Link Against tiny-cuda-nn in C++ Program

[half-potato]

I didn't see any instructions on how to link against tiny-cuda-nn. In fact, it doesn't even have an install. I saw that iNGP just uses add_subdirectory, but I get the following error when I do that with my program:

/home/amai/splinetracers/support/tiny-cuda-nn/include/tiny-cuda-nn/common_device.h(96): error: no suitable constructor exists to convert from "float" to "__half"
   return (half)relu<float>((float)val);
                ^

/home/amai/splinetracers/support/tiny-cuda-nn/include/tiny-cuda-nn/vec.h(214): error: no suitable conversion function from "__half" to "float" exists
   return fmaf(a, b, c);
               ^

Inspecting the code around this error shows that there are some switches based on the CUDA arch, which might not be set correctly:

#ifdef __CUDACC__
inline TCNN_DEVICE __half fma(__half a, __half b, __half c) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 600
	return __hfma(a, b, c);
#else
	return fmaf(a, b, c);
#endif
}
#endif

Based on the error, I don't think CUDA_ARCH is set correctly. I have an NVIDIA GTX3090 with CUDA 12.3. This corresponds to compute_86, which is greater than 60. I'm not sure why it's not finding the arch correctly. My CMakeList.txt file is pretty cursed though, as it is integrating Optix, CUDA, and Pytorch.

[half-potato]

I just wanted to add that tiny-cuda-nn does build outside of my project, just not inside it.

[cogwheel]

You need to set a minimum architecture with TCNN_MIN_GPU_ARCH. E.g. I used -DTCNN_MIN_GPU_ARCH=75 to enable features on RTX 2000 series and later cards. And then make sure your cuda compiler is set to build for at least that instruction set.

I recently got TCNN building in our bazel project. See here for details: https://github.com/fbriggs/lifecast_public/commit/aaa1000ccfcd9bf94a143fc21300fe6607636342#diff-aef1b984940ceb407dca09e98a080c07a1cecbb1ae6b386caa0028e03e45bc48 - Note that this does not build any of the MLP code, only the encoders. It should just be a matter of adding the other .cu sources.

Also note: I basically had to reimplement all the functionality from the python bindings in C++ in order to interface with the C++ version of torch.