replace `@.` in `hess_op`

[tmigot]

using NLPModels, NLPModelsTest # https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/pull/60
nlp = BROWNDEN()
print_nlp_allocations(nlp, test_allocs_nlpmodels(nlp))

returns

  Problem name: BROWNDEN_manual
                   obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
              hess_op!: ████████████████████ 1344.0
           hess_coord!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                 grad!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
       hess_structure!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                hprod!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
         hess_op_prod!: ██⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 96.0 

and after this PR, it returns

  Problem name: BROWNDEN_manual
                   obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
              hess_op!: ████████████████████ 1248.0
           hess_coord!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                 grad!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
       hess_structure!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                hprod!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
         hess_op_prod!: ██⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 96.0  

We can do the same for jac_op, jac_lin_op, and jac_nln_op.

@geoffroyleconte @amontoison @dpo @abelsiqueira I am slightly surprised the hess_op! allocates and using the resulting linear operator for products also allocates. Any idea, where this is coming from? And, can we improve?

[codecov[bot]]

Codecov Report

Base: 99.49% // Head: 99.49% // No change to project coverage :thumbsup:

Coverage data is based on head (d1f7308) compared to base (f9388ab). Patch coverage: 100.00% of modified lines in pull request are covered.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #416   +/-   ##
=======================================
  Coverage   99.49%   99.49%           
=======================================
  Files          13       13           
  Lines         786      786           
=======================================
  Hits          782      782           
  Misses          4        4           

Impacted Files	Coverage Δ
src/nlp/api.jl	99.75% <100.00%> (ø)

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.

:umbrella: View full report at Codecov.
:loudspeaker: Do you have feedback about the report comment? Let us know in this issue.

[amontoison]

@tmigot Can you try this function to see if it allocates please.

function hess_op!(
  nlp::AbstractNLPModel,
  x::AbstractVector,
  Hv::AbstractVector,
  obj_weight::Real = one(eltype(x)),
)
  @lencheck nlp.meta.nvar x Hv
  prod = (res, v) -> hprod!(nlp, x, v, res; obj_weight = obj_weight)
  return LinearOperator{eltype(x)}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod, prod, prod)
end

[dpo]

You allocate an array here: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/pull/416/files#diff-3d235ab1458b43a45704d5735013ca8e8a94befce081da5f56722f0b95f71576R1141

[github-actions[bot]]

Package name	latest	stable
ADNLPModels.jl
AmplNLReader.jl
CUTEst.jl
CaNNOLeS.jl
DCISolver.jl
JSOSolvers.jl
LLSModels.jl
NLPModelsIpopt.jl
NLPModelsJuMP.jl
NLPModelsModifiers.jl
NLPModelsTest.jl
PDENLPModels.jl
Percival.jl
QuadraticModels.jl
SolverBenchmark.jl
SolverCore.jl
SolverTest.jl
SolverTools.jl

[tmigot]

@tmigot Can you try this function to see if it allocates please.

function hess_op!(
  nlp::AbstractNLPModel,
  x::AbstractVector,
  Hv::AbstractVector,
  obj_weight::Real = one(eltype(x)),
)
  @lencheck nlp.meta.nvar x Hv
  prod = (res, v) -> hprod!(nlp, x, v, res; obj_weight = obj_weight)
  return LinearOperator{eltype(x)}(nlp.meta.nvar, nlp.meta.nvar, true, true, prod, prod, prod)
end

  Problem name: BROWNDEN_manual
                   obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
              hess_op!: ████████████████████ 1136.0
           hess_coord!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                 grad!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
       hess_structure!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
                hprod!: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0.0   
         hess_op_prod!: ██⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 96.0  

[tmigot]

You allocate an array here: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/pull/416/files#diff-3d235ab1458b43a45704d5735013ca8e8a94befce081da5f56722f0b95f71576R1141

I am not sure to see which vector we allocate there

[amontoison]

The creation of a linear operator structure just allocates maybe and it can't be avoided.

[dpo]

Hm, not sure why my link didn't work. I mean that hprod!() without the multipliers calls hprod!() with multipliers by allocating a vector of zero multipliers: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/blob/main/src/nlp/api.jl#L1141. Could that be it?

[dpo]

The creation of a linear operator structure just allocates maybe and it can't be avoided.

That's probably true. And in fact I would expect hess_op!() to take an operator as argument and modify its prod, tprod, and ctprod.

[tmigot]

Hm, not sure why my link didn't work. I mean that hprod!() without the multipliers calls hprod!() with multipliers by allocating a vector of zero multipliers: https://github.com/JuliaSmoothOptimizers/NLPModels.jl/blob/main/src/nlp/api.jl#L1141. Could that be it?

You are right, this allocates in general. But not here as we now redefine hprod in the test problems of NLPModelsTest https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/blob/32ff072119733adc0c8637c2cc8380d108108939/src/nlp/problems/brownden.jl#L107

[tmigot]

The creation of a linear operator structure just allocates maybe and it can't be avoided.

Okay, but still what is strange is that computing a product H * v also allocates (this is hess_op_prod!)

[dpo]

Ok. Did you confirm that https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/blob/32ff072119733adc0c8637c2cc8380d108108939/src/nlp/problems/brownden.jl#L107 does not allocate?

[tmigot]

Ok. Did you confirm that https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/blob/32ff072119733adc0c8637c2cc8380d108108939/src/nlp/problems/brownden.jl#L107 does not allocate?

Yes, I updated https://github.com/JuliaSmoothOptimizers/NLPModelsTest.jl/pull/60#issuecomment-1231887261

[amontoison]

All the allocations are in LinearOperators.jl. The main issue is this line: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L75 We also always allocate two empty vectors: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L74

using NLPModelsTest
nlp = eval(Meta.parse(NLPModelsTest.nlp_problems[1]))()
test_allocs_nlpmodels(nlp)

using Profile, PProf
Profile.Allocs.clear()
Profile.Allocs.@profile sample_rate=1 test_allocs_nlpmodels(nlp)
PProf.Allocs.pprof(from_c = false)

[dpo]

All the allocations are in LinearOperators.jl. The main issue is this line: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L75 We also always allocate two empty vectors: https://github.com/JuliaSmoothOptimizers/LinearOperators.jl/blob/main/src/abstract.jl#L74

I would say that those allocations are somewhat expected. This is the reason why I think hess_op!() needs to modify the operator in place, instead of creating a new one.