Gahow Wang
e1e75fc7f6
Phase 6 — Model Loading (xserv-model): - safetensors parser with single/sharded file support - ModelConfig with dual naming (GPT-2 n_embd/n_head + modern HF naming) - Weight loading flow: safetensors → mmap → CPU Tensor → GPU Phase 7 — BPE Tokenizer (xserv-tokenizer): - Full BPE encode/decode from tokenizer.json - GPT-2 byte-to-unicode mapping (printable ASCII identity + shifted bytes) - Pre-tokenization regex, special token handling - Chat template support structure Phase 8 — GPT-2 Complete Inference: - GPT-2 model definition: wte, wpe, 12 transformer blocks, ln_f - Forward pass: embedding → (LayerNorm → MHA → residual → LayerNorm → MLP → residual) × 12 → LN → logits - QKV split with correct [batch, heads, seq, dim] layout (fixed reshape bug) - Greedy sampling from last-position logits - Interactive CLI: xserv-cli <model-dir> [--max-tokens N] Verified: GPT-2 124M generates coherent English text on RTX 5090. "The future of AI is uncertain. The future of AI is uncertain..." "Once upon a time, the world was a place of great beauty..." Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
58 lines
2.3 KiB
Markdown
58 lines
2.3 KiB
Markdown
# Phase 7: BPE Tokenizer — Design Document
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## Goal
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从零实现 Byte-Pair Encoding tokenizer，兼容 HuggingFace `tokenizer.json` 格式。支持 GPT-2 和 Qwen3。
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## Crate: `xserv-tokenizer`
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```
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crates/xserv-tokenizer/src/
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├── lib.rs
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├── bpe.rs # BPE encode/decode core algorithm
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└── chat.rs # Chat template formatting
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```
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## Dependencies
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- `serde` + `serde_json`: parse tokenizer.json
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- `regex`: pre-tokenization patterns
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## BPE Algorithm
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### Encode
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1. Pre-tokenize: split text by regex (GPT-2 pattern)
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2. Each word → byte sequence → initial token list (one token per byte)
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3. Repeatedly merge highest-priority pair until no more merges
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4. Map merged tokens to IDs via vocab
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### Decode
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Token IDs → lookup vocab → concatenate bytes → UTF-8 decode
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## Key Data Structures
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```rust
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pub struct Tokenizer {
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vocab: HashMap<Vec<u8>, u32>, // token bytes → ID
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vocab_rev: Vec<Vec<u8>>, // ID → token bytes
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merges: Vec<(Vec<u8>, Vec<u8>)>, // ordered merge rules
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merge_ranks: HashMap<(u32, u32), usize>, // (id_a, id_b) → priority
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special_tokens: HashMap<String, u32>,
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pre_tokenize_regex: Regex,
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}
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```
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## Test Plan
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- [x] Encode + decode roundtrip verified (GPT-2 tokenizer, English text)
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- [x] Special tokens handled (endoftext)
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- [x] Integrated into GPT-2 inference pipeline, generates coherent text
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## Takeaways
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1. **GPT-2 byte-to-unicode 映射**：GPT-2 的 vocab 中，每个 byte 都映射到一个 Unicode 字符。可打印 ASCII (0x21-0x7E) 映射到自身，其余字节（空格、控制字符等）映射到 U+0100 以上的 Unicode 码点。解码时需要反向映射。这个映射表是 BPE tokenizer 正确性的关键。
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2. **Rust regex 不支持 lookahead**：GPT-2 的 pre-tokenization regex 使用了 `(?!\S)` lookahead，Rust 的 `regex` crate 不支持。简化为去掉 lookahead 后功能等价（whitespace 仍然被正确分词）。如果需要精确匹配 Python 行为，需要 `fancy-regex` crate。
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3. **BPE merge 的 O(n²) 复杂度**：当前实现每次 merge 扫描整个 token 序列找最高优先级 pair，复杂度 O(n² × |merges|)。对于短文本够用，长文本需要 priority queue 优化。推理场景中 prompt 通常 < 10K tokens，暂时可接受。
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