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Repeated literal operator arguments allocate separate EValue slots, and integer lists also duplicate persistent boxed-list storage. Pool read-only integer literals and lists made entirely from literal integers within each execution plan, using the argument schema to exclude out and aliased values. Preserve existing abstract values, method inputs and outputs, symbolic values, and opaque delegate arguments. Construct references with their final pooled IDs during emission so no serialization-time remapping is needed. Add tests for type distinctions, signed 64-bit limits, list references, mutation and aliasing boundaries, plan scope, and repeated control flow. Update the keyword-argument test to follow instruction argument IDs. Validation: 78 emitter tests and 14 subtests passed, with two existing skips. All four trained MLPerf Tiny models produced exact before/after outputs on 67,029 Cortex-M55 FVP validation inputs. Operator calls and planned memory were unchanged, and independent re-exports were byte-identical. DS-CNN persistent method metadata decreased from 8,008 to 5,056 measured bytes. Authored with OpenAI Codex.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22915
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September 17, 2026 22:36
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Summary
Repeated literal operator arguments allocate separate EValue slots, and integer lists also duplicate persistent boxed-list storage. Pool read-only integer literals and lists made entirely from literal integers within each execution plan, using the argument schema to exclude out and aliased values.
Preserve existing abstract values, method inputs and outputs, symbolic values, and opaque delegate arguments. Construct references with their final pooled IDs during emission so no serialization-time remapping is needed.
Add tests for type distinctions, signed 64-bit limits, list references, mutation and aliasing boundaries, plan scope, and repeated control flow. Update the keyword-argument test to follow instruction argument IDs.
Test plan
Validation: 78 emitter tests and 14 subtests passed, with two existing skips. All four trained MLPerf Tiny models produced exact before/after outputs on 67,029 Cortex-M55 FVP validation inputs. Operator calls and planned memory were unchanged, and independent re-exports were byte-identical. DS-CNN persistent method metadata decreased from 8,008 to 5,056 measured bytes.
Authored with OpenAI Codex.