LKJCholesky does not work with compiled ReverseDiff.jl
#2091
Comments
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Fixed by #2097? Run without warning on my end. versioninfoJulia Version 1.10.0-beta3
Commit 404750f8586 (2023-10-03 12:53 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 10 × Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-15.0.7 (ORCJIT, skylake)
Threads: 8 on 10 virtual cores
Environment:
LD_LIBRARY_PATH = /usr/local/cuda/lib64:
JULIA_NUM_THREADS = 6
JULIA_EDITOR = code |
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Are you on master? Because we haven't made a realize with that PR yet |
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No, on your branch |
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Oh sure, then this will be fixed by #2097 yes |
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Fixed by #2097, which is now being released |
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I'm still getting some weird behaviour when using I know I made a similar issue about this, but actually the MWE here is enough to reproduce some behaviour that looks weird to me. That is, in the code below, I would expect Am I missing anything obvious here? using Turing, Random, StatsPlots, TuringBenchmarking, Memoization
@model demo() = L ~ LKJCholesky(3, 1.0)
Turing.setadbackend(:reversediff)
Turing.setrdcache(false)
Random.seed!(1234)
chn_rd = sample(demo(), NUTS(), 1000)
Turing.setrdcache(true)
Random.seed!(1234)
chn_rd_compiled = sample(demo(), NUTS(), 1000)
Turing.setadbackend(:forwarddiff)
Random.seed!(1234)
chn_fd = sample(demo(), NUTS(), 1000)
StatsPlots.plot(chn_rd) # looks healthy
StatsPlots.plot(chn_rd_compiled) # looks not great
using TuringBenchmarking
benchmark_model(demo(); check = true, adbackends=[:forwarddiff, :reversediff, :reversediff_compiled]) # no warnings |
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Hmmm, yeah this is strange. I get the following: julia> using Random, Turing, ReverseDiff
julia> @model demo() = L ~ LKJCholesky(3, 1.0)
demo (generic function with 2 methods)
julia> Turing.setadbackend(:reversediff)
:reversediff
julia> Turing.setrdcache(false)
false
julia> Random.seed!(1234)
TaskLocalRNG()
julia> chn_rd = sample(demo(), NUTS(), 1000)
┌ Info: Found initial step size
└ ϵ = 0.8125
Sampling 100%|███████████████████████████████████████████████████████████| Time: 0:00:15
Chains MCMC chain (1000×18×1 Array{Float64, 3}):
Iterations = 501:1:1500
Number of chains = 1
Samples per chain = 1000
Wall duration = 16.14 seconds
Compute duration = 16.14 seconds
parameters = L.L[1,1], L.L[2,1], L.L[3,1], L.L[2,2], L.L[3,2], L.L[3,3]
internals = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size
Summary Statistics
parameters mean std mcse ess_bulk ess_tail rhat ess_per_sec
Symbol Float64 Float64 Float64 Float64 Float64 Float64 Float64
L.L[1,1] 1.0000 0.0000 NaN NaN NaN NaN NaN
L.L[2,1] -0.0220 0.5100 0.0149 1108.5225 588.9220 0.9999 68.6817
L.L[3,1] -0.0018 0.4925 0.0134 1301.7727 754.2240 0.9997 80.6551
L.L[2,2] 0.8417 0.1770 0.0082 513.2173 506.4421 0.9993 31.7978
L.L[3,2] 0.0038 0.5100 0.0127 1463.0641 699.8792 1.0006 90.6483
L.L[3,3] 0.6646 0.2369 0.0111 446.5069 698.9634 1.0020 27.6646
Quantiles
parameters 2.5% 25.0% 50.0% 75.0% 97.5%
