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attention-variants-from-papers

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Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

tasks-extra/diff-transformer_impl/environment/skills/attention-variants-from-papers/SKILL.md

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# Attention Variants from Papers

Use this skill when a paper changes how attention scores, branches, normalization, or head sharing work, but the module still needs to behave like a drop-in transformer attention block.

## Workflow

1. Read the paper for invariants, not names.
   Use [paper-to-implementation.md](references/paper-to-implementation.md) to extract the external contract, the changed computation, and the training-time constraints.
2. Build a shape ledger before coding.
   Use [shape-ledger.md](references/shape-ledger.md) to track projections, head grouping, branch count, and output width.
3. Choose the mechanism pattern.
   Use [mechanism-patterns.md](references/mechanism-patterns.md) for subtractive attention, branch mixing, learned gates, and extra normalization.
4. Preserve the module boundary.
   Keep the same input and output shape, mask semantics, positional encoding flow, and cache behavior unless the task explicitly changes them.
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