Mixture-of-Transformers: A Sparse and Scalable Architecture for
  Multi-Modal Foundation Models
The development of large language models (LLMs) has expanded to multi-modal
systems capable of processing text, images, and speech within a unified
framework. Training these models demands significantly larger datasets and
computational resources compared to text-only LLMs. To address the scaling
challenges, we introduce Mixture-of-Transformers (MoT), a sparse multi-modal
transformer architecture that significantly reduces pretraining computational
costs. MoT decouples non-embedding parameters of the model by modality --
including feed-forward networks, attention matrices, and layer normalization --
enabling modality-specific processing with global self-attention over the full
input sequence. We evaluate MoT across multiple settings and model scales. In
the Chameleon 7B setting (autoregressive text-and-image generation), MoT
matches the dense baseline's performance using only 55.8\% of the FLOPs. When
extended to include speech, MoT reaches speech performance comparable to the
dense baseline with only 37.2\% of the FLOPs. In the Transfusion setting, where
text and image are trained with different objectives, a 7B MoT model matches
the image modality performance of the dense baseline with one third of the
FLOPs, and a 760M MoT model outperforms a 1.4B dense baseline across key image
generation metrics. System profiling further highlights MoT's practical
benefits, achieving dense baseline image quality in 47.2\% of the wall-clock
time and text quality in 75.6\% of the wall-clock time (measured on AWS
p4de.24xlarge instances with NVIDIA A100 GPUs).
672d87680c2b474d28e60295
Weixin Liang
extracted_pending
2024-11-08T03:37:15.688Z
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Wx
user
WxWx
672d87680c2b474d28e60296
Lili Yu
672d87680c2b474d28e60297
Liang Luo
672d87680c2b474d28e60298
Srinivasan Iyer
672d87680c2b474d28e60299
Ning Dong
672d87680c2b474d28e6029a
Chunting Zhou
672d87680c2b474d28e6029b
Gargi Ghosh
672d87680c2b474d28e6029c
Mike Lewis
672d87680c2b474d28e6029d
Wen-tau Yih
672d87680c2b474d28e6029e
Luke Zettlemoyer
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Xi Victoria Lin
claimed_verified
2024-12-02T08:51:16.868Z
61df349e34b589fbaf2e93c4
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Victoria Lin
user
VictoriaLinML
Mixture-of-Transformers: A Sparse and Scalable Architecture for
  Multi-Modal Foundation Models
The development of large language models (LLMs) has expanded to multi-modal
systems capable of processing text, images, and speech within a unified
framework. Training these models demands significantly larger datasets and
computational resources compared to text-only LLMs. To address the scaling
challenges, we introduce Mixture-of-Transformers (MoT), a sparse multi-modal
transformer architecture that significantly reduces pretraining computational
costs. MoT decouples non-embedding parameters of the model by modality --
including feed-forward networks, attention matrices, and layer normalization --
enabling modality-specific processing with global self-attention over the full
input sequence. We evaluate MoT across multiple settings and model scales. In
the Chameleon 7B setting (autoregressive text-and-image generation), MoT
matches the dense baseline's performance using only 55.8\% of the FLOPs. When
extended to include speech, MoT reaches speech performance comparable to the
dense baseline with only 37.2\% of the FLOPs. In the Transfusion setting, where
text and image are trained with different objectives, a 7B MoT model matches
the image modality performance of the dense baseline with one third of the
FLOPs, and a 760M MoT model outperforms a 1.4B dense baseline across key image
generation metrics. System profiling further highlights MoT's practical
benefits, achieving dense baseline image quality in 47.2\% of the wall-clock
time and text quality in 75.6\% of the wall-clock time (measured on AWS
p4de.24xlarge instances with NVIDIA A100 GPUs).
Mixture-of-Transformers (MoT) reduces pretraining computational costs for multi-modal large language models by decoupling non-embedding parameters by modality, enabling efficient processing and competitive performance across text, image, and speech.
Mixture-of-Transformers MoT multi-modal transformer feed-forward networks attention matrices layer normalization global self-attention Chameleon autoregressive text-and-image generation speech Transfusion image generation metrics FLOPs wall-clock time AWS p4de.24xlarge NVIDIA A100 GPUs