Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[email protected] Compatibility Fix #494

Merged
merged 17 commits into from
Jul 21, 2023
Merged

[email protected] Compatibility Fix #494

merged 17 commits into from
Jul 21, 2023

Conversation

sunxd3
Copy link
Member

@sunxd3 sunxd3 commented Jul 3, 2023

The test is failing at two main points:

  1. @test inspace(@varname(M[:, 1:4]), space)
    This test should arguably be marked as broken, unless there is a specific reason for its inclusion?
  2. any(DynamicPPL.subsumes(vn_left, vn) for vn_left in keys(vals))
    with model
    @model function demo_assume_matrix_dot_observe_matrix(
    x=transpose([1.5 2.0;]), ::Type{TV}=Array{Float64}
    ) where {TV}
    n = length(x)
    d = n ÷ 2
    s ~ reshape(product_distribution(fill(InverseGamma(2, 3), n)), d, 2)
    s_vec = vec(s)
    m ~ MvNormal(zeros(n), Diagonal(s_vec))
    # Dotted observe for `Matrix`.
    x .~ MvNormal(m, Diagonal(s_vec))
    return (; s=s, m=m, x=x, logp=getlogp(__varinfo__))
    end
    Two potential solutions come to mind. We could either use == instead of subsume (or in) in the test, or we could modify subsume to allow two equal varnames to subsume each other.

The issue of concretization runs a bit deeper, and the following concerns warrant discussion:

  1. At https://github.com/TuringLang/AbstractPPL.jl/blob/0f289206b22da5feee03218c157afb28706ed8ec/src/varname.jl#L609 the decision to concretize is based on Setfield.need_dynamic_lens, but expressions with Colon return false. Should this be changed?

  2. There should be no issues using offset arrays and infinite dimensional models. However, the concern lies in user-friendliness, which is linked to the next point.

  3. The automatic concretization could potentially confuse intermediate users and lead to silent failures in code pieces we gave to users. This is especially problematic when the functions use varname as keys and key comparison becomes more complex because of the concretize v.s. unconretized dichotomy, use of offset arrays can potentially make this worse. Additionally, (c.f. [Merged by Bors] - Concretize IndexLens using to_indices AbstractPPL.jl#43 (comment)), say one doesn't have x in the environment, but does @varname(x[end]), it will error, and this could be potentially very confusing and explain why by default the concretize was set to false.

Fix #440; Fix #470

@sunxd3
Copy link
Member Author

sunxd3 commented Jul 3, 2023

In DPPL, do we need concrete varname to guarantee correctness or concreteness is just a nice feature?

@yebai
Copy link
Member

yebai commented Jul 3, 2023

@torfjelde @devmotion, what are your thoughts here?

@torfjelde
Copy link
Member

Will have a look on Friday.

src/compiler.jl Outdated Show resolved Hide resolved
src/compiler.jl Show resolved Hide resolved
test/varinfo.jl Outdated Show resolved Hide resolved
src/compiler.jl Outdated Show resolved Hide resolved
@sunxd3
Copy link
Member Author

sunxd3 commented Jul 7, 2023

Okay, I have narrowed down the fail cases in SimpleVarInfo tests to

DynamicPPL.jl/src/utils.jl

Lines 338 to 341 in e6dd4ef

function set!!(obj, vn::VarName{sym}, value) where {sym}
lens = BangBang.prefermutation(Setfield.PropertyLens{sym}() AbstractPPL.getlens(vn))
return Setfield.set(obj, lens, value)
end

which is called from
function BangBang.setindex!!(vi::SimpleVarInfo, val, vn::VarName)
# For `NamedTuple` we treat the symbol in `vn` as the _property_ to set.
return Setfield.@set vi.values = set!!(vi.values, vn, val)
end

BangBang.set!! silently modify the eltype of some array from Float64 to Any, MWE

julia> model = DynamicPPL.TestUtils.demo_dot_assume_matrix_dot_observe_matrix();

julia> values_constrained = rand(NamedTuple, model)
(s = [1.1595043641715013 2.778153502600599], m = [0.32505356054876894, 0.3961233885884393])

julia> vi = SimpleVarInfo(values_constrained);

julia> vn = DynamicPPL.TestUtils.varnames(model)[1]
s[:, 1] # this is conretized

julia> DynamicPPL.setindex!!(vi, [1.0], vn)
SimpleVarInfo((s = Any[1.0 1.3154718042147129], m = [2.129624970695997, 0.849323875002674]), 0.0)

julia> AbstractPPL.set(vi.values, vn, [1.0])
(s = [1.0 1.3154718042147129], m = [2.129624970695997, 0.849323875002674])

@sunxd3
Copy link
Member Author

sunxd3 commented Jul 7, 2023

@torfjelde any chance you seen this before?

