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

Compiler optimization for ScopedValues #51352

Closed
vchuravy opened this issue Sep 16, 2023 · 7 comments
Closed

Compiler optimization for ScopedValues #51352

vchuravy opened this issue Sep 16, 2023 · 7 comments
Assignees
Labels
compiler:optimizer Optimization passes (mostly in base/compiler/ssair/) help wanted Indicates that a maintainer wants help on an issue or pull request

Comments

@vchuravy
Copy link
Member

It might be beneficial to perform some optimization over scoped values.

In particular it would be beneficial for the value lookup to be coalesced within a dynamical scope.
This would require them to be CSE'd or for GVN (#51120) to be aware of their semantics.

We would need to annotate in the IR the extent of a dynamical scope, since
since ScopedValue are only constant within that region.

token = Expr(:dynamical_scope_enter, args::Pair{ScopedValue, Any}...)
...
Expr(:dynamical_scope_exit, token)

This might be a bit frustrating since currently we have:

current_scope = current_task().scope
current_task().scope = Scope(...)
try
finally
      current_task().scope = current_scope
end

and thus we would introduce a "silent" new form of a try...finally block which seems not ideal.

But maybe a representation like:

old_scope = Expr(:dynamical_scope_enter, args::Pair{ScopedValue, Any}...)
try
finally
    Expr(:dynamical_scope_exit, old_scope)
end

Could still work?

Of course this is predicated on how folks are using ScopedValues in the wild to determine if this would be useful at all.
Java seems to do this kind of optimization + a very small cache.

@vchuravy vchuravy added help wanted Indicates that a maintainer wants help on an issue or pull request compiler:optimizer Optimization passes (mostly in base/compiler/ssair/) labels Sep 16, 2023
@simonbyrne
Copy link
Contributor

Somewhat related, but we could make scoped variables a language feature (possibly in Julia 2.0?). i.e. instead of

const var = ScopedValue{T}(default)
with(var => newval) do
   x = var[]
end

we could have

scoped var::T = default
with var = newval
   x = var
end

@serenity4
Copy link
Contributor

What type of code would benefit from these optimizations? Would that include e.g. the following:

const S = ScopedValue(0)

g() = S[] + 1

function f()
  @with S => 1 begin
    g()
  end
end

f()

which would be able to const-prop the result to 2?

It seems to me that, for cases where e.g. g() is not inlineable, it might require a separate compilation for every possible scoped value and MethodInstance pair (S, g()) within a given world age, which might negatively affect compilation times.

Although, if we are able to detect that a hypothetical method called by g(), say h(x), does not rely on scoped values at all, we don't need to compile h(x) twice for two different scoped values S. I guess with some cleverness the problem then goes away.

@vchuravy
Copy link
Member Author

vchuravy commented Oct 9, 2023

which would be able to const-prop the result to 2?

Note that scoped values are constant within a certain scope, so your code as written does not work.

But yes the goal would be that we could constant partially const-prop.

If g is not inlineable, we wouldn't know the value, but we would still be able to merge multiple accesses of the same scoped value.

@serenity4
Copy link
Contributor

Note that scoped values are constant within a certain scope, so your code as written does not work.

What do you mean? This seems to work fine:

julia> const S = ScopedValue(0)
ScopedValue{Int64}(0)

julia> g() = S[] + 1
g (generic function with 1 method)

julia> function f()
         @with S => 1 begin
           g()
         end
       end
f (generic function with 1 method)

julia> f()
2

@vchuravy
Copy link
Member Author

Ah sorry I read g() = S[] + 1 as g() = S[] += 1

@Keno Keno self-assigned this Nov 1, 2023
@Keno
Copy link
Member

Keno commented Nov 1, 2023

I'm planning to address this as follows:
Once #51754 is merged, extend the :enter form to take an additional argument that implicitly sets the scope. Then we can extend SROA to forward scope enters to scope reads (and exception throws to :the_exception). :leave would pop the current scope automatically and direct setfield of the scope on a running task would be considered illegal.

Keno added a commit that referenced this issue Nov 2, 2023
This is part of the work to address #51352 by attempting to allow
the compiler to perform SRAO on persistent data structures like
`PersistentDict` as if they were regular immutable data structures.
These sorts of data structures have very complicated internals
(with lots of mutation, memory sharing, etc.), but a relatively
simple interface. As such, it is unlikely that our compiler will
have sufficient power to optimize this interface by analyzing
the implementation.

