Understanding `Error: Enzyme aligned size and Julia size disagree` #999

MilesCranmer · 2023-08-13T18:31:02Z

I've seen this error a few times while writing an Enzyme backend for SymbolicRegression.jl – MilesCranmer/SymbolicRegression.jl#254 – and I was just wondering what sort of issue it could indicate? I can't reproduce it reliably right now, unfortunately. This is on Julia 1.10-beta1, so perhaps it could be some unimplemented functionality for the latest Julia version.

┌ Error: Enzyme aligned size and Julia size disagree
│   AlignedSize = 2192
│   esizeof(TT) = 2136
│   fieldtypes(TT) = (Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, @NamedTuple{1, 2, 3, 4, 5::Bool, 6::UInt64, 7::Core.LLVMPtr{UInt64, 0}, 8::UInt64, 9::UInt64}, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, @NamedTuple{1, 2, 3, 4, 5, 6::NTuple{4, Float32}, 7::NTuple{4, Float32}, 8::NTuple{4, Float32}, 9::NTuple{4, Float32}, 10, 11::Core.LLVMPtr{UInt8, 0}, 12, 13, 14::UInt64, 15::UInt64, 16::UInt64, 17::UInt64, 18::UInt64, 19::Bool, 20::Float32, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6::NTuple{4, Float32}, 7::NTuple{4, Float32}, 8::NTuple{4, Float32}, 9::NTuple{4, Float32}, 10, 11::Core.LLVMPtr{UInt8, 0}, 12, 13, 14::UInt64, 15::UInt64, 16::UInt64, 17::UInt64, 18::UInt64, 19::Bool, 20::Float32, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6::NTuple{4, Float32}, 7::NTuple{4, Float32}, 8::NTuple{4, Float32}, 9::NTuple{4, Float32}, 10, 11::Core.LLVMPtr{UInt8, 0}, 12, 13, 14::UInt64, 15::UInt64, 16::UInt64, 17::UInt64, 18::UInt64, 19::Bool, 20::Float32, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6::NTuple{4, Float32}, 7::NTuple{4, Float32}, 8::NTuple{4, Float32}, 9::NTuple{4, Float32}, 10, 11::Core.LLVMPtr{UInt8, 0}, 12, 13, 14::UInt64, 15::UInt64, 16::UInt64, 17::UInt64, 18::UInt64, 19::Bool, 20::Float32, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2::UInt64}, Tuple{Core.LLVMPtr{UInt8, 0}, NTuple{4, Float32}, NTuple{4, Float32}, NTuple{4, Float32}, NTuple{4, Float32}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Vararg{NTuple{4, Float32}, 6}}, Tuple{Core.LLVMPtr{UInt8, 0}, NTuple{4, Float32}, NTuple{4, Float32}, NTuple{4, Float32}, NTuple{4, Float32}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Core.LLVMPtr{NTuple{4, Float32}, 0}, Vararg{NTuple{4, Float32}, 6}}, Any, Any, Any, Any, Any, Any, Any, @NamedTuple{1, 2::UInt64}, Any, Any, @NamedTuple{1, 2::UInt64}, Any, Any, @NamedTuple{1, 2::UInt64}, Any, @NamedTuple{1, 2::UInt64}, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, @NamedTuple{1, 2, 3, 4, 5, 6, 7::UInt8, 8, 9, 10, 11::Bool}, @NamedTuple{1, 2, 3, 4, 5, 6, 7::UInt8, 8, 9, 10, 11::Bool}, UInt8, Any, UInt8, Any, Any, Any, Bool, Bool, UInt64, UInt64, UInt64, UInt8, UInt8, Any, Bool, UInt8, Any, Any, Bool, Bool, Bool, Any, Bool, UInt8, Bool, Bool, UInt8, Bool, Bool, Bool, Bool)
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/YO8Uj/src/utils.jl:56
Assertion failed: (isa<To>(Val) && "cast<Ty>() argument of incompatible type!"), function cast, file /opt/aarch64-apple-darwin20/aarch64-apple-darwin20/sys-root/usr/local/include/llvm/Support/Casting.h, line 578.

I will update this if I can reproduce it reliably.

vchuravy · 2023-08-14T16:03:42Z

A MWE would be great, this error means that one of Enzyme's assumptions got violated.

MilesCranmer · 2023-08-14T17:01:39Z

Here is a working example. It's far from minimal but it does reliably generate this issue.