@sunxd3
Copy link
Member Author

sunxd3 commented Jul 7, 2023

It might be desirable to set concretize to false by default at https://github.com/TuringLang/AbstractPPL.jl/blob/0f289206b22da5feee03218c157afb28706ed8ec/src/varname.jl#L609
so that it don't error if someone does @varname(s[end]) when there is no s in the environment. But this would requires we modify getindexs to allow indexing with un-concretized varnames.

@github-actions
Copy link
Contributor

github-actions bot commented Jul 8, 2023

Pull Request Test Coverage Report for Build 5621778213

  • 12 of 12 (100.0%) changed or added relevant lines in 3 files are covered.
  • 2 unchanged lines in 1 file lost coverage.
  • Overall coverage increased (+0.02%) to 76.325%

Files with Coverage Reduction New Missed Lines %
src/utils.jl 2 76.96%
Totals Coverage Status
Change from base Build 5621457734: 0.02%
Covered Lines: 2060
Relevant Lines: 2699

💛 - Coveralls

@codecov
Copy link

codecov bot commented Jul 8, 2023

Codecov Report

Patch coverage: 100.00% and project coverage change: +0.02 🎉

Comparison is base (0d5c463) 76.30% compared to head (dddda4d) 76.32%.

Additional details and impacted files
@@            Coverage Diff             @@
##           master     #494      +/-   ##
==========================================
+ Coverage   76.30%   76.32%   +0.02%     
==========================================
  Files          22       22              
  Lines        2688     2699      +11     
==========================================
+ Hits         2051     2060       +9     
- Misses        637      639       +2     
Impacted Files Coverage Δ
src/compiler.jl 93.20% <100.00%> (+0.27%) ⬆️
src/test_utils.jl 83.24% <100.00%> (+0.04%) ⬆️
src/utils.jl 76.95% <100.00%> (-0.72%) ⬇️

☔ View full report in Codecov by Sentry.
📢 Do you have feedback about the report comment? Let us know in this issue.

@sunxd3
Copy link
Member Author

sunxd3 commented Jul 8, 2023

Okay, I modify the set!! function like #494 (comment). The tests are passing. But this is not a good fix as maybe we want to modify the underlying object with set!!?

@yebai
Copy link
Member

yebai commented Jul 10, 2023

@devmotion @torfjelde what are your thoughts on @sunxd3's proposed fixes?

@torfjelde
Copy link
Member

Having a look now 👍

@torfjelde
Copy link
Member

torfjelde commented Jul 10, 2023

Okay, I modify the set!! function like #494 (comment). The tests are passing. But this is not a good fix as maybe we want to modify the underlying object with set!!?

Yeah, I don't think this is the way to go 😕

What's the actual issue?

So a few things:

  1. If we hit the mutating branch in set!!, then it will not change the type (since it will then try to mutate the buffer).
  2. But set!! can also hit set if it cannot find a mutating version.

This means that we're hitting scenario (2).

If you use something like Cthulhu.jl to step through the call in your example where set!! results in an incorrect type, following the resulting set calls, you eventually see the following:

_setindex(xs::AbstractArray, v, I...) @ BangBang.NoBang ~/.julia/packages/BangBang/FUkah/src/NoBang/base.jl:129
129 function _setindex(xs::Matrix{Float64}::AbstractArray, v::Vector{Float64}, I::Tuple{AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}...)::Matrix{Any}
130     T::Core.Const(Any) = promote_type(eltype(xs::Matrix{Float64})::Type{Float64}, typeof(v::Vector{Float64})::Type{Vector{Float64}})::Type{Any}
131     ys::Matrix{Any} = similar(xs::Matrix{Float64}, T::Type{Any})::Matrix{Any}
132     if (eltype(xs::Matrix{Float64})::Type{Float64} !== Union::Type{Union}{}::Type{Union{}})::Bool
133         copy!(ys::Matrix{Any}, xs::Matrix{Float64})
134     end
135     ys::Matrix{Any}[I::Tuple{AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}...] = v
136     return ys::Matrix{Any}
137 end
Select a call to descend into or  to ascend. [q]uit. [b]ookmark.
Toggles: [w]arn, [h]ide type-stable statements, [t]ype annotations, [s]yntax highlight for Source/LLVM/Native.
Show: [S]ource code, [A]ST, [T]yped code, [L]LVM IR, [N]ative code
Actions: [E]dit source code, [R]evise and redisplay
   eltype(xs::Matrix{Float64})
   promote_type(eltype(xs::Matrix{Float64})::Type{Float64}, typeof(v::Vector{Float64})::Type{Vector{Float64}})
   similar(xs::Matrix{Float64}, T::Type{Any})
   eltype(xs::Matrix{Float64})
    eltype(xs::Matrix{Float64})::Type{Float64} !== Union::Type{Union}{}::Type{Union{}}
   %9 = copy!(::Matrix{Any},::Matrix{Float64})::Any
   %12 = setindex!(::Matrix{Any},::Vector{Float64},::AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}},::Int64)::Any

