Fix getindex invalidation from AbstractArray + BlockIndices

[ChrisRackauckas-Claude]

Summary

• getindex(::AbstractArray{T,N}, ::BlockIndices{N}) caused 12,082 method invalidations when loading BlockArrays
• Removed the AbstractArray fallback and added specialized unblock methods in views.jl that handle BlockIndices on non-blocked axes (e.g. Base.OneTo) by decomposing into block-level + sub-indexing
• The LayoutArray specializations (covering all AbstractBlockArray types) and AbstractBlockedUnitRange methods are unchanged

Context

When profiling the TTFX of ModelingToolkit's DAE pipeline, BlockArrays was identified as the #2 source of method invalidations. The getindex(::AbstractArray{T,N}, ::BlockIndices{N}) method invalidated compiled instances of getindex for all AbstractArray types because BlockIndices <: AbstractArray, triggering recompilation of fancy-indexing paths.

Changes

• src/blockaxis.jl: Removed getindex(b::AbstractArray{T,N}, K::BlockIndices{N}) fallback (line 10)
• src/views.jl: Added two unblock specializations:
  • For AbstractUnitRange{<:Integer} axes: decomposes BlockIndices into block() + regular sub-indices
  • For AbstractBlockedUnitRange axes: preserves original direct-indexing behavior

Design rationale

The AbstractArray fallback was needed because unblock in to_indices recursively calls getindex(axis, BlockIndices). For non-blocked axes like Base.OneTo, there was no handler, causing infinite recursion without the fallback. The fix intercepts at the unblock level instead, using getindex(::AbstractUnitRange, ::Block{1}) (which returns the whole range for non-blocked types) followed by standard sub-indexing.

Test plan

• Full test suite passes locally (2,318 pass, 2 broken (pre-existing), 0 fail)

🤖 Generated with Claude Code

[dlfivefifty]

@ChrisRackauckas looks fine assuming the tests pass but I had two comments:

1. Ideally there would be a test to avoid a future regression
2. Are we confident that it doesn't cause a (runtime) performance regression?

[ChrisRackauckas]

There aren't great ways to test for invalidations.

[ChrisRackauckas-Claude]

Runtime Performance Benchmarks

I ran comprehensive benchmarks comparing the fix against the original code (Julia 1.12.4). All benchmarks use BenchmarkTools.@benchmark with 3s per benchmark.

Summary: No regression anywhere. 2x speedup on one path.

Benchmark	Original (ns)	Fixed (ns)	allocs	bytes	Return Type
Unchanged paths (LayoutArray / Block):
BA[Block(2)[1:10], Block(1)[1:20]]	560	494	2	1680	Same
BA[Block(2,1)]	1100	1101	3	10136	Same
BlockedAxis[Block(2)[1:10]]	46	47	1	32	Same (UnitRange)
BlockedAxis[Block(2)]	47	46	1	32	Same (UnitRange)
Changed paths (new unblock + UnitRange method):
OneTo(100)[Block(1)[1:50]]	46	49	1	32	Same (UnitRange)
OneTo(100)[Block(1)[10:90]]	45	46	1	32	Same (UnitRange)
OneTo(1000)[Block(1)[1:500]]	45	46	1	32	Same (UnitRange)
Vector(100)[Block(1)[1:50]]	167	82	2 (was 4)	480 (was 1408)	Same (Vector)
view(A,100x50)[Block(1)[..]]	298	337	2	1680	Same (Matrix)
End-to-end operations:
mul!(view(C,:,1:50), A, B, 1, 2)	27050	26800	7	40280	Same
mul!(BlockArray, BA, BA)	68420	68170	1	32	Same
sum(BlockMatrix(100x100))	135059	135329	1	16	Same
norm(BlockVector(100))	908	950	2	48	Same

Key observations:

1. All existing paths are within noise — BlockArray indexing, BlockedUnitRange indexing, views, mul!, reductions
2. OneTo[BlockIndexRange] is identical — same ~46ns timing, same return type (UnitRange{Int64}), same allocations (1, 32 bytes). This is handled by the new narrow getindex(::AbstractUnitRange{<:Integer}, ::BlockIndices{1}) method
3. Vector[BlockIndexRange] is 2x faster — 167ns → 82ns. The original code did Vector[Block(1)][1:50] which copies the entire vector first, then sub-indexes. The new to_indices path indexes directly with a BlockSlice, avoiding the intermediate full copy
4. Correctness checks all pass — OneTo(n)[Block(1)[a:b]] == a:b, Vector[Block(1)[1:50]] == Vector[1:50], etc.

Design notes:

The initial version of this PR caused a regression for OneTo[BlockIndexRange] (returned Vector instead of UnitRange with O(n) allocation). This was fixed by adding a narrow getindex(::AbstractUnitRange{<:Integer}, ::BlockIndices{1}) method which is specific enough to avoid the mass invalidation that the original getindex(::AbstractArray{T,N}, ::BlockIndices{N}) caused, while preserving the efficient decomposition into block() + sub-indices.

Types that are not LayoutArray (and thus affected by the change): Vector, Matrix, SubArray, UnitRange, OneTo, and all other non-block arrays. Only AbstractBlockArray subtypes are LayoutArray.

Benchmark script

using BlockArrays, LinearAlgebra, BenchmarkTools
BenchmarkTools.DEFAULT_PARAMETERS.seconds = 3

A_med = BlockArray(rand(100, 100), [25, 25, 25, 25], [50, 50])
v_med = BlockArray(rand(100), [25, 25, 25, 25])
ax_med = axes(A_med, 1)
ot100 = Base.OneTo(100)
ot1000 = Base.OneTo(1000)
R_med = rand(100, 100)
r_med = rand(100)
V_med = view(Array(A_med), :, 1:50)

function run_bench(label, f)
    f(); val = f(); T = typeof(val)
    b = @benchmark $f()
    t = round(Int, median(b).time)
    println("$(rpad(label, 52)) $(lpad(t, 8))  $(b.allocs)  $(b.memory)  $T")
end

run_bench("BA[Block(2)[1:10], Block(1)[1:20]]", () -> A_med[Block(2)[1:10], Block(1)[1:20]])
run_bench("BA[Block(2,1)]", () -> A_med[Block(2,1)])
run_bench("BlockedAxis[Block(2)[1:10]]", () -> ax_med[Block(2)[1:10]])
run_bench("OneTo(100)[Block(1)[1:50]]", () -> ot100[Block(1)[1:50]])
run_bench("OneTo(1000)[Block(1)[1:500]]", () -> ot1000[Block(1)[1:500]])
run_bench("Vector(100)[Block(1)[1:50]]", () -> r_med[Block(1)[1:50]])
run_bench("view(A,100x50)[Block(1)[1:10],Block(1)[1:20]]", () -> V_med[Block(1)[1:10], Block(1)[1:20]])
C = rand(100,50); A = rand(100,50); B = rand(50,50)
run_bench("mul!(view(C,:,1:50), A, B, 1, 2)", () -> mul!(view(copy(C),:,1:50), A, B, 1, 2))

[ChrisRackauckas]

Seems like there is no regression from this and it passes tests.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 94.39%. Comparing base (7ebbea7) to head (3cef108).
⚠️ Report is 8 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #499      +/-   ##
==========================================
- Coverage   94.52%   94.39%   -0.13%     
==========================================
  Files          19       19              
  Lines        1789     1819      +30     
==========================================
+ Hits         1691     1717      +26     
- Misses         98      102       +4     

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[dlfivefifty]

The performance benchmark tests are amazing! Do I need to pay for Claude to do this myself?