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Blockwise Parallel Transformer: fused attention and feedforward blocks

Contribution grade

Memory-efficient Transformer training · Human-designed research method or resource

Reference score65,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Fused blockwise exact-attention computation with feedforward computation on completed query blocks, reducing intermediate activation memory without materializing whole-sequence feedforward intermediates.

Scientific scope

Fused blockwise exact-attention computation with feedforward computation on completed query blocks, reducing intermediate activation memory without materializing whole-sequence feedforward intermediates. Inherits online softmax, FlashAttention and memory-efficient exact attention. The contribution extends blockwise fusion to feedforward activations, not invention of exact attention or subquadratic dense-attention compute. Tested context-length gains depend on model, checkpointing and accelerator memory.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits online softmax, FlashAttention and memory-efficient exact attention. The contribution extends blockwise fusion to feedforward activations, not invention of exact attention or subquadratic dense-attention compute. Tested context-length gains depend on model, checkpointing and accelerator memory.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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