We thus need to come up with some other mechanism that gives the
compiler license to perform the requisite optimization. One way
would be to just hardcode `PersistentDict` into the compiler,
optimizing it like any of the other builtin datatypes. However,
this is of course very unsatisfying. At the other end of the
spectrum would be something like a generic rewrite rule system
(e-graphs anyone?) that would let the PersistentDict
implementation declare its interface to the compiler and the
compiler would use this for optimization (in a perfect world,
the actual rewrite would then be checked using some sort of
formal methods). I think that would be interesting, but we're
very far from even being able to design something like that
(at least in Base - experiments with external AbstractInterpreters
in this direction are encouraged).

This PR tries to come up with a reasonable middle ground, where
the compiler gets some knowledge of the protocol hardcoded without
having to know about the implementation details of the data structure.

The basic ideas is that `Core` provides some magic generic functions
that implementations can extend. Semantically, they are not special.
They dispatch as usual, and implementations are expected to work
properly even in the absence of any compiler optimizations.

However, the compiler is semantically permitted to perform structural
optimization using these magic generic functions. In the concrete
case, this PR introduces the `KeyValue` interface which consists
of two generic functions, `get` and `set`. The core optimization
is that the compiler is allowed to rewrite any occurrence of
`get(set(x, k, v), k)` into `v` without additional legality checks.
In particular, the compiler performs no type checks, conversions, etc.
The higher level implementation code is expected to do all that.

This approach closely matches the general direction we've been taking
in external AbstractInterpreters for embedding additional semantics
and optimization opportunities into Julia code (although we generally
use methods there, rather than full generic functions), so I think
we have some evidence that this sort of approach works reasonably well.

Nevertheless, this is certainly an experiment and the interface is
explicitly declared unstable.

## Current Status

This is fully working and implemented, but the optimization currently
bails on anything but the simplest cases. Filling all those cases in
is not particularly hard, but should be done along with a more invasive
refactoring of SROA, so we should figure out the general direction
here first and then we can finish all that up in a follow-up cleanup.

## Obligatory benchmark
Before:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  32.940 ns …  28.754 μs  ┊ GC (min … max):  0.00% … 99.76%
 Time  (median):     49.647 ns               ┊ GC (median):     0.00%
 Time  (mean ± σ):   57.519 ns ± 333.275 ns  ┊ GC (mean ± σ):  10.81% ±  2.22%

        ▃█▅               ▁▃▅▅▃▁                ▁▃▂   ▂
  ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃
  32.9 ns         Histogram: frequency by time         68.6 ns <

 Memory estimate: 128 bytes, allocs estimate: 4.

julia> @code_typed foo()
CodeInfo(
1 ─ %1  = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %2  = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64}
│   %3  = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64}
│   %4  = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %5  = $(Expr(:boundscheck, true))::Bool
└──       goto #5 if not %5
2 ─ %7  = Base.sub_int(1, 1)::Int64
│   %8  = Base.bitcast(UInt64, %7)::UInt64
│   %9  = Base.getfield(%4, :size)::Tuple{Int64}
│   %10 = $(Expr(:boundscheck, true))::Bool
│   %11 = Base.getfield(%9, 1, %10)::Int64
│   %12 = Base.bitcast(UInt64, %11)::UInt64
│   %13 = Base.ult_int(%8, %12)::Bool
└──       goto #4 if not %13
3 ─       goto #5
4 ─ %16 = Core.tuple(1)::Tuple{Int64}
│         invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{}
└──       unreachable
5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│         Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
└──       goto #6
6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32
│   %24 = Base.or_int(%23, 0x00010000)::UInt32
│         Base.setfield!(%2, :bitmap, %24)::UInt32
└──       goto #7
7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64}
└──       goto #8
8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64
└──       return %29
```

After:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  2.459 ns … 11.320 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     2.460 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.469 ns ±  0.183 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂    █                                              ▁    █ ▂
  █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █
  2.46 ns      Histogram: log(frequency) by time     2.47 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @code_typed foo()
CodeInfo(
1 ─     return 1
```
aviatesk pushed a commit that referenced this issue Nov 16, 2023
This is part of the work to address #51352 by attempting to allow
the compiler to perform SRAO on persistent data structures like
`PersistentDict` as if they were regular immutable data structures.
These sorts of data structures have very complicated internals
(with lots of mutation, memory sharing, etc.), but a relatively
simple interface. As such, it is unlikely that our compiler will
have sufficient power to optimize this interface by analyzing
the implementation.

We thus need to come up with some other mechanism that gives the
compiler license to perform the requisite optimization. One way
would be to just hardcode `PersistentDict` into the compiler,
optimizing it like any of the other builtin datatypes. However,
this is of course very unsatisfying. At the other end of the
spectrum would be something like a generic rewrite rule system
(e-graphs anyone?) that would let the PersistentDict
implementation declare its interface to the compiler and the
compiler would use this for optimization (in a perfect world,
the actual rewrite would then be checked using some sort of
formal methods). I think that would be interesting, but we're
very far from even being able to design something like that
(at least in Base - experiments with external AbstractInterpreters
in this direction are encouraged).

