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Bump the all-julia-packages group across 1 directory with 2 updates - #278

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Updates the requirements on NormalizingFlows and QuasiMonteCarlo to permit the latest version.
Updates NormalizingFlows to 0.4.0

Release notes

Sourced from NormalizingFlows's releases.

v0.4.0

NormalizingFlows v0.4.0

Diff since v0.3.0

Breaking changes

DifferentiationInterface has been removed as a dependency. Automatic differentiation is now routed through AbstractPPL's evaluator interface (AbstractPPL.prepare and AbstractPPL.value_and_gradient!!), following the rest of the Turing ecosystem.

The AD backend package must now be loaded so that its AbstractPPL.prepare method is available:

  • AutoForwardDiff works with using ForwardDiff.
  • AutoMooncake works with using Mooncake.
  • Other backends routed through DifferentiationInterface (AutoZygote, AutoReverseDiff, AutoEnzyme) additionally require using DifferentiationInterface alongside the concrete backend package.

AutoReverseDiff(; compile=true) stays rejected, and the rejection now matters: through the evaluator interface a compiled tape does take effect and bakes the objective's context into itself, so the gradient would be taken against the first iteration's random draws. Under the previous DifferentiationInterface path the flag was silently dropped instead.

Merged pull requests:

  • Bump deps to latest, route AD through AbstractPPL instead of DifferentiationInterface (#61) (@​shravanngoswamii)
Changelog

Sourced from NormalizingFlows's changelog.

0.4.0

Breaking changes

DifferentiationInterface has been removed as a dependency. Automatic differentiation is now routed through AbstractPPL's evaluator interface (AbstractPPL.prepare and AbstractPPL.value_and_gradient!!), following the rest of the Turing ecosystem.

The AD backend package must now be loaded so that its AbstractPPL.prepare method is available:

  • AutoForwardDiff works with using ForwardDiff.
  • AutoMooncake works with using Mooncake.
  • Other backends routed through DifferentiationInterface (AutoZygote, AutoReverseDiff, AutoEnzyme) additionally require using DifferentiationInterface alongside the concrete backend package.

AutoReverseDiff(; compile=true) stays rejected, and the rejection now matters: through the evaluator interface a compiled tape does take effect and bakes the objective's context into itself, so the gradient would be taken against the first iteration's random draws. Under the previous DifferentiationInterface path the flag was silently dropped instead.

0.3.0

Breaking changes

nsf now builds its rational quadratic splines with a batched implementation in this package, and MonotonicSplines is no longer a dependency. The spline is written with whole-array operations, so the flow runs on the GPU and trains under Zygote, ForwardDiff, ReverseDiff, Mooncake, and, on Julia 1.11 and newer, Enzyme.

The spline boundary B is now honoured when scaling the knots. The previous implementation always scaled the knots into (-5, 5] regardless of B, so flows built with any other B define a different transform than before.

Bin widths, heights, and derivatives now have a floor of 1e-3, so no bin or slope can underflow to zero and produce NaN. This shifts the constrained knots slightly, so the transform differs from previous releases at every B, B = 5 included, and weights trained with an older version define a different flow.

AutoReverseDiff(; compile=true) is now rejected rather than silently ignored. DifferentiationInterface drops the compile flag when context arguments are present, which is how the objective's arguments are passed here, and a tape that did take effect would freeze the random number generator.

NeuralSplineCoupling requires B > 0. A negative boundary silently broke invertibility and zero silently made the layer the identity.

Other changes

NSF_layer rejects dim < 2 and NeuralSplineCoupling validates its mask, instead of failing later with a DivideError.

Commits
  • 1cd5e25 Bump deps to latest, route AD through AbstractPPL instead of DifferentiationI...
  • c82284c Use the Bijectors batched RQS for nsf and drop MonotonicSplines (#63)
  • e0a4fbd Bump the all-julia-packages group across 5 directories with 25 updates (#62)
  • 911f295 Bump actions/checkout in the all-github-actions-packages group (#60)
  • 471dcb8 Update Buildkite pipeline for the new JuliaGPU cluster. (#59)
  • 68bcaca Bump the all-github-actions-packages group with 3 updates (#57)
  • fb019dc Replace CompatHelper with Dependabot (#56)
  • See full diff in compare view

Updates QuasiMonteCarlo to 0.4.0

Release notes

Sourced from QuasiMonteCarlo's releases.

v0.4.0

QuasiMonteCarlo v0.4.0

Diff since v0.3.8

Breaking changes

0.x minor bump. Documented sampling APIs were publicized and hardened. See the changelog.

Merged pull requests:

Commits

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@dependabot
Bump the all-julia-packages group across 1 directory with 2 updates
afd6e91 
Updates the requirements on [NormalizingFlows](https://github.com/TuringLang/NormalizingFlows.jl) and [QuasiMonteCarlo](https://github.com/SciML/QuasiMonteCarlo.jl) to permit the latest version.

Updates `NormalizingFlows` to 0.4.0
- [Release notes](https://github.com/TuringLang/NormalizingFlows.jl/releases)
- [Changelog](https://github.com/TuringLang/NormalizingFlows.jl/blob/main/HISTORY.md)
- [Commits](TuringLang/NormalizingFlows.jl@v0.2.2...v0.4.0)

Updates `QuasiMonteCarlo` to 0.4.0
- [Release notes](https://github.com/SciML/QuasiMonteCarlo.jl/releases)
- [Commits](SciML/QuasiMonteCarlo.jl@v0.3.0...v0.4.0)

---
updated-dependencies:
- dependency-name: NormalizingFlows
  dependency-version: 0.4.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
- dependency-name: QuasiMonteCarlo
  dependency-version: 0.4.0
  dependency-type: direct:production
  dependency-group: all-julia-packages
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot added dependencies Pull requests that update a dependency file julia Pull requests that update julia code labels Aug 21, 2026
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Benchmark Results

Details
Benchmark suite Current: afd6e91 Previous: 476d58c Ratio
normal/RepGradELBO/fullrank/Mooncake 720426739 ns 483718381 ns 1.49
normal/RepGradELBO/fullrank/ReverseDiff 609483023 ns 529788084.5 ns 1.15
normal/RepGradELBO/meanfield/Mooncake 267950354 ns 212767152 ns 1.26
normal/RepGradELBO/meanfield/ReverseDiff 315964158 ns 276854192 ns 1.14
normal/RepGradELBO + STL/fullrank/Mooncake 900388419.5 ns 602453885 ns 1.49
normal/RepGradELBO + STL/fullrank/ReverseDiff 1168058578 ns 1084775704 ns 1.08
normal/RepGradELBO + STL/meanfield/Mooncake 416157964 ns 313216545 ns 1.33
normal/RepGradELBO + STL/meanfield/ReverseDiff 624233276 ns 544930417.5 ns 1.15

This comment was automatically generated by workflow using github-action-benchmark.

@github-actions

github-actions Bot commented Aug 21, 2026

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AdvancedVI.jl documentation for PR #278 is available at:
https://TuringLang.github.io/AdvancedVI.jl/previews/PR278/

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