Allow generation of `ParamsWithStats` from `FastLDF` plus parameters, and also `bundle_samples` #1129

penelopeysm · 2025-11-08T17:50:48Z

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

is faster to evaluate than regular unflatten + evaluate!!;
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 · 2025-11-08T17:55:30Z

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 · 2025-11-08T18:01:59Z

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

codecov · 2025-11-08T18:12:01Z

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

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penelopeysm · 2025-11-21T11:50:59Z

ping @sunxd3 :)

sunxd3

looks good, couple of tiny questions

sunxd3 · 2025-11-21T14:33:37Z

src/chains.jl

+            DynamicPPL.LogPriorAccumulator(),
+            DynamicPPL.LogLikelihoodAccumulator(),
+            DynamicPPL.ValuesAsInModelAccumulator(include_colon_eq),


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

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'.

ext/DynamicPPLMCMCChainsExt.jl

penelopeysm changed the base branch from main to py/fastldf November 8, 2025 17:50

github-actions bot assigned penelopeysm Nov 8, 2025

penelopeysm mentioned this pull request Nov 8, 2025

Path to FastLDF #1120

Closed

penelopeysm force-pushed the py/params-from-ldf branch from c475f96 to 92cbb20 Compare November 13, 2025 16:46

penelopeysm marked this pull request as ready for review November 13, 2025 16:46

penelopeysm changed the base branch from py/fastldf to breaking November 13, 2025 16:46

penelopeysm force-pushed the py/params-from-ldf branch from 92cbb20 to 39df719 Compare November 13, 2025 16:46

penelopeysm changed the title ~~Allow generation of ParamsWithStats from FastLDF plus parameters~~ Allow generation of ParamsWithStats from FastLDF plus parameters, and also bundle_samples Nov 13, 2025

penelopeysm requested a review from sunxd3 November 13, 2025 18:46

penelopeysm mentioned this pull request Nov 13, 2025

Turing compatibility nsiccha/NUTS.jl#1

Open

penelopeysm force-pushed the py/params-from-ldf branch from 3e646ca to 0dd2f70 Compare November 14, 2025 00:35

penelopeysm added 2 commits November 14, 2025 00:40

Implement ParamsWithStats for FastLDF

fa32c88

Add comments

2bf5b18

penelopeysm force-pushed the py/params-from-ldf branch from 0dd2f70 to 077457f Compare November 14, 2025 00:40

Implement bundle_samples for ParamsWithStats -> MCMCChains

2fad97b

penelopeysm force-pushed the py/params-from-ldf branch from 0150ef4 to 2fad97b Compare November 14, 2025 02:06

penelopeysm added 2 commits November 18, 2025 01:42

Merge branch 'breaking' into py/params-from-ldf

8129340

Merge branch 'breaking' into py/params-from-ldf

5d4527a

sunxd3 reviewed Nov 21, 2025

View reviewed changes

Remove redundant comment

82616cd

penelopeysm requested a review from sunxd3 November 21, 2025 18:59

Merge branch 'breaking' into py/params-from-ldf

60dbab1

sunxd3 approved these changes Nov 21, 2025

View reviewed changes

don't need Statistics?

0a9557e

penelopeysm merged commit 4a11560 into breaking Nov 22, 2025
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penelopeysm deleted the py/params-from-ldf branch November 22, 2025 00:26

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Allow generation of `ParamsWithStats` from `FastLDF` plus parameters, and also `bundle_samples` #1129

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	"""
	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(),))

Allow generation of ParamsWithStats from FastLDF plus parameters, and also bundle_samples #1129

Allow generation of ParamsWithStats from FastLDF plus parameters, and also bundle_samples #1129

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Allow generation of `ParamsWithStats` from `FastLDF` plus parameters, and also `bundle_samples` #1129

Allow generation of `ParamsWithStats` from `FastLDF` plus parameters, and also `bundle_samples` #1129

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