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Revise 0.15.0 HISTORY.md (#162)
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## 0.15.0
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New evaluator-preparation and AD interface: `prepare` binds a callable to a sample input (vector or `NamedTuple`); `value_and_gradient!!` / `value_and_jacobian!!` return value-and-derivative pairs from the resulting `Prepared` wrapper. The `!!` suffix signals the returned derivative may alias the cache copy if you need to keep it.
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Added a new evaluator-preparation interface for AD backends. `prepare` binds a callable to a sample input, either a vector or a `NamedTuple`, and returns a `Prepared` wrapper. `value_and_gradient!!` and `value_and_jacobian!!` then compute value-and-derivative pairs from that wrapper. The `!!` suffix means the returned derivative may alias backend cache storage, so copy it if you need to keep it across calls.
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```julia
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using ADTypes, Mooncake # or DifferentiationInterface + ForwardDiff
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# val == -7.0; grad == [-1.0, -2.0, -3.0]
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```
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Two new AD-backend extensions ship with it: `AbstractPPLDifferentiationInterfaceExt` (any DI backend) and `AbstractPPLMooncakeExt` (`AutoMooncake`, `AutoMooncakeForward`). `AbstractPPLTestExt` gains a conformance harness via `generate_testcases` / `run_testcases` (reserved groups: `:vector`, `:namedtuple`, `:edge`, `:cache_reuse`).
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Added two AD-backend extensions:
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- `AbstractPPLDifferentiationInterfaceExt`, supporting DifferentiationInterface backends.
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- `AbstractPPLMooncakeExt`, supporting `AutoMooncake` and `AutoMooncakeForward`.
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The new evaluator path supports scalar gradients, vector-output Jacobians, `NamedTuple` inputs, reusable AD caches, input-shape checks, and constant context arguments via `context::Tuple`.
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`AbstractPPLTestExt` now provides a small AD conformance harness through `generate_testcases` and `run_testcases`, with reserved groups `:vector`, `:namedtuple`, `:edge`, and `:cache_reuse`.
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See [`docs/src/evaluators.md`](docs/src/evaluators.md) for the full interface, the `check_dims` and `context::Tuple` options, the `NamedTuple` input path, and extension-author guidance.
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