Try #147:

Project.toml

@@ -8,6 +8,7 @@ AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
+NaturalSort = "c020b1a1-e9b0-503a-9c33-f039bfc54a85"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ZygoteRules = "700de1a5-db45-46bc-99cf-38207098b444"
 
@@ -16,6 +17,7 @@ AbstractMCMC = "1"
 Bijectors = "0.5.2, 0.6, 0.7, 0.8"
 Distributions = "0.22, 0.23"
 MacroTools = "0.5.1"
+NaturalSort = "1"
 ZygoteRules = "0.2"
 julia = "1"
 

src/DynamicPPL.jl

@@ -5,6 +5,7 @@ using Distributions
 using Bijectors
 
 import AbstractMCMC
+import NaturalSort
 import MacroTools
 import ZygoteRules
 

src/prob_macro.jl

@@ -190,12 +190,13 @@ function Distributions.loglikelihood(
         # Element-wise likelihood for each value in chain
         chain = right.chain
         ctx = LikelihoodContext()
-        return map(1:length(chain)) do i
-            c = chain[i]
-            _setval!(vi, c)
+        iters = Iterators.product(1:size(chain, 1), 1:size(chain, 3))
+        logps = map(iters) do (sample_idx, chain_idx)
+            setval!(vi, chain, sample_idx, chain_idx)
             model(vi, SampleFromPrior(), ctx)
             return getlogp(vi)
         end
+        return reshape(logps, size(chain, 1), size(chain, 3))
     else
         # Likelihood without chain
         # Rhs values are used in the context
@@ -231,16 +232,3 @@ end
     return :(Model{$(Tuple(missings))}(model.f, $(to_namedtuple_expr(argnames, argvals)),
                                        model.defaults))
 end
-
-_setval!(vi::TypedVarInfo, c::AbstractChains) = _setval!(vi.metadata, vi, c)
-@generated function _setval!(md::NamedTuple{names}, vi, c) where {names}
-    return Expr(:block, map(names) do n
-        quote
-            for vn in md.$n.vns
-                val = vec(c[Symbol(vn)])
-                setval!(vi, val, vn)
-                settrans!(vi, false, vn)
-            end
-        end
-    end...)
-end

src/varinfo.jl

@@ -279,7 +279,7 @@ Set the value(s) of `vn` in the metadata of `vi` to `val`.
 
 The values may or may not be transformed to Euclidean space.
 """
-setval!(vi::VarInfo, val, vn::VarName) = getmetadata(vi, vn).vals[getrange(vi, vn)] = val
+setval!(vi::VarInfo, val, vn::VarName) = getmetadata(vi, vn).vals[getrange(vi, vn)] = [val;]
 
 """
     getval(vi::VarInfo, vns::Vector{<:VarName})
@@ -1144,3 +1144,49 @@ function updategid!(vi::AbstractVarInfo, vn::VarName, spl::Sampler)
         setgid!(vi, spl.selector, vn)
     end
 end
+
+setval!(vi::AbstractVarInfo, x) = _setval!(vi, values(x), keys(x))
+function setval!(vi::AbstractVarInfo, chains::AbstractChains, sample_idx::Int, chain_idx::Int)
+    return _setval!(vi, chains.value[sample_idx, :, chain_idx], keys(chains))
+end
+
+function _setval!(vi::AbstractVarInfo, values, keys)
+    for vn in Base.keys(vi)
+        _setval_kernel!(vi, vn, values, keys)
+    end
+    return vi
+end
+_setval!(vi::TypedVarInfo, values, keys) = _typed_setval!(vi, vi.metadata, values, keys)
+@generated function _typed_setval!(
+    vi::TypedVarInfo,
+    metadata::NamedTuple{names},
+    values,
+    keys
+) where {names}
+    updates = map(names) do n
+        quote
+            for vn in metadata.$n.vns
+                _setval_kernel!(vi, vn, values, keys)
+            end
+        end
+    end
+
+    return quote
+        $(updates...)
+        return vi
+    end
+end
+
+function _setval_kernel!(vi::AbstractVarInfo, vn::VarName, values, keys)
+    sym = Symbol(vn)
+    regex = Regex("^$sym\$|^$sym\\[")
+    indices = findall(x -> match(regex, string(x)) !== nothing, keys)
+    if !isempty(indices)
+        sorted_indices = sort!(indices; by=i -> string(keys[i]), lt=NaturalSort.natural)
+        val = mapreduce(vcat, sorted_indices) do i
+            values[i]
+        end
+        setval!(vi, val, vn)
+        settrans!(vi, false, vn)
+    end
+end

test/prob_macro.jl

@@ -10,7 +10,7 @@ Random.seed!(129)
 
