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Remove getmodel and setmodel after transferred to DynamicPPL #2292

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Merged
merged 2 commits into from
Jul 29, 2024
Merged

Remove getmodel and setmodel after transferred to DynamicPPL #2292

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df3f6d9
remove `getmodel` and `setmodel` after transferred to DynamicPPL
sunxd3 Jul 22, 2024
1f075fd
bump version
sunxd3 Jul 22, 2024
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4 changes: 2 additions & 2 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "Turing"
uuid = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
version = "0.33.2"
version = "0.33.3"

[deps]
ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
Expand Down Expand Up @@ -63,7 +63,7 @@ Distributions = "0.23.3, 0.24, 0.25"
DistributionsAD = "0.6"
DocStringExtensions = "0.8, 0.9"
DynamicHMC = "3.4"
DynamicPPL = "0.28.1"
DynamicPPL = "0.28.2"
Compat = "4.15.0"
EllipticalSliceSampling = "0.5, 1, 2"
ForwardDiff = "0.10.3"
Expand Down
41 changes: 2 additions & 39 deletions src/mcmc/abstractmcmc.jl
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Expand Up @@ -17,50 +17,13 @@ function transition_to_turing(f::LogDensityProblemsAD.ADGradientWrapper, transit
return transition_to_turing(parent(f), transition)
end

"""
getmodel(f)

Return the `DynamicPPL.Model` wrapped in the given log-density function `f`.
"""
getmodel(f::LogDensityProblemsAD.ADGradientWrapper) = getmodel(parent(f))
getmodel(f::DynamicPPL.LogDensityFunction) = f.model

"""
setmodel(f, model[, adtype])

Set the `DynamicPPL.Model` in the given log-density function `f` to `model`.

!!! warning
Note that if `f` is a `LogDensityProblemsAD.ADGradientWrapper` wrapping a
`DynamicPPL.LogDensityFunction`, performing an update of the `model` in `f`
might require recompilation of the gradient tape, depending on the AD backend.
"""
function setmodel(
f::LogDensityProblemsAD.ADGradientWrapper,
model::DynamicPPL.Model,
adtype::ADTypes.AbstractADType
)
# TODO: Should we handle `SciMLBase.NoAD`?
# For an `ADGradientWrapper` we do the following:
# 1. Update the `Model` in the underlying `LogDensityFunction`.
# 2. Re-construct the `ADGradientWrapper` using `ADgradient` using the provided `adtype`
# to ensure that the recompilation of gradient tapes, etc. also occur. For example,
# ReverseDiff.jl in compiled mode will cache the compiled tape, which means that just
# replacing the corresponding field with the new model won't be sufficient to obtain
# the correct gradients.
return LogDensityProblemsAD.ADgradient(adtype, setmodel(parent(f), model))
end
function setmodel(f::DynamicPPL.LogDensityFunction, model::DynamicPPL.Model)
return Accessors.@set f.model = model
end

function varinfo_from_logdensityfn(f::LogDensityProblemsAD.ADGradientWrapper)
return varinfo_from_logdensityfn(parent(f))
end
varinfo_from_logdensityfn(f::DynamicPPL.LogDensityFunction) = f.varinfo

function varinfo(state::TuringState)
θ = getparams(getmodel(state.logdensity), state.state)
θ = getparams(DynamicPPL.getmodel(state.logdensity), state.state)
# TODO: Do we need to link here first?
return DynamicPPL.unflatten(varinfo_from_logdensityfn(state.logdensity), θ)
end
Expand Down Expand Up @@ -97,7 +60,7 @@ function recompute_logprob!!(
)
# Re-using the log-density function from the `state` and updating only the `model` field,
# since the `model` might now contain different conditioning values.
f = setmodel(state.logdensity, model, sampler.alg.adtype)
f = DynamicPPL.setmodel(state.logdensity, model, sampler.alg.adtype)
# Recompute the log-probability with the new `model`.
state_inner = recompute_logprob!!(
rng, AbstractMCMC.LogDensityModel(f), sampler.alg.sampler, state.state
Expand Down