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Setup tests for driving with Enzyme #905

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Setup tests for driving with Enzyme #905

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ChrisRackauckas
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@ChrisRackauckas
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MWE:

using OrdinaryDiffEq, SciMLSensitivity, Zygote, Enzyme
prob = ODEProblem((u, p, t) -> u .* p, [2.0], (0.0, 1.0), [3.0])

struct senseloss{T}
    sense::T
end
function (f::senseloss)(u0p)
    sum(solve(prob, Tsit5(), u0 = u0p[1:1], p = u0p[2:2], abstol = 1e-12,
        reltol = 1e-12, saveat = 0.1, sensealg = f.sense))
end
u0p = [2.0, 3.0]
dup = Zygote.gradient(senseloss(InterpolatingAdjoint()), u0p)[1]
Enzyme.gradient(Reverse, senseloss(InterpolatingAdjoint()), u0p) ≈ dup

@ChrisRackauckas
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The error is:

ERROR: Duplicated Returns not yet handled
Stacktrace:
 [1] autodiff(#unused#::ReverseMode{false, FFIABI}, f::Const{senseloss{InterpolatingAdjoint{0, true, Val{:central}, Nothing}}}, #unused#::Type{Duplicated{Any}}, args::Duplicated{Vector{Float64}})
   @ Enzyme C:\Users\accou\.julia\packages\Enzyme\VS5jo\src\Enzyme.jl:205
 [2] autodiff
   @ C:\Users\accou\.julia\packages\Enzyme\VS5jo\src\Enzyme.jl:236 [inlined]
 [3] autodiff
   @ C:\Users\accou\.julia\packages\Enzyme\VS5jo\src\Enzyme.jl:222 [inlined]
 [4] gradient(#unused#::ReverseMode{false, FFIABI}, f::senseloss{InterpolatingAdjoint{0, true, Val{:central}, Nothing}}, x::Vector{Float64})
   @ Enzyme C:\Users\accou\.julia\packages\Enzyme\VS5jo\src\Enzyme.jl:809
 [5] top-level scope
   @ c:\Users\accou\OneDrive\Computer\Desktop\test.jl:94

Not sure why it would throw that since the result is a scalar and I can't get a stacktrace on it.

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codecov bot commented Sep 23, 2023 •
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Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (bd6e048) 59.16% compared to head (d95e542) 59.59%.

❗ Current head d95e542 differs from pull request most recent head e3f5850. Consider uploading reports for the commit e3f5850 to get more accurate results

Additional details and impacted files 
@@            Coverage Diff             @@
##           master     #905      +/-   ##
==========================================
+ Coverage   59.16%   59.59%   +0.43%     
==========================================
  Files          19       20       +1     
  Lines        4481     4482       +1     
==========================================
+ Hits         2651     2671      +20     
+ Misses       1830     1811      -19

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@wsmoses
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wsmoses commented Sep 24, 2023

Julia failed to auto deduce type, claiming the return type was any.

What happens if you do Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p))

@ChrisRackauckas
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Interesting, so it seems Enzyme has issues with remake. But ignoring that, the issue boils down to:

using OrdinaryDiffEq, SciMLSensitivity, Zygote, Enzyme
struct senseloss{T}
    sense::T
end
function (f::senseloss)(u0p)
	prob = ODEProblem{false}((u, p, t) -> u .* p, u0p[1:1], (0.0, 1.0), u0p[2:2])
    sum(solve(prob, Tsit5(), abstol = 1e-12, reltol = 1e-12, saveat = 0.1))
end
u0p = [2.0, 3.0]
du0p = zeros(2)
dup = Zygote.gradient(senseloss(InterpolatingAdjoint()), u0p)[1]
Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) ≈ dup

@code_warntype senseloss(InterpolatingAdjoint())(u0p)

I can code_warntype and see that it infers now. But it kills my terminal with its segfault so I can't seem to actually be able to read the stacktrace.

@ChrisRackauckas ChrisRackauckas mentioned this pull request Oct 10, 2023
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@frankschae
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Here's the output from @code_warntype senseloss(InterpolatingAdjoint())(u0p) from Chris' MWE

MethodInstance for (::senseloss{InterpolatingAdjoint{0, true, Val{:central}, Nothing}})(::Vector{Float64})
  from (f::senseloss)(u0p) @ Main ~/Enzyme905/MWE.jl:13
Arguments
  f::senseloss{InterpolatingAdjoint{0, true, Val{:central}, Nothing}}
  u0p::Vector{Float64}
Locals
  #5::var"#5#6"
  prob::ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}
Body::Float64
1 ─ %1  = Core.apply_type(Main.ODEProblem, false)::Core.Const(ODEProblem{false})
│         (#5 = %new(Main.:(var"#5#6")))
│   %3  = #5::Core.Const(var"#5#6"())
│   %4  = (1:1)::Core.Const(1:1)
│   %5  = Base.getindex(u0p, %4)::Vector{Float64}
│   %6  = Core.tuple(0.0, 1.0)::Core.Const((0.0, 1.0))
│   %7  = (2:2)::Core.Const(2:2)
│   %8  = Base.getindex(u0p, %7)::Vector{Float64}
│         (prob = (%1)(%3, %5, %6, %8))
│   %10 = Main.Tsit5()::Core.Const(Tsit5(; stage_limiter! = trivial_limiter!, step_limiter! = trivial_limiter!, thread = static(false),))
│   %11 = (:abstol, :reltol, :saveat)::Core.Const((:abstol, :reltol, :saveat))
│   %12 = Core.apply_type(Core.NamedTuple, %11)::Core.Const(NamedTuple{(:abstol, :reltol, :saveat)})
│   %13 = Core.tuple(1.0e-12, 1.0e-12, 0.1)::Core.Const((1.0e-12, 1.0e-12, 0.1))
│   %14 = (%12)(%13)::Core.Const((abstol = 1.0e-12, reltol = 1.0e-12, saveat = 0.1))
│   %15 = Core.kwcall(%14, Main.solve, prob, %10)::ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Tsit5ConstantCache, Nothing}, SciMLBase.DEStats, Nothing}
│   %16 = Main.sum(%15)::Float64
└──       return %16

and here's Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) ≈ dup with Enzyme Master

