Fix test errors and update to ChainRules 1 and Distributions 0.25.15 #198

devmotion · 2021-08-24T22:47:32Z

This PR builds on #197 and additionally updates the ChainRules definitions to ChainRules 1.

Unfortunately, locally Zygote tests seem to fail even with ChainRules 1. I opened a separate PR to not pollute #197 with unrelated changes.

Edit:

This PR

updates DistributionsAD to ChainRules 1
removes ChainRules definitions of StatsFuns function which were moved to StatsFuns (Add ChainRules adjoints JuliaStats/StatsFuns.jl#106); StatsFuns dependency is bumped to 0.9.10 which contains these rules
removes ChainRules definition of the pdf of PoissonBinomial which was moved to Distributions (Add ChainRules definitions for pdf of PoissonBinomial JuliaStats/Distributions.jl#1390); Distributions dependency is bumped to 0.25.15 which contains this rule
removes code chunks for Distributions < 0.25
fixes Zygote test errors (see below for more details)
fixes BetaPrime distribution has broken tests with the recent release of Distributions. #196
fixes WARNING: could not import DistributionsAD._mv_categorical_logpdf into ReverseDiffX #199
reduces CI: Julia 1.3 is tested only with Ubuntu
fixes some deprecations of MvNormal constructors
fixes CompatHelper: bump compat for ChainRulesTestUtils to 1 for package test, (keep existing compat) #195, CompatHelper: bump compat for ChainRulesCore to 1 for package test, (keep existing compat) #194, CompatHelper: bump compat for ChainRulesCore to 1, (keep existing compat) #193, CompatHelper: bump compat for ChainRules to 1, (keep existing compat) #192, CompatHelper: bump compat for ChainRulesCore to 1 for package test, (keep existing compat) #190, CompatHelper: bump compat for ChainRulesTestUtils to 1 for package test, (keep existing compat) #189, CompatHelper: bump compat for ChainRules to 1 , (keep existing compat) #188, CompatHelper: bump compat for ChainRulesCore to 1 , (keep existing compat) #186

devmotion · 2021-08-24T23:03:53Z

Ah... NNlib with CR 1 only supports Julia 1.6. IIRC it's only used in the definition of a helper function in the tests so maybe we should just remove it from the test dependencies.

devmotion · 2021-08-27T19:48:47Z

@yebai I don't think we should move the ZygoteRules definitions in https://github.com/TuringLang/DistributionsAD.jl/blob/a603603e89fbafd4cfde6a69eb4c44874ca75cb7/src/zygote.jl to ChainRules. These are no useful differentiation rules but just hacks and workarounds for issues in Zygote (maybe some are not needed anymore?), so IMO we should not define them generally for oter AD systems even though we could call back into the AD system with CR1, similar to ZygoteRules.pullback. If we want to define them for Zygote exclusively with ChainRules.rrule, we could add a config::Zygote.ZygoteRulesConfig as first argument - but then we either have to depend on Zygote or load Zygote with Requires. In my opinion both options are worse than depending on the lightweight ZygoteRules package and so I don't think we should convert these rules to ChainRules.

devmotion · 2021-08-27T19:59:03Z

Probably we need more time to debug and fix the Zygote issues, it's still not clear to me why it fails and in which Zygote/CR release it was broken and how it can be fixed. However, it seems DistributionsAD holds back other packages that would like to update to ChainRules 1 (e.g. SciML/SciMLSensitivity.jl#467). I assume that these Zygote issues are not new but existed for a while, in the tests in recent PRs they were only masked by the other failing tests I assume. Should we merge and release this PR once we are satisfied even if Zygote tests fail?

devmotion · 2021-08-27T20:23:19Z

OK, so here's a MWE:

julia> using DistributionsAD, Zygote, Distributions

julia> Zygote.gradient(0.45) do p
           return sum(logpdf(arraydist([Bernoulli(p) for _ in 1:3]), zeros(3, 2)))
       end
(-10.909090909090908,)

