diff --git a/ext/DiffEqBaseForwardDiffExt.jl b/ext/DiffEqBaseForwardDiffExt.jl
index adfb044de..3f3fbc2ee 100644
--- a/ext/DiffEqBaseForwardDiffExt.jl
+++ b/ext/DiffEqBaseForwardDiffExt.jl
@@ -2,8 +2,9 @@ module DiffEqBaseForwardDiffExt
 
 using DiffEqBase, ForwardDiff
 using DiffEqBase.ArrayInterface
-using DiffEqBase: Void, FunctionWrappersWrappers, OrdinaryDiffEqTag, AbstractTimeseriesSolution,
-    RecursiveArrayTools, reduce_tup, _promote_tspan, has_continuous_callback
+using DiffEqBase: Void, FunctionWrappersWrappers, OrdinaryDiffEqTag,
+                  AbstractTimeseriesSolution,
+                  RecursiveArrayTools, reduce_tup, _promote_tspan, has_continuous_callback
 import DiffEqBase: hasdualpromote, wrapfun_oop, wrapfun_iip, prob2dtmin,
                    promote_tspan, anyeltypedual, isdualtype, value, ODE_DEFAULT_NORM,
                    InternalITP, nextfloat_tdir, DualEltypeChecker, sse
@@ -502,7 +503,8 @@ unitfulvalue(x::ForwardDiff.Dual) = unitfulvalue(ForwardDiff.unitfulvalue(x))
 
 sse(x::ForwardDiff.Dual) = sse(ForwardDiff.value(x)) + sum(sse, ForwardDiff.partials(x))
 function DiffEqBase.totallength(x::ForwardDiff.Dual)
-    return DiffEqBase.totallength(ForwardDiff.value(x)) + sum(DiffEqBase.totallength, ForwardDiff.partials(x))
+    return DiffEqBase.totallength(ForwardDiff.value(x)) +
+           sum(DiffEqBase.totallength, ForwardDiff.partials(x))
 end
 
 @inline ODE_DEFAULT_NORM(u::ForwardDiff.Dual, ::Any) = sqrt(sse(u))
diff --git a/ext/DiffEqBaseGTPSAExt.jl b/ext/DiffEqBaseGTPSAExt.jl
index 8214a48e9..65c383f73 100644
--- a/ext/DiffEqBaseGTPSAExt.jl
+++ b/ext/DiffEqBaseGTPSAExt.jl
@@ -35,4 +35,4 @@ function ODE_DEFAULT_NORM(f::F, u::AbstractArray{<:TPS}, t) where {F}
     Base.FastMath.sqrt_fast(x / max(length(u), 1))
 end
 
-end
\ No newline at end of file
+end
diff --git a/ext/DiffEqBaseMonteCarloMeasurementsExt.jl b/ext/DiffEqBaseMonteCarloMeasurementsExt.jl
index 2ddb724d8..2a5d0ed84 100644
--- a/ext/DiffEqBaseMonteCarloMeasurementsExt.jl
+++ b/ext/DiffEqBaseMonteCarloMeasurementsExt.jl
@@ -26,7 +26,10 @@ end
 
 DiffEqBase.value(x::Type{MonteCarloMeasurements.AbstractParticles{T, N}}) where {T, N} = T
 DiffEqBase.value(x::MonteCarloMeasurements.AbstractParticles) = mean(x.particles)
-DiffEqBase.unitfulvalue(x::Type{MonteCarloMeasurements.AbstractParticles{T, N}}) where {T, N} = T
+function DiffEqBase.unitfulvalue(x::Type{MonteCarloMeasurements.AbstractParticles{
+        T, N}}) where {T, N}
+    T
+end
 DiffEqBase.unitfulvalue(x::MonteCarloMeasurements.AbstractParticles) = mean(x.particles)
 
 # Support adaptive steps should be errorless
diff --git a/ext/DiffEqBaseReverseDiffExt.jl b/ext/DiffEqBaseReverseDiffExt.jl
index 872d519a1..879989d30 100644
--- a/ext/DiffEqBaseReverseDiffExt.jl
+++ b/ext/DiffEqBaseReverseDiffExt.jl
@@ -22,7 +22,6 @@ end
 DiffEqBase.value(x::ReverseDiff.TrackedReal) = x.value
 DiffEqBase.value(x::ReverseDiff.TrackedArray) = x.value
 
