Compatibility with DynamicPPL 0.38 + InitContext #2676

penelopeysm · 2025-09-24T18:05:52Z

This PR is being tested against this DynamicPPL branch: TuringLang/DynamicPPL.jl#1057

It should be noted that due to the changes in DynamicPPL's src/sampler.jl, the results of running MCMC sampling on this branch will pretty much always differ from that on the main branch. Thus there is no (easy) way to test full reproducibility of MCMC results (we have to rely instead on statistics for converged chains).

TODO:

pMCMC (it at least runs and gives sensible results on simple models, proper tests will have to wait for CI to run)
Gibbs (same as above)
fix initial_params argument for most samplers to require AbstractInitStrategy
fix tests
changelog

Separate PRs:

use InitStrategy for optimisation as well

Note that the three pre-existing InitStrategies can be used directly with optimisation. However, to handle constraints properly, it seems necessary to introduce a new subtype of AbstractInitStrategy. I think this should be a separate PR because it's a fair bit of work.
fix docs for that argument, wherever it is (there's probably some in AbstractMCMC but it should probably be documented on the main site) EDIT: https://turinglang.org/docs/usage/sampling-options/#specifying-initial-parameters

penelopeysm · 2025-09-24T22:32:28Z

src/mcmc/gibbs.jl

-    # Get the initial values for this component sampler.
-    initial_params_local = if initial_params === nothing
-        nothing
-    else
-        DynamicPPL.subset(vi, varnames)[:]
-    end


I was quite pleased with this discovery. Previously the initial params had to be subsetted to be the correct length for the conditioned model. That's not only a faff, but also I get a bit scared whenever there's direct VarInfo manipulation like this.

Now, if you use InitFromParams with a NamedTuple/Dict that has extra params, the extra params are just ignored. So no need to subset it at all, just pass it through directly!

penelopeysm · 2025-09-24T22:33:47Z

src/mcmc/mh.jl

-# TODO(DPPL0.38/penelopeysm): This function should no longer be needed
-# once InitContext is merged.


unfortunately set_namedtuple! is used elsewhere in this file (though it won't appear in this diff) so we can't delete it (yet)

penelopeysm · 2025-09-24T22:34:53Z

src/mcmc/mh.jl

+function DynamicPPL.tilde_assume!!(
+    context::MHContext, right::Distribution, vn::VarName, vi::AbstractVarInfo
 )
-    # Just defer to `SampleFromPrior`.
-    retval = DynamicPPL.assume(rng, SampleFromPrior(), dist, vn, vi)
-    return retval
+    # Allow MH to sample new variables from the prior if it's not already present in the
+    # VarInfo.
+    dispatch_ctx = if haskey(vi, vn)
+        DynamicPPL.DefaultContext()
+    else
+        DynamicPPL.InitContext(context.rng, DynamicPPL.InitFromPrior())
+    end
+    return DynamicPPL.tilde_assume!!(dispatch_ctx, right, vn, vi)


The behaviour of SampleFromPrior used to be: if the key is present, don't actually sample, and if it was absent, sample. This if/else replicates the old behaviour.

penelopeysm · 2025-09-24T22:39:46Z

src/mcmc/particle_mcmc.jl

-    sampler::S
    varinfo::V
    evaluator::E
+    resample::Bool


For pMCMC, this Boolean field essentially replaces the del flag. Instead of set_all_del and unset_all_del we construct new TracedModel with this set to true and false respectively.

penelopeysm · 2025-09-24T23:02:07Z

test/mcmc/ess.jl

-        @test sample(StableRNG(23), xy(), spl_xy, num_samples).value ≈
-            sample(StableRNG(23), x12(), spl_x, num_samples).value
+        chn1 = sample(StableRNG(23), xy(), spl_xy, num_samples)
+        chn2 = sample(StableRNG(23), x12(), spl_x, num_samples)
+
+        @test mean(chn1[:z]) ≈ mean(chn2[:z]) atol = 0.05
+        @test mean(chn1[:x]) ≈ mean(chn2["x[1]"]) atol = 0.05
+        @test mean(chn1[:y]) ≈ mean(chn2["x[2]"]) atol = 0.05


The values are no longer exactly the same (it has something to do with initialisation behaviour which is different for the two models). But we can still check that the results are sensibly similar, which is probably also more meaningful anyway as it means that ESS not only works on both models but also consistently converges regardless of how the model is specified.

While we are at it, shall we add a ≈ -3.0 on the last line?

It's a shame that these don't match exactly any more, but I guess if something about the order of sampling changes in Gibbs or some such then what can you do.

