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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24943
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to_native takes metadata, the package's counterpart to a PTE's constant methods, and save writes it as a metadata.json member: names mapped to a bool, number, string, or flat list of those. Package validates the member at load and reads integers and integer lists by name; exir.native's read_metadata reads it back in Python. LLM exports can then publish facts the graph does not carry, such as the per-layer sliding windows of an off-graph KV cache, under the names extension/llm/export/model_metadata.py already defines.
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Off-graph KV cache for the native ggml engine, through serving Muse Glimmer on it. Review bottom-up; each PR builds and passes its tests on its own.
Summary
A
.ptnhad no way to carry facts the graph doesn't, such as an LLM's per-layer sliding windows, which a PTE publishes as constant methods. This adds the counterpart:to_native(programs, metadata={...})saves ametadata.jsonmember: names mapped to a bool, number, string, or a flat list of those.write_ptnvalidates it and rejects non-finite floats.exir.native.read_metadata(path)reads it back.Packagevalidates the member's structure at load and reads values by name withmetadata_int/metadata_ints, which reject integers outsideint64.backends/native/README.mddocuments the member. LLM exports reuse the constant-method names fromextension/llm/export/model_metadata.py(get_windows, ...).Test plan
exir/native/test/test_native.py: metadata round-trips, no member without metadata, invalid names and values rejected.test_package.cpp: reads by name, rejects wrong types and malformed members. Run through a local CMake build; that file is only built by Buck.Authored with Claude Code.