 @testset "prob_macro" begin
     @testset "scalar" begin
-        @model demo(x) = begin
+        @model function demo(x)
             m ~ Normal()
             x ~ Normal(m, 1)
         end
@@ -29,37 +29,44 @@ Random.seed!(129)
         @test logprob"x = xval | m = mval, model = model" == loglike
         @test logprob"x = xval, m = mval | model = model" == logjoint
 
+        varinfo = VarInfo(demo(missing))
+        @test logprob"x = xval, m = mval | model = model, varinfo = varinfo" == logjoint
+
         varinfo = VarInfo(demo(xval))
         @test logprob"m = mval | model = model, varinfo = varinfo" == logprior
         @test logprob"m = mval | x = xval, model = model, varinfo = varinfo" == logprior
         @test logprob"x = xval | m = mval, model = model, varinfo = varinfo" == loglike
-        varinfo = VarInfo(demo(missing))
-        @test logprob"x = xval, m = mval | model = model, varinfo = varinfo" == logjoint
 
         chain = sample(demo(xval), IS(), iters; save_state = true)
         chain2 = Chains(chain.value, chain.logevidence, chain.name_map, NamedTuple())
-        lps = logpdf.(Normal.(vec(chain["m"]), 1), xval)
+        lps = logpdf.(Normal.(chain["m"], 1), xval)
         @test logprob"x = xval | chain = chain" == lps
         @test logprob"x = xval | chain = chain2, model = model" == lps
-        varinfo = VarInfo(demo(xval))
         @test logprob"x = xval | chain = chain, varinfo = varinfo" == lps
         @test logprob"x = xval | chain = chain2, model = model, varinfo = varinfo" == lps
+
+        # multiple chains
+        pchain = chainscat(chain, chain)
+        pchain2 = chainscat(chain2, chain2)
+        plps = repeat(lps, 1, 2)
+        @test logprob"x = xval | chain = pchain" == plps
+        @test logprob"x = xval | chain = pchain2, model = model" == plps
+        @test logprob"x = xval | chain = pchain, varinfo = varinfo" == plps
+        @test logprob"x = xval | chain = pchain2, model = model, varinfo = varinfo" == plps
     end
 
     @testset "vector" begin
         n = 5
-        @model demo(x, n = n, ::Type{T} = Float64) where {T} = begin
-            m = Vector{T}(undef, n)
-            @. m ~ Normal()
-            @. x ~ Normal.(m, 1)
+        @model function demo(x, n = n)
+            m ~ MvNormal(n, 1.0)
+            x ~ MvNormal(m, 1.0)
         end
         mval = rand(n)
         xval = rand(n)
         iters = 1000
 
-        logprior = sum(logpdf.(Normal(), mval))
-        like(m, x) = sum(logpdf.(Normal.(m, 1), x))
-        loglike = like(mval, xval)
+        logprior = logpdf(MvNormal(n, 1.0), mval)
+        loglike = logpdf(MvNormal(mval, 1.0), xval)
         logjoint = logprior + loglike
 
         model = demo(xval)
@@ -76,12 +83,49 @@ Random.seed!(129)
         chain2 = Chains(chain.value, chain.logevidence, chain.name_map, NamedTuple())
 
