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Add new backends with DifferentiationInterface.jl
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‎.buildkite/pipeline.yml‎

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@@ -8,5 +8,225 @@ steps:
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cuda: "*"
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command: |
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julia --color=yes --project -e 'using Pkg; Pkg.add("CUDA"); Pkg.add("NLPModels"); Pkg.add("NLPModelsTest"); Pkg.instantiate()'
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julia --color=yes --project -e 'include("test/gpu.jl")'
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julia --color=yes --project -e 'include("test/gpu/nvidia.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- ForwardDiff.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("ForwardDiff")
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Pkg.instantiate()
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include("test/backend/ForwardDiff.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- ReverseDiff.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("ReverseDiff")
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Pkg.instantiate()
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include("test/backend/ReverseDiff.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- Enzyme.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("Enzyme")
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Pkg.instantiate()
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include("test/backend/Enzyme.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- Zygote.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("Zygote")
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Pkg.instantiate()
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include("test/backend/Zygote.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- Mooncake.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("Mooncake")
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Pkg.instantiate()
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include("test/backend/Mooncake.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- Diffractor.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("Diffractor")
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Pkg.instantiate()
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include("test/backend/Diffractor.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- Tracker.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("Tracker")
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Pkg.instantiate()
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include("test/backend/Tracker.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- Symbolics.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("Symbolics")
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Pkg.instantiate()
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include("test/backend/Symbolics.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- ChainRules.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("ChainRules")
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Pkg.instantiate()
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include("test/backend/ChainRules.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- FastDifferentiation.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("FastDifferentiation")
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Pkg.instantiate()
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include("test/backend/FastDifferentiation.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- FiniteDiff.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("FiniteDiff")
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Pkg.instantiate()
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include("test/backend/FiniteDiff.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- FiniteDifferences.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("FiniteDifferences")
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Pkg.instantiate()
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include("test/backend/FiniteDifferences.jl")'
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timeout_in_minutes: 30
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- label: "CPUs -- PolyesterForwardDiff.jl"
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plugins:
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- JuliaCI/julia#v1:
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version: "1.10"
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agents:
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queue: "juliaecosystem"
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os: "linux"
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arch: "x86_64"
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command: |
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julia --color=yes --project -e '
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using Pkg
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Pkg.add("OptimizationProblems")
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Pkg.add("PolyesterForwardDiff")
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Pkg.instantiate()
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include("test/backend/PolyesterForwardDiff.jl")'

‎.github/workflows/CI.yml‎

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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: actions/cache@v1
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- uses: actions/cache@v4
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env:
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cache-name: cache-artifacts
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with:

‎Project.toml‎

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[deps]
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ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
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DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
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Requires = "ae029012-a4dd-5104-9daa-d747884805df"
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ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
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SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
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SparseMatrixColorings = "0a514795-09f3-496d-8182-132a7b665d35"
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[compat]
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ADTypes = "1.2.1"
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ForwardDiff = "0.9.0, 0.10.0"
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NLPModels = "0.18, 0.19, 0.20, 0.21"
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Requires = "1"
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ReverseDiff = "1"
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ADTypes = "1.9.0"
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DifferentiationInterface = "0.6.1"
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ForwardDiff = "0.10.36"
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NLPModels = "0.21.3"
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ReverseDiff = "1.15.3"
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SparseConnectivityTracer = "0.6.1"
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SparseMatrixColorings = "0.4.0"
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julia = "^1.6"
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julia = "1.6"

‎docs/Project.toml‎

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@@ -16,10 +16,10 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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DataFrames = "1"
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Documenter = "1.0"
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ManualNLPModels = "0.1"
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NLPModels = "0.21"
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NLPModelsJuMP = "0.13"
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OptimizationProblems = "0.8"
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NLPModels = "0.21.3"
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NLPModelsJuMP = "0.13.2"
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OptimizationProblems = "0.9"
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Percival = "0.7"
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Plots = "1"
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SolverBenchmark = "0.6"
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Zygote = "0.6.62"
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Zygote = "0.6.70"

