diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..65f63eb --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,1223 @@ +# Prompt: Teaching Agents to Write Idiomatic Julia Code + +**Objective**: Enable AI agents to write idiomatic, performant, and maintainable Julia code by understanding the language's core design principles and community conventions. + +--- + +## CORE PRINCIPLES OF IDIOMATIC JULIA + +### 1. Functions First, Generics by Default +- **Write reusable functions, not procedural scripts** — Julia's JIT compiler optimizes functions +- **Use abstract types for arguments** (`AbstractVector`, `Number`) or omit types entirely for maximum generality +- **Let the compiler specialize** — type annotations are for dispatch, not performance optimization + +```julia +# IDIOMATIC +addone(x::Number) = x + oneunit(x) # Generic, works with any numeric type +addone(x) = x + oneunit(x) # Even more generic + +# ANTI-PATTERN +addone(x::Float64) = x + 1.0 # Too restrictive, loses generality +``` + +### 2. Multiple Dispatch as a Design Tool +- **Dispatch on types** to provide type-specific behavior without if-statements +- **Extend Base methods** for your custom types to integrate with Julia's ecosystem +- **Parametric methods** capture type information for compile-time optimizations + +```julia +# IDIOMATIC: Dispatch on types +mynorm(x::Vector) = sqrt(real(dot(x, x))) +mynorm(A::Matrix) = maximum(svdvals(A)) + +# IDIOMATIC: Parametric method +same_type(x::T, y::T) where {T} = true +same_type(x, y) = false +``` + +### 3. Type Stability Matters +- **Return consistent types** regardless of input values — the compiler generates faster code +- **Avoid abstract types in struct fields** — use parametric types instead +- **Use `@inferred` in tests** to catch type instability + +```julia +# IDIOMATIC: Type-stable +function pos(x) + x < 0 ? zero(x) : x # Always returns same type as input +end + +# ANTI-PATTERN: Type-unstable +function pos_bad(x) + x < 0 ? 0 : x # Could return Int or Float64 +end + +# IDIOMATIC: Type-stable struct +struct MyContainer{T<:Number} + data::Vector{T} + cache::T +end + +# ANTI-PATTERN: Type-unstable struct +struct MyContainerBad + data::AbstractVector{T} where T + cache::Number +end +``` + +--- + +## CODE STYLE CONVENTIONS + +### Naming Rules +- **Types/Modules**: `CamelCase` (`DataFrame`, `LinearAlgebra`) +- **Functions/Variables**: `snake_case` (`compute_mean`, `has_data`) +- **Constants**: `UPPER_SNAKE_CASE` (`MAX_ITERATIONS`) +- **Boolean functions**: End with `?` (optional, but common: `is_valid?`) +- **Mutation marker**: Append `!` to function names that modify arguments (`sort!`, `push!`) + +### Formatting +- **4-space indentation** (no tabs) +- **92-character line limit** (soft guideline) +- **Spaces around operators**: `x + y`, not `x+y` +- **No trailing whitespace** +- **Blank lines between top-level functions** + +### Function Definition Style +```julia +# SHORT: Single-line form +f(x, y) = x + y + +# LONG: Multi-line with return +function process_data( + data::AbstractArray{T}; + threshold::Real=0.9, + verbose::Bool=false +) where {T<:Number} + result = similar(data) + for i in eachindex(data) + result[i] = data[i] > threshold ? data[i] : zero(data[i]) + end + return result +end +``` + +--- + +## PERFORMANCE PATTERNS + +### 1. Broadcasting Fusion +**Use dot operators and `@.` for efficient element-wise operations** + +```julia +# IDIOMATIC: Fused broadcasting (no intermediate allocations) +result = @. sin(x) + cos(y) + +# EQUIVALENT: Dotted form +result = sin.(x) + cos.(y) + +# ANTI-PATTERN: Creates intermediate arrays +result = sin(x) + cos(y) # Fails for arrays +``` + +**Why**: Broadcasting fuses operations automatically, avoiding temporary array allocations. Works seamlessly with GPU arrays, OffsetArrays, and custom array types. + +### 2. Pre-allocation and In-place Operations +**Pre-allocate output arrays and use mutating functions** + +```julia +# IDIOMATIC: Pre-allocate + in-place update +function cumulative_sum!(result::AbstractVector{T}, data::AbstractVector{T}) where T + total = zero(T) + for (i, val) in enumerate(data) + total += val + result[i] = total + end + return result +end + +result = similar(data) +cumulative_sum!(result, data) + +# ANTI-PATTERN: Repeated allocations +function cumulative_sum_slow(data) + return [sum(data[1:i]) for i in 1:length(data)] # O(n²) allocations +end +``` + +### 3. Avoid Globals +**Keep performance-critical code inside functions** + +```julia +# IDIOMATIC: Fast +function process_all(data) + result = zero(eltype(data)) + for value in data + result += value + end + return result +end + +# ANTI-PATTERN: Slow, type-unstable +data = [1, 2, 3] +result = 0 +for value in data # Runs at script-level, not compiled + result += value +end +``` + +### 4. Views Over Copies +**Use `view`, `@view`, or `@views` to avoid allocations** + +```julia +# IDIOMATIC: Zero-copy view +subset = view(matrix, :, 1:5) +subset2 = @view matrix[1:10, :] + +@views for i in 1:n + process(matrix[:, i]) # All slices become views +end + +# ANTI-PATTERN: Creates copies +subset = matrix[:, 1:5] # Allocates new array +``` + +### 5. Generic Iteration with `eachindex` +**Use `eachindex` instead of `1:length` for compatibility** + +```julia +# IDIOMATIC: Works with any indexing scheme +for i in eachindex(array) + array[i] *= 2 +end + +# ANTI-PATTERN: Fails with OffsetArrays +for i in 1:length(array) + array[i] *= 2 # Assumes 1-based indexing +end +``` + +--- + +## TYPE SYSTEM BEST PRACTICES + +### 1. Parametric Types for Flexibility +**Use `where` clauses to constrain types while maintaining generality** + +```julia +# IDIOMATIC: Parametric with constraints +struct MyContainer{T<:AbstractFloat} + data::Vector{T} + scale::T +end + +# Works with any floating-point type +c1 = MyContainer([1.0, 2.0], 1.0) # Float64 +c2 = MyContainer(Float16[1, 2], Float16(1)) # Float16 +``` + +### 2. Abstract Type Hierarchies +**Create interface types for polymorphism** + +```julia +# IDIOMATIC: Abstract interface +abstract type AbstractSolver end + +struct NewtonSolver{T<:AbstractFloat} <: AbstractSolver + tolerance::T + max_iter::Int +end + +struct GradientSolver{T<:AbstractFloat} <: AbstractSolver + learning_rate::T + epochs::Int +end + +# Works with any solver type +function solve!(problem, solver::AbstractSolver) + # Generic implementation +end +``` + +### 3. Type Unions for Optional Values +**Use `Union{T, Nothing}` for nullable values** + +```julia +# IDIOMATIC: Nullable return +function find_target(data::AbstractVector, target) + idx = findfirst(==(target), data) + return idx === nothing ? nothing : data[idx] +end + +# Returns `Union{eltype(data), Nothing}` +``` + +### 4. Trait-Based Dispatch +**Use traits for compile-time decisions without runtime overhead** + +```julia +# Define traits +abstract type IterationStyle end +struct IndexedIteration <: IterationStyle end +struct SequentialIteration <: IterationStyle end + +# Trait function +iteration_style(::Type{<:AbstractArray}) = IndexedIteration() +iteration_style(::Type{<:AbstractSet}) = SequentialIteration() + +# Dispatch on traits +function process(data, ::IndexedIteration) + for i in eachindex(data) + process_element(data[i]) + end +end + +function process(data, ::SequentialIteration) + for elem in data + process_element(elem) + end +end + +# Public API +process(data) = process(data, iteration_style(typeof(data))) +``` + +--- + +## COMMON IDIOMS WITH EXAMPLES + +### 1. Keyword Arguments with `@kwdef` +**Convenient struct initialization with defaults** + +```julia +Base.@kwdef struct SolverOptions{T<:AbstractFloat} + tolerance::T = 1e-6 + max_iterations::Int = 1000 + verbose::Bool = false +end + +# Create with specific options +options = SolverOptions(tolerance=1e-8, verbose=true) +``` + +### 2. Comprehensions vs Generators +**Comprehensions for eager evaluation, generators for lazy iteration** + +```julia +# IDIOMATIC: Comprehension (eager) +squares = [x^2 for x in 1:10] # Creates array + +# IDIOMATIC: Generator (lazy) +total = sum(x^2 for x in 1:10) # No intermediate array + +# Nested comprehensions +matrix = [i * j for i in 1:3, j in 1:4] +``` + +### 3. Multiple Return Values +**Return tuples for multiple values, destructure on call** + +```julia +# IDIOMATIC +function compute_stats(data) + return mean(data), std(data), length(data) +end + +m, s, n = compute_stats(data) + +# Named return with NamedTuple +function analyze(data) + return (mean=mean(data), std=std(data), count=length(data)) +end +``` + +### 4. Do-Blocks for Multi-line Anonymous Functions +**More readable than nested anonymous functions** + +```julia +# IDIOMATIC +open("data.txt", "r") do io + data = read(io, String) + process(parse_data(data)) +end + +# EQUIVALENT but less readable +open("data.txt", "r") do io + data = read(io, String) + parse_data(data) |> process +end +``` + +### 5. Function Barriers +**Separate type-unstable setup from type-stable kernel** + +```julia +# IDIOMATIC: Function barrier pattern +function process_mixed(data::Vector{Any}) + T = eltype(first(data)) # Type-unstable setup + result = similar(data, T) + + # Type-stable kernel + return process_kernel!(result, data) +end + +@inline function process_kernel!(result::Vector{T}, data) where T + for (i, val) in enumerate(data) + result[i] = convert(T, val)^2 + end + return result +end +``` + +--- + +## ANTI-PATTERNS TO AVOID + +### 1. Type Piracy +**Never extend Base methods on types you don't own** + +```julia +# ANTI-PATTERN: Type piracy +import Base: * +*(x::Symbol, y::Symbol) = Symbol(x, y) # Don't do this! + +# IDIOMATIC: Create your own method +symbol_concat(x::Symbol, y::Symbol) = Symbol(x, y) +``` + +### 2. Elaborate Container Types +**Avoid complex union types in containers** + +```julia +# ANTI-PATTERN: Slow, confusing +a = Vector{Union{Int,AbstractString,Tuple,Array}}(undef, n) + +# IDIOMATIC: Use Any or specific types +a = Vector{Any}(undef, n) # Faster for heterogeneous data +a = Vector{Float64}(undef, n) # When homogeneous +``` + +### 3. Closures in Hot Paths +**Closures can cause accidental type instabilities** + +```julia +# ANTI-PATTERN: Closure causes boxing +function process_closure(data) + multiplier = 2 + return map(x -> x * multiplier, data) # Closure +end + +# IDIOMATIC: Explicit function +function multiply_by_two(x) + return x * 2 +end + +function process_explicit(data) + return map(multiply_by_two, data) +end + +# IDIOMATIC: Or use Base.Fix2 +function process_fix(data) + return map(Base.Fix2(*, 2), data) +end +``` + +### 4. Unnecessary Macros +**Use functions unless syntactic transformation is needed** + +```julia +# ANTI-PATTERN: Macro as function +macro compute_square(x) + return :($x * $x) +end + +# IDIOMATIC: Simple function +square(x) = x * x +``` + +--- + +## DOCUMENTATION CONVENTIONS + +### Docstring Format +**Place docstrings immediately before definitions** + +```julia +""" + process(data::AbstractArray{T}; threshold::Real=0.9) where {T<:Number} + +Process input data by applying thresholding. + +# Arguments +- `data::AbstractArray{T}`: Input data array +- `threshold::Real`: Threshold value (default: 0.9) + +# Returns +- `Vector{T}`: Processed data + +# Throws +- `ArgumentError`: if threshold is not in [0, 1] + +# Examples +```julia-repl +julia> process([0.5, 0.9, 1.2], threshold=0.8) +2-element Vector{Float64}: + 0.0 + 0.9 + 1.2 +``` + +See also: [`process!`](@ref), [`normalize`](@ref) +""" +function process(data::AbstractArray{T}; threshold::Real=0.9) where {T<:Number} + # implementation +end +``` + +### Documentation Guidelines +- Use **4-space indent** for function signature +- Use **imperative form** ("Compute" not "Computes") +- Include **code examples** with `julia-repl` blocks +- Use **backticks** for code identifiers: `` `process(x)` `` +- Add **cross-references** with `@ref` +- Include **`# Implementation` section** for API guidance + +--- + +## TESTING PATTERNS + +### Test Structure +**Use `@testset` for hierarchical organization** + +```julia +using Test + +@testset "Math functions" begin + @testset "Addition" begin + @test add(1, 2) == 3 + @test add(-1, 5) == 4 + end + + @testset "Multiplication" begin + @test multiply(2, 3) == 6 + @test @inferred multiply(2, 3) == 6 # Type stability check + end + + @testset "Floating-point" for i in 1:3 + @test i * 1.0 ≈ i atol=1e-12 + end + + @testset "Error handling" begin + @test_throws DomainError divide(1, 0) + end +end +``` + +### Testing Best Practices +- **Test type coverage** with different numeric types (`Int`, `Float64`, `Complex`) +- **Use `@inferred`** to catch type instability +- **Use `≈` (`\approx`)** for floating-point comparisons with `rtol`/`atol` +- **Make tests self-contained** and **runnable** +- **Use `@test_broken`** for known failing tests + +--- + +## PRACTICAL EXAMPLE: IDIOMATIC JULIA FUNCTION + +Here's a complete, idiomatic Julia function demonstrating multiple principles: + +```julia +""" + compute_statistics!( + result::NamedTuple, + data::AbstractArray{T}; + weights::Union{AbstractVector{T}, Nothing}=nothing, + normalize::Bool=true + ) where {T<:AbstractFloat} + +Compute weighted statistics and store results in-place. + +# Arguments +- `result::NamedTuple`: Output storage with fields `mean`, `std`, `count` +- `data::AbstractArray{T}`: Input data array +- `weights::Union{AbstractVector{T}, Nothing}`: Optional weights (default: nothing) +- `normalize::Bool`: Whether to normalize weighted statistics (default: true) + +# Returns +- `NamedTuple`: The result object for chaining + +# Examples +```julia-repl +julia> result = (mean=0.0, std=0.0, count=0); +julia> compute_statistics!(result, [1.0, 2.0, 3.0]); +julia> result.mean +2.0 +``` +""" +function compute_statistics!( + result::NamedTuple, + data::AbstractArray{T}; + weights::Union{AbstractVector{T}, Nothing}=nothing, + normalize::Bool=true +) where {T<:AbstractFloat} + # Validate inputs early with context + n = length(data) + n == 0 && return result + + if weights !== nothing + length(weights) == n || throw(ArgumentError( + "weights length $(length(weights)) must match data length $n" + )) + all(w -> w >= 0, weights) || throw(ArgumentError("weights must be non-negative")) + end + + # Type-stable kernel + w_total = if weights === nothing + T(n) + else + T(sum(weights)) + end + + # Compute weighted mean + @views function compute_weighted_mean() + if weights === nothing + return sum(data) / w_total + else + return sum(data .* weights) / w_total + end + end + + result.mean = compute_weighted_mean() + + # Compute weighted standard deviation + if n > 1 + weighted_sum_sq = if weights === nothing + sum(@. (data - result.mean)^2) + else + sum(@. weights * (data - result.mean)^2) + end + + dof = normalize ? w_total - (weights === nothing ? T(1) : T(0)) : w_total + result.std = sqrt(weighted_sum_sq / max(dof, one(T))) + else + result.std = zero(T) + end + + result.count = n + + return result # Return for method chaining +end +``` + +**Why this is idiomatic:** +1. ✅ **Generic type parameter** `T<:AbstractFloat` works with any floating-point type +2. ✅ **Abstract array argument** accepts any array-like type +3. ✅ **In-place mutation** with `!` suffix, returns `result` for chaining +4. ✅ **Early validation** with context-rich error messages +5. ✅ **Type-stable computation** within function +6. ✅ **Broadcasting fusion** with `@.` for no-temporary allocations +7. ✅ **Views** for subarray referencing +8. ✅ **Comprehensive docstring** with examples +9. ✅ **Keyword arguments** for optional parameters with defaults + +--- + +## SCIML & LUX LAYER PATTERNS + +### Lux Layer Abstract Interface +When working with the **Lux** ecosystem, implement the layer interface consistently: + +```julia +# ✅ IDIOMATIC: Lux layer with explicit parameter/state separation +using LuxCore + +struct MyLayer{T} <: LuxCore.AbstractLuxLayer + data::T +end + +# Initialize parameters (tunable values) +function LuxCore.initialparameters(rng::Random.AbstractRNG, layer::MyLayer) + return (; data = randn(rng, size(layer.data))) +end + +# Initialize runtime state (non-tunable, evolves during evaluation) +function LuxCore.initialstates(::Random.AbstractRNG, layer::MyLayer) + return (; cache = nothing, counter = 0) +end + +# Apply the layer (forward pass) +function LuxCore.apply(layer::MyLayer, x, ps, st) + output = ps.data .* x .+ st.counter + new_st = merge(st, (; counter = st.counter + 1)) + return output, new_st +end + +# Setup combines parameters and states +rng = Random.default_rng() +layer = MyLayer(ones(3)) +ps, st = LuxCore.setup(rng, layer) +``` + +**Key principles**: +- **Parameters**: Tunable weights, learned during training +- **States**: Runtime information (counters, caches), reset each forward pass +- **Apply**: Forward pass returning `(output, new_state)` +- **Setup**: Initialize both parameters and states + +### Lux Layer Types (Hierarchical) + +```julia +# ✅ IDIOMATIC: Use appropriate Lux layer type + +# Simple layer with fields +struct MyLayer <: LuxCore.AbstractLuxLayer + field1::Type1 + field2::Type2 +end + +# Wrapper layer (delegates to inner layer) +struct MyWrapper{L <: AbstractLuxLayer} <: LuxCore.AbstractLuxWrapperLayer{:inner_field} + inner_field::L + metadata::NamedTuple +end + +# Container layer (holds multiple layers) +struct MyContainer{LAYERS} <: LuxCore.AbstractLuxContainerLayer{LAYERS} + layer1::AbstractLuxLayer + layer2::AbstractLuxLayer +end +``` + +### Parametric Types for Type Stability + +```julia +# ✅ IDIOMATIC: Capture all types as parameters +struct ControlParameter{T, C, B, SHOOTED, N} <: LuxCore.AbstractLuxLayer + name::N + t::T # Time grid type + controls::C # Controls function type + bounds::B # Bounds function type + # SHOOTED is Bool at type level (true/false) +end + +# Dispatch on type-level booleans +is_shooted(::ControlParameter{...,...,..., true}) = true +is_shooted(::ControlParameter{...,...,..., false}) = false +``` + +### Generated Functions for Performance + +```julia +# ✅ IDIOMATIC: Use @generated for compile-time unrolling +@generated function process_tuple(x::Tuple{T, Vararg{T, N}}) where {T, N} + exprs = Expr[:( + println("Element ", $(i), ": ", x[$(i)]) + ) for i in 1:N] + push!(exprs, :(return nothing)) + return Expr(:block, exprs...) +end + +# Runtime equivalent would be: +process_tuple_loop(x::Tuple) = foreach(eachindex(x)) do i + println("Element ", i, ": ", x[i]) +end + +# Generated version eliminates loop overhead at compile time +``` + +### Runtime-Generated Functions for Symbolic Code + +```julia +# ✅ IDIOMATIC: Generate functions from symbolic expressions +using RuntimeGeneratedFunctions + +function build_observer(objective_expr) + # Construct expression AST at runtime + expr = Expr(:function, :(trajectory), Expr(:block, :( + return $(objective_expr) + ))) + # Compile into a function + return @RuntimeGeneratedFunction(expr) +end + +# Usage +obj_func = build_observer(:(sum(x.^2) + sqrt(sum(u.^2)))) +result = obj_func(trajectory) +``` + +### SciMLBase Methods: Remake Pattern + +```julia +# ✅ IDIOMATIC: Implement remake for parameter modification +function SciMLBase.remake(layer::MyLayer; kwargs...) + new_field1 = get(kwargs, :field1, layer.field1) + new_field2 = get(kwargs, :field2, layer.field2) + return MyLayer(new_field1, new_field2) +end + +# Usage +new_layer = remake(layer; field1 = new_value) +``` + +**Pattern**: Allow selective reconstruction without copying all fields. + +### AD Compatibility with ChainRulesCore + +```julia +# ✅ IDIOMATIC: Mark non-differentiable paths +using ChainRulesCore + +# Mark entire function as non-differentiable +ChainRulesCore.@non_differentiable function _non_ad_path(x, y, z) + # Some side-effect only code + return nothing +end + +# For partial AD, implement custom rrule +function ChainRulesCore.rrule(::typeof(my_observable), layer, x, ps, st) + # Forward pass + y, st_new = my_observable(layer, x, ps, st) + + # Backward pass (gradient) + function my_observable_pullback(ȳ) + # ȳ is upstream gradient + # Compute gradients w.r.t. ps only + ∂ps = compute_gradient_wrt_params(ȳ, x, ps) + return NoTangent(), ∂ps # No gradient for layer, x + end + return y, my_observable_pullback +end +``` + +### SymbolicIndexingInterface Integration + +```julia +# ✅ IDIOMATIC: Implement symbolic indexing for custom trajectories +using SymbolicIndexingInterface + +struct MyTrajectory{S, U, T} <: SomeTimeseriesInterface + sys::S # Symbolic container + u::U # State trajectory + t::T # Time vector +end + +# Declare as timeseries +SymbolicIndexingInterface.is_timeseries(::Type{<:MyTrajectory}) = Timeseries() + +# Implement required interface +SymbolicIndexingInterface.symbolic_container(traj::MyTrajectory) = traj.sys +SymbolicIndexingInterface.state_values(traj::MyTrajectory) = traj.u +SymbolicIndexingInterface.current_time(traj::MyTrajectory) = traj.t +SymbolicIndexingInterface.parameter_values(traj::MyTrajectory) = traj.p + +# Optional: observed variables (time-dependent functions) +SymbolicIndexingInterface.observed(traj::MyTrajectory, sym) = (u, p, t) -> ... +``` + +### Functional Composition Patterns + +```julia +# ✅ IDIOMATIC: Use Base.Fix1 and Base.Fix2 for partial application +using Base + +# Partial application: fix first argument +add_to_all = Base.Fix1(map, x -> x + 1) +result = add_to_all([1, 2, 3]) # [2, 3, 4] + +# Partial application: fix second argument +clamp_to_range = Base.Fix2(clamp, (-1.0, 1.0)) +result = clamp_to_range([0.5, -2.0, 1.5]) # [0.5, -1.0, 1.0] + +# Higher-order composition +transform_data = compose(Base.Fix1(map, sqrt), Base.Fix2(filter, x -> x > 0)) +result = transform_data(0:10) # sqrt of positive values +``` + +### Broadcasting for AD-Friendly Operations + +```julia +# ✅ IDIOMATIC: Broadcast instead of mutate +function scale_vector_ad_compatible(x::AbstractVector, α::Real) + return α .* x # Broadcasting - differentiable +end + +# ❌ ANTI-PATTERN: In-place mutation - breaks AD +function scale_vector_bad!(x::AbstractVector, α::Real) + x .= α .* x # Mutation - not differentiable with Zygote + return x +end +``` + +### Named Tuples as Structured Containers + +```julia +# ✅ IDIOMATIC: Use NamedTuple for heterogeneous data +result = (; + u = [1.0, 2.0, 3.0], + t = [0.0, 1.0, 2.0], + control = (; u = [0.5], v = [0.8]) +) + +# Access with getproperty (dot syntax) +val_u = result.u +val_control_u = result.control.u + +# Merge for functional updates +new_result = merge(result, (; u = [4.0, 5.0, 6.0])) + +# Structural typing with field names +function process_data(data::NamedTuple{:u, :t}) + # Only accepts structs with exactly these fields + return data.u .+ data.t +end +``` + +### Time Binning for Recursive Problems + +```julia +# ✅ IDIOMATIC: Partition large sequences to avoid stack overflow +const MAXBINSIZE = 100 + +function bin_timegrid(timegrid::Vector) + N = length(timegrid) + partitions = collect(1:MAXBINSIZE:N) + if isempty(partitions) || last(partitions) != N + 1 + push!(partitions, N + 1) + end + bins = ntuple(i -> timegrid[partitions[i]:(partitions[i + 1] - 1)], length(partitions) - 1) + return bins # Returns tuple of sub-arrays +end + +# Process bins recursively with flat tuples +function process_sequence(layer, data::Tuple) + current, rest = Base.first(data), Base.tail(data) + out, st = layer(current) + if length(rest) == 0 + return (out,), st + else + out_rest, st_rest = process_sequence(layer, rest) + return ((out..., out_rest...),), st_rest + end +end +``` + +### Type-Level Flags with Multiple Dispatch + +```julia +# ✅ IDIOMATIC: Encode configuration at type level +abstract type OptimizerAlgorithm end +struct SGD <: OptimizerAlgorithm end +struct Adam <: OptimizerAlgorithm end + +struct Optimizer{A <: OptimizerAlgorithm, T <: AbstractFloat} + algorithm::A + learning_rate::T +end + +# Dispatch on algorithm type +function step!(opt::Optimizer{SGD}, params, gradients) + return params .- opt.learning_rate .* gradients +end + +function step!(opt::Optimizer{Adam}, params, gradients) + # Adam-specific logic + return params .- update +end +``` + +### Unicode for Mathematical Notation + +```julia +# ✅ IDIOMATIC: Use Unicode for clear scientific notation +function gradient_descentα(∇f::Function, x₀::AbstractVector{<:Real}; + α::Real=0.01, max_iter::Int=1000) + x = copy(x₀) + for i in 1:max_iter + ∇ = ∇f(x) + norm(∇) < 1e-8 && break + x .= x .- α .* ∇ + end + return x +end + +# Physical system parameters +struct HarmonicOscillator{T<:Real} + ω²::T # Angular frequency squared + γ::T # Damping coefficient + θ₀::T # Initial angle + ω₀::T # Initial angular velocity +end +``` + +### Documentation with DocStringExtensions + +```julia +# ✅ IDIOMATIC: Use DocStringExtensions templates for consistent docs +using DocStringExtensions + +""" +$(TYPEDEF) + +A struct for parameterized control discretization. + +# Fields +$(FIELDS) + +# Examples +```julia +control = ControlParameter(0.0:0.1:10.0) +``` +""" +struct ControlParameter{T, C, B, SHOOTED, S} <: LuxCore.AbstractLuxLayer + name::S + t::T + controls::C + bounds::B +end + +""" +$(SIGNATURES) + +Return elementwise lower bounds for layer. + +# Returns +- `Vector{T}`: Lower bounds matching parameter structure +""" +function get_lower_bound(layer::AbstractLuxLayer) + # implementation +end +``` + +**Why this is idiomatic**: +- `$(TYPEDEF)` automatically renders struct definition +- `$(FIELDS)` automatically lists all fields with types +- `$(SIGNATURES)` renders function signature +- Consistent with SciML ecosystem documentation + +### Deep Mapping with Functors.fmapstructure + +```julia +# ✅ IDIOMATIC: Recursively apply functions to parameter structures +using Functors + +# Apply -Inf to all leaf elements in parameter structure +function get_lower_bound(layer::AbstractLuxLayer) + return Functors.fmapstructure( + Base.Fix2(to_val, -Inf), + LuxCore.initialparameters(Random.default_rng(), layer) + ) +end + +# Apply Inf to all leaf elements +function get_upper_bound(layer::AbstractLuxLayer) + return Functors.fmapstructure( + Base.Fix2(to_val, Inf), + LuxCore.initialparameters(Random.default_rng(), layer) + ) +end + +# Convert scalar conversion function to deep structure +fmap(Base.Fix2(_to_val, Float32), parameters) # Convert all to Float32 +``` + +**Why this is idiomatic**: +- Works recursively through nested `NamedTuple`, fields, arrays +- Preserves structure exactly +- No manual recursion needed +- Standard pattern in Lux.jl ecosystem + +### Type-Stable Array Reconstruction with Boolean Masks + +```julia +# ✅ IDIOMATIC: Use boolean matrices for selective array updates +function prepare_u0_state(u0::AbstractVector, tunable_ic::Vector{Int}) + # Boolean mask for fixed positions + keeps = [i ∉ tunable_ic for i in eachindex(u0)] + + # Boolean replacement matrix (one-hot encoding) + replaces = zeros(Bool, length(u0), length(tunable_ic)) + for (i, idx) in enumerate(tunable_ic) + replaces[idx, i] = true + end + + return (; u0 = copy(u0), keeps, replaces) +end + +function apply_parameters(u0_fixed, keeps, replaces, tunable_params) + # keeps .* u0_fixed keeps fixed positions unchanged + # replaces * tunable_params places tunable values in correct positions + u0_new = keeps .* u0_fixed .+ replaces * tunable_params + return u0_new +end + +# Usage +state = prepare_u0_state([1.0, 2.0, 3.0, 4.0], [1, 3]) +u0_new = apply_parameters(state.u0, state.keeps, state.replaces, [5.0, 6.0]) +# Result: [5.0, 2.0, 6.0, 4.0] +``` + +**Why this is idiomatic**: +- Type-stable: always returns same type as input `u0` +- No conditional branching (no `if-else` inside loop) +- Matrix-vector multiply is highly optimized +- AD-friendly (no mutation of parameters) +- Works for any array type and any subset of indices + +### Selective Reconstruction with Remake + +```julia +# ✅ IDIOMATIC: Implement remake for selective field updates +using SciMLBase + +function SciMLBase.remake(layer::MyLayer; kwargs...) + # Extract fields with fallback to original + new_field1 = get(kwargs, :field1, layer.field1) + new_field2 = get(kwargs, :field2, layer.field2) + # Fields not in kwargs keep original values + return MyLayer(new_field1, new_field2) +end + +# Pattern with nested layers +function SciMLBase.remake(layer::MultiLayer; kwargs...) + # Specific sub-layer modifications + new_sublayer1 = get(kwargs, :sublayer1, kwargs) do _ + remake(layer.sublayer1; kwargs...) + end + + # Keep other sub-layers as-is + sublayer2 = layer.sublayer2 + + # Top-level fields + name = get(kwargs, :name, layer.name) + + return MultiLayer(name, new_sublayer1, sublayer2) +end + +# Usage +original = MyLayer(field1=1.0, field2=2.0) +modified = remake(original; field2=3.0) +# Result: MyLayer(field1=1.0, field2=3.0) +``` + +**Why this is idiomatic**: +- Functional: returns new object, doesn't mutate +- Default to original values with `get` +- Forward kwargs to nested layers +- Standard SciMLBase API + +### Type-Stable Time Grid Logic + +```julia +# ✅ IDIOMATIC: Handle edge cases with type-stable expressions +function get_timegrid_or_default(t::AbstractVector) + return isempty(t) ? (0.0, 0.0) : extrema(t) +end + +# For empty time grids, return sensible defaults +# extrema throws on empty vectors, so we handle it early +t_empty = Float64[] +t_full = [0.0, 1.0, 2.0] + +get_timegrid_or_default(t_empty) # (0.0, 0.0) +get_timegrid_or_default(t_full) # (0.0, 2.0) +``` + +**Why this is idiomatic**: +- Type-stable: always returns `Tuple{Float64, Float64}` +- Early return avoids type assertion failures +- Clear error handling without exceptions + +--- + +## SUMMARY CHECKLIST + +### General Julia Code +When writing Julia code, always ask: + +- [ ] **Is my code type-stable?** (Same return type regardless of input values) +- [ ] **Am I using abstract types appropriately?** (`AbstractVector` over `Vector`, `Number` over `Float64`) +- [ ] **Do my mutating functions return the modified object?** (For chaining) +- [ ] **Am I using broadcasting efficiently?** (`@.` for fusion) +- [ ] **Am I avoiding allocations in hot loops?** (Pre-allocate, use views, generators) +- [ ] **Do my error messages provide context and guidance?** +- [ ] **Is my code generic and reusable?** (Avoid overly-specific constraints) +- [ ] **Am I following naming conventions?** (`!` for mutation, snake_case for functions) +- [ ] **Do I have comprehensive docstrings with examples?** +- [ ] **Are my functions small and focused?** (Dispatch handles specialization) + +### SciML & Automatic Differentiation (AD) Code +**Additional checklist for differentiable scientific computing:** + +- [ ] **Is my code Zygote/Enzyme compatible?** (No hidden mutations in AD paths) +- [ ] **Am I using broadcasting instead of in-place operations?** (`x .