Add a benchmark folder

.github/workflows/Benchmark.yml

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+name: Run benchmarks
+
+on:
+  pull_request:
+    types: [labeled, opened, synchronize, reopened]
+
+# Only trigger the benchmark job when you add `run benchmark` label to the PR
+jobs:
+  Benchmark:
+    runs-on: ubuntu-latest
+    if: contains(github.event.pull_request.labels.*.name, 'run benchmark')
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: lts
+      - uses: julia-actions/julia-buildpkg@latest
+      - name: Install dependencies
+        run: julia -e 'using Pkg; pkg"add PkgBenchmark BenchmarkCI"'
+      - name: Run benchmarks
+        run: julia -e 'using BenchmarkCI; BenchmarkCI.judge(;baseline = "origin/main", script = joinpath(pwd(), "benchmark", "benchmarks.jl"))'
+      - name: Post results
+        run: julia -e 'using BenchmarkCI; BenchmarkCI.postjudge()'
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

benchmark/Project.toml

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+[deps]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
+OptimizationProblems = "5049e819-d29b-5fba-b941-0eee7e64c1c6"
+PkgBenchmark = "32113eaa-f34f-5b0d-bd6c-c81e245fc73d"

benchmark/README.md

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+# Benchmarks for OptimizationProblems

benchmark/benchmarks.jl

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+# Include useful packages
+using ADNLPModels, NLPModels, OptimizationProblems
+using BenchmarkTools
+# Most likely benchmark with JuMP as well
+using JuMP, NLPModelsJuMP
+
+const list_problems = Symbol.(OptimizationProblems.meta[!, :name])
+
+# Should match the runtests
+const list_problems_not_ADNLPProblems =
+  Symbol[:catmix, :gasoil, :glider, :methanol, :minsurf, :pinene, :rocket, :steering, :torsion]
+const list_problems_ADNLPProblems = setdiff(list_problems, list_problems_not_ADNLPProblems)
+const list_problems_not_PureJuMP = Symbol[]
+const list_problems_PureJuMP = setdiff(list_problems, list_problems_not_PureJuMP)
+
+# Run locally with `tune!(SUITE)` and then `run(SUITE)`
+const SUITE = BenchmarkGroup()
+
+const SAMPLES = 5
+const EVALS   = 1
+
+SUITE["ADNLPProblems"] = BenchmarkGroup()
+SUITE["ADNLPProblems"]["NLP"] = BenchmarkGroup()
+SUITE["ADNLPProblems"]["NLP"]["constructor"] = BenchmarkGroup()
+SUITE["ADNLPProblems"]["NLP"]["obj"] = BenchmarkGroup()
+for pb in list_problems_ADNLPProblems
+  problem_constructor = getproperty(OptimizationProblems.ADNLPProblems, Symbol(pb))
+  SUITE["ADNLPProblems"]["NLP"]["constructor"][pb] = @benchmarkable $(problem_constructor)() samples=SAMPLES evals=EVALS
+  SUITE["ADNLPProblems"]["NLP"]["obj"][pb] = @benchmarkable obj(nlp, nlp.meta.x0) samples=SAMPLES evals=EVALS setup = (nlp = $(problem_constructor)())
+end
+
+SUITE["ADNLPProblems"]["NLS"] = BenchmarkGroup()
+SUITE["ADNLPProblems"]["NLS"]["constructor"] = BenchmarkGroup()
+SUITE["ADNLPProblems"]["NLS"]["obj"] = BenchmarkGroup()
+list_problems_nls_ADNLPProblems = intersect(Symbol.(OptimizationProblems.meta[OptimizationProblems.meta.objtype .== :least_squares, :name]), list_problems_ADNLPProblems)
+for pb in list_problems_nls_ADNLPProblems
+  problem_constructor = getproperty(OptimizationProblems.ADNLPProblems, Symbol(pb))
+  SUITE["ADNLPProblems"]["NLS"]["constructor"][pb] = @benchmarkable $(problem_constructor)(use_nls = true) samples=SAMPLES evals=EVALS
+  SUITE["ADNLPProblems"]["NLS"]["obj"][pb] = @benchmarkable obj(nlp, nlp.meta.x0) samples=SAMPLES evals=EVALS setup = (nlp = $(problem_constructor)(use_nls = true))
+end
+SUITE["PureJuMP"] = BenchmarkGroup()
+SUITE["PureJuMP"]["constructor"] = BenchmarkGroup()
+SUITE["PureJuMP"]["obj"] = BenchmarkGroup()
+for pb in list_problems_PureJuMP
+    problem_constructor = getproperty(OptimizationProblems.PureJuMP, Symbol(pb))
+    SUITE["PureJuMP"]["constructor"][pb] = @benchmarkable $(problem_constructor)() samples=SAMPLES evals=EVALS
+    SUITE["PureJuMP"]["obj"][pb] = @benchmarkable obj(nlp, nlp.meta.x0) samples=SAMPLES evals=EVALS setup = (
+        nlp = MathOptNLPModel($(problem_constructor)())
+    )
+end

benchmark/run_local.jl

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+using Pkg
+Pkg.activate("benchmark")
+Pkg.instantiate()
+using Logging, JLD2, Dates
+
+path = dirname(@__FILE__)
+skip_tune = true
+
+@info "INITIALIZE"
+include("benchmarks.jl")
+
+list_of_benchmark = keys(SUITE)
+for benchmark_in_suite in list_of_benchmark
+  @info "$(benchmark_in_suite)"
+end
+
+@info "TUNE"
+if !skip_tune
+  @time with_logger(ConsoleLogger(Error)) do
+    tune!(SUITE)
+    BenchmarkTools.save("params.json", params(suite))
+  end
+else
+  @info "Skip tuning"
+  # https://juliaci.github.io/BenchmarkTools.jl/dev/manual/
+  BenchmarkTools.DEFAULT_PARAMETERS.evals = 1
+end
+
+@info "RUN"
+@time result = with_logger(ConsoleLogger(Error)) do
+  if "params.json" in (path == "" ? readdir() : readdir(path))
+    loadparams!(suite, BenchmarkTools.load("params.json")[1], :evals, :samples)
+  end
+  run(SUITE, verbose = true)
+end
+
+@info "SAVE BENCHMARK RESULT"
+name = "$(today())_optimizationproblems_benchmark"
+BenchmarkTools.save("$name.json", result)