Merge pull request #4718 from SciML/as/codegen-options

refactor: use `Symbolics.CodegenOptions`, cleanup kwargs

Author: AayushSabharwal

Project.toml

@@ -122,7 +122,7 @@ StaticArrays = "1.9.14"
 StochasticDiffEq = "6.82.0, 7"
 SymbolicIndexingInterface = "0.3.46"
 SymbolicUtils = "4.31"
-Symbolics = "7"
+Symbolics = "7.30"
 UnPack = "0.1, 1.0"
 julia = "1.10"
 BenchmarkTools = "1"

lib/ModelingToolkitBase/Project.toml

@@ -188,7 +188,7 @@ StochasticDiffEq = "6.82.0, 7"
 Sundials = "6"
 SymbolicIndexingInterface = "0.3.39"
 SymbolicUtils = "4.35.3"
-Symbolics = "7.24"
+Symbolics = "7.30"
 TaskLocalValues = "0.1.3"
 Test = "1"
 Tracker = "0.2.30"

lib/ModelingToolkitBase/src/inputoutput.jl

@@ -274,7 +274,7 @@ function generate_control_function(
     )
 
     if !isscheduled(sys)
-        sys = mtkcompile(sys; inputs, disturbance_inputs = all_disturbances, split)
+        sys = mtkcompile(sys; inputs, disturbance_inputs = all_disturbances, split, simplify)
     end
 
     # Add all disturbances to inputs for the purposes of io processing

lib/ModelingToolkitBase/src/problems/bvproblem.jl

@@ -1,6 +1,6 @@
 @fallback_iip_specialize function SciMLBase.BVProblem{iip, spec}(
         sys::System, op, tspan;
-        check_compatibility = true, cse = true,
+        check_compatibility = true,
         checkbounds = false, eval_expression = false, eval_module = @__MODULE__,
         expression = Val{false}, guesses = Dict(), callback = nothing,
         kwargs...
@@ -19,21 +19,21 @@
     fode, u0,
         p = process_SciMLProblem(
         ODEFunction{_iip, spec}, sys, _op; guesses,
-        t = tspan !== nothing ? tspan[1] : tspan, check_compatibility = false, cse,
+        t = tspan !== nothing ? tspan[1] : tspan, check_compatibility = false,
         checkbounds, time_dependent_init = false, expression, kwargs...
     )
 
     fcost = generate_bvp_cost(
         sys; expression = Val{false}, wrap_gfw = Val{false},
-        eval_expression, eval_module, cse, checkbounds
+        eval_expression, eval_module, checkbounds
     )
 
     stidxmap = Dict([v => i for (i, v) in enumerate(dvs)])
     u0_idxs = has_alg_eqs(sys) ? collect(1:length(dvs)) :
         [stidxmap[k] for (k, v) in op if haskey(stidxmap, k)]
     fbc = generate_boundary_conditions(
         sys, u0, u0_idxs, tspan[1]; expression = Val{false},
-        wrap_gfw = Val{true}, cse, checkbounds
+        wrap_gfw = Val{true}, checkbounds
     )
 
     n_controls = length(default_codegen_inputs(sys))

lib/ModelingToolkitBase/src/problems/daeproblem.jl

@@ -2,15 +2,15 @@
         sys::System; u0 = nothing, p = nothing, tgrad = false, jac = false,
         t = nothing, eval_expression = false, eval_module = @__MODULE__, sparse = false,
         steady_state = false, checkbounds = false, sparsity = false, analytic = nothing,
-        simplify = false, cse = true, initialization_data = nothing,
+        simplify = false, initialization_data = nothing,
         expression = Val{false}, check_compatibility = true, kwargs...
     ) where {iip, spec}
     check_complete(sys, DAEFunction)
     check_compatibility && check_compatible_system(DAEFunction, sys)
 
     f = generate_rhs(
         sys; expression, wrap_gfw = Val{true},
-        implicit_dae = true, eval_expression, eval_module, checkbounds = checkbounds, cse,
+        implicit_dae = true, eval_expression, eval_module, checkbounds = checkbounds,
         kwargs...
     )
 
