Error: `corrupted size vs. prev_size`

[ArrogantGao]

Meet error when contracting of tropical tensor network:

julia> using TropicalGEMM, TensorInference, JLD, OMEinsum

julia> tn = load("./ObjectDetection_32.jld")["tn"]
TensorNetworkModel{Int64, SlicedEinsum{Int64, DynamicNestedEinsum{Int64}}, Array{Float64}}
variables: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60
contraction time = 2^25.941, space = 2^20.0, read-write = 2^23.128

julia> most_probable_config(tn)
corrupted size vs. prev_size

[3870633] signal (6.-6): Aborted
in expression starting at REPL[3]:1

version of package

(tensorinference-vs-merlin) pkg> st
Status `~/work/probabilistic-inference-in-the-era-of-tensor-networks-and-differential-programming/scripts/benchmarks/map/tensorinference-vs-merlin/Project.toml`
  [c7e460c6] ArgParse v1.1.4
  [6e4b80f9] BenchmarkTools v1.4.0
  [336ed68f] CSV v0.10.12
  [a93c6f00] DataFrames v1.6.1
  [b732b382] JunctionTrees v0.4.2
⌃ [8314cec4] PGFPlotsX v1.5.1
  [2913bbd2] StatsBase v0.34.2
  [c2297e78] TensorInference v0.4.1
  [a4ad3063] TropicalGEMM v0.2.0
  [56f22d72] Artifacts
Info Packages marked with ⌃ have new versions available and may be upgradable.

julia> versioninfo()
Julia Version 1.10.0
Commit 3120989f39b (2023-12-25 18:01 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 64 × Intel(R) Xeon(R) Platinum 8358P CPU @ 2.60GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, icelake-server)
  Threads: 1 on 64 virtual cores
Environment:
  LD_LIBRARY_PATH = /opt/slurm/lib
  JULIA_PKG_SERVER = https://mirrors.nju.edu.cn/julia

[ArrogantGao]

ObjectDetection_32.jld.zip This is the jld file I used.

[ArrogantGao]

The error also happens on a server with cpu Intel(R) Xeon(R) Platinum 8378A CPU @ 3.00GHz, but did not happen on our laptop with an apple m2 chip, an Intel(R) Core(TM) i7-10510U, or CPU: 16 × 13th Gen Intel(R) Core(TM) i5-13400F

[GiggleLiu]

This problem seems to depend on specific CPU architecture. @chriselrod do you have any clue?