🤖 Format .jl files

Author: amontoison

src/enzyme.jl

@@ -23,7 +23,7 @@ function EnzymeReverseADJacobian(
   return EnzymeReverseADJacobian()
 end
 
-struct EnzymeReverseADHessian{T,F} <: ADBackend
+struct EnzymeReverseADHessian{T, F} <: ADBackend
   seed::Vector{T}
   Hv::Vector{T}
   f::F
@@ -383,7 +383,12 @@ end
 
     function ADNLPModels.gradient(::EnzymeReverseADGradient, f, x)
       g = similar(x)
-      Enzyme.autodiff(Enzyme.set_runtime_activity(Enzyme.Reverse), Enzyme.Const(f), Enzyme.Active, Enzyme.Duplicated(x, g))
+      Enzyme.autodiff(
+        Enzyme.set_runtime_activity(Enzyme.Reverse),
+        Enzyme.Const(f),
+        Enzyme.Active,
+        Enzyme.Duplicated(x, g),
+      )
       return g
     end
 
@@ -505,7 +510,12 @@ end
       end
       _hvp!(
         Enzyme.DuplicatedNoNeed(b.grad, b.hvbuf),
-        b.ℓ, b.xbuf, b.vbuf, b.ybuf, zero(eltype(x)), b.cx,
+        b.ℓ,
+        b.xbuf,
+        b.vbuf,
+        b.ybuf,
+        zero(eltype(x)),
+        b.cx,
       )
       copyto!(Hv, b.hvbuf)
       return Hv

test/enzyme.jl

@@ -18,8 +18,16 @@ _noop_ℓ(x, y, obj_weight, cx) = zero(eltype(x))
 # no constraints (ncon = 0).  Fields: grad, hvbuf, xbuf, vbuf, cx, ybuf, f, c!, ℓ, ncon.
 function _make_enzyme_hvprod(n)
   ADNLPModels.EnzymeReverseADHvprod(
-    zeros(n), zeros(n), zeros(n), zeros(n), zeros(0), zeros(0),
-    identity, _noop_c!, _noop_ℓ, 0,
+    zeros(n),
+    zeros(n),
+    zeros(n),
+    zeros(n),
+    zeros(0),
+    zeros(0),
+    identity,
+    _noop_c!,
+    _noop_ℓ,
+    0,
   )
 end
 

test/sparse_hessian.jl

@@ -71,13 +71,7 @@ function sparse_hessian(backend, info, kw)
       n = length(x)
       sum(100 * (x[i + 1] - x[i]^2)^2 + (x[i] - 1)^2 for i = 1:(n - 1))
     end
-    nlp = ADNLPModel(
-      f,
-      x0,
-      hessian_backend = backend,
-      name = "Extended Rosenbrock";
-      kw...,
-    )
+    nlp = ADNLPModel(f, x0, hessian_backend = backend, name = "Extended Rosenbrock"; kw...)
     @test hess(nlp, x0) == T[802 -400 0 0; -400 1002 -400 0; 0 -400 1002 -400; 0 0 -400 200]
 
     x = ones(T, 2)