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SciML / SciMLSensitivity.jl

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
https://docs.sciml.ai/SciMLSensitivity/stable/
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Changing parameter component array in callback fails for ReverseDiffVJP() #1073

Open m-bossart opened 4 months ago

m-bossart commented 4 months ago

Describe the bug 🐞

I'm trying to implement a discrete callback which changes the value of a parameter. I'm using ComponentArrays for the parameters as my application is for large systems with complicated parameter handling. Indexing into the component array by symbol inside the callback fails when using the ReverseDiffVJP() option during sensitivity analysis.

Expected behavior

I expect to be able to index and modify the componentarray parameters using symbol indexing.

Minimal Reproducible Example 👇 The MWE shows a version that works which is derived from an example in the tests. In the lower version, the callback attempts to modify p using the symbol :a instead of the numerical index. This causes a failure during sensitivity analysis. 

using OrdinaryDiffEq, Zygote
using SciMLSensitivity, Test, ForwardDiff

#Version from the test (works)
function fiip(du, u, p, t)
    du[1] = dx = p[1] * u[1] - p[2] * u[1] * u[2]
    du[2] = dy = -p[3] * u[2] + p[4] * u[1] * u[2]
end

p = [1.5, 1.0, 3.0, 1.0]
u0 = [1.0; 1.0]

condition(u, t, integrator) = t == 5
affect!(integrator) = (integrator.p[1] = 2 * integrator.p[1] .- 0.5)
cb = DiscreteCallback(condition, affect!, save_positions = (false, false))
tstops = [5.0]

prob = ODEProblem(fiip, u0, (0.0, 10.0), p)
du01, dp1 = Zygote.gradient(
    (u0, p) -> sum(solve(prob, Tsit5(), u0 = u0, p = p,
        callback = cb, tstops = tstops,
        saveat = 0.5,
        sensealg = BacksolveAdjoint(autojacvec=ReverseDiffVJP()))),
    u0, p)

## Version using ComponentArrays 
using ComponentArrays
function fiip(du, u, p, t)
    du[1] = dx = p[:a] * u[1] - p[:b] * u[1] * u[2]
    du[2] = dy = -p[:c] * u[2] + p[:d] * u[1] * u[2]
end

p = ComponentArray(a = 1.5, b = 1.0, c = 3.0, d= 1.0)
u0 = [1.0; 1.0]

condition(u, t, integrator) = t == 5
affect!(integrator) = (integrator.p[:a] = 2 * integrator.p[1] .- 0.5)      #DIFFERENCE HERE: Index with :a instead of 1
cb = DiscreteCallback(condition, affect!, save_positions = (false, false))
tstops = [5.0]

prob = ODEProblem(fiip, u0, (0.0, 10.0), p)
du01, dp1 = Zygote.gradient(
    (u0, p) -> sum(solve(prob, Tsit5(), u0 = u0, p = p,
        callback = cb, tstops = tstops,
        saveat = 0.5,
        sensealg = BacksolveAdjoint(autojacvec=ReverseDiffVJP()))),
    u0, p)