 Warning: TypeAnalysisDepthLimit
│ {[]:Pointer, [0]:Pointer, [0,0]:Pointer, [0,0,0]:Pointer, [0,0,0,0]:Pointer, [0,0,0,0,0]:Pointer, [0,0,0,0,0,-1]:Float@double, [0,0,0,0,8]:Integer, [0,0,0,0,9]:Integer, [0,0,0,0,10]:Integer, [0,0,0,0,11]:Integer, [0,0,0,0,12]:Integer, [0,0,0,0,13]:Integer, [0,0,0,0,14]:Integer, [0,0,0,0,15]:Integer, [0,0,0,0,16]:Integer, [0,0,0,0,17]:Integer, [0,0,0,0,18]:Integer, [0,0,0,0,19]:Integer, [0,0,0,0,20]:Integer, [0,0,0,0,21]:Integer, [0,0,0,0,22]:Integer, [0,0,0,0,23]:Integer, [0,0,0,0,24]:Integer, [0,0,0,0,25]:Integer, [0,0,0,0,26]:Integer, [0,0,0,0,27]:Integer, [0,0,0,0,28]:Integer, [0,0,0,0,29]:Integer, [0,0,0,0,30]:Integer, [0,0,0,0,31]:Integer, [0,0,0,0,32]:Integer, [0,0,0,0,33]:Integer, [0,0,0,0,34]:Integer, [0,0,0,0,35]:Integer, [0,0,0,0,36]:Integer, [0,0,0,0,37]:Integer, [0,0,0,0,38]:Integer, [0,0,0,0,39]:Integer, [0,0,8]:Integer, [0,0,9]:Integer, [0,0,10]:Integer, [0,0,11]:Integer, [0,0,12]:Integer, [0,0,13]:Integer, [0,0,14]:Integer, [0,0,15]:Integer, [0,0,16]:Integer, [0,0,17]:Integer, [0,0,18]:Integer, [0,0,19]:Integer, [0,0,20]:Integer, [0,0,21]:Integer, [0,0,22]:Integer, [0,0,23]:Integer, [0,0,24]:Integer, [0,0,25]:Integer, [0,0,26]:Integer, [0,0,27]:Integer, [0,0,28]:Integer, [0,0,29]:Integer, [0,0,30]:Integer, [0,0,31]:Integer, [0,0,32]:Integer, [0,0,33]:Integer, [0,0,34]:Integer, [0,0,35]:Integer, [0,0,36]:Integer, [0,0,37]:Integer, [0,0,38]:Integer, [0,0,39]:Integer, [8]:Integer, [9]:Integer, [10]:Integer, [11]:Integer, [12]:Integer, [13]:Integer, [14]:Integer, [15]:Integer, [16]:Integer, [17]:Integer, [18]:Integer, [19]:Integer, [20]:Integer, [21]:Integer, [22]:Integer, [23]:Integer, [24]:Integer, [25]:Integer, [26]:Integer, [27]:Integer, [28]:Integer, [29]:Integer, [30]:Integer, [31]:Integer, [32]:Integer, [33]:Integer, [34]:Integer, [35]:Integer, [36]:Integer, [37]:Integer, [38]:Integer, [39]:Integer}
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/U36Ed/src/utils.jl:59
┌ Warning: TypeAnalysisDepthLimit
│ {[]:Pointer, [0]:Pointer, [0,0]:Pointer, [0,0,0]:Pointer, [0,0,0,0]:Pointer, [0,0,0,0,0]:Pointer, [0,0,0,0,0,-1]:Float@double, [0,0,0,0,8]:Integer, [0,0,0,0,9]:Integer, [0,0,0,0,10]:Integer, [0,0,0,0,11]:Integer, [0,0,0,0,12]:Integer, [0,0,0,0,13]:Integer, [0,0,0,0,14]:Integer, [0,0,0,0,15]:Integer, [0,0,0,0,16]:Integer, [0,0,0,0,17]:Integer, [0,0,0,0,18]:Integer, [0,0,0,0,19]:Integer, [0,0,0,0,20]:Integer, [0,0,0,0,21]:Integer, [0,0,0,0,22]:Integer, [0,0,0,0,23]:Integer, [0,0,0,0,24]:Integer, [0,0,0,0,25]:Integer, [0,0,0,0,26]:Integer, [0,0,0,0,27]:Integer, [0,0,0,0,28]:Integer, [0,0,0,0,29]:Integer, [0,0,0,0,30]:Integer, [0,0,0,0,31]:Integer, [0,0,0,0,32]:Integer, [0,0,0,0,33]:Integer, [0,0,0,0,34]:Integer, [0,0,0,0,35]:Integer, [0,0,0,0,36]:Integer, [0,0,0,0,37]:Integer, [0,0,0,0,38]:Integer, [0,0,0,0,39]:Integer, [0,0,8]:Integer, [0,0,9]:Integer, [0,0,10]:Integer, [0,0,11]:Integer, [0,0,12]:Integer, [0,0,13]:Integer, [0,0,14]:Integer, [0,0,15]:Integer, [0,0,16]:Integer, [0,0,17]:Integer, [0,0,18]:Integer, [0,0,19]:Integer, [0,0,20]:Integer, [0,0,21]:Integer, [0,0,22]:Integer, [0,0,23]:Integer, [0,0,24]:Integer, [0,0,25]:Integer, [0,0,26]:Integer, [0,0,27]:Integer, [0,0,28]:Integer, [0,0,29]:Integer, [0,0,30]:Integer, [0,0,31]:Integer, [0,0,32]:Integer, [0,0,33]:Integer, [0,0,34]:Integer, [0,0,35]:Integer, [0,0,36]:Integer, [0,0,37]:Integer, [0,0,38]:Integer, [0,0,39]:Integer, [8]:Integer, [9]:Integer, [10]:Integer, [11]:Integer, [12]:Integer, [13]:Integer, [14]:Integer, [15]:Integer, [16]:Integer, [17]:Integer, [18]:Integer, [19]:Integer, [20]:Integer, [21]:Integer, [22]:Integer, [23]:Integer, [24]:Integer, [25]:Integer, [26]:Integer, [27]:Integer, [28]:Integer, [29]:Integer, [30]:Integer, [31]:Integer, [32]:Integer, [33]:Integer, [34]:Integer, [35]:Integer, [36]:Integer, [37]:Integer, [38]:Integer, [39]:Integer}
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/U36Ed/src/utils.jl:59
┌ Warning: TypeAnalysisDepthLimit
│ {[]:Pointer, [0]:Pointer, [0,0]:Pointer, [0,0,0]:Pointer, [0,0,0,0]:Pointer, [0,0,0,0,-1]:Float@double, [0,0,0,8]:Integer, [0,0,0,9]:Integer, [0,0,0,10]:Integer, [0,0,0,11]:Integer, [0,0,0,12]:Integer, [0,0,0,13]:Integer, [0,0,0,14]:Integer, [0,0,0,15]:Integer, [0,0,0,16]:Integer, [0,0,0,17]:Integer, [0,0,0,18]:Integer, [0,0,0,19]:Integer, [0,0,0,20]:Integer, [0,0,0,21]:Integer, [0,0,0,22]:Integer, [0,0,0,23]:Integer, [0,0,0,24]:Integer, [0,0,0,25]:Integer, [0,0,0,26]:Integer, [0,0,0,27]:Integer, [0,0,0,28]:Integer, [0,0,0,29]:Integer, [0,0,0,30]:Integer, [0,0,0,31]:Integer, [0,0,0,32]:Integer, [0,0,0,33]:Integer, [0,0,0,34]:Integer, [0,0,0,35]:Integer, [0,0,0,36]:Integer, [0,0,0,37]:Integer, [0,0,0,38]:Integer, [0,0,0,39]:Integer, [0,8]:Integer, [0,9]:Integer, [0,10]:Integer, [0,11]:Integer, [0,12]:Integer, [0,13]:Integer, [0,14]:Integer, [0,15]:Integer, [0,16]:Integer, [0,17]:Integer, [0,18]:Integer, [0,19]:Integer, [0,20]:Integer, [0,21]:Integer, [0,22]:Integer, [0,23]:Integer, 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[48,19]:Integer, [48,20]:Integer, [48,21]:Integer, [48,22]:Integer, [48,23]:Integer, [48,24]:Integer, [48,25]:Integer, [48,26]:Integer, [48,27]:Integer, [48,28]:Integer, [48,29]:Integer, [48,30]:Integer, [48,31]:Integer, [48,32]:Integer, [48,33]:Integer, [48,34]:Integer, [48,35]:Integer, [48,36]:Integer, [48,37]:Integer, [48,38]:Integer, [48,39]:Integer, [56]:Pointer, [56,0]:Pointer, [56,0,0]:Pointer, [56,0,0,0]:Pointer, [56,0,0,0,0]:Pointer, [56,0,0,0,0,0]:Pointer, [56,0,0,0,0,8]:Integer, [56,0,0,0,0,9]:Integer, [56,0,0,0,0,10]:Integer, [56,0,0,0,0,11]:Integer, [56,0,0,0,0,12]:Integer, [56,0,0,0,0,13]:Integer, [56,0,0,0,0,14]:Integer, [56,0,0,0,0,15]:Integer, [56,0,0,0,0,16]:Integer, [56,0,0,0,0,17]:Integer, [56,0,0,0,0,18]:Integer, [56,0,0,0,0,19]:Integer, [56,0,0,0,0,20]:Integer, [56,0,0,0,0,21]:Integer, [56,0,0,0,0,22]:Integer, [56,0,0,0,0,23]:Integer, [56,0,0,0,0,24]:Integer, [56,0,0,0,0,25]:Integer, [56,0,0,0,0,26]:Integer, [56,0,0,0,0,27]:Integer, [56,0,0,0,0,28]:Integer, [56,0,0,0,0,29]:Integer, [56,0,0,0,0,30]:Integer, [56,0,0,0,0,31]:Integer, [56,0,0,0,0,32]:Integer, [56,0,0,0,0,33]:Integer, [56,0,0,0,0,34]:Integer, [56,0,0,0,0,35]:Integer, [56,0,0,0,0,36]:Integer, [56,0,0,0,0,37]:Integer, [56,0,0,0,0,38]:Integer, [56,0,0,0,0,39]:Integer, [56,0,0,8]:Integer, [56,0,0,9]:Integer, [56,0,0,10]:Integer, [56,0,0,11]:Integer, [56,0,0,12]:Integer, [56,0,0,13]:Integer, [56,0,0,14]:Integer, [56,0,0,15]:Integer, [56,0,0,16]:Integer, [56,0,0,17]:Integer, [56,0,0,18]:Integer, [56,0,0,19]:Integer, [56,0,0,20]:Integer, [56,0,0,21]:Integer, [56,0,0,22]:Integer, [56,0,0,23]:Integer, [56,0,0,24]:Integer, [56,0,0,25]:Integer, [56,0,0,26]:Integer, [56,0,0,27]:Integer, [56,0,0,28]:Integer, [56,0,0,29]:Integer, [56,0,0,30]:Integer, [56,0,0,31]:Integer, [56,0,0,32]:Integer, [56,0,0,33]:Integer, [56,0,0,34]:Integer, [56,0,0,35]:Integer, [56,0,0,36]:Integer, [56,0,0,37]:Integer, [56,0,0,38]:Integer, [56,0,0,39]:Integer, [56,8]:Integer, [56,9]:Integer, [56,10]:Integer, [56,11]:Integer, [56,12]:Integer, [56,13]:Integer, [56,14]:Integer, [56,15]:Integer, [56,16]:Integer, [56,17]:Integer, [56,18]:Integer, [56,19]:Integer, [56,20]:Integer, [56,21]:Integer, [56,22]:Integer, [56,23]:Integer, [56,24]:Integer, [56,25]:Integer, [56,26]:Integer, [56,27]:Integer, [56,28]:Integer, [56,29]:Integer, [56,30]:Integer, [56,31]:Integer, [56,32]:Integer, [56,33]:Integer, [56,34]:Integer, [56,35]:Integer, [56,36]:Integer, [56,37]:Integer, [56,38]:Integer, [56,39]:Integer, [64]:Integer, [72]:Integer, [80]:Integer, [81]:Integer, [82]:Integer, [83]:Integer, [84]:Integer, [85]:Integer, [86]:Integer, [87]:Integer, [88]:Pointer, [88,0]:Integer, [88,1]:Integer, [88,2]:Integer, [88,3]:Integer, [88,4]:Integer, [88,5]:Integer, [88,6]:Integer, [88,7]:Integer, [88,8]:Integer, [88,9]:Integer, [88,10]:Integer, [88,11]:Integer, [88,12]:Integer, [88,13]:Integer, [88,14]:Integer, [88,15]:Integer, [88,16]:Integer, [88,17]:Integer, [88,18]:Integer, [88,19]:Integer, [88,20]:Integer, [88,21]:Integer, [88,22]:Integer, [88,23]:Integer, [88,24]:Integer, [88,25]:Integer, [88,26]:Integer, [88,27]:Integer, [88,28]:Integer, [88,29]:Integer, [88,30]:Integer, [88,31]:Integer, [88,32]:Integer, [88,33]:Integer, [88,34]:Integer, [88,35]:Integer, [88,36]:Integer, [88,37]:Integer, [88,38]:Integer, [88,39]:Integer, [88,40]:Integer, [88,41]:Integer, [88,42]:Integer, [88,43]:Integer, [88,44]:Integer, [88,45]:Integer, [88,46]:Integer, [88,47]:Integer, [88,48]:Integer, [88,49]:Integer, [88,50]:Integer, [88,51]:Integer, [88,52]:Integer, [88,53]:Integer, [88,54]:Integer, [88,55]:Integer, [88,56]:Integer, [88,57]:Integer, [88,58]:Integer, [88,59]:Integer, [88,60]:Integer, [88,61]:Integer, [88,62]:Integer, [88,63]:Integer, [88,64]:Integer, [88,65]:Integer, [88,66]:Integer, [88,67]:Integer, [88,68]:Integer, [88,69]:Integer, [88,70]:Integer, [88,71]:Integer, [88,72]:Integer, [88,73]:Integer, [88,74]:Integer, [88,75]:Integer, [88,76]:Integer, [88,77]:Integer, [88,78]:Integer, [88,79]:Integer, [88,80]:Float@double}
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/U36Ed/src/utils.jl:59
warning: didn't implement memmove, using memcpy as fallback which can result in errors
warning: didn't implement memmove, using memcpy as fallback which can result in errors
warning: didn't implement memmove, using memcpy as fallback which can result in errors
ERROR: Enzyme execution failed.
Enzyme: No custom reverse rule was appliable for Tuple{@NamedTuple{abstol::Float64, reltol::Float64, saveat::Float64}, typeof(EnzymeCore.EnzymeRules.reverse), EnzymeCore.EnzymeRules.ConfigWidth{1, true, true, (false, true, false, true, true, false)}, Const{typeof(DiffEqBase.solve_up)}, Type{Duplicated{ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Tsit5ConstantCache, Nothing}, SciMLBase.DEStats, Nothing}}}, Any, Duplicated{ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}}, Const{Nothing}, Duplicated{Vector{Float64}}, Duplicated{Vector{Float64}}, Const{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}}
Stacktrace:
 [1] #solve#40
   @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:977
 [2] solve
   @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:967
 [3] senseloss
 [4] senseloss