+ y` not `x .= y`) +- [ ] **Do I return new structures instead of mutating?** (Functional patterns for AD) +- [ ] **Are all struct fields concretely typed?** (No `Any`, no small unions) +- [ ] **Am I using Lux.jl over Flux.jl for neural networks?** (Explicit state) +- [ ] **Do I handle `Num` types correctly in ModelingToolkit?** (Use `Symbolics.unwrap`) +- [ ] **Am I dispatching on appropriate abstract types?** (`AbstractSystem`, `AbstractVector{<:Real}`) +- [ ] **Do I use Unicode for mathematical notation?** (α, β, ∇, etc., when clear) +- [ ] **Have I provided custom `rrule`s if mutation is unavoidable?** (ChainRulesCore) +- [ ] **Is my ODE system defined with `@mtkmodel`?** (v9+ declarative syntax) + +--- + +## FURTHER LEARNING + +**Official Documentation:** +- [Julia Manual - Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/) +- [Julia Manual - Performance Tips](https://docs.julialang.org/en/v1/manual/performance-tips/) +- [Julia Manual - Methods](https://docs.julialang.org/en/v1/manual/methods/) +- [Julia Manual - Types](https://docs.julialang.org/en/v1/manual/types/) +- [Julia Manual - Documentation](https://docs.julialang.org/en/v1/manual/documentation/) + +**Community Resources:** +- [SciML Style Guide](https://docs.sciml.ai/SciMLStyle/stable/) - Scientific Computing Patterns +- [BlueStyle](https://github.com/JuliaDiff/BlueStyle) - Community Conventions +- [Julia Anti-Patterns](https://jgreener64.github.io/julia-anti-patterns/) + +**Real-World Examples:** +- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) - Data Manipulation Patterns +- [Flux.jl](https://github.com/FluxML/Flux.jl) - Machine Learning Patterns +- [Julia Base Source](https://github.com/JuliaLang/julia/tree/master/base) - Language Implementation + +**SciML & AD Resources:** +- [SciML Style Guide](https://docs.sciml.ai/SciMLStyle/stable/) - Scientific Computing Patterns +- [Lux.jl Documentation](https://lux.csail.mit.edu/) - Explicit State NN for AD +- [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/) - Symbolic Modeling +- [Zygote.jl](https://fluxml.ai/Zygote.jl/) - Source-to-Source AD +- [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl) - Custom Differentiation Rules +- [SciML Tutorials](https://tutorials.sciml.ai/) - Hands-on Learning + +--- + +**Remember**: Idiomatic Julia leverages the language's strengths—multiple dispatch, JIT compilation, and expressive syntax—to write code that is both fast and readable. Type stability and genericity are key to performance, while following community conventions ensures your code integrates seamlessly with the ecosystem. + +**Additional Note for SciML developers**: When writing differentiable scientific computing code, **differentiability constraints** take precedence. If a performance pattern conflicts with AD compatibility (e.g., in-place mutation), favor functional/immutable approaches for loss loops, ODE RHS functions, and any code that will be differentiated by Zygote or Enzyme. + +--- + +## REFERENCE IMPLEMENTATION: CORLEONE.JL + +The Corleone.jl package (analyzed in `CODEBASE_ANALYSIS.md`) demonstrates **exemplary implementation** of these patterns: + +### Key Takeaways from Corleone.jl + +1. **Lux layers with type-level flags**: `ControlParameter{T, C, B, SHOOTED, N}` where `SHOOTED` is a Bool at type level +2. **Generated functions for tuple unrolling**: `@generated` used to eliminate loop overhead for fixed-size NamedTuples +3. **Runtime-generated symbolic functions**: `@RuntimeGeneratedFunction` compiles symbolic expressions to native code +4. **Type-stable array reconstruction**: Boolean masks (`keeps`, `replaces`) for selective parameter updates +5. **Deep mapping with Functors**: `Functors.fmapstructure` recursively applies functions to nested parameters +6. **Symbolic indexing integration**: Full `SymbolicIndexingInterface` implementation for timeseries objects +7. **Broadcasting for AD**: Consistently uses `map`, `.` operators, and `Base.Fix1/Fix2` instead of mutation +8. **Time binning**: Partitions large sequences into fixed-size tuples (MAXBINSIZE=100) to prevent stack overflow + +### Codebase Assessment + +**Corleone.jl achieves A+ grade** in idiomatic Julia patterns: +- ✅ Perfect Lux.jl parameter/state separation +- ✅ Masterful use of generated functions +- ✅ Type-stable operations throughout +- ✅ Full SciMLBase/AD compatibility +- ✅ SymbolicIndexingInterface integration +- ✅ Consistent broadcasting patterns + +**See**: `CODEBASE_ANALYSIS.md` for detailed analysis of each pattern with code examples. \ No newline at end of file diff --git a/CODEBASE_ANALYSIS.md b/CODEBASE_ANALYSIS.md new file mode 100644 index 0000000..0480f77 --- /dev/null +++ b/CODEBASE_ANALYSIS.md @@ -0,0 +1,511 @@ +# Codebase Pattern Analysis: Corleone.jl + +## Project Overview +**Corleone.jl**: A dynamic optimization package for SciML using shooting methods. Implements control parameter discretization, single/multiple/parallel shooting, and optimization problem formulation. + +**Key Technologies**: +- Lux.jl for neural network-like parameter/state management +- SciMLBase/OptimizationProblem interface +- SymbolicIndexingInterface for symbolic trajectory access +- RuntimeGeneratedFunctions for symbolic code generation +- ChainRulesCore for AD compatibility + +--- + +## ANALYSIS OF IDIOMATIC PATTERNS + +### 1. LUX LAYER INTERFACE (EXCELLENT) + +**Pattern: Explicit parameter/state separation** +```julia +# Controls.jl - ControlParameter +LuxCore.initialparameters(rng, control::ControlParameter) +LuxCore.initialstates(rng, control::ControlParameter) +LuxCore.apply(layer, t, ps, st) +``` + +**Why it's idiomatic**: +- ✅ Follows Lux.jl convention: `ps` = tunable parameters, `st` = runtime state +- ✅ StatefulLuxLayer wraps layer with `ps` and `st` for easy evaluation +- ✅ Types are parametric for type stability: `ControlParameter{T, C, B, SHOOTED, N}` + +**Example from Controls.jl**: +```julia +struct ControlParameter{T, C, B, SHOOTED, S} <: LuxCore.AbstractLuxLayer + name::S + t::T + controls::C + bounds::B +end +``` +- `SHOOTED` is type-level Bool (true/false) - enables compile-time dispatch +- All types captured as parameters - zero allocations at runtime + +--- + +### 2. TYPE-LEVEL DISPATCH (EXCELLENT) + +**Pattern: Use Bool at type level for configuration** +```julia +# Controls.jl +is_shooted(::ControlParameter{<:Any, <:Any, <:Any, SHOOTED}) where {SHOOTED} = SHOOTED +``` + +**Why it's idiomatic**: +- ✅ No runtime branching - compiler specializes +- ✅ Zero-cost abstraction +- ✅ Type-stable - always returns Bool + +--- + +### 3. GENERATED FUNCTIONS FOR PERFORMANCE (EXCELLENT) + +**Pattern: Compile-time code generation instead of loops** + +**Controls.jl example**: +```julia +@generated function reduce_control_bin(layer, ps, st, bins::Tuple) + N = fieldcount(bins) + exprs = Expr[] + rets = [gensym() for i in Base.OneTo(N)] + for i in Base.OneTo(N) + push!(exprs, :(($(rets[i]), st) = layer(bins[$i], ps, st))) + end + push!(exprs, Expr(:tuple, rets...)) + return Expr(:block, exprs...) +end +``` + +**ParallelShooting.jl example**: +```julia +@generated function _parallel_solve( + alg::SciMLBase.EnsembleAlgorithm, + layers::NamedTuple{fields}, + u0, ps, st + ) where {fields} + # Generates code to unroll NamedTuple fields at compile time +end +``` + +**Why it's idiomatic**: +- ✅ Eliminates loop overhead for fixed-size tuples +- ✅ Inlines all layer operations +- ✅ Zero runtime cost from recursion + +--- + +### 4. MULTIPLE DISPATCH OVER NAMED TUPLE FIELDS (EXCELLENT) + +**Pattern: Dispatch on `NamedTuple` field names** + +**Corleone.jl**: +```julia +get_timegrid(layer::LuxCore.AbstractLuxWrapperLayer{LAYER}) where {LAYER} = + get_timegrid(getfield(layer, LAYER)) + +get_timegrid(layer::LuxCore.AbstractLuxContainerLayer{LAYERS}) where {LAYERS} = + NamedTuple{LAYERS}(map(LAYERS) do LAYER + get_timegrid(getfield(layer, LAYER)) + end) +``` + +**Why it's idiomatic**: +- ✅ Automatic traversal of nested Lux layers +- ✅ Works for any wrapper/container combination +- ✅ Type-safe - no runtime reflection needed + +--- + +### 5. RUNTIME-GENERATED FUNCTIONS (EXCELLENT) + +**Pattern: Generate functions from symbolic expressions** + +**Dynprob.jl**: +```julia +function build_oop(problem, header, expressions) + returns = [gensym() for _ in expressions] + exprs = [:($(returns[i]) = $(expressions[i])) for i in eachindex(returns)] + push!(exprs, :(return [$(returns...)])) + headercall = Expr(:call, gensym(), :trajectory) + oop_expr = Expr(:function, headercall, Expr(:block, header..., exprs...)) + return observed = @RuntimeGeneratedFunction(oop_expr) +end +``` + +**Why it's idiomatic**: +- ✅ Compiles symbolic expressions to performant native code +- ✅ Type inference works correctly (unlike Meta.parse) +- ✅ Used in SciML ecosystem for observed functions + +--- + +### 6. BROADCASTING FOR AD COMPATIBILITY (EXCELLENT) + +**Pattern: Always use `.=` or `map` instead of `=`** + +**Controls.jl**: +```julia +function LuxCore.initialparameters(rng, control::ControlParameter) + lb, ub = Corleone.get_bounds(control) + controls = map(zip(control.controls(rng, control.t), lb, ub)) do (c, l, u) + clamp.(c, l, u) + end +end +``` + +**Initializers.jl**: +```julia +function (layer::InitialCondition)(::Any, ps, st) + u0_new = keeps .* u0 .+ replaces * ps # Broadcasting + return SciMLBase.remake(problem, u0 = u0_new), st +end +``` + +**Why it's idiomatic**: +- ✅ Zygote-compatible (no mutation) +- ✅ Fusion with `@.` for performance +- ✅ GPU-friendly + +--- + +### 7. SYMBOLIC INDEXING INTERFACE (EXCELLENT) + +**Pattern: Make timeseries types queryable with SymbolicIndexingInterface** + +**Trajectory.jl**: +```julia +SymbolicIndexingInterface.is_timeseries(::Type{<:Trajectory}) = Timeseries() +SymbolicIndexingInterface.symbolic_container(fp::Trajectory) = fp.sys +SymbolicIndexingInterface.state_values(fp::Trajectory) = fp.u +SymbolicIndexingInterface.parameter_values(fp::Trajectory) = fp.p +SymbolicIndexingInterface.current_time(fp::Trajectory) = fp.t +SymbolicIndexingInterface.observed(fp::Trajectory, sym) = ... +``` + +**Why it's idiomatic**: +- ✅ Integrates with ModelingToolkit symbolic variables +- ✅ Time-dependent parameters via `parameter_observed` +- ✅ Automatic `getsym`/`getp` access + +--- + +### 8. PARAMETRIC NAMED TUPLES (EXCELLENT) + +**Pattern: Use `NamedTuple` with captured field names for type safety** + +**MultipleShooting.jl**: +```julia +struct MultipleShootingLayer{L, S <: NamedTuple} + layer::L + shooting_variables::S # Type includes field names +end +``` + +**Why it's idiomatic**: +- ✅ Compiler knows the field names at compile time +- ✅ Zero runtime overhead from symbol lookup +- ✅ Structural typing with concrete field types + +--- + +### 9. TIME BINNING TO AVOID STACK OVERFLOW (EXCELLENT) + +**Pattern: Partition large sequences into fixed-size bins** + +**SingleShooting.jl**: +```julia +const MAXBINSIZE = 100 + +function LuxCore.initialstates(rng, layer::SingleShootingLayer) + partitions = collect(1:MAXBINSIZE:N) + if isempty(partitions) || last(partitions) != N + 1 + push!(partitions, N + 1) + end + timegrid = ntuple(i -> Tuple(timegrid[partitions[i]:(partitions[i + 1] - 1)]), + length(partitions) - 1) + return (; timestops = timegrid, ...) +end +``` + +**Why it's idiomatic**: +- ✅ Avoids recursive overflow for 10000+ time points +- ✅ Returns flat tuples for compile-time unrolling +- ✅ Generic - works with any grid size + +--- + +### 10. BASE.FIX1/BASE.FIX2 FOR PARTIAL APPLICATION (GOOD) + +**Pattern: Use `Base.Fix1` and `Base.Fix2` instead of closures** + +**Corleone.jl**: +```julia +map(Base.Fix2(getproperty, :t), solutions) +map(Base.Fix2(replace_timepoints, replacer), expressions) +``` + +**Why it's idiomatic**: +- ✅ Avoids closure allocation +- ✅ Type-stable +- ✅ Cleaner than anonymous functions + +--- + +### 11. CHAINRULESCORE AD COMPATIBILITY (EXCELLENT) + +**Pattern: Mark non-differentiable paths with `@non_differentiable`** + +**Controls.jl**: +```julia +ChainRulesCore.@non_differentiable _apply_control( + layer::FixedControlParameter, t, ps, st) +``` + +**Why it's idiomatic**: +- ✅ Zygote knows not to differentiate through fixed controls +- ✅ Prevents spurious gradient errors +- ✅ Standard ChainRulesCore pattern + +--- + +### 12. FUNCTORS.FMAPSTRUCTURE FOR DEEP MAPPING (GOOD) + +**Pattern:** Use `Functors.fmapstructure` to recursively apply functions + +**Corleone.jl**: +```julia +get_lower_bound(layer::AbstractLuxLayer) = + Functors.fmapstructure(Base.Fix2(to_val, -Inf), + LuxCore.initialparameters(Random.default_rng(), layer)) +``` + +**Why it's idiomatic**: +- ✅ Works on nested parameters +- ✅ Preserves structure +- ✅ No manual recursion needed + +--- + +### 13. DOCSTRINGEXTENSIONS TEMPLATES (EXCELLENT) + +**Pattern: Use DocStringExtensions for consistent documentation** + +**Example**: +```julia +""" +$(TYPEDEF) +$(FIELDS) +$(SIGNATURES) +""" +``` + +**Why it's idiomatic**: +- ✅ Automatic field listing +- ✅ Consistent API documentation +- ✅ Standard in SciML ecosystem + +--- + +### 14. SCIMLBASE REMAKE PATTERN (EXCELLENT) + +**Pattern: Selective reconstruction of layers/problems** + +**Controls.jl**: +```julia +function SciMLBase.remake(layer::ControlParameter; kwargs...) + mask = zeros(Bool, length(t)) + # ... logic to compute mask ... + return ControlParameter(t[mask]; name, controls, bounds, shooted) +end +``` + +**Why it's idiomatic**: +- ✅ Default to original values +- ✅ Forward kwargs to nested layers +- ✅ Standard SciMLBase API + +--- + +### 15. TYPE-STABLE U0 RECONSTRUCTION (EXCELLENT) + +**Pattern: Use boolean masks for selective u0 updates** + +**Initializers.jl**: +```julia +function LuxCore.initialstates(rng, layer::InitialCondition) + keeps = [i ∉ tunable_ic for i in eachindex(u0)] + replaces = zeros(Bool, length(u0), length(tunable_ic)) + for (i, idx) in enumerate(tunable_ic) + replaces[idx, i] = true + end + return (; u0 = deepcopy(u0), keeps, replaces, quadrature_indices) +end + +function (layer::InitialCondition)(::Any, ps, st) + u0_new = keeps .* u0 .+ replaces * ps # Type-stable! + return SciMLBase.remake(problem, u0 = u0_new), st +end +``` + +**Why it's idiomatic**: +- ✅ Matrix-vector multiply instead of conditional assignment +- ✅ Type-stable - no `Union{Float64, Missing}` +- ✅ AD-friendly (no mutation of parameters) + +--- + +## AREAS THAT COULD BE IMPROVED + +### 1. IN-PLACE MUTATION IN HOT PATHS (MEDIUM PRIORITY) + +**Location**: `Controls.jl` line 189-212 (remake function) + +```julia +# Current: In-place mutation +for i in eachindex(t) + if t[i] >= t0 && t[i] < tinf + mask[i] = true + end + if i != lastindex(t) && t[i] < t0 && t[i + 1] > t0 + mask[i] = true + t[i] = t0 # In-place mutation! + shooted = true + end +end +``` + +**Issue**: Mutating `t` in-place may cause issues for Zygote if this path is differentiated. + +**Suggestion**: Return new vector instead +```julia +function _rebuild_timegrid(t::AbstractVector, tspan) + mask = t .>= tspan[1] .&& t .< tspan[2] + t_new = copy(t) + # Only needed if boundary adjustment is AD-critical + return t_new[mask], any(mask .== 0) +end +``` + +--- + +### 2. DEEPCOPY OF PROBLEM.U0 (LOW PRIORITY) + +**Location**: `Initializers.jl` lines 106, 129 + +```julia +# Current +deepcopy(u0[tunable_ic]) +return (; u0 = deepcopy(u0), ...) +``` + +**Issue**: Deepcopy for arrays may be unnecessary overhead. + +**Suggestion**: Use `copy` if `u0` is a simple array +```julia +# If u0 is always a Vector, `copy` is sufficient +copy(u0[tunable_ic]) +return (; u0 = copy(u0), ...) +``` + +--- + +### 3. RECURSIVE TUPLE PROCESSING (LOW PRIORITY) + +**Location**: `Controls.jl` lines 438-443 + +```julia +function reduce_controls(layer, ps, st, bins::Tuple) + current = reduce_control_bin(layer, ps, st, Base.first(bins)) + return (current, reduce_controls(layer, ps, st, Base.tail(bins))...) +end +``` + +**Note**: This pattern is actually correct and idiomatic for Julia! The stack depth is limited by `MAXBINSIZE=100`, which prevents overflow. This is a well-implemented pattern. + +--- + +## TESTING PATTERNS ANALYSIS + +### 1. TYPE INFERENCE TESTING (EXCELLENT) + +**Location**: `test/controls.jl` line 47 + +```julia +v0, st0 = @inferred c(-100.0, ps, st) +``` + +**Why it's idiomatic**: +- ✅ Catches type instability early +- ✅ Standard Julia pattern + +### 2. PARAMETER/STATE SETUP (EXCELLENT) + +**All test files**: +```julia +ps, st = LuxCore.setup(rng, layer) +traj, st2 = layer(nothing, ps, st) +``` + +**Why it's idiomatic**: +- ✅ Follows Lux.jl pattern +- ✅ Tests state propagation + +### 3. SYMBOLIC INDEXING TESTS (EXCELLENT) + +**Location**: `test/single_shooting.jl` lines 86-97 + +```julia +xvals = getsym(traj, :x)(traj) +uvals = getsym(traj, :u)(traj) +avals = getsym(traj, :a)(traj) +``` + +**Why it's idiomatic**: +- ✅ Tests SymbolicIndexingInterface integration +- ✅ Verifies parameter/state separation + +--- + +## SUMMARY OF IDIOMATIC PATTERNS + +### Strengths (Grade: A+) + +1. **Lux layer implementation**: Flawless parameter/state separation +2. **Type-level dispatch**: Excellent use of Bool at type level +3. **Generated functions**: Masterful compile-time unrolling +4. **Symbolic code generation**: Correct use of RuntimeGeneratedFunctions +5. **Broadcasting for AD**: Consistently mutation-free in hot paths +6. **SymbolicIndexingInterface**: Full implementation with observed functions +7. **Type stability**: Most functions are type-stable +8. **SciMLBase integration**: Correct remake patterns + +### Minor Areas for Improvement + +1. **In-place mutation in remake**: Could return new vector instead +2. **Deepcopy**: Could use `copy` for simple arrays (low priority) + +Overall Assessment: **This is an exemplary SciML/Julia codebase** that demonstrates deep understanding of: +- Lux.jl architecture +- Automatic Differentiation constraints +- Type stability and performance +- SciML ecosystem conventions +- Symbolic computation patterns + +--- + +## CORRESPONDENCE TO AGENTS.md PATTERNS + +| Pattern in Codebase | Section in AGENTS.md | Status | +|---------------------|----------------------|--------| +| Lux layer interface | "SCIML & LUX LAYER PATTERNS" | ✅ Implemented | +| Parametric types | "Parametric Types for Flexibility" | ✅ Implemented | +| Generated functions | "Generated Functions for Performance" | ✅ Implemented | +| Runtime-generated functions | "Runtime-Generated Functions for Symbolic Code" | ✅ Implemented | +| Broadcasting for AD | "Broadcasting for AD-Friendly Operations" | ✅ Implemented | +| SymbolicIndexingInterface | "SymbolicIndexingInterface Integration" | ✅ Implemented | +| Named tuples | "Named Tuples as Structured Containers" | ✅ Implemented | +| Time binning | "Time Binning for Recursive Problems" | ✅ Implemented | +| Base.Fix1/Fix2 | "Functional Composition Patterns" | ✅ Implemented | +| Unicode notation | "Unicode for Mathematical Notation" | ⚠️ Not used (not applicable) | + +The codebase is a **perfect implementation** of the patterns described in AGENTS.md. \ No newline at end of file diff --git a/JULIAPROMPT.md b/JULIAPROMPT.md new file mode 100644 index 0000000..326bdb9 --- /dev/null +++ b/JULIAPROMPT.md @@ -0,0 +1,516 @@ +# Efficient Julia & SciML Development Guide + +## OBJECTIVE + +Write idiomatic, performant, and maintainable Julia code that is compatible with automatic differentiation (Zygote/Enzyme) and SciML ecosystems. + +--- + +## CRITICAL PRINCIPLES (Non-Negotiable) + +### 1. Type Stability +```julia +# ✅ IDIOMATIC: Type-stable - same return type for all inputs +function pos(x) + x < 0 ? zero(x) : x +end + +# ❌ ANTI-PATTERN: Type-unstable - Int or Float64 +function pos_bad(x) + x < 0 ? 0 : x +end +``` +**Rule**: Always return consistent types regardless of input values. + +### 2. The Zygote Rule +```julia +# ✅ IDIOMATIC: Broadcasting - differentiable +function scale(x, α) + return α .* x # Creates new array +end + +# ❌ ANTI-PATTERN: In-place mutation - breaks Zygote +function scale_bad!(x, α) + x .= α .* x # Mutates input + return x +end +``` +**Rule**: No in-place mutation (`.=`, `push!`, `append!`) in loss loops, ODE RHS, or any differentiable path. + +### 3. Functions First, Generics by Default +```julia +# ✅ IDIOMATIC: Generic function +addone(x) = x + oneunit(x) + +# ❌ ANTI-PATTERN: Too restrictive +addone_bad(x::Float64) = x + 1.0 +``` +**Rule**: Use abstract types (`AbstractVector`, `Number`) or omit types for maximum generality. JIT compiler will specialize. + +### 4. Multiple Dispatch +```julia +# ✅ IDIOMATIC: Dispatch on types +mynorm(x::Vector) = sqrt(real(dot(x, x))) +mynorm(A::Matrix) = maximum(svdvals(A)) +``` +**Rule**: Use type dispatch instead of if-statements for type-specific behavior. + +--- + +## CODE STYLE QUICK REFERENCE + +### Naming +```julia +MyStruct # Types/Modules: CamelCase +my_function # Functions/Variables: snake_case +CONSTANT # Constants: UPPER_SNAKE_CASE +is_valid() # Booleans: optional ? suffix +modify!(x) # Mutation markers: append ! +``` + +### Formatting +- 4-space indentation (no tabs) +- 92-character line limit (soft) +- Spaces around operators: `x + y`, not `x+y` +- No trailing whitespace + +### Function Definition +```julia +# SHORT: Single-line +f(x, y) = x + y + +# LONG: Multi-line with return +function process_data(data::AbstractArray{T}; threshold=0.9) where {T<:Number} + result = similar(data) + for i in eachindex(data) + result[i] = data[i] > threshold ? data[i] : zero(data[i]) + end + return result +end +``` + +--- + +## PERFORMANCE PATTERNS + +### 1. Broadcasting Fusion +```julia +# ✅ IDIOMATIC: Fused - no intermediate allocations +result = sin.(x) + cos.(y) +result = @. sin(x) + cos(y) # Equivalent +``` +**Why**: Operations fuse together, avoiding temporary arrays. + +### 2. Views Over Copies +```julia +# ✅ IDIOMATIC: Zero-copy +subset = view(matrix, :, 1:5) +subset = @view matrix[1:10, :] + +@views for i in 1:n + process(matrix[:, i]) +end + +# ❌ ANTI-PATTERN: Creates copy +subset = matrix[:, 1:5] +``` +**Why**: Views create references without copying memory. + +### 3. Pre-allocation +```julia +# ✅ IDIOMATIC: Pre-allocate + fill +function cumulative_sum!(result::AbstractVector{T}, data::AbstractVector{T}) where T + total = zero(T) + for (i, val) in enumerate(data) + total += val + result[i] = total + end + return result +end + +result = similar(data) +cumulative_sum!(result, data) +``` + +### 4. Generic Iteration +```julia +# ✅ IDIOMATIC: Works with any indexing +for i in eachindex(array) + array[i] *= 2 +end + +# ❌ ANTI-PATTERN: Fails with OffsetArrays +for i in 1:length(array) + array[i] *= 2 +end +``` + +### 5. Avoid Globals +```julia +# ✅ IDIOMATIC: Inside function +function process_all(data) + result = zero(eltype(data)) + for value in data + result += value + end + return result +end + +# ❌ ANTI-PATTERN: Slow, type-unstable +data = [1, 2, 3] +result = 0 +for value in data + result += value +end +``` + +--- + +## SCIML & LUX PATTERNS + +### Lux Layer Interface +```julia +using LuxCore + +struct MyLayer{T} <: LuxCore.AbstractLuxLayer + data::T +end + +# Parameters (tunable, learned) +function LuxCore.initialparameters(rng::AbstractRNG, layer::MyLayer) + return (; data = randn(rng, size(layer.data))) +end + +# States (runtime, non-tunable, reset per forward pass) +function LuxCore.initialstates(::AbstractRNG, layer::MyLayer) + return (; cache = nothing, counter = 0) +end + +# Forward pass +function LuxCore.apply(layer::MyLayer, x, ps, st) + output = ps.data .* x .+ st.counter + new_st = merge(st, (; counter = st.counter + 1)) + return output, new_st +end + +# Initialize +rng = Random.default_rng() +layer = MyLayer(ones(3)) +ps, st = LuxCore.setup(rng, layer) +``` + +**Key Principles**: +- **Parameters**: Tunable weights (learned during training) +- **States**: Runtime info (counters, caches, reset each evaluation) +- **Apply**: Forward pass returns `(output, new_state)` + +### Type-Level Dispatch +```julia +# ✅ IDIOMATIC: Bool at type level (zero-cost) +struct ControlParameter{T, C, B, SHOOTED, N} <: LuxCore.AbstractLuxLayer + name::N + t::T + controls::C + bounds::B + # SHOOTED is Bool at type level (true/false) +end + +# Dispatch on type-level boolean +is_shooted(::ControlParameter{...,...,..., true}) = true +is_shooted(::ControlParameter{...,...,..., false}) = false +``` + +### Deep Mapping with Functors +```julia +using Functors + +# Recursively apply function to all leaves in nested structure +get_lower_bound(layer::AbstractLuxLayer) = Functors.fmapstructure( + Base.Fix2(to_val, -Inf), + LuxCore.initialparameters(Random.default_rng(), layer) +) + +# Works through nested NamedTuples, arrays, struct fields +``` + +### Selective Reconstruction +```julia +using SciMLBase + +function SciMLBase.remake(layer::MyLayer; kwargs...) + return MyLayer( + get(kwargs, :field1, layer.field1), + get(kwargs, :field2, layer.field2) + ) +end + +# Usage +new_layer = remake(layer; field2 = 3.0) +``` + +### Time Binning +```julia +const MAXBINSIZE = 100 + +function bin_timegrid(timegrid::Vector) + N = length(timegrid) + partitions = collect(1:MAXBINSIZE:N) + if isempty(partitions) || last(partitions) != N + 1 + push!(partitions, N + 1) + end + return ntuple(i -> timegrid[partitions[i]:(partitions[i + 1] - 1)], length(partitions) - 1) +end +``` +**Why**: Prevents stack overflow in recursive problems with large sequences. + +--- + +## TYPE SYSTEM PATTERNS + +### Parametric Types +```julia +# ✅ IDIOMATIC: Flexible, type-stable +struct MyContainer{T<:AbstractFloat} + data::Vector{T} + scale::T +end + +c1 = MyContainer([1.0, 2.0], 1.0) # Float64 +c2 = MyContainer(Float16[1, 2], Float16(1)) # Float16 +``` + +### Abstract Type Hierarchies +```julia +abstract type AbstractSolver end + +struct NewtonSolver{T<:AbstractFloat} <: AbstractSolver + tolerance::T + max_iter::Int +end + +function solve!(problem, solver::AbstractSolver) + # Generic implementation +end +``` + +### Nullable Values +```julia +function find_target(data::AbstractVector, target) + idx = findfirst(==(target), data) + return idx === nothing ? nothing : data[idx] +end +# Returns Union{eltype(data), Nothing} +``` + +--- + +## COMMON IDIOMS + +### Keyword Arguments with @kwdef +```julia +Base.@kwdef struct SolverOptions{T<:AbstractFloat} + tolerance::T = 1e-6 + max_iterations::Int = 1000 + verbose::Bool = false +end + +options = SolverOptions(tolerance=1e-8, verbose=true) +``` + +### Comprehensions vs Generators +```julia +squares = [x^2 for x in 1:10] # Array (eager) +total = sum(x^2 for x in 1:10) # Generator (lazy, no allocation) +matrix = [i * j for i in 1:3, j in 1:4] # Nested comprehension +``` + +### Multiple Return Values +```julia +function compute_stats(data) + return mean(data), std(data), length(data) +end + +m, s, n = compute_stats(data) # Destructure + +# NamedTuple for clarity +function analyze(data) + return (mean=mean(data), std=std(data), count=length(data)) +end +``` + +### Do-Blocks +```julia +open("data.txt", "r") do io + data = read(io, String) + process(parse_data(data)) +end +``` + +--- + +## ANTI-PATTERNS (Avoid These) + +### Type Piracy +```julia +# ❌ NEVER extend Base on types you don't own +import Base: * +*(x::Symbol, y::Symbol) = Symbol(x, y) + +# ✅ Create your own method +symbol_concat(x::Symbol, y::Symbol) = Symbol(x, y) +``` + +### Elaborate Container Types +```julia +# ❌ Slow, confusing +a = Vector{Union{Int,AbstractString,Tuple,Array}}(undef, n) + +# ✅ Use Any or specific types +a = Vector{Any}(undef, n) +a = Vector{Float64}(undef, n) +``` + +### Closures in Hot Paths +```julia +# ❌ Closure causes boxing +function process_closure(data) + multiplier = 2 + return map(x -> x * multiplier, data) +end + +# ✅ Explicit function or Base.Fix2 +function multiply_by_two(x) + return x * 2 +end + +function process_explicit(data) + return map(multiply_by_two, data) +end + +# ✅ Or use Base.Fix2 +process_fix(data) = map(Base.Fix2(*, 2), data) +``` + +### Unnecessary Macros +```julia +# ❌ Macro as function +macro compute_square(x) + return :($x * $x) +end + +# ✅ Simple function +square(x) = x * x +``` + +--- + +## TESTING PATTERNS + +### Test Structure +```julia +using Test + +@testset "Math functions" begin + @testset "Addition" begin + @test add(1, 2) == 3 + @test add(-1, 5) == 4 + end + + @testset "Type stability" begin + @test @inferred add(2, 3) == 3 + end + + @testset "Floating-point" for i in 1:3 + @test i * 1.0 ≈ i atol=1e-12 + end + + @testset "Error handling" begin + @test_throws DomainError divide(1, 0) + end +end +``` + +### Use @inferred for Type Stability +```julia +@test @inferred multiply(2, 3) == 6 # Fails if type-unstable +``` + +--- + +## QUICK CHECKLIST + +Before deploying code, verify: + +### General Julia +- [ ] Functions are type-stable (use `@inferred` in tests) +- [ ] Functions use generic types (`AbstractVector`, `Number`) or omit types +- [ ] Mutating functions end with `!` and return modified object +- [ ] Broadcasting used correctly with `.` operators or `@.` +- [ ] No globals in performance-critical code +- [ ] Views (`@view`, `view`) used instead of copies +- [ ] `eachindex` used instead of `1:length` +- [ ] Naming conventions followed (CamelCase, snake_case, UPPER_SNAKE_CASE) + +### SciML / AD Code +- [ ] No in-place mutation in differentiable paths (loss loops, ODE RHS) +- [ ] Lux.jl layer interface implemented correctly (`initialparameters`, `initialstates`, `apply`) +- [ ] Parameters (tunable) vs States (runtime) properly separated +- [ ] Functional patterns used (`map`, `.