torch-performance-optimization

Optimize or review eager CPU-only Albucore PyTorch runtime paths with benchmark-backed decisions. Use when adding or changing Torch CPU kernels, Tensor/NumPy bridges, Torch backend routing, tensor layouts, allocations, threading, profiling, memory-format candidates, or Torch performance benchmarks.

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Repositório: albumentations-team/albucore
Última atividade na origem: 5 de agosto de 2026 às 08:27
Idioma detectado do SKILL.md: inglês
Estrelas: 123
Forks: 11

Opções de instalação

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npx skills add https://github.com/albumentations-team/albucore --skill torch-performance-optimization

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SKILL.md

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albucore-conventions

Albucore image processing conventions - shapes (H,W,C), dtypes (uint8/float32), benchmark-driven backend routing (OpenCV, NumPy, Torch CPU, LUT, NumKong), tests, and lockfile discipline. Use when implementing or modifying albucore modules, writing tests, or reviewing image-processing code.

2026-08-27

albucore-benchmarks

Running Albucore micro-benchmarks under benchmarks/, synthetic router timings, and comparing PyPI releases with uv --no-project. Use when adding benchmarks, comparing performance across versions, or documenting benchmark workflow.

2026-08-05

performance-optimization

Systematic performance audit for Albucore runtime code. Use whenever implementing, reviewing, profiling, or optimizing atomic image operations, backend routing, reductions, label maps, LUTs, random generation, dtype conversions, allocation-heavy paths, batch or volume kernels, or in-place behavior.

2026-08-05

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Torch Performance Optimization

Read docs/torch-performance-optimization.md completely before inspecting or editing a Torch path. Also read ../performance-optimization/SKILL.md, its canonical performance guide, and ../albucore-benchmarks/SKILL.md completely.

Workflow

Establish a correctness baseline and benchmark the existing public path. State container, layout, dtype, shape, parameter, stride, thread, and allocation contracts.

Audit unnecessary Python work, full-volume passes, conversions, materializations, and output repairs before choosing a Torch operator.

Profile only to discover candidates. Benchmark every viable implementation end to end, including NumPy↔Torch bridges and layout conversion.

Compare NumPy, OpenCV, NumKong, StringZilla, and Torch where they share semantics. Treat fused operators, layout, channels_last_3d, and in-place reuse as hypotheses with their own correctness and performance matrices.

Select a route or threshold only from stable public-path evidence. Preserve rejected candidates in the benchmark report when they clarify a boundary.

Add correctness tests for each accepted route and update the benchmark evidence, public contract, and canonical guide when a reusable rule or limitation is discovered.

Albucore Contract

Torch is a required dependency. Assume it is already imported for benchmarks.

Current public Tensor routes are eager CPU paths that do not record autograd inside the primitive and do not use torch.compile. Do not add device routes, graph-preserving primitive fallbacks, compilation, or their benchmark candidates.

Tensor layouts are explicit and independent of NumPy layouts. Do not infer NCHW versus CDHW from shape sizes.

Caller-prevalidated 3D routers leave validation outside the hot path. Do not add it back while optimizing.

A Tensor route must preserve the documented container, layout, border, interpolation, rounding, mutation, and aliasing behavior. Its dtype contract must distinguish supported dtype preservation from any explicit fallback conversion.

Required Handoff

Report the full-path baseline, selected route, removed work, allocation and copy changes, all viable backends and Torch-specific candidates considered, thread and benchmark matrix, correctness/memory evidence, regressions, rejected regions, and remaining follow-ups.