Implement a trie-based tokenizer
Overview
This question evaluates a candidate's competency in designing and implementing a production-grade subword tokenizer, covering trie-based longest-prefix matching, Unicode-aware text processing, normalization and casing impacts, whitespace/punctuation rules, fallback strategies, performance and memory trade-offs, versioning, and testing for edge cases. It is commonly asked in ML System Design interviews to assess practical implementation skills and conceptual trade-off reasoning for deterministic, scalable LLM preprocessing, and tests both practical application and conceptual understanding within the ML System Design domain.
Design and Implement a Trie-Based Subword Tokenizer for LLM Pretraining
Context
You are building a subword tokenizer for a large-scale LLM pretraining pipeline. The tokenizer must be deterministic, fast, memory-efficient, Unicode-safe, and production-ready.
Requirements
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API
- build(vocab, config) -> Tokenizer
- tokenize(text) -> List[int] (token IDs)
- detokenize(ids) -> str (text)
- Optional: encode(text) -> List[str] (pieces), decode(pieces) -> str
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Core Algorithm
- Implement greedy longest-prefix matching using a prefix trie over a fixed vocabulary of subword strings.
- Match over Unicode code points (not bytes) to correctly handle multi-byte UTF-8, emojis, and CJK.
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Unicode Handling
- Correctly process multi-byte UTF-8, emojis (including ZWJ sequences and variation selectors), CJK, RTL scripts, combining marks.
- Specify whether matching operates on bytes, code points, or grapheme clusters (and why).
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Normalization and Casing
- Define normalization choices (e.g., NFKC). Discuss reversibility impact.
- Define casing policy (e.g., preserve case vs. lowercasing). Call out implications for detokenization fidelity.
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Whitespace and Punctuation
- Define rules (e.g., SentencePiece-style "▁" for spaces or GPT-style leading-space tokens).
- Clarify how multiple spaces, tabs, newlines, and punctuation are tokenized.
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Unknown-Token Fallback
- Provide a robust fallback when no subword matches at a position.
- Options: dedicated <unk> vs. byte-level fallback to ensure total coverage and reversibility.
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Complexity Analysis
- Time and space complexity for build, tokenize, detokenize.
- Practical memory estimates and throughput considerations.
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Incremental Vocabulary Updates
- How to add tokens incrementally without breaking determinism.
- Versioning, hot-swap strategies, and implications for previously tokenized data.
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Testing Strategy
- Propose comprehensive tests for tricky inputs: emojis (ZWJ/skin tones), CJK without spaces, combining marks, RTL, invalid UTF-8, whitespace runs, URLs, code, etc.
- Comparison to Alternatives
- Compare this trie-based approach to BPE/WordPiece/Unigram.
- Discuss trade-offs for throughput and memory in production.
Solution
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