Allow generation of ParamsWithStats from FastLDF plus parameters, and also bundle_samples

[penelopeysm]

This has no real impact on DynamicPPL, but using this in Turing should lead to some nice speedups because this:

1. is faster to evaluate than regular unflatten + evaluate!!;
2. avoids deepcopying VarInfo.

Closes #1119.

Since the intention of this is to replace Turing.Inference.Transition with ParamsWithStats, I also added the necessary AbstractMCMC.bundle_samples method here. MCMCChainsExt is the natural place for it to live (it could be defined in Turing, but that would be piracy).

[github-actions]

Benchmark Report

• this PR's head: 0a9557ec0633bb85a42b0124727b7bfe8de82e12
• base branch: 3cd8d3431e14ebc581266c1323d1db8a5bd4c0eb

Computer Information

Julia Version 1.11.7
Commit f2b3dbda30a (2025-09-08 12:10 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Benchmark Results

┌───────────────────────┬───────┬─────────────┬───────────────────┬────────┬─────────────────────────────────┬───────────────────────────┬──────────────────────────────────┐
│                       │       │             │                   │        │        t(eval) / t(ref)         │     t(grad) / t(eval)     │         t(grad) / t(ref)         │
│                       │       │             │                   │        │ ──────────┬───────────┬──────── │ ──────┬─────────┬──────── │ ──────────┬────────────┬──────── │
│                 Model │   Dim │  AD Backend │           VarInfo │ Linked │      base │   this PR │ speedup │  base │ this PR │ speedup │      base │    this PR │ speedup │
├───────────────────────┼───────┼─────────────┼───────────────────┼────────┼───────────┼───────────┼─────────┼───────┼─────────┼─────────┼───────────┼────────────┼─────────┤
│               Dynamic │    10 │    mooncake │             typed │   true │    504.53 │    519.32 │    0.97 │  7.67 │    7.35 │    1.04 │   3870.25 │    3819.24 │    1.01 │
│                   LDA │    12 │ reversediff │             typed │   true │   2915.66 │   2983.56 │    0.98 │  2.27 │    2.12 │    1.07 │   6611.06 │    6319.26 │    1.05 │
│   Loop univariate 10k │ 10000 │    mooncake │             typed │   true │ 162397.29 │ 163322.69 │    0.99 │  5.90 │    6.35 │    0.93 │ 957822.51 │ 1036552.28 │    0.92 │
├───────────────────────┼───────┼─────────────┼───────────────────┼────────┼───────────┼───────────┼─────────┼───────┼─────────┼─────────┼───────────┼────────────┼─────────┤
│    Loop univariate 1k │  1000 │    mooncake │             typed │   true │  14807.28 │  14817.98 │    1.00 │  6.12 │    6.01 │    1.02 │  90690.53 │   89119.79 │    1.02 │
│      Multivariate 10k │ 10000 │    mooncake │             typed │   true │  45304.69 │  47745.23 │    0.95 │  7.62 │    7.47 │    1.02 │ 345166.70 │  356839.74 │    0.97 │
│       Multivariate 1k │  1000 │    mooncake │             typed │   true │   5185.70 │   9561.27 │    0.54 │  6.77 │    4.17 │    1.62 │  35121.79 │   39884.18 │    0.88 │
├───────────────────────┼───────┼─────────────┼───────────────────┼────────┼───────────┼───────────┼─────────┼───────┼─────────┼─────────┼───────────┼────────────┼─────────┤
│ Simple assume observe │     1 │ forwarddiff │             typed │  false │     20.42 │     18.86 │    1.08 │  1.72 │    1.91 │    0.90 │     35.08 │      35.94 │    0.98 │
│           Smorgasbord │   201 │ forwarddiff │             typed │  false │   2990.16 │   3078.52 │    0.97 │ 47.17 │   44.42 │    1.06 │ 141046.21 │  136747.70 │    1.03 │
│           Smorgasbord │   201 │ forwarddiff │       simple_dict │   true │  25176.26 │  26895.89 │    0.94 │ 28.33 │   28.60 │    0.99 │ 713344.90 │  769187.00 │    0.93 │
├───────────────────────┼───────┼─────────────┼───────────────────┼────────┼───────────┼───────────┼─────────┼───────┼─────────┼─────────┼───────────┼────────────┼─────────┤
│           Smorgasbord │   201 │ forwarddiff │ simple_namedtuple │   true │   1305.72 │   1308.92 │    1.00 │ 81.47 │   81.82 │    1.00 │ 106373.25 │  107092.84 │    0.99 │
│           Smorgasbord │   201 │      enzyme │             typed │   true │   2943.02 │   3020.11 │    0.97 │  4.70 │    4.76 │    0.99 │  13840.55 │   14372.07 │    0.96 │
│           Smorgasbord │   201 │    mooncake │             typed │   true │   2935.20 │   3052.52 │    0.96 │  4.93 │    4.92 │    1.00 │  14464.31 │   15021.72 │    0.96 │
├───────────────────────┼───────┼─────────────┼───────────────────┼────────┼───────────┼───────────┼─────────┼───────┼─────────┼─────────┼───────────┼────────────┼─────────┤
│           Smorgasbord │   201 │ reversediff │             typed │   true │   2959.98 │   3091.69 │    0.96 │ 57.09 │   55.54 │    1.03 │ 168991.72 │  171717.17 │    0.98 │
│           Smorgasbord │   201 │ forwarddiff │      typed_vector │   true │   3033.14 │   3099.62 │    0.98 │ 77.26 │   48.93 │    1.58 │ 234328.22 │  151663.75 │    1.55 │
│           Smorgasbord │   201 │ forwarddiff │           untyped │   true │   2600.79 │   2620.72 │    0.99 │ 51.58 │   49.99 │    1.03 │ 134142.57 │  131022.47 │    1.02 │
├───────────────────────┼───────┼─────────────┼───────────────────┼────────┼───────────┼───────────┼─────────┼───────┼─────────┼─────────┼───────────┼────────────┼─────────┤
│           Smorgasbord │   201 │ forwarddiff │    untyped_vector │   true │   2696.82 │   2721.07 │    0.99 │ 49.60 │   51.44 │    0.96 │ 133755.53 │  139972.91 │    0.96 │
│              Submodel │     1 │    mooncake │             typed │   true │     28.12 │     28.85 │    0.97 │  5.14 │    6.64 │    0.78 │    144.64 │     191.46 │    0.76 │
└───────────────────────┴───────┴─────────────┴───────────────────┴────────┴───────────┴───────────┴─────────┴───────┴─────────┴─────────┴───────────┴────────────┴─────────┘

