xla
Megatron-LM
xla | Megatron-LM | |
---|---|---|
8 | 19 | |
2,296 | 8,645 | |
1.7% | 4.7% | |
9.9 | 9.9 | |
5 days ago | 5 days ago | |
C++ | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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xla
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Who uses Google TPUs for inference in production?
> The PyTorch/XLA Team at Google
Meanwhile you have an issue from 5 years ago with 0 support
https://github.com/pytorch/xla/issues/202
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Google TPU v5p beats Nvidia H100
PyTorch has had an XLA backend for years. I don't know how performant it is though. https://pytorch.org/xla
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Why Did Google Brain Exist?
It's curtains for XLA, to be precise. And PyTorch officially supports XLA backend nowadays too ([1]), which kind of makes JAX and PyTorch standing on the same foundation.
1. https://github.com/pytorch/xla
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Accelerating AI inference?
Pytorch supports other kinds of accelerators (e.g. FPGA, and https://github.com/pytorch/glow), but unless you want to become a ML systems engineer and have money and time to throw away, or a business case to fund it, it is not worth it. In general, both pytorch and tensorflow have hardware abstractions that will compile down to device code. (XLA, https://github.com/pytorch/xla, https://github.com/pytorch/glow). TPUs and GPUs have very different strengths; so getting top performance requires a lot of manual optimizations. Considering the the cost of training LLM, it is time well spent.
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[D] Colab TPU low performance
While apparently TPUs can theoretically achieve great speedups, getting to the point where they beat a single GPU requires a lot of fiddling around and debugging. A specific setup is required to make it work properly. E.g., here it says that to exploit TPUs you might need a better CPU to keep the TPU busy, than the one in colab. The tutorials I looked at oversimplified the whole matter, the same goes for pytorch-lightning which implies switching to TPU is as easy as changing a single parameter. Furthermore, none of the tutorials I saw (even after specifically searching for that) went into detail about why and how to set up a GCS bucket for data loading.
- How to train large deep learning models as a startup
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Distributed Training Made Easy with PyTorch-Ignite
XLA on TPUs via pytorch/xla.
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[P] PyTorch for TensorFlow Users - A Minimal Diff
I don't know of any such trick except for using TensorFlow. In fact, I benchmarked PyTorch XLA vs TensorFlow and found that the former's performance was quite abysmal: PyTorch XLA is very slow on Google Colab. The developers' explanation, as I understood it, was that TF was using features not available to the PyTorch XLA developers and that they therefore could not compete on performance. The situation may be different today, I don't know really.
Megatron-LM
- FLaNK AI Weekly for 29 April 2024
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Apple releases CoreNet, a library for training deep neural networks
https://github.com/NVIDIA/Megatron-LM
This is probably a good baseline to start thinking about LLM training at scale.
- Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
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Large Language Models: Compairing Gen2/Gen3 Models (GPT-3, GPT-J, MT5 and More)
This 20B model was trained on the same datasets as its predecessor, aptly named The Pile. Furthermore, the libraries Megatron and DeepSpeed were used to achieve better computing resource utilization, and eventually GPT-NeoX evolved into its own framework for training other LLMs. It was used, for example, as the foundation for Llemma, an open-source model specializing on theorem proving.
- Why async gradient update doesn't get popular in LLM community?
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[D] Distributes pre-training and fine-tuning
Deepspeed Megatron-LM
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Why Did Google Brain Exist?
GPU cluster scaling has come a long way. Just checkout the scaling plot here: https://github.com/NVIDIA/Megatron-LM
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Does Megatron-LM really not communicate during multi-head attention operations?
I found their code that the softmax function conduct all-reduce before they work.
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I asked ChatGPT to rate the intelligence level of current AI systems out there.
Google's PaLM, Facebook's LLaMA, Nvidia's Megatron, I am missing some surely and Apple sure has something cooking as well but these are the big ones, of course none of them are publicly available, but research papers are reputable. All of the ones mentioned should beat GPT-3 although GPT-3.5 (chatGPT) should be bit better and ability to search (Bing) should level the playing field even further, but Google's PaLM with search functionality should be clearly ahead. This is why people are excited about GPT-4, GPT-3 was way ahead of anyone else when it came out but others were able to catch up since, we'll see if GPT-4 will be another bing jump among LLMs.
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GPT-4 Will Be 500x Smaller Than People Think - Here Is Why
Found relevant code at https://github.com/nvidia/megatron-lm + all code implementations here
What are some alternatives?
NCCL - Optimized primitives for collective multi-GPU communication
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
ColossalAI - Making large AI models cheaper, faster and more accessible
why-ignite - Why should we use PyTorch-Ignite ?
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
pocketsphinx - A small speech recognizer
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
ompi - Open MPI main development repository
ChatGPT-Siri - Shortcuts for Siri using ChatGPT API gpt-3.5-turbo & gpt-4 model, supports continuous conversations, configure the API key & save chat records. 由 ChatGPT API gpt-3.5-turbo & gpt-4 模型驱动的智能 Siri,支持连续对话,配置API key,配置系统prompt,保存聊天记录。