JuliaDiff / JuliaDiff/ReverseDiff.jl

DimensionMismatch error

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Description

I asked this question in the #autodiff channel on Slack but haven't found out if I'm doing something wrong, hitting a bug in ReverseDiff, or hitting an incompatibility with DifferentialEquations.

I’m trying to differentiate through a system of ODEs. The function F returns simulations F(x) and I’m trying to obtain the product J(x)*u and J(x)ᵀ*v where J is the Jacobian of F. It works with ForwardDiff, but ReverseDiff throws an error…

using DifferentialEquations, DiffEqSensitivity, ForwardDiff, ReverseDiff

function ODE(dx, x, p, t)
  V, W = x
  I, μ, a, b, c = p
  dx[1] = (V - V^3 / 3 - W + I) / μ
  dx[2] = μ * (a * V - b * W + c)
end

prob = ODEProblem(ODE, [2.0; 0.0], (0.0, 20.0), [0.5, 0.08, 1.0, 0.8, 0.7])

function F(x)
  temp_prob = remake(prob, p = x)
  sol = solve(temp_prob, Vern9(), saveat = 0.2)
  return vec(sol)
end

jprod_fwd(f, x, u) = ForwardDiff.derivative(t -> f(x + t * u), 0)
jprod_fwd(F, rand(5), rand(5))  # works

jprod_rev(f, x, u) = ReverseDiff.jacobian(t -> f(x + t[1] * u), [zero(eltype(x))])
jprod_rev(F, rand(5), rand(5))  # throws DimensionMismatch ?!

jtprod_rev(f, x, u) = ReverseDiff.gradient(z -> dot(f(z), u), x)
jtprod_rev (generic function with 1 method)

jtprod_rev(F, rand(5), rand(202))
ERROR: DimensionMismatch("arrays could not be broadcast to a common size; got a dimension with lengths 5 and 6")

Here is the full error message: https://gist.github.com/7544d5c680995d4634cab19a2cdccfd6

pkg> status
      Status `~/dev/julia/BLA/myenv/Project.toml`
  [41bf760c] DiffEqSensitivity v6.57.0
  [0c46a032] DifferentialEquations v6.18.0
  [f6369f11] ForwardDiff v0.10.19
  [37e2e3b7] ReverseDiff v1.9.0

Many thanks in advance!

ps: I am aware that using ReverseDiff for J(x)*u isn't the most efficient in many cases, but it should work. It works with Zygote.

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First steps

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  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by running the provided ODE example with the listed DifferentialEquations, DiffEqSensitivity, ForwardDiff, and ReverseDiff versions, focusing on the ReverseDiff.jacobian and gradient calls. Compare the full error from the linked gist with the working ForwardDiff and Zygote cases; done means identifying whether the mismatch is a ReverseDiff bug or a package incompatibility and capturing a minimal reproducer.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
devtools
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
30/100

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