Reorganize how the binOp and genBinOp functions work.
I've spent quite a while here reading exactly through the spec and so many
tests are enabled because of several critical issues the old design had.
There are some regressions that will take a long time to figure out individually
so I will ignore them for now, and pray they get fixed by themselves. When
we're closer to 100% passing is when I will start diving into them one-by-one.
The old vectorization helper (WipElementWise) was clunky and a bit
annoying to use, and it wasn't really flexible enough.
This introduces a new vectorization helper, which uses Temporary and
Operation types to deduce a Vectorization to perform the operation
in a reasonably efficient manner. It removes the outer loop
required by WipElementWise so that implementations of AIR instructions
are cleaner. This helps with sanity when we start to introduce support
for composite integers.
airShift, convertToDirect, convertToIndirect, and normalize are initially
implemented using this new method.
Besides the Intel OpenCL CPU runtime, we can now run the
behavior tests using the Portable Computing Language. This
implementation is open-source, so it will be easier for us
to patch in updated versions of spirv-llvm-translator that
have bug fixes etc.
In general, I don't like the idea of std.meta.trait, and so I am
providing some guidance by deleting the entire namespace from the
standard library and compiler codebase.
My main criticism is that it's overcomplicated machinery that bloats
compile times and is ultimately unnecessary given the existence of Zig's
strong type system and reference traces.
Users who want this can create a third party package that provides this
functionality.
closes#18051
This reverts commit 0c99ba1eab, reversing
changes made to 5f92b070bf.
This caused a CI failure when it landed in master branch due to a
128-bit `@byteSwap` in std.mem.
as explainded at https://llvm.org/docs/LangRef.html#vector-type :
"In general vector elements are laid out in memory in the same way as array types.
Such an analogy works fine as long as the vector elements are byte sized.
However, when the elements of the vector aren’t byte sized it gets a bit more complicated.
One way to describe the layout is by describing what happens when a vector such
as <N x iM> is bitcasted to an integer type with N*M bits, and then following the
rules for storing such an integer to memory."
"When <N*M> isn’t evenly divisible by the byte size the exact memory layout
is unspecified (just like it is for an integral type of the same size)."
