ncnn VS HIPIFY

ncnn uses Vulkan for GPU acceleration, I've seen it used in a few projects to get AMD hardware support.

https://github.com/Tencent/ncnn

They're using NCNN to package the model. Have a look. https://github.com/Tencent/NCNN

Hi. Here is my prefered android app for realtime objet detection: https://github.com/nihui/ncnn-android-nanodet ; https://github.com/Tencent/ncnn contains a lot of android demo app for a lot of models.

ncnn home page: https://github.com/Tencent/ncnn

ncnn

Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.

As the leading open-source inference framework in China and in the world, what we like are its almost zero cost cross-platform capability, high inference speed, and minimal deployment volume. (Project address: https://github.com/Tencent/ncnn)

If you’re not planning on training the neural net on an embedded device and just do inference, this might interest you: https://github.com/Tencent/ncnn

Right. I can't speak to its correctness/completeness as I've only done a quick installation and smoke test of the ROCm/HIP/MIOpen stack, but there's even a tool that automates the translation [1].

[1] https://github.com/ROCm-Developer-Tools/HIPIFY

From https://news.ycombinator.com/item?id=32904285 re: AMD Rocm, HIPIFY, :

>> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :

>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

> AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...

> Thus, the idea is that through typically negligible effort porting to HiP, your code becomes vendor-independent.

Here, the big AMD mistake was to rename those function prefixes in the first place. It's a mistake that they could have avoided...

What a lot of SW codebases did to support AMD (see PyTorch code notably): codebase is still CUDA, have the conversion pass to HIP done at build time.

See https://github.com/ROCm-Developer-Tools/HIPIFY/blob/amd-stag... for the Perl script to do it.

Then comes the problem of AMD not supporting ROCm HIP on most of their hardware or user base.

On Windows, the ROCm HIP SDK is private and only available under NDA. This means that while you can use Blender w/ HIP on Windows, the Blender builds that you compile yourself will not be able to use ROCm HIP.

On Linux, the supported GPUs are few and far between, Vega20 onwards are supported today. APUs, RDNA1, and lower end RDNA2 w/o unsupported hacks (6700 XT and below) are excluded.

https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni...

RadeonOpenCompute/ROCm_Documentation: https://github.com/RadeonOpenCompute/ROCm_Documentation

ROCm-Developer-Tools/HIPIFYhttps://github.com/ROCm-Developer-Tools/HIPIFY :

> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced.

ROCmSoftwarePlatform/gpufort: https://github.com/ROCmSoftwarePlatform/gpufort :

> GPUFORT: S2S translation tool for CUDA Fortran and Fortran+X in the spirit of hipify

ROCm-Developer-Tools/HIP https://github.com/ROCm-Developer-Tools/HIP:

> HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code. [...] Key features include:

> - HIP is very thin and has little or no performance impact over coding directly in CUDA mode.

> - HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.

> - HIP allows developers to use the "best" development environment and tools on each target platform.

> - The [HIPIFY] tools automatically convert source from CUDA to HIP.

> - * Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.*

并且有一个自动转换工具 https://github.com/ROCm-Developer-Tools/HIPIFY https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-porting-guide.html

It might be worth checking out HIPIFY, which lets you automatically convert CUDA code to vendor neutral code that can be run on any GPU. Disclaimer, I have never used it and have no idea how it works.

CUDA zu AMD HIP conversion: https://github.com/ROCm-Developer-Tools/HIPIFY