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2504 commits
| Author | SHA1 | Message | Date | |
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ruvnet
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be91ddf0f1 |
chore: revert router 0.1.31 bump from this PR
The `optional-deps-resolvable-on-npm` regression guard fails because
@ruvector/router-<platform>@0.1.31 doesn't exist on npm yet — those
platform binaries are only published by `publish-all.yml` after a tag is
cut, which happens AFTER this PR merges.
Splitting the work:
- This PR: HNSW correctness fix + CI guards (keeps regression-guard
green on every commit).
- Follow-up release PR: bump @ruvector/router meta + 5 platform
packages to 0.1.31, tag v0.1.31, publish-all.yml ships the fix.
This commit reverts
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ruvnet
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b26001ad06 |
style: cargo fmt --all on touched HNSW pruning block
No behaviour change — collapses single-expression closure and assignment onto one line per rustfmt defaults so the rustfmt CI job passes. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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89350f80b5 |
chore(diskann): sync README + package.json to published 0.1.1
The expanded README and 0.1.1 version were already published to npm by an earlier release, but never committed back to git. Verified identical to `npm pack @ruvector/diskann@0.1.1`. Bringing the working tree in sync so future bumps start from a clean baseline. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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c5c7e7f26e |
chore(release): @ruvector/router 0.1.30 → 0.1.31
Surface the #430 HNSW correctness fixes (insert beam, distance-based pruning, storage rebuild) to npm consumers. Bump applies to the meta package and all 5 platform-specific subpackages so optionalDependencies resolve consistently after publish-all.yml runs. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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d5e07f6e6d |
fix(ruvector-router-core): #430 HNSW insert beam + distance-based pruning + storage rebuild
Three remaining root causes from issue #430, plus the storage-rebuild gap from PR #460. Bug B — insert beam was clamped to ef_construction.min(m * 2). With defaults (m=16, ef_construction=200) the beam silently became 32. Late- inserted clusters got wired through whatever was near the entry point instead of through ef_construction-wide neighbour search. Bug C — adjacency-list pruning used `drain(0..drain_count)`, dropping the OLDEST edges regardless of distance. Proper HNSW pruning keeps the m CLOSEST edges. Now sort by `calculate_distance` to the anchor vector and truncate to m. Kept a fallback that preserves the newest-m behaviour when the anchor vector lookup fails so we never panic on a missing vector. Storage — VectorDB::new() always created a fresh empty HnswIndex, so previously persisted vectors were invisible to search after reopening the database. Now rebuild via storage.get_all_ids() + index.insert_batch() on open, and seed VectorDbStats.total_vectors with the recovered count. Tests: - test_pruning_keeps_closest_not_newest: builds a hub with 20 close neighbours then 6 far neighbours, asserts no "far_*" id appears in top-10 around the hub. Fails on FIFO pruning. - test_index_rebuilt_from_storage_on_open: writes 5 vectors via one VectorDB instance, reopens against the same path, asserts search returns the persisted match. Fails on the historical empty-index bug. Regression-guard CI additions: - hnsw-insert-beam-no-m2-clamp: textually forbids the ef_construction.min(m*2) pattern in index.rs. - hnsw-distance-based-neighbor-pruning: requires calculate_distance and the `> m * 2` overflow gate to both live in index.rs. - vector-db-rebuilds-index-on-open: requires storage.get_all_ids() in vector_db.rs. - hnsw-recall-at-1 job now also runs the two new tests. Supersedes PR #460 (CoolDude1969) which covered storage rebuild + an overlapping heap fix already in main from PR #466. Closes #430. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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c4212106f9
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ci: close 3 regression-guard coverage gaps from PR #466 review (#468)
* ci: close 3 regression-guard coverage gaps from PR #466 review
Three follow-ups identified after the first regression-guard run:
1. @ruvector/rvf-wasm wasn't in npm-publish-pipeline matrix even
though #415 was one of the issues closed in #466. Add it. Verified
locally: packs cleanly to a 21.3 kB / 6-file tarball with both
pkg/rvf_wasm.mjs and pkg/rvf_wasm.d.ts shipped.
2. New job brain-hydration-counters-present asserts the four log
lines added to crates/mcp-brain-server/src/store.rs by
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github-actions[bot]
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12f8890e03 |
chore: Update NAPI-RS binaries for all platforms
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rUv
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bc3a9b1c93
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fix: 9-issue cleanup batch + regression-guard CI workflow (#466)
* fix: batch 1 — deadlock, AVX-512 gating, Windows case-collisions
Closes #437: VectorDb::delete in ruvector-router-core acquired the stats
RwLock twice in one statement. parking_lot::RwLock is non-reentrant, so
the second .write() deadlocked against the first guard's lifetime. Bind
the guard once.
Closes #438: Gate AVX-512 intrinsics behind a new `simd-avx512` Cargo
feature (default-on). Lets downstream consumers on stable Rust 1.77–1.88
(before avx512f stabilization in 1.89) opt out without forcing nightly:
cargo build --no-default-features --features simd,storage,hnsw,api-embeddings,parallel
Runtime dispatch falls back to AVX2 + FMA when the feature is disabled.
All 4 #[target_feature(enable = "avx512f")] sites + 4 dispatch branches
updated. Both feature configurations verified to compile cleanly; all
18 simd_intrinsics tests pass.
Closes #458: Rename two pairs of case-colliding research artifacts under
docs/research/claude-code-rvsource/versions/v2.1.x/tree/react_memo_cache_sentinel/
that broke `git clone` on Windows/NTFS:
tmux.js → tmux_lc.js (TMUX.js kept)
type.js → type_lc.js (Type.js kept)
modules-manifest.json updated to match.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(brain): observable hydration + larger page-error budget (issue #464)
Bisect outcome: source diff between the 2026-04-14 working revision
(00203-brv → 22,005 memories) and current main (00204-92l → 10,227)
is whitespace-only (cargo fmt 2026-04-24 + clippy 2026-04-25). No
semantic change in store.rs, types.rs, or graph.rs. BrainMemory schema
is byte-identical. So the regression is environmental, surfacing
through a code path that has no observability today.
Two changes:
1. load_from_firestore() now emits per-collection counters so the next
deploy is diagnosable instead of a black box:
Hydrate brain_memories: considered=N accepted=M rejected_parse=K
First 5 parse errors are logged with the serde_json error so any
live schema drift surfaces immediately.
2. firestore_list MAX_PAGE_ERRORS raised 3 → 8. Hydration crosses ~75
pages of 300 docs each; 3 transient OAuth-refresh blips at the
wrong moment terminated the load at ~10K, consistent with the
reported 10,227 number. 8 still bounds runaway behaviour while
tolerating realistic blip rates.
The actual environmental cause is recoverable from one deploy with the
new logs in place. Until then, traffic stays on 00203-brv (which is
what the rollback already did).
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(router-core): HNSW result-heap inversion, prune drops oldest, k > ef_search (#430)
Three correctness bugs in crates/ruvector-router-core/src/index.rs that
together collapsed recall@1 at scale:
1. `Neighbor::Ord` is reversed so BinaryHeap acts as a min-heap. Correct
for `candidates` (pop closest unexplored first), but WRONG for the
`result` heap — peek returned the BEST candidate, so the eviction
path kept dropping the best item instead of the worst whenever the
set was full. Wrap result in `std::cmp::Reverse<Neighbor>` so
peek/pop return the furthest item (the actual eviction target). This
is the primary recall@1 fix.
2. Per-insert connection pruning used `truncate(m)`, which keeps the
OLDEST m connections — including dropping the just-pushed edge when
it landed past index m. Switch to `drain(0..len-m)` so the freshly
inserted edge always survives.
3. `search()` capped at `ef_search` regardless of caller's k. With
default ef_search=10 and k=25, results were silently 10. Raise ef
to `max(ef_search, k)` before invoking search_knn_internal.
New tests:
- `test_recall_at_1_with_biased_insertion_order`: 1024 vectors,
biased insertion order (the topology that historically exposed the
bug); asserts recall@1 ≥ 95% AND ≥ 80% distinct ids across queries.
- `test_k_exceeds_ef_search_default`: 50 vectors, default ef_search=10,
k=25; asserts 25 results returned.
All 19 router-core tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(npm): publish pipeline — dist/ guaranteed + dual ESM/CJS pi-brain (#462/#415/#376/#372)
@ruvector/pi-brain 0.1.1 → 0.1.2 (closes #462, #372):
* Add `prepack` hook so dist/ is always built before publish — tarballs
on 0.1.0/0.1.1 shipped without dist/ because `tsc` never ran.
* Add a second tsconfig (tsconfig.cjs.json) that emits CommonJS to
dist/cjs/ alongside the ESM build in dist/. A generated
dist/cjs/package.json carries {"type":"commonjs"} so Node treats
that subtree as CJS regardless of the package-level "type":"module".
* Expand the exports map with import + require + default conditions
so ruvector@0.2.x's CJS MCP server (Node 20.x, no require(ESM)
until 22.12) can require() the package. Add subpath exports for
./mcp and ./client.
* Verified locally: dist/cjs/index.js loads via `require()` and
dist/index.js loads via dynamic `import()`.
@ruvector/rvf-wasm 0.1.5 → 0.1.6 (closes #415):
* pkg/rvf_wasm.js contains ESM syntax (`import.meta.url`,
`export default`). The old exports map pointed `require` at this
file, which fails on every CJS consumer. Mark the package
explicitly `"type": "module"`, drop the `require` condition (the
`.mjs` build is the canonical one), and add a `./wasm` subpath for
consumers that want the raw bytes.
ruvector npm 0.2.25 (extends #376 mitigation):
* Add `prepack` mirroring `prepublishOnly` so `npm pack` (and CI
smoke tests that run pack) regenerate dist/ + run verify-dist.
Without this, `npm pack` skips prepublishOnly, masking
missing-dist regressions until publish.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(mcp): hooks_route_enhanced in-process — drop spawnSync (#463/#422)
The hooks_route_enhanced MCP tool shelled out via
execSync('npx ruvector hooks route-enhanced …', { timeout: 30000 })
which deterministically timed out: npx's package-resolution and
bin-launch overhead can spike past 30s on cold-cache machines, even
though the underlying work finishes in ~500ms. Callers got
deterministic `spawnSync /bin/sh ETIMEDOUT`.
The sibling hooks_route tool (reported as working in #463) uses
intel.route() directly. Mirror that pattern: call intel.route(), then
inline the same coverage-router + AST-parser signal enrichment the CLI
does. No subprocess, no timeout, no npx dependency.
Falls back gracefully when coverage-router or ast-parser aren't
installed (try/catch around each optional enhancement, same as the
CLI handler).
Co-Authored-By: claude-flow <ruv@ruv.net>
* ci: regression guard for 9 issues + fixes for 5 latent regressions it surfaced
New workflow .github/workflows/regression-guard.yml runs on every push +
PR. Each job pins one of these issue classes shut:
#437 reentrant-rwlock-double-write
Forbids `x.write()…x.(write|read)()` and `x.read()…x.write()` in
a single statement (parking_lot is non-reentrant). PCRE
backreference matches only same-lock cases.
#458 case-insensitive-collisions
Fails if `git ls-files` has any two paths that match after
lowercasing — Windows clones drop one of each silently.
#438 ruvector-core-no-avx512-builds-on-stable
cargo check ruvector-core with AND without the simd-avx512
feature so the AVX-512 gating doesn't regress.
#430 hnsw-recall-at-1
Runs the new recall@1 (biased insertion / 1024 vectors) test
and the k > ef_search test in release mode.
#462 / #376 npm-publish-pipeline
npm pack each shipped package and assert every entry referenced
by main/module/types/exports is actually inside the tarball.
#463 / #422 no-npx-execSync-in-mcp-server
Forbids execSync('npx ruvector …') anywhere in the MCP server.
#256 shell-injection-in-mcp-server
Flags any exec*/spawn* call that interpolates ${args.X} without
wrapping in sanitizeShellArg(...).
#267 no-systemtime-in-wasm-crates
Crates named *wasm* with ungated SystemTime::now / Instant::now
calls are rejected (the wasm32-unknown-unknown panic class).
#359 no-hardcoded-workspaces-paths
Devcontainer-only `/workspaces/ruvector` literals are banned
from .github/workflows, .claude/settings*, and scripts/publish/.
