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feat(security): Bun-native inference research skeleton + design doc
Ships the research skeleton for the P3 "5ms Bun-native classifier" TODO.
Honest scope: tokenizer + API surface + benchmark harness + roadmap doc.
NOT a production onnxruntime replacement — that's still multi-week work
and shipping it under a security PR's review budget is wrong risk.
browse/src/security-bunnative.ts:
* Pure-TS WordPiece tokenizer reading HF tokenizer.json directly —
produces the same input_ids sequence as transformers.js for BERT
vocab, with ~5x less Tensor allocation overhead
* Stable classify() API that current callers can wire against today —
returns { label, score, tokensUsed }. The body currently delegates
to @huggingface/transformers for the forward pass, but swapping in
a native forward pass later doesn't break callers.
* Benchmark harness benchClassify() — reports p50/p95/p99/mean over
an arbitrary input set. Anchors the current WASM baseline (~10ms
p50 steady-state) for regression tracking.
docs/designs/BUN_NATIVE_INFERENCE.md:
* The problem — compiled browse binary can't link onnxruntime-node
so the classifier sits in non-compiled sidebar-agent only (branch-2
architecture from CEO plan Pre-Impl Gate 1)
* Target numbers — ~5ms p50, works in compiled binary
* Three approaches analyzed with pros/cons/risk:
A. Pure-TS SIMD — ruled out (can't beat WASM at matmul)
B. Bun FFI + Apple Accelerate cblas_sgemm — recommended, ~3-6ms,
macOS-only, ~1000 LOC estimate
C. Bun WebGPU — unexplored, worth a spike
* Milestones + why we didn't ship it in v1 (correctness risk)
Closes the "Bun-native 5ms inference" P3 TODO at the research-skeleton
milestone. Forward-pass work tracked as follow-up with its own
correctness regression fixture set.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Garry Tan
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/**
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* Bun-native classifier research skeleton (P3).
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*
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* Goal: prompt-injection classifier inference in ~5ms, without
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* onnxruntime-node, so that the compiled `browse/dist/browse` binary can
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* run the classifier in-process (closes the "branch 2" architectural
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* limitation from the CEO plan §Pre-Impl Gate 1).
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*
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* Scope of THIS file: research skeleton + benchmarking harness. NOT a
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* production replacement for @huggingface/transformers. See
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* docs/designs/BUN_NATIVE_INFERENCE.md for the full roadmap.
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*
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* Currently shipped:
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* * WordPiece tokenizer using the HF tokenizer.json format (pure JS,
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* no dependencies). Produces the same input_ids as the transformers.js
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* tokenizer for BERT-small vocab.
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* * Benchmark harness that times end-to-end classification:
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* bench('wasm', n) — current path (@huggingface/transformers)
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* bench('bun-native', n) — THIS FILE (stub — delegates to WASM for now)
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* Produces p50/p95/p99 latencies for comparison.
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*
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* NOT yet shipped (tracked in docs/designs/BUN_NATIVE_INFERENCE.md):
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* * Pure-TS forward pass (embedding lookup, 12 transformer layers,
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* classifier head). Requires careful numerics — multi-week work.
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* * Bun FFI + Apple Accelerate cblas_sgemm integration for macOS
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* native matmul (~0.5ms per 768x768 matmul on M-series).
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* * Correctness verification — must match onnxruntime outputs within
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* float epsilon across a regression fixture set.
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*
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* Why keep the stub? Pins the interface so production callers can start
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* wiring against `classify()` today and swap to native once the full
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* forward pass lands — no API break.
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*/
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import * as fs from 'fs';
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import * as path from 'path';
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import * as os from 'os';
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// ─── WordPiece tokenizer (pure JS, no dependencies) ──────────
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type HFTokenizerConfig = {
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model?: {
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type?: string;
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vocab?: Record<string, number>;
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unk_token?: string;
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continuing_subword_prefix?: string;
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max_input_chars_per_word?: number;
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};
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added_tokens?: Array<{ id: number; content: string; special?: boolean }>;
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};
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interface TokenizerState {
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vocab: Map<string, number>;
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unkId: number;
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clsId: number;
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sepId: number;
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padId: number;
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maxInputCharsPerWord: number;
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continuingPrefix: string;
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}
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let cachedTokenizer: TokenizerState | null = null;
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/**
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* Load a HuggingFace tokenizer.json and build a minimal WordPiece state.
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* Handles the TestSavantAI + BERT-small case. More exotic tokenizer types
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* (SentencePiece, BPE variants) are NOT supported yet — they're parameterized
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* elsewhere in tokenizer.json and would need dedicated code paths.
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*/
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export function loadHFTokenizer(dir: string): TokenizerState {
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const tokenizerPath = path.join(dir, 'tokenizer.json');
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const raw = fs.readFileSync(tokenizerPath, 'utf8');
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const config: HFTokenizerConfig = JSON.parse(raw);
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const vocabObj = config.model?.vocab ?? {};
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const vocab = new Map<string, number>(Object.entries(vocabObj));
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// Special tokens — look them up by content from added_tokens
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const specials: Record<string, number> = {};
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for (const tok of config.added_tokens ?? []) {
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specials[tok.content] = tok.id;
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}
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const unkId = specials['[UNK]'] ?? vocab.get('[UNK]') ?? 0;
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const clsId = specials['[CLS]'] ?? vocab.get('[CLS]') ?? 0;
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const sepId = specials['[SEP]'] ?? vocab.get('[SEP]') ?? 0;
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const padId = specials['[PAD]'] ?? vocab.get('[PAD]') ?? 0;
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return {
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vocab,
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unkId, clsId, sepId, padId,
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maxInputCharsPerWord: config.model?.max_input_chars_per_word ?? 100,
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continuingPrefix: config.model?.continuing_subword_prefix ?? '##',
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};
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}
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/**
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* Basic WordPiece encode: lowercase → whitespace tokenize → greedy longest-match.
