Revolutionary Megalodon Architecture Surpasses Transformer with Infinite Context Processing

Discover the breakthrough Megalodon architecture by Meta, USC, CMU, and UCSD, outperforming Llama2 with superior efficiency in handling 2 trillion tokens.

The reign of the Transformer architecture is being challenged! Meta, USC, CMU, and UCSD have collaborated to introduce an innovative architecture called Megalodon.

This new model can handle unlimited context lengths and boasts remarkable efficiency in managing a training task of 2 trillion tokens, outperforming the Llama2-7B model.

megalodon llm pre training architecture

After Mamba, Megalodon emerges as yet another contender in the realm of architecture daring to rival Transformer. This collaborative effort by Meta, USC, CMU, and UCSD has resulted in the new Megalodon architecture.

Megalodon is specifically designed to efficiently manage both pre-training and inference for large language models (LLMs) across potentially unlimited contexts.

The widely used Transformer model struggles with quadratic complexity and limited ability to handle long contexts due to its design.

While alternative approaches like linear attention and state-space models exist, they generally fall short of Transformer in terms of pre-training efficiency and downstream task accuracy.

Megalodon addresses the challenge of effectively processing limitless contexts.

Moreover, it is engineered for both efficient training, reducing both computation and communication demands, and efficient inference by maintaining a constant key-value (KV) cache.

It’s notable that Megalodon not only trains more efficiently but also shows better accuracy compared to the Transformer model when managing 7 billion parameters and 2 trillion training tokens.

In terms of training loss, Megalodon records a score of 1.70, which positions it between Llama2-7B at 1.75 and the 13B model at 1.67.

This groundbreaking innovation marks a significant advancement in AI, positioning Megalodon at the forefront of computational efficiency and performance.

This achievement is heralded as one of the most significant milestones since the introduction of GPT-3, with online commentators noting the continuous advancements by Google and now Meta in the field of infinite context processing, paving the way for LLMs to reach their full potential.

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Observers have also noted that the capability for unlimited context length could be a game-changer.

“Meta’s Megalodon represents a significant breakthrough, mirroring human cognitive processes with its unlimited context handling and enabling smooth transitions between tasks,” remarked industry experts.

Hao Zhang, the paper’s author, describes Megalodon as a completely novel architecture that could potentially replace Transformer.

Tri Dao, an assistant professor at Princeton, commented, “Merging SSM/RNN/EMA with attention strategies enhances longer context processing and speeds up reasoning. Megalodon, along with predecessors like Griffin, Jamba, and Zamba, exemplifies this.”

Megalodon utilizes a revolutionary design for enhanced stability during training.

Based on the MEGA architecture, it incorporates several innovative technical components, including a complex exponential moving average (CEMA) that expands the conventional exponential moving average techniques into the complex domain, improving the model’s capacity to handle complex data.

Additionally, a novel normalization method, termed “time step normalization layer,” has been proposed. This method extends traditional group normalization to autoregressive sequence modeling, facilitating effective normalization in sequence data processing.

Traditional “layer normalization” has shown effectiveness with Transformer models but does not address internal covariate shift in time or ordinal dimensions.

While “Group Normalization” surpasses “Layer Normalization” in computer vision tasks, its application in Transformer-based autoregressive sequence modeling is limited due to potential leakage of future information through the mean and variance of the time step dimension.

Below, in the figure, c illustrates both the layer normalization and time step normalization techniques utilized in Megalodon.

megalodon figure 2

To further boost the stability of large-scale LLM pre-training, a configuration combining normalized attention and pre-normalization with a two-hop residual has been proposed, optimizing the learning process and enhancing training stability.

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Figure 3 outlines a comprehensive structural diagram of Megalodon, with subsequent diagrams detailing configurations of pre-normalization and pre-normalization with two-hop residuals.

megalodon layer output

In detailed experimental assessments, Megalodon was scaled to 7 billion parameters for extensive LLM pre-training involving 2 trillion tokens.

Furthermore, the authors conducted additional tests across various benchmark tasks, including Long Range Arena (LRA), speech classification on Speech Commands, image classification on ImageNet-1K, and language modeling on WikiText-103 and PG19, where Megalodon significantly outperformed all baseline models across these data types.

figure 4

From the training loss data and multiple benchmark results, it is evident that Megalodon surpasses Transformer in data learning efficiency under the condition of 7 billion parameters.

The architecture’s computational efficiency also proves robust across different context lengths, including 4K and 32K tokens.

In evaluations on academic benchmarks with shorter contexts (4K tokens), Megalodon-7B significantly outperformed Llama2-7B after training with the same 2 trillion tokens.

table 1

In assessments involving various context lengths, Megalodon demonstrated its ability to accurately predict the next token in exceedingly long contexts.

figure 5 ppl

As depicted in Figure 5, the perplexity (PPL) across various context lengths ranging from 4K to 2M tokens is shown.

long range arena megalodon accuracy

In a long-context question-answering task within the Scroll dataset, Megalodon achieved the highest F1 score on NaQA and showed competitive performance against Llama 2 Long.

text accuracy comparison megalodon

Results from other evaluations, including original speech classification, ImageNet-1K, WikiText-103, and PG-19 are also highlighted.

Quotes from the study’s original author reflect the lengthy and challenging journey from initial idea to final realization of this project, encompassing nearly two years of work marked by several setbacks and valuable lessons about conducting scientific research in the era of large-scale pre-training.

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This project has also illuminated critical considerations for developing new model architectures in the era of large models, emphasizing the importance of using identical datasets for credible comparisons between models. Even small data discrepancies can significantly affect both training loss and downstream task results.

For substantial and credible comparisons between different large model architectures, ensuring adequate training with comparable datasets is crucial. Some models might perform well with lesser data but falter as data scales up. Therefore, comparisons should be based on sufficient data to yield convincing results.

With increasing diversity in model architectures, traditional comparisons based on flops are becoming less relevant. The actual computational speed of models with different architectures can vary significantly, even if they share similar flops, making it essential to consider both data learning efficiency and computational efficiency in evaluations.

This approach places high demands on the engineering capabilities of researchers, as developing new algorithms in the era of large models often involves integration with systems and other technical aspects.

Megalodon Paper address : https://arxiv.org/abs/2404.08801

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Aditya Nath Jha is an Engineer from New Delhi, India. His areas of interest include Gadgets, WordPress, speed optimization & latest technology. When he is not busy blogging he loves to write poetry, compose his own songs and has a taste for music! Find him on Facebook, Twitter, Linked in, Instagram. And watch his videos on YouTube.


Revolutionary Megalodon Architecture Surpasses Transformer with Infinite Context Processing

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