@@ -28,15 +28,15 @@
     if jac
         _jac = generate_dae_jacobian(
             sys; expression,
-            wrap_gfw = Val{true}, simplify, sparse, cse, eval_expression, eval_module,
+            wrap_gfw = Val{true}, simplify, sparse, eval_expression, eval_module,
             checkbounds, kwargs...
         )
     else
         _jac = nothing
     end
 
     observedfun = ObservedFunctionCache(
-        sys; expression, steady_state, eval_expression, eval_module, checkbounds, cse
+        sys; expression, steady_state, eval_expression, eval_module, checkbounds
     )
 
     jac_prototype = if sparse

lib/ModelingToolkitBase/src/problems/ddeproblem.jl

@@ -1,15 +1,15 @@
 @fallback_iip_specialize function SciMLBase.DDEFunction{iip, spec}(
         sys::System; u0 = nothing, p = nothing, eval_expression = false,
         eval_module = @__MODULE__, expression = Val{false}, checkbounds = false,
-        initialization_data = nothing, cse = true, check_compatibility = true,
+        initialization_data = nothing, check_compatibility = true,
         sparse = false, simplify = false, analytic = nothing, kwargs...
     ) where {iip, spec}
     check_complete(sys, DDEFunction)
     check_compatibility && check_compatible_system(DDEFunction, sys)
 
     f = generate_rhs(
         sys; expression, wrap_gfw = Val{true},
-        eval_expression, eval_module, checkbounds = checkbounds, cse,
+        eval_expression, eval_module, checkbounds = checkbounds,
         kwargs...
     )
 
@@ -28,7 +28,7 @@
     _M = concrete_massmatrix(M; sparse, u0)
 
     observedfun = ObservedFunctionCache(
-        sys; expression, eval_expression, eval_module, checkbounds, cse
+        sys; expression, eval_expression, eval_module, checkbounds
     )
 
     kwargs = (;
@@ -45,7 +45,7 @@ end
 
 @fallback_iip_specialize function SciMLBase.DDEProblem{iip, spec}(
         sys::System, op, tspan;
-        callback = nothing, check_length = true, cse = true, checkbounds = false,
+        callback = nothing, check_length = true, checkbounds = false,
         eval_expression = false, eval_module = @__MODULE__, check_compatibility = true,
         u0_constructor = identity, expression = Val{false}, kwargs...
     ) where {iip, spec}
@@ -56,13 +56,13 @@ end
     f, u0,
         p = process_SciMLProblem(
         DDEFunction{_iip, spec}, sys, op;
-        t = tspan !== nothing ? tspan[1] : tspan, check_length, cse, checkbounds,
+        t = tspan !== nothing ? tspan[1] : tspan, check_length, checkbounds,
         eval_expression, eval_module, check_compatibility, symbolic_u0 = true,
         expression, u0_constructor, kwargs...
     )
 
     h = generate_history(
-        sys, u0; expression, wrap_gfw = Val{true}, cse, eval_expression, eval_module,
+        sys, u0; expression, wrap_gfw = Val{true}, eval_expression, eval_module,
         checkbounds
     )
 

lib/ModelingToolkitBase/src/problems/discreteproblem.jl

@@ -1,7 +1,7 @@
 @fallback_iip_specialize function SciMLBase.DiscreteFunction{iip, spec}(
         sys::System; u0 = nothing, p = nothing, t = nothing,
         eval_expression = false, eval_module = @__MODULE__, expression = Val{false},
-        checkbounds = false, analytic = nothing, simplify = false, cse = true,
+        checkbounds = false, analytic = nothing, simplify = false,
         initialization_data = nothing, check_compatibility = true,
         kwargs...
     ) where {iip, spec}
@@ -10,7 +10,7 @@
 
     f = generate_rhs(
         sys; expression, wrap_gfw = Val{true},
-        eval_expression, eval_module, checkbounds = checkbounds, cse,
+        eval_expression, eval_module, checkbounds = checkbounds,
         kwargs...
     )
 
