From 8f7b6ee53800ad6695ba8a9cc68a7418eb594d27 Mon Sep 17 00:00:00 2001 From: altair823 Date: Mon, 1 Jun 2026 14:52:25 +0000 Subject: [PATCH] =?UTF-8?q?feat(embed):=20candle=20=EC=9E=84=EB=B2=A0?= =?UTF-8?q?=EB=94=A9=20provider=20(NUMA-=EC=95=88=EC=A0=84,=20opt-in)=20+?= =?UTF-8?q?=20v0.22.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit duo-socket NUMA 서버에서 fastembed(onnxruntime)가 intra-op 스레드를 48개로 하드코딩해 NUMA 힙 손상 → double-free 로 ingest 가 죽는 문제를 회피하기 위해, 같은 multilingual-e5-large 모델을 순수 Rust(candle)로 돌리는 opt-in 임베딩 provider 를 추가한다. - 신규 crate kebab-embed-candle: CandleEmbedder (kebab_core::Embedder). hf-hub safetensors → XLMRobertaModel forward → mask mean-pool → L2 → e5 prefix. candle 의존성 트리를 이 crate 에 격리 (core/config 외 kebab-* 의존 0). - 스레드 캡: [models.embedding].num_threads + env KEBAB_EMBED_THREADS → 글로벌 rayon 풀 1회 캡 (NUMA-안전 레버). - kebab-app::embedder() 가 provider 분기 (fastembed/onnx/"" → 기존 경로 불변, candle → CandleEmbedder, 미지값 → 에러). - Phase 0 스파이크 crate 제거 (production 흡수). - 버전 0.21.1 → 0.22.0 (신규 config surface, pre-1.0 minor bump). 패리티: cosine_min=1.000000, max abs diff=2.01e-7 (< 1e-5) → embedding_version 유지, 재색인 0. fastembed default 동작/벡터 불변. wire schema 변경 없음. 검증(파일+exit code): clippy -D warnings EXIT=0(warning 0), test EXIT=0 (candle unit 5 + thread_cap rayon=4 + config 68), parity #[ignore] EXIT=0. Co-Authored-By: Claude Opus 4.8 (1M context) --- Cargo.lock | 81 ++-- Cargo.toml | 5 +- HANDOFF.md | 1 + IMPL_REPORT.md | 85 ++++ README.md | 8 + crates/kebab-app/Cargo.toml | 1 + crates/kebab-app/src/app.rs | 24 +- crates/kebab-config/src/lib.rs | 16 + crates/kebab-embed-candle/Cargo.toml | 39 ++ crates/kebab-embed-candle/src/lib.rs | 363 ++++++++++++++++++ crates/kebab-embed-candle/tests/parity.rs | 88 +++++ crates/kebab-embed-candle/tests/thread_cap.rs | 32 ++ crates/spike-embed-candle/Cargo.toml | 32 -- crates/spike-embed-candle/src/main.rs | 251 ------------ docs/ARCHITECTURE.md | 7 +- docs/SMOKE.md | 4 +- docs/release-notes/v0.22.0-draft.md | 72 ++++ tasks/HOTFIXES.md | 46 +++ 18 files changed, 825 insertions(+), 330 deletions(-) create mode 100644 IMPL_REPORT.md create mode 100644 crates/kebab-embed-candle/Cargo.toml create mode 100644 crates/kebab-embed-candle/src/lib.rs create mode 100644 crates/kebab-embed-candle/tests/parity.rs create mode 100644 crates/kebab-embed-candle/tests/thread_cap.rs delete mode 100644 crates/spike-embed-candle/Cargo.toml delete mode 100644 crates/spike-embed-candle/src/main.rs create mode 100644 docs/release-notes/v0.22.0-draft.md diff --git a/Cargo.lock b/Cargo.lock index 94edd70..f7b4da6 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -4530,7 +4530,7 @@ dependencies = [ [[package]] name = "kebab-app" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "base64 0.22.1", @@ -4543,6 +4543,7 @@ dependencies = [ "kebab-config", "kebab-core", "kebab-embed", + "kebab-embed-candle", "kebab-embed-local", "kebab-llm", "kebab-llm-local", @@ -4576,7 +4577,7 @@ dependencies = [ [[package]] name = "kebab-chunk" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4594,7 +4595,7 @@ dependencies = [ [[package]] name = "kebab-cli" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "clap", @@ -4615,7 +4616,7 @@ dependencies = [ [[package]] name = "kebab-config" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "dirs 5.0.1", @@ -4631,7 +4632,7 @@ dependencies = [ [[package]] name = "kebab-core" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4645,7 +4646,7 @@ dependencies = [ [[package]] name = "kebab-embed" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4657,9 +4658,28 @@ dependencies = [ "tracing", ] +[[package]] +name = "kebab-embed-candle" +version = "0.22.0" +dependencies = [ + "anyhow", + "candle-core", + "candle-nn", + "candle-transformers", + "hf-hub", + "kebab-config", + "kebab-core", + "kebab-embed-local", + "rayon", + "serde_json", + "tempfile", + "tokenizers 0.21.4", + "tracing", +] + [[package]] name = "kebab-embed-local" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "fastembed", @@ -4672,7 +4692,7 @@ dependencies = [ [[package]] name = "kebab-eval" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "kebab-app", @@ -4691,7 +4711,7 @@ dependencies = [ [[package]] name = "kebab-llm" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "kebab-core", @@ -4700,7 +4720,7 @@ dependencies = [ [[package]] name = "kebab-llm-local" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "kebab-config", @@ -4717,7 +4737,7 @@ dependencies = [ [[package]] name = "kebab-mcp" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "kebab-app", @@ -4735,7 +4755,7 @@ dependencies = [ [[package]] name = "kebab-nli" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "hf-hub", @@ -4750,7 +4770,7 @@ dependencies = [ [[package]] name = "kebab-parse-code" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "gix", @@ -4773,7 +4793,7 @@ dependencies = [ [[package]] name = "kebab-parse-image" -version = "0.21.1" +version = "0.22.0" dependencies = [ "ab_glyph", "anyhow", @@ -4797,7 +4817,7 @@ dependencies = [ [[package]] name = "kebab-parse-md" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "kebab-core", @@ -4814,7 +4834,7 @@ dependencies = [ [[package]] name = "kebab-parse-pdf" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4829,7 +4849,7 @@ dependencies = [ [[package]] name = "kebab-rag" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4851,7 +4871,7 @@ dependencies = [ [[package]] name = "kebab-search" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "globset", @@ -4870,7 +4890,7 @@ dependencies = [ [[package]] name = "kebab-source-fs" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4888,7 +4908,7 @@ dependencies = [ [[package]] name = "kebab-store-sqlite" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "blake3", @@ -4908,7 +4928,7 @@ dependencies = [ [[package]] name = "kebab-store-vector" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "arrow", @@ -4932,7 +4952,7 @@ dependencies = [ [[package]] name = "kebab-tui" -version = "0.21.1" +version = "0.22.0" dependencies = [ "anyhow", "crossterm", @@ -8397,23 +8417,6 @@ dependencies = [ "smallvec", ] -[[package]] -name = "spike-embed-candle" -version = "0.0.0" -dependencies = [ - "anyhow", - "candle-core", - "candle-nn", - "candle-transformers", - "hf-hub", - "kebab-config", - "kebab-embed", - "kebab-embed-local", - "rayon", - "serde_json", - "tokenizers 0.21.4", -] - [[package]] name = "spm_precompiled" version = "0.1.4" diff --git a/Cargo.toml b/Cargo.toml index 4789a65..d968f73 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -11,6 +11,7 @@ members = [ "crates/kebab-search", "crates/kebab-embed", "crates/kebab-embed-local", + "crates/kebab-embed-candle", "crates/kebab-llm", "crates/kebab-llm-local", "crates/kebab-rag", @@ -23,8 +24,6 @@ members = [ "crates/kebab-mcp", "crates/kebab-parse-code", "crates/kebab-nli", - # Track 1 / Phase 0 feasibility spike (throwaway; candle deps isolated here). - "crates/spike-embed-candle", ] [workspace.package] @@ -32,7 +31,7 @@ edition = "2024" rust-version = "1.85" license = "MIT OR Apache-2.0" repository = "https://github.com/altair823/kebab" -version = "0.21.1" # v0.21.1 — config 마이그레이션(kebab config migrate): 기존 config.toml 에 빠진 섹션 주석과 함께 추가 + deprecated 정리 + schema_version 1→2 — CLAUDE.md §Release 도그푸딩 트리거 +version = "0.22.0" # v0.22.0 — candle 임베딩 provider (NUMA-안전, opt-in `provider=candle` + `num_threads`/KEBAB_EMBED_THREADS). fastembed default 불변, embedding_version 유지(재색인 0). — CLAUDE.md §Release 도그푸딩 트리거 # pre-v0.18 workspace-wide cleanup: enable clippy::pedantic group with # intentional allow-list. The allowed lints are either cosmetic (doc style), diff --git a/HANDOFF.md b/HANDOFF.md index a34853f..0e7882f 100644 --- a/HANDOFF.md +++ b/HANDOFF.md @@ -30,6 +30,7 @@ P0~P5 직렬. P6~P9 P5 이후 병렬 가능. ## 머지 후 발견된 버그 / 결정 (요약) +- **candle 임베딩 백엔드 다변화** (2026-06-01, Track 1, v0.22.0): `provider = "candle"` opt-in 추가 — 같은 `multilingual-e5-large` 모델을 순수 Rust(candle)로 돌려 듀얼소켓 NUMA 서버의 onnxruntime 48-스레드 double-free 를 회피. `[models.embedding].num_threads`(+env `KEBAB_EMBED_THREADS`)로 CPU 스레드 캡. fastembed default 동작·벡터 불변, `embedding_version` 유지(재색인 0). Phase 0 스파이크 패리티 cosine 1.000000. 상세 HOTFIXES 동일 일자. - **config 마이그레이션** (2026-05-31, PR #198): `kebab config migrate` 추가 — 기존 config.toml 에 빠진 섹션을 주석과 함께 채우고 deprecated 정리(멱등·`.bak`·dry-run, 값/주석 보존). `schema_version` 1→2, `init` 도 섹션 주석 포함, doctor 에 `config_migration` 체크. 상세 HOTFIXES 동일 일자. 머지 후 발견된 모든 deviation / hotfix 의 dated 로그는 [tasks/HOTFIXES.md](tasks/HOTFIXES.md). 본 요약은 \"누군가가 인수받을 때 알아두면 시간을 많이 절약하는\" 항목만: diff --git a/IMPL_REPORT.md b/IMPL_REPORT.md new file mode 100644 index 0000000..bd272e6 --- /dev/null +++ b/IMPL_REPORT.md @@ -0,0 +1,85 @@ +# Track 1 / Phase 1 — candle 임베딩 provider 구현 보고서 + +- 날짜: 2026-06-01 +- 브랜치: `feat/embed-candle` (worktree `/build/out/kebab-worktrees/embed-candle`) +- 스펙: `docs/superpowers/specs/2026-06-01-embed-candle-track-spec.md` +- 버전: 0.21.1 → **0.22.0** + +## 1. 변경 요약 + +| 영역 | 변경 | +|------|------| +| 신규 crate | `crates/kebab-embed-candle` — `CandleEmbedder` (`kebab_core::Embedder` impl). 스파이크 파이프라인 흡수: safetensors via hf-hub → `XLMRobertaModel` forward(`Device::Cpu`) → attention-mask mean pooling → L2 → e5 prefix(`passage:`/`query:`). 모델 캐시 `{model_dir}/candle/`. deps = candle-core/nn/transformers 0.10.2, tokenizers, hf-hub, serde_json, rayon, anyhow, tracing + `kebab-core`/`kebab-config` 만 (design §8 경계 준수). | +| 스레드 캡 | `[models.embedding].num_threads: u32`(default 0=auto) + env `KEBAB_EMBED_THREADS`(우선). `apply_thread_cap()` 가 글로벌 rayon 풀 1회 캡 (이미 init 시 no-op). | +| 주입 분기 | `kebab-app::App::embedder()` 가 `provider` 분기 — `fastembed`/`onnx`/`""` → 기존 `FastembedEmbedder`(불변), `candle` → `CandleEmbedder`, 미지값 → 에러. `none` 은 기존 lexical-only. `kebab-app/Cargo.toml` 에 dep 추가. | +| config | `EmbeddingModelCfg.num_threads`(`#[serde(default)]` — 옛 config 호환) + `KEBAB_MODELS_EMBEDDING_NUM_THREADS` env + `Config::defaults()`. | +| 스파이크 제거 | `crates/spike-embed-candle` 삭제 + 워크스페이스 멤버 제거 + `spike_build.log`/`spike_run.log` 정리. | +| 문서/버전 | README Configuration, `docs/SMOKE.md` config 예시, `docs/ARCHITECTURE.md`(crate 그래프+트리), HANDOFF 한 줄, `tasks/HOTFIXES.md` 2026-06-01 dated entry, workspace `version` 0.22.0, `docs/release-notes/v0.22.0-draft.md`. | + +## 2. 검증 게이트 결과 (모두 파일 출력 + exit code 로 검증) + +> ⚠️ 주의: background shell 의 notification "exit 0" 은 wrapper 의 종료코드라 +> 신뢰 불가. 실제 결과는 각 로그의 `*_EXIT=` 라인 값으로 확정했다 +> ([[project_rerank_experiment]] 교훈). 실제로 첫 빌드는 wrapper 가 exit 0 을 +> 보고했지만 로그의 `BUILD_EXIT=101`(serde_json 미선언)이었고, dep 추가 후 통과. + +| 게이트 | 명령 | 결과 | 로그 | +|--------|------|------|------| +| 빌드 + clippy | `cargo clippy --workspace --all-targets -j 4 -- -D warnings` | **`CLIPPY_EXIT=0`**, warning 0 | `clippy.log` | +| 단위/통합 테스트 | `cargo test -p kebab-embed-candle -p kebab-config -j 4` | **`TEST_EXIT=0`** — candle lib unit 5, `thread_cap` 1 passed(rayon current=4 검증), config 68 passed, parity 1 ignored | `test_units.log` | +| config 회귀 | (위 동일 run, `kebab-config` 68 tests) | 0 failed | `test_units.log` | +| 패리티 `#[ignore]` 수동 1회 | `cargo test -p kebab-embed-candle --release -- --ignored --nocapture` | **`PARITY_EXIT=0`**, 1 passed (32.53s) | `test_parity.log` | + +## 3. 패리티 수치 (재색인 결정 근거 — 스펙 D-reindex) + +10 문장(한/영 혼합) candle vs `FastembedEmbedder`(onnxruntime): + +``` +PARITY_SUMMARY cosine_min=1.000000 max_abs_diff=2.011657e-7 +``` + +- 코사인 최소 **1.000000** (≥ 0.9999 게이트 통과). +- 차원별 **max 절대오차 = 2.01e-7** — 스펙이 정한 "사실상 동일" 기준 + (max abs diff < 1e-5) 보다 **약 50배 작다**. +- **결론: `embedding_version` 유지 = 재색인 0.** candle 과 onnxruntime 의 + 벡터는 f32 반올림 수준에서만 다르며 (e-7), 기존 LanceDB 색인을 그대로 + 재사용해도 검색 결과가 바뀌지 않는다. version bump / cascade 트리거 안 함. + +## 4. 잔여 리스크 + +- **CPU latency**: candle 는 순수 Rust 라 onnxruntime 의 네이티브 커널보다 + 느리다 (Phase 0 스파이크 ~4×). 그래서 default 는 fastembed 유지, candle 은 + NUMA 환경 opt-in. 단일 워크스테이션 사용자에게는 권하지 않음 (README 명시). +- **모델 다운로드**: candle 은 `{model_dir}/candle/` 에 safetensors(~2GB)를 + 별도 캐시 (onnx 캐시와 공유 안 함). 첫 ingest 시 ~2GB 다운로드 발생. +- **잔여 게이트 (사용자 실행, Claude 불가, meta-spec §4.3)**: 그 듀얼소켓 + NUMA 서버에서 `provider=candle` 로 5150-doc ingest 가 double-free 없이 + EXIT=0 완주하는지 — 이 머신은 GPU/NUMA 없는 단일 VM 이라 재현 불가. PR + 머지 전/후 사용자 검증 예약. +- **골든 스위트 회귀 0 (스펙 §8)**: provider=candle 로 `kebab-eval` 골든 + 스위트 실행은 본 worktree 범위 밖(사용자 도그푸딩 단계). 패리티 e-7 로 + 벡터 동일성이 입증돼 회귀 위험은 낮음. + +## 5. 재현 명령 + +```bash +cd /build/out/kebab-worktrees/embed-candle +export CARGO_TARGET_DIR=/build/out/cargo-target/target + +# 빌드 + clippy (warning 0) +cargo clippy --workspace --all-targets -j 4 -- -D warnings + +# 단위 + config 회귀 +cargo test -p kebab-embed-candle -p kebab-config -j 4 + +# 패리티 (모델 ~2GB 다운로드 + 양쪽 추론, /build/dogfood/config.toml 필요) +cargo test -p kebab-embed-candle --release -j 4 -- --ignored --nocapture +# → PARITY_SUMMARY cosine_min=1.000000 max_abs_diff=2.011657e-7 + +# candle provider 로 ingest (사용자 NUMA 검증) +KEBAB_EMBED_THREADS=8 kebab ingest --config /path/to/candle-config.toml +``` + +## 6. 커밋 + +`feat/embed-candle` 에 커밋 완료. push / PR 은 메인 세션이 처리 (본 worker 는 하지 않음). diff --git a/README.md b/README.md index 0f115cb..704928a 100644 --- a/README.md +++ b/README.md @@ -97,9 +97,17 @@ root = "~/KnowledgeBase" # 색인할 폴더. 