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SciML · Dec 9, 2024 · f810b33 · f810b33
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diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "SciMLSensitivity"
 uuid = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 authors = ["Christopher Rackauckas <[email protected]>", "Yingbo Ma <[email protected]>"]
-version = "7.71.2"
+version = "7.72.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -43,6 +43,12 @@ SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
+[weakdeps]
+Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
+
+[extensions]
+SciMLSensitivityMooncakeExt = "Mooncake"
+
 [compat]
 ADTypes = "1.9"
 Accessors = "0.1.36"
@@ -72,7 +78,7 @@ LinearSolve = "2"
 Lux = "1"
 Markdown = "1.10"
 ModelingToolkit = "9.42"
-Mooncake = "0.4.44"
+Mooncake = "0.4.52"
 NLsolve = "4.5.1"
 NonlinearSolve = "3.0.1"
 Optimization = "4"
@@ -112,6 +118,7 @@ DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
+Mooncake = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
@@ -125,4 +132,4 @@ StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["AlgebraicMultigrid", "Aqua", "Calculus", "ComponentArrays", "DelayDiffEq", "Distributed", "Lux", "ModelingToolkit", "NLsolve", "NonlinearSolve", "Optimization", "OptimizationOptimisers", "OrdinaryDiffEq", "Pkg", "SafeTestsets", "SparseArrays", "SteadyStateDiffEq", "StochasticDiffEq", "Test"]
+test = ["AlgebraicMultigrid", "Aqua", "Calculus", "ComponentArrays", "DelayDiffEq", "Distributed", "Lux", "ModelingToolkit", "Mooncake", "NLsolve", "NonlinearSolve", "Optimization", "OptimizationOptimisers", "OrdinaryDiffEq", "Pkg", "SafeTestsets", "SparseArrays", "SteadyStateDiffEq", "StochasticDiffEq", "Test"]
diff --git a/docs/src/manual/differential_equation_sensitivities.md b/docs/src/manual/differential_equation_sensitivities.md
@@ -212,6 +212,7 @@ ZygoteVJP
 EnzymeVJP
 TrackerVJP
 ReverseDiffVJP
+MooncakeVJP
 ```
 
 ## More Details on Sensitivity Algorithm Choices

diff --git a/ext/SciMLSensitivityMooncakeExt.jl b/ext/SciMLSensitivityMooncakeExt.jl
@@ -0,0 +1,22 @@
+module SciMLSensitivityMooncakeExt
+
+using SciMLSensitivity, Mooncake
+import SciMLSensitivity: get_paramjac_config, mooncake_run_ad, MooncakeVJP, MooncakeLoaded
+
+function get_paramjac_config(::MooncakeLoaded, ::MooncakeVJP, pf, p, f, y, _t)
+    dy_mem = zero(y)
+    λ_mem = zero(y)
+    cache = Mooncake.prepare_pullback_cache(pf, dy_mem, y, p, _t)
+    return cache, pf, λ_mem, dy_mem
+end
+
+function mooncake_run_ad(paramjac_config::Tuple, y, p, t, λ)
+    cache, pf, λ_mem, dy_mem = paramjac_config
+    λ_mem .= λ
+    dy, _ = Mooncake.value_and_pullback!!(cache, λ_mem, pf, dy_mem, y, p, t)
+    y_grad = cache.tangents[3]
+    p_grad = cache.tangents[4]
+    return dy, y_grad, p_grad
+end
+
+end
diff --git a/src/adjoint_common.jl b/src/adjoint_common.jl
@@ -211,6 +211,9 @@ function adjointdiffcache(g::G, sensealg, discrete, sol, dgdu::DG1, dgdp::DG2, f
         paramjac_config = get_paramjac_config(autojacvec, p, f, y, _p, _t; numindvar, alg)
         pf = get_pf(autojacvec; _f = unwrappedf, isinplace = isinplace, isRODE = isRODE)
         paramjac_config = (paramjac_config..., Enzyme.make_zero(pf))
+    elseif autojacvec isa MooncakeVJP
+        pf = get_pf(autojacvec, prob, unwrappedf)
+        paramjac_config = get_paramjac_config(MooncakeLoaded(), autojacvec, pf, p, f, y, _t)
     elseif SciMLBase.has_paramjac(f) || quad || !(autojacvec isa Bool) ||
            autojacvec isa EnzymeVJP
         paramjac_config = nothing
@@ -460,6 +463,15 @@ function get_paramjac_config(autojacvec::EnzymeVJP, p, f, y, _p, _t; numindvar,
     return paramjac_config
 end
 
+# Dispatched on inside extension.