This PR tries to come up with a reasonable middle ground, where
the compiler gets some knowledge of the protocol hardcoded without
having to know about the implementation details of the data structure.

The basic ideas is that `Core` provides some magic generic functions
that implementations can extend. Semantically, they are not special.
They dispatch as usual, and implementations are expected to work
properly even in the absence of any compiler optimizations.

However, the compiler is semantically permitted to perform structural
optimization using these magic generic functions. In the concrete
case, this PR introduces the `KeyValue` interface which consists
of two generic functions, `get` and `set`. The core optimization
is that the compiler is allowed to rewrite any occurrence of
`get(set(x, k, v), k)` into `v` without additional legality checks.
In particular, the compiler performs no type checks, conversions, etc.
The higher level implementation code is expected to do all that.

This approach closely matches the general direction we've been taking
in external AbstractInterpreters for embedding additional semantics
and optimization opportunities into Julia code (although we generally
use methods there, rather than full generic functions), so I think
we have some evidence that this sort of approach works reasonably well.

Nevertheless, this is certainly an experiment and the interface is
explicitly declared unstable.

This is fully working and implemented, but the optimization currently
bails on anything but the simplest cases. Filling all those cases in
is not particularly hard, but should be done along with a more invasive
refactoring of SROA, so we should figure out the general direction
here first and then we can finish all that up in a follow-up cleanup.

Before:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  32.940 ns …  28.754 μs  ┊ GC (min … max):  0.00% … 99.76%
 Time  (median):     49.647 ns               ┊ GC (median):     0.00%
 Time  (mean ± σ):   57.519 ns ± 333.275 ns  ┊ GC (mean ± σ):  10.81% ±  2.22%

        ▃█▅               ▁▃▅▅▃▁                ▁▃▂   ▂
  ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃
  32.9 ns         Histogram: frequency by time         68.6 ns <

 Memory estimate: 128 bytes, allocs estimate: 4.

julia> @code_typed foo()
CodeInfo(
1 ─ %1  = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %2  = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64}
│   %3  = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64}
│   %4  = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %5  = $(Expr(:boundscheck, true))::Bool
└──       goto #5 if not %5
2 ─ %7  = Base.sub_int(1, 1)::Int64
│   %8  = Base.bitcast(UInt64, %7)::UInt64
│   %9  = Base.getfield(%4, :size)::Tuple{Int64}
│   %10 = $(Expr(:boundscheck, true))::Bool
│   %11 = Base.getfield(%9, 1, %10)::Int64
│   %12 = Base.bitcast(UInt64, %11)::UInt64
│   %13 = Base.ult_int(%8, %12)::Bool
└──       goto #4 if not %13
3 ─       goto #5
4 ─ %16 = Core.tuple(1)::Tuple{Int64}
│         invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{}
└──       unreachable
5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│         Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
└──       goto #6
6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32
│   %24 = Base.or_int(%23, 0x00010000)::UInt32
│         Base.setfield!(%2, :bitmap, %24)::UInt32
└──       goto #7
7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64}
└──       goto #8
8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64
└──       return %29
```

After:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  2.459 ns … 11.320 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     2.460 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.469 ns ±  0.183 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂    █                                              ▁    █ ▂
  █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █
  2.46 ns      Histogram: log(frequency) by time     2.47 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @code_typed foo()
CodeInfo(
1 ─     return 1
```
Keno added a commit that referenced this issue Nov 20, 2023
This is part of the work to address #51352 by attempting to allow
the compiler to perform SRAO on persistent data structures like
`PersistentDict` as if they were regular immutable data structures.
These sorts of data structures have very complicated internals
(with lots of mutation, memory sharing, etc.), but a relatively
simple interface. As such, it is unlikely that our compiler will
have sufficient power to optimize this interface by analyzing
the implementation.

We thus need to come up with some other mechanism that gives the
compiler license to perform the requisite optimization. One way
would be to just hardcode `PersistentDict` into the compiler,
optimizing it like any of the other builtin datatypes. However,
this is of course very unsatisfying. At the other end of the
spectrum would be something like a generic rewrite rule system
(e-graphs anyone?) that would let the PersistentDict
implementation declare its interface to the compiler and the
compiler would use this for optimization (in a perfect world,
the actual rewrite would then be checked using some sort of
formal methods). I think that would be interesting, but we're
very far from even being able to design something like that
(at least in Base - experiments with external AbstractInterpreters
in this direction are encouraged).