Symbol Float64 Float64 Float64 Float64 Float64
L.L[1,1] 1.0000 1.0000 1.0000 1.0000 1.0000
L.L[2,1] -0.8724 -0.4387 -0.0179 0.3828 0.8772
L.L[3,1] -0.8948 -0.3826 -0.0162 0.3757 0.8773
L.L[2,2] 0.3821 0.7527 0.9118 0.9802 0.9997
L.L[3,2] -0.8838 -0.4094 -0.0122 0.4311 0.8668
L.L[3,3] 0.1783 0.4838 0.7020 0.8775 0.9914
julia> Turing.setrdcache(true)
true
julia> Random.seed!(1234)
TaskLocalRNG()
julia> chn_rd_compiled = sample(demo(), NUTS(), 1000)
┌ Info: Found initial step size
└ ϵ = 0.8
Sampling 100%|███████████████████████████████████████████████████████████| Time: 0:00:05
Chains MCMC chain (1000×18×1 Array{Float64, 3}):
Iterations = 501:1:1500
Number of chains = 1
Samples per chain = 1000
Wall duration = 5.84 seconds
Compute duration = 5.84 seconds
parameters = L.L[1,1], L.L[2,1], L.L[3,1], L.L[2,2], L.L[3,2], L.L[3,3]
internals = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size
Summary Statistics
parameters mean std mcse ess_bulk ess_tail rhat ess_per_sec
Symbol Float64 Float64 Float64 Float64 Float64 Float64 Float64
L.L[1,1] 1.0000 0.0000 NaN NaN NaN NaN NaN
L.L[2,1] -0.0084 0.4925 0.0324 228.6450 338.8731 1.0121 39.1851
L.L[3,1] -0.0309 0.5025 0.0258 340.1116 282.3064 1.0081 58.2882
L.L[2,2] 0.8551 0.1626 0.0089 370.7538 497.9914 1.0130 63.5396
L.L[3,2] -0.0147 0.4512 0.0227 392.0160 419.2180 1.0097 67.1836
L.L[3,3] 0.7074 0.2068 0.0118 340.1865 475.6237 0.9994 58.3010
Quantiles
parameters 2.5% 25.0% 50.0% 75.0% 97.5%
Symbol Float64 Float64 Float64 Float64 Float64
L.L[1,1] 1.0000 1.0000 1.0000 1.0000 1.0000
L.L[2,1] -0.8647 -0.3878 -0.0117 0.3833 0.8483
L.L[3,1] -0.8775 -0.4332 -0.0412 0.3793 0.8522
L.L[2,2] 0.4263 0.7698 0.9232 0.9847 0.9998
L.L[3,2] -0.8352 -0.3499 -0.0043 0.3450 0.7960
L.L[3,3] 0.2748 0.5634 0.7456 0.8834 0.9902So it seems the resulting parameter estimates are roughly the same but the ESS is different. Also note that the chosen step-size is slightly different. Compiled ReverseDiff will produce different results if we have if-statements in the computation which relies on the values of the random variables we're inferring, and so I'm wondering if maybe there's a conditional somewhere in the computation graph that is not correctly included. TuringBenchmarking.jl just checks a single value, hence it might work correctly for that particular value. julia> using Test, LogDensityProblems, LogDensityProblemsAD
julia> model = demo();
julia> varinfo = DynamicPPL.link(DynamicPPL.VarInfo(model), model);
julia> f = DynamicPPL.LogDensityFunction(model, varinfo);
julia> f_rd = LogDensityProblemsAD.ADgradient(Turing.Essential.ReverseDiffAD{false}(), deepcopy(f));
julia> f_crd = LogDensityProblemsAD.ADgradient(Turing.Essential.ReverseDiffAD{true}(), deepcopy(f));
julia> # Let's check if they're the same on the input we compiled for.
result_rd = LogDensityProblems.logdensity_and_gradient(f_rd, varinfo[:])
(-2.0957855104582483, [1.4082609718596935, -0.09191446506506586, 0.6847718985562207])
julia> result_crd = LogDensityProblems.logdensity_and_gradient(f_crd, varinfo[:])
(-2.0957855104582483, [1.4082609718596935, -0.09191446506506586, 0.6847718985562207])
julia> @test result_rd[1] ≈ result_crd[1]
Test Passed
julia> @test result_rd[2] ≈ result_crd[2]
Test Passed
julia> # Now with inputs that it was not compiled on.
