Disable at-nospecialize for GPU codegen

Now that we have a GPU runtime library that can allocate and box, I tried to get rid of this hack: https://github.com/JuliaGPU/CUDAnative.jl/blob/53368d48b6405ee962e54d4c3b9f90e3eb623310/src/compiler/irgen.jl#L229-L236

Turns out we still need it, as the argument to `throw` often is the value returned by the `BoundsError` constructor, which has a `@nospecialize` resulting in a `jl_invoke`. LLVM obviously can't remove this function by itself, so we end up with GPU-incompatible code. Just try to `cu([1]) .+ cu([2])` with `--check-bounds=yes` (and the above hack disabled, of course).

We could try and redefine `BoundsError `or `throw_boundserror` as it occurs for GPU code, e.g.:

```julia
using LinearAlgebra

for (W, _) in (:AT => (A,mut)->mut(A), Adapt.wrappers...)
@eval begin
# BoundsError has a @nospecialize constructor resulting in `invoke`
Base.throw_boundserror(A::$W where {AT <: CuDeviceArray}, I) = throw(nothing)
end
end
```

But then we still miss cases thrown from the broadcasting code (and I couldn't find a way to dispatch on `throw_boundserror(Broadcasted{...CuDeviceArray...}`. It would be best to just get rid of `@nospecialize` for GPU code altogether.

Not sure whether that would need to happen at the inference/optimizer/codegen level though. Monkey-patching `MethodInstance`s from within CUDAnative's `emit_function` hook didn't seem to work.

@Keno did you do something similar for XLA, since you worked on more exhaustive inference there?
Are those interfaces public already?