Seems you can't chain comparisons under @test when using atol. I'm superficial enough that that makes me lean towards not checking against -3.0.

github-actions · 2025-09-24T23:03:38Z

Turing.jl documentation for PR #2676 is available at:
https://TuringLang.github.io/Turing.jl/previews/PR2676/

codecov · 2025-09-24T23:48:24Z

Codecov Report

❌ Patch coverage is 16.09195% with 73 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (breaking@ff8d01e). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
src/mcmc/particle_mcmc.jl	0.00%	25 Missing ⚠️
src/mcmc/emcee.jl	0.00%	11 Missing ⚠️
src/mcmc/mh.jl	0.00%	11 Missing ⚠️
src/mcmc/is.jl	0.00%	8 Missing ⚠️
src/mcmc/repeat_sampler.jl	0.00%	6 Missing ⚠️
src/mcmc/external_sampler.jl	0.00%	3 Missing ⚠️
src/mcmc/ess.jl	0.00%	2 Missing ⚠️
src/mcmc/hmc.jl	50.00%	2 Missing ⚠️
ext/TuringOptimExt.jl	0.00%	1 Missing ⚠️
src/mcmc/Inference.jl	0.00%	1 Missing ⚠️
... and 3 more

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penelopeysm · 2025-09-25T00:59:49Z

test/stdlib/distributions.jl

+                            seed = if dist isa GeneralizedExtremeValue
+                                # GEV is prone to giving really wacky results that are quite
+                                # seed-dependent.
+                                StableRNG(469)
+                            else
+                                StableRNG(468)
+                            end
+                            chn = sample(seed, m(), HMC(0.05, 20), n_samples)


Case in point:

julia> using Turing, StableRNGs julia> dist = GeneralizedExtremeValue(0, 1, 0.5); @model m() = x ~ dist m (generic function with 2 methods) julia> mean(dist) 1.5449077018110322 julia> mean(sample(StableRNG(468), m(), HMC(0.05, 20), 10000; progress=false)) Mean parameters mean Symbol Float64 x 3.9024 julia> mean(sample(StableRNG(469), m(), HMC(0.05, 20), 10000; progress=false)) Mean parameters mean Symbol Float64 x 1.5868

penelopeysm · 2025-09-26T20:51:53Z

For the record, 11 failing CI jobs is the expected number:

8x failing jobs because [sources] is not understood on 1.10
3x failing jobs because Libtask 1.12

There is also the failing job caused by base Julia segfault (#2655), but that's on 1.10 so overlaps with the first category.

penelopeysm · 2025-10-14T06:38:26Z

@mhauru, I haven't run CI against the latest revisions like removal of the del flag, but I think this might be meaty enough as it stands and also any adjustments arising from that PR (like renaming islinked) should be quite trivial.

mhauru

Good stuff, some minor comments.

I'm wondering about how to merge this. Should be review the code here, but then hold off merging to breaking before all the 0.38 compat fixes are in and a release of 0.38 is out, so all the temporary source stuff etc. can go and we can see tests pass?

mhauru · 2025-10-14T13:58:50Z

src/mcmc/abstractmcmc.jl

+    new_context = DynamicPPL.setleafcontext(model.context, DynamicPPL.InitContext())
+    new_model = DynamicPPL.contextualize(model, new_context)


Isn't there a short-hand for these two lines now?

mhauru · 2025-10-14T14:27:10Z

src/mcmc/mh.jl

+end
+DynamicPPL.NodeTrait(::MHContext) = DynamicPPL.IsLeaf()
+
+function DynamicPPL.tilde_assume!!(


Why is this needed? Doesn't MH just use init to get initial values like any other sampler, and then evaluate logpdfs on proposed steps?

Also, this looks like dynamical dispatch, since the context type depends on haskey(vi, vn), which could be a performance issue.

mhauru · 2025-10-14T14:37:46Z

src/mcmc/particle_mcmc.jl

+    trng = get_trace_local_rng_maybe(ctx.rng)
+    resample = get_trace_local_resampled_maybe(true)
+
+    dispatch_ctx = if ~haskey(vi, vn) || resample


Like with MH, I wonder about dynamic dispatch here. Might be unavoidable and/or inconsequential in this case though.

mhauru · 2025-10-14T14:52:19Z

src/mcmc/repeat_sampler.jl

+    return DynamicPPL.init_strategy(spl.sampler)
+end
+
+function AbstractMCMC.sample(


I don't see why these became necessary now. Is it something about the type hierarchy around Sampler?

mhauru · 2025-10-14T14:58:53Z

test/mcmc/ess.jl

-        @test sample(StableRNG(23), xy(), spl_xy, num_samples).value ≈
-            sample(StableRNG(23), x12(), spl_x, num_samples).value
+        chn1 = sample(StableRNG(23), xy(), spl_xy, num_samples)
+        chn2 = sample(StableRNG(23), x12(), spl_x, num_samples)
+
+        @test mean(chn1[:z]) ≈ mean(chn2[:z]) atol = 0.05
+        @test mean(chn1[:x]) ≈ mean(chn2["x[1]"]) atol = 0.05
+        @test mean(chn1[:y]) ≈ mean(chn2["x[2]"]) atol = 0.05


While we are at it, shall we add a ≈ -3.0 on the last line?

