Revolutionizing Large AI Models: Colossal-AI and Hugging Face
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Chapter 1: The Rise of Large AI Models
Recently, Forbes News highlighted large AI models as one of the key AI trends to monitor in 2022. As these expansive models excel across various fields, new trends arise, fostering unique and efficient AI applications that are transforming the landscape of technology.
One noteworthy innovation is GitHub's partnership with OpenAI to introduce Copilot, an AI-assisted coding tool that provides real-time code suggestions and function completions, simplifying programming tasks significantly.
Another significant release from OpenAI is DALL-E 2, a robust tool capable of generating original and lifelike images from simple textual prompts. Shortly thereafter, Google unveiled its own impressive text-to-image diffusion model, Imagen, which has further intensified the competition in the realm of large AI models.
Section 1.1: Challenges of Scaling AI Models
In recent years, the impressive capabilities of model scaling have led to increasingly larger pre-trained models. However, the financial burden of training or fine-tuning these extensive AI systems often poses a significant challenge, frequently requiring dozens of GPUs. Current deep learning frameworks like PyTorch and TensorFlow struggle to provide adequate support for such large models, while advanced knowledge of AI systems is often necessary for proper configuration and optimization. As a result, many AI practitioners, especially those in small to medium-sized enterprises, find themselves daunted by the complexities associated with large AI models.
Section 1.2: Colossal-AI's Innovative Solutions
The rising costs associated with large AI models primarily stem from GPU memory limitations and the challenges in accommodating such extensive models. In response, Colossal-AI has introduced the Gemini module, which optimally manages and utilizes both GPU and CPU memory, aiming to alleviate these issues. What’s more, it is open-source and requires minimal changes to existing deep learning projects, allowing for the training of significantly larger models on standard consumer-grade graphics cards. This accessibility simplifies downstream tasks like fine-tuning and inference, even enabling users to train AI models at home.
Chapter 2: Hugging Face and the Democratization of AI
Hugging Face is a vibrant AI community dedicated to promoting the advancement and democratization of AI through open-source initiatives. They have successfully aggregated a vast array of large-scale models, boasting over 50,000 entries, including popular models like GPT and OPT.
The first video, "Colossal AI: Scaling AI Models in Big Model Era," delves into the efficiency of Colossal-AI in scaling large AI models, detailing its groundbreaking approaches and technologies.
Colossal-AI, the flagship open-source large-scale AI system from HPC-AI Tech, now enables Hugging Face users to develop their machine learning models in a streamlined and distributed manner. In the following sections, we will illustrate how to train and fine-tune one of the most sought-after models from the Hugging Face Hub—OPT from Meta—at a low cost with minimal code changes.
Section 2.1: Understanding the Open Pretrained Transformer (OPT)
Meta's Open Pretrained Transformer (OPT), a 175-billion parameter AI language model, has been released to promote AI democratization. By providing both the code and trained model weights, Meta encourages AI developers to engage in various downstream tasks and applications. We will now explore fine-tuning for Casual Language Modeling using the pre-training weights of the OPT model available on the Hugging Face Hub.
Section 2.2: Configuring with Colossal-AI
Utilizing the advanced features of Colossal-AI is straightforward. Users only need to create a simple configuration file without altering their training logic to incorporate desired features such as mixed-precision training and gradient accumulation.
For instance, if we aim to train OPT on a single GPU, we can leverage Colossal-AI's heterogeneous training by merely adding relevant settings in the configuration files. The tensor_placement_policy can be set to cuda, cpu, or auto to dictate our training strategy, each with its unique benefits.
Subsection 2.2.1: Launching with Colossal-AI
To initiate the training, a single line of code in the configuration file is all that’s needed to awaken Colossal-AI, which will automatically set up the distributed environment and apply the configuration settings to its components.
colossalai.launch_from_torch(config='./configs/colossalai_zero.py')
Following this, users can define their datasets, models, optimizers, and loss functions as usual, or using raw PyTorch code, ensuring their models are initialized under ZeroInitContext.
Section 2.3: Achieving Remarkable Performance
Colossal-AI's automatic strategy boasts impressive performance improvements through the ZeRO Offloading strategy from Microsoft DeepSpeed. Users can experience performance boosts of up to 40% across various model scales. However, traditional frameworks like PyTorch often fall short for training models at such scales with a single GPU.
Switching to distributed training with eight GPUs is as simple as adding a parameter to the Colossal-AI training command.
The second video, "ColossalAI: Making Large AI Models Cheaper, Faster, and More Accessible!" discusses the strategies and benefits of using Colossal-AI for efficient training of large models.
Chapter 3: Behind the Technology
The remarkable advancements are attributable to Colossal-AI's efficient heterogeneous memory management system, Gemini. This system employs warmup steps during training to gather memory usage data from PyTorch computational graphs. After the warmup phase, Gemini pre-allocates memory for operations based on previous usage records while reallocating some tensors from GPU to CPU memory.
The built-in memory manager assigns a state to each tensor and adjusts their positions based on real-time memory usage, ensuring optimal memory utilization while balancing training speed with minimal hardware resources.
For large models like GPT, Colossal-AI can train up to 1.5 billion parameters on a gaming laptop equipped with an RTX 2060, and up to 18 billion parameters on a PC with an RTX 3090.
Chapter 4: The Future of AI Training
To expedite the training of the largest AI models, efficient parallel and distributed technologies are crucial. Colossal-AI leverages advanced multi-dimensional parallelism to enable rapid deployment of large AI models with minimal code alterations.
In theory, the potential savings are significant, as Colossal-AI requires only half the computing resources compared to NVIDIA solutions for training extensive models like GPT-3. In practice, Colossal-AI has demonstrated its effectiveness across diverse industries, including autonomous driving and healthcare.
Colossal-AI also embraces the open-source ethos, offering detailed tutorials and supporting cutting-edge applications like PaLM and AlphaFold. The community is encouraged to engage in discussions and provide feedback, ensuring the project continues to evolve and improve.
For more information, check out the open-source code on GitHub.
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