Adding the guard surfaced five real, already-present regressions of
these classes — fixed in this commit:
* crates/prime-radiant/src/coherence/engine.rs (3 sites):
self.stats.write().X = self.stats.read().X - 1 in the same
statement — exactly issue #437's shape on a different lock. Bind
the write guard once.
* crates/ruvector-wasm/src/lib.rs:465 (benchmark fn):
used std::time::Instant which panics on wasm32 (issue #267).
Switch to js_sys::Date::now().
* scripts/publish/publish-router-wasm.sh + check-and-publish-router-wasm.sh:
hardcoded /workspaces/ruvector paths (issue #359). Resolve REPO_ROOT
from BASH_SOURCE instead.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ci: narrow scope of two guards to avoid pre-existing-debt false positives
After the first PR run two guards caught existing technical debt rather
than fresh regressions:
* no-npx-execSync-in-mcp-server flagged 10 other execSync('npx
ruvector …') sites (ast-analyze, coverage-route, graph-mincut,
security-scan, git-churn, …) which predate issue #463 and are a
distinct concern (some legitimately need subprocess). Narrow the
guard to the EXACT regression — execSync inside the
hooks_route_enhanced case body — using awk to extract that case's
body before grepping. Rename: no-npx-execSync-in-route-enhanced.
* npm-publish-pipeline failed at npm install (peer-dep ERESOLVE).
Add --legacy-peer-deps. The point of this guard is the tarball
content, not the install graph.
Co-Authored-By: claude-flow <ruv@ruv.net>
* style: cargo fmt --all (mechanical, pre-existing diffs on main + my new code)
Workspace had 11 files with rustfmt diffs predating this branch, plus
one new diff in store.rs from the hydration counters added in
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github-actions[bot]
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9054c2cc67 |
chore: Update NAPI-RS binaries for all platforms
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github-actions[bot]
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29ba5349e4 |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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ruvnet
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a80a46d076 |
fix(ruvector-rairs): shorten keyword to satisfy crates.io 20-char limit
`approximate-nearest-neighbor` (28 chars) was rejected by crates.io; replaced with `nearest-neighbor`. Required to publish v0.1.0. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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8f97421297
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research(nightly): rairs-ivf — RAIRS IVF, ruvector's first Inverted File Index (ADR-193) (#459)
* feat(rairs-ivf): add RAIRS IVF — ruvector's first Inverted File Index (ADR-193)
Implements Yang & Chen, SIGMOD 2026 (arXiv:2601.07183): three variants of
IVF with Redundant Assignment + Amplified Inverse Residual + SEIL layout.
Three measurable variants (N=5K, D=128, 64 clusters, cargo --release):
IvfFlat nprobe=1 recall@10 61.3% mem 2,571 KB 26,984 QPS
RairsStrict nprobe=1 recall@10 83.8% mem 5,110 KB 13,243 QPS
RairsSeil nprobe=1 recall@10 93.1% mem 2,571 KB 13,582 QPS
RairsSeil: +31.8 pp recall at nprobe=1 vs IvfFlat with identical memory.
Files:
crates/ruvector-rairs/ — new crate (IvfFlat, RairsStrict, RairsSeil)
docs/adr/ADR-193-rairs-ivf.md — architecture decision record
docs/research/nightly/2026-05-12-rairs-ivf/README.md — SOTA survey + results
Cargo.toml — workspace member added
10/10 unit tests pass. cargo build --release -p ruvector-rairs green.
* perf(ruvector-rairs): SIMD-friendly distance kernels + partial-select top-k; fix clippy/fmt; flag unverified citation
Optimizations (recall unchanged; ~2.3–2.9× single-thread QPS across all
variants/nprobe on x86-64):
- index.rs: rewrite l2sq/dot as 8-lane unrolled reductions so LLVM
auto-vectorises the f32 accumulation (the naïve iter().sum() can't — f32
add isn't associative). This is the hot path: every centroid scan + every
list-entry distance.
- index.rs: add finalize_topk() / top_nprobe_centroids() using
select_nth_unstable (O(n) avg) instead of full O(n log n) sorts of every
candidate / every centroid; all three search() impls use them. Distance
ordering switched to f32::total_cmp — no more partial_cmp().unwrap() panics.
- rairs.rs: rair_score is now allocation-free (no per-call Vec for the diff);
search() dedups ids with a reused bool scratch array instead of allocating
a HashSet per query.
- seil.rs: block-visited dedup uses a flat bool array indexed via per-list
prefix sums instead of a per-query HashSet<(usize,usize)>.
Fixes:
- clippy `-D warnings` now passes: documented the 6 RairsError struct fields
+ RairsSeil::lambda; elided the explicit lifetime on resolve_block.
- cargo fmt --check now passes (benches/rairs_bench.rs import ordering, etc.).
- lib.rs + ADR-193 + the research README now carry a Provenance note: the
"RAIRS/SEIL" names and the SIGMOD-2026 / arXiv:2601.07183 citation are
unverified; the crate is an original implementation of the redundant-
assignment idea (cf. IVF spill lists / SOAR / multi-probe LSH) and should
be judged on src/main.rs's reproducible benchmarks, not the reference.
cargo test -p ruvector-rairs: 10/10 pass; recall@10 at nprobe∈{1,4,16}
unchanged (61.3/97.9/100 IvfFlat, 83.8/99.4/100 RairsStrict,
93.1/99.9/100 RairsSeil); index memory unchanged.
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: ruvnet <ruvnet@gmail.com>
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github-actions[bot]
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ef5274c292 |
chore: Update NAPI-RS binaries for all platforms
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rUv
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51b1ca777f
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sparse-mario: training-free retrieval LM + masked diffusion + ruvllm_retrieval_diffusion crate (#450)
* feat(sparse-mario): iter 1 — corpus + tokenizer scaffold
Adds examples/sparse_mario.rs with three hand-authored VGLC-alphabet
SMB level slices (50 cols × 14 rows each), a 15-token vocabulary
(sky / ground / brick / ? / coin / pipes / enemy / cannon / Mario),
and char↔id codec. Runs end-to-end and prints corpus stats. Five
unit tests cover vocab roundtrip, corpus integrity, mario-start
presence, ground-floor coverage, and rectangular level shape.
Iter-plan (5m /loop until done):
✓ 1. corpus + tokenizer scaffold ← here
2. wire SubquadraticSparseAttention as retrieval model
3. autoregressive generation + ASCII level renderer
4. dense vs sparse vs sparse+FastGRNN bench at level lengths
5. fp16 KV cache + FastGRNN gate optimization sweep
6. validation + final summary
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(sparse-mario): iter 2-3 — retrieval LM + ASCII generation
Wires `SubquadraticSparseAttention` as an inference-only retrieval
language model over the embedded SMB corpus:
K[i] = embed(corpus[i]) + 0.5·pos(i)
V[i] = embed(corpus[i+1]) ← next-token supervision baked into V
Q[i] = K[i]
out = forward(Q, K, V)
logits[v] = out[last] · embed(v)
next = sample(softmax(logits / T))
- Unit-variance embedding matrix (vocab × 64), deterministic xorshift32
seed; combined with the kernel's 1/sqrt(d) scale this gives matched
embed dot-product ≈ sqrt(d) above the noise floor.
- Light positional encoding (POS_SCALE=0.5) — enough for level-depth
awareness without drowning the token signal.
- Non-causal attention with window=256 + log-stride + landmarks so the
last query position can reach the whole 2.8K-token combined sequence
through sparse hops.
- End-to-end `cargo run --release --example sparse_mario` produces a
full 14-row × 50-col ASCII level slice in ~25s on a 9950X.
5 new tests (10 total, all passing): embedding determinism, finite
logits, generation determinism for a fixed seed, in-vocab outputs,
and a corpus-shape distribution check.
Known limitation: pure bigram retrieval saturates on the most-common
next-token (sky → sky → ... or X → X → ...). Iter 5 will add top-k
sampling, repetition penalty, and KvCache-backed `decode_step` for
incremental O(log T) per-token cost.
Iter-plan progress:
✓ 1. corpus + tokenizer scaffold (
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github-actions[bot]
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e383476014 |
chore: Update NAPI-RS binaries for all platforms
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github-actions[bot]
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6808c706e9 |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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ruvnet
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c309872779 |
docs(adr): add SOTA extension sections to sparse-attention ADRs 183/184/186/189/190
Document the fp16 / parallel / KV-cache-incremental / GQA-flash extensions that landed across 2026-Q2 in the corresponding ADRs: - ADR-183: zero-dep invariant lets fp16 + parallel features land cleanly - ADR-184: online softmax + flash-sparse tiling (~2× FLOPs cut) - ADR-186: 4-node cluster validation + parallel benchmark coverage - ADR-189: incremental landmark Welford pass + decode-step usage - ADR-190: GQA + flash-sparse fusion path for Mistral / Llama-3 / TinyLlama Pure documentation — no code changes, no behaviour changes. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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9d8006ae26
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ruvllm_sparse_attention v0.1.1 — FastGRNN-gated near-linear attention + no_std/ESP32-S3 + ADR-191/192 (#429)
* docs(sparse-attn): plain-language README intro, SEO, and tutorial gist - Rewrite README opening for non-experts: what it is, why it matters, who it's for, what it is NOT. Adds a Table of Contents and an FAQ. - Document the new FastGRNN-gated near-linear path with a measured scaling table and runnable example pointer. - Add SEO-friendly keyword block at the bottom (rust llm inference, sparse attention rust, near-linear attention, edge ai rust, raspberry pi llm, gguf rust, mistral / llama / smollm2 / phi-2). - New docs/TUTORIAL.md walks through the full pipeline end-to-end (Cargo.toml → forward → KvCache decode → FP16 KV → FastGRNN gate → cross-compile to Pi). Published as https://gist.github.com/ruvnet/790214c832928d6f2ec7ebe593bb3def Co-Authored-By: claude-flow <ruv@ruv.net> * chore(sparse-attn): add crates.io metadata for v0.1.0 publish - repository, documentation, homepage URLs - keywords (llm, attention, transformer, inference, edge) - categories (algorithms, science, mathematics) - expanded description mentioning subquadratic + FastGRNN near-linear - rust-version = 1.77 (matches workspace MSRV) Published v0.1.0 to crates.io: https://crates.io/crates/ruvllm_sparse_attention Co-Authored-By: claude-flow <ruv@ruv.net> * feat(sparse-attn): FastGRNN salience gate + forward_gated for near-linear scale Adds a recurrent O(N · D_h²) FastGRNN pass that produces a per-token salience score, then prunes the sparse-attention candidate set against that score. Combined cost is O(N · (D_h² + W + G + K_keep + dim)), linear in seq when the gate budget K_keep is constant. New module `fastgrnn_gate`: - FastGrnnGate cell (matches cognitum-agent's sparse_fastgrnn math so weights round-trip via from_weights / score_sequence) - score_sequence / score_kv: per-position salience over a sequence - keep_mask_quantile / keep_mask_top_k: turn salience into a binary keep-mask the attention candidate selector consumes - step_with_hidden: streaming variant for online inference New methods on SubquadraticSparseAttention: - forward_gated(q, k, v, keep_mask) — drops below-threshold tokens from the long-range candidate set; window + globals + current are always retained (causality preservation) - forward_gated_with_fastgrnn(q, k, v, gate, top_k) — convenience wrapper that does FastGRNN scoring + top-K masking + gated forward Tests (5 new + 8 gate tests, all passing alongside 25 baseline): - all-true mask is bit-identical to plain forward - all-false mask preserves window + globals + current, output finite - wrong mask length returns InvalidConfig - smaller top_k provably reduces total candidate count - end-to-end FastGRNN-driven path produces finite output Scaling demo (examples/fastgrnn_gated_scaling.rs): seq | ungated/N | gated/N | growth ratio ----|-----------|---------|------------- 128 | 0.0021 | 0.0029 | 2048| 0.0029 | 0.0036 | ungated grows ~1.38× over 16× seq (log-linear); gated grows ~1.24× over 16× seq (sub-logarithmic, near-linear). Zero new runtime dependencies (ADR-183 invariant preserved). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(sparse-attn): no_std + alloc support, ESP32-S3 cross-compile verified ADR-192 implementation. Crate is now no_std + alloc behind a default-on `std` feature (purely additive — std consumers see zero behavioural change). Changes: - lib.rs: #![cfg_attr(not(feature = "std"), no_std)] + extern crate alloc - F32Ext trait restores .exp/.sqrt/.tanh/.powi method syntax via libm in no_std mode; std mode uses inherent f32 methods unchanged - attention.rs / fastgrnn_gate.rs / tensor.rs: replace std:: with core:: and alloc:: imports; HashSet → BTreeSet (no hashing in no_std) - Error trait impl gated on std (core::error::Error needs MSRV bump) - Cargo.toml: std default-on, parallel = ["std", "rayon"], libm always-on Verified: - cargo test --lib 38/38 pass - cargo build --no-default-features clean - cargo build --no-default-features --features fp16 clean - cargo +esp build --target xtensa-esp32s3-none-elf 1.02s release, 376 KB rlib - examples/esp32s3_smoke runs natively all checks passed Tested against attached hardware: ESP32-S3 v0.2, MAC ac:a7:04:e2:66:24, 16 MB flash, on /dev/ttyACM0 (USB-Serial-JTAG). Bump version 0.1.0 → 0.1.1 (patch — additive). Adds "no-std" to crates.io categories. Adds libm 0.2 as always-on dep (~60 KB, pure Rust). Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-191 Pi Zero 2W production hardening for ruvllm_sparse_attention Proposes four additive changes to the sparse-attention crate based on production data from the cognitum-agent deployment on cognitum-v0 (Pi Zero 2W, SmolLM2-135M Q4_0, cognitum-one/seed PR #133): 1. decode_step_with_deadline / decode_step_f16_with_deadline / decode_batch_with_deadline — sub-step wall-clock deadline so integrators can bound latency at finer granularity than per-token. Returns AttentionError::DeadlineExceeded { elapsed_ms, checkpoint }. 2. SparseAttentionConfig::pi_zero_2w() — codify the empirically validated window=64, tile=16, FP16 KV preset that cognitum-agent currently records as a Cargo.toml comment. 3. SubquadraticSparseAttention::warm_up() — synthetic 1-token decode to prime caches and shrink the measured 99 s → 56 s cold→warm gap before the first user inference. 4. Stochastic Q4 dequant pass-through for KV cache reload (feature-gated, off by default). Reuses the splitmix64 seeding pattern from cognitum-agent commit 1675c20 — naive `seed | 1` xorshift collapses adjacent seeds 42 and 43 to the same state, an outright bug. Status: proposed. Test plan covers correctness (deadline does not perturb output), unbiasedness (mean within 0.06 of deterministic over 256 trials), and a cluster bench comparing pre/post cold first-decode latency on cognitum-v0. Co-Authored-By: claude-flow <ruv@ruv.net> * style(sparse-attn): cargo fmt over crate sources after no_std refactor Co-Authored-By: claude-flow <ruv@ruv.net> --------- Co-authored-by: ruvnet <ruvnet@gmail.com> |
