Check the input length in logdensity

[shravanngoswamii]

Fixes #1445.

LogDensityProblems.logdensity now checks the input length against ldf._dim and throws the same ArgumentError that the two-argument InitFromVector(vect, ldf) constructor already throws. Previously a shorter vector gave a BoundsError from inside model evaluation and a longer one silently returned the log density of the first dimension(ldf) elements.

ldf._dim is already stored, so the check costs nothing measurable. On a 21-dimensional model, logdensity takes 48.3 ns before and 48.2 ns after.

HISTORY.md also gains the missing entry for #1448, so the 0.42.5 section covers everything merged since 0.42.4. Project.toml is already at 0.42.5 on main.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 81.85%. Comparing base (e65fee2) to head (b5f3ffb).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1446      +/-   ##
==========================================
+ Coverage   81.84%   81.85%   +0.01%     
==========================================
  Files          50       50              
  Lines        3581     3583       +2     
==========================================
+ Hits         2931     2933       +2     
  Misses        650      650              

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[github-actions]

DynamicPPL.jl documentation for PR #1446 is available at:
https://TuringLang.github.io/DynamicPPL.jl/previews/PR1446/

[github-actions]

Benchmarks @ b5f3ffb

Performance Ratio: gradient time divided by log-density time.

For very small models these ratios are noisy across runs and machines; raw primal and gradient timings are more reliable. The benchmarks are aimed at DynamicPPL developers and mainly catch obvious allocation or type-stability regressions. See benchmark notes for details.

===================================================================================================
                                               eval                       gradient                 
                                            ----------  -------------------------------------------
Model                        dim    linked      primal     FwdDiff    RvsDiff    Mooncake    Enzyme
---------------------------------------------------------------------------------------------------
Simple assume observe*         1     false     4.94 ns       12.26    1367.36       39.67     14.71
Simple assume observe*         1      true     5.05 ns       11.61    1493.81       38.99     15.71
Smorgasbord                  201     false     5.72 μs       75.10     140.81        7.18      7.61
Smorgasbord                  201      true     7.46 μs       72.26     142.43        6.35      5.57
Loop univariate 1k          1000     false     16.5 μs     1137.54     316.36       10.09      7.78
Loop univariate 1k          1000      true     19.7 μs     1037.78     269.67        8.08      6.63
Multivariate 1k             1000     false     23.9 μs      323.83      69.57        8.30      2.03
Multivariate 1k             1000      true     22.6 μs      278.14      74.24        9.74      2.12
Loop univariate 10k        10000     false    159.0 μs    15549.87     358.41        9.95      7.83
Loop univariate 10k        10000      true    194.0 μs    14276.55     303.14        8.28      6.60
Multivariate 10k           10000     false    213.0 μs     5824.32      80.37       10.18      1.91
Multivariate 10k           10000      true    214.0 μs     5685.59      83.33       10.28      1.93
Dynamic                       15     false     1.39 μs         err      44.44       14.57     11.28
Dynamic                       10      true     1.99 μs        1.90      59.57       11.17     18.00
Submodel*                      1     false     4.93 ns       12.62    1709.85       43.43     15.09
Submodel*                      1      true     4.93 ns       12.75    1729.48       39.08     16.79
LDA                           12      true     24.4 μs        0.64       2.04       28.43       err
===================================================================================================

Main @ e65fee2

===================================================================================================
                                               eval                       gradient                 
                                            ----------  -------------------------------------------
Model                        dim    linked      primal     FwdDiff    RvsDiff    Mooncake    Enzyme
---------------------------------------------------------------------------------------------------
Simple assume observe*         1     false     4.63 ns       12.77    1584.01       39.79     12.35
Simple assume observe*         1      true     4.63 ns       13.27    1742.87       40.20     12.14
Smorgasbord                  201     false     5.95 μs       69.69     135.12        6.95      9.65
Smorgasbord                  201      true     7.71 μs       67.83     144.32        6.22      7.02
Loop univariate 1k          1000     false     17.8 μs      996.73     314.24        8.57      6.80
Loop univariate 1k          1000      true     19.0 μs     1489.64     297.09        7.66      6.25
Multivariate 1k             1000     false     21.9 μs      371.25      81.24        8.92      2.90
Multivariate 1k             1000      true     29.4 μs      320.52      57.18        7.82      3.26
Loop univariate 10k        10000     false    174.0 μs    12383.01     347.33        8.43      6.78
Loop univariate 10k        10000      true    186.0 μs    12670.40     325.69        7.89      6.29
Multivariate 10k           10000     false    198.0 μs     5048.19      91.02       11.59      2.38
Multivariate 10k           10000      true    198.0 μs     5131.65      92.14       11.54      2.35
Dynamic                       15     false     1.43 μs         err      43.74       15.42     11.83
Dynamic                       10      true      2.0 μs        1.77      57.80       19.17     20.12
Submodel*                      1     false     4.64 ns       12.62    1787.35       40.14     12.64
Submodel*                      1      true     4.64 ns       12.66    1955.32       40.44     12.26
LDA                           12      true     24.2 μs        0.62       2.23       30.92       err
===================================================================================================

Environment

Julia Version 1.11.9
Commit 53a02c0720c (2026-02-06 00:27 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 9V74 80-Core Processor
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver4)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

[sunxd3]

looks fine