FluxML/NNlib.jl erreicht einen Gesundheitsindex von 71 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (80/100) ab, am schwächsten bei Security (52/100). Zuletzt heute aktualisiert. 4 Mitwirkende tragen den Großteil der jüngsten Arbeit.
71
gesamt / 100
Gut
Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
71
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Liefert Releases aus — 100 Releases veröffentlicht
36/36
Release-Aktualität — letztes Release vor 14 Tagen
19.8/27
Release-Rhythmus — ein Release etwa alle 51,4 Tage
0/10
OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count
100
latest_release_tag
v0.9.38
releases_from_tags
nein
days_since_latest_release
14
mean_days_between_releases
51,4
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
Typprüfbarer Code — Julia ohne Typprüfungs-Konfiguration
55/55
Handhabbare Dateigrößen — 0/1 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_language
Julia
largest_source_bytes
1.275
source_files_sampled
1
oversized_source_files
0
Eckdaten
255GitHub-Sterne
94Mitwirkende
74Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
4Bus-Faktor
36offene Issues
—Paket-Ökosysteme
Warnungen zur Datenerhebung
No resolved dependencies carried a version and a supported ecosystem
Weitere Details
Stern- und Fork-Verlauf 255 ★ / 134 ⇿
255Sterne
134Forks
100Releases
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Major 0Minor 3Patch 97
Jeder Punkt umfasst 9 Tage.
OpenSSF Scorecard 5.2 / 10
5.2Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).Scorecard v5.5.0 · 2026-07-22 03:21 UTC
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 2 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
Registry
Paket
Version
Beziehung
PyPI
numpy
—
indirekt
PyPI
torch
—
indirekt
Abhängigkeits-Advisories nicht bewertet
Der Advisory-Abgleich konnte für diesen Bericht nicht ausgeführt werden: No resolved dependencies carried a version and a supported ecosystem
JSON-Rohbericht maschinenlesbar
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"body": "…e (#734) (#735)\n\n* Accept `alpha`/`beta` in cuDNN pooling backward (Enzyme accumulation)\n\nThe `EnzymeRules.reverse` rule for `maxpool!`/`meanpool!` (in\nNNlibEnzymeCoreExt) calls `∇maxpool!(dx, dy, y, x, pdims; alpha=1, beta=1)`\nto accumulate the gradient into the input shadow. The cuDNN\n`∇maxpool!`\n[…]\nnput+grid)\nand unfold/fold (spatial ranks 1/2/3).\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Enzyme rules for cuDNN pooling/softmax/activations + imrotate/upsampl…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-07-01T12:34:06Z",
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"oid": "04fb06a5a5a69d28e40d04caaf195ec311c3494a",
"body": "…(#733)\n\n* Support multiple AD backends in `test_gradients`, default to Zygote + Enzyme\n\n`test_gradients` now accepts a list of AD types for `compare` and defaults to\n`[AutoZygote(), AutoEnzyme()]`, so gradients are checked against both backends.\n\n- Add `NNLIB_TEST_ENZYME` env flag; when false, `Tes\n[…]\nthored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n* exclude enzyme on gpu and on windows\n\n* or\n\n* ifelse\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Compare gradients against both Zygote and Enzyme in `test_gradients` …",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-07-01T04:44:15Z",
"body_truncated": true,
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"oid": "b340fdd2d8a33a249c105757c8b72c009e9298e4",
"body": "* rename Test_Enzyme\n\n* Migrate GPU `gputest` calls to the shared `test_gradients`\n\nReplace per-backend `gputest` calls with the unified `test_gradients`\nhelper (test_module.jl) wherever the test checks a differentiable\nforward+gradient, continuing the migration started in #730.\n\nCUDA (verified loca\n[…]\ns, and the ForwardDiff `Dual` forward-mode tests.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Migrate GPU `gputest` calls to the shared `test_gradients` (#732)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-29T05:26:36Z",
"body_truncated": true,
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"oid": "bba9c2209f770355a917eaed97830a204a740192",
"body": "* Enable scatter common_testsuite on the Metal backend\n\nActivate the shared `scatter` suite on Metal and make it Metal-compatible.\nOther shared suites stay skipped on Metal pending their own fixes (they hit\nFloat64 / scalar-indexing / complex-activation / ImageTransformations limits).\n\nscatter.jl Me\n[…]\nle/gather/fold are 0-error on both Metal and CPU.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Enable scatter, gather, fold, upsample common_testsuites on Metal (#731)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-28T14:38:23Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "e0831c03e4bb5a39205988db0f6b7613e2de13f9",