-
 DiffEqBase.unitfulvalue(x::Type{ReverseDiff.TrackedReal{V, D, O}}) where {V, D, O} = V
 function DiffEqBase.unitfulvalue(x::Type{
         ReverseDiff.TrackedArray{V, D, N, VA, DA},
diff --git a/ext/DiffEqBaseTrackerExt.jl b/ext/DiffEqBaseTrackerExt.jl
index 406927ded..6d77714ba 100644
--- a/ext/DiffEqBaseTrackerExt.jl
+++ b/ext/DiffEqBaseTrackerExt.jl
@@ -16,7 +16,9 @@ DiffEqBase.value(x::Tracker.TrackedReal) = x.data
 DiffEqBase.value(x::Tracker.TrackedArray) = x.data
 
 DiffEqBase.unitfulvalue(x::Type{Tracker.TrackedReal{T}}) where {T} = T
-DiffEqBase.unitfulvalue(x::Type{Tracker.TrackedArray{T, N, A}}) where {T, N, A} = Array{T, N}
+function DiffEqBase.unitfulvalue(x::Type{Tracker.TrackedArray{T, N, A}}) where {T, N, A}
+    Array{T, N}
+end
 DiffEqBase.unitfulvalue(x::Tracker.TrackedReal) = x.data
 DiffEqBase.unitfulvalue(x::Tracker.TrackedArray) = x.data
 
diff --git a/src/norecompile.jl b/src/norecompile.jl
index 273fb14ce..d20d22a76 100644
--- a/src/norecompile.jl
+++ b/src/norecompile.jl
@@ -23,9 +23,11 @@ end
 
 # Default dispatch assumes no ForwardDiff, gets added in the new dispatch
 function wrapfun_iip(ff, inputs)
-    FunctionWrappersWrappers.FunctionWrappersWrapper(Void(ff), (typeof(inputs),), (Nothing,))
+    FunctionWrappersWrappers.FunctionWrappersWrapper(
+        Void(ff), (typeof(inputs),), (Nothing,))
 end
 
 function wrapfun_oop(ff, inputs)
-    FunctionWrappersWrappers.FunctionWrappersWrapper(ff, (typeof(inputs),), (typeof(inputs[1]),))
-end
\ No newline at end of file
+    FunctionWrappersWrappers.FunctionWrappersWrapper(
+        ff, (typeof(inputs),), (typeof(inputs[1]),))
+end
diff --git a/test/downstream/gtpsa.jl b/test/downstream/gtpsa.jl
index 496ebc10d..6b506a0e7 100644
--- a/test/downstream/gtpsa.jl
+++ b/test/downstream/gtpsa.jl
@@ -9,68 +9,69 @@ x = [1.0, 2.0, 3.0]
 p = [4.0, 5.0, 6.0]
 
 prob = ODEProblem(f!, x, (0.0, 1.0), p)
-sol = solve(prob, Tsit5(), reltol=1e-16, abstol=1e-16)
+sol = solve(prob, Tsit5(), reltol = 1e-16, abstol = 1e-16)
 
 # Parametric GTPSA map
 desc = Descriptor(3, 2, 3, 2) # 3 variables 3 parameters, both to 2nd order
 dx = @vars(desc)
 dp = @params(desc)
 prob_GTPSA = ODEProblem(f!, x .+ dx, (0.0, 1.0), p .+ dp)
-sol_GTPSA = solve(prob_GTPSA, Tsit5(), reltol=1e-16, abstol=1e-16)
+sol_GTPSA = solve(prob_GTPSA, Tsit5(), reltol = 1e-16, abstol = 1e-16)
 
 @test sol.u[end] ≈ scalar.(sol_GTPSA.u[end]) # scalar gets 0th order part
 
 # Compare Jacobian against ForwardDiff
 J_FD = ForwardDiff.jacobian([x..., p...]) do t
     prob = ODEProblem(f!, t[1:3], (0.0, 1.0), t[4:6])
-    sol = solve(prob, Tsit5(), reltol=1e-16, abstol=1e-16)
+    sol = solve(prob, Tsit5(), reltol = 1e-16, abstol = 1e-16)
     sol.u[end]
 end
 
-@test J_FD ≈ GTPSA.jacobian(sol_GTPSA.u[end], include_params=true)
+@test J_FD ≈ GTPSA.jacobian(sol_GTPSA.u[end], include_params = true)
 
 # Compare Hessians against ForwardDiff
 for i in 1:3
     Hi_FD = ForwardDiff.hessian([x..., p...]) do t
         prob = ODEProblem(f!, t[1:3], (0.0, 1.0), t[4:6])
-        sol = solve(prob, Tsit5(), reltol=1e-16, abstol=1e-16)
+        sol = solve(prob, Tsit5(), reltol = 1e-16, abstol = 1e-16)
         sol.u[end][i]
     end
-    @test Hi_FD ≈ GTPSA.hessian(sol_GTPSA.u[end][i], include_params=true)
+    @test Hi_FD ≈ GTPSA.hessian(sol_GTPSA.u[end][i], include_params = true)
 end
 