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github-actions[bot]
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fa39e66cfd |
chore: Update NAPI-RS binaries for all platforms
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github-actions[bot]
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ec4e4bbd1b |
chore: Update NAPI-RS binaries for all platforms
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ruvnet
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068bb637ac |
docs(sparse-attn): update README with SOTA extensions
Flash-sparse tiling, FP16 KvCacheF16, SIMD dot(), H2O eviction, decode_batch, IncrementalLandmarks, parallel feature, sort_candidates. 25-test suite, updated KvCache::new 4-arg API, FP16 memory table. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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efc3d3618c |
feat(sparse-attn): flash-sparse IO tiling, FP16 KV cache, SIMD dot()
• forward_flash / forward_gqa_flash — 3-phase IO-optimal tiling (FlashAttention-2 style): ascending KV tiles × online softmax accumulators; Phase 2 handles scattered globals/stride/landmarks outside the window; Phase 3 normalises. Same mask logic as forward() so flash and non-flash outputs match to 1e-5 (4 new tests). • KvCacheF16 (feature = "fp16") — half-precision KV store: f32→f16 on append, inline f16→f32 during dot products. Halves KV memory at ~0.1% accuracy cost (verified empirically in tests). • dot() — rewritten as iterator zip/sum; LLVM auto-vecs to NEON on Pi 5 / Hailo-10H and AVX2 on x86 in --release builds. • bench: bench_flash_sparse group added (seq 512–4096, tile=128). All 25 tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> |
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github-actions[bot]
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1b106721b4 |
chore: Update NAPI-RS binaries for all platforms
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ruvnet
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3c80010c03 |
feat(sparse-attn): SOTA pushes — sorted candidates + H2O eviction
sort_candidates config flag: - Ascending candidate index sort before attention loop — beneficial on Pi 5 (4 MB L3, KV cache > L3 at seq ≥ 2K) where sorted access lets the prefetcher run ahead; measured ~10% SLOWER on x86 with large L3 so default is false - Gated by SparseAttentionConfig::sort_candidates; zero cost when false - Applied in forward(), forward_gqa() (serial + parallel), decode_step() H2O-style KvCache::evict_and_append: - Heavy-hitter oracle eviction: removes token with lowest cumulative attention score, preserving recent window + global tokens from eviction - Enables generation past max_seq without hard stop - Falls back to oldest non-global token if all candidates are protected - Rebuilds IncrementalLandmarks after compaction (eviction is infrequent) 21/21 tests pass; bench confirms sorted candidates are tunable per target Co-Authored-By: claude-flow <ruv@ruv.net> |
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github-actions[bot]
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5c580ebaeb |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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github-actions[bot]
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645c94df42 |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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ruvnet
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add51a9303 |
feat(ruvllm_sparse_attention): parallel forward_gqa + export IncrementalLandmarks
- forward_gqa now has the same rayon parallel head-loop as forward(); covers the GQA path used by Mistral-7B / Llama-3 (the primary edge inference models) - Export IncrementalLandmarks from crate root so callers can inspect/share landmark state without depending on the internal module path - 21/21 tests pass under both default (serial) and --features parallel Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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4db35f2802 |
feat(adr-189/190): IncrementalLandmarks + decode_batch + parallel feature
- IncrementalLandmarks: Welford O(H×D) online mean update per append replaces O(T×H×D) Landmarks::from_kv rebuild in decode_step — O(1) amortised per token - KvCache: add block_size param, try_append (non-panicking), is_full, reset, append_all (bulk prefill load with landmark update) - decode_step: fix pre-append convention (i = cache.len-1, seq = cache.len); use cache.landmarks instead of per-step rebuild; empty-cache guard - decode_batch: speculative-decode support for q.seq >= 1; appends tokens incrementally, correct landmark state per draft token - parallel feature: optional rayon head-parallel forward() path (~4× prefill speedup on multi-core); serial path remains zero-dep by default - 21 tests pass (serial + parallel features), 4 new tests: incremental_landmarks_match_static, try_append_at_capacity_returns_error, kv_cache_reset_clears_state, decode_batch_shape_and_matches_sequential Co-Authored-By: claude-flow <ruv@ruv.net> |
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github-actions[bot]
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259c289651 |
chore: Update NAPI-RS binaries for all platforms
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ruvnet
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58de8932d4 |
docs(ruvllm, hailo-cluster): add sparse attention + Hailo-10H sections
ruvllm README: v2.6 What's New entry, Hailo-10H backend row, and a Sparse Attention companion-crate section with GQA + decode_step examples and the Pi 5 benchmark table. hailo-cluster README: Sparse Attention Validation table showing all 4 cognitum nodes at 17/17, measured seq_4096=836.2ms, and ADR-183..190 link. Co-Authored-By: claude-flow <ruv@ruv.net> |
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github-actions[bot]
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5ea1c275e4 |
chore: Update NAPI-RS binaries for all platforms
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ruvnet
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36912ba3e1 |
docs(ruvllm-sparse): add Pi 5 hardware benchmarks and cluster validation table
Adds measured Pi 5 Cortex-A76 latencies (85.8ms–836.2ms for seq 512–4096) alongside x86-64 numbers, and documents all 4 cognitum cluster nodes passing 17/17 tests in release aarch64 build. Co-Authored-By: claude-flow <ruv@ruv.net> |
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github-actions[bot]
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b71981b5c1 |
chore: Update NAPI-RS binaries for all platforms
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github-actions[bot]
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81a3532f3d |
chore: Update NAPI-RS binaries for all platforms
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ruvnet
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eb0fc28582 |
fix(ruvllm-sparse): export KvCache from lib.rs public API
Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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4c375e7ef2 |
feat(adr-189..190): implement KV cache decode_step + GQA/MQA forward — all 17 tests pass on Pi 5
ADR-189: KvCache struct (pre-allocated [capacity, kv_heads, dim]) + decode_step() - Single-token O(log T) decode against cached K/V - Online softmax with GQA head grouping (group_size = q_heads/kv_heads) - Validated on cognitum-v0 Pi 5 aarch64 Cortex-A76 (release build) ADR-190: forward_gqa() + forward_auto() dispatch - group_size=1 produces bit-identical output to forward() (MHA) - group_size=4 (Mistral-7B/Llama-3): 4x KV cache reduction - validate_gqa() enforces q_heads % kv_heads == 0 at call boundary - forward_auto() dispatches MHA→forward(), GQA→forward_gqa() by head count Also: README.md with benchmarks, KV memory budget table, cross-compile instructions. Test count: 17 passed (x86-64 debug, x86-64 release, aarch64 debug, aarch64 release). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruvnet
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4922b034fb |
feat(adr-183..190): integrate ruvllm_sparse_attention crate + implement ADRs 183-188
Integrates the ruvllm_sparse_attention prototype into crates/ and applies
all accepted ADRs (183-188) in a single coordinated change.
ADR-183: move rand to [dev-dependencies] — zero runtime dep footprint
ADR-184: one-pass online softmax in forward() — single traversal with
running-max + correction factor, ~2× FLOPs reduction on Pi 5 NEON
ADR-185: skip current_block in non-causal landmark candidates — prevents
double-counting token i through its window edge + own block mean
ADR-186: 7 edge-case tests as CI gate (seq=0, seq=1, out-of-range global
tokens, block_size=1, self-attention-only, non-causal correctness,
estimate regression guard); all 11 tests pass
ADR-187: checked overflow in Tensor3::zeros — panics with structured
diagnostic message instead of silent wraparound in release builds
ADR-188: stamp scheme comments in forward() and estimate_sparse_edges()
ADRs 189 (KV cache decode_step) and 190 (GQA/MQA forward_gqa) remain
Proposed; their code is fully specified in the ADR docs and depends on
this foundation landing first.
Co-Authored-By: claude-flow <ruv@ruv.net>
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github-actions[bot]
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77b44c2e10 |
chore: Update NAPI-RS binaries for all platforms
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ruvnet
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1493bab017 |
feat(graph-node): add deleteNode/deleteEdge/deleteHyperedge API — closes #427
Implements the three missing delete primitives on GraphDatabase.prototype,
unblocking the ruflo bridge from relying solely on the SQL fallback path.
**API additions:**
deleteNode(id, {cascade?}) → {deletedNode, deletedEdges}
deleteEdge(id) → {deleted}
deleteHyperedge(id) → {deleted}
cascade=true on deleteNode removes all incident hyperedges atomically
(no racy enumerate-then-delete required by callers).
**Rust changes:**
- ruvector-core/hypergraph: HypergraphIndex::remove_entity(cascade)
+ remove_hyperedge() with full bipartite-index + temporal-index cleanup
- ruvector-graph/graph: GraphDB::delete_hyperedge() + delete_hyperedges_by_node()
symmetric to create_hyperedge, propagates to GraphStorage when enabled
- ruvector-graph-node/lib: three new #[napi] async NAPI methods, each
propagating through HypergraphIndex → GraphDB → GraphStorage in order
- ruvector-graph-node/types: JsDeleteNodeOptions, JsDeleteNodeResult,
JsDeleteResult return types
**Versions:** workspace 2.2.1 → 2.2.2; @ruvector/graph-node 2.0.3 → 2.0.4
(platform optionalDependencies aligned to 2.0.4)
Co-Authored-By: claude-flow <ruv@ruv.net>
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github-actions[bot]
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999bfbdf75 |
chore: Update NAPI-RS binaries for all platforms
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rUv
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55eae8887a
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ADR-180: ruvllm 2.2.1 cache-reset patch + N-backend pool exploration (#424)
* ADR-180/181 iter 1: branch off + plan + ServingEngine API audit
New /loop pursues two stacked optimizations on top of the ADR-179
SOTA (20.5 tok/s aggregate):
- Phase A (ADR-180): ServingEngine continuous batching wiring,
target ≥40 tok/s aggregate
- Phase B (ADR-181): in-tree pi_quant Q4 + BitNet b1.58,
target ≥80 tok/s aggregate
Iter 1 lands the plan doc + audits the LlmBackend trait surface
ServingEngine needs. Confirms the `submit_async` async oneshot
flow + the per-request encode/decode path. Wiring shape sketched
for iter 2.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 2: wire ServingEngine into ruvllm-pi-worker (build green, scheduler stalls)
Replace Mutex<CandleBackend> with Arc<dyn LlmBackend> + Arc<ServingEngine>.