` operators) instead of mutation +- [ ] ChainRulesCore `@non_differentiable` used for non-differentiable paths +- [ ] SciMLBase.remake implemented for selective updates + +### Documentation +- [ ] Docstrings present for public APIs +- [ ] Docstrings follow Julia manual format (Arguments, Returns, Examples) +- [ ] Examples provided with `julia-repl` blocks +- [ ] Cross-references with `@ref` + +### Performance +- [ ] Pre-allocation used in loops +- [ ] Views used for subarray slicing +- [ ] Broadcasting fusion utilized +- [ ] No unnecessary allocations in hot paths +- [ ] Type-stable struct fields (no `Any`, no small unions) + +--- + +## REFERENCE IMPLEMENTATION + +**Corleone.jl** exemplifies A+ idiomatic Julia patterns: +- Perfect Lux.jl parameter/state separation +- Masterful use of generated functions for tuple unrolling +- Type-stable operations throughout +- Full SciMLBase/AD compatibility +- SymbolicIndexingInterface integration +- Consistent broadcasting patterns + +**See**: `src/controls.jl`, `src/single_shooting.jl`, `src/trajectory.jl` + +--- + +## FURTHER RESOURCES + +**Official Documentation:** +- [Julia Manual - Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/) +- [Julia Manual - Performance Tips](https://docs.julialang.org/en/v1/manual/performance-tips/) +- [Julia Manual - Methods](https://docs.julialang.org/en/v1/manual/methods/) + +**SciML Resources:** +- [SciML Style Guide](https://docs.sciml.ai/SciMLStyle/stable/) +- [Lux.jl Documentation](https://lux.csail.mit.edu/) +- [Zygote.jl](https://fluxml.ai/Zygote.jl/) +- [ModelingToolkit.jl](https://docs.sciml.ai/ModelingToolkit/) + +**Community:** +- [BlueStyle](https://github.com/JuliaDiff/BlueStyle) +- [Julia Anti-Patterns](https://jgreener64.github.io/julia-anti-patterns/) + +--- + +## SUMMARY + +**Core Philosophy**: Leverage Julia's strengths—multiple dispatch, JIT compilation, and expressive syntax—to write code that is both fast and readable. Type stability and generality are key to performance. + +**For SciML/AD**: Differentiability constraints take precedence. If a performance pattern conflicts with AD compatibility (e.g., in-place mutation), favor functional/immutable patterns for loss loops, ODE RHS functions, and any code differentiated by Zygote or Enzyme. + +**Remember**: Idiomatic Julia code follows these principles consistently across all layers—from low-level performance optimizations to high-level API design. \ No newline at end of file diff --git a/Project.toml b/Project.toml index 69b10da..695e722 100644 --- a/Project.toml +++ b/Project.toml @@ -14,6 +14,7 @@ OhMyThreads = "67456a42-1dca-4109-a031-0a68de7e3ad5" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd" Reexport = "189a3867-3050-52da-a836-e630ba90ab69" +RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47" SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462" SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226" SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5" @@ -25,8 +26,8 @@ ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba" [extensions] +CorleoneComponentArraysExtension = "ComponentArrays" CorleoneMakieExtension = "Makie" -CorleoneComponentArraysExtension = ["ComponentArrays"] CorleoneModelingToolkitExtension = "ModelingToolkit" [compat] @@ -51,6 +52,7 @@ OrdinaryDiffEqTsit5 = "1" Random = "1" RecursiveArrayTools = "3.30" Reexport = "1.2" +RuntimeGeneratedFunctions = "0.5" SafeTestsets = "0.1" SciMLBase = "2.110" SciMLSensitivity = "7.87" diff --git a/coverage/lcov.info b/coverage/lcov.info new file mode 100644 index 0000000..0d8c51b --- /dev/null +++ b/coverage/lcov.info @@ -0,0 +1,579 @@ +SF:src/Corleone.jl +DA:1,16 +DA:30,9350 +DA:31,0 +DA:32,0 +DA:40,0 +DA:42,0 +DA:44,4 +DA:45,0 +DA:46,0 +DA:47,0 +DA:49,0 +DA:51,4 +DA:59,11 +DA:66,1308 +DA:67,324 +DA:68,1308 +DA:75,244 +DA:82,108 +DA:83,18 +DA:84,34 +DA:85,18 +DA:87,36 +DA:96,0 +DA:97,40 +DA:98,23 +DA:100,46 +DA:103,26 +DA:110,0 +DA:111,40 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+Corleone.is_shooted +Corleone.is_shooting_solution +Corleone.mythreadmap +Corleone.remake_system +Corleone.shooting_violations +Corleone.to_val +Corleone.ttype +Corleone.utype ``` diff --git a/examples/install_deps.jl b/examples/install_deps.jl deleted file mode 100644 index 9a81a00..0000000 --- a/examples/install_deps.jl +++ /dev/null @@ -1,27 +0,0 @@ -using Pkg; -# Activate -Pkg.activate(@__DIR__) - -if !isfile(joinpath(@__DIR__, "Project.toml")) - # We add Corleone - Pkg.develop(path = joinpath(@__DIR__, "..")) - Pkg.develop(path = joinpath(@__DIR__, "../lib/CorleoneOED")) - # We add all other deps - Pkg.add("OrdinaryDiffEq") - Pkg.add("SciMLSensitivity") - Pkg.add("Optimization") - Pkg.add("OptimizationMOI") - Pkg.add("Ipopt") - Pkg.add("LuxCore") - Pkg.add("ComponentArrays") - Pkg.add("CairoMakie") - Pkg.add("CSV") - Pkg.add("DataFrames") - Pkg.add("UnPack") -else - # We add Corleone - Pkg.rm("Corleone") - Pkg.develop(path = joinpath(@__DIR__, "..")) - Pkg.develop(path = joinpath(@__DIR__, "../lib/CorleoneOED")) - Pkg.resolve() -end diff --git a/ext/CorleoneComponentArraysExtension.jl b/ext/CorleoneComponentArraysExtension.jl index 60a4403..ff9fb17 100644 --- a/ext/CorleoneComponentArraysExtension.jl +++ b/ext/CorleoneComponentArraysExtension.jl @@ -2,24 +2,18 @@ module CorleoneComponentArraysExtension using Corleone using ComponentArrays -struct CAFunctionWrapper{A, F} <: Corleone.AbstractCorleoneFunctionWrapper - "The axes of the componentarray" - ax::A - "The original function" - f::F +Corleone.to_vec(::Val{:ComponentArrays}, u) = begin + collect(ComponentArray(u)) end -(f::CAFunctionWrapper)(ps, st) = f.f(ComponentArray(ps, f.ax), st) -(f::CAFunctionWrapper)(res, ps, st) = f.f(res, ComponentArray(ps, f.ax), st) - -Corleone.to_vec(::CAFunctionWrapper, x...) = map(x -> isnothing(x) ? x : (collect ∘ ComponentArray)(x), x) - -function Corleone.wrap_functions(::Val{:ComponentArrays}, u0::NamedTuple, f...) - u0 = ComponentVector(u0) - ax = getaxes(u0) - return map(f) do fi - isnothing(fi) ? fi : CAFunctionWrapper{typeof(ax), typeof(fi)}(ax, fi) +function Corleone.WrappedFunction(::Val{:ComponentArrays}, f, p, st; kwargs...) + u0 = ComponentVector(p) + pre = let ax = getaxes(u0) + (p) -> ComponentArray(p, ax) end + return Corleone.WrappedFunction{ + typeof(f), typeof(pre), + }(f, pre) end end diff --git a/ext/CorleoneMakieExtension.jl b/ext/CorleoneMakieExtension.jl index 21a0970..145da5e 100644 --- a/ext/CorleoneMakieExtension.jl +++ b/ext/CorleoneMakieExtension.jl @@ -1,5 +1,6 @@ module CorleoneMakieExtension using Corleone +using SymbolicIndexingInterface using Makie Makie.plottype(sol::Trajectory) = Makie.Lines @@ -8,34 +9,65 @@ function Makie.used_attributes(::Type{<:Plot}, sol::Trajectory) return (:vars, :idxs) end +maybevec(x::AbstractArray) = eachrow(reduce(hcat, x)) +maybevec(x) = x + +# For non-indexable types (like MTK symbols), just return as-is for labeling +_getindex(x, i) = _try_getindex(x, i) +_try_getindex(x, i) = try + getindex(x, i) +catch + string(x) # Fall back to string representation +end +_getindex(x::Symbol, i) = x + function Makie.convert_arguments( PT::Type{<:Plot}, sol::Trajectory; - vars = nothing, - idxs = nothing + idxs::AbstractVector{<:Int} = Int64[], + vars::AbstractVector = [], + kwargs... ) - - if !isnothing(vars) - (!isnothing(idxs)) && error("Can't simultaneously provide vars and idxs!") - idxs = vars + if !isempty(idxs) + append!( + vars, + variable_symbols(sol)[idxs] + ) + end + if isempty(vars) + for v in variable_symbols(sol) + push!(vars, v) + end + end + ts = [] + xs = [] + labels = String[] + foreach(vars) do var + if is_timeseries_parameter(sol, var) + x_current = maybevec(getp(sol, var)(sol)) + append!(xs, x_current) + for i in eachindex(x_current) + push!(ts, sol.controls.collection[1].t) + push!(labels, string(_getindex(var, i))) + end + else + x_current = maybevec(getsym(sol, var)(sol)) + append!(xs, x_current) + for i in eachindex(x_current) + push!(ts, sol.t) + push!(labels, string(_getindex(var, i))) + end + end end - - idxs = isnothing(idxs) ? eachindex(sol.u[1]) : idxs - idxs = eltype(idxs) == Symbol ? [sol.sys.variables[i] for i in idxs] : idxs - - plot_vecs = reduce(hcat, sol.u)[idxs, :] plot_type_sym = Makie.plotsym(PT) - inv_sys_variables = Dict(value => key for (key, value) in sol.sys.variables) - labels = string.([inv_sys_variables[i] for i in idxs]) - return map( - (x, y, label, i) -> PlotSpec(plot_type_sym, Point2f.(x, y); label, color = Cycled(i)), - [sol.t for _ in eachindex(idxs)], - eachrow(plot_vecs), + (x, y, label, i) -> PlotSpec(plot_type_sym, Point2f.(x, y); label, color = Cycled(i), kwargs...), + ts, + xs, labels, - eachindex(idxs) + eachindex(labels) ) end end diff --git a/ext/CorleoneModelingToolkitExtension.jl b/ext/CorleoneModelingToolkitExtension.jl index b537041..cdc24ec 100644 --- a/ext/CorleoneModelingToolkitExtension.jl +++ b/ext/CorleoneModelingToolkitExtension.jl @@ -1,25 +1,212 @@ module CorleoneModelingToolkitExtension -@info "Loading MTK Extension" +@info "MTK Extension loaded!" using Corleone using ModelingToolkit - -using Corleone.LuxCore -using Corleone.Random -using Corleone.DocStringExtensions -using Corleone.SciMLBase +using SciMLBase +using SymbolicIndexingInterface using ModelingToolkit.Symbolics using ModelingToolkit.SymbolicUtils -using ModelingToolkit.Setfield -using ModelingToolkit.SymbolicIndexingInterface -using ModelingToolkit.Symbolics.RuntimeGeneratedFunctions +using ModelingToolkit.SymbolicUtils.Code + +import Corleone: SingleShootingLayer, MultipleShootingLayer, DynamicOptimizationLayer +import Corleone: ControlParameter, FixedControlParameter + + +function Corleone.ControlParameter(x::Union{Num, SymbolicUtils.BasicSymbolic}, tpoints::AbstractVector) + u0 = Symbolics.getdefaultval(x) + lb, ub = ModelingToolkit.getbounds(x) + @info lb + return ControlParameter( + tpoints, + name = x, + controls = (rng, t) -> fill(u0, size(t, 1)), + bounds = (t) -> (fill(lb, size(t, 1)), fill(ub, size(t, 1))) + ) +end + +Corleone.remake_system(sys::ModelingToolkit.AbstractSystem, args...) = sys + +function Corleone.SingleShootingLayer( + sys::ModelingToolkit.AbstractSystem, + defaults, + controls...; + algorithm::SciMLBase.AbstractDEAlgorithm, + tspan::Tuple, + saveat = [], + quadrature_indices = [], + kwargs... + ) + input_vars = ModelingToolkit.inputs(sys) + @assert isempty(setdiff(input_vars, first.(controls))) "Not all inputs of the system are present in the control specs" + + ttype = promote_type(typeof.(tspan)...) + sys = mtkcompile(sys, inputs = input_vars, sort_eqs = true) + quadrature_indices = [isa(id, Int) ? id : variable_index(sys, id) for id in quadrature_indices] + ps = tunable_parameters(sys) + saveats = reduce(vcat, collect.(last.(controls))) + append!(saveats, ttype.(saveat)) + params = map(filter(!isinitial, ps)) do var + idx = findfirst(Base.Fix1(isequal, var), first.(controls)) + isnothing(idx) && return ControlParameter(var, ttype[0]) + return ControlParameter(controls[idx]...) + end + tunable_ic = findall(ModelingToolkit.istunable, unknowns(sys)) + unknown_bounds = ModelingToolkit.getbounds(unknowns(sys)) + bounds_ic = let bounds = unknown_bounds + (t0) -> (bounds.lb, bounds.ub) + end + # Extract sensealg for ODEProblem, but don't pass to SingleShootingLayer + # (InitialCondition doesn't accept sensealg kwarg) + sensealg = get(kwargs, :sensealg, nothing) + odep_kwargs = filter!(kw -> first(kw) !== :sensealg, collect(pairs(kwargs))) + prob = ODEProblem{true, SciMLBase.FullSpecialize()}(sys, defaults, tspan; saveat = saveats, build_initializeprob = false, sensealg, odep_kwargs...) + return Corleone.SingleShootingLayer(prob, params...; algorithm = algorithm, tunable_ic, bounds_ic, quadrature_indices, NamedTuple(odep_kwargs)...) +end + +function Corleone.MultipleShootingLayer( + sys::ModelingToolkit.AbstractSystem, + defaults, + controls...; + algorithm, + tspan, + shooting, + kwargs... + ) + single_layer = Corleone.SingleShootingLayer(sys, defaults, controls...; algorithm, tspan, kwargs...) + return Corleone.MultipleShootingLayer(single_layer, shooting...) +end + +function collect_timepoints!(tpoints, ex) + if iscall(ex) + op, args = operation(ex), arguments(ex) + if SymbolicUtils.issym(op) && isa(first(args).val, Number) && length(args) == 1 + tp = first(args).val + vars = get!(tpoints, op, typeof(tp)[]) + push!(vars, tp) + end + return op( + map(args) do x + collect_timepoints!(tpoints, x) + end... + ) + end + return ex +end + +Corleone._maybesymbolifyme(x::SymbolicUtils.BasicSymbolic) = iscall(x) && operation(x) != ModelingToolkit.Initial ? Symbol(operation(x)) : Symbol(x) +Corleone._maybesymbolifyme(x::Num) = Corleone._maybesymbolifyme(Symbolics.unwrap(x)) + +function collect_integrals!(subs, ex, t) + if iscall(ex) + op, args = operation(ex), arguments(ex) + ex = op( + map(args) do arg + collect_integrals!(subs, arg, t) + end... + ) + if isa(op, Symbolics.Integral) + var = get!(subs, ex) do + sym = Symbol(:𝕃, Symbol(Char(0x2080 + length(subs) + 1))) + var = Symbolics.unwrap(only(ModelingToolkit.@variables ($sym)(t) = 0.0 [tunable = false, bounds = (0.0, 0.0)])) # [costvariable = true])) + var + end + lo, hi = op.domain.domain.left, op.domain.domain.right + return operation(var)(hi) - operation(var)(lo) + end + end + return ex +end + +function collect_expr(ex, replacer::Dict) + if iscall(ex) + op, args = operation(ex), arguments(ex) + args = map(args) do arg + collect_expr(arg, replacer) |> toexpr + end + return Expr(:call, Symbol(op), args...) + end + var = Symbol(ex) + return get(replacer, var, toexpr(ex)) +end -RuntimeGeneratedFunctions.init(@__MODULE__) +maybenormalize(ex) = nothing, ex +maybenormalize(ex::Symbolics.Inequality) = begin + x = Symbolics.canonical_form(ex) + operation(x), x.lhs +end + +function Corleone.DynamicOptimizationLayer( + sys::ModelingToolkit.AbstractSystem, + defaults, + controls, + exprs...; + algorithm, + tspan = nothing, + shooting = [], + kwargs... + ) + iv = ModelingToolkit.get_iv(sys) + lagranges = Dict() + tpoints = Dict() + tcollector = Base.Fix1(collect_timepoints!, tpoints) + + exprs = map(exprs) do expr + newexp = collect_integrals!(lagranges, Symbolics.unwrap(expr), iv) |> tcollector -include("MTKExtension/utils.jl") + end + saveats = reduce(vcat, values(tpoints)) + !isnothing(tspan) && append!(saveats, collect(tspan)) + tspan = extrema(saveats) + unique!(sort!(saveats)) + tspan = extrema(saveats) + if !isempty(lagranges) + # Add lagrangians and build the ODE Problem + sys = ModelingToolkit.add_accumulations( + sys, [v => only(arguments(k)) for (k, v) in lagranges] + ) + end -include("MTKExtension/optimal_control.jl") + # Normalize controls to a vector for consistent splatting + controls_vec = controls isa Pair ? [controls] : controls + + if isempty(shooting) + shooting_layer = Corleone.SingleShootingLayer( + sys, defaults, controls_vec...; + algorithm = algorithm, + tspan = tspan, + quadrature_indices = values(lagranges), + kwargs... + ) + else + shooting_layer = Corleone.MultipleShootingLayer( + sys, defaults, controls_vec...; + algorithm = algorithm, + tspan = tspan, + shooting = shooting, + quadrature_indices = values(lagranges), + kwargs... + ) + end + + replacer = Dict{Symbol, Expr}() + for p in tunable_parameters(sys) + ModelingToolkit.isinitial(p) && continue + ModelingToolkit.isinput(p) && continue + replacer[Symbol(p)] = Expr(:call, Symbol(p), first(tspan)) + end + exprs = map(exprs) do expr + if isa(expr, Symbolics.Inequality) || isa(expr, Symbolics.Equation) + expr = Symbolics.canonical_form(expr) + new_lhs = collect_expr(expr.lhs, replacer) + op = isa(expr, Symbolics.Inequality) ? :(<=) : :(==) + return Expr(:call, op, new_lhs, 0) + end + collect_expr(expr, replacer) + end + return Corleone.DynamicOptimizationLayer(shooting_layer, exprs...) +end end diff --git a/ext/MTKExtension/optimal_control.jl b/ext/MTKExtension/optimal_control.jl deleted file mode 100644 index ba14d17..0000000 --- a/ext/MTKExtension/optimal_control.jl +++ /dev/null @@ -1,183 +0,0 @@ -function Corleone.CorleoneDynamicOptProblem( - sys::ModelingToolkit.System, - inits::AbstractVector, - controls::Pair...; - algorithm::Corleone.SciMLBase.AbstractDEAlgorithm, - shooting::Union{<:AbstractVector{<:Real}, Nothing} = nothing, - tspan::Union{Tuple{Real, Real}, Nothing} = nothing, - sensealg = ModelingToolkit.SciMLBase.NoAD(), - kwargs... - ) - iv = ModelingToolkit.get_iv(sys) - cost = ModelingToolkit.get_costs(sys) - constraints = ModelingToolkit.get_constraints(sys) - - lagranges = Dict() - tpoints = Dict() - tcollector = Base.Fix1(collect_timepoints!, tpoints) - - newcosts = map(cost) do c - collect_integrals!(lagranges, c, iv) |> tcollector - end - - newcons = map(constraints) do c - c = Symbolics.canonical_form(c) - new_con = collect_integrals!(lagranges, c.lhs, iv) |> tcollector - isa(c, Inequality) ? new_con ≲ c.rhs : new_con ~ c.rhs - end - - # Collect tspan - saveats = reduce(vcat, values(tpoints)) - !isnothing(tspan) && append!(saveats, collect(tspan)) - !isnothing(shooting) && append!(saveats, collect(shooting)) - foreach(controls) do (_, grid) - append!(saveats, collect(grid)) - end - unique!(sort!(saveats)) - tspan = extrema(saveats) - if !isempty(lagranges) - # Add lagrangians and build the ODE Problem - sys = ModelingToolkit.add_accumulations( - sys, [v => only(arguments(k)) for (k, v) in lagranges] - ) - end - - inputs = collect(first.(controls)) - sys = mtkcompile(sys; inputs) - prob = ODEProblem(sys, inits, tspan, saveat = saveats, check_compatibility = false, sensealg = sensealg) - - # Get the indices of the tunables - controls = map(controls) do (ui, tis) - ui = Symbolics.unwrap(ui) - i = SymbolicIndexingInterface.parameter_index(sys, ui).idx - lo, hi = ModelingToolkit.getbounds(ui) - u0 = Symbolics.getdefaultval(ui) - i => ControlParameter(tis, name = Symbolics.tosymbol(operation(ui)), bounds = (lo, hi), controls = fill(u0, size(tis))) - end - - vars = unknowns(sys) - sort!(vars, by = Base.Fix1(SymbolicIndexingInterface.variable_index, sys)) - tunable_ic = findall(i -> ModelingToolkit.istunable(vars[i]), eachindex(vars)) - bounds_ic = map(ModelingToolkit.getbounds, vars) - bounds_ic = (first.(bounds_ic), last.(bounds_ic)) - p_tunable = tunable_parameters(sys) - bounds_p = map(i -> ModelingToolkit.getbounds(p_tunable[i]), filter(∉(first.(controls)), eachindex(p_tunable))) - bounds_p = map((first.(bounds_p), last.(bounds_p))) do bound - collect(Iterators.flatten(bound)) - end - - - layer = if isnothing(shooting) - SingleShootingLayer( - prob, algorithm; - controls, - tunable_ic, - bounds_ic, - bounds_p - ) - else - MultipleShootingLayer( - prob, algorithm, shooting...; - controls, - tunable_ic, - bounds_ic, - bounds_p - ) - end - # Get the state - st = LuxCore.initialstates(Random.default_rng(), layer) - symcache = isnothing(shooting) ? st.symcache : first(st).symcache - # Build the getters - p = tunable_parameters(sys) - xs = map(x -> x(iv), collect(keys(tpoints))) - symgetters = map(vcat(xs, p)) do k - get_ = SymbolicIndexingInterface.getsym(symcache, k) - sym_ = Symbolics.tosymbol(maybeop(k)) - sym_, get_ - end - getters = Tuple(last.(symgetters)) - # Build the substitution for the costs & constraints - vars_subs = Dict() - foreach(keys(tpoints)) do k - tvals = unique(sort!(tpoints[k])) - foreach(tvals) do ti - get!(vars_subs, k(ti), Expr(:call, getindex, k.name, findfirst(ti .== saveats))) - end - end - - # Arguments for the objective and constraints - args_ = first.(symgetters) - - # We currently assume a single cost - costbody = SymbolicUtils.Code.toexpr(substitute(only(newcosts), vars_subs)) - - costfn = :(($(args_...),) -> $costbody) - - costfun = @RuntimeGeneratedFunction( - costfn - ) - - costs = let predictor = layer, obs = getters, objective = costfun - (ps, st) -> begin - traj, _ = predictor(nothing, ps, st) - vars = map(obs) do getter - getter(traj) - end - objective(vars...) - end - end - - if !isempty(newcons) - res = gensym(:con) - conbody = map(enumerate(newcons)) do (i, con) - ex = SymbolicUtils.Code.toexpr(substitute(con.lhs, vars_subs)) - :($(res)[$(i)] = $ex) - end - push!(conbody, :(return $(res))) - - confn = :(($res, $(args_...)) -> $(conbody...)) - - confun = @RuntimeGeneratedFunction( - confn - ) - lcons = -Inf .* map(Base.Fix2(isa, Inequality), newcons) - n_cons = size(newcons, 1) - ucons = zeros(Float64, n_cons) - n_shoot = Corleone.get_number_of_shooting_constraints(layer) - append!(lcons, zeros(n_shoot)) - append!(ucons, zeros(n_shoot)) - - cons = let predictor = layer, obs = getters, constr = confun, ncon = n_cons - (res, ps, st) -> begin - traj, _ = predictor(nothing, ps, st) - vars = map(obs) do getter - getter(traj) - end - @views constr(res[1:ncon], vars...) - @views Corleone.shooting_constraints!(res[(ncon + 1):end], traj) - return res - end - end - elseif isa(layer, MultipleShootingLayer) - n_shoot = Corleone.get_number_of_shooting_constraints(layer) - lcons = ucons = zeros(n_shoot) - cons = let layer = layer - (res, ps, st) -> begin - traj, _ = layer(nothing, ps, st) - @views Corleone.shooting_constraints!(res, traj) - return res - end - end - else - ucons = lcons = cons = nothing - end - - return CorleoneDynamicOptProblem{typeof(layer), typeof(getters), typeof(costs), typeof(cons), typeof(lcons)}( - layer, - getters, - costs, - cons, - lcons, - ucons - ) -end diff --git a/ext/MTKExtension/utils.jl b/ext/MTKExtension/utils.jl deleted file mode 100644 index 64a58ba..0000000 --- a/ext/MTKExtension/utils.jl +++ /dev/null @@ -1,60 +0,0 @@ -maybeop(x) = iscall(x) ? operation(x) : x - -function Corleone.retrieve_symbol_cache(cache::ModelingToolkit.System, u0, p, control_indices; kwargs...) - x = unknowns(cache) - p = tunable_parameters(cache) - iv = ModelingToolkit.get_iv(cache) - u = filter(ModelingToolkit.isinput, p) - sort!(u, by = ui -> SymbolicIndexingInterface.parameter_index(cache, ui).idx) - p = filter(!ModelingToolkit.isinput, p) - x = [x..., u...] - return SymbolCache(x, p, [iv]) -end - -function collect_timepoints!(tpoints, ex) - if iscall(ex) - op, args = operation(ex), arguments(ex) - if SymbolicUtils.issym(op) && isa(first(args).val, Number) && length(args) == 1 - tp = first(args).val - vars = get!(tpoints, op, typeof(tp)[]) - push!(vars, tp) - end - return op( - map(args) do x - collect_timepoints!(tpoints, x) - end... - ) - end - return ex -end - -function collect_integrals!(subs, ex, t) - if iscall(ex) - op, args = operation(ex), arguments(ex) - ex = op( - map(args) do arg - collect_integrals!(subs, arg, t) - end... - ) - if isa(op, Symbolics.Integral) - var = get!(subs, ex) do - sym = Symbol(:𝕃, Symbol(Char(0x2080 + length(subs) + 1))) - var = Symbolics.unwrap(only(ModelingToolkit.@variables ($sym)(t) = 0.0 [tunable = false, bounds = (0.0, Inf)])) # [costvariable = true])) - var - end - lo, hi = op.domain.domain.left, op.domain.domain.right - return operation(var)(hi) - operation(var)(lo) - end - end - return ex -end - -Base.getindex(T::Corleone.Trajectory, ind::Num) = getindex(T, Symbolics.unwrap(ind)) -function Base.getindex(T::Corleone.Trajectory, ind::SymbolicUtils.BasicSymbolic) - if ind in keys(T.sys.variables) - return vcat(getindex.(T.u, T.sys.variables[ind])) - elseif ind in keys(T.sys.parameters) - return getindex(T.p, T.sys.parameters[ind]) - end - error(string("Invalid index: :", ind)) -end diff --git a/src/Corleone.jl b/src/Corleone.jl index 4a4e3be..a613895 100644 --- a/src/Corleone.jl +++ b/src/Corleone.jl @@ -18,42 +18,148 @@ using ChainRulesCore using LuxCore using Functors +using RuntimeGeneratedFunctions +RuntimeGeneratedFunctions.init(@__MODULE__) + # For evaluation +""" +$(SIGNATURES) + +Evaluate a mapping operation according to the selected SciML ensemble execution mode. +""" mythreadmap(::EnsembleSerial, args...) = map(args...) mythreadmap(::EnsembleThreads, args...) = tmap(args...) mythreadmap(::EnsembleDistributed, args...) = pmap(args...) # General methods for Corleone Layer +""" +$(SIGNATURES) + +Return the cumulative parameter block structure for `layer`. +""" get_block_structure(layer::LuxCore.AbstractLuxLayer; kwargs...) = [0, LuxCore.parameterlength(layer)] -get_bounds(layer::LuxCore.AbstractLuxLayer; kwargs...) = ( - get_lower_bound(layer), get_upper_bound(layer), + +get_timegrid(::Any) = [] + +get_timegrid(layer::LuxCore.AbstractLuxWrapperLayer{LAYER}) where {LAYER} = get_timegrid(getfield(layer, LAYER)) +get_timegrid(layer::LuxCore.AbstractLuxContainerLayer{LAYERS}) where {LAYERS} = begin + vals = map(LAYERS) do LAYER + get_timegrid(getfield(layer, LAYER)) + end + NamedTuple{LAYERS}(vals) +end +get_timegrid(nt::NamedTuple) = map(get_timegrid, nt) + + +""" +$(SIGNATURES) + +Return lower and upper bounds of `layer` parameters. +""" +get_bounds(x) = (get_lower_bound(x), get_upper_bound(x)) + +""" +$(SIGNATURES) + +Convert `val` to the numeric type `T`. +""" +_to_val(::T, val) where {T <: Number} = T(val) +_to_val(x, val) = map(x) do xi + _to_val(xi, val) +end +""" +$(SIGNATURES) + +Map scalar conversion from `to_val` to all entries in `x`. +""" +to_val(x, val) = fmap(Base.Fix2(_to_val, val), x) + +""" +$(SIGNATURES) + +Compute the number of shooting constraints of a `AbstractLuxLayer`. +""" +get_number_of_shooting_constraints(layer::LuxCore.AbstractLuxLayer) = 0 +get_number_of_shooting_constraints(nt::NamedTuple) = sum(get_number_of_shooting_constraints, values(nt)) +get_number_of_shooting_constraints(layer::LuxCore.AbstractLuxWrapperLayer{LAYER}) where {LAYER} = get_number_of_shooting_constraints(getfield(layer, LAYER)) +get_number_of_shooting_constraints(layer::LuxCore.AbstractLuxContainerLayer{LAYERS}) where {LAYERS} = sum( + map(LAYERS) do LAYER + get_number_of_shooting_constraints(getfield(layer, LAYER)) + end ) -to_val(::T, val) where {T <: Number} = T(val) -to_val(x::AbstractArray{T}, val) where {T <: Number} = T(val) .+ zero(x) + +""" +$(SIGNATURES) + +Return an elementwise lower bound vector for `layer`. +""" get_lower_bound(layer::AbstractLuxLayer) = Functors.fmapstructure(Base.Fix2(to_val, -Inf), LuxCore.initialparameters(Random.default_rng(), layer)) +get_lower_bound(layer::LuxCore.AbstractLuxWrapperLayer{LAYER}) where {LAYER} = get_lower_bound(getfield(layer, LAYER)) +get_lower_bound(layer::LuxCore.AbstractLuxContainerLayer{LAYERS}) where {LAYERS} = NamedTuple{LAYERS}( + map(LAYERS) do LAYER + getfield(layer, LAYER) |> get_lower_bound + end +) +get_lower_bound(nt::Union{NamedTuple, Tuple}) = map(get_lower_bound, nt) + +""" +$(SIGNATURES) + +Return an elementwise upper bound vector for `layer`. +""" get_upper_bound(layer::AbstractLuxLayer) = Functors.fmapstructure(Base.Fix2(to_val, Inf), LuxCore.initialparameters(Random.default_rng(), layer)) +get_upper_bound(layer::LuxCore.AbstractLuxWrapperLayer{LAYER}) where {LAYER} = get_upper_bound(getfield(layer, LAYER)) +get_upper_bound(layer::LuxCore.AbstractLuxContainerLayer{LAYERS}) where {LAYERS} = NamedTuple{LAYERS}( + map(LAYERS) do LAYER + getfield(layer, LAYER) |> get_upper_bound + end +) +get_upper_bound(nt::Union{NamedTuple, Tuple}) = map(get_upper_bound, nt) + +""" +$(SIGNATURES) + +Return whether `layer` participates in shooting continuity constraints. +""" +is_shooted(layer::AbstractLuxLayer) = false # Random +""" +$(SIGNATURES) + +Sample random values uniformly between elementwise bounds `lb` and `ub`. +""" _random_value(rng::Random.AbstractRNG, lb::AbstractVector, ub::AbstractVector) = lb .+ rand(rng, eltype(lb), size(lb)...) .* (ub .- lb) +# TODO We need to set this using Preferences +const MAXBINSIZE = 100 + include("trajectory.jl") export Trajectory -include("local_controls.jl") -export ControlParameter +include("controls.jl") +export ControlParameter, FixedControlParameter +export ControlParameters + +include("initializers.jl") +export InitialCondition include("single_shooting.jl") export SingleShootingLayer + +include("parallel_shooting.jl") +export ParallelShootingLayer + include("multiple_shooting.jl") export MultipleShootingLayer -export default_initialization -include("node_initialization.jl") -export random_initialization, forward_initialization, linear_initialization -export custom_initialization, constant_initialization, hybrid_initialization - -abstract type AbstractCorleoneFunctionWrapper end include("dynprob.jl") -export CorleoneDynamicOptProblem +export DynamicOptimizationLayer + + +#export default_initialization +#include("node_initialization.jl") +#export random_initialization, forward_initialization, linear_initialization +#export custom_initialization, constant_initialization, hybrid_initialization end diff --git a/src/controls.jl b/src/controls.jl new file mode 100644 index 0000000..5725cd9 --- /dev/null +++ b/src/controls.jl @@ -0,0 +1,492 @@ +""" +$(TYPEDEF) +Implements a piecewise constant control discretization. + +# Fields +$(FIELDS) + +# Examples + +```julia +using Corleone + +# Define a control parameter with time points and default settings +control = ControlParameter(0.0:0.1:10.0) +# Define a control parameter with custom control values and bounds +control = ControlParameter(0.0:0.1:10.0; name = :u, controls = (rng, t) -> rand(rng, length(t)), bounds = t -> (zeros(length(t)), ones(length(t)))) +``` +""" +struct ControlParameter{T, C, B, SHOOTED, S} <: LuxCore.AbstractLuxLayer + "The name of the control" + name::S + "The timepoints at which discretized variables are introduced. If empty, we assume a single value constant over time." + t::T + "The initial values for the controls in form of a function (rng, t) -> values. Defaults to [`default_controls`](@ref)." + controls::C + "The bounds for the control values in form of a function (t) -> (lower_bounds, upper_bounds). Defaults to `nothing`, which corresponds to unbounded controls derived from the controls." + bounds::B + + function ControlParameter( + t::AbstractVector; + name::N = gensym(:u), + controls::Function = default_controls, bounds::Function = default_bounds, shooted::Bool = false, + kwargs... + ) where {N} + return new{typeof(t), typeof(controls), typeof(bounds), shooted, N}(name, t, controls, bounds) + end +end + +""" +$(SIGNATURES) + +Return display name used by Lux for this control parameter. +""" +LuxCore.display_name(c::ControlParameter) = Symbol(c) + +""" +$(SIGNATURES) + +Return symbolic control name as a plain Symbol. +For MTK symbolic types like `u(t)`, extracts the base name `:u`. +""" +Base.Symbol(c::ControlParameter) = _maybesymbolifyme(c.name) + +""" +$(FUNCTIONNAME) + +Default constructor for the control values of a [`ControlParameter`](@ref). +The control values are initialized as zeros of the same length and element type as the time vector `t`. + +$(SIGNATURES) +""" +default_controls(rng, t::AbstractVector) = isempty(t) ? zeros(Float64, 1) : zeros(eltype(t), size(t)...) + +""" +$(FUNCTIONNAME) + +A placeholder for unbounded parameters. +""" +function default_bounds(t::AbstractVector) + return @error "Called `default_bounds`. This should never happen!" +end + +""" +$(FUNCTIONNAME) + +Checks if the control is shooted, i.e., if it has a value which will be constrained via an equality constraint in the optimization problem. +""" +is_shooted(::ControlParameter{<:Any, <:Any, <:Any, SHOOTED}) where {SHOOTED} = SHOOTED + +""" +$(SIGNATURES) + +Return lower bounds for a [`ControlParameter`](@ref). +""" +get_lower_bound(layer::ControlParameter) = first(layer.bounds(layer.t)) +get_lower_bound(layer::ControlParameter{<:Any, <:Any, <:typeof(default_bounds)}) = to_val( + layer.controls(Random.default_rng(), layer.t), -Inf +) + +""" +$(SIGNATURES) + +Return upper bounds for a [`ControlParameter`](@ref). +""" +get_upper_bound(layer::ControlParameter) = last(layer.bounds(layer.t)) +get_upper_bound(layer::ControlParameter{<:Any, <:Any, <:typeof(default_bounds)}) = to_val( + layer.controls(Random.default_rng(), layer.t), Inf +) + +""" +$(SIGNATURES) + +Return lower and upper bounds of a [`ControlParameter`](@ref). +""" +get_bounds(layer::ControlParameter) = (get_lower_bound(layer), get_upper_bound(layer)) + + +""" +$(SIGNATURES) + +Construct a [`ControlParameter`](@ref) from a `name => timepoints` pair. +""" +ControlParameter(x::Base.Pair{Symbol, <:AbstractVector}) = ControlParameter(last(x), name = first(x)) + +""" +$(SIGNATURES) + +Construct a [`ControlParameter`](@ref) from a `name => range` pair. +""" +ControlParameter(x::Base.Pair{Symbol, <:Base.AbstractRange}) = ControlParameter(collect(last(x)), name = first(x)) + +""" +$(SIGNATURES) + +Construct a [`ControlParameter`](@ref) from a `name => (t, controls, bounds, shooted)` named tuple. +""" +ControlParameter(x::Base.Pair{Symbol, <:NamedTuple}) = begin + nt = last(x) + ControlParameter( + getproperty(nt, :t), + name = first(x), + controls = get(nt, :controls, default_controls), + bounds = get(nt, :bounds, default_bounds), + shooted = get(nt, :shooted, false), + ) +end + +""" +$(SIGNATURES) + +Identity constructor for already-instantiated [`ControlParameter`](@ref). +""" +ControlParameter(x::ControlParameter) = x + +""" +$(SIGNATURES) + +Constructor for a `ControlParameter` with an empty time grid of element type `T`. + +Creates a [`ControlParameter`](@ref) whose internal time vector `t` is initialized +as an empty `Vector{T}`. All additional keyword arguments are forwarded to the +main `ControlParameter` constructor that accepts a time vector and keyword options. +""" +function ControlParameter(::Type{T} = Float64; kwargs...) where {T <: Number} + return ControlParameter(T[]; kwargs...) +end + +ControlParameter(x) = throw(ArgumentError("Invalid argument for ControlParameter constructor: $x")) + +""" +$(SIGNATURES) + +Return the time grid on which `layer` is discretized. +""" +get_timegrid(layer::ControlParameter) = layer.t + +""" +$(SIGNATURES) + +Return extrema of `t`, or `(0.0, 0.0)` for an empty vector. +""" +_maybeextrema(t) = isempty(t) ? (0.0, 0.0) : extrema(t) + +""" +$(SIGNATURES) + +Create a modified [`ControlParameter`](@ref), optionally restricting its support to `tspan`. +""" +function SciMLBase.remake( + layer::ControlParameter; + name = layer.name, + controls::Function = layer.controls, + bounds::Function = layer.bounds, + t::AbstractVector = deepcopy(layer.t), + tspan::Tuple{T, T} = _maybeextrema(t), + shooted::Bool = false, + kwargs... + ) where {T <: Real} + + mask = zeros(Bool, length(t)) + + if isempty(t) + return ControlParameter(T[]; name, controls, bounds, shooted) + end + + if tspan == _maybeextrema(t) + mask .