perf: build merge candidates with itertools.chain#37
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UnconnectedGraphs.get_merges yielded candidates with a Python-level `for sg: yield from sg.get_merges()` loop, run every training step to feed the Counter. itertools.chain.from_iterable does the same flattening in C. Same candidates, same order — output identical. ~2-3% faster across BPE/BNE/Boundless; memory unchanged. 137 tests pass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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UnconnectedGraphs.get_mergesis the source the trainer'sCounterconsumes every training step. It flattened subgraph candidates with a Python-level loop:itertools.chain.from_iterabledoes the same flattening in C:Same candidates, same order → output identical (137 tests pass).
Measured back-to-back (50 texts / 200 merges; best of 3):
Memory unchanged.
🤖 Generated with Claude Code