Add support for Turing v0.33

Project.toml

@@ -1,7 +1,7 @@
 name = "Pathfinder"
 uuid = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 authors = ["Seth Axen <[email protected]> and contributors"]
-version = "0.9.0-DEV"
+version = "0.9.0"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
@@ -41,8 +41,8 @@ ADTypes = "0.2, 1"
 Accessors = "0.1.12"
 Distributions = "0.25.87"
 DynamicHMC = "3.4.0"
-DynamicPPL = "0.24.7, 0.25, 0.27"
-Folds = "0.2.2"
+DynamicPPL = "0.25.2, 0.27"
+Folds = "0.2.9"
 ForwardDiff = "0.10.19"
 IrrationalConstants = "0.1.1, 0.2"
 LinearAlgebra = "1.6"
@@ -61,8 +61,8 @@ ReverseDiff = "1.4.5"
 SciMLBase = "1.95.0, 2"
 Statistics = "1.6"
 StatsBase = "0.33.7, 0.34"
-Transducers = "0.4.66"
-Turing = "0.30.5, 0.31, 0.32"
+Transducers = "0.4.81"
+Turing = "0.31.4, 0.32, 0.33"
 UnPack = "1"
 julia = "1.6"
 

docs/Project.toml

@@ -30,4 +30,4 @@ StatsFuns = "1"
 StatsPlots = "0.14.21, 0.15"
 TransformVariables = "0.6.2, 0.7, 0.8"
 TransformedLogDensities = "1.0.2"
-Turing = "0.30.5, 0.31, 0.32"
+Turing = "0.31.4, 0.32, 0.33"

ext/PathfinderTuringExt.jl

@@ -2,149 +2,86 @@ module PathfinderTuringExt
 
 if isdefined(Base, :get_extension)
     using Accessors: Accessors
-    using ADTypes: ADTypes
     using DynamicPPL: DynamicPPL
     using MCMCChains: MCMCChains
     using Pathfinder: Pathfinder
-    using Random: Random
     using Turing: Turing
-    import Pathfinder: flattened_varnames_list
 else  # using Requires
     using ..Accessors: Accessors
-    using ..ADTypes: ADTypes
     using ..DynamicPPL: DynamicPPL
     using ..MCMCChains: MCMCChains
     using ..Pathfinder: Pathfinder
-    using ..Random: Random
     using ..Turing: Turing
-    import ..Pathfinder: flattened_varnames_list
 end
 
-# utilities for working with Turing model parameter names using only the DynamicPPL API
-
-function Pathfinder.flattened_varnames_list(model::DynamicPPL.Model)
-    varnames_ranges = varnames_to_ranges(model)
-    nsyms = maximum(maximum, values(varnames_ranges))
-    syms = Vector{Symbol}(undef, nsyms)
-    for (var_name, range) in varnames_to_ranges(model)
-        sym = Symbol(var_name)
-        if length(range) == 1
-            syms[range[begin]] = sym
-            continue
-        end
-        for i in eachindex(range)
-            syms[range[i]] = Symbol("$sym[$i]")
-        end
-    end
-    return syms
-end
-
-# code snippet shared by @torfjelde
 """
-    varnames_to_ranges(model::DynamicPPL.Model)
-    varnames_to_ranges(model::DynamicPPL.VarInfo)
-    varnames_to_ranges(model::DynamicPPL.Metadata)
-
-Get `Dict` mapping variable names in model to their ranges in a corresponding parameter vector.
+    create_log_density_problem(model::DynamicPPL.Model)
 
-# Examples
+Create a log density problem from a `model`.
 