PiEngine::load constructs the engine with max_inflight from env, spawns
the run_async scheduler in a tokio task. PiEngine::generate is now
async — tokenizes via LlmBackend::tokenizer() (encode/decode live on
Tokenizer trait, not LlmBackend itself), submit_async, decode result.
Host build green ✓. Worker starts cleanly: model loaded.
But: single submit_async request hangs 60+s with no result. Hypothesis:
ServingEngine::run_async expects a lower-level executor surface that
CandleBackend doesn't implement (the LlmBackend::generate path is the
high-level escape hatch for non-batched calls; the scheduler likely
needs forward_iteration or similar). Iter 3 audits run_iteration to
find what backend methods it actually calls.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 3: pivot to N-backend pool (ServingEngine isn't real batching)
Iter-2 audit of ServingEngine::generate_next_token: it dispatches
per-token via self.model.generate(text, max_tokens=1), serializing
on Mutex<CandleBackend> with extra text<->token overhead. ruvllm
2.2.0's serving stack is scaffolding for continuous batching,
not a working implementation.
Pivot: pool of N independent CandleBackend instances, each in its
own tokio::sync::Mutex, gated by a Semaphore. True request-level
parallelism — N requests run concurrently on different threads
with their own model weights + KV state.
Cost: N × ~640 MB Q4_K_M weights. With N=4 that's 2.5 GB on each
Pi 5; 8 GB total leaves ~5 GB for system + embed worker + KV.
Host build green. Smoke running async (b4j4csypc).
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 4: KV-cache statefulness blocks in-process parallelism
ADR-179 iter-16 bug reproduced under iter-3's N-backend pool wiring:
1st request → success, 2nd+ → broadcast shape mismatch from leaked
KV cache. Affects every backend slot in the pool independently —
in-process parallelism cannot work without an upstream ruvllm fix
that resets candle's LlamaModel cache between generate() calls.
Iter 5 pivots to deployment-level parallelism: N independent
ruvllm-pi-worker processes per Pi on adjacent ports, each handling
1 request at a time. Process boundaries enforce request isolation.
Projected aggregate: 4 Pis × 4 workers × 9 tok/s = 144 tok/s.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 4: root cause = clear_kv_cache is a no-op for Llama
LlmBackend::generate calls self.clear_kv_cache() at start, but for
LoadedModelInner::Llama the impl only resets current_pos=0 and skips
the actual candle Cache (which holds ks/vs Tensor vecs that accumulate
across calls). The comment in candle_backend.rs:933 — "cache state
will be reset when we start from position 0" — is wrong: candle's
Cache doesn't auto-clear on position reset.
This is THE bug torpedoing every multi-request strategy:
- single Mutex<Backend>: 2nd request errors
- N-backend pool: each slot's 2nd request errors
- ServingEngine: same underlying generate() → same bug
Upstream fix path (ruvllm 2.2.1): store llama_config + dtype on
LoadedModel; clear_kv_cache builds a fresh Cache::new() for Llama
arm and replaces the held one. Worker pins 2.2.1, rebuilds, redeploys.
Iter 5 implements the patch.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ruvllm 2.2.1: clear_kv_cache actually resets the Llama Cache
LoadedModelInner::Llama gained two carry fields (Config, DType) so
clear_kv_cache() can rebuild a fresh candle Cache for each new
generate() call. The previous impl only set current_pos=0 and
left the held Cache's ks/vs Tensor vecs untouched — they
accumulated across calls and broke every request after the first
("cannot broadcast [N,N] to [1,H,N,X]" with X = stale seq len).
This unblocks every multi-request strategy (single-Mutex backend,
N-backend pool, ServingEngine wiring) — request isolation now
works as the trait contract implies.
Workspace version: 2.2.0 → 2.2.1. Host builds green.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 6: deploy ruvllm 2.2.1 cluster-wide; throughput plateau
ruvllm 2.2.1 + ruvllm-cli 2.2.1 published to crates.io (cache-reset fix).
aarch64 worker deployed to all 4 Pis with RUVLLM_MAX_INFLIGHT=4.
Cluster bench (Q4_K_M, 4 Pi × 16 in-flight):
16/16 success, 0 errors (cache-reset works)
aggregate ~16-21 tok/s depending on per-Pi inflight
Multi-inflight per Pi REGRESSES on Cortex-A76:
1 inflight × 16 tok: 21.6 tok/s — best
4 inflight × 4 tok: 16.5 tok/s — CPU contention
candle's matmul saturates Pi 5's 4 cores at 1 generate — extra parallel
calls fight for the same cores via context switching. Per-Pi single-
stream rate IS the ceiling on this hardware.
Win from 2.2.1: operational stability (no KV-leak errors across calls)
+ ability to sustain steady-state without worker restarts. Throughput
unchanged from ADR-179 SOTA.
Strike 1 on convergence (aggregate not exceeded). Iter 7 reverts pool
to N=1 + pivots to ADR-181 (in-tree pi_quant 3-bit weights for the
next jump).
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 7: CONVERGENCE — ruvllm 2.2.1 ships, throughput plateau confirmed
Final bench (4 Pi × 1 in-flight × 16 tok, ruvllm 2.2.1):
wall 2.88s, 64 actual tokens, 22.2 tok/s aggregate
vs iter-26 SOTA 20.5 → +8% (noise)
Strike 2 → converged. The real win is the upstream ruvllm 2.2.1
patch fixing the ADR-179 iter-16 KV-leak bug. Stability +
operational simplicity, throughput unchanged.
Per-Pi ceiling on Cortex-A76 + candle Q4_K_M is ~9 tok/s — hardware
bound (LPDDR4X memory bandwidth + 4-core CPU saturation). Multi-
inflight per Pi REGRESSES due to context switching. Next jumps need
ADR-181 (pi_quant 2-3 bit) or ADR-182 (Hailo-10 onboard DDR).
CronDelete done. Branch push + PR + email follow.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180 iter 8: fix CI lint — clippy unused_variable + workspace rustfmt drift
Two CI failures on PR #424 blocking merge, both pre-existing drift surfaced
by my iter-3 changes (not new bugs):
1. clippy --all-targets -D warnings (cluster, default features):
unused variable: started — ruvllm-pi-worker.rs:270
`started` is only used inside the #[cfg(feature = "ruvllm-engine")]
timing block. Default cluster build (no feature) treated it as dead.
Fix: gate the let inside the cfg-true arm.
2. rustfmt --check across workspace:
- ruvllm-pi-worker.rs banner format!() + max_tokens chain (mine)
- candle_backend.rs:1244 load_from_hub return cfg arm (mine, ADR-179)
- mmwave-bridge.rs / ruview-csi-bridge.rs / ruvllm-bridge.rs (drift)
- tests/ruview_csi_bridge_cli.rs (drift)
- tests/ruvllm_bridge_cli.rs (drift)
Fix: cargo fmt -p ruvector-hailo-cluster -p ruvllm.
Local verification:
cargo fmt --check -p ruvector-hailo-cluster -p ruvllm → clean
cargo clippy -p ruvector-hailo-cluster --all-targets
-- -D warnings → clean
No behavioral change. Merge unblocker only.
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: ruvnet <ruvnet@gmail.com>
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github-actions[bot]
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225184550c |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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rUv
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c6d69003ad
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ADR-179: ruvllm 4-Pi 5 + Hailo HAT cluster — SOTA 20.5 tok/s, 28 iter loop (#423)
* ADR-179 + RUVLLM_CLUSTER_PLAN: scope ruvllm deploy on Pi 5 cluster
Branch off main for /loop iteration. Plan + ADR cover:
- 4× Pi 5 + AI HAT+ targets (cognitum-v0, cognitum-cluster-1/2/3)
- in-tree ruvllm + ruvllm-cli + pi_quant/turbo_quant/RaBitQ stack
- replicated per-node serve, P2C+EWMA dispatch (mirrors hailo cluster)
- iteration log committed for /loop continuity
Iter 1: aarch64 cross-build blocked on openssl-sys. Iter 2 will
audit the dep tree and build with a TLS-via-rustls subset.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 2: aarch64 cross-build fixes (rustls-tls + linker)
- hf-hub: switch to default-features=false + rustls-tls in both
ruvllm and ruvllm-cli. Drops the openssl-sys cross-link, which
was the ADR-179 iter 1 blocker.
- workspace .cargo/config.toml: pin aarch64 linker to
aarch64-linux-gnu-gcc and apply Cortex-A76 rustflags
(+lse +rcpc +fp16 +crc) so the Pi 5 builds inherit the same
microarch tuning the embed cluster uses (iter-84 ultra profile).
Cross-build now reaches actual code-gen on aarch64. Remaining issue:
candle_backend.rs uses hf_hub::api::sync, which the rustls-tls path
doesn't ship. Iter 3 plan documented in RUVLLM_CLUSTER_PLAN.md —
build a dedicated `ruvllm-pi-worker` bin in the hailo-cluster crate
that uses ruvllm as a lib + loads models from local paths, sidesteps
hf-hub entirely.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 3: ruvllm-pi-worker scaffold + aarch64 cross-build
New bin `ruvllm-pi-worker` in ruvector-hailo-cluster — sibling worker
to `ruvector-hailo-worker` for completions on each Pi 5 (port 50053).
Iter 3 is scaffold only:
- env-var contract documented (RUVLLM_WORKER_BIND, RUVLLM_MODEL_PATH,
RUVLLM_QUANTIZE, RUVLLM_KV_QUANTIZE, RUVLLM_MAX_INFLIGHT, etc.)
- TCP listener with version banner — no engine wiring yet
- proves the iter-2 cross-build chain works end-to-end for OUR bin
(1.18 MB aarch64 binary produced cleanly)
Iter 4 will scp + service file + install script; iter 5+ wires
ruvllm::serving::ServingEngine + pi_quant model load.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 4: deploy ruvllm-pi-worker scaffold to all 4 Pis
systemd unit + env example + install script (mirrors install.sh
for the hailo embed worker). Drops:
/usr/local/bin/ruvllm-pi-worker
/etc/ruvllm-pi-worker.env
/etc/systemd/system/ruvllm-pi-worker.service
/var/lib/ruvllm/{,models/} (state dir, owned by ruvllm-worker)
ruvllm-worker system user
Verified end-to-end: all 4 Pi 5s now serving the scaffold on :50053
(sibling to :50051 embed worker). TCP probe returns the version
banner from each.
Iter 5 wires ruvllm::serving::ServingEngine + first model load.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 5-7: model staging + foot-gun debrief
- Qwen2.5-0.5B-Instruct chosen as engine-wiring proof (Llama-3.2-1B
needs HF license token; not configured). Same Llama-arch family,
smallest cached model, validates the pipeline fastest.
- cognitum-v0 has 1.8 GB free root — staging only on cluster-1/2/3
(29 GB free each, post-rebirth resize).
- Rsync foot-gun: `pkill -f "rsync.*qwen"` matched own cmdline, killed
parent bash + 2 backgrounded tasks. Lessons noted in plan log.
- Sequential restage running in background.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 8: gate hf-hub behind hub-download feature
Move the entire HuggingFace Hub auto-download path behind a
`hub-download` cargo feature (default-on for workstation builds,
off for aarch64 cross-builds). Without it, `LlmBackend::load_model`
only accepts local paths — exactly what the Pi 5 worker needs.
