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feat(ad): linear-operator pullback rules (DESIGN §6) — re-verify against current Enzyme

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Tranquilo
Stack tecnológico
julia
Área
backend, performance, testing-qa

Línea de trabajo

Start with DESIGN.md §6 and test/autodiff.jl, then rerun the mixed pointer/isbits rule-argument MWE using Enzyme at least 0.13.202 under Julia 1.12. Done means establishing whether the calling-convention blocker remains; if it does, document the result against EnzymeAD/Enzyme.jl#3322 before revisiting the rule implementation.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

enhancement

Status update 2026-09-30. This is a performance item, not a correctness one: gradients are already exact via the tape (see below). The upstream blocker still needs re-verification. The tracker named below, EnzymeAD/Enzyme.jl#2707, was closed as completed on 2026-02-01, before this issue was filed. No Manifest is committed any more; the test suite is currently on Enzyme 0.13.204 (the version cited in the 1.13 skip comments at test/autodiff.jl:89,198 and test/enzyme_rules.jl:133), and the current release is 0.13.208. CI now runs Julia 1.10 and 1.13 only (1.12 dropped in d989b79), so the 1.12 CallingConventionMismatchError below is never exercised in CI. So "blocked" is unverified: re-run the mixed pointer/isbits rule-argument MWE on current Enzyme under Julia 1.12 and 1.13 before treating this as blocked. If it still fails, EnzymeAD/Enzyme.jl#3322 (CallingConventionMismatchError in classify_arguments for a mixed pointer/isbits struct, still open; its title scopes it to nested differentiation on Julia ≤ 1.11) is the closest live upstream issue to point at instead. Scope is Enzyme only: the Mooncake rrule!! half of §6 is out, since Mooncake rule parity (#44) was closed as not planned; Mooncake stays a test-time CPU cross-check.

DESIGN.md §6 specifies custom rules that route the reverse-mode pullback of a linear operator through its already-declared apply_adjoint!, instead of taping the leaf bodies. That is still the right design, and it is still unbuilt — ext/MatrixFreeOperatorsEnzymeCoreExt.jl registers rules only for the _exchange_storage!/_bc_storage! seams and their transposes — but the blocker turned out to be upstream, not us.

Why it is blocked

From Julia 1.12, an Enzyme custom-rule argument may not have a type that mixes GC-tracked pointers with inline floats. It is a hard error:

CallingConventionMismatchError: ... has mismatch between roots_activep (DFT_DUP_ARG) and activep (DFT_CONSTANT).
  - Non-rooted types: (Int64, ..., Float64, Float64, ...)
  - Rooted types: (Vector{Matrix{Float64}}, ..., Base.RefValue{...})
Hint: You are currently on Julia 1.12, which changed its calling convention.

Tracking issue: https://github.com/EnzymeAD/Enzyme.jl/issues/2707

Field, BlockField, PackedBlockField and BlockForest all qualify, through the grid they embed. That disqualifies every seam the §6 plan named:

Intended seam Blocked because
apply(L, x) takes and returns a Field — cannot be worked around by re-parameterising arguments
apply!(y, L, x, g, α, β) Field arguments
mul!(y, ::PreparedOperator, x, α, β) PreparedOperator argument
_forest_sweep! / _forest_adjoint_sweep! AbstractBlockField arguments

The halo/BC sweeps were reachable only because they could be re-cut against raw storage (_exchange_storage! / _bc_storage! over a block vector or packed array plus isbits descriptors) — see _storage/_layout/BlockLayout. apply cannot be re-cut that way while it returns a field.

What we lose meanwhile

Not correctness — the tape still produces the right gradients, verified against the declared adjoint throughout test/autodiff.jl (at rtol=1e-10 on single grids, 1e-8 on forests). What we lose is the efficiency win of skipping the leaf tape entirely, and the structural guarantee for @kernel-authored leaves that §6 calls mandatory (the exchange sweeps now have it; the operator sweeps do not).

When to revisit

Once the MWE above passes on current Enzyme (i.e. once Enzyme.jl#2707's fix is confirmed to cover this case). The scoping work is already done and should be reused:

  • gate on operator type, one rule per parameter-free linear leaf (Laplacian, Derivative, Gradient, Divergence, IdentityOp, Restriction, Prolongation) — a rule cannot decline and fall back to taping, so a trait branch inside the rule body is not an option;
  • deliberately exclude ScalingOp and Advection{_,<:AbstractField}, whose coefficient fields are differentiable parameters — a rule firing there would silently zero dκ (test/autodiff.jl guards this, but its ScalingOp κ testset is skipped on Julia 1.13, so in CI only the 1.10 job runs it until #108 re-enables it);
  • accumulate with apply_adjoint!(x̄, L, ȳ, g, α, true), and keep ghost cotangents exactly zero;
  • rule bodies must not allocate (see #40).

—
🤖 Claude brought this status note forward from the Julia 1.12 era.
claude-opus-5-5[1m] (high) · claude-code 2.1.285 · unreviewed

Lenguaje dominante
Julia
Estrellas
3
Forks
1
Merge medio
1 d 28 min
PR fusionados (30 d)
6

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