Add benchmark suite

.gitignore

@@ -7,8 +7,7 @@ __pycache__
 .ipynb*
 Manifest.toml
 .vscode
-benchmark
 experimental
 refs
 *.mathcha
-
+/benchmark/results/

benchmark/Project.toml

@@ -0,0 +1,4 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+TensorKit = "07d1fe3e-3e46-537d-9eac-e9e13d0d4cec"

benchmark/README.md

@@ -0,0 +1,31 @@
+# TensorKit Benchmarks
+
+This directory contains a benchmark suite for TensorKit.
+Most of the benchmarks are designed to capture performance characteristics of the library, and are not intended to be used as a benchmark suite for comparing different libraries.
+In particular, the main goal here is to catch performance regressions and/or improvements between different versions of TensorKit.
+
+## Running the benchmarks
+
+The benchmarks are written using `BenchmarkTools.jl`, and the full suite can be found in the `SUITE` global variable defined in `benchmarks.jl`.
+Sometimes, it is useful to run only a subset of the benchmarks.
+To do this, you can use the `--modules` flag to specify which modules to run.
+Alternatively, you can use the `TensorKitBenchmarks` module directly, which is designed after `BaseBenchmarks` to allow for conditional loading of the benchmarks.
+
+For a more streamlined CLI experience, you can use [`AirspeedVelocity.jl`](https://github.com/MilesCranmer/AirspeedVelocity.jl) to run the benchmarks.
+The following command will run the benchmarks and compare with the current master branch:
+
+```bash
+benchpkg TensorKit \
+    --rev=dirty,master \
+    -o benchmark/results/ \
+    -exeflags="--threads=4"
+```
+
+To compare with previous results, the following command can be used:
+
+```bash
+benchpkgtable TensorKit \
+    --rev=dirty,master \
+    -i benchmark/results/ \
+    -o benchmark/results/ \
+```

benchmark/TensorKitBenchmarks/TensorKitBenchmarks.jl

@@ -0,0 +1,55 @@
+module TensorKitBenchmarks
+
+using BenchmarkTools
+using TensorKit
+using TOML
+
+BenchmarkTools.DEFAULT_PARAMETERS.seconds = 1.0
+BenchmarkTools.DEFAULT_PARAMETERS.samples = 10000
+BenchmarkTools.DEFAULT_PARAMETERS.time_tolerance = 0.15
+BenchmarkTools.DEFAULT_PARAMETERS.memory_tolerance = 0.01
+
+const PARAMS_PATH = joinpath(@__DIR__, "etc", "params.json")
+const SUITE = BenchmarkGroup()
+const MODULES = Dict("linalg" => :LinalgBenchmarks,
+                     "indexmanipulations" => :IndexManipulationBenchmarks,
+                     "tensornetworks" => :TensorNetworkBenchmarks)
+
+load!(id::AbstractString; kwargs...) = load!(SUITE, id; kwargs...)
+
+function load!(group::BenchmarkGroup, id::AbstractString; tune::Bool=false)
+    modsym = MODULES[id]
+    modpath = joinpath(dirname(@__FILE__), id, "$(modsym).jl")
+    Core.eval(@__MODULE__, :(include($modpath)))
+    mod = Core.eval(@__MODULE__, modsym)
+    modsuite = @invokelatest getglobal(mod, :SUITE)
+    group[id] = modsuite
+    if tune
+        results = BenchmarkTools.load(PARAMS_PATH)[1]
+        haskey(results, id) && loadparams!(modsuite, results[id], :evals)
+    end
+    return group
+end
+
+loadall!(; kwargs...) = loadall!(SUITE; kwargs...)
+
+function loadall!(group::BenchmarkGroup; verbose::Bool=true, tune::Bool=false)
+    for id in keys(MODULES)
+        if verbose
+            print("loading group $(repr(id))... ")
+            time = @elapsed load!(group, id, tune=false)
+            println("done (took $time seconds)")
+        else
+            load!(group, id; tune=false)
+        end
+    end
+    if tune
+        results = BenchmarkTools.load(PARAMS_PATH)[1]
+        for (id, suite) in group
+            haskey(results, id) && loadparams!(suite, results[id], :evals)
+        end
+    end
+    return group
+end
+
+end

benchmark/TensorKitBenchmarks/indexmanipulations/IndexManipulationBenchmarks.jl

@@ -0,0 +1,55 @@
+module IndexManipulationBenchmarks
+
+include(joinpath(@__DIR__, "..", "utils", "BenchUtils.jl"))
+
+using .BenchUtils
+using BenchmarkTools
+using TensorKit
+using TOML
+
+const SUITE = BenchmarkGroup()
+const all_parameters = TOML.parsefile(joinpath(@__DIR__, "benchparams.toml"))
+
+# permute!
+# --------
+function init_permute_tensors(T, W, p)
+    C = randn(T, permute(W, p))
+    A = randn(T, W)
+    return C, A
+end
+function benchmark_permute!(benchgroup, params::Dict)
+    haskey(benchgroup, "permute") || addgroup!(benchgroup, "permute")
+    bench = benchgroup["permute"]
+    for kwargs in expand_kwargs(params)
+        benchmark_permute!(bench; kwargs...)
+    end
+    return nothing
+end
+function benchmark_permute!(bench; sigmas=nothing, T="Float64", I="Trivial", dims, p)
+    T_ = parse_type(T)
+    I_ = parse_type(I)
+
+    p_ = (Tuple(p[1]), Tuple(p[2]))
+    Vs = generate_space.(I_, dims, sigmas)
+
+    codomain = mapreduce(Base.Fix1(getindex, Vs), ⊗, p_[1]; init=one(eltype(Vs)))
+    domain = mapreduce(Base.Fix1(getindex, Vs), ⊗, p_[2]; init=one(eltype(Vs)))
+    init() = init_permute_tensors(T_, codomain ← domain, p_)
+
+    bench[T, I, dims, sigmas, p] = @benchmarkable permute!(C, A, $p_) setup = ((C, A) = $init())
+    return nothing
+end
+
+if haskey(all_parameters, "permute")
+    g = addgroup!(SUITE, "permute")
+    for params in all_parameters["permute"]
+        benchmark_permute!(g, params)
+    end
+end
+
+# transpose!
+# ----------
+
+# TODO
+
+end