‎docs/src/backend.md‎

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# How to switch backend in ADNLPModels
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`ADNLPModels` allows the use of different backends to compute the derivatives required within NLPModel API.
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It uses `ForwardDiff.jl`, `ReverseDiff.jl`, and more via optional depencies.
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It uses `ForwardDiff.jl`, `ReverseDiff.jl`, and more via optional dependencies.
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The backend information is in a structure [`ADNLPModels.ADModelBackend`](@ref) in the attribute `adbackend` of a `ADNLPModel`, it can also be accessed with [`get_adbackend`](@ref).
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The functions used internally to define the NLPModel API and the possible backends are defined in the following table:
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| Functions | FowardDiff backends | ReverseDiff backends | Zygote backends | Enzyme backend | Sparse backend |
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| --------- | ------------------- | -------------------- | --------------- | -------------- | -------------- |
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| `gradient` and `gradient!` | `ForwardDiffADGradient`/`GenericForwardDiffADGradient` | `ReverseDiffADGradient`/`GenericReverseDiffADGradient` | `ZygoteADGradient` | `EnzymeADGradient` | -- |
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| `jacobian` | `ForwardDiffADJacobian` | `ReverseDiffADJacobian` | `ZygoteADJacobian` | -- | `SparseADJacobian` |
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| `hessian` | `ForwardDiffADHessian` | `ReverseDiffADHessian` | `ZygoteADHessian` | -- | `SparseADHessian`/`SparseReverseADHessian` |
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| `Jprod` | `ForwardDiffADJprod`/`GenericForwardDiffADJprod` | `ReverseDiffADJprod`/`GenericReverseDiffADJprod` | `ZygoteADJprod` | -- | -- |
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| `Jtprod` | `ForwardDiffADJtprod`/`GenericForwardDiffADJtprod` | `ReverseDiffADJtprod`/`GenericReverseDiffADJtprod` | `ZygoteADJtprod` | -- | -- |
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| `Hvprod` | `ForwardDiffADHvprod`/`GenericForwardDiffADHvprod` | `ReverseDiffADHvprod`/`GenericReverseDiffADHvprod` | -- | -- | -- |
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| `directional_second_derivative` | `ForwardDiffADGHjvprod` | -- | -- | -- | -- |
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The functions `hess_structure!`, `hess_coord!`, `jac_structure!` and `jac_coord!` defined in `ad.jl` are generic to all the backends for now.
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| package | ForwardDiff.jl | ReverseDiff.jl | Enzyme.jl | Zygote.jl | Mooncake.jl | Diffractor.jl | Tracker.jl | Symbolics.jl | ChainRules.jl | FastDifferentiation.jl | FiniteDiff.jl | FiniteDifferences.jl | PolyesterForwardDiff.jl |
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|--------|----------------|----------------|-----------|-----------|-------------|---------------|------------|--------------|----------------------|------------------------|---------------|----------------------|-------------------------|
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| $\nabla f(x)$ | `ForwardDiffADGradient` | `ReverseDiffADGradient` | `EnzymeADGradient` | `ZygoteADGradient` | `MooncakeADGradient` | `DiffractorADGradient` | `TrackerADGradient` | `SymbolicsADGradient` | `ChainRulesADGradient` | `FastDifferentiationADGradient` | `FiniteDiffADGradient` | `FiniteDifferencesADGradient` | `PolyesterForwardDiffADGradient` |
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| $J_c(x)*v$ | `ForwardDiffADJprod` | `ReverseDiffADJprod` | `EnzymeADJprod` | `ZygoteADJprod` | `MooncakeADJprod` | `DiffractorADJprod` | `TrackerADJprod` | `SymbolicsADJprod` | `ChainRulesADJprod` | `FastDifferentiationADJprod` | `FiniteDiffADJprod` | `FiniteDifferencesADJprod` | `PolyesterForwardDiffADJprod` |
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| $J^T_c(x)*v$ | `ForwardDiffADJtprod` | `ReverseDiffADJtprod` | `EnzymeADJtprod` | `ZygoteADJtprod` | `MooncakeADJtprod` | `DiffractorADJtprod` | `TrackerADJtprod` | `SymbolicsADJtprod` | `ChainRulesADJtprod` | `FastDifferentiationADJtprod` | `FiniteDiffADJtprod` | `FiniteDifferencesADJtprod` | `PolyesterForwardDiffADJtprod` |
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| $J_c(x)$ | `ForwardDiffADJacobian` | `ReverseDiffADJacobian` | `EnzymeADJacobian` | `ZygoteADJacobian` | `MooncakeADJacobian` | `DiffractorADJacobian` | `TrackerADJacobian` | `SymbolicsADJacobian` | `ChainRulesADJacobian` | `FastDifferentiationADJacobian` | `FiniteDiffADJacobian` | `FiniteDifferencesADJacobian` | `PolyesterForwardDiffADJacobian` |
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| $\nabla^2 \mathcal{L}(x)*v$ | `ForwardDiffADHvprod` | `ReverseDiffADHvprod` | `EnzymeADHvprod` | `ZygoteADHvprod` | `MooncakeADHvprod` | `DiffractorADHvprod` | `TrackerADHvprod` | `SymbolicsADHvprod` | `ChainRulesADHvprod` | `FastDifferentiationADHvprod` | `FiniteDiffADHvprod` | `FiniteDifferencesADHvprod` | `PolyesterForwardDiffADHvprod` |
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| $\nabla^2 \mathcal{L}(x)$ | `ForwardDiffADHessian` | `ReverseDiffADHessian` | `EnzymeADHessian` | `ZygoteADHessian` | `MooncakeADHessian` | `DiffractorADHessian` | `TrackerADHessian` | `SymbolicsADHessian` | `ChainRulesADHessian` | `FastDifferentiationADHessian` | `FiniteDiffADHessian` | `FiniteDifferencesADHessian` | `PolyesterForwardDiffADHessian` |
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$\mathcal{L}(x)$ denotes the Lagrangian $f(x) + \lambda^T c(x)$.
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Except for the backends based on `ForwardDiff.jl` and `ReverseDiff.jl`, all other backends require the associated AD package to be manually installed by the user to work.
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Note that the Jacobians and Hessians computed by the backends above are dense.
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The backends `SparseADJacobian`, `SparseADHessian`, and `SparseReverseADHessian` should be used instead if sparse Jacobians and Hessians are required.
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```@example ex1
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using ADNLPModels