+struct MooncakeLoaded end
+
+function get_paramjac_config(::Any, ::MooncakeVJP, pf, p, f, y, _t)
+    msg = "MooncakeVJP requires Mooncake.jl is loaded. Install the package and do " *
+          "`using Mooncake` to use this functionality"
+    error(msg)
+end
+
 function get_pf(autojacvec::ReverseDiffVJP; _f = nothing, isinplace = nothing,
         isRODE = nothing)
     nothing
@@ -492,6 +504,41 @@ function get_pf(autojacvec::EnzymeVJP; _f, isinplace, isRODE)
     end
 end
 
+function get_pf(::MooncakeVJP, prob, _f)
+    isinplace = DiffEqBase.isinplace(prob)
+    isRODE = isa(prob, RODEProblem)
+    pf = let f = _f
+        if isinplace && isRODE
+            function (out, u, _p, t, W)
+                f(out, u, _p, t, W)
+                return out
+            end
+        elseif isinplace
+            function (out, u, _p, t)
+                f(out, u, _p, t)
+                return out
+            end
+        elseif !isinplace && isRODE
+            function (out, u, _p, t, W)
+                out .= f(u, _p, t, W)
+                return out
+            end
+        else
+            # !isinplace
+            function (out, u, _p, t)
+                out .= f(u, _p, t)
+                return out
+            end
+        end
+    end
+end
+
+function mooncake_run_ad(paramjac_config, y, p, t, λ)
+    msg = "MooncakeVJP requires Mooncake.jl is loaded. Install the package and do " *
+          "`using Mooncake` to use this functionality"
+    error(msg)
+end
+
 function getprob(S::SensitivityFunction)
     (S isa ODEBacksolveSensitivityFunction) ? S.prob : S.sol.prob
 end

diff --git a/src/derivative_wrappers.jl b/src/derivative_wrappers.jl
@@ -751,6 +751,14 @@ function _vecjacobian!(dλ, y, λ, p, t, S::TS, isautojacvec::EnzymeVJP, dgrad,
     return
 end
 
+function _vecjacobian!(dλ, y, λ, p, t, S::SensitivityFunction, ::MooncakeVJP, dgrad, dy, W)
+    _dy, y_grad, p_grad = mooncake_run_ad(S.diffcache.paramjac_config, y, p, t, λ)
+    dy !== nothing && recursive_copyto!(dy, _dy)
+    dλ !== nothing && recursive_copyto!(dλ, y_grad)
+    dgrad !== nothing && recursive_copyto!(dgrad, p_grad)
+    return
+end
+
 function jacNoise!(λ, y, p, t, S::SensitivityFunction;
         dgrad = nothing, dλ = nothing, dy = nothing)
     _jacNoise!(λ, y, p, t, S, S.sensealg.autojacvec, dgrad, dλ, dy)

diff --git a/src/gauss_adjoint.jl b/src/gauss_adjoint.jl
@@ -428,6 +428,11 @@ function GaussIntegrand(sol, sensealg, checkpoints, dgdp = nothing)
         end
         paramjac_config = zero(y), zero(y), Enzyme.make_zero(pf)
         pJ = nothing
+    elseif sensealg.autojacvec isa MooncakeVJP
+        pf = get_pf(sensealg.autojacvec, prob, f)
+        paramjac_config = get_paramjac_config(
+            MooncakeLoaded(), sensealg.autojacvec, pf, p, f, y, tspan[2])
+        pJ = nothing
     elseif isautojacvec # Zygote
         paramjac_config = nothing
         pf = nothing
@@ -500,6 +505,9 @@ function vec_pjac!(out, λ, y, t, S::GaussIntegrand)
             Enzyme.Reverse, Enzyme.Duplicated(pf, tmp6), Enzyme.Const,
             Enzyme.Duplicated(tmp3, tmp4),
             Enzyme.Const(y), Enzyme.Duplicated(p, out), Enzyme.Const(t))
+    elseif sensealg.autojacvec isa MooncakeVJP
+        _, _, p_grad = mooncake_run_ad(paramjac_config, y, p, t, λ)
+        out .= p_grad
     else
         error("autojacvec choice $(sensealg.autojacvec) is not supported by GaussAdjoint")
     end

diff --git a/src/quadrature_adjoint.jl b/src/quadrature_adjoint.jl
@@ -235,6 +235,11 @@ function AdjointSensitivityIntegrand(sol, adj_sol, sensealg, dgdp = nothing)
         end
         paramjac_config = zero(y), zero(y), Enzyme.make_zero(pf)
         pJ = nothing
+    elseif sensealg.autojacvec isa MooncakeVJP