This PR tries to come up with a reasonable middle ground, where
the compiler gets some knowledge of the protocol hardcoded without
having to know about the implementation details of the data structure.

The basic ideas is that `Core` provides some magic generic functions
that implementations can extend. Semantically, they are not special.
They dispatch as usual, and implementations are expected to work
properly even in the absence of any compiler optimizations.

However, the compiler is semantically permitted to perform structural
optimization using these magic generic functions. In the concrete
case, this PR introduces the `KeyValue` interface which consists
of two generic functions, `get` and `set`. The core optimization
is that the compiler is allowed to rewrite any occurrence of
`get(set(x, k, v), k)` into `v` without additional legality checks.
In particular, the compiler performs no type checks, conversions, etc.
The higher level implementation code is expected to do all that.

This approach closely matches the general direction we've been taking
in external AbstractInterpreters for embedding additional semantics
and optimization opportunities into Julia code (although we generally
use methods there, rather than full generic functions), so I think
we have some evidence that this sort of approach works reasonably well.

Nevertheless, this is certainly an experiment and the interface is
explicitly declared unstable.

This is fully working and implemented, but the optimization currently
bails on anything but the simplest cases. Filling all those cases in
is not particularly hard, but should be done along with a more invasive
refactoring of SROA, so we should figure out the general direction
here first and then we can finish all that up in a follow-up cleanup.

Before:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  32.940 ns …  28.754 μs  ┊ GC (min … max):  0.00% … 99.76%
 Time  (median):     49.647 ns               ┊ GC (median):     0.00%
 Time  (mean ± σ):   57.519 ns ± 333.275 ns  ┊ GC (mean ± σ):  10.81% ±  2.22%

        ▃█▅               ▁▃▅▅▃▁                ▁▃▂   ▂
  ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃
  32.9 ns         Histogram: frequency by time         68.6 ns <

 Memory estimate: 128 bytes, allocs estimate: 4.

julia> @code_typed foo()
CodeInfo(
1 ─ %1  = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %2  = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64}
│   %3  = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64}
│   %4  = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %5  = $(Expr(:boundscheck, true))::Bool
└──       goto #5 if not %5
2 ─ %7  = Base.sub_int(1, 1)::Int64
│   %8  = Base.bitcast(UInt64, %7)::UInt64
│   %9  = Base.getfield(%4, :size)::Tuple{Int64}
│   %10 = $(Expr(:boundscheck, true))::Bool
│   %11 = Base.getfield(%9, 1, %10)::Int64
│   %12 = Base.bitcast(UInt64, %11)::UInt64
│   %13 = Base.ult_int(%8, %12)::Bool
└──       goto #4 if not %13
3 ─       goto #5
4 ─ %16 = Core.tuple(1)::Tuple{Int64}
│         invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{}
└──       unreachable
5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│         Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
└──       goto #6
6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32
│   %24 = Base.or_int(%23, 0x00010000)::UInt32
│         Base.setfield!(%2, :bitmap, %24)::UInt32
└──       goto #7
7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64}
└──       goto #8
8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64
└──       return %29
```

After:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  2.459 ns … 11.320 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     2.460 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.469 ns ±  0.183 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂    █                                              ▁    █ ▂
  █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █
  2.46 ns      Histogram: log(frequency) by time     2.47 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @code_typed foo()
CodeInfo(
1 ─     return 1
```
Keno added a commit that referenced this issue Nov 25, 2023
This is a prepratory commit in anticipation of giving :enter additional
responsibilities of entering and restoring dynamic scopes for ScopedValue
(c.f. #51352). This commit simply turns `:enter` into its own node type
(like the other terminators). The changes are largely mechanical from
the `Expr` version, but will make it easier to add additional semantics
in a follow up PR.
Keno added a commit that referenced this issue Nov 25, 2023
This is a prepratory commit in anticipation of giving :enter additional
responsibilities of entering and restoring dynamic scopes for ScopedValue
(c.f. #51352). This commit simply turns `:enter` into its own node type
(like the other terminators). The changes are largely mechanical from
the `Expr` version, but will make it easier to add additional semantics
in a follow up PR.
Keno added a commit that referenced this issue Nov 26, 2023
This is a prepratory commit in anticipation of giving :enter additional
responsibilities of entering and restoring dynamic scopes for
ScopedValue (c.f. #51352). This commit simply turns `:enter` into its
own node type (like the other terminators). The changes are largely
mechanical from the `Expr` version, but will make it easier to add
additional semantics in a follow up PR.
Keno added a commit that referenced this issue Nov 26, 2023
As discussed in #51352, this gives `EnterNode` the ability to set
(and restore on leave or catch edge) jl_current_task->scope. Manual
modifications of the task field after the task has started are
considered undefined behavior. In addition, we gain a new intrinsic
to access current_task->scope and both inference and the optimizer
will forward scopes from EnterNodes to this intrinsic (non-interprocedurally).
Together with #51993 this is sufficient to fully optimize ScopedValues
(non-interprocedurally at least).
Keno added a commit that referenced this issue Nov 26, 2023
As discussed in #51352, this gives `EnterNode` the ability to set
(and restore on leave or catch edge) jl_current_task->scope. Manual
modifications of the task field after the task has started are
considered undefined behavior. In addition, we gain a new intrinsic
to access current_task->scope and both inference and the optimizer
will forward scopes from EnterNodes to this intrinsic (non-interprocedurally).
Together with #51993 this is sufficient to fully optimize ScopedValues
(non-interprocedurally at least).
Keno added a commit that referenced this issue Nov 26, 2023
This is part of the work to address #51352 by attempting to allow
the compiler to perform SRAO on persistent data structures like
`PersistentDict` as if they were regular immutable data structures.
These sorts of data structures have very complicated internals
(with lots of mutation, memory sharing, etc.), but a relatively
simple interface. As such, it is unlikely that our compiler will
have sufficient power to optimize this interface by analyzing
the implementation.