This is very verbose, but notice in particular the following part

   eltype(xs::Matrix{Float64})
   promote_type(eltype(xs::Matrix{Float64})::Type{Float64}, typeof(v::Vector{Float64})::Type{Vector{Float64}})
   similar(xs::Matrix{Float64}, T::Type{Any})

Here it becomes clear why we're ending up with Any: promote_type is called on eltype(xs::Matrix{Float64})::Type{Float64} and typeof(v::Vector{Float64})::Type{Vector{Float64}}, thus trying to promote a Vector{Float64} into a Float64, which returns Type{Any}.

So that answers the questions of where the Any comes from. It does not however answer the question

Why are we even hitting this non-mutating version in the first place?

When you step through using Cthulhu.jl, before you reach the _setindex we saw above, you'll also hit this:

may(mutate, args...) @ BangBang ~/.julia/packages/BangBang/FUkah/src/core.jl:7
┌ Warning: Some line information is missing, type-assignment may be incomplete
└ @ Cthulhu ~/.julia/packages/Cthulhu/vJrGb/src/codeview.jl:142
 7  may(mutate::typeof(BangBang._setindex!), args::Tuple{Matrix{Float64}, Vector{Float64}, AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}...)::Matrix{Any} =
 8     if possible(mutate::typeof(BangBang._setindex!), args::Tuple{Matrix{Float64}, Vector{Float64}, AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}...)
 9         mutate(args...)
10     else
11         pure(mutate::typeof(BangBang._setindex!))::typeof(BangBang.NoBang._setindex)(args::Tuple{Matrix{Float64}, Vector{Float64}, AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}...)::Matrix{Any}
12     end
Select a call to descend into or  to ascend. [q]uit. [b]ookmark.
Toggles: [w]arn, [h]ide type-stable statements, [t]ype annotations, [s]yntax highlight for Source/LLVM/Native.
Show: [S]ource code, [A]ST, [T]yped code, [L]LVM IR, [N]ative code
Actions: [E]dit source code, [R]evise and redisplay
   %4 = possible(::#_setindex!,::Array{…},::Array{…},::ConcretizedSlice{…},::Int64)::Core.Const(false)
   pure(mutate::typeof(BangBang._setindex!))
 • pure(mutate::typeof(BangBang._setindex!))::typeof(BangBang.NoBang._setindex)(args::Tuple{Matrix{Float64}, Vector{Float64}, A
   

where, in particular,

   %4 = possible(::#_setindex!,::Array{…},::Array{…},::ConcretizedSlice{…},::Int64)::Core.Const(false)

where we see this BangBang.possible function returns false, i.e. it considers it not possible to mutate.

Looking at the source in the above, i.e.

 8     if possible(mutate::typeof(BangBang._setindex!), args::Tuple{Matrix{Float64}, Vector{Float64}, AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}...)
 9         mutate(args...)

we see that BangBang.possible is given args::Tuple{Matrix{Float64}, Vector{Float64}, AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}, and given what we saw before with Any, I'm at this poin suspecting that a similar thing is causing possible to return false: Vector{Float64} is not considered a valid element of Matrix{Float64}, even with a ConcretizedSlice using Base.OneTo.

But let's check. Stepping into possible

possible(::typeof(BangBang._setindex!), ::C, ::T, ...) where {C<:AbstractArray, T} @ BangBang ~/.julia/packages/BangBang/FUkah/src/base.jl:484
484 (possible(::(typeof(_setindex!))::typeof(BangBang._setindex!), ::(C)::Matrix{Float64}, ::(T)::Vector{Float64}, ::(Vararg)::Tuple{AbstractPPL.ConcretizedSlice{Int64, Base.OneTo{Int64}}, Int64}) where {C <: AbstractArray, T})::Core.Const(false) =
485     implements(setindex!, C::Type{Matrix{Float64}}) && promote_type(eltype(C::Type{Matrix{Float64}}), T::Type{Vector{Float64}})::Type{Any} <: eltype(C::Type{Matrix{Float64}})::Bool
Select a call to descend into or  to ascend. [q]uit. [b]ookmark.
Toggles: [w]arn, [h]ide type-stable statements, [t]ype annotations, [s]yntax highlight for Source/LLVM/Native.
Show: [S]ource code, [A]ST, [T]yped code, [L]LVM IR, [N]ative code
Actions: [E]dit source code, [R]evise and redisplay
 • %1 = implements(::typeof(setindex!),::Type{Matrix{Float64}})::Core.Const(true)
   %3 = eltype(::Type{Matrix{Float64}})::Core.Const(Float64)
   promote_type(eltype(C::Type{Matrix{Float64}}), T::Type{Vector{Float64}})
   %5 = eltype(::Type{Matrix{Float64}})::Core.Const(Float64)
   

