Improving unigram tokenizer CPU performance

A performance-focused reimplementation of the XLM-RoBERTa Unigram tokenizer substantially reduced CPU-side inference overhead by replacing allocation-heavy and pointer-chasing components in Hugging Face’s Rust tokenizer with a specialized zero-allocation Viterbi encoder. Unigram tokenization selects the highest-scoring segmentation through dynamic programming, but profiling showed the reference implementation spent significant time creating strings, performing hash-map lookups, allocating memory for each token match, and traversing heap-scattered trie nodes. The new implementation first eliminated redundant allocations and stored token IDs and scores directly in trie nodes, then replaced the hash-map trie with a cache-friendly double-array trie, added bitmap-based child validation and cache-line-aligned node packing, and used 2 MB huge pages to reduce translation lookaside buffer overhead. At production-scale inputs, the resulting encoder achieved roughly 5× lower median latency than Hugging Face tokenizers, about 2× faster performance than native SentencePiece, and about 1.5× faster than IREE, while reducing instructions per encode from 3.66 million to 1.04 million and preserving token-exact output. In deployment, these improvements cut preprocessing CPU utilization by 5–6× and removed double-digit milliseconds from reranker latency, illustrating that tokenizer design, memory allocation, cache locality, and wrapper overhead can materially affect performance for small-model inference workloads.

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