Stacktrace:
  [1] throwerr(cstr::Cstring)
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:1342
  [2] augmented_primal
    @ ~/.julia/packages/DiffEqBase/nKsvb/ext/DiffEqBaseEnzymeExt.jl:9 [inlined]
  [3] #solve#40
    @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:977 [inlined]
  [4] solve
    @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:967 [inlined]
  [5] senseloss
    @ ~/Enzyme905/MWE.jl:15 [inlined]
  [6] senseloss
    @ ~/Enzyme905/MWE.jl:0 [inlined]
  [7] diffejulia_senseloss_7763_inner_1wrap
    @ ~/Enzyme905/MWE.jl:0
  [8] macro expansion
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:5301 [inlined]
  [9] enzyme_call
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:4979 [inlined]
 [10] CombinedAdjointThunk
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:4944 [inlined]
 [11] autodiff(::ReverseMode{false, FFIABI}, f::Const{senseloss{…}}, ::Type{Active}, args::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/dev/Enzyme/src/Enzyme.jl:215
 [12] autodiff(::ReverseMode{false, FFIABI}, ::senseloss{InterpolatingAdjoint{…}}, ::Type, ::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/dev/Enzyme/src/Enzyme.jl:224
 [13] top-level scope
Some type information was truncated. Use `show(err)` to see complete types.

Interestingly, when I ran it again, it slightly changed. I get

ERROR: Enzyme execution failed.
Enzyme: No custom reverse rule was appliable for Tuple{@NamedTuple{abstol::Float64, reltol::Float64, saveat::Float64}, typeof(EnzymeCore.EnzymeRules.reverse), EnzymeCore.EnzymeRules.ConfigWidth{1, true, true, (false, true, false, true, true, false)}, Const{typeof(DiffEqBase.solve_up)}, Type{Duplicated{ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Tsit5ConstantCache, Nothing}, SciMLBase.DEStats, Nothing}}}, Any, Duplicated{ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#5#6", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}}, Const{Nothing}, Duplicated{Vector{Float64}}, Duplicated{Vector{Float64}}, Const{Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}}}
Stacktrace:
 [1] #solve#40
   @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:977
 [2] solve
   @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:967
 [3] senseloss
   @ ~/Enzyme905/MWE.jl:15
 [4] senseloss
   @ ~/Enzyme905/MWE.jl:0

Stacktrace:
  [1] throwerr(cstr::Cstring)
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:1342
  [2] augmented_primal
    @ ~/.julia/packages/DiffEqBase/nKsvb/ext/DiffEqBaseEnzymeExt.jl:9 [inlined]
  [3] #solve#40
    @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:977 [inlined]
  [4] solve
    @ ~/.julia/packages/DiffEqBase/nKsvb/src/solve.jl:967 [inlined]
  [5] senseloss
    @ ~/Enzyme905/MWE.jl:15 [inlined]
  [6] senseloss
    @ ~/Enzyme905/MWE.jl:0 [inlined]
  [7] diffejulia_senseloss_7763_inner_1wrap
    @ ~/Enzyme905/MWE.jl:0
  [8] macro expansion
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:5301 [inlined]
  [9] enzyme_call
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:4979 [inlined]
 [10] CombinedAdjointThunk
    @ Enzyme.Compiler ~/.julia/dev/Enzyme/src/compiler.jl:4944 [inlined]
 [11] autodiff(::ReverseMode{false, FFIABI}, f::Const{senseloss{…}}, ::Type{Active}, args::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/dev/Enzyme/src/Enzyme.jl:215
 [12] autodiff(::ReverseMode{false, FFIABI}, ::senseloss{InterpolatingAdjoint{…}}, ::Type, ::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/dev/Enzyme/src/Enzyme.jl:224
 [13] top-level scope
Some type information was truncated. Use `show(err)` to see complete types.

No segfault!

@wsmoses
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wsmoses commented Dec 19, 2023

That error message basically says "you implemented a custom rule for this function, but not for these activity states". So this presumably is an issue in SciML to complete the rule.

@wsmoses
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wsmoses commented Dec 19, 2023

I think the error is on this line:

https://github.com/SciML/DiffEqBase.jl/blob/37814405e9b8ed5f31f099cdd3c00ada02c0a25d/ext/DiffEqBaseEnzymeExt.jl#L33C136-L33C136

It should probably be "Type{Duplicated{RT}}" rahter than "Type{<:Duplicated{RT}}"

@ChrisRackauckas
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Locally I made that change and it didn't make a difference. Type{<:Duplicated{RT}} captures strictly more cases than Type{Duplicated{RT}} so I'm not surprised, I would've been surprised if that did work 😅

Can we get an error message that says which activity is not handled? I'm pretty confused as to what it might be calling.

@wsmoses
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wsmoses commented Dec 27, 2023

I think you need to release SciML/DiffEqBase.jl#982 for this to pass. Not sure, but I'm not confident that the test would use your manifest here.

ChrisRackauckas
ChrisRackauckas commented Dec 28, 2023
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@ChrisRackauckas
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The manifest does work, but because it's harmless I merged and tagged and so now this is setup without a manifest. Same issue.

@ChrisRackauckas
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There's a tracker issue that just came up in here that we can ignore for now, but the Enzyme one is more intriguing.

https://github.com/SciML/SciMLSensitivity.jl/actions/runs/7385603005/job/20090674633?pr=905#step:6:814

TypeError: in typeassert, expected SciMLBase.ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, SciMLBase.ODEFunction{false, SciMLBase.AutoSpecialize, typeof(Main.var"##Alternative AD Frontend#335".odef), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{SciMLBase.ODEFunction{false, SciMLBase.AutoSpecialize, typeof(Main.var"##Alternative AD Frontend#335".odef), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, OrdinaryDiffEq.Tsit5ConstantCache, Nothing}, SciMLBase.DEStats, Nothing}, got a value of type SciMLBase.ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Nothing, SciMLBase.ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, SciMLBase.ODEFunction{false, SciMLBase.AutoSpecialize, typeof(Main.var"##Alternative AD Frontend#335".odef), LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}}, SciMLBase.StandardODEProblem}, OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, SciMLBase.SensitivityInterpolation{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Vector{Float64}}}, SciMLBase.DEStats, Nothing}

This is by design. In the ODE solution object, sol(t) does an interpolation using internal solver values. If one uses the adjoint method then you cannot differentiate with respect to these values (it actually becomes equivalent to having to differentiate the solver), and so when someone is using the adjoint method we disable this by replacing the interpolation object from the solution before passing it on when in reverse mode AD https://github.com/SciML/SciMLBase.jl/blob/350e3d81db91d8f901a9c5d8e8977adb3a9273d5/src/solutions/ode_solutions.jl#L388-L404 so that way it throws a customized error on interpolations which violate what is differentiable.

Is there a way to make Enzyme understand that?

@wsmoses
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wsmoses commented Jan 2, 2024

No idea where that's coming from but Enzyme has an invariant that the primal and shadow are the same type/shape.

@ChrisRackauckas
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No idea where that's coming from but Enzyme has an invariant that the primal and shadow are the same type/shape.

But that cannot or should not be true in this case for correctness checking. How can we get around that?