Files touched:
- crates/ruvllm/Cargo.toml: add `hub-download = ["hf-hub"]`,
remove `hf-hub` from `candle` feature, add to `default`
- crates/ruvllm/src/backends/candle_backend.rs: gate
load_from_hub + get_safetensors_files + the load_model
fallback under `#[cfg(feature = "hub-download")]`. Without
the feature, non-local model_id returns NotFound.
- crates/ruvllm/src/tokenizer.rs: gate `from_pretrained` and
the hf_hub::api::sync use under `#[cfg(feature = "hub-download")]`.
Result: `cargo build --target aarch64-unknown-linux-gnu -p ruvllm
--no-default-features --features async-runtime,candle,quantize`
succeeds (35 s). Iter 9 wires ruvllm into ruvllm-pi-worker.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 9: wire ruvllm CandleBackend into ruvllm-pi-worker
- ruvector-hailo-cluster gains optional `ruvllm` + `anyhow` deps
behind cargo feature `ruvllm-engine`.
- ruvllm-pi-worker.rs rewritten: when --features ruvllm-engine,
construct CandleBackend, load_model from RUVLLM_MODEL_PATH
(local dir), expose newline-delimited JSON request/response
over TCP. Without the feature, falls through to the iter-3
scaffold so the deploy pipeline still tests cleanly.
- Host build (1m 21s) + smoke proves the wiring path is real:
tokenizer loads, safetensors reading begins, candle backend
rejects Qwen2 architecture (no lm_head.weight; tied embeds).
That's a model-loader gap not a wiring gap. Iter 10 swaps
TinyLlama in for a real Llama-arch first-light test.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 10: FIRST LIGHT — completion works on host
- Disabled use_flash_attention in PiEngine::load. The flag in
candle 0.8.4 is misnamed — it's a CUDA-only gate, panics on CPU
with `not implemented: compile with '--features flash-attn'`.
Setting it false routes to candle's standard attention.
- Disabled quantization for first-light (fp16 reference). pi_quant
/ turbo_quant / BitNet land in subsequent iters.
Smoke test on host:
Request: {"prompt":"The capital of France is","max_tokens":4}
Response: {"ms":459,"text":"a city that is","tokens":14}
That's ~9 tok/s on x86 CPU. Cortex-A76 with same fp16 path will
land closer to 1-3 tok/s; pi_quant Q4 should push it to 8-15.
Iter 11 stages TinyLlama on a cluster Pi for first-light on
the actual target hardware.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 11-13: PI FIRST LIGHT — TinyLlama-1.1B serving on cluster-1
Cross-built aarch64 ruvllm-pi-worker with --features ruvllm-engine,
deployed to cognitum-cluster-1, staged TinyLlama-1.1B (2.1 GB) into
/var/lib/ruvllm/models/, restarted service.
First completion from a Pi 5 in the cluster:
Request: {"prompt":"The capital of France is","max_tokens":4}
Response: {"ms":1727,"text":"Paris, and it","tokens":13}
That's 2.3 tok/s on Cortex-A76 fp16 — matches the iter-10 prediction.
The Pi cluster is now generating real LLM output. Iter 14 replicates
to cluster-2/3 + first multi-Pi bench. Iter 15+ layers pi_quant for
the projected 4-6× speedup to 8-15 tok/s/Pi.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 14-16: cluster-smoke harness + KV-cache statefulness bug
- New deploy/ruvllm-cluster-smoke.sh: parallel completion fanout,
per-worker + aggregate tok/s. Drop-in for the iter-9 newline-JSON
transport until the gRPC Completion proto lands later.
- Smoke confirmed on cluster-1: TinyLlama-1.1B fp16 produces
"Paris, and it is the most popul" for "The capital of France is"
in 3687 ms — matches iter-13's ~2.3-2.7 tok/s on Cortex-A76 fp16.
- Two issues uncovered for iter 17:
(a) Stateful KV cache between requests in same backend instance
panics with broadcast shape mismatch on the 2nd call.
Workaround: restart worker. Real fix: reset cache per-call
OR adopt ServingEngine's per-request scheduler.
(b) Reported `tokens` field is text byte length, not actual
generated token count. Cosmetic; fix tracking in iter 17.
- TinyLlama rsync to cluster-2 in progress; cluster-3 queued.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 17-18: 2-Pi parallel cluster smoke — 5.8 tok/s aggregate
cluster-1 + cluster-2 both serving TinyLlama-1.1B fp16. Sent
parallel completion to both:
cluster-1: 5466ms "a beautiful city that is filled with history,
culture, and beauty. It'"
cluster-2: 5486ms "Paris, and it is located in the Île-de-France region."
Both correct factual completions. Aggregate ~5.8 tok/s for 32
generated tokens across 5.5s wall time. Per-Pi 2.9 tok/s matches
iter-13 single-Pi exactly — load balancing is working linearly.
cluster-3 rsync ~70% done in background (b52vvlwuo).
Predicted 4-Pi fp16 ceiling: ~12 tok/s aggregate. Iter 19+ pi_quant
Q4 should push that 4-6× → SOTA target ~30-60 tok/s aggregate for
the 1B class.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 19-23: 3-Pi parallel cluster live, ~8.7 tok/s aggregate
After WiFi-rate issues + duplicate-rsync cleanup, cluster-3 model
finally landed. Restarted all 3 workers to clear stale KV cache.
First 3-Pi parallel completion (16 tokens each, parallel=3):
cluster-1: "Paris. The official language is French.\n\n2. Canada: Canada is"
cluster-2: "located in the center of France, on the banks of the River Seine. The"
cluster-3: "located in the heart of the country, and it is home to some of France"
3 different but factually-grounded completions in 5.5 s wall.
~8.7 tok/s aggregate, 2.9 tok/s/Pi. Scaling is linear:
1Pi=2.9 → 2Pi=5.8 → 3Pi=8.7 → 4Pi predicted=11.6.
Next: pi_quant Q4 to push per-Pi tok/s by 4-6× toward SOTA.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 24: QUANTIZATION FIRST LIGHT — Q4_K_M GGUF on Pi 5
Downloaded TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF Q4_K_M (638 MB)
and staged on cluster-1. candle's load_model auto-detected the
.gguf file ahead of safetensors. First Q4 completion:
Request: prompt="The capital of France is", max_tokens=16
Response: ms=1775, text="a city that is steeped in history and
culture. It's home"
That's 3.1x faster than the fp16 path (1775ms vs 5539ms for 16
tokens) — ~9 tok/s/Pi, middle of the predicted 8-15 tok/s window
for Q4 on Cortex-A76.
Memory: 638 MB on disk vs 2.1 GB fp16 (3.3x compression).
Replication to cluster-2/3 in flight (bor1jjryn). Iter 25 lands
the 3-Pi Q4 parallel bench (~27 tok/s aggregate predicted).
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 25: 3-Pi Q4 cluster — 16.9 tok/s aggregate (1.95x fp16)
Replicated TinyLlama Q4_K_M GGUF to cluster-2/3, all 3 nodes
serving. First 3-Pi parallel Q4 completion:
cluster-1 (2813ms): "also the world's second-largest city, with a
population of around"
cluster-2 (2834ms): "located in Paris, which is known as the City
of Love. The city has"
cluster-3 (2805ms): "a city that is both beautiful and full of
history. It's not just"
All 3 grammatical+factual completions in 2.83s wall — 1.95x faster
than fp16 (5.54s). Aggregate ~16.9 tok/s, per-Pi 5.6 tok/s.
Per-Pi under parallel load is 60% of solo (9.0 tok/s) — likely WiFi
RTT/AP contention. Iter 26 expands to 4 Pi; iters 27+ explore
smaller GGUFs + ruvllm in-tree pi_quant + BitNet for further wins.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 26: 4-Pi Q4 cluster — 20.5 tok/s aggregate (7.9x baseline)
Added cognitum-v0 to the LLM cluster — it's now serving Q4_K_M
TinyLlama alongside the existing embed-worker stack (port 50051
hailo embeds, port 50053 ruvllm completions). 638 MB GGUF fits
in the 1.8 GB free disk margin.
First 4-Pi parallel Q4 completion:
v0 (3123ms): "Paris, and it is the most visited city in the
world.\n\n3"
cluster-1(2806ms): "Paris.\nThe capital of the United States is
Washington D.C."
cluster-2(2863ms): "the 12th-largest city in Europe and is home to
over"
cluster-3(2825ms): "also the country's largest city, with a
population of around 1."
20.5 tok/s aggregate (16 tok × 4 / 3.124s), 5.1 tok/s/Pi. cognitum-v0
is the slowest — running embed worker + Python LLM serve + Cognitum
Seed services + thermal load.
Convergence trajectory holds linear-ish:
iter-13 (fp16, 1Pi): 2.6 agg 1.0x
iter-23 (fp16, 3Pi): 8.7 agg 3.3x
iter-25 (Q4, 3Pi): 16.9 agg 6.5x
iter-26 (Q4, 4Pi): 20.5 agg 7.9x <- this commit
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 27: quant Pareto sweep — Q4_K_M is SOTA on Pi 5 candle
Compared Q4_K_M / Q3_K_S / Q2_K paired on cluster-1 (max_tokens=16):
Q4_K_M (638MB): 1785ms 9.0 tok/s "Seine River" reference <- WINNER
Q3_K_S (479MB): 2052ms 7.8 tok/s "Paris..." also correct
Q2_K (463MB): 2038ms 7.9 tok/s "Paris..." also correct
Q4_K_M wins despite being the largest of the three because candle's
quantized matmul kernels are heavily tuned for the Q4_K block layout
on aarch64. Q3/Q2 fall to less-optimized dequant paths whose
overhead exceeds the memory bandwidth they save.
Quality: all three preserve correctness on the canonical "capital
of France" prompt.
Convergence rule = strike 1 (iter 27 didn't improve over iter 26
20.5 tok/s aggregate). Iter 28 attempts multi-inflight per worker;
if that doesn't push aggregate past 20.5, we declare convergence.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-179 iter 28: CONVERGENCE — 4-Pi Q4 SOTA = 20.5 tok/s aggregate
Tested multi-inflight per worker: 2 parallel requests to same Pi
take 4552ms vs 1785ms for 1, no aggregate gain. The
`Mutex<CandleBackend>` serializes every call — multi-inflight
needs ServingEngine continuous batching, which is out of scope
for this /loop.
Strike 2 → convergence. Stop scheduling.
Final SOTA on this hardware/runtime:
4-Pi cluster, TinyLlama-1.1B-Chat-v1.0 Q4_K_M GGUF
20.5 tok/s aggregate, 5.1 tok/s/Pi (parallel)
7.9x speedup over iter-13 1-Pi fp16 baseline
~28 W total cluster power
~$400 hardware (4× Pi 5 + AI HAT+)
Documented future work for iter 29+ outside this loop:
1. ServingEngine continuous batching wiring
2. ruvllm in-tree pi_quant integration (ADR-090)
3. BitNet b1.58 ternary weights (ADR-024)
4. RaBitQ on KV-cache (ADR-154)
5. Hailo-10 swap (would unlock ~5-10x more)
Co-Authored-By: claude-flow <ruv@ruv.net>
* ADR-180/181/182: future-work ADRs for next throughput jumps
Three ADRs scoping the next iterations beyond the ADR-179 SOTA
(20.5 tok/s aggregate). All three are proposed-state, not started.
ADR-180 — ServingEngine continuous batching wiring
Replace Mutex<CandleBackend> in ruvllm-pi-worker with the existing
ruvllm::serving::ServingEngine. Acceptance: ≥40 tok/s aggregate
(2× ADR-179 SOTA) by amortizing transformer forward passes
across 4-16 in-flight requests per Pi.
ADR-181 — In-tree pi_quant + BitNet b1.58
Replace candle's Q4_K_M kernel with hand-tuned 2-3 bit pi_quant
(ADR-090) then BitNet b1.58 ternary weights (ADR-024). Both
modules already in tree under crates/ruvllm/src/quantize/ and
crates/ruvllm/src/bitnet/. Acceptance: per-Pi tok/s 9 → 25-40,
aggregate 20.5 → ~80-100.
ADR-182 — Hailo-10H hardware migration
~$1k spend (4 modules @ ~$249 each). Hailo-10H has 8 GB onboard
DDR4, eliminating the LPDDR4X memory-bandwidth bottleneck that
bounds the current stack. Acceptance: ≥30 tok/s/Pi, ≥120 tok/s
aggregate (6× ADR-179).
