inference: make throw block deoptimization concrete-eval friendly

[aviatesk]

The deoptimization can sometimes destroy the effects analysis and disable [semi-]concrete evaluation that is otherwise possible. This is because the deoptimization was designed with the type domain profitability in mind (#35982), and hasn't been adequately considering the effects domain.

This commit makes the deoptimization aware of the effects domain more and enables the throw block deoptimization only when the effects already known to be ineligible for concrete-evaluation.

In our current effect system, ALWAYS_FALSE/false means that the effect can not be refined to ALWAYS_TRUE/true anymore (unless given user annotation later). Therefore we can enable the throw block deoptimization without hindering the chance of concrete-evaluation when any of the following conditions are met:

• effects.consistent === ALWAYS_FALSE
• effects.effect_free === ALWAYS_FALSE
• effects.terminates === false
• effects.nonoverlayed === false

Here are some numbers:

Metric	master	this commit	#35982 reverted (set unoptimize_throw_blocks=false)
Base (seconds)	15.579300	15.206645	15.296319
Stdlibs (seconds)	17.919013	17.667094	17.738128
Total (seconds)	33.499279	32.874737	33.035448
Precompilation (seconds)	49.967516	49.421121	49.999998
First time plot(rand(10,3)) 1	2.476678 seconds (11.74 M allocations)	2.430355 seconds (11.77 M allocations)	2.514874 seconds (11.64 M allocations)
First time solve(prob, QNDF())(5.0) 2	4.469492 seconds (15.32 M allocations)	4.499217 seconds (15.41 M allocations)	4.470772 seconds (15.38 M allocations)

These numbers made me question if we are getting any actual benefit from
the throw block deoptimization anymore. Since it is sometimes harmful
for the effects analysis, we probably want to either merge this commit
or remove the throw block deoptimization completely.

Footnotes

1. With disabling precompilation of Plots.jl. ↩

2. With disabling precompilation of OrdinaryDiffEq. ↩

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your job failed.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - no performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[aviatesk]

Given that this appears to be a clear improvement, I'll go ahead and merge it once the CI checks come back clean.

[nanosoldier]

Your benchmark job has completed - no performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.

[aviatesk]

@nanosoldier runbenchmarks("inference", vs=":master")

[nanosoldier]

Your benchmark job has completed - possible performance regressions were detected. A full report can be found here.