We thus need to come up with some other mechanism that gives the
compiler license to perform the requisite optimization. One way
would be to just hardcode `PersistentDict` into the compiler,
optimizing it like any of the other builtin datatypes. However,
this is of course very unsatisfying. At the other end of the
spectrum would be something like a generic rewrite rule system
(e-graphs anyone?) that would let the PersistentDict
implementation declare its interface to the compiler and the
compiler would use this for optimization (in a perfect world,
the actual rewrite would then be checked using some sort of
formal methods). I think that would be interesting, but we're
very far from even being able to design something like that
(at least in Base - experiments with external AbstractInterpreters
in this direction are encouraged).

This PR tries to come up with a reasonable middle ground, where
the compiler gets some knowledge of the protocol hardcoded without
having to know about the implementation details of the data structure.

The basic ideas is that `Core` provides some magic generic functions
that implementations can extend. Semantically, they are not special.
They dispatch as usual, and implementations are expected to work
properly even in the absence of any compiler optimizations.

However, the compiler is semantically permitted to perform structural
optimization using these magic generic functions. In the concrete
case, this PR introduces the `KeyValue` interface which consists
of two generic functions, `get` and `set`. The core optimization
is that the compiler is allowed to rewrite any occurrence of
`get(set(x, k, v), k)` into `v` without additional legality checks.
In particular, the compiler performs no type checks, conversions, etc.
The higher level implementation code is expected to do all that.

This approach closely matches the general direction we've been taking
in external AbstractInterpreters for embedding additional semantics
and optimization opportunities into Julia code (although we generally
use methods there, rather than full generic functions), so I think
we have some evidence that this sort of approach works reasonably well.

Nevertheless, this is certainly an experiment and the interface is
explicitly declared unstable.

This is fully working and implemented, but the optimization currently
bails on anything but the simplest cases. Filling all those cases in
is not particularly hard, but should be done along with a more invasive
refactoring of SROA, so we should figure out the general direction
here first and then we can finish all that up in a follow-up cleanup.

Before:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  32.940 ns …  28.754 μs  ┊ GC (min … max):  0.00% … 99.76%
 Time  (median):     49.647 ns               ┊ GC (median):     0.00%
 Time  (mean ± σ):   57.519 ns ± 333.275 ns  ┊ GC (mean ± σ):  10.81% ±  2.22%

        ▃█▅               ▁▃▅▅▃▁                ▁▃▂   ▂
  ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃
  32.9 ns         Histogram: frequency by time         68.6 ns <

 Memory estimate: 128 bytes, allocs estimate: 4.

