Preparing for inplace NLP

jbcaillau commented 1 month ago

@PierreMartinon aiming to providing inplace function as a result of nlp_constraints!, having inplace signatures for ξ, η, ψ, ϕ, θ:

julia> (ξl, ξ, ξu), (ηl, η, ηu), (ψl, ψ, ψu), (ϕl, ϕ, ϕu), (θl, θ, θu),
    (ul, uind, uu), (xl, xind, xu), (vl, vind, vu) = nlp_constraints!(ocp)

@ocots I'll need some help to update the unit tests.

jbcaillau commented 1 month ago

@jbcaillau To update:

In model.jl

5 possible nonlinear (= defined by functions, now inplace) constraints to treat, associated respectively with ξ, η, ψ, ϕ, θ:

        (type, f::BoundaryConstraint, lb, ub) && if type ∈ [:initial, :final, :boundary] end # ϕ
        (:control, f::ControlConstraint, lb, ub) # ξ
        (:state, f::StateConstraint, lb, ub) # η
        (:mixed, f::MixedConstraint, lb, ub) # ψ
        (:variable, f::VariableConstraint, lb, ub) # θ

Also (see fun_range):

function constraint!(
    ocp::OptimalControlModel{T, V}, 
    type::Symbol;
    rg::Union{OrdinalRange{<:Integer}, Index, Integer, Nothing}=nothing, 
    f::Union{Function, Nothing}=nothing, 
    lb::Union{ctVector, Nothing}=nothing, 
    ub::Union{ctVector, Nothing}=nothing,
    val::Union{ctVector, Nothing}=nothing,
    label::Symbol=__constraint_label()) where {T <: TimeDependence, V <: VariableDependence}

Also:

function constraint(ocp::OptimalControlModel{T, V}, label::Symbol) where {T <: TimeDependence, V <: VariableDependence}

In onepass.jl

p_constraint!(p, ocp, e1, e2, e3, label=gensym(); log=false)

p_dynamics!(p, ocp, x, t, e, label=nothing; log=false)

p_lagrange!(p, ocp, e, type; log=false)

p_mayer!(p, ocp, e, type; log=false)

p_bolza!(p, ocp, e1, e2, type; log=false)

NB.

use function for long funs (blue style)
code = then __wrap(code, p.lnum, p.line) in every p_...
even better(?): factor out generated code for alternative future generated code

Misc

check allocations with @allocates
use views, see https://github.com/control-toolbox/CTBase.jl/pull/271#discussion_r1741275180
is_in_place(ocp) now available
if false (default for functional syntax), nothing changed
should always be true for abstract, ensuring to map problems towards efficient functional code
first (maybe useless thanks to second step): add in-place functions from abstract, but cheat ocp getters to provide out of place and pass the tests as before, and build in CTDirect.jl and before
second: cheat getters in tests to return out of place funs (while in place will be passed)
end: remove all this

Type changes

also Dynamics, Lagrange, Mayer (in addition to the 5 nonlinear constraint types)
Type -> Type_ for all optimal_control_model-setters
enrich union type in the ocp model

add

Mayer_(ocp, f_::Function; variable::Bool = false) = is_in_place(ocp) ? Mayer!(f_; variable) : Mayer(f_; variable)

NB : actually, Mayer_ = is_in_place(ocp) ? Mayer! : Mayer also works
check all getters/setters for types

jbcaillau commented 1 month ago

@PierreMartinon any preliminary comments?

@PierreMartinon aiming to providing inplace function as a result of nlp_constraints!, having inplace signatures for ξ, η, ψ, ϕ, θ:
julia> (ξl, ξ, ξu), (ηl, η, ηu), (ψl, ψ, ψu), (ϕl, ϕ, ϕu), (θl, θ, θu),
    (ul, uind, uu), (xl, xind, xu), (vl, vind, vu) = nlp_constraints!(ocp)
Related to: control-toolbox/CTDirect.jl#188

jbcaillau commented 3 weeks ago

@PierreMartinon back on this issue. on your side, ready for inplace in CTDirect.jl with trapezoidal scheme? Target above: https://github.com/control-toolbox/CTBase.jl/issues/232#issue-2458120989

jbcaillau commented 3 weeks ago

@ocots I'll need some help to update the unit tests.

