May 17, 2024
4 mins

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Megalodon by Meta AI is a new LLM architecture that tackles the problems in transformers and can support unlimited context length using a new attention technique.
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Key Takeaways

  • Megalodon is a new neural network architecture designed for efficient sequence modeling with the ability to handle sequences of any length, unlike traditional Transformers which struggle with longer sequences.
  • Megalodon builds upon the MEGA architecture by introducing several improvements like CEMA, timestep normalization, normalized attention, and a two-hop residual configuration.
  • Megalodon outperforms LLAMA2 (a state-of-the-art Transformer model) in terms of training efficiency and accuracy on downstream tasks, even with fewer parameters.
  • Megalodon shows promising results on tasks requiring long-context understanding, like long document question answering, opening up possibilities for applications like analyzing legal documents or medical records.


Today, Large Language Models (LLMs) mostly based on transformers architecture that enables them to predict next word, but are hard to scale. These models often struggle with processing long sequences of data, like lengthy documents or extended conversations. This limitation arises from the quadratic complexity of the Transformer architecture. In simpler terms, as the sequence length grows, the computational resources required by the model increase exponentially, making it impractical for real-world applications that deal with extensive data.

Megalodon from Meta AI addresses this challenge. It proposes a new architecture that efficiently handles sequences of any length while maintaining high accuracy on various tasks. This enables LLMs based on Megalodon to support tasks that were out of scope for previous transformer based LLMs like processing long sequential data, understanding internal long range dynamics, and generate coherent output over that length.

Megalodon builds upon MEGA architecture, which harnesses gated attention with the classical Exponential Moving Average (EMA) approach (from Hunter, 1986). Meta AI then adds novel technical components, complex exponential moving average (CEMA) and then a timestep normalization layer.


MEGA introduced two key components:

Damped EMA

The Exponential Moving Average (EMA) is a statistical technique used to smooth out time-series data. MEGA adopts a multi-dimensional damped EMA to capture contextual information within a sequence. Imagine you're reading a sentence – understanding the meaning of each word depends on the context provided by the preceding words. EMA helps the model do just that by maintaining a "memory" of sorts, where the representation of each word is influenced by the representations of the words that came before it.

Moving Average Equipped Gated Attention

In the gated attention mechanism in MEGA, the output from EMA is used to compute the shared representation, because it encodes contextual information through EMA.Subsequently, MEGA introduces the reset gate, the update gate , and computes the candidate activation with the update gate and the residual connection.

Problems with MEGA

While MEGA made significant strides in efficient sequence modeling, it still faced some limitations:

  • Performance Gap: The chunk-wise attention mechanism used by MEGA to reduce computational complexity resulted in a performance gap compared to models using full attention.
  • Architectural Divergence: Different tasks and data types required different architectural configurations in MEGA, making it less versatile.
  • Scalability Concerns: The scalability of MEGA for large-scale pre-training remained unproven.


Megalodon addresses the limitations of MEGA by introducing several key improvements:


CEMA, or Complex Exponential Moving Average, extends the EMA concept to the complex domain. This seemingly technical change allows the model to capture more intricate relationships within sequences, leading to better performance and efficiency.

Normalized Attention

Megalodon incorporates normalized attention mechanisms to improve stability during training. In essence, this modification prevents the attention weights from becoming too large or too small, ensuring smoother learning and preventing the model from getting "stuck" in undesirable states.

Pre-norm with Two-hop Residual

The way information flows within a neural network is crucial for its performance. Megalodon adopts a "pre-norm with two-hop residual" configuration, which optimizes this flow and improves stability, particularly when training larger models.

Parallelism in Distributed Pre-training

Training large language models requires massive computational resources. Megalodon leverages a 4-dimensional parallelism strategy, enabling efficient distributed training even with very long sequences. This advancement allows for scaling up the model without compromising training speed or efficiency.


The researchers conducted a series of experiments to evaluate the performance of Megalodon across various tasks and data modalities:

LLM Pretraining

Megalodon-7B was pre-trained on the same 2 trillion tokens as LLAMA2-7B, but with a context length of 32K tokens, which is 8 times longer than LLAMA2. The results show that Megalodon achieves a significantly lower training loss, indicating better data efficiency. Furthermore, Megalodon demonstrates superior computational efficiency when dealing with longer sequences.

Short-Context Evaluation

On standard academic benchmarks with shorter contexts, Megalodon-7B consistently outperforms LLAMA2-7B and even rivals the performance of LLAMA2-13B (a larger model) on several tasks. This demonstrates the effectiveness of Megalodon's architectural improvements.

Long-Context Evaluation

Megalodon excels in tasks involving long sequences, such as long document question answering. It achieves state-of-the-art results on the NarrativeQA task from the Scrolls dataset, showcasing its ability to process and understand extensive information.

Instruction Finetuning

Megalodon also demonstrates strong performance on instruction-based tasks after fine-tuning. It achieves comparable results to LLAMA2-Chat (which uses additional techniques) on the MT-Bench benchmark, indicating its ability to follow instructions and align with user intent.

Medium-Scale Benchmarks

Beyond language-based tasks, Megalodon exhibits strong performance on medium-scale benchmarks involving image and audio data, such as ImageNet classification and raw speech classification. This versatility highlights the robustness of the Megalodon architecture across different data modalities.

Business Implications

The ability of Megalodon to efficiently handle long sequences unlocks new possibilities for businesses and researchers:

Analyzing Complex Documents

Megalodon could be used to analyze legal documents, medical records, or financial reports, extracting key insights and identifying patterns that were previously difficult to capture with traditional LLMs.

Modeling Customer Interactions

Understanding extended customer interactions, such as chat logs or customer service calls, becomes feasible with Megalodon, enabling businesses to gain a deeper understanding of customer needs and behavior.

Scientific Research

Megalodon could be applied to analyze large datasets in scientific research, such as genomic sequences or climate data, facilitating new discoveries and advancing scientific understanding.


Megalodon presents a significant step forward in the evolution of LLMs, offering an architecture that efficiently handles long sequences while maintaining high accuracy on various tasks. Its ability to process and understand extensive information paves the way for exciting new applications across different industries and research domains. As Megalodon continues to develop, we can expect to see its impact grow, further bridging the gap between human and machine intelligence.

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