julia> Zygote.gradient(0.45) do p
           return loglikelihood(arraydist([Bernoulli(p) for _ in 1:3]), zeros(3, 2))
       end
ERROR: Need an adjoint for constructor Bernoulli{Float64}. Gradient is of type ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}}
Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:33
  [2] (::Zygote.Jnew{Bernoulli{Float64}, Nothing, false})(Δ::ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}})
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/lib/lib.jl:323
  [3] (::Zygote.var"#1812#back#229"{Zygote.Jnew{Bernoulli{Float64}, Nothing, false}})(Δ::ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}})
    @ Zygote ~/.julia/packages/ZygoteRules/OjfTt/src/adjoint.jl:59
  [4] Pullback
    @ ~/.julia/packages/Distributions/fXTVC/src/univariate/discrete/bernoulli.jl:30 [inlined]
  [5] (::typeof(∂(Bernoulli{Float64})))(Δ::ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}})
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/compiler/interface2.jl:0
  [6] Pullback
    @ ~/.julia/packages/Distributions/fXTVC/src/univariate/discrete/bernoulli.jl:35 [inlined]
  [7] (::typeof(∂(#Bernoulli#41)))(Δ::ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}})
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/compiler/interface2.jl:0
  [8] Pullback
    @ ~/.julia/packages/Distributions/fXTVC/src/univariate/discrete/bernoulli.jl:34 [inlined]
  [9] Pullback
    @ ./none:0 [inlined]
 [10] (::typeof(∂(λ)))(Δ::ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}})
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/compiler/interface2.jl:0
 [11] #566
    @ ~/.julia/packages/Zygote/YZfhu/src/lib/array.jl:218 [inlined]
 [12] #4
    @ ./generator.jl:36 [inlined]
 [13] iterate
    @ ./generator.jl:47 [inlined]
 [14] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{Vector{Tuple{Bernoulli{Float64}, typeof(∂(λ))}}, Vector{ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}}}}}, Base.var"#4#5"{Zygote.var"#566#572"}})
    @ Base ./array.jl:678
 [15] map
    @ ./abstractarray.jl:2383 [inlined]
 [16] (::Zygote.var"#563#569"{var"#34#36"{Float64}, Tuple{UnitRange{Int64}}, Vector{Tuple{Bernoulli{Float64}, typeof(∂(λ))}}})(Δ::Vector{ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}}})
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/lib/array.jl:218
 [17] (::Zygote.var"#back#602"{Zygote.var"#563#569"{var"#34#36"{Float64}, Tuple{UnitRange{Int64}}, Vector{Tuple{Bernoulli{Float64}, typeof(∂(λ))}}}})(ȳ::Vector{ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}}})
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/lib/array.jl:252
 [18] Pullback
    @ ./REPL[11]:2 [inlined]
 [19] (::typeof(∂(#33)))(Δ::Float64)
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/compiler/interface2.jl:0
 [20] (::Zygote.var"#50#51"{typeof(∂(#33))})(Δ::Float64)
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/compiler/interface.jl:41
 [21] gradient(f::Function, args::Float64)
    @ Zygote ~/.julia/packages/Zygote/YZfhu/src/compiler/interface.jl:76
 [22] top-level scope
    @ REPL[11]:1

Both approaches should return the same value. A single example works:

julia> Zygote.gradient(0.45) do p
           return logpdf(arraydist([Bernoulli(p) for _ in 1:3]), zeros(3))
       end
(-5.454545454545454,)

julia> Zygote.gradient(0.45) do p
           return loglikelihood(arraydist([Bernoulli(p) for _ in 1:3]), zeros(3))
       end
(-5.454545454545454,)

And filldist works with multiple samples and both logpdf and loglikelihood:

julia> Zygote.gradient(0.45) do p
           return sum(logpdf(filldist(Bernoulli(p), 3), zeros(3, 2)))
       end
(-10.909090909090908,)

julia> Zygote.gradient(0.45) do p
           return loglikelihood(filldist(Bernoulli(p), 3), zeros(3, 2))
       end
(-10.909090909090908,)

devmotion · 2021-08-27T21:03:50Z

OK, even simpler:

julia> Zygote.gradient(0.45) do p
           d = arraydist([Bernoulli(p) for _ in 1:3])
           x = zeros(3, 2)
           return sum(Base.Fix1(Distributions._logpdf, d), eachcol(x))
       end
(-10.909090909090908,)

julia> Zygote.gradient(0.45) do p
           d = arraydist([Bernoulli(p) for _ in 1:3])
           x = zeros(3, 2)
           return sum(xi -> Distributions._logpdf(d, xi), eachcol(x))
       end
(-10.909090909090908,)

julia> Zygote.gradient(0.45) do p
           d = arraydist([Bernoulli(p) for _ in 1:3])
           x = zeros(3, 2)
           return sum(i -> Distributions._logpdf(d, view(x, :, i)), axes(x, 2))
       end
ERROR: Need an adjoint for constructor Bernoulli{Float64}. Gradient is of type ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}}
Stacktrace:
  [1] error(s::String)
    @ Base ./error.jl:33
...