@ChrisRackauckas
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I set it up with runtime checks so that the type invariant can hold, but I get an error message with no information deep in the compiler? 😅

Alternative AD Frontend: Error During Test at /home/runner/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:30
  Got exception outside of a @test
  LoadError: AssertionError: value_type(lhs_v) == value_type(rhs_v)

@wsmoses
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wsmoses commented Jan 7, 2024

@ChrisRackauckas I just merged EnzymeAD/Enzyme.jl#1237 to main which should provide some more information, can you rerun with that. In essence something is going wrong when analyzing the GC behavior of the custom rule code being injected and we're throwing up our hands in advance rather than risk a segfault.

@ArnoStrouwen
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Running the following example, which uses a lower level interface than the above examples:

using OrdinaryDiffEq, SciMLSensitivity, Zygote, Enzyme
struct senseloss{T}
    sense::T
end
function (f::senseloss)(u0p)
	prob = ODEProblem{false}((u, p, t) -> u .* p, u0p[1:1], (0.0, 1.0), u0p[2:2])
    integrator = init(prob, Tsit5())
    integrator.u[1]
end
u0p = [2.0, 3.0]
du0p = zeros(2)
#dup = Zygote.gradient(senseloss(InterpolatingAdjoint()), u0p)
Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p))
Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) # need to run twice to get below output

gives

julia> Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) # need to run twice to get below output
ERROR: Enzyme execution failed.
Enzyme compilation failed.
Current scope: 
; Function Attrs: mustprogress willreturn
define double @preprocess_julia_senseloss_3007_inner.1([1 x { i8, i8 }] %0, {} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %1) local_unnamed_addr #165 !dbg !8783 {
entry:
  %newstruct.i = alloca [1 x [2 x i64]], align 8
  %newstruct2.i = alloca [1 x [2 x i64]], align 8
  %2 = alloca [2 x [2 x {} addrspace(10)*]], align 8
  %3 = bitcast [1 x [2 x i64]]* %newstruct.i to i8*
  call void @llvm.lifetime.start.p0i8(i64 noundef 16, i8* noundef nonnull dereferenceable(16) %3) #183
  %4 = bitcast [1 x [2 x i64]]* %newstruct2.i to i8*
  call void @llvm.lifetime.start.p0i8(i64 noundef 16, i8* noundef nonnull dereferenceable(16) %4) #183
  %5 = bitcast [2 x [2 x {} addrspace(10)*]]* %2 to i8*
  call void @llvm.lifetime.start.p0i8(i64 noundef 32, i8* noundef nonnull dereferenceable(32) %5) #183
  %6 = call {}*** @julia.get_pgcstack() #183
  %current_task1.i16 = getelementptr inbounds {}**, {}*** %6, i64 -14
  %current_task1.i = bitcast {}*** %current_task1.i16 to {}**
  %ptls_field.i17 = getelementptr inbounds {}**, {}*** %6, i64 2
  %7 = bitcast {}*** %ptls_field.i17 to i64***
  %ptls_load.i1819 = load i64**, i64*** %7, align 8, !tbaa !159
  %8 = getelementptr inbounds i64*, i64** %ptls_load.i1819, i64 2
  %safepoint.i = load i64*, i64** %8, align 8, !tbaa !163, !invariant.load !158
  fence syncscope("singlethread") seq_cst
  call void @julia.safepoint(i64* %safepoint.i) #183, !dbg !8784
  fence syncscope("singlethread") seq_cst
  call void @llvm.memcpy.p0i8.p0i8.i64(i8* noundef nonnull writeonly align 8 dereferenceable(16) %3, i8* noundef nonnull readonly align 8 dereferenceable(16) bitcast ([2 x i64]* @_j_const1 to i8*), i64 noundef 16, i1 noundef false) #183, !dbg !8786, !tbaa !301, !alias.scope !4349, !noalias !8789
  %9 = addrspacecast {} addrspace(10)* %1 to { i8 addrspace(13)*, i64, i16, i16, i32 } addrspace(11)*, !dbg !8792
  %arraylen_ptr.i = getelementptr inbounds { i8 addrspace(13)*, i64, i16, i16, i32 }, { i8 addrspace(13)*, i64, i16, i16, i32 } addrspace(11)* %9, i64 0, i32 1, !dbg !8792
  %arraylen.i = load i64, i64 addrspace(11)* %arraylen_ptr.i, align 8, !dbg !8792, !tbaa !204, !range !207, !alias.scope !208, !noalias !209
  %.not = icmp eq i64 %arraylen.i, 0, !dbg !8798
  br i1 %.not, label %L19.i, label %L18.i, !dbg !8801

L18.i:                                            ; preds = %entry
  %10 = call noalias nonnull {} addrspace(10)* @ijl_alloc_array_1d({} addrspace(10)* noundef addrspacecast ({}* inttoptr (i64 139751658212592 to {}*) to {} addrspace(10)*), i64 noundef 1) #184, !dbg !8802
  call fastcc void @julia__copyto_impl__3570({} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %10, {} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %1, i64 noundef signext 1, i64 noundef signext 1) #183, !dbg !8807
  call void @llvm.memcpy.p0i8.p0i8.i64(i8* noundef nonnull align 8 dereferenceable(16) %4, i8* noundef nonnull align 8 dereferenceable(16) bitcast ([2 x i64]* @_j_const2 to i8*), i64 noundef 16, i1 noundef false) #183, !dbg !8786, !tbaa !301, !alias.scope !4349, !noalias !8789
  %arraylen4.i = load i64, i64 addrspace(11)* %arraylen_ptr.i, align 8, !dbg !8792, !tbaa !204, !range !207, !alias.scope !208, !noalias !209
  %11 = icmp ult i64 %arraylen4.i, 2, !dbg !8798
  br i1 %11, label %L51.i, label %L50.i, !dbg !8801

L19.i:                                            ; preds = %entry
  %12 = addrspacecast [1 x [2 x i64]]* %newstruct.i to [1 x [2 x i64]] addrspace(11)*, !dbg !8801
  call fastcc void @julia_throw_boundserror_3015({} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %1, [1 x [2 x i64]] addrspace(11)* nocapture nofree noundef nonnull readonly align 8 dereferenceable(16) %12) #185, !dbg !8801
  unreachable, !dbg !8801