         names = namesingroup(chain, "m")
-        lps = map(1:iters) do iter
-            like([chain[iter, name, 1] for name in names], xval)
-        end
+        lps = [
+            logpdf(MvNormal(chain.value[i, names, j], 1.0), xval)
+            for i in 1:size(chain, 1), j in 1:size(chain, 3)
+        ]
         @test logprob"x = xval | chain = chain" == lps
         @test logprob"x = xval | chain = chain2, model = model" == lps
         @test logprob"x = xval | chain = chain, varinfo = varinfo" == lps
         @test logprob"x = xval | chain = chain2, model = model, varinfo = varinfo" == lps
+
+        # multiple chains
+        pchain = chainscat(chain, chain)
+        pchain2 = chainscat(chain2, chain2)
+        plps = repeat(lps, 1, 2)
+        @test logprob"x = xval | chain = pchain" == plps
+        @test logprob"x = xval | chain = pchain2, model = model" == plps
+        @test logprob"x = xval | chain = pchain, varinfo = varinfo" == plps
+        @test logprob"x = xval | chain = pchain2, model = model, varinfo = varinfo" == plps
+    end
+
+    @testset "issue#137" begin
+        @model function model1(y, group, n_groups)
+            σ ~ truncated(Cauchy(0, 1), 0, Inf)
+            α ~ filldist(Normal(0, 10), n_groups)
+            μ = α[group]
+            y ~ MvNormal(μ, σ)
+        end
+
+        y = randn(100)
+        group = rand(1:4, 100)
+        n_groups = 4
+
+        chain1 = sample(model1(y, group, n_groups), NUTS(0.65), 2_000; save_state=true)
+        logprob"y = y[[1]] | group = group[[1]], n_groups = n_groups, chain = chain1"
+
+        @model function model2(y, group, n_groups)
+            σ ~ truncated(Cauchy(0, 1), 0, Inf)
+            α ~ filldist(Normal(0, 10), n_groups)
+            for i in 1:length(y)
+                y[i] ~ Normal(α[group[i]], σ)
+            end
+        end
+
+        chain2 = sample(model2(y, group, n_groups), NUTS(0.65), 2_000; save_state=true)
+        logprob"y = y[[1]] | group = group[[1]], n_groups = n_groups, chain = chain2"
     end
 end

test/varinfo.jl

@@ -505,4 +505,49 @@ include(dir*"/test/test_utils/AllUtils.jl")
         @test vi.metadata.w.gids[1] == Set([hmc.selector])
         @test vi.metadata.u.gids[1] == Set([hmc.selector])
     end
+
+    @testset "setval!" begin
+        @model function testmodel(x)
+            n = length(x)
+            s ~ truncated(Normal(), 0, Inf)
+            m ~ MvNormal(n, 1.0)
+            x ~ MvNormal(m, s)
+        end
+
+        x = randn(5)
+        model = testmodel(x)
+
+        # UntypedVarInfo
+        vi = VarInfo()
+        model(vi, SampleFromPrior())
+
+        vicopy = deepcopy(vi)
+        DynamicPPL.setval!(vicopy, (m = zeros(5),))
+        @test vicopy[@varname(m)] == zeros(5)
+        @test vicopy[@varname(s)] == vi[@varname(s)]
+
+        DynamicPPL.setval!(vicopy, (; (Symbol("m[$i]") => i for i in (1, 3, 5, 4, 2))...))
+        @test vicopy[@varname(m)] == 1:5
+        @test vicopy[@varname(s)] == vi[@varname(s)]
+
+        DynamicPPL.setval!(vicopy, (s = 42,))
+        @test vicopy[@varname(m)] == 1:5
+        @test vicopy[@varname(s)] == 42
+
+        # TypedVarInfo
+        vi = VarInfo(model)
+
+        vicopy = deepcopy(vi)
+        DynamicPPL.setval!(vicopy, (m = zeros(5),))
+        @test vicopy[@varname(m)] == zeros(5)
+        @test vicopy[@varname(s)] == vi[@varname(s)]
+
+        DynamicPPL.setval!(vicopy, (; (Symbol("m[$i]") => i for i in (1, 3, 5, 4, 2))...))
+        @test vicopy[@varname(m)] == 1:5
+        @test vicopy[@varname(s)] == vi[@varname(s)]
+
+        DynamicPPL.setval!(vicopy, (s = 42,))
+        @test vicopy[@varname(m)] == 1:5
+        @test vicopy[@varname(s)] == 42
+    end
 end