Allow generation of ParamsWithStats from FastLDF plus parameters, and also bundle_samples (#1129)

* Implement `ParamsWithStats` for `FastLDF`

* Add comments

* Implement `bundle_samples` for ParamsWithStats -> MCMCChains

* Remove redundant comment

* don't need Statistics?

Author: penelopeysm

ext/DynamicPPLMCMCChainsExt.jl

@@ -140,6 +140,43 @@ function AbstractMCMC.to_samples(
     end
 end
 
+function AbstractMCMC.bundle_samples(
+    ts::Vector{<:DynamicPPL.ParamsWithStats},
+    model::DynamicPPL.Model,
+    spl::AbstractMCMC.AbstractSampler,
+    state,
+    chain_type::Type{MCMCChains.Chains};
+    save_state=false,
+    stats=missing,
+    sort_chain=false,
+    discard_initial=0,
+    thinning=1,
+    kwargs...,
+)
+    bare_chain = AbstractMCMC.from_samples(MCMCChains.Chains, reshape(ts, :, 1))
+
+    # Add additional MCMC-specific info
+    info = bare_chain.info
+    if save_state
+        info = merge(info, (model=model, sampler=spl, samplerstate=state))
+    end
+    if !ismissing(stats)
+        info = merge(info, (start_time=stats.start, stop_time=stats.stop))
+    end
+
+    # Reconstruct the chain with the extra information
+    # Yeah, this is quite ugly. Blame MCMCChains.
+    chain = MCMCChains.Chains(
+        bare_chain.value.data,
+        names(bare_chain),
+        bare_chain.name_map;
+        info=info,
+        start=discard_initial + 1,
+        thin=thinning,
+    )
+    return sort_chain ? sort(chain) : chain
+end
+
 """
     predict([rng::AbstractRNG,] model::Model, chain::MCMCChains.Chains; include_all=false)
 

src/DynamicPPL.jl

@@ -202,14 +202,14 @@ include("logdensityfunction.jl")
 include("model_utils.jl")
 include("extract_priors.jl")
 include("values_as_in_model.jl")
+include("experimental.jl")
 include("chains.jl")
 include("bijector.jl")
 
 include("debug_utils.jl")
 using .DebugUtils
 include("test_utils.jl")
 
-include("experimental.jl")
 include("deprecated.jl")
 
 if isdefined(Base.Experimental, :register_error_hint)

src/chains.jl

@@ -133,3 +133,60 @@ function ParamsWithStats(
     end
     return ParamsWithStats(params, stats)
 end
+
+"""
+    ParamsWithStats(
+        param_vector::AbstractVector,
+        ldf::DynamicPPL.Experimental.FastLDF,
+        stats::NamedTuple=NamedTuple();
+        include_colon_eq::Bool=true,
+        include_log_probs::Bool=true,
+    )
+
+Generate a `ParamsWithStats` by re-evaluating the given `ldf` with the provided
+`param_vector`.
+
+This method is intended to replace the old method of obtaining parameters and statistics
+via `unflatten` plus re-evaluation. It is faster for two reasons:
+
+1. It does not rely on `deepcopy`-ing the VarInfo object (this used to be mandatory as
+   otherwise re-evaluation would mutate the VarInfo, rendering it unusable for subsequent
+   MCMC iterations).
+2. The re-evaluation is faster as it uses `OnlyAccsVarInfo`.
+"""
+function ParamsWithStats(
+    param_vector::AbstractVector,
+    ldf::DynamicPPL.Experimental.FastLDF,
+    stats::NamedTuple=NamedTuple();
+    include_colon_eq::Bool=true,
+    include_log_probs::Bool=true,
+)
+    strategy = InitFromParams(
+        VectorWithRanges(ldf._iden_varname_ranges, ldf._varname_ranges, param_vector),
+        nothing,
+    )
+    accs = if include_log_probs
+        (
+            DynamicPPL.LogPriorAccumulator(),
+            DynamicPPL.LogLikelihoodAccumulator(),
+            DynamicPPL.ValuesAsInModelAccumulator(include_colon_eq),
+        )
+    else
+        (DynamicPPL.ValuesAsInModelAccumulator(include_colon_eq),)
+    end
+    _, vi = DynamicPPL.Experimental.fast_evaluate!!(
+        ldf.model, strategy, AccumulatorTuple(accs)
+    )
+    params = DynamicPPL.getacc(vi, Val(:ValuesAsInModel)).values
+    if include_log_probs
+        stats = merge(
+            stats,
+            (
+                logprior=DynamicPPL.getlogprior(vi),
+                loglikelihood=DynamicPPL.getloglikelihood(vi),
+                lp=DynamicPPL.getlogjoint(vi),
+            ),
+        )
+    end
+    return ParamsWithStats(params, stats)
+end

src/fasteval.jl

@@ -3,6 +3,7 @@ using DynamicPPL:
     AccumulatorTuple,
     InitContext,
     InitFromParams,
+    AbstractInitStrategy,
     LogJacobianAccumulator,
     LogLikelihoodAccumulator,
     LogPriorAccumulator,
@@ -28,6 +29,60 @@ using LogDensityProblems: LogDensityProblems
 import DifferentiationInterface as DI
 using Random: Random
 
