Update variational inference interface to match [email protected]

HISTORY.md

@@ -1,5 +1,55 @@
 # 0.42.0
 
+## Breaking Changes
+
+**AdvancedVI 0.5**
+
+Turing.jl v0.42 updates `AdvancedVI.jl` compatibility to 0.5.
+Most of the changes introduced in `[email protected]` are structural, with some changes spilling out into the interface.
+The summary of the changes below are the things that affect the end-users of Turing.
+For a more comprehensive list of changes, please refer to the [changelogs](https://github.com/TuringLang/AdvancedVI.jl/blob/main/HISTORY.md) in `AdvancedVI`.
+
+A new level of interface for defining different variational algorithms has been introduced in `AdvancedVI` v0.5. As a result, the function `Turing.vi` now receives a keyword argument `algorithm`. The object `algorithm <: AdvancedVI.AbstractVariationalAlgorithm` should now contain all the algorithm-specific configurations. Therefore, keyword arguments of `vi` that were algorithm-specific such as `objective`, `operator`, `averager` and so on, have been moved as fields of the relevant `<: AdvancedVI.AbstractVariationalAlgorithm` structs.
+For example,
+
+```julia
+vi(model, q, n_iters; objective=RepGradELBO(10), operator=AdvancedVI.ClipScale())
+```
+
+is now
+
+```julia
+vi(
+    model,
+    q,
+    n_iters;
+    algorithm=KLMinRepGradDescent(adtype; n_samples=10, operator=AdvancedVI.ClipScale()),
+)
+```
+
+Similarly,
+
+```julia
+vi(
+    model,
+    q,
+    n_iters;
+    objective=RepGradELBO(10; entropy=AdvancedVI.ClosedFormEntropyZeroGradient()),
+    operator=AdvancedVI.ProximalLocationScaleEntropy(),
+)
+```
+
+is now
+
+```julia
+vi(model, q, n_iters; algorithm=KLMinRepGradProxDescent(adtype; n_samples=10))
+```
+
+Additionally,
+
+  - The default hyperparameters of `DoG`and `DoWG` have been altered.
+  - The deprecated `[email protected]`-era interface is now removed.
+
 # 0.41.0
 
 ## DynamicPPL 0.38

Project.toml

@@ -55,7 +55,7 @@ Accessors = "0.1"
 AdvancedHMC = "0.3.0, 0.4.0, 0.5.2, 0.6, 0.7, 0.8"
 AdvancedMH = "0.8"
 AdvancedPS = "0.7"
-AdvancedVI = "0.4"
+AdvancedVI = "0.5"
 BangBang = "0.4.2"
 Bijectors = "0.14, 0.15"
 Compat = "4.15.0"

src/Turing.jl

@@ -116,10 +116,12 @@ export
     externalsampler,
     # Variational inference - AdvancedVI
     vi,
-    ADVI,
     q_locationscale,
     q_meanfield_gaussian,
     q_fullrank_gaussian,
+    KLMinRepGradProxDescent,
+    KLMinRepGradDescent,
+    KLMinScoreGradDescent,
     # ADTypes
     AutoForwardDiff,
     AutoReverseDiff,

src/variational/VariationalInference.jl

@@ -1,21 +1,24 @@
 
 module Variational
 
-using DynamicPPL
+using AdvancedVI:
+    AdvancedVI, KLMinRepGradDescent, KLMinRepGradProxDescent, KLMinScoreGradDescent
 using ADTypes
+using Bijectors: Bijectors
 using Distributions
+using DynamicPPL
 using LinearAlgebra
 using LogDensityProblems
 using Random
+using ..Turing: DEFAULT_ADTYPE, PROGRESS
 
-import ..Turing: DEFAULT_ADTYPE, PROGRESS
-
-import AdvancedVI
-import Bijectors
-
-export vi, q_locationscale, q_meanfield_gaussian, q_fullrank_gaussian
-
-include("deprecated.jl")
+export vi,
+    q_locationscale,
+    q_meanfield_gaussian,
+    q_fullrank_gaussian,
+    KLMinRepGradProxDescent,
+    KLMinRepGradDescent,
+    KLMinScoreGradDescent
 
 """
     q_initialize_scale(
@@ -248,76 +251,66 @@ end
 """
     vi(
         [rng::Random.AbstractRNG,]
-        model::DynamicPPL.Model;
+        model::DynamicPPL.Model,
         q,
-        n_iterations::Int;
-        objective::AdvancedVI.AbstractVariationalObjective = AdvancedVI.RepGradELBO(
-            10; entropy = AdvancedVI.ClosedFormEntropyZeroGradient()
+        max_iter::Int;
+        adtype::ADTypes.AbstractADType=DEFAULT_ADTYPE,
+        algorithm::AdvancedVI.AbstractVariationalAlgorithm = KLMinRepGradProxDescent(
+            adtype; n_samples=10
         ),
         show_progress::Bool = Turing.PROGRESS[],
-        optimizer::Optimisers.AbstractRule = AdvancedVI.DoWG(),
-        averager::AdvancedVI.AbstractAverager = AdvancedVI.PolynomialAveraging(),
-        operator::AdvancedVI.AbstractOperator = AdvancedVI.ProximalLocationScaleEntropy(),
-        adtype::ADTypes.AbstractADType = Turing.DEFAULT_ADTYPE,
         kwargs...
     )
 
