MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
We introduce MoMa, a novel modality-aware mixture-of-experts (MoE) architecture designed for pre-training mixed-modal, early-fusion language models. MoMa processes images and text in arbitrary sequences by dividing expert modules into modality-specific groups. These groups exclusively process design...
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creator | Lin, Xi Victoria Shrivastava, Akshat Luo, Liang Iyer, Srinivasan Lewis, Mike Ghosh, Gargi Zettlemoyer, Luke Aghajanyan, Armen |
description | We introduce MoMa, a novel modality-aware mixture-of-experts (MoE)
architecture designed for pre-training mixed-modal, early-fusion language
models. MoMa processes images and text in arbitrary sequences by dividing
expert modules into modality-specific groups. These groups exclusively process
designated tokens while employing learned routing within each group to maintain
semantically informed adaptivity. Our empirical results reveal substantial
pre-training efficiency gains through this modality-specific parameter
allocation. Under a 1-trillion-token training budget, the MoMa 1.4B model,
featuring 4 text experts and 4 image experts, achieves impressive FLOPs
savings: 3.7x overall, with 2.6x for text and 5.2x for image processing
compared to a compute-equivalent dense baseline, measured by pre-training loss.
This outperforms the standard expert-choice MoE with 8 mixed-modal experts,
which achieves 3x overall FLOPs savings (3x for text, 2.8x for image).
Combining MoMa with mixture-of-depths (MoD) further improves pre-training FLOPs
savings to 4.2x overall (text: 3.4x, image: 5.3x), although this combination
hurts performance in causal inference due to increased sensitivity to router
accuracy. These results demonstrate MoMa's potential to significantly advance
the efficiency of mixed-modal, early-fusion language model pre-training, paving
the way for more resource-efficient and capable multimodal AI systems. |
doi_str_mv | 10.48550/arxiv.2407.21770 |
format | Article |
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architecture designed for pre-training mixed-modal, early-fusion language
models. MoMa processes images and text in arbitrary sequences by dividing
expert modules into modality-specific groups. These groups exclusively process
designated tokens while employing learned routing within each group to maintain
semantically informed adaptivity. Our empirical results reveal substantial
pre-training efficiency gains through this modality-specific parameter
allocation. Under a 1-trillion-token training budget, the MoMa 1.4B model,
featuring 4 text experts and 4 image experts, achieves impressive FLOPs
savings: 3.7x overall, with 2.6x for text and 5.2x for image processing
compared to a compute-equivalent dense baseline, measured by pre-training loss.
This outperforms the standard expert-choice MoE with 8 mixed-modal experts,
which achieves 3x overall FLOPs savings (3x for text, 2.8x for image).
Combining MoMa with mixture-of-depths (MoD) further improves pre-training FLOPs
savings to 4.2x overall (text: 3.4x, image: 5.3x), although this combination
hurts performance in causal inference due to increased sensitivity to router
accuracy. These results demonstrate MoMa's potential to significantly advance
the efficiency of mixed-modal, early-fusion language model pre-training, paving
the way for more resource-efficient and capable multimodal AI systems.</description><identifier>DOI: 10.48550/arxiv.2407.21770</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Learning</subject><creationdate>2024-07</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2407.21770$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2407.21770$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Xi Victoria</creatorcontrib><creatorcontrib>Shrivastava, Akshat</creatorcontrib><creatorcontrib>Luo, Liang</creatorcontrib><creatorcontrib>Iyer, Srinivasan</creatorcontrib><creatorcontrib>Lewis, Mike</creatorcontrib><creatorcontrib>Ghosh, Gargi</creatorcontrib><creatorcontrib>Zettlemoyer, Luke</creatorcontrib><creatorcontrib>Aghajanyan, Armen</creatorcontrib><title>MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts</title><description>We introduce MoMa, a novel modality-aware mixture-of-experts (MoE)
architecture designed for pre-training mixed-modal, early-fusion language
models. MoMa processes images and text in arbitrary sequences by dividing
expert modules into modality-specific groups. These groups exclusively process
designated tokens while employing learned routing within each group to maintain
semantically informed adaptivity. Our empirical results reveal substantial
pre-training efficiency gains through this modality-specific parameter
allocation. Under a 1-trillion-token training budget, the MoMa 1.4B model,
featuring 4 text experts and 4 image experts, achieves impressive FLOPs
savings: 3.7x overall, with 2.6x for text and 5.2x for image processing
compared to a compute-equivalent dense baseline, measured by pre-training loss.