d = length(varinfo[:]);
julia> x = rand(d);
julia> result_unseen_rd = LogDensityProblems.logdensity_and_gradient(f_rd, x)
(-2.4459590133007008, [-1.870827529402606, -0.17639514549826948, -0.6335288221522503])
julia> result_unseen_crd = LogDensityProblems.logdensity_and_gradient(f_crd, x)
(-2.4459590133007008, [-0.8316199935369052, -0.1968884416775799, -0.6343288727116665])
julia> @test result_unseen_rd[1] ≈ result_unseen_crd[1]
Test Passed
julia> @test result_unseen_rd[2] ≈ result_unseen_crd[2]
Test Failed at REPL[282]:1
Expression: result_unseen_rd[2] ≈ result_unseen_crd[2]
Evaluated: [-1.870827529402606, -0.17639514549826948, -0.6335288221522503] ≈ [-0.8316199935369052, -0.1968884416775799, -0.6343288727116665]
ERROR: There was an error during testingwhich immediately fails. And so this is likely caused by some conditional somewhere. My immediate question is if this conditional is present in the linking or just the log-prob computation: julia> varinfo = DynamicPPL.VarInfo(model); # without linking
julia> f = DynamicPPL.LogDensityFunction(model, varinfo);
julia> f_rd = LogDensityProblemsAD.ADgradient(Turing.Essential.ReverseDiffAD{false}(), deepcopy(f));
julia> f_crd = LogDensityProblemsAD.ADgradient(Turing.Essential.ReverseDiffAD{true}(), deepcopy(f));
julia> # Let's check if they're the same on the input we compiled for.
result_rd = LogDensityProblems.logdensity_and_gradient(f_rd, varinfo[:])
(-1.6501987419955653, [0.0, 0.0, 0.0, 0.0, 1.0553644429775089, 0.0, 0.0, 0.0, 0.0])
julia> result_crd = LogDensityProblems.logdensity_and_gradient(f_crd, varinfo[:])
(-1.6501987419955653, [0.0, 0.0, 0.0, 0.0, 1.0553644429775089, 0.0, 0.0, 0.0, 0.0])
julia> @test result_rd[1] ≈ result_crd[1]
Test Passed
julia> @test result_rd[2] ≈ result_crd[2]
Test Passed
julia> # Unseen.
x = DynamicPPL.vectorize(LKJCholesky(3, 1.0), model())
9-element Vector{Float64}:
1.0
0.3623020689503904
0.2946552179906151
0.0
0.932060733447272
0.7153149803851709
0.0
0.0
0.6336424712307923
julia> result_unseen_rd = LogDensityProblems.logdensity_and_gradient(f_rd, x)
(-1.6666698929155281, [0.0, 0.0, 0.0, 0.0, 1.072891458801672, 0.0, 0.0, 0.0, 0.0])
julia> result_unseen_crd = LogDensityProblems.logdensity_and_gradient(f_crd, x)
(-1.6666698929155281, [0.0, 0.0, 0.0, 0.0, 1.072891458801672, 0.0, 0.0, 0.0, 0.0])
julia> @test result_unseen_rd[1] ≈ result_unseen_crd[1]
Test Passed
julia> @test result_unseen_rd[2] ≈ result_unseen_crd[2]
Test PassedOkay, so this works nicely while the linked version fails, indicating that it's an issue with the linking itself. Will have a look at this. I'll also make it so that TuringBenchmarking.jl runs the gradient checks on inputs which it is not compiled on so we catch these things too. Manifest(jl_w7AqOd) pkg> st --manifest
Status `/tmp/jl_w7AqOd/Manifest.toml`
⌃ [47edcb42] ADTypes v0.1.6
[621f4979] AbstractFFTs v1.5.0
[80f14c24] AbstractMCMC v4.5.0
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[1520ce14] AbstractTrees v0.4.4
[79e6a3ab] Adapt v3.7.0
[0bf59076] AdvancedHMC v0.5.5
