Remove Sampler and initialstep
#1037
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Note that, apart from being simpler code, Distributions.Uniform also doesn't allow the lower and upper bounds to be exactly equal (but we might like to keep that option open in DynamicPPL, e.g. if the user wants to initialise all values to the same value in linked space).
This should have been changed in #940, but slipped through as the file wasn't listed as one of the changed files.
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penelopeysm
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Sep 8, 2025
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| function AbstractMCMC.sample( | ||
| rng::Random.AbstractRNG, | ||
| model::Model, | ||
| sampler::Sampler, | ||
| N::Integer; | ||
| chain_type=default_chain_type(sampler), | ||
| resume_from=nothing, | ||
| initial_state=loadstate(resume_from), | ||
| kwargs..., | ||
| ) | ||
| return AbstractMCMC.mcmcsample( | ||
| rng, model, sampler, N; chain_type, initial_state, kwargs... | ||
| ) | ||
| end |
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This method (or to be precise its default kwargs) will have to be shifted upwards to Turing. The same is true for the multiple-chain sampling one.
penelopeysm
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Sep 8, 2025
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| function AbstractMCMC.step( | ||
| rng::Random.AbstractRNG, | ||
| model::Model, | ||
| spl::Sampler; | ||
| initial_params::AbstractInitStrategy=init_strategy(spl), | ||
| kwargs..., | ||
| ) |
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This one requires some subtlety. It ensures that for all Samplers the first step goes via initialstep because the rest of the code is shared. We can do the same using an internal Turing.Inference.initialstep for now; in the long term that function will also be removed if we use LogDensityFunction in sample.
Benchmark Report for Commit 35d0be2Computer InformationBenchmark Results |
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As title. The challenge is not here, of course, it's making sure that Turing still works correctly. I'm about 99% sure that this can be done fairly straightforwardly because I've actually done all the work before, see TuringLang/Turing.jl#2588.
Because I branched off the InitContext branches it will have to wait until those PRs are merged.