L50.i:                                            ; preds = %L18.i
  %13 = call noalias nonnull {} addrspace(10)* @ijl_alloc_array_1d({} addrspace(10)* noundef addrspacecast ({}* inttoptr (i64 139751658212592 to {}*) to {} addrspace(10)*), i64 noundef 1) #184, !dbg !8802
  call fastcc void @julia__copyto_impl__3570({} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %13, {} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %1, i64 noundef signext 2, i64 noundef signext 1) #183, !dbg !8807
  call fastcc void @julia____14_3594() #183, !dbg !8809
  %newstruct7.i = call noalias nonnull dereferenceable(40) {} addrspace(10)* @julia.gc_alloc_obj({}** nonnull %current_task1.i, i64 noundef 40, {} addrspace(10)* noundef addrspacecast ({}* inttoptr (i64 139749124209808 to {}*) to {} addrspace(10)*)) #186, !dbg !8812
  %14 = addrspacecast {} addrspace(10)* %newstruct7.i to {} addrspace(10)* addrspace(11)*, !dbg !8812
  %15 = getelementptr inbounds {} addrspace(10)*, {} addrspace(10)* addrspace(11)* %14, i64 1, !dbg !8812
  store {} addrspace(10)* null, {} addrspace(10)* addrspace(11)* %15, align 8, !dbg !8812, !tbaa !1751, !alias.scope !255, !noalias !8818
  %16 = getelementptr inbounds {} addrspace(10)*, {} addrspace(10)* addrspace(11)* %14, i64 4, !dbg !8812
  store {} addrspace(10)* null, {} addrspace(10)* addrspace(11)* %16, align 8, !dbg !8812, !tbaa !1751, !alias.scope !255, !noalias !8818
  %17 = bitcast {} addrspace(10)* %newstruct7.i to i8 addrspace(10)*, !dbg !8812
  store i8 1, i8 addrspace(10)* %17, align 8, !dbg !8812
  %18 = addrspacecast {} addrspace(10)* %newstruct7.i to i8 addrspace(11)*, !dbg !8812
  %19 = getelementptr inbounds i8, i8 addrspace(11)* %18, i64 8, !dbg !8812
  %20 = bitcast i8 addrspace(11)* %19 to {} addrspace(10)* addrspace(11)*, !dbg !8812
  store atomic {} addrspace(10)* %10, {} addrspace(10)* addrspace(11)* %20 release, align 8, !dbg !8812, !tbaa !1751, !alias.scope !255, !noalias !8818
  %21 = getelementptr inbounds i8, i8 addrspace(11)* %18, i64 16, !dbg !8812
  call void @llvm.memcpy.p11i8.p0i8.i64(i8 addrspace(11)* noundef align 8 dereferenceable(16) dereferenceable_or_null(24) %21, i8* noundef nonnull align 8 dereferenceable(16) bitcast ([2 x double]* @_j_const5 to i8*), i64 noundef 16, i1 noundef false) #183, !dbg !8812, !tbaa !2607, !alias.scope !255, !noalias !8818
  %22 = getelementptr inbounds i8, i8 addrspace(11)* %18, i64 32, !dbg !8812
  %23 = bitcast i8 addrspace(11)* %22 to {} addrspace(10)* addrspace(11)*, !dbg !8812
  store atomic {} addrspace(10)* %13, {} addrspace(10)* addrspace(11)* %23 release, align 8, !dbg !8812, !tbaa !1751, !alias.scope !255, !noalias !8818
  %unbox.i.unpack = load {} addrspace(10)*, {} addrspace(10)** inttoptr (i64 139749814316464 to {} addrspace(10)**), align 16, !dbg !8819, !tbaa !414, !alias.scope !255, !noalias !256
  %unbox.i.unpack20 = load {} addrspace(10)*, {} addrspace(10)** inttoptr (i64 139749814316472 to {} addrspace(10)**), align 8, !dbg !8819, !tbaa !414, !alias.scope !255, !noalias !256
  %.fca.0.0.gep = getelementptr inbounds [2 x [2 x {} addrspace(10)*]], [2 x [2 x {} addrspace(10)*]]* %2, i64 0, i64 0, i64 0, !dbg !8827
  store {} addrspace(10)* %10, {} addrspace(10)** %.fca.0.0.gep, align 8, !dbg !8827, !noalias !8828
  %.fca.0.1.gep = getelementptr inbounds [2 x [2 x {} addrspace(10)*]], [2 x [2 x {} addrspace(10)*]]* %2, i64 0, i64 0, i64 1, !dbg !8827
  store {} addrspace(10)* %13, {} addrspace(10)** %.fca.0.1.gep, align 8, !dbg !8827, !noalias !8828
  %.fca.1.0.gep = getelementptr inbounds [2 x [2 x {} addrspace(10)*]], [2 x [2 x {} addrspace(10)*]]* %2, i64 0, i64 1, i64 0, !dbg !8827
  store {} addrspace(10)* %unbox.i.unpack, {} addrspace(10)** %.fca.1.0.gep, align 8, !dbg !8827, !noalias !8828
  %.fca.1.1.gep = getelementptr inbounds [2 x [2 x {} addrspace(10)*]], [2 x [2 x {} addrspace(10)*]]* %2, i64 0, i64 1, i64 1, !dbg !8827
  store {} addrspace(10)* %unbox.i.unpack20, {} addrspace(10)** %.fca.1.1.gep, align 8, !dbg !8827, !noalias !8828
  %24 = addrspacecast [2 x [2 x {} addrspace(10)*]]* %2 to [2 x [2 x {} addrspace(10)*]] addrspace(11)*, !dbg !8827
  %25 = call fastcc nonnull {} addrspace(10)* @julia__get_concrete_problem_58_3589([2 x [2 x {} addrspace(10)*]] addrspace(11)* nocapture noundef nonnull readonly align 8 dereferenceable(32) %24, {} addrspace(10)* noundef nonnull align 8 dereferenceable(40) %newstruct7.i) #183, !dbg !8827
  %26 = addrspacecast {} addrspace(10)* %25 to i8 addrspace(11)*, !dbg !8829
  %aggregate_load_box.i.sroa.0.0..sroa_idx = getelementptr inbounds i8, i8 addrspace(11)* %26, i64 16, !dbg !8829
  %aggregate_load_box.i.sroa.0.0..sroa_cast = bitcast i8 addrspace(11)* %aggregate_load_box.i.sroa.0.0..sroa_idx to double addrspace(11)*, !dbg !8829
  %aggregate_load_box.i.sroa.3.0..sroa_idx = getelementptr inbounds i8, i8 addrspace(11)* %26, i64 24, !dbg !8829
  %aggregate_load_box.i.sroa.3.0..sroa_cast = bitcast i8 addrspace(11)* %aggregate_load_box.i.sroa.3.0..sroa_idx to double addrspace(11)*, !dbg !8829
  %aggregate_load_box.i.sroa.3.0.copyload = load double, double addrspace(11)* %aggregate_load_box.i.sroa.3.0..sroa_cast, align 1, !dbg !8829, !tbaa !301, !alias.scope !4349, !noalias !8789
  %aggregate_load_box10.i.sroa.0.0.copyload = load double, double addrspace(11)* %aggregate_load_box.i.sroa.0.0..sroa_cast, align 1, !dbg !8829, !tbaa !301, !alias.scope !4349, !noalias !8789
  %27 = fsub double %aggregate_load_box.i.sroa.3.0.copyload, %aggregate_load_box10.i.sroa.0.0.copyload, !dbg !8838
  %28 = call nonnull "enzyme_inactive" {}* @julia.pointer_from_objref({} addrspace(11)* noundef addrspacecast ({}* inttoptr (i64 139749438057424 to {}*) to {} addrspace(11)*)) #187, !dbg !8839
  %29 = bitcast {}* %28 to {} addrspace(10)**, !dbg !8839
  %30 = getelementptr inbounds {} addrspace(10)*, {} addrspace(10)** %29, i64 1, !dbg !8839
  %string_ptr.i = ptrtoint {} addrspace(10)** %30 to i64, !dbg !8839
  %31 = call nonnull {} addrspace(10)* @ijl_tagged_gensym(i64 %string_ptr.i, i64 noundef 14) #183, !dbg !8841
  %32 = call fastcc nonnull {} addrspace(10)* @julia____init_747_3017(double %27, {} addrspace(10)* nofree noundef nonnull %31, {} addrspace(10)* noundef nonnull align 8 dereferenceable(40) %25) #183, !dbg !8842
  %33 = addrspacecast {} addrspace(10)* %32 to i8 addrspace(11)*, !dbg !8843
  %getfield_addr13.i = getelementptr inbounds i8, i8 addrspace(11)* %33, i64 104, !dbg !8843
  %34 = bitcast i8 addrspace(11)* %getfield_addr13.i to {} addrspace(10)* addrspace(11)*, !dbg !8843
  %getfield14.i = load atomic {} addrspace(10)*, {} addrspace(10)* addrspace(11)* %34 unordered, align 8, !dbg !8843, !tbaa !1751, !alias.scope !255, !noalias !256, !nonnull !158, !dereferenceable !266, !align !267
  %35 = addrspacecast {} addrspace(10)* %getfield14.i to { i8 addrspace(13)*, i64, i16, i16, i32 } addrspace(11)*, !dbg !8845
  %arraylen_ptr15.i = getelementptr inbounds { i8 addrspace(13)*, i64, i16, i16, i32 }, { i8 addrspace(13)*, i64, i16, i16, i32 } addrspace(11)* %35, i64 0, i32 1, !dbg !8845
  %arraylen16.i = load i64, i64 addrspace(11)* %arraylen_ptr15.i, align 8, !dbg !8845, !tbaa !204, !range !207, !alias.scope !208, !noalias !209
  %inbounds.i.not = icmp eq i64 %arraylen16.i, 0, !dbg !8845
  br i1 %inbounds.i.not, label %oob.i, label %julia_senseloss_3007_inner.exit, !dbg !8845

L51.i:                                            ; preds = %L18.i
  %36 = addrspacecast [1 x [2 x i64]]* %newstruct2.i to [1 x [2 x i64]] addrspace(11)*, !dbg !8801
  call fastcc void @julia_throw_boundserror_3015({} addrspace(10)* noundef nonnull align 16 dereferenceable(40) %1, [1 x [2 x i64]] addrspace(11)* nocapture nofree noundef nonnull readonly align 8 dereferenceable(16) %36) #185, !dbg !8801
  unreachable, !dbg !8801

oob.i:                                            ; preds = %L50.i
  %errorbox.i = alloca i64, align 8, !dbg !8845
  store i64 1, i64* %errorbox.i, align 8, !dbg !8845, !noalias !8828
  %37 = addrspacecast {} addrspace(10)* %getfield14.i to {} addrspace(12)*, !dbg !8845
  call void @ijl_bounds_error_ints({} addrspace(12)* %37, i64* nonnull align 8 %errorbox.i, i64 1) #183, !dbg !8845
  unreachable, !dbg !8845

julia_senseloss_3007_inner.exit:                  ; preds = %L50.i
  %38 = addrspacecast {} addrspace(10)* %getfield14.i to double addrspace(13)* addrspace(11)*, !dbg !8845
  %arrayptr.i22 = load double addrspace(13)*, double addrspace(13)* addrspace(11)* %38, align 16, !dbg !8845, !tbaa !247, !alias.scope !8846, !noalias !209, !nonnull !158
  %arrayref.i = load double, double addrspace(13)* %arrayptr.i22, align 8, !dbg !8845, !tbaa !1172, !alias.scope !255, !noalias !256
  call void @llvm.lifetime.end.p0i8(i64 16, i8* nonnull %3) #183, !dbg !8847
  call void @llvm.lifetime.end.p0i8(i64 16, i8* nonnull %4) #183, !dbg !8847
  call void @llvm.lifetime.end.p0i8(i64 32, i8* nonnull %5) #183, !dbg !8847
  ret double %arrayref.i, !dbg !8848
}

No augmented forward pass found for ijl_tagged_gensym
 at context:   %31 = call nonnull {} addrspace(10)* @ijl_tagged_gensym(i64 %string_ptr.i, i64 noundef 14) #183, !dbg !290

Stacktrace:
  [1] gensym
    @ ./expr.jl:16
  [2] __init (repeats 5 times)
    @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/solve.jl:10
  [3] #init_call#30
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:529
  [4] init_call
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:503
  [5] #init_up#33
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:562
  [6] init_up
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:549
  [7] #init#31
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:542
  [8] init
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:533
  [9] senseloss
    @ ./REPL[4]:3
 [10] senseloss
    @ ./REPL[4]:0


Stacktrace:
  [1] throwerr(cstr::Cstring)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:1317
  [2] gensym
    @ ./expr.jl:16 [inlined]
  [3] __init (repeats 5 times)
    @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/solve.jl:10 [inlined]
  [4] #init_call#30
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:529 [inlined]
  [5] init_call
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:503 [inlined]
  [6] #init_up#33
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:562 [inlined]
  [7] init_up
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:549 [inlined]
  [8] #init#31
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:542 [inlined]
  [9] init
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:533 [inlined]
 [10] senseloss
    @ ./REPL[4]:3 [inlined]
 [11] senseloss
    @ ./REPL[4]:0 [inlined]
 [12] diffejulia_senseloss_3007_inner_1wrap
    @ ./REPL[4]:0
 [13] macro expansion
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:5299 [inlined]
 [14] enzyme_call
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:4977 [inlined]
 [15] CombinedAdjointThunk
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/Dd2LU/src/compiler.jl:4919 [inlined]
 [16] autodiff(::ReverseMode{false, FFIABI}, f::Const{senseloss{…}}, ::Type{Active}, args::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/Dd2LU/src/Enzyme.jl:215
 [17] autodiff(::ReverseMode{false, FFIABI}, ::senseloss{InterpolatingAdjoint{…}}, ::Type, ::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/Dd2LU/src/Enzyme.jl:224
 [18] top-level scope
    @ REPL[8]:1
Some type information was truncated. Use `show(err)` to see complete types.