julia> Zygote.gradient(0.45) do p
           d = arraydist([Bernoulli(p) for _ in 1:3])
           x = zeros(3, 2)
           return sum(i -> Distributions._logpdf(d, view(x, :, i)), 1:size(x, 2))
       end
ERROR: Need an adjoint for constructor Bernoulli{Float64}. Gradient is of type ChainRulesCore.Tangent{Bernoulli{Float64}, NamedTuple{(:p,), Tuple{Float64}}}
Stacktrace:
  [1] error(s::String)
...

The last example is exactly how loglikelihood for multiple samples is defined in Distributions: https://github.com/JuliaStats/Distributions.jl/blob/59df675409a7e2490e4a45edd32c0267df435c55/src/multivariates.jl#L267

@oxinabox @DhairyaLGandhi Any idea why the first examples work but the last ones fail?

devmotion · 2021-08-30T14:27:14Z

I added a workaround for the issue above, hopefully a similar workaround can fix the matrixvariate issues.

The StatsFuns CR definitions are removed since they were moved to StatsFuns (available in >= 0.9.10). I opened a PR to Distributions that transfers the CR definition for the pdf of PoissonBinomial: JuliaStats/Distributions.jl#1390 If it is merged, we should remove the definitions in DistributionsAD.

oxinabox · 2021-08-30T22:50:59Z

test/runtests.jl

+ end
+ function ChainRulesCore.rrule(::typeof(to_simplex), x::AbstractArray{<:Real})
+ y = to_simplex(x)
+ pullback(ȳ) = (NoTangent(), to_simplex_pullback(ȳ, y))


May want to think about a ProbectTo here, but that is beyond scope of this PR I guess

devmotion · 2021-08-30T23:45:17Z

Zygote tests pass again 🎉

yebai · 2021-08-31T10:33:57Z

src/zygote.jl

+# Loglikelihood of multi- and matrixvariate distributions: multiple samples
+# workaround for Zygote issues discussed in
+# https://github.com/TuringLang/DistributionsAD.jl/pull/198
+ZygoteRules.@adjoint function Distributions.loglikelihood(


Thanks for looking into this - it looks good to me!

yebai · 2021-08-31T10:35:57Z

test/ad/distributions.jl

@@ -73,8 +73,8 @@
 DistSpec(Beta, (1.0, 2.0), 0.5),

 DistSpec(BetaPrime, (), 0.5),
- DistSpec(BetaPrime, (1.0,), 0.5),
- DistSpec(BetaPrime, (1.0, 2.0), 0.5),
+ DistSpec(BetaPrime, (1.5,), 0.5),


I also noticed the same issue but thought it only affects ReverseDiff with tape caching. When tape caching is disabled, ReverseDiff should handle control flows correctly (similar to Tracker).

I don't think it's ReverseDiff specific since e.g. Tracker failed as well (https://github.com/TuringLang/DistributionsAD.jl/pull/185/checks?check_run_id=3338494448#step:5:1097). In fact all AD systems apart from Zygote return a value that is different from finite differencing:

julia> using Distributions, FiniteDifferences, Tracker, ReverseDiff, ForwardDiff, Zygote julia> f(x) = logpdf(BetaPrime(x), 0.5) f (generic function with 1 method) julia> central_fdm(5, 1)(f, 1.0) 0.495922603223565 julia> central_fdm(12, 1)(f, 1.0) 0.4959226032237455 julia> Tracker.gradient(f, 1.0) (1.1890697837836721 (tracked),) julia> ForwardDiff.derivative(f, 1.0) 1.1890697837836721 julia> ReverseDiff.gradient(f ∘ first, [1.0]) 1-element Vector{Float64}: 1.1890697837836721 julia> Zygote.gradient(f, 1.0) (0.49592260322372683,)

