v0.2.0

Project.toml

@@ -1,15 +1,15 @@
 name = "FMISensitivity"
 uuid = "3e748fe5-cd7f-4615-8419-3159287187d2"
 authors = ["TT <[email protected]>", "LM <[email protected]>"]
-version = "0.1.4"
+version = "0.2.0"
 
 [deps]
-FMICore = "8af89139-c281-408e-bce2-3005eb87462f"
+FMIBase = "900ee838-d029-460e-b485-d98a826ceef2"
 ForwardDiffChainRules = "c9556dd2-1aed-4cfe-8560-1557cf593001"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 
 [compat]
-FMICore = "0.20.0"
+FMIBase = "1.0.0"
 ForwardDiffChainRules = "0.2.0"
-SciMLSensitivity = "6.51.2 - 7.51"
+SciMLSensitivity = "7.0 - 7.59"
 julia = "1.6"

README.md

@@ -59,10 +59,11 @@ To keep dependencies nice and clean, the original package [*FMI.jl*](https://git
 - [*FMI.jl*](https://github.com/ThummeTo/FMI.jl): High level loading, manipulating, saving or building entire FMUs from scratch
 - [*FMIImport.jl*](https://github.com/ThummeTo/FMIImport.jl): Importing FMUs into Julia
 - [*FMIExport.jl*](https://github.com/ThummeTo/FMIExport.jl): Exporting stand-alone FMUs from Julia Code
+- [*FMIBase.jl*](https://github.com/ThummeTo/FMIBase.jl): Common concepts for import and export of FMUs
 - [*FMICore.jl*](https://github.com/ThummeTo/FMICore.jl): C-code wrapper for the FMI-standard
 - [*FMISensitivity.jl*](https://github.com/ThummeTo/FMISensitivity.jl): Static and dynamic sensitivities over FMUs
 - [*FMIBuild.jl*](https://github.com/ThummeTo/FMIBuild.jl): Compiler/Compilation dependencies for FMIExport.jl
-- [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): Machine Learning with FMUs (differentiation over FMUs)
+- [*FMIFlux.jl*](https://github.com/ThummeTo/FMIFlux.jl): Machine Learning with FMUs
 - [*FMIZoo.jl*](https://github.com/ThummeTo/FMIZoo.jl): A collection of testing and example FMUs
 
 ## What Platforms are supported?

src/FMISensitivity.jl

@@ -11,40 +11,23 @@ import SciMLSensitivity: ForwardDiff
 import SciMLSensitivity: FiniteDiff
 import SciMLSensitivity: ReverseDiff
 import SciMLSensitivity: Zygote 
-import FMICore.ChainRulesCore
+
+import FMIBase.ChainRulesCore
+using FMIBase.ChainRulesCore: ZeroTangent, NoTangent, @thunk
 
 import ForwardDiffChainRules
-import ForwardDiffChainRules: @ForwardDiff_frule
-import SciMLSensitivity.ReverseDiff: @grad_from_chainrules
-import FMICore.ChainRulesCore: ZeroTangent, NoTangent, @thunk
-using FMICore: undual, unsense, untrack
+using ForwardDiffChainRules: @ForwardDiff_frule
+using SciMLSensitivity.ReverseDiff: @grad_from_chainrules
 
 using SciMLSensitivity.LinearAlgebra
 import SciMLSensitivity.SparseDiffTools
 
-import FMICore: invalidate!, check_invalidate!
-
-using FMICore
-
-function isZeroTangent(d)
-    return false
-end
-function isZeroTangent(d::ZeroTangent)
-    return true
-end
-function isZeroTangent(d::AbstractArray{<:ZeroTangent})
-    return true
-end
-
-# additional dispatch for ReverseDiff.jl 
-# import SciMLSensitivity.ReverseDiff: increment_deriv!, ZeroTangent
-# function ReverseDiff.increment_deriv!(::ReverseDiff.TrackedReal, ::ZeroTangent)
-#     return nothing 
-# end
-# function ReverseDiff.increment_deriv!(::ReverseDiff.TrackedArray, ::ZeroTangent, ::Int64)
-#     return nothing 
-# end
-
-include("FMI2.jl")
+using FMIBase
+using FMIBase.FMICore
+using FMIBase: undual, unsense, untrack
+
+include("utils.jl")
+include("sense.jl")
+include("hotfixes.jl")
 
 end # module

src/hotfixes.jl

@@ -0,0 +1,12 @@
+#
+# Copyright (c) 2023 Tobias Thummerer, Lars Mikelsons
+# Licensed under the MIT license. See LICENSE file in the project root for details.
+#
+
+import SciMLSensitivity.Zygote: grad_mut, Context
+import FMIBase: FMUEvaluationOutput
+#grad_mut(av::AbstractVector) = invoke(grad_mut, Tuple{Any}, av)
+grad_mut(av::FMUEvaluationOutput) = invoke(grad_mut, Tuple{Any}, av)
+#grad_mut(c::Zygote.Context, av::AbstractVector) = invoke(grad_mut, Tuple{Zygote.Context, Any}, c, av)
+grad_mut(c::Zygote.Context, av::FMUEvaluationOutput) = invoke(grad_mut, Tuple{Zygote.Context, Any}, c, av)
+#grad_mut(av::AbstractVector) = []