I don't have much experience reading output like this, but it seems to point to:
https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/src/solve.jl#L66 ?

@wsmoses
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wsmoses commented Jan 27, 2024 via email

@ArnoStrouwen
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Indeed, with that branch of Enzyme, I now get the different error:

julia> Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) # need to run twice to get below output
ERROR: Enzyme execution failed.
Mismatched activity for:   store atomic {} addrspace(10)* %1, {} addrspace(10)* addrspace(11)* %108 release, align 8, !dbg !380, !tbaa !213, !alias.scope !217, !noalias !242 const val: {} addrspace(10)* %1
Type tree: {[-1]:Pointer}
You may be using a constant variable as temporary storage for active memory (https://enzyme.mit.edu/julia/stable/#Activity-of-temporary-storage). If not, please open an issue, and either rewrite this variable to not be conditionally active or use Enzyme.API.runtimeActivity!(true) as a workaround for now

Stacktrace:
 [1] DEOptions
   @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/integrators/type.jl:4
 [2] #__init#747
   @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/solve.jl:366

Stacktrace:
  [1] throwerr(cstr::Cstring)
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/cL7zZ/src/compiler.jl:1319
  [2] DEOptions
    @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/integrators/type.jl:4 [inlined]
  [3] #__init#747
    @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/solve.jl:366
  [4] __init (repeats 5 times)
    @ ~/.julia/packages/OrdinaryDiffEq/2nLli/src/solve.jl:10 [inlined]
  [5] #init_call#30
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:529 [inlined]
  [6] init_call
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:503 [inlined]
  [7] #init_up#33
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:562 [inlined]
  [8] init_up
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:549 [inlined]
  [9] #init#31
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:542 [inlined]
 [10] init
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:533 [inlined]
 [11] senseloss
    @ ~/SciML/SciMLSensitivity.jl/src/tester.jl:7 [inlined]
 [12] senseloss
    @ ~/SciML/SciMLSensitivity.jl/src/tester.jl:0 [inlined]
 [13] diffejulia_senseloss_3992_inner_1wrap
    @ ~/SciML/SciMLSensitivity.jl/src/tester.jl:0
 [14] macro expansion
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/cL7zZ/src/compiler.jl:5308 [inlined]
 [15] enzyme_call
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/cL7zZ/src/compiler.jl:4986 [inlined]
 [16] CombinedAdjointThunk
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/cL7zZ/src/compiler.jl:4928 [inlined]
 [17] autodiff(::ReverseMode{false, FFIABI}, f::Const{senseloss{…}}, ::Type{Active}, args::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/cL7zZ/src/Enzyme.jl:215
 [18] autodiff(::ReverseMode{false, FFIABI}, ::senseloss{InterpolatingAdjoint{…}}, ::Type, ::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/cL7zZ/src/Enzyme.jl:224
 [19] top-level scope
    @ REPL[14]:1
Some type information was truncated. Use `show(err)` to see complete types.

Curiously, with the below example, I got the correct answer (1.0, 0.0) once. But it is not reproducible.

using OrdinaryDiffEq, SciMLSensitivity, Zygote, Enzyme
function g(u0p)
	prob = ODEProblem{false}((u, p, t) -> u .* p, u0p[1:1], (0.0, 1.0), u0p[2:2])
    integrator = init(prob, Tsit5())
    integrator.u[1]
end
u0p = [2.0, 3.0]
du0p = zeros(2)
Enzyme.gradient(Reverse, g, u0p)

@ChrisRackauckas
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It shouldn't be differentiating into the solve. That means it's not hitting the rule for some reason.

@ArnoStrouwen
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Enzyme master works for me now!

using OrdinaryDiffEq, SciMLSensitivity, Zygote, Enzyme
struct senseloss{T}
    sense::T
end
function (f::senseloss)(u0p)
	prob = ODEProblem{false}((u, p, t) -> u .* p, u0p[1:1], (0.0, 1.0), u0p[2:2])
    sum(solve(prob, Tsit5(), abstol = 1e-12, reltol = 1e-12, saveat = 0.1))
end
u0p = [2.0, 3.0]
du0p = zeros(2)
dup = Zygote.gradient(senseloss(InterpolatingAdjoint()), u0p)[1]
Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) 
du0p ≈ dup # gives true

It still prints quite a few of warnings about TypeAnalysisDepthLimit, however.
It seems like adding the gensym rule was sufficient.

@ArnoStrouwen
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When manually specifying the sensealg, instead of relying on the automatic algorithm (which chooses forward mode here), there is still an error, but this seems like an issue on the SciML side:

using OrdinaryDiffEq, SciMLSensitivity, Zygote, Enzyme
Enzyme.API.runtimeActivity!(true)
struct senseloss{T}
    sense::T
end
function (f::senseloss)(u0p)
	prob = ODEProblem{true}((du, u, p, t) -> begin du .= u .* p  end, u0p[1:1], (0.0, 1.0), u0p[2:2])
    sum(solve(prob, Tsit5(), abstol = 1e-12, reltol = 1e-12, saveat = 0.1, sensealg = f.sense)) # not specifying sensealg works
end
u0p = [2.0, 3.0]
du0p = zeros(2)
senseloss(InterpolatingAdjoint())(u0p)
dup = Zygote.gradient(senseloss(InterpolatingAdjoint()), u0p)[1]
Enzyme.autodiff(Reverse, senseloss(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) 
du0p ≈ dup #fails
ERROR: TypeError: in typeassert, expected ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, Vector{Float64}, Vector{Vector{Vector{Float64}}}, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#13#14", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{false, SciMLBase.AutoSpecialize, var"#13#14", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, Nothing, OrdinaryDiffEq.Tsit5ConstantCache, Nothing}, SciMLBase.DEStats, Nothing}, got a value of type ODESolution{Float64, 2, Vector{Vector{Float64}}, Nothing, Nothing, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Nothing, ODEProblem{Vector{Float64}, Tuple{Float64, Float64}, false, Vector{Float64}, ODEFunction{false, SciMLBase.AutoSpecialize, var"#13#14", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Base.Pairs{Symbol, Union{}, Tuple{}, @NamedTuple{}}, SciMLBase.StandardODEProblem}, Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, OrdinaryDiffEq.InterpolationData{ODEFunction{false, SciMLBase.AutoSpecialize, var"#13#14", LinearAlgebra.UniformScaling{Bool}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED), Nothing, SymbolicIndexingInterface.SymbolCache{Nothing, Nothing, Nothing}}, Vector{Vector{Float64}}, Vector{Float64}, Vector{Vector{Vector{Float64}}}, Nothing, OrdinaryDiffEq.Tsit5ConstantCache, Nothing}, SciMLBase.DEStats, Nothing}
Stacktrace:
  [1] #augmented_primal#1
    @ ~/.julia/packages/DiffEqBase/eLhx9/ext/DiffEqBaseEnzymeExt.jl:25
  [2] augmented_primal
    @ ~/.julia/packages/DiffEqBase/eLhx9/ext/DiffEqBaseEnzymeExt.jl:9 [inlined]
  [3] #solve#40
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:981 [inlined]
  [4] solve
    @ ~/.julia/packages/DiffEqBase/eLhx9/src/solve.jl:971 [inlined]
  [5] senseloss
    @ ./REPL[28]:3 [inlined]
  [6] senseloss
    @ ./REPL[28]:0 [inlined]
  [7] diffejulia_senseloss_18987_inner_1wrap
    @ ./REPL[28]:0
  [8] macro expansion
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/RN4Ys/src/compiler.jl:5308 [inlined]
  [9] enzyme_call
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/RN4Ys/src/compiler.jl:4986 [inlined]
 [10] CombinedAdjointThunk
    @ Enzyme.Compiler ~/.julia/packages/Enzyme/RN4Ys/src/compiler.jl:4928 [inlined]
 [11] autodiff(::ReverseMode{false, FFIABI}, f::Const{senseloss{…}}, ::Type{Active}, args::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/RN4Ys/src/Enzyme.jl:215
 [12] autodiff(::ReverseMode{false, FFIABI}, ::senseloss{InterpolatingAdjoint{…}}, ::Type, ::Duplicated{Vector{…}})
    @ Enzyme ~/.julia/packages/Enzyme/RN4Ys/src/Enzyme.jl:224
 [13] top-level scope
    @ REPL[32]:1
Some type information was truncated. Use `show(err)` to see complete types.

@ChrisRackauckas
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Can you diff the two types? What's actually different?

