Summary Scaling Transformers to 1000000000 Tokens arxiv.org
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The L ONG N ET Transformer variant has the ability to process sequences up to 1 billion tokens with dilated attention while still performing well on shorter sequences.
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Key Points
- L ONG N ET is a Transformer variant that can scale the sequence length to over 1 billion tokens without sacrificing performance on shorter sequences.
- Dilated attention is proposed as a way to expand the attentive field exponentially as the distance grows.
- Scaling the sequence length can be achieved by implementing sliding windows or convolution modules over the attention, but this sacrifices the ability to recall early tokens.
- Dilated attention splits input into segments and sparsifies them along the sequence dimension to reduce computation cost while capturing long-range and short-range information.
- Longformer is a transformer model that addresses the challenge of scaling sequence length to 1 billion tokens by reducing the computation complexity of dilated attention.
- L ONG N ET is compared to vanilla Transformer and sparse Transformers, with differences observed in the attention layers.
- The torchscale codebase is used for all experiments.
- The list of references includes papers and articles related to scaling transformers and neural language models from conferences and journals such as ICLR, NeurIPS, and ICML.
Summaries
23 word summary
The L ONG N ET Transformer variant can handle sequences up to 1 billion tokens using dilated attention, maintaining performance on shorter sequences.
44 word summary
L ONG N ET is a Transformer variant that can scale the sequence length to 1 billion tokens and beyond without sacrificing performance on shorter sequences. It achieves this through dilated attention, which expands the attentive field exponentially as the distance grows. This reduces
337 word summary
This article introduces L ONG N ET , a Transformer variant that can scale sequence length to over 1 billion tokens without sacrificing performance on shorter sequences. The authors propose dilated attention, which expands the attentive field exponentially as the distance grows. L O
One approach to scaling the sequence length is to decrease the complexity of Transformers. This can be done by implementing sliding windows or convolution modules over the attention, which reduces the complexity to nearly linear. However, this sacrifices the ability to recall early tokens at the
The text describes the concept of dilated attention, which splits input into segments and sparsifies them along the sequence dimension. This reduces computation cost while capturing both long-range and short-range information. The text also explains the implementation of a mixture of dil
Longformer is a transformer model that addresses the challenge of scaling sequence length to 1 billion tokens. The computation complexity of dilated attention has been reduced to O(Nd), but it is still infeasible to scale the sequence length on a single
The torchscale codebase is used for all experiments. The paper compares L ONG N ET with vanilla Transformer and sparse Transformers, noting that the differences lie in the attention layers. The sequence length of the models is scaled from 2K to
L ONG N ET is a Transformer variant that can scale the sequence length to 1 billion tokens and beyond without losing performance on shorter sequences. It achieves this through dilated attention, which reduces computation complexity. L ONG N ET can be used
This text excerpt is a list of references to papers and articles related to scaling transformers and neural language models. The references include papers from various conferences and journals, such as ICLR, NeurIPS, and ICML. Some of the key papers mentioned
This document provides a list of references to papers related to scaling transformers and language models. The references include papers on various topics such as grounding multimodal large language models, linear transformers, length-extrapolatable transformers, training multi-billion parameter language models,