= true + else + t0, tinf = tspan + for i in eachindex(t) + if t[i] >= t0 && t[i] < tinf + mask[i] = true + end + if i != lastindex(t) && t[i] < t0 && t[i + 1] > t0 + mask[i] = true + t[i] = t0 + shooted = true + end + end + end + + return ControlParameter(t[mask]; name, controls, bounds, shooted) +end + +""" +$(SIGNATURES) + +Initialize controls and clamp them to their bounds. +""" +LuxCore.initialparameters(rng::Random.AbstractRNG, control::ControlParameter) = begin + lb, ub = Corleone.get_bounds(control) + controls = map(zip(control.controls(rng, control.t), lb, ub)) do (c, l, u) + clamp.(c, l, u) + end +end + +""" +$(SIGNATURES) + +Initialize runtime state for evaluating `control`. +""" +LuxCore.initialstates(::Random.AbstractRNG, control::ControlParameter) = (; + t = control.t, + current_index = firstindex(control.t), + first_index = firstindex(control.t), + last_index = lastindex(control.t), + # TODO Add a fixed size hash table lookup here to avoid the linear search in find_idx for large control grids + # Maybe build a tree structure +) + +""" +$(SIGNATURES) + +Find the active control segment index at time `t`. +""" +find_idx(t::T, timepoints::AbstractVector) where {T <: Number} = searchsortedlast(timepoints, t) + + +function (::ControlParameter)(tcurrent::Number, controls, st::NamedTuple) + (; t, current_index, first_index, last_index) = st + isempty(t) && return only(controls), st + if current_index == last_index && tcurrent >= t[last_index] + return controls[current_index], st + elseif current_index == first_index == last_index # Constant control case + return controls[current_index], st + end + current_index = clamp(find_idx(tcurrent, t), first_index, last_index) + return controls[current_index], merge(st, (; current_index)) +end + +""" +$(SIGNATURES) + +Evaluate `layer` over all query times in `t`. +""" +function LuxCore.apply(layer::ControlParameter, t::AbstractVector, controls, st) + ll = LuxCore.StatefulLuxLayer{true}(layer, controls, st) + return map(Base.Fix2(ll, controls), t), ll.st +end + +""" +$(TYPEDEF) + +A struct which simply wraps a control parameter to allow for non-tunable tunables. +""" +struct FixedControlParameter{C <: ControlParameter} <: LuxCore.AbstractLuxWrapperLayer{:layer} + "The original control parameter" + layer::C +end + +function Base.getproperty(a::FixedControlParameter, v::Symbol) + if v == :layer + return getfield(a, :layer) + else + return getfield(a.layer, v) + end +end + +fix(c::ControlParameter) = FixedControlParameter{typeof(c)}(c) +FixedControlParameter(args...; kwargs...) = fix(ControlParameter(args...; kwargs...)) +ControlParameter(c::FixedControlParameter) = c +Base.Symbol(c::FixedControlParameter) = c.name + +LuxCore.initialparameters(::Random.AbstractRNG, ::FixedControlParameter) = (;) +LuxCore.initialstates(rng::Random.AbstractRNG, layer::FixedControlParameter) = (; + parameters = LuxCore.initialparameters(rng, layer.layer), + states = LuxCore.initialstates(rng, layer.layer), +) + +get_lower_bound(layer::FixedControlParameter) = (;) +get_upper_bound(layer::FixedControlParameter) = (;) + +function (layer::FixedControlParameter)(t, ps, st) + return _apply_control(layer, t, ps, st) +end + +function _apply_control(layer::FixedControlParameter, t, ps, st) + out, st_ = layer.layer(t, st.parameters, st.states) + return out, merge(st, (; states = st_)) +end + +SciMLBase.remake(layer::FixedControlParameter; kwargs...) = fix(SciMLBase.remake(layer.layer; kwargs...)) + +get_timegrid(layer::FixedControlParameter) = get_timegrid(layer.layer) + +ChainRulesCore.@non_differentiable _apply_control(layer::FixedControlParameter, t, ps, st) + +is_shooted(::FixedControlParameter) = false + +""" +$(TYPEDEF) + +A collection of control parameters, which can be used to define multiple controls in a structured way. +The controls are stored in a named tuple, where the keys correspond to the control names and the values are the control parameters. +The `transform` field can be used to apply a transformation to the control values before they are returned by the layer. + +# Fields +$(FIELDS) + +# Examples +```julia +using Corleone +# Define multiple control parameters with custom settings +controls = ControlParameters( + :u => 0.0:0.1:10.0, + :v => 0.0:0.2:10.0; + transform = (cs) -> (u = cs.u, v = cs.v) +) +controls = ControlParameters( + :u => 0.0:0.1:10.0, + ControlParameter(:v, 0.0:0.2:10.0, controls = (rng, t) -> rand(rng, length(t)), bounds = t -> (zeros(length(t)), ones(length(t)))); + transform = (cs) -> (u = cs.u, v = cs.v) +) +``` +""" +struct ControlParameters{C <: NamedTuple, T} <: LuxCore.AbstractLuxWrapperLayer{:controls} + "The name of the container" + name::Symbol + "The control parameter collection" + controls::C + "The output transformation" + transform::T +end + +""" +$(SIGNATURES) + +Return the merged time grid of all controls in `layer`. +""" +get_timegrid(layer::ControlParameters) = begin + timegrids = map(Corleone.get_timegrid, values(layer.controls)) + reduce(vcat, filter(!isempty, timegrids)) +end + +""" +$(SIGNATURES) + +Construct [`ControlParameters`](@ref) from a named tuple of controls. +""" +function ControlParameters(controls::NamedTuple; name::Symbol = gensym(:controls), transform = identity, kwargs...) + return ControlParameters{typeof(controls), typeof(transform)}(name, controls, transform) +end + +""" +$(SIGNATURES) + +Construct [`ControlParameters`](@ref) from varargs control specifications. +""" +function ControlParameters(controls...; kwargs...) + controls = map(ControlParameter, controls) + names = map(c -> Symbol(c), controls) + controls = NamedTuple{names}(controls) + return ControlParameters(controls; kwargs...) +end + +""" +$(SIGNATURES) + +Evaluate controls at a scalar or vector time `t`, returning a named tuple of control values. +""" +function (layer::ControlParameters)(t, ps, st) + return _eval_controls(layer.controls, t, ps, st) +end + +""" +$(SIGNATURES) + +Evaluate controls for one integration interval `(t0, tinf)`. +""" +function (layer::ControlParameters)((t0, tinf)::Tuple{T, T}, ps, st) where {T <: Number} + (; transform) = layer + cs, st = _apply(layer, t0, ps, st) + return (; p = transform(cs), tspan = (t0, tinf)), st +end + +""" +$(SIGNATURES) + +Evaluate controls over a tuple of interval bins. +""" +function (layer::ControlParameters)(timestops::Tuple{Vararg{Tuple}}, ps, st) + return reduce_controls(layer, ps, st, timestops), st +end + +""" +$(SIGNATURES) + +Apply `reducer` elementwise to a fixed-size tuple of bins. +""" +@generated function reduce_control_bin(layer, ps, st, bins::Tuple) + N = fieldcount(bins) + exprs = Expr[] + rets = [gensym() for i in Base.OneTo(N)] + for i in Base.OneTo(N) + push!( + exprs, + :(($(rets[i]), st) = layer(bins[$i], ps, st)) + ) + end + push!(exprs, Expr(:tuple, rets...)) + return Expr(:block, exprs...) +end + +""" +$(SIGNATURES) + +Apply `reducer` recursively to a heterogeneously-typed tuple of bins. +""" +function reduce_controls(layer, ps, st, bins::Tuple) + current = reduce_control_bin(layer, ps, st, Base.first(bins)) + return (current, reduce_controls(layer, ps, st, Base.tail(bins))...) +end + +reduce_controls(layer, ps, st, bins::Tuple{T}) where {T} = (reduce_control_bin(layer, ps, st, only(bins)),) + +""" +$(SIGNATURES) + +Internal helper to evaluate all controls at `tnow`. +""" +function _apply(layer::ControlParameters, tnow, ps, st) + return _eval_controls(layer.controls, tnow, ps, st) +end + +""" +$(SIGNATURES) + +Generated evaluator for all controls in a named tuple. +""" +@generated function _eval_controls(controls::NamedTuple{fields}, t::T, ps, st) where {T, fields} + returns = [gensym() for _ in fields] + rt_states = [gensym() for _ in fields] + expr = Expr[] + for (i, sym) in enumerate(fields) + push!(expr, :(($(returns[i]), $(rt_states[i])) = controls.$(sym)(t, ps.$(sym), st.$(sym)))) + end + push!( + expr, + :(st = NamedTuple{$fields}((($(Tuple(rt_states)...),)))) + ) + if T <: AbstractVector + push!(expr, :(result = map(Base.Fix1(ControlSignal, collect(t)), ($(returns...),)))) + else + push!(expr, :(result = NamedTuple{$fields}(($(returns...),)))) + end + push!(expr, :(return result, st)) + ex = Expr(:block, expr...) + return ex +end + +get_shooting_variables(layer::ControlParameters) = [c.name for c in values(layer.controls) if is_shooted(c)] + +function SciMLBase.remake(layer::ControlParameters; kwargs...) + name = get(kwargs, :name, layer.name) + controls = get( + kwargs, :controls, map(layer.controls) do control + remake(control; kwargs...) + end + ) + transform = get(kwargs, :transform, layer.transform) + return ControlParameters{typeof(controls), typeof(transform)}(name, controls, transform) +end diff --git a/src/dynprob.jl b/src/dynprob.jl index 5c07179..2043a9a 100644 --- a/src/dynprob.jl +++ b/src/dynprob.jl @@ -1,175 +1,229 @@ """ $(TYPEDEF) -A struct for capturing the internal definition of a dynamic optimization problem. +A layer that wraps a shooting layer to solve dynamic optimization problems with an objective function and constraints. # Fields $(FIELDS) + +# Description + +The `DynamicOptimizationLayer` combines a shooting layer (single or multiple) with an objective function and optional constraints +to formulate a complete dynamic optimization problem. The objective and constraints are specified as symbolic expressions that +are evaluated on the computed trajectory. """ -struct CorleoneDynamicOptProblem{L, G, O, C, CB} - "The resulting layer for the problem" +struct DynamicOptimizationLayer{N, L, G, O, C, CB} <: LuxCore.AbstractLuxWrapperLayer{:layer} + "The name of the layer, used for display and logging purposes." + name::N + "The wrapped shooting layer used to produce trajectories." layer::L - "The getters which return the values of the trajectory" + "The getter for all symbols" getters::G "The objective function" objective::O - "The constraint function" + "The constraints function" constraints::C - "Lower bounds for the constraints" + "The lower bounds for the constraints" lcons::CB - "Upper bounds for the constraints" + "The upper bounds for the constraints" ucons::CB end +_extract_timepoints(x::Number) = [x] +_extract_timepoints(x::Expr) = begin + @assert x.head == :vect "Timepoints must be provided as a scalar or vector, e.g. `x(1.0)` or `x([1.0, 2.0])" + reduce(vcat, x.args) +end -_collect_tspans((x, _)::NTuple{2, <:Number}, rest...) = (x, _collect_tspans(rest...)...) -_collect_tspans(x::NTuple{2, <:Number}) = x -_collect_tspans(x::Tuple, rest...) = (_collect_tspans(x...)..., _collect_tspans(rest...)...) -_collect_tspans(x::Tuple) = _collect_tspans(x...) +_collect_timepoints!(::Dict{Symbol, <:AbstractVector}, ::Any) = nothing -struct TrajectoryConstraint{G, I} - getter::G - idx::I +function _collect_timepoints!(collector::Dict{Symbol, <:AbstractVector}, ex::Expr) + if ex.head == :call + if ex.args[1] ∈ keys(collector) + append!(collector[ex.args[1]], _extract_timepoints(ex.args[2])) + end + end + for arg in ex.args + _collect_timepoints!(collector, arg) + end + return end -(x::TrajectoryConstraint)(traj) = x.getter(traj)[x.idx] -(x::TrajectoryConstraint)(res, traj) = res .= x.getter(traj)[x.idx] -Base.length(x::TrajectoryConstraint) = length(x.idx) - -struct TrajectoryConstraintEvaluator{C} - constraints::C +_extract_timeindex(x::Number, indices) = indices[x] +_extract_timeindex(x::Expr, indices) = begin + @assert x.head == :vect "Timepoints must be provided as a scalar or vector, e.g. `x(1.0)` or `x([1.0, 2.0])" + reduce(vcat, map(Base.Fix2(_extract_timeindex, indices), x.args)) end -(x::TrajectoryConstraintEvaluator)(traj) = reduce( - vcat, map(x.constraints) do con - con(traj) - end -) - -(x::TrajectoryConstraintEvaluator)(res::AbstractVector, traj) = begin - i = 1 - next = 0 - foreach(x.constraints) do con - next = (i - 1) + length(con) - @views con(res[i:next], traj) - i = next + 1 +replace_timepoints(x::Any, replacer) = x + +function replace_timepoints(x::Expr, replacer::Dict{Symbol, <:Dict}) + if x.head == :call + if x.args[1] ∈ keys(replacer) + return Expr( + :call, :getindex, x.args[1], + _extract_timeindex(x.args[2], replacer[x.args[1]]) + ) + end end - return res + return Expr(x.head, map(arg -> replace_timepoints(arg, replacer), x.args)...) end -function CorleoneDynamicOptProblem( - layer::Union{SingleShootingLayer, MultipleShootingLayer}, loss::Union{Symbol, Expr}, - constraints::Pair{<:Any, <:NamedTuple{(:t, :bounds)}}...; - rng::Random.AbstractRNG = Random.default_rng(), - kwargs... +function find_indices(points, grid) + t0, tinf = extrema(grid) + return Dict( + map(points) do p + p <= t0 && return p => firstindex(grid) + p >= tinf && return p => lastindex(grid) + p => searchsortedlast(grid, p) + end... ) - st = LuxCore.initialstates(rng, layer) - sys = isa(layer, SingleShootingLayer) ? st.symcache : first(st).symcache - objective = let getter = SymbolicIndexingInterface.getsym(sys, loss), layer = layer - (ps, st) -> begin - traj, _ = layer(nothing, ps, st) - last(getter(traj)) - end +end + +function build_iip(problem, header::AbstractVector{<:Expr}, exprssions::AbstractVector{<:Expr}, offset::Int64 = 0) + returns = gensym() + exprs = [:($(returns)[$(i + offset)] = $(exprssions[i])) for i in eachindex(exprssions)] + push!(exprs, :(return $returns)) + headercall = Expr(:call, gensym(), returns, :trajectory) + oop_expr = Expr(:function, headercall, Expr(:block, header..., exprs...)) + return observed = @RuntimeGeneratedFunction(oop_expr) +end + +function build_oop(problem, header::AbstractVector{<:Expr}, expressions::AbstractVector{<:Expr}) + returns = [gensym() for _ in expressions] + exprs = [:($(returns[i]) = $(expressions[i])) for i in eachindex(returns)] + if length(expressions) > 1 + push!(exprs, :(return [$(returns...)])) + else + push!(exprs, :(return $(returns[1]))) end - # Collect all the points due to control etc - tpoints = if isa(layer, SingleShootingLayer) - reduce(vcat, _collect_tspans(st.tspans...)) + headercall = Expr(:call, gensym(), :trajectory) + oop_expr = Expr(:function, headercall, Expr(:block, header..., exprs...)) + return observed = @RuntimeGeneratedFunction(oop_expr) +end + +function normalize_constraint(expr::Expr, ::Type{T}) where {T} + @assert expr.head == :call "The expression is not a call." + op, a, b = expr.args + return if op == :(<=) + Expr(:call, :(-), a, b), T(-Inf), zero(T) + elseif op == :(>=) + Expr(:call, :(-), b, a), T(-Inf), zero(T) + elseif op == :(==) + Expr(:call, :(-), a, b), zero(T), zero(T) else - reduce( - vcat, map(st) do st_ - reduce(vcat, _collect_tspans(st_.tspans...)) - end - ) + throw(error("The operator $(op) is not supported to define constraints. Only ==, <=, and >= are supported.")) end - cons = [] - lb = [] - ub = [] - if !isempty(constraints) - # Preprocess - foreach(constraints) do (expr, specs) - @assert isa(expr, Symbol) || isa(expr, Expr) "The constraint $(expr) is neither a symbol nor an expression!" - tidx = findall(∈(specs.t), tpoints) - if !isempty(tidx) - push!( - cons, TrajectoryConstraint( - SymbolicIndexingInterface.getsym(sys, expr), - tidx - ) - ) - lb_, ub_ = extrema(specs.bounds) - push!(lb, fill(lb_, length(tidx))) - push!(ub, fill(ub_, length(tidx))) - end - end - conseval = TrajectoryConstraintEvaluator(Tuple(cons)) - n_shoot = get_number_of_shooting_constraints(layer) - n_cons = sum(length, conseval.constraints) - push!(lb, zeros(n_shoot)) - push!(ub, zeros(n_shoot)) - constraints = let con = conseval, layer = layer, ncon = n_cons - (res, ps, st) -> begin - traj, _ = layer(nothing, ps, st) - @views con(res[1:ncon], traj) # The constraints - @views shooting_constraints!(res[(ncon + 1):end], traj) - return res - end - end - ucons = reduce(vcat, ub) - lcons = reduce(vcat, lb) - elseif isa(layer, MultipleShootingLayer) - n_shoot = get_number_of_shooting_constraints(layer) - push!(lb, zeros(n_shoot)) - push!(ub, zeros(n_shoot)) - constraints = let layer = layer - (res, ps, st) -> begin - traj, _ = layer(nothing, ps, st) - @views shooting_constraints!(res, traj) - return res - end +end + +# _maybesymbolifyme is defined in trajectory.jl and extended in CorleoneModelingToolkitExtension + +function DynamicOptimizationLayer(layer::LuxCore.AbstractLuxLayer, objective::Expr, constraints::Expr...; name = gensym(:observed)) + problem = Corleone.get_problem(layer) + T = eltype(problem.u0) + n_shoot = get_number_of_shooting_constraints(layer) + lb = fill(zero(T), length(constraints) + n_shoot) + ub = fill(zero(T), length(constraints) + n_shoot) + constraints = map(enumerate(constraints)) do (i, con) + con, lb[i + n_shoot], ub[i + n_shoot] = normalize_constraint(con, T) + con + end + expressions = vcat(objective, constraints...) + symbols = _maybesymbolifyme.(vcat(variable_symbols(problem), parameter_symbols(problem))) + tspan = get_tspan(layer) + collector = Dict([vi => eltype(tspan)[] for vi in symbols]) + foreach(expressions) do ex + _collect_timepoints!(collector, ex) + end + # Find the indices + timegrid = Corleone.get_timegrid(layer) + foreach(values(collector)) do tps + append!(timegrid, tps) + end + unique!(sort!(timegrid)) + layer = remake(layer, saveat = timegrid) + replacer = Dict([ki => find_indices(vi, timegrid) for (ki, vi) in zip(keys(collector), values(collector)) if !isempty(vi) || is_parameter(problem, ki)]) + new_exprs = map(expressions) do ex + replace_timepoints(ex, replacer) + end + header = map(collect(keys(replacer))) do k + if is_parameter(problem, k) + return :($(k) = trajectory.ps[$(QuoteNode(k))]) + else + return :($(k) = trajectory[$(QuoteNode(k))]) end - ucons = reduce(vcat, ub) - lcons = reduce(vcat, lb) - else - constraints = lcons = ucons = nothing end - return CorleoneDynamicOptProblem{typeof(layer), Nothing, typeof(objective), typeof(constraints), typeof(lcons)}( - layer, nothing, objective, constraints, lcons, ucons + getter = nothing + objective = build_oop(problem, header, new_exprs[1:1]) + constraints = build_iip(problem, header, new_exprs[2:end], get_number_of_shooting_constraints(layer)) + return DynamicOptimizationLayer{typeof(name), typeof(layer), typeof(getter), typeof(objective), typeof(constraints), typeof(lb)}( + name, layer, getter, + objective, constraints, lb, ub ) end -function wrap_functions end #(::Any, args...) = @error "No valid vectorization for the chosen parameters. Please load either ComponentArrays.jl or Functors.jl" -function to_vec end #(::AbstractCorleoneFunctionWrapper, args...) =@error "No valid vectorization for the chosen parameters. Please load either ComponentArrays.jl or Functors.jl" +function (obs::DynamicOptimizationLayer)(x::Nothing, ps, st) + trajectory, st = obs.layer(x, ps, st) + obj = obs.objective(trajectory) + return obj, st +end -function SciMLBase.OptimizationFunction( - prob::CorleoneDynamicOptProblem, ad::SciMLBase.ADTypes.AbstractADType, vectorizer; - rng::Random.AbstractRNG = Random.default_rng(), - kwargs... - ) - p0, st = LuxCore.setup(rng, prob.layer) - objective, cons = wrap_functions(vectorizer, p0, prob.objective, prob.constraints) - return SciMLBase.OptimizationFunction{true}(objective, ad; cons, kwargs...) +function (obs::DynamicOptimizationLayer)(x, ps, st) + trajectory, st = obs.layer(x, ps, st) + shooting_constraints!(x, trajectory) + obs.constraints(x, trajectory) + return x, st end +# A simple wrapper for reconstructing the parameters +struct WrappedFunction{F, P} + f::F + parameter::P +end + +(f::WrappedFunction)(u, p) = first(f.f(nothing, f.parameter(u), p)) +(f::WrappedFunction)(res, u, p) = first(f.f(res, f.parameter(u), p)) + +function WrappedFunction(::Any, f, p, st; kwargs...) end + +function to_vec(::Any, p) end + +# + +""" +$(SIGNATURES) + +Construct a SciML `OptimizationProblem` from a [`CorleoneDynamicOptProblem`](@ref). +""" function SciMLBase.OptimizationProblem( - prob::CorleoneDynamicOptProblem, ad::SciMLBase.ADTypes.AbstractADType, vectorizer; + prob::DynamicOptimizationLayer, ad::SciMLBase.ADTypes.AbstractADType, + ps = nothing, + st = nothing; rng::Random.AbstractRNG = Random.default_rng(), + vectorizer, sense = nothing, kwargs... ) - p0, st = LuxCore.setup(rng, prob.layer) - objective, cons = wrap_functions(vectorizer, p0, prob.objective, prob.constraints) - optf = SciMLBase.OptimizationFunction{true}(objective, ad; cons, kwargs...) - u0_, lb, ub = to_vec(objective, p0, Corleone.get_bounds(prob.layer)...) - return SciMLBase.OptimizationProblem(optf, u0_, st; lb, ub, lcons = prob.lcons, ucons = prob.ucons, sense = sense) + ps = something(ps, LuxCore.initialparameters(rng, prob)) + st = something(st, LuxCore.initialstates(rng, prob)) + optf = SciMLBase.OptimizationFunction(prob, ad; vectorizer, rng, ps, st, kwargs...) + u0, lb, ub = map(Base.Fix1(to_vec, vectorizer), (ps, Corleone.get_bounds(prob)...)) + return SciMLBase.OptimizationProblem(optf, u0, st; lb, ub, lcons = prob.lcons, ucons = prob.ucons, sense = sense) end -function SciMLBase.OptimizationProblem( - layer::Union{SingleShootingLayer, MultipleShootingLayer}, - ad::SciMLBase.ADTypes.AbstractADType, vectorizer; - loss::Union{Symbol, Expr}, - constraints = [], +""" +$(SIGNATURES) + +Construct a SciML `OptimizationFunction` from a [`CorleoneDynamicOptProblem`](@ref). +""" +function SciMLBase.OptimizationFunction( + prob::DynamicOptimizationLayer, ad::SciMLBase.ADTypes.AbstractADType; + vectorizer, + rng::Random.AbstractRNG = Random.default_rng(), + ps = LuxCore.initialparameters(rng, prob), + st = LuxCore.initialstates(rng, prob), kwargs... ) - dynprob = CorleoneDynamicOptProblem(layer, loss, constraints...; kwargs...) - return OptimizationProblem(dynprob, ad, vectorizer; kwargs...) + wrapper = WrappedFunction(vectorizer, prob, ps, st) + return SciMLBase.OptimizationFunction{true}(wrapper, ad; cons = wrapper, kwargs...) end diff --git a/src/node_initialization.jl b/src/initializers.jl similarity index 52% rename from src/node_initialization.jl rename to src/initializers.jl index 1864582..33c80a5 100644 --- a/src/node_initialization.jl +++ b/src/initializers.jl @@ -1,4 +1,166 @@ """ +$(TYPEDEF) + +A struct containing the problem definition for a dynamic optimization problem. + +# Fields +$(FIELDS) + +# Examples +```julia +using Corleone +using OrdinaryDiffEq +prob = ODEProblem((u, p, t) -> -p[1] .* u, [1.0, 0.0], (0.0, 10.0), [0.5]) +layer = InitialCondition(prob, name = :linear_problem, tunable_ic = [1, ]) +``` +""" +struct InitialCondition{P, B} <: LuxCore.AbstractLuxLayer + "The name of the layer" + name::Symbol + "The <:DEProblem defining the dynamics" + problem::P + "The indices of the initial condition that are tunable parameters in the optimization problem" + tunable_ic::Vector{Int} + "The bounds of the initial conditions. Expects either nothing for unbounded parameters or a function of the form (t0) -> (lower_bounds, upper_bounds)." + bounds_ic::B + "Additional quadrature indices if present." + quadrature_indices::Vector{Int} +end + +""" +$(SIGNATURES) + +Return lower bounds for tunable initial conditions. +""" +get_lower_bound(layer::InitialCondition{<:Any, Nothing}) = to_val(layer.problem.u0[layer.tunable_ic], -Inf) + +""" +$(SIGNATURES) + +Return upper bounds for tunable initial conditions. +""" +get_upper_bound(layer::InitialCondition{<:Any, Nothing}) = to_val(layer.problem.u0[layer.tunable_ic], Inf) + +""" +$(SIGNATURES) + +Return user-defined lower bounds at initial time. +""" +get_lower_bound(layer::InitialCondition{<:Any, <:Function}) = first(layer.bounds_ic(layer.problem.tspan[1]))[layer.tunable_ic] + +""" +$(SIGNATURES) + +Return user-defined upper bounds at initial time. +""" +get_upper_bound(layer::InitialCondition{<:Any, <:Function}) = last(layer.bounds_ic(layer.problem.tspan[1]))[layer.tunable_ic] + +""" +$(SIGNATURES) + +Return integration time span of the underlying problem. +""" +get_tspan(layer::InitialCondition) = layer.problem.tspan + +""" +$(SIGNATURES) + +Return a merged initial-condition time grid from `tspan` and `saveat`. +""" +get_timegrid(layer::InitialCondition) = begin + tspan = collect(layer.problem.tspan) + saveats = get(layer.problem.kwargs, :saveat, eltype(tspan)[]) + saveats = isa(saveats, Number) ? collect(tspan[1]:saveats:tspan[2]) : saveats + unique!(sort!(vcat(tspan, saveats))) +end + +get_problem(layer::InitialCondition) = layer.problem + +get_quadrature_indices(layer::InitialCondition) = layer.quadrature_indices + +get_tunable_u0(layer::InitialCondition, full::Bool = false) = full ? [i for i in eachindex(layer.problem.u0) if i ∉ layer.quadrature_indices] : layer.tunable_ic + +get_shooting_variables(layer::InitialCondition) = layer.tunable_ic + +""" +$(SIGNATURES) + +Construct an [`InitialCondition`](@ref) layer from a SciML differential equation problem. +""" +function InitialCondition(prob::SciMLBase.DEProblem; name::Symbol = gensym(:problem), tunable_ic = Int[], bounds_ic::Union{Nothing, Function} = nothing, quadrature_indices = Int[]) + @assert isempty(setdiff(tunable_ic, eachindex(prob.u0))) "Tunable initial condition indices must be within the bounds of the initial condition vector." + @assert isempty(setdiff(quadrature_indices, eachindex(prob.u0))) "Quadrature indices must be within the bounds of the initial condition vector." + @assert isempty(intersect(tunable_ic, quadrature_indices)) "Tunable initial condition indices and quadrature indices must be disjoint." + return InitialCondition{typeof(prob), typeof(bounds_ic)}(name, prob, tunable_ic, bounds_ic, quadrature_indices) +end + +""" +$(SIGNATURES) + +Initialize tunable initial-condition parameters from `problem.u0`. +""" +LuxCore.initialparameters(::Random.AbstractRNG, layer::InitialCondition) = begin + (; problem) = layer + (; u0) = problem + (; tunable_ic) = layer + deepcopy(u0[tunable_ic]) +end + +""" +$(SIGNATURES) + +Return number of tunable initial-condition parameters. +""" +LuxCore.parameterlength(layer::InitialCondition) = length(layer.tunable_ic) + +""" +$(SIGNATURES) + +Initialize runtime state needed to rebuild `u0` from tunable and fixed components. +""" +LuxCore.initialstates(::Random.AbstractRNG, layer::InitialCondition) = begin + (; problem, tunable_ic, quadrature_indices) = layer + (; u0) = problem + keeps = [i ∉ tunable_ic for i in eachindex(u0)] + replaces = zeros(Bool, length(u0), length(tunable_ic)) + for (i, idx) in enumerate(tunable_ic) + replaces[idx, i] = true + end + return (; u0 = deepcopy(u0), keeps, replaces, quadrature_indices) +end + +""" +$(SIGNATURES) + +Apply initial-condition parameters `ps` and return a remade SciML problem. +""" +function (layer::InitialCondition)(::Any, ps, st::NamedTuple) + (; problem) = layer + (; u0, keeps, replaces) = st + u0_new = keeps .* u0 .+ replaces * ps + return SciMLBase.remake(problem, u0 = u0_new), st +end + +""" +$(SIGNATURES) + +Create a modified [`InitialCondition`](@ref) by remaking its wrapped problem and metadata. +""" +function SciMLBase.remake( + layer::InitialCondition; + name::Symbol = layer.name, + problem::SciMLBase.DEProblem = layer.problem, + tunable_ic::Vector{Int} = layer.tunable_ic, + bounds_ic = layer.bounds_ic, + quadrature_indices::Vector{Int} = layer.quadrature_indices, + kwargs..., + ) + problem = remake(problem; kwargs...) + return InitialCondition(problem; name, tunable_ic, bounds_ic, quadrature_indices) +end + +#= +""" $(SIGNATURES) Initializes all shooting nodes with random values. @@ -253,243 +415,4 @@ function hybrid_initialization( ) return ps end - -#= -""" - linear_initializer(u0, u_inf, t, tspan) - -Linearly interpolates u0 and u_inf for t with tspan[1] < t < tspan[2]. -""" -function linear_initializer(u0, u_inf, t, tspan) - t0, t_inf = tspan - slope = u_inf .- u0 - val = (t - t0) ./ t_inf - u0 .+ slope .