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Merged
marius311 merged 7 commits into from
Aug 6, 2025
Merged

fix tests on Julia v1.10, upgrade to CUDA.jl v5 #91

marius311 merged 7 commits into from
Aug 6, 2025

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@xzackli
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@xzackli xzackli commented Jun 18, 2025

This PR fixes the tests on Julia 1.10 LTS, many of which were broken (see #89) from changes to adjoints through StaticArrays. It also allows CUDA.jl v5.

The most important part to review is the changes to AD rules in autodiff.jl on SVector. Most of the breaking tests were from this. @marius311 I'd give these a good long stare before accepting those changes, but the tests do pass now.

The method ambiguity between Base.reshape and ComponentArrays.reshape for 0-dim arrays persists, but CUDA._derived_array appears to no longer exist. I've replaced it with GPUArrays.derive,

function GPUArrays.derive(::Type{T}, a::JLArray, dims::Dims{N}, offset::Int) where {T,N}

Zygote.@nograd has been deprecated for ChainRulesCore.@non_differentiable. Also, I took that AbstractDifferentiation ForwardDiff tag which doesn't exist / have an associated PR. Finally, in recent Julia versions the act of method overwriting is looked down upon, so I have renamed Loess.loess to cmblensing_loess and dispatched appropriately.

xzackli
xzackli commented Jun 18, 2025
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src/autodiff.jl
end
return result, svector_field_pullback
end

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I confess this function was heavily AI-assisted...

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xzackli commented Jun 18, 2025 •
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Four minor notes:

  • Without the changes to the AD rules, the pinv(L)' term in logdet was breaking all over the place. The field wrapper gets stripped off somewhere in the adjoint due to some fallback, and you are left with a bare array showing up in the adjoint chain when you need the field wrapper for Zfac.
  • I split off bilinear lensing which was also breaking tests; it no longer is a closure on a trait but instead just dispatches on the array type. Previously it was using CUDA outside of the extension.
  • I have allowed Zygote 0.7. The big change is unthunking only happens when necessary, but it looks like the custom rules in CMBLensing.jl are fairly vanilla.
  • Something that confused me was the use of both Zygote.Chainrules and ChainRules in the imports. Sadly no ∇getindex in ChainRulesCore so you can't drop the dependency. Maybe I should just consolidate to ChainRules?

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marius311 commented Jun 18, 2025 •
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Thanks @xzackli, will take a look. I will say changing AD versions and CUDA were some of the most brittle things unfort and I dont have the best tests, but we'll see.

One question, does CUDA.jl 5.0 allow using CUDA version 11.4? Unfort there's a super insidisous FFT regression in 11.5 which makes MAPs for large-ish maps fail in subtle ways, which we never figured out.

@shuimufge you should take a look as well if you plan to use such an upgraded version, please let us know if you see any issues

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xzackli commented Jun 19, 2025

I could run a regression test on CPU with StableRNGs to compare between 1.8 and this PR on 1.10. Maybe what really matters are gradients of a log density agreeing with something like FiniteDiff?

The FFT issue sounds bad; we should test. In the release notes for 11.5 they say

FFTs of certain sizes in single and double precision (multiples of size 6) could fail on
future devices. This issue will be fixed in an upcoming release.

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xzackli commented Jun 19, 2025

@marius311 am I hitting some kind of Chesterton's fence by introducing changes that made six @test_broken now succeed?

https://github.com/marius311/CMBLensing.jl/actions/runs/15723860216/job/44390286671

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marius311 commented Jun 19, 2025

Most likely Zygote improved, def feel free to turn any @test_broken's into @tests seems like its pretty close to all good then.

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xzackli commented Jun 19, 2025

Ok great. I've marked that @test_broken to @test.

The MKL runs above failed with a small numerical difference for one test. Works fine for FFTW though?

