Cgml
HIP
Cgml | HIP | |
---|---|---|
22 | 30 | |
39 | 3,462 | |
- | 1.5% | |
8.6 | 8.9 | |
4 months ago | 6 days ago | |
C++ | C++ | |
GNU Lesser General Public License v3.0 only | MIT License |
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Cgml
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Asynchronous Programming in C#
> Meant no offense
None taken.
> computervison project in c#
Yeah, for CV applications nuget.org is indeed not particularly great. Very few people are using C# for these things, people typically choose something else like Python and OpenCV.
BTW, same applies to ML libraries, most folks are using Python/Torch/CUDA stack. For that hobby project https://github.com/Const-me/Cgml/ I had to re-implement the entire tech stack in C#/C++/HLSL.
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Groq CEO: 'We No Longer Sell Hardware'
> If there is a future with this idea, its gotta be just shipping the LLM with game right?
That might be a nice application for this library of mine: https://github.com/Const-me/Cgml/
That’s an open source Mistral ML model implementation which runs on GPUs (all of them, not just nVidia), takes 4.5GB on disk, uses under 6GB of VRAM, and optimized for interactive single-user use case. Probably fast enough for that application.
You wouldn’t want in-game dialogues with the original model though. Game developers would need to finetune, retrain and/or do something else with these weights and/or my implementation.
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Ask HN: How to get started with local language models?
If you just want to run Mistral on Windows, you could try my port: https://github.com/Const-me/Cgml/tree/master/Mistral/Mistral...
The setup is relatively easy: install .NET runtime, download 4.5 GB model file from BitTorrent, unpack a small ZIP file and run the EXE.
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OpenAI postmortem – Unexpected responses from ChatGPT
Speaking about random sampling during inference, most ML models are doing it rather inefficiently.
Here’s a better way: https://github.com/Const-me/Cgml/blob/master/Readme.md#rando...
My HLSL is easily portable to CUDA, which has `__syncthreads` and `atomicInc` intrinsics.
- Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I did a few times with Direct3D 11 compute shaders. Here’s an open-source example: https://github.com/Const-me/Cgml
Pretty sure Vulkan gonna work equally well, at the very least there’s an open source DXVK project which implements D3D11 on top of Vulkan.
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Brave Leo now uses Mixtral 8x7B as default
Here’s an example of a custom 4 bits/weight codec for ML weights:
https://github.com/Const-me/Cgml/blob/master/Readme.md#bcml1...
llama.cpp does it slightly differently but still, AFAIK their quantized data formats are conceptually similar to my codec.
- Efficient LLM inference solution on Intel GPU
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Vcc – The Vulkan Clang Compiler
> the API was high-friction due to the shader language, and the glue between shader and CPU
Direct3D 11 compute shaders share these things with Vulkan, yet D3D11 is relatively easy to use. For example, see that library which implements ML-targeted compute shaders for C# with minimal friction: https://github.com/Const-me/Cgml The backend implemented in C++ is rather simple, just binds resources and dispatches these shaders.
I think the main usability issue with Vulkan is API design. Vulkan was only designed with AAA game engines in mind. The developers of these game engines have borderline unlimited budgets, and their requirements are very different from ordinary folks who want to leverage GPU hardware.
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I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros
Minor update https://github.com/Const-me/Cgml/releases/tag/1.1a Can’t edit that comment anymore, too late.
HIP
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Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP
>ROCm or HIP?
I'm not sure that's even the right question to ask. Afaik ROCm is the name of that entire tech stack and HIP is AMD's equivalent to CUDA C++ (they basically replicated the API and replaced every "CUDA" by "hip", they have functions called "hipmalloc" and "hipmemcpy").
The repository is located at https://github.com/ROCm/HIP.
- Hip: Runtime API and Kernel Language for Portable Apps for AMD and Nvidia GPUs
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Open-source project ZLUDA lets CUDA apps run on AMD GPUs
Is it perhaps because they want people to use HIP?
> HIP is very thin and has little or no performance impact over coding directly in CUDA mode.
> The HIPIFY tools automatically convert source from CUDA to HIP.
1. https://github.com/ROCm/HIP
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AMD's Next GPU Is a 3D-Integrated Superchip
AMD has released HIP and a tool called HIPIFY which kind of behaves like this but at the source level¹. Rather than try and just translate CUDA to work on AMD compute they are more focused on higher level tooling.
Currently they seem to have a particular focus on AI frameworks and tools like PyTorch/Tensorflow/ONNX. They have sponsored and helped with a lot of PyTorch development for example, so PyTorch support for AMD is much better than it was this time last year².
¹(https://github.com/ROCm/HIP)
²(https://pytorch.org/blog/experience-power-pytorch-2.0/)
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
> what would be the point for someone to add ROCm support to various pieces of software which currently require CUDA
It isn't just old cards though, CUDA is a point of centralization on a single provider during a time when access to that providers higher end cards isn't even available and that is causing people to look elsewhere.
ROCm supports CUDA through the included HIP projects...
https://github.com/ROCm/HIP
https://github.com/ROCm/HIPCC
https://github.com/ROCm/HIPIFY
The later will regex replace your CUDA methods with HIP methods. If it is as easy as running hipify on your codebase (or just coding to HIP apis), it certainly makes sense to do so.
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Nvidia on the Mountaintop
AMD's equivalent is HIP [1], for sufficiently flexible definitions of "equivalent". I can't speak to how complete/correct/performant it is (I'm just a guy running tutorial/toy-level ML stuff on an RDNA1 card), but part of AMD's problem is that it might not practically matter how well they do this because the broader ecosystem support specifically for the CUDA stack is so entrenched.
[1] https://github.com/ROCm-Developer-Tools/HIP
- Stable Diffusion in pure C/C++
- Would love to hear your information and knowledge to simplify my understanding on AMD's positioning in the AI market
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Ask HN: C++ still dominates on GPUs, why not Rust?
From what I know, modern GPUs are still programmed with C++ exclusively. See CUDA [0] for Nvidia and ROCm [1] for AMD.
Why is this? Why Rust is not loved there?
[0] https://docs.nvidia.com/cuda/
[1] https://github.com/ROCm-Developer-Tools/HIP
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[P] RWKV C++ Cuda library with no dependencies, no torch, and no python
Go ahead and try to ship ROCm code that works on multiple consumer graphics cards on Linux, MacOS, and Windows. As an example of how much AMD cares about it, the installation notes linked to in the readme returns a 404.
What are some alternatives?
PowerInfer - High-speed Large Language Model Serving on PCs with Consumer-grade GPUs
AdaptiveCpp - Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
ZLUDA - CUDA on AMD GPUs
mlx - MLX: An array framework for Apple silicon
futhark - :boom::computer::boom: A data-parallel functional programming language
EmotiVoice - EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
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.
llamafile - Distribute and run LLMs with a single file.
ginkgo - Numerical linear algebra software package
clspv - Clspv is a compiler for OpenCL C to Vulkan compute shaders
rocm-arch - A collection of Arch Linux PKGBUILDS for the ROCm platform