Install this branch of DynamicExpressions.jl: Enzyme compatibility SymbolicML/DynamicExpressions.jl#52
Install this branch of SymbolicRegression.jl: [WIP] Use Enzyme.jl for constant optimization MilesCranmer/SymbolicRegression.jl#254

The enzyme code is here: https://github.com/MilesCranmer/SymbolicRegression.jl/blob/38c3f0c9d3e22ed7a2ab52acc414049e824b23f6/ext/SymbolicRegressionEnzymeExt.jl

Run the following code

using SymbolicRegression
using Enzyme

options = Options(binary_operators=[+, -, *], unary_operators=[cos, exp], enable_enzyme=true)

X = randn(10, 100)
y = randn(100)
dataset = Dataset(X, y)
tree = Node(2, Node(val=0.5))
member = PopMember(tree, 100.0, 100.0, options)
SymbolicRegression.ConstantOptimizationModule.optimize_constants(dataset, member, options)

which generates the following error:

┌ Error: Enzyme aligned size and Julia size disagree
│   AlignedSize = 12752
│   esizeof(TT) = 12488
│   fieldtypes(TT) = (Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, @NamedTuple{1, 2, 3, 4, 5::Tuple{Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}}, 6, 7, 8, 9::Bool, 10::UInt64, 11::Core.LLVMPtr{UInt64, 0}, 12::UInt64, 13::Bool, 14, 15::Bool, 16::UInt64, 17::UInt64}, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, @NamedTuple{1, 2, 3, 4, 5, 6, 7, 8::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 9::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 10::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 11::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 12::Tuple{Float64, Float64}, 13::Tuple{Float64, Float64}, 14::Tuple{Float64, Float64}, 15::Tuple{Float64, Float64}, 16::Tuple{Float64, Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Tuple{Float64, Float64}, 20::Tuple{Float64, Float64}, 21::Tuple{Float64, Float64}, 22, 23::Core.LLVMPtr{UInt8, 0}, 24, 25, 26::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 27::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 28::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 29::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 30::Tuple{Float64, Float64}, 31::Tuple{Float64, Float64}, 32::Tuple{Float64, Float64}, 33::Tuple{Float64, Float64}, 34::Tuple{Float64, Float64}, 35::Tuple{Float64, Float64}, 36::Tuple{Float64, Float64}, 37::Tuple{Float64, Float64}, 38::Tuple{Float64, Float64}, 39::Tuple{Float64, Float64}, 40, 41, 42::Core.LLVMPtr{UInt8, 0}, 43, 44, 45, 46::Core.LLVMPtr{UInt8, 0}, 47, 48, 49::UInt64, 50::UInt64, 51::UInt64, 52::UInt64, 53::UInt64, 54::UInt64, 55::UInt64, 56::UInt64, 57::UInt64, 58::Core.LLVMPtr{UInt8, 0}, 59::Tuple{Float64, Float64}, 60::Tuple{Float64, Float64}, 61::Tuple{Float64, Float64}, 62::Tuple{Float64, Float64}, 63::Bool, 64::Bool, 65::Float64, 66::Float64, 67::Float64, 68::UInt64, 69::Core.LLVMPtr{UInt8, 0}, 70::Bool, 71::Float64, 72::UInt64, 73::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7, 8::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 9::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 10::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 11::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 12::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 13::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 14::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 15::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 16::Tuple{Float64, Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Tuple{Float64, Float64}, 20::Tuple{Float64, Float64}, 21::Tuple{Float64, Float64}, 22::Tuple{Float64, Float64}, 23::Tuple{Float64, Float64}, 24::Tuple{Float64, Float64}, 25::Tuple{Float64, Float64}, 26::Tuple{Float64, Float64}, 27::Tuple{Float64, Float64}, 28::Tuple{Float64, Float64}, 29::Tuple{Float64, Float64}, 30::Tuple{Float64, Float64}, 31::Tuple{Float64, Float64}, 32::Tuple{Float64, Float64}, 33::Tuple{Float64, Float64}, 34::Tuple{Float64, Float64}, 35::Tuple{Float64, Float64}, 36, 37::Core.LLVMPtr{UInt8, 0}, 38, 39, 40::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 41::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 42::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 43::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 44::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 45::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 46::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 47::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 48::Tuple{Float64, Float64}, 49::Tuple{Float64, Float64}, 50::Tuple{Float64, Float64}, 51::Tuple{Float64, Float64}, 52::Tuple{Float64, Float64}, 53::Tuple{Float64, Float64}, 54::Tuple{Float64, Float64}, 55::Tuple{Float64, Float64}, 56::Tuple{Float64, Float64}, 57::Tuple{Float64, Float64}, 58::Tuple{Float64, Float64}, 59::Tuple{Float64, Float64}, 60::Tuple{Float64, Float64}, 61::Tuple{Float64, Float64}, 62::Tuple{Float64, Float64}, 63::Tuple{Float64, Float64}, 64::Tuple{Float64, Float64}, 65::Tuple{Float64, Float64}, 66::Tuple{Float64, Float64}, 67::Tuple{Float64, Float64}, 68, 69, 70::Core.LLVMPtr{UInt8, 0}, 71, 