@@ -26,8 +26,7 @@
     end
 
     observedfun = ObservedFunctionCache(
-        sys; steady_state = false, expression, eval_expression, eval_module, checkbounds,
-        cse
+        sys; steady_state = false, expression, eval_expression, eval_module, checkbounds
     )
 
     kwargs = (;

lib/ModelingToolkitBase/src/problems/docs.jl

@@ -248,7 +248,6 @@ function function_docstring(
     $EVAL_EXPR_MOD_KWARGS
     - `checkbounds`: Whether to enable bounds checking in the generated code.
     - `simplify`: Whether to `simplify` any symbolically computed jacobians/hessians/etc.
-    - `cse`: Whether to enable Common Subexpression Elimination (CSE) on the generated code.
       This typically improves performance of the generated code but reduces readability.
     - `sparse`: Whether to generate jacobian/hessian/etc. functions that return/operate on
       sparse matrices. Also controls whether the mass matrix is sparse, wherever applicable.
@@ -387,7 +386,6 @@ subtype indicating the level of specialization of the $func.
 $EVAL_EXPR_MOD_KWARGS
 - `checkbounds`: Whether to enable bounds checking in the generated code.
 - `simplify`: Whether to `simplify` any symbolically computed jacobians/hessians/etc.
-- `cse`: Whether to enable Common Subexpression Elimination (CSE) on the generated code.
   This typically improves performance of the generated code but reduces readability.
 - `fraction_cancel_fn`: The function to use to simplify fractions in the polynomial
   expression. A more powerful function can increase processing time but be able to

lib/ModelingToolkitBase/src/problems/implicitdiscreteproblem.jl

@@ -1,7 +1,7 @@
 @fallback_iip_specialize function SciMLBase.ImplicitDiscreteFunction{iip, spec}(
         sys::System; u0 = nothing, p = nothing, t = nothing, eval_expression = false,
         eval_module = @__MODULE__, expression = Val{false},
-        checkbounds = false, analytic = nothing, simplify = false, cse = true,
+        checkbounds = false, analytic = nothing, simplify = false,
         initialization_data = nothing, check_compatibility = true, kwargs...
     ) where {
         iip, spec,
@@ -13,7 +13,7 @@
     dvs = unknowns(sys)
     f = generate_rhs(
         sys; expression, wrap_gfw = Val{true},
-        implicit_dae = true, eval_expression, eval_module, checkbounds = checkbounds, cse,
+        implicit_dae = true, eval_expression, eval_module, checkbounds = checkbounds,
         override_discrete = true, kwargs...
     )
 
@@ -31,7 +31,7 @@
     end
 
     observedfun = ObservedFunctionCache(
-        sys; steady_state = false, expression, eval_expression, eval_module, checkbounds, cse
+        sys; steady_state = false, expression, eval_expression, eval_module, checkbounds
     )
 
     args = (; f)

lib/ModelingToolkitBase/src/problems/initializationproblem.jl

@@ -24,7 +24,13 @@ All other keyword arguments are forwarded to the wrapped problem constructor.
         t, op = Dict();
         fast_path = false,
         guesses = [],
-        check_length = true,
+        # `check_length` defaults to `nothing`, meaning "no opinion — let the underlying
+        # problem constructor apply its own default". It is only forwarded below when the
+        # caller explicitly sets it. This matters because the initialization problem types
+        # have different defaults (`NonlinearProblem` uses `true` for square systems,
+        # `NonlinearLeastSquaresProblem` uses `false` for non-square); forwarding a single
+        # value unconditionally would override and break one of those cases.
+        check_length = nothing,
         warn_initialize_determined = true,
         initialization_eqs = [],
         fully_determined = nothing,
@@ -144,7 +150,13 @@ All other keyword arguments are forwarded to the wrapped problem constructor.
         sys, isys; warn_initialize_determined,
         kwargs...
     )
-    TProb{_iip}(isys, op; kwargs..., build_initializeprob = false, is_initializeprob = true)
+    # Only forward `check_length` when the caller explicitly set it; otherwise let the
+    # underlying problem type apply its own default (see the keyword's definition above).
+    check_length_kw = check_length === nothing ? (;) : (; check_length)
+    TProb{_iip}(
+        isys, op; kwargs..., check_length_kw...,
+        build_initializeprob = false, is_initializeprob = true
+    )
 end
 
 function overdetermined_initialization_message(neqs::Integer, nunknown::Integer, extra::AbstractString)