-```julia
-julia> @model function demo()
-           s ~ Dirac(1)
-           x = Matrix{Float64}(undef, 2, 4)
-           x[1, 1] ~ Dirac(2)
-           x[2, 1] ~ Dirac(3)
-           x[3] ~ Dirac(4)
-           y ~ Dirac(5)
-           x[4] ~ Dirac(6)
-           x[:, 3] ~ arraydist([Dirac(7), Dirac(8)])
-           x[[2, 1], 4] ~ arraydist([Dirac(9), Dirac(10)])
-           return s, x, y
-       end
-demo (generic function with 2 methods)
-
-julia> demo()()
-(1, Any[2.0 4.0 7 10; 3.0 6.0 8 9], 5)
+The return value is an object implementing the LogDensityProblems API whose log-density is
+that of the `model` transformed to unconstrained space with the appropriate log-density
+adjustment due to change of variables.
+"""
+function create_log_density_problem(model::DynamicPPL.Model)
+    # create an unconstrained VarInfo
+    varinfo = DynamicPPL.link(DynamicPPL.VarInfo(model), model)
+    # DefaultContext ensures that the log-density adjustment is computed
+    prob = DynamicPPL.LogDensityFunction(varinfo, model, DynamicPPL.DefaultContext())
+    return prob
+end
 
-julia> varnames_to_ranges(demo())
-Dict{AbstractPPL.VarName, UnitRange{Int64}} with 8 entries:
-  s           => 1:1
-  x[4]        => 5:5
-  x[:,3]      => 6:7
-  x[1,1]      => 2:2
-  x[2,1]      => 3:3
-  x[[2, 1],4] => 8:9
-  x[3]        => 4:4
-  y           => 10:10
-```
 """
-function varnames_to_ranges end
+    draws_to_chains(model::DynamicPPL.Model, draws) -> MCMCChains.Chains
 
-varnames_to_ranges(model::DynamicPPL.Model) = varnames_to_ranges(DynamicPPL.VarInfo(model))
-function varnames_to_ranges(varinfo::DynamicPPL.UntypedVarInfo)
-    return varnames_to_ranges(varinfo.metadata)
-end
-function varnames_to_ranges(varinfo::DynamicPPL.TypedVarInfo)
-    offset = 0
-    dicts = map(varinfo.metadata) do md
-        vns2ranges = varnames_to_ranges(md)
-        vals = collect(values(vns2ranges))
-        vals_offset = map(r -> offset .+ r, vals)
-        offset += reduce((curr, r) -> max(curr, r[end]), vals; init=0)
-        Dict(zip(keys(vns2ranges), vals_offset))
+Convert a `(nparams, ndraws)` matrix of unconstrained `draws` to an `MCMCChains.Chains`
+object with corresponding constrained draws and names according to `model`.
+"""
+function draws_to_chains(model::DynamicPPL.Model, draws::AbstractMatrix)
+    varinfo = DynamicPPL.link(DynamicPPL.VarInfo(model), model)
+    draw_con_varinfos = map(eachcol(draws)) do draw
+        # this re-evaluates the model but allows supporting dynamic bijectors
+        # https://github.com/TuringLang/Turing.jl/issues/2195
+        return Turing.Inference.getparams(model, DynamicPPL.unflatten(varinfo, draw))
     end
-
-    return reduce(merge, dicts)
-end
-function varnames_to_ranges(metadata::DynamicPPL.Metadata)
-    idcs = map(Base.Fix1(getindex, metadata.idcs), metadata.vns)
-    ranges = metadata.ranges[idcs]
-    return Dict(zip(metadata.vns, ranges))
+    param_con_names = map(first, first(draw_con_varinfos))
+    draws_con = reduce(
+        vcat, Iterators.map(transpose ∘ Base.Fix1(map, last), draw_con_varinfos)
+    )
+    chns = MCMCChains.Chains(draws_con, param_con_names)
+    return chns
 end
 
