Bump bijectors compat

Project.toml

@@ -1,6 +1,6 @@
 name = "Turing"
 uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
-version = "0.26.4"
+version = "0.27"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -16,7 +16,6 @@ DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
 EllipticalSliceSampling = "cad2338a-1db2-11e9-3401-43bc07c9ede2"
-FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -44,20 +43,20 @@ AdvancedMH = "0.6.8, 0.7"
 AdvancedPS = "0.4"
 AdvancedVI = "0.2"
 BangBang = "0.3"
-Bijectors = "0.12"
+Bijectors = "0.13.2"
 DataStructures = "0.18"
 Distributions = "0.23.3, 0.24, 0.25"
 DistributionsAD = "0.6"
 DocStringExtensions = "0.8, 0.9"
 DynamicPPL = "0.23"
 EllipticalSliceSampling = "0.5, 1"
-FillArrays = "=1.0.0"
 ForwardDiff = "0.10.3"
 Libtask = "0.7, 0.8"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1.4"
 MCMCChains = "5, 6"
 NamedArrays = "0.9"
+Optim = "1"
 Reexport = "0.2, 1"
 Requires = "0.5, 1.0"
 SciMLBase = "1.37.1"
@@ -68,3 +67,12 @@ StatsBase = "0.32, 0.33, 0.34"
 StatsFuns = "0.8, 0.9, 1"
 Tracker = "0.2.3"
 julia = "1.7"
+
+[weakdeps]
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+
+[extensions]
+TuringOptimExt = "Optim"
+
+[extras]
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"

src/modes/OptimInterface.jl → ext/TuringOptimExt.jl

@@ -1,14 +1,14 @@
-using Setfield
-using DynamicPPL: DefaultContext, LikelihoodContext
-using DynamicPPL: DynamicPPL
-import .Optim
-import .Optim: optimize
-import ..ForwardDiff
-import NamedArrays
-import StatsBase
-import Printf
-import StatsAPI
-
+module TuringOptimExt
+
+if isdefined(Base, :get_extension)
+ import Turing
+ import Turing: Distributions, DynamicPPL, ForwardDiff, NamedArrays, Printf, Setfield, Statistics, StatsAPI, StatsBase 
+ import Optim
+else
+ import ..Turing
+ import ..Turing: Distributions, DynamicPPL, ForwardDiff, NamedArrays, Printf, Setfield, Statistics, StatsAPI, StatsBase
+ import ..Optim
+end
 
 """
  ModeResult{
@@ -23,7 +23,7 @@ A wrapper struct to store various results from a MAP or MLE estimation.
 struct ModeResult{
  V<:NamedArrays.NamedArray,
  O<:Optim.MultivariateOptimizationResults,
- M<:OptimLogDensity
+ M<:Turing.OptimLogDensity
 } <: StatsBase.StatisticalModel
  "A vector with the resulting point estimates."
  values::V
@@ -57,10 +57,10 @@ function StatsBase.coeftable(m::ModeResult; level::Real=0.95)
  estimates = m.values.array[:, 1]
  stderrors = StatsBase.stderror(m)
  zscore = estimates ./ stderrors
- p = map(z -> StatsAPI.pvalue(Normal(), z; tail=:both), zscore)
+ p = map(z -> StatsAPI.pvalue(Distributions.Normal(), z; tail=:both), zscore)
 
  # Confidence interval (CI)
- q = quantile(Normal(), (1 + level) / 2)
+ q = Statistics.quantile(Distributions.Normal(), (1 + level) / 2)
  ci_low = estimates .- q .* stderrors
  ci_high = estimates .+ q .* stderrors
 
@@ -80,7 +80,7 @@ function StatsBase.informationmatrix(m::ModeResult; hessian_function=ForwardDiff
  # Hessian is computed with respect to the untransformed parameters.
  linked = DynamicPPL.istrans(m.f.varinfo)
  if linked
- @set! m.f.varinfo = invlink!!(m.f.varinfo, m.f.model)
+ Setfield.@set! m.f.varinfo = DynamicPPL.invlink!!(m.f.varinfo, m.f.model)
  end
 