72, 73, 74::Core.LLVMPtr{UInt8, 0}, 75, 76, 77::UInt64, 78::UInt64, 79::UInt64, 80::UInt64, 81::UInt64, 82::UInt64, 83::UInt64, 84::UInt64, 85::UInt64, 86::Core.LLVMPtr{UInt8, 0}, 87::Tuple{Float64, Float64}, 88::Tuple{Float64, Float64}, 89::Tuple{Float64, Float64}, 90::Tuple{Float64, Float64}, 91::Bool, 92::Bool, 93::Float64, 94::Float64, 95::Float64, 96::UInt64, 97::Core.LLVMPtr{UInt8, 0}, 98::Bool, 99::Float64, 100::UInt64, 101::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7, 8, 9::Core.LLVMPtr{UInt8, 0}, 10, 11, 12, 13, 14::Core.LLVMPtr{UInt8, 0}, 15, 16, 17, 18::Core.LLVMPtr{UInt8, 0}, 19, 20, 21::UInt64, 22::UInt64, 23::UInt64, 24::UInt64, 25::UInt64, 26::UInt64, 27::UInt64, 28::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 29::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 30::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 31::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 32::Tuple{Float64, Float64}, 33::Tuple{Float64, Float64}, 34::Tuple{Float64, Float64}, 35::Tuple{Float64, Float64}, 36::Tuple{Float64, Float64}, 37::Tuple{Float64, Float64}, 38::Tuple{Float64, Float64}, 39::Tuple{Float64, Float64}, 40::Tuple{Float64, Float64}, 41::Tuple{Float64, Float64}, 42::UInt64, 43::UInt64, 44::Core.LLVMPtr{UInt8, 0}, 45::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 46::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 47::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 48::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 49::Tuple{Float64, Float64}, 50::Tuple{Float64, Float64}, 51::Tuple{Float64, Float64}, 52::Tuple{Float64, Float64}, 53::Tuple{Float64, Float64}, 54::Tuple{Float64, Float64}, 55::Tuple{Float64, Float64}, 56::Tuple{Float64, Float64}, 57::Tuple{Float64, Float64}, 58::Tuple{Float64, Float64}, 59::Tuple{Float64, Float64}, 60::Tuple{Float64, Float64}, 61::Tuple{Float64, Float64}, 62::Tuple{Float64, Float64}, 63::Bool, 64::Bool, 65::Float64, 66::Float64, 67::Float64, 68::UInt64, 69::Core.LLVMPtr{UInt8, 0}, 70::Bool, 71::Float64, 72::UInt64, 73::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7, 8::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 9::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 10::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 11::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 12::Tuple{Float64, Float64}, 13::Tuple{Float64, Float64}, 14::Tuple{Float64, Float64}, 15::Tuple{Float64, Float64}, 16::Tuple{Float64, Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Tuple{Float64, Float64}, 20::Tuple{Float64, Float64}, 21::Tuple{Float64, Float64}, 22, 23::Core.LLVMPtr{UInt8, 0}, 24, 25, 26::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 27::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 28::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 29::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 30::Tuple{Float64, Float64}, 31::Tuple{Float64, Float64}, 32::Tuple{Float64, Float64}, 33::Tuple{Float64, Float64}, 34::Tuple{Float64, Float64}, 35::Tuple{Float64, Float64}, 36::Tuple{Float64, Float64}, 37::Tuple{Float64, Float64}, 38::Tuple{Float64, Float64}, 39::Tuple{Float64, Float64}, 40, 41, 42::Core.LLVMPtr{UInt8, 0}, 43, 44, 45, 46::Core.LLVMPtr{UInt8, 0}, 47, 48, 49::UInt64, 50::UInt64, 51::UInt64, 52::UInt64, 53::UInt64, 54::UInt64, 55::UInt64, 56::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 57::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 58::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 59::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 60::Tuple{Float64, Float64}, 61::Tuple{Float64, Float64}, 62::Tuple{Float64, Float64}, 63::Tuple{Float64, Float64}, 64::Tuple{Float64, Float64}, 65::Tuple{Float64, Float64}, 66::Tuple{Float64, Float64}, 67::Tuple{Float64, Float64}, 68::Tuple{Float64, Float64}, 69::Tuple{Float64, Float64}, 70::UInt64, 71::UInt64, 72::Core.LLVMPtr{UInt8, 0}, 73::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 74::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 75::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 76::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 77::Tuple{Float64, Float64}, 78::Tuple{Float64, Float64}, 79::Tuple{Float64, Float64}, 80::Tuple{Float64, Float64}, 81::Tuple{Float64, Float64}, 82::Tuple{Float64, Float64}, 83::Tuple{Float64, Float64}, 84::Tuple{Float64, Float64}, 85::Tuple{Float64, Float64}, 86::Tuple{Float64, Float64}, 87::Tuple{Float64, Float64}, 88::Tuple{Float64, Float64}, 89::Tuple{Float64, Float64}, 90::Tuple{Float64, Float64}, 91::Bool, 92::Bool, 93::Float64, 94::Float64, 95::Float64, 96::UInt64, 97::Core.LLVMPtr{UInt8, 0}, 98::Bool, 99::Float64, 100::UInt64, 101::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7, 8, 9::Core.LLVMPtr{UInt8, 0}, 10, 11, 12, 13, 14::Core.LLVMPtr{UInt8, 0}, 15, 16, 17, 18::Core.LLVMPtr{UInt8, 0}, 19, 20, 21::UInt64, 22::UInt64, 23::UInt64, 24::UInt64, 25::UInt64, 26::UInt64, 27::UInt64, 28::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 