[github-actions]

DynamicPPL.jl documentation for PR #1129 is available at:
https://TuringLang.github.io/DynamicPPL.jl/previews/PR1129/

[codecov]

Codecov Report

❌ Patch coverage is 72.72727% with 9 lines in your changes missing coverage. Please review.
✅ Project coverage is 81.59%. Comparing base (3cd8d34) to head (0a9557e).
⚠️ Report is 1 commits behind head on breaking.

Files with missing lines	Patch %	Lines
ext/DynamicPPLMCMCChainsExt.jl	0.00%	9 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##           breaking    #1129      +/-   ##
============================================
- Coverage     81.67%   81.59%   -0.09%     
============================================
  Files            42       42              
  Lines          3930     3955      +25     
============================================
+ Hits           3210     3227      +17     
- Misses          720      728       +8     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[penelopeysm]

ping @sunxd3 :)

[sunxd3]

looks good, couple of tiny questions

[sunxd3]

I am looking at

DynamicPPL.jl/src/fasteval.jl

Lines 247 to 262 in 5d4527a

	"""
	fast_ldf_accs(getlogdensity::Function)
	Determine which accumulators are needed for fast evaluation with the given
	`getlogdensity` function.
	"""
	fast_ldf_accs(::Function) = default_accumulators()
	fast_ldf_accs(::typeof(getlogjoint_internal)) = default_accumulators()
	function fast_ldf_accs(::typeof(getlogjoint))
	return AccumulatorTuple((LogPriorAccumulator(), LogLikelihoodAccumulator()))
	end
	function fast_ldf_accs(::typeof(getlogprior_internal))
	return AccumulatorTuple((LogPriorAccumulator(), LogJacobianAccumulator()))
	end
	fast_ldf_accs(::typeof(getlogprior)) = AccumulatorTuple((LogPriorAccumulator(),))
	fast_ldf_accs(::typeof(getloglikelihood)) = AccumulatorTuple((LogLikelihoodAccumulator(),))

and wonder if there are time we need the LogJacobianAccumulator

[penelopeysm]

Ahh, right. I think it doesn't matter for the present purposes because we always want the output to not include the Jacobian term i.e. logprior and logjoint are 'as seen in the model'.