Update Turing requirement from 0.33, 0.34, 0.35, 0.36, 0.45 to 0.33, 0.34, 0.35, 0.36, 0.45, 0.46 in the all-julia-packages group across 1 directory

[dependabot]

Updates the requirements on Turing to permit the latest version.
Updates Turing to 0.46.0

Release notes

Sourced from Turing's releases.

v0.46.0

Turing v0.46.0

Diff since v0.45.0

Breaking changes

DynamicPPL 0.42

Turing.jl v0.46 brings with it all the underlying changes in DynamicPPL 0.42. Most notably, gradient preparation and evaluation now go through AbstractPPL's prepare / value_and_gradient!! interface. This is an internal change and does not affect sampling results. Please see the DynamicPPL changelog for full details, and the AdvancedVI section below for the user-facing changes in this release.

AdvancedVI 0.7

Turing.jl v0.46 also brings in the changes in AdvancedVI 0.7. Please see the AdvancedVI changelog for full details; the changes most pertinent to users of vi are:

• AutoReverseDiff(; compile=true) is no longer supported for VI, and is rejected with an ArgumentError, as compiled tapes can silently produce incorrect gradients when reused across optimisation steps. Use AutoReverseDiff(; compile=false), or a different reverse-mode backend such as AutoMooncake(), instead.
• vi with KLMinScoreGradDescent now optimises in unconstrained (linked) space, making it consistent with the other KLMin... algorithms. If you use it with a model that has constrained parameters, results may differ slightly from previous releases.

Other changes

DifferentiationInterface removed as a direct dependency

informationmatrix (and hence vcov) now computes its Hessian through AbstractPPL's second-order interface instead of DifferentiationInterface. There is no change in behaviour for users.

Performance of nested submodels

DynamicPPL 0.42.1 fixes a type-inference failure that made nested submodels (a x ~ to_submodel(...) statement inside a model that is itself used as a submodel) many times slower to evaluate and differentiate; see TuringLang/Turing.jl#2844.

MCMCChains extension fix

Turing v0.45.0 was accidentally released without declaring the TuringMCMCChainsExt package extension, meaning that the extension did not load when MCMCChains was imported. This broke some MCMCChains-specific functionality, such as loadstate (for resuming sampling from a previous chain) and the post-sampling divergence warnings for Hamiltonian samplers; this is now fixed.

Merged pull requests:

• CompatHelper: add new compat entry for MCMCChains at version 7 for package test, (keep existing compat) (#2824) (@​github-actions[bot])
• CompatHelper: bump compat for AbstractPPL to 0.15, (keep existing compat) (#2826) (@​github-actions[bot])
• CompatHelper: bump compat for AbstractPPL to 0.15 for package test, (keep existing compat) (#2827) (@​github-actions[bot])
• Update README.md (#2829) (@​yebai)
• CompatHelper: bump compat for Bijectors to 0.16, (keep existing compat) (#2830) (@​github-actions[bot])
• CompatHelper: bump compat for Bijectors to 0.16 for package test, (keep existing compat) (#2831) (@​github-actions[bot])
• CompatHelper: bump compat for DynamicPPL to 0.42, (keep existing compat) (#2832) (@​github-actions[bot])
• CompatHelper: bump compat for DynamicPPL to 0.42 for package test, (keep existing compat) (#2833) (@​github-actions[bot])
• CompatHelper: bump compat for StatsFuns to 2, (keep existing compat) (#2834) (@​github-actions[bot])
• CompatHelper: bump compat for StatsFuns to 2 for package test, (keep existing compat) (#2835) (@​github-actions[bot])

... (truncated)

Changelog

Sourced from Turing's changelog.

0.46.0

Breaking changes

DynamicPPL 0.42

Turing.jl v0.46 brings with it all the underlying changes in DynamicPPL 0.42. Most notably, gradient preparation and evaluation now go through AbstractPPL's prepare / value_and_gradient!! interface. This is an internal change and does not affect sampling results. Please see the DynamicPPL changelog for full details, and the AdvancedVI section below for the user-facing changes in this release.

AdvancedVI 0.7

Turing.jl v0.46 also brings in the changes in AdvancedVI 0.7. Please see the AdvancedVI changelog for full details; the changes most pertinent to users of vi are:

• AutoReverseDiff(; compile=true) is no longer supported for VI, and is rejected with an ArgumentError, as compiled tapes can silently produce incorrect gradients when reused across optimisation steps. Use AutoReverseDiff(; compile=false), or a different reverse-mode backend such as AutoMooncake(), instead.
• vi with KLMinScoreGradDescent now optimises in unconstrained (linked) space, making it consistent with the other KLMin... algorithms. If you use it with a model that has constrained parameters, results may differ slightly from previous releases.

Other changes

DifferentiationInterface removed as a direct dependency

informationmatrix (and hence vcov) now computes its Hessian through AbstractPPL's second-order interface instead of DifferentiationInterface. There is no change in behaviour for users.

Performance of nested submodels

DynamicPPL 0.42.1 fixes a type-inference failure that made nested submodels (a x ~ to_submodel(...) statement inside a model that is itself used as a submodel) many times slower to evaluate and differentiate; see TuringLang/Turing.jl#2844.

MCMCChains extension fix

Turing v0.45.0 was accidentally released without declaring the TuringMCMCChainsExt package extension, meaning that the extension did not load when MCMCChains was imported. This broke some MCMCChains-specific functionality, such as loadstate (for resuming sampling from a previous chain) and the post-sampling divergence warnings for Hamiltonian samplers; this is now fixed.

0.45.0

Breaking changes

Make FlexiChains the default chain type for MCMC sampling.

MCMCChains is still fully supported: you can specify chain_type=MCMCChains.Chains in the sample function to use it instead. However, it is no longer loaded as a dependency of Turing and re-exported (it is now an extension). That means that if you were previously importing MCMCChains via Turing, you will now have to import it directly.

0.44.5

Allow users to disable the post-sample hook by passing verbose=false keyword argument to sample.

... (truncated)

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