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[executorch][native] Record embedding op samples - #25023
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/25023
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Stack from ghstack (oldest at bottom):
Embedding becomes the second op in the op samples, with a case module like linear's: the backend test suite's embedding models, LLM vocabulary and token shapes, leading batch dims, a scalar index, the first and last rows, int32 indices,
padding_idx, one table per GGUF super-scale family, and dynamic token and batch dims. It fits the float, int4 weight-only and GGUF schemes.drawsandchaintake asampler, standard normal by default, so a case can draw integer indices.Parallelmoves tocases.pyfor both ops, and its modules are nowbranches, which renames the constants of linear'sscale_familiespackages; their values are unchanged. Only weight-only packages with annn.Linearin a model that fits the scheme getgolden-int8_act, because without one it would repeatgolden;Scheme.adds_int8_actdecides this from the modulesint8_activationsquantizes.The native test workflow (
test-native.yml) and the CMake sample rule now also depend on the backend test suite'stest_embedding.py, so a change there rebuilds and retests the samples, astest_linear.pyalready does.A new ggml-CPU sample test,
ggml_sample_test_no_linearin Buck andnative_ggml_sample_test_no_linearin CTest and the OSS ggml job, judges every op but linear without activation quantization, so CI also covers embedding on plain ggml-CPU. Linear keeps only the act_quant test. Until the next diff, both CPU CI targets exclude the 22 int4 weight-only f32 embedding tests that ggml fails.Authored with Claude Code.
Differential Revision: D130869556
NOTE FOR REVIEWERS: This PR has internal Meta-specific changes or comments, please review them on Phabricator!