T :: Float64: Test Failed at /home/runner/work/CMBLensing.jl/CMBLensing.jl/test/runtests.jl:567
  Expression: f' * (Lϕ * g) ≈ (f' * Lϕ) * g
   Evaluated: 7872.802622360745 ≈ 7872.800659616416

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marius311 commented Jun 19, 2025 •
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I would do it myself but I ran into some snags on ARM, but could you verify on the old code / 1.9 and on the new code / 1.10 with MKL / FFT / CUDA that the distribution of errors doesn't change? Using something like eg this script:

using CMBLensing, Random, Plots, JLD2

Cℓ = camb().unlensed_total

L = LenseFlow
Ny, Nx = 128, 128
T = Float64
storage = Array

rng = Xoshiro(1)

proj = ProjLambert(;Ny,Nx,T,storage)
Cϕ = Cℓ_to_Cov(:I, proj, Cℓ.ϕϕ)
Cf = Cℓ_to_Cov(:I, proj, Cℓ.TT)

x = map(1:500) do i
    ϕ = simulate(rng,Cϕ)
    f = simulate(rng,Cf)
    g = simulate(rng,Cf)
    Lϕ = precompute!!(LenseFlow(ϕ),f)
    f' * (Lϕ * g) - (f' * Lϕ) * g
end

@save ...

I'm just slightly surprised that difference is so large for Float64 and the CG solver is a bit sensitive to that transpose property being numerically accurate. If the distribution doesn't actually change and we just got unlucky, feel free to bump up that tolerance a bit though.

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xzackli commented Jun 19, 2025

I tried a variety of tests on Julia 1.9.4 and can't replicate what's happening on the test machine. For your test script, here's my outputs below.

your script, save with julia ver + fft backend 
using CMBLensing, Random, DelimitedFiles

Cℓ = camb().unlensed_total

L = LenseFlow
Ny, Nx = 128, 128
T = Float64
storage = Array

rng = Xoshiro(1)

proj = ProjLambert(;Ny,Nx,T,storage)
Cϕ = Cℓ_to_Cov(:I, proj, Cℓ.ϕϕ)
Cf = Cℓ_to_Cov(:I, proj, Cℓ.TT)

x = map(1:500) do i
    ϕ = simulate(rng,Cϕ)
    f = simulate(rng,Cf)
    g = simulate(rng,Cf)
    Lϕ = precompute!!(LenseFlow(ϕ),f)
    f' * (Lϕ * g) - (f' * Lϕ) * g
end

# get FFT backend and Julia version here 
fft_backend = CMBLensing.FFTW.get_provider()
julia_ver = replace(string(VERSION), "." => "_")
filename = "precision_test_julia$(julia_ver)_$(fft_backend).txt"
writedlm(filename, hcat(x...))