@ArnoStrouwen
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Capture

@ChrisRackauckas
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Interesting. I think after the t is subbed in for the saveat output it's turned into a range while the solvers always make it a vector. Force the t in the sensitivity_solution to be a vector

@ArnoStrouwen ArnoStrouwen mentioned this pull request Feb 4, 2024
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@ArnoStrouwen
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Enzyme + TrackerAdjoint has the issue that all the real numbers in the solution are tracked, while enzyme expects them not to be.
Screenshot_20240214_120355

@ArnoStrouwen
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This one reads to me like it should be handled in Enzyme @wsmoses

  LoadError: Enzyme execution failed.
  Enzyme: Not yet implemented augmented forward for jl_f__apply_iterate (true, true, iterate, Core.apply_type, 7, 6)
  Stacktrace:
   [1] signature_type
     @ ./reflection.jl:962
   [2] _methods
     @ ./reflection.jl:1020
  
  Stacktrace:
    [1] throwerr(cstr::Cstring)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:1287
    [2] signature_type
      @ ./reflection.jl:962 [inlined]
    [3] _methods
      @ ./reflection.jl:1020 [inlined]
    [4] augmented_julia__methods_39623wrap
      @ ./reflection.jl:0
    [5] macro expansion
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5376 [inlined]
    [6] enzyme_call
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5054 [inlined]
    [7] AugmentedForwardThunk
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5007 [inlined]
    [8] runtime_generic_augfwd(activity::Type{Val{(false, false, false, false, false)}}, width::Val{1}, ModifiedBetween::Val{(true, true, true, true, true)}, RT::Val{@NamedTuple{1, 2, 3}}, f::typeof(Base._methods), df::Nothing, primal_1::typeof(Main.var"##Alternative AD Frontend#225".f_aug), shadow_1_1::Nothing, primal_2::Type{Tuple}, shadow_2_1::Nothing, primal_3::Int64, shadow_3_1::Nothing, primal_4::UInt64, shadow_4_1::Nothing)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/rules/jitrules.jl:179
    [9] methods
      @ ./reflection.jl:1072 [inlined]
   [10] augmented_julia_methods_39596wrap
      @ ./reflection.jl:0
   [11] macro expansion
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5376 [inlined]
   [12] enzyme_call(::Val{false}, ::Ptr{Nothing}, ::Type{Enzyme.Compiler.AugmentedForwardThunk}, ::Type{Val{1}}, ::Val{true}, ::Type{Tuple{EnzymeCore.Const{typeof(Main.var"##Alternative AD Frontend#225".f_aug)}, EnzymeCore.Const{Type{Tuple}}, EnzymeCore.Const{Nothing}}}, ::Type{EnzymeCore.Duplicated{Base.MethodList}}, ::EnzymeCore.Const{typeof(methods)}, ::Type{@NamedTuple{1, 2, 3, 4, 5, 6::UInt64, 7, 8, 9, 10::Bool, 11, 12::Core.LLVMPtr{UInt64, 0}, 13::Core.LLVMPtr{Bool, 0}, 14::Core.LLVMPtr{Bool, 0}, 15}}, ::EnzymeCore.Const{typeof(Main.var"##Alternative AD Frontend#225".f_aug)}, ::EnzymeCore.Const{Type{Tuple}}, ::EnzymeCore.Const{Nothing})
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5054
   [13] (::Enzyme.Compiler.AugmentedForwardThunk{Ptr{Nothing}, EnzymeCore.Const{typeof(methods)}, EnzymeCore.Duplicated{Base.MethodList}, Tuple{EnzymeCore.Const{typeof(Main.var"##Alternative AD Frontend#225".f_aug)}, EnzymeCore.Const{Type{Tuple}}, EnzymeCore.Const{Nothing}}, Val{1}, Val{true}(), @NamedTuple{1, 2, 3, 4, 5, 6::UInt64, 7, 8, 9, 10::Bool, 11, 12::Core.LLVMPtr{UInt64, 0}, 13::Core.LLVMPtr{Bool, 0}, 14::Core.LLVMPtr{Bool, 0}, 15}})(::EnzymeCore.Const{typeof(methods)}, ::EnzymeCore.Const{typeof(Main.var"##Alternative AD Frontend#225".f_aug)}, ::Vararg{Any})
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5007
   [14] runtime_generic_augfwd(activity::Type{Val{(false, false, false, false)}}, width::Val{1}, ModifiedBetween::Val{(true, true, true, true)}, RT::Val{@NamedTuple{1, 2, 3}}, f::typeof(methods), df::Nothing, primal_1::typeof(Main.var"##Alternative AD Frontend#225".f_aug), shadow_1_1::Nothing, primal_2::Type{Tuple}, shadow_2_1::Nothing, primal_3::Nothing, shadow_3_1::Nothing)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/rules/jitrules.jl:179
   [15] methods (repeats 2 times)
      @ ./reflection.jl:1094 [inlined]
   [16] numargs
      @ ~/.julia/packages/SciMLBase/QSc1r/src/utils.jl:12 [inlined]
   [17] #isinplace#3
      @ ~/.julia/packages/SciMLBase/QSc1r/src/utils.jl:244 [inlined]
   [18] augmented_julia__isinplace_3_38717_inner_1wrap
      @ ~/.julia/packages/SciMLBase/QSc1r/src/utils.jl:0
   [19] macro expansion
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5376 [inlined]
   [20] enzyme_call
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5054 [inlined]
   [21] AugmentedForwardThunk
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5007 [inlined]
   [22] runtime_generic_augfwd(activity::Type{Val{(false, false, false, false, false, false, false, false)}}, width::Val{1}, ModifiedBetween::Val{(true, true, true, true, true, true, true, true)}, RT::Val{@NamedTuple{1, 2, 3}}, f::SciMLBase.var"##isinplace#3", df::Nothing, primal_1::Bool, shadow_1_1::Nothing, primal_2::Bool, shadow_2_1::Nothing, primal_3::typeof(SciMLBase.isinplace), shadow_3_1::Nothing, primal_4::typeof(Main.var"##Alternative AD Frontend#225".f_aug), shadow_4_1::Nothing, primal_5::Int64, shadow_5_1::Nothing, primal_6::String, shadow_6_1::Nothing, primal_7::Bool, shadow_7_1::Nothing)
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/rules/jitrules.jl:179
   [23] isinplace (repeats 2 times)
      @ ~/.julia/packages/SciMLBase/QSc1r/src/utils.jl:242 [inlined]
   [24] #ODEProblem#307
      @ ~/.julia/packages/SciMLBase/QSc1r/src/problems/ode_problems.jl:188 [inlined]
   [25] augmented_julia__ODEProblem_307_38622_inner_1wrap
      @ ~/.julia/packages/SciMLBase/QSc1r/src/problems/ode_problems.jl:0
   [26] macro expansion
      @ ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5376 [inlined]
   [27] enzyme_call
      @ ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5054 [inlined]
   [28] AugmentedForwardThunk
      @ ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5007 [inlined]
   [29] runtime_generic_augfwd(activity::Type{Val{(false, false, false, false, false, true, true)}}, width::Val{1}, ModifiedBetween::Val{(true, true, true, true, true, true, true)}, RT::Val{@NamedTuple{1, 2, 3}}, f::SciMLBase.var"##ODEProblem#307", df::Nothing, primal_1::@Kwargs{alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, Nothing}}, shadow_1_1::Nothing, primal_2::Type{SciMLBase.ODEProblem}, shadow_2_1::Nothing, primal_3::typeof(Main.var"##Alternative AD Frontend#225".f_aug), shadow_3_1::Nothing, primal_4::Matrix{Float64}, shadow_4_1::Nothing, primal_5::Tuple{Float64, Float64}, shadow_5_1::Base.RefValue{Tuple{Float64, Float64}}, primal_6::Vector{Float64}, shadow_6_1::Vector{Float64})
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/rules/jitrules.jl:179
   [30] ODEProblem
      @ ~/.julia/packages/SciMLBase/QSc1r/src/problems/ode_problems.jl:187 [inlined]
   [31] ODEProblem
      @ ~/.julia/packages/SciMLBase/QSc1r/src/problems/ode_problems.jl:0 [inlined]
   [32] augmented_julia_ODEProblem_38465_inner_1wrap
      @ ~/.julia/packages/SciMLBase/QSc1r/src/problems/ode_problems.jl:0
   [33] macro expansion
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5376 [inlined]
   [34] enzyme_call
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5054 [inlined]
   [35] AugmentedForwardThunk
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5007 [inlined]
   [36] runtime_generic_augfwd(activity::Type{Val{(false, true, false, false, false, false, true)}}, width::Val{1}, ModifiedBetween::Val{(true, true, true, true, true, true, true)}, RT::Val{@NamedTuple{1, 2, 3}}, f::typeof(Core.kwcall), df::Nothing, primal_1::@NamedTuple{alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, Nothing}}, shadow_1_1::@NamedTuple{alg::OrdinaryDiffEq.Tsit5{typeof(OrdinaryDiffEq.trivial_limiter!), typeof(OrdinaryDiffEq.trivial_limiter!), Static.False}, sensealg::InterpolatingAdjoint{0, true, Val{:central}, Nothing}}, primal_2::Type{SciMLBase.ODEProblem}, shadow_2_1::Nothing, primal_3::typeof(Main.var"##Alternative AD Frontend#225".f_aug), shadow_3_1::Nothing, primal_4::Matrix{Float64}, shadow_4_1::Nothing, primal_5::Tuple{Float64, Float64}, shadow_5_1::Nothing, primal_6::Vector{Float64}, shadow_6_1::Vector{Float64})
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/rules/jitrules.jl:179
   [37] loss
      @ ~/SciML/SciMLSensitivity.jl/test/alternative_ad_frontend.jl:171 [inlined]
   [38] augmented_julia_loss_38280wrap
      @ ~/SciML/SciMLSensitivity.jl/test/alternative_ad_frontend.jl:0
   [39] macro expansion
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5376 [inlined]
   [40] enzyme_call
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5054 [inlined]
   [41] AugmentedForwardThunk
      @ Enzyme.Compiler ~/.julia/packages/Enzyme/M8KTx/src/compiler.jl:5007 [inlined]
   [42] autodiff(::EnzymeCore.ReverseMode{false, EnzymeCore.FFIABI}, f::EnzymeCore.Const{typeof(Main.var"##Alternative AD Frontend#225".loss)}, ::Type{EnzymeCore.Active}, args::EnzymeCore.Duplicated{Vector{Float64}})
      @ Enzyme ~/.julia/packages/Enzyme/M8KTx/src/Enzyme.jl:198
   [43] autodiff
      @ ~/.julia/packages/Enzyme/M8KTx/src/Enzyme.jl:224 [inlined]
   [44] gradient(::EnzymeCore.ReverseMode{false, EnzymeCore.FFIABI}, f::Function, x::Vector{Float64})
      @ Enzyme ~/.julia/packages/Enzyme/M8KTx/src/Enzyme.jl:805
   [45] top-level scope
      @ ~/SciML/SciMLSensitivity.jl/test/alternative_ad_frontend.jl:191
   [46] include(mod::Module, _path::String)
      @ Base ./Base.jl:495
   [47] include(x::String)
      @ Main.var"##Alternative AD Frontend#225" ~/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:28
   [48] macro expansion
      @ ~/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:24 [inlined]
   [49] macro expansion
      @ ~/.julia/juliaup/julia-1.10.0+0.x64.linux.gnu/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined]
   [50] top-level scope
      @ ~/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:24
   [51] eval(m::Module, e::Any)
      @ Core ./boot.jl:385
   [52] macro expansion
      @ ~/.julia/packages/SafeTestsets/raUNr/src/SafeTestsets.jl:28 [inlined]
   [53] macro expansion
      @ ./timing.jl:279 [inlined]
   [54] macro expansion
      @ ~/SciML/SciMLSensitivity.jl/test/runtests.jl:15 [inlined]
   [55] macro expansion
      @ ~/.julia/juliaup/julia-1.10.0+0.x64.linux.gnu/share/julia/stdlib/v1.10/Test/src/Test.jl:1577 [inlined]
  --- the last 2 lines are repeated 1 more time ---
   [58] macro expansion
      @ ~/SciML/SciMLSensitivity.jl/test/runtests.jl:13 [inlined]
   [59] macro expansion
      @ ./timing.jl:279 [inlined]
   [60] top-level scope
      @ ~/SciML/SciMLSensitivity.jl/test/runtests.jl:269
   [61] include(fname::String)
      @ Base.MainInclude ./client.jl:489
   [62] top-level scope
      @ none:6
   [63] eval
      @ Core ./boot.jl:385 [inlined]
   [64] exec_options(opts::Base.JLOptions)
      @ Base ./client.jl:291
  in expression starting at /home/arno/SciML/SciMLSensitivity.jl/test/alternative_ad_frontend.jl:191

