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      "statement": "# DeepSpeed Ulysses: all-to-all sequence and head redistribution\n\nSharded tokens across devices and used all-to-all redistribution to give each device full sequence access for a subset of attention heads, followed by inverse redistribution and ZeRO-compatible training.\n\n## Scientific scope\n\nInherits sequence/model parallelism, ZeRO and attention kernels. Constant communication volume assumes proportional sequence/device scaling in the paper's model; it is not constant end-to-end runtime. Head-count divisibility/scalability and network topology matter. Original seven-author Microsoft report controls attribution.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nSam Ade Jacobs; Masahiro Tanaka; Chengming Zhang; Minjia Zhang; Leon Song; Samyam Rajbhandari; Yuxiong He\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://arxiv.org/abs/2309.14509v1)\n",
      "summary": "Sharded tokens across devices and used all-to-all redistribution to give each device full sequence access for a subset of attention heads, followed by inverse redistribution and ZeRO-compatible training.",
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