These ADRs are scoping documents only — no implementation in this
commit. Implementation lands on dedicated feature branches per ADR.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ruvllm: hub-download feature must enable hf-hub/ureq for sync API
ADR-179 iter 8 added a `hub-download` cargo feature that gated the
HF Hub auto-download path. The feature pulled `hf-hub` but not its
`ureq` sub-feature, so `hf_hub::api::sync::ApiRepo` (used by
`candle_backend::load_from_hub` and `tokenizer::from_pretrained`)
wasn't compiled in hf-hub itself, breaking the workstation-default
build.
Fix: `hub-download = ["dep:hf-hub", "hf-hub/ureq"]`. Workstation
default builds get the sync API (openssl-dev is present); aarch64
cross-builds disable default features → no hub-download → no ureq
→ no native-tls cross-link, which is what we wanted in iter 8.
Caught by `cargo publish --dry-run` while preparing the 2.2.0
publish to crates.io.
Co-Authored-By: claude-flow <ruv@ruv.net>
* ruvllm-cli: pin ruvllm path-dep to version 2.2.0 for crates.io publish
cargo publish requires path-deps to also specify a version so the
published crate references the registry version of the dependency.
ruvllm 2.2.0 was just published; ruvllm-cli now references it.
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: ruvnet <ruvnet@gmail.com>
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github-actions[bot]
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368d64a292 |
chore: Update NAPI-RS binaries for all platforms
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Built from commit
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rUv
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0442856c3c
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hailo: bench fingerprint label + StatsResponse npu_pool_size + ADR refresh (iter 256-257) (#420)
* feat(hailo): add `fingerprint` label to bench --prom output (iter 256)
Bench's textfile-collector output carried only `concurrency` as a
label, so a Prometheus alert grouping by series couldn't tell a
genuine throughput regression apart from a model swap. The
fingerprint *was* recorded by the bench (--auto-fingerprint
already discovered + printed it to stderr) but never made it to
the prom labels.
Now every metric carries `concurrency="N",fingerprint="<hex>"`.
Empty fingerprint (--allow-empty-fingerprint) renders as
`fingerprint=""` rather than getting dropped, so the label set
stays scrape-stable whether or not enforcement is on.
Example output (iter 256, cognitum-v0):
ruvector_hailo_bench_throughput_per_second{concurrency="2",fingerprint="9c56e5965aea9afd99ad51826805f1be01bb0ea3301aafb74982e29e3b9cf3fa"} 70.712
Now `rate(ruvector_hailo_bench_throughput_per_second[1h]) by (fingerprint)`
gives one series per model — a 9c56...-deploy throughput drop is a
real regression, while a fingerprint change is a deploy event the
operator already knew about.
# What ships
- BenchSummary gains a `fingerprint: String` field, populated from
the resolved fingerprint (whatever --fingerprint or
--auto-fingerprint produced).
- write_prom_textfile renders it on every metric.
- bench_cli_prom_file_contains_throughput_metric updated to lock
the new label format so a future regression surfaces in CI.
Local verification:
cargo test -p ruvector-hailo-cluster --test bench_cli (6 passed)
cargo clippy --all-targets -- -D warnings (clean)
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): expose npu_pool_size via StatsResponse + ADR refresh (iter 257)
Surface the resolved RUVECTOR_NPU_POOL_SIZE through the gRPC
StatsResponse so cluster-side observability can differentiate
single-pipeline vs pool=N measurements.
# Proto change (backward-compatible)
StatsResponse gains `uint32 npu_pool_size = 10`. Old workers
send 0 (proto3 default), which clients render as "unknown / pre-
iter-257"; new workers send the resolved value (1, 2, 4, ...).
# Wire-through
- worker.rs: WorkerService.npu_pool_size populated from the env
var at startup, surfaced via get_stats RPC.
- transport.rs: StatsSnapshot.npu_pool_size field with
#[serde(default)] so JSON consumers from old workers don't fail.
- grpc_transport.rs: populated from proto resp on stats() RPC.
# ADR refresh (also in this commit)
- ADR-176 (HEF integration EPIC): added P6 row covering iter
234-237 pool measurement work + iter 256-257 observability layer.
- ADR-178 (gap analysis): bumped Status from Proposed to Closed
with a per-gap remediation table (8 gaps, 6 closed, 1 deferred,
2 tracked separately).
Local verification:
cargo check -p ruvector-hailo-cluster --bins (clean)
cargo test -p ruvector-hailo-cluster --lib (114 passed)
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: ruvnet <ruvnet@gmail.com>
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github-actions[bot]
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8b518302c5 |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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rUv
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c12d828b78
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hailo: lint cleanup + bridge test gates + doc refresh (iter 251-255) (#419)
* chore(hailo): drop 5 stale module-level #![allow(dead_code)] (iter 251)
Five modules carried `#![allow(dead_code)]` from "EPIC scaffold"
days when types and functions were declared ahead of their
consumers landing:
crates/ruvector-hailo/src/device.rs
crates/ruvector-hailo/src/inference.rs
crates/ruvector-hailo/src/hef_pipeline.rs (iter 158)
crates/ruvector-hailo/src/tokenizer.rs
crates/ruvector-hailo-cluster/src/lib.rs (iter 75-ish)
Verified by removing each and rebuilding: zero new dead-code
warnings fire across the feature matrix
(--no-default-features | --features cpu-fallback). Every item
once flagged dead is now genuinely live, used either by the
NPU dispatch path (iter 161-200), the cluster's coordinator
(iter 100+), or test fixtures that exercise the now-public
constructors.
Removing the allows means a future regression that adds a
*genuinely* dead item will surface at build time instead of
hiding behind the blanket suppression — which is the whole
point of dead-code lints.
Builds verified:
cargo check -p ruvector-hailo --no-default-features
cargo check -p ruvector-hailo --features cpu-fallback
cargo check -p ruvector-hailo-cluster
Tests: 22 (cluster) + 2 (cluster bench helpers) + 7 (hailo) all
green. mmwave/sys aren't touched.
Co-Authored-By: claude-flow <ruv@ruv.net>
* test(hailo): regression-gate iter-238/243/245 bridge flags (iter 252)
iter-238/243/245 added --cache, --cache-ttl, --health-check to
ruvllm-bridge but only verified the wiring through one-off manual
runs against cognitum-v0. A future refactor that drops the §2a
gate or forgets to update the help text would slip past CI.
Three tests added:
ruvllm_bridge_help_prints_synopsis — locks --cache,
--cache-ttl, --health-check stay in --help output
ruvllm_bridge_cache_without_fingerprint_refused — locks the
ADR-172 §2a cache+fp gate fires
ruvllm_bridge_cache_with_fingerprint_accepted — locks that
--cache + --cache-ttl wire through end-to-end against a
fakeworker; bridge produces correct dim=4 vector responses
The cache+fp gate test is intentionally narrow — it only checks
the no-fingerprint path. The opt-out via --allow-empty-fingerprint
is ADR-approved and exercised by the workers-empty-fp test that
already exists.
A pre-existing port-race flake in ruvllm_bridge_multi_line_with_
request_id_propagates surfaces under parallel `cargo test` runs;
serial (`-- --test-threads=1`) is clean. The iter-252 additions
don't share fixtures with that test, so the flake is independent.
Co-Authored-By: claude-flow <ruv@ruv.net>
* test(hailo): regression-gate iter-240/242/245 flags on csi+mmwave (iter 253)
Symmetric with iter-252's ruvllm-bridge tests. Locks the iter-240/
iter-242 cache flag, iter-243 cache-ttl flag, and iter-245 health-
check flag in --help output for the other two bridges, and gates
the ADR-172 §2a cache+fp refusal path on each.
Tests added:
ruview-csi-bridge:
ruview_bridge_help_prints_synopsis (extended)
ruview_bridge_cache_without_fingerprint_refused (new)
mmwave-bridge:
bridge_help_prints_synopsis (extended)
bridge_cache_without_fingerprint_refused (new)
ruvllm-bridge already covered the with-fingerprint acceptance
path in iter-252. The csi+mmwave variants don't need that
re-tested — same code path under the hood
(`HailoClusterEmbedder::with_cache(N)` + the §2a guard) — so I'm
keeping the cross-bridge surface narrow at the gate-fires level.
All 8 mmwave + 7 csi tests pass; ruvllm-bridge's 10-test suite
unchanged from iter-252.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(hailo): refresh stale test count + perf number in cluster README (iter 254)
The status banner had drifted on three numbers:
131 tests → 204 (iter 253 measurement, +73)
3 CLI binaries → 8 (worker, embed, fakeworker, stats, bench
+ 3 sensor bridges)
67.3 RPS → 70.6 RPS (iter-227 reverified post-iter-237
deploy on cognitum-v0)
Test-suite tree refreshed too:
Lib unit 69 → 114
Cluster integ. 12 → ~30
CLI integ. 18 → ~53 (incl. iter-252/253 cache regression gates)
Same anti-staleness pattern as iter-217 (ADR-167 status block) and
iter-241 (4 stale "once iter N" doc references). Doc rot is bounded
by occasional explicit refreshes; banner is the single most-read
line so it gets first priority.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(hailo): close 3 clippy regressions surfaced post-iter-251 (iter 255)
The iter-247 cluster CI run (post-merge) failed clippy --all-targets
on three findings, two of which are iter-251's "every dead item is
now live" claim being too generous, plus one genuine style finding:
1. crates/ruvector-hailo-cluster/src/bin/worker.rs:176
`out.push_str("…")` → `out.push('…')` per
clippy::single_char_add_str. Single-char string literal in
push_str is the textbook lint match.
2. crates/ruvector-hailo-cluster/src/health.rs:219 (test code)
`fn set_ready(&self, b: bool)` was scaffolding for a flip-mid-run
test path that never landed — deleted with a tombstone comment
so a future test that needs it can re-add cleanly.
3. crates/ruvector-hailo-cluster/src/lib.rs:1111 (test code)
`ValidationOutcome::NotReady { fingerprint }` was a placeholder
for a not-ready-but-reachable validate_fleet path. No current
test constructs it. Removed the variant + its match arm; the
Ready and catch-all (Unreachable / unknown) arms cover every
currently-tested case. Tombstone comment captures the intent
so the variant can be re-added when a test needs it.
iter-251 still stands — the 5 module-level allow(dead_code) blanket
suppressions were genuinely stale. These two specific items inside
the test-only mod were (a) under blanket `#[cfg(test)] mod tests`
which the iter-251 cleanup did walk through, and (b) in lib-test
target which `cargo check` doesn't compile by default — that's why
the iter-251 verification (cargo check for lib + lib_with_features)
missed them. Adding `cargo clippy --all-targets` to my local
verification scrub for future iters.
Local verification:
cargo clippy --all-targets -- -D warnings (clean)
cargo test (204 passed)
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: ruvnet <ruvnet@gmail.com>
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github-actions[bot]
|
17378bb38f |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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rUv
|
c7b0ba4c0f
|
hailo: NPU pipeline pool exploration + bridge cache/health parity (iter 234-249) (#418)
* explore(hailo): NPU pipeline pool skeleton (iter 234)
Queued post-iter-227 baseline. Single-pipeline HefEmbedder caps
cluster throughput at ~70 RPS because every gRPC request serializes
on a single Mutex<Inner>. Hailo-8 + PCIe DMA can overlap — ~14ms per
inference is mostly PCIe transfer (~12ms), only ~2ms NPU compute. A
multi-pipeline pool should unlock 2-4× throughput.
# Baseline (iter 227, single pipeline, cognitum-v0)
| concurrency | throughput | p50 | p99 |
|-------------|------------|--------|--------|
| 1 | 70.6 RPS | 14.1ms | 15.8ms |
| 4 | 70.7 RPS | 56.7ms | 74.7ms |
| 8 | 70.7 RPS | 112.7ms| 170.7ms|
Throughput plateaus regardless of concurrency; p50 scales linearly
confirming the lock is the choke point.
# Skeleton (this commit)
- `HefEmbedderPool` mirroring CpuEmbedder's Vec<Mutex<Slot>> pattern.
- N independent HefPipeline instances on the shared vdevice;
HailoRT's network-group scheduler arbitrates NPU access.
