Revert Statistics checksum change #7
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Fixed upstream.
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Tested this and it now works. Also |
udesou
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Oct 20, 2023
…k#34) Backports PR JuliaLang#50337 for RAI julia v1.9.2 Original description: =================== Pass the types to the allocator functions. ------- Before this PR, we were missing the types for allocations in two cases: 1. allocations from codegen 2. allocations in `gc_managed_realloc_` The second one is easy: those are always used for buffers, right? For the first one: we extend the allocation functions called from codegen, to take the type as a parameter, and set the tag there. I kept the old interfaces around, since I think that they cannot be removed due to supporting legacy code? ------ An example of the generated code: ```julia %ptls_field6 = getelementptr inbounds {}**, {}*** %4, i64 2 %13 = bitcast {}*** %ptls_field6 to i8** %ptls_load78 = load i8*, i8** %13, align 8 %box = call noalias nonnull dereferenceable(32) {}* @ijl_gc_pool_alloc_typed(i8* %ptls_load78, i32 1184, i32 32, i64 4366152144) mmtk#7 ``` Fixes JuliaLang#43688. Fixes JuliaLang#45268. Co-authored-by: Valentin Churavy <vchuravy@users.noreply.github.com>
qinsoon
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May 2, 2024
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 mmtk#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 mmtk#4 if not %13 3 ─ goto mmtk#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 mmtk#6 6 ─ %23 = Base.getfield(%2, :bitmap)::UInt32 │ %24 = Base.or_int(%23, 0x00010000)::UInt32 │ Base.setfield!(%2, :bitmap, %24)::UInt32 └── goto mmtk#7 7 ─ %27 = %new(Base.PersistentDict{Symbol, Int64}, %2)::Base.PersistentDict{Symbol, Int64} └── goto mmtk#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 ```
udesou
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Oct 16, 2024
E.g. this allows `finalizer` inlining in the following case:
```julia
mutable struct ForeignBuffer{T}
const ptr::Ptr{T}
end
const foreign_buffer_finalized = Ref(false)
function foreign_alloc(::Type{T}, length) where T
ptr = Libc.malloc(sizeof(T) * length)
ptr = Base.unsafe_convert(Ptr{T}, ptr)
obj = ForeignBuffer{T}(ptr)
return finalizer(obj) do obj
Base.@assume_effects :notaskstate :nothrow
foreign_buffer_finalized[] = true
Libc.free(obj.ptr)
end
end
function f_EA_finalizer(N::Int)
workspace = foreign_alloc(Float64, N)
GC.@preserve workspace begin
(;ptr) = workspace
Base.@assume_effects :nothrow @noinline println(devnull, "ptr = ", ptr)
end
end
```
```julia
julia> @code_typed f_EA_finalizer(42)
CodeInfo(
1 ── %1 = Base.mul_int(8, N)::Int64
│ %2 = Core.lshr_int(%1, 63)::Int64
│ %3 = Core.trunc_int(Core.UInt8, %2)::UInt8
│ %4 = Core.eq_int(%3, 0x01)::Bool
└─── goto mmtk#3 if not %4
2 ── invoke Core.throw_inexacterror(:convert::Symbol, UInt64::Type, %1::Int64)::Union{}
└─── unreachable
3 ── goto mmtk#4
4 ── %9 = Core.bitcast(Core.UInt64, %1)::UInt64
└─── goto mmtk#5
5 ── goto mmtk#6
6 ── goto mmtk#7
7 ── goto mmtk#8
8 ── %14 = $(Expr(:foreigncall, :(:malloc), Ptr{Nothing}, svec(UInt64), 0, :(:ccall), :(%9), :(%9)))::Ptr{Nothing}
└─── goto mmtk#9
9 ── %16 = Base.bitcast(Ptr{Float64}, %14)::Ptr{Float64}
│ %17 = %new(ForeignBuffer{Float64}, %16)::ForeignBuffer{Float64}
└─── goto mmtk#10
10 ─ %19 = $(Expr(:gc_preserve_begin, :(%17)))
│ %20 = Base.getfield(%17, :ptr)::Ptr{Float64}
│ invoke Main.println(Main.devnull::Base.DevNull, "ptr = "::String, %20::Ptr{Float64})::Nothing
│ $(Expr(:gc_preserve_end, :(%19)))
│ %23 = Main.foreign_buffer_finalized::Base.RefValue{Bool}
│ Base.setfield!(%23, :x, true)::Bool
│ %25 = Base.getfield(%17, :ptr)::Ptr{Float64}
│ %26 = Base.bitcast(Ptr{Nothing}, %25)::Ptr{Nothing}
│ $(Expr(:foreigncall, :(:free), Nothing, svec(Ptr{Nothing}), 0, :(:ccall), :(%26), :(%25)))::Nothing
└─── return nothing
) => Nothing
```
However, this is still a WIP. Before merging, I want to improve EA's
precision a bit and at least fix the test case that is currently marked
as `broken`. I also need to check its impact on compiler performance.
Additionally, I believe this feature is not yet practical. In
particular, there is still significant room for improvement in the
following areas:
- EA's interprocedural capabilities: currently EA is performed ad-hoc
for limited frames because of latency reasons, which significantly
reduces its precision in the presence of interprocedural calls.
- Relaxing the `:nothrow` check for finalizer inlining: the current
algorithm requires `:nothrow`-ness on all paths from the allocation of
the mutable struct to its last use, which is not practical for
real-world cases. Even when `:nothrow` cannot be guaranteed, auxiliary
optimizations such as inserting a `finalize` call after the last use
might still be possible (JuliaLang#55990).
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Fixed upstream; see JuliaLang#48698.