+        pf = get_pf(sensealg.autojacvec, prob, f)
+        paramjac_config = get_paramjac_config(
+            MooncakeLoaded(), sensealg.autojacvec, pf, p, f, y, tspan[2])
+        pJ = nothing
     elseif isautojacvec # Zygote
         paramjac_config = nothing
         pf = nothing
@@ -288,6 +293,9 @@ function vec_pjac!(out, λ, y, t, S::AdjointSensitivityIntegrand)
         else
             out[:] .= vec(tmp[1])
         end
+    elseif sensealg.autojacvec isa MooncakeVJP
+        _, _, p_grad = mooncake_run_ad(paramjac_config, y, p, t, λ)
+        out .= p_grad
     elseif sensealg.autojacvec isa EnzymeVJP
         tmp3, tmp4, tmp6 = paramjac_config
         tmp4 .= λ

diff --git a/src/sensitivity_algorithms.jl b/src/sensitivity_algorithms.jl
@@ -1226,6 +1226,23 @@ struct ReverseDiffVJP{compile} <: VJPChoice
     ReverseDiffVJP(compile = false) = new{compile}()
 end
 
+"""
+```julia
+MooncakeVJP <: VJPChoice
+```
+
+Uses Mooncake.jl to compute the vector-Jacobian products.
+
+Does not support GPUs (CuArrays).
+
+## Constructor
+
+```julia
+MooncakeVJP()
+```
+"""
+struct MooncakeVJP <: VJPChoice end
+
 @inline convert_tspan(::ForwardDiffSensitivity{CS, CTS}) where {CS, CTS} = CTS
 @inline convert_tspan(::Any) = nothing
 @inline function alg_autodiff(alg::AbstractSensitivityAlgorithm{

diff --git a/test/adjoint.jl b/test/adjoint.jl
@@ -135,6 +135,14 @@ _, easy_res14 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
     sensealg = GaussAdjoint())
+_, easy_res15 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = InterpolatingAdjoint(autojacvec = SciMLSensitivity.MooncakeVJP()))
+_, easy_res16 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = QuadratureAdjoint(autojacvec = SciMLSensitivity.MooncakeVJP()))
 _, easy_res142 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
@@ -158,6 +166,10 @@ _, easy_res146 = adjoint_sensitivities(sol_nodense, Tsit5(), t = t, dgdu_discret
     sensealg = GaussAdjoint(checkpointing = true,
         autojacvec = false),
     checkpoints = sol.t[1:500:end])
+_, easy_res147 = adjoint_sensitivities(solb, Tsit5(), t = t, dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = GaussAdjoint(autojacvec = SciMLSensitivity.MooncakeVJP()))
 adj_prob = ODEAdjointProblem(sol,
     QuadratureAdjoint(abstol = 1e-14, reltol = 1e-14,
         autojacvec = SciMLSensitivity.ReverseDiffVJP()),
@@ -189,11 +201,14 @@ res, err = quadgk(integrand, 0.0, 10.0, atol = 1e-14, rtol = 1e-12)
 @test isapprox(res, easy_res12, rtol = 1e-9)
 @test isapprox(res, easy_res13, rtol = 1e-9)
 @test isapprox(res, easy_res14, rtol = 1e-9)
+@test isapprox(res, easy_res15, rtol = 1e-9)
+@test isapprox(res, easy_res16, rtol = 1e-9)
 @test isapprox(res, easy_res142, rtol = 1e-9)
 @test isapprox(res, easy_res143, rtol = 1e-9)
 @test isapprox(res, easy_res144, rtol = 1e-9)
 @test isapprox(res, easy_res145, rtol = 1e-9)
 @test isapprox(res, easy_res146, rtol = 1e-9)
+@test isapprox(res, easy_res147, rtol = 1e-9)
 
 println("OOP adjoint sensitivities ")
 
@@ -203,14 +218,11 @@ _, easy_res = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
 _, easy_res2 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
-    sensealg = QuadratureAdjoint(abstol = 1e-14,
-        reltol = 1e-14))
+    sensealg = QuadratureAdjoint(abstol = 1e-14, reltol = 1e-14))
 _, easy_res22 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
-    sensealg = QuadratureAdjoint(autojacvec = false,
-        abstol = 1e-14,
-        reltol = 1e-14))
+    sensealg = QuadratureAdjoint(autojacvec = false, abstol = 1e-14, reltol = 1e-14))
 _, easy_res2 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
@@ -224,17 +236,15 @@ _, easy_res3 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