FFTW

1.4901161193847656e-8	0.0	-7.450580596923828e-9	0.0	5.587935447692871e-9	-2.7939677238464355e-9	1.862645149230957e-9	1.6763806343078613e-8	-1.1175870895385742e-8	0.0	0.0	1.862645149230957e-9	-6.51925802230835e-9	3.725290298461914e-9	7.450580596923828e-9	3.725290298461914e-9	-7.450580596923828e-9	-7.450580596923828e-9	-3.725290298461914e-9	7.450580596923828e-9	-3.725290298461914e-9	-1.1175870895385742e-8	-7.450580596923828e-9	3.725290298461914e-9	3.725290298461914e-9	-3.725290298461914e-9	-3.725290298461914e-8	-2.2351741790771484e-8	-3.725290298461914e-9	3.725290298461914e-9	-1.4901161193847656e-8	1.4901161193847656e-8	7.450580596923828e-9	-1.30385160446167e-8	3.725290298461914e-9	-9.313225746154785e-9	-3.725290298461914e-9	1.862645149230957e-9	7.450580596923828e-9	1.1175870895385742e-8	-3.725290298461914e-9	-7.450580596923828e-9	-1.862645149230957e-9	-1.1175870895385742e-8	5.587935447692871e-9	2.9802322387695312e-8	0.0	-3.725290298461914e-8	-1.1175870895385742e-8	-6.51925802230835e-9	3.725290298461914e-9	1.1175870895385742e-8	2.3283064365386963e-9	-9.313225746154785e-10	9.313225746154785e-9	-5.587935447692871e-9	7.450580596923828e-9	9.313225746154785e-9	0.0	-9.313225746154785e-10	0.0	-7.450580596923828e-9	1.862645149230957e-9	-2.7939677238464355e-9	0.0	2.2351741790771484e-8	-5.21540641784668e-8	5.587935447692871e-9	1.1175870895385742e-8	1.4901161193847656e-8	1.4901161193847656e-8	0.0	-1.862645149230957e-9	-7.450580596923828e-9	1.1175870895385742e-8	1.4901161193847656e-8	1.862645149230957e-9	-7.916241884231567e-9	-5.587935447692871e-9	4.656612873077393e-10	-1.862645149230957e-9	-3.725290298461914e-9	-7.450580596923828e-9	0.0	7.450580596923828e-9	0.0	-7.450580596923828e-9	1.862645149230957e-9	7.450580596923828e-9	-3.725290298461914e-9	0.0	-5.587935447692871e-9	6.51925802230835e-9	-5.587935447692871e-9	5.587935447692871e-9	-3.725290298461914e-9	-2.9802322387695312e-8	-7.450580596923828e-9	-1.4901161193847656e-8	2.9802322387695312e-8	1.30385160446167e-8	-1.1175870895385742e-8	0.0	1.1175870895385742e-8	-2.3283064365386963e-9	1.862645149230957e-9	-1.862645149230957e-9	0.0	-1.862645149230957e-8	7.450580596923828e-9	-3.725290298461914e-9	7.450580596923828e-9	-5.587935447692871e-9	-7.450580596923828e-9	1.210719347000122e-8	2.7939677238464355e-9	-3.725290298461914e-9	-1.210719347000122e-8	-7.450580596923828e-9	1.4901161193847656e-8	1.862645149230957e-9	3.725290298461914e-9	7.450580596923828e-9	1.1175870895385742e-8	-2.2351741790771484e-8	-9.313225746154785e-9	1.862645149230957e-9	1.3969838619232178e-9	7.450580596923828e-9	-1.4901161193847656e-8	-1.3969838619232178e-9	-1.0244548320770264e-8	-2.2351741790771484e-8	1.862645149230957e-9	2.60770320892334e-8	-3.725290298461914e-9	-1.3969838619232178e-8	5.9371814131736755e-9	3.725290298461914e-9	-5.587935447692871e-9	0.0	-2.7939677238464355e-9	4.6566128730773926e-9	7.450580596923828e-9	-3.725290298461914e-9	-3.725290298461914e-9	1.3969838619232178e-8	3.725290298461914e-9	3.725290298461914e-9	1.4901161193847656e-8	-1.30385160446167e-8	0.0	-7.450580596923828e-9	-1.862645149230957e-9	7.450580596923828e-9	2.2351741790771484e-8	7.450580596923828e-9	-1.862645149230957e-9	3.725290298461914e-9	7.450580596923828e-9	2.2351741790771484e-8	0.0	0.0	-1.1175870895385742e-8	-9.313225746154785e-9	-7.450580596923828e-9	-2.7939677238464355e-9	-4.656612873077393e-10	3.725290298461914e-9	0.0	-7.450580596923828e-9	0.0	-3.725290298461914e-9	-7.450580596923828e-9	7.450580596923828e-9	-3.725290298461914e-9	-2.7939677238464355e-9	-1.4901161193847656e-8	-3.725290298461914e-9	-1.4901161193847656e-8	-2.2351741790771484e-8	-5.587935447692871e-9	1.3969838619232178e-9	-8.381903171539307e-9	7.450580596923828e-9	5.587935447692871e-9	3.725290298461914e-9	-7.450580596923828e-9	-3.725290298461914e-9	7.450580596923828e-9	-4.656612873077393e-10	-2.7939677238464355e-9	1.862645149230957e-9	7.450580596923828e-9	0.0	2.2351741790771484e-8	-6.51925802230835e-9	-1.862645149230957e-9	7.450580596923828e-9	9.313225746154785e-10	2.60770320892334e-8	1.4901161193847656e-8	-1.4901161193847656e-8	1.4901161193847656e-8	-7.450580596923828e-9	3.3527612686157227e-8	-3.725290298461914e-9	1.862645149230957e-9	0.0	3.725290298461914e-9	-2.2351741790771484e-8	-1.1175870895385742e-8	-7.450580596923828e-9	0.0	1.862645149230957e-9	-2.9802322387695312e-8	2.2351741790771484e-8	0.0	1.1175870895385742e-8	0.0	7.450580596923828e-9	3.725290298461914e-9	-3.725290298461914e-9	-5.587935447692871e-9	-5.587935447692871e-9	-9.313225746154785e-9	-3.725290298461914e-9	-2.561137080192566e-9	9.313225746154785e-9	-7.450580596923828e-9	5.587935447692871e-9	-3.725290298461914e-9	-1.4901161193847656e-8	-7.450580596923828e-9	9.313225746154785e-10	-9.313225746154785e-10	-7.450580596923828e-9	-1.1175870895385742e-8	-1.6763806343078613e-8	-3.4924596548080444e-9	4.6566128730773926e-9	7.450580596923828e-9	-1.4901161193847656e-8	0.0	3.725290298461914e-9	0.0	0.0	7.450580596923828e-9	6.984919309616089e-10	1.1175870895385742e-8	1.862645149230957e-9	6.51925802230835e-9	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MKL