This outperforms the standard expert-choice MoE with 8 mixed-modal experts,
which achieves 3x overall FLOPs savings (3x for text, 2.8x for image).
Combining MoMa with mixture-of-depths (MoD) further improves pre-training FLOPs
savings to 4.2x overall (text: 3.4x, image: 5.3x), although this combination
hurts performance in causal inference due to increased sensitivity to router
accuracy. These results demonstrate MoMa's potential to significantly advance
the efficiency of mixed-modal, early-fusion language model pre-training, paving
the way for more resource-efficient and capable multimodal AI systems.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFjrEKwjAURbM4iPoBTuYHUtPaUnETSXEJdHAvD030QU3Ka2rbv1eLu9OBy4F7GFvHMkr3WSa3QAO-oiSVeZTEeS7nrNRew4Era_GKxgWugOpRFF2L3vGSjAgE6NDdeY_hwTUOoSPDveXa36DGMIpjD59FDY2h0C7ZzELdmtWPC7Yp1OV0FtN31RA-gcbq21BNDbv_xhuv8Twn</recordid><startdate>20240731</startdate><enddate>20240731</enddate><creator>Lin, Xi Victoria</creator><creator>Shrivastava, Akshat</creator><creator>Luo, Liang</creator><creator>Iyer, Srinivasan</creator><creator>Lewis, Mike</creator><creator>Ghosh, Gargi</creator><creator>Zettlemoyer, Luke</creator><creator>Aghajanyan, Armen</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240731</creationdate><title>MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts</title><author>Lin, Xi Victoria ; Shrivastava, Akshat ; Luo, Liang ; Iyer, Srinivasan ; Lewis, Mike ; Ghosh, Gargi ; Zettlemoyer, Luke ; Aghajanyan, Armen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2407_217703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Lin, Xi Victoria</creatorcontrib><creatorcontrib>Shrivastava, Akshat</creatorcontrib><creatorcontrib>Luo, Liang</creatorcontrib><creatorcontrib>Iyer, Srinivasan</creatorcontrib><creatorcontrib>Lewis, Mike</creatorcontrib><creatorcontrib>Ghosh, Gargi</creatorcontrib><creatorcontrib>Zettlemoyer, Luke</creatorcontrib><creatorcontrib>Aghajanyan, Armen</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lin, Xi Victoria</au><au>Shrivastava, Akshat</au><au>Luo, Liang</au><au>Iyer, Srinivasan</au><au>Lewis, Mike</au><au>Ghosh, Gargi</au><au>Zettlemoyer, Luke</au><au>Aghajanyan, Armen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts</atitle><date>2024-07-31</date><risdate>2024</risdate><abstract>We introduce MoMa, a novel modality-aware mixture-of-experts (MoE)
architecture designed for pre-training mixed-modal, early-fusion language
models. MoMa processes images and text in arbitrary sequences by dividing
expert modules into modality-specific groups. These groups exclusively process
designated tokens while employing learned routing within each group to maintain
semantically informed adaptivity. Our empirical results reveal substantial
pre-training efficiency gains through this modality-specific parameter
allocation. Under a 1-trillion-token training budget, the MoMa 1.4B model,
featuring 4 text experts and 4 image experts, achieves impressive FLOPs
savings: 3.7x overall, with 2.6x for text and 5.2x for image processing
compared to a compute-equivalent dense baseline, measured by pre-training loss.
This outperforms the standard expert-choice MoE with 8 mixed-modal experts,
which achieves 3x overall FLOPs savings (3x for text, 2.8x for image).
Combining MoMa with mixture-of-depths (MoD) further improves pre-training FLOPs
savings to 4.2x overall (text: 3.4x, image: 5.3x), although this combination
hurts performance in causal inference due to increased sensitivity to router
accuracy. These results demonstrate MoMa's potential to significantly advance
the efficiency of mixed-modal, early-fusion language model pre-training, paving
the way for more resource-efficient and capable multimodal AI systems.</abstract><doi>10.48550/arxiv.2407.21770</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Learning |
title | MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts |
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