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    Announcement

    SGL project adds layer_boundary to make features reusable across models

    A contributor says the shared stage boundary covers communication, residual handling, token movement and normalization.

    Banghua ZhuBZ
    Cheng WanCW
    2 Sources, 3h ago, first seen 3h ago

    TLDR

    An SGL project contributor says layer_boundary puts TP/DP/CP communication, residual handling, token movement and normalization behind a shared stage boundary. They say features such as Attention DP × TP × CP can now be integrated once and reused across many model architectures instead of being reimplemented for each model.

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    2 Sources, first seen 3h ago

    Combined views

    1.9K

    2 Sources, first seen 3h ago

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    2 Sources

    Cheng Wan@ChengWan17We just landed layer_boundary in @sgl_project. The biggest win: feature compatibility across models. Instead of re-implementing TP/DP/CP communication, residual handling, token movement, and normalization for every model, we put these semantics behind a shared stage boundary. Now features like Attention DP × TP × CP can be integrated once at the boundary level and reused across many model architectures. Less model-specific plumbing. Much broader feature compatibility.3h
    Banghua Zhu@BanghuaZRT @ChengWan17: We just landed layer_boundary in @sgl_project. The biggest win: feature compatibility across models. Instead of re-implem…1h

    2 Sources

    Cheng Wan@ChengWan17We just landed layer_boundary in @sgl_project. The biggest win: feature compatibility across models. Instead of re-implementing TP/DP/CP communication, residual handling, token movement, and normalization for every model, we put these semantics behind a shared stage boundary. Now features like Attention DP × TP × CP can be integrated once at the boundary level and reused across many model architectures. Less model-specific plumbing. Much broader feature compatibility.3h
    Banghua Zhu@BanghuaZRT @ChengWan17: We just landed layer_boundary in @sgl_project. The biggest win: feature compatibility across models. Instead of re-implem…1h