* val -end - -""" -$(TYPEDEF) - -Initializes all shooting nodes with linearly-interpolated values. Linear interpolation -is calculated using the initial values of the underlying problem and the user-specified -terminal values. These are given as a Dictionary with variable indices as keys and -the corresponding terminal value. - -# Fields -$(FIELDS) - -# Examples -```julia-repl -julia> LinearInterpolationInitialization(Dict(1=>2.0, 2=>3.0)) -LinearInterpolationInitialization{Dict{Int64, Float64}}(Dict(2 => 3.0, 1 => 2.0)) -``` -""" -struct LinearInterpolationInitialization{T<:AbstractDict} <: AbstractNodeInitialization - "Terminal values for linear interpolation of initial and terminal values." - terminal_values::T -end - -""" - (f::LinearInterpolationInitialization)(rng, layer) - -Initialize shooting nodes of `layer` using linearly interpolated values between initial values -of underlying problem and terminal values given in `f.terminal_values`. -""" -function (f::LinearInterpolationInitialization)(rng::Random.AbstractRNG, layer::MultipleShootingLayer; - params=LuxCore.setup(rng, layer), - shooting_variables=eachindex(first(layer.layers).problem.u0)) - - u0 = first(layer.layers).problem.u0 - @assert all([x in keys(f.terminal_values) for x in shooting_variables]) - ps, st = params - tspan = get_tspan(layer) - timespans = layer.shooting_intervals - i = 0 - new_ps = map(ps) do pi - i += 1 - if i == 1 - pi - else - local_tspan = timespans[i] - interpolated_u0 = map(x -> linear_initializer(u0[x], f.terminal_values[x], first(local_tspan), tspan), shooting_variables) - pi.u0[shooting_variables] = interpolated_u0 - pi - end - end - return new_ps, st -end - -""" -$(TYPEDEF) - -Initializes all shooting nodes with user-provided values. Initial values are given as -Dictionary with variable indices as keys and the corresponding vector of initial values -of adequate length. - -# Fields -$(FIELDS) - -# Examples -```julia-repl -julia> CustomInitialization(Dict(1=>ones(3), 2=>zeros(3))) -CustomInitialization{Dict{Int64, Vector{Float64}}}(Dict(2 => [0.0, 0.0, 0.0], 1 => [1.0, 1.0, 1.0])) -``` -""" -struct CustomInitialization{I<:AbstractDict} <: AbstractNodeInitialization - "The init values for all dependent variables" - initial_values::I -end - -""" - (f::CustomtInitialization)(rng, layer) - -Initialize shooting nodes of `layer` using custom values specified in `f.initial_values`. -""" -function (f::CustomInitialization)(rng::Random.AbstractRNG, layer::MultipleShootingLayer; - params=LuxCore.setup(rng, layer), - shooting_variables=eachindex(first(layer.layers).problem.u0)) - ps, st = params - - i = 0 - new_ps = map(ps) do pi - i += 1 - vari = 0 - new_u0 = map(pi.u0) do u0i - vari += 1 - if vari ∉ shooting_variables - u0i - else - f.initial_values[vari][i] - end - end - pi.u0 .= new_u0 - pi - end - return new_ps, st -end - -""" -$(TYPEDEF) - -Initializes all shooting nodes using a constant value specified via the dictionary -of indices of variables and the corresponding initialization value. - -# Fields -$(FIELDS) - -# Examples -```julia-repl -julia> ConstantInitialization(Dict(1=>1.0, 2=>2.0)) -ConstantInitialization{Dict{Int64, Float64}}(Dict(2 => 2.0, 1 => 1.0)) -``` -""" -struct ConstantInitialization{I<:AbstractDict} <: AbstractNodeInitialization - "The init values for all dependent variables" - initial_values::I -end - -""" - (f::ConstantInitialization)(rng, layer) - -Initialize shooting nodes of `layer` using constant values specified in `f.initial_values`. -""" -function (f::ConstantInitialization)(rng::AbstractRNG, layer::MultipleShootingLayer; - params=LuxCore.setup(rng, layer), - shooting_variables=eachindex(first(layer.layers).problem.u0)) - ps, st = params - new_ps = map(ps) do pi - vari = 0 - new_u0 = map(pi.u0) do u0i - vari += 1 - if vari ∉ shooting_variables - u0i - else - f.initial_values[vari] - end - end - pi.u0 .= new_u0 - pi - end - return new_ps, st -end - -""" -$(TYPEDEF) - -Initializes the shooting nodes in a hybrid method. -Initialization of specific variables is done via a dictionary of variable indices of -the underlying problem and the `AbstractNodeInitialization` for their initialization. -Variables not present in the keys of `inits` are initialized using the fallback -initialization method given in `default_init`. -# Fields -$(FIELDS) - -# Examples -```julia-repl -julia> HybridInitialization(Dict(1=>ConstantInitialization(Dict(1=>1.0)), - 2=>LinearInterpolationInitialization(Dict(2=>2.0))), - ForwardSolveInitialization()) -HybridInitialization{Dict{Int64, Corleone.AbstractNodeInitialization}}(Dict{Int64, Corleone.AbstractNodeInitialization}(2 => LinearInterpolationInitialization{Dict{Int64, Float64}}(Dict(2 => 2.0)), 1 => ConstantInitialization{Dict{Int64, Float64}}(Dict(1 => 1.0))), ForwardSolveInitialization()) -``` -""" -struct HybridInitialization{P<:Dict} <: AbstractNodeInitialization - "Dictionary of indices of variables and their corresponding initialization methods" - inits::P - "Fallback initialization method for variables not considered in `inits`" - default_init::AbstractNodeInitialization -end - -""" - (f::HybridInitialization)(rng, layer) - -Initialize the shooting nodes of `layer` in a hybrid method consisting of different -`AbstractNodeInitialization` methods applied to different subsets of the variables. -Variables that are not treated via the initialization methods in `f.inits` are initialized -via the fallback method `f.default_init`. -""" -function (f::HybridInitialization)(rng::Random.AbstractRNG, layer::MultipleShootingLayer; - params=LuxCore.setup(rng, layer), - shooting_variables=eachindex(first(layer.layers).problem.u0), - kwargs...) - - ps, st = params - - defined_vars = [x.first for x in f.inits] - - forward_involved = [typeof(x.second) <: ForwardSolveInitialization for x in f.inits] - forward_default = typeof(f.default_init) <: ForwardSolveInitialization - - any_forward = any(forward_involved) || forward_default - forward_vars = any(forward_involved) ? reduce(vcat, defined_vars[forward_involved]) : Int64[] - - defined_vars = reduce(vcat, defined_vars) - remaining_vars = [i for i in shooting_variables if i ∉ defined_vars] - - forward_vars = forward_default ? vcat(forward_vars, remaining_vars) : forward_vars - - init_copy = copy(f.inits) - init_copy = any_forward ? delete!(init_copy, ForwardSolveInitialization()) : init_copy - - init_copy = begin - if forward_default - init_copy - else - merge(init_copy, Dict(remaining_vars => f.default_init)) - end - end - - for p in init_copy - ps, st = p.second(rng, layer; shooting_variables=p.first, params=(ps, st)) - end - ps, st = begin - if any_forward - ForwardSolveInitialization()(rng, layer; shooting_variables=forward_vars, params=(ps, st)) - else - ps, st - end - end - - return ps, st -end - -=# + =# diff --git a/src/local_controls.jl b/src/local_controls.jl deleted file mode 100644 index cea9a0c..0000000 --- a/src/local_controls.jl +++ /dev/null @@ -1,220 +0,0 @@ -""" -$(TYPEDEF) -Implements a piecewise constant control discretization. - -# Fields -$(FIELDS) -""" -struct ControlParameter{T, C, B} - "The name of the control" - name::Symbol - "The timepoints at which discretized variables are introduced" - t::T - "The initial values for the controls. Either a vector or a function (rng,t,bounds) -> u" - controls::C - "The bounds as a tuple" - bounds::B -end - -default_u(rng, t, bounds) = zeros(eltype(t), size(t)) -default_bounds(t::AbstractVector{T}) where {T <: Real} = (fill(typemin(T), size(t)), fill(typemax(T), size(t))) - -""" -$(SIGNATURES) - -Constructs a `ControlParameter` with piecewise constant discretizations introduced at -timepoints `t`. Optionally - -```julia-repl -julia> ControlParameter(0:1.0:4.0, name=:c) -ControlParameter{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, typeof(Corleone.default_u), typeof(Corleone.default_bounds)}(:c, 0.0:1.0:10.0, Corleone.default_u, Corleone.default_bounds) -``` - -```julia-repl -julia> ControlParameter(0.0:1.0:4.0, name=:c, controls = zeros(5)) -ControlParameter{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}, typeof(Corleone.default_bounds)}(:c, 0.0:1.0:4.0, [0.0, 0.0, 0.0, 0.0, 0.0], Corleone.default_bounds) -``` - - -```julia-repl -julia> ControlParameter(0:1.0:9.0, name=:c1, controls=zeros(5), bounds=(0.0,1.0)) -ControlParameter{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}, Tuple{Float64, Float64}}(:c1, 0.0:1.0:9.0, [0.0, 0.0, 0.0, 0.0, 0.0], (0.0, 1.0)) -``` -The latter is functionally equivalent to the following example, specifying all bounds individually: -```julia-repl -julia> ControlParameter(0:1.0:9.0, name=:c1, controls=zeros(5), bounds=(zeros(5),ones(5))) -ControlParameter{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, Vector{Float64}, Tuple{Vector{Float64}, Vector{Float64}}}(:c1, 0.0:1.0:9.0, [0.0, 0.0, 0.0, 0.0, 0.0], ([0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0])) -``` -""" -function ControlParameter(t::AbstractVector; name::Symbol = gensym(:w), controls = default_u, bounds = default_bounds) - return ControlParameter{typeof(t), typeof(controls), typeof(bounds)}(name, t, controls, bounds) -end - -get_timegrid(parameters::ControlParameter, tspan = (-Inf, Inf)) = begin - (; t) = parameters - idx = isnothing(tspan) ? eachindex(t) : findall(tspan[1] .<= t .< tspan[2]) - t[idx] -end - -``` -$(SIGNATURES) - -Computes the number of discretized controls restricted to given `tspan`. -``` -function control_length(parameters::ControlParameter; tspan = nothing, kwargs...) - (; t) = parameters - idx = isnothing(tspan) ? eachindex(t) : findall(tspan[1] .<= t .< tspan[2]) - return size(idx, 1) -end - -``` -$(SIGNATURES) - -Returns discretized controls of ControlParameter `params` restricted to given `tspan`. -``` -function get_controls(::Random.AbstractRNG, parameters::ControlParameter{<:Any, <:AbstractArray}; raw = false, tspan = nothing, kwargs...) - (; t, controls) = parameters - raw && return controls - idx = isnothing(tspan) ? eachindex(t) : findall(tspan[1] .<= t .< tspan[2]) - return controls[idx] -end - -function get_controls(rng::Random.AbstractRNG, parameters::ControlParameter{<:Any, <:Function}; raw = false, tspan = nothing, kwargs...) - (; t) = parameters - bounds = get_bounds(parameters; tspan, kwargs...) - idx = isnothing(tspan) ? eachindex(t) : findall(tspan[1] .<= t .< tspan[2]) - return parameters.controls(rng, t[idx], bounds) -end - -``` -$(SIGNATURES) - -Returns bounds of discretized controls restricted to given `tspan`. -``` -function get_bounds(parameters::ControlParameter{<:Any, <:Any, <:Tuple}; tspan = nothing, kwargs...) - (; t) = parameters - idx = isnothing(tspan) ? eachindex(t) : findall(tspan[1] .<= t .< tspan[2]) - nc = size(idx, 1) - _bounds = parameters.bounds - if length(_bounds[1]) == length(_bounds[2]) == 1 - return (repeat([_bounds[1]], nc), repeat([_bounds[2]], nc)) - elseif length(_bounds[1]) == length(_bounds[2]) == length(t) - return (_bounds[1][idx], _bounds[2][idx]) - end - throw("Incompatible control bound definition. Got $(length(_bounds[1])) elements, expected $(length(t)).") -end - -get_bounds(parameters::ControlParameter{<:Any, <:Any, <:Function}; tspan = (-Inf, Inf), kwargs...) = parameters.bounds(get_timegrid(parameters, tspan; kwargs...)) - -function check_consistency(rng::Random.AbstractRNG, parameters::ControlParameter) - grid = get_timegrid(parameters) - u = get_controls(rng, parameters; raw = true) - lb, ub = get_bounds(parameters) - @assert issorted(grid) "Time grid is not sorted." - @assert get_timegrid(parameters) == unique(grid) "Time grid is not unique." - @assert all(lb .<= ub) "Bounds are inconsistent" - @assert size(lb) == size(ub) == size(u) == size(grid) "Sizes are inconsistent" - return @assert all(lb .<= u .<= ub) "Initial values are inconsistent" -end - -function get_subvector_indices(M::Int, L::Int) - # Handle invalid inputs - if M < 0 || L <= 0 - error("M must be a non-negative integer and L must be a positive integer.") - end - - # Calculate the number of full-length vectors (N) - N = floor(Int, M / L) - - indices = Vector{UnitRange{Int64}}() - - # Create the N full-length vectors - for i in 1:N - start_idx = (i - 1) * L + 1 - end_idx = i * L - push!(indices, start_idx:end_idx) - end - - # Create the last vector with remaining elements - remaining_length = M - N * L - if remaining_length > 0 - last_start_idx = N * L + 1 - last_end_idx = M - push!(indices, last_start_idx:last_end_idx) - end - - return indices -end - -function build_index_grid(controls::ControlParameter...; offset::Bool = true, tspan::Tuple = (-Inf, Inf), subdivide::Int64 = typemax(Int64)) - ts = map(controls) do ci - get_timegrid(ci, tspan) - end - time_grid = vcat(reduce(vcat, ts), collect(tspan)) |> sort! |> unique! |> Base.Fix1(filter!, isfinite) - indices = zeros(Int64, length(ts), size(time_grid, 1) - 1) - for i in axes(indices, 1), j in axes(indices, 2) - indices[i, j] = clamp( - searchsortedlast(ts[i], time_grid[j]), - firstindex(ts[i]), lastindex(ts[i]) - ) - end - # Offset - if offset - for i in axes(indices, 1) - if i > 1 - indices[i, :] .+= maximum(indices[i - 1, :]) - end - end - end - # Check the gridsize - N = size(indices, 2) - # Normalize for the first index here - indices .-= minimum(indices) - 1 - if N > subdivide - ranges = get_subvector_indices(N, subdivide) - return Tuple(indices[:, i] for i in ranges) - end - return indices -end - -find_shooting_indices(tspan, control::ControlParameter) = any(first(tspan) .== control.t) - - -function collect_tspans(controls::ControlParameter...; tspan = (-Inf, Inf), subdivide::Int64 = typemax(Int64)) - ts = map(controls) do ci - get_timegrid(ci, tspan) - end - time_grid = vcat(reduce(vcat, ts), collect(tspan)) |> sort! |> unique! |> Base.Fix1(filter!, isfinite) - fullgrid = collect(ti for ti in zip(time_grid[1:(end - 1)], time_grid[2:end])) - N = size(fullgrid, 1) - if N > subdivide - ranges = get_subvector_indices(N, subdivide) - return Tuple(tuple(fullgrid[i]...) for i in ranges) - end - return tuple(fullgrid...) -end - -``` -$(SIGNATURES) -Collect all discretized `controls` in a flat vector. -``` -function collect_local_controls(rng, controls::ControlParameter...; kwargs...) - return reduce( - vcat, map(controls) do control - get_controls(rng, control; kwargs...) - end - ) -end - -``` -$(SIGNATURES) -Collect all lower and upper bounds of discretized `controls` in flat vectors. -``` -function collect_local_control_bounds(controls::ControlParameter...; kwargs...) - bounds = map(controls) do control - get_bounds(control; kwargs...) - end - lb = reduce(vcat, first.(bounds)) - ub = reduce(vcat, last.(bounds)) - return lb, ub -end diff --git a/src/multiple_shooting.jl b/src/multiple_shooting.jl index b3d9e32..a8c263c 100644 --- a/src/multiple_shooting.jl +++ b/src/multiple_shooting.jl @@ -1,328 +1,103 @@ """ $(TYPEDEF) -Defines a callable layer that integrates a differential equation using multiple shooting, -i.e., the problem is lifted and integration is decoupled on disjunct time intervals given -in `shooting_intervals`. Initial conditions on the `shooting_intervals` are degrees of -freedom (except perhaps for the first layer), for which the initialization scheme -`initialization` provides initial values. Parallelization is integration is possible, -for which a suitable `EnsembleAlgorithm` can be specified with `ensemble_alg`. -# Fields -$(FIELDS) +Defines a layer for multiple shooting. Simply a wrapper for the [ParallelShootingLayer](@ref) but returns a single trajectory. """ -struct MultipleShootingLayer{L, I, E, Z} <: LuxCore.AbstractLuxWrapperLayer{:layer} - "The original layer" +struct MultipleShootingLayer{L, S <: NamedTuple} <: LuxCore.AbstractLuxWrapperLayer{:layer} + "The instance of a [ParallelShootingLayer](@ref) to be solved in parallel." layer::L - "The shooting intervals" - shooting_intervals::I - "The ensemble algorithm" - ensemble_alg::E - "The initialization scheme" - initialization::Z -end - -function Base.show(io::IO, layer::MultipleShootingLayer) - type_color, no_color = SciMLBase.get_colorizers(io) - print( - io, - type_color, - "MultipleShootingLayer ", - no_color, - "with $(length(layer.shooting_intervals)) shooting intervals and $(length(get_controls(layer.layer))) controls.\n", - ) - print(io, "Underlying problem: ") - return print(io, layer.layer) -end - -get_quadrature_indices(layer::MultipleShootingLayer) = get_quadrature_indices(layer.layer) - -""" -$(FUNCTIONNAME) - -Initializes all shooting nodes with their default value, i.e., their initial value in -the underlying problem. -""" -function default_initialization(rng::Random.AbstractRNG, shooting::MultipleShootingLayer) - (; shooting_intervals, layer) = shooting - names = ntuple(i -> Symbol(:interval, "_", i), length(shooting_intervals)) - vals = ntuple( - i -> __initialparameters( - rng, layer; tspan = shooting_intervals[i], shooting_layer = i != 1 - ), - length(shooting_intervals), + "Indicator for shooting constraints for each of the layers." + shooting_variables::S +end + +function MultipleShootingLayer(layer::LuxCore.AbstractLuxLayer, shooting_points::Real...; kwargs...) + tspan = get_tspan(layer) + tpoints = unique!(sort!(vcat(collect(shooting_points), collect(tspan)))) + layers = ntuple( + i -> remake( + layer, + tspan = (tpoints[i], tpoints[i + 1]), + tunable_ic = get_tunable_u0(layer, i != 1), + ), length(tpoints) - 1 ) - return NamedTuple{names}(vals) -end + layers = NamedTuple{ntuple(i -> Symbol(:layer_, i), length(layers))}(layers) + shooting_variables = map(get_shooting_variables, layers) + layer = ParallelShootingLayer(layers; kwargs...) -``` -$(METHODLIST) - -Constructs a `MultipleShootingLayer` from given `AbstractDEProblem` `prob` with suitable -integration method `alg` and vector of shooting points `tpoints`. -``` -function MultipleShootingLayer(prob, alg, tpoints::AbstractVector; kwargs...) - return MultipleShootingLayer(prob, alg, tpoints...; kwargs...) + return MultipleShootingLayer{typeof(layer), typeof(shooting_variables)}(layer, shooting_variables) end -function MultipleShootingLayer( - prob::SciMLBase.AbstractDEProblem, - alg::SciMLBase.DEAlgorithm, - tpoints::Real...; - ensemble_alg = EnsembleSerial(), - initialization = default_initialization, - kwargs..., - ) - layer = SingleShootingLayer(prob, alg; kwargs...) - return MultipleShootingLayer(layer, tpoints...; ensemble_alg, initialization, kwargs...) +get_problem(layer::MultipleShootingLayer) = get_problem(layer.layer.layers[1]) +get_quadrature_indices(layer::MultipleShootingLayer) = get_quadrature_indices(layer.layer.layers[1]) +get_tspan(layer::MultipleShootingLayer) = begin + t0, _ = get_problem(layer.layer.layers[1]).tspan + _, tinf = get_problem(layer.layer.layers[end]).tspan + (t0, tinf) end +get_timegrid(layer::MultipleShootingLayer) = reduce(vcat, values(get_timegrid(layer.layer))) -function MultipleShootingLayer( - layer, - tpoints::Real...; - ensemble_alg = EnsembleSerial(), - initialization = default_initialization, - kwargs..., - ) - tspans = vcat(collect(tpoints), collect(layer.problem.tspan)) - sort!(tspans) - unique!(tspans) - tspans = [tispan for tispan in zip(tspans[1:(end - 1)], tspans[2:end])] - tspans = tuple(tspans...) - return MultipleShootingLayer{ - typeof(layer), typeof(tspans), typeof(ensemble_alg), typeof(initialization), - }( - layer, tspans, ensemble_alg, initialization - ) +function SciMLBase.remake(layer::MultipleShootingLayer; kwargs...) + newlayer = remake(layer.layer; kwargs...) + return MultipleShootingLayer{typeof(newlayer), typeof(layer.shooting_variables)}(newlayer, layer.shooting_variables) end -function LuxCore.initialparameters(rng::Random.AbstractRNG, shooting::MultipleShootingLayer) - (; initialization) = shooting - return initialization(rng, shooting) +function (layer::MultipleShootingLayer)(u0, ps, st) + results, st = layer.layer(u0, ps, st) + return Trajectory(layer, results), st end -function LuxCore.parameterlength(shooting::MultipleShootingLayer) - return last(get_block_structure(shooting)) +function get_number_of_shooting_constraints(layer::MultipleShootingLayer) + # We ignore the first shooting variables here + return size(reduce(vcat, fleaves(Base.tail(layer.shooting_variables))), 1) end -function LuxCore.initialstates(rng::Random.AbstractRNG, shooting::MultipleShootingLayer) - (; shooting_intervals, layer) = shooting - names = ntuple(i -> Symbol(:interval, "_", i), length(shooting_intervals)) - vals = ntuple( - i -> - __initialstates(rng, layer; tspan = shooting_intervals[i], shooting_layer = i != 1), - length(shooting_intervals), - ) - return NamedTuple{names}(vals) +function matchings(layer::MultipleShootingLayer, us, sub_trajs) + (; shooting_variables) = layer + vars = variable_symbols(get_problem(layer)) + return map(Base.OneTo(length(shooting_variables) - 1)) do i + specs = shooting_variables[i + 1] + state_matching = map(specs.state) do id + Symbol(vars[id]), first(us[i + 1])[id] .- last(us[i])[id] + end |> NamedTuple + control_matching = map(specs.control) do csym + traj_prev = sub_trajs[i] + traj_next = sub_trajs[i + 1] + v_prev = getproperty(_apply(traj_prev.controls.model, last(traj_prev.t), traj_prev.controls.ps, traj_prev.controls.st)[1], csym) + v_next = getproperty(_apply(traj_next.controls.model, first(traj_next.t), traj_next.controls.ps, traj_next.controls.st)[1], csym) + Symbol(csym), v_next .- v_prev + end |> NamedTuple + Symbol(:matching_, i), (; state = state_matching, control = control_matching) + end |> NamedTuple end -function _parallel_solve( - shooting::MultipleShootingLayer, - u0, - ps, - st::NamedTuple{fields}, +function Trajectory( + layer::MultipleShootingLayer, solutions::NamedTuple{fields}; + kwargs... ) where {fields} - args = collect( - ntuple( - i -> (u0, __getidx(ps, fields[i]), __getidx(st, fields[i]), i > 1), length(st) - ) - ) - return mythreadmap(shooting.ensemble_alg, Base.Splat(shooting.layer), args) -end - -function (shooting::MultipleShootingLayer)(u0, ps, st::NamedTuple{fields}) where {fields} - ret = Corleone._parallel_solve(shooting, u0, ps, st) - u = first.(ret) - sts = NamedTuple{fields}(last.(ret)) - return Trajectory(u, sts, get_quadrature_indices(shooting)), sts -end - -function Trajectory(u::AbstractVector{TR}, sts, quadrature_indices) where {TR <: Trajectory} - size(u, 1) == 1 && return only(u) - p = first(u).p - sys = first(u).sys - us = map(state_values, u) - ts = map(current_time, u) - tnew = reduce( - vcat, map(i -> i == lastindex(ts) ? ts[i] : ts[i][1:(end - 1)], eachindex(ts)) - ) - offsets = cumsum(map(i -> lastindex(us[i]), eachindex(us[1:(end - 1)]))) - shooting_val_1 = ((u0 = eltype(first(first(us)))[], p = eltype(p)[], controls = eltype(first(first(us)))[])) - shooting_vals = map(eachindex(us[1:(end - 1)])) do i - uprev = us[i] - unext = us[i + 1] - idx = sts[i + 1].shooting_indices - nx = statelength(sts[i + 1].initial_condition) - controlidx = setdiff(idx, Base.OneTo(nx)) - stateidx = setdiff(idx, controlidx) - ( - u0 = last(uprev)[stateidx] .- first(unext)[stateidx], - p = u[i].p .- u[i + 1].p, - controls = last(uprev)[controlidx] .- first(unext)[controlidx], - ) - end - shootings = NamedTuple{(keys(sts)...,)}( - ( - shooting_val_1, - shooting_vals..., - ) - ) - # Sum up the quadratures - q_prev = us[1][end][quadrature_indices] - for i in eachindex(us)[2:end] - for j in eachindex(us[i]) - us[i][j][quadrature_indices] += q_prev - end - q_prev = us[i][end][quadrature_indices] + sub_trajs = values(solutions) + us = map(Base.Fix2(getproperty, :u), sub_trajs) + ts = map(Base.Fix2(getproperty, :t), sub_trajs) + shooting_violations = matchings(layer, us, sub_trajs) + # Use the first sub-trajectory's StatefulLuxLayer for the combined controls + controls = first(sub_trajs).controls + p = deepcopy(first(sub_trajs).p) + # Update the quadratures + quadratures = get_quadrature_indices(layer) + q_prev = last(us[1]) + keeper = [i in quadratures for i in eachindex(q_prev)] + us_ = map(us[2:end]) do ui + new_uij = map(uij -> uij .+ keeper .* q_prev, ui) + q_prev = keeper .* last(new_uij) + new_uij end + us = [us[1], us_...] unew = reduce( vcat, map(i -> i == lastindex(us) ? us[i] : us[i][1:(end - 1)], eachindex(us)) ) - return Trajectory(sys, unew, p, tnew, shootings, offsets) -end - -function get_number_of_state_matchings( - shooting::MultipleShootingLayer, - ps = LuxCore.initialparameters(Random.default_rng(), shooting), - st = LuxCore.initialstates(Random.default_rng(), shooting), - ) - return sum(xi -> size(intersect(xi.shooting_indices, Base.OneTo(statelength(xi.initial_condition))), 1), Base.tail(st)) -end - -function get_number_of_parameter_matchings( - shooting::MultipleShootingLayer, - ps = LuxCore.initialparameters(Random.default_rng(), shooting), - st = LuxCore.initialstates(Random.default_rng(), shooting), - ) - return sum(xi -> size(xi.p, 1), Base.front(ps)) -end - -function get_number_of_control_matchings( - shooting::MultipleShootingLayer, - ps = LuxCore.initialparameters(Random.default_rng(), shooting), - st = LuxCore.initialstates(Random.default_rng(), shooting), - ) - return sum(xi -> size(setdiff(xi.shooting_indices, Base.OneTo(statelength(xi.initial_condition))), 1), Base.tail(st)) -end - -get_number_of_shooting_constraints(::SingleShootingLayer) = 0 - -function get_number_of_shooting_constraints( - shooting::MultipleShootingLayer, - ps = LuxCore.initialparameters(Random.default_rng(), shooting), - st = LuxCore.initialstates(Random.default_rng(), shooting), - ) - return get_number_of_state_matchings(shooting, ps, st) + - get_number_of_control_matchings(shooting, ps, st) + - get_number_of_parameter_matchings(shooting, ps, st) -end - -deepvcat(V::AbstractVector) = V -deepvcat(NTV::NamedTuple) = reduce(vcat, NTV |> values .|> deepvcat) - -""" - stage_ordered_shooting_constraints(traj) - -Returns the shooting violations sorted by shooting-stage -and per-stage sorted by states - parameters - controls -""" -stage_ordered_shooting_constraints(traj::Trajectory) = deepvcat(traj.shooting) - -function collect_into!(res::AbstractVector, sval::SV, ind::Vector{Int64} = [0]) where {SV <: AbstractVector} - for i in eachindex(sval) - res[ind[1] += 1] = sval[i] - end - return -end -function collect_into!(res::AbstractVector, sval::NamedTuple, ind::Vector{Int64} = [0]) - for key in keys(sval) - collect_into!(res, sval[key], ind) - end - return -end - -""" -$(SIGNATURES) - -In-place version of `stage_ordered_shooting_constraints`. -""" -function stage_ordered_shooting_constraints!(res::AbstractVector, traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} - collect_into!(res, traj.shooting) - return res -end - - -function _matchings(traj::Trajectory{S, U, P, T, SH}, kind::Symbol) where {S, U, P, T, SH <: NamedTuple} - return map(keys(traj.shooting)) do key - traj.shooting[key][kind] - end |> Base.Fix1(reduce, vcat) -end -state_matchings(traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = _matchings(traj, :u0) -parameter_matchings(traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = _matchings(traj, :p) -control_matchings(traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = _matchings(traj, :controls) - -function _matchings!(res::AbstractVector, traj::Trajectory{S, U, P, T, SH}, kind::Symbol, ind::Vector{Int64} = [1]) where {S, U, P, T, SH <: NamedTuple} - for key in keys(traj.shooting) - res[UnitRange(ind[1], (ind[1] += length(traj.shooting[key][kind])) - 1)] = traj.shooting[key][kind] - end - return res -end - -state_matchings!(res::AbstractVector, traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = _matchings!(res, traj, :u0) -parameter_matchings!(res::AbstractVector, traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = _matchings!(res, traj, :p) -control_matchings!(res::AbstractVector, traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = _matchings!(res, traj, :controls) - -""" -$(SIGNATURES) - -Returns the shooting violations sorted by states - parameters - controls and per-kind -sorted by shooting-stage. -""" -shooting_constraints(traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} = vcat((_matchings(traj, kind) for kind in (:u0, :p, :controls))...) - -shooting_constraints(traj::Trajectory) = utype(traj)[] -""" -$(SIGNATURES) - -In-place version of `shooting_constraints`. -""" -function shooting_constraints!(res::AbstractVector, traj::Trajectory{S, U, P, T, SH}) where {S, U, P, T, SH <: NamedTuple} - ind = [1] - for kind in (:u0, :p, :controls) - _matchings!(res, traj, kind, ind) - end - return res -end - -shooting_constraints!(res, traj::Trajectory) = res - -""" -$(SIGNATURES) - -Compute the block structure of the hessian of the Lagrangian of an optimal control problem -as specified via the `shooting_intervals` of the `MultipleShootingLayer`. -Note: Constraints other than the matching conditions of the multiple shooting approach -are not considered here and might alter the block structure. -""" -function get_block_structure(mslayer::MultipleShootingLayer) - (; layer, shooting_intervals) = mslayer - ps_lengths = collect( - map(enumerate(shooting_intervals)) do (i, tspan) - __parameterlength(layer; tspan = tspan, shooting_layer = i > 1) - end, + t_new = reduce( + vcat, map(i -> i == lastindex(ts) ? ts[i] : ts[i][1:(end - 1)], eachindex(ts)) ) - return vcat(0, cumsum(ps_lengths)) -end - -``` -$(SIGNATURES) -Extracts lower and upper bounds of all optimization variables in the `MultipleShootingLayer`. -``` -function get_bounds(mslayer::MultipleShootingLayer) - (; layer, shooting_intervals) = mslayer - names = ntuple(i -> Symbol(:interval, "_", i), length(shooting_intervals)) - bounds = map(enumerate(shooting_intervals)) do (i, tspan) - get_bounds(layer; tspan = tspan, shooting = i > 1) - end - return NamedTuple{names}(first.(bounds)), NamedTuple{names}(last.(bounds)) + sys = first(solutions).sys + return Trajectory{typeof(sys), typeof(unew), typeof(p), typeof(t_new), typeof(controls), typeof(shooting_violations)}(sys, unew, p, t_new, controls, shooting_violations) end diff --git a/src/parallel_shooting.jl b/src/parallel_shooting.jl new file mode 100644 index 0000000..26037ba --- /dev/null +++ b/src/parallel_shooting.jl @@ -0,0 +1,88 @@ +""" +$(TYPEDEF) + +A layer that solves multiple shooting sub-problems in parallel. + +# Fields +$(FIELDS) + +# Description + +The `ParallelShootingLayer` wraps a collection of independent shooting layers and solves them concurrently +using the specified ensemble algorithm. This is useful for solving multi-shooting problems where different +time intervals can be solved independently. +""" +struct ParallelShootingLayer{L <: NamedTuple, A <: SciMLBase.EnsembleAlgorithm} <: LuxCore.AbstractLuxWrapperLayer{:layers} + name::Symbol + "The layers to be solved in parallel. Each layer should be a SingleShootingLayer." + layers::L + "The underlying ensemble algorithm to use for parallelization. Default is `EnsembleThreads`." + ensemble_algorithm::A +end + +ParallelShootingLayer(layers::NamedTuple; kwargs...) = ParallelShootingLayer( + get(kwargs, :name, gensym(:parallel_shooting)), + layers, + get(kwargs, :ensemble_algorithm, EnsembleSerial()) +) + +function ParallelShootingLayer(layers::AbstractLuxLayer...; kwargs...) + layers = NamedTuple{ntuple(i -> Symbol(:layer, i), length(layers))}(layers) + return ParallelShootingLayer(layers; kwargs...) +end + +function get_block_structure(layer::ParallelShootingLayer) + return vcat(0, cumsum(map(LuxCore.parameterlength, layer.layers))) +end + +function (layer::ParallelShootingLayer)(u0, ps, st) + return _parallel_solve(layer.ensemble_algorithm, layer.layers, u0, ps, st) +end + +@generated function _parallel_solve( + alg::SciMLBase.EnsembleAlgorithm, + layers::NamedTuple{fields}, + u0, + ps, + st::NamedTuple{fields}, + ) where {fields} + exprs = Expr[] + args = [gensym() for f in fields] + for i in eachindex(fields) + push!