improve RepGradELBO to not redefine integrand

Author: Red-Portal

src/AdvancedVI.jl

@@ -37,6 +37,9 @@ Evaluate the value and gradient of a function `f` at `θ` using the automatic di
 """
 function value_and_gradient! end
 
+stop_gradient(x) = x
+
+
 # Update for gradient descent step
 """
     update_variational_params!(family_type, opt_st, params, restructure, grad)
@@ -92,9 +95,9 @@ This function needs to be implemented only if `obj` is stateful.
 init(
     ::Random.AbstractRNG,
     ::AbstractVariationalObjective,
-    ::Any
     ::Any,
-    ::Any
+    ::Any,
+    ::Any,
 ) = nothing
 
 """

src/objectives/elbo/repgradelbo.jl

@@ -46,24 +46,18 @@ function Base.show(io::IO, obj::RepGradELBO)
     print(io, ")")
 end
 
-function estimate_entropy_maybe_stl(entropy_estimator::AbstractEntropyEstimator, samples, q, q_stop)
-    q_maybe_stop = maybe_stop_entropy_score(entropy_estimator, q, q_stop)
-    estimate_entropy(entropy_estimator, samples, q_maybe_stop)
-end
-
 function estimate_energy_with_samples(prob, samples)
     mean(Base.Fix1(LogDensityProblems.logdensity, prob), eachsample(samples))
 end
 
 """
-    reparam_with_entropy(rng, q, q_stop, n_samples, ent_est)
+    reparam_with_entropy(rng, q, n_samples, ent_est)
 
 Draw `n_samples` from `q` and compute its entropy.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.
 - `q`: Variational approximation.
-- `q_stop`: `q` but with its gradient stopped.
 - `n_samples::Int`: Number of Monte Carlo samples 
 - `ent_est`: The entropy estimation strategy. (See `estimate_entropy`.)
 
@@ -72,10 +66,10 @@ Draw `n_samples` from `q` and compute its entropy.
 - `entropy`: An estimate (or exact value) of the differential entropy of `q`.
 """
 function reparam_with_entropy(
-    rng::Random.AbstractRNG, q, q_stop, n_samples::Int, ent_est::AbstractEntropyEstimator
+    rng::Random.AbstractRNG, q, n_samples::Int, ent_est::AbstractEntropyEstimator
 )
     samples = rand(rng, q, n_samples)
-    entropy = estimate_entropy_maybe_stl(ent_est, samples, q, q_stop)
+    entropy = estimate_entropy(ent_est, samples, q)
     samples, entropy
 end
 
@@ -86,14 +80,39 @@ function estimate_objective(
     prob;
     n_samples::Int = obj.n_samples
 )
-    samples, entropy = reparam_with_entropy(rng, q, q, n_samples, obj.entropy)
+    samples, entropy = reparam_with_entropy(rng, q, n_samples, obj.entropy)
     energy = estimate_energy_with_samples(prob, samples)
     energy + entropy
 end
 
 estimate_objective(obj::RepGradELBO, q, prob; n_samples::Int = obj.n_samples) =
     estimate_objective(Random.default_rng(), obj, q, prob; n_samples)
 
+function init(rng::Random.AbstractRNG, obj::RepGradELBO, problem, params, restructure)
+    function obj_integrand(params′)
+        q = restructure(params′)
+        samples, entropy = reparam_with_entropy(rng, q, obj.n_samples, obj.entropy)
+        energy = estimate_energy_with_samples(problem, samples)
+        elbo = energy + entropy
+        -elbo
+    end
+    obj_integrand
+end
+
+function estimate_objective_restructure(
+    rng::Random.AbstractRNG,
+    obj::RepGradELBO,
+    params,
+    restructure,
+    prob;
+    n_samples::Int = obj.n_samples
+)
+    q = restructure(params)
+    samples, entropy = reparam_with_entropy(rng, q, n_samples, obj.entropy)
+    energy = estimate_energy_with_samples(prob, samples)
+    energy + entropy
+end
+
 function estimate_gradient!(
     rng   ::Random.AbstractRNG,
     obj   ::RepGradELBO,
@@ -104,18 +123,11 @@ function estimate_gradient!(
     restructure,
     state,
 )
-    q_stop = restructure(λ)
-    function f(λ′)
-        q = restructure(λ′)
-        samples, entropy = reparam_with_entropy(rng, q, q_stop, obj.n_samples, obj.entropy)
-        energy = estimate_energy_with_samples(prob, samples)
-        elbo = energy + entropy
-        -elbo
-    end
-    value_and_gradient!(adtype, f, λ, out)
+    obj_integrand = state
+    value_and_gradient!(adtype, obj_integrand, λ, out)
 
     nelbo = DiffResults.value(out)
     stat  = (elbo=-nelbo,)
 
-    out, nothing, stat
+    out, state, stat
 end