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Manifest.toml 
# This file is machine-generated - editing it directly is not advised

julia_version = "1.9.4"
manifest_format = "2.0"
project_hash = "9a9d6d83119b3354a13d5b1fead7181c57e01bb3"

[[deps.AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.5.0"
weakdeps = ["ChainRulesCore", "Test"]

    [deps.AbstractFFTs.extensions]
    AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
    AbstractFFTsTestExt = "Test"

[[deps.AbstractNFFTs]]
deps = ["LinearAlgebra", "Printf"]
git-tree-sha1 = "292e21e99dedb8621c15f185b8fdb4260bb3c429"
uuid = "7f219486-4aa7-41d6-80a7-e08ef20ceed7"
version = "0.8.2"

[[deps.Accessors]]
deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
git-tree-sha1 = "0ba8f4c1f06707985ffb4804fdad1bf97b233897"
uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
version = "0.1.41"

    [deps.Accessors.extensions]
    AxisKeysExt = "AxisKeys"
    IntervalSetsExt = "IntervalSets"
    LinearAlgebraExt = "LinearAlgebra"
    StaticArraysExt = "StaticArrays"
    StructArraysExt = "StructArrays"
    TestExt = "Test"
    UnitfulExt = "Unitful"

    [deps.Accessors.weakdeps]
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    Requires = "ae029012-a4dd-5104-9daa-d747884805df"
    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
    StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
    Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
    Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"

[[deps.Adapt]]
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version = "3.7.2"
weakdeps = ["StaticArrays"]

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[[deps.AliasTables]]
deps = ["PtrArrays", "Random"]
git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff"
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version = "1.1.3"

[[deps.ArgCheck]]
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version = "2.4.0"

[[deps.ArgTools]]
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version = "1.1.1"

[[deps.ArrayInterface]]
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git-tree-sha1 = "c5aeb516a84459e0318a02507d2261edad97eb75"
uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
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    [deps.ArrayInterface.extensions]
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    ArrayInterfaceBlockBandedMatricesExt = "BlockBandedMatrices"
    ArrayInterfaceCUDAExt = "CUDA"
    ArrayInterfaceGPUArraysCoreExt = "GPUArraysCore"
    ArrayInterfaceStaticArraysCoreExt = "StaticArraysCore"
    ArrayInterfaceTrackerExt = "Tracker"

    [deps.ArrayInterface.weakdeps]
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    BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
    CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
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    Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"

[[deps.Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"

[[deps.Atomix]]
deps = ["UnsafeAtomics"]
git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be"
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version = "0.1.0"

[[deps.AxisAlgorithms]]
deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"]
git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7"
uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950"
version = "1.0.1"

[[deps.AxisArrays]]
deps = ["Dates", "IntervalSets", "IterTools", "RangeArrays"]
git-tree-sha1 = "16351be62963a67ac4083f748fdb3cca58bfd52f"
uuid = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
version = "0.4.7"

[[deps.BFloat16s]]
deps = ["LinearAlgebra", "Printf", "Random", "Test"]
git-tree-sha1 = "dbf84058d0a8cbbadee18d25cf606934b22d7c66"
uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b"
version = "0.4.2"

[[deps.BangBang]]
deps = ["Accessors", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires"]
git-tree-sha1 = "e2144b631226d9eeab2d746ca8880b7ccff504ae"
uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"
version = "0.4.3"

    [deps.BangBang.extensions]
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    BangBangDataFramesExt = "DataFrames"
    BangBangStaticArraysExt = "StaticArrays"
    BangBangStructArraysExt = "StructArrays"
    BangBangTablesExt = "Tables"
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@xzackli
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xzackli commented Jun 21, 2025 •
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I've lowered the difficulty for MKL to rtol=3e-7 on Float64 for that failing test. In the commit history above I briefly experimented with raising some other compat limits: raising StaticArrays 1.0 to 1.9 led to test failures only on Julia 1.8 and 1.9, so let's cross that bridge later.

@marius311
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marius311 commented Jun 22, 2025

PInged Fei will give him a couple of days but otherwise I think this is probably good to go many thank for this.

@marius311 marius311 merged commit 8e75a7c into marius311:master Aug 6, 2025
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@xzackli @marius311