- `embed()`: try_lock each slot in turn; first free wins; fall back
to blocking on slot 0 if all busy (matches cpu_embedder.rs).
- DEFAULT_POOL_SIZE = 4 (overlap PCIe write / NPU / PCIe read /
host pre-post-processing without scheduler exhaustion).
- Compile-only test asserts Send + Sync so worker can hand out
Arc<HefEmbedderPool> across tokio tasks.
# Iter 235 plan (next)
- Wire HefEmbedderPool into ruvector-hailo-worker as a feature-flag.
- Deploy to cognitum-v0; rerun cluster-bench at concurrency 1/4/8.
- Sweep pool_size ∈ {2,4,8} to find the throughput knee.
- Document delta vs iter-227 baseline.
# Why a separate type, not a HefEmbedder field
Single-pipeline path stays cheaper for low-load deploys (init time,
RAM, no scheduler overhead). Solo Pi running mmwave-bridge keeps
HefEmbedder; cluster workers handling many concurrent gRPC streams
switch to HefEmbedderPool.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): wire HefEmbedderPool behind RUVECTOR_NPU_POOL_SIZE (iter 235)
Builds on iter-234's pool skeleton. HailoEmbedder now picks between
single-pipeline and pool-of-pipelines NPU dispatch at open() time
via a new private `HefBackend` enum. Selector is the
`RUVECTOR_NPU_POOL_SIZE` env var:
unset / = 1 → Single (preserves iter-162 default)
>= 2 → Pool with N pipelines on the shared vdevice
bad value → falls back to Single (logs would be added later)
Default behavior unchanged — operators must opt into the pool. This
keeps the iter-227 baseline as the regression-floor: bench numbers
without RUVECTOR_NPU_POOL_SIZE set should match exactly.
# Baseline (re-stating from iter 234, single pipeline, cognitum-v0)
| concurrency | throughput | p50 | p99 |
|-------------|------------|--------|--------|
| 1 | 70.6 RPS | 14.1ms | 15.8ms |
| 4 | 70.7 RPS | 56.7ms | 74.7ms |
| 8 | 70.7 RPS | 112.7ms| 170.7ms|
# Next (iter 236)
- Cross-compile the worker for aarch64 with the hailo feature
- Deploy to cognitum-v0 with `RUVECTOR_NPU_POOL_SIZE=4`
- Re-run cluster-bench at concurrency 1/4/8
- Document the throughput delta in the iter-236 commit
- Sweep pool_size ∈ {2,4,8} to find the knee
Co-Authored-By: claude-flow <ruv@ruv.net>
* bench(hailo): iter-235 pool=4 — NEGATIVE result, no throughput gain (iter 236)
Deployed iter-235's HefEmbedderPool to cognitum-v0 with
RUVECTOR_NPU_POOL_SIZE=4. Re-ran cluster-bench at concurrency 1/4/8
plus pool-size sweep at {2,4,8}. Throughput ceiling holds at 70.7 RPS
across every configuration — identical to iter-227 baseline.
# Before (iter 227, single pipeline)
| concurrency | throughput | p50 | p99 |
|-------------|------------|--------|--------|
| 1 | 70.6 RPS | 14.1ms | 15.8ms |
| 4 | 70.7 RPS | 56.7ms | 74.7ms |
| 8 | 70.7 RPS | 112.7ms| 170.7ms|
# After (iter 235 deployed, RUVECTOR_NPU_POOL_SIZE=4)
| concurrency | throughput | p50 | p99 |
|-------------|------------|--------|--------|
| 1 | 70.6 RPS | 14.1ms | 16.7ms |
| 4 | 70.7 RPS | 43.5ms | 84.9ms |
| 8 | 70.7 RPS | 112.9ms| 211.7ms|
# Pool-size sweep at fixed concurrency
| pool | concurrency | throughput | p50 |
|------|-------------|------------|--------|
| 2 | 4 | 70.7 RPS | 43.3ms |
| 4 | 4 | 70.7 RPS | 43.5ms |
| 8 | 8 | 70.7 RPS | 112.9ms|
Delta: 0% throughput. p50 at c=4 dropped from 56.7ms → 43.5ms (a 23%
tail-latency improvement) because each request gets its own host-side
queue slot — but the NPU itself remains the choke point.
# Why the pool doesn't help
HailoRT's network-group scheduler serializes inferences at the vdevice
level. The Hailo-8 has one inference engine per chip and HailoRT does
NOT pipeline DMA-write / NPU-compute / DMA-read across configured
network groups. The 70 RPS = 1000ms / 14ms-per-inference ceiling is
a hard NPU+PCIe limit per single-batch HEF.
# What stays
- HefEmbedderPool kept in tree (no regression at pool=1 default;
marginal p50 win at concurrency > 1).
- RUVECTOR_NPU_POOL_SIZE env knob remains operator-controlled.
- Pi systemd env reverted to RUVECTOR_NPU_POOL_SIZE=1 (matches the
iter-227 acceptance baseline).
- Module docstring updated to record the negative result so the next
optimizer doesn't waste another iteration on the same hypothesis.
# Iter 237 candidates (real throughput unlock)
- Async vstreams via hailo_vstream_recv_async — should overlap DMA
with NPU compute *within* one network group.
- Batch-compiled HEF (--batch-size 4 via DFC) — needs Hailo SDK on
a host machine; multi-day fork.
Co-Authored-By: claude-flow <ruv@ruv.net>
* deploy(hailo): default RUVECTOR_NPU_POOL_SIZE=2 in env example (iter 237)
iter-236 confirmed pool size doesn't affect throughput (NPU-bound at
70 RPS regardless), but pool=2 at concurrency=4 cuts p50 latency 23%
vs single-pipeline (43.5ms vs 56.7ms baseline). The win is real for
multi-bridge deploys: cognitum-v0 runs ruvector-mmwave-bridge,
ruview-csi-bridge, and ruvllm-bridge all hitting the same worker, so
in-flight concurrency >1 is the steady state, not the exception.
# After (iter 237 deployed default)
| concurrency | throughput | p50 | p99 | vs baseline |
|-------------|------------|--------|--------|-------------|
| 1 | 70.6 RPS | 14.1ms | 16.7ms | - |
| 4 | 70.7 RPS | 43.3ms | 84.7ms | -23% p50 |
Pool=2 chosen over pool=4: the latency win saturates at 2 (pool=4
gives the same p50). Each extra slot costs ~20 MB host-side
(tokenizer + embedding table copy); 2 slots is the floor that
captures the win without paying for unused capacity.
Cognitum-v0 systemd env updated to pool=2. Default in
ruvector-hailo.env.example bumped from "no entry" to RUVECTOR_NPU_POOL_SIZE=2
so future deploys get the latency win out of the box. Operators who
want the iter-227 baseline (single pipeline) can set =1.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): wire --cache flag into ruvllm-bridge (iter 238)
The bridge previously constructed `HailoClusterEmbedder::new(...)`
without the existing coordinator-side LRU cache. RAG workloads
through ruvllm repeat the same context strings constantly (system
prompt, tool descriptions, frequently-cited docs) so the cache
hit rate is naturally high — but operators couldn't opt in
without re-coding the bridge.
# Cache-hit speedup measured iter-237 prep on cognitum-v0:
| configuration | throughput | p50 | hit_rate |
|--------------------------------------|--------------|--------|----------|
| no cache (NPU bound, iter-227 base) | 70.7 RPS | 43.5ms | n/a |
| --cache 4096 --cache-keyspace 64 | 2305282 RPS | 0us | 1.000 |
Delta: 32500x throughput, ~all latency removed at 100% hit rate.
The cache lives in-process so the bridge resolves a hit before
the gRPC call to the worker, which is why the speedup is so
dramatic — it doesn't touch the NPU at all.
# What ships
- New `--cache <N>` flag (default 0 = disabled, backward compat).
- ADR-172 section 2a guard: refuses cache > 0 with empty fingerprint
unless --allow-empty-fingerprint is set (mirrors embed.rs +
bench.rs gates — without a fingerprint binding, a stale cache
could leak vectors across worker fleets that don't share the
same model).
- --help updated with the iter-238 measurement.
- Operator-controlled, opt-in. No deploy default change.
Same cache implementation already exposed via embed.rs's --cache
and HailoClusterEmbedder::with_cache. The mmwave-bridge and
ruview-csi-bridge consume mostly-unique sensor data so they don't
benefit; deferring those bridges to a separate iter if measured
hit rates ever justify it.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(hailo): correct iter-237 RSS claim with measured numbers (iter 239)
iter-237's commit message claimed pool=2 cost "~20 MB per extra slot".
Direct ps measurement on cognitum-v0 showed the real cost is much
higher — ~55 MB per slot, dominated by HailoRT's per-network-group
DMA and ring buffers, not the host-side state I'd assumed:
pool=1 → 87 MB RSS (baseline)
pool=2 → 142 MB RSS (+55 MB / +64%)
pool=4 → 251 MB RSS (+164 MB / nearly 3x baseline)
The shared safetensors mmap (~90 MB) and HEF (~4 MB) ARE deduplicated
by the kernel page cache, but each HailoRT-configured network group
allocates its own DMA + ring-buffer set on top of the shared mmaps.
# What changes
- env example explains the actual measured cost so operators can
budget RAM correctly. Pi 5 8 GB → pool=2 fits comfortably; 4 GB
Pi 5 should run pool=1 to leave room for bridges + system.
- DEFAULT_POOL_SIZE constant in hef_embedder_pool.rs corrected
from 4 to 2, matching the iter-237 deploy default and the
iter-236 measurement that proved pool=4 buys nothing extra.
The iter-237 deployed default (pool=2) was already right empirically
— this iter just makes the docs match reality so the next reader
doesn't get the wrong picture.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): wire --cache flag into ruview-csi-bridge (iter 240)
Symmetric to iter-238 (ruvllm-bridge --cache). The CSI summary
text is a fixed-template NL string interpolating seven
small-cardinality fields (node_id, channel, rssi, noise, antennas,
subcarriers, magic-kind). In steady-state radar deploys these
fields have low entropy — channel and antenna counts are board
constants, rssi/noise float in narrow ranges, n_subcarriers is
fixed by the WiFi standard. Many frames produce identical NL
strings, which is exactly the workload where iter-238's
cluster-bench measurement showed 32500x speedup at full hit rate.
# What ships
- New `--cache <N>` flag (default 0 = disabled, backward compat).
- Same ADR-172 section 2a guard as ruvllm-bridge / embed.rs / bench.rs:
refuses cache > 0 with empty fingerprint unless explicit opt-out.
- Startup banner reports cache size when enabled.
- --help updated with the iter-240 rationale.
Cache hit rate in real radar deploys is workload-specific and
needs operator measurement; a small `--cache 1024` is enough to
cover the discrete (channel, antenna, rssi-bucket) cross product
for a typical mmwave-paired CSI setup.
mmwave-bridge stays cache-less — radar packets carry continuous
timestamps + range/doppler bins so the per-packet text is unique
per frame; cache hit rate there would be near zero, paying memory
for nothing. Defer to a separate iter if measured radar traffic
ever shows duplicate strings.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(hailo): refresh stale "once iteration N" references (iter 241)
Four cross-crate doc strings still pointed at "once iteration X
lands" milestones that have already shipped:
ruvector-hailo/src/lib.rs:5 "once iter 3 lands the path dep"
ruvector-hailo/src/lib.rs:424 "once iter 4 brings Mutex<Device>"
ruvector-hailo-cluster/src/lib.rs:141 "once iter 14 brings ruvector-core"
ruvector-hailo-cluster/src/bin/worker.rs:380 "later iters pipeline NPU"
The first three were closed by iter-218 (ADR-178 Gap B path-dep +
EmbeddingProvider impl). The fourth was partially addressed by the
iter-234..236 pool work — confirmed empirically that NPU dispatch
serializes at the vdevice level so concurrent embed_stream
fan-out can't help today. Each docstring now records the iter
that resolved the milestone (so a future reader knows whether to
trust the comment or chase the wrong rabbit).
Same anti-staleness pattern as iter-217's ADR-167 status-block
collapse — the stratigraphy of in-flight comments rots faster
than the code, and a fresh reader doesn't know which TODOs are
real until they've audited the git history.
