force batch sizes of ReshufflingBatchSubsampling to have the same size

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedVI"
 uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
-version = "0.6"
+version = "0.6.1"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/algorithms/subsampledobjective.jl

@@ -25,12 +25,22 @@ function init(
     adtype::ADTypes.AbstractADType,
     q_init,
     prob,
-    params,
-    restructure,
+    ::Any,
+    ::Any,
 )
     (; objective, subsampling) = subobj
     sub_st = init(rng, subsampling)
-    obj_st = AdvancedVI.init(rng, objective, adtype, q_init, prob, params, restructure)
+
+    # This is necessary to ensure that `init` sees the type "conditioned" on a minibatch
+    # when calling `DifferentiationInterface.prepare_*` inside it.
+    batch, _, _ = step(rng, subsampling, sub_st, true)
+    prob_sub = subsample(prob, batch)
+    q_init_sub = subsample(q_init, batch)
+    params_sub, re_sub = Optimisers.destructure(q_init_sub)
+
+    obj_st = AdvancedVI.init(
+        rng, objective, adtype, q_init_sub, prob_sub, params_sub, re_sub
+    )
     return SubsampledObjectiveState(prob, sub_st, obj_st)
 end
 
@@ -66,7 +76,7 @@ function estimate_gradient!(
     (; prob, sub_st, obj_st) = state
     q = restructure(params)
 
-    batch, sub_st′, sub_inf = step(rng, subsampling, sub_st)
+    batch, sub_st′, sub_inf = step(rng, subsampling, sub_st, true)
     prob_sub = subsample(prob, batch)
     q_sub = subsample(q, batch)
     params_sub, re_sub = Optimisers.destructure(q_sub)

src/reshuffling.jl

@@ -20,7 +20,9 @@ struct ReshufflingBatchSubsamplingState{It}
     iterator::It
 end
 
-Base.length(sub::ReshufflingBatchSubsampling) = ceil(Int, length(sub.dataset)/sub.batchsize)
+function Base.length(sub::ReshufflingBatchSubsampling)
+    return ceil(Int, length(sub.dataset) / sub.batchsize)
+end
 
 function reshuffle_batches(rng::Random.AbstractRNG, sub::ReshufflingBatchSubsampling)
     (; dataset, batchsize) = sub
@@ -37,15 +39,22 @@ function step(
     rng::Random.AbstractRNG,
     sub::ReshufflingBatchSubsampling,
     state::ReshufflingBatchSubsamplingState,
+    drop_trailing_batch_if_too_small::Bool=false,
 )
     (; epoch, iterator) = state
-    (sub_step, batch), batch_it′ = Iterators.peel(iterator)
-    epoch′, iterator′′ = if isempty(batch_it′)
-        epoch + 1, reshuffle_batches(rng, sub)
-    else
-        epoch, batch_it′
+    (sub_step, batch), iterator = Iterators.peel(iterator)
+    if isempty(iterator)
+        iterator = reshuffle_batches(rng, sub)
+        if drop_trailing_batch_if_too_small && length(batch) < sub.batchsize
+            # Ignore the trailing batch if its size is smaller than `batchsize`.
+            # This should only be used when estimating gradients during optimization.
+            # This is necessary to ensure that all batches have the same size.
+            # Otherwise, `DifferentiationInterface.prepare_*` behaves incorrectly.
+            (sub_step, batch), iterator = Iterators.peel(iterator)
+        end
+        epoch = epoch + 1
     end
     info = (epoch=epoch, step=sub_step)
-    state′ = ReshufflingBatchSubsamplingState(epoch′, iterator′′)
-    return batch, state′, info
+    state = ReshufflingBatchSubsamplingState(epoch, iterator)
+    return batch, state, info
 end