  # Calculate the Hessian.
@@ -90,7 +90,7 @@ function StatsBase.informationmatrix(m::ModeResult; hessian_function=ForwardDiff
 
  # Link it back if we invlinked it.
  if linked
- @set! m.f.varinfo = link!!(m.f.varinfo, m.f.model)
+ Setfield.@set! m.f.varinfo = DynamicPPL.link!!(m.f.varinfo, m.f.model)
  end
 
  return NamedArrays.NamedArray(info, (varnames, varnames))
@@ -126,18 +126,18 @@ mle = optimize(model, MLE())
 mle = optimize(model, MLE(), NelderMead())
 ```
 """
-function Optim.optimize(model::Model, ::MLE, options::Optim.Options=Optim.Options(); kwargs...)
+function Optim.optimize(model::DynamicPPL.Model, ::Turing.MLE, options::Optim.Options=Optim.Options(); kwargs...)
  return _mle_optimize(model, options; kwargs...)
 end
-function Optim.optimize(model::Model, ::MLE, init_vals::AbstractArray, options::Optim.Options=Optim.Options(); kwargs...)
+function Optim.optimize(model::DynamicPPL.Model, ::Turing.MLE, init_vals::AbstractArray, options::Optim.Options=Optim.Options(); kwargs...)
  return _mle_optimize(model, init_vals, options; kwargs...)
 end
-function Optim.optimize(model::Model, ::MLE, optimizer::Optim.AbstractOptimizer, options::Optim.Options=Optim.Options(); kwargs...)
+function Optim.optimize(model::DynamicPPL.Model, ::Turing.MLE, optimizer::Optim.AbstractOptimizer, options::Optim.Options=Optim.Options(); kwargs...)
  return _mle_optimize(model, optimizer, options; kwargs...)
 end
 function Optim.optimize(
- model::Model,
- ::MLE,
+ model::DynamicPPL.Model,
+ ::Turing.MLE,
  init_vals::AbstractArray,
  optimizer::Optim.AbstractOptimizer,
  options::Optim.Options=Optim.Options();
@@ -146,9 +146,9 @@ function Optim.optimize(
  return _mle_optimize(model, init_vals, optimizer, options; kwargs...)
 end
 
-function _mle_optimize(model::Model, args...; kwargs...)
- ctx = OptimizationContext(DynamicPPL.LikelihoodContext())
- return _optimize(model, OptimLogDensity(model, ctx), args...; kwargs...)
+function _mle_optimize(model::DynamicPPL.Model, args...; kwargs...)
+ ctx = Turing.OptimizationContext(DynamicPPL.LikelihoodContext())
+ return _optimize(model, Turing.OptimLogDensity(model, ctx), args...; kwargs...)
 end
 
 """
@@ -172,18 +172,18 @@ map_est = optimize(model, MAP(), NelderMead())
 ```
 """
 
-function Optim.optimize(model::Model, ::MAP, options::Optim.Options=Optim.Options(); kwargs...)
+function Optim.optimize(model::DynamicPPL.Model, ::Turing.MAP, options::Optim.Options=Optim.Options(); kwargs...)
  return _map_optimize(model, options; kwargs...)
 end
-function Optim.optimize(model::Model, ::MAP, init_vals::AbstractArray, options::Optim.Options=Optim.Options(); kwargs...)
+function Optim.optimize(model::DynamicPPL.Model, ::Turing.MAP, init_vals::AbstractArray, options::Optim.Options=Optim.Options(); kwargs...)
  return _map_optimize(model, init_vals, options; kwargs...)
 end
-function Optim.optimize(model::Model, ::MAP, optimizer::Optim.AbstractOptimizer, options::Optim.Options=Optim.Options(); kwargs...)
+function Optim.optimize(model::DynamicPPL.Model, ::Turing.MAP, optimizer::Optim.AbstractOptimizer, options::Optim.Options=Optim.Options(); kwargs...)
  return _map_optimize(model, optimizer, options; kwargs...)
 end
 function Optim.optimize(
- model::Model,
- ::MAP,
+ model::DynamicPPL.Model,
+ ::Turing.MAP,
  init_vals::AbstractArray,
  optimizer::Optim.AbstractOptimizer,
  options::Optim.Options=Optim.Options();
@@ -192,9 +192,9 @@ function Optim.optimize(
  return _map_optimize(model, init_vals, optimizer, options; kwargs...)
 end
 
