bug fix, formatter

src/quadrature_adjoint.jl

@@ -106,7 +106,13 @@ end
  (dgdu_continuous === nothing && dgdp_continuous === nothing ||
  g !== nothing))
 
- λ = zero(u0)
+ if ArrayInterfaceCore.ismutable(u0)
+ len = length(u0)
+ λ = similar(u0, len)
+ λ .= false
+ else
+ λ = zero(u0)
+ end
  sense = ODEQuadratureAdjointSensitivityFunction(g, sensealg, discrete, sol,
  dgdu_continuous, dgdp_continuous)
 
@@ -334,10 +340,10 @@ function _adjoint_sensitivities(sol, sensealg::QuadratureAdjoint, alg; t = nothi
  for i in (length(t) - 1):-1:1
  if ArrayInterfaceCore.ismutable(res)
  res .+= quadgk(integrand, t[i], t[i + 1],
- atol = abstol, rtol = reltol)[1]
+  atol = abstol, rtol = reltol)[1]
  else
  res += quadgk(integrand, t[i], t[i + 1],
- atol = abstol, rtol = reltol)[1]
+  atol = abstol, rtol = reltol)[1]
  end
  if t[i] == t[i + 1]
  for cb in callback.discrete_callbacks

test/adjoint_oop.jl

@@ -11,11 +11,11 @@ datasize = 20
 tsteps = range(tspan[1], tspan[2], length = datasize)
 
 function f(u, p, t)
- p*u
+ p * u
 end
 
 prob = ODEProblem(f, u0, tspan, p)
-sol = solve(prob, Tsit5(), saveat=tsteps, abstol = 1e-12, reltol = 1e-12)
+sol = solve(prob, Tsit5(), saveat = tsteps, abstol = 1e-12, reltol = 1e-12)
 
 ## Discrete Case
 dg_disc(u, p, t, i; outtype = nothing) = u .- 1
@@ -27,15 +27,15 @@ du0, dp = adjoint_sensitivities(sol, Tsit5(); t = tsteps, dgdu_discrete = dg_dis
 function G_p(p)
  tmp_prob = remake(prob, p = p)
  u = Array(solve(tmp_prob, Tsit5(), saveat = tsteps,
- sensealg = SensitivityADPassThrough(), abstol=1e-12, reltol=1e-12))
+ sensealg = SensitivityADPassThrough(), abstol = 1e-12, reltol = 1e-12))
 
  return sum(((1 .- u) .^ 2) ./ 2)
 end
 
 function G_u(u0)
  tmp_prob = remake(prob, u0 = u0)
  u = Array(solve(tmp_prob, Tsit5(), saveat = tsteps,
- sensealg = SensitivityADPassThrough(), abstol=1e-12, reltol=1e-12))
+ sensealg = SensitivityADPassThrough(), abstol = 1e-12, reltol = 1e-12))
  return sum(((1 .- u) .^ 2) ./ 2)
 end
 
@@ -44,13 +44,13 @@ G_u(u0)
 n_dp = ForwardDiff.gradient(G_p, p)
 n_du0 = ForwardDiff.gradient(G_u, u0)
 
-@test n_du0 ≈ du0 rtol = 1e-3
-@test_broken n_dp ≈ dp' rtol = 1e-3
+@test n_du0≈du0 rtol=1e-3
+@test_broken n_dp≈dp' rtol=1e-3
 @test sum(n_dp - dp') < 8.0
 
 ## Continuous Case
 
-g(u, p, t) = sum((u.^2)./2)
+g(u, p, t) = sum((u .^ 2) ./ 2)
 
 function dg(u, p, t)
  u
@@ -67,24 +67,24 @@ du0, dp = adjoint_sensitivities(sol, Tsit5(); dgdu_continuous = dg, g = g,
 function G_p(p)
  tmp_prob = remake(prob, p = p)
  sol = solve(tmp_prob, Tsit5(), abstol = 1e-12, reltol = 1e-12)
- res, err = quadgk((t) -> (sum((sol(t).^2)./2)), 0.0, 5.0, atol = 1e-12,
+ res, err = quadgk((t) -> (sum((sol(t) .^ 2) ./ 2)), 0.0, 5.0, atol = 1e-12,
  rtol = 1e-12)
  res
 end
 