29::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 30::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 31::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 32::Tuple{Float64, Float64}, 33::Tuple{Float64, Float64}, 34::Tuple{Float64, Float64}, 35::Tuple{Float64, Float64}, 36::Tuple{Float64, Float64}, 37::Tuple{Float64, Float64}, 38::Tuple{Float64, Float64}, 39::Tuple{Float64, Float64}, 40::Tuple{Float64, Float64}, 41::Tuple{Float64, Float64}, 42::UInt64, 43::UInt64, 44::Core.LLVMPtr{UInt8, 0}, 45::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 46::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 47::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 48::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 49::Tuple{Float64, Float64}, 50::Tuple{Float64, Float64}, 51::Tuple{Float64, Float64}, 52::Tuple{Float64, Float64}, 53::Tuple{Float64, Float64}, 54::Tuple{Float64, Float64}, 55::Tuple{Float64, Float64}, 56::Tuple{Float64, Float64}, 57::Tuple{Float64, Float64}, 58::Tuple{Float64, Float64}, 59::Tuple{Float64, Float64}, 60::Tuple{Float64, Float64}, 61::Tuple{Float64, Float64}, 62::Tuple{Float64, Float64}, 63::Bool, 64::Bool, 65::Float64, 66::Float64, 67::Float64, 68::UInt64, 69::Core.LLVMPtr{UInt8, 0}, 70::Bool, 71::Float64, 72::UInt64, 73::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7, 8::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 9::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 10::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 11::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 12::Tuple{Float64, Float64}, 13::Tuple{Float64, Float64}, 14::Tuple{Float64, Float64}, 15::Tuple{Float64, Float64}, 16::Tuple{Float64, Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Tuple{Float64, Float64}, 20::Tuple{Float64, Float64}, 21::Tuple{Float64, Float64}, 22, 23::Core.LLVMPtr{UInt8, 0}, 24, 25, 26::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 27::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 28::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 29::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 30::Tuple{Float64, Float64}, 31::Tuple{Float64, Float64}, 32::Tuple{Float64, Float64}, 33::Tuple{Float64, Float64}, 34::Tuple{Float64, Float64}, 35::Tuple{Float64, Float64}, 36::Tuple{Float64, Float64}, 37::Tuple{Float64, Float64}, 38::Tuple{Float64, Float64}, 39::Tuple{Float64, Float64}, 40, 41, 42::Core.LLVMPtr{UInt8, 0}, 43, 44, 45, 46::Core.LLVMPtr{UInt8, 0}, 47, 48, 49::UInt64, 50::UInt64, 51::UInt64, 52::UInt64, 53::UInt64, 54::UInt64, 55::UInt64, 56::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 57::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 58::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 59::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 60::Tuple{Float64, Float64}, 61::Tuple{Float64, Float64}, 62::Tuple{Float64, Float64}, 63::Tuple{Float64, Float64}, 64::Tuple{Float64, Float64}, 65::Tuple{Float64, Float64}, 66::Tuple{Float64, Float64}, 67::Tuple{Float64, Float64}, 68::Tuple{Float64, Float64}, 69::Tuple{Float64, Float64}, 70::UInt64, 71::UInt64, 72::Core.LLVMPtr{UInt8, 0}, 73::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 74::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 75::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 76::Core.LLVMPtr{Tuple{Float64, Float64}, 0}, 77::Tuple{Float64, Float64}, 78::Tuple{Float64, 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Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Bool, 20::Float64, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7::Core.LLVMPtr{UInt8, 0}, 8, 9, 10::UInt64, 11::UInt64, 12::UInt64, 13::UInt64, 14::UInt64, 15::Tuple{Float64, Float64}, 16::Tuple{Float64, Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Bool, 20::Float64, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2, 3, 4, 5, 6, 7::Core.LLVMPtr{UInt8, 0}, 8, 9, 10::UInt64, 11::UInt64, 12::UInt64, 13::UInt64, 14::UInt64, 15::Tuple{Float64, Float64}, 16::Tuple{Float64, Float64}, 17::Tuple{Float64, Float64}, 18::Tuple{Float64, Float64}, 19::Bool, 20::Float64, 21::UInt64, 22::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2::UInt64}, Tuple{Core.LLVMPtr{UInt8, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, 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11::Tuple{Float64, Float64}, 12::Tuple{Float64, Float64}, 13::Tuple{Float64, Float64}, 14::Tuple{Float64, Float64}, 15::Tuple{Float64, Float64}, 16::Bool, 17::Float64, 18::UInt64, 19::Core.LLVMPtr{UInt8, 0}}, @NamedTuple{1, 2::UInt64}, Any, @NamedTuple{1, 2::UInt64}, Tuple{Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Vararg{Tuple{Float64, Float64}, 12}}, Tuple{Core.LLVMPtr{UInt8, 0}, 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└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/YO8Uj/src/utils.jl:56
Assertion failed: (isa<To>(Val) && "cast<Ty>() argument of incompatible type!"), function cast, file /opt/aarch64-apple-darwin20/aarch64-apple-darwin20/sys-root/usr/local/include/llvm/Support/Casting.h, line 578.

