Training large language models (LLMs) can often be both resource-demanding and time-consuming. Even with the right resources, the implementation can be quite challenging. To assist smaller companies, enthusiasts, and innovators, various open-source frameworks have emerged to tackle these complexities. Here are some of the most notable frameworks that can aid in training and fine-tuning LLMs. I encourage you to delve into these tools, as they can streamline and enhance the training process, enabling you to achieve superior results with greater efficiency.

1. DeepSpeed

   https://www.deepspeed.ai/

   DeepSpeed is an advanced deep learning optimization library that simplifies distributed training and inference, making implementation straightforward and effective. It enhances the training of models similar to ChatGPT with a one-click setup, achieving a 15x speed improvement over state-of-the-art reinforcement learning from human feedback (RLHF) systems, while also drastically reducing costs across all scales.

   DeepSpeed-Training

   DeepSpeed combines various system innovations that enhance the effectiveness and efficiency of large-scale deep learning training, significantly improving usability and redefining the training landscape. Innovations under this umbrella include ZeRO, 3D Parallelism, DeepSpeed-MoE, and ZeRO-Infinity. For more details, visit: DeepSpeed-Training.

   DeepSpeed-Inference

   This aspect of DeepSpeed merges parallelism technologies—such as tensor, pipeline, expert, and ZeRO parallelism—with high-performance custom inference kernels, communication optimizations, and heterogeneous memory technologies. This allows for inference at an unprecedented scale, achieving unmatched latency, throughput, and cost efficiency. Discover more at: DeepSpeed-Inference.

   DeepSpeed-Compression

   To boost inference efficiency further, DeepSpeed offers flexible compression techniques that allow researchers to compress their models while maintaining speed, reducing model size, and minimizing compression costs. Innovations like ZeroQuant and XTC fall under DeepSpeed-Compression. Learn more at: DeepSpeed-Compression.

   Included models:

   • Megatron-Turing NLG (530B)
   • Jurassic-1 (178B)
   • BLOOM (176B)
   • GLM (130B)
   • YaLM (100B)
   • GPT-NeoX (20B)
   • AlexaTM (20B)
   • Turing NLG (17B)
   • METRO-LM (5.4B)

   DeepSpeed is compatible with several popular open-source deep learning frameworks, including:

   • Transformers with DeepSpeed
   • Accelerate with DeepSpeed
   • Lightning with DeepSpeed
   • MosaicML with DeepSpeed

2. Megatron-DeepSpeed

   https://huggingface.co/blog/bloom-megatron-deepspeed

   This is a DeepSpeed variant of NVIDIA’s Megatron-LM, providing additional support for mixture of experts (MoE) model training, curriculum learning, 3D parallelism, and other advanced features.

   Megatron-DeepSpeed implements 3D Parallelism to facilitate efficient training of large models. The components of 3D Parallelism include:

   • DataParallel (DP): Replicates the same setup multiple times, each receiving a slice of the data to process in parallel, synchronizing at each training step.
   • TensorParallel (TP): Splits each tensor into chunks, distributing them across designated GPUs, allowing separate processing of each shard in parallel.
   • PipelineParallel (PP): Vertically splits the model across multiple GPUs, so that individual layers are processed in parallel on different GPUs.
   • Zero Redundancy Optimizer (ZeRO): Similar to TP, this method shards tensors but reconstructs the entire tensor for computations, supporting various offloading techniques for limited GPU memory.

3. FairScale

   FairScale is a PyTorch extension aimed at high-performance, large-scale training, allowing researchers to train models more efficiently. This library enhances basic PyTorch functionalities while incorporating state-of-the-art scaling techniques. FairScale provides distributed training techniques through composable modules and user-friendly APIs, essential for researchers aiming to scale models with limited resources.

   Categories for efficiency improvements include:

   • Parallelism: Techniques for layer and tensor parallelism.
   • Sharding Methods: Reduces memory utilization and optimizes computation by sharding model layers or parameters and gradients.
   • Optimization: Focuses on optimizing memory usage, training without hyperparameter tuning, and enhancing training performance.

4. Megatron-LM

   Megatron-LM is a widely-used tool for pre-training large transformer models, developed by NVIDIA's Applied Deep Learning Research team. Though it can be complex for beginners compared to other frameworks, it is optimized for GPU training and can offer significant speed improvements.

   To learn more, read:

   • Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
   • How to train a Language Model with Megatron-LM

5. Colossal-AI

   Colossal-AI aims to make large AI models more accessible, faster, and cost-effective. It offers a suite of parallel components and user-friendly tools for distributed training and inference with minimal configuration.

   Parallelism strategies include:

   • Data Parallelism
   • Pipeline Parallelism
   • 1D, 2D, 2.5D, 3D Tensor Parallelism
   • Sequence Parallelism
   • Zero Redundancy Optimizer (ZeRO)
   • Auto-Parallelism

   Heterogeneous memory management includes:

   • PatrickStar

   Friendly Usage:

   • Configuration-based parallelism

   Inference:

   • Energon-AI

   Watch a quick talk about Colossal-AI by Prof. James Demmel here:

6. BMTrain

   BMTrain is a toolkit designed for efficient training of large models with tens of billions of parameters, enabling distributed training while keeping the code simple. It serves as a nearly drop-in replacement for default models.

   For more details: https://github.com/OpenBMB/BMTrain

7. Mesh TensorFlow

   Mesh TensorFlow (mtf) is a language for distributed deep learning that allows specification of a wide range of distributed tensor computations. Its purpose is to formalize and implement distribution strategies across hardware and processors.

   The Mesh TensorFlow approach includes:

   • An n-dimensional array of processors connected by a network.
   • Distribution of tensors across processors in a mesh.
   • User-defined layout rules to ensure consistent splitting of tensor dimensions across mesh dimensions.
   • Operations involve parallel computation across all processors, often requiring collective communication.

   Watch this video for more information.

8. MaxText

   MaxText is a high-performance, scalable, open-source LLM written in pure Python/Jax, specifically targeting Google Cloud TPUs. It achieves 55% to 60% model-flop utilization and can scale from single hosts to extensive clusters without unnecessary complexity.

   For more information, visit: https://github.com/EleutherAI/maxtext

9. Alpa

   Alpa aims to make large models accessible to everyone, simplifying the training and serving of massive machine learning models like GPT-3. As a fully open-source system, it was initially developed by the Sky Lab at UC Berkeley. Advanced techniques used in Alpa were published in a paper at OSDI’2022, and the community continues to grow with contributors from Google, Amazon, AnyScale, and more.

   Learn more about Alpa at the Stanford MLSys Seminar:

10. Bonus: GPT-NeoX 2.0

    GPT-NeoX is another implementation based on NVIDIA’s Megatron Language Model. Following the migration to the latest DeepSpeed version, two versioned releases have been introduced for both GPT-NeoX and DeeperSpeed:

    • Version 1.0: Maintains snapshots of the older stable versions.
    • Version 2.0: Built on the latest DeepSpeed and will continue to be maintained going forward.

    Examples: https://blog.eleuther.ai/announcing-20b/

This article is a compilation of information sourced from various resources, including LinkedIn posts, messages, and GitHub documentation. Credit for the information belongs to the original authors; I merely compiled it.

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