 function G_u(u0)
  tmp_prob = remake(prob, u0 = u0)
  sol = solve(tmp_prob, Tsit5(), abstol = 1e-12, reltol = 1e-12)
- res, err = quadgk((t) -> (sum((sol(t).^2)./2)), 0.0, 5.0, atol = 1e-12,
+ res, err = quadgk((t) -> (sum((sol(t) .^ 2) ./ 2)), 0.0, 5.0, atol = 1e-12,
  rtol = 1e-12)
  res
 end
 
 n_du0 = ForwardDiff.gradient(G_u, u0)
 n_dp = ForwardDiff.gradient(G_p, p)
 
-@test_broken n_du0 ≈ du0 rtol=1e-3
-@test_broken n_dp ≈ dp' rtol=1e-3
+@test_broken n_du0≈du0 rtol=1e-3
+@test_broken n_dp≈dp' rtol=1e-3
 
 @test sum(n_du0 - du0) < 1.0
 @test sum(n_dp - dp) < 5.0
@@ -100,9 +100,6 @@ du0, dp = Zygote.gradient((u0, p) -> sum(concrete_solve(prob, Tsit5(), u0, p,
 @test !iszero(du0)
 @test !iszero(dp)
 
-
-
-
 ##Neural ODE adjoint with SimpleChains
 u0 = @SArray Float32[2.0, 0.0]
 datasize = 30
@@ -158,8 +155,8 @@ G_u(u0)
 n_dp = ForwardDiff.gradient(G_p, p_nn)
 n_du0 = ForwardDiff.gradient(G_u, u0)
 
-@test n_du0 ≈ du0 rtol = 1e-3
-@test n_dp ≈ dp' rtol = 1e-3
+@test n_du0≈du0 rtol=1e-3
+@test n_dp≈dp' rtol=1e-3
 
 ## Continuous case
 
@@ -179,24 +176,26 @@ du0, dp = adjoint_sensitivities(sol, Tsit5(); dgdu_continuous = dg, g = G,
 function G_p(p)
  tmp_prob = remake(prob_nn, p = p)
  sol = solve(tmp_prob, Tsit5(), abstol = 1e-12, reltol = 1e-12)
- res, err = quadgk((t) -> (sum(((sol_n(t) .- sol(t)).^2)./2)), 0.0, 1.5, atol = 1e-12,
+ res, err = quadgk((t) -> (sum(((sol_n(t) .- sol(t)) .^ 2) ./ 2)), 0.0, 1.5,
+ atol = 1e-12,
  rtol = 1e-12) # sol_n(t):numerical solution/data(above)
  res
 end
 
 function G_u(u0)
  tmp_prob = remake(prob_nn, u0 = u0)
  sol = solve(tmp_prob, Tsit5(), abstol = 1e-12, reltol = 1e-12)
- res, err = quadgk((t) -> (sum(((sol_n(t) .- sol(t)).^2)./2)), 0.0, 1.5, atol = 1e-12,
+ res, err = quadgk((t) -> (sum(((sol_n(t) .- sol(t)) .^ 2) ./ 2)), 0.0, 1.5,
+ atol = 1e-12,
  rtol = 1e-12) # sol_n(t):numerical solution/data(above)
  res
 end
 
 n_du0 = ForwardDiff.gradient(G_u, u0)
 n_dp = ForwardDiff.gradient(G_p, p_nn)
 
-@test n_du0 ≈ du0 rtol=1e-3
-@test n_dp ≈ dp' rtol=1e-3
+@test n_du0≈du0 rtol=1e-3
+@test n_dp≈dp' rtol=1e-3
 
 #concrete_solve
 
@@ -207,4 +206,4 @@ du0, dp = Zygote.gradient((u0, p) -> sum(concrete_solve(prob_nn, Tsit5(), u0, p,
  u0, p_nn)
 
 @test !iszero(du0)
-@test !iszero(dp)
+@test !iszero(dp)