절대 / tilde / env / 상대 경 # 상대 경로의 base 는 config.toml 위치 (cwd 무관). [models.embedding] +provider = "fastembed" # "fastembed"(기본, onnxruntime) / "candle"(순수 Rust) + # / "none"(lexical-only). candle 는 같은 모델·같은 벡터를 + # 순수 Rust 로 돌려 NUMA 서버의 onnxruntime 48-스레드 + # double-free 를 피하는 opt-in 백엔드 (재색인 불필요). model = "multilingual-e5-large" # 다국어 sentence embedding (1024-dim). # 첫 ingest 시 ONNX (~1.3GB) 자동 다운로드. + # candle provider 는 safetensors (~2GB) 다운로드. dimensions = 1024 # config 와 LanceDB stored dim 불일치 시 검색 0건. +num_threads = 0 # candle 전용 CPU 스레드 캡 (0=auto=#cores). + # env KEBAB_EMBED_THREADS 가 우선. NUMA 노드 바인딩은 + # numactl 과 조합. fastembed provider 는 무시. [models.llm] endpoint = "http://localhost:11434" # Ollama host:port diff --git a/crates/kebab-app/Cargo.toml b/crates/kebab-app/Cargo.toml index 6c2d637..c80e1d7 100644 --- a/crates/kebab-app/Cargo.toml +++ b/crates/kebab-app/Cargo.toml @@ -18,6 +18,7 @@ kebab-store-vector = { path = "../kebab-store-vector" } kebab-search = { path = "../kebab-search" } kebab-embed = { path = "../kebab-embed" } kebab-embed-local = { path = "../kebab-embed-local" } +kebab-embed-candle = { path = "../kebab-embed-candle" } kebab-llm = { path = "../kebab-llm" } kebab-llm-local = { path = "../kebab-llm-local" } kebab-rag = { path = "../kebab-rag" } diff --git a/crates/kebab-app/src/app.rs b/crates/kebab-app/src/app.rs index a47a80c..7860f70 100644 --- a/crates/kebab-app/src/app.rs +++ b/crates/kebab-app/src/app.rs @@ -43,6 +43,7 @@ use kebab_core::{ Answer, DocumentStore, Embedder, ExtractContext, Extractor, IndexVersion, LanguageModel, MediaType, Retriever, SearchHit, SearchMode, SearchOpts, SearchQuery, VectorStore, }; +use kebab_embed_candle::CandleEmbedder; use kebab_embed_local::FastembedEmbedder; use kebab_llm_local::OllamaLanguageModel; use kebab_parse_code::{ @@ -833,9 +834,26 @@ impl App { if let Some(e) = self.embedder.get() { return Ok(Some(e.clone())); } - let emb: Arc = Arc::new( - FastembedEmbedder::new(&self.config).context("kb-app: load FastembedEmbedder")?, - ); + // Provider branch (Track 1 spec §3). `embeddings_disabled()` above + // already handled `"none"`; here we route the live providers. + // `fastembed`/`onnx`/(empty) keep the default onnxruntime path + // (vectors unchanged — `embedding_version` is preserved); `candle` + // selects the pure-Rust NUMA-safe backend. + let provider = self.config.models.embedding.provider.as_str(); + let emb: Arc = match provider { + "fastembed" | "onnx" | "" => Arc::new( + FastembedEmbedder::new(&self.config).context("kb-app: load FastembedEmbedder")?, + ), + "candle" => Arc::new( + CandleEmbedder::new(&self.config).context("kb-app: load CandleEmbedder")?, + ), + other => { + return Err(anyhow!( + "kb-app: unknown embedding provider {other:?}; expected one of \ + `fastembed` (default), `candle`, or `none` (lexical-only)" + )); + } + }; // `set` returns Err if another thread won the race; in that case // the loser still returns the (now-cached) winner via `get()`. let _ = self.embedder.set(emb.clone()); diff --git a/crates/kebab-config/src/lib.rs b/crates/kebab-config/src/lib.rs index 8e66bca..e2d18fe 100644 --- a/crates/kebab-config/src/lib.rs +++ b/crates/kebab-config/src/lib.rs @@ -155,11 +155,21 @@ impl NliCfg { #[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] pub struct EmbeddingModelCfg { + /// `fastembed` (default, onnxruntime) or `candle` (pure-Rust, + /// NUMA-safe). `none` disables embeddings (lexical-only). Unknown + /// values error at embedder construction. pub provider: String, pub model: String, pub version: String, pub dimensions: usize, pub batch_size: usize, + /// Cap on the CPU worker threads the `candle` provider spins up + /// (sizes the global rayon pool; env `KEBAB_EMBED_THREADS` overrides). + /// `0` = auto (rayon default = #cores). Lever to sidestep the + /// onnxruntime 48-thread NUMA double-free; ignored by the `fastembed` + /// provider. Defaulted on load so pre-0.22 config files still parse. + #[serde(default)] + pub num_threads: u32, } #[derive(Clone, Debug, PartialEq, Serialize, Deserialize)] @@ -707,6 +717,7 @@ impl Config { version: "v1".to_string(), dimensions: 1024, batch_size: 64, + num_threads: 0, }, llm: LlmCfg { provider: "ollama".to_string(), @@ -964,6 +975,11 @@ impl Config { self.models.embedding.batch_size = n; } } + "KEBAB_MODELS_EMBEDDING_NUM_THREADS" => { + if let Ok(n) = v.parse::() { + self.models.embedding.num_threads = n; + } + } // models.llm "KEBAB_MODELS_LLM_PROVIDER" => self.models.llm.provider = v.clone(), diff --git a/crates/kebab-embed-candle/Cargo.toml b/crates/kebab-embed-candle/Cargo.toml new file mode 100644 index 0000000..079d624 --- /dev/null +++ b/crates/kebab-embed-candle/Cargo.toml @@ -0,0 +1,39 @@ +[package] +name = "kebab-embed-candle" +version = { workspace = true } +edition = { workspace = true } +rust-version = { workspace = true } +license = { workspace = true } +repository = { workspace = true } +description = "Pure-Rust candle adapter implementing kb_core::Embedder (multilingual-e5-large, NUMA-safe thread cap)" + +[dependencies] +kebab-core = { path = "../kebab-core" } +kebab-config = { path = "../kebab-config" } +# candle stack — pinned to the workspace-locked crates.io release (0.10.x), +# same versions the Phase 0 spike compiled so build artifacts are reused. +candle-core = "0.10.2" +candle-nn = "0.10.2" +candle-transformers = "0.10.2" +tokenizers = "0.21" +hf-hub = { version = "0.4", features = ["ureq"] } +serde_json = { workspace = true } +# Thread cap: a one-shot global rayon pool sizes candle's CPU threads +# (the Phase 0 spike proved RAYON_NUM_THREADS caps candle), so a NUMA host +# can keep onnxruntime's hard-coded 48-intra-op heap corruption at bay. +rayon = "1" +anyhow = { workspace = true } +tracing = { workspace = true } + +[dev-dependencies] +# Integration-test binaries can only see the library's public API + these, +# not the library's own (non-dev) dependencies — so rayon/kebab-config/kebab-core +# are repeated here for tests/parity.rs and tests/thread_cap.rs. +kebab-embed-local = { path = "../kebab-embed-local" } +kebab-config = { path = "../kebab-config" } +kebab-core = { path = "../kebab-core" } +rayon = "1" +tempfile = { workspace = true } + +[lints] +workspace = true diff --git a/crates/kebab-embed-candle/src/lib.rs b/crates/kebab-embed-candle/src/lib.rs new file mode 100644 index 0000000..5adee8b --- /dev/null +++ b/crates/kebab-embed-candle/src/lib.rs @@ -0,0 +1,363 @@ +//! `kebab-embed-candle` — [`CandleEmbedder`], a pure-Rust (candle) +//! implementation of [`Embedder`](kebab_core::Embedder). +//! +//! Runs the same `intfloat/multilingual-e5-large` model as the default +//! [`FastembedEmbedder`](kebab_embed_local) but through `candle` +//! (`candle-transformers`' XLM-RoBERTa) instead of onnxruntime. Motivation: +//! fastembed 4.9's onnxruntime hard-codes 48 intra-op threads, which corrupts +//! the heap (double-free) on dual-socket NUMA hosts. candle's CPU backend +//! sizes its threads off the global rayon pool, so a one-shot +//! [`rayon::ThreadPoolBuilder`] cap (config `num_threads` / env +//! `KEBAB_EMBED_THREADS`) keeps the worker count NUMA-safe. +//! +//! Output parity with the onnxruntime path was proven by the Phase 0 spike +//! (cosine 1.000000); this crate absorbs that pipeline verbatim: +//! +//! 1. e5 prefix (`passage: ` for documents, `query: ` for queries — the same +//! convention as `kebab-embed-local`'s `prefix_input`); +//! 2. tokenize (max_len 512, batch-longest padding, special tokens); +//! 3. XLM-RoBERTa forward on `Device::Cpu`; +//! 4. attention-mask-weighted mean pooling; +//! 5. L2 normalization. +//! +//! Model files (`config.json`, `tokenizer.json`, `model.safetensors`) are +//! fetched via `hf-hub` into `{config.storage.model_dir}/candle/`. +//! +//! This crate is **opt-in** (`config.models.embedding.provider = "candle"`); +//! the default provider stays `fastembed`. See +//! `docs/superpowers/specs/2026-06-01-embed-candle-track-spec.md`. + +use std::sync::Mutex; + +use anyhow::{Context, Result}; +use candle_core::{DType, Device, Tensor}; +use candle_nn::VarBuilder; +use candle_transformers::models::xlm_roberta::{Config as XlmConfig, XLMRobertaModel}; +use kebab_config::{Config, expand_path}; +use kebab_core::{Embedder, EmbeddingInput, EmbeddingKind, EmbeddingModelId, EmbeddingVersion}; +use tokenizers::{PaddingParams, PaddingStrategy, Tokenizer, TruncationParams}; + +/// Subdirectory under `config.storage.model_dir` where the candle adapter +/// caches safetensors + tokenizer. Mirrors `kebab-embed-local`'s +/// `fastembed/` subdir so the two backends never collide. +const CANDLE_CACHE_SUBDIR: &str = "candle"; + +/// HuggingFace repo id for the multilingual e5 large model. Same weights the +/// onnxruntime path uses, just the safetensors variant candle can read. +const HF_MODEL: &str = "intfloat/multilingual-e5-large"; + +/// Token truncation length (e5 was trained at 512). +const MAX_LEN: usize = 512; + +/// Env var that overrides `config.models.embedding.num_threads`. Read once in +/// [`CandleEmbedder::new`]; `0`/unset/unparseable means "leave rayon default". +const ENV_EMBED_THREADS: &str = "KEBAB_EMBED_THREADS"; + +/// Pure-Rust candle adapter. Construct via [`CandleEmbedder::new`]; the +/// constructor downloads the model on first use, so share one instance. +pub struct CandleEmbedder { + // candle's `forward` is `&self`, but `XLMRobertaModel` is not guaranteed + // `Sync`; the `Mutex` both supplies that bound and serializes inference + // (callers batch sequentially anyway — same rationale as + // `FastembedEmbedder`). + model: Mutex, + tokenizer: Tokenizer, + device: Device, + model_id: EmbeddingModelId, + version: EmbeddingVersion, + dimensions: usize, + batch_size: usize, +} + +impl CandleEmbedder { + /// Build an embedder from `Config`. Applies the NUMA thread cap, fetches + /// the model into `{model_dir}/candle/`, and validates that the model's + /// hidden size matches `config.models.embedding.dimensions` before + /// returning. + pub fn new(config: &Config) -> Result { + // 1. NUMA thread cap. env `KEBAB_EMBED_THREADS` wins over the config + // field; `0`/unset leaves rayon's default. `build_global` errors if + // the pool was already initialized — intentionally ignored so a + // second embedder (or a prior rayon user) is a no-op, not a failure. + let n_threads = std::env::var(ENV_EMBED_THREADS) + .ok() + .and_then(|v| v.parse::().ok()) + .unwrap_or(config.models.embedding.num_threads as usize); + if n_threads > 0 { + if apply_thread_cap(n_threads) { + tracing::info!( + target: "kebab-embed-candle", + num_threads = n_threads, + "capped global rayon pool for candle CPU backend" + ); + } else { + tracing::debug!( + target: "kebab-embed-candle", + requested = n_threads, + "global rayon pool already initialized; thread cap not applied" + ); + } + } + + // 2. Resolve `{data_dir}/models/candle/` exactly like the fastembed + // adapter resolves its own subdir. + let data_dir = expand_path(&config.storage.data_dir, ""); + let model_dir = expand_path(&config.storage.model_dir, &data_dir.to_string_lossy()); + let cache_dir = model_dir.join(CANDLE_CACHE_SUBDIR); + std::fs::create_dir_all(&cache_dir) + .with_context(|| format!("create candle cache dir {}", cache_dir.display()))?; + + let device = Device::Cpu; + + // 3. Fetch model files via hf-hub into the candle cache. + tracing::info!( + target: "kebab-embed-candle", + cache_dir = %cache_dir.display(), + model = HF_MODEL, + "loading candle embedding model (first run downloads ~2GB safetensors)" + ); + let api = hf_hub::api::sync::ApiBuilder::new() + .with_cache_dir(cache_dir.clone()) + .build() + .context("kb-embed-candle: build hf-hub api")?; + let repo = api.model(HF_MODEL.to_string()); + let config_path = repo.get("config.json").context("download config.json")?; + let tokenizer_path = repo + .get("tokenizer.json") + .context("download tokenizer.json")?; + let weights_path = repo + .get("model.safetensors") + .context("download model.safetensors")?; + + // 4. Build the candle XLM-RoBERTa model. + let cfg_json = std::fs::read_to_string(&config_path) + .with_context(|| format!("read {}", config_path.display()))?; + let cfg: XlmConfig = + serde_json::from_str(&cfg_json).context("kb-embed-candle: parse XLM-R config")?; + + // Validate dim BEFORE building the model so a misconfigured + // `dimensions` fails cheaply (matches FastembedEmbedder's contract). + check_dim(cfg.hidden_size, config.models.embedding.dimensions)?; + + let vb = unsafe { + VarBuilder::from_mmaped_safetensors(&[weights_path], DType::F32, &device) + .context("kb-embed-candle: mmap safetensors")? + }; + let model = + XLMRobertaModel::new(&cfg, vb).context("kb-embed-candle: build XLMRobertaModel")?; + + let mut tokenizer = Tokenizer::from_file(&tokenizer_path) + .map_err(|e| anyhow::anyhow!("kb-embed-candle: load tokenizer: {e}"))?; + tokenizer + .with_padding(Some(PaddingParams { + strategy: PaddingStrategy::BatchLongest, + ..Default::default() + })) + .with_truncation(Some(TruncationParams { + max_length: MAX_LEN, + ..Default::default() + })) + .map_err(|e| anyhow::anyhow!("kb-embed-candle: set truncation: {e}"))?; + + tracing::info!( + target: "kebab-embed-candle", + dimensions = cfg.hidden_size, + layers = cfg.num_hidden_layers, + "candle embedding model loaded" + ); + + Ok(Self { + model: Mutex::new(model), + tokenizer, + device, + model_id: EmbeddingModelId(config.models.embedding.model.clone()), + version: EmbeddingVersion(config.models.embedding.version.clone()), + dimensions: cfg.hidden_size, + batch_size: config.models.embedding.batch_size.max(1), + }) + } + + /// Embed one batch (already prefixed) through the candle pipeline: + /// tokenize → forward → masked mean pool → L2 normalize. + fn embed_batch(&self, prefixed: &[String]) -> Result>> { + let encodings = self + .tokenizer + .encode_batch(prefixed.to_vec(), true) + .map_err(|e| anyhow::anyhow!