"body": "* Migrate gradient tests from `gradtest` to `test_gradients`\n\nAdd a multi-backend `test_gradients` utility (adapted from Flux) to\n`test/test_module.jl` and migrate the bulk of `gradtest` call sites to it.\n\n`test_gradients`:\n- compares a Zygote gradient against a FiniteDifferences reference (inputs\n \n[…]\nfloat coverage. Verified on CPU and CUDA locally.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Migrate gradient tests from `gradtest` to `test_gradients` (#730)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-28T04:39:05Z",
"body_truncated": true,
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"oid": "cda262f97fa8e8afdbabf614784bb1024b9a8bb8",
"body": null,
"is_bot": false,
"headline": "add AGENTS.md",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-27T05:43:20Z",
"body_truncated": false,
"is_coding_agent": false
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{
"oid": "ad332c694e934804bccdbc2bfa94f1c5c47f0e34",
"body": "* Migrate test suite to ParallelTestRunner (#533)\n\nRun each test file in its own ParallelTestRunner worker so the suite runs\nin parallel. Shared imports/helpers move into test/test_module.jl (loaded in\nevery worker via init_code); the backend-parametrized testsuite/ suites are\ndriven with one entry \n[…]\nember (Metal 1.9.3 lands in the shared manifest).\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Migrate test suite to ParallelTestRunner (#533) (#729)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-27T05:39:16Z",
"body_truncated": true,
"is_coding_agent": true
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"oid": "4726413e4956b47f4169d87daf4347e4d85d906d",
"body": "* bachnorm rules\n\n* rm cuda, cudnn deps\n\n* fix batchnorm rrule!! backward with nothing running stats\n\n* cache fwd mean/ivar to make backward deterministic\n\n* fix dims\n\n* note about caching mean & var",
"is_bot": false,
"headline": "Mooncake rule for `NNlib.batchnorm`. (#727)",
"author_name": "Astitva Aggarwal",
"author_login": "AstitvaAggarwal",
"committed_at": "2026-06-26T15:04:55Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "fdc06d1d0717939ac57d067fba9df00067092e0c",
"body": "The `dropout` rrule built `@. ((keep > p) * val) * A` with `p` passed\nthrough unchanged. When `p` is a `Float64` (e.g. the `Dropout(0.0)`\ndefault inside `MultiHeadAttention`) and `A` is a `Float32` GPU array,\nthe `keep > p` comparison promoted the broadcast to `Float64`, which\nfails to compile on Me\n[…]\nays\nin the element type. Adds a Metal regression test covering `p = 0.0`\n(the MHA default) and `p = 0.3`, with and without `dims`.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix dropout rrule on Metal: keep `p` in the array's float type (#726)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-25T06:16:31Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "ba135536be31fa2b00ad6712305f36a6708ef26e",
"body": "Bumps [actions/checkout](https://github.com/actions/checkout) from 6 to 7.\n- [Release notes](https://github.com/actions/checkout/releases)\n- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/actions/checkout/compare/v6...v7)\n\n---\nupdated-dependenc\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
"is_bot": true,
"headline": "Bump actions/checkout from 6 to 7 (#724)",
"author_name": "dependabot[bot]",
"author_login": "dependabot[bot]",
"committed_at": "2026-06-22T12:43:51Z",
"body_truncated": true,
"is_coding_agent": false
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{
"oid": "9ef1fd04fbefb68f9688e55f3a3e834a6bc81944",
"body": "* Update Buildkite pipeline for the new JuliaGPU cluster.\n\nSwitch to the per-backend queues of the new JuliaGPU cluster.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>\n\n* Move the Metal job(s) to the new metal queue.\n\nThe JuliaGPU cluster now has a macOS agent with an Apple Silicon GPU,\nso Metal jobs no longer need a separate pipeline on the ecosystem\ncluster.\n\nCo-Authored-By: Claude Fable 5 <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Fable 5 <noreply@anthropic.com>",
"is_bot": false,
"headline": "Update Buildkite pipeline for the new JuliaGPU cluster (#723)",
"author_name": "Tim Besard",
"author_login": "maleadt",
"committed_at": "2026-06-14T19:11:52Z",
"body_truncated": false,
"is_coding_agent": true
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{
"oid": "f71b71c7f497b849c6553e098ba1966d36418225",
