fix bug for bijector with 1 MC sample with tests

Author: Red-Portal

ext/AdvancedVIBijectorsExt.jl

@@ -11,35 +11,45 @@ else
     using ..Random
 end
 
-function AdvancedVI.reparam_with_entropy(
-    rng      ::Random.AbstractRNG,
-    q        ::Bijectors.TransformedDistribution,
-    q_stop   ::Bijectors.TransformedDistribution,
-    n_samples::Int,
-    ent_est  ::AdvancedVI.AbstractEntropyEstimator
-)
-    transform    = q.transform
-    q_base       = q.dist
-    q_base_stop  = q_stop.dist
-    base_samples = rand(rng, q_base, n_samples)
-    it           = AdvancedVI.eachsample(base_samples)
-    sample_init  = first(it)
+function transform_samples_with_jacobian(unconst_samples, transform, n_samples)
+    unconst_iter = AdvancedVI.eachsample(unconst_samples)
+    unconst_init = first(unconst_iter)
+
+    samples_init, logjac_init = with_logabsdet_jacobian(transform, unconst_init)
 
     samples_and_logjac = mapreduce(
         AdvancedVI.catsamples_and_acc,
-        Iterators.drop(it, 1);
-        init=with_logabsdet_jacobian(transform, sample_init)
+        Iterators.drop(unconst_iter, 1);
+        init=(AdvancedVI.samples_expand_dim(samples_init), logjac_init)
     ) do sample
         with_logabsdet_jacobian(transform, sample)
     end
     samples = first(samples_and_logjac)
-    logjac  = last(samples_and_logjac)
+    logjac  = last(samples_and_logjac)/n_samples
+    samples, logjac
+end
 
-    entropy_base = AdvancedVI.estimate_entropy_maybe_stl(
-        ent_est, base_samples, q_base, q_base_stop
+function AdvancedVI.reparam_with_entropy(
+    rng      ::Random.AbstractRNG,
+    q        ::Bijectors.TransformedDistribution,
+    q_stop   ::Bijectors.TransformedDistribution,
+    n_samples::Int,
+    ent_est  ::AdvancedVI.AbstractEntropyEstimator
+)
+    transform       = q.transform
+    q_unconst       = q.dist
+    q_unconst_stop  = q_stop.dist
+
+    # Draw samples and compute entropy of the uncontrained distribution
+    unconst_samples, unconst_entropy = AdvancedVI.reparam_with_entropy(
+        rng, q_unconst, q_unconst_stop, n_samples, ent_est
     )
 
-    entropy = entropy_base + logjac/n_samples
+    # Apply bijector to samples while estimating its jacobian
+    samples, logjac = transform_samples_with_jacobian(
+        unconst_samples, transform, n_samples
+    )
+    entropy = unconst_entropy + logjac
     samples, entropy
 end
 end

src/utils.jl

@@ -34,3 +34,7 @@ function catsamples_and_acc(
     return (x, ∑y)
 end
 
+function samples_expand_dim(x::AbstractVector)
+    reshape(x, (:,1))
+end
+

test/inference/repgradelbo_distributionsad.jl

@@ -9,9 +9,10 @@ using Test
         (modelname, modelconstr) ∈ Dict(
             :Normal=> normal_meanfield,
         ),
-        (objname, objective) ∈ Dict(
-            :RepGradELBOClosedFormEntropy  => RepGradELBO(10),
-            :RepGradELBOStickingTheLanding => RepGradELBO(10, entropy = StickingTheLandingEntropy()),
+        n_montecarlo in [1, 10],
+        (objname, objective) in Dict(
+            :RepGradELBOClosedFormEntropy  => RepGradELBO(n_montecarlo),
+            :RepGradELBOStickingTheLanding => RepGradELBO(n_montecarlo, entropy = StickingTheLandingEntropy()),
         ),
         (adbackname, adbackend) ∈ Dict(
             :ForwarDiff  => AutoForwardDiff(),
@@ -33,7 +34,7 @@ using Test
         q0 = TuringDiagMvNormal(μ0, diag(L0))
 
         @testset "convergence" begin
-            Δλ₀ = sum(abs2, μ0 - μ_true) + sum(abs2, L0 - L_true)
+            Δλ0 = sum(abs2, μ0 - μ_true) + sum(abs2, L0 - L_true)
             q, stats, _ = optimize(
                 rng, model, objective, q0, T;
                 optimizer     = Optimisers.Adam(realtype(η)),
@@ -45,7 +46,7 @@ using Test
             L  = sqrt(cov(q))
             Δλ = sum(abs2, μ - μ_true) + sum(abs2, L - L_true)
 