where in particular, we see the line

485     implements(setindex!, C::Type{Matrix{Float64}}) && promote_type(eltype(C::Type{Matrix{Float64}}), T::Type{Vector{Float64}})::Type{Any} <: eltype(C::Type{Matrix{Float64}})::Bool

where indeed Any pops up in a similar pomote_type as we saw earlier.

The solution

To solve this, it seems that we need to tell BangBang that it is indeed okay to call setindex! on AbstractArray{N,T} with element being of type AbstractArray{M,T} if given a ConcretizedSlice with M entries being slices.

More concretely, we need to implement BangBang.possible for this combination of inputs. As seen above, the current implementation we're hitting is

possible(::typeof(_setindex!), ::C, ::T, ::Vararg) where {C <: AbstractArray, T} =
    implements(setindex!, C) && promote_type(eltype(C), T) <: eltype(C)

and so maybe doing something like

_dimension(index) = 0
_dimension(::AbstractPPL.ConcretizedSlice) = 1
_dimension(indices::Tuple) = sum(map(_dimension, indices))


function BangBang.possible(
    ::typeof(BangBang._setindex!),
    ::C,
    ::T,
    indices::Vararg
) where {N, M, C <: AbstractArray{<:Any,N}, T <: AbstractArray{<:Any,M}}
    return BangBang.implements(setindex!, C) &&
        promote_type(eltype(C), eltype(T)) <: eltype(C) &&
        _dimension(indices) == M
end

With this method impleemnted, we have

julia> model = DynamicPPL.TestUtils.demo_dot_assume_matrix_dot_observe_matrix();

julia> values_constrained = rand(NamedTuple, model)
(s = [4.895152314885813 2.111286552161793], m = [0.8664750151981476, 1.6153565968318373])

julia> vi = SimpleVarInfo(values_constrained);

julia> vn = DynamicPPL.TestUtils.varnames(model)[1]
s[:,1]

julia> DynamicPPL.setindex!!(vi, [1.0], vn)
SimpleVarInfo((s = [1.0 2.111286552161793], m = [0.8664750151981476, 1.6153565968318373]), 0.0)

as wanted!

But this should really be in BangBang.jl IMO. I'll make a PR.

@torfjelde
Copy link
Member

torfjelde commented Jul 10, 2023

Related: JuliaFolds/BangBang.jl#238

Once that is added, we really only need to overload _index_dimension for ConcretizedSlice in AbstractPPL, and we should be gucci.

@sunxd3
Copy link
Member Author

sunxd3 commented Jul 11, 2023

Nice 🕵️‍♂️ work, I'll monitor the PR and follow up later.

@yebai
Copy link
Member

yebai commented Jul 11, 2023

thanks, @sunxd3 and @torfjelde for the investigations! Let's aim to fix this compact issue quickly since it has been causing various issues for a while.

@sunxd3
Copy link
Member Author

sunxd3 commented Jul 19, 2023

While recreating the debugging process of @torfjelde, I realized I didn't answer why with unconcretized varname, things worked fine. After a little bit digging, I found a function Tor wrote to make it work.
Maybe the BangBang PR is a better solution in the long run, but in the short term, a similar function to above should do for concretized varname.

@sunxd3 sunxd3 marked this pull request as ready for review July 19, 2023 09:36
Copy link
Member

@yebai yebai left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Good work @sunxd3 — I left one clarification comment below.

src/compiler.jl Show resolved Hide resolved
@sunxd3 sunxd3 requested a review from torfjelde July 19, 2023 13:04
@yebai
Copy link
Member

yebai commented Jul 20, 2023

@torfjelde Can you take a quick look before I merge this PR? Let's aim to merge it in the next day or two.

@yebai yebai enabled auto-merge July 21, 2023 12:06
@yebai yebai added this pull request to the merge queue Jul 21, 2023
Merged via the queue into master with commit e241dca Jul 21, 2023
12 of 13 checks passed
@yebai yebai deleted the abstractppl_compat_fix branch July 21, 2023 12:31
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants