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[executorch][native] Record op packages with bundled golden and precise samples - #24532
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…se samples Pull Request resolved: #24357 Engines need op-level reference data that doesn't depend on any engine or device. `op_samples` writes one .ptn per op and quantization scheme, bundling `golden` (eager) and `precise` (float64, weights dequantized, activations unquantized) samples over the same inputs. Linear is the first op. Schemes are float, int4wo g32/g128 and 8da4w g32/g128, each with f32, f16 and bf16 methods; int8 weight-only is left out because no native engine runs it. Linear cases are the backend test suite's models, reused through a stand-in runner, plus decode, prefill, large-K, bias-free leading-batch, outlier-channel and zero-row cases. With CMake, the `native_op_samples` target writes them; it is not in ALL, so a plain build never needs torch. Ops and schemes are independent axes. An op is an `Op` in `ops()`, a function returning `Case`s plus the dtypes to record them in; a scheme is a `Scheme` in `schemes()` that decides which cases it fits, quantizes a model and decodes its weights. `generate` records every op under every scheme, one spawned process per package, and writes no package when a scheme fits none of an op's cases. `cases.py` holds the case type and the backend test suite adapter, `schemes.py` the quantization, and `record.py` the packaging, which knows neither torchao nor any op. Authored with Claude Code. @bypass-github-export-checks @bypass-github-pytorch-ci-checks @bypass-github-executorch-ci-checks ghstack-source-id: 463657757 @exported-using-ghexport Differential Revision: [D128254877](https://our.internmc.facebook.com/intern/diff/D128254877/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24532
Note: Links to docs will display an error until the docs builds have been completed. ❌ 3 New FailuresAs of commit 3502615 with merge base 0f8e096 ( NEW FAILURES - The following jobs have failed:
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #24357 by @digantdesai
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/digantdesai/184/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/184/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/digantdesai/183/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/digantdesai/184/orig
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