Bump Julia lower bound to v1.10 (LTS)

.github/workflows/CI.yml

@@ -20,7 +20,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6'
+          - 'lts'
           - '1'
           - 'pre'
         os:
@@ -68,7 +68,7 @@ jobs:
         include:
           - version: '1'
             downgrade: false
-          - version: '1.7'
+          - version: 'lts'
             downgrade: true
     steps:
       - uses: actions/checkout@v4

Project.toml

@@ -1,7 +1,7 @@
 name = "Pathfinder"
 uuid = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 authors = ["Seth Axen <[email protected]> and contributors"]
-version = "0.9.8"
+version = "0.9.9"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -23,7 +23,6 @@ SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Transducers = "28d57a85-8fef-5791-bfe6-a80928e7c999"
-UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [weakdeps]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
@@ -37,34 +36,34 @@ PathfinderDynamicHMCExt = "DynamicHMC"
 PathfinderTuringExt = ["Accessors", "DynamicPPL", "MCMCChains", "Turing"]
 
 [compat]
-ADTypes = "0.2, 1"
+ADTypes = "0.2.5, 1"
 Accessors = "0.1.12"
 Distributions = "0.25.87"
 DynamicHMC = "3.4.0"
 DynamicPPL = "0.25.2, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32"
 Folds = "0.2.9"
-ForwardDiff = "0.10.19"
+ForwardDiff = "0.10.26"
 IrrationalConstants = "0.1.1, 0.2"
-LinearAlgebra = "1.6"
+LinearAlgebra = "1"
 LogDensityProblems = "2.1.0"
 LogDensityProblemsAD = "1.7.0"
 MCMCChains = "6.0.2"
 Optim = "1.7.2"
-Optimization = "3.16.0, 4"
-OptimizationOptimJL = "0.1.7, 0.2, 0.3, 0.4"
+Optimization = "3.21, 4"
+OptimizationNLopt = "0.2, 0.3"
+OptimizationOptimJL = "0.2.1, 0.3, 0.4"
 PDMats = "0.11.26"
 PSIS = "0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9"
 ProgressLogging = "0.1.4"
-Random = "1.6"
+Random = "1"
 Requires = "1.1"
-ReverseDiff = "1.4.5"
-SciMLBase = "1.95.0, 2"
-Statistics = "1.6"
-StatsBase = "0.33.7, 0.34"
+ReverseDiff = "1.15"
+SciMLBase = "2.30"
+Statistics = "1"
+StatsBase = "0.33.17, 0.34"
 Transducers = "0.4.81"
 Turing = "0.31.4, 0.32, 0.33, 0.34, 0.35"
-UnPack = "1"
-julia = "1.6"
+julia = "1.10"
 
 [extras]
 Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"

docs/Project.toml

@@ -19,19 +19,19 @@ TransformedLogDensities = "f9bc47f6-f3f8-4f3b-ab21-f8bc73906f26"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
-ADTypes = "0.2, 1"
+ADTypes = "0.2.5, 1"
 AdvancedHMC = "0.6"
 Documenter = "1"
 DocumenterCitations = "1.2"
 DocumenterInterLinks = "1"
 DynamicHMC = "3.4.0"
-ForwardDiff = "0.10.19"
+ForwardDiff = "0.10.26"
 LogDensityProblems = "2.1.0"
 LogDensityProblemsAD = "1.7"
 Pathfinder = "0.9"
-ReverseDiff = "1.4.5"
+ReverseDiff = "1.15"
 StatsFuns = "1"
-StatsPlots = "0.14.21, 0.15"
+StatsPlots = "0.15.2"
 TransformVariables = "0.6.2, 0.7, 0.8"
 TransformedLogDensities = "1.0.2"
 Turing = "0.31.4, 0.32, 0.33, 0.34, 0.35"

src/Pathfinder.jl

@@ -19,7 +19,6 @@ using SciMLBase: SciMLBase
 using Statistics: Statistics
 using StatsBase: StatsBase
 using Transducers: Transducers
-using UnPack: @unpack
 
 export PathfinderResult, MultiPathfinderResult
 export pathfinder, multipathfinder

src/optimize.jl

@@ -98,54 +98,28 @@ struct OptimizationCallback{X,FX,∇FX,ID,CB}
     fail_on_nonfinite::Bool
 end
 