("kb-embed-candle: encode_batch: {e}"))?; + + let bsz = encodings.len(); + let seq = encodings[0].get_ids().len(); + + let mut ids = Vec::with_capacity(bsz * seq); + let mut mask = Vec::with_capacity(bsz * seq); + for enc in &encodings { + ids.extend(enc.get_ids().iter().copied()); + mask.extend(enc.get_attention_mask().iter().map(|&m| m as f32)); + } + + let input_ids = Tensor::from_vec(ids, (bsz, seq), &self.device)?; + let attn_f32 = Tensor::from_vec(mask, (bsz, seq), &self.device)?; + let token_type_ids = input_ids.zeros_like()?; + + let hidden = { + let guard = self + .model + .lock() + .unwrap_or_else(std::sync::PoisonError::into_inner); + // forward: (input_ids, attention_mask, token_type_ids, past, + // encoder_hidden, encoder_mask) + guard.forward(&input_ids, &attn_f32, &token_type_ids, None, None, None)? + }; + + // attention-mask-weighted mean pooling + let mask3 = attn_f32.unsqueeze(2)?; // (b, seq, 1) + let summed = hidden.broadcast_mul(&mask3)?.sum(1)?; // (b, hidden) + let counts = mask3.sum(1)?; // (b, 1) + let mean = summed.broadcast_div(&counts)?; + + // L2 normalize + let norm = mean.sqr()?.sum_keepdim(1)?.sqrt()?; + let normalized = mean.broadcast_div(&norm)?; + + Ok(normalized.to_vec2::()?) + } +} + +impl Embedder for CandleEmbedder { + fn model_id(&self) -> EmbeddingModelId { + self.model_id.clone() + } + + fn model_version(&self) -> EmbeddingVersion { + self.version.clone() + } + + fn dimensions(&self) -> usize { + self.dimensions + } + + fn embed(&self, inputs: &[EmbeddingInput<'_>]) -> Result>> { + if inputs.is_empty() { + return Ok(Vec::new()); + } + + // e5 prefix per §11.3 BEFORE tokenization (same convention as + // FastembedEmbedder so the two backends produce comparable vectors). + let prefixed: Vec = inputs.iter().map(prefix_input).collect(); + + let mut out: Vec> = Vec::with_capacity(prefixed.len()); + for chunk in prefixed.chunks(self.batch_size) { + let batch = self.embed_batch(chunk)?; + for v in &batch { + if v.len() != self.dimensions { + anyhow::bail!( + "candle returned vector of length {} but adapter expects {}", + v.len(), + self.dimensions + ); + } + } + out.extend(batch); + } + + debug_assert_eq!(out.len(), inputs.len()); + Ok(out) + } +} + +/// Build the e5-prefixed string for one [`EmbeddingInput`]. Free function so +/// a unit test can pin the format without loading the model. Byte-identical to +/// `kebab-embed-local`'s `prefix_input` — the two backends MUST agree here or +/// their vectors diverge. +fn prefix_input(input: &EmbeddingInput<'_>) -> String { + match input.kind { + EmbeddingKind::Document => format!("passage: {}", input.text), + EmbeddingKind::Query => format!("query: {}", input.text), + } +} + +/// Apply a one-shot global rayon thread cap (the NUMA-safety lever). Returns +/// `true` if this call set the pool, `false` if it was already initialized +/// (cap not applied) or `n_threads == 0`. `#[doc(hidden)] pub` so the +/// thread-cap test can drive it without loading the 2GB model. +#[doc(hidden)] +pub fn apply_thread_cap(n_threads: usize) -> bool { + if n_threads == 0 { + return false; + } + rayon::ThreadPoolBuilder::new() + .num_threads(n_threads) + .build_global() + .is_ok() +} + +/// Compare model hidden size against the configured dim. Extracted so a unit +/// test can exercise the error branch without loading the model. +pub(crate) fn check_dim(model_dim: usize, cfg_dim: usize) -> Result<()> { + if model_dim != cfg_dim { + anyhow::bail!( + "dimension mismatch: model={model_dim}, config={cfg_dim}; \ + update `config.models.embedding.dimensions` to match the model \ + (or pick a different model)." + ); + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::*; + + // ── prefix_input ───────────────────────────────────────────────── + // Pin the exact e5 prefix strings; these MUST match + // kebab-embed-local::prefix_input or candle vs fastembed parity breaks. + + #[test] + fn prefix_document_uses_passage() { + let input = EmbeddingInput { + text: "hello world", + kind: EmbeddingKind::Document, + }; + assert_eq!(prefix_input(&input), "passage: hello world"); + } + + #[test] + fn prefix_query_uses_query() { + let input = EmbeddingInput { + text: "hello world", + kind: EmbeddingKind::Query, + }; + assert_eq!(prefix_input(&input), "query: hello world"); + } + + #[test] + fn prefix_handles_empty_text() { + let doc = EmbeddingInput { + text: "", + kind: EmbeddingKind::Document, + }; + let qry = EmbeddingInput { + text: "", + kind: EmbeddingKind::Query, + }; + assert_eq!(prefix_input(&doc), "passage: "); + assert_eq!(prefix_input(&qry), "query: "); + } + + // ── check_dim ──────────────────────────────────────────────────── + + #[test] + fn check_dim_passes_for_1024() { + check_dim(1024, 1024).expect("matching dims must pass"); + } + + #[test] + fn check_dim_rejects_384_vs_1024() { + let err = check_dim(384, 1024).expect_err("dim mismatch must error"); + let msg = format!("{err}"); + assert!( + msg.contains("384") && msg.contains("1024"), + "error must mention both dims, got: {msg}" + ); + } +} diff --git a/crates/kebab-embed-candle/tests/parity.rs b/crates/kebab-embed-candle/tests/parity.rs new file mode 100644 index 0000000..8eeaeef --- /dev/null +++ b/crates/kebab-embed-candle/tests/parity.rs @@ -0,0 +1,88 @@ +//! Parity test (spec §7, `#[ignore]` — needs the ~2GB model + network). +//! +//! Confirms the candle backend reproduces the onnxruntime `FastembedEmbedder` +//! vectors closely enough that no re-index is required (spec D-reindex): +//! per-sentence cosine ≥ 0.9999, and reports the dimension-wise max absolute +//! difference (the number the re-index decision hangs on). +//! +//! Run manually: +//! CARGO_TARGET_DIR=/build/out/cargo-target/target \ +//! cargo test -p kebab-embed-candle --release -- --ignored --nocapture +//! +//! Uses the canonical dogfood config so both backends resolve the same model +//! identifiers and cache roots. + +use kebab_config::Config; +use kebab_core::{Embedder, EmbeddingInput, EmbeddingKind}; +use kebab_embed_candle::CandleEmbedder; +use kebab_embed_local::FastembedEmbedder; + +const DOGFOOD_CONFIG: &str = "/build/dogfood/config.toml"; + +/// Mixed Korean / English parity set (≥ 8 sentences, mirrors the Phase 0 spike). +const SENTENCES: &[&str] = &[ + "The quick brown fox jumps over the lazy dog.", + "오늘 날씨가 정말 좋아서 산책을 나가고 싶다.", + "Rust is a systems programming language focused on safety and performance.", + "벡터 검색은 임베딩 사이의 코사인 유사도를 이용한다.", + "Machine learning models require large amounts of training data.", + "한국어와 영어가 섞인 문장도 멀티링구얼 모델은 잘 처리한다.", + "The capital of France is Paris, a city known for its art and culture.", + "이 프로젝트는 로컬 우선 지식 베이스와 검색 증강 생성을 목표로 한다.", + "Database indexing dramatically speeds up query performance.", + "임베딩 모델을 candle 로 옮기면 NUMA 서버에서 안전하게 돌릴 수 있다.", +]; + +fn cosine(a: &[f32], b: &[f32]) -> f32 { + let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum(); + let na: f32 = a.iter().map(|x| x * x).sum::().sqrt(); + let nb: f32 = b.iter().map(|x| x * x).sum::().sqrt(); + dot / (na * nb) +} + +#[test] +#[ignore = "needs ~2GB model + network; run manually for the re-index decision"] +fn candle_matches_fastembed() { + let config = Config::load(Some(std::path::Path::new(DOGFOOD_CONFIG))) + .expect("load dogfood config for parity baseline"); + + let candle = CandleEmbedder::new(&config).expect("build CandleEmbedder"); + let fastembed = FastembedEmbedder::new(&config).expect("build FastembedEmbedder"); + + let inputs: Vec = SENTENCES + .iter() + .map(|s| EmbeddingInput { + text: s, + kind: EmbeddingKind::Document, + }) + .collect(); + + let cv = candle.embed(&inputs).expect("candle embed"); + let fv = fastembed.embed(&inputs).expect("fastembed embed"); + + assert_eq!(cv.len(), fv.len(), "embedding counts must match"); + assert_eq!(candle.dimensions(), 1024); + + let mut min_cos = f32::INFINITY; + let mut max_abs_diff = 0f32; + for (i, s) in SENTENCES.iter().enumerate() { + assert_eq!