-function _map_optimize(model::Model, args...; kwargs...)
- ctx = OptimizationContext(DynamicPPL.DefaultContext())
- return _optimize(model, OptimLogDensity(model, ctx), args...; kwargs...)
+function _map_optimize(model::DynamicPPL.Model, args...; kwargs...)
+ ctx = Turing.OptimizationContext(DynamicPPL.DefaultContext())
+ return _optimize(model, Turing.OptimLogDensity(model, ctx), args...; kwargs...)
 end
 
 """
@@ -203,8 +203,8 @@ end
 Estimate a mode, i.e., compute a MLE or MAP estimate.
 """
 function _optimize(
- model::Model,
- f::OptimLogDensity,
+ model::DynamicPPL.Model,
+ f::Turing.OptimLogDensity,
  optimizer::Optim.AbstractOptimizer=Optim.LBFGS(),
  args...;
  kwargs...
@@ -213,8 +213,8 @@ function _optimize(
 end
 
 function _optimize(
- model::Model,
- f::OptimLogDensity,
+ model::DynamicPPL.Model,
+ f::Turing.OptimLogDensity,
  options::Optim.Options=Optim.Options(),
  args...;
  kwargs...
@@ -223,8 +223,8 @@ function _optimize(
 end
 
 function _optimize(
- model::Model,
- f::OptimLogDensity,
+ model::DynamicPPL.Model,
+ f::Turing.OptimLogDensity,
  init_vals::AbstractArray=DynamicPPL.getparams(f),
  options::Optim.Options=Optim.Options(),
  args...;
@@ -234,8 +234,8 @@ function _optimize(
 end
 
 function _optimize(
- model::Model,
- f::OptimLogDensity,
+ model::DynamicPPL.Model,
+ f::Turing.OptimLogDensity,
  init_vals::AbstractArray=DynamicPPL.getparams(f),
  optimizer::Optim.AbstractOptimizer=Optim.LBFGS(),
  options::Optim.Options=Optim.Options(),
@@ -244,8 +244,8 @@ function _optimize(
 )
  # Convert the initial values, since it is assumed that users provide them
  # in the constrained space.
- @set! f.varinfo = DynamicPPL.unflatten(f.varinfo, init_vals)
- @set! f.varinfo = DynamicPPL.link!!(f.varinfo, model)
+ Setfield.@set! f.varinfo = DynamicPPL.unflatten(f.varinfo, init_vals)
+ Setfield.@set! f.varinfo = DynamicPPL.link!!(f.varinfo, model)
  init_vals = DynamicPPL.getparams(f)
 
  # Optimize!
@@ -258,10 +258,10 @@ function _optimize(
 
  # Get the VarInfo at the MLE/MAP point, and run the model to ensure
  # correct dimensionality.
- @set! f.varinfo = DynamicPPL.unflatten(f.varinfo, M.minimizer)
- @set! f.varinfo = invlink!!(f.varinfo, model)
+ Setfield.@set! f.varinfo = DynamicPPL.unflatten(f.varinfo, M.minimizer)
+ Setfield.@set! f.varinfo = DynamicPPL.invlink!!(f.varinfo, model)
  vals = DynamicPPL.getparams(f)
- @set! f.varinfo = link!!(f.varinfo, model)
+ Setfield.@set! f.varinfo = DynamicPPL.link!!(f.varinfo, model)
 
  # Make one transition to get the parameter names.
  ts = [Turing.Inference.Transition(
@@ -275,3 +275,5 @@ function _optimize(
 
  return ModeResult(vmat, M, -M.minimum, f)
 end
+
+end # module

src/Turing.jl

@@ -11,6 +11,12 @@ import AdvancedVI
 using DynamicPPL: DynamicPPL, LogDensityFunction
 import DynamicPPL: getspace, NoDist, NamedDist
 import LogDensityProblems
+import NamedArrays
+import Setfield
+import StatsAPI
+import StatsBase
+
+import Printf
 import Random
 
 const PROGRESS = Ref(true)
@@ -48,26 +54,9 @@ using .Inference
 include("variational/VariationalInference.jl")
 using .Variational
 
