HIPIFY
arch4edu
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HIPIFY
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AMD Hip SDK: Making CUDA Applications Run Across Consumer, Pro GPUs and APUs
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
- How to run Llama 13B with a 6GB graphics card
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How Nvidia’s CUDA Monopoly in Machine Learning Is Breaking
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...
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Stable Diffusion on AMD RDNA3
> 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.
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AI Seamless Texture Generator Built-In to Blender
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.*
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单位要求五一之后上缴旧电脑,统一换国产新电脑、新系统,由于不兼容windows软件,所以还要装个windows模拟器,导致办公效率倒退10年。主任吐槽说,这不是用落后代替先进么,我心说连他都看出来了。
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
- Hipify: Convert CUDA to Portable C++ Code
- Hipify: Convert CUDA to Portable Hip C++ Code
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Deep Learning options on Radeon RX 6800
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.
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Will NVIDIA's cryptocurrency limiter interfere with nouveau drivers?
CUDA zu AMD HIP conversion: https://github.com/ROCm-Developer-Tools/HIPIFY
arch4edu
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This started as a complete accident, took 8 hours of my life but I couldn't be happier with the result. Best one yet!
Along with --medvram --always-batch-cond-uncond as my launch parameters. Just took inspiration from what I could find online and kept what worked after some trial and error. Latest kernel with amdgpu-experimental on Manjaro (Too many things need fixing and touch-up to my taste when using Arch from scratch and I was seeing crazy glitches in it like popup password prompts filling the entire screen with a blurry mess), mesa-git and hip-runtime-amd from arch4edu. Just went wild with the "latest" version of everything basically, hoping I'd stop seeing my computer freeze and crash after ~50 minutes of messing in SD, typically sticking to 512x512 and lower.
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Stable Diffusion on AMD RDNA3
For anyone on Arch, there is a third-party repository called arch4edu[0] that provides up to date builds of ROCm and its dependencies. On my iGPU, OpenCL sometimes work, sometimes crashes. Even finding a list of supported hardware is close to impossible. The whole situation is just ridiculous and makes AMD look bad.
[0] https://github.com/arch4edu/arch4edu
What are some alternatives?
ZLUDA - CUDA on AMD GPUs
Packages - Aim to be the bioinformatics repository with more and newer packages
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
stable-diffusion-webui - Stable Diffusion web UI
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
llama-cpp-python - Python bindings for llama.cpp
rocm-build - build scripts for ROCm
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
Numba - NumPy aware dynamic Python compiler using LLVM
coriander - Build NVIDIA® CUDA™ code for OpenCL™ 1.2 devices
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]