(cv[i].len(), 1024, "candle dim"); + assert_eq!(fv[i].len(), 1024, "fastembed dim"); + let c = cosine(&cv[i], &fv[i]); + min_cos = min_cos.min(c); + let diff = cv[i] + .iter() + .zip(&fv[i]) + .map(|(a, b)| (a - b).abs()) + .fold(0f32, f32::max); + max_abs_diff = max_abs_diff.max(diff); + let preview: String = s.chars().take(40).collect(); + println!("[{i:>2}] cos={c:.6} max_abs_diff={diff:.6e} {preview}"); + } + + println!("PARITY_SUMMARY cosine_min={min_cos:.6} max_abs_diff={max_abs_diff:.6e}"); + assert!( + min_cos >= 0.9999, + "candle vs fastembed cosine_min={min_cos:.6} < 0.9999 — investigate before merge" + ); +} diff --git a/crates/kebab-embed-candle/tests/thread_cap.rs b/crates/kebab-embed-candle/tests/thread_cap.rs new file mode 100644 index 0000000..7845721 --- /dev/null +++ b/crates/kebab-embed-candle/tests/thread_cap.rs @@ -0,0 +1,32 @@ +//! Thread-cap test (spec §7). Own integration binary → clean process, so the +//! one-shot global rayon pool is initialized exactly once, by us. +//! +//! Verifies that `apply_thread_cap(4)` sizes the global rayon pool to 4, which +//! is the lever that keeps candle's CPU backend NUMA-safe (vs onnxruntime's +//! hard-coded 48 intra-op threads). + +use kebab_embed_candle::apply_thread_cap; + +#[test] +fn thread_cap_sizes_global_rayon_pool() { + // Must run before any other rayon use in this process. As the only test in + // this binary that touches rayon, that holds. + let applied = apply_thread_cap(4); + assert!(applied, "first build_global call should succeed"); + assert_eq!( + rayon::current_num_threads(), + 4, + "global rayon pool must be capped at the requested 4 threads" + ); + + // A second cap attempt is a no-op (pool already built), not a panic. + assert!( + !apply_thread_cap(8), + "second build_global must report not-applied" + ); + assert_eq!( + rayon::current_num_threads(), + 4, + "thread count must stay at the first cap" + ); +} diff --git a/crates/spike-embed-candle/Cargo.toml b/crates/spike-embed-candle/Cargo.toml deleted file mode 100644 index 7057360..0000000 --- a/crates/spike-embed-candle/Cargo.toml +++ /dev/null @@ -1,32 +0,0 @@ -# Track 1 / Phase 0 feasibility SPIKE — NOT production. -# Isolated binary that loads multilingual-e5-large via candle (pure Rust) -# and compares its output against the existing onnxruntime FastembedEmbedder. -# candle deps live ONLY here so the production crates stay untouched. -[package] -name = "spike-embed-candle" -version = "0.0.0" -edition = "2024" -publish = false - -[[bin]] -name = "spike-embed-candle" -path = "src/main.rs" - -[dependencies] -anyhow = "1" -serde_json = "1" -# candle stack — pinned to the current crates.io release (0.10.2). -candle-core = "0.10.2" -candle-nn = "0.10.2" -candle-transformers = "0.10.2" -# Align with workspace-locked versions so we reuse compiled artifacts. -tokenizers = "0.21" -hf-hub = { version = "0.4", features = ["ureq"] } -rayon = "1" -# Parity baseline: reuse the real production embedder + its config loader. -kebab-config = { path = "../kebab-config" } -kebab-embed = { path = "../kebab-embed" } -kebab-embed-local = { path = "../kebab-embed-local" } - -# Keep the spike out of the workspace pedantic-lint gate; it is throwaway. -[lints] diff --git a/crates/spike-embed-candle/src/main.rs b/crates/spike-embed-candle/src/main.rs deleted file mode 100644 index 60d76bd..0000000 --- a/crates/spike-embed-candle/src/main.rs +++ /dev/null @@ -1,251 +0,0 @@ -//! Track 1 / Phase 0 feasibility SPIKE (NOT production code). -//! -//! Proves whether candle (pure Rust) can run `intfloat/multilingual-e5-large` -//! with output parity against the existing onnxruntime `FastembedEmbedder`, -//! so the NUMA double-free in fastembed 4.9.1 can be sidestepped. -//! -//! What it checks (see SPIKE_BRIEF.md): -//! 1. numeric parity — per-sentence cosine vs FastembedEmbedder -//! 2. padding_idx — XLM-R position ids start at pad_token_id+1 -//! 3. thread control — RAYON_NUM_THREADS caps candle's CPU threads -//! 4. CPU latency — batch wall-clock, rough vs onnxruntime -//! -//! Run: -//! CARGO_TARGET_DIR=/build/out/cargo-target/target \ -//! HF_HOME=/build/cache/huggingface \ -//! RAYON_NUM_THREADS=4 \ -//! cargo run -j 4 -p spike-embed-candle --release - -use std::path::PathBuf; -use std::time::Instant; - -use anyhow::{Context, Result}; -use candle_core::{DType, Device, Tensor}; -use candle_nn::VarBuilder; -use candle_transformers::models::xlm_roberta::{Config as XlmConfig, XLMRobertaModel}; -use tokenizers::{PaddingParams, PaddingStrategy, Tokenizer, TruncationParams}; - -use kebab_embed::{Embedder, EmbeddingInput, EmbeddingKind}; -use kebab_embed_local::FastembedEmbedder; - -const HF_MODEL: &str = "intfloat/multilingual-e5-large"; -const DOGFOOD_CONFIG: &str = "/build/dogfood/config.toml"; -const MAX_LEN: usize = 512; - -/// Mixed Korean / English parity set (≥ 8, brief §3). -const SENTENCES: &[&str] = &[ - "The quick brown fox jumps over the lazy dog.", - "오늘 날씨가 정말 좋아서 산책을 나가고 싶다.", - "Rust is a systems programming language focused on safety and performance.", - "벡터 검색은 임베딩 사이의 코사인 유사도를 이용한다.", - "Machine learning models require large amounts of training data.", - "한국어와 영어가 섞인 문장도 멀티링구얼 모델은 잘 처리한다.", - "The capital of France is Paris, a city known for its art and culture.", - "이 프로젝트는 로컬 우선 지식 베이스와 검색 증강 생성을 목표로 한다.", - "Database indexing dramatically speeds up query performance.", - "임베딩 모델을 candle 로 옮기면 NUMA 서버에서 안전하게 돌릴 수 있다.", -]; - -fn main() -> Result<()> { - // Touch the rayon global pool early so RAYON_NUM_THREADS is honored and - // reportable before any candle compute spins it up. - let rayon_threads = rayon::current_num_threads(); - let avail = std::thread::available_parallelism() - .map(|n| n.get()) - .unwrap_or(0); - let rayon_env = std::env::var("RAYON_NUM_THREADS").unwrap_or_else(|_| "".into()); - - println!("== spike-embed-candle =="); - println!("available_parallelism = {avail}"); - println!("RAYON_NUM_THREADS env = {rayon_env}"); - println!("rayon::current_num_threads() = {rayon_threads}"); - - let device = Device::Cpu; - - // ── 1. Fetch model files (candle reads safetensors, not the ONNX cache) ── - let cache_dir = std::env::var("HF_HOME") - .map(PathBuf::from) - .unwrap_or_else(|_| PathBuf::from("/build/cache/huggingface")); - let api = hf_hub::api::sync::ApiBuilder::new() - .with_cache_dir(cache_dir.clone()) - .build() - .context("build hf-hub api")?; - let repo = api.model(HF_MODEL.to_string()); - println!("\n[load] fetching {HF_MODEL} into {} ...", cache_dir.display()); - let config_path = repo.get("config.json").context("download config.json")?; - let tokenizer_path = repo.get("tokenizer.json").context("download tokenizer.json")?; - let weights_path = repo - .get("model.safetensors") - .context("download model.safetensors")?; - println!("[load] config = {}", config_path.display()); - println!("[load] tokenizer = {}", tokenizer_path.display()); - println!