This was used for the old interop code. It's dead code after #143491

Summary:
This check is unnecessarily restrictive and currently incorrectly fires
for any size less than eight bytes. Just remove it, we do sanity checks
elsewhere and at some point need to trust the ABI.

This implements two pieces to restore the interop functionality (that I
broke) when the 6.0 interfaces were added:

* A set of wrappers that support the old interfaces on top of the new
ones
* The same level of interop support for the CUDA amd AMD plugins

Co-authored-by: Joseph Huber <huberjn@outlook.com>

Summary:
This was originally added in as a hack to work around CUDA's limitation
on allocation. The `libc` implementation now isn't even used for CUDA so
this code is never hit. Even if this case, this code never truly worked.

A true solution would be to use CUDA's virtual memory API instead to
allocate 2MiB slabs independenctly from the normal memory management
done in the stream.

Summary:
I made the GPU flags accept more of the default LLVM warnings, which
triggered some new cases. Clean those up and fix some other ones while
I'm at it.

Summary:
Currently we have this `__tgt_device_image` indirection which just takes
a reference to some pointers. This was all find and good when the only
usage of this was from a section of GPU code that came from an ELF
constant section. However, we have expanded beyond that and now need to
worry about managing lifetimes. We have code that references the image
even after it was loaded internally. This patch changes the
implementation to instaed copy the memory buffer and manage it locally.

This PR reworks the JIT and other image handling to directly manage its
own memory. We now don't need to duplicate this behavior externally at
the Offload API level. Also we actually free these if the user unloads
them.

Upside, less likely to crash and burn. Downside, more latency when
loading an image.

This is the total number of work items that the device supports (the
equivalent work group properties are for only a single work group).

On my system, this will be "Radeon RX 7900 GRE" rather than "gfx1100". For Nvidia, the product name and device name are identical.