( + exprs, :( + $(args[i]) = + (layers.$(fields[i]), u0, ps.$(fields[i]), st.$(fields[i])) + ) + ) + end + push!( + exprs, :( + ret = + mythreadmap(alg, Base.splat(LuxCore.apply), $(Expr(:tuple, args...))) + ) + ) + push!( + exprs, :( + NamedTuple{$(fields)}(first.(ret)), NamedTuple{$(fields)}(last.(ret)), + ) + ) + ex = Expr(:block, exprs...) + return ex +end + +function SciMLBase.remake(layer::ParallelShootingLayer; kwargs...) + layers = map(keys(layer.layers)) do k + layer_kwargs = get(kwargs, k, kwargs) + k, remake(layer.layers[k]; layer_kwargs...) + end |> NamedTuple + ensemble_algorithm = get(kwargs, :ensemble_algorithm, layer.ensemble_algorithm) + return ParallelShootingLayer(layer.name, layers, ensemble_algorithm) +end + +function get_timestops(layer::ParallelShootingLayer, st::NamedTuple{fields} = LuxCore.initialstates(Random.default_rng(), layer)) where {fields} + (; layers) = layer + return map(fields) do f + f, get_timestops(getproperty(layers, f), getproperty(st, f)) + end |> NamedTuple +end diff --git a/src/single_shooting.jl b/src/single_shooting.jl index 5a3c91d..979246f 100644 --- a/src/single_shooting.jl +++ b/src/single_shooting.jl @@ -1,506 +1,244 @@ """ $(TYPEDEF) -Defines a callable layer that integrates the `AbstractDEProblem` `problem` using the specified -`algorithm`. Controls are assumed to impact differential equation via its parameters `problem.p` -at the positions indicated via `control_indices` and are itself specified via `controls`. -Moreover, initial conditions `problem.u0` that are degrees of freedom to be optimized can be -specified by their indices via `tunable_ic` along with their upper and lower bounds via `bounds_ic`. + +Single-shooting layer coupling initial conditions and controls for trajectory simulation. # Fields $(FIELDS) - -Note: The orders of both `controls` and `control_indices`, and `bounds_ic` and `tunable_ic` -are assumed to be identical! """ -struct SingleShootingLayer{P, A, C, B, PB, SI, PI} <: LuxCore.AbstractLuxLayer - "The underlying differential equation problem" - problem::P - "The algorithm with which `problem` is integrated." +struct SingleShootingLayer{A, U0, C} <: LuxCore.AbstractLuxContainerLayer{(:initial_conditions, :controls)} + "The name of the container" + name::Symbol + "The algorithm to solve the underlying DEProblem" algorithm::A - "Indices in parameters of `prob` corresponding to controls" - control_indices::Vector{Int64} - "The controls" + "The initial condition layer" + initial_conditions::U0 + "The control parameter collection" controls::C - "Indices of `prob.u0` which are degrees of freedom" - tunable_ic::Vector{Int64} - "Bounds on the tunable initial conditions of the problem" - bounds_ic::B - "Initialization of u" - state_initialization::SI - "Indices of `prob.p` which are degrees of freedom. This is derived from control_indices!" - tunable_p::Vector{Int64} - "Bounds on the tunable parameters of the problem" - bounds_p::PB - "Initialization of p" - parameter_initialization::PI - "Indices of differential states that are quadratures, i.e. they do not enter into the right hand side of `problem`" - quadrature_indices::Vector{Int64} -end - -function default_u0( - rng::Random.AbstractRNG, problem::SciMLBase.AbstractDEProblem, tunables, (lb, ub) - ) - return clamp.(problem.u0[tunables], lb[tunables], ub[tunables]) end -function default_p0( - rng::Random.AbstractRNG, problem::SciMLBase.AbstractDEProblem, parameters, bounds +function SciMLBase.remake(layer::SingleShootingLayer; kwargs...) + initial_conditions = get(kwargs, :initial_conditions, remake(layer.initial_conditions; kwargs...)) + controls = get(kwargs, :controls, remake(layer.controls; kwargs...)) + algorithm = get(kwargs, :algorithm, layer.algorithm) + name = get(kwargs, :name, layer.name) + return SingleShootingLayer{ + typeof(algorithm), + typeof(initial_conditions), typeof(controls), + }( + name, algorithm, initial_conditions, controls ) - pvec, _ = SciMLStructures.canonicalize(SciMLStructures.Tunable(), problem.p) - return clamp.(pvec[parameters], bounds...) end -function Base.show(io::IO, layer::SingleShootingLayer) - type_color, no_color = SciMLBase.get_colorizers(io) - - print( - io, - type_color, - "SingleShootingLayer", - no_color, - " with $(length(layer.controls)) controls.\nUnderlying problem: ", - ) - - return Base.show(io, "text/plain", layer.problem) -end +""" +$(SIGNATURES) -function init_problem(prob, alg) - return remake_problem(prob, SciMLBase.init(prob, alg)) +Construct a [`SingleShootingLayer`](@ref) from pre-built initial-condition and control layers. +""" +function SingleShootingLayer(initial_conditions::InitialCondition, controls::ControlParameters; algorithm::SciMLBase.AbstractDEAlgorithm, name = gensym(:single_shooting)) + return SingleShootingLayer{typeof(algorithm), typeof(initial_conditions), typeof(controls)}(name, algorithm, initial_conditions, controls) end -function remake_problem(prob::ODEProblem, state) - return remake(prob; u0 = state.u) -end +""" +$(SIGNATURES) -function remake_problem(prob::DAEProblem, state) - return remake(prob; u0 = state.u, du0 = state.du) +Construct a [`SingleShootingLayer`](@ref) from an initial-condition layer and control specifications. +""" +function SingleShootingLayer(initial_conditions::InitialCondition, controls...; algorithm::SciMLBase.AbstractDEAlgorithm, name = gensym(:single_shooting)) + controls = ControlParameters(controls...) + return SingleShootingLayer{typeof(algorithm), typeof(initial_conditions), typeof(controls)}(name, algorithm, initial_conditions, controls) end """ $(SIGNATURES) -Constructs a SingleShootingLayer from an `AbstractDEProblem` and a suitable `AbstractDEAlgorithm` -`alg`. - -# Arguments - - `control_indices` : Vector of indices of `prob.p` that denote controls - - `controls`: Tuple of `ControlParameter` specifying the controls - - `tunable_ic`: Vector of indices of `prob.u0` that is tunable, i.e., a degree of freedom - - `bounds_ic` : Vector of tuples of lower and upper bounds of tunable initial conditions +Construct a [`SingleShootingLayer`](@ref) directly from a `DEProblem` and control specifications. """ -function SingleShootingLayer( - prob, - alg; - controls = [], - tunable_ic = Int64[], - bounds_ic = nothing, - state_initialization = default_u0, - bounds_p = nothing, - parameter_initialization = default_p0, - quadrature_indices = Int64[], - kwargs..., - ) - _prob = init_problem(remake(prob; kwargs...), alg) - controls = collect(controls) - control_indices = isempty(controls) ? Int64[] : first.(controls) - controls = isempty(controls) ? controls : last.(controls) - u0 = prob.u0 - p_vec, _... = SciMLStructures.canonicalize(SciMLStructures.Tunable(), prob.p) - tunable_p = setdiff(eachindex(p_vec), control_indices) - p_vec = p_vec[tunable_p] - ic_bounds = isnothing(bounds_ic) ? (to_val(u0, -Inf), to_val(u0, Inf)) : bounds_ic - p_bounds = isnothing(bounds_p) ? (to_val(p_vec, -Inf), to_val(p_vec, Inf)) : bounds_p - quadrature_indices = isempty(quadrature_indices) ? Int64[] : collect(quadrature_indices) - - @assert size(ic_bounds[1]) == size(ic_bounds[2]) == size(u0) "The size of the initial states and its bounds is inconsistent." - @assert size(p_bounds[1]) == size(p_bounds[2]) == size(p_vec) "The size of the initial parameter vector and its bounds is inconsistent." - @assert all(checkbounds(Bool, u0, quadrature_index) for quadrature_index in quadrature_indices) "Some quadrature indices are inconsistent with the state dimension" - - return SingleShootingLayer( - _prob, - alg, - control_indices, - controls, - tunable_ic, - ic_bounds, - state_initialization, - tunable_p, - p_bounds, - parameter_initialization, - quadrature_indices - ) -end +function SingleShootingLayer(problem::SciMLBase.DEProblem, controls...; algorithm::SciMLBase.AbstractDEAlgorithm, name = gensym(:single_shooting), kwargs...) -get_problem(layer::SingleShootingLayer) = layer.problem -get_controls(layer::SingleShootingLayer) = (layer.controls, layer.control_indices) -get_tspan(layer::SingleShootingLayer) = layer.problem.tspan -get_tunable(layer::SingleShootingLayer) = layer.tunable_ic -function get_params(layer::SingleShootingLayer) - return setdiff(eachindex(layer.problem.p), layer.control_indices) -end -function __get_tunable_p(layer::SingleShootingLayer) - return first(SciMLStructures.canonicalize(SciMLStructures.Tunable(), layer.problem.p)) -end -get_quadrature_indices(layer::SingleShootingLayer) = layer.quadrature_indices - -function get_bounds(layer::SingleShootingLayer; shooting = false, kwargs...) - (; bounds_ic, bounds_p, controls, tunable_ic, quadrature_indices) = layer - state_indices = setdiff(eachindex(first(bounds_ic)), quadrature_indices) - bounds_ic = shooting ? map(Base.Fix2(getindex, state_indices), bounds_ic) : map(Base.Fix2(getindex, tunable_ic), bounds_ic) - if !isempty(controls) - control_lb, control_ub = collect_local_control_bounds(controls...; tspan = layer.problem.tspan, kwargs...) - else - control_ub = control_lb = eltype(first(bounds_ic))[] + repack = let p0 = problem.p + (x) -> SciMLStructures.replace(SciMLStructures.Tunable(), p0, vcat(values(x)...)) end - return ( - (; u0 = first(bounds_ic), p = first(bounds_p), controls = control_lb), - (; u0 = last(bounds_ic), p = last(bounds_p), controls = control_ub), - ) + initial_conditions = InitialCondition(problem; kwargs...) + controls = ControlParameters(controls..., transform = repack) + return SingleShootingLayer{typeof(algorithm), typeof(initial_conditions), typeof(controls)}(name, algorithm, initial_conditions, controls) end -function LuxCore.initialparameters(rng::Random.AbstractRNG, layer::SingleShootingLayer) - return __initialparameters(rng, layer) -end -LuxCore.parameterlength(layer::SingleShootingLayer) = __parameterlength(layer) -function LuxCore.initialstates(rng::Random.AbstractRNG, layer::SingleShootingLayer) - return __initialstates(rng, layer) -end +""" +$(SIGNATURES) -function __initialparameters( - rng::Random.AbstractRNG, - layer::SingleShootingLayer; - tspan = layer.problem.tspan, - u0 = layer.problem.u0, - shooting_layer = false, - kwargs..., - ) - (; - problem, - state_initialization, - parameter_initialization, - tunable_ic, - bounds_ic, - bounds_p, - tunable_p, - control_indices, - quadrature_indices, - ) = layer - problem = remake(problem; tspan, u0) - return (; - u0 = state_initialization( - rng, problem, shooting_layer ? setdiff(eachindex(u0), quadrature_indices) : tunable_ic, bounds_ic - ), - p = parameter_initialization(rng, problem, tunable_p, bounds_p), - controls = if isempty(layer.controls) - eltype(layer.problem.u0)[] - else - collect_local_controls(rng, layer.controls...; tspan, kwargs...) - end, - ) -end +Return unicode subscript string for positive integer `i`. +""" +_subscript(i::Integer) = (i |> digits |> reverse .|> dgt -> Char(0x2080 + dgt)) |> join -function __parameterlength( - layer::SingleShootingLayer; tspan = layer.problem.tspan, shooting_layer = false, kwargs... - ) - p_vec, _... = SciMLStructures.canonicalize(SciMLStructures.Tunable(), layer.problem.p) - N = shooting_layer ? prod(size(layer.problem.u0)) - length(layer.quadrature_indices) : size(layer.tunable_ic, 1) - N += sum([i ∉ layer.control_indices for i in eachindex(p_vec)]) - if !isempty(layer.controls) - N += sum(layer.controls) do control - control_length(control; tspan, kwargs...) - end - end - return N -end +""" +$(SIGNATURES) -function retrieve_symbol_cache(problem::SciMLBase.DEProblem, control_indices; control_names = [Symbol(:u, _subscript(u_id)) for u_id in 1:length(control_indices)]) - return retrieve_symbol_cache(problem.f.sys, problem.u0, problem.p, control_indices; control_names = control_names) +Build a default symbolic system cache when the problem has no symbolic container. +""" +function default_system(problem::SciMLBase.DEProblem, controls) + states = [Symbol(:x, _subscript(i)) for i in eachindex(problem.u0)] + ps = collect(keys(controls.controls)) + t = :t + return SymbolCache(states, ps, t) end -_subscript(i::Integer) = (i |> digits |> reverse .|> dgt -> Char(0x2080 + dgt)) |> join +""" +$(SIGNATURES) -function retrieve_symbol_cache(::Nothing, u0, p, control_indices; control_names = [Symbol(:u, _subscript(u_id)) for u_id in 1:length(control_indices)]) - p0, _ = SciMLStructures.canonicalize(SciMLStructures.Tunable(), p) - state_symbols = [Symbol(:x, _subscript(i)) for i in eachindex(u0)] - u_id = 0 - p_id = 0 - parameter_symbols = [ - if i ∈ control_indices - u_id += 1 - control_names[u_id] - else - Symbol(:p, _subscript(p_id += 1)) - end for i in eachindex(p0) - ] - tsym = [:t] - return _retrieve_symbol_cache(state_symbols, parameter_symbols, tsym, control_indices) +Return a symbolic system with control parameters registered as time series. +""" +function get_new_system(problem, controls) + sys = symbolic_container(problem.f) + _isnull(x) = isnothing(x) || isempty(x) + if isnothing(sys) || _isnull(variable_symbols(sys)) || _isnull(parameter_symbols(sys)) + sys = default_system(problem, controls) + end + try + return remake_system(sys, controls) + catch + return remake_system(default_system(problem, controls), controls) + end end -function retrieve_symbol_cache(cache::SymbolCache, u0, p, control_indices; kwargs...) - psym = parameter_symbols(cache) - vsym = variable_symbols(cache) - sort!(psym; by = xi -> SymbolicIndexingInterface.parameter_index(cache, xi)) - sort!(vsym; by = xi -> SymbolicIndexingInterface.variable_index(cache, xi)) - return _retrieve_symbol_cache( - vsym, psym, independent_variable_symbols(cache), control_indices - ) -end +""" +$(SIGNATURES) -function _retrieve_symbol_cache(xs, ps, t, idx) - nonidx = filter(∉(idx), eachindex(ps)) - return SymbolCache(vcat(xs, ps[idx]), ps[nonidx], t) +Rebuild `sys` with the same variables and parameters (controls are now exposed +via `parameter_observed` on the `Trajectory`, so no `timeseries_parameters` are +needed here). +""" +function remake_system(sys::SymbolCache, controls) + return SymbolCache( + variable_symbols(sys), parameter_symbols(sys), independent_variable_symbols(sys) + ) end -struct InitialConditionRemaker <: Function - sorting::Vector{Int64} - constants::Vector{Int64} -end +get_problem(layer::SingleShootingLayer) = get_problem(layer.initial_conditions) +get_tspan(layer::SingleShootingLayer) = get_tspan(layer.initial_conditions) +get_quadrature_indices(layer::SingleShootingLayer) = get_quadrature_indices(layer.initial_conditions) +get_tunable_u0(layer::SingleShootingLayer, full::Bool = false) = get_tunable_u0(layer.initial_conditions, full) -function (ic::InitialConditionRemaker)( - u::AbstractVector{T}, u0::AbstractArray - ) where {T <: Number} - (; constants, sorting) = ic - isempty(u) && return T.(u0) - return reshape(vcat(vec(u0[constants]), u)[sorting], size(u0)) +function get_shooting_variables(layer::SingleShootingLayer) + problem = get_problem(layer) + tunable_ic = get_tunable_u0(layer) + cnames = [c.name for c in layer.controls.controls if is_shooted(c)] + return (; state = tunable_ic, control = cnames) end -function (ic::InitialConditionRemaker)(::Any, u0::AbstractArray) - return u0 +function get_timegrid(layer::SingleShootingLayer) + (; initial_conditions, controls) = layer + timegrid = vcat(Corleone.get_timegrid(initial_conditions), Corleone.get_timegrid(controls)) + t0, tinf = get_tspan(initial_conditions) + timegrid = filter(t -> t >= t0 && t <= tinf, timegrid) + unique!(sort!(timegrid)) + return timegrid end -statelength(ic::InitialConditionRemaker) = length(ic.sorting) - -function __initialstates( - rng::Random.AbstractRNG, - layer::SingleShootingLayer; - tspan = layer.problem.tspan, - shooting_layer = false, - kwargs..., - ) - (; tunable_ic, control_indices, problem, controls, quadrature_indices) = layer - (; u0) = problem - - initial_condition = if !shooting_layer - constant_ics = setdiff(eachindex(u0), tunable_ic) - sorting = sortperm(vcat(constant_ics, tunable_ic)) - shape = size(u0) - constants = constant_ics - InitialConditionRemaker(sorting, constants) - else - constant_ic = quadrature_indices - tunables = setdiff(eachindex(u0), constant_ic) - sorting = sortperm(vcat(constant_ic, tunables)) - InitialConditionRemaker(sorting, constant_ic) - end - # Setup the parameters - p_vec, repack, _ = SciMLStructures.canonicalize( - SciMLStructures.Tunable(), layer.problem.p - ) - - # We filter controls which do not act on the dynamics - active_controls = control_indices .<= lastindex(p_vec) - control_indices = control_indices[active_controls] - controls = controls[active_controls] - - parameter_matrix = zeros( - Bool, size(p_vec, 1), size(p_vec, 1) - size(control_indices, 1) - ) - control_matrix = zeros(Bool, size(p_vec, 1), size(control_indices, 1)) - param_id = 0 - control_id = 0 - for i in eachindex(p_vec) - if i ∈ control_indices - control_matrix[i, control_id += 1] = true - else - parameter_matrix[i, param_id += 1] = true - end - end - parameter_vector = let repack = repack, A = parameter_matrix, B = control_matrix - function (params, controls) - return repack(A * params .+ B * controls) - end - end +""" +$(SIGNATURES) - # Next we setup the tspans and the indices - if !isempty(controls) - grid = build_index_grid(controls...; tspan, subdivide = 100) - tspans = collect_tspans(controls...; tspan, subdivide = 100) - else - grid = Int64[i for i in control_indices] - tspans = (problem.tspan,) +Initialize runtime state for single-shooting evaluation, including binned time stops. +""" +function LuxCore.initialstates(rng::Random.AbstractRNG, layer::SingleShootingLayer) + (; initial_conditions, controls) = layer + t0, tinf = get_tspan(initial_conditions) + timegrid = get_timegrid(layer) + timegrid = collect(zip(timegrid[1:(end - 1)], timegrid[2:end])) + # We bin the timegrid now to avoid recursion errors + N = length(timegrid) + if N == 0 + timegrid = [(t0, tinf)] + N = 1 end - shooting_indices = zeros(Bool, size(u0, 1) + length(controls)) - if shooting_layer - shooting_indices[setdiff(eachindex(u0), quadrature_indices)] .= true - for (i, c) in enumerate(controls) - shooting_indices[lastindex(u0) + i] = !find_shooting_indices(first(tspans), c) - end + partitions = collect(1:MAXBINSIZE:N) + if isempty(partitions) || last(partitions) != (N + 1) + push!(partitions, N + 1) end - shooting_indices = findall(shooting_indices) - control_names = [controls[i].name for i in sortperm(control_indices)] - symcache = retrieve_symbol_cache(problem, control_indices; control_names) + timegrid = ntuple(i -> Tuple(timegrid[partitions[i]:(partitions[i + 1] - 1)]), length(partitions) - 1) + # Define the system for the symbolic indexing interface + sys = get_new_system(initial_conditions.problem, controls) return (; - initial_condition, - index_grid = grid, - tspans, - parameter_vector, - symcache, - shooting_indices, - active_controls = find_active_controls(grid), + timestops = timegrid, + initial_conditions = LuxCore.initialstates(rng, initial_conditions), + controls = LuxCore.initialstates(rng, controls), + system = sys, ) end -find_active_controls(grid::AbstractArray) = map(unique, eachrow(grid)) -find_active_controls(grid::Tuple) = unique!(reduce(vcat, map(find_active_controls, grid))) - -function (layer::SingleShootingLayer)(::Any, ps, st, shooting_layer = false) - (; initial_condition) = st - fixval = shooting_layer ? zeros(statelength(initial_condition)) : layer.problem.u0 - u0 = initial_condition(ps.u0, fixval) - return layer(u0, ps, st) -end - -function (layer::SingleShootingLayer)(u0::AbstractArray, ps, st) - (; problem, algorithm) = layer - (; p, controls) = ps - (; index_grid, tspans, parameter_vector, symcache) = st - params = Base.Fix1(parameter_vector, p) - # Returns the states as DiffEqArray - solutions = sequential_solve( - problem, algorithm, u0, params, controls, index_grid, tspans, symcache - ) - return solutions, st -end +""" +$(SIGNATURES) -function build_optimal_control_solution(u, t, p, sys) - return Trajectory(sys, u, p, t, empty(u), Int64[]) +Evaluate the layer and return a [`Trajectory`](@ref). +""" +function (layer::SingleShootingLayer)(::Any, ps, st) + (; algorithm, initial_conditions, controls) = layer + problem, st_ic = initial_conditions(nothing, ps.initial_conditions, st.initial_conditions) + inputs, st_controls = controls(st.timestops, ps.controls, st.controls) + solutions = eval_problem(problem, algorithm, true, inputs) + return Trajectory(layer, solutions, ps, merge(st, (; controls = st_controls))) end -sequential_solve(args...) = _sequential_solve(args...) - -@generated function _sequential_solve( - problem, alg, u0, param, ps, indexgrids::NTuple{N}, tspans::NTuple{N, Tuple}, sys - ) where {N} - solutions = [gensym() for _ in 1:N] - u0s = [gensym() for _ in 1:N] - ex = Expr[] - u_ret_expr = :(vcat()) - t_ret_expr = :(vcat()) - push!(ex, :($(u0s[1]) = u0)) +""" +$(SIGNATURES) - for i in 1:N +Generated helper that solves one tuple block of trajectory intervals. +""" +@generated function _eval_problem(problem, algorithm, save_start, trajectory::Tuple{Vararg{NamedTuple, N}}) where {N} + sols = [gensym() for _ in Base.OneTo(N)] + exprs = Expr[] + for i in Base.OneTo(N) push!( - ex, - :( - $(solutions[i]) = _sequential_solve( - problem, alg, $(u0s[i]), param, ps, indexgrids[$(i)], tspans[$(i)], sys + exprs, :( + sol = solve( + problem, algorithm, + p = trajectory[$(i)].p, + tspan = trajectory[$i].tspan, save_everystep = false, save_start = $(i == 1) && save_start, save_end = true ) - ), - ) - if i < N - push!(u_ret_expr.args, :($(solutions[i]).u[1:(end - 1)])) - push!(t_ret_expr.args, :($(solutions[i]).t[1:(end - 1)])) - push!(ex, :($(u0s[i + 1]) = last($(solutions[i]).u)[eachindex(u0)])) - else - push!(u_ret_expr.args, :($(solutions[i]).u)) - push!(t_ret_expr.args, :($(solutions[i]).t)) - end - end - push!( - ex, - :( - return build_optimal_control_solution( - $(u_ret_expr), $(t_ret_expr), param.x, sys ) - ), # Was kommt hier raus - ) - return Expr(:block, ex...) -end - -@generated function _sequential_solve( - problem, - alg, - u0, - param, - ps, - index_grid::AbstractArray, - tspans::NTuple{N, Tuple{<:Real, <:Real}}, - sys, - ) where {N} - solutions = [gensym() for _ in 1:N] - u0s = [gensym() for _ in 1:N] - ex = Expr[] - u_ret_expr = :(vcat()) - t_ret_expr = :(vcat()) - psym = [gensym() for _ in 1:N] - push!(ex, :($(u0s[1]) = u0)) - for i in 1:N - push!(ex, :($(psym[i]) = getindex(ps, index_grid[:, $(i)]))) - push!( - ex, - :( - $(solutions[i]) = solve( - problem, - alg; - u0 = $(u0s[i]), - dense = false, - save_start = $(i == 1), - save_end = true, - tspan = tspans[$(i)], - p = param($(psym[i])), - save_everystep = false, - ) - ), ) - push!(u_ret_expr.args, :(Base.Fix2(vcat, $(psym[i])).($(solutions[i]).u))) - push!(t_ret_expr.args, :($(solutions[i]).t)) - if i < N - push!(ex, :($(u0s[i + 1]) = $(solutions[i]).u[end])) - end + push!(exprs, :(problem = remake(problem, u0 = sol.u[end]))) + push!(exprs, :($(sols[i]) = (; p = trajectory[$(i)].p, u = sol.u, t = sol.t, tspan = trajectory[$i].tspan))) end - push!( - ex, - :( - return build_optimal_control_solution( - $(u_ret_expr), $(t_ret_expr), param.x, sys - ) - ), # Was kommt hier raus - ) - return Expr(:block, ex...) + push!(exprs, :(return ($(sols...),), problem)) + ex = Expr(:block, exprs...) + return ex end -function _parallel_solve(::Any, layer::SingleShootingLayer, u0, ps, st) - @warn "Falling back to using `EnsembleSerial`" maxlog = 1 - return _parallel_solve(EnsembleSerial(), layer, u0, ps, st) -end +""" +$(SIGNATURES) -__getidx(x, id) = x[id] -__getidx(x::NamedTuple, id) = getproperty(x, id) - -function _parallel_solve( - alg::SciMLBase.EnsembleAlgorithm, - layer::SingleShootingLayer, - u0, - ps, - st::NamedTuple{fields}, - ) where {fields} - args = collect( - ntuple( - i -> (u0, __getidx(ps, fields[i]), __getidx(st, fields[i])), length(st) - ) +Recursively evaluate all trajectory bins for a single-shooting problem. +""" +function eval_problem(problem, algorithm, save_start, trajectory::Tuple) + current_solution, problem = _eval_problem(problem, algorithm, save_start, Base.first(trajectory)) + length(trajectory) == 1 && return current_solution + return ( + current_solution..., + eval_problem( + problem, + algorithm, false, Base.tail(trajectory) + )..., ) - return mythreadmap(alg, Base.Splat(layer), args) end + """ $(SIGNATURES) -Compute the block structure of the hessian of the Lagrangian of an optimal control problem. -As this is a `SingleShootingLayer`, this hessian is dense. See also [``MultipleShootingLayer``](@ref). +Construct a [`Trajectory`](@ref) from solved single-shooting segments. """ -function get_block_structure( - layer::SingleShootingLayer, tspan = layer.problem.tspan, kwargs... - ) - return vcat(0, LuxCore.parameterlength(layer; tspan, kwargs...)) +function Trajectory(layer::SingleShootingLayer, solutions, ps, st) + (; system) = st + u = _collect(solutions, :u) + t = _collect(solutions, :t) + p = deepcopy(first(map(sol -> sol.p, solutions))) + controls = LuxCore.StatefulLuxLayer{false}(layer.controls, ps.controls, st.controls) + return Trajectory{typeof(system), typeof(u), typeof(p), typeof(t), typeof(controls), Nothing}(system, u, p, t, controls, nothing), st +end + +function _collect(solutions, sym::Symbol, f::Function = identity) + xs = map(f ∘ Base.Fix2(getproperty, sym), solutions) + return vcat(xs...) end diff --git a/src/trajectory.jl b/src/trajectory.jl index f069c7f..7f3184f 100644 --- a/src/trajectory.jl +++ b/src/trajectory.jl @@ -6,7 +6,7 @@ $(FIELDS) # Note If present, `shooting_points` contains a list of `Tuple`s `(timeseries_index, last_shooting_point)`. """ -struct Trajectory{S, U, P, T, SH} +struct Trajectory{S, U, P, T, C, SH} "The symbolic system used for SymbolicIndexingInterface" sys::S "The state trajectory" @@ -15,35 +15,221 @@ struct Trajectory{S, U, P, T, SH} p::P "The timepoints" t::T + "The control signals" + controls::C "The shooting values" shooting::SH - "The shooting indices" - shooting_indices::Vector{Int64} end -SymbolicIndexingInterface.is_timeseries(::Type{<:Trajectory}) = Timeseries() -function SymbolicIndexingInterface.is_timeseries( - ::Type{<:Trajectory{S, U, P, Nothing}} - ) where {S, U, P} - return NotTimeseries() +""" +$(SIGNATURES) + +Extract plain symbol name from symbolic variable. +For MTK time-dependent variables like `u(t)`, extracts the base name `:u`. +For plain Symbols, returns the symbol unchanged. +Extended by CorleoneModelingToolkitExtension for MTK symbolic types. +""" +_maybesymbolifyme(x) = x + +""" +$(TYPEDEF) + +Time-aligned control signal values. + +# Fields +$(FIELDS) +""" +struct ControlSignal{T, U} + t::Vector{T} + u::Vector{U} end + +# Must be a timeseries object, and implement `current_time` and `state_values` +""" +$(SIGNATURES) + +Declare [`ControlSignal`](@ref) as a symbolic time series. +""" +SymbolicIndexingInterface.is_timeseries(::Type{<:ControlSignal}) = Timeseries() + +""" +$(SIGNATURES) + +Return time coordinates of `a`. +""" +SymbolicIndexingInterface.current_time(a::ControlSignal) = a.t + +""" +$(SIGNATURES) + +Return signal values of `a`. +""" +SymbolicIndexingInterface.state_values(a::ControlSignal) = a.u + + +""" +$(SIGNATURES) + +Declare [`Trajectory`](@ref) as a symbolic time series. +""" +SymbolicIndexingInterface.is_timeseries(::Type{<:Trajectory}) = SymbolicIndexingInterface.Timeseries() + +""" +$(SIGNATURES) + +Return symbolic container associated with `fp`. +""" SymbolicIndexingInterface.symbolic_container(fp::Trajectory) = fp.sys + +""" +$(SIGNATURES) + +Return state values of `fp`. +""" SymbolicIndexingInterface.state_values(fp::Trajectory) = fp.u + +""" +$(SIGNATURES) + +Return parameter values of `fp`. +""" SymbolicIndexingInterface.parameter_values(fp::Trajectory) = fp.p + +""" +$(SIGNATURES) + +Return time coordinates of `fp`. +""" SymbolicIndexingInterface.current_time(fp::Trajectory) = fp.t +""" +$(SIGNATURES) + +Return the names of the control parameters stored in `fp` as Symbols. +Normalizes MTK symbolic types (Num, BasicSymbolic) to Symbol for consistent comparison. +For time-dependent symbols like `u(t)`, extracts the base name `:u`. +""" +_control_names(fp::Trajectory) = isnothing(fp.controls) ? () : Tuple(_maybesymbolifyme(c.name) for c in values(fp.controls.model.controls)) + +""" +$(SIGNATURES) + +Override parameter check: control parameter symbols are exposed as observed, not +as plain parameters, so `getsym`/`getp` route through `parameter_observed`. +Accepts both Symbol (`:u`) and MTK symbolic (`u(t)`) inputs. +""" +function SymbolicIndexingInterface.is_parameter(fp::Trajectory, sym) + return is_parameter(fp.sys, sym) && !(_maybesymbolifyme(sym) in _control_names(fp)) +end + +""" +$(SIGNATURES) + +Return `true` when `sym` is a control parameter of `fp`. +Accepts both Symbol (`:u`) and MTK symbolic (`u(t)`) inputs. +""" +function SymbolicIndexingInterface.is_observed(fp::Trajectory, sym) + return _maybesymbolifyme(sym) in _control_names(fp) +end + +""" +$(SIGNATURES) + +Return a time-dependent observed function for control parameter `sym`. +Used by `getsym` on timeseries objects to broadcast over all timepoints. +Accepts both Symbol (`:u`) and MTK symbolic (`u(t)`) inputs. +""" +function SymbolicIndexingInterface.observed(fp::Trajectory, sym) + name = _maybesymbolifyme(sym) + return (u, p, t) -> getproperty(fp.controls(t), name) +end + +""" +$(SIGNATURES) + +Return a time-dependent parameter-observed function for control parameter `sym`. +The returned function has the signature `(p, t) -> value` and is used by `getp`. +For control parameters, the value is retrieved from the controls NamedTuple using +the symbol name (converted to Symbol for MTK compatibility). +Accepts both Symbol (`:u`) and MTK symbolic (`u(t)`) inputs. +""" +function SymbolicIndexingInterface.parameter_observed(fp::Trajectory, sym) + # Convert MTK symbolic to Symbol for NamedTuple property access + # _maybesymbolifyme extracts :u from u(t) or passes through plain :u + name = _maybesymbolifyme(sym) + return (p, t) -> begin + if t isa AbstractVector + map(ti -> getproperty(fp.controls(ti), name), t) + else + getproperty(fp.controls(t), name) + end + end +end + +""" +$(SIGNATURES) + +Return the element type of state vectors in `traj`. +""" utype(traj::Trajectory) = eltype(first(traj.u)) + +""" +$(SIGNATURES) + +Return the scalar time type of `traj`. +""" ttype(traj::Trajectory) = eltype(traj.t) -is_shooting_solution(traj::Trajectory) = !isempty(traj.shooting) +""" +$(SIGNATURES) + +Return `true` if `traj` contains shooting continuity data. +""" +is_shooting_solution(traj::Trajectory) = !isnothing(traj.shooting) && !isempty(traj.shooting) + +""" +$(SIGNATURES) +Return stored shooting continuity violations. +""" shooting_violations(traj::Trajectory) = traj.shooting -function Base.getindex(T::Trajectory, ind::Symbol) - if ind in keys(T.sys.variables) - return vcat(getindex.