julia> @code_typed foo()
CodeInfo(
1 ─ %1  = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %2  = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64}
│   %3  = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64}
│   %4  = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %5  = $(Expr(:boundscheck, true))::Bool
└──       goto #5 if not %5
2 ─ %7  = Base.sub_int(1, 1)::Int64
│   %8  = Base.bitcast(UInt64, %7)::UInt64
│   %9  = Base.getfield(%4, :size)::Tuple{Int64}
│   %10 = $(Expr(:boundscheck, true))::Bool
│   %11 = Base.getfield(%9, 1, %10)::Int64
│   %12 = Base.bitcast(UInt64, %11)::UInt64
│   %13 = Base.ult_int(%8, %12)::Bool
└──       goto #4 if not %13
3 ─       goto #5
4 ─ %16 = Core.tuple(1)::Tuple{Int64}
│         invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{}
└──       unreachable
5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│         Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
└──       goto #6
6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32
│   %24 = Base.or_int(%23, 0x00010000)::UInt32
│         Base.setfield!(%2, :bitmap, %24)::UInt32
└──       goto #7
7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64}
└──       goto #8
8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64
└──       return %29
```

After:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  2.459 ns … 11.320 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     2.460 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.469 ns ±  0.183 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂    █                                              ▁    █ ▂
  █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █
  2.46 ns      Histogram: log(frequency) by time     2.47 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @code_typed foo()
CodeInfo(
1 ─     return 1
```
Keno added a commit that referenced this issue Nov 27, 2023
This is part of the work to address #51352 by attempting to allow the
compiler to perform SRAO on persistent data structures like
`PersistentDict` as if they were regular immutable data structures.
These sorts of data structures have very complicated internals (with
lots of mutation, memory sharing, etc.), but a relatively simple
interface. As such, it is unlikely that our compiler will have
sufficient power to optimize this interface by analyzing the
implementation.

We thus need to come up with some other mechanism that gives the
compiler license to perform the requisite optimization. One way would be
to just hardcode `PersistentDict` into the compiler, optimizing it like
any of the other builtin datatypes. However, this is of course very
unsatisfying. At the other end of the spectrum would be something like a
generic rewrite rule system (e-graphs anyone?) that would let the
PersistentDict implementation declare its interface to the compiler and
the compiler would use this for optimization (in a perfect world, the
actual rewrite would then be checked using some sort of formal methods).
I think that would be interesting, but we're very far from even being
able to design something like that (at least in Base - experiments with
external AbstractInterpreters in this direction are encouraged).

This PR tries to come up with a reasonable middle ground, where the
compiler gets some knowledge of the protocol hardcoded without having to
know about the implementation details of the data structure.

The basic ideas is that `Core` provides some magic generic functions
that implementations can extend. Semantically, they are not special.
They dispatch as usual, and implementations are expected to work
properly even in the absence of any compiler optimizations.

However, the compiler is semantically permitted to perform structural
optimization using these magic generic functions. In the concrete case,
this PR introduces the `KeyValue` interface which consists of two
generic functions, `get` and `set`. The core optimization is that the
compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)`
into `v` without additional legality checks. In particular, the compiler
performs no type checks, conversions, etc. The higher level
implementation code is expected to do all that.

This approach closely matches the general direction we've been taking in
external AbstractInterpreters for embedding additional semantics and
optimization opportunities into Julia code (although we generally use
methods there, rather than full generic functions), so I think we have
some evidence that this sort of approach works reasonably well.

Nevertheless, this is certainly an experiment and the interface is
explicitly declared unstable.

## Current Status

This is fully working and implemented, but the optimization currently
bails on anything but the simplest cases. Filling all those cases in is
not particularly hard, but should be done along with a more invasive
refactoring of SROA, so we should figure out the general direction here
first and then we can finish all that up in a follow-up cleanup.

## Obligatory benchmark
Before:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  32.940 ns …  28.754 μs  ┊ GC (min … max):  0.00% … 99.76%
 Time  (median):     49.647 ns               ┊ GC (median):     0.00%
 Time  (mean ± σ):   57.519 ns ± 333.275 ns  ┊ GC (mean ± σ):  10.81% ±  2.22%

        ▃█▅               ▁▃▅▅▃▁                ▁▃▂   ▂
  ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃
  32.9 ns         Histogram: frequency by time         68.6 ns <

 Memory estimate: 128 bytes, allocs estimate: 4.