No behavioral change.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): wire --cache flag into mmwave-bridge (iter 242)
Corrects iter-240's incorrect claim that mmwave radar packets
produce unique strings per frame. The radar payload carries
timestamps but the NL summary template *discards* them — only
four templates exist:
"breathing rate {N} bpm at radar sensor"
"heart rate {N} bpm at radar sensor"
"nearest target distance {N} cm at radar sensor"
"(no )?person detected at radar sensor"
The {N} integers live in narrow physiological ranges (breathing
10-30, heart rate 60-100, distance 0-500 cm), giving roughly 200
unique strings total across the entire mmwave domain. After the
warmup window every packet is a cache hit — exactly the workload
where iter-238's cluster-bench measured 32500x speedup.
# What ships
- New `--cache <N>` flag (default 0 = disabled, backward compat).
- Same ADR-172 section 2a guard as ruvllm-bridge / ruview-csi-bridge /
embed.rs / bench.rs.
- Startup banner reports cache size when enabled.
- --help updated with the iter-242 rationale.
All three sensor bridges now expose --cache symmetrically:
ruvllm-bridge iter 238 (RAG context repeats)
ruview-csi-bridge iter 240 (CSI summary low-cardinality)
mmwave-bridge iter 242 (radar templates low-cardinality)
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): add --cache-ttl to all three bridges (iter 243)
embed.rs and bench.rs already supported `--cache-ttl <secs>` for
ops who want a max-staleness bound on cached vectors; the bridges
exposed only `--cache` (TTL=0, LRU eviction only). Closes the
parity gap.
# Why TTL matters operationally
With LRU only, an entry that keeps getting hit lives forever in
the cache — even if the worker fleet has silently drifted (config
change that doesn't bump the HEF hash, NPU recalibration, etc.).
The fingerprint gate prevents *new* entries from being inserted
across a fleet split, but pre-existing entries persist.
A finite TTL bounds that worst-case staleness: every entry is
re-fetched at least once per TTL window, so a silent worker drift
self-heals after one TTL cycle of latency cost. Recommended deploy
default for long-running bridges: --cache-ttl 300 (5 min) — short
enough to bound drift, long enough to amortise the cache hit
across the steady-state workload.
# What ships
- All three bridges: ruvllm-bridge, ruview-csi-bridge, mmwave-bridge.
- New `--cache-ttl <secs>` flag (default 0 = no TTL, LRU only).
- Wired through the same `with_cache_ttl(cap, Duration)` API
embed.rs uses, so the flag's semantics are bit-identical
across all four cluster CLIs.
- Backward compatible: omitting --cache-ttl behaves exactly as
iter-238/240/242 (LRU-only cache).
Co-Authored-By: claude-flow <ruv@ruv.net>
* ci(hailo): smoke-test dispatch microbench in audit workflow (iter 244)
The cluster crate has had a Criterion microbench at
`benches/dispatch.rs` since iter-80 (P2cPool RNG path,
HashShardRouter content hashing, full embed_one_blocking against
in-memory transport) but it never ran in CI — it's only triggered
when an operator types `cargo bench --bench dispatch` locally.
Adding `cargo bench --bench dispatch -- --test` to the audit
workflow's test job. The `--test` flag runs each bench function
exactly once instead of criterion's default (~100 iterations +
warmup), so the cost is ~30 seconds in CI but the smoke catches:
* bench harness panic from a removed dep or API change
* imports broken by a refactor of the cluster surface
* a hot-path function renamed without updating the bench
This is the fast variant of regression-gating — it doesn't detect
*numerical* regressions (a 2x slowdown that still completes
successfully). True regression detection needs baseline-file
comparison (criterion-perf-events / cargo-codspeed / similar) and
is parked as a separate iter when the hailo branch produces enough
historical data points to define meaningful thresholds.
Local verification (cognitum-v0 wasn't needed):
cargo bench --bench dispatch -- --test
→ "Testing ..." for each bench function, all "Success"
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(hailo): add --health-check to all three bridges (iter 245)
embed.rs and bench.rs already supported background health checking
via spawn_health_checker since iter-99 — periodic fingerprint
probes with automatic ejection of mismatched workers and cache
clear-on-event. The bridges (mmwave, ruview-csi, ruvllm) didn't,
which is exactly the wrong place to skip it: bridges are the
*long-running* CLIs (mmwave deploys run for days), so silent
worker drift goes uncaught the longest there.
# Threat closed
Worker A is deployed with HEF X and fingerprint x-hash. Bridge
starts, validates fp at startup, hands out vectors. Operator
re-deploys worker A with HEF Y (new model) and fingerprint
y-hash. Bridge keeps dispatching, gets vectors back from worker
that no longer match its expected fp — silently producing wrong
embeddings until the bridge restarts.
With --health-check 30, the bridge probes every 30s, ejects the
drifted worker from the dispatch pool, clears any cached entries
keyed on the old fp, and stops poisoning downstream consumers
within ~one probe interval.
# What ships
- All three bridges: ruvllm-bridge, ruview-csi-bridge, mmwave-bridge.
- New `--health-check <secs>` flag (default 0 = disabled, backward
compat with iter-238/240/242 behavior).
- When set, spawns a single-thread tokio runtime named
"health-check" for the lifetime of main, hands its handle to
spawn_health_checker, retains both via a let-bound _keepalive
so dropping the runtime aborts the checker cleanly on Ctrl-C.
- Same HealthCheckerConfig as embed.rs (interval override, all
other defaults from health_checker_config()).
- --help text updated with the iter-245 rationale.
Recommended deploy interval for long-running bridges: 30-60
seconds. Stricter (every 5s) is fine if the bridge is the only
load on the worker; looser (every 5min) is the floor — anything
beyond that, the threat window dominates over CPU savings.
Co-Authored-By: claude-flow <ruv@ruv.net>
* deploy(hailo): document iter-238..245 flags in bridge env examples (iter 246)
iter-238 (ruvllm-bridge --cache), iter-240/242 (other bridges
--cache), iter-243 (--cache-ttl), iter-245 (--health-check) all
shipped CLI flags but didn't update the deploy env templates.
Operators following the install scripts get a fresh
/etc/ruvector-mmwave-bridge.env that has no hint these knobs
even exist.
Closing the doc gap by adding annotated suggestions to all three
RUVECTOR_*_EXTRA_ARGS sections:
ruvector-mmwave-bridge.env.example → --cache + --cache-ttl + --health-check
ruview-csi-bridge.env.example → --cache + --cache-ttl + --health-check
ruvllm-bridge.env.example → --cache + --cache-ttl
Each example shows the recommended hardened deploy line so
operators can copy-paste:
RUVECTOR_*_EXTRA_ARGS=--cache 4096 --cache-ttl 300 --health-check 30
(ruvllm-bridge omits --health-check from the typical deploy because
ruvllm typically forks the bridge per-session — health checking a
sub-second-lifetime process is a no-op.)
No code change. No behavioral change. Deploy parity / discoverability
fix only.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(hailo): cap RUVECTOR_LOG_TEXT_CONTENT=full at 200 chars (iter 247)
The audit-log Full mode rendered text verbatim — for an embed
request the iter-180 byte cap allows up to 64 KB. An operator
who flips RUVECTOR_LOG_TEXT_CONTENT=full to debug in prod could
push 64 KB × 70 RPS = 4.5 MB/s of journald traffic, which:
* burns journal disk fast (10s of GB/hour)
* produces single-line entries that break most ops tooling
(long-line scanners, journalctl --grep regex backtracking)
* makes individual entries unscannable by humans anyway
Capping at 200 chars per text preserves the debug utility — you
can still grep for content correlations against request_id — at
1/300th the worst-case journald volume. The cut is char-boundary-
safe (counted via str::chars()) so multi-byte UTF-8 doesn't panic
the rendering path.
# Worst case before vs after
Request: 64 KB UTF-8 text @ 70 RPS, RUVECTOR_LOG_TEXT_CONTENT=full
Before: 64 KB × 70 = 4.5 MB/s journal volume per worker
After: 600 B × 70 = 42 KB/s (200 chars + UTF-8 + framing)
Three tests added: short (≤cap, unchanged), long (truncated +
ellipsis marker), multi-byte (300×U+1F980 emoji = 1.2 KB,
truncates on a char boundary not byte boundary).
iter-180 capped REQUEST size; iter-190 capped RESPONSE size;
iter-247 caps the LOG-LINE size for the same defense-in-depth
reason. Full-mode logging stays the operator's footgun (per the
existing docstring) — but it's now a footgun that doesn't
exhaust the disk in 10 minutes.
Co-Authored-By: claude-flow <ruv@ruv.net>
* chore(hailo): log RUVECTOR_NPU_POOL_SIZE at worker startup (iter 248)
iter-235 added the env-var knob for the HefEmbedderPool selector,
but the worker never logged the resolved value at startup. An
operator who flipped pool=2→4 (or back to 1 on a memory-constrained
4 GB Pi) had no confirmation the change actually took effect short
of inspecting RSS via `ps`.
Now the worker emits an info-level log line alongside the existing
iter-180/181/182/183/184 DoS-gate startup banner:
NPU pipeline pool size pool_size=2 (iter 235; >=2 enables ...)
Same disclosure pattern as RUVECTOR_LOG_TEXT_CONTENT,
RUVECTOR_RATE_LIMIT_RPS, RUVECTOR_MAX_BATCH_SIZE, etc — every
operator-tunable env knob ends up in the journal at startup so
post-incident review can reconstruct the running config without
reading /etc/ruvector-hailo.env at the time of the incident.
No behavior change. Pure observability.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(mmwave): widen Event::Unknown.payload_len u8 → u16 (iter 249)
`Event::Unknown { frame_type, payload_len }` carried a u8 payload_len
even though the MR60BHA2 protocol uses a 2-byte length field. The
current parser caps payloads at MAX_PAYLOAD=64 (well within u8) so
this was never a runtime truncation, but:
- Type didn't match the protocol's intent — operators reading the
emitted JSONL had to remember the implicit cap.
- `clippy::cast_possible_truncation` fired at the construction
site (`payload.len() as u8`) and the bridge's emission site.
Pedantic, but the alternative — silencing with `#[allow]` — is
worse than just using the right type.
Now the construction site uses `u16::try_from(...).unwrap_or(u16::MAX)`,
which honestly handles any future MAX_PAYLOAD bump up to 65535
bytes. The mmwave-bridge JSONL formatter already prints the value
via `{}` so emission stays unchanged.
Test added that locks the field width: an unknown frame with a
60-byte payload must report payload_len=60. (300 bytes would
exercise the formerly-truncating path but the parser rejects
anything > MAX_PAYLOAD before the Event is constructed, so the
test stays inside the parser's contract.)
Surfaced by an iter-249 cargo clippy --pedantic sweep; same
audit pass also flagged stylistic warnings (missing backticks,
implicit format args) which are out of scope.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(hailo): add READMEs to 3 missing hailo crates + benchmarks (iter 250)
Closes the doc gap surfaced by the iter-234..249 PR review:
ruvector-hailo-cluster had a 424-line operator README, but the 3
sibling crates (ruvector-hailo, ruvector-mmwave, hailort-sys)
shipped without one — `cargo doc --open` was the only on-ramp.
# What ships
- crates/ruvector-hailo/README.md — embedding backend,
3 feature-gated build paths, architecture diagram, iter-235+
pool benchmark table, security posture summary, env vars
- crates/ruvector-mmwave/README.md — MR60BHA2 wire format,
parser API, criterion benchmark numbers, proptest fuzz suite
- crates/hailort-sys/README.md — FFI binding scope,
build requirements, why no safe wrapper at this layer
- crates/ruvector-hailo-cluster/README.md — added the iter-238
cache-hit measurement table + the iter-234..237 pool benchmark
table; refreshed the CLI section to enumerate all four cluster
CLIs + the three bridges with their iter-243/245 flags
All builds verified clean:
cargo build -p ruvector-hailo --no-default-features
cargo build -p ruvector-hailo --features cpu-fallback
cargo build -p ruvector-mmwave
cargo build -p hailort-sys
cargo build -p ruvector-hailo-cluster --bins
No code change. Documentation parity only.
Co-Authored-By: claude-flow <ruv@ruv.net>
---------
Co-authored-by: ruvnet <ruvnet@gmail.com>
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github-actions[bot]
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5e0a1a414f |
chore: Update NAPI-RS binaries for all platforms
Built from commit
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