[83445] signal (6): Abort trap: 6
in expression starting at /Users/mcranmer/Documents/SymbolicRegression.jl/src/rep.jl:11
__pthread_kill at /usr/lib/system/libsystem_kernel.dylib (unknown line)
Allocations: 73003985 (Pool: 72946943; Big: 57042); GC: 18

versioninfo():

Julia Version 1.10.0-beta1
Commit 6616549950e (2023-07-25 17:43 UTC)
Platform Info:
  OS: macOS (arm64-apple-darwin22.4.0)
  CPU: 8 × Apple M1 Pro
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, apple-m1)
  Threads: 8 on 6 virtual cores
Environment:
  JULIA_NUM_THREADS = auto
  JULIA_FORMATTER_SO = /Users/mcranmer/julia_formatter.so
  JULIA_EDITOR = code

wsmoses · 2023-08-15T03:32:52Z

Having just released a new tag, mind double checking if the error persits on it?

MilesCranmer · 2023-08-15T04:06:02Z

Just checked. Unfortunately it still occurs with Enzyme 0.11.7

MilesCranmer · 2023-08-15T04:13:25Z

Not sure if this is helpful at all but here's the Julia 1.9 version. Seems like AlignedSize and esizeof(TT) change on 1.9 but are still not equal to eachother.

