
  part of a Monoid", as for binary.minus.
- A.reduce_rowwise(ibo(1)): AttributeError -> NotImplementedException, as
  for binary.minus.