@ocots we'll need to be able to run tests like

    @def o begin
        t ∈ [ 0, 1 ], time
        x = [ r, v ] ∈ R², state
        u ∈ R, control
        w = r + 2v
        r(0) == 0,    (1)
        v(0) == 1,    (♡)
        ẋ(t) == [ v(t), w(t)^2 ]
        ∫( u(t)^2 + x₁(t) ) → min
    end
    x = [ 1, 2 ]
    x0  = 2 * x
    xf  = 3 * x
    u = 3
    @test constraint(o, :eq1)(x0, xf) == x0[1]
    @test constraint(o, Symbol("♡"))(x0, xf) == x0[2]
    @test o.dynamics(x, u) == [ x[2], (x[1] + 2x[2])^2 ]
    @test o.lagrange(x, u) == u^2 + x[1]

It is better not to rewrite test but to update the getters (such as constraint) so that they take care of the functions now being in place. My question: can you please remind me what getters are available for dynamics and the three different costs? (Using getters in the test would be nicer, too.)

ocots commented 3 weeks ago

Getters:

https://github.com/control-toolbox/CTBase.jl/blob/5db9d88a096e8a515aa289dcbd30c72435679b5f/src/optimal_control_model-getters.jl#L565-L589

jbcaillau commented 3 weeks ago

@ocots excellent. do think you could vscode update test_onepass.jl to replace field accesses by these getters? Then it should be fairly easy to reuse the existing tests with in place functions.

jbcaillau commented 3 weeks ago

Getters: [...]

@ocots just for the record: no bolza getter, subsumed by lagrange + mayer ones, ~~I suppose?~~ YES

PierreMartinon commented 3 weeks ago

Hi guys, While you're at it, can you make it so that initial conditions are always before final conditions when building the boundary conditions ? Also, iirc mayer exists only in autonomous version ?

jbcaillau commented 3 weeks ago

@PierreMartinon

Hi guys, While you're at it, can you make it so that initial conditions are always before final conditions when building the boundary conditions ?

OK

Also, iirc mayer exists only in autonomous version ?

Not sure that the question makes sense:

either $t_0$ and $t_f$ are fixed and the mayer cost function is $g(x_0,x_f[,v])$ (where $v$ might be a variable)
or $t_f$, for instance, is free and then is (a component of) the variable, so $g(x_0, x_f, t_f)$ is expected

Same thing for (nonlinear) boundary conditions, BTW.

Am I missing sth?

jbcaillau commented 3 weeks ago

@PierreMartinon meeting tomorrow (tuesday) 14:00 https://inria.webex.com/meet/jean-baptiste.caillau

@ocots @joseph-gergaud welcome to join if you're around!

ocots commented 2 weeks ago

@PierreMartinon Parametric type:

struct TypeParamExample{T<:Number}
    x::T
end

and for a function:

julia> function f(x::T, y::T) where {T <: Number}
       return x + y
       end
f (generic function with 1 method)

julia> f(1, 2)
3

julia> f(1, 2.0)
ERROR: MethodError: no method matching f(::Int64, ::Float64)

Closest candidates are:
  f(::T, ::T) where T<:Number
   @ Main REPL[5]:1

Stacktrace:
 [1] top-level scope
   @ REPL[7]:1

joseph-gergaud commented 2 weeks ago

@jbcaillau The scalar case for the constraints must be treated.

ocots commented 2 weeks ago

@PierreMartinon Voir :

jbcaillau commented 2 weeks ago

@jbcaillau The scalar case for the constraints must be treated.

@joseph-gergaud @PierreMartinon should be OK provided in place functions returned by nlp_constraints! are called with a (slice of) vector as first arg. may be an exception for Lagrange cost, to be checked. in any case, the possible generated error should be clear enough 🤞🏾

jbcaillau commented 2 weeks ago

[x] @jbcaillau adapt getters (constraint, dynamics, lagrange, mayer) to transform in place into out of place inside unitary tests
impacted tests:
- [x] test_onepass
- [x] ~~test_model~~ (useless)

jbcaillau commented 2 weeks ago

@PierreMartinon catch-up this Monday 9/9 at 14:00 on zoom https://univ-cotedazur.zoom.us/my/caillau

@ocots @joseph-gergaud welcome if you're around!

jbcaillau commented 2 weeks ago

Hi guys, While you're at it, can you make it so that initial conditions are always before final conditions when building the boundary conditions ? Also, iirc mayer exists only in autonomous version ?

@PierreMartinon closing this issue, in favour of https://github.com/control-toolbox/CTBase.jl/issues/288 (please answer there!)

control-toolbox / CTBase.jl