┌ Error: Enzyme aligned size and Julia size disagree
│   AlignedSize = 12672
│   esizeof(TT) = 12408
│   fieldtypes(TT) = (Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, NamedTuple{(Symbol("1"), Symbol("2"), Symbol("3"), Symbol("4"), Symbol("5"), Symbol("6"), Symbol("7"), Symbol("8"), Symbol("9"), Symbol("10"), Symbol("11"), Symbol("12"), Symbol("13"), Symbol("14"), Symbol("15"), Symbol("16"), Symbol("17")), Tuple{Any, Any, Any, Any, Tuple{Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}}, Any, Any, Any, Bool, UInt64, Core.LLVMPtr{UInt64, 0}, UInt64, Bool, Any, Bool, UInt64, UInt64}}, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, NamedTuple{(Symbol("1"), Symbol("2"), Symbol("3"), Symbol("4"), Symbol("5"), Symbol("6"), Symbol("7"), Symbol("8"), Symbol("9"), Symbol("10"), Symbol("11"), Symbol("12"), Symbol("13"), Symbol("14"), Symbol("15"), Symbol("16"), Symbol("17"), Symbol("18"), Symbol("19"), Symbol("20"), Symbol("21"), 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Symbol("9"), Symbol("10"), Symbol("11"), Symbol("12"), Symbol("13"), Symbol("14"), Symbol("15"), Symbol("16"), Symbol("17"), Symbol("18"), Symbol("19"), Symbol("20"), Symbol("21"), Symbol("22"), Symbol("23"), Symbol("24"), Symbol("25"), Symbol("26"), Symbol("27"), Symbol("28"), Symbol("29"), Symbol("30"), Symbol("31"), Symbol("32"), Symbol("33"), Symbol("34"), Symbol("35"), Symbol("36"), Symbol("37"), Symbol("38"), Symbol("39"), Symbol("40"), Symbol("41"), Symbol("42"), Symbol("43")), Tuple{Any, Any, Core.LLVMPtr{UInt8, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Core.LLVMPtr{UInt8, 0}, UInt64, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Bool, Float64, UInt64, Core.LLVMPtr{UInt8, 0}}}, NamedTuple{(Symbol("1"), Symbol("2"), Symbol("3"), Symbol("4"), Symbol("5"), Symbol("6"), Symbol("7"), Symbol("8"), Symbol("9"), Symbol("10"), Symbol("11"), Symbol("12"), Symbol("13"), Symbol("14"), Symbol("15"), Symbol("16"), Symbol("17"), Symbol("18"), Symbol("19")), Tuple{Any, Any, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, UInt64, UInt64, Core.LLVMPtr{UInt8, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Bool, Float64, UInt64, Core.LLVMPtr{UInt8, 0}}}, NamedTuple{(Symbol("1"), Symbol("2"), Symbol("3"), Symbol("4"), Symbol("5"), Symbol("6"), Symbol("7"), Symbol("8"), Symbol("9"), Symbol("10"), Symbol("11"), Symbol("12"), Symbol("13"), Symbol("14"), Symbol("15"), Symbol("16"), Symbol("17"), Symbol("18"), Symbol("19")), Tuple{Any, Any, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, UInt64, UInt64, Core.LLVMPtr{UInt8, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Bool, Float64, UInt64, Core.LLVMPtr{UInt8, 0}}}, Any, Tuple{Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, Tuple{Float64, Float64}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Core.LLVMPtr{Tuple{Float64, Float64}, 0}, Vararg{Tuple{Float64, Float64}, 12}}, Tuple{Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, Vararg{Tuple{Float64, Float64}, 4}}, Tuple{Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, UInt64, Core.LLVMPtr{UInt8, 0}, Core.LLVMPtr{UInt8, 0}, Vararg{Tuple{Float64, Float64}, 4}}, Any, Any, Any, Any, Any, Any, Any, NamedTuple{(Symbol("1"), Symbol("2"), Symbol("3"), Symbol("4"), Symbol("5"), Symbol("6"), Symbol("7"), Symbol("8"), Symbol("9"), Symbol("10"), Symbol("11")), Tuple{Any, Any, Any, Any, Any, Any, UInt8, Any, Any, Any, Bool}}, NamedTuple{(Symbol("1"), Symbol("2"), Symbol("3"), Symbol("4"), Symbol("5"), Symbol("6"), Symbol("7"), Symbol("8"), Symbol("9"), Symbol("10"), Symbol("11")), Tuple{Any, Any, Any, Any, Any, Any, UInt8, Any, Any, Any, Bool}}, UInt8, Any, UInt8, Any, Any, Any, Bool, Bool, UInt64, UInt64, UInt8, Any, Bool, Any, Any, Bool, Bool, Bool, Any, Bool, UInt8, Bool, Bool, Bool, Bool, Bool, Bool)
└ @ Enzyme.Compiler ~/.julia/packages/GPUCompiler/YO8Uj/src/utils.jl:56
Assertion failed: (isa<X>(Val) && "cast<Ty>() argument of incompatible type!"), function cast, file /opt/aarch64-apple-darwin20/aarch64-apple-darwin20/sys-root/usr/local/include/llvm/Support/Casting.h, line 269.