julia> @code_typed foo()
CodeInfo(
1 ─ %1  = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %2  = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64}
│   %3  = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64}
│   %4  = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %5  = $(Expr(:boundscheck, true))::Bool
└──       goto #5 if not %5
2 ─ %7  = Base.sub_int(1, 1)::Int64
│   %8  = Base.bitcast(UInt64, %7)::UInt64
│   %9  = Base.getfield(%4, :size)::Tuple{Int64}
│   %10 = $(Expr(:boundscheck, true))::Bool
│   %11 = Base.getfield(%9, 1, %10)::Int64
│   %12 = Base.bitcast(UInt64, %11)::UInt64
│   %13 = Base.ult_int(%8, %12)::Bool
└──       goto #4 if not %13
3 ─       goto #5
4 ─ %16 = Core.tuple(1)::Tuple{Int64}
│         invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{}
└──       unreachable
5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│         Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
└──       goto #6
6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32
│   %24 = Base.or_int(%23, 0x00010000)::UInt32
│         Base.setfield!(%2, :bitmap, %24)::UInt32
└──       goto #7
7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64}
└──       goto #8
8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64
└──       return %29
```

After:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  2.459 ns … 11.320 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     2.460 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.469 ns ±  0.183 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂    █                                              ▁    █ ▂
  █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █
  2.46 ns      Histogram: log(frequency) by time     2.47 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @code_typed foo()
CodeInfo(
1 ─     return 1
```
Keno added a commit that referenced this issue Nov 28, 2023
As discussed in #51352, this gives `EnterNode` the ability to set
(and restore on leave or catch edge) jl_current_task->scope. Manual
modifications of the task field after the task has started are
considered undefined behavior. In addition, we gain a new intrinsic
to access current_task->scope and both inference and the optimizer
will forward scopes from EnterNodes to this intrinsic (non-interprocedurally).
Together with #51993 this is sufficient to fully optimize ScopedValues
(non-interprocedurally at least).
mkitti pushed a commit to mkitti/julia that referenced this issue Dec 9, 2023
This is a prepratory commit in anticipation of giving :enter additional
responsibilities of entering and restoring dynamic scopes for
ScopedValue (c.f. JuliaLang#51352). This commit simply turns `:enter` into its
own node type (like the other terminators). The changes are largely
mechanical from the `Expr` version, but will make it easier to add
additional semantics in a follow up PR.
mkitti pushed a commit to mkitti/julia that referenced this issue Dec 9, 2023
This is part of the work to address JuliaLang#51352 by attempting to allow the
compiler to perform SRAO on persistent data structures like
`PersistentDict` as if they were regular immutable data structures.
These sorts of data structures have very complicated internals (with
lots of mutation, memory sharing, etc.), but a relatively simple
interface. As such, it is unlikely that our compiler will have
sufficient power to optimize this interface by analyzing the
implementation.

We thus need to come up with some other mechanism that gives the
compiler license to perform the requisite optimization. One way would be
to just hardcode `PersistentDict` into the compiler, optimizing it like
any of the other builtin datatypes. However, this is of course very
unsatisfying. At the other end of the spectrum would be something like a
generic rewrite rule system (e-graphs anyone?) that would let the
PersistentDict implementation declare its interface to the compiler and
the compiler would use this for optimization (in a perfect world, the
actual rewrite would then be checked using some sort of formal methods).
I think that would be interesting, but we're very far from even being
able to design something like that (at least in Base - experiments with
external AbstractInterpreters in this direction are encouraged).

This PR tries to come up with a reasonable middle ground, where the
compiler gets some knowledge of the protocol hardcoded without having to
know about the implementation details of the data structure.

The basic ideas is that `Core` provides some magic generic functions
that implementations can extend. Semantically, they are not special.
They dispatch as usual, and implementations are expected to work
properly even in the absence of any compiler optimizations.

However, the compiler is semantically permitted to perform structural
optimization using these magic generic functions. In the concrete case,
this PR introduces the `KeyValue` interface which consists of two
generic functions, `get` and `set`. The core optimization is that the
compiler is allowed to rewrite any occurrence of `get(set(x, k, v), k)`
into `v` without additional legality checks. In particular, the compiler
performs no type checks, conversions, etc. The higher level
implementation code is expected to do all that.

This approach closely matches the general direction we've been taking in
external AbstractInterpreters for embedding additional semantics and
optimization opportunities into Julia code (although we generally use
methods there, rather than full generic functions), so I think we have
some evidence that this sort of approach works reasonably well.

Nevertheless, this is certainly an experiment and the interface is
explicitly declared unstable.

## Current Status

This is fully working and implemented, but the optimization currently
bails on anything but the simplest cases. Filling all those cases in is
not particularly hard, but should be done along with a more invasive
refactoring of SROA, so we should figure out the general direction here
first and then we can finish all that up in a follow-up cleanup.

## Obligatory benchmark
Before:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 993 evaluations.
 Range (min … max):  32.940 ns …  28.754 μs  ┊ GC (min … max):  0.00% … 99.76%
 Time  (median):     49.647 ns               ┊ GC (median):     0.00%
 Time  (mean ± σ):   57.519 ns ± 333.275 ns  ┊ GC (mean ± σ):  10.81% ±  2.22%

        ▃█▅               ▁▃▅▅▃▁                ▁▃▂   ▂
  ▁▂▄▃▅▇███▇▃▁▂▁▁▁▁▁▁▁▁▂▂▅██████▅▂▁▁▁▁▁▁▁▁▁▁▂▃▃▇███▇▆███▆▄▃▃▂▂ ▃
  32.9 ns         Histogram: frequency by time         68.6 ns <

