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mathoptinterface_api.jl
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mathoptinterface_api.jl
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# Copyright (c) 2017: Miles Lubin and contributors
# Copyright (c) 2017: Google Inc.
#
# Use of this source code is governed by an MIT-style license that can be found
# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
_no_hessian(op::MOI.Nonlinear._UnivariateOperator) = op.f′′ === nothing
_no_hessian(op::MOI.Nonlinear._MultivariateOperator) = op.∇²f === nothing
function MOI.features_available(d::NLPEvaluator)
# Check if we are missing any hessians for user-defined operators, in which
# case we need to disable :Hess and :HessVec.
d.disable_2ndorder =
any(_no_hessian, d.data.operators.registered_univariate_operators) ||
any(_no_hessian, d.data.operators.registered_multivariate_operators)
if d.disable_2ndorder
return [:Grad, :Jac, :JacVec]
end
return [:Grad, :Jac, :JacVec, :Hess, :HessVec]
end
function MOI.initialize(d::NLPEvaluator, requested_features::Vector{Symbol})
# Check that we support the features requested by the user.
available_features = MOI.features_available(d)
for feature in requested_features
if !(feature in available_features)
error("Unsupported feature $feature")
end
end
moi_index_to_consecutive_index =
Dict(x => i for (i, x) in enumerate(d.ordered_variables))
N = length(moi_index_to_consecutive_index)
#
largest_user_input_dimension = 1
for op in d.data.operators.registered_multivariate_operators
largest_user_input_dimension = max(largest_user_input_dimension, op.N)
end
d.objective = nothing
d.user_output_buffer = zeros(largest_user_input_dimension)
d.jac_storage = zeros(max(N, largest_user_input_dimension))
d.constraints = _FunctionStorage[]
d.last_x = fill(NaN, N)
d.want_hess = :Hess in requested_features
want_hess_storage = (:HessVec in requested_features) || d.want_hess
coloring_storage = Coloring.IndexedSet(N)
max_expr_length = 0
#
main_expressions = [c.expression.nodes for (_, c) in d.data.constraints]
if d.data.objective !== nothing
pushfirst!(main_expressions, something(d.data.objective).nodes)
end
d.subexpression_order, individual_order = _order_subexpressions(
main_expressions,
[expr.nodes for expr in d.data.expressions],
)
num_subexpressions = length(d.data.expressions)
d.subexpression_linearity = Vector{Linearity}(undef, num_subexpressions)
subexpression_variables = Vector{Vector{Int}}(undef, num_subexpressions)
subexpression_edgelist =
Vector{Set{Tuple{Int,Int}}}(undef, num_subexpressions)
d.subexpressions = Vector{_SubexpressionStorage}(undef, num_subexpressions)
d.subexpression_forward_values = zeros(num_subexpressions)
d.subexpression_reverse_values = zeros(num_subexpressions)
for k in d.subexpression_order
# Only load expressions which actually are used
d.subexpression_forward_values[k] = NaN
subex = _SubexpressionStorage(
d.data.expressions[k],
d.subexpression_linearity,
moi_index_to_consecutive_index,
)
d.subexpressions[k] = subex
d.subexpression_linearity[k] = subex.linearity
if d.want_hess
empty!(coloring_storage)
_compute_gradient_sparsity!(coloring_storage, subex.nodes)
# union with all dependent expressions
for idx in _list_subexpressions(subex.nodes)
union!(coloring_storage, subexpression_variables[idx])
end
subexpression_variables[k] = collect(coloring_storage)
empty!(coloring_storage)
linearity = _classify_linearity(
subex.nodes,
subex.adj,
d.subexpression_linearity,
)
edgelist = _compute_hessian_sparsity(
subex.nodes,
subex.adj,
linearity,
coloring_storage,
subexpression_edgelist,
subexpression_variables,
)
subexpression_edgelist[k] = edgelist
end
end
max_chunk = 1
if d.data.objective !== nothing
objective = _FunctionStorage(
main_expressions[1],
something(d.data.objective).values,
N,