It's a shame that these don't match exactly any more, but I guess if something about the order of sampling changes in Gibbs or some such then what can you do.

mhauru · 2025-10-14T15:06:41Z

test/mcmc/is.jl

+        N = 1000
+        model = normal()
+        chain = sample(StableRNG(468), model, alg, N)
+        ref = reference(N)


Getting quite nit-picky, but could the RNG be an argument to reference, to ensure the two instance of StableRNG(468) remain in sync?

mhauru · 2025-10-14T15:11:34Z

test/Project.toml

 julia = "1.10"
+
+[sources]
+DynamicPPL = {url = "https://github.com/TuringLang/DynamicPPL.jl", rev = "breaking"}


Leaving this comment just as a reminder that this needs to be removed before merging.

mhauru · 2025-10-14T15:12:01Z

Project.toml

 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+
+[sources]
+DynamicPPL = {url = "https://github.com/TuringLang/DynamicPPL.jl", rev = "breaking"}


Likewise this.

mhauru · 2025-10-14T15:12:37Z

HISTORY.md

+You still need to use the `initial_params` keyword argument to `sample`, but the allowed values are different.
+For almost all samplers in Turing.jl (except `Emcee`) this should now be a `DynamicPPL.AbstractInitStrategy`.
+
+TODO LINK TO DPPL DOCS WHEN THIS IS LIVE


Likewise a reminder comment.

mhauru · 2025-10-14T15:15:20Z

HISTORY.md

+
+  - `InitFromPrior()`: Sample from the prior distribution. This is the default for most samplers in Turing.jl (if you don't specify `initial_params`).
+  - `InitFromUniform(a, b)`: Sample uniformly from `[a, b]` in linked space. This is the default for Hamiltonian samplers. If `a` and `b` are not specified it defaults to `[-2, 2]`, which preserves the behaviour in previous versions (and mimics that of Stan).
+  - `InitFromParams(p)`: Explicitly provide a set of initial parameters. **Note: `p` must be either a `NamedTuple` or a `Dict{<:VarName}`; it can no longer be a `Vector`.** Parameters must be provided in unlinked space, even if the sampler later performs linking.


Do I recall correctly that you did end up implementing the option of providing an unwrapped NamedTuple or Dict as well?

Oh, also, just came to mind: Does it need to be a Dict, or can it be an AbstractDict?

penelopeysm marked this pull request as draft September 24, 2025 18:06

penelopeysm force-pushed the py/dppl-0.38 branch from 9658a3e to ed43a02 Compare September 24, 2025 18:12

Import varname_leaves etc from AbstractPPL instead

10f960e

penelopeysm force-pushed the py/dppl-0.38 branch from ed43a02 to bf18516 Compare September 24, 2025 18:12

[no ci] initial updates for InitContext

3a04643

penelopeysm force-pushed the py/dppl-0.38 branch from bf18516 to 3a04643 Compare September 24, 2025 18:16

penelopeysm added 6 commits September 24, 2025 20:58

[no ci] More fixes

7e522a6

[no ci] Fix pMCMC

9bc58c8

[no ci] Fix Gibbs

02d1d0e

[no ci] More fixes, reexport InitFrom

27b0096

Fix a bunch of tests; I'll let CI tell me what's still broken...

7f12c3e

Remove comment

ed197f9

penelopeysm commented Sep 24, 2025

View reviewed changes

Fix more tests

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penelopeysm added 3 commits September 25, 2025 00:57

More test fixes

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Fix more tests

ba4da83

fix GeneralizedExtremeValue numerical test

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penelopeysm commented Sep 25, 2025

View reviewed changes

penelopeysm added 6 commits September 25, 2025 02:05

fix sample method

b5d82c9

fix ESS reproducibility

c315993

Fix externalsampler test correctly

3afd807

Fix everything (I _think_)

25c6513

Add changelog

d4aaa18

Fix remaining tests (for real this time)

aa3cfcf

penelopeysm mentioned this pull request Sep 25, 2025

Fixes for Turing 0.41 TuringLang/DynamicPPL.jl#1057

Merged

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penelopeysm marked this pull request as ready for review September 25, 2025 13:24

Specify default chain type in Turing

c0ea6e0

fix DPPL revision

b0badc2

penelopeysm force-pushed the py/dppl-0.38 branch from 0d42641 to b0badc2 Compare October 3, 2025 09:43

penelopeysm requested a review from mhauru October 14, 2025 06:36

mhauru requested changes Oct 14, 2025

View reviewed changes

		# TODO(DPPL0.38/penelopeysm): This function should no longer be needed
		# once InitContext is merged.

		new_context = DynamicPPL.setleafcontext(model.context, DynamicPPL.InitContext())
		new_model = DynamicPPL.contextualize(model, new_context)

Compatibility with DynamicPPL 0.38 + InitContext #2676

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