benchmark/TensorKitBenchmarks/indexmanipulations/benchparams.toml

@@ -0,0 +1,12 @@
+[[permute]]
+T = ["Float64"]
+I = "Trivial"
+p = [[[2, 1], []]]
+dims = [[7264, 7264], [43408, 1216]]
+
+[[permute]]
+T = ["Float64"]
+I = "Z2Irrep"
+p = [[[2, 1], []]]
+dims = [[7264, 7264], [43408, 1216]]
+sigmas = [[0.5, 0.5]]

benchmark/TensorKitBenchmarks/linalg/LinalgBenchmarks.jl

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+module LinalgBenchmarks
+
+include(joinpath(@__DIR__, "..", "utils", "BenchUtils.jl"))
+
+using .BenchUtils
+using BenchmarkTools
+using TensorKit
+using TOML
+
+const SUITE = BenchmarkGroup()
+const all_parameters = TOML.parsefile(joinpath(@__DIR__, "benchparams.toml"))
+
+# mul!
+# ----
+function init_mul_tensors(T, V)
+    A = randn(T, V[1] ← V[2])
+    B = randn(T, V[2] ← V[3])
+    C = randn(T, V[1] ← V[3])
+    return A, B, C
+end
+
+function benchmark_mul!(benchgroup, params::Dict)
+    haskey(benchgroup, "mul") || addgroup!(benchgroup, "mul")
+    bench = benchgroup["mul"]
+    for kwargs in expand_kwargs(params)
+        benchmark_mul!(bench; kwargs...)
+    end
+    return nothing
+end
+
+function benchmark_mul!(bench; sigmas=nothing, T="Float64", I="Trivial", dims)
+    T_ = parse_type(T)
+    I_ = parse_type(I)
+
+    Vs = generate_space.(I_, dims, sigmas)
+    init() = init_mul_tensors(T_, Vs)
+
+    bench[T, I, dims, sigmas] = @benchmarkable mul!(C, A, B) setup = ((A, B, C) = $init())
+
+    return nothing
+end
+
+if haskey(all_parameters, "mul")
+    g = addgroup!(SUITE, "mul")
+    for params in all_parameters["mul"]
+        benchmark_mul!(g, params)
+    end
+end
+
+# svd!
+# ----
+function init_svd_tensor(T, V)
+    A = randn(T, V[1] ← V[2])
+    return A
+end
+
+function benchmark_svd!(benchgroup, params::Dict)
+    haskey(benchgroup, "svd") || addgroup!(benchgroup, "svd")
+    bench = benchgroup["svd"]
+    for kwargs in expand_kwargs(params)
+        benchmark_svd!(bench; kwargs...)
+    end
+    return nothing
+end
+function benchmark_svd!(bench; sigmas=nothing, T="Float64", I="Trivial", dims)
+    T_ = parse_type(T)
+    I_ = parse_type(I)
+    Vs = generate_space.(I_, dims, sigmas)
+    init() = init_svd_tensor(T_, Vs)
+    bench[T, I, dims, sigmas] = @benchmarkable tsvd!(A) setup = (A = $init())
+    return nothing
+end
+
+if haskey(all_parameters, "svd")
+    g = addgroup!(SUITE, "svd")
+    for params in all_parameters["svd"]
+        benchmark_svd!(g, params)
+    end
+end
+
+end

benchmark/TensorKitBenchmarks/linalg/benchparams.toml

@@ -0,0 +1,21 @@
+[[mul]]
+T = ["Float64", "ComplexF64"]
+I = "Trivial"
+dims = [[2, 2, 2], [8, 8, 8], [32, 32, 32], [64, 64, 64], [128, 128, 128]]
+
+[[mul]]
+T = ["Float64", "ComplexF64"]
+I = "Z2Irrep"
+dims = [[2, 2, 2], [8, 8, 8], [32, 32, 32], [64, 64, 64], [128, 128, 128]]
+sigmas = [[0.5, 0.5, 0.5]]
+
+[[svd]]
+T = ["Float64", "ComplexF64"]
+I = "Trivial"
+dims = [[2, 2], [8, 8], [32, 32], [64, 64], [128, 128]]
+
+[[svd]]
+T = ["Float64", "ComplexF64"]
+I = "Z2Irrep"
+dims = [[2, 2], [8, 8], [32, 32], [64, 64], [128, 128]]
+sigmas = [[0.5, 0.5]]