Batch API

.gitignore

@@ -2,4 +2,4 @@
 *.jl.mem
 docs/build
 docs/site
-Manifest.toml
+Manifest*.toml

src/NLPModels.jl

@@ -42,4 +42,8 @@ for f in ["utils", "api", "counters", "meta", "show", "tools"]
   include("nls/$f.jl")
 end
 
+include("nlp/batch/api.jl")
+include("nlp/batch/foreach.jl")
+include("nlp/batch/inplace.jl")
+
 end # module

src/nlp/batch/api.jl

@@ -0,0 +1,82 @@
+export AbstractBatchNLPModel
+
+abstract type AbstractBatchNLPModel end
+
+function NLPModels.increment!(bnlp::AbstractBatchNLPModel, fun::Symbol)
+  NLPModels.increment!(bnlp, Val(fun))
+end
+
+for fun in (
+    :batch_obj,
+    :batch_grad,
+    :batch_grad!,
+    :batch_objgrad,
+    :batch_objgrad!,
+    :batch_objcons,
+    :batch_objcons!,
+    :batch_cons,
+    :batch_cons!,
+    :batch_cons_lin,
+    :batch_cons_lin!,
+    :batch_cons_nln,
+    :batch_cons_nln!,
+    :batch_jth_con,
+    :batch_jth_congrad,
+    :batch_jth_congrad!,
+    :batch_jth_sparse_congrad,
+    :batch_jac_structure!,
+    :batch_jac_structure,
+    :batch_jac_coord!,
+    :batch_jac_coord,
+    :batch_jac,
+    :batch_jprod,
+    :batch_jprod!,
+    :batch_jtprod,
+    :batch_jtprod!,
+    :batch_jac_op,
+    :batch_jac_op!,
+    :batch_jac_lin_structure!,
+    :batch_jac_lin_structure,
+    :batch_jac_lin_coord!,
+    :batch_jac_lin_coord,
+    :batch_jac_lin,
+    :batch_jprod_lin,
+    :batch_jprod_lin!,
+    :batch_jtprod_lin,
+    :batch_jtprod_lin!,
+    :batch_jac_lin_op,
+    :batch_jac_lin_op!,
+    :batch_jac_nln_structure!,
+    :batch_jac_nln_structure,
+    :batch_jac_nln_coord!,
+    :batch_jac_nln_coord,
+    :batch_jac_nln,
+    :batch_jprod_nln,
+    :batch_jprod_nln!,
+    :batch_jtprod_nln,
+    :batch_jtprod_nln!,
+    :batch_jac_nln_op,
+    :batch_jac_nln_op!,
+    :batch_jth_hess_coord,
+    :batch_jth_hess_coord!,
+    :batch_jth_hess,
+    :batch_jth_hprod,
+    :batch_jth_hprod!,
+    :batch_ghjvprod,
+    :batch_ghjvprod!,
+    :batch_hess_structure!,
+    :batch_hess_structure,
+    :batch_hess_coord!,
+    :batch_hess_coord,
+    :batch_hess,
+    :batch_hprod,
+    :batch_hprod!,
+    :batch_hess_op,
+    :batch_hess_op!,
+    :batch_varscale,
+    :batch_lagscale,
+    :batch_conscale,
+)
+    @eval export $fun
+    @eval function $fun end
+end