[1253] signal (6): Abort trap: 6
in expression starting at /Users/mcranmer/Documents/SymbolicRegression.jl/src/rep.jl:11
__pthread_kill at /usr/lib/system/libsystem_kernel.dylib (unknown line)
Allocations: 117534982 (Pool: 117465318; Big: 69664); GC: 108

wsmoses · 2023-08-16T00:20:24Z

Alas, relatedly @MilesCranmer can you make the MWE a single function passed to an Enzyme autodiff. Otherwise I have no idea how those packages use Enzyme in order to debug.

MilesCranmer · 2023-08-16T00:30:25Z

Here is a MWE. Keep in mind you should still install those specific branches I mentioned above.

using SymbolicRegression
using Enzyme

function opt_func!(result, x, dataset, tree, constant_nodes, options, idx)
    _set_constants!(x, constant_nodes)
    result[1] = SymbolicRegression.LossFunctionsModule.eval_loss(tree, dataset, options; regularization=false, idx=idx)
    return nothing
end
function _set_constants!(x::AbstractArray{T}, constant_nodes) where {T}
    for (xi, node) in zip(x, constant_nodes)
        node.val::T = xi
    end
    return nothing
end

options = Options(binary_operators=[+, -, *], unary_operators=[cos, exp], enable_enzyme=true)

X = randn(10, 100)
y = randn(100)
dataset = Dataset(X, y)

tree = Node(2, Node(val=0.5))  # == exp(x), where x=0.5

# Copy tree which is used for storage.
ctree = copy(tree)
# Set all constants to 0.0:
foreach(t -> t.degree == 0 && t.constant && (t.val = 0.0), ctree)

constant_nodes = filter(t -> t.degree == 0 && t.constant, tree)
c_constant_nodes = filter(t -> t.degree == 0 && t.constant, ctree)

x = map(t -> t.val::Float64, constant_nodes)
dx = map(t -> 1.0, constant_nodes)

result = [0.0]
dresult = [1.0]

idx = nothing

autodiff(
    Reverse,
    opt_func!,
    Duplicated(result, dresult),
    Duplicated(x, dx),
    Const(dataset),
    Duplicated(tree, ctree),
    Duplicated(constant_nodes, c_constant_nodes),
    Const(options),
    Const(idx),
)

wsmoses · 2023-08-16T00:42:32Z

That inlining, and even more would be quite helpful. Unfortunately I don’t see the cause of the error from the source as is and would need a smaller reproducer (even without external packages if you’re able to find an example) would allow me to assess the cause and fix it.

…

On Wed, Aug 16, 2023 at 9:30 AM Miles Cranmer ***@***.***> wrote: And with some of the called functions inlined: using SymbolicRegressionusing Enzyme function opt_func!(result, x, dataset, tree, constant_nodes, options, idx) _set_constants!(x, constant_nodes) result[1] = SymbolicRegression.LossFunctionsModule.eval_loss(tree, dataset, options; regularization=false, idx=idx) return nothingendfunction _set_constants!(x::AbstractArray{T}, constant_nodes) where {T} for (xi, node) in zip(x, constant_nodes) node.val::T = xi end return nothingend options = Options(binary_operators=[+, -, *], unary_operators=[cos, exp], enable_enzyme=true) X = randn(10, 100) y = randn(100) dataset = Dataset(X, y) tree = Node(2, Node(val=0.5)) # == exp(x), where x=0.5 # Copy tree which is used for storage. ctree = copy(tree)# Set all constants to 0.0:foreach(t -> t.degree == 0 && t.constant && (t.val = 0.0), ctree) constant_nodes = filter(t -> t.degree == 0 && t.constant, tree) c_constant_nodes = filter(t -> t.degree == 0 && t.constant, ctree) x = map(t -> t.val::Float64, constant_nodes) dx = map(t -> 1.0, constant_nodes) result = [0.0] dresult = [1.0] idx = nothing autodiff( Reverse, opt_func!, Duplicated(result, dresult), Duplicated(x, dx), Const(dataset), Duplicated(tree, ctree), Duplicated(constant_nodes, c_constant_nodes), Const(options), Const(idx), ) — Reply to this email directly, view it on GitHub <#999 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAJTUXHK24BQBX26CQUR6XTXVQICXANCNFSM6AAAAAA3OZ2VO4> . You are receiving this because you commented.Message ID: <EnzymeAD/Enzyme. ***@***.***>

MilesCranmer · 2023-08-16T03:28:46Z

Hm, it will be a bit of work to in-line everything. Maybe if I understand the error a bit better I will know what part of my code is most likely the cause. What is this error due to?