-            @test Δλ ≤ Δλ₀/T^(1/4)
+            @test Δλ ≤ Δλ0/T^(1/4)
             @test eltype(μ) == eltype(μ_true)
             @test eltype(L) == eltype(L_true)
         end

test/inference/repgradelbo_locationscale.jl

@@ -5,16 +5,17 @@ using Test
 
 @testset "inference RepGradELBO VILocationScale" begin
     @testset "$(modelname) $(objname) $(realtype) $(adbackname)"  for
-        realtype ∈ [Float64, Float32],
-        (modelname, modelconstr) ∈ Dict(
+        realtype in [Float64, Float32],
+        (modelname, modelconstr) in Dict(
             :Normal=> normal_meanfield,
             :Normal=> normal_fullrank,
         ),
-        (objname, objective) ∈ Dict(
-            :RepGradELBOClosedFormEntropy  => RepGradELBO(10),
-            :RepGradELBOStickingTheLanding => RepGradELBO(10, entropy = StickingTheLandingEntropy()),
+        n_montecarlo in [1, 10],
+        (objname, objective) in Dict(
+            :RepGradELBOClosedFormEntropy  => RepGradELBO(n_montecarlo),
+            :RepGradELBOStickingTheLanding => RepGradELBO(n_montecarlo, entropy = StickingTheLandingEntropy()),
         ),
-        (adbackname, adbackend) ∈ Dict(
+        (adbackname, adbackend) in Dict(
             :ForwarDiff  => AutoForwardDiff(),
             :ReverseDiff => AutoReverseDiff(),
             :Zygote      => AutoZygote(), 
@@ -37,7 +38,7 @@ using Test
         end
 
         @testset "convergence" begin
-            Δλ₀ = sum(abs2, q0.location - μ_true) + sum(abs2, q0.scale - L_true)
+            Δλ0 = sum(abs2, q0.location - μ_true) + sum(abs2, q0.scale - L_true)
             q, stats, _ = optimize(
                 rng, model, objective, q0, T;
                 optimizer     = Optimisers.Adam(realtype(η)),
@@ -49,7 +50,7 @@ using Test
             L  = q.scale
             Δλ = sum(abs2, μ - μ_true) + sum(abs2, L - L_true)
 
-            @test Δλ ≤ Δλ₀/T^(1/4)
+            @test Δλ ≤ Δλ0/T^(1/4)
             @test eltype(μ) == eltype(μ_true)
             @test eltype(L) == eltype(L_true)
         end

test/inference/repgradelbo_locationscale_bijectors.jl

@@ -5,15 +5,16 @@ using Test
 
 @testset "inference RepGradELBO VILocationScale Bijectors" begin
     @testset "$(modelname) $(objname) $(realtype) $(adbackname)"  for
-        realtype ∈ [Float64, Float32],
-        (modelname, modelconstr) ∈ Dict(
+        realtype in [Float64, Float32],
+        (modelname, modelconstr) in Dict(
             :NormalLogNormalMeanField => normallognormal_meanfield,
         ),
-        (objname, objective) ∈ Dict(
-            :RepGradELBOClosedFormEntropy  => RepGradELBO(10),
-            :RepGradELBOStickingTheLanding => RepGradELBO(10, entropy = StickingTheLandingEntropy()),
+        n_montecarlo in [1, 10],
+        (objname, objective) in Dict(
+            :RepGradELBOClosedFormEntropy  => RepGradELBO(n_montecarlo),
+            :RepGradELBOStickingTheLanding => RepGradELBO(n_montecarlo, entropy = StickingTheLandingEntropy()),
         ),
-        (adbackname, adbackend) ∈ Dict(
+        (adbackname, adbackend) in Dict(
             :ForwarDiff  => AutoForwardDiff(),
             :ReverseDiff => AutoReverseDiff(),
             #:Zygote      => AutoZygote(), 
@@ -42,7 +43,7 @@ using Test
         q0_z = Bijectors.transformed(q0_η, b⁻¹)
 
         @testset "convergence" begin
-            Δλ₀ = sum(abs2, μ0 - μ_true) + sum(abs2, L0 - L_true)
+            Δλ0 = sum(abs2, μ0 - μ_true) + sum(abs2, L0 - L_true)
             q, stats, _ = optimize(
                 rng, model, objective, q0_z, T;
                 optimizer     = Optimisers.Adam(realtype(η)),
@@ -54,7 +55,7 @@ using Test
             L  = q.dist.scale
             Δλ = sum(abs2, μ - μ_true) + sum(abs2, L - L_true)
 
-            @test Δλ ≤ Δλ₀/T^(1/4)
+            @test Δλ ≤ Δλ0/T^(1/4)
             @test eltype(μ) == eltype(μ_true)
             @test eltype(L) == eltype(L_true)
         end