("[load] weights = {}", weights_path.display()); - - // ── 2. Build the candle XLM-RoBERTa model ── - let cfg_json = std::fs::read_to_string(&config_path)?; - let cfg: XlmConfig = serde_json::from_str(&cfg_json).context("parse XLM-R config")?; - println!( - "[load] config: hidden={} layers={} heads={} pad_token_id={} max_pos={} pos_emb={}", - cfg.hidden_size, - cfg.num_hidden_layers, - cfg.num_attention_heads, - cfg.pad_token_id, - cfg.max_position_embeddings, - cfg.position_embedding_type, - ); - let vb = unsafe { - VarBuilder::from_mmaped_safetensors(&[weights_path], DType::F32, &device) - .context("mmap safetensors")? - }; - let model = XLMRobertaModel::new(&cfg, vb).context("build XLMRobertaModel")?; - - let mut tokenizer = Tokenizer::from_file(&tokenizer_path) - .map_err(|e| anyhow::anyhow!("load tokenizer: {e}"))?; - tokenizer - .with_padding(Some(PaddingParams { - strategy: PaddingStrategy::BatchLongest, - ..Default::default() - })) - .with_truncation(Some(TruncationParams { - max_length: MAX_LEN, - ..Default::default() - })) - .map_err(|e| anyhow::anyhow!("set truncation: {e}"))?; - - let pad_id = cfg.pad_token_id; - - // ── 3. candle embedding path (passage prefix, masked mean pool, L2) ── - let candle_vecs = candle_embed(&model, &tokenizer, &device, pad_id, SENTENCES)?; - println!("\n[candle] embedded {} sentences, dim={}", candle_vecs.len(), candle_vecs[0].len()); - // L2 norm sanity (should be ~1.0 after normalization) - let norm0 = l2(&candle_vecs[0]); - println!("[candle] ‖v0‖ = {norm0:.6}"); - - // ── 4. FastembedEmbedder (onnxruntime) baseline ── - println!("\n[fastembed] loading FastembedEmbedder from {DOGFOOD_CONFIG} ..."); - let config = kebab_config::Config::load(Some(std::path::Path::new(DOGFOOD_CONFIG))) - .context("load dogfood config")?; - let fb_t0 = Instant::now(); - let fb = FastembedEmbedder::new(&config).context("build FastembedEmbedder")?; - println!("[fastembed] model loaded in {:.2}s", fb_t0.elapsed().as_secs_f64()); - let fb_inputs: Vec = SENTENCES - .iter() - .map(|s| EmbeddingInput { text: s, kind: EmbeddingKind::Document }) - .collect(); - let fb_vecs = fb.embed(&fb_inputs).context("fastembed embed")?; - - // ── 5. Per-sentence parity (both L2-normalized → cosine = dot) ── - println!("\n== PARITY (candle vs fastembed, EmbeddingKind::Document / passage:) =="); - let mut cosines = Vec::with_capacity(SENTENCES.len()); - for (i, s) in SENTENCES.iter().enumerate() { - let c = cosine(&candle_vecs[i], &fb_vecs[i]); - cosines.push(c); - let preview: String = s.chars().take(40).collect(); - println!(" [{i:>2}] cos={c:.6} {preview}"); - } - let min = cosines.iter().cloned().fold(f32::INFINITY, f32::min); - let mean = cosines.iter().sum::() / cosines.len() as f32; - println!(" --> cosine min={min:.6} mean={mean:.6}"); - - // ── 6. Latency: batch of 32 (replicated) through candle ── - let batch: Vec<&str> = SENTENCES.iter().cloned().cycle().take(32).collect(); - // warmup - let _ = candle_embed(&model, &tokenizer, &device, pad_id, &batch[..4])?; - let t0 = Instant::now(); - let _ = candle_embed(&model, &tokenizer, &device, pad_id, &batch)?; - let candle_lat = t0.elapsed(); - - let fb_batch: Vec = batch - .iter() - .map(|s| EmbeddingInput { text: s, kind: EmbeddingKind::Document }) - .collect(); - let t1 = Instant::now(); - let _ = fb.embed(&fb_batch)?; - let fb_lat = t1.elapsed(); - - let peak_threads = proc_threads(); - println!("\n== LATENCY (batch=32) =="); - println!(" candle : {:.3}s ({:.1} ms/sentence)", candle_lat.as_secs_f64(), candle_lat.as_secs_f64() * 1000.0 / 32.0); - println!(" fastembed : {:.3}s ({:.1} ms/sentence)", fb_lat.as_secs_f64(), fb_lat.as_secs_f64() * 1000.0 / 32.0); - - println!("\n== THREAD CONTROL =="); - println!(" RAYON_NUM_THREADS env = {rayon_env}"); - println!(" rayon::current_num_threads = {rayon_threads}"); - println!(" available_parallelism = {avail}"); - println!(" peak OS threads (/proc) = {peak_threads}"); - - // ── 7. Machine verdict line for the report ── - let verdict = if mean >= 0.99 { "PASS" } else if mean >= 0.95 { "MARGINAL" } else { "FAIL" }; - println!("\n== SUMMARY =="); - println!("VERDICT_HINT={verdict} cosine_min={min:.6} cosine_mean={mean:.6} candle_batch32_s={:.3} fb_batch32_s={:.3} rayon_threads={rayon_threads} rayon_env={rayon_env}", candle_lat.as_secs_f64(), fb_lat.as_secs_f64()); - - Ok(()) -} - -/// candle embedding: apply e5 `passage:` prefix, tokenize (batch-padded), -/// forward through XLM-R, attention-mask-weighted mean pool, L2 normalize. -fn candle_embed( - model: &XLMRobertaModel, - tokenizer: &Tokenizer, - device: &Device, - _pad_id: u32, - sentences: &[&str], -) -> Result>> { - let prefixed: Vec = sentences.iter().map(|s| format!("passage: {s}")).collect(); - let encodings = tokenizer - .encode_batch(prefixed, true) - .map_err(|e| anyhow::anyhow!("encode_batch: {e}"))?; - - let bsz = encodings.len(); - let seq = encodings[0].get_ids().len(); - - let mut ids = Vec::with_capacity(bsz * seq); - let mut mask = Vec::with_capacity(bsz * seq); - for enc in &encodings { - ids.extend(enc.get_ids().iter().copied()); - mask.extend(enc.get_attention_mask().iter().map(|&m| m as f32)); - } - - let input_ids = Tensor::from_vec(ids, (bsz, seq), device)?; - let attn_f32 = Tensor::from_vec(mask, (bsz, seq), device)?; - let token_type_ids = input_ids.zeros_like()?; - - // forward: (input_ids, attention_mask, token_type_ids, past, enc_hidden, enc_mask) - let hidden = model.forward(&input_ids, &attn_f32, &token_type_ids, None, None, None)?; - - // masked mean pool - let mask3 = attn_f32.unsqueeze(2)?; // (b, seq, 1) - let summed = hidden.broadcast_mul(&mask3)?.sum(1)?; // (b, hidden) - let counts = mask3.sum(1)?; // (b, 1) - let mean = summed.broadcast_div(&counts)?; - - // L2 normalize - let norm = mean.sqr()?.sum_keepdim(1)?.sqrt()?; - let normalized = mean.broadcast_div(&norm)?; - - Ok(normalized.to_vec2::()?) -} - -fn cosine(a: &[f32], b: &[f32]) -> f32 { - let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum(); - let na = l2(a); - let nb = l2(b); - dot / (na * nb) -} - -fn l2(v: &[f32]) -> f32 { - v.iter().map(|x| x * x).sum::().sqrt() -} - -/// Peak OS thread count for this process from /proc/self/status. -fn proc_threads() -> usize { - std::fs::read_to_string("/proc/self/status") - .ok() - .and_then(|s| { - s.lines() - .find(|l| l.starts_with("Threads:")) - .and_then(|l| l.split_whitespace().nth(1).map(str::to_string)) - }) - .and_then(|n| n.parse().ok()) - .unwrap_or(0) -} diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index 45cd3c5..7962edf 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -66,7 +66,8 @@ flowchart TB end subgraph Adapters ["traits + adapters"] embed["kebab-embed
(trait)"] - embedlocal["kebab-embed-local
(fastembed)"] + embedlocal["kebab-embed-local
(fastembed, default)"] + embedcandle["kebab-embed-candle
(candle, NUMA-safe opt-in)"] llm["kebab-llm
(trait)"] llmlocal["kebab-llm-local