MilesCranmer · 2023-08-16T03:52:07Z

If it helps, I think the error occurs when it hits this branch of the code: https://github.com/SymbolicML/DynamicExpressions.jl/blob/4629571123dde66d1270fedd39fe689af56ac9d6/src/EvaluateEquation.jl#L100

Is there a way the error can be triggered but not crash? Maybe it will be easier to backtrace it.

wsmoses · 2023-08-16T05:38:33Z

The error means that the expected size of the Julia object representing data to preserve for the reverse pass does match the actual size of the data to be preserved

…

On Wed, Aug 16, 2023 at 12:52 PM Miles Cranmer ***@***.***> wrote: If it helps, I think the error occurs when it hits this branch of the code: https://github.com/SymbolicML/DynamicExpressions.jl/blob/4629571123dde66d1270fedd39fe689af56ac9d6/src/EvaluateEquation.jl#L100 Is there a way the error can be triggered but not crash? Maybe it will be easier to backtrace it. — Reply to this email directly, view it on GitHub <#999 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAJTUXH6UWFR6VTHDDRBFGDXVQ7XDANCNFSM6AAAAAA3OZ2VO4> . You are receiving this because you commented.Message ID: <EnzymeAD/Enzyme. ***@***.***>

MilesCranmer · 2023-08-16T09:37:47Z

Thanks.

I tried building a more minimal MWE today but didn’t have luck reproducing the error, sorry.

One other note is that earlier I noticed via Cthulhu that there was type inference issues with return statements like:

result, flag = ...

Where result is an array and flag is a bool.

It seems like because Julia allows things like:

x, y = (1, 2, 3)

To set x=1 and y=2 (IIRC; am just getting off the plane so can’t check). Because of this allowed syntax it could be that result, flag = (::Array, ::Bool) might be causing weird inference issues for Enzyme. What do you think?

MilesCranmer · 2023-08-17T00:19:31Z

I'm doing a binary search over the code by commenting out segments of the libraries until the error goes away. In doing so I saw this warning: warning: didn't implement memmove, using memcpy as fallback which can result in errors. Is this some internal thing in Enzyme or is it a problem with my library?

wsmoses · 2023-08-17T00:20:27Z

Internal to Enzyme warning, it can be ignored here

MilesCranmer · 2023-08-17T00:56:15Z

Okay, I figured out. Sorry for wasting your time on what I thought could be an Enzyme bug; turns out it's just interference between packages that wasn't being removed by the optimization before autodiff.

Here's the postmortem:

So, I remember reading about the incompatibility between Enzyme.jl and LoopVectorization.jl. I do use LoopVectorization.jl, but I didn't think this would be an issue here, because my evaluation kernels are declared like this:

function my_kernel(x, ::Val{turbo}) where {turbo}
    if turbo
        @turbo for i in eachindex(x)
            x[i] = x[i] ^ 2
        end
    else
        for i in eachindex(x)
            x[i] = x[i] ^ 2
        end
    end
end

so because I have called my evaluation loop with turbo=false inside the autodiff, I had thought the entire branch would be removed by the -O2 pass before the differentiation occurs. But since the assignment turbo -> Val(turbo) happens inside the autodiff call, I think Enzyme was actually trying to differentiate both branches of this – and ran into the LoopVectorization issue. Does this sound plausible to you?

I was able to temporarily fix this issue by completely removing the turbo branches. Is that AlignedSize issue generally indicative of interfering with other packages that manipulate the LLVM in incompatible ways?

So I think a general fix for me could be to have turbo -> Val(turbo) outside of the autodiff, which I assume would cause the optimization to remove the LoopVectorization.jl code.

wsmoses · 2023-08-17T00:58:33Z

Independent of turbo not working correctly, I think we still need to figure out and fix the source of this, since this is a state that should never occur for the caching infrastructure.

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MilesCranmer closed this as completed Aug 17, 2023

wsmoses reopened this Aug 17, 2023

wsmoses mentioned this issue Aug 17, 2023

support wide ints in tape #1002

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jgreener64 mentioned this issue May 31, 2024

Higher order AD error #1487

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Understanding `Error: Enzyme aligned size and Julia size disagree` #999

Understanding `Error: Enzyme aligned size and Julia size disagree` #999

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Understanding Error: Enzyme aligned size and Julia size disagree #999

Understanding Error: Enzyme aligned size and Julia size disagree #999

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Understanding `Error: Enzyme aligned size and Julia size disagree` #999

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