coloring_storage,
d.want_hess,
d.subexpressions,
individual_order[1],
d.subexpression_linearity,
subexpression_edgelist,
subexpression_variables,
moi_index_to_consecutive_index,
)
max_expr_length = max(max_expr_length, length(objective.nodes))
max_chunk = max(max_chunk, size(objective.seed_matrix, 2))
d.objective = objective
end
for (k, (_, constraint)) in enumerate(d.data.constraints)
idx = d.data.objective !== nothing ? k + 1 : k
push!(
d.constraints,
_FunctionStorage(
main_expressions[idx],
constraint.expression.values,
N,
coloring_storage,
d.want_hess,
d.subexpressions,
individual_order[idx],
d.subexpression_linearity,
subexpression_edgelist,
subexpression_variables,
moi_index_to_consecutive_index,
),
)
max_expr_length = max(max_expr_length, length(d.constraints[end].nodes))
max_chunk = max(max_chunk, size(d.constraints[end].seed_matrix, 2))
end
# 10 is hardcoded upper bound to avoid excess memory allocation
max_chunk = min(max_chunk, 10)
if d.want_hess || want_hess_storage
d.input_ϵ = zeros(max_chunk * N)
d.output_ϵ = zeros(max_chunk * N)
#
len = max_chunk * max_expr_length
d.forward_storage_ϵ = zeros(len)
d.partials_storage_ϵ = zeros(len)
d.reverse_storage_ϵ = zeros(len)
#
len = max_chunk * length(d.subexpressions)
d.subexpression_forward_values_ϵ = zeros(len)
d.subexpression_reverse_values_ϵ = zeros(len)
#
for k in d.subexpression_order
len = max_chunk * length(d.subexpressions[k].nodes)
d.subexpressions[k].forward_storage_ϵ = zeros(len)
d.subexpressions[k].partials_storage_ϵ = zeros(len)
d.subexpressions[k].reverse_storage_ϵ = zeros(len)
end
d.max_chunk = max_chunk
if d.want_hess
d.hessian_sparsity = Tuple{Int64,Int64}[]
obj = d.objective
if obj !== nothing
append!(d.hessian_sparsity, zip(obj.hess_I, obj.hess_J))
end
for c in d.constraints
append!(d.hessian_sparsity, zip(c.hess_I, c.hess_J))
end
end
end
return
end
function MOI.eval_objective(d::NLPEvaluator, x)
if d.objective === nothing
error("No nonlinear objective.")
end
_reverse_mode(d, x)
return something(d.objective).forward_storage[1]
end
function MOI.eval_objective_gradient(d::NLPEvaluator, g, x)
if d.objective === nothing
error("No nonlinear objective.")
end
_reverse_mode(d, x)
fill!(g, 0.0)
_extract_reverse_pass(g, d, something(d.objective))
return
end
function MOI.eval_constraint(d::NLPEvaluator, g, x)
_reverse_mode(d, x)
for i in 1:length(d.constraints)
g[i] = d.constraints[i].forward_storage[1]
end
return
end
function MOI.jacobian_structure(d::NLPEvaluator)
J = Tuple{Int64,Int64}[]
for (row, constraint) in enumerate(d.constraints)
for col in constraint.grad_sparsity
push!(J, (row, col))
end
end
return J
end
function MOI.constraint_gradient_structure(d::NLPEvaluator, i)
return copy(d.constraints[i].grad_sparsity)
end
function MOI.eval_constraint_gradient(d::NLPEvaluator, ∇g, x, i)
_reverse_mode(d, x)
for k in d.constraints[i].grad_sparsity
d.jac_storage[k] = 0.0
end
_extract_reverse_pass(d.jac_storage, d, d.constraints[i])
for (j, k) in enumerate(d.constraints[i].grad_sparsity)
∇g[j] = d.jac_storage[k]
end
return
end
function MOI.eval_constraint_jacobian(d::NLPEvaluator, J, x)
_reverse_mode(d, x)
fill!(J, 0.0)
offset = 0
for ex in d.constraints
for i in ex.grad_sparsity
d.jac_storage[i] = 0.0
end
_extract_reverse_pass(d.jac_storage, d, ex)
for (k, idx) in enumerate(ex.grad_sparsity)
J[offset+k] = d.jac_storage[idx]
end
offset += length(ex.grad_sparsity)
end
return
end
function MOI.eval_constraint_jacobian_product(d::NLPEvaluator, y, x, w)
_reverse_mode(d, x)
fill!(y, 0.0)
for (row, expr) in enumerate(d.constraints)
for col in expr.grad_sparsity
d.jac_storage[col] = 0.0
end
_extract_reverse_pass(d.jac_storage, d, expr)
for col in expr.grad_sparsity
y[row] += d.jac_storage[col] * w[col]
end
end
return
end
function MOI.eval_constraint_jacobian_transpose_product(
d::NLPEvaluator,