src/nlp/batch/foreach.jl

@@ -0,0 +1,259 @@
+export ForEachBatchNLPModel
+struct ForEachBatchNLPModel{M} <: AbstractBatchNLPModel
+  models::M
+  counters::Counters
+  batch_size::Int
+end
+function ForEachBatchNLPModel(models::M) where {M}
+  isempty(models) && error("Cannot create ForEachBatchNLPModel from empty collection.")
+  ForEachBatchNLPModel{M}(models, Counters(), length(models))
+end
+Base.length(vnlp::ForEachBatchNLPModel) = vnlp.batch_size
+Base.getindex(vnlp::ForEachBatchNLPModel, i::Integer) = vnlp.models[i]
+Base.iterate(vnlp::ForEachBatchNLPModel, state::Integer = 1) = iterate(vnlp.models, state)
+
+
+function _batch_map(f::F, bnlp::ForEachBatchNLPModel, xs::Vararg{T,N}) where {F,T,N}
+  n = bnlp.batch_size
+  @lencheck_tup n xs
+  results = []
+  resize!(results, n)
+  for i = 1:n
+    args_i = (x[i] for x in xs)
+    results[i] = f(bnlp[i], args_i...)
+  end
+  return results
+end
+
+function _batch_map!(f::F, bnlp::ForEachBatchNLPModel, xs::Vararg{T,N}) where {F,T,N}
+  n = bnlp.batch_size
+  length(xs) == 0 && error("Cannot call _batch_map! without providing arguments.")
+  @lencheck_tup n xs
+  outputs = xs[end]
+  inputs = length(xs) == 1 ? () : Base.ntuple(i -> xs[i], length(xs) - 1)
+  @lencheck n outputs
+  for i = 1:n
+    args_i = (x[i] for x in inputs)
+    f(bnlp[i], args_i..., outputs[i])
+  end
+  return outputs
+end
+
+function _batch_map_weight(f::F, bnlp::ForEachBatchNLPModel, obj_weights, xs::Vararg{T,N}) where {F,T,N}
+  n = bnlp.batch_size
+  @lencheck_tup n xs
+  @lencheck n obj_weights
+  results = []
+  resize!(results, n)
+  for i = 1:n
+    args_i = (x[i] for x in xs)
+    results[i] = f(bnlp[i], args_i...; obj_weight = obj_weights[i])
+  end
+  return results
+end
+
+function _batch_map_weight!(f::F, bnlp::ForEachBatchNLPModel, obj_weights, xs::Vararg{T,N}) where {F,T,N}
+  n = bnlp.batch_size
+  length(xs) == 0 && error("Cannot call _batch_map_weight! without providing arguments.")
+  @lencheck_tup n xs
+  @lencheck n obj_weights
+  outputs = xs[end]
+  inputs = length(xs) == 1 ? () : Base.ntuple(i -> xs[i], length(xs) - 1)
+  @lencheck n outputs
+  for i = 1:n
+    args_i = (x[i] for x in inputs)
+    f(bnlp[i], args_i..., outputs[i]; obj_weight = obj_weights[i])
+  end
+  return outputs
+end
+
+function _batch_map_tuple(f::F, bnlp::ForEachBatchNLPModel, xs::Vararg{T,N}) where {F,T,N}
+  n = bnlp.batch_size
+  @lencheck_tup n xs
+  results = _batch_map(f, bnlp, xs...)
+
+  first_result = first(results)
+  T1, T2 = typeof(first_result[1]), typeof(first_result[2])
+  vec1, vec2 = Vector{T1}(undef, n), Vector{T2}(undef, n)
+  for i = 1:n
+    vec1[i], vec2[i] = results[i]
+  end
+  return vec1, vec2
+end
+