"body": "* Refactor softmax gradients to not capture x\n\nThe softmax/logsoftmax backward passes never use the input `x` for its\nvalues: the generic path computes from `dy` and `y` alone, and the cuDNN\npath uses `x` only for shape/eltype metadata, which `y` carries equally.\nCapturing `x` in the rrule pullback \n[…]\nnoreply@anthropic.com>\n\n* Remove deprecation test\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Refactor softmax and hook cudnn backward (#718)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T16:07:28Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "fd8bf8364ec2cc9f2d22cd03b1daa6d542d47f16",
"body": "…mark (#513) (#717)\n\n* Add GPU softmax benchmark (cuDNN vs custom kernels, issue #513)\n\nBenchmarks softmax/logsoftmax forward and gradient on the GPU, comparing\nthe cuDNN routines against NNlib's generic broadcast kernels (and\nLogExpFunctions.softmax as a cross-check), for dims=1 and dims=2.\n\nReprod\n[…]\nustom policy, and a\n\"why not permute\" comparison.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix slow cuDNN softmax backward via INSTANCE mode + GPU softmax bench…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T09:55:49Z",
"body_truncated": true,
"is_coding_agent": true
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{
"oid": "5a36dcfc4cafc6b5b02653107ca9cbbcd94cb999",
"body": "* skip enzyme gather test on amdgpu\n\n* deactivate enzyme tests on amdgpu",
"is_bot": false,
"headline": "skip enzyme tests on amdgpu (#709)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T09:16:44Z",
"body_truncated": false,
"is_coding_agent": false
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{
"oid": "f91c34dbc4086e47aee9c6e9b326b9d7c645ec38",
"body": "Adds `NNlib._batched_gemm!(::Type{<:MtlArray}, ...)` to the Metal extension,\nthe dispatch hole that made `batched_mul` (and thus `dot_product_attention`)\nraise a MethodError on `MtlArray`s.\n\nReal `Float16`/`Float32` operands with matching batch sizes go through Metal\nPerformance Shaders' batched `ma\n[…]\nperands,\nmatrix×batch broadcasting, batched_vec, tiny arrays, Float16, gradients, the\nin-place 5-arg form, and display/conversion.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support batched_mul on Metal via MPS (fixes #581) (#714)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T06:13:20Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "2a3c123146c852a01ac52bd4cab60b1cc6e724a6",
"body": "The cuDNN softmax algorithm was selected from CUDA.math_mode: under\nCUDA.FAST_MATH it used CUDNN_SOFTMAX_FAST, which skips the row-max\nsubtraction and overflows to NaN on masked / large-magnitude inputs\n(e.g. attention masks of -1000 or -Inf), and is unsafe in Float16.\n\nsoftmaxalgo() now always retu\n[…]\nse overflow-safe.\n\nAdds a regression test exercising both DEFAULT_MATH and FAST_MATH with\nlarge-negative and -Inf attention masks.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Always use accurate cuDNN softmax algorithm (fixes #506) (#715)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T06:07:43Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "4ed25976bb6c67a4f2d04630e26fd1f37fbde6eb",
"body": null,
"is_bot": false,
"headline": "Bump version from 0.9.35 to 0.9.36",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T03:50:57Z",
"body_truncated": false,
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{
"oid": "8d831105dc79e56005d97523595593be16b505e5",
"body": "* Support complex inputs in meanpool on GPU (fixes #610)\n\nMean pooling is linear, so the cuDNN extension supports complex inputs by\npooling the real and imaginary parts independently and recombining, mirroring\nthe existing complex-conv pattern. This already worked on the CPU path via the\ngeneric imp\n[…]\nCUDA/CPU complex tests to\nalso cover the AD path.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support complex inputs in meanpool across backends (fixes #610) (#711)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T03:49:56Z",
"body_truncated": true,
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{
"oid": "ba68bbbaaf039f1fd1ef791ce895a18a850f8398",
"body": "The `@inferred stft(x; n_fft=16)` test in test/testsuite/spectral.jl was\ndisabled with a \"TODO fix type stability for pad_reflect\" comment. There\nwere two distinct sources of instability:\n\n1. The single-dimension padding kernels (`pad_reflect`, `pad_symmetric`,\n `pad_circular`, `pad_repeat`) forwa\n[…]\nng docstring examples match\nexactly, gradients agree with finite differences, and the padding.jl and\nspectral CPU testsuites pass.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Make padding functions and stft type-stable (#702) (#713)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T03:45:10Z",
"body_truncated": true,
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{
"oid": "6af48f575444f9ee3e9d8c40101c36849b2121af",