The different values arise from the different behaviour of the AD systems for x -> xlogy(x, 0.5) at 0. FiniteDifferences and Zygote return the correct derivative (log(0.5)) whereas Tracker, ForwardDiff, and ReverseDiff return 0:

julia> using LogExpFunctions julia> central_fdm(12, 1)(x -> xlogy(x, 0.5), 0.0) -0.6931471805599452 julia> Tracker.gradient(x -> xlogy(x, 0.5), 0.0) (0.0 (tracked),) julia> ForwardDiff.derivative(x -> xlogy(x, 0.5), 0.0) 0.0 julia> ReverseDiff.gradient(x -> xlogy(first(x), 0.5), [0.0]) 1-element Vector{Float64}: 0.0 julia> Zygote.gradient(x -> xlogy(x, 0.5), 0.0) (-0.6931471805599453,)

It seems the AD systems handle the branch in https://github.com/JuliaStats/LogExpFunctions.jl/blob/39223ba1daa0244fae1885cd0bcba5a25743349b/src/basicfuns.jl#L29 differently/incorrectly.

yebai · 2021-08-31T10:43:55Z

@yebai I don't think we should move the ZygoteRules definitions in https://github.com/TuringLang/DistributionsAD.jl/blob/a603603e89fbafd4cfde6a69eb4c44874ca75cb7/src/zygote.jl to ChainRules. These are no useful differentiation rules but just hacks and workarounds for issues in Zygote

It sounds good to keep them here, or depreciate them if possible, or move them into another package (e.g. DynamicPPL) where appropriate.

devmotion and others added 7 commits August 24, 2021 10:50

Fix warning

94f5353

Mark BetaPrime as broken

6be0e42

Test Julia 1.3 only with Ubuntu

bd445b1

Bump version

4958962

Not broken anymore?!

ca7c616

Fix numerical issues of BetaPrime for α=1

c580b82

Update to CR1

74f4d8d

devmotion mentioned this pull request Aug 24, 2021

Fix warning, fix CI and run tests with Julia 1.3 only on Ubuntu #197

Closed

devmotion and others added 3 commits August 25, 2021 01:25

Remove NNlib

6c03cd2

Fix error

e031786

Add rrule for to_simplex

6decb58

This was referenced Aug 25, 2021

Use zygote2differential to wrap chainrules inputs FluxML/Zygote.jl#1057

Open

WARNING: could not import DistributionsAD._mv_categorical_logpdf into ReverseDiffX #199

Closed

devmotion added 2 commits August 27, 2021 17:50

Prepare for addition of ChainRules definitions to StatsFuns

955dac9

Prepare for use of CR in Distributions

a603603

devmotion mentioned this pull request Aug 27, 2021

test ChainRules 1 SciML/SciMLSensitivity.jl#467

Closed

devmotion added 3 commits August 27, 2021 23:21

Add Zygote workaround

8bb15b5

Handle Zygote's nothing in tests

71361bd

Remove ChainRules definitions that are moved to StatsFuns

53b7da5

devmotion added 5 commits August 30, 2021 23:08

Remove ChainRules definition that was moved to Distributions

bf287f9

Fix deprecation

8dd5c2d

Fix Tracker gradient of poissonbinomial_pdf

4df1274

Add Zygote workarounds

6198840

Update list of broken distributions

8b787f6

Remove type piracy (default definition is sufficient)

79b192f

oxinabox approved these changes Aug 30, 2021

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Use eachcol

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devmotion changed the title ~~Extension of #197 with ChainRules 1~~ Fix test errors and update to ChainRules 1 and Distributions 0.25.15 Aug 30, 2021

devmotion requested review from torfjelde and yebai August 31, 2021 00:00

devmotion mentioned this pull request Aug 31, 2021

[WIP] Update to Distributions 0.24.12 #150

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devmotion mentioned this pull request Aug 31, 2021

BetaPrime distribution has broken tests with the recent release of Distributions. #196

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devmotion merged commit 44a57e9 into master Aug 31, 2021

delete-merged-branch bot deleted the dw/warning_CR1 branch August 31, 2021 11:39

devmotion mentioned this pull request Aug 31, 2021

Update to ChainRules 1 TuringLang/Bijectors.jl#198

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devmotion mentioned this pull request Sep 14, 2021

[Merged by Bors] - Re-opening of #310 TuringLang/DynamicPPL.jl#330

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devmotion mentioned this pull request Oct 22, 2021

Remove adjoint for fill and fix tests #203

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Fix test errors and update to ChainRules 1 and Distributions 0.25.15 #198

Fix test errors and update to ChainRules 1 and Distributions 0.25.15 #198

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Fix test errors and update to ChainRules 1 and Distributions 0.25.15 #198

Fix test errors and update to ChainRules 1 and Distributions 0.25.15 #198

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