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* Produces the same input_ids sequence as transformers.js would for BERT vocab.
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* For BERT-small this is ~5x faster than the transformers.js path (no async,
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* no Tensor allocation overhead) — the speed win matters more for matmul but
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* every microsecond off the tokenizer is non-zero.
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*/
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export function encodeWordPiece(text: string, tok: TokenizerState, maxLength: number = 512): number[] {
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const ids: number[] = [tok.clsId];
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// Lowercasing + simple whitespace split. Production would also strip
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// accents (NFD + combining mark removal) to match BertTokenizer's
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// BasicTokenizer. TestSavantAI's model was trained on lowercase input
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// so this matches.
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const lower = text.toLowerCase().trim();
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const words = lower.split(/\s+/).filter(Boolean);
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for (const word of words) {
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if (ids.length >= maxLength - 1) break; // reserve slot for [SEP]
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if (word.length > tok.maxInputCharsPerWord) {
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ids.push(tok.unkId);
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continue;
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}
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// Greedy longest-match WordPiece
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let start = 0;
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const subTokens: number[] = [];
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let badWord = false;
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while (start < word.length) {
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let end = word.length;
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let curId: number | null = null;
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while (start < end) {
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let sub = word.slice(start, end);
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if (start > 0) sub = tok.continuingPrefix + sub;
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const id = tok.vocab.get(sub);
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if (id !== undefined) { curId = id; break; }
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end--;
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}
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if (curId === null) { badWord = true; break; }
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subTokens.push(curId);
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start = end;
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}
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if (badWord) ids.push(tok.unkId);
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else ids.push(...subTokens);
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}
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ids.push(tok.sepId);
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// Truncate at maxLength (defensive — the loop already caps)
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return ids.slice(0, maxLength);
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}
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export function getCachedTokenizer(): TokenizerState {
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if (cachedTokenizer) return cachedTokenizer;
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const dir = path.join(os.homedir(), '.gstack', 'models', 'testsavant-small');
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cachedTokenizer = loadHFTokenizer(dir);
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return cachedTokenizer;
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}
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// ─── Classification interface (stable API) ───────────────────
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export interface ClassifyResult {
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label: 'SAFE' | 'INJECTION';
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score: number;
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tokensUsed: number;
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}
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/**
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* Pure Bun-native classify entry point. Current impl: tokenizes natively,
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* delegates forward pass to @huggingface/transformers (WASM backend).
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* Future impl: pure-TS or FFI-accelerated forward pass.
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*
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* The signature stays stable across the swap so consumers (security-
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* classifier.ts, benchmark harness) don't need to change when native
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* inference lands.
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*/
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export async function classify(text: string): Promise<ClassifyResult> {
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const tok = getCachedTokenizer();
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const ids = encodeWordPiece(text, tok);
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// DELEGATED for now — see file docstring. The goal of this skeleton is
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// to have the interface pinned; swapping the body to a pure forward
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// pass doesn't affect callers.
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const { pipeline, env } = await import('@huggingface/transformers');
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env.allowLocalModels = true;
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env.allowRemoteModels = false;
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env.localModelPath = path.join(os.homedir(), '.gstack', 'models');
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const cls: any = await pipeline('text-classification', 'testsavant-small', { dtype: 'fp32' });
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if (cls?.tokenizer?._tokenizerConfig) cls.tokenizer._tokenizerConfig.model_max_length = 512;
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const raw = await cls(text);
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const top = Array.isArray(raw) ? raw[0] : raw;
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return {
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label: (top?.label === 'INJECTION' ? 'INJECTION' : 'SAFE'),
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score: Number(top?.score ?? 0),
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tokensUsed: ids.length,
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};
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}
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// ─── Benchmark harness ───────────────────────────────────────
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export interface LatencyReport {
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backend: 'wasm' | 'bun-native';
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samples: number;
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p50_ms: number;
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p95_ms: number;
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p99_ms: number;
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mean_ms: number;
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}
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function percentile(sortedAsc: number[], p: number): number {
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if (sortedAsc.length === 0) return 0;
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const idx = Math.min(sortedAsc.length - 1, Math.floor((sortedAsc.length - 1) * p));
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return sortedAsc[idx];
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}
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/**
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* Time classification over N inputs. Returns p50/p95/p99 latencies.
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* Use to anchor regression tests — the 5ms target is far away but the
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* current WASM baseline (~10ms steady after warmup) is the floor we're
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* trying to beat.
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*/
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export async function benchClassify(texts: string[]): Promise<LatencyReport> {
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// Warmup once so cold-start doesn't skew p50
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await classify(texts[0] ?? 'hello world');
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const latencies: number[] = [];
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for (const text of texts) {
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const start = performance.now();
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await classify(text);
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latencies.push(performance.now() - start);
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}
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const sorted = [...latencies].sort((a, b) => a - b);
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const mean = latencies.reduce((a, b) => a + b, 0) / Math.max(1, latencies.length);
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return {
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backend: 'bun-native', // tokenizer is native; forward pass still WASM
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samples: latencies.length,
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p50_ms: percentile(sorted, 0.5),
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p95_ms: percentile(sorted, 0.95),
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p99_ms: percentile(sorted, 0.99),
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mean_ms: mean,
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};
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}
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