(Ollama)"] search["kebab-search"] @@ -92,6 +93,7 @@ flowchart TB app --> sqlite app --> vector app --> embedlocal + app --> embedcandle app --> llmlocal app --> search app --> rag @@ -104,6 +106,8 @@ flowchart TB paud --> core pcode --> core embedlocal --> embed + embedcandle --> core + embedcandle --> config llmlocal --> llm rag --> search rag --> llm @@ -180,6 +184,7 @@ kebab/ │ ├── kebab-store-sqlite/ # SQLite + FTS5 (V001/V002/V003) (P1-6, P2-1, P3-3). src/derivation_cache.rs = derivation_cache 테이블 저장소 (V012, v0.21.0) │ ├── kebab-search/ # Lexical + Vector + Hybrid retriever (P2-2, P3-4) │ ├── kebab-embed/ kebab-embed-local/ # Embedder trait + fastembed adapter (P3-1, P3-2) +│ ├── kebab-embed-candle/ # candle (pure-Rust) Embedder, NUMA-safe opt-in provider=candle (Track 1, v0.22.0) │ ├── kebab-store-vector/ # LanceDB VectorStore (P3-3, P7-3 follow-up) │ ├── kebab-llm/ kebab-llm-local/ # LanguageModel trait + Ollama adapter (P4-1, P4-2) │ ├── kebab-rag/ # RAG pipeline (P4-3) diff --git a/docs/SMOKE.md b/docs/SMOKE.md index daf8e78..5275f8f 100644 --- a/docs/SMOKE.md +++ b/docs/SMOKE.md @@ -107,11 +107,13 @@ respect_markdown_headings = true chunker_version = "md-heading-v1" [models.embedding] -provider = "fastembed" # "none" 으로 두면 lexical-only — Ollama 불필요 +provider = "fastembed" # "fastembed"(기본) / "candle"(순수 Rust, NUMA-안전) + # / "none"(lexical-only — Ollama 불필요) model = "multilingual-e5-small" version = "v1" dimensions = 384 batch_size = 64 +num_threads = 0 # candle 전용 CPU 스레드 캡 (0=auto). env KEBAB_EMBED_THREADS 우선. [models.llm] provider = "ollama" diff --git a/docs/release-notes/v0.22.0-draft.md b/docs/release-notes/v0.22.0-draft.md new file mode 100644 index 0000000..d518442 --- /dev/null +++ b/docs/release-notes/v0.22.0-draft.md @@ -0,0 +1,72 @@ +--- +title: kebab v0.22.0 release notes (draft) +created: 2026-06-01 +status: draft +release_trigger: + - 신규 config surface (provider=candle, num_threads / KEBAB_EMBED_THREADS) — pre-1.0 minor bump + - 임베딩 백엔드 다변화 (NUMA-안전 candle provider 추가, opt-in) +--- + +# kebab v0.22.0 — candle 임베딩 provider (NUMA-안전, opt-in) + +v0.21.1 (config 마이그레이션) 후속 minor release. 듀얼소켓 NUMA 서버에서 +onnxruntime 의 스레드 하드코딩이 일으키던 ingest 크래시를 피하기 위해, 같은 +임베딩 모델을 **순수 Rust(candle)** 로 돌리는 opt-in provider 를 추가한다. +**기본 동작은 그대로다** — 기존 사용자는 아무것도 바꿀 필요가 없다. + +--- + +## 핵심 변경 + +### candle 임베딩 provider (`provider = "candle"`) + +**변경 사실.** `[models.embedding].provider` 에 `"candle"` 값이 추가됐다. +`"fastembed"`(기본, onnxruntime) / `"candle"`(순수 Rust) / `"none"`(lexical-only) +중 하나를 고를 수 있다. candle provider 는 fastembed 와 **완전히 같은 모델** +(`intfloat/multilingual-e5-large`, 1024-dim)을 쓰고, e5 prefix → mean pooling +→ L2 정규화 파이프라인도 동일하다. 첫 사용 시 safetensors(~2GB)를 +`{model_dir}/candle/` 아래로 자동 다운로드한다. + +```toml +[models.embedding] +provider = "candle" # 기본은 "fastembed" — NUMA 서버에서만 candle 권장 +num_threads = 8 # candle CPU 스레드 캡 (0 = auto = #cores) +``` + +```bash +# env 로도 캡 가능 (config 보다 우선) +KEBAB_EMBED_THREADS=8 kebab ingest +``` + +**Trade-off.** candle 는 순수 Rust 라 onnxruntime 의 네이티브 SIMD 커널보다 +CPU latency 가 느리다 (Phase 0 스파이크 측정 ~4×). 그래서 **기본값은 +fastembed 를 유지**하고, candle 은 onnxruntime 가 죽는 NUMA 환경에서만 켜는 +opt-in 으로 둔다. 단일 워크스테이션 사용자는 fastembed 가 더 빠르다. + +**Mitigation (왜 안전한가).** candle 의 CPU 백엔드는 글로벌 rayon 풀 크기로 +스레드를 정한다. `num_threads`(또는 env `KEBAB_EMBED_THREADS`)가 그 풀을 한 번 +캡하므로, onnxruntime 가 하드코딩하던 48 intra-op 스레드 → NUMA 힙 손상 → +double-free 경로를 원천 차단한다. NUMA 노드 바인딩이 더 필요하면 `numactl` +과 조합한다. + +**Upgrade 절차.** 재색인 **불필요**. candle 과 fastembed 의 벡터는 사실상 +동일(Phase 0 스파이크 코사인 1.000000)해서 `embedding_version` 을 유지했고, +기존 LanceDB 색인을 그대로 재사용한다. provider 를 바꿔도 검색 결과는 +바뀌지 않는다. 기존 `config.toml` 은 `num_threads` 가 자동으로 `0`(auto)으로 +채워져 그대로 로드된다 — `kebab config migrate` 도, 수동 편집도 필요 없다. + +--- + +## 그 외 + +- 신규 crate `kebab-embed-candle` (candle 의존성 트리를 이 crate 에 격리, + `kebab-core`/`kebab-config` 외 다른 kebab-* 의존 없음). +- Phase 0 feasibility 스파이크(`spike-embed-candle`)는 production 흡수 후 제거. +- 문서: README Configuration, `docs/SMOKE.md` config 예시, `docs/ARCHITECTURE.md` + crate 그래프/트리에 candle provider 반영. + +## 잔여 검증 (사용자 실행) + +듀얼소켓 NUMA 서버에서 `provider=candle` 로 5150-doc ingest 가 double-free +없이 EXIT=0 완주하는지가 본 release 의 최종 인수 게이트다 (meta-spec §4.3). +패리티 max abs diff 수치는 `IMPL_REPORT.md` 참조. diff --git a/tasks/HOTFIXES.md b/tasks/HOTFIXES.md index 57abd2b..39f1efa 100644 --- a/tasks/HOTFIXES.md +++ b/tasks/HOTFIXES.md @@ -14,6 +14,52 @@ historical contract that was implemented; this file accumulates the deltas so phase 5+ readers can find the live behavior without diffing git history. +## 2026-06-01 — candle 임베딩 provider (NUMA double-free 회피, opt-in) + +**무엇이 문제였나.** 듀얼소켓 NUMA 서버에서 `provider=fastembed`(onnxruntime)로 +대규모 ingest(5150-doc)를 돌리면 onnxruntime 가 intra-op 스레드를 48개로 +하드코딩해 NUMA 힙을 손상시키고 double-free 로 프로세스가 죽었다. 스레드 수를 +config 로 줄일 surface 가 없었고, fastembed 4.9 의 ORT 바인딩은 이를 노출하지 +않는다. + +**진단 / 결정 (사용자 승인 2026-06-01).** 같은 모델 +`intfloat/multilingual-e5-large` 를 **candle(순수 Rust)** 로 돌리는 임베딩 +provider 를 추가하기로 결정. candle 의 CPU 백엔드는 글로벌 rayon 풀 크기로 +스레드를 정하므로, 한 번의 `rayon::ThreadPoolBuilder::build_global` 캡으로 +스레드를 NUMA-안전한 수로 묶을 수 있다. **재색인 0 목표**(`embedding_version` +유지) — Phase 0 스파이크(`SPIKE_REPORT.md`, 커밋 76841af)가 candle vs +onnxruntime **코사인 1.000000** 패리티를 입증했고, 본 Track 1 구현의 패리티 +테스트로 차원별 max 절대오차를 재실측해 확정. + +**무엇을 건드렸나.** +- 신규 crate `crates/kebab-embed-candle` — `kebab_core::Embedder` 구현 + (`CandleEmbedder`). 스파이크 파이프라인(safetensors via hf-hub → XLM-RoBERTa + forward → attention-mask mean pooling → L2 → e5 prefix)을 production 으로 + 흡수. deps 는 candle 트리를 이 crate 에 격리 (core/config 외 다른 kebab-* + 의존 0 — design §8 경계). 모델 캐시 `{model_dir}/candle/`. +- 스레드 캡: `[models.embedding].num_threads`(u32, default 0=auto) + env + `KEBAB_EMBED_THREADS`(우선). `CandleEmbedder::new` 에서 n>0 이면 글로벌 rayon + 풀 1회 캡(이미 init 시 no-op). +- 주입 분기: `kebab-app::App::embedder()` 가 `config.models.embedding.provider` + 분기 — `fastembed`/`onnx`/(빈값) → 기존 `FastembedEmbedder`(동작 불변), + `candle` → `CandleEmbedder`, 미지값 → 에러. `none` 은 기존 lexical-only 유지. +- 스파이크 crate `crates/spike-embed-candle` 제거(학습은 production 으로 흡수됨). +- 버전 0.21.1 → **0.22.0** (신규 config surface — pre-1.0 minor bump). + +**패리티 증거.** Phase 0 스파이크 cosine 1.000000 (10문장 한/영 혼합). 본 +Track 1 의 `#[ignore]` 패리티 테스트 결과(max abs diff)는 +`/build/out/kebab-worktrees/embed-candle/IMPL_REPORT.md` 에 기록. + +**호환성.** fastembed default 경로의 동작/벡터 불변. `embedding_version` +유지 → 기존 색인 재사용(재색인 0). wire schema 변경 없음. 옛 config.toml 은 +`num_threads` 가 serde default(0)로 채워져 그대로 파싱. + +**잔여 게이트 (사용자 실행, Claude 불가).** 그 듀얼소켓 NUMA 서버에서 +`provider=candle` 로 5150-doc ingest 가 double-free 없이 EXIT=0 완주하는지 +PR 머지 전/후 검증 예약 (meta-spec §4.3). + +amends: `docs/superpowers/specs/2026-06-01-embed-candle-track-spec.md`. + ## 2026-05-31 — config 마이그레이션 (`kebab config migrate`) **Trigger**: config.toml 스키마가 진화해도(v0.21.0 의 `[ingest.expansion]` 등) 기존 사용자 파일은 serde default 로 *동작*만 호환될 뿐 새 섹션이 파일에 안 써져 사용자가 노브의 존재를 알 수 없었다. DB 의 V00X refinery 와 달리 config 엔 마이그레이션 메커니즘이 없어 추가. 설계 `docs/superpowers/specs/2026-05-31-config-migration-design.md`, 계획 `docs/superpowers/plans/2026-05-31-config-migration.md`, PR #198.