"body": "* Enable Enzyme tests on Julia 1.12\n\nEnzyme is functional on Julia 1.12 (verified against Enzyme 0.13.154):\nconv!/∇conv_data!/∇conv_filter!/depthwiseconv! (spatial ranks 1-3),\nmaxpool!/meanpool!, gather!, scatter! and dropout all pass.\n\n- runtests.jl: extend the `Test_Enzyme` guard to `v\"1.13-\"` so \n[…]\nhttps://github.com/EnzymeAD/Enzyme.jl/issues/3150\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Enable Enzyme tests on Julia 1.12 (#708)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-10T01:58:17Z",
"body_truncated": true,
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{
"oid": "49948aeea8174d6105236a40d2f912d6651da83c",
"body": "…nbreak tests (#706)\n\nAudit follow-up to #702.\n\nsrc/activations.jl:\n- hardσ: the broadcasted/scalar derivative returned `oftf(Ω, 1)` (i.e. 1)\n in its saturated branch instead of 0, so the gradient was wrong for |x|>3\n on every backend. The existing gradtests only probe ±2±rand, always inside\n har\n[…]\nactivations.jl: add a regression test checking saturated-region gradients\n of hardσ/hardtanh/relu6/hardswish against ForwardDiff.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix clamping-activation gradients (incl. hardσ correctness bug) and u…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T16:47:35Z",
"body_truncated": true,
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{
"oid": "8918579a075e3e77d01a7c22d90712c94ba5e766",
"body": "…ing (#705)\n\nWhen `gather`/`scatter` are called with the source/destination on the GPU\nbut the index array left on the CPU, dispatch previously fell through to the\ngeneric method, which scalar-indexes the GPU array — slow, or an error under\n`allowscalar(false)`. Add methods that move such an index o\n[…]\nr GPU) against the\nindexed dimensions before launching the kernel, for both the generic-KA and\nCUDA-specific scatter paths (#416).\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support CPU index with GPU source in gather/scatter, add bounds check…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T16:47:08Z",
"body_truncated": true,
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"oid": "1992a728e9f986d13dcf7640f202adefe171126a",
"body": "…03) (#704)\n\n* Support all scatter reduction ops on Metal (#534)\n\nThe shared GPU scatter kernel performed its atomic update via\n`Atomix.modify!`, whose Metal backend only implements a fixed op list.\nOn Metal this meant:\n\n- `max`/`min` on floats failed to compile (no Float32 atomic_fetch_max/min,\n s\n[…]\noverride. The Metal grad\ntests now cover all ops.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix scatter on Metal: all reduction ops and gradients (fixes #534, #7…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T15:47:10Z",
"body_truncated": true,
"is_coding_agent": true
},
{
"oid": "33528494bed3403a26a609cbc930f96a41d8b3ab",
"body": "cuDNN's TRUE_HALF_CONFIG (accumulate in Float16) is unsupported for 3D\nconvolutions and for many backward-filter shapes, surfacing as\nCUDNN_STATUS_NOT_SUPPORTED / BAD_PARAM. Set the convolution descriptor's\ncompute type to Float32 for Float16 inputs (PSEUDO_HALF_CONFIG), which is\nbroadly supported a\n[…]\nhin Float16\nrounding), plus explicit regression guards for the #505 (5D forward) and\n#515 (3=>64, 1x1-output ∇conv_filter) shapes.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Fix Float16 GPU conv via PSEUDO_HALF_CONFIG (fixes #505, #515) (#701)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T15:09:03Z",
"body_truncated": true,
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{
"oid": "8ee3540eccd991597e6c89d2fe33f608035e29fe",
"body": "…pace (#520) (#700)\n\nGrouped convolutions (and, since #698, depthwise convolutions) allocated a\nfresh im2col `col` workspace per group, so a depthwise conv with N channels\nallocated O(N) buffers. They also nested two levels of threading (parallel\nover groups x per-image batch threading inside each g\n[…]\n to share).\n\nThe #520 MWE drops from ~12.6k to ~1.2k allocations and the redundant per-group\ncol buffers (~13 MiB) are eliminated.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Reduce grouped/depthwise conv allocations by sharing the im2col works…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T13:17:03Z",
"body_truncated": true,
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{
"oid": "7c006e7bcc74eb75438e2c420cadaee6f1c73bf2",
"body": "…698)\n\nA depthwise convolution is exactly a grouped convolution with\n`groups == channels_in`. The specialized depthwise im2col/direct kernels\nbenchmarked consistently slower than the generic grouped-convolution path\n(forward, ∇data, and especially ∇filter which was ~4.5x slower), so route\nevery dept\n[…]\ng test is repurposed to check `depthwiseconv` against an\nindependently (explicit-loop) reordered grouped convolution.\n\nCloses #402\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Implement depthwiseconv via grouped conv, drop specialized kernels (#…",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T09:56:33Z",