+function _batch_map_tuple!(f::F, bnlp::ForEachBatchNLPModel, outputs, xs::Vararg{T,N}) where {F,T,N}
+  n = bnlp.batch_size
+  @lencheck_tup n xs
+  @lencheck n outputs
+  firsts = []
+  resize!(firsts, n)
+  for i = 1:n
+    args_i = (x[i] for x in xs)
+    firsts[i], _ = f(bnlp[i], args_i..., outputs[i])
+  end
+  return firsts, outputs
+end
+
+for fun in fieldnames(Counters)
+  @eval function NLPModels.increment!(bnlp::ForEachBatchNLPModel, ::Val{$(Meta.quot(fun))})
+    # sub-model counters are already incremented since we call their methods
+    bnlp.counters.$fun += 1
+  end
+end
+for (batch_fun, fun) in (
+        (:batch_jac_structure, :jac_structure),
+        (:batch_jac_lin_structure, :jac_lin_structure),
+        (:batch_jac_nln_structure, :jac_nln_structure),
+        (:batch_hess_structure, :hess_structure),
+        (:batch_varscale, :varscale),
+        (:batch_lagscale, :lagscale),
+        (:batch_conscale, :conscale),
+    )
+    @eval $batch_fun(bnlp::ForEachBatchNLPModel) = _batch_map($fun, bnlp)
+end
+for (batch_fun, fun) in (
+        (:batch_obj, :obj),
+        (:batch_grad, :grad),
+        (:batch_cons, :cons),
+        (:batch_cons_lin, :cons_lin),
+        (:batch_cons_nln, :cons_nln),
+        (:batch_jac, :jac),
+        (:batch_jac_lin, :jac_lin),
+        (:batch_jac_nln, :jac_nln),
+        (:batch_jac_lin_coord, :jac_lin_coord),
+        (:batch_jac_coord, :jac_coord),
+        (:batch_jac_nln_coord, :jac_nln_coord),
+    )
+    @eval $batch_fun(bnlp::ForEachBatchNLPModel, x) = _batch_map($fun, bnlp, x)
+end
+for (batch_fun, fun) in (
+        (:batch_jprod, :jprod),
+        (:batch_jtprod, :jtprod),
+        (:batch_jprod_nln, :jprod_nln),
+        (:batch_jtprod_nln, :jtprod_nln),
+        (:batch_jprod_lin, :jprod_lin),
+        (:batch_jtprod_lin, :jtprod_lin),
+    )
+    @eval $batch_fun(bnlp::ForEachBatchNLPModel, x, y) = _batch_map($fun, bnlp, x, y)
+end
+for (batch_fun, fun) in (
+        (:batch_ghjvprod, :ghjvprod),
+    )
+    @eval $batch_fun(bnlp::ForEachBatchNLPModel, x, y, z) = _batch_map($fun, bnlp, x, y, z)
+end
+
+batch_jac_structure!(bnlp::ForEachBatchNLPModel, rowss, colss) =
+  _batch_map!(jac_structure!, bnlp, rowss, colss)
+batch_jac_lin_structure!(bnlp::ForEachBatchNLPModel, rowss, colss) =
+  _batch_map!(jac_lin_structure!, bnlp, rowss, colss)
+batch_jac_nln_structure!(bnlp::ForEachBatchNLPModel, rowss, colss) =
+  _batch_map!(jac_nln_structure!, bnlp, rowss, colss)
+batch_hess_structure!(bnlp::ForEachBatchNLPModel, rowss, colss) =
+  _batch_map!(hess_structure!, bnlp, rowss, colss)
+batch_jac_lin_coord!(bnlp::ForEachBatchNLPModel, xs, valss) =
+  _batch_map!(jac_lin_coord!, bnlp, xs, valss)