y::AbstractVector{Float64},
x::AbstractVector{Float64},
w::AbstractVector{Float64},
)
_reverse_mode(d, x)
fill!(y, 0.0)
for (row, expr) in enumerate(d.constraints)
for col in expr.grad_sparsity
d.jac_storage[col] = 0.0
end
_extract_reverse_pass(d.jac_storage, d, expr)
for col in expr.grad_sparsity
y[col] += d.jac_storage[col] * w[row]
end
end
return
end
function MOI.hessian_objective_structure(d::NLPEvaluator)
@assert d.want_hess
ret = Tuple{Int64,Int64}[]
obj = d.objective
if obj !== nothing
for (i, j) in zip(obj.hess_I, obj.hess_J)
push!(ret, (i, j))
end
end
return ret
end
function MOI.hessian_constraint_structure(d::NLPEvaluator, c::Integer)
@assert d.want_hess
con = d.constraints[c]
return Tuple{Int64,Int64}[(i, j) for (i, j) in zip(con.hess_I, con.hess_J)]
end
function MOI.hessian_lagrangian_structure(d::NLPEvaluator)
@assert d.want_hess
return d.hessian_sparsity
end
function MOI.eval_hessian_objective(d::NLPEvaluator, H, x)
@assert d.want_hess
_reverse_mode(d, x)
fill!(d.input_ϵ, 0.0)
if d.objective !== nothing
_eval_hessian(d, something(d.objective), H, 1.0, 0)
end
return
end
function MOI.eval_hessian_constraint(d::NLPEvaluator, H, x, i)
@assert d.want_hess
_reverse_mode(d, x)
fill!(d.input_ϵ, 0.0)
_eval_hessian(d, d.constraints[i], H, 1.0, 0)
return
end
function MOI.eval_hessian_lagrangian(d::NLPEvaluator, H, x, σ, μ)
@assert d.want_hess
_reverse_mode(d, x)
fill!(d.input_ϵ, 0.0)
offset = 0
if d.objective !== nothing
offset += _eval_hessian(d, something(d.objective), H, σ, offset)::Int
end
for (i, ex) in enumerate(d.constraints)
offset += _eval_hessian(d, ex, H, μ[i], offset)::Int
end
return
end
function MOI.eval_hessian_lagrangian_product(d::NLPEvaluator, h, x, v, σ, μ)
_reverse_mode(d, x)
fill!(h, 0.0)
T = ForwardDiff.Partials{1,Float64}
input_ϵ = reinterpret(T, d.input_ϵ)
output_ϵ = reinterpret(T, d.output_ϵ)
for i in 1:length(x)
input_ϵ[i] = ForwardDiff.Partials((v[i],))
end
# forward evaluate all subexpressions once
subexpr_forward_values_ϵ = reinterpret(T, d.subexpression_forward_values_ϵ)
for i in d.subexpression_order
subexpr = d.subexpressions[i]
subexpr_forward_values_ϵ[i] = _forward_eval_ϵ(
d,
subexpr,
reinterpret(T, subexpr.forward_storage_ϵ),
reinterpret(T, subexpr.partials_storage_ϵ),
input_ϵ,
subexpr_forward_values_ϵ,
d.data.operators,
)
end
# we only need to do one reverse pass through the subexpressions as well
subexpr_reverse_values_ϵ = reinterpret(T, d.subexpression_reverse_values_ϵ)
fill!(subexpr_reverse_values_ϵ, zero(T))
fill!(d.subexpression_reverse_values, 0.0)
fill!(d.reverse_storage_ϵ, 0.0)
fill!(output_ϵ, zero(T))
if d.objective !== nothing
_forward_eval_ϵ(
d,
something(d.objective),
reinterpret(T, d.forward_storage_ϵ),
reinterpret(T, d.partials_storage_ϵ),
input_ϵ,
subexpr_forward_values_ϵ,
d.data.operators,
)
_reverse_eval_ϵ(
output_ϵ,
something(d.objective),
reinterpret(T, d.reverse_storage_ϵ),
reinterpret(T, d.partials_storage_ϵ),
d.subexpression_reverse_values,
subexpr_reverse_values_ϵ,
σ,
zero(T),
)
end
for (i, con) in enumerate(d.constraints)
_forward_eval_ϵ(
d,
con,
reinterpret(T, d.forward_storage_ϵ),
reinterpret(T, d.partials_storage_ϵ),
input_ϵ,
subexpr_forward_values_ϵ,
d.data.operators,
)
_reverse_eval_ϵ(
output_ϵ,
con,
reinterpret(T, d.reverse_storage_ϵ),
reinterpret(T, d.partials_storage_ϵ),
d.subexpression_reverse_values,
subexpr_reverse_values_ϵ,
μ[i],
zero(T),
)
end
for i in length(d.subexpression_order):-1:1
j = d.subexpression_order[i]
subexpr = d.subexpressions[j]
_reverse_eval_ϵ(
output_ϵ,
subexpr,
reinterpret(T, subexpr.reverse_storage_ϵ),
reinterpret(T, subexpr.partials_storage_ϵ),
d.subexpression_reverse_values,
subexpr_reverse_values_ϵ,
d.subexpression_reverse_values[j],
subexpr_reverse_values_ϵ[j],
)
end
for i in 1:length(x)
h[i] += output_ϵ[i].values[1]
end
return
end