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The full Weka corpus has 338M hash ID occurrences but only 5.7M distinct values when counted per trace. Reusing one int object per distinct value within a trace cuts preparation memory; values, ordering and serialization are unchanged. Signed-off-by: Robert Washbourne <rawashbourne@gmail.com>
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Try out this PRQuick install: pip install --upgrade --force-reinstall git+https://github.com/ai-dynamo/aiperf.git@d95c1f2768d9a704729e417ea1ae6f42e9221e44Recommended with virtual environment (using uv): uv venv --python 3.12 && source .venv/bin/activate
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| @model_validator(mode="after") | ||
| def _share_hash_id_values(self) -> Self: | ||
| """Reuse immutable IDs within their trace-local namespace.""" |
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This validator adds an internal Weka loading and memory-management mechanism, but the commit does not update any documentation. For internal-only mechanics, the repository requires documentation under docs/reference/ rather than skipping it, so this change remains incomplete until that reference (and its docs/index.yml entry if it is new) is added.
AGENTS.md reference: AGENTS.md:L164-L164
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The full Weka corpus has 338M hash ID occurrences but only 5.7M distinct values when counted per trace. Reusing one int object per distinct value within a trace cuts preparation memory. Values, ordering and serialization are unchanged; adds tests.