"body_truncated": true,
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"oid": "9f5ada1bd0bdb038fede4248c0a2907bd7d01c57",
"body": "* Thread CPU conv within a single image at small batch (#234)\n\n`conv_im2col!` only parallelized over the batch dimension, so with\n`batch < nthreads` the extra threads sat idle — the main reason CPU conv\nlagged PyTorch at small batch in #234.\n\nWhen there are fewer images than tasks, also split each i\n[…]\nin a fair 4-thread config and report\nthe minimum.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Thread CPU conv within a single image at small batch (#234) (#697)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2026-06-09T09:55:21Z",
"body_truncated": true,
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"oid": "741381c577e0b389afaf5a2ea09a6840a8f5be08",
"body": "* Add conv perf analysis to benchmark README\n\nSummarizes why NNlib's CPU conv is slower than PyTorch's: NNlib threads\nonly over the batch dimension (idle cores when batch < nthreads), while\nPyTorch dispatches float32 convs to oneDNN's JIT direct convolution, which\nparallelizes within a single image.\n[…]\nright fix, not a from-scratch\ndirect convolution.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Add CPU conv performance analysis to benchmark README (#696)",
"author_name": "Carlo Lucibello",
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"body": "cuDNN exposes two average-pooling modes: one that includes padding zeros\nin the averaging denominator and one that excludes them. NNlib's meanpool\nonly implemented the include-padding mode.\n\nAdd a `count_include_pad` keyword (default `true`, so the behavior is\nunchanged) to `meanpool`. When `false`,\n[…]\nyword.\n- Tests: CPU forward/gradient vs a brute-force reference across spatial\n ranks, and CPU-vs-cuDNN agreement for both modes.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Add count_include_pad option to meanpool (issue #218) (#694)",
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"body": "Adds benchmark/conv_vs_pytorch/ with matched forward-conv benchmarks for\nNNlib and PyTorch on CPU (4 threads, Float32), reproducing the ~2x slowdown\nreported in #234. The Python side is managed with uv (CPU-only torch).\n\nConfirms NNlib is ~2-3x slower for common conv shapes, with the gap nearly\nclosing for 1x1 convs (pure GEMM), pointing at the im2col path and\nallocations rather than the matmul.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Add CPU conv benchmark comparing NNlib vs PyTorch (issue #234) (#695)",
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"body": "* activations: support complex inputs, with stable forward and correct gradients\n\nHolomorphic activations (σ, swish, softplus, logσ, mish, logcosh, tanh_fast,\nlisht, tanhshrink, gelu_*) now work on complex inputs. Only σ(::Complex) and\nsoftplus(::Complex) had to be added; the functions defined in te\n[…]\nrectly\nadjacent to the definition for Documenter.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>\n\n---------\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Support complex inputs in activation functions (#691)",
"author_name": "Carlo Lucibello",
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"body": "Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 6 to 7.\n- [Release notes](https://github.com/codecov/codecov-action/releases)\n- [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/codecov/codecov-action/compare/v\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
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"headline": "Bump codecov/codecov-action from 6 to 7 (#688)",
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"oid": "bc1d3fa20dce885dfea4d2b22fc83d74b57db0a0",
"body": "batched_mul already reshaped trailing batch dimensions to dispatch to\nthe 3D implementation, but batched_mul! only had a 3D method. Add a\ngeneric N-D method that reshapes to 3D (sharing data, no copy) and\nvalidates batch sizes.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
"is_bot": false,
"headline": "Support N-D arrays in batched_mul! (#529) (#685)",
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"oid": "01a3d38211a12323f78dda0138c6bd0cca98d4bc",
"body": "Thread the dimension count through `gen_pad` as `Val{N}` instead of a\nruntime `Int`, so the internal `ntuple` calls infer to fixed-length\ntuples. Previously `pad_constant`/`pad_zeros` with a tuple pad and the\ndefault `dims = :` inferred to `Any`.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Fix type instability in pad_constant/pad_zeros (#409) (#684)",
