AltUp is a novel method that addresses the challenge of scaling up token representation in Transformer neural networks without increasing computational complexity. It partitions the representation vector into blocks and processes one block at each layer, utilizing a prediction-correction mechanism to infer outputs for non-processed blocks. AltUp outperforms dense models in benchmark tasks and shows promise in efficiently scaling up Transformers. The researchers also introduce Recycled-AltUp, which replicates embeddings instead of widening them, achieving improved pre-training performance without slowing down. These methods contribute to making large-scale Transformer models more accessible and practical.
Google AI Introduces AltUp: Efficient Scaling of Transformer Networks
In the field of deep learning, Transformer neural networks have gained attention for their effectiveness in various domains. However, the increasing scale of these models brings about a rise in compute cost and inference latency. AltUp is a novel method introduced to address this challenge.
Key Features of AltUp:
- Augments token representation without increasing computational overhead
- Partitions a widened representation vector into equal-sized blocks
- Processes only one block at each layer
- Uses a prediction-correction mechanism to infer outputs for non-processed blocks
AltUp’s mechanics focus on widening token embeddings without increasing computational complexity. It involves invoking a 1x width transformer layer for one block, employing a lightweight predictor, and using a correction mechanism to update inactivated blocks based on the activated ones.
Evaluation of AltUp on T5 models demonstrates its ability to outperform dense models at the same accuracy. Notably, a T5 Large model augmented with AltUp achieves notable speedups on benchmark language tasks.
Recycled-AltUp:
Recycled-AltUp, an extension of AltUp, replicates embeddings instead of widening initial token embeddings. It demonstrates improvements in pre-training performance without introducing slowdown.
Benefits of AltUp:
- Efficient scaling of Transformer neural networks
- Augments token representation without increasing computational cost
- Promising solution for various applications
- Seamless integration with other techniques like MoE
AltUp signifies a breakthrough in efficiently scaling up Transformer networks, making large-scale models more accessible and practical for various applications.
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