[AutoDiff] Canonicalize JVP/VJP types to re-enable LoadableByAddress.#27298
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dan-zheng wants to merge 9 commits intotensorflowfrom
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[AutoDiff] Canonicalize JVP/VJP types to re-enable LoadableByAddress.#27298dan-zheng wants to merge 9 commits intotensorflowfrom
dan-zheng wants to merge 9 commits intotensorflowfrom
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Canonicalize JVPs/VJPs to return maximally abstracted linear map functions with `@in_guaranteed` parameters and an `@out` result. This is a necessary step towards re-enabling LoadableByAddress: linear map type is no longer computed based on the original function type. See TF-11 for more info regarding LoadableByAddress. See TF-625 for more info regarding maximally abstracted linear maps.
- Change differential generation to use only tangent buffers.
- Change pullback visitors to use only adjoint buffers.
- Change adjoint of active value propagation to use only adjoint buffers.
- Mark all tangent/adjoint value helpers as `[[deprecated]]`.
- They are not deleted because helpers may become useful after
SIL opaque values are introduced.
Will re-add in follow-up for separation of concerns.
Canonicalize JVP/VJP types so the returned original result is always indirect. This makes it so JVP/VJP type no longer depends on the abstraction of the original function type, making it possible to re-enable LoadableByAddress. Resolves TF-626.
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A less invasive approach for re-enabling LoadableByAddress was found: #27923 Maximal indirection will not be pursued further: the differentiation transform should not be forced to generate maximally indirect code to workaround issues caused by LoadableByAddress. |
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Canonicalize JVP/VJP types so the returned original result is always indirect.
This makes it so JVP/VJP type no longer depends on the abstraction of the
original function type, making it possible to re-enable LoadableByAddress.
Resolves TF-626.
Note: this PR depends on #26709.