-@init @require DynamicHMC="bbc10e6e-7c05-544b-b16e-64fede858acb" begin
- @eval Inference begin
- import ..DynamicHMC
-
- if isdefined(DynamicHMC, :mcmc_with_warmup)
- include("contrib/inference/dynamichmc.jl")
- else
- error("Please update DynamicHMC, v1.x is no longer supported")
- end
- end
-end
-
 include("modes/ModeEstimation.jl")
 using .ModeEstimation
 
-@init @require Optim="429524aa-4258-5aef-a3af-852621145aeb" @eval begin
- include("modes/OptimInterface.jl")
- export optimize
-end
-
 ###########
 # Exports #
 ###########
@@ -145,4 +134,22 @@ export @model, # modelling
  optim_objective,
  optim_function,
  optim_problem
+
+function __init__()
+ @static if !isdefined(Base, :get_extension)
+ @require Optim="429524aa-4258-5aef-a3af-852621145aeb" include("../ext/TuringOptimExt.jl")
+ end
+ @require DynamicHMC="bbc10e6e-7c05-544b-b16e-64fede858acb" begin
+ @eval Inference begin
+ import ..DynamicHMC
+
+ if isdefined(DynamicHMC, :mcmc_with_warmup)
+ include("contrib/inference/dynamichmc.jl")
+ else
+ error("Please update DynamicHMC, v1.x is no longer supported")
+ end
+ end
+ end
+end
+
 end

src/contrib/inference/abstractmcmc.jl

@@ -19,7 +19,6 @@ getparams(transition::AdvancedHMC.Transition) = transition.z.θ
 getstats(transition::AdvancedHMC.Transition) = transition.stat
 
 getparams(transition::AdvancedMH.Transition) = transition.params
-getstats(transition) = NamedTuple()
 
 getvarinfo(f::DynamicPPL.LogDensityFunction) = f.varinfo
 getvarinfo(f::LogDensityProblemsAD.ADGradientWrapper) = getvarinfo(parent(f))

test/Project.toml

@@ -8,7 +8,6 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DistributionsAD = "ced4e74d-a319-5a8a-b0ac-84af2272839c"
 DynamicHMC = "bbc10e6e-7c05-544b-b16e-64fede858acb"
 DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
-FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -42,14 +41,13 @@ Distributions = "0.25"
 DistributionsAD = "0.6.3"
 DynamicHMC = "2.1.6, 3.0"
 DynamicPPL = "0.23"
-FillArrays = "=1.0.0"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1.4"
 MCMCChains = "5, 6"
 NamedArrays = "0.9.4"
-Optim = "0.22, 1.0"
+Optim = "1"
 Optimization = "3.5"
 OptimizationOptimJL = "0.1"
 PDMats = "0.10, 0.11"

test/contrib/inference/abstractmcmc.jl

@@ -41,7 +41,7 @@ function initialize_mh(model)
 end
 
 @testset "External samplers" begin
- @testset "AdvancedHMC.jl" begin
+ @turing_testset "AdvancedHMC.jl" begin
  for model in DynamicPPL.TestUtils.DEMO_MODELS
  # Need some functionality to initialize the sampler.
  # TODO: Remove this once the constructors in the respective packages become "lazy".
@@ -52,12 +52,13 @@ end
  5_000;
  nadapts=1_000,
  discard_initial=1_000,
- rtol=0.2
+ rtol=0.2,
+ sampler_name="AdvancedHMC"
  )
  end
  end
 
- @testset "AdvancedMH.jl" begin
+ @turing_testset "AdvancedMH.jl" begin
  for model in DynamicPPL.TestUtils.DEMO_MODELS
  # Need some functionality to initialize the sampler.
  # TODO: Remove this once the constructors in the respective packages become "lazy".
@@ -68,7 +69,8 @@ end
  10_000;
  discard_initial=1_000,
  thinning=10,
- rtol=0.2
+ rtol=0.2,
+ sampler_name="AdvancedMH"
  )
  end
  end