-@static if isdefined(Optimization, :OptimizationState)
-    # Optimization v3.21.0 and later
-    function (cb::OptimizationCallback)(state::Optimization.OptimizationState, args...)
-        @unpack (
-            xs, fxs, ∇fxs, progress_name, progress_id, maxiters, callback, fail_on_nonfinite
-        ) = cb
-        ret = callback !== nothing && callback(state, args...)
-        iteration = state.iter
-        Base.@logmsg ProgressLogging.ProgressLevel progress_name progress =
-            iteration / maxiters _id = progress_id
-
-        x = copy(state.u)
-        fx = -state.objective
-        ∇fx = state.grad === nothing ? nothing : -state.grad
-
-        # some backends mutate x, so we must copy it
-        push!(xs, x)
-        push!(fxs, fx)
-        push!(∇fxs, ∇fx)
-
-        if fail_on_nonfinite && !ret
-            ret = (isnan(fx) || fx == Inf || (∇fx !== nothing && any(!isfinite, ∇fx)))::Bool
-        end
-
-        return ret
+function (cb::OptimizationCallback)(state::Optimization.OptimizationState, args...)
+    (; xs, fxs, ∇fxs, progress_name, progress_id, maxiters, callback, fail_on_nonfinite) =
+        cb
+    ret = callback !== nothing && callback(state, args...)
+    iteration = state.iter
+    Base.@logmsg ProgressLogging.ProgressLevel progress_name progress = iteration / maxiters _id =
+        progress_id
+
+    x = copy(state.u)
+    fx = -state.objective
+    ∇fx = state.grad === nothing ? nothing : -state.grad
+
+    # some backends mutate x, so we must copy it
+    push!(xs, x)
+    push!(fxs, fx)
+    push!(∇fxs, ∇fx)
+
+    if fail_on_nonfinite && !ret
+        ret = (isnan(fx) || fx == Inf || (∇fx !== nothing && any(!isfinite, ∇fx)))::Bool
     end
-else
-    # Optimization v3.20.X and earlier
-    function (cb::OptimizationCallback)(x, nfx, args...)
-        @unpack (
-            xs, fxs, ∇fxs, progress_name, progress_id, maxiters, callback, fail_on_nonfinite
-        ) = cb
-        ret = callback !== nothing && callback(x, nfx, args...)
-        iteration = length(cb.xs)
-        Base.@logmsg ProgressLogging.ProgressLevel progress_name progress =
-            iteration / maxiters _id = progress_id
 
-        # some backends mutate x, so we must copy it
-        push!(xs, copy(x))
-        push!(fxs, -nfx)
-        push!(∇fxs, nothing)
-
-        if fail_on_nonfinite && !ret
-            ret = (isnan(nfx) || nfx == -Inf)::Bool
-        end
-
-        return ret
-    end
+    return ret
 end
 
 struct OptimizationTrace{P,L}

src/singlepath.jl

@@ -202,7 +202,7 @@ function pathfinder(
             kwargs...,
         )
     end
-    @unpack (
+    (;
         itry,
         success,
         optim_prob,

src/woodbury.jl

@@ -292,7 +292,7 @@ function Base.AbstractMatrix{T}(W::WoodburyPDMat) where {T}
     F = pdfactorize(W)
     Fnew = WoodburyPDFactorization(
         convert(AbstractMatrix{T}, F.U),
-        convert(AbstractMatrix{T}, F.Q),
+        convert(LinearAlgebra.AbstractQ{T}, F.Q),
         convert(AbstractMatrix{T}, F.V),
     )
     return WoodburyPDMat(

test/elbo.jl

@@ -34,11 +34,7 @@ using Transducers
         logp(x) = logpdf(target_dist, x[1])
         σs = [1e-3, 0.05, σ_target, 1.0, 1.1, 1.2, 5.0, 10.0]
         dists = Normal.(0, σs)
-        if VERSION ≥ v"1.7.0"
-            executors = [SequentialEx(), ThreadedEx()]
-        else
-            executors = [SequentialEx()]
-        end
+        executors = [SequentialEx(), ThreadedEx()]
         @testset "$executor" for executor in executors
             rng = Random.seed!(Random.default_rng(), 42)
             lopt, estimates = @inferred Pathfinder.maximize_elbo(