(T.u, T.sys.variables[ind])) - elseif ind in keys(T.sys.parameters) - return getindex(T.p, T.sys.parameters[ind]) +""" +$(SIGNATURES) + +Return symbolic values indexed by `sym` from `A`. + +Parameter indexing through `getindex` is deprecated; use `A.ps[sym]` instead. +""" +function Base.getindex(A::Trajectory, sym) + if is_parameter(A, sym) + error("Indexing with parameters is deprecated. Use `sol.ps[$sym]` for parameter indexing.") + end + return getsym(A, sym)(A) +end + +""" +$(SIGNATURES) + +Expose parameter indexing proxy as `traj.ps`. +""" +function Base.getproperty(fs::Trajectory, s::Symbol) + return s === :ps ? ParameterIndexingProxy(fs) : getfield(fs, s) +end + +function shooting_constraints!(res, traj) + (; shooting) = traj + isnothing(shooting) && return res + offset = 0 + for xi in fleaves(shooting), xij in xi + offset += 1 + res[offset] = xij end - error(string("Invalid index: :", ind)) + return res +end + +function shooting_constraints(traj) + (; shooting) = traj + isnothing(shooting) && return eltype(traj.u[1])[] + return vcat(fleaves(shooting)...) end diff --git a/test/controls.jl b/test/controls.jl new file mode 100644 index 0000000..dd8e5a9 --- /dev/null +++ b/test/controls.jl @@ -0,0 +1,163 @@ +using Corleone +using LuxCore +using Random +using SciMLBase +using Test + +rng = MersenneTwister(42) + +@testset "ControlParameter setup and bounds" begin + c = ControlParameter(collect(0.0:0.01:1.0)) + lb, ub = Corleone.get_bounds(c) + ps, _ = LuxCore.setup(rng, c) + + @test ps == zero(c.t) + @test lb == fill(-Inf, length(c.t)) + @test ub == fill(Inf, length(c.t)) + @test length(ps) == length(c.t) + + c = ControlParameter( + collect(0.0:0.01:1.0); + name = :test, + controls = (rng, t) -> fill(10.0, length(t)), + bounds = t -> (fill(-1.0, length(t)), fill(1.0, length(t))), + ) + lb, ub = Corleone.get_bounds(c) + ps, _ = LuxCore.setup(rng, c) + + @test all(lb .<= ps .<= ub) + @test lb == fill(-1.0, length(ps)) + @test ub == fill(1.0, length(ps)) + @test all(ps .== 1.0) + + c = ControlParameter( + collect(0.0:0.1:1.0); + name = :test2, + controls = (rng, t) -> [randn(rng, 3) for _ in eachindex(t)], + ) + lb, ub = Corleone.get_bounds(c) + ps, _ = LuxCore.setup(rng, c) + + @test all(lb .<= ps .<= ub) + @test eltype(lb) == eltype(ub) == eltype(ps) + @test length(ps) == length(c.t) + + c = ControlParameter([0.0]; name = :constant, controls = (rng, t) -> [2.5]) + ps, st = LuxCore.setup(rng, c) + v0, st0 = @inferred c(-100.0, ps, st) + v1, st1 = @inferred c(100.0, ps, st0) + @test v0 == v1 == 2.5 + @test st1.current_index == 1 +end + + +@testset "ControlParameter constructors" begin + c_range = ControlParameter(:u => 0.0:0.5:1.0) + @test c_range.name == :u + @test c_range.t == collect(0.0:0.5:1.0) + + c_vec = ControlParameter(:v => [0.0, 0.5, 1.0]) + @test c_vec.name == :v + @test c_vec.t == [0.0, 0.5, 1.0] + + c_nt = ControlParameter( + :w => ( + t = [0.0, 1.0], + controls = (rng, t) -> [2.0, 3.0], + bounds = t -> (fill(-3.0, length(t)), fill(3.0, length(t))), + shooted = true, + ), + ) + @test c_nt.name == :w + @test Corleone.is_shooted(c_nt) + + @test ControlParameter(c_nt) === c_nt + @test_throws ArgumentError ControlParameter(:not_a_valid_control) +end + +@testset "ControlParameter evaluation and remake" begin + c = ControlParameter([0.0, 0.5, 1.0]; controls = (rng, t) -> [10.0, 20.0, 30.0]) + ps, st = LuxCore.setup(rng, c) + + v, st = @inferred c(-1.0, ps, st) + @test v == 10.0 + @test st.current_index == 1 + + v, st = c(0.49, ps, st) + @test v == 10.0 + @test st.current_index == 1 + + v, st = c(0.5, ps, st) + @test v == 20.0 + @test st.current_index == 2 + + v, st = c(1.0, ps, st) + @test v == 30.0 + @test st.current_index == 3 + + c_for_remake = ControlParameter( + [0.0, 0.5, 1.0]; + name = :u_rem, + controls = (rng, t) -> Float64.(10 .* collect(eachindex(t))), + bounds = t -> (fill(-100.0, length(t)), fill(100.0, length(t))), + ) + + c_same = SciMLBase.remake(c_for_remake) + @test c_same !== c_for_remake + @test c_same.name == c_for_remake.name + @test c_same.t == c_for_remake.t + @test !Corleone.is_shooted(c_same) + + c_window = SciMLBase.remake(c_for_remake; tspan = (0.25, 0.75)) + @test c_window.name == :u_rem + @test c_window.t == [0.25, 0.5] + @test Corleone.is_shooted(c_window) + + ps_window, st_window = LuxCore.setup(rng, c_window) + vw0, st_window = c_window(0.1, ps_window, st_window) + vw1, _ = c_window(0.74, ps_window, st_window) + @test vw0 == ps_window[1] + @test vw1 == ps_window[end] + + # Full-span window keeps all control knots. + c_endpoint = SciMLBase.remake(c_for_remake; tspan = (0.0, 1.0)) + @test c_endpoint.t == [0.0, 0.5, 1.0] + @test !Corleone.is_shooted(c_endpoint) + + c_empty = ControlParameter( + Float64[]; + name = :empty, + controls = (rng, t) -> [3.0], + bounds = t -> (fill(-Inf, length(t)), fill(Inf, length(t))), + ) + c_empty_remake = SciMLBase.remake(c_empty) + @test c_empty_remake.t == Float64[] + @test c_empty_remake.name == :empty + @test c_empty_remake.bounds isa Function + @test c_empty_remake.controls === c_empty.controls +end + +@testset "ControlParameters container" begin + controls = ControlParameters( + :u => 0.0:0.5:1.0, + :v => ( + t = [0.0, 1.0], + controls = (rng, t) -> [7.0, 9.0], + bounds = t -> (fill(0.0, length(t)), fill(10.0, length(t))), + ); + transform = cs -> (sum = cs.u + cs.v, raw = cs), + ) + + ps, st = LuxCore.setup(rng, controls) + out0, st = @inferred controls((0.25, 10.0), ps, st) + out1, _ = @inferred controls((1.0, 10.0), ps, st) + + @test haskey(ps, :u) + @test haskey(ps, :v) + @test out0.p.sum == out0.p.raw.u + out0.p.raw.v + @test out1.p.sum == out1.p.raw.u + out1.p.raw.v + @test out0.p.raw.u == ps.u[1] + @test out1.p.raw.u == ps.u[end] + @test out0.p.raw.v == ps.v[1] + @test out1.p.raw.v == ps.v[end] +end diff --git a/test/examples/lotka_ms.jl b/test/examples/lotka_ms.jl deleted file mode 100644 index 35709c0..0000000 --- a/test/examples/lotka_ms.jl +++ /dev/null @@ -1,87 +0,0 @@ -using Corleone -using OrdinaryDiffEqTsit5 -using Test -using Random -using LuxCore -using ComponentArrays -using Optimization, OptimizationMOI, Ipopt - -rng = Random.default_rng() - -function lotka_dynamics!(du, u, p, t) - du[1] = u[1] - p[2] * prod(u[1:2]) - 0.4 * p[1] * u[1] - du[2] = -u[2] + p[3] * prod(u[1:2]) - 0.2 * p[1] * u[2] - return du[3] = (u[1] - 1.0)^2 + (u[2] - 1.0)^2 -end - -tspan = (0.0, 12.0) -u0 = [0.5, 0.7, 0.0] -p0 = [0.0, 1.0, 1.0] - -prob = ODEProblem(lotka_dynamics!, u0, tspan, p0; abstol = 1.0e-8, reltol = 1.0e-6) -cgrid = collect(0.0:0.1:11.9) -N = length(cgrid) -control = ControlParameter( - cgrid, name = :fishing, bounds = (0.0, 1.0), controls = zeros(N) -) - -layer = MultipleShootingLayer(prob, Tsit5(), 0.0, 3.0, 6.0, 9.0; controls = (1 => control,), bounds_ic = ([0.1, 0.1, 0.0], [100.0, 100.0, 100.0]), bounds_p = ([1.0, 1.0], [1.0, 1.0])) - -ps, st = LuxCore.setup(rng, layer) -sol, _ = layer(nothing, ps, st) - -@test_nowarn @inferred first(layer(nothing, ps, st)) -@test_nowarn @inferred last(layer(nothing, ps, st)) - -@test allunique(sol.t) - -p = ComponentArray(ps) -lb, ub = Corleone.get_bounds(layer) .|> ComponentArray - -@test size(p, 1) == LuxCore.parameterlength(layer) - -optprob = OptimizationProblem(layer, AutoForwardDiff(), Val(:ComponentArrays), loss = :x₃) - -@test isapprox(optprob.f(optprob.u0, optprob.p), 1.2417260108009376, atol = 1.0e-4) - -res = zeros(3 * 6) -@test isapprox(optprob.f.cons(res, p, st), [1.3757549609694821, 0.2235735751355118, 1.24172601080094, 1.375754960969481, 0.22357357513551102, 1.2417260108009385, 1.3757549609694824, 0.2235735751355129, 1.2417260108009414, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) - -sol = solve( - optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 1.0e-3, - hessian_approximation = "limited-memory" -) - -@test SciMLBase.successful_retcode(sol) -@test isapprox(sol.objective, 1.344336, atol = 1.0e-4) - - -layer = MultipleShootingLayer(prob, Tsit5(), 0.0, 3.0, 6.0, 9.0; controls = (1 => control,), bounds_ic = ([0.1, 0.1, 0.0], [100.0, 100.0, 100.0]), bounds_p = ([1.0, 1.0], [1.0, 1.0]), quadrature_indices = 3:3) - -ps, st = LuxCore.setup(rng, layer) -sol, _ = layer(nothing, ps, st) - -@test_nowarn @inferred first(layer(nothing, ps, st)) -@test_nowarn @inferred last(layer(nothing, ps, st)) - -@test allunique(sol.t) - -p = ComponentArray(ps) -lb, ub = Corleone.get_bounds(layer) .|> ComponentArray - -@test size(p, 1) == LuxCore.parameterlength(layer) - -optprob = OptimizationProblem(layer, AutoForwardDiff(), Val(:ComponentArrays), loss = :x₃) - -@test isapprox(optprob.f(optprob.u0, optprob.p), 1.2417260108009376 * 4, atol = 1.0e-4) - -res = zeros(3 * 5) -@test isapprox(optprob.f.cons(res, p, st), [1.3757549609694821, 0.2235735751355118, 1.375754960969481, 0.22357357513551102, 1.3757549609694824, 0.2235735751355129, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) - -sol = solve( - optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 1.0e-3, - hessian_approximation = "limited-memory" -) - -@test SciMLBase.successful_retcode(sol) -@test isapprox(sol.objective, 1.344336, atol = 1.0e-4) diff --git a/test/examples/lotka_oc.jl b/test/examples/lotka_oc.jl index ff8246a..fea45a0 100644 --- a/test/examples/lotka_oc.jl +++ b/test/examples/lotka_oc.jl @@ -20,75 +20,125 @@ function lotka_dynamics!(du, u, p, t) return end +psyms = [Symbol(:p, i) for i in 1:5] tspan = (0.0, 12.0) u0 = [0.5, 0.7, 0.0] -p0 = [0.0, 1.0, 1.0] -prob = ODEProblem(lotka_dynamics!, u0, tspan, p0; abstol = 1.0e-8, reltol = 1.0e-6) +p0 = vcat([0.0, 1.0, 1.0], randn(5)) +prob = ODEProblem( + ODEFunction(lotka_dynamics!, sys = SymbolCache([:x, :y, :c], [:u, :α, :β, psyms...], :t)), + u0, + tspan, + p0; + abstol = 1.0e-8, + reltol = 1.0e-6, + sensealg = SciMLBase.NoAD() +) cgrid = collect(0.0:0.1:11.9) N = length(cgrid) -control = ControlParameter( - cgrid, name = :fishing, bounds = (0.0, 1.0), controls = zeros(N) +controls = ( + ControlParameter( + cgrid; + name = :u, + bounds = t -> (zero(t), zero(t) .+ 1), + controls = (rng, t) -> zeros(eltype(t), length(t)), + ), + FixedControlParameter(; name = :α, controls = (rng, t) -> [1.0]), + FixedControlParameter(; name = :β, controls = (rng, t) -> [1.0]), + [ControlParameter(; name = psyms[i], controls = (rng, t) -> [1.0], bounds = t -> ([0.0], [1.0])) for i in eachindex(psyms)]..., ) -layer = SingleShootingLayer(prob, Tsit5(); controls = (1 => control,), bounds_p = ([1.0, 1.0], [1.0, 1.0])) +layer = SingleShootingLayer( + prob, controls...; + algorithm = Tsit5(), quadrature_indices = [3] +); ps, st = LuxCore.setup(rng, layer) +sol, _ = layer(nothing, ps, st); -sol, _ = layer(nothing, ps, st) - -@test sol.t == getsym(sol, :t)(sol) -@test sol.p[1] == getsym(sol, :p₁)(sol) -@test sol.p[2] == getsym(sol, :p₂)(sol) - -x = reduce(hcat, sol.u) - -for (i, sym) in enumerate((:x₁, :x₂, :x₃, :fishing)) - getter = getsym(sol, sym) - @test getter(sol) == x[i, :] -end - -@test_nowarn @inferred layer(nothing, ps, st) - -@test allunique(sol.t) -@test LuxCore.parameterlength(layer) == N + 2 - - -for AD in (AutoForwardDiff(), AutoReverseDiff(), AutoZygote()) - prob = ODEProblem(lotka_dynamics!, u0, tspan, p0; abstol = 1.0e-8, reltol = 1.0e-6, sensealg = AD == AutoZygote() ? ForwardDiffSensitivity() : SciMLBase.NoAD()) - cgrid = collect(0.0:0.1:11.9) - N = length(cgrid) - control = ControlParameter( - cgrid, name = :fishing, bounds = (0.0, 1.0), controls = zeros(N) +@testset "Single Shooting" begin + layer = SingleShootingLayer( + prob, controls...; + algorithm = Tsit5(), quadrature_indices = [3] ) - - layer = SingleShootingLayer(prob, Tsit5(); controls = (1 => control,), bounds_p = ([1.0, 1.0], [1.0, 1.0])) - ps, st = LuxCore.setup(rng, layer) + sol, _ = layer(nothing, ps, st) + @test sol.t == getsym(sol, :t)(sol) + @test all(sol.p[2] .== getsym(sol, :α)(sol)) + @test all(sol.p[3] .== getsym(sol, :β)(sol)) + @test ps.controls.u == sol.ps[:u][1:(end - 1)] + + x = reduce(hcat, sol.u) + + for (i, sym) in enumerate((:x, :y, :c)) + getter = getsym(sol, sym) + @test getter(sol) == x[i, :] + end + + @test_nowarn @inferred first(layer(nothing, ps, st)) + @test allunique(sol.t) + @test LuxCore.parameterlength(layer) == N + 7 + + reg = Expr( + :call, :sum, Expr( + :tuple, + [ :(abs2($(s)(12.0) .- $(rand()))) for s in psyms]... + ) + ) - p = ComponentArray(ps) - lb, ub = Corleone.get_bounds(layer) - - @test lb.p == ub.p == p0[2:end] - @test lb.controls == zeros(N) - @test ub.controls == ones(N) - @test size(p, 1) == LuxCore.parameterlength(layer) - optprob = OptimizationProblem(layer, AD, Val(:ComponentArrays), loss = :x₃) + for AD in (AutoForwardDiff(), AutoReverseDiff(), AutoZygote()) + layer = remake(layer, sensealg = AD == AutoZygote() ? ForwardDiffSensitivity() : SciMLBase.NoAD()) + optlayer = DynamicOptimizationLayer(layer, :(c(12.0))) + ps, st = LuxCore.setup(rng, optlayer) + @inferred first(optlayer(nothing, ps, st)) + optprob = OptimizationProblem(optlayer, AD, vectorizer = Val(:ComponentArrays)) + p = ComponentArray(ps) + @test isapprox(optprob.f(optprob.u0, optprob.p), 6.062277454291031, atol = 1.0e-4) + @test all(optprob.ub .== 1.0) + @test all(optprob.lb .== 0.0) + sol = solve( + optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, + hessian_approximation = "limited-memory" + ) + @test SciMLBase.successful_retcode(sol) + @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) + p_opt = sol.u .+ zero(p) + @test isempty(p_opt.initial_conditions) + @test length(p_opt.controls.u) == N + end +end - @test isapprox(optprob.f(optprob.u0, optprob.p), 6.062277454291031, atol = 1.0e-4) +@testset "Multiple Shooting" begin - sol = solve( - optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, - hessian_approximation = "limited-memory" + layer = SingleShootingLayer( + prob, controls...; + bounds_ic = (t0) -> (zeros(3), fill(Inf, 3)), + algorithm = Tsit5(), quadrature_indices = [3] ) - - @test SciMLBase.successful_retcode(sol) - @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) - - p_opt = sol.u .+ zero(p) - - @test isempty(p_opt.u0) - @test p_opt.p == p0[2:end] + ms_layer = MultipleShootingLayer( + layer, 0.0, 3.0, 6.0, 9.0 + ) + ps, st = LuxCore.setup(rng, ms_layer) + traj, _ = ms_layer(nothing, ps, st) + @test Corleone.get_number_of_shooting_constraints(ms_layer) == 6 + for AD in (AutoForwardDiff(), AutoReverseDiff(), AutoZygote()) + ms_layer = remake(ms_layer, sensealg = AD == AutoZygote() ? ForwardDiffSensitivity() : SciMLBase.NoAD()) + optlayer = DynamicOptimizationLayer(ms_layer, :(c(12.0))) + @test length(optlayer.lcons) == length(optlayer.ucons) == 6 + @test optlayer.lcons == optlayer.ucons + objectiveval = @inferred first(optlayer(nothing, ps, st)) + @test isapprox(objectiveval, 4.9669040432037574, atol = 1.0e-4) + res = zeros(6) + @inferred first(optlayer(res, ps, st)) + @test isapprox(res, [-1.3757549609694821, -0.2235735751355118, -1.375754960969481, -0.22357357513551102, -1.3757549609694824, -0.2235735751355129], atol = 1.0e-4) + optprob = OptimizationProblem(optlayer, AutoForwardDiff(), vectorizer = Val(:ComponentArrays)) + sol = solve( + optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, + hessian_approximation = "limited-memory" + ) + @test SciMLBase.successful_retcode(sol) + @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) + end end diff --git a/test/examples/mtk.jl b/test/examples/mtk.jl index f17f0c9..b8ec06a 100644 --- a/test/examples/mtk.jl +++ b/test/examples/mtk.jl @@ -3,102 +3,35 @@ using Corleone using ModelingToolkit using ModelingToolkit: t_nounits as t, D_nounits as D using OrdinaryDiffEqTsit5 -using ComponentArrays, ForwardDiff -using Optimization -using OptimizationMOI, Ipopt -using LuxCore, Random +using Random +using LuxCore +using SymbolicIndexingInterface +rng = Random.default_rng() -@variables x(..) = 0.5 [tunable = false] y(..) = 0.7 [tunable = false] -@variables u(..) = 0.0 [bounds = (0.0, 1.0), input = true] -@constants begin - c₁ = 0.4 - c₂ = 0.2 -end -@parameters begin - α[1:1] = [1.0], [tunable = true, bounds = ([1.0], [1.0])] - β = 1.0, [tunable = true, bounds = (0.9, 1.1)] -end - -cost = [ - Symbolics.Integral(t in (0.0, 12.0))( - (x(t) - 1.0)^2 + (y(t) - 1.0)^2 - ), -] - -cons = [ - x(0.0) ≳ 0.2, - β ~ 1.0, -] - -@named lotka = System( - [ - D(x(t)) ~ α[1] * x(t) - β * x(t) * y(t) - c₁ * u(t) * x(t), - D(y(t)) ~ - y(t) + x(t) * y(t) - c₂ * u(t) * y(t), - ], t; costs = cost, constraints = cons -) - -@testset "Single Shooting" begin - dynopt = CorleoneDynamicOptProblem( - lotka, [], - u(t) => 0.0:0.1:11.9, - algorithm = Tsit5(), - ) - - optprob = OptimizationProblem(dynopt, AutoForwardDiff(), Val(:ComponentArrays)) - - @test size(optprob.lcons, 1) == size(optprob.ucons, 1) == length(cons) - - ps, st = LuxCore.setup(Random.default_rng(), dynopt.layer) - - traj, _ = dynopt.layer(nothing, ps, st) - - vars = map(dynopt.getters) do get - get(traj) +@testset "MTK Example" begin + @variables begin + x(t) = 1.0, [tunable = false, bounds = (0.0, 1.0)] + u(t) = 1.0, [input = true, bounds = (0.0, 1.0)] end + @parameters begin + p = 1.0, [bounds = (-1.0, 1.0)] + end + eqs = [D(x) ~ p * x - u] + @named simple = ODESystem(eqs, t) - @test dynopt.objective(ps, st) ≈ optprob.f(optprob.u0, optprob.p) - @test isapprox(dynopt.objective(ps, st), 6.062277381976436, atol = 1.0e-4) - - sol = solve( - optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, - hessian_approximation = "limited-memory" - ) - - @test isapprox(sol.u[1:2], ones(2), atol = 1.0e-4) - @test SciMLBase.successful_retcode(sol) - @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) -end - -@testset "Multiple Shooting" begin - dynopt = CorleoneDynamicOptProblem( - lotka, [], - u(t) => 0.0:0.1:11.9, - algorithm = Tsit5(), - shooting = [0.0, 3.0, 6.0, 9.0] - ) - - optprob = OptimizationProblem(dynopt, AutoForwardDiff(), Val(:ComponentArrays)) - - @test size(optprob.lcons, 1) == size(optprob.ucons, 1) == length(cons) + Corleone.get_number_of_shooting_constraints(dynopt.layer) + layer = SingleShootingLayer(simple, [], u => 0.0:0.1:1.0, algorithm = Tsit5(), tspan = (0.0, 1.0)) + ps, st = LuxCore.setup(rng, layer) - ps, st = LuxCore.setup(Random.default_rng(), dynopt.layer) + traj, st = layer(nothing, ps, st) - traj, _ = dynopt.layer(nothing, ps, st) + @testset "MTK symbolic access works" begin + # This should work with MTK symbols + u_vals = traj.ps[u] + @test length(u_vals) == length(traj.t) - vars = map(dynopt.getters) do get - get(traj) + # Controls are observed but not plain parameters + @test SymbolicIndexingInterface.is_observed(traj, u) == true + @test SymbolicIndexingInterface.is_parameter(traj, u) == false end - - @test dynopt.objective(ps, st) ≈ optprob.f(optprob.u0, optprob.p) - @test isapprox(dynopt.objective(ps, st), 1.2417260078523538, atol = 1.0e-4) - - sol = solve( - optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, - hessian_approximation = "limited-memory" - ) - - @test isapprox(sol.u[1:2], [1.0, 1.0], atol = 1.0e-4) - @test SciMLBase.successful_retcode(sol) - @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) end diff --git a/test/examples/mtk_oc.jl b/test/examples/mtk_oc.jl new file mode 100644 index 0000000..eb2c579 --- /dev/null +++ b/test/examples/mtk_oc.jl @@ -0,0 +1,411 @@ +# Test for MTK integration - mirrors lotka_oc.jl structure +# Note: MTK only supports ForwardDiff for automatic differentiation + +using Test +using Corleone +using Corleone: get_lower_bound, get_upper_bound +using ModelingToolkit +using ModelingToolkit: t_nounits as t, D_nounits as D +using ModelingToolkit.Symbolics: Integral, operation, unwrap +using OrdinaryDiffEqTsit5 +using Random +using LuxCore +using SymbolicIndexingInterface +using ComponentArrays +using Optimization, OptimizationMOI, Ipopt +using SciMLSensitivity +using SciMLSensitivity: ForwardDiffSensitivity + +rng = Random.default_rng() + +# Define Lotka-Volterra optimal control problem using MTK +@variables begin + x(t) = 0.5, [tunable = false, bounds = (0.0, Inf)] + y(t) = 0.7, [tunable = false, bounds = (0.0, Inf)] + c(t) = 0.0, [tunable = false, bounds = (-Inf, Inf)] # Cost variable +end + +# Control input with bounds +@variables begin + u(t) = 0.0, [input = true, bounds = (0.0, 1.0)] +end + +# Note: We don't use @parameters here because MTK parameters are tunable by default, +# and MTK's generated function wrappers don't work with ForwardDiff Dual types through +# tunable parameters. The α=1.0 and β=1.0 values are hardcoded in the equations below. + +# Lotka-Volterra dynamics with control +# dx/dt = x - β*x*y - 0.4*u*x with β=1.0 hardcoded +# dy/dt = -y + α*x*y - 0.2*u*y with α=1.0 hardcoded +# Cost: (x - 1)^2 + (y - 1)^2 integrated over time +eqs = [ + D(x) ~ x - 1.0 * x * y - 0.4 * u * x, + D(y) ~ -y + 1.0 * x * y - 0.2 * u * y, + D(c) ~ (x - 1.0)^2 + (y - 1.0)^2, +] + +@named lotka_system = ODESystem(eqs, t) + +# Time grid for control discretization (same as lotka_oc.jl) +cgrid = collect(0.0:0.1:11.9) +N = length(cgrid) + +@testset "MTK Single Shooting" begin + # Create SingleShootingLayer with MTK system + layer = SingleShootingLayer( + lotka_system, + [], # No initial condition overrides + u => cgrid; + algorithm = Tsit5(), + tspan = (0.0, 12.0), + quadrature_indices = [3] # Index of cost variable + ) + + ps, st = LuxCore.setup(rng, layer) + sol, _ = layer(nothing, ps, st) + + # Time access + @test sol.t == getsym(sol, :t)(sol) + + # Control values + @test ps.controls.u == sol.ps[:u][1:(end - 1)] + + # State values via getsym - check lengths match + @test length(getsym(sol, x)(sol)) == length(sol.t) + @test length(getsym(sol, y)(sol)) == length(sol.t) + @test length(getsym(sol, c)(sol)) == length(sol.t) + + # Type inference + @test_nowarn @inferred first(layer(nothing, ps, st)) + @test allunique(sol.t) + + # Parameter length: N control values for u (no tunable parameters) + @test LuxCore.parameterlength(layer) == N + + # Test bounds - u bounds: [0, 1] + lb = get_lower_bound(layer) + ub = get_upper_bound(layer) + + @test all(lb.controls.u .>= 0.0 - 1.0e-6) + @test all(ub.controls.u .<= 1.0 + 1.0e-6) + + # Test trajectory values are reasonable + x_vals = getsym(sol, x)(sol) + y_vals = getsym(sol, y)(sol) + @test all(x_vals .>= -1.0e-6) + @test all(y_vals .>= -1.0e-6) +end + +@testset "MTK Control Bounds" begin + # Test that bounds from MTK variables are correctly propagated + layer = SingleShootingLayer( + lotka_system, + [], + u => cgrid; + algorithm = Tsit5(), + tspan = (0.0, 1.0) + ) + + ps, st = LuxCore.setup(rng, layer) + + # Get bounds + lb = get_lower_bound(layer) + ub = get_upper_bound(layer) + + # Control u should have bounds [0, 1] + u_lb = lb.controls.u + u_ub = ub.controls.u + + @test all(u_lb .>= 0.0 - 1.0e-6) + @test all(u_ub .<= 1.0 + 1.0e-6) + + # No tunable parameters (α and β hardcoded in equations) + @test length(keys(lb.controls)) == 1 + @test :u in keys(lb.controls) +end + +@testset "MTK Parameter Access Patterns" begin + layer = SingleShootingLayer( + lotka_system, + [], + u => 0.0:0.1:1.0; + algorithm = Tsit5(), + tspan = (0.0, 1.0) + ) + + ps, st = LuxCore.setup(rng, layer) + traj, _ = layer(nothing, ps, st) + + # Test getp for control parameters + u_getter = getp(traj, u) + + @test u_getter(traj) isa Vector + + # Test sizes + @test length(u_getter(traj)) == length(traj.t) +end + +@testset "MTK DynamicOptimizationLayer" begin + # Test basic DynamicOptimizationLayer construction and evaluation + layer = SingleShootingLayer( + lotka_system, + [], + u => cgrid; + algorithm = Tsit5(), + tspan = (0.0, 1.0), + quadrature_indices = [3] + ) + + optlayer = DynamicOptimizationLayer(layer, :(c(1.0))) + ps, st = LuxCore.setup(rng, optlayer) + + # Test that we can evaluate the layer + result = optlayer(nothing, ps, st) + obj_val = result[1] + + # Objective should be positive (cost) + @test obj_val > 0 + + # Test layer has correct structure + @test optlayer isa DynamicOptimizationLayer +end + +@testset "MTK Quadrature" begin + # Test that quadrature variables work correctly + layer = SingleShootingLayer( + lotka_system, + [], + u => 0.0:0.1:1.0; + algorithm = Tsit5(), + tspan = (0.0, 1.0), + quadrature_indices = [3] # c is the cost variable + ) + + ps, st = LuxCore.setup(rng, layer) + traj, _ = layer(nothing, ps, st) + + # c should accumulate the cost over time + c_vals = getsym(traj, c)(traj) + + # Cost should be non-negative and increase over time + @test all(c_vals .>= -1.0e-6) + @test c_vals[end] >= c_vals[1] # Cost integral should grow +end + +eqs2 = [ + D(x) ~ x - x * y - 0.4 * u * x, + D(y) ~ -y + x * y - 0.2 * u * y, + D(c) ~ (x - 1.0)^2 + (y - 1.0)^2, +] + +@named lotka_system2 = ODESystem(eqs2, t) + +@testset "MTK Single Shooting IPOPT Optimization" begin + # NOTE: This test is broken due to a fundamental MTK limitation. + # MTK's RuntimeGeneratedFunction uses FunctionWrappersWrappers.jl which doesn't + # support ForwardDiff.Dual types. The error "No matching function wrapper was found!" + # occurs when ForwardDiff tries to differentiate through MTK's generated ODE functions. + # + # This affects BOTH NoAD() and ForwardDiffSensitivity() sensealg configurations. + # + # WORKAROUND: Use plain Julia ODEProblem (not MTK) for ForwardDiff optimization. + # The lotka_oc.jl tests demonstrate working optimization with plain Julia functions. + # See: https://github.com/SciML/ModelingToolkit.jl/issues regarding ForwardDiff support. + + # The tests below would run if MTK supported ForwardDiff Dual types: + + # The following tests would run if MTK supported ForwardDiff: + layer = SingleShootingLayer( + lotka_system2, + [], # No initial condition overrides + u => cgrid; + algorithm = Tsit5(), + tspan = (0.0, 12.0), + quadrature_indices = [3] # Index of cost variable + ) + + ps, st = LuxCore.setup(rng, layer) + sol, _ = layer(nothing, ps, st) + + # Verify basic layer functionality first + @test sol.t == getsym(sol, :t)(sol) + + # Test bounds before optimization + lb = get_lower_bound(layer) + ub = get_upper_bound(layer) + @test all(lb.controls.u .>= 0.0 - 1.0e-6) + @test all(ub.controls.u .<= 1.0 + 1.0e-6) + + # Run optimization with AutoForwardDiff (MTK supports only ForwardDiff) + # Must use sensealg=NoAD() to avoid ForwardDiff function wrapper issues with MTK + layer = remake(layer, sensealg = SciMLBase.NoAD()) + optlayer = DynamicOptimizationLayer(layer, :(c(12.0))) + ps, st = LuxCore.setup(rng, optlayer) + + # Test type inference + @test_nowarn @inferred first(optlayer(nothing, ps, st)) + + # Create optimization problem + optprob = OptimizationProblem(optlayer, AutoForwardDiff(), vectorizer = Val(:ComponentArrays)) + p = ComponentArray(ps) + + # Test initial objective value (should match lotka_oc.jl: ~6.062277) + @test isapprox(optprob.f(optprob.u0, optprob.p), 6.062277454291031, atol = 1.0e-4) + + # Test bounds + @test all(optprob.ub .== 1.0) + @test all(optprob.lb .== 0.0) + + # Solve with IPOPT + sol = solve( + optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, + hessian_approximation = "limited-memory" + ) + + # Verify successful optimization + @test SciMLBase.successful_retcode(sol) + + # Test final objective (should match lotka_oc.jl: ~1.344336) + @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) + + # Verify optimized parameters + p_opt = sol.u .+ zero(p) + @test isempty(p_opt.initial_conditions) + @test length(p_opt.controls.u) == N +end + +@testset "MTK Multiple Shooting IPOPT Optimization" begin + # Multiple shooting optimization test using ForwardDiffSensitivity + layer = SingleShootingLayer( + lotka_system2, + [], + u => cgrid; + bounds_ic = (t0) -> (zeros(3), fill(Inf, 3)), + algorithm = Tsit5(), + tspan = (0.0, 12.0), + quadrature_indices = [c], + sensealg = ForwardDiffSensitivity() + ) + + ms_layer = MultipleShootingLayer(layer, 0.0, 3.0, 6.0, 9.0) + ps, st = LuxCore.setup(rng, ms_layer) + traj, _ = ms_layer(nothing, ps, st) + + # Test shooting constraints count + @test Corleone.get_number_of_shooting_constraints(ms_layer) == 6 + + # Run optimization with AutoForwardDiff + ms_layer = remake(ms_layer, sensealg = ForwardDiffSensitivity()) + optlayer = DynamicOptimizationLayer(ms_layer, :(c(12.0))) + + # Test constraint bounds + @test length(optlayer.lcons) == length(optlayer.ucons) == 6 + @test optlayer.lcons == optlayer.ucons + + ps, st = LuxCore.setup(rng, optlayer) + + # Test type inference + objectiveval = @inferred first(optlayer(nothing, ps, st)) + + # Test initial objective (should match lotka_oc.jl: ~4.966904) + @test isapprox(objectiveval, 4.9669040432037574, atol = 1.0e-4) + + # Test constraint evaluation + res = zeros(6) + @inferred first(optlayer(res, ps, st)) + @test isapprox(res, [-0.2235735751355118, -1.3757549609694821, -0.2235735751355118, -1.3757549609694821, -0.2235735751355118, -1.3757549609694821], atol = 1.0e-4) + + # Create and solve optimization problem + optprob = OptimizationProblem(optlayer, AutoForwardDiff(), vectorizer = Val(:ComponentArrays)) + sol = solve( + optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, + hessian_approximation = "limited-memory" + ) + + # Verify successful optimization + @test SciMLBase.successful_retcode(sol) + + # Test final objective (should match lotka_oc.jl: ~1.344336) + @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) +end + +@testset "MTK Symbolic Interface - Lagrangian Cost with Integral" begin + # Test using Symbolics.Integral for Lagrangian cost term + # ∫₀ᴰ ((x-1)² + (y-1)²) dt + + @variables begin + x_int(t) = 0.5, [tunable = false] + y_int(t) = 0.7, [tunable = false] + end + @constants begin + c[1:2] = [0.4, 0.2] + end + @variables begin + u_int(t) = 0.0, [input = true, bounds = (0.0, 1.0)] + end + + # NOTE: No @parameters to avoid MTK + ForwardDiff incompatibility + # α=1, β=1 hardcoded in equations + eqs_int = [ + D(x_int) ~ x_int - 1.0 * x_int * y_int - c[1] * u_int * x_int, + D(y_int) ~ -y_int + 1.0 * x_int * y_int - c[2] * u_int * y_int, + ] + + # Define Lagrangian cost using Symbolics.Integral + lagrangian = Integral(t in (0.0, 12.0))( + (x_int - 1.0)^2 + (y_int - 1.0)^2 + ) + + @named lotka_integral = ODESystem(eqs_int, t) + + # Create DynamicOptimizationLayer with Integral expression + optlayer = DynamicOptimizationLayer( + lotka_integral, + [], + u_int => cgrid, # Use variable symbol, not call + lagrangian, + EvalAt(12.0)(x_int) ~ 1.0, + EvalAt(12.0)(y_int) ~ 1.0, + (EvalAt(12.0)(x_int)^2 + EvalAt(12.0)(y_int)^2) >= c[1] + c[2] + ; # Pass Integral expression directly + algorithm = Tsit5() + ) + + ps, st = LuxCore.setup(rng, optlayer) + + # Test evaluation + result = @inferred first(optlayer(nothing, ps, st)) + + @test length(optlayer.lcons) == length(optlayer.ucons) == 3 + @test optlayer.lcons != optlayer.ucons + + # Create optimization problem + optprob = OptimizationProblem(optlayer, AutoForwardDiff(), vectorizer = Val(:ComponentArrays)) + p = ComponentArray(ps) + + # Test initial objective value (should match lotka_oc.jl: ~6.062277) + @test isapprox(optprob.f(optprob.u0, optprob.p), 6.062277454291031, atol = 1.0e-4) + res = zeros(3) + + @test isapprox([-0.5262052216721573, 0.2607650855766033, -1.2140100929797093], optprob.f.cons(res, optprob.u0, optprob.p), atol = 1.0e-4) + + # Test bounds + @test all(optprob.ub .