 @test easy_res32 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
-    sensealg = InterpolatingAdjoint(autojacvec = false))[1] isa
-                   AbstractArray
+    sensealg = InterpolatingAdjoint(autojacvec = false))[1] isa AbstractArray
 _, easy_res4 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
     sensealg = BacksolveAdjoint())
 @test easy_res42 = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
     abstol = 1e-14,
     reltol = 1e-14,
-    sensealg = BacksolveAdjoint(autojacvec = false))[1] isa
-                   AbstractArray
+    sensealg = BacksolveAdjoint(autojacvec = false))[1] isa AbstractArray
 _, easy_res5 = adjoint_sensitivities(soloop,
     Kvaerno5(nlsolve = NLAnderson(), smooth_est = false),
     t = t, dgdu_discrete = dg, abstol = 1e-12,
@@ -248,8 +258,7 @@ _, easy_res6 = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t, dgdu_discre
 _, easy_res62 = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t,
     dgdu_discrete = dg, abstol = 1e-14,
     reltol = 1e-14,
-    sensealg = InterpolatingAdjoint(checkpointing = true,
-        autojacvec = false),
+    sensealg = InterpolatingAdjoint(checkpointing = true, autojacvec = false),
     checkpoints = soloop_nodense.t[1:5:end])
 
 _, easy_res8 = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t, dgdu_discrete = dg,
@@ -289,6 +298,39 @@ _, easy_res123 = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t, dgdu_disc
     reltol = 1e-14,
     sensealg = GaussAdjoint(checkpointing = true),
     checkpoints = soloop_nodense.t[1:5:end])
+
+_, easy_res2_mc_quad = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = QuadratureAdjoint(
+        abstol = 1e-14, reltol = 1e-14, autojacvec = SciMLSensitivity.MooncakeVJP()))
+_, easy_res2_mc_interp = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = InterpolatingAdjoint(autojacvec = SciMLSensitivity.MooncakeVJP()))
+_, easy_res2_mc_back = adjoint_sensitivities(soloop, Tsit5(), t = t, dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = BacksolveAdjoint(autojacvec = SciMLSensitivity.MooncakeVJP()))
+_, easy_res6_mc_quad = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t,
+    dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = QuadratureAdjoint(
+        abstol = 1e-14, reltol = 1e-14, autojacvec = SciMLSensitivity.MooncakeVJP()))
+_, easy_res6_mc_interp = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t,
+    dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = InterpolatingAdjoint(checkpointing = true,
+        autojacvec = SciMLSensitivity.MooncakeVJP()),
+    checkpoints = soloop_nodense.t[1:5:end])
+_, easy_res6_mc_back = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t,
+    dgdu_discrete = dg,
+    abstol = 1e-14,
+    reltol = 1e-14,
+    sensealg = BacksolveAdjoint(autojacvec = SciMLSensitivity.MooncakeVJP()))
+
 @test isapprox(res, easy_res, rtol = 1e-10)
 @test isapprox(res, easy_res2, rtol = 1e-10)
 @test isapprox(res, easy_res22, rtol = 1e-10)
@@ -309,6 +351,12 @@ _, easy_res123 = adjoint_sensitivities(soloop_nodense, Tsit5(), t = t, dgdu_disc
 @test isapprox(res, easy_res12, rtol = 1e-9)
 @test isapprox(res, easy_res122, rtol = 1e-9)
 @test isapprox(res, easy_res123, rtol = 1e-4)
+@test isapprox(res, easy_res2_mc_quad, rtol = 1e-9)
+@test isapprox(res, easy_res2_mc_interp, rtol = 1e-9)
+@test isapprox(res, easy_res2_mc_back, rtol = 1e-9)
+@test isapprox(res, easy_res6_mc_quad, rtol = 1e-4)
+@test isapprox(res, easy_res6_mc_interp, rtol = 1e-9)
+@test isapprox(res, easy_res6_mc_back, rtol = 1e-9)
 
 println("Calculate adjoint sensitivities ")
 

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1,5 +1,6 @@
 using SciMLSensitivity, SafeTestsets
 using Test, Pkg
+import Mooncake
 
 const GROUP = get(ENV, "GROUP", "All")