+"""
+    DynamicPPL.Experimental.fast_evaluate!!(
+        [rng::Random.AbstractRNG,]
+        model::Model,
+        strategy::AbstractInitStrategy,
+        accs::AccumulatorTuple, params::AbstractVector{<:Real}
+    )
+
+Evaluate a model using parameters obtained via `strategy`, and only computing the results in
+the provided accumulators.
+
+It is assumed that the accumulators passed in have been initialised to appropriate values,
+as this function will not reset them. The default constructors for each accumulator will do
+this for you correctly.
+
+Returns a tuple of the model's return value, plus an `OnlyAccsVarInfo`. Note that the `accs`
+argument may be mutated (depending on how the accumulators are implemented); hence the `!!`
+in the function name.
+"""
+@inline function fast_evaluate!!(
+    # Note that this `@inline` is mandatory for performance. If it's not inlined, it leads
+    # to extra allocations (even for trivial models) and much slower runtime.
+    rng::Random.AbstractRNG,
+    model::Model,
+    strategy::AbstractInitStrategy,
+    accs::AccumulatorTuple,
+)
+    ctx = InitContext(rng, strategy)
+    model = DynamicPPL.setleafcontext(model, ctx)
+    # Calling `evaluate!!` would be fine, but would lead to an extra call to resetaccs!!,
+    # which is unnecessary. So we shortcircuit this by simply calling `_evaluate!!`
+    # directly. To preserve thread-safety we need to reproduce the ThreadSafeVarInfo logic
+    # here.
+    # TODO(penelopeysm): This should _not_ check Threads.nthreads(). I still don't know what
+    # it _should_ do, but this is wrong regardless.
+    # https://github.com/TuringLang/DynamicPPL.jl/issues/1086
+    vi = if Threads.nthreads() > 1
+        param_eltype = DynamicPPL.get_param_eltype(strategy)
+        accs = map(accs) do acc
+            DynamicPPL.convert_eltype(float_type_with_fallback(param_eltype), acc)
+        end
+        ThreadSafeVarInfo(OnlyAccsVarInfo(accs))
+    else
+        OnlyAccsVarInfo(accs)
+    end
+    return DynamicPPL._evaluate!!(model, vi)
+end
+@inline function fast_evaluate!!(
+    model::Model, strategy::AbstractInitStrategy, accs::AccumulatorTuple
+)
+    # This `@inline` is also mandatory for performance
+    return fast_evaluate!!(Random.default_rng(), model, strategy, accs)
+end
+
 """
     FastLDF(
         model::Model,
@@ -213,31 +268,11 @@ struct FastLogDensityAt{M<:Model,F<:Function,N<:NamedTuple}
     varname_ranges::Dict{VarName,RangeAndLinked}
 end
 function (f::FastLogDensityAt)(params::AbstractVector{<:Real})
-    ctx = InitContext(
-        Random.default_rng(),
-        InitFromParams(
-            VectorWithRanges(f.iden_varname_ranges, f.varname_ranges, params), nothing
-        ),
+    strategy = InitFromParams(
+        VectorWithRanges(f.iden_varname_ranges, f.varname_ranges, params), nothing
     )
-    model = DynamicPPL.setleafcontext(f.model, ctx)
     accs = fast_ldf_accs(f.getlogdensity)
-    # Calling `evaluate!!` would be fine, but would lead to an extra call to resetaccs!!,
-    # which is unnecessary. So we shortcircuit this by simply calling `_evaluate!!`
-    # directly. To preserve thread-safety we need to reproduce the ThreadSafeVarInfo logic
-    # here.
-    # TODO(penelopeysm): This should _not_ check Threads.nthreads(). I still don't know what
-    # it _should_ do, but this is wrong regardless.
-    # https://github.com/TuringLang/DynamicPPL.jl/issues/1086
-    vi = if Threads.nthreads() > 1
-        accs = map(
-            acc -> DynamicPPL.convert_eltype(float_type_with_fallback(eltype(params)), acc),
-            accs,
-        )
-        ThreadSafeVarInfo(OnlyAccsVarInfo(accs))
-    else
-        OnlyAccsVarInfo(accs)
-    end
-    _, vi = DynamicPPL._evaluate!!(model, vi)
+    _, vi = fast_evaluate!!(f.model, strategy, accs)
     return f.getlogdensity(vi)
 end
 

test/chains.jl

@@ -4,7 +4,7 @@ using DynamicPPL
 using Distributions
 using Test
 
-@testset "ParamsWithStats" begin
+@testset "ParamsWithStats from VarInfo" begin
     @model function f(z)
         x ~ Normal()
         y := x + 1
@@ -66,4 +66,30 @@ using Test
     end
 end
 
+@testset "ParamsWithStats from FastLDF" begin
+    @testset "$(m.f)" for m in DynamicPPL.TestUtils.DEMO_MODELS
+        unlinked_vi = VarInfo(m)
+        @testset "$islinked" for islinked in (false, true)
+            vi = if islinked
+                DynamicPPL.link!!(unlinked_vi, m)
+            else
+                unlinked_vi
+            end
+            params = [x for x in vi[:]]
+
+            # Get the ParamsWithStats using FastLDF
+            fldf = DynamicPPL.Experimental.FastLDF(m, getlogjoint, vi)
+            ps = ParamsWithStats(params, fldf)
+
+            # Check that length of parameters is as expected
+            @test length(ps.params) == length(keys(vi))
+
+            # Iterate over all variables to check that their values match
+            for vn in keys(vi)
+                @test ps.params[vn] == vi[vn]
+            end
+        end
+    end
+end
+
 end # module