-function Pathfinder.pathfinder(
-    model::DynamicPPL.Model;
-    rng=Random.GLOBAL_RNG,
-    init_scale=2,
-    init_sampler=Pathfinder.UniformSampler(init_scale),
-    init=nothing,
-    adtype::ADTypes.AbstractADType=Pathfinder.default_ad(),
-    kwargs...,
-)
-    var_names = flattened_varnames_list(model)
-    prob = Turing.optim_problem(
-        model, Turing.MAP(); constrained=false, init_theta=init, adtype
-    )
-    init_sampler(rng, prob.prob.u0)
-    result = Pathfinder.pathfinder(prob.prob; rng, input=model, kwargs...)
-    draws = reduce(vcat, transpose.(prob.transform.(eachcol(result.draws))))
-    chns = MCMCChains.Chains(draws, var_names; info=(; pathfinder_result=result))
-    result_new = Accessors.@set result.draws_transformed = chns
+function Pathfinder.pathfinder(model::DynamicPPL.Model; kwargs...)
+    log_density_problem = create_log_density_problem(model)
+    result = Pathfinder.pathfinder(log_density_problem; input=model, kwargs...)
+
+    # add transformed draws as Chains
+    chains_info = (; pathfinder_result=result)
+    chains = Accessors.@set draws_to_chains(model, result.draws).info = chains_info
+    result_new = Accessors.@set result.draws_transformed = chains
     return result_new
 end
 
-function Pathfinder.multipathfinder(
-    model::DynamicPPL.Model,
-    ndraws::Int;
-    rng=Random.GLOBAL_RNG,
-    init_scale=2,
-    init_sampler=Pathfinder.UniformSampler(init_scale),
-    nruns::Int,
-    adtype=Pathfinder.default_ad(),
-    kwargs...,
-)
-    var_names = flattened_varnames_list(model)
-    fun = Turing.optim_function(model, Turing.MAP(); constrained=false, adtype)
-    init1 = fun.init()
-    init = [init_sampler(rng, init1)]
-    for _ in 2:nruns
-        push!(init, init_sampler(rng, deepcopy(init1)))
+function Pathfinder.multipathfinder(model::DynamicPPL.Model, ndraws::Int; kwargs...)
+    log_density_problem = create_log_density_problem(model)
+    result = Pathfinder.multipathfinder(log_density_problem, ndraws; input=model, kwargs...)
+
+    # add transformed draws as Chains
+    chains_info = (; pathfinder_result=result)
+    chains = Accessors.@set draws_to_chains(model, result.draws).info = chains_info
+
+    # add transformed draws as Chains for each individual path
+    single_path_results_new = map(result.pathfinder_results) do r
+        single_chains_info = (; pathfinder_result=r)
+        single_chains = Accessors.@set draws_to_chains(model, r.draws).info =
+            single_chains_info
+        r_new = Accessors.@set r.draws_transformed = single_chains
+        return r_new
     end
-    result = Pathfinder.multipathfinder(fun.func, ndraws; rng, input=model, init, kwargs...)
-    draws = reduce(vcat, transpose.(fun.transform.(eachcol(result.draws))))
-    chns = MCMCChains.Chains(draws, var_names; info=(; pathfinder_result=result))
-    result_new = Accessors.@set result.draws_transformed = chns
+
+    result_new = Accessors.@set (Accessors.@set result.draws_transformed =
+        chains).pathfinder_results = single_path_results_new
     return result_new
 end
 

src/Pathfinder.jl

@@ -50,8 +50,6 @@ include("resample.jl")
 include("singlepath.jl")
 include("multipath.jl")
 
-include("integration/turing.jl")
-
 function __init__()
     Requires.@require AdvancedHMC = "0bf59076-c3b1-5ca4-86bd-e02cd72cde3d" begin
         include("integration/advancedhmc.jl")

test/integration/AdvancedHMC/Project.toml

@@ -16,7 +16,7 @@ TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
 TransformedLogDensities = "f9bc47f6-f3f8-4f3b-ab21-f8bc73906f26"
 
 [compat]
-AdvancedHMC = "0.4, 0.5.2, 0.6"
+AdvancedHMC = "0.6"
 Distributions = "0.25.87"
 ForwardDiff = "0.10.19"
 LogDensityProblems = "2.1.0"

test/integration/Turing/Project.toml

@@ -1,10 +1,13 @@
 [deps]
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
 Pathfinder = "b1d3bc72-d0e7-4279-b92f-7fa5d6d2d454"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
+LogDensityProblems = "2.1.0"
 Pathfinder = "0.9"
-Turing = "0.30.5, 0.31, 0.32"
+Turing = "0.31.4, 0.32, 0.33"
 julia = "1.6"