Inference performance regression vs 1.1 (MDDatasets.jl)

[KristofferC]

For MDDatasets the following code

using MDDatasets

xingsrise = CrossType(:rise)

#Required mostly to test cross function:
y = [0,0,0,0,1,-3,4,5,6,7,-10,-5,0,0,0,-5,-10,10,-3,1,-4,0,0,0,0,0,0]
d10=DataF1(collect(1:length(y)), y)
@time _x = meas(:xcross, Event, d10, allow=xingsrise)

takes 80 seconds in 1.1 but seems to hang indefinitely on 1.2 and 1.3. Interrupting gives a stack trace deep into inference.

In addition, just loading MDDatasets make the REPL become extremely sluggish for a while on 1.2 and 1.3 (everything getting invalidated?). This is also noticeable on 1.1 but to a much smaller degree.

Here is an example that shows the REPL experience after loading the package (note that I show key pressed on the keyboard). After loading the package and typing 1 it takes about 6 seconds for it to appear in the REPL input field. Pressing enter then takes ~10 seconds for the result to show:

Edit:

1.2 finished:

julia> @time _x = meas(:xcross, Event, d10, allow=xingsrise)
6430.483092 seconds (8.14 G allocations: 511.835 GiB, 18.72% gc time)
DataF1(x=[1, 2, 3],y=[6.428571428571429, 17.5, 19.75])

1.3 finished:

julia> @time _x = meas(:xcross, Event, d10, allow=xingsrise)
	8414.099232 seconds (10.91 G allocations: 684.973 GiB, 4.87% gc time)
DataF1(x=[1, 2, 3],y=[6.428571428571429, 17.5, 19.75])

[maleadt]

Bisected to 8c44566/#31191 (cc @vtjnash). I had my script timeout at 300s, and right before 8c44566 the script takes 152s vs. about 80 on v1.1.0, so there's another earlier regression, but at least this is the most problematic one. I'll di another bisect overnight.

[maleadt]

Further bisect points to #30577 as the cause for the slowdown (cc @JeffBezanson):

commit e456a72b033e3da8ade872a856ad4415a8924663
Author: Jeff Bezanson <jeff.bezanson@gmail.com>
Date:   Sat Feb 2 14:52:32 2019 -0500

    define core NamedTuple constructor without generated functions

[KristofferC]

Further bisect points to #30577 as the cause for the slowdown

You mean the one from 80s -> 152s?

[maleadt]

Yes.

[JeffBezanson]

That's probably because that commit allows us to better infer NamedTuple constructors, so inference is doing more work.

[KristofferC]

I don't know if we should but a 1.3 milestone on this. It is not a regression since 1.2 but if we would have found it in 1.2 we probably would have milestoned it.

[JeffBezanson]

The operator definitions in MDDatasets (src/datasetop_reg.jl) seem to be causing nearly everything to be invalidated.

[KristofferC]

Even with everything invalidated, it takes 2 hours to run now. Should be enough to recompile everything many times over?

[JeffBezanson]

Yes there are probably multiple issues here.

[JeffBezanson]

When inferring meas there is a lot of repetitive work; for example here is part of an inference trace piped through sort, uniq -c:

    172 Tuple{typeof(Base.:(>)), MDDatasets.DataF1{TX, TY} where TY<:Number where TX<:Number, Int64}
    119 Tuple{typeof(Base.:(!=)), Int64, MDDatasets.DataF1{TX, TY} where TY<:Number where TX<:Number}
     62 Tuple{typeof(MDDatasets.error_mismatchedsweep), Array{MDDatasets.PSweep{T} where T, 1}, Array{MDDatasets.PSweep{T} where T, 1}}
     62 Tuple{typeof(Base.string), String, Array{MDDatasets.PSweep{T} where T, 1}, String, Array{MDDatasets.PSweep{T} where T, 1}}
     32 Tuple{typeof(MDDatasets._broadcast), Type{T} where T, Array{MDDatasets.PSweep{T} where T, 1}, typeof(Base.:(>)), MDDatasets.DataHR{T1} where T1, Int64}
     32 Tuple{typeof(MDDatasets._broadcast), Type, Array{MDDatasets.PSweep{T} where T, 1}, typeof(Base.:(!=)), Int64, MDDatasets.DataHR{T2} where T2}
     32 Tuple{typeof(MDDatasets.broadcastMDSweep), Array{MDDatasets.PSweep{T} where T, 1}, Int64}
     32 Tuple{typeof(MDDatasets.broadcastMD), MDDatasets.CastType2{Number, 1, Number, 2}, typeof(Base.:(>)), MDDatasets.DataHR{T1}, Int64} where T1
     32 Tuple{typeof(MDDatasets.broadcastMD), MDDatasets.CastType2{Number, 1, Number, 2}, typeof(Base.:(!=)), Int64, MDDatasets.DataHR{T2}} where T2
     32 Tuple{typeof(Base.unsafe_convert), Type{Ptr{T}}, AbstractArray{T, N} where N} where T
     32 Tuple{typeof(Base.unsafe_convert), Type{Ptr{Nothing}}, Union{Array{Int128, 1}, Array{Int16, 1}, Array{Int32, 1}, Array{Int64, 1}, Array{Int8, 1}, Array{UInt128, 1}, Array{UInt16, 1}, Array{UInt32, 1}, Array{UInt64, 1}, Array{UInt8, 1}}}
     32 Tuple{typeof(Base.unsafe_convert), Type{Ptr{_A}} where _A, Union{Array{Int128, 1}, Array{Int16, 1}, Array{Int32, 1}, Array{Int64, 1}, Array{Int8, 1}, Array{UInt128, 1}, Array{UInt16, 1}, Array{UInt32, 1}, Array{UInt64, 1}, Array{UInt8, 1}}}
     32 Tuple{typeof(Base.string), String, Type{#s64} where #s64<:AbstractArray{T, N} where N where T}
     32 Tuple{typeof(Base.show_datatype), Base.GenericIOBuffer{Array{UInt8, 1}}, Type{#s64} where #s64<:AbstractArray{T, N} where N where T}
     32 Tuple{typeof(Base.show), Base.GenericIOBuffer{Array{UInt8, 1}}, Type{#s64} where #s64<:AbstractArray{T, N} where N where T}
     32 Tuple{typeof(Base.print_to_string), String, Type{#s64} where #s64<:AbstractArray{T, N} where N where T}
     32 Tuple{typeof(Base.print), Base.GenericIOBuffer{Array{UInt8, 1}}, Type{#s64} where #s64<:AbstractArray{T, N} where N where T}
     32 Tuple{typeof(Base._memcmp), Union{Array{Int128, 1}, Array{Int16, 1}, Array{Int32, 1}, Array{Int64, 1}, Array{Int8, 1}, Array{UInt128, 1}, Array{UInt16, 1}, Array{UInt32, 1}, Array{UInt64, 1}, Array{UInt8, 1}}, Union{Array{Int128, 1}, Array{Int16, 1}, Array{Int32, 1}, Array{Int64, 1}, Array{Int8, 1}, Array{UInt128, 1}, Array{UInt16, 1}, Array{UInt32, 1}, Array{UInt64, 1}, Array{UInt8, 1}}, Int64}

Maybe there is a large cycle of calls that don't get cached?

[JeffBezanson]

One issue is that MDDatasets defines methods of == and (much worse) != that do not seem to return booleans. We almost always rely on != just calling ==, but the package changes that and also the type. I don't think it's the whole cause of this problem, but it's an abusive definition I happened to notice while investigating.