Error & Stacktrace ⚠️

ERROR: ArgumentError: invalid index: :a of type Symbol
Stacktrace:
  [1] to_index(i::Symbol)
    @ Base .\indices.jl:300
  [2] to_index(A::Vector{ReverseDiff.TrackedReal{Float64, Float64, ReverseDiff.TrackedArray{…}}}, i::Symbol)
    @ Base .\indices.jl:277
  [3] _to_indices1(A::Vector{ReverseDiff.TrackedReal{…}}, inds::Tuple{Base.OneTo{…}}, I1::Symbol)
    @ Base .\indices.jl:359
  [4] to_indices
    @ .\indices.jl:354 [inlined]
  [5] to_indices
    @ .\indices.jl:345 [inlined]
  [6] setindex!
    @ .\abstractarray.jl:1396 [inlined]
  [7] affect!(integrator::SciMLSensitivity.FakeIntegrator{Vector{…}, Vector{…}, ReverseDiff.TrackedReal{…}, Float64})
    @ Main c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\TestingEnzyme\enzyme_mwe_callback_indexing_bug.jl:38
  [8] (::SciMLSensitivity.var"#273#275"{…})(du::Vector{…}, u::ReverseDiff.TrackedArray{…}, p::ReverseDiff.TrackedArray{…}, t::ReverseDiff.TrackedReal{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\callback_tracking.jl:389
  [9] (::SciMLSensitivity.var"#123#127"{…})(u::ReverseDiff.TrackedArray{…}, p::ReverseDiff.TrackedArray{…}, t::ReverseDiff.TrackedArray{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\adjoint_common.jl:366
 [10] ReverseDiff.GradientTape(f::Function, input::Tuple{…}, cfg::ReverseDiff.GradientConfig{…})
    @ ReverseDiff C:\Users\Matt Bossart\.julia\packages\ReverseDiff\p1MzG\src\api\tape.jl:207
 [11] ReverseDiff.GradientTape(f::Function, input::Tuple{Vector{…}, ComponentVector{…}, Vector{…}})
    @ ReverseDiff C:\Users\Matt Bossart\.julia\packages\ReverseDiff\p1MzG\src\api\tape.jl:204
 [12] get_paramjac_config(autojacvec::ReverseDiffVJP{…}, p::ComponentVector{…}, f::SciMLSensitivity.var"#273#275"{…}, y::Vector{…}, _p::ComponentVector{…}, _t::Float64; numindvar::Int64, alg::Nothing, isinplace::Bool, isRODE::Bool, _W::Nothing)
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\adjoint_common.jl:362
 [13] get_cb_diffcaches
    @ C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\callback_tracking.jl:468 [inlined]
 [14] _setup_reverse_callbacks(cb::DiscreteCallback{…}, affect::SciMLSensitivity.TrackedAffect{…}, sensealg::BacksolveAdjoint{…}, dgdu::Function, dgdp::Nothing, loss_ref::Base.RefValue{…}, terminated::Bool)
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\callback_tracking.jl:255
 [15] _setup_reverse_callbacks
    @ C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\callback_tracking.jl:219 [inlined]
 [16] _broadcast_getindex_evalf
    @ .\broadcast.jl:709 [inlined]
 [17] _broadcast_getindex
    @ .\broadcast.jl:682 [inlined]
 [18] (::Base.Broadcast.var"#31#32"{Base.Broadcast.Broadcasted{…}})(k::Int64)
    @ Base.Broadcast .\broadcast.jl:1118
 [19] ntuple
    @ .\ntuple.jl:48 [inlined]
 [20] copy
    @ .\broadcast.jl:1118 [inlined]
 [21] materialize
    @ .\broadcast.jl:903 [inlined]
 [22] setup_reverse_callbacks(cb::CallbackSet{…}, sensealg::BacksolveAdjoint{…}, dgdu::Function, dgdp::Nothing, cur_time::Base.RefValue{…}, terminated::Bool)
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\callback_tracking.jl:200
 [23] generate_callbacks(sensefun::SciMLSensitivity.ODEBacksolveSensitivityFunction{…}, dgdu::Function, dgdp::Nothing, λ::Vector{…}, t::Vector{…}, t0::Float64, callback::CallbackSet{…}, init_cb::Bool, terminated::Bool)
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\adjoint_common.jl:593
 [24] ODEAdjointProblem(sol::ODESolution{…}, sensealg::BacksolveAdjoint{…}, alg::Tsit5{…}, t::Vector{…}, dgdu_discrete::SciMLSensitivity.var"#df_iip#315"{…}, dgdp_discrete::Nothing, dgdu_continuous::Nothing, dgdp_continuous::Nothing, g::Nothing, ::Val{…}; checkpoints::Vector{…}, callback::CallbackSet{…}, z0::Nothing, M::Nothing, nilss::Nothing, tspan::Tuple{…}, kwargs::@Kwargs{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\backsolve_adjoint.jl:189
 [25] _adjoint_sensitivities(sol::ODESolution{…}, sensealg::BacksolveAdjoint{…}, alg::Tsit5{…}; t::Vector{…}, dgdu_discrete::Function, dgdp_discrete::Nothing, dgdu_continuous::Nothing, dgdp_continuous::Nothing, g::Nothing, abstol::Float64, reltol::Float64, checkpoints::Vector{…}, corfunc_analytical::Nothing, callback::CallbackSet{…}, kwargs::@Kwargs{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\sensitivity_interface.jl:406
 [26] _adjoint_sensitivities
    @ C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\sensitivity_interface.jl:390 [inlined]
 [27] #adjoint_sensitivities#63
    @ C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\sensitivity_interface.jl:386 [inlined]
 [28] (::SciMLSensitivity.var"#adjoint_sensitivity_backpass#314"{…})(Δ::ODESolution{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\waEMv\src\concrete_solve.jl:582
 [29] ZBack
    @ C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:211 [inlined]
 [30] (::Zygote.var"#kw_zpullback#53"{…})(dy::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:237
 [31] #291
    @ C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\lib\lib.jl:206 [inlined]
 [32] (::Zygote.var"#2169#back#293"{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\ZygoteRules\M4xmc\src\adjoint.jl:72
 [33] #solve#51
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1003 [inlined]
 [34] (::Zygote.Pullback{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [35] #291
    @ C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\lib\lib.jl:206 [inlined]
 [36] (::Zygote.var"#2169#back#293"{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\ZygoteRules\M4xmc\src\adjoint.jl:72
 [37] solve
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:993 [inlined]
 [38] (::Zygote.Pullback{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [39] #97
    @ c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\TestingEnzyme\enzyme_mwe_callback_indexing_bug.jl:44 [inlined]
 [40] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Float64)
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [41] (::Zygote.var"#75#76"{Zygote.Pullback{Tuple{…}, Tuple{…}}})(Δ::Float64)
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface.jl:91
 [42] gradient(::Function, ::Vector{Float64}, ::Vararg{Any})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface.jl:148
 [43] top-level scope
    @ c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\TestingEnzyme\enzyme_mwe_callback_indexing_bug.jl:43
Some type information was truncated. Use `show(err)` to see complete types.

Environment (please complete the following information):

  [b0b7db55] ComponentArrays v0.15.14
  [f6369f11] ForwardDiff v0.10.36
  [1dea7af3] OrdinaryDiffEq v6.85.0
  [1ed8b502] SciMLSensitivity v7.62.0
  [e88e6eb3] Zygote v0.6.70
  [47edcb42] ADTypes v1.5.3
  [621f4979] AbstractFFTs v1.5.0
  [7d9f7c33] Accessors v0.1.36
  [79e6a3ab] Adapt v4.0.4
  [66dad0bd] AliasTables v1.1.3
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.11.0
  [4c555306] ArrayLayouts v1.10.0
  [a9b6321e] Atomix v0.1.0
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.6
  [49dc2e85] Calculus v0.5.1
  [7057c7e9] Cassette v0.3.13
  [082447d4] ChainRules v1.69.0
  [d360d2e6] ChainRulesCore v1.24.0
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.0
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.15.0
  [b0b7db55] ComponentArrays v0.15.14
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [187b0558] ConstructionBase v1.5.5
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  [e2d170a0] DataValueInterfaces v1.0.0
  [2b5f629d] DiffEqBase v6.151.5
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  [31c24e10] Distributions v0.25.109
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  [f151be2c] EnzymeCore v0.7.6
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  [6a86dc24] FiniteDiff v2.23.1
  [f6369f11] ForwardDiff v0.10.36
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  [d41bc354] NLSolversBase v7.8.3
  [2774e3e8] NLsolve v4.5.1
  [872c559c] NNlib v0.9.18
  [77ba4419] NaNMath v1.0.2
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  [429524aa] Optim v1.9.4
  [3bd65402] Optimisers v0.3.3
  [bac558e1] OrderedCollections v1.6.3
  [1dea7af3] OrdinaryDiffEq v6.85.0
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  [b77e0a4c] InteractiveUtils
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  [3f19e933] p7zip_jll v17.4.0+2
Julia Version 1.10.4
Commit 48d4fd4843 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 16 × 11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, tigerlake)
Threads: 1 default, 0 interactive, 1 GC (on 16 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS =
ChrisRackauckas commented 4 months ago

I think this is fundamental to ReverseDiffVJP since it needs to run on the Array. We should just try to get this working with Enzyme.