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"body": "The referenced ChainRulesTestUtils.jl PR #166 has been merged.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Remove obsolete TODO comment in test_utils (#428) (#687)",
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"body": "Document input/output array layouts, the output size formula, keyword\narguments, and add a runnable example.\n\nFixes #544\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Improve conv docstring with dimensions and examples (#683)",
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"body": "…#682)\n\nUnicodePlots 3.5 (April 2023) changed the function-plot signature from\nlineplot(f, lo, hi) to lineplot(lo, hi, f). Update the docstring examples\nin activations.jl accordingly.\n\nFixes #623\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Update lineplot syntax in activations.jl docs for UnicodePlots 3.5+ (…",
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"body": "Mark the documented but non-exported functions (gather, gather!, scatter,\nscatter!, fold, unfold, glu, within_gradient) with the `public` keyword.\nParsed lazily via Meta.parse since `public` is only valid syntax on Julia 1.11+.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"oid": "1f2619de7519f7166ae50ecb963e0f733dc84c69",
"body": "`gather` had a multi-index method `gather(src, IJK...)` but the\nmutating `gather!` was missing the equivalent, causing a MethodError\nfor `gather!(dst, src, I, J, ...)`.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"body": "The previous definition `log1p(exp(-abs(x))) + relu(x)` has kinks from\n`abs`/`relu` at x=0, so AD (e.g. ForwardDiff for Hessians) computed a\nwrong gradient there (-0.5 instead of 0.5), breaking convexity of\nInput-Convex NNs at the origin.\n\nSwitch to the logaddexp formulation, branching on the sign of x so each\nbranch is smooth and analytic. Values and numerical stability are\nunchanged.\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Fix softplus to be smooth at the origin (#671) (#679)",
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"headline": "Bump version from 0.9.34 to 0.9.35",
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"body": "- Replace removed CUDA.adapt with Adapt.adapt in ctc.jl\n- Move all using/import statements to the module file\n\nCo-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"headline": "Fix CUDA.adapt and centralize imports in NNlibCUDAExt (#678)",
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"body": "Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 5 to 6.\n- [Release notes](https://github.com/codecov/codecov-action/releases)\n- [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/codecov/codecov-action/compare/v\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
"is_bot": true,
"headline": "Bump codecov/codecov-action from 5 to 6 (#674)",
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"body": "Bumps [julia-actions/cache](https://github.com/julia-actions/cache) from 2 to 3.\n- [Release notes](https://github.com/julia-actions/cache/releases)\n- [Commits](https://github.com/julia-actions/cache/compare/v2...v3)\n\n---\nupdated-dependencies:\n- dependency-name: julia-actions/cache\n dependency-versi\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
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"headline": "Bump julia-actions/cache from 2 to 3 (#673)",
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"body": "Bumps [julia-actions/setup-julia](https://github.com/julia-actions/setup-julia) from 2 to 3.\n- [Release notes](https://github.com/julia-actions/setup-julia/releases)\n- [Commits](https://github.com/julia-actions/setup-julia/compare/v2...v3)\n\n---\nupdated-dependencies:\n- dependency-name: julia-actions/\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
"is_bot": true,
"headline": "Bump julia-actions/setup-julia from 2 to 3 (#677)",
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"headline": "chore: bump version from 0.9.33 to 0.9.34",
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"body": "… compat) (#675)\n\nCo-authored-by: CompatHelper Julia <compathelper_noreply@julialang.org>",
"is_bot": true,
"headline": "CompatHelper: bump compat for CUDA in [weakdeps] to 6, (keep existing…",
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"is_bot": true,
"headline": "CompatHelper: bump compat for cuDNN in [weakdeps] to 6, (keep existin…",
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"headline": "Bump version from 0.9.32 to 0.9.33",
"author_name": "Carlo Lucibello",