‎src/ADNLPModels.jl‎

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using LinearAlgebra, SparseArrays
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# external
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using ADTypes: ADTypes, AbstractColoringAlgorithm, AbstractSparsityDetector
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import DifferentiationInterface
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using ADTypes: ADTypes, AbstractADType, AbstractColoringAlgorithm, AbstractSparsityDetector, AutoEnzyme, AutoZygote
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using ADTypes: AutoForwardDiff, AutoReverseDiff, AutoMooncake, AutoDiffractor, AutoTracker, AutoSymbolics
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using ADTypes: AutoChainRules, AutoFastDifferentiation, AutoFiniteDiff, AutoFiniteDifferences, AutoPolyesterForwardDiff
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using SparseConnectivityTracer: TracerSparsityDetector
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using SparseMatrixColorings
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using ForwardDiff, ReverseDiff
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# JSO
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using NLPModels
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using Requires
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abstract type AbstractADNLPModel{T, S} <: AbstractNLPModel{T, S} end
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abstract type AbstractADNLSModel{T, S} <: AbstractNLSModel{T, S} end
@@ -27,8 +29,7 @@ include("sparse_hessian.jl")
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include("forward.jl")
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include("reverse.jl")
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include("enzyme.jl")
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include("zygote.jl")
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include("di.jl")
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include("predefined_backend.jl")
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include("nlp.jl")
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