-Approximating the target `model` via variational inference by optimizing `objective` with the initialization `q`.
+Approximate the target `model` via the variational inference algorithm `algorithm` by starting from the initial variational approximation `q`.
 This is a thin wrapper around `AdvancedVI.optimize`.
+The default `algorithm`, `KLMinRepGradProxDescent` ([relevant docs](https://turinglang.org/AdvancedVI.jl/dev/klminrepgradproxdescent/)), assumes `q` uses `AdvancedVI.MvLocationScale`, which can be constructed by invoking `q_fullrank_gaussian` or `q_meanfield_gaussian`.
+For other variational families, refer to `AdvancedVI` to determine the best algorithm and options.
 
 # Arguments
 - `model`: The target `DynamicPPL.Model`.
 - `q`: The initial variational approximation.
-- `n_iterations`: Number of optimization steps.
+- `max_iter`: Maximum number of steps.
 
 # Keyword Arguments
-- `objective`: Variational objective to be optimized.
+- `adtype`: Automatic differentiation backend to be applied to the log-density. The default value for `algorithm` also uses this backend for differentiation the variational objective.
+- `algorithm`: Variational inference algorithm.
 - `show_progress`: Whether to show the progress bar.
-- `optimizer`: Optimization algorithm.
-- `averager`: Parameter averaging strategy.
-- `operator`: Operator applied after each optimization step.
-- `adtype`: Automatic differentiation backend.
 
 See the docs of `AdvancedVI.optimize` for additional keyword arguments.
 
 # Returns 
-- `q`: Variational distribution formed by the last iterate of the optimization run.
-- `q_avg`: Variational distribution formed by the averaged iterates according to `averager`.
-- `state`: Collection of states used for optimization. This can be used to resume from a past call to `vi`.
-- `info`: Information generated during the optimization run.
+- `q`: Output variational distribution of `algorithm`.
+- `state`: Collection of states used by `algorithm`. This can be used to resume from a past call to `vi`.
+- `info`: Information generated while executing `algorithm`.
 """
 function vi(
     rng::Random.AbstractRNG,
     model::DynamicPPL.Model,
     q,
-    n_iterations::Int;
-    objective=AdvancedVI.RepGradELBO(
-        10; entropy=AdvancedVI.ClosedFormEntropyZeroGradient()
+    max_iter::Int,
+    args...;
+    adtype::ADTypes.AbstractADType=DEFAULT_ADTYPE,
+    algorithm::AdvancedVI.AbstractVariationalAlgorithm=KLMinRepGradProxDescent(
+        adtype; n_samples=10
     ),
     show_progress::Bool=PROGRESS[],
-    optimizer=AdvancedVI.DoWG(),
-    averager=AdvancedVI.PolynomialAveraging(),
-    operator=AdvancedVI.ProximalLocationScaleEntropy(),
-    adtype::ADTypes.AbstractADType=DEFAULT_ADTYPE,
     kwargs...,
 )
     return AdvancedVI.optimize(
         rng,
-        LogDensityFunction(model),
-        objective,
+        algorithm,
+        max_iter,
+        LogDensityFunction(model; adtype),
         q,
-        n_iterations;
+        args...;
         show_progress=show_progress,
-        adtype,
-        optimizer,
-        averager,
-        operator,
         kwargs...,
     )
 end
 
-function vi(model::DynamicPPL.Model, q, n_iterations::Int; kwargs...)
-    return vi(Random.default_rng(), model, q, n_iterations; kwargs...)
+function vi(model::DynamicPPL.Model, q, max_iter::Int; kwargs...)
+    return vi(Random.default_rng(), model, q, max_iter; kwargs...)
 end
 
 end

test/Project.toml

@@ -44,7 +44,7 @@ AbstractMCMC = "5"
 AbstractPPL = "0.11, 0.12, 0.13"
 AdvancedMH = "0.6, 0.7, 0.8"
 AdvancedPS = "0.7"
-AdvancedVI = "0.4"
+AdvancedVI = "0.5"
 Aqua = "0.8"
 BangBang = "0.4"
 Bijectors = "0.14, 0.15"
@@ -53,6 +53,7 @@ Combinatorics = "1"
 Distributions = "0.25"
 DistributionsAD = "0.6.3"
 DynamicHMC = "2.1.6, 3.0"
+DynamicPPL = "0.38"
 FiniteDifferences = "0.10.8, 0.11, 0.12"
 ForwardDiff = "0.10.12 - 0.10.32, 0.10, 1"
 HypothesisTests = "0.11"