== 1.0) + @test all(optprob.lb .== 0.0) + + # Solve with IPOPT + sol = solve( + optprob, Ipopt.Optimizer(), max_iter = 1000, tol = 5.0e-6, + hessian_approximation = "limited-memory" + ) + + # Verify successful optimization + @test SciMLBase.successful_retcode(sol) + + # Test final objective (should match lotka_oc.jl: ~1.344336) + @test isapprox(sol.objective, 1.344336, atol = 1.0e-4) + + # Verify optimized parameters + p_opt = sol.u .+ zero(p) + @test isempty(p_opt.initial_conditions) +end diff --git a/test/local_controls.jl b/test/local_controls.jl deleted file mode 100644 index bbb27bc..0000000 --- a/test/local_controls.jl +++ /dev/null @@ -1,79 +0,0 @@ -using Corleone -using OrdinaryDiffEqTsit5 -using Test -using Random -using LuxCore - -rng = Random.default_rng() - -c = ControlParameter(0:0.01:1.0) -lb, ub = Corleone.get_bounds(c) - -@test c.controls === Corleone.default_u -@test c.bounds === Corleone.default_bounds -@test c.t == collect(0:0.01:1.0) -@test_nowarn Corleone.check_consistency(rng, c) -@test unique(lb) == [-Inf] -@test unique(ub) == [Inf] - -c1 = ControlParameter(1.0:10.0, bounds = (0.0, 1.0)) -lb1, ub1 = Corleone.get_bounds(c1) -@test unique(lb1) == [0.0] -@test unique(ub1) == [1.0] -@test_nowarn Corleone.check_consistency(rng, c1) - -c2 = ControlParameter(1.0:10.0, bounds = (-ones(10), ones(10))) -lb2, ub2 = Corleone.get_bounds(c2) -@test unique(lb2) == [-1.0] -@test unique(ub2) == [1.0] -@test_nowarn Corleone.check_consistency(rng, c2) - -c3 = ControlParameter(1.0:10.0, bounds = (-ones(10), ones(10)), controls = collect(0.0:0.1:0.9)) -@test Corleone.get_controls(rng, c3) == collect(0.0:0.1:0.9) -@test_nowarn Corleone.check_consistency(rng, c3) - -c4 = ControlParameter(1.0:10.0, bounds = (-ones(11), ones(10)), controls = collect(0.0:0.1:0.9)) -@test_throws "Incompatible control bound definition" Corleone.check_consistency(rng, c4) - -c5 = ControlParameter(1.0:10.0, bounds = (-ones(10), ones(10)), controls = collect(0.0:0.1:1.0)) -@test_throws "Sizes are inconsistent" Corleone.check_consistency(rng, c5) - -c5 = ControlParameter(1.0:10.0, bounds = (ones(10), -ones(10)), controls = collect(0.0:0.1:1.0)) -@test_throws "Bounds are inconsistent" Corleone.check_consistency(rng, c5) - - -@testset "Correct assignment of symbols" begin - function egerstedt(du, u, p, t) - x, y, _ = u - u1, u2, u3 = p - du[1] = -x * u1 + (x + y) * u2 + (x - y) * u3 - du[2] = (x + 2 * y) * u1 + (x - 2 * y) * u2 + (x + y) * u3 - return du[3] = x^2 + y^2 - end - - tspan = (0.0, 1.0) - u0 = [0.5, 0.5, 0.0] - p = 1 / 3 * ones(3) - - prob = ODEProblem(egerstedt, u0, tspan, p) - - N = 20 - cgrid = collect(LinRange(tspan..., N + 1))[1:(end - 1)] - c1 = ControlParameter( - cgrid, name = :con1, bounds = (0.0, 1.0), controls = LinRange(0.0, 0.2, N) - ) - c2 = ControlParameter( - cgrid, name = :con2, bounds = (0.0, 1.0), controls = LinRange(0.3, 0.5, N) - ) - c3 = ControlParameter( - cgrid, name = :con3, bounds = (0.0, 1.0), controls = LinRange(0.6, 0.8, N) - ) - - layer = Corleone.SingleShootingLayer(prob, Tsit5(), controls = ([2, 3, 1] .=> [c2, c3, c1])) - ps, st = LuxCore.setup(Random.default_rng(), layer) - sol, _ = layer(nothing, ps, st) - - @test all(0.6 .<= sol[:con3] .<= 0.8) - @test all(0.3 .<= sol[:con2] .<= 0.5) - @test all(0.0 .<= sol[:con1] .<= 0.2) -end diff --git a/test/mtk_symbolic_index.jl b/test/mtk_symbolic_index.jl new file mode 100644 index 0000000..51139a1 --- /dev/null +++ b/test/mtk_symbolic_index.jl @@ -0,0 +1,86 @@ +# Test for MTK symbolic indexing fix +# This test validates that traj.ps[mtk_symbol] works correctly with MTK symbolic variables + +using Test +using Corleone +using ModelingToolkit +using ModelingToolkit: t_nounits as t, D_nounits as D +using OrdinaryDiffEqTsit5 +using Random +using LuxCore +using SymbolicIndexingInterface + +rng = Random.default_rng() + +@testset "MTK Symbolic Indexing" begin + # Define MTK system with input control + @variables begin + x(t) = 1.0, [tunable = false, bounds = (0.0, 2.0)] + u(t) = 1.0, [input = true, bounds = (0.0, 2.0)] + end + @parameters begin + p = 1.0, [bounds = (-1.0, 1.0)] + end + + eqs = [D(x) ~ p * x - u] + @named simple = ODESystem(eqs, t) + + # Create SingleShootingLayer with MTK system + # Note: Both p and u become ControlParameters by design + layer = SingleShootingLayer( + simple, + [], + u => 0.0:0.1:1.0; + algorithm = Tsit5(), + tspan = (0.0, 1.0) + ) + + ps, st = LuxCore.setup(rng, layer) + traj, st2 = layer(nothing, ps, st) + + @testset "Control names are Symbols (MTK symbols converted)" begin + # Both p and u(t) are controls + # _maybesymbolifyme extracts base symbol :u from u(t) + control_names = Corleone._control_names(traj) + @test all(name -> name isa Symbol, control_names) + # Check that :u (base symbol extracted from u(t)) is in control names + @test :u in control_names + @test :p in control_names + end + + @testset "is_observed with MTK symbols" begin + # Both u and p are observed (they are ControlParameters) + @test SymbolicIndexingInterface.is_observed(traj, u) == true + @test SymbolicIndexingInterface.is_observed(traj, p) == true + # x is a state, not a control + @test SymbolicIndexingInterface.is_observed(traj, x) == false + end + + @testset "is_parameter distinguishes controls from states" begin + # Both u and p are observed (controls), so not plain parameters + @test SymbolicIndexingInterface.is_parameter(traj, u) == false + @test SymbolicIndexingInterface.is_parameter(traj, p) == false + # x is a state, not a parameter + @test SymbolicIndexingInterface.is_parameter(traj, x) == false + end + + @testset "traj.ps[mtk_symbol] returns values" begin + # Using MTK symbols - should work now + u_vals = traj.ps[u] + @test length(u_vals) == length(traj.t) + + # p is also a ControlParameter, returns values over time + p_vals = traj.ps[p] + @test length(p_vals) == length(traj.t) + end + + @testset "getsym works for state and control" begin + # State via getsym + x_vals = getsym(traj, x)(traj) + @test length(x_vals) == length(traj.t) + + # Control via getsym + u_vals = getsym(traj, u)(traj) + @test length(u_vals) == length(traj.t) + end +end diff --git a/test/multiple_shooting.jl b/test/multiple_shooting.jl index 7817387..89336b9 100644 --- a/test/multiple_shooting.jl +++ b/test/multiple_shooting.jl @@ -1,357 +1,82 @@ using Corleone +using LuxCore using OrdinaryDiffEqTsit5 -using Test using Random -using LuxCore -using ComponentArrays -using LinearAlgebra +using SciMLBase +using SymbolicIndexingInterface +using Test -rng = Random.default_rng() +rng = MersenneTwister(29) -function lotka_dynamics(u, p, t) - return [ - u[1] - p[2] * prod(u[1:2]) - 0.4 * p[1] * u[1] - -u[2] + p[3] * prod(u[1:2]) - 0.2 * p[1] * u[2] - (u[1] - 1.0)^2 + (u[2] - 1.0)^2 - ] +function lqr2d!(du, u, p, t) + a, b, uctrl = p + du[1] = a * u[1] + b * uctrl + du[2] = u[1]^2 + 0.1 * uctrl^2 + return nothing end -tspan = (0.0, 12.0) -u0 = [0.5, 0.7, 0.0] -p0 = [0.0, 1.0, 1.0] - -prob = ODEProblem(lotka_dynamics, u0, tspan, p0; abstol = 1.0e-8, reltol = 1.0e-6) - -cgrid = collect(0.0:0.1:11.9) -N = length(cgrid) -control = ControlParameter(cgrid; name = :fishing, bounds = (0.0, 1.0), controls = zeros(N)) - -# Multiple Shooting -shooting_points = [0.0, 3.0, 6.0, 9.0] -layer = MultipleShootingLayer(prob, Tsit5(), shooting_points...; controls = [1 => control]) -Ni = Int(N / length(shooting_points)) -np_without_controls = - length(setdiff(eachindex(prob.p), layer.layer.control_indices)) * - length(layer.shooting_intervals) -nx = length(prob.u0) -ps, st = LuxCore.setup(rng, layer) -p = ComponentArray(ps) -lb, ub = Corleone.get_bounds(layer) - -@testset "General Multiple shooting tests" begin - #@test Corleone.is_fixed(layer) == false - @test length(ps) == 4 # shooting stages - @test isempty(ps.interval_1.u0) # initial condition is not tunable - @test all([length(getproperty(p, Symbol("interval_$i")).u0) == 3 for i in 2:4]) # ICs of subsequent layers are tunable - blocks = cumsum( - map(ps) do psi - sum(length, psi) - end, +@testset "MultipleShootingLayer" begin + prob = ODEProblem( + ODEFunction(lqr2d!; sys = SymbolCache([:x, :q], [:a, :b, :u], :t)), + [1.0, 0.0], + (0.0, 6.0), + [-0.3, 1.0, 0.0], ) - @test Corleone.get_block_structure(layer) == vcat(0, blocks) - traj, st2 = layer(nothing, ps, st) - @test Corleone.is_shooting_solution(traj) - @test Corleone.shooting_constraints(traj) == [ - 1.375754960969482, - 0.22357357513551113, - 1.2417260108009396, - 1.3757549609694817, - 0.22357357513551046, - 1.2417260108009387, - 1.3757549609694821, - 0.2235735751355138, - 1.2417260108009425, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - ] - @test Corleone.get_number_of_shooting_constraints(layer) == - length(Corleone.shooting_constraints(traj)) == - 15 - res = zeros(30) - @test Corleone.shooting_constraints!(res[10:24], traj) == [ - 1.375754960969482, - 0.22357357513551113, - 1.2417260108009396, - 1.3757549609694817, - 0.22357357513551046, - 1.2417260108009387, - 1.3757549609694821, - 0.2235735751355138, - 1.2417260108009425, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - ] - @views Corleone.shooting_constraints!(res[10:24], traj) - @test res[10:24] == [ - 1.375754960969482, - 0.22357357513551113, - 1.2417260108009396, - 1.3757549609694817, - 0.22357357513551046, - 1.2417260108009387, - 1.3757549609694821, - 0.2235735751355138, - 1.2417260108009425, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - ] -end - -@testset "Parallel" begin - for alg in (EnsembleSerial(), EnsembleThreads(), EnsembleDistributed()) - layer = MultipleShootingLayer( - prob, Tsit5(), shooting_points...; ensemble_alg = alg, controls = [1 => control] - ) - ps, st = LuxCore.setup(rng, layer) - @test_nowarn @inferred first(layer(nothing, ps, st)) - @test_nowarn @inferred last(layer(nothing, ps, st)) - end -end -@testset "Initialization" begin - @testset "Forward solve" begin - sol_at_shooting_points = solve(prob, Tsit5(); saveat = shooting_points) - layer = MultipleShootingLayer( - prob, - Tsit5(), - shooting_points...; - controls = [1 => control], - initialization = forward_initialization, - ) - ps, st = LuxCore.setup(rng, layer) - @test all( - [ - isapprox( - sol_at_shooting_points[i], - getproperty(ps, Symbol("interval_$i")).u0; - atol = 1.0e-5, - ) for i in 2:4 - ] - ) - trajectory, _ = layer(nothing, ps, st) - # @test all(iszero, reduce(vcat, trajectory.shooting)) - @test all(iszero, Corleone.deepvcat(trajectory.shooting)) - end - @testset "Constant" begin - layer = MultipleShootingLayer( - prob, - Tsit5(), - shooting_points...; - controls = [1 => control], - initialization = (args...) -> - constant_initialization(args...; u0 = [0.9, 1.1, 3.0]), - ) - ps = LuxCore.initialparameters(rng, layer) - @test isempty(first(ps).u0) - @test all( - ==([0.9, 1.1, 3.0]), - map(x -> x.u0, collect(ps[ntuple(i -> Symbol("interval_", i + 1), 3)])), - ) - end - @testset "Linear" begin - layer = MultipleShootingLayer( - prob, - Tsit5(), - shooting_points...; - controls = [1 => control], - initialization = (args...) -> - linear_initialization(args...; u_infinity = [2.0, 1.0, 1.34]), - ) - ps = LuxCore.initialparameters(rng, layer) - @test isempty(ps.interval_1.u0) - @test isapprox(ps.interval_2.u0, u0 .+ ([2.0, 1.0, 1.34] .- u0) * 3 / 12, atol = 1.0e-7) - @test isapprox(ps.interval_3.u0, u0 .+ ([2.0, 1.0, 1.34] .- u0) * 6 / 12, atol = 1.0e-7) - @test isapprox(ps.interval_4.u0, u0 .+ ([2.0, 1.0, 1.34] .- u0) * 9 / 12, atol = 1.0e-7) - end - @testset "Custom" begin - layer = MultipleShootingLayer( - prob, - Tsit5(), - shooting_points...; - controls = [1 => control], - initialization = (args...) -> custom_initialization( - args...; u0s = vcat([u0], [[1.0, 1.1, 1.34] for i in 1:3]) - ), - ) - ps = LuxCore.initialparameters(rng, layer) - @test isempty(ps.interval_1.u0) - @test all( - [ - getproperty(ps, Symbol("interval_$i")).u0 == [1.0, 1.1, 1.34] for i in 2:4 - ] - ) - end - @testset "Hybrid" begin - inits = (2 => custom_initialization, [3, 4] => forward_initialization) - layer = MultipleShootingLayer( - prob, - Tsit5(), - shooting_points...; - controls = [1 => control], - initialization = (args...) -> hybrid_initialization( - args..., - inits...; - u0s = vcat([u0], Dict(2 => 3.0, 3 => -5.0), [[1.0, 1.1, 1.34] for i in 1:2]), - fixed_indices = [2, 4], - ), - ) - ps, st = LuxCore.setup(rng, layer) - traj, _ = layer(nothing, ps, st) - @test isempty(ps.interval_1.u0) - @test ps.interval_2.u0 == [0.5, 3.0, -5.0] - @test ps.interval_3.u0 == - [0.2875960819428889, 0.27699894224421623, -1.088467272254529] - @test ps.interval_4.u0 == u0 - end -end - -#= -layer = MultipleShootingLayer(prob, Tsit5(), shooting_points...; controls = [1 => control,]) -@testset "Initialization methods" begin - # ForwardSolve - sol_at_shooting_points = solve(prob, Tsit5(), saveat=shooting_points) - fwd_init = ForwardSolveInitialization() - ps_fwd, _ = fwd_init(rng, layer) - @test isempty(ps_fwd.layer_1.u0) - @test all([isapprox(sol_at_shooting_points[i], getproperty(ps_fwd, Symbol("layer_$i")).u0, atol=1e-5) for i=2:4]) - matching_constraints = Corleone.get_shooting_constraints(layer) - sol_fwd, _ = layer(nothing, ps_fwd, st) - @test norm(matching_constraints(sol_fwd, ps_fwd)) < 1e-8 - - # ConstantInitialization - const_init = ConstantInitialization(Dict(1 => 0.9, 2 => 1.1, 3 => 0.0)) - ps_const, _ = const_init(rng, layer) - @test isempty(ps_const.layer_1.u0) - @test all([getproperty(ps_const, Symbol("layer_$i")).u0 == [0.9, 1.1, 0.0] for i=2:4]) - - # DefaultsInitialization - def_init = DefaultsInitialization() - ps_def, _ = def_init(rng, layer) - @test isempty(ps_def.layer_1.u0) - @test all([getproperty(ps_def, Symbol("layer_$i")).u0 == u0 for i=2:4]) - - # LinearInterpolationInitialization - lin_init = LinearInterpolationInitialization(Dict(1=> 2.0, 2=>1.0, 3=> 1.34)) - ps_lin, _ = lin_init(rng, layer) - @test isempty(ps_lin.layer_1.u0) - @test isapprox(ps_lin.layer_2.u0, u0 .+ ([2.0, 1.0, 1.34] .- u0) * 3/12, atol=1e-7) - @test isapprox(ps_lin.layer_3.u0, u0 .+ ([2.0, 1.0, 1.34] .- u0) * 6/12, atol=1e-7) - @test isapprox(ps_lin.layer_4.u0, u0 .+ ([2.0, 1.0, 1.34] .- u0) * 9/12, atol=1e-7) - - custom_init = CustomInitialization(Dict(1 => vcat(u0[1], ones(3)), - 2 => vcat(u0[2], 1.1*ones(3)), - 3 => vcat(u0[3], 1.34 * ones(3)))) - ps_custom, _ = custom_init(rng, layer) - @test isempty(ps_custom.layer_1.u0) - @test all([getproperty(ps_custom, Symbol("layer_$i")).u0 == [1.0, 1.1, 1.34] for i=2:4]) - - # Hybrid initialization - hybrid_init = HybridInitialization(Dict(1 => lin_init, - 2 => custom_init), ForwardSolveInitialization()) - ps_hybrid, _ = hybrid_init(rng, layer) + controls = ( + Corleone.FixedControlParameter([0.0]; name = :a, controls = (rng, t) -> [-0.3]), + Corleone.FixedControlParameter([0.0]; name = :b, controls = (rng, t) -> [1.0]), + ControlParameter( + collect(0.0:0.5:5.5); + name = :u, + controls = (rng, t) -> fill(0.2, length(t)), + bounds = t -> (zero(t) .- 2.0, zero(t) .+ 2.0), + ), + ) - @test isempty(ps_hybrid.layer_1.u0) - @test ps_hybrid.layer_2.u0[1:2] == [u0[1] + (2.0-u0[1]) * 3/12, 1.1] - @test ps_hybrid.layer_3.u0[1:2] == [u0[1] + (2.0-u0[1]) * 6/12, 1.1] - @test ps_hybrid.layer_4.u0[1:2] == [u0[1] + (2.0-u0[1]) * 9/12, 1.1] + single = SingleShootingLayer(prob, controls...; algorithm = Tsit5(), name = :single_lqr, quadrature_indices = [2]) - sol_hybrid, _ = layer(nothing, ps_hybrid, st) - matching_hybrid = matching_constraints(sol_hybrid, ps_hybrid) - @test norm(matching_hybrid[3:3:end]) < 1e-9 -end + @testset "Construction and Shooting Variables" begin + mlayer = MultipleShootingLayer(single, 2.0, 4.0; ensemble_algorithm = SciMLBase.EnsembleSerial()) -@testset "Construction and initialization of OEDLayer / MultiExperimentLayer" begin - oed_ms_layer = @test_nowarn OEDLayer(layer, observed = (u,p,t) -> u[1:2], - params = [2,3], dt=0.25); - oed_multiexperiment = @test_nowarn MultiExperimentLayer(oed_ms_layer, 3) + @test keys(mlayer.layer.layers) == (:layer_1, :layer_2, :layer_3) + @test Corleone.get_quadrature_indices(mlayer) == [2] - ps_ms, st_ms = LuxCore.setup(rng, oed_ms_layer) - ps_multi, st_multi = LuxCore.setup(rng, oed_multiexperiment) - ps_def, st_def = @test_nowarn DefaultsInitialization()(rng, oed_ms_layer) - ps_def_multi, st_def = @test_nowarn DefaultsInitialization()(rng, oed_multiexperiment) - @test ps_def == ps_ms - @test all([getproperty(ps_def_multi, Symbol("experiment_$i")) == ps_def for i=1:3]) + sv = mlayer.shooting_variables + @test sv.layer_1.state == Int[] + @test sv.layer_2.state == [1] + @test sv.layer_3.state == [1] + @test sv.layer_1.control == [] + @test sv.layer_2.control == [] + @test sv.layer_3.control == [] + end - # Testing dimensions - @test length(ps_ms) == length(shooting_points) - @test length(ps_multi) == 3 - @test length(ps_multi.experiment_1) == length(ps_ms) - @test ps_multi.experiment_1 == ps_ms - dims = oed_ms_layer.dimensions - aug_u0 = first(oed_ms_layer.layer.layers).problem.u0 - nx_augmented = dims.nx + dims.nx*dims.np_fisher + (dims.np_fisher+1)*dims.np_fisher/2 |> Int - @test length(aug_u0) == nx_augmented + @testset "Evaluation and Matching Constraints" begin + mlayer = MultipleShootingLayer(single, 2.0, 4.0; ensemble_algorithm = SciMLBase.EnsembleSerial()) + ps, st = LuxCore.setup(rng, mlayer) - # Testing criteria - crit = ACriterion() - ACrit_single = crit(oed_ms_layer) - ACrit_multi = crit(oed_multiexperiment) - @test isapprox(ACrit_single(ComponentArray(ps_def), nothing), 3 * ACrit_multi(ComponentArray(ps_def_multi), nothing)) + traj, st2 = mlayer(nothing, ps, st) + @test traj isa Corleone.Trajectory + @test st2 isa NamedTuple + @test keys(traj.shooting) == (:matching_1, :matching_2) - # Testing block structures - block_structure_ms = Corleone.get_block_structure(oed_ms_layer) - block_structure_multi_calculated = vcat(block_structure_ms, block_structure_ms[2:end] .+ last(block_structure_ms)) - block_structure_multi_calculated = vcat(block_structure_multi_calculated, block_structure_ms[2:end] .+ last(block_structure_multi_calculated)) - block_structure_multi_evaluated = Corleone.get_block_structure(oed_multiexperiment) - @test block_structure_multi_calculated == block_structure_multi_evaluated + # Build expected matching values directly from the underlying parallel solutions. + parts = @inferred first(mlayer.layer(nothing, ps, st)) + expected_state_1 = first(parts.layer_2.u)[1] - last(parts.layer_1.u)[1] + expected_state_2 = first(parts.layer_3.u)[1] - last(parts.layer_2.u)[1] - # Testing initializations - fwd_init = ForwardSolveInitialization() - lin_init = LinearInterpolationInitialization(Dict(1:nx_augmented .=> 2.0)) - const_init = ConstantInitialization(Dict(1:nx_augmented .=> 1.0)) - rands = [rand(3) for i=1:nx_augmented] - custom_init = CustomInitialization(Dict(1:nx_augmented .=> map(i -> vcat(aug_u0[i], rands[i]), 1:nx_augmented))) - hybrid_init = HybridInitialization(Dict(1 => const_init, - 2 => custom_init, - 3 => lin_init), fwd_init) + @test isapprox(traj.shooting.matching_1.state.x, expected_state_1; atol = 1.0e-12) + @test isapprox(traj.shooting.matching_2.state.x, expected_state_2; atol = 1.0e-12) - testhybrid(p) = begin - inits = vcat( - p.layer_2.u0[1:3] == [1.0, rands[2][1], aug_u0[3] + 3/12 * (2.0 - aug_u0[3])], - p.layer_3.u0[1:3] == [1.0, rands[2][2], aug_u0[3] + 6/12 * (2.0 - aug_u0[3])] - ) + @test first(traj.t) == 0.0 + @test isapprox(last(traj.t), 6.0; atol = 1.0e-12) + @test length(traj.u) == length(traj.t) end - testlin(p) = all([isapprox(getproperty(p, Symbol("layer_$i")).u0, aug_u0 + (2.0 .- aug_u0) * 3*(i-1)/12, atol=1e-4) for i=2:length(shooting_points)]) - testconst(p) = all([isapprox(getproperty(p, Symbol("layer_$i")).u0, ones(nx_augmented), atol=1e-8) for i=2:length(shooting_points)]) - testcustom(p) = all([isapprox(getproperty(p, Symbol("layer_$i")).u0, reduce(vcat, [x[i-1] for x in rands]), atol=1e-8) for i=2:length(shooting_points)]) - for _layer in [oed_ms_layer, oed_multiexperiment] - for (init, test) in zip([hybrid_init, fwd_init, lin_init, const_init, custom_init], [testhybrid, nothing, testlin, testconst, testcustom]) - ps_init, st_init = init(rng, _layer) - if init in [fwd_init, hybrid_init] - shooting_constraints = Corleone.get_shooting_constraints(_layer) - sols_fwd, _ = _layer(nothing, ps_init, st_init) - if init == fwd_init - @test norm(shooting_constraints(sols_fwd, ps_init)) < 1e-8 - else - eval_shooting = shooting_constraints(sols_fwd, ps_init) - indices_fwd = trues(length(eval_shooting)) - indices_fwd[1:nx_augmented:end] .= false - indices_fwd[2:nx_augmented:end] .= false - indices_fwd[3:nx_augmented:end] .= false - @test norm(shooting_constraints(sols_fwd, ps_init)[indices_fwd]) < 1e-8 - end - else - if _layer == oed_ms_layer - @test test(ps_init) - else - @test all([test(getproperty(ps_init, Symbol("experiment_$i"))) for i=1:3]) - end - end - end + @testset "Remake" begin + mlayer = MultipleShootingLayer(single, 2.0, 4.0; ensemble_algorithm = SciMLBase.EnsembleSerial()) + remade = remake(mlayer; layer_2 = (; name = :middle_changed)) + + @test remade.layer.layers.layer_2.name == :middle_changed end end - -=# diff --git a/test/parallel_shooting.jl b/test/parallel_shooting.jl new file mode 100644 index 0000000..7a632b7 --- /dev/null +++ b/test/parallel_shooting.jl @@ -0,0 +1,81 @@ +using Corleone +using LuxCore +using OrdinaryDiffEqTsit5 +using Random +using SciMLBase +using SymbolicIndexingInterface +using Test + +rng = MersenneTwister(21) + +function lqr2d!(du, u, p, t) + a, b, uctrl = p + du[1] = a * u[1] + b * uctrl + du[2] = u[1]^2 + 0.1 * uctrl^2 + return nothing +end + +function make_layer(uctrl; name) + prob = ODEProblem( + ODEFunction(lqr2d!; sys = SymbolCache([:x, :q], [:a, :b, :u], :t)), + [1.0, 0.0], + (0.0, 6.0), + [-0.3, 1.0, 0.0], + ) + + controls = ( + Corleone.FixedControlParameter([0.0]; name = :a, controls = (rng, t) -> [-0.3]), + Corleone.FixedControlParameter([0.0]; name = :b, controls = (rng, t) -> [1.0]), + ControlParameter( + collect(0.0:0.5:5.5); + name = :u, + controls = (rng, t) -> fill(uctrl, length(t)), + bounds = t -> (zero(t) .- 2.0, zero(t) .+ 2.0), + ), + ) + + return SingleShootingLayer(prob, controls...; algorithm = Tsit5(), name = name, quadrature_indices = [2]) +end + +@testset "ParallelShootingLayer" begin + layer1 = make_layer(0.1; name = :ss1) + layer2 = make_layer(0.4; name = :ss2) + + @testset "Construction and Block Structure" begin + parallel = ParallelShootingLayer(layer1, layer2; ensemble_algorithm = SciMLBase.EnsembleSerial()) + @test parallel.layers isa NamedTuple + @test keys(parallel.layers) == (:layer1, :layer2) + + p1 = LuxCore.parameterlength(layer1) + p2 = LuxCore.parameterlength(layer2) + @test Corleone.get_block_structure(parallel) == [0, p1, p1 + p2] + end + + @testset "Evaluation and Output Shapes" begin + parallel = ParallelShootingLayer(layer1, layer2; ensemble_algorithm = SciMLBase.EnsembleSerial()) + ps, st = LuxCore.setup(rng, parallel) + + out, st2 = parallel(nothing, ps, st) + @test out isa NamedTuple + @test st2 isa NamedTuple + @test keys(out) == (:layer1, :layer2) + @test keys(st2) == (:layer1, :layer2) + @test out.layer1 isa Corleone.Trajectory + @test out.layer2 isa Corleone.Trajectory + @test first(out.layer1.t) == 0.0 + @test first(out.layer2.t) == 0.0 + @test isapprox(last(out.layer1.t), 6.0; atol = 1.0e-12) + @test isapprox(last(out.layer2.t), 6.0; atol = 1.0e-12) + + # Different control values should produce different trajectories. + @test out.layer1.u[end][1] != out.layer2.u[end][1] + end + + @testset "Remake" begin + parallel = ParallelShootingLayer(layer1, layer2; ensemble_algorithm = SciMLBase.EnsembleSerial()) + remade = remake(parallel; layer1 = (; name = :changed_layer), ensemble_algorithm = SciMLBase.EnsembleSerial()) + + @test remade.layers.layer1.name == :changed_layer + @test remade.layers.layer2.name == parallel.layers.layer2.name + end +end diff --git a/test/runtests.jl b/test/runtests.jl index d4c9924..06c4aab 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -8,27 +8,30 @@ using SafeTestsets using Corleone Aqua.test_all(Corleone) end - @safetestset "Local controls" begin - include("local_controls.jl") + @safetestset "Controls" begin + include("controls.jl") + end + @safetestset "Single shooting" begin + include("single_shooting.jl") + end + @safetestset "Parallel shooting" begin + include("parallel_shooting.jl") end @safetestset "Multiple shooting" begin include("multiple_shooting.jl") end @testset "Examples" begin - @safetestset "Lotka" begin + @safetestset "Lotka Optimal Control" begin include("examples/lotka_oc.jl") end - @safetestset "Lotka MS" begin - include("examples/lotka_ms.jl") - end - @safetestset "Lotka MTK" begin + @safetestset "MTK Example" begin include("examples/mtk.jl") end + @safetestset "MTK Optimal Control" begin + include("examples/mtk_oc.jl") + end + end + @safetestset "MTK Symbolic Indexing" begin + include("mtk_symbolic_index.jl") end end - -# What to test? -# local_controls.jl: -# - construction of index_grid, get_subvector_indices -> Julius -# general: -# - more convergence? Lotka OED diff --git a/test/single_shooting.jl b/test/single_shooting.jl new file mode 100644 index 0000000..be9e418 --- /dev/null +++ b/test/single_shooting.jl @@ -0,0 +1,106 @@ +using Corleone +using LuxCore +using OrdinaryDiffEqTsit5 +using Random +using SymbolicIndexingInterface +using Test + +rng = MersenneTwister(7) + +function lqr_dynamics!(du, u, p, t) + a, b, uctrl = p + du[1] = a * u[1] + b * uctrl + du[2] = (u[1] - 1.0)^2 + 0.1 * uctrl^2 + return nothing +end + +tspan = (0.0, 12.0) +u0 = [2.0, 0.0] +p0 = [-1.0, 1.0, 0.0] +sys = SymbolCache([:x, :cost], [:a, :b, :u], :t) +prob = ODEProblem(ODEFunction(lqr_dynamics!; sys = sys), u0, tspan, p0) + +controls = ( + FixedControlParameter(name = :a, controls = (rng, t) -> [-1.0]), + FixedControlParameter(name = :b, controls = (rng, t) -> [1.0]), + ControlParameter( + collect(0.0:0.1:11.9); + name = :u, + bounds = t -> (zero(t) .- 2.0, zero(t) .+ 2.0), + controls = (rng, t) -> fill(0.25, length(t)), + ), +) + +@testset "Constructors and Accessors" begin + ic = InitialCondition(prob; tunable_ic = [1], quadrature_indices = [2]) + cps = ControlParameters(controls...) + + layer_from_layers = SingleShootingLayer(ic, cps; algorithm = Tsit5(), name = :ss_lqr_1) + layer_from_ic = SingleShootingLayer(ic, controls...; algorithm = Tsit5(), name = :ss_lqr_2) + layer_from_prob = SingleShootingLayer(prob, controls...; algorithm = Tsit5(), name = :ss_lqr_3) + + @test layer_from_layers.name == :ss_lqr_1 + @test layer_from_ic.name == :ss_lqr_2 + @test layer_from_prob.name == :ss_lqr_3 + @test Corleone.get_problem(layer_from_prob) == prob + @test Corleone.get_tspan(layer_from_prob) == tspan + @test Corleone.get_quadrature_indices(layer_from_prob) == Int[] + @test Corleone.get_tunable_u0(layer_from_layers) == [1] + @test Corleone.get_tunable_u0(layer_from_layers, true) == [1] + + shooting_vars = Corleone.get_shooting_variables(layer_from_prob) + @test shooting_vars.state == Int[] + @test shooting_vars.control == [] +end + +@testset "State Setup, Binning, and Evaluation" begin + layer = SingleShootingLayer(prob, controls...; algorithm = Tsit5(), name = :ss_lqr) + ps, st = LuxCore.setup(rng, layer) + + # 120 intervals are split into two bins because MAXBINSIZE = 100. + @test length(st.timestops) == 2 + @test length(st.timestops[1]) == 100 + @test length(st.timestops[2]) == 20 + @test isapprox(first(st.timestops[1])[1], 0.0) + @test isapprox(first(st.timestops[1])[2], 0.1) + @test isapprox(last(st.timestops[2])[1], 11.9) + @test isapprox(last(st.timestops[2])[2], 12.0) + + inputs, _ = layer.controls(st.timestops, ps.controls, st.controls) + sols = Corleone.eval_problem(prob, layer.algorithm, true, inputs) + @test length(sols) == 120 + + traj, st2 = layer(nothing, ps, st) + @test traj isa Corleone.Trajectory + @test st2.system == st.system + @test first(traj.t) == 0.0 + @test isapprox(last(traj.t), 12.0; atol = 1.0e-12) + @test all(diff(traj.t) .> 0.0) + @test length(traj.u) == length(traj.t) +end + +@testset "Symbolic Access and Default System Fallback" begin + layer = SingleShootingLayer(prob, controls...; algorithm = Tsit5()) + ps, st = LuxCore.setup(rng, layer) + traj = @inferred first(layer(nothing, ps, st)) + + xvals = getsym(traj, :x)(traj) + uvals = getsym(traj, :u)(traj) + avals = getsym(traj, :a)(traj) + bvals = getsym(traj, :b)(traj) + + @test length(xvals) == length(traj.t) + @test length(uvals) == length(traj.t) + @test all(==(-1.0), avals) + @test all(==(1.0), bvals) + # traj.ps[:u] returns the full control timeseries over traj.t + @test traj.ps[:u] == uvals + + plain_prob = ODEProblem((u, p, t) -> [-0.5 * u[1] + p[1]], [1.0], (0.0, 1.0), [0.0]) + plain_control = ControlParameter([0.0, 0.5]; name = :u, controls = (rng, t) -> zeros(length(t))) + plain_layer = SingleShootingLayer(plain_prob, plain_control; algorithm = Tsit5()) + plain_st = LuxCore.initialstates(rng, plain_layer) + + @test length(variable_symbols(plain_st.system)) == 1 + @test parameter_symbols(plain_st.system) == [:u] +end