-
 # ODEProblem 2 =======================
-pdot!(dq, p, q, params, t) = dq .= [0.0, 0.0, 0.0] 
-qdot!(dp, p, q, params, t) = dp .= [p[1] / sqrt((1 + p[3])^2 - p[1]^2 - p[2]^2), 
-                                    p[2] / sqrt((1 + p[3])^2 - p[1]^2 - p[2]^2),
-                                    p[3] / sqrt(1 + p[3]^2) - (p[3] + 1)/sqrt((1 + p[3])^2 - p[1]^2 - p[2]^2)]
+pdot!(dq, p, q, params, t) = dq .= [0.0, 0.0, 0.0]
+function qdot!(dp, p, q, params, t)
+    dp .= [p[1] / sqrt((1 + p[3])^2 - p[1]^2 - p[2]^2),
+        p[2] / sqrt((1 + p[3])^2 - p[1]^2 - p[2]^2),
+        p[3] / sqrt(1 + p[3]^2) - (p[3] + 1) / sqrt((1 + p[3])^2 - p[1]^2 - p[2]^2)]
+end
 
 prob = DynamicalODEProblem(pdot!, qdot!, [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], (0.0, 25.0))
-sol = solve(prob, Yoshida6(), dt = 1.0, reltol=1e-16, abstol=1e-16)
+sol = solve(prob, Yoshida6(), dt = 1.0, reltol = 1e-16, abstol = 1e-16)
 
 desc = Descriptor(6, 2) # 6 variables to 2nd order
-dx  = @vars(desc) # identity map
+dx = @vars(desc) # identity map
 prob_GTPSA = DynamicalODEProblem(pdot!, qdot!, dx[1:3], dx[4:6], (0.0, 25.0))
-sol_GTPSA = solve(prob_GTPSA, Yoshida6(), dt = 1.0, reltol=1e-16, abstol=1e-16)
+sol_GTPSA = solve(prob_GTPSA, Yoshida6(), dt = 1.0, reltol = 1e-16, abstol = 1e-16)
 
 @test sol.u[end] ≈ scalar.(sol_GTPSA.u[end]) # scalar gets 0th order part
 
 # Compare Jacobian against ForwardDiff
 J_FD = ForwardDiff.jacobian(zeros(6)) do t
     prob = DynamicalODEProblem(pdot!, qdot!, t[1:3], t[4:6], (0.0, 25.0))
-    sol = solve(prob, Yoshida6(), dt = 1.0, reltol=1e-16, abstol=1e-16)
+    sol = solve(prob, Yoshida6(), dt = 1.0, reltol = 1e-16, abstol = 1e-16)
     sol.u[end]
 end
 
-@test J_FD ≈ GTPSA.jacobian(sol_GTPSA.u[end], include_params=true)
+@test J_FD ≈ GTPSA.jacobian(sol_GTPSA.u[end], include_params = true)
 
 # Compare Hessians against ForwardDiff
 for i in 1:6
     Hi_FD = ForwardDiff.hessian(zeros(6)) do t
-        prob =  DynamicalODEProblem(pdot!, qdot!, t[1:3], t[4:6], (0.0, 25.0))
-        sol = solve(prob, Yoshida6(), dt = 1.0, reltol=1e-16, abstol=1e-16)
+        prob = DynamicalODEProblem(pdot!, qdot!, t[1:3], t[4:6], (0.0, 25.0))
+        sol = solve(prob, Yoshida6(), dt = 1.0, reltol = 1e-16, abstol = 1e-16)
         sol.u[end][i]
     end
-    @test Hi_FD ≈ GTPSA.hessian(sol_GTPSA.u[end][i], include_params=true)
+    @test Hi_FD ≈ GTPSA.hessian(sol_GTPSA.u[end][i], include_params = true)
 end
diff --git a/test/forwarddiff_dual_detection.jl b/test/forwarddiff_dual_detection.jl
index 3685164ea..1fc50fefd 100644
--- a/test/forwarddiff_dual_detection.jl
+++ b/test/forwarddiff_dual_detection.jl
@@ -387,5 +387,6 @@ u = Dual.(val, par)
 @test DiffEqBase.totallength(val[1]) == 1
 @test DiffEqBase.totallength(val) == length(val)
 @test DiffEqBase.totallength(par) == length(par)
-@test DiffEqBase.totallength(u[1]) == DiffEqBase.totallength(val[1]) + DiffEqBase.totallength(par[1])
+@test DiffEqBase.totallength(u[1]) ==
+      DiffEqBase.totallength(val[1]) + DiffEqBase.totallength(par[1])
 @test DiffEqBase.totallength(u) == sum(DiffEqBase.totallength, u)