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"body": "* fix: Zero case in gather\n\n* test: Test zero-sized array\n\n* fix: scatter! skip empty\n\n---------\n\nCo-authored-by: Carlo Lucibello <carlo.lucibello@gmail.com>",
"is_bot": false,
"headline": "fix: `gather` and `scatter` on empty arrays (#668)",
"author_name": "Leni Aniva",
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"headline": "revert 648 (#670)",
"author_name": "Carlo Lucibello",
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"body": "* Expanded dispatch of scatter! to include AbstractCuSparseArray\n\n* Fix incorrect import\n\n* restore formatting\n\n* Typos\n\n* Adding tests for scatter on CUSPARSE arrays",
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"headline": "Extending scatter! to work with CUDA sparse arrays (#648)",
"author_name": "Alonso Cisneros",
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"committed_at": "2025-12-21T09:03:00Z",
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"headline": "Add Metal queue specification for Buildkite agents (#667)",
"author_name": "Carlo Lucibello",
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"committed_at": "2025-12-21T09:00:04Z",
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"oid": "b50c226b8a9294bb49abe5b43efb937898ac2ce6",
"body": "* metal ext\n\n* tests\n\n* add test\n\n* don't test on nightly",
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"headline": "Metal Extension with fix for tanh_fast (#666)",
"author_name": "Carlo Lucibello",
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"oid": "f880a87c1e2714b575de8ac5c4303d51be44e90e",
"body": "* Initial plan\n\n* Implement generalized batched_vec for N-D batches\n\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\n\n* Update src/batched/batchedmul.jl\n\n* Update test/batchedmul.jl\n\n---------\n\nCo-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\nCo-authored-by: Carlo Lucibello <carlo.lucibello@gmail.com>",
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"headline": "Generalize batched_vec to support N-D batches (#652)",
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"oid": "b265a0a360c4ed3083aa35fd8e8e9650ab5eb4e2",
"body": "* pipeline\n\n* fix\n\n* fixlabel\n\n* fix 1.10\n\n* fix cdims\n\n* fix enzyme\n\n* fix",
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"headline": "fix CI (#665)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2025-12-06T09:44:25Z",
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"oid": "b35749e4a040235c6b47789601034d5a2159de8e",
"body": "* Initial plan\n\n* Implement gelu_sigmoid and update gelu_tanh to use tanh\n\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\n\n* Add gelu_sigmoid to documentation\n\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>\n\n---------\n\nCo-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>\nCo-authored-by: CarloLucibello <7014210+CarloLucibello@users.noreply.github.com>",
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"headline": "Refactor `gelu_tanh` to use `tanh` for compiler pattern matching (#653)",
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"committed_at": "2025-12-06T09:12:42Z",
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"oid": "2ef26dd6d49dc826c9da2b7051521168f6600407",
"body": null,
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"headline": "update min julia version (#661)",
"author_name": "Carlo Lucibello",
"author_login": "CarloLucibello",
"committed_at": "2025-12-02T18:00:39Z",
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"oid": "9841d3827f09989ad39988618ba67e2dedd756be",
"body": "Bumps [actions/checkout](https://github.com/actions/checkout) from 5 to 6.\n- [Release notes](https://github.com/actions/checkout/releases)\n- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)\n- [Commits](https://github.com/actions/checkout/compare/v5...v6)\n\n---\nupdated-dependenc\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
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"headline": "Bump actions/checkout from 5 to 6 (#663)",
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"body": "Bumps [tibdex/github-app-token](https://github.com/tibdex/github-app-token) from 1 to 2.\n- [Release notes](https://github.com/tibdex/github-app-token/releases)\n- [Commits](https://github.com/tibdex/github-app-token/compare/v1...v2)\n\n---\nupdated-dependencies:\n- dependency-name: tibdex/github-app-toke\n[…]\nirect:production\n update-type: version-update:semver-major\n...\n\nSigned-off-by: dependabot[bot] <support@github.com>\nCo-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>",
"is_bot": true,
"headline": "Bump tibdex/github-app-token from 1 to 2 (#664)",
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