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Fix cache reuse safety in non-allocating finite_difference_jacobian#214

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ChrisRackauckas merged 2 commits intoJuliaDiff:masterfrom
ChrisRackauckas-Claude:fix-cache-updates
Apr 30, 2026
Merged

Fix cache reuse safety in non-allocating finite_difference_jacobian#214
ChrisRackauckas merged 2 commits intoJuliaDiff:masterfrom
ChrisRackauckas-Claude:fix-cache-updates

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Summary

  • Fixes Fix cache updates #213. The non-allocating cached finite_difference_jacobian was reading the unperturbed components of x from cache.x1 (which can be uninitialized when the cache is built via similar(x), or stale when the cache is reused at a different x) instead of from the input x. This produced junk Jacobians in :central mode — most visibly via DifferentiationInterface.jl (see FiniteDiff with :central includes junk DifferentiationInterface.jl#983).
  • The fix has the :central dense and sparse paths perturb around vecx (the input) directly. The in-place ! version was already safe via copyto!(x1, x). A defensive copyto!(cache.x1, x) is added to the non-allocating entry point too (guarded for immutability), so the cache is observably consistent with x after the call.
  • Audited the broader caveat from @gdalle that the issue might be wider: GradientCache, HessianCache, JVPCache already update their scratch fields from x correctly. DerivativeCache has no x cache. New tests pin all of those down to prevent regressions.

Reproducer (from JuliaDiff/DifferentiationInterface.jl#983)

julia> using DifferentiationInterface, FiniteDiff

julia> foo(x) = [2x[1], 3x[2], 4x[1]];

julia> jacobian(foo, AutoFiniteDiff(fdjtype=Val(:central)), zeros(2))
# before:
# 3×2 Matrix{Float64}:
#  2.0           3.94723e-309
#  6.73119e-317  3.0
#  4.0           7.89446e-309
# after:
# 3×2 Matrix{Float64}:
#  2.0  0.0
#  0.0  3.0
#  4.0  0.0

Test plan

  • New test/cache_reuse_tests.jl (27 tests) covers:
    • JacobianCache reuse, out-of-place + in-place, :forward/:central/:complex, dense + sparse, including: fresh cache reused at a new x, explicitly-poisoned (fill!(.., 1e10)) cache, and a direct DI-style similar(x) reproduction of #983.
    • GradientCache, JVPCache, HessianCache reuse with poisoned scratch buffers (these were already safe but lacked regression coverage).
    • In-place :central sparse path: verifies x is not mutated across the call.
  • Wired the new file into the Core test group in test/runtests.jl.
  • Full Core suite passes locally: Standard 219 + Color 39 + OutOfPlace 25 + new CacheReuse 27 = 310 passing (plus 1 pre-existing @test_broken).
  • CI green.

🤖 Generated with Claude Code

The non-allocating cached `finite_difference_jacobian` was reading the
unperturbed components of x from `cache.x1` instead of the input `x`.
When a caller (e.g. DifferentiationInterface) builds the cache via
`similar(x)` (uninitialized memory) or reuses it at a different x than
it was constructed for, the `:central` mode produced wildly wrong
Jacobians, while `:forward` and `:complex` were unaffected because they
already read from the input directly.

The in-place `finite_difference_jacobian!` was already safe due to its
upfront `copyto!(x1, x)`. Other caches (`GradientCache`, `HessianCache`,
`JVPCache`, `DerivativeCache`) were also already safe and now have
regression tests.

Fixes JuliaDiff#213. Closes the upstream report at
JuliaDiff/DifferentiationInterface.jl#983.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
@ChrisRackauckas-Claude
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CI status:

Check Result
test (Core, 1) ✅ pass — includes the new 27-test cache-reuse safety set
build ✅ pass
OrdinaryDiffEq.jl/InterfaceII/1 ✅ pass
codecov/patch, codecov/project ✅ pass
test (Downstream, 1) ❌ fail — pre-existing on master, unrelated to this PR

The Downstream failure is UndefVarError: Rodas4P not defined in test/downstream/ordinarydiffeq_tridiagonal_solve.jl, caused by an upstream rename in OrdinaryDiffEq. Same failure on master in run 24500721998 (2026-04-16, before this PR). Worth fixing separately by either re-importing Rodas4P from its new location or replacing it with whatever solver superseded it.

Two upstream breaking changes had silently broken the Tridiagonal
solve test on master since (at least) 2026-04-16:

- `Rodas4P` is no longer reexported by the OrdinaryDiffEq meta-package;
  it now lives in OrdinaryDiffEqRosenbrock.
- `autodiff::Bool` is no longer accepted; you must pass an ADTypes
  specifier such as `AutoFiniteDiff()`.

Switch to those new APIs (and update the Project.toml deps) so the
downstream test exercises the FiniteDiff path again. The expected
gradient value drifts slightly with solver internals between releases,
so use a loose `atol=1e-2` — this test is a smoke test that
differentiation through a Rosenbrock solver with a Tridiagonal jacobian
prototype works, not a tight numeric regression.

Co-Authored-By: Chris Rackauckas <accounts@chrisrackauckas.com>
@ChrisRackauckas ChrisRackauckas merged commit 88355ea into JuliaDiff:master Apr 30, 2026
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Fix cache updates

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