HIPIFY
llama-cpp-python
HIPIFY | llama-cpp-python | |
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11 | 55 | |
318 | 6,658 | |
- | - | |
0.0 | 9.8 | |
5 months ago | 2 days ago | |
C++ | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
llama-cpp-python
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Ollama v0.1.33 with Llama 3, Phi 3, and Qwen 110B
There's a Python binding for llama.cpp which is actively maintained and has worked well for me: https://github.com/abetlen/llama-cpp-python
- FLaNK AI for 11 March 2024
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OpenAI: Memory and New Controls for ChatGPT
I'll share the core bit that took a while to figure out the right format, my main script is a hot mess using embeddings with SentenceTransformer, so I won't share that yet. E.g: last night I did a PR for llama-cpp-python that shows how Phi might be used with JSON only for the author to write almost exactly the same code at pretty much the same time. https://github.com/abetlen/llama-cpp-python/pull/1184
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TinyLlama LLM: A Step-by-Step Guide to Implementing the 1.1B Model on Google Colab
Python Bindings for llama.cpp
- Mistral-8x7B-Chat
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Running Mistral LLM on Apple Silicon Using Apple's MLX Framework Is Much Faster
If the model could be made to work with llama.cpp, then https://github.com/abetlen/llama-cpp-python might be more compact. llama.cpp only supports a limited list of model types though.
- Run ChatGPT-like LLMs on your laptop in 3 lines of code
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Code Llama, a state-of-the-art large language model for coding
https://github.com/abetlen/llama-cpp-python has a web server mode that replicates openai's API iirc and the readme shows it has docker builds already.
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Meta: Code Llama, an AI Tool for Coding
LocalAI https://localai.io/ and LMStudio https://lmstudio.ai/ both have fairly complete OpenAI compatibility layers. llama-cpp-python has a FastAPI server as well: https://github.com/abetlen/llama-cpp-python/blob/main/llama_... (as of this moment it hasn't merged GGUF update yet though)
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First steps with llama
I went with Python, llama-cpp-python, since my goal is just to get a small project up and running locally.
What are some alternatives?
ZLUDA - CUDA on AMD GPUs
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
llama.cpp - LLM inference in C/C++
rocm-build - build scripts for ROCm
text-generation-inference - Large Language Model Text Generation Inference
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.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.