 Memory estimate: 128 bytes, allocs estimate: 4.

julia> @code_typed foo()
CodeInfo(
1 ─ %1  = invoke Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}(Base.HashArrayMappedTries.undef::UndefInitializer, 1::Int64)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %2  = %new(Base.HashArrayMappedTries.HAMT{Symbol, Int64}, %1, 0x00000000)::Base.HashArrayMappedTries.HAMT{Symbol, Int64}
│   %3  = %new(Base.HashArrayMappedTries.Leaf{Symbol, Int64}, :a, 1)::Base.HashArrayMappedTries.Leaf{Symbol, Int64}
│   %4  = Base.getfield(%2, :data)::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %5  = $(Expr(:boundscheck, true))::Bool
└──       goto JuliaLang#5 if not %5
2 ─ %7  = Base.sub_int(1, 1)::Int64
│   %8  = Base.bitcast(UInt64, %7)::UInt64
│   %9  = Base.getfield(%4, :size)::Tuple{Int64}
│   %10 = $(Expr(:boundscheck, true))::Bool
│   %11 = Base.getfield(%9, 1, %10)::Int64
│   %12 = Base.bitcast(UInt64, %11)::UInt64
│   %13 = Base.ult_int(%8, %12)::Bool
└──       goto JuliaLang#4 if not %13
3 ─       goto JuliaLang#5
4 ─ %16 = Core.tuple(1)::Tuple{Int64}
│         invoke Base.throw_boundserror(%4::Vector{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}, %16::Tuple{Int64})::Union{}
└──       unreachable
5 ┄ %19 = Base.getfield(%4, :ref)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│   %20 = Base.memoryref(%19, 1, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
│         Base.memoryrefset!(%20, %3, :not_atomic, false)::MemoryRef{Union{Base.HashArrayMappedTries.HAMT{Symbol, Int64}, Base.HashArrayMappedTries.Leaf{Symbol, Int64}}}
└──       goto JuliaLang#6
6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32
│   %24 = Base.or_int(%23, 0x00010000)::UInt32
│         Base.setfield!(%2, :bitmap, %24)::UInt32
└──       goto JuliaLang#7
7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64}
└──       goto JuliaLang#8
8 ─ %29 = invoke Base.getindex(%27::Base.PersistentDict{Symbol, Int64}, 🅰️:Symbol)::Int64
└──       return %29
```

After:
```
julia> using BenchmarkTools

julia> function foo()
           a = Base.PersistentDict(:a => 1)
           return a[:a]
       end
foo (generic function with 1 method)

julia> @benchmark foo()
BenchmarkTools.Trial: 10000 samples with 1000 evaluations.
 Range (min … max):  2.459 ns … 11.320 ns  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     2.460 ns              ┊ GC (median):    0.00%
 Time  (mean ± σ):   2.469 ns ±  0.183 ns  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▂    █                                              ▁    █ ▂
  █▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁█ █
  2.46 ns      Histogram: log(frequency) by time     2.47 ns <

 Memory estimate: 0 bytes, allocs estimate: 0.

julia> @code_typed foo()
CodeInfo(
1 ─     return 1
```
Keno added a commit that referenced this issue Dec 11, 2023
As discussed in #51352, this gives `EnterNode` the ability to set
(and restore on leave or catch edge) jl_current_task->scope. Manual
modifications of the task field after the task has started are
considered undefined behavior. In addition, we gain a new intrinsic
to access current_task->scope and both inference and the optimizer
will forward scopes from EnterNodes to this intrinsic (non-interprocedurally).
Together with #51993 this is sufficient to fully optimize ScopedValues
(non-interprocedurally at least).
Keno added a commit that referenced this issue Dec 13, 2023
As discussed in #51352, this gives `EnterNode` the ability to set (and
restore on leave or catch edge) jl_current_task->scope. Manual
modifications of the task field after the task has started are
considered undefined behavior. In addition, we gain a new intrinsic to
access current_task->scope and both inference and the optimizer will
forward scopes from EnterNodes to this intrinsic
(non-interprocedurally). Together with #51993 this is sufficient to
fully optimize ScopedValues (non-interprocedurally at least).
@Keno
Copy link
Member

Keno commented Dec 23, 2023

I'm gonna call this addressed. ScopedValues are pretty well optimizable at this point.

@Keno Keno closed this as completed Dec 23, 2023
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
compiler:optimizer Optimization passes (mostly in base/compiler/ssair/) help wanted Indicates that a maintainer wants help on an issue or pull request
Projects
None yet
Development

No branches or pull requests

4 participants