One way to do this is to use Julia. Julia's CUDAnative.jl allows for automated recompilation of pretty general code to GPUs using the LLVM PTX backend. It just works on standard Julia code, so types, dispatches, etc. are all fine: most cases you shouldn't have to alter your code from the original to make it work. This has demonstrated to be performance competitive and many times even better than the original CUDA compiler, so it's a nice high level but fast environment to work in.
On top of that, there's many layers of abstraction one could use. GPUifyLoops.jl has some extra tooling for if you want to just compile scalar loops, and KernelAbstractions.jl is the next iteration of this. Additionally, if you just want array primitives, you can make use of CuArrays.jl which will make use of BLAS and all of that. Additionally, it tends to perform very well in comparison to other high level GPU libraries since it's not just calling preconstructed kernels but is rather using the codegen tools, so for example
E .= A .* B .+ C .+ sin.(D) will generate and compile a single non-allocating GPU kernel instead of calling 5 kernels and allocating temporaries like in things like cuPy, PyTorch, etc. (since those are calling pre-written CUDA code on binary operators). Julia also has a stack like this for AMD GPUs with AMDGPUnative.jl and ROCArrays.jl.
You can also use ArrayFire.jl if you wanted to stick to ArrayFire, but it doesn't perform codegen so it might not be as performant in all cases.
The nice thing about the Julia stack is that tools in Julia generally are compatible, which means you can take these arrays and use them in other existing codes! So for example, if you make your initial condition to DifferentialEquations.jl be a CuArray, then the whole differential equation solver recompiles to perform each of its actions on the GPU. Thus in many cases (i.e. cases where you weren't directly defining scalar indexing), moving to the GPU is simply calling
cu(x) on the input to a function.
Now, the thing to be more generally worried about with OpenCL is the performance of the kernels. In 2020, there's still some major advantages to using CUDA. CuBLAS(xt) is quite well optimized, and then CuDNN really doesn't have a substitute. What this means is that, even if a card is rated fast, it doesn't mean that it will be as fast as using the latest NVIDIA card with CUDA simply because if all of the time is spent in a convolution kernel (i.e. a convolutional neural network), then CuDNN can give a flat 10x speedup over current alternatives, and that's the reason why people stick with CUDA, not necessarily the hardware. That said, at this point other BLAS implementations are okay (not on par, but okay), so using OpenCL to do a bunch of matmuls is fine (and you can dig up loads of performance numbers on this)