@ChrisRackauckas
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Enzyme + TrackerAdjoint has the issue that all the real numbers in the solution are tracked, while enzyme expects them not to be.

The return is supposed to strip tracking information https://github.com/SciML/SciMLSensitivity.jl/blob/master/src/concrete_solve.jl#L1198-L1199. We probably just need to strip off more, though it's very strange that is showing tracker on the u part.

@ArnoStrouwen
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The final set of CI failures are of the form:

julia> ReverseDiff.gradient(senseloss(ForwardSensitivity()),
           u0p)≈dup
ERROR: MethodError: no method matching increment_deriv!(::ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}}, ::ChainRulesCore.NotImplemented, ::Int64)

Closest candidates are:
  increment_deriv!(::AbstractArray, ::Any)
   @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/derivatives/propagation.jl:38
  increment_deriv!(::ReverseDiff.TrackedArray, ::Real, ::Any)
   @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/derivatives/propagation.jl:34
  increment_deriv!(::AbstractArray, ::Real, ::Any)
   @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/derivatives/propagation.jl:36
  ...

Stacktrace:
  [1] increment_deriv!
    @ ~/.julia/packages/ReverseDiff/UJhiD/src/derivatives/propagation.jl:40 [inlined]
  [2] _add_to_deriv!
    @ ~/.julia/packages/ReverseDiff/UJhiD/src/macros.jl:381 [inlined]
  [3] _broadcast_getindex_evalf
    @ ./broadcast.jl:709 [inlined]
  [4] _broadcast_getindex
    @ ./broadcast.jl:682 [inlined]
  [5] (::Base.Broadcast.var"#31#32")(k::Any)
    @ Base.Broadcast ./broadcast.jl:1118 [inlined]
  [6] macro expansion
    @ ./ntuple.jl:72 [inlined]
  [7] ntuple
    @ ./ntuple.jl:69 [inlined]
  [8] copy
    @ ./broadcast.jl:1118 [inlined]
  [9] materialize
    @ ./broadcast.jl:903 [inlined]
 [10] special_reverse_exec!(instruction::ReverseDiff.SpecialInstruction{typeof(DiffEqBase.solve_up), Tuple{…}, ReverseDiff.TrackedArray{…}, Tuple{…}})
    @ DiffEqBaseReverseDiffExt ~/.julia/packages/ReverseDiff/UJhiD/src/macros.jl:218
 [11] reverse_exec!(instruction::ReverseDiff.SpecialInstruction{typeof(DiffEqBase.solve_up), Tuple{…}, ReverseDiff.TrackedArray{…}, Tuple{…}})
    @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/tape.jl:93
 [12] reverse_pass!(tape::Vector{ReverseDiff.AbstractInstruction})
    @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/tape.jl:87
 [13] reverse_pass!
    @ ~/.julia/packages/ReverseDiff/UJhiD/src/api/tape.jl:36 [inlined]
 [14] seeded_reverse_pass!(result::Vector{…}, output::ReverseDiff.TrackedReal{…}, input::ReverseDiff.TrackedArray{…}, tape::ReverseDiff.GradientTape{…})
    @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/api/utils.jl:31
 [15] seeded_reverse_pass!(result::Vector{Float64}, t::ReverseDiff.GradientTape{senseloss{ForwardSensitivity{…}}, ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{…}, Vector{…}}, ReverseDiff.TrackedReal{Float64, Float64, Nothing}})
    @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/api/tape.jl:47
 [16] gradient(f::senseloss{ForwardSensitivity{0, true, Val{:central}}}, input::Vector{Float64}, cfg::ReverseDiff.GradientConfig{ReverseDiff.TrackedArray{Float64, Float64, 1, Vector{Float64}, Vector{Float64}}})
    @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/api/gradients.jl:24
 [17] gradient(f::senseloss{ForwardSensitivity{0, true, Val{:central}}}, input::Vector{Float64})
    @ ReverseDiff ~/.julia/packages/ReverseDiff/UJhiD/src/api/gradients.jl:22
 [18] top-level scope
    @ REPL[25]:1

while instead a ForwardSensitivityOutOfPlaceError is expected.

@ChrisRackauckas
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That means ReverseDiff isn't getting captured into the adjoint system?

@ArnoStrouwen
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For the tracker error, in this specific example it should be:

julia> [Tracker.data.(u) for u in sol.u]
11-element Vector{Vector{Float64}}:
 [2.0]
 [2.6997176151520326]
 [3.644237600781067]
 [4.919206222313961]
 [6.6402338454731975]
 [8.96337814067623]
 [12.099294928825994]
 [16.33233982513538]
 [22.046352761283476]
 [29.759463449746153]
 [40.17107384637566]

But that might break the non-Enzyme AD systems.

For the reversediff error, I have no clue. How can this PR break that, it does not touch Enzyme at all?

@ChrisRackauckas
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The return from the adjoint should be untracked, so that should be fine.

For the reversediff error, I have no clue. How can this PR break that, it does not touch Enzyme at all?

That one is really odd. I'd setup a master test and see where we are at.

ArnoStrouwen
ArnoStrouwen reviewed Feb 26, 2024
View reviewed changes
test/alternative_ad_frontend.jl
@test_broken Enzyme.gradient(Reverse, senseloss(ReverseDiffAdjoint()), u0p) ≈ dup
@test Enzyme.gradient(Reverse, senseloss(TrackerAdjoint()), u0p) ≈ dup
@test Enzyme.gradient(Reverse, senseloss(ForwardDiffSensitivity()), u0p) ≈ dup
@test_throws SciMLSensitivity.ForwardSensitivityOutOfPlaceError ReverseDiff.gradient(Reverse, senseloss(ForwardSensitivity()),
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Why an oop error? The problem is in position. It gives a different error. Is Reversediff + iip + ForwardSensitivity known to be broken, or should this work?

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I think this had more to do with ReverseDiff requiring AbstractArray returns, so reversediff is known to fail in this case (see it marked as failing in the rest of the file too)

@ChrisRackauckas ChrisRackauckas mentioned this pull request Feb 26, 2024
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@ArnoStrouwen ArnoStrouwen mentioned this pull request Feb 27, 2024
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@ChrisRackauckas
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Merged as #1021

@ChrisRackauckas ChrisRackauckas deleted the enzyme branch February 27, 2024 06:35
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@ChrisRackauckas @wsmoses @frankschae @ArnoStrouwen