test/integration/AdvancedHMC/Project.toml

@@ -18,7 +18,7 @@ TransformedLogDensities = "f9bc47f6-f3f8-4f3b-ab21-f8bc73906f26"
 [compat]
 AdvancedHMC = "0.6"
 Distributions = "0.25.87"
-ForwardDiff = "0.10.19"
+ForwardDiff = "0.10.26"
 LogDensityProblems = "2.1.0"
 LogDensityProblemsAD = "1.7"
 MCMCDiagnosticTools = "0.3"
@@ -28,4 +28,4 @@ Statistics = "1"
 StatsFuns = "1"
 TransformVariables = "0.6.2, 0.7, 0.8"
 TransformedLogDensities = "1.0.2"
-julia = "1.6"
+julia = "1.10"

test/integration/DynamicHMC/Project.toml

@@ -24,4 +24,4 @@ Statistics = "1"
 StatsFuns = "1"
 TransformVariables = "0.6.2, 0.7, 0.8"
 TransformedLogDensities = "1.0.2"
-julia = "1.6"
+julia = "1.10"

test/integration/Turing/Project.toml

@@ -10,4 +10,4 @@ Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 LogDensityProblems = "2.1.0"
 Pathfinder = "0.9"
 Turing = "0.31.4, 0.32, 0.33, 0.34, 0.35"
-julia = "1.6"
+julia = "1.10"

test/multipath.jl

@@ -22,11 +22,7 @@ using Transducers
         d = MvNormal(μ, Σ)
         logp(x) = logpdf(d, x)
         ℓ = build_logdensityproblem(logp, dim, 2)
-        rngs = if VERSION ≥ v"1.7"
-            [MersenneTwister(), Random.default_rng()]
-        else
-            [MersenneTwister()]
-        end
+        rngs = [MersenneTwister(), Random.default_rng()]
         seed = 76
         @testset for rng in rngs
             executor = rng isa MersenneTwister ? SequentialEx() : ThreadedEx()

test/optimize.jl

@@ -73,18 +73,13 @@ end
                         (isdefined(Optimization, :OptimizationState) && !isfinite(gval))
                     )
                 )
-            if isdefined(Optimization, :OptimizationState)
-                # Optimization v3.21.0 and later
-                callback = (state, args...) -> cbfail
-                state = Optimization.OptimizationState(;
-                    iter=0, u=x, objective=-fval, grad=-∇f(x)
-                )
-                cb_args = (state, -fval)
-            else
-                # Optimization v3.20.X and earlier
-                callback = (x, fx, args...) -> cbfail
-                cb_args = (x, -fval)
-            end
+
+            callback = (state, args...) -> cbfail
+            state = Optimization.OptimizationState(;
+                iter=0, u=x, objective=-fval, grad=-∇f(x)
+            )
+            cb_args = (state, -fval)
+
             cb = Pathfinder.OptimizationCallback(
                 xs,
                 fxs,

test/singlepath.jl

@@ -16,11 +16,7 @@ using Transducers
         # here pathfinder finds the exact solution after 1 iteration
         logp(x) = -sum(abs2, x) / 2
         ndraws = 100
-        rngs = if VERSION ≥ v"1.7"
-            [MersenneTwister(), Random.default_rng()]
-        else
-            [MersenneTwister()]
-        end
+        rngs = [MersenneTwister(), Random.default_rng()]
         seed = 42
         @testset for dim in [1, 5, 10, 100], rng in rngs
             executor = rng isa MersenneTwister ? SequentialEx() : ThreadedEx()
@@ -83,11 +79,7 @@ using Transducers
         dim = 5
         ℓ = build_logdensityproblem(logp, dim, 2)
         ndraws_elbo = 100
-        rngs = if VERSION ≥ v"1.7"
-            [MersenneTwister(), Random.default_rng()]
-        else
-            [MersenneTwister()]
-        end
+        rngs = [MersenneTwister(), Random.default_rng()]
         x = randn(dim)
         seed = 38
         optimizer = Optim.LBFGS(; m=6)