[5b7e9947] AdvancedMH v0.7.5
⌅ [576499cb] AdvancedPS v0.4.3
[b5ca4192] AdvancedVI v0.2.4
[dce04be8] ArgCheck v2.3.0
[4fba245c] ArrayInterface v7.4.11
[a9b6321e] Atomix v0.1.0
[13072b0f] AxisAlgorithms v1.0.1
[39de3d68] AxisArrays v0.4.7
[198e06fe] BangBang v0.3.39
[9718e550] Baselet v0.1.1
[6e4b80f9] BenchmarkTools v1.3.2
[76274a88] Bijectors v0.13.7
⌅ [fa961155] CEnum v0.4.2
[49dc2e85] Calculus v0.5.1
[082447d4] ChainRules v1.56.0
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[163ba53b] DiffResults v1.1.0
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Info Packages marked with ⌃ and ⌅ have new versions available, but those with ⌅ are restricted by
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Ah, I thought we had chainrules for everything relating to the transformation of Though I'm a bit uncertain why this would cause issues.. |
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Hmm, this is very strange: I can't seem to reproduce the issue when looking just at the transformation. julia> using Turing, ReverseDiff
julia> dist = LKJCholesky(3, 1.0)
LKJCholesky{Float64}(
d: 3
η: 1.0
uplo: L
)
julia> b = bijector(dist)
Bijectors.VecCholeskyBijector(:L)
julia> binv = inverse(b)
Inverse{Bijectors.VecCholeskyBijector}(Bijectors.VecCholeskyBijector(:L))
julia> x = rand(dist)
LinearAlgebra.Cholesky{Float64, Matrix{Float64}}
L factor:
3×3 LinearAlgebra.LowerTriangular{Float64, Matrix{Float64}}:
1.0 ⋅ ⋅
0.10674 0.994287 ⋅
0.390942 0.661192 0.640304
julia> y = b(x)
3-element Vector{Float64}:
0.10714823845189496
0.4129118569882736
0.9042537275936676
julia> function f(y)
x, logjac = with_logabsdet_jacobian(binv, y)
return logpdf(dist, x) + logjac
end
f (generic function with 1 method)
julia> # ReverseDiff.
f_tape = ReverseDiff.GradientTape(f, (y,))
typename(ReverseDiff.GradientTape)(f)
julia> f_tape_compiled = ReverseDiff.compile(f_tape)
typename(ReverseDiff.CompiledTape)(f)
julia> inputs = (y,);
julia> buffers = (DiffResults.GradientResult(similar(y)),);
julia> cfg = ReverseDiff.GradientConfig(inputs);
julia> ReverseDiff.gradient!(buffers, f_tape, inputs)
(MutableDiffResult(-2.588055647847676, ([-0.32022019679930697, -1.1728269159715612, -1.4367255598238464],)),)
julia> ReverseDiff.gradient!(buffers, f_tape_compiled, inputs)
(MutableDiffResult(-2.588055647847676, ([-0.32022019679930697, -1.1728269159715612, -1.4367255598238464],)),)
julia> # New inputs.
inputs = (randn(length(y)),);
julia> buffers = (DiffResults.GradientResult(similar(y)),);
julia> cfg = ReverseDiff.GradientConfig(inputs);
julia> ReverseDiff.gradient!(buffers, f_tape, inputs)
(MutableDiffResult(-3.7413960226614016, ([-0.15691122445568645, 8.57759603199995, -1.7830917058959441],)),)
julia> ReverseDiff.gradient!(buffers, f_tape_compiled, inputs)
(MutableDiffResult(-3.7413960226614016, ([-0.15691122445568645, 8.57759603199995, -1.7830917058959441],)),) |
|
Any news on a fix for this issue? |
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MWE:
Manifest.toml
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