+batch_grad!(bnlp::ForEachBatchNLPModel, xs, gs) =
+  _batch_map!(grad!, bnlp, xs, gs)
+batch_cons!(bnlp::ForEachBatchNLPModel, xs, cs) =
+  _batch_map!(cons!, bnlp, xs, cs)
+batch_cons_lin!(bnlp::ForEachBatchNLPModel, xs, cs) =
+  _batch_map!(cons_lin!, bnlp, xs, cs)
+batch_cons_nln!(bnlp::ForEachBatchNLPModel, xs, cs) =
+  _batch_map!(cons_nln!, bnlp, xs, cs)
+batch_jac_coord!(bnlp::ForEachBatchNLPModel, xs, valss) =
+  _batch_map!(jac_coord!, bnlp, xs, valss)
+batch_jac_nln_coord!(bnlp::ForEachBatchNLPModel, xs, valss) =
+  _batch_map!(jac_nln_coord!, bnlp, xs, valss)
+batch_jprod!(bnlp::ForEachBatchNLPModel, xs, vs, Jvs) =
+  _batch_map!(jprod!, bnlp, xs, vs, Jvs)
+batch_jtprod!(bnlp::ForEachBatchNLPModel, xs, vs, Jtvs) =
+  _batch_map!(jtprod!, bnlp, xs, vs, Jtvs)
+batch_jprod_nln!(bnlp::ForEachBatchNLPModel, xs, vs, Jvs) =
+  _batch_map!(jprod_nln!, bnlp, xs, vs, Jvs)
+batch_jtprod_nln!(bnlp::ForEachBatchNLPModel, xs, vs, Jtvs) =
+  _batch_map!(jtprod_nln!, bnlp, xs, vs, Jtvs)
+batch_jprod_lin!(bnlp::ForEachBatchNLPModel, xs, vs, Jvs) =
+  _batch_map!(jprod_lin!, bnlp, xs, vs, Jvs)
+batch_jtprod_lin!(bnlp::ForEachBatchNLPModel, xs, vs, Jtvs) =
+  _batch_map!(jtprod_lin!, bnlp, xs, vs, Jtvs)
+batch_ghjvprod!(bnlp::ForEachBatchNLPModel, xs, gs, vs, gHvs) =
+  _batch_map!(ghjvprod!, bnlp, xs, gs, vs, gHvs)
+
+## jth
+batch_jth_con(bnlp::ForEachBatchNLPModel, xs, j::Integer) =
+  _batch_map((m, x) -> jth_con(m, x, j), bnlp, xs)
+batch_jth_congrad(bnlp::ForEachBatchNLPModel, xs, j::Integer) =
+  _batch_map((m, x) -> jth_congrad(m, x, j), bnlp, xs)
+batch_jth_sparse_congrad(bnlp::ForEachBatchNLPModel, xs, j::Integer) =
+  _batch_map((m, x) -> jth_sparse_congrad(m, x, j), bnlp, xs)
+batch_jth_hess_coord(bnlp::ForEachBatchNLPModel, xs, j::Integer) =
+  _batch_map((m, x) -> jth_hess_coord(m, x, j), bnlp, xs)
+batch_jth_hess(bnlp::ForEachBatchNLPModel, xs, j::Integer) =
+  _batch_map((m, x) -> jth_hess(m, x, j), bnlp, xs)
+batch_jth_hprod(bnlp::ForEachBatchNLPModel, xs, vs, j::Integer) =
+  _batch_map((m, x, v) -> jth_hprod(m, x, v, j), bnlp, xs, vs)
+
+batch_jth_congrad!(bnlp::ForEachBatchNLPModel, xs, j::Integer, outputs) =
+  _batch_map!((m, x, out) -> jth_congrad!(m, x, j, out), bnlp, xs, outputs)
+batch_jth_hess_coord!(bnlp::ForEachBatchNLPModel, xs, j::Integer, outputs) =
+  _batch_map!((m, x, out) -> jth_hess_coord!(m, x, j, out), bnlp, xs, outputs)
+batch_jth_hprod!(bnlp::ForEachBatchNLPModel, xs, vs, j::Integer, outputs) =
+  _batch_map!((m, x, v, out) -> jth_hprod!(m, x, v, j, out), bnlp, xs, vs, outputs)
+
+# hess (need to treat obj_weight)  FIXME: obj_weights is required in batch API
+batch_hprod(bnlp::ForEachBatchNLPModel, xs, vs; obj_weights) =
+  _batch_map_weight(hprod, bnlp, obj_weights, xs, vs)
+batch_hprod(bnlp::ForEachBatchNLPModel, xs, ys, vs; obj_weights) =
+  _batch_map_weight(hprod, bnlp, obj_weights, xs, ys, vs)
+batch_hess_coord(bnlp::ForEachBatchNLPModel, xs; obj_weights) =
+  _batch_map_weight(hess_coord, bnlp, obj_weights, xs)
+batch_hess_coord(bnlp::ForEachBatchNLPModel, xs, ys; obj_weights) =
+  _batch_map_weight(hess_coord, bnlp, obj_weights, xs, ys)
+batch_hess_op(bnlp::ForEachBatchNLPModel, xs; obj_weights) =
+  _batch_map_weight(hess_op, bnlp, obj_weights, xs)
+batch_hess_op(bnlp::ForEachBatchNLPModel, xs, ys; obj_weights) =
+  _batch_map_weight(hess_op, bnlp, obj_weights, xs, ys)
+
+batch_hprod!(bnlp::ForEachBatchNLPModel, xs, vs, outputs; obj_weights) =
+  _batch_map_weight!(hprod!, bnlp, obj_weights, xs, vs, outputs)
+batch_hprod!(bnlp::ForEachBatchNLPModel, xs, ys, vs, outputs; obj_weights) =
+  _batch_map_weight!(hprod!, bnlp, obj_weights, xs, ys, vs, outputs)
+batch_hess_coord!(bnlp::ForEachBatchNLPModel, xs, outputs; obj_weights) =
+  _batch_map_weight!(hess_coord!, bnlp, obj_weights, xs, outputs)
+batch_hess_coord!(bnlp::ForEachBatchNLPModel, xs, ys, outputs; obj_weights) =
+  _batch_map_weight!(hess_coord!, bnlp, obj_weights, xs, ys, outputs)
+batch_hess_op!(bnlp::ForEachBatchNLPModel, xs, Hvs; obj_weights) =
+  _batch_map_weight(hess_op!, bnlp, obj_weights, xs, Hvs)
+batch_hess_op!(bnlp::ForEachBatchNLPModel, xs, ys, Hvs; obj_weights) =
+  _batch_map_weight(hess_op!, bnlp, obj_weights, xs, ys, Hvs)
+
+batch_hess(bnlp::ForEachBatchNLPModel, xs; obj_weights) =
+  _batch_map_weight(hess, bnlp, obj_weights, xs)
+batch_hess(bnlp::ForEachBatchNLPModel, xs, ys; obj_weights) =
+  _batch_map_weight(hess, bnlp, obj_weights, xs, ys)
+
+## operators
+batch_jac_op(bnlp::ForEachBatchNLPModel, xs) =
+  _batch_map(jac_op, bnlp, xs)
+batch_jac_lin_op(bnlp::ForEachBatchNLPModel, xs) =
+  _batch_map(jac_lin_op, bnlp, xs)
+batch_jac_nln_op(bnlp::ForEachBatchNLPModel, xs) =
+  _batch_map(jac_nln_op, bnlp, xs)
+
+batch_jac_op!(bnlp::ForEachBatchNLPModel, xs, Jvs, Jtvs) =
+  _batch_map(jac_op!, bnlp, xs, Jvs, Jtvs)
+batch_jac_lin_op!(bnlp::ForEachBatchNLPModel, xs, Jvs, Jtvs) =
+  _batch_map(jac_lin_op!, bnlp, xs, Jvs, Jtvs)
+batch_jac_nln_op!(bnlp::ForEachBatchNLPModel, xs, Jvs, Jtvs) =
+  _batch_map(jac_nln_op!, bnlp, xs, Jvs, Jtvs)
+
+## tuple functions
+batch_objgrad(bnlp::ForEachBatchNLPModel, xs) =
+  _batch_map_tuple(objgrad, bnlp, xs)
+batch_objcons(bnlp::ForEachBatchNLPModel, xs) =
+  _batch_map_tuple(objcons, bnlp, xs)
+
+batch_objgrad!(bnlp::ForEachBatchNLPModel, xs, gs) =
+  _batch_map_tuple!(objgrad!, bnlp, gs, xs)
+batch_objcons!(